From 117206ba3945de15fc380588de3c779526977c7f Mon Sep 17 00:00:00 2001
From: leestott <leestott@users.noreply.github.com>
Date: Sat, 6 Sep 2025 15:23:27 +0000
Subject: [PATCH] =?UTF-8?q?=F0=9F=8C=90=20Update=20translations=20via=20Co?=
 =?UTF-8?q?-op=20Translator?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
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 .../3-Linear/solution/notebook.ipynb          | 1111 +++++++
 .../sk/2-Regression/4-Logistic/notebook.ipynb |  269 ++
 .../4-Logistic/solution/R/lesson_4-R.ipynb    |  686 ++++
 .../4-Logistic/solution/notebook.ipynb        | 1255 ++++++++
 .../sk/3-Web-App/1-Web-App/notebook.ipynb     |    0
 .../1-Web-App/solution/notebook.ipynb         |  267 ++
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 .../solution/R/lesson_11-R.ipynb              | 1285 ++++++++
 .../2-Classifiers-1/solution/notebook.ipynb   |  279 ++
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 .../3-Classifiers-2/solution/notebook.ipynb   |  302 ++
 .../4-Classification/4-Applied/notebook.ipynb |   39 +
 .../4-Applied/solution/notebook.ipynb         |  290 ++
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 .../2-K-Means/solution/notebook.ipynb         |  544 ++++
 .../2-K-Means/solution/tester.ipynb           |  341 ++
 .../solution/notebook.ipynb                   |  100 +
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 .../solution/1-notebook.ipynb                 |  172 +
 .../solution/2-notebook.ipynb                 |  137 +
 .../solution/3-notebook.ipynb                 |  260 ++
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 .../3-Linear/solution/notebook.ipynb          | 1113 +++++++
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 .../4-Logistic/solution/notebook.ipynb        | 1258 ++++++++
 .../sl/3-Web-App/1-Web-App/notebook.ipynb     |    0
 .../1-Web-App/solution/notebook.ipynb         |  267 ++
 .../1-Introduction/notebook.ipynb             |   39 +
 .../solution/R/lesson_10-R.ipynb              |  724 +++++
 .../1-Introduction/solution/notebook.ipynb    |  699 ++++
 .../2-Classifiers-1/notebook.ipynb            |   41 +
 .../solution/R/lesson_11-R.ipynb              | 1302 ++++++++
 .../2-Classifiers-1/solution/notebook.ipynb   |  281 ++
 .../3-Classifiers-2/notebook.ipynb            |  165 +
 .../solution/R/lesson_12-R.ipynb              |  647 ++++
 .../3-Classifiers-2/solution/notebook.ipynb   |  304 ++
 .../4-Classification/4-Applied/notebook.ipynb |   41 +
 .../4-Applied/solution/notebook.ipynb         |  292 ++
 .../5-Clustering/1-Visualize/notebook.ipynb   |   50 +
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 .../1-Visualize/solution/notebook.ipynb       |  892 ++++++
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 .../2-K-Means/solution/notebook.ipynb         |  550 ++++
 .../2-K-Means/solution/tester.ipynb           |  343 ++
 .../solution/notebook.ipynb                   |  100 +
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 .../4-Hotel-Reviews-1/solution/notebook.ipynb |  174 +
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 .../solution/1-notebook.ipynb                 |  172 +
 .../solution/2-notebook.ipynb                 |  137 +
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 .../1-Introduction/working/notebook.ipynb     |   63 +
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 .../3-Linear/solution/notebook.ipynb          | 1113 +++++++
 .../sr/2-Regression/4-Logistic/notebook.ipynb |  269 ++
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 .../4-Logistic/solution/notebook.ipynb        | 1255 ++++++++
 .../sr/3-Web-App/1-Web-App/notebook.ipynb     |    0
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 .../solution/R/lesson_10-R.ipynb              |  725 +++++
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 .../solution/R/lesson_11-R.ipynb              | 1298 ++++++++
 .../2-Classifiers-1/solution/notebook.ipynb   |  281 ++
 .../3-Classifiers-2/notebook.ipynb            |  163 +
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 .../3-Classifiers-2/solution/notebook.ipynb   |  302 ++
 .../4-Classification/4-Applied/notebook.ipynb |   39 +
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 .../1-Visualize/solution/notebook.ipynb       |  882 +++++
 .../sr/5-Clustering/2-K-Means/notebook.ipynb  |  231 ++
 .../2-K-Means/solution/R/lesson_15-R.ipynb    |  642 ++++
 .../2-K-Means/solution/notebook.ipynb         |  548 ++++
 .../2-K-Means/solution/tester.ipynb           |  343 ++
 .../solution/notebook.ipynb                   |  100 +
 .../sr/6-NLP/4-Hotel-Reviews-1/notebook.ipynb |    0
 .../4-Hotel-Reviews-1/solution/notebook.ipynb |  174 +
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 .../solution/1-notebook.ipynb                 |  172 +
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 translations/sr/PyTorch_Fundamentals.ipynb    | 2830 +++++++++++++++++
 .../sv/2-Regression/1-Tools/notebook.ipynb    |    0
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 .../3-Linear/solution/notebook.ipynb          | 1113 +++++++
 .../sv/2-Regression/4-Logistic/notebook.ipynb |  269 ++
 .../4-Logistic/solution/R/lesson_4-R.ipynb    |  686 ++++
 .../4-Logistic/solution/notebook.ipynb        | 1257 ++++++++
 .../sv/3-Web-App/1-Web-App/notebook.ipynb     |    0
 .../1-Web-App/solution/notebook.ipynb         |  267 ++
 .../1-Introduction/notebook.ipynb             |   39 +
 .../solution/R/lesson_10-R.ipynb              |  727 +++++
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 .../solution/R/lesson_11-R.ipynb              | 1298 ++++++++
 .../2-Classifiers-1/solution/notebook.ipynb   |  281 ++
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 .../3-Classifiers-2/solution/notebook.ipynb   |  302 ++
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 .../4-Applied/solution/notebook.ipynb         |  290 ++
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 .../1-Visualize/solution/notebook.ipynb       |  821 +++++
 .../sv/5-Clustering/2-K-Means/notebook.ipynb  |  231 ++
 .../2-K-Means/solution/R/lesson_15-R.ipynb    |  640 ++++
 .../2-K-Means/solution/notebook.ipynb         |  550 ++++
 .../2-K-Means/solution/tester.ipynb           |  343 ++
 .../solution/notebook.ipynb                   |  100 +
 .../sv/6-NLP/4-Hotel-Reviews-1/notebook.ipynb |    0
 .../4-Hotel-Reviews-1/solution/notebook.ipynb |  174 +
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 .../solution/1-notebook.ipynb                 |  172 +
 .../solution/2-notebook.ipynb                 |  137 +
 .../solution/3-notebook.ipynb                 |  260 ++
 .../1-Introduction/solution/notebook.ipynb    |  170 +
 .../1-Introduction/working/notebook.ipynb     |   63 +
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 .../7-TimeSeries/3-SVR/working/notebook.ipynb |  701 ++++
 .../1-QLearning/notebook.ipynb                |  411 +++
 .../solution/assignment-solution.ipynb        |  469 +++
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 .../sv/8-Reinforcement/2-Gym/notebook.ipynb   |  394 +++
 .../2-Gym/solution/notebook.ipynb             |  526 +++
 translations/sv/PyTorch_Fundamentals.ipynb    | 2830 +++++++++++++++++
 .../sw/2-Regression/1-Tools/notebook.ipynb    |    0
 .../1-Tools/solution/R/lesson_1-R.ipynb       |  448 +++
 .../1-Tools/solution/notebook.ipynb           |  671 ++++
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 .../2-Data/solution/notebook.ipynb            |  437 +++
 .../sw/2-Regression/3-Linear/notebook.ipynb   |  128 +
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 .../3-Linear/solution/notebook.ipynb          | 1111 +++++++
 .../sw/2-Regression/4-Logistic/notebook.ipynb |  269 ++
 .../4-Logistic/solution/R/lesson_4-R.ipynb    |  686 ++++
 .../4-Logistic/solution/notebook.ipynb        | 1255 ++++++++
 .../sw/3-Web-App/1-Web-App/notebook.ipynb     |    0
 .../1-Web-App/solution/notebook.ipynb         |  267 ++
 .../1-Introduction/notebook.ipynb             |   39 +
 .../solution/R/lesson_10-R.ipynb              |  721 +++++
 .../1-Introduction/solution/notebook.ipynb    |  739 +++++
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 .../solution/R/lesson_11-R.ipynb              | 1294 ++++++++
 .../2-Classifiers-1/solution/notebook.ipynb   |  279 ++
 .../3-Classifiers-2/notebook.ipynb            |  163 +
 .../solution/R/lesson_12-R.ipynb              |  648 ++++
 .../3-Classifiers-2/solution/notebook.ipynb   |  302 ++
 .../4-Classification/4-Applied/notebook.ipynb |   39 +
 .../4-Applied/solution/notebook.ipynb         |  290 ++
 .../5-Clustering/1-Visualize/notebook.ipynb   |   50 +
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 .../1-Visualize/solution/notebook.ipynb       |  817 +++++
 .../sw/5-Clustering/2-K-Means/notebook.ipynb  |  229 ++
 .../2-K-Means/solution/R/lesson_15-R.ipynb    |  642 ++++
 .../2-K-Means/solution/notebook.ipynb         |  544 ++++
 .../2-K-Means/solution/tester.ipynb           |  341 ++
 .../solution/notebook.ipynb                   |  100 +
 .../sw/6-NLP/4-Hotel-Reviews-1/notebook.ipynb |    0
 .../4-Hotel-Reviews-1/solution/notebook.ipynb |  174 +
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 .../solution/1-notebook.ipynb                 |  172 +
 .../solution/2-notebook.ipynb                 |  137 +
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 .../1-Introduction/solution/notebook.ipynb    |  162 +
 .../1-Introduction/working/notebook.ipynb     |   63 +
 .../2-ARIMA/solution/notebook.ipynb           | 1131 +++++++
 .../2-ARIMA/working/notebook.ipynb            |   61 +
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 .../7-TimeSeries/3-SVR/working/notebook.ipynb |  693 ++++
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 .../solution/assignment-solution.ipynb        |  425 +++
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 .../sw/8-Reinforcement/2-Gym/notebook.ipynb   |  392 +++
 .../2-Gym/solution/notebook.ipynb             |  524 +++
 translations/sw/PyTorch_Fundamentals.ipynb    | 2828 ++++++++++++++++
 .../th/2-Regression/1-Tools/notebook.ipynb    |    0
 .../1-Tools/solution/R/lesson_1-R.ipynb       |  448 +++
 .../1-Tools/solution/notebook.ipynb           |  669 ++++
 .../th/2-Regression/2-Data/notebook.ipynb     |   46 +
 .../2-Data/solution/R/lesson_2-R.ipynb        |  672 ++++
 .../2-Data/solution/notebook.ipynb            |  437 +++
 .../th/2-Regression/3-Linear/notebook.ipynb   |  128 +
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 .../3-Linear/solution/notebook.ipynb          | 1109 +++++++
 .../th/2-Regression/4-Logistic/notebook.ipynb |  269 ++
 .../4-Logistic/solution/R/lesson_4-R.ipynb    |  686 ++++
 .../4-Logistic/solution/notebook.ipynb        | 1255 ++++++++
 .../th/3-Web-App/1-Web-App/notebook.ipynb     |    0
 .../1-Web-App/solution/notebook.ipynb         |  267 ++
 .../1-Introduction/notebook.ipynb             |   39 +
 .../solution/R/lesson_10-R.ipynb              |  716 +++++
 .../1-Introduction/solution/notebook.ipynb    |  700 ++++
 .../2-Classifiers-1/notebook.ipynb            |   39 +
 .../solution/R/lesson_11-R.ipynb              | 1294 ++++++++
 .../2-Classifiers-1/solution/notebook.ipynb   |  279 ++
 .../3-Classifiers-2/notebook.ipynb            |  163 +
 .../solution/R/lesson_12-R.ipynb              |  648 ++++
 .../3-Classifiers-2/solution/notebook.ipynb   |  300 ++
 .../4-Classification/4-Applied/notebook.ipynb |   39 +
 .../4-Applied/solution/notebook.ipynb         |  290 ++
 .../5-Clustering/1-Visualize/notebook.ipynb   |   50 +
 .../1-Visualize/solution/R/lesson_14-R.ipynb  |  493 +++
 .../1-Visualize/solution/notebook.ipynb       |  817 +++++
 .../th/5-Clustering/2-K-Means/notebook.ipynb  |  229 ++
 .../2-K-Means/solution/R/lesson_15-R.ipynb    |  639 ++++
 .../2-K-Means/solution/notebook.ipynb         |  544 ++++
 .../2-K-Means/solution/tester.ipynb           |  341 ++
 .../solution/notebook.ipynb                   |  100 +
 .../th/6-NLP/4-Hotel-Reviews-1/notebook.ipynb |    0
 .../4-Hotel-Reviews-1/solution/notebook.ipynb |  174 +
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 .../solution/1-notebook.ipynb                 |  172 +
 .../solution/2-notebook.ipynb                 |  137 +
 .../solution/3-notebook.ipynb                 |  260 ++
 .../1-Introduction/solution/notebook.ipynb    |  169 +
 .../1-Introduction/working/notebook.ipynb     |   64 +
 .../2-ARIMA/solution/notebook.ipynb           | 1095 +++++++
 .../2-ARIMA/working/notebook.ipynb            |   61 +
 .../3-SVR/solution/notebook.ipynb             | 1013 ++++++
 .../7-TimeSeries/3-SVR/working/notebook.ipynb |  689 ++++
 .../1-QLearning/notebook.ipynb                |  411 +++
 .../solution/assignment-solution.ipynb        |  447 +++
 .../1-QLearning/solution/notebook.ipynb       |  577 ++++
 .../th/8-Reinforcement/2-Gym/notebook.ipynb   |  392 +++
 .../2-Gym/solution/notebook.ipynb             |  524 +++
 translations/th/PyTorch_Fundamentals.ipynb    | 2828 ++++++++++++++++
 .../tr/2-Regression/1-Tools/notebook.ipynb    |    0
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 .../tr/2-Regression/2-Data/notebook.ipynb     |   46 +
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 .../2-Data/solution/notebook.ipynb            |  437 +++
 .../tr/2-Regression/3-Linear/notebook.ipynb   |  128 +
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 .../3-Linear/solution/notebook.ipynb          | 1113 +++++++
 .../tr/2-Regression/4-Logistic/notebook.ipynb |  269 ++
 .../4-Logistic/solution/R/lesson_4-R.ipynb    |  686 ++++
 .../4-Logistic/solution/notebook.ipynb        | 1257 ++++++++
 .../tr/3-Web-App/1-Web-App/notebook.ipynb     |    0
 .../1-Web-App/solution/notebook.ipynb         |  267 ++
 .../1-Introduction/notebook.ipynb             |   39 +
 .../solution/R/lesson_10-R.ipynb              |  727 +++++
 .../1-Introduction/solution/notebook.ipynb    |  717 +++++
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+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_1-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "c18d3bd0bd8ae3878597e89dcd1fa5c1",
+   "translation_date": "2025-09-06T13:40:43+00:00",
+   "source_file": "2-Regression/1-Tools/solution/R/lesson_1-R.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "YJUHCXqK57yz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Úvod do regresie - Lekcia 1\n",
+    "\n",
+    "#### Uvedenie do kontextu\n",
+    "\n",
+    "✅ Existuje mnoho typov regresných metód a výber tej správnej závisí od odpovede, ktorú hľadáte. Ak chcete predpovedať pravdepodobnú výšku osoby v určitom veku, použijete `lineárnu regresiu`, pretože hľadáte **číselnú hodnotu**. Ak vás zaujíma, či by určitý typ kuchyne mal byť považovaný za vegánsky alebo nie, hľadáte **priradenie kategórie**, a preto by ste použili `logistickú regresiu`. O logistickej regresii sa dozviete viac neskôr. Zamyslite sa nad niektorými otázkami, ktoré môžete klásť na základe údajov, a nad tým, ktorá z týchto metód by bola vhodnejšia.\n",
+    "\n",
+    "V tejto časti budete pracovať s [malým datasetom o cukrovke](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Predstavte si, že by ste chceli otestovať liečbu pre pacientov s cukrovkou. Modely strojového učenia by vám mohli pomôcť určiť, ktorí pacienti by na liečbu reagovali lepšie, na základe kombinácií premenných. Dokonca aj veľmi základný regresný model, keď je vizualizovaný, môže ukázať informácie o premenných, ktoré by vám mohli pomôcť zorganizovať vaše teoretické klinické štúdie.\n",
+    "\n",
+    "Tak teda, poďme sa pustiť do tejto úlohy!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/encouRage.jpg\"\n",
+    "   width=\"630\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n",
+    "\n",
+    "<!--![Ilustrácia od \\@allison_horst](../../../../../../2-Regression/1-Tools/images/encouRage.jpg)<br>Ilustrácia od @allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "LWNNzfqd6feZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Načítanie našej sady nástrojov\n",
+    "\n",
+    "Pre túto úlohu budeme potrebovať nasledujúce balíky:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [kolekcia balíkov v R](https://www.tidyverse.org/packages), ktorá je navrhnutá tak, aby robila dátovú vedu rýchlejšou, jednoduchšou a zábavnejšou!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je [rámec balíkov](https://www.tidymodels.org/packages/) určený na modelovanie a strojové učenie.\n",
+    "\n",
+    "Môžete ich nainštalovať pomocou:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+    "\n",
+    "Nasledujúci skript skontroluje, či máte nainštalované balíky potrebné na dokončenie tohto modulu, a v prípade, že niektoré chýbajú, ich pre vás nainštaluje.\n"
+   ],
+   "metadata": {
+    "id": "FIo2YhO26wI9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse, tidymodels)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "id": "cIA9fz9v7Dss",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Teraz načítajme tieto úžasné balíky a sprístupnime ich v našej aktuálnej R relácii. (Toto je len na ilustráciu, `pacman::p_load()` to už za vás urobil)\n"
+   ],
+   "metadata": {
+    "id": "gpO_P_6f9WUG"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# load the core Tidyverse packages\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# load the core Tidymodels packages\r\n",
+    "library(tidymodels)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NLMycgG-9ezO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Dataset o cukrovke\n",
+    "\n",
+    "V tomto cvičení si precvičíme naše schopnosti regresie tým, že budeme robiť predpovede na datasete o cukrovke. [Dataset o cukrovke](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) obsahuje `442 vzoriek` údajov týkajúcich sa cukrovky, s 10 prediktívnymi premennými: `vek`, `pohlavie`, `index telesnej hmotnosti`, `priemerný krvný tlak` a `šesť meraní krvného séra`, ako aj výstupnú premennú `y`: kvantitatívne meranie progresie ochorenia jeden rok po základnom vyšetrení.\n",
+    "\n",
+    "|Počet pozorovaní|442|\n",
+    "|----------------|:---|\n",
+    "|Počet prediktorov|Prvých 10 stĺpcov sú číselné prediktívne premenné|\n",
+    "|Výstup/Cieľ|Stĺpec 11 je kvantitatívne meranie progresie ochorenia jeden rok po základnom vyšetrení|\n",
+    "|Informácie o prediktoroch|- vek v rokoch\n",
+    "||- pohlavie\n",
+    "||- bmi index telesnej hmotnosti\n",
+    "||- bp priemerný krvný tlak\n",
+    "||- s1 tc, celkový cholesterol v sére\n",
+    "||- s2 ldl, lipoproteíny s nízkou hustotou\n",
+    "||- s3 hdl, lipoproteíny s vysokou hustotou\n",
+    "||- s4 tch, celkový cholesterol / HDL\n",
+    "||- s5 ltg, pravdepodobne logaritmus hladiny triglyceridov v sére\n",
+    "||- s6 glu, hladina cukru v krvi|\n",
+    "\n",
+    "> 🎓 Pamätajte, že ide o učenie s učiteľom (supervised learning), a potrebujeme cieľovú premennú nazvanú 'y'.\n",
+    "\n",
+    "Predtým, než budete môcť manipulovať s údajmi v R, musíte údaje importovať do pamäte R alebo vytvoriť spojenie s údajmi, ktoré R môže použiť na vzdialený prístup k údajom.\n",
+    "\n",
+    "> Balík [readr](https://readr.tidyverse.org/), ktorý je súčasťou Tidyverse, poskytuje rýchly a priateľský spôsob, ako načítať obdĺžnikové údaje do R.\n",
+    "\n",
+    "Teraz načítajme dataset o cukrovke z poskytnutého zdrojového URL: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+    "\n",
+    "Tiež vykonáme kontrolu údajov pomocou `glimpse()` a zobrazíme prvých 5 riadkov pomocou `slice()`.\n",
+    "\n",
+    "Predtým, než budeme pokračovať ďalej, predstavme si niečo, s čím sa často stretnete v kóde R 🥁🥁: operátor pipe `%>%`\n",
+    "\n",
+    "Operátor pipe (`%>%`) vykonáva operácie v logickej postupnosti tým, že posúva objekt ďalej do funkcie alebo výrazu. Môžete si operátor pipe predstaviť ako \"a potom\" vo vašom kóde.\n"
+   ],
+   "metadata": {
+    "id": "KM6iXLH996Cl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import the data set\r\n",
+    "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Get a glimpse and dimensions of the data\r\n",
+    "glimpse(diabetes)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Select the first 5 rows of the data\r\n",
+    "diabetes %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Z1geAMhM-bSP"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`glimpse()` nám ukazuje, že tieto dáta obsahujú 442 riadkov a 11 stĺpcov, pričom všetky stĺpce majú dátový typ `double`.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "> glimpse() a slice() sú funkcie v [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, ktorý je súčasťou Tidyverse, predstavuje gramatiku manipulácie s dátami a poskytuje konzistentnú sadu slovies, ktoré vám pomôžu riešiť najbežnejšie výzvy pri práci s dátami.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Teraz, keď máme dáta, zamerajme sa na jednu vlastnosť (`bmi`), ktorú použijeme ako cieľ pre toto cvičenie. To si bude vyžadovať výber požadovaných stĺpcov. Ako to môžeme urobiť?\n",
+    "\n",
+    "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) nám umožňuje *vybrať* (a prípadne premenovať) stĺpce v dátovom rámci.\n"
+   ],
+   "metadata": {
+    "id": "UwjVT1Hz-c3Z"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select predictor feature `bmi` and outcome `y`\r\n",
+    "diabetes_select <- diabetes %>% \r\n",
+    "  select(c(bmi, y))\r\n",
+    "\r\n",
+    "# Print the first 5 rows\r\n",
+    "diabetes_select %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RDY1oAKI-m80"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Tréningové a testovacie dáta\n",
+    "\n",
+    "V supervidovanom učení je bežnou praxou *rozdeliť* dáta na dva podmnožiny; (zvyčajne väčšiu) množinu, s ktorou sa model trénuje, a menšiu „rezervnú“ množinu, na ktorej sa overí, ako model fungoval.\n",
+    "\n",
+    "Teraz, keď máme dáta pripravené, môžeme zistiť, či nám stroj dokáže pomôcť určiť logické rozdelenie čísel v tejto dátovej sade. Môžeme použiť balík [rsample](https://tidymodels.github.io/rsample/), ktorý je súčasťou rámca Tidymodels, na vytvorenie objektu obsahujúceho informácie o *tom, ako* rozdeliť dáta, a potom ďalšie dve funkcie rsample na extrakciu vytvorených tréningových a testovacích množín:\n"
+   ],
+   "metadata": {
+    "id": "SDk668xK-tc3"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\r\n",
+    "# Split 67% of the data for training and the rest for tesing\r\n",
+    "diabetes_split <- diabetes_select %>% \r\n",
+    "  initial_split(prop = 0.67)\r\n",
+    "\r\n",
+    "# Extract the resulting train and test sets\r\n",
+    "diabetes_train <- training(diabetes_split)\r\n",
+    "diabetes_test <- testing(diabetes_split)\r\n",
+    "\r\n",
+    "# Print the first 3 rows of the training set\r\n",
+    "diabetes_train %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "EqtHx129-1h-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Natrénujte lineárny regresný model pomocou Tidymodels\n",
+    "\n",
+    "Teraz sme pripravení natrénovať náš model!\n",
+    "\n",
+    "V Tidymodels špecifikujete modely pomocou `parsnip()` tým, že definujete tri koncepty:\n",
+    "\n",
+    "-   **Typ** modelu rozlišuje medzi modelmi, ako sú lineárna regresia, logistická regresia, modely rozhodovacích stromov a podobne.\n",
+    "\n",
+    "-   **Režim** modelu zahŕňa bežné možnosti, ako sú regresia a klasifikácia; niektoré typy modelov podporujú obe možnosti, zatiaľ čo iné majú iba jeden režim.\n",
+    "\n",
+    "-   **Engine** modelu je výpočtový nástroj, ktorý sa použije na natrénovanie modelu. Často ide o balíky v R, ako napríklad **`\"lm\"`** alebo **`\"ranger\"`**\n",
+    "\n",
+    "Tieto informácie o modelovaní sú zachytené v špecifikácii modelu, takže si jednu vytvorme!\n"
+   ],
+   "metadata": {
+    "id": "sBOS-XhB-6v7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- \r\n",
+    "  # Type\r\n",
+    "  linear_reg() %>% \r\n",
+    "  # Engine\r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  # Mode\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the model specification\r\n",
+    "lm_spec"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "20OwEw20--t3"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Po tom, čo je model *špecifikovaný*, môže byť `odhadnutý` alebo `natrénovaný` pomocou funkcie [`fit()`](https://parsnip.tidymodels.org/reference/fit.html), zvyčajne s použitím vzorca a nejakých údajov.\n",
+    "\n",
+    "`y ~ .` znamená, že budeme prispôsobovať `y` ako predpovedanú hodnotu/cieľ, vysvetlenú všetkými prediktormi/vlastnosťami, teda `.` (v tomto prípade máme iba jeden prediktor: `bmi`).\n"
+   ],
+   "metadata": {
+    "id": "_oDHs89k_CJj"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>%\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Train a linear regression model\r\n",
+    "lm_mod <- lm_spec %>% \r\n",
+    "  fit(y ~ ., data = diabetes_train)\r\n",
+    "\r\n",
+    "# Print the model\r\n",
+    "lm_mod"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YlsHqd-q_GJQ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Z modelového výstupu môžeme vidieť koeficienty naučené počas tréningu. Predstavujú koeficienty priamky najlepšieho prispôsobenia, ktorá nám poskytuje najnižšiu celkovú chybu medzi skutočnou a predpovedanou premennou.\n",
+    "<br>\n",
+    "\n",
+    "## 5. Predikcia na testovacej množine\n",
+    "\n",
+    "Teraz, keď sme natrénovali model, môžeme ho použiť na predikciu progresie ochorenia y pre testovaciu množinu dát pomocou [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Toto sa použije na vykreslenie čiary medzi skupinami dát.\n"
+   ],
+   "metadata": {
+    "id": "kGZ22RQj_Olu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\r\n",
+    "predictions <- lm_mod %>% \r\n",
+    "  predict(new_data = diabetes_test)\r\n",
+    "\r\n",
+    "# Print out some of the predictions\r\n",
+    "predictions %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "nXHbY7M2_aao"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hurá! 💃🕺 Práve sme natrénovali model a použili ho na vytváranie predpovedí!\n",
+    "\n",
+    "Pri vytváraní predpovedí je v súlade s konvenciou tidymodels vždy vytvoriť tibble/dátový rámec s výsledkami, ktoré majú štandardizované názvy stĺpcov. To umožňuje jednoducho skombinovať pôvodné dáta a predpovede do použiteľného formátu pre ďalšie operácie, ako je napríklad vizualizácia.\n",
+    "\n",
+    "`dplyr::bind_cols()` efektívne spája viacero dátových rámcov podľa stĺpcov.\n"
+   ],
+   "metadata": {
+    "id": "R_JstwUY_bIs"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Combine the predictions and the original test set\r\n",
+    "results <- diabetes_test %>% \r\n",
+    "  bind_cols(predictions)\r\n",
+    "\r\n",
+    "\r\n",
+    "results %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RybsMJR7_iI8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 6. Zobrazenie výsledkov modelovania\n",
+    "\n",
+    "Teraz je čas vidieť to vizuálne 📈. Vytvoríme bodový graf všetkých hodnôt `y` a `bmi` z testovacej množiny a potom použijeme predpovede na nakreslenie čiary na najvhodnejšom mieste medzi skupinami údajov modelu.\n",
+    "\n",
+    "R má niekoľko systémov na tvorbu grafov, ale `ggplot2` je jedným z najelegantnejších a najvšestrannejších. Umožňuje vám vytvárať grafy **kombinovaním nezávislých komponentov**.\n"
+   ],
+   "metadata": {
+    "id": "XJbYbMZW_n_s"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plot\r\n",
+    "theme_set(theme_light())\r\n",
+    "# Create a scatter plot\r\n",
+    "results %>% \r\n",
+    "  ggplot(aes(x = bmi)) +\r\n",
+    "  # Add a scatter plot\r\n",
+    "  geom_point(aes(y = y), size = 1.6) +\r\n",
+    "  # Add a line plot\r\n",
+    "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R9tYp3VW_sTn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ Zamyslite sa trochu nad tým, čo sa tu deje. Priama čiara prechádza cez množstvo malých bodov údajov, ale čo presne robí? Vidíte, ako by ste mali byť schopní použiť túto čiaru na predpovedanie, kam by mal nový, nevidený údaj zapadnúť vo vzťahu k osi y grafu? Skúste slovami vyjadriť praktické využitie tohto modelu.\n",
+    "\n",
+    "Gratulujeme, vytvorili ste svoj prvý model lineárnej regresie, urobili ste s ním predpoveď a zobrazili ste ju v grafe!\n"
+   ],
+   "metadata": {
+    "id": "zrPtHIxx_tNI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/2-Regression/1-Tools/solution/notebook.ipynb b/translations/sk/2-Regression/1-Tools/solution/notebook.ipynb
new file mode 100644
index 000000000..aa7c5d8ca
--- /dev/null
+++ b/translations/sk/2-Regression/1-Tools/solution/notebook.ipynb
@@ -0,0 +1,673 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Importujte potrebné knižnice\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model, model_selection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Načítajte dataset diabetes, rozdelený na údaje `X` a vlastnosti `y`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "print(X.shape)\n",
+    "print(X[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vyberte iba jednu funkciu, na ktorú sa zameriate pri tomto cvičení\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selecting the 3rd feature\n",
+    "X = X[:, 2]\n",
+    "print(X.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 1)\n",
+      "[[ 0.06169621]\n",
+      " [-0.05147406]\n",
+      " [ 0.04445121]\n",
+      " [-0.01159501]\n",
+      " [-0.03638469]\n",
+      " [-0.04069594]\n",
+      " [-0.04716281]\n",
+      " [-0.00189471]\n",
+      " [ 0.06169621]\n",
+      " [ 0.03906215]\n",
+      " [-0.08380842]\n",
+      " [ 0.01750591]\n",
+      " [-0.02884001]\n",
+      " [-0.00189471]\n",
+      " [-0.02560657]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [ 0.01211685]\n",
+      " [-0.0105172 ]\n",
+      " [-0.01806189]\n",
+      " [-0.05686312]\n",
+      " [-0.02237314]\n",
+      " [-0.00405033]\n",
+      " [ 0.06061839]\n",
+      " [ 0.03582872]\n",
+      " [-0.01267283]\n",
+      " [-0.07734155]\n",
+      " [ 0.05954058]\n",
+      " [-0.02129532]\n",
+      " [-0.00620595]\n",
+      " [ 0.04445121]\n",
+      " [-0.06548562]\n",
+      " [ 0.12528712]\n",
+      " [-0.05039625]\n",
+      " [-0.06332999]\n",
+      " [-0.03099563]\n",
+      " [ 0.02289497]\n",
+      " [ 0.01103904]\n",
+      " [ 0.07139652]\n",
+      " [ 0.01427248]\n",
+      " [-0.00836158]\n",
+      " [-0.06764124]\n",
+      " [-0.0105172 ]\n",
+      " [-0.02345095]\n",
+      " [ 0.06816308]\n",
+      " [-0.03530688]\n",
+      " [-0.01159501]\n",
+      " [-0.0730303 ]\n",
+      " [-0.04177375]\n",
+      " [ 0.01427248]\n",
+      " [-0.00728377]\n",
+      " [ 0.0164281 ]\n",
+      " [-0.00943939]\n",
+      " [-0.01590626]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.04931844]\n",
+      " [ 0.04121778]\n",
+      " [-0.06332999]\n",
+      " [-0.06440781]\n",
+      " [-0.02560657]\n",
+      " [-0.00405033]\n",
+      " [ 0.00457217]\n",
+      " [-0.00728377]\n",
+      " [-0.0374625 ]\n",
+      " [-0.02560657]\n",
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+      " [-0.01482845]\n",
+      " [-0.02991782]\n",
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+      " [-0.06979687]\n",
+      " [ 0.03367309]\n",
+      " [-0.00405033]\n",
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+      " [ 0.00241654]\n",
+      " [-0.03099563]\n",
+      " [ 0.02828403]\n",
+      " [-0.03638469]\n",
+      " [-0.05794093]\n",
+      " [-0.0374625 ]\n",
+      " [ 0.01211685]\n",
+      " [-0.02237314]\n",
+      " [-0.03530688]\n",
+      " [ 0.00996123]\n",
+      " [-0.03961813]\n",
+      " [ 0.07139652]\n",
+      " [-0.07518593]\n",
+      " [-0.00620595]\n",
+      " [-0.04069594]\n",
+      " [-0.04824063]\n",
+      " [-0.02560657]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.00457217]\n",
+      " [-0.06440781]\n",
+      " [-0.01698407]\n",
+      " [-0.05794093]\n",
+      " [ 0.00996123]\n",
+      " [ 0.08864151]\n",
+      " [-0.00512814]\n",
+      " [-0.06440781]\n",
+      " [ 0.01750591]\n",
+      " [-0.04500719]\n",
+      " [ 0.02828403]\n",
+      " [ 0.04121778]\n",
+      " [ 0.06492964]\n",
+      " [-0.03207344]\n",
+      " [-0.07626374]\n",
+      " [ 0.04984027]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03207344]\n",
+      " [ 0.00457217]\n",
+      " [ 0.02073935]\n",
+      " [ 0.01427248]\n",
+      " [ 0.11019775]\n",
+      " [ 0.00133873]\n",
+      " [ 0.05846277]\n",
+      " [-0.02129532]\n",
+      " [-0.0105172 ]\n",
+      " [-0.04716281]\n",
+      " [ 0.00457217]\n",
+      " [ 0.01750591]\n",
+      " [ 0.08109682]\n",
+      " [ 0.0347509 ]\n",
+      " [ 0.02397278]\n",
+      " [-0.00836158]\n",
+      " [-0.06117437]\n",
+      " [-0.00189471]\n",
+      " [-0.06225218]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.09618619]\n",
+      " [-0.06979687]\n",
+      " [-0.02129532]\n",
+      " [-0.05362969]\n",
+      " [ 0.0433734 ]\n",
+      " [ 0.05630715]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04984027]\n",
+      " [ 0.11127556]\n",
+      " [ 0.06169621]\n",
+      " [ 0.01427248]\n",
+      " [ 0.04768465]\n",
+      " [ 0.01211685]\n",
+      " [ 0.00564998]\n",
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+      " [ 0.12852056]\n",
+      " [ 0.05954058]\n",
+      " [ 0.09295276]\n",
+      " [ 0.01535029]\n",
+      " [-0.00512814]\n",
+      " [ 0.0703187 ]\n",
+      " [-0.00405033]\n",
+      " [-0.00081689]\n",
+      " [-0.04392938]\n",
+      " [ 0.02073935]\n",
+      " [ 0.06061839]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03315126]\n",
+      " [-0.06548562]\n",
+      " [ 0.0433734 ]\n",
+      " [-0.06225218]\n",
+      " [ 0.06385183]\n",
+      " [ 0.03043966]\n",
+      " [ 0.07247433]\n",
+      " [-0.0191397 ]\n",
+      " [-0.06656343]\n",
+      " [-0.06009656]\n",
+      " [ 0.06924089]\n",
+      " [ 0.05954058]\n",
+      " [-0.02668438]\n",
+      " [-0.02021751]\n",
+      " [-0.046085  ]\n",
+      " [ 0.07139652]\n",
+      " [-0.07949718]\n",
+      " [ 0.00996123]\n",
+      " [-0.03854032]\n",
+      " [ 0.01966154]\n",
+      " [ 0.02720622]\n",
+      " [-0.00836158]\n",
+      " [-0.01590626]\n",
+      " [ 0.00457217]\n",
+      " [-0.04285156]\n",
+      " [ 0.00564998]\n",
+      " [-0.03530688]\n",
+      " [ 0.02397278]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00297252]\n",
+      " [-0.06656343]\n",
+      " [-0.01267283]\n",
+      " [-0.04177375]\n",
+      " [-0.03099563]\n",
+      " [-0.00512814]\n",
+      " [-0.05901875]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00349435]\n",
+      " [ 0.05415152]\n",
+      " [-0.04500719]\n",
+      " [-0.05794093]\n",
+      " [-0.05578531]\n",
+      " [ 0.00133873]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00672779]\n",
+      " [ 0.04660684]\n",
+      " [ 0.02612841]\n",
+      " [ 0.04552903]\n",
+      " [ 0.04013997]\n",
+      " [-0.01806189]\n",
+      " [ 0.01427248]\n",
+      " [ 0.03690653]\n",
+      " [ 0.00349435]\n",
+      " [-0.07087468]\n",
+      " [-0.03315126]\n",
+      " [ 0.09403057]\n",
+      " [ 0.03582872]\n",
+      " [ 0.03151747]\n",
+      " [-0.06548562]\n",
+      " [-0.04177375]\n",
+      " [-0.03961813]\n",
+      " [-0.03854032]\n",
+      " [-0.02560657]\n",
+      " [-0.02345095]\n",
+      " [-0.06656343]\n",
+      " [ 0.03259528]\n",
+      " [-0.046085  ]\n",
+      " [-0.02991782]\n",
+      " [-0.01267283]\n",
+      " [-0.01590626]\n",
+      " [ 0.07139652]\n",
+      " [-0.03099563]\n",
+      " [ 0.00026092]\n",
+      " [ 0.03690653]\n",
+      " [ 0.03906215]\n",
+      " [-0.01482845]\n",
+      " [ 0.00672779]\n",
+      " [-0.06871905]\n",
+      " [-0.00943939]\n",
+      " [ 0.01966154]\n",
+      " [ 0.07462995]\n",
+      " [-0.00836158]\n",
+      " [-0.02345095]\n",
+      " [-0.046085  ]\n",
+      " [ 0.05415152]\n",
+      " [-0.03530688]\n",
+      " [-0.03207344]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04768465]\n",
+      " [ 0.06061839]\n",
+      " [ 0.05630715]\n",
+      " [ 0.09834182]\n",
+      " [ 0.05954058]\n",
+      " [ 0.03367309]\n",
+      " [ 0.05630715]\n",
+      " [-0.06548562]\n",
+      " [ 0.16085492]\n",
+      " [-0.05578531]\n",
+      " [-0.02452876]\n",
+      " [-0.03638469]\n",
+      " [-0.00836158]\n",
+      " [-0.04177375]\n",
+      " [ 0.12744274]\n",
+      " [-0.07734155]\n",
+      " [ 0.02828403]\n",
+      " [-0.02560657]\n",
+      " [-0.06225218]\n",
+      " [-0.00081689]\n",
+      " [ 0.08864151]\n",
+      " [-0.03207344]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00888341]\n",
+      " [ 0.00672779]\n",
+      " [-0.02021751]\n",
+      " [-0.02452876]\n",
+      " [-0.01159501]\n",
+      " [ 0.02612841]\n",
+      " [-0.05901875]\n",
+      " [-0.03638469]\n",
+      " [-0.02452876]\n",
+      " [ 0.01858372]\n",
+      " [-0.0902753 ]\n",
+      " [-0.00512814]\n",
+      " [-0.05255187]\n",
+      " [-0.02237314]\n",
+      " [-0.02021751]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00620595]\n",
+      " [-0.01698407]\n",
+      " [ 0.05522933]\n",
+      " [ 0.07678558]\n",
+      " [ 0.01858372]\n",
+      " [-0.02237314]\n",
+      " [ 0.09295276]\n",
+      " [-0.03099563]\n",
+      " [ 0.03906215]\n",
+      " [-0.06117437]\n",
+      " [-0.00836158]\n",
+      " [-0.0374625 ]\n",
+      " [-0.01375064]\n",
+      " [ 0.07355214]\n",
+      " [-0.02452876]\n",
+      " [ 0.03367309]\n",
+      " [ 0.0347509 ]\n",
+      " [-0.03854032]\n",
+      " [-0.03961813]\n",
+      " [-0.00189471]\n",
+      " [-0.03099563]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06492964]\n",
+      " [ 0.04013997]\n",
+      " [-0.02345095]\n",
+      " [ 0.05307371]\n",
+      " [ 0.04013997]\n",
+      " [-0.02021751]\n",
+      " [ 0.01427248]\n",
+      " [-0.03422907]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00457217]\n",
+      " [ 0.03043966]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.06169621]\n",
+      " [-0.00728377]\n",
+      " [ 0.00564998]\n",
+      " [ 0.05415152]\n",
+      " [-0.00836158]\n",
+      " [ 0.114509  ]\n",
+      " [ 0.06708527]\n",
+      " [-0.05578531]\n",
+      " [ 0.03043966]\n",
+      " [-0.02560657]\n",
+      " [ 0.10480869]\n",
+      " [-0.00620595]\n",
+      " [-0.04716281]\n",
+      " [-0.04824063]\n",
+      " [ 0.08540807]\n",
+      " [-0.01267283]\n",
+      " [-0.03315126]\n",
+      " [-0.00728377]\n",
+      " [-0.01375064]\n",
+      " [ 0.05954058]\n",
+      " [ 0.02181716]\n",
+      " [ 0.01858372]\n",
+      " [-0.01159501]\n",
+      " [-0.00297252]\n",
+      " [ 0.01750591]\n",
+      " [-0.02991782]\n",
+      " [-0.02021751]\n",
+      " [-0.05794093]\n",
+      " [ 0.06061839]\n",
+      " [-0.04069594]\n",
+      " [-0.07195249]\n",
+      " [-0.05578531]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03315126]\n",
+      " [ 0.04984027]\n",
+      " [-0.08488624]\n",
+      " [ 0.00564998]\n",
+      " [ 0.02073935]\n",
+      " [-0.00728377]\n",
+      " [ 0.10480869]\n",
+      " [-0.02452876]\n",
+      " [-0.00620595]\n",
+      " [-0.03854032]\n",
+      " [ 0.13714305]\n",
+      " [ 0.17055523]\n",
+      " [ 0.00241654]\n",
+      " [ 0.03798434]\n",
+      " [-0.05794093]\n",
+      " [-0.00943939]\n",
+      " [-0.02345095]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03422907]\n",
+      " [-0.00297252]\n",
+      " [ 0.06816308]\n",
+      " [ 0.00996123]\n",
+      " [ 0.00241654]\n",
+      " [-0.03854032]\n",
+      " [ 0.02612841]\n",
+      " [-0.08919748]\n",
+      " [ 0.06061839]\n",
+      " [-0.02884001]\n",
+      " [-0.02991782]\n",
+      " [-0.0191397 ]\n",
+      " [-0.04069594]\n",
+      " [ 0.01535029]\n",
+      " [-0.02452876]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06924089]\n",
+      " [-0.06979687]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [ 0.01858372]\n",
+      " [ 0.00133873]\n",
+      " [-0.03099563]\n",
+      " [-0.00405033]\n",
+      " [ 0.01535029]\n",
+      " [ 0.02289497]\n",
+      " [ 0.04552903]\n",
+      " [-0.04500719]\n",
+      " [-0.03315126]\n",
+      " [ 0.097264  ]\n",
+      " [ 0.05415152]\n",
+      " [ 0.12313149]\n",
+      " [-0.08057499]\n",
+      " [ 0.09295276]\n",
+      " [-0.05039625]\n",
+      " [-0.01159501]\n",
+      " [-0.0277622 ]\n",
+      " [ 0.05846277]\n",
+      " [ 0.08540807]\n",
+      " [-0.00081689]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00888341]\n",
+      " [ 0.08001901]\n",
+      " [ 0.07139652]\n",
+      " [-0.02452876]\n",
+      " [-0.0547075 ]\n",
+      " [-0.03638469]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.07786339]\n",
+      " [-0.03961813]\n",
+      " [ 0.01103904]\n",
+      " [-0.04069594]\n",
+      " [-0.03422907]\n",
+      " [ 0.00564998]\n",
+      " [ 0.08864151]\n",
+      " [-0.03315126]\n",
+      " [-0.05686312]\n",
+      " [-0.03099563]\n",
+      " [ 0.05522933]\n",
+      " [-0.06009656]\n",
+      " [ 0.00133873]\n",
+      " [-0.02345095]\n",
+      " [-0.07410811]\n",
+      " [ 0.01966154]\n",
+      " [-0.01590626]\n",
+      " [-0.01590626]\n",
+      " [ 0.03906215]\n",
+      " [-0.0730303 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Reshaping to get a 2D array\n",
+    "X = X.reshape(-1, 1)\n",
+    "print(X.shape)\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vyberte model a natrénujte ho na tréningových údajoch\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Použite testovacie údaje na predpovedanie línie\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zobrazte výsledky v grafe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test,  color='black')\n",
+    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nenesieme zodpovednosť za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "16ff1a974f6e4348e869e4a7d366b86a",
+   "translation_date": "2025-09-06T13:38:26+00:00",
+   "source_file": "2-Regression/1-Tools/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sk/2-Regression/2-Data/notebook.ipynb b/translations/sk/2-Regression/2-Data/notebook.ipynb
new file mode 100644
index 000000000..53729346b
--- /dev/null
+++ b/translations/sk/2-Regression/2-Data/notebook.ipynb
@@ -0,0 +1,46 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  },
+  "coopTranslator": {
+   "original_hash": "1b2ab303ac6c604a34c6ca7a49077fc7",
+   "translation_date": "2025-09-06T13:45:49+00:00",
+   "source_file": "2-Regression/2-Data/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/sk/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
new file mode 100644
index 000000000..a3110e2cc
--- /dev/null
+++ b/translations/sk/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -0,0 +1,685 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_2-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "f3c335f9940cfd76528b3ef918b9b342",
+   "translation_date": "2025-09-06T13:47:59+00:00",
+   "source_file": "2-Regression/2-Data/solution/R/lesson_2-R.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Vytvorenie regresného modelu: príprava a vizualizácia dát\n",
+    "\n",
+    "## **Lineárna regresia pre tekvice - Lekcia 2**\n",
+    "#### Úvod\n",
+    "\n",
+    "Teraz, keď máte pripravené nástroje na budovanie modelov strojového učenia pomocou Tidymodels a Tidyverse, ste pripravení začať klásť otázky o svojich dátach. Pri práci s dátami a aplikovaní riešení strojového učenia je veľmi dôležité vedieť, ako položiť správnu otázku, aby ste mohli plne využiť potenciál svojho datasetu.\n",
+    "\n",
+    "V tejto lekcii sa naučíte:\n",
+    "\n",
+    "-   Ako pripraviť svoje dáta na budovanie modelov.\n",
+    "\n",
+    "-   Ako používať `ggplot2` na vizualizáciu dát.\n",
+    "\n",
+    "Otázka, na ktorú potrebujete odpoveď, určí, aký typ algoritmov strojového učenia budete používať. Kvalita odpovede, ktorú dostanete, bude výrazne závisieť od povahy vašich dát.\n",
+    "\n",
+    "Pozrime sa na to prostredníctvom praktického cvičenia.\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/unruly_data.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Ilustrácia od \\@allison_horst](../../../../../../2-Regression/2-Data/images/unruly_data.jpg)<br>Ilustrácia od \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "Pg5aexcOPqAZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Importovanie údajov o tekviciach a privolanie Tidyverse\n",
+    "\n",
+    "Na spracovanie tejto lekcie budeme potrebovať nasledujúce balíky:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [kolekcia balíkov pre R](https://www.tidyverse.org/packages), ktorá je navrhnutá tak, aby robila dátovú vedu rýchlejšou, jednoduchšou a zábavnejšou!\n",
+    "\n",
+    "Môžete ich nainštalovať pomocou:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\"))`\n",
+    "\n",
+    "Nasledujúci skript skontroluje, či máte nainštalované balíky potrebné na dokončenie tohto modulu, a v prípade, že niektoré chýbajú, ich pre vás nainštaluje.\n"
+   ],
+   "metadata": {
+    "id": "dc5WhyVdXAjR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "GqPYUZgfXOBt"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Teraz si spustime niektoré balíčky a načítajme [dáta](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) poskytnuté pre túto lekciu!\n"
+   ],
+   "metadata": {
+    "id": "kvjDTPDSXRr2"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n =50)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VMri-t2zXqgD"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Rýchly pohľad pomocou `glimpse()` okamžite ukáže, že existujú prázdne hodnoty a mix reťazcov (`chr`) a číselných údajov (`dbl`). `Date` je typu znakový reťazec a je tu aj zvláštny stĺpec s názvom `Package`, kde sú údaje zmiešané medzi `sacks`, `bins` a inými hodnotami. Údaje sú, úprimne povedané, trochu chaotické 😤.\n",
+    "\n",
+    "V skutočnosti nie je veľmi bežné dostať dataset, ktorý je úplne pripravený na použitie na vytvorenie ML modelu hneď po rozbalení. Ale nebojte sa, v tejto lekcii sa naučíte, ako pripraviť surový dataset pomocou štandardných knižníc v R 🧑‍🔧. Taktiež sa naučíte rôzne techniky na vizualizáciu údajov. 📈📊\n",
+    "<br>\n",
+    "\n",
+    "> Pripomenutie: Operátor pipe (`%>%`) vykonáva operácie v logickej postupnosti tým, že posúva objekt ďalej do funkcie alebo výrazu. Môžete si operátor pipe predstaviť ako výraz „a potom“ vo vašom kóde.\n"
+   ],
+   "metadata": {
+    "id": "REWcIv9yX29v"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Kontrola chýbajúcich údajov\n",
+    "\n",
+    "Jedným z najbežnejších problémov, s ktorými sa dátoví analytici musia vysporiadať, sú neúplné alebo chýbajúce údaje. R reprezentuje chýbajúce alebo neznáme hodnoty pomocou špeciálnej hodnoty: `NA` (Not Available).\n",
+    "\n",
+    "Ako teda zistíme, že dátový rámec obsahuje chýbajúce hodnoty?\n",
+    "<br>\n",
+    "-   Jedným z priamych spôsobov je použitie základnej funkcie R `anyNA`, ktorá vracia logické objekty `TRUE` alebo `FALSE`.\n"
+   ],
+   "metadata": {
+    "id": "Zxfb3AM5YbUe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  anyNA()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "G--DQutAYltj"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdá sa, že niektoré údaje chýbajú! To je dobré miesto, kde začať.\n",
+    "\n",
+    "-   Ďalším spôsobom by bolo použiť funkciu `is.na()`, ktorá označuje, ktoré jednotlivé prvky stĺpcov chýbajú pomocou logickej hodnoty `TRUE`.\n"
+   ],
+   "metadata": {
+    "id": "mU-7-SB6YokF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "W-DxDOR4YxSW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Dobre, práca je hotová, ale s takým veľkým dátovým rámcom by bolo neefektívne a prakticky nemožné kontrolovať všetky riadky a stĺpce jednotlivo😴.\n",
+    "\n",
+    "-   Intuitívnejší spôsob by bol vypočítať súčet chýbajúcich hodnôt pre každý stĺpec:\n"
+   ],
+   "metadata": {
+    "id": "xUWxipKYY0o7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  colSums()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZRBWV6P9ZArL"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Oveľa lepšie! Chýbajú niektoré údaje, ale možno to nebude mať vplyv na danú úlohu. Uvidíme, aké ďalšie analýzy prinesú výsledky.\n",
+    "\n",
+    "> Okrem skvelých balíkov a funkcií má R veľmi dobrú dokumentáciu. Napríklad použite `help(colSums)` alebo `?colSums`, aby ste sa dozvedeli viac o funkcii.\n"
+   ],
+   "metadata": {
+    "id": "9gv-crB6ZD1Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Dplyr: Gramatika manipulácie s dátami\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_wrangling.png\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Ilustrácia od \\@allison_horst](../../../../../../2-Regression/2-Data/images/dplyr_wrangling.png)<br/>Ilustrácia od \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "o4jLY5-VZO2C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`dplyr`](https://dplyr.tidyverse.org/), balík v Tidyverse, je gramatika manipulácie s dátami, ktorá poskytuje konzistentnú sadu slovies, ktoré vám pomôžu vyriešiť najbežnejšie výzvy pri manipulácii s dátami. V tejto sekcii preskúmame niektoré zo slovies dplyr!\n"
+   ],
+   "metadata": {
+    "id": "i5o33MQBZWWw"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::select()\n",
+    "\n",
+    "`select()` je funkcia v balíku `dplyr`, ktorá vám pomáha vybrať stĺpce na ponechanie alebo vylúčenie.\n",
+    "\n",
+    "Aby ste uľahčili prácu s vaším dátovým rámcom, odstráňte niekoľko jeho stĺpcov pomocou `select()`, pričom ponechajte iba tie, ktoré potrebujete.\n",
+    "\n",
+    "Napríklad v tomto cvičení bude naša analýza zahŕňať stĺpce `Package`, `Low Price`, `High Price` a `Date`. Vyberme tieto stĺpce.\n"
+   ],
+   "metadata": {
+    "id": "x3VGMAGBZiUr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(Package, `Low Price`, `High Price`, Date)\n",
+    "\n",
+    "\n",
+    "# Print data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "F_FgxQnVZnM0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::mutate()\n",
+    "\n",
+    "`mutate()` je funkcia v balíku `dplyr`, ktorá vám umožňuje vytvárať alebo upravovať stĺpce, pričom zachováva existujúce stĺpce.\n",
+    "\n",
+    "Všeobecná štruktúra funkcie mutate je:\n",
+    "\n",
+    "`data %>%   mutate(new_column_name = what_it_contains)`\n",
+    "\n",
+    "Poďme si vyskúšať `mutate` na stĺpci `Date` vykonaním nasledujúcich operácií:\n",
+    "\n",
+    "1.  Konvertovať dátumy (aktuálne typu character) na formát mesiaca (ide o americké dátumy, takže formát je `MM/DD/YYYY`).\n",
+    "\n",
+    "2.  Extrahovať mesiac z dátumov do nového stĺpca.\n",
+    "\n",
+    "V R balík [lubridate](https://lubridate.tidyverse.org/) uľahčuje prácu s dátumovo-časovými údajmi. Takže použijeme `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` a pozrieme sa, ako dosiahnuť vyššie uvedené ciele. Stĺpec Date môžeme vynechať, pretože ho už nebudeme potrebovať v ďalších operáciách.\n"
+   ],
+   "metadata": {
+    "id": "2KKo0Ed9Z1VB"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load lubridate\n",
+    "library(lubridate)\n",
+    "\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  # Convert the Date column to a date object\n",
+    "  mutate(Date = mdy(Date)) %>% \n",
+    "  # Extract month from Date\n",
+    "  mutate(Month = month(Date)) %>% \n",
+    "  # Drop Date column\n",
+    "  select(-Date)\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5joszIVSZ6xe"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hurá! 🤩\n",
+    "\n",
+    "Ďalej vytvorme nový stĺpec `Price`, ktorý bude predstavovať priemernú cenu tekvice. Teraz vypočítajme priemer stĺpcov `Low Price` a `High Price`, aby sme naplnili nový stĺpec Price.  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "nIgLjNMCZ-6Y"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new column Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Zo0BsqqtaJw2"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Yeees!💪\n",
+    "\n",
+    "„Ale počkaj!“, poviete si po rýchlom prehliadnutí celého dátového súboru pomocou `View(pumpkins)`, „Tu je niečo zvláštne!“🤔\n",
+    "\n",
+    "Ak sa pozriete na stĺpec `Package`, tekvice sa predávajú v rôznych konfiguráciách. Niektoré sa predávajú v mierach `1 1/9 bushel`, niektoré v mierach `1/2 bushel`, niektoré na kusy, niektoré na váhu a niektoré vo veľkých škatuliach s rôznymi šírkami.\n",
+    "\n",
+    "Poďme si to overiť:\n"
+   ],
+   "metadata": {
+    "id": "p77WZr-9aQAR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Verify the distinct observations in Package column\n",
+    "pumpkins %>% \n",
+    "  distinct(Package)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "XISGfh0IaUy6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Úžasné!👏\n",
+    "\n",
+    "Zdá sa, že tekvice je veľmi ťažké vážiť konzistentne, takže ich vyfiltrujme tak, že vyberieme iba tekvice, ktoré obsahujú reťazec *bushel* v stĺpci `Package`, a uložme to do nového dátového rámca `new_pumpkins`.\n"
+   ],
+   "metadata": {
+    "id": "7sMjiVujaZxY"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::filter() a stringr::str_detect()\n",
+    "\n",
+    "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): vytvára podmnožinu údajov obsahujúcu iba **riadky**, ktoré spĺňajú vaše podmienky, v tomto prípade tekvice so slovom *bushel* v stĺpci `Package`.\n",
+    "\n",
+    "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): zisťuje prítomnosť alebo neprítomnosť vzoru v reťazci.\n",
+    "\n",
+    "Balík [`stringr`](https://github.com/tidyverse/stringr) poskytuje jednoduché funkcie pre bežné operácie s reťazcami.\n"
+   ],
+   "metadata": {
+    "id": "L8Qfcs92ageF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Retain only pumpkins with \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "       filter(str_detect(Package, \"bushel\"))\n",
+    "\n",
+    "# Get the dimensions of the new data\n",
+    "dim(new_pumpkins)\n",
+    "\n",
+    "# View a few rows of the new data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "hy_SGYREampd"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Môžete vidieť, že sme zúžili výber na približne 415 riadkov údajov obsahujúcich tekvice na veľké množstvo.🤩  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "VrDwF031avlR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::case_when()\n",
+    "\n",
+    "**Ale počkajte! Ešte je tu jedna vec, ktorú treba urobiť**\n",
+    "\n",
+    "Všimli ste si, že množstvo v bušloch sa líši v jednotlivých riadkoch? Musíte normalizovať ceny tak, aby ste zobrazili cenu za bušel, nie za 1 1/9 alebo 1/2 bušela. Je čas na trochu matematiky, aby ste to štandardizovali.\n",
+    "\n",
+    "Použijeme funkciu [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) na *mutáciu* stĺpca Price v závislosti od určitých podmienok. `case_when` umožňuje vektorovo spracovať viacero `if_else()` vyjadrení.\n"
+   ],
+   "metadata": {
+    "id": "mLpw2jH4a0tx"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(Price = case_when(\n",
+    "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+    "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+    "    TRUE ~ Price))\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 30)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "P68kLVQmbM6I"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Teraz môžeme analyzovať cenu za jednotku na základe ich merania v bušloch. Celá táto štúdia o bušloch tekvíc však ukazuje, aké veľmi `dôležité` je `pochopiť povahu vašich údajov`!\n",
+    "\n",
+    "> ✅ Podľa [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308) hmotnosť bušľa závisí od typu produktu, pretože ide o objemové meranie. \"Bušel paradajok, napríklad, by mal vážiť 56 libier... Listy a zelené rastliny zaberajú viac miesta s menšou hmotnosťou, takže bušel špenátu váži iba 20 libier.\" Je to všetko dosť komplikované! Nezaoberajme sa konverziou bušlov na libry, namiesto toho stanovme cenu za bušel. Celá táto štúdia o bušloch tekvíc však ukazuje, aké veľmi dôležité je pochopiť povahu vašich údajov!\n",
+    ">\n",
+    "> ✅ Všimli ste si, že tekvice predávané na pol bušľa sú veľmi drahé? Dokážete zistiť prečo? Tip: malé tekvice sú oveľa drahšie ako veľké, pravdepodobne preto, že ich je oveľa viac na bušel, vzhľadom na nevyužitý priestor, ktorý zaberá jedna veľká dutá tekvica na koláč.\n"
+   ],
+   "metadata": {
+    "id": "pS2GNPagbSdb"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "DÔLEŽITÉ PRAVIDLÁ:  \n",
+    "1. NEPRIDÁVAJTE '''markdown alebo akékoľvek iné značky okolo prekladu  \n",
+    "2. Uistite sa, že preklad neznie príliš doslovne  \n",
+    "3. Preložte aj komentáre  \n",
+    "4. Tento súbor je napísaný vo formáte Markdown - nezaobchádzajte s ním ako s XML alebo HTML  \n",
+    "5. Neprekladajte:  \n",
+    "   - [!NOTE], [!WARNING], [!TIP], [!IMPORTANT], [!CAUTION]  \n",
+    "   - Názvy premenných, funkcií, tried  \n",
+    "   - Zástupné symboly ako @@INLINE_CODE_x@@ alebo @@CODE_BLOCK_x@@  \n",
+    "   - URL alebo cesty  \n",
+    "6. Zachovajte všetko pôvodné formátovanie Markdownu  \n",
+    "7. Vráťte IBA preložený obsah bez akýchkoľvek dodatočných značiek alebo formátovania  \n",
+    "\n",
+    "A teraz nakoniec, len pre dobrodružstvo 💁‍♀️, presuňme stĺpec Month na prvú pozíciu, teda `pred` stĺpec `Package`.  \n",
+    "\n",
+    "Na zmenu pozícií stĺpcov sa používa `dplyr::relocate()`.  \n"
+   ],
+   "metadata": {
+    "id": "qql1SowfbdnP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new data frame new_pumpkins\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(Month, .before = Package)\n",
+    "\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "JJ1x6kw8bixF"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Skvelá práca!👌 Teraz máte čistý, uprataný dataset, na ktorom môžete postaviť svoj nový regresný model!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "y8TJ0Za_bn5Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Vizualizácia dát s ggplot2\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/data-visualization.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografika od Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografika od Dasani Madipalli](../../../../../../2-Regression/2-Data/images/data-visualization.png){width=\"600\"}-->\n",
+    "\n",
+    "Existuje *múdre* príslovie, ktoré hovorí:\n",
+    "\n",
+    "> \"Jednoduchý graf priniesol analytikovi dát viac informácií než akékoľvek iné zariadenie.\" --- John Tukey\n",
+    "\n",
+    "Úlohou dátového vedca je ukázať kvalitu a charakter dát, s ktorými pracuje. Na tento účel často vytvára zaujímavé vizualizácie, ako sú grafy, diagramy a tabuľky, ktoré zobrazujú rôzne aspekty dát. Týmto spôsobom dokáže vizuálne ukázať vzťahy a medzery, ktoré by inak bolo ťažké odhaliť.\n",
+    "\n",
+    "Vizualizácie môžu tiež pomôcť určiť, ktorá technika strojového učenia je pre dané dáta najvhodnejšia. Napríklad bodový graf, ktorý sa zdá sledovať líniu, naznačuje, že dáta sú vhodným kandidátom na cvičenie s lineárnou regresiou.\n",
+    "\n",
+    "R ponúka niekoľko systémov na tvorbu grafov, ale [`ggplot2`](https://ggplot2.tidyverse.org/index.html) je jedným z najelegantnejších a najuniverzálnejších. `ggplot2` vám umožňuje vytvárať grafy **kombinovaním nezávislých komponentov**.\n",
+    "\n",
+    "Začnime jednoduchým bodovým grafom pre stĺpce Price a Month.\n",
+    "\n",
+    "V tomto prípade začneme s [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), poskytneme dataset a estetické mapovanie (pomocou [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)), a potom pridáme vrstvy (ako [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) pre bodové grafy.\n"
+   ],
+   "metadata": {
+    "id": "mYSH6-EtbvNa"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plots\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+    "p + geom_point()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "g2YjnGeOcLo4"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Je to užitočný graf 🤷? Prekvapilo vás na ňom niečo?\n",
+    "\n",
+    "Nie je obzvlášť užitočný, pretože iba zobrazuje vaše údaje ako rozptyl bodov v danom mesiaci.  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "Ml7SDCLQcPvE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Ako to urobiť užitočným?**\n",
+    "\n",
+    "Aby grafy zobrazovali užitočné údaje, zvyčajne je potrebné údaje nejako zoskupiť. Napríklad v našom prípade by zistenie priemernej ceny tekvíc za každý mesiac poskytlo viac informácií o základných vzoroch v našich údajoch. To nás privádza k ďalšiemu preletu nad **dplyr**:\n",
+    "\n",
+    "#### `dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "Zoskupenú agregáciu v R je možné jednoducho vypočítať pomocou\n",
+    "\n",
+    "`dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "-   `dplyr::group_by()` mení jednotku analýzy z celého datasetu na jednotlivé skupiny, napríklad podľa mesiacov.\n",
+    "\n",
+    "-   `dplyr::summarize()` vytvára nový dátový rámec s jedným stĺpcom pre každú zoskupovaciu premennú a jedným stĺpcom pre každú štatistiku, ktorú ste špecifikovali.\n",
+    "\n",
+    "Napríklad môžeme použiť `dplyr::group_by() %>% summarize()` na zoskupenie tekvíc do skupín na základe stĺpca **Month** a potom vypočítať **priemernú cenu** za každý mesiac.\n"
+   ],
+   "metadata": {
+    "id": "jMakvJZIcVkh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "6kVSUa2Bcilf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Stručne!✨\n",
+    "\n",
+    "Kategorické prvky, ako sú mesiace, sú lepšie znázornené pomocou stĺpcového grafu 📊. Vrstvy zodpovedné za stĺpcové grafy sú `geom_bar()` a `geom_col()`. Viac informácií nájdete v `?geom_bar`.\n",
+    "\n",
+    "Poďme si jeden vytvoriť!\n"
+   ],
+   "metadata": {
+    "id": "Kds48GUBcj3W"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price)) %>% \r\n",
+    "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"Pumpkin Price\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VNbU1S3BcrxO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Toto je užitočnejšia vizualizácia dát! Zdá sa, že najvyššia cena za tekvice sa vyskytuje v septembri a októbri. Zodpovedá to vašim očakávaniam? Prečo áno alebo prečo nie?\n",
+    "\n",
+    "Gratulujeme k dokončeniu druhej lekcie 👏! Pripravili ste svoje dáta na vytváranie modelov a potom ste odhalili ďalšie poznatky pomocou vizualizácií!\n"
+   ],
+   "metadata": {
+    "id": "zDm0VOzzcuzR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/2-Regression/2-Data/solution/notebook.ipynb b/translations/sk/2-Regression/2-Data/solution/notebook.ipynb
new file mode 100644
index 000000000..b4cd214e7
--- /dev/null
+++ b/translations/sk/2-Regression/2-Data/solution/notebook.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/8/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    City Name Type               Package   Variety Sub Variety  Grade  \\\n",
+       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "\n",
+       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
+       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
+       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "\n",
+       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
+       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "\n",
+       "    Unnamed: 25  \n",
+       "70          NaN  \n",
+       "71          NaN  \n",
+       "72          NaN  \n",
+       "73          NaN  \n",
+       "74          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Month               Package  Low Price  High Price  Price\n",
+      "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
+      "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "...     ...                   ...        ...         ...    ...\n",
+      "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
+      "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
+      "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
+      "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Get an average between low and high price for the base pumpkin price\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "# Convert the date to its month only\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "\n",
+    "# Create a new dataframe with this basic data\n",
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
+    "\n",
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
+    "\n",
+    "print(new_pumpkins)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "price = new_pumpkins.Price\n",
+    "month = new_pumpkins.Month\n",
+    "plt.scatter(price, month)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "95726f0b8283628d5356a4f8eb8b4b76",
+   "translation_date": "2025-09-06T13:46:11+00:00",
+   "source_file": "2-Regression/2-Data/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sk/2-Regression/3-Linear/notebook.ipynb b/translations/sk/2-Regression/3-Linear/notebook.ipynb
new file mode 100644
index 000000000..ee1028c0b
--- /dev/null
+++ b/translations/sk/2-Regression/3-Linear/notebook.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ceny tekvíc\n",
+    "\n",
+    "Načítajte potrebné knižnice a dataset. Preveďte údaje do dátového rámca obsahujúceho podmnožinu údajov:\n",
+    "\n",
+    "- Zahrňte iba tekvice ocenené na základe bušlov\n",
+    "- Preveďte dátum na mesiac\n",
+    "- Vypočítajte cenu ako priemer vysokých a nízkych cien\n",
+    "- Preveďte cenu tak, aby odrážala cenu podľa množstva v bušloch\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Základný bodový graf nám pripomína, že máme údaje iba od augusta do decembra. Pravdepodobne potrebujeme viac údajov, aby sme mohli vyvodiť závery lineárnym spôsobom.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter('Month','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "b032d371c75279373507f003439a577e",
+   "translation_date": "2025-09-06T13:08:32+00:00",
+   "source_file": "2-Regression/3-Linear/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sk/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/sk/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
new file mode 100644
index 000000000..1fa36b98c
--- /dev/null
+++ b/translations/sk/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -0,0 +1,1081 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_3-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "5015d65d61ba75a223bfc56c273aa174",
+   "translation_date": "2025-09-06T13:14:27+00:00",
+   "source_file": "2-Regression/3-Linear/solution/R/lesson_3-R.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "EgQw8osnsUV-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Lineárna a polynomiálna regresia pre stanovenie cien tekvíc - Lekcia 3\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Infografika od Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "#### Úvod\n",
+    "\n",
+    "Doteraz ste preskúmali, čo je regresia, na základe vzorových údajov zo súboru údajov o cenách tekvíc, ktorý budeme používať počas celej tejto lekcie. Vizualizovali ste ju pomocou `ggplot2`.💪\n",
+    "\n",
+    "Teraz ste pripravení ponoriť sa hlbšie do regresie pre strojové učenie. V tejto lekcii sa dozviete viac o dvoch typoch regresie: *základná lineárna regresia* a *polynomiálna regresia*, spolu s niektorými matematickými základmi týchto techník.\n",
+    "\n",
+    "> Počas celého kurzu predpokladáme minimálne znalosti matematiky a snažíme sa ju sprístupniť študentom z iných oblastí, preto sledujte poznámky, 🧮 upozornenia, diagramy a ďalšie nástroje na učenie, ktoré vám pomôžu pochopiť.\n",
+    "\n",
+    "#### Príprava\n",
+    "\n",
+    "Pripomeňme si, že tieto údaje načítavate, aby ste si mohli klásť otázky.\n",
+    "\n",
+    "-   Kedy je najlepší čas na kúpu tekvíc?\n",
+    "\n",
+    "-   Akú cenu môžem očakávať za balenie miniatúrnych tekvíc?\n",
+    "\n",
+    "-   Mal by som ich kúpiť v polovičných košoch alebo v krabici 1 1/9 bušlu? Poďme sa hlbšie ponoriť do týchto údajov.\n",
+    "\n",
+    "V predchádzajúcej lekcii ste vytvorili `tibble` (moderné prepracovanie dátového rámca) a naplnili ho časťou pôvodného súboru údajov, pričom ste štandardizovali ceny podľa bušlu. Týmto spôsobom ste však dokázali zhromaždiť iba približne 400 údajových bodov a iba pre jesenné mesiace. Možno môžeme získať trochu viac detailov o povahe údajov ich dôkladnejším čistením? Uvidíme... 🕵️‍♀️\n",
+    "\n",
+    "Pre túto úlohu budeme potrebovať nasledujúce balíky:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [kolekcia balíkov pre R](https://www.tidyverse.org/packages), ktorá robí dátovú vedu rýchlejšou, jednoduchšou a zábavnejšou!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je [rámec balíkov](https://www.tidymodels.org/packages/) pre modelovanie a strojové učenie.\n",
+    "\n",
+    "-   `janitor`: [janitor balík](https://github.com/sfirke/janitor) poskytuje jednoduché nástroje na skúmanie a čistenie špinavých údajov.\n",
+    "\n",
+    "-   `corrplot`: [corrplot balík](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) poskytuje vizuálny prieskumný nástroj na korelačnú maticu, ktorý podporuje automatické preusporiadanie premenných na odhalenie skrytých vzorov medzi premennými.\n",
+    "\n",
+    "Môžete ich nainštalovať pomocou:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\n",
+    "\n",
+    "Nasledujúci skript skontroluje, či máte balíky potrebné na dokončenie tohto modulu, a v prípade ich absencie ich nainštaluje.\n"
+   ],
+   "metadata": {
+    "id": "WqQPS1OAsg3H"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "tA4C2WN3skCf",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Neskôr načítame tieto skvelé balíčky a sprístupníme ich v našej aktuálnej R relácii. (Toto je len pre ilustráciu, `pacman::p_load()` to už za vás urobil)\n",
+    "\n",
+    "## 1. Lineárna regresná priamka\n",
+    "\n",
+    "Ako ste sa naučili v Lekcii 1, cieľom cvičenia s lineárnou regresiou je byť schopný vykresliť *priamku* *najlepšieho prispôsobenia*, aby sme:\n",
+    "\n",
+    "-   **Ukázali vzťahy medzi premennými**. Zobraziť vzťah medzi premennými.\n",
+    "\n",
+    "-   **Vytvárali predpovede**. Presne predpovedať, kde by nový dátový bod mohol spadnúť vo vzťahu k tejto priamke.\n",
+    "\n",
+    "Na vykreslenie tohto typu priamky používame štatistickú techniku nazývanú **Regresia najmenších štvorcov**. Termín `najmenšie štvorce` znamená, že všetky dátové body okolo regresnej priamky sú umocnené na druhú a potom sčítané. Ideálne je, aby tento konečný súčet bol čo najmenší, pretože chceme mať čo najmenej chýb, teda `najmenšie štvorce`. Preto je priamka najlepšieho prispôsobenia tá priamka, ktorá nám dáva najnižšiu hodnotu súčtu štvorcov chýb - odtiaľ názov *regresia najmenších štvorcov*.\n",
+    "\n",
+    "Robíme to preto, že chceme modelovať priamku, ktorá má najmenšiu kumulatívnu vzdialenosť od všetkých našich dátových bodov. Tiež umocňujeme hodnoty na druhú pred ich sčítaním, pretože nás zaujíma ich veľkosť, nie ich smer.\n",
+    "\n",
+    "> **🧮 Ukáž mi matematiku**\n",
+    ">\n",
+    "> Táto priamka, nazývaná *priamka najlepšieho prispôsobenia*, môže byť vyjadrená [rovnicou](https://en.wikipedia.org/wiki/Simple_linear_regression):\n",
+    ">\n",
+    ">     Y = a + bX\n",
+    ">\n",
+    "> `X` je '`vysvetľujúca premenná` alebo `prediktor`'. `Y` je '`závislá premenná` alebo `výsledok`'. Sklon priamky je `b` a `a` je priesečník s osou y, ktorý označuje hodnotu `Y`, keď `X = 0`.\n",
+    ">\n",
+    "\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/slope.png \"sklon = $y/x$\")\n",
+    "    Infografika od Jen Looper\n",
+    ">\n",
+    "> Najprv vypočítajte sklon `b`.\n",
+    ">\n",
+    "> Inými slovami, a odkazujúc na pôvodnú otázku o údajoch o tekviciach: \"predpovedajte cenu tekvice za bušel podľa mesiaca\", `X` by označovalo cenu a `Y` by označovalo mesiac predaja.\n",
+    ">\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/calculation.png)\n",
+    "    Infografika od Jen Looper\n",
+    "> \n",
+    "> Vypočítajte hodnotu Y. Ak platíte okolo 4 dolárov, musí byť apríl!\n",
+    ">\n",
+    "> Matematika, ktorá vypočíta priamku, musí ukázať sklon priamky, ktorý tiež závisí od priesečníka, teda od toho, kde sa `Y` nachádza, keď `X = 0`.\n",
+    ">\n",
+    "> Metódu výpočtu týchto hodnôt si môžete pozrieť na webovej stránke [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html). Navštívte tiež [tento kalkulátor najmenších štvorcov](https://www.mathsisfun.com/data/least-squares-calculator.html), aby ste videli, ako hodnoty čísel ovplyvňujú priamku.\n",
+    "\n",
+    "Nie je to také strašidelné, však? 🤓\n",
+    "\n",
+    "#### Korelácia\n",
+    "\n",
+    "Ešte jeden pojem, ktorý treba pochopiť, je **Korelačný koeficient** medzi danými premennými X a Y. Pomocou bodového grafu môžete tento koeficient rýchlo vizualizovať. Graf s bodmi usporiadanými do úhľadnej priamky má vysokú koreláciu, ale graf s bodmi roztrúsenými všade medzi X a Y má nízku koreláciu.\n",
+    "\n",
+    "Dobrý model lineárnej regresie bude taký, ktorý má vysoký (bližší k 1 ako k 0) Korelačný koeficient, použitím metódy Regresie najmenších štvorcov s regresnou priamkou.\n"
+   ],
+   "metadata": {
+    "id": "cdX5FRpvsoP5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **2. Tanec s dátami: vytvorenie dátového rámca na modelovanie**\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/janitor.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Ilustrácia od \\@allison_horst](../../../../../../2-Regression/3-Linear/images/janitor.jpg){width=\"700\"}-->\n"
+   ],
+   "metadata": {
+    "id": "WdUKXk7Bs8-V"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Načítajte potrebné knižnice a dataset. Konvertujte údaje na dátový rámec obsahujúci podmnožinu údajov:\n",
+    "\n",
+    "-   Získajte iba tekvice ocenené na základe ceny za bušel\n",
+    "\n",
+    "-   Konvertujte dátum na mesiac\n",
+    "\n",
+    "-   Vypočítajte cenu ako priemer vysokých a nízkych cien\n",
+    "\n",
+    "-   Konvertujte cenu tak, aby odrážala cenu za množstvo v bušeloch\n",
+    "\n",
+    "> Tieto kroky sme pokryli v [predchádzajúcej lekcii](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb).\n"
+   ],
+   "metadata": {
+    "id": "fMCtu2G2s-p8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "library(lubridate)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ryMVZEEPtERn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "V duchu čírej dobrodružnosti sa poďme pozrieť na [`janitor package`](../../../../../../2-Regression/3-Linear/solution/R/github.com/sfirke/janitor), ktorý poskytuje jednoduché funkcie na skúmanie a čistenie nečistých údajov. Napríklad sa pozrime na názvy stĺpcov našich údajov:\n"
+   ],
+   "metadata": {
+    "id": "xcNxM70EtJjb"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5XtpaIigtPfW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤔 Môžeme to urobiť lepšie. Poďme zmeniť názvy týchto stĺpcov na `friendR` konvertovaním na konvenciu [snake_case](https://en.wikipedia.org/wiki/Snake_case) pomocou `janitor::clean_names`. Ak sa chcete dozvedieť viac o tejto funkcii: `?clean_names`\n"
+   ],
+   "metadata": {
+    "id": "IbIqrMINtSHe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Clean names to the snake_case convention\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  clean_names(case = \"snake\")\n",
+    "\n",
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "a2uYvclYtWvX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Veľa poriadku s tidyR 🧹! Teraz si zatancujeme s dátami pomocou `dplyr`, ako v predchádzajúcej lekcii! 💃\n"
+   ],
+   "metadata": {
+    "id": "HfhnuzDDtaDd"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(variety, city_name, package, low_price, high_price, date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Extract the month from the dates to a new column\n",
+    "pumpkins <- pumpkins %>%\n",
+    "  mutate(date = mdy(date),\n",
+    "         month = month(date)) %>% \n",
+    "  select(-date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a new column for average Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(price = (low_price + high_price)/2)\n",
+    "\n",
+    "\n",
+    "# Retain only pumpkins with the string \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "  filter(str_detect(string = package, pattern = \"bushel\"))\n",
+    "\n",
+    "\n",
+    "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(price = case_when(\n",
+    "    str_detect(package, \"1 1/9\") ~ price/(1.1),\n",
+    "    str_detect(package, \"1/2\") ~ price*2,\n",
+    "    TRUE ~ price))\n",
+    "\n",
+    "# Relocate column positions\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(month, .before = variety)\n",
+    "\n",
+    "\n",
+    "# Display the first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "X0wU3gQvtd9f"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Dobrá práca!👌 Teraz máte čistý a uprataný dátový súbor, na ktorom môžete postaviť svoj nový regresný model!\n",
+    "\n",
+    "Čo tak rozptylový graf?\n"
+   ],
+   "metadata": {
+    "id": "UpaIwaxqth82"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set theme\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Make a scatter plot of month and price\n",
+    "new_pumpkins %>% \n",
+    "  ggplot(mapping = aes(x = month, y = price)) +\n",
+    "  geom_point(size = 1.6)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "DXgU-j37tl5K"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bodový graf nám pripomína, že máme údaje za mesiace iba od augusta do decembra. Pravdepodobne potrebujeme viac údajov, aby sme mohli vyvodiť závery lineárnym spôsobom.\n",
+    "\n",
+    "Pozrime sa znova na naše modelovacie údaje:\n"
+   ],
+   "metadata": {
+    "id": "Ve64wVbwtobI"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Display first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HFQX2ng1tuSJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Čo ak by sme chceli predpovedať `cenu` tekvice na základe stĺpcov `mesto` alebo `balenie`, ktoré sú typu znakový reťazec? Alebo ešte jednoduchšie, ako by sme mohli nájsť koreláciu (ktorá vyžaduje, aby oba vstupy boli numerické) medzi, povedzme, `balenie` a `cena`? 🤷🤷\n",
+    "\n",
+    "Modely strojového učenia fungujú najlepšie s numerickými vlastnosťami namiesto textových hodnôt, takže vo všeobecnosti je potrebné previesť kategóriálne vlastnosti na numerické reprezentácie.\n",
+    "\n",
+    "To znamená, že musíme nájsť spôsob, ako upraviť naše prediktory, aby ich model mohol efektívne používať, proces známy ako `inžinierstvo vlastností`.\n"
+   ],
+   "metadata": {
+    "id": "7hsHoxsStyjJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Predspracovanie údajov na modelovanie pomocou receptov 👩‍🍳👨‍🍳\n",
+    "\n",
+    "Aktivity, ktoré upravujú hodnoty prediktorov, aby ich model mohol efektívne využívať, sa nazývajú `inžinierstvo vlastností`.\n",
+    "\n",
+    "Rôzne modely majú rôzne požiadavky na predspracovanie. Napríklad metóda najmenších štvorcov vyžaduje `kódovanie kategóriálnych premenných`, ako sú mesiac, odroda a city_name. To jednoducho zahŕňa `preklad` stĺpca s `kategóriálnymi hodnotami` na jeden alebo viac `numerických stĺpcov`, ktoré nahradia pôvodný.\n",
+    "\n",
+    "Napríklad, predpokladajme, že vaše údaje obsahujú nasledujúcu kategóriálnu vlastnosť:\n",
+    "\n",
+    "|  mesto   |\n",
+    "|:--------:|\n",
+    "| Denver   |\n",
+    "| Nairobi  |\n",
+    "|  Tokio   |\n",
+    "\n",
+    "Môžete použiť *ordinálne kódovanie*, aby ste nahradili každú kategóriu jedinečnou celočíselnou hodnotou, napríklad takto:\n",
+    "\n",
+    "| mesto |\n",
+    "|:-----:|\n",
+    "|   0   |\n",
+    "|   1   |\n",
+    "|   2   |\n",
+    "\n",
+    "A presne to urobíme s našimi údajmi!\n",
+    "\n",
+    "V tejto časti preskúmame ďalší úžasný balík Tidymodels: [recipes](https://tidymodels.github.io/recipes/) - ktorý je navrhnutý tak, aby vám pomohol predspracovať vaše údaje **predtým**, než začnete trénovať váš model. V jadre receptu je objekt, ktorý definuje, aké kroky by sa mali aplikovať na dátovú množinu, aby bola pripravená na modelovanie.\n",
+    "\n",
+    "Teraz si vytvoríme recept, ktorý pripraví naše údaje na modelovanie tým, že nahradí jedinečné celé číslo za všetky pozorovania v stĺpcoch prediktorov:\n"
+   ],
+   "metadata": {
+    "id": "AD5kQbcvt3Xl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\n",
+    "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "# Print out the recipe\n",
+    "pumpkins_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "BNaFKXfRt9TU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Úžasné! 👏 Práve sme vytvorili náš prvý recept, ktorý špecifikuje výsledok (cenu) a jeho zodpovedajúce prediktory, pričom všetky stĺpce prediktorov by mali byť zakódované ako sada celých čísel 🙌! Poďme si to rýchlo rozobrať:\n",
+    "\n",
+    "-   Volanie `recipe()` s formulou určuje receptu *úlohy* premenných pomocou údajov `new_pumpkins` ako referencie. Napríklad stĺpec `price` bol priradený úlohe `outcome`, zatiaľ čo ostatné stĺpce boli priradené úlohe `predictor`.\n",
+    "\n",
+    "-   `step_integer(all_predictors(), zero_based = TRUE)` špecifikuje, že všetky prediktory by mali byť konvertované na sadu celých čísel, pričom číslovanie začína od 0.\n",
+    "\n",
+    "Sme si istí, že vás možno napadli myšlienky ako: \"Toto je tak super!! Ale čo ak by som potreboval potvrdiť, že recepty robia presne to, čo od nich očakávam? 🤔\"\n",
+    "\n",
+    "To je skvelá myšlienka! Vidíte, keď je váš recept definovaný, môžete odhadnúť parametre potrebné na skutočné predspracovanie údajov a potom extrahovať spracované údaje. Zvyčajne to nemusíte robiť, keď používate Tidymodels (o chvíľu uvidíme bežnú konvenciu -> `workflows`), ale môže to byť užitočné, keď chcete vykonať určitú kontrolu správnosti, aby ste si potvrdili, že recepty robia to, čo očakávate.\n",
+    "\n",
+    "Na to budete potrebovať ďalšie dva príkazy: `prep()` a `bake()`, a ako vždy, naši malí R kamaráti od [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) vám pomôžu lepšie pochopiť túto tému!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "KEiO0v7kuC9O"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): odhaduje potrebné parametre z tréningovej množiny, ktoré môžu byť neskôr aplikované na iné dátové množiny. Napríklad, pre daný stĺpec prediktora, ktorá hodnota bude priradená ako celé číslo 0, 1, 2 atď.\n",
+    "\n",
+    "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): vezme pripravený recept a aplikuje operácie na akúkoľvek dátovú množinu.\n",
+    "\n",
+    "Takže, poďme pripraviť a aplikovať naše recepty, aby sme naozaj potvrdili, že v pozadí budú stĺpce prediktorov najskôr zakódované pred tým, ako sa model prispôsobí.\n"
+   ],
+   "metadata": {
+    "id": "Q1xtzebuuTCP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep the recipe\n",
+    "pumpkins_prep <- prep(pumpkins_recipe)\n",
+    "\n",
+    "# Bake the recipe to extract a preprocessed new_pumpkins data\n",
+    "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\n",
+    "\n",
+    "# Print out the baked data set\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FGBbJbP_uUUn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hurá! 🥳 Spracované dáta `baked_pumpkins` majú všetky svoje prediktory zakódované, čo potvrdzuje, že kroky predspracovania definované ako náš recept fungujú podľa očakávania. Pre vás to môže byť ťažšie na čítanie, ale pre Tidymodels je to oveľa zrozumiteľnejšie! Nájdite si chvíľu na zistenie, ktorá pozorovaná hodnota bola namapovaná na zodpovedajúce celé číslo.\n",
+    "\n",
+    "Stojí tiež za zmienku, že `baked_pumpkins` je dátový rámec, na ktorom môžeme vykonávať výpočty.\n",
+    "\n",
+    "Napríklad, poďme sa pokúsiť nájsť dobrú koreláciu medzi dvoma bodmi vašich dát, aby sme potenciálne vytvorili dobrý prediktívny model. Na to použijeme funkciu `cor()`. Napíšte `?cor()`, aby ste sa dozvedeli viac o tejto funkcii.\n"
+   ],
+   "metadata": {
+    "id": "1dvP0LBUueAW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the correlation between the city_name and the price\n",
+    "cor(baked_pumpkins$city_name, baked_pumpkins$price)\n",
+    "\n",
+    "# Find the correlation between the package and the price\n",
+    "cor(baked_pumpkins$package, baked_pumpkins$price)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "3bQzXCjFuiSV"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ako sa ukazuje, medzi mestom a cenou existuje iba slabá korelácia. Avšak medzi balíkom a jeho cenou je o niečo lepšia korelácia. To dáva zmysel, však? Zvyčajne platí, že čím väčšia je krabica s produktmi, tým vyššia je cena.\n",
+    "\n",
+    "Keď už sme pri tom, poďme sa pokúsiť vizualizovať korelačnú maticu všetkých stĺpcov pomocou balíka `corrplot`.\n"
+   ],
+   "metadata": {
+    "id": "BToPWbgjuoZw"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the corrplot package\n",
+    "library(corrplot)\n",
+    "\n",
+    "# Obtain correlation matrix\n",
+    "corr_mat <- cor(baked_pumpkins %>% \n",
+    "                  # Drop columns that are not really informative\n",
+    "                  select(-c(low_price, high_price)))\n",
+    "\n",
+    "# Make a correlation plot between the variables\n",
+    "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZwAL3ksmutVR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Oveľa lepšie.\n",
+    "\n",
+    "Dobrá otázka, ktorú si teraz môžeme položiť na základe týchto údajov, je: '`Akú cenu môžem očakávať za daný balík tekvíc?`' Poďme na to!\n",
+    "\n",
+    "> Poznámka: Keď **`bake()`** použijete na pripravený recept **`pumpkins_prep`** s **`new_data = NULL`**, získate spracované (t.j. zakódované) tréningové dáta. Ak by ste mali iný súbor údajov, napríklad testovaciu množinu, a chceli by ste vidieť, ako by recept tieto údaje predspracoval, jednoducho by ste použili **`bake()`** na **`pumpkins_prep`** s **`new_data = test_set`**.\n",
+    "\n",
+    "## 4. Vytvorenie lineárneho regresného modelu\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Infografika od Dasani Madipalli</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "YqXjLuWavNxW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Teraz, keď sme vytvorili recept a skutočne potvrdili, že údaje budú správne predspracované, poďme vytvoriť regresný model, aby sme odpovedali na otázku: `Akú cenu môžem očakávať za dané balenie tekvice?`\n",
+    "\n",
+    "#### Natrénujte lineárny regresný model pomocou tréningovej množiny\n",
+    "\n",
+    "Ako ste už pravdepodobne zistili, stĺpec *price* je `výsledná` premenná, zatiaľ čo stĺpec *package* je `prediktorová` premenná.\n",
+    "\n",
+    "Na to najskôr rozdelíme údaje tak, že 80 % pôjde do tréningovej množiny a 20 % do testovacej množiny, potom definujeme recept, ktorý zakóduje stĺpec prediktora do množiny celých čísel, a následne vytvoríme špecifikáciu modelu. Recept nebudeme pripravovať a piecť, pretože už vieme, že údaje predspracuje podľa očakávania.\n"
+   ],
+   "metadata": {
+    "id": "Pq0bSzCevW-h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\n",
+    "# Split the data into training and test sets\n",
+    "pumpkins_split <- new_pumpkins %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "\n",
+    "# Extract training and test data\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a recipe for preprocessing the data\n",
+    "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a linear model specification\n",
+    "lm_spec <- linear_reg() %>% \n",
+    "  set_engine(\"lm\") %>% \n",
+    "  set_mode(\"regression\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "CyoEh_wuvcLv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Skvelá práca! Teraz, keď máme recept a špecifikáciu modelu, musíme nájsť spôsob, ako ich spojiť do objektu, ktorý najprv predspracuje dáta (v zákulisí prep+bake), natrénuje model na predspracovaných dátach a zároveň umožní aj prípadné aktivity po spracovaní. Čo poviete na takýto pokoj na duši!🤩\n",
+    "\n",
+    "V Tidymodels sa tento praktický objekt nazýva [`workflow`](https://workflows.tidymodels.org/) a pohodlne uchováva vaše modelovacie komponenty! Toto by sme v *Python-e* nazvali *pipelines*.\n",
+    "\n",
+    "Tak poďme všetko zabaliť do workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "G3zF_3DqviFJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Hold modelling components in a workflow\n",
+    "lm_wf <- workflow() %>% \n",
+    "  add_recipe(lm_pumpkins_recipe) %>% \n",
+    "  add_model(lm_spec)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "lm_wf"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "T3olroU3v-WX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "👌 Navyše, pracovný postup môže byť prispôsobený/vytrénovaný podobne ako model.\n"
+   ],
+   "metadata": {
+    "id": "zd1A5tgOwEPX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Train the model\n",
+    "lm_wf_fit <- lm_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the model coefficients learned \n",
+    "lm_wf_fit"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NhJagFumwFHf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Z výstupu modelu môžeme vidieť koeficienty naučené počas tréningu. Predstavujú koeficienty priamky najlepšieho prispôsobenia, ktorá nám poskytuje najnižšiu celkovú chybu medzi skutočnou a predpovedanou premennou.\n",
+    "\n",
+    "#### Vyhodnotenie výkonu modelu pomocou testovacej množiny\n",
+    "\n",
+    "Je čas zistiť, ako si model viedol 📏! Ako to urobíme?\n",
+    "\n",
+    "Teraz, keď sme model natrénovali, môžeme ho použiť na predpovede pre testovaciu množinu pomocou `parsnip::predict()`. Potom môžeme tieto predpovede porovnať so skutočnými hodnotami štítkov, aby sme zhodnotili, ako dobre (alebo nie!) model funguje.\n",
+    "\n",
+    "Začnime vytváraním predpovedí pre testovaciu množinu a následným pripojením stĺpcov k testovacej množine.\n"
+   ],
+   "metadata": {
+    "id": "_4QkGtBTwItF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\n",
+    "predictions <- lm_wf_fit %>% \n",
+    "  predict(new_data = pumpkins_test)\n",
+    "\n",
+    "\n",
+    "# Bind predictions to the test set\n",
+    "lm_results <- pumpkins_test %>% \n",
+    "  select(c(package, price)) %>% \n",
+    "  bind_cols(predictions)\n",
+    "\n",
+    "\n",
+    "# Print the first ten rows of the tibble\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "UFZzTG0gwTs9"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Áno, práve ste natrénovali model a použili ho na predikcie!🔮 Je dobrý? Poďme vyhodnotiť výkon modelu!\n",
+    "\n",
+    "V Tidymodels to robíme pomocou `yardstick::metrics()`! Pre lineárnu regresiu sa zamerajme na nasledujúce metriky:\n",
+    "\n",
+    "-   `Root Mean Square Error (RMSE)`: Odmocnina z [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). Poskytuje absolútnu metriku v rovnakých jednotkách ako cieľová hodnota (v tomto prípade cena tekvice). Čím menšia hodnota, tým lepší model (v jednoduchom zmysle predstavuje priemernú cenu, o ktorú sa predikcie mýlia!).\n",
+    "\n",
+    "-   `Coefficient of Determination (zvyčajne známy ako R-squared alebo R2)`: Relatívna metrika, pri ktorej vyššia hodnota znamená lepšie prispôsobenie modelu. V podstate táto metrika reprezentuje, koľko variability medzi predikovanými a skutočnými hodnotami cieľovej premenné dokáže model vysvetliť.\n"
+   ],
+   "metadata": {
+    "id": "0A5MjzM7wW9M"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Evaluate performance of linear regression\n",
+    "metrics(data = lm_results,\n",
+    "        truth = price,\n",
+    "        estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "reJ0UIhQwcEH"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tam ide výkon modelu. Pozrime sa, či môžeme získať lepšiu predstavu vizualizáciou bodového grafu balíka a ceny, a potom použijeme predpovede na prekrytie najlepšej prispôsobenej čiary.\n",
+    "\n",
+    "To znamená, že budeme musieť pripraviť a spracovať testovaciu množinu, aby sme zakódovali stĺpec balíka, a potom to spojiť s predpoveďami vytvorenými naším modelom.\n"
+   ],
+   "metadata": {
+    "id": "fdgjzjkBwfWt"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Encode package column\n",
+    "package_encode <- lm_pumpkins_recipe %>% \n",
+    "  prep() %>% \n",
+    "  bake(new_data = pumpkins_test) %>% \n",
+    "  select(package)\n",
+    "\n",
+    "\n",
+    "# Bind encoded package column to the results\n",
+    "lm_results <- lm_results %>% \n",
+    "  bind_cols(package_encode %>% \n",
+    "              rename(package_integer = package)) %>% \n",
+    "  relocate(package_integer, .after = package)\n",
+    "\n",
+    "\n",
+    "# Print new results data frame\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 5)\n",
+    "\n",
+    "\n",
+    "# Make a scatter plot\n",
+    "lm_results %>% \n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\n",
+    "  geom_point(size = 1.6) +\n",
+    "  # Overlay a line of best fit\n",
+    "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\n",
+    "  xlab(\"package\")\n",
+    "  \n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R0nw719lwkHE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Skvelé! Ako môžete vidieť, lineárny regresný model nedokáže veľmi dobre generalizovať vzťah medzi balíkom a jeho zodpovedajúcou cenou.\n",
+    "\n",
+    "🎃 Gratulujeme, práve ste vytvorili model, ktorý dokáže predpovedať cenu niekoľkých druhov tekvíc. Vaša sviatočná tekvicová záhrada bude nádherná. Ale pravdepodobne dokážete vytvoriť ešte lepší model!\n",
+    "\n",
+    "## 5. Vytvorte polynomiálny regresný model\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Infografika od Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografika od Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "HOCqJXLTwtWI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Niekedy naše údaje nemusia mať lineárny vzťah, no aj tak chceme predpovedať výsledok. Polynomická regresia nám môže pomôcť robiť predpovede pre zložitejšie nelineárne vzťahy.\n",
+    "\n",
+    "Vezmime si napríklad vzťah medzi balením a cenou v našej dátovej sade tekvíc. Zatiaľ čo niekedy existuje lineárny vzťah medzi premennými – čím väčší objem tekvice, tým vyššia cena – niekedy tieto vzťahy nemožno znázorniť ako rovinu alebo priamku.\n",
+    "\n",
+    "> ✅ Tu sú [ďalšie príklady](https://online.stat.psu.edu/stat501/lesson/9/9.8) údajov, ktoré by mohli využiť polynomickú regresiu\n",
+    ">\n",
+    "> Pozrite sa znova na vzťah medzi odrodou a cenou v predchádzajúcom grafe. Zdá sa, že by tento bodový graf mal byť nevyhnutne analyzovaný priamkou? Možno nie. V tomto prípade môžete vyskúšať polynomickú regresiu.\n",
+    ">\n",
+    "> ✅ Polynómy sú matematické výrazy, ktoré môžu pozostávať z jednej alebo viacerých premenných a koeficientov\n",
+    "\n",
+    "#### Natrénujte model polynomickej regresie pomocou tréningovej množiny\n",
+    "\n",
+    "Polynomická regresia vytvára *zakrivenú čiaru*, ktorá lepšie zodpovedá nelineárnym údajom.\n",
+    "\n",
+    "Pozrime sa, či polynomický model bude lepší pri predpovedaní. Budeme postupovať podobným spôsobom ako predtým:\n",
+    "\n",
+    "-   Vytvorte recept, ktorý špecifikuje kroky predspracovania, ktoré by sa mali vykonať na našich údajoch, aby boli pripravené na modelovanie, t. j.: kódovanie prediktorov a výpočet polynómov stupňa *n*\n",
+    "\n",
+    "-   Vytvorte špecifikáciu modelu\n",
+    "\n",
+    "-   Spojte recept a špecifikáciu modelu do pracovného postupu\n",
+    "\n",
+    "-   Vytvorte model prispôsobením pracovného postupu\n",
+    "\n",
+    "-   Vyhodnoťte, ako dobre model funguje na testovacích údajoch\n",
+    "\n",
+    "Poďme na to!\n"
+   ],
+   "metadata": {
+    "id": "VcEIpRV9wzYr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\r\n",
+    "poly_pumpkins_recipe <-\r\n",
+    "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+    "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+    "  step_poly(all_predictors(), degree = 4)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model specification\r\n",
+    "poly_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Bundle recipe and model spec into a workflow\r\n",
+    "poly_wf <- workflow() %>% \r\n",
+    "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+    "  add_model(poly_spec)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model\r\n",
+    "poly_wf_fit <- poly_wf %>% \r\n",
+    "  fit(data = pumpkins_train)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print learned model coefficients\r\n",
+    "poly_wf_fit\r\n",
+    "\r\n",
+    "  "
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "63n_YyRXw3CC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Vyhodnotenie výkonu modelu\n",
+    "\n",
+    "👏👏Vytvorili ste polynomiálny model, poďme urobiť predpovede na testovacej množine!\n"
+   ],
+   "metadata": {
+    "id": "-LHZtztSxDP0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make price predictions on test data\r\n",
+    "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+    "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+    "  relocate(.pred, .after = last_col())\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the results\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YUFpQ_dKxJGx"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hurá, poďme vyhodnotiť, ako model fungoval na testovacej množine pomocou `yardstick::metrics()`.\n"
+   ],
+   "metadata": {
+    "id": "qxdyj86bxNGZ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "metrics(data = poly_results, truth = price, estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "8AW5ltkBxXDm"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Oveľa lepší výkon.\n",
+    "\n",
+    "`rmse` kleslo z približne 7 na približne 3, čo naznačuje zníženie chyby medzi skutočnou cenou a predpovedanou cenou. Môžete to *voľne* interpretovať tak, že nesprávne predpovede sú v priemere nesprávne o približne 3 $. `rsq` sa zvýšilo z približne 0,4 na 0,8.\n",
+    "\n",
+    "Všetky tieto metriky naznačujú, že polynomiálny model funguje oveľa lepšie ako lineárny model. Skvelá práca!\n",
+    "\n",
+    "Pozrime sa, či to dokážeme vizualizovať!\n"
+   ],
+   "metadata": {
+    "id": "6gLHNZDwxYaS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Bind encoded package column to the results\r\n",
+    "poly_results <- poly_results %>% \r\n",
+    "  bind_cols(package_encode %>% \r\n",
+    "              rename(package_integer = package)) %>% \r\n",
+    "  relocate(package_integer, .after = package)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print new results data frame\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+    "  xlab(\"package\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "A83U16frxdF1"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Môžete vidieť zakrivenú čiaru, ktorá lepšie zodpovedá vašim údajom! 🤩\n",
+    "\n",
+    "Môžete ju urobiť ešte plynulejšou tým, že do `geom_smooth` zadáte polynomiálny vzorec, napríklad takto:\n"
+   ],
+   "metadata": {
+    "id": "4U-7aHOVxlGU"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+    "  xlab(\"package\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5vzNT0Uexm-w"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Podobne ako hladká krivka!🤩\n",
+    "\n",
+    "Tu je postup, ako vytvoriť novú predikciu:\n"
+   ],
+   "metadata": {
+    "id": "v9u-wwyLxq4G"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a hypothetical data frame\r\n",
+    "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+    "\r\n",
+    "# Make predictions using linear model\r\n",
+    "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Make predictions using polynomial model\r\n",
+    "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Return predictions in a list\r\n",
+    "list(\"linear model prediction\" = lm_pred, \r\n",
+    "     \"polynomial model prediction\" = poly_pred)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "jRPSyfQGxuQv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Predikcia pomocou `polynomial model` dáva zmysel, vzhľadom na bodové grafy `price` a `package`! A ak je tento model lepší ako ten predchádzajúci, pri pohľade na tie isté údaje, budete musieť plánovať rozpočet na tieto drahšie tekvice!\n",
+    "\n",
+    "🏆 Skvelá práca! Vytvorili ste dva regresné modely v jednej lekcii. V poslednej časti o regresii sa naučíte o logistickej regresii na určenie kategórií.\n",
+    "\n",
+    "## **🚀Výzva**\n",
+    "\n",
+    "Otestujte niekoľko rôznych premenných v tomto notebooku, aby ste zistili, ako korelácia súvisí s presnosťou modelu.\n",
+    "\n",
+    "## [**Kvíz po prednáške**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)\n",
+    "\n",
+    "## **Prehľad & Samoštúdium**\n",
+    "\n",
+    "V tejto lekcii sme sa naučili o lineárnej regresii. Existujú aj iné dôležité typy regresie. Prečítajte si o technikách Stepwise, Ridge, Lasso a Elasticnet. Dobrou možnosťou na štúdium je [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning).\n",
+    "\n",
+    "Ak sa chcete dozvedieť viac o používaní úžasného frameworku Tidymodels, pozrite si nasledujúce zdroje:\n",
+    "\n",
+    "-   Webová stránka Tidymodels: [Začnite s Tidymodels](https://www.tidymodels.org/start/)\n",
+    "\n",
+    "-   Max Kuhn a Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+    "\n",
+    "###### **ĎAKUJEME:**\n",
+    "\n",
+    "[Allison Horst](https://twitter.com/allison_horst?lang=en) za vytvorenie úžasných ilustrácií, ktoré robia R prístupnejším a zábavnejším. Viac ilustrácií nájdete v jej [galérii](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n"
+   ],
+   "metadata": {
+    "id": "8zOLOWqMxzk5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/2-Regression/3-Linear/solution/notebook.ipynb b/translations/sk/2-Regression/3-Linear/solution/notebook.ipynb
new file mode 100644
index 000000000..f41dd74ef
--- /dev/null
+++ b/translations/sk/2-Regression/3-Linear/solution/notebook.ipynb
@@ -0,0 +1,1111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lineárna a polynomiálna regresia pre stanovenie cien tekvíc - Lekcia 3\n",
+    "\n",
+    "Načítajte potrebné knižnice a dataset. Konvertujte údaje na dataframe obsahujúci podmnožinu údajov:\n",
+    "\n",
+    "- Získajte iba tekvice ocenené na základe ceny za koš\n",
+    "- Konvertujte dátum na mesiac\n",
+    "- Vypočítajte cenu ako priemer vysokých a nízkych cien\n",
+    "- Konvertujte cenu tak, aby odrážala cenu za množstvo v koši\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bodový graf nám pripomína, že máme údaje iba od augusta do decembra. Pravdepodobne potrebujeme viac údajov, aby sme mohli vyvodiť závery lineárnym spôsobom.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Month', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('Month','Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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LNJln4ZzTQtU7Ea92nHBSTTW144R7rpnHPdecSzXxN2E1ONnasJr44myu4klwX/KMd/0Hz+SUCdUpZadMqOb6D57pRbwl5zU5nVBXaf9nSo1txJIl15NvXMdzLdNknt2HulOOax92P5P4bBGJZ1CNp9O7ntxJciv1Pzy5M68JOf9z9Y6heAPB/XziLV+1bWhSmAb3fZowNHF8auf4SeOrRzl6bK5n9FTa/5lSYlf8OWg7s57PLZrt7A3nOp5r9XW1nNs0ZSjph+0ATHTu9vYP8qv3BujtH+Tz39/mtDPRdWfnfRt2c6w/NRUe61fu27Dbi/q57ox1HS+h0v7PlIqsEr+InC0i60Xk5eD+PBH562irZnwTtgMwXUfpYIbLy7Cdia47O1dneFym8rH43hnrOp7xW7ZX/N8EvgD0QXzEDvB7UVXK+ClsB2C6jtIqSX9s2M5E152dSzI8LlP5WHzvjHUdz/gt28R/kqr+bFhZf9ojTdZKbeXCsB2A6TqLv3LtuU47E113dn76srOYOC71r9PEccKnLzvLi/q57ox1Hc/4LatF2kTkR8DNwPdV9TwRuQa4UVU/EnUFoTwXaSvllQtXb+ngiR0HuWruaTklhq6e3hGdxWFjZfLIC3tYs/0Ai+fNCJ1Uk923YTertx9gybwZoZN+VPVbs/UNPve9rSjxPvN7f68179+569+HKa5Mi7Rlm/h/Dbgf+CBwBNgDXK+qex3XM61yS/y2cqHJl/3OTTbyWp1TVX+hqlcADcD7VfVDhUr65cj3WaZh2AYZheXD79yUrqzG8YvIXcCKYJVNRGQq8XX0bWRPCL7PMs2VbZBReMX+nZvSlm3n7kcSSR8g2Dil4vbKdcX1NneF3jYvuTzTgm7ZXvn73sHta7zE73x8NdRWVzG+Or+ZxQntnd2sinXQ3pndxDxTmrKduVstIrWq2gsQLLNsDYl5cL3NXaG2zRte/qeXtlBTVcVxTlx9Vouw4dVDXPb+UwEy1sn3Dm7f48X2vsV7A0Dw2sdefyuveLev3sFDm/YN3V96UTN3Lp4bOp7xV7adu7cCVwP/l/jM7k8Bj6vqimirF1dunbu+y9Rx+MTNH+Kqrz+XUl47TgChd9gSy3W11RzvG0BEmDCuekSi872D2/d47Z3dXHHvsyPKn77lEloaJxc9nvFDvp27K4C/B34d+A3gbwuV9E3hZeo43NpxdET5+Opqbr6shQk1VUxKWiump3eA/kHoG9C0TUC+d3D7Hm9rx9Gcygsdz/gt60XaVPVHwI8irIvxRKaOw9amKWnL/+DCZv7gwmY2vHqIO/79FXp6U9ekSUgkuvq6Wu87uH2P19o0JafyQsczfhv1il9Engu+d4vIO0lf3SLyzhiPnSAiPxORbSLyioh8KSifJiLrRGR38H2qu9OpbK728D3RcVjF+GphfHW8s7ilcXKwvHLV0FeiQ/HIu+/R09tP30DmpsNj7/XzlbWvsXpLB0fefY+Pf2Am4wRqqoXx1eKkg7umCqqroKbKzbLM46vFaf1cxWtpnMzSi5pTypZe1By6WcZ1POO3Ua/4VfVDwfcwv/1e4HJV7RGRGuC5YAbw7wLrVfXLInIbcBuwPER8k8T1Hr4rYx28N5C4QlW+H+vg6tbTie19K6U9P/b6W8T2vpXSKZhJv8Z3TBqxa1LwxyLxHGF9fcNuEk3oA8A3NuzOK178NTjxhyzf+rmO53od5TsXz2Xp/Fls7ThKa9MUS/plbMw2fhGpSqzKmQuN6wnu1gRfCiwGHgzKHwSW5BrbpCrUnsCrt3SMSPAPbdyXVdLPRj51Xr/zYNo9WdfvDLe6pO/7LLd3dqf9XeQ7DLOlcTLXtDVZ0i9zYyZ+VR0EtolI81jHDici1SKyFThEfLP1F4HGxD67wfdTMzx2mYjERCR2+PDhXJ+6ohRqf9NCLNEbts6Z9gPOVB62Hr7sGWudsSYf2U7gmgG8EqzJ/3jia6wHqeqAqrYCM4ELROScbCumqverapuqtjU0NGT7sIpUqP1NC7FEb9g6Z9oPOFN52Hr4smesdcaafGSb+L8EXAXcCXwl6SsrwazfZ4ArgU4RmQEQfD+UfXVNOoXa33TJeU1pOwCHl4WVT50XzjmN2Y2TUspmN05i4Zxwf6x83zPWOmNNPkadwCUiE4A/AVqAHcADqprVOvwi0gD0qerRYKbvWuBu4LeArqTO3WmqeutosWwCV3Zc70eaKV57Z/eIDsDkMiDt7U3/9ebQksTvn3Eyz+5+k1PrxnOo5z1ndV6/8yBrd3ayaE5j6KSfrFCvaVjpfhfGJIRalllEHiW+69ZPgY8Ar6vqZ7N8wnnEO2+riX+yWKmqd4pIPbASaAb2Adeq6lujxbLEX/qSlys41tefcUavMcadTIl/rAlcc1R1bhDgAWD4LlwZBdszfiBNeRewMNs4pvQlL+R2Yk0fpW8g/uHx1se2s6Bluq0jb0yBjNXG35e4kW0TjzHDpVuuIJmtI29MYY2V+M9Nnq0LzMt25q4pX7ku3ZtuuYJkx/sH6OtPv8xD1HWzeKYSjTVzt3q0n5vKE2bp3sRyBbcOa+OH+CJufQPKNfdtynsZYNfLCldaPFM5sh3OaUzWs0WHb9SyreMoC1qm8/zyy3nkpgt58YtX8N2bLhyxrk8+M09dz2SttHimsmS9Oqcxo80WTQwlzHb0zoZX00/fSI7lum4Wz5g4u+I3WRtrtujwbRhHW4/f92WFKy2eqSyW+E3WxpotmsvoHd+XFa60eKayZLX1YrHZBC6/ZJotmm57wWTpthp0PfPU4hlzQqiZu76wxF86Ht/6xojROzZD15jiCDtz15icXN16OgtaprP/yLGhbQUTt21mrjF+sMRvnKuvq01J8pbwjfGLde4aY0yFsSt+UzBdPb3WBGSMByzxm4KwZZmN8Yc19ZjI5TKxyxgTvcgSv4g0icgGEdklIq+IyGeD8jtE5A0R2Rp8fTSqOpjCSV6fZzhbltkYv0TZ1NMPfF5Vt4jIZGCziKwLfnavqv5jhM9tCii5GSdd081YyzL3DQ4OtfsbY6IX2RW/qh5Q1S3B7W5gF2ANuWVmeDNOuqabxLLME2qqmFw7jnFVUFMtTK4dx4SaKlZ8fJ518BpTQAXp3BWRWcS3YXwRWADcLCJLgRjxTwVH0jxmGbAMoLm5efiPjScSzTgntlQ80XSTnMxtYpcx/oi8c1dE6oDHgD9X1XeAfwbeB7QCB4CvpHucqt6vqm2q2tbQ0BB1NU1I6ZpxMjXd1NfVcm7TlKEJXonbxpjCijTxi0gN8aT/HVX9AYCqdqrqgKoOAt8ELoiyDiZaw5txRmu6Sd4m0LYMNKZ4Imvqkfjeeg8Au1T1q0nlM1T1QHD3Y8DLUdXBFMbwZpx0SX/4NoHJbMtAYworyjb+BcAngB0isjUo+yLw+yLSCiiwF/h0hHUwBTJ8fZ5k6bYJTPbQxn0snT/LlhU2pkAiS/yq+hwgaX70ZFTPafyUaZvA4cdY4jemMGzmrolcNtsB2paBxhSOJX4TuXTbBCazLQONKSxbpM0UxJ2L57J0/qyhbQIB2zLQmCKxxG8KpqVxckqSt4RvTHFYU48xxlQYS/zGGFNhLPGbjEZbatmHeMaYcKyN36Q11lLLxY5njAnPrvjNCNkstVzMeMaY/FjiNyOk2zErn12yXMczxuTHEr8ZIZellosRzxiTH0v8ZoRcllouRjxjTH5EVYtdhzG1tbVpLBYrdjUqTldPr9NdslzHM8aMTkQ2q2rb8HIb1WMyGm2pZR/ipeP7HyuLZ/F8YInflA3fh6BaPIvni8ja+EWkSUQ2iMguEXlFRD4blE8TkXUisjv4PjWqOpjK4fsQVItn8XwSZeduP/B5Vf11YD7wpyIyB7gNWK+qZwHrg/sVyfeZsaU009b3IagWz+L5JModuA4AB4Lb3SKyCzgdWAxcGhz2IPAMsDyqevjK94+ZpfSxFfwfgmrxLJ5PCjKcU0RmAR8AXgQaE5utB99PLUQdfOL7x8xS+9gK/g9BtXgWzyeRD+cUkTrgP4C/V9UfiMhRVZ2S9PMjqjqinV9ElgHLAJqbm89//fXXI61nIW3rOMr133qR7t7+obLJteN45KYLOTfEFoS+xysk30dpWDyLV0hFGc4pIjXAY8B3VPUHQXGniMxQ1QMiMgM4lO6xqno/cD/Ex/FHWc9C8/1jZql9bE3m+xBUi2fxfBDlqB4BHgB2qepXk370OHBDcPsGYE1UdfBV4mNh7bgqThpfTe04Nx8z3ccTTqqppnacpMQL2+mb7nG+d0hbPFOOorziXwB8AtghIluDsi8CXwZWisiNwD7g2gjr4C1N/KsydM+/eAJCEDMubKdvuscpeN0hbfH87tA34dmSDUXQ1dPLgrt/wvG+E80pE2qqeH755aGu0gsV74mbP8RVX38u5+dJF692XBWg9PafeP+Vwmtg8UwpydTGX9aLtPn6Mdj3McSZ4m3tOBrqedLFq64SqqX0XgOLZ8pB2S7Z4PPH4JlTJ3K8fyCl7Hj/QF6dsT1JI3AAenr7ncdrbZoSqtM33fn2DQxSJanH+dQhbfFKp0Pf5K4sr/hLYVz78Ca2fJrcjrz73ohWfQ3KXcYDQo9VTne+t//33/B2HLXF83scuslPWV7xJz62HufEFUziY2uYN3IU8SbWjEsZJz+xZlzoeFs7jmYsb2mc7DTeNW1NLGiZntNY5Uzne85/O4Xnl1/ubNzz1a2n51w3ixddPOOvskz8vn8Mdh2vNcOkqkzl+cbLdazyaOfr+zhqi2fKUVk29Yw1Dj1sPF8/Vrc0TmbpRc0pZUsvag51tR9FPNfzDIwx+SnLK37IPA49LN8/Vp9/xjS+97N9CFUog7SdMc2reK7nGRSK71P6Ky2ecaMsx/FX2phk38d0l+rvw+eRYZUYz+SuosbxV9qYZN/HdJfi78P3kWGVFs+4VZaJP6oxyb5OCCt053Ou9R4tnuvXdPWWDm568CVWb+nIK47vf/wqLZ5xqyzb+BOdibcO+5iZT7OCzx+D6+tqaZ42kZ93vjtUdsa08G2q9XW1tJ0xlefau4bKfvOMqdTX1Yaqd31dLdedP5OHNu0bKruubSbPtb/p9DWdf9c6Dr4Tn7vw9K5D3P3jV9n4xQ+HijVz6kR+1Zc66exXfflNsvN5ZJjv8YxbZXnFD/HO0+eXX84jN13I88svzyuh+P4xOLanKyXpA7zW+S6xPV0ZHjG69s7ulKQP8NP2LmJ7ukLVu6unl5Wb96eUPfrSfm5dtc3Za7B6S8dQ0k848M57oa/8j7z7HgODqf1fA4MaelJcfV0t17XNTCm7rm2mNyPDfI9n3CrLK/4EV2OSfZ8Q9uzuNzOWt51Zn3O8TBO4nt39Zqh6pzvf6ioJRvicuKrO5zX44dZfZixfcl5TzvGeaz+csTzMsNaunl5WxlL/+K2M7eezC88O/R71faSZTQjzV9le8bvk+8fgS86anlP5WDJN4LrkrOkc60tdw+dY39hrAqU734FBHdGU0p3H+kIfyFDnTOVjmV43IafysUTV5l1fV8u5TVOcJVXf4xk3LPFnwfePwW1n1nNxS+qV/cUt9aGu9gGmThofvyJPUl0lTDlpPPH9dU4Yfj+ddM0cl53dkPbYPYd7cqxt3NzTT8mpfCwTa9L/18hUPpao2rxddWYnrN95kOWrtrF+50En8WJ7uvjq2tdCNztGHa+9s5tVsQ7aO7srIl5CWTf1uOT7x+CHb5pPbE8Xz+5+k0vOmh466UP86vSkmuqUtXVOqqlma8fRtO3eYzXPpGvmWLerM+2xYZuntu1/O2P5wjmnFT1efV0ttdXC8b4TZbXVktfv3WVnNsCie58Z6it6NLaf2Y2TeOqWS0PHu/5bm4b6iv7pJ+1c3FLPwzfN9ybe7at3pAw4WHpRM3cunlu28ZJFufXit0XkkIi8nFR2h4i8ISJbg6+PRvX8UfD9Y3DbmfV8btHsvJI+ZL46HVcFw/I+gwpvdh8fNV66Zo5xGd55vzb9pJzrO9rjfIm3eksHbx9Pbdp6+/hA6Ct1153Z63ceTDtAIOyVf2xPV8YBAj7Ea+/sTkmqAA9t3Bf6ytr3eMNF2dTzr8CVacrvVdXW4OvJCJ/fhJSpKeoXb/4q7fGZro4T0rbxk76JqH8wbfGYMj3Ol3hP7EifQDOVFzre2p3pP4FlKh/LaAMOfIg32gq05RhvuMgSv6o+C7wVVXwTratbT+eRT13AHy2YxSOfuoCrW08P3YmcvEhb4uvW356d9tioVhQtdryr5qZvHspUPpZLM7zmmcrHsmhOY07lY3E94KBQAxh8eb+4jjdcMTp3bxaR7UFT0NRMB4nIMhGJiUjs8OH0Q+tMdG5fvYNr7tvEP/2knWvu28Tta3bk1Ykc2/sWvf2DQ1/7j/zK6xVFXcdbcl4TM04en1I24+TxoYaaAsxtSv9fJ1P5WBbOOY3ZjZNSymY3TgrVnwHuBxy4juf7+8V1vOEiXaRNRGYBT6jqOcH9RuBN4ssz/i0wQ1U/NVacctts3Xftnd1cce+zI8qfvuUSWhons3pLB0/sOMhVc0/LKnGNFu/1rndZu7OTRXMaQyeZZOt3HvQ63n0bdrN6+wGWzJvBpy87K3ScqBa+e+SFPazZfoDF82Zw/QfPDB0nIdf3SqHj+f5+yXfARqZF2go6qkdVhxoMReSbwBOFfH6TndHaFx/auHeo0+npXYfY0nF0zJEGmeLdvuZlXvhFvDXw0dh+p6MgfI+360A3b7xzPHS80ZbVcFG/l/Ye4eeHe5ydb7bvlWLF8/H9krw8yv0//YXT1U0L2tQjIjOS7n4MeDnTsaZ4MrUjTj2pJtRIg0zxEkk/l1iZ+D6qIop46Ua5+FQ/ixc+XtSrm0Y5nPO7wEZgtojsF5EbgRUiskNEtgOXAbdE9fwmvEzti0d+1Zf2+LFGGqSL98H3pd/YxZdREBbP4hUzXtSrm0bW1KOqv5+m+IGons+4defiuSydP4utHUdpbZpCS+PkjFcv2Yw0OP+MaTz60n4S+29devapvPBfIwd9+TIKwuJZvGLGi3p1U1uywWTU0jiZa9qahkYSTJ00fsToewnKR5P42NrbP8jxYFTPV5/+Ode1pbZX5jNqIWzdLF5cS+PktKNm8hmVUmnxotinOqrVTW3JBpO1/UeOUVc7LmUph7racaFW56ypquIPL5zFsovfl/KpotB1s3hxXT29vPT6kZSyl14/QldPr8XLUrpPyfmIcnVTS/wma2E/fo72uPq6Widjk31fQdX3eL4vPe57vISWxsnOxtqDu6Xlh7OmHpO1sB8/C7Eph+8rqPoez/c/TL7HKzWRTuByxSZw+aWrpzfUx8+wjytE3SwePL71jRHbleYzbrzS4vko0wQuS/zGmCE+/2EqhXi+8WLmrjGlptwTw3Cu25QrLV6psMRvTAbJU+bLtSnAVCbr3DUmjainzBtTTJb4jUkj6inzxhSTJX5j0qj04X6mvFniNyaNqOYetHd2syrW4WzvVNfxunp62dZx1Jq0ypx17hqTgesp88nrtQNO1393Ec86syuHXfEbM4r6ulrObZri5Eq/ktd/N36xxG9MAVT6+u/GL1FuxPJtETkkIi8nlU0TkXUisjv4Hm5naGNKTKWv/278EuUV/78CVw4ruw1Yr6pnAeuD+8aUPdfrtZfa+u/GL5Gu1SMis4AnVPWc4P5rwKWqeiDYf/cZVZ09Vhxbq8eUi/bObmfrtUcRr9KWqCh3vqzV06iqBwCC5H9qpgNFZBmwDKC5uTnTYcaUFNfrtZfK+u/GL9527qrq/arapqptDQ0Nxa6OMcaUjUIn/s6giYfg+6ECP78xxlS8Qif+x4Ebgts3AGsK/PzGGFPxohzO+V1gIzBbRPaLyI3Al4EPi8hu4MPBfWOMMQUUWeeuqv5+hh8tjOo5jTHGjK0ktl4UkcPA6xE+xXTgzQjj+6Dcz7Hczw/sHMtFIc/xDFUdMTqmJBJ/1EQklm6sazkp93Ms9/MDO8dy4cM5ejuc0xhjTDQs8RtjTIWxxB93f7ErUADlfo7lfn5g51guin6O1sZvjDEVxq74jTGmwljiN8aYClP2iV9EmkRkg4jsEpFXROSzw37+FyKiIjI9qewLItIuIq+JyG8Xvta5Ge0cReTPgvN4RURWJJWXxTmKSKuIbBKRrSISE5ELkh5Tauc4QUR+JiLbgnP8UlCecQOjUjrHUc7vHhF5VUS2i8gPRWRK0mNK5vwg8zkm/dyPfKOqZf0FzADOC25PBn4OzAnuNwFPEZ8cNj0omwNsA2qBM4H/AqqLfR5hzhG4DHgaqA1+dmoZnuNa4CNB+UeJ7/FQqucoQF1wuwZ4EZgPrABuC8pvA+4uxXMc5fwWAeOC8rtL9fxGO8fgvjf5puyv+FX1gKpuCW53A7uA04Mf3wvcCiT3cC8Gvqeqvaq6B2gHLsBjo5zjZ4Avq2pv8LPEaqjldI4KnBwcdgrwy+B2KZ6jqmpPcLcm+FLi5/JgUP4gsCS4XVLnmOn8VHWtqvYH5ZuAmcHtkjo/GPV3CB7lm7JP/MmCHcE+ALwoIlcDb6jqtmGHnQ50JN3fz4k/FN5LPkfgbOBiEXlRRP5DRH4zOKyczvHPgXtEpAP4R+ALwWEleY4iUi0iW4kvWb5OVV9k2AZGQGIDo5I7xwznl+xTwI+C2yV3fpD+HH3LNxWT+EWkDniMeKLoB/4KuD3doWnKSmLMa/I5quo7xBfhm0r84/RfAitFRCivc/wMcIuqNgG3AA8kDk3zcO/PUVUHVLWV+FXvBSJyziiHl9w5jnZ+IvJXxP9vfidRlC5E5JXMU5pznIdn+aYiEr+I1BBPFt9R1R8A7yPenrZNRPYS/wVtEZHTiP/FbUp6+ExONB94K805QvxcfhB8/PwZMEh8gahyOscbgMTt73PiY3JJnmOCqh4FngGuJPMGRiV7jsPODxG5AbgK+EMNGr8p4fODlHNcjG/5ppgdIYX4Iv4X9SHga6Mcs5cTnS2/QWpnyy8ojQ6lEecI/AlwZ3D7bOIfKaXMznEXcGlweyGwuYR/jw3AlOD2ROCnxJPhPaR27q4oxXMc5fyuBHYCDcOOL6nzG+0chx1T9HxT6M3Wi2EB8AlgR9DuBvBFVX0y3cGq+oqIrCT+RuwH/lRVBwpS0/DSniPwbeDbIvIy8B5wg8bfbeV0jn8M/C8RGQccB5ZByf4eZwAPikg18U/jK1X1CRHZSLyZ7kZgH3AtlOQ5Zjq/duKJb128JZJNqvonJXh+kOEcMx1crHO0JRuMMabCVEQbvzHGmBMs8RtjTIWxxG+MMRXGEr8xxlQYS/zGGFNhLPGbsiUiA8Gqna8EqyV+TkRCv+dF5EPByouvBl/Lkn7WECyN8Z8SX0X0M0k/uzBYebIShk+bEmBvRFPOjml86jwicirwb8QXcvubXAMFsyz/DViiqluCZXWfEpE3VPX/EZ889qqq3iAijcBGEVkFdAFfB/6HnliILNfnFuJDrwfDPN6Y4WwcvylbItKjqnVJ938NeIn4shVnAA8Dk4If36yqL4jIw8AqVV0TPOY7wKPAbxJffPH2pHgLgTuAPwMeJz5T8w3gIuCPgse8BJxPfGLZl4FLiU9W+oaq3hesPbSG+JpKNcBfq+qaYCG6HwEbgnhLVPV1l6+PqVyW+E3ZGp74g7IjwPuBbmBQVY+LyFnAd1W1TUR+i/iib0tE5BRgK3AWsBJ4MPEHIYh1CrBHVaeJyCeBNlW9OfhZFbCR+EqabcDHie+H8HciUgs8T3wGbgdwkqq+E3yK2BQ83xnEp+9/UFU3RfICmYplTT2m0iRWQ6wBvi4ircAA8bWMUNX/EJFvBE1Dvws8pqr9QXNLuquktFdOqjooIvcR/2PQJSKLgHkick1wyCnEE/x+4C4RuYT4InqnA43BMa9b0jdRsMRvKkbQ1DNAfHXLvwE6gXOJD3I4nnTow8AfAr9HfH14gFeIX7k/nnTc+cTXWMlkMPiC+B+cP1PVp4bV6ZPEF/Y6X1X7gtUbJwQ/fjf7szMmezaqx1QEEWkA/gX4erBQ3SnAgaDD9BNAddLh/0p83wZU9ZWg7BvAJ4NPCIhIPfFtAleQnaeAzwRLSyMiZ4vIpKAeh4KkfxnxJh5jImVX/KacTQxW8qwhvvLhw8BXg5/9H+AxEbmWeAfq0NW1qnaKyC5gdVLZARG5HvimiEwmfgX/NVX99yzr8i1gFvF12AU4THwLxe8A/y4iMeL9Ca+GOVFjcmGdu8YMIyInATuIb+7+drHrY4xr1tRjTBIRuYL4Vff/tqRvypVd8RtjTIWxK35jjKkwlviNMabCWOI3xpgKY4nfGGMqjCV+Y4ypMP8fFF03YlhPduQAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zdá sa, že korelácia je dosť malá, ale existuje nejaký iný dôležitejší vzťah - pretože cenové body v grafe vyššie sa zdajú mať niekoľko odlišných klastrov. Poďme vytvoriť graf, ktorý ukáže rôzne odrody tekvíc:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax=None\n",
+    "colors = ['red','blue','green','yellow']\n",
+    "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    ax = new_pumpkins[new_pumpkins['Variety']==var].plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAGKCAYAAAAVEBpAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcxklEQVR4nO3df5h3dV3n8ecrwEBEARm4bhW8UxEDfwDekqztpiiFmiGGCW5GZhduLRVpJaX5a7e9bP25musurAiRgqaopGEiF+TiKnrzQ4SQIEMSEG615BbzB/DeP8538sswv+c7c85n5vm4rrnm/PjOzIvx/r488znnfE6qCklSe36s7wCSpOWxwCWpURa4JDXKApekRlngktSoHdfyh+211161efPmtfyRktS8yy677BtVNTVz+5oW+ObNm9m6deta/khJal6Sr862fcEhlCQ7J/l8ki8muSbJ60bbX5vk5iRXjj6eNenQkqS5LeYI/PvAEVX1nSQ7AZckOX+0761V9abViydJmsuCBV7drZrfGa3uNPrw9k1J6tmirkJJskOSK4HbgQuq6tLRrpOSXJXk9CR7rFZISdJ9LarAq+ruqjoYeBhwWJLHAu8CHgkcDNwKvHm2r01yYpKtSbZu27ZtIqElSUu8Dryq/gW4GDiqqm4bFfs9wGnAYXN8zalVtaWqtkxN3ecqGEnSMi3mKpSpJLuPlncBngF8OcmmsZcdA1y9KgklSbNazFUom4Azk+xAV/gfqKqPJTkrycF0JzRvBF66aiklSfexmKtQrgIOmWX7i1YlkSRpUdb0TkwN3+ZTPt53hAXd+IZn9x1BGgQns5KkRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1KjmH6nWwiPAwMeASZo8j8AlqVEWuCQ1asECT7Jzks8n+WKSa5K8brR9zyQXJLl+9HmP1Y8rSZq2mCPw7wNHVNUTgIOBo5I8GTgFuLCq9gcuHK1LktbIggVene+MVncafRRwNHDmaPuZwHNXI6AkaXaLGgNPskOSK4HbgQuq6lJgn6q6FWD0ee85vvbEJFuTbN22bduEYkuSFlXgVXV3VR0MPAw4LMljF/sDqurUqtpSVVumpqaWGVOSNNOSrkKpqn8BLgaOAm5Lsglg9Pn2SYeTJM1tMVehTCXZfbS8C/AM4MvAecAJo5edAHx0lTJKkmaxmDsxNwFnJtmBrvA/UFUfS/JZ4ANJXgLcBDx/FXNKkmZYsMCr6irgkFm2fxN4+mqEkiQtzDsxJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhq1YIEn2TfJRUmuTXJNkt8ZbX9tkpuTXDn6eNbqx5UkTdtxEa+5C3h5VV2eZDfgsiQXjPa9taretHrxJElzWbDAq+pW4NbR8vYk1wIPXe1gkqT5LWkMPMlm4BDg0tGmk5JcleT0JHvM8TUnJtmaZOu2bdtWllaS9G8WXeBJHgB8CDi5qu4A3gU8EjiY7gj9zbN9XVWdWlVbqmrL1NTUyhNLkoBFFniSnejK+71VdS5AVd1WVXdX1T3AacBhqxdTkjTTYq5CCfBu4NqqesvY9k1jLzsGuHry8SRJc1nMVShPAV4EfCnJlaNtfwQcn+RgoIAbgZeuQj5J0hwWcxXKJUBm2fXXk48jSVos78SUpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJatSCBZ5k3yQXJbk2yTVJfme0fc8kFyS5fvR5j9WPK0matpgj8LuAl1fVTwJPBv5zkgOBU4ALq2p/4MLRuiRpjSxY4FV1a1VdPlreDlwLPBQ4Gjhz9LIzgeeuUkZJ0iyWNAaeZDNwCHApsE9V3QpdyQN7TzydJGlOiy7wJA8APgScXFV3LOHrTkyyNcnWbdu2LSejJGkWiyrwJDvRlfd7q+rc0ebbkmwa7d8E3D7b11bVqVW1paq2TE1NTSKzJInFXYUS4N3AtVX1lrFd5wEnjJZPAD46+XiSpLnsuIjXPAV4EfClJFeOtv0R8AbgA0leAtwEPH9VEkqSZrVggVfVJUDm2P30ycaRJC2Wd2JKUqMscElqlAUuSY2ywCWpURa4JDXKApekRi3mOnBJy7D5lI/3HWFRbnzDs/uOoGXyCFySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjXIyK0lNcHKw+/IIXJIaZYFLUqMWLPAkpye5PcnVY9tem+TmJFeOPp61ujElSTMt5gj8DOCoWba/taoOHn389WRjSZIWsmCBV9WngW+tQRZJ0hKsZAz8pCRXjYZY9pjrRUlOTLI1ydZt27at4MdJksYtt8DfBTwSOBi4FXjzXC+sqlOraktVbZmamlrmj5MkzbSsAq+q26rq7qq6BzgNOGyysSRJC1lWgSfZNLZ6DHD1XK+VJK2OBe/ETHI28FRgryRfA14DPDXJwUABNwIvXb2IkqTZLFjgVXX8LJvfvQpZJElL4J2YktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY1asMCTnJ7k9iRXj23bM8kFSa4ffd5jdWNKkmZazBH4GcBRM7adAlxYVfsDF47WJUlraMECr6pPA9+asflo4MzR8pnAcycbS5K0kOWOge9TVbcCjD7vPdcLk5yYZGuSrdu2bVvmj5MkzbTqJzGr6tSq2lJVW6amplb7x0nShrHcAr8tySaA0efbJxdJkrQYyy3w84ATRssnAB+dTBxJ0mIt5jLCs4HPAgck+VqSlwBvAI5Mcj1w5GhdkrSGdlzoBVV1/By7nj7hLJKkJfBOTElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGLfhU+vkkuRHYDtwN3FVVWyYRSpK0sBUV+MjTquobE/g+kqQlcAhFkhq10gIv4JNJLkty4iQCSZIWZ6VDKE+pqluS7A1ckOTLVfXp8ReMiv1EgP3222+FP06SNG1FR+BVdcvo8+3Ah4HDZnnNqVW1paq2TE1NreTHSZLGLLvAk+yaZLfpZeBngasnFUySNL+VDKHsA3w4yfT3eV9VfWIiqSRJC1p2gVfVV4AnTDCLJGkJvIxQkhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1akUFnuSoJNcluSHJKZMKJUla2LILPMkOwDuBZwIHAscnOXBSwSRJ81vJEfhhwA1V9ZWq+gFwDnD0ZGJJkhaSqlreFybHAkdV1a+P1l8E/FRVnTTjdScCJ45WDwCuW37cNbMX8I2+Q6wj/j4nx9/lZLXy+3x4VU3N3LjjCr5hZtl2n/83qKpTgVNX8HPWXJKtVbWl7xzrhb/PyfF3OVmt/z5XMoTyNWDfsfWHAbesLI4kabFWUuBfAPZP8hNJ7gccB5w3mViSpIUsewilqu5KchLwN8AOwOlVdc3EkvWrqSGfBvj7nBx/l5PV9O9z2ScxJUn98k5MSWqUBS5JjbLAJalRG77Akzxwnn37rWUWaS5JHpzkmCRP7DtLi5J8YGz5T2fs++TaJ5qMDV/gwMXTC0kunLHvI2uapHHr9U3ShyQfS/LY0fIm4Grg14CzkpzcZ7ZG7T+2fOSMffe5w7EVFvi97yjdc559Wti6fJP05Ceq6urR8ouBC6rqOcBP0RW5lma+y+2avRRvJbfSrxc1x/Js65rfunyT9OSHY8tPB04DqKrtSe7pJ1LT7p/kELqD1l1Gyxl97NJrshWwwGHvJC+j+x9yepnRukeNS7Mu3yQ9+ackv0U3ZcWhwCcAkuwC7NRnsEZ9HXjLLMvT603a8DfyJHnNfPur6nVrlaV1SS6ab39VPW2tsrQuyd7A64FNwDur6pOj7U8DnlhVb+ozn4Zhwxf4fJKcXFVv6zuHNC7JjlV1V985WpJkf+CNwKOALwG/V1U395tq5TyJOb+XLfwSLSTJkUku6DtHS5JcMrZ81ozdn1/jOOvB6cDHgV8ELgfe0W+cybDA5+dVKEuQ5Igkf5/kO0n+IsmBSbYCbwDe1Xe+xuw6tnzQjH3+u1y63arqtKq6rqreCGzuO9AkeBJzfo4vLc2b6Z6+9Fm6Z6V+DvjjqvofvaZqk1f0TNbOYyfV4d4n2amqy3tLtgIbvsCTbGf2N0SA+69xnNZVVV08Wv5Ikm2W97LtnuQYur+Sd0/yvNH2AA/qL1azZrvyZHq9gCPWPNEEeBJTE5PkK8DvjW160/h6VZ275qEaleQ98+2vqhevVZb1IMkDq+qOvnNMmgU+iyS7As8FXlhVz+45TjMWKJ2qKu8gVC+S/APwyqo6p+8sk2SBj4weC/cs4IXAUcCHgHOr6q96DaYNaeyGsllV1Vvm2697S/Jw4G3AA4DfqKob+k00GY6BJ0cCxwM/B1wEnAUc5p+oSzdL6RTwDeCSqvrHHiK1bLe+A6wnVfVV4JgkRwGfSfIF4J6x/b/QW7gV2PAFTvdMz/8L/PR0ySTxxNvyzFY6m4FXJnntevvzdZV9s6r+rO8Q60mSA4A/oHu/v5OxAm/Vhh9CGV1KdBxwLPAV4Bzg1VX18F6DrSNJ9gQ+VVWH9p2lFUku9/c1OUneAPwC8PKqOr/vPJOy4W/kqaorquoVVfVI4LXAIcD9kpyf5MR+060PVfUtvPlE/doCHLqeyhss8Hs9aKCqPlNVJwEPpTvhcXhfudaTJEcA/9x3jsY8Pskds3xsT7LuLodbA3tW1ff6DjFpjoHPMmVsVd1DNzb+N2sfp11JvsR9b4raE7gF+JW1T9S0L1XVIX2H0LBZ4PCgsbvc7sObT5bkecAPxtaL7mTcnT3lkaY9Isl5c+30KpR2PQj4eWYfoy3AAl+893vibWL+su8A68w2url61hULHG7yDsGJ8UTl5Bw8vZDkT6vqFWPrn6yqn+0lVbu2V9Xf9h1i0ixwZ3abpKn57iD07sEledTY8pHAK8bWfdTf0t3Yd4DVYIHDt9brRDc92IHuVmWPxFeXBx1LVFVznudqmQUOnwQuS/Kaqnpf32Ead2tVvb7vEOuED4jWgjb8nZgASR5KNzfwXnRPjhmfI8GTmIuU5IrZLn1Lsi9w3OhJKFqEJBczz5G2D4gWeAQOQFXdnOTjwJ8Az+FHBe5VKEvz9OmFJHsBz6ebKOxh+Htckqp6at8Z1pMkv1xVfzFafkpVfWZs30mtzjuz4Y/AkxxEd9R9C/C7VXVrz5GalWQ34Bi6KXkfDXwYeEFVPazXYA2a794E8C/DpRqfW2bmPDMtzzvjETh8EDi5qu5z12WSXb0JZUlup3ti+qvoppCt0WPBtHTPmWeffxkuXeZYnm29GRZ4d73tXkm2AFdV1Q+S7A2cDPwq8JD+ojXnj+hmdnwX8L4k7+85T7Ocj37iao7l2dabseEnswL+E3Al8A7gc0lOAK6lO9P/xB5zNaeq3lpVP0U3bWeAjwAPSfKKJI/uNVyDkuwwOpcwvX6/JCcmubbPXI16TJKrRvP1TC9Prx/Qd7jlcgw8+Tu6hzl8K8l+wA3Af6iqz/UcbV1I8ji6MfFfGk3Zq0VIchzwv4E7gevppjo+C/gC8F+q6vL+0rVn9Ei1OY2e2NMcC/y+JzSurqrH9plJSnI18NyquiHJocBn6S7F/HDP0TQgFnhyO91TeKYdN75eVb+95qEalWQ7s48nhu6p9A9c40jNmuXA4stV9Zg+M7Vs7N/m9AnL6X+nTf/b9CQm/P6M9ct6SbEOVJUP4p2cvWfMK/OA8XXnlVma9fpvc8MfgUtDlOQ18+wupyxYmiQ7012w8CjgKuD0qrqr31Qrt+ELPMlfMf8ty01O9N6HWf5MZbS+I3C/qvIvvglI8qSq+kLfOVoyuqT1h3RPpH8m8NWq+p1+U62cbyh4U98B1ouZf6aO7sz8TeCldHdlapmSHEh3fuZ44Nt0D+nV4h1YVY8DSPJuuhvOmrfhC3yuSd6nJ2AC1t0k8Kstye50N0L9CvA+4ElV9c0+M7VodOnb8aOPu4CHA1uq6sY+czXqh9MLVXVX0uzNl/ey4Qt83IwJmB6KR41LMvr9vRx4AXA6cEhVfbvfVG1K8v/oHvd3DnBsVV2f5B8t72V7QpLpOf9DN0XvHXgVStvmmIDpEU7AtCxfpXv24HuA7wIvGT/S8cqJJdlGN4vjPnRP4Lmehm/57ltV7dB3htWw4QscJ2CapDfyo5JZl5dtrZWqOjrJg4BfBF6X5FHA7kkOq6p1MX6rlfMqlOR36ca6d6Ubr30/cEFVPaLXYNrQkuxUVT8cW9+bbmjqeGDfqtq3t3AajA1f4NOSPILuzXEcsD/wGuDDVfX3vQZrSJK3z7ffu1oXb3SH8EeBs4GLauyNmuThrc7docna8AWeZL+qumnGtsfRlfkLnIBp8UYzOc6pqs5cqyytS/Jg4Fh+dEDxQeDsqrq012AaFAv83k/q+FBV/WLfmaRxSR5Cd3XUccDewDlV9cp+U2kILPCxB/HO9VBeLU6S8+bb712ty5fkAcDzgJcBm6pqn54jaQC8CmX+J3VoaQ4H/olu3PZSGn5U1RCM5u94Dt1w3lOATwB/CHyyz1waDo/Ak7vpJs0P3VN4vju9i4Yv8O9Dkh2AI+kK5/HAx+nGba/pNViDkrwPeAbwabqbeT5WVd/rN5WGZsMXuFZHkh+nK/I3Aq+vqnf0HKkpoxPC51bV9r6zaLgscE3UqLifTVfem4Hz6KbuvLnPXNJ6ZIFrYpKcCTwWOJ/uSomre44krWsWuCYmyT105xPg3ieEPZ8grQILXBqgJM+bb39VnbtWWTRcFrg0QKO/Zq4cfcCMpxxV1a+tdSYNjwUuDdBoRswX0D3D8aN0l2Pe0G8qDY0FLg1Ykl2Bo+nK/MHAK+d6ipQ2nh/rO4CkeX2P7hmYd9BNebxzv3E0JB6BSwOU5Gl019IfBnyK7rLMrf2m0tBY4NIAjU5iXgVcQndJ5r3eqM6tLnAyK2moXtx3AA2fR+DSwI2mkq2qunPBF2tD8SSmNFBJfiPJTcBXgZuSfDXJb/adS8NhgUsDlORVdHOBP7WqHlxVDwaeBjxztE9yCEUaoiTXAU+YOQd4kl2AL1bVo/tJpiHxCFwaqNke4FBV/wrc00McDZAFLg3T15I8febGJEcAt/aQRwPkEIo0QEkOopsD5RLgMrrrwJ9E92zMo31MncAClwZr9FDjFwIH0c1GeA3wXp+NqWneyCMNVFV9L8lFwO10R+DXWt4a5xG4NEBJHgj8H+CJdHOC/xjwBLrhlJdU1R39pdNQWODSACU5A7gReH1V3TPaFuCPgUdV1a/0l05DYYFLA5Tk+qraf6n7tLF4GaE0TFn4JdroLHBpmD6T5NWjYZN/k+SPgc/1lEkD4xCKNECjk5jvBg6lO4lZwCHAFXQnMb/dXzoNhQUuDViSRwIHMroOvKr+IcnJVfW2fpNpCCxwqTFJbqqq/frOof45Bi61xxOcAixwqUX+2SzAW+mlQUqyndmLOsAuaxxHA+UYuCQ1yiEUqSFJdk/yyr5zaBgscGmAkuyb5NQkH0vy60nun+TNwPXA3n3n0zA4Bi4N058Dfwt8CDiK7u7La4DHVdXX+wym4XAMXBqgJF+sqieMrd8G7FdV3+8xlgbGI3BpoJLswY+u+f46cP8kuwJU1bd6C6bB8AhcGqAkN9I9fX62m3aqqh6xtok0RBa4JDXKIRRpgJIcOt/+qrp8rbJouDwClwZo9DDjuVRVHbFmYTRYFrgkNcobeaQBSvIHY8vPn7Hvv619Ig2RBS4N03Fjy384Y99RaxlEw2WBS8OUOZZnW9cGZYFLw1RzLM+2rg3Kk5jSACW5G7iTH83//d3pXcDOVbVTX9k0HBa4JDXKG3mkAUqy53z7nQtF4BG4NEhJ/pFurDvAJuAWfnTy0rlQBFjg0uAluaKqDuk7h4bHq1Ck4fMoS7OywCWpUZ7ElAYoycvGVveesU5VvWWNI2mALHBpmHYbWz5txroEeBJTkprlGLgkNcoCl6RGWeCS1ChPYkoDleQA4ETgMaNN1wKnVdV1/aXSkHgELg1QksOBi4HtwKl0V6LcCVyU5Mk9RtOAeBWKNEBJzgf+tKounrH9Z4BTquqZvQTToFjg0gAl+fuqevQc+66rqgPWOpOGxyEUaZi2z7PvzjVLoUHzJKY0TPsmefss2wM8dK3DaJgscGmYfn+efVvXLIUGzTFwqTFJdqyqu/rOof45Bi4NUJJLxpbPmrH782scRwNlgUvDtOvY8kEz9gUJC1waqvnGNh33FOBJTGmodk9yDN1B1u5JnjfaHuBB/cXSkHgSUxqgJGcwz5F2Vb147dJoqCxwSWqUY+DSACV5R5L7PEYtyWOSfKqPTBoeC1wapq8DVyZ5IUCS+yf578B5wDt7TabBcAhFGqgkPwH8Gd0DjR8CfAD4r1X13V6DaTA8ApeGa/roake69+q1lrfGWeDSACV5FfAp4M+r6t8B/x44OsnfJjmw33QaCq8Dl4ZpCjikqrYDVNXNwLFJngl8CPjJPsNpGBwDlxqT5Mer6vt951D/PAKXBmiOucDH/faaBNGgWeDSMF3WdwANn0MoktQoj8ClAUpy3nz7q+oX1iqLhssCl4bpcOCfgLOBS3EOcM3CIRRpgJLsABwJHA88Hvg4cHZVXdNrMA2KN/JIA1RVd1fVJ6rqBODJwA3AxUl+q+doGhCHUKSBSvLjwLPpjsI3A28Hzu0zk4bFIRRpgJKcCTwWOB84p6qu7jmSBsgClwYoyT3AnaPV8TdpgKqqB659Kg2NBS5JjfIkpiQ1ygKXpEZZ4JLUKAtczUtycZKfm7Ht5CT/c5Ff//okz1jgNb+a5CErySlNmgWu9eBs4LgZ244bbZ9Xkh2q6tVVtdCT3n+V7rmU0mBY4FoPPgj8/OjGF5JspivbFybZmuSaJK+bfnGSG5O8OsklwPOTnJHk2NG+J44eW3ZZkr9Jsmm0bwvw3iRXJnl2kg+Pfb8jk3iDjdacBa7mVdU3gc8DR402HQe8H3hlVW2hm0vkZ5I8fuzLvldVP11V50xvSLIT8A7g2Kp6InA68CdV9UFgK/Afq+pg4K+Bn0wyNfrSFwPvWbX/QGkOFrjWi/FhlOnhk19KcjlwBXAQMP4w4PfP8j0OoLv78YIkVwKvAh4280XV3TxxFvDLSXanmznw/In8V0hL4FwoWi8+ArwlyaHALsA/A78HPKmq/jnJGcDOY6+/8z7fobvL8ZqqOnwRP+89wF8B3wP+sqruWkF2aVk8Ate6UFXfAS6mG/Y4G3ggXUl/O8k+wDMX8W2uA6aSHA7dkEqSg0b7tgO7jf28W4Bb6I7Sz5jMf4W0NB6Baz05m262vuOq6stJrgCuAb4CfGahL66qH4xOWL49yYPo3h9vG32PM4D/leRfgcOr6l+B9wJTVfV3q/EfIy3EuVCkZUryZ8AVVfXuvrNoY7LApWVIchndEM2RVfX9vvNoY7LAJalRnsSUpEZZ4JLUKAtckhplgUtSoyxwSWrU/wdO32Yxjk19aAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.2669192282197318\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n",
+    "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))\n",
+    "pie_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lineárna regresia\n",
+    "\n",
+    "Použijeme Scikit Learn na natrénovanie modelu lineárnej regresie:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a3232f8dc0>]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD4CAYAAAATpHZ6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXXElEQVR4nO3df3BV5Z3H8feXEDGKEF0QTYCmdTEtv4R6tW5/rL+qYVerjF21bmeHrW5ZXWvXtmJFO63tdEdrnFY722mHGRl1a9mqpGzX1kZrtdqtQC+gBtBYLYgkKFGM+CMCCd/9495gSHJzf+Se3HuefF4zjDfPPfecJ1/hc899znOfY+6OiIiEYUypOyAiIsWjUBcRCYhCXUQkIAp1EZGAKNRFRAIydiQPNmnSJK+rqxvJQ4qIxN66detec/fJuWw7oqFeV1dHMpkcyUOKiMSemb2U67YafhERCYhCXUQkIAp1EZGAKNRFRAKiUBcRCUjW2S9mNg24GzgG2A8sc/fbzawR+AywF3gR+IK7d0bYVxkBqza00djcSntnFzXVVSxpqGfh/NpSd0tEcpTLmXo38DV3/whwCnClmc0EHgZmu/tc4HlgaXTdlJGwakMbS5taaOvswoG2zi6WNrWwakNbqbsmIjnKGuruvsPd16cfvwU8C9S6+0Pu3p3ebDUwNbpuykhobG6la1/PQW1d+3pobG4tUY9EJF95jambWR0wH1jT76lLgQczvGaxmSXNLNnR0VFQJ2VktHd25dUuIuUn51A3s/HASuBqd9/dp/0GUkM09wz2Ondf5u4Jd09MnpzTt1ylRGqqq/JqF5Hyk1Oom1klqUC/x92b+rQvAs4FPu+6hVLsLWmop6qy4qC2qsoKljTUl6hHIpKvXGa/GHAH8Ky7f79P+wLg68Cp7v5udF2UkdI7y0WzX0Tiy7KdYJvZJ4EngBZSUxoBrgd+CIwDXk+3rXb3y4faVyKRcC3oJSKSHzNb5+6JXLbNeqbu7n8AbJCnfp1vx0REJFr6RqmISEAU6iIiAVGoi4gERKEuIhIQhbqISEAU6iIiAVGoi4gERKEuIhIQhbqISEAU6iIiAVGoi4gERKEuIhIQhbqISEAU6iIiAVGoi4gERKEuIhIQhbqISEAU6iIiAVGoi4gEJOs9SkWitmpDG43NrbR3dlFTXcWShnoWzq8t+33LQKp36SnUpaRWbWhjaVMLXft6AGjr7GJpUwvAsMMgyn3LQKp3edDwi5RUY3PrgRDo1bWvh8bm1rLetwykepcHhbqUVHtnV17t5bJvGUj1Lg8KdSmpmuqqvNrLZd8ykOpdHhTqUlJLGuqpqqw4qK2qsoIlDfVlvW8ZSPUuD7pQKiXVewEtihkTUe5bBlK9y4O5+4gdLJFIeDKZHLHjiYiEwMzWuXsil22znqmb2TTgbuAYYD+wzN1vN7OjgJ8DdcBW4CJ3f6PQTpeC5tSKSGhyGVPvBr7m7h8BTgGuNLOZwHXAI+4+A3gk/XNs9M6pbevswnl/Tu2qDW2l7pqISMGyhrq773D39enHbwHPArXA+cBd6c3uAhZG1MdIaE6tiIQor9kvZlYHzAfWAFPcfQekgh84OsNrFptZ0sySHR0dw+xu8WhOrYiEKOdQN7PxwErganffnevr3H2ZuyfcPTF58uRC+hgJzakVkRDlFOpmVkkq0O9x96Z086tmdmz6+WOBndF0MRqaUysiIcoa6mZmwB3As+7+/T5P/RJYlH68CPif4ncvOgvn13LTBXOora7CgNrqKm66YI5mv4hIrGWdp25mnwSeAFpITWkEuJ7UuPq9wHRgG3Chu+8aal+apy4ikr+izlN39z8AluHpM/PpmIiIREtrv4iIBEShLiISEIW6iEhAFOoiIgFRqIuIBEShLiISEIW6iEhAFOoiIgFRqIuIBET3KBUpkO6cJeVIoS5SgN47Z/XeaKX3zlmAgl1KSsMvIgXQnbOkXCnURQqgO2dJuVKoixRAd86ScqVQFymA7pwl5UoXSkUK0HsxVLNfpNwo1EUKtHB+rUJcyo6GX0REAqJQFxEJiEJdRCQgCnURkYAo1EVEAqJQFxEJSNlPaYzrSnhx7beIxFtZh3pcV8KLa79FJP7KevglrivhxbXfIhJ/WUPdzJab2U4z29inbZ6ZrTazp8wsaWYnR9G5uK6EF9d+i0j85XKmfiewoF/bLcC33X0e8M30z0UX15Xw4tpvEYm/rKHu7o8Du/o3AxPSjycC7UXuFxDflfDi2m8Rib9CL5ReDTSb2a2k3hg+XrQe9RHXlfDi2m8RiT9z9+wbmdUBD7j77PTPPwR+7+4rzewiYLG7fzrDaxcDiwGmT59+4ksvvVSsvouIjApmts7dE7lsW+jsl0VAU/rxfUDGC6XuvszdE+6emDx5coGHExGRXBQa6u3AqenHZwB/Lk53RERkOLKOqZvZCuA0YJKZbQe+BXwRuN3MxgLvkR5eERGR0soa6u5+SYanTixyX0REZJjK+hulIiKSH4W6iEhAFOoiIgFRqIuIBEShLiISEIW6iEhAFOoiIgFRqIuIBEShLiISEIW6iEhAFOoiIgFRqIuIBEShLiISEIW6iEhACr1H6Yhq3vQK//pf6w5qO/yQCmbVTGRmzQRm1UxgVs1EZkwZT2WF3qdEZPSKRahPPbJqQNs7e3tYu3UXa7fuyvr6D006nJk1E9JvABOZVTOBSePHRdFVEZGSyunG08WSSCQ8mUwWZV9vvLOXzTt2s6n9TTa172ZT+25e2Pl2Ufbd6/JTj+NLZ/w148fF4r1PRAKVz42nYxvqueru2c+LHe+wecebbGrbnX4DeJPd73UX7Rizaydw42dmkag7qmj7FBHppVAv0Notu/jOA5vY2LY7smM0zJrCrReewBGHVkZ2DBEJi0I9Ym/v6ebHj73Ajx59MdLj3PmFkzit/uhIjyEi5U+hXkb+9+l2rlqxIdJjfOaEGm6+YA6Ha+xfJEgK9Ryt2tBGY3Mr7Z1d1FRXsaShnoXza0vSl9ff3sPVP3+KJ/78WqTH+d5n53DxSdMjPYaIFJdCPQerNrSxtKmFrn09B9qqKiu46YI5JQv2XK1ct52v3fd0pMeoPqySJ649XWP/ImVAoZ6DT9z8O9o6uwa011ZX8X/XnVGCHhXfltfe4fRbH4v8OLd/bh7nzyvvN0KROFOo5+CD1/2KwX5zA7bcfM5Id6fkbvzlJu7849ZIj3HMhEP53TWnctghGvsXyUc+oT5q/3XVVFcNeqZeUz3w26ujwY3nzeLG82Zl3e75V9/i7B88XtAxXtn9HjO/2ZzTtj+8ZD7nnVBT0HFERrNRe6Ye5zH1OHB3rr3/Ge5btz3S4xx1+CH88bozOLSyItLjiJRSUYdfzGw5cC6w091n92m/CvgS0A38yt2vzXawcgp1KK/ZL6NVpmsbUfj+RSdwwUenjsixRIqp2KH+t8DbwN29oW5mpwM3AOe4+x4zO9rdd2Y7WLmFupRePtc29u93/vnOP/H48x2R96v1uwsYN1Zn/1Ieijqm7u6Pm1ldv+YrgJvdfU96m6yBLtGL4yePfK5tjBlj3H3pyTntN7l1F//wkycL7lf9N36T03Y/+sePcs7cYws+jkixFXqh9HjgU2b2H8B7wDXu/qfBNjSzxcBigOnT9aWXqPS/RtDW2cXSphaAsg720z88mZ+u3jZo+3Ak6o7itovnZb1u0t2zn0/d8ig73nyvoONc+bP1XPmz7NtVVVbwzI1na71/iVyhoT4WOBI4BTgJuNfMPuSDjOW4+zJgGaSGXwrtqAytsbn1oPAC6NrXQ2Nza1mH+qPPDT6Ukqk9H7nUZGzFGJ5cemZO+/vt5lf5l7sLGz7s2tfDjBsezGnbOxYlOPMjUwo6jkihob4daEqH+Foz2w9MAqIf7JRBtWe42JipvVxE2e9i7/vTM6ewNYfvMOzt3s+J332Ytwpc3vmyu3J745g/vZr7L/84FWOsoONImAoN9VXAGcBjZnY8cAgQ7aIlMqS4zruPst+lqskhY8fQcmNDTts+8Ew7X/pZYQu+bdjWyXHX/zqnbVde8Tec+AGt9z8aZA11M1sBnAZMMrPtwLeA5cByM9sI7AUWDTb0IiNnSUP9oOPHSxrqS9ir7KLsdxxqcu7cGs6dm/1LVnu6e1hw2xNsee2dgo7z2R/ndtH4ytOP45qz6zHT2X9cjdovH4UojrNfINp+x7Umw/GbjTu4/KfrIz1G9WGVrPq3T1A36fBIjyMpWvtFRLLa093D9U0bWbk+mm/9jh1jXJiYxkWJqcybVq2z/2HQ2i+jVFzPSuPa7yiN5KeX2y6el3Xfa/7yOhcvW53Xcbr3OyvWbmPF2oFTVvubP72aixLTOHfusVrueZh0ph6IuK5lE9d+RynKmkRd7/f29XDzg8/x3Cu7Wf2XXcPeX3/jxo7hosQ0LkpMY3bthFFz9q/hl1EoruvDx7XfUYqyJuVU7+1vvEvT+jbuTb7M9jeKP/U28YEjuTAxlXPm1jA+5rd61PDLKKR56uGI09z94Zh65GF8+cwZfPnMGUNut69nP4+1dnBf8mUe2vxqzvtPvvQGyZfe4OsrW4bc7vBDKrgwMY3PnTyNDx8zIef9lyuFeiA0Tz0cIc7dH47KijGcNXMKZ83M/i3bba+/y/3rt3N/8mXac1z64Z29Pdz5x60ZbxIzftxYZtZMYFbNBGYeO4FZNROZMWV82S75oFAPRBzmZA8mrv2O0mifuz8c0//qML561vF89azjh9xub/d+fvfcTu5Lvswjzw29HuHbe7pZu2UXa7dkv0bwoUmHp98AJqbeBGomMGn8uLx+h+HSmHpA4jqLJK79jpLm7pePznf3snnHbja372ZT+242tb/J86++nfd+Hrjqk8yunVhQH3ShVERkhHX37GfLa+8cCP7NO1JvAp3v7gPgD18/nalHHlbQvnWhVERkhI2tGMOMKUcwY8oRJf3kU54j/SIiUhCFuohIQBTqIiIBUaiLiAREoS4iEhCFuohIQBTqIiIB0Tx1OYi+bSgSbwp1OaD/WtttnV0sbUqtcKdgF4kHDb/IAY3NrQct9ATQta+HxubWEvVIRPKlUJcDymmtbREpjEJdDsi0pnY5r7UtIgdTqMsBSxrqqaqsOKgtpLW2RUYDXSiVA3ovhmr2i0h8KdTlIAvn1yrERWJMoS4lp7nxA8X1zkf6fznQSNdEoS4lpbnxA0VZk7juO65KUZOsF0rNbLmZ7TSzjYM8d42ZuZlNiqR3EjzNjR8oyprEdd9xVYqa5DL75U5gQf9GM5sGnAVsK3KfZBTR3PiBoqxJXPcdV6WoSdZQd/fHgV2DPPUD4Fpg5O5cLcHR3PiBoqxJXPcdV6WoSUHz1M3sPKDN3Z/OYdvFZpY0s2RHR0chh5OAaW78QFHWJK77jqtS1CTvC6VmdhhwA3B2Ltu7+zJgGUAikdBZvRxEc+MHirImcd13XJWiJuaePWfNrA54wN1nm9kc4BHg3fTTU4F24GR3f2Wo/SQSCU8mk8PrsYjIKGNm69w9kcu2eZ+pu3sLcHSfg20FEu7+Wr77Eoma5k3LaJPLlMYVwJNAvZltN7PLou+WyPD1zhFu6+zCeX+O8KoNbaXumkhksp6pu/slWZ6vK1pvRIpoqDnCOluXUGmVRgmW5k3LaKRlAiRYNdVVtA0S4KN53nTU4noNI679HozO1CVYmjc9suJ6DSOu/c5EoS7BWji/lpsumENtdRUG1FZXcdMFc2J7Blbu4rr2S1z7nYmGXyRoWh9+5MT1GkZc+52JztRFpCjiuvZLXPudiUJdRIoirtcw4trvTDT8IiJFEde1X+La70xyWvulWLT2i4hI/vJZ+0XDLyIiAVGoi4gERKEuIhIQhbqISEAU6iIiAVGoi4gERKEuIhIQhbqISEAU6iIiAVGoi4gERKEuIhIQhbqISEAU6iIiAVGoi4gERKEuIhIQhbqISEAU6iIiAcka6ma23Mx2mtnGPm2NZvacmT1jZr8ws+pIeykiIjnJ5Uz9TmBBv7aHgdnuPhd4Hlha5H6JiEgBsoa6uz8O7OrX9pC7d6d/XA1MjaBvIiKSp2KMqV8KPJjpSTNbbGZJM0t2dHQU4XAiIpLJsELdzG4AuoF7Mm3j7svcPeHuicmTJw/ncCIiksXYQl9oZouAc4Ez3d2L1yURESlUQaFuZguArwOnuvu7xe2SiIgUKpcpjSuAJ4F6M9tuZpcB/wkcATxsZk+Z2U8i7qeIiOQg65m6u18ySPMdEfRFRESGSd8oFREJiEJdRCQgCnURkYAo1EVEAqJQFxEJiEJdRCQgCnURkYAo1EVEAqJQFxEJiEJdRCQgCnURkYAUvPSuiETnG6taWLHmZXrcqTDjko9N47sL5xRl36s2tNHY3Ep7Zxc11VUsaahn4fzaouxbSk+hLlJmvrGqhZ+u3nbg5x73Az8PN9hXbWhjaVMLXft6AGjr7GJpUwuAgj0QGn4RKTMr1rycV3s+GptbDwR6r659PTQ2tw5731IeFOoiZaYnw43EMrXno72zK692iR+FukiZqTDLqz0fNdVVebVL/CjURcrMJR+blld7PpY01FNVWXFQW1VlBUsa6oe9bykPulAqUmZ6L4ZGMful92KoZr+Ey7wI43S5SiQSnkwmR+x4IiIhMLN17p7IZVsNv4iIBEShLiISEIW6iEhAFOoiIgFRqIuIBGREZ7+YWQfwEjAJeG3EDly+VAfVAFSDXqpD5hp8wN0n57KDEQ31Awc1S+Y6PSdkqoNqAKpBL9WhODXQ8IuISEAU6iIiASlVqC8r0XHLjeqgGoBq0Et1KEINSjKmLiIi0dDwi4hIQBTqIiIBKXqom9k0M3vUzJ41s01m9u/9nr/GzNzMJvVpW2pmL5hZq5k1FLtPpTBUHczsqvTvusnMbunTHlQdMtXAzOaZ2Woze8rMkmZ2cp/XBFUDADM71MzWmtnT6Tp8O91+lJk9bGZ/Tv/3yD6vCaoOQ9Sg0cyeM7NnzOwXZlbd5zVB1QAy16HP88PPR3cv6h/gWOCj6cdHAM8DM9M/TwOaSX8BKd02E3gaGAd8EHgRqCh2v0b6T6Y6AKcDvwXGpZ87OtQ6DFGDh4C/S7f/PfBYqDVI/14GjE8/rgTWAKcAtwDXpduvA74Xah2GqMHZwNh0+/dCrsFQdUj/XJR8LPqZurvvcPf16cdvAc8CvSvw/wC4Fuh7dfZ84L/dfY+7bwFeAE4m5oaowxXAze6+J/3czvRLgqvDEDVwYEJ6s4lAe/pxcDUA8JS30z9Wpv84qd/3rnT7XcDC9OPg6pCpBu7+kLt3p9tXA1PTj4OrAQz5dwGKlI+RjqmbWR0wH1hjZucBbe7+dL/NaoG+t0nfzvtvAkHoWwfgeOBTZrbGzH5vZielNwu6Dv1qcDXQaGYvA7cCS9ObBVsDM6sws6eAncDD7r4GmOLuOyD1Bggcnd48yDpkqEFflwIPph8HWQMYvA7FzMfIQt3MxgMrSf0D7gZuAL452KaDtAUzz7JvHdx9N6lbCB5J6qPnEuBeMzMCrsMgNbgC+Iq7TwO+AtzRu+kgLw+iBu7e4+7zSJ2Jnmxms4fYPMg6DFUDM7uBVE7c09s02C4i7+QIGKQOcyliPkYS6mZWSeof8T3u3gQcR2o86Gkz20rql1lvZseQeufpe0fdqbz/cTzWBqkDpH7fpvTHsLXAflKL+ARZhww1WAT0Pr6P9z9OBlmDvty9E3gMWAC8ambHAqT/2zsUF3Qd+tUAM1sEnAt83tMDyQReAzioDudTzHyM6ELA3cBtQ2yzlfcvBMzi4AsBfyGcCyID6gBcDnwn/fh4Uh+tLMQ6DFGDZ4HT0o/PBNYF/ndhMlCdflwFPEEqxBo5+ELpLaHWYYgaLAA2A5P7bR9cDYaqQ79thpWPY4fI+0J9AvgnoCU9bgRwvbv/erCN3X2Tmd1L6n9sN3Clu/dE0K+RNmgdgOXAcjPbCOwFFnnq/16IdchUgy8Ct5vZWOA9YDEE/XfhWOAuM6sg9en4Xnd/wMyeJDX8dhmwDbgQgq1Dphq8QCqwHk6NQrLa3S8PtAaQoQ6ZNi6kDlomQEQkIPpGqYhIQBTqIiIBUaiLiAREoS4iEhCFuohIQBTqIiIBUaiLiATk/wHell9jjLoNCQAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(X_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sklon priamky možno určiť z koeficientov lineárnej regresie:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-0.01751876]), 21.133734359909326)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.coef_, lin_reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.64893156])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pumpkin price on programmer's day\n",
+    "\n",
+    "lin_reg.predict([[256]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Polynomická regresia\n",
+    "\n",
+    "Niekedy je vzťah medzi vlastnosťami a výsledkami prirodzene nelineárny. Napríklad ceny tekvíc môžu byť vysoké v zime (mesiace=1,2), potom klesnúť počas leta (mesiace=5-7) a následne opäť stúpnuť. Lineárna regresia nedokáže tento vzťah presne zachytiť.\n",
+    "\n",
+    "V takom prípade môžeme zvážiť pridanie ďalších vlastností. Jednoduchým spôsobom je použiť polynómy z vstupných vlastností, čo by viedlo k **polynomickej regresii**. V knižnici Scikit Learn môžeme automaticky predpočítať polynomiálne vlastnosti pomocou pipeline:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.73 (17.0%)\n",
+      "Model determination:  0.07639977655280217\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a323368ac0>]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)\n",
+    "\n",
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kódovanie odrôd\n",
+    "\n",
+    "V ideálnom svete by sme chceli byť schopní predpovedať ceny pre rôzne odrody tekvíc pomocou toho istého modelu. Aby sme zohľadnili odrodu, musíme ju najprv previesť do číselnej podoby, alebo ju **zakódovať**. Existuje niekoľko spôsobov, ako to môžeme urobiť:\n",
+    "\n",
+    "* Jednoduché číselné kódovanie, ktoré vytvorí tabuľku rôznych odrôd a potom nahradí názov odrody indexom v tejto tabuľke. Toto nie je najlepší nápad pre lineárnu regresiu, pretože lineárna regresia berie do úvahy číselnú hodnotu indexu, a tá pravdepodobne nebude číselne korelovať s cenou.\n",
+    "* One-hot kódovanie, ktoré nahradí stĺpec `Variety` štyrmi rôznymi stĺpcami, každý pre jednu odrodu, pričom obsahuje hodnotu 1, ak daný riadok patrí danej odrode, a 0 v opačnom prípade.\n",
+    "\n",
+    "Kód nižšie ukazuje, ako môžeme zakódovať odrodu pomocou one-hot kódovania:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FAIRYTALE</th>\n",
+       "      <th>MINIATURE</th>\n",
+       "      <th>MIXED HEIRLOOM VARIETIES</th>\n",
+       "      <th>PIE TYPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE\n",
+       "70            0          0                         0         1\n",
+       "71            0          0                         0         1\n",
+       "72            0          0                         0         1\n",
+       "73            0          0                         0         1\n",
+       "74            0          0                         0         1\n",
+       "...         ...        ...                       ...       ...\n",
+       "1738          0          1                         0         0\n",
+       "1739          0          1                         0         0\n",
+       "1740          0          1                         0         0\n",
+       "1741          0          1                         0         0\n",
+       "1742          0          1                         0         0\n",
+       "\n",
+       "[415 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(new_pumpkins['Variety'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Lineárna regresia na odrode\n",
+    "\n",
+    "Teraz použijeme ten istý kód ako vyššie, ale namiesto `DayOfYear` použijeme našu one-hot-enkódovanú odrodu ako vstup:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety'])\n",
+    "y = new_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 5.24 (19.7%)\n",
+      "Model determination:  0.774085281105197\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_linear_regression(X,y):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "    lin_reg = LinearRegression()\n",
+    "    lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "    pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "    mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "    print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "    score = lin_reg.score(X_train,y_train)\n",
+    "    print('Model determination: ', score)\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Môžeme tiež skúsiť použiť ďalšie funkcie rovnakým spôsobom a skombinovať ich s číselnými funkciami, ako sú `Month` alebo `DayOfYear`:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.84 (10.5%)\n",
+      "Model determination:  0.9401096672643048\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['Package']))\n",
+    "y = new_pumpkins['Price']\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Polynomická regresia\n",
+    "\n",
+    "Polynomická regresia môže byť použitá aj s kategóriálnymi črtami, ktoré sú zakódované pomocou one-hot-encoding. Kód na trénovanie polynomickej regresie by bol v podstate rovnaký, ako sme videli vyššie.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.23 (8.25%)\n",
+      "Model determination:  0.9652870784724543\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d77bd89ae7e79780c68c58bab91f13f8",
+   "translation_date": "2025-09-06T13:09:45+00:00",
+   "source_file": "2-Regression/3-Linear/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sk/2-Regression/4-Logistic/notebook.ipynb b/translations/sk/2-Regression/4-Logistic/notebook.ipynb
new file mode 100644
index 000000000..d5867c2dd
--- /dev/null
+++ b/translations/sk/2-Regression/4-Logistic/notebook.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Druhy tekvíc a farba\n",
+    "\n",
+    "Načítajte potrebné knižnice a dataset. Preveďte údaje na dataframe obsahujúci podmnožinu údajov:\n",
+    "\n",
+    "Pozrime sa na vzťah medzi farbou a druhom.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
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@@ -0,0 +1,686 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vytvorenie modelu logistickej regresie - Lekcia 4\n",
+    "\n",
+    "![Infografika: Logistická vs. lineárna regresia](../../../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)\n",
+    "\n",
+    "#### **[Kvíz pred prednáškou](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
+    "\n",
+    "#### Úvod\n",
+    "\n",
+    "V tejto záverečnej lekcii o regresii, jednej zo základných *klasických* techník strojového učenia, sa pozrieme na logistickú regresiu. Túto techniku by ste použili na objavenie vzorcov na predpovedanie binárnych kategórií. Je táto cukrovinka čokoláda alebo nie? Je táto choroba nákazlivá alebo nie? Vyberie si tento zákazník tento produkt alebo nie?\n",
+    "\n",
+    "V tejto lekcii sa naučíte:\n",
+    "\n",
+    "-   Techniky logistickej regresie\n",
+    "\n",
+    "✅ Prehĺbte si svoje porozumenie práce s týmto typom regresie v tomto [učebnom module](https://learn.microsoft.com/training/modules/introduction-classification-models/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "## Predpoklady\n",
+    "\n",
+    "Keďže sme pracovali s údajmi o tekviciach, sme už dostatočne oboznámení s tým, že existuje jedna binárna kategória, s ktorou môžeme pracovať: `Color`.\n",
+    "\n",
+    "Vytvorme model logistickej regresie na predpovedanie toho, *akú farbu bude mať daná tekvica* (oranžová 🎃 alebo biela 👻).\n",
+    "\n",
+    "> Prečo hovoríme o binárnej klasifikácii v lekcii zameranej na regresiu? Len z jazykového pohodlia, keďže logistická regresia je [v skutočnosti metóda klasifikácie](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), hoci založená na lineárnom prístupe. O ďalších spôsoboch klasifikácie údajov sa dozviete v nasledujúcej skupine lekcií.\n",
+    "\n",
+    "Pre túto lekciu budeme potrebovať nasledujúce balíky:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [kolekcia balíkov pre R](https://www.tidyverse.org/packages), ktorá robí dátovú vedu rýchlejšou, jednoduchšou a zábavnejšou!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je rámec [kolekcie balíkov](https://www.tidymodels.org/packages/) na modelovanie a strojové učenie.\n",
+    "\n",
+    "-   `janitor`: Balík [janitor](https://github.com/sfirke/janitor) poskytuje jednoduché nástroje na skúmanie a čistenie nečistých údajov.\n",
+    "\n",
+    "-   `ggbeeswarm`: Balík [ggbeeswarm](https://github.com/eclarke/ggbeeswarm) poskytuje metódy na vytváranie grafov v štýle \"beeswarm\" pomocou ggplot2.\n",
+    "\n",
+    "Môžete ich nainštalovať pomocou:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+    "\n",
+    "Alternatívne, skript nižšie skontroluje, či máte balíky potrebné na dokončenie tohto modulu, a nainštaluje ich za vás, ak chýbajú.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## **Definovanie otázky**\n",
+    "\n",
+    "Pre naše účely vyjadríme otázku ako binárnu: 'Biela' alebo 'Nie biela'. V našej dátovej sade sa nachádza aj kategória 'pruhovaná', ale má len málo záznamov, takže ju nebudeme používať. Táto kategória aj tak zmizne, keď odstránime nulové hodnoty z dátovej sady.\n",
+    "\n",
+    "> 🎃 Zaujímavosť: biele tekvice niekedy nazývame 'duchové' tekvice. Nie sú veľmi ľahké na vyrezávanie, takže nie sú také populárne ako oranžové, ale vyzerajú zaujímavo! Mohli by sme teda našu otázku preformulovať ako: 'Duch' alebo 'Nie duch'. 👻\n",
+    "\n",
+    "## **O logistickej regresii**\n",
+    "\n",
+    "Logistická regresia sa v niekoľkých dôležitých aspektoch líši od lineárnej regresie, ktorú ste sa učili predtým.\n",
+    "\n",
+    "#### **Binárna klasifikácia**\n",
+    "\n",
+    "Logistická regresia neponúka rovnaké funkcie ako lineárna regresia. Prvá ponúka predikciu o `binárnej kategórii` (\"oranžová alebo nie oranžová\"), zatiaľ čo druhá dokáže predpovedať `kontinuálne hodnoty`, napríklad na základe pôvodu tekvice a času zberu *o koľko sa zvýši jej cena*.\n",
+    "\n",
+    "![Infografika od Dasani Madipalli](../../../../../../2-Regression/4-Logistic/images/pumpkin-classifier.png)\n",
+    "\n",
+    "### Iné typy klasifikácií\n",
+    "\n",
+    "Existujú aj iné typy logistickej regresie, vrátane multinomiálnej a ordinálnej:\n",
+    "\n",
+    "- **Multinomiálna**, ktorá zahŕňa viac ako jednu kategóriu - \"Oranžová, Biela a Pruhovaná\".\n",
+    "\n",
+    "- **Ordinálna**, ktorá zahŕňa usporiadané kategórie, užitočné, ak chceme logicky zoradiť naše výsledky, napríklad naše tekvice zoradené podľa konečného počtu veľkostí (mini, malá, stredná, veľká, XL, XXL).\n",
+    "\n",
+    "![Multinomiálna vs ordinálna regresia](../../../../../../2-Regression/4-Logistic/images/multinomial-vs-ordinal.png)\n",
+    "\n",
+    "#### **Premenné NEMUSIA korelovať**\n",
+    "\n",
+    "Pamätáte si, ako lineárna regresia fungovala lepšie s viac korelovanými premennými? Logistická regresia je opakom - premenné nemusia byť v súlade. To je výhodné pre tieto dáta, ktoré majú pomerne slabé korelácie.\n",
+    "\n",
+    "#### **Potrebujete veľa čistých dát**\n",
+    "\n",
+    "Logistická regresia poskytne presnejšie výsledky, ak použijete viac dát; naša malá dátová sada nie je pre túto úlohu optimálna, takže to majte na pamäti.\n",
+    "\n",
+    "✅ Zamyslite sa nad typmi dát, ktoré by sa dobre hodili na logistickú regresiu.\n",
+    "\n",
+    "## Cvičenie - upravte dáta\n",
+    "\n",
+    "Najprv trochu upravte dáta, odstráňte nulové hodnoty a vyberte len niektoré stĺpce:\n",
+    "\n",
+    "1. Pridajte nasledujúci kód:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the core tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data and clean column names\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \n",
+    "  clean_names()\n",
+    "\n",
+    "# Select desired columns\n",
+    "pumpkins_select <- pumpkins %>% \n",
+    "  select(c(city_name, package, variety, origin, item_size, color)) \n",
+    "\n",
+    "# Drop rows containing missing values and encode color as factor (category)\n",
+    "pumpkins_select <- pumpkins_select %>% \n",
+    "  drop_na() %>% \n",
+    "  mutate(color = factor(color))\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins_select %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vždy sa môžete pozrieť na svoj nový dataframe pomocou funkcie [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html), ako je uvedené nižšie:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pumpkins_select %>% \n",
+    "  glimpse()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Poďme si potvrdiť, že sa skutočne budeme zaoberať problémom binárnej klasifikácie:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Subset distinct observations in outcome column\n",
+    "pumpkins_select %>% \n",
+    "  distinct(color)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vizualizácia - kategóriálny graf\n",
+    "Teraz ste znova načítali údaje o tekviciach a vyčistili ich tak, aby ste zachovali dataset obsahujúci niekoľko premenných, vrátane farby (Color). Poďme si vizualizovať dataframe v notebooku pomocou knižnice ggplot.\n",
+    "\n",
+    "Knižnica ggplot ponúka niekoľko skvelých spôsobov, ako vizualizovať vaše údaje. Napríklad môžete porovnať rozdelenia údajov pre každú odrodu (Variety) a farbu (Color) v kategóriálnom grafe.\n",
+    "\n",
+    "1. Vytvorte takýto graf pomocou funkcie geombar, pričom použijete naše údaje o tekviciach a špecifikujete farebné mapovanie pre každú kategóriu tekvíc (oranžová alebo biela):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Specify colors for each value of the hue variable\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# Create the bar plot\n",
+    "ggplot(pumpkins_select, aes(y = variety, fill = color)) +\n",
+    "  geom_bar(position = \"dodge\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(y = \"Variety\", fill = \"Color\") +\n",
+    "  theme_minimal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pozorovaním údajov môžete vidieť, ako sa údaje o Farbe vzťahujú k Odrode.\n",
+    "\n",
+    "✅ Na základe tohto kategóriálneho grafu, aké zaujímavé skúmania si dokážete predstaviť?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predspracovanie údajov: kódovanie vlastností\n",
+    "\n",
+    "Náš dataset tekvíc obsahuje textové hodnoty vo všetkých svojich stĺpcoch. Práca s kategorizovanými údajmi je pre ľudí intuitívna, ale pre stroje nie. Algoritmy strojového učenia pracujú dobre s číslami. Preto je kódovanie veľmi dôležitým krokom vo fáze predspracovania údajov, pretože nám umožňuje premeniť kategorizované údaje na číselné údaje bez straty informácií. Dobré kódovanie vedie k vytvoreniu dobrého modelu.\n",
+    "\n",
+    "Pre kódovanie vlastností existujú dva hlavné typy kóderov:\n",
+    "\n",
+    "1. Ordinálny kóder: je vhodný pre ordinálne premenné, čo sú kategorizované premenné, kde ich údaje nasledujú logické poradie, ako napríklad stĺpec `item_size` v našom datasete. Vytvára mapovanie, kde každá kategória je reprezentovaná číslom, ktoré zodpovedá poradiu kategórie v stĺpci.\n",
+    "\n",
+    "2. Kategorizovaný kóder: je vhodný pre nominálne premenné, čo sú kategorizované premenné, kde ich údaje nenasledujú logické poradie, ako všetky vlastnosti okrem `item_size` v našom datasete. Ide o kódovanie typu one-hot, čo znamená, že každá kategória je reprezentovaná binárnym stĺpcom: kódovaná premenná je rovná 1, ak tekvica patrí do danej Variety, a 0 v opačnom prípade.\n",
+    "\n",
+    "Tidymodels poskytuje ďalší šikovný balík: [recipes](https://recipes.tidymodels.org/) - balík na predspracovanie údajov. Definujeme `recipe`, ktorý špecifikuje, že všetky stĺpce prediktorov by mali byť kódované do množiny celých čísel, `prep` na odhad potrebných množstiev a štatistík potrebných pre akékoľvek operácie a nakoniec `bake` na aplikáciu výpočtov na nové údaje.\n",
+    "\n",
+    "> Bežne sa recipes zvyčajne používa ako predspracovateľ pre modelovanie, kde definuje, aké kroky by sa mali aplikovať na dataset, aby bol pripravený na modelovanie. V takom prípade je **veľmi odporúčané**, aby ste použili `workflow()` namiesto manuálneho odhadu receptu pomocou prep a bake. Všetko toto si ukážeme už čoskoro.\n",
+    ">\n",
+    "> Avšak momentálne používame recipes + prep + bake na špecifikáciu krokov, ktoré by sa mali aplikovať na dataset, aby bol pripravený na analýzu údajov, a následne extrahujeme predspracované údaje s aplikovanými krokmi.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Preprocess and extract data to allow some data analysis\n",
+    "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>%\n",
+    "  # Define ordering for item_size column\n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  # Convert factors to numbers using the order defined above (Ordinal encoding)\n",
+    "  step_integer(item_size, zero_based = F) %>%\n",
+    "  # Encode all other predictors using one hot encoding\n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE) %>%\n",
+    "  prep(data = pumpkin_select) %>%\n",
+    "  bake(new_data = NULL)\n",
+    "\n",
+    "# Display the first few rows of preprocessed data\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "✅ Aké sú výhody použitia ordinálneho enkodéra pre stĺpec Item Size?\n",
+    "\n",
+    "### Analyzujte vzťahy medzi premennými\n",
+    "\n",
+    "Teraz, keď sme predspracovali naše údaje, môžeme analyzovať vzťahy medzi vlastnosťami a štítkom, aby sme získali predstavu o tom, ako dobre bude model schopný predpovedať štítok na základe vlastností. Najlepší spôsob, ako vykonať tento typ analýzy, je vizualizácia údajov. \n",
+    "Opäť použijeme funkciu ggplot geom_boxplot_, aby sme zobrazili vzťahy medzi Item Size, Variety a Color v kategóriálnom grafe. Na lepšiu vizualizáciu údajov použijeme enkódovaný stĺpec Item Size a neenkódovaný stĺpec Variety.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define the color palette\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins_select_plot<-pumpkins_select\n",
+    "pumpkins_select_plot$item_size <- baked_pumpkins$item_size\n",
+    "\n",
+    "# Create the grouped box plot\n",
+    "ggplot(pumpkins_select_plot, aes(x = `item_size`, y = color, fill = color)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_grid(variety ~ ., scales = \"free_x\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(x = \"Item Size\", y = \"\") +\n",
+    "  theme_minimal() +\n",
+    "  theme(strip.text = element_text(size = 12)) +\n",
+    "  theme(axis.text.x = element_text(size = 10)) +\n",
+    "  theme(axis.title.x = element_text(size = 12)) +\n",
+    "  theme(axis.title.y = element_blank()) +\n",
+    "  theme(legend.position = \"bottom\") +\n",
+    "  guides(fill = guide_legend(title = \"Color\")) +\n",
+    "  theme(panel.spacing = unit(0.5, \"lines\"))+\n",
+    "  theme(strip.text.y = element_text(size = 4, hjust = 0)) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Použitie swarm grafu\n",
+    "\n",
+    "Keďže Color je binárna kategória (Biela alebo Nie), vyžaduje si '[špeciálny prístup](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf)' k vizualizácii.\n",
+    "\n",
+    "Vyskúšajte `swarm graf`, aby ste zobrazili rozloženie farby vo vzťahu k item_size.\n",
+    "\n",
+    "Použijeme balík [ggbeeswarm](https://github.com/eclarke/ggbeeswarm), ktorý poskytuje metódy na vytváranie grafov v štýle beeswarm pomocou ggplot2. Beeswarm grafy sú spôsob, ako zobrazovať body, ktoré by sa za normálnych okolností prekrývali, tak, aby boli vedľa seba.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create beeswarm plots of color and item_size\n",
+    "baked_pumpkins %>% \n",
+    "  mutate(color = factor(color)) %>% \n",
+    "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\n",
+    "  geom_quasirandom() +\n",
+    "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\n",
+    "  theme(legend.position = \"none\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Teraz, keď máme predstavu o vzťahu medzi binárnymi kategóriami farieb a väčšou skupinou veľkostí, poďme preskúmať logistickú regresiu na určenie pravdepodobnej farby danej tekvice.\n",
+    "\n",
+    "## Vytvorte svoj model\n",
+    "\n",
+    "Vyberte premenné, ktoré chcete použiť vo svojom klasifikačnom modeli, a rozdeľte údaje na tréningovú a testovaciu množinu. [rsample](https://rsample.tidymodels.org/), balík v rámci Tidymodels, poskytuje infraštruktúru na efektívne delenie a resamplovanie údajov:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Split data into 80% for training and 20% for testing\n",
+    "set.seed(2056)\n",
+    "pumpkins_split <- pumpkins_select %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "# Print out the first 5 rows of the training set\n",
+    "pumpkins_train %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🙌 Teraz sme pripravení trénovať model prispôsobením tréningových vlastností tréningovej značke (farba).\n",
+    "\n",
+    "Začneme vytvorením receptu, ktorý špecifikuje kroky predspracovania, ktoré by sa mali vykonať na našich údajoch, aby boli pripravené na modelovanie, t.j. kódovanie kategóriálnych premenných na množinu celých čísel. Rovnako ako `baked_pumpkins`, vytvoríme `pumpkins_recipe`, ale nebudeme používať `prep` a `bake`, pretože to bude zahrnuté do pracovného toku, čo uvidíte o pár krokov neskôr.\n",
+    "\n",
+    "Existuje pomerne veľa spôsobov, ako špecifikovať logistickú regresiu v Tidymodels. Pozrite si `?logistic_reg()`. Zatiaľ špecifikujeme logistickú regresiu prostredníctvom predvoleného enginu `stats::glm()`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a recipe that specifies preprocessing steps for modelling\n",
+    "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  step_integer(item_size, zero_based = F) %>%  \n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)\n",
+    "\n",
+    "# Create a logistic model specification\n",
+    "log_reg <- logistic_reg() %>% \n",
+    "  set_engine(\"glm\") %>% \n",
+    "  set_mode(\"classification\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Teraz, keď máme recept a špecifikáciu modelu, musíme nájsť spôsob, ako ich spojiť do objektu, ktorý najskôr predspracuje údaje (príprava + pečenie na pozadí), prispôsobí model na predspracovaných údajoch a zároveň umožní potenciálne aktivity po spracovaní.\n",
+    "\n",
+    "V Tidymodels sa tento praktický objekt nazýva [`workflow`](https://workflows.tidymodels.org/) a pohodlne uchováva vaše modelovacie komponenty.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Bundle modelling components in a workflow\n",
+    "log_reg_wf <- workflow() %>% \n",
+    "  add_recipe(pumpkins_recipe) %>% \n",
+    "  add_model(log_reg)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "log_reg_wf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Po definovaní *pracovného postupu* môže byť model `natrénovaný` pomocou funkcie [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html). Pracovný postup odhadne recept a pred spracovaním údajov ich predpripraví, takže to nebudeme musieť robiť manuálne pomocou funkcií prep a bake.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "wf_fit <- log_reg_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the trained workflow\n",
+    "wf_fit\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Model vytlačí koeficienty naučené počas tréningu.\n",
+    "\n",
+    "Teraz, keď sme model natrénovali pomocou tréningových dát, môžeme robiť predpovede na testovacích dátach pomocou [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Začnime tým, že použijeme model na predpovedanie štítkov pre náš testovací súbor a pravdepodobností pre každý štítok. Keď je pravdepodobnosť vyššia ako 0.5, predpovedaná trieda je `WHITE`, inak `ORANGE`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions for color and corresponding probabilities\n",
+    "results <- pumpkins_test %>% select(color) %>% \n",
+    "  bind_cols(wf_fit %>% \n",
+    "              predict(new_data = pumpkins_test)) %>%\n",
+    "  bind_cols(wf_fit %>%\n",
+    "              predict(new_data = pumpkins_test, type = \"prob\"))\n",
+    "\n",
+    "# Compare predictions\n",
+    "results %>% \n",
+    "  slice_head(n = 10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Veľmi pekné! Toto poskytuje ďalší pohľad na to, ako funguje logistická regresia.\n",
+    "\n",
+    "### Lepšie pochopenie pomocou matice zámien\n",
+    "\n",
+    "Porovnávanie každej predikcie s jej zodpovedajúcou „skutočnou hodnotou“ nie je veľmi efektívny spôsob, ako určiť, ako dobre model predikuje. Našťastie má Tidymodels v rukáve ešte niekoľko trikov: [`yardstick`](https://yardstick.tidymodels.org/) - balík používaný na meranie efektívnosti modelov pomocou metrík výkonnosti.\n",
+    "\n",
+    "Jednou z metrík výkonnosti spojených s klasifikačnými problémami je [`matica zámien`](https://wikipedia.org/wiki/Confusion_matrix). Matica zámien opisuje, ako dobre klasifikačný model funguje. Matica zámien zaznamenáva, koľko príkladov v každej triede bolo modelom správne klasifikovaných. V našom prípade vám ukáže, koľko oranžových tekvíc bolo klasifikovaných ako oranžové a koľko bielych tekvíc bolo klasifikovaných ako biele; matica zámien vám tiež ukáže, koľko ich bolo klasifikovaných do **nesprávnych** kategórií.\n",
+    "\n",
+    "Funkcia [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) z yardstick vypočíta túto krížovú tabuľku pozorovaných a predikovaných tried.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Confusion matrix for prediction results\n",
+    "conf_mat(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Poďme interpretovať maticu zámien. Náš model má za úlohu klasifikovať tekvice do dvoch binárnych kategórií, kategórie `biela` a kategórie `nie-biela`.\n",
+    "\n",
+    "-   Ak váš model predpovedá tekvicu ako bielu a v skutočnosti patrí do kategórie 'biela', nazývame to `pravý pozitívny`, čo je znázornené číslom v ľavom hornom rohu.\n",
+    "\n",
+    "-   Ak váš model predpovedá tekvicu ako nie-bielu a v skutočnosti patrí do kategórie 'biela', nazývame to `falošný negatívny`, čo je znázornené číslom v ľavom dolnom rohu.\n",
+    "\n",
+    "-   Ak váš model predpovedá tekvicu ako bielu a v skutočnosti patrí do kategórie 'nie-biela', nazývame to `falošný pozitívny`, čo je znázornené číslom v pravom hornom rohu.\n",
+    "\n",
+    "-   Ak váš model predpovedá tekvicu ako nie-bielu a v skutočnosti patrí do kategórie 'nie-biela', nazývame to `pravý negatívny`, čo je znázornené číslom v pravom dolnom rohu.\n",
+    "\n",
+    "| Pravda |\n",
+    "|:-----:|\n",
+    "\n",
+    "\n",
+    "|               |        |       |\n",
+    "|---------------|--------|-------|\n",
+    "| **Predikcia** | BIELA | ORANŽOVÁ |\n",
+    "| BIELA         | TP     | FP    |\n",
+    "| ORANŽOVÁ      | FN     | TN    |\n",
+    "\n",
+    "Ako ste možno uhádli, je preferované mať vyšší počet pravých pozitívnych a pravých negatívnych a nižší počet falošných pozitívnych a falošných negatívnych, čo naznačuje, že model funguje lepšie.\n",
+    "\n",
+    "Matica zámien je užitočná, pretože umožňuje odvodiť ďalšie metriky, ktoré nám môžu pomôcť lepšie vyhodnotiť výkon klasifikačného modelu. Poďme si ich prejsť:\n",
+    "\n",
+    "🎓 Presnosť: `TP/(TP + FP)` definovaná ako podiel predpokladaných pozitívnych, ktoré sú skutočne pozitívne. Tiež nazývaná [pozitívna prediktívna hodnota](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\").\n",
+    "\n",
+    "🎓 Návratnosť: `TP/(TP + FN)` definovaná ako podiel pozitívnych výsledkov z počtu vzoriek, ktoré boli skutočne pozitívne. Tiež známa ako `citlivosť`.\n",
+    "\n",
+    "🎓 Špecifickosť: `TN/(TN + FP)` definovaná ako podiel negatívnych výsledkov z počtu vzoriek, ktoré boli skutočne negatívne.\n",
+    "\n",
+    "🎓 Presnosť modelu: `TP + TN/(TP + TN + FP + FN)` Percento správne predpovedaných označení pre vzorku.\n",
+    "\n",
+    "🎓 F Miera: Vážený priemer presnosti a návratnosti, pričom najlepšia hodnota je 1 a najhoršia je 0.\n",
+    "\n",
+    "Poďme vypočítať tieto metriky!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Combine metric functions and calculate them all at once\n",
+    "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\n",
+    "eval_metrics(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vizualizácia ROC krivky tohto modelu\n",
+    "\n",
+    "Urobme ešte jednu vizualizáciu, aby sme si pozreli tzv. [`ROC krivku`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make a roc_curve\n",
+    "results %>% \n",
+    "  roc_curve(color, .pred_ORANGE) %>% \n",
+    "  autoplot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ROC krivky sa často používajú na zobrazenie výstupu klasifikátora z hľadiska jeho skutočných vs. falošných pozitívnych výsledkov. ROC krivky zvyčajne zobrazujú `True Positive Rate`/citlivosť na osi Y a `False Positive Rate`/1-špecifickosť na osi X. Preto je dôležitá strmosť krivky a priestor medzi stredovou čiarou a krivkou: chcete krivku, ktorá rýchlo stúpa a prechádza nad čiaru. V našom prípade sú na začiatku falošné pozitívne výsledky, a potom krivka správne stúpa a prechádza nad čiaru.\n",
+    "\n",
+    "Nakoniec použime `yardstick::roc_auc()` na výpočet skutočnej plochy pod krivkou (Area Under the Curve). Jedným zo spôsobov interpretácie AUC je pravdepodobnosť, že model hodnotí náhodný pozitívny príklad vyššie ako náhodný negatívny príklad.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate area under curve\n",
+    "results %>% \n",
+    "  roc_auc(color, .pred_ORANGE)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Výsledok je približne `0.975`. Keďže AUC sa pohybuje v rozmedzí od 0 do 1, chcete dosiahnuť vysoké skóre, pretože model, ktorý je na 100 % presný vo svojich predpovediach, bude mať AUC rovné 1; v tomto prípade je model *celkom dobrý*.\n",
+    "\n",
+    "V budúcich lekciách o klasifikáciách sa naučíte, ako zlepšiť skóre vášho modelu (napríklad riešením problémov s nevyváženými údajmi, ako v tomto prípade).\n",
+    "\n",
+    "## 🚀Výzva\n",
+    "\n",
+    "Logistická regresia ponúka veľa možností na preskúmanie! Najlepší spôsob, ako sa ju naučiť, je experimentovať. Nájdite dataset, ktorý sa hodí na tento typ analýzy, a vytvorte s ním model. Čo ste sa naučili? tip: skúste [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) pre zaujímavé datasety.\n",
+    "\n",
+    "## Prehľad a samostatné štúdium\n",
+    "\n",
+    "Prečítajte si prvé strany [tohto článku zo Stanfordu](https://web.stanford.edu/~jurafsky/slp3/5.pdf) o niektorých praktických využitiach logistickej regresie. Zamyslite sa nad úlohami, ktoré sú lepšie prispôsobené jednému alebo druhému typu regresných úloh, ktoré sme doteraz študovali. Čo by fungovalo najlepšie?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "langauge": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "feaf125f481a89c468fa115bf2aed580",
+   "translation_date": "2025-09-06T13:29:46+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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diff --git a/translations/sk/2-Regression/4-Logistic/solution/notebook.ipynb b/translations/sk/2-Regression/4-Logistic/solution/notebook.ipynb
new file mode 100644
index 000000000..869648e29
--- /dev/null
+++ b/translations/sk/2-Regression/4-Logistic/solution/notebook.ipynb
@@ -0,0 +1,1255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logistická regresia - Lekcia 4\n",
+    "\n",
+    "Načítajte potrebné knižnice a dataset. Konvertujte údaje na dataframe obsahujúci podmnožinu údajov:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date   \n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17  \\\n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition   \n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN  \\\n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Origin</th>\n",
+       "      <th>Item Size</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>DELAWARE</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>VIRGINIA</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>36 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name       Package      Variety    Origin Item Size   Color\n",
+       "2  BALTIMORE  24 inch bins  HOWDEN TYPE  DELAWARE       med  ORANGE\n",
+       "3  BALTIMORE  24 inch bins  HOWDEN TYPE  VIRGINIA       med  ORANGE\n",
+       "4  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "5  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "6  BALTIMORE  36 inch bins  HOWDEN TYPE  MARYLAND       med  ORANGE"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select the columns we want to use\n",
+    "columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Drop rows with missing values\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pozrime sa na naše dáta!\n",
+    "\n",
+    "Vizualizujme ich pomocou Seaborn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56d0c650>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 609.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# Specify colors for each values of the hue variable\n",
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# Plot a bar plot to visualize how many pumpkins of each variety are orange or white\n",
+    "sns.catplot(\n",
+    "    data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
+    "    palette=palette, \n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Predspracovanie údajov\n",
+    "\n",
+    "Zakódujme črty a štítky, aby sme mohli lepšie vizualizovať údaje a trénovať model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the different values of the 'Item Size' column\n",
+    "pumpkins['Item Size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "# Encode the 'Item Size' column using ordinal encoding\n",
+    "item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
+    "ordinal_features = ['Item Size']\n",
+    "ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "# Encode all the other features using one-hot encoding\n",
+    "categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
+    "categorical_encoder = OneHotEncoder(sparse_output=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_MICHIGAN</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_MICHIGAN  cat__Origin_NEW JERSEY   \n",
+       "2                   0.0  ...                   0.0                     0.0  \\\n",
+       "3                   0.0  ...                   0.0                     0.0   \n",
+       "4                   0.0  ...                   0.0                     0.0   \n",
+       "5                   0.0  ...                   0.0                     0.0   \n",
+       "6                   0.0  ...                   0.0                     0.0   \n",
+       "\n",
+       "   cat__Origin_NEW YORK  cat__Origin_NORTH CAROLINA  cat__Origin_OHIO   \n",
+       "2                   0.0                         0.0               0.0  \\\n",
+       "3                   0.0                         0.0               0.0   \n",
+       "4                   0.0                         0.0               0.0   \n",
+       "5                   0.0                         0.0               0.0   \n",
+       "6                   0.0                         0.0               0.0   \n",
+       "\n",
+       "   cat__Origin_PENNSYLVANIA  cat__Origin_TENNESSEE  cat__Origin_TEXAS   \n",
+       "2                       0.0                    0.0                0.0  \\\n",
+       "3                       0.0                    0.0                0.0   \n",
+       "4                       0.0                    0.0                0.0   \n",
+       "5                       0.0                    0.0                0.0   \n",
+       "6                       0.0                    0.0                0.0   \n",
+       "\n",
+       "   cat__Origin_VERMONT  cat__Origin_VIRGINIA  \n",
+       "2                  0.0                   0.0  \n",
+       "3                  0.0                   1.0  \n",
+       "4                  0.0                   0.0  \n",
+       "5                  0.0                   0.0  \n",
+       "6                  0.0                   0.0  \n",
+       "\n",
+       "[5 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "ct = ColumnTransformer(transformers=[\n",
+    "     ('ord', ordinal_encoder, ordinal_features),\n",
+    "     ('cat', categorical_encoder, categorical_features)\n",
+    "     ])\n",
+    "# Get the encoded features as a pandas DataFrame\n",
+    "ct.set_output(transform='pandas')\n",
+    "encoded_features = ct.fit_transform(pumpkins)\n",
+    "encoded_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_NEW JERSEY  cat__Origin_NEW YORK   \n",
+       "2                   0.0  ...                     0.0                   0.0  \\\n",
+       "3                   0.0  ...                     0.0                   0.0   \n",
+       "4                   0.0  ...                     0.0                   0.0   \n",
+       "5                   0.0  ...                     0.0                   0.0   \n",
+       "6                   0.0  ...                     0.0                   0.0   \n",
+       "\n",
+       "   cat__Origin_NORTH CAROLINA  cat__Origin_OHIO  cat__Origin_PENNSYLVANIA   \n",
+       "2                         0.0               0.0                       0.0  \\\n",
+       "3                         0.0               0.0                       0.0   \n",
+       "4                         0.0               0.0                       0.0   \n",
+       "5                         0.0               0.0                       0.0   \n",
+       "6                         0.0               0.0                       0.0   \n",
+       "\n",
+       "   cat__Origin_TENNESSEE  cat__Origin_TEXAS  cat__Origin_VERMONT   \n",
+       "2                    0.0                0.0                  0.0  \\\n",
+       "3                    0.0                0.0                  0.0   \n",
+       "4                    0.0                0.0                  0.0   \n",
+       "5                    0.0                0.0                  0.0   \n",
+       "6                    0.0                0.0                  0.0   \n",
+       "\n",
+       "   cat__Origin_VIRGINIA  Color  \n",
+       "2                   0.0      0  \n",
+       "3                   1.0      0  \n",
+       "4                   0.0      0  \n",
+       "5                   0.0      0  \n",
+       "6                   0.0      0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "# Encode the 'Color' column using label encoding\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n",
+    "encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
+    "encoded_pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ORANGE', 'WHITE']"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the mapping between the encoded values and the original values\n",
+    "list(label_encoder.inverse_transform([0, 1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56322210>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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brs8FAADAtXXo0KHEvi+++ELTp0/XoUOH9Nhjj2nq1Kku91flIbZ169YKDAxUYmKiI8QmJiZq8ODB2rBhg7Zu3eoYkU1MTFSfPn3K/VmNGjVyvK5fv74kyd/fv9RwWrduXQUEBJT7swAAAFA+27dv17Rp0/Tll1/qwQcf1Jo1a9SwYcMy9VEtS2z16dNHCQkJjvcJCQmKiopSZGSkY//58+f19ddfVyjEAgAA4Jfr4MGDGjZsmHr27KnAwEAdOHBAr7zySpkDrFSNIXbz5s0qLCxUbm6udu3apcjISPXu3VuJiYmSpC1btqigoMApxD7++OPy8fFx2p555plKqWn48OEl+j569OhVjy8oKFBOTo7TBgAAANc8/PDDateunbKzs7V9+3b961//UvPmzcvdX7WsThAVFaVz585p+/btysrKUnh4uBo1aqTIyEjFxMQoPz9fiYmJatGihZo1a+Y479FHH3XcjHXZyy+/rI0bN1a4prlz56pfv35O+4KCgq56fFxcnGbNmlXhzwUAAHBHr7/+ury9vZWZmamYmJirHrdr1y6X+quWEBsWFqamTZsqISFBWVlZioyMlHQpNAYHB+urr75SQkKC+vbt63Rew4YNFRYW5rTv8lzXigoICCjR97VMnz5dU6ZMcbzPyclRcHBwpdQCAADwazdjxoxK7a/a1ont06ePEhMTlZWVpUcffdSxv3fv3lq1apW2bdumcePGVVc5Zebl5SUvLy+zywAAALCkv/3tb5XaX7WG2NjYWF28eNExEitJkZGRGj9+vC5cuFCtN3WdPXtWGRkZTvt8fX1Vu3btaqsBAAAA5VMtN3ZJl0Ls+fPnFRYWpsaNGzv2R0ZGKjc317EUV3WJiYlRYGCg0/bKK69U2+cDAACg/GzGlc/5gstycnLk5+en7Oxsx8MUAAAAUD2qbSQWAAAAqCyEWAAAAJju7NmzZbo/ihALAAAA0124cEFJSUkuH0+IBQAAgOVU2xJbvzaX74fj8bMAAMCd+Pr6ymazlfm8H3744Zrtp06dKlN/rE5QTt9//71atmxpdhkAAADVKjMzU40aNSrzeXa7XYZhyGazqbT4eXl/cXGxS/0xEltOlx9/e/ToUfn5+Zlcjfu5/NjfY8eOscSZCbj+5uL6m4drby6uv7kuX39PT89ynb9r165rtp85c0Z9+/Z1uT9CbDnVqHFpOrGfnx//IZmoTp06XH8Tcf3NxfU3D9feXFx/c5VnKoEkdejQ4ZrtmZmZZeqPG7sAAADwi1CWgEyIBQAAgOk8PT3Vtm1bl48nxJaTl5eXZsyYIS8vL7NLcUtcf3Nx/c3F9TcP195cXH9zVfX137Rpk06fPu3y8axOAAAAANPk5uZq0qRJevfddzVjxgxNnz7dpfO4sQsAAACmSEhI0JgxY1S/fn3t2LFD7dq1c/lcQiwAAACqXN++fZ3Why0sLNTWrVv1xBNP6G9/+5vsdnuZ+iPEAgAAoMp16tTJ6X1hYaH27Nmj/fv368yZM2V+gAJzYgEAAGCK48ePKyYmRt9++63mz5+vIUOGuHwuqxOU04IFCxQaGipvb29169ZN27ZtM7skt7Bx40ZFR0crKChINptNH3/8sdkluY24uDjdfPPN8vX1lb+/v+666y6lpqaaXZbbePXVV9WhQwfHIu89evTQqlWrzC7Lbc2ZM0c2m02TJ082uxS3MHPmTNlsNqctIiLC7LLcyo8//qh7771XDRo0UK1atXTDDTcoOTm5wv02bdpUa9eu1d/+9jfFxMRo6NChLp9LiC2H9957T1OmTNGMGTO0c+dOdezYUQMGDCjzkyZQdufOnVPHjh21YMECs0txO0lJSYqNjdXWrVu1du1aXbx4UbfffrvOnTtndmluoWnTppozZ4527Nih5ORk9e3bV4MHD9bevXvNLs3tbN++Xa+99tp1nz6EytWuXTulp6c7ti+//NLsktxGVlaWevXqpZo1a2rVqlXat2+fXnjhBdWrV6/SPiM2NlYpKSk6fvy4y+cwnaAcunXrpptvvlnz58+XJBUXFys4OFgTJkzQtGnTTK7OfdhsNq1cuVJ33XWX2aW4pVOnTsnf319JSUnq3bu32eW4pfr16+u5557T/fffb3YpbiMvL0833nijFi5cqNmzZ6tTp06aN2+e2WX96s2cOVMff/yxUlJSzC7FLU2bNk2bN2/Wpk2bqvyzDMNw+ald3NhVRhcuXNCOHTuc1jCrUaOG+vXrpy1btphYGVC9srOzJV0KUqheRUVF+uCDD3Tu3Dn16NHD7HLcSmxsrO644w7169dPs2fPNrsct3Lw4EEFBQXJ29tbPXr0UFxcnJo1a2Z2WW7h008/1YABAzRkyBAlJSWpSZMm+vOf/6yxY8eWqZ9Zs2Zd9xjDMDRz5kyX+iPEltHp06dVVFSkxo0bO+1v3LixDhw4YFJVQPUqLi7W5MmT1atXL7Vv397sctzG7t271aNHD+Xn58vHx0crV64s0yMaUTHLly/Xzp07tX37drNLcTvdunXTm2++qdatWys9PV2zZs3Srbfeqj179sjX19fs8n71vv/+e7366quaMmWKnnjiCW3fvl0TJ06Up6enRo0a5XI/n3zyieP1hQsXdODAAadpOZdXKyDEAqgysbGx2rNnD3PSqlnr1q2VkpKi7Oxsffjhhxo1apSSkpIIstXg2LFjmjRpktauXStvb2+zy3E7gwYNcrzu0KGDunXrppCQEL3//vtMp6kGxcXF6tKli5555hlJUufOnbVnzx4tWrSoTCF2586djtdpaWnq2LGj075Tp04pICDA5f64sauMGjZsKLvdrpMnTzrtP3nyZJkuPGBV48eP1+eff66EhAQ1bdrU7HLciqenp8LCwnTTTTcpLi5OHTt21EsvvWR2WW5hx44dyszM1I033igPDw95eHgoKSlJL7/8sjw8PFRUVGR2iW6lbt26Cg8P16FDh8wuxS0EBgaW+GW5TZs2Onr0aLn7tNvtKiwsdNp38eJF1ajhejQlxJaRp6enbrrpJq1fv96xr7i4WOvXr2duGn7VDMPQ+PHjtXLlSm3YsEHNmzc3uyS3V1xcrIKCArPLcAu33Xabdu/erZSUFMfWpUsXjRgxQikpKWV+0hAqJi8vT4cPH1ZgYKDZpbiFXr16lVhS8bvvvlNISEi5+wwKCtLFixe1Y8cOx77NmzeXaXCE6QTlMGXKFI0aNUpdunRR165dNW/ePJ07d04xMTFml/arl5eX5/Sbd1pamlJSUlS/fn0m+Fex2NhYvfvuu/rkk0/k6+urjIwMSZKfn59q1aplcnW/ftOnT9egQYPUrFkz5ebm6t1331ViYqLWrFljdmluwdfXt8T879q1a6tBgwbMC68GU6dOVXR0tEJCQnTixAnNmDFDdrtdw4cPN7s0t/DII4+oZ8+eeuaZZzR06FBt27ZNixcv1uLFi8vdp4eHh373u99p0KBBGj58uPLz87V06VI9+OCDrndioFxeeeUVo1mzZoanp6fRtWtXY+vWrWaX5BYSEhIMSSW2UaNGmV3ar15p112SsWTJErNLcwtjxowxQkJCDE9PT6NRo0bGbbfdZnzxxRdml+XWIiMjjUmTJpldhlsYNmyYERgYaHh6ehpNmjQxhg0bZhw6dMjsstzKZ599ZrRv397w8vIyIiIijMWLF1e4z8zMTGPYsGFGw4YNjaCgIOPhhx828vLyXD6fdWIBAABgOUwnAAAAQJX74YcfXDrO1bm2jMQCAACgytntdscTuUqLn5f3FxcXu9QfI7EAAACoFuvWrVPDhg0lScePH9fQoUP11VdfSZLOnDmjvn37utwXIRYAAADVol27do6nnvr4+Mhmszme2pWZmVmmvlgnFgAAAJZDiAUAAECVq+zbsAixAAAAqHI2m+26+0o75moIsQDgBo4cOSKbzaaUlBSzSwHgppYtW6a6des63rdo0UI5OTmO9w0aNNCWLVtc7o8QCwDlMHr0aN11112O91FRUZo8ebJp9aSlpelPf/qTgoKC5O3traZNm2rw4ME6cOCAJCk4OFjp6ek8IhWAaYYOHSovL6+rttvtdnXt2tXl/lidAAAs7uLFi+rfv79at26tFStWKDAwUMePH9eqVat09uxZSZd+OAQEBJhbKABUIkZiAaCCRo8eraSkJL300kuy2Wyy2Ww6cuSIJGnPnj0aNGiQfHx81LhxY40cOVKnT592nBsVFaUJEyZo8uTJqlevnho3bqzXX39d586dU0xMjHx9fRUWFqZVq1Zd9fP37t2rw4cPa+HCherevbtCQkLUq1cvzZ49W927d5dUcjrB6NGjHbVeuSUmJkqSCgoKNHXqVDVp0kS1a9dWt27dHG0A8EtAiAWACnrppZfUo0cPjR07Vunp6UpPT1dwcLDOnj2rvn37qnPnzkpOTtbq1at18uRJDR061On8t956Sw0bNtS2bds0YcIEjRs3TkOGDFHPnj21c+dO3X777Ro5cqT++9//lvr5jRo1Uo0aNfThhx+qqKjI5Zov15qenq5JkybJ399fERERkqTx48dry5YtWr58ub799lsNGTJEAwcO1MGDByt2sQCgkvDYWQAoh9GjR+vs2bP6+OOPJV0aUe3UqZPmzZvnOGb27NnatGmT1qxZ49h3/PhxBQcHKzU1VeHh4YqKilJRUZE2bdokSSoqKpKfn5/uvvtuvf3225KkjIwMBQYGasuWLY6R1Z9bsGCBHnvsMdntdnXp0kV9+vTRiBEj1KJFC0mXRmKbN2+uXbt2qVOnTk7nrlixQiNGjNC6devUq1cvHT16VC1atNDRo0cVFBTkOK5fv37q2rWrnnnmmYpePgCoMEZiAaCKfPPNN0pISJCPj49juzzSefjwYcdxl59WI12au9qgQQPdcMMNjn2Xn25zrafZxMbGKiMjQ0uXLlWPHj30wQcfqF27dlq7du01a9y1a5dGjhyp+fPnq1evXpKk3bt3q6ioSOHh4U61JyUlOdUNAGbixi4AqCJ5eXmKjo7Ws88+W6ItMDDQ8bpmzZpObTabzWnf5XUTi4uLr/l5vr6+io6OVnR0tGbPnq0BAwZo9uzZ6t+/f6nHZ2Rk6M4779QDDzyg+++/36luu92uHTt2yG63O53j4+NzzRoAoLoQYgGgEnh6epaYj3rjjTfqo48+UmhoqDw8qvfbrc1mU0REhL766qtS2/Pz8zV48GBFREToxRdfdGrr3LmzioqKlJmZqVtvvbU6ygWAMmM6AQBUgtDQUH399dc6cuSITp8+reLiYsXGxurMmTMaPny4tm/frsOHD2vNmjWKiYlx+QYsV6SkpGjw4MH68MMPtW/fPh06dEjx8fF64403NHjw4FLPeeihh3Ts2DG9/PLLOnXqlDIyMpSRkaELFy4oPDxcI0aM0H333acVK1YoLS1N27ZtU1xcnP79739XWt0AUBGMxAJAJZg6dapGjRqltm3b6vz580pLS1NoaKg2b96sxx9/XLfffrsKCgoUEhKigQMHqkaNyhtDaNq0qUJDQzVr1izHUlqX3z/yyCOlnpOUlKT09HS1bdvWaX9CQoKioqK0ZMkSzZ49W3/5y1/0448/qmHDhurevbt+97vfVVrdAFARrE4AAAAAy2E6AQAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBwPswsAAADAr19MTIxLxy1ZssSl42yGYRgVKQgAAAC4nrvvvtvx+ty5c9qwYYOio6Md+woKCrRq1SoVFxe71B8hFgAAANUqLS1NHTp0UG5urmPfqVOnFBAQoKKiIpf6YE4sAAAAqlXNmjV18eJFp335+fny8HB9pishtpwMw1BOTo4YyAYAACiboKAgGYahdevWOfb95z//UbNmzVzugxu7yik3N1d+fn46efKk6tSpY3Y5bsUwDBUUFEiSvLy8ZLPZTK7IfXH9AQDlUaNGDY0YMULR0dEaMGCAzp8/r3Xr1mnmzJku98Gc2HLKycmRn5+fBgwYoJo1a5pdDmCKDz74QN7e3maXAQCwoPPnz2vWrFlav369PD09deedd+rRRx9VjRquTRRgJBYAAADVrlatWpozZ065zyfEVtCM/t+pvo/ZVbiXgkKbnlzdVpL09MB98vLgjwnV6UJRDT2xqo3ZZQAALCYpKcml4yIjI106jhBbQTVrFMvLgzmBZvHyMAix1c619fuAXxvm4wMV07dvXxmGcc3/dgzDcHmdWEJsBV0o4psYALiDgoICDRkyRBLzwYHyyMrKqtT+CLEAAACocpW9mlO51ok9duyYxowZo6CgIHl6eiokJESTJk3STz/95DgmKipKNptNNptN3t7eCg8PV1xcXKnrqm7ZskV2u1133HFHibYjR47IZrPJ39/f6akOktSpU6cSSzEcOnRIY8aMUbNmzeTl5aUmTZrotttu09KlS1VYWOg47nJtP9+WL19enksCAACAa0hKSnJpc1WZR2K///579ejRQ+Hh4Vq2bJmaN2+uvXv36tFHH9WqVau0detW1a9fX5I0duxYPfXUUyooKNCGDRv04IMPqm7duho3bpxTn/Hx8ZowYYLi4+N14sQJBQUFlfjc3NxcPf/885o1a9ZVa9u2bZv69eundu3aacGCBYqIiJAkJScna8GCBWrfvr06duzoOH7JkiUaOHCgUx9169Yt6yUBAADAdZQ2J7a091U2JzY2Nlaenp764osvVKtWLUlSs2bN1LlzZ7Vs2VJPPvmkXn31VUnSb37zGwUEBEiSYmJiNH/+fK1du9YpxObl5em9995TcnKyMjIy9Oabb+qJJ54o8bkTJkzQiy++qNjYWPn7+5doNwxDo0ePVnh4uDZv3uy0xlirVq00fPjwEqPAdevWddQHwDVX/meUn59vXiFANbvy3ztLrAPlk5qaqsaNG0u69Nf2W265RceOHZPNZtOpU6cUHh7ucl9lCrFnzpzRmjVr9PTTTzsC7GUBAQEaMWKE3nvvPS1cuNCpzTAMffnllzpw4IBatWrl1Pb+++8rIiJCrVu31r333qvJkydr+vTpJe5cGz58uNauXaunnnpK8+fPL1FbSkqK9u/fr2XLll11kdyK3ElaUFDguCtVuvSwA8AdXXkz48iRI02sBDBPQUFBiZ+DAK6vTp06jrmxPj4+MgxDfn5+ki79oliWXxDLNCf24MGDMgxDbdqUvkZkmzZtlJWVpVOnTkmSFi5cKB8fH3l5eal3794qLi7WxIkTnc6Jj4/XvffeK0kaOHCgsrOzS50PYbPZNGfOHC1evFiHDx8u0f7dd99Jklq3bu3Yl5mZKR8fH8f283A9fPhwp3YfHx8dPXq01K8tLi5Ofn5+ji04OPhqlwkAAABVrFyrE7iakkeMGKEnn3xSWVlZmjFjhnr27KmePXs62lNTU7Vt2zatXLnyUjEeHho2bJji4+MVFRVVor8BAwbolltu0V//+le9++671/38Bg0aKCUlRdKlG80uXLjg1D537lz169fPaV9p83Elafr06ZoyZYrjfU5ODkEWbsnT/r///t955x2WGYLbyM/Pd/z1wcvLy+RqAOup7Gk4ZQqxYWFhstls2r9/v37/+9+XaN+/f7/q1aunRo0aSZL8/PwUFhYm6dK0gbCwMHXv3t0RHOPj41VYWOgUHA3DkJeXl+bPn+8YXr7SnDlz1KNHDz366KNO+y9PU0hNTVXnzp0lSXa73fH5Hh4lv9SAgABH+/V4eXnxTQuQdOWsHG9vb0Is3BIPOgDK7uf/3dSsWVOhoaHXPOZayjSdoEGDBurfv78WLlyo8+fPO7VlZGRo6dKlGjZsWKkF+Pj4aNKkSZo6daoMw1BhYaHefvttvfDCC0pJSXFs33zzjYKCgrRs2bJSa+jatavuvvtuTZs2zWl/586dFRERoeeff97lu9oAAABQPbZs2aIGDRo43gcHB2v37t2O9/7+/kpPT3e5vzJPJ5g/f7569uypAQMGaPbs2U5LbDVp0kRPP/30Vc996KGH9Pe//10fffSRPDw8lJWVpfvvv7/EiOs999yj+Ph4Pfzww6X28/TTT6tdu3ZOo6s2m01LlixR//791atXL02fPl1t2rTRxYsXtXHjRp06dUp2u92pn7NnzyojI8Npn6+vr2rXrl3WywIAAIBr6Nq163WPKW0Fqqsp88MOWrVqpeTkZLVo0UJDhw5Vy5Yt9eCDD6pPnz7asmWLY43Y0tSvX1/33XefZs6cqfj4ePXr16/UKQP33HOPkpOT9e2335baT3h4uMaMGVNieZ/u3btrx44dat26tWJjY9W2bVv17NlTy5Yt09y5c0usTxsTE6PAwECn7ZVXXinT9bhyfiAA4NfLy8tLH3zwgT744AOmlwG/ADaDxe7KJScnR35+fkqeG6IGvsyNqk4FhTZN/bydJOn53+2Vlwf/hKvTldef58cDAMxSrtUJ8D8XimqooPD6x6HyFBTaSn2N6nGhqFxPqwYAoFIRYito1tpw1axZ0+wy3NaTq9uaXQIAAKgE//3vf/Xcc89pxowZLh3PkAoAAABMl5eXp1mzZrl8PCOxFfT22287Hp+G6mEYhuMRwF5eXqzXaCJubgEAVKay/EwnxFYQi72bg2eWm+fKXyIAAKhMZVlvgBALoEwKCgo0ZMgQSaxOAABwXYsWLa4ZUouKisrUHyEWQJlcuT5zfn4+IRYA4JLJkydfsz0vL0//93//53J/hFgAAABUuYkTJ16zPTMzs0whltUJAAAAYDmEWABlUlxcXOprAAAqqiyrExBiAZRJbm5uqa8BAKiICxcuqG/fvi4fX+UhdtGiRfL19VVh4f+ezZqXl6eaNWsqKirK6djExETZbDYdPnxYoaGhmjdvXon+Zs6cqU6dOpX6PjQ0VDab7arb6NGjJemq7cuXL6/krx4AAADX8/bbb6tDhw6y2+0un1PlN3b16dNHeXl5Sk5OVvfu3SVJmzZtUkBAgL7++munu5sTEhLUrFkztWzZslyftX37dsfyDF999ZXuuecepaamOh5GcOXaokuWLNHAgQOdzq9bt265PhcAAABld+rUKT300ENat26dXnjhBY0dO9blc6s8xLZu3VqBgYFKTEx0hNjExEQNHjxYGzZs0NatWx0jsomJierTp0+5P6tRo0aO1/Xr15ck+fv7lxpO69atq4CAgHJ/FgAAAFz380fKFhYW6rXXXlP79u21e/duhYSElKm/alliq0+fPkpISNC0adMkXRpxfeyxx1RUVKSEhARFRUXp/Pnz+vrrrzVmzJjqKKnMCgoKnJ5SlJOTY2I1AAAA1vLJJ584vS8sLFRWVpbuvvvuMgdYqRpD7OTJk1VYWKjz589r165dioyM1MWLF7Vo0SJJ0pYtW1RQUOA0Evv444+XWC/swoULatu2bYVrGj58eIl5F/v27VOzZs1KPT4uLq7EbxAAAABwzc6dO0vs++yzzzR27FitWLFC8fHxat68ucv9VcvqBFFRUTp37py2b9+uTZs2KTw8XI0aNVJkZKRjXmxiYqJatGjhFCIfffRRpaSkOG0PP/xwpdQ0d+7cEn0HBQVd9fjp06crOzvbsR07dqxS6gAAAHBX0dHR2rt3rxo0aKAOHTpo4cKFLp9bLSOxYWFhatq0qRISEpSVlaXIyEhJUlBQkIKDg/XVV18pISGhxLIKDRs2VFhYmNO+y3NdKyogIKBE39fi5eUlLy+vSvlsAAAAXNKgQQN98MEHevfddxUbG6s///nPLp1XbevE9unTR4mJiUpMTHRaWqt3795atWqVtm3bVqGbugAAAGBdf/rTn7R3716Xj6+WkVjpUoiNjY3VxYsXHSOxkhQZGanx48frwoUL1Rpiz549q4yMDKd9vr6+ql27drXVAAAA4C6SkpJcOu5a0zuvVK0h9vz584qIiFDjxo0d+yMjI5Wbm+tYiqu6xMTElNgXFxfnWEEBAAAAladv374yDMPxaNkrX19mGIbLjzS3GYZhVHqVbiAnJ0d+fn7Kzs52PEwBcAdnzpzRqFGjJElvvfVWpc1TBwD8ul25POmRI0d0yy236Pjx4459p06dUnh4uOPBVddTbSOxAH4datSoUeprAACu5cpBP19fXxUXFzvtO3/+vMoytspPIAAAAFQrf39/nT9/Xunp6Y59Bw8elL+/v8t9MBILoEy8vb1LfQ0AgKtq166tTp066Q9/+IOmTp2q/Px8PfXUU7rllltc7oM5seXEnFi4K8MwHI9g9vLyKjEpHwAAV2zfvl333HOPfvzxR0lS27Zt9emnn7r81C5CbDllZ2erbt26OnbsGCEWAAC4DV9f30obwCgsLFRqaqo8PT0VFhZWpn4JseX0/fffq2XLlmaXAQAAUK0yMzPVqFEjs8tgTmx5XV5W6OjRo/Lz8zO5GveTk5Oj4OBgRsJNwvU3F9ffPFx7c3H9zXX5+nt6epbrfFemCRiGoSNHjrjUHyG2nC4vLeTn58d/SCaqU6cO199EXH9zcf3Nw7U3F9ffXOWdSnD06FE99dRT8vX1lSSdPn1azz33nJ599llJUl5env7v//7P5f4IsQAAAKgWDzzwgOPJrd9//73mzp2riRMnSro0TaEsIZZ1YgEAAGA5hNhy8vLy0owZM+Tl5WV2KW6J628urr+5uP7m4dqbi+tvrl/a9Wd1AgAAAFQ5u92uEydOOE0n6NSpk3JyciRdmk4QGBiooqIil/pjJBYAAABV7qGHHtJvfvMbx/smTZpo1apVjve+vr6Ki4tzuT9GYgEAAGA5jMQCAADAcgix5bRgwQKFhobK29tb3bp107Zt28wuyS1s3LhR0dHRCgoKks1m08cff2x2SW4jLi5ON998s3x9feXv76+77rpLqampZpflNl599VV16NDBsT5mjx49nP4Mh+o1Z84c2Ww2TZ482exS3MLMmTNls9mctoiICLPLcis//vij7r33XjVo0EC1atXSDTfcoOTkZFNrIsSWw3vvvacpU6ZoxowZ2rlzpzp27KgBAwYoMzPT7NJ+9c6dO6eOHTtqwYIFZpfidpKSkhQbG6utW7dq7dq1unjxom6//XadO3fO7NLcQtOmTTVnzhzt2LFDycnJ6tu3rwYPHqy9e/eaXZrb2b59u1577TV16NDB7FLcSrt27ZSenu7YvvzyS7NLchtZWVnq1auXatasqVWrVmnfvn164YUXVK9ePVPrYk5sOXTr1k0333yz5s+fL0kqLi5WcHCwJkyYoGnTpplcnfuw2WxauXKl7rrrLrNLcUunTp2Sv7+/kpKS1Lt3b7PLcUv169fXc889p/vvv9/sUtxGXl6ebrzxRi1cuFCzZ89Wp06dNG/ePLPL+tWbOXOmPv74Y6WkpJhdiluaNm2aNm/erE2bNpldihNGYsvowoUL2rFjh/r16+fYV6NGDfXr109btmwxsTKgemVnZ0u6FKRQvYqKirR8+XKdO3dOPXr0MLsctxIbG6s77rjD6WcAqsfBgwcVFBSkFi1aaMSIETp69KjZJbmNTz/9VF26dNGQIUPk7++vzp076/XXXze7LEJsWZ0+fVpFRUWONc4ua9y4sTIyMkyqCqhexcXFmjx5snr16qX27dubXY7b2L17t3x8fOTl5aWHH35YK1euVNu2bc0uy20sX75cO3fuLNMSQKgc3bp105tvvqnVq1fr1VdfVVpamm699Vbl5uaaXZpb+P777/Xqq6+qVatWWrNmjcaNG6eJEyfqrbfeMrUuD1M/HYAlxcbGas+ePcxJq2atW7dWSkqKsrOz9eGHH2rUqFFKSkoiyFaDY8eOadKkSVq7dq28vb3NLsftDBo0yPG6Q4cO6tatm0JCQvT+++8znaYaFBcXq0uXLnrmmWckSZ07d9aePXu0aNEijRo1yrS6GIkto4YNG8put+vkyZNO+0+ePKmAgACTqgKqz/jx4/X5558rISFBTZs2Nbsct+Lp6amwsDDddNNNiouLU8eOHfXSSy+ZXZZb2LFjhzIzM3XjjTfKw8NDHh4eSkpK0ssvvywPDw+XnzCEylG3bl2Fh4fr0KFDZpfiFgIDA0v8stymTRvTp3QQYsvI09NTN910k9avX+/YV1xcrPXr1zM3Db9qhmFo/PjxWrlypTZs2KDmzZubXZLbKy4uVkFBgdlluIXbbrtNu3fvVkpKimPr0qWLRowYoZSUFNntdrNLdCt5eXk6fPiwAgMDzS7FLfTq1avEkorfffedQkJCTKroEqYTlMOUKVM0atQodenSRV27dtW8efN07tw5xcTEmF3ar15eXp7Tb95paWlKSUlR/fr11axZMxMr+/WLjY3Vu+++q08++US+vr6OOeB+fn6qVauWydX9+k2fPl2DBg1Ss2bNlJubq3fffVeJiYlas2aN2aW5BV9f3xLzv2vXrq0GDRowL7waTJ06VdHR0QoJCdGJEyc0Y8YM2e12DR8+3OzS3MIjjzyinj176plnntHQoUO1bds2LV68WIsXLza3MAPl8sorrxjNmjUzPD09ja5duxpbt241uyS3kJCQYEgqsY0aNcrs0n71SrvukowlS5aYXZpbGDNmjBESEmJ4enoajRo1Mm677Tbjiy++MLsstxYZGWlMmjTJ7DLcwrBhw4zAwEDD09PTaNKkiTFs2DDj0KFDZpflVj777DOjffv2hpeXlxEREWEsXrzY7JIM1okFAACA5TAnFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgDcwJEjR2Sz2ZSSkmJ2KQBQKQixAFAOo0eP1l133eV4HxUVpcmTJ5tWT1pamv70pz8pKChI3t7eatq0qQYPHqwDBw5IkoKDg5Wenq727dubViMAVCYPswsAAFTMxYsX1b9/f7Vu3VorVqxQYGCgjh8/rlWrVuns2bOSJLvdroCAAHMLBYBKxEgsAFTQ6NGjlZSUpJdeekk2m002m01HjhyRJO3Zs0eDBg2Sj4+PGjdurJEjR+r06dOOc6OiojRhwgRNnjxZ9erVU+PGjfX666/r3LlziomJka+vr8LCwrRq1aqrfv7evXt1+PBhLVy4UN27d1dISIh69eql2bNnq3v37pJKTicYPXq0o9Yrt8TERElSQUGBpk6dqiZNmqh27drq1q2bow0AfgkIsQBQQS+99JJ69OihsWPHKj09Xenp6QoODtbZs2fVt29fde7cWcnJyVq9erVOnjypoUOHOp3/1ltvqWHDhtq2bZsmTJigcePGaciQIerZs6d27typ22+/XSNHjtR///vfUj+/UaNGqlGjhj788EMVFRW5XPPlWtPT0zVp0iT5+/srIiJCkjR+/Hht2bJFy5cv17fffqshQ4Zo4MCBOnjwYMUuFgBUEpthGIbZRQCA1YwePVpnz57Vxx9/LOnSiGqnTp00b948xzGzZ8/Wpk2btGbNGse+48ePKzg4WKmpqQoPD1dUVJSKioq0adMmSVJRUZH8/Px099136+2335YkZWRkKDAwUFu2bHGMrP7cggUL9Nhjj8lut6tLly7q06ePRowYoRYtWki6NBLbvHlz7dq1S506dXI6d8WKFRoxYoTWrVunXr166ejRo2rRooWOHj2qoKAgx3H9+vVT165d9cwzz1T08gFAhTESCwBV5JtvvlFCQoJ8fHwc2+WRzsOHDzuO69Chg+O13W5XgwYNdMMNNzj2NW7cWJKUmZl51c+KjY1VRkaGli5dqh49euiDDz5Qu3bttHbt2mvWuGvXLo0cOVLz589Xr169JEm7d+9WUVGRwsPDnWpPSkpyqhsAzMSNXQBQRfLy8hQdHa1nn322RFtgYKDjdc2aNZ3abDab0z6bzSZJKi4uvubn+fr6Kjo6WtHR0Zo9e7YGDBig2bNnq3///qUen5GRoTvvvFMPPPCA7r//fqe67Xa7duzYIbvd7nSOj4/PNWsAgOpCiAWASuDp6VliPuqNN96ojz76SKGhofLwqN5vtzabTREREfrqq69Kbc/Pz9fgwYMVERGhF1980amtc+fOKioqUmZmpm699dbqKBcAyozpBABQCUJDQ/X111/ryJEjOn36tIqLixUbG6szZ85o+PDh2r59uw4fPqw1a9YoJibG5RuwXJGSkqLBgwfrww8/1L59+3To0CHFx8frjTfe0ODBg0s956GHHtKxY8f08ssv69SpU8rIyFBGRoYuXLig8PBwjRgxQvfdd59WrFihtLQ0bdu2TXFxcfr3v/9daXUDQEUwEgsAlWDq1KkaNWqU2rZtq/PnzystLU2hoaHavHmzHn/8cd1+++0qKChQSEiIBg4cqBo1Km8MoWnTpgoNDdWsWbMcS2ldfv/II4+Uek5SUpLS09PVtm1bp/0JCQmKiorSkiVLNHv2bP3lL3/Rjz/+qIYNG6p79+763e9+V2l1A0BFsDoBAAAALIfpBAAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAy/EwuwAAAAD8+sXExLh03JIlS1w6zmYYhlGRggAAAIDrsdvtGjhwoLy8vCRJ586d04YNGxQdHS1JKigo0KpVq1RcXOxSf4RYAAAAVDm73a4TJ06ocePGkqS0tDR16NBBubm5kqRTp06pcePGLodY5sQCAACg2v18HLWs46qEWAAAAFQ5X19fZWVlOd5nZWXp3LlzysvLkyRlZGSofv36LvdHiAUAAECVi4iI0CuvvKLi4mIVFxdr4cKFCgoK0tSpU7V582Y9+eSTuvnmm13ujzmxAAAAqHIff/yx/vCHP6h27doqLi5W7dq1tXr1av3xj3/UwYMHFRwcrM8++0w33HCDS/0RYgEAAFAtNm7cqM8++0y1atXS2LFjFRwcLEn66aef1KBBgzL1RYgFAACA5TAnFgAAAJbDE7sAAABQ5ex2u0vLaLm6TiwhFgAAAFVu5cqVldofc2IBAABgOYzEAgAAoNocO3ZMH374oQ4ePChJatWqlf7whz84VipwFSOxAAAAqBbz58/XX/7yFxUWFsrPz0+GYSgnJ0ceHh6aO3eu/vznP7vcF6sTAAAAoMpt2LBBkydP1vjx45Wenq4zZ84oKytL6enpmjhxoiZMmKCEhASX+2MkFgAAAFXud7/7nRo2bKg333yz1PYxY8bo1KlT+uyzz1zqj5FYAAAAVLmvv/5ao0ePvmr7fffdp6+//trl/gixAAAAqHI5OTlq3ry54/1///tfrVixwvG+ZcuWys3Ndbk/QiwAAACqXIMGDXTmzBnH+4yMDI0aNcrxPjs7W02bNnW5P5bYAgAAQJXr0aOH3nnnHdWrV082m03Hjx93at+wYYO6dOnicn/c2AUAAIAqt27dOg0YMMDx6Fmbzabf/OY3jikEN998s1588UXdeuutLvVHiAUAAEC12Lt3r4qKihzv7Xa72rVrV66+CLEAAACwHG7sAgAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOILSfDMJSTkyPuiwMAAKh+POygnHJzc+Xn56fs7GzVqVPH7HIAAAB+0Vq0aHHdwT/DMHTkyBGX+iPEAoCFGIahgoICs8twS1de+zp16qhGDf6YCZTF5MmTr9p28uRJLV26VEePHnW5P0IsAFhIQUGBhgwZYnYZbu+dd95R3bp1zS4DsJSJEyc6vS8qKtK///1vLVmyRGvWrFFkZKT+8Y9/uNwfIRYAAADV5sCBA3rjjTf0r3/9S7Vr11ZMTIzmz5+vJk2alKkfQiwAWNQzg/bL015sdhluI6/ArplrI8wuA7C0rKwstWvXTr1799by5cvVu3fvcvdFiAUAC7nypoiaNYrl5cEKKdWloJBfGMx05ZxkLy8v2Ww2kytCefzmN7/RH//4R3388ceaMWOGYmJiNGTIENWqVavMfTErHQAs5Mqbui4U8UMc7uPyfPAhQ4Zwc6OFeXl5aenSpUpPT9fQoUP18ssvKzAwUA899JC+/vrrMvVVrhB77NgxjRkzRkFBQfL09FRISIgmTZqkn376yXFMVFSUbDabbDabvL29FR4erri4uFKXVtiyZYvsdrvuuOOOEm1HjhyRzWaTv7+/cnNzndo6deqkmTNnOu07dOiQxowZo2bNmsnLy0tNmjTRbbfdpqVLl6qwsNBx3OXafr4tX768TNdi7ty5ZToelePZZ59VdHS0nn32WbNLAQAAZVSnTh2NGzdOycnJ2rhxo2rVqqU77rhD7dq1c7mPMofY77//Xl26dNHBgwe1bNkyHTp0SIsWLdL69evVo0cPnTlzxnHs2LFjlZ6ertTUVE2fPl1/+9vftGjRohJ9xsfHa8KECdq4caNOnDhR6ufm5ubq+eefv2Zt27Zt04033qj9+/drwYIF2rNnjxITE/XAAw/o1Vdf1d69e52OX7JkidLT0522u+66q0zXY8uWLcrMzCzTOaiYzMxMffnll5KkL7/8kusPAIAF1K9fX/Xq1SuxRUZG6u2339aFCxe0f/9+l/sr85zY2NhYeXp66osvvnDMX2jWrJk6d+6sli1b6sknn9Srr74q6dK8h4CAAEly3Hm2du1ajRs3ztFfXl6e3nvvPSUnJysjI0NvvvmmnnjiiRKfO2HCBL344ouKjY2Vv79/iXbDMDR69GiFh4dr8+bNTuv3tWrVSsOHDy8xCly3bl1HfRXx+OOPa8mSJRXuB655/PHHS7zn+gMA8Mv20ksvVeqTTssUYs+cOaM1a9bo6aefLjEBNyAgQCNGjNB7772nhQsXOrUZhqEvv/xSBw4cUKtWrZza3n//fUVERKh169a69957NXnyZE2fPr3EhO3hw4dr7dq1euqppzR//vwStaWkpGj//v1atmzZVRegrqpJ4KdPn9b69et12223VUn/+J/169fr9OnTTvu4/gCqxRU/e/Pz85Wfn29eLW7oyuvNI9+t6Y9//KNq1qxZaf2VKcQePHhQhmGoTZs2pba3adNGWVlZOnXqlCRp4cKF+uc//6kLFy7o4sWL8vb2LrHQbXx8vO69915J0sCBA5Wdna2kpCRFRUU5HWez2TRnzhxFR0frkUceUcuWLZ3av/vuO0lS69atHfsyMzPVokULx/t//OMf+vOf/+x4P3z4cNntdqd+9u3bp2bNmpX42goKCpwmkufk5Di1v/LKK4qKiirRHypPUVGRXnnllVLbuP4AqtqVN9KNHTvWxEpQUFBQrrvZYa4mTZroT3/6k+6//37dcMMNFe6vXDd2ufob0IgRI5SSkqLNmzdr0KBBevLJJ9WzZ09He2pqqrZt26bhw4dLkjw8PDRs2DDFx8eX2t+AAQN0yy236K9//atLn9+gQQOlpKQoJSVFdevW1YULF5za586d62i/vAUFBZXaV1xcnPz8/BxbcHCwU3tRUZFWr17tUl0on9WrV6uoqKjUNq4/AAC/bNOmTdO2bdvUuXNndevWTa+99lqJm/bLokwjsWFhYbLZbNq/f79+//vfl2jfv3+/6tWrp0aNGkmS/Pz8FBYWJunStIGwsDB1795d/fr1k3RpFLawsNApOBqGIS8vL82fP19+fn4lPmPOnDnq0aOHHn30Uaf9l6cppKamqnPnzpIku93u+HwPj5JfakBAgKP9eqZPn64pU6Y43ufk5DgFWbvdroEDB7rUF8pn4MCBev3110sNslx/AFXN0/6/AZzXX3+dx85Ws/z8fI0cOVLSpWWaYD1TpkzRlClTtHnzZkVGRio/P19TpkzRH/7wB40ZM0aRkZFl6q9MIbZBgwbq37+/Fi5cqEceecRpKD8jI0NLly7VfffdV+rcUx8fH02aNElTp07Vrl27VFRUpLffflsvvPCCbr/9dqdj77rrLi1btkwPP/xwiX66du2qu+++W9OmTXPa37lzZ0VEROj555/X0KFDrzovtry8vLyu+R/NxIkT+VN2FbPb7ZowYYLmzZtXoo3rD6DKXfGjzdvbW97e3ubV4uZ40IG11atXTzabTd9884327Nmjd955RyNHjpS3t7diYmI0ffp0l/opc9KbP3++CgoKNGDAAG3cuFHHjh3T6tWr1b9/fzVp0kRPP/30Vc996KGH9N133+mjjz7S559/rqysLN1///1q376903bPPfdcdUqBJD399NPasGGDUlNTHftsNpuWLFmi1NRU9erVS59++qkOHjyoffv2adGiRTp16lSJkHP27FllZGQ4befOnSvrJVHDhg3Vt2/fMp+HsrvtttvUsGFDp31cfwAArKl9+/Z69tlnlZaWpiFDhrg8ZVQqR4ht1aqVkpOT1aJFCw0dOlQtW7bUgw8+qD59+mjLli2qX7/+Vc+tX7++7rvvPs2cOVPx8fHq169fqVMG7rnnHiUnJ+vbb78ttZ/w8HCNGTOmxJ2h3bt3144dO9S6dWvFxsaqbdu26tmzp5YtW6a5c+c6Le0lXVr2KzAw0Gm72o1D18KC+9Xr59eb6w8AgDV98803evTRRxUSEqKPP/64TD/Ty7xOrCSFhITozTffvOYxiYmJpe4v7WEHP9e1a1enm8dKu5Hstdde02uvvVZif3h4+HVru1qf5dGjR49S161F1fH399ctt9yiL7/8UrfccgvXH27lymlNV87RBH7tvLy89MEHHzhew7p++uknGYahG264QUePHtWQIUP04Ycfqnv37mXqp1whFv/zyCOPmF2CW3r88cdLPPQAcAdXzgVkWiDcyeXH2MO6nnvuOX300UdKTk5Wjx49NGbMGA0bNky/+c1vytUfIRYALOpCUQ1JxWaX4TYuFlXuDcOAu3nxxRc1cuRIvf322woPD69wf4RYALCoJ1aV/uAZAPglOn78uOMm+6ysLB08eFA2m01hYWGqV69emfvj10oAAABUObvdrrS0NP32t79Vw4YN1b17d3Xr1k0NGzbUb3/7W/3www9l6s9m8ADicsnJyZGfn5+ys7NVp04ds8sB4CYMw3B6BDaqz5XXvk6dOpW+Hjnwa3fy5EndeOONstvtio2NVUREhKRLD6qaP3++ioqKtHPnTjVu3Nil/gix5USIBWAGQizcFb9EWN/EiROVkJCgbdu2OT0wS7r0RLabb75ZUVFRLi93SogtJ0IsADPk5+dryJAhZpcBmOqdd97hsb8W1KJFCz3//PO6++67S23/+OOP9Ze//EWHDx92qT9+jQEAAECVS09PV4cOHa7a3r59e/34448u98fqBABgUa/8fay8PGuaXQZQLXLy/qupf3/T7DJQAY0aNVJhYeFV2y9evOjyfFiJEAsLunJelJeXl9Pi74A78fKsKS8vQizcg2fB/yILMyGt6aabbtIXX3zhuKHr51avXq2OHTu63B/TCWA5BQUFGjJkiIYMGcINLgDgJi5c/N8IHt/7remRRx7Ra6+9puzs7BJtOTk5ev311zV58mSX+2MkFgAAAFWud+/e2rt3b6ltderU0b59+8rUX5WPxC5atEi+vr5OcyDy8vJUs2ZNRUVFOR2bmJgom82mw4cPKzQ0VPPmzSvR38yZM9WpU6dS34eGhspms111Gz16tCRdtX358uWV/NUDAACgKlT5SGyfPn2Ul5en5ORkde/eXZK0adMmBQQE6Ouvv1Z+fr68vb0lSQkJCWrWrJlatmxZrs/avn27ioqKJElfffWV7rnnHqWmpjqWwLpyTbIlS5Zo4MCBTuezXAcAAEDVaNGihUvzmdPS0lzqr8pDbOvWrRUYGKjExERHiE1MTNTgwYO1YcMGbd261TEim5iYqD59+pT7sxo1auR4Xb9+fUmSv79/qeG0bt26CggIKPdnwTxX/geQn59vYiVA9bvy3zw3twCwkp/Pd12/fr3WrFmjv/71r/L19S1zf9UyJ7ZPnz5KSEjQtGnTJF0acX3sscdUVFSkhIQERUVF6fz58/r66681ZsyY6iipzAoKCpwmkufk5JhYjXu78v+HkSNHmlgJYK4LFwvl7e1pdhkA4JKJEyc6Xq9bt07Tp09XnTp1lJiYqH//+9/y9Czb97NqWZ2gT58+2rx5swoLC5Wbm6tdu3YpMjJSvXv3VmJioiRpy5YtKigocBqJffzxx+Xj4+O0PfPMM5VS0/Dhw0v0ffTo0aseHxcXJz8/P8cWHBxcKXUAAAC4k/Xr1+vOO+/UQw89pP379+vHH3/UH//4RxUXF5epn2oZiY2KitK5c+e0fft2ZWVlKTw8XI0aNVJkZKRiYmKUn5+vxMREtWjRQs2aNXOc9+ijjzpuxrrs5Zdf1saNGytc09y5c9WvXz+nfUFBQVc9fvr06ZoyZYrjfU5ODkHWJF5eXo7X77zzjmNONeAO8vPzHX+B8KzJAjMArCUhIUF33nmnHnzwQb344ouSLo3K9uzZUw888IDeeOMNl/uqlu+AYWFhatq0qRISEpSVlaXIyEhJl0JjcHCwvvrqKyUkJKhv375O5zVs2FBhYWFO+y7Pda2ogICAEn1fi5eXl1N4gnmufLiBt7c3IRZuiwd9ALCSpKQkRUdH64EHHnBagSooKEjr1q3TLbfcoqlTp+r55593qb9qe9hBnz59lJiYqMTERKeltXr37q1Vq1Zp27ZtFbqpCwAAAL9c0dHRiomJ0UsvvVSiLSwsTKtXr9Y///lPl/urtr9F9enTR7Gxsbp48aJjJFaSIiMjNX78eF24cKFaQ+zZs2eVkZHhtM/X11e1a9euthoAAADcxahRo/TKK69ctb1Tp0769NNPXe6vWkPs+fPnFRERocaNGzv2R0ZGKjc317EUV3WJiYkpsS8uLs6xggJ+uby8vPTBBx84XgMAfv2unAPO935rulaAvax3794u92czWGiwXHJycuTn56fs7GzHwxQAoKrl5+dryJAhkqTFz/5ZXl41Ta4IqB45uf/VhL++LunSTb08oAjc2goAFlVw4aLZJQDVhn/v+DlCLABY1OVRKQBwR9W2OgEAAABwNT/99JOaN2/u8vHMiS2n7Oxs1a1bV8eOHWNOLIBqYxiG06OXAXdx5b/9OnXqqEYNxuHM4uvrWyXrVGdmZiogIMDlJ3cxnaCcfvrpJ0niqV0AAMCtZGZmqlGjRmaXQYgtr8tPDjt69Kj8/PxMrsb9XH7sLyPh5uD6m4vrbx6uvbm4/ua6fP09PT3LdX5SUtI128+cOVOm/gix5XT5zxh+fn78h2SiOnXqcP1NxPU3F9ffPFx7c3H9zVXeqQR9+/aVYRiVNhWBEAsAAIAql5WVdc32U6dOqVWrVi73R4gFAABAlbve6Hl+fn6Z+uPWvnLy8vLSjBkzePSdSbj+5uL6m4vrbx6uvbm4/uaqjutflqkGLLEFAAAA0+Xm5urhhx/W0qVLXTqekVgAAACY7q233tLHH3/s8vHMiQUAAIBpjh49qjFjxiglJUXx8fEun8dILAAAAEzx1ltvqWPHjqpVq5b27t2rP/7xjy6fy0gsAAAAqlyLFi105a1YhYWFSk9P16JFi/TAAw+UuT9GYstpwYIFCg0Nlbe3t7p166Zt27aZXZJb2Lhxo6KjoxUUFCSbzVamuTOomLi4ON18883y9fWVv7+/7rrrLqWmpppdltt49dVX1aFDB8ci7z169NCqVavMLsttzZkzRzabTZMnTza7FLcwc+ZM2Ww2py0iIsLsstzKjz/+qHvvvVcNGjRQrVq1dMMNNyg5OblMfUyePFmPPPKI0xYcHKz4+HgdOHCgzDUxElsO7733nqZMmaJFixapW7dumjdvngYMGKDU1FT5+/ubXd6v2rlz59SxY0eNGTNGd999t9nluJWkpCTFxsbq5ptvVmFhoZ544gndfvvt2rdvn2rXrm12eb96TZs21Zw5c9SqVSsZhqG33npLgwcP1q5du9SuXTuzy3Mr27dv12uvvaYOHTqYXYpbadeundatW+d47+FBhKkuWVlZ6tWrl/r06aNVq1apUaNGOnjwoOrVq1emfiZOnFhi34MPPqjJkyfrxhtv1KxZszR16lSXl9liia1y6Natm26++WbNnz9fklRcXKzg4GBNmDBB06ZNM7k692Gz2bRy5UrdddddZpfilk6dOiV/f38lJSWpd+/eZpfjlurXr6/nnntO999/v9mluI28vDzdeOONWrhwoWbPnq1OnTpp3rx5Zpf1qzdz5kx9/PHHSklJMbsUtzRt2jRt3rxZmzZtqrLP+Pe//62xY8cqNDRUX331lUvnMJ2gjC5cuKAdO3aoX79+jn01atRQv379tGXLFhMrA6pXdna2pEtBCtWrqKhIy5cv17lz59SjRw+zy3ErsbGxuuOOO5x+BqB6HDx4UEFBQWrRooVGjBiho0ePml2S2/j000/VpUsXDRkyRP7+/urcubNef/31Sv2MO+64Q3v37lXTpk1dPocQW0anT59WUVGRGjdu7LS/cePGysjIMKkqoHoVFxdr8uTJ6tWrl9q3b292OW5j9+7d8vHxkZeXlx5++GGtXLlSbdu2Nbsst7F8+XLt3LlTcXFxZpfidrp166Y333xTq1ev1quvvqq0tDTdeuutys3NNbs0t/D999/r1VdfVatWrbRmzRqNGzdOEydO1FtvvVWpn1OvXj29//77Lh/PhBIAZRYbG6s9e/boyy+/NLsUt9K6dWulpKQoOztbH374oUaNGqWkpCSCbDU4duyYJk2apLVr18rb29vsctzOoEGDHK87dOigbt26KSQkRO+//z7TaapBcXGxunTpomeeeUaS1LlzZ+3Zs0eLFi3SqFGjXO4nJibmuscYhqE333zTpf4YiS2jhg0bym636+TJk077T548qYCAAJOqAqrP+PHj9fnnnyshIaFMf/ZBxXl6eiosLEw33XST4uLi1LFjR7300ktml+UWduzYoczMTN14443y8PCQh4eHkpKS9PLLL8vDw0NFRUVml+hW6tatq/DwcB06dMjsUtxCYGBgiV+W27RpU+YpHdnZ2Y7txIkT+te//uW0LzMzU2+//bbL/TESW0aenp666aabtH79escNRcXFxVq/fr3Gjx9vbnFAFTIMQxMmTNDKlSuVmJio5s2bm12S2ysuLlZBQYHZZbiF2267Tbt373baFxMTo4iICD3++OOy2+0mVeae8vLydPjwYY0cOdLsUtxCr169Siyp+N133ykkJKRM/axYscLxOi0tTR06dHDad+rUqTINCBJiy2HKlCkaNWqUunTpoq5du2revHk6d+6cS8PkqJi8vDyn37zT0tKUkpKi+vXrq1mzZiZW9usXGxurd999V5988ol8fX0dc8D9/PxUq1Ytk6v79Zs+fboGDRqkZs2aKTc3V++++64SExO1Zs0as0tzC76+viXmf9euXVsNGjRgXng1mDp1qqKjoxUSEqITJ05oxowZstvtGj58uNmluYVHHnlEPXv21DPPPKOhQ4dq27ZtWrx4sRYvXlzuPmvWrKmLFy867cvPzy/b0mkGyuWVV14xmjVrZnh6ehpdu3Y1tm7danZJbiEhIcGQVGIbNWqU2aX96pV23SUZS5YsMbs0tzBmzBgjJCTE8PT0NBo1amTcdtttxhdffGF2WW4tMjLSmDRpktlluIVhw4YZgYGBhqenp9GkSRNj2LBhxqFDh8wuy6189tlnRvv27Q0vLy8jIiLCWLx4cYX6KyoqMjw9PY21a9c69i1atMgICwtzuQ/WiQUAAEC1GzNmjJYtW6YBAwbo/PnzWrdunWbOnKm//vWvLp1PiAUAAEC1O3/+vGbNmqX169fL09NTd955px599FHVqOHaugOEWAAAAFgON3YBAACgyiUlJbl0XGRkpEvHMRILAACAKme322UYhmw2m2Nfae+Li4td6o+HHQAAAKBapKamKisrS1lZWdq1a5d8fHx05swZZWVl6bvvvnMKtNfDdAIAAABUizp16qhOnTqSJB8fHxmGIT8/P0mX1oktywQBRmIBAABgOYRYAAAAVLnKvg2LEAsAbuDIkSOy2WxKSUkxuxQAburn811r1qyp0NDQax5zLYRYACiH0aNH66677nK8j4qK0uTJk02rJy0tTX/6058UFBQkb29vNW3aVIMHD9aBAwckScHBwUpPT1f79u1NqxGAe9uyZYsaNGjgeB8cHKzdu3c73vv7+ys9Pd3l/rixCwAs7uLFi+rfv79at26tFStWKDAwUMePH9eqVat09uxZSZeWtgkICDC3UABurWvXrtc9xt/f3+X+GIkFgAoaPXq0kpKS9NJLL8lms8lms+nIkSOSpD179mjQoEHy8fFR48aNNXLkSJ0+fdpxblRUlCZMmKDJkyerXr16aty4sV5//XWdO3dOMTEx8vX1VVhYmFatWnXVz9+7d68OHz6shQsXqnv37goJCVGvXr00e/Zsde/eXVLJ6QSjR4921HrllpiYKEkqKCjQ1KlT1aRJE9WuXVvdunVztAHALwEhFgAq6KWXXlKPHj00duxYpaenKz09XcHBwTp79qz69u2rzp07Kzk5WatXr9bJkyc1dOhQp/PfeustNWzYUNu2bdOECRM0btw4DRkyRD179tTOnTt1++23a+TIkfrvf/9b6uc3atRINWrU0IcffqiioiKXa75ca3p6uiZNmiR/f39FRERIksaPH68tW7Zo+fLl+vbbbzVkyBANHDhQBw8erNjFAoBKwhO7AKAcRo8erbNnz+rjjz+WdGlEtVOnTpo3b57jmNmzZ2vTpk1as2aNY9/x48cVHBys1NRUhYeHKyoqSkVFRdq0aZMkqaioSH5+frr77rv19ttvS5IyMjIUGBioLVu2OEZWf27BggV67LHHZLfb1aVLF/Xp00cjRoxQixYtJF0aiW3evLl27dqlTp06OZ27YsUKjRgxQuvWrVOvXr109OhRtWjRQkePHlVQUJDjuH79+qlr16565plnKnr5AKDCGIkFgCryzTffKCEhQT4+Po7t8kjn4cOHHcd16NDB8dput6tBgwa64YYbHPsaN24sScrMzLzqZ8XGxiojI0NLly5Vjx499MEHH6hdu3Zau3btNWvctWuXRo4cqfnz56tXr16SpN27d6uoqEjh4eFOtSclJTnVDQBm4sYuAKgieXl5io6O1rPPPluiLTAw0PG6Zs2aTm02m81p3+UlZ673PHFfX19FR0crOjpas2fP1oABAzR79mz179+/1OMzMjJ055136oEHHtD999/vVLfdbteOHTtkt9udzvHx8blmDQBQXQixAFAJPD09S8xHvfHGG/XRRx8pNDRUHh7V++3WZrMpIiJCX331Vant+fn5Gjx4sCIiIvTiiy86tXXu3FlFRUXKzMzUrbfeWh3lAkCZMZ0AACpBaGiovv76ax05ckSnT59WcXGxYmNjdebMGQ0fPlzbt2/X4cOHtWbNGsXExLh8A5YrUlJSNHjwYH344Yfat2+fDh06pPj4eL3xxhsaPHhwqec89NBDOnbsmF5++WWdOnVKGRkZysjI0IULFxQeHq4RI0bovvvu04oVK5SWlqZt27YpLi5O//73vyutbgCoCEZiAaASTJ06VaNGjVLbtm11/vx5paWlKTQ0VJs3b9bjjz+u22+/XQUFBQoJCdHAgQNVo0bljSE0bdpUoaGhmjVrlmMprcvvH3nkkVLPSUpKUnp6utq2beu0PyEhQVFRUVqyZIlmz56tv/zlL/rxxx/VsGFDde/eXb/73e8qrW4AqAhWJwAAAIDlMJ0AAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOV4mF0AAAAA3EdMTMx1jzEMQ2+++eY1j7EZhmFUUk0AAADANd19991XbTMMQ7t27dLRo0dVXFx8zX4YiQUAAEC1WbFiRYl9p0+f1tKlS/XGG28oJydHDz/88HX7YSQWAAAA1a64uFirV6/WkiVL9Pnnn+uWW27RmDFjdPfdd8vLy+u65xNiAQAAUK3y8vIUEREhT09PjR49WjExMQoODi5TH6xOAAAAgGpnt9tls9lkGMZ157+WhhALAACAauXj46MjR45o4cKF2rt3r9q2bav+/ftr2bJlKigocKkPphMAAADAVGfOnNG//vUvLVmyRD/88IOGDx+uBQsWXPMcQiwAAACqze9///urttlsNu3atUs//PADS2wBAADgl6N+/fq61hhqnz59XOqHkVgAAABYDiOxAAAAqDauPHZWkpYsWXLNdkIsAAAAqk12dnal9MN0gnIyDEO5ubny9fWVzWYzuxwAAAC3wjqx5ZSbmys/Pz/l5OSYXQoAAIDbYTpBBbm6IC8AAABcmxNrGIbefPPNax5DiK2g/Px8s0sAAACwjGvNiS0qKtK6det0/vx5QiwAAAB+OVasWFHq/k8++URPPPGEvL29NWPGjOv2w5xYAAAAmGbTpk3q2bOnhg8frt/97nf6/vvv9dhjj133PEJsBV3vkWgAAAAoac+ePYqOjtZtt92mdu3a6dChQ3r22Wfl5+fn0vmE2ArKy8szuwQAbmbcuHGKjo7WuHHjzC4FAMrshx9+0KhRo9SpUyd5eHho9+7dev311xUUFFSmfsoVYo8dO6YxY8YoKChInp6eCgkJ0aRJk/TTTz85jomKipLNZpPNZpO3t7fCw8MVFxdX6rNyt2zZIrvdrjvuuKNE25EjR2Sz2eTv76/c3Fyntk6dOmnmzJlO+w4dOqQxY8aoWbNm8vLyUpMmTXTbbbdp6dKlKiwsdBx3ubafb8uXLy/PJQGAanH48GEdP35cknT8+HEdPnzY5IoAoGxat26tDz74QFOnTtXo0aN14MABffLJJyW26ynzjV3ff/+9evToofDwcC1btkzNmzfX3r179eijj2rVqlXaunWr6tevL0kaO3asnnrqKRUUFGjDhg168MEHVbdu3RKjB/Hx8ZowYYLi4+N14sSJUpN4bm6unn/+ec2aNeuqtW3btk39+vVTu3bttGDBAkVEREiSkpOTtWDBArVv314dO3Z0HL9kyRINHDjQqY+6deuW9ZIAQLWZOnVqifcrV640qRoAKLvCwkIZhqHnnnvuqscYhnHdKZtlHomNjY2Vp6envvjiC0VGRqpZs2YaNGiQ1q1bpx9//FFPPvmk49jf/OY3CggIUEhIiGJiYtShQwetXbvWqb+8vDy99957GjdunO64446rLqcwYcIEvfjii8rMzCy13TAMjR49WuHh4dq8ebOio6PVqlUrtWrVSsOHD9eXX36pDh06OJ1Tt25dBQQEOG3e3t5lvSQAUC2WLFni9Bcl6dIPg+s9XxwAfkkKCwtVVFR0zc2Ve47KFGLPnDmjNWvW6M9//rNq1arl1BYQEKARI0bovffeKzFlwDAMbdq0SQcOHJCnp6dT2/vvv6+IiAi1bt1a9957r954441SpxwMHz5cYWFheuqpp0qtLSUlRfv379fUqVNVo0bpX1ZFHg9bUFCgnJwcpw0AqsvFixevuizNihUrdPHixWquCADMVabpBAcPHpRhGGrTpk2p7W3atFFWVpZOnTolSVq4cKH++c9/6sKFC7p48aK8vb01ceJEp3Pi4+N17733SpIGDhyo7OxsJSUlKSoqyuk4m82mOXPmKDo6Wo888ohatmzp1P7dd99JujTP4rLMzEy1aNHC8f4f//iH/vznPzveDx8+XHa73amfffv2qVmzZiW+tri4uGtOZQCAqvT6669ft/3K728A8EuVlJTk0nGRkZHXbC/Xww5KGyktzYgRI/Tkk08qKytLM2bMUM+ePdWzZ09He2pqqrZt2+aYz+Xh4aFhw4YpPj6+RIiVpAEDBuiWW27RX//6V7377rvX/fwGDRooJSVF0qUbzS5cuODUPnfuXPXr189p39XujJs+fbqmTJnieJ+Tk6Pg4ODr1gAAlWHs2LFatWrVNdsBwAr69u0rwzCu+RdyV+bElinEhoWFyWazaf/+/fr9739fon3//v2qV6+eGjVqJEny8/NTWFiYpEvTBsLCwtS9e3dHcIyPj1dhYaFTcDQMQ15eXpo/f36p64TNmTNHPXr00KOPPuq0v1WrVpIuBePOnTtLkux2u+PzPTxKfqkBAQGO9uvx8vKSl5eXS8cCQGWrWbOm7r777lKnFNxzzz2qWbOmCVUBQNllZWVVSj9lmhPboEED9e/fXwsXLtT58+ed2jIyMrR06VINGzas1GTt4+OjSZMmaerUqTIMQ4WFhXr77bf1wgsvKCUlxbF98803CgoK0rJly0qtoWvXrrr77rs1bdo0p/2dO3dWRESEnn/+eR5AAOBXKSYmpsQv5B4eHho9erQ5BQFAOaxbt061atVSnTp1rrldT5lXJ5g/f74KCgo0YMAAbdy4UceOHdPq1avVv39/NWnSRE8//fRVz33ooYf03Xff6aOPPtLnn3+urKws3X///Wrfvr3Tds899yg+Pv6q/Tz99NPasGGDUlNTHftsNpuWLFmi1NRU9erVS59++qkOHjyoffv2adGiRTp16lSJ+a9nz55VRkaG03bu3LmyXhIAqDbPP//8Nd8DwC/dsGHD1LRpU02dOlX79+8vdz9lDrGtWrVScnKyWrRooaFDh6ply5Z68MEH1adPH23ZssWxRmxp6tevr/vuu08zZ85UfHy8+vXrV+qUgXvuuUfJycn69ttvS+0nPDxcY8aMUX5+vtP+7t27a8eOHWrdurViY2PVtm1b9ezZU8uWLdPcuXNLrE8bExOjwMBAp+2VV14p6yUBgGrTsmVLNW3aVJLUtGnTEje5AsAv3YkTJzR79mzt2LFD7dq1U8+ePfX666+X+SmoNsPVu7TgJCcnR35+fkpLS1NoaKjZ5QAAAFjK/v371aFDB/3jH//Qv/71L3333XcaMmSI7r//fvXq1eu655frsbP4n6utSQsAAICruzyO+sgjj2jHjh3avn27AgMDde+99zqeunotJDAAAACYLjw8XL1799Ytt9yiH3744brHE2IriMfUAgAAlF9ycrKmTJmiJk2aaNKkSbrhhhtcCrHletgB/oe1YwEAAMrmwIEDeuONN1RcXKyoqCgNHTpUH374oUtzYS8jxFbQtZ42AQAAAGft27fXvn371L17dy1evFh//OMfVbt2bUd7YWGhNm/eXDWPncX/sLgDAACA6wYOHKgPPvhAbdq0KbX9zJkz6tOnT+U+dhYlFRQUmF0CAACAZbjykBZX/tLNjV0V9PMHLgAAAKBiXPlLNyOxAAAAqDYxMTHXbD9//rxL/RBiK+h68zUAAADwP9nZ2ddsd3WqJiG2gsr6nF8AAAB3tmLFimu2nzp1So0bN75uP1U+J3bRokXy9fVVYWGhY19eXp5q1qypqKgop2MTExNls9l0+PBhhYaGat68eSX6mzlzpjp16lTq+9DQUNlstqtuo0ePlqSrti9fvrySv3oAAACUhasrP1X5SGyfPn2Ul5en5ORkde/eXZK0adMmBQQE6Ouvv1Z+fr7jqVcJCQlq1qyZWrZsWa7P2r59u4qKiiRJX331le655x6lpqaqTp06kqRatWo5jl2yZIkGDhzodH7dunXL9bkAAACoPK6sTlDlIbZ169YKDAxUYmKiI8QmJiZq8ODB2rBhg7Zu3eoYkU1MTFSfPn3K/VmNGjVyvK5fv74kyd/fv9RwWrduXQUEBJT7swAAAFD56tevr4SEhOseVy1zYvv06aOEhARNmzZN0qUR18cee0xFRUVKSEhQVFSUzp8/r6+//lpjxoypjpIAAABgkqKiIq1Zs0apqanKyckp9ZjevXtfs49qC7GTJ09WYWGhzp8/r127dikyMlIXL17UokWLJElbtmxRQUGB00js448/rv/7v/9z6uvChQtq27ZthWsaPny47Ha70759+/apWbNmpR5fUFDgdLfc1S44AAAAri4jI0O33367UlNT1bRpU/n5+ZU4xjAMzZgx45r9VEuIjYqK0rlz57R9+3ZlZWUpPDxcjRo1UmRkpGJiYpSfn6/ExES1aNHCKUQ++uijjpuxLnv55Ze1cePGCtc0d+5c9evXz2lfUFDQVY+Pi4vTrFmzKvy5AAAA7uyJJ56Qv7+/1q9f7zQVtKyqJcSGhYWpadOmSkhIUFZWliIjIyVdCo3BwcH66quvlJCQoL59+zqd17BhQ4WFhTntuzzXtaICAgJK9H0t06dP15QpUxzvc3JyFBwcXCm1AAAAuIuEhAS9++67FQqwUjWuE9unTx8lJiYqKytLjz76qGN/7969tWrVKm3btk3jxo2rrnLKzMvLS15eXmaXAQAAYGmnTp2qlJvrqzXExsbG6uLFi46RWEmKjIzU+PHjdeHChQqtTFBWZ8+eVUZGhtM+X19f1a5du9pqAAAAcDfNmzfXjh071Lx58wr1U60h9vz584qIiHB6CkNkZKRyc3MdS3FVl9Ke2xsXF+dYQQEAAACVb9SoUZo0aZJycnJ00003XXWd/pCQkGv2YzNcfSwCnOTk5MjPz09paWkKDQ01uxwAAABLKCoq0t/+9jfNmzdP+fn5JZ7QZbPZZBiGiouLr9kPIbacLofYH3744arLcgEAAKB0hmHo6NGjys7OLrW9Q4cO1zy/2qYTAAAAAJfZbLbrThm4FkIsAAAAqk1SUpJLx125EEBpCLEV5O3tbXYJAAAAltG3b18ZhiGbzebYV9r7682JJcSW0+WpxAUFBTyCFgAAuA1fX1+nwFlWWVlZTu+PHDmiW265RceOHZPNZtOpU6cUHh5+3X4IseX0008/SRI3dQEAALeSmZlZoadt1alTx+l9rVq1ZBiG/Pz8JKnUFQtKQ4gtp8uPvz169KjjoqP6XH7s77Fjx0r8x4Cqx/U3F9ffPFx7c3H9zXX5+nt6elZqv1u3btW5c+eUnZ0tPz8/nThxQg0bNrzueYTYcqpRo4Ykyc/Pj/+QTFSnTh2uv4m4/ubi+puHa28urr+5KjKV4Ernz5/XggULFB8fL0n685//rOHDh2vevHm6+eabr3t+jUqpAgAAAHDB8ePHNW3aNDVt2lSfffaZEhMTddddd2n58uUaPHiwDh06pGefffa6/TASCwAAgGrTsmVLRURE6I033tDgwYMlSStWrNB3332nCxcuKCIiQh4e14+ohNhy8vLy0owZM+Tl5WV2KW6J628urr+5uP7m4dqbi+tvrsq6/kuXLtUf/vCHEvtdWZHgSjx2FgAAAJbDSCwAAACqTUxMjEvHLVmy5JrtjMQCAACg2tjtdg0cONAxLeHcuXPasGGDoqOjJV16kNSqVauu+8QuQiwAAACqjd1u14kTJ9S4cWNJUlpamjp06KDc3FxJ0qlTpxQQEKCioqJr9sMSWwAAADDNz8dTDcNw6YldhNhyWrBggUJDQ+Xt7a1u3bpp27ZtZpfkFjZu3Kjo6GgFBQXJZrPp448/NrsktxEXF6ebb75Zvr6+8vf311133aXU1FSzy3Ibr776qjp06OBY5L1Hjx5atWqV2WW5rTlz5shms2ny5Mlml+IWZs6cKZvN5rRFRESYXZZb+fHHH3XvvfeqQYMGqlWrlm644QYlJyebWhMhthzee+89TZkyRTNmzNDOnTvVsWNHDRgwQJmZmWaX9qt37tw5dezYUQsWLDC7FLeTlJSk2NhYbd26VWvXrtXFixd1++2369y5c2aX5haaNm2qOXPmaMeOHUpOTlbfvn01ePBg7d271+zS3M727dv12muvqUOHDmaX4lbatWun9PR0x/bll1+aXZLbyMrKUq9evVSzZk2tWrVK+/bt0wsvvKB69epV2mf8/ClgLj0VzECZde3a1YiNjXW8LyoqMoKCgoy4uDgTq3I/koyVK1eaXYbbyszMNCQZSUlJZpfiturVq2f885//NLsMt5Kbm2u0atXKWLt2rREZGWlMmjTJ7JLcwowZM4yOHTuaXYbbevzxx41bbrml0vpr06aNcfr0acf7rKwspwx19uxZY9CgQdfth5HYMrpw4YJ27Nihfv36OfbVqFFD/fr105YtW0ysDKhe2dnZkqT69eubXIn7KSoq0vLly3Xu3Dn16NHD7HLcSmxsrO644w6nnwGoHgcPHlRQUJBatGihESNG6OjRo2aX5DY+/fRTdenSRUOGDJG/v786d+6s119/vdz97du3Tw0aNHC8r1u3rqZNm+Z47+fnp//85z/X7YcQW0anT59WUVGR4466yxo3bqyMjAyTqgKqV3FxsSZPnqxevXqpffv2ZpfjNnbv3i0fHx95eXnp4Ycf1sqVK9W2bVuzy3Iby5cv186dOxUXF2d2KW6nW7duevPNN7V69Wq9+uqrSktL06233uq4mx1V6/vvv9err76qVq1aac2aNRo3bpwmTpyot956y9S6eNgBgDKLjY3Vnj17mJNWzVq3bq2UlBRlZ2frww8/1KhRo5SUlESQrQbHjh3TpEmTtHbtWnl7e5tdjtsZNGiQ43WHDh3UrVs3hYSE6P3339f9999vYmXuobi4WF26dNEzzzwjSercubP27NmjRYsWadSoUabVxUhsGTVs2FB2u10nT5502n/y5EkFBASYVBVQfcaPH6/PP/9cCQkJatq0qdnluBVPT0+FhYXppptuUlxcnDp27KiXXnrJ7LLcwo4dO5SZmakbb7xRHh4e8vDwUFJSkl5++WV5eHhcdz1LVK66desqPDxchw4dMrsUtxAYGFjil+U2bdqYPqWDEFtGnp6euummm7R+/XrHvuLiYq1fv565afhVMwxD48eP18qVK7VhwwY1b97c7JLcXnFxsQoKCswuwy3cdttt2r17t1JSUhxbly5dNGLECKWkpMhut5tdolvJy8vT4cOHFRgYaHYpbqFXr14lllT87rvvFBISYlJFlzCdoBymTJmiUaNGqUuXLuratavmzZunc+fOufwsYJRfXl6e02/eaWlpSklJUf369dWsWTMTK/v1i42N1bvvvqtPPvlEvr6+jjngfn5+qlWrlsnV/fpNnz5dgwYNUrNmzZSbm6t3331XiYmJWrNmjdmluQVfX98S879r166tBg0aMC+8GkydOlXR0dEKCQnRiRMnNGPGDNntdg0fPtzs0tzCI488op49e+qZZ57R0KFDtW3bNi1evFiLFy82t7BKWy/BzbzyyitGs2bNDE9PT6Nr167G1q1bzS7JLSQkJBiSSmyjRo0yu7RfvdKuuyRjyZIlZpfmFsaMGWOEhIQYnp6eRqNGjYzbbrvN+OKLL8wuy62xxFb1GTZsmBEYGGh4enoaTZo0MYYNG2YcOnTI7LLcymeffWa0b9/e8PLyMiIiIozFixebXZJhMwwXnusFAAAA/IIwJxYAAACWQ4gFAACA5RBiAQAAYDmEWAAAAFgOIRYAAACWQ4gFAACA5RBiAQAAYDmEWABwA0eOHJHNZlNKSorZpQBApSDEAkA5jB49WnfddZfjfVRUlCZPnmxaPWlpafrTn/6koKAgeXt7q2nTpho8eLAOHDggSQoODlZ6ejqPSAXwq+FhdgEAgIq5ePGi+vfvr9atW2vFihUKDAzU8ePHtWrVKp09e1aSZLfbFRAQYG6hAFCJGIkFgAoaPXq0kpKS9NJLL8lms8lms+nIkSOSpD179mjQoEHy8fFR48aNNXLkSJ0+fdpxblRUlCZMmKDJkyerXr16aty4sV5//XWdO3dOMTEx8vX1VVhYmFatWnXVz9+7d68OHz6shQsXqnv37goJCVGvXr00e/Zsde/eXVLJ6QSjR4921HrllpiYKEkqKCjQ1KlT1aRJE9WuXVvdunVztAHALwEhFgAq6KWXXlKPHj00duxYpaenKz09XcHBwTp79qz69u2rzp07Kzk5WatXr9bJkyc1dOhQp/PfeustNWzYUNu2bdOECRM0btw4DRkyRD179tTOnTt1++23a+TIkfrvf/9b6uc3atRINWrU0IcffqiioiKXa75ca3p6uiZNmiR/f39FRERIksaPH68tW7Zo+fLl+vbbbzVkyBANHDhQBw8erNjFAoBKYjMMwzC7CACwmtGjR+vs2bP6+OOPJV0aUe3UqZPmzZvnOGb27NnatGmT1qxZ49h3/PhxBQcHKzU1VeHh4YqKilJRUZE2bdokSSoqKpKfn5/uvvtuvf3225KkjIwMBQYGasuWLY6R1Z9bsGCBHnvsMdntdnXp0kV9+vTRiBEj1KJFC0mXRmKbN2+uXbt2qVOnTk7nrlixQiNGjNC6devUq1cvHT16VC1atNDRo0cVFBTkOK5fv37q2rWrnnnmmYpePgCoMEZiAaCKfPPNN0pISJCPj49juzzSefjwYcdxHTp0cLy22+1q0KCBbrjhBse+xo0bS5IyMzOv+lmxsbHKyMjQ0qVL1aNHD33wwQdq166d1q5de80ad+3apZEjR2r+/Pnq1auXJGn37t0qKipSeHi4U+1JSUlOdQOAmbixCwCqSF5enqKjo/Xss8+WaAsMDHS8rlmzplObzWZz2mez2SRJxcXF1/w8X19fRUdHKzo6WrNnz9aAAQM0e/Zs9e/fv9TjMzIydOedd+qBBx7Q/fff71S33W7Xjh07ZLfbnc7x8fG5Zg0AUF0IsQBQCTw9PUvMR73xxhv10UcfKTQ0VB4e1fvt1mazKSIiQl999VWp7fn5+Ro8eLAiIiL04osvOrV17txZRUVFyszM1K233lod5QJAmTGdAAAqQWhoqL7++msdOXJEp0+fVnFxsWJjY3XmzBkNHz5c27dv1+HDh7VmzRrFxMS4fAOWK1JSUjR48GB9+OGH2rdvnw4dOqT4+Hi98cYbGjx4cKnnPPTQQzp27JhefvllnTp1ShkZGcrIyNCFCxcUHh6uESNG6L777tOKFSuUlpambdu2KS4uTv/+978rrW4AqAhGYgGgEkydOlWjRo1S27Ztdf78eaWlpSk0NFSbN2/W448/rttvv10FBQUKCQnRwIEDVaNG5Y0hNG3aVKGhoZo1a5ZjKa3L7x955JFSz0lKSlJ6erratm3rtD8hIUFRUVFasmSJZs+erb/85S/68ccf1bBhQ3Xv3l2/+93vKq1uAKgIVicAAACA5TCdAAAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWI6H2QUAAADA/WRlZengwYOy2WwKCwtTvXr1ynQ+I7EAAACoNmlpafrtb3+rhg0bqnv37urWrZsaNmyo3/72t/rhhx9c7sdmGIZRhXUCAAAAkqSTJ0/qxhtvlN1uV2xsrCIiIiRJqampmj9/voqKirRz5041btz4un0RYgEAAFAtJk6cqISEBG3btk21atVyasvPz9fNN9+sqKgovfLKK9fti+kEAAAAqBaff/65Zs2aVSLASpK3t7f+/ve/6z//+Y9LfRFiAQAAUC3S09PVoUOHq7a3b99eP/74o0t9EWIBAABQLRo1aqTCwsKrtl+8eNGl+bASIRYAAADV5KabbtIXX3xx1fbVq1erY8eOLvVFiAUAAEC1mDJlil577TVlZ2eXaMvJydHrr7+uyZMnu9QXqxMAAADAcnhiFwAAAKpFixYt5Mr4aVpa2nWPIcQCAACgWrg6VcAVTCcAAACA5XBjFwAAACyH6QQAAACoFq7MiTUMQ0eOHLluX4RYAAAAVItrzYlNS0vT4sWLdf78eZf6Yk4sAAAATHP69Gk9/fTTWrRokbp3765nn31WXbt2ve55jMQCAACg2p07d04vvviinn/+eTVv3lwrV67UwIEDXT6fEAsAAIBqU1hYqMWLF2v27NmqVauWFi5cqBEjRpS5H0IsAAAAqsXy5cv117/+VdnZ2XryyScVGxsrD4/yxVHmxJaTYRjKzc2Vr6+vbDab2eUAAAD84tntdnl5eelPf/qTfH19r3rc3Llzr9sXI7HllJubKz8/P2VnZ6tOnTpmlwMAqGKGYaigoECS5OXlxQAGUA5RUVEyDEPff//9VY9xdXyVkdhyysnJIcQCgBvJz8/XkCFDJEkffPCBvL29Ta4IcG88sQsAABfk5+eX+hqAOZhOAAAAgGoxa9Ysl46bMWPGdY8hxAIA4ILi4uJSXwNw3VNPPaV27dpddUWCwsJC7dmzhxALAEBlyc3NdXpdv359E6sBrGvt2rVq3LhxqW2nTp1SQECAS/2Ua07ssWPHNGbMGAUFBcnT01MhISGaNGmSfvrpJ8cxUVFRstlsstls8vb2Vnh4uOLi4kq942zLli2y2+264447SrQdOXJENptN/v7+Tt9AJKlTp06aOXOm075Dhw5pzJgxatasmby8vNSkSRPddtttWrp0qQoLCx3HXa7t59vy5cvLc0kAAABwHXa7/Zp/ySgqKlKNGq7F0zKH2O+//15dunTRwYMHtWzZMh06dEiLFi3S+vXr1aNHD505c8Zx7NixY5Wenq7U1FRNnz5df/vb37Ro0aISfcbHx2vChAnauHGjTpw4Uern5ubm/j979x5WVZX/cfxzPAiaECpeAC8gIuAlFTMVKQHT1CnTLHMYNEQrc9DUxkprJq1UbKaL5TUbMptKLc2aZgbNFNBMRUzM+y1MTRCvXBxFgf37w8fz6wTqAYEzp/N+Pc9+Ovu2zpf9mH5crr2WXn/99RvWlpaWpk6dOmnv3r2aO3eudu3apZSUFD3++OOaP3++du/ebXX9okWLlJWVZbUNHDiwvI8EAAAANvD09LTq9Py1M2fOqG7duja1Ve7hBPHx8XJ1ddXXX3+t2rVrS5KaN2+u0NBQtWzZUi+++KLmz58vSbrtttssXcJxcXGaM2eO1qxZo9GjR1vaKygo0LJly5Senq7s7Gx98MEHeuGFF0p979ixY/Xmm28qPj5ejRo1KnXeMAwNHz5cQUFB2rhxo1WKb9WqlaKjo0v1AtetW9fmLmsAAADcmjZt2ig5OVnt2rUr83xKSoratGljU1vl6ok9e/asVq9erT/+8Y+WAHuNt7e3YmJitGzZslJh0TAMbdiwQfv27ZOrq6vVuU8//VQhISEKDg7W0KFD9f7775c55CA6OlqBgYF65ZVXyqwtIyNDe/fu1cSJE6/bDc3E1AAAAPbzyCOPaPr06dq3b1+pc/v379err76qQYMG2dRWuULswYMHZRiGWrduXeb51q1b69y5czp16pQkad68eXJ3d5ebm5t69OihkpISPf3001b3JCYmaujQoZKkvn37Kjc3V6mpqaXaNplMmjlzphYuXKjDhw+XOn/gwAFJUnBwsOVYTk6O3N3dLdu8efOs7omOjrY67+7urqNHj5b5sxUWFiovL89qAwAAgO2eeuoptWjRQh06dNCAAQM0adIkTZ48WQ899JA6dOigpk2bWv2L/Y1U6MUuWxf5iomJUUZGhjZu3Kh+/frpxRdfVPfu3S3n9+/fr7S0NEVHR0uSXFxcNGTIECUmJpbZXp8+fXT33XfrL3/5i03f7+XlpYyMDGVkZKhu3bq6fPmy1fm33nrLcv7a5uvrW2ZbCQkJ8vT0tGzNmjWzqQYAAABcVbNmTa1du1YvvviiMjMzNXfuXM2ePVsHDx7Uc889p9TU1FL/an895RoTGxgYKJPJpL179+qhhx4qdX7v3r2qV6+eGjZsKOnq4N3AwEBJV4cNBAYGqlu3burVq5ekq72wRUVFVsHRMAy5ublpzpw58vT0LPUdM2fOVFhYmJ599lmr461atZJ0NRiHhoZKuvoG3LXvL2s+Mm9vb8v5m5k8ebKeeeYZy35eXh5BFgAAoJxuu+02vfTSS3rppZduqZ1y9cR6eXmpd+/emjdvni5evGh1Ljs7Wx9//LGGDBlS5thTd3d3jRs3ThMnTpRhGCoqKtKHH36oN954w6ondMeOHfL19dWSJUvKrKFLly4aNGiQJk2aZHU8NDRUISEhev3116tkEmo3NzfdfvvtVhsAAADso9yzE8yZM0fdu3dXnz59NG3aNLVo0UK7d+/Ws88+qyZNmmj69OnXvXfUqFF69dVXtWLFCrm4uOjcuXMaOXJkqR7Xhx9+WImJiXrqqafKbGf69OmlVnswmUxatGiRevfurfDwcE2ePFmtW7fWlStXtH79ep06dUpms9mqnfPnzys7O9vqmIeHh+rUqVPexwIAAICbiIqKuuk1hmEoJSXlpteVe0xsq1atlJ6eroCAAD366KNq2bKlnnzySUVFRWnTpk03XMGkfv36euyxxzR16lQlJiaqV69eZQ4ZePjhh5Wenq4ffvihzHaCgoI0YsQIXbp0yep4t27dtG3bNgUHBys+Pl5t2rRR9+7dtWTJEr311lulBgrHxcXJx8fHaps9e3Z5HwkAAABssH79egUHBys0NFShoaEKCAjQd999Z9kPDg7W+vXrbWrLZNj6lhas5OXlydPTU7m5uQwtAAAncPbsWcXGxkqSFi9ezLKzQAWYzWadOHHCsuzsjz/+qA4dOlhWZc3JyZG3t7dNQ0MrNDsBAADO5pdzkNu6LCaAqsP/hQAAAHA4hFgAAADYza9ntbJ1hVVCLAAANqhVq1aZnwHYrk+fPnJzc7PsN27cWO+++65l/7bbbtOoUaNsaosXuyqIF7sAwLkYhqHCwkJJV+cOt7W3CEDVoCcWAAAADqfcix0AAOCMCgsLNXjwYEnSZ599xpACoALMZrNsGQRgyxRbhFgAAGzwywV2Ll26RIgFKmDlypVW+ydPntT48eO1ZMkSSVJubq5lPuabIcQCAACgWjz44INW+z/++KNq1KhhOZ6Tk2NzW4yJBQAAgF38+OOPunjxooqLiyVdfXHew8PDpnsJsQAA2OCXY/RsGa8H4Ma2bt2qCRMmqKSkRLNnz1ZBQYHmzp2r4OBgm+4nxAIAYINra7v/+jOA8lm1apX69u2r3/3ud3rnnXcUEBCgZ555Rrfffrvmzp2rP//5zza1U+UhdsGCBfLw8FBRUZHlWEFBgWrWrKnIyEira1NSUmQymXT48GH5+/tr1qxZpdqbOnWqOnbsWOa+v7+/TCbTdbfhw4dL0nXPL126tJJ/egAAAFzTtm1bPfTQQ2rdurX27t2rqKgopaena8GCBXrnnXe0ffv2UuNmr6fKX+yKiopSQUGB0tPT1a1bN0nShg0b5O3trS1btli94ZmcnKzmzZurZcuWFfqurVu3WsZUfPfdd3r44Ye1f/9+y2IEtWvXtly7aNEi9e3b1+r+unXrVuh7AQAAcHNRUVH65ptv5OPjYzlWt25dPfnkk+Vuq8pDbHBwsHx8fJSSkmIJsSkpKRowYIDWrVunzZs3W3pkU1JSFBUVVeHvatiwoeVz/fr1JUmNGjUqM5zWrVtX3t7eFf4uAAAAlM+cOXMqra1qmWIrKipKycnJmjRpkqSrPa7PPfeciouLlZycrMjISF28eFFbtmzRiBEjqqOkcissLLQsNyhdfXsOAAAAtlu8eLFN19kyV2y1hdjx48erqKhIFy9e1Pbt2xUREaErV65owYIFkqRNmzapsLDQqif2+eefLzW49/Lly2rTps0t1xQdHS2z2Wx1bM+ePWrevHmZ1yckJOjll1++5e8FAABwViNGjNDtt98uk8kk6epMH3l5eZZ/NTcMw+YFD6olxEZGRurChQvaunWrzp07p6CgIDVs2FARERGKi4vTpUuXlJKSooCAAKsQ+eyzz1pexrrmnXfe0fr162+5prfeeku9evWyOubr63vd6ydPnqxnnnnGsp+Xl6dmzZrdch0AAADOZN++fWrcuLEkKTMzUx06dNDZs2clSadOnbJ5uGe1hNjAwEA1bdpUycnJOnfunCIiIiRdDY3NmjXTd999p+TkZPXs2dPqvgYNGigwMNDq2LWxrrfK29u7VNs34ubmJjc3t0r5bgAAAFzteTUM47r7N1Jt88RGRUUpJSVFKSkpVlNr9ejRQ0lJSUpLS7ull7oAAADgPKqlJ1a6GmLj4+N15coVS0+sJEVERGjMmDG6fPlytYbY8+fPKzs72+qYh4eH6tSpU201AAAAOBNbe1ltUa09sRcvXlRgYKBlHIR0NcTm5+dbpuKqLnFxcfLx8bHaZs+eXW3fDwAA4GyuvdB1Te3atdWjRw+r89fWD7hpW0ZlRmInkpeXJ09PT+Xm5loWUwAA/HadPXvW8sb04sWLK+0dDcCZ5OTkqFGjRpXSVrX1xAIA4Mhq1KhR5mcAtqusACtV45hYAAAAOLe4uDibrlu0aNFNryHEAgBgg1+O07N1zB4Aax9++KEiIiIsixvcCsbEVlBubq7q1q2rY8eOMSYWAJyAYRiW5cfd3NxKvaACOAsPD48K//o3m836/vvv1aFDh1uug57YCjpz5owksWoXAABwKjk5OWrYsKG9yyDEVtS1t1KPHj0qT09PO1fjfK4t+0tPuH3w/O2L528/PHv74vnb17Xn7+rqau9SJBFiK+zam6menp78j2RHt99+O8/fjnj+9sXztx+evX3x/O3rf2UoDXOEAAAAoFqMHDlSXl5eldIWPbEAAACoFgsXLqy0tgixFeTm5qYpU6bIzc3N3qU4JZ6/ffH87Yvnbz88e/vi+dtXZTz/Fi1a2HRdZmbmTa9hii0AAABUC7PZrIkTJ6pJkyY3vO7pp5++aVuEWAAAAFSLypwnlhe7AAAA4HAIsQAAAHA4hFgAAAA4HEJsBc2dO1f+/v6qVauWunbtqrS0NHuX5BTWr1+v/v37y9fXVyaTSV988YW9S3IaCQkJuuuuu+Th4aFGjRpp4MCB2r9/v73Lchrz589X+/btLZO8h4WFKSkpyd5lOa2ZM2fKZDJp/Pjx9i7FKUydOlUmk8lqCwkJsXdZTuXnn3/W0KFD5eXlpdq1a+uOO+5Qenp6udv5xz/+IT8/v0qpiRBbAcuWLdMzzzyjKVOmWAYn9+nTRzk5OfYu7TfvwoUL6tChg+bOnWvvUpxOamqq4uPjtXnzZq1Zs0ZXrlzRfffdpwsXLti7NKfQtGlTzZw5U9u2bVN6erp69uypAQMGaPfu3fYuzels3bpV7777rtq3b2/vUpxK27ZtlZWVZdm+/fZbe5fkNM6dO6fw8HDVrFlTSUlJ2rNnj9544w3Vq1ev3G399a9/1QcffKCzZ8/eemEGyq1Lly5GfHy8Zb+4uNjw9fU1EhIS7FiV85FkrFy50t5lOK2cnBxDkpGammrvUpxWvXr1jL///e/2LsOp5OfnG61atTLWrFljREREGOPGjbN3SU5hypQpRocOHexdhtN6/vnnjbvvvrtS2ho7dqzRpEkTw83NzRg8eLCxatUqo6SkpEJt0RNbTpcvX9a2bdvUq1cvy7EaNWqoV69e2rRpkx0rA6pXbm6uJKl+/fp2rsT5FBcXa+nSpbpw4YLCwsLsXY5TiY+P1/3332/1ZwCqx8GDB+Xr66uAgADFxMTo6NGj9i7Jafzzn/9U586dNXjwYDVq1EihoaF67733KtTWO++8o+PHj+ujjz7SihUrNHToUPn7+2vKlCk2LXDwS4TYcjp9+rSKi4vVuHFjq+ONGzdWdna2naoCqldJSYnGjx+v8PBwtWvXzt7lOI2dO3fK3d1dbm5ueuqpp7Ry5Uq1adPG3mU5jaVLl+r7779XQkKCvUtxOl27dtUHH3ygVatWaf78+crMzNQ999yj/Px8e5fmFH788UfNnz9frVq10urVqzV69Gg9/fTTWrx4cYXbbNOmjWrUqKHs7GzNnTtX+/bt0x133KHevXtryZIlNrXBsrMAyi0+Pl67du1iTFo1Cw4OVkZGhnJzc7V8+XLFxsYqNTWVIFsNjh07pnHjxmnNmjWqVauWvctxOv369bN8bt++vbp27So/Pz99+umnGjlypB0rcw4lJSXq3LmzZsyYIUkKDQ3Vrl27tGDBAsXGxt5S22azWQ888IAeeOAB5efna+bMmRo2bJiio6Nvei8htpwaNGggs9mskydPWh0/efKkvL297VQVUH3GjBmjf/3rX1q/fr2aNm1q73KciqurqwIDAyVJd955p7Zu3aq3335b7777rp0r++3btm2bcnJy1KlTJ8ux4uJirV+/XnPmzFFhYaHMZrMdK3QudevWVVBQkA4dOmTvUpyCj49Pqb8st27dWitWrKiU9s+cOaOlS5fqo48+0sGDBzV69Gib7mM4QTm5urrqzjvv1Nq1ay3HSkpKtHbtWsam4TfNMAyNGTNGK1eu1Lp169SiRQt7l+T0SkpKVFhYaO8ynMK9996rnTt3KiMjw7J17txZMTExysjIIMBWs4KCAh0+fFg+Pj72LsUphIeHl5pS8cCBA7c0VVZxcbEMw1D//v3VtGlTrVy5UuPGjdOJEyc0e/Zsm9qgJ7YCnnnmGcXGxqpz587q0qWLZs2apQsXLiguLs7epf3mFRQUWP3NOzMzUxkZGapfv76aN29ux8p+++Lj4/XJJ5/oyy+/lIeHh2UMuKenp2rXrm3n6n77Jk+erH79+ql58+bKz8/XJ598opSUFK1evdrepTkFDw+PUuO/69SpIy8vL8aFV4OJEyeqf//+8vPz04kTJzRlyhSZzWab/skZt27ChAnq3r27ZsyYoUcffVRpaWlauHChFi5cWO62PvvsM61YsUJJSUlq2rSpOnXqpDlz5lQsEFfKfAlOaPbs2Ubz5s0NV1dXo0uXLsbmzZvtXZJTSE5ONiSV2mJjY+1d2m9eWc9dkrFo0SJ7l+YURowYYfj5+Rmurq5Gw4YNjXvvvdf4+uuv7V2WU2OKreozZMgQw8fHx3B1dTWaNGliDBkyxDh06JC9y3IqX331ldGuXTvDzc3NCAkJMRYuXFihdmrVqmUMHjzYWL169S3XZDIMwyh/9AUAAADK5+zZs5U2NSMhFgAAAA6HF7sAAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCgBM4cuSITCaTMjIy7F0KAFQKQiwAVMDw4cM1cOBAy35kZKTGjx9vt3oyMzP1hz/8Qb6+vqpVq5aaNm2qAQMGaN++fZKkZs2aKSsrS+3atbNbjQBQmVzsXQAA4NZcuXJFvXv3VnBwsD7//HP5+Pjo+PHjSkpK0vnz5yVJZrNZ3t7e9i0UACoRPbEAcIuGDx+u1NRUvf322zKZTDKZTDpy5IgkadeuXerXr5/c3d3VuHFjDRs2TKdPn7bcGxkZqbFjx2r8+PGqV6+eGjdurPfee08XLlxQXFycPDw8FBgYqKSkpOt+/+7du3X48GHNmzdP3bp1k5+fn8LDwzVt2jR169ZNUunhBMOHD7fU+sstJSVFklRYWKiJEyeqSZMmqlOnjrp27Wo5BwD/CwixAHCL3n77bYWFhemJJ55QVlaWsrKy1KxZM50/f149e/ZUaGio0tPTtWrVKp08eVKPPvqo1f2LFy9WgwYNlJaWprFjx2r06NEaPHiwunfvru+//1733Xefhg0bpv/+979lfn/Dhg1Vo0YNLV++XMXFxTbXfK3WrKwsjRs3To0aNVJISIgkacyYMdq0aZOWLl2qH374QYMHD1bfvn118ODBW3tYAFBJTIZhGPYuAgAczfDhw3X+/Hl98cUXkq72qHbs2FGzZs2yXDNt2jRt2LBBq1evthw7fvy4mjVrpv379ysoKEiRkZEqLi7Whg0bJEnFxcXy9PTUoEGD9OGHH0qSsrOz5ePjo02bNll6Vn9t7ty5eu6552Q2m9W5c2dFRUUpJiZGAQEBkq72xLZo0ULbt29Xx44dre79/PPPFRMTo2+++Ubh4eE6evSoAgICdPToUfn6+lqu69Wrl7p06aIZM2bc6uMDgFtGTywAVJEdO3YoOTlZ7u7ulu1aT+fhw4ct17Vv397y2Ww2y8vLS3fccYflWOPGjSVJOTk51/2u+Ph4ZWdn6+OPP1ZYWJg+++wztW3bVmvWrLlhjdu3b9ewYcM0Z84chYeHS5J27typ4uJiBQUFWdWemppqVTcA2BMvdgFAFSkoKFD//v312muvlTrn4+Nj+VyzZk2rcyaTyeqYyWSSJJWUlNzw+zw8PNS/f3/1799f06ZNU58+fTRt2jT17t27zOuzs7P14IMP6vHHH9fIkSOt6jabzdq2bZvMZrPVPe7u7jesAQCqCyEWACqBq6trqfGonTp10ooVK+Tv7y8Xl+r97dZkMikkJETfffddmecvXbqkAQMGKCQkRG+++abVudDQUBUXFysnJ0f33HNPdZQLAOXGcAIAqAT+/v7asmWLjhw5otOnT6ukpETx8fE6e/asoqOjtXXrVh0+fFirV69WXFyczS9g2SIjI0MDBgzQ8uXLtWfPHh06dEiJiYl6//33NWDAgDLvGTVqlI4dO6Z33nlHp06dUnZ2trKzs3X58mUFBQUpJiZGjz32mD7//HNlZmYqLS1NCQkJ+ve//11pdQPAraAnFgAqwcSJExUbG6s2bdro4sWLyszMlL+/vzZu3Kjnn39e9913nwoLC+Xn56e+ffuqRo3K60No2rSp/P399fLLL1um0rq2P2HChDLvSU1NVVZWltq0aWN1PDk5WZGRkVq0aJGmTZumP/3pT/r555/VoEEDdevWTQ888ECl1Q0At4LZCQAAAOBwGE4AAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADsfF3gUAAADAecTFxdl03aJFi2543mQYhlEZBQEAAAA3M2jQIKv9CxcuaN26derfv78kqbCwUElJSSopKblhO4RYAAAA2E1mZqbat2+v/Px8SdKpU6fk7e2t4uLiG97HmFgAAADYza/7Uw3DKHWsLIRYAAAAVJsrV65USjuEWAAAAFSbJk2aaPz48dq5c6ckqU6dOvrd735ndY3JZLppO4RYAAAAVJtJkyYpLS1NoaGh6tq1q7744gv9/e9/t5xv2LChDh48eNN2eLELAAAA1W7jxo2KiIhQ27ZtdejQIT3yyCMaMWKEIiIibLqfnlgAAABUu3r16slkMmnHjh3asmWLvL29NWzYMAUFBSkhIeGm99MTCwAAgGq3Z88edejQwepFr+LiYr300kt67bXXVFRUdMP7WbELAAAAdrVjxw599NFHWrJkiTw9PfXaa6/d9B5CbAUZhqH8/Hx5eHjY9AYdAAAA/t+ZM2dkGIbuuOMOHT16VIMHD9by5cvVrVs3m+4nxFZQfn6+PD09dfLkSd1+++32LgeoNoZhqLCwUJLk5ubGX+LglPi1D1Tc3/72N61YsULp6ekKCwvTiBEjNGTIEN12223laocxsRWUl5cnT09P9enTRzVr1rR3OQCAavTZZ5+pVq1a9i4DcEg+Pj567LHHNHLkSAUFBVW4HXpiAQAAUG2OHz8us9l8y+0QYm/RlN4HVN/d3lUA1aewyKQXV7WRJE3vu0duLvxjDpzD5eIaeiGptb3LABzeRx99ZNN1sbGxNzxPiL1FruYSubkwLgrOyc3FIMTCiZTYuwDgN2HChAk3vcYwDEJsVWNEMQA4h1/+fs/rJEDFnT17tlLaYcWuW3S5mF5YAHAGv/z9/toMHQDsp0Ih9tixYxoxYoR8fX3l6uoqPz8/jRs3TmfOnLFcExkZKZPJJJPJpFq1almWECvrb6+bNm2S2WzW/fffX+rckSNHZDKZ1KhRI+Xn51ud69ixo6ZOnWp17NChQxoxYoSaN28uNzc3NWnSRPfee68+/vhjq5UfrtX2623p0qUVeSQAAACwweLFi23abqbcwwl+/PFHhYWFKSgoSEuWLFGLFi20e/duPfvss0pKStLmzZtVv359SdITTzyhV155RYWFhVq3bp2efPJJ1a1bV6NHj7ZqMzExUWPHjlViYqJOnDghX1/fUt+bn5+v119/XS+//PJ1a0tLS1OvXr3Utm1bzZ07VyEhIZKk9PR0zZ07V+3atVOHDh0s1y9atEh9+/a1aqNu3brlfSQAAACw0a/HxJaUlCgvL8+SwQzDUG5ubuWPiY2Pj5erq6u+/vpr1a5dW5LUvHlzhYaGqmXLlnrxxRc1f/58SdJtt90mb29vSVJcXJzmzJmjNWvWWIXYgoICLVu2TOnp6crOztYHH3ygF154odT3jh07Vm+++abi4+PVqFGjUucNw9Dw4cMVFBSkjRs3qkaN/+9kbtWqlaKjo0v1AtetW9dSHwAAAKrer8fEZmZmqkOHDpbjp06dsimflWs4wdmzZ7V69Wr98Y9/tATYa7y9vRUTE6Nly5aVCouGYWjDhg3at2+fXF1drc59+umnCgkJUXBwsIYOHar333+/zCEH0dHRCgwM1CuvvFJmbRkZGdq7d68mTpxoFWB/6VZWVyksLFReXp7VBgAAgFtz+fJllZT8/+wfv/x8I+UKsQcPHpRhGGrduux58lq3bq1z587p1KlTkqR58+bJ3d1dbm5u6tGjh0pKSvT0009b3ZOYmKihQ4dKkvr27avc3FylpqaWattkMmnmzJlauHChDh8+XOr8gQMHJEnBwcGWYzk5OXJ3d7ds8+bNs7onOjra6ry7u7uOHj1a5s+WkJAgT09Py9asWbPrPSYAAADYKCkpSf/973+Vk5Mj6WrPbOPGjW96X4Ve7LJ1apGYmBhlZGRo48aN6tevn1588UV1797dcn7//v1KS0tTdHS0JMnFxUVDhgxRYmJime316dNHd999t/7yl7/Y9P1eXl7KyMhQRkaG6tatq8uXL1udf+uttyznr21ljceVpMmTJys3N9eyHTt2zKYaAAAAUNrx48c1ZswYffzxxzKZTPr973+vOXPmaPTo0erRo8dN7y9XiA0MDJTJZNLevXvLPL93717Vq1dPDRs2lCR5enoqMDBQd911lz799FPNmTNH33zzjeX6xMREFRUVydfXVy4uLnJxcdH8+fO1YsUK5ebmlvkdM2fO1LJly7R9+3ar461atZJ0NRhfYzabFRgYqMDAQLm4lB7+6+3tbTl/o+skyc3NTbfffrvVBgAAgPLZsmWLfv/736tly5bKy8tTamqqxo0bp2+//Vbjxo1TrVq19Prrr9+0nXKFWC8vL/Xu3Vvz5s3TxYsXrc5lZ2fr448/1pAhQ8oce+ru7q5x48Zp4sSJMgxDRUVF+vDDD/XGG29Y9YTu2LFDvr6+WrJkSZk1dOnSRYMGDdKkSZOsjoeGhiokJESvv/66zWMpAAAAUL26d++uCxcuaMuWLfrwww9122236c0337T8a/emTZvUtGnTm7ZT7tkJ5syZo+7du6tPnz6aNm2a1RRbTZo00fTp069776hRo/Tqq69qxYoVcnFx0blz5zRy5Eh5enpaXffwww8rMTFRTz31VJntTJ8+XW3btrXqNTWZTFq0aJF69+6t8PBwTZ48Wa1bt9aVK1e0fv16nTp1Smaz2aqd8+fPKzs72+qYh4eH6tSpU97HAgAAABts2bJFnTt3LnX82qQBx48fV1xcnNasWXPDdso9JrZVq1ZKT09XQECAHn30UbVs2VJPPvmkoqKitGnTJsscsWWpX7++HnvsMU2dOlWJiYnq1atXqQArXQ2x6enp+uGHH8psJygoSCNGjNClS5esjnfr1k3btm1TcHCw4uPj1aZNG3Xv3l1LlizRW2+9VWp+2ri4OPn4+Fhts2fPLu8jAQAAgI3KCrDXfPjhh2rfvn2pjseymAwWgK6QvLw8eXp6auubfmpwO0vPwnkUFpk08V9tJUmvP7Bbbi78FgLncOmKSc/+++qv/U8//bTUVJMAKu7UqVMaNWqUvvnmG73xxht64oknbnpPuYcTwNqVkhoqLLr5dcBvRWGRqczPwG/dlZL//8fLW5l3HHB2v159taioSO+++67atWunnTt3ys/Pz6Z2CLG36OU1QapZs6a9ywDs4sVVbexdAgDAwXz55ZdW+0VFRTp37pwGDRpkc4CVCLEAAACoRt9//32pY1999ZWeeOIJff7550pMTFSLFi1u2g5jYivo2pjYkydPMmcsnIphGCosLJR0df5k/lkVzohf+0DlO3v2rEaNGqVVq1bptdde0x//+McbXk+IraBrITY3N5cQCwAAUEk++eQTxcfH69y5cze8rkLLzgIA4GwMw9ClS5d06dIlm5dfB1B+/fv3v+E0XNcQYgEAsEFhYaEGDx6swYMHW4bUAKh8Fy9e1Nq1a296HSEWAAAb/HKBnV8vtgOg+hFiAQAA4HCYYgsAABuUlJSU+RlA+ZjN5koZV06IBQDABvn5+Vaf69evb8dqAMe1cuXKG57Pzc1VbGzsTdup8uEECxYskIeHh4qK/n9t1oKCAtWsWVORkZFW16akpMhkMunw4cPy9/fXrFmzSrU3depUdezYscx9f39/mUym627Dhw+XpOueX7p0aSX/9AAAAPilBx988IZbnz59bGqnyntio6KiVFBQoPT0dHXr1k2StGHDBnl7e2vLli26dOmSatWqJUlKTk5W8+bN1bJlywp919atW1VcXCxJ+u677/Twww9r//79lnlca9eubbl20aJF6tu3r9X9devWrdD3AgAAwDZXrlxRzZo1b7mdKu+JDQ4Olo+Pj1JSUizHUlJSNGDAALVo0UKbN2+2Oh4VFVXh72rYsKG8vb3l7e1t+WeeRo0aWY55enparq1bt67l+LXtWpgGAABA1WjSpInGjx+vnTt3lnnebDbL39//pu1Uy+wEUVFRSk5OtuwnJycrMjJSERERluMXL17Uli1bbinEAgAA4H/bpEmTlJaWptDQUHXt2lXvvvuu1ZhzLy8v/fjjjzdtp9pC7MaNG1VUVKT8/Hxt375dERER6tGjh6WHdtOmTSosLLQKsc8//7zc3d2tthkzZlRKTdHR0aXaPnr06HWvLywsVF5entUGAACA8nnmmWf03XffKTU1Vdu2bdO8efPk7e2t2NhYpaam2txOtYTYyMhIXbhwQVu3btWGDRsUFBSkhg0bKiIiwjIuNiUlRQEBAWrevLnlvmeffVYZGRlW21NPPVUpNb311lul2vb19b3u9QkJCfL09LRszZo1q5Q6AAAAnFG9evVkMpm0Y8cObdmyRd7e3ho2bJiCgoKUkJBw0/urJcQGBgaqadOmSk5OVnJysiIiIiRJvr6+atasmb777jslJyerZ8+eVvc1aNBAgYGBVltlTWni7e1dqm0Xl+u/5zZ58mTl5uZatmPHjlVKHQAAAM6uXbt2eu2115SZmanBgwfrL3/5y03vqbZ5YqOiopSSkqJz587p2WeftRzv0aOHkpKSlJaWptGjR1dXOeXm5uYmNzc3e5cBAADwm7Njxw599NFHWrJkiTw9PfXaa6/d9J5qDbHx8fG6cuWKpSdWkiIiIjRmzBhdvny5Wl/qOn/+vLKzs62OeXh4qE6dOtVWAwAAgLM6c+aMDMPQHXfcoaNHj2rw4MFavny5ZUrWm6nWEHvx4kWFhISocePGluMRERHKz8+3TMVVXeLi4kodS0hI0KRJk6qtBgAAAGfzt7/9TStWrFB6errCwsI0YsQIDRkyRLfddlu52jEZlbF4rRPKy8uTp6encnNzLYspAAB+u86ePWtZCnPx4sUsOwtUkI+Pjx577DGNHDlSQUFBFW6n2npiAQBwZDVq1CjzM4DyOX78uMxm8y23Q4gFAABAtfnoo49suu7av3xcDyEWAAAA1WbEiBGqU6fODac2NQyDEAsAQGWoVatWmZ8BlN+GDRvUoUOHW2qDEFtB196HY/lZAHAOhmEoMTFR0tWlyC9fvmznigD78PDwkMlksncZhNiKOnPmjCSx/CwAAHAqOTk5atiwob3LIMRW1LWpVY4ePSpPT087V+N88vLy1KxZMx07dowpzuyA529fPH/74dnbF8/fvq49f1dXV3uXIokQW2HXplfx9PTkfyQ7uv3223n+dsTzty+ev/3w7O2L529ftzqUoGXLlnJzc7vlOgixAAAAqDYHDhyolHYIsQAAAKg2cXFxNl23aNGiG55nyZEKcnNz05QpUyqlOxzlx/O3L56/ffH87Ydnb188f/uqrOf/4YcfKicnR7m5ucrNzdWJEyf00UcfWfZzcnK0ePHim7ZjMq7NFQUAAABUMbPZrBMnTqhx48aSpMzMTLVv3175+fmSpFOnTsnb21vFxcU3bIeeWAAAANjNr/tTDcModawshFgAAAA4HEIsAAAA7OrX03bZMo0XIRYAAADVJjg4WC4u/z9BVv369fXCCy9Y9t3c3NSnT5+btkOIraC5c+fK399ftWrVUteuXZWWlmbvkpzC+vXr1b9/f/n6+spkMumLL76wd0lOIyEhQXfddZc8PDzUqFEjDRw4UPv377d3WU5j/vz5at++vWWS97CwMCUlJdm7LKc1c+ZMmUwmjR8/3t6lOIWpU6fKZDJZbSEhIfYuy6n8/PPPGjp0qLy8vFS7dm3dcccdSk9Pr1Bbe/bskZeXl2W/bt26mjRpkmXf09NT//nPf27aDiG2ApYtW6ZnnnlGU6ZM0ffff68OHTqoT58+ysnJsXdpv3kXLlxQhw4dNHfuXHuX4nRSU1MVHx+vzZs3a82aNbpy5Yruu+8+Xbhwwd6lOYWmTZtq5syZ2rZtm9LT09WzZ08NGDBAu3fvtndpTmfr1q1699131b59e3uX4lTatm2rrKwsy/btt9/auySnce7cOYWHh6tmzZpKSkrSnj179MYbb6hevXp2rYsptiqga9euuuuuuzRnzhxJUklJiZo1a6axY8da/U0CVctkMmnlypUaOHCgvUtxSqdOnVKjRo2UmpqqHj162Lscp1S/fn397W9/08iRI+1ditMoKChQp06dNG/ePE2bNk0dO3bUrFmz7F3Wb97UqVP1xRdfKCMjw96lOKVJkyZp48aN2rBhg71LsUJPbDldvnxZ27ZtU69evSzHatSooV69emnTpk12rAyoXrm5uZKuBilUr+LiYi1dulQXLlxQWFiYvctxKvHx8br//vut/gxA9Th48KB8fX0VEBCgmJgYHT161N4lOY1//vOf6ty5swYPHqxGjRopNDRU7733nr3LIsSW1+nTp1VcXGyZoPeaxo0bKzs7205VAdWrpKRE48ePV3h4uNq1a2fvcpzGzp075e7uLjc3Nz311FNauXKl2rRpY++ynMbSpUv1/fffKyEhwd6lOJ2uXbvqgw8+0KpVqzR//nxlZmbqnnvusUyOj6r1448/av78+WrVqpVWr16t0aNH6+mnn7ZpVa2q5HLzSwDAWnx8vHbt2sWYtGoWHBysjIwM5ebmavny5YqNjVVqaipBthocO3ZM48aN05o1a1SrVi17l+N0+vXrZ/ncvn17de3aVX5+fvr0008ZTlMNSkpK1LlzZ82YMUOSFBoaql27dmnBggWKjY21W130xJZTgwYNZDabdfLkSavjJ0+elLe3t52qAqrPmDFj9K9//UvJyclq2rSpvctxKq6urgoMDNSdd96phIQEdejQQW+//ba9y3IK27ZtU05Ojjp16iQXFxe5uLgoNTVV77zzjlxcXG66PCYqV926dRUUFKRDhw7ZuxSn4OPjU+ovy61bt7b7kA5CbDm5urrqzjvv1Nq1ay3HSkpKtHbtWsam4TfNMAyNGTNGK1eu1Lp169SiRQt7l+T0SkpKVFhYaO8ynMK9996rnTt3KiMjw7J17txZMTExysjIkNlstneJTqWgoECHDx+Wj4+PvUtxCuHh4aWmVDxw4ID8/PzsVNFVDCeogGeeeUaxsbHq3LmzunTpolmzZunChQuKi4uzd2m/eQUFBVZ/887MzFRGRobq16+v5s2b27Gy3774+Hh98skn+vLLL+Xh4WEZA+7p6anatWvbubrfvsmTJ6tfv35q3ry58vPz9cknnyglJUWrV6+2d2lOwcPDo9T47zp16sjLy4tx4dVg4sSJ6t+/v/z8/HTixAlNmTJFZrNZ0dHR9i7NKUyYMEHdu3fXjBkz9OijjyotLU0LFy7UwoUL7VuYgQqZPXu20bx5c8PV1dXo0qWLsXnzZnuX5BSSk5MNSaW22NhYe5f2m1fWc5dkLFq0yN6lOYURI0YYfn5+hqurq9GwYUPj3nvvNb7++mt7l+XUIiIijHHjxtm7DKcwZMgQw8fHx3B1dTWaNGliDBkyxDh06JC9y3IqX331ldGuXTvDzc3NCAkJMRYuXGjvkgzmiQUAAIDDYUwsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAHACR44ckclkUkZGhr1LAYBKQYgFgAoYPny4Bg4caNmPjIzU+PHj7VZPZmam/vCHP8jX11e1atVS06ZNNWDAAO3bt0+S1KxZM2VlZbFEKoDfDBd7FwAAuDVXrlxR7969FRwcrM8//1w+Pj46fvy4kpKSdP78eUmS2WyWt7e3fQsFgEpETywA3KLhw4crNTVVb7/9tkwmk0wmk44cOSJJ2rVrl/r16yd3d3c1btxYw4YN0+nTpy33RkZGauzYsRo/frzq1aunxo0b67333tOFCxcUFxcnDw8PBQYGKikp6brfv3v3bh0+fFjz5s1Tt27d5Ofnp/DwcE2bNk3dunWTVHo4wfDhwy21/nJLSUmRJBUWFmrixIlq0qSJ6tSpo65du1rOAcD/AkIsANyit99+W2FhYXriiSeUlZWlrKwsNWvWTOfPn1fPnj0VGhqq9PR0rVq1SidPntSjjz5qdf/ixYvVoEEDpaWlaezYsRo9erQGDx6s7t276/vvv9d9992nYcOG6b///W+Z39+wYUPVqFFDy5cvV3Fxsc01X6s1KytL48aNU6NGjRQSEiJJGjNmjDZt2qSlS5fqhx9+0ODBg9W3b18dPHjw1h4WAFQSk2EYhr2LAABHM3z4cJ0/f15ffPGFpKs9qh07dtSsWbMs10ybNk0bNmzQ6tWrLceOHz+uZs2aaf/+/QoKClJkZKSKi4u1YcMGSVJxcbE8PT01aNAgffjhh5Kk7Oxs+fj4aNOmTZae1V+bO3eunnvuOZnNZnXu3FlRUVGKiYlRQECApKs9sS1atND27dvVsWNHq3s///xzxcTE6JtvvlF4eLiOHj2qgIAAHT16VL6+vpbrevXqpS5dumjGjBm3+vgA4JbREwsAVWTHjh1KTk6Wu7u7ZbvW03n48GHLde3bt7d8NpvN8vLy0h133GE51rhxY0lSTk7Odb8rPj5e2dnZ+vjjjxUWFqbPPvtMbdu21Zo1a25Y4/bt2zVs2DDNmTNH4eHhkqSdO3equLhYQUFBVrWnpqZa1Q0A9sSLXQBQRQoKCtS/f3+99tprpc75+PhYPtesWdPqnMlksjpmMpkkSSUlJTf8Pg8PD/Xv31/9+/fXtGnT1KdPH02bNk29e/cu8/rs7Gw9+OCDevzxxzVy5Eirus1ms7Zt2yaz2Wx1j7u7+w1rAIDqQogFgErg6upaajxqp06dtGLFCvn7+8vFpXp/uzWZTAoJCdF3331X5vlLly5pwIABCgkJ0Ztvvml1LjQ0VMXFxcrJydE999xTHeUCQLkxnAAAKoG/v7+2bNmiI0eO6PTp0yopKVF8fLzOnj2r6Ohobd26VYcPH9bq1asVFxdn8wtYtsjIyNCAAQO0fPly7dmzR4cOHVJiYqLef/99DRgwoMx7Ro0apWPHjumdd97RqVOnlJ2drezsbF2+fFlBQUGKiYnRY489ps8//1yZmZlKS0tTQkKC/v3vf1da3QBwK+iJBYBKMHHiRMXGxqpNmza6ePGiMjMz5e/vr40bN+r555/Xfffdp8LCQvn5+alv376qUaPy+hCaNm0qf39/vfzyy5aptK7tT5gwocx7UlNTlZWVpTZt2lgdT05OVmRkpBYtWqRp06bpT3/6k37++Wc1aNBA3bp10wMPPFBpdQPArWB2AgAAADgchhMAAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAw3GxdwEAAAD47YuLi7PpukWLFtl0nckwDONWCgIAAABuxmw2q2/fvnJzc5MkXbhwQevWrVP//v0lSYWFhUpKSlJJSYlN7RFiAQAAUOXMZrNOnDihxo0bS5IyMzPVvn175efnS5JOnTqlxo0b2xxiGRMLAACAavfrftTy9qsSYgEAAFDlPDw8dO7cOcv+uXPndOHCBRUUFEiSsrOzVb9+fZvbI8QCAACgyoWEhGj27NkqKSlRSUmJ5s2bJ19fX02cOFEbN27Uiy++qLvuusvm9hgTCwAAgCr3xRdf6JFHHlGdOnVUUlKiOnXqaNWqVfr973+vgwcPqlmzZvrqq690xx132NQeIRYAAADVYv369frqq69Uu3ZtPfHEE2rWrJkk6cyZM/Ly8ipXW4RYAAAAOBzGxAIAAMDhsGIXAAAAqpzZbLZpGi1b54klxAIAAKBavPXWW2rRokWltMWY2AoyDEP5+fny8PCQyWSydzkAAAD/08xms77//nt16NChUtpjTGwF5efny9PT07JUGgDgt80wDF26dEmXLl0q98pCACofIRYAABsUFhZq8ODBGjx4sAoLC+1dDuD0CLEAANjg0qVLZX4GYB+82AUAAIAqt2HDBrVq1UqSdO7cOeXl5ZV5nZ+fn03tEWIBAABQ5cLCwvTaa6/pzTff1OnTp0udN5lMMgyDKbYAAKhMv/yD1dY/ZAH8v3nz5unNN9/UCy+8oDvvvFOenp631B4hFgAAG/xyNpr8/HzVr1/fjtUAjue9997TrFmz9Ic//KFS2qvQi13Hjh3TiBEj5OvrK1dXV/n5+WncuHE6c+aM5ZrIyEiZTCaZTCbVqlVLQUFBSkhIKHNakk2bNslsNuv+++8vde7IkSMymUxq1KhRqemsOnbsqKlTp1odO3TokEaMGKHmzZvLzc1NTZo00b333quPP/5YRUVFluuu1fbrbenSpRV5JAAAALiBw4cPq1u3bpXWXrlD7I8//qjOnTvr4MGDWrJkiQ4dOqQFCxZo7dq1CgsL09mzZy3XPvHEE8rKytL+/fs1efJkvfTSS1qwYEGpNhMTEzV27FitX79eJ06cKPN78/Pz9frrr9+wtrS0NHXq1El79+7V3LlztWvXLqWkpOjxxx/X/PnztXv3bqvrFy1apKysLKtt4MCB5X0kAAAAuIn69euXORa2osodYuPj4+Xq6qqvv/5aERERat68ufr166dvvvlGP//8s1588UXLtbfddpu8vb3l5+enuLg4tW/fXmvWrLFqr6CgQMuWLdPo0aN1//3364MPPijze8eOHas333xTOTk5ZZ43DEPDhw9XUFCQNm7cqP79+6tVq1Zq1aqVoqOj9e2336p9+/ZW99StW1fe3t5WW61atcr7SAAAAHAT4eHhevXVV687K0F5lSvEnj17VqtXr9Yf//hH1a5d2+qct7e3YmJitGzZslJDBgzD0IYNG7Rv3z65urpanfv0008VEhKi4OBgDR06VO+//36ZQw6io6MVGBioV155pczaMjIytHfvXk2cOFE1apT9Y93K8rCFhYXKy8uz2gAAAGCbmTNnau/evWrSpIlCQ0MVFRVV5marcoXYgwcPyjAMtW7duszzrVu31rlz53Tq1ClJV99Cc3d3l5ubm3r06KGSkhI9/fTTVvckJiZq6NChkqS+ffsqNzdXqamppdo2mUyaOXOmFi5cqMOHD5c6f+DAAUlScHCw5VhOTo7c3d0t27x586zuiY6Otjrv7u6uo0ePlvmzJSQkyNPT07I1a9bseo8JAAAAv9K8eXPt3r1bCxcu1MCBAxUaGlrmZqsKzU5g65rRMTExevHFF3Xu3DlNmTJF3bt3V/fu3S3n9+/fr7S0NK1cufJqMS4uGjJkiBITExUZGVmqvT59+ujuu+/WX/7yF33yySc3/X4vLy9lZGRIuvqi2eXLl63Ov/XWW+rVq5fVMV9f3zLbmjx5sp555hnLfl5eHkEWAACgHNzc3BQdHV0pbZUrxAYGBspkMmnv3r166KGHSp3fu3ev6tWrp4YNG0qSPD09FRgYKOnqsIHAwEB169bNEhwTExNVVFRkFRwNw5Cbm5vmzJlT5vxhM2fOVFhYmJ599lmr49dWgNi/f78lxZvNZsv3u7iU/lG9vb0t52/Gzc1Nbm5uNl0LAACAqlWu4QReXl7q3bu35s2bp4sXL1qdy87O1scff6whQ4aUOfbU3d1d48aN08SJE2UYhoqKivThhx/qjTfeUEZGhmXbsWOHfH19tWTJkjJr6NKliwYNGqRJkyZZHQ8NDVVISIhef/11JqEGAAD4H9OiRYubbv7+/ja3V+7hBHPmzFH37t3Vp08fTZs2TS1atNDu3bv17LPPqkmTJpo+ffp17x01apReffVVrVixQi4uLjp37pxGjhxZqsf14YcfVmJiop566qky25k+fbratm1r1btqMpm0aNEi9e7dW+Hh4Zo8ebJat26tK1euaP369Tp16pTMZrNVO+fPn1d2drbVMQ8PD9WpU6e8jwUAAAA3cPToUb3yyivy8PCQJJ0+fVp/+9vf9Nprr0m6OmPVn//8Z9sbNCrgyJEjRmxsrNG4cWOjZs2aRrNmzYyxY8cap0+ftlwTERFhjBs3rtS9o0aNMtq2bWs88MADxu9+97sy29+yZYshydixY4eRmZlpSDK2b99udc2TTz5pSDKmTJlidXz//v1GbGys0bRpU8PFxcXw9PQ0evToYbz77rvGlStXLNdJKnNLSEiw6Rnk5uYakozc3FybrgcAOLYjR44YDzzwgPHAAw8YR44csXc5gMOpUaOGkZ2dbdk/fPiw4e7ubtk/efKkYTKZbG7PZBg2vqUFK3l5efL09FRubq5uv/12e5cDAKhiZ8+eVWxsrCRp8eLFLDsLlJPZbNaJEyfUuHFjSVcX0OrQoYNlRdacnBx5e3vbPCy0QsvOAgDgbH45B/n15iMHUH34vxAAAAB28evJAMqzMBUhFgAAG/xyWXKWKAfKb9SoUbrtttss+02aNFFSUpJl38PDQwkJCTa3x5jYCmJMLAA4F8MwVFhYKOnq3OG3spQ5gFtXoRW7AABwNiaTiR5Y4H8IwwkAAADgcAixAAAAcDiEWAAAANidYRj66aefbL6eEAsAAAC7O3XqlFq0aGHz9YRYAAAA/E9gnlgAAAA4nPLM/FrlU2wtWLBAzz77rM6dOycXl6tfV1BQoHr16ik8PFwpKSmWa1NSUhQVFaVDhw7p3nvv1fjx4zV+/Hir9qZOnaovvvhCGRkZpfb9/f1vOJYiNjZWH3zwwXVT/pIlS/T73//+ln5eAAAAlPbyyy/f8HxBQUG52qvyEBsVFaWCggKlp6erW7dukqQNGzbI29tbW7Zs0aVLlyzz7iUnJ6t58+Zq2bJlhb5r69atKi4uliR99913evjhh7V//37LYgS1a9e2XLto0SL17dvX6v66detW6HsBAABwY19++eUNzxcVFZWrvSoPscHBwfLx8VFKSoolxKakpGjAgAFat26dNm/erMjISMvxqKioCn9Xw4YNLZ/r168vSWrUqFGZ4bRu3bry9vau8HcBAADAdt9///0Nz586dUqNGze2ub1qGRMbFRWl5ORky35ycrIiIyMVERFhOX7x4kVt2bLllkJsVSosLFReXp7VBgAAgMpRnvGwUjWG2I0bN6qoqEj5+fnavn27IiIi1KNHD8uY2E2bNqmwsNAqxD7//PNyd3e32mbMmFEpNUVHR5dq++jRo9e9PiEhQZ6enpatWbNmlVIHAAAArirP7ARVPpxAkiIjI3XhwgVt3bpV586dU1BQkBo2bKiIiAjFxcXp0qVLSklJUUBAgJo3b26579lnn9Xw4cOt2nrnnXe0fv36W67prbfeUq9evayO+fr6Xvf6yZMn65lnnrHs5+XlEWQBAAAqiaen501f/vqlagmxgYGBatq0qZKTk3Xu3DlFRERIuhoamzVrpu+++07Jycnq2bOn1X0NGjRQYGCg1bFrY11vlbe3d6m2b8TNzU1ubm6V8t0AAAD4f6dPn9ZTTz2lVatW6c9//rNN91TbPLFRUVFKSUlRSkqK5UUuSerRo4eSkpKUlpb2PzseFgAAAFXjyy+/VLt27XTy5En98MMPNt9XLT2x0tUQGx8frytXrlh6YiUpIiJCY8aM0eXLl6s1xJ4/f17Z2dlWxzw8PFSnTp1qqwEAAMBZ/Hou/6KiIr366qv67LPP9Oqrr1oN27RFtYbYixcvKiQkxGr6hIiICOXn51um4qoucXFxpY4lJCRo0qRJ1VYDAACAswgICJBhGDKZTJb/mkwmrVq1qtR7SrYwGeWdzwCSrr7Y5enpqdzcXMtiCgAAACjbr4cKFBUVafr06UpOTtabb75Z6mX+myHEVhAhFgAA4NZ99NFHevrppxUWFqb33nvvhrNF/VK1vdgFAAAA/NrQoUO1a9cuFRUVqV27djbfV21jYgEAAICy+Pr6avXq1Zo3b57N9zCcoIIYTgAAAGA/9MRW0LXsn5eXZ+dKAAAAqo+Hh0e5loe95trsBDdiGIaOHDliU3uE2Ao6c+aMJLH0LAAAcCo5OTlq2LBhue8bP3685fPp06f1t7/9Ta+99prlWEFBgc2rdUkMJ6iw8+fPq169ejp69Kg8PT3tXY7TycvLU7NmzXTs2DGGc9gBz9++eP72w7O3L56/fV17/ufPn7/l7PPjjz+qQ4cOys/PtxzLycmRt7e3SkpKbGqDntgKqlHj6sQOnp6e/I9kR7fffjvP3454/vbF87cfnr198fztqyJDCX7N3d1dly5d0uXLl+Xq6irpaki+7bbbbG6DKbYAAABQrRo1aiQPDw+98cYbkqTi4mK9/vrrCg4OtrkNemIBAABQ7V544QU9//zz+utf/6orV67o4sWLWrZsmc33E2IryM3NTVOmTJGbm5u9S3FKPH/74vnbF8/ffnj29sXzt6/Kfv4TJ05Uu3bttHbtWrm6uqp///7q1q2bzffzYhcAAAAcDj2xAAAAqHIvv/yyTddNmTLFpuvoiQUAAECVM5vNatu2rVxcrvahXr58Wfv27VP79u0lSUVFRdq1a5fNU2wRYgEAAFDlzGazTpw4ocaNG0uSMjMz1b59e8tcsadOnZK3t7eKi4ttao8ptgAAAFDtft2PahjGTZel/SVCbAXNnTtX/v7+qlWrlrp27aq0tDR7l+QU1q9fr/79+8vX11cmk0lffPGFvUtyGgkJCbrrrrvk4eGhRo0aaeDAgdq/f7+9y3Ia8+fPV/v27S2TvIeFhSkpKcneZTmtmTNnymQyWS2jiaozdepUmUwmqy0kJMTeZTmVn3/+WUOHDpWXl5dq166tO+64Q+np6XatiRBbAcuWLdMzzzyjKVOm6Pvvv1eHDh3Up08f5eTk2Lu037wLFy6oQ4cOmjt3rr1LcTqpqamKj4/X5s2btWbNGl25ckX33XefLly4YO/SnELTpk01c+ZMbdu2Tenp6erZs6cGDBig3bt327s0p7N161a9++67lnF8qB5t27ZVVlaWZfv222/tXZLTOHfunMLDw1WzZk0lJSVpz549euONN1SvXr1ytVNWL+strf5loNy6dOlixMfHW/aLi4sNX19fIyEhwY5VOR9JxsqVK+1dhtPKyckxJBmpqan2LsVp1atXz/j73/9u7zKcSn5+vtGqVStjzZo1RkREhDFu3Dh7l+QUpkyZYnTo0MHeZTit559/3rj77rtvuZ3atWsbJ0+etOyfOnXKGD16tGX/zJkzRuvWrW1uj57Ycrp8+bK2bdumXr16WY7VqFFDvXr10qZNm+xYGVC9cnNzJUn169e3cyXOp7i4WEuXLtWFCxcUFhZm73KcSnx8vO6//36rPwNQPQ4ePChfX18FBAQoJiZGR48etXdJTuOf//ynOnfurMGDB6tRo0YKDQ3Ve++9V+52/vvf/6pRo0aW/QYNGmjevHmW/fr162vPnj02t0eILafTp0+ruLjY8mbdNY0bN1Z2dradqgKqV0lJicaPH6/w8HC1a9fO3uU4jZ07d8rd3V1ubm566qmntHLlSrVp08beZTmNpUuX6vvvv1dCQoK9S3E6Xbt21QcffKBVq1Zp/vz5yszM1D333GN5qx1V68cff9T8+fPVqlUrrV69WqNHj9bTTz+txYsX27UuFjsAUG7x8fHatWsXY9KqWXBwsDIyMpSbm6vly5crNjZWqampBNlqcOzYMY0bN05r1qxRrVq17F2O0+nXr5/lc/v27dW1a1f5+fnp008/1ciRI+1YmXMoKSlR586dNWPGDElSaGiodu3apQULFig2NtZuddETW04NGjSQ2WzWyZMnrY6fPHlS3t7edqoKqD5jxozRv/71LyUnJ6tp06b2LsepuLq6KjAwUHfeeacSEhLUoUMHvf322/Yuyyls27ZNOTk56tSpk1xcXOTi4qLU1FS98847cnFxsXleS1SOunXrKigoSIcOHbJ3KU7Bx8en1F+WW7dubfchHYTYcnJ1ddWdd96ptWvXWo6VlJRo7dq1jE3Db5phGBozZoxWrlypdevWqUWLFvYuyemVlJSosLDQ3mU4hXvvvVc7d+5URkaGZevcubNiYmKUkZEhs9ls7xKdSkFBgQ4fPiwfHx97l+IUwsPDS02peODAAfn5+dmpoqsYTlABzzzzjGJjY9W5c2d16dJFs2bN0oULFxQXF2fv0n7zCgoKrP7mnZmZqYyMDNWvX1/Nmze3Y2W/ffHx8frkk0/05ZdfysPDwzIG3NPTU7Vr17Zzdb99kydPVr9+/dS8eXPl5+frk08+UUpKilavXm3v0pyCh4dHqfHfderUkZeXF+PCq8HEiRPVv39/+fn56cSJE5oyZYrMZrOio6PtXZpTmDBhgrp3764ZM2bo0UcfVVpamhYuXKiFCxfat7Bbni/BSc2ePdto3ry54erqanTp0sXYvHmzvUtyCsnJyYakUltsbKy9S/vNK+u5SzIWLVpk79KcwogRIww/Pz/D1dXVaNiwoXHvvfcaX3/9tb3LcmpMsVV9hgwZYvj4+Biurq5GkyZNjCFDhhiHDh2yd1lO5auvvjLatWtnuLm5GSEhIcbChQvtXZJhMoxyrO8FAAAA/A9gTCwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAcAJHjhyRyWRSRkaGvUsBgEpBiAWAChg+fLgGDhxo2Y+MjNT48ePtVk9mZqb+8Ic/yNfXV7Vq1VLTpk01YMAA7du3T5LUrFkzZWVlsUQqgN8MF3sXAAC4NVeuXFHv3r0VHByszz//XD4+Pjp+/LiSkpJ0/vx5SZLZbJa3t7d9CwWASkRPLADcouHDhys1NVVvv/22TCaTTCaTjhw5IknatWuX+vXrJ3d3dzVu3FjDhg3T6dOnLfdGRkZq7NixGj9+vOrVq6fGjRvrvffe04ULFxQXFycPDw8FBgYqKSnput+/e/duHT58WPPmzVO3bt3k5+en8PBwTZs2Td26dZNUejjB8OHDLbX+cktJSZEkFRYWauLEiWrSpInq1Kmjrl27Ws4BwP8CQiwA3KK3335bYWFheuKJJ5SVlaWsrCw1a9ZM58+fV8+ePRUaGqr09HStWrVKJ0+e1KOPPmp1/+LFi9WgQQOlpaVp7NixGj16tAYPHqzu3bvr+++/13333adhw4bpv//9b5nf37BhQ9WoUUPLly9XcXGxzTVfqzUrK0vjxo1To0aNFBISIkkaM2aMNm3apKVLl+qHH37Q4MGD1bdvXx08ePDWHhYAVBKTYRiGvYsAAEczfPhwnT9/Xl988YWkqz2qHTt21KxZsyzXTJs2TRs2bNDq1astx44fP65mzZpp//79CgoKUmRkpIqLi7VhwwZJUnFxsTw9PTVo0CB9+OGHkqTs7Gz5+Pho06ZNlp7VX5s7d66ee+45mc1mde7cWVFRUYqJiVFAQICkqz2xLVq00Pbt29WxY0erez///HPFxMTom2++UXh4uI4ePaqAgAAdPXpUvr6+lut69eqlLl26aMaMGbf6+ADgltETCwBVZMeOHUpOTpa7u7tlu9bTefjwYct17du3t3w2m83y8vLSHXfcYTnWuHFjSVJOTs51vys+Pl7Z2dn6+OOPFRYWps8++0xt27bVmjVrbljj9u3bNWzYMM2ZM0fh4eGSpJ07d6q4uFhBQUFWtaemplrVDQD2xItdAFBFCgoK1L9/f7322mulzvn4+Fg+16xZ0+qcyWSyOmYymSRJJSUlN/w+Dw8P9e/fX/3799e0adPUp08fTZs2Tb179y7z+uzsbD344IN6/PHHNXLkSKu6zWaztm3bJrPZbHWPu7v7DWsAgOpCiAWASuDq6lpqPGqnTp20YsUK+fv7y8Wlen+7NZlMCgkJ0XfffVfm+UuXLmnAgAEKCQnRm2++aXUuNDRUxcXFysnJ0T333FMd5QJAuTGcAAAqgb+/v7Zs2aIjR47o9OnTKikpUXx8vM6ePavo6Ght3bpVhw8f1urVqxUXF2fzC1i2yMjI0IABA7R8+XLt2bNHhw4dUmJiot5//30NGDCgzHtGjRqlY8eO6Z133tGpU6eUnZ2t7OxsXb58WUFBQYqJidFjjz2mzz//XJmZmUpLS1NCQoL+/e9/V1rdAHAr6IkFgEowceJExcbGqk2bNrp48aIyMzPl7++vjRs36vnnn9d9992nwsJC+fn5qW/fvqpRo/L6EJo2bSp/f3+9/PLLlqm0ru1PmDChzHtSU1OVlZWlNm3aWB1PTk5WZGSkFi1apGnTpulPf/qTfv75ZzVo0EDdunXTAw88UGl1A8CtYHYCAAAAOByGEwAAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACH42LvAgAAAPDbFxcXZ9N1ixYtsuk6QiwAAACqXG5urtX+zz//rB07duh3v/tdhdojxAIAAKDKff7555bPx44dU2RkpK5cuaKgoCDNnDmz3O0xJhYAAADV5vjx44qMjFSTJk20du1azZ8/X3/961/L3Q49sQAAAKgWJ06cUFRUlHx9fZWUlKQ6deroyy+/1P3336/69evr8ccft7ktQiwAAACqXFZWliIjI9W4cWOtWrVKderUkSRFRkZqyZIlGjJkiOrWratHHnnEpvZMhmEYVVkwAAAAEBISIi8vL61evVru7u6lzn/44YcaNWqULl68aFN79MQCAACgynl5eWnVqlVlBlhJeuyxx3TmzBmb26MnFgAAAFWuoKDgugG2IgixAAAAqHK2LHZgGIY++OADm9ojxAIAAKDKDRo06LrniouL9c033+jixYsqKSmxqT3GxAIAAKDK/XKxg1/68ssv9cILL6hWrVqaMmWKze2x2AEAAACq3YYNG9S9e3dFR0frgQce0I8//qjnnnvO5vsJsQAAAKg2u3btUv/+/XXvvfeqbdu2OnTokF577TV5enqWqx1CLAAAAKrcTz/9pNjYWHXs2FEuLi7auXOn3nvvPfn6+laoPV7sAgAAQJWrVauWatSooaefflphYWHXvW7AgAE2tUeIBQAAQJVzcXHRzWKnYRg2z05AiAUAAIDDYUwsAAAAHA7zxAIAAKDa7NmzR/v371deXl6Z52NjY21qh+EEAAAAqHJ5eXl69NFHtWbNGrm4uKhOnTqlrjEMQ+fOnbOpPXpiAQAAUOWmTJminJwcbdu2TR07drzl9uiJBQAAQJULCgrSggUL1LNnz0ppjxe7AAAAUOVOnDihgICASmuPEAsAAIAq17RpU+3bt6/S2mNMLAAAAKrcoEGDNGHCBLm6uurOO++Up6fnLbXHmFgAAABUuf/+97964okntHTp0huu3MWKXQAAAPifk5WVpQMHDig3N7fM8w8++KBN7RBiK8gwDOXn58vDw0Mmk8ne5QAAADgVxsRWUH5+vjw9PZWbm6vbb7/d3uUAAAA4hLS0NH3yySc6cOCATCaTAgMDFRMToy5dupSrHWYnAADABoZh6NKlS7p06dINx/MBuL5JkyYpLCxMixYtUlZWlk6cOKEPPvhA3bp104svvliutgixAADYoLCwUIMHD9bgwYNVWFho73IAh7N8+XK9+eabmjVrls6ePavt27dr+/btOnv2rN555x399a9/1YoVK2xujxALAIANLl26VOZnALaZO3euJkyYoLFjx8psNluOm81mjRkzRn/60580Z84cm9sjxAIAAKDKbd++XQ899NB1zw8cOFDbt2+3uT1CLAAANvjl3JW2zmMJ4P+VlJTI19f3uud9fX1VXFxsc3uEWAAAbJCfn1/mZwC2admypQ4ePHjd8wcPHlTLli1tbq9CIfbYsWMaMWKEfH195erqKj8/P40bN05nzpyxXBMZGSmTySSTyaRatWopKChICQkJZb7RuWnTJpnNZt1///2lzh05ckQmk0mNGjUq9ZtGx44dNXXqVKtjhw4d0ogRI9S8eXO5ubmpSZMmuvfee/Xxxx+rqKjIct212n69LV26tCKPBAAAADfwyCOP6N13373u+QULFujhhx+2ub1yh9gff/xRnTt31sGDB7VkyRIdOnRICxYs0Nq1axUWFqazZ89arn3iiSeUlZWl/fv3a/LkyXrppZe0YMGCUm0mJiZq7NixWr9+vU6cOFHm9+bn5+v111+/YW1paWnq1KmT9u7dq7lz52rXrl1KSUnR448/rvnz52v37t1W11+b3uGX28CBA8v7SAAAAHATY8eO1T333FPmSl15eXnq0aOHxowZY3N75V7sID4+Xq6urvr6669Vu3ZtSVLz5s0VGhqqli1b6sUXX9T8+fMlSbfddpu8vb0lSXFxcZozZ47WrFmj0aNHW9orKCjQsmXLlJ6eruzsbH3wwQd64YUXSn3v2LFj9eabbyo+Pl6NGjUqdd4wDA0fPlxBQUHauHGjatT4/3zeqlUrRUdHl+oFrlu3rqU+AAAAVJ3bb79dY8eOLfe56ylXiD179qxWr16t6dOnWwLsNd7e3oqJidGyZcs0b948q3OGYejbb7/Vvn371KpVK6tzn376qUJCQhQcHKyhQ4dq/Pjxmjx5cqmlXKOjo7VmzRq98sorZU6/kJGRob1792rJkiVWAfaXWB4WAADAPhYvXmzTdbGxsTZdV64Qe/DgQRmGodatW5d5vnXr1jp37pxOnTolSZo3b57+/ve/6/Lly7py5Ypq1aqlp59+2uqexMREDR06VJLUt29f5ebmKjU1VZGRkVbXmUwmzZw5U/3799eECRNKDfw9cOCAJCk4ONhyLCcnRwEBAZb9v/71r/rjH/9o2Y+Ojraap0yS9uzZo+bNm5f62QoLC60mt87LyyvzGQAAAKC0CRMmWO0XFRXp4sWL8vDwsBwzDMPmEFuhF7tsXW4vJiZGGRkZ2rhxo/r166cXX3xR3bt3t5zfv3+/0tLSFB0dLUlycXHRkCFDlJiYWGZ7ffr00d13362//OUvNn2/l5eXMjIylJGRobp16+ry5ctW59966y3L+Wvb9aZ+SEhIkKenp2Vr1qyZTTUAAADg6r/oX9tOnjype+65R5L0/vvvW46fO3fO5vbKFWIDAwNlMpm0d+/eMs/v3btX9erVU8OGDSVJnp6eCgwM1F133aVPP/1Uc+bM0TfffGO5PjExUUVFRfL19ZWLi4tcXFw0f/58rVixosxBv5I0c+ZMLVu2rNRkuNeGKezfv99yzGw2KzAwUIGBgXJxKd3p7O3tbTl/o+skafLkycrNzbVsx44du8GTAgAAQFkuX76shx56SBkZGXrxxRf1hz/8QevWrSt3O+UKsV5eXurdu7fmzZunixcvWp3Lzs7Wxx9/rCFDhpQ59tTd3V3jxo3TxIkTZRiGioqK9OGHH+qNN96w6gndsWOHfH19tWTJkjJr6NKliwYNGqRJkyZZHQ8NDVVISIhef/31KpmE2s3NTbfffrvVBgAAANsVFRXpkUce0bZt27R27VpNnTpVr7zyigYOHKitW7eWq61yz04wZ84cde/eXX369NG0adPUokUL7d69W88++6yaNGmi6dOnX/feUaNG6dVXX9WKFSvk4uKic+fOaeTIkfL09LS67uGHH1ZiYqKeeuqpMtuZPn262rZta9VrajKZtGjRIvXu3Vvh4eGaPHmyWrdurStXrmj9+vU6depUqfGv58+fV3Z2ttUxDw8P1alTp7yPBQAAADdQXFysRx55RFu3blVycrJCQkIkSRMnTtTZs2f1u9/9TuvXr7/uu1e/Vu4xsa1atVJ6eroCAgL06KOPqmXLlnryyScVFRWlTZs2qX79+te9t379+nrsscc0depUJSYmqlevXqUCrHQ1xKanp+uHH34os52goCCNGDFCly5dsjrerVs3bdu2TcHBwYqPj1ebNm3UvXt3LVmyRG+99ZbV1F7S1Wm/fHx8rLbZs2eX95EAAADgJgYPHqzNmzdr3bp1lgB7zYwZM/Twww+rT58+NrdnMmx9SwtW8vLy5OnpqdzcXIYWAIATOHv2rOWt6cWLF9+w0wZAad7e3lq3bp3atGlz3WuGDBmiZcuW2dRehWYnAADA2fxyDvLrzUcO4PpuFmAl6eOPP7a5vXKPiQUAAADKa+vWrTd9eevaCqy2IMQCAACgyo0YMUJ16tS57nSmEiEWAIBKV6tWrTI/A7Ddhg0b1KFDh0ppixALAIAN3Nzc9Nlnn1k+A7AvQiwAADYwmUz0wAL/Q3i9EgAAAA6HEAsAAIAq17Jly0odisNwAgAAAFS5AwcOVGp7hFgAAABUubi4OJuuW7RokU3XsexsBbHsLAAAgO3MZrP69u1rGVJw4cIFrVu3Tv3795ckFRYWKikpSSUlJTa1R4itIEIsAACA7cxms06cOKHGjRtLkjIzM9W+fXvl5+dLkk6dOiVvb28VFxfb1F6Vv9i1YMECeXh4qKioyHKsoKBANWvWVGRkpNW1KSkpMplMOnz4sPz9/TVr1qxS7U2dOlUdO3Ysc9/f318mk+m627UVIK53funSpZX80wMAAKAsv+5HNQyj1LEbqfIxsVFRUSooKFB6erq6desm6epqDd7e3tqyZYsuXbpkmXcvOTlZzZs3V8uWLSv0XVu3brWk9++++04PP/yw9u/fb+kprV27tuXaRYsWqW/fvlb3161bt0LfCwAAgOpV5SE2ODhYPj4+SklJsYTYlJQUDRgwQOvWrdPmzZstPbIpKSmKioqq8Hc1bNjQ8rl+/fqSpEaNGpUZTuvWrStvb+8KfxcAAABujclkuuH+jVTLPLFRUVFKTk627CcnJysyMlIRERGW4xcvXtSWLVtuKcRWpcLCQuXl5VltAAAAsE1wcLBcXP6//7R+/fp64YUXLPtubm7q06ePze1VW4jduHGjioqKlJ+fr+3btysiIkI9evRQSkqKJGnTpk0qLCy0CrHPP/+83N3drbYZM2ZUSk3R0dGl2j569Oh1r09ISJCnp6dla9asWaXUAQAA4Az27NkjLy8vy37dunU1adIky76np6f+85//2NxetcwTGxkZqQsXLmjr1q06d+6cgoKC1LBhQ0VERCguLk6XLl1SSkqKAgIC1Lx5c8t9zz77rOVlrGveeecdrV+//pZreuutt9SrVy+rY76+vte9fvLkyXrmmWcs+3l5eQRZAAAAO6mWEBsYGKimTZsqOTlZ586dU0REhKSrobFZs2b67rvvlJycrJ49e1rd16BBAwUGBloduzbW9VZ5e3uXavtG3NzcKnWpNAAAAFRctQwnkK4OKUhJSVFKSorV1Fo9evRQUlKS0tLS/mfHwwIAAOB/S7UtOxsVFaX4+HhduXLF0hMrSRERERozZowuX75crSH2/Pnzys7Otjrm4eGhOnXqVFsNAAAAqJhq7Ym9ePGiAgMDLSs1SFdDbH5+vmUqruoSFxcnHx8fq2327NnV9v0AAACoOJadrSCWnQUAALDdyy+/bNN1U6ZMsek6QmwFEWIBAABs16lTJ6v9y5cva9++fWrfvr3lmGEY2r59u03tEWIriBALAABQcZmZmWrfvr3y8/MrdH+1jYkFAAAArrnVftRqm53gt+bag2f5WQAA4Ew8PDxkMpnsXQYhtqLOnDkjSazaBQAAnEpOTo4aNmxo7zIIsRV1beWwo0ePytPT087VOJ9ry/4eO3aMMcl2wPO3L56//fDs7Yvnb1/Xnr+rq2uF7v/pp5+s9o8fPy7DMHTkyBGrnl0/Pz+b2iPEVlCNGleHE3t6evI/kh3dfvvtPH874vnbF8/ffnj29sXzt6+KDiUICAiwGgd7rZ2AgADLvmEYKikpsak9QiwAAACqnK1TZ9mKEAsAAIAq98v5YCsDIbaC3NzcNGXKFLm5udm7FKfE87cvnr998fzth2dvXzx/+7rV5//rMbHXY+uYWBY7AAAAQJUzm80yDMMy9vXXGBMLAACA/0nffPONGjRoIOnq7ASPPvqovvvuO0nS2bNn1bNnT5vbIsQCAACgWrRt21aNGzeWJLm7u8tkMlnGyubk5JSrLZadBQAAgMMhxAIAAKDKVfZrWITYCpo7d678/f1Vq1Ytde3aVWlpafYuySmsX79e/fv3l6+vr0wmk7744gt7l+Q0EhISdNddd8nDw0ONGjXSwIEDtX//fnuX5TTmz5+v9u3bWyZ5DwsLU1JSkr3LclozZ86UyWTS+PHj7V2KU5g6dapMJpPVFhISYu+ynMrPP/+soUOHysvLS7Vr19Ydd9yh9PT0crVR1iIJvz5WnoUUCLEVsGzZMj3zzDOaMmWKvv/+e3Xo0EF9+vQp91gOlN+FCxfUoUMHzZ07196lOJ3U1FTFx8dr8+bNWrNmja5cuaL77rtPFy5csHdpTqFp06aaOXOmtm3bpvT0dPXs2VMDBgzQ7t277V2a09m6davefffdSp/zEjfWtm1bZWVlWbZvv/3W3iU5jXPnzik8PFw1a9ZUUlKS9uzZozfeeEP16tUrVztLlixR3bp1LfsBAQHKy8uz7Ht5eWnTpk02t8cUWxXQtWtX3XXXXZozZ44kqaSkRM2aNdPYsWM1adIkO1fnPEwmk1auXKmBAwfauxSndOrUKTVq1Eipqanq0aOHvctxSvXr19ff/vY3jRw50t6lOI2CggJ16tRJ8+bN07Rp09SxY0fNmjXL3mX95k2dOlVffPGFMjIy7F2KU5o0aZI2btyoDRs23FI7b775poYNG6aGDRtWSl30xJbT5cuXtW3bNvXq1ctyrEaNGurVq1e5/vYAOLrc3FxJV4MUqldxcbGWLl2qCxcuKCwszN7lOJX4+Hjdf//9Vn8GoHocPHhQvr6+CggIUExMjI4ePWrvkpzGP//5T3Xu3FmDBw9Wo0aNFBoaqvfee6/c7UyfPl1NmzbVI488oqSkJJvng70eQmw5nT59WsXFxZbpIa5p3LixsrOz7VQVUL1KSko0fvx4hYeHq127dvYux2ns3LlT7u7ucnNz01NPPaWVK1eqTZs29i7LaSxdulTff/+9EhIS7F2K0+natas++OADrVq1SvPnz1dmZqbuuece5efn27s0p/Djjz9q/vz5atWqlVavXq3Ro0fr6aef1uLFi8vVTk5Ojv71r3/Jzc1N999/v/z8/PTnP/9Zhw8frlBdzBMLoNzi4+O1a9cuxqRVs+DgYGVkZCg3N1fLly9XbGysUlNTCbLV4NixYxo3bpzWrFmjWrVq2bscp9OvXz/L5/bt26tr167y8/PTp59+ynCaalBSUqLOnTtrxowZkqTQ0FDt2rVLCxYsUGxsrM3tmM1m9e7dW02aNNFnn32md955Rx999JHuuOMOdevWTSNHjtTDDz9s8/9j9MSWU4MGDWQ2m3Xy5Emr4ydPnpS3t7edqgKqz5gxY/Svf/1LycnJatq0qb3LcSqurq4KDAzUnXfeqYSEBHXo0EFvv/22vctyCtu2bVNOTo46deokFxcXubi4KDU1Ve+8845cXFxUXFxs7xKdSt26dRUUFKRDhw7ZuxSn4OPjU+ovy61bt76lIR2GYeihhx7SihUrlJ2drejoaC1cuFC+vr42t0GILSdXV1fdeeedWrt2reVYSUmJ1q5dy9g0/KYZhqExY8Zo5cqVWrdunVq0aGHvkpxeSUmJCgsL7V2GU7j33nu1c+dOZWRkWLbOnTsrJiZGGRkZMpvN9i7RqRQUFOjw4cPy8fGxdylOITw8vNSUigcOHJCfn1+ltF9cXCzDMGQYhlxcbB8kwHCCCnjmmWcUGxurzp07q0uXLpo1a5YuXLiguLg4e5f2m1dQUGD1N+/MzExlZGSofv36at68uR0r++2Lj4/XJ598oi+//FIeHh6WMeCenp6qXbu2nav77Zs8ebL69eun5s2bKz8/X5988olSUlK0evVqe5fmFDw8PEqN/65Tp468vLwYF14NJk6cqP79+8vPz08nTpzQlClTZDabFR0dbe/SnMKECRPUvXt3zZgxQ48++qjS0tK0cOFCLVy48JbaXb58uT755BOtWrVK99xzj8aOHVu+GYcMVMjs2bON5s2bG66urkaXLl2MzZs327skp5CcnGxIKrXFxsbau7TfvLKeuyRj0aJF9i7NKYwYMcLw8/MzXF1djYYNGxr33nuv8fXXX9u7LKcWERFhjBs3zt5lOIUhQ4YYPj4+hqurq9GkSRNjyJAhxqFDh+xdllP56quvjHbt2hlubm5GSEiIsXDhwnK3cfnyZSMpKcn4wx/+YNSoUcPw9/c3Xn75ZePo0aMVqol5YgEAAFDlvLy89N///lcPPfSQRo4cqXvvvfeW2iPEAgAAoMrNmTNHQ4cOtVq161YQYgEAAOBweLELAAAAVc6WWW0Mw9CRI0dsao+eWAAAAFQ5s9msV155RR4eHmWeLygo0J///Gebl6MlxAIAAKDKmc1mnThxQo0bNy7zfE5Ojry9vW0OsSx2AAAAgCrn6uqqK1euXPf85cuXy7WsMyEWAAAAVc7b21uZmZnXPX/kyJHr9tKWhRALAACAKtetWzf94x//uO75f/zjH+rSpYvN7RFiAcAJHDlyRCaTSRkZGfYuBYCTGj16tN5//3299NJLOnv2rOX4uXPnNHXqVP3973/XU089ZXN7hFgAqIDhw4dbrfEdGRmp8ePH262ezMxM/eEPf5Cvr69q1aqlpk2basCAAdq3b58kqVmzZsrKylK7du3sViMA59ajRw/NnDlTM2fOVMOGDeXt7S0fHx81aNBA06dP17Rp0xQVFWVze8wTCwAO7sqVK+rdu7eCg4P1+eefy8fHR8ePH1dSUpLOnz8v6epbwd7e3vYtFIDTmzhxogYNGqQvvvhCmZmZMgxD/v7+GjBggFq1alW+xgwAQLnFxsYaAwYMsHyWZLVlZmYahmEYO3fuNPr27WvUqVPHaNSokTF06FDj1KlTlnYiIiKMMWPGGOPGjTPq1q1rNGrUyFi4cKFRUFBgDB8+3HB3dzdatmxp/Oc//7luLdu3bzckGUeOHLnuNZmZmYYkY/v27detWZKRnJxsGIZhXLp0yfjTn/5k+Pr6GrfddpvRpUsXyzkA+F/AcAIAuEVvv/22wsLC9MQTTygrK0tZWVlq1qyZzp8/r549eyo0NFTp6elatWqVTp48qUcffdTq/sWLF6tBgwZKS0vT2LFjNXr0aA0ePFjdu3fX999/r/vuu0/Dhg3Tf//73zK/v2HDhqpRo4aWL1+u4uJim2u+VmtWVpbGjRunRo0aKSQkRJI0ZswYbdq0SUuXLtUPP/ygwYMHq2/fvjp48OCtPSwAqCQsdgAAFTB8+HCdP39eX3zxhaSrY2I7duyoWbNmWa6ZNm2aNmzYoNWrV1uOHT9+XM2aNdP+/fsVFBSkyMhIFRcXa8OGDZKk4uJieXp6atCgQfrwww8lSdnZ2fLx8dGmTZvUrVu3MuuZO3eunnvuOZnNZnXu3FlRUVGKiYlRQECApKsvdrVo0ULbt29Xx44dre79/PPPFRMTo2+++Ubh4eE6evSoAgICdPToUfn6+lqu69Wrl7p06aIZM2bc6uMDgFtGTywAVJEdO3YoOTlZ7u7ulu1aT+fhw4ct17Vv397y2Ww2y8vLS3fccYfl2LV5E3Nycq77XfHx8crOztbHH3+ssLAwffbZZ2rbtq3WrFlzwxq3b9+uYcOGac6cOQoPD5ck7dy5U8XFxQoKCrKqPTU11apuALAnXuwCgCpSUFCg/v3767XXXit1zsfHx/K5Zs2aVudMJpPVMZPJJEk3XYrRw8ND/fv3V//+/TVt2jT16dNH06ZNU+/evcu8Pjs7Ww8++KAef/xxjRw50qpus9msbdu2yWw2W93j7u5+wxoAoLoQYgGgEri6upYaj9qpUyetWLFC/v7+cnGp3t9uTSaTQkJC9N1335V5/tKlSxowYIBCQkL05ptvWp0LDQ1VcXGxcnJydM8991RHuQBQbgwnAIBK4O/vry1btujIkSM6ffq0SkpKFB8fr7Nnzyo6Olpbt27V4cOHtXr1asXFxdn8ApYtMjIyNGDAAC1fvlx79uzRoUOHlJiYqPfff18DBgwo855Ro0bp2LFjeuedd3Tq1CllZ2crOztbly9fVlBQkGJiYvTYY4/p888/V2ZmptLS0pSQkKB///vflVY3ANwKemIBoBJMnDhRsbGxatOmjS5evKjMzEz5+/tr48aNev7553XfffepsLBQfn5+6tu3r2rUqLw+hKZNm8rf318vv/yyZWWua/sTJkwo857U1FRlZWWpTZs2VseTk5MVGRmpRYsWadq0afrTn/6kn3/+WQ0aNFC3bt30wAMPVFrdAHArmJ0AAAAADofhBAAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcFzsXQAAAAB+++Li4my6btGiRTZdR4gFAABAlcvNzbXav3DhgtatW6f+/ftXqD2TYRhGZRQGAAAA2CozM1Pt27dXfn5+he5nTCwAAACq3a32oxJiAQAA4HAIsQAAAHA4vNgFAACAKpeammq1//PPP6u4uFgpKSkymUyW4xERETa1x4tdAAAAqHJms1mGYVgF1l8zDEMlJSU2tUdPLAAAAKrcuXPnKrU9emIBAADgcHixCwAAANVm6dKlGjhwoNq0aaM2bdpo4MCBWrZsWbnboScWAAAAVa6kpESDBw/WF198oVatWql169YymUzau3ev9u/fr4cffljLli1TjRq29bEyJhYAAABVbtasWUpNTdU///lP3X///Vbn/vOf/2jYsGF6++23NWHCBJvaoycWAAAAVa59+/YaP368RowYUeb5RYsW6a233tIPP/xgU3uEWAAAAFS52rVra9++ffLz8yvz/E8//aSQkBBdvHjRpvZ4sQsAAABVrlatWsrNzb3u+by8PNWuXdvm9gixAAAAqHJhYWGaO3fudc/PmTNH3bp1s7k9XuwCAABAlXvppZcUERGh06dP609/+pPatGkjSdq7d6/eeOMN/fOf/1RKSorN7TEmFgAAANXiq6++0siRI3X69Gmr4w0aNNDf//53Pfjggza3RYgFAABAtbl48aLWrFmjAwcOSJKCgoLUu3fvco2HlQixAAAA+B+xf/9+BQcH23QtY2IBAABgF4cPH1ZycrJly87OVklJiU33EmIBAABQLY4cOWIJrCkpKTp+/Ljc3d119913a/z48YqMjLS5LYYTAAAAoMq1aNFCP/30k+rUqaPw8HBFRUUpMjJSd911l2rUKP+sr4RYAAAAVDkXFxe5u7srLi5OvXv31j333CMPD48Kt0eIBQAAQJXLyclRamqqUlNTlZKSogMHDig0NFSRkZGKiorS3XffLXd3d5vbI8QCAACg2p05c0YpKSmWULt//36FhoZq8+bNNt3Pi10AAACodl5eXgoPD1dJSYlKSkqUm5urHTt22Hw/PbEAAACoFseOHVNqaqrWr1+v9evX66efflLXrl3Vs2dPRUZGqlu3bnJ1dbWpLUIsAAAAqlxAQIBOnDihrl27KjIyUj179lRYWJjNofXXyj+fAQAAAFBOR48elclkkmEYMgzDMoygouiJBQAAQJU7efKkUlJSLIsdHDp0SK6ururSpYuioqIUERGh7t27y83Nzab2CLEAAACodj///LPVkrM//fSTXF1ddfHiRZvuJ8QCAADA7o4ePaq1a9cqLi7OpusJsQAAAHA4zBMLAACAKmdLD6thGPrggw9sao+eWAAAAFQ5s9msvn37XvfFrcLCQiUlJdk8YwEhFgAAAFXObDbrxIkTaty4cZnnT506JW9vbxUXF9vUHvPEAgAAoMq5uLjcMKAWFRXJbDbb3B4hFgAAAFWuXr16Onny5HXPnzx5UvXr17e5PUIsAAAAqlyHDh2UlJR03fOrVq1S+/btbW6PEAsAAIAqFxMTo5kzZ2rdunWlziUnJ2vGjBmKjo62uT1e7AIAAEC1GDhwoP75z3/qjjvuUOvWrWUymbRv3z7t2LFDv/vd7/TVV1/JZDLZ1BYhFgAAANXCMAz94x//0PLly5WZmSnDMOTv769BgwZp+PDhqlHD9kEChFgAAAA4HMbEAgAAwOGw7CwAAACqXIsWLW56jWEYOnLkiE3tMZwAAAAAVc5sNuuVV16Rh4eHJOn06dP629/+ptdee02SVFBQoD//+c8sOwsAAID/Hb9edvbHH39Uhw4dlJ+fL0nKycmRt7e3zSGWMbEAAABwOIRYAAAAOBxCbAUZhqG8vDwxGgMAAKBifr2wga0LHUiE2ArLz8+Xp6enZRwHAAAArm/UqFG67bbbLPtNmjRRUlKSZd/Dw0MJCQk2t8eLXRWUl5cnT09P5ebm6vbbb7d3OQAAAE6FeWIBAABQ5X766SebrvPz87PpOkIsAAAAqlxAQIAMw5DJZLJ6p+jX+7ZOsUWIBQAAQJXbvn17mccNw9CSJUs0e/Zsy0IItiDEAgAAoMq1b9++1LGvv/5akydP1qFDh/Tcc89p4sSJNrdHiAUAAEC12rp1qyZNmqRvv/1WTz75pFavXq0GDRqUq40KTbF17NgxjRgxQr6+vnJ1dZWfn5/GjRunM2fOWK6JjIyUyWSSyWRSrVq1FBQUpISEhDLnVd20aZPMZrPuv//+UueOHDkik8mkRo0alZrOqmPHjpo6darVsUOHDmnEiBFq3ry53Nzc1KRJE9177736+OOPVVRUZLnuWm2/3pYuXVqRRwIAAICbOHjwoIYMGaLu3bvLx8dH+/bt0+zZs8sdYKUKhNgff/xRnTt31sGDB7VkyRIdOnRICxYs0Nq1axUWFqazZ89arn3iiSeUlZWl/fv3a/LkyXrppZe0YMGCUm0mJiZq7NixWr9+vU6cOFHm9+bn5+v111+/YW1paWnq1KmT9u7dq7lz52rXrl1KSUnR448/rvnz52v37t1W1y9atEhZWVlW28CBA8v7SAAAAHATTz31lNq2bavc3Fxt3bpVH330kVq0aFHh9so9T2y/fv20a9cuHThwQLVr17Ycz87OVsuWLfXYY49p/vz5ioyMVMeOHTVr1izLNXfeeaf8/Pz0+eefW44VFBTIx8dH6enpmjJlitq3b68XXnjBcv7IkSNq0aKFnn32Wc2fP1+HDx9Wo0aNJF3tiR04cKCmTp0qwzDUtm1b3XbbbUpLS1ONGqXz+bU34qSrPbErV66scGhlnlgAAADbmc1m1apVS8HBwTdc8fR6L4D9WrnGxJ49e1arV6/W9OnTrQKsJHl7eysmJkbLli3TvHnzrM4ZhqFvv/1W+/btU6tWrazOffrppwoJCVFwcLCGDh2q8ePHa/LkyaWWHYuOjtaaNWv0yiuvaM6cOaVqy8jI0N69e7VkyZIyA6xUvqXMfq2wsFCFhYWW/by8vAq3BQAA4GymTJlSqe2VK8QePHhQhmGodevWZZ5v3bq1zp07p1OnTkmS5s2bp7///e+6fPmyrly5olq1aunpp5+2uicxMVFDhw6VJPXt21e5ublKTU1VZGSk1XUmk0kzZ85U//79NWHCBLVs2dLq/IEDByRJwcHBlmM5OTkKCAiw7P/1r3/VH//4R8t+dHS0zGazVTt79uxR8+bNS/1sCQkJevnll8v8uQEAAHBjL730UqW2V6EXu2wdgRATE6OMjAxt3LhR/fr104svvqju3btbzu/fv19paWmKjo6WJLm4uGjIkCFKTEwss70+ffro7rvv1l/+8hebvt/Ly0sZGRnKyMhQ3bp1dfnyZavzb731luX8tc3X17fMtiZPnqzc3FzLduzYMZtqAAAAQOUrV09sYGCgTCaT9u7dq4ceeqjU+b1796pevXpq2LChJMnT01OBgYGSrg4bCAwMVLdu3dSrVy9JV3thi4qKrIKjYRhyc3PTnDlz5OnpWeo7Zs6cqbCwMD377LNWx68NU9i/f79CQ0MlXR17ce37XVxK/6je3t6W8zfj5uYmNzc3m64FAABA1SpXT6yXl5d69+6tefPm6eLFi1bnsrOz9fHHH2vIkCFljj11d3fXuHHjNHHiRBmGoaKiIn344Yd64403rHpCd+zYIV9fXy1ZsqTMGrp06aJBgwZp0qRJVsdDQ0MVEhKi119/3eblygAAAOCYyr3YwZw5c9S9e3f16dNH06ZNU4sWLbR79249++yzatKkiaZPn37de0eNGqVXX31VK1askIuLi86dO6eRI0eW6nF9+OGHlZiYqKeeeqrMdqZPn662bdta9a6aTCYtWrRIvXv3Vnh4uCZPnqzWrVvrypUrWr9+vU6dOlVq/Ov58+eVnZ1tdczDw0N16tQp72MBAABANSr3mNhWrVopPT1dAQEBevTRR9WyZUs9+eSTioqK0qZNm1S/fv3r3lu/fn099thjmjp1qhITE9WrV68yhww8/PDDSk9P1w8//FBmO0FBQRoxYoQuXbpkdbxbt27atm2bgoODFR8frzZt2qh79+5asmSJ3nrrLY0ePdrq+ri4OPn4+Fhts2fPLu8jAQAAQCXYv3+/zdeWe55YXMU8sQAAALfm8OHDSk5OtmzZ2dk2Dwst93ACAAAAoCKOHDliCawpKSk6fvy43N3ddffdd2v8+PGlpli9EUIsAAAAqlyLFi30008/qU6dOgoPD1d8fLwiIyN11113XXehqhshxAIAAKDKHTt2TLfffrvi4uLUu3dv3XPPPfLw8KhwexVa7AAAAAAojxMnTui9997TlStX9Nxzz8nLy0tdu3bV888/r1WrVqmgoKBc7fFiVwXxYhcAAEDFnTlzRikpKUpNTVVKSoplwarNmzfbdD/DCeBwDMNQYWGhvcsA7OKXv/7d3NzKXFwG+K3j1/5vg5eXl8LDw1VSUqKSkhLl5uZqx44dNt9PiIXDKSws1ODBg+1dBgDATj777DPVqlXL3mWgAo4dO6bU1FStX79e69ev108//aSuXbuqZ8+e+sc//qFu3brZ3BYhFgAAAFUuICBAJ06cUNeuXRUZGal3331XYWFhcnV1rVB7hFg4tNmvPiE315r2LgOoNoWXr2jsX96TxK9/OJdf/tqHYzp69Khq1qwpwzBkGIZlGEFFEWJvEe/F2Zeba025ufGHOJwTv/7hTH755y1/9jqmn3/+WSkpKUpOTtayZcs0ffp0ubq6qkuXLoqKilJERIS6d+8uNzc3m9ojxN4iXjACAKDqXb5SZPlcWFio2rVr27EaVETjxo01ZMgQ/V97dx7U1PX2AfwbAgEUBBcIIEtcitQqoKKojLKIWyvitKOORQxorbVxQes6OhV/MmBra9W64liq4zhWrVq7oLWa4C6KUrepVcBxA8EphkUIGPL+4ZDXFNSwJLeY72cmM7kn9548nkmbJ4fnnjN+/HgAz5Pa2t27duzYgRUrVkAikaCiosKo/ky+TuzmzZvh6OiIZ8/+/8NXVlYGGxubOluLqVQqiEQi5OTkQCaTYc2aNXX6S0xMRGBgYL3HMpkMIpHopY+4uDgAeOnru3fvbuZ/PRERERHVp2PHjpg4cSK2bduG3Nxc5OXlYePGjUZfb/KZ2PDwcJSVleHixYv6O85OnjwJNzc3nD9/HpWVlfo7DJVKJby9vdGlS5dGvdeFCxeg1WoBAGfOnMEHH3yAmzdv6tdxffFXW1paGkaMGGFwvbOzc6Pel4iIiIiaxtvbG/Hx8Uafb/Iktlu3bnB3d4dKpdInsSqVCtHR0Th+/DjOnTunn5FVqVQIDw9v9Hu5uLjon7dr1w4A4OrqWm9y6uzsDDc3t0a/B2svWgAAEfNJREFUFxEREREZz9gENS0tzajzzFITGx4eDqVSiUWLFgF4PuO6YMECaLVaKJVKhIWFoaKiAufPn8fkyZPNEVKzqaysRGVlpdBhWJQXx5vF/URERC2DWq02OC4vL8fx48cRFRXVqP7MlsQmJCTg2bNnqKiowOXLlxEaGorq6mps3rwZAHD27FloNBqDmdiFCxdi6dKlBn1VVVWhe/fuTY5pwoQJEIvFBm03btyAt7d3vedrNBqDm7hKSkoAAB9//DFsbHh3sFCqqp/Bzq5x68sRERGR+ezfv9/gOC8vD/7+/nXajWWWJDYsLAzl5eW4cOECiouL4evrCxcXF4SGhiI+Ph6VlZVQqVTo3LmzQRI5f/58/c1YtdatW4cTJ040OaZvvvkGkZGRBm0eHh4vPT8lJQXLly9v8vsSERERUdP/mmqWJLZr167w9PSEUqlEcXExQkNDATxPGr28vHDmzBkolUpEREQYXNehQwd07drVoK221rWp3Nzc6vT9KosXL8bcuXP1xyUlJfDy8kJqaipcXV2bJSYyTmVlJWJjYwEAEhuuEkdERGSJzJYBhIeHQ6VSobi4GPPnz9e3Dx48GOnp6cjMzMT06dPNFU6D2dra1rv4rp2dHfdvFpBIJBI6BCIiIhKAWZNYhUKB6upq/UwsAISGhmLGjBmoqqpq0soEDfXkyRMUFBQYtDk6OqJ169Zmi4GIiIjIUmRkZBgcP3jwAFqtVr9PQK0X88RXMWsSW1FRAT8/P0ilUn17aGgoSktL9UtxmUt9yzykpKToV1AgIiIiouYTEREBnU5X56+oQ4YM0T/X6XSoqakxqj+zJbEymazeAl4fH5962+/cuVNvP4mJiUhMTHzpca2wsLCXFgw357JMxu7vS0RERI334j0Q/O5tmYqLi5u1P94V00SsySQiIjK9F79v+d3bMtXuoNpcmMRSi6apqhY6BCKzevEzz88/WRJ+3lu+f9fEvoyxNbEiHbc8apSSkhI4OTlBrVY3+y8LerXKykqMHTtW6DCIiEgge/fu5cpALZBYLK63JvZF/8maWCIiIiKyXM1dE8uZ2EZSq9VwdnbGvXv3OBNrZjqdzmALYCJL8uLn39bWlrWBZJH42ReWo6OjSca/tLQUs2bNQlpamlHnM4ltpNzcXHTp0kXoMIiIiIjMqrCwEC4uLibp183NjeUEpla7/e3du3fh5OQkcDSWp3bbX86EC4PjLyyOv3A49sLi+AurdvwlEonJ3qMhM7xMYhvJysoKAODk5MT/kATUpk0bjr+AOP7C4vgLh2MvLI6/sExZytGQAgEmsURERERkcrWrEzQXJrFEREREZHIHDhx45etqtRpyudzo/pjENpKtrS2WLVvGre8EwvEXFsdfWBx/4XDshcXxF1ZTx3/06NGvfL2wsLBB/XF1AiIiIiISXENXJ7AycTxERERERK8lFoshk8mMPp8zsURERETU4rAmloiIiIhMLjw8/LXn6HQ6qFQqo/rjTCwRERERmZxYLMbUqVPRqlUrAM9XI9i5cycUCgUA4OnTp0hNTWVNrKlt2LABMpkMdnZ2CA4ORmZmptAhWYQTJ04gKioKHh4eEIlEOHjwoNAhWYyUlBT07dsXjo6OcHV1xZgxY3Dz5k2hw7IYmzZtgr+/v36R9wEDBiA9PV3osCzWypUrIRKJkJCQIHQoFiExMREikcjg4efnJ3RYFuXBgweYOHEi2rdvD3t7e/Ts2RMXL15scD/Lly/H6tWrsXr1aixZsgQSiUR//L///a9BfTGJbYQffvgBc+fOxbJly3Dp0iUEBARg+PDhDV4aghquvLwcAQEB2LBhg9ChWJyMjAwoFAqcO3cOR48eRXV1NYYNG4by8nKhQ7MInp6eWLlyJbKysnDx4kVEREQgOjoa169fFzo0i3PhwgVs2bIF/v7+QodiUd555x3k5+frH6dOnRI6JItRXFyMkJAQ2NjYID09HTdu3MDXX3+Ntm3bChoXywkaITg4GH379sX69esBADU1NfDy8sLMmTOxaNEigaOzHCKRCAcOHMCYMWOEDsUiFRUVwdXVFRkZGRg8eLDQ4Vikdu3aYdWqVZgyZYrQoViMsrIy9O7dGxs3bkRSUhICAwOxZs0aocN64yUmJuLgwYPIzs4WOhSLtGjRIpw+fRonT55sUj9isRgPHz6EVCoFAOTm5iIgIAClpaUAuMSWyVVVVSErKwuRkZH6NisrK0RGRuLs2bMCRkZkXmq1GsDzRIrMS6vVYvfu3SgvL8eAAQOEDseiKBQKvPfeewbfAWQet27dgoeHBzp37oyYmBjcvXtX6JAsxqFDhxAUFISxY8fC1dUVvXr1wtatW5ulb5FI9MrjV2ES20CPHz+GVqvV/4qoJZVKUVBQIFBUROZVU1ODhIQEhISEoEePHkKHYzGuXr0KBwcH2Nra4pNPPsGBAwfQvXt3ocOyGLt378alS5eQkpIidCgWJzg4GN9//z0OHz6MTZs2IS8vD4MGDdLP4JFp5ebmYtOmTXjrrbdw5MgRTJ8+HbNmzcL27dsb1M/w4cMNdvuSSqXYsmWL/rhVq1aYNm2a0f1xiS0iajCFQoFr166xJs3MunXrhuzsbKjVauzbtw9yuRwZGRlMZM3g3r17mD17No4ePQo7Ozuhw7E4I0eO1D/39/dHcHAwfHx8sGfPHpbTmEFNTQ2CgoKQnJwMAOjVqxeuXbuGzZs3Qy6XG93Pb7/9ZnDcunVrTJgwQX/s4OCAjRs3Gt0fZ2IbqEOHDhCLxXj06JFB+6NHj+Dm5iZQVETmM2PGDPzyyy9QKpXw9PQUOhyLIpFI0LVrV/Tp0wcpKSkICAjA2rVrhQ7LImRlZaGwsBC9e/eGtbU1rK2tkZGRgXXr1sHa2hparVboEC2Ks7MzfH19cfv2baFDsQju7u51fiy//fbbgpd0MIltIIlEgj59+uDYsWP6tpqaGhw7doy1afRG0+l0mDFjBg4cOIDjx4+jU6dOQodk8WpqaqDRaIQOwyIMGTIEV69eRXZ2tv4RFBSEmJgYZGdnQywWCx2iRSkrK0NOTg7c3d2FDsUihISE1FlS8e+//4aPj49AET3HcoJGmDt3LuRyOYKCgtCvXz+sWbMG5eXliI+PFzq0N15ZWZnBL++8vDxkZ2ejXbt28Pb2FjCyN59CocCuXbvw008/wdHRUV8D7uTkBHt7e4Gje/MtXrwYI0eOhLe3N0pLS7Fr1y6oVCocOXJE6NAsgqOjY53679atW6N9+/asCzeDefPmISoqCj4+Pnj48CGWLVsGsVhs8KdoMp05c+Zg4MCBSE5Oxrhx45CZmYnU1FSkpqYKG5iOGuXbb7/VeXt76yQSia5fv366c+fOCR2SRVAqlToAdR5yuVzo0N549Y07AF1aWprQoVmEyZMn63x8fHQSiUTn4uKiGzJkiO73338XOiyLFhoaqps9e7bQYViE8ePH69zd3XUSiUTXsWNH3fjx43W3b98WOiyL8vPPP+t69Oihs7W11fn5+elSU1OFDknHdWKJiIiISHBcJ5aIiIiIWiSuE0tERERELU5DCgR4YxcRERERmdzrNkeo3QnSWKyJJSIiIiKTe9025TqdDmq12uiaWCaxRERERCS4oqIiSKVS3thFRERERC1HQ+dVmcQSERER0X8CVycgIiIDd+7cgUgkQnZ2ttChEBHVy8HBAaGhoUafzySWiKgR4uLiMGbMGP1xWFgYEhISBIsnLy8PH374ITw8PGBnZwdPT09ER0fjr7/+AgB4eXkhPz+fW6QS0X+SRqPB559/jlOnThl9DZfYIiJq4aqrqzF06FB069YN+/fvh7u7O+7fv4/09HQ8efIEACAWi+Hm5iZsoERE9cjKyoJcLodGo4FKpTL6Os7EEhE1UVxcHDIyMrB27VqIRCKIRCLcuXMHAHDt2jWMHDkSDg4OkEqliI2NxePHj/XXhoWFYebMmUhISEDbtm0hlUqxdetWlJeXIz4+Ho6OjujatSvS09Nf+v7Xr19HTk4ONm7ciP79+8PHxwchISFISkpC//79AdQtJ4iLi9PH+uKj9gtEo9Fg3rx56NixI1q3bo3g4OAGfbkQEb2OVqvF8uXLERISgoiICFy5cgUDBw40+nomsURETbR27VoMGDAAU6dORX5+PvLz8+Hl5YUnT54gIiICvXr1wsWLF3H48GE8evQI48aNM7h++/bt6NChAzIzMzFz5kxMnz4dY8eOxcCBA3Hp0iUMGzYMsbGxePr0ab3v7+LiAisrK+zbtw9ardbomGtjzc/Px+zZs+Hq6go/Pz8AwIwZM3D27Fns3r0bV65cwdixYzFixAjcunWraYNFRBZLLBbDyspK/7CxscGKFSuwZ88erFu3Dvb29g3qj+vEEhE1QlxcHJ48eYKDBw8CeD6jGhgYiDVr1ujPSUpKwsmTJ3HkyBF92/379+Hl5YWbN2/C19cXYWFh0Gq1OHnyJIDnMxNOTk54//33sWPHDgBAQUEB3N3dcfbsWf3M6r9t2LABCxYsgFgsRlBQEMLDwxETE4POnTsDeD4T26lTJ1y+fBmBgYEG1+7fvx8xMTH4448/EBISgrt376Jz5864e/cuPDw89OdFRkaiX79+SE5OburwEZEFOnTokMGxVqvFypUrUVRUhG3btiE8PLxB/bEmlojIRP78808olUo4ODjUeS0nJwe+vr4AAH9/f327WCxG+/bt0bNnT32bVCoFABQWFr70vRQKBSZNmgSVSoVz585h7969SE5OxqFDhzB06NCXXnf58mXExsZi/fr1CAkJAQBcvXoVWq1WH18tjUaD9u3bG/EvJyKqa/To0XXaoqOjkZycjHfffReTJ0/GqlWr0KpVK6P6YxJLRGQiZWVliIqKwhdffFHnNXd3d/1zGxsbg9dEIpFBW+26ia/bxcbR0RFRUVGIiopCUlIShg8fjqSkpJcmsQUFBRg9ejQ++ugjTJkyxSBusViMrKwsiMVig2vqS8iJiBrLysoKS5cuxahRoxAbG4sePXogNzfXqGuZxBIRNQOJRFKnHrV379748ccfIZPJYG1t3v/dikQi+Pn54cyZM/W+XllZiejoaPj5+WH16tUGr/Xq1QtarRaFhYUYNGiQOcIlIgsXGBiIS5cuYcmSJUZfwxu7iIiagUwmw/nz53Hnzh08fvwYNTU1UCgU+OeffzBhwgRcuHABOTk5OHLkCOLj442+AcsY2dnZiI6Oxr59+3Djxg3cvn0b27Ztw3fffYfo6Oh6r5k2bRru3buHdevWoaioCAUFBSgoKEBVVRV8fX0RExODSZMmYf/+/cjLy0NmZiZSUlLw66+/NlvcREQvsrGxwZdffmn0+ZyJJSJqBvPmzYNcLkf37t1RUVGBvLw8yGQynD59GgsXLsSwYcOg0Wjg4+ODESNGwMqq+eYQPD09IZPJsHz5cv1SWrXHc+bMqfeajIwM5Ofno3v37gbtSqUSYWFhSEtLQ1JSEj777DM8ePAAHTp0QP/+/TFq1Khmi5uILEtERARet56ATqczejk/rk5ARERERCY3d+5c/XO1Wo2dO3dCoVDo254+fYrU1NTX1v/XYhJLRERERGaVm5uLgIAAlJaW6tuKiooglUqNTmJZE0tEREREZmVvb4+qqiqDhLW8vBy2trZG98EkloiIiIjMyt3dHdbW1ti1a5e+bfv27foNWozBG7uIiIiIyOw+/fRTyOVyfPXVV6ioqMCtW7ewYcMGo69nTSwRERERCWLz5s04duwYJBIJRo8ejfHjxxt9LZNYIiIiImpxWE5ARERERCa3fft2o86Ty+VGnceZWCIiIiIyObFYjDZt2kAkEgEAampqUFJSAmdnZwDPNzpQq9VcJ5aIiIiI/jvEYjEePnwIqVQKAMjLy0NAQABKSkoAPF8n1s3NzehtubnEFhERERGZnU6nM9iG9t/Hr8MkloiIiIhaHCaxRERERGRyzV3ByiSWiIiIiEyu9oauWvb29hg8eLDB63Z2dsb3xxu7iIiIiMjUCgsL4eLiUieZbSwmsURERETU4rCcgIiIiIhaHCaxRERERNTiMIklIiIiohaHSSwRERERtThMYomIiIioxWESS0REREQtDpNYIiIiImpxmMQSERERUYvzf6ZZxmSdoxyiAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 720x1620 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=pumpkins,\n",
+    "    x=\"Item Size\", y=\"Color\", row='Variety',\n",
+    "    kind=\"box\", orient=\"h\",\n",
+    "    sharex=False, margin_titles=True,\n",
+    "    height=1.8, aspect=4, palette=palette,\n",
+    ")\n",
+    "# Defining axis labels \n",
+    "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
+    "g.set_titles(row_template=\"{row_name}\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Color', ylabel='ord__Item Size'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Suppressing warning message claiming that a portion of points cannot be placed into the plot due to the high number of data points\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')\n",
+    "\n",
+    "palette = {\n",
+    "    0: 'orange',\n",
+    "    1: 'wheat'\n",
+    "}\n",
+    "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pozor**: Ignorovanie varovaní NIE je najlepšou praxou a malo by sa vyhnúť, kedykoľvek je to možné. Varovania často obsahujú užitočné správy, ktoré nám umožňujú zlepšiť náš kód a vyriešiť problém.  \n",
+    "Dôvod, prečo ignorujeme toto konkrétne varovanie, je zabezpečenie čitateľnosti grafu. Zobrazenie všetkých dátových bodov s menšou veľkosťou značky, pri zachovaní konzistencie s farbou palety, vytvára nejasnú vizualizáciu.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# X is the encoded features\n",
+    "X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
+    "# y is the encoded label\n",
+    "y = encoded_pumpkins['Color']\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.94      0.98      0.96       166\n",
+      "           1       0.85      0.67      0.75        33\n",
+      "\n",
+      "    accuracy                           0.92       199\n",
+      "   macro avg       0.89      0.82      0.85       199\n",
+      "weighted avg       0.92      0.92      0.92       199\n",
+      "\n",
+      "Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
+      " 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
+      " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+      " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
+      "F1-score:  0.7457627118644068\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Train a logistic regression model on the pumpkin dataset\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model and print the results\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('F1-score: ', f1_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[162,   4],\n",
+       "       [ 11,  22]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "y_scores = model.predict_proba(X_test)\n",
+    "# calculate ROC curve\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "# Plot the diagonal 50% line\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "# Plot the FPR and TPR achieved by our model\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9749908725812341\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate AUC score\n",
+    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+    "print(auc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "vscode": {
+   "interpreter": {
+    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "ef50cc584e0b79412610cc7da15e1f86",
+   "translation_date": "2025-09-06T13:27:04+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sk/3-Web-App/1-Web-App/notebook.ipynb b/translations/sk/3-Web-App/1-Web-App/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sk/3-Web-App/1-Web-App/solution/notebook.ipynb b/translations/sk/3-Web-App/1-Web-App/solution/notebook.ipynb
new file mode 100644
index 000000000..305096828
--- /dev/null
+++ b/translations/sk/3-Web-App/1-Web-App/solution/notebook.ipynb
@@ -0,0 +1,267 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5fa2e8f4584c78250ca9729b46562ceb",
+   "translation_date": "2025-09-06T14:32:02+00:00",
+   "source_file": "3-Web-App/1-Web-App/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           datetime                  city state country     shape  \\\n",
+       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
+       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
+       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
+       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
+       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
+       "\n",
+       "   duration (seconds) duration (hours/min)  \\\n",
+       "0              2700.0           45 minutes   \n",
+       "1              7200.0              1-2 hrs   \n",
+       "2                20.0           20 seconds   \n",
+       "3                20.0             1/2 hour   \n",
+       "4               900.0           15 minutes   \n",
+       "\n",
+       "                                            comments date posted   latitude  \\\n",
+       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
+       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
+       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
+       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
+       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
+       "\n",
+       "    longitude  \n",
+       "0  -97.941111  \n",
+       "1  -98.581082  \n",
+       "2   -2.916667  \n",
+       "3  -96.645833  \n",
+       "4 -157.803611  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ufos = pd.read_csv('../data/ufos.csv')\n",
+    "ufos.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "\n",
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()\n",
+    "\n",
+    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufos.dropna(inplace=True)\n",
+    "\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "\n",
+    "X = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       1.00      0.02      0.05       250\n",
+      "           2       0.00      0.00      0.00         8\n",
+      "           3       0.94      1.00      0.97       131\n",
+      "           4       0.95      1.00      0.97      4743\n",
+      "\n",
+      "    accuracy                           0.95      5173\n",
+      "   macro avg       0.78      0.60      0.60      5173\n",
+      "weighted avg       0.95      0.95      0.93      5173\n",
+      "\n",
+      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
+      "Accuracy:  0.9512855209742895\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "model_filename = 'ufo-model.pkl'\n",
+    "pickle.dump(model, open(model_filename,'wb'))\n",
+    "\n",
+    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
+    "print(model.predict([[50,44,-12]]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/1-Introduction/notebook.ipynb b/translations/sk/4-Classification/1-Introduction/notebook.ipynb
new file mode 100644
index 000000000..8e9c16fe2
--- /dev/null
+++ b/translations/sk/4-Classification/1-Introduction/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d544ef384b7ba73757d830a72372a7f2",
+   "translation_date": "2025-09-06T14:50:40+00:00",
+   "source_file": "4-Classification/1-Introduction/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/sk/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
new file mode 100644
index 000000000..ebabb1cbc
--- /dev/null
+++ b/translations/sk/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -0,0 +1,721 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_10-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "2621e24705e8100893c9bf84e0fc8aef",
+   "translation_date": "2025-09-06T14:54:24+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ItETB4tSFprR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Úvod do klasifikácie: Čistenie, príprava a vizualizácia dát\n",
+    "\n",
+    "V týchto štyroch lekciách sa zameriame na základný aspekt klasického strojového učenia - *klasifikáciu*. Prejdeme si používanie rôznych klasifikačných algoritmov na dátovom súbore o všetkých úžasných kuchyniach Ázie a Indie. Dúfame, že máte chuť na jedlo!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/pinch.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Oslávte panázijské kuchyne v týchto lekciách! Obrázok od Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Oslávte panázijské kuchyne v týchto lekciách! Obrázok od Jen Looper](../../../../../../4-Classification/1-Introduction/solution/R/images/pinch.png)-->\n",
+    "\n",
+    "Klasifikácia je forma [supervised learning](https://wikipedia.org/wiki/Supervised_learning), ktorá má veľa spoločného s regresnými technikami. Pri klasifikácii trénujete model, aby predpovedal, do akej `kategórie` položka patrí. Ak je strojové učenie o predpovedaní hodnôt alebo názvov vecí pomocou dátových súborov, potom klasifikácia všeobecne spadá do dvoch skupín: *binárna klasifikácia* a *multiklasová klasifikácia*.\n",
+    "\n",
+    "Pamätajte:\n",
+    "\n",
+    "-   **Lineárna regresia** vám pomohla predpovedať vzťahy medzi premennými a presne určiť, kde by nový dátový bod spadal vo vzťahu k tejto línii. Napríklad ste mohli predpovedať číselné hodnoty, ako *aká bude cena tekvice v septembri vs. decembri*.\n",
+    "\n",
+    "-   **Logistická regresia** vám pomohla objaviť \"binárne kategórie\": pri tejto cenovej úrovni, *je táto tekvica oranžová alebo nie-oranžová*?\n",
+    "\n",
+    "Klasifikácia používa rôzne algoritmy na určenie ďalších spôsobov, ako priradiť dátovému bodu štítok alebo triedu. Poďme pracovať s týmito dátami o kuchyniach, aby sme zistili, či na základe skupiny ingrediencií dokážeme určiť jej pôvodnú kuchyňu.\n",
+    "\n",
+    "### [**Kvíz pred prednáškou**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)\n",
+    "\n",
+    "### **Úvod**\n",
+    "\n",
+    "Klasifikácia je jednou zo základných aktivít výskumníka strojového učenia a dátového vedca. Od základnej klasifikácie binárnej hodnoty (\"je tento e-mail spam alebo nie?\") až po komplexnú klasifikáciu a segmentáciu obrázkov pomocou počítačového videnia, je vždy užitočné vedieť triediť dáta do tried a klásť im otázky.\n",
+    "\n",
+    "Ak to vyjadríme vedeckejšie, vaša klasifikačná metóda vytvára prediktívny model, ktorý vám umožňuje mapovať vzťah medzi vstupnými premennými a výstupnými premennými.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/binary-multiclass.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Binárne vs. multiklasové problémy, ktoré klasifikačné algoritmy riešia. Infografika od Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Predtým, než začneme proces čistenia našich dát, ich vizualizácie a prípravy na úlohy strojového učenia, poďme sa naučiť niečo o rôznych spôsoboch, ako môže byť strojové učenie využité na klasifikáciu dát.\n",
+    "\n",
+    "Odvodené zo [štatistiky](https://wikipedia.org/wiki/Statistical_classification), klasifikácia pomocou klasického strojového učenia používa vlastnosti, ako `fajčiar`, `hmotnosť` a `vek`, na určenie *pravdepodobnosti vývoja X choroby*. Ako technika supervised learning podobná regresným cvičeniam, ktoré ste vykonávali skôr, vaše dáta sú označené a algoritmy strojového učenia používajú tieto označenia na klasifikáciu a predpovedanie tried (alebo 'vlastností') dátového súboru a ich priradenie do skupiny alebo výsledku.\n",
+    "\n",
+    "✅ Predstavte si na chvíľu dátový súbor o kuchyniach. Na čo by mohol odpovedať multiklasový model? Na čo by mohol odpovedať binárny model? Čo ak by ste chceli určiť, či daná kuchyňa pravdepodobne používa senovku grécku? Čo ak by ste chceli zistiť, či by ste na základe daru tašky s potravinami plnej badiánu, artičokov, karfiolu a chrenu mohli vytvoriť typické indické jedlo?\n",
+    "\n",
+    "### **Ahoj 'klasifikátor'**\n",
+    "\n",
+    "Otázka, ktorú chceme položiť tomuto dátovému súboru o kuchyniach, je vlastne **multiklasová otázka**, pretože máme niekoľko potenciálnych národných kuchýň, s ktorými môžeme pracovať. Na základe dávky ingrediencií, do ktorej z týchto mnohých tried budú dáta patriť?\n",
+    "\n",
+    "Tidymodels ponúka niekoľko rôznych algoritmov na klasifikáciu dát, v závislosti od typu problému, ktorý chcete vyriešiť. V nasledujúcich dvoch lekciách sa naučíte o niekoľkých z týchto algoritmov.\n",
+    "\n",
+    "#### **Predpoklad**\n",
+    "\n",
+    "Pre túto lekciu budeme potrebovať nasledujúce balíky na čistenie, prípravu a vizualizáciu našich dát:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [kolekcia balíkov pre R](https://www.tidyverse.org/packages), ktorá robí dátovú vedu rýchlejšou, jednoduchšou a zábavnejšou!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je rámec [kolekcie balíkov](https://www.tidymodels.org/packages/) na modelovanie a strojové učenie.\n",
+    "\n",
+    "-   `DataExplorer`: Balík [DataExplorer](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) je určený na zjednodušenie a automatizáciu procesu EDA a generovania správ.\n",
+    "\n",
+    "-   `themis`: Balík [themis](https://themis.tidymodels.org/) poskytuje extra kroky receptov na riešenie nevyvážených dát.\n",
+    "\n",
+    "Môžete ich nainštalovať pomocou:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Alternatívne, skript nižšie skontroluje, či máte balíky potrebné na dokončenie tohto modulu, a nainštaluje ich za vás, ak chýbajú.\n"
+   ],
+   "metadata": {
+    "id": "ri5bQxZ-Fz_0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "KIPxa4elGAPI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Neskôr načítame tieto úžasné balíky a sprístupníme ich v našej aktuálnej R relácii. (Toto je len na ilustráciu, `pacman::p_load()` to už za vás urobil)\n"
+   ],
+   "metadata": {
+    "id": "YkKAxOJvGD4C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Cvičenie - vyčistite a vyvážte svoje dáta\n",
+    "\n",
+    "Prvým krokom pred začatím tohto projektu je vyčistiť a **vyvážiť** svoje dáta, aby ste dosiahli lepšie výsledky.\n",
+    "\n",
+    "Zoznámme sa s dátami! 🕵️\n"
+   ],
+   "metadata": {
+    "id": "PFkQDlk0GN5O"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Qccw7okxGT0S"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zaujímavé! Podľa vzhľadu to vyzerá, že prvý stĺpec je akýsi stĺpec `id`. Poďme získať trochu viac informácií o údajoch.\n"
+   ],
+   "metadata": {
+    "id": "XrWnlgSrGVmR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Basic information about the data\r\n",
+    "df %>%\r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# Visualize basic information above\r\n",
+    "df %>% \r\n",
+    "  plot_intro(ggtheme = theme_light())"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "4UcGmxRxGieA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Z výstupu môžeme okamžite vidieť, že máme `2448` riadkov a `385` stĺpcov a `0` chýbajúcich hodnôt. Máme tiež 1 diskrétny stĺpec, *cuisine*.\n",
+    "\n",
+    "## Cvičenie - učenie sa o kuchyniach\n",
+    "\n",
+    "Teraz sa práca začína stávať zaujímavejšou. Poďme objaviť rozdelenie údajov podľa kuchyne.\n"
+   ],
+   "metadata": {
+    "id": "AaPubl__GmH5"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Count observations per cuisine\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(n)\r\n",
+    "\r\n",
+    "# Plot the distribution\r\n",
+    "theme_set(theme_light())\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"cuisine\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FRsBVy5eGrrv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Existuje konečný počet kuchýň, ale rozloženie údajov je nerovnomerné. Môžete to napraviť! Predtým však preskúmajte trochu viac.\n",
+    "\n",
+    "Ďalej priraďme každú kuchyňu do jej vlastného tibble a zistime, koľko údajov je k dispozícii (riadky, stĺpce) na jednu kuchyňu.\n",
+    "\n",
+    "> [Tibble](https://tibble.tidyverse.org/) je moderný dátový rámec.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_filter.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "vVvyDb1kG2in"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create individual tibble for the cuisines\r\n",
+    "thai_df <- df %>% \r\n",
+    "  filter(cuisine == \"thai\")\r\n",
+    "japanese_df <- df %>% \r\n",
+    "  filter(cuisine == \"japanese\")\r\n",
+    "chinese_df <- df %>% \r\n",
+    "  filter(cuisine == \"chinese\")\r\n",
+    "indian_df <- df %>% \r\n",
+    "  filter(cuisine == \"indian\")\r\n",
+    "korean_df <- df %>% \r\n",
+    "  filter(cuisine == \"korean\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Find out how much data is available per cuisine\r\n",
+    "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
+    "    \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
+    "    \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
+    "    \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
+    "    \"korean_df:\", dim(korean_df))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "0TvXUxD3G8Bk"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Cvičenie - Objavovanie hlavných ingrediencií podľa kuchyne pomocou dplyr**\n",
+    "\n",
+    "Teraz sa môžete hlbšie ponoriť do údajov a zistiť, aké sú typické ingrediencie pre jednotlivé kuchyne. Mali by ste odstrániť opakujúce sa údaje, ktoré spôsobujú zmätok medzi kuchyňami, takže sa poďme pozrieť na tento problém.\n",
+    "\n",
+    "Vytvorte funkciu `create_ingredient()` v R, ktorá vráti dataframe s ingredienciami. Táto funkcia začne odstránením nepotrebného stĺpca a zoradí ingrediencie podľa ich počtu.\n",
+    "\n",
+    "Základná štruktúra funkcie v R je:\n",
+    "\n",
+    "`myFunction <- function(arglist){`\n",
+    "\n",
+    "**`...`**\n",
+    "\n",
+    "**`return`**`(value)`\n",
+    "\n",
+    "`}`\n",
+    "\n",
+    "Úhľadný úvod do funkcií v R nájdete [tu](https://skirmer.github.io/presentations/functions_with_r.html#1).\n",
+    "\n",
+    "Poďme na to! Využijeme [dplyr slovesá](https://dplyr.tidyverse.org/), ktoré sme sa učili v predchádzajúcich lekciách. Na zopakovanie:\n",
+    "\n",
+    "-   `dplyr::select()`: pomáha vám vybrať, ktoré **stĺpce** chcete ponechať alebo vylúčiť.\n",
+    "\n",
+    "-   `dplyr::pivot_longer()`: pomáha \"predĺžiť\" údaje, čím sa zvýši počet riadkov a zníži počet stĺpcov.\n",
+    "\n",
+    "-   `dplyr::group_by()` a `dplyr::summarise()`: pomáha nájsť štatistické súhrny pre rôzne skupiny a usporiadať ich do prehľadnej tabuľky.\n",
+    "\n",
+    "-   `dplyr::filter()`: vytvára podmnožinu údajov obsahujúcu iba riadky, ktoré spĺňajú vaše podmienky.\n",
+    "\n",
+    "-   `dplyr::mutate()`: pomáha vytvárať alebo upravovať stĺpce.\n",
+    "\n",
+    "Pozrite si tento [*umelecky* ladený learnr tutoriál](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) od Allison Horst, ktorý predstavuje niektoré užitočné funkcie na spracovanie údajov v dplyr *(súčasť Tidyverse)*.\n"
+   ],
+   "metadata": {
+    "id": "K3RF5bSCHC76"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Creates a functions that returns the top ingredients by class\r\n",
+    "\r\n",
+    "create_ingredient <- function(df){\r\n",
+    "  \r\n",
+    "  # Drop the id column which is the first colum\r\n",
+    "  ingredient_df = df %>% select(-1) %>% \r\n",
+    "  # Transpose data to a long format\r\n",
+    "    pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
+    "  # Find the top most ingredients for a particular cuisine\r\n",
+    "    group_by(ingredients) %>% \r\n",
+    "    summarise(n_instances = sum(count)) %>% \r\n",
+    "    filter(n_instances != 0) %>% \r\n",
+    "  # Arrange by descending order\r\n",
+    "    arrange(desc(n_instances)) %>% \r\n",
+    "    mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
+    "  \r\n",
+    "  \r\n",
+    "  return(ingredient_df)\r\n",
+    "} # End of function"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "uB_0JR82HTPa"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Teraz môžeme použiť funkciu na získanie predstavy o desiatich najpopulárnejších ingredienciách podľa kuchyne. Poďme si to vyskúšať s `thai_df`.\n"
+   ],
+   "metadata": {
+    "id": "h9794WF8HWmc"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Call create_ingredient and display popular ingredients\r\n",
+    "thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
+    "\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "agQ-1HrcHaEA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "V predchádzajúcej časti sme použili `geom_col()`, pozrime sa, ako môžete použiť aj `geom_bar` na vytvorenie stĺpcových grafov. Použite `?geom_bar` na ďalšie čítanie.\n"
+   ],
+   "metadata": {
+    "id": "kHu9ffGjHdcX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a bar chart for popular thai cuisines\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10) %>% \r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "fb3Bx_3DHj6e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "RHP_xgdkHnvM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
+    "create_ingredient(df = japanese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "019v8F0XHrRU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Čo tak čínska kuchyňa?\n"
+   ],
+   "metadata": {
+    "id": "iIGM7vO8Hu3v"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
+    "create_ingredient(df = chinese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lHd9_gd2HyzU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ir8qyQbNH1c7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Indian cuisines and make bar chart\r\n",
+    "create_ingredient(df = indian_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ApukQtKjH5FO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qv30cwY1H-FM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Korean cuisines and make bar chart\r\n",
+    "create_ingredient(df = korean_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lumgk9cHIBie"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Z vizualizácií dát môžeme teraz vynechať najbežnejšie ingrediencie, ktoré spôsobujú zmätok medzi odlišnými kuchyňami, pomocou `dplyr::select()`.\n",
+    "\n",
+    "Každý miluje ryžu, cesnak a zázvor!\n"
+   ],
+   "metadata": {
+    "id": "iO4veMXuIEta"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "iHJPiG6rIUcK"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Predspracovanie údajov pomocou receptov 👩‍🍳👨‍🍳 - Riešenie nevyvážených údajov ⚖️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n",
+    "\n",
+    "Keďže táto lekcia je o kuchyniach, musíme dať `recepty` do kontextu.\n",
+    "\n",
+    "Tidymodels poskytuje ďalší šikovný balík: `recipes` - balík na predspracovanie údajov.\n"
+   ],
+   "metadata": {
+    "id": "kkFd-JxdIaL6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Pozrime sa znova na rozdelenie našich kuchýň.\n"
+   ],
+   "metadata": {
+    "id": "6l2ubtTPJAhY"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "old_label_count <- df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "old_label_count"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "1e-E9cb7JDVi"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ako vidíte, počet kuchýň je dosť nerovnomerne rozdelený. Kórejské kuchyne sú takmer 3-krát početnejšie ako thajské kuchyne. Nevyvážené údaje často negatívne ovplyvňujú výkon modelu. Zamyslite sa nad binárnou klasifikáciou. Ak väčšina vašich údajov patrí do jednej triedy, model strojového učenia bude túto triedu predpovedať častejšie, jednoducho preto, že má k dispozícii viac údajov. Vyváženie údajov odstraňuje akúkoľvek skreslenosť a pomáha eliminovať túto nerovnováhu. Mnohé modely dosahujú najlepšie výsledky, keď je počet pozorovaní rovnaký, a preto majú tendenciu zápasiť s nevyváženými údajmi.\n",
+    "\n",
+    "Existujú dva hlavné spôsoby, ako sa vysporiadať s nevyváženými dátovými súbormi:\n",
+    "\n",
+    "-   pridanie pozorovaní do minoritnej triedy: `Over-sampling`, napr. pomocou algoritmu SMOTE\n",
+    "\n",
+    "-   odstránenie pozorovaní z majoritnej triedy: `Under-sampling`\n",
+    "\n",
+    "Teraz si ukážeme, ako pracovať s nevyváženými dátovými súbormi pomocou `receptu`. Recept si môžete predstaviť ako plán, ktorý popisuje, aké kroky by sa mali aplikovať na dátový súbor, aby bol pripravený na analýzu údajov.\n"
+   ],
+   "metadata": {
+    "id": "soAw6826JKx9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HS41brUIJVJy"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Poďme si rozobrať naše kroky predspracovania.\n",
+    "\n",
+    "-   Volanie funkcie `recipe()` s formulou určuje *úlohy* premenných pomocou údajov `df_select` ako referencie. Napríklad stĺpec `cuisine` bol priradený úlohe `outcome`, zatiaľ čo ostatné stĺpce boli priradené úlohe `predictor`.\n",
+    "\n",
+    "-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html) vytvára *špecifikáciu* kroku receptu, ktorý synteticky generuje nové príklady minoritnej triedy pomocou najbližších susedov týchto prípadov.\n",
+    "\n",
+    "Ak by sme teraz chceli vidieť predspracované údaje, museli by sme [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) a [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) náš recept.\n",
+    "\n",
+    "`prep()`: odhaduje potrebné parametre z tréningovej množiny, ktoré môžu byť neskôr aplikované na iné množiny údajov.\n",
+    "\n",
+    "`bake()`: vezme pripravený recept a aplikuje operácie na akúkoľvek množinu údajov.\n"
+   ],
+   "metadata": {
+    "id": "Yb-7t7XcJaC8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep and bake the recipe\r\n",
+    "preprocessed_df <- cuisines_recipe %>% \r\n",
+    "  prep() %>% \r\n",
+    "  bake(new_data = NULL) %>% \r\n",
+    "  relocate(cuisine)\r\n",
+    "\r\n",
+    "# Display data\r\n",
+    "preprocessed_df %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Quick summary stats\r\n",
+    "preprocessed_df %>% \r\n",
+    "  introduce()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "9QhSgdpxJl44"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Poďme teraz skontrolovať rozdelenie našich kuchýň a porovnať ich s nevyváženými údajmi.\n"
+   ],
+   "metadata": {
+    "id": "dmidELh_LdV7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "new_label_count <- preprocessed_df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "list(new_label_count = new_label_count,\r\n",
+    "     old_label_count = old_label_count)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "aSh23klBLwDz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mňam! Dáta sú pekné, čisté, vyvážené a veľmi chutné 😋!\n",
+    "\n",
+    "> Zvyčajne sa recept používa ako predspracovateľ pre modelovanie, kde definuje, aké kroky by sa mali aplikovať na dátovú sadu, aby bola pripravená na modelovanie. V takom prípade sa typicky používa `workflow()` (ako sme už videli v našich predchádzajúcich lekciách) namiesto manuálneho odhadovania receptu.\n",
+    ">\n",
+    "> Preto zvyčajne nepotrebujete **`prep()`** a **`bake()`** recepty, keď používate tidymodels, ale sú to užitočné funkcie, ktoré môžete mať vo svojej výbave na potvrdenie, že recepty robia to, čo očakávate, ako v našom prípade.\n",
+    ">\n",
+    "> Keď **`bake()`** predspracovaný recept s **`new_data = NULL`**, dostanete späť dáta, ktoré ste poskytli pri definovaní receptu, ale už prešli krokmi predspracovania.\n",
+    "\n",
+    "Teraz si uložíme kópiu týchto dát na použitie v budúcich lekciách:\n"
+   ],
+   "metadata": {
+    "id": "HEu80HZ8L7ae"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Save preprocessed data\r\n",
+    "write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "cBmCbIgrMOI6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tento nový CSV súbor sa teraz nachádza v hlavnom priečinku s dátami.\n",
+    "\n",
+    "**🚀Výzva**\n",
+    "\n",
+    "Tento učebný plán obsahuje niekoľko zaujímavých datasetov. Prezrite si priečinky `data` a zistite, či niektoré obsahujú datasety vhodné na binárnu alebo viactriednu klasifikáciu. Aké otázky by ste mohli položiť tomuto datasetu?\n",
+    "\n",
+    "## [**Kvíz po prednáške**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)\n",
+    "\n",
+    "## **Prehľad a samostatné štúdium**\n",
+    "\n",
+    "-   Pozrite si [balík themis](https://github.com/tidymodels/themis). Aké ďalšie techniky by sme mohli použiť na riešenie nevyvážených dát?\n",
+    "\n",
+    "-   Referenčná stránka [Tidy models](https://www.tidymodels.org/start/).\n",
+    "\n",
+    "-   H. Wickham a G. Grolemund, [*R for Data Science: Vizualizácia, modelovanie, transformácia, úprava a import dát*](https://r4ds.had.co.nz/).\n",
+    "\n",
+    "#### ĎAKUJEME:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) za vytvorenie úžasných ilustrácií, ktoré robia R prístupnejším a zábavnejším. Viac ilustrácií nájdete v jej [galérii](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) a [Jen Looper](https://www.twitter.com/jenlooper) za vytvorenie pôvodnej verzie tohto modulu v Pythone ♥️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "WQs5621pMGwf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/1-Introduction/solution/notebook.ipynb b/translations/sk/4-Classification/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..e29c69a24
--- /dev/null
+++ b/translations/sk/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,672 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Lahodné ázijské a indické jedlá\n",
+    "\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Nainštalujte Imblearn, ktorý umožní SMOTE. Toto je balík Scikit-learn, ktorý pomáha spracovávať nevyvážené údaje pri vykonávaní klasifikácie. (https://imbalanced-learn.org/stable/)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
+      "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df  = pd.read_csv('../../data/cuisines.csv')"
+   ]
+  },
+  {
+   "source": [
+    "Táto množina údajov obsahuje 385 stĺpcov, ktoré označujú všetky druhy ingrediencií v rôznych kuchyniach z daného súboru kuchýň.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0          65  indian       0         0      0           0      0   \n",
+       "1          66  indian       1         0      0           0      0   \n",
+       "2          67  indian       0         0      0           0      0   \n",
+       "3          68  indian       0         0      0           0      0   \n",
+       "4          69  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 385 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>67</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>69</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 385 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2448 entries, 0 to 2447\nColumns: 385 entries, Unnamed: 0 to zucchini\ndtypes: int64(384), object(1)\nmemory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "korean      799\n",
+       "indian      598\n",
+       "chinese     442\n",
+       "japanese    320\n",
+       "thai        289\n",
+       "Name: cuisine, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts()"
+   ]
+  },
+  {
+   "source": [
+    "Zobrazte kuchyne v stĺpcovom grafe\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18799710>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "thai df: (289, 385)\njapanese df: (320, 385)\nchinese df: (442, 385)\nindian df: (598, 385)\nkorean df: (799, 385)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "thai_df = df[(df.cuisine == \"thai\")]\n",
+    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
+    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
+    "indian_df = df[(df.cuisine == \"indian\")]\n",
+    "korean_df = df[(df.cuisine == \"korean\")]\n",
+    "\n",
+    "print(f'thai df: {thai_df.shape}')\n",
+    "print(f'japanese df: {japanese_df.shape}')\n",
+    "print(f'chinese df: {chinese_df.shape}')\n",
+    "print(f'indian df: {indian_df.shape}')\n",
+    "print(f'korean df: {korean_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ingredient_df(df):\n",
+    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    # drop ingredients that have a 0 sum\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    # sort df\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
+    "    return ingredient_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d5917db00>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
+    "thai_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d389634a8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+   ],
+   "source": [
+    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
+    "japanese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18817f98>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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bq8lGWo16Aysl1QDjmsQxRlI3SfsB+wJNE9B9wASlYZmk4a306ZHG9iV9jGxk5YRmZpYDm0xMEbGILGEsj4iVEfFb4L+AOZLqgZlA74h4iuye0QLgHqAeaHwqYBzwdUnzgUVA44MJtwIXpAcQ9gO+CzxBluSeaxLK/weeTG2fnaYMS30fqAEWSFqUPrfk50C3FP9twBkRsW5TfwszM+t4img6I7YFjUm9IuLt9DTdw8D4iJhbthPkVKFQiGKxWOkwzMyqiqS6iCg0LS/3u/KmpC/M9gSmdYWkZGZm5VXWxBQRXylne1tK0lHAZU2KX4yIL1UiHjMz27St+u3iEXEff/vIuZmZ5VylX0lkZmb2N5yYzMwsV5yYzMwsV5yYzMwsV5yYzMwsV5yYzMwsV5yYzMwsV7bq7zF1lvrlDdROmlXpMMw6xNJLj6t0CNbFeMRkZma54sRkZma54sTUhKSzJZ2WtqdKGl3pmMzMuhLfY2oiIq6tdAxmZl1ZWUdMkraXNEvSfEkLJY2VNFLSQ5LqJN0nqV+qe56kZyQtkHRrKjtI0py0cODjkj6eys+Q9CtJ90taKulcSd9K9X4vaadUbz9J96ZzPSJpYCux1kp6MJ3/AUl7pfLJkia2oa/jJRUlFTesadhUdTMza6NyT+UdDayIiKERMRi4F7gKGB0RI4HrgR+kupOA4RFxAHB2KnsO+HREDAe+B/xrSduDgb8DDkxtrEn15gCnpTpTgAnpXBPJVqptyVVka0YdANwMXNmejkbElIgoRESh+3Z92nOomZm1otxTefXAjyRdBvwGeIMsodwvCaA7sDLVXQDcLOlXwK9SWR9gmqQBQJAtld5odkSsBlZLagB+XXLOAyT1Ag4GZqRzAfRoJdZRZIkO4Ebg8vZ318zMyq3cCwUukTQCOBa4BHgQWBQRo5qpfhzwGeAE4EJJQ4DvkyWgL0mqBX5XUn9dyfbGks8byfrRDXgzIoaVrUNmZtbpyn2PaXeyKbabgCuATwJ9JY1K+2skDZLUDdgzImYD/0Q2UuqVfi9PzZ3RnnNHxFvAi5LGpHNJ0tBWDnkcOCVtjwMeac/5zMysY5R7Km8IcIWkjcB64BzgXeBKSX3S+X4KLAFuSmUCroyINyVdTjaVdxGwOa9SGAdck46vAW4F5rdQdwJwg6QLgFXA1zbjfAAM6d+Hor8db2ZWFoqISsdQ9QqFQhSLxUqHYWZWVSTVRUShabm/YGtmZrmy1X/BVtKFwJgmxTMi4gfN1Tczs8ra6hNTSkBOQmZmVcJTeWZmlitOTGZmlitOTGZmlitOTGZmlitOTGZmlitOTGZmlitb/ePinaF+eQO1kzbnDUpmtqWW+nVgWx2PmMzMLFecmMzMLFe2usSUlkxfmLY/J+k3afuLkiZVNjozM9uULnOPKSLuBu6udBxmZta63I2YJG0vaZak+ZIWShor6UBJj6eyJyX1TiOjRyTNTT8Hb6LdMyT9LG3XSnpQ0gJJD0jaK5VPlXRlOtcfJY3ujD6bmdn78jhiOhpYERHHAaTFBOcBYyPiKUk7AO8ArwBHRsRaSQOAW4APrOvRgquAaRExTdKZwJXASWlfP+BQYCDZCGtmcw1IGg+MB+i+Q9/299LMzJqVuxETUA8cKekySZ8G9gJWRsRTkC2hHhHvkq1Q+wtJ9cAMYP92nGMU8F9p+0ayRNToVxGxMSKeAT7SUgMRMSUiChFR6L5dn3ac2szMWpO7EVNELJE0AjgWuAR4sIWq3wReBoaSJdi1ZQphXcm2ytSmmZm1Ue5GTJJ2B9ZExE3AFcAngX6SDkz7e0vaBuhDNpLaCJwKdG/HaR4HTknb44BHyhW/mZltmdyNmIAhwBWSNgLrgXPIRi5XSfoQ2f2lI4CfA7dLOg24F/hLO84xAbhB0gXAKuBrZYzfzMy2gCKi0jFUvUKhEMVisdJhmJlVFUl1EfGBh9ZyN5VnZmZdmxOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlSh7flVd16pc3UDtpVqXDMLMyWnrpcZUOocvyiMnMzHKlyyamJkutn53eUm5mZhXWJafy0npO74mIaysVi5mZ/a2qTkySvgt8lWxNpT8BdUADMB7YFngBODUi1kiaSrbK7XDgMWBBSTuTgbcj4oeSPgpcC/QFNgBjIuIPndUnM7Ourmqn8tKKtl8mW1r9GKBxTY87IuLAiBgKPAt8veSwPYCDI+JbrTR9M3B1Ov5gYGUL5x8vqSipuGFNwxb2xszMGlXziOkQ4K6IWAuslfTrVD5Y0iXAjkAv4L6SY2ZExIaWGpTUG+gfEXcCpLabFRFTgCkAPfoN8GqLZmZlUrUjplZMBc6NiCHAxUDPkn3tWX7dzMwqoJoT02PACZJ6SuoFHJ/KewMrJdUA49rTYESsBpZJOglAUg9J25UzaDMza13VJqaIeAq4m+whhnuAerIHH74LPEGWuJ7bjKZPBc6TtAB4HNitLAGbmVmbKKJ6b49I6hURb6dRzcPA+IiY29lx9Og3IPqd/tPOPq2ZdSC/+aHjSaqLiELT8mp++AFgiqT9ye4jTatEUgIY0r8PRf8jNjMri6pOTBHxlUrHYGZm5VW195jMzGzr5MRkZma54sRkZma54sRkZma54sRkZma54sRkZma54sRkZma54sRkZma54sRkZma5UtVvfsiL+uUN1E6aVekwzKwD+J15nc8jJjMzy5WqT0yS/kXSEZWOw8zMyqPqp/Ii4nsdfQ5J3Vtbkt3MzMqnqkZMkr4rabGkRyXdImmipKmSRqf9SyVdLGmupHpJA1N5X0n3S1ok6ZeSXpK0S9r3VUlPSnpa0nWSuqfytyX9SNJ8YFTFOm1m1sVUTWKSdCDwZWAocAzwgcWlklcjYgRwDTAxlf0z8GBEDAJmAnulNj8BjAUOiYhhwAbeX459e+CJiBgaEY82E894SUVJxQ1rGsrSRzMzq66pvEOAuyJiLbBW0q9bqHdH+l0H/F3aPhT4EkBE3CvpjVR+ODASeEoSwIeAV9K+DcDtLQUTEVOAKZCtYLs5HTIzsw+qpsTUVuvS7w1sun8iW/n2O83sW+v7SmZmna9qpvKAx4ATJPWU1As4vp3Hngwg6QvAh1P5A8BoSbumfTtJ2ruMMZuZWTtVzYgpIp6SdDewAHgZqAfaenPnYuAWSacCc4A/A6sj4lVJFwG/ldQNWA98A3ip7B0wM7M2qZrElPwwIiZL2g54GKiLiF807oyI2pLtIvC59LEBOCoi3pU0CjgwItalercBtzU9UUT06rBemJlZi6otMU2RtD/Qk+ze0Nw2HrcXMD2Niv4KnFXOoIb070PRry0xMyuLqkpMEfGVzTzueWB4mcMxM7MOUE0PP5iZWRfgxGRmZrnixGRmZrnixGRmZrnixGRmZrnixGRmZrnixGRmZrnixGRmZrlSVV+wzav65Q3UTppV6TDMzDrV0g56441HTGZmlitOTGZmlitOTGZmlitOTGZmlisdmpgkbS9plqT5khZKGivpcEnzJNVLul5SD0mHSfpVyXFHSrqzhTa7S5qa2quX9M1Ufpakp9K5bk9rNpHqji45/u2S7X9KbcyXdGkq20/SvZLqJD0iaWBH/X3MzOyDOnrEdDSwIiKGRsRg4F5gKjA2IoaQPRV4DjAbGCipbzrua8D1LbQ5DOgfEYNTGzek8jsi4sCIGAo8C3y9tcAkHQOcCHwyHXN52jUFmBARI4GJwM9bOH68pKKk4oY1bV1I18zMNqWjE1M9cKSkyyR9GqgFXoyIJWn/NOAzERHAjcBXJe0IjALuaaHNPwL7SrpK0tHAW6l8cBrh1APjgEGbiO0I4IaIWAMQEa9L6gUcDMyQ9DRwHdCvuYMjYkpEFCKi0H27Ppv6O5iZWRt16PeYImKJpBHAscAlwIOtVL8B+DWwFpgREe+20OYbkoYCRwFnAycDZ5KNxE6KiPmSzuD9ZdXfJSXgtILttq3E0A14MyKGtaV/ZmZWfh19j2l3YE1E3ARcQTYSqpX00VTlVOAhgIhYAawALuL96bnm2twF6BYRt6e6I9Ku3sBKSTVkI6ZGS4GRafuLQE3avh/4Wsm9qJ0i4i3gRUljUplSEjQzs07S0W9+GAJcIWkjsJ7sflIfsqmybYCngGtL6t8M9I2IZ1tpsz9wQxr9AHwn/f4u8ASwKv3uncp/AdwlaT7ZPa6/AETEvZKGAUVJfwX+G/h/ZEntGkkXkSWxW4H5m9l/MzNrJ2W3d/JB0s+AeRHxH5WOpT0KhUIUi8VKh2FmVlUk1UVEoWl5bt6VJ6mObDTz7UrHYmZmlZObxJQez/4bkp4AejQpPjUi6jsnKjMz62y5SUzNiYhPVjoGMzPrXH4lkZmZ5YoTk5mZ5UqunsqrVpJWA4srHUcZ7QK8Wukgysx9yr+trT+w9fWp3P3ZOyL6Ni3M9T2mKrK4uUceq5Wk4tbUH3CfqsHW1h/Y+vrUWf3xVJ6ZmeWKE5OZmeWKE1N5TKl0AGW2tfUH3KdqsLX1B7a+PnVKf/zwg5mZ5YpHTGZmlitOTGZmlitOTFtA0tGSFkt6QdKkSsezuSQtlVQv6WlJxVS2k6T7JT2ffn+40nG2RtL1kl6RtLCkrNk+pHW2rkzXbUFazDJXWujPZEnL03V6WtKxJfu+k/qzWNJRlYm6ZZL2lDRb0jOSFkn6x1RezdeopT5V83XqKelJSfNTny5O5ftIeiLFfpukbVN5j/T5hbS/tiyBRIR/NuMH6A78AdiXbFXc+cD+lY5rM/uyFNilSdnlwKS0PQm4rNJxbqIPnyFbNHLhpvpAtqLyPYCATwFPVDr+NvZnMjCxmbr7p39/PYB90r/L7pXuQ5MY+wEj0nZvYEmKu5qvUUt9qubrJKBX2q4hW9vuU8B04JRUfi1wTtr+B+DatH0KcFs54vCIafMdBLwQEX+MiL+SLSh4YoVjKqcTgWlpexpwUgVj2aSIeBh4vUlxS304EfjPyPwe2FFSv86JtG1a6E9LTgRujYh1EfEi8ALZv8/ciIiVETE3ba8GniVb9LOar1FLfWpJNVyniIi308ea9BPAYcDMVN70OjVev5nA4ZK0pXE4MW2+/sCfSj4vo/V/lHkWwG8l1Ukan8o+EhEr0/afgY9UJrQt0lIfqvnanZumtq4vmV6tqv6k6Z7hZP9vfKu4Rk36BFV8nSR1l/Q08ApwP9nI7s2IeDdVKY37vT6l/Q3AzlsagxOTARwaESOAY4BvSPpM6c7IxulV/b2CraEPwDXAfsAwYCXwo8qG036SegG3A+dHxFul+6r1GjXTp6q+ThGxISKGAXuQjegGdnYMTkybbzmwZ8nnPVJZ1YmI5en3K8CdZP8YX26cOkm/X6lchJutpT5U5bWLiJfTfzQ2Ar/g/WmgquiPpBqy/4DfHBF3pOKqvkbN9anar1OjiHgTmA2MIptKbXy3amnc7/Up7e8DvLal53Zi2nxPAQPS0yrbkt34u7vCMbWbpO0l9W7cBr4ALCTry+mp2unAXZWJcIu01Ie7gdPSk1+fAhpKppNyq8k9li+RXSfI+nNKekJqH2AA8GRnx9eadN/hP4BnI+LHJbuq9hq11Kcqv059Je2Ytj8EHEl272w2MDpVa3qdGq/faODBNPLdMpV+CqSaf8ieHFpCNgd7YaXj2cw+7Ev2pNB8YFFjP8jmiR8Angf+B9ip0rFuoh+3kE2brCebA/96S30ge/Lo6nTd6oFCpeNvY39uTPEuSP9B6FdS/8LUn8XAMZWOv5n+HEo2TbcAeDr9HFvl16ilPlXzdToAmJdiXwh8L5XvS5ZEXwBmAD1Sec/0+YW0f99yxOFXEpmZWa54Ks/MzHLFicnMzHLFicnMzHLFicnMzHLFicnMzHLFicnMzHLFicnMzHLlfwHH5sUVMquziAAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
+    "chinese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1880a550>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
+    "indian_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1896ce80>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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QYmpX1VE5cWaxwzBrV89ddlyxQ7AS4hmpmZlZDk6kOzhJ/5VeDG5mZjsgL+3uwCQJOD4iNhc7FjMza5xnpDsYSZWSlkv6BbAY2CRpz7TvdEmLJC2UdGsq6yPp15Lmp5+Dixm/mVmp8Yx0xzQAOCMi/ijpOQBJg4CLgdER8Yqk3VPdq4EfR8SjkvYBZgOfLGxM0nhgPED33fp00CmYmZUGJ9Id0/MR8ccGZUcA0yPiFYCIeC2VHwXsl60CA7CbpF4Rsba+ICImA5MBelQM8JvczczakBPpjuntVtTtBhwUEevbKxgzM2uar5F2HnOAMZL2AChY2v09MKG+kqRhRYjNzKxkOZF2EhGxBLgUeEjSQuDKtOt8oCrdhLQUOKdYMZqZlSIv7e5gIuI5YHDB58qC7anA1Ab1XwHGdlB4ZmbWgBNpiRnSt5xqPz7NzKzNeGnXzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAcnUjMzsxycSM3MzHJwIjUzM8vBidTMzCwHJ1IzM7Mc/IjAElO7qo7KiTOLHYZZUT3nx2RaG/KM1MzMLAcnUjMzsxy6XCKVVClpcSPlD0qq2o72zpR0XdtEZ2ZmXU2XS6QGknzt28ysg3TVRLqTpNslLZM0Q9IuhTsl3SipWtISSd8vKB8p6XFJCyU9Kal3g+OOkzRP0p6NdSppiqSbUtvPSDo+lXeXdIWk+ZIWSfrXVH64pIclzZS0PB3bLe1bK+nHKcYHJPVJ5ftKmiWpRtIjkgY26PsJ4IcN4hqfYqretK4u9+CamdkWXTWRfgK4ISI+CbwJ/O8G+y+KiCpgf+AwSftL2hm4E/haRAwFjgL+Xn+ApC8AE4FjI+KVZvquBA4EjgNuktQTOBuoi4iRwEjgq5L6p/oHAhOA/YB9gX9O5bsC1RExCHgI+F4qnwxMiIgRwIXADQV99wNGR8Q3CgOKiMkRURURVd13KW8mdDMza62uugT4QkQ8lrZvA85vsP9USePJzr+CLIkFsDoi5gNExJsAkgCOAKqAz9SXN2NaRGwG/izpWWAg8Blgf0mnpDrlwADgH8CTEfFs6utXwCHADGAzWWKvP4ffSOoFjAamp7gAehT0PT0iNm0jPjMza0NdNZFGU5/TTPBCYGREvC5pCtBzG+39Ffgo8HGgejv6FtkscnbhDkmHNxdrI+XdgDciYlgTdd7eRmxmZtbGuurS7j6SRqXtLwGPFuzbjSzh1EnaC/hcKl8OVEgaCSCpd8FNO88DJwO/kDRoG32PkdRN0r5kyXc5MBs4V1JZavvjknZN9Q+U1D9dGx1bEGs3oH4G+yXg0TQbXiFpTGpHkoa2dFDMzKztddVEuhz4N0nLgA8AN9bviIiFwALgT8AvgcdS+T/IEtm1khYC91MwU42IPwHjyJZV922m7/8GngR+B5wTEeuBnwFLgafSV3N+wpbVgPnAdcAyYAVwVyp/myzJLiZbWv7PVD4OODvFuAQ4sVUjY2ZmbUoRTa0kWmulZeL7ImJGC+sfDlwYEcc3sm9tRPRq2wihqqoqqqu3tTptZmaFJNWkm1Tfo6vOSM3MzDpEV73ZqF1JuggY06B4ekSc2Zp2IuJB4MEm9rX5bNTMzNqeE+l2iIhLgUuLHYeZmRWfl3bNzMxycCI1MzPLwYnUzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAd/j7TE1K6qo3LizGKHYWbNeO6y44odgrWCZ6RmZmY5OJGamZnl4ETayUk6R9LpaXuKpFO2dYyZmbUdXyPt5CLipmLHYGZWykp6RippV0kzJS2UtFjSWEkjJD0kqUbSbEkVqe75kpZKWiTpjlR2oKR5khZIelzSJ1L5mZLulnS/pOcknSfpG6neHyXtnurtK2lW6usRSQObibVS0pzU/wOS9knlkyRduI3zHC+pWlL1pnV1bTV8ZmZGiSdS4BjgxYgYGhGDgVnAtcApETECuJktb3mZCAyPiP2Bc1LZn4BPR8Rw4P8A/7eg7cHAPwMjUxvrUr15wOmpzmRgQurrQuCGZmK9Fpia+r8duKalJxkRkyOiKiKquu9S3tLDzMysBUp9abcW+P+SLgfuA14nS4D3SwLoDqxOdRcBt0u6G7g7lZUDUyUNAAIoK2h7bkS8BbwlqQ74bUGf+0vqBYwGpqe+AHo0E+sossQMcCvww9afrpmZtbWSTqQR8YykA4BjgUuAOcCSiBjVSPXjgEOBE4CLJA0BfkCWML8gqZKtX9K9oWB7c8HnzWTj3g14IyKGtdkJmZlZhyvppV1JHyZbcr0NuAL4FNBH0qi0v0zSIEndgL0jYi7wbbKZaK/0e1Vq7szW9B0RbwIrJI1JfUnS0GYOeRz4YtoeBzzSmv7MzKx9lPSMFBgCXCFpM7AROBd4B7hGUjnZ+FwFPAPclsoEXBMRb0j6IdnS7sXA9jwuaBxwYzq+DLgDWNhE3QnALZK+CawBvrId/TGkbznVfmqKmVmbUUQUOwbrQFVVVVFdXV3sMMzMOhVJNRFR1di+kl7aNTMzy6vUl3Z3OJIuAsY0KJ4eEZc2Vt/MzIrLiXQHkxKmk6aZWSfhpV0zM7McnEjNzMxycCI1MzPLwYnUzMwsBydSMzOzHJxIzczMcvDXX0pM7ao6Kiduz9MMzayUPOdHibaYZ6RmZmY5OJGamZnl4ERqZmaWgxOpmZlZDk6kBSTtKmmmpIWSFksaK+lISQsk1Uq6WVIPSUdIurvguKMl3dVEm90lTUnt1Ur691T+VUnzU1+/lrRLKp8i6ZSC49cWbH87tbFQ0mWpbF9JsyTVSHpE0sD2Gh8zM3svJ9KtHQO8GBFDI2IwMAuYAoyNiCFkdzmfC8wFBkrqk477CnBzE20OA/pGxODUxi2p/DcRMTIihgLLgLObC0zS54ATgU+lY36Ydk0GJkTECOBC4IZGjh0vqVpS9aZ1ddseBTMzazEn0q3VAkdLulzSp4FKYEVEPJP2TwUOjext6LcCX5b0fmAU8Lsm2nwW+KikayUdA7yZygenGWQtMA4YtI3YjgJuiYh1ABHxmqRewGhguqSngZ8AFQ0PjIjJEVEVEVXddylvyTiYmVkL+XukBSLiGUkHAMcClwBzmql+C/BbYD3Z+0LfaaLN1yUNBT4LnAOcCpxFNtM9KSIWSjoTODwd8g7pPziSugE7NxNDN+CNiBjWkvMzM7O25xlpAUkfBtZFxG3AFWQzzUpJH0tVTgMeAoiIF4EXgYvZslzbWJt7At0i4tep7gFpV29gtaQyshlpveeAEWn780BZ2r4f+ErBtdTdI+JNYIWkMalMKWmbmVkH8Yx0a0OAKyRtBjaSXQ8tJ1s63QmYD9xUUP92oE9ELGumzb7ALWl2CfAf6fd3gSeANel371T+U+AeSQvJrtG+DRARsyQNA6ol/QP4L+A7ZEn4RkkXkyXdO4CF23n+ZmbWSsou99n2kHQdsCAifl7sWFqqqqoqqqurix2GmVmnIqkmIqoa2+cZ6XaSVEM2W7yg2LGYmVnxOJFup/R1k61IegLo0aD4tIio7ZiozMysozmRtqGI+FSxYzAzs47lu3bNzMxycCI1MzPLwYnUzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAd/j7TE1K6qo3LizGKHYWZdzHOXHVfsEIrGM1IzM7McnEjNzMxycCI1MzPLwYm0lSSdLmmRpIWSbpV0gqQnJC2Q9AdJe0nqJunPkvqkY7pJ+oukPunn15Lmp5+DU51Jkm6W9KCkZyWdn8orJS2T9FNJSyT9XtL70r59Jc2SVCPpEUkDizcyZmalyYm0FSQNAi4GjoiIocDXgEeBgyJiONlLtb8VEZuB28heug1wFLAwItYAVwM/joiRwMnAzwq6GAh8FjgQ+J6kslQ+ALg+IgYBb6TjACYDE9KbaC4Ebmgi7vGSqiVVb1pXl3sczMxsC9+12zpHANMj4hWAiHhN0hDgTkkVwM7AilT3ZuAe4CrgLOCWVH4UsJ+k+jZ3k9Qrbc+MiA3ABkkvA3ul8hUR8XTargEq0zGjgekFbTV8hRspzslkSZceFQP8JnczszbkRJrftcCVEXGvpMOBSQAR8YKklyQdQTbDrJ+ddiObwa4vbCQlww0FRZvY8ufTsPx9qZ03ImJYm56NmZm1ipd2W2cOMEbSHgCSdgfKgVVp/xkN6v+MbIl3ekRsSmW/BybUV5C0XYkwIt4EVkgak9qRpKHb05aZmW0/J9JWiIglwKXAQ5IWAleSzUCnS6oBXmlwyL1AL7Ys6wKcD1SlG5aWAufkCGkccHaKZQlwYo62zMxsOyjCl8zai6QqshuLPl3sWOr1qBgQFWdcVewwzKyL6epPNpJUExFVje3zNdJ2ImkicC5bro3uEIb0Lae6i/+FNzPrSF7abScRcVlEfCQiHi12LGZm1n6cSM3MzHJwIjUzM8vBidTMzCwHJ1IzM7McnEjNzMxycCI1MzPLwYnUzMwsBydSMzOzHJxIzczMcvAjAktM7ao6KifOLHYYZlYCuvrzd+t5RmpmZpaDE2mRSaqUtDhtHy7pvrT9+fTgezMz24F5aXcHFRH3kr3P1MzMdmCekeYkaVdJMyUtlLRY0lhJIyU9nsqelNQ7zTwfkfRU+hm9jXbPlHRd2q6UNCe9DPwBSfuk8imSrkl9PSvplI44ZzMz28Iz0vyOAV6MiOMAJJUDC4CxETFf0m7A34GXgaMjYr2kAcCvgEZfEtuIa4GpETFV0lnANcBJaV8FcAgwkGwGO6PhwZLGA+MBuu/WZ/vO0szMGuUZaX61wNGSLpf0aWAfYHVEzAeIiDcj4h2gDPippFpgOrBfK/oYBfwybd9Kljjr3R0RmyNiKbBXYwdHxOSIqIqIqu67lLfq5MzMrHmekeYUEc9IOgA4FrgEmNNE1X8HXgKGkv0HZn0bhbChYFtt1KaZmbWQZ6Q5SfowsC4ibgOuAD4FVEgamfb3lrQTUE42U90MnAZ0b0U3jwNfTNvjgEfaKn4zM8vHM9L8hgBXSNoMbATOJZsZXivpfWTXR48CbgB+Lel0YBbwdiv6mADcIumbwBrgK20Yv5mZ5aCIKHYM1oF6VAyIijOuKnYYZlYCutKTjSTVRESjN4h6RlpihvQtp7oL/eU2Mys2XyM1MzPLwYnUzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAcnUjMzsxycSM3MzHJwIjUzM8vBTzYqMbWr6qicOLPYYZiZdaj2fFyhZ6RmZmY5OJGamZnl4ETaSUg6U9J1afuc9Do2MzMrMl8j7QTSi8HfFRE3FSsWMzPbmhNpB5L0XeDLZC/nfgGoAeqA8cDOwF+A0yJinaQpwHpgOPAYsKignUnA2oj4kaSPATcBfYBNwJiI+GtHnZOZWanz0m4HkTQSOBkYCnwOqH9B7G8iYmREDAWWAWcXHNYPGB0R32im6duB69Pxo4HVjfQ9XlK1pOpN6+ra4GzMzKyeZ6Qd52DgnohYD6yX9NtUPljSJcD7gV7A7IJjpkfEpqYalNQb6BsRdwGktt8jIiYDkwF6VAyI3GdiZmbv8oy0+KYA50XEEOD7QM+CfW8XJSIzM2sxJ9KO8xhwgqSeknoBx6fy3sBqSWXAuNY0GBFvASslnQQgqYekXdoyaDMza54TaQeJiPnAvWQ3Df0OqCW70ei7wBNkifZP29H0acD5khYBjwMfapOAzcysRRThS2YdRVKviFibZo0PA+Mj4qmOjKGqqiqqq6s7skszs05PUk1EVDW2zzcbdazJkvYjuw46taOTqJmZtT0n0g4UEV8qdgxmZta2fI3UzMwsBydSMzOzHJxIzczMcvBduyVG0lvA8mLHsQPZE3il2EHsQDweW3gstlbq4/GRiOjT2A7fbFR6ljd1C3cpklTt8djC47GFx2JrHo+meWnXzMwsBydSMzOzHJxIS8/kYgewg/F4bM3jsYXHYmsejyb4ZiMzM7McPCM1MzPLwYnUzMwsByfSEiLpGEnLJf1F0sRix9MRJN0s6WVJiwvKdpd0v6Q/p98fSOWSdE0an0WSDihe5G1P0t6S5kpaKmmJpK+l8pIbj/Re4CclLUxj8f1U3l/SE+mc75S0cyrvkT7/Je2vLLtjiJIAAALmSURBVGb87UVSd0kLJN2XPpf0eLSUE2mJkNQduB74HLAf8C/pTTRd3RTgmAZlE4EHImIA8ED6DNnYDEg/44EbOyjGjvIOcEFE7AccBPxb+jtQiuOxATgiIoYCw4BjJB0EXA78OCI+BrwOnJ3qnw28nsp/nOp1RV8DlhV8LvXxaBEn0tJxIPCXiHg2Iv4B3AGcWOSY2l1EPAy81qD4RGBq2p4KnFRQ/ovI/BF4v6SKjom0/UXE6vpX90XEW2T/YPalBMcjndPa9LEs/QRwBDAjlTcci/oxmgEcKUkdFG6HkNQPOA74WfosSng8WsOJtHT0BV4o+LwylZWivSJiddr+G7BX2i6ZMUpLccOBJyjR8UjLmE8DLwP3A38F3oiId1KVwvN9dyzS/jpgj46NuN1dBXwL2Jw+70Fpj0eLOZFaSYvs+18l9R0wSb2AXwNfj4g3C/eV0nhExKaIGAb0I1uxGVjkkIpG0vHAyxFRU+xYOiMn0tKxCti74HO/VFaKXqpfoky/X07lXX6MJJWRJdHbI+I3qbhkxwMgIt4A5gKjyJav659BXni+745F2l8OvNrBobang4HPS3qO7LLPEcDVlO54tIoTaemYDwxId+HtDHwRuLfIMRXLvcAZafsM4J6C8tPT3aoHAXUFS56dXrqG9XNgWURcWbCr5MZDUh9J70/b7wOOJrtmPBc4JVVrOBb1Y3QKMCe60NNsIuI/IqJfRFSS/dswJyLGUaLj0Vp+slEJkXQs2XWQ7sDNEXFpkUNqd5J+BRxO9gqol4DvAXcD04B9gOeBUyPitZRoriO7y3cd8JWIqC5G3O1B0iHAI0AtW66DfYfsOmlJjYek/clululONqGYFhH/KemjZDOy3YEFwJcjYoOknsCtZNeVXwO+GBHPFif69iXpcODCiDje49EyTqRmZmY5eGnXzMwsBydSMzOzHJxIzczMcnAiNTMzy8GJ1MzMLAcnUjMzsxycSM3MzHL4Hx9S2FkSapv/AAAAAElFTkSuQmCC\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
+    "korean_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\n",
+    "labels_df = df.cuisine #.unique()\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "source": [
+    "Vyvážte údaje pomocou SMOTE oversamplingu na najvyššiu triedu. Viac informácií nájdete tu: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oversample = SMOTE()\n",
+    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "new label count: korean      799\nchinese     799\njapanese    799\nindian      799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
+    "print(f'old label count: {df.cuisine.value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "source": [
+    "transformed_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "     cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  \\\n",
+       "0     indian       0         0      0           0      0             0   \n",
+       "1     indian       1         0      0           0      0             0   \n",
+       "2     indian       0         0      0           0      0             0   \n",
+       "3     indian       0         0      0           0      0             0   \n",
+       "4     indian       0         0      0           0      0             0   \n",
+       "...      ...     ...       ...    ...         ...    ...           ...   \n",
+       "3990    thai       0         0      0           0      0             0   \n",
+       "3991    thai       0         0      0           0      0             0   \n",
+       "3992    thai       0         0      0           0      0             0   \n",
+       "3993    thai       0         0      0           0      0             0   \n",
+       "3994    thai       0         0      0           0      0             0   \n",
+       "\n",
+       "      apricot  armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
+       "0           0         0          0  ...        0            0           0   \n",
+       "1           0         0          0  ...        0            0           0   \n",
+       "2           0         0          0  ...        0            0           0   \n",
+       "3           0         0          0  ...        0            0           0   \n",
+       "4           0         0          0  ...        0            0           0   \n",
+       "...       ...       ...        ...  ...      ...          ...         ...   \n",
+       "3990        0         0          0  ...        0            0           0   \n",
+       "3991        0         0          0  ...        0            0           0   \n",
+       "3992        0         0          0  ...        0            0           0   \n",
+       "3993        0         0          0  ...        0            0           0   \n",
+       "3994        0         0          0  ...        0            0           0   \n",
+       "\n",
+       "      whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                           0     0     0    0      0       0         0  \n",
+       "1                           0     0     0    0      0       0         0  \n",
+       "2                           0     0     0    0      0       0         0  \n",
+       "3                           0     0     0    0      0       0         0  \n",
+       "4                           0     0     0    0      0       1         0  \n",
+       "...                       ...   ...   ...  ...    ...     ...       ...  \n",
+       "3990                        0     0     0    0      0       0         0  \n",
+       "3991                        0     0     0    0      0       0         0  \n",
+       "3992                        0     0     0    0      0       0         0  \n",
+       "3993                        0     0     0    0      0       0         0  \n",
+       "3994                        0     0     0    0      0       0         0  \n",
+       "\n",
+       "[3995 rows x 381 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": [
+    "# export transformed data to new df for classification\n",
+    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
+    "transformed_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df.info()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "1da12ed6d238756959b8de9cac2a35a2",
+   "translation_date": "2025-09-06T14:51:13+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/2-Classifiers-1/notebook.ipynb b/translations/sk/4-Classification/2-Classifiers-1/notebook.ipynb
new file mode 100644
index 000000000..4d7e9bb24
--- /dev/null
+++ b/translations/sk/4-Classification/2-Classifiers-1/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "68829b06b4dcd512d3327849191f4d7f",
+   "translation_date": "2025-09-06T14:32:28+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/sk/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
new file mode 100644
index 000000000..423e84ad7
--- /dev/null
+++ b/translations/sk/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -0,0 +1,1285 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_11-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "6ea6a5171b1b99b7b5a55f7469c048d2",
+   "translation_date": "2025-09-06T14:34:22+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "zs2woWv_HoE8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Klasifikátory kuchýň 1\n",
+    "\n",
+    "V tejto lekcii preskúmame rôzne klasifikátory na *predpovedanie národnej kuchyne na základe skupiny ingrediencií.* Pri tom sa dozvieme viac o spôsoboch, akými môžu byť algoritmy využívané na úlohy klasifikácie.\n",
+    "\n",
+    "### [**Kvíz pred prednáškou**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
+    "\n",
+    "### **Príprava**\n",
+    "\n",
+    "Táto lekcia nadväzuje na našu [predchádzajúcu lekciu](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb), kde sme:\n",
+    "\n",
+    "-   Jemne uviedli klasifikácie pomocou datasetu o všetkých úžasných kuchyniach Ázie a Indie 😋.\n",
+    "\n",
+    "-   Preskúmali niektoré [slovesá dplyr](https://dplyr.tidyverse.org/) na prípravu a čistenie našich dát.\n",
+    "\n",
+    "-   Vytvorili krásne vizualizácie pomocou ggplot2.\n",
+    "\n",
+    "-   Ukázali, ako sa vysporiadať s nevyváženými dátami ich predspracovaním pomocou [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html).\n",
+    "\n",
+    "-   Demonštrovali, ako `prep` a `bake` náš recept, aby sme si overili, že funguje podľa očakávaní.\n",
+    "\n",
+    "#### **Predpoklady**\n",
+    "\n",
+    "Na túto lekciu budeme potrebovať nasledujúce balíky na čistenie, prípravu a vizualizáciu našich dát:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [kolekcia balíkov pre R](https://www.tidyverse.org/packages), ktorá robí dátovú vedu rýchlejšou, jednoduchšou a zábavnejšou!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je rámec [kolekcie balíkov](https://www.tidymodels.org/packages/) na modelovanie a strojové učenie.\n",
+    "\n",
+    "-   `themis`: [balík themis](https://themis.tidymodels.org/) poskytuje dodatočné kroky receptov na riešenie nevyvážených dát.\n",
+    "\n",
+    "-   `nnet`: [balík nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) poskytuje funkcie na odhadovanie dopredných neurónových sietí s jednou skrytou vrstvou a na modely multinomiálnej logistickej regresie.\n",
+    "\n",
+    "Môžete ich nainštalovať takto:\n"
+   ],
+   "metadata": {
+    "id": "iDFOb3ebHwQC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Alternatívne, nasledujúci skript skontroluje, či máte nainštalované balíky potrebné na dokončenie tohto modulu, a v prípade, že chýbajú, ich nainštaluje za vás.\n"
+   ],
+   "metadata": {
+    "id": "4V85BGCjII7F"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, here)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "an5NPyyKIKNR",
+    "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Rozdeľte údaje na tréningovú a testovaciu množinu.\n",
+    "\n",
+    "Začneme výberom niekoľkých krokov z našej predchádzajúcej lekcie.\n",
+    "\n",
+    "### Odstráňte najbežnejšie ingrediencie, ktoré spôsobujú zmätok medzi rôznymi kuchyňami, pomocou `dplyr::select()`.\n",
+    "\n",
+    "Každý miluje ryžu, cesnak a zázvor!\n"
+   ],
+   "metadata": {
+    "id": "0ax9GQLBINVv"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# Load the original cuisines data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
+    "  # Encode cuisine column as categorical\r\n",
+    "  mutate(cuisine = factor(cuisine))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Display distribution of cuisines\r\n",
+    "df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "New names:\n",
+      "* `` -> ...1\n",
+      "\n",
+      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
+      "\n",
+      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+      "\u001b[31mchr\u001b[39m   (1): cuisine\n",
+      "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
+      "\n",
+      "\n",
+      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
+      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 indian  0      0        0     0          0     0            0       0       \n",
+       "2 indian  1      0        0     0          0     0            0       0       \n",
+       "3 indian  0      0        0     0          0     0            0       0       \n",
+       "4 indian  0      0        0     0          0     0            0       0       \n",
+       "5 indian  0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       0    0   \n",
+       "2 0         ⋯ 0       0           0          0                       0    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     1      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   799\n",
+       "2 indian   598\n",
+       "3 chinese  442\n",
+       "4 japanese 320\n",
+       "5 thai     289"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 799 |\n",
+       "| indian   | 598 |\n",
+       "| chinese  | 442 |\n",
+       "| japanese | 320 |\n",
+       "| thai     | 289 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 799\\\\\n",
+       "\t indian   & 598\\\\\n",
+       "\t chinese  & 442\\\\\n",
+       "\t japanese & 320\\\\\n",
+       "\t thai     & 289\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>799</td></tr>\n",
+       "\t<tr><td>indian  </td><td>598</td></tr>\n",
+       "\t<tr><td>chinese </td><td>442</td></tr>\n",
+       "\t<tr><td>japanese</td><td>320</td></tr>\n",
+       "\t<tr><td>thai    </td><td>289</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 735
+    },
+    "id": "jhCrrH22IWVR",
+    "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Perfektné! Teraz je čas rozdeliť údaje tak, aby 70 % údajov išlo na tréning a 30 % na testovanie. Pri rozdeľovaní údajov použijeme aj techniku `stratifikácie`, aby sme `zachovali pomer jednotlivých kuchýň` v tréningových a validačných datasetoch.\n",
+    "\n",
+    "[rsample](https://rsample.tidymodels.org/), balík v Tidymodels, poskytuje infraštruktúru na efektívne rozdeľovanie a resampling údajov:\n"
+   ],
+   "metadata": {
+    "id": "AYTjVyajIdny"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# Load the core Tidymodels packages into R session\r\n",
+    "library(tidymodels)\r\n",
+    "\r\n",
+    "# Create split specification\r\n",
+    "set.seed(2056)\r\n",
+    "cuisines_split <- initial_split(data = df_select,\r\n",
+    "                                strata = cuisine,\r\n",
+    "                                prop = 0.7)\r\n",
+    "\r\n",
+    "# Extract the data in each split\r\n",
+    "cuisines_train <- training(cuisines_split)\r\n",
+    "cuisines_test <- testing(cuisines_split)\r\n",
+    "\r\n",
+    "# Print the number of cases in each split\r\n",
+    "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
+    "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
+    "\r\n",
+    "# Display the first few rows of the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Display distribution of cuisines in the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training cases: 1712\n",
+      "Test cases: 736"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 chinese 0      0        0     0          0     0            0       0       \n",
+       "2 chinese 0      0        0     0          0     0            0       0       \n",
+       "3 chinese 0      0        0     0          0     0            0       0       \n",
+       "4 chinese 0      0        0     0          0     0            0       0       \n",
+       "5 chinese 0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       1    0   \n",
+       "2 0         ⋯ 0       0           0          0                       1    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     0      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   559\n",
+       "2 indian   418\n",
+       "3 chinese  309\n",
+       "4 japanese 224\n",
+       "5 thai     202"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 559 |\n",
+       "| indian   | 418 |\n",
+       "| chinese  | 309 |\n",
+       "| japanese | 224 |\n",
+       "| thai     | 202 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 559\\\\\n",
+       "\t indian   & 418\\\\\n",
+       "\t chinese  & 309\\\\\n",
+       "\t japanese & 224\\\\\n",
+       "\t thai     & 202\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>559</td></tr>\n",
+       "\t<tr><td>indian  </td><td>418</td></tr>\n",
+       "\t<tr><td>chinese </td><td>309</td></tr>\n",
+       "\t<tr><td>japanese</td><td>224</td></tr>\n",
+       "\t<tr><td>thai    </td><td>202</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 535
+    },
+    "id": "w5FWIkEiIjdN",
+    "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Riešenie nevyvážených údajov\n",
+    "\n",
+    "Ako ste si mohli všimnúť v pôvodnej dátovej sade, ako aj v našej tréningovej sade, existuje pomerne nerovnomerné rozdelenie počtu kuchýň. Kórejské kuchyne sú *takmer* 3-krát častejšie ako thajské kuchyne. Nevývážené údaje často negatívne ovplyvňujú výkon modelu. Mnohé modely dosahujú najlepšie výsledky, keď je počet pozorovaní rovnaký, a preto majú tendenciu mať problémy s nevyváženými údajmi.\n",
+    "\n",
+    "Existujú dva hlavné spôsoby, ako riešiť nevyvážené dátové sady:\n",
+    "\n",
+    "-   pridanie pozorovaní do menšinovej triedy: `Over-sampling`, napríklad pomocou algoritmu SMOTE, ktorý synteticky generuje nové príklady menšinovej triedy pomocou najbližších susedov týchto prípadov.\n",
+    "\n",
+    "-   odstránenie pozorovaní z väčšinovej triedy: `Under-sampling`\n",
+    "\n",
+    "V našej predchádzajúcej lekcii sme ukázali, ako riešiť nevyvážené dátové sady pomocou `receptu`. Recept si môžeme predstaviť ako plán, ktorý popisuje, aké kroky by sa mali aplikovať na dátovú sadu, aby bola pripravená na analýzu údajov. V našom prípade chceme dosiahnuť rovnomerné rozdelenie počtu našich kuchýň pre našu `tréningovú sadu`. Poďme sa do toho pustiť.\n"
+   ],
+   "metadata": {
+    "id": "daBi9qJNIwqW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing training data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "# Print recipe\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Data Recipe\n",
+       "\n",
+       "Inputs:\n",
+       "\n",
+       "      role #variables\n",
+       "   outcome          1\n",
+       " predictor        380\n",
+       "\n",
+       "Operations:\n",
+       "\n",
+       "SMOTE based on cuisine"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 200
+    },
+    "id": "Az6LFBGxI1X0",
+    "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Môžete samozrejme potvrdiť (pomocou prípravy a pečenia), že recept bude fungovať tak, ako očakávate - všetky označenia kuchyne majú `559` pozorovaní.\n",
+    "\n",
+    "Keďže tento recept budeme používať ako predspracovanie pre modelovanie, `workflow()` za nás vykoná všetku prípravu a pečenie, takže recept nebudeme musieť manuálne odhadovať.\n",
+    "\n",
+    "Teraz sme pripravení trénovať model 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Výber klasifikátora\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/parsnip.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "NBL3PqIWJBBB"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Teraz musíme rozhodnúť, ktorý algoritmus použiť na túto úlohu 🤔.\n",
+    "\n",
+    "V Tidymodels poskytuje [`parsnip package`](https://parsnip.tidymodels.org/index.html) konzistentné rozhranie na prácu s modelmi naprieč rôznymi enginmi (balíčkami). Pozrite si dokumentáciu k parsnip, aby ste preskúmali [typy modelov a enginy](https://www.tidymodels.org/find/parsnip/#models) a ich zodpovedajúce [argumenty modelov](https://www.tidymodels.org/find/parsnip/#model-args). Rozmanitosť môže byť na prvý pohľad dosť mätúca. Napríklad nasledujúce metódy zahŕňajú techniky klasifikácie:\n",
+    "\n",
+    "-   C5.0 modely založené na pravidlách\n",
+    "-   Flexibilné diskriminačné modely\n",
+    "-   Lineárne diskriminačné modely\n",
+    "-   Regularizované diskriminačné modely\n",
+    "-   Modely logistickej regresie\n",
+    "-   Modely multinomiálnej regresie\n",
+    "-   Modely naivného Bayesa\n",
+    "-   Podporné vektorové stroje\n",
+    "-   Najbližší susedia\n",
+    "-   Rozhodovacie stromy\n",
+    "-   Ensemble metódy\n",
+    "-   Neurónové siete\n",
+    "\n",
+    "A zoznam pokračuje!\n",
+    "\n",
+    "### **Aký klasifikátor zvoliť?**\n",
+    "\n",
+    "Takže, ktorý klasifikátor by ste si mali vybrať? Často je dobrým spôsobom testovania prejsť viacerými a hľadať dobrý výsledok.\n",
+    "\n",
+    "> AutoML tento problém elegantne rieši tým, že vykonáva tieto porovnania v cloude, čo vám umožňuje vybrať najlepší algoritmus pre vaše dáta. Vyskúšajte to [tu](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "Výber klasifikátora však závisí aj od nášho problému. Napríklad, keď výsledok môže byť kategorizovaný do `viac ako dvoch tried`, ako v našom prípade, musíte použiť `algoritmus pre multiklasifikáciu` namiesto `binárnej klasifikácie.`\n",
+    "\n",
+    "### **Lepší prístup**\n",
+    "\n",
+    "Lepším spôsobom ako náhodne hádať je však riadiť sa nápadmi z tohto stiahnuteľného [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Tu zistíme, že pre náš problém s multiklasifikáciou máme niekoľko možností:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/cheatsheet.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Časť Microsoftovho prehľadu algoritmov, ktorá podrobne opisuje možnosti multiklasifikácie</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "a6DLAZ3vJZ14"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Úvaha**\n",
+    "\n",
+    "Pozrime sa, či dokážeme logicky zhodnotiť rôzne prístupy vzhľadom na obmedzenia, ktoré máme:\n",
+    "\n",
+    "-   **Hlboké neurónové siete sú príliš náročné**. Vzhľadom na náš čistý, ale minimálny dataset a fakt, že trénovanie prebieha lokálne cez notebooky, sú hlboké neurónové siete pre túto úlohu príliš náročné.\n",
+    "\n",
+    "-   **Žiadny dvojtriedny klasifikátor**. Nepoužívame dvojtriedny klasifikátor, takže možnosť one-vs-all je vylúčená.\n",
+    "\n",
+    "-   **Rozhodovací strom alebo logistická regresia by mohli fungovať**. Rozhodovací strom by mohol fungovať, rovnako ako multinomiálna regresia/multitriedna logistická regresia pre multitriedne dáta.\n",
+    "\n",
+    "-   **Multitriedne Boosted Decision Trees riešia iný problém**. Multitriedny Boosted Decision Tree je najvhodnejší pre neparametrické úlohy, napríklad úlohy zamerané na vytváranie rebríčkov, takže pre nás nie je užitočný.\n",
+    "\n",
+    "Okrem toho, predtým než sa pustíme do zložitejších modelov strojového učenia, ako sú ensemble metódy, je zvyčajne dobré začať s najjednoduchším možným modelom, aby sme získali predstavu o tom, čo sa deje. Preto v tejto lekcii začneme s modelom `multinomiálnej regresie`.\n",
+    "\n",
+    "> Logistická regresia je technika používaná, keď je výstupná premenná kategóriálna (alebo nominálna). Pri binárnej logistickej regresii je počet výstupných premenných dva, zatiaľ čo pri multinomiálnej logistickej regresii je počet výstupných premenných viac ako dva. Viac informácií nájdete v [Pokročilé regresné metódy](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html).\n",
+    "\n",
+    "## 4. Trénovanie a hodnotenie modelu multinomiálnej logistickej regresie\n",
+    "\n",
+    "V Tidymodels, `parsnip::multinom_reg()`, definuje model, ktorý používa lineárne prediktory na predpovedanie multitriednych dát pomocou multinomiálneho rozdelenia. Pozrite si `?multinom_reg()` pre rôzne spôsoby/enginy, ktoré môžete použiť na fitovanie tohto modelu.\n",
+    "\n",
+    "V tomto príklade budeme fitovať model multinomiálnej regresie cez predvolený engine [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf).\n",
+    "\n",
+    "> Hodnotu pre `penalty` som vybral tak trochu náhodne. Existujú lepšie spôsoby, ako túto hodnotu zvoliť, napríklad pomocou `resamplingu` a `ladenia` modelu, o ktorých budeme hovoriť neskôr.\n",
+    ">\n",
+    "> Pozrite si [Tidymodels: Začnite](https://www.tidymodels.org/start/tuning/), ak sa chcete dozvedieť viac o ladení hyperparametrov modelu.\n"
+   ],
+   "metadata": {
+    "id": "gWMsVcbBJemu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# Create a multinomial regression model specification\r\n",
+    "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
+    "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
+    "  set_mode(\"classification\")\r\n",
+    "\r\n",
+    "# Print model specification\r\n",
+    "mr_spec"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 166
+    },
+    "id": "Wq_fcyQiJvfG",
+    "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Skvelá práca 🥳! Teraz, keď máme recept a špecifikáciu modelu, musíme nájsť spôsob, ako ich spojiť do objektu, ktorý najprv predspracuje dáta, potom na predspracovaných dátach natrénuje model a zároveň umožní aj prípadné aktivity po spracovaní. V Tidymodels sa tento praktický objekt nazýva [`workflow`](https://workflows.tidymodels.org/) a pohodlne uchováva vaše modelovacie komponenty! Toto by sme v *Pythone* nazvali *pipelines*.\n",
+    "\n",
+    "Takže poďme všetko zabaliť do workflowu!📦\n"
+   ],
+   "metadata": {
+    "id": "NlSbzDfgJ0zh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# Bundle recipe and model specification\r\n",
+    "mr_wf <- workflow() %>% \r\n",
+    "  add_recipe(cuisines_recipe) %>% \r\n",
+    "  add_model(mr_spec)\r\n",
+    "\r\n",
+    "# Print out workflow\r\n",
+    "mr_wf"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow ════════════════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 333
+    },
+    "id": "Sc1TfPA4Ke3_",
+    "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Pracovné postupy 👌👌! **`workflow()`** môže byť nastavený podobne ako model. Takže, je čas trénovať model!\n"
+   ],
+   "metadata": {
+    "id": "TNQ8i85aKf9L"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# Train a multinomial regression model\n",
+    "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
+    "\n",
+    "mr_fit"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Call:\n",
+       "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
+       "    trace = FALSE)\n",
+       "\n",
+       "Coefficients:\n",
+       "         (Intercept)     almond angelica         anise anise_seed       apple\n",
+       "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
+       "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
+       "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
+       "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
+       "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
+       "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
+       "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
+       "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
+       "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
+       "             avocado       bacon baked_potato balm     banana     barley\n",
+       "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
+       "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
+       "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
+       "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
+       "         bartlett_pear      basil        bay       bean         beech\n",
+       "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
+       "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
+       "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
+       "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
+       "               beef beef_broth   beef_liver         beer        beet\n",
+       "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
+       "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
+       "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
+       "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
+       "         bell_pepper bergamot       berry bitter_orange black_bean\n",
+       "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
+       "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
+       "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
+       "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
+       "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
+       "indian               0             0.38935801   -0.4453495               0\n",
+       "japanese             0            -0.05452887   -0.5440869               0\n",
+       "korean               0            -0.03929970    0.8025454               0\n",
+       "thai                 0            -0.21498372   -0.9854806               0\n",
+       "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
+       "indian          -0.2759246  0.3079977  0.191256164                 0\n",
+       "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
+       "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
+       "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
+       "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
+       "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
+       "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
+       "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
+       "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
+       "\n",
+       "...\n",
+       "and 308 more lines."
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GMbdfVmTKkJI",
+    "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Výstup zobrazuje koeficienty, ktoré model naučil počas tréningu.\n",
+    "\n",
+    "### Vyhodnotenie vytrénovaného modelu\n",
+    "\n",
+    "Je čas zistiť, ako si model viedol 📏, vyhodnotením na testovacej množine! Začnime tým, že urobíme predpovede na testovacej množine.\n"
+   ],
+   "metadata": {
+    "id": "tt2BfOxrKmcJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# Make predictions on the test set\n",
+    "results <- cuisines_test %>% select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
+    "\n",
+    "# Print out results\n",
+    "results %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class\n",
+       "1 indian  thai       \n",
+       "2 indian  indian     \n",
+       "3 indian  indian     \n",
+       "4 indian  indian     \n",
+       "5 indian  indian     "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
+       "|---|---|\n",
+       "| indian | thai   |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & .pred\\_class\\\\\n",
+       " <fct> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai  \\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "CqtckvtsKqax",
+    "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Skvelá práca! V Tidymodels je hodnotenie výkonu modelu možné pomocou [yardstick](https://yardstick.tidymodels.org/) - balíka používaného na meranie efektívnosti modelov pomocou metrík výkonu. Ako sme to urobili v našej lekcii o logistickej regresii, začnime výpočtom matice zámien.\n"
+   ],
+   "metadata": {
+    "id": "8w5N6XsBKss7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "# Confusion matrix for categorical data\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "          Truth\n",
+       "Prediction chinese indian japanese korean thai\n",
+       "  chinese       83      1        8     15   10\n",
+       "  indian         4    163        1      2    6\n",
+       "  japanese      21      5       73     25    1\n",
+       "  korean        15      0       11    191    0\n",
+       "  thai          10     11        3      7   70"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 133
+    },
+    "id": "YvODvsLkK0iG",
+    "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Pri práci s viacerými triedami je vo všeobecnosti intuitívnejšie vizualizovať to ako tepelnú mapu, napríklad takto:\n"
+   ],
+   "metadata": {
+    "id": "c0HfPL16Lr6U"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
+    "# Visualize confusion matrix\n",
+    "results %>% \n",
+    "  conf_mat(cuisine, .pred_class) %>% \n",
+    "  autoplot(type = \"heatmap\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "plot without title"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {
+      "image/png": {
+       "width": 420,
+       "height": 420
+      }
+     }
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 436
+    },
+    "id": "HsAtwukyLsvt",
+    "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tmavšie štvorce v grafe matice zámien naznačujú vysoký počet prípadov, a dúfajme, že vidíte diagonálnu líniu tmavších štvorcov, ktorá označuje prípady, kde predpovedaná a skutočná značka sú rovnaké.\n",
+    "\n",
+    "Teraz vypočítajme súhrnné štatistiky pre maticu zámien.\n"
+   ],
+   "metadata": {
+    "id": "oOJC87dkLwPr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# Summary stats for confusion matrix\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
+    "summary()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "   .metric              .estimator .estimate\n",
+       "1  accuracy             multiclass 0.7880435\n",
+       "2  kap                  multiclass 0.7276583\n",
+       "3  sens                 macro      0.7780927\n",
+       "4  spec                 macro      0.9477598\n",
+       "5  ppv                  macro      0.7585583\n",
+       "6  npv                  macro      0.9460080\n",
+       "7  mcc                  multiclass 0.7292724\n",
+       "8  j_index              macro      0.7258524\n",
+       "9  bal_accuracy         macro      0.8629262\n",
+       "10 detection_prevalence macro      0.2000000\n",
+       "11 precision            macro      0.7585583\n",
+       "12 recall               macro      0.7780927\n",
+       "13 f_meas               macro      0.7641862"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 13 × 3\n",
+       "\n",
+       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| accuracy             | multiclass | 0.7880435 |\n",
+       "| kap                  | multiclass | 0.7276583 |\n",
+       "| sens                 | macro      | 0.7780927 |\n",
+       "| spec                 | macro      | 0.9477598 |\n",
+       "| ppv                  | macro      | 0.7585583 |\n",
+       "| npv                  | macro      | 0.9460080 |\n",
+       "| mcc                  | multiclass | 0.7292724 |\n",
+       "| j_index              | macro      | 0.7258524 |\n",
+       "| bal_accuracy         | macro      | 0.8629262 |\n",
+       "| detection_prevalence | macro      | 0.2000000 |\n",
+       "| precision            | macro      | 0.7585583 |\n",
+       "| recall               | macro      | 0.7780927 |\n",
+       "| f_meas               | macro      | 0.7641862 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 13 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " .metric & .estimator & .estimate\\\\\n",
+       " <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t accuracy             & multiclass & 0.7880435\\\\\n",
+       "\t kap                  & multiclass & 0.7276583\\\\\n",
+       "\t sens                 & macro      & 0.7780927\\\\\n",
+       "\t spec                 & macro      & 0.9477598\\\\\n",
+       "\t ppv                  & macro      & 0.7585583\\\\\n",
+       "\t npv                  & macro      & 0.9460080\\\\\n",
+       "\t mcc                  & multiclass & 0.7292724\\\\\n",
+       "\t j\\_index              & macro      & 0.7258524\\\\\n",
+       "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
+       "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
+       "\t precision            & macro      & 0.7585583\\\\\n",
+       "\t recall               & macro      & 0.7780927\\\\\n",
+       "\t f\\_meas               & macro      & 0.7641862\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 13 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
+       "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
+       "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
+       "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
+       "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
+       "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
+       "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
+       "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
+       "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 494
+    },
+    "id": "OYqetUyzL5Wz",
+    "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ak sa zameriame na niektoré metriky, ako presnosť, citlivosť, ppv, na začiatok na tom nie sme zle 🥳!\n",
+    "\n",
+    "## 4. Hlbšie skúmanie\n",
+    "\n",
+    "Položme si jednu jemnú otázku: Aké kritériá sa používajú na určenie konkrétneho typu kuchyne ako predpokladaného výsledku?\n",
+    "\n",
+    "No, štatistické algoritmy strojového učenia, ako logistická regresia, sú založené na `pravdepodobnosti`; takže to, čo klasifikátor skutočne predpovedá, je pravdepodobnostné rozdelenie nad množinou možných výsledkov. Trieda s najvyššou pravdepodobnosťou je potom vybraná ako najpravdepodobnejší výsledok pre dané pozorovania.\n",
+    "\n",
+    "Pozrime sa na to v praxi tým, že urobíme tvrdé predikcie tried a aj pravdepodobnosti.\n"
+   ],
+   "metadata": {
+    "id": "43t7vz8vMJtW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "# Make hard class prediction and probabilities\n",
+    "results_prob <- cuisines_test %>%\n",
+    "  select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
+    "\n",
+    "# Print out results\n",
+    "results_prob %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
+       "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
+       "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
+       "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
+       "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
+       "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
+       "  .pred_thai  \n",
+       "1 5.388194e-01\n",
+       "2 1.577948e-06\n",
+       "3 6.874989e-03\n",
+       "4 3.863391e-03\n",
+       "5 5.653283e-03"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 7\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
+       "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
+       "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
+       "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
+       "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 7\n",
+       "\\begin{tabular}{lllllll}\n",
+       " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
+       " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
+       "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
+       "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
+       "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
+       "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "xdKNs-ZPMTJL",
+    "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ Môžete vysvetliť, prečo si model celkom iste myslí, že prvé pozorovanie je thajské?\n",
+    "\n",
+    "## **🚀Výzva**\n",
+    "\n",
+    "V tejto lekcii ste použili svoje vyčistené dáta na vytvorenie modelu strojového učenia, ktorý dokáže predpovedať národnú kuchyňu na základe série ingrediencií. Nájdite si čas na preštudovanie [mnohých možností](https://www.tidymodels.org/find/parsnip/#models), ktoré Tidymodels ponúka na klasifikáciu dát, a [iných spôsobov](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models), ako prispôsobiť multinomiálnu regresiu.\n",
+    "\n",
+    "#### POĎAKOVANIE:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) za vytvorenie úžasných ilustrácií, ktoré robia R prístupnejším a pútavejším. Viac ilustrácií nájdete v jej [galérii](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) a [Jen Looper](https://www.twitter.com/jenlooper) za vytvorenie pôvodnej verzie tohto modulu v Pythone ♥️\n",
+    "\n",
+    "<br>\n",
+    "Pridal by som nejaké vtipy, ale nerozumiem jedlým slovným hračkám 😅.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Šťastné učenie,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Zlatý ambasádor Microsoft Learn.\n"
+   ],
+   "metadata": {
+    "id": "2tWVHMeLMYdM"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/translations/sk/4-Classification/2-Classifiers-1/solution/notebook.ipynb
new file mode 100644
index 000000000..38e1198e1
--- /dev/null
+++ b/translations/sk/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy is 0.8181818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print (\"Accuracy is {}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n       'vegetable_oil'],\n      dtype='object')\ncuisine: korean\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test an item\n",
+    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[50]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                 0\n",
+       "korean    0.392231\n",
+       "chinese   0.372872\n",
+       "japanese  0.218825\n",
+       "thai      0.013427\n",
+       "indian    0.002645"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>korean</th>\n      <td>0.392231</td>\n    </tr>\n    <tr>\n      <th>chinese</th>\n      <td>0.372872</td>\n    </tr>\n    <tr>\n      <th>japanese</th>\n      <td>0.218825</td>\n    </tr>\n    <tr>\n      <th>thai</th>\n      <td>0.013427</td>\n    </tr>\n    <tr>\n      <th>indian</th>\n      <td>0.002645</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "#rehsape to 2d array and transpose\n",
+    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
+    "# predict with score\n",
+    "proba = model.predict_proba(test)\n",
+    "classes = model.classes_\n",
+    "# create df with classes and scores\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
+    "\n",
+    "# create df to show results\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.75      0.73      0.74       223\n      indian       0.93      0.88      0.90       255\n    japanese       0.78      0.78      0.78       253\n      korean       0.87      0.86      0.86       236\n        thai       0.76      0.84      0.80       232\n\n    accuracy                           0.82      1199\n   macro avg       0.82      0.82      0.82      1199\nweighted avg       0.82      0.82      0.82      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\r\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "9408506dd864f2b6e334c62f80c0cfcc",
+   "translation_date": "2025-09-06T14:32:57+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/3-Classifiers-2/notebook.ipynb b/translations/sk/4-Classification/3-Classifiers-2/notebook.ipynb
new file mode 100644
index 000000000..f52d16c69
--- /dev/null
+++ b/translations/sk/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
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diff --git a/translations/sk/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/translations/sk/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
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--- /dev/null
+++ b/translations/sk/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -0,0 +1,648 @@
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+   "collapsed_sections": []
+  },
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+   "display_name": "R"
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+   "name": "R"
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+   "source_file": "4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jsFutf_ygqSx"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HD54bEefgtNO"
+   },
+   "source": [
+    "## Klasifikátory kuchýň 2\n",
+    "\n",
+    "V tejto druhej lekcii o klasifikácii sa pozrieme na `ďalšie spôsoby`, ako klasifikovať kategóriálne údaje. Tiež sa oboznámime s dôsledkami výberu jedného klasifikátora oproti inému.\n",
+    "\n",
+    "### [**Kvíz pred prednáškou**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
+    "\n",
+    "### **Predpoklady**\n",
+    "\n",
+    "Predpokladáme, že ste dokončili predchádzajúce lekcie, pretože budeme nadväzovať na niektoré koncepty, ktoré sme sa už naučili.\n",
+    "\n",
+    "Na túto lekciu budeme potrebovať nasledujúce balíky:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [kolekcia balíkov pre R](https://www.tidyverse.org/packages), ktorá robí dátovú vedu rýchlejšou, jednoduchšou a zábavnejšou!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je [rámec balíkov](https://www.tidymodels.org/packages/) určený na modelovanie a strojové učenie.\n",
+    "\n",
+    "-   `themis`: [balík themis](https://themis.tidymodels.org/) poskytuje dodatočné kroky pre recepty na riešenie nevyvážených údajov.\n",
+    "\n",
+    "Môžete ich nainštalovať pomocou:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\n",
+    "\n",
+    "Prípadne, nasledujúci skript skontroluje, či máte nainštalované potrebné balíky na dokončenie tohto modulu, a v prípade, že chýbajú, ich nainštaluje za vás.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vZ57IuUxgyQt"
+   },
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z22M-pj4g07x"
+   },
+   "source": [
+    "## **1. Mapa klasifikácie**\n",
+    "\n",
+    "V našej [predchádzajúcej lekcii](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1) sme sa snažili odpovedať na otázku: ako si vybrať medzi viacerými modelmi? Do veľkej miery to závisí od charakteristík údajov a typu problému, ktorý chceme vyriešiť (napríklad klasifikácia alebo regresia?).\n",
+    "\n",
+    "Predtým sme sa naučili o rôznych možnostiach, ktoré máte pri klasifikácii údajov, pomocou prehľadovej tabuľky od Microsoftu. Pythonovský rámec pre strojové učenie, Scikit-learn, ponúka podobnú, ale podrobnejšiu prehľadovú tabuľku, ktorá vám môže ďalej pomôcť zúžiť výber vašich odhadovačov (iný výraz pre klasifikátory):\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/map.png\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption></figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u1i3xRIVg7vG"
+   },
+   "source": [
+    "> Tip: [navštívte túto mapu online](https://scikit-learn.org/stable/tutorial/machine_learning_map/) a kliknite na cestu, aby ste si prečítali dokumentáciu.\n",
+    ">\n",
+    "> [Referenčná stránka Tidymodels](https://www.tidymodels.org/find/parsnip/#models) tiež poskytuje vynikajúcu dokumentáciu o rôznych typoch modelov.\n",
+    "\n",
+    "### **Plán** 🗺️\n",
+    "\n",
+    "Táto mapa je veľmi užitočná, ak máte jasné pochopenie svojich údajov, pretože sa môžete „prejsť“ po jej cestách k rozhodnutiu:\n",
+    "\n",
+    "-   Máme viac ako 50 vzoriek\n",
+    "\n",
+    "-   Chceme predpovedať kategóriu\n",
+    "\n",
+    "-   Máme označené údaje\n",
+    "\n",
+    "-   Máme menej ako 100K vzoriek\n",
+    "\n",
+    "-   ✨ Môžeme si vybrať Linear SVC\n",
+    "\n",
+    "-   Ak to nefunguje, keďže máme numerické údaje\n",
+    "\n",
+    "    -   Môžeme skúsiť ✨ KNeighbors Classifier\n",
+    "\n",
+    "        -   Ak to nefunguje, skúste ✨ SVC a ✨ Ensemble Classifiers\n",
+    "\n",
+    "Toto je veľmi užitočná cesta, ktorú sa oplatí nasledovať. Teraz sa do toho pustíme pomocou [tidymodels](https://www.tidymodels.org/) frameworku na modelovanie: konzistentnej a flexibilnej kolekcie balíkov v R, vyvinutej na podporu správnej štatistickej praxe 😊.\n",
+    "\n",
+    "## 2. Rozdelenie údajov a riešenie nevyváženého dátového súboru.\n",
+    "\n",
+    "Z našich predchádzajúcich lekcií sme sa naučili, že existuje súbor spoločných ingrediencií naprieč našimi kuchyňami. Tiež sme si všimli, že rozdelenie počtu kuchýň bolo dosť nerovnomerné.\n",
+    "\n",
+    "S týmto sa vysporiadame takto:\n",
+    "\n",
+    "-   Odstránime najbežnejšie ingrediencie, ktoré spôsobujú zmätok medzi odlišnými kuchyňami, pomocou `dplyr::select()`.\n",
+    "\n",
+    "-   Použijeme `recipe`, ktorý predspracuje údaje, aby boli pripravené na modelovanie, aplikovaním algoritmu `over-sampling`.\n",
+    "\n",
+    "Toto sme už prešli v predchádzajúcej lekcii, takže to bude hračka 🥳!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "6tj_rN00hClA"
+   },
+   "source": [
+    "# Load the core Tidyverse and Tidymodels packages\n",
+    "library(tidyverse)\n",
+    "library(tidymodels)\n",
+    "\n",
+    "# Load the original cuisines data\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
+    "\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\n",
+    "df_select <- df %>% \n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\n",
+    "  # Encode cuisine column as categorical\n",
+    "  mutate(cuisine = factor(cuisine))\n",
+    "\n",
+    "\n",
+    "# Create data split specification\n",
+    "set.seed(2056)\n",
+    "cuisines_split <- initial_split(data = df_select,\n",
+    "                                strata = cuisine,\n",
+    "                                prop = 0.7)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "cuisines_train <- training(cuisines_split)\n",
+    "cuisines_test <- testing(cuisines_split)\n",
+    "\n",
+    "# Display distribution of cuisines in the training set\n",
+    "cuisines_train %>% \n",
+    "  count(cuisine) %>% \n",
+    "  arrange(desc(n))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFin5yw3hHb1"
+   },
+   "source": [
+    "### Riešenie nevyvážených údajov\n",
+    "\n",
+    "Nevyvážené údaje často negatívne ovplyvňujú výkon modelu. Mnohé modely dosahujú najlepšie výsledky, keď je počet pozorovaní rovnaký, a preto majú tendenciu zápasiť s nevyváženými údajmi.\n",
+    "\n",
+    "Existujú dva hlavné spôsoby, ako riešiť nevyvážené dátové súbory:\n",
+    "\n",
+    "-   pridanie pozorovaní do minoritnej triedy: `Over-sampling`, napríklad pomocou algoritmu SMOTE, ktorý synteticky generuje nové príklady minoritnej triedy na základe najbližších susedov týchto prípadov.\n",
+    "\n",
+    "-   odstránenie pozorovaní z majoritnej triedy: `Under-sampling`\n",
+    "\n",
+    "V našej predchádzajúcej lekcii sme ukázali, ako riešiť nevyvážené dátové súbory pomocou `recipe`. Recipe si môžete predstaviť ako plán, ktorý popisuje, aké kroky by sa mali aplikovať na dátový súbor, aby bol pripravený na analýzu údajov. V našom prípade chceme dosiahnuť rovnomerné rozdelenie počtu našich kuchýň pre náš `training set`. Poďme na to.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "cRzTnHolhLWd"
+   },
+   "source": [
+    "# Load themis package for dealing with imbalanced data\n",
+    "library(themis)\n",
+    "\n",
+    "# Create a recipe for preprocessing training data\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
+    "  step_smote(cuisine) \n",
+    "\n",
+    "# Print recipe\n",
+    "cuisines_recipe"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KxOQ2ORhhO81"
+   },
+   "source": [
+    "Teraz sme pripravení trénovať modely 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Nad rámec multinomických regresných modelov\n",
+    "\n",
+    "V našej predchádzajúcej lekcii sme sa zaoberali multinomickými regresnými modelmi. Poďme preskúmať niektoré flexibilnejšie modely pre klasifikáciu.\n",
+    "\n",
+    "### Support Vector Machines.\n",
+    "\n",
+    "V kontexte klasifikácie je `Support Vector Machines` technika strojového učenia, ktorá sa snaží nájsť *hyperrovinu*, ktorá \"najlepšie\" oddeľuje triedy. Pozrime sa na jednoduchý príklad:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/svm.png\"\n",
+    "   width=\"300\"/>\n",
+    "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C4Wsd0vZhXYu"
+   },
+   "source": [
+    "H1~ neoddeľuje triedy. H2~ ich oddeľuje, ale iba s malým okrajom. H3~ ich oddeľuje s maximálnym okrajom.\n",
+    "\n",
+    "#### Lineárny Support Vector Classifier\n",
+    "\n",
+    "Support-Vector clustering (SVC) je súčasťou rodiny techník strojového učenia Support-Vector machines. V SVC je hyperplocha vybraná tak, aby správne oddelila `väčšinu` tréningových pozorovaní, ale `môže nesprávne klasifikovať` niektoré pozorovania. Tým, že umožníme niektorým bodom byť na nesprávnej strane, SVM sa stáva odolnejším voči odľahlým hodnotám, a tým lepšie generalizuje na nové dáta. Parameter, ktorý reguluje toto porušenie, sa nazýva `cost` a má predvolenú hodnotu 1 (pozri `help(\"svm_poly\")`).\n",
+    "\n",
+    "Vytvorme lineárny SVC nastavením `degree = 1` v polynomiálnom SVM modeli.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vJpp6nuChlBz"
+   },
+   "source": [
+    "# Make a linear SVC specification\n",
+    "svc_linear_spec <- svm_poly(degree = 1) %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svc_linear_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svc_linear_spec)\n",
+    "\n",
+    "# Print out workflow\n",
+    "svc_linear_wf"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rDs8cWNkhoqu"
+   },
+   "source": [
+    "Teraz, keď sme zachytili kroky predspracovania a špecifikáciu modelu do *workflow*, môžeme pokračovať a natrénovať lineárny SVC a zároveň vyhodnotiť výsledky. Pre metriky výkonu vytvorme súbor metrík, ktorý bude hodnotiť: `presnosť`, `citlivosť`, `pozitívnu prediktívnu hodnotu` a `F mieru`.\n",
+    "\n",
+    "> `augment()` pridá stĺpec/stĺpce s predikciami k daným údajom.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "81wiqcwuhrnq"
+   },
+   "source": [
+    "# Train a linear SVC model\n",
+    "svc_linear_fit <- svc_linear_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svc_linear_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0UFQvHf-huo3"
+   },
+   "source": [
+    "#### Support Vector Machine\n",
+    "\n",
+    "Support vector machine (SVM) je rozšírením klasifikátora podporných vektorov, ktoré umožňuje zohľadniť nelineárnu hranicu medzi triedami. V podstate SVM využívajú *kernel trick* na rozšírenie priestoru vlastností, aby sa prispôsobili nelineárnym vzťahom medzi triedami. Jednou z populárnych a mimoriadne flexibilných funkcií jadra, ktorú SVM využívajú, je *Radial basis function.* Pozrime sa, ako bude fungovať na našich údajoch.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-KX4S8mzhzmp"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Make an RBF SVM specification\n",
+    "svm_rbf_spec <- svm_rbf() %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svm_rbf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svm_rbf_spec)\n",
+    "\n",
+    "\n",
+    "# Train an RBF model\n",
+    "svm_rbf_fit <- svm_rbf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svm_rbf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QBFSa7WSh4HQ"
+   },
+   "source": [
+    "Oveľa lepšie 🤩!\n",
+    "\n",
+    "> ✅ Pozrite si:\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://bradleyboehmke.github.io/HOML/svm.html), Hands-on Machine Learning with R\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://www.statlearning.com/), An Introduction to Statistical Learning with Applications in R\n",
+    ">\n",
+    "> pre ďalšie čítanie.\n",
+    "\n",
+    "### Klasifikátory najbližšieho suseda\n",
+    "\n",
+    "Algoritmus *K*-najbližšieho suseda (KNN) predpovedá každé pozorovanie na základe jeho *podobnosti* s ostatnými pozorovaniami.\n",
+    "\n",
+    "Poďme ho aplikovať na naše dáta.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "k4BxxBcdh9Ka"
+   },
+   "source": [
+    "# Make a KNN specification\n",
+    "knn_spec <- nearest_neighbor() %>% \n",
+    "  set_engine(\"kknn\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "knn_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(knn_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "knn_wf_fit <- knn_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "knn_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HaegQseriAcj"
+   },
+   "source": [
+    "Zdá sa, že tento model nefunguje až tak dobre. Pravdepodobne zmena argumentov modelu (pozrite si `help(\"nearest_neighbor\")`) zlepší jeho výkon. Určite to vyskúšajte.\n",
+    "\n",
+    "> ✅ Pozrite si:\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> aby ste sa dozvedeli viac o klasifikátoroch *K*-Najbližších Susedov.\n",
+    "\n",
+    "### Ensemble klasifikátory\n",
+    "\n",
+    "Ensemble algoritmy fungujú tak, že kombinujú viacero základných odhadov, aby vytvorili optimálny model, buď:\n",
+    "\n",
+    "`bagging`: aplikáciou *priemerovacej funkcie* na kolekciu základných modelov\n",
+    "\n",
+    "`boosting`: vytváraním sekvencie modelov, ktoré na seba nadväzujú, aby zlepšili prediktívny výkon.\n",
+    "\n",
+    "Začnime tým, že vyskúšame model Random Forest, ktorý vytvára veľkú kolekciu rozhodovacích stromov a potom aplikuje priemerovaciu funkciu na vytvorenie lepšieho celkového modelu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "49DPoVs6iK1M"
+   },
+   "source": [
+    "# Make a random forest specification\n",
+    "rf_spec <- rand_forest() %>% \n",
+    "  set_engine(\"ranger\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "rf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(rf_spec)\n",
+    "\n",
+    "# Train a random forest model\n",
+    "rf_wf_fit <- rf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "rf_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RGVYwC_aiUWc"
+   },
+   "source": [
+    "Dobrá práca 👏!\n",
+    "\n",
+    "Poďme tiež experimentovať s modelom Boosted Tree.\n",
+    "\n",
+    "Boosted Tree definuje ensemble metódu, ktorá vytvára sériu sekvenčných rozhodovacích stromov, kde každý strom závisí od výsledkov predchádzajúcich stromov v snahe postupne znižovať chybu. Zameriava sa na váhy nesprávne klasifikovaných položiek a upravuje prispôsobenie pre ďalší klasifikátor, aby ich opravil.\n",
+    "\n",
+    "Existujú rôzne spôsoby, ako tento model prispôsobiť (pozri `help(\"boost_tree\")`). V tomto príklade prispôsobíme Boosted stromy pomocou enginu `xgboost`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Py1YWo-micWs"
+   },
+   "source": [
+    "# Make a boosted tree specification\n",
+    "boost_spec <- boost_tree(trees = 200) %>% \n",
+    "  set_engine(\"xgboost\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "boost_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(boost_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "boost_wf_fit <- boost_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "boost_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zNQnbuejigZM"
+   },
+   "source": [
+    "> ✅ Pozrite si:\n",
+    ">\n",
+    "> -   [Machine Learning for Social Scientists](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> -   <https://algotech.netlify.app/blog/xgboost/> - Skúma model AdaBoost, ktorý je dobrou alternatívou k xgboost.\n",
+    ">\n",
+    "> pre viac informácií o Ensemble klasifikátoroch.\n",
+    "\n",
+    "## 4. Extra - porovnanie viacerých modelov\n",
+    "\n",
+    "V tomto cvičení sme vytvorili pomerne veľké množstvo modelov 🙌. Môže byť únavné alebo náročné vytvárať množstvo workflowov z rôznych sád predspracovania a/alebo špecifikácií modelov a potom jeden po druhom vypočítavať metriky výkonnosti.\n",
+    "\n",
+    "Pozrime sa, či to môžeme vyriešiť vytvorením funkcie, ktorá aplikuje zoznam workflowov na tréningovú množinu a následne vráti metriky výkonnosti na základe testovacej množiny. Použijeme `map()` a `map_dfr()` z balíka [purrr](https://purrr.tidyverse.org/) na aplikáciu funkcií na každý prvok zoznamu.\n",
+    "\n",
+    "> Funkcie [`map()`](https://purrr.tidyverse.org/reference/map.html) vám umožňujú nahradiť mnoho for-cyklov kódom, ktorý je stručnejší a ľahšie čitateľný. Najlepším miestom na učenie sa o funkciách [`map()`](https://purrr.tidyverse.org/reference/map.html) je [kapitola o iterácii](http://r4ds.had.co.nz/iteration.html) v knihe R for Data Science.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Qzb7LyZnimd2"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "# Define a function that returns performance metrics\n",
+    "compare_models <- function(workflow_list, train_set, test_set){\n",
+    "  \n",
+    "  suppressWarnings(\n",
+    "    # Fit each model to the train_set\n",
+    "    map(workflow_list, fit, data = train_set) %>% \n",
+    "    # Make predictions on the test set\n",
+    "      map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
+    "    # Select desired columns\n",
+    "      select(model, cuisine, .pred_class) %>% \n",
+    "    # Evaluate model performance\n",
+    "      group_by(model) %>% \n",
+    "      eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
+    "      ungroup()\n",
+    "  )\n",
+    "  \n",
+    "} # End of function"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fwa712sNisDA"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "3i4VJOi2iu-a"
+   },
+   "source": [
+    "# Make a list of workflows\n",
+    "workflow_list <- list(\n",
+    "  \"svc\" = svc_linear_wf,\n",
+    "  \"svm\" = svm_rbf_wf,\n",
+    "  \"knn\" = knn_wf,\n",
+    "  \"random_forest\" = rf_wf,\n",
+    "  \"xgboost\" = boost_wf)\n",
+    "\n",
+    "# Call the function\n",
+    "set.seed(2056)\n",
+    "perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
+    "\n",
+    "# Print out performance metrics\n",
+    "perf_metrics %>% \n",
+    "  group_by(.metric) %>% \n",
+    "  arrange(desc(.estimate)) %>% \n",
+    "  slice_head(n=7)\n",
+    "\n",
+    "# Compare accuracy\n",
+    "perf_metrics %>% \n",
+    "  filter(.metric == \"accuracy\") %>% \n",
+    "  arrange(desc(.estimate))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KuWK_lEli4nW"
+   },
+   "source": [
+    "[**workflowset**](https://workflowsets.tidymodels.org/) balík umožňuje používateľom vytvárať a jednoducho prispôsobovať veľké množstvo modelov, ale je primárne navrhnutý na prácu s technikami resamplingu, ako je `krížová validácia`, ktorú si ešte len preberieme.\n",
+    "\n",
+    "## **🚀Výzva**\n",
+    "\n",
+    "Každá z týchto techník má veľké množstvo parametrov, ktoré môžete upravovať, napríklad `cost` v SVM, `neighbors` v KNN, `mtry` (náhodne vybrané prediktory) v Random Forest.\n",
+    "\n",
+    "Preskúmajte predvolené parametre každého z nich a zamyslite sa nad tým, čo by úprava týchto parametrov znamenala pre kvalitu modelu.\n",
+    "\n",
+    "Ak chcete zistiť viac o konkrétnom modeli a jeho parametroch, použite: `help(\"model\")`, napr. `help(\"rand_forest\")`.\n",
+    "\n",
+    "> V praxi zvyčajne *odhadujeme* *najlepšie hodnoty* týchto parametrov trénovaním mnohých modelov na `simulovanom dátovom súbore` a meraním, ako dobre tieto modely fungujú. Tento proces sa nazýva **ladenie**.\n",
+    "\n",
+    "### [**Kvíz po prednáške**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
+    "\n",
+    "### **Prehľad a samostatné štúdium**\n",
+    "\n",
+    "V týchto lekciách je veľa odborných výrazov, preto si nájdite chvíľu na preštudovanie [tohto zoznamu](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) užitočnej terminológie!\n",
+    "\n",
+    "#### POĎAKOVANIE:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) za vytvorenie úžasných ilustrácií, ktoré robia R prístupnejším a pútavejším. Viac ilustrácií nájdete v jej [galérii](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) a [Jen Looper](https://www.twitter.com/jenlooper) za vytvorenie pôvodnej verzie tohto modulu v Pythone ♥️\n",
+    "\n",
+    "Šťastné učenie,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Zlatý študentský ambasádor Microsoft Learn.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/sk/4-Classification/3-Classifiers-2/solution/notebook.ipynb
new file mode 100644
index 000000000..9d86ba1cb
--- /dev/null
+++ b/translations/sk/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vyskúšajte rôzne klasifikátory\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "C = 10\n",
+    "# Create different classifiers.\n",
+    "classifiers = {\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
+    "    'KNN classifier': KNeighborsClassifier(C),\n",
+    "    'SVC': SVC(),\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "    \n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy (train) for Linear SVC: 76.4% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.64      0.66      0.65       242\n",
+      "      indian       0.91      0.86      0.89       236\n",
+      "    japanese       0.72      0.73      0.73       245\n",
+      "      korean       0.83      0.75      0.79       234\n",
+      "        thai       0.75      0.82      0.78       242\n",
+      "\n",
+      "    accuracy                           0.76      1199\n",
+      "   macro avg       0.77      0.76      0.77      1199\n",
+      "weighted avg       0.77      0.76      0.77      1199\n",
+      "\n",
+      "Accuracy (train) for KNN classifier: 70.7% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.65      0.63      0.64       242\n",
+      "      indian       0.84      0.81      0.82       236\n",
+      "    japanese       0.60      0.81      0.69       245\n",
+      "      korean       0.89      0.53      0.67       234\n",
+      "        thai       0.69      0.75      0.72       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.73      0.71      0.71      1199\n",
+      "weighted avg       0.73      0.71      0.71      1199\n",
+      "\n",
+      "Accuracy (train) for SVC: 80.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.71      0.69      0.70       242\n",
+      "      indian       0.92      0.92      0.92       236\n",
+      "    japanese       0.77      0.78      0.77       245\n",
+      "      korean       0.87      0.77      0.82       234\n",
+      "        thai       0.75      0.86      0.80       242\n",
+      "\n",
+      "    accuracy                           0.80      1199\n",
+      "   macro avg       0.80      0.80      0.80      1199\n",
+      "weighted avg       0.80      0.80      0.80      1199\n",
+      "\n",
+      "Accuracy (train) for RFST: 82.8% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.80      0.75      0.77       242\n",
+      "      indian       0.90      0.91      0.90       236\n",
+      "    japanese       0.82      0.78      0.80       245\n",
+      "      korean       0.85      0.82      0.83       234\n",
+      "        thai       0.78      0.89      0.83       242\n",
+      "\n",
+      "    accuracy                           0.83      1199\n",
+      "   macro avg       0.83      0.83      0.83      1199\n",
+      "weighted avg       0.83      0.83      0.83      1199\n",
+      "\n",
+      "Accuracy (train) for ADA: 71.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.60      0.57      0.58       242\n",
+      "      indian       0.87      0.84      0.86       236\n",
+      "    japanese       0.71      0.60      0.65       245\n",
+      "      korean       0.68      0.78      0.72       234\n",
+      "        thai       0.70      0.78      0.74       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.71      0.71      0.71      1199\n",
+      "weighted avg       0.71      0.71      0.71      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "    y_pred = classifier.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
+    "    print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "7ea2b714669c823a596d986ba2d5739f",
+   "translation_date": "2025-09-06T14:42:38+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/4-Applied/notebook.ipynb b/translations/sk/4-Classification/4-Applied/notebook.ipynb
new file mode 100644
index 000000000..959db7ee8
--- /dev/null
+++ b/translations/sk/4-Classification/4-Applied/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2f3e0d9e9ac5c301558fb8bf733ac0cb",
+   "translation_date": "2025-09-06T14:41:22+00:00",
+   "source_file": "4-Classification/4-Applied/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/4-Classification/4-Applied/solution/notebook.ipynb b/translations/sk/4-Classification/4-Applied/solution/notebook.ipynb
new file mode 100644
index 000000000..342d57c27
--- /dev/null
+++ b/translations/sk/4-Classification/4-Applied/solution/notebook.ipynb
@@ -0,0 +1,290 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "49325d6dd12a3628fc64fa7ccb1a80ff",
+   "translation_date": "2025-09-06T14:41:47+00:00",
+   "source_file": "4-Classification/4-Applied/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: skl2onnx in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.8.0)\n",
+      "Requirement already satisfied: protobuf in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (3.8.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.19.2)\n",
+      "Requirement already satisfied: onnx>=1.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.9.0)\n",
+      "Requirement already satisfied: six in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from skl2onnx) (1.12.0)\n",
+      "Requirement already satisfied: onnxconverter-common<1.9,>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.8.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.19 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (0.24.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.4.1)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->skl2onnx) (45.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnx>=1.2.1->skl2onnx) (3.10.0.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (0.16.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install skl2onnx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 60
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 61
+    }
+   ],
+   "source": [
+    "X = data.iloc[:,2:]\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "  cuisine\n",
+       "0  indian\n",
+       "1  indian\n",
+       "2  indian\n",
+       "3  indian\n",
+       "4  indian"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 62
+    }
+   ],
+   "source": [
+    "y = data[['cuisine']]\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 65
+    }
+   ],
+   "source": [
+    "model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
+    "model.fit(X_train,y_train.values.ravel())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.72      0.70      0.71       236\n      indian       0.91      0.88      0.89       243\n    japanese       0.80      0.75      0.77       240\n      korean       0.80      0.81      0.81       230\n        thai       0.76      0.85      0.80       250\n\n    accuracy                           0.80      1199\n   macro avg       0.80      0.80      0.80      1199\nweighted avg       0.80      0.80      0.80      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}\n",
+    "onx = convert_sklearn(model, initial_types=initial_type, options=options)\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
+    "    f.write(onx.SerializeToString())\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za záväzný zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/5-Clustering/1-Visualize/notebook.ipynb b/translations/sk/5-Clustering/1-Visualize/notebook.ipynb
new file mode 100644
index 000000000..a1646504b
--- /dev/null
+++ b/translations/sk/5-Clustering/1-Visualize/notebook.ipynb
@@ -0,0 +1,50 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python383jvsc74a57bd0e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95",
+   "display_name": "Python 3.8.3 64-bit (conda)"
+  },
+  "coopTranslator": {
+   "original_hash": "40e0707e96b3e1899a912776006264f9",
+   "translation_date": "2025-09-06T14:07:53+00:00",
+   "source_file": "5-Clustering/1-Visualize/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
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@@ -0,0 +1,499 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Nigerijská hudba zozbieraná zo Spotify - analýza**\n",
+    "\n",
+    "Clustering je typ [neučenej metódy](https://wikipedia.org/wiki/Unsupervised_learning), ktorá predpokladá, že dataset nie je označený alebo že jeho vstupy nie sú spojené s preddefinovanými výstupmi. Používa rôzne algoritmy na triedenie neoznačených dát a poskytuje skupiny podľa vzorov, ktoré rozpozná v dátach.\n",
+    "\n",
+    "[**Kvíz pred prednáškou**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)\n",
+    "\n",
+    "### **Úvod**\n",
+    "\n",
+    "[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) je veľmi užitočný na prieskum dát. Pozrime sa, či nám môže pomôcť objaviť trendy a vzory v tom, ako nigérijské publikum konzumuje hudbu.\n",
+    "\n",
+    "> ✅ Zamyslite sa na chvíľu nad využitím clusteringu. V reálnom živote clustering nastáva vždy, keď máte kopu bielizne a potrebujete roztriediť oblečenie členov rodiny 🧦👕👖🩲. V dátovej vede clustering nastáva pri analýze preferencií používateľov alebo pri určovaní charakteristík akéhokoľvek neoznačeného datasetu. Clustering pomáha urobiť poriadok v chaose, ako napríklad v zásuvke na ponožky.\n",
+    "\n",
+    "V profesionálnom prostredí sa clustering môže použiť na určenie vecí, ako je segmentácia trhu, napríklad na zistenie, ktoré vekové skupiny kupujú aké produkty. Ďalším využitím by mohlo byť odhaľovanie anomálií, napríklad na detekciu podvodov v datasete transakcií kreditných kariet. Alebo by ste mohli použiť clustering na identifikáciu nádorov v dávke medicínskych skenov.\n",
+    "\n",
+    "✅ Zamyslite sa na chvíľu nad tým, ako ste sa mohli stretnúť s clusteringom „v divočine“, napríklad v bankovníctve, e-commerce alebo podnikateľskom prostredí.\n",
+    "\n",
+    "> 🎓 Zaujímavé je, že analýza klastrov vznikla v oblasti antropológie a psychológie v 30. rokoch 20. storočia. Dokážete si predstaviť, ako mohla byť použitá?\n",
+    "\n",
+    "Alternatívne by ste ju mohli použiť na zoskupovanie výsledkov vyhľadávania – napríklad podľa nákupných odkazov, obrázkov alebo recenzií. Clustering je užitočný, keď máte veľký dataset, ktorý chcete zmenšiť a na ktorom chcete vykonať podrobnejšiu analýzu, takže táto technika môže byť použitá na získanie informácií o dátach pred vytvorením iných modelov.\n",
+    "\n",
+    "✅ Keď sú vaše dáta organizované v klastroch, priradíte im identifikátor klastru. Táto technika môže byť užitočná pri zachovaní súkromia datasetu; namiesto toho môžete odkazovať na dátový bod podľa jeho identifikátora klastru, namiesto odhaľovania identifikovateľných údajov. Dokážete si predstaviť ďalšie dôvody, prečo by ste odkazovali na identifikátor klastru namiesto iných prvkov klastru na jeho identifikáciu?\n",
+    "\n",
+    "### Začíname s clusteringom\n",
+    "\n",
+    "> 🎓 Spôsob, akým vytvárame klastre, má veľa spoločného s tým, ako zhromažďujeme dátové body do skupín. Poďme si rozobrať niektoré pojmy:\n",
+    ">\n",
+    "> 🎓 ['Transduktívny' vs. 'induktívny'](https://wikipedia.org/wiki/Transduction_(machine_learning))\n",
+    ">\n",
+    "> Transduktívna inferencia je odvodená z pozorovaných tréningových prípadov, ktoré sa mapujú na konkrétne testovacie prípady. Induktívna inferencia je odvodená z tréningových prípadov, ktoré sa mapujú na všeobecné pravidlá, ktoré sa až potom aplikujú na testovacie prípady.\n",
+    ">\n",
+    "> Príklad: Predstavte si, že máte dataset, ktorý je len čiastočne označený. Niektoré veci sú „platne“, niektoré „CD“ a niektoré sú prázdne. Vašou úlohou je poskytnúť označenia pre prázdne miesta. Ak si zvolíte induktívny prístup, trénovali by ste model hľadajúci „platne“ a „CD“ a aplikovali tieto označenia na neoznačené dáta. Tento prístup bude mať problém klasifikovať veci, ktoré sú vlastne „kazety“. Transduktívny prístup na druhej strane efektívnejšie spracováva tieto neznáme dáta, pretože pracuje na zoskupovaní podobných položiek a potom aplikuje označenie na skupinu. V tomto prípade by klastre mohli odrážať „okrúhle hudobné veci“ a „štvorcové hudobné veci“.\n",
+    ">\n",
+    "> 🎓 ['Nerovinná' vs. 'rovinná' geometria](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)\n",
+    ">\n",
+    "> Odvodené z matematickej terminológie, nerovinná vs. rovinná geometria sa týka merania vzdialeností medzi bodmi buď „rovinnými“ ([Euklidovskými](https://wikipedia.org/wiki/Euclidean_geometry)) alebo „nerovinnými“ (ne-Euklidovskými) geometrickými metódami.\n",
+    ">\n",
+    "> „Rovinná“ v tomto kontexte odkazuje na Euklidovskú geometriu (časti ktorej sa učia ako „plánová“ geometria) a nerovinná odkazuje na ne-Euklidovskú geometriu. Čo má geometria spoločné s machine learningom? No, ako dve oblasti zakorenené v matematike, musí existovať spoločný spôsob merania vzdialeností medzi bodmi v klastroch, a to sa dá urobiť „rovinným“ alebo „nerovinným“ spôsobom, v závislosti od povahy dát. [Euklidovské vzdialenosti](https://wikipedia.org/wiki/Euclidean_distance) sa merajú ako dĺžka úsečky medzi dvoma bodmi. [Ne-Euklidovské vzdialenosti](https://wikipedia.org/wiki/Non-Euclidean_geometry) sa merajú pozdĺž krivky. Ak vaše dáta, vizualizované, neexistujú na rovine, možno budete potrebovať špecializovaný algoritmus na ich spracovanie.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/flat-nonflat.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografika od Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "> 🎓 ['Vzdialenosti'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)\n",
+    ">\n",
+    "> Klastre sú definované ich maticou vzdialeností, napr. vzdialenosťami medzi bodmi. Táto vzdialenosť sa dá merať niekoľkými spôsobmi. Euklidovské klastre sú definované priemerom hodnôt bodov a obsahujú „centroid“ alebo stredový bod. Vzdialenosti sa teda merajú podľa vzdialenosti od tohto centroidu. Ne-Euklidovské vzdialenosti odkazujú na „clustroidy“, bod najbližší k ostatným bodom. Clustroidy môžu byť definované rôznymi spôsobmi.\n",
+    ">\n",
+    "> 🎓 ['Obmedzené'](https://wikipedia.org/wiki/Constrained_clustering)\n",
+    ">\n",
+    "> [Obmedzený clustering](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) zavádza „semi-supervised“ učenie do tejto neučenej metódy. Vzťahy medzi bodmi sú označené ako „nemôže byť prepojené“ alebo „musí byť prepojené“, takže na dataset sú aplikované určité pravidlá.\n",
+    ">\n",
+    "> Príklad: Ak je algoritmus voľne aplikovaný na dávku neoznačených alebo čiastočne označených dát, klastre, ktoré vytvorí, môžu byť nekvalitné. V príklade vyššie by klastre mohli zoskupovať „okrúhle hudobné veci“, „štvorcové hudobné veci“, „trojuholníkové veci“ a „sušienky“. Ak by boli dané určité obmedzenia alebo pravidlá („položka musí byť vyrobená z plastu“, „položka musí byť schopná produkovať hudbu“), mohlo by to pomôcť „obmedziť“ algoritmus na lepšie rozhodnutia.\n",
+    ">\n",
+    "> 🎓 'Hustota'\n",
+    ">\n",
+    "> Dáta, ktoré sú „šumové“, sa považujú za „husté“. Vzdialenosti medzi bodmi v každom z jeho klastrov môžu byť pri skúmaní viac alebo menej husté, alebo „preplnené“, a preto tieto dáta potrebujú byť analyzované vhodnou metódou clusteringu. [Tento článok](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) demonštruje rozdiel medzi použitím K-Means clusteringu a HDBSCAN algoritmov na preskúmanie šumového datasetu s nerovnomernou hustotou klastrov.\n",
+    "\n",
+    "Prehĺbte svoje pochopenie techník clusteringu v tomto [učebnom module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "### **Algoritmy clusteringu**\n",
+    "\n",
+    "Existuje viac ako 100 algoritmov clusteringu a ich použitie závisí od povahy dát. Poďme si prejsť niektoré hlavné:\n",
+    "\n",
+    "-   **Hierarchický clustering**. Ak je objekt klasifikovaný podľa jeho blízkosti k blízkemu objektu, namiesto vzdialeného, klastre sa tvoria na základe vzdialenosti členov od ostatných objektov. Hierarchický clustering sa vyznačuje opakovaným spájaním dvoch klastrov.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/hierarchical.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografika od Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "-   **Centroid clustering**. Tento populárny algoritmus vyžaduje výber „k“, alebo počet klastrov, ktoré sa majú vytvoriť, po ktorom algoritmus určí stredový bod klastru a zhromaždí dáta okolo tohto bodu. [K-means clustering](https://wikipedia.org/wiki/K-means_clustering) je populárna verzia centroid clusteringu, ktorá rozdeľuje dataset na preddefinované K skupiny. Stred je určený najbližším priemerom, odtiaľ názov. Štvorcová vzdialenosť od klastru je minimalizovaná.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/centroid.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografika od Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "-   **Clustering založený na distribúcii**. Založený na štatistickom modelovaní, clustering založený na distribúcii sa sústreďuje na určenie pravdepodobnosti, že dátový bod patrí do klastru, a priradenie ho podľa toho. Metódy Gaussovskej zmesi patria do tohto typu.\n",
+    "\n",
+    "-   **Clustering založený na hustote**. Dátové body sú priradené do klastrov na základe ich hustoty, alebo ich zoskupenia okolo seba. Dátové body vzdialené od skupiny sú považované za odľahlé alebo šumové. DBSCAN, Mean-shift a OPTICS patria do tohto typu clusteringu.\n",
+    "\n",
+    "-   **Clustering založený na mriežke**. Pre multidimenzionálne datasety sa vytvorí mriežka a dáta sa rozdelia medzi bunky mriežky, čím sa vytvoria klastre.\n",
+    "\n",
+    "Najlepší spôsob, ako sa naučiť o clusteringu, je vyskúšať si ho sami, a presne to urobíte v tomto cvičení.\n",
+    "\n",
+    "Budeme potrebovať niektoré balíčky na dokončenie tohto modulu. Môžete ich nainštalovať pomocou: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`\n",
+    "\n",
+    "Alternatívne, skript nižšie skontroluje, či máte balíčky potrebné na dokončenie tohto modulu, a nainštaluje ich za vás, ak niektoré chýbajú.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Cvičenie - zoskupte svoje údaje\n",
+    "\n",
+    "Zoskupovanie ako technika je výrazne podporené správnou vizualizáciou, takže začnime vizualizáciou našich hudobných údajov. Toto cvičenie nám pomôže rozhodnúť, ktorý z metód zoskupovania by sme mali najefektívnejšie použiť vzhľadom na povahu týchto údajov.\n",
+    "\n",
+    "Začnime hneď tým, že importujeme údaje.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# Import the data into a tibble\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows of the data set\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Niekedy môžeme chcieť získať trochu viac informácií o našich údajoch. Môžeme sa pozrieť na `údaje` a `ich štruktúru` pomocou funkcie [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Glimpse into the data set\r\n",
+    "df %>% \r\n",
+    "  glimpse()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Dobrá práca!💪\n",
+    "\n",
+    "Môžeme si všimnúť, že `glimpse()` vám poskytne celkový počet riadkov (pozorovaní) a stĺpcov (premenných), potom prvé záznamy každej premennej v riadku za názvom premennej. Okrem toho sa *dátový typ* premennej zobrazí hneď za názvom premennej vo vnútri `< >`.\n",
+    "\n",
+    "`DataExplorer::introduce()` dokáže túto informáciu prehľadne zhrnúť:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe basic information for our data\r\n",
+    "df %>% \r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# A visual display of the same\r\n",
+    "df %>% \r\n",
+    "  plot_intro()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Skvelé! Práve sme zistili, že naše údaje nemajú žiadne chýbajúce hodnoty.\n",
+    "\n",
+    "Keď už sme pri tom, môžeme preskúmať bežné štatistiky centrálnej tendencie (napr. [priemer](https://en.wikipedia.org/wiki/Arithmetic_mean) a [medián](https://en.wikipedia.org/wiki/Median)) a miery rozptylu (napr. [štandardná odchýlka](https://en.wikipedia.org/wiki/Standard_deviation)) pomocou `summarytools::descr()`.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe common statistics\r\n",
+    "df %>% \r\n",
+    "  descr(stats = \"common\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Pozrime sa na všeobecné hodnoty údajov. Všimnite si, že popularita môže byť `0`, čo znamená piesne, ktoré nemajú žiadne hodnotenie. Tieto údaje čoskoro odstránime.\n",
+    "\n",
+    "> 🤔 Ak pracujeme s klastrovaním, nesupervidovanou metódou, ktorá nevyžaduje označené údaje, prečo zobrazujeme tieto údaje s označeniami? Počas fázy skúmania údajov sú užitočné, ale pre fungovanie algoritmov klastrovania nie sú nevyhnutné.\n",
+    "\n",
+    "### 1. Preskúmajte populárne žánre\n",
+    "\n",
+    "Poďme zistiť najpopulárnejšie žánre 🎶 tým, že spočítame, koľkokrát sa vyskytujú.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Popular genres\r\n",
+    "top_genres <- df %>% \r\n",
+    "  count(artist_top_genre, sort = TRUE) %>% \r\n",
+    "# Encode to categorical and reorder the according to count\r\n",
+    "  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())\r\n",
+    "\r\n",
+    "# Print the top genres\r\n",
+    "top_genres\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "To dopadlo dobre! Hovorí sa, že obrázok má hodnotu tisíc riadkov dátového rámca (vlastne to nikto nikdy nehovorí 😅). Ale chápete, čo tým myslím, však?\n",
+    "\n",
+    "Jedným zo spôsobov, ako vizualizovať kategóriálne údaje (znakové alebo faktorové premenné), je použitie stĺpcových grafov. Poďme vytvoriť stĺpcový graf pre top 10 žánrov:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Change the default gray theme\r\n",
+    "theme_set(theme_light())\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Teraz je oveľa jednoduchšie identifikovať, že máme `chýbajúce` žánre 🧐!\n",
+    "\n",
+    "> Dobrá vizualizácia vám ukáže veci, ktoré ste neočakávali, alebo vyvolá nové otázky o údajoch - Hadley Wickham a Garrett Grolemund, [R For Data Science](https://r4ds.had.co.nz/introduction.html)\n",
+    "\n",
+    "Poznámka: Keď je najvyšší žáner označený ako `Chýbajúci`, znamená to, že ho Spotify neklasifikoval, takže sa ho zbavme.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  filter(artist_top_genre != \"Missing\") %>% \r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Z krátkeho prieskumu údajov sme zistili, že tri najpopulárnejšie žánre dominujú tomuto datasetu. Zamerajme sa na `afro dancehall`, `afropop` a `nigerian pop`, a navyše filtrujme dataset tak, aby sme odstránili všetko s hodnotou popularity 0 (čo znamená, že nebolo klasifikované s popularitou v datasete a môže byť považované za šum pre naše účely):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "nigerian_songs <- df %>% \r\n",
+    "  # Concentrate on top 3 genres\r\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \r\n",
+    "  # Remove unclassified observations\r\n",
+    "  filter(popularity != 0)\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "nigerian_songs %>%\r\n",
+    "  count(artist_top_genre) %>%\r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Pozrime sa, či existuje nejaký zjavný lineárny vzťah medzi číselnými premennými v našej dátovej sade. Tento vzťah je kvantifikovaný matematicky pomocou [korelačnej štatistiky](https://en.wikipedia.org/wiki/Correlation).\n",
+    "\n",
+    "Korelačná štatistika je hodnota medzi -1 a 1, ktorá naznačuje silu vzťahu. Hodnoty nad 0 indikujú *pozitívnu* koreláciu (vysoké hodnoty jednej premennej majú tendenciu zhodovať sa s vysokými hodnotami druhej), zatiaľ čo hodnoty pod 0 indikujú *negatívnu* koreláciu (vysoké hodnoty jednej premennej majú tendenciu zhodovať sa s nízkymi hodnotami druhej).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Narrow down to numeric variables and fid correlation\r\n",
+    "corr_mat <- nigerian_songs %>% \r\n",
+    "  select(where(is.numeric)) %>% \r\n",
+    "  cor()\r\n",
+    "\r\n",
+    "# Visualize correlation matrix\r\n",
+    "corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  \r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Údaje nie sú silne korelované, okrem `energy` a `loudness`, čo dáva zmysel, keďže hlasná hudba je zvyčajne dosť energická. `Popularity` má súvislosť s `release date`, čo tiež dáva zmysel, pretože novšie skladby sú pravdepodobne populárnejšie. Zdá sa, že dĺžka a energia majú tiež určitú koreláciu.\n",
+    "\n",
+    "Bude zaujímavé zistiť, čo dokáže algoritmus zhlukovania urobiť s týmito údajmi!\n",
+    "\n",
+    "> 🎓 Upozorňujeme, že korelácia neznamená kauzalitu! Máme dôkaz o korelácii, ale žiadny dôkaz o kauzalite. [Zábavná webová stránka](https://tylervigen.com/spurious-correlations) obsahuje niekoľko vizuálov, ktoré tento bod zdôrazňujú.\n",
+    "\n",
+    "### 2. Preskúmajte distribúciu údajov\n",
+    "\n",
+    "Položme si niekoľko jemnejších otázok. Sú žánre výrazne odlišné v ich vnímaní tanečnosti na základe ich popularity? Preskúmajme distribúciu údajov našich troch najpopulárnejších žánrov z hľadiska popularity a tanečnosti pozdĺž danej osi x a y pomocou [hustotných grafov](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Perform 2D kernel density estimation\r\n",
+    "density_estimate_2d <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +\r\n",
+    "  geom_density_2d(bins = 5, size = 1) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  xlim(-20, 80) +\r\n",
+    "  ylim(0, 1.2)\r\n",
+    "\r\n",
+    "# Density plot based on the popularity\r\n",
+    "density_estimate_pop <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  theme(legend.position = \"none\")\r\n",
+    "\r\n",
+    "# Density plot based on the danceability\r\n",
+    "density_estimate_dance <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Patch everything together\r\n",
+    "library(patchwork)\r\n",
+    "density_estimate_2d / (density_estimate_pop + density_estimate_dance)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vidíme, že existujú sústredené kruhy, ktoré sa zhodujú, bez ohľadu na žáner. Môže to byť tak, že nigerijské chute sa zbiehajú na určitej úrovni tanečnosti pre tento žáner?\n",
+    "\n",
+    "Vo všeobecnosti sa tri žánre zhodujú, pokiaľ ide o ich popularitu a tanečnosť. Určenie klastrov v týchto voľne zarovnaných údajoch bude výzvou. Pozrime sa, či nám rozptylový graf môže v tomto pomôcť.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# A scatter plot of popularity and danceability\r\n",
+    "scatter_plot <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +\r\n",
+    "  geom_point(size = 2, alpha = 0.8) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"futurevisions::mars\")\r\n",
+    "\r\n",
+    "# Add a touch of interactivity\r\n",
+    "ggplotly(scatter_plot)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Rozptylový graf rovnakých osí ukazuje podobný vzor konvergencie.\n",
+    "\n",
+    "Vo všeobecnosti môžete pri zhlukovaní použiť rozptylové grafy na zobrazenie zhlukov údajov, takže zvládnutie tohto typu vizualizácie je veľmi užitočné. V ďalšej lekcii použijeme tieto filtrované údaje a metódu k-means na objavenie skupín v týchto údajoch, ktoré sa zaujímavo prekrývajú.\n",
+    "\n",
+    "## **🚀 Výzva**\n",
+    "\n",
+    "V rámci prípravy na ďalšiu lekciu vytvorte graf o rôznych algoritmoch zhlukovania, ktoré môžete objaviť a použiť v produkčnom prostredí. Aké typy problémov sa zhlukovanie snaží riešiť?\n",
+    "\n",
+    "## [**Kvíz po prednáške**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)\n",
+    "\n",
+    "## **Prehľad a samostatné štúdium**\n",
+    "\n",
+    "Predtým, než použijete algoritmy zhlukovania, ako sme sa naučili, je dobré pochopiť povahu vášho datasetu. Prečítajte si viac na túto tému [tu](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html).\n",
+    "\n",
+    "Prehĺbte si svoje znalosti o technikách zhlukovania:\n",
+    "\n",
+    "-   [Trénovanie a hodnotenie modelov zhlukovania pomocou Tidymodels a priateľov](https://rpubs.com/eR_ic/clustering)\n",
+    "\n",
+    "-   Bradley Boehmke & Brandon Greenwell, [*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/)*.*\n",
+    "\n",
+    "## **Úloha**\n",
+    "\n",
+    "[Preskúmajte ďalšie vizualizácie pre zhlukovanie](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)\n",
+    "\n",
+    "## POĎAKOVANIE:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) za vytvorenie pôvodnej verzie tohto modulu v Pythone ♥️\n",
+    "\n",
+    "[`Dasani Madipalli`](https://twitter.com/dasani_decoded) za vytvorenie úžasných ilustrácií, ktoré robia koncepty strojového učenia zrozumiteľnejšími a ľahšie pochopiteľnými.\n",
+    "\n",
+    "Šťastné učenie,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "99c36449cad3708a435f6798cfa39972",
+   "translation_date": "2025-09-06T14:11:16+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: seaborn in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (0.11.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (3.5.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.21.4)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.3.4)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.7.2)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (4.28.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (21.2)\n",
+      "Requirement already satisfied: setuptools-scm>=4 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (6.3.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
+      "Requirement already satisfied: tomli>=1.0.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (1.2.2)\n",
+      "Requirement already satisfied: setuptools in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (59.1.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Získajte informácie o dátovom rámci\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 530 entries, 0 to 529\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   name              530 non-null    object \n",
+      " 1   album             530 non-null    object \n",
+      " 2   artist            530 non-null    object \n",
+      " 3   artist_top_genre  530 non-null    object \n",
+      " 4   release_date      530 non-null    int64  \n",
+      " 5   length            530 non-null    int64  \n",
+      " 6   popularity        530 non-null    int64  \n",
+      " 7   danceability      530 non-null    float64\n",
+      " 8   acousticness      530 non-null    float64\n",
+      " 9   energy            530 non-null    float64\n",
+      " 10  instrumentalness  530 non-null    float64\n",
+      " 11  liveness          530 non-null    float64\n",
+      " 12  loudness          530 non-null    float64\n",
+      " 13  speechiness       530 non-null    float64\n",
+      " 14  tempo             530 non-null    float64\n",
+      " 15  time_signature    530 non-null    int64  \n",
+      "dtypes: float64(8), int64(4), object(4)\n",
+      "memory usage: 66.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name                0\n",
+       "album               0\n",
+       "artist              0\n",
+       "artist_top_genre    0\n",
+       "release_date        0\n",
+       "length              0\n",
+       "popularity          0\n",
+       "danceability        0\n",
+       "acousticness        0\n",
+       "energy              0\n",
+       "instrumentalness    0\n",
+       "liveness            0\n",
+       "loudness            0\n",
+       "speechiness         0\n",
+       "tempo               0\n",
+       "time_signature      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pozrite sa na všeobecné hodnoty údajov. Všimnite si, že popularita môže byť „0“ - a existuje veľa riadkov s touto hodnotou\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2015.390566</td>\n",
+       "      <td>222298.169811</td>\n",
+       "      <td>17.507547</td>\n",
+       "      <td>0.741619</td>\n",
+       "      <td>0.265412</td>\n",
+       "      <td>0.760623</td>\n",
+       "      <td>0.016305</td>\n",
+       "      <td>0.147308</td>\n",
+       "      <td>-4.953011</td>\n",
+       "      <td>0.130748</td>\n",
+       "      <td>116.487864</td>\n",
+       "      <td>3.986792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.131688</td>\n",
+       "      <td>39696.822259</td>\n",
+       "      <td>18.992212</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.208342</td>\n",
+       "      <td>0.148533</td>\n",
+       "      <td>0.090321</td>\n",
+       "      <td>0.123588</td>\n",
+       "      <td>2.464186</td>\n",
+       "      <td>0.092939</td>\n",
+       "      <td>23.518601</td>\n",
+       "      <td>0.333701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>89488.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>0.111000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028300</td>\n",
+       "      <td>-19.362000</td>\n",
+       "      <td>0.027800</td>\n",
+       "      <td>61.695000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>199305.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681000</td>\n",
+       "      <td>0.089525</td>\n",
+       "      <td>0.669000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.075650</td>\n",
+       "      <td>-6.298750</td>\n",
+       "      <td>0.059100</td>\n",
+       "      <td>102.961250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>218509.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.761000</td>\n",
+       "      <td>0.220500</td>\n",
+       "      <td>0.784500</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.103500</td>\n",
+       "      <td>-4.558500</td>\n",
+       "      <td>0.097950</td>\n",
+       "      <td>112.714500</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>242098.500000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.829500</td>\n",
+       "      <td>0.403000</td>\n",
+       "      <td>0.875750</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.164000</td>\n",
+       "      <td>-3.331000</td>\n",
+       "      <td>0.177000</td>\n",
+       "      <td>125.039250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>511738.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>0.966000</td>\n",
+       "      <td>0.954000</td>\n",
+       "      <td>0.995000</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.811000</td>\n",
+       "      <td>0.582000</td>\n",
+       "      <td>0.514000</td>\n",
+       "      <td>206.007000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       release_date         length  popularity  danceability  acousticness  \\\n",
+       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
+       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
+       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
+       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
+       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
+       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
+       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
+       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
+       "\n",
+       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
+       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
+       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
+       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
+       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
+       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
+       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
+       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
+       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
+       "\n",
+       "            tempo  time_signature  \n",
+       "count  530.000000      530.000000  \n",
+       "mean   116.487864        3.986792  \n",
+       "std     23.518601        0.333701  \n",
+       "min     61.695000        3.000000  \n",
+       "25%    102.961250        4.000000  \n",
+       "50%    112.714500        4.000000  \n",
+       "75%    125.039250        4.000000  \n",
+       "max    206.007000        5.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DÔLEŽITÉ PRAVIDLÁ:  \n",
+    "1. NEPRIDÁVAJTE '''markdown alebo akékoľvek iné značky okolo prekladu  \n",
+    "2. Uistite sa, že preklad neznie príliš doslovne  \n",
+    "3. Preložte aj komentáre  \n",
+    "4. Tento súbor je napísaný vo formáte Markdown - nezaobchádzajte s ním ako s XML alebo HTML  \n",
+    "5. Neprekladajte:  \n",
+    "   - [!NOTE], [!WARNING], [!TIP], [!IMPORTANT], [!CAUTION]  \n",
+    "   - Názvy premenných, názvy funkcií, názvy tried  \n",
+    "   - Zástupné symboly ako @@INLINE_CODE_x@@ alebo @@CODE_BLOCK_x@@  \n",
+    "   - URL adresy alebo cesty  \n",
+    "6. Zachovajte všetko pôvodné formátovanie Markdownu  \n",
+    "7. Vráťte IBA preložený obsah bez akýchkoľvek ďalších značiek alebo formátovania  \n",
+    "\n",
+    "Odstráňte žánre „Missing“, pretože nie sú klasifikované v Spotify\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[df['artist_top_genre'] != 'Missing']\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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A+4ATkuwDXA7s0p9+CrAjcAlwE/DyxjVLkiSNpGUKVlW12xIObbuYcwvYbyJFSZIkTUXuvC5JktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIzNW9IFJtgSOH2jaHDgYWAd4JbCwbz+oqk5Z0deRJEmaKlY4WFXVxcBMgCSrAlcBXwFeDnywqj7QokBJkqSpotVQ4LbApVV1eaPnkyRJmnJaBatdgeMG7u+f5LwkxyRZd3EPSDInybwk8xYuXLi4UyRJkqaUCQerJKsBLwS+1DcdBTyUbphwPnDY4h5XVXOralZVzRobG5toGZIkSUPXosdqB+CcqroGoKquqao7qupO4Ghg6wavIUmSNPJaBKvdGBgGTLLRwLEXARc0eA1JkqSRt8KrAgGSrAk8F3jVQPN/JJkJFHDZIsckSZKmrQkFq6q6EVh/kbY9J1SRJEnSFOXO65IkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNTJj2AVIWn6/PeTRwy5B08yDDj5/2CVI04I9VpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIzMm+gRJLgP+BNwB3F5Vs5KsBxwPbAZcBuxSVddP9LUkSZJGWaseq2dV1cyqmtXfPxA4vaq2AE7v70uSJE1rkzUUOBs4tr99LLDTJL2OJEnSyGgRrAo4NcnZSeb0bRtW1fz+9tXAhos+KMmcJPOSzFu4cGGDMiRJkoZrwnOsgKdW1VVJ7g+cluSiwYNVVUlq0QdV1VxgLsCsWbP+5rgkSdJUM+Eeq6q6qv93AfAVYGvgmiQbAfT/Lpjo60iSJI26CQWrJGsmud/4beB5wAXAycBe/Wl7AV+dyOtIkiRNBRMdCtwQ+EqS8ef6QlX9b5KfACck2Qe4HNhlgq8jSZI08iYUrKrq18DfL6b9WmDbiTy3JEnSVOPO65IkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1ssLBKsmmSc5IcmGSnyd5Xd/+ziRXJTm3/9mxXbmSJEmja8YEHns78KaqOifJ/YCzk5zWH/tgVX1g4uVJkiRNHSscrKpqPjC/v/2nJL8ANm5VmCRJ0lTTZI5Vks2AxwI/6pv2T3JekmOSrLuEx8xJMi/JvIULF7YoQ5IkaagmHKyS3Bc4CXh9Vd0AHAU8FJhJ16N12OIeV1Vzq2pWVc0aGxubaBmSJElDN6FgleRedKHq81X1ZYCquqaq7qiqO4Gjga0nXqYkSdLom8iqwACfBH5RVYcPtG80cNqLgAtWvDxJkqSpYyKrAp8C7Amcn+Tcvu0gYLckM4ECLgNeNYHXkCRJmjImsirwu0AWc+iUFS9HkiRp6nLndUmSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWpkItstSJI0aZ7ykacMuwRNM997zfcm/TXssZIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGJi1YJdk+ycVJLkly4GS9jiRJ0qiYlGCVZFXgSGAHYCtgtyRbTcZrSZIkjYrJ6rHaGrikqn5dVX8BvgjMnqTXkiRJGgmpqvZPmvw/YPuqekV/f0/giVW1/8A5c4A5/d0tgYubF6K7swHw+2EXIU0y3+daGfg+v+c9uKrGFndgxj1dybiqmgvMHdbrr+ySzKuqWcOuQ5pMvs+1MvB9PlomayjwKmDTgfub9G2SJEnT1mQFq58AWyR5SJLVgF2BkyfptSRJkkbCpAwFVtXtSfYHvgGsChxTVT+fjNfSCnMYVisD3+daGfg+HyGTMnldkiRpZeTO65IkSY0YrCRJkhoxWKmJJJsnWX3YdUiSNEwGK01YknWBA4C3G64kaepJkmHXMF0YrDQhSTarquuBk4C1gQMMV5rK/IDRyiZJqqqSPCXJPkm27bdK0gowWGmFJVkH+ECSt1fV6cCXgQdiuNIUMB6gkjy6/0DZGKD/gDFcaaXRv+efBXwWeBjwIeC1SR421MKmKLdb0ApLci/gScC+wDlV9YEkzwB2AX4HfKCqbh1mjdLiDHxDfw7wEWA+cBnwc+Dw8g+jViJJtgQOAT5fVScneRzwKuDcqjpquNVNPfZYabmNf5uvqtuAHwJHAtskOaCqzgROAO4PHGzPlUZRH6oeB7wZ2Kmqng18ie5SXLOHWpx0D0kPeDrwUGC7JGtW1TnAccCcfg6tloPBSstl/Jt+f3ttgKr6HnA48KSBcPU1YDXgvkMrVlqCfv7IM4BnARv3zd8FbgCeMKy6pHvCwFD3BsCMqjoaeA8QukvQAVwN/Klv03KYlEvaaPoaCFWvBbYFrk1yalV9sf9dfV2Sg6vqkCTfraqbh1mvNG5g+O9ewG10Pa3rAm9Ncn1V/TTJucDuSdYAbnZIUNNR/3uwI93w31VJbgT2AdYE9kqyO10++I+qum6IpU5JBisttyT7Av8I7AEcChyWZP2qOjLJDGCfJOv5C6lR0n+YvAB4Ed1Q9aF0w9bXAicnOYauB+vQqrppeJVKkyvJI4F3A/sD5wJfAD5VVbsmuQXYDji/qr7Wnx+/ZCw7g5WWS5J7A7fSfTi9DLgP8FLgk0nurKqjkvy4qm4ZZp3SopI8Hng/8Aq6OSV7AmfQrYR6AF0P7FFV9d9JVq2qO4ZWrDS5bgUupFt0dAuwU5Izk+wHfAJYh25qx67A8Yaq5eMcK92tJH/1HqmqW6rqU3RdxtsD+1XVGcDPgH9Oso6hSiPqkcCPq+r7VfU+4H+B1wP3Bg6j673aJ8mjDFWaTga2Flm1Hwq/DtgImDVw2heBO6rqduBY4CzgDEPV8jNYaYmSbFJVd/a3X5Pk8CQHJFmLuyY2PiTJHOAa4NlV9YfhVSzdZeDDZPzv3IXAaklmAVTVicBFwBZV9Xu6VYFfBv44hHKlSdMPg8+m+/JwPN2XjCOBjyTZP8kr6IYFL+nPv62qjq2qa4ZW9BTmPlb6G/0H0lp0Y+/vBc4DjgD+E3gM3UqSvej2OXkc8Hhgj6o6bxj1SkvS71P1JOAPwGl0e67NB64CLqZbUv7i8feuQ4CajpI8gu7v93vp/n6/k24o/Da6+VSbACdW1anDqnE6MVhpiZI8Gfgk3Tf9I6vqW0keCLyVLnjtV1U3JVm7qvyWr5GS5CnAMXTDfC+j2wLkcmAMeDLdcPbR/YaITs7VtJTkUXS/AxdX1Wv7tu2ATwNPq6pLhljetORQoP7KwPBJqur7dBPT/w7YEaCqfge8D7gDODLJKoYqjZokDwf+GTiiquYCOwHrA4+pqiOqaldgb0OVVgK/pJu68cgkWyRZvaq+QXd917HhljY92WOl/7PI5p/PARbSDZmsC/wP3TL0T/THH0A3dO8YvEZOkh2A19KtfnpDVf2mnxt4JvCSqvrlUAuU7gHjQ9v9hPVPAjcD3wQW0A2Dz66qnwyzxunIYKW/keQNdN/wz6DbnXoXYHO6lSIfr6oPDa04aTEGNv/cHLiJbtXTlnTzAH9L9+08dJPT/6GqrhhasdI9YOB3YkZV3d5fbeBIuisLnAl8vapOtce2PYcC9VeSbAVsV1XPANYGrgf+UFU/opunsmeSdYZYovQ3+g+QHejmUR0O/IRudd9xdEvKv0T3ofJWQ5Wmo4FpHFv0IwoA9KFqRlX9hW54fB6wBnCOoWpyGKxWcgPXjBr3F+C3SQ4CHg7sXlW3JZldVT8GnuyWCho1/QfJu4FXVtXudEHqZOBXdCuhvg98m64XdnHve2nKGuid2o7uff9fwH5JHgZ/Fa5uowtX9wfehpuETwqD1UpskTlVs/tVgL8GNgT2pht/v6Xf4+SA/rI1tw6vYuku4/tTJXkC3WKKc+lW/VFV7wa+AxxUVT+lmyP4ULoNQGf4LV3TSR+qZtFdVeAFwJvoFh3ttEi4WrXvudoZOKwPWmrMYLUSGwhV+wP/BizoNwT9MN23+xOTHEi3cdyrq+raYdUqjUtyH4CqujPJU+km5T6Kbn+efxg49bt0k3Wpqq/TbY54Yr+ztDRtJLkf8HLgcVV1SVV9j+76f5sDL+lXydJPZF+lqv7Sr/DWJLAbcCXXf5vZGdh+4BftO8D5dFstXE+3iuriIZUo/Z9+T54PJXk+3SU5/hX4SFWdkeTPwNwkD6XbEPQlwEHjj3XzQ00ngyMOVfWnJEcAD0tyJPCaqvpOklWBPeg2AqU/987hVLzycFXgSmbRyYr9RPSPAwcCvwNu67uVNwMud8hEo6JfMn4U8CPgv4Hn0Q17rAvsVVVXJXkk3UrW9YEf9JvaOkFX08rAnKrnAg+i+yz/z75n6kDgBuCNfa/uulV1/VALXsk4FLgSWWRO1Qb9kMqNwOrADn33cCXZnW5i4xpDLFdanKvogtOX6FY3HUI3t+o1STaqql9U1cer6j1V9S24a8hbmg76obxK8g90O6pfCRyS5AP9/mz/DjwA+Gj/EDdwvofZY7WSWCRUvRHYDfgZ3eqRn9J9UF0E3A5sDexZVecPp1rpry3yDf2zwLf73dPHL13zfLovAv/uprWajpI8BFilqi5NsgHd78Eb6FZvHwhsDHyzqvZJsiWwenn91qGwx2olMRCqnkB34eTXAF8HXk+3z8/z6Cb3/oDuorSGKo2ERTb/HANeDKyS5N39MMf3gFPovhSsM8RSpcn0ZGD9/pI0vwfmAKsB7wKeCjwLeHmSj1bVxYaq4XHy+kqkX0H1ZeDDVfXDfijwT8CbgQdW1VFDLVBajD5UvZBu2O8Sui1BPkG3pPy1SY7oJ+qe7x5rmq6q6vNJ7gv8JMlLq+q8JA8Ezu7nUj2AbnPcbwy3UtljNY0tugliVX2Xrvt4lyQbVNXNwFl02ys8N8m6bpyoUZNkG7rVf9sBX6Hb4PB5wAfo5lu9qd+b6g9DK1KaJAM7qm9Ht63IZ4Gj+xWylwFrJ/kY3WWbvlpVp/l3fLicYzVNLTKnalvgfsBpVXVjkg8C29BtALqgX211r6q6aYglS4uVZBO6rRXWpdtdfXe6lazXAZ8GFpYXktU0lmRrui/Ab+hHG95I93uwU3/KY4Abq+rMIZWoAQaraS7dBZX/ke7SHhsC76+qM5McSjfh9+lVtXCYNUrLIsl76Dax/XCSlwGvBf6xqi4fcmnSpEmyKfBB4LqqmjPQ/kbglcDOVXXBsOrT33KO1TTTj7NfX1W39iuonltVT+13UH8y3eTGqqo3J7kVWBMwWGkqOB94Vd/D+mLgdYYqrQRuB84DZifZvqr+F6CqDu83AF1nmMXpb9ljNU30Y+pjwInAfwKfoxs6uS/dipG9gBcCnwIeBrylqs4YTrXS8kuyFvAiuvfxMf1laqRpZWAV7JPo/qb/lm7Bxt501/87oapOH2KJWgonr08fqaoFdNf8242ue/ja/hv9w4H/qapbgO/RDQvadawppapuqKpj6S6x9HUn6Go66kPV84Bj6KZvnA08BTiZrudq7/64RpRDgdPEwPWf1gDuAxyTZI2q+hTwfeBj/eUOnkQXuhz+01R1B7ijuqafJKvQDe29iq53dj3gQuCcqromyZforpQxf2hFaqkcCpxGkrwEeCuwA91qkZcBHwOOp/vG81zgc1V10bBqlCTdvSRvBdYGng3s0e+2vjfd9jiXeSHl0WaP1fSyPnBWf0mPTyS5FpgLrFFVRwMuxZWkEZRkJt0WOO+iW1S0J/CsPlT9Pd2X5kuq6tdDLFPLwGA1vfwWmNnv+3NVVZ3YL0t/QZIvVtWfhlyfJKk3MFH9acDOwHZJFlTVwUkeAbwjye3ATOCt/SbPGnEOBU4jSdam2zjxcroLK69BNyT4uqq6bHiVSZLGLbKB89OBzwP7A48AHg98v6o+1F+GbF3gd1V19uDjNLoMVlPMkn6x+kt63J5kPbqJjw8EtgAOcPM4SRoN/fX9Hgl8u6ruSPJSYOOqen9/LcCZwPvptlX48BBL1QoyWE0hi3zLmQ0EuLOqTu7bxsPVKv1FOdesqhuHWbMk6S793+5fAVfSbf65Ld3O6s+tqt/0KwOPpZtndWJVfWFoxWqFuI/VFDIQqval269qS+CjSd7SH7+9/6UcT8te+0+SRkhVfRW4mm7F9k7AqXRTOD7cz6t6DN21MX8FbDykMjUBTl6fAhbpqbo3sAuwV1X9NMkJwDeS/LmqPja4DNexeEkaDYN/x6vquiRnAs8D/gJ8hW4E4rN0vVj7AI8Dnttfwul2/55PHQarKaTvqboY+CVwn37I7zdJ9gd2HW51kqQl6Vf/PQN4NPCtqjo6yZ/pLtF0Z1UdmuRj/elbAwcDL6qq24ZUslaQQ4EjLMmW8H+/kC+mW477c+Ba4A1015EC2BRYo78gpyRpRIxfeinJE+mG/54BvCXJK6vqOOBrwEuT7ALcAtwbeDLdnlY/H1LZmgAnr4+oJNsBR9F1B28EHAZcWlWv6Y8fQ7cM90a6uVYvd/WfJI2eJFsDhwBvqarzkuxKF57O73uudgMurKqf9eevVlV/GWLJmgCHAkdQkhl0XcH/CmxFt/z2DGB2khdU1X9X1T/134BWpdsM9PKhFSxJujvrAM+hu6zYecCJwJ30c6iq6mNw1zwsQ9XUZrAaQf3qvkuBf6G74Oyz6LqIb6bbRf2Oqjqlqn40zDolSUtXVaf20znem+R3VXVckhPpvhj/bOA8h5CmAYPV6DqPbruEG4C1q+r3Sb5M9y1nzyS3VtXpQ61QkrRMqurk/vI0/9YP9R0LHDfsutSec6xGxCJbKqwG3NHvynsA3RXO31FVP+mvA7gD8LWqmj/EkiVJyynJC4H30Q0NXj24RY6mB4PVCFgkVO1PN6/qBuCdVXVLkoOAJwLvq6ofJFm1qu4YYsmSpBWUZKyqFg67Dk0Og9UISfLPwEuA3YFzgG8CB1fVpUneDTwM2LuqbhlimZIkaQkMViMiyVrA4XQrAXcGdgQW0G218OqquiTJ+lV17RDLlCRJd8NgNUKSrA48AvhQVT2r31huIfAJumFBd+CVJGmEuSpwhFTVrUluAmYkeTTwYOB04BOGKkmSRp89ViOm77V6Pd2KkQcCO1fVhUMtSpIkLROD1Qjqr2b+ALoLc1417HokSdKyMVhJkiQ1ssqwC5AkSZouDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpkf8PwQ8MlHUQ3OMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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k/rLi2kNX/X3drkOY0qVrXd91CFP65g5v6TqEST31grd3HcKU3v+gQ7sOYUo7585dhzClW3reFe2S1W7sOoQpbV5rdR3ClM6qa7sOYUq/Wb3/7+Oat6zTdQiT2q3rAFrlPK6SJElSf1lxlSRJGhr7uEqSJEn9ZcVVkiRpaJzHVZIkSeovK66SJElDYx9XSZIkqb+suEqSJA2N87hKkiRJ/WXFVZIkaWhmaR9XE1dJkqShcTosSZIkqb+suEqSJA3NLO0qYMVVkiRJg2DFVZIkaWDK6bD6I8lhSf6jb+dPcs8kx7bbj07y7XZ77yQHt9tPTbLdHRuxJEnSzGfFdTlU1ZXAvhO0Hw8c3959KvBt4OI7LjJJkjSr2Me1W0kOSfLLJD8B7t+2vSTJWUnOT/K1JOu07Ucm+VCSnya5PMm+I8d5Y5IL2ucc0bZtleTEJGcn+XGSbdr2pyT5WZJzk/xPkruNhLRjktOTXJrkJe3+WyS5cILYD0jykSQPA/YG3pvkvPa854zst/XofUmSJC27XlRckzwIeDYwlyamc4Czga9X1Sfbfd4BvAj4cPu0ewC7A9vQVDuPTfLPwD7AQ6rqhiR3afedB7ysqi5N8hDgY8BjgZ8Au1VVJXkx8Abg39vnPBDYDVgXODfJd6Z6HVX10yTHA9+uqrEuBdcmmVtV5wH/Cnx2Rd4jSZKk28zSimsvElfgEcA3quoGgDb5A9i+TVg3AtYDThp5zjerahFw8UildA/gs2PHqaq/JFkPeBhwTJKx567Vfr0XcHSSewBrAr8aOf5xVfUP4B9JfgjsCpy3Aq/tU8C/Jnk9sF97nCUkORA4EOC16z+IJ99pqxU4lSRJ0szVm64CS3Ek8G9VtQPwNmDtkcduGtkOS7cacE1VzR25bds+9mHgI+3xXzru+OM/yqzoR5uvAf8MPBk4u6r+PNFOVTWvqnapql1MWiVJ0qRq0fTeeqovieupwFOT3CnJ+sBT2vb1gd8nWQPYfxmO8z2a6uZYX9i7VNXfgF8leWbbliQ7tvtvCPyu3X7BuGPtk2TtJP8EPBo4axlfy3Vt3ABU1Y00leKPYzcBSZKkFdaLxLWqzgGOBs4HvsvtSeJbgJ8BpwGXLMNxTqTp7zo/yXnA2JRW+wMvSnI+cBFNP1iAw2i6EJwN/Gnc4RYAPwTOAN7eziiwLL4CHNQO+BornR4FLAJOXsZjSJIkLd2imt5bT/WljytV9U7gnRM89PEJ9j1g3P31RraPAI4Y9/ivgL0mOM5xwHETtB+2lBivALZvt08BTmm3j6Tp1kBVnQaMn8d1d5q+twsnOq4kSZKm1pvEdaZK8g1gK5pZDCRJklZa9bgqOp1MXKdZVT2t6xgkSZJmAhNXSZKkoZmlFddeDM6SJEmSpmLiKkmSNDSLFk3vbRkk2SvJL5JcluTgSfZ7RpJKssvKvmwTV0mSJC2XJHOAj9IssrQd8Jwk42dVop2f/zU005uuNBNXSZKkoel+Htddgcuq6vKquplmHvt9Jtjv7cC7gRtXxcs2cZUkSRqa7hPXTYHfjNz/bdt2myQ7A5tV1XdW1cs2cZUkSdJikhyYZP7I7cDlfP5qwH8B/74q43I6LEmSpIGpmt7psKpqHjBvkl1+B2w2cv9ebduY9WlWGz0lCcDdgeOT7F1V81c0LiuukiRJWl5nAVsn2TLJmsCzgePHHqyqa6tq46raoqq2AM4AVippBSuukiRJw9PxAgRVdWuSfwNOAuYAn6mqi5IcDsyvquMnP8KKMXGVJEnScquqE4ATxrUdupR9H70qzmniKkmSNDQu+SpJkiT1lxVXSZKkgalZWnE1ce2hNbJsawR36eFs2HUIU/pzz3+63/+gCbsB9cq/n3141yFM6SE7PL/rEKb0+LU2m3qnDj38lrW7DmFKp62xShbdmVbPvaX/fxdPXfOWrkOY0mVr9P9/oLrT83/tkiRJWsIsrbjax1WSJEmDYMVVkiRpaGZpjworrpIkSRoEK66SJEkDM1tnFbDiKkmSpEGw4ipJkjQ0VlwlSZKk/rLiKkmSNDTOKiBJkiT1lxVXSZKkgXFWAUmSJKnHrLhKkiQNzSzt42riKkmSNDB2FZAkSZJ6bFYlrklem2SdkfsnJNmow5AkSZKW36JpvvXUrEpcgdcCtyWuVfXEqrqms2gkSZK0zDpNXJN8M8nZSS5KcmDbtleSc5Kcn+T7bdtd2n0XJDkjyQPb9sOS/MfI8S5MskWSdZN8pz3GhUn2S/Jq4J7AD5P8sN3/iiQbt9vPb49/fpIvtG1HJvlQkp8muTzJviPnOijJWe1z3ta2LXHetv2IJBe3+77vjnhvJUnSzFWLpvfWV10PznphVf0lyZ2As5IcB3wSeGRV/SrJXdr93gacW1VPTfJY4PPA3EmOuxdwZVU9CSDJhlV1bZLXA4+pqj+N7pzkAcCbgYdV1Z9GzgtwD2B3YBvgeODYJHsCWwO7AgGOT/JIYJPx503yT8DTgG2qquyaIEmStGK67irw6iTnA2cAmwEHAqdW1a8Aquov7X67A19o234A/FOSDSY57gXA45O8O8kjquraKeJ4LHDMWEI7cl6Ab1bVoqq6GLhb27ZnezsXOIcmqd16Kee9FrgR+HSSpwM3TBRAkgOTzE8y//gbLp8iXEmSNKvZx/WOleTRwB7AQ6tqR5ok8LzlPMytLP4a1gaoql8CO9Mkku9IcuhKhHrTyHZGvr6rqua2t/tW1acnOm9V3UpTmT0WeDJw4kQnqap5VbVLVe2y9zr3WYlwJUmSZqYuK64bAn+tqhuSbAPsRpN4PjLJltD0bW33/TGwf9v2aOBPVfU34AqaRJEkOwNjz7sncENVfRF479g+wHXA+hPE8gPgme1lfcZ1FZjIScALk6zX7r9pkrtOdN52nw2r6gTgdcCOy/b2SJIkTcw+rne8E4GXJfk58Aua7gJ/pOku8PUkqwFXA48HDgM+k2QBzaX2F7TH+Brw/CQXAT8Dftm27wC8N8ki4Bbg5W37PODEJFdW1WPGAqmqi5K8E/hRkoU01d8DlhZ4VZ2cZFvg9CQA1wPPA+47wXnXB45LsjZNpfb1K/BeSZIkzXqdJa5VdRPwz0t5+Lvj9v0L8NQJjvEPmr6m411BUxUdv/+HgQ+P3N9iZPtzwOfG7X/AuPvrjWx/EPjguFP870TnpekqIEmStGr0uCo6nboenCVJkiQtk66nw5IkSdJy6nM/1OlkxVWSJEmDYMVVkiRpYKy4SpIkST1mxVWSJGlgrLhKkiRJPWbFVZIkaWgqU+8zA1lxlSRJ0iBYcZUkSRqY2drH1cRVkiRpYGqRXQUkSZKk3rLiKkmSNDCztauAFVdJkiQNghVXSZKkgSmnw5IkSZL6y4prD/37ald1HcKUPr/Wel2HMKXX3lhdhzCpnXPnrkOY0kN2eH7XIUzpZxd8vusQpvT1Hd7SdQiTunKNriOY2ke/8/KuQ5jSB5/42a5DmNITb5zTdQhTWrDW7KwkLi/7uEqSJEk9ZsVVkiRpYJzHVZIkSeoxK66SJEkDU/0exjFtrLhKkiRpEKy4SpIkDYx9XCVJkqQes+IqSZI0MFZcJUmSpB6z4ipJkjQwziogSZIk9ZgVV0mSpIGxj6skSZLUY1ZcJUmSBqZqdlZcTVwlSZIGphZ1HUE37CqwCiTxA4AkSdI0m5WJa5LnJTkzyXlJPpFkTpLrk7wzyflJzkhyt3bfTZJ8LclZ7e3hbfthSb6Q5DTgC+1+30tyUZJPJfl1ko2THJ7ktSPnfmeS13TzyiVJ0kywqDKtt76adYlrkm2B/YCHV9VcYCGwP7AucEZV7QicCrykfcoHgf+uqgcDzwA+NXK47YA9quo5wFuBH1TVA4Bjgc3bfT4DPL8992rAs4EvTtsLlCRJmqFm4yXuxwEPAs5KAnAn4GrgZuDb7T5nA49vt/cAtmv3BdggyXrt9vFV9Y92e3fgaQBVdWKSv7bbVyT5c5KdgLsB51bVn6frxUmSpJnPwVmzR4DPVdWbFmtM/qPqtnUoFnL7e7MasFtV3Thuf4C/L+M5PwUcANydpgK7ZFDJgcCBAPfe8L5sss49lvHQkiRJs8Os6yoAfB/YN8ldAZLcJcm9J9n/ZOBVY3eSzF3KfqcBz2r32RO488hj3wD2Ah4MnDTRk6tqXlXtUlW7mLRKkqTJ1KJM662vZl3iWlUXA28GTk6yAPgeMFmm+GpglyQLklwMvGwp+70N2DPJhcAzgT8A17XnvBn4IfDVqlq4al6JJEnS7DIbuwpQVUcDR49rXm/k8WNpBlhRVX+iGcw1/hiHjWu6FnhCVd2a5KHAg6vqJrhtUNZuNAmtJEnSSrmtc+MsMysT12myOfDVNkm9mXZWgiTb0Qz6+kZVXdphfJIkSYNm4rqKtEnpThO0Xwzc546PSJIkzVR96IeaZC+aaUPnAJ+qqiPGPf564MXArcAfgRdW1a9X5pyzro+rJEmSVk6SOcBHgX+mmdf+Oe1V5lHnArtU1QNpumC+Z2XPa8VVkiRpYHqwutWuwGVVdTlAkq8A+wAXj+1QVT8c2f8M4Hkre1IrrpIkSVpemwK/Gbn/27ZtaV4EfHdlT2rFVZIkaWCme+Ws0YWRWvOqat4KHut5wC7Ao1Y2LhNXSZIkLaZNUidLVH8HbDZy/15t22KS7AEcAjxqbJrQlWHiKkmSNDA9mMf1LGDrJFvSJKzPBp47ukOSnYBPAHtV1dWr4qT2cZUkSdJyqapbgX+jWcr+5zSrg16U5PAke7e7vZdmgadjkpyX5PiVPa8VV0mSpIHpwawCVNUJwAnj2g4d2d5jVZ/TiqskSZIGwYqrJEnSwEz3rAJ9ZeIqSZI0MD0YnNUJuwpIkiRpEKy4SpIkDUwfBmd1wYqrJEmSBsGKaw+9aM7mXYcwpQ/cstKLX0y7J2atrkOY1C2Luo5gao9fa7Opd+rY13d4S9chTOnpF7y96xAm9dUHHjr1Th37ryd+tusQpvSP9L/T4W/WmNN1CFPaYAB/G/tgtg7OsuIqSZKkQbDiKkmSNDD2cZUkSZJ6zIqrJEnSwPS/R/X0sOIqSZKkQbDiKkmSNDD2cZUkSZJ6zIqrJEnSwDiPqyRJktRjVlwlSZIGZrYuMGbFVZIkSYNgxVWSJGlgCvu4SpIkSb1lxVWSJGlgFs3SpbOsuEqSJGkQpkxck/x0RQ6c5KlJtluR506HJBslecUy7nv9dMcjSZK0ohaRab311ZSJa1U9bAWP/VRgwsQ1SRddFDYClilxlSRJUv8sS8X1+vbro5OckuTYJJckOSpJ2seOSHJxkgVJ3pfkYcDewHuTnJdkq/a5H0gyH3hNkiOT7LuU8/woyXFJLm+PvX+SM5NckGSrdr9NknwtyVnt7eFt+2FJPtOe7/Ikr25PcQSwVRvPe5Osl+T7Sc5pj7vPBK99stf8oDbOs5OclOQebfurR96Lr7Rtj2rPe16Sc5Osv4LfL0mSJIpM662vlrfyuRPwAOBK4DTg4Ul+DjwN2KaqKslGVXVNkuOBb1fVsQBtvrdmVe3S3j9ykvPsCGwL/AW4HPhUVe2a5DXAq4DXAh8E/ruqfpJkc+Ck9jkA2wCPAdYHfpHk48DBwPZVNbc9/+rA06rqb0k2Bs5IcnxVje/uPNFr/hnwYWCfqvpjkv2AdwIvbM+zZVXdlGSj9hj/Abyyqk5Lsh5w49RvtSRJ0sRm6wIEy5u4nllVvwVIch6wBXAGTSL26STfBr49yfOPXsbznFVVv2/P87/AyW37BTQJKcAewHZtQgywQZsUAnynqm4CbkpyNXC3Cc4R4D+TPJLm+79pu98fxu030Wu+Btge+F57/jnA79v9FwBHJfkm8M227TTgv5IcBXx97HiSJEladss7q8BNI9sLgdWr6lZgV+BY4MnAiZM8/+8j27eOnT/JasCaSznPopH7i7g92V4N2K2q5ra3Tavq+gmev5CJE/T9gU2AB7VV2KuAtSfYb6JjBbho5Nw7VNWe7T5PAj4K7AyclWT1qjoCeDFwJ+C0JNuMP0mSA5PMTzL/x9dfOkEYkiRJjdnaVWClp8Nqq5wbVtUJwOtoLvMDXEdzqX5prgAe1G7vDayxnKc+mabbwFgcc6fYf3w8GwJXV9UtSR4D3Hs5zv0LYJMkD23PvUaSB7QJ+GZV9UPgje051kuyVVVdUFXvBs6i6cqwmKqaV1W7VNUuj1hv6+UIRZIkaXZYFaP71weOS7I2TSXy9W37V4BPtoOj9p3geZ9sn3c+TZX27xPsM5lXAx9NsoDmdZwKvGxpO1fVn5OcluRC4LvAu4FvJbkAmA9csqwnrqqb24FlH0qyYXv+DwC/BL7YtgX4UNvf9+1tcrwIuKg9vyRJ0gqZrX1cs+RYJHXt/232vN5/U86dc9PUO3Vsu0VrdR3CpG7pOoBl8MfVFnYdwpR2vqn/66g8/YK3dx3CpL76wEO7DmFK/7e81+Q68I/0/k83d1/Y30vAY/r+V+dVv/liL97EE+/27Gn9gdvrqq/04nWO55KvkiRJAzNbK679L1VIkiRJWHGVJEkanD6P/J9OVlwlSZI0CFZcJUmSBmbR7Cy4WnGVJEnSMFhxlSRJGphF9nGVJEmS+suKqyRJ0sD0f7mL6WHFVZIkSYNgxVWSJGlgXDlLkiRJ6jErrpIkSQOzKM4qIEmSJPWWFVdJkqSBma2zCpi4SpIkDcxsHZxl4tpDV87p/+eoHRau1XUIU1rY8+4/l6x2Y9chTOnht6zddQhTunKNriOY2lcfeGjXIUzqWQsO7zqEKR2yyyFdhzClHW+e03UIU7pijf7/f7l73/94q1MmrpIkSQOzaJbm9w7OkiRJ0iBYcZUkSRqYRczOkqsVV0mSJA2CFVdJkqSB6f8wu+lhxVWSJEmDYMVVkiRpYJxVQJIkSeoxK66SJEkDM1tXzrLiKkmSpEGw4ipJkjQwziogSZIk9ZgVV0mSpIFxVgFJkiSpx6y4SpIkDYyzCswwSa5vv94zybFdxyNJkqSVM+MrrlV1JbBv13FIkiStKlZcZ6gkWyS5sN0+I8kDRh47JckuSdZN8pkkZyY5N8k+7eMHJPl6khOTXJrkPSPP3TPJ6UnOSXJMkvXa9iOSXJxkQZL3tW3PTHJhkvOTnHrHvgOSJEkzw4yvuI5zNPAs4K1J7gHco6rmJ/lP4AdV9cIkGwFnJvmf9jlzgZ2Am4BfJPkw8A/gzcAeVfX3JG8EXp/ko8DTgG2qqtpjARwKPKGqfjfSJkmStELKWQVmha9ye7eBZwFjfV/3BA5Och5wCrA2sHn72Per6tqquhG4GLg3sBuwHXBa+5wXtO3XAjcCn07ydOCG9hinAUcmeQkwZ6LAkhyYZH6S+edcd9mqebWSJGlGWjTNt76aVYlrVf0O+HOSBwL70VRgAQI8o6rmtrfNq+rn7WM3jRxiIU2VOsD3RvbfrqpeVFW3ArvSJMRPBk5sz/symgrtZsDZSf5pgtjmVdUuVbXLzuvfd5W/dkmSpKGbVYlr62jgDcCGVbWgbTsJeFWSACTZaYpjnAE8PMl92/3XTXK/tp/rhlV1AvA6YMf28a2q6mdVdSjwR5oEVpIkaYX0oeKaZK8kv0hyWZKDJ3h8rSRHt4//LMkWK/hybzMbE9djgWfTdBsY83ZgDWBBkova+0tVVX8EDgC+nGQBcDqwDbA+8O227SfA69unvDfJBe0gsZ8C56+6lyNJknTHSjIH+CjwzzTdJ5+TZLtxu70I+GtV3Rf4b+DdK3veGTs4q6rWa79eAWw/0n4V4153Vf0DeOkExzgSOHLk/pNHtn8APHiCU+86wXGevpzhS5IkLVV1HUCT71xWVZcDJPkKsA/NeKAx+wCHtdvHAh9Jkqpa4fBnY8VVkiRJkxgdNN7eDhy3y6bAb0bu/7Ztm3CfdhzQtcAS43yWx4ytuEqSJM1Ui6Z5OqyqmgfMm96zLD8rrpIkSVpev2Pxweb3atsm3CfJ6sCGwJ9X5qQmrpIkSQPTg1kFzgK2TrJlkjVpBr4fP26f42nmuodmHv0frEz/VrCrgCRJkpZTVd2a5N9ophSdA3ymqi5Kcjgwv6qOBz4NfCHJZcBfaJLblWLiKkmSNDB9WN2qnbf+hHFth45s3wg8c1We064CkiRJGgQrrpIkSQPTg3lcO2HFVZIkSYNgxVWSJGlgpnse176y4ipJkqRBsOIqSZI0MH2YVaALVlwlSZI0CFZcJUmSBsZZBSRJkqQes+IqSZI0MItmac3VxLWHruLmrkOY0hNu6f8vzNFrz+k6hEltXmt1HcKUTlvjxq5DmNJHv/PyrkOY0n898bNdhzCpQ3Y5pOsQpvTO+e/sOoQpHfGgt3QdwpTm3tT/IT2Xrdnvv9190f/v5PSwq4AkSZIGwYqrJEnSwPT/uuf0sOIqSZKkQbDiKkmSNDD2cZUkSZJ6zIqrJEnSwCxK1xF0w4qrJEmSBsGKqyRJ0sDM1gUIrLhKkiRpEKy4SpIkDczsrLdacZUkSdJAWHGVJEkaGOdxlSRJknrMiqskSdLAOKuAJEmS1GMzJnFNcv0qOs6jk3x7VRxLkiRpOtQ03/pqxiSukiRJmtlmXOKaxnuTXJjkgiT7te2LVVKTfCTJAe32XkkuSXIO8PSRfQ5L8pkkpyS5PMmrRx57XpIzk5yX5BNJ5rS3I0fO/bp231cnuTjJgiRfuaPeC0mSNDMtmuZbX83EwVlPB+YCOwIbA2clOXVpOydZG/gk8FjgMuDocbtsAzwGWB/4RZKPA/cF9gMeXlW3JPkYsD9wEbBpVW3fHnuj9hgHA1tW1U0jbZIkSVoOM67iCuwOfLmqFlbVVcCPgAdPsv82wK+q6tKqKuCL4x7/TlXdVFV/Aq4G7gY8DngQTVJ8Xnv/PsDlwH2SfDjJXsDf2mMsAI5K8jzg1omCSHJgkvlJ5v/8ustX4GVLkqTZYhE1rbe+momJ69LcyuKvd+1lfN5NI9sLaarUAT5XVXPb2/2r6rCq+itNpfcU4GXAp9rnPQn4KLAzTbK7RKW7quZV1S5Vtcu2699neV6XJEnSrDATE9cfA/u1/U03AR4JnAn8GtguyVrt5frHtftfAmyRZKv2/nOW4RzfB/ZNcleAJHdJcu8kGwOrVdXXgDcDOydZDdisqn4IvBHYEFhvlbxSSZI0K83WWQVmYh/XbwAPBc6nee/fUFV/AEjyVeBC4FfAuQBVdWOSA4HvJLmBJvFdf7ITVNXFSd4MnNwmprcArwT+AXy2bQN4EzAH+GKSDWkqtR+qqmtW4euVJEmzTJ8HUE2nGZO4VtV67dcCDmpv4/d5A/CGCdpPpOnrOr79sHH3tx/ZPpolB3JB0x1gvN0nj16SJElTmTGJqyRJ0mxRvb6gP31mYh9XSZIkzUBWXCVJkgZmtvZxteIqSZKkQbDiKkmSNDB9XiRgOllxlSRJ0iBYcZUkSRqY2VlvteIqSZKkgbDiKkmSNDD2cZUkSZJ6zIqrJEnSwDiPqyRJktRjVlwlSZIGpuzjKkmSJPWXFVdJkqSBsY+rJEmS1GNWXHvoXrVm1yEsg5u6DmBKGzCn6xAmdVZd23UIU3ruLRt2HcKUPvjEz3YdwpT+kX73Rdvx5n7/rgAc8aC3dB3ClA4+++1dhzClo3Y8tOsQprTebC0lLif7uEqSJEk9ZsVVkiRpYGZrYdrEVZIkaWAWlV0FJEmSpN6y4ipJkjQws7PeasVVkiRJA2HFVZIkaWAWzdKaqxVXSZIkDYIVV0mSpIFxAQJJkiSpx6y4SpIkDcxsXYDAiqskSZIGwYqrJEnSwDirgCRJktRjVlwlSZIGxlkFZpkkVyTZeIL2vZMc3EVMkiRJWrpZm7guTVUdX1VHdB2HJEnS0iya5tvKSHKXJN9Lcmn79c4T7DM3yelJLkqyIMl+y3LsXiSuSdZN8p0k5ye5MMl+bUX0PUkuSHJmkvu2+26S5GtJzmpvDx85xmfafc9Nsk/bPifJ+9rjLkjyqpFTvyrJOe05tmn3PyDJR9rtI5N8KMlPk1yeZN+RmA9qz78gyduW9jra9iOSXNzu+7475E2VJEnqxsHA96tqa+D77f3xbgCeX1UPAPYCPpBko6kO3Jc+rnsBV1bVkwCSbAi8G7i2qnZI8nzgA8CTgQ8C/11VP0myOXASsC1wCPCDqnph+8LPTPI/wPOBLYC5VXVrkruMnPdPVbVzklcA/wG8eILY7gHsDmwDHA8cm2RPYGtgVyDA8UkeCWwy/nUk+SfgacA2VVXL8k2RJEmaTFWv+7juAzy63f4ccArwxtEdquqXI9tXJrmaJo+6ZrID96LiClwAPD7Ju5M8oqqubdu/PPL1oe32HsBHkpxHk0hukGQ9YE/g4Lb9FGBtYPN2/09U1a0AVfWXkfN+vf16Nk1yO5FvVtWiqroYuFvbtmd7Oxc4hyap3Xopr+Na4Ebg00meTvMJYwlJDkwyP8n8+ddftvR3SpIkqd/uVlW/b7f/wO3504SS7AqsCfzvVAfuRcW1qn6ZZGfgicA7knx/7KHR3dqvqwG7VdWNo8dIEuAZVfWLce2Tnfqm9utClv5e3DSynZGv76qqT4zfefzrqKrD22/I44B9gX8DHjv+eVU1D5gH8PZ779/rj1GSJKlb0z2Pa5IDgQNHmua1ucrY4/8D3H2Cpx4yeqe92rzUYJPcA/gC8IKqmrJ7bS8qrknuCdxQVV8E3gvs3D6038jX09vtk4FXjTx3brt5Ek2f1bTtO7Xt3wNemmT1tn20q8CKOgl4YVvpJcmmSe460eto99mwqk4AXgfsuArOL0mSNG2qal5V7TJymzfu8T2qavsJbscBV7UJ6VhievVE50iyAfAd4JCqOmNZ4upFxRXYAXhvkkXALcDLgWOBOydZQFP1fE6776uBj7btqwOnAi8D3k7TD3ZBktWAX9H0if0UcL+2/Rbgk8BHVibYqjo5ybbA6W2efD3wPOC+E7yO9YHjkqxNU6l9/cqcW5IkaWVH/k+z44EXAEe0X48bv0OSNYFvAJ+vqmOX9cC9SFyr6iSaKuZt2oTwvVU1vjPvn7i9Ejva/g/gpRO030qTLL5+XPsWI9vzaTsRV9WRwJHt9gHjnrPeyPYHaQaKjfrf8a+jtesEbZIkSSuk5wsQHAF8NcmLgF8DzwJIsgvwsqp6cdv2SOCfkhzQPu+AqjpvsgP3InGVJEnSzFBVf6YZ2zO+fT7tDE5tt8ovLu+xe5u4jlZEJUmSdLvpHpzVV70YnCVJkiRNpbcVV0mSJE2s5wsQTBsrrpIkSRoEK66SJEkD0/PpsKaNFVdJkiQNghVXSZKkgen5PK7TxoqrJEmSBsGKqyRJ0sA4j6skSZLUY1ZcJUmSBsZ5XCVJkqQes+IqSZI0MPZxlSRJknrMimsP3XVhug5hSqevuXbXIUxps1u7jmByv1l9ra5DmNKpa97SdQhTeuKNc7oOYUq/WaPfMV6xRv8rN3Nv6v86QUfteGjXIUxp//MP7zqEKX19h7d0HcIgOI+rJEmS1GNWXCVJkgZmkbMKSJIkSf1lxVWSJGlgZme91cRVkiRpcJwOS5IkSeoxK66SJEkDY8VVkiRJ6jErrpIkSQNTToclSZIk9ZcVV0mSpIGxj6skSZLUY1ZcJUmSBqasuEqSJEn9ZcVVkiRpYJxVYJZJslGSV3QdhyRJkpbNrE1cgY0AE1dJkjQ4i6hpvfXVbE5cjwC2SnJekvcmOSjJWUkWJHkbQJItklyS5Mgkv0xyVJI9kpyW5NIku7b7HZbkC0lOb9tf0ranPfaFSS5Isl+Hr1eSJGnQZnMf14OB7atqbpI9gX2BXYEAxyd5JPB/wH2BZwIvBM4CngvsDuwN/H/AU9vjPRDYDVgXODfJd4CHAnOBHYGNgbOSnFpVv78jXqAkSZqZ7OM6u+3Z3s4FzgG2AbZuH/tVVV1QVYuAi4DvV/PTcgGwxcgxjquqf1TVn4Af0iTBuwNfrqqFVXUV8CPgwXfEC5IkSZppZnPFdVSAd1XVJxZrTLYAbhppWjRyfxGLv3/jP/os10ehJAcCBwLsv9GuPGLdrad4hiRJmq363A91Os3miut1wPrt9knAC5OsB5Bk0yR3Xc7j7ZNk7ST/BDyaplvBj4H9ksxJsgnwSODMiZ5cVfOqapeq2sWkVZIkaUmztuJaVX9uB1ldCHwX+BJwehKA64HnAQuX45ALaLoIbAy8vaquTPINmn6u59NUYN9QVX9YhS9DkiTNQrN15axZm7gCVNVzxzV9cILdth/Z/4CR7StGHwMWVNXzxx2/gIPamyRJklbCrE5cJUmShmjRLJ1VwMR1Faiqw7qOQZIkaaYzcZUkSRoY+7hKkiRpEGZrV4HZPB2WJEmSBsSKqyRJ0sDM1q4CVlwlSZI0CFZcJUmSBsY+rpIkSVKPWXGVJEkaGPu4SpIkST1mxVWSJGlg7OMqSZIk9ZgVV0mSpIGxj6skSZLUY1ZcJUmSBqZqUdchdMLEtYd+t3r/y/9b3pKuQ5jStnVD1yFMas1b1uk6hCldtkb//zAuWKv/P4sb9PxtvPvC/r+Hl605p+sQprRez7/PAF/f4S1dhzClp1/w9q5DUI+ZuEqSJA3MIvu4SpIkSf1lxVWSJGlgynlcJUmSpP6y4ipJkjQw9nGVJEmSesyKqyRJ0sDM1j6uJq6SJEkDs2iWJq52FZAkSdIgWHGVJEkamHJwliRJktRfVlwlSZIGZrYOzrLiKkmSpEEwcZUkSRqYRdS03lZGkrsk+V6SS9uvd55k3w2S/DbJR5bl2CaukiRJWpUOBr5fVVsD32/vL83bgVOX9cAmrpIkSQNTVdN6W0n7AJ9rtz8HPHWinZI8CLgbcPKyHniVJ65JNkryinb7nkmOXdXnmOL8uyT50DQc96lJtlvVx5UkSZph7lZVv2+3/0CTnC4myWrA+4H/WJ4DT8esAhsBrwA+VlVXAvtOwzmWqqrmA/On4dBPBb4NXLysT0iyelXdOg2xSJKkWWy6V85KciBw4EjTvKqaN/L4/wB3n+Cph4zeqapKMlGwrwBOqKrfJlnmuKYjcT0C2CrJecClwLZVtX2SA2iSv3WBrYH3AWsC/wLcBDyxqv6SZCvgo8AmwA3AS6rqkolOlOSZwFuBhcC1VfXIJI8G/qOqnpxkE+BLwD2B04HHAw8C1gO+C/wEeBjwO2CfqvpHkpfQfKPWBC5r45sL7A08KsmbgWcAn27PMz/JxsD8qtqifZ1Pb88xJ8kTgQ8D2wNrAIdV1XEr/O5KkiRNszZJnTfJ43ss7bEkVyW5R1X9Psk9gKsn2O2hwCPaq/TrAWsmub6qJusPOy19XA8G/req5gIHjXtse5qk7sHAO4EbqmonmqTy+e0+84BXVdWDaMrHH5vkXIcCT6iqHWkSy/HeCvygqh4AHAtsPvLY1sBH28euoUlGAb5eVQ9uj/lz4EVV9VPgeOCgqppbVf87xXuwM7BvVT2K5pPHD6pqV+AxwHuTrDvF8yVJkpaq531cjwde0G6/AFiiYFdV+1fV5lW1BU2+9/mpkla44wdn/bCqrquqPwLXAt9q2y8AtkiyHk0F9Ji2YvsJ4B6THO804Mi2Sjpngsd3B74CUFUnAn8deexXVXVeu302sEW7vX2SHye5ANgfeMByvcLG96rqL+32nsDB7es5BVibxRNooCnJJ5mfZP451122AqeUJEnqhSOAxye5FNijvT82DulTK3PgO3rlrJtGtheN3F/UxrIacE1brZ1SVb0syUOAJwFnt6PTViSWhcCd2u0jgadW1fntZf9HL+X5t3J74r/2uMf+PrId4BlV9YvJghktyR+6xf6zczkMSZK0TFZ2rtXpVFV/Bh43Qft84MUTtB9Jk39NaToqrtcB66/IE6vqb8Cv2r6rpLHj0vZPslVV/ayqDgX+CGw2bpfTgGe1++4JLHUC3BHrA79PsgZNxXXM+Nd1BU1/WZh8ANpJwKvS9jxOstMyxCBJkqRxVnni2mbZpyW5EHjvChxif+BFSc4HLqKZC2xp3pvkgvZcPwXOH/f424A928efSTMlw3VTnP8twM9okt7RQWFfAQ5Kcm47gOx9wMuTnAtsPMnx3k4zKGtBkova+5IkSSus531cp036HNzKSrIWsLCqbk3yUODjy9oNoUtD6Cqw5S3LPnVFV7atG7oOYVI/zzpdhzCly9ZY1HUIU1qn+v+zuMGifse4Tu//4sDfBrBcznr9/3Vhg4X9/2Y//YJ+13fW2Pg+vfiF3mDd+0zrN/Nvf7+8F69zvDu6j+sdbXPgq+0ktzcDL+k4HkmSpJU23fO49tUgEtckh9Bc6h91TFW9c7LnVdWlgH1KJUmSZoBBJK5tgjppkipJkjRbVI9nFZhOg0hcJUmSdLvZ2lVgAN3dJUmSJCuukiRJgzOTZ4WajBVXSZIkDYIVV0mSpIGZrYOzrLhKkiRpEKy4SpIkDYx9XCVJkqQes+IqSZI0MFZcJUmSpB6z4ipJkjQws7PeasVVkiRJA5HZ2kdiNklyYFXN6zqOyRjjqmGMK6/v8YExrip9j7Hv8YEx6o5nxXV2OLDrAJaBMa4axrjy+h4fGOOq0vcY+x4fGKPuYCaukiRJGgQTV0mSJA2CievsMIS+Pca4ahjjyut7fGCMq0rfY+x7fGCMuoM5OEuSJEmDYMVVkiRJg2DiKkmSpEEwcZUkSdIgmLjOcEl2T/Kv7fYmSbbsOqZRSeYkuWeSzcduXcc0KskOXceg6ZfkVUnu3HUck0mybpLV2u37Jdk7yRpdx7U0Se6c5IFdxzGZJKsl2aDrOIYmyd2SPLm93bXreJYmyZ2S3L/rOLRqmbjOYEneCrwReFPbtAbwxe4iWlySVwFXAd8DvtPevt1pUEv6WJIzk7wiyYZdBzNekrOTvLLPSVeS9yTZIMkaSb6f5I9Jntd1XOPcDTgryVeT7JUkXQc0gVOBtZNsCpwM/AtwZKcRjZPklPZ7fRfgHOCTSf6r67hGJflSG+O6wIXAxUkO6jquMX3/fUnyLOBM4JnAs4CfJdm326iWlOQpwHnAie39uUmO7zQorRImrjPb04C9gb8DVNWVwPqdRrS41wD3r6oHVNUO7a1XFZqqegSwP7AZcHb7T+/xHYc1aj/gnjRJ11eSPKGHSdeeVfU34MnAFcB9gd4kCgBV9WZga+DTwAHApUn+M8lWnQa2uFTVDcDTgY9V1TOBB3Qc03gbtt/rpwOfr6qHAHt0HNN427UxPhX4LrAlzYeAvuj778shwIOr6gVV9XxgV+AtHcc0kcNoYrsGoKrOo/lea+BMXGe2m6uZ76ygudTYcTzj/Qa4tusgplJVlwJvpqlePwr4UJJLkjy928igqi6rqkOA+wFfAj4D/DrJ29qqVx+s3n59EnBMVfXye97+rvyhvd0K3Bk4Nsl7Og3sdknyUJoPUt9p2+Z0GM9EVk9yD5pKXN+unoxZo+1i8VTg+Kq6hfZvZE/0/fdltaq6euT+n+lnLnHLBO9dn77PWkGrT72LBuyrST4BbJTkJcALgU91HBNJXt9uXg6ckuQ7wE1jj1dVby4ttn30/pXmn8j3gKdU1TlJ7gmcDny9y/hgsRifCHwNOArYHfgBMLe7yG7z7SSXAP8AXp5kE+DGjmNaTJLXAM8H/kTzO3JQVd3S9im9FHhDl/G1XkvT7ecbVXVRkvsAP+w2pCUcDpwE/KSqzmpjvLTjmMb7BE0l83zg1CT3Bv7WaUSL6/vvy4lJTgK+3N7fDzihw3iW5qIkzwXmJNkaeDXw045j0irgAgQzXHtZe08gwElV9b2OQxrre7s0VVWH32HBTCHJj2gSmWOr6h/jHvuXqvpCN5HdFsPZNJfCPg18rapuGnns61XVeVUYoK3+XltVC5OsA2xQVX/oOq4xSd4GfKaqfj3BY9tW1c87CGup2oR6vfaSslZSktWr6tau4xgzgN+Xp9N8OAb4cVV9o8t4JtK+b4fQ/P+D5gPVO6qqTx8CtAJMXGewJO+uqjdO1daVJM+sqmOmautSktdW1QfGtb2mqj7YUUiLSXKfqrp8XNuWVfWrrmIaL8kzgROr6rokbwZ2pvkHck7Hod1mKd0qrmsvI/dCki8BLwMWAmcBGwAfrKr3dhrYiLZbxTtoqoUnAg8EXldVfRoU+hrgs8B1NB9KdwIOrqqTOw2sNZDfl7sDDwEWAWf1KamGZrYa4H+q6jFdx6JVr4/9UrTqTDSI6J/v8CiW7k3L2Nal50/QdsAdHcQkjl3Gti69pf0nvDvNQJ1PAx/vOKbxzgH+CPyS5tL2H4ErkpyT5EGdRna7vg8qgv4PLAJ4YRvjnjT9mP8FOKLbkBbT69+XJC+mmVXgacC+wBlJXthtVIurqoXAoj7OBKOVZx/XGSjJy4FXAPdJsmDkofWB07qJ6nZJ/pmmP+amST408tAGNINiOpfkOcBzgS3HTaGyPvCXbqK6XZJtaEaUbzhukNgGwNrdRLVUC9uvTwLmVdV3kryjy4Am8D2a7iAnASTZE3gGTWXuYzTVpa6NDir6SNsHt2+XzJYYWNS/SS4YC+iJwBfa/sJ9CrLvvy8HATtV1Z8BkvwTTd/Rz3Qa1ZKuBy5I8j3amXUAqurV3YWkVcHEdWb6Ek1F5l3AwSPt11VV50kXcCUwn2aqrrNH2q8DXtdJREv6KfB7YGPg/SPt1wELJnzGHev+NFWtjYCnjLRfB7yki4Am8bt2kODjgXcnWYv+Xe3Zrapue9+q6uQk76uql7bx9kHfBxVB/wcWQTOt3ck0Fes3JVmf5pJ3X/T99+XPNH9nxlzXtvXN1+nB4FmtevZxnQXSrGxyWxWuqv6vw3Buk2SNPvUhHKIkD62q07uOYzLtIIm9gAuq6tJ2uqQd+tKnEKBNZL4PfKVt2o8mcdiLpg/fzl3FNpm+DSqCJQYWrQus36c+kO3AtrnA5VV1TVsx3LSq+vCBtPe/L0k+D+wAHEczvdQ+NB/mF0C/ZoXRzGTFdQZrVw75L5oJ6q8G7g38nP5MWn7OBJc6r6Wpxr5j7FJUF5L8pKp2T3Idi8/9F5qZDzpdJjLJG6rqPcBz224Ni+nT5bCquiHJ1TSjkC+l6Q7StymSngu8Ffgmzff7tLZtDs2cpJ1LcjfgP4F7VtU/J9kOeChNH8heaJOuVwCbAwfS/O25P/2a07WA7WiuWBwOrEuPutcM4Pflf9vbmOPar31a3IYkv2KCeVur6j4dhKNVyIrrDJbkfOCxNKMrd0ryGOB5VfWijkMDbhuBvJCmawPAs4F1aCaA372qnrK05852SZ5SVd9K8oKJHq+qz93RMS1NO/3ZLjSrpN2vnQP3mKp6eMehAbeNQP58Ve3fdSyTSfJdmj63h1TVjklWB86tqh06Du02SY6m6f7z/Kravk1kf1pVc7uN7HZJPk7TNeCxVbVtmuWST66qB3ccGtD/35ehaCvpY9amWaL2LlV1aEchaRWx4jqz3VJVf06yWpLVquqHST7QdVAj9hh3CfaCJOdU1c7pwdrcbUJzUVVt03Us41XVt9qvvUlQJ/E0mimHzoFm6eG2X2EvtJe0751kzaq6uet4JrFxVX01yZsAqurWJAunetIdbKuq2m/sKkBbPezTwCeAh7R/Y84FqKq/Jlmz66BG9Pr3JckuNPOj3puRHKL6t1z3+Ct2H0gz77WJ68CZuM5s1yRZDzgVOKq9/PT3KZ5zR5qTZNeqOhMgyYO5fQnLzvvttQnNL5Js3pd+wWOSfItJli+sqr3vwHCmcnNV1Vi3kPRv6WFoVnE7rZ1BYnQEcp/66/29rSKNvY+70b8lk29Ociduj3ErRlbF64lb2g+lYzFuQr8GZ/X99+UompkFLqBf79tikowWRVajqWKb88wAfhNntn1oRvS+jmZ98w1p+nT1xYuBz7TJdWhGSL+4/UP9rk4ju92daZYOPJPFE5quE8P3dXz+5THR0sOf7Dim8cb67a1Gz/rqjXg9cDywVZLTgE1o5tHsk7fSLDywWZKjgIfTr3mPAT4EfAO4a5J30ryHb+42pMX0/fflj1V1/NS7dW50NphbgV/Rk/7qWjn2cVXnxiaJrqq+VY9I8qiJ2qvqR3d0LEOWHi49PJEk61TVDV3HsTRtv9b707yPv+jjrBxtVXg3mhjPqKo/dRzSEtp5kB9HE+P3e7ikb29/X5I8DngOzSwct1XTq6pXU09lAKsKasWYuM5AE4yEX0zXI+LHtPMTPgPYgsX7SvWpKtxLSb5aVc9KcgETz3rQq/5mfZdkbHT+elW1eZIdgZdW1Ss6Dm0xSR7Gkr8vn+8soAkk2ZQl+z+e2l1ES2q7CtyNxWPsVXegvkryRWAb4CJu7ypQVdWr1bPGxkuMazu7qvqyEp5WkF0FZqCqWh8gydtpJtH/Ak1Csz9wjw5DG+84mj56Z9O/fnDAbf0IPwxsC6xJ0wf37z1I/l/Tfn1yp1EsgzQre70buCvNz2EvphQb5wPAE2guxVNV5yd5ZKcRjZPkC8BWwHncvrpSAb1JXJO8m2YO3MWSGpp+9r2Q5FU0XRquonkfQxNjLz7sDeD35cFVdf+ug1iaDGtVQa0AE9eZbe+q2nHk/sfbKbL6MqryXlW1V9dBTOEjNNN0HUPTuf/5wP06jQioqt+3X3+d5O7ArjT/fM/q02TvrfcAT+nb5djxquo34wbA923E/i7AdtXvy2RPpZnGqZcfRFuvoYmxj6s9Qf9/X36aZLuqurjrQJZiSKsKagWYuM5sf0+yP81qQEXTL6lPswr8NMkOVXVB14FMpqouSzKnqhYCn22n0XlT13EBJHkxzQeRH9BUZj6c5PCq6tO64Vf1+J/wmN+0l+EryRo0yU3fYr4QuDvNVZS+uhxYg55eQWn9hv7NxjCq778vuwHntRP830TPuidV1XHAcRnAqoJaMSauM9tzgQ+2t9HVgPpid+CAvv4BbN3QzvF4Xrtgwu/p17rhBwE7jVWP2oExPwX6lLjObyem/yb9HczxMprfk02B3wEnA6/sNKIlbQxc3M5wMfo+dj3DxagbaH5Xxg/c6c1KbjTJ9SlJvsPiMfZl6rO+/770/SrZmHOTvJKm28Dokue96our5WfiOoNV1RU0U2JNKMmbqqrLaaf+ucNzL6t/oenX+m8004ptRjOgrC/+THMJbMx1bVufbECT0Ow50lZAX/4R04587/XKWcBhXQewDI5vb332f+1tzfbWN73+fWm7J+0ObF1Vn23nwV2v67gm8AXgEpq+64fT/H73uZKtZeSsArPYRKMuO4hhiT+ATlcytSSvbzfnAjvQDHQrmg8qC6rqgG4iG6b2Z+8lLDli3+rMcmoXINi8qn7RdSxa9YayJG2Sc6tZ6nxBVT2w7QL046rarevYtHKsuM5unS7FOPoHkGYN9jWAL9JMWt6pCaaZWkwPujOMTZI/NnH+mOM6iGVSST4HvKaqrmnv3xl4f8+SwuOAHwP/Q88GZU0yvV3fRpuT5Ck0i2OsCWyZZC5weB+6M/R9tbkkb6iq9yT5MBPE2aPuFr1eknbE2BzH1yTZHvgDzUwNGjgT19mt63J7n/8A9nqaqap6W9cxLIcHjiWtcNva8Dt1GM9E1qmqN3YdxETGprcbiMNoZrg4BaCqzktyny4DGjG22tzTaQa5fbG9/xyaqbG6NnYZe36nUUyt70vSjpnXfkh+C033lfXoz4w6WgkmrrNbpxVXevwHsKp+3XUMy6K9xP0GlhyA8NjOglrSaknuXFV/BUhyF/r3t+fbSZ5YVSd0HchUktyVxb/XfZo4/5aqunbctGK9WM9+bLW7JO+vql1GHvpWks6Txar6Vvv1c13HMoW+L0kLQFV9qt38EdCXD09aBfr2z0N3rGM6Pn/v/wCOu0y7Jk13hj4sQDDmKOBomgrxy4AXAH/sNKIlvR84PckxNB+W9gXe2W1IS3gN8KYkN9NcYuzjZfi9ad7LewJX06xO9XOaDy19cVGS5wJzkmwNvJpmlos+WXd0OdAkWwK9+dCc5H7Af7Bkf+u+fBjdBDgW+BtNN69DgT06jWgCrsw4czk4awZr/wB+HLhbVW2f5IE0ixK8o+PQbtPnNbnHS1NG2gfYraoO7joeuH0Jw7EBCG3bWVX14K5jG5VkO2DsH+8P+jZ5eZLVaEYdb1lVhyfZHLhHVf2s49Bu0y4e8ljgf9pBJ48BnldVL+o4tNskWQc4hNtHxJ8EvKOqbuwuqsUl2QuYRzMtVmg+ALy0qk7qNLBW+33+fzQrCt7W37qqzu4sqBFLWUr1tr8/fZHkRG5fmXH0fXx/Z0FplTBxncGS/Ihmns9PVNVObduFVbV9t5EN29ho1a7jAEhyRlXtluQk4EPAlcCxVbVVx6GRZIOq+lvbNWAJVfWXOzqmpUnycZpL2o+tqm3bvnEn9+kDQJL5VbVLm9jsVFWLkpw/bnW8TiXZuarO6TqOqbTVuG3au5f0aaWvsQ+jXccxXpKXA6+guew+OiB0feC0qnpeJ4Ethf/rZi67Csxs61TVmeP6m93aVTBjBjZKenSt69VoZkHoTfUIeEeSDYF/Bz5MMwfk67oN6TZfounCcDaLf7/H1obvU7+zh1TVzu2qaGMDyPo2x+c1SdYDTgWOSnI1/VoJD+D9aZYgPhY4uqou7Dqg8ZI8f1zTjkmoqs93ElBr5APet5K8AvgGiy9A0PUHvS8B3wXeBYxecbquB7FNZBArM2r5WXGdwZJ8l2bi/GPaf8r7Ai+qqiFM/N8LST47cvdW4Argk1V1dTcRaTok+RnwMOCs9ndlE5qKay8q63Db4MV/0HyA2h/YEPhi35KGNnF9FrAfzQepo3vWPenDI3fXBh4HnFNV+3YUEgBpVhAsJh40W1XVpw96vZfkYuC+QJ9XZtQKMHGdwdppaObR/EP+K80v8PPaFbU0AwykH/P3q+pxU7V1Kcn+NInWzsDnaAaQvbmquh7AeJsk7x4/ZddEbX2RZAeaGS/2q6q+Va9vk2Qj4CtVNZSlTLUMktx7ovahzBijpevTmutaxarq8qrag2YU6DZVtbtJ6/JJcp8k30ryxyRXJzmuR/NSQjMLw5toJ9uuqgXAszuNqJVk7fby58ZJ7pzkLu1tC2DTjsNbTFUdRZNkvQv4PfDUPiWtrcdP0NarqydJtk1yWJoFPD5MM6PAvToOayp/B7bsOogxSV7ZJtNj9+/cdh3Q8rlugtuVnUakVcI+rjNYktfQrEh1HfDJJDsDB1fVyd1GNihfAj5Ks1gCNEnhl4GHdBbR4nrZj7n1UuC1NNM3nc3tl0D/Bnyko5iWqqouoVnbvFdGB8UkWTDy0PrAad1EtVSfoZme7QlV1cskIYuvoDUH2Bb4ancRLeElVfXRsTttf+uXAB/rMKYhOgfYjOZqY4CNgD8kuYrmPe7FLA1afiauM9sLq+qDSZ4A/BPwL8AXABPXZbdOVX1h5P4XkxzUWTRL+lOSrWj/Ebf9mH/fbUiNqvog8MEkr6qqD0/5BC3NYAbFVNVDu45hGbxvZPtW4NdV9duugpnAnCSpth9fkjk0c0hr+XyPZoaVkwCS7Ekzr+tnaT4E9KX4oOVkV4GZbazC9UTg81V1ERN3/NfSfTfJwUm2SHLvJG8AThi77N11cMArgU8A2yT5HU2F8+WdRrSkP6RdyjfJm5N8va3+axlU1bVVdUVVPQf4LU23kALWa+eb7VySr7ZfL0iyYOR2wbgqcefaFbQuoalY3xm4uduIlnAicHSSxyV5HM0VnhM7jmmIdhudm7e90vjQqjoDWKu7sLSyHJw1g7Uj4jel6b+1I81lsVP6OEdgX7UjfZemNyN92xHnq1XVdV3HMt7Y5ORJdgfeAbwXOLSqrHgshyT/BhwGXMXty6j2YpR0kntU1e+HMCAmybNofgZPofkg/wjgoKo6tsu4xqRZDOOlNLMdQFM5/FRVLVz6szRekpOB7wNfaZv2o+knvhft7CFdxaaVY+I6g7V/AOcCl1fVNUn+Cdi0HcCjGSDJfwLvqapr2vt3Bv69qt7caWAjxhZsSPIu4IKq+lJ6tIjDUCS5jGa+2T93HcuQtQs4PH5sSrt26rP/6dlCDncCNq+qX3Qdy1Al2Rh4K7B723Qa8Daa1bQ2r6rLuopNK8fEdYZrE5mtaeYrBKCqTu0uomFJsgbNpfdHtk2n0KxEdktnQY2YKAHMBEsydinJt4Hf0VQ7dqaZi/TMPiUKQ5DkhzQJV18G391mYIuKXFBVO4zcXw04f7StS0n2pqkIr1lVWyaZCxxeVXt3G5nUDw7OmsGSvBh4Dc10NOcBuwGnc/ua8Zrax4E1uH1E77+0bS/uLKLFzUmy1tiSlW2lpm/9t55Fc3nufW3l/x40SxFr+VwOnJLkOyy+otJ/dRfSbTGs33UMy+HENEskf7m9vx9wQofxjPdWYFeaD8lU1XlJejNdV98l+UBVvXbc7BG38QPA8Jm4zmyvAR4MnFFVj0myDfCfHcc0NA8eVxn8QXupsS+OAr6f21f4+leaCfT7ZGNgPsDIYKLeTTs1AP/X3tbEUeYrrKoOSvIM4OFt07yq+kaXMY1zS1VdO26KOy+NLruxWWDeN+leGiwT15ntxqq6MQltVe6SJPfvOqiBWZhkq6r6X7htNbLeDJKoqne3o7bHBnK8fXQkbU98h9uXslybZrDgL4AHdBnU0FTV2wCSrFNVN3Qdz5BV1deAr3Udx1JclOS5NFdTtgZeTbOQg5bB2Pys7ewRwG1d5jZzfMfMYOI6s/22XYHlm8D3kvwV6M3o3oE4CPhhksvb+1vQVDV7o6q+SzPPZy+N7zvYToXlSkDLKclDgU8D6wGbJ9kReGlV+V4ugwH1w30VcAhNd5AvASfRzMah5ZDkFGBvmjznbODqJKdV1es7DUwrzcFZs0SSRwEbAidWVd/mLeytJGsD/05T0bwGOAv476q6scu4xiR5OvBu4K40/4D79k94QuMHyGhqSX4G7AscPzYgL8mFVbV9t5FpOlhZXzkjs5m8mKba+taxqfm6jk0rx4rrDNfOnbl1VX22nfZlU2CyuUm1uM/TLFH69vb+c2n6UD2zs4gW9x7gKVX1864DWZokoxWO1WhmFujlcqB9V1W/Gdf3sTfdVrRqJHkY8CmsrK+s1duBoM+iqWBrhjBxncGSvBXYBbg/zTJ3awBf5PZBCZra9lW13cj9Hya5uLNolnRVn5PW1uiI81tp+rz2tX9hn/2mTWqqnabtNUDfv/dafv8NPAE4HqCqzk/yyMmfogkcTtPN4idVdVY7PuHSjmPSKmDiOrM9DdgJOAegqq4cW3pTy+ycJLu1ywSS5CG0I+R7Yn6So2n6MY9OkfT1ziIaZ2RQ0Xrt/eu7jWiwXgZ8kOaqye+Ak2mW/NUMY2V95VXVMcAxI/cvB54xdj/Jm6rqXV3EppVj4jqz3VxVlaTgtmVBtXweBPw0yf+19zcHfpHkAvqx3OYGwA3AniNtBfQmcU2yPU33iru09/8EvKCqLuw0sIGpqj8B+3cdh6adlfU7xjMBE9cBMnGd2b6a5BPARkleArwQ+GTHMQ3NXl0HMJmq6tUMB0sxD3h9Vf0QIMmj27aHdRjT4LST0L+KZmaL2/52O6H6jDNaWb+S5nK3lfVVL1Pvoj5yVoEZLsnjaapxAU6qqu91HJJWoXbWgxfRzIk6uqzvCzsLapwk549f3nWiNk2uXfji08AFwKKx9tH5KiUtm74tja1lZ8V1hmsTVZPVmesLNKtQPYFmMML+9O+y4uVJ3sLtK9o8j2b5Ui2fG6vqQ10HoenVDiL6IM0S3UWzTPfr2j6aWnWsuA6UFdcZaEATbWsljcxVuKCqHtj2iftxVe3WdWxj2lVr3gbsTvNz+WPgbVX1104DG5h2NaWtaQZljQ7EO6ezoLTKJTkD+Cjw5bbp2cCrquoh3UU18yT5/6rKJdAHyMRVGrAkZ1bVrklOpVmN6g/AmVV1n45D0yqW5F3AvwD/y+1dBaqqHttdVFrVJpok3641yy/J/YCPA3erqu2TPBDYu6pchWzgVus6AE2vJLsn+dd2e+N2gIdmjnltRfPNNPM+XkyzklZvJPleu/Tw2P07Jzmpw5CG6pnAfarqUVX1mPZm0jrzfDfJwUm2SHLvJG8ATkhylyR36Tq4Afkk8CbgFoCqWkBTvdbA2cd1BptgAYI1cQGCGWHcalRjMwt8tP3at2nPNq6qa8buVNVfk9y1w3iG6kJgI+DqjuPQ9HpW+/Wl3N7lKzRJVwFeTVk261TVmePmw721q2C06pi4zmwuQDBzjX0f7w88mHaVHeApwJmdRLR0i5JsXlX/B5BkCybug63JbQRckuQsFu/j6nRYM8sbgROr6m/toMadgbfbl3m5/SnJVrR/a5LsC/y+25C0Kpi4zmwuQDBDjaxGdSqwc1Vd194/jGZJ1T45BPhJkh/RVI4eARzYbUiD9NauA9Ad4s1V9dUkuwOPBd5H01fTwVnL55U080Vvk+R3wK9oZjTRwJm4zlBpro982wUIZry7ATeP3L+5beuNqjoxyS40yeq5NMvT/qPToAbI+VpnjbHlXZ8EfLKqvpPEAUXLqZ0+bI+2YLPa2Id7DZ+J6wzVVlqfCbwe+BvNJeVDXYBgxvk8cGaSb7T3nwoc2Vk0E0jyYpplK+8FnEczP+XpNNUkLaNx09ytCawB/N3p7Wac37UFh8cD706yFg6kXm7tgNDn0640N9bXtape3V1UWhWcDmsGS/I54CNVdVbXsWj6JNmZ5vI7wKlVdW6X8YyX5AKafrhnVNXcJNsA/1lVT+84tMFqr6jsA+xWVQd3HY9WnSTr0Cw1fUFVXZrkHsAOVXVyx6ENSpKfAmew5Epzn+ssKK0SJq4zWJJLgPsCvwb+PtY+fo5AaTolOauqHpzkPOAhVXVTkouq6gFdxzZ0YwtQdB2H1Dcu6Tpz2VVgZntC1wFIwG/by3bfBL6X5K80H6a0HJKMVqhXo5nq7saOwpH67gvt2I5vs/gsHH/pLiStClZcJd1hkjwK2JBmup+bp9pft0vy2ZG7twJX0AzecV5XaZwkrwTeCVzD7X3Dy1UFh8/EVZJ6Lskc4NVV9d9dxyINQZLLgV2r6k9dx6JVy5GKktRzVbUQeE7XcUgDchlwQ9dBaNWzj6skDcNpST4CHM3igy1dUUla0t+B85L8kMX7uDod1sDZVUCSBqD9BzxeVZXz4UrjJHnBRO1OhzV8Jq6SNABJ7tOuBjRpmyTNZCaukjQAE81LmeTsqnpQVzFJfZPkq1X1rHbhk/EJTlXVjl3EpVXHPq6S1GPtSmMPADYcN5frBsDa3UQl9dZr2q8/Bw4aaQ/wnjs+HK1qJq6S1G/3B54MbAQ8ZaT9OuAlXQQk9VVV/b7dvG9VLbbQSfshUANnVwFJGoAkD62q07uOQ+qzJC8HXgHcB/jfkYfWB06rqud1EphWGRNXSRqAJO8B3gH8AzgReCDwuqr6YqeBST2SZEPgzsC7gINHHrrO5V5nBhNXSRqAJOdV1dwkT6PpOvB64FQHm0iaTVw5S5KGYY3265OAY6rq2i6DkaQuODhLkobhW0kuoekq8PIkmwA3dhyTJN2h7CogSQOR5C7AtVW1MMk6wAZV9Yeu45KkO4oVV0kajm2ALZKM/u3+fFfBSNIdzcRVkgYgyReArYDzgIVtc2HiKmkWsauAJA1Akp8D25V/tCXNYs4qIEnDcCFw966DkKQu2VVAkoZhY+DiJGcCN401VtXe3YUkSXcsE1dJGobDug5AkrpmH1dJkiQNghVXSeqxJD+pqt2TXEczi8BtDwFVVRt0FJok3eGsuEqSJGkQnFVAkiRJg2DiKkmSpEEwcZUkSdIgmLhKkiRpEExcJUmSNAj/Px/6v9Jq3TQTAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 864x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corrmat = df.corr()\n",
+    "f, ax = plt.subplots(figsize=(12, 9))\n",
+    "sns.heatmap(corrmat, vmax=.8, square=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "\n",
+    "# Show the joint distribution using kernel density estimation\n",
+    "g = sns.jointplot(\n",
+    "    data=df,\n",
+    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
+    "    kind=\"kde\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1350b1a90>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 468.975x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
+    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
+    "   .add_legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "c61deff2839902ac8cb4ed411eb10fee",
+   "translation_date": "2025-09-06T14:08:28+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sk/5-Clustering/2-K-Means/notebook.ipynb b/translations/sk/5-Clustering/2-K-Means/notebook.ipynb
new file mode 100644
index 000000000..9b772a894
--- /dev/null
+++ b/translations/sk/5-Clustering/2-K-Means/notebook.ipynb
@@ -0,0 +1,229 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "3e5c8ab363e8d88f566d4365efc7e0bd",
+   "translation_date": "2025-09-06T14:19:22+00:00",
+   "source_file": "5-Clustering/2-K-Means/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Zameriame sa iba na 3 žánre. Možno dokážeme vytvoriť 3 klastre!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/translations/sk/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
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--- /dev/null
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@@ -0,0 +1,642 @@
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+   "display_name": "R",
+   "language": "R",
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+   "toc_visible": true
+  },
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+   "original_hash": "ad65fb4aad0a156b42216e4929f490fc",
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+   "source_file": "5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GULATlQXLXyR"
+   },
+   "source": [
+    "## Preskúmajte K-Means zhlukovanie pomocou R a princípov Tidy dát.\n",
+    "\n",
+    "### [**Kvíz pred prednáškou**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)\n",
+    "\n",
+    "V tejto lekcii sa naučíte, ako vytvárať zhluky pomocou balíka Tidymodels a ďalších balíkov v ekosystéme R (nazveme ich priatelia 🧑‍🤝‍🧑) a datasetu nigérijskej hudby, ktorý ste importovali skôr. Pokryjeme základy K-Means pre zhlukovanie. Majte na pamäti, že ako ste sa naučili v predchádzajúcej lekcii, existuje mnoho spôsobov, ako pracovať so zhlukmi, a metóda, ktorú použijete, závisí od vašich dát. Skúsime K-Means, pretože je to najbežnejšia technika zhlukovania. Poďme na to!\n",
+    "\n",
+    "Pojmy, o ktorých sa dozviete:\n",
+    "\n",
+    "-   Silhouette skórovanie\n",
+    "\n",
+    "-   Metóda lakťa\n",
+    "\n",
+    "-   Inercia\n",
+    "\n",
+    "-   Variancia\n",
+    "\n",
+    "### **Úvod**\n",
+    "\n",
+    "[K-Means zhlukovanie](https://wikipedia.org/wiki/K-means_clustering) je metóda odvodená z oblasti spracovania signálov. Používa sa na rozdelenie a rozčlenenie skupín dát do `k zhlukov` na základe podobností ich vlastností.\n",
+    "\n",
+    "Zhluky môžu byť vizualizované ako [Voronoi diagramy](https://wikipedia.org/wiki/Voronoi_diagram), ktoré zahŕňajú bod (alebo 'semienko') a jeho zodpovedajúcu oblasť.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/voronoi.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Infografika od Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "K-Means zhlukovanie má nasledujúce kroky:\n",
+    "\n",
+    "1.  Data scientist začne tým, že špecifikuje požadovaný počet zhlukov, ktoré sa majú vytvoriť.\n",
+    "\n",
+    "2.  Následne algoritmus náhodne vyberie K pozorovaní z datasetu, ktoré budú slúžiť ako počiatočné centrá zhlukov (t.j. centroidy).\n",
+    "\n",
+    "3.  Potom sa každé zvyšné pozorovanie priradí k najbližšiemu centroidu.\n",
+    "\n",
+    "4.  Následne sa vypočíta nový priemer každého zhluku a centroid sa presunie na tento priemer.\n",
+    "\n",
+    "5.  Teraz, keď boli centrá prepočítané, každé pozorovanie sa opäť skontroluje, či by nemohlo byť bližšie k inému zhluku. Všetky objekty sa znovu priradia pomocou aktualizovaných priemerov zhlukov. Kroky priraďovania zhlukov a aktualizácie centroidov sa iteratívne opakujú, kým sa priradenia zhlukov prestanú meniť (t.j. keď sa dosiahne konvergencia). Typicky algoritmus končí, keď každá nová iterácia vedie k zanedbateľnému pohybu centroidov a zhluky sa stanú statickými.\n",
+    "\n",
+    "<div>\n",
+    "\n",
+    "> Upozorňujeme, že kvôli náhodnosti počiatočných k pozorovaní použitých ako východiskové centroidy môžeme získať mierne odlišné výsledky pri každom použití postupu. Z tohto dôvodu väčšina algoritmov používa niekoľko *náhodných začiatkov* a vyberie iteráciu s najnižším WCSS. Preto sa dôrazne odporúča vždy spustiť K-Means s viacerými hodnotami *nstart*, aby sa predišlo *nežiaducemu lokálnemu optimu.*\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Táto krátka animácia využívajúca [ilustrácie](https://github.com/allisonhorst/stats-illustrations) od Allison Horst vysvetľuje proces zhlukovania:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/kmeans.gif\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Základná otázka, ktorá pri zhlukovaní vyvstáva, je táto: ako viete, na koľko zhlukov rozdeliť vaše dáta? Jednou z nevýhod použitia K-Means je skutočnosť, že budete musieť určiť `k`, teda počet `centroidov`. Našťastie `metóda lakťa` pomáha odhadnúť dobrú východiskovú hodnotu pre `k`. Vyskúšate si to o chvíľu.\n",
+    "\n",
+    "### \n",
+    "\n",
+    "**Predpoklady**\n",
+    "\n",
+    "Nadviažeme presne tam, kde sme skončili v [predchádzajúcej lekcii](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb), kde sme analyzovali dataset, vytvorili množstvo vizualizácií a filtrovali dataset na pozorovania, ktoré nás zaujímajú. Určite si ju pozrite!\n",
+    "\n",
+    "Budeme potrebovať niekoľko balíkov na dokončenie tohto modulu. Môžete si ich nainštalovať pomocou: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`\n",
+    "\n",
+    "Alternatívne, nasledujúci skript skontroluje, či máte balíky potrebné na dokončenie tohto modulu, a nainštaluje ich za vás, ak niektoré chýbajú.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ah_tBi58LXyi"
+   },
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7e--UCUTLXym"
+   },
+   "source": [
+    "Poďme na to!\n",
+    "\n",
+    "## 1. Tanec s dátami: Zúžme to na 3 najpopulárnejšie hudobné žánre\n",
+    "\n",
+    "Toto je zhrnutie toho, čo sme robili v predchádzajúcej lekcii. Poďme si trochu pohrať s dátami!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Ycamx7GGLXyn"
+   },
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data into a tibble\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\", show_col_types = FALSE)\n",
+    "\n",
+    "# Narrow down to top 3 popular genres\n",
+    "nigerian_songs <- df %>% \n",
+    "  # Concentrate on top 3 genres\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \n",
+    "  # Remove unclassified observations\n",
+    "  filter(popularity != 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Visualize popular genres using bar plots\n",
+    "theme_set(theme_light())\n",
+    "nigerian_songs %>%\n",
+    "  count(artist_top_genre) %>%\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\n",
+    "                       fill = artist_top_genre)) +\n",
+    "  geom_col(alpha = 0.8) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\n",
+    "  ggtitle(\"Top genres\") +\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b5h5zmkPLXyp"
+   },
+   "source": [
+    "🤩 To išlo skvele!\n",
+    "\n",
+    "## 2. Viac prieskumu údajov.\n",
+    "\n",
+    "Aké čisté sú tieto údaje? Poďme skontrolovať odľahlé hodnoty pomocou boxplotov. Zameriame sa na číselné stĺpce s menším počtom odľahlých hodnôt (aj keď by ste mohli odľahlé hodnoty vyčistiť). Boxploty môžu ukázať rozsah údajov a pomôžu pri výbere stĺpcov na použitie. Upozorňujeme, že boxploty neukazujú rozptyl, čo je dôležitý prvok pre dobré zoskupiteľné údaje. Pre ďalšie čítanie si pozrite [túto diskusiu](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot).\n",
+    "\n",
+    "[Boxploty](https://en.wikipedia.org/wiki/Box_plot) sa používajú na grafické znázornenie distribúcie `číselných` údajov, takže začnime *výberom* všetkých číselných stĺpcov spolu s populárnymi hudobnými žánrami.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HhNreJKLLXyq"
+   },
+   "source": [
+    "# Select top genre column and all other numeric columns\n",
+    "df_numeric <- nigerian_songs %>% \n",
+    "  select(artist_top_genre, where(is.numeric)) \n",
+    "\n",
+    "# Display the data\n",
+    "df_numeric %>% \n",
+    "  slice_head(n = 5)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uYXrwJRaLXyq"
+   },
+   "source": [
+    "Pozrite sa, ako výberový pomocník `where` uľahčuje tento proces 💁. Preskúmajte ďalšie podobné funkcie [tu](https://tidyselect.r-lib.org/).\n",
+    "\n",
+    "Keďže budeme vytvárať boxplot pre každú číselnú vlastnosť a chceme sa vyhnúť používaniu slučiek, preformátujme naše údaje do *dlhšieho* formátu, ktorý nám umožní využiť `facets` - podgrafy, z ktorých každý zobrazuje jednu podmnožinu údajov.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "gd5bR3f8LXys"
+   },
+   "source": [
+    "# Pivot data from wide to long\n",
+    "df_numeric_long <- df_numeric %>% \n",
+    "  pivot_longer(!artist_top_genre, names_to = \"feature_names\", values_to = \"values\") \n",
+    "\n",
+    "# Print out data\n",
+    "df_numeric_long %>% \n",
+    "  slice_head(n = 15)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-7tE1swnLXyv"
+   },
+   "source": [
+    "Oveľa dlhšie! Teraz je čas na nejaké `ggplots`! Aký `geom` použijeme?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "r88bIsyuLXyy"
+   },
+   "source": [
+    "# Make a box plot\n",
+    "df_numeric_long %>% \n",
+    "  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_wrap(~ feature_names, ncol = 4, scales = \"free\") +\n",
+    "  theme(legend.position = \"none\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EYVyKIUELXyz"
+   },
+   "source": [
+    "Jednoduché-gg!\n",
+    "\n",
+    "Teraz môžeme vidieť, že tieto údaje sú trochu šumivé: pri pozorovaní jednotlivých stĺpcov ako boxplotov môžete vidieť odľahlé hodnoty. Mohli by ste prejsť dataset a odstrániť tieto odľahlé hodnoty, ale to by údaje dosť zredukovalo.\n",
+    "\n",
+    "Zatiaľ si vyberme, ktoré stĺpce použijeme na náš klastrovací cvičenie. Vyberme si číselné stĺpce s podobnými rozsahmi. Mohli by sme zakódovať `artist_top_genre` ako číselnú hodnotu, ale zatiaľ ho vynecháme.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-wkpINyZLXy0"
+   },
+   "source": [
+    "# Select variables with similar ranges\n",
+    "df_numeric_select <- df_numeric %>% \n",
+    "  select(popularity, danceability, acousticness, loudness, energy) \n",
+    "\n",
+    "# Normalize data\n",
+    "# df_numeric_select <- scale(df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D7dLzgpqLXy1"
+   },
+   "source": [
+    "## 3. Výpočet k-means zoskupovania v R\n",
+    "\n",
+    "K-means môžeme vypočítať v R pomocou zabudovanej funkcie `kmeans`, pozrite si `help(\"kmeans()\")`. Funkcia `kmeans()` prijíma dátový rámec so všetkými číselnými stĺpcami ako svoj hlavný argument.\n",
+    "\n",
+    "Prvým krokom pri používaní k-means zoskupovania je určiť počet klastrov (k), ktoré budú vytvorené vo finálnom riešení. Vieme, že v dátovom súbore sme identifikovali 3 hudobné žánre, takže skúsme 3:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "uC4EQ5w7LXy5"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Kmeans clustering for 3 clusters\n",
+    "kclust <- kmeans(\n",
+    "  df_numeric_select,\n",
+    "  # Specify the number of clusters\n",
+    "  centers = 3,\n",
+    "  # How many random initial configurations\n",
+    "  nstart = 25\n",
+    ")\n",
+    "\n",
+    "# Display clustering object\n",
+    "kclust\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hzfhscWrLXy-"
+   },
+   "source": [
+    "Objekt kmeans obsahuje niekoľko informácií, ktoré sú dobre vysvetlené v `help(\"kmeans()\")`. Momentálne sa zamerajme na niekoľko z nich. Vidíme, že údaje boli rozdelené do 3 klastrov s veľkosťami 65, 110, 111. Výstup tiež obsahuje centrá klastrov (priemery) pre 3 skupiny naprieč 5 premennými.\n",
+    "\n",
+    "Vektor klastrovania je priradenie klastrov pre každé pozorovanie. Použime funkciu `augment`, aby sme pridali priradenie klastrov do pôvodného súboru údajov.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "0XwwpFGQLXy_"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "augment(kclust, df_numeric_select) %>% \n",
+    "  relocate(.cluster) %>% \n",
+    "  slice_head(n = 10)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NXIVXXACLXzA"
+   },
+   "source": [
+    "Perfektné, práve sme rozdelili náš dátový súbor do 3 skupín. Takže, aké dobré je naše zhlukovanie 🤷? Pozrime sa na `Silhouette skóre`.\n",
+    "\n",
+    "### **Silhouette skóre**\n",
+    "\n",
+    "[Silhouette analýza](https://en.wikipedia.org/wiki/Silhouette_(clustering)) môže byť použitá na štúdium vzdialenosti medzi výslednými zhlukmi. Toto skóre sa pohybuje od -1 do 1, pričom skóre blízke 1 znamená, že zhluk je hustý a dobre oddelený od ostatných zhlukov. Hodnota blízka 0 predstavuje prekrývajúce sa zhluky so vzorkami veľmi blízko rozhodovacej hranice susedných zhlukov. [zdroj](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).\n",
+    "\n",
+    "Metóda priemerného silhouette skóre vypočíta priemerné silhouette skóre pozorovaní pre rôzne hodnoty *k*. Vysoké priemerné silhouette skóre naznačuje dobré zhlukovanie.\n",
+    "\n",
+    "Funkcia `silhouette` v balíku cluster sa používa na výpočet priemernej šírky silhouette.\n",
+    "\n",
+    "> Silhouette môže byť vypočítané s akoukoľvek [vzdialenosťou](https://en.wikipedia.org/wiki/Distance \"Distance\"), ako napríklad [Euklidovská vzdialenosť](https://en.wikipedia.org/wiki/Euclidean_distance \"Euclidean distance\") alebo [Manhattanská vzdialenosť](https://en.wikipedia.org/wiki/Manhattan_distance \"Manhattan distance\"), o ktorých sme hovorili v [predchádzajúcej lekcii](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Jn0McL28LXzB"
+   },
+   "source": [
+    "# Load cluster package\n",
+    "library(cluster)\n",
+    "\n",
+    "# Compute average silhouette score\n",
+    "ss <- silhouette(kclust$cluster,\n",
+    "                 # Compute euclidean distance\n",
+    "                 dist = dist(df_numeric_select))\n",
+    "mean(ss[, 3])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QyQRn97nLXzC"
+   },
+   "source": [
+    "Náš skóre je **0,549**, teda presne v strede. To naznačuje, že naše údaje nie sú obzvlášť vhodné pre tento typ zoskupovania. Pozrime sa, či môžeme tento predpoklad vizuálne potvrdiť. Balík [factoextra](https://rpkgs.datanovia.com/factoextra/index.html) poskytuje funkcie (`fviz_cluster()`) na vizualizáciu zoskupovania.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "7a6Km1_FLXzD"
+   },
+   "source": [
+    "library(factoextra)\n",
+    "\n",
+    "# Visualize clustering results\n",
+    "fviz_cluster(kclust, df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IBwCWt-0LXzD"
+   },
+   "source": [
+    "Prekrývanie klastrov naznačuje, že naše údaje nie sú obzvlášť vhodné pre tento typ klastrovania, ale poďme pokračovať.\n",
+    "\n",
+    "## 4. Určenie optimálneho počtu klastrov\n",
+    "\n",
+    "Základná otázka, ktorá často vyvstáva pri K-Means klastrovaní, je táto - bez známych triednych označení, ako zistíte, na koľko klastrov rozdeliť vaše údaje?\n",
+    "\n",
+    "Jedným zo spôsobov, ako to môžeme zistiť, je použiť vzorku údajov na `vytvorenie série modelov klastrovania` s narastajúcim počtom klastrov (napr. od 1 do 10) a vyhodnotiť metriky klastrovania, ako je **Silhouette skóre.**\n",
+    "\n",
+    "Určme optimálny počet klastrov výpočtom algoritmu klastrovania pre rôzne hodnoty *k* a vyhodnotením **Súčtu štvorcov v rámci klastrov** (WCSS). Celkový súčet štvorcov v rámci klastrov (WCSS) meria kompaktnosť klastrovania a chceme, aby bol čo najmenší, pričom nižšie hodnoty znamenajú, že dátové body sú bližšie.\n",
+    "\n",
+    "Preskúmajme vplyv rôznych volieb `k`, od 1 do 10, na toto klastrovanie.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "hSeIiylDLXzE"
+   },
+   "source": [
+    "# Create a series of clustering models\n",
+    "kclusts <- tibble(k = 1:10) %>% \n",
+    "  # Perform kmeans clustering for 1,2,3 ... ,10 clusters\n",
+    "  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),\n",
+    "  # Farm out clustering metrics eg WCSS\n",
+    "         glanced = map(model, ~ glance(.x))) %>% \n",
+    "  unnest(cols = glanced)\n",
+    "  \n",
+    "\n",
+    "# View clustering rsulsts\n",
+    "kclusts\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m7rS2U1eLXzE"
+   },
+   "source": [
+    "Teraz, keď máme celkový súčet štvorcov v rámci klastrov (tot.withinss) pre každý algoritmus zhlukovania s centrom *k*, použijeme [metódu lakťa](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) na nájdenie optimálneho počtu klastrov. Táto metóda spočíva v zobrazení WCSS ako funkcie počtu klastrov a výbere [lakťa krivky](https://en.wikipedia.org/wiki/Elbow_of_the_curve \"Elbow of the curve\") ako počtu klastrov, ktoré sa majú použiť.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "o_DjHGItLXzF"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Use elbow method to determine optimum number of clusters\n",
+    "kclusts %>% \n",
+    "  ggplot(mapping = aes(x = k, y = tot.withinss)) +\n",
+    "  geom_line(size = 1.2, alpha = 0.8, color = \"#FF7F0EFF\") +\n",
+    "  geom_point(size = 2, color = \"#FF7F0EFF\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLYyt5XSLXzG"
+   },
+   "source": [
+    "Graf ukazuje výrazné zníženie WCSS (teda väčšiu *súdržnosť*) pri zvýšení počtu klastrov z jedného na dva, a ďalšie výrazné zníženie z dvoch na tri klastre. Potom je zníženie menej výrazné, čo vedie k vytvoreniu `laktu` 💪 na grafe približne pri troch klastroch. Toto je dobrý indikátor, že existujú dva až tri rozumne dobre oddelené klastre dátových bodov.\n",
+    "\n",
+    "Teraz môžeme pokračovať a extrahovať model klastrovania, kde `k = 3`:\n",
+    "\n",
+    "> `pull()`: používa sa na extrakciu jedného stĺpca\n",
+    ">\n",
+    "> `pluck()`: používa sa na indexovanie dátových štruktúr, ako sú zoznamy\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JP_JPKBILXzG"
+   },
+   "source": [
+    "# Extract k = 3 clustering\n",
+    "final_kmeans <- kclusts %>% \n",
+    "  filter(k == 3) %>% \n",
+    "  pull(model) %>% \n",
+    "  pluck(1)\n",
+    "\n",
+    "\n",
+    "final_kmeans\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l_PDTu8tLXzI"
+   },
+   "source": [
+    "Skvelé! Poďme si vizualizovať získané klastre. Máte záujem o interaktivitu pomocou `plotly`?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dNcleFe-LXzJ"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "results <-  augment(final_kmeans, df_numeric_select) %>% \n",
+    "  bind_cols(df_numeric %>% select(artist_top_genre)) \n",
+    "\n",
+    "# Plot cluster assignments\n",
+    "clust_plt <- results %>% \n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +\n",
+    "  geom_point(size = 2, alpha = 0.8) +\n",
+    "  paletteer::scale_color_paletteer_d(\"ggthemes::Tableau_10\")\n",
+    "\n",
+    "ggplotly(clust_plt)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6JUM_51VLXzK"
+   },
+   "source": [
+    "Možno by sme očakávali, že každý klaster (reprezentovaný rôznymi farbami) bude mať odlišné žánre (reprezentované rôznymi tvarmi).\n",
+    "\n",
+    "Pozrime sa na presnosť modelu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HdIMUGq7LXzL"
+   },
+   "source": [
+    "# Assign genres to predefined integers\n",
+    "label_count <- results %>% \n",
+    "  group_by(artist_top_genre) %>% \n",
+    "  mutate(id = cur_group_id()) %>% \n",
+    "  ungroup() %>% \n",
+    "  summarise(correct_labels = sum(.cluster == id))\n",
+    "\n",
+    "\n",
+    "# Print results  \n",
+    "cat(\"Result:\", label_count$correct_labels, \"out of\", nrow(results), \"samples were correctly labeled.\")\n",
+    "\n",
+    "cat(\"\\nAccuracy score:\", label_count$correct_labels/nrow(results))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C50wvaAOLXzM"
+   },
+   "source": [
+    "Presnosť tohto modelu nie je zlá, ale ani výborná. Môže to byť spôsobené tým, že dáta nie sú vhodné pre K-Means Clustering. Tieto dáta sú príliš nevyvážené, málo korelované a medzi hodnotami stĺpcov je príliš veľká variabilita na to, aby sa dali dobre zoskupiť. V skutočnosti sú vytvorené klastre pravdepodobne silne ovplyvnené alebo skreslené tromi kategóriami žánrov, ktoré sme definovali vyššie.\n",
+    "\n",
+    "Napriek tomu to bol celkom poučný proces!\n",
+    "\n",
+    "V dokumentácii Scikit-learn môžete vidieť, že model ako tento, s klastrami, ktoré nie sú veľmi dobre ohraničené, má problém s „variabilitou“:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/problems.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Infografika zo Scikit-learn</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "## **Variabilita**\n",
+    "\n",
+    "Variabilita je definovaná ako „priemer štvorcových rozdielov od priemeru“ [zdroj](https://www.mathsisfun.com/data/standard-deviation.html). V kontexte tohto problému zoskupovania sa vzťahuje na dáta, pri ktorých hodnoty v našej dátovej sade majú tendenciu odchýliť sa príliš od priemeru.\n",
+    "\n",
+    "✅ Toto je skvelý moment na zamyslenie sa nad všetkými spôsobmi, ako by ste mohli tento problém napraviť. Upraviť dáta trochu viac? Použiť iné stĺpce? Použiť iný algoritmus? Tip: Skúste [škálovať svoje dáta](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) na ich normalizáciu a otestujte iné stĺpce.\n",
+    "\n",
+    "> Skúste tento '[kalkulátor variability](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)', aby ste lepšie pochopili tento koncept.\n",
+    "\n",
+    "------------------------------------------------------------------------\n",
+    "\n",
+    "## **🚀Výzva**\n",
+    "\n",
+    "Strávte nejaký čas s týmto notebookom a upravujte parametre. Dokážete zlepšiť presnosť modelu tým, že dáta viac vyčistíte (napríklad odstránením odľahlých hodnôt)? Môžete použiť váhy na pridanie väčšej váhy určitým vzorkám dát. Čo ešte môžete urobiť, aby ste vytvorili lepšie klastre?\n",
+    "\n",
+    "Tip: Skúste škálovať svoje dáta. V notebooku je komentovaný kód, ktorý pridáva štandardné škálovanie, aby sa stĺpce dát viac podobali z hľadiska rozsahu. Zistíte, že zatiaľ čo skóre siluety klesá, „zlom“ v grafe lakťa sa vyhladzuje. Je to preto, že ponechanie dát neškálovaných umožňuje dátam s menšou variabilitou niesť väčšiu váhu. Prečítajte si o tomto probléme viac [tu](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
+    "\n",
+    "## [**Kvíz po prednáške**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)\n",
+    "\n",
+    "## **Recenzia a samostatné štúdium**\n",
+    "\n",
+    "-   Pozrite si simulátor K-Means [ako tento](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). Tento nástroj môžete použiť na vizualizáciu vzorových dátových bodov a určenie ich centroidov. Môžete upraviť náhodnosť dát, počet klastrov a počet centroidov. Pomáha vám to získať predstavu o tom, ako sa dáta môžu zoskupovať?\n",
+    "\n",
+    "-   Pozrite si tiež [tento materiál o K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) zo Stanfordu.\n",
+    "\n",
+    "Chcete si vyskúšať svoje novo nadobudnuté zručnosti zoskupovania na dátových sadách, ktoré sa dobre hodia pre K-Means clustering? Pozrite si:\n",
+    "\n",
+    "-   [Trénovanie a hodnotenie modelov zoskupovania](https://rpubs.com/eR_ic/clustering) pomocou Tidymodels a priateľov\n",
+    "\n",
+    "-   [Analýza klastrov K-Means](https://uc-r.github.io/kmeans_clustering), UC Business Analytics R Programming Guide\n",
+    "\n",
+    "- [K-Means clustering s princípmi upravených dát](https://www.tidymodels.org/learn/statistics/k-means/)\n",
+    "\n",
+    "## **Úloha**\n",
+    "\n",
+    "[Skúste rôzne metódy zoskupovania](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)\n",
+    "\n",
+    "## ĎAKUJEME:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) za vytvorenie pôvodnej verzie tohto modulu v Pythone ♥️\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) za vytvorenie úžasných ilustrácií, ktoré robia R prístupnejším a zábavnejším. Viac ilustrácií nájdete v jej [galérii](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "Šťastné učenie,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Ilustrácia od @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
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+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
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+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Zameriame sa iba na 3 žánre. Možno dokážeme vytvoriť 3 klastre!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Aké čisté sú tieto údaje? Skontrolujte odľahlé hodnoty pomocou boxplotov. Zameriame sa na stĺpce s menším počtom odľahlých hodnôt (aj keď by ste mohli odľahlé hodnoty odstrániť). Boxploty môžu ukázať rozsah údajov a pomôžu vybrať, ktoré stĺpce použiť. Upozorňujeme, že boxploty neukazujú rozptyl, čo je dôležitý prvok dobrých zhlukovateľných údajov (https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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/91pV9fBNpvm9MdOPEuPFmcqxdMz7zruAXamqQ6vqe6rqN9PdyHNw/9ZTdlGcfdiUTTk+27WTa+M3rap/q6ovV9VFVXV2VZ1aVQ8ac9PYvjPj+Oz2moXzvMy97vxMkkP78b9urbVtzDOoulNVRyZ5av/yxNba+ZOm36HdHDMOTvI9uylnP9adGcdnu9aOPZdk/fvdzI9V1buq6sKq+npVfaKqXlJVP7Xfnno9z9hU1Wuq6tz+uHNuVZ1RVY+pqmtsMeta3bmgT3Ydq7V2TtavM6k7e7ebJyMNpu707l9V7+8/64VV9ZGqen5V3XmP5TruTCChgpXQ9zq11hvRZo+ZSZK01r6UrjfgJLn+LNcLxlmm7bWqfijrNwW9t7UmoYJvWdS22v9waVXVkpyX5K1JfjlJpbtA/tOttS/vZRnsL/PeVvsf1WsNko9prZ23m3IYpgWeBxyX5DuTXCnJtZL8SLpEtY9W1a/ssWyAldY3Av9iuh5Qbp/kzP7i3o9W1V2r6g/SXZg8JMm/59tvzP2ukfGJ+/Yknx4Z1yaxPKYVw92UUxvmY0G22UayFquvbeN36VqMr11VV544JVM1hd9s4ry8Ri/EHUjXg9mvJzly5P+HJLlrktdX1YmblCPGy+3/JnlBP/6MqnpOVd27qm5dVferqlOy3ivin7TWXjumDDFegKo6kOQxI/966ZjJnHcBM1dVJ4xcZ/pauqc8PDnJdfpJnpDkRbso2j5sCmYYn+0se6fXxq+T5LZJjkjXu/j1ktwn3Y1976qqpb/pa6fmGJ/jsrtrFoM9z1tg3Rm9qfUFm051RUOrO09McnSStyR53pTLnvaxZ3B1J7ONz5aq6l5JfrB/+eoNnTWNc8N0HUpcLclV0z3p6gFJTknypn3WWd48Y3O3JNdOd9y5drokl8cn+XhV3XfCfGt1Z6v6l6zXnf1y7WchdadPfviF/uU3kvzdNmcdUt1JunbSm6X7rFdLcpN0x+zXV9UpVXXELst13Jng4K0ngaVw9ZHxr25j+q+le9z71WazOjDRUmyv/QHouUnWel993ITJGaal2FZH/EWSP15QjwEst3lvq09K1xD4tiTP2WUZDNe8t9ePJ3lZuu117YfojZL853S9Cl0lybOqqrXW/nKXywBYea2106rqVumSJR6Sb+/x5vPpekJ+Tmvt6xve28m+/Wsj49oklse0YmhbWFE7aCNZi/F2z+PWXC3JN3e3duzCXn+zifPyuubI+Inpfs/8c5LfT/KeJIen+63zhHQ37jyhqj7YWjt1QzlivMRaa5cleXBVvSLJ7yR5aD+MekOSP90kmSIR40V5VLob55LkZa21d46ZxnkXsEjvSvKw1tqZu5zfPmy29hqfiXZ4bfzyJK9L8k9J3p3kC+ni9p+S/Eq6m9dunuQNVXXb1tqnZrHOS2Za8dnrNQvned9uZnWnqr476z3rv7W1tlXP+oOrO1V1x3S/Vy5N8vBtPsFjJ6Z97BlU3ZlDfLZa/jWTPKN/eVm69ovNXJzktCSvSfK+JBek60Di2CSPSHez8u2TnF5Vx7bWLpjVes/DHGPz3iQvT/L2JJ9Nl1DxfUl+Psnd033H/1BV926tvWrM/LupOyt/zrbgunOHdOcGSXJKa+3CLaYfVN1J1zncaemOtx9Mt22uJQk9PF2i6k8lObWq7tZau2SH5TvuTCChglVxlZHxi7cx/Vqlu+oM1gW2sizb69OT3Loff35r7RVTLp/Vt6ht9ZfS3Txc6U5yb53uJPfXktyoqh7aWvv8HpfB/jK3bbWqfizdE1MW0ujCvjDPfesp6Y7xG7fTM5O8pKqOT3fh4kpJ/ryqTpv0qFKA/ayqDknXc8t9052HbnSddD3ifCJdQ+WonezbRxsBtUksj2nF0LawurbbRrIW452cxyViPDdT+s0mzsvrsJHxqyQ5Pcnx/Q34Sfek02dV1fvSPV3qQJLH97912oZ5EzFeWn2PtQ9KcotNJjk2yUOq6gOttc+MeV+M56yq7pQumSlJzk3XnjuO8y5gHl6e7klWSVfvb5yud9qfTvLiqvqN1tord1Gufdh0zCo+W9nJtfH7bdKT7puq6pnpErcfnK696ClJ7jfVNV2sWcZnGtcshnyet4i68wtZbyvdztMpBlV3+jblv0z3Hf15a+19M1jMtI89g6k7c4rPpOUflOSFSW7Q/+t/ttb+34RZbrtJ/Tmjqp6e5O/TJQDcLMkfJPnNaa7vPM0xNk9prZ005v//luQF/RORnpUu2fK5VXXj1tpFG6bdTd1Z2XqTLL7upHui/JrtHHsGU3d619vk855eVU9L8qokt0yXYPGIdJ0X74TjzgQHFr0CsE2jB7NDtjH92qNhvjGDdYGtLHx7rarHZr1XrzOT/Oq0ymZfWci22lr7RGvtfa2197bW3tRa+/N0j0D8pyTHJzmzqvbFY42Zmrlsq33vRWs/HJ/aWnvPTuaH3tz2ra21CybdQNY3rP9R//LQdD2yAwxOVR2W5LVJHpuu5+snpmtYvXK63q3vnuTN6S66v7yqNja27mTfPvqoWm0Sy2NaMbQtrKAdtpGsxXgn53GJGM/FFH+zifPy2nhR+8SRZIpvaa29Od2NWEl3TN94U74YL7G+F8S3Jbl3ks+ku5B+dLp4XT/dfvrrSR6Y5O1V9f1jihHjOepjcEq6jvouSnL/1tq5m0zuvAuYudbal/vrTO9rrZ3ZWvvb1tr90iXr3Shdb60n7KJo+7ApmGF8NrXTa+Ob3KS29t4lfVkf6v/101V1vWms5zKYZXymdM1isOd5i6g7Wb+p9ZtJXrKddZzw3n6sO7+T5KZJPpXkD2e0jGkfe4ZUd+YRn0memeQn+vFXJvnjSRNvUX8uTJdA9cX+Xw/rb3pfVXOJzaTvtH//2Ume17+8brqnJW20m7qzyvUmWWDdqaqrJLl///Kz6a7dTTSwurPV5/18uid+rT2V4pG7WITjzgQSKlgVo4/22c5jk9Z6strOI2Vg2ha6vfYZtn/av/xgknu21r42YRaGa2n2rX0W+C+lu1h6/XQ3ucGaeW2rj0v3+MdPp8tch91Ymn1r7y+TrF3AuNOkCQH2sZOS3LEff0hr7cTW2gdbaxe31r7SWjs9yZ2TvCHdTbpPqqofGpl/J/v20Z61tUksj2nF0LawYnbRRrIW452cxyViPC/T+s0mzstrdD973hY9O756ZPw2m5QjxkumT4x6cbqk1s8l+dHW2t+01j7fWruktXZ2a+2ZSX4s3YXZ6yZ5/piixHhOquqGSV6T5BpJLkvywNbav0yYxXkXsDCttb9O8nfp7oN5elVdc4dF2IfN0BTiM9Ysro231i7N+g2YyQDa1mcVnzG2umbhPG+DGdad26a7oTZJTtvqxuTt2E91p6pumq6DniR55AzvuZn2sWcQdWeO8dls+Y9P8rD+5ZuSPGBchxA70Vq7IMnf9i8Py/pTl1bKomMzxrNHxqd13FnJepMsRXzuk+TIfvyFe603yf6pO9vVWvt4uqf6JslNquq6OyzCcWeCgxe9ArAdrbWLquoLSa6VZGKv5VV1jaxXwk/Pet1go0Vur1X1X9JlQSfJJ5PcrbV2/l7LZX9atn1ra+38qnpLkrsluW9VXanvyYKBm+O2emL/97VJ7l1V46ZZK/uwqnpgP35ua+31O1wW+9QS7lvP7dfnqCSr3hMQwI5Vd0D/5f7lh1tr427IS2vt0qr6vXRPqjiQ5IQkj+rfPntk0q2epHb9kXFtEstjWjHcWM6k39tr5bQN8zEnu2wjOTvJj6Q73z9yi5sJ1mJ8XmvtmxOmY3qm9ZtNnJfX6H53q33n6LTX3vCeGC+vn8j6b9OntdY+N26i1tp/VNXfpOvh9lZV9UOttXePTCLGc9BflH9tusSWluSXW2unbjGb8y5g0U5N10vtYemOOy/awbz2YbO3l/h8mxlfG3//yPhQ2tanGp9xtnHNwnneeLOIzYNGxl8whfLW7Je686h0vW5/PMmhI20Mo35gZPzHq+rofvwVO7hJeeMx4x0Tpt3q2DOkujOv+HybqjoxyWP6l/+e5PjW2rR6XN8P9WdhsdnEVt/p2Umuk63P/ZL1urPK134WHR/Hnul4f5J79uPXS/e0j+1y3JlAQgWr5P3pepS8SVUd3Gc2j3PTkfEPzH61YKy5b69VdZ90JxsHkpyT5C6ttSE0vLE3y7ZvPa//e2i6hrRzZrgsVss8ttW1R9H9Uj9MclS6HhWT5I1JJFQwatn2rZs+YhtgAK6TZK3Xtkm9XCfJO0fGR/fR79/k/+Nok1hO04rhxnLetY1yPr0EvXANzh7aSN6f9Ue/3zTJv25S/sFJbty/VNfnZ1q/2cR5ef3HyPhBW0w7+v7G31xivLxuNjL+71tM+850CRVJF8fRhAoxnrGqOipdj4c36v/1yNbadm52cN4FLNp5I+M32OG89mGzt5f4XMEcro0PsV19avHZwqTv1nneeFONTVVdKcm3OiFI8s97LXPEfqk7V+7/3ijr7QuT/N7I+A2TbHefvptjz6VJPjKmnCHVnXnF5wqq6r8leUL/8gNJ7tFa+6RtGjwAACAASURBVMpuytrEfqg/C4nNBFt9p+9PcqskR1TV0Zt1/FBV35nk8P6lurMLVfUdSe7Rv/z31tr7dlvWGPuh7uzEXj6v484EBxa9ArADb+7/HpbuQLaZ0cczvWV2qwMTzXV7raq7JHlpukS5L6TrfeNjuy2PQVm2fetopvBSP+aLuVu2bRUmWZrttaqune5msmRnPRMA7BejN1hu1bHIlTaZ7xNZ34du9Zj6H+v/fibJWVutHHMzrRi+eWR803L6Hpu+t3/pnHTO9thGsq0Yp3ts+NqTEMR49YjzkmqtfTLJp/qXx9QmjyHp3Xhk/DMb3hPj5TWNc7NEjGeqqo5I8uokN+//9ZjW2jO2ObvzLmDR9nKdyT5s9qZyHXBO18ZvPjI+lLb1mV+n3cY1C+d54007NvdK96T1JHnRhI7BdmO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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "# scaler = StandardScaler()\n",
+    "\n",
+    "X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
+    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "y = le.transform(y)\n",
+    "\n"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 0, 2, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0,\n",
+       "       2, 0, 0, 1, 1, 2, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2,\n",
+       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 0,\n",
+       "       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 2,\n",
+       "       2, 1, 1, 0, 1, 2, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0,\n",
+       "       1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       1, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 1, 1, 2, 1],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "source": [
+    "Tieto čísla pre nás veľa neznamenajú, takže si poďme zistiť 'silhouette skóre', aby sme videli presnosť. Naše skóre je v strede.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "source": [
+    "Použite tento model na určenie najlepšieho počtu klastrov na základe metódy lakťa.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Looks like 3 is a good number after all. Fit the model again and create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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ksjcIIdLEoaTiR4BPq3E+APzfb9Mp8HrRg0uwDri1ALdPm1LpZ248eMD0cGxVVXYXFmAxydU3Q5OSOTui08GpHZ9A38YnYVdD0y8dFgsjuhwKwi3bs5sLv/2KZu+8TocPRvHmgr9Kq5x35efzy+aNYe6lAq+Hb1atjGoeC6csRVXDf438Xj8zvp4T1TMOZB3kpetG4XX5cBW4w4xACT63j/mTjdPg4p+XH9YIgOHrj9YYbVq6jYA//NRmtVvJ3LCb1HopISJ2FdHgZPPqYjP++HYuj5//AstmrGLP1n3M/GYut3V5iB3rMqN+xvFCSbwXkTYVkfgQIulJRO25CHtPpLYbGdhmSEFru0E/CLIQApmQOxxZMBJcnyALnkfuP8toUVkBUi9Gun9CFn+O9IenIsc4OqrNEEgpA8AdwHRgHTBOSrlGCPGMEKIkh60PsEEIsRGoAzxfXfMpYUGWed/T7fm5eE1cN9HQKi0Ns6Xer2lkJCbRo0GjqKs5kx2OqMd9/Zxzuahla+yqikVRqJ+UxHsDL6BtbcM/v+ngQYZO+J5V2fvQpaTA6+XDpUt4YubvAKzdn43fRPpCk5KZ27dGNQdN002/NymJaqEG+HPcfFMp5/IIASl1awCQWPPwrjy700a7Xm1o2r5xVPNo3qmJqZqo3+unfst00pvWpWWXplhsFR+k7U4bt7wSXaN1TdN47+7P8LoOnTJ0TcdT5GXM499G9YzjjbA0RMQPRcQNBj0X/cCFyP3nIA9cBPs7Q9EokHmADtqmMqqgAG6QeciCxyM+X/qWI/efgcwfiSx8BXnwMvT8h0/8CuljSLXWEUgpp0kpW0gpm0opnw9ee1JKOSn47/FSyubBe4ZJKcMTrquY8gqgZamsIbiz62k4LKGLg9Ni4aq27Ui023Farbw38AIcqorDYsGqKMZBulyw0GmxMKxj56jHdVisvNDvbFaMuJPFw27lz+uG0bvxod3v6CULQ3b7AJ5AgInr15LjdlHk90VsDGgWezCj28COpdkwZbE5bfS+7PSonuEp9kZlNGxOGxfdYbjNLr3v/LC6AdWqUDMjleS0JNLq1+TKhy/i6YkPRjUHgMF3D8LqKKck6rRx6oCOpcHvpyY8SPverbHarTgTHCSkxHPF/11Iiy5NSUiJp/XpLXh+6qN07t8+qjFz9+WHxCRKkFKy+q91Uc+99H2BzUjvQqRezRIWZmPLADJnCAQ2YNQNuIML/uF+l3TwLUKaZBdJqSHzbgVZhKEd5AM84P4ZvNOr+luocqS2Fz1/JPr+/ugHr0Z6Zh7vKZnyn6ssTrDZKDJZ5FQhiLfZKvXM1rVqM+aiS3jmz5msO7CfZLudG4NZQyXUS0ykZlw8+13FKEJgV1XqJyWTWViAVVHwaRpDTmnPpZWQdrCpKjY1vEJ37YH9pobPpqrsys+nht1hBHhN7qkZRXwAIKVODW598wZG3zsGLaChazo2h43+1/biQOZBJrw9jdant6Rll8iBwVPP7cA3L04I0yMSwpCIUK0qWkDnllevoW2PVgD0vbIH21fv5Ic3pmC1Wwn4AjTr2IRnJv0fSamJUc29PLUb1uLNOc8y6s5PWDPPyBoaOLwfNzx3Vek9SamJvDj9CXL35VGYW0xGs7pHVR0dnxwX8TSUcgT1D1I7gMwdDoHNpSmbMuEOlGNZWeubG1ywKyNhXhJjKId/FUgzV6Eb6RqHcFRvhfLRILV9yAMXBD+TAGg7kHlrkIn3osRff7ynF8J/zhDc2+10np/7Z8jiJ4Ahp7Q3VeKMxAGXi+/WrGTDgQO0q1OXy1q3ZfJV5rVwmq4z9Mfv2e8qDtmBZxYW8N7A87GrFlqmpUUVnD0STk6rxeacg2ELvVfTaJCcTIPkZCxBI1QWu6pyXouWUY9z3vD+dDyzLTO//Qu/x0+Tdo14546PmTF2DgGfhqIqdOp3CiPHP2C6aLbo3JRmnZqwZu76kOsd+rZlxOvXU5hTRItTm+KMP+Q2E0Jw4/NXc+n957N1xQ7SMlKrpEdwfHIcaRmpJNSIIy7R+LfFGj7nlDo1SKlT46jHc8Y7OPPqnsz89q+QgLs93s7Vj1xc+rXU9iILXwPvbBBOiBuCiL8BIYw/YZl3OwTWA4FDC2fxe0hLC4Sj6tRWK0TLjpAuejgsYO9V+r2EEgBTxysgK3eCP1bI4g8PGYFS3FD4BjLuitKA+j+B/5whuL5DJ3I8bj5eagQddQmXnNyax87oE/UzNh48wGXff4tPC+DVNH7ftoX3/17ExCuGUD8pvJBrfuYuiv3+MDeMX9OYtWM7T/WuniYmt3bpyq9bNoWktTosFi5o0arU6NzZtTvvLV5Yeo/DYqFRcg0Gnxy52ElKyboFG1k4bSlxiU76XtmDjGb1GPr4pUgpubH1PRQeLKSs/Vn6+yqmfvg7F9x2Ttjz9mzbx6YlW8Kur5m3gcTUBE5qZ657tG/HfsY+/wMr/1xDrQZpXPXwxXTqV3k5idx9edza5SGKc4vRdUnBwSI+e/xbdqzN5L4PR1T6uYfjrneH4ff6mfPDQiw2FalLhj55GX2u6AGA1POQBy8GPQ/QQOZC0TvIwDpEjdeRgUzwryWsGYt0I12fVbkhkHoe+DeAWi+0j7C1IxHlbEMQGMqgunF6UVIRSc+a32pth7kH24mI+4eLyHnnE94gBxAqBLaAteqEHY+W/2zPYpffT2ZBPnUTEkiyRx+gBbjs+29Yumd3yK+8KgRnNWnK++ddGHb/lI3refj3X3EFTNQxVZWArtM8tSaP9+pDjwZVJ/YGRtbQU3/+wersfSTa7VzXviN3dj0tJJ11zs7tfLliOXleDwObteCKNqfgtJqrokopeeWGd5kzfgEetxeL1YKiKjz02e30vvx0dm/Zy/D294cEP0to2qEx7y99Jez6uFd+4rMnviVQLiPH5rAy7MWhXHzXwLD37Nm2j1s7P4SnyIMWMHah9jg7t799A+feeNYRfUYljHnyW8a9MimsLsHqsPLF5lGkpRtpuWvmbWDCO9PI2ZNL9/O6MGh4P+KTKj7NSWmksVpsFmrVN5cAKcgpJGdPHvVOqh3S6lIv+gCK3iW8v4AdkTYNZAEy55rg7rMcanOUWlONOfhXGgJw/lUgEiDuGkTCbRF24ubfgyx8FVxfgLCB9IO1PSLlPYRiuOP03HvAO5NDtQV2YywZAArA0goSH0GgQWAjqI3BfkaFc5De2cjcOwEN8IGIA2tnRMoHUc/9eKDn3Gi4y8KwIWr9jlDrHtP5HK86gn8Ufk3DHQiQaLMhhCDOaqVFzbQjfk5A11m2d0/YvkeTktk7t5u+p1PddFMjAJS6ZTYcPMDNkyfy5cWX0rleaOphkc+HVVGwW478x9WxXjo/XVlx85czGjbmjCgb5Cz+ZTlzflhQWsFbsni/cuO7nHpuRwJ+LWLFbMBvHhAO+DVTP7muy4jv+eqZ8bgLPSGBaq/Lywf3f0H/a3pH7CUspWTt/I0cyMqhRZeTQiqgV81ZZy5lbbeybeUO0tJTmfrRb4y+93N8bi9SGtXLU97/ldF/v0R8snku/Nr5G3hhyFvkZucjdUmDVhk8Me4+MpqF1gkkpSaaxzd8Swg3Ahi76cA6sPfBfCduA4dx2pSBrUFjEVygZT4Uf4zU9yKS/2c67zA8E8H1FeCFkrwO/zJk/oOIlPeNKdV4DekaB+5vjHscgxDxNyIUkywve4+ohhX2XlDrV6R7MugHEfaeYDut2uWsjxYRfzPS9zehBXc2sHU95kbgcJzwhsCvabz412y+Wb2SgK6TFhfHYz37ALD2QDZNaqQwqHnLiDvg8ihCoEYIsJbP6S9ha14uFqEQOIz/1BMI8Mb8v/hq8OUALN2zm0dm/MrW3FwUAec0bc5zZ/YnyX78GqPPGDvHVE1Ttags/X0lPS7qSmJqQtg9NqeN/teY6/6cfuGpjH3+h7DMIUURnH6h6QaGFbPWmGYraZrO3m3ZpvGCg3tyefCsp9ifmYMiBAF/gL5X9eS+j0agKAoNW2Wweu76sOcGfAHqNK6Nx+Xl/fs+Dwlq+9w+DmTl8NN707n6kcFhY+buy+Phc54LyQzaunIH9/V6krE7Rkc0WCFYmoJvHuHtFzVQ6yOEDZn4BBQ8hZGtIwG7of0ffyNQ4q8OF3/DPQmZ+ABCOXwRoiz+lLAqYnzgnYvU8xFKMkKoiPirIP4qs0dUGqHWQSQMq9JnVjfCfhoy6TEofBGjZ7LfMGA1Xj/eUwvjhDcEI2fNYOL6dXg0Y+e6t6iIu36Zgk1V8WoacVYrL/01hx8vvzoqoTZFCAY2b8G0TZvw64cWLruqRsz4yfO4sVssBPyHT8ncmGPILO3Kz+eaCeNxB08SmoTpWzax/sB+GiQnE9B1Lm7VhvNbtDyiIPfRoloij6WoCkIIHvvmXh4Z8BxaQMPn8eNMcNDw5AwuutO8YrpxmwZcev/5/PDa5NKCLavNwlWPDg7ZNWdu2sOPb0xh+5pdYW6kEjS/RlKaedbQ81e9QdamvSEL/azv5nFyt+YMGt6fwfcM4rcvZ4cs9Fa7hZanNsMRb2fRz8tMTyh+r5854+ebGoJfv5gVZuCkLnEXe1n08zJOv+BU07mWRcQNQbq/MRaSQzMDtTnCasRylLjBSEsTZPFnRs9e2xmI+GsQSjCg7V+L4Vop/3A7BHaALYpqdD1S0b8SdEslG7n93llI93cgPQjH+eC8ACGOXwOmo0EGthlGWCSC/awjroBW4i5HOi8yPmMlBaEeuRfiWHBCG4ICr5cf163Fp5f7Q4TS/HqX348nEODhGdMZG9yJH46n+/RjW14em3MOIjBqEzrWTef+08yPul3SMwjo0RVXNUsx/Mefr1iKTwtd7Py6zubcHDbn5gCwZPduJm9cx8fnX1ytAmYuv5+J69eyKCsTxzn1UX9LQNsT6o+WuiwN1LY5vSVfbBnFjLFz2J+ZQ7szTqbbeZ1QI5yYAG545krOGNyNOeMXIISg9+Wn0eSUQ/GSNfM28PA5z+L3+tECuqlBsjqsdBvYydS9krsvj/ULN4ft9r0uLxNH/cyg4f1p0DKD56c8wmvDRrM/0zDI7fq0IXvHAW5oeRdSyogGyBWhGjp754HSPs1l8Xv9HMzKifh5lEVYGkDKp8j8R0ELVhvbe4e5dIStI8LW0fwhlpMNn3z51E7pBbWB6VvCsPcM9hYo97usJIBiGGxZ+CK4vqXk5CB9y4ymNKmfYyjT/zsw4iHPgWuccUFYgKcg5SOELfpaHwh2TLM2r/I5ViUntCHYX1xkWjlbHl1KFmVl4g0EovLDJ9ntTLj8albu28u2vFxa1kyrUKeobkIiN3ToxOcrlpfu8C2KgqbrIZ5dh8XCPd2NIqwV+/aiHSaQ7w74WZiVyfzMXZzeIHpV1vUH9vPrls1YFIWBzVvQuEbkfPVct5sLvv2KHLcbd8CPTVWRD7Yl/f31OHcUBbWB4Inv78cRd8hlVaNWMpfcc17UcwJo1qEJzTqYy0G8ccsHIe6mkgCxoirY42wEfBqnntOBh8bcbvp+T7EXRTU3lmXdNu37tOHzTe9QcLAQCdx08t0U5hRxuJyKSIa4bqNaptcDvsARyVAIW2dI+yVYoWtHKEeWaiwSbkZ6fiHUteMw+vgGd6lSSmMnX/QB6MXg6A2Jj6KoKcFn3GV0IpPFGIVdCmBDJD2LEAoysAtcX2O4p0pwQ2A1eGeBo3JB/OOC709wjaf0ewnGRGTuCKg97197wonECW0IFCGQUaWzAYgj2lULIWhftx7t60YnDPbg6WfQOT2Dr1Yup8jnY1DzlhT7fHy6/G9yPR6apqTyRK++dM0wmpWYCcqZ4fL7mb1je9SG4NV5c/h0+VL8moYiBKMWLeDhnr24tr35TvKthfPILmNQfZoGCgQe6MJNRRk4E5z0HNy10kVc0eBxedm1Psv0NavDyltzn6NGnRqk1I7s2qvbpDaJKQl4XaG7cIvNQs9y/YyFECSnJTFj7Bz83sBhjQBAk1PMP//c7ALT64qqkL3D6JsgpWTy6Ol8/cIE8vbl06h1fW557To6nRXaxlIIAaJyTXaEpRmkfo4seBoCa0HEg/NqROLdpffIvLtCq3U9P4FnOnqtuShqEk0NypcAACAASURBVEKth6z5FeQ9adQsqDUh4T5EyQLvW4hpqqd0Ib0zD933L0C6xhMeDwEIgG8p2I9PD+zq4oQ2BGayz2aoQtCzYUPT6tyqQgTTS89qElphe3vX7qZNUOKt0VU521SVFGd06a9r92fz6fKlpZ+LJiV+dF6Y+ydnN21G3YTwxXz6lk2mp6oDXjc9h/WhTkLl5bujxVqBto+qKCEupEgIIXjo8zt44oKX0PwBAn4Ne5yd5FqJpr59gANZOaZunfCHw3m3mPd/MAtog/E9lcQbvn1xAl//78fSE8/WlTt48oIXeXH647TtWXWd44StAyJtgunvmx7IiiDZ4IHC56DGy0htH+RcazShxwuaG/IfRirxCHtvUJJAKCYJTBaIIhj9j8KsmQ5g1EBE8TvxL+OfnX91lFgPE0S1KArxVht14hN48axDhU5+TePzFcs4/5svOe/rLxizfGnUO/TKYHYS6dO4ScQspLIoQhDQJW/M/4u/du2oUIhr2qaN+Ew0fYQQzNh2SGRu6Z7d3Dx5Av2//MxUjgOMQjx7FTWej4pK1Lvous5vX/zJnac9ys3t7mPt/A28Pe85LrzzXHpc1JVhLw7ho5Wvk1TT/DTTqlszrPbD75UsFpVNS7eZvnbGJd2xx4UbdV2XdB3YEb/PzzcvTAjLsvK6fXz2RKjonJRepG8Z0r8pKsE16d+AnncP+v4B6Ll3If2GdpHpydddQXNA75/G84pGgZ7PIdePDniQ+Y8hpQ723pjvLS0I55H1pjjeCOcFRgV3GDrYzDPZ/s2c0CeC+snJEfsnNUhKZsgp7WlcowZ9G5+ENbjoSikZNnkCS3ZnlVbbvjxvDr9t3cxXF192zLpKXdn2FMasWMpBl6t0R25TVQSUzlXTJbrUeXex0Q8hbrmVjnXr8ekFg0vvKYsiBMLkAykrgPf71s3c9ctUvIEAEvPiflUIOtdLp4ajakvkc/flsXDqUhCC7ud1okYtw9Xj84ZXZZegaTq7t+wlOS0xLI//zVs+ZOa3c0sX2a83/8isb+fx7pKXSjuOVUS7Xq1peWoz1i/chNcdOeMr4NeY++NCrvy/8ErXlqc2xRHvCCuwa9Q6g7T0VA5kHYx4atix9pAMte6eDAVPAgKkBmo6pHwQUtkr/auRxV+CtsdIOXX/gOHL10HbjvT+CamfIMwWMrUCl5N0oe8/G7S9mFbK6gWg70GoGZA6Bpl7c7BeQRhjJ72IsFRtoWS14zgXPJMNd5d0AVZAhaSXEeLIClD/DZzQhqDA60URwjToWuD1MHP7VlrXqk2X9IxSyYXFu7P4e8/uEFkGTyDAin17KwzKegMBpm3ayF+7dpCRlMTlbU45oi5f5UmyO5h85TW8t2Qhv23dTKLNELK7oEUrlu/bg1/TueeXqRxwH+oZ6/L7WbpnN+PWrGJIu/BWgOe1aMnHy5aglXOZ6VLS76SmSCl5cuaMEJdaySenCIFDtSAE1IqP580B4dW+R8PPn85g1B2foAR7G7xz+0fc+9EI+g3phTPeQUbzemRu2B32Pr/Pzy0dHkALaPQc3J37Px6B3Wkna/MeZoydHeLa8Xn87N2ezZ/j5tH/mt6HnZMQgv/9/BgT357GL5/+gdftI2dPrmkKaXIt81PF/ElLwpr2AOxav5utK3dQv2U6IkK/igYtjVoI6V8H+Y8RUlSmbUPmXg9pvyOEgu6eAvmPUrrw+xcSavF1wI0seBaR9lP4YI5LoOAZzAXjvKBtN51jcDJGzAEQ1jZQaw74VxgBVlvHals4pdQNgyPiqnyDJoQKNd4H3zzDgCo1EM4LDWN3jJHSjXRNBv8SsDRGOC9FqJVrohWJE9oQFPv9WBUVTQvfxeR7vSzMymRhViafL1/K95ddRcd66SzZnWUaW3D7/SzZnWVqCIp8Pi4Z9zVZhQW4/EZmzcdLl/DR+RcfUTZPeWrGxfFEr7480StUK+bU9Pqs3Z9tWq3sDgQYv26NqSFoUTONu7qexlsL5wHGQiel5Nm+/cj3eFiTvY8ct1mADOIsVp7qcybpiUl0zagfJqF9NOzdns2oOz4J88e/cfP7dOjblrT0VO4ZPZzHznsBv9doAq+oCrqmowd0PAFjx//XhIUAPDr2btbO2xgUuAt9pqfYy9+/rohoCApyCvnh9SnM+2kxiakJDL5nEJc/eCGXP2hIh9zc7j52rssK2cU74u1cfNcg0+ctn7XaVGYaYM1f6zmpXSMuvf98vn91ckj9gt1p47qnrwBAur4mXMpZBz3XqOy1ngIFIwmtPo5whgqsN40RKIodPfl1yL+PI1MPtRpFUsohAT4hVKMTWclM9ELwTEVq2QhbJ7CdflRVwVJKZPEnwRaYLlBqIBPuQ4m7rNLPNEMIAfYeiCgroKsDqecgD14CWg5G8NpmFAemfoGwVl5XqzwntCFolFyDRLsNj6vioLEO3PDTDywfcSdpcXE4LBZc/tAFxGGxUis+1PXg9vvJLi5m/LrV7MzPK61NKIkn3Dd9GvNuuqVKF80SKtoBVfTaiC5dGdi8Bb9v3YJFUWhXpw4P//4ruwryUYQIq7kooVZ8PINPPny7x8ow+/v56BGkmMc8/i2tujWnw5lteWfB//j+1UnsWLOLA1k55OwNLXDyefzM/XEhhblFRgMbk8/BYlOp1cC8qKc4v5hbOz9E7t680paVm5ZuZfN927j+6SsBeH7KIzw84Dn27zqIalHxe/0MefwSupxt3n8gLSMVm8MaZuRUi1raZOfakZfjKfLw06hf8PsCJKTGc8fbN9Ghb7BAUduH+eIsQM+BwKYIr5vhiPj7oTgHIu09kK7vjE5ingoa44jEoNZQK0SNVyPepvvWQM6VlMQVZLECluZQczxCVK5CXhZ/CkXvUJrVox+EgufQsSNsHYzCLaX6stiOJbLwneDPv2QN8xkS43kPIWr9UmXjnNCGQBGCV/qfy4ipPxHQNAIVBNgKfD6KfT4GNm/J83Nmhb2uCsGg5i0AQ1b6xbmzGbt6BYoQuE2URQGK/D625ebQNNVcZOxI8QYCjF6yiB/WrUGLUB/htFi44jA9DRom1+DGjp2RUtL3i0/ILCiosGGP02JhROfDV8BWloBfQ5r4yX0eP398M4dZ4/5C6pJzbzqLBz+7HSEEQ5vcZvos1aqSv7+Ajme1JT7ZiafYE6JjpFpUBt5snsY45YPfyMsuCOlb7Cn28v0rkxh81yCSaiZSu2EtPlnzJpuXbSP/QCEtT21KYkrkzKn+1/bh6+d/DLkmhJH22nWgsWte9sdqJr//G1rwM/C5fLx/7xja9WptCNTZ+wZTM8ud1qTPUPyUbiNuEA3iMJWxIgFh64BUalVgCASixpug1EVUUCglpYTc6witK9AhsAFZ8AoiOXJXsgqfWfw+4amdbih4CIkd0JCOsxHJz/+jpJ4rhfdXTOMyWiZSO4hQq2ZtOaGzhgB6NWrMlKuuYWi7jmGpm+XxBPwk2GyMHXw59ZOScFqsxFmtpCcm8uXgy0pVSt9ZtICvV6/AEwjgimAEwAjmOixVU3gipeTaieP5cOlisgoL2FtcZNQCYCzUFkXBabHSo0EjLomyuc2yvXs44HKZGgFFCBJsNhwWC8M6dalUw5xoOe2CLqgmmv8Afm8Ar8uHz+Nn+piZLJjyNwBterQqjSeEzFsR1GlcC1VVeW3m0zRu0wC704YzwUGNWkmM/OHBEKG5siz+ZbmpP99is7ChjEy2EILmnU6iy9ntKzQCAGnpqTw35RFS69XAkeDAHmcjo0U6r816GpvdipSS14a9h9flLXU3+Tx+CnOL+Hzkd8Z4cReDmgGU9bU7IX4YQk0zKo8tzYEosrjkwYgZR9K/Cbm/NzL3FiiMIAsNYDkFYT+jQiNgPG8DSPM6Cjw/HH6upniN3semGHEQ8IHnN2Tew5Uc459EpFOTNEQHq4hqPREIIQYAb2H8hn4spXyx3OsNgc+BGsF7HpZSTqvqeZyUksqTvQ0/e9O3X4u4cCcGF/q2tevw53XD2JKbg5TQLDW19DgtpeTTZX+HBJPNUISgaUoKGUmVDxiXZcmeLNbszw6JX/h1nTirlUtatSY9KYmu6fXpULde1IGzHLcrotuqe0YDHunZi0Y1UkioZOe2w5G5cTcrZq0hLskZMWBaFk+xl2kf/c5p53fh2qcuY8GUJXiKPKVuJUecnZv+dzVWm/EHkt60Lh+ueI2szXvwunw0alO/QpmLWg3TEIoIU0LVNZ2UOkYGk6ZpfPviRCa+M43ifDdtTm/BrW/cELFnAkD73m34ZtcH7FibidVmIaP5oZ9Rzt48cvflh71HC+gsmrYUwAi21vzeiBV4poOShIi7JqTPgEh5D5l7Y1CCQjWXpAZjx2/yM5dSN96vZ0f8PgziIGX0Ye4Jou+P/Fqlu9LaQUmLYp5e8M5A6rkIpXJFeFJ6ka4Jxq5cSUHEDTFiHMeSuCtMJMhVsHVGKFWztkA1GgJhCIu8C/QHMoHFQohJUsq1ZW57HKOp/WghRGtgGtC4uuYEMLB5C6Zu2hh2/ZTadUIKyoQQNDNx6fh1neIKxOPirFYEgkS7jfcGXVA1kwZW7ttHwMQd5PL7sVss3NK56xE/s0PddNP6CKfFwoBmzWlT23znfLRIKXnrtg/57YvZCGHoFEVVuAV4PcZnn9GsHu8ufokvnx7HqjnrSMuoyVWPXMxp54enRpaXe47ExXeey5zv54ekiiqqQr2T6tC0fWMA3hrxIX98M7c0HXT5zDXc0/NxPlj+KvVOivx5KYpCk7bhiQOOeHvEVpXxyYdkJIQSj0i4GRJuNr1XqHWg5hRDllo/gCz+Dny/hd9o624+Qf+yCMZDgNrU0COyd0fEXR29b9/awXi/2dZLqVw6qRACmfBAMJXWPAh/6GarET+ohCGQ0os8eAUEtmGcMgTS8xsy8X6U+OsqM/VKIeJvRPqXgXeeUayHACUNkRze1+NoqM4TQVdgs5RyK4AQ4lvgQqCsIZBAiVlLBsLzA6uY58/sz7oD+9mZn09A17EoCikOJ+8PCm8oY4ZNVWmQnMzO/PBdXKuaadzYsTO14xPo0aBhlaqCZiQmYVXUsIXbabHQMLlyLRPT4uK4pfOpfLz071INJLuqUi8xsdoCwwBzfljAjK/mmLphKsIRb+esq88o/bp+83o88tXdEe+XUjLzm7lMGj0dT7GXPlf04MI7BoS0vCxLi85Nuf+T23jr1g/RdYkW0DjplIaM/PFBhBDkZufz+1dzwnoW+Dx+xr3yE3ePHn5E3w9AfFIcXc7pwJLpy0ME7exxdi4yachTEUIICKqRyvxnzG/yrTG/LoswrxqRoNZDSf3giOYCoKiJ6PZzwVv+kC8guQLX0+GeG3cRUnEiC98EbXewSU4BpgYnWkG9ckjXhDJGgOCzPVD4KtI5+JgFo4WwIlLeR/rXg3+NUT9i61blvRiq0xBkALvKfJ0JlBfoeAr4VQhxJxAP9DN7kBBiODAcoGHDyqdjgpGfP33I9czeuZ11+/fTuEYK/U5qekTyEiN7n8nt0yaXumkEhmDcM3370SW9evKMz2xyEvE2K+6AP8Snb1EUEu12vl29klPTM0wD05qus7eoiES7PayXwb3de9Cudl0+X7mMfI+Hc5u1IMFmY8BXY8h2FdMstSaP9ux9VGmw5Zn64e+mPQ3MUC0qWkDDkeDg5O7NOWvIGYd/U5C3bvuQGV8d6p+wa0MWM7+ZyzsLX4hYUNb3yh6ccUk3tq/eRUJKPHUbH8rXztywG5vDGmYItIAWEkM4Uh4aczuPn/cCW1bswGJV8Xn99Bt6BuePOLvSz0TPNL8ud5umj2LtVE7mOohwHlWDeFHjNWRhBrg+B3yGSqnzCigciZ67A9S6kHAvitM8/Tbicx3nIByGGoAM7EQevChY+KWXzpuEeyqdmWQEaU1SqYUV/EuDVdTHDmFtBdZW1ff86mpVKYS4FBggpRwW/PoaoJuU8o4y99wXnMNrQojTgE+AtlJG7uBSVa0qD8fyvXuYtGE9Esn5LVrRqV5oo5M/tm3ludl/sK+4mPqJSTxzZn+6BQXjqov5u3YybPKE0viERQgcVitI0KShZDqoeUte6ndOqe9/2qYNPDlrBi6/YUD6NWnKS/3OIT6C33/M8qW8Mm9OWJ/jMRdeUiqId7Tce8YTrP5rfYX3WG0WajeqRd+relBwsJCu53bi1AEdUKI8Ze3Zuo9hbe8Nczk54u3cPXo4/YaaN8mpiAO7c7i26R1hhkBRFfpf04sHPjVXPo2W7Wt2sW97Nk07NCYt4+iyQfTsPqCbHLCV2ii1zdongl48FgpforQoTThBbYao+Y0hpXwUGOuMZhRo5d5BqFvHAUkjUeIqL0MhAzuQRW+DbxEotREJtyIcpvvKqNDz7gfPFMLL8OMRKZ9Glvv+B1NRq8rqzBrKAsqey+oHr5XlJmAcgJRyPkZaxHHv3PDyX7O5+sdxfLFyGV+sWMY1E77nf2VSSrfm5nD/rz+zt7gYdyBAZmEhd/08hT2FkbIZjh5vIMCt0yaFLNABKSny+Sjy+3AHAniC1c2TNhiL7N97snjwt1/IcbvxBAL4NI0Z27Zw1y9TTMfQdJ03F84LC4R7AgFemTenyr6XM4ecgT0ufKdmsVmIS3KSUCOec27oy6iFL3DdU1dw5zvD6DawU9RGAGD1X+tNexZ4ir0smb68UvNOS0/ltAu6YHOGLopWu5XLHozOtVgRjds0oNugzkdtBABIuJvQLCMAJyTcFfEtSvwQROqX4LwI7GciEkdWiREAw20lhAVZ+Arhvn0PFL0elX5SRLQd4F9pBKi1LYYmk4xOdNJ0vnFDCM/YESCSwGpeM/JvpjpdQ4uB5kKIJhgG4Erg6nL37ATOAsYIIU7G+M2tINWg+tmcc5AxK5aFZOe4AwG+WrWCwSe3oVVaLR774zcKvJ7SvYI74MenBXh+zixGDTy/WuY1ddMGCryHd6e4A36+Xr2Ci1qdzAdLFodVSXs1jXm7drK3qJC6CYn4NI13Fs3n61UrKPb5I7bT3BTsnFYVDLixL398PYcty7fjLvJgtVlQLApPfHcf3QZF3/Rj7YKNfPjAF2xaupXkWklc/uCFJNSI589x83AVutG18IXFYrVELCgDw300+r7PWTlrDc5EB+ffNoCrH7m4tKXk/31xJx899CU/f/IHPrePxqc05K53h9Ho5CM7LXlcXhZOXYq70E2n/u2oXcGcKoMSdzE6ASh6IxgwTYX4u1HiKm6+JGztEbZqXOgC282v6wcx6g2OXI5C+paHnjKkC1xfIvX9RnBc2MDex7xvcgSErRMy8X4ofDWYpilBJCFSP/3H90quDNVmCKSUASHEHcB0jNTQT6WUa4QQzwBLpJSTgPuBj4QQ92Kcwa6X1eCryi4u4vu1q8kqKKBb/Qac26xFxJjAH9u2mhZr+TWNGdu20iy1Jot3Z5k2r5+5fWvY+6qKP3dsj/reksV/Z0G+aaqsVVXZV1xM3YREbp36E39u345+mL4NjSoZkDbDarPy0m9P8sVT45g/eQk1aiVxw3NX0rZH9JLLm5dv46F+z5TKMuzfdZD37vkMRVXQSrSATGKfqlVl4DDzgrIDWQe5s/ujuArcSCnxun2Me2kimRt28+hYIyhts1u5/a0bue3NG9ACGharhaK8Yia8PZVtq3fRvGMTzhrai7jEyIVMq+eu47FBLyCRSF2iBXSufPgirh0ZXYe8aFHiLoO4y5DSB1iPmWBihah1jd17eUQikXPmK0YWjcL0lOGZiPROx3B86FBjFMIefYxJib8O6RxsxASCJ4ET0QhANdcRBGsCppW79mSZf68FqlXIY8nuLK6Z8D1+TUMHxq9dzdsL5/PTlUNN8+MjGQghBPag+qcSoXm9Ral6WeZV2fuYu3M72UXRuZ0cFgsXtTQW1K7pGWzNzQlLO/VrOk1TUtmWm8Os7dsO27rHYbFwb/fQH9OWFdtZ/PMyHAkOel92Gil1ojcUPq+fh/o9zZYVO/AUedhtU3n47Od4/Lv76H5edCeCL54ah88dekKSukQrK5ERlE+12a2oFhWLzcL/fXFnxDTPCW//jM/tC3FReN0+5k5YSPbO/dRueKjbmBACi9VC1uY93HXao3jdPrwuHzPj7Xz5zPeMWvSi6S7f5/Xz+Pkv4ioMDUSOe2USJ7VvTFp6Cg1aZRCfdGQdyCqiIteODGwH7wxAAcc5CDU94r1VgtrY3BAodStvqDRzCXAgqIIa/GfeHVDrryM7GSiJxzwwfDw4oSUmpJQMnzyhVAMIDL/6trxcHp0xnR4NGtG4RgpdM+qX/hKeVr+BaSOWgK5zWn0jJfScps34dcvmkPtsqsrgk1tXOB+338/M7Vtx+f30bNjItBHM6ux9rMneR/2kZCasX8u0zRvxa5ppYl8JJYYpzmqlWWpNrj7FEKMa0aUrkzaup8jrK93xOy0Wbul8Kgk2G9+t3hrRCAiMxa5uQgKP9uxNz4aNyCosoIbdwSf3fs6vn88i4NNQrSof/99XPPrNPVE1YgeY/tlMNi/bVpqLH/BpBNB48Zq3+X7fx6UFYRWxZfn2qFoUOOIdDHlsMF3O7kCTUxoGhejMWb9oE36TnsQ2u5UdazNDDEEJb474kMLc4tJaAE+xF5/Hz+h7xjDyhwfC7l/+x2pTX7jX5eWZS1/FmeAg4NO47MHzue6pK6p1F68XvR8sVtIBAYWvIxMfMpQt9SKjbqCq1Tb9y8yva5uRMoAQlViSLCcHC+kO9wuhGL0VjjBD6b/ACW0ItuXmkhfBrz5l00ZmbNuKIgTpiUl8c8nlpDrjWLJnN6qJdLWKYMmeTNrUrs2zffuxOSeHXQX5pal4LWumcUOHTvyxbSu14+NpU6t2yB/xoqxMbpo0obTZvSZ17ujandtPNQp8vIEAN0+eyN97jHi6LiU+TYuq0WaduHjibDb6NG7CLZ1PZfzaNazdn03rWrX5ZvAVfLR0MfMyd5LmjGN451M5v4WRhmapIPiakZjI79fehE1V+Wb1Srp89B5+TUPTdOLz9pHi9SM0I9ce4IUhbzFu78cRc/TL8sfYOWH6/GAY7o1LttLm9JaHfUaDlulk7zxw2PsURdCwVX2adTTvhVyWJqc0ZM28DYdcS0H8Xj/pzeqGzHPD4s3kZhewYtYa00rkRT8vNR2jfMZRWaQucRUYO9gfXp9CRrN6UcllVwbp3xQ0AuX+PgqfReI0BJEKNGTcdShJ4Qat8kQK4EqOTPX0ECLhDqR3DuatJcuPUdmK5hObE9oQZLuKK3y9JDtme14uj/3xG6MHXchBl8u0f4GG5IDL0P6v4XAy7eprWZSVyba8XFrUrMnPmzZyzldjsKkqmi5pkJzM5xddQu34hOAiPyGsIvm9xQs5rX5DOtVL570lC1m8OzPk9BIt+90uAsVFZBUWMGb5UmyqijsQwGmxEGe1MuGKIbyeFF6c1L9pM56ZPdPU2JzfohU2VeX3rZt5dvbMUGmLjqlofo1a47eXXlNUhWW/r+L0Cw9/KrCZdOwCYyG0OaLTTxn65GWsnru+woYxYFT0dhkQKsl9YHcOOXtyadAyHWfCIV/+4LsHMf2zmSGGwOaw0r5Pm9Lq5L3bs/m/s58ld28eiHA5ihJKgsvl6dC3jWk/g/J4ir2Me+Wn6jMEnulEXpTdhzbX7i+R9tOqTorZ3hc8vwBlPwMB1k6Vzk4S1laQ+gWy8CXwrwIRFyyQK2d0pQa26GME/yVOzMhHkBY1a1boUinBr+vM2LYVv6axsyAv4n078w69JoSgW/0GXNm2HVmFhXy9ehVeTaPQ58MV8LM55yC3T5sMwNxdO0zdGJ5AgO/XrgZg3JrVlTICQGkMwBMIoElZauDcgQC5Hg8jZ80wfV96YhIXt2qNWs79UMPhYHhQsuKdRQvCMo+kTaWwW210W+ivT7Rx/vOG98cRHx4YTEiJj2rnDtC2Ryue+P5+0pvWQVEEzgQH3c/vjM1hJS7JSVyik+S0RF6c/nhp8Zir0M1j5/2Pa5vdwYNnPc1ldYbx7UsTSp+Z3rQur8wYSbOOjVEUgdVupd/QXjw5/oHS7+/x815g79Z9uIs8uAvNd6BWuyVi4Vt8cjztelfsQiwhLzu8er3qiDInQ7oNWeoqQiT+X7B/cYkBdoBIRFRQaazrPvSDN6DvbYW+twX6vu7onlmhz7W1R6n5NUrdVYjaC4wOY6XKo4oxTuL9CDXcvRfjBD8RpDrj6Fa/AQsydx32Xl1KdCnJLop8isguNn/NEKErV20qJauz97G3qBCvSZ9gMP4U3cG+B9H2RLapKrXj4vFoAfI9HtN4Rll0KZmz0yQ4F+Tl/gPoULceY5Yvpcjno99JTbm72+kkOwwXT8TaCCnR4ywowZ7GAb/GytlrWT5rDb0u6U7bnq1M/dsBf4AeF3el34ze/DpmJkJRUFUFi03l2UkPH5FPvNvATnQb2Amfx4fFZkFRFNxFblbOXofdaeOUM04OiQm8fP0ols1Yhd8bwB8sNBv73A9kNKvHGZcYLrpWXZsz+u9X8Hn9WKxqSO3CjrWZ7Nu+37R3gqIqWO3GHBq2rs/NLw81nfOB3TmsnBVB5qHs8xRB+z7Vp/gqHOcgiz8idGceAXk4l8sRjKvWhbRfke5JEFgFluYI58UhjW3CODgoNMAscyBvOHrq9ygmqa5CCEh+BXwLjJOPcBrdxaqxMvffzgltCADeH3Qht02dxOLdmShCwa8ZvXjL/ikLoH2dutgtFlrXqsXcXeYL58m1zHcTkfL7VUWh0OujR4OG+E0avsRZrQxqbvjDzz6pGT+sXxOW4VMStC3pV/zewAvo3bgJGw8e4OLvxh7WEABYKkh5U4RgaLsODDXpaAbQvm49/ti2JWz/KAISa3EAq8OKpunoAY2J7/yM1HV++WQGfa7owX0fjShd2H//6k8+zyHhdgAAIABJREFUfngsOXtySa6VzDUjL+OD5a+ycvY6kmom0HVgp6j6CJthcxxyKTgTnHQbGK4QWZBTyKJpy0J6DYDhgvnu5YmlhqD0mSZzKc53oZgUqQHUb1GPwXcPolGbBrQ5vWVEg7Z23gYsNkuFInuqVcURZ+eG566MeM/RIqwtkAkjoGg0hjEQhLlSAHAinOeVfiWlBN8CoxWlmg6O/kes+S+UeET8VcBVh71X9601zzICoytb2kRjXtp+pOszQ5xNTUfE34Swn4awn3ZEc/uvcsIbgiS7na8GX0ZWYQH7i4tJttu56sdxFHp9uAN+HBYLNlXlxbMM3ZI7u57GJ8v+No0TTNu0kXFrV9Mtoz6P9Oxdqk7a/6SmfLZ8Wdhib1ctnJSSgqoojOx9Js/MnmkEXIMZPj0aNOSsk4weCfef3pM5O7eT5/Xg8vtxqBYsqsKzfc4is7CARJudQc1bUjPOSCtsHmWzG6uicl6LwwdfI3H/aT2Yt2tHSLWxTVW5rV0H0kY2R9d0xv7vBwLlmrnM+u4v+l/bm3a9WjPru794c8RHpTn/edn5fPjglwx/eSgX3FZ5HZsjoTCnCNWi4Dex2eU7nUWiWcfGpqcBm9PG2df1YdDw/od9Ro3ayaYuNEUR1G5UC0ecnVN6ncwVD11EnUbV68ZQEm5DOgaA5//ZO8/wKKo2DN9ntqZ3IEDoTXoTEFFRARVQUBAQ7KBi7+WzYu8FCypiwYKAgAoCIoh0lI50pNdACOnJtpnz/ZhkSbKzyZJkQ8t9XVyY2Z2Zs7iZd+YtzzMPhAmJGbLeRa8deIBQsLYFu95lI6UDefxW8GzVZaSFHTJfhbgJCHPJXh9lxrXM/2v5w2lSPYI8ds2JuoBnC9K5BBn5Mkpo+Se+zwXO+kBQQK2ISK+Z/J833c4v27awPvkwTeLiGdi8BdF2/a4mzGrll8HDuPXXaaTmG8MX3I0XFJ8X7NnNykMHmT3sFmpFRHJXh078tn0bxx26lINJCCwmE69f3tOrQDqkZWvaJ9Zk6pZNZLuc9GrQmIvq1vNqAsWHhjL3ptv4ddsW1iYfokF0LAObt/Re+IuT4XT49UQQgM1sRhGC+tExPFvM8/hkiAsJ9RafC9CkpFPrJnTpncTvX83HbDYVCQSgB4OXB72HxaYPXBX24wW9XfLbF38qdyBIO5LOjrW7ia8VS/1WRaWNVVVlxqd/MOPTP3DmOr0OYIVRTArtewTm/WoLsXHnWzfy8f1fFplajogN5+q7rwjoGC27NSMiJhxHjrNIodlis/Dy9Keo16JsapmBINVD+vCVcykosYiwEWDvjQjXVVMFIG0XI/OmgpaOsF0Gtkt0D2JA5ozTFTALOm9kDpCLTH8EEf9rcBZtMX5SBfThNEBmf5pvVlPwHSxQCn0ZGdIbUYEGLmcrQROdCxaVJToH+kRyam4u106e4JPDtygKQ1u14YVLLgP09NCkjf+yeN9eakVGckubdjSLL/mOzuFx88fOHRzKyqJ19RpcUDsp4Bz5T5s28OSffxi+Zsl/AmkUG8f5NWuVqxf9vlkzmLXD178hxm5n1R338OcPi/nwni/8GrSXhBAwy/Gj3w6bkpBS8vmj45n+6R9YbGZUj0adZrV4bfbTRCfoJjKv3vA+y2es9gYhk9mEqqrevKDJbCIkws5na94O+O77f1e9wrr5m/C4C0lGh1j5YMkrJRa6NU3zGtMIRfB8vzc5svcYJpOCUASPfDGSiwcGnsaQUtMN4XMnAW6w90OEDvTbeaPfNffNv2su+C7bwXoByHTAjAi9HuzX+J2e1VIuB9Wo3mZFJPwVtEKsdrSL7s1cnOgvUOyX+F+XCEXETUGYGwVlXWcaJYnOnTNPBCdDptPJO8sWM2P7Njyaavgo79Y0Zu/Yzv6MDNrUqMHQlm24o8P53BGgt++utOMMnjIRR75YnM1spll8At9fOzAge8ukqCi/r4VbrQxtdaKIpknJsdwcwq02Qi0nd3c0b7exvHKaw8HBrCy69O3AByNPXqseIDYxpkxBAGDe94uY+cU83E63tzd/1797efWGD3h73gvs23qQZdNXFfE8UD0qVruFWo0TkZqkdfcWDH6iX8A6P0f2pvDvws1FggDo08KT3v6VZyY8ZLjf+oWbeO2GD8jN1v2TE5LiGDX1MRCCvKw8GratF9AQXWFkxpO6VHJBIde9BemYBbHfGl7IZc6X+TLNhW9oHOD668R7MjeCYwEi5gM/Jy3ppjGIN5TxsyD1hkITxFaIeArFnt9aq8QZBwLpAVE2d7JzjapAUAxV0xg0ZSJ70tNK7eQ5lpPD/JxdLN2/l6/WruHnwUOpFx3YF++h32dyPC/P++uT63az6egRPl+9kgc7dzXcx6WqfLNuDZM3b0DVJBZFMSwWj2h/IujP3bmD5xbMI8Ohi+Rd1agJr17WM+CAYOSKVkCOy0nt+ASenfgIrwx5D0VRUD1qQG5jtlArt79WXIMwcKZ98JuPp4HqUdm0dBtpRzPYtmKHoaexy+Gmfuu6/O87/yqc/ji67xgWm8Xn80lNcvC/w4b7HDt0nGf7vl5krQe3H+LRS0cxYd9nZSqQS/c23bKyiL6OAzwbwbUIbN19d3L9g3ExuPCB88D5F9K9CWExMCYK6Qc54yg6lCXAXE+fRg4SihILCXPQNBeQi1Ksw0iEDUemP0HRgTILWDtVmLn72c5ZPUdQFhbu3cPBzIyA2jkLLpH6/ICTVxcvDOgcx3Jz2X481eceyqmqTNls3FoopWTE9J/54J9l7EpLY29GOkjpMyfRq0Ej7u6o+/+sSz7Mg3NmcjQnB6eq4lJVft+xnYfnzAxonQDVQsMMtwsgKV+IrkvfDny6+i26XdeZDr3aYPZjRF+Y869sV65hqazjxq28ikmQk5FLfO1YjDJiFpu5REvJkqjborbhZLDZaqL1xcaCeXO/XeA1pS9ASnA73F5P4pPG9Q+Gd+AyF+n0U1w11cbYgaw4Hl3T3wARdgeYG+sDWwCE6oqcUe8FcNzyoyhWnyAAeiss4SPRZxLCAZs+oBb9fqWs62zgnH0i8Ggax/NyibaHFBGa23IshTy3cRG2YPDKqKNIk5IlJfTrF0aW8Bjt77W1yYdZffhQ0QlfKQmzWBjRrgMJYeH0atiY+ELF5U9XrcBpIEO9cO8ejmRnUz28dPGt5y65lAdm/1bkMwv0yeOCp4q/Ji3lndvH6PIZmoaqaigmxecCWJgl0/7hiye/5863bip1DUbUbpLIkb2+iuWqW6Vmw+rUbFid6IQonLmuIuswmf2rj5ZGZGwE/e67iulj5njrDooisIfZGfiIsfz4sYPHDZ+QVI+qTyfnM+/7RfzwylRSDx2nYdt63PHWTTTv0sR4IUosCLOBAbwFXOvR0u4G68WI0P7e1k4RNgLpXELpMgwWvx6/QgmFuCngWoR0/asL1NmvQijGNwuViRJ+NzL0JvBsByUBYQ5e0f1s5Jx8Ivhm3Ro6jB1D9/Ff0u7zT3hn2RKvmmjdqChCDPLWYRYL93Xqwie9r8biR2XUaD8jEkLDaBAd43N/ZjOZuLap8dTpuuTDhrMIOW43uW4PQ1u1KRIEAPZmpBuGFavJRHIxNVOZP1AHsGz/PoZOnUy3r8fy2/atDG/XAatiQhECBejTuAlv9tC7ZDJTs3jntjG48lw481y4nZ78bhiJ2WIynCAuYOr7xgY5geCv5VMCGceyUBSFd/4aRbPOjbDYzNhCrFSrE89rs54pl/b/HW/eyL2jb6POebWIrhbFJYO7MmbVm37NZNp2b0lIuLH+UosL9QGnaR/O5IORYzmw/RB52Q42LtnKEz1eYtvKHcaLsPdAV3Yvjhs863U10aw3kMf6IzXdkF5Y20HU6/k58xDAguGvv1DA5r8NVggFYeuOEvGAXpw+DYJAAUIJR1jbVwWBMnDOPRFM27LJx4rx63WrsSgKD3bpSs8GjXh18QKvXAPoQ1cOj4exq1diVhRUTfURprOZTAxu0SrgdXxwZR8GT5mIS9XI87gJs1ioFx3D3eefsHXOcbmYsGE9v+/8j1y322++PsRs/L+xY2Itdh1PxVPsCcatajSIiQX0yebXlixk6pZNOD0e6kXHcDAr05saK5gstppMaJrEpphYsHcPO9OO0zyhGv/MXGPoBCYl9L6jBwMe7sstje83XJ+majjznNhCTl6HPi/LuEvJYjWTk5FLTLUoqiXFM3rJq6QdSceZ56J63QTdhP5IOmlHMqjVuIbhuXeu38OaeRuIiAmja//zWTFrLb9/NR9N0+h1y6X0uqU7Vw0P7Kmia7/zSWpWiz2b9nsL1/YwG136dqBB67p43B6+fWGyYXvtV8/+yJtznvM5phB2XVsnbWS+abvILwQXFm7LA/UQMnc8Ily30FRCeiPtV4B6EJRIcG9Fpj+Ibk1Jvg3jmKBf3KVnPzLnK91RzNIYETai1M4e6VyGzBmvdw/ZL0OE3lhpBvLnAudcIPhoxd8+/fd5Hg9frl3N/Z0vwGY2M2XQUJ6aN8crTWE1mXB4PEX2U6TEajJhNZlwqxoXJtXhIT9FXiOaxMWz+LY7mfnfNg5lZtK6Rg26163vnTvIc7u5dtIP7M/MxKmWbLnnz1Xs7o6dmLF9CznuE4b3IWYzw9t1JCLfxP6u334tIna3Oz2tyDEKQkjB605NxelSeXjOLObceCuapvlNZ5mtZmo2rFFimqjwVPDJ0Llve2Z+PtdHwM1kNvH1MxMIiQjhytsvo3H7+mz5+z8cuU4kks8eHs/K39dhsZnRVI1bXhzsTetIKXl3+BgWTF6G6tEwW0y8f9fnuqF8fnrnv9W7WDx1Oa/M+J+3LVdKydYVOziw/RD1WiTRuH2DIut5b+GL/Prx7/z5w2LMVjNXj+xFz1v0+kj60Qw8BtLXALvW7/H7+YWlOSQs1Ien3NshcxSQW+xdTnDMhvATXspCmMBcR//B1gWqLQPPJsAM5mZBN16R7u3I44Pz01oe8GxG5s2G2C8RVsPORrScryBrNN60VvZWZN4UiPv1pLwFqvDPORcIjuRkG27P87hxejyEWCzUiojku2uvx+nxsPVYCkOn/eRzqdOAdjUSual1O5rGxdEwwEnfwoRbrX6fIqZu2cTBrNKDAICRAKaUklqRkfwy5EbeXbaEvw/uJzYklDvbd2TAeXpHyM7jqaw6fLBMYnd7M9JJycmhU+/2aPd84fO61W6l+2BdsbL74K7Mn+BrmN6+Z+siMw471u5m4U/LEIpC90FdadC6rs8+BQx7ZgCLp/5Ddlo2Lodb7xCSEpfDzaIpfyOEyD+nxGKzIKXEkeNEUQSqR/MWfce/MInEBtW5sH8nlvy8goU/LS/kk6D/27sKBTFHjpN/F27m34WbadO9BTkZOTzZ6xX2bt6PEAJNkzQ9vyGvznwae74vsy3ExqDH+zHIwNc4Ms7/XW2N+iUXtYUQYGkOIgzpT8JZlHzXLIQJLIEN1FUEMuv1/EG0AlQgD5n5AiLet4lBatmQ9T5FO5WcoB5F5k7wDsOVaS3OJfowmnoIrB0Q4fchzPXKfLwzmXMuEDSLi2fdkWSf7SFmCzf+/BNxIaHc2rY9XZPqYDObcaoqJsW428KtqvRu7KegV07+3L3T7+RwYULMFno0ODHeP23LJt5ZvoTk7GwSwyN4rGu3Ij7KblVl8uaN/LxlE5lOZ5kNw6WUmBRBTLUo7vt4OB/f9yWaJtFUDYvVTN+RPTmvc2MAnhh/H2lHMlg7f4P3EaNZ58bc+tIQprw3g8i4CPZs2s/0T37XL9BCMO3937jhf9cy7NmBhuePqR7NFxve5bfP/mDNvA2YzCY2Lt3qTb9IKb0X+8L6QmqxqOnIcTLxjZ+5sH8n/vjmL5+WVCMcOU7WLdhIm+4t+OTBr9m1fk8RQ5stf//H2Ce+5eq7ehGVEElsDf8txVa7lWvuvYLpY/4okh6yhVq5+YXrS10LgDDXRZrrguc/imr6hyBCy1aMDxqu1cbbPTuR0okQxVJ17o2657BPYdwJzr+gjIFAy50KmS/hfcpwHEY6/4S4aQhzYAq4ZxNBDQRCiCuB0eiVrXFSyjeKvf4+UKB/EApUk1JWnDmuAU91u4Rbf51apPtGAE7Vw9pkvRd86f69PNzlQka070jLatUNPYztJhO9GjYO2jqrhYX7tcQsINRs4aK6dflt+1bGrVlFhM3GjG1bcBTk97OzeGb+XACubdYcTUqGT/+Z1YcPBhRkSiIpKorYEL04fdXtl9P20pYsnLwct9PNBdd0pFHbE79MJpOJt+Y+T9qRdA5sP0z1egmMe+p7Hr/8RVSPismkFPMV0P2CJ7z+M5cMvpDajRMN1xAZG8HQpwcw9OkBvDt8TJHhsZMh9bCeDlP9qMQWxxpiJSouEiklf01c6pPacTvdzBjzB/O+W4zH5aHtZS15ZsKDhEUVzb3n5ThwO9wMf30YZquZXz6cjcvpJjo+kjvfvZnzr2wX8GcQ0Z8ij9+UbwKfXysIHazLMZ9OKBGgGdV3LPl/ir8/BmPfBAFK2Yr+Unog63WKdlBpuuR29ofnZNtp0AKB0AVKPgF6AgeAlUKI6fk+xQBIKR8u9P77gcC/+WWkU63afH/t9byzbAlbU1Owm82k5uYWGczK83h4d/lSBrVoRaTNxjMXdfcWkCW6h29ieESR6d2K5sbWbZmxfWuRgKUIQUJoGJfVa4AqNRLDw/l8zSqvkJ3At7vc4fHw7vIl9G96Hkv27WVN8qESg4BAdy4TgNlkwuF2GyYdMhwONCm9WkmJ9asz5Mn+JX6mmOrRxFSPZv6ExSyfvsp7B+xvNZqqsXz6Kq5/1Lg1szBh0WGltqwaoZgU2l6qyz23urg5K39fV+o+UpPMGf8XUz/4zW9+H/D6Faybv4FXh47mtZlPA3qn1du3j2HVHP1cifWr8dhX93DLi4NxZDsIjQwtgyyIlp9ycaJ/CxRQDWQZTjWhN0H2GIoOw9kgZKBxfcLcRJ+B8Oyi6FS0HRF2S9nWoCaDNBqu08BVOfI1pxsBBQIhxGrgK2CClDKttPfn0wnYIaXclX+MiUA/YLOf998AvBDgsctF+8SaTBgwCIAhUyeRnO1bN7CaFDYcTebCpLoMbdWGZvEJjF+/hqM5OfRs0IjBLVoRZi1boTMQWlWrzsuX9uCFBX+iCIGqadSMiOTLa66lTlQ0blXl/C8+LRIo/D07HMrKotFH72H2M4ks0AviqpR0qZ3ECxdfSkJYOMdycxgydZLXma0wOW43R7KzSYwwzkE785ws/WUlacnpNO/alGadGnkvbrO/nB9QCkZRREDDaQBX3HYpv332R6mOZT5IyfWPXQPAvq0H/b7NFmpDMQk0VUNqkh1rSjBML4bb6WHd/I2kHk4jtkY0T/Z6mT0b93kL3fu3HeLJK17hy43vGfoiB/QxUgfnawYVoIFzOjK3LSLM2BvhVCDC7kCq+yHvVxA2kC6wdUdEPmX8fiEgZhwy7U7w7ANh0p3GIp7yW1wuFSUavz4MQZyQPp0J9IlgMHAb+l39KuBr4A9ZcoK5FlBYAOQA0NnojUKIukB9YH6A66kwEkJDDe+kPZokxn5CZ719Yk3aJ9as1LUNOK8FfRo3YePRo0TYbDSJjfNeTLccS0H10y1khAS/3gUSePLCi7m1bVEd/0ibjWh7iGEg0KQk3E8g3L1xH49d+gJulwe304PZYqJN9xa8+PMTJ4TfAuSiAYZfGR/qt6zD3R/cypgHv8FsNeFxqwGlikwWE4umLKd+yzqk+PFADo0M4daXBlO3RR2eu/p1wwGxgqcRIYwlecxWM+lHMzh2IJUD2w/5dDt5XB6mfzqHEa/rF+2czFzmfP0XGxZvIalpTfqO7OV3/kFzbwGZavwBsz+FcgQCKSW41+laPpbm5RZwE8KEiHoVGf6wrh1kStLNakrax5SIiJ+B9OwALR3MzfXhtrKuQQlH2q/Kt8wsfEMSgggbWer++myGPKvaVwPqFZNS7pBSPgM0ASagPx3sFUK8KISIrYB1DAGmSCkNrxBCiDuFEKuEEKtSUnynScvDLW3aYy/Wh28SgloREZxXinpoaTg9HlYcPMC65MMl5vpLwm620LFmLZrGxRdJF4RZLOXO8xfG3/ruaNfBZ07BajJxab363hbUwkgpeWngO2SmZpOX5cDj8ujF1b828tvnulpqr5u7eztqimMLtWEPtWG1W3jg0zv8DmoZ0eeOnkw6NJbHv76X/vdd6XeQqzBup4c/f1gMQMdebbCG+AY3j8vDpTd0w2ozY7EZ3zvF1ojm0iEXct4FTYq4ohUgpSSpaU2S96QUcT0rfI79Ww8BcDw5jeHNH+KrZ35kybR/mPLuDIa3eIhN859HS+mJduxaZN7PJwr9nv/8f0BZdrtLqaUhU/sh025DZr6APHYdWtpdSFm2WkxhhCkeYT2/1CBQZB9zI4S1Y7mCgPdYUS+DvSdg1SUzRJhuZWn3P0wn1UNoqcOQRzshj3ZGOzYQ6dlV7rWcDgRcIxBCtEZ/KugNTAV+ALqh38UbiYYfBAqP+NXO32bEEOBeP68hpRwLjAVdhjrQNQdCx5q1ePqi7ry2eAFmRcGjaSRFRvFlv+vKJd88d+cOHp07G9AvAmFWK+OuvpaW1cqmc1Mcm8nst+PHoiioUmISIiAHM5vJ5FeEbmDzluw4fpxv/12L1WTCpWp0SKzJWz2MfQQO7Uwm5YDv3akz18V3L/7E/AlLqNsiicYd6vPf2j04sh1Y7RYUReHRr+4hJz0HIQQXXNORmOon3zcQHh1Gt2s706JrU375aHZA+7idHqa+/xvV6iYQERtGZorm7QKyhVpp36M1rw0bjSPb6W0tLYwQcF6XJjw94SFSDqRyV9vHyM3M8xaf7aE2Rrw+FKvdSsO29fAYFKVtIVZadtMnjb9+9kfSj2Z693e7PLhdHt4duY5xi3QZE5kxClwbEFHPg6WD/w9nKvtTrMx4Gjw7KFLFcS5DZn+OiDAeEjxTEMKOiH4PqWXoBXZTbb8S3gBSuvT0m5aCtzPLswGZOgQS5p/x8wwB+RHk1wjSgS+BqVKe6OUSQkyTUl5nsI8Z2A5cjh4AVgJDpZSbir2vGfA7UL+UVBMQPD+CXLebjUePEG230yTu5LsRth5L4c0li1idfIhom53DWZk+WchIq5V/RtyNzc8k8MmwPvkww37+iVy3b5qiaVw8s4fdwiNzZjFj+1ZDbaTC2Ewmltx2p18THIC0vDy2px4jMSKCOlH+L9D7tx3kno5PllgDMJkVLDYLt782jOPJacQkRHHp0G7EVPMvrV0Wprw3g2+en4jb4UbTJEIRRcxgQL+ImyxmvThuMxMWFcYFV3dg7fyNRMaGgxDsWr/H+3mEIkBSJAjbQq28Ne8FrzZQyoFUfnx9GmvmbSC+ViyDn+hXpAPo5cHv8c9vq731DJNZITIugq+2jCY8OoyB1YeTkZLp83nMVo0JqzcTFVfwzbIiEuYhTDXQjl2bPxhW5NNB9NgTcs0ngZQO5JH2GJbylWoo1XznQs5mpGMOMuOpYjMQAKGIyKcRoYNOybpOhorwI7i+oOhb6KD1pZS7jYIAgJTSI4S4D5iD3j76lZRykxDiJWCVlHJ6/luHABMDCQLBJNRioVOt2mXad1facfpP/B5X/t13tsv40Tnb7eavPbu5slH5204bxcYZpnMsikK3Ovog1t0dOzPrv20+gnGgO5gJ9JTQWz2uLDEIAMSEhNC5dukaLrWb1CQyLqLEQKB6NFSPkz++mc+nq98u9ZhlZeAjV5PYsAY/vj4NR46TTr3bMXf8Apx5LjwuFU1V0VTp7fxxuzw4c10c3nWEr7eMZvvqnTxyyQtF+vtlfkAxm02YbRbMFhP3fzKiiEBcQu04HvjkDr/revqHB/np3RnM+GwOjhwnXfp04LZXbyA8Wm8vtYfaMEzoSLDYCv0/F1ZdpsFUAxH7HTLtHnCvzH/RAhHPlCkI6OcqIe3o09N/ZiK1THDMQHoO6FpMtsvQ718NUA/4+dy5SHVfQLqupzOBBoIpQHFH8ClACc+kIKWcBcwqtu35Yj+PCnANFcahrEy+WruadUeSaRIbx4j2Hb3aO2Vh1II/vUGgJDQp2Xg0uUICQZjVykOduzL6n2XeWoFZUYiw2bizvW6Ok+fRh7MKowjBRXXqcln9hlgUhR4NGpUaBE4GIQTPTHyYp654Gc2jldjFs3PdXn2OwCCnXhH88e0CRt/9BZpHRfVoJO8+yiWDLqBz7/akHkpj/AuTyMkoWgjXVI21f27E5XCxbv5GHxMa0INB/4evoveIHtRsWOOk128ymxjyZH+/7bZ9R/bi+5d/KpKGMps12l2cTWh44e+ZBoqeahRKOCLuW6R2HLQ0MNUpl0WjUMKR5obg2VZ89WAru/Xp6YJ0b9bnLqQHyEPmhYKpDsT+aKy1ZG6eP9hW7PsgQhGWlpWy5mBSYiDIT9u0AKKEEIXv/COB0itxpwlSSlYeOsjRnGyi7XbumTUDp8eDW9NYn3yYX7dtYXz/gXSsWatMx1916FDA7y1emC4Pd3Y4nwYxMYxds4qUnBwurluPezp2JiFM/yJ/tmoF7mIdOqqULD+wnzd7XOl9X0XTvEsTvt81hvkTlnDsYCq/jpmDw8DK0mw1G5rHBIoj18m87xaxfsFGqtdLoO9dvahRT2//y0rLZvTIL3A5TlxMnblOFv20nMuHXczFAy/g2xcn+zmyRNMkkXERWKxm1GIdPla7hep1qpHUtGzfl9K4/tGr2b56Z76gnwmpqSTWyeSxD/YVepcJlEQfeQihxOoy1QEitex8g3gT2Lp6ZasBRNTr+RdLN7owXQgoYYiIR8v1+U4HZPoj+T7HBRtywbMLmTMWEfGw7w7WLmBqlB8YC54MLKDUAFvZZM1PJ0q7KjUF+gLRQOGpnizA/7PvacSF1AToAAAgAElEQVThrCyG/TyZozk5CPRhscIpFVVK8jwenpk/lzk33lq2kwT4XGgSgjY1jKdky0qPBo3o0aBoS19ydhZ70tPZfvyY4WyBBAb+9CNhVitDWrRiaKs2mA06WcpDZFwE/e/Xp1oX/rScZINAEBJhL3NBPistm3vPf4q0I+k4cpyYrSZ+/eh3Xp7xFG0vbcmqOesxWZSic0vo8hALJi6h/eWtqNciiY1LtvocO7paFPZQGxcN6MyYh7/2eV0xKXQfErjA4MliMpt4fvKj7N92kB1r91C9XgLN2h6EzKf1C5ZUwdISET26XA0NWt5MyPif7m2gb4HoDxG2iwH0O934ObovsrpDN3sJGXDGF0alekRP9fjggrzpYBAIhBC64mvOJ5D3i/7/wN4HEfFguZ68ThdKDARSyl+BX4UQF0gpl1fSmiqU+2bPYH9GRqkF051px3F43AH5BRenY2Itluwv3ZRGk5J2NYI3i+BSVR6fO5s/du7AajL5rVW4VJX9mXoW+s2li1i8bw9fXH1t0NZl1EUEkHksq8ypoYlv/ELKgVRvft/jUvG4VN685SMm7P3MUBob9F9oU/6QWsp+47mBzOPZqB6VsKgwnpnwEC8OfMerV2S2mHjsq3uITqjYwrYRSU1rFXrqaIK0LQF1ry4XbSpf95lUD0LGU4CzyBCNTLsfqi1E5DuBCVO1M75DyJcSvm/C/2tCCUVEPA4RjwdhTaeWEm8DhRBP5P/nUCHEh8X/VML6ysWR7Gw2pxwtNQiAnl/3ZzhTGqO6X4bNVPq+ihAs2BO8vuP3li9l7q6d+daZroDsxPM8Hpbt38e/BkJ8FYXZz4VeMSllTg0tnvq3obxD1vFsDu86Qscr2qKpvv8C1hArPW7UC6iZqcZKtJpHw5HrRPWofPrIeNRCshWapjH28e9wGVhWVjSappGVlu1tIxXChDA38AYBKR1Ix19Ixx9eA5pAkXm/gZF4iAAcc8u58tMbYYrXLTd9Ln92CDEWOTzbKe23cEv+36uA1QZ/TmvyPO6AsjY2k4lrmzX3egGcLA1iYvl58DC6161PhNXq1yhGlZJ9GWUf8CmNCRvXF5GcCBRVSlYfDrzOcdL4SV+UI6uBzc9AmqZJbKE2QiNCeG7Sw9hCrNjD9AE1q93CgIf70KJrU0BXQDUirlYMoREhrJi9luPJaWieQoFAlWQdz2bpz8a+vhXF3O8WMqTWnQxKvIP+sbfy1bMTikxkS+cy5NELkBmPIjOeQh69AC13eglHLIbMwbA1VKr5JjdnNyL6A13QToQBFhAhYGmLCLv9VC/tlFBaamhG/t/jK2c5FUudqGgfd67CRFituFSNrklJPH9x+TohmsUn8FU/vZ5++6/TWLDXWItGKc/VrxSMZgoCwaIoVA9S4RjwKwSnqbpsdVlSQ/3uvZLPHh1fpLVTMSk0aluPuERd9rlznw5M2P8ZS39egSPHyflXteXvGau5PnEEWalZJDaogdVuwe3yeOcLbKFW7vtwOEII9m4+YChTkZftYM+m/T7bK4q/f1vN6LvHFvFFmPbBLKQqGf76MKSWhUy/G2Qx/+HMZ5DWtogC45kSELbuuuOXj4exAraLKuaDnMYIc13d2Mc5H9TDetHd0r5cNZczmdK6hmbgX8sMKeU1Fb6iCuRYbo6hhDRA9bAw3ul1FXUio0mKqth8b4uEaizcu9vnH86iKNSNDp7Kdutq1Q29FkpCF5wzc3n9hqW+t6y0uLAp6+Zv9NneoE3dMreO9r7jcjYt38aiycv0YwiIqRbFs5MeKfK+yNgIr63k18/+yNQPZnqDx4Hth7DaLbS9tCXJu49Qq3Eiw54dSMt8L+E6zWphDbH62GKGhNupe15wOoYAvh01yWeC2Znr5JePZ3Pzi4Mwq/NAGl2wVGTedETEfaWfxNIO7FeA84/8JwAB2CF0CMLcoLS9y4XUMpF5U/UZCHMTRMgghOnkjZ3KixBWsBtPyJ9rlNY19E6lrCJIHMjM9BvF0hwOLkzy74BVHga1aMW4tat8nL9CLBYuqxe8X7IXL+3BDVMn4fJ48EiJWQjMJhOtEqqzNTWFmJAQrmjYmN+2bSXd6UACtSIiGdP7mgqZdvbHPe/fyoPdnsPlcKG6VRSTomsJfTKizMdUFIUnv7mPG58dwLYVO4irFUuri84z1PEBvdW0cBAowOVwY7Ga+XbHJz77dO7TnuiEKFx5bm+e3mRWCIsOo9uALmVee2kk7zXW09JUjey0HKLDczDM76OCDKxWIISAqDfB1QeZNwOECWG/FmEL3ucCXa9Hpg4ALQe9pWseMmccxE5EWILn71FFyZSWGlpYWQsJBpEGomgFBFLcLStJUVF8cEUfHps7GyEEUkpCLVbGXXNtUC+4rapVZ+YNN/PFmpVsTjlKi2rVGdGuo89TyFMXXszu9DTMilKiXERFUb9VXcauf4cp781g28qdNGhdh+sfvYbaTcrfQVWrUSK1GpXeknvs4HEUP05zuzfsM9xuMpsYvexVPr7/S5b9uhKpSTr3bc/9Hw3Hagtey2DDNvUMn6CsIVYi4yNAXgxZb/ruKEIQ9sB72oUQYLsEYSvj9HEZkJmv6wNv3kDmBOlCZj6LiJtUaeuooiilpYYmSykHCSE2UDRFJAAppaw8s9MyUC86hjCLhRyD3PkVhdzFPJrGHzv/48/du4gPCWVwy1blmjQGuKJRY7rXq8/a5MPYTCba1EgMan2ggLrR0bxymX8FRdAvAOX9fCdLjXrVuO/D4ZV6zsLE1YzxW6uo28JXWiQvOw+z1UxMtSiem/SIV1uoMnLIt786lMcvH1UkPWQLtXHbKzdgMpmAOsjQWyD3O/S7aqkraFq7g6WMGv2VhWsRvk8zEtzrkdJVovBbFcGjRNE5IUSilPJwvl+AD1LK0pvnK5iTFZ2buX0bj86djSs/TaMIQbTNxqxht1AtLByXqjJs2mS2HEsh1+32plPe6nEFfZs0C9bHqOIUMPaJ75g+Zo6PN3BhwbhNy7bx/p2fcWD7IRRF4ZLBXXngkxGEhIf4O2xQ2Lx8G+Oe+oGd6/cQXyuWm18YxCWDig6xSdcKPdcu3Qj71frdvZHL12mEduR8P9LYFkT19f61fqooNyWJzgWkPpp/kBrormMSWCmlDF7jeQmURX10/ZFkxq1ZxYHMDC5MqsOtbTsQn6+v8+OG9byyeIGPtn+oxcKqO+4u04BZFSdPekoGnz78jd6WKQQXD+zCyHdvITKuZPMPj9vDkb0pRMVHekXb/KFpGhPf+Jkp784gKy2HOs1rc+8Ht9G+h/5ge+C/w9zd/vEignkWm4WW3Zrx1tzn/R22ipNAy3wDcn+gqCGMBexXoES/d6qWdU5Q7kAghBgBPI/uPSCAS4CXpJRfVeRCA6GiZaiHTZvM8gO+rYDhViuf9+nHBUmlt+JVUT7cLje3n/cQKftTvUVZs8VEjfrVGLfxfb+dRTO/mMsXT3yPmi8q1+26zjzyxUi/pjeFkVL6pHk+um8cM8fO8zGxt4VY+Wzt2xVS0zjXkdKBTLsLXOtODJKY6iFixyOU4E9rn8tUhAz140A7KXU/PCFEHLAM3ansjCbUYpyTlFIS4sespSxIKZm+fSvfrl9LjttN70ZNuL1dB792j+cSy35dRUZKZpELsMetkno4jX9mraHrNef77LNi9lo+ffibInn0pT//g5SSZyY8VOo5jXL9ezbt9wkCoIvjHd51pCoQVABC2BGx45HuzbqAm6k+WNqcs/37pwuBBoJUdKG5ArLyt53xDG3VmmX79+mSzYWIsNpoXT1wG73SGLVwPlM2b/SmoPampzFj+1Zm3HDjGZ9+klKyYvZaZo/7E6fDxeVDL+LSIReWOiOgqipH9x5j+6qd5BmI0jlzXezddMAwEEx4bZpPr73L4WbpzyvISssmIubkhdGaX9CELX9v9+oKFeB2uqnXsurJsCIRluZgaX6ql1FFPqV1DRVM5+wA/hFC/IpeI+gH/BvktVUK3evW56bWbRm/fg0mRUFBYDGZ+LLfdRXW5XMwM5PJmzYUmStwqiqHsrL4ddtWBrdoVSHnOVV8/th4Zo6d582tb1y8hXnfL+K1WU/77etfPO0fRt89FkeOE4/L4zV/L4wt1ErtpsZ34cf8CNmZLCYyUjLLFAj639+b3z6bi8etFpk0vmjgBSTUrvyBpyqqqCxKeyIoqNTtzP9TwK/BWU5wyHI6mfXfNpJzsmlXoybd6tT1XuSFEDzV7WJuatOWFQcOEGW3c1GdulgqcM5gTfIhzIriM2CW53GzcO/uMzoQHNqZzIxP/8DlOPFE5chxsmnZNlbNWU+nq9r57LNt1U7evPlDQ//fAkxmE5GxEVxwtbH3UYtuzUg5kOoTPBRFUL1eQpk+S1xiDB+veJ0vnvyetfM2EBIRQv/7ruT6x07rAfoqqig3pQ2UvVhZCwkWm1OOcsPUyaiaRq7HTajFQrO4eH64blCR4a5aEZFce15wHlUTQo27WcyKQq2IyKCcs7JY++cGhMFdvyPbwd+/rTIMBFPem4Erz3e2Q1EECD04d+nbgQfGjMBsMf6K3vzC9fw9YxWOHKc3GNhCbQx/fRgWa9lTbbUaJTJq6tknM1xFFSURUI1ACJEAPIHuVuZ1JpNSXlbKflcCo9EFwMdJKd8weM8gYBR6ymm9lHJooIsvDSkl98/+jSzXiVa1XLebzSkpfLl2Nfec37miTlUinWrVJtoe4mOKY1YUhrZqUylrCBbhMeEoJt8Umtli8tv6mbzrCEbdaiHhdl785QlaXdzcb0qpgFqNEhmz6k2+HfUTGxZvJqF2HDf87zq69C3RPbWKKqowINBi8Q/AJHS3spHALYCxIEo+QggT8AnQEzgArBRCTJdSbi70nsbA/4ALpZRpQohqJ/8R/HMwK5PD2Vk+2x2qh2lbN1VaIFCEYMJ1g7jrt1/Yk5GOSeh1iHd6XkX96JhKWYM/NE3jzWWLmbjxX1yqSrsaNXm755XUigzsSaVzn/aGF22T2cQVtxorura5tAU71+3BXcxPwO3y0KBNvVKDQAG1GiXyv+8fCOi9VVRRhX8CDQRxUsovhRAP5usPLRRCrCxln07ADinlLgAhxET0IvPmQu+5A/hESpkGIKU8enLLLxmBQNWM5yQCnKOrMJKiopg17Bb2pqeT63HTODauwu0hy8LAn34solj698H9XPrtlyy97Q4SwkovuNpDbbz++7M8d/UbuJ1uELo42uNf30tiA2MXrQEP9WX2l/PR0nNQ87X+7WE2rn2gd5mKvFVUUUX5CDQQFCR0Dwsh+gCHgNLEamoBhSe1DgDFb8GbAAghlqKnj0ZJKX8PcE2lUjMiAn8q2pUhtmZEMGWoT5atKSmGstUeTWPUwvl80juwIul5nRsz6dBYNi/fjtvppsWFTbGF+B/qiqkezWdr3ua7l35i1Zx1RMVHMPCRa7hsaLcyf5Yqqqii7AQaCF4RQkQBjwIfAZGAr8Nz2c7fGOgO1AYWCSFaSSnTC79JCHEncCdAnTqB93MfzMr0O6iyLyPdcPu5xG//+Rq3F/D3ASNzb/+YzCZaXXRewO9PqB3HI2NHntQ5qqiiiuAQUCCQUv6W/58ZQKBWXgeBpEI/187fVpgDwD9SSjewWwixHT0wFEk7SSnHAmNBl5gI8PwIhN9ZgMpQAj3dKempKDak7CJrUkp+/nAWk9+ZTlZqFk06NmTke7fStGPJ5jcZxzJZMWstCL32EBlbss5QFacHUkrQjoCwIZRTW/OqomwElKQWQjQRQvwphNiY/3NrIcSzpey2EmgshKgvdG3ZIUBxU9Vf0J8GEELEo6eKKszdvVZkpGF7pt1sZuB5LSrqNGcsA89r4bdO8XCXC8t83HFPfc9Xz/xI6sHjuBxuNi7ZymPdXyjR3vGPbxcwtM5IPrpvHB/dO46hSSP5a9LSMq/hZJBS4nK4DDuZqigZ6VqFPNYDmdITebQbWuqNSLVCS31VVAKBViu/QO/ucQNIKf9Fv7D7RUrpAe4D5gBbgMlSyk1CiJeEEAXJ5zlAqhBiM/AX8HiBnlFF8dFVfYmy2Qi1WFCEINRioXX1Gtzatn1FnuaMRFEUJg4Y7GPSc3vb9vRu3KRMx8zNyuOXj343cAJz8cMrUwz3ObovhdEjx+JyuMnLdpCX7cCZ5+Kd28aQejitTOsIlD8nLOaGpLu4OvxGrou/jUlv/1oVEAJEqoeQacNB3Y+uJuoG92rk8Zuq/g3PMAKtEYRKKVcUy7d7/L25ACnlLGBWsW3PF/pvCTyS/ycoNItPYOntdzF7x3aSs7NpXyORLrWTqkSu8mmfWJMt9z7Esv37SMnJpmeDRoSWQwgvefdRzFYTrmLSQZom+W+N8cPewsnLvZIORRCweOrf9L/vqjKvpySW/bqS9+/8zDvhnJ2Ww/cv/QRSMviJ/kE559mEzJ0IsvhlQNXTRO5VYPXViKri9CTQQHBMCNGQ/BYcIcRA4HDQVlXBhFosDKhKBZVI1wqS205IivMRbQNdcbjOeb5OYAAupxuPgeqn6lFxO3wnkCuKr5/70UfmwpHj5MfXf2bgo1fnu4FV4Rd1LycaCgsjQK1cuxIpXeD4A+n6B0y1ECHXIUwVOpZ0VhNoauhe4HOgmRDiIPAQcHfQVlXFGUtETDg9b7oYW2jRpwpriJVhzwww3Kdz7/aGgx2aR6NTn+Cl8JL3GM9EOnOdOAzUUKsohqUTYNBUID1gaVlpy5BaNjL1OmTGs5A3CbI/Rh7rhXRVnG/J2U5AgUBKuUtK2QNIAJpJKbtJKfcEdWVVBIRbVflz904mb9rA7vTg5tMD5f5PRtDv3iuxh9kQiqB2k0Re/PkJmp7fyPD9qYfTUAwkq01mhdRDwftMdZoZK5uGRYUSEnHiApednsP8CYuZ9/0iMlN9J9XPVURIf1BigMLaTnaw90CY61faOmTOV+DZC+Tmb3GBzEWmP1JVqwiQQLWGXgPeKujvF0LEAI9KKUvrHKoiiOw4nsoNUyfj9HhQpUSTGv2bNue1y3ue0hqI2WLmjjdvYvjrw/C4Vay2kkXgtq3Yger2TQ1pmmT7yh20vzw46qzDX7+R5695A2deMZP4V2/wylwsmrKcN2/5GJNJ/1n1qDz42Z30url7UNZ0JiGUMIifhsz+BBxzQdghdBgi9MbKXYhjJkWtL/PRMvT0lble5a7nDCTQ1NBVhYe88iUhegdnSVUEgpSSO2f8wvG8XLLdLvI8bpyqyoztW5m+3f+gWGWiKEqpQQD0uoI9zHcS2RZiJSEpPhhLA6D95a148ZcnaNimHla7hZqNavDI2Lvoc0dPANKOpPPmLR/jynN5u5lcDjejR47lyN4SpbbOGYQSixL5HEq1RSgJf6CE3YIuM1aZi/DX3KCV8FoVhQk0EJiEEN7fVCFECFC6MWwVQWPH8eMcycnxEdDI9bj5YcP6U7KmsnLJoK6YrWYKP8QIIbDYLXS7rlNQz92hZxs+W/s2M3MnMH77R1w29CLva4un/oPRc5WmSRZOXhbUdVVxEtj83JMqsQhTlb1oIAQaCH4A/hRCDBdCDAfmAuODt6wqSsOpevxORzs8weu0CQahESG8t/Al6raog8VmwWKz0KB1Xd5f9HKJmkXBxuVwoWmaz3bNo+J0+DfVAUhPyWDF7LXsWLu7Kk8dbNTDYBSytSx00YIqSiNQiYk3hRD/Apfnb3pZSjkneMuqojSaxSdgMSk+3Xt2s5lrmgSu+XO6UL9lHb74911SD6chBMTWOPVSBZ37tOfr5yb6bLfYLVzQt6PhPlJKvnpmAtM+mInFZkH1qNSoX5035jxLXOKp/0xnJe6lGIpLCi2/RmDcpFDFCQLWQZZSzpZSPpb/pyoInGLMisL7vXoTYjZjUfScbKjFQqPYOG5sfeaa3cQlxpwWQQAgqWktBjzUB1uo3v0khMAeauOq4ZfTqJ1xV8ziqX/zy0ezcTnc5GTk4shxsm/LAV4c8HYlr/4cQvHjJy09IE6P79LpTqBdQ9cBbwLV0J/BBPpg8Jnts3iGc0m9+sy58VYmb9rIkZxsLqpTlysaNq5Qv+WKYvvqnYx/YTK7/91LUrOa3PTCIFpe2OxUL6tUbn91KBdccz5//rAITZNcNuRCWpSw7qkfzMSRU7SDRVM1dq7bw9F9KVSrUzY/5bIgtRxk3m/g2QLmpoiQqxHK2ef3IMKGI9OfAPIKbbWAtTPC5CdIVFGEQCeL3wKullJuCeZiqjh5akdG8cgFZReIqww2Lt3KU1e8givPiZSQciCVTUu38cLUxzj/Sl9P49ON8zo35rzOjQN6b3ZatuF2k8VEdnou1SpmgLtUpHoYmToAtBz0C2QIMns0xE1BmI0nvM9UhP0KZPhuyB4DwgzSDZa2iOj3TvXSzhgCDQRHqoJAUaSUrEk+xJ70dJrExdOqmrEbV0Wfc+n+fUzdshGPJunXtBmX1W942ktqf/boeB8ROmeei08e/Jpvtp3+geBk6NrvfA7vPOJjw2kym6jbvPIuwDLzJdCOAwXF7jyQTmTmKETsuEpbR2WhhI9Eht4Inv9ASTjrgl2wCTQQrBJCTEKXjfb+RksppwVlVac5mU4Hw6b95J3klVLSqnoNvr7mOkIspffNl5VXFy/gx40byMvvCvprzy4uq9eA0Vf2Oa1F9Hat32O4/dCOw6geFZPBVPGZyvWPXcP8CUtIT8nEledCUfQ22Ic+u6tyP6dzESeCQAEauJYipTytvy9lRSjhYD27biwqi0ADQST6/HavQtskcE4Gguf/+pPtqcdwF2otXJ98mHeWL+G5iwP17Tk5dqUd54cN/+JUT9xp5rrd/Ll7F6sOH+T8mqfvHVBkfCSpB4/7bA+JCEExnXrf5ookMjaCsevfYebYeayas45qdeLp/0BvGrWtPMkFHRPGgnBnT9AtCSldyNzJ4JgBwo4IvQFsV5yVAbAiCLR99LZgL+RMQZOS2Tu2FwkCAE5VZeqWTUELBIv27sGoRS7P4+av3btO60Aw5Mn+jHvqhyLpIVuojese6ntW/mKGRYUx6PF+DHq836lbREgfyJtO0WBgAfvZfzGU0oM8fiO4t1FQQJaudRDyNyJq1Cld2+lKoF1DdmA40AKwF2yXUt4epHWdtmhSovoZEHKpvno5FUWY1YpJUaDYOSyKQrj19B7y7nfvlWQcy+Snd2agKAJN1eh7Zw9ufM5YjbSs5GTkMPHNX1j403Jsdit97+5F37t6npNy0iLif0j3Zr2PXqogTGCqjYh8vvSdz3Sc88CznaJdRHmQNxUZdhvCXLdchy8YEDybAmqgqaHvgK3AFcBLwDB017FzDrOi0L5GTVYfPljk/lxBcEndekE77xUNGzFqwXyf7SZFoV/T03uATAjBLaMGM+TJ/qQcOE5czRhCwuyl73gSuBwu7uvyNEf2pOB26nfBXzzxPRsXb+GZHx+u0HOdCQglEuJ+AdcKvYBqbqi3U4qzKxVnhHQuAZlr8IoCrpVQxkAgtePIjFF6oEEibRcjIl9EmGqUZ7mnBYF+KxpJKZ8DcqSU44E+QOfgLev05rXLexJhs2E363E0xGwmJsTOs0FKCwFE2ux83rcf4Var90+I2cw7Pa+kVuSZMc5hC7FRu3FihQcBgL8mLuXYgVRvEADdV2D59FXs23ow4OO4XW7mfb+Ilwa+w+h7xrLTT6H7TEAIgbB1RoTdiLBdcE4EASB/wMygaUMo+bLZJ4+UKjJ1SH4Q8AAqOBciUwci5ZnvXRHoE0HBb1e6EKIlkIw+XHZO0ig2jvk3385PmzayNfUYratXZ8B5LYm0BTdF061OXVaOuJvlB/ajahpdaicRVg5bybOJdX9t9BnkAhCKYMvf26nTrFapx3A53Tx6yfPs2bQfR44TxaQwd/xC7vt4OFfedlkwll1FEBAhA5E5X+NbLLeC7SKjXUrHuQi0FIo69Gogs8HxO4Sc2damgQaCsfkeBM8C04Fw4LnSdhJCXAmMRm9VGCelfKPY67cCbwMFt2wfSynPiCbn2JBQ7uoYXGVMI2xmM93rVXYHyulPjfrVsNjMPjaZQhHE14oN6Bhzxy9g98b93qK2pmo481x8fP9XXDKoa1CeZKqoeIQ5CaJHIzMeQ2+w0EBEI2I+R5RVllrdBdLA80DmIt3/IQyM2s4kSgwEQojCpvIFnUOf5P8dVsq+pvz39gQOACuFENOllJuLvXWSlPK+wJd8+uBWVTKdTqLtdr2QW8Upo/eIHkx5d0aRQKAogojYcNpeFpht4sKflvsMvoHulLZ52TY69DxzNZzONYT9UrD9De6NIGxgPq98xV1zI/04spgftwhFWJqWb7GnAaU9EUTk/90UOB/9aQDgamBFKft2AnZIKXcBCCEmAv2A4oHgjENKyUcr/uaLNStxaxp2k5kHu3TltrbB89etomQSasfx6syneeOmD8lMzUJTJQ1a1+G5yY8G3DUUHm18byOlLGJdWcWZgRCWihsws3YDpYbeheVND5lARIH9ioo5xymkxEAgpXwRQAixCGgvpczK/3kUMLOUY9cC9hf6+QDGBeYBQoiLge3Aw1LK/cXfIIS4E7gToE6dShJrKYHPV6/k89UryPPoXwiXqvLOssVEWK0MbF55pt1G7E1PZ9zaVWxKOUqLhGqMaNeRutHRp3RNlUXri5vzw55PSd59FIvdQnzNwFJCBfQd2YuVs9fiKPZUEB4VRrNOVVLG5zJCmCDuR2Tma3pNAA1slyEin6WQZ9cZS6D5jOpAYScOV/628jIDqCelbE0JZjdSyrFSyo5Syo4JCZWn3uhnLXxWKAgUkOfx8OGKv0/RqnQ2Hj1Cnx+/ZdLGf1mXfJhJG/+lz4/fsvHokVO6rspECEFig+onHQRAt64c/GQ/LHYLoREhhEaEEFM9mtdmP+P1MK7i3EUo0SjRb6HU+BelxkaUmKyH6lIAABJdSURBVA8RprOjZybQYvG3wAohxM/5P/cHvilln4NAUqGfa3OiKAyAlDK10I/j0FVOT2vcmkaW06BoBBzNMVaerCxeWPAnue4TnRIeKfG43Yxa8CdTBg09hSurHHIyc/np3RksnLQMW4iVq+/uxVUjLj+pi/iNz11Pnzt78u+iLUTEhNGme4tyawTl5TiY+t4M5v+4FLPFRJ87e9D3rl5nlcZSFWc2gUpMvCqEmA0U9F7dJqVcW8puK4HGQoj66AFgCFDkaiSESJRSHs7/8RpO0yE1l6rywd/L+GHDOnLcbsyK4iMxAXpb6alk/ZFkw+3r/Gw/m3A5XDxwwdMc3n0Ut0MPhp8+Mp5/F2/hf989cFLHioyPoF7LJMKjw8p9sfa4PTx80XPs33oQV/66vnjyB9bO38ioqY+X69hVVFFRBPpEgJRyDbDmJN7vEULcB8xBbx/9Skq5SQjxErBKSjkdeEAIcQ169eU4cOvJLL6yeHzu78zdtQNHfjpIM5CYsJvNPN3tkspeWhFCLRayXb5eumFBVEQtTF6OA03VCIsMrZTzFWbh5OUc3XfMGwRAHyhbMu0f9j97kKSmpc8RACz5+R/ev+tzXA43qkelWafGPDf5EWKqRZVpXUt/WcmhHcneIFCwrlVz1rFj3e5TIEZXRRW+BDXxKaWcJaVsIqVsKKV8NX/b8/lBACnl/6SULaSUbaSUl0optwZzPWUhOTuLP3b+5w0CBShCEGsPIdJmo0NiTb7pN4CuSae2kH1Dy9beaecC7GYzw1q1Dep5Uw+n8b8rX+Ha2FsZEH8793R8gt0b9gb1nMVZM3+D4UCZogg2L98e0DF2rNutdx0dy8KR7cDtcLN5+TaevurVMq/r30WbyMs2mDyVsCXAdVVRRbAJ+IngXGVPejpWkxlnMbE3TUrqREUxbfCwU7QyXx69oBuHs7KYu2sHVpMJl6rSs0FDHurSNWjnVFWVRy5+jiN7U1A9errsvzW7efiS5/l2x8dExkaUcoSKoXrdBCxWs48hjKIoxAVYOP7lw1lFnigAVLfK/m2H2PXvXhq0PnmNmoSkeKx2S5EnAtCNagJdVxVVBJuqVohSqBcdXcQDoACzEDRPOL06BqwmEx9e1Zf5Nw/nsz79mH/zcEZf2RdrENU31/65kbSjGd4gUIDH6eGP8QuCdt7i9B5xOSZL0c+pKIKw6FDaXR5YS2/ynhQ0zTftZ7aYSD3k66cQCL1uvsSnziCEwBZq4/yrgvukVkUVgVIVCEqhRngEVzRs5JNysZrNjGjf8RStqmQSIyK4IKkOiRHBvxs/vOsImse3cO7Mc3Fg22GDPYJDtaR4Xp7+FHE1Y7CH2bDaLTRoU493F7wY8EBZ+56tsdp9JQjcTjeNOzQo07pia8Tw2uxnSEiKxx5qwxpipV7LJN5b+CIWa+XUbqqoojTOidSQ0+Phrz27OZKTRbsaNWld/eRkY9/ueRXv/72UCRvWk+N206Z6DUZ1v5x60WVTMjybaNSuPkLxHd23h9k4r0tghu8VRdtLWzJh32cc2pGM1W6hWp2Tmzm5emQvpo+ZQ0ZKJp78FJM9zMY1915JdELZisUALS9sxg97xnBwRzIWq5nqdU/tLEwVVRRHSD8mK6crHTt2lKtWrQr4/bvSjjN4yiQcHjceTUMRgs61k/i8Tz8s56BhSUUjpeTxy19ky9/bvXlwk8VEfM1Yvtz8PraQM2vqMj0lg4lv/sLyX1cRERvGgIevpvvgrmeVCUkV5yZCiNVSSsM0xlkfCHr/MJ5tqceKmMiEmM081rUbt7XtUPELPAdxOVx8/8oU5nz9Fx6XyoXXduL2V28o1110FVVUUbGcs4HgUFYml3/7lU/HD0Dj2Djm3HhrBa+uiiqqqOL0pKRAcFYXiz2a5veR3mMwGfz/9u4/yKryvuP4+7PL7soiIhTGqFB3VaISlR9FA0karUqLP4KdmEZMmpBEy8SqI8ROitKxE1NnqjP1RyamDqYmnalKq8aUAadqiWmjbQQUURQRlE2BIGADQfm1LPvtH+esOSzrsqR7Oefe83nN3Ln3POe5dz/cs8v3nufc8xwzszKq6UIw6pghjGg+eGrhpvr6wl/n18zsSKnpQiCJ70y9lEENjR98/bO5oYFThg4r7Fc/rfqtemENN0y+halN0/mTj1zN/DueoNN7oFZgNX2MoMuvdu/ix2+sYuN7OzjnhJFcdPIpDPC0wlYB61b+DzdMuuWAK501NTdxyTUX8uf3fLWXZ5pVVm/HCEpxHsGwgc18bby/IWSV9/Dtj9O+58CJ//bu2suiec8w41ufZ9CQXq/wapaLUhQCq007fvUeC+9/muWLV/KRlhF8dtaltJ51+PMB9ae1y9uInqapaBzApnVbPNuoFZILgVWlbZu38/UJ3+T9be/TvmcfdfV1PDv/eebOn83kz+R3/KflzFFsXLOJ7kOuHe0dPqPYCssD5VaVHr79R+x4d8cHZzN37u9k7+527vqz+3M9MPvFuVfQOPDAOYSamhuZ8uXzGDz06JxSmfXOhcCq0s8XvkjHvoNPFNyzcw+/fCu/azSfOr6V2xfdQutZybUpmo8ZyBWzP8MN370mt0xmh+KhIatKRw9thraD2/d3dDJoyJG/QlrW2PM+xrwVf0dnZ6cvem9Vwb+lVpU+O+syjhp04IR2Axrq+dgnT/utLyvZ31wErFpU9DdV0lRJqyWtlTSnl35XSApJPsvL+uSiP/00l86cQsNRDQwaMpCm5iZazz6JuY/MyjuaWdWp2AllkuqBN4EpwAZgKXBVRLzerd9gYBHQCFwfEb2eLfbbnFBmtWvb5u2sXb6O4ScOy/2ro2ZFltekc+cCayPi7YhoB+YDl/fQ79vAHUAPV/g2693Q447lnKnjXQTM/h8qWQhOBNZnljekbR+QNAEYFRGLKpjDzMx6kdvRLEl1wF3ATX3oO1PSMknLtm7dWvlwZmYlUslCsBEYlVkembZ1GQycCfxUUhswCVjQ0wHjiJgXERMjYuKIET4708ysP1WyECwFRktqldQITAcWdK2MiF9HxPCIaImIFuDnwLRDHSw2M7P+VbFCEBEdwPXAU8Aq4F8i4jVJt0maVqmfa2Zmh6eiZxZHxJPAk93abv2QvudXMouZmfXMpz6amZWcC4GZWcm5EJiZlZwLgZlZybkQmJmVnAuBmVnJuRCYmZWcC4GZWcm5EJiZlZwLgZlZybkQmJmVnAuBmVnJuRCYmZWcC4GZWcm5EJiZlZwLgZlZybkQmJmVnAuBmVnJVbQQSJoqabWktZLm9LD+65JelfSypOckjalkHjMzO1jFCoGkeuA+4GJgDHBVD//RPxwRZ0XEOOBO4K5K5TEzs55Vco/gXGBtRLwdEe3AfODybIeI2JFZHAREBfOYmVkPBlTwtU8E1meWNwAf795J0nXAN4BG4IIK5jEzsx7kfrA4Iu6LiFOAvwT+qqc+kmZKWiZp2datW49sQDOzGlfJQrARGJVZHpm2fZj5wB/3tCIi5kXExIiYOGLEiH6MaGZmlSwES4HRklolNQLTgQXZDpJGZxYvBdZUMI+ZmfWgYscIIqJD0vXAU0A98GBEvCbpNmBZRCwArpd0EbAP2AbMqFQeMzPrWSUPFhMRTwJPdmu7NfP4xkr+fDMzO7TcDxabmVm+XAjMzErOhcDMrOQqeozArD9FBGteeptfrn2H1rNP4qQzRuYdyawmuBBYVXh/+07m/NHf8IvX11NXV8f+jv2Mu+BM/vrxv6ChsSHveGZVzUNDVhXuvXYeb61oY8/Ovex6bzd7d7ez/Ccreejbj+UdzazquRBY4XXs6+C5J5bQ0d5xQHv77nYWPbA4p1RmtcOFwApvf8d+Ovd39rhu7+69RziNWe1xIbDCaxrYxMljTzqova5OnDN1fA6JzGqLC4FVhZseuJaBgwfS0JQcGG4c2MjgYYOZeeeXck5mVv38rSGrCqeOb+XBVfew8P6naVu5njMmjebiay7kmGGD845mVvVcCKxqDD9hGF+5bXreMcxqjoeGzMxKzoXAzKzkXAjMzErOhcDMrORcCMzMSs6FwMys5FwIzMxKzoXAzKzkXAjMzEpOEZF3hsMiaSvwiz52Hw68W8E4/aVackL1ZHXO/lUtOaF6sh7pnCdFxIieVlRdITgckpZFxMS8cxxKteSE6snqnP2rWnJC9WQtUk4PDZmZlZwLgZlZydV6IZiXd4A+qpacUD1ZnbN/VUtOqJ6shclZ08cIzMzs0Gp9j8DMzA6hZguBpKmSVktaK2lO3nm6SHpQ0hZJKzNtwyQ9I2lNej80z4xpplGSnpX0uqTXJN1YxKySjpK0RNKKNOe30vZWSS+k2/+fJTXmmbOLpHpJyyUtTJeLmrNN0quSXpa0LG0r1LZPMx0r6TFJb0haJWly0XJKOi19H7tuOyTNKlLOmiwEkuqB+4CLgTHAVZLG5JvqAz8EpnZrmwMsjojRwOJ0OW8dwE0RMQaYBFyXvodFy7oXuCAixgLjgKmSJgF3AHdHxKnANuDqHDNm3QisyiwXNSfAH0TEuMxXHIu27QHuBf4tIk4HxpK8t4XKGRGr0/dxHPB7wC7gCYqUMyJq7gZMBp7KLN8M3Jx3rkyeFmBlZnk1cHz6+Hhgdd4Ze8j8r8CUImcFmoGXgI+TnKgzoKffhxzzjST5g78AWAioiDnTLG3A8G5thdr2wBBgHemxzqLm7JbtD4Hni5azJvcIgBOB9ZnlDWlbUR0XEZvSx+8Ax+UZpjtJLcB44AUKmDUdbnkZ2AI8A7wFbI+IjrRLUbb/PcA3gc50+XcoZk6AAJ6W9KKkmWlb0bZ9K7AV+EE63PZ9SYMoXs6s6cAj6ePC5KzVQlC1Ivl4UJivckk6GngcmBURO7LripI1IvZHsts9EjgXOD3nSAeRdBmwJSJezDtLH30qIiaQDK9eJ+nT2ZUF2fYDgAnA30fEeGAn3YZXCpITgPT4zzTg0e7r8s5Zq4VgIzAqszwybSuqzZKOB0jvt+ScBwBJDSRF4KGI+FHaXMisABGxHXiWZIjlWEkD0lVF2P6fBKZJagPmkwwP3UvxcgIQERvT+y0k49nnUrxtvwHYEBEvpMuPkRSGouXscjHwUkRsTpcLk7NWC8FSYHT6jYxGkt2xBTln6s0CYEb6eAbJeHyuJAn4B2BVRNyVWVWorJJGSDo2fTyQ5DjGKpKC8Lm0W+45I+LmiBgZES0kv48/iYgvUrCcAJIGSRrc9ZhkXHslBdv2EfEOsF7SaWnThcDrFCxnxlX8ZlgIipQz74MnFTwocwnwJsl48dy882RyPQJsAvaRfKK5mmSseDGwBvh3YFgBcn6KZFf1FeDl9HZJ0bICZwPL05wrgVvT9pOBJcBakl3xprzf00zm84GFRc2ZZlqR3l7r+vsp2rZPM40DlqXb/8fA0ILmHAT8LzAk01aYnD6z2Mys5Gp1aMjMzPrIhcDMrORcCMzMSs6FwMys5FwIzMxKzoXArJ9IasnOKnsYz/uvzPO/0P/JzHrnQmCWk64ziiPiE2lTC+BCYEecC4GVRvqJ+w1JD6Vz1z8mqVnShemkZa+m14toSvu3SbozbV8i6dS0/YeSPpd53fc/5Gf9TNJL6e0Tafv5afsCkrNgs8//W+D30znrZ0v6T0njMq/5nKSxFXuDrLRcCKxsTgO+FxFnADuAb5BcI+LKiDiLZCKzazP9f522f5dk9tC+2gJMiWTitiuB72TWTQBujIiPdnvOHOBnkcxdfzfJFB9fAZD0UeCoiFhxGBnM+sSFwMpmfUQ8nz7+J5L5adZFxJtp2z8C2Zk2H8ncTz6Mn9MAPCDpVZKpI7IXRloSEev68BqPApelk/99jaRgmfW7AYfuYlZTus+psp1kzpe+9O963EH6IUpSHdDT5SVnA5tJrppVB+zJrNvZp6ARuyQ9A1wOfJ7k6lZm/c57BFY2vyup65P9F0gmLGvpGv8HvgT8R6b/lZn7/04ft/Gb/5SnkXz6724IsCkiOtPXrO9DtveAwd3avk8yrLQ0Irb14TXMDpsLgZXNapILrawimanybuCrwKPpME4ncH+m/1BJr5Bca3h22vYAcJ6kFSTDRT19wv8eMCPtc/qH9OnuFWC/pBWSZgNEciGbHcAPDu+fadZ3nn3USiO95ObCiDizj/3bgIkR8W4FYx0qwwnAT4HT070Ls37nPQKzgpL0ZZLrRM91EbBK8h6BmVnJeY/AzKzkXAjMzErOhcDMrORcCMzMSs6FwMys5FwIzMxK7v8A+me/ab3a2nMAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Presnosť tohto modelu nie je zlá, ale ani výnimočná. Môže to byť tým, že údaje nemusia byť vhodné pre zhlukovanie metódou K-Means. Môžete vyskúšať inú metódu.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 811,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/5-Clustering/2-K-Means/solution/tester.ipynb b/translations/sk/5-Clustering/2-K-Means/solution/tester.ipynb
new file mode 100644
index 000000000..286219db2
--- /dev/null
+++ b/translations/sk/5-Clustering/2-K-Means/solution/tester.ipynb
@@ -0,0 +1,341 @@
+{
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+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 105
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Zameriame sa iba na 3 žánre. Možno dokážeme vytvoriť 3 klastre!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 106
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 107
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# X = df.loc[:, ('danceability','energy')]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Unknown label type: 'continuous'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-110-3756ba7b0bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# we create an instance of SVM and fit out data. We do not scale our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# data since we want to plot the support vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mls30\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 30% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mls50\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 50% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mls100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 100% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m# actual graph construction (implementations should override this)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    181\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    182\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.semi_supervised import LabelSpreading\n",
+    "from sklearn.semi_supervised import SelfTrainingClassifier\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "X = df[['danceability','acousticness']].values\n",
+    "y = df['energy'].values\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "# step size in the mesh\n",
+    "h = .02\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "y_rand = rng.rand(y.shape[0])\n",
+    "y_30 = np.copy(y)\n",
+    "y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled\n",
+    "y_50 = np.copy(y)\n",
+    "y_50[y_rand < 0.5] = -1\n",
+    "# we create an instance of SVM and fit out data. We do not scale our\n",
+    "# data since we want to plot the support vectors\n",
+    "ls30 = (LabelSpreading().fit(X, y_30), y_30, 'Label Spreading 30% data')\n",
+    "ls50 = (LabelSpreading().fit(X, y_50), y_50, 'Label Spreading 50% data')\n",
+    "ls100 = (LabelSpreading().fit(X, y), y, 'Label Spreading 100% data')\n",
+    "\n",
+    "# the base classifier for self-training is identical to the SVC\n",
+    "base_classifier = SVC(kernel='rbf', gamma=.5, probability=True)\n",
+    "st30 = (SelfTrainingClassifier(base_classifier).fit(X, y_30),\n",
+    "        y_30, 'Self-training 30% data')\n",
+    "st50 = (SelfTrainingClassifier(base_classifier).fit(X, y_50),\n",
+    "        y_50, 'Self-training 50% data')\n",
+    "\n",
+    "rbf_svc = (SVC(kernel='rbf', gamma=.5).fit(X, y), y, 'SVC with rbf kernel')\n",
+    "\n",
+    "# create a mesh to plot in\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                     np.arange(y_min, y_max, h))\n",
+    "\n",
+    "color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}\n",
+    "\n",
+    "classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)\n",
+    "for i, (clf, y_train, title) in enumerate(classifiers):\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    colors = [color_map[y] for y in y_train]\n",
+    "    plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')\n",
+    "\n",
+    "    plt.title(title)\n",
+    "\n",
+    "plt.suptitle(\"Unlabeled points are colored white\", y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb b/translations/sk/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
new file mode 100644
index 000000000..9a5525681
--- /dev/null
+++ b/translations/sk/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
@@ -0,0 +1,100 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "27de2abc0235ebd22080fc8f1107454d",
+   "translation_date": "2025-09-06T15:22:04+00:00",
+   "source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You should download the book text, clean it, and import it here\n",
+    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
+    "    file_contents = f.read()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "book_pride = TextBlob(file_contents)\n",
+    "positive_sentiment_sentences = []\n",
+    "negative_sentiment_sentences = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sentence in book_pride.sentences:\n",
+    "    if sentence.sentiment.polarity == 1:\n",
+    "        positive_sentiment_sentences.append(sentence)\n",
+    "    if sentence.sentiment.polarity == -1:\n",
+    "        negative_sentiment_sentences.append(sentence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
+    "for sentence in positive_sentiment_sentences:\n",
+    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
+    "for sentence in negative_sentiment_sentences:\n",
+    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nenesieme zodpovednosť za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/6-NLP/4-Hotel-Reviews-1/notebook.ipynb b/translations/sk/6-NLP/4-Hotel-Reviews-1/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sk/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb b/translations/sk/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
new file mode 100644
index 000000000..94cf5b1c9
--- /dev/null
+++ b/translations/sk/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
@@ -0,0 +1,174 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2d05e7db439376aa824f4b387f8324ca",
+   "translation_date": "2025-09-06T15:21:44+00:00",
+   "source_file": "6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EDA\n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_difference_review_avg(row):\n",
+    "    return row[\"Average_Score\"] - row[\"Calc_Average_Score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "print(\"Loading data file now, this could take a while depending on file size\")\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n",
+    "end = time.time()\n",
+    "print(\"Loading took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What shape is the data (rows, columns)?\n",
+    "print(\"The shape of the data (rows, cols) is \" + str(df.shape))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# value_counts() creates a Series object that has index and values\n",
+    "#                in this case, the country and the frequency they occur in reviewer nationality\n",
+    "nationality_freq = df[\"Reviewer_Nationality\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What reviewer nationality is the most common in the dataset?\n",
+    "print(\"The highest frequency reviewer nationality is \" + str(nationality_freq.index[0]).strip() + \" with \" + str(nationality_freq[0]) + \" reviews.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the top 10 most common nationalities and their frequencies?\n",
+    "print(\"The top 10 highest frequency reviewer nationalities are:\")\n",
+    "print(nationality_freq[0:10].to_string())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many unique nationalities are there?\n",
+    "print(\"There are \" + str(nationality_freq.index.size) + \" unique nationalities in the dataset\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What was the most frequently reviewed hotel for the top 10 nationalities - print the hotel and number of reviews\n",
+    "for nat in nationality_freq[:10].index:\n",
+    "   # First, extract all the rows that match the criteria into a new dataframe\n",
+    "   nat_df = df[df[\"Reviewer_Nationality\"] == nat]   \n",
+    "   # Now get the hotel freq\n",
+    "   freq = nat_df[\"Hotel_Name\"].value_counts()\n",
+    "   print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many reviews are there per hotel (frequency count of hotel) and do the results match the value in `Total_Number_of_Reviews`?\n",
+    "# First create a new dataframe based on the old one, removing the uneeded columns\n",
+    "hotel_freq_df = df.drop([\"Hotel_Address\", \"Additional_Number_of_Scoring\", \"Review_Date\", \"Average_Score\", \"Reviewer_Nationality\", \"Negative_Review\", \"Review_Total_Negative_Word_Counts\", \"Positive_Review\", \"Review_Total_Positive_Word_Counts\", \"Total_Number_of_Reviews_Reviewer_Has_Given\", \"Reviewer_Score\", \"Tags\", \"days_since_review\", \"lat\", \"lng\"], axis = 1)\n",
+    "# Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found\n",
+    "hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')\n",
+    "# Get rid of all the duplicated rows\n",
+    "hotel_freq_df = hotel_freq_df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "print()\n",
+    "print(hotel_freq_df.to_string())\n",
+    "print(str(hotel_freq_df.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# While there is an `Average_Score` for each hotel according to the dataset, \n",
+    "# you can also calculate an average score (getting the average of all reviewer scores in the dataset for each hotel)\n",
+    "# Add a new column to your dataframe with the column header `Calc_Average_Score` that contains that calculated average. \n",
+    "df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n",
+    "# Add a new column with the difference between the two average scores\n",
+    "df[\"Average_Score_Difference\"] = df.apply(get_difference_review_avg, axis = 1)\n",
+    "# Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)\n",
+    "review_scores_df = df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "# Sort the dataframe to find the lowest and highest average score difference\n",
+    "review_scores_df = review_scores_df.sort_values(by=[\"Average_Score_Difference\"])\n",
+    "print(review_scores_df[[\"Average_Score_Difference\", \"Average_Score\", \"Calc_Average_Score\", \"Hotel_Name\"]])\n",
+    "# Do any hotels have the same (rounded to 1 decimal place) `Average_Score` and `Calc_Average_Score`?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/6-NLP/5-Hotel-Reviews-2/notebook.ipynb b/translations/sk/6-NLP/5-Hotel-Reviews-2/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sk/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb b/translations/sk/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
new file mode 100644
index 000000000..3c8f9b89c
--- /dev/null
+++ b/translations/sk/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
@@ -0,0 +1,172 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "033cb89c85500224b3c63fd04f49b4aa",
+   "translation_date": "2025-09-06T15:22:26+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import time\n",
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replace_address(row):\n",
+    "    if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Amsterdam, Netherlands\"\n",
+    "    elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Barcelona, Spain\"\n",
+    "    elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
+    "        return \"London, United Kingdom\"\n",
+    "    elif \"Milan\" in row[\"Hotel_Address\"]:        \n",
+    "        return \"Milan, Italy\"\n",
+    "    elif \"France\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Paris, France\"\n",
+    "    elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Vienna, Austria\" \n",
+    "    else:\n",
+    "        return row.Hotel_Address\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dropping columns we will not use:\n",
+    "df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace all the addresses with a shortened, more useful form\n",
+    "df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop `Additional_Number_of_Scoring`\n",
+    "df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
+    "# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
+    "df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
+    "df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process the Tags into new columns\n",
+    "# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
+    "# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
+    "# Family with young children, Family with older children, With a pet\n",
+    "df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
+    "df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
+    "df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
+    "df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
+    "df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
+    "df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
+    "df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
+    "df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No longer need any of these columns\n",
+    "df.drop([\"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_Filtered.csv\n",
+      "Filtering took 23.74 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Saving new data file with calculated columns\n",
+    "print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
+    "df.to_csv(r'../../data/Hotel_Reviews_Filtered.csv', index = False)\n",
+    "end = time.time()\n",
+    "print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb b/translations/sk/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
new file mode 100644
index 000000000..76d65ca9c
--- /dev/null
+++ b/translations/sk/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
@@ -0,0 +1,137 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "341efc86325ec2a214f682f57a189dfd",
+   "translation_date": "2025-09-06T15:22:47+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV (you can )\n",
+    "import pandas as pd \n",
+    "\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We want to find the most useful tags to keep\n",
+    "# Remove opening and closing brackets\n",
+    "df.Tags = df.Tags.str.strip(\"[']\")\n",
+    "# remove all quotes too\n",
+    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
+    "# Now split the strings into a list\n",
+    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove leading and trailing spaces\n",
+    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
+    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
+    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
+    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
+    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
+    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the 6 columns into one with melt\n",
+    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "The shape of the tags with no filtering: (2514684, 2)\n",
+      "                        index   count\n",
+      "0                Leisure trip  338423\n",
+      "1                      Couple  205305\n",
+      "2               Solo traveler   89779\n",
+      "3               Business trip   68176\n",
+      "4                       Group   51593\n",
+      "5  Family with young children   49318\n",
+      "6  Family with older children   21509\n",
+      "7      Travelers with friends    1610\n",
+      "8                  With a pet    1078\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the value counts\n",
+    "tag_vc = df_tags.value.value_counts()\n",
+    "# print(tag_vc)\n",
+    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
+    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
+    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
+    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
+    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
+    "print(tag_vc[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nenesieme zodpovednosť za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb b/translations/sk/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
new file mode 100644
index 000000000..00c499297
--- /dev/null
+++ b/translations/sk/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
@@ -0,0 +1,260 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "705bf02633759f689abc37b19749a16d",
+   "translation_date": "2025-09-06T15:23:07+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[nltk_data] Downloading package vader_lexicon to\n[nltk_data]     /Users/jenlooper/nltk_data...\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import pandas as pd\n",
+    "import nltk as nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    "nltk.download('vader_lexicon')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vader_sentiment = SentimentIntensityAnalyzer()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are 3 possibilities of input for a review:\n",
+    "# It could be \"No Negative\", in which case, return 0\n",
+    "# It could be \"No Positive\", in which case, return 0\n",
+    "# It could be a review, in which case calculate the sentiment\n",
+    "def calc_sentiment(review):    \n",
+    "    if review == \"No Negative\" or review == \"No Positive\":\n",
+    "        return 0\n",
+    "    return vader_sentiment.polarity_scores(review)[\"compound\"]    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "df = pd.read_csv(\"../../data/Hotel_Reviews_Filtered.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove stop words - can be slow for a lot of text!\n",
+    "# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches\n",
+    "# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends\n",
+    "start = time.time()\n",
+    "cache = set(stopwords.words(\"english\"))\n",
+    "def remove_stopwords(review):\n",
+    "    text = \" \".join([word for word in review.split() if word not in cache])\n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the stop words from both columns\n",
+    "df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   \n",
+    "df.Positive_Review = df.Positive_Review.apply(remove_stopwords)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Removing stop words took 5.77 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "end = time.time()\n",
+    "print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Calculating sentiment columns for both positive and negative reviews\n",
+      "Calculating sentiment took 201.07 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a negative sentiment and positive sentiment column\n",
+    "print(\"Calculating sentiment columns for both positive and negative reviews\")\n",
+    "start = time.time()\n",
+    "df[\"Negative_Sentiment\"] = df.Negative_Review.apply(calc_sentiment)\n",
+    "df[\"Positive_Sentiment\"] = df.Positive_Review.apply(calc_sentiment)\n",
+    "end = time.time()\n",
+    "print(\"Calculating sentiment took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                          Negative_Review  Negative_Sentiment\n",
+      "186584  So bad experience memories I hotel The first n...             -0.9920\n",
+      "129503  First charged twice room booked booking second...             -0.9896\n",
+      "307286  The staff Had bad experience even booking Janu...             -0.9889\n",
+      "452092  No WLAN room Incredibly rude restaurant staff ...             -0.9884\n",
+      "201293  We usually traveling Paris 2 3 times year busi...             -0.9873\n",
+      "...                                                   ...                 ...\n",
+      "26899   I would say however one night expensive even d...              0.9933\n",
+      "138365  Wifi terribly slow I speed test network upload...              0.9938\n",
+      "79215   I find anything hotel first I walked past hote...              0.9938\n",
+      "278506  The property great location There bakery next ...              0.9945\n",
+      "339189  Guys I like hotel I wish return next year Howe...              0.9948\n",
+      "\n",
+      "[515738 rows x 2 columns]\n",
+      "                                          Positive_Review  Positive_Sentiment\n",
+      "137893  Bathroom Shower We going stay twice hotel 2 ni...             -0.9820\n",
+      "5839    I completely disappointed mad since reception ...             -0.9780\n",
+      "64158   get everything extra internet parking breakfas...             -0.9751\n",
+      "124178  I didnt like anythig Room small Asked upgrade ...             -0.9721\n",
+      "489137  Very rude manager abusive staff reception Dirt...             -0.9703\n",
+      "...                                                   ...                 ...\n",
+      "331570  Everything This recently renovated hotel class...              0.9984\n",
+      "322920  From moment stepped doors Guesthouse Hotel sta...              0.9985\n",
+      "293710  This place surprise expected good actually gre...              0.9985\n",
+      "417442  We celebrated wedding night Langham I commend ...              0.9985\n",
+      "132492  We arrived super cute boutique hotel area expl...              0.9987\n",
+      "\n",
+      "[515738 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=[\"Negative_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Negative_Review\", \"Negative_Sentiment\"]])\n",
+    "df = df.sort_values(by=[\"Positive_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Positive_Review\", \"Positive_Sentiment\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)\n",
+    "df = df.reindex([\"Hotel_Name\", \"Hotel_Address\", \"Total_Number_of_Reviews\", \"Average_Score\", \"Reviewer_Score\", \"Negative_Sentiment\", \"Positive_Sentiment\", \"Reviewer_Nationality\", \"Leisure_trip\", \"Couple\", \"Solo_traveler\", \"Business_trip\", \"Group\", \"Family_with_young_children\", \"Family_with_older_children\", \"With_a_pet\", \"Negative_Review\", \"Positive_Review\"], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_NLP.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
+    "df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/7-TimeSeries/1-Introduction/solution/notebook.ipynb b/translations/sk/7-TimeSeries/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..c40687482
--- /dev/null
+++ b/translations/sk/7-TimeSeries/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli a Rob J. Hyndman, \"Pravdepodobnostné energetické predpovedanie: Globálna súťaž v energetickom predpovedaní 2014 a ďalej\", International Journal of Forecasting, zv.32, č.3, str. 896-913, júl-september, 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "from common.utils import load_data\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Načítajte údaje z CSV do Pandas dataframe.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2698.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2558.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2444.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2402.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2403.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "data_dir = './data'\n",
+    "energy = load_data(data_dir)[['load']]\n",
+    "energy.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zobraz všetky dostupné údaje o zaťažení (január 2012 až december 2014)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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231.185656 \nL 91.475143 185.318661 \nL 91.506964 197.827841 \nL 91.761536 330.381263 \nL 91.825179 313.153708 \nL 91.984286 214.726208 \nL 92.047929 223.065662 \nL 92.143394 231.953764 \nL 92.238858 182.026771 \nL 92.302501 208.252159 \nL 92.525251 302.290472 \nL 92.557073 297.901286 \nL 92.652537 247.645104 \nL 92.779823 197.169464 \nL 92.811645 201.229461 \nL 92.875287 210.446752 \nL 92.907109 207.81324 \nL 93.002573 138.13491 \nL 93.066216 169.84678 \nL 93.257145 279.905623 \nL 93.288967 277.820759 \nL 93.352609 237.440247 \nL 93.416252 165.896512 \nL 93.479895 170.175969 \nL 93.670824 198.925138 \nL 93.702646 189.488388 \nL 93.766288 144.3895 \nL 93.79811 156.679221 \nL 94.052682 299.65696 \nL 94.084503 288.245076 \nL 94.179968 188.500821 \nL 94.275432 193.109466 \nL 94.434539 214.506749 \nL 94.466361 204.850539 \nL 94.530004 162.714352 \nL 94.561825 175.881911 \nL 94.816397 318.969379 \nL 94.848218 308.545062 \nL 95.039147 197.169464 \nL 95.134611 205.947836 \nL 95.198254 212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 197.217872 308.106144 \nL 197.440622 167.981376 \nL 197.59973 160.410029 \nL 197.727015 185.318661 \nL 197.917944 322.480728 \nL 197.981587 297.352638 \nL 198.236159 139.122477 \nL 198.331623 119.919787 \nL 198.363445 120.578165 \nL 198.490731 168.420294 \nL 198.713481 322.261269 \nL 198.745302 321.383432 \nL 199.063517 158.325166 \nL 199.12716 147.791119 \nL 199.158981 152.290035 \nL 199.286267 213.519182 \nL 199.477196 317.542894 \nL 199.509017 319.847217 \nL 199.604482 229.539711 \nL 199.763589 153.82625 \nL 199.827232 152.180305 \nL 199.890875 142.194907 \nL 199.922697 149.437064 \nL 200.049982 220.212691 \nL 200.20909 311.398033 \nL 200.272733 292.305074 \nL 200.495483 131.441401 \nL 200.527304 126.064648 \nL 200.590947 133.965183 \nL 200.622769 133.087346 \nL 200.65459 133.306805 \nL 200.781876 176.2111 \nL 200.972805 319.627757 \nL 201.036448 302.94885 \nL 201.227377 151.851116 \nL 201.259198 152.399765 \nL 201.29102 152.180305 \nL 201.322841 152.619224 \nL 201.354662 152.948413 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 218.983754 162.055974 \nL 219.015576 162.385163 \nL 219.11104 187.622984 \nL 219.301969 310.300737 \nL 219.365612 281.880756 \nL 219.556541 157.995977 \nL 219.683826 161.726785 \nL 219.620183 157.666788 \nL 219.715648 160.410029 \nL 219.747469 161.178137 \nL 219.874755 183.562986 \nL 220.065684 319.737487 \nL 220.097505 319.079109 \nL 220.161148 297.462367 \nL 220.352077 172.80948 \nL 220.383899 175.442992 \nL 220.63847 211.763507 \nL 220.829399 325.22397 \nL 220.861221 324.016944 \nL 220.924864 310.191007 \nL 221.115792 208.471618 \nL 221.147614 210.117563 \nL 221.179435 211.214859 \nL 221.211257 210.88567 \nL 221.2749 200.461353 \nL 221.306721 201.44892 \nL 221.434007 256.533206 \nL 221.593114 330.271534 \nL 221.656757 303.168309 \nL 221.911329 131.55113 \nL 222.006793 125.40627 \nL 222.038615 127.052215 \nL 222.165901 168.420294 \nL 222.356829 309.861818 \nL 222.420472 285.392105 \nL 222.675044 125.845188 \nL 222.738687 129.246808 \nL 222.865973 155.252736 \nL 222.897794 153.058143 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227.702836 315.238571 \nL 227.766479 291.427236 \nL 228.02105 135.391668 \nL 228.116515 126.503566 \nL 228.148336 128.039781 \nL 228.275622 168.200835 \nL 228.466551 311.727222 \nL 228.530194 285.831024 \nL 228.784765 105.764662 \nL 228.848408 100.278179 \nL 228.912051 102.143583 \nL 229.039337 142.085177 \nL 229.230266 295.377504 \nL 229.262087 291.536966 \nL 229.548481 84.696568 \nL 229.643945 97.864127 \nL 229.803052 143.182474 \nL 229.993981 278.369407 \nL 230.057624 251.156453 \nL 230.280374 103.789528 \nL 230.344017 117.396005 \nL 230.598589 209.239725 \nL 230.757696 299.108312 \nL 230.789518 298.559664 \nL 230.853161 280.564001 \nL 231.075911 157.666788 \nL 231.26684 168.310565 \nL 231.298661 167.761916 \nL 231.553233 301.632094 \nL 231.585054 296.4748 \nL 231.648697 262.239148 \nL 231.839626 164.908945 \nL 231.935091 146.254904 \nL 231.966912 138.573828 \nL 232.030555 148.120308 \nL 232.062376 146.035445 \nL 232.285127 300.863987 \nL 232.316948 295.048315 \nL 232.635163 98.961423 \nL 232.698806 100.168449 \nL 232.730627 101.704665 \nL 232.857913 151.302468 \nL 233.048842 295.487234 \nL 233.080663 291.207777 \nL 233.367056 101.046287 \nL 233.430699 107.191147 \nL 233.557985 130.014915 \nL 233.589807 127.710593 \nL 233.812557 294.280207 \nL 233.844378 289.561832 \nL 234.194415 111.690063 \nL 234.258057 119.480868 \nL 234.385343 155.801384 \nL 234.576272 292.414803 \nL 234.608094 288.245076 \nL 234.894487 125.625729 \nL 234.926308 127.491133 \nL 235.085416 148.559227 \nL 235.117237 147.68139 \nL 235.339987 312.166141 \nL 235.371809 306.569929 \nL 235.721845 120.687895 \nL 235.753666 121.346273 \nL 235.880952 144.608959 \nL 236.135524 304.046147 \nL 236.167345 296.4748 \nL 236.549203 193.877574 \nL 236.676489 220.322421 \nL 236.899239 337.513691 \nL 236.962882 327.528292 \nL 237.185632 225.589444 \nL 237.217454 226.79647 \nL 237.249275 224.272688 \nL 237.408383 203.314324 \nL 237.631133 332.466127 \nL 237.662954 326.211537 \nL 237.949347 160.958678 \nL 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345.414226 \nL 247.654894 330.600723 \nL 247.845823 238.208354 \nL 247.877645 242.048892 \nL 247.941288 246.438078 \nL 247.973109 242.59754 \nL 248.068573 234.916464 \nL 248.100395 239.41538 \nL 248.323145 354.192598 \nL 248.354967 351.120168 \nL 248.482252 275.296977 \nL 248.577717 229.539711 \nL 248.64136 231.075927 \nL 248.673181 230.966197 \nL 248.736824 220.651609 \nL 248.832289 201.010001 \nL 248.86411 206.496484 \nL 249.08686 340.586121 \nL 249.150503 302.94885 \nL 249.309611 191.573251 \nL 249.405075 187.513254 \nL 249.468718 192.01217 \nL 249.500539 189.378658 \nL 249.596004 178.844611 \nL 249.691468 261.6905 \nL 249.850575 324.785051 \nL 249.914218 283.526701 \nL 250.073326 161.507326 \nL 250.16879 158.764085 \nL 250.264255 163.043541 \nL 250.296076 161.946245 \nL 250.327897 161.507326 \nL 250.359719 153.387332 \nL 250.39154 165.567323 \nL 250.614291 321.602891 \nL 250.677934 284.953187 \nL 250.868862 168.420294 \nL 250.996148 160.958678 \nL 251.02797 161.946245 \nL 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216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 315.75283 171.382995 \nL 315.880116 189.488388 \nL 315.911937 182.026771 \nL 315.97558 135.281939 \nL 316.039223 155.252736 \nL 316.134688 222.955932 \nL 316.293795 295.487234 \nL 316.357438 280.125082 \nL 316.516545 184.221364 \nL 316.548367 185.977039 \nL 316.643831 197.279193 \nL 316.675652 191.682981 \nL 316.739295 154.704087 \nL 316.802938 177.198666 \nL 317.05751 323.578025 \nL 317.152974 296.4748 \nL 317.312082 212.531615 \nL 317.343903 215.165127 \nL 317.407546 223.065662 \nL 317.439368 216.262423 \nL 317.503011 165.238134 \nL 317.566653 190.256495 \nL 317.789404 338.610988 \nL 317.853047 324.785051 \nL 318.043975 214.28729 \nL 318.171261 228.332685 \nL 318.203083 224.272688 \nL 318.266726 161.397596 \nL 318.330369 174.674884 \nL 318.553119 330.381263 \nL 318.616762 319.518028 \nL 318.807691 200.790542 \nL 318.839512 203.424054 \nL 318.934976 209.349455 \nL 318.966798 204.411621 \nL 319.030441 154.923547 \nL 319.094084 172.480291 \nL 319.348656 336.745583 \nL 319.380477 327.089374 \nL 319.475941 219.993232 \nL 319.603227 220.212691 \nL 319.635049 220.871069 \nL 319.66687 219.225124 \nL 319.698692 219.334854 \nL 319.825978 144.27977 \nL 319.88962 169.956509 \nL 320.080549 296.145611 \nL 320.112371 295.158045 \nL 320.176014 248.852131 \nL 320.239657 173.906777 \nL 320.303299 176.979207 \nL 320.462407 199.583516 \nL 320.494228 195.413789 \nL 320.557871 129.246808 \nL 320.621514 138.13491 \nL 320.844264 294.719126 \nL 320.907907 283.087783 \nL 321.003372 177.308396 \nL 321.098836 183.233797 \nL 321.226122 204.74081 \nL 321.257943 199.144597 \nL 321.321586 157.227869 \nL 321.385229 179.722449 \nL 321.639801 331.039641 \nL 321.703444 314.360734 \nL 321.862551 207.264592 \nL 321.926194 212.641345 \nL 321.989837 222.297554 \nL 322.021659 217.46945 \nL 322.085302 175.881911 \nL 322.148944 200.900272 \nL 322.403516 336.196935 \nL 322.467159 323.248836 \nL 322.658088 232.392682 \nL 322.689909 235.245653 \nL 322.753552 243.475378 \nL 322.785374 235.026194 \nL 322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 340.923609 150.973279 \nL 340.95543 151.083009 \nL 341.082716 162.933811 \nL 341.114538 159.312733 \nL 341.178181 98.741964 \nL 341.241824 109.38574 \nL 341.464574 253.460776 \nL 341.528217 240.293218 \nL 341.623681 133.087346 \nL 341.719146 138.464099 \nL 341.846431 158.434896 \nL 341.878253 156.350032 \nL 341.941896 100.05872 \nL 342.005539 111.141415 \nL 342.26011 265.201849 \nL 342.291932 254.448343 \nL 342.451039 145.377067 \nL 342.482861 145.596526 \nL 342.641968 174.674884 \nL 342.705611 132.209508 \nL 342.769254 155.362465 \nL 343.023826 302.619661 \nL 343.087469 286.489402 \nL 343.246576 199.802975 \nL 343.310219 208.910536 \nL 343.373862 219.225124 \nL 343.405683 214.616478 \nL 343.469326 153.716521 \nL 343.532969 171.053806 \nL 343.787541 298.011016 \nL 343.851184 287.476969 \nL 344.010291 198.705679 \nL 344.073934 203.862972 \nL 344.137577 216.372153 \nL 344.169398 214.17756 \nL 344.233041 148.668956 \nL 344.296684 173.138669 \nL 344.519435 307.228306 \nL 344.551256 304.375335 \nL 344.614899 260.593204 \nL 344.774006 175.662451 \nL 344.901292 193.658115 \nL 344.933114 188.83001 \nL 345.028578 123.650595 \nL 345.060399 137.695991 \nL 345.28315 282.868323 \nL 345.346793 271.456439 \nL 345.474078 169.298131 \nL 345.537721 177.418126 \nL 345.665007 199.473786 \nL 345.696829 196.291626 \nL 345.760472 135.940317 \nL 345.824115 150.973279 \nL 346.046865 299.87642 \nL 346.110508 287.03805 \nL 346.205972 176.10137 \nL 346.301437 181.148934 \nL 346.428722 187.952173 \nL 346.460544 184.550553 \nL 346.524187 138.354369 \nL 346.58783 157.557058 \nL 346.842401 309.532629 \nL 346.874223 297.681827 \nL 347.065152 190.146766 \nL 347.192438 207.264592 \nL 347.224259 204.960269 \nL 347.287902 148.339767 \nL 347.351545 162.494893 \nL 347.574295 305.801821 \nL 347.637938 294.170478 \nL 347.797045 186.196498 \nL 347.828867 187.513254 \nL 347.956153 199.583516 \nL 347.987974 199.473786 \nL 348.051617 159.641922 \nL 348.11526 183.014338 \nL 348.369832 324.565592 \nL 348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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159.203003 \nL 389.3877 188.72028 \nL 389.578629 316.884516 \nL 389.610451 316.555327 \nL 389.674094 271.12725 \nL 389.833201 188.61055 \nL 389.865022 189.488388 \nL 389.992308 201.55865 \nL 390.087773 157.337599 \nL 390.151416 185.099202 \nL 390.342344 310.959115 \nL 390.374166 310.520196 \nL 390.437809 269.152117 \nL 390.596916 186.745146 \nL 390.660559 196.181897 \nL 390.756024 215.494316 \nL 390.787845 210.666211 \nL 390.851488 177.747315 \nL 390.883309 188.939739 \nL 391.10606 317.981813 \nL 391.201524 297.462367 \nL 391.32881 229.320252 \nL 391.392453 237.111058 \nL 391.519739 263.007256 \nL 391.55156 256.094288 \nL 391.615203 206.606214 \nL 391.678846 227.015929 \nL 391.901596 326.211537 \nL 391.933418 325.22397 \nL 392.028882 286.92832 \nL 392.156168 237.440247 \nL 392.187989 242.158622 \nL 392.283454 261.141852 \nL 392.315275 256.423477 \nL 392.41074 198.486219 \nL 392.442561 210.446752 \nL 392.63349 331.47856 \nL 392.665311 330.161804 \nL 392.728954 288.793725 \nL 392.888062 207.045132 \nL 392.983526 220.43215 \nL 393.047169 227.345118 \nL 393.07899 222.078095 \nL 393.174455 181.807312 \nL 393.206276 196.511086 \nL 393.429027 329.283967 \nL 393.49267 289.232643 \nL 393.651777 198.37649 \nL 393.683598 194.097033 \nL 393.747241 199.254327 \nL 393.810884 210.117563 \nL 393.842706 202.875406 \nL 393.93817 178.625152 \nL 393.969991 193.548385 \nL 394.192742 330.929912 \nL 394.256385 290.11048 \nL 394.415492 207.154862 \nL 394.542778 223.394851 \nL 394.574599 229.8689 \nL 394.606421 226.9062 \nL 394.701885 188.391091 \nL 394.733707 197.059734 \nL 394.924635 317.542894 \nL 394.956457 316.006679 \nL 395.0201 274.748329 \nL 395.147386 195.633248 \nL 395.179207 195.19433 \nL 395.338314 210.446752 \nL 395.370136 207.045132 \nL 395.4656 164.908945 \nL 395.497422 172.041373 \nL 395.688351 294.170478 \nL 395.720172 293.621829 \nL 395.783815 253.460776 \nL 395.879279 177.966774 \nL 395.911101 178.734882 \nL 395.974744 187.622984 \nL 396.10203 221.309987 \nL 396.133851 219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 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214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 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319.737487 \nL 413.540193 227.564578 \nL 413.603836 244.024026 \nL 413.667478 254.119154 \nL 413.6993 249.400779 \nL 413.794764 219.883502 \nL 413.826586 221.748906 \nL 414.017515 342.561255 \nL 414.049336 341.463959 \nL 414.112979 295.926152 \nL 414.272086 212.860804 \nL 414.463015 243.255918 \nL 414.494837 237.659706 \nL 414.558479 213.409452 \nL 414.590301 214.06783 \nL 414.78123 341.244499 \nL 414.813051 340.91531 \nL 414.876694 297.572097 \nL 415.035801 207.264592 \nL 415.099444 211.982967 \nL 415.22673 231.075927 \nL 415.290373 222.846203 \nL 415.322195 208.471618 \nL 415.354016 210.007833 \nL 415.544945 343.658552 \nL 415.576766 343.329363 \nL 415.640409 301.741824 \nL 415.767695 215.823505 \nL 415.799517 216.701342 \nL 415.958624 234.367816 \nL 415.990445 231.734305 \nL 416.08591 204.850539 \nL 416.117731 213.738641 \nL 416.30866 345.194767 \nL 416.340482 343.768281 \nL 416.404124 299.327771 \nL 416.53141 221.858636 \nL 416.563232 221.968365 \nL 416.595053 222.517014 \nL 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331.58829 \nL 498.089826 330.381263 \nL 498.153469 308.435333 \nL 498.344397 190.914873 \nL 498.376219 191.024603 \nL 498.439862 185.099202 \nL 498.503505 173.797047 \nL 498.535326 177.418126 \nL 498.662612 228.661874 \nL 498.853541 322.151539 \nL 498.917184 307.886684 \nL 499.171756 188.171632 \nL 499.203577 190.037036 \nL 499.26722 177.637585 \nL 499.330863 181.039204 \nL 499.362684 183.233797 \nL 499.585435 312.824519 \nL 499.617256 303.716958 \nL 499.935471 127.381404 \nL 499.967292 128.039781 \nL 499.999114 127.600863 \nL 500.1264 151.192738 \nL 500.34915 310.849385 \nL 500.412793 287.696428 \nL 500.635543 147.791119 \nL 500.79465 143.072744 \nL 500.921936 184.331094 \nL 501.112865 318.530461 \nL 501.176508 292.743992 \nL 501.399258 149.875983 \nL 501.526544 138.573828 \nL 501.558365 140.439232 \nL 501.685651 179.832178 \nL 501.87658 315.896949 \nL 501.940223 291.317507 \nL 502.162973 136.269506 \nL 502.194795 131.66086 \nL 502.226616 135.830587 \nL 502.449366 185.977039 \nL 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168.639753 \nL 528.288396 168.639753 \nL 528.352039 166.66462 \nL 528.383861 176.2111 \nL 528.606611 328.625589 \nL 528.670254 294.389937 \nL 528.829361 206.057565 \nL 528.861183 203.972702 \nL 528.924826 205.508917 \nL 528.988468 210.666211 \nL 529.083933 210.007833 \nL 529.115754 200.790542 \nL 529.147576 207.374321 \nL 529.370326 349.913142 \nL 529.433969 315.56776 \nL 529.593076 224.382418 \nL 529.688541 222.187825 \nL 529.784005 230.966197 \nL 529.847648 229.97863 \nL 529.879469 222.517014 \nL 529.911291 228.442415 \nL 530.134041 351.449357 \nL 530.165863 348.376927 \nL 530.261327 296.58453 \nL 530.388613 240.841866 \nL 530.420434 241.061325 \nL 530.515899 245.560241 \nL 530.643185 222.078095 \nL 530.675006 231.734305 \nL 530.897756 350.46179 \nL 530.929578 348.486656 \nL 531.025042 307.776955 \nL 531.18415 239.195921 \nL 531.279614 254.228884 \nL 531.311435 247.315915 \nL 531.4069 224.272688 \nL 531.438721 234.477546 \nL 531.62965 357.484488 \nL 531.661472 357.045569 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 569.910873 275.626166 \nL 569.974516 190.037036 \nL 570.038159 197.827841 \nL 570.197266 223.61431 \nL 570.229087 227.125659 \nL 570.260909 219.444583 \nL 570.324552 164.689486 \nL 570.388195 190.037036 \nL 570.610945 322.04181 \nL 570.674588 280.234812 \nL 570.738231 193.109466 \nL 570.801874 195.633248 \nL 570.833695 195.523519 \nL 570.992803 226.68674 \nL 571.024624 222.407284 \nL 571.088267 172.480291 \nL 571.15191 197.498652 \nL 571.342839 325.333699 \nL 571.37466 325.00451 \nL 571.438303 282.539134 \nL 571.59741 195.962437 \nL 571.629232 195.852708 \nL 571.756518 203.094865 \nL 571.820161 186.086768 \nL 571.883804 197.388923 \nL 571.979268 275.845625 \nL 572.138375 340.476392 \nL 572.202018 305.143443 \nL 572.361126 204.192161 \nL 572.392947 204.850539 \nL 572.552054 230.966197 \nL 572.615697 199.254327 \nL 572.647519 214.06783 \nL 572.90209 352.436924 \nL 572.965733 333.782883 \nL 573.124841 247.535375 \nL 573.156662 250.498075 \nL 573.283948 267.835361 \nL 573.31577 258.947259 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199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 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150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 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693.537265 325.443429 \nL 693.569087 321.493162 \nL 693.63273 277.71103 \nL 693.760015 206.386754 \nL 693.791837 206.606214 \nL 693.919123 221.858636 \nL 693.950944 226.357551 \nL 693.982766 225.369985 \nL 694.110052 198.26676 \nL 694.30098 312.934248 \nL 694.332802 310.849385 \nL 694.396445 266.628335 \nL 694.460088 201.229461 \nL 694.523731 207.92297 \nL 694.714659 233.819168 \nL 694.778302 226.9062 \nL 694.873767 204.960269 \nL 695.096517 328.625589 \nL 695.16016 286.92832 \nL 695.255624 214.616478 \nL 695.287446 217.798638 \nL 695.351089 223.72404 \nL 695.478375 242.487811 \nL 695.542017 237.549976 \nL 695.60566 226.79647 \nL 695.637482 228.661874 \nL 695.828411 340.476392 \nL 695.892054 335.648287 \nL 696.051161 243.804566 \nL 696.146625 257.081855 \nL 696.24209 271.23698 \nL 696.273911 268.384009 \nL 696.401197 244.024026 \nL 696.592126 346.511523 \nL 696.655769 344.975307 \nL 696.751233 293.182911 \nL 696.878519 221.968365 \nL 696.910341 223.285121 \nL 697.005805 239.964029 \nL 697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 \nL 707.729639 249.839697 \nL 707.856925 227.893767 \nL 708.047853 338.062339 \nL 708.079675 334.66072 \nL 708.143318 293.621829 \nL 708.302425 218.566746 \nL 708.461532 233.489979 \nL 708.493354 229.649441 \nL 708.588818 213.738641 \nL 708.62064 214.616478 \nL 708.84339 336.635854 \nL 708.907033 300.425068 \nL 709.034319 224.272688 \nL 709.06614 225.589444 \nL 709.225248 245.99916 \nL 709.28889 238.866732 \nL 709.384355 216.701342 \nL 709.607105 343.878011 \nL 709.670748 303.497498 \nL 709.829855 209.568914 \nL 709.92532 215.933234 \nL 709.988963 221.419717 \nL 710.020784 216.701342 \nL 710.116249 201.33919 \nL 710.14807 205.728377 \nL 710.338999 329.722886 \nL 710.37082 328.515859 \nL 710.434463 290.000751 \nL 710.593571 208.910536 \nL 710.657213 216.481883 \nL 710.752678 232.502412 \nL 710.848142 231.514845 \nL 710.911785 218.127827 \nL 711.102714 340.91531 \nL 711.166357 331.259101 \nL 711.357286 232.941331 \nL 711.389107 233.819168 \nL 711.611857 256.752666 \nL 711.6755 241.171055 \nL 711.707322 255.106721 \nL 711.898251 345.962874 \nL 711.961894 333.343964 \nL 712.121001 255.984558 \nL 712.184644 263.775363 \nL 712.248287 274.089951 \nL 712.31193 270.578602 \nL 712.439216 250.607805 \nL 712.661966 354.302328 \nL 712.693787 352.327194 \nL 712.789252 308.764522 \nL 712.916538 252.473209 \nL 712.948359 253.460776 \nL 713.043823 260.702933 \nL 713.202931 237.879165 \nL 713.393859 344.31693 \nL 713.425681 342.341796 \nL 713.489324 302.290472 \nL 713.648431 219.773772 \nL 713.680253 220.102961 \nL 713.807539 240.402947 \nL 713.871181 235.245653 \nL 713.934824 229.97863 \nL 713.966646 220.322421 \nL 714.157575 340.805581 \nL 714.189396 338.391528 \nL 714.253039 301.522364 \nL 714.412146 231.514845 \nL 714.443968 229.649441 \nL 714.475789 234.148357 \nL 714.571254 240.841866 \nL 714.603075 239.63484 \nL 714.730361 218.566746 \nL 714.92129 339.927743 \nL 714.984933 325.772618 \nL 715.112219 234.367816 \nL 715.175862 237.001328 \nL 715.36679 245.121322 \nL 715.462255 236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 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158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 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339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 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\nL 834.569999 204.850539 \nL 834.633642 221.090528 \nL 834.856392 341.13477 \nL 834.888214 337.73315 \nL 835.0155 250.607805 \nL 835.079143 228.332685 \nL 835.142785 239.41538 \nL 835.23825 262.348878 \nL 835.270071 256.423477 \nL 835.333714 219.554313 \nL 835.397357 235.684572 \nL 835.588286 339.598554 \nL 835.651929 335.977476 \nL 835.747393 285.392105 \nL 835.811036 245.88943 \nL 835.874679 254.777532 \nL 835.970144 270.578602 \nL 836.001965 269.920224 \nL 836.097429 215.494316 \nL 836.161072 235.026194 \nL 836.383823 340.366662 \nL 836.447466 308.654792 \nL 836.606573 211.324589 \nL 836.76568 228.113226 \nL 836.797502 223.61431 \nL 836.861145 186.415957 \nL 836.892966 193.767844 \nL 837.147538 345.523956 \nL 837.179359 334.880179 \nL 837.40211 216.262423 \nL 837.497574 219.225124 \nL 837.529395 221.858636 \nL 837.561217 219.554313 \nL 837.62486 186.854876 \nL 837.688503 213.189993 \nL 837.879431 339.379095 \nL 837.911253 339.269365 \nL 837.974896 301.193176 \nL 838.134003 204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 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222.626743 \nL 852.103626 191.682981 \nL 852.135448 200.461353 \nL 852.39002 332.356397 \nL 852.421841 330.052075 \nL 852.485484 283.85589 \nL 852.61277 217.908368 \nL 852.644591 218.127827 \nL 852.676413 219.444583 \nL 852.771877 228.881334 \nL 852.83552 202.217028 \nL 852.867342 172.480291 \nL 852.930985 188.939739 \nL 853.153735 324.016944 \nL 853.217378 310.191007 \nL 853.408307 208.032699 \nL 853.535592 220.761339 \nL 853.567414 217.140261 \nL 853.631057 177.308396 \nL 853.6947 192.451088 \nL 853.91745 332.356397 \nL 853.981093 320.725054 \nL 854.108379 216.481883 \nL 854.203843 220.980798 \nL 854.299308 232.831601 \nL 854.331129 231.624575 \nL 854.394772 181.258664 \nL 854.458415 197.169464 \nL 854.681165 338.940176 \nL 854.744808 325.882348 \nL 854.872094 210.9954 \nL 854.935737 211.214859 \nL 854.967558 210.775941 \nL 854.99938 215.274856 \nL 855.063023 214.06783 \nL 855.126666 188.281362 \nL 855.158487 172.041373 \nL 855.22213 193.109466 \nL 855.44488 330.052075 \nL 855.508523 320.834784 \nL 855.699452 202.875406 \nL 855.731273 204.850539 \nL 855.826738 214.506749 \nL 855.858559 211.653778 \nL 855.922202 171.931643 \nL 855.985845 192.341359 \nL 856.240417 316.555327 \nL 856.30406 296.365071 \nL 856.431346 226.028363 \nL 856.494989 233.489979 \nL 856.590453 250.498075 \nL 856.622275 243.914296 \nL 856.685917 189.268928 \nL 856.74956 213.738641 \nL 857.004132 334.002342 \nL 857.067775 323.468295 \nL 857.258704 241.500244 \nL 857.290525 247.645104 \nL 857.354168 254.996991 \nL 857.38599 247.535375 \nL 857.449633 187.952173 \nL 857.513276 210.117563 \nL 857.736026 333.782883 \nL 857.799669 320.615324 \nL 857.990598 211.434319 \nL 858.117883 227.015929 \nL 858.149705 226.028363 \nL 858.213348 175.772181 \nL 858.276991 193.877574 \nL 858.499741 328.625589 \nL 858.563384 316.116408 \nL 858.754313 219.554313 \nL 858.786134 224.492147 \nL 858.881599 231.734305 \nL 858.91342 228.661874 \nL 858.977063 177.308396 \nL 859.040706 198.486219 \nL 859.263456 338.610988 \nL 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nie sme zodpovední za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "dddca9ad9e34435494e0933c218e1579",
+   "translation_date": "2025-09-06T14:01:03+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sk/7-TimeSeries/1-Introduction/working/notebook.ipynb b/translations/sk/7-TimeSeries/1-Introduction/working/notebook.ipynb
new file mode 100644
index 000000000..745ae224b
--- /dev/null
+++ b/translations/sk/7-TimeSeries/1-Introduction/working/notebook.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Nastavenie údajov\n",
+    "\n",
+    "V tomto notebooku ukážeme, ako:\n",
+    "\n",
+    "nastaviť časové rady údajov pre tento modul\n",
+    "vizualizovať údaje\n",
+    "Údaje v tomto príklade pochádzajú zo súťaže GEFCom2014 v predpovedaní1. Obsahujú 3 roky hodinových hodnôt spotreby elektrickej energie a teploty medzi rokmi 2012 a 2014.\n",
+    "\n",
+    "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli a Rob J. Hyndman, \"Pravdepodobnostné predpovedanie energie: Globálna súťaž v predpovedaní energie 2014 a ďalej\", International Journal of Forecasting, zv.32, č.3, str. 896-913, júl-september, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5e2bbe594906dce3aaaa736d6dac6683",
+   "translation_date": "2025-09-06T14:02:08+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/working/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sk/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/translations/sk/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
new file mode 100644
index 000000000..db6484f62
--- /dev/null
+++ b/translations/sk/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -0,0 +1,1124 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli a Rob J. Hyndman, \"Pravdepodobnostné predpovedanie energie: Globálna súťaž v predpovedaní energie 2014 a ďalej\", International Journal of Forecasting, zv.32, č.3, str. 896-913, júl-september, 2016.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Inštalácia závislostí\n",
+    "Začnite inštaláciou niektorých potrebných závislostí. Tieto knižnice s ich príslušnými verziami sú známe tým, že fungujú pre toto riešenie:\n",
+    "\n",
+    "* `statsmodels == 0.12.2`\n",
+    "* `matplotlib == 3.4.2`\n",
+    "* `scikit-learn == 0.24.2`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "!pip install statsmodels"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "/bin/sh: pip: command not found\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from pandas.plotting import autocorrelation_plot\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "%matplotlib inline\n",
+    "pd.options.display.float_format = '{:,.2f}'.format\n",
+    "np.set_printoptions(precision=2)\n",
+    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load\n",
+       "2012-01-01 00:00:00 2,698.00\n",
+       "2012-01-01 01:00:00 2,558.00\n",
+       "2012-01-01 02:00:00 2,444.00\n",
+       "2012-01-01 03:00:00 2,402.00\n",
+       "2012-01-01 04:00:00 2,403.00\n",
+       "2012-01-01 05:00:00 2,453.00\n",
+       "2012-01-01 06:00:00 2,560.00\n",
+       "2012-01-01 07:00:00 2,719.00\n",
+       "2012-01-01 08:00:00 2,916.00\n",
+       "2012-01-01 09:00:00 3,105.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zobrazte všetky dostupné údaje o zaťažení (január 2012 až december 2014)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Vytvorte tréningové a testovacie dátové sady\n",
+    "\n",
+    "### Rozdelenie údajov na tréningové a testovacie sady\n",
+    "Rozdelenie údajov na tréningové a testovacie sady je kľúčovým krokom pri vývoji modelov strojového učenia. Tréningová sada sa používa na naučenie modelu, zatiaľ čo testovacia sada slúži na vyhodnotenie jeho výkonu na neznámych údajoch.\n",
+    "\n",
+    "[!TIP] Uistite sa, že testovacia sada je reprezentatívna pre skutočné údaje, s ktorými sa model stretne v praxi.\n",
+    "\n",
+    "### Ako rozdeliť údaje\n",
+    "Existuje niekoľko spôsobov, ako rozdeliť údaje na tréningové a testovacie sady:\n",
+    "\n",
+    "1. **Manuálne rozdelenie**: Ručne vyberte podmnožinu údajov pre tréning a testovanie.\n",
+    "2. **Použitie knižníc**: Využite knižnice ako `scikit-learn`, ktoré poskytujú funkcie na jednoduché rozdelenie údajov, napríklad `train_test_split`.\n",
+    "\n",
+    "```python\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Rozdelenie údajov na tréningovú a testovaciu sadu\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "```\n",
+    "\n",
+    "[!NOTE] Parameter `test_size` určuje, aké percento údajov bude použitých na testovanie. V tomto prípade je to 20 %.\n",
+    "\n",
+    "### Vyváženie údajov\n",
+    "Ak máte nevyvážené údaje (napríklad v prípade klasifikácie, kde jedna trieda výrazne prevláda), je dôležité zabezpečiť, aby tréningová a testovacia sada obsahovali reprezentatívne vzorky všetkých tried.\n",
+    "\n",
+    "[!WARNING] Nevyvážené údaje môžu viesť k zaujatým modelom, ktoré nebudú dobre generalizovať.\n",
+    "\n",
+    "### Kedy použiť validačnú sadu\n",
+    "Okrem tréningovej a testovacej sady môžete potrebovať aj validačnú sadu na ladenie hyperparametrov modelu. Validačná sada by mala byť oddelená od tréningovej sady, aby sa predišlo nadmernému prispôsobeniu.\n",
+    "\n",
+    "```python\n",
+    "# Rozdelenie údajov na tréningovú, validačnú a testovaciu sadu\n",
+    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
+    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
+    "```\n",
+    "\n",
+    "V tomto príklade je 60 % údajov použitých na tréning, 20 % na validáciu a 20 % na testovanie.\n",
+    "\n",
+    "[!IMPORTANT] Nikdy nepoužívajte testovaciu sadu na ladenie modelu. Testovacia sada by mala byť použitá výhradne na konečné vyhodnotenie výkonu modelu.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-11-01 00:00:00  0.10\n",
+       "2014-11-01 01:00:00  0.07\n",
+       "2014-11-01 02:00:00  0.05\n",
+       "2014-11-01 03:00:00  0.04\n",
+       "2014-11-01 04:00:00  0.06\n",
+       "2014-11-01 05:00:00  0.10\n",
+       "2014-11-01 06:00:00  0.19\n",
+       "2014-11-01 07:00:00  0.31\n",
+       "2014-11-01 08:00:00  0.40\n",
+       "2014-11-01 09:00:00  0.48"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Pôvodné vs škálované údaje:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Poďme tiež škálovať testovacie dáta\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-12-30 00:00:00  0.33\n",
+       "2014-12-30 01:00:00  0.29\n",
+       "2014-12-30 02:00:00  0.27\n",
+       "2014-12-30 03:00:00  0.27\n",
+       "2014-12-30 04:00:00  0.30"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Forecasting horizon: 3 hours\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "order = (4, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
+    "results = model.fit()\n",
+    "\n",
+    "print(results.summary())\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Thu, 30 Sep 2021   AIC                          -6942.477\n",
+      "Time:                                    14:36:28   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vytvorte testovací dátový bod pre každý krok HORIZON.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load  load+1  load+2\n",
+       "2014-12-30 00:00:00  0.33    0.29    0.27\n",
+       "2014-12-30 01:00:00  0.29    0.27    0.27\n",
+       "2014-12-30 02:00:00  0.27    0.27    0.30\n",
+       "2014-12-30 03:00:00  0.27    0.30    0.41\n",
+       "2014-12-30 04:00:00  0.30    0.41    0.57"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2014-12-30 00:00:00\n",
+      "1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
+      "2014-12-30 01:00:00\n",
+      "2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
+      "2014-12-30 02:00:00\n",
+      "3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
+      "2014-12-30 03:00:00\n",
+      "4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
+      "2014-12-30 04:00:00\n",
+      "5 : predicted = [0.3  0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
+      "2014-12-30 05:00:00\n",
+      "6 : predicted = [0.4  0.55 0.66] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
+      "2014-12-30 06:00:00\n",
+      "7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
+      "2014-12-30 07:00:00\n",
+      "8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
+      "2014-12-30 08:00:00\n",
+      "9 : predicted = [0.75 0.8  0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
+      "2014-12-30 09:00:00\n",
+      "10 : predicted = [0.77 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
+      "2014-12-30 10:00:00\n",
+      "11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
+      "2014-12-30 11:00:00\n",
+      "12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
+      "2014-12-30 12:00:00\n",
+      "13 : predicted = [0.7  0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
+      "2014-12-30 13:00:00\n",
+      "14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
+      "2014-12-30 14:00:00\n",
+      "15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
+      "2014-12-30 15:00:00\n",
+      "16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
+      "2014-12-30 16:00:00\n",
+      "17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
+      "2014-12-30 17:00:00\n",
+      "18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
+      "2014-12-30 18:00:00\n",
+      "19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
+      "2014-12-30 19:00:00\n",
+      "20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
+      "2014-12-30 20:00:00\n",
+      "21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
+      "2014-12-30 21:00:00\n",
+      "22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
+      "2014-12-30 22:00:00\n",
+      "23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
+      "2014-12-30 23:00:00\n",
+      "24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
+      "2014-12-31 00:00:00\n",
+      "25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
+      "2014-12-31 01:00:00\n",
+      "26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
+      "2014-12-31 02:00:00\n",
+      "27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
+      "2014-12-31 03:00:00\n",
+      "28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
+      "2014-12-31 04:00:00\n",
+      "29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
+      "2014-12-31 05:00:00\n",
+      "30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
+      "2014-12-31 06:00:00\n",
+      "31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
+      "2014-12-31 07:00:00\n",
+      "32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
+      "2014-12-31 08:00:00\n",
+      "33 : predicted = [0.79 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
+      "2014-12-31 09:00:00\n",
+      "34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
+      "2014-12-31 10:00:00\n",
+      "35 : predicted = [0.8  0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
+      "2014-12-31 11:00:00\n",
+      "36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
+      "2014-12-31 12:00:00\n",
+      "37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
+      "2014-12-31 13:00:00\n",
+      "38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
+      "2014-12-31 14:00:00\n",
+      "39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
+      "2014-12-31 15:00:00\n",
+      "40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
+      "2014-12-31 16:00:00\n",
+      "41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
+      "2014-12-31 17:00:00\n",
+      "42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
+      "2014-12-31 18:00:00\n",
+      "43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
+      "2014-12-31 19:00:00\n",
+      "44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
+      "2014-12-31 20:00:00\n",
+      "45 : predicted = [0.7  0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
+      "2014-12-31 21:00:00\n",
+      "46 : predicted = [0.6  0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
+      "CPU times: user 12min 15s, sys: 2min 39s, total: 14min 54s\n",
+      "Wall time: 2min 36s\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": true
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Porovnajte predpovede so skutočným zaťažením\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp    h  prediction   actual\n",
+       "0 2014-12-30 00:00:00  t+1    3,008.74 3,023.00\n",
+       "1 2014-12-30 01:00:00  t+1    2,955.53 2,935.00\n",
+       "2 2014-12-30 02:00:00  t+1    2,900.17 2,899.00\n",
+       "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
+       "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vypočítajte **strednú absolútnu percentuálnu chybu (MAPE)** pre všetky predpovede\n",
+    "\n",
+    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "One step forecast MAPE:  0.5570581332313952 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Multi-step forecast MAPE:  1.1460048657704118 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vykreslite predpovede oproti skutočným hodnotám za prvý týždeň testovacej množiny\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "if(HORIZON == 1):\n",
+    "    ## Plotting single step forecast\n",
+    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
+    "\n",
+    "else:\n",
+    "    ## Plotting multi step forecast\n",
+    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
+    "\n",
+    "    fig = plt.figure(figsize=(15, 8))\n",
+    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        x = plot_df['timestamp'][(t-1):]\n",
+    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
+    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
+    "    \n",
+    "    ax.legend(loc='best')\n",
+    "    \n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "c193140200b9684da27e3890211391b6",
+   "translation_date": "2025-09-06T13:56:55+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sk/7-TimeSeries/2-ARIMA/working/notebook.ipynb b/translations/sk/7-TimeSeries/2-ARIMA/working/notebook.ipynb
new file mode 100644
index 000000000..5a18ef99d
--- /dev/null
+++ b/translations/sk/7-TimeSeries/2-ARIMA/working/notebook.ipynb
@@ -0,0 +1,59 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "523ec472196307b3c4235337353c9ceb",
+   "translation_date": "2025-09-06T14:00:10+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/working/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Prognózovanie časových radov pomocou ARIMA\n",
+    "\n",
+    "V tomto zápisníku si ukážeme, ako:\n",
+    "- pripraviť údaje časových radov na trénovanie modelu ARIMA pre prognózovanie časových radov\n",
+    "- implementovať jednoduchý model ARIMA na predpovedanie nasledujúcich HORIZON krokov dopredu (od času *t+1* po *t+HORIZON*) v časovom rade\n",
+    "- vyhodnotiť model\n",
+    "\n",
+    "Údaje v tomto príklade pochádzajú zo súťaže GEFCom2014 v prognózovaní. Skladajú sa z 3 rokov hodinových hodnôt elektrického zaťaženia a teploty medzi rokmi 2012 a 2014. Úlohou je predpovedať budúce hodnoty elektrického zaťaženia. V tomto príklade ukážeme, ako predpovedať jeden časový krok dopredu, pričom použijeme iba historické údaje o zaťažení.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli a Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, str. 896-913, júl-september, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install statsmodels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/7-TimeSeries/3-SVR/solution/notebook.ipynb b/translations/sk/7-TimeSeries/3-SVR/solution/notebook.ipynb
new file mode 100644
index 000000000..05c4ee9de
--- /dev/null
+++ b/translations/sk/7-TimeSeries/3-SVR/solution/notebook.ipynb
@@ -0,0 +1,1019 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "V tomto zápisníku demonštrujeme, ako:\n",
+    "\n",
+    "- pripraviť 2D časové rady na trénovanie modelu regresora SVM\n",
+    "- implementovať SVR pomocou RBF jadra\n",
+    "- vyhodnotiť model pomocou grafov a MAPE\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importovanie modulov\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Načítať údaje\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Teraz musíte pripraviť údaje na trénovanie vykonaním filtrovania a škálovania vašich údajov.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zmeňte rozsah údajov na (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.101611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.065801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.046106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.042525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.059087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-11-01 00:00:00  0.101611\n",
+       "2014-11-01 01:00:00  0.065801\n",
+       "2014-11-01 02:00:00  0.046106\n",
+       "2014-11-01 03:00:00  0.042525\n",
+       "2014-11-01 04:00:00  0.059087"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.329454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.290063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.273948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.268129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.302596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-12-30 00:00:00  0.329454\n",
+       "2014-12-30 01:00:00  0.290063\n",
+       "2014-12-30 02:00:00  0.273948\n",
+       "2014-12-30 03:00:00  0.268129\n",
+       "2014-12-30 04:00:00  0.302596"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "Pre náš SVR transformujeme vstupné dáta do formy `[batch, timesteps]`. Preto preusporiadame existujúce `train_data` a `test_data` tak, aby existovala nová dimenzia, ktorá sa vzťahuje na časové kroky. V našom príklade berieme `timesteps = 5`. Takže vstupy do modelu sú dáta pre prvé 4 časové kroky a výstup budú dáta pre 5. časový krok.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1412, 5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=np.array([[j for j in train_data[i:i+timesteps]] for i in range(0,len(train_data)-timesteps+1)])[:,:,0]\n",
+    "train_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(44, 5)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=np.array([[j for j in test_data[i:i+timesteps]] for i in range(0,len(test_data)-timesteps+1)])[:,:,0]\n",
+    "test_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 4) (1412, 1)\n",
+      "(44, 4) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train, y_train = train_data_timesteps[:,:timesteps-1],train_data_timesteps[:,[timesteps-1]]\n",
+    "x_test, y_test = test_data_timesteps[:,:timesteps-1],test_data_timesteps[:,[timesteps-1]]\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = SVR(kernel='rbf',gamma=0.5, C=10, epsilon = 0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVR(C=10, cache_size=200, coef0=0.0, degree=3, epsilon=0.05, gamma=0.5,\n",
+       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit model on training data\n",
+    "\n",
+    "model.fit(x_train, y_train[:,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Vytvoriť predikciu modelu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 1) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = model.predict(x_train).reshape(-1,1)\n",
+    "y_test_pred = model.predict(x_test).reshape(-1,1)\n",
+    "\n",
+    "print(y_train_pred.shape, y_test_pred.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
+    "\n",
+    "print(len(y_train_pred), len(y_test_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)\n",
+    "\n",
+    "print(len(y_train), len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)].index[timesteps-1:]\n",
+    "test_timestamps = energy[test_start_dt:].index[timesteps-1:]\n",
+    "\n",
+    "print(len(train_timestamps), len(test_timestamps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "plt.plot(train_timestamps, y_train, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(train_timestamps, y_train_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for training data:  1.7195710200875551 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "plt.plot(test_timestamps, y_test, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(test_timestamps, y_test_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for testing data:  1.2623790187854018 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Predikcia úplného súboru údajov\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor shape:  (26300, 5)\n",
+      "X shape:  (26300, 4) \n",
+      "Y shape:  (26300, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = energy.copy().values\n",
+    "\n",
+    "# Scaling\n",
+    "data = scaler.transform(data)\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=np.array([[j for j in data[i:i+timesteps]] for i in range(0,len(data)-timesteps+1)])[:,:,0]\n",
+    "print(\"Tensor shape: \", data_timesteps.shape)\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = data_timesteps[:,:timesteps-1],data_timesteps[:,[timesteps-1]]\n",
+    "print(\"X shape: \", X.shape,\"\\nY shape: \", Y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "Y_pred = model.predict(X).reshape(-1,1)\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = scaler.inverse_transform(Y_pred)\n",
+    "Y = scaler.inverse_transform(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EEEISbN4MvPwyMHVq/tqoqgIeegi45578tUEIIYQQQgghhBQDVHARQkggqOAihBCSYMgQYPp04NVX89fG/PmJ/83N+WuDEEIIKWUWLwaefTbhEa0SCtUIIYSQwsP5lxBCAkEFFyGEkASbN+e/DV3PfxuEEEJIKfPUU8C8eZnk8KqggI0QQggpPGF7cHH+J4QUOVRwEUIISaBpYfeAEEIIIbKsXRt2Dwgh+aa5mQZihJQ6VDARQkggqOAihBCSwKjgamzMTxtcvBNCCCHRhHM0IdGiuRm47TbgwQfD7gkhpFDQg4sQQjxDBRchhJAERgXXHXcA69eH1xdCCCGEOLP//mH3gBCST1asSPxfty7cfhBC8kvYIQoJIaTIoYKLEEKINVOnqq+TC3ZCCCEkMR/GYsHrUAnnaEKiBcOHE1IecP4lhJBAUMFFCCEkQYVpSggqeCOEEEKINU8/DdxyC9DSEnZPMlDARki0oIKLEFIIOP8TQoocKrgIIYQkML9E86WaEEIIyQ+LFyf+v/VWIs+OH+jBRUhpw7U4IeUH52JCCPEMFVyEEEISFELBxQU7IYQQkmHyZOD998PuBSGEEEIIIYQUJVRwEUJI2EyeDMyZE3YvCmMlSgUXIYQQko3fNQA9uAgpbejBRUj5EcZczPmfEFLktAu7A4QQUtY0NwO9eyc+v/IKUFkZXl8YopAQQggpHqjgIqS04VqckPKgtTXzmXMxIYR4hh5chBASJm1t1p/DgC/RhBBCSOGhMIsQYgXX5oSUPvX1wP33h9sHrkMIIUUOFVyEEBImup75HI+H1w+AHlyEEEJIGERFsBSVfhBCCCHlwtSp2d/t5uIFC4DHHgNWrMh7lwghpNiggosQQsLEqOAyfg6DQii4KDwjhBBCsvE7N3JOJSQabNoEvPwyMHt22D0hhBQbsu/c//kPsHw58Oqr6vvA9QQhpMihgosQQsLEqNR6912gpSW8vhBCCCEk/0RVkBTVfhESdb74Apg+HXj++bB7Qggpdtzm4uZm9W2GbWhLCCEBoYKLEELCxBiWcNo04JtvwuuLGYYoJIQQQtRjFiT5FSwpUkhpfXpjSf12VHAR4pfNm8PuASGkVAhjLub8TwgpcqjgIoSQsGhpAfr2zd5WWxtOX4DCLGy5eCaEEFLuqLKU9jinPj7lHNww+veW+zY0d+EcTYhfKivD7gEhpFjxOvdyriaEkByo4CKEkLAYNQpYujR7W6l7TXFBTgghpNwxem8DBZv7X551Ml6feyK0Pr1z9lVonJ8J8U2pr98JIYXD7X2Z4QQJISQHKrgIISQsmppytxXyBbmqCnjhhVwlW4oKThGEEEKIclQpuBQajVDBRQghhBQBNBglhJAcKL0khJAoUUgFV//+wKxZwBNPFK5NLsgJIYSUO2br6wh4f1RqtAgnhBBCQieIB9fGjUAsprQ7hBBSDFDBRQghUaKQQq716533R0DgRgghhJQcZg+uCKDRg0sOGuoQQghRiXlecZtn7PZXVwN33QU8/LCafhFCSBFBBRchhISF1eK0kIITvwmxN2wAXnsNWLnS23Hz5gGffeavTUIIIaRUMM/1IYQo/NOYK/HdygOU1VcW6Drw2GPAO++E3RMSNWgURggpFHYeXAsWJP5XVRWuL4QQEhGo4CKEkChRDMKlvn2BiROBp5/2dtyzz+anP4QQQkgxE4KCq9eck/Ha3BN9H1+WLFsGrFgB/PBD2D0h5UifPkBLS9i9IISoxqsHl52Ci4p2QkgZQwUXIYREiSgpuCpspoiamsR/vmQTQggh3gljrndpUwgtWmuQKELhIQmTSZOA777L3rZyJdDQEE5/CCHRgnM4IaSMaRd2BwghpGyxWoQ6JY3NN7KLYjvFFyGEEEK8E4IHFwB8vPgojHp3v0RVoPLGE0JQ4UUKj1GZtW4d8Mgjic+9e4fTH0KIevzO7UHkCFSOEUKKHEopCSGEeIMCHUIIIcQ/qnJwKaBq8zahta2EhobCebAYr1PKm50QoHDPcDye+bxmTWHaJIQUFjdlE9/FCSEkByq4CCEkSoRpPWVu+/33gfnzw+kHrcgIIYSUCyF5cBU9QgB33pn4K/S5oICRhE1lZdg9IISoQNX8ZayHqQQIIWUGFVyEEELs+e9/c7flU4gkBPDMM8Czz+avDUIIIaQUUD0fF7PCjAouQoLT3JwIdzh9etg9IaR88TufGY/761+BtjbXQ2J6BVrjVJYTQoofKrgIISQsrBavxSBcMvZx2jS1dcfjwKJFCc+xYjgXhBBCSDHgMqcW5ZRr7HQsVti2qeAipcjQodBuvgmj/jUk7J4QQuywm3/ME/nata5V/d/w67D3h48p6BQhhIQLFVyEEFKuqJBmvfJK8DrsKLSwihBCCCkCnpx6Fir6vOr5OCedjIAGDBgQoFchoOuZz1wzkDJkSf122NjSWV2FjY0AgOk1u6mrkxDiDRUeXED2HGnDpA17YFVjV3/tEUJIhKCCixBCokQxmFDnO0RhColFOSGksCya04q+z9UDs2Yxvj8hfgk4j07esAcEKtTPx3PnAuvWqa0znxh/f6HXTxV8jSbhs/eHj+P/hl8XdjcIIUFIzl+//uZWPDHlbP/zmfndWaKeCq0IZA+EECIBV+aEEBIWYSuzzKbcYfcHyF6YR6E/hJAsnrxiKq6/Y2sse+y9/HpwElJORGm+KybFdaEVXFG6TiRaaBoW129f2FskuY6vbd1CTX31SeMVQkhhSQ4c36w4FP0WHe0+18iGKJRAA+c1QkhpQAUXIYSERdiCkrDbtyJMa2xCiDutCeH3Xv2ewIKfakLuDCElgsf5Li3a8nycRPmZMz3VGSqF9vo2tkcPLmJE17HPh4/h25UHh90T/9x/f9qDkytwQgqIYf6K6RXAf/+b8Ki2Q6GCS8mxhBASAbgyJ4SQKFEMi8tChSgshnNBSBnTHG8XdhcIKVqmVe+GNU1b+zo2rajyMk+6lE3vnTLFV59CgR5cJAp8/jkwdiwAoKGtY37byuM9WL2xEv8af0He6ieE2GB4ruMiKaLt0ydQPZbfLWCIQkJIqUAFFyGEhMyMml1Q39op8aUYhCdUcBFCCCGB6Pnp/bjsuxt9Hav5FEjZ2HwDAITQUh981R0KRq+tQntwFdN5Ivnlm2/SH+N68YpXhqw8CI9P/XXY3SCk/DDMX2kFlx8vYR8KLoYoJISUCsW7AiOEkGInueg8rP+DuOuni0PuTESg8IgQQkipk5zfWnx6QaYtrj16cEkpxo4/3lefQoEeXCRi6I5qZAUYQ5PZhSlTQGNbR1RV5a16QogRq7nFyWhDVYjCXr3yOYwUjro6oKEh7F4QQkKGCi5CCIkAzfH2YXfBmi0sElfTg4sQQggJjJ6y1Pabg8ujZMrJUlukat1yS091hoYQwIwZ2d8L0SYhDqQ9IRVy8ZCbce/4C50Lvf460NKirM17J1yE7t2VVUcIcUKVB7IXDy4hgClTit+DKx4H/vlP4M47w+4JISRkqOAihJAIUKElF7ZhCk+EwNyNO+W9mf9MOwOt8UrbPlh+JoQQQkqF5PxW6FlOSh9WLHPv1KnAm29mvhciRCEpb1pagCeeAIYNsy2i50HBNWDpEXh/4TG5O5IP9I9V+0C74Xpg+HDlbRNCCouUkrytDfj4Y6uD5RuKxQA4hy4uCpqbM5+LZf1CCMkLVHARQkhYGBZhHyw8Bl3feja09lMc+PHD2NjS2bFM0MXjP376LeZulDAL5SKVEEJICePX2yOvFtfFMvfOm5f9nR5cJN/88AOwdKm1YDlJvkIULt+0Hfb78GHne7CpKVAbvLsJCQk/c4uDol2K1lYA/nN6Roakog5AwpuLEFK2+Av8TgghRCnN8Q5ojncIV3iStARt1V2mBgV9lFpMU5BESOQoektPQiJE3vP1mJBSjBXL3GsWZBXag6tYzhNRh43wVAhgScP2eW9+Yb1LlAXek4QUPYGeYrObttOYUCrKoLa2zGd6chNS1tCDixBCwiIP3lGB2k9+jxstyvPUnwq75TtDFBJSNOQj1wgh5cS06t2x1ZvP+5/vqqqAgQPlygrhqE4ruinXLJwruh9ASoVvVhyCfT58DEB5zIvV1cDq1WH3gpASx2OOTctjnObFpDKo6HNwJT3RAHAdQEiZQwUXIYSQLOK6y9SgYPFYUezhEAiJADU1wLp1YfeCEBKETW2dPB+T5QX99ddK+pFWfxWLgMgYlggojOV2sZwbUjC++w6YvGGPsLsBAHh31B5BoxTmYuHlcdppwK67Km6HEJKNCgWXE8n5zE8zkYIeXISQJFRwEUJIuWLrwZX/qUHThLugyLxI3biRwiVCDJx4bCt22qkEXk4JKTds5t98U1Lhgc0KLubgIvnGYrI94wzg/okXhNAZ5PTn6peOwTffKG5jxYqcTTSsIUQxYcwtpTKfyUR/EYLKL0LKACq4CCEkLKIWojC1GRpWbOqK3w//o6fjvDUtIZE3tjN7NnDXXcBHHwVum5CSIB7H6uVt7uUUsvPOwNvzjytom4QQEytX5m6rrZU6tOhDERmhsIqEwMK6HRDXNQgBHH987n7nQKBy1NQYHKc8rrkL+Rqxfn3h2iKkZInFgAUL8lO304CQ8uBKrQuKVeFl7LfduuDFF4G//S07nCEhpOSggosQQkgWQgDfrToQ7y881lEJd+e4S7CxpbO/NqwEAG1twDvvWB8weHDi/4gRvtojpOQwv8StXZv3JteuBVr19nlvh5Cyw4tg6ZFHcrfdfTewaJHzcU1Nct6epSDkKkQbxXqeSCD2++hRvDHveOg6MHZsftrYbruEPDYSONznO+5YwH4QUqq8/z4wb56aurzMS2kFV5Ejo+CaNQtobgaWLi1Ilwgh4UAFFyGElCNjxyaS01sgoElZef93+pkYs3Y/X80LgdxF+OTJwIwZme/GRWplZebzZ5/5apOQksZOOUwIKTksBVKff+4s3PrnPx3ndqprCJGjavPWtvv6Lz4SL78U/GlatSpwFWqwGFMYFpkQhSQ15Vqf3gCcvUD7LTw6XS4wpWik4fabSvE3E0LSSCu4NE2r1DRtiqZpXyW/v6Vp2hJN06Ym/3omt2uapr2gadpCTdOma5p2pKGOP2iatiD59wflv4YQQoqdQi283n7bvgseqtFlQg1atmFxnDmov/FctGuX+fztt77aJKSksUgETwgpTSxzac2fD0yd6rvORyafiz9//7viFQAVImRhsZ4bEhwhgMZGAIm1r52SZ9CKQ3HrXzT/90qyjbSGy6keapoIKV0snu+5G7s7HyMEZtbskvXdqWyimRKa19zWAZzDiZnx44Hvvgu7F0QRXjy4bgMwx7TtH0KInsm/qclt5wDYL/l3I4BXAUDTtG4AHgRwLIBjADyoaVrXAH0nhJDiJuwcXDZtC6E5L3YNfYwLf47AAsgVyPNFnRBCSDkQcK63nS1HjfJ3HICvlx+GV2efUjwCoDDWDAxRWL689RYwaFD6q+vl37jRXztDhybqnzULeO457/fZlCn+2rXhvc+7KK2PEOKM0yPfvsL07mxR+ND+D2Jpw3bulZVKiEIjbgquOWZxNil7+vYFPvnE/5xNIoWUZFLTtN0AnAvgdYniFwB4RyQYB2BbTdN2BnAWgKFCiBohRC2AoQDO9tlv4sLLLwOnHtMI3HcfY80SQixZv7mLZZiD8wbfiocnnZf44vJi7duDS2jA3/+eSKxrx3PPZSxZmUyekJLhqquAjz4KuxeERAiPQmzbUINOcyokLbWLVXFTrP0mxcG4cemPTiHE0rg8i7YY17tz5wLLl9uXtVLyjh/vr10brvq7c6Kt004DPv5YaZOEEBsqNbn34ZZ4O/dCpfJu7cXwJJXTu6EB+PHHRP5vQgCgpSXsHhAFyJre/w/APwGYR8HHkmEIn9M0rWNy264AVhjKrExus9tO8kD//sDICVsC69cntF2EkOKggAKaplgHy+3z6rpjccMO9gcq8eDSEguJNWty9n29/BBMWLcnUFsLfPllTpuW3wkhRcN77wFvvBF2LwgJEc5hwZH14Fq6NDHg1NcHb5PXjaQYMcJ5v9+wwcacs4Dne64gd2hba/rjiBHAgAGFaJSQMsNijstRcNm8Hw9deSAGrziYIQrtePHFhFfuF1/ktTukiOD6riRwlUxqmnYegHVCiEmmXfcAOADA0QC6AbhLRYc0TbtR07SJmqZNXL9+vYoqiV8LMkJIftE0NLZZK5kKQYdKibEhT2EUszy/TPWdN/gvuHL4dYkvTU3JA0rEyowQVQiR/e4bwsJ87eat4XepxvcIQgx49eCyE0i5KH1sPb+KEfNvtTqHDQ3AE08AP/1EKTwJxIpNXdNRD4asPBD//VeNmordnv0CTpZCIirDd98BsU3NBegNIeVJ2kPUYj6vkFRG/WXs73DON391aSgVorCE1gWy4+WyZYn/M2bkry+kKHhx5qnFnX+WZCFjen88gPM1TVsK4EMAp2ma9p4QYk0yDGELgDeRyKsFAKsA7G44frfkNrvtWQgh+gghjhJCHLXDDg4eBMSZdesyn/mwEhJJ2mIaurz5YvbGAj6vlVoq55b3YxfW7eD7WMBgYdrcDNx9N9CvX1ZlMZG0YE1ZwJobosKLkNA5a9DtOPZYNXWtWcM0fITIknpU3l9wjKPBSM5xMs9Ysb43WPX7zjsznzdtUtLGmDX7oq61U/GeJ+KL5Zu6pT+PrdoXd42/xPkAmfvjgw+Ae+5xDo0UgftMCGDgwMT4ccYZwPrmrcPuEiFlSY6CK8j4YM7BFYGxxhfGfnuVD/j1tCUlwwszTy2u/LPEEVcFlxDiHiHEbkKIvQBcAWC4EOL3ybxa0DRNA3AhgJnJQwYCuFpLcByAOiHEGgDfAjhT07SumqZ1BXBmchvJA9pGg1WZz4f1sMPotUtIPpGK4Z/P9pNDQ5temb3BheUNXbHfR48GbDv526dOTST1HDkya39MT05PdgtVLkJIuRORZ8CvB5dZ0F6jyBiekHIg5cH1+xHXYXXjNpkdbgquUrLUTg4iM2p2weL67d3HRBUadCFw0pf/wKOTzw1eFyGjRiXCcU+fntmWvE9jemUij86SJZ6qlPHCssPOM1QIYN48f3XW1tJJghCvNMXaW273rOAqhxCFXnJwEWIiZfBN4+nSwF/ylATva5o2A8AMANsDSEk7BwFYDGAhgNcA/BkAhBA1AB4BMCH593ByG8kju7//hG/DhBkzgCFD1PaHEJLBcg0WwsIsreCSpDWeKe9XSZc+zphrwOjB5aZ04yKEkEjg24uT76CEeMdscQ3TPOyq4JJvo1g4rP+DOO5zi0j55nWCwt9VXGeIBEFF6jZX2toyn5MKrudnno5ubz8LTJhgf1wRuD3/+c8Jo1k7hABOP71w/SEk6izftB22fOMly30V5hxcZrzkrC4FD66mJuCrrzLfKR8gHkk/U8V4/5McPCm4hBAjhRDnJT+fJoQ4VAhxiBDi90KITcntQghxixBin+T+iYbj3xBC7Jv8e1PtTyFWrGzshljc/+J3wgTgoYfU9YcQUiCEcJ2oU0KxtHAsT/m2LNtOVVthPQ3F3SxQuQghpLA0Niqtbvly4MknlVZJSPHgRQhlJCm8MXpiCQAT1++ZyA9kV08pzpkG4X5zvH3ubzQqDQA1gq9SPI/EkW22AX79a+Bhr157Cu6VpljHwHWoIMhPaXZJ1xWLAcOH+6+fkJLFKgeX2bwiYIjCVY3bYnPc2lvMb50F5aOPgLlz/bfPOb3sqSx2D0aSRRAPLlIGTJgA/PvfYfeCkNIkb2sqXU9Ijvv0cW4/VdxjOBNNZIREfn+CpeeX4YTEBUMUEhIWq1cDL5mNR01hRM0MGpSINirLvHmJ1COEEA+kFFyGF3IhNCyo2zFrvx1FH4rISEUFbh97GYDkmsK8LjCHsOC6gfjkm2+AISsPDrsbAIC1Gzvl5VZuaAA2xzqor9gBXWcKHEK8kBOi0IyXwUEI7Pb+U5hWvXuwTqUYOhT45z+BDRvU1CeDOYQrPbiIR9IeXCF4RC9dyqWpaqjgItY0NeVsmjYN+OyzooiGQEhRkLcQhWvXJmbMyZNd2k95cHlr2m8Oj+emn479Pnw4q027N9uYW9jEWMxXH7LYsCERh9Vs5U2IQhQ7PtmjcIX82mvAX/7i7ZhzzwUefhhYvFhZNwgpH2Sf3+ScaQ5RmDZUUeHBVSxv25qG52cmYptJradUe3AVy3kikeXDhUdhyzdeyN7o8qK9819+iw8+UN+X3XcHrh99teW+fN3qt9wC7LSTe7lVq4ApU4BHHslPPwgpFpTm4FIdxrd//0RM1y++CFaPD/b78GH8bwZjnRLvpKfcENZ0PXoAX35Z8GZLGiq4iDXDhmV/13X07AlcckkovSGkJAlbNpLyotKFw1TgFubQg/fXyDU/w8L6nbKPMyqqDG3pTmETAeDvfwcWLJBu25IXXwQ+/RQYMCBYPYTYMHUq0KVLniov9AAiYd3y3HPAPvsUoC+ElCsWShohXEING47za6ASScwhjt3CPiocM2nrVyb88IP/YyXutx/X7e0ehrBj7v71M9YkPljMy35z49bV2e/L13Jj4sRMjjOnNnbbDTjySOCBB/LTD0KKBc8KLify9WAX8v0k2dbC+p0wbNUBvo8nJCwaGsLuQWlBBRexxuxVsWhROP0gpIQReuFyXlm2L6z/u/XHr4Ds6+WHpj+7hUVMN+t0Pj780Fc/0qxdm/i/cGGwegixYf36AjaWx7FD14HPf5QwsyaE+MNrDi4t2yAkPae6hPUtqSgMbj8mHwouCsPKixA8EcwsWr9V7sbRYwrfkYBoGrBpk/1+PlqEuJMOp2aHF8/lUlBwGWSWUvKJvfbKX18IIaFDBRexxmwVyXi2hBSGQiq4Uh5c0DwFAc5Obm+R98KGuMiEHUxbmNoIqBpjnaD16Z2p26qcijCFhOSRoMLk++7LWDd7RdfVTd3z5gEXPfpzy32NjcF/Z2MjLdhImeFnrt+8GXj9dQCmEIVGgxGXEIVSAqBikTQb3lUENPdwSwp/l1BcH4koQSY3D/fHwhcGJeLwWbS574eP+e+DIsTUaf6PNZwGp5DNfJwIMWEefxYtQoV5DlfpwVWMD6Fh3tc04f4bevTI/l6Mv5mUBqkUGQXLZVAeUMFFrHEL+0EICYzfMCKuGIU8Ds9uxklKA554AqLOpyRdcnz400Ej058vHPInXD3imuzFuxBoirWXr1tVZuqSMmknpcRjjwHff+/v2OOPB847T21/8sUppwC/+EXYvSAk4nz1FTBnDoBcQ5P0d1cFlwTFsuY3KrisvMLNCi+FObjiOl+hS52aGmDqul0K0tZ+Hz2KaXe+6/3AQj2rH32kpBqn7hbLsENIPpC6/59+OjdEYZCKS+GhM6wDnNY3G1s6Y1H99rk7SuEckOJk0KDEfybhUgpX5yVKtmWnjwqo4CIk7+TtsXJLgp7clhIIpRVttbVS1WsiIyQSAtJCo207bE5/3tC8FT5Z/HOgogIPTTwPVU1bAUJgyzdecqyjTa/IhGLiuEQijnkq9YPtbe5y/48bB4wa5b29p57yd5xfNC2RhyMNPcZJOeBn/jIkyTHZhmSEXql6N2wAHnkEmDIla7vmVTgWVVpbgeXL018L7VEVd8pdSkqCv/4VOOKt23wf/9ubunkq39zsELEgZERLq+djUjZoTj8naxwbWcCFByERQ9botTIZovCiITdjbdPWwXJP5suDq5Dv55Lj5Z+/vzLhDUvZATERWm7aVIoMenAphatzQggJCdmcV55x82wytZFSGEnn4PKZ4Na8gEi1++/Jv8EXy3o69lUI4I25v8TWbz6PO8f91lO7lrz1lv9jCZFEhZyq0Pqeu+8GRowwbSxkHs7vvitcW4REBZn5zFAmx4MrNS+nBozhw4GVK4FevdLbn59xGiZv2NN732pro6d4fuklYPHi9FdL4WA+BFkpDy4quEqe+AY5oy87Ph20haKehI/XiBPz5gHt2nls5OOPPR5ASOkg+w6eMmb5fOkR+LFqb/d5rtQ9uCRpaOtkvaOMzgGJFq2610mSyMDVeRmgJAxa1F5sCSkB8ramMj6vVs9ucltqbAjcDcnxwRxWQUADhgxJfBawPiHJbfXNHXDd6D+gOd4B49ftlbXPFz/+6P9YQmRZswZAQtHVr5+/Kvx6cCnlnXcK19bs2YVri5AixWhoImCI3JAaF8we2ULg+ZmnyVVuHFtmzUpovV97zW9X88O8eVlfLdcQybXJ8k1dccIX/1AzZibriDFEYcnTfvHcgrZnGWbTVz1KqgnEunWJ/6efDowZI3fMZ4t7YsaM/PWJkCijS8rrjO/SMStDCy9eWfmS74XkweXkyW3boygMmCQaFPBe+PproOM//XuIE3u4OieEkCihUADjxIcLj8LLs04BAOjJBbKsMlzLWkxq8h5cJgWXLkz1WHmeJetujVemN9WnrLC4KCURYuFCoKkpe5vW74P051mzfFQIQIyVVMZaPA9F+YgUZacJKQBZHlzGzYYcXCmBVceOOcf6MngbPTrxf/Jk78cWGhvB3vh1PfBD1b5Km4qJCo5VJU5lWOE8vbh+F+gedB07bCJHDB8OVFcb6nHo7hXDbsCf/+yjc4QUObrQsMQqP5QFRgVXXA84D5kVXMU4pxllEg5GArb7ivE3k7yw6zGFybkJAEuXFqypsoMKrjJAybhtrmTTJl/VHHooUFWloD+ElAB5W1MZXzRtGrntx8vxn+ln5rcfRtracl6PjS/MutCAWCz3uJS3maGPbXpS2eW34/mKOU7Kmv32A+65x36/59ssmXxWnzzVd5+8EIsB++9fkKac8XiiYjFgwoQ89YWQAnHL6Muwfr18+dwQhakvNnl8hPA31VnNyxHE0tgmlXdMZX4Frhciz4Rek/DEFdOica1WrXLcHb1sWw44uVdNm+arSothyh1DaFJCSoFPFv8c+3/8SO4OyxCFGaVUTFTm7M/ByUur2CM0DRyYcReFN6NbUj5cey0wZ457udVV7XItVUnRQQVXiRI4ibSL9djQJyf50nHNnAnMLWzEB0Iii6+cEUOGAE88ATQ325cxLlht6mtfkVGCpcIi+MkJJj3S3HprbohC88GSgrS0FVaqAq8LdCq4SJ6oq8v+niWE9nCbtbQA01bvACDb0/G997JlOyoFt5s3A/PnK6rMaLJtxvAyaonH57FfP+CYYzwdQkj4mO7zV2aenJv7zkxybV7dvGWWYEs3enDZPT9+5zm3nJ4RwdI6m3N7WfL008C9Hx2eyEEXNqkceAVESWoCc50C0ObPsy8Q96cI95WndNkyX20RElU2tnSWLlthWPfHgnpw5Wt+L9Tc+/XX0kUZorB8eestYMAAycLvv5/PrpACQAVXGTBq9X7BhVYmYfqZj52MF1/0VxXnEUIS+HoWPv004df8/ff2ZdwUXEJkKbi8xv3PqdLth2zeDCBXGC8MU5CABrRvb9uY8YVdGPf16wfcdhtQXy/T9QSlEJKBFJxhw9zLqMrl/MILQM+nfgcgOy7/VVcBDz8sX09otLTY73NxU5m/bltpA7rTTwfuustDvwiJMGKe3GJ9+3eexUuzTs0cB0PYIjsPLi+GIMbBqkgUXAAsjVfu/ukivDrnZOVtqMqXRPKI0zwkgRIDksbG4HVY4LSeGLn6Z/jsM8XteVSa+VJcgctxQtzIycHlZrTp5aEq0gewtmULAM5Gt+kxTMsY9U5Yt2fR/mbiDenLPM/BkCNPBHZMIVlQwVUG/PqrP+Pyy7O3tbQAXbt6qMTCs4LzASHBELq18kmKtjb7fRIeXB0qMs90ykNE+gVWMt51mqSAzOzBZUQXGtDZwoLNQliX7qcQwMiRQGsrMG6cez/s4GBGXGhqAn71K/dyZhmyUcgj4vIC5qROOHGc6RlLt1GI+1ZRG62thi8ukq/9n74O996b+Lxxo3O9w4cDa9YE6hopR4QAxo+P3s0zapTzfptn54WZp+HS725KfLF7ZuNx+Tm+COdEy+Tyuo6npp2NYasOVNiQsP5MokcUwm/5uUcktUNCAOfdlJszpM/ck3DJJR7bzJMizozb6Vi1Cnj88YJ0hZDoYzHfaOYcXF7q8LKvSKhr7YRubz8HwFsOrs+X9sQxn99bEueAuBOF5UAWxWQ8VmRQwVWm1Ne7C47yhV+rLkKIAacHyWOIwrTQSzKufY6QzG1xKJEHQwDWk33aWtqiOYnfacfqxm0wdOWB0Pr09nQcIV7IClE49kfp4yocVme2i/R8vKQ55dxw4PTLumXZxXTsCHz7bfKLxCKgoSHxv2tXJuIleWDRIqBvX+Chh8LuSRauj7DNs7NiU7fMl9QAoWnY0Lxl1nZfXkcVFfhu5QGRlwFJhXxW+CPyEQaOBEfTgGGrD0h8iYAAydKQzQ3JF2VdB74e2cV7/VasXeu4u1DP/7JlwL/+ld82Zs/2aORLSJ6xfeSND56u44Jv/4SLhvw5vSkmKqXfwaVQ9aAXcMHQGm/nq+3qli3dC5GSwe62+P57IC5CUIkMHlz4NssEKrjKhfo61yK/+x1QW5tQfp12d/4SWkT9JZmQQhHoWVCo4Ep7cEn2J0vZ5EHIY9XlIz/9V7JOTdq8JrBgSQicMPAfOHPQ7envhDjhpHAy4mgo2SCfuDLL8ws2Hlwy6AEFfDU1vg4b/kOnHIPw5cv9daFAhuWknKiqCrsHlvid23bqbAjRa/B63uGdZ/HdyqSw36/5qqbhjEF/w8L6Hf0dXyCEQF4VWuY6uWpQz+bNwIYNweupjZDgsmgUoZWV7mXygcwzqjiX2pQp4Rn5EmJFpWY9P2c9HkJg6MqDsvbHrDy4QswzHdMrMHXDbgVrD8j2aHMOUZhN2vuNMoCywM7Y5MQTgRk1hb1nAWSl1nhq6lm4+mreh6qggqtEyfGUMGe+t+DDD4GpUxOGrSOmb2eqsEgW6IQUEZZrKtmFlouC64kpZ2NR/fYOIQotPLgk8fvCbuXBNaV6j0ydkh5ci+p3THheBViU1rcawiFycUtckJ0CzbdS9otXMGWwXRuWpEKYxgIquCIX04EQBUTAs8PqQXZ9tG0GhpjR+tRU74bmpIdHPC6vlLHoW5sekgBcEgGJXCQq2xMa1w6KueYaYIcdwu5FBiV5McK8R7y07WLF4/Vn2K1hBgwwpRBeIWH5smqVt8YJKTLaVVivSXSj17XFejxulYMrRN6ZfxyO+Oz+0Prk5T1LLxbjAxKM5PyhjxsvVdyX17UPjDKx6TW74913uaZUBRVcZUJgC7KIKbgWLOAYQIofqZA6rpWI3Bx5uo57J1yEXrNtEqsLkR2iUCT+amStXrNycDmUmzABeO45YPp0ABI5uKwqc2qgqcmtp7ZkCS44mJQ9jzwCbLONezmVkUCccFRwpV5ynRpTpZjKh4JLYj0R9Dw2Nwc7npQ4xaq4tXl2Plh4bOaLIUQhYBDi6Lq/d4Gk4LsopskCenC9vzB/kS7KFb9evlEmX8+NgOZet5fGXeZlVb/j1luBq64yNCtjbGDuW8RkEoQEpdLm/dis4DKXiukShh1O6x3Fc2ZjrKNzAV0HhgwBqqsDtWOHbRjmn37K2detY1PqoLz0hUSECRMAAGL9eqnioXpdR8H4rgSggqtMcVobWu6L2GLyZz8Dvvgi7F4QEgwlIQr79AFuuSXbSzO5mG3TK20XtlkhCqHhrfm/xK7vP51TrrGtQ26/sz47WJy8/jowdy7wzjuJLjvYj+tCA0aOzN2R8uDSXKYrtxhypiReWX3h4rbsGTcuK1qALUEUXF7uMsvbORm7SZ8+y3vjMuT5OUhVv2Rl+7y209QEdO7sXo6UMVEY8608uPzkyJKoF0DCgyvAzy5Ka+c8Xue4iLZHG0EknnNfwjLJd27XnxdRRb7UZVmxIvNZtQwiJeiM6Pkh5UeFTYjCrFvUwkglFrF5yDWX0WuvAZ9+Ctx7r7I2jesmy6GlrQ14442cfbtvWZOqQFlfSATRUmk4LOYRC0WrrmIdTkKFCi6ShYoxvrY2eB0ybJJPZ0JI6TJ5cvZ/IP0g24YUMnlwPT7lHPxx1B8si65q3Db3cMPU4Ulo7+bBZYXLoKQLDc2xds4vwC0twP33A/37+2qDlCarVgE77ZT4LCs/sZKH6LrB4KK1xf5YD0lsK9auzt04f36inpbWxHeJ+3ZzvIP0b9M++lC2e75pagL2PmOf/FTe0AAgE52REFsiaiVpKQzfuBF46ilg0iS5gco0LqRf6r38ZmMdTsKBlhbg8cfTXtqhU0APLpI/ImZTGQiv4Y7mzgV2uOwURY2ru1cLbtU+c2b+6h44MPE/K1YiIeHRzk7BZZx3LZ5nSw8uM46Wd2rns3iqv3b1GmUVisiRH7h4sDU2J2QjqXnm+u+ukMnkQoqVpMWo8T5Ztiwpr07NBUa4xit6qOAiclg87FOmAO+9l1u0Wzdg8OAC9ImQIsfyxVfhxNqmV1rXN2hQloLr0yVH2taxyS3cgCTLN3VFtUMIRDfrcLvTcu/4C9H5jZftJSJNTcD//pew2Bw6NF1ZKQlQiD/mzwfWrfN2jNV9uGgRcOGFic+tk2dhzZrMPr+3mfbj2Nw206HC8nTz/vhj7jaF45Fmk2ZPGV99lW6HkGLE8nEbOBBYvDjhra1pifyTMpWkQhSmxgu/3gopBZfVaNa7d0JS8PLL/uoOQHOsHeZv3NG5UAFzcpEIYLawDHi9VUwlXhVDU6YAG+pyIydY1j1nrnMBm2e+uhr4xS88dStSHHD1MaipcS4zYYLEWkAyZBUh+abSRsEV100hCk1DmqUha5B5L+CY6cWQTwUxvQLN8UxUCEtj2eRvSuUjzTqnAPrO/gWmvD87f50k4ZJ6dzbMxXvtBVx6qXVxLhOLHyq4iG/698+Oo23Eac0Yi+WmDCKkHPFjEbm2aWs8P+M0i8psksKbt69bBwwebMrB5a0fxn7LJlrf84Mn8eTUc2z32y6KXYRyM2p2TXywC1HYr19COGiCIQpJpeG90FUQkoxfKBpyXYfbGyLufbb0SOyyi3UV3tJhZAr/bvgNOOUUmU76p60N2BzLb+jAvJN03aKCixQrlmsC44JZ5ua2y9Gn61jd1FWyI1YeXBblOqoxgPHDE1PPwf4fP5K90UvuEb8Irh0Kga9TmzJiKmJk56/X5p6A/7uw0bmQzUmcPTsRltkLbtfDtzGPQ2QHO+at2MJqWZ9m6lSmMSDFhV2EEzcPro2tWxQuObAE8QKHd/v98D9ir35PpL/bpU2ob+2E2bU7A7AWF+iDaJkfJVpb7YPueMZmUq2pgXt6i0LDNaUSInZViSpcp5fRoxP/582Tq9DlgauvT0QqkeGMM4rbcowQVVg+Vi7P2r8mXIDbf7zc+ThjiEJzfc3NALJzcDm1aDWW5GP+dYt5bCX4i+sGNZXdImW2tVVW1eZtDJVzQVGOtGsnX1a88SYAQO/7Zs6+9pJ6odQ9PHJkItF6r172Zc13+6hRsFykG7e0xf0v6f7wB2Cnd5/xfXwkSD7HVHCRUuOJKWcnQvH6ID13+nWfdPLg2n57f3UqoL61U+5GC8v1Si2PbqN9+uSv7jLH17IsgoO/198h+xPmbtwZ/Zf83HN/amsTUU+9kq/fIVWBRWVO/TniCOCxx5JfnJTcXPuTiNCuwl8OrriMkWkBPbjieoWSemSZtGGPrO+WBru6jpO+vBOteuJlLSU5yBKbFGOO0RLm229zPaySaai9k17DWmAhO3K7F+bOBfr2td7X2AisXeuxf0Q5VHCVK198nvj/8cdKqttmG+Dqq+XLT5wYvE2uS0mxYzmJutzYb8w7Qbp+SwVXcrVstBZzUi5Z6uA0Yw4uOQ8uN/zk4Gr3ei9MTi1u7d6mrRLyMGwRQbYHlysLFgAA9Lm5RiHarBm527RcT+bUXXbqqYmIXv/7n4f2U5U60OGR+20X3W7MmJGdW6+YiaCMk0QNTcOqxm3DTSZtMe/YzUT3TrgIU6t393Vzp+sMGKKwKBJvWyq4dPv9AdtoWUZJRqSI4ODv7vlU2PVnz57A+edbdcT53P17/Nn4ZsUhSvvSowcwcf2eruU2Nwe4rqNG+T+WEJXMnQu8+aalRbhdiELduC63GEzSCqUoMGqUu6Jo662VvnKbhy3LqoXAwrodco4x9rUo1jdlTF0dsMMO7uVkyBJ9e/TgisWAhx8Grr8+8f3ZZ4F33snsv+46YOedA3SO8iglRGhUJKGQeuFNBbOuqoLWvNlXVQsXZj4X4vmsrQVWr85/O4TkjSAPisSLvGVs7rSCK7OYjgeImb2pTU2IIjcFl92pSnti2ZwPEdfx+dLDcdXwa/H+gmOsy3hMAk6Kn/p6oHH6IgDAb89vcc97nA7TlX2fjRgBfPnINMtDzFZcL8w8HdtIOg5aCr3MnhQWFcyYWt7xf//7X+CKKxKf+Z5AnNjt/afw1rxohROwtD5OzYHQpOb9HJsWP/O7VYhCK8GVuT8tLcB//pNwU80zUroMIVDpIwSaW50pThp4JweaPKHEgytoDi4F907BPZ9cGl++3F9Vz089BcNXHyB/QDJahBNLlwJtuo1navJErFsHbHFirqea9HldtEiyICF55rnnErFBLRLV2ym6zTm4zMREhbvRZqG8GD/4IGM8a1fvIYeg4rXemFUTRAuQwXzenHJwpY+xcvKigivSWNkq++XyZBAku1vU6ZFo3z6R+SLF3/8O3Hln5rsxB7cvuJ5UAhVcZYLt85IKWfJmMuzS++8Db73loYLwuO02YNddw+4FIf5R8VjN27gT/jjyasvKWvXK3IVtspzRgysu7F1Z3EIU/n2cTZZOAK/OPgk9P70PG1s625ZJodtZfcmeJBupQLPoiIuG/BnvLTwOfecdb90EQxOUFTfeCOy2G3D6jfsAAD79sqO0sYT5Xrn4YuDm739vXdbi1k2m8rLdn8LqdtaOPsq9fz+Ndy3jCYVzfyGipLzxBvDVV8HqIOVDbesWobZfYbLadvLq1oU/BVf6u98HwikHF4AmY+6+sWMT3q5GCUCesBQIWvx48zlWyfj1PfKT56tc2VgrVeyHH4CqKosdHrVD8+YBDQ2Z7/feC9x3n6cqXInCPNTcnG2EWhA++sh2l5dz0miTZsy2jlq5eyhVySOP2OcTJyQveIgRag5RaM7VZWnAEmTQ8Xts8jg7T7Q0yTG6avPW/toxV2fuhlVUGSFMucNTZU3HkciiwvBDCC3rgVI1N7e0AIMGJT576acKAxpiDRVc5U7q6TZOtj6DnEZhEU9IMeEnB9cNB4zJ+v7pkiPx5vzjE2/8QOLlLjXTWpHy4AoQFkV2ITho+aGYVr07ur79P/c63XJwuXVXYgBKt2Eqq8c5eJUTr72WLdRy49NPgUcm/RpA7m3mtJjNd97nufMtlnCb/Xlg54sNG4BHHw1Wh+x5iuta1vUQcQqeiQ3JGyXIPKikGx7al7UwzjHeDiq4cfLgqqjAlm+8hNFr9kt8j4XsQWphua48ap0X63jiCa22Jv3Zadw/4YREHsvcCrxd7AMOAG6/PfF5jz2AJ54AHn/cUxWu3Dvu/PTy3C1XtXIha/IkPv44sN9+aqs2M379Xrjppsx3rUpN+E7Pz6/HMeid11vw3ntqPQQIsWLFpq7Y84PHLe9Ru2c/K0ShrluvWfL9siFDdTUAw5rGrs3kA61EuC8E5tV1z9ok48GV2Zyr9CLRYtSoxNisbB33xRfZ363yO3qch+vrgXPPTXz2GPEwF96ISqCCq0xJPbpvTTsCAwYAj4w5JczuEFKW+JnHOlRkL4zTdaRcUJ57Ltu01UYYU2mT0Danj1YbFYeBAfzl4JIhy2rLa9uEAHjoIeDxKecAyF34ui26nQTYxlt7/PiEIk3quOT/A4/uguqWLu6Vh8gXXwD335+nyk2/sd0fr8asWYbdAz7PU8OkVDBbQxeaHOtjh+7IhijUTYYcqkIUOs2Ta5qcQwVbsnAhfndpDD/95L17iabkrl3elZhUcDnSqxfwyis+Dhw92nG35bPiQwqW8qpesSK73uXLbcJ8e+T56afixRcTnzt1CsGTCon8JflmUf2O6NNHXX3PfbanJ0MkO4569AIMH26zU4i0gW+HDsHbIsSJmTW7YPmm7Tytz83zrqVHchQUXMmkxq5rqjwbF1kay0rM0YENgUheOOUUU86soJgnAwUKLqvqvv/edxVEAVRwlTnXfnkxLr4YeGDU6flvzGKCveWWhGCPkHJExZqzvi0Z/u/4ZPg9c9wWm3hFsotLK+FYPtbKcVGButZOuTuM+Ud8oGsZAYVd7iLKp0gai5vBuP71ogxdtw72Cihk34bXXQf89rfGNv09ZHaekJqWrUCz2p9v1q3zJ9wbNw7WOdLMSc5MiNFjHPcTUqGJhPdzGJOAEDnPuZ2XFAC0xNt5U3DZfJftWxqnNv0OHLNmAc88gw/7t8MnTy32V4cVFuudLGMeFYsXt3wnJIs//SnxrucVMeibwG1XHHaw72P33BN4e/4vA/cByL5FamrsywGFmYvTRMzj28gdfQ7Ad9/5ONB0Aict2wHffmtd9K73D0Nty5Y+GiHEO2nlj8V6wy2TSKKQ8GeU45RsyLyO9jufJX+T5paDK+3B5a8ZM2fuNivru2WrQlh6axnXW74MgUj+WDA//TEWU+AZheT1bpfJ+ej5Vp8/371MkhNPdPfYtoTrSSXwaSY5SMW194OFJuuVV4C337Y/pK4OWLIkeNOEFA0uz5pZAPb0tLMSHzpZKIes6hMC8zbuhNVN2/rujtDUTx1vz/8Ftn3refsO+FwNG8M72An/qeAqD2bOBB5/zGUumzPHcbcXD64zzwTO/9ZesuckQ3a624XQfIUCmzrV8yGBMf7G++8HjjxS7jjj+bjhBuDkk10qt9pNi0xiR8qKWBPAO+8AP/4YSjfM+Sos7+jkm31TrIPUPW2r4FLoDW2LpqGxrQPiuktZwzirL8yjgssupJPiNoh6VISYFULLuVxLlyLb07fA8iS39pQquNwa++ILNDYmzkkhWb4cOOggubJ250NsbrbcfvUtuUZFDQvXWhrXPD3wAEcjJEJU4qT8sX0/NYlqLRVcpvqEbirz8cfWwvlJkzJJa53o1w+46SbnuU42/UFyPeMlPLMtQqCdaQ2liwpLuYdbRghGcokYQ4amPwoRcF405pGV0JTZrnXNBuQWCSKN/cxSTstCBZcSqOAickg8cK6Dzxhv1tSaBmy/PbD33p4OI6RosJpEXSMNmL63r3ARdFtUeMDHD+PblXKWrX766Ie1m7ex6YCQa9OmQJaCy+ZQV2EcKQleegn4130u19piRZqV18lDDi4veK7nGxfrdovnoU+fRO6SYkAIAM3N2d89QrkzcSMtZFm1qvCNC+GekB0w5MCSew5EPJ41jgVW9CYFAk65LdIhCgF0efNFPDDxfOc6x40zV6EGC8GWZUgnlVAgkR9cBvAfxui44ALv1ZxyCnDIIf675QdPt0hTU976kUN1Nf76V6BHj8I1mcLFlsgV8fEnltvf/TjX2O/Vz7rnPQcZIW6kZ1AvIQqN45eFB5d0Ta++mrvNKo6aVd9Gjkz8f+89+/pTCq5U/2bPBqZMyS1nNC5SgNT5kMnRraQ3JB+IhYvUVZZjTWqxrrV7ITd4fwGwvK+8KLgoecofVHCVKEqSNxpYvKpj8EpMA8a++7ofYmmk7svnk5DoYbXm8mpFdMtBIxMfbM0cg40FUglb8y3gEcJ3EgFLDy5ziEJaboXOgAH5D82jIsTBru8/hYcfznwP0ufUbbj77sD06R4P9uGOtW4d0snuC4JdXCAZGhqAT6wFWISoIr1WNr+4FohKs3DGai4yhBEzW3NboYsK4N//Nnz34cFlZWHuUK45njx/tbUAgIX1OzrXb0iu41cBJ3uU8nmFHlwFwdaDa948AMDadRUYONC0zyqfhulytbU57w8b7dP+hWtM111DJkaWVN7hAjJmDNCtW8GbJSVC2qDGMkShjQeXOQeXhCpGekzzOndNm2a/Lx7HFd9djxdmnpbZ1qtXbrlUiEII5/okMXuCCaG5yiisQhTaedCR8NHH/ACtbmPii4XXlCvJe25R/Q4YujRj6WD3nDwy7lfWObRM64s9922fvb+tjR5cEYEKrjLFzjW477zj8ers3FhA+1z2c0/1jxoFfPihc5lFSYW852f56689HkBINPGj4DIvwnLe581S/DyEJSpo6C9dB955B2JtlXtZq8PNHlzJ87Fth8wiifKp8Jk9O/9tSCm4XCyymuMdspyRgwhP9Vhi9btyZe4+p/Adq5u2wRdz97fcZ5dnzpVNNtncA4wfQXKoNE+dgyUTq50LuZx8p65feGEw/RspcpL3TjrvwoIFPt9Gg2H2LrKcWysNeSQlwrZtaO6SCKWS/I3xoLklzOfKiPEhGzMGGJoIKxPT5dv0a2AiFU5diCyB4IjFe/pqyxEuIArLihWeihsvz7Bhmcd83pzEjuWz6vHWW9nHXHFFgP5ZYLwtXadUlR5cycbspsqaxo55G/ZUhCBzCk2VDzmgW50//pjW4RPimfS9bBmi0PoYc4SRnDWDhELHFq9zV/v29vt0HR8tPhqLG3ZwriN5EpZt2g4tL/YJ9iBb5DHVrdZQQtjIMgzHUcEVWQQA9O6d+PyWQ14bFz5beiTO/PSm7I0WE8yT40/H7bcnHBY1zT6E7/Iqk/PHoEFZX0N4pSBJqOAqU+wE1B8sPBa95lglu3AgudoThknqjTeA3/3Od/ek2iOkFPG6yEq/RKbCCOmA1qd3pkBAbyvLHFzmbXm2OBE/jMXXyw/1dWy2gkszJMI1lKF8qizwq4wyH6fqdhe19l6JTn2dsL4HLvzgMjWdSOHTQ9INv+fqw0XHYO8Xbs9b2198AXz0UaDqSQmQnm8XL07cFAVGamxJh+nVpOaq9ZuTOWVGjw7WuRTG/AU2feu53cqs8EWfLZVMtof8G8xUVmRO2qp6Bfl26MFVEJTNs4Z6fvWrTBqNAw5KrA0nztsa116bfUw+54aC5uByqXu7e27El18WoOE8kI9xwzVEfMoSy+BVS4gsTh5cduR4cMnk4DIVOX/wn20saj3OXb/4hf0+2bqS48yVw6/H0QPuCaYFELmjgF3UmayfP2YMMHdudhEGjIssQmhpj119gcJwhQ737KRJwFVXJT7L5qjsfOm5GDEi813UbvTdNRIMKrhKkIJrjFMKJ58eFp4pskU4IV5w9eAyfTdbL7XFXYZ1j1IDS2uoQiIElm3aDn8Z66Ixt/ldQjOFKNT1HKtuz/KpZcuATz8FWls9HkjsKIRXvpQHl8XN4DTlaHGXHHg+UZKAOWQaYx3DfUQY6oG4kHWHjB0bVjekEJBTcG3XKTuEi6/HQIhEKIb33zfkALMfCM1tHLfjYuf6t946bYij1HLaxYMrL1baVHA54veVzfZ+81hhFC6P0zOYkxJE5dxfAnOg0lf+tEebz/MyJykUN+QQJESW9H1nGQJYIkShdA6u7K1fLj/cukNWAsNNm3I2/fn73+Hnn93r6sElg9HodEbNbsoHaJm0CkIAeO65rIg49OCKFsZ58JXZJ+PMQbcBUKuIFKvXSE0w6dvHpWxzW7vs1Dp9+/roVPHP2VGACq4SpF07YMjKg7O2mQcEpY/PwoWJ/xT2EuKJQCEKNS37mUuHETIdb148evbgKkyIwp232Gi7L8jCM6evKQWXIcyD5/X1448DQ4YwxplKUmGH8mihIaPg0o48IifNo+OatslHPPAk+VjGph/vCCySd3v/Kdx6q5q6GhuBnXbydozbOBWBU0RCRggNrfFKnP7V38JoPDd/hNU9u/vuqeLQ4+43rXm+9B229IMPEl5gybHZ8ujk5Gnu9xHbL3eu25DzzNdjOG2a9bhs8Ruz5nq7tYQQCTNdc4Imu7JGpkxxP4ZIIaXc8ZhMM+oKLuO+q0Zci8em/Dr/HSoBfI0bVvGgjXW6eXD5aZOQJOnZxzIHlzX+PLgkcmfb9ANz5sCc3HDYqgMweYNLeF9TXV8uOwzNsdzcpuaQi0EHaPP50K1CNpra+O13N2FzrH3WuoUKrmhhvDZTq/fAT+v2TmzXCz8K+31XPK3P5Xj00QI1RrKggosEpq4xnOTcslxwATBypPfjhgzJ1dnNnAkce6ySbhHiKQ63JX/9a444bGNrZ+djPC4mpXJwRWFCtumDcdEqbMr5zlGyfr2/40gO2uRJiQ8TJ+atjYrNcsooYw7bt9+2j9738stAc9zBotEFJwWMXwtjHVrJRs9Zt05tfVEYtkhIGAxCNjR3wfDVB0TihrDsQSr8sKQH17Tq3bCmaWvnOl07kps0yCkJu2fhkC6hdLJDCOCVV+z3mTAKwGx/w6hRwBNPAH36eOsLkCMMJGqwfRzNmk03pcV3w9R0KAANDYmQR4DzMLOofkdMchMk54n11WpFQr69pAw8+ijwpz9Z73Maj2xxG0CjoA0lJUuFgweXHTk5uKxm9Ndf97d+sbvfTbnuLZVqLnWd/+0t+GjxUTnFchRcgUMUmhVc1rlCje9bQ1YejJWNXbOLhB2thkgRxnXyOy3MqNkN/fur7QuRgwquMqFNr8zyqPe1MLRh24tPkytoYwYfj3vMqesxXsHAgcANN3jPCXbWWcDHH2dvGzUKGD/eWz2E2OHLg8tojW1c3CWfi08Xm3JfBMzBZdUf2Sq8tORkWR3kNVlUVGa3kVyprG/OCABlrOIt4cuwepqb81a19uMPno+55hpgkSnkd+r+v/VWoK51C9/9cQxb5LPOV2efgi22ACq3lu9XlUt04WeeAe6dcKHPHoWHqzV2+PoMEhbJi69DS8+pYViGmnGyvJbNwfXb727GlcOuT3/XRUWwmz3pbWUpWEidx2S/F9TtmO4rvvnGvl3DdkuBlBPJY42eWU4YhXO25+/HHxP/p0+Xbt+9UhIIu3vH/A64Zo1jNfrn4Ssghw4FjkrKemtrgY0bkzuWLPFvYCWDh+d+x8N3zl8/fDJlSo6sXQl2MhAx6Bu54wLGTdR1YMaMQFWQIsQpB5fdPWlelpjnPSE0YNUqYMECwzaLiqzereLx7JzdNvhRcAFAdXNuzsscfZbi+TMu671m2tzQ1klpP0ieUPjiJgClIQqt4PIwHKjgKhPmbtzZMTdkmPTpA2y5ZX7bWLgQ+PBD78eZPbgoECP5JqibvKt3ldcQhYF6I09Mr3Qv5ITdAtZwPgQ0y9WG7ncq5ICgHp5TJehmK0kHundPxsK34dFHgc+XHqGiWwWFCi7ixquzT8YpX94BIBovok7WqfeMv0haCTe2au/sDUOGeOtIWxuOGXA3rh5xTTrvhuXzklK+QYMQwM8+eiT5HcDnn9srjLbbLlOFt56lJWSVMkI3U86SvIQh8hgyj8ghnYMr5pwHM2rD/HnnAT//efLL6tV4cZakkahPFiwANq2ozWsbkcHNQMpl0hdjf3Ter8h74PPPgcMOU1IVKSL85eAyzC8WObjSjB6dVc7M8zNOw5CBpudDctFTKWNMYlFXbUuuoV1O1wKGpTd7itp6cLmkW/jz9x6t4Eko+DJEs1FMyY7nQd4VPd/efDFVAlflpUaID8a8up1w1lnB61m2DDjgAA8H1OZv4S4Tjp8Qv/jx4EqTnLA3xTpmVRbTTcN6UAVXgXJwtdkpuKTdxdxDFCY26DllvQo259R2R49+j3Ehkg/yeE5l79r5812NwpVg9Rxdn3S8UJpovlzh80nsSN4bi+p3xML6RHK3giu4hNxMeuHDCeXy4oYdpKtu1dunLbOFAPDZZ5l8uTLU1GDC+h4YtuqAtBe07uLBZbl2SbuqmNh1V0MVHtcTLS14ceapmFGzi21/jBhDOilRcJnboIJLinwZWdY3FF9oqcWLkx8KMEf97GdA38+6uhcsBT74INjxBVozeIpaQ0oPD4uNrJB+FgquxlhHzN+4I1BdnSlmMVff/uPl+Me17mH1jeGNU1Rtzt2Wg8WzYxWm1DIvdgDMv1SHRQ4uieeaObiihd07sN2qOT2nJhk9Wt1wTg+u4oOr8lJj7FjXIiNGAO8tUJ9IqinW0dlIVHJgmDoVmDdPvt0HH+sgX9gjUQhZQ8oL9xCFGZpi7bGsYbus/XttVW06oAAhCq3qlBTepbBVcEFSAGbnwRWLG4rYeHB5WNgKAQxffQCWNmxPAXpA2tospoUInNNf/AL4dQHyvFvdd3375r/dYmbiREjnGHMbgUSbteX/0qWuTgGk2LEYZ6LwIprTrQ0b8MWPO2X2+1iT6kgqn1atkj/IoLRJ9cnOMjqxT0NMZObwxfU74IWZp0qGKPQoNKiqwl/HXoGBy3o6l1u0CHjllayQTkIA+OknoFevHAs2rU9vLK7f3r19Krh8YQyTL4PsUmCbs47Dhg1O9URYcBkw1F2UCeWXzZ4d6HBpr2/TdXvuuYTsghAn0sYWHvJvm+dHs4JrwNIjsP/HjwBHeI+y0BbLfUp3ee8ZTFi3Z9a2dT4VXMfvtMiioIkgCy8hLDy4rA1xrE6vMWf5o0eHH8qWZPAiQZo7F9hnn+xtJ58MLFmSMFjVzjrT/mCZEIWN/i0S4i32L5M0ZM0fXJWXGvPnO+8XAjfeCNwx7rLC9CfJTz8Bv3n9AtdyCxdKvIObBqOH/5uH+IZJDZv2/WiXgoSoRXqtJwSuHnEtBq04NPE1KfjaeYu6nHL+Gkge7vc11aOiIuaQhyCIx5he15D5LCwsu+DtlHzwAXDrD8lQBiUsnCgERiVCVm65CNDYaL9PmVWYlaVhksjcWhG5HimOPhp49lnJwnYncfXqxP/Jky139+gBvPSS976RIsJqHojAK1HOXPfGG9n7fTyO94y/GId88oA3RUyyoZZ4+/QmJ0WUDi3Le3zoqoNw29grpBRcnuf3Dg5Gbcb2nnsOAHJDFL7xRiK5z6RJmbLJsWK9Rc4QVyIzWJcWXu6LtNGDxbUIIym9NPm+dyI2f6vE8rq6nc/k+bDyLDHu99QmgDvuAJ56yrlpIHA0NhJlqqtd75/U7SniFjm4bEMUmhRcdkJx470vEbIfAOpbOlqWa4xZb1eB0hCFdoa0c+Zkf9+wAXGRbUT7yORf47Lvbkp/36KdKScJiSRWt7ad0aOu53p2GVnW0A039znSvkCqzU8/A+DT8LGm2r1MVmOlO2cXkvDf5oha3BZ3M2cWph8mPv8c+Gr23q7l9tsPuOWW/PfHle+/T/xfvsJyt6Yl3o8JCYKfEIVpa9RPPkl4EZnqyjk+4GQpgITbZ9Y2iTY8ths40baxvaoq4N57gR9+gNg1k1tIAJYhCuMe8hWlZOMAKNjKB1zcAYiWZVfUbvOWFrlyZm+XxkagoQEJsz6L/UaqPb6TkCIjCh5clnk4TDQ0ZO/3OSzM2bgLsPvu8gckhU7VLV3S873l2iR50oTQrOdwu5MaxINL8kKJ1jbcP+H8rNVKVlvmJLvw6S0etQEyYng5PVJFLSqMgvelLyoD5p51g+spT1gpHrL2O+yTuc/btQPq6tzLkSJi3DjgppsS75wuyd47VCQk5LVN8gqkXA8um3vUwuvaDTulmq/3cb9jTeAQhdnttsbboe6r0dlWihZGMV8tz06Cx6EyOkybBqxq3NZyn5frJATQ0eFRa4x1Qu+h7rJpUVOLpUuB9r88Wr7xJJ7f5XkjKoEKrlLDbYW1cmVh+qGYX/0qei8wASMhEGKJlxCFWZ9TCi435ZOfHFymRbtUFTYhAeywDH8En2FCv/02IaF+5x3reN8+c3Bt3myy4KFgSwktLZn7vs9Xu+Qt96GXhWYh1pjrNm8NrcL6HrK1MFbBpk3yZYWAs1gnupif/VNOAQ4+GHLx+CO23iD5J5LX3OR1Vf/jLP91eRnULE6GkyfM2/N/gR/W7pO7w846O8uDy2PfPFh8PzrlXDTGMsItXWgYtXo/vDjz1OyCQebynXf2f2xZ4G/+CHi7+qqnkJx2Gko6vGW+jXRWNm7rKeqqDNL3SoDxornZ96Ekgog33sTYtXsnPJhHjnQsm1rXt7TlPvd2t55bDi5D5Vnl7Duc2ZeTs1vicCONjS5NWawZzOVjrQEVXKbzsaKxG7Z96/ns9xwLQ4LaluzoT5H29C0zevYE/vT9/1nu83KdhFAjprn0uxt95xDlfRUOpbuyKlck3fOLjWHDDO+0Fr/xwAMZUogUH5aOTx4mQ6MyzDFPRtZB3haTljm4zH3s1cs5plsAdKHJDVvG39U5E1c76xzBJgeX5FS49dYJI700JSycKCRbbgk8OOl8AMBNzx+EGTPy0w71kUnK0D1J0xL5u1YYnLKdhhWGEipxouDBhVzhTI4HkWnQ+vmAf/lvzMsPNCqg7LzDDTsHrTgUvx78V8d6sjbrxvqt52WZvuUwdWrGq8xKuAbgXxMuxF/HXpG9w4snjbn9gw6SP7bciMcL8t4pJWRdsCDv/fDCiBHgoiQA/zf8ehx+uGljnmUgKvK58ZKXFgvqdsTxA+9C+9dfxfTqXaWO8bK+NM+7trePlisPcGvczoB0ty617h1raUGXLsB7z613L2vA3Lf2e8mdM7vKbM+H8SQXqeyTBEOVgqsp1hFr1wavhxQOSudIXqmpkS87Zozzfqf5ae5cYOhQ+bacGDkS0K6/Tk1lhHhENkSh1qc3plbvkdm+bDnQ2Jh1/JQNFiGJPC70hq46CHWtnWz3/2aPacDy5cCXXwZqxw6/1i9an94YV9UjqxtCwFrBFZfra078Zb6pKsH8smcROargLFyYUGhaUfTvSh4Cie9/w4moqyvO+9ztMjkJq4zDxJo1wLp1avpEIoLFQ+wlVG2+cJvvzFbH3ir36SWVHC88hxIEbBVXxs26jeGJVN9MvNJ/x0TIKFgr5nRdQ/uK5PHG85FUcEmtN4p+AiggmzZJe/IsXJgdGsvWe99jiMJ0LcOHS/WjoOQ5RKHWddu81m/FnrvHsWFDYdqqlZDDe8Ht0U6PDxb3YCyWiI5OyosWvV36c9Vmm5eGJKk1p1UIQLv1qPmetA1RaLgnf/u/45064dhHq75c87OxuYWGDQMALO/3g209a5ucz4cKbKNdZCV5lvjNivpD8kvJL79K/gcWBiq4Sg03jwIhoG1uKkxfAGy3XeK/jBz4pJOCtaVqTJjlEAGG4w5RiZ8cXLZ1zV8A3HVX1vGrm7qits40Jni8iZ+b8StcP+rq7CoMQqDtOyXDAJhyhQAOyXA9IIQPoVPy86zaXbKFW6IiWaEpRKFfq0x6cOUFpxCFa9YUTohgcUsXjPBF7Qnmr+oSdhdySD++eVQwGwWm++wDHHts3poiYWDlwSVp6KCKWQs6YEPzVlnbEhFB89QPL0okQ9nLe5+W2ORnnrT5LcauCKF5NGm3/x23/HBlTlbxZZsyuUp14aLg8nHqy/W9YPFiYK+9XAq1a2e7q1ev7Mu+337AuHWZfBieohk43NpXjfgj3nwT0TRIKsE15PKVlXjjDWD02v0K33jAa/zilBMwerTNTpcUD598AnTv7t6GppXvmFGKvDr7ZOmyqTHNyphGdryrlAhR+PUUa6+oGTW7ZQ2Wdvehea5vV2ExP2/cmN2sRWXXjromZ5vqe1/Kg0sCFd6ZJP94WRf84x/Af5/MU84DEmlKb2VFnBEC2BSi1K4IcFwf19fLlSPEJ+45uKz3d+z7CuItsZzju/28h6kC76vL/kt+bluFgJbwFLOQMKjII6Tr/hVQFZqAqDeNd1YeXH4XthwECs7++wNHHRVuHwohnMhn/gptX4tcOUVE+vy7XAi7F9ZYm8NxqVCrS5ekN23ejIJZpJPwKGiIwrY2HHLuntb78jXAeM3OneSzpUcmNvlRu0t4cAmHcpa4Ca6SCeWt+pul4DLiZS73mcOz1JgyBVi2LHHqvv3WppDDef3Tn4DVq+3rn13THeutol9Z1Dl3rr3+YfCKQ/Daa859CY0SVHABwF13AXeOuzTv7XgeKl0OuGvMefiXXRTYKVPw0KTfeGwwl+OOA26+OXA1JCKs2NRNumzq9rPy4HI7JvXFNgeX7MNgDFFoM6ebt6fX0sY2kmOX07vKEdstl+vTwoVy5cwIYd++h0gVJEJ4yRHtwpdfAl8Obq+sPlI8lObKqpyJ4gJeNXn6jam0JI7vG4MG5aVtUp748eByWsI2xTokwv2Y2bzZuVEXurQ3ZUU21PFj1d448rP7ch8cp4W4B+qaO8oJ1rJNwtP/RENmsWQnSIvLKLhoclk4HASYDQ3A+vXZuZRS3Hqrcy7GfCc9J4VF22Vnx/1240b70060PygVxsqUr6UcllZlhZUHVyEteOfOtdwskEfzfg+amMbNuQthlR5cxiFeFxWZDTNnAq+/DrS02Nfp9jtSObj2zfUgEQIZBZcxLnpSse1LiVemawPjz545U339x3/2d1x+uVzZCy4ATj/dfr8QiKYyKYp9KjIMdqfuRGAiX7wYaEoF0mkqXEQdkh+Mo7+sgapl/m07vZVpTrI1HE1VMH26Yx+s8muambRhD/z0U+a75e9KKbgc3vOv3T83tKFlmwHyI9q27zEHl990CCTB7NmKKnKJO1vyy62S/4GFgSsrUjJYjgmrVkkfv/32idBYEVj/kjImiJCtRW+XEBaZSSbDi8eBb8d5j4l98V5Tsr4bF4Lz6pIxOSxe1FU8SiNW7Ov9nCQHg9rWLbCycdvMZkOuj0O6ZsYGy3NmZsYMb30g/nExJGhpAfbYI3f7yy8Dzz+fpz4VmHzPQ+XqdUAIgPAVXDaC7RwjDJUvux7qWlW7BXbsnC05tjw/bgOVVw+uF18EJkxwzpckOXiJrbfJPVRoGcMbg8vP4pptAQCXfnejRMXZ55GCsfzhRf5vtOOyJIovd1RwBUIIYJvcx9z5ABWoupe++05NPSQ0jJEC2nS5nHpWc4atN5XJg8vVUO+zz5z3Syh+rhl5LY47zrma1DOQ7nWQNZXHcIJphMPs6zlEob8ukAQHHww0N7uXc2PA4M6O+9+fd5Q6ZVoU4Y2oBK6syo1ye3DmzfNUXNez3zei+D5EShvHBaHL89tr9smOllY/fl6Fs+88NEj3Et2wWlJWVOC11wyPnBD2yXA90L4iLrdItjg3d467FGcNuj27iK4DQmC3LhkrIV1mKnSVnhCv2N7ONt4N5US+vc3KIXqHiuXOxImJ/1wLlBgWN4eXsEGBsVNwCZMHl8o1uwettoZcQZqld7jPNo2bdaHllnPSbLj9jtQ5swlHXN/WKfHlrLMSMfbmzcM+zyRihq1p2ta5bmP9JFLoOjBr+VaW+4QAB/FyII/XeMZS63srEOWwECtxjLNBS9w+56Cx7KTVztEH7FsAtu1g8y4qMb/v1LkOF1yRUSDIGmekyxmfr7SCy34+tFTk6R7c16T6ZreD83Sh8XvKR47MRIe/+MbtHcv+edTluOcef+2Q8oEKrnJDxvojorgNnC0twV1khXBeHxfnmSNRxWqh53ifC+GYCPWbFQc736PvvC3dNycs+1hRgRtvBJ58MrNJxatmlw6t3gWPNicxvdg259AwntM2+YSktY0dvPWL5B2n50dFTjgAGDUq/7IyVX21w6/BZBSQfYlS4Vlx9NHyZdescT6vzz4LXHll4C6RPFFQDy4bjF7GiQ3heHBpeu6NbOXp7FqlTYHsn5j5zY1tHbBu81b2CZUA98ErFaLQouljd1uFDhVJoXJlJfD44xD/fda5PhcoRyswNpPvqlXAIX851fNxocKbpzgQAof99ZTM9yjeSyQUjOvM7ls4x8tMvb//X/+LsHixuR7nY9w74j6WVG3eBgO/yeQkcj3E7JJjPCCl4NIs9iWRXlPJGN/YvJvbRmDJ1zqKKOfUU4FnnpEvn3M5izQkCaeR/CEtNdQ0rVLTtCmapn2V/N5D07SfNE1bqGnaR5qmdUhu75j8vjC5fy9DHfckt8/TNO0s5b+GRBYVD7Hb/DR0aMJF1q3hRYvsIxcKwYgRJFzcFoROgu+YXmm92EtO/hX5VNFqubHFVeTg6tKuWUrBta7WkEjUTsFlOLfGz+lzPmlSIpHT6NE5x7a25tY3fNZOrv0iDhTporQU4PueR4T7vbrLLsCrr9rv79sX6NdPYZ+IfywegJdmnYoRI0Loi4G0l3EKlWOkFw8ui/vdcszwubjP+okGpd7/Db8OO737H6C9Q2Jwt8HLkIPTTIfKODqkcnAlF/uBFeEcTO2RPTeFyEVEaVLpo+AaazELQbpNkrmWFuAPVyiIy0WKFuO7ZKdKZwNJ41xj1tfYKbKy5ifFc43r3Dd/vv0+KQ8ui21WHlwua5NB/xyJe475Dli92lSZsG/d49qpFEMNT58OPPVU2L2wZ8aMzJDtJbxhzmPw2aeJ/9OmKelXqHA9qQQvovzbAMwxfH8KwHNCiH0B1AK4Lrn9OgC1ye3PJctB07SDAFwB4GAAZwN4RdM0uWC1hPjFNFBoGrDvvsAJJ1jPfUIAWL++MH0jZY/VgsotRKHTEsw2/vfGjQD8K5yMSrWffkok/7ZDjJ+Q/KAmROEN316CqdW7uffRuMy19eCyJn3O330XA5b0hP7eBzllRk72nruMOCM+/Eh5nRs3Ap9/rrzaglLQ9e3y5QVsrLCc/83NeFuN0yq0Frm3L7vlw6RJnpxDSb6xeMj+M/1M/POf4bUPJNcE+QpR6MWDS+iobu6Stc0qRKFfoZDRCcsYovCLZT0TGw85xFe9iU7ZK7gAoENl0oNL0zB34074ernHsM0UQKhn4EDLzap0UpG9ZJHtWJEicT5/+1ug79wT7AtszihbdT05b9tYxa5eDbzzUSevvSQRZ8IEoHdv78fJ5uACPEQikB0i/BjDBBl/kgYiTka3Vko7yzWDS9+feW9nPDn1HFhZINl6uHlcR5XiSPzcc8Ddd4fdC3uM+spAU+HcZG6MMWOweXMi8nRUKNaoacWOlIJL07TdAJwL4PXkdw3AaQD6J4u8DeDC5OcLkt+R3H96svwFAD4UQrQIIZYAWAjgGAW/gZQJKt8Dli5NRCexbGPQIKk6aAxI8kGQHFw6NOsiHyQUNpVBFE5JF6ZRo2z2//BD4r8hprwKD67Ztbvg3QVu2W6BdhXuvy0tPDSdpLSHmBC4eOifMLs2Nz56xYZ1ch0ucu65p4ApsMaOVV5ldTVw0UXKqy04+Z5e0o+A7QMdXZ56Cth7b/dy46r2xvvvB2jIh8DAbl1w1FHAggUB+kLUYuvlW+B+mNvP2RBeiMKYyF4kW4YodBupJEIUDlzWM+dZO+rGI6X66dimxfMrdJHxZNc0XDfqapz/7S3+24KNRTrxRr11aC+VXoOEAMCnnwLDVx9gX8Bwz/35z8BOOyH3nrOIWEFKh3vvBW6+Wa6s8RZ4bMqvncsaCuuN2bm0CupBlLTEcr1/rQRlKczPhGyIQqtGXdbaaVmChWe3qhxc0qEgiTI6dvR3nO2l1XXccw+w115+e6Qeu+f6hReASev3KHBvygdZD67/AfgngNQItB2AjUKIlCRzJYBdk593BbACAJL765Ll09stjiGq4MI/G8kJLlWsuhrYHGdeHVIYrAQj7h5c9vf0MTssxe+GX5+7o6YGcx/+GIsbdvDTzWTHdOM/W95Z8Iv0Z1WjUatL4l4gswDWdaBqo/WqKcugy7A9dc5bGhNTWsfK3MTPFVMmOVdo4txzgQsucOl0BHnySSjzenHDbuFntz3o9FYss6MQ+bf6EmvW5rX+fBKPA0uWyJUNJIAyeBXQCo8UAiGyPbiUKk+8KGwtykoLq2SqN9dlqmfSvGzvMU9tpjy4bEb8tLBMCPv8HRL1lzvG09CvH3DTTdbljFfhgguAMWOsCuV3dhYi/22QCKA4H8LEiUBtbW69Hw/bLki1aZau28JzPST/aI2bpMsaFSOLG7Z3LmscDU2x4+ymFWnFl5d56Ztv5A5JKrgs17/pEIUOXbLy+rZq02VtMq8umQ6gnUkW4JSXnCEKSxbb+zYeR3V1Qbvim9tuA/rMPSl3B9eXSnBd2Wuadh6AdUIIC+meejRNu1HTtImapk1cz1BxxEAhnvk99wRu/eF3Dn3ITID932nEf/6T/z6R8sJWwaXrCe2DA9+tOgBNMQvljq7jwAcvwxXDbgjQMfvE7QCg9emdXVZRiEIAaJFQcKU61vflZnS/9bfWRZIL2BWrKlDf2jm9PXXOU7+to0UcdStvNKchadAg4Kuv3LtNCkcxKSnyLovzE3+ljHhj3vHY4lJna1wrKEMtEmylSQXKCygZonDuum6F6Y8Elvk0fA6pObKn2lp/FTlUbqccTM3lIhZH3GK9VV8np0BLf6VgDJMmAX36uJcbOBD47DOLHXYDZ31doH6liKzMKLIdK1+0OsNY1NCQ3Jh9fz76diJsupfLJ9pyDed+ee/JnvtH8k/FimSMs2R4fyfO2T2Tn83V9sLwuTUuF84wq0qnBrwodIwxgp2w8eCaORPQLjgfgPN7lVtO8UxB576vauya+NC5c84+q9DJ5jpf/7SrXD9KjGKaXvz09frrgTrTEqHo38GK6aJFGBnTteMBnK9p2lIAHyIRmvB5ANtqmpaSOu4GIBWgeBWA3QEguX8bANXG7RbHpBFC9BFCHCWEOGqHHQJ4GxBrwnhwZs1SVpUQQGOLhLDbR71e+WzwlvjHP5R3hZQ5tgvCRYuweWkVYg4Wx3E/1sgS1LZkrAxFnXUomSyWLIFbvjBP7be6WzmmhFnrvxpnW2ZR/Y6AENijZzeMW5eJcWZeIMf13PNoqeBiSINAKJ2OWlqyvmpaIoFtMVKIaVo0yFuoFjNO51IAaGoybUy+HdW2bInNMe/e3EX/clUu2N0Yy1dYby8Q/5pwYdb3WSu3UVe5h4HFyovZ0ttJ0psqp664abtVaPDp0z3VKYNR6XXwX07FhPU9cspssy0fYmUEncxUmmNzcC57vh2dKyDPwZAsU9TUJD6YFgp+bmvxQb+cbWtqO+Onn7zXRfJLOq9USsHpwK5bbkx/9vLW+9KsU7O+20a0kH3PdPFcziKZSsD1Pq6wliksXZr57DSsqvLgsu2PkweXoaEbHt7duoxbv0gg3ngDeOUV+/1Z944Hp5bUterbF5gyxV/fSGnjKg0VQtwjhNhNCLEXgCsADBdC/B+AEQBSZvJ/APBF8vPA5Hck9w8XQojk9is0TeuoaVoPAPsBGK/sl5DoMmCAkmr22gvo0QPocuOVSuojJGysFn+2Cq54HNu+9RzeNYQAlD42IAOX9cwsnocNdz8guQhVkYMLAJa6hH3Ianpjjef6U4K7zm+8DABYvinXar6dlmvxlq/zTeQYOdKg1zKbcSFaiWajRrm8zLW2AvPmWe/7cNEx2HLL/Pfhlcc3Kq1PCHkDXGKD0wMQy1XuFBRD35R6nXrJwWXRrtV859uDyyVEIQBguMRaw4rUWsWiyufHH5f22pqzcmtf1WsXZsceZg4ub/zvf8A2WycFmlVVif8SyqdFixJ/fhRVDFFIAODsP+7iqXxaeJ4z4drfS6efnmNvlajrB+ucs+NNkrBYDFi40EMniXLS85+E4sX4Hr9th8225aZOTeSUTtHQ1slUjwQSE67UnOzi5exWuWUbQXJwNTZ664cMqWsnm6qEnti+sTvFf/4zcItFitOffrI4ZvJkf4YDpmM4zRNAPgeXFXcBuEPTtIVI5Njqm9zeF8B2ye13ALgbAIQQswB8DGA2gMEAbhFC8BU9BAr+7CuSpK1fnz+BpZhmbSlaTCGtSGlgu8iKxdCq5yZYNZJXhUvyObYNB2CkQ8LzQVWIQhlSw0ylm1LNZhE+eXLm+yNTzs0p061T7gKcgq1g2Obakhx3Tz0VeOcd+/0eQ7BHhkLk4CoXDdeYMcABDvnkZZF9abIqN27A6uAdMHDvvSiIYq4cEUD4Ci4DQsuPV7ZruxbDg6WCy209YDPOxHVrwdfle0/IfG9tda7bDgfh3YC5B2Ftk0KvOOLIF19VWq4Z6xuS9/Wbbyb+SwywBx2U+PNFg0TkAVL0qBZS262SZi7ewnaaGD48IcA1ernE48DRA+6xLG++9V97DdhvP89dJQr5aV2uZ68dxnnx1oNH2JY74gjgpjG/T3+XXYHL3tMff7s19LjI9Y62QssOy2/fuE0o5dVrMlUZfonZEHRja67HpGWVk5yz4HSqtF8L2P6E1AsgXSTzTuqamq+t2eFu0ybg+++B445L/Nc2ZzxjX5h1Gk44wVt7OW1qWvEruMrk3TzfeHpzEkKMFEKcl/y8WAhxjBBiXyHEpUKIluT25uT3fZP7FxuOf0wIsY8QYn8hxDdqfwoBEEnVdSweTp9qaoD9/mDv7ZJFr1757QwhFlgJYGyVVBJCN7uE6deNuspTvyxJW25JPM9CKA1RKEWyf5U+lGpxUYF6g/wjZhGiEFuUtkR52bLoTB9e7hynx4KWXfZwDe2NIArH9g3evUqdmDTJ2kKcyNPn2z0tt0+v3k2Je5wQwJo1LgUsOLH7gvw9nB7qlVVw+cWyrngcndsZ8l/a5kmTM2KxKyZlpOOBY/5+otL6osCgQe6PgcztdOHvOtuuSwFkctxITM6trYm/pmbvSl9RU+teKAw4EatFwfm0nOst6v36K2Hb3MknA4cdlvn+r38BU6v3kGpfIu0TyTM1LV2kyxrnsutHX4ULL5Q7zhwK3+7dWvaWvvyFE7Dy5S/kDPtS463PEMOWiRQtyv53+pkSnXHPR/b3w76z3uEYgzy5zy6Eg7m4VCnixrBhmdvLrOB65hngxORyKRYD8P776X0t8fYYa+3kmkNVFdAvGfFVCGBzLGP8zfd8AgTz4CLFSAiL6fb/vg+PP17wZrFkCbBwlXvuHoCuySQ6pBbLmzcDzz5r2CEh0bQT3LwxT9IsRgIpIVdynFEVolAK2TYtQiHoqMjK9xHTcxfbYptt7ZosCZYsyXwu1AJR9vSNGwfceKNNHRLvN8VGQXJwiUSqiXlr/IXpIiZqkwJUg9n21KkJxbHqcbBY7+socVOvIyy3x0SlkhM8YgSwi7doWACA7lvUYcKkzKvZVh2aA/cljZffZVHWMp+GmxezXQ4u8zpip50AXc+eE/y64NqZEyfp3tnem+dXu8723NyMZaU3hp57bmLeTbHffsAXX9iXLyRbnnqM52OE0CIp+XrmTffw207eCyQb9R5c9vWJWNxxSG02DN3zpstbpHB+D5+9t0rmA5K4GMa5rDnewXqctHjvbF8hZ0hjvgedetQwZip+XL6rVL2AvAdXOidZyvPLw3N22WXAr3/t3OaCuh0d60hFg/l+5rbS7abXD8UayqOIMC655s9PfP7Zz3Jv+xyDVIvUAjJMmQJcmcxWs24d8KfvM6lrIjjNkxCggouULCoGuYa2TtiwIXg9hFjhZCU9dizw978bdkhYlZstwpTiJYFtckFZyBCfmRCFLovZmTNzNulCQ8vgTGiJmIXFsaW3nUtTXGip4e23E2FbvGJ8vnbcEXhj3i/VdSqf+Fz0e0IIvPQScMBdF7iXJdjYuoWtkhUA8PXXAABt+rT0piOOAM45B6hQPA5SAJZnFJzgmlWbfdX1yeKjcMzpW6W/b9UuHAWXdIhCn6cqp66ePRMKLuP2gAouO+Xbjp0bbA+1Mm4hiZxAOSnRli8PpS9+iOqQ+c/ndnYt06myzbUMSVLARbemO7+TZXVl0ya5cuD8HgX26JL0upfJeSXzTpwygDJwyd6Ts75L1ePSn3snXIhT3r/BvZ6UosqrgUpaDmDYZOi3VW2ffAJ88w3gJExrc5l3Uy2ceOexjuWy8KjgkopOU0RoWuLdOfW5qcm5vGoWLChMO1deCUyv2T3xJaVdK2Y4ASiBCi5Scsyb5318sJvk/z7uUnTvbihHTy+SZ1KCn5z3NBlLsnzenxYLW8eyQoTiweWHuNDw78nnGb5bTI2SFu2lQCwGNNjIAJub1elfVLxQOF1243vN+vXAmqZtA7dXCETfNzIWk/lqA/bXuOxocxcg6qLCWclq8xI9Zw7QZ+5JPjtmDd9/8oyKE/zJJ4n/U6cG60pYc4xdwnhFN5/l46LYg8vu3Dn9hJFr9k98cIwvWfykTu38+cCECdZl6upcwqUNHqy6WznoQsOKFYnPQXQXWtAKQqRU15l+2dJK6e83X58FxrPtNFZowtmDK4sK+WsoFi5KfFD4m4g30u+uEnOQZVQTifjoZsMn24i8rj3IMLV6dw+l5T24zHyz/JD05/Tvd6lM9O6T+G8hb4vt6OzubmssKxPCQ/IhLfVxNp92kx5Pdf6Ix4t1ms8Q+kksDajgIiXHAQcA48erq09BOgZCpLFVcPldaKvCSw6ulAdXARVcqTWBpXJKgo0tmWS4Vp5wVovyUl2HPPMMsLVN1KWrrwa23bag3XFE1xMKuV0P385yX1FSAMt4oRetvE89EycGryN5MmW8VmVzNNhRquNOFNi2Q6OSEyw2Jz24JPM/5LByZbIihRc7qAeXVYhCN6GQbIhCAIjHs/I1TZpiM5e7/Y7UwG9TTmqN8Pnn7mWKkBdfTEQGqEwazJ9xBnCMTcS/c89NONYVBJvJaML6HthDLnWRIxwyS5xBgwConxtT45H220ty9rktn7JvafvSOR5cqSgTP/3k3kGSF1Ih8byGKMxsNL18WIxvxrlz40Zgft1OlvV7MQRcvin3PciSVHIknzm4vq/aN1NErkW0rd9oW6Xbujnr9KXWRg79A+C6Dih3mprCt+PJR5SfSL7X8h4sOFRwkZJk82Zvg1ypW26QaGI155329h+wcqU/Dy6/yh0pku27KdE6VrZlPLgKKVaQ7J8dxvOdDk9hxEJb4nZJ4nGgutpXd5Tz6KPAzTfb75ddfy1apKY/gLpxt6UFWL3WIm9aka4pCzIfCRHNF4EwMFqx+L1pNBvDBAuC5rIp1vs6Mji4pWzRrjU0pVIWScmDykvda/RB0mWtum0laHMNcWTB2rXAqIUWeUJ0PWv+PmrAvzzXneiUc4hCqXWShFdnFionRh+0tQE1FssWM3/9aya3669+5W5LsWxZ5nOOIL7A702B5ytOeCWB5WUMQVLrdjtl7XcomxuiMLmB1rWh4cmDy+rimo773+tb5hQxzk433aDjqWlnW9afNc4qXvx59eDSfnMeBg8GZtdmPK50SbmD3pBIyLSpuV3OPlcFl3H/wIGmLto8XEL+GqaLm36vn4hQfpg/Pyt9b14wjzM33eQvT6wVqXP0wAOZ4AWEhAkVXOVGGUlGZN5lzjkHmDbNvRwhhWTCBECrSWpGUpmKJRZpec3BlcRNqCEE0n0taIjCJH7yFZgXyGfsNie3jE8Prr339tydvPDii0Dv3onPCxda5jzOIWzrLhmEsB/ry2i688zYdftg1aqwexENBv3YlfdKGbHpmVdt97WviCsZOH5a18O5gE/L6SD8qV+wUJm+QhRalL/tNuDW/qdYNKDniLlSjnBudVo2bbNWkTKCmTVLqo00IUt17rsP2E7SeD/FsGFy5YIq5FVB/RSxJfnOoVLp+tBDwOqmbWz3a8L5nUzWg8vMg5POT3yooIguLNK5nKUip1hcJ5Ny8m8P5t5HxvfO+okFzh2UysHlwwP7nHNci1iypGF7NDUBB9x9Yc4+N1mBcf8RD18oF6XJaw4u5K5vDjgAOOoo4KqrpKrwzf77A8d6SC/mRkuLxcZ167K+5uP9/rnngBEj7PcXYg4v+nUCX0aVwNmzhBACaGzJtYwwFypk2LCoM3hwIjd8Y6xj2F0hZYjdPBabMQfa228lvqSslfxakqki2b6bcMi4SAwjRGH3LeqdC0qsfuJWluo+f0q9S3fCYL/9gDvvdC+3yy72VuEqFpEq1nGlqOASev5t43/zzS3o1SvPjRQJ5/7zYM/y7Bw8hCgMSuq+1jSm6fBD68p1zgUUxDbdodOmYBV4CQmcB6wMOixDFPq43SvX2UhWdD1HWNinj/f63fJB9J13gvdK3fDq8aUYo6cVkPAcv+MONXUHDakaGYpU8hXWGBBVLOdYhdc29d7y738DNS1dHEoK+fHPgwcXCZ+0QkXiAlsWkVhDGFf5Wtx+/siq36U/O3WWTLQke9PJ/P7U7xDCUWE2o2ZX+2nSpR3jMz+1apcs4wzbNhWFKJw8GXjvvUBVSNHWpkbptHw50KmTxY6UhWse8HOKBw4E+i85Um1Hfv5zoMUiR2PYFKswooihgquEGDgQ6HLtpY5lJizsmt8wZkXIww8D3d56VqosxyhSCMToMRnrwNWrE//DzsGVxE303qa3w73/7ZZY7BbyxVw2RKHVyk/TsrzfLD3hfIQojAo//3mO8VaO4s3ut5hfSArxm1vi7dKOi24UbZ4tF6Zs8JYsmgSjWJ5lILuvlh4uxJE2PTecaRYKboaOlbHEB7/a95BvyHk1O+RsMyqfGhqAFSuAqjorSYoB0+9YtgzQl6+0LWeev/1E6VKyDtoyN6SUIyFrms23y8iRCWvqUoKKAOJGIYdNDYD4ZrD9ftn71a7TvOFDIx1eX1GIQitk71Uv5m5e5z4Vz4tsmy1xewN8x35YhFNPl1eYg0sky771FjBlitQhSqmtVRMy0DYCt09r2+Zm93WYn/vof/8DXph5uq8+2bLrrtAmTFBbp0Luugs47TQAmwIawBFXqOkoIVZavDOaOeauU7HAJpFlKeFk1W/G0pWXkALgtCbIcdn3m+xWFWmLcveiT/TZPn/9sCH1EuB6DiqtczXdeOCY9HcrIwCr310sypXJkzOfHdLPeKZPH2DUKGDqVH/H2724/fqbv+Dgg+XrKTU5gICGZ6afFXY3yorAL/rpHFz5lbAtWpStdC61e78QFELBFdiTLw8ht7xwVv8bcrYZQxRecw2wxx7APv+42FO9e+0FfLT4aNv95rNmOce6XJ/UGiDQZfR6cMj5cgoh2M/XWNPUBLz1/b55bV9AK9rBsr6tc9hdiBQNETkf2uYm4MexsqXtdxkHOeODXKT3aymgefDgMnsdb9uh0bMHl3M5eaQN2GVDFEr8ju/XJsduiXNld0t3ahezP8ii3qxHxu44jyEKdVEBCIFrrwXuvlvqkFBYvBgYM0bt8PC3vwFvv229r3PnRL4uR71RnYvnYAEtDwoRRcMvgwYlQzguXGhfqJisLSMMFVwlBNdCGaZNyxbqFjuy3gykNNA0kZvkNmwPrpSCS1bgtnJlKMI5t3PwxGc/s9y+ZbuMpjtm9ZJgseiwXYbU1jr24ccfgfXrHYt4ZvZsIObwjpCia1d1bd50E3DKKcARR6irEwBiohKLF8uVdVoLFus6UTDuXMFRpazO94i3776J8SPdHtd9nmnV7S2JNQ1qFFxuik63NpIKkyiNYcauVFXJdWz+mq0kK095cGXPvZN/8j4Wpg23gzyN225rvb2hwXp7AS6UrtvLkcK4TyzblEnuaeLrr4Fr+7qHjQwy1hUiPy0Jl1WrgJHLC5f0Vvt8gPN+4/0qG6LQmFyIk3s4CIv3bwfM75wCWvZxNsYPxuHTMo+XD5QruFLpBhyKDFpxqFSTTm1NXbOT/Rym6znnWGa9Xr+pQr4wkj81SgsuC2bPTuRAOylYOtUsvvwy4U3Vq5e9o0TfvsAxx9jXIV5+xbkRGeGECiI4Zi5r6Iabbk58TttXO/Uz4vdgscAVXwkRwec6NP72N+CPfwy7F+ro3Llw8wOJBhXmJLdSFmF5HNLTCi5JCq1hllTA3fvuQTnb/jf9VLwy++T0dythiFW9tiEYXay5f/lL4PbbHYt45uCD7S2wgpDPtZaKqh37VywudiaYc6N4OPJI4IorgJ73nONemESC1gJ4cFXYjG4//ggsWSLRRiyGTz8FTvlKIlligUhZOAOAWL5C6phVNR68LYTIEWT1699B/vgkSjy4Tj7ZevsXX1hvL4BQ4uWX7fVu5uZDk5FE1BpvY2tnviSXOH/+M3Dmh8Ff/GXvEq3VOfxL9u0m6cG1dq1k6ySfpK+WhGdujvJFmBRcqTzaJozrfCfDzKz3AUnv5SjipD+68rPfYqydM6QQaF+RfR3i1RvT++zel1as65guI9c/Q060iJ7Ggw8G5s9XW+f55yf+jxsH7O4SGV/TgKoqix1uIcTKWGnTqrdHn9dMMqWo3mAlBBVcJQSflzwSgcH5/fcTExApHYSN/F0ILSMgSy2wQwyBs/82a9O5XqQtzTStoI9Nqi0/C/xxVXtjek1mZScbojDI78uHwrqpyf+xERjilCOGfhd2F3wRVliyckaYhltZpkwBPvoImLY8uGvkhg3e13Fc93mnICEKba7LL38JXHaZRBu6jvFjo2XVtLAuk5dLtNplizfh8VyqmIaUCPna2Xj52SW9K8AEKhOG/r77EgIoO0ezIFTPXZflYGJJHs9DuYYoJHIovbwSxlGa5pxn2OjF69Q3baFBYs0QhZEgbWAqMZ6Z1+s5Ci4bzY3xOMcUVB7eBzx7cLn9PF3H8uXAovrcnJw5uFS2Y6cGiGXLbfc/8YR9HzpUZK+FxPffu7fpMURhXFQUrVGkEencbj6n6urqXPlFlN5Z8x0mPggc0gsHFVzElU6VDJeEmhr7fQVyrbrmGuCWWwrSFAkZAS0TIiECCq55dd2xxV47AvCwKCq0xsQmSb0fckJOCOC1abn++bY/USZuex7W0eW4eBJLl9qf7hX2L1NRphSVjVFH14Hp04F2F/1G+hirmPR3j/eWkyhFfT0wd27m+9y5wEsv+aqKuNDqkOwcgBoPLs1lgHdp45y7D4c+anTgfqjkvYXHpT//WLWP1DFeT6WUAY2bFXvy1TaQ0MWuDZvtIp5/wViFw6lJdeuxx4Du3fMTweLdwTvi2GPV1yt1j+g6gqg/OaeWOJqmdv2bEqA7Nemyv6FBw5VXuhfWjNEuSkDAXvQIw8zhI0RhXFRkv6/X11set7JxW0Md9oN7jneuw80kreCSqCvFsccCI9fs76leK7p2bIJ4403b/XPm2OywSg+QPOfHnbU1vljW0/owXT7MJADEI+z9pgJVU+DZZ8P7OkAInHQS8Mar0fTwLjgyizkSCCq4iCtR0syHhsOAc+ElFZg2LWHVUOCmSZFjd211oaWtUNKCk4jcCLrseKDrBR070h5ceWgzFgNemfKL3DbdOmP3HWr0lZdfDjz0UPB6gLByeAS/VmLCJNsXyGKFc27hEcIm9IYDTjHpvXLSScCJJyY+//OfwG9/C/zlL+7HlaNSOyiOObgg1HhwOeyTyfUweOL2iLdGT9i5ZLHwdM9lCf+c3IpsDFTO3n0mHnnESw8VhSj0eLCTDO3bbxO584JSDDKR4aNcvCP9MnFiIGNCzqkljmoF16xZ7k1KiIz79cuUloIeXJEg9f6tHX0U9JjzPGyes2J6pZRS5Z7xF2PRIus6/OK1Htd5QwhPaRUdcyJDg15rk0QSQLzRRvlhIUtIff9pUnv7BnVvspOYXhnKRDpqZEQmb0lWrMjNQOH6C4TAmDHAmppO+epWmqIYNTm25x0quEoIPi/5x+ol6YuBFejZE7j55jy1WVxzH1GAcZGaVoRE4UbQdXmlhK4rsxjyghKliek5t1NG3fzBybjqqtztw8eYFt1tmXBOqXFaVsF1112JsENWfPxxfvJuGcnXvFJVBczZuLNc4XXrbHcJAeC119R0ipQ1Uvd6i3PeDekyJhYvznx+5hkp+RrxSXNBPLiEbV2bN8u1sX5zl8D9UM2atQEEaAMGuBY2n5XBKw7BAw+Y0phEMA+JU5vDhiEtyPTL6acD771nvz8Ky0MAOP23wUO1WrJxY6DD1zRti2Xrt1DTFxI9hDfFuyuSlUkrTmX7RgVXJDDm0Gz57GvHsjkRP4BsBddOO9keu+++AGIxR8NM4z02e0F7VDdvaVvWa4hCV4SAkJlcZEI5ChdFSOMm6+0WsgSp+S6VL1TSuzpmyL1dWwt89pnUYYE55dTsa3HZZcC0aXLHLlsmH46YuZ3Dh0N64aCCq4Tgg5NHJGbT1jxFcozKiytRj5MHV4oQIxPmMm6c/AtdSKE28iHcim+0X0FaCZ1yhDwWAm+xbr1U208/DTz1lFRRPPYYcMopcmX79QO6dTNsmD5d6ji/49EbbwBDhmS+X3QRcPKXd0odq82d7dynJUustxfpgn7d5q3C7kL5sXSpo4dEmm+/dS2yse+nrmXmzMmOfMx5vnA8MeUc5wJ5DlE4e7ZcG8aQgFFBxmvBSNbP9KH4TXHBBbapTHJIG27nw5bX5ro5rdOCLIV0PWG/MXw4sDwZcddK1yNWrPDfiE9cz6/ED5e+mxQ8k2c+elLgOkhECWEC9SZzsS9845ir0jZwH/2wq98GiCqEyMrjo//wo3Nxtxxc22zj3N6IEY7vKsY7++DzemBq9R62ZaVzZKeUP25juBDK0hIIaC6/Mzc1QCwGfPJpBT5f2tOxrCXJa5AOVehWHJnrNn48cMklUocp55NPgM8/lyu7117ATTdlbws8FPrwlHZ91y6gPCjKObhST/OgMdbv+Cd0X8CXQUVQwVVC5Gst1BJ3cAEuF4TA1KnAKkPMZDNO51/XgTp7z2y3pkmZYVwIxmP2luAFp7FRvhvxeEEVDPkMURh/uVfACnKlX2L1GunDHcM+GPatXQuMGiVfZ22t4fuw4dL98cN11wF/+lPme1OTmnrfXXAc7vjxUjWVRYT9Pno07C6UHx98ILeGkvAkqJ8wz7XMQQclnokUURjey4XL9pnoXCBfIQqN9RZprhVt5Ajfx26usLc8B+B63ueOtvfkNaIkRKFHnC5nkH6sWwfceGP2tq5dgRkzTG1syFOM9CB8951rESE0T9b4QYjpVBiULELIGahIICsg1eBB+O9CczI62xX/y4RCX1vTAQDQ2AjsuaeadogEQmQ8sAHE23V0LJ7rwVXhaX7/7b8PxcT19hc4n+/RbvdvY5OP0Jp2ReDNAPXpp4H27YHLrtkC36/dL6cuWfS4XOkg53m9nL2qNF7kucb3eCekDX5kLYmS3HcfsN07zzqW0bYsnPd0pGf51asBAOf+yVpJvU2HzXwZVAQVXCUEjX3yyxFHAM/OOMPXsb17A9tu61xG04ANG6TS95ASYXmttRWH8ZKndCORuA8qKjzl4AoDaQs2B8ynOr5idbAKLc5FGGGU/KDyvsuHN+LM2l3Re87J6ismZYcqAZns2KdCyRuJeaHI6NLOxZNIwdxVYSUkXbYs89kQtraY0KZM8lTeeH9ucf2VruW7tLe/Nt1G9M+t1IK0giuIqMOuDTsPLgflSZBntNImpZVZqBVJb+UpU9TVpWKgi1Q4BKIUXS+4DMSTp4APB4edf30Err4auOOOjPcmKQzGy6VXOIc0tnyXM1xQNw+iT3/ojs3xDl66FxzJ8bTLyT9HY6MaJZcQmuOcbJ7DZs50rku2P8MWymmHheEYL4wfD+y4o+fDlOF33LP9qV6SriGhD6MjhBxa3Nk7LpLruCKFCi5CCoBRruHE3nsn4u8aoQCrNHnnHeC3b/zacp8uKtILwUi9k1dWyk/A8XhBc3ClnpN8PC/xoIsOi7fXDc1d8OCDcoe7LWCLxRmgWPpJyhNlCi7JQcj4XHOeLxxOQhYNUOTBZVGHcQAsUgWXZ2TPZbJc50r787LLFnJhEELJwWW4tOvWAUuXZr7nQ8FVKkgrIYXwHB4zp4qWPMWRJ6Gz38NXYfBgNXXF9QosXO8SVg4ePQWqnL1P7caId98F+vRxr37evEARYMuOmhrnSGzGEMPpKCo2WBp9GhVcAZcTazdvnbd3p7yE8bVBF5rj3PyLnRbb7rOqSxZP59/HxTKGGldFPpT1OT+taq1kweIi6DqBlAZUcJUQ9OAqfhoagNGjs7cV+VxTFPzrX8DXznlkPTN0aDKZvA3jx9vvEyJz3edt2A6xWETug4oK6YWl20tBvlARotCsxIvpclKmzZuT+VXMWGgpx6/vgYcf9tO7bJYtKx4hWKEVXIV8eSPFj6c11LBh9vUIuRtdhYLLy3GqBIClTIWmK8rBZVFHe4OVa6QsV+TxOqKmTuVXX0mWd9hnTALvhJIQhR4PNnpwnXkm0KOHXFXTpwPPP2+/307pnhPpQebKRGIRmc3Gls5oHP6Te0FaxxAHFm7YFg32qXI9MXLN/tjvgd+5lvMkSHXJURB0OjjgAOCZZ4LVUU5st51DbmORPZr6MpgwXNCg7yF3jrsUffsGqsKStjZg7Oq91FSWzullP0fHRCVa4vbecMfumJ1L2WGJLXVOv5i8GwBgi0o5za8w506TJB9TUzyeCVvqFelpXiLcutL2iOtd69eLkORCBRchinASjgUZrzjW5Z/HHweefFJtnWeeCbzxhv1+x7xKhmnwuHf+jP/9z+WAQlFRIb1Yn7xsu8K6WyfPj4oQhWbiknU++ihw8MEWOwKugAtx6QtxdxWpTJeUAZ6fsYEDbXd9uqgnUO2eE6fQHlznnFM+jkNOOJ1qTYPSi5FVlXEALNLB0Kt1bOr3u6ZjMlr12BATcpYca+q2yGpbKTaVGvN8pMIHbtqU3Ocw/T/5JHD77Qq6JVNonntuQOVtunDEZ/fjuj7HSjQWvDUavBCVaJq6eyq+aGngOurrnffrOtC9e+BmSoYNG+z3ZeXgcjHWtHzPNQz6KhQg+fAS6tcP+MtoRfmLhYB2nPM4/quv/4a9P3zcdv/fx12aNcy3OjjcyswG933SE3PnAttvIRcLXEBzDqtbQBnMv/8NdO4sV7atDZg/372c9FgVEW+JLu19aviKGK5R1EEFVwkRkTGJKCYKeo1ywClcQT7qdLquuilWdXU1sL7eOdFtQdDkxVtusYZVk8+FgWyIQlsL0gJbHzt5DnrloYeAt99WU5fxNBR0XFuxooCNkWLkbz9ehn//W6KgxI37tx8vA9bahP9QzMEHA7fc4l4u1e18zHPFhpPhhQZ3RYsXsqy/jQNgkXqkeH3PSM3Lk3+S06w6XZuvlh2aKuRYxwNfHZPVtiqqqoALXj3bcp/V5dwqmWLV0Zgpue+TT4A5c3LDmcumApMyJpJdGDhJfpPMnAksqd9erj4FfPL9ztKe9HbwXYqoRINQZhim93ldUU32xOOJMazsSVr5bFFvv0Yzvum6TdVu84yKkLmVG6qUG944KZDC4mRDOmUtZt9BITSp87F2LSBaPVh1paxSJNC0hGxG5rK4KZ+D8N13wP77q69X14Gn78tjx13YZYuNvo6jLJwAVHCVFHyo84jEDFZfDwwfnr1t2jTpw4M0TRSwerWaeoRIePIAzsJEfZV9g+YF8TPPAN2vP09F9wIj65WlLV9WWGuUVIgEBc+Lud+L6ncIVqHDG1I+PCq22ML7MXbXtXdvKAmlCBR+jkrfC6tWFbZhUnT8ULWvu5eJEbeBxmNCL+FT4bFiBTBypHu5VPX04CpMDq5UDVmXtcgVXFfu+5PvU1O5bo1UOadr03vOSZJ1KMDih44bBwyc0cOicHaIwlzlk3szl10GHHQQcMIJ/rpn18TPfpbotxszZhi+SMTDOvRQ4L6JF1rumz9fSkfmicuePkrag88OWkeTqKI3e9c26Lq/aaTsZQqjRgEA2k+dYL3fdIJ8OVsb6hC6gpDH348GFsvnqHJDF5ra+0BRZWPGZD5rLfYePLrQHCxKsxFxuYfENTycxb7f/z4j72lszC0Sjyfm1m22SXjMqZIz+aGutbMX/R3q6oC7Hts6fx1yoVW3D2dZqsgqbok7VHCVEFRw5RGJAWf4cOD00zPfa2qAnj3z0/Rf/0pLLNUsX574v88+Uu/3trS2Avffn/jstDDuPXAX2326KRBQZKIZCRFKAncZUs9JPoQYp331d7nG7XB4C+3QITfa2fz5wLXXZr5rVvlcCoiquSW0dRsnRxJB0rdlWxtETGKQD+AVRg+uDE7jUIXmU2pow4TF22W+JOs9uOuqolRwCaF5znGZEu5pzcHdik/dZV6qI85tqsjBZVmv/T477+Tnnwcm2MhRreo0K6BFo4fwShYsWGAQGjrMg4cdZviSirHok/33TyjsCCllVK7L/bxXnXIKcNZZHtrQs/+XLY2Nnoq7vX9bzguGjcremRW6XAldRF6OXunwfMm+58c3NkiX9ZNSYfDgjAFJly650U4++igzt155JbDrrp6b8IzddT20/4M49VRjOeffG/b9sbTBn4e41zDakSPsE18iUMFFiATazt4DVxsFSao9uF58MTenQXMzMGuW/3bKFtMJXrwY+PZb/9WpCMNW1bQ1jvv8Hv+dyBe6Lr1Y1DR14TsKjfG6KQn3l7wp7O6HJdm5dfH558BbbwGHHpo4IBbTsOWWCvpRbARc6KXvVY/eNIQUlFhMblwdMsRys4z+Vt+YCDWi1zonuy8HHD24lCnzExUd/9AZGQfSpKRM95nMPGz6LToGfedKuhglSZ3rCs0tzlMiNKTTiP+bPadLtZkSKAYxdnn5812xcKF8eaMHl5HbbwfGj7c/zjzFme8/8eZb8p1QhYKHwKMMuSAUNCcsKXlU3k1xUZEbn9SFMWPSzkgA3JfLqf2RMZiMKkKgU7uMpYGvEIVGDy5Vd4ri0MlR9OAyUllhf+Jdva2S6K/JWysLAA2N1u+KGuQMn8yPsNU8mDW9qnZ1BoDRo213eVnTyFKq+pgt26nPAeZmFFGipzIUKPUpIWikHh3Gjwe+/lqu7OTJwOP2eTdtJw/z9X76aeCQQ+TaJAYM2oXUOf3mG//VGV8e9GX2uX+6drSXAMyv28l/B/KJrktbo4U1HKleIPzhDwoqcVFwmUmVmzkzcxab5Ay584JxrLELtzF3bqLf9fUK5yIhsO++wLsLjgtUjZ3wkZC8IfEQpIpMm67l3TNWvJ540dffVJRQr4hxGoZVWX8ahVrpNUGRhygEgNfmnujrOBXnNabLvbKquIK3vvAzPPecfHnj8ys7z59yioQxlaRESkp5IzEm6TqwsUUyu32wpggpajQIqefulFOA0Wv2cyyjC82Xh7aX5yw1LhXp1FNQjNfVSSF44YUCD0+2SB+g2INL9XutritWTORBy3HgtvZhjXVRIdVmXFRId21zrAO2vuLXlvuEpLjc/Dy6PmuvvSZVryfG2Cu4/IwXTvz0k9qc3ypQ5Vnr5EHol6L3LisiqOAiJA/87nfAH/+Y+e40Ufz3v8C//mW/XyywfsE1OyV4ia1bbmyzjUMeAouL49fCbcSIhHdduuqJk2zLtjkkzI5qGEBPi9jOnQtqMZsvK6KlS533X7HPeOkQhVG2dJK2MlxjHUT8wAOBoUOBdetkG5Q4GbqORYuATW2dJCvNZe1aoN1xR/k+nhAjQ2d2x7RpwLtzj3Yu6OFNsudxnRCXyS8TwBPxd71PAeBsdOHEl18mBHWlTj5mrPRQl5oHoCHeVh5SRpH88bttuVGmsOOa4ebvf58u51xN4UMUxn0ouEaNyk0+n+PBJdkXpya9nIeXXgK6PveA/AGElCmyU/yoUUB1SxfHMjr85V7xIrBOCds/+8xzMyVJi12OH5MnsZOS4osv3C+AihxcqimGfIS/2nWu7T4hIPW86EKT/q1OMplMo854VnDlw4NLgpNPBsav3ytwPccd5+yhHgaq7uzGWAdFNcnDHFzqoIKrhKDFXHTwkmTatS6DxiQWA6Yno7TwestTX++QB6FD7iQWj/kTPp12GnDvvZnvdoqqeBy4ej/7zN9e81wUDF2Xtj/R9HDiYKhQqnlZ/HfrKOFalQpN1WZ9TnKEWgrWN17rcCqf1T+HFbvRkuvMMy0KxA1xW1evsijgoVOS1DEiG1HImf85Cz17AlcPvcq5oAcPrqDY1fPgg8CwYYnPA5YeAcC/8cQ332SHQypmXOcIBeOOsQqzgksXGtpdfkngNooBkZwuju+eh9g4NqgIUei9zczrdJDbJ3ctoC5/yCV/38u1zAp/+m9Cyg4NQtkYE5f0Ts3pgw+PjP/7v0RKg3Lnyann2O7L8uDyEwHC8J6kxINLsbFoMYQodPJ2EZAL89ylfYt011RcJ8/v8l6M1iR/iMy9Mno00BTraL2ziIWLKm9DKaNDL5itmSygaksdVHCVEmvt3XlJ4dC03Jw6dqxenZtU2oxxspo+HTj88Ew72eU8dLIMGTMGeP11ix0WCq6OaFHSpt2CqV07YHXTNp6PCx0hsgQ5TuhxeWWYCna/7WI88EA+hFpyv+L554Fp0+yqSNQhPukv12IJPMtDhxq+pBZ2hjiLUr8xcA6uol6rkxJnzhzghhvyV//DDydCFxuJS47fZkopB6DrHKF4ALZScJUbsr9YxZlPne9CzqMy11Qm2qDsfOXFgyvFZ8Ps15yEkPDwa9QYjwMDBiQ+jx4N3H+/fVnjmHHddb6aK3piMaDv0D1cyxnXCHo82ESiJkShYgWX4hCFWmf/UTZykOiYAKRC7XSsdBGwZdWpXsGlNByorYAhm6C/4+d/+QXmzQtURXgIEd0wgGPHRtV0vSShgquU+GZw2D0gFnz6KTJJxk3suivwySfOx9stjvxGKxo7VnqeLCk++cRGmGixmLrhgO+VtOm0sF3VuK3tPr9CyLzjIXbjy9NPLHhS70ceUVNPVgJbmbcAIXD77Q65XZPnTYyyj41tqs6SF15wD5noVocftE3ulkdWfP01cPPNSJuLtsYrMWKEh3Y7Bg8RQAUXCYWqKtciixbZGF3Y4eNmNh/ywszTPNcBAO3b+zoskjh7q6rPwZUmOQ/EVFuGRpgLXvwV3nvPXch32T17K2szdQWrm4LlkvJiRNZnzomu5fbbzz2cT26IQuvzdsstwE03uZcjGVQ924Sopq7Vfaxavz53W1sbcPHFic/jxwOPPmp/vFHYvmCBxw6WCHPnAte/fIRzISGy5qtY3GZsdZgQup5wUKaYgrFZebhdaJEMnQggE/HE4bzpQpOSRzwx5Rwl5//q/X70dRGUXrepUwNXIfMKMXnhNhg7tjgNbSPdZwltp/AZqpbkElEpKvFDZLXWZc5vfwt89JH/4+0mZ7uX786dgZUr7es7/nib8GHlisVk0rmdvNWPE49MOde+WbfFWxSJx6XHmeUNXfPcGWuUvwjIvARI5uCyu66ygrTbbgNefdW9O16ZMQOYvXFn2/1a0vOqRcKx0dj3Pn2A3r2R/oEbW7fEaf7k64QUFdpJJ7oXUtWWw3Rh3vfq7JPy25kiwGnuzYdRRmpM3P+SQwAAMZ/hqIqVXr2AdhXOL/efDNsOI39ojw8XHeNa3/g5Lvlsktfw2OevlO9kQJ6dcUb6s9NywC0hu+xaYN68xPyaKafmvi1l2UqhDa5I6aPqeTnu83tcy+y4Y7A2xA9j05/dIseUKrLXy1jszbm/sC7koGDZ2JDJ76UrELWqFnzrOiDG/qisPqUkf6dT2E4hqeBq0dv5uuZmtmovF9NT1oNr9epkQJOITrjFahgaWaUtIO/OF9F7otgor7esEifsAamL5ARQzjz/lPdzZDfU2V3v5uaEZXi509bmPwyaKgWTLiowYwZw8MFW+5wUXBEdmj14cFU3d0F1S+HjWt0x7jK1FbrcQ1JLkeTCRnbZIp0Py2cdZg47DLhz3KUObSYq69QJmFa9u3S9lZrK+AyEEBmamzPjhGaSA/i1Zi2ldy7Xn6IiB5fhcyoc0PzliTA+rsnMS4wffgC2bOduHXHjnVtJ1XfsLUc77heq84t4RGr+Xr3aen9jg682J67f03efPLNhg2uRsN9HCck3iWBYxXOji48z4WLK9fk0joMX7zXZskz1BoGphvec03eZY11ZLGa93dymFr33eSEAsWx52N2wJrlg7b6FfeQQHXIKrpheIf2Mzq/bybmAxCSao+CqWmdZbtddk9FNXNh7b+nbDACwaRNQ3xo8XKTU+BDRl4LIjm0yHlw0wlFG9EZd4ht6cEWf2+/uJOUFYcRuwHMbxNeujfBAXwA6dADuu0+ioMUk/YRD8lmvHHYYMHt27tzmpOCK6pM8Ye5WWNko55k1pXoPDFzWM78dyhNCKHYTTym4ZBcvra22u9KhSRsbA3Yqwc72jlueMY83FevWWu8oEEJoZT0GkvLAfI8bE8h/800i52MK38Yb1dWJ/x6MHEqVn37yVl6sWJk1ncTKTMEFyClWFyyzSXruEV2Rgsvv3CHV9tix1ttNc7+scK6+LVg4Rk9s3Fi4thTCEIVENUrvqDwJjP/xD+C114AhKzNh88p1XWw8xZ8tPRK3355b5qa/dsDkDRmDAWNEl3ffNdQhqXlQk4NLLbqIsMQwucbsUGF/fmU9uLwYC8/faK/g8qPIvv9+YOh79qHK161zf+SXLDF4fUuMDyeeCJz+9R0eemmNVqSR8qJucMA1SOGggosoozVefi/tfvAsHLELUVi30fE4pzCF5cLjjwNY7mKlpGoWFwK/2Mnedc6ss5habZ/oNqpWHMf88xSMXLN/2N0oKG63h9S1cglRmFPnkKG2+9Ivpi6xjoQAli0D7v2Ls0X42rXu/fH7KlSxeZPtvkLd4+X6Ik9KhBUr0h+HTO9uWcT8fDqNWb49uMZPSHywE8x75J57gEmTlFTlGaexx/gC+vzzwK23Zu9fvRo47jj3vhvbEL16Z6Vkay1HBVcB3+sFFCd3LyA5z7LEMS3N7qWUnn/ZRKARY/mm7cLuAikhhIjuu5qR//wHuPFG4IphmSTUKtbF69cDDf4cTkNj4cLs788/n1umpSX75Jw56HZcey1w6KHA1VcbDIgk4zxG0YMr4VUe0Xs3HfHEvn9rN28tpeDae+v10nOfU84vWYzP1aOPAt+sONSxrMx63MuzOm+efNnAbUZRAyZEpJ093E5rVv53EojojbqkaGnVSygLeR6p8PjU2Y11Wv9PbMtVVdlGQCk/vvoq62uszXRCVU0mLostB6ecHFQstIh/PAuB3e6h5L0hnU8vZn8veQlR+OGHwBMvyYV9UoXxVFTSWomQYNTVpT8OnWnjbrm5Sbq6wNbE9fZhY7zw5JOJ3ExBeeopYMwYb8fI5uC6/Xbg5Zez96cUJ2ecAUeMbejQMHduZl9DIb1tIsLvht/gXkgRQuTH0VA6l0eAac+P4LlTZ+eDlBt5SGgPaVhCiDfGzZAP6T5zZrC2VDyfO+4InHde8HoKSd++7mWExQD+1lsW51zypV6JB5diZVSkZejp92V7vl5+mNQkf8wOS30ZdbU3eY/Jni6vz5Wn61Dgi+baXAStiCJ9X3NRVFCo4Coh6PpYHFR6NN61FYq3mUKZGC7/5ZcDF1zgtWelycgFu2Z9z/F8k5gRGxqAqVNdCrlYjngJTakqBxgJQPq+cL4/pEbdZF3SObgcFuTpNVIBV3Ke1mUGV4V0F8MKUQiGKCRFjsxLpGkscPTgcsr9qEt4mCt8oFRUdffdSU9tD7gOnQ4FUn328m7/zfJD8NBD8uVJMHShKZG9+A9R6H7/2N1jud6Yap43lcuFleslQ0ly8iUlTHO8PRpjasKqAkBTs7tILqVTef+9aMh7ytGQNq1XWbNGqryKuUh16LVEiMKIjs+yJ0xCwdWmV/rKfW057xoLrLPOreVlytM0uZ8axjSqbawtfKOKiKwsXGIRFlmvyiKECi5CCkyFx8HXrjTfHR0wTCSn/i9b06fpcduydjzwAHDEES6FXFYqaQWXMUGKXVWc5CKD24JDCA3fDXO5XimLNJu6zBbnUknqXe7bRPgU527lhZdeMvYihA4QUkIY5hU7AwovITmcvINHjkyE37MiH8KQoGsYu/GtXTvgiSccjnP7LRIKCre+G6u4Y9xlGD3auTyR45rfu4eFqtBEqKnixGb3NZ4d5ttKxXO3fDkwblzgatLs/vuT5QpG2pyakGCcO/hWnPzlncrq69DOXdqdzist8R7phNYS7Ph0PUX2qipSuUSdysgF5AAGDHAumAzlqmIMX7Gpq9LxdG1dZ9S1RtSTXDZntaSCSxZhEImb18k5fXn1Vcs6vCq4VK6rBw92PyVCAJgxw7UubcBn7g1GcH4XemTVtgkjeBf5L0MUqoMKrhIisg81yaJyU517IQN2k3xMr0BTk7FckF6VGA45AnLOk8WJ+799s83Y7d4lNmwwyB9158CCrVVJi5gvv3QolSDuITEqUY8QMNwXzg/W8sZuOOOcdlL12iku//pXoLshvY6UB1cBkRWg2/YtLA8uUXwv4IRkoTgMiNM6wcnLOB/ri6DP5scfW2+Px4Hx4+2PC/JTjH1WFK2ReODt991Dod9y8qy8RM9xegaeObZ/plw8WqF7XnoJOOEEubJ8jyBEjuZ4B6X1dWznLrAfMiSh5Hry+YDKibVy3kdueE25EDoSXlczZzv/qLQSYc89nSuaPRuAGoPVXnMkjQokOeKhC/DQpN8orVMZLiH900iEiIyJCl9eMa7zoI2i1MuadsIE4Iz+N7mWk63znHPcT0k8DmDiRIk2uRBQjqzVFRdhSii2qYmQoqdi6WLH/Tn5eGwm+Uu/uxG77+6vD5oGrF3r79iiwCn5q3mSsZhMzAJ9uwXGDjsYcnTouuOioOXtDxMfNmyw71u6S5TKFwsxXW4abWkB1m7exna/xG0BwFsOrjDIGq9sQhT+6lfA3I3dUQio4CJFjeEGtlU0e0grKRyW/U7HLajfMac/YWOOElNfn+me07tkkPk1Vf/GjcA22wAPPmjTBk3OQmOLDjHoLe6eXp4xe/8b6Nwu2Z7LxOv2+JjXkMUq69Dq6yI1VhASdTqM/961zLRpwGOPBW9L1ZNZbI+4zNy/fIWkguvQQ90aM/4jsshapzS5555NhCj0oeAyrZOb4+3xt39kDFmFHvyirl8PjF61j3tfhPmDf+JxSD20mkxzmzYF7o9qhPAW0aKgyIQo5HuDMqjgIqTAVA7+2nG/eQy0GxPjohI1Ne7lrKiqAna2yVefL267DTj//MK2acVxv+6avcHixJknGaf1QDoGuYsHV8uqpAZDwuTNuSYSJaQWJELgjjuAgz95SLJOezRR+NhLqu/GYcOAmPCYjJCQMsduHsrN26O+7c+XusXo9U5Q4Vgqn+ngwQmj1IaGzL5AIeqcFmG12bkJHn44kZ9z8+YA7RHlxEe5C4vdyBGUfOW8dk8h9fzJ5uAq1rXghvVh94CQoqKQeW9UeWgUnQeXBVdd5a289NoiOcYrSzlQLpqydEh/l3KxmGtVbXqlktO2oG5H/O8lg4LLpk5t5YrgjeURiVOWwbigtmLYsEB9KTvyEVaA2FICUxNJEVmtNcmiQvM2yOXzBXf48IQRRiHyFbzwglR0Pk80NgJ/+pPFjkp74fnEaaawEhYrlfcXHpv13UkQlz7cZfJq1du59i0Fc3CFi4CWvrCTpzmHH5S6VkJg1SoP7TvUWfHj2HSdLk0qex/y/TJcLi9khBQCm+dpyqKts5yW/T52UscpNNkOWlU7w9A8fLh7+ZUrgcMPBx6adJ5r2X7vG+Zzw9wu+n2YU/aII3KFZBz5wkMIQB/4VfCKZs3K/u4Q3spoZR0kxLB5P9eChJBCMGWK92O0NvcwcVEibjGevvce8PbbCY8aqTpWJK1a3QTWKQ8uVTKccnmfkghR2LVjo3OkniRecnA5IZHZAgCg/aQw2aW5LQXXPxaDpAeXAHr1ciyjt4WY6NSGSD8iuu4qpxdCi/iPKB6o4Cohis1VvFypcBngckMU5o/TTwe22grYbrs8NpJHZs2ymYMllEhpPEwmVh7ZsgquRfU7JD4kTd7aV9ib0jAHV/EgK4BSpmyqrXEvpJgZNbtJzS85ZVIPTEgLNgGN8yIpC6qqPBS2eR7tHtPLLjN8CcFk+/rrgR9+yN1uVHDlOF1ZhJDp0QOYPh1Y37y1a5v/figxn1doeraCa/ESy/LLl5vap2IiVJSsodZlP1RSs5gQyq79xx8D/5l+ppK6Ck0Yd3+HijyEpSSkQKjOe7NiBfAbmzRL5udz2TLgyCMTnxsbgZkznetePimhDdICempedpnHtUtAYjYKj2uuAV57Ta6O+FvvJj5IvtcoiGbnqb2iR2GuIr8hCnOaMtVh997/6ZIjcO21gZvLbV8Aq2q3UFKPDJoGiKp1jmXeGb1X4P4oR4jovvO7RHkiaqEUlZACU+GyiM2NjpP/IbGuLnfb3LnR9qitqlLUPyGw3zbOK+zUhDljhuXhCVw68++U1XhSQOikGKFwLFy8vEbILp69vJs4WoBL9k6lB5cXrJpsnu2cdzCfRHaxS4gEk2Z3Tj/HWmuL1DGqPbg++cTwReED9e67UnnC0bcv8M47uduNdiw5fV+4MKe8THiWVr0SG2oq0gOZEMgSuNjN2xMm2OfjIoVF6CI0z6fp04Sll4Asxvn9rruAl2adqqJbBaeutTO+X7JrQdvssVV1QdsjJMqMGgV8JenIatQpPPSQe3qphjUJ4zWniDS1tYl8YR99lMiPacUnnwDjDE4v990HvPhi8HcXXbeuY8v29muojz+Wqzu+KRmP2K2TqRCFuqK5KMoCGZWkPbjs0SD3gquLCiXvwWaZjN07+tiqffHWW1D68h2PA59+Cuz2zysD16XrQEubu+hfg3CVNGxsbB+4P6qJtA5Y4vmNcveLDSq4SgiGKCwOKt1CFJpc/vMhQJfhwAOBzz93LlNVJZXn0xMPPADsv79zmdWrge7dHeYLT9oE97JOMr2RI+XqueeIbxIfkgquOHMQlQSyIQq93JJOdabuxVMu6mpbJmp0PvGosLtASFFy1O8PwPz5yS8O1tLG8cVprLn5wFGZ8KuTgfvv99ghhR5cmzYBX3zhGPkNf/xj4n9rKzBiRPa+yok/pT+3tZki1rQ0++rT/Lru2OHkg7BiTWJ+FqjIkv45rccefhhS5Uj+UaHgmrR+T+myqet9+M/boTHWyb5gyprM4SF1856wo3tnC0u1kJhZuytO7PV/BW2Tb8CkmDmsv1oLCad1gNlbzOiB7JZ6B8gsA6bX7G7rgfX11wmF1RVXJIxZ7PuS+fzYY8Bf/wqMHu3eBye6dQPuvDN3+95bbbA9Zto0ubrTywEXgXXKi5whCr0hYqkcXC7nTVLhJ3v+nTyAZUMUpkkv2oOz7bY2qTh80ukG96RzMt6kUbwdBbTil4VH8cQWIVRwlRCq3dtJfnA1gG7OFsyEOdZZheQz0r17RgClimHD3NcGqYTugwYl/j/yiCldguKTpunZpt9z5mSu44QJyY0ui632FclVsUT4xCAWwEQR0uEnCnutUou3UT92cCxnFaKr0FDIS0hwZCK27LFH4n9bW84SIotuHRvTY9vLLwOPPprxoip0Di4gEaLokENytz/6KLDzzsCbbya+v/cecNpp2WXazZ6e/vzAA4kQhKpoasr8zqNPzCgswh9ViQwqQhQubtg+67sKz3rtw36O++fXdU97T0g9aoaHtrIiuIU/ZSuElAZeHH5OTTqKrl0LtEg4ihvtXB54wL19J7sYq3FOIr2SI3V1wMSJudvP2G1OsIphWI+5DJZzlm8JQOE7YmOjmnoizpwFidjTrvmzJG7w6TW74sQP5LRD3bewcTOEhQeX2zwZ9AY2scFeL5s33Nc7EVwsCBFdWfhOO7kWYQ4udVDBRUgIrFolLycKy4NLts6VK73VqcLLPnXuHnss8f+BB0y5uNw6vi47trCbxYeWjKGgVSdWGRbRjwBdd6xFTwlcJCzgmYMrXLwsMvITotAe2cWb+HqQfIMligBDFJLiJ3UPy9zKV18N7LWX/X4duWNbx47AvfdmQuwNHizRmQAsWpT9vcYireDo0QlhWwqr8IJOAn2VyvWJUzKCFlklB99Rw2Pxhq0xv85dmOCGa7QFA9KXO+nBtWD1lq5FpR61n35yL1Mm0KCGECSSb8HFg8tmxNp5ZyRCrCGRk8ouQssB52SsSazGqblz092wLeOEivnTqk3XeiUajqdCDrqUFUlhhzJjQxnNYwkgYnHMmAHc8oN9SD4NkBImrWrcVrpdJ7mL+Qq6Ki2LfAGowX0+FRGMmCkE8MHCY8LuhjVdu7rKb4r7rokW7dyLEEJUIoRzSJ6c8h7qVY2MMsqrwirVz7q6hADLLRyhFa6LZbdODRyYyFwvSUVrwiS+esZq4Lztrdt3aTO9IJJQcIWVP4IkeHbGGai8N4an/+teNh8hCp2EqNJ3xpAhED3OlW9UAbqencQ5Cos1KrhIsePlHnYLs2McW4z1PvFE5vM55zisJwI+UIsWAfvu615OZrxs50EBQcqHu774JYBfBq7HnC9XlQJl1izgkDvOcy0n9aitWAHguET5SMy44cF1MyFA85padNp9dxcFlzs33gjssgtwruE1YtYsYNIkU10WlR14oHsZmX1e+e4751dxobm8f7e1AXCOjpGu3zUHV7K8qnGpTHJwaULHqFESBSUWiV68rh3zohuemLo6YMb63aXrLUY+WHgMBq84OOxueEcILN+0Xdi9sKbIlZ7FBt0ESggu7UsD8xgoPUFnxehTg8x66scfvdX54ouJMIS33AIccIC/frkuiN0mEmPMJ2EjtjDUkRIcnHfvYZg3z0ZHpVDBRQ+u8Hnm2XZy1nwFvlayQizXF7k8cPHFQM9P70t/r27uUvA+EFJqZDy4gsfF14OGwJDMwTVvnvX2VHhhFXjxsFGFtMcuV+RFj/l5cxSASa7TNU0+b61XwW+533EqQkgSUuz8u3d3AGr0IZ1M6QTvvRf4wx+yt8mMU3fcYR9C2JenlQ1nnAGcdZb9fqFAyJx+53M7wUJxDq5yUXDpcTz5pES5iy50LaN7OPdO88fE9XulP999N3Di5393q0y63SgyYOkR6DvvBMcyKp4l1USwS54QFhE2iD8oRS0hyt16r1hwW+ysb94aY8bIl0+Xq1rnXsgjytZThor+9rfEAsEuv5fMYjmwB1cq6YgDd91lbC/zbNXWZrd/yj7J7LxC5Fj8ZnUptXiSysHFoblYkBKqePXgcnjmpYVeIbguxeNAXGTu7+GrfWqwFUGBFykF/vCHROJ1GVwVXB6FLRs3mjZoGl55Bfilg4PMpk32xit2/TMPVzLjpYqcQ4TYsWzT9jjGEO1GlSW+UvmF4cEJnHuiuto98W6E4RswIUBtfWIN7jTO/FC1r1SaILM9i1Wdr77qXk9TE7B0qf3+trZE3k2VWCvOXMZwXUf7Cot4yAbSObJdF1uJ9Qk9uLyh6RJJZyXxcu5lc5875bhNUw5KijL4iaqhRKJwUIpKSAi4zX0nnSRf1hMSip28YFqYBZW9Wx2ftU0IdKx0WL0bY1nbJKV8+hmD4MCwva0te9HfqTWTmLR7Z/skpWnBoowHl86hORJIPHyyC2jZd5PmZufkutIeXFx8AmCIQlL8/PQT8Oab7uWWLpXx4PI2t4wYnl2hdtKJuP9+Z89tq3xZXpFScIWQTFp2XOXwWxpMmJD4v3Spcz4PWUO0Sk2Xvodk5q7Rs7eXq0yCd/86AXMGL1VWHyGk8KTeSdzGmY8+cq/LPAZ9+aXPTjkQjydCF99/v9p6R45M9N+os3cdp4VA147OLrZCUsGV2q0qBVe5KLgqoLvOfdK5qD2FKKTcxQt3fnhU2F3IIdJyD8UhNYkzzMFFSIHxOv5+ufwwZW2vmFgFQD52sDLhsGlgF8LZmitFPG7t8CQTorBjRQwt8faWux8afBweutO9/XR7hqt28slAO8PIqUsudtOLJwkF17y67vKdI/lD1+FmB+LVI8KNLl2AePxXtvudvASN7Pv+Q6gOSZ8dKVasgJcxj5AosmkT8PjUXzuWsQsBZMQYolBmfm+3aSOArlnbamrcj7NDWkEkUc5bXkP5so71SAjIoGl8US0xEs/WJYHr6TX7JDT8J3A1aU5+8JT056BRPK7+4OyAvQkXhgUlBIjrcgqudLhgh/ecfBqIpTIFXHyxVWoG62Pq6hI5zL2kN9h118RxTvWm0d0NEGTf+VMKKRVrgRO7L8hOrVDCVAj339nY1lGqLi8zouy7fKSVKKT44Q2mBKqrSwhaqpcejzwC3DnuUtdyP/0EVG3e2rXcstXWCh9fbNggX9ZkeTRxIjBtmvMhsVi2IsmI6/jvUqDP+MOzvrsJBozPlhDICu1g9OBxqsVLDi4SEYTAVu2dk8ZIrUU8hCh0e4fZ2NoZM2e617Nu89bl8j7kiPbSi2F3gZDIIABPL1DtNO+DiFP1Kt/dnATaZqFSwYyfZeI+kaKgQ4X8tZS9r9+cfzz691dYIUmjLBQYIUVMymPIbQi58cbkB4fILvmUK/Xqlfgfj+fOz2LWbMtj/vxn4MADvel66g2BVVyV4LruWmZd81ZS7WY8uIKfRAGUjQeXttk9SeXmeAepurwYPcheJ07N4WMXRjTS18YmWlRWkWQ5EhxXSaumaZ00TRuvado0TdNmaZr27+T2tzRNW6Jp2tTkX8/kdk3TtBc0TVuoadp0TdOONNT1B03TFiT//mDTJCEljRdrnqFD5coddxzQf8nPrXcaFkWVFXID54ABif+Oi9uqKrnOAb4G7NQC1nxodTWwetp61/acWlzTILdATVHhkMw+vYASwvHaesnBRSKCEK4LZJmwBgdecwyGDJFozxg604ZHJp+HQw+VqIvQopsQE17DsMiuGYyk5uw//hE5wnzZpcD337uX8ZRfQZGyX3Tt5lwgqeDi2FP8pK7h+PEhND7bWsDrhKzQr1Sh1yQpVs7bY7qyuvSkB5e0PsRhUvaj4Pr2W7lyjiKE777L2fTAA8CkSYnP553nvV+AhEePy3s8AFw7Uk58qdLAUAitbBRcFUMGK1OselkjKjWQoJKCkFCRedNtAXCaEOJwAD0BnK1p2nHJff8QQvRM/k1NbjsHwH7JvxsBvAoAmqZ1A/AggGMBHAPgQU3TsuOekEAEDU9BCkdB575hw9If7TyizIz7MdnBRkUJp30szNLWT6ZDzzgD+MX5OzgeO2t+ezS0dZZuyDXes8M+6RxMqXJ0tSweJDyvZB7lucu3lGtPQsFF5Bmx+mdo1RmJmZAUxkTaUiEKAzw+b74JvP66fPnRozOfZRyhvCiRAgubUtK47i7hg8PKc0ryxrHHhtDorFkQHpPZrZOI4lDK8A2YFCsqhesrqxKRWqTlDA7v5xXw/u4uk34AcA51bNX3Rx4B5s1LfB482N+rtKsSXNddx5H0O4V0iEK5vjkhgLIJUdi+Iq5MTOItB5dCDy4quPKK3XUVyhLe5QEhXOX0NNJRh6uCSyRISbnbJ/+crtAFAN5JHjcOwLaapu0M4CwAQ4UQNUKIWgBDARR3wG9CfFBwy965c9MfZZ2Htli3BACgDRxoX8hLqD0fk/30pEGbeU23aJH7sYdcsr90O7MWdMC6zc4eXU6TknSIQoOnFykSVq509XhQavVF5adSPlx0DD5ZbOPZSki5kpyDZKaidpX+PbiA3DWHU5v/z95Zh8lRpH/8WzOz7prsJht3d3dPiId4QgTiOZwASZCgh7veoXf84LAD7rA73B0O9wQICZIQ192Z/v3RPT3dPS3VMrrv53nyZLu7uqpmprvkVbtW2XZCFbs2fn75ZbFKqw20pOCiaT71sbNWj8m6fudO7+tMY7YesPCuJIgkxctcvi+8nYc//vBGwcUE+xMn7zbmppuMr8VKTsKTQ9OqDK8xeW6mPQMFMwTUHw8uxrxbP9l5r7iisbQHPvvMTY+IWJLU626OzlGIQu/gklAzxvyMsY8A/AZRSfW2dOliKQzhNYyxcMa/RgB+Uty+VTpndJ4g6hfdu8d3/FJoiHitsXP3SyEAzcyoDVaxR44AS5bwdk6n2t27AESsZrUKLq8NpDtNaYkdRjG1pR/KbMHOG/LJbmgoIgnYtctyK0O/a3LjJIcQQaQrSgtBs3XI3o++BwD4X3nRVv333AOcfnrk2I6Cy64Dq2kOLkAlEArWuhQOSYsAq7Xb79u9E2oRiaUuxB9OOiQwLuMrbhijMJcEUU/w2nI/GPRIweXALzKZ00xbfiehkGUZv0UeHbmtoPi9vr+1AVd5S+qJBxcA/PSTdRkegiH+hzHI8Q5++SXwwQcclZGSIjEkcRSczvO74Js9lYnuRr2B680XBCEoCEI3AI0B9GGMdQJwNoB2AHoDKAVwphcdYowtZ4y9xxh77/ffLfLsECqsktcRyYGdeS+8QHIDO+Vk3Hef+DevB1d2QBTSHP/KIhw0yvdpsIrduhW4+271uWAd/4d+7TN15NKtW9XX4+rk8r0o5DPPwQXxR+XNwUWkDn6/pZDJ01GXPLg8x0fzIkFEcfQo8OOPxtevvl60hqnbbE/ScNll6vl/61bg8sv5+2QHszlVm7Mi+MST9ip3yI5fRSEUKSfqF3/7pi9atfKwQloLEES9wev9oS0vGDMF1zdfO2rbS+6+G8jjifLOoQAym5fbF2/j8uBqXfQbR2fEr/Wbb4A1/xzFVd6M+pSDS+jjXUxgOx5cR0MZnrVLxBajoU34zH7u0njx6fe52HbQPDMT7Ru8w5adhSAIuwG8CGCcIAjbpTCERwDcBTGvFgD8DKBGcVtj6ZzReW0btwuC0EsQhF4VFeZ5dgg19FqkITZj8Bvxdtjn8kk+IY9yHbXIKJ+qchWrCIM4dGh00U2XmCwcdu827UubNupjIyWd3UX1rl0chQ4csCwSEhjXwpoUXClIZqbl7+bl73r2+fU7SXwsoAUjQUQIbwyzsoD//tesoFhy2L9PNylkeJvMhx8CZ55pfN02Ci2YpQeXYl4OvfCSq2Y/3FJi2SYAoK4O334rKjyI+sOuI5x5NnlhjPIxEEQ9wcsQhYBof3riibyNmyi4/m2SpsDoHs6P0rZ8h+E15TLh7rthbGirZPt2yyJmy4/irENAKGS5p5vd8j2pMot8OiHBKxFOlEd6OvPOTxZ5Tm2QKLlLOA8eEWe8euESCXn/eYKlgosxVsEYK5b+zgEwGsCXUl4tMMYYgKkAPpVueQLAcUykH4A9giBsB/AsgDGMsRLGWAmAMdI5wiOcuJIT8WfiNSN0lUB6CB4taMILztDWKJ2yLsFQZFHw8MMGzlqKkzffJODDD8W/f9Zp4utvTYaaPXvkP0dxGDp5Ff6glCdcf4a4SDELQxcSfLIgzUz4JS+0aPJKHXw+SyGTlyEK/3wVKbgIgogdt30xFG+8ab3p50nWvHat+ri6WmXrol+vy+mPZWXi1Vet69LmrAi6HKf/2M85NtfWYtMm4KOdTVy1R6QWsVjV0UqRIOoH7/7WzNP6bBl8mhho/uuHrvLfFraoMlx7awBtC3/hKielv7SGQzhgtp8TBIYnnrE2agyHslt0eUfTcp7ro+qJgmvGlQM8q8vtus8pNRM6J6Td+oKRrE2oS+0wnoLgYQK6eg7Pm18F4EXG2McA3oWYg+vfAO5jjH0C4BMA5QAukso/BeB7AN8C+AuA1QAgCMIfAC6U6ngXwAXSOYKoV2zZUZCwMLG8wvgqQa2l0h1vFSvoNTe2x6ZNZu2aNKao/PnnrftmFi7QczJFoZZZbGZuDy7yJEk9QiHrHFz0uyY1tFYkCDVXXMMRq5jjxXnsMfUxhwG1J+/j11LUJFMPLm2IQpfLhrDQyzIBfZ1J3lIibfHaUnz3wUzyPiaIesLe2hxP67NlCGqiOLn4wwny3716mSXt3GujQZEPfq02vObIe5VDq2c2pn67twJTFhXjQF22aR3hvE73Pmfcf0A0EvIqXKPAKWcg1Px2qDDRXSAIIgEErAoIgvAxgO4650cYlBcArDG4dieAO232keCEQranI9Y/Ks+aJ7zY5RUuCbv3WBfSrKDNFtSmhkc2rZLY4UMAPA4HY4T0hZklKg2ByRl9zb5eLz19iDgRDMY1RCHhPSQkJAg1PFMujweXkzXnypX27zHCbOx9YVs7ILRXUdbd/Btuq90F88wLBoMkh6qHeL2+Kzlukqf1EQRB6MK5B//uO5MJ/4kngAULAJivC66/PhI60ZP8uHv3AoWSEoPLg8v42o7DBVxN8noFfbG9GF24SloT0nikEwQRTarv98ke1ztI4koQKU7AUk2tCFHIOfhzbdY1q1htbixlfo+QmcDHroIrnlOAtBquzNlnWCSksBQ3szoz8wIjkpRQCILFNEkKLoIgUgkeQxeBo9DWrfbb/uAD+/c4RqFpcjv/BoOceTsFAc8956opIgUhwQRBEClHKGS5Bz/lFOtq2MIFuPde6e/33zMs94Qipdf8Vu8YluMWVPOEfQGAw4ft1WsC757vwqd6uG5L3TApuAgCMJFRepXDJEFQiELvSO0ngSDSHK/HOVPFlaIxvQXcunXAAw/olweAQ4eAb7+NHI8Zo2zXpFMefcjrrwf27/ekKplwerCcgHHYITkUksXnkL97mrxSB44NRapbDPFQnHkg0V1wDL1tBKGGS07C+eLsM7b9iDmWY68qRKG77Q537mpB4FIOEukFGboQBJEscE9Bb75pGQbm2mv5qvrsM+mPt982LKNMz5AXMM7VMPlZ3UBQ0fz2W+Rvs4WNtJn3YmZ+5ZfWcphkM7xcBggCw1fbKdweQZgh+DjCrycxtHPwDlJwpRFx9WwhUoqws5XpgkuxUNTbrF9xhfjPiCefBFq31r9mKlCzswoUBFOh1ldf8VfFw8dfZISbNeTDnU0AQUCncY3x+A/dDMuRACQF4ZAE14ff1UzBm+w4iuVPEGnMk89Yu33zhCgEYmOvEdrrjaXKVTdm4eBBqU6XhghHa/mMQ1koSDYs9ZD6YOhCEESKwDsJ/fabZ5N4WM5wJGS8vlDq0rxIqyEok2uafY5QCIcOAYtfWuK6zf9s7YjZszn65uE64EBdpnV4ZIKo51TcckGiu+AKWkd6Bym40gjmRTxjIql44/0sT+rhClFYGxFiGwntnS5I3ebguvlm6Q+LFWOvXvx94oI3JIAg4LNvMs2rEni0jERSwZFMpT4ouAiCqF/c91I1mje3Lse7Jjh6lL/tw3c/YF0I1lPp6efn4403xL/denBt25nNN3XT/F4voXUAQRDJgvDFl1zlXvuiTOVV5QUrXl1geE3pCe3FmBkKKuZbs/26IODnn103ZwtBYJ4o8QD+vF8EQaQuggDaQ3gEjZgEUQ9g+8Vk63uP5pgUiqzEjBRhysVaOHyfFaEQ8OSzGeYFLHjwQfH/3X+EsOtIHl/DXiBIubWsrCo4JiQSgKQeZ97Q2LJMfbC4SWUvqPrw+xCE12z5NRdbtliX4xXgZGUBH33EV7Z280+etKmk7YMX2r9JwUl/64Vnn+UoSHky6iU0zxAEkSwc2X2Iq9zgDUNw54P5luVOPtm6rvC8bLZHDyu4gkFg874y60rDLtgGLPnbiMhBMIge5T/oFxSEuMuNvWyPIjQRRPojgHJweQUpuAiiHsAOi4vdSWZxrZUKLgNrIaVgqdmQGq62X3nFogCHQOjll8X/L/0zV5P81JqHXmO8wiqeUHYkAEk5Lr+vkWWZ+qC4TOXlVn34fQgiUYTXBMr8m0Yo02WYwatQ51Eq3HYbX5s87NzJUSgBgjQi8dA8QxBEstBielfusjzbXMt9vIK8wGHDa+GgGHfcAdz51SDryt57z/Tyc19FjBBvvDsfH+xoql/Q40l51y5PqyMIgkhpY+JkgxRcaQS9FoQR4WfjSNDEk0oBT4jC3Xv5kjkOH25RwMbCM2QdMc4eVqtU2YPLAi4PLhpu05H6INhKZet0snwkiNizeLF1GV7PKy9lURbyMVsowysZwQTy4KqPkGCCIIhk4dARvv05wDcv8+SfDHNOj6cMr9XVivPjH39wVmbRue17I95nV99RaFzQY8OTHwwcxVRNetdcSu/BCILgg6QV3kES1zSCBHmEEX/s8eOtt8zL3PdoJHyh1zm4TJFWnVx1ey082rbN9DITBGzfDvzjO4vkXnZCFJJ5d1pRLxRc9MgSBGECjwDsjjv46nr86/bmBfbtA8C3GfRyzcKRkhEIhWi8rIeQhz5BEKmIz8exf+XYejMG/O9/wFnvTDcsU2ceNCUaG5o10z5yTd7e4uU6oD7sM4nY0yiPXA+TmZBAIQq9ghRcBFEP+Otj5ejf37zMaRdErJ+MFlNvv23D8oqTYC2/0spz42iL7Lo7d/tx6aXAG7+2Mq+HY0I6GvJTeo40pD5Y1qXyZ0zlvhNEqsAjh/rHP/jqWvLMbPMCQQ5XKom4K7hoc1ovIQEkQRCpCM/I9b//8dVlFcb3629tih1tTOBWCq5UzsFFHsKEF9DyNLkheYV3kIIrjYiJdw2RUFoX/Rq3tn793S+npDKzRv37371t98AB/knX88nZbx7GYcoZbXDDDRz1cHTs+k9H4owzOPtFpAz1QbC143BBortAEEQSEp76fCyO1htCuG3rsbeuDti925tmX3ie4zOSFUtakZ9hnE9GCQkgCYJIRXzMOw8uHt55B9j6E+dm3oZgy3QbnhAPLu+8Mb7fV+FJPUT9Zl9tdqK7QJhA60jvIAUXQSQx8ba2CC9izfJF8eShsENdXQKtSrKyvKmHU6j14YfeNEckD14puPpVfu9JPQRBEPHGt3NH/BqzsWD48UegpMSbZp9+hmPL5HGuDyKxNMjZy1WOQhQSBJGKeGkczVNX377ATTdzFKyrs9U5Mx3WZ1/6sWcPd1WeIAAQ/vPf+DZKECbsq82xLkQkDAEgNzuPIAUXQSQp558P/Li/NK5thsdVM6G9HQXX1q3WZR5+rthTD67fvt7NVxngnVUX5wcQBP6yRGpgpgy2A+OwoiQIgkg2vvwSeP6TSk/qsmMpnpThPMiDK63gtaitD57cBEGkHzbSXJnCmMeRhL78kqvC/fvF/0Mh47LfbA7guuu86hgnAhB67Y04N0oQRKoigHJweUUg0R0gvINxpdwmUoVNmwAgI65tRjy4jBeKv/3GX19NjXWZzT9ncsuEeMb9Bm2L+SoD4q7gItKPQ8FMT+oJhsjehCCI1EIQgNde866+a6/lKBQK4fBh4L9b23vXsEe0XTUi0V0gPIR3ZUehZQiCSEW8lB15uRXetiMTS86yFiIUFIjtmjVde1SIexqP7/4owc7DefFtlCCIlIVEid5BErU0gjwACLfweHBddZW3bWYEQjY8uDx+xj1ScB0+wFePIACf/5jvSZtEekEhjmIDzYoEEUM8Dsl3zz08bYbw4IPAvd/0965hgtDh5wPFXOXqBPN8rgRBEMmIVx5cgLeprlpPaIX/vF3EXT504JDhtWBdYnYCH+7ksPIlCIIA0KbIhgcBYQopuAiCkAl7UsUz9E+XFgc8DVFoC48SiuV0aM5VThCAjn8iC28iGvLgIggi1RBq6zyNyvfxx9Zl9uxh2MuXGokgXHE0FN8oCnZokr8z0V0gCCLF8dKDy8sc3QcP2dsTmYaJDYXi7sEFeBfCnqjflGfvS3QXiDjw5Iw7gYqKRHcjLaAQhQRByCQifURl8dHEpa3w0tyMg1deiWtzRApBHlyxgb5VgogdQ3vtxw97SuLa5imX0AaQIDJ9HkqTPSbLX4sjweRVDhIEIbLozIae1MMO7kdtbWIilIjKK+O2hWBihAyUm5HwAgqBXE847jigRYtE9yItIAVXGkHDH+GWYBD4z3+As96ZHr9GOUMcffYZsGu3x095nBVcBGHEnqM5ie5CWkIhCgkidny0Jb7KLbtkBoI4Wkfh44j0I56RFuxCuSQIon7BDuxHgwbJGYI/FIx/Di4AOBIkMSvhHppOCcIeNPKmEWOeOjnRXSDSgK++inODO3dyeXB16gQAmZ42feOjVZ7WRxBO2bKvPNFdIAiCSCv8QhAAKbiI9IO8AwiCSCZy4myn17HkZ3y2q5FlOSEYQtu2ceiQhvXvTot/o0TaQXM9QdiDgsMSBJFQhPfeT5i1559uSMCKlyAIgiCImMMY2b4SRLxJ5txlBEF4D0P8PTe5vVhDIbTL3hLTvhBErEhmb22CSEZIwUUQhEyiFE2JaDfTVxv/RgmCIAiCiAsH67IS3QWCiAkk9CIIIlk4eNiHH36Ib5uf76rmKicEQxCeeTbGvSGI2EA5uAjCHqTgIghCBU+c6mee8a49AYwrRCFBEIRdgiFa5hAEQRDpBYU1JggiWbjy3kpMnpzoXugjhATKY0SkLPTsEoQ9SPJDEISMEOKbRseP97hdmr0JgogBG9+bmuguEARBEARBOCbAgtj8/h+J7gZBpBw1+bsS3QWCcAx5axOEPUjBRRBEhARomp7YPwLr1sW9WVowEGlPjv9oortAEARBEARBuIAxAc2akjUgQdil9JOXKcwbQRBEPSGQ6A4QBJE8CMEQ4q33vual7sBLcW0SAMU0JtIfEoUQBEEQBEGkD9W5u7DtYEnM2ynLOYCdh/Ji3g5BxJKgwFAb8ie6GwThiP212YnuAkGkFOTBRRBEhFCIKwdXOhAkBReR5pCXIkEQBEEQRHpQVwdMa/ZR1Pnzp/3P03buvkvA1yuu9rROgkgEPR/diAUvHp/obhAEQRBxgBRcBEHIiB5c9QOBhj8izaHcdgRBEARBEOmBX8cRxcdCOG/6JwCATF+tJ+0EMhgy/PVnT0gQBEEQROpDEl6CICKEaDNDEARBmNO/9e+J7gJBEB7hZ0HdvwmCSCKkEBtm3vk3DnwAX8w613VTRUVA5tIFrushCIIgCMKC+hJCKw6QgosgiAi//ELjK0GkCSGBpngiNrxx3n8S3QWCIDyiUd5u+W8/I0Mngkg2GARDAZggQHbZ9zH3wak3n34TjjkGyOrQEj9+ssdlbQRBEARBEPGBpF8EQcgIt9ya6C4QBOERdQIlVSZiR+O8P1CevS/R3SAIwgXC1degOpeE2ASR0kjKLx9zH5u6WckeWZdWU+O6OoIgCIIgiLhACi6CICKEQintwVWYcSjRXSAIgqgXfP3hQTw6mowiCCJdWNvxxUR3gSAIC7QqLOW+jUEAKy+La38IgiAIgiCSAVJwEQQh4z6wRWLZW5uT6C4QBEHUC3JaN0amvy7R3SCSnExfbaK7QHByw+AHE90FgiCM4LBA9MKDy26bBEEQBEEQyUAg0R0gCCJ56PDQ+ah9LNG9IAiCIAgiHTgaykh0Fwhexo0Dbk90JwiCsIOg0GmJObg8VnIRBEEQBBE7yJjEM0jBRRCEzK4jecCRRPeCIAiCSAVoOU4QBEEQsUUp+xIEnZnXJwbl8bFQ7BomCIIgCIJIYihEYbpQSyFgCIIgCIIgCIJwAAmzCSJ5MXg/GYtcy/bX0WtMEARBEES9hBRc6UJtLY5t/n6ie0EQBEEQRD2BQiERBEEQRPzQzrqCACAkem5l+2tpXiYIgiAIol5CCi6CIAiCIIgUZduCMxLdBYIgUhhZHE6uHwSRvJi9n8OGAQDyB3aNX5sEQRAEQRBJBCm40gVBIHstgiAIgqhnVOXujX+jUlZ7kn0RROoTFGg7SBBeUJBxKDENFxfj9dcEDN4whOZlgiAIgiDqJbSjIQiCIAiCIAiCqG8IAmpDfvFvH20LCcIph49fg/4Nvo9J3UeCGfLfgqDWYIUVWgMGMu+VW6QtIwiCIAgiRaCdTLogCFELXrc0yNmD1R1e8rROgiAIgiDSA8r1QRApDmOoC9F2kCDcEvP50EDZJAjqa677IdC8ThAEQRBE6kE7mnRBECDAWwVXr4ofcNOg+z2tkyAIgiAID+nePf5tklU3QaQN+2uzEt0Fgkg5dv71n+oTVVWeG5uGyfLXyn97vd8nCIIgCIJIB0jBRRhCy2eCIAiCSHJat45bU2Mbf4bgspVAhhguiTGy9CaIVOOa/g9iY/cn5ePSrIPICxwGQMJzguCltKA26lwsZsRs/1HcOug+7vKe2p/EyZjlzSl/xpSmH8WlLYIgCIJIKshw1DNIwZUuCILni2ofC3lcI0EQBEEQnhLHRbGPheBjQmK8xgiC8IRRjb7Ayg6voFnBDgDA8xOvwZZ56xPcK4JIbfw+AUJNE8/rvXHgA1jc9s2kFIDdN+KvnqQzaF30m7i2IAiCIIj6BoUG9gxScKURXodFSL5lNEEQ8WL7gjNwepf/JLobBEFYEUehFwOA886TvcZonUAQqQeDgEadS7F57gage3eUZB1EefYBIBCgvHoEwYtm7vUxAUJuvvfNaN5J3TfUoxxc387ZoP5cFuuLea3eRbviXxy3F8bPQjTyEARBEAThikCiO0B4RAw8uBhZaRNEvaUg4zCy/HWJ7gZBEEkEYwJQWZnobhAE4ZaTTgJ++AFo0waoqwO+/RZo25ZCFBIELz61nXCsbE1kzyazBpQKLhf9aFm4A0Ce6lzzgt+xeV+F80o5CPhCMctfRhAEQRBJTRJ6aKcq5MGVLui4NY5q9LmrKn0Q6GUjiHoK5dYhCEILAzyzFCcIwh39Kr/HrBbv2b6PMQDZ2UDbtuLBqFHAypW05icIOwSi7YRjEWVIXo/7/VIbFu+p205IOTalxvH+9Etwi0kOMC9CC/pZCKE4Krh+WXA6RlR/Gbf2CIIgCIKIPaTgSiO0C97/HnOdq/pIwE0Q9RsKB0wQRBQkBCeIpGB2y/dwbo9/276PFNME4QGdO6uPBSEm6+aavF3iHzoKNT1cv98LF6oOS7IOoix7v2FxL3J2+1kIoTh6jzbI3UdyDoIgCIJIM0jBlS4IgudhRUiERRD1F3r/CSK1yPTVxrwNreCMdF0EkTg8F6bTC00Q/DRrpj5mzPN38tDSNRjR6CtgwQJDBZcXHlQy55wDNGwYdVpvZHhv2sVAx45ixBeX+FkIQSF2YqkMn3HIdWH5ipi1SxB26d/gu0R3gSAIImUhBVca4fU+19MFM0EQulTl7k7KzRUrLEh0FwiC4EESSucE4qDg0qwLPt9VFfM2CYIwxolOijwXCMIDdF4+rVOXU4ozD+CcCe8jOyApZgYPlq8dCaoVXVGhg30eKqpNBpieExoAJ5zgibzAxwRkmSih2hT9gq9nn+O4/qMnrIGfBfUvcnrGEUQ8eGPK5YnuAkEQRMpCCi7CEAphQhCxJ2nfsxh4hRIEETuaF+yIeRvaEWH7wSIAQHXurpi3TRCEGs/naPLgIgjnMIZrrwV+3XiD66r6Vm7BBZPe1b3292/7qY6jQgS6cSPTjgFS3i9dhgwBcnM9UXAxBlw74EGc3e1p3euFmYfRuug3V21ox0s5tcO0aa7qJQiCIAgiOSAFV7ogCNFJZydMcFWlnoXnwaVrXdVJEERqwCAkq+qNIAgNvx93Kk7p/HzM22FMUAnAxtV8BgDYMm895rZ8J+btEwShxomRTG3IRGhNEIQp1bm7cOyxiFYGMQa/H8gKGHsi2WLIEPH/3r1Ni0V1w83q3Y6CS8KTiC9VVWhWsBOX9HlMvw0IQLt27tshCIIgiCTh+Lav4ZvZGxPdjbSCFFzpguC9MFpvwRqPEEgEUe/o2zfRPYiCCe6TRhMEEQd8PpRnH4Dfg0TvVmj9O8qyxMTzfiagzkb+jPbF25IyNCtBpBIMApCVZfu+w3UZMegNQdQPJjT5FA89ZHydeZWIq1Ur4MorgeOPV52e3uwDdXuxNEdjDJg40bSNKA8yJ6xaZXrZ7wsBRUW2qy3MOIR+ld9HX+jUSXX43DHX4MROsTcSIghLGjdOdA8IgogThZmH0arodyBEcjevIAUXYQgFKiGI2EMRgQiCcEVxcdya0gq5wiF/fEwgrxCCiDMBn7MNcXXeHo97QhD1B18c4hvI3tIFBVEbhRaF6nDEPo1ntef7ikmTVIcntHsVj46+JdK+y++juKAOaNDAtIzTNs7u/jTenHoZMHSo+oLmSxrZ6Es0yt3tqA2C8JQePRLdA4Ig4oS8r/bKMIawVnAxxrIZY+8wxv7HGPuMMbZJOt+cMfY2Y+xbxtg/GGOZ0vks6fhb6XozRV1nS+e/YoyNjdmnqo/EIF+OJxZZBEGkJOTBRRApQk4OsHGjblhhr9GGKKwLRZaRpOAiiPjiZyHb3hvC8hVolLdb/yJj0eHOCYJQYRiST5obY51bV1s/g0bB5WWIQh16lP+Iac0/ko/thCgslby+lex6+RPxj/HjDe9zGgYxP3BE/EPnc8k1kmCRSCYSYPn68Khb494mQRAKaB7yDB4PriMARgiC0BVANwDjGGP9AFwG4BpBEFoB2AUg7D9/PIBd0vlrpHJgjHUAMAdARwDjANzMGCNpiIdEbUpdTpC0xSWI+gtjOmMKQRDJB2NATU18mtIcl2fvR/8G3wGwp+DSLk+mNP3IXccIop5Rk/cHBkjvHkEQ8WFOy3cwr1Uk3+RnM8/HX4fcG9c+6KT+SgxSSCVe5dO/xt6If429ybjA1KnAuHG6l5yEYP7k2E1Y0eEVICMD6NYtSn4YCu9xcnMBwHNDYYJIFWa0+DDRXSCIeolsnJpBocO9wlLBJYiEzW0ypH8CgBEAHpbO3wNgqvT3FOkY0vWRjDEmnX9AEIQjgiBsBvAtgD5efAgC+jm43Cq48vOiT44Z46pOgiBSgwAL0maPIAgVWi+x7EAd3phyOQCbCi7FiuXi3o/h4dG3edNBgqgn/Dj/bHQr3+p5vfHwBCWIVOX+kXdgcNW38nGHku1oVrBTPAjvu+Nsia0N3+dq+89xc5avTvxD+py8yqfcwFFk+oPmhQIB3dNOPLg6lW5Dhi8EXHcdkBct0wiF84aWlwOLFtmunyDShjiGOicIQkOrVkCHDonuRdrAlYOLMeZnjH0E4DcA/wXwHYDdgiBIKxxsBdBI+rsRgJ8AQLq+B0CZ8rzOPYRbYrCY9nXpZF2IIAj3JGMirjht0DN9tehVsSUubREEETvchCgM+EL4bs4GD3tDEPWAtWvl5cOqDi9hfqu3XVdJntsEYcGaNZG/zzsvslyWXkYvls/asINKMsMKJol4pBRQdiU7UCv+kZMjtc//gZ1+N64+o19cm6i+TkFAUFAoJAcMiENmNYJIUvqQzwFBJIwzzpDnKcI9XAouQRCCgiB0A9AYotdVu1h1iDG2nDH2HmPsvd9//z1WzaQlXm5Kx9V8imUruB4PgiBcEOtY/U54Y8plcVO69WuwGS9OvDoubREE4Y7N+8oNr5kpuMY2/gwbuj8lHzMAGDYMgOgtCgAtCnd40UWCqB906AB07iwfrmj/KtoU/equzmQ0tiGIZMLnA7p0iRxXV0dFOzgajK2g6sxuz+L1yZfJx4wB8czBBUCcv5s2BeA8P5YMh9bLdRuM6YQoVMs5nMpRijIPojp3l9OeEYSaRMzDNPcTBJEm2NJgCIKwG8CLAPoDKGaMhf3IGwP4Wfr7ZwA1ACBdLwKwU3le5x5lG7cLgtBLEIReFRUVdrpXv9ELUeiC6/r/A/37e1ghQRDGJNnCsn+D73U3g7GAQUhKJR9BpAxxDIf03u/NDK91Ld2K3HBCdw3dy3/Enzq+IB8zFrFOD/hib31OEGlH+L2nxNQEITOh5hPsXnxS7BrQWa/LuZykayXZh3Bpn0e5q5zW7EMw8M+D+ceOw4CG30e6lIg19Ny58ufNM5j3tXCFPzUYz5zk4AIArFpleCmoUWjxhGXvXbEZWf5a1bmN3Z/C+9MvcdY/gkgGfGTUThCJwE9hwT3HcjRjjFUwxoqlv3MAjAbwBURF17FSsUUAHpf+fkI6hnT9BUEQBOn8HMZYFmOsOYDWACJZWgnvcSE0N7w1yQTxBJEW1GMBFY0oBJEGZGTglsH/h98Wnm5YxGj5kOGzyMlBEIQhYeG2LSF39+4GldGMTKQ2Gb4gijIPx7VNrWLE7xNwVrdnue9/dMytqMlXewAxwPh9nDABaNFCPmxVqI54E+scXNoSYxp/jo+P3cRVfbviXzCrxXu2u+XIg6thQ6BbN8PLWg8uHt6Z9meUZe1XnfOzEBnqEakNKbgIIiFkawwmCPfwjGZVAF5kjH0M4F0A/xUE4d8AzgRwKmPsW4g5tu6Qyt8BoEw6fyqAswBAEITPADwI4HMAzwBYIwgCSTW8QhC4LI94oYUaQcSXWS3eQ1HmwUR3I0KcBF2U0J4gXCJtTGOROyc/w0JQOGqU+P/IkfAxAZn+OvPyEso1RsAXBM47D2jf3mk3CYLgpXt34PjjDS/TnEykMrF8fv8z4VouD65w+F07OJ2/n51wLZ6feI13IQodwBjQuXSbdTkABZlH8I9Rf7HdhmMPLrlxBkEj9ory4HKcH4zGTCLFIeMWgkgIpODyHksFlyAIHwuC0F0QhC6CIHQSBOEC6fz3giD0EQShlSAIMwVBOCKdPywdt5Kuf6+o62JBEFoKgtBWEISnY/ex6iGC4Klwiza4BBFf/jHqLxha9XWiuxE3BjT4Vv7baLwp1VhJEgQRe5rk78S3tz2PPYtPwqCGkfc0238UT467QV14xgxgwwZgyhRg7FhTMxul0E25l87wBYHqamDePI8+AUHUP7jX7c2bAxkZhpdjoSgniHgRy6d3dOMvdM/Lb154YhswAPjTn1y1pQzja0ZR5iHkZ2hCBMY4KkSBleGLAVyKN4O+O1IiWXwPsmLSzvd1zDFRp/wsRPoBwjs8epiE5Svi3iZBxIqXJl6J+0b81ZO6CjMOyX9PbvoRdp+buFzwWZyGoQQ/5I+aRni5nLWa5rqW/WRt2U0QROqyerWpV2iGz92E/PqUKwAYjzWDG36DAQ2+N7hKEISMxxvTbH8tWlYdRKEmzNOWuesxocmn6sI+H9Ckifj/9OnwtWllu73ybHeKbFubeIJIM0guRRARfCykCt/nOYFA1KkopTBj4rxoA603kW2UHlwxDFG4ee56HNPkExcN6GChYFrf7Slc0vsx7uoCLIjgspWWYde0IQq5IuEwphOSkvKIEikOhSgkkhBlONhGebtRlHnIpDQfT467Ac8dc4187GNCQpVMAQrT7zk0mqULHltrGVpZSQvf7mU/Yd+Sk1ReGPGmOneXdSGCSHKU26SksZzeuBFo1cpUaX5ipxewfcEZtqu+uPdjYpgXCcb0t5SvTL4STfN32q6fIAh3GFlK81hQM5/+GMag9jAJrzG+nn0OJjX52H4nCYJQ4cnqgbRlRIpjmrvKC1q3jjqlu4q12QfdfFAcdXi+bzBoMzxnNyvY6e7r9ftt37Kg9dtoX/ILfxO+kLheUbal7bQgRJSKkgzFqSTFzwQgP9/h3QShIRHzMGPo3+C7+LfrMb8sMM4DTKQW70y9BDNbvC8f+wzkRXbJ9NWpcj8netXrp6hpnkMKrjRC14LMIVa3+qRY2GEvjHhzSufn8L9jL0xI2wThOR06JLoHcaNn+Q+qMC9mIUu8zCtIEAQfRu8kl4KL85UNF2td9Bv8PlrcEwRBEO4xC9VZknUAgxt+47zyXr2AJUuiTru2MW3dOsqDy/bqV+GBEfc82gbhGFe2fxkjGynW+wzA2WcD5eW2qrebNsEX/vwDByoriSoX5cHFoyxkLKqcj4XAMqI9+wgiZfD58OqkK/DYmJsT3RNXNMjdl+guEB7RtOAP1UzmZyFP5jbG1NOBgMTadrnOL0lEQQqudEEQPA5RaO7BlQwJVUn0TaQNbdqIXlPJAsdM7+n717697unEjzIEkQJ4vDJnTL9Ot/M+q662LHN2t6dx/4i/4LOZ57tqiyDqC+H1etyF2gSRhPhM3oM/dXwRr0y+0nnlxxwD5OVFnR5c9S1O6vQ8EHQYaqikBEE9D65kZf589XGnTsDYsVHFNvZ4Cu2KNJ5XNTXAwoW2mpN/U05Not8XAtasAYYPNy3nOixkuD3OfGkEEVemTuUvyxj8PoGE7URSoTQm8Er27NN5xkMJjKCUDDL1dCOFVlOEKYIQ7e3gyoPL/GUz20DEC9rME2lFTU2iexBBGjusrBmdvIPascVAlg4gsQsOgkgZvFZwQdCNx+9jIeCUU5xXrBBOMabThiDgkj6PYU6r99ChZLvzdgiiHvHLwULvKiMhLZHiMBNlg5M16/UDHrDMOV2SdRDXDnhQrYCx+S7pKrgcvo+xzMGFqipgyBC+qpzs03WUWIwBWCHm2nxv2sXoVbHFtAo/CwHNm1t+FtmDSw5RyPfFaXvoZyHbbnwfH7sJNXl/2LqHqF/4mcvcPHaeSWk9btdbMhXJDRyR/17c5g1c1ueRBPaGMIJBQEgxJvtZyJMlKkP03JTB6tAkf6f7d84meYHD6G0xnxH2IQVXOuNGwWVxPdHaZlJuEelIMoTkK8vaH1Mhl1ZpZraYTpqcZASRAng1fvgUAkLlXOvzMaBdO8f1Rs3b2lwcTuM8DR3q7D6CSAPalfwKgHRTBAF4H93jT51eRJbPIgF9OApBr17OGmEsyqCLd58rgAHnn++s3RgTbdBm8Jks5n4fCwE9egCMoWfFj5bfjU9PyakzQDry4NKpx0norM6l20iSQejDGOpOWIn13Z82LLKs3at4dfLlnrZZX2hWEMnvPaz6K8xv/U4Ce0MYwZgQ5cHlSYhCnToCfgE/zFvvum677Fx0GrqX/xT3dtMdUnClC4IQLQzWscDmxWoASYZFWT2ai4l0J0ke5rO6PY2vZp/L3R9WWmK7jbqwxeSiRWIdRgUrK5NC4UcQSY/J+/rpsefbrw4CkJ0ddd5N6BI/CyHTr0nqG9DkrHCq4EqS8ZMgEkHAJ76Xnhh+0bvEzd+G35noLhA6mHpwxerxXrVK9G4ePdpxY65CFFZVqQ5djQWG351get1o/lYWL8064KxLNsvzrlVO7/JfnNzpOfmYawmi8/nDYzBBeILPB7/PfAdclHkI7Yt/MSkBIGTjufT5LEN6pgP/m3EBVrZ/RT7W8+YhkgMG9bzoVfhMMQeXEH2SSBtIwZVGRA3PjGHz3PV4b9rFmNL0I1t1Gb7n0gV5wOnd21a9XkGCbyIdSfQSqzDjMMqy+TagTheEdSE/MGUKUFkJAPh+XzmtKwiCE4YQfllwOnf5jqUOQ/317Al07ao6xeW5bfAy+1kI2f5aRTGBy4OrOJNvPFrT4UW0LvqVqyxBJBMvTbwSp3X5j+P7Ex1Rob6yoPXbie4CoYNZCH0GAWjc2Hadco1Gi9WsLNG72YVhqZchuRMisNULLajoy4Gla9FxQXf5WlXubu6q5c/D+f0GWIhLYLm200u4ZsBD8rET2cLK9i9jbOPPuPYxNwy4HwtbvykeDB1KsgzCMVzCfrtGY3PmpP0TWZR5KGrNVB/CMqYqQa0Hlwe/FYMAZjCXxHtMJuVqbCAFV7qgN4n5fGhWsBM9K37EY2NvsVWd2Qs3oeYTTGv2kdxGomA50RbmBJHKJDrnlKy4lt7rWEz0tSE/MGGCPGZ9s6eBYVma9glCDWNAg9x90Sc9xMcEICMDWL06+rxD/L4QAn5FDi4IQH6+6T2hZStQlbuHq/4bBz2ALqVbHfePIBLF0OpvcHHvx/HFrHMd3R9ehnslpKF5l0hlzDy4AAAFBbbrjIfQy58VsC4UDzxcTzAI8rohd9xQ0bhNYtuCM9GnYjNXPT4r7zEFl/V5BK9P0QndZnavnIOLD+XzsKH7UyjOOsR139pOL0XCozHm2GmdSE/O6PIsHh9zE5diims9bseDq55AyqzUgZWWyHKpO4bcg9KsA2mngKXnMTaQgitd0AtR6CYHl8kL9+T4GzGhyafiQVmZ4zYIglATchOixANkBVduLgDrNbaTIaY2pPbaMNvgJlrhRxApgccKLqaoU1mzKwVXVC4OAAMHAn37ysnjtesJ7o9FLqBEipPlr0PT/D8c3Rt2hKS3AGiQw6cQJ9IX5fwVdc3hHBYPRcRH//Ph8Yl/kY/dCL5i2l8bIQoZUxjM6hi06HZTpx7ZW0Vq22yv1Ln0Z7Qo3GF4XRdpEK3T7E900Xx+xgCccw53U8FQpO/kwUUoGdTwW0xu9jHXCxwUfGBFheaFBAF5gcO2+pDyHiVDhti+hd7C5IT5ffJ4ubTdG55t9ZgmlxcDoiOKxAkGABs3JqTtdIYUXGlE1JSUkeE4ITzXGDJxIjBggKP63cIgkEyLSAuUm9hELyuDAgPWrOGycHX6/rULxwwPb1TBDBfUUUp7gqjnaN8Vn0cxyVVtGAjW3Ci4AiwYfdLvB5YuFZPHA2KYp8suA664AmjRQuqL4yYJIqVwKlgKvyMNc/fwCUzNhGeMpaywR1i+AoMbfpvobhAJxtKDK34d4S87ZgxatQLalvwWuZ3zVr3X2ZXixGMPLrO+8LbkD+e4ysgAYG78Jndf+zm0x8ovThJuXvnxaFii8bxieblA48bc43edEBGk0h6H0KWBcWSTMFzPmyBg1+JTcFKn5z3oVIpgEVmKIXrMTHmlXprCWHRuylj8VgIAtG/veb08sHZtgZqahLSdzpCCK10QDBaRp5ziyMvKMLavcoE4aVJCQxQSRNogvVf/2drR02qXtn3NlgA8GPIBXbpwl7e70BCWr0D38p9U58w2qmTdSBDmXNL7MXn80JVbz5hhu07DFJwuNhZhAdVNA/9PrMtICFlcDBQWyjn6uEgGYSZBuMTpY5yZIWBxmzdQlGnPUtsIEvUQqYzZa+ToFcvOjqxFPQ8HHEKjRpG8YE7mWD3Dk4R4cOkVLcjHgbpMk6qkjmo6LCxfgXE1n8rHsgf4mDFAWZnptyR/hwb9DLAgDh+/RjwYMUI0pmnVCgBQG7IfJtLWI+HzoS5EchPCgiZNgJUrDS+/NPFKrO/+tPWzl5eHDF+IFDgW0BYieanTKrg8+K2UuSFlxowBjjkm7mFj6d2MDTTL1gdOPx2oqrJ1S4ZPx9oa0DF7SNysQIMCQZhzx9C/oSiTLzY8EG0pE1MFE2PoXvYjRjf6wjjiSexaJ4iUZ1m7V3Fmt2fleTmk9746mKOVyielNxdvVf834q/RdUr/r+74surYE4IG6xWCSAXKy13d7vMBdw27h/+GJJHmVGTvxRV9H050N4g0g8EbD66K7L14ZdIVQJs2MfO0mVDzKbZujdTtpNu6Cq4YrN0rsvebFzAIUTi/1TtY0f5l3VvMeqkM5Scb3ZaVARdeyBe+PKBRVinWNFn+OvHc7NnAmWfaNtZ1/P36/ZF9FiMpBqFG9f537274fAxs+B0KeQxamjYFJk605dmdJMsD53B8AGUReguTFwZBNQ9g1CjP6lUfQ/Ti7d/fcZ0ndnoeJ7R71X5fUv19S1JIwZUu6OXgClNaCowfb6s6QwVXmzbi/1L4IIIgPCBJZrhXfmkd1/Y+mHEx/tz3n4bXKXwHQajRfSPCCi69vBROFFxuNnyMYW6rd6NOv/1bc+d1WtG1KwAaL4gUpVDMo+H4vbMbSSHeJqoGNM7bjdO7/jfR3SDSDLMQhXbesaLMQxhcJYa8jNUbE5VDx8G76bkHl8F3N7Dhd/hj0Smma4rL+jyC9sXbVFUNq/4atw7+PwMFmF58RfGc0uBOFVWGMdMcXIwJYm5PKZyhSUHz65z3hQ+5cqb5/eTBRbgmPI4ZyurkggyYNIlUOAoYhGhb/ZLihPSFMIcxRBS5kyYBxx7LPYczmEcvUg7jgt5Jm/y5zz/RpfRn2/clifgv7aBZNl0QBNsT2KhGn6NTif7LGPAZDAwFBcC11wJnnCEeJ9KDiwYFgrDEzob+3d+aqY7Ncu7EIw8eLcoJggNpt+aV3NoXgxwm81q9Ey2I57G05BkFysvFdQlBpColJc7v9fJdZQxBITHJtr2A1gyE2fxl51WRyzoNxW/R2PDqL7HqsmbqW5SJ7zlzXuqFIY+FggsASrIOGt8nCFjX7T/IDdRad0Q67wt/XuV3LLUfVBisaNMm6Hqrh28HxNyehteNQxhe1Osxw/ss6+PB71eF3KIw7IQunGNObkateQF5b8DxnCVIqJbps/gMcYC8uJITBgG3DLoPm+euB/LzxRyxLnJBy/Uynd/c5fMf8AVxNJS6a+d0gxRc9ZiHR9+ma2kNmFiFMAbk5CRF7i2akIh0INHPcfOC33Fx78cAAOf1/LfqWkz7ptj4GrVDHhkEocbM4tlM6GOrDbEhZzdrwwIBeHnSlZjU9GPVusFyk1Jrc9Obk2OvPEEkC4wBF1wAluvwGfZyPU6WY/xkZSW6B4QODPDknZCVL0Y5ri07Yn7PTQPvtxtcRRefzvrZJyQgbG9YmK44pfoKQqGovxe1eRPTm32g/r2kv3MUijK/xujWLESh4b5FDlGo1zmR3MBRw3oN27PzaFRVqTzTksSZlkgSGAQxTFpRkXk56ZkzM0J11Yc44vf6M1i8kLqX6UVMWgoyj6BZwU71/MGB9nfO8NUZlvUpwhoLDlUjfibgSNDCa5iIG4nXUhDeYBai0IAMX9BwkegzSPzqllM7/xePjr7F0zoJgnBOadZBrO/+NAD7C1tHsrDzzhP/5xhbzIQKtw3+Oz6feZ6DDkT4/bhTMbuFvpKfIJIRBgGYODFyomVLMSE1vFMIq/Ju2b25Q4eoU70qtohrCqWCy2qsmTDBbssyKw3yfRBE0pKZKeaXcYLHHlzxIhYipbgaxUybFr+2CG4YBKCwECvav6zyDtjY/UnRk5gT2TNq6NC4yT+9ysFVnnsQ70272IMeKQh3rrKS/xajt1wSVi5r/xoeGXObuI7R8Lfhd+KNKZcB0AjBGePLwWXZOechCvXGGbOwiTInnIA6svInDGBMABYvti4HThmdrHROXg8uPQ/UeMIY2fUkK6rfRdo/8v5U2rlnUpOPcWbXZ+Rrut7SLh4EHxNwQrvXcOdQG/lwiZhBCq40wu76O8MXRFHmIXs3aV9+m4NB59Kf0aP8R3ttcnaFIAj7KIXZ5Zok0mZeFo7cxEeNAqqrxb85rHG0m9jwQvjXhafjhHavqSw8nVCefSDKMpSovwjLV6BPxWbTMp1Lt8apNyYoJ7916+SFf3gTe2jpGjTI2RNdlrd6hTWbapPAU9ewYWLidlV9Enas6hs3Bq65xtHG/JbB/4dNPZ/AvFZv87dHEAmG18CkffE2/G/GBZETXnpwxTE6g+fW5y1bUsivNEGevxzAmACMG4dbxz2OTH/Ek+mCXk+IluCc+JgAnH8+0LFjTJ4rvTW0cgzQ88zSQ/c9Ki9HzwqHe22jef6cc4AhQ4C5c/Wv6+bYUhwo1/xKD5Urr5TzEAIA/KICqDz7ANoXbxdPaUMUmnlwGV3Srml0CnItlzSfM1xfMCPb+t6SElXfabwi3PLz/HW65xvl7ZLn8/d3NIlnl2zh+TrA4iXWjiVA4iPpEPqofhfJsMLpb1WSdVDO+a6de716Bitz9mFJ2zc8qYtwBym40gUHIRT8TMCqDi+LsU1N6k1G8jKOJLoLBOEd0oJserMP4t502BJx24IzsKz9a6prvBtsbpQLT4W1utF6VBvPuEPxdgjLV6AyZ19MQjMQhNlT9ftxp+L2wX+PW1/0MNu7hQUn2YE6V4ITwxwmPNIfnw/o1cuyGFfv/H7HS5Bzez6JxW3edHYzQSQxjAFdyn5Wn5BQvi/ti7fhpE7P26s8jgouBojhmFIVsrKLGZ/PPB8PjbrN0b0+JojCsE2bVMoExqBWpPDUU1wMIDaegVY18q5xdcstWWLbI7Qsaz9mtnjPuECjRsD8+WIubhOU35WhgUyTJsCiRcCZZ0bXN2IE0KyZ6pRWKG22vuEWgLrwwFSFYQwruOYuML3nlyffj7qXwrATSri9UxiASy8FAFTn6RsDzGz+vvz3m79Ge0ga4ZXStTJnL1c5z98Ai3lZLwULTeXJCWMANmwQ55327SPneO5V/L2h+1M4s9uz0XVLKEMUOkYnPD+ROEjBlS44kAL5mICAL2Ru0ebxqC/AmwSBuYGjZHFBpB2eW/ONGsVdY1Xu3qiNstXr7+odLC8HTj8duDg6jMqQqq8BAD3KfwKDsYcVjQFEPCnIOIJADDz+svy1OKvb0/w3GLyYX+5uKP/tNITPk+NuwP0j/qrIV6F4xxxqm7TvqZ8F0an0Z4PSasjCmagv2F5uh5VRCqWUMg/flf0ewdK2rzurMw4wJgB5ea7rqcrdjS9mnSuGDjMos6bDi67bIeJDaNkKlGYfRLbfvof+9GYfYFm7iKFWlALhoou469JVHNmZA516T5u1r4NuiK+yMmDVKltt3zXsHjw46i+e7vsZA5AteTa1bau+OGAA0KJF9E25ucDZZwOIzP/aLpl7cHF8b5dcAlRVRd/Ls6fQdIb5xONgZXR9ShqUiTlgSKlFGKF9/jJ1lDEAgDZtgNJSw3f13B7/xjUDHpKvdyrhW297xTPjr8M/OdOReCETBIDjWr+JncedYjl+BYy+UyI5adJE9By2MS+VZB1Ah5Jt8vEJ7V5D66Lf5GMGqOZyMphOP0jBlUaYLpo0i/KyrP0GBTUToXazG0czh0Z5u3BVv4d0r2X7jZMFEkQqoXylPJ9iXb6vnsS5V6IVDrRuLSq6FP1kCOHlSVcBjOHsbk+j9oTV8ga+jifGve0u0WaT4CPA+DZGOf6jaCiFWPpx3ll4cNRtWN3hJcPyQxp+g0v7PMZVd6avzvC9fmRzd/lv+d21KbDuW7kZjfN361/s3l3/PC+SRfm+JSfhxn825rqF6/2UkwOry3o+fhFELJDmRdsGG+FxQDEeBEM+TRGbdfp8OKPLsyjJOmDvvgTCIKBd8a+GuXEA4MZBD6gEHt40TONLLHDztV7e7xF0L/9JPlYqfJGfD2RlcdflZyHF3CLhYVQTq/fdcjyQ+mIYacHii2xd9CuKMg/Kx/J86fQH6Nw5ugsQgAsvFI3Z2rSxXWVQ731mDOf0eApzWvLnUwvfJ/YJhuui49q8hbuG3m1ej0HEnLIyoF1ra9mE8l4SqxJmnNL5Obw6+XLjApZWqFJ0mOYfetgrc35beBrG1nyObM4UAl4pF3IDR1GafdByz6PrwUVvYlKi97vw/FZ/LDrV1HmDQVC9Ol7k4HK8NqB1ZEwgBVe6IAi2hucdi04zvPbJTHVs/7enXmrsauzIQo2PSU0+xqldntO9lu2vpTGBIDxALx51GKsxxat3UHfB4vOBMcDvExBcJlqiHg2SCziROHxM4FpcHzz+T+hQIuaOqMnfhZktPkBHj4Srmb6g4YtXqwjp6VS5I1s36rWxcCFfJXr3hnNzDRyInPPPRKAtR8gUxtQCSr0iCBl+H/H2/upW9iPO7/mvuLZJuOeZ8ddhcZvExM3/cta5XPkodQkLcpQGIm4fecZweb9HcW6PJ11WxNGUF2FhCMIEN/oopeBVnkucvqs6WEUBthT8hhVcDgXEX88+F1ObfSQfuzYI6dQJOE0tW2AQxLCQrVs7qjJkYNS2pO0buLDXE7rXrD6FmdK/JOsgFrfVD208t+U72L/kTzrtifXl5ABfvKkfLk6J/D0LAv44km9Znqg/aMeEvIyj6FbmIu+vXi5di7JudfgVOcYG9HroKZxcYeXBpZeDizx4khL9OZLvtzIzjtTWy4wa4+QfI28Hgg6f4yRNBZTqkIIrjYiJJ4Lfjz6VWxxbiNnhwl6P474Rf7Usd1GvxzC75Xs0KBBph9b62jUc72fAFzSMqX9ej3/jvWnRIQQBMYdfLJC7rNP3IyG1giscGsQNNIrUb8Y0/gxtin6xLCcsXwHGbCyuo47tP6vNCnaIiaIV+JixQufsbs/IfxsJhqww3WxmcyRR14E1qQHmzBHHmeOOA2pqOG9kltO8ygpP863He4lwUqcXsKL9K/FtlHBNx5JtKE2Qx1Lb4l9lobnt5XQ45n/Ys7JFi+hcNXb3BXEMUQjAWwWXSYhCALhr6D04rwcpoNOB4dVf4qd5Z0adVz1NjKnnXZvPmnLfK88ldiYVB++S8gnmWWuc0+PfaGqWZsAC5fjg2oMLAFq18nRNbeY1b/T9WAnz8x3m8M701yEv4ygAYNeRSGhVu2Gr5e/ZQ2Upkb648i6S3uVEhGDjGUXennopOnlk/Me7N9O+r+S9lby4+W2Uz7yVJ5gc5tfh3Der5fvWhYi4QgqudMGpJGfgQPPr0gJdrt3lZtTHQoYDVuui39AsP7JQN/pEazu9iJKsgzQpEemD9F71rtwSg6rN35OALwSMHat7rSDziOyJEnUfZ7g2FQbjlO67nJurOpzR/H3MaqFYRJxxhv32CULDP8fcgr6Vmz2pa0LNJ/hj0SkA7AmWGROABg2izm+euwG9K7aozvmYsddDA4WntZXnkx4lWQeQE857ogznA+DK1d/brk+Fw7WDlWJQJYTUlI23BxdZgaYm/hjk1bMFp6BzfM0n6hOjRkkXxov5dv70J5WnKIN+KC1Twmt+xaN859B78PwxV9urhwPP306TEIUA0KdyS1xDNRGxI8MXROP83fjg3MfQVmGgolpLap8Hm/tkpphrT+r0gv08boEARjf6HCOqv9SvX2/dq8wLwhGi8IJe/0KG0fjFMecqW5DXDAHvIiW41V+XZh/Ez/PXAYMHu2+TMcxo/j5O7/Jf40Lr1hnWq+f5AZhHwdBDHpPJSJfQYFuuZbWnlvYVybo27eOhzEN+oy3eKz1lH/mRJwcndnoehRmHTMtw/VZt25oqdbXvmWe/vxNPZYpiEBNIwZUu2AxRKLNwobmSq6TEaY908WninirRxkQ1Ilwky1+H2wf/zbO+EUTCkB78c3o8hQ+m8yfB9oIAC5lamhotuHnDtang3dCVlwOLFqlOPTz6dlzd/yGgqAi44AKgVSt7bevRtKn7OoiUxs8EQ8GFHkZT1MOjbsX9I/+Kkiwxp4X2Sbd8Vxo14m/fIOwIcy7LAyDGLTeag0+bYyNJtTYJuwtjFCtFITNS+FVWJsQEhuXmJKBVwg2WguRYY5GDa3LTj/DutEvw1PgbIyf//GegStXMAAAApaxJREFUpRTmMxAAunUDcnMxq+X7CC5bqa2aH521AIOA8mx7YYd48EroZub1Has2idhjppwNzwvdm/6h+k21j4ATQ48wSgHZ5f0exY2DHpDzSHLBGP7zTjHOUnhWqy/rCFoV3Y2l4v2xMTcD0Hpw+YD27YGMDFd1K383V4aol14KnHIKqtctAGbOjLps+5dlDA+Pvh1ndP2PcZmWLYGRI3UvyYqszEz1eZ9aqWrWPuCdwTCRfuiPCQ7foUsuAfJET0OuNU4insfGjT1bfcljjYPNDxnMJwfXDXgQOYGj8rFuiEKe3yovL7rc1Knyn4eCGdh+sEjRjke//8knA4358kvLkKFDTCAFVxrhyFqZMTFwtJaw98TYseLGWVneBVqNeoeSbeYJNHUID1qMAcvav+aqP06ItwKCSG+iLUk8nOx4QxTGa2HLMZEzCMCZZwKVlfoFLrtMYZXmRZdok1mf8bMQrhnwID6beT4wfLh54fXrDS/V5O9CYeZhR30wewJ1n08DhfTUZh/hibE3AVCEKHTykvTrZ/+eGGE1YmT5dZK6l5QAp5ziOEyjU3yanEKdS7di+wLyNE12EhG+R4WFB1fT/D/Qq+IH9UmNh7OS8OfRfTeskKM2iM/x8navYFSjL+zXw4HbvAdh/Cwkhj+dM8fWPqhZwQ5MavI/5w2TgDo28AqICgqAyZOj18wLFsh/usrBpaz3oouAjRtN3ztdqqttrehtrf/txO/VMKWZ+Nwr35eQwEQBnZvnWhNW2NUrUloKtGsHdOkCZGVFN2XwXdnJN6SL4gNU50ZCRAd8IaBDB2DgQNw++G9yuFO7IQrXdnwJZ3R5Vvx8BKFA76k0fYUmTDC+plDG21njeBX5gPc99GoPzhiAqiqgb19n9xJJhxt5WJ1i/8cyAmKkg06dAABHghmq6CjyXM/xIHQq+RnC8hXRFwYMEI3NGjZ03GfCO0jBlS44WcV37Kh/b1YWcNJJkb81nhQqGEOP8h/Qo/wH4zIKtF4fZVkHMKjhd1JVOh4hOl4aiZ6Iupf/lNgOEOmHl0nibWLlwWWEn4U866utenQKfz9nPW4d9HdvOkOkPWd3exovTbwSgPgcF2UeFkNxanN2KJk6FWja1Fqowhiwdq39TaLBS1Ab8gMAXpx4FQB1Di6t1VmWvw6Tmn4MQG25PrTqK/Rv8B1/X5YsUXTLwdpC68HldJxgzDLxfX4gkk9D7unkyUBpadwN48QcbZHjDF8QDXP3Gt9AJAUJV3DF6EHNDRzVH4fM2tO8rLcNuQ+N83d72zG5Kfefe1qzD3Fej3+L1uo6YV7N6FSyDU+Muxl+J+GWAbK8jRUGHjRKGBPE37ykJNpjSBHOTjAQc8xq8Z5lG6pwwBUV/LkjNRgZWujObIpnKh5bgU6lEc9sq7mWF+XnTYRnBNd4zqHg+s+Ea/HomFvl0wFfUJSNZGZiWfvXMK35RwA0IQo5FjtDrpiEy9ftiISYJQgJ7vclPE4MHQqceGJ0PUx7nLxzlaceXOefr5sreG3HF/HtnA0etUTEC10PLs5p6tweT2JF+5d1r4UEhuxAnZzH0877oVt26lRg/nzuOjQVOruPMIUUXGmErb1Wu3ZizH49Tj4ZaNaMu6r3p1+CGwc+wFXWz0Kuwx/IyQBd1kMQyYinG0Kfz7S+pvk7MLXZR6bvkt7cu7zdKziuzVvc3eha9hP2Lj6Ra5AKCn7uCT/82QoyDzuzVifqJW2KfkWnUjFXjepRKy+3vNcwxG74/M03A50727dK1FT89PjrAQBHQmI+jGHVX0cVM/f8ivz90qSr0b5YP5desmOmKOxVsQWX931EbzdveW+iOHqCwbqLSBh+Fkrss6KMksALxxzZKG+3faG1Qb2xEJB5sda5ot/DWNruDflYW+Owqq9U1rbKT3egTgw1Vrdstet+EB7i8wHXXce3p9UYQaieU5N35B+j/mLdDYUxiRuMPode7iZbzblVsA4fjjO7PotaaU4Kh1d2BVO/1bGU3RnVbZgTi9eQT/pecwJHVeGr/ZoxMPz12/6MbduKwlCSYRAaHIVk4/AEjPvqxiKPX17gMB4fI0ab8MxjjBm/kH4WQqbP2JCFQhQmlm5lP+I7TgUk72/Vs+JHrOn4ku618JohbLxl5wnUNaBo3Vr1zD886laUZ+/jq5AMpWICKbjSBcFmMumePSOLKzsvlwchCpWDkyp2Om8Xwn+0aeOqL47p0SMx7RLpCzPYoHtQr9m48NbUy8Qwn336iBO0Tpx7PdZ3fxo1+busC4a7AQEFmUesC2rhsOR1SzIKwYnYkumvUwt+160DxowRrSGNsJgnGQSga9doIcqECfZjcgMYV/MZAOBIUL1RZBCsBTXdukVZjcc7XJ8KFwt4M0Xhhu5PYWGbt+VjuaQ0nnplkc6LONqar2kyYphXhXCGjwlxf1ZkxowBJk3iK6sMvWOxFheWrzCeoznW8amy5VaFltEJd2S2ngomckwkzNGxwo9Ceo6DKo8h7/DKs9MoD5heaDvlOGTZuo0QhUvavK6+Nm0aMGMGGBP78cuC0zG+5lOrFrnwLAeXGVdcYfjxDX835bqJM0Sh8rMENJ6etscPstQnAFze9xH8U8qBZwlXYnrrMj3Kf+Sux/Ubm5cHbNpk2q3irEOY3EyMNhGPNAEMAmryd+GrWedEX3OST5zwlCx/HVoU7og679Xvoq1HO3bbmet58tnNaPEhd31EbKDVfbogCOaThNlCWBBQk/eH8XXGjDcNOoK2woxDmNfqbZ3CBoOIZH2S7a/lEjLIA9X8+XI81VgwruZT/GPk7TGrnyBklAqueDYLAZg3T1R2n356dMiMRo3075PeY88WhUYr4VmzRGtHq9thLsgCxLjJBAEAVbl71HNNy5bAjBmWVoeASd4HJgCrI54AcqmmTcXcHU5o1AgV2fs17cMwRKES+UrHjkDHjqrPW5hxiLsL8rrChXDG8TjBOO802JjHW3nNmKD6mnwsFAkFTSQtDAlUcHXrZjnuyO+5wXxshtN3IB4KcQa4tl7lHpaqqqQ2FcJrt785CaxjCu+zq/Lg8lBQ6ZWCy+g50/M0Ohr081esk5dKj0Z5u3DnsHvVJ7t3V3kQNcjd59njHBeD9MJC1An635VXHlyaP+HXKCR1FVw2w703yd+Jhjl7bN1DpDbl2fvRrvgX3WtR49dll4FlZbpuc3TjL/TzBsWC4cMto2EoQ7p7tfbimc/bFP/m6D4ithiN2fohCm2EE9Q516rwV1VoXrGcuTsuQ6R/lusCadLgNoCgdWRMIAUXAQD4cf7ZaGyi5DJ8ncMJqRUFVnd8CX8d8jf94iyksXAWxHCJEDX4UQOCzkpZHgtyc0WhZAx4fMxNeGT0rZjV8v2Y1E8QRnhqScSYaX2MwdxrZf16sMqK6PvsdsNmeRV5eZG/+/TR1Mv3Xc1u8S4+mnGh7jWy26pf7Fp0MoZXf21oVW31TNn2sNQmZjKq06DMvcPvwvYFZwAAzuz6DM7u/nREwWXSprxp9PuBE0+U59ZjmnyMnMBR486sXas6dLT59HDBHhZyvjxJzJk2r9XbCC5bydV+3HNwaRrVhjRSbpKI5GBJm9eRE+AzrkooTZpwGXtocfQOrFiBoNYTykE18UArmNB+XLnfHToAZ5+tutaldGtM+0bEB8N3123EERjPy3YwUtTpCfW0HtumLFokRjI5/XTTYqZrGg6jHruovJ5i6LFs9LsbCiD9CoWYhQfXxb0fQ8/yH9Gj/Ec5lFpA83uF9Ix/fD5k+03WVxq+mnUuHhsb7c1D+YLqJ1H7i6IisDatrW6KXYdcYCp7kP/wTuJRF5YfOvg+yIMrsSjnCSsPYCdPu/KR+GbOuWiiiW5gFt7ytsF/x+/HReZYQwOKMCHxOveegkIUxgRScKULgiAPAw1y9uDeYXfKLxkA8wFfulYnJbM3ymehey2s4FINSMbounZKdWb7a1EbsrZei8dENLnZx8gN1OpfpMGI8BClRwYuuMDjyl0ufAMBoKQkulrpHeSt3vYbo6x49mzRC+KUU4Djj9fUK5azWnBoPStUdSS7YJPwlOIs0XvJ6Hd36vGgvUtbv+ViVxDwztRLok7nZxxBw9y9AIA/9/0nVnV4RaHgMnizMjIgQL3ZC7f/73E3Gfdh2TKgc2d1v12ItvMzDiu7YB+Fom5I1Tfy6SgBlkEDcffg0oQoDGhi/iepHKJec+ewexHwhVw9567gfSg2bJCF0YJgcp/GW1T3HbBaw/boER8PLg88ZGSFgM8Hq5DMytzCP847C9cOeNBd47QXSCyS8YgqRKHZ62Txe63q8BJWd3hJPvbOg0v/vN66VRnKuyrXwrOnQQPgtNPE8OJ6cOz7cdFFaiMyD1CufWI55xlZyLsOUcgY1nd/GjmBWvh9ghxKLUcjE9Bt3+/HrkWn4Iq+D5v2PUx2oC5KcQYALXXCdRHpASlT1AoFr9YawRBnPV26qA59JvIBIj4o50Ivl1VR75rBD200Z7wz9RIsafs68gKRedly3SpFJaMQ2ImFvv00Iryx61z6s5iXQjlKmI0YYQFYeGOoMwCYWYqLbWvL67ennUiU9Wb56yIWGCYwJkRZYlpxXo9/2SpPEAmhQQOwfHebzRcnXoVTO/+XqyzPQltvPeDVxp+L4mLgxBNlT08l4QWEn5mLJ2ntSmjxSpEVOW/+TpgtdsP39q78Qe3lY7TrkkMUas4ffzxQUwNMnRp1i9M3VtdK2YpAAB8fuwkPehDiV6so1FVMGoQFeuu35q7btwNjUH1PfhZSHTMIlqFbiMTQsWR7orvgDTU1qrlS+d5PafoRnj/maq5qHviul6tubJO8Ts0IvxmmIdItEEz2LSqCQVWxvMCRmHqXEO4xm7OUv7ZhiEKL+VNL74ofcEGvJ+Rjn5WlNidGq1O9dXST/D8QWrYC383ZgPN7/ttdwzxzdkkJ0Lu3u3Y0xGt3oPUyDeM6RKFOuanNPsSUph9p2tepLxBAdqAOfSq3cHtsJ733MOE5xvsIvXP23yhtvrhEYKYIkMc+n8+z5z/I48HVo0eUoayPcnAlHHnMrqxUKTx1QxS6+a0MZOFGT0zvyh+i8ib7mGAeen7SJGD4cP7nmrSrMYEUXOmCXlJUzYvcseRn/dwb4US9JtYPgqasTEYGkKmOD8xgbA2hKxiXCucGjqr6YGRhz0pLI5aYHANDt7IfcX4v/o3C+9Mv4i5LEJ6gFYK6YECD73BFv0ewf8mfYjZxhheuyve5JOsAhlR9HVX2lM7P4eLej8ekH1V5omeLNjY+QVhRXXIIz4y/zrP6tK+ads588Pue5hVIHteCclmmDKkzfbp1J/r0ET04SkpwQa/HcVGvx+RLKit365oi3XJiheb3o3PpNlTm7LN/r4YXJl4te7at7vASlrV/NbqQViAlhyiMvweXclOjXe/4mACMGRPXPhF8rO7wEn6Yd1ZiO+GV6apiMFK+A/kZRzCi0VdcVWw7UBw5OPtsy3WJNu9uleR1at5Nsc4f55+tu3bgQTvOat95WcimCcXmydKIPLhiCk9EDwAagZjJbxKeJwxyV2k9cH0moYPtYDQPGRmKMQa0KNyBLH+d67YBg/le+bk8fo5VxkMxfEeMZBZcHlxmoRl1FFz/HHMrOpaqjSB0BZiZmcDKlRhS9Q1Cy1fh6Amr0KX0J+O2YKAoo3VC2mLXc9mJTCBDEz0Aa9ZwNOTRutkqhDoUn8nDEIVmRoTymDB+PJCd7VGLhFfIv0+TJoaGC04IT3PyUzZunPj/qFGqucnKaDtUFcl/64Ngng83JweYM4ffg4vWkTGBFFzpgiBEvyOaRdqnMy9Ax9Jt0fdKCiozDy7TGOdXX809IWo3EIwJgCDg9cmXoWPJtuiksYKAq/uJYUTC1nR29xt2rEMYQuhRbr4YJQjP8VDB5WMCfExAXsZRbdU6zXK0pZcHT/pfaSlZnbsbL0+6Kqrs6V3+gwlNPg03aNlcTd4f3C952ALbb2GBxUyu09Ii8ZRn70PnOOdDYQX5GFvzubgYtXMf5xOj9RD7aX+pSZ0Rnhp3PZ4ad714UKHIf1dSInpmTZ1qHaKQMZzT4yls6PG0vA5QzoN2nnlH74fUZnh8cTymMYb2Jb+gd+UPAICbBt2P4dU6gnA5F6h6DTOx6cem1bco+J27KzxlGRNUgoWAxoOrb+VmstZLUhgDSjIPJrobuvgUwiAZnvBjUAt9ZI+UkLVByLdzNuKvQ+4Fhg1ThfbTcna3p/HFrHNx34g7LeuM6qYHsy/X3qN1a1mwIYdXVrZtZo1r3riz+wguDtZlWhdizL5ATCcagFiV+vdsnLeL3+vHBCNvcV2hWgyeKeXnKs/eh6rc3eoCHOOBHeL1WhgJEA09uJRjpt9Eear8zRcsMCxWnr1f/0L37vKfGb6QpaCTJ3INkdy8OPEqvDAxev9rhNHc55W6J0qW1qUL0LWrJ3XzwpWDKzfXsxCFdSZG+owJwAkniLlMNfx3a3vy4Eow8v5UEGIbHrt1a+C664CZM1WntblcteRm1uHZCdcC4I9gxB0yk4gJ9O2nEeFFtPyiFhYqLpq8kH36ADBXYpkOOBkZ2uKmoR30JpIBDb8HY/ohB07p8jxennQl/j78TsP7zfA8DwdjGNjgW7QrTpOQNkRSYVf++c3sjcjyR2LDqzZ3jNlLWq3XHyH6XWYQgNWrVRO9UbflMpWVovWUCc8dcw0+mbmJu2/hui1zcHHXSCSCFgU78PGxF8a30VWrxATtp52mOm1lEGEY9chE2QSoc0iZMb7JZxjf5DOgRQtgzpzIhaZNxfdH8Q5xKag5rCnNcBQ+xCsljkU98lUDa+yWheZKqe/mbjS8VpJ1QPW3WVllf4qzD0NYvgKA5FkqfYY3p/wZL0y8GgiF8PbUSzGt2YeW9RH1AI53RZ5DHSi4lF4w8lkOgXZx1iEc3+51y+Y6l/6MdsW/WtZniEtpuHZ9r1vb6acDBQWqU6qx0+V41SBnj6v7CX0sBV1yfkmb3slDhgCTJ5tViW/nbMC1/R/0ZC4zmkO9CoFoiMYQ5s99HsX3czZg89wN6s8V9DacWbxyX7Yo3IGuZdEGqYYCSMZEy/3hw809uJQ5yQYPBlav1i3WqXQbtnOEYjVVcDVooC8IJUOYlKI48yAKpZyzbtANyWb1KNgxSrfbuAvMEgcwJoh7mnnzPAlRuL7bU1jd8WXD6z4IhqFYj4b8unIOIn7Ic7ggeJoPV3dfHvbgq6yMlFPMGVW5u3VDfI5p/AUA/nmb+3PQWB8TSMGVRoQtGQUwcQGvsCLSllFRVQVccQVemXQl3pp6qe6GM6ixjLbCX1Gme54xtTBaW1udQUiKIVXfoGn+TukemwouG5Mnb0z+16ZcgYdGuc8vQhAAXHlwtSpSC3G1r+ieo7mG9/ocWi2x4iKga1f4fRpvTL02wucvvBAoKjKtNy9wBEWZh/knfEGAsHwF5dJIccLPyOI2b6BZAV9y7XuG3YWFrd901mBmppir5rTTxP8VON1saZ9/7bxTrbWa1tKjh/p48WJRKHvllcA554gJ5SOVW3UmquxNg+7H21MvBYqLbW0f3AirwnfGdP0+d25UmOQwTfJ3ycomO5ze5T/4Y9Gp8jHvOKkdu/0sJAvSyrP3i7HcQyH0qdyCnMBR2/1S8tGMOCuE6wEJ2WfaUXDxEvZIqqxEqUJRK49RHgu0naL85E7DIlVk7wPOMg4tqX0nddcpGRnoV/m9rrDclFAIvy08DRu6P23vPoILXt2nUpDElYPL5wP69Ys6nacYkxvm7EV2wJsQgUYKDt15xak3oRWFhTiz27MoyDwihj5UGqaahVtyAHdoJpeUZB3ERzOiUwqYCiBnzlQbDukxcqTo8bJ8uXist7447jgAQMPcvZZjuJlnCdav1xeEktAzpWCMf61sVoo3LKsVbvJk630Os3X0l7PO1T1v9lkYBODUU4HSUk8UGhf3eRz9G3wvVR5dn/b7EFRzBpFInhp3Pe4adrd4kJdn6fnEvSc9/3z5T93h9MQTxRzvALJ8kbn+uzkb8aCJfJcBXIsTbk80igQQE0jBlS5oX5BZs3TfaMPXqLAQXcp+Rt/KLQYKLv4BRxuGUItPm3hV0c+jSmtTk/jk+gf68EyejaUk11aeIHKb55xDLs1ETHCyr7HyHjHCae6q8GKRZxFtx0o1ZFORrhyrTA3adfICDq3iy0VCxJ7wWH/XsHtEgSUHx7V5CzV5u5w1eKGxcsDSg8swtIjRDQrDE8MiAtCrF7BhQ+Rk+NkuKAAaN7bXJgB06iQq7yRP7sqcfehTuQXo2TPqE0xv9oFheBXH1pWLFzu7zwYCIIZQk2B6ni4GzGrxHnc7TsbJIVVfY1aL94GMDPyx6JSIIYL0e7gVZHQti29ITyJx2BZWDR8OrFwJrFuHLoMKcWipmH9DFqh7GJLMzTpYucZ3UouwfAUqcvYDzZvbbzv8R5MmwOTJeHPqZbisz6O266nI2R+d74TwBMu9m07+aO71s6bg5zPPw/TZGfLz7EZArMUohKJuG4sXi8oVr2nZMvL3jBlqY7Nhw0SZwQUXeNLUln3lntTjFNehobKyxJxFPaW8qXrj5cCB3NVFhYtTkp0NIaCjQNN5kE/p/Bz+MZIMa5MRu/Og0Th1WCfiipM5VveeGAnS2xp4cBsZrAPqsS8e8v1oBVeEloW/kzwvgQyp+gZN8neJThnHHONa4dmqUHoeq6rMC1ZUAPPm4dwe/8bycG5nnw85gVpTo2kv1wZE7CAFVxoRfuV0B2o7ySO1sw1jlkKuqFtMJotc/xFF1YLqZr9q0mNA//7mfeXYzSg180b8NP9sAJqBq7paP/cAY6LA0WbuFoIwxGUOLqfLAR7PBL3+KF+7a/o/aHE/P7Ii3YGCy7QPOguSlyZdLVVB9luJJlMxRtux/tWW7VL6E16ZdIX5TS1ayFZbejiN/619T7RPHNeTqowPz5H7w3SsWLtWVJhp3yUd743WRb/p57aCCwVX//6RfDdevGJLlkT+lnKoaAXL5/Z4EjcOvJ+rOjthlJVeMOZ1RtYzL0+6Cse1eQvIyEBJlpTb6dJLZU88x8pZot6h+6yavVQ+nygsKCgAVq5E9ogB0i0OPLis8v25pU4c+72ah+3kAwYgjpHSfMCV80lJOPefvbsITnjnYkMPLi0mIT7b9yuC74Slcng6L593o8+hKyjLyxND4g0eDCxa5K5ho887Zoy6XCAgei0pvcRd8MWsc3FBr8c9qcsUg5xC8nzrFRaRJ3T3IUuXisYs552HXuU/mObw1LU30Bnfu5f9iGnN3Yc2jnlozHqIjwncihozAbne/OVk/ezI6MJjr0HevVw8Qppqv/OwEvz96RfhnB5PUojCBCL/NitXArm5lgYKZs9VRfZefDMn4lHIE8Z/U69/RQwQ9ULXbtggj/Ezmr+PRW0cRo4xgrx1YwIpuNIFQZAFUbqThfRycs2/OslXrQTPvBNUSdYBZPqDandnxarg2Obv45NjN0lNCcCgQeJC0SGvT74MT46/gbu87MHVty+wfj3AGGpPWKVblsYkwguUeVoA/udqUMNv8PiYm8wLxciDS7n5P7nz8wCA/bVZumV5rV0YQmhWsFM6iEHs4m7ddE/HK18AYUymYjNmGs5F4qlx1wOIXugWZBzB4KpvzW+2eGYME9YLYWUN3/MsaLwR9TauU5p+ZNwvt8ntGdP/rOXlUU+8/A7o7NA9SfjrxkRz+XIx0XvfvsD06cApp8iX8jRh/rqX/4Q1HV/SrWZD96dw5PjVuKrfQ+ha9hMm1HwKANi24Ax0LPnZtAtDq/SVf1q0BjsAItb45eVAaanoVTdrFv7c91HsWXwSV71EjJk3L3Ft84QotEiAbUp2tiwIfuNXyYvDQ5Np7fzerexHbiUzA4ChQ8UuedQfbT3ab0wVNnX4cPFA2vMcCmbAFpq8XoS3mBlXKOdhw3J25tCwIleqyscEy5yxvNjy4Ap3YsECYMAAdw0rP5Pb9YQN2hX/ivE1n8W+oSVLgH798Mz46zCpyf8AKLw6vaRxY2DZMmCjdR5Omb59gSuuAKqr8cDIv+DL2fph3AB+h1q/z30wt0+O3YRFTkN7E4YwCNx7SbNIQV7kzirL2o+BDb+LLsc577teHkj9yTUJw62N+uQpuiEK1d95+E3qUf4TGNOJLEV4xosG0UHCaOdBq1HuaNB+9AvDZ0z7sEtrQfl0u3ai4al04uHRt4uGi0TSQwqudEHQTK7aAZ5nxpo8WYxLrrQil+qyM+kyJuhOML8tPE0MgagsC40Hl09Ap9JtAKRBz+eTBUROBNEdSrajKnevqCjTYWjVV/j9uEiuDT8LiUK0RYvkGOVRrqqk2SI8RLvY5V3s5QWOYnKzj80L6XkgKvDCgyvMEZ3QCoC0sJSEWIYIAkLLV6Emf5dxA7qdi5QzG+LojU1uMv11QEkJAD6rv/FNPosqe/vgv5nGzebFSqFj9CxpH9kuZVuR449s8PTm0MfG3mLckI6hiYysbDMuEsXGjcDUqcDAgcZvvY5nh5sE0J4oj3v2FK3ZGQPGjhU3HNLnDxhZqep8MX4WQqY/iFO7PIePZlyEhW3eBgBxfWBy+yfHbsJNnAJ7ANHSqi5dgNNPV4efHDkSGb4QCjOdJSWvUuRzK83yWJhX3zjnHHl+SniYGkHA34ffEXU6ajnv8L36cneVaKE6YoStPpmhXR+Pq/lMVDJzCOcFQAwjc/XVUdfmtXobj442GR9dwrKzIrl4JOF/JWd4XJnSUmDhQo97lp4cXLrW9j2jG3+Oac1MPFbCIQoVc7bKAMVsDjVCacgydar9+3UwskiP63iTa5yLNxZwhft3S04OMHgwxtZ8jsbhvUOs6NUrKl8rL36fIObfNEAI6TwHOgpJM4UBIHovWNGi8HduIy2CHztrcR8zTuOhJ9y3K3Lasei0SD4qDooyD6Ig45BpHwCgUd4uZPv5c8d2LduKn+ev070WEiIGeJ6FfJs71/CS9hNp93mMAaFl9vP1EtYMM4gOEkY1VzBmuQe3o+a3LGmg4IrCJOoLkZyQgiuNCAuizBbNZpbaOOYY0SJKZza19uDSnhCiklLKVlXatg0sy2ThuwvLM3ngNNikZ/nrUJ6tTMINcWPuZGNEEA7wM2ceXJablI0bgepqDGxg7NHCtbDUeXfkMUa6dmzz9zG75ftiWE9tGxCsLeS1QuFYeHDF4n7CEzJ9QaB3bwDmcdu1KL29upZtRXXeHtd9cWojq513bxn0f/hj0SlyyCOlUPqMLs/i1cmXK+5VMHiwqBThWFB3K/uJv4M1NaJFutncpqPgcrP1dKMc4yFgJESz+V6bGRnkBI7C7xOPh1nk7fvlYJFuiGe0bu2ZgPHqfg9i6/yzAADbF5yBP3V80ZN66y0ZNr12Ysz81u9EndN6tWrXDLa4/nrRm5AXaUwwWm8EmHrMkMP8VVbytxEIRCntWhTswLTmH/HXIWGl/NPdH0lj4qjGX+Lw8WvsNWhgPEeoyQnU2r6nZeEOPDrmVstyNw/8P5zV7WkAmtBcjOHuYXdhdYeXom/Svj+aNSiP8Rcv4TXFG1MuQyNFaNq4Lj/HjgU6dxZzS8WBbmUc4aI9JNZrDVNc7leMPLiaK8IafjD9Ikxu+rGhfOe6AQ/g05nWOdR8ebmu838S0YgeXHyYff+OIia4DOk/q8X72LvkZNHbGwYK+VGjsHX+Wbiy3yO2uma0JxMEJsv2eN/ci3s/hpGNvtC/WFkZycer68Gl8RLSjhdCdJ5uIj7EI6cVtzGJUc7shQvtrWkhelISiYMUXOmCIkShLpmZUjFnI3jQLEmqBjstMABo2lRxInK3WVJIvfJ62B04lVYlhsQ6JwFRr3DqwWW6AV+6VBRoM4bXplyB+0b8NapIcNlKZPqdJUeX3yvpXXho9O24uv9DkaTMemXNUC68mzXTj4OsR6dO4v/t25sKtvwspL+490V/i+XZ+3DDAGuPjePbvhafPANpTpfSnzCn5bvyZscwRKAOSsttq6cs238UW+aebR2i0CJEotH7qRUABxbNR/bw/vL8lheI5J7sWrYVg/RCiABiaKI1ayz7KSxf4SxUAmPRIQrDXdeRtITs5sVTYCefmi0EAWd0eRZdyrZy32I2rmq9UJS/pTxCjBqFFydFe5oo+W5vRbQHvMcwFhlTG+bupU25V5x3XmLa5fgBtWsEV94RvMZbYQV769amxeTuz5mDhjl70Kv8B+6uhBRhXJX7l8v6PIKVHV7hq+TEE1WH2j2OkWJO9bUrDrL81jl7ieRiabs3sLrDywDU4Y7BGBa1eQuTm/5PPjZEmvs8zRspEV5T9G/wvTondOPG3jWih/Qh/CwE5OeLeTnDIXNjDGMQw0WPGhXbhqTFiyehlJ3i0iBWd+/StCm+mHW+nOO4+9x2ouePwXPpZwJXbi0GAeNrPjM1fCTsw5jALV978sdOuvNSw5w96FJqvaY9qdPz3mvHw4Z1iOG6XUEIEVnbP0b9BQ+M/IvlPas7vGTfy1pCu2biCYVPxAfV3owj+pjZe6a9PVx3VCSuMHr5FYcMid4tlpUBF1wghvvmnNO+nH0uXlMYshLxhd7wNCK8SBIUE4dM9+5Au3aRl9bm5PiXIffinmF3GbetGHBMk1sOGhQ9YA0eDMyfD1x4IbAu4s4sC8YzM4G2bQ0HtaVtX8PYxvrxvrWCeC3y2fHjsbL9y+ImiVPBRRBe0LroN0ceXE/91Fn8o0cP9WRcXi7GfwciSdB13h3+3FgmHlxadBYjtq1zzjqL/0tYvFgMM7RsmamCP9NIaKXT34rsfVjb6SXLpntXbMH8VtHW9oQ9/jPhOsxq+b5uuCErlMow+RmfMkW3rI8JaFrwh+Wz5T7LgcSgQaKySmpvQ4+n8Omx58t9SRjMRNXjcYhCwxCCHnB5v0eRa+QVoPMbm/3sdwy5Fxf3fkw+fvj7HvLfcp7CwkLLPh3b4n1gxQqgY0fLsvIY7RSpPwm1XE8Hwg9GdTVYzx7mZWONgYW11vvF9vjhZM16zjmi8qhPH/Oqw6NJTg62L1wnh/7k6pZB/1Z3fBmN8nZbVzB/vuW7ZvTJjRRcRGqgncXCx6b7T7PfOfzuhUMUemjAqHxfHxt7Mx6efK94cNJJnrVhhqGnc6yZM0f0HIsDdgyjkg3tHD5t1F6gc2dk9esOf9tW4sljjzWtg/d5ZQyY2+pdvDYlft519QGfjRxci9vo50DbvnAdWhX9rntNybUDHrTVNysEQNyrSIalumtKD/N2Dq36Cuf3/Jc8Hrcu+s0yOgIgKhEv6v24o9DF2vdjb22OuoCHn4+wh960nOGrE6OA6RhdVuUaR2oxGgcNjcKaNwcmTQJOPjlybv58fXkzY8Dq1cDMmQBEI1OzEPHl2QdQknXQ8DoRW0jBlS5oPbi0I0YgAJxySlTiey4YwwntXhetxQ1DFGrV5jrlmjcXJ9EGDdTnfT5gyBDR/bNFC1FABI1ySpFYXtvOHUP/JgqWdJAFAxyT1y2D/w+X9HmM+7thvtRdUBPJwSOjb8Xdw+5RnbO9sdY+28pjDxNLM0USVvkV4XhXuD6PchFjZ2zKzRUTxUth4IwIW/UKy1eoQyoJ0VZ3ZgKSHuU/4E8dXwAA/HrIWuBNWKMyZACngmv9egDqcIYNcqT8Ay6taa3yHBg9nlZPbW6gFh1LtwNQfGYL7whDXG7GtPny5Pk7Jyeq7MhGX6J98TZH7eRZfJeO4R0jFGsN3XFICmnSrXwrJjaJ5DNsV/yrmOsL9kJVlWfvBwoKgBkzrAsvWSIaHjll1Spg1ixScKU6ymdZ573+YPpFOK3Lf1Xn/HYVXE7Gi/x8UXkkRyzQhzEBuOIKR0oiv8Kq1ml0CS1WnzTcTS8VGF71neBHa8gZNkzRE2QZCZ5Xtn8Z/SqlXDXSGjRc1kud56KlftmSu3PpNgz9q5S3jcNowgv8PhchTd3QqFHc2k2IB9f8+er/rTBYm4ZyIvuX1R1ewqP/LRS/txNOQKib2ujCLIIAzzdNkWdiA2NA26JfMIDDM85qj8GFk729IOCCXo+jUJFvSzytfnLM9mC6nk8VFQCA3hWb8fKkKy3f+U09/4VFJvJEIxiAFoUGoYttRnMa1PBb9b6GFFxJhZlzQvuSX6JS4BgRvt3Qg4sxYOJEoH17J910FhI7qhJ69mIBKbjSBSFiPWKegyu2PD3+eqzt+KL+5JuVJQ4ma9bIyVr9JolXtYkHdQeScFgrM3fjcBgzM2wq/AjCC5oX7EB+xhHVOd7H65we/xb/MArgDniaS04pVJPHGEkpEbnA8MO8szCq0efyKS5rc96QhCaYLTSUHlyq3tj0OGtZ+DuuH/gPAECn0m20WfQAOVTHiBFAx454YuzN1uEq8vKAm27CrJbvYX6rtyEsX4HmhTvFawbPvPxbWbxgx7V+C/kZh+18BHX9RjRrJv/pYyHg5puBadMAOAg5ZvbOc3A4qM47JACioljKg6bkkj6P4fNZmxy107xgB1bp5T9xi9HmvmtXcSyRlFM4/XRuJaJyUz+8OmJNyustc2DpWrQs3CEeSLkMTGFMXBPZRB6y6uqAkSO5rYYJA5J8Pde9/KcoDy5TLxUTYhGWqjDjsGNBvXLcU75lXs6r2lBQ4fcnKhThtdd61ibhEZrwk4YMHiwbu3C/zj4fbhn8f3h50pV4a+qlEQVXDJZ0mYvmYuBxLeXjuI3Z0pfhZT4xJ+3Hg4R4cA0ZAtx4I5/HNmCs4Dpmkvy3duzjXeoxQNdAKapcck93KQuDgNLsg3idwzPOx6mMlLEalBjDjObvY1zNp5ZVndPjKWT51euJqPB9ZgouvWuDBwMAKrL3Y0jVN5Z9sGMgq7rPZh4lJdp1/KSmH0f2NfPmkZIh0axdK/7PGHpXbMaQht/Ix3YwKm2o4DKA9w01fWxW8CnhaFCODaTgSiPClrxcL6adF4rDUiT8ko+r+QzFWYfkhVyGMt54uM0GDYDjjgMAZPsNQgxZXJPhEeCvXWv9eZX1cFrGxFu43aLA2nWdSC1shf9TsH3BGTinx5PigXaGVT7r4RCFLjbU4f4o47vLTRx/fCTxppS4vkn+LtVigktAPHcuUF0NrFzpuJ9mC40MX1AuoPouGjaMKmvWX+VGoHvZj/Y7SUQhC1+ys4ETT0TXsq0YWvW1YflMnzQvBAIY0/gL/H3EnZoKLcZvi7ngzG7P4sWJxrmWjJ4zyyn11FPFZxzSIlwx59hWcOmEEuSGsSgPLgBiOCEPPT4BINMfxM2DrPPZ2cboy161Crj++oigp7BQDgUoC7qHD9etR2mZqszZZxXmOIwqXGJZmai8XLaM48M4pFZsL5VDMyUFybS5tOqLIKBFwe8YzyHI0qs32yikJ08VOuuSH+edJebZkfqmoqAAT467AWd3e1q3vn+NvRF3D7tbPvZK6K+1RtcK95sX7sBV/R6K/qo5hMOECwxCB+sSfpY6dgSmTtUtonoeFyxAXXa+ZXXqCsQHINMfRN/KLVEeXJ7CGFBaKh96YM9ls3khMeNcPNqUc7cmSJyVkWFdJpzP0CDnWqi0XP5b+5V17qw4GDnSJIKAwLV3ZEKCwlWmOUa5HnXLeiE70nh+Pzz6dhzf9nWuW7VjXLQRiM5DJkVJUUbN0PZFDkke3kf066fbvixLsJmawUhJISxfYduDS2bgQGDoUOvGidgRCKgGujenXIanx18vHjidQzSTrKu8tXpoI5Hp0aOH5bjw9exzPOoQoYUUXOmCVYjCcLHwxBZrjyUp9M/2BeuwvttTOh0RX3pHCi6dEGxmnmBgzNo6w0iwp3dfAjYKP847C8vavxr3don4w7P4bZi7Fxk6z/zBpWvVz7KVAnj0aO5+KReI8t+NGom5866/HjjvPPm6UljMpeCqrBTvdxGyS5k7qUn+TsxtGcmPpUw8Lr/SbdoAs2dH5QkqzTpg2EbYi21eq7dRnbcnqWSjqYqPCbY8AHIDR43H4PXrrcdnp2E19RSkVvcoycoSw/Qi+p2wnVNH4Q3mhPtH3iHGwLdDMj3shlIeFj3myTlVJAYNEsetzp1lpTygFpApfw95HWLXG3bcOKBXL3v32EHqe0JCM6UpCXnEmfW6Xcl3czfi3J5P2m7m0dG34PoB/7B9nxk1+bv0L4wfD3TpgglNPsWI6i91i/Rr8D2qcqWwsoyphGp2hIVarO7M8IVwapfnHNcfpip3t6LNJBobE8CZXZ/BY2NuTmgfdENnAWL4zDDK90vrZSsZnwgsRuOpou2SEmDr1tg0o9cmQ4IUXPHwipCiSHQs2R4553J95DkbNwIXXKBScipRiTQ0gthRoxTXZ80CmjbFkjbRigzen5cx8EW1IWxh5+0yXe/ref/r/bjKh0ZWzlugHA9M+hNWBrw77RJ8PvM8PDL6VnHQWrFCf5yVZA5yrr9wJIjjjhPDv2m7AajPn3aaVc8BAJk+g1zaAIeCy0LBQR5cSYPfJ8Dvk34PKcoJL6qIMIg863Y9uCwdLPr2BUaPjqz9HEYxaF30m6NIHoQ1tDNOI3g2WfIY7rGCK6rtoUOBk09GWfaBSLxhnXqa5v9hWKfW2lR3/pGETgtavY0nx92gutRc4/EkLF+BAk3sYZk2bSJ/K/tZpzOhykH847hhyM5GrZ7lDJF2WOV2m99Kk8SdMTncTk6gVq3gMntG58zhyxUjobSAiRLmZ2WJG02p7WOafIpGebv0y8YIZZjSmwf9Hzb2iCjWlQtjITztzZoF5OWpEnA/O+Fa/MMkPF74O7hvxJ3RIY4IRzAmAJdeqj5n8sx0LjXJB9W0qUk70h8uw/t58TRrN1u2rctatRLzUl52mf3GGcPslu9hdst37d+bLNjxNJMVXAIwcqSo3DrnHDFU8rBhojAekdAr81q9jQlNPgEEAbsXnyR6pE+dKluw/rHoFLw08UpvPofTTfXpp8sWhLQvd4lyPEj0l8nhweWUac0/QgelENhrlH2bOlV+R3lzFqbSY/yXIfdiy9z1ie5G0tC66DeMbKSvyIwXhvsjI8FTRgawYYPo9Tt2LDB7NoAYDgHhHB9SvppGjWLUjg4Js01RGJDEjMaNge7dcXa3p3H0hFWicuvkk2Pfrh0KCkwt/qVHAj4WQp+KLeZ1MYY7h90bddrHQly/s88HMapNjx6WZes7V/Z7CENMokko0e5Z5EgTOoghCg0GmnPPjT6nNyh17Rp1ytLYKayI0gj7tV7O1Xl7AAC9Kn5A+5JfML35h2IfevRQjbO3Dvo7XpkUyb8Z8IWAK68UIxgAonyuSZPojwMAkyJhOdGmDVBUZN53AP7iAuOLFoZChmFaw3uJRK/90hkrhbpO1CEAolGAzXGKQRANCiRjUrlam0ZToxp9gYdG3Wb8XGRmAscei9xiKU1HOJJRVH8syMiQ1x6Et5CCK10IhWQPrpgKlA2Eg/0qv8eZXZ+JnGBMtogLGXiNbV9wBi7o9UR0ZdKAkqONE+yLdmsOK7iyA3WY0EQdtuX7uRvV/TFi3TqgZUv9ax06qA7HNP5M/lvPkqVhzh7jdlzAhJC+aziR0ug9llbvb1RINkHAB9Mvxlezzomu1Oy5r67m2/lK76MqB5fFbSd3fh5vTrmMq6xXKD24GICdhyOJm5sX7pDDujGoxxGl92fzgp0ozT5o2IbW24tycLnHB4E7Zs9V/R6KhC4wwmqj0ratZTtOPAi47gmHvdM8N6YeyEa0axcJfeMS3ZAk0YWcN+BSqRiFnUElvJ4I1IpKbcYi/wIBOQRWaZb43t834k45l1ZRppSLTfJIQYMGKMk6iN6VW/TbuvhiJ5/GPoq8YqE4eI+MafwZdh53SszbqffMnGl+PYFCGMtXTjsWWdygnTtVHlyAvZB2yno074PhN6bXPxMDCSV+JiDT7yJMbBrRNH8H+lV+H7sGOJ/5diW/4txwXlptFUZjZJMmQLduwPTpsgFDKFZikQYNgEsuUUU6iCvx1HJdeilw9tmi10esYQxYuRKMid6Z6NQp5cKNDhkC7P3kBwSXrcL81u+YFzZ4H5jJtejCLOW+o0SwtuNLkVxAFmgF6GYGkDVGht0DB0aUQ2bMmiVHSQIgv9shq3V8vhjGVZvDUzs0jGz0Jf5YpL/eqxMicqg2xb+KIYrDkZRYiCv/bFBHdsY1PNlY/7Qv3qbKOWpZvySvJNzRsvC36JOqOKsWKBVcTscoxX1OowFk+oM4tsUHlg/OW+8G8K1JalvLd/Kssww9ewl3kIIrjRBkBResQxQ6zbUhhQPQUpB5BH/u+0/1SXnS9amOwzTM3Wu4SZzb8p0ohZXsCaYkKyti/uSUli2NB7HJk+WNdrOCHXh2wvXyYKSn4Nq+cJ0675iHkAdX/SD8KK7t+CKWt3vF+oZQCC0Kd6BN8W/qCrwirODymXhwhVGE8Yq3KE5pvcYg4Pt9ovXo3sUnYm7Ld+X497KiThtWAdYKq94VP3jZZQIGSl6DR7g48yDyMkxCFJq1A0FUVIwbZ/teJUbKIDchSjyPD24GU6wTJLhCbLnJz+W1YN7m7//M+OuwssPLpmU6lGwXQ7yG0fY5N1e0KDSiVy9XFusV2XuxrJ39MMTxCFGY7a81VfynNIrfOeEhCtu3B665xrhsMlsZt2snCt7WrROPLcYL1RjI1DOvLzMATJjgyMuAS1lvxEknqZTHhDVb5m1Ax9Ltjo19nOYW1v7KWYEgNvWyCLvL8YKXNM6LjpLgFWVlfDmbvMIgJFnMKS1NvjCBSU5BPudvZGAsZBq+TUkyhZpOMNqoP1rsrMsZE1Q51owUXMLyFajK3av/M/DO7+3b60ZqUQnTL7ww+r7p04G2baMUXHrh+0qyNOs9qQ11rlpBdS3gC3I9XzExytIY9n4+axM2dI9EcTEMURiO0nTCCRQqziWfzzwPtwz6v+gLTsccG3tOYfkK3fOxdgxo0sTYPwIguW0iIQVXuiAI8qRhprEe2/hz9Ch3IKQ96SQxlEO+cSJfI8bXfIqhVV+pT5pN5Izh/0begWYFO1WnZat3Qb0x1gqd/CyICTWf6FatFeTpLmCU3gQZGXKS+gALin+PHQsAqA3qvz6uNtgGMB8zjjFPpCx6G8/wuSVtX496B6IIBMQYxccco6gg+vkLevBMKr1ODNcrgwYBNTUAYvMemKFc3Ad8EY/HgswjYn+POQaYOjXKE9Sn8kwzHpd+XXg6VrRXKxzd5AohRPQ2HkYCGfm505s/li61bqxvX9cZ3h3n4FLgOgeXByj7y7WvdqPg8tqDiyfBbxhBwNiaz5EbsIipDsnLK0ytcXndJ8Ch8uHZCdcCEMPC3D7k75blozxU4vDopKtIzCyMUNzQTqYZGeJa00vi8pAwMQRoeLcvjRdGTTMmqEKJKZ9rLz+/ofJebxGTlwf07YsDS9diXM2n0dfDt5Lntid8fOwm3Dfijpi3Y+fXCsyYgr9PfABYuTJm/Ykn7Yu3ieEjSbGR1lTm7KPf2CZaI2otPiZw51dkAHDqqbLBNbfC0QskjxV5D9yhg37ItIIC4KSTVEadgEn4PiXSRK4U2MvyM+m50yrOlBzX+k38uc+jAAw8uHj6EAoBK/QVGXrPvvK3i/qM/fqJ/w8eLP5fWQksXGjdB8IQHxOsPZZ4GDRIzMeel2ddVg/Fs9Cq8Df8fbiDNUbY66x/f2d9kLBUcNGYHTMsJRaMsRrG2IuMsc8ZY58xxk6Szp/PGPuZMfaR9G+C4p6zGWPfMsa+YoyNVZwfJ537ljF2Vmw+Uj1F4BtYruj3CN6ffon9l6pDBzGUgxGTJ0efk9roU7kFL026mr9Ngx2xoRBQIXg7od2r2DJ3PZ4cf6NpEzcMuB+ARsF1+eViaAVtMnllfOE5c2QvtqOhiKB0Qs0n+Gb2RsQMQSBLgHRFM4mHF3pci84bbxStxpTvn8575sWzo/TgMnwX8/LE+MfgcM32mKDCkyHgC+rnCRszJmpRXpgZyctn1uOoGPdDhrjsMQHoPOdSTiRTtM/4vHmyIYIXmD0HXsiKk8GDS9U+T4jEZPLgmjgRGD1aHmtct63dNOfmRt7vcMLsGDGm8RcAIu/BVf0eQvtikzxzGoJx8OBKhAI2HmT6g94rXzm5tv8/8O60S3Sv/Xbc6Vjf7Snda47w4DOGXyPuZzNsVW5oEIBIXiLGVK+p1fM2utHneO4YfU837ifVZD+SG6i1Frg1aCAb8xDOjH18EKzv4507vJpj2rUDrr5aFLClOpInw5X9HnE3f6cSCRrPXePiOf95/jqMafw5356LBKpc9KrYYmvd42OCuP/t1QtAdJ6rTF9txGPMruBe+5trf8O8PGDlSj5lHGM6Hlz8n7NOsd6U75PCKvqZYPh8tSv+BWd2exaAizWrlAdMVk4p0aQT0RL1GRcvFsd5M/cbwhZZ/jr9MUjzTNw48H45nYuwfEX03LRwoWhgYjRWrTfOf6q9xe8TrMO+6rFypZir2aVcgRRciYNnlKkDcJogCB0A9AOwhjEWHkmuEQShm/TvKQCQrs0B0BHAOAA3M8b8jDE/gJsAjAfQAcBcRT2EWwTBnseE1y/VMceI+SfKyoxj57tsk2cD9Zchf0fj/N2W5dZ2egmARrBXVGQYC/WZ8dfhgZF/VZ3rVLoNf+r4AgAgL+MIWhWJoTZi4X7NIKhiHxNpxOWXqw7Dr4mZNVRUYUDOJYNp06KKuQpjJQiY0/IdlVU1j7VVvOdtlQcXC2FczWfRhRiLUnBd2uefeHHiVQAUi2CdsKdy7aWlwKZNwLx5aevZEE+inpOpU8EK9ZMJy89deEF89tliKKuBA63b0W3MPoYeXHamX837kxAPLkV/TZXpBdJv4WYj6LWCKysLOPZYPgEzT9vaUGiXXgoMGACcf76uZ6Du+Ofy2Qo/A6d2eU4Mw8lJPJSjhuFdUpgr+j6MD6dflLCwfx1LtqGXQcjbkqyD+iGOtMZXvHgg9D0iGXR9PmsT3w1SX/WstQGdHFzKHJoWr1Kzgp3cXimG+yKj3106byks3LRJDjeXzJEj44WfYw7rXvajKoS79udrV7wdV/V7yFkHTH4E+RngHaPTRejEm483HQgLIrt0SWw/nMI7RivSRIxu9DkeHHUbqvP2gDGbhow0aJny7rRLbZXP8UtrtrBBtGbNVJO/S/QYKywENm2y5wXM81t1785tVBqWLYTDunUp26ouoCfUlz7XuT2elI1L/Cwk5hAqKgIQnaNaiXKPo9tPi8/48bGbVO9IOPIBAFHhpTTyDXuPG7QPQPw8Tj2ECF2y/bX6ykvN3LOm40sozT5geN2Spk2BKVPw3DHXYExjHTmPFwQCovG4y3mzLg4GiIQ+lt+8IAjbBUH4QPp7H4AvADQyuWUKgAcEQTgiCMJmAN8C6CP9+1YQhO8FQTgK4AGpLOEFyhCFPOVjsdgtLxeT6E6QnPnMNuNBE+G9Qd9kIZyLvmvnUC4vGcYwtuZzdC3bqmq7MOsIrh/4D7GIoniscmLUUojCtIMxISpkWnjha6rgatsWWLBAfW78eOCGG8Qky0quu0713F/Y63F8OetcW+/R/SPVLt48t8oL/jihDVFoJPjVKrhyA7VoUSgqp+VNh5l31qWXAg0bAoxRqCIP0BWem212BgyQN1Ro1kw0qOANO8j5zJv9rsrnbEjV17JVppsQhdkmCak9Rycvh6kH1yWXAJdd5i5hfCKFKXbb9vvFRNmMAVVVsbV8V/RNfg9atLDl/ZqfccTrXkURfl7P6fFvZPmTIKyfB7Qr/kU2SgoTz/Hcaig6vt3ruH7AA+JBONyQU4GMBwquRrm7MajhNwCAJW1ej1ww8qL0+4GTTzb24NKctmOgZ/Y7aesJz+06BU3PW/ZH8QF4w1ilLYsXI8tfh63zzzQt9sGMi7Gu63/kYx9TPx1Tmv4Pp3Z5Tv3b+Hy4f8Rf0Ltis3kfSGAfjVOFeCqycKG4TmnePNE9cQbvfuy44+RcnwUZhzGzxQfypQzGsY5Md0VngsgOh7eW1ouGa+r27SNGYxCNp2e3eNeTPvCuG0c1+gKtCn8FICq5FrV5S11g8WLgoouA226LnJOem7LsA6JxCYAGOXtV71uAhYzld0oFl858afVYdi7dphrjw5EPAABDh6pzG0r7QeUcnq5RCJKJTB+fBxegma4dzt0jG30ZpUhmMPAiTND6QM/ooGPJz5EDGo9jhq2dO2OsGYDuAMIZWNcyxj5mjN3JGAtLPxoB+Elx21bpnNF5wgsEge/97dtXFA527BjzLiEnxzjMg1kydoMPYpp/xSgZ9bBh4v8DBgBAlBdUZc4+436YdkLdDx7ByHdzNthrS4NyoLx+wAN4ZPStruojkhNLD65LLhFjfYdjRytRWPfJZGerFpTVubvRtvhX2xOrXSFOg9x9eGuqPSs4N6hCFLIgKnP24Z5hd0WVm9DkUwyr+kpXcE05teKP3lNl9GgyJgCLFsV2UVhcbHpZ+Zz1rdiMMY0/j/SNE+W8M6L6S8xo/oFJ6dhjauiRmWn5nRhSVSX+n2ZhQDx7/BSKB9kDomFDW8L++a3fxvFtX+Mq62dBjGr0ua0uApHn44Je/4q74UKskN/XBG18fRpDCxmpP43yduNPnV4UQ+Gdd554rahIFD796U/2GmvSRPxfKQSySWn2Qbw6+UoAENcPgJhzy8yLsn17w3WMyrCBaVbQFi+Y2VirvPLa5Mtx3YB/GBS08uDip16vGpo3F/NUZGWhUd5u43I6yhYG9d5JVwjp82FOq/eQG7AYd0jBFU1enrgHnjDBsmjKk5HhfJ2SDNTUiP238kCrqhKj5SB6HCzLOYhdi06OTf8IU7LCnqnS3FWZzSdbGlvzOe4cdg8+PfZ8/sYM5seAz2BNoeHyfo/imznnGhfw+aKjmGhyegnLV6BpwR/67eugHOeNvLo9IxAwlgkSMSM3cFQla+pdsRl/GXKvZX40N+O21mPMMFKLmVNFDNFbt3w68wIw8L2rhHO4RxnGWD6ARwCcLAjCXgC3AGgJoBuA7QCu8qJDjLHljLH3GGPv/f67geUdEY0goCTrIAALQduSJcCf/yzmmIg1jKkT9SoHmKIiMfzPlVdG3xe2CNFYq7Yo+B2rOryk39by5dFxeSdNAmbNEv9u2RK48kocCUY2+F/MOhdX9nuY73NYFrHeXLUo3GHdlkkXVnd4GRu7PwkA6Fz6M1oU0PuRjig9uLyyDG5esDNSP9P+YdUhsZyTnFp9K7fYvscpWg8uxoDjwpZpkyaJ/zOG+0bciRcm6ucEZACwQV8RrafE5vkKx9d8giFVX1sXrKd4blnn9+OliVdirF7oAp4fTBIeGFGn2Jwp+/77If2wilp+WXC66A0MAIKA5ydeg2HV8X8+lHNWzKwbTz4ZmDEDmDs3NvXzYNd7xYbHVuO8P6wLmaHom4+FRM/RmTMRlMayt3UMBLSeqe2Kf8Vfh/6Nq7kGOXvx32Ous91N5fORdt4qCsF4PPNGGiqVtUqooiK1h2r//tFe2laUlwPnnit6OHjASZ2exztT9fOHack0UHBpv2k7zxVvyaLMQ8gwErpZKETsKJlthYdPN3gVSzpzb8AXVIfK1ZuHJMXYy9vbOukdAMWzVR+FSXPnGqcNIJKHjAwxOsTq1c7rYAzFWYcMLweXKfLaeKQQLudU5KQknAYhL028En5fJHx63Qkr0bzAQN4jrfmUY11uoBYdS7cbN2CVg0tiSNU3GNjgW/PO2h0DzzxTjBZjZqQW9u7K2h9dv87ndZyDS2ctz6DjNcZYVF5dsn+ILcLyFcgO1GFE9Vf465B7AYgeVie0e93Ag0s6V1wMrFplv0GpTjkEYLt2+uXChjUJegD6Vm7R9WwXwuqX+rgmiRNcowxjLAOicus+QRAeBQBBEH4VBCEoCEIIwF8ghiAEgJ8BKE36GkvnjM6rEAThdkEQegmC0KtCJw8KYYAg4Kf5Z1uXYyz+yWaPO050yT72WPX5qiqVq7ZMaSlwwQWii7SC7EAdbh50v34bjImxjZU0a6a2GpTayvSJruTtin+1letCbkeHeLg/96z4ERf2fgKAKOSKRa4vIvEoPbiCnO7eVoyr+Qy1J4iLCFlRwzvhS4ngbQk2Bw2y0z1PUAonVZbEJSXAxImqsozBQMElRKzd9Tj99OjyFjw1/kbU5O2yLFdf0fsOXUXUCgQwtPob56HUAgHTV8ynUQyF+1+evd+67rIyNMjdF3n2EpUQ/corVVaUMcvjVFwMjBkjenMnihjE2Q//5l/NPhfn9/yXeNLJBkoZ3sUXAubNA3JzZW/tPjoGAma/1QntXsX0ZsbegDkBZ++ESsGVbsJ8Rd7VnMz4WXkajjGMiaGGvaZRI8/ehexAHXpX/sC1Fsn06YfNisrBZeP1MffgsghLtGaN+D0YCZI5PLi07aed0tcJc+aYX8/OVh2+PfVStCzcoQ6VGx7bNCEKuVi9Wvxd16yJulSdu9teXQSRCHw+5wLPmhrLQVQ1HoZCcg4mp/Rv8B1+P+5064JJiDKntCGcv8XQ6m9U9/h9gvGcIP1GjfN24fXJ3hichGlWsBOvTbnC0zrRooV+tBhAlNNJfDtnA85QhJ+VkT5veJxvU/QLupX9FF2OByFadmG0lxevmYdFJLynMPMwjm8nhrCWv3HG8N8J1+CEdq/K5eTfY82aSKQPB8j7WGVoWuXzUF0tdUwjH44jpp7tRMywXO0xxhiAOwB8IQjC1YrzyidyGoBPpb+fADCHMZbFGGsOoDWAdwC8C6A1Y6w5YywTwBypLOEFNsPlxZWBA4ErrogMNDw0aGDsZWa0SdEu7nSUZ+NrPlHFrObCaJGj+s5ji/Y39bNQzHJ9EYlF6cHlpSt/OHyAvOjj3exPnAjMmGHPun3hQpu9c0/YKkxYvoIv9Kjiva6TBMpm+xm/LwS0bq06xysw1n53fp7NVT1BN1y2mxFVJ/66U3Yedwqaazxle5T/KHsvMAiylWfD3L3WFY4YAfTpEwkxligFV0GBKlxvWsen79dPXINYWQmGDaoaNDAv17u3/KcPAs7q9gy2zD3b2W85YgQwdy5OaPcqVrV/WX4ZlJ7mWsx+q+XtXsX67k8bXs9xqPT1sZCceDytnpR4RTPQQV7T6T03ylDDiRojeOAQAHYr/wnX9o8OE5jpV8yBNoW6ZqWV477uXqhLF+Cqq8Q8prqVW/dFO7fE0/MvaRkwIBJKUw9pfA1vm/SU9/LYpnLr4lyntmsn/q46Id56ru6LoyesAmbP5quLIFKJ6mrgrLP4ytrw4Bpe/SWKMw+46FhysqnnE9i9+GSusrbtlqTxyvC2/v0BiD/DgIbfu2zMAV55jEyapFortyzcoZ7TNYTH9q9mn4fmhTujrh8NcuQL1FFwATD8TEeDEc/3xvVYyeBWka3HnUPvsSyjnM9HNf4SNw/6P3w9+xyxT24fdWmNrPUGDGgjBqxcKe7ZTjnFZYPe4zPJWUe4hycz+0AACwF8whj7SDq3HsBcxlg3iOP4FgArAEAQhM8YYw8C+BxAHYA1giAEAYAxthbAswD8AO4UBEEnhhDhiGT3v/XyJTaqq3HjyN+zZwNNm0YVeWr8je7aM7IUgQDceCPw1VfA7fabsIvfF6JNdRpg9guKHlyRyTvbfxRl2Qe8eZd69hQts3gIBIAhQxASOBIYJyPKsdHgXd5xOF88ZSK+9esIlvMCRxx1qVfFD3hr6mVgt99mXbge4mpskxROWos9xgySz+oQfg6KMg9h8z61JzljEL0XIHlwVYj5JLksQnNzgeOPjxwncN4O1pcEzH6/6EVuxYknAk89BRxzjHm5448H+0hcuvqYgEx/UMxFEGxsfp8egQAwbBj+cs87gK+7fPpw0HhpbvZbGSY2l8h26MGlfGvSylulpCRhTWeHlY1WCqwUV3DlBmpxUucXcPKbauWC9jlWPVcWQlizeVqZ28vwXTF10Q0LKPmfc1qLS2RlGV8z+M5DqnlI51k3yBet68Vn9LuOGoWMoUNd5aAjiKSjRPI+PvZYcS1hZ29o4VUvLF+BYIjhH9/3wvwXTtAtk3TG1Jxk+IJ80XsYsx9OT/oNdA3sLrwwKpeVCqO5nlOhExfOOw/44ANg7Fj96xwhCvVomLsX/xp7IyY9u9a4UDjUKuea6LBkKHZw6VrH0QsIfZa0fQNLX15kWsan8cjO8IXQuug36ZrLsWPQIOCzzxB8XP28ReWBKy8HTtAfvxKNae5rwjWWI7cgCK8JgsAEQegiCEI36d9TgiAsFAShs3R+siAI2xX3XCwIQktBENoKgvC04vxTgiC0ka6ZJ7og7BFHb6KEY2TR16sXMHw4sG6daBlthlU4DZ62FYkRGZMs+Vu1clavTfxMoKExTVGGKFRu/u8dfhc2z13vun5BYGLOOpuL5HDYLG7ivAgvyDisf8FIiaDoXzg0jqnwWEf4kukPyiFPzaB31R5GAkOuJ6pLF6BFC/0Npl1PAYvi2nCFtjHb7MaYoEEusXpLZSWweHF0cm0tjMmCUtWY4CaJcZ8+4vpFom3xr6jJ+0PMX6bB6Ld6cNRt6Fq61bQZpx5cjAmR0G3p/KjE8cPJAhcrYY3f5rwbazSh5pzw+3GnRp2zoySS3wGdvCAZCgteRwJYSfBr1h8KUajA5pyq/a7ChiglWQcwoIHk0VBeHilQUQFccQXaFZvkqOGBlFtEmtFuXDMxr2JYCcyT1iMst5g82TKXo98npF9IYiWLF5tfD4VU+977RvwV67o+a36P5BmsO/MoxzU9ahOkhLETXam6WozqohxPzdZN0rXSLHNPQMaAiU0/MS5w4oliuHNAtWZiMDZcnNbsQzw6+hbXyq22Rb+4uj9d2dTzCYxq9LnhdVmBo/N85AZspofRkpUFnHhiRAEtPQMBFkwur6hp0wwvkQdXbKEYZ2mIWXz6tCYQEBVXZskww9iJxa4cgJTChlWr5M1+rAWE2jGQQhQmN7NavIef5kUnluRBGaJQKQwIsJCYMN3lhOh0fJjZ4n0MaviNdcFIQ47acUqU90KzZuL/HKGIwu+vmUAsyvVd4sgJJhZnhCNcWcQHAsCZZ3oSc131PBjkbPMxAW9MuczZ415WBpx6KnD++Y776BRliMKY5eBKc7Q5LbziqXE34MvZ5wI+Hy7u/RjGNI4EOzD6rWa2+CCS6FyHGwbcj/tG3OGoP4LAVAo4ALim/4OO6koWdF9XQZBzVcaanPAG32gtGo5A0LVrXPrDjVJYYUMh2K8yEo6pPJsz7JWUJ+atqZdGh8Q57zxdwZzSgpcxACNHcvcRgOjd3qePdTkFaa301RAOMSTDEeECgPwlqTwi8vJkAfofi07FyEZfAvPnA23aqO8tLCQvOYJQcPAgsOkCpjK0xfLl5vlsiooi3gz5+ZFQ2SaYKe9T3jDKauBu316l4BrV6EsMbPCtftlwRJSmTYFzz3VmYGdiJPX7caciwyCfpe12wkydKoZp3riRr7yT9rKz8eO8szC31bvu2igtjbSjCBNu1n52oA7Tmn/kqtkPpl+Evwz5m6s6Ug3eFArn9nxS9sgyRec982o2jw5RmGRKo3HjgKVLdS/5bESVIexDEvJ0IZnDmHhFeCAoK3Nflx0Fl1KppRyMGjSQExozvetaPExyKCq4aGBMVooyD6Fx/m7LcnoKlfCGQkxUqyjr0WbC6VNzx9C/4aWJV+HsbsY5XlSEFc1Nmjhs0R4ZWgXU2rWiwnvePP0bwu/qkiXyuyQrRXQWZHohCnlJayvIGBD1W0rYscjXjo927q2TFs2q4VznmQgLU/s3+D7qGjdt27pKsuuUehOiMAaEx2jV8+HGg0tDdqAOuYFaICcH67s/jTkt35OvRf1WGgtBI8OXAQ2/Q03+Lued6tIFOO88+bOf3Pl553UlAbrPvCBEhziJEbIFq1JAqeTkk8X1pV0FTayYO1f9P2BLs/Pm1MvQNH+H4fXwczWk6uuo+vtWbsGStm/IuSRCAjO0Oj+72zM4vu1rACQL2VmzuPso3uQDjj/e1pxdnzy4TAVaZvsfKdfdLZ8PEY+zs4GNG6O/uyFDdG+vT98xQViRk6Pj3FteDqxebXzTmjV8Xl6AHF3AbIgPpLNhVMeOwOLFKgVXgAX1x6GuXdXKwkaNoopwrbHrDBRYgoDy7AP6OQrd0LOnGKbZrZe4WX86dULN2A7wLV1s7jFox0qke3d1vkevvg+pD4MbfiMbcBVkHK5Xc88vC06PeFJLTGzyMT6cfqF83LroV/lvs3WS/L011oRv79fPEwNUAKjT5KlPyjGpoEB9XFMDgPbdsYYUXETqsGGDmOjcyq2cBzsKLmXCby2CteeHjFXYRBMYBJWVjY8JpOBKYtx4RCgn7GObf4A5Ld8BoJi4nZgLn3GG/GeLwt/t388iSrdL+jzGd8+yZcD48eYbLg9Z0uYNvDDxqsiJggIxZKlRrPnworhfPwQLigGow7ZpcbMY0S6Q6c01p0HuPnw/x104TjchCr/a3VD+e0bz98MViv9LYWjntXobM1u8L1rCAqLnWAqhDlGYhJuCJCaQ6UObcNiS1q3F/3v08K6BGTNEhZIkEFAm4I6aW8aNA/r1kw9jti5golIhXZT1WQ7DNdphUMNvMLnpR7rXijMPAevXR29+w+Tmis+AnbVqLBk2DLj+eqB//8g5m2PewTqTtbTEy5OusixTmGkQjhjAyEZfYlOvfwFwN8/aEcDQWpyDpk2BGTOwr1Zajw0fDpSWcn93T467Ec9OuDZ2/SOIdMArYb8krzAT7vt9IXFeSDH8ir30RzMuxLCqr6ILjRgB5OWpFFx+X0hf0tO1q6zAD6P93vzacGR6kYaMFFwSDXL2oiJ7r/VvzKvE9CoHaWWl6GHVvn30NcbEHLj9+pmvZTp3NrwU5VHEjA1cvKBv5Wac3Pl57F18IloVOZCXuOSyPo84jgBkRFHmQeRy5AxvkLsvak4uzDyEbuVi+PP3p1+Er2efK1/rUGIcOlgAxJQx2t9KEDC35buY2ORj7v4bERWi0JdkIQr16NIFZ3R5Fuf2eDL5+5rCJMnOiXBNfcjBVVMjunoaWbzaQbMYMSUQEENIXXihYREu7xq3A1lNjRxruzp3t/3kp0TcKM46yFVO75EozDyMEdVfAgB6VvyI+0eKYaUqcvaJBZwouFq1AkaMgLB8BQY1/M7+/U4oKhJDIHi1iLYgO1CH4dVfWxcMo0iI3qL4DwDR7u5eof3FSKFggSCgeeHOqNN2hlDbOeMUDKmSQnEWF2NI1TdomLMn4iW9ejXQrRvuG3En2hb/CixZIoZUWptaoSr7VG7B/434KwB33on1Ed+qFfhqzQ1iXoA//Qk47TRvBT1jxojW1pJlrfJZtlK08wqMV7R/Wd5Ef3LsJqzq8JJ8rTjTOIScV5aXiSZTz0tUMbeWZe3HK5OucNXGpX3+iSv6PhLdzPIVYl6IcBjCVCE8Z4aNtQy8bYxY0PodzG/1tu61gF5YHIPIFNkWysmwwVm8LGTrk4V3FDqTcstCAy+vMWMiHnrSfbzjVeui39C3cnPkhNYqnCCIKITlK0wF22VZ++W/N89dj87hPJ6SR7rZCJqqoa3leaFhQ3Qt24rZLd8zzPGnXHvl2cgbFL3n05xZuRKYMEGdl8tig/PO1Evx6cwLjAusWwcMHCjuu8249lrgyiu9y0uYkQFcfLG4HnbK7Nm6OWdXd3gJfxt+l+4t7Yu3oXPpz3xGQJwyv+PbviavUQoyxfcm3iGIG+XtRkXOfuuCNvj5yf/h/3RClC9s/WbUuahnVzpzcqfn0L5YnY9sbccXcdQgrHdI8ImKXO1zHQrhzG7P4l/jbuL/AAbcM+xu/GvsjXIbRtFfkgrGcHm/R3Fmt2dJwRVDSEKeLqgUXCSsMiTsit2li+gNNns2331VVbLLvgrZg4sDFwOZcnEkLF+B0uyDUa65APD8MVc7boPwjmw/Z6xsHTL8ITw/8RrVuZ/nr0Pfyi3iQSISPmif3WRPOjFpkvG1uXPFsEWKxX15jijQlRVcMf58pFCIPad2eQ4buj8lH9sZfhvl7ZZDMpzY6UVsX7gu8kzk5ak8ZlBWJio49KwXk5iALyTHxc/xu0z4W99o1kzc0HfsKAr927SJzUZFqlOt4NIRKilCvxgJjOWzUk6nAAuhKPMQAKBN0a8ozxY31LUnrELPih/l+woyDqGfImRJuqwvs/TmaMW4n5dxBIOrDPJt1HdmzwZuvtl2uPCr+z+Ev4+4UzyQ8teGyfTrCCZ0vNtenXw5TuvyX6723DyrdjwVyYMrwr4lJ+Kqfg8bXm+lUX45Vg6WlDq7jyDSGZ11iDw+6exrvp+7AfcOE8fkZgU7I7KGggKgSxfTcdDPQsm/F9TBz0LAOefI89fKDq/gi1nn65YNr71enXw5Ar4Q97ygLRelDCwsBKZMEdeRK1eKMiaLcPoNcvehMmzoqkfLlqK3lGZujSInx9hz3Ck+n7WiyWyNnJUle4ev7/YUXpSisRzX5k1xn6Jz73vTL8HrUy7n69/FF1uXYQx/Hfo32VspTPVwgzzeHvH65Mtw86D7AIhKZqf5yh4dfQseHHVb9IVJk5A3qr9uHvF7h98NAGiYs0f2ZNSGOQ+PCdcMeEg0zALkZ5UxiLnhAdEob9kysd5hd2JtxxcdfQ47dC//CRObfiIfJ10OLitSqa8pRmrF1CGMScFFRkK49lrxu/L7DRP/2cLnw1+H3IuhVV8DGGQ+WHmh4FLUoedtMqKRjqs9EXd6lv/g/Gad56Q6b0/kwOm77ibUkfZeD/PNeE7btsDEicbXDTwtupf9iAY5e8UDve/Ywfs7vdkHUnXqe5PFgysvcBgH6iw2Q4nA4Lu2I7Cc1PRjTGr6MS7+cIJlvXp0K98KCEWRE2ma5/KD6RehU+nPABYkuiuEFmkcUim49N6Bnj1FT/Nbb7X2sFKEmPNLG1Nl3qmAL5Lfc3j1l3hBNraYL7afJsp53RCFinDUmbwJ3U34cX8pGpgJpFIZN3k7srNVYZMBjeVtOG/WjBnAoUPARx/Jl+x4oLvJW2p2p/YdTI83wgMYQ37GEV1BWhh5LSTNxUEbykFlSUlPTxCEEp29i6xE1ln/FmYejgitoQhdzRiwZg2Eh+4xbCpVDfV8TBA9QHdZ5yTNzxC9eMLzDq9CXvvNmO5duncX/xmh3X+nqlCcQwF25PjVCPhC8jrT7JPmhp9bq++jcWM+Dy6DelqfMhFC9xfBRgy3rsMBpdkHZO/AZgVi5BKevW7fyu/x/d5y/H64UKwn6wD2HDVIyQDj/LwA0KdyMx4fe4tUTiuv0OmL9rc8+eSIkefvv2PhY49Frmm/1+bNgXclJZ7HsuukzMGl/fyp+v6mGOTBlS6QgouPzExVaDLXMIbj270e8zi9svWPYmDsXv4TxjT+LKbt1jeiYj07QFi+QmVRYobuIiZWk5+b0J6ppOByIngTBHww42LkZRyVj5UsbvOG7SrbF2/DI2NuAwoKrMNVJIj9S0/SDw0VJ3YtOln/QgzyzjAI9t8t5XNg9HeK0738p4gFHpFcSF6mx7d7Xbay9uv9Vj6fKCBZt87QwphBEDf5incg/JePCSphVbiOFzSexEB0UudURTdE4YwZsqd+WLDlhunNPiTvHj1WrYoKMScrFNesAQYNEv8uKBDL2iSs2NJVBnNi6rmgeQfTJS+dFR1KtrmuQ6uAP1ynCJNVau6VFV4vH1q6xixiPEHUX3TWpiHBWMEFqJUxdZqcNmZL3ZT24AK4ZEFX938I383ZIB/zftxoDy4X31Oy5OF0y5gx4v8jRxoWyfQHVftjrr2y1b6Od99n9D0z5r3HmwI/ExytVN6aehlOV3izZ/qDUfPr17PP4QoH3LboV/lvbR26BrnK72rdOnUEE+337feLRscDB4qeX8NjoChMpRxcSlKprylGmoyaBDp1ki1A3FgtEjbh0Mz3rtiMZ8Zf50opoKfgqszZh2cnXK8uqAi5OKP5+3hv2sWeJHKsDwjLV6Bb2VbrgrHGSkHj1JNk2DBgwAAxZ4xdtM+1RTLchOJkwWCxa7lr2D2265UX5hUV1uEqEkgiZ4virEP6F6R34PXJl+H4tq/Jp91Z5Lt8LtLUg4tIYrKygLVrUZZ9AAvbiHkBTIX2LVuaP+dXXin/6WOCymtG+bfZW3Y0lOGJIUii0Q1RWFICnCnmJVNatTtBWL4C2YE6HHWRCzCtUApEdOZbWeGYY2yBbBc3sgM7Hly7jtjI6ZuinNL5OXw2c1O0cFLnSzb72rVroeHVX+GuoXeLB5s2Gd+oUIhm+evSRuZLEF7z2czzcVa3p+VjKwVXO0VeHa0BS3g9ISxfga9nn4P3p1+Ef465GUBy7WPsIO/NcnPF/fC6dWI+LB3yM46gReEO+Zh3H3E4qM5v5Uouly6DXYsWwPXXRzy0tWiez009n0Cnkp+t67Wa6HmNXvXqCStuHChyBzb4Fr8tPM2yXIAFMa3ZR7hFClMI8D9np3X5L/YsPgmA+D4q59dWhb+iddFvCm9p9XMUzs23a9HJ+HPff0batmuw07Kl+ljve5w0SQyfOXSo5VrQDUnpwaWFlFpxIU1GTQKNGonCayK+cCw8upf9hLE1n7tSCnB7fEgDZ/OC3/Hw6NvRs+LHhOXM6Fr2U0LadUNx1kHvKnOaQN5KEWoVX9uIjAxg0SJVvhZutBNyMntweaHg4l10mbRl9s7S8kaBXu6qBWK4vAENv0eFyxBfV/Z7KHLgxoNLKXht0UL8X5kgmiBiQefOqkOn3p+/HipUbfQzfEExp8TyFQCAEzu9gOeOET22osIfa5J/J7Ngq03RL/hi1rmW5eRwtFqkMcKrz6j0UJnY5GMcWrrGk3pThlatxP+VihEdYwHD3wMALrpIzJvLydGgGH3fVQ4uk1la68H1168GOW6Hl9zAETTN32FdMEb4WEhcO06eDADIzzjMJbzTov1FsgN1WNz2TdF7SxEiNIqzzpIty0k+RBDGdCjZjlLFXtZUWL5gATqXbpPXAXWCtEYIe3ApirYu+g09yn/C1Gb/AyCNg6nswQWIY5pWQG8C76c9pFFwuYrakS4hCgFzrznNuuDcnk8iOyDJzczkZ1YKLLN5xaR9DBzoyIM8TIYviIqc/ZblAr4QCjMPY2WHV+RzvK+V3yegMPOwVE8QHUu2oTp3FzJ8dWha8IeqrNKD69TO/8Xvx4m5o4uzDkWezzZtop5x3dCGZt+5HYWsV4ajUpsp4cGl7F+y9zWFIQVXOiFYx6wlPKa6WlQcVFWJxzqDlV7+LLvIC7JevcT/O3aUQ+mI14P65RHtbhwPOpb8jI9mXBT3dt3yyOhbsX3BGdYFeeCwGtK16grH4u7RIyqMD5YuFZPTJgJlDOtkVnA1aGD/Hh4Fl97727GjYZXyex8KRW0wQ2DOFZUek9D5omlTYPny6POdOiEcg0hpTeakr6d1eU6618EG0+cTE0B37QqMGhU5X1wMXHWVucU5QcQAXc8jBeGh6/s56zGj+fvy+fYl21XlAhohfX7GEYxs9CUA4ECtRggRDi0D4ImxN+GJsTcDCxbg2zkbcErn5+x+hJiS5a9Du+JfTcsIy1cYe2hJG2W7Y83T46/XPX80FEl17GehiMCmvrBmDXDCCcCUKZE1q47xz1+G/A3fz1mvX0dFBbB6NTB3LleT4Zx1Wit6O5hZMWs9uD6ecQHO6/Evx23xMLz6K2yZt8G6YIzwQRC9HSRhIYMgCu9s7mvO7PosrhvwQPQay6qejAyE/JyCSoKor0jvlTK3XUgTdlDF4MGqQ60HV1AvJHFAnNPMjEBO6/If7FtyIk+P445umGdJ0K6bmzPMeeep5oWTOj1vaLByz7C7xcg9Em7C5aaVgssMIyVWu3aiEb+WQYPEvVlZmf59K1eKMroFnPmFa2oif48aJd4XVsjFUJEb8AWBU08V971z5wIXX4zsBkV4aeKV0WVZECOqv0TdCSujrlXl7kGb4t/w84Kz8OvCM/C45Gmp58GV6a8Tc5hpDUwLC6NCGermo+3UCWjTBhg7NvqanefT7fc6YIC4H5cMATNM8n8mDaTgigsB6yJEqkEhCuNIdjZw+eURCxEdpQZjgphUccgQx834fdJv2qePOGE3bAjs2QPcfjuE5Ssw87/L8fDmnpHyimcgyho7DiTLkN2j/Ad8sEMtTCnP3ocdh/XjKRdlHkaRZA2j5cyuz2BUoy8w+qlTPO+nihkzxIVDx47i5HfXXcAHH4jX+vaNbdtmXHaZs/CG8WLjRuCtt0RXeLvwhDDQW4gsXaoK+6UqHt7Q6Czg6kK+iLI6wSR0tujd2zj5r/SbOM5hM3Ys8OyzDjsmkZdnnAA6P99d3Ymma1fgf6IlruEGkUgqXp98Gfo12GxaJqxMb164E03zIxac2hwQhqFfxo/HnvuNw8RNahoOedwDLQt3xGVD2bP8B/Rv8D1u/Mw6dr/rECWSMEk374AJ42r086EqQxSq1uanxHgdkSzk5orjPACcfz5w5IjumF+QeQQFmSZ5zxgDioqs21u9GsFLHgEAFGYahMDlwDREoeZdal64E+f3+jc2fcC39nhx4lUY/m973k+J9pqUFeKyZ4c0LyvXThzCqo6l29GxdDsgjLfdh0TsZQgiFZEVU61bY0T1l9JYWGF+U00NHh19K3YdzQUgeqXqGgmcfDJwszTXGrzzfiYgL+A+j2Us0FU2SZ/jjiH3SnP5UvX1CROA6mp53Lu492NY2PotQ4OVmvxdqs/vyoOrb1/gww8jx+kqFM/LUx83bQqcfbbx51240Lw+o72bEZWVotFiYWH0GsWGImbr/DPR+L7LRMWVwhBdyQW9HsfC1m+j+f2XiGvztm3Ff2EyMzG0+puo++4dfhdmt3wPvnWniwbQmzcD114re2CGKdGJRhTSMxbt0AF49FHx7+7dgdmzUXheJGTpixOvQvfyH0UP+o+ltf+cOeL+9zSDNYwdA+iASzXEcceJ/+8Qvdv7Vm4BWAd3dcYa5ZrJSc54ggtaLaYhlNQ6zuTmRgZpPQ8uCMBZZwG5ubhp4P9hebtXospYofICq6kRvcbKy+WcW0GNhZbSQknXAivGJIuS9f3pl6iOt8w9G1Oa/s9RXW2KfkUHjRW8KRYLounNPlAJIGUyMkTvrawsUXG6dCkwfjywIXHWuwDEvrRpI/7dpEli+6JHTQ0wc6Yzz6hweNfwYph3MZuXp/bsUaDc0Ghrq7OZl6Uo08PQmUpikezVDhwbNUe5swBg+vSIZ62N9gCIC/fmzfW9y9KF448H1q4FzjtP/EckPQMafm8pKFFevbD34/hpnphTSikg37v4RCxo/bY6xObGjcC4ccAxx6Bd8S9oUfC7eWc4ktB7xeyW7+GGgQ9wlW1d9JvhtcENv8H+JRZGGrKCi++DFWUeRH6GvlEMIIaoXtbuVfXJqVNFq+T6ht9vbNAQxuyB4nnYunZFXYkoyC3PPmCjc9qmnIUf5mFY9de279EqqONBfsZhWXCmVbDJ+wqlUDLGwpqCzCNomLMnpm0QREoje3BJ72d+Pp475ho8OvpW6/VvRQV6V/6AMY2/kE8tbfs6Hgt7ggDAJZfIxl1m46CPhcAYogTvyYCuB5f0vWX7a1GmN29oQqmt7/40avJ3cbU3sMG3WNr2ddv9lOneHThXEXY5w7lnclKTmQn8+c+R44KC+CvzGja0XqMYcG3/f2D34pPQKG83AOC5nztER+GRKM06iGYFO/HkuBtQka0Thv+EE3SVYxm+oPjelZWJso727cVQimZI76vSQEQAxHxoBQpj75UrgcJCPDr6Vnw3R5Q3dS79WTT6XrVKNHC+5hpruUE4sk11tXGZ8ePF59qtLIkx+V/tCatwepf/uKsvhrQr3o4XJl6lPkkKrphBCq4042/D78TFvR9PdDcIBfL87PdjdceXMUqxeOTFakN9df+H8OyEawEAzx1zDf4x8i/ihVatcMvg+3D/iL/YbtMNRuHATuvyH/Qo/yGufVHStOAPVx4r3ML29u0BQYCwfAUaGGzIHxlzG18i+4wMUSCWDEqlZctES7bVqxPdE2+ZOFG0SlwqWe3pCdJyjL0a9JDf2T59ooRltSE/t2TYz4LYvdietf9nM8/HjQPv1732yqQr8M3sjfhm9sao0Bd3DLkHS9u+hprC3Zja7EPd++OG9P24EusxjcUa72apTRvRKMFsgZ7qZGWJYR2qq83j4hOJ55xzHN2WG6hF4/zdANRriILMI+KroFRw1dQA06YBGRl4bOzN+HzW+eJ5I69hrSeHQ3gEYO2L+QxLXp18Oe4edrfh9YAviLyMo+aVSBvOHLNQRRI3D7oPP8w7G78uPN2wTHHWIdw+5O8AksezPWXhtAxuUrgbfSu/d9WU2bzz1m/NHder9Axc1eElvDDxKvSpMPfK1N4XL5TGktqQprJ1+qxZkZPSu8MVDjgspA2HlA7najMhKxDE9oXrrOsmiPqKtKZXhsYNy4ANWb8e6NdPHQJW2u8UZB7BlGYKo1CFt3+d4EvKHFw3DrwfXUqNc4DrenBpcwFpP5e0VnLycR8ZfSuWtX/N/o1KlHsR3pxSqUhJSeTvZPJU4/jhM/11hhGAlJzY83VMafoRAGBCk0/1P2bjxsDZZ+Pg0rW4sFdEputjghj2Wfk9ma2LRo4U320AhRkaj/asLN17y7IPoEXhDnQp/QkFYeMtn08MS8ij/GvcWFSErzcIOQ2IMq2VK737jX0+BHyiUj2pnhuJwoxDmNbsIwwPGzfNmiXmM02SVBXpCCm40owFrd9Gp9Jtie4GoUDemDIGjBvnLhazFmlR1qxgp2x1NbLRl5FnoEsXtCzcgfFNPvWuTQ6M5pc/9/knupUZLzzjgW7CzDAdOojxfHVgjHNxW1kJnHSSfPjV7HNx97C77HUyWSksFHNpKBdX6YDfLyolwxsHvR96/nzu6kY3+hwnd3peXIiOGKF64+8f8RfcNEhf+aSHE2vx5gU7kG0goB1c9S1aFf2OVkW/R72oS9u9gTuG/g0/nnQ1/jnmVu72hOUr0DhPxxtRh6fHX4/pzT7gqFRScKnCKtj8LhS/o67VJkGkAo0bc3tbluqEJgGgPx7MmSN6Ep15pup0hi8k5voaPDgSAsSAWIu3Di5di4lNP+Eq2zBnr8poZGP3J/Ha5MvlY65tL2P45NhNuGf4XcDcuXhn6iWGwrJVHV5BUeZhMZcBT9XhbyuZc1gmGjPhRNga2iKsTUHmEbw19TJX3TBT3B6uc25BH1Dky21d+BuGV3+Nt6f9GRf3fgz/HHMzHh6lP+86Cb0pLF+BwQ2jwxzxUpolejL4WAh9KyUlHGPoU7FZzNd32mnq9XJuLtCli3ne35kzRWFteDw78URR0DNvnnWHyNqZIMwpLwdatsS4mk8xvkYzbxqNrU2bAkuWiPu7FSvEqBQdzMN8PTP+OlzW59FIVA8N8rp95kx8euz5OLfHv+1+Eses6fgSbht8H24edJ/udTMPrqjjjRtFwx/JS6ZF4Q7ufoS/bk/CODMmjp2zZqWvB1eYsBK1U6fE9sMmBRn6ITn/N+MC5AUiiq/rRv1LNjwzJTMTOYHaiJIJ0hqySxd1OW0kBqXhquJ5mdT0Y/ww7yypHonSUlH2ocnFBwD/O/YiZPodPrtlZfF9TrV56pKM3YtPxsW9H4ucGDkSOOaYhPWnPkA5uAgixqiWlNOmYcpTq/DetIvR658ehJzTCkqUVkjLlomh7h59NO6Ww0aCaD8LmYZ+iQcNcvYaXGgghuwysTrhygHg96u0YUWZh9GphJTOKc2qVUC3btzFbxl8H1oW7gDazI9aeM1p9Z74h6CTNFcH20od2NtQ6QryOBaLC1u/iU/+aISPdoqehbyhccfVfIZHN0uhIM2EmR55iIQRBYskICNSFE7T4a5lW7Fn8Umqc/uX/Enfc6mqyjwXVKtWlsoEu+FW7aJUWGX46lAbMu6PNoxaWfYBtCn6VT7m8oJhLGIgFAqhd+UPrkPSRaqW6iGrTWPM5p6qKtGb0crAxgOvgqNB4+fajbGE0hNKOf2t7/604T2PjbkZvSu2WNbdqeRnfLqrERrm7JE9nZyGrH909C2yUiu4bJXq2ltTw2GkNEpExoA1a1Dx3KXGFY8apQ7rXF7OL+jJzQUOOc+rRhBpj88HrFuHQStW4KnxNwJQ5CDi8Wzo0UP8Z8HYms/FP/r0wbZnPsYvtz+Bj3bWYOnLiwBI487GjUDjxuj40EP4U+4L2HUkFzd8NsKy7i6lP+HjP2qs+2pCvwab0aviB6x+Ldow0cyDq2GuJB8Iy1ZqasR/En0qt9gOu+hZnlKDcPhpx1lnAd98Yy9/VqzRevhp+G7OBjQr2Kl7rUvZz+oQw7wGToEAMGcOVgUfwqhGX6DTw+frr0XHjQMOHAB69hSf1ZNPBq6+OqoYY0ATbVhNxsTyqY5y3ZiEHlxJ2KW0J7lVnoQ9ktBVnIj2wAj4QuhZ8aM3lWt/86wsoGVL0fqqV6+EWTVoP3NYsMSYdwJrXtZ3ewrruz0lH1/Q6wlsX3BGdMH8fFMLUQbBnqBr6lT5z54VPyK4bCX/vURiEQSc0eVZVOdKi0Gz90hn3A0ovTZhnM/j1cmXo9JI4eoC7oT0Rp/LwnoTAK7t/yBGVH8lHx81ETxrORzkKCv1bUT1l2gtCaltLxIV8c/9TKBVJpG6VFgkiA8zdCgKMw/L+TkBWIflM8LsfZGEPrUxVnApOXrCGtPrWsVDpq9OlQ/Udh4jaWznVRK8OPEqtaV6ixbRhXr1AoYMsdeP+oTVGN24cXQy+hhwOBixPn5/+kW4YUDE63pc489s1cUQeS6VczNv2MFjmnyC6rxIqOs5Ld/RDUH8ycwL8M3sjXhnWkTBZOpNJfHtnGhjuxaFO1RtKtfGViHPeldskUOme8ry5eK4c4bO+p0gCHOceFT07m1+3edD1dgu6F7+E4Yr9gPNCnaK76o0UJRnH8D1A/9h2dxfh9yLlyZFC+e9xMhA4dDSNRjY8DvxwEgJoTfwGQyGYeNEbXhXwoLCQlFZk+ReOUpaFO7Qlw/16QNAM9fX1Ymfj4fhw5HpD6Jj6XZk+WvRUc9YOiNDjMbQujV3f20ZzqbCvpm8uwkNqTN6ENaQgispyfTVcZcd3ehznNjpef7Km+vkAjjjDDHshwInXiBuUE6Hfx9+B16ddEXC+nJxn8dxZrdn5eNMfzBipaWHwXvEmICa/F3YMvdsADDMrSXToQNwVSShpFdW4ER8uLzfo8gNcAiGdZ4X2WJPWqAbKXUHNfwO/5txId6eamzx7MT6z/V6dPJklYLWso1hw3CgNpLH6d/jbsCSNuqkysWZB2TLRy6vD2kzPrHpJ/h69rkWhQ2YPRsYMECszisrSoJIBMOGifkPzeLaA+JG97zzuEMamqI3F7ZuLYYfqa4Grr8eR03e5U4lP2NVh5fc9aF9e+6iWsvsTL967cWYICbAbtwYOP5444pKSsRwtVIeEl6jnGHVX6MmXwrVWlEhhnBT9o8Jomd9OufQcIsXYW082Asdqov8RhXZ++W1gLB8BUY1/pK7nhM7PY+6ZWLO0juH3oPHxtwiX+MNO6hUih3X+k3cNPB+TG76P92yrYp+R43CSttIOXvb4L/LireWOmG35DZnzAAuuEDXItwIxiCHTPeUZs1ErxCOfF0EQWgoKrJ/z9KlEQ+Prl3F/w3G6PCwe2DpWqzq8LL9tmBPXmJFWNYwpOprOczwXUPvxjFN9EMeZwcUbRt57GzYEJ2PuW1b/falPT/tPdIAQcBNA/8PFdlq2dGEmk8iHn0nnigq5yR6V2wW35nzz0dFzn5VXVi2DDjhBPGY01Pt8PFr0WqZB+t6ACUGocwBANOnq4+vucaTNmOKUiCR7Ao5Wv/HBVJwEUQMWdbuVSxs/RZX2U4lP2Nlh1dw3YAH+RvIzgZuvDFyHDatNBngCzIOYVGbN/jbcIDSaros+4AqzNAV/R7B65Pd5Ufgpl07AEBh5mF8P2c9/r+9O4+Tojr3P/59elYYBhj2QYYdQRBkU0GFACK4gAsKroDgLipuUVzBuPyMGpNoEhOTeNUbE5NobhKNiZrExOQmmrhdjRrXaJS4xIgLLiDM+f1xqnuqu6uXmemZnh4+79eL13RXVVdVD9PVp85zzvOotjavl5014b60ZfHf6JDad9V43An611Hn6uOVp+hX+3xVPSs/atow3DDu1s3X5EJpCe7UXvygf4tenhixl0izFyEIfg3o+oEGdE0Pln5wtA9St/TmKCqQfENqTvqGhuiAc3W171DPsf/tat7zTwYO1OBu76ouqNkxvd/Lqg2K7T666DJJLXgftbXSwoVJi57eUN/8fQSN9fLY1o7f8AUyKS/39Q+HDMm+XSzmg09m0rp16etb2/l/1lm+w7u8XKqq0uYsszGXjHhEX57+48h1M+uf1wWT7olcl+T00/Ouf5g+g2uruoRqj8XkfCfERRclRtZGuuIKf1MfXC9um/Nd/Wqfr6ZvN3p0WorHxIy2AQPS0jsWtP5qZzNjhg9cbJdf6t6sChHgCs3g6l75SV4zoaKUW6Ni5vTtmbfq6O3/pDnbNc1yyHemdfhr65bZN6tX9cdZA8thqQGuLmU+UHf8Dn/QESP/qoWDowNlCePH+/TdpNUESo+Z/y67+uqWDR6Ixfwgk699zadql3xa0fnzpcMPT9o0PhCka/lnsl6hNLLNCI5Xlm1NqlNYCHfu9U1dMvUuSdLRo/8cXTMzdbZQpu+QhgYfnIi78sqmmlEpupX7mkxl1phW5xSl5+Rxv08aPCKF+rp22MEPngp8ZfoP9aVpd/gn9fX6w8Kr9c8j1uiZxWt9bW4zPzvyssv8zOR8jBzpaze10iuHn6fV47MMpJ8/P7muV2pAtyMKz+DqaDP/wg24+npp2rTincs2pIP9FQCl74tflL4w9WeSpBtnfk/jer2R1+seXXS5Fg17vPkHzKPRGr6+Tui1XjfPuqX5x2mGcKd5mTUm3WT3rv5Iuw14WXfM/aZ6VGYZRVIIobROw7r/J6+aP5J0zbQ701YnRmYHm8bMqUv5Z5rf8ExidO/6I8/x08/DLrkk8zHnzcvxBlAULWggnb7jr/XnA3xtisQNWra/t9AxoraqrYzfHPnP0q79XlZDzbsRW3rnT7xHjxx0eWI0WVSH3IljH/QPhg71o8emTk3clD4WBKKaTsrUUPOuzppwnzYsPz1p1X6Dn1Rtxadava5O//7hb6UZM/TQgVfq6cXr/FsLBbgn93lNvao2amxd03UwsTZXwGnBguzrm4FRlNjm1Nf7ovGSNCeofdHazn+zpMBNthSFMbmMM7bPGP9rXbbzz7Ie6td3BoH/mTPTC2tHSKQgDNK/VMS2qqZis54/9CJ/PvnOoo7Fkt7j+F7/StQceeqQS/TPI9boi7vc6YONY8b47/igrZH2+wil02MWdxZHHeWzDxSic6IAAa5+XT5Uj8qPtXHFqepR+am2NmY5rywp8+JBrGPH/G/a111ranllCyxLSgQKU+vGTuv/cqKN8L05N+nne38j8uX8pQKdQEWFr10XmlnS4v2EL2CLFqUNgku6Zlx6adPjPFPKrptyl+YPelq1lZuaXecqm6rYFjXmqp+9667Jz7P9vsLfL1nqQZbFnNzxJ/hfW1S6YpSODG2KxKCleEAo2G71+N9qRv2Lic9M/64fqqHbBu1ww2nJqQT79s2/zZNvuybT3+7220vyg7QrOlvazPDvsCOnK1y3jsFC7YQAV2fC6PQO4ZxzpHN2ui+5FkMeWtX5MWuWn60wcWLk6kydTEO6vaNlo/6sO+Z+M23drbNuih61nEE4x7BJ0uzZmlX/nHbq/XraTbYkHTw8ezCvR+XH6l6Ru6B0mW2NrGVw1MiImXMZGghHjHy4aV3ENh+uOE2zBz6f8Rzi729gzfvpubsjGi8LB/+ftGJF+lRwdAz1fqbQQwf+P31n5q3Zt3VOnx6zStdO/7Gm9fdF2VNzrkfW4MqzURv/237owC9qfK/1Gbfboe7NpNp+GTuey8ul887zo8fMNLH3a9qxbr0m9fEpPHTUUf5nZaX+ueqLumbanepZlfw5vHvvr6ss5lQ2f676LJkjxWLqWfWJ+lT7NAxVKanBXj78At2zz/Xp55LPd9b++0uShtX+W6eM+13u7TMco4IZXNgWTZ7sR28vWeKfFziVddYAV5Y2TaJjYMkSPbvkYp067rdp2+w5M2KkdRaJdsCIEZKa0h2N6vF28vp8RXQUlMe2qqHbBp1zWfemGaYDBiTSOKWlXz3zzMTDvGsjouj+sPBqvXjYhYn6dVlncI0cmTElaLYgVrk1+s9nXH2OGcqhWYeje76ln+x1Q+ZtzzpLGj487/pxqRJtlmzfmXmk2rlo8t16tg2yFQLIQyFmxOZj6tTk+5wWzBZbO+XutHuNbMb3el1njr8/fcXAgdK55yYuXZVlW3NfB4cMka65Rjr5ZGmffRLZX1rs5JNb93p0LEG7OfVe3sz57/6DDvILcvXptCa4kW/bvb5eWr5cOvvs5OUnneRnZHdGpZSiEO2CAFdnMmhQsc8AgaqyLYkp8fnK1BlUm0eQR4cf7juxunZNX7d4cdLT+A335Tv/VC8cdpFumX1zZLCpZ9Un+dUgkvSNPW7ThuVnqDpIf+IkqbZWDyy8Vv26fKgt4dGvQeeTlKHjP/DYosv1pQzpjcK2HHeyXjj0Iv1+4TWJZYNq3tV/z/kv/yScTqCx0Td+U9w256amJxGjP7pVbMp+DuFOrUzFaQOXTv2ZHzVbW8sXcUe1887SEUdo136v6Jgx/5tz86qyLUn/leWxrdL06Ym/pVc39kp+wdln5x3gMpOvvaP00dhhvas2Jj0fGEp7OKDL+zpuzB/8k5Qg+B/3v9oXpW9o8NNPZ8zwK2Ixn4qsGeI1RVJnS/Wo/NSnBenvUz5m+9yn2W8/afp0vXz4herX5cNmnY+k5AAXsC3q3r3puyZTbYkoeVyjsgW48grolJVpTM+3VJPyHVvWgjRFZeakq65KdAR0SUlF1OxBRGPHps0iTcwS22uv5A7+4He1JXWmz6BB0okntuz4aJnUWfQt0Kv6Y/Wpbko9nbNuZIbOp8g6W0Ha6pg5n35z7lz/Xbtunf++yyT0eYyZ00HDnsi8bXW1dO65aR27O0YVqY8671ijrw3SPyJN87Jl/ryj7jdS7Nb/5Vb3FQNopnPP9RlCCpDSLC/HHitX3b6pzGYPfE5fmn5H9MrhwxMDBipjW/IL9NfW+ppJBx6Y/d48nzbUTjvlHrCAkjas9t/a+/jBvu5tfJbibrslp6OM/x2tWOH76fKczRipOW333XZLnikm+e/rqVPze32BB8K1ufJy3yZZvrzYZ5IuyyxPtB0CXJ1Jnz7+wnr55cU+E7Qgx2qmWVYfrDg9vy+bTA2yuXNlQwYnnlbGtkhLl+r8Sb9smqacIYd0rkZhPCVZddkWda/8VJ8cc6qkHJ0BZ56ZCMZmGxVbHtuqZaMe0h/3vyrrOUjS8O7vaFSPtxLPkzrWwl/osZh0/vm+Mz9V/PcXBBOiTyo6LczW8O9p+vSs55r4f84RCEMRmUmf+1yLX15ujdLRRyeeP70hZRTlqFFJnVXZPmfh68KWDAGu1488V3s3PN20YMgQ7T/k//TRylMkSTfMuE03zvyef0/LloV2bupS/pnvCD7xRKlnz+QdV1UlHj635CLVd30v43kGu9PfDlmXNoMt4Ygjsr4+o1amrRpXt97/fggoY1vXnELzedSsPHDoE76YdoSYuaR6nGGJj2IwonXT1uQR32mf1Ayf3XN2uld/PegKScH3fvD+/rD/VZo/6OmkbZtdA8ssrQ5gmTX6zqsM57fFRbd9DhvxFx016uHmHR8ts+OO/mcBIytJgcsc7fuu5Zu0cYVvC0d+FwaDTJzk67QuXtwUSDLTj+d+S0Nr35GkpvbvpZfm/v5assR/vy9blhhcEx4U8+GK03RtfNBYuF28fLmOGPmw9h/yRGJRdVnQJog65u67+7ShOWxYfnpyuwRA+xg+XDr44Iz3rAVnlr2bIkN2mUi9euXeRrm/z2Pm9N7Rq2UmfRpuX6xcmf+5RBkc9Kek3i+h84rP4AotevnwC3XS+RF/q926pS+bNi1nXeucWhMca654n2Aeg1g6jJkzfWCvo+nf39dZO++8Yp/JNoUAV2czfHhycUAUx5FH+pQ1xx4bXeg9cPyYB/X4Ip+rOuu9a3NGbkSI19k5eezvdNqOD/gvgRUrmjaIT68OKbPGnIW143V1UoNzvauTZ5NM6L1ehw7/q39SXp4YwZqtQVxujaos26rdB7wUuT5mjTp8xF/8KOu0dSk7Xr3afy5OPtmnTgiOH2nGjPSp3XEZGsaJTq0DD/T/UoXT0CRe1PpRxmgnedZuk6Sder+W1qk1uc+r6a8L5WQvL8v8QbDQMVJnB4zv9bo2LD9d29W85ze5+mo/42DVKpkpUUw5cSP4uc8lBa2SZPowBh1Z2/d8O2MQPixrzcHBgxOzGZqllQGuvy3+gr7QzBm1QKc0apR0yCE+hVkmxx7rZ5XkESA4evSf9cCC6CLyMXMZWxD9u3zgjzFypKTMA3wSFi+O7FBqqHlXU4Lra2IPjY3aY8BLKosl77PFM6hCQa4ya4xOPxSkZGqMSu3WpYt+sOd3tdcgcrW1i8MO80GeEwpXxyXRxtt556TBK1HKrFE1FZt18tjf6dARf/V1NiLa2JlmMh8y/DF1CbIhJNq//frl11m9++7+XyA8eKZbxaamtomZbytMmyYNHKjb5tykGQNelCQ9s3ithnd/J/excmhOujEApS3rd/gxx/hZzxEm9v5ncs2tTPcoKarLc6cw7lH5qSSpZ7ze98CB6fW2mqtnT+nKK5PrjKFzC+5Bp/T5Z44N1er71TSrV/v6WUuXFna/2Rx9tB+sfc457XfMzmzKFF/7HO2GABfQFiorpR128DfDWaapD+/+jkZ0/3fu/bXyCzM+Wejre/xAC4c86fcXHoUakUu/zBpzF2aVdMfcb2rJiEcSz9cfeY7u2OtbSdt0q9ik2+d+p2lB0JmeabSzlGHka8jeg57W9/f8buJ5uKsgLTXS2LF+ZmNEoVdTyrZmUt++emzRZbp615T0B1OmSF//uu9AGDYssThRgHzMmOgClytXptdoIMDVOaR8Np84+LK0jtRf7nN9er2MyZN9+swrrtB23d7XC4demLw+GIVu5hJpPVNThppccidS96AuTMosjcT5ZOsgyxTgOuIIn+pEBZgAVV0tTZqUCLjnvcPWXP+YtQU0MfMdTUHB6Ug77+wDSi347PTv8r7eXX6GJH/dKa+M6Z1lZ+q7M2/RPg1PSZI2rjhVu/R7JSl98ropdyUG+/jXpnwv9+0rXXaZduv/onqF0rGa+X9f2/0Hqo2nOcxwLcs0myynBQsS6V4ypl3s1k06+ujoLr7Ro32nWrwOGtpWdbVvoxVw9HFiln5FRc7PRUO3DZJ8e3tMz7ekyy6T5s9v2qCxUSfu8HvNG/RM805i4ULfQRuehS1p8fBHdO++X4l8ScY2/OzZfn+hgW7x7+Ud6t5s3nkB2OaVZ0srHPSJjOz+VtqM76T7pfjs2xweOehyXTjpnrzPbeGQJ/XJylV5b59TXV1eNQjRSQQDsW6c+T39fcnF7XvssWP9gLT2THXXu7cPcpFmEyWKABfQ3k47LfEwZk5JY5yrq32B01SHHtqqQ9ZWbdaP534rfcWee0oTJkTWpYqZS/QT7bXdM/r1fl/W84delLbdwcMfT8wUUc+eGljzvh81lUdaxePG/DEpLUpYamM5PqJVks6feI8unnK3fxJ0NoSPlnOkdujcKsuC40yalLTJpD6vaVDNhogTK/cdDGvWJNLYbWrMMbK2okIaOVKXTf2plm7/kF9Gw6F0ZPtbjkpHkKJflw81uNu7yQvNfJqgIBXAyB4pge66Ot251zf1/Tnf9aO3167V92bflPQZTOpni/gMN20XnH+2FAeZZomatSgVR9ro9CuuSASqPr/Tfbpi5//Jf2eFGhEXFXwG0CrhwFHMnOqqPg4eN0qDBql39UdaOeZPmt7/ZUlSTUUoUB9cW2srN2lin9eT9hPlj/tfrR/PvbHp2ME3/6pxv4t+TSjY3+wUhRHKsg28mT5dO/d9NXrQzMqV7VcPBa2Xkuo93xpcn59wr/64/9Xp68Nf1lu26IYZ39fAmvfTt4sSf22PHtLatUkztCRpdI+3NC/D7MBbZ/+X7pj7TY3v1fTZ0jXXJNXCbdN6GxEz1wB0PlVluQdtvnDYxWmzYBJXxoYG6ZRT8krfP6XvP5PbEfK1tz89JjqIZSZVlzOoFC00YIC0dq1i5lTf9X3VlH+aeduOXL+qS/vWyQOKhQAX0N7GjUsULo9ZY3KXyzXXpNe9WL487Ya2ucx82pM0S5ZIq1ZFjkiNmUukKLxvv69qz+3+rlE93s5+oHB6vqgv+fhxgjy51+9+u67Y+aeRuwrP4Nq44lQtGPJk4vnp43+jXfu94p9MmCBde626VzQ1OPJJRfTFXe6UFNQkO/jgyM6n/l0/yL6T4P00upjKso1eC1ww+Zca3G2DdMYZ0XXA0DFlC7CMHetna+aQV5HjFIuGPa5ZA5/3f2cDB6pX9cdJn8FEh21DQ8b8zj+a+y3NGficTw2YLRgX1MKJFPVZjgrEZ7J8eVKdv6l9X9V5k36V/41AoQJchU4dASBJ+Ls3Jpf0Gd/aGPH5i7gGrJtyV3TaQzOZJV9LI5Mghvd54YWJ0bdZg1O5BPvMFSTbu+FpNR5/UsuPg44hntJakhYuTEsPnCb4++hd/VEiwJtRHgMtMif3TBefMRZll36v6ODhj+vJQ4LZkfPnp99jxAeJFbpfbtw4ae+9C7xTAB3RwG4f6MlDLsm5XWot4cQAmW7d/LUozxSFOvdcqbxc39jjNt025zu6edYtPsgWv75mm6XeHlpZWgIdTDCItHvlp9q4cnXm7erq/N9xhtr2RTV+fHqJEqATaqfqkwCixFJvYysq0u8y27OwZEjMGrUly8wkU6N08cXS/ff7BmnPnsk1OyZOlO6+WxoyRPrPf6SNG5tmgkyYIF1yibR2bcY0heHRYDUVm5NmhCSCX6tX+2PGYqqpjckdf4Lsxm/lDnCZ6ZyJ92lTY7nqKj+WdpqTHOQLHs8e+LzeWpqhHleK6rLc+cATClj8HO0gW2CkosLX28tR7yOffPEDu27Qvz6OSEOQITBl5nxw69RT09NlVFdLn36qxfHAdsoMxYTDDpM++CD7LK3Ua9KyZfkXc12wIPO2+d4Ajh0r/eY3LSvqHP6/I8AFtKlwACh1QExSTc+gtl+8dlXYvEHPaFr/f6Qtj9sa6iDb6swHI94J1QwKX6+6d/epEM9RM0IGmbW4jhdKz047SdddJ1VVac8br9bPX90pZ3rC/l1SBkXNmdP0+KSTpPfekzYnzzzIKcv33jvLzswdUAtbtCh9WfCetuaRkjwvkydLjz3m69kC2DY0Nmp8r3/l3ixlsF9a7a5jjpG+/OWkRY8uukyL7jtRr24M1ZgfPlwaO1YnbXkw+fXxdv7ChU3pai8O0srlU8ewUEaMkN56y7dP0Pl07x69vLxcuv76jpkxJBbzA06BTo4AF1BEZn40yOOLLm3qoG7r6c2hgulh29VsULfyTXru/QGSpLLU9ImSD1oFqsu2+M6pcMHtDaGRpA0NvhBrba30+uvSnXdKhx/etH6AP07UyNjG407w99zr1kl//7t0++1JTeDK2BZpjz18x3dc6PeWqJMRUXNLUuKG/qLJ8Rzec6K3k08vJ0k6OyLQFYw0m97/JZ/O0GjIdkr5BEYuvVS6KD2FZ9z4Xv/SS4ddEL0y+Ntdf9QaHf/gUfr232f4ZVdcIW3alHF2lUnS5z8fPeJx1SrpS1/Kfd6pteGynF/iRnTgwPSOvj339EGotJMsQLfyuHH+fbYkrWc48EeAC2hT8dpZW4870QeD3ODEukQ9oHHjpCOP9I/r6nyb5K67JEn/PGJNdGpgKTRjuumaEpNLvz727Zv13FokOHaXPAYqoBMJvltn1L+oxw6+XFL2bAp9qoP6cFdckT6CO95+vidL7ZigU2zmgBea/s5Dac1T9a7+qOnJ6NFZzy2j4Htxc3hA2zHHtGxfkq8tumFDxxzBDqBt5FlXOjWQnvg2j7fV6+ulc87Rlb//iR7412jd+/o4Te7zmvp3+VCvbuwjd3xoMGFUf0m/fv5nbW16QL89A1yHHCL16tXqDDzoQEaPlp57Tho0SDr22MzbRQzcAtB+6O0BiihmjdK6dZr4uR6+A1dKb7AVooM4PFMiSI+Y6vUj12jpqIeSzi0trZpzunXWTerf5X2N6/Wv9HNLLepdV+cblEOH+iKZEXWCUtMVSKHd1tdHNmArYlulpUuTF4beY8ycH6UyfXraa5MPkOF5quHDE0Xmk8yfL02YoD8dcJV+NPfbHTv3MpqvVy//c/Dg7NtJ/qYqniYwQ8284d3fiVwe/rtJPIqnOAh/ZlauTHqZmcv8t7v99oUbQZ3PNWjJkvzzey9e7APg+d74mfk0Yy2ZzRqL+c/pvHmFuZYCSBL+VMVnOMXM+TpG4RSF8fZEaidTqE3S0G1D5o9psCK+fvseb2pG/Yu+o6G+viml3JQp/hpzQfKAgr7x4ENLOKfNx56kbhWb8tue9ESdU6Y2nnN6d/kZ2m/wU/55tg6u+GzqqNTGs2ZJgwbpWzNv03OHrvUzw6Pqa555ZvLzCy/0nW4tEcwQO3jYYzpm9B99bdBddmnZviT/nUtwC9i2zJ3rf2aqNRlcO9NmcJnz167Fi5OWnzvxXk3p82ri+c/mf0MvH3Z+8j7rUjJeTJ2aPJA2VXvOqqmp8YN34veRKH2nny5ddZUfzEoddaDDYgYXUERl5vyXZJYRmgXplM0z8BJOIVRmTqN7vtm08vzzpV/8Qku3f1j7D/0/f+52cfIOqqqkNWuaNVOiIpajdlVw7uEUhZGvCb1Hk/OdXJl+d7l+p/mef02NnymTIz1ds/aJjuPSS306odTAbSZDh0o33ND8/2uz/D6ju+4q/eUviVScY3q8Kdl2zTtWSwSfl9p4nbvWXpPmzm26GW4PUWmZABTcPz4MZk8NHepT8yxY4K+Jkk8H3BrBdTU+Q/u5Q9f65fX1frZ3nFny9eWzz/TqEWvUr/pDSXu1+PAVzanhledodpSYLIHLRKpAs+z1Luvr/ezqqHZF166+8yxXm3L0aH/fcN11/nmmmq4HH+yzJ8RfE6W2Vjr7bO14zTX6zuf+W9LU7McGgFQHHeTvuzMNCAyunakzuGLmfIA+4r4i3CcxIKom9oEHSh9+KD3+uH++fHl6uvawjpg2DqUjFpN69Cj2WQDIgQAXUESRKXOc028XfEmfbKnU25/USlaAm81co4kTI6uaGp5l1qgR3d/x6QB69vS1tILtelRm6egeNqxZpzax9+u6a/7XtPDeU7JuZ6HaF5H960mzYHJ0wOf6fbR0enm2/RLgKj3l5c1PaZHr/3mffdKXnX56Ws75XEGkD44+zdepy3Z92JojeJyv4IbxNwu+rE9GTpCGHBW9HbMWgG3LqlXSV29MPB3f63X/YOed/c+JE6Vrr5XOPFNnTPi1Fg55UlL/3PutqkofnR2Y2vdVLR315/zPccAADe62oXXpiZo7O5sAV+eUz3fc9dfnbgdkC4AV0rx5fkbF00/7Wd2ZhDMUMNMZQHPFYn5gSybB/Uh4BlfPyo80vd/LklLKCcTrAkaUMEhSUyOdeKL0xhs+nXum4NaKFT7QnzJLDADQ+dDjChRRLLW4qiQ5p9kDn9e+g/+mo0f/uTA3m/F0IxMmRK+PSB0QM9d0kx4fadoGKfhi5rRgyFPpK+I33MGIq3jwbfn2f4reUejcGp1l/72lpm5LHdVVXZ1cqyzf/4NsAYV3MqSnw7Zj3jzpgAPSl48Z42dISprc55+qjOWu81JbuUmVZTkCWP3z6EjOx/Tp0tixqj9uoYavXdrymZEAOpcJE2QjR0iS3jzqbD15yKXS8cdLc0J1LWtqpLVrVRFr1A51b0bvJ16TK+7aa6NHW0+dqrqqj3Xr7JvzP8faWl8PNJ+ahJnk2/aJ1yRtTQ0jdFyZ0vCG/z4KWX+jEN+pZWW+7Z+hlicAtLmIwa9vLztbXzrvnYzXudTZXgmheuCS/KzYbMG1adN8armWpnEFAJQMAlxAEeWaiSSpMDN/5s/3NbCOOy56fUTqgDJrlM4913e+x1/XFueWom/1B75GwamnBgt8yqMu5ZslSTfPuiX6hcG57NL3H9q34W/ZDzJmjHTJJU3PozrSMtQqy+qzLIGJsWObvz90LvX1OTusThr7oDYdm302Y9723NMH1Naubd1+Kiqk1aulmTOzb3fKKf7zetZZrTsegJITr7+lsWPT2wbhOkJR7YaZM307JS7TbKtMbZhc6urap4N/+nTpW9+i7kZnc+KJvmZW1ACVYqGQPYBS0bOndNVVSZliYnLRNbOC+6SFQ57Unts9K02e3LRu/nw/I6u5GIAHANsEUhQCxRB08ESmKExViEZZLJY9PUmmGVxDh0pnnNH642cydqz0zDOJpxdPvlvnTfylNGqRT1EU3+aww3T9p7fr4sm/yLyv4Pf08EFXBs8zFLqN69ev6XGuQF2+/wfZUiANHOjzjNPxte3Klrs79W8w37+5bDnlKyqkfffNbz+FMGqUdNllwZPX2++4AIoukUY407Xr4IOlu+4qbJCgvWr5kX512zZpkv+XSRtkN8hp1Chfl7OZacFzKsZ7AdD59eihJcMf0YefVemXr41vGhSTKmhDzBr4vGYNfF5SKMC1++7MRgUAZESACyiiHpWf5N6oPUYdde8uKTnAVZahPlhBnXaaHxkbGNXjbVWXp9SuMJNmz1av229Xr+osReqnT5cefDD/Y4cDCrlqc+QKgC1YIL30UnIdgyiZCoGjczv1VP/3kW0WX2oHaj6f++uu6/ijEjv6+QEoiMQnPdNnft48aa+9Mq9vbhCpV6/2q6kxcqT0yivJA2OAuLb6nss2gMVMWrmyfY8JAK1w8PDHdfDwx7NvlO0alG2gIABgm0eACyiGXXfVi4ddoGGzI0ZepgaR2qODuKZGWrNGjQc+n1gUObKq0AGu0Hv7aOUp6lL2WdryvC1Z4msO/fjH+b+mrk765JPMdRXq6qQNG3xqmmzC9bqAVDvu6P9l09Dg/732Wv77jc9yBIAiiX9dJ9oM2QaEZPtu37Il87oobZAiOaP99/fpV1NrfwBtqRjBJgJcANrDfvtFL09Nvzptmv8O3ryZ2VsAgKyowQUUQ58+GnHzxYodF1GIvFjpQYYNU2PTGGz1rtqYvk0bBt+6ln/WtLtcI7mXLk1fVlEhjR/fvHO7/HJfeD7TtmvWSMuW+ZHnQFuKxaQLLsi9HemDAHRApuDalGtGdCY77+x/jhlTmBMqpKoqadYsX0cESFXo7+UxY3y7dMSIwu43m3i6z/ZK+wlg27Xnnj5oFSUc4Lr0UmmnnXwN4yFD2ufcAAAlixlcQLFkmn2ReqPcjrUf4sVf3fEnRG/QrVv7nMjIkZnXLVok7bFH9LrwiO58RqHm2qZnT5/vG2gP4UBrXV30Nlu3ts+5FAopCoFtgpmTTjih5Z/5ESOkK6/MPwUR1xZ0FPPmSf/7v9I++xRmf6ef7r/rWxosbonFi6WDDmrfYwLYNmXLQBG+N+/bt+3PBQDQadCKBTqarl2Tnw8Y0G6H3upydBgtWSJt2iQ9+aR/XogUQd26SRuD2WLnn+/3ma1WVbab73AwkM4vlKLPf1569FE/ujHKkCHS3/8u1da273k1kxOfP2BbYI0+6B6TkyZPzrF1DpkC+2E77CA9+2zrjwUUyoAB0g03FC5tpllxAk0EtwAUm5l00kl+wC/38gCAZqAlC3Q0M2ZIL73kO3FGjWrXlDjbdX0v+wbdu0urVvlR2lJhbobPOEO6+24/MyufAu7ZGruZamkBpWLkyOwzGBcs8NeEDl4LhkyKwDYimFXabv1QJ57oA1zhlMRAsbVnTTgA6Mw6+D0OAKBjIsAFdDQVFdJxxxXl0GdNuF8njf29fzJ0aO4XFKLY66BBvsMqX9k6Ebp39/vq3r315wV0RJWV0pw5xT6LnLY6OvuAbcIRR0gXS3b08vY5XnW1NGlS+xwLAAAUFjOzAABtgAAXgISymFNt5SZfA+OMMzJveO21Ph1gRxyxSscXUHQEuIBtQ8XwBs2YIVXtNqXYpwIAADq6jth/AAAoeQS4AKTbYYfss7NqatrvXFIx6gvo8HLW8wPQKZhJDz5Y7LMAAAAlYcSIYp8BAKATIsAFoLSUlRX7DADksKWRzykAAAAASVdcIa1f7wfSAgBQYMwPBpCuI6YOiDeGd9yxuOcBIKfN8QAXMy4BAACAbVvv3tKECcU+CwB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+      "text/plain": [
+       "<Figure size 2160x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "plt.plot(Y, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(Y_pred, color = 'blue', linewidth=1)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE:  2.0572089029888656 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za záväzný zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "f8f3967282314d3995245835bdaa8418",
+   "translation_date": "2025-09-06T14:03:07+00:00",
+   "source_file": "7-TimeSeries/3-SVR/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sk/7-TimeSeries/3-SVR/working/notebook.ipynb b/translations/sk/7-TimeSeries/3-SVR/working/notebook.ipynb
new file mode 100644
index 000000000..edb34cdb2
--- /dev/null
+++ b/translations/sk/7-TimeSeries/3-SVR/working/notebook.ipynb
@@ -0,0 +1,695 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "V tomto zápisníku ukážeme, ako:\n",
+    "\n",
+    "- pripraviť 2D časové rady na trénovanie modelu regresora SVM\n",
+    "- implementovať SVR pomocou RBF jadra\n",
+    "- vyhodnotiť model pomocou grafov a MAPE\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importovanie modulov\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Načítať údaje\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Teraz musíte pripraviť údaje na trénovanie vykonaním filtrovania a škálovania vašich údajov.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zmeňte údaje tak, aby boli v rozsahu (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "Pre náš SVR transformujeme vstupné údaje do formy `[batch, timesteps]`. Takže preformátujeme existujúce `train_data` a `test_data` tak, aby existoval nový rozmer, ktorý sa vzťahuje na časové kroky. V našom príklade berieme `timesteps = 5`. Vstupy do modelu sú teda údaje za prvé 4 časové kroky a výstup budú údaje za 5. časový krok.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [],
+   "source": [
+    "x_train, y_train = None\n",
+    "x_test, y_test = None\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit model on training data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Vytvoriť predikciu modelu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = None\n",
+    "y_test_pred = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = None\n",
+    "test_timestamps = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Predikcia celého súboru údajov\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = None\n",
+    "\n",
+    "# Scaling\n",
+    "data = None\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=None\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = None, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = None\n",
+    "Y = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "e86ce102239a14c44585623b9b924a74",
+   "translation_date": "2025-09-06T14:05:37+00:00",
+   "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sk/8-Reinforcement/1-QLearning/notebook.ipynb b/translations/sk/8-Reinforcement/1-QLearning/notebook.ipynb
new file mode 100644
index 000000000..c1277fa60
--- /dev/null
+++ b/translations/sk/8-Reinforcement/1-QLearning/notebook.ipynb
@@ -0,0 +1,411 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "17e5a668646eabf5aabd0e9bfcf17876",
+   "translation_date": "2025-09-06T15:02:49+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter a vlk: Úvod do posilňovacieho učenia\n",
+    "\n",
+    "V tomto návode sa naučíme, ako aplikovať posilňovacie učenie na problém hľadania cesty. Prostredie je inšpirované hudobnou rozprávkou [Peter a vlk](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) od ruského skladateľa [Sergeja Prokofieva](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Je to príbeh o mladom pionierovi Petrovi, ktorý odvážne opustí svoj dom a vydá sa na lesnú čistinu, aby prenasledoval vlka. Vytrénujeme algoritmy strojového učenia, ktoré Petrovi pomôžu preskúmať okolitú oblasť a vytvoriť optimálnu navigačnú mapu.\n",
+    "\n",
+    "Najskôr si importujeme niekoľko užitočných knižníc:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Prehľad posilňovacieho učenia\n",
+    "\n",
+    "**Posilňovacie učenie** (RL) je technika učenia, ktorá nám umožňuje naučiť sa optimálne správanie **agenta** v určitom **prostredí** prostredníctvom vykonávania mnohých experimentov. Agent v tomto prostredí by mal mať nejaký **cieľ**, definovaný pomocou **funkcie odmeny**.\n",
+    "\n",
+    "## Prostredie\n",
+    "\n",
+    "Pre jednoduchosť si predstavme Petrov svet ako štvorcovú dosku veľkosti `width` x `height`. Každé pole na tejto doske môže byť:\n",
+    "* **zem**, po ktorej Peter a ostatné bytosti môžu chodiť\n",
+    "* **voda**, po ktorej, samozrejme, nemôžete chodiť\n",
+    "* **strom** alebo **tráva** - miesto, kde si môžete oddýchnuť\n",
+    "* **jablko**, ktoré predstavuje niečo, čo by Peter rád našiel, aby sa nakŕmil\n",
+    "* **vlk**, ktorý je nebezpečný a treba sa mu vyhnúť\n",
+    "\n",
+    "Na prácu s prostredím definujeme triedu s názvom `Board`. Aby sme tento notebook príliš nezahltili, presunuli sme všetok kód na prácu s doskou do samostatného modulu `rlboard`, ktorý teraz importujeme. Môžete sa pozrieť do tohto modulu, aby ste získali viac podrobností o interných mechanizmoch implementácie.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Poďme teraz vytvoriť náhodnú dosku a pozrieť sa, ako vyzerá:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Akcie a Politika\n",
+    "\n",
+    "V našom príklade by Peterovým cieľom bolo nájsť jablko, pričom by sa mal vyhnúť vlkovi a iným prekážkam. Definujte tieto akcie ako slovník a priraďte ich k dvojiciam zodpovedajúcich zmien súradníc.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 2"
+   ]
+  },
+  {
+   "source": [
+    "Strategia nášho agenta (Peter) je definovaná takzvanou **politikou**. Pozrime sa na najjednoduchšiu politiku nazývanú **náhodná prechádzka**.\n",
+    "\n",
+    "## Náhodná prechádzka\n",
+    "\n",
+    "Najprv vyriešme náš problém implementáciou stratégie náhodnej prechádzky.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Let's run a random walk experiment several times and see the average number of steps taken: code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 4"
+   ]
+  },
+  {
+   "source": [
+    "## Funkcia odmeny\n",
+    "\n",
+    "Aby sme našu politiku urobili inteligentnejšou, musíme pochopiť, ktoré ťahy sú „lepšie“ ako ostatné.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning\n",
+    "\n",
+    "Vytvorte Q-Tabuľku alebo viacrozmerné pole. Keďže naša hracia plocha má rozmery `width` x `height`, môžeme Q-Tabuľku reprezentovať pomocou numpy poľa s tvarom `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Odošlite Q-Tabuľku do funkcie `plot`, aby ste zobrazili tabuľku na doske:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'm' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8b8a9e4cb2b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Podstata Q-Learning: Bellmanova rovnica a učebný algoritmus\n",
+    "\n",
+    "Napíšte pseudokód pre náš učebný algoritmus:\n",
+    "\n",
+    "* Inicializujte Q-Tabuľku Q rovnakými hodnotami pre všetky stavy a akcie\n",
+    "* Nastavte rýchlosť učenia $\\alpha\\leftarrow 1$\n",
+    "* Opakujte simuláciu mnohokrát\n",
+    "   1. Začnite na náhodnej pozícii\n",
+    "   1. Opakujte\n",
+    "        1. Vyberte akciu $a$ v stave $s$\n",
+    "        2. Vykonajte akciu presunutím sa do nového stavu $s'$\n",
+    "        3. Ak narazíme na podmienku konca hry alebo je celková odmena príliš malá - ukončite simuláciu  \n",
+    "        4. Vypočítajte odmenu $r$ v novom stave\n",
+    "        5. Aktualizujte Q-Funkciu podľa Bellmanovej rovnice: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Aktualizujte celkovú odmenu a znížte $\\alpha$.\n",
+    "\n",
+    "## Využívanie vs. Preskúmavanie\n",
+    "\n",
+    "Najlepší prístup je nájsť rovnováhu medzi preskúmavaním a využívaním. Ako sa dozvedáme viac o našom prostredí, budeme pravdepodobne nasledovať optimálnu cestu, avšak občas si zvolíme nepreskúmanú cestu.\n",
+    "\n",
+    "## Implementácia v Pythone\n",
+    "\n",
+    "Teraz sme pripravení implementovať učebný algoritmus. Predtým však potrebujeme funkciu, ktorá premení ľubovoľné čísla v Q-Tabuľke na vektor pravdepodobností pre zodpovedajúce akcie:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 7"
+   ]
+  },
+  {
+   "source": [
+    "K pôvodnému vektoru pridávame malé množstvo `eps`, aby sme sa vyhli deleniu nulou v počiatočnom prípade, keď sú všetky komponenty vektora identické.\n",
+    "\n",
+    "Skutočný učebný algoritmus budeme spúšťať počas 5000 experimentov, tiež nazývaných **epochy**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "# code block 8"
+   ]
+  },
+  {
+   "source": [
+    "Po vykonaní tohto algoritmu by mala byť Q-Tabuľka aktualizovaná hodnotami, ktoré definujú atraktivitu rôznych akcií v každom kroku. Vizualizujte tabuľku tu:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kontrola politiky\n",
+    "\n",
+    "Keďže Q-Tabuľka uvádza „atraktivitu“ každej akcie v každom stave, je pomerne jednoduché použiť ju na definovanie efektívnej navigácie v našom svete. V najjednoduchšom prípade môžeme jednoducho vybrať akciu, ktorá zodpovedá najvyššej hodnote v Q-Tabuľke:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "# code block 9"
+   ]
+  },
+  {
+   "source": [
+    "Ak vyskúšate vyššie uvedený kód niekoľkokrát, môžete si všimnúť, že sa niekedy jednoducho \"zasekne\" a musíte stlačiť tlačidlo STOP v notebooku, aby ste ho prerušili.\n",
+    "\n",
+    "> **Úloha 1:** Upraviť funkciu `walk` tak, aby obmedzila maximálnu dĺžku cesty na určitý počet krokov (napríklad 100), a sledovať, ako vyššie uvedený kód občas vráti túto hodnotu.\n",
+    "\n",
+    "> **Úloha 2:** Upraviť funkciu `walk` tak, aby sa nevracala na miesta, kde už predtým bola. Tým sa zabráni tomu, aby sa `walk` dostala do slučky, avšak agent sa stále môže ocitnúť \"uväznený\" na mieste, z ktorého sa nedokáže dostať.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 5.31, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# code block 10"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26dab295a48>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 57
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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109.232964 214.756364 \r\nL 109.263403 214.085931 \r\nL 109.293843 201.703888 \r\nL 109.385161 202.919046 \r\nL 109.44604 214.756364 \r\nL 109.476479 192.192132 \r\nL 109.537358 214.735413 \r\nL 109.598237 214.756364 \r\nL 109.719994 198.100315 \r\nL 109.811312 214.756364 \r\nL 109.841752 210.230947 \r\nL 109.872191 214.735413 \r\nL 109.93307 208.63867 \r\nL 110.054828 214.756364 \r\nL 110.085267 200.509681 \r\nL 110.146146 208.052042 \r\nL 110.207025 214.756364 \r\nL 110.267904 213.268842 \r\nL 110.298343 193.470143 \r\nL 110.328782 214.756364 \r\nL 110.359222 209.811927 \r\nL 110.420101 214.756364 \r\nL 110.480979 214.044029 \r\nL 110.541858 214.756364 \r\nL 110.572298 212.032733 \r\nL 110.633176 214.756364 \r\nL 110.663616 214.756364 \r\nL 110.694055 199.441179 \r\nL 110.785374 202.981899 \r\nL 110.907131 214.756364 \r\nL 110.937571 211.110889 \r\nL 110.998449 214.756364 \r\nL 111.089768 209.916682 \r\nL 111.181086 214.756364 \r\nL 111.211525 203.882792 \r\nL 111.272404 207.842532 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115.016451 213.43645 \r\nL 115.046891 214.46305 \r\nL 115.07733 207.339708 \r\nL 115.168648 209.66527 \r\nL 115.229527 213.268842 \r\nL 115.290406 211.215644 \r\nL 115.320845 205.642677 \r\nL 115.351285 214.756364 \r\nL 115.381724 209.749074 \r\nL 115.533921 214.756364 \r\nL 115.564361 213.687862 \r\nL 115.655679 206.606423 \r\nL 115.746997 214.756364 \r\nL 115.716558 193.700604 \r\nL 115.777436 214.735413 \r\nL 115.838315 193.763457 \r\nL 115.868755 193.910114 \r\nL 115.899194 214.756364 \r\nL 115.990512 212.346998 \r\nL 116.051391 210.754722 \r\nL 116.11227 214.756364 \r\nL 116.142709 211.76037 \r\nL 116.203588 214.756364 \r\nL 116.234027 214.756364 \r\nL 116.264467 213.478352 \r\nL 116.294906 214.756364 \r\nL 116.325346 184.670721 \r\nL 116.386225 214.756364 \r\nL 116.416664 214.044029 \r\nL 116.447103 214.756364 \r\nL 116.477543 196.047117 \r\nL 116.538422 212.786969 \r\nL 116.5993 210.964232 \r\nL 116.690619 214.756364 \r\nL 116.812376 200.404926 \r\nL 116.903694 214.756364 \r\nL 116.934134 196.403284 \r\nL 116.995013 214.756364 \r\nL 117.055891 214.756364 \r\nL 117.086331 213.939274 \r\nL 117.11677 199.650689 \r\nL 117.177649 212.158439 \r\nL 117.208089 214.756364 \r\nL 117.238528 213.939274 \r\nL 117.268967 205.684579 \r\nL 117.329846 214.756364 \r\nL 117.360286 214.484001 \r\nL 117.421164 195.774754 \r\nL 117.451604 210.000486 \r\nL 117.512483 214.756364 \r\nL 117.542922 176.709339 \r\nL 117.63424 184.64977 \r\nL 117.725558 183.874583 \r\nL 117.755998 214.756364 \r\nL 117.786437 214.756364 \r\nL 117.877756 198.959306 \r\nL 117.908195 210.566163 \r\nL 117.938634 212.766018 \r\nL 117.969074 206.522619 \r\nL 118.029953 214.756364 \r\nL 118.060392 214.756364 \r\nL 118.18215 199.399277 \r\nL 118.273468 214.756364 \r\nL 118.303907 205.035098 \r\nL 118.364786 214.756364 \r\nL 118.456104 214.232589 \r\nL 118.486544 214.756364 \r\nL 118.516983 209.476711 \r\nL 118.577862 214.756364 \r\nL 118.608301 213.352646 \r\nL 118.66918 214.735413 \r\nL 118.69962 186.786773 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200.090661 \r\nL 122.595864 211.927978 \r\nL 122.687182 211.383252 \r\nL 122.717621 214.756364 \r\nL 122.7785 214.756364 \r\nL 122.80894 210.335702 \r\nL 122.900258 212.493655 \r\nL 122.930697 214.756364 \r\nL 123.022015 183.958387 \r\nL 123.052455 208.533915 \r\nL 123.082894 214.756364 \r\nL 123.113334 201.703888 \r\nL 123.143773 210.482359 \r\nL 123.174212 210.419506 \r\nL 123.265531 200.48873 \r\nL 123.29597 214.756364 \r\nL 123.326409 214.735413 \r\nL 123.356849 187.017234 \r\nL 123.417728 214.756364 \r\nL 123.569925 191.291239 \r\nL 123.691682 214.756364 \r\nL 123.722122 214.735413 \r\nL 123.783001 205.579824 \r\nL 123.81344 189.279942 \r\nL 123.874319 214.735413 \r\nL 123.935198 214.735413 \r\nL 123.996076 214.756364 \r\nL 124.026516 206.669276 \r\nL 124.148273 214.756364 \r\nL 124.178713 214.756364 \r\nL 124.239592 209.581466 \r\nL 124.270031 211.96988 \r\nL 124.300471 214.023078 \r\nL 124.33091 201.201064 \r\nL 124.422228 208.471062 \r\nL 124.513546 214.756364 \r\nL 124.543986 212.326047 \r\nL 124.604865 199.818297 \r\nL 124.635304 207.360659 \r\nL 124.726622 214.756364 \r\nL 124.787501 174.467582 \r\nL 124.81794 214.756364 \r\nL 124.909259 214.735413 \r\nL 124.939698 211.173742 \r\nL 124.970137 186.283949 \r\nL 125.031016 211.907027 \r\nL 125.091895 214.756364 \r\nL 125.152774 214.735413 \r\nL 125.274532 173.252424 \r\nL 125.36585 214.756364 \r\nL 125.396289 214.127834 \r\nL 125.426729 214.735413 \r\nL 125.457168 198.267923 \r\nL 125.518047 214.106882 \r\nL 125.548486 210.775673 \r\nL 125.609365 214.756364 \r\nL 125.639804 211.006134 \r\nL 125.731123 214.735413 \r\nL 125.761562 214.421148 \r\nL 125.822441 214.735413 \r\nL 125.913759 173.399081 \r\nL 125.974638 214.756364 \r\nL 126.035517 206.648325 \r\nL 126.065956 206.585472 \r\nL 126.187714 214.756364 \r\nL 126.248593 214.756364 \r\nL 126.309471 213.562156 \r\nL 126.339911 205.852187 \r\nL 126.37035 214.735413 \r\nL 126.40079 212.996479 \r\nL 126.431229 214.756364 \r\nL 126.461668 214.588756 \r\nL 126.522547 198.41458 \r\nL 126.552987 213.059332 \r\nL 126.705184 214.756364 \r\nL 126.735623 192.192132 \r\nL 126.796502 213.750715 \r\nL 126.91826 214.756364 \r\nL 126.979138 210.021437 \r\nL 127.009578 214.756364 \r\nL 127.040017 214.756364 \r\nL 127.131335 210.461408 \r\nL 127.192214 207.402561 \r\nL 127.253093 214.756364 \r\nL 127.283532 210.377604 \r\nL 127.344411 214.735413 \r\nL 127.374851 214.756364 \r\nL 127.40529 214.46305 \r\nL 127.466169 214.756364 \r\nL 127.527048 199.524983 \r\nL 127.557487 214.735413 \r\nL 127.587927 211.404203 \r\nL 127.618366 177.673086 \r\nL 127.648805 214.756364 \r\nL 127.679245 184.251701 \r\nL 127.801002 214.756364 \r\nL 127.861881 214.756364 \r\nL 127.953199 207.612071 \r\nL 128.014078 214.756364 \r\nL 128.044518 204.532273 \r\nL 128.074957 198.309825 \r\nL 128.105396 214.756364 \r\nL 128.166275 214.756364 \r\nL 128.257593 210.105241 \r\nL 128.348912 214.756364 \r\nL 128.501109 186.430606 \r\nL 128.531548 210.314751 \r\nL 128.592427 206.459766 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214.693511 \r\nL 130.236155 214.735413 \r\nL 130.297034 204.972245 \r\nL 130.388352 214.756364 \r\nL 130.449231 190.620806 \r\nL 130.47967 213.331695 \r\nL 130.540549 214.756364 \r\nL 130.601428 214.358295 \r\nL 130.692746 203.547576 \r\nL 130.753625 197.953658 \r\nL 130.814504 214.756364 \r\nL 130.844943 210.649967 \r\nL 130.905822 211.425154 \r\nL 130.936261 214.756364 \r\nL 130.99714 214.546854 \r\nL 131.058019 203.945645 \r\nL 131.088458 213.939274 \r\nL 131.149337 214.756364 \r\nL 131.179777 207.653973 \r\nL 131.240655 214.756364 \r\nL 131.51461 214.756364 \r\nL 131.54505 182.827033 \r\nL 131.605928 214.106882 \r\nL 131.636368 214.756364 \r\nL 131.758125 205.998844 \r\nL 131.788565 214.756364 \r\nL 131.819004 200.111612 \r\nL 131.849444 213.813568 \r\nL 131.879883 194.643399 \r\nL 131.910322 214.756364 \r\nL 131.940762 212.849822 \r\nL 131.971201 214.756364 \r\nL 132.092959 153.16041 \r\nL 132.184277 214.756364 \r\nL 132.214716 191.752161 \r\nL 132.306035 214.756364 \r\nL 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154.952954 214.756364 \r\nL 155.074712 200.823946 \r\nL 155.16603 214.756364 \r\nL 155.196469 214.735413 \r\nL 155.257348 214.735413 \r\nL 155.379106 197.869854 \r\nL 155.500863 214.756364 \r\nL 155.531303 209.141494 \r\nL 155.622621 211.634664 \r\nL 155.65306 209.204347 \r\nL 155.6835 214.756364 \r\nL 155.713939 211.383252 \r\nL 155.744379 213.813568 \r\nL 155.774818 195.167174 \r\nL 155.805257 214.756364 \r\nL 155.835697 213.729764 \r\nL 155.866136 214.756364 \r\nL 156.018333 198.770747 \r\nL 156.048773 210.398555 \r\nL 156.079212 135.854881 \r\nL 156.109651 214.756364 \r\nL 156.140091 195.984264 \r\nL 156.20097 214.756364 \r\nL 156.231409 210.754722 \r\nL 156.261848 183.706975 \r\nL 156.322727 214.756364 \r\nL 156.353167 214.756364 \r\nL 156.474924 196.466137 \r\nL 156.627121 214.756364 \r\nL 156.657561 214.756364 \r\nL 156.688 192.485446 \r\nL 156.748879 214.735413 \r\nL 156.779318 213.520254 \r\nL 156.840197 214.735413 \r\nL 156.870637 214.735413 \r\nL 156.901076 213.289793 \r\nL 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162.83676 214.756364 \r\nL 162.897639 198.016511 \r\nL 162.988958 214.756364 \r\nL 163.019397 209.392906 \r\nL 163.049836 209.351004 \r\nL 163.080276 211.508958 \r\nL 163.141155 204.951294 \r\nL 163.171594 214.735413 \r\nL 163.232473 214.756364 \r\nL 163.262912 211.299448 \r\nL 163.323791 210.649967 \r\nL 163.35423 214.756364 \r\nL 163.415109 201.641035 \r\nL 163.445549 214.735413 \r\nL 163.506427 214.756364 \r\nL 163.536867 213.729764 \r\nL 163.597746 214.756364 \r\nL 163.628185 214.756364 \r\nL 163.719503 191.815014 \r\nL 163.780382 214.756364 \r\nL 163.841261 214.023078 \r\nL 163.8717 202.688585 \r\nL 163.932579 208.743425 \r\nL 163.963019 214.756364 \r\nL 164.023897 212.912675 \r\nL 164.054337 202.939997 \r\nL 164.084776 214.756364 \r\nL 164.145655 205.097951 \r\nL 164.206534 214.756364 \r\nL 164.236973 202.541928 \r\nL 164.267413 189.971325 \r\nL 164.297852 214.756364 \r\nL 164.328291 205.852187 \r\nL 164.38917 214.756364 \r\nL 164.480489 212.870773 \r\nL 164.510928 212.59841 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168.589809 199.88115 \r\nL 168.650687 214.756364 \r\nL 168.772445 205.056049 \r\nL 168.833324 207.255904 \r\nL 168.955081 214.756364 \r\nL 169.076839 204.720832 \r\nL 169.168157 214.756364 \r\nL 169.198597 212.912675 \r\nL 169.289915 214.756364 \r\nL 169.320354 214.735413 \r\nL 169.442112 194.287232 \r\nL 169.56387 214.756364 \r\nL 169.685627 181.884238 \r\nL 169.807385 214.756364 \r\nL 169.837824 200.048758 \r\nL 169.898703 208.072993 \r\nL 169.959582 214.756364 \r\nL 169.990021 207.695875 \r\nL 170.020461 210.189045 \r\nL 170.111779 214.756364 \r\nL 170.142218 187.059136 \r\nL 170.203097 213.059332 \r\nL 170.233537 210.796624 \r\nL 170.263976 213.331695 \r\nL 170.324855 211.404203 \r\nL 170.385734 214.756364 \r\nL 170.446612 214.756364 \r\nL 170.477052 210.733771 \r\nL 170.537931 214.735413 \r\nL 170.629249 214.756364 \r\nL 170.659688 204.406567 \r\nL 170.720567 208.21965 \r\nL 170.842325 214.756364 \r\nL 170.933643 196.298529 \r\nL 170.964082 211.446105 \r\nL 170.994522 214.756364 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178.695692 214.756364 \r\nL 178.726131 205.077 \r\nL 178.78701 214.693511 \r\nL 178.847889 214.756364 \r\nL 178.878328 214.421148 \r\nL 178.908768 214.714462 \r\nL 178.939207 211.844174 \r\nL 178.969647 193.051123 \r\nL 179.030525 214.756364 \r\nL 179.060965 207.255904 \r\nL 179.121844 214.756364 \r\nL 179.213162 211.027085 \r\nL 179.274041 192.548299 \r\nL 179.30448 213.750715 \r\nL 179.334919 214.756364 \r\nL 179.426238 214.358295 \r\nL 179.456677 214.756364 \r\nL 179.487116 214.609707 \r\nL 179.547995 189.887521 \r\nL 179.578435 214.756364 \r\nL 179.639313 214.756364 \r\nL 179.669753 214.504952 \r\nL 179.700192 205.349363 \r\nL 179.730632 214.756364 \r\nL 179.761071 214.756364 \r\nL 179.882829 200.237318 \r\nL 179.913268 214.756364 \r\nL 180.004586 206.564521 \r\nL 180.065465 205.097951 \r\nL 180.095905 195.062419 \r\nL 180.126344 208.994837 \r\nL 180.156783 203.715184 \r\nL 180.278541 214.756364 \r\nL 180.400299 206.145501 \r\nL 180.430738 214.756364 \r\nL 180.461177 208.114895 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200.551583 \r\nL 184.266103 214.756364 \r\nL 184.296543 214.756364 \r\nL 184.387861 203.191409 \r\nL 184.509619 214.756364 \r\nL 184.540058 208.994837 \r\nL 184.600937 214.756364 \r\nL 184.631376 214.756364 \r\nL 184.661816 195.523342 \r\nL 184.722695 214.756364 \r\nL 184.783573 214.756364 \r\nL 184.93577 200.404926 \r\nL 184.96621 214.735413 \r\nL 185.057528 213.541205 \r\nL 185.118407 214.044029 \r\nL 185.240164 192.275936 \r\nL 185.331483 187.373401 \r\nL 185.361922 214.756364 \r\nL 185.392362 203.882792 \r\nL 185.45324 213.981176 \r\nL 185.48368 214.756364 \r\nL 185.544559 180.103402 \r\nL 185.574998 214.756364 \r\nL 185.605437 214.756364 \r\nL 185.757634 190.599855 \r\nL 185.788074 192.171181 \r\nL 185.818513 181.695679 \r\nL 185.909831 214.756364 \r\nL 185.940271 203.149507 \r\nL 185.97071 214.756364 \r\nL 186.031589 202.16481 \r\nL 186.062028 213.22694 \r\nL 186.092468 195.271929 \r\nL 186.153347 207.130198 \r\nL 186.214226 214.756364 \r\nL 186.244665 207.402561 \r\nL 186.305544 213.981176 \r\nL 186.396862 211.236595 \r\nL 186.427301 207.758728 \r\nL 186.457741 214.756364 \r\nL 186.48818 212.661263 \r\nL 186.51862 210.733771 \r\nL 186.609938 214.756364 \r\nL 186.640377 168.035624 \r\nL 186.701256 210.461408 \r\nL 186.762135 208.303454 \r\nL 186.792574 195.942362 \r\nL 186.823014 214.756364 \r\nL 186.853453 198.184119 \r\nL 186.975211 214.756364 \r\nL 187.00565 191.668357 \r\nL 187.066529 209.476711 \r\nL 187.096968 214.756364 \r\nL 187.188287 170.486891 \r\nL 187.310044 214.756364 \r\nL 187.340484 214.756364 \r\nL 187.431802 200.928701 \r\nL 187.462241 214.316393 \r\nL 187.492681 199.294522 \r\nL 187.553559 208.84818 \r\nL 187.614438 210.901379 \r\nL 187.644878 194.978615 \r\nL 187.705756 212.80792 \r\nL 187.736196 210.042388 \r\nL 187.857954 183.853632 \r\nL 187.827514 210.92233 \r\nL 187.888393 186.996283 \r\nL 187.949272 214.756364 \r\nL 187.979711 214.735413 \r\nL 188.010151 192.024524 \r\nL 188.04059 214.756364 \r\nL 188.071029 214.756364 \r\nL 188.162348 206.648325 \r\nL 188.192787 164.432051 \r\nL 188.253666 208.701523 \r\nL 188.284105 207.653973 \r\nL 188.314545 213.897372 \r\nL 188.344984 202.332418 \r\nL 188.405863 210.000486 \r\nL 188.436302 214.756364 \r\nL 188.466742 190.055129 \r\nL 188.52762 214.756364 \r\nL 188.55806 214.044029 \r\nL 188.588499 214.756364 \r\nL 188.710257 204.993196 \r\nL 188.801575 214.756364 \r\nL 188.832015 214.735413 \r\nL 188.862454 214.567805 \r\nL 188.892893 199.105963 \r\nL 188.984212 206.585472 \r\nL 189.136409 214.756364 \r\nL 189.166848 164.369198 \r\nL 189.227727 214.756364 \r\nL 189.319045 200.048758 \r\nL 189.440803 214.756364 \r\nL 189.471242 214.023078 \r\nL 189.501682 204.322763 \r\nL 189.56256 212.724116 \r\nL 189.623439 214.756364 \r\nL 189.653879 194.475791 \r\nL 189.714757 214.756364 \r\nL 189.836515 194.203428 \r\nL 189.866954 205.894089 \r\nL 189.927833 194.894811 \r\nL 189.958273 214.756364 \r\nL 189.988712 193.931065 \r\nL 190.049591 214.274491 \r\nL 190.08003 214.756364 \r\nL 190.11047 202.772389 \r\nL 190.201788 207.59112 \r\nL 190.323546 214.756364 \r\nL 190.353985 214.421148 \r\nL 190.384424 214.756364 \r\nL 190.414864 197.953658 \r\nL 190.475743 209.183396 \r\nL 190.62794 214.756364 \r\nL 190.658379 214.756364 \r\nL 190.688818 189.824668 \r\nL 190.749697 214.714462 \r\nL 190.810576 214.756364 \r\nL 190.932334 201.578182 \r\nL 191.023652 214.756364 \r\nL 191.084531 211.802272 \r\nL 191.175849 214.756364 \r\nL 191.236728 206.836884 \r\nL 191.267167 213.771666 \r\nL 191.328046 214.756364 \r\nL 191.419364 208.680572 \r\nL 191.480243 214.756364 \r\nL 191.541122 214.735413 \r\nL 191.571561 214.756364 \r\nL 191.63244 180.962393 \r\nL 191.662879 212.3889 \r\nL 191.693319 214.756364 \r\nL 191.815077 180.627177 \r\nL 191.936834 214.756364 \r\nL 191.967274 214.756364 \r\nL 191.997713 214.190687 \r\nL 192.028152 179.831039 \r\nL 192.058592 214.756364 \r\nL 192.089031 214.756364 \r\nL 192.14991 210.2938 \r\nL 192.180349 213.331695 \r\nL 192.271668 214.756364 \r\nL 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194.311108 208.931984 \r\nL 194.341547 208.177748 \r\nL 194.463305 214.756364 \r\nL 194.493744 194.203428 \r\nL 194.585063 195.586195 \r\nL 194.73726 214.756364 \r\nL 194.767699 212.053684 \r\nL 194.828578 214.756364 \r\nL 194.859017 214.756364 \r\nL 194.919896 191.144582 \r\nL 194.950335 195.020517 \r\nL 194.980775 214.756364 \r\nL 195.072093 202.479075 \r\nL 195.193851 214.756364 \r\nL 195.315608 201.515329 \r\nL 195.376487 201.431525 \r\nL 195.467805 214.756364 \r\nL 195.589563 203.966596 \r\nL 195.650442 196.382333 \r\nL 195.74176 214.756364 \r\nL 195.833078 206.606423 \r\nL 195.863518 213.562156 \r\nL 195.893957 214.756364 \r\nL 195.924397 213.43645 \r\nL 195.954836 211.425154 \r\nL 195.985275 212.640312 \r\nL 196.015715 194.685301 \r\nL 196.076594 214.756364 \r\nL 196.167912 214.756364 \r\nL 196.25923 192.736858 \r\nL 196.289669 206.711178 \r\nL 196.380988 202.79334 \r\nL 196.411427 214.756364 \r\nL 196.502745 214.756364 \r\nL 196.624503 197.932707 \r\nL 196.715821 214.756364 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200.520747 199.441179 \r\nL 200.612065 203.023801 \r\nL 200.703384 214.67256 \r\nL 200.672944 201.368672 \r\nL 200.733823 208.533915 \r\nL 200.764262 205.077 \r\nL 200.794702 214.756364 \r\nL 200.825141 195.104321 \r\nL 200.855581 160.87038 \r\nL 200.88602 214.756364 \r\nL 200.916459 207.423512 \r\nL 200.977338 214.735413 \r\nL 201.038217 177.777841 \r\nL 201.129535 212.17939 \r\nL 201.159975 210.126192 \r\nL 201.251293 214.232589 \r\nL 201.281732 184.545015 \r\nL 201.40349 213.604058 \r\nL 201.464369 208.911033 \r\nL 201.494808 198.393629 \r\nL 201.525248 214.756364 \r\nL 201.555687 214.756364 \r\nL 201.586126 214.421148 \r\nL 201.738323 146.89606 \r\nL 201.829642 214.756364 \r\nL 201.860081 208.63867 \r\nL 201.89052 206.040746 \r\nL 201.92096 212.053684 \r\nL 201.951399 212.493655 \r\nL 201.981839 214.756364 \r\nL 202.012278 214.735413 \r\nL 202.042717 142.140182 \r\nL 202.103596 159.843781 \r\nL 202.164475 214.756364 \r\nL 202.225354 211.844174 \r\nL 202.255793 157.664876 \r\nL 202.286233 214.756364 \r\nL 202.316672 213.85547 \r\nL 202.377551 205.642677 \r\nL 202.40799 214.756364 \r\nL 202.43843 214.484001 \r\nL 202.468869 188.588559 \r\nL 202.499309 214.756364 \r\nL 202.529748 214.484001 \r\nL 202.651506 198.330776 \r\nL 202.681945 214.756364 \r\nL 202.742824 212.849822 \r\nL 202.773263 182.994641 \r\nL 202.834142 190.055129 \r\nL 202.92546 214.756364 \r\nL 202.9559 214.735413 \r\nL 202.986339 170.067871 \r\nL 203.047218 214.756364 \r\nL 203.077657 208.869131 \r\nL 203.168976 211.089938 \r\nL 203.229854 214.337344 \r\nL 203.260294 211.739419 \r\nL 203.321173 211.948929 \r\nL 203.351612 193.449192 \r\nL 203.382051 214.756364 \r\nL 203.47337 202.311467 \r\nL 203.503809 203.547576 \r\nL 203.534248 214.735413 \r\nL 203.564688 165.66816 \r\nL 203.656006 192.715907 \r\nL 203.686445 193.009221 \r\nL 203.716885 214.400197 \r\nL 203.747324 206.627374 \r\nL 203.777764 170.549744 \r\nL 203.808203 214.756364 \r\nL 203.838643 214.756364 \r\nL 203.869082 208.198699 \r\nL 203.929961 209.288151 \r\nL 203.99084 214.756364 \r\nL 204.021279 210.964232 \r\nL 204.082158 176.353172 \r\nL 204.112597 214.756364 \r\nL 204.173476 213.918323 \r\nL 204.203915 214.756364 \r\nL 204.234355 210.482359 \r\nL 204.295234 214.567805 \r\nL 204.356112 211.96988 \r\nL 204.386552 214.756364 \r\nL 204.447431 186.975332 \r\nL 204.47787 211.446105 \r\nL 204.508309 212.870773 \r\nL 204.538749 209.2672 \r\nL 204.569188 207.989189 \r\nL 204.599628 190.159884 \r\nL 204.660507 214.756364 \r\nL 204.690946 214.756364 \r\nL 204.721385 211.907027 \r\nL 204.782264 214.379246 \r\nL 204.812704 192.213083 \r\nL 204.843143 214.756364 \r\nL 204.904022 205.91504 \r\nL 204.934461 205.244608 \r\nL 204.99534 214.756364 \r\nL 205.056219 211.488007 \r\nL 205.086658 213.771666 \r\nL 205.117098 213.352646 \r\nL 205.147537 171.052568 \r\nL 205.208416 207.549218 \r\nL 205.238855 213.394548 \r\nL 205.299734 204.86749 \r\nL 205.360613 214.442099 \r\nL 205.391052 157.74868 \r\nL 205.421492 214.756364 \r\nL 205.451931 214.756364 \r\nL 205.51281 202.688585 \r\nL 205.543249 213.373597 \r\nL 205.604128 211.865125 \r\nL 205.634568 214.756364 \r\nL 205.665007 199.734493 \r\nL 205.725886 214.756364 \r\nL 205.756325 213.310744 \r\nL 205.786765 193.428241 \r\nL 205.847643 214.756364 \r\nL 205.878083 212.849822 \r\nL 205.908522 212.975528 \r\nL 205.969401 188.777118 \r\nL 206.03028 199.650689 \r\nL 206.091159 214.756364 \r\nL 206.152037 205.077 \r\nL 206.212916 214.756364 \r\nL 206.243356 182.76418 \r\nL 206.304235 214.756364 \r\nL 206.425992 191.354092 \r\nL 206.456432 214.756364 \r\nL 206.51731 204.427518 \r\nL 206.54775 198.142217 \r\nL 206.578189 214.756364 \r\nL 206.669507 197.031814 \r\nL 206.760826 214.756364 \r\nL 206.791265 196.256627 \r\nL 206.852144 210.943281 \r\nL 206.882583 214.756364 \r\nL 206.913023 188.588559 \r\nL 207.004341 205.28651 \r\nL 207.03478 214.756364 \r\nL 207.06522 213.373597 \r\nL 207.095659 198.435531 \r\nL 207.156538 205.768383 \r\nL 207.278296 214.756364 \r\nL 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "## Cvičenie\n",
+    "## Realistickejší svet Petra a vlka\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Za autoritatívny zdroj by sa mal považovať pôvodný dokument v jeho pôvodnom jazyku. Pre dôležité informácie odporúčame profesionálny preklad vykonaný človekom. Nezodpovedáme za žiadne nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/translations/sk/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
new file mode 100644
index 000000000..f256bf704
--- /dev/null
+++ b/translations/sk/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
@@ -0,0 +1,458 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "eadbd20d2a075efb602615ad90b1e97a",
+   "translation_date": "2025-09-06T15:13:37+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter a vlk: Realistické prostredie\n",
+    "\n",
+    "V našej situácii sa Peter mohol pohybovať takmer bez toho, aby sa unavil alebo vyhladol. V realistickejšom svete si však musí občas sadnúť a oddýchnuť si, a tiež sa najesť. Urobme náš svet realistickejším zavedením nasledujúcich pravidiel:\n",
+    "\n",
+    "1. Pri presune z jedného miesta na druhé Peter stráca **energiu** a získava **únavu**.\n",
+    "2. Peter môže získať viac energie jedením jabĺk.\n",
+    "3. Peter sa môže zbaviť únavy odpočinkom pod stromom alebo na tráve (t.j. vstúpením na políčko s stromom alebo trávou - zelené pole).\n",
+    "4. Peter musí nájsť a zabiť vlka.\n",
+    "5. Aby Peter dokázal zabiť vlka, musí mať určité úrovne energie a únavy, inak prehrá súboj.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "from rlboard import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "## Definovanie stavu\n",
+    "\n",
+    "V našich nových pravidlách hry musíme sledovať energiu a únavu v každom stave hracej plochy. Preto vytvoríme objekt `state`, ktorý bude obsahovať všetky potrebné informácie o aktuálnom stave problému, vrátane stavu hracej plochy, aktuálnych úrovní energie a únavy, a či môžeme poraziť vlka v terminálnom stave:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class state:\n",
+    "    def __init__(self,board,energy=10,fatigue=0,init=True):\n",
+    "        self.board = board\n",
+    "        self.energy = energy\n",
+    "        self.fatigue = fatigue\n",
+    "        self.dead = False\n",
+    "        if init:\n",
+    "            self.board.random_start()\n",
+    "        self.update()\n",
+    "\n",
+    "    def at(self):\n",
+    "        return self.board.at()\n",
+    "\n",
+    "    def update(self):\n",
+    "        if self.at() == Board.Cell.water:\n",
+    "            self.dead = True\n",
+    "            return\n",
+    "        if self.at() == Board.Cell.tree:\n",
+    "            self.fatigue = 0\n",
+    "        if self.at() == Board.Cell.apple:\n",
+    "            self.energy = 10\n",
+    "\n",
+    "    def move(self,a):\n",
+    "        self.board.move(a)\n",
+    "        self.energy -= 1\n",
+    "        self.fatigue += 1\n",
+    "        self.update()\n",
+    "\n",
+    "    def is_winning(self):\n",
+    "        return self.energy > self.fatigue"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(state):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(board,policy):\n",
+    "    n = 0 # number of steps\n",
+    "    s = state(board)\n",
+    "    while True:\n",
+    "        if s.at() == Board.Cell.wolf:\n",
+    "            if s.is_winning():\n",
+    "                return n # success!\n",
+    "            else:\n",
+    "                return -n # failure!\n",
+    "        if s.at() == Board.Cell.water:\n",
+    "            return 0 # died\n",
+    "        a = actions[policy(m)]\n",
+    "        s.move(a)\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 5, won: 1 times, drown: 94 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        elif z==0:\n",
+    "            n+=1\n",
+    "        else:\n",
+    "            s+=1\n",
+    "    print(f\"Killed by wolf = {w}, won: {s} times, drown: {n} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Funkcia odmeny\n",
+    "\n",
+    "### Úvod\n",
+    "Funkcia odmeny je kľúčovým prvkom pri navrhovaní systémov posilňovacieho učenia. Definuje cieľ, ktorý sa agent snaží dosiahnuť, a poskytuje spätnú väzbu na základe jeho akcií.\n",
+    "\n",
+    "### Základné princípy\n",
+    "- **Jasnosť**: Funkcia odmeny by mala byť jednoduchá a ľahko pochopiteľná.\n",
+    "- **Konzistentnosť**: Mala by byť konzistentná s cieľmi systému.\n",
+    "- **Vyváženosť**: Treba zabezpečiť, aby odmeny neboli príliš vysoké alebo nízke, čo by mohlo ovplyvniť správanie agenta.\n",
+    "\n",
+    "### Príklad\n",
+    "Nasledujúci príklad ukazuje, ako môže byť funkcia odmeny implementovaná:\n",
+    "\n",
+    "```python\n",
+    "def reward_function(state, action):\n",
+    "    if state == \"goal_state\":\n",
+    "        return 100\n",
+    "    elif action == \"invalid_action\":\n",
+    "        return -10\n",
+    "    else:\n",
+    "        return 0\n",
+    "```\n",
+    "\n",
+    "### Bežné chyby\n",
+    "- **Príliš zložité funkcie odmeny**: Môžu zmiasť agenta a spomaliť proces učenia.\n",
+    "- **Nedostatočné odmeny**: Ak agent nedostáva dostatočnú spätnú väzbu, môže mať problém naučiť sa správne správanie.\n",
+    "- **Neúmyselné odmeny**: Nesprávne navrhnutá funkcia môže odmeňovať neželané správanie.\n",
+    "\n",
+    "### Tipy na optimalizáciu\n",
+    "- Testujte funkciu odmeny v rôznych scenároch.\n",
+    "- Sledujte správanie agenta a upravujte odmeny podľa potreby.\n",
+    "- Zvážte použitie kombinácie pozitívnych a negatívnych odmien na dosiahnutie rovnováhy.\n",
+    "\n",
+    "### Záver\n",
+    "Funkcia odmeny je základným stavebným prvkom úspešného systému posilňovacieho učenia. Dôkladné plánovanie a testovanie sú nevyhnutné na zabezpečenie jej efektívnosti.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reward(s):\n",
+    "    r = s.energy-s.fatigue\n",
+    "    if s.at()==Board.Cell.wolf:\n",
+    "        return 100 if s.is_winning() else -100\n",
+    "    if s.at()==Board.Cell.water:\n",
+    "        return -100\n",
+    "    return r"
+   ]
+  },
+  {
+   "source": [
+    "## Algoritmus Q-Learning\n",
+    "\n",
+    "Samotný algoritmus učenia zostáva takmer nezmenený, len používame `state` namiesto samotnej pozície na hracej ploche.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    s = state(m)\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = s.board.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        while True:\n",
+    "            a = random.choices(list(actions),weights=v)[0]\n",
+    "            dpos = actions[a]\n",
+    "            if s.board.is_valid(s.board.move_pos(s.board.human,dpos)):\n",
+    "                break \n",
+    "        s.move(dpos)\n",
+    "        r = reward(s)\n",
+    "        if abs(r)==100: # end of game\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Výsledky\n",
+    "\n",
+    "Pozrime sa, či sme boli úspešní v tréningu Petra, aby bojoval proti vlkovi!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 1, won: 9 times, drown: 90 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [
+    "Teraz vidíme oveľa menej prípadov utopenia, ale Peter stále nie je vždy schopný zabiť vlka. Skúste experimentovať a zistiť, či môžete tento výsledok zlepšiť úpravou hyperparametrov.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0987e7dd8>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nenesieme zodpovednosť za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/translations/sk/8-Reinforcement/1-QLearning/solution/notebook.ipynb
new file mode 100644
index 000000000..7830bcc9a
--- /dev/null
+++ b/translations/sk/8-Reinforcement/1-QLearning/solution/notebook.ipynb
@@ -0,0 +1,577 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "488431336543f71f14d4aaf0399e3381",
+   "translation_date": "2025-09-06T15:07:53+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter a vlk: Úvod do posilňovacieho učenia\n",
+    "\n",
+    "V tomto tutoriáli sa naučíme, ako aplikovať posilňovacie učenie na problém hľadania cesty. Prostredie je inšpirované hudobnou rozprávkou [Peter a vlk](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) od ruského skladateľa [Sergeja Prokofieva](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Je to príbeh o mladom pionierovi Petrovi, ktorý odvážne opustí svoj dom a vydá sa na lesnú čistinu, aby prenasledoval vlka. Vytrénujeme algoritmy strojového učenia, ktoré Petrovi pomôžu preskúmať okolitú oblasť a vytvoriť optimálnu navigačnú mapu.\n",
+    "\n",
+    "Najskôr si importujeme niekoľko užitočných knižníc:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Prehľad posilňovacieho učenia\n",
+    "\n",
+    "**Posilňovacie učenie** (RL) je technika učenia, ktorá nám umožňuje naučiť sa optimálne správanie **agenta** v určitom **prostredí** prostredníctvom vykonávania mnohých experimentov. Agent v tomto prostredí by mal mať nejaký **cieľ**, definovaný pomocou **funkcie odmeny**.\n",
+    "\n",
+    "## Prostredie\n",
+    "\n",
+    "Pre zjednodušenie si predstavme Petrov svet ako štvorcovú dosku veľkosti `width` x `height`. Každé pole na tejto doske môže byť:\n",
+    "* **zem**, po ktorej Peter a ostatné bytosti môžu chodiť\n",
+    "* **voda**, po ktorej, samozrejme, nemôžete chodiť\n",
+    "* **strom** alebo **tráva** - miesto, kde si môžete oddýchnuť\n",
+    "* **jablko**, ktoré predstavuje niečo, čo by Peter rád našiel, aby sa nakŕmil\n",
+    "* **vlk**, ktorý je nebezpečný a treba sa mu vyhnúť\n",
+    "\n",
+    "Na prácu s prostredím definujeme triedu nazvanú `Board`. Aby sme tento zápisník príliš nezahltili, presunuli sme všetok kód na prácu s doskou do samostatného modulu `rlboard`, ktorý teraz importujeme. Môžete sa pozrieť do tohto modulu, aby ste získali viac podrobností o interných častiach implementácie.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rlboard import *"
+   ]
+  },
+  {
+   "source": [
+    "Poďme teraz vytvoriť náhodnú dosku a pozrieť sa, ako vyzerá:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "source": [
+    "## Akcie a politika\n",
+    "\n",
+    "V našom príklade by Peterovým cieľom bolo nájsť jablko, pričom sa vyhne vlkovi a ďalším prekážkam. Na dosiahnutie tohto cieľa môže v podstate chodiť, kým nenájde jablko. Preto si na každej pozícii môže vybrať jednu z nasledujúcich akcií: hore, dole, doľava a doprava. Tieto akcie definujeme ako slovník a priradíme ich k dvojiciam zodpovedajúcich zmien súradníc. Napríklad pohyb doprava (`R`) by zodpovedal dvojici `(1,0)`.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "Strategia nášho agenta (Petra) je definovaná takzvanou **politikou**. Pozrime sa na najjednoduchšiu politiku nazývanú **náhodná prechádzka**.\n",
+    "\n",
+    "## Náhodná prechádzka\n",
+    "\n",
+    "Najprv vyriešme náš problém implementáciou stratégie náhodnej prechádzky.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(m):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(m,policy,start_position=None):\n",
+    "    n = 0 # number of steps\n",
+    "    # set initial position\n",
+    "    if start_position:\n",
+    "        m.human = start_position \n",
+    "    else:\n",
+    "        m.random_start()\n",
+    "    while True:\n",
+    "        if m.at() == Board.Cell.apple:\n",
+    "            return n # success!\n",
+    "        if m.at() in [Board.Cell.wolf, Board.Cell.water]:\n",
+    "            return -1 # eaten by wolf or drowned\n",
+    "        while True:\n",
+    "            a = actions[policy(m)]\n",
+    "            new_pos = m.move_pos(m.human,a)\n",
+    "            if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:\n",
+    "                m.move(a) # do the actual move\n",
+    "                break\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "Poďme vykonať experiment náhodnej prechádzky niekoľkokrát a pozrieť sa na priemerný počet vykonaných krokov:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 32.87096774193548, eaten by wolf: 7 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        else:\n",
+    "            s += z\n",
+    "            n += 1\n",
+    "    print(f\"Average path length = {s/n}, eaten by wolf: {w} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Funkcia odmeny\n",
+    "\n",
+    "Aby sme našu politiku urobili inteligentnejšou, musíme pochopiť, ktoré ťahy sú „lepšie“ ako ostatné.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "move_reward = -0.1\n",
+    "goal_reward = 10\n",
+    "end_reward = -10\n",
+    "\n",
+    "def reward(m,pos=None):\n",
+    "    pos = pos or m.human\n",
+    "    if not m.is_valid(pos):\n",
+    "        return end_reward\n",
+    "    x = m.at(pos)\n",
+    "    if x==Board.Cell.water or x == Board.Cell.wolf:\n",
+    "        return end_reward\n",
+    "    if x==Board.Cell.apple:\n",
+    "        return goal_reward\n",
+    "    return move_reward"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning\n",
+    "\n",
+    "Vytvorte Q-Tabuľku alebo viacrozmerné pole. Keďže naša hracia plocha má rozmery `šírka` x `výška`, môžeme Q-Tabuľku reprezentovať ako numpy pole s tvarom `šírka` x `výška` x `len(akcie)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "source": [
+    "Preneste Q-Tabuľku do funkcie na vykreslenie, aby ste vizualizovali tabuľku na doske:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p267d475764)\">\n    <image height=\"327\" id=\"image00b8757863\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Podstata Q-Learning: Bellmanova rovnica a učebný algoritmus\n",
+    "\n",
+    "Napíšte pseudokód pre náš učebný algoritmus:\n",
+    "\n",
+    "* Inicializujte Q-Tabuľku Q rovnakými hodnotami pre všetky stavy a akcie\n",
+    "* Nastavte rýchlosť učenia $\\alpha\\leftarrow 1$\n",
+    "* Opakujte simuláciu mnohokrát\n",
+    "   1. Začnite na náhodnej pozícii\n",
+    "   1. Opakujte\n",
+    "        1. Vyberte akciu $a$ v stave $s$\n",
+    "        2. Vykonajte akciu presunutím do nového stavu $s'$\n",
+    "        3. Ak narazíme na podmienku konca hry alebo je celková odmena príliš malá - ukončite simuláciu  \n",
+    "        4. Vypočítajte odmenu $r$ v novom stave\n",
+    "        5. Aktualizujte Q-Funkciu podľa Bellmanovej rovnice: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Aktualizujte celkovú odmenu a znížte $\\alpha$.\n",
+    "\n",
+    "## Využívanie vs. Preskúmavanie\n",
+    "\n",
+    "Najlepší prístup je nájsť rovnováhu medzi preskúmavaním a využívaním. Ako sa dozvedáme viac o našom prostredí, budeme pravdepodobne nasledovať optimálnu cestu, avšak občas si zvolíme nepreskúmanú cestu.\n",
+    "\n",
+    "## Implementácia v Pythone\n",
+    "\n",
+    "Teraz sme pripravení implementovať učebný algoritmus. Predtým však potrebujeme funkciu, ktorá premení ľubovoľné čísla v Q-Tabuľke na vektor pravdepodobností pre zodpovedajúce akcie:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "source": [
+    "Pridáme malé množstvo `eps` k pôvodnému vektoru, aby sme sa vyhli deleniu nulou v počiatočnom prípade, keď sú všetky komponenty vektora identické.\n",
+    "\n",
+    "Skutočný učebný algoritmus budeme spúšťať počas 5000 experimentov, nazývaných aj **epochy**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    m.random_start()\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        dpos = actions[a]\n",
+    "        m.move(dpos,check_correctness=False) # we allow player to move outside the board, which terminates episode\n",
+    "        r = reward(m)\n",
+    "        cum_reward += r\n",
+    "        if r==end_reward or cum_reward < -1000:\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "source": [
+    "Po vykonaní tohto algoritmu by mala byť Q-Tabuľka aktualizovaná hodnotami, ktoré definujú atraktivitu rôznych akcií v každom kroku. Vizualizujte tabuľku tu:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p31952a86ca)\">\n    <image height=\"327\" id=\"image3fd90cd4cb\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kontrola politiky\n",
+    "\n",
+    "Keďže Q-Tabuľka uvádza „atraktivitu“ každej akcie v každom stave, je pomerne jednoduché použiť ju na definovanie efektívnej navigácie v našom svete. V najjednoduchšom prípade môžeme jednoducho vybrať akciu zodpovedajúcu najvyššej hodnote v Q-Tabuľke:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "def qpolicy_strict(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = list(actions)[np.argmax(v)]\n",
+    "        return a\n",
+    "\n",
+    "walk(m,qpolicy_strict)"
+   ]
+  },
+  {
+   "source": [
+    "Ak vyskúšate vyššie uvedený kód niekoľkokrát, môžete si všimnúť, že sa niekedy jednoducho \"zasekne\" a musíte stlačiť tlačidlo STOP v notebooku, aby ste ho prerušili.\n",
+    "\n",
+    "> **Úloha 1:** Upraviť funkciu `walk` tak, aby obmedzila maximálnu dĺžku cesty na určitý počet krokov (napríklad 100), a sledovať, ako vyššie uvedený kód občas vráti túto hodnotu.\n",
+    "\n",
+    "> **Úloha 2:** Upraviť funkciu `walk` tak, aby sa nevracala na miesta, kde už predtým bola. Tým sa zabráni tomu, aby sa `walk` dostal do slučky, avšak agent sa stále môže ocitnúť \"uväznený\" na mieste, z ktorého sa nedokáže dostať.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 3.45, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbab852fd68>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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133.949763 214.756364 \nL 133.980202 214.756364 \nL 134.132399 206.226264 \nL 134.254157 214.756364 \nL 134.284596 214.685867 \nL 134.315036 198.00165 \nL 134.345475 214.756364 \nL 134.375914 199.106098 \nL 134.436793 214.756364 \nL 134.528111 214.497876 \nL 134.558551 214.309885 \nL 134.58899 214.756364 \nL 134.649869 214.685867 \nL 134.680308 213.252434 \nL 134.710748 214.756364 \nL 134.741187 214.756364 \nL 134.802066 212.946949 \nL 134.832506 214.756364 \nL 134.954263 193.184376 \nL 135.076021 214.756364 \nL 135.10646 212.594465 \nL 135.167339 214.756364 \nL 135.289097 214.756364 \nL 135.502172 214.756364 \nL 135.532612 206.766738 \nL 135.593491 214.756364 \nL 135.62393 214.662368 \nL 135.65437 214.756364 \nL 135.745688 195.722257 \nL 135.806567 214.756364 \nL 135.867445 203.923372 \nL 135.989203 214.756364 \nL 136.050082 213.111441 \nL 136.080521 214.756364 \nL 136.110961 214.756364 \nL 136.202279 206.837235 \nL 136.293597 214.756364 \nL 136.324036 213.510922 \nL 136.384915 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138.150401 209.375116 \nL 138.21128 214.756364 \nL 138.363477 206.672743 \nL 138.454795 214.756364 \nL 138.485234 205.967776 \nL 138.546113 210.338571 \nL 138.576553 214.732865 \nL 138.637431 211.41952 \nL 138.667871 207.659697 \nL 138.69831 213.604918 \nL 138.789629 213.299432 \nL 138.820068 214.756364 \nL 138.911386 201.737974 \nL 139.002704 214.756364 \nL 139.033144 213.322931 \nL 139.094023 214.756364 \nL 139.124462 210.033085 \nL 139.21578 210.503063 \nL 139.276659 209.7276 \nL 139.337538 214.756364 \nL 139.398417 212.92345 \nL 139.428856 214.756364 \nL 139.459295 214.756364 \nL 139.520174 204.863328 \nL 139.611493 211.114034 \nL 139.641932 211.255028 \nL 139.672371 210.738052 \nL 139.702811 214.756364 \nL 139.794129 213.957401 \nL 139.824568 214.756364 \nL 139.855008 213.581419 \nL 139.885447 207.119222 \nL 139.915887 214.756364 \nL 139.946326 214.004399 \nL 140.037644 211.325525 \nL 140.128962 214.756364 \nL 140.159402 207.260215 \nL 140.220281 214.380381 \nL 140.25072 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147.221344 214.591871 \nL 147.312663 210.57356 \nL 147.373541 211.231529 \nL 147.403981 214.756364 \nL 147.495299 214.19239 \nL 147.525738 214.732865 \nL 147.556178 208.317666 \nL 147.617057 214.262887 \nL 147.677936 213.816408 \nL 147.799693 214.756364 \nL 147.95189 201.502985 \nL 148.043208 214.756364 \nL 148.073648 211.513516 \nL 148.104087 210.78505 \nL 148.134527 197.79016 \nL 148.164966 214.756364 \nL 148.195405 214.568372 \nL 148.225845 214.756364 \nL 148.347602 211.8425 \nL 148.438921 214.756364 \nL 148.46936 209.093129 \nL 148.530239 212.617964 \nL 148.651997 214.756364 \nL 148.682436 201.549983 \nL 148.743315 214.756364 \nL 148.773754 213.698913 \nL 148.804194 214.756364 \nL 148.834633 202.724928 \nL 148.895512 212.335977 \nL 149.017269 214.756364 \nL 149.047709 214.662368 \nL 149.078148 213.698913 \nL 149.108588 207.213217 \nL 149.169466 214.027898 \nL 149.199906 211.137533 \nL 149.291224 208.787644 \nL 149.321664 214.756364 \nL 149.352103 214.756364 \nL 149.382542 188.484596 \nL 149.443421 206.860734 \nL 149.534739 214.756364 \nL 149.565179 207.401209 \nL 149.626058 214.756364 \nL 149.656497 212.547467 \nL 149.717376 214.756364 \nL 149.778255 214.756364 \nL 149.869573 212.312478 \nL 149.900012 214.262887 \nL 149.930452 207.518703 \nL 149.99133 214.685867 \nL 150.02177 208.740646 \nL 150.052209 206.226264 \nL 150.082649 209.7276 \nL 150.234846 214.756364 \nL 150.265285 201.573482 \nL 150.326164 213.9809 \nL 150.356603 213.792909 \nL 150.387043 214.756364 \nL 150.417482 209.539608 \nL 150.478361 213.651915 \nL 150.5088 213.487423 \nL 150.53924 214.756364 \nL 150.569679 208.811143 \nL 150.630558 214.051397 \nL 150.660997 214.756364 \nL 150.691437 207.659697 \nL 150.752316 214.756364 \nL 150.843634 212.852953 \nL 150.904513 213.628417 \nL 150.934952 214.756364 \nL 151.02627 208.388162 \nL 151.05671 209.116628 \nL 151.117589 214.756364 \nL 151.148028 213.463924 \nL 151.178467 204.275855 \nL 151.239346 214.732865 \nL 151.269786 208.01218 \nL 151.300225 211.889498 \nL 151.330664 190.435005 \nL 151.361104 214.756364 \nL 151.391543 211.748505 \nL 151.421983 212.429973 \nL 151.452422 204.322853 \nL 151.482861 214.756364 \nL 151.513301 207.80069 \nL 151.665498 214.756364 \nL 151.726377 208.975635 \nL 151.756816 210.15058 \nL 151.787256 214.756364 \nL 151.817695 211.748505 \nL 151.848134 206.155767 \nL 151.909013 214.521375 \nL 151.969892 214.756364 \nL 152.000331 214.145392 \nL 152.06121 214.756364 \nL 152.182968 197.625667 \nL 152.243847 214.756364 \nL 152.304725 213.181937 \nL 152.335165 212.71196 \nL 152.365604 214.756364 \nL 152.426483 205.897279 \nL 152.456923 214.756364 \nL 152.548241 208.458659 \nL 152.57868 213.111441 \nL 152.60912 214.756364 \nL 152.700438 214.19239 \nL 152.730877 214.215889 \nL 152.822195 214.756364 \nL 152.852635 208.364663 \nL 152.913514 214.121893 \nL 152.943953 184.654276 \nL 153.065711 214.756364 \nL 153.09615 210.926043 \nL 153.157029 214.756364 \nL 153.187468 214.756364 \nL 153.248347 206.320259 \nL 153.309226 207.706694 \nL 153.370105 214.756364 \nL 153.430984 213.158439 \nL 153.461423 213.816408 \nL 153.491862 207.518703 \nL 153.522302 214.756364 \nL 153.552741 212.617964 \nL 153.644059 214.756364 \nL 153.674499 214.356882 \nL 153.704938 214.756364 \nL 153.735378 213.839907 \nL 153.765817 207.542202 \nL 153.796256 214.756364 \nL 153.826696 214.239388 \nL 153.887575 214.756364 \nL 153.918014 204.839829 \nL 153.978893 213.792909 \nL 154.039772 214.756364 \nL 154.070211 213.322931 \nL 154.100651 188.790082 \nL 154.161529 214.215889 \nL 154.191969 214.756364 \nL 154.222408 200.11655 \nL 154.283287 202.630932 \nL 154.405045 214.756364 \nL 154.435484 214.756364 \nL 154.465923 214.591871 \nL 154.496363 208.975635 \nL 154.526802 214.756364 \nL 154.557242 214.756364 \nL 154.587681 214.756364 \nL 154.678999 213.698913 \nL 154.739878 214.756364 \nL 154.770317 214.145392 \nL 154.800757 213.13494 \nL 154.831196 214.756364 \nL 154.861636 214.756364 \nL 154.892075 205.02782 \nL 154.952954 214.756364 \nL 155.013833 213.534421 \nL 155.044272 206.461253 \nL 155.105151 214.756364 \nL 155.16603 214.756364 \nL 155.196469 204.487345 \nL 155.257348 214.756364 \nL 155.379106 213.228935 \nL 155.439984 211.959995 \nL 155.561742 214.756364 \nL 155.622621 214.756364 \nL 155.713939 205.215811 \nL 155.805257 214.756364 \nL 155.835697 202.865921 \nL 155.896576 212.641463 \nL 156.018333 214.756364 \nL 156.048773 213.322931 \nL 156.109651 214.756364 \nL 156.140091 196.356727 \nL 156.261848 214.756364 \nL 156.322727 207.777191 \nL 156.353167 213.040944 \nL 156.414045 214.756364 \nL 156.566243 204.017367 \nL 156.657561 214.756364 \nL 156.688 214.239388 \nL 156.748879 214.756364 \nL 156.779318 208.035679 \nL 156.840197 207.80069 \nL 156.870637 214.756364 \nL 156.961955 202.912919 \nL 157.022834 214.756364 \nL 157.083712 214.356882 \nL 157.175031 200.539531 \nL 157.23591 214.756364 \nL 157.296788 207.847688 \nL 157.357667 214.756364 \nL 157.388107 204.463846 \nL 157.418546 211.278527 \nL 157.448985 210.103582 \nL 157.479425 213.816408 \nL 157.570743 213.534421 \nL 157.631622 214.756364 \nL 157.692501 207.988681 \nL 157.753379 214.756364 \nL 157.814258 214.521375 \nL 157.844698 214.756364 \nL 157.996895 205.403802 \nL 158.027334 205.521297 \nL 158.149092 214.732865 \nL 158.179531 214.756364 \nL 158.209971 204.510844 \nL 158.270849 211.983494 \nL 158.392607 214.756364 \nL 158.453486 214.756364 \nL 158.483925 212.970447 \nL 158.544804 213.416926 \nL 158.605683 214.756364 \nL 158.666562 210.503063 \nL 158.697001 214.756364 \nL 158.72744 213.816408 \nL 158.788319 214.756364 \nL 158.818759 214.756364 \nL 158.940516 210.832048 \nL 158.970956 211.372522 \nL 159.092713 192.620402 \nL 159.031835 213.76941 \nL 159.123153 200.32804 \nL 159.24491 214.756364 \nL 159.366668 212.194984 \nL 159.397107 212.805955 \nL 159.427547 212.335977 \nL 159.457986 214.756364 \nL 159.518865 212.852953 \nL 159.549304 212.429973 \nL 159.640623 214.756364 \nL 159.671062 214.709366 \nL 159.701502 213.675414 \nL 159.731941 214.756364 \nL 159.76238 214.685867 \nL 159.79282 202.25495 \nL 159.853699 210.855547 \nL 159.975456 214.756364 \nL 160.066774 211.865999 \nL 160.158093 214.756364 \nL 160.27985 211.325525 \nL 160.340729 207.565701 \nL 160.371168 214.756364 \nL 160.492926 200.657025 \nL 160.584244 214.168891 \nL 160.614684 209.28112 \nL 160.736441 214.732865 \nL 160.675563 208.670149 \nL 160.766881 214.568372 \nL 160.858199 214.756364 \nL 160.888638 197.155689 \nL 161.010396 214.756364 \nL 161.071275 214.756364 \nL 161.101714 208.905138 \nL 161.162593 214.756364 \nL 161.284351 211.678008 \nL 161.375669 211.231529 \nL 161.406108 214.756364 \nL 161.527866 209.892092 \nL 161.588745 208.411661 \nL 161.649624 214.756364 \nL 161.740942 213.416926 \nL 161.771381 214.756364 \nL 161.801821 213.440425 \nL 161.83226 207.142721 \nL 161.893139 214.756364 \nL 162.045336 214.756364 \nL 162.167094 208.153173 \nL 162.319291 214.662368 \nL 162.410609 214.756364 \nL 162.441048 205.192312 \nL 162.593245 214.756364 \nL 162.745442 205.591794 \nL 162.775882 214.756364 \nL 162.8672 212.899951 \nL 162.958518 214.756364 \nL 162.928079 212.171485 \nL 162.988958 213.675414 \nL 163.049836 214.756364 \nL 163.080276 213.55792 \nL 163.110715 210.949542 \nL 163.171594 214.756364 \nL 163.202033 214.662368 \nL 163.232473 213.040944 \nL 163.262912 197.696164 \nL 163.323791 214.756364 \nL 163.35423 208.693648 \nL 163.415109 213.910403 \nL 163.445549 214.756364 \nL 163.567306 207.119222 \nL 163.719503 214.756364 \nL 163.749943 214.638869 \nL 163.780382 208.059178 \nL 163.841261 214.756364 \nL 163.8717 212.852953 \nL 163.90214 214.756364 \nL 163.932579 199.670071 \nL 163.993458 209.234123 \nL 164.023897 213.76941 \nL 164.115216 212.476971 \nL 164.145655 214.61537 \nL 164.176094 211.63101 \nL 164.206534 212.735458 \nL 164.236973 212.641463 \nL 164.328291 214.756364 \nL 164.358731 213.510922 \nL 164.38917 214.40388 \nL 164.480489 209.915591 \nL 164.510928 211.725006 \nL 164.541367 214.40388 \nL 164.571807 207.424708 \nL 164.602246 212.735458 \nL 164.663125 209.915591 \nL 164.693564 212.71196 \nL 164.724004 212.970447 \nL 164.784883 211.255028 \nL 164.845761 214.756364 \nL 164.876201 214.756364 \nL 165.028398 193.983338 \nL 165.119716 214.756364 \nL 165.150155 213.393428 \nL 165.180595 211.889498 \nL 165.211034 213.487423 \nL 165.241474 214.756364 \nL 165.271913 207.683195 \nL 165.363231 212.147986 \nL 165.393671 214.756364 \nL 165.42411 200.915513 \nL 165.484989 214.756364 \nL 165.515428 214.756364 \nL 165.576307 210.291573 \nL 165.637186 212.382975 \nL 165.758944 214.756364 \nL 165.819822 212.241982 \nL 165.850262 214.756364 \nL 165.880701 214.756364 \nL 165.911141 213.581419 \nL 165.972019 214.662368 \nL 166.032898 214.756364 \nL 166.063338 214.309885 \nL 166.124217 214.756364 \nL 166.154656 214.61537 \nL 166.185095 214.756364 \nL 166.215535 212.006993 \nL 166.276414 213.487423 \nL 166.306853 214.756364 \nL 166.337292 211.912997 \nL 166.367732 214.239388 \nL 166.428611 214.709366 \nL 166.45905 206.273262 \nL 166.580808 214.756364 \nL 166.611247 212.288979 \nL 166.672126 213.55792 \nL 166.733005 214.756364 \nL 166.885202 209.445613 \nL 167.037399 214.756364 \nL 167.159156 212.500469 \nL 167.220035 214.756364 \nL 167.250475 204.957323 \nL 167.311353 214.756364 \nL 167.433111 212.547467 \nL 167.46355 214.40388 \nL 167.49399 207.80069 \nL 167.585308 207.965182 \nL 167.615747 214.756364 \nL 167.646187 195.22878 \nL 167.707066 214.756364 \nL 167.767945 214.756364 \nL 167.798384 211.865999 \nL 167.859263 214.098395 \nL 167.920142 214.756364 \nL 167.950581 213.299432 \nL 168.01146 214.756364 \nL 168.041899 214.732865 \nL 168.072339 214.333383 \nL 168.163657 193.818846 \nL 168.285414 214.756364 \nL 168.315854 208.787644 \nL 168.376733 211.184531 \nL 168.407172 214.756364 \nL 168.437611 214.450878 \nL 168.468051 203.429895 \nL 168.52893 214.756364 \nL 168.589809 212.100988 \nL 168.620248 212.147986 \nL 168.650687 214.756364 \nL 168.681127 211.513516 \nL 168.742006 213.463924 \nL 168.772445 210.526562 \nL 168.802884 184.325291 \nL 168.863763 214.685867 \nL 168.924642 214.756364 \nL 168.955081 214.027898 \nL 168.985521 209.351617 \nL 169.0464 214.756364 \nL 169.076839 214.756364 \nL 169.107278 207.612699 \nL 169.168157 214.591871 \nL 169.198597 213.628417 \nL 169.229036 214.756364 \nL 169.259475 214.756364 \nL 169.320354 214.027898 \nL 169.442112 200.398537 \nL 169.381233 214.756364 \nL 169.472551 206.132268 \nL 169.56387 214.756364 \nL 169.685627 200.586528 \nL 169.807385 214.756364 \nL 169.868264 214.756364 \nL 169.898703 214.568372 \nL 169.929142 207.824189 \nL 169.959582 214.756364 \nL 169.990021 214.756364 \nL 170.0509 211.396021 \nL 170.081339 214.756364 \nL 170.233537 195.886749 \nL 170.263976 214.756364 \nL 170.355294 213.228935 \nL 170.416173 212.26548 \nL 170.446612 213.76941 \nL 170.477052 213.510922 \nL 170.56837 200.563029 \nL 170.629249 203.782378 \nL 170.751006 214.756364 \nL 170.842325 206.907732 \nL 170.872764 207.824189 \nL 170.994522 214.756364 \nL 171.024961 207.753692 \nL 171.08584 214.662368 \nL 171.116279 214.756364 \nL 171.268476 212.71196 \nL 171.298916 214.638869 \nL 171.329355 210.738052 \nL 171.359795 209.351617 \nL 171.481552 214.709366 \nL 171.511992 214.756364 \nL 171.542431 214.286386 \nL 171.57287 206.884233 \nL 171.60331 214.756364 \nL 171.664189 209.116628 \nL 171.755507 214.756364 \nL 171.785946 214.286386 \nL 171.877265 214.756364 \nL 171.907704 212.124487 \nL 171.938143 214.756364 \nL 171.968583 206.437754 \nL 172.059901 211.396021 \nL 172.09034 213.651915 \nL 172.12078 213.534421 \nL 172.151219 205.756286 \nL 172.181659 214.756364 \nL 172.212098 214.756364 \nL 172.272977 214.756364 \nL 172.303416 213.252434 \nL 172.364295 214.333383 \nL 172.394734 214.756364 \nL 172.486053 208.43516 \nL 172.577371 206.343758 \nL 172.60781 214.756364 \nL 172.63825 191.680446 \nL 172.699129 214.756364 \nL 172.729568 214.333383 \nL 172.760007 214.756364 \nL 172.790447 211.466518 \nL 172.851326 214.756364 \nL 172.912204 214.756364 \nL 172.942644 208.623151 \nL 173.033962 211.772004 \nL 173.12528 214.756364 \nL 173.15572 212.030492 \nL 173.216598 214.756364 \nL 173.338356 214.709366 \nL 173.368796 202.724928 \nL 173.429674 214.756364 \nL 173.460114 214.756364 \nL 173.581871 205.709288 \nL 173.703629 214.756364 \nL 173.734068 214.756364 \nL 173.764508 214.544874 \nL 173.855826 214.662368 \nL 173.977584 214.756364 \nL 174.129781 210.855547 \nL 174.16022 204.134862 \nL 174.19066 214.638869 \nL 174.221099 214.215889 \nL 174.251538 210.620558 \nL 174.281978 214.756364 \nL 174.312417 214.756364 \nL 174.342857 214.756364 \nL 174.464614 204.745833 \nL 174.495054 207.871187 \nL 174.616811 214.756364 \nL 174.647251 214.709366 \nL 174.67769 214.145392 \nL 174.708129 214.709366 \nL 174.769008 203.429895 \nL 174.799448 214.756364 \nL 174.890766 214.756364 \nL 174.982084 213.416926 \nL 175.012524 214.756364 \nL 175.042963 211.560514 \nL 175.073402 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177.112843 214.756364 \nL 177.143282 214.027898 \nL 177.173721 208.270668 \nL 177.2346 208.811143 \nL 177.295479 214.756364 \nL 177.356358 212.241982 \nL 177.417237 214.756364 \nL 177.447676 214.568372 \nL 177.508555 208.106176 \nL 177.538994 214.756364 \nL 177.569434 212.359476 \nL 177.599873 212.335977 \nL 177.660752 214.756364 \nL 177.691191 200.022555 \nL 177.78251 214.756364 \nL 177.812949 213.087942 \nL 177.843388 214.756364 \nL 177.873828 214.638869 \nL 177.904267 196.427223 \nL 177.965146 214.756364 \nL 177.995585 208.599652 \nL 178.056464 209.892092 \nL 178.178222 214.756364 \nL 178.208661 214.756364 \nL 178.29998 208.482158 \nL 178.421737 214.756364 \nL 178.482616 211.443019 \nL 178.513055 214.756364 \nL 178.573934 212.030492 \nL 178.634813 213.158439 \nL 178.665252 214.756364 \nL 178.756571 205.380303 \nL 178.847889 214.756364 \nL 178.878328 207.683195 \nL 178.939207 214.756364 \nL 178.969647 214.756364 \nL 179.030525 210.879046 \nL 179.060965 214.19239 \nL 179.121844 212.993946 \nL 179.182722 210.78505 \nL 179.213162 214.756364 \nL 179.243601 210.432566 \nL 179.274041 202.395943 \nL 179.30448 214.756364 \nL 179.334919 214.756364 \nL 179.365359 214.756364 \nL 179.395798 208.129674 \nL 179.456677 214.215889 \nL 179.487116 214.756364 \nL 179.517556 211.114034 \nL 179.578435 214.756364 \nL 179.608874 214.756364 \nL 179.669753 196.544718 \nL 179.730632 212.05399 \nL 179.791511 214.732865 \nL 179.82195 214.168891 \nL 179.852389 200.821517 \nL 179.913268 214.756364 \nL 180.035026 208.576154 \nL 180.126344 214.756364 \nL 180.156783 214.732865 \nL 180.187223 214.427379 \nL 180.217662 214.756364 \nL 180.278541 203.735381 \nL 180.30898 213.158439 \nL 180.369859 214.756364 \nL 180.400299 214.239388 \nL 180.430738 204.769332 \nL 180.491617 213.933902 \nL 180.552496 214.756364 \nL 180.674253 205.920778 \nL 180.735132 214.756364 \nL 180.765572 211.748505 \nL 180.796011 204.275855 \nL 180.82645 214.756364 \nL 180.85689 211.278527 \nL 180.887329 213.910403 \nL 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204.299354 \nL 187.066529 210.879046 \nL 187.218726 214.756364 \nL 187.340484 211.748505 \nL 187.370923 214.380381 \nL 187.431802 201.808471 \nL 187.462241 214.756364 \nL 187.492681 211.137533 \nL 187.52312 214.756364 \nL 187.553559 209.06963 \nL 187.614438 214.709366 \nL 187.644878 195.369773 \nL 187.736196 196.403725 \nL 187.827514 214.756364 \nL 187.857954 213.675414 \nL 187.888393 214.756364 \nL 188.010151 209.892092 \nL 188.04059 214.544874 \nL 188.101469 187.732631 \nL 188.131908 214.756364 \nL 188.162348 213.017445 \nL 188.192787 202.88942 \nL 188.223226 214.427379 \nL 188.253666 213.158439 \nL 188.344984 214.756364 \nL 188.405863 200.069553 \nL 188.466742 212.359476 \nL 188.52762 214.756364 \nL 188.588499 187.591638 \nL 188.679818 214.756364 \nL 188.710257 214.380381 \nL 188.771136 211.63101 \nL 188.801575 214.286386 \nL 188.832015 214.756364 \nL 188.862454 214.732865 \nL 188.923333 214.756364 \nL 188.984212 202.771926 \nL 189.07553 214.756364 \nL 189.105969 211.278527 \nL 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214.756364 \nL 191.023652 214.356882 \nL 191.054091 201.385491 \nL 191.084531 214.568372 \nL 191.14541 208.64665 \nL 191.267167 214.732865 \nL 191.328046 211.959995 \nL 191.358485 214.756364 \nL 191.388925 214.427379 \nL 191.449804 214.638869 \nL 191.541122 203.594387 \nL 191.571561 213.675414 \nL 191.63244 202.701429 \nL 191.662879 198.283636 \nL 191.784637 214.732865 \nL 191.815077 208.200171 \nL 191.845516 214.756364 \nL 191.906395 210.291573 \nL 191.936834 214.756364 \nL 191.967274 210.174079 \nL 191.997713 199.294089 \nL 192.058592 214.756364 \nL 192.119471 213.205436 \nL 192.14991 214.756364 \nL 192.210789 214.309885 \nL 192.241228 214.756364 \nL 192.271668 203.805877 \nL 192.332546 210.197577 \nL 192.454304 214.756364 \nL 192.606501 208.928637 \nL 192.728259 214.756364 \nL 192.789138 214.286386 \nL 192.819577 201.1975 \nL 192.850016 214.756364 \nL 192.880456 213.651915 \nL 192.941335 211.231529 \nL 192.971774 212.26548 \nL 193.032653 214.756364 \nL 193.15441 190.622996 \nL 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209.774597 \nL 195.467805 208.881639 \nL 195.498245 214.756364 \nL 195.528684 205.944277 \nL 195.589563 214.638869 \nL 195.680881 206.508251 \nL 195.711321 214.756364 \nL 195.802639 211.490017 \nL 195.833078 214.756364 \nL 195.863518 188.837079 \nL 195.924397 214.544874 \nL 196.046154 181.411428 \nL 196.167912 214.756364 \nL 196.289669 200.281043 \nL 196.380988 214.756364 \nL 196.350548 179.696008 \nL 196.411427 213.181937 \nL 196.441866 211.114034 \nL 196.472306 214.756364 \nL 196.502745 212.641463 \nL 196.533185 212.547467 \nL 196.563624 213.76941 \nL 196.594063 211.983494 \nL 196.624503 210.385569 \nL 196.685382 214.756364 \nL 196.746261 213.393428 \nL 196.807139 214.756364 \nL 196.837579 214.568372 \nL 196.898458 214.756364 \nL 196.928897 209.304619 \nL 197.020215 214.756364 \nL 197.050655 212.171485 \nL 197.141973 214.756364 \nL 197.172412 183.526329 \nL 197.29417 214.756364 \nL 197.415928 213.675414 \nL 197.476806 214.756364 \nL 197.507246 214.333383 \nL 197.568125 205.051319 \nL 197.598564 206.555248 \nL 197.629003 214.756364 \nL 197.689882 207.260215 \nL 197.720322 196.803206 \nL 197.750761 214.756364 \nL 197.7812 214.756364 \nL 197.81164 211.302026 \nL 197.872519 214.756364 \nL 197.902958 214.756364 \nL 197.933397 175.842189 \nL 197.994276 213.487423 \nL 198.055155 212.382975 \nL 198.116034 214.756364 \nL 198.176913 187.168658 \nL 198.237792 204.910326 \nL 198.268231 214.756364 \nL 198.32911 206.061771 \nL 198.359549 203.594387 \nL 198.389989 186.111207 \nL 198.450867 214.756364 \nL 198.542186 202.654431 \nL 198.572625 213.55792 \nL 198.603064 213.463924 \nL 198.633504 214.756364 \nL 198.663943 207.542202 \nL 198.724822 214.568372 \nL 198.81614 214.756364 \nL 198.877019 206.320259 \nL 198.907458 214.521375 \nL 198.998777 213.886904 \nL 199.029216 200.516032 \nL 199.090095 201.291495 \nL 199.181413 214.756364 \nL 199.211853 213.087942 \nL 199.242292 214.756364 \nL 199.272731 213.863406 \nL 199.303171 189.753537 \nL 199.394489 205.004321 \nL 199.485807 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205.02782 \nL 204.325673 202.536937 \nL 204.447431 214.756364 \nL 204.47787 214.756364 \nL 204.508309 211.114034 \nL 204.569188 212.664962 \nL 204.660507 214.756364 \nL 204.751825 206.484752 \nL 204.843143 214.756364 \nL 204.873582 213.322931 \nL 204.904022 211.20803 \nL 204.934461 214.756364 \nL 204.964901 183.338337 \nL 205.025779 214.756364 \nL 205.056219 214.521375 \nL 205.117098 214.756364 \nL 205.147537 201.455988 \nL 205.208416 214.756364 \nL 205.360613 214.756364 \nL 205.391052 199.0591 \nL 205.451931 214.756364 \nL 205.482371 213.722412 \nL 205.543249 214.709366 \nL 205.573689 214.756364 \nL 205.665007 207.448206 \nL 205.695446 189.988525 \nL 205.756325 212.617964 \nL 205.786765 214.756364 \nL 205.817204 203.852875 \nL 205.878083 211.443019 \nL 205.938962 214.756364 \nL 205.99984 214.662368 \nL 206.03028 214.756364 \nL 206.152037 191.492455 \nL 206.273795 214.756364 \nL 206.304235 197.625667 \nL 206.365113 214.685867 \nL 206.395553 214.544874 \nL 206.425992 208.411661 \nL 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209.657103 \nL 208.404554 214.756364 \nL 208.434993 210.268074 \nL 208.465432 207.941683 \nL 208.495872 213.416926 \nL 208.556751 214.756364 \nL 208.58719 203.500392 \nL 208.678508 207.354211 \nL 208.708948 206.29676 \nL 208.800266 213.463924 \nL 208.830705 212.429973 \nL 208.861145 203.946871 \nL 208.891584 214.732865 \nL 208.952463 208.317666 \nL 208.982902 213.792909 \nL 209.013342 207.777191 \nL 209.043781 212.899951 \nL 209.135099 214.756364 \nL 209.165539 194.688305 \nL 209.226418 214.756364 \nL 209.287296 212.899951 \nL 209.317736 214.756364 \nL 209.348175 209.586606 \nL 209.409054 213.463924 \nL 209.439493 214.756364 \nL 209.469933 201.573482 \nL 209.530812 213.534421 \nL 209.561251 214.286386 \nL 209.591691 204.064365 \nL 209.652569 209.774597 \nL 209.713448 207.424708 \nL 209.774327 214.756364 \nL 209.804766 206.578747 \nL 209.865645 214.450878 \nL 209.896085 214.756364 \nL 209.987403 208.670149 \nL 210.017842 214.756364 \nL 210.10916 214.215889 \nL 210.1396 208.082677 \nL 210.200479 212.312478 \nL 210.261357 214.756364 \nL 210.291797 213.628417 \nL 210.322236 197.625667 \nL 210.352676 214.756364 \nL 210.383115 214.756364 \nL 210.413555 208.22367 \nL 210.474433 214.544874 \nL 210.504873 214.756364 \nL 210.535312 214.121893 \nL 210.596191 208.85814 \nL 210.62663 210.244575 \nL 210.717949 214.756364 \nL 210.748388 193.066881 \nL 210.809267 214.756364 \nL 210.931024 206.108769 \nL 211.052782 214.756364 \nL 211.113661 214.756364 \nL 211.204979 209.328118 \nL 211.296297 214.756364 \nL 211.326737 209.022633 \nL 211.418055 210.244575 \nL 211.448494 211.701507 \nL 211.509373 213.275933 \nL 211.539813 189.965027 \nL 211.570252 214.756364 \nL 211.66157 201.573482 \nL 211.69201 214.732865 \nL 211.722449 191.915435 \nL 211.752888 211.396021 \nL 211.813767 195.722257 \nL 211.844207 214.756364 \nL 211.874646 214.40388 \nL 211.905086 206.014774 \nL 211.935525 214.756364 \nL 211.965964 214.027898 \nL 211.996404 213.252434 \nL 212.026843 214.756364 \nL 212.057283 198.84761 \nL 212.118161 212.899951 \nL 212.148601 211.701507 \nL 212.17904 199.411583 \nL 212.20948 214.756364 \nL 212.239919 214.333383 \nL 212.300798 214.756364 \nL 212.331237 199.952058 \nL 212.392116 208.85814 \nL 212.422555 211.983494 \nL 212.452995 182.468878 \nL 212.483434 213.158439 \nL 212.513874 203.194906 \nL 212.544313 214.756364 \nL 212.635631 212.735458 \nL 212.666071 206.437754 \nL 212.72695 210.409068 \nL 212.757389 214.756364 \nL 212.818268 213.322931 \nL 212.848707 211.725006 \nL 212.879147 198.236639 \nL 212.909586 214.756364 \nL 212.940025 199.223592 \nL 213.000904 214.756364 \nL 213.061783 212.171485 \nL 213.092222 207.307213 \nL 213.122662 214.756364 \nL 213.153101 210.009586 \nL 213.183541 214.756364 \nL 213.244419 208.341165 \nL 213.274859 214.568372 \nL 213.335738 214.756364 \nL 213.396616 213.510922 \nL 213.427056 214.756364 \nL 213.457495 212.735458 \nL 213.487935 213.9809 \nL 213.518374 209.234123 \nL 213.579253 214.756364 \nL 213.609692 214.756364 \nL 213.761889 197.155689 \nL 213.883647 214.756364 \nL 213.914086 214.756364 \nL 213.944526 214.497876 \nL 214.005405 214.756364 \nL 214.035844 214.756364 \nL 214.066283 214.61537 \nL 214.127162 214.756364 \nL 214.188041 209.422114 \nL 214.309799 214.756364 \nL 214.370678 209.539608 \nL 214.401117 214.662368 \nL 214.431556 214.756364 \nL 214.522875 194.218327 \nL 214.553314 210.244575 \nL 214.614193 209.539608 \nL 214.675072 214.756364 \nL 214.76639 213.275933 \nL 214.857708 214.756364 \nL 214.827269 212.92345 \nL 214.888147 214.450878 \nL 214.918587 204.087864 \nL 215.009905 210.197577 \nL 215.101223 214.756364 \nL 215.131663 214.732865 \nL 215.192542 214.756364 \nL 215.25342 206.484752 \nL 215.28386 212.05399 \nL 215.314299 195.745755 \nL 215.375178 203.006915 \nL 215.496936 214.756364 \nL 215.527375 206.531749 \nL 215.588254 214.756364 \nL 215.618693 214.756364 \nL 215.649133 206.790237 \nL 215.710011 214.732865 \nL 215.77089 214.756364 \nL 215.831769 213.534421 \nL 215.862208 199.90506 \nL 215.923087 208.717147 \nL 216.014406 214.756364 \nL 216.044845 212.92345 \nL 216.105724 202.842423 \nL 216.136163 214.756364 \nL 216.227481 206.978229 \nL 216.257921 214.756364 \nL 216.349239 214.709366 \nL 216.440557 202.372445 \nL 216.410118 214.756364 \nL 216.470997 211.889498 \nL 216.531875 209.868593 \nL 216.592754 214.215889 \nL 216.684072 205.544796 \nL 216.775391 214.756364 \nL 216.80583 206.790237 \nL 216.866709 214.756364 \nL 216.927588 211.396021 \nL 216.958027 186.628183 \nL 216.988467 214.591871 \nL 217.018906 212.758957 \nL 217.049345 209.398615 \nL 217.110224 213.863406 \nL 217.262421 214.756364 \nL 217.292861 201.549983 \nL 217.353739 213.463924 \nL 217.384179 213.675414 \nL 217.414618 200.398537 \nL 217.445058 214.521375 \nL 217.475497 211.560514 \nL 217.505937 214.756364 \nL 217.597255 212.735458 \nL 217.658134 214.756364 \nL 217.719012 202.489939 \nL 217.779891 212.077489 \nL 217.84077 210.832048 \nL 217.932088 214.756364 \nL 217.962528 211.349023 \nL 218.023406 212.876452 \nL 218.114725 214.756364 \nL 218.145164 214.732865 \nL 218.206043 214.756364 \nL 218.297361 200.610027 \nL 218.388679 214.756364 \nL 218.419119 207.095723 \nL 218.540876 214.756364 \nL 218.571316 214.591871 \nL 218.632195 203.077411 \nL 218.662634 211.678008 \nL 218.693073 214.756364 \nL 218.723513 201.385491 \nL 218.784392 211.043538 \nL 218.87571 214.756364 \nL 218.936589 201.878968 \nL 218.967028 214.756364 \nL 218.997467 214.756364 \nL 219.027907 201.737974 \nL 219.088786 214.756364 \nL 219.149665 214.756364 \nL 219.180104 197.696164 \nL 219.240983 214.756364 \nL 219.271422 213.087942 \nL 219.301862 214.450878 \nL 219.332301 195.581263 \nL 219.39318 210.503063 \nL 219.423619 210.080083 \nL 219.454059 212.030492 \nL 219.514937 202.771926 \nL 219.545377 214.756364 \nL 219.575816 212.547467 \nL 219.636695 214.756364 \nL 219.697574 214.756364 \nL 219.819331 213.487423 \nL 219.91065 214.756364 \nL 219.971529 195.722257 \nL 220.001968 214.732865 \nL 220.032407 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222.102287 214.756364 \nL 222.132726 213.228935 \nL 222.193605 214.756364 \nL 222.224045 197.437676 \nL 222.284923 211.889498 \nL 222.345802 210.78505 \nL 222.406681 214.756364 \nL 222.437121 214.497876 \nL 222.46756 212.406474 \nL 222.497999 214.756364 \nL 222.528439 213.722412 \nL 222.650196 214.756364 \nL 222.680636 214.004399 \nL 222.802393 203.147908 \nL 222.832833 206.08527 \nL 222.863272 214.685867 \nL 222.924151 208.64665 \nL 222.95459 206.437754 \nL 223.076348 214.756364 \nL 223.106787 214.732865 \nL 223.137227 213.205436 \nL 223.167666 194.241826 \nL 223.228545 211.278527 \nL 223.289424 210.597059 \nL 223.350303 214.756364 \nL 223.380742 189.236561 \nL 223.47206 204.722334 \nL 223.532939 211.114034 \nL 223.563379 189.941528 \nL 223.593818 214.732865 \nL 223.624257 211.63101 \nL 223.715576 214.756364 \nL 223.746015 203.735381 \nL 223.806894 211.584012 \nL 223.837333 214.756364 \nL 223.867773 206.08527 \nL 223.928652 209.798096 \nL 223.98953 214.756364 \nL 224.01997 197.672665 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228.068411 214.756364 \nL 228.09885 210.291573 \nL 228.159729 214.756364 \nL 228.190169 214.756364 \nL 228.220608 214.380381 \nL 228.251047 199.505579 \nL 228.281487 214.756364 \nL 228.311926 211.560514 \nL 228.342366 212.852953 \nL 228.372805 190.998978 \nL 228.433684 214.521375 \nL 228.464123 214.61537 \nL 228.494563 214.239388 \nL 228.555441 214.756364 \nL 228.61632 203.335899 \nL 228.677199 192.808393 \nL 228.738078 214.756364 \nL 228.890275 207.330712 \nL 228.920714 213.087942 \nL 228.951154 197.437676 \nL 228.981593 214.756364 \nL 229.012033 208.458659 \nL 229.042472 214.756364 \nL 229.13379 213.322931 \nL 229.225108 214.756364 \nL 229.255548 213.440425 \nL 229.285987 214.756364 \nL 229.346866 203.335899 \nL 229.377305 208.43516 \nL 229.468624 214.756364 \nL 229.529502 191.844938 \nL 229.559942 196.192234 \nL 229.6817 214.756364 \nL 229.712139 213.181937 \nL 229.773018 214.732865 \nL 229.864336 213.228935 \nL 229.894775 214.40388 \nL 229.925215 214.732865 \nL 229.955654 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231.72114 210.221076 \nL 231.782019 214.756364 \nL 231.812458 213.76941 \nL 231.903776 200.892014 \nL 231.934216 212.382975 \nL 231.964655 214.756364 \nL 232.055973 213.910403 \nL 232.086413 214.168891 \nL 232.177731 214.239388 \nL 232.20817 196.450722 \nL 232.23861 214.756364 \nL 232.329928 214.286386 \nL 232.360367 214.732865 \nL 232.451686 200.633526 \nL 232.421246 214.756364 \nL 232.482125 208.43516 \nL 232.543004 214.756364 \nL 232.603883 213.745911 \nL 232.664761 213.275933 \nL 232.72564 197.155689 \nL 232.786519 206.132268 \nL 232.877837 214.756364 \nL 232.847398 198.518625 \nL 232.908277 213.252434 \nL 232.999595 214.756364 \nL 233.030034 211.278527 \nL 233.121353 213.275933 \nL 233.24311 200.563029 \nL 233.27355 210.479564 \nL 233.303989 214.756364 \nL 233.395307 212.570966 \nL 233.425747 208.200171 \nL 233.456186 186.581185 \nL 233.517065 211.20803 \nL 233.547504 214.756364 \nL 233.608383 209.140127 \nL 233.638823 207.871187 \nL 233.669262 188.249607 \nL 233.730141 214.756364 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237.839461 199.881561 \nL 237.8699 214.756364 \nL 237.90034 214.685867 \nL 238.052537 203.382897 \nL 238.143855 214.756364 \nL 238.174294 205.615292 \nL 238.235173 211.137533 \nL 238.296052 214.756364 \nL 238.326491 212.547467 \nL 238.356931 198.84761 \nL 238.38737 214.756364 \nL 238.41781 205.967776 \nL 238.539567 214.756364 \nL 238.661325 198.072146 \nL 238.691764 214.756364 \nL 238.783082 212.241982 \nL 238.813522 200.093051 \nL 238.874401 214.756364 \nL 238.90484 203.547389 \nL 239.057037 214.756364 \nL 239.087476 207.213217 \nL 239.148355 211.090536 \nL 239.270113 214.756364 \nL 239.361431 191.539453 \nL 239.42231 214.756364 \nL 239.483189 205.87378 \nL 239.544068 214.756364 \nL 239.604946 210.009586 \nL 239.635386 214.427379 \nL 239.665825 201.385491 \nL 239.696265 214.756364 \nL 239.726704 211.41952 \nL 239.787583 214.450878 \nL 239.818022 209.445613 \nL 239.878901 214.756364 \nL 240.000659 209.375116 \nL 240.031098 191.492455 \nL 240.091977 214.662368 \nL 240.122416 214.61537 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254.733332 214.756364 \nL 254.763771 214.756364 \nL 254.794211 198.730115 \nL 254.885529 206.813736 \nL 254.946408 214.756364 \nL 254.976847 213.487423 \nL 255.007287 141.345807 \nL 255.068165 214.756364 \nL 255.098605 213.886904 \nL 255.159484 196.239232 \nL 255.189923 212.664962 \nL 255.220362 210.808549 \nL 255.250802 212.735458 \nL 255.281241 212.030492 \nL 255.311681 214.662368 \nL 255.34212 201.925966 \nL 255.402999 214.756364 \nL 255.463878 211.513516 \nL 255.494317 199.599575 \nL 255.555196 214.756364 \nL 255.616075 214.756364 \nL 255.646514 213.275933 \nL 255.676954 213.698913 \nL 255.707393 201.033007 \nL 255.737832 214.732865 \nL 255.768272 211.067037 \nL 255.798711 214.145392 \nL 255.829151 194.194828 \nL 255.85959 214.756364 \nL 255.890029 214.756364 \nL 255.920469 214.756364 \nL 255.950908 191.210468 \nL 256.011787 212.71196 \nL 256.042226 208.811143 \nL 256.103105 212.946949 \nL 256.133545 214.756364 \nL 256.163984 214.638869 \nL 256.224863 167.923061 \nL 256.255302 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288.947226 213.064443 \nL 288.977665 214.756364 \nL 289.038544 214.215889 \nL 289.099423 204.111363 \nL 289.129863 208.811143 \nL 289.190741 214.756364 \nL 289.25162 212.876452 \nL 289.28206 214.756364 \nL 289.434257 196.521219 \nL 289.556014 214.756364 \nL 289.586454 212.218483 \nL 289.677772 212.735458 \nL 289.708211 189.04857 \nL 289.76909 212.852953 \nL 289.829969 214.756364 \nL 289.860408 205.051319 \nL 289.951727 211.231529 \nL 289.982166 210.526562 \nL 290.012605 211.043538 \nL 290.043045 212.735458 \nL 290.073484 208.059178 \nL 290.103924 214.732865 \nL 290.134363 212.030492 \nL 290.195242 214.286386 \nL 290.225681 207.213217 \nL 290.28656 214.756364 \nL 290.408318 199.552577 \nL 290.469196 198.283636 \nL 290.530075 214.756364 \nL 290.621394 214.685867 \nL 290.651833 203.523891 \nL 290.712712 214.756364 \nL 290.743151 214.756364 \nL 290.864909 204.557842 \nL 290.925788 214.756364 \nL 291.017106 214.427379 \nL 291.047545 214.356882 \nL 291.077985 190.834486 \nL 291.108424 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297.561578 209.351617 \nL 297.592018 214.756364 \nL 297.622457 201.174001 \nL 297.652897 213.322931 \nL 297.805094 203.899873 \nL 297.896412 214.756364 \nL 297.926851 203.194906 \nL 298.01817 209.962589 \nL 298.139927 214.756364 \nL 298.170367 205.192312 \nL 298.231245 214.756364 \nL 298.261685 214.756364 \nL 298.292124 205.779785 \nL 298.353003 206.696242 \nL 298.383442 214.121893 \nL 298.474761 213.181937 \nL 298.535639 214.756364 \nL 298.566079 211.325525 \nL 298.626958 214.544874 \nL 298.687837 214.756364 \nL 298.748715 213.205436 \nL 298.809594 214.756364 \nL 298.840034 213.886904 \nL 298.870473 203.194906 \nL 298.931352 208.176672 \nL 299.02267 214.756364 \nL 299.053109 208.035679 \nL 299.113988 214.756364 \nL 299.144428 214.662368 \nL 299.235746 207.424708 \nL 299.327064 214.756364 \nL 299.357503 211.772004 \nL 299.387943 211.41952 \nL 299.418382 206.531749 \nL 299.448822 207.612699 \nL 299.479261 214.756364 \nL 299.509701 202.019961 \nL 299.54014 212.641463 \nL 299.601019 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301.366504 207.777191 \nL 301.396944 214.756364 \nL 301.427383 211.090536 \nL 301.457823 202.583935 \nL 301.488262 212.218483 \nL 301.518701 203.923372 \nL 301.640459 214.756364 \nL 301.670898 214.098395 \nL 301.701338 214.756364 \nL 301.731777 196.779707 \nL 301.823095 204.534343 \nL 301.883974 214.756364 \nL 301.914414 211.161032 \nL 301.944853 199.247091 \nL 302.005732 214.732865 \nL 302.036171 209.892092 \nL 302.066611 214.756364 \nL 302.12749 211.137533 \nL 302.157929 210.926043 \nL 302.188368 197.061694 \nL 302.249247 211.748505 \nL 302.279687 208.975635 \nL 302.310126 214.756364 \nL 302.340565 213.910403 \nL 302.401444 214.756364 \nL 302.431884 202.25495 \nL 302.492762 207.918184 \nL 302.553641 214.756364 \nL 302.584081 208.811143 \nL 302.61452 196.004243 \nL 302.644959 214.756364 \nL 302.705838 202.513438 \nL 302.736278 202.536937 \nL 302.888475 214.756364 \nL 303.010232 203.453394 \nL 303.13199 214.756364 \nL 303.192869 206.061771 \nL 303.223308 211.278527 \nL 303.314626 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "To, čo tu vidíme, je, že na začiatku sa priemerná dĺžka cesty zvýšila. Pravdepodobne je to spôsobené tým, že keď o prostredí nič nevieme, máme tendenciu uviaznuť v zlých stavoch, ako je voda alebo vlk. Keď sa však začneme učiť a využívať získané poznatky, dokážeme prostredie skúmať dlhšie, no stále nevieme presne, kde sa nachádzajú jablká.\n",
+    "\n",
+    "Keď sa naučíme dostatok informácií, agentovi sa cieľ dosahuje ľahšie a dĺžka cesty sa začne skracovať. Napriek tomu sme stále otvorení skúmaniu, takže často odbočíme z najlepšej cesty a skúmame nové možnosti, čo spôsobí, že cesta je dlhšia, než by bola optimálna.\n",
+    "\n",
+    "Na grafe tiež pozorujeme, že v určitom bode sa dĺžka náhle zvýšila. To poukazuje na stochastickú povahu procesu a na to, že v určitom momente môžeme „pokaziť“ koeficienty Q-Tabuľky tým, že ich prepíšeme novými hodnotami. Ideálne by sa tomu malo predísť znižovaním rýchlosti učenia (t.j. ku koncu tréningu upravujeme hodnoty Q-Tabuľky len o malé hodnoty).\n",
+    "\n",
+    "Celkovo je dôležité pamätať na to, že úspech a kvalita procesu učenia výrazne závisí od parametrov, ako sú rýchlosť učenia, pokles rýchlosti učenia a diskontný faktor. Tieto parametre sa často nazývajú **hyperparametre**, aby sa odlíšili od **parametrov**, ktoré optimalizujeme počas tréningu (napr. koeficienty Q-Tabuľky). Proces hľadania najlepších hodnôt hyperparametrov sa nazýva **optimalizácia hyperparametrov** a zaslúži si samostatnú tému.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## Cvičenie\n",
+    "#### Realistickejší svet Petra a vlka\n",
+    "\n",
+    "V našej situácii sa Peter mohol pohybovať takmer bez toho, aby sa unavil alebo vyhladol. V realistickejšom svete si však musí z času na čas sadnúť a oddýchnuť si, a tiež sa najesť. Urobme náš svet realistickejším zavedením nasledujúcich pravidiel:\n",
+    "\n",
+    "1. Pri presune z jedného miesta na druhé Peter stráca **energiu** a získava **únavu**.\n",
+    "2. Peter môže získať viac energie jedením jabĺk.\n",
+    "3. Peter sa môže zbaviť únavy odpočinkom pod stromom alebo na tráve (t. j. vstupom na políčko s umiestneným stromom alebo trávou - zelené pole).\n",
+    "4. Peter musí nájsť a zabiť vlka.\n",
+    "5. Aby Peter dokázal zabiť vlka, musí mať určitú úroveň energie a únavy, inak prehrá boj.\n",
+    "\n",
+    "Upravte funkciu odmeny podľa pravidiel hry, spustite algoritmus posilňovaného učenia na naučenie najlepšej stratégie na výhru v hre a porovnajte výsledky náhodného pohybu s vaším algoritmom z hľadiska počtu vyhraných a prehratých hier.\n",
+    "\n",
+    "> **Poznámka**: Možno bude potrebné upraviť hyperparametre, aby to fungovalo, najmä počet epoch. Keďže úspech v hre (boj s vlkom) je zriedkavá udalosť, môžete očakávať oveľa dlhší čas trénovania.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/8-Reinforcement/2-Gym/notebook.ipynb b/translations/sk/8-Reinforcement/2-Gym/notebook.ipynb
new file mode 100644
index 000000000..e80ff99bd
--- /dev/null
+++ b/translations/sk/8-Reinforcement/2-Gym/notebook.ipynb
@@ -0,0 +1,392 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.4 64-bit ('base': conda)"
+  },
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "coopTranslator": {
+   "original_hash": "f22f8f3daed4b6d34648d1254763105b",
+   "translation_date": "2025-09-06T15:16:11+00:00",
+   "source_file": "8-Reinforcement/2-Gym/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Korčuľovanie na CartPole\n",
+    "\n",
+    "> **Problém**: Ak chce Peter uniknúť vlkovi, musí sa pohybovať rýchlejšie ako on. Uvidíme, ako sa Peter môže naučiť korčuľovať, konkrétne udržiavať rovnováhu, pomocou Q-Learningu.\n",
+    "\n",
+    "Najskôr nainštalujme knižnicu gym a importujme potrebné knižnice:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Vytvorte prostredie cartpole\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 2"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Aby sme videli, ako prostredie funguje, spustime krátku simuláciu na 100 krokov.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Počas simulácie potrebujeme získať pozorovania, aby sme sa rozhodli, ako konať. V skutočnosti nám funkcia `step` vracia aktuálne pozorovania, funkciu odmeny a príznak `done`, ktorý naznačuje, či má zmysel pokračovať v simulácii alebo nie:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 4"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Môžeme získať minimálnu a maximálnu hodnotu týchto čísel:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Poďme tiež preskúmať inú metódu diskrétizácie pomocou intervalov:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Poďme teraz spustiť krátku simuláciu a pozorovať tieto diskrétne hodnoty prostredia.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -2, -2)\n(0, 1, -2, -5)\n(0, 2, -3, -8)\n(0, 3, -5, -11)\n(0, 3, -7, -14)\n(0, 4, -10, -17)\n(0, 3, -14, -15)\n(0, 3, -17, -12)\n(0, 3, -20, -16)\n(0, 4, -23, -19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 8"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 9"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 22.0, alpha=0.3, epsilon=0.9\n",
+      "5000: 70.1384, alpha=0.3, epsilon=0.9\n",
+      "10000: 121.8586, alpha=0.3, epsilon=0.9\n",
+      "15000: 149.6368, alpha=0.3, epsilon=0.9\n",
+      "20000: 168.2782, alpha=0.3, epsilon=0.9\n",
+      "25000: 196.7356, alpha=0.3, epsilon=0.9\n",
+      "30000: 220.7614, alpha=0.3, epsilon=0.9\n",
+      "35000: 233.2138, alpha=0.3, epsilon=0.9\n",
+      "40000: 248.22, alpha=0.3, epsilon=0.9\n",
+      "45000: 264.636, alpha=0.3, epsilon=0.9\n",
+      "50000: 276.926, alpha=0.3, epsilon=0.9\n",
+      "55000: 277.9438, alpha=0.3, epsilon=0.9\n",
+      "60000: 248.881, alpha=0.3, epsilon=0.9\n",
+      "65000: 272.529, alpha=0.3, epsilon=0.9\n",
+      "70000: 281.7972, alpha=0.3, epsilon=0.9\n",
+      "75000: 284.2844, alpha=0.3, epsilon=0.9\n",
+      "80000: 269.667, alpha=0.3, epsilon=0.9\n",
+      "85000: 273.8652, alpha=0.3, epsilon=0.9\n",
+      "90000: 278.2466, alpha=0.3, epsilon=0.9\n",
+      "95000: 269.1736, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 11"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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125.656548 212.345721 \r\nL 125.738727 132.392727 \r\nL 125.811775 195.872993 \r\nL 125.805688 47.216674 \r\nL 125.817862 130.383858 \r\nL 125.820906 33.958137 \r\nL 125.924391 194.667672 \r\nL 125.991351 17.083636 \r\nL 125.970046 202.301375 \r\nL 126.046137 122.750155 \r\nL 126.137447 186.230421 \r\nL 126.122229 70.519557 \r\nL 126.155709 132.794501 \r\nL 126.16484 85.786962 \r\nL 126.177015 195.069446 \r\nL 126.259194 131.187406 \r\nL 126.271369 203.104922 \r\nL 126.353548 85.786962 \r\nL 126.37181 178.998492 \r\nL 126.453989 86.992284 \r\nL 126.472251 209.533304 \r\nL 126.55443 81.769224 \r\nL 126.584866 133.196275 \r\nL 126.633565 199.087184 \r\nL 126.603128 63.689401 \r\nL 126.694438 104.268559 \r\nL 126.706613 209.13153 \r\nL 126.7127 70.117783 \r\nL 126.807054 158.9098 \r\nL 126.886189 78.153259 \r\nL 126.840534 201.497827 \r\nL 126.916626 132.392727 \r\nL 127.001849 201.497827 \r\nL 126.931844 82.974545 \r\nL 127.023154 138.017561 \r\nL 127.074897 102.661463 \r\nL 127.111421 205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 128.423241 194.265898 \r\nL 128.514551 142.035299 \r\nL 128.56325 208.729756 \r\nL 128.566293 51.234412 \r\nL 128.624123 150.47255 \r\nL 128.730651 46.011353 \r\nL 128.715433 208.327982 \r\nL 128.736739 103.465011 \r\nL 128.751957 172.971885 \r\nL 128.74587 80.162129 \r\nL 128.803699 140.428204 \r\nL 128.858485 33.958137 \r\nL 128.864573 204.712018 \r\nL 128.913271 151.276098 \r\nL 128.919359 192.658803 \r\nL 128.998494 63.287627 \r\nL 129.016756 110.295166 \r\nL 129.077629 60.876984 \r\nL 129.025887 186.230421 \r\nL 129.114153 117.928869 \r\nL 129.117197 166.945277 \r\nL 129.175027 50.832639 \r\nL 129.223725 161.320443 \r\nL 129.321123 63.287627 \r\nL 129.308948 199.890732 \r\nL 129.333297 84.983415 \r\nL 129.357647 198.283636 \r\nL 129.409389 53.243282 \r\nL 129.442869 145.24949 \r\nL 129.518961 20.297827 \r\nL 129.491568 197.078315 \r\nL 129.549398 161.722217 \r\nL 129.640708 202.301375 \r\nL 129.610271 33.556364 \r\nL 129.655926 179.80204 \r\nL 129.716799 19.092506 \r\nL 129.668101 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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51.636186 \r\nL 330.930568 164.13286 \r\nL 330.994485 63.689401 \r\nL 330.970136 197.881863 \r\nL 331.043184 113.911131 \r\nL 331.101013 59.269889 \r\nL 331.13145 192.257029 \r\nL 331.143625 140.02643 \r\nL 331.213629 200.292506 \r\nL 331.22276 49.627317 \r\nL 331.234935 174.980754 \r\nL 331.317114 38.779424 \r\nL 331.262328 199.488958 \r\nL 331.344507 81.36745 \r\nL 331.435817 199.890732 \r\nL 331.444948 50.832639 \r\nL 331.454079 185.0251 \r\nL 331.551476 43.198936 \r\nL 331.536258 208.729756 \r\nL 331.566694 101.456142 \r\nL 331.578869 193.864124 \r\nL 331.633655 76.14439 \r\nL 331.64583 93.018891 \r\nL 331.648873 21.503149 \r\nL 331.664092 202.703149 \r\nL 331.755402 46.8149 \r\nL 331.816275 210.336851 \r\nL 331.831493 46.011353 \r\nL 331.868017 153.284967 \r\nL 331.962371 17.083636 \r\nL 331.910629 201.096053 \r\nL 331.977589 82.974545 \r\nL 332.059768 194.265898 \r\nL 331.989764 58.064568 \r\nL 332.090205 170.963016 \r\nL 332.117598 23.512018 \r\nL 332.141947 188.23929 \r\nL 332.202821 102.661463 \r\nL 332.236301 199.488958 \r\nL 332.306305 31.547494 \r\nL 332.358048 17.083636 \r\nL 332.415877 204.712018 \r\nL 332.522406 17.083636 \r\nL 332.491969 209.533304 \r\nL 332.531537 43.60071 \r\nL 332.635021 209.533304 \r\nL 332.543711 17.083636 \r\nL 332.644152 87.795831 \r\nL 332.711113 44.002483 \r\nL 332.662414 199.890732 \r\nL 332.735462 117.928869 \r\nL 332.738506 196.274767 \r\nL 332.750681 17.083636 \r\nL 332.841991 81.769224 \r\nL 332.863296 192.257029 \r\nL 332.930257 17.083636 \r\nL 332.954606 193.864124 \r\nL 333.039829 80.965676 \r\nL 333.042873 17.083636 \r\nL 333.061135 206.720887 \r\nL 333.149401 39.582971 \r\nL 333.161576 180.605588 \r\nL 333.240711 17.083636 \r\nL 333.258973 87.795831 \r\nL 333.262017 17.083636 \r\nL 333.344196 193.46235 \r\nL 333.365501 76.546164 \r\nL 333.462899 206.319113 \r\nL 333.371589 17.083636 \r\nL 333.475073 121.14306 \r\nL 333.578558 47.618448 \r\nL 333.560296 195.872993 \r\nL 333.587689 55.653925 \r\nL 333.685086 199.087184 \r\nL 333.639431 26.726208 \r\nL 333.697261 81.36745 \r\nL 333.72161 193.060576 \r\nL 333.712479 17.083636 \r\nL 333.785527 147.660133 \r\nL 333.87075 17.083636 \r\nL 333.809877 187.033969 \r\nL 333.895099 185.828647 \r\nL 333.898143 187.033969 \r\nL 333.949885 17.083636 \r\nL 333.955973 197.480089 \r\nL 334.010759 78.956807 \r\nL 334.062501 17.083636 \r\nL 334.065545 203.90847 \r\nL 334.095981 86.188736 \r\nL 334.175117 195.069446 \r\nL 334.105112 17.083636 \r\nL 334.205553 70.92133 \r\nL 334.275558 197.881863 \r\nL 334.23599 17.083636 \r\nL 334.302951 47.618448 \r\nL 334.354693 197.078315 \r\nL 334.403392 17.083636 \r\nL 334.47644 189.042838 \r\nL 334.512964 66.501818 \r\nL 334.592099 189.042838 \r\nL 334.573837 17.083636 \r\nL 334.619492 83.376319 \r\nL 334.689496 17.083636 \r\nL 334.69254 199.087184 \r\nL 334.719933 97.438404 \r\nL 334.722977 202.301375 \r\nL 334.738195 17.083636 \r\nL 334.826461 126.36612 \r\nL 334.893422 17.083636 \r\nL 334.917771 193.864124 \r\nL 334.93299 84.581641 \r\nL 334.990819 192.658803 \r\nL 334.996907 23.913792 \r\nL 335.042562 86.188736 \r\nL 335.127784 198.68541 \r\nL 335.082129 17.083636 \r\nL 335.155177 144.044169 \r\nL 335.228225 17.083636 \r\nL 335.203876 202.301375 \r\nL 335.261706 65.698271 \r\nL 335.362147 196.274767 \r\nL 335.307361 17.083636 \r\nL 335.371278 166.141729 \r\nL 335.453457 17.083636 \r\nL 335.380409 208.729756 \r\nL 335.483893 78.153259 \r\nL 335.587378 195.069446 \r\nL 335.520417 40.386519 \r\nL 335.593465 81.36745 \r\nL 335.617815 192.658803 \r\nL 335.690863 17.083636 \r\nL 335.794347 198.283636 \r\nL 335.803478 147.258359 \r\nL 335.84609 195.069446 \r\nL 335.818697 33.15459 \r\nL 335.891745 115.116452 \r\nL 335.967836 17.083636 \r\nL 335.995229 209.13153 \r\nL 335.998273 163.731086 \r\nL 336.077408 32.752816 \r\nL 336.007404 205.515565 \r\nL 336.123063 69.314235 \r\nL 336.199155 191.051707 \r\nL 336.156544 25.520887 \r\nL 336.235679 101.456142 \r\nL 336.269159 33.15459 \r\nL 336.314814 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Z tohto grafu nie je možné nič povedať, pretože kvôli povahe stochastického tréningového procesu sa dĺžka tréningových relácií veľmi líši. Aby sme tento graf lepšie pochopili, môžeme vypočítať **bežiaci priemer** cez sériu experimentov, povedzme 100. To sa dá pohodlne urobiť pomocou `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "#code block 12"
+   ]
+  },
+  {
+   "source": [
+    "## Menenie hyperparametrov a sledovanie výsledkov v praxi\n",
+    "\n",
+    "Teraz by bolo zaujímavé skutočne vidieť, ako sa správa natrénovaný model. Spustime simuláciu a budeme postupovať podľa rovnakej stratégie výberu akcií ako počas tréningu: vzorkovanie podľa pravdepodobnostného rozdelenia v Q-Tabuľke:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 13"
+   ]
+  },
+  {
+   "source": [
+    "## Uloženie výsledku ako animovaný GIF\n",
+    "\n",
+    "Ak chcete zapôsobiť na svojich priateľov, môžete im poslať animovaný GIF obrázok vyvažovacej tyče. Na to môžeme použiť `env.render` na vytvorenie obrazového rámu a následne ich uložiť ako animovaný GIF pomocou knižnice PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nenesieme zodpovednosť za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/8-Reinforcement/2-Gym/solution/notebook.ipynb b/translations/sk/8-Reinforcement/2-Gym/solution/notebook.ipynb
new file mode 100644
index 000000000..dff270c8b
--- /dev/null
+++ b/translations/sk/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -0,0 +1,524 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "5c0e485e58d63c506f1791c4dbf990ce",
+   "translation_date": "2025-09-06T15:19:04+00:00",
+   "source_file": "8-Reinforcement/2-Gym/solution/notebook.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Korčuľovanie na CartPole\n",
+    "\n",
+    "> **Problém**: Ak chce Peter uniknúť vlkovi, musí sa pohybovať rýchlejšie ako on. Uvidíme, ako sa Peter môže naučiť korčuľovať, konkrétne udržiavať rovnováhu, pomocou Q-Learningu.\n",
+    "\n",
+    "Najskôr nainštalujme knižnicu gym a importujme potrebné knižnice:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: gym in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.18.3)\n",
+      "Requirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
+      "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
+      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.6.0)\n",
+      "Requirement already satisfied: pyglet<=1.5.15,>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.5.15)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!pip install gym \n",
+    "\n",
+    "import gym\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "source": [
+    "## Vytvorte prostredie cartpole\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Aby sme videli, ako prostredie funguje, spustime krátku simuláciu na 100 krokov.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Počas simulácie potrebujeme získať pozorovania, aby sme rozhodli, ako konať. V skutočnosti nám funkcia `step` vracia aktuálne pozorovania, funkciu odmeny a príznak `done`, ktorý naznačuje, či má zmysel pokračovať v simulácii alebo nie:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[ 0.03044442 -0.19543914 -0.04496216  0.28125618] -> 1.0\n",
+      "[ 0.02653564 -0.38989186 -0.03933704  0.55942606] -> 1.0\n",
+      "[ 0.0187378  -0.19424049 -0.02814852  0.25461393] -> 1.0\n",
+      "[ 0.01485299 -0.38894946 -0.02305624  0.53828712] -> 1.0\n",
+      "[ 0.007074   -0.19351108 -0.0122905   0.23842953] -> 1.0\n",
+      "[ 0.00320378  0.00178427 -0.00752191 -0.05810469] -> 1.0\n",
+      "[ 0.00323946  0.19701326 -0.008684   -0.35315131] -> 1.0\n",
+      "[ 0.00717973  0.00201587 -0.01574703 -0.06321931] -> 1.0\n",
+      "[ 0.00722005  0.19736001 -0.01701141 -0.36082863] -> 1.0\n",
+      "[ 0.01116725  0.39271958 -0.02422798 -0.65882671] -> 1.0\n",
+      "[ 0.01902164  0.19794307 -0.03740452 -0.37387001] -> 1.0\n",
+      "[ 0.0229805   0.39357584 -0.04488192 -0.67810827] -> 1.0\n",
+      "[ 0.03085202  0.58929164 -0.05844408 -0.98457719] -> 1.0\n",
+      "[ 0.04263785  0.78514572 -0.07813563 -1.2950295 ] -> 1.0\n",
+      "[ 0.05834076  0.98116859 -0.10403622 -1.61111521] -> 1.0\n",
+      "[ 0.07796413  0.78741784 -0.13625852 -1.35259196] -> 1.0\n",
+      "[ 0.09371249  0.98396202 -0.16331036 -1.68461179] -> 1.0\n",
+      "[ 0.11339173  0.79106371 -0.1970026  -1.44691436] -> 1.0\n",
+      "[ 0.12921301  0.59883361 -0.22594088 -1.22169133] -> 1.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Môžeme získať minimálnu a maximálnu hodnotu týchto čísel:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(env.observation_space.low)\n",
+    "print(env.observation_space.high)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretize(x):\n",
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+   ]
+  },
+  {
+   "source": [
+    "Poďme tiež preskúmať inú metódu diskrétizácie pomocou intervalov:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_bins(i,num):\n",
+    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
+    "\n",
+    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
+    "\n",
+    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
+    "nbins = [20,20,10,10] # number of bins for each parameter\n",
+    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
+    "\n",
+    "def discretize_bins(x):\n",
+    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+   ]
+  },
+  {
+   "source": [
+    "Poďme teraz spustiť krátku simuláciu a pozorovať tieto diskrétne hodnoty prostredia.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 2, -4, -9)\n(0, 3, -6, -12)\n(0, 2, -8, -9)\n(0, 3, -10, -13)\n(0, 4, -13, -16)\n(0, 4, -16, -19)\n(0, 4, -20, -17)\n(0, 4, -24, -20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   #env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   #print(discretize_bins(obs))\n",
+    "   print(discretize(obs))\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = {}\n",
+    "actions = (0,1)\n",
+    "\n",
+    "def qvalues(state):\n",
+    "    return [Q.get((state,a),0) for a in actions]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hyperparameters\n",
+    "alpha = 0.3\n",
+    "gamma = 0.9\n",
+    "epsilon = 0.90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 108.0, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v\n",
+    "\n",
+    "Qmax = 0\n",
+    "cum_rewards = []\n",
+    "rewards = []\n",
+    "for epoch in range(100000):\n",
+    "    obs = env.reset()\n",
+    "    done = False\n",
+    "    cum_reward=0\n",
+    "    # == do the simulation ==\n",
+    "    while not done:\n",
+    "        s = discretize(obs)\n",
+    "        if random.random()<epsilon:\n",
+    "            # exploitation - chose the action according to Q-Table probabilities\n",
+    "            v = probs(np.array(qvalues(s)))\n",
+    "            a = random.choices(actions,weights=v)[0]\n",
+    "        else:\n",
+    "            # exploration - randomly chose the action\n",
+    "            a = np.random.randint(env.action_space.n)\n",
+    "\n",
+    "        obs, rew, done, info = env.step(a)\n",
+    "        cum_reward+=rew\n",
+    "        ns = discretize(obs)\n",
+    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
+    "    cum_rewards.append(cum_reward)\n",
+    "    rewards.append(cum_reward)\n",
+    "    # == Periodically print results and calculate average reward ==\n",
+    "    if epoch%5000==0:\n",
+    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
+    "        if np.average(cum_rewards) > Qmax:\n",
+    "            Qmax = np.average(cum_rewards)\n",
+    "            Qbest = Q\n",
+    "        cum_rewards=[]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Z tohto grafu nie je možné nič povedať, pretože kvôli povahe stochastického tréningového procesu sa dĺžka tréningových relácií veľmi líši. Aby sme tento graf lepšie pochopili, môžeme vypočítať **bežiaci priemer** cez sériu experimentov, povedzme 100. To sa dá pohodlne urobiť pomocou `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "def running_average(x,window):\n",
+    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
+    "\n",
+    "plt.plot(running_average(rewards,100))"
+   ]
+  },
+  {
+   "source": [
+    "## Menenie hyperparametrov a sledovanie výsledkov v praxi\n",
+    "\n",
+    "Teraz by bolo zaujímavé skutočne vidieť, ako sa správa natrénovaný model. Spustime simuláciu a budeme postupovať podľa rovnakej stratégie výberu akcií ako počas tréningu: vzorkovanie podľa pravdepodobnostného rozdelenia v Q-Tabuľke:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset()\n",
+    "done = False\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   env.render()\n",
+    "   v = probs(np.array(qvalues(s)))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Uloženie výsledku do animovaného GIF\n",
+    "\n",
+    "Ak chcete ohúriť svojich priateľov, môžete im poslať animovaný GIF obrázok balansujúcej tyče. Na to môžeme použiť `env.render` na vytvorenie obrazového rámca a následne ich uložiť do animovaného GIF pomocou knižnice PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, prosím, berte na vedomie, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho rodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nenesieme zodpovednosť za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sk/PyTorch_Fundamentals.ipynb b/translations/sk/PyTorch_Fundamentals.ipynb
new file mode 100644
index 000000000..51ed05d29
--- /dev/null
+++ b/translations/sk/PyTorch_Fundamentals.ipynb
@@ -0,0 +1,2828 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4",
+   "authorship_tag": "ABX9TyOgv0AozH1FKQBD+RkgT2bV",
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "coopTranslator": {
+   "original_hash": "0ca21b6ee62904d616f2e36dc1cf0da7",
+   "translation_date": "2025-09-06T13:07:49+00:00",
+   "source_file": "PyTorch_Fundamentals.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "view-in-github",
+    "colab_type": "text"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EHh5JllMh1rG",
+    "outputId": "f55755ad-c369-414c-85ec-6e9d4f061a02",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'2.2.1+cu121'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(\"I am excited to run this\")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UPlb-duwXAfz",
+    "outputId": "cfd687e4-1238-49f4-ab6b-ee1305b740d2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "I am excited to run this\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(torch.__version__)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "byWVlJ9wXDSk",
+    "outputId": "fd74a5c4-4d4a-41b2-ef3c-562ea3e4811f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2.2.1+cu121\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "Osm80zoEYklS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# scalar\n",
+    "scalar = torch.tensor(7)\n",
+    "scalar"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-o8wvJ-VXZmI",
+    "outputId": "558816f5-1205-4de1-fe1f-2f96e9bd79e6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(7)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mCZ2tXC4Y_Sg",
+    "outputId": "2d86dbdc-56e1-45c6-d3dd-14515f2a457a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ssN00By0ZQgS",
+    "outputId": "490f40d1-5135-4969-a6d3-c8c902cdc473"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# vector\n",
+    "vector = torch.tensor([7, 7])\n",
+    "vector\n",
+    "#vector.ndim\n",
+    "#vector.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Bws__5wlZnmF",
+    "outputId": "944e38f9-5ba1-4ddc-a9c6-cfb6a19bb488"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([7, 7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "vector.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9pjCvnsZZzNG",
+    "outputId": "e030a4da-8f81-4858-fbce-86da2aaafe52"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([2])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Matrix\n",
+    "MATRIX = torch.tensor([[7, 8],[9, 10]])\n",
+    "MATRIX"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a747hI9SaBGW",
+    "outputId": "af835ddb-81ff-4981-badb-441567194d15"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 7,  8],\n",
+       "        [ 9, 10]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XdTfFa7vaRUj",
+    "outputId": "0fbbab9c-8263-4cad-a380-0d2a16ca499e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX[0]\n",
+    "MATRIX[1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TFeD3jSDafm7",
+    "outputId": "69b44ab3-5ba7-451a-c6b2-f019a03d0c96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Tensor\n",
+    "TENSOR = torch.tensor([[[1, 2, 3],[3,6,9], [2,4,5]]])\n",
+    "TENSOR"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ic3cE47tah42",
+    "outputId": "f250e295-91de-43ec-9d80-588a6fe0abde"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 2, 3],\n",
+       "         [3, 6, 9],\n",
+       "         [2, 4, 5]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wvjf5fczbAM1",
+    "outputId": "9c72b5b8-bafe-4ae7-9883-b051e209eada"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mwtXZwiMbN3m",
+    "outputId": "331a5e36-b1b0-4a5f-a9b8-e7049cbaa8f9"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vzdZu_IfbP3J",
+    "outputId": "e24e7e71-e365-412d-ff50-fc094b56d2f3"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [3, 6, 9],\n",
+       "        [2, 4, 5]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "A8OL9eWfcRrJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor = torch.rand(3,4)\n",
+    "random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hAqSDE1EcVS_",
+    "outputId": "946171c3-d054-400c-f893-79110356888c"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.4414, 0.7681, 0.8385, 0.3166],\n",
+       "        [0.0468, 0.5812, 0.0670, 0.9173],\n",
+       "        [0.2959, 0.3276, 0.7411, 0.4643]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "g4fvPE5GcwzP",
+    "outputId": "8737f36b-6864-4059-eaed-6f9156c22306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XsAg99QmdAU6",
+    "outputId": "35467c11-257c-4f16-99aa-eca930bcbc36"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.size()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cii1pNdVdB68",
+    "outputId": "fc8d2de6-9215-43de-99f7-7b0d7f7d20fa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_image_tensor = torch.rand(size=(3, 224, 224)) #color channels, height, width\n",
+    "random_image_tensor.ndim, random_image_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aTKq2j0cdDjb",
+    "outputId": "6be42057-20b9-4faf-d79d-8b65c42cc27e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([3, 224, 224]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor_ofownsize = torch.rand(size=(5,10,10))\n",
+    "random_tensor_ofownsize.ndim, random_tensor_ofownsize.shape\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IyhDdj-Pd6nC",
+    "outputId": "43e5e334-6d4d-4b67-f87d-7d364c6d8c67"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([5, 10, 10]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "UOJW08uOert_"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero = torch.zeros(size=(3, 4))\n",
+    "zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGvXtaXyefie",
+    "outputId": "d40d3e28-8667-4d2f-8b62-f0829c6162ad"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero*random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OyUkUPkDe0uH",
+    "outputId": "26c2e4be-36ba-4c6c-9a90-2704ec135828"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones = torch.ones(size=(3, 4))\n",
+    "ones\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "y_Ac62Aqe82G",
+    "outputId": "291de5d9-b9df-49de-c9d1-d098e3e9f4d8"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TvGOA9odfIEO",
+    "outputId": "45949ef4-6649-4b6c-d6af-2d4bfb8de832"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones*zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "--pTyge-fI-8",
+    "outputId": "c4d9bb7e-829b-43db-e2db-b1a2d64e61f0"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qDcc7Z36fSJF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "one_to_ten = torch.arange(start = 1, end = 11, step = 1)\n",
+    "one_to_ten"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w3CZB4zUfR1s",
+    "outputId": "197fcba1-da0a-4b4a-ed11-3974bd6c01aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ten_zeros = torch.zeros_like(one_to_ten)\n",
+    "ten_zeros"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WZh99BwVfRy8",
+    "outputId": "51ef8bfb-6fa0-4099-ff66-b97d65b2ddea"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "pGGhgsbUgqbW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor = torch.tensor([3.0, 6.0,9.0], dtype = None, device = None, requires_grad = False)\n",
+    "float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JORJl4XkfRsx",
+    "outputId": "71114171-0f49-481f-b6fc-6cb48e2fb895"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3., 6., 9.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6wOPPwGyfRLn",
+    "outputId": "f23776a1-b682-404a-9f67-d5bcb0402666"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor = float_32_tensor.type(torch.float16)\n",
+    "float_16_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tFsHCvmZfOYe",
+    "outputId": "d3aa305a-7591-47f5-97fd-61bff60b44bd"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float16"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 31
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TQiCGTPuwq0q",
+    "outputId": "98750fce-1ca3-4889-e269-8b753efdea96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor = torch.tensor([3, 6, 9], dtype = torch.int32)\n",
+    "int_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5hlrLvGUw5D_",
+    "outputId": "41d890a0-9aee-446c-d906-631ce2ab0995"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9], dtype=torch.int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ihApD9u3xTNW",
+    "outputId": "d295eed0-6996-4e0f-8502-ff4b55cd1373"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(0,100,10)"
+   ],
+   "metadata": {
+    "id": "utKhlb_KxWDQ"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p78D74E9Rj7Y",
+    "outputId": "781a1614-a900-41f5-9e5d-358f0b2390aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.min()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4BcSs5NeRkcj",
+    "outputId": "3f24a8dc-58e9-4a5f-9834-e85856a34f9d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.max()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hinqvXVLRm4q",
+    "outputId": "5c7d8a53-3913-4ac1-bba3-5ba8ff68250a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 38
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mean(x.type(torch.float32))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k7okc0_vRpnB",
+    "outputId": "91e5494f-dc57-417c-ea4d-25dbc547c893"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.type(torch.float32).mean()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "29QcDTjHRq10",
+    "outputId": "62937c6c-78e0-49f2-dde3-1543ee8f7907"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wlpY_G_sbdKF",
+    "outputId": "475d8258-af65-4011-a258-b93d4d8142d4"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(450)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmax()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GT6HJzwhbk4n",
+    "outputId": "2e455c20-c322-4bcf-d07c-1259d3ccefc6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmin()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "egL3oi2Mb19P",
+    "outputId": "f71fb32f-6338-44a3-b377-75bea0a3ab54"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p2U8DZKib3DP",
+    "outputId": "b9f613b9-74e9-45f4-ed01-05babb6a6793"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[9]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24qBFlGYcABe",
+    "outputId": "5813cfcb-7f63-4bd7-ee46-f95ccbfda939"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(1, 10)\n",
+    "x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0GPOxEzkcBHO",
+    "outputId": "aefbd903-4f4c-4d2c-c90f-eccd682fe018"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped = x.reshape(1,9)\n",
+    "x_reshaped, x_reshaped.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "spmRgQjwddgp",
+    "outputId": "85a7c55c-2909-4ea2-fc68-386dddc65742"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), torch.Size([1, 9]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped.view(1,9)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tH2ahWGydqqP",
+    "outputId": "65d92263-4fc4-434a-c06d-c5e08436f7fe"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked = torch.stack([x, x, x, x], dim = 1)\n",
+    "x_stacked"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jgCeJcaud_-1",
+    "outputId": "7f293a37-6ef1-43b6-aee5-9d6d91c94f9e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XhJHIK6cfPse",
+    "outputId": "06c47b89-3a9e-453e-bcc3-00cbcb0b8b49"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=1)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ej2c3Xxzf0tq",
+    "outputId": "94024061-eb37-446d-c4a8-e4d16cb6de81"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 52
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4DJYo1a0f5M0",
+    "outputId": "efca2b47-1b14-44de-9a9a-2c83629d153f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 53
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=-2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J4iEjn2ah2HL",
+    "outputId": "22395593-7c16-4162-beae-dd2bbe7bda35"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 55
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "tensor = torch.tensor([1, 2, 3])\n",
+    "tensor = tensor - 10\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cFfiD7Nth7Z_",
+    "outputId": "1139e1f8-fc1a-46ca-d636-f2bc4fd2eef6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-9, -8, -7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mul(tensor, 10)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dyA7BM_GHhqE",
+    "outputId": "0e3b9671-d9e8-4a32-87bb-59bc05986142"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-90, -80, -70])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.sub(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "owtUsZ1KNegI",
+    "outputId": "189b7b23-0041-4e09-b991-cd209a48506a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-109, -108, -107])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.add(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K5STXlQONsyc",
+    "outputId": "00cbb79a-0a1d-4e21-86ec-5c91c37a2d01"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([91, 92, 93])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.divide(tensor, 2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xqMGnzIUNvp0",
+    "outputId": "c894cf3e-f148-45f8-cfc8-d78740735306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-4.5000, -4.0000, -3.5000])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(tensor, tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ruGzKpV8NyBc",
+    "outputId": "fddb63bf-006f-48b6-ae28-287fbcda8bc5"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8GS3r9yTeGfD",
+    "outputId": "c80b12ac-30b5-4f3d-c38c-9e41ba511b0e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QmuYHqXTemC0",
+    "outputId": "402fe3ba-70b5-4bb2-c83b-254db84ff810"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 622 µs, sys: 0 ns, total: 622 µs\n",
+      "Wall time: 516 µs\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "torch.matmul(tensor,tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dGr1fzdNepd8",
+    "outputId": "97bd6c91-bc25-4b38-cdf5-f22dcdef243e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 424 µs, sys: 998 µs, total: 1.42 ms\n",
+      "Wall time: 1.43 ms\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.rand(3,2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pGYDoK2gevfo",
+    "outputId": "2c8783d5-0453-47c5-c7ed-af10d25d6989"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5999, 0.0073],\n",
+       "        [0.9321, 0.3026],\n",
+       "        [0.3463, 0.3872]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(torch.rand(3,2), torch.rand(2,3))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KGBGQoB8e2DP",
+    "outputId": "4c2ef361-a2d0-41ee-c328-3992cbbc138d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.3528, 0.1893, 0.0714],\n",
+       "        [1.2791, 0.7110, 0.2563],\n",
+       "        [0.8812, 0.4553, 0.1803]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch"
+   ],
+   "metadata": {
+    "id": "ib8DMtkBe_LJ"
+   },
+   "execution_count": 1,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.rand(2,9)"
+   ],
+   "metadata": {
+    "id": "nJo8ZBdrQY1b"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wi6oRv4MQfgf",
+    "outputId": "55c99f55-31f6-4cf5-ba4e-19a47c3a0167"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5894, 0.4391, 0.2018, 0.5417, 0.3844, 0.3592, 0.9209, 0.9269, 0.0681],\n",
+       "        [0.0746, 0.1740, 0.6821, 0.6890, 0.0999, 0.7444, 0.2391, 0.4625, 0.8302]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "y=torch.randn(2,3,5)\n",
+    "y"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zpx8myAUQgoc",
+    "outputId": "07756d70-56bd-437c-c74e-9aecc1a77311"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[ 1.5552, -0.4877,  0.5175, -1.7958, -0.6187],\n",
+       "         [-0.3359, -1.9710,  0.0112, -1.7578, -1.5295],\n",
+       "         [ 0.0932,  1.4079,  0.9108,  0.3328, -0.6978]],\n",
+       "\n",
+       "        [[-0.9406, -1.0809, -0.2595,  0.1282,  1.6605],\n",
+       "         [ 1.1624,  1.0902,  1.7092, -0.2842, -1.3780],\n",
+       "         [-0.1534, -1.2795, -0.5495,  0.9902,  0.1822]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original = torch.rand(size=(224,224,3))\n",
+    "x_original"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s4U-X9bJQnWe",
+    "outputId": "657a7a76-962c-4b41-a76b-902d0482266c"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[0.4549, 0.6809, 0.2118],\n",
+       "         [0.4824, 0.9008, 0.8741],\n",
+       "         [0.1715, 0.1757, 0.1845],\n",
+       "         ...,\n",
+       "         [0.8741, 0.6594, 0.2610],\n",
+       "         [0.0092, 0.1984, 0.1955],\n",
+       "         [0.4236, 0.4182, 0.0251]],\n",
+       "\n",
+       "        [[0.9174, 0.1661, 0.5852],\n",
+       "         [0.1837, 0.2351, 0.3810],\n",
+       "         [0.3726, 0.4808, 0.8732],\n",
+       "         ...,\n",
+       "         [0.6794, 0.0554, 0.9202],\n",
+       "         [0.0864, 0.8750, 0.3558],\n",
+       "         [0.8445, 0.9759, 0.4934]],\n",
+       "\n",
+       "        [[0.1600, 0.2635, 0.7194],\n",
+       "         [0.9488, 0.3405, 0.3647],\n",
+       "         [0.6683, 0.5168, 0.9592],\n",
+       "         ...,\n",
+       "         [0.0521, 0.0140, 0.2445],\n",
+       "         [0.3596, 0.3999, 0.2730],\n",
+       "         [0.5926, 0.9877, 0.7784]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[0.4794, 0.5635, 0.3764],\n",
+       "         [0.9124, 0.6094, 0.5059],\n",
+       "         [0.4528, 0.4447, 0.5021],\n",
+       "         ...,\n",
+       "         [0.0089, 0.4816, 0.8727],\n",
+       "         [0.2173, 0.6296, 0.2347],\n",
+       "         [0.2028, 0.9931, 0.7201]],\n",
+       "\n",
+       "        [[0.3116, 0.6459, 0.4703],\n",
+       "         [0.0148, 0.2345, 0.7149],\n",
+       "         [0.8393, 0.5804, 0.6691],\n",
+       "         ...,\n",
+       "         [0.2105, 0.9460, 0.2696],\n",
+       "         [0.5918, 0.9295, 0.2616],\n",
+       "         [0.2537, 0.7819, 0.4700]],\n",
+       "\n",
+       "        [[0.6654, 0.1200, 0.5841],\n",
+       "         [0.9147, 0.5522, 0.6529],\n",
+       "         [0.1799, 0.5276, 0.5415],\n",
+       "         ...,\n",
+       "         [0.7536, 0.4346, 0.8793],\n",
+       "         [0.3793, 0.1750, 0.7792],\n",
+       "         [0.9266, 0.8325, 0.9974]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted=x_original.permute(2, 0, 1)\n",
+    "print(x_original.shape)\n",
+    "print(x_permuted.shape)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DD19_zvbQzHo",
+    "outputId": "1d64ce1b-eb48-47e3-90b6-7f1340e7f2b2"
+   },
+   "execution_count": 9,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "torch.Size([224, 224, 3])\n",
+      "torch.Size([3, 224, 224])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NnPmMk4ZRF7w",
+    "outputId": "2cd5da7f-4a23-4a76-8c4a-bb982113f2a4"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z0ylNoAARgTo",
+    "outputId": "ddca0298-cddf-4048-9b71-a791655e5bed"
+   },
+   "execution_count": 11,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]=0.989"
+   ],
+   "metadata": {
+    "id": "RXw0xXsDRi4L"
+   },
+   "execution_count": 13,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1sFdV6wzRo3f",
+    "outputId": "1cf87d2c-6d88-453a-d136-0f625a2800f1"
+   },
+   "execution_count": 14,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xTX-hx2SR1wp",
+    "outputId": "0d4908c4-c3bc-44e3-8ec6-1487104cc209"
+   },
+   "execution_count": 15,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x=torch.arange(1,10).reshape(1,3,3)\n",
+    "x, x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mZomOe7gR4Q8",
+    "outputId": "0b3c922f-ec11-46de-b8a5-9f9533d866ad"
+   },
+   "execution_count": 18,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[[1, 2, 3],\n",
+       "          [4, 5, 6],\n",
+       "          [7, 8, 9]]]),\n",
+       " torch.Size([1, 3, 3]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3y7v4SQvSBs1",
+    "outputId": "8c53307d-e628-404d-db66-56c6bdffab7c"
+   },
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [4, 5, 6],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hf9uG4xLSNya",
+    "outputId": "3075bc42-9ffa-426b-8a86-95628ffcd824"
+   },
+   "execution_count": 21,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zA4G2Se4SRB3",
+    "outputId": "324312d2-ed0a-49eb-f81f-e904e53992fe"
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(1)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][2][2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mwy3zmKKSdbk",
+    "outputId": "d35172c3-b099-40a6-ddf1-a453c2adfa44"
+   },
+   "execution_count": 23,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[:,1,1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fE3nCM1KS7XT",
+    "outputId": "01f5d755-9737-4235-9f73-dce89ff6ba16"
+   },
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([5])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,0,:]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "luNDINKNTTxp",
+    "outputId": "091195ef-2f71-4602-e95f-529a69193150"
+   },
+   "execution_count": 25,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,:,2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KG8A4xbfThCL",
+    "outputId": "5866bc41-9241-4619-be7b-e9206b3f80ab"
+   },
+   "execution_count": 26,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import numpy as np"
+   ],
+   "metadata": {
+    "id": "CZ3PX0qlTwHJ"
+   },
+   "execution_count": 27,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array = np.arange(1.0, 8.0)"
+   ],
+   "metadata": {
+    "id": "UOBeTumiT3Lf"
+   },
+   "execution_count": 28,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RzcO32E9UCQl",
+    "outputId": "430def24-c42c-461f-e5e7-398544c695d3"
+   },
+   "execution_count": 29,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 4., 5., 6., 7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.from_numpy(array)\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JJIL0q1DUC6O",
+    "outputId": "8a3b1d7c-4482-4d32-f34f-9212d9d3a177"
+   },
+   "execution_count": 32,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1., 2., 3., 4., 5., 6., 7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array[3]=11.0"
+   ],
+   "metadata": {
+    "id": "j3Ce6q3DUIEK"
+   },
+   "execution_count": 33,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dc_BCVdjUsCc",
+    "outputId": "65537325-8b11-4f36-fc73-e56f30d6a036"
+   },
+   "execution_count": 34,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3., 11.,  5.,  6.,  7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VG1e_eITUta2",
+    "outputId": "a26c5198-23b6-4a6d-d73a-ba20cd9782b8"
+   },
+   "execution_count": 35,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1.,  2.,  3., 11.,  5.,  6.,  7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.ones(7)\n",
+    "tensor, tensor.dtype\n",
+    "numpy_tensor = tensor.numpy()\n",
+    "numpy_tensor, numpy_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Swt8JF8vUuev",
+    "outputId": "c9e5bf6a-6d2c-41d6-8327-366867ffdd2d"
+   },
+   "execution_count": 37,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(array([1., 1., 1., 1., 1., 1., 1.], dtype=float32), dtype('float32'))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "random_tensor_A = torch.rand(3,4)\n",
+    "random_tensor_B = torch.rand(3,4)\n",
+    "print(random_tensor_A)\n",
+    "print(random_tensor_B)\n",
+    "print(random_tensor_A == random_tensor_B)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGcagTteVFTD",
+    "outputId": "49405790-08e7-4210-b7f1-f00b904c7eb9"
+   },
+   "execution_count": 38,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.9870, 0.6636, 0.6873, 0.8863],\n",
+      "        [0.8386, 0.4169, 0.3587, 0.0265],\n",
+      "        [0.2981, 0.6025, 0.5652, 0.5840]])\n",
+      "tensor([[0.9821, 0.3481, 0.0913, 0.4940],\n",
+      "        [0.7495, 0.4387, 0.9582, 0.8659],\n",
+      "        [0.5064, 0.6919, 0.0809, 0.9771]])\n",
+      "tensor([[False, False, False, False],\n",
+      "        [False, False, False, False],\n",
+      "        [False, False, False, False]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "RANDOM_SEED = 42\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_C = torch.rand(3,4)\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_D = torch.rand(3,4)\n",
+    "print(random_tensor_C)\n",
+    "print(random_tensor_D)\n",
+    "print(random_tensor_C == random_tensor_D)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HznyXyEaWjLM",
+    "outputId": "25956434-01b6-4059-9054-c9978884ddc1"
+   },
+   "execution_count": 46,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[True, True, True, True],\n",
+      "        [True, True, True, True],\n",
+      "        [True, True, True, True]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!nvidia-smi"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vltPTh0YXJSt",
+    "outputId": "807af6dc-a9ca-4301-ec32-b688dbde8be8"
+   },
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Thu May 23 02:57:59 2024       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   60C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|  No running processes found                                                           |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "torch.cuda.is_available()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L6mMyPDyYh1j",
+    "outputId": "279c5dd8-c2a8-4fbd-f321-2f5d7c6e90e6"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "oOdiYa7ZYytx",
+    "outputId": "d73b04fc-8963-4826-9722-08d118d5ab91"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.cuda.device_count()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vOdsazLqZFM5",
+    "outputId": "8189cd6a-9017-4663-a652-3e15c517d9c3"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.tensor([1,2,3], device = \"cpu\")\n",
+    "print(tensor, tensor.device)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cdik9Vw3ZMv0",
+    "outputId": "044a68fd-83a1-409d-8e3b-655142ca0270"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([1, 2, 3]) cpu\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu = tensor.to(device)\n",
+    "tensor_on_gpu"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zmp835rrZp-z",
+    "outputId": "37fa3413-18a3-47bf-ae51-5b36ff85a3ef"
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3], device='cuda:0')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu.numpy()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 159
+    },
+    "id": "jhriaa8uZ1yM",
+    "outputId": "bc5a3226-1a12-4fea-8769-a44f21cdc323"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "TypeError",
+     "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-e0c96c7436fd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtensor_on_gpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_cpu = tensor_on_gpu.cpu().numpy()"
+   ],
+   "metadata": {
+    "id": "LHGXK3GgaOzL"
+   },
+   "execution_count": 12,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "id": "j-El4LlCajfq"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby na automatický preklad [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatické preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za záväzný zdroj. Pre dôležité informácie odporúčame profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/2-Regression/1-Tools/notebook.ipynb b/translations/sl/2-Regression/1-Tools/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sl/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/sl/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
new file mode 100644
index 000000000..e2ac16414
--- /dev/null
+++ b/translations/sl/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -0,0 +1,447 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_1-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "c18d3bd0bd8ae3878597e89dcd1fa5c1",
+   "translation_date": "2025-09-06T13:41:25+00:00",
+   "source_file": "2-Regression/1-Tools/solution/R/lesson_1-R.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "YJUHCXqK57yz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Uvod v regresijo - Lekcija 1\n",
+    "\n",
+    "#### Postavljanje v perspektivo\n",
+    "\n",
+    "✅ Obstaja veliko vrst regresijskih metod, izbira pa je odvisna od odgovora, ki ga iščete. Če želite napovedati verjetno višino osebe glede na njeno starost, bi uporabili `linearno regresijo`, saj iščete **številčno vrednost**. Če vas zanima, ali naj se določena vrsta kuhinje šteje za vegansko ali ne, iščete **dodelitev kategorije**, zato bi uporabili `logistično regresijo`. Več o logistični regresiji boste izvedeli kasneje. Razmislite o nekaterih vprašanjih, ki jih lahko zastavite podatkom, in o tem, katera od teh metod bi bila bolj primerna.\n",
+    "\n",
+    "V tem razdelku boste delali z [majhnim naborom podatkov o diabetesu](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Predstavljajte si, da želite preizkusiti zdravljenje za diabetične bolnike. Modeli strojnega učenja vam lahko pomagajo določiti, kateri bolniki bi se na zdravljenje bolje odzvali, glede na kombinacije spremenljivk. Tudi zelo osnovni regresijski model, ko je vizualiziran, lahko pokaže informacije o spremenljivkah, ki bi vam pomagale organizirati teoretične klinične preizkuse.\n",
+    "\n",
+    "Pa začnimo s to nalogo!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/encouRage.jpg\"\n",
+    "   width=\"630\"/>\n",
+    "   <figcaption>Umetniško delo @allison_horst</figcaption>\n",
+    "\n",
+    "<!--![Umetniško delo \\@allison_horst](../../../../../../2-Regression/1-Tools/images/encouRage.jpg)<br>Umetniško delo @allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "LWNNzfqd6feZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Nalaganje našega nabora orodij\n",
+    "\n",
+    "Za to nalogo bomo potrebovali naslednje pakete:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [zbirka paketov za R](https://www.tidyverse.org/packages), zasnovana za hitrejše, enostavnejše in bolj zabavno podatkovno znanost!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je [zbirka paketov](https://www.tidymodels.org/packages/) za modeliranje in strojno učenje.\n",
+    "\n",
+    "Namestite jih lahko z ukazom:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+    "\n",
+    "Spodnji skript preveri, ali imate nameščene pakete, potrebne za dokončanje tega modula, in jih po potrebi namesti.\n"
+   ],
+   "metadata": {
+    "id": "FIo2YhO26wI9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse, tidymodels)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "id": "cIA9fz9v7Dss",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdaj naložimo te odlične pakete in jih naredimo dostopne v naši trenutni R seji. (To je zgolj za ponazoritev, `pacman::p_load()` je to že naredil namesto vas)\n"
+   ],
+   "metadata": {
+    "id": "gpO_P_6f9WUG"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# load the core Tidyverse packages\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# load the core Tidymodels packages\r\n",
+    "library(tidymodels)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NLMycgG-9ezO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Dataset o diabetesu\n",
+    "\n",
+    "V tej vaji bomo uporabili svoje regresijske veščine za napovedovanje na podlagi podatkov o diabetesu. [Dataset o diabetesu](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) vključuje `442 vzorce` podatkov o diabetesu, z 10 spremenljivkami napovedovalnih značilnosti: `starost`, `spol`, `indeks telesne mase`, `povprečni krvni tlak` in `šest meritev krvnega seruma`, ter izhodno spremenljivko `y`: kvantitativno merilo napredovanja bolezni eno leto po začetnem stanju.\n",
+    "\n",
+    "|Število opazovanj|442|\n",
+    "|-----------------|:---|\n",
+    "|Število napovedovalcev|Prvih 10 stolpcev je numeričnih napovedovalnih|\n",
+    "|Izhodna tarča|Stolpec 11 je kvantitativno merilo napredovanja bolezni eno leto po začetnem stanju|\n",
+    "|Informacije o napovedovalcih|- starost v letih\n",
+    "||- spol\n",
+    "||- bmi indeks telesne mase\n",
+    "||- bp povprečni krvni tlak\n",
+    "||- s1 tc, skupni serumski holesterol\n",
+    "||- s2 ldl, lipoproteini nizke gostote\n",
+    "||- s3 hdl, lipoproteini visoke gostote\n",
+    "||- s4 tch, skupni holesterol / HDL\n",
+    "||- s5 ltg, verjetno logaritem ravni serumskih trigliceridov\n",
+    "||- s6 glu, raven krvnega sladkorja|\n",
+    "\n",
+    "> 🎓 Zapomnite si, to je nadzorovano učenje, zato potrebujemo ciljno spremenljivko 'y'.\n",
+    "\n",
+    "Preden lahko manipulirate s podatki v R-ju, jih morate uvoziti v pomnilnik R-ja ali vzpostaviti povezavo do podatkov, ki jih R lahko uporablja za dostop do podatkov na daljavo.\n",
+    "\n",
+    "> Paket [readr](https://readr.tidyverse.org/), ki je del Tidyverse, ponuja hiter in prijazen način za branje pravokotnih podatkov v R.\n",
+    "\n",
+    "Zdaj naložimo dataset o diabetesu, ki je na voljo na tem URL-ju: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+    "\n",
+    "Prav tako bomo izvedli osnovni pregled podatkov z uporabo `glimpse()` in prikazali prvih 5 vrstic z uporabo `slice()`.\n",
+    "\n",
+    "Preden nadaljujemo, naj predstavimo nekaj, kar boste pogosto srečali v kodi R 🥁🥁: operator cevi `%>%`\n",
+    "\n",
+    "Operator cevi (`%>%`) izvaja operacije v logičnem zaporedju tako, da objekt posreduje naprej v funkcijo ali izraz klica. Operator cevi si lahko predstavljate kot \"in nato\" v vaši kodi.\n"
+   ],
+   "metadata": {
+    "id": "KM6iXLH996Cl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import the data set\r\n",
+    "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Get a glimpse and dimensions of the data\r\n",
+    "glimpse(diabetes)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Select the first 5 rows of the data\r\n",
+    "diabetes %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Z1geAMhM-bSP"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`glimpse()` nam pokaže, da ima ta podatkovni niz 442 vrstic in 11 stolpcev, pri čemer so vsi stolpci tipa podatkov `double`.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "> `glimpse()` in `slice()` sta funkciji v knjižnici [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, ki je del Tidyverse, je slovnica za manipulacijo podatkov, ki ponuja dosleden nabor glagolov za reševanje najpogostejših izzivov pri obdelavi podatkov.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Zdaj, ko imamo podatke, se osredotočimo na eno značilnost (`bmi`), ki jo bomo uporabili za to vajo. To zahteva, da izberemo želene stolpce. Kako to storimo?\n",
+    "\n",
+    "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) nam omogoča, da *izberemo* (in po želji preimenujemo) stolpce v podatkovnem okviru.\n"
+   ],
+   "metadata": {
+    "id": "UwjVT1Hz-c3Z"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select predictor feature `bmi` and outcome `y`\r\n",
+    "diabetes_select <- diabetes %>% \r\n",
+    "  select(c(bmi, y))\r\n",
+    "\r\n",
+    "# Print the first 5 rows\r\n",
+    "diabetes_select %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RDY1oAKI-m80"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Podatki za učenje in testiranje\n",
+    "\n",
+    "Pri nadzorovanem učenju je običajna praksa, da podatke *razdelimo* na dva podsklopa; (običajno večji) sklop, s katerim treniramo model, in manjši \"rezervni\" sklop, s katerim preverimo, kako se je model odrezal.\n",
+    "\n",
+    "Zdaj, ko imamo podatke pripravljene, lahko preverimo, ali nam stroj lahko pomaga določiti logično razdelitev med številkami v tem naboru podatkov. Uporabimo lahko paket [rsample](https://tidymodels.github.io/rsample/), ki je del okvira Tidymodels, za ustvarjanje objekta, ki vsebuje informacije o *načinu* razdelitve podatkov, nato pa še dve funkciji rsample za pridobitev ustvarjenih učnih in testnih sklopov:\n"
+   ],
+   "metadata": {
+    "id": "SDk668xK-tc3"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\r\n",
+    "# Split 67% of the data for training and the rest for tesing\r\n",
+    "diabetes_split <- diabetes_select %>% \r\n",
+    "  initial_split(prop = 0.67)\r\n",
+    "\r\n",
+    "# Extract the resulting train and test sets\r\n",
+    "diabetes_train <- training(diabetes_split)\r\n",
+    "diabetes_test <- testing(diabetes_split)\r\n",
+    "\r\n",
+    "# Print the first 3 rows of the training set\r\n",
+    "diabetes_train %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "EqtHx129-1h-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Učite model linearne regresije s Tidymodels\n",
+    "\n",
+    "Zdaj smo pripravljeni, da naučimo naš model!\n",
+    "\n",
+    "V Tidymodels modele določite z uporabo `parsnip()` tako, da določite tri koncepte:\n",
+    "\n",
+    "-   **Tip** modela razlikuje med modeli, kot so linearna regresija, logistična regresija, modeli odločitvenih dreves in podobno.\n",
+    "\n",
+    "-   **Način** modela vključuje pogoste možnosti, kot sta regresija in klasifikacija; nekateri tipi modelov podpirajo oba načina, medtem ko imajo drugi le en način.\n",
+    "\n",
+    "-   **Pogon** modela je računsko orodje, ki bo uporabljeno za prilagoditev modela. Pogosto so to R paketi, kot sta **`\"lm\"`** ali **`\"ranger\"`**.\n",
+    "\n",
+    "Te informacije o modeliranju so zajete v specifikaciji modela, zato jo ustvarimo!\n"
+   ],
+   "metadata": {
+    "id": "sBOS-XhB-6v7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- \r\n",
+    "  # Type\r\n",
+    "  linear_reg() %>% \r\n",
+    "  # Engine\r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  # Mode\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the model specification\r\n",
+    "lm_spec"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "20OwEw20--t3"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ko je model *določen*, ga je mogoče `oceniti` ali `usposobiti` z uporabo funkcije [`fit()`](https://parsnip.tidymodels.org/reference/fit.html), običajno z uporabo formule in nekaterih podatkov.\n",
+    "\n",
+    "`y ~ .` pomeni, da bomo prilagodili `y` kot napovedano količino/cilj, ki ga pojasnjujejo vsi napovedniki/lastnosti, tj. `.` (v tem primeru imamo samo en napovednik: `bmi`).\n"
+   ],
+   "metadata": {
+    "id": "_oDHs89k_CJj"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>%\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Train a linear regression model\r\n",
+    "lm_mod <- lm_spec %>% \r\n",
+    "  fit(y ~ ., data = diabetes_train)\r\n",
+    "\r\n",
+    "# Print the model\r\n",
+    "lm_mod"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YlsHqd-q_GJQ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Iz modelnega izhoda lahko vidimo koeficiente, pridobljene med učenjem. Ti predstavljajo koeficiente premice najboljše prileganja, ki nam daje najnižjo skupno napako med dejansko in napovedano spremenljivko.\n",
+    "<br>\n",
+    "\n",
+    "## 5. Napovedovanje na testnem naboru\n",
+    "\n",
+    "Zdaj, ko smo izurili model, ga lahko uporabimo za napovedovanje napredovanja bolezni y za testni nabor podatkov z uporabo [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). To bo uporabljeno za risanje premice med skupinami podatkov.\n"
+   ],
+   "metadata": {
+    "id": "kGZ22RQj_Olu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\r\n",
+    "predictions <- lm_mod %>% \r\n",
+    "  predict(new_data = diabetes_test)\r\n",
+    "\r\n",
+    "# Print out some of the predictions\r\n",
+    "predictions %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "nXHbY7M2_aao"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Juhu! 💃🕺 Pravkar smo izurili model in ga uporabili za napovedovanje!\n",
+    "\n",
+    "Pri napovedovanju je konvencija tidymodels vedno ustvariti tibble/podatkovni okvir z rezultati in standardiziranimi imeni stolpcev. To omogoča enostavno združevanje izvirnih podatkov in napovedi v uporabni obliki za nadaljnje operacije, kot je risanje grafov.\n",
+    "\n",
+    "`dplyr::bind_cols()` učinkovito združi več podatkovnih okvirjev po stolpcih.\n"
+   ],
+   "metadata": {
+    "id": "R_JstwUY_bIs"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Combine the predictions and the original test set\r\n",
+    "results <- diabetes_test %>% \r\n",
+    "  bind_cols(predictions)\r\n",
+    "\r\n",
+    "\r\n",
+    "results %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RybsMJR7_iI8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 6. Prikaz rezultatov modeliranja\n",
+    "\n",
+    "Zdaj je čas, da to vidimo vizualno 📈. Ustvarili bomo razpršen diagram vseh vrednosti `y` in `bmi` iz testnega nabora, nato pa uporabili napovedi za risanje črte na najbolj ustreznem mestu med skupinami podatkov modela.\n",
+    "\n",
+    "R ima več sistemov za izdelavo grafov, vendar je `ggplot2` eden najbolj elegantnih in najbolj vsestranskih. Omogoča vam sestavljanje grafov z **združevanjem neodvisnih komponent**.\n"
+   ],
+   "metadata": {
+    "id": "XJbYbMZW_n_s"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plot\r\n",
+    "theme_set(theme_light())\r\n",
+    "# Create a scatter plot\r\n",
+    "results %>% \r\n",
+    "  ggplot(aes(x = bmi)) +\r\n",
+    "  # Add a scatter plot\r\n",
+    "  geom_point(aes(y = y), size = 1.6) +\r\n",
+    "  # Add a line plot\r\n",
+    "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R9tYp3VW_sTn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ Malo razmislite, kaj se tukaj dogaja. Ravna črta poteka skozi številne majhne točke podatkov, vendar kaj točno počne? Ali vidite, kako bi morali biti sposobni uporabiti to črto za napovedovanje, kje bi se nova, nevidena podatkovna točka morala uvrstiti glede na y-os grafa? Poskusite z besedami opisati praktično uporabo tega modela.\n",
+    "\n",
+    "Čestitke, zgradili ste svoj prvi model linearne regresije, z njim ustvarili napoved in jo prikazali na grafu!\n"
+   ],
+   "metadata": {
+    "id": "zrPtHIxx_tNI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/2-Regression/1-Tools/solution/notebook.ipynb b/translations/sl/2-Regression/1-Tools/solution/notebook.ipynb
new file mode 100644
index 000000000..e35aec234
--- /dev/null
+++ b/translations/sl/2-Regression/1-Tools/solution/notebook.ipynb
@@ -0,0 +1,677 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linearna regresija za podatkovno zbirko Diabetes - Lekcija 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Uvozi potrebne knjižnice\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model, model_selection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Naložite podatkovni niz za diabetes, razdeljen na podatke `X` in značilnosti `y`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "print(X.shape)\n",
+    "print(X[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Izberite samo eno funkcijo za ciljanje v tej vaji\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selecting the 3rd feature\n",
+    "X = X[:, 2]\n",
+    "print(X.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 1)\n",
+      "[[ 0.06169621]\n",
+      " [-0.05147406]\n",
+      " [ 0.04445121]\n",
+      " [-0.01159501]\n",
+      " [-0.03638469]\n",
+      " [-0.04069594]\n",
+      " [-0.04716281]\n",
+      " [-0.00189471]\n",
+      " [ 0.06169621]\n",
+      " [ 0.03906215]\n",
+      " [-0.08380842]\n",
+      " [ 0.01750591]\n",
+      " [-0.02884001]\n",
+      " [-0.00189471]\n",
+      " [-0.02560657]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [ 0.01211685]\n",
+      " [-0.0105172 ]\n",
+      " [-0.01806189]\n",
+      " [-0.05686312]\n",
+      " [-0.02237314]\n",
+      " [-0.00405033]\n",
+      " [ 0.06061839]\n",
+      " [ 0.03582872]\n",
+      " [-0.01267283]\n",
+      " [-0.07734155]\n",
+      " [ 0.05954058]\n",
+      " [-0.02129532]\n",
+      " [-0.00620595]\n",
+      " [ 0.04445121]\n",
+      " [-0.06548562]\n",
+      " [ 0.12528712]\n",
+      " [-0.05039625]\n",
+      " [-0.06332999]\n",
+      " [-0.03099563]\n",
+      " [ 0.02289497]\n",
+      " [ 0.01103904]\n",
+      " [ 0.07139652]\n",
+      " [ 0.01427248]\n",
+      " [-0.00836158]\n",
+      " [-0.06764124]\n",
+      " [-0.0105172 ]\n",
+      " [-0.02345095]\n",
+      " [ 0.06816308]\n",
+      " [-0.03530688]\n",
+      " [-0.01159501]\n",
+      " [-0.0730303 ]\n",
+      " [-0.04177375]\n",
+      " [ 0.01427248]\n",
+      " [-0.00728377]\n",
+      " [ 0.0164281 ]\n",
+      " [-0.00943939]\n",
+      " [-0.01590626]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.04931844]\n",
+      " [ 0.04121778]\n",
+      " [-0.06332999]\n",
+      " [-0.06440781]\n",
+      " [-0.02560657]\n",
+      " [-0.00405033]\n",
+      " [ 0.00457217]\n",
+      " [-0.00728377]\n",
+      " [-0.0374625 ]\n",
+      " [-0.02560657]\n",
+      " [-0.02452876]\n",
+      " [-0.01806189]\n",
+      " [-0.01482845]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [-0.06979687]\n",
+      " [ 0.03367309]\n",
+      " [-0.00405033]\n",
+      " [-0.02021751]\n",
+      " [ 0.00241654]\n",
+      " [-0.03099563]\n",
+      " [ 0.02828403]\n",
+      " [-0.03638469]\n",
+      " [-0.05794093]\n",
+      " [-0.0374625 ]\n",
+      " [ 0.01211685]\n",
+      " [-0.02237314]\n",
+      " [-0.03530688]\n",
+      " [ 0.00996123]\n",
+      " [-0.03961813]\n",
+      " [ 0.07139652]\n",
+      " [-0.07518593]\n",
+      " [-0.00620595]\n",
+      " [-0.04069594]\n",
+      " [-0.04824063]\n",
+      " [-0.02560657]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.00457217]\n",
+      " [-0.06440781]\n",
+      " [-0.01698407]\n",
+      " [-0.05794093]\n",
+      " [ 0.00996123]\n",
+      " [ 0.08864151]\n",
+      " [-0.00512814]\n",
+      " [-0.06440781]\n",
+      " [ 0.01750591]\n",
+      " [-0.04500719]\n",
+      " [ 0.02828403]\n",
+      " [ 0.04121778]\n",
+      " [ 0.06492964]\n",
+      " [-0.03207344]\n",
+      " [-0.07626374]\n",
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+      " [ 0.04552903]\n",
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+      " [-0.03207344]\n",
+      " [ 0.00457217]\n",
+      " [ 0.02073935]\n",
+      " [ 0.01427248]\n",
+      " [ 0.11019775]\n",
+      " [ 0.00133873]\n",
+      " [ 0.05846277]\n",
+      " [-0.02129532]\n",
+      " [-0.0105172 ]\n",
+      " [-0.04716281]\n",
+      " [ 0.00457217]\n",
+      " [ 0.01750591]\n",
+      " [ 0.08109682]\n",
+      " [ 0.0347509 ]\n",
+      " [ 0.02397278]\n",
+      " [-0.00836158]\n",
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+      " [-0.00189471]\n",
+      " [-0.06225218]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.09618619]\n",
+      " [-0.06979687]\n",
+      " [-0.02129532]\n",
+      " [-0.05362969]\n",
+      " [ 0.0433734 ]\n",
+      " [ 0.05630715]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04984027]\n",
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+      " [ 0.12852056]\n",
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+      " [ 0.09295276]\n",
+      " [ 0.01535029]\n",
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+      " [ 0.0703187 ]\n",
+      " [-0.00405033]\n",
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+      " [-0.0105172 ]\n",
+      " [-0.03315126]\n",
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+      " [ 0.0433734 ]\n",
+      " [-0.06225218]\n",
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+      " [ 0.03043966]\n",
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+      " [-0.0191397 ]\n",
+      " [-0.06656343]\n",
+      " [-0.06009656]\n",
+      " [ 0.06924089]\n",
+      " [ 0.05954058]\n",
+      " [-0.02668438]\n",
+      " [-0.02021751]\n",
+      " [-0.046085  ]\n",
+      " [ 0.07139652]\n",
+      " [-0.07949718]\n",
+      " [ 0.00996123]\n",
+      " [-0.03854032]\n",
+      " [ 0.01966154]\n",
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+      " [-0.00836158]\n",
+      " [-0.01590626]\n",
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+      " [ 0.00564998]\n",
+      " [-0.03530688]\n",
+      " [ 0.02397278]\n",
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+      " [-0.0547075 ]\n",
+      " [-0.00297252]\n",
+      " [-0.06656343]\n",
+      " [-0.01267283]\n",
+      " [-0.04177375]\n",
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+      " [-0.00512814]\n",
+      " [-0.05901875]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00349435]\n",
+      " [ 0.05415152]\n",
+      " [-0.04500719]\n",
+      " [-0.05794093]\n",
+      " [-0.05578531]\n",
+      " [ 0.00133873]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00672779]\n",
+      " [ 0.04660684]\n",
+      " [ 0.02612841]\n",
+      " [ 0.04552903]\n",
+      " [ 0.04013997]\n",
+      " [-0.01806189]\n",
+      " [ 0.01427248]\n",
+      " [ 0.03690653]\n",
+      " [ 0.00349435]\n",
+      " [-0.07087468]\n",
+      " [-0.03315126]\n",
+      " [ 0.09403057]\n",
+      " [ 0.03582872]\n",
+      " [ 0.03151747]\n",
+      " [-0.06548562]\n",
+      " [-0.04177375]\n",
+      " [-0.03961813]\n",
+      " [-0.03854032]\n",
+      " [-0.02560657]\n",
+      " [-0.02345095]\n",
+      " [-0.06656343]\n",
+      " [ 0.03259528]\n",
+      " [-0.046085  ]\n",
+      " [-0.02991782]\n",
+      " [-0.01267283]\n",
+      " [-0.01590626]\n",
+      " [ 0.07139652]\n",
+      " [-0.03099563]\n",
+      " [ 0.00026092]\n",
+      " [ 0.03690653]\n",
+      " [ 0.03906215]\n",
+      " [-0.01482845]\n",
+      " [ 0.00672779]\n",
+      " [-0.06871905]\n",
+      " [-0.00943939]\n",
+      " [ 0.01966154]\n",
+      " [ 0.07462995]\n",
+      " [-0.00836158]\n",
+      " [-0.02345095]\n",
+      " [-0.046085  ]\n",
+      " [ 0.05415152]\n",
+      " [-0.03530688]\n",
+      " [-0.03207344]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04768465]\n",
+      " [ 0.06061839]\n",
+      " [ 0.05630715]\n",
+      " [ 0.09834182]\n",
+      " [ 0.05954058]\n",
+      " [ 0.03367309]\n",
+      " [ 0.05630715]\n",
+      " [-0.06548562]\n",
+      " [ 0.16085492]\n",
+      " [-0.05578531]\n",
+      " [-0.02452876]\n",
+      " [-0.03638469]\n",
+      " [-0.00836158]\n",
+      " [-0.04177375]\n",
+      " [ 0.12744274]\n",
+      " [-0.07734155]\n",
+      " [ 0.02828403]\n",
+      " [-0.02560657]\n",
+      " [-0.06225218]\n",
+      " [-0.00081689]\n",
+      " [ 0.08864151]\n",
+      " [-0.03207344]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00888341]\n",
+      " [ 0.00672779]\n",
+      " [-0.02021751]\n",
+      " [-0.02452876]\n",
+      " [-0.01159501]\n",
+      " [ 0.02612841]\n",
+      " [-0.05901875]\n",
+      " [-0.03638469]\n",
+      " [-0.02452876]\n",
+      " [ 0.01858372]\n",
+      " [-0.0902753 ]\n",
+      " [-0.00512814]\n",
+      " [-0.05255187]\n",
+      " [-0.02237314]\n",
+      " [-0.02021751]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00620595]\n",
+      " [-0.01698407]\n",
+      " [ 0.05522933]\n",
+      " [ 0.07678558]\n",
+      " [ 0.01858372]\n",
+      " [-0.02237314]\n",
+      " [ 0.09295276]\n",
+      " [-0.03099563]\n",
+      " [ 0.03906215]\n",
+      " [-0.06117437]\n",
+      " [-0.00836158]\n",
+      " [-0.0374625 ]\n",
+      " [-0.01375064]\n",
+      " [ 0.07355214]\n",
+      " [-0.02452876]\n",
+      " [ 0.03367309]\n",
+      " [ 0.0347509 ]\n",
+      " [-0.03854032]\n",
+      " [-0.03961813]\n",
+      " [-0.00189471]\n",
+      " [-0.03099563]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06492964]\n",
+      " [ 0.04013997]\n",
+      " [-0.02345095]\n",
+      " [ 0.05307371]\n",
+      " [ 0.04013997]\n",
+      " [-0.02021751]\n",
+      " [ 0.01427248]\n",
+      " [-0.03422907]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00457217]\n",
+      " [ 0.03043966]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.06169621]\n",
+      " [-0.00728377]\n",
+      " [ 0.00564998]\n",
+      " [ 0.05415152]\n",
+      " [-0.00836158]\n",
+      " [ 0.114509  ]\n",
+      " [ 0.06708527]\n",
+      " [-0.05578531]\n",
+      " [ 0.03043966]\n",
+      " [-0.02560657]\n",
+      " [ 0.10480869]\n",
+      " [-0.00620595]\n",
+      " [-0.04716281]\n",
+      " [-0.04824063]\n",
+      " [ 0.08540807]\n",
+      " [-0.01267283]\n",
+      " [-0.03315126]\n",
+      " [-0.00728377]\n",
+      " [-0.01375064]\n",
+      " [ 0.05954058]\n",
+      " [ 0.02181716]\n",
+      " [ 0.01858372]\n",
+      " [-0.01159501]\n",
+      " [-0.00297252]\n",
+      " [ 0.01750591]\n",
+      " [-0.02991782]\n",
+      " [-0.02021751]\n",
+      " [-0.05794093]\n",
+      " [ 0.06061839]\n",
+      " [-0.04069594]\n",
+      " [-0.07195249]\n",
+      " [-0.05578531]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03315126]\n",
+      " [ 0.04984027]\n",
+      " [-0.08488624]\n",
+      " [ 0.00564998]\n",
+      " [ 0.02073935]\n",
+      " [-0.00728377]\n",
+      " [ 0.10480869]\n",
+      " [-0.02452876]\n",
+      " [-0.00620595]\n",
+      " [-0.03854032]\n",
+      " [ 0.13714305]\n",
+      " [ 0.17055523]\n",
+      " [ 0.00241654]\n",
+      " [ 0.03798434]\n",
+      " [-0.05794093]\n",
+      " [-0.00943939]\n",
+      " [-0.02345095]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03422907]\n",
+      " [-0.00297252]\n",
+      " [ 0.06816308]\n",
+      " [ 0.00996123]\n",
+      " [ 0.00241654]\n",
+      " [-0.03854032]\n",
+      " [ 0.02612841]\n",
+      " [-0.08919748]\n",
+      " [ 0.06061839]\n",
+      " [-0.02884001]\n",
+      " [-0.02991782]\n",
+      " [-0.0191397 ]\n",
+      " [-0.04069594]\n",
+      " [ 0.01535029]\n",
+      " [-0.02452876]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06924089]\n",
+      " [-0.06979687]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [ 0.01858372]\n",
+      " [ 0.00133873]\n",
+      " [-0.03099563]\n",
+      " [-0.00405033]\n",
+      " [ 0.01535029]\n",
+      " [ 0.02289497]\n",
+      " [ 0.04552903]\n",
+      " [-0.04500719]\n",
+      " [-0.03315126]\n",
+      " [ 0.097264  ]\n",
+      " [ 0.05415152]\n",
+      " [ 0.12313149]\n",
+      " [-0.08057499]\n",
+      " [ 0.09295276]\n",
+      " [-0.05039625]\n",
+      " [-0.01159501]\n",
+      " [-0.0277622 ]\n",
+      " [ 0.05846277]\n",
+      " [ 0.08540807]\n",
+      " [-0.00081689]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00888341]\n",
+      " [ 0.08001901]\n",
+      " [ 0.07139652]\n",
+      " [-0.02452876]\n",
+      " [-0.0547075 ]\n",
+      " [-0.03638469]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.07786339]\n",
+      " [-0.03961813]\n",
+      " [ 0.01103904]\n",
+      " [-0.04069594]\n",
+      " [-0.03422907]\n",
+      " [ 0.00564998]\n",
+      " [ 0.08864151]\n",
+      " [-0.03315126]\n",
+      " [-0.05686312]\n",
+      " [-0.03099563]\n",
+      " [ 0.05522933]\n",
+      " [-0.06009656]\n",
+      " [ 0.00133873]\n",
+      " [-0.02345095]\n",
+      " [-0.07410811]\n",
+      " [ 0.01966154]\n",
+      " [-0.01590626]\n",
+      " [-0.01590626]\n",
+      " [ 0.03906215]\n",
+      " [-0.0730303 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Reshaping to get a 2D array\n",
+    "X = X.reshape(-1, 1)\n",
+    "print(X.shape)\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Razdeli podatke za usposabljanje in testiranje za `X` in `y`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Izberite model in ga prilagodite učnim podatkom\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Uporabite testne podatke za napovedovanje črte\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prikaži rezultate v grafu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test,  color='black')\n",
+    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "16ff1a974f6e4348e869e4a7d366b86a",
+   "translation_date": "2025-09-06T13:38:40+00:00",
+   "source_file": "2-Regression/1-Tools/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sl/2-Regression/2-Data/notebook.ipynb b/translations/sl/2-Regression/2-Data/notebook.ipynb
new file mode 100644
index 000000000..c7567a3c1
--- /dev/null
+++ b/translations/sl/2-Regression/2-Data/notebook.ipynb
@@ -0,0 +1,46 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  },
+  "coopTranslator": {
+   "original_hash": "1b2ab303ac6c604a34c6ca7a49077fc7",
+   "translation_date": "2025-09-06T13:45:51+00:00",
+   "source_file": "2-Regression/2-Data/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/sl/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
new file mode 100644
index 000000000..19c889e01
--- /dev/null
+++ b/translations/sl/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -0,0 +1,684 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_2-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "f3c335f9940cfd76528b3ef918b9b342",
+   "translation_date": "2025-09-06T13:49:10+00:00",
+   "source_file": "2-Regression/2-Data/solution/R/lesson_2-R.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Zgradite regresijski model: priprava in vizualizacija podatkov\n",
+    "\n",
+    "## **Linearna regresija za buče - Lekcija 2**\n",
+    "#### Uvod\n",
+    "\n",
+    "Zdaj, ko imate na voljo orodja, ki jih potrebujete za začetek gradnje modelov strojnega učenja z Tidymodels in Tidyverse, ste pripravljeni začeti postavljati vprašanja o svojih podatkih. Ko delate s podatki in uporabljate rešitve strojnega učenja, je zelo pomembno, da znate postaviti prava vprašanja, da pravilno izkoristite potencial svojega nabora podatkov.\n",
+    "\n",
+    "V tej lekciji boste spoznali:\n",
+    "\n",
+    "-   Kako pripraviti podatke za gradnjo modela.\n",
+    "\n",
+    "-   Kako uporabiti `ggplot2` za vizualizacijo podatkov.\n",
+    "\n",
+    "Vprašanje, na katerega želite dobiti odgovor, bo določilo, katere vrste algoritmov strojnega učenja boste uporabili. Kakovost odgovora, ki ga dobite, pa bo močno odvisna od narave vaših podatkov.\n",
+    "\n",
+    "Poglejmo to skozi praktično vajo.\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/unruly_data.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Umetniško delo @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Umetniško delo \\@allison_horst](../../../../../../2-Regression/2-Data/images/unruly_data.jpg)<br>Umetniško delo \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "Pg5aexcOPqAZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Uvoz podatkov o bučah in priklic Tidyverse\n",
+    "\n",
+    "Za obdelavo te lekcije bomo potrebovali naslednje pakete:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [zbirka paketov za R](https://www.tidyverse.org/packages), zasnovana za hitrejše, enostavnejše in bolj zabavno podatkovno znanost!\n",
+    "\n",
+    "Namestite jih lahko z ukazom:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\"))`\n",
+    "\n",
+    "Spodnji skript preveri, ali imate nameščene pakete, potrebne za dokončanje tega modula, in jih namesti, če kateri manjka.\n"
+   ],
+   "metadata": {
+    "id": "dc5WhyVdXAjR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "GqPYUZgfXOBt"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdaj zaženimo nekaj paketov in naložimo [podatke](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv), ki so na voljo za to lekcijo!\n"
+   ],
+   "metadata": {
+    "id": "kvjDTPDSXRr2"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n =50)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VMri-t2zXqgD"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hitro `glimpse()` takoj pokaže, da so prisotne prazne vrednosti ter mešanica nizov (`chr`) in numeričnih podatkov (`dbl`). `Date` je tipa znak, poleg tega pa je tu še nenavadni stolpec z imenom `Package`, kjer so podatki mešanica med `sacks`, `bins` in drugimi vrednostmi. Podatki so, pravzaprav, precej zmedeni 😤.\n",
+    "\n",
+    "Pravzaprav ni ravno pogosto, da bi dobili podatkovni niz, ki je popolnoma pripravljen za uporabo pri ustvarjanju modela strojnega učenja kar takoj. A brez skrbi, v tej lekciji se boste naučili, kako pripraviti surov podatkovni niz z uporabo standardnih knjižnic v R 🧑‍🔧. Prav tako se boste naučili različnih tehnik za vizualizacijo podatkov.📈📊\n",
+    "<br>\n",
+    "\n",
+    "> Osvežitev: Operator cevi (`%>%`) izvaja operacije v logičnem zaporedju tako, da objekt posreduje naprej v funkcijo ali izraz klica. Operator cevi si lahko predstavljate kot \"in potem\" v vaši kodi.\n"
+   ],
+   "metadata": {
+    "id": "REWcIv9yX29v"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Preverjanje manjkajočih podatkov\n",
+    "\n",
+    "Ena najpogostejših težav, s katerimi se soočajo podatkovni znanstveniki, so nepopolni ali manjkajoči podatki. R predstavlja manjkajoče ali neznane vrednosti s posebnim označevalcem: `NA` (Not Available).\n",
+    "\n",
+    "Kako torej ugotovimo, da podatkovni okvir vsebuje manjkajoče vrednosti?\n",
+    "<br>\n",
+    "-   Eden od preprostih načinov je uporaba osnovne R funkcije `anyNA`, ki vrne logične vrednosti `TRUE` ali `FALSE`.\n"
+   ],
+   "metadata": {
+    "id": "Zxfb3AM5YbUe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  anyNA()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "G--DQutAYltj"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično, zdi se, da manjkajo nekateri podatki! To je dobro izhodišče.\n",
+    "\n",
+    "-   Druga možnost bi bila uporaba funkcije `is.na()`, ki pokaže, kateri posamezni elementi stolpcev manjkajo, z logično vrednostjo `TRUE`.\n"
+   ],
+   "metadata": {
+    "id": "mU-7-SB6YokF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "W-DxDOR4YxSW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "V redu, naloga je opravljena, vendar bi bilo z veliko podatkovno tabelo, kot je ta, neučinkovito in praktično nemogoče pregledati vse vrstice in stolpce posamično😴.\n",
+    "\n",
+    "-   Bolj intuitiven način bi bil izračunati vsoto manjkajočih vrednosti za vsak stolpec:\n"
+   ],
+   "metadata": {
+    "id": "xUWxipKYY0o7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  colSums()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZRBWV6P9ZArL"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Veliko bolje! Manjkajo podatki, vendar morda to ne bo pomembno za nalogo. Poglejmo, kaj bo prinesla nadaljnja analiza.\n",
+    "\n",
+    "> Poleg odličnih paketov in funkcij ima R zelo dobro dokumentacijo. Na primer, uporabite `help(colSums)` ali `?colSums`, da izveste več o funkciji.\n"
+   ],
+   "metadata": {
+    "id": "9gv-crB6ZD1Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Dplyr: Slovnica za manipulacijo podatkov\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_wrangling.png\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Ilustracija avtorice @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Ilustracija avtorice \\@allison_horst](../../../../../../2-Regression/2-Data/images/dplyr_wrangling.png)<br/>Ilustracija avtorice \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "o4jLY5-VZO2C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`dplyr`](https://dplyr.tidyverse.org/), paket v Tidyverse, je slovnica za obdelavo podatkov, ki ponuja dosleden nabor glagolov, s katerimi lahko rešite najpogostejše izzive pri obdelavi podatkov. V tem razdelku bomo raziskali nekatere glagole dplyr!\n"
+   ],
+   "metadata": {
+    "id": "i5o33MQBZWWw"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::select()\n",
+    "\n",
+    "`select()` je funkcija v paketu `dplyr`, ki vam pomaga izbrati stolpce za ohranitev ali izključitev.\n",
+    "\n",
+    "Da bo vaš podatkovni okvir lažji za delo, odstranite več njegovih stolpcev z uporabo `select()` in obdržite samo tiste stolpce, ki jih potrebujete.\n",
+    "\n",
+    "Na primer, v tej vaji bo naša analiza vključevala stolpce `Package`, `Low Price`, `High Price` in `Date`. Izberimo te stolpce.\n"
+   ],
+   "metadata": {
+    "id": "x3VGMAGBZiUr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(Package, `Low Price`, `High Price`, Date)\n",
+    "\n",
+    "\n",
+    "# Print data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "F_FgxQnVZnM0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::mutate()\n",
+    "\n",
+    "`mutate()` je funkcija v paketu `dplyr`, ki vam omogoča ustvarjanje ali spreminjanje stolpcev, pri čemer obstoječi stolpci ostanejo nespremenjeni.\n",
+    "\n",
+    "Splošna struktura funkcije mutate je:\n",
+    "\n",
+    "`data %>%   mutate(new_column_name = what_it_contains)`\n",
+    "\n",
+    "Poglejmo, kako deluje `mutate`, z uporabo stolpca `Date` in izvedbo naslednjih operacij:\n",
+    "\n",
+    "1.  Pretvorba datumov (trenutno tipa znak) v format meseca (to so ameriški datumi, zato je format `MM/DD/YYYY`).\n",
+    "\n",
+    "2.  Izvleček meseca iz datumov v nov stolpec.\n",
+    "\n",
+    "V jeziku R paket [lubridate](https://lubridate.tidyverse.org/) olajša delo s podatki tipa Datum-čas. Zato uporabimo `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` in preverimo, kako doseči zgoraj navedene cilje. Stolpec Date lahko odstranimo, saj ga v nadaljnjih operacijah ne bomo več potrebovali.\n"
+   ],
+   "metadata": {
+    "id": "2KKo0Ed9Z1VB"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load lubridate\n",
+    "library(lubridate)\n",
+    "\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  # Convert the Date column to a date object\n",
+    "  mutate(Date = mdy(Date)) %>% \n",
+    "  # Extract month from Date\n",
+    "  mutate(Month = month(Date)) %>% \n",
+    "  # Drop Date column\n",
+    "  select(-Date)\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5joszIVSZ6xe"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Juhu! 🤩\n",
+    "\n",
+    "Zdaj ustvarimo nov stolpec `Price`, ki predstavlja povprečno ceno buče. Nato izračunajmo povprečje stolpcev `Low Price` in `High Price`, da zapolnimo novi stolpec Price.  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "nIgLjNMCZ-6Y"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new column Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Zo0BsqqtaJw2"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ja!💪\n",
+    "\n",
+    "\"Počakaj malo!\", boš rekel po hitrem pregledu celotnega nabora podatkov z `View(pumpkins)`, \"Tukaj je nekaj nenavadnega!\"🤔\n",
+    "\n",
+    "Če pogledaš stolpec `Package`, so buče prodane v različnih konfiguracijah. Nekatere so prodane v merah `1 1/9 bushel`, nekatere v merah `1/2 bushel`, nekatere na bučo, nekatere na funt, in nekatere v velikih škatlah z različnimi širinami.\n",
+    "\n",
+    "Preverimo to:\n"
+   ],
+   "metadata": {
+    "id": "p77WZr-9aQAR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Verify the distinct observations in Package column\n",
+    "pumpkins %>% \n",
+    "  distinct(Package)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "XISGfh0IaUy6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Neverjetno!👏\n",
+    "\n",
+    "Buče je očitno zelo težko dosledno tehtati, zato jih filtrirajmo tako, da izberemo samo buče z nizom *bushel* v stolpcu `Package` in jih shranimo v nov podatkovni okvir `new_pumpkins`.\n"
+   ],
+   "metadata": {
+    "id": "7sMjiVujaZxY"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::filter() in stringr::str_detect()\n",
+    "\n",
+    "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): ustvari podmnožico podatkov, ki vsebuje **vrstice**, ki ustrezajo vašim pogojem, v tem primeru buče z nizom *bushel* v stolpcu `Package`.\n",
+    "\n",
+    "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): zazna prisotnost ali odsotnost vzorca v nizu.\n",
+    "\n",
+    "Paket [`stringr`](https://github.com/tidyverse/stringr) ponuja enostavne funkcije za pogoste operacije z nizi.\n"
+   ],
+   "metadata": {
+    "id": "L8Qfcs92ageF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Retain only pumpkins with \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "       filter(str_detect(Package, \"bushel\"))\n",
+    "\n",
+    "# Get the dimensions of the new data\n",
+    "dim(new_pumpkins)\n",
+    "\n",
+    "# View a few rows of the new data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "hy_SGYREampd"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vidite, da smo zožili izbor na približno 415 vrstic podatkov, ki vsebujejo buče po koših.🤩\n"
+   ],
+   "metadata": {
+    "id": "VrDwF031avlR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::case_when()\n",
+    "\n",
+    "**Ampak počakajte! Še nekaj je treba narediti**\n",
+    "\n",
+    "Ste opazili, da se količina košev razlikuje po vrsticah? Potrebno je normalizirati cene, da bodo prikazane na koš, ne pa na 1 1/9 ali 1/2 koša. Čas je za nekaj matematike, da to standardiziramo.\n",
+    "\n",
+    "Uporabili bomo funkcijo [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html), da *spremenimo* stolpec Price glede na določene pogoje. `case_when` omogoča vektorizacijo več `if_else()` stavkov.\n"
+   ],
+   "metadata": {
+    "id": "mLpw2jH4a0tx"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(Price = case_when(\n",
+    "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+    "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+    "    TRUE ~ Price))\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 30)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "P68kLVQmbM6I"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdaj lahko analiziramo ceno na enoto glede na njihovo meritev v bušlnih. Vsa ta študija o bušlnih buč pa kaže, kako zelo `pomembno` je `razumeti naravo svojih podatkov`!\n",
+    "\n",
+    "> ✅ Po navedbah [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308) teža bušlja zavisi od vrste pridelka, saj gre za meritev prostornine. \"Bušelj paradižnikov, na primer, naj bi tehtal 56 funtov... Listi in zelenjava zavzamejo več prostora z manjšo težo, zato bušelj špinače tehta le 20 funtov.\" Vse skupaj je precej zapleteno! Ne ukvarjajmo se s pretvorbo iz bušljev v funte, ampak raje določimo ceno po bušlju. Vsa ta študija o bušlnih buč pa kaže, kako zelo pomembno je razumeti naravo svojih podatkov!\n",
+    ">\n",
+    "> ✅ Ste opazili, da so buče, prodane po pol bušlja, zelo drage? Ali lahko ugotovite, zakaj? Namig: majhne buče so precej dražje od velikih, verjetno zato, ker jih je v bušlju veliko več, glede na neizkoriščen prostor, ki ga zavzame ena velika votla buča za pito.\n"
+   ],
+   "metadata": {
+    "id": "pS2GNPagbSdb"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Pomembno pravilo:  \n",
+    "1. NE dodajajte '''markdown ali kakršnih koli drugih oznak okoli prevoda  \n",
+    "2. Poskrbite, da prevod ne bo zvenel preveč dobesedno  \n",
+    "3. Prevedite tudi komentarje  \n",
+    "4. Ta datoteka je napisana v obliki Markdown - ne obravnavajte je kot XML ali HTML  \n",
+    "5. Ne prevajajte:  \n",
+    "   - [!NOTE], [!WARNING], [!TIP], [!IMPORTANT], [!CAUTION]  \n",
+    "   - Imen spremenljivk, funkcij, razredov  \n",
+    "   - Oznak, kot so @@INLINE_CODE_x@@ ali @@CODE_BLOCK_x@@  \n",
+    "   - URL-jev ali poti  \n",
+    "6. Ohranite vso izvirno oblikovanje Markdown nespremenjeno  \n",
+    "7. Vrni SAMO prevedeno vsebino brez dodatnih oznak ali oblikovanja  \n",
+    "\n",
+    "Zdaj pa, za čisto avanturo 💁‍♀️, premaknimo stolpec Month na prvo mesto, torej `pred` stolpec `Package`.  \n",
+    "\n",
+    "Za spremembo položajev stolpcev se uporablja `dplyr::relocate()`.  \n"
+   ],
+   "metadata": {
+    "id": "qql1SowfbdnP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new data frame new_pumpkins\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(Month, .before = Package)\n",
+    "\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "JJ1x6kw8bixF"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično delo!👌 Zdaj imate čist in urejen nabor podatkov, na katerem lahko zgradite svoj novi regresijski model!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "y8TJ0Za_bn5Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Vizualizacija podatkov z ggplot2\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/data-visualization.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografika avtorja Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografika avtorja Dasani Madipalli](../../../../../../2-Regression/2-Data/images/data-visualization.png){width=\"600\"}-->\n",
+    "\n",
+    "Obstaja *modri* rek, ki pravi:\n",
+    "\n",
+    "> \"Preprost graf je analitiku podatkov prinesel več informacij kot katerakoli druga naprava.\" --- John Tukey\n",
+    "\n",
+    "Del vloge podatkovnega znanstvenika je prikazati kakovost in naravo podatkov, s katerimi dela. To pogosto dosežejo z ustvarjanjem zanimivih vizualizacij, kot so grafi, diagrami in prikazi, ki pokažejo različne vidike podatkov. Na ta način lahko vizualno prikažejo odnose in vrzeli, ki jih je sicer težko odkriti.\n",
+    "\n",
+    "Vizualizacije lahko pomagajo tudi pri določanju najbolj primerne tehnike strojnega učenja za podatke. Na primer, razpršen diagram, ki sledi črti, nakazuje, da so podatki primerni za nalogo linearne regresije.\n",
+    "\n",
+    "R ponuja več sistemov za izdelavo grafov, vendar je [`ggplot2`](https://ggplot2.tidyverse.org/index.html) eden najbolj elegantnih in vsestranskih. `ggplot2` omogoča sestavljanje grafov z **združevanjem neodvisnih komponent**.\n",
+    "\n",
+    "Začnimo s preprostim razpršenim diagramom za stolpca Price in Month.\n",
+    "\n",
+    "V tem primeru bomo začeli z [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), podali podatkovni niz in estetsko preslikavo (z [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)), nato pa dodali sloje (kot je [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) za razpršene diagrame.\n"
+   ],
+   "metadata": {
+    "id": "mYSH6-EtbvNa"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plots\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+    "p + geom_point()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "g2YjnGeOcLo4"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Je to uporaben graf 🤷? Ali te kaj na njem preseneča?\n",
+    "\n",
+    "Ni posebej uporaben, saj zgolj prikazuje tvoje podatke kot razpored točk v določenem mesecu.\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "Ml7SDCLQcPvE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Kako naredimo podatke uporabne?**\n",
+    "\n",
+    "Da bi prikazali koristne podatke na grafikonih, je običajno potrebno podatke nekako združiti. Na primer, v našem primeru bi iskanje povprečne cene buč za vsak mesec omogočilo boljši vpogled v osnovne vzorce v naših podatkih. To nas pripelje do še ene hitre predstavitve **dplyr**:\n",
+    "\n",
+    "#### `dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "Združeno agregiranje v R lahko enostavno izvedemo z\n",
+    "\n",
+    "`dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "-   `dplyr::group_by()` spremeni enoto analize iz celotnega nabora podatkov na posamezne skupine, kot so na primer meseci.\n",
+    "\n",
+    "-   `dplyr::summarize()` ustvari nov podatkovni okvir z enim stolpcem za vsako spremenljivko skupine in enim stolpcem za vsako statistiko povzetka, ki ste jo določili.\n",
+    "\n",
+    "Na primer, lahko uporabimo `dplyr::group_by() %>% summarize()` za združevanje buč v skupine na podlagi stolpca **Month** in nato izračunamo **povprečno ceno** za vsak mesec.\n"
+   ],
+   "metadata": {
+    "id": "jMakvJZIcVkh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "6kVSUa2Bcilf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Jedrnato!✨\n",
+    "\n",
+    "Kategorijske značilnosti, kot so meseci, so bolje prikazane z uporabo stolpčnega diagrama 📊. Plasti, ki so odgovorne za stolpčne diagrame, so `geom_bar()` in `geom_col()`. Preverite `?geom_bar` za več informacij.\n",
+    "\n",
+    "Pa ga ustvarimo!\n"
+   ],
+   "metadata": {
+    "id": "Kds48GUBcj3W"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price)) %>% \r\n",
+    "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"Pumpkin Price\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VNbU1S3BcrxO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 To je bolj uporabna vizualizacija podatkov! Zdi se, da kaže, da so najvišje cene buč v septembru in oktobru. Ali to ustreza vašim pričakovanjem? Zakaj ali zakaj ne?\n",
+    "\n",
+    "Čestitke za zaključek druge lekcije 👏! Pripravili ste svoje podatke za gradnjo modela in nato odkrili več vpogledov z uporabo vizualizacij!\n"
+   ],
+   "metadata": {
+    "id": "zDm0VOzzcuzR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/2-Regression/2-Data/solution/notebook.ipynb b/translations/sl/2-Regression/2-Data/solution/notebook.ipynb
new file mode 100644
index 000000000..31d66e397
--- /dev/null
+++ b/translations/sl/2-Regression/2-Data/solution/notebook.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linearna regresija za buče - Lekcija 2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/8/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    City Name Type               Package   Variety Sub Variety  Grade  \\\n",
+       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "\n",
+       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
+       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
+       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "\n",
+       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
+       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "\n",
+       "    Unnamed: 25  \n",
+       "70          NaN  \n",
+       "71          NaN  \n",
+       "72          NaN  \n",
+       "73          NaN  \n",
+       "74          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Month               Package  Low Price  High Price  Price\n",
+      "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
+      "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "...     ...                   ...        ...         ...    ...\n",
+      "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
+      "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
+      "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
+      "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Get an average between low and high price for the base pumpkin price\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "# Convert the date to its month only\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "\n",
+    "# Create a new dataframe with this basic data\n",
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
+    "\n",
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
+    "\n",
+    "print(new_pumpkins)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "price = new_pumpkins.Price\n",
+    "month = new_pumpkins.Month\n",
+    "plt.scatter(price, month)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "95726f0b8283628d5356a4f8eb8b4b76",
+   "translation_date": "2025-09-06T13:46:15+00:00",
+   "source_file": "2-Regression/2-Data/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sl/2-Regression/3-Linear/notebook.ipynb b/translations/sl/2-Regression/3-Linear/notebook.ipynb
new file mode 100644
index 000000000..c0701506d
--- /dev/null
+++ b/translations/sl/2-Regression/3-Linear/notebook.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cene buč\n",
+    "\n",
+    "Naložite potrebne knjižnice in podatkovni niz. Pretvorite podatke v podatkovni okvir, ki vsebuje podnabor podatkov:\n",
+    "\n",
+    "- Pridobite samo buče, ki so ocenjene po ceni na koš\n",
+    "- Pretvorite datum v mesec\n",
+    "- Izračunajte ceno kot povprečje med najvišjo in najnižjo ceno\n",
+    "- Pretvorite ceno, da odraža ceno glede na količino v košu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Osnovni raztreseni diagram nas opominja, da imamo podatke o mesecih samo od avgusta do decembra. Verjetno potrebujemo več podatkov, da bi lahko sklepali na linearen način.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter('Month','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "b032d371c75279373507f003439a577e",
+   "translation_date": "2025-09-06T13:08:38+00:00",
+   "source_file": "2-Regression/3-Linear/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sl/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/sl/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
new file mode 100644
index 000000000..f95001d6c
--- /dev/null
+++ b/translations/sl/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -0,0 +1,1089 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_3-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "5015d65d61ba75a223bfc56c273aa174",
+   "translation_date": "2025-09-06T13:16:09+00:00",
+   "source_file": "2-Regression/3-Linear/solution/R/lesson_3-R.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Zgradite regresijski model: linearni in polinomski regresijski modeli\n"
+   ],
+   "metadata": {
+    "id": "EgQw8osnsUV-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Linearna in polinomska regresija za določanje cen buč - Lekcija 3\n",
+    "<p>\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Infografika: Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografika: Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n",
+    "\n",
+    "#### Uvod\n",
+    "\n",
+    "Do sedaj ste raziskali, kaj regresija je, z vzorčnimi podatki, zbranimi iz nabora podatkov o cenah buč, ki ga bomo uporabljali skozi celotno lekcijo. Prav tako ste ga vizualizirali z uporabo `ggplot2`. 💪\n",
+    "\n",
+    "Zdaj ste pripravljeni, da se poglobite v regresijo za strojno učenje. V tej lekciji boste izvedeli več o dveh vrstah regresije: *osnovni linearni regresiji* in *polinomski regresiji*, skupaj z nekaj matematike, ki stoji za temi tehnikami.\n",
+    "\n",
+    "> V tem učnem načrtu predpostavljamo minimalno matematično predznanje in si prizadevamo, da bi bila vsebina dostopna študentom iz drugih področij. Zato bodite pozorni na opombe, 🧮 poudarke, diagrame in druga učna orodja, ki vam bodo pomagala pri razumevanju.\n",
+    "\n",
+    "#### Priprava\n",
+    "\n",
+    "Naj vas spomnimo, da nalagate te podatke, da bi si zastavili vprašanja o njih.\n",
+    "\n",
+    "-   Kdaj je najboljši čas za nakup buč?\n",
+    "\n",
+    "-   Kakšno ceno lahko pričakujem za zabojček miniaturnih buč?\n",
+    "\n",
+    "-   Ali naj jih kupim v polovičnih košarah ali v škatlah velikosti 1 1/9 busha? Poglobimo se v te podatke.\n",
+    "\n",
+    "V prejšnji lekciji ste ustvarili `tibble` (sodobno reinterpretacijo podatkovnega okvira) in ga napolnili z delom izvirnega nabora podatkov, pri čemer ste standardizirali cene glede na bushel. S tem ste sicer pridobili približno 400 podatkovnih točk, vendar le za jesenske mesece. Morda lahko pridobimo še malo več podrobnosti o naravi podatkov, če jih še bolj očistimo? Bomo videli... 🕵️‍♀️\n",
+    "\n",
+    "Za to nalogo bomo potrebovali naslednje pakete:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [zbirka paketov za R](https://www.tidyverse.org/packages), zasnovana za hitrejše, enostavnejše in zabavnejše delo z znanostjo o podatkih!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je [okvir paketov](https://www.tidymodels.org/packages/) za modeliranje in strojno učenje.\n",
+    "\n",
+    "-   `janitor`: Paket [janitor](https://github.com/sfirke/janitor) ponuja preprosta orodja za pregledovanje in čiščenje \"umazanih\" podatkov.\n",
+    "\n",
+    "-   `corrplot`: Paket [corrplot](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) omogoča vizualno raziskovanje korelacijske matrike, ki podpira samodejno preurejanje spremenljivk za odkrivanje skritih vzorcev med njimi.\n",
+    "\n",
+    "Pakete lahko namestite z naslednjim ukazom:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\n",
+    "\n",
+    "Spodnji skript preveri, ali imate nameščene potrebne pakete za dokončanje tega modula, in jih po potrebi namesti.\n"
+   ],
+   "metadata": {
+    "id": "WqQPS1OAsg3H"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "tA4C2WN3skCf",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Linearna regresijska premica\n",
+    "\n",
+    "Kot ste se naučili v Lekciji 1, je cilj linearne regresije narisati *premico* *najboljše prileganje*, da:\n",
+    "\n",
+    "-   **Prikažete odnose med spremenljivkami**. Prikažete odnos med spremenljivkami.\n",
+    "\n",
+    "-   **Napovedujete**. Naredite natančne napovedi, kje bi nov podatek padel v odnosu do te premice.\n",
+    "\n",
+    "Za risanje takšne premice uporabljamo statistično tehniko, imenovano **Regresija najmanjših kvadratov**. Izraz `najmanjši kvadrati` pomeni, da so vsi podatkovni točki okoli regresijske premice kvadrirani in nato sešteveni. Idealno je, da je končni seštevek čim manjši, saj želimo nizko število napak oziroma `najmanjše kvadrate`. Tako je premica najboljše prileganje tista, ki nam daje najnižjo vrednost za vsoto kvadriranih napak - od tod ime *regresija najmanjših kvadratov*.\n",
+    "\n",
+    "To počnemo, ker želimo modelirati premico, ki ima najmanjšo kumulativno razdaljo od vseh naših podatkovnih točk. Prav tako kvadriramo izraze pred seštevanjem, saj nas zanima njihova velikost in ne smer.\n",
+    "\n",
+    "> **🧮 Pokaži mi matematiko**\n",
+    ">\n",
+    "> Ta premica, imenovana *premica najboljše prileganje*, se lahko izrazi z [enačbo](https://en.wikipedia.org/wiki/Simple_linear_regression):\n",
+    ">\n",
+    ">     Y = a + bX\n",
+    ">\n",
+    "> `X` je '`pojasnjevalna spremenljivka` ali `napovednik`'. `Y` je '`odvisna spremenljivka` ali `rezultat`'. Naklon premice je `b`, `a` pa je presečišče z osjo y, kar se nanaša na vrednost `Y`, ko `X = 0`.\n",
+    ">\n",
+    "\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/slope.png \"naklon = $y/x$\")\n",
+    "    Infografika: Jen Looper\n",
+    ">\n",
+    "> Najprej izračunajte naklon `b`.\n",
+    ">\n",
+    "> Z drugimi besedami, in glede na prvotno vprašanje o podatkih o bučah: \"napovedati ceno buče na koš po mesecih\", bi `X` pomenil ceno, `Y` pa mesec prodaje.\n",
+    ">\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/calculation.png)\n",
+    "    Infografika: Jen Looper\n",
+    "> \n",
+    "> Izračunajte vrednost Y. Če plačujete približno 4 USD, mora biti april!\n",
+    ">\n",
+    "> Matematika, ki izračuna premico, mora prikazati naklon premice, ki je odvisen tudi od presečišča, oziroma kje se `Y` nahaja, ko `X = 0`.\n",
+    ">\n",
+    "> Metodo izračuna teh vrednosti si lahko ogledate na spletni strani [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html). Obiščite tudi [ta kalkulator najmanjših kvadratov](https://www.mathsisfun.com/data/least-squares-calculator.html), da vidite, kako vrednosti številk vplivajo na premico.\n",
+    "\n",
+    "Ni tako strašno, kajne? 🤓\n",
+    "\n",
+    "#### Korelacija\n",
+    "\n",
+    "Še en izraz, ki ga je treba razumeti, je **Koeficient korelacije** med danima spremenljivkama X in Y. S pomočjo razsevnega diagrama lahko hitro vizualizirate ta koeficient. Diagram s podatkovnimi točkami, razporejenimi v urejeni premici, ima visoko korelacijo, medtem ko diagram s podatkovnimi točkami, razpršenimi povsod med X in Y, ima nizko korelacijo.\n",
+    "\n",
+    "Dober model linearne regresije bo tisti, ki ima visok (bližje 1 kot 0) koeficient korelacije z uporabo metode regresije najmanjših kvadratov s premico regresije.\n"
+   ],
+   "metadata": {
+    "id": "cdX5FRpvsoP5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **2. Ples s podatki: ustvarjanje podatkovnega okvira za modeliranje**\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/janitor.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Umetniško delo @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Umetniško delo \\@allison_horst](../../../../../../2-Regression/3-Linear/images/janitor.jpg){width=\"700\"}-->\n"
+   ],
+   "metadata": {
+    "id": "WdUKXk7Bs8-V"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Naložite potrebne knjižnice in podatkovni niz. Podatke pretvorite v podatkovni okvir, ki vsebuje podmnožico podatkov:\n",
+    "\n",
+    "-   Uporabite samo buče, katerih cena je določena na sod.\n",
+    "\n",
+    "-   Datum pretvorite v mesec.\n",
+    "\n",
+    "-   Izračunajte ceno kot povprečje najvišje in najnižje cene.\n",
+    "\n",
+    "-   Ceno prilagodite tako, da odraža določanje cen glede na količino v sodu.\n",
+    "\n",
+    "> Te korake smo obravnavali v [prejšnji lekciji](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb).\n"
+   ],
+   "metadata": {
+    "id": "fMCtu2G2s-p8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "library(lubridate)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ryMVZEEPtERn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "V duhu čiste avanture raziščimo [`paket janitor`](../../../../../../2-Regression/3-Linear/solution/R/github.com/sfirke/janitor), ki ponuja preproste funkcije za pregledovanje in čiščenje neurejenih podatkov. Na primer, poglejmo imena stolpcev za naše podatke:\n"
+   ],
+   "metadata": {
+    "id": "xcNxM70EtJjb"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5XtpaIigtPfW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤔 Lahko naredimo bolje. Spremenimo ta imena stolpcev v `friendR` tako, da jih pretvorimo v konvencijo [snake_case](https://en.wikipedia.org/wiki/Snake_case) z uporabo `janitor::clean_names`. Če želite izvedeti več o tej funkciji: `?clean_names`\n"
+   ],
+   "metadata": {
+    "id": "IbIqrMINtSHe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Clean names to the snake_case convention\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  clean_names(case = \"snake\")\n",
+    "\n",
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "a2uYvclYtWvX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Veliko urejenosti 🧹! Zdaj pa ples s podatki z uporabo `dplyr`, kot v prejšnji lekciji! 💃\n"
+   ],
+   "metadata": {
+    "id": "HfhnuzDDtaDd"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(variety, city_name, package, low_price, high_price, date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Extract the month from the dates to a new column\n",
+    "pumpkins <- pumpkins %>%\n",
+    "  mutate(date = mdy(date),\n",
+    "         month = month(date)) %>% \n",
+    "  select(-date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a new column for average Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(price = (low_price + high_price)/2)\n",
+    "\n",
+    "\n",
+    "# Retain only pumpkins with the string \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "  filter(str_detect(string = package, pattern = \"bushel\"))\n",
+    "\n",
+    "\n",
+    "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(price = case_when(\n",
+    "    str_detect(package, \"1 1/9\") ~ price/(1.1),\n",
+    "    str_detect(package, \"1/2\") ~ price*2,\n",
+    "    TRUE ~ price))\n",
+    "\n",
+    "# Relocate column positions\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(month, .before = variety)\n",
+    "\n",
+    "\n",
+    "# Display the first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "X0wU3gQvtd9f"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično delo!👌 Zdaj imate čist in urejen nabor podatkov, na katerem lahko zgradite svoj novi regresijski model!\n",
+    "\n",
+    "Kaj pravite na razpršeni diagram?\n"
+   ],
+   "metadata": {
+    "id": "UpaIwaxqth82"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set theme\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Make a scatter plot of month and price\n",
+    "new_pumpkins %>% \n",
+    "  ggplot(mapping = aes(x = month, y = price)) +\n",
+    "  geom_point(size = 1.6)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "DXgU-j37tl5K"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Raztreseni diagram nas spomni, da imamo podatke le za mesece od avgusta do decembra. Verjetno potrebujemo več podatkov, da bi lahko sklepali na linearen način.\n",
+    "\n",
+    "Poglejmo si še enkrat naše podatke za modeliranje:\n"
+   ],
+   "metadata": {
+    "id": "Ve64wVbwtobI"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Display first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HFQX2ng1tuSJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kaj če bi želeli napovedati `ceno` buče na podlagi stolpcev `mesto` ali `paket`, ki sta tipa znak? Ali pa še bolj preprosto, kako bi lahko našli korelacijo (ki zahteva, da sta oba njena vnosa številska) med, recimo, `paketom` in `ceno`? 🤷🤷\n",
+    "\n",
+    "Modeli strojnega učenja najbolje delujejo s številski lastnostmi namesto z besedilnimi vrednostmi, zato je običajno potrebno pretvoriti kategorijske lastnosti v številske predstavitve.\n",
+    "\n",
+    "To pomeni, da moramo najti način za preoblikovanje naših napovednih spremenljivk, da jih model lahko učinkoviteje uporabi, kar imenujemo `inženiring lastnosti`.\n"
+   ],
+   "metadata": {
+    "id": "7hsHoxsStyjJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Predobdelava podatkov za modeliranje z recepti 👩‍🍳👨‍🍳\n",
+    "\n",
+    "Dejavnosti, ki preoblikujejo vrednosti napovedovalcev, da jih model lažje učinkovito uporabi, se imenujejo `inženiring značilnosti`.\n",
+    "\n",
+    "Različni modeli imajo različne zahteve glede predobdelave. Na primer, metoda najmanjših kvadratov zahteva `kodiranje kategornih spremenljivk`, kot so mesec, sorta in ime_mesta. To preprosto pomeni `pretvorbo` stolpca s `kategorničnimi vrednostmi` v enega ali več `številskih stolpcev`, ki nadomestijo izvirni stolpec.\n",
+    "\n",
+    "Na primer, predpostavimo, da vaši podatki vključujejo naslednjo kategornično značilnost:\n",
+    "\n",
+    "|   mesto   |\n",
+    "|:---------:|\n",
+    "|  Denver   |\n",
+    "|  Nairobi  |\n",
+    "|   Tokio   |\n",
+    "\n",
+    "Uporabite lahko *ordinalno kodiranje*, da vsaki kategoriji dodelite edinstveno celoštevilsko vrednost, kot je to:\n",
+    "\n",
+    "| mesto |\n",
+    "|:-----:|\n",
+    "|   0   |\n",
+    "|   1   |\n",
+    "|   2   |\n",
+    "\n",
+    "In to bomo storili s svojimi podatki!\n",
+    "\n",
+    "V tem razdelku bomo raziskali še en izjemen paket Tidymodels: [recipes](https://tidymodels.github.io/recipes/) - ki je zasnovan za pomoč pri predobdelavi podatkov **preden** treniramo model. V svojem jedru je recept objekt, ki določa, katere korake je treba uporabiti na podatkovnem naboru, da ga pripravimo za modeliranje.\n",
+    "\n",
+    "Zdaj pa ustvarimo recept, ki pripravi naše podatke za modeliranje tako, da za vse opazovanja v stolpcih napovedovalcev nadomesti edinstveno celoštevilsko vrednost:\n"
+   ],
+   "metadata": {
+    "id": "AD5kQbcvt3Xl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\n",
+    "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "# Print out the recipe\n",
+    "pumpkins_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "BNaFKXfRt9TU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Super! 👏 Pravkar smo ustvarili naš prvi recept, ki določa izid (ceno) in ustrezne napovedne spremenljivke ter da morajo biti vsi stolpci napovednih spremenljivk kodirani v nabor celih števil 🙌! Hitro si poglejmo podrobnosti:\n",
+    "\n",
+    "-   Klic funkcije `recipe()` s formulo pove receptu *vloge* spremenljivk, pri čemer uporablja podatke `new_pumpkins` kot referenco. Na primer, stolpec `price` je bil dodeljen vlogi `outcome` (izid), medtem ko so bili preostali stolpci dodeljeni vlogi `predictor` (napovednik).\n",
+    "\n",
+    "-   `step_integer(all_predictors(), zero_based = TRUE)` določa, da morajo biti vsi napovedniki pretvorjeni v nabor celih števil, pri čemer se številčenje začne pri 0.\n",
+    "\n",
+    "Prepričani smo, da imate misli, kot so: \"To je tako kul!! Ampak kaj, če bi moral potrditi, da recepti dejansko delajo točno to, kar pričakujem? 🤔\"\n",
+    "\n",
+    "To je odlična misel! Vidite, ko je vaš recept definiran, lahko ocenite parametre, potrebne za dejansko predobdelavo podatkov, in nato pridobite obdelane podatke. Tega običajno ne potrebujete, ko uporabljate Tidymodels (v trenutku bomo videli običajno prakso -> `workflows`), vendar je to lahko koristno, ko želite narediti nekakšen pregled za potrditev, da recepti delajo, kar pričakujete.\n",
+    "\n",
+    "Za to boste potrebovali še dva glagola: `prep()` in `bake()`, in kot vedno, vam naši mali R prijatelji, ki jih je ustvarila [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations), pomagajo bolje razumeti to temo!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Umetniško delo @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Umetniško delo \\@allison_horst](../../../../../../2-Regression/3-Linear/images/recipes.png){width=\"550\"}-->\n"
+   ],
+   "metadata": {
+    "id": "KEiO0v7kuC9O"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): oceni potrebne parametre iz učnega nabora, ki jih je mogoče kasneje uporabiti na drugih podatkovnih naborih. Na primer, za določen stolpec napovednika, kateremu opazovanju bo dodeljena cela števila 0, 1, 2 itd.\n",
+    "\n",
+    "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): vzame pripravljeni recept in uporabi operacije na katerem koli podatkovnem naboru.\n",
+    "\n",
+    "Torej, pripravimo in uporabimo naše recepte, da resnično potrdimo, da bodo stolpci napovednikov v ozadju najprej kodirani, preden se model prilega.\n"
+   ],
+   "metadata": {
+    "id": "Q1xtzebuuTCP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep the recipe\n",
+    "pumpkins_prep <- prep(pumpkins_recipe)\n",
+    "\n",
+    "# Bake the recipe to extract a preprocessed new_pumpkins data\n",
+    "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\n",
+    "\n",
+    "# Print out the baked data set\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FGBbJbP_uUUn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woo-hoo!🥳 Obdelani podatki `baked_pumpkins` imajo vse napovedne spremenljivke kodirane, kar potrjuje, da bodo koraki predobdelave, opredeljeni kot naš recept, delovali po pričakovanjih. To sicer otežuje branje, a je veliko bolj razumljivo za Tidymodels! Vzemite si čas, da ugotovite, katera opazovanja so bila preslikana v ustrezne celoštevilske vrednosti.\n",
+    "\n",
+    "Prav tako je vredno omeniti, da je `baked_pumpkins` podatkovni okvir, na katerem lahko izvajamo izračune.\n",
+    "\n",
+    "Na primer, poskusimo najti dobro korelacijo med dvema točkama vaših podatkov, da bi potencialno zgradili dober napovedni model. Za to bomo uporabili funkcijo `cor()`. Vnesite `?cor()`, da izveste več o funkciji.\n"
+   ],
+   "metadata": {
+    "id": "1dvP0LBUueAW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the correlation between the city_name and the price\n",
+    "cor(baked_pumpkins$city_name, baked_pumpkins$price)\n",
+    "\n",
+    "# Find the correlation between the package and the price\n",
+    "cor(baked_pumpkins$package, baked_pumpkins$price)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "3bQzXCjFuiSV"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kot se izkaže, obstaja le šibka povezava med mestom in ceno. Vendar pa je nekoliko boljša povezava med paketom in njegovo ceno. To ima smisel, kajne? Običajno velja, da večja kot je škatla s pridelki, višja je cena.\n",
+    "\n",
+    "Medtem pa poskusimo vizualizirati tudi korelacijsko matriko vseh stolpcev z uporabo paketa `corrplot`.\n"
+   ],
+   "metadata": {
+    "id": "BToPWbgjuoZw"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the corrplot package\n",
+    "library(corrplot)\n",
+    "\n",
+    "# Obtain correlation matrix\n",
+    "corr_mat <- cor(baked_pumpkins %>% \n",
+    "                  # Drop columns that are not really informative\n",
+    "                  select(-c(low_price, high_price)))\n",
+    "\n",
+    "# Make a correlation plot between the variables\n",
+    "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZwAL3ksmutVR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Veliko bolje.\n",
+    "\n",
+    "Dobro vprašanje, ki si ga lahko zdaj zastavimo glede teh podatkov, je: '`Kakšno ceno lahko pričakujem za določen paket buč?`' Pojdimo kar takoj k stvari!\n",
+    "\n",
+    "> Opomba: Ko **`bake()`** uporabite na pripravljenem receptu **`pumpkins_prep`** z **`new_data = NULL`**, pridobite obdelane (tj. kodirane) podatke za učenje. Če bi imeli drug nabor podatkov, na primer testni nabor, in bi želeli videti, kako bi recept predhodno obdelal te podatke, bi preprosto uporabili **`bake`** na **`pumpkins_prep`** z **`new_data = test_set`**.\n",
+    "\n",
+    "## 4. Izdelava modela linearne regresije\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Infografika avtorja Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografika avtorja Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "YqXjLuWavNxW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdaj, ko smo sestavili recept in dejansko potrdili, da bodo podatki ustrezno predobdelani, bomo zgradili regresijski model, da odgovorimo na vprašanje: `Kakšno ceno lahko pričakujem za določen paket buč?`\n",
+    "\n",
+    "#### Učenje linearnega regresijskega modela z uporabo učnega nabora\n",
+    "\n",
+    "Kot ste verjetno že ugotovili, je stolpec *price* `izhodna` spremenljivka, medtem ko je stolpec *package* `napovedna` spremenljivka.\n",
+    "\n",
+    "Za to bomo najprej razdelili podatke tako, da bo 80 % šlo v učni nabor in 20 % v testni nabor, nato pa definirali recept, ki bo kodiral napovedni stolpec v niz celih števil, in zgradili specifikacijo modela. Recepta ne bomo pripravili in uporabili, saj že vemo, da bo podatke predobdelal, kot je pričakovano.\n"
+   ],
+   "metadata": {
+    "id": "Pq0bSzCevW-h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\n",
+    "# Split the data into training and test sets\n",
+    "pumpkins_split <- new_pumpkins %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "\n",
+    "# Extract training and test data\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a recipe for preprocessing the data\n",
+    "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a linear model specification\n",
+    "lm_spec <- linear_reg() %>% \n",
+    "  set_engine(\"lm\") %>% \n",
+    "  set_mode(\"regression\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "CyoEh_wuvcLv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično! Zdaj, ko imamo recept in specifikacijo modela, moramo najti način, kako ju združiti v objekt, ki bo najprej predobdelal podatke (v ozadju prep+bake), nato prilegal model na predobdelane podatke in omogočal tudi morebitne aktivnosti po obdelavi. Kako ti je to všeč za mirno vest!🤩\n",
+    "\n",
+    "V Tidymodels se ta priročen objekt imenuje [`workflow`](https://workflows.tidymodels.org/) in priročno združuje tvoje komponente modeliranja! To je tisto, kar bi v *Pythonu* imenovali *pipelines*.\n",
+    "\n",
+    "Torej, združimo vse skupaj v workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "G3zF_3DqviFJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Hold modelling components in a workflow\n",
+    "lm_wf <- workflow() %>% \n",
+    "  add_recipe(lm_pumpkins_recipe) %>% \n",
+    "  add_model(lm_spec)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "lm_wf"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "T3olroU3v-WX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Poleg tega je mogoče potek dela prilagoditi/usposobiti na zelo podoben način, kot se lahko model.\n"
+   ],
+   "metadata": {
+    "id": "zd1A5tgOwEPX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Train the model\n",
+    "lm_wf_fit <- lm_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the model coefficients learned \n",
+    "lm_wf_fit"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NhJagFumwFHf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Iz modelnega izhoda lahko vidimo koeficiente, pridobljene med učenjem. Ti predstavljajo koeficiente premice najboljše prileganja, ki nam daje najnižjo skupno napako med dejansko in napovedano spremenljivko.\n",
+    "\n",
+    "#### Ocenjevanje uspešnosti modela z uporabo testnega nabora\n",
+    "\n",
+    "Čas je, da preverimo, kako se je model odrezal 📏! Kako to storimo?\n",
+    "\n",
+    "Zdaj, ko smo model naučili, ga lahko uporabimo za napovedovanje na testnem_naboru z uporabo `parsnip::predict()`. Nato lahko te napovedi primerjamo z dejanskimi vrednostmi oznak, da ocenimo, kako dobro (ali ne!) model deluje.\n",
+    "\n",
+    "Začnimo z izdelavo napovedi za testni nabor in nato združimo stolpce s testnim naborom.\n"
+   ],
+   "metadata": {
+    "id": "_4QkGtBTwItF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\n",
+    "predictions <- lm_wf_fit %>% \n",
+    "  predict(new_data = pumpkins_test)\n",
+    "\n",
+    "\n",
+    "# Bind predictions to the test set\n",
+    "lm_results <- pumpkins_test %>% \n",
+    "  select(c(package, price)) %>% \n",
+    "  bind_cols(predictions)\n",
+    "\n",
+    "\n",
+    "# Print the first ten rows of the tibble\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "UFZzTG0gwTs9"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Da, pravkar ste trenirali model in ga uporabili za napovedovanje! 🔮 Je dober? Poglejmo, kako dobro deluje model!\n",
+    "\n",
+    "V Tidymodels to naredimo z uporabo `yardstick::metrics()`! Pri linearni regresiji se osredotočimo na naslednje metrike:\n",
+    "\n",
+    "-   `Root Mean Square Error (RMSE)`: Kvadratni koren [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). To daje absolutno metriko v isti enoti kot oznaka (v tem primeru cena buče). Manjša kot je vrednost, boljši je model (v preprostem smislu predstavlja povprečno ceno, za katero so napovedi napačne!).\n",
+    "\n",
+    "-   `Coefficient of Determination (običajno znan kot R-squared ali R2)`: Relativna metrika, pri kateri višja vrednost pomeni boljše prileganje modela. V bistvu ta metrika predstavlja, koliko variance med napovedanimi in dejanskimi vrednostmi oznak model lahko pojasni.\n"
+   ],
+   "metadata": {
+    "id": "0A5MjzM7wW9M"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Evaluate performance of linear regression\n",
+    "metrics(data = lm_results,\n",
+    "        truth = price,\n",
+    "        estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "reJ0UIhQwcEH"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tam gre uspešnost modela. Poglejmo, ali lahko dobimo boljšo indikacijo z vizualizacijo razpršenega grafa paketa in cene, nato pa uporabimo napovedi za dodajanje črte najboljše prileganje.\n",
+    "\n",
+    "To pomeni, da bomo morali pripraviti in obdelati testni niz, da kodiramo stolpec paketa, nato pa to povežemo z napovedmi, ki jih je ustvaril naš model.\n"
+   ],
+   "metadata": {
+    "id": "fdgjzjkBwfWt"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Encode package column\n",
+    "package_encode <- lm_pumpkins_recipe %>% \n",
+    "  prep() %>% \n",
+    "  bake(new_data = pumpkins_test) %>% \n",
+    "  select(package)\n",
+    "\n",
+    "\n",
+    "# Bind encoded package column to the results\n",
+    "lm_results <- lm_results %>% \n",
+    "  bind_cols(package_encode %>% \n",
+    "              rename(package_integer = package)) %>% \n",
+    "  relocate(package_integer, .after = package)\n",
+    "\n",
+    "\n",
+    "# Print new results data frame\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 5)\n",
+    "\n",
+    "\n",
+    "# Make a scatter plot\n",
+    "lm_results %>% \n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\n",
+    "  geom_point(size = 1.6) +\n",
+    "  # Overlay a line of best fit\n",
+    "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\n",
+    "  xlab(\"package\")\n",
+    "  \n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R0nw719lwkHE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično! Kot lahko vidiš, linearni regresijski model ne posploši najbolje odnosa med paketom in njegovo ustrezno ceno.\n",
+    "\n",
+    "🎃 Čestitke, pravkar si ustvaril model, ki lahko pomaga napovedati ceno nekaj vrst buč. Tvoja praznična bučna njiva bo čudovita. Ampak verjetno lahko ustvariš še boljši model!\n",
+    "\n",
+    "## 5. Ustvari polinomski regresijski model\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Infografika: Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografika: Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "HOCqJXLTwtWI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Včasih naši podatki morda nimajo linearne povezave, vendar še vedno želimo napovedati rezultat. Polinomska regresija nam lahko pomaga pri napovedovanju bolj zapletenih nelinearnih povezav.\n",
+    "\n",
+    "Vzemimo na primer povezavo med embalažo in ceno v našem naboru podatkov o bučah. Čeprav je včasih med spremenljivkami linearna povezava - večja kot je buča po volumnu, višja je cena - te povezave včasih ni mogoče prikazati kot ravnino ali ravno črto.\n",
+    "\n",
+    "> ✅ Tukaj so [nekateri dodatni primeri](https://online.stat.psu.edu/stat501/lesson/9/9.8) podatkov, ki bi lahko uporabili polinomsko regresijo\n",
+    ">\n",
+    "> Ponovno si oglejte povezavo med sorto in ceno na prejšnjem grafu. Ali se vam zdi, da bi moral ta raztros nujno analizirati z ravno črto? Morda ne. V tem primeru lahko poskusite polinomsko regresijo.\n",
+    ">\n",
+    "> ✅ Polinomi so matematični izrazi, ki lahko vsebujejo eno ali več spremenljivk in koeficientov\n",
+    "\n",
+    "#### Učimo polinomski regresijski model z uporabo učnega nabora\n",
+    "\n",
+    "Polinomska regresija ustvari *ukrivljeno črto*, ki bolje ustreza nelinearnim podatkom.\n",
+    "\n",
+    "Poglejmo, ali bo polinomski model boljši pri napovedovanju. Sledili bomo nekoliko podobnemu postopku kot prej:\n",
+    "\n",
+    "-   Ustvarite recept, ki določa korake predobdelave, ki jih je treba izvesti na naših podatkih, da jih pripravimo za modeliranje, tj. kodiranje napovedovalcev in izračunavanje polinomov stopnje *n*\n",
+    "\n",
+    "-   Zgradite specifikacijo modela\n",
+    "\n",
+    "-   Združite recept in specifikacijo modela v delovni tok\n",
+    "\n",
+    "-   Ustvarite model z ujemanjem delovnega toka\n",
+    "\n",
+    "-   Ocenite, kako dobro model deluje na testnih podatkih\n",
+    "\n",
+    "Pojdimo kar v akcijo!\n"
+   ],
+   "metadata": {
+    "id": "VcEIpRV9wzYr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\r\n",
+    "poly_pumpkins_recipe <-\r\n",
+    "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+    "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+    "  step_poly(all_predictors(), degree = 4)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model specification\r\n",
+    "poly_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Bundle recipe and model spec into a workflow\r\n",
+    "poly_wf <- workflow() %>% \r\n",
+    "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+    "  add_model(poly_spec)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model\r\n",
+    "poly_wf_fit <- poly_wf %>% \r\n",
+    "  fit(data = pumpkins_train)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print learned model coefficients\r\n",
+    "poly_wf_fit\r\n",
+    "\r\n",
+    "  "
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "63n_YyRXw3CC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Ocenjevanje zmogljivosti modela\n",
+    "\n",
+    "👏👏 Ustvarili ste polinomski model, zdaj pa naredimo napovedi na testnem naboru!\n"
+   ],
+   "metadata": {
+    "id": "-LHZtztSxDP0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make price predictions on test data\r\n",
+    "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+    "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+    "  relocate(.pred, .after = last_col())\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the results\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YUFpQ_dKxJGx"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Juhu, ocenimo, kako se je model odrezal na testnem naboru z uporabo `yardstick::metrics()`.\n"
+   ],
+   "metadata": {
+    "id": "qxdyj86bxNGZ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "metrics(data = poly_results, truth = price, estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "8AW5ltkBxXDm"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Veliko boljša zmogljivost.\n",
+    "\n",
+    "`rmse` se je zmanjšal s približno 7 na približno 3, kar kaže na zmanjšano napako med dejansko ceno in napovedano ceno. To lahko *približno* interpretiramo kot povprečno napako napovedi, ki znaša približno 3 USD. `rsq` se je povečal s približno 0,4 na 0,8.\n",
+    "\n",
+    "Vse te metrike kažejo, da polinomski model deluje veliko bolje kot linearni model. Odlično delo!\n",
+    "\n",
+    "Poglejmo, ali lahko to vizualiziramo!\n"
+   ],
+   "metadata": {
+    "id": "6gLHNZDwxYaS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Bind encoded package column to the results\r\n",
+    "poly_results <- poly_results %>% \r\n",
+    "  bind_cols(package_encode %>% \r\n",
+    "              rename(package_integer = package)) %>% \r\n",
+    "  relocate(package_integer, .after = package)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print new results data frame\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+    "  xlab(\"package\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "A83U16frxdF1"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Lahko vidite ukrivljeno črto, ki se bolje prilega vašim podatkom! 🤩\n",
+    "\n",
+    "To lahko naredite še bolj gladko, če podate polinomsko formulo funkciji `geom_smooth`, kot to:\n"
+   ],
+   "metadata": {
+    "id": "4U-7aHOVxlGU"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+    "  xlab(\"package\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5vzNT0Uexm-w"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tako kot gladka krivulja!🤩\n",
+    "\n",
+    "Tukaj je, kako bi naredili novo napoved:\n"
+   ],
+   "metadata": {
+    "id": "v9u-wwyLxq4G"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a hypothetical data frame\r\n",
+    "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+    "\r\n",
+    "# Make predictions using linear model\r\n",
+    "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Make predictions using polynomial model\r\n",
+    "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Return predictions in a list\r\n",
+    "list(\"linear model prediction\" = lm_pred, \r\n",
+    "     \"polynomial model prediction\" = poly_pred)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "jRPSyfQGxuQv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Napoved modela `polynomial` je smiselna, glede na razpršene grafe `price` in `package`! In, če je to boljši model kot prejšnji, ob pogledu na iste podatke, morate načrtovati proračun za te dražje buče!\n",
+    "\n",
+    "🏆 Odlično opravljeno! Ustvarili ste dva regresijska modela v eni lekciji. V zadnjem delu o regresiji se boste naučili o logistični regresiji za določanje kategorij.\n",
+    "\n",
+    "## **🚀Izziv**\n",
+    "\n",
+    "Preizkusite več različnih spremenljivk v tej beležki, da vidite, kako korelacija vpliva na natančnost modela.\n",
+    "\n",
+    "## [**Kvizi po predavanju**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)\n",
+    "\n",
+    "## **Pregled in samostojno učenje**\n",
+    "\n",
+    "V tej lekciji smo se naučili o linearni regresiji. Obstajajo tudi druge pomembne vrste regresije. Preberite o tehnikah Stepwise, Ridge, Lasso in Elasticnet. Dober tečaj za poglobljeno učenje je [Stanfordov tečaj statističnega učenja](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning).\n",
+    "\n",
+    "Če želite izvedeti več o uporabi izjemnega okvira Tidymodels, si oglejte naslednje vire:\n",
+    "\n",
+    "-   Spletna stran Tidymodels: [Začnite z Tidymodels](https://www.tidymodels.org/start/)\n",
+    "\n",
+    "-   Max Kuhn in Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+    "\n",
+    "###### **POSEBNA ZAHVALA:**\n",
+    "\n",
+    "[Allison Horst](https://twitter.com/allison_horst?lang=en) za ustvarjanje izjemnih ilustracij, ki naredijo R bolj prijazen in privlačen. Več ilustracij najdete v njeni [galeriji](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n"
+   ],
+   "metadata": {
+    "id": "8zOLOWqMxzk5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/2-Regression/3-Linear/solution/notebook.ipynb b/translations/sl/2-Regression/3-Linear/solution/notebook.ipynb
new file mode 100644
index 000000000..c9f3ac327
--- /dev/null
+++ b/translations/sl/2-Regression/3-Linear/solution/notebook.ipynb
@@ -0,0 +1,1113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linearna in polinomska regresija za določanje cen buč - Lekcija 3\n",
+    "\n",
+    "Naložite potrebne knjižnice in podatkovni niz. Podatke pretvorite v podatkovni okvir, ki vsebuje podmnožico podatkov:\n",
+    "\n",
+    "- Uporabite samo buče, katerih cena je določena na sod\n",
+    "- Datum pretvorite v mesec\n",
+    "- Ceno izračunajte kot povprečje najvišje in najnižje cene\n",
+    "- Ceno pretvorite tako, da odraža določanje cen glede na količino v sodih\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Raztreseni diagram nas opominja, da imamo podatke le od avgusta do decembra. Verjetno potrebujemo več podatkov, da bi lahko sklepali na linearen način.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Month', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('Month','Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zdi se, da je korelacija precej majhna, vendar obstaja neka druga, bolj pomembna povezava - ker se zdi, da imajo cenovne točke na zgornjem grafu več ločenih grozdov. Naredimo graf, ki bo prikazal različne sorte buč:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax=None\n",
+    "colors = ['red','blue','green','yellow']\n",
+    "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    ax = new_pumpkins[new_pumpkins['Variety']==var].plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.2669192282197318\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n",
+    "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))\n",
+    "pie_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linearna regresija\n",
+    "\n",
+    "Za učenje modela linearne regresije bomo uporabili Scikit Learn:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a3232f8dc0>]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(X_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Naklon premice lahko določimo iz koeficientov linearne regresije:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-0.01751876]), 21.133734359909326)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.coef_, lin_reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Usposobljen model lahko uporabimo za napovedovanje cene:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.64893156])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pumpkin price on programmer's day\n",
+    "\n",
+    "lin_reg.predict([[256]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Polinomska regresija\n",
+    "\n",
+    "Včasih je razmerje med značilnostmi in rezultati po naravi nelinearno. Na primer, cene buč so lahko visoke pozimi (meseci=1,2), nato padejo poleti (meseci=5-7) in nato ponovno narastejo. Linearna regresija tega razmerja ne more natančno zajeti.\n",
+    "\n",
+    "V tem primeru lahko razmislimo o dodajanju dodatnih značilnosti. Preprost način je uporaba polinomov iz vhodnih značilnosti, kar vodi do **polinomske regresije**. V Scikit Learn lahko avtomatsko predračunamo polinomske značilnosti z uporabo cevovodov:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.73 (17.0%)\n",
+      "Model determination:  0.07639977655280217\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a323368ac0>]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)\n",
+    "\n",
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kodiranje sort\n",
+    "\n",
+    "V idealnem svetu bi želeli napovedati cene za različne sorte buč z uporabo istega modela. Da upoštevamo sorto, jo moramo najprej pretvoriti v številčno obliko, oziroma **kodirati**. Obstaja več načinov, kako to storiti:\n",
+    "\n",
+    "* Preprosto številčno kodiranje, ki ustvari tabelo različnih sort in nato ime sorte zamenja z indeksom v tej tabeli. To ni najboljša ideja za linearno regresijo, saj linearna regresija upošteva številčno vrednost indeksa, ta pa verjetno ne bo numerično korelirala s ceno.\n",
+    "* Kodiranje s tehniko \"one-hot\", ki stolpec `Variety` zamenja s 4 različnimi stolpci, po enim za vsako sorto, ki vsebujejo 1, če ustrezna vrstica pripada določeni sorti, in 0 sicer.\n",
+    "\n",
+    "Spodnja koda prikazuje, kako lahko kodiramo sorto s tehniko \"one-hot\":\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FAIRYTALE</th>\n",
+       "      <th>MINIATURE</th>\n",
+       "      <th>MIXED HEIRLOOM VARIETIES</th>\n",
+       "      <th>PIE TYPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE\n",
+       "70            0          0                         0         1\n",
+       "71            0          0                         0         1\n",
+       "72            0          0                         0         1\n",
+       "73            0          0                         0         1\n",
+       "74            0          0                         0         1\n",
+       "...         ...        ...                       ...       ...\n",
+       "1738          0          1                         0         0\n",
+       "1739          0          1                         0         0\n",
+       "1740          0          1                         0         0\n",
+       "1741          0          1                         0         0\n",
+       "1742          0          1                         0         0\n",
+       "\n",
+       "[415 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(new_pumpkins['Variety'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linearna regresija na sorti\n",
+    "\n",
+    "Zdaj bomo uporabili isto kodo kot zgoraj, vendar bomo namesto `DayOfYear` uporabili našo eno-vroče kodirano sorto kot vhod:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety'])\n",
+    "y = new_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 5.24 (19.7%)\n",
+      "Model determination:  0.774085281105197\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_linear_regression(X,y):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "    lin_reg = LinearRegression()\n",
+    "    lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "    pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "    mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "    print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "    score = lin_reg.score(X_train,y_train)\n",
+    "    print('Model determination: ', score)\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prav tako lahko poskusimo uporabiti druge značilnosti na enak način in jih združiti s številskimi značilnostmi, kot sta `Month` ali `DayOfYear`:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.84 (10.5%)\n",
+      "Model determination:  0.9401096672643048\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['Package']))\n",
+    "y = new_pumpkins['Price']\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Polinomska regresija\n",
+    "\n",
+    "Polinomska regresija se lahko uporablja tudi s kategorialnimi značilnostmi, ki so enovročno kodirane (one-hot-encoded). Koda za učenje polinomske regresije bi bila v bistvu enaka kot tista, ki smo jo videli zgoraj.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.23 (8.25%)\n",
+      "Model determination:  0.9652870784724543\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d77bd89ae7e79780c68c58bab91f13f8",
+   "translation_date": "2025-09-06T13:10:10+00:00",
+   "source_file": "2-Regression/3-Linear/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sl/2-Regression/4-Logistic/notebook.ipynb b/translations/sl/2-Regression/4-Logistic/notebook.ipynb
new file mode 100644
index 000000000..1f0e117ab
--- /dev/null
+++ b/translations/sl/2-Regression/4-Logistic/notebook.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sorte buč in barva\n",
+    "\n",
+    "Naložite potrebne knjižnice in podatkovni niz. Podatke pretvorite v podatkovni okvir, ki vsebuje podmnožico podatkov:\n",
+    "\n",
+    "Poglejmo razmerje med barvo in sorto.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ustvarite model logistične regresije - Lekcija 4\n",
+    "\n",
+    "![Infografika: Logistična vs. linearna regresija](../../../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)\n",
+    "\n",
+    "#### **[Predhodni kviz](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
+    "\n",
+    "#### Uvod\n",
+    "\n",
+    "V tej zadnji lekciji o regresiji, eni izmed osnovnih *klasičnih* tehnik strojnega učenja, si bomo ogledali logistično regresijo. To tehniko bi uporabili za odkrivanje vzorcev za napovedovanje binarnih kategorij. Ali je ta sladkarija čokolada ali ne? Ali je ta bolezen nalezljiva ali ne? Ali bo ta stranka izbrala ta izdelek ali ne?\n",
+    "\n",
+    "V tej lekciji boste spoznali:\n",
+    "\n",
+    "-   Tehnike za logistično regresijo\n",
+    "\n",
+    "✅ Poglobite svoje razumevanje dela s to vrsto regresije v tem [učnem modulu](https://learn.microsoft.com/training/modules/introduction-classification-models/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "## Predpogoji\n",
+    "\n",
+    "Ker smo že delali s podatki o bučah, smo zdaj dovolj seznanjeni z njimi, da ugotovimo, da obstaja ena binarna kategorija, s katero lahko delamo: `Barva`.\n",
+    "\n",
+    "Zgradimo model logistične regresije, da napovemo, glede na nekatere spremenljivke, *kakšne barve bo določena buča verjetno* (oranžna 🎃 ali bela 👻).\n",
+    "\n",
+    "> Zakaj govorimo o binarni klasifikaciji v lekciji, ki se ukvarja z regresijo? Izključno zaradi jezikovne priročnosti, saj je logistična regresija [pravzaprav metoda klasifikacije](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), čeprav temelji na linearni metodi. Več o drugih načinih klasifikacije podatkov boste izvedeli v naslednji skupini lekcij.\n",
+    "\n",
+    "Za to lekcijo bomo potrebovali naslednje pakete:\n",
+    "\n",
+    "-   `tidyverse`: [Tidyverse](https://www.tidyverse.org/) je [zbirka paketov za R](https://www.tidyverse.org/packages), zasnovana za hitrejše, lažje in bolj zabavno delo z podatki!\n",
+    "\n",
+    "-   `tidymodels`: [Tidymodels](https://www.tidymodels.org/) je okvir [zbirke paketov](https://www.tidymodels.org/packages/) za modeliranje in strojno učenje.\n",
+    "\n",
+    "-   `janitor`: Paket [janitor](https://github.com/sfirke/janitor) ponuja preprosta orodja za pregledovanje in čiščenje umazanih podatkov.\n",
+    "\n",
+    "-   `ggbeeswarm`: Paket [ggbeeswarm](https://github.com/eclarke/ggbeeswarm) omogoča ustvarjanje grafov v slogu \"beeswarm\" z uporabo ggplot2.\n",
+    "\n",
+    "Namestite jih lahko z ukazom:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+    "\n",
+    "Alternativno, spodnji skript preveri, ali imate potrebne pakete za dokončanje tega modula, in jih namesti, če manjkajo.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## **Določite vprašanje**\n",
+    "\n",
+    "Za naše namene bomo to izrazili kot binarno: 'Bela' ali 'Ne bela'. V našem naboru podatkov obstaja tudi kategorija 'črtasta', vendar je primerov te kategorije malo, zato je ne bomo uporabili. Kategorija tako ali tako izgine, ko odstranimo ničelne vrednosti iz nabora podatkov.\n",
+    "\n",
+    "> 🎃 Zabavno dejstvo: bele buče včasih imenujemo 'duhove' buče. Niso zelo enostavne za rezljanje, zato niso tako priljubljene kot oranžne, vendar so videti kul! Tako bi lahko naše vprašanje preoblikovali tudi kot: 'Duh' ali 'Ne duh'. 👻\n",
+    "\n",
+    "## **O logistični regresiji**\n",
+    "\n",
+    "Logistična regresija se od linearne regresije, o kateri ste se že učili, razlikuje v nekaj pomembnih vidikih.\n",
+    "\n",
+    "#### **Binarna klasifikacija**\n",
+    "\n",
+    "Logistična regresija ne ponuja enakih funkcij kot linearna regresija. Prva ponuja napoved o `binarni kategoriji` (\"oranžna ali ne oranžna\"), medtem ko je druga sposobna napovedovati `neprekinjene vrednosti`, na primer glede na izvor buče in čas žetve, *koliko se bo njena cena zvišala*.\n",
+    "\n",
+    "![Infografika avtorja Dasani Madipalli](../../../../../../2-Regression/4-Logistic/images/pumpkin-classifier.png)\n",
+    "\n",
+    "### Druge klasifikacije\n",
+    "\n",
+    "Obstajajo tudi druge vrste logistične regresije, vključno z multinomno in ordinalno:\n",
+    "\n",
+    "- **Multinomna**, ki vključuje več kot eno kategorijo - \"Oranžna, Bela in Črtasta\".\n",
+    "\n",
+    "- **Ordinalna**, ki vključuje urejene kategorije, uporabna, če želimo logično urediti naše rezultate, na primer naše buče, ki so razvrščene po končnem številu velikosti (mini,sm,med,lg,xl,xxl).\n",
+    "\n",
+    "![Multinomna vs ordinalna regresija](../../../../../../2-Regression/4-Logistic/images/multinomial-vs-ordinal.png)\n",
+    "\n",
+    "#### **Spremenljivke NI NUJNO, da so povezane**\n",
+    "\n",
+    "Se spomnite, kako je linearna regresija bolje delovala z bolj povezanimi spremenljivkami? Logistična regresija je nasprotje - spremenljivke ni nujno, da se ujemajo. To ustreza tem podatkom, ki imajo nekoliko šibke korelacije.\n",
+    "\n",
+    "#### **Potrebujete veliko čistih podatkov**\n",
+    "\n",
+    "Logistična regresija bo dala natančnejše rezultate, če uporabite več podatkov; naš majhen nabor podatkov ni optimalen za to nalogo, zato imejte to v mislih.\n",
+    "\n",
+    "✅ Razmislite o vrstah podatkov, ki bi bile primerne za logistično regresijo.\n",
+    "\n",
+    "## Naloga - uredite podatke\n",
+    "\n",
+    "Najprej nekoliko očistite podatke, odstranite ničelne vrednosti in izberite le nekatere stolpce:\n",
+    "\n",
+    "1. Dodajte naslednjo kodo:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the core tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data and clean column names\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \n",
+    "  clean_names()\n",
+    "\n",
+    "# Select desired columns\n",
+    "pumpkins_select <- pumpkins %>% \n",
+    "  select(c(city_name, package, variety, origin, item_size, color)) \n",
+    "\n",
+    "# Drop rows containing missing values and encode color as factor (category)\n",
+    "pumpkins_select <- pumpkins_select %>% \n",
+    "  drop_na() %>% \n",
+    "  mutate(color = factor(color))\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins_select %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vedno lahko pogledate svoj novi podatkovni okvir z uporabo funkcije [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html), kot je prikazano spodaj:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pumpkins_select %>% \n",
+    "  glimpse()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Potrdimo, da bomo dejansko reševali problem binarne klasifikacije:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Subset distinct observations in outcome column\n",
+    "pumpkins_select %>% \n",
+    "  distinct(color)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vizualizacija - kategorni graf\n",
+    "Do sedaj ste znova naložili podatke o bučah in jih očistili, da ste ohranili podatkovni niz, ki vsebuje nekaj spremenljivk, vključno z Barvo. Vizualizirajmo podatkovni okvir v zvezku z uporabo knjižnice ggplot.\n",
+    "\n",
+    "Knjižnica ggplot ponuja nekaj odličnih načinov za vizualizacijo vaših podatkov. Na primer, lahko primerjate porazdelitve podatkov za vsako Sorto in Barvo v kategorni graf.\n",
+    "\n",
+    "1. Ustvarite takšen graf z uporabo funkcije geombar, pri čemer uporabite naše podatke o bučah in določite barvno preslikavo za vsako kategorijo buč (oranžna ali bela):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Specify colors for each value of the hue variable\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# Create the bar plot\n",
+    "ggplot(pumpkins_select, aes(y = variety, fill = color)) +\n",
+    "  geom_bar(position = \"dodge\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(y = \"Variety\", fill = \"Color\") +\n",
+    "  theme_minimal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Z opazovanjem podatkov lahko vidite, kako so podatki o barvi povezani z vrsto.\n",
+    "\n",
+    "✅ Glede na ta kategorni graf, katere zanimive raziskave si lahko predstavljate?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predobdelava podatkov: kodiranje značilnosti\n",
+    "\n",
+    "Naš nabor podatkov o bučah vsebuje nize kot vrednosti za vse stolpce. Delo s kategorialnimi podatki je za ljudi intuitivno, za računalnike pa ne. Algoritmi strojnega učenja delujejo dobro s številkami. Zato je kodiranje zelo pomemben korak v fazi predobdelave podatkov, saj nam omogoča, da kategorialne podatke pretvorimo v številske, ne da bi pri tem izgubili informacije. Dobro kodiranje vodi do gradnje dobrega modela.\n",
+    "\n",
+    "Za kodiranje značilnosti obstajata dve glavni vrsti kodirnikov:\n",
+    "\n",
+    "1. **Ordinalni kodirnik**: Ta je primeren za ordinalne spremenljivke, torej kategorialne spremenljivke, kjer podatki sledijo logičnemu vrstnemu redu, kot je stolpec `item_size` v našem naboru podatkov. Ustvari preslikavo, kjer je vsaka kategorija predstavljena s številko, ki ustreza vrstnemu redu kategorije v stolpcu.\n",
+    "\n",
+    "2. **Kategorialni kodirnik**: Ta je primeren za nominalne spremenljivke, torej kategorialne spremenljivke, kjer podatki ne sledijo logičnemu vrstnemu redu, kot so vse značilnosti, ki niso `item_size` v našem naboru podatkov. Gre za kodiranje \"ena-vroča\" (one-hot encoding), kar pomeni, da je vsaka kategorija predstavljena z binarnim stolpcem: kodirana spremenljivka je enaka 1, če buča pripada tej sorti, in 0 sicer.\n",
+    "\n",
+    "Tidymodels ponuja še eno uporabno knjižnico: [recipes](https://recipes.tidymodels.org/) - knjižnico za predobdelavo podatkov. Določili bomo `recipe`, ki opredeljuje, da je treba vse stolpce napovedovalcev zakodirati v nabor celih števil, nato pa ga `prep`-irati, da ocenimo potrebne količine in statistike za posamezne operacije, in na koncu `bake`, da uporabimo izračune na novih podatkih.\n",
+    "\n",
+    "> Običajno se recipes uporablja kot predprocesor za modeliranje, kjer določa, kateri koraki naj se uporabijo na naboru podatkov, da ga pripravimo za modeliranje. V tem primeru je **zelo priporočljivo**, da uporabite `workflow()` namesto ročnega ocenjevanja recepta z uporabo prep in bake. Vse to bomo videli v kratkem.\n",
+    ">\n",
+    "> Zaenkrat pa uporabljamo recipes + prep + bake, da določimo, kateri koraki naj se uporabijo na naboru podatkov, da ga pripravimo za analizo podatkov, in nato izvlečemo predobdelane podatke z uporabljenimi koraki.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Preprocess and extract data to allow some data analysis\n",
+    "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>%\n",
+    "  # Define ordering for item_size column\n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  # Convert factors to numbers using the order defined above (Ordinal encoding)\n",
+    "  step_integer(item_size, zero_based = F) %>%\n",
+    "  # Encode all other predictors using one hot encoding\n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE) %>%\n",
+    "  prep(data = pumpkin_select) %>%\n",
+    "  bake(new_data = NULL)\n",
+    "\n",
+    "# Display the first few rows of preprocessed data\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "✅ Kakšne so prednosti uporabe ordinalnega kodirnika za stolpec Velikost artikla?\n",
+    "\n",
+    "### Analizirajte odnose med spremenljivkami\n",
+    "\n",
+    "Zdaj, ko smo predhodno obdelali naše podatke, lahko analiziramo odnose med značilnostmi in oznako, da dobimo predstavo o tem, kako dobro bo model lahko napovedal oznako glede na značilnosti. Najboljši način za izvedbo takšne analize je vizualizacija podatkov. \n",
+    "Ponovno bomo uporabili funkcijo ggplot geom_boxplot_, da vizualiziramo odnose med Velikostjo artikla, Sorto in Barvo v kategorijskem grafu. Za boljšo vizualizacijo podatkov bomo uporabili kodiran stolpec Velikost artikla in nekodiran stolpec Sorta.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define the color palette\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins_select_plot<-pumpkins_select\n",
+    "pumpkins_select_plot$item_size <- baked_pumpkins$item_size\n",
+    "\n",
+    "# Create the grouped box plot\n",
+    "ggplot(pumpkins_select_plot, aes(x = `item_size`, y = color, fill = color)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_grid(variety ~ ., scales = \"free_x\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(x = \"Item Size\", y = \"\") +\n",
+    "  theme_minimal() +\n",
+    "  theme(strip.text = element_text(size = 12)) +\n",
+    "  theme(axis.text.x = element_text(size = 10)) +\n",
+    "  theme(axis.title.x = element_text(size = 12)) +\n",
+    "  theme(axis.title.y = element_blank()) +\n",
+    "  theme(legend.position = \"bottom\") +\n",
+    "  guides(fill = guide_legend(title = \"Color\")) +\n",
+    "  theme(panel.spacing = unit(0.5, \"lines\"))+\n",
+    "  theme(strip.text.y = element_text(size = 4, hjust = 0)) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Uporaba swarm plota\n",
+    "\n",
+    "Ker je Barva binarna kategorija (Bela ali Ne), zahteva 'poseben pristop [k vizualizaciji](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf)'.\n",
+    "\n",
+    "Poskusite uporabiti `swarm plot`, da prikažete porazdelitev barve glede na velikost predmeta.\n",
+    "\n",
+    "Uporabili bomo [paket ggbeeswarm](https://github.com/eclarke/ggbeeswarm), ki ponuja metode za ustvarjanje grafov v slogu beeswarm z uporabo ggplot2. Beeswarm grafi so način prikaza točk, ki bi se sicer prekrivale, tako da so razporejene ena poleg druge.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create beeswarm plots of color and item_size\n",
+    "baked_pumpkins %>% \n",
+    "  mutate(color = factor(color)) %>% \n",
+    "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\n",
+    "  geom_quasirandom() +\n",
+    "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\n",
+    "  theme(legend.position = \"none\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zdaj, ko imamo predstavo o razmerju med binarnimi kategorijami barve in večjo skupino velikosti, raziščimo logistično regresijo za določitev verjetne barve določene buče.\n",
+    "\n",
+    "## Ustvarite svoj model\n",
+    "\n",
+    "Izberite spremenljivke, ki jih želite uporabiti v svojem klasifikacijskem modelu, in razdelite podatke na učne in testne sklope. [rsample](https://rsample.tidymodels.org/), paket v Tidymodels, zagotavlja infrastrukturo za učinkovito razdeljevanje podatkov in ponovno vzorčenje:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Split data into 80% for training and 20% for testing\n",
+    "set.seed(2056)\n",
+    "pumpkins_split <- pumpkins_select %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "# Print out the first 5 rows of the training set\n",
+    "pumpkins_train %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🙌 Zdaj smo pripravljeni, da model naučimo tako, da prilagodimo učne značilnosti učni oznaki (barvi).\n",
+    "\n",
+    "Začeli bomo z ustvarjanjem recepta, ki določa korake predobdelave, ki jih je treba izvesti na naših podatkih, da jih pripravimo za modeliranje, tj. kodiranje kategornih spremenljivk v niz celih števil. Tako kot `baked_pumpkins`, ustvarimo `pumpkins_recipe`, vendar ga ne `prep` in `bake`, saj bo vključen v delovni tok, kar boste videli v le nekaj korakih.\n",
+    "\n",
+    "Obstaja kar nekaj načinov za določitev logistične regresije v Tidymodels. Oglejte si `?logistic_reg()`. Za zdaj bomo določili model logistične regresije prek privzetega pogona `stats::glm()`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a recipe that specifies preprocessing steps for modelling\n",
+    "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  step_integer(item_size, zero_based = F) %>%  \n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)\n",
+    "\n",
+    "# Create a logistic model specification\n",
+    "log_reg <- logistic_reg() %>% \n",
+    "  set_engine(\"glm\") %>% \n",
+    "  set_mode(\"classification\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zdaj, ko imamo recept in specifikacijo modela, moramo najti način, kako ju združiti v objekt, ki bo najprej predobdelal podatke (v ozadju uporabil funkciji prep + bake), nato prilagodil model na predobdelane podatke in omogočil tudi morebitne aktivnosti po obdelavi.\n",
+    "\n",
+    "V Tidymodels se ta priročen objekt imenuje [`workflow`](https://workflows.tidymodels.org/) in priročno združuje vaše komponente modeliranja.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Bundle modelling components in a workflow\n",
+    "log_reg_wf <- workflow() %>% \n",
+    "  add_recipe(pumpkins_recipe) %>% \n",
+    "  add_model(log_reg)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "log_reg_wf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ko je potek dela *določen*, lahko model `usposobimo` z uporabo funkcije [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html). Potek dela bo ocenil recept in predhodno obdelal podatke pred usposabljanjem, zato tega ne bo treba ročno narediti z uporabo funkcij prep in bake.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "wf_fit <- log_reg_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the trained workflow\n",
+    "wf_fit\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Model izpiše koeficiente, pridobljene med usposabljanjem.\n",
+    "\n",
+    "Zdaj, ko smo model usposobili z uporabo učnih podatkov, lahko naredimo napovedi na testnih podatkih z uporabo [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Začnimo z uporabo modela za napovedovanje oznak za naš testni niz in verjetnosti za vsako oznako. Ko je verjetnost večja od 0.5, je napovedan razred `WHITE`, sicer `ORANGE`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions for color and corresponding probabilities\n",
+    "results <- pumpkins_test %>% select(color) %>% \n",
+    "  bind_cols(wf_fit %>% \n",
+    "              predict(new_data = pumpkins_test)) %>%\n",
+    "  bind_cols(wf_fit %>%\n",
+    "              predict(new_data = pumpkins_test, type = \"prob\"))\n",
+    "\n",
+    "# Compare predictions\n",
+    "results %>% \n",
+    "  slice_head(n = 10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zelo lepo! To ponuja nekaj več vpogleda v delovanje logistične regresije.\n",
+    "\n",
+    "### Boljše razumevanje prek matrike zmede\n",
+    "\n",
+    "Primerjanje vsake napovedi z ustrezno \"resnično vrednostjo\" ni zelo učinkovit način za določanje, kako dobro model napoveduje. Na srečo ima Tidymodels še nekaj trikov v rokavu: [`yardstick`](https://yardstick.tidymodels.org/) - paket, ki se uporablja za merjenje učinkovitosti modelov z uporabo metrik uspešnosti.\n",
+    "\n",
+    "Ena od metrik uspešnosti, povezanih s klasifikacijskimi problemi, je [`matrika zmede`](https://wikipedia.org/wiki/Confusion_matrix). Matrika zmede opisuje, kako dobro klasifikacijski model deluje. Matrika zmede prikazuje, koliko primerov v vsakem razredu je model pravilno klasificiral. V našem primeru bo pokazala, koliko oranžnih buč je bilo klasificiranih kot oranžne in koliko belih buč kot bele; matrika zmede prav tako prikazuje, koliko jih je bilo klasificiranih v **napačne** kategorije.\n",
+    "\n",
+    "Funkcija [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) iz paketa yardstick izračuna to križno tabelo opazovanih in napovedanih razredov.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Confusion matrix for prediction results\n",
+    "conf_mat(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Poglejmo, kako interpretirati matriko zmede. Naš model mora razvrstiti buče v dve binarni kategoriji, kategorijo `bela` in kategorijo `ne-bela`.\n",
+    "\n",
+    "-   Če vaš model napove, da je buča bela, in ta v resnici spada v kategorijo 'bela', temu pravimo `prava pozitivna napoved` (true positive), kar je prikazano s številom v zgornjem levem kotu.\n",
+    "\n",
+    "-   Če vaš model napove, da buča ni bela, in ta v resnici spada v kategorijo 'bela', temu pravimo `napačna negativna napoved` (false negative), kar je prikazano s številom v spodnjem levem kotu.\n",
+    "\n",
+    "-   Če vaš model napove, da je buča bela, in ta v resnici spada v kategorijo 'ne-bela', temu pravimo `napačna pozitivna napoved` (false positive), kar je prikazano s številom v zgornjem desnem kotu.\n",
+    "\n",
+    "-   Če vaš model napove, da buča ni bela, in ta v resnici spada v kategorijo 'ne-bela', temu pravimo `prava negativna napoved` (true negative), kar je prikazano s številom v spodnjem desnem kotu.\n",
+    "\n",
+    "| Resnica |\n",
+    "|:-------:|\n",
+    "\n",
+    "|               |        |       |\n",
+    "|---------------|--------|-------|\n",
+    "| **Napovedano** | BELA   | ORANŽNA |\n",
+    "| BELA          | TP     | FP    |\n",
+    "| ORANŽNA       | FN     | TN    |\n",
+    "\n",
+    "Kot ste morda uganili, je zaželeno imeti večje število pravih pozitivnih in pravih negativnih napovedi ter manjše število napačnih pozitivnih in napačnih negativnih napovedi, saj to pomeni, da model deluje bolje.\n",
+    "\n",
+    "Matrika zmede je koristna, saj omogoča izračun drugih metrik, ki nam pomagajo bolje oceniti uspešnost klasifikacijskega modela. Poglejmo si nekatere od njih:\n",
+    "\n",
+    "🎓 Natančnost (Precision): `TP/(TP + FP)` je definirana kot delež napovedanih pozitivnih primerov, ki so dejansko pozitivni. Imenujemo jo tudi [pozitivna napovedna vrednost](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\").\n",
+    "\n",
+    "🎓 Priklic (Recall): `TP/(TP + FN)` je definiran kot delež pozitivnih rezultatov glede na število vzorcev, ki so dejansko pozitivni. Imenujemo ga tudi `občutljivost` (sensitivity).\n",
+    "\n",
+    "🎓 Specifičnost (Specificity): `TN/(TN + FP)` je definirana kot delež negativnih rezultatov glede na število vzorcev, ki so dejansko negativni.\n",
+    "\n",
+    "🎓 Točnost (Accuracy): `TP + TN/(TP + TN + FP + FN)` je odstotek oznak, ki so bile pravilno napovedane za vzorec.\n",
+    "\n",
+    "🎓 F-metrika (F Measure): Tehtano povprečje natančnosti in priklica, kjer je najboljša vrednost 1, najslabša pa 0.\n",
+    "\n",
+    "Izračunajmo te metrike!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Combine metric functions and calculate them all at once\n",
+    "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\n",
+    "eval_metrics(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vizualizirajmo ROC krivuljo tega modela\n",
+    "\n",
+    "Naredimo še eno vizualizacijo, da si ogledamo tako imenovano [`ROC krivuljo`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make a roc_curve\n",
+    "results %>% \n",
+    "  roc_curve(color, .pred_ORANGE) %>% \n",
+    "  autoplot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ROC krivulje se pogosto uporabljajo za prikaz rezultatov klasifikatorja glede na njegove prave in lažne pozitivne zadetke. ROC krivulje običajno prikazujejo `True Positive Rate`/občutljivost na Y osi in `False Positive Rate`/1-specifičnost na X osi. Zato sta strmina krivulje in prostor med sredinsko črto ter krivuljo pomembna: želite krivuljo, ki se hitro dvigne in preseže črto. V našem primeru se začne z lažnimi pozitivnimi zadetki, nato pa se črta pravilno dvigne in preseže.\n",
+    "\n",
+    "Na koncu uporabimo `yardstick::roc_auc()` za izračun dejanskega območja pod krivuljo (Area Under the Curve). Eden od načinov interpretacije AUC je kot verjetnost, da model naključno pozitivni primer razvrsti višje kot naključno negativni primer.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate area under curve\n",
+    "results %>% \n",
+    "  roc_auc(color, .pred_ORANGE)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rezultat je približno `0.975`. Ker se AUC giblje med 0 in 1, si želite visok rezultat, saj bo model, ki je 100 % natančen v svojih napovedih, imel AUC vrednost 1; v tem primeru je model *kar dober*.\n",
+    "\n",
+    "V prihodnjih lekcijah o klasifikacijah boste spoznali, kako izboljšati rezultate svojega modela (na primer obravnavanje neuravnoteženih podatkov v tem primeru).\n",
+    "\n",
+    "## 🚀Izziv\n",
+    "\n",
+    "Logistična regresija ponuja še veliko več! Najboljši način za učenje je eksperimentiranje. Poiščite podatkovni niz, ki je primeren za tovrstno analizo, in zgradite model z njim. Kaj ste se naučili? namig: poskusite [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) za zanimive podatkovne nize.\n",
+    "\n",
+    "## Pregled in samostojno učenje\n",
+    "\n",
+    "Preberite prvih nekaj strani [tega dokumenta iz Stanforda](https://web.stanford.edu/~jurafsky/slp3/5.pdf) o praktični uporabi logistične regresije. Razmislite o nalogah, ki so bolj primerne za eno ali drugo vrsto regresijskih nalog, ki smo jih preučevali do zdaj. Kaj bi delovalo najbolje?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "langauge": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "feaf125f481a89c468fa115bf2aed580",
+   "translation_date": "2025-09-06T13:31:00+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
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diff --git a/translations/sl/2-Regression/4-Logistic/solution/notebook.ipynb b/translations/sl/2-Regression/4-Logistic/solution/notebook.ipynb
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index 000000000..47f11ea88
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+++ b/translations/sl/2-Regression/4-Logistic/solution/notebook.ipynb
@@ -0,0 +1,1258 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logistična regresija - Lekcija 4\n",
+    "\n",
+    "Naložite potrebne knjižnice in podatkovni niz. Podatke pretvorite v podatkovni okvir, ki vsebuje podmnožico podatkov:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date   \n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17  \\\n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition   \n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN  \\\n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Origin</th>\n",
+       "      <th>Item Size</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>DELAWARE</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>VIRGINIA</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>36 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name       Package      Variety    Origin Item Size   Color\n",
+       "2  BALTIMORE  24 inch bins  HOWDEN TYPE  DELAWARE       med  ORANGE\n",
+       "3  BALTIMORE  24 inch bins  HOWDEN TYPE  VIRGINIA       med  ORANGE\n",
+       "4  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "5  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "6  BALTIMORE  36 inch bins  HOWDEN TYPE  MARYLAND       med  ORANGE"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select the columns we want to use\n",
+    "columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Drop rows with missing values\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Poglejmo si naše podatke!\n",
+    "\n",
+    "S pomočjo vizualizacije s Seaborn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56d0c650>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 609.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# Specify colors for each values of the hue variable\n",
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# Plot a bar plot to visualize how many pumpkins of each variety are orange or white\n",
+    "sns.catplot(\n",
+    "    data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
+    "    palette=palette, \n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Predobdelava podatkov\n",
+    "\n",
+    "Šifrirajmo značilnosti in oznake, da bomo lažje prikazali podatke in usposobili model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the different values of the 'Item Size' column\n",
+    "pumpkins['Item Size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "# Encode the 'Item Size' column using ordinal encoding\n",
+    "item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
+    "ordinal_features = ['Item Size']\n",
+    "ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "# Encode all the other features using one-hot encoding\n",
+    "categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
+    "categorical_encoder = OneHotEncoder(sparse_output=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_MICHIGAN</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_MICHIGAN  cat__Origin_NEW JERSEY   \n",
+       "2                   0.0  ...                   0.0                     0.0  \\\n",
+       "3                   0.0  ...                   0.0                     0.0   \n",
+       "4                   0.0  ...                   0.0                     0.0   \n",
+       "5                   0.0  ...                   0.0                     0.0   \n",
+       "6                   0.0  ...                   0.0                     0.0   \n",
+       "\n",
+       "   cat__Origin_NEW YORK  cat__Origin_NORTH CAROLINA  cat__Origin_OHIO   \n",
+       "2                   0.0                         0.0               0.0  \\\n",
+       "3                   0.0                         0.0               0.0   \n",
+       "4                   0.0                         0.0               0.0   \n",
+       "5                   0.0                         0.0               0.0   \n",
+       "6                   0.0                         0.0               0.0   \n",
+       "\n",
+       "   cat__Origin_PENNSYLVANIA  cat__Origin_TENNESSEE  cat__Origin_TEXAS   \n",
+       "2                       0.0                    0.0                0.0  \\\n",
+       "3                       0.0                    0.0                0.0   \n",
+       "4                       0.0                    0.0                0.0   \n",
+       "5                       0.0                    0.0                0.0   \n",
+       "6                       0.0                    0.0                0.0   \n",
+       "\n",
+       "   cat__Origin_VERMONT  cat__Origin_VIRGINIA  \n",
+       "2                  0.0                   0.0  \n",
+       "3                  0.0                   1.0  \n",
+       "4                  0.0                   0.0  \n",
+       "5                  0.0                   0.0  \n",
+       "6                  0.0                   0.0  \n",
+       "\n",
+       "[5 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "ct = ColumnTransformer(transformers=[\n",
+    "     ('ord', ordinal_encoder, ordinal_features),\n",
+    "     ('cat', categorical_encoder, categorical_features)\n",
+    "     ])\n",
+    "# Get the encoded features as a pandas DataFrame\n",
+    "ct.set_output(transform='pandas')\n",
+    "encoded_features = ct.fit_transform(pumpkins)\n",
+    "encoded_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_NEW JERSEY  cat__Origin_NEW YORK   \n",
+       "2                   0.0  ...                     0.0                   0.0  \\\n",
+       "3                   0.0  ...                     0.0                   0.0   \n",
+       "4                   0.0  ...                     0.0                   0.0   \n",
+       "5                   0.0  ...                     0.0                   0.0   \n",
+       "6                   0.0  ...                     0.0                   0.0   \n",
+       "\n",
+       "   cat__Origin_NORTH CAROLINA  cat__Origin_OHIO  cat__Origin_PENNSYLVANIA   \n",
+       "2                         0.0               0.0                       0.0  \\\n",
+       "3                         0.0               0.0                       0.0   \n",
+       "4                         0.0               0.0                       0.0   \n",
+       "5                         0.0               0.0                       0.0   \n",
+       "6                         0.0               0.0                       0.0   \n",
+       "\n",
+       "   cat__Origin_TENNESSEE  cat__Origin_TEXAS  cat__Origin_VERMONT   \n",
+       "2                    0.0                0.0                  0.0  \\\n",
+       "3                    0.0                0.0                  0.0   \n",
+       "4                    0.0                0.0                  0.0   \n",
+       "5                    0.0                0.0                  0.0   \n",
+       "6                    0.0                0.0                  0.0   \n",
+       "\n",
+       "   cat__Origin_VIRGINIA  Color  \n",
+       "2                   0.0      0  \n",
+       "3                   1.0      0  \n",
+       "4                   0.0      0  \n",
+       "5                   0.0      0  \n",
+       "6                   0.0      0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "# Encode the 'Color' column using label encoding\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n",
+    "encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
+    "encoded_pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ORANGE', 'WHITE']"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the mapping between the encoded values and the original values\n",
+    "list(label_encoder.inverse_transform([0, 1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analiza odnosov med značilnostmi in oznako\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56322210>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1620 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=pumpkins,\n",
+    "    x=\"Item Size\", y=\"Color\", row='Variety',\n",
+    "    kind=\"box\", orient=\"h\",\n",
+    "    sharex=False, margin_titles=True,\n",
+    "    height=1.8, aspect=4, palette=palette,\n",
+    ")\n",
+    "# Defining axis labels \n",
+    "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
+    "g.set_titles(row_template=\"{row_name}\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Color', ylabel='ord__Item Size'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Suppressing warning message claiming that a portion of points cannot be placed into the plot due to the high number of data points\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')\n",
+    "\n",
+    "palette = {\n",
+    "    0: 'orange',\n",
+    "    1: 'wheat'\n",
+    "}\n",
+    "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pazi**: Ignoriranje opozoril NI najboljša praksa in se je treba temu izogibati, kadar je le mogoče. Opozorila pogosto vsebujejo koristna sporočila, ki nam pomagajo izboljšati kodo in rešiti težavo. Razlog, zakaj ignoriramo to specifično opozorilo, je zagotavljanje berljivosti grafa. Prikaz vseh podatkovnih točk z zmanjšano velikostjo označevalca, ob ohranjanju skladnosti s paleto barv, ustvarja nejasno vizualizacijo.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Zgradite svoj model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# X is the encoded features\n",
+    "X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
+    "# y is the encoded label\n",
+    "y = encoded_pumpkins['Color']\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.94      0.98      0.96       166\n",
+      "           1       0.85      0.67      0.75        33\n",
+      "\n",
+      "    accuracy                           0.92       199\n",
+      "   macro avg       0.89      0.82      0.85       199\n",
+      "weighted avg       0.92      0.92      0.92       199\n",
+      "\n",
+      "Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
+      " 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
+      " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+      " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
+      "F1-score:  0.7457627118644068\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Train a logistic regression model on the pumpkin dataset\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model and print the results\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('F1-score: ', f1_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[162,   4],\n",
+       "       [ 11,  22]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "y_scores = model.predict_proba(X_test)\n",
+    "# calculate ROC curve\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "# Plot the diagonal 50% line\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "# Plot the FPR and TPR achieved by our model\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9749908725812341\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate AUC score\n",
+    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+    "print(auc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "vscode": {
+   "interpreter": {
+    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "ef50cc584e0b79412610cc7da15e1f86",
+   "translation_date": "2025-09-06T13:27:16+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sl/3-Web-App/1-Web-App/notebook.ipynb b/translations/sl/3-Web-App/1-Web-App/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sl/3-Web-App/1-Web-App/solution/notebook.ipynb b/translations/sl/3-Web-App/1-Web-App/solution/notebook.ipynb
new file mode 100644
index 000000000..0f8778dc3
--- /dev/null
+++ b/translations/sl/3-Web-App/1-Web-App/solution/notebook.ipynb
@@ -0,0 +1,267 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5fa2e8f4584c78250ca9729b46562ceb",
+   "translation_date": "2025-09-06T14:32:05+00:00",
+   "source_file": "3-Web-App/1-Web-App/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           datetime                  city state country     shape  \\\n",
+       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
+       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
+       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
+       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
+       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
+       "\n",
+       "   duration (seconds) duration (hours/min)  \\\n",
+       "0              2700.0           45 minutes   \n",
+       "1              7200.0              1-2 hrs   \n",
+       "2                20.0           20 seconds   \n",
+       "3                20.0             1/2 hour   \n",
+       "4               900.0           15 minutes   \n",
+       "\n",
+       "                                            comments date posted   latitude  \\\n",
+       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
+       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
+       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
+       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
+       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
+       "\n",
+       "    longitude  \n",
+       "0  -97.941111  \n",
+       "1  -98.581082  \n",
+       "2   -2.916667  \n",
+       "3  -96.645833  \n",
+       "4 -157.803611  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ufos = pd.read_csv('../data/ufos.csv')\n",
+    "ufos.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "\n",
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()\n",
+    "\n",
+    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufos.dropna(inplace=True)\n",
+    "\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "\n",
+    "X = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       1.00      0.02      0.05       250\n",
+      "           2       0.00      0.00      0.00         8\n",
+      "           3       0.94      1.00      0.97       131\n",
+      "           4       0.95      1.00      0.97      4743\n",
+      "\n",
+      "    accuracy                           0.95      5173\n",
+      "   macro avg       0.78      0.60      0.60      5173\n",
+      "weighted avg       0.95      0.95      0.93      5173\n",
+      "\n",
+      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
+      "Accuracy:  0.9512855209742895\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "model_filename = 'ufo-model.pkl'\n",
+    "pickle.dump(model, open(model_filename,'wb'))\n",
+    "\n",
+    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
+    "print(model.predict([[50,44,-12]]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/1-Introduction/notebook.ipynb b/translations/sl/4-Classification/1-Introduction/notebook.ipynb
new file mode 100644
index 000000000..b63c56b53
--- /dev/null
+++ b/translations/sl/4-Classification/1-Introduction/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d544ef384b7ba73757d830a72372a7f2",
+   "translation_date": "2025-09-06T14:50:43+00:00",
+   "source_file": "4-Classification/1-Introduction/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas opozarjamo, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/sl/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
new file mode 100644
index 000000000..462119e14
--- /dev/null
+++ b/translations/sl/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -0,0 +1,724 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_10-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "2621e24705e8100893c9bf84e0fc8aef",
+   "translation_date": "2025-09-06T14:55:33+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Zgradite klasifikacijski model: Slastne azijske in indijske kuhinje\n"
+   ],
+   "metadata": {
+    "id": "ItETB4tSFprR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Uvod v klasifikacijo: Čiščenje, priprava in vizualizacija podatkov\n",
+    "\n",
+    "V teh štirih lekcijah boste raziskovali temeljni vidik klasičnega strojnega učenja - *klasifikacijo*. Preučili bomo uporabo različnih algoritmov za klasifikacijo na podatkovnem naboru o vseh čudovitih kuhinjah Azije in Indije. Upam, da ste lačni!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/pinch.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Proslavite panazijske kuhinje v teh lekcijah! Slika: Jen Looper</figcaption>\n",
+    "\n",
+    "<!--![Proslavite panazijske kuhinje v teh lekcijah! Slika: Jen Looper](../../../../../../4-Classification/1-Introduction/solution/R/images/pinch.png)-->\n",
+    "\n",
+    "Klasifikacija je oblika [nadzorovanega učenja](https://wikipedia.org/wiki/Supervised_learning), ki ima veliko skupnega s tehnikami regresije. Pri klasifikaciji trenirate model, da napove, v katero `kategorijo` spada določen element. Če je strojno učenje namenjeno napovedovanju vrednosti ali imen stvari z uporabo podatkovnih nizov, potem klasifikacija običajno spada v dve skupini: *binarna klasifikacija* in *večrazredna klasifikacija*.\n",
+    "\n",
+    "Zapomnite si:\n",
+    "\n",
+    "-   **Linearna regresija** vam je pomagala napovedati odnose med spremenljivkami in narediti natančne napovedi, kje bi nov podatkovni element padel v odnosu do te črte. Na primer, lahko bi napovedali numerične vrednosti, kot je *kakšna bo cena buče septembra v primerjavi z decembrom*.\n",
+    "\n",
+    "-   **Logistična regresija** vam je pomagala odkriti \"binarne kategorije\": pri tej cenovni točki, *ali je buča oranžna ali ne-oranžna*?\n",
+    "\n",
+    "Klasifikacija uporablja različne algoritme za določanje drugih načinov določanja oznake ali razreda podatkovne točke. Delajmo s temi podatki o kuhinjah, da vidimo, ali lahko z opazovanjem skupine sestavin določimo izvorno kuhinjo.\n",
+    "\n",
+    "### [**Predlekcijski kviz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)\n",
+    "\n",
+    "### **Uvod**\n",
+    "\n",
+    "Klasifikacija je ena temeljnih dejavnosti raziskovalca strojnega učenja in podatkovnega znanstvenika. Od osnovne klasifikacije binarne vrednosti (\"ali je ta e-pošta spam ali ne?\") do kompleksne klasifikacije slik in segmentacije z uporabo računalniškega vida, je vedno koristno, da lahko podatke razvrstimo v razrede in zastavimo vprašanja o njih.\n",
+    "\n",
+    "Če proces opišemo na bolj znanstven način, vaša metoda klasifikacije ustvari napovedni model, ki vam omogoča mapiranje odnosa med vhodnimi spremenljivkami in izhodnimi spremenljivkami.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/binary-multiclass.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Binarni vs. večrazredni problemi, ki jih algoritmi za klasifikacijo obravnavajo. Infografika: Jen Looper</figcaption>\n",
+    "\n",
+    "Preden začnemo s procesom čiščenja podatkov, njihove vizualizacije in priprave za naloge strojnega učenja, se naučimo nekaj o različnih načinih, kako lahko strojno učenje uporabimo za klasifikacijo podatkov.\n",
+    "\n",
+    "Izpeljana iz [statistike](https://wikipedia.org/wiki/Statistical_classification), klasifikacija z uporabo klasičnega strojnega učenja uporablja značilnosti, kot so `kadilec`, `teža` in `starost`, za določanje *verjetnosti razvoja X bolezni*. Kot tehnika nadzorovanega učenja, podobna regresijskim vajam, ki ste jih izvajali prej, so vaši podatki označeni, algoritmi strojnega učenja pa te oznake uporabljajo za klasifikacijo in napovedovanje razredov (ali 'značilnosti') podatkovnega niza ter njihovo dodelitev skupini ali izidu.\n",
+    "\n",
+    "✅ Vzemite trenutek in si zamislite podatkovni niz o kuhinjah. Kaj bi lahko odgovoril večrazredni model? Kaj bi lahko odgovoril binarni model? Kaj če bi želeli ugotoviti, ali določena kuhinja verjetno uporablja piskavico? Kaj če bi želeli videti, ali bi lahko iz vrečke zvezdastega janeža, artičok, cvetače in hrena pripravili tipično indijsko jed?\n",
+    "\n",
+    "### **Pozdravljeni 'klasifikator'**\n",
+    "\n",
+    "Vprašanje, ki ga želimo zastaviti o tem podatkovnem naboru kuhinj, je pravzaprav **večrazredno vprašanje**, saj imamo na voljo več potencialnih nacionalnih kuhinj. Glede na skupino sestavin, v katerega od teh razredov bodo podatki ustrezali?\n",
+    "\n",
+    "Tidymodels ponuja več različnih algoritmov za klasifikacijo podatkov, odvisno od vrste problema, ki ga želite rešiti. V naslednjih dveh lekcijah se boste naučili o nekaterih od teh algoritmov.\n",
+    "\n",
+    "#### **Predpogoj**\n",
+    "\n",
+    "Za to lekcijo bomo potrebovali naslednje pakete za čiščenje, pripravo in vizualizacijo naših podatkov:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [zbirka paketov za R](https://www.tidyverse.org/packages), zasnovana za hitrejše, lažje in bolj zabavno podatkovno znanost!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je okvir [zbirke paketov](https://www.tidymodels.org/packages/) za modeliranje in strojno učenje.\n",
+    "\n",
+    "-   `DataExplorer`: Paket [DataExplorer](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) je namenjen poenostavitvi in avtomatizaciji procesa EDA ter generiranju poročil.\n",
+    "\n",
+    "-   `themis`: Paket [themis](https://themis.tidymodels.org/) ponuja dodatne korake receptov za obravnavo neuravnoteženih podatkov.\n",
+    "\n",
+    "Namestite jih lahko z:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Alternativno, spodnji skript preveri, ali imate pakete, potrebne za dokončanje tega modula, in jih namesti, če manjkajo.\n"
+   ],
+   "metadata": {
+    "id": "ri5bQxZ-Fz_0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "KIPxa4elGAPI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kasneje bomo naložili te odlične pakete in jih naredili dostopne v naši trenutni R seji. (To je zgolj za ponazoritev, `pacman::p_load()` je to že naredil namesto vas)\n"
+   ],
+   "metadata": {
+    "id": "YkKAxOJvGD4C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Vaja - očistite in uravnotežite svoje podatke\n",
+    "\n",
+    "Prva naloga, preden začnete s tem projektom, je očistiti in **uravnotežiti** svoje podatke za boljše rezultate.\n",
+    "\n",
+    "Spoznajmo podatke! 🕵️\n"
+   ],
+   "metadata": {
+    "id": "PFkQDlk0GN5O"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Qccw7okxGT0S"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zanimivo! Po videzu sodeč je prvi stolpec nekakšen stolpec `id`. Poglejmo si malo več informacij o podatkih.\n"
+   ],
+   "metadata": {
+    "id": "XrWnlgSrGVmR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Basic information about the data\r\n",
+    "df %>%\r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# Visualize basic information above\r\n",
+    "df %>% \r\n",
+    "  plot_intro(ggtheme = theme_light())"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "4UcGmxRxGieA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Iz izpisa lahko takoj vidimo, da imamo `2448` vrstic in `385` stolpcev ter `0` manjkajočih vrednosti. Imamo tudi 1 diskretni stolpec, *cuisine*.\n",
+    "\n",
+    "## Naloga - spoznavanje kuhinj\n",
+    "\n",
+    "Zdaj delo postaja bolj zanimivo. Odkrijmo porazdelitev podatkov glede na kuhinjo.\n"
+   ],
+   "metadata": {
+    "id": "AaPubl__GmH5"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Count observations per cuisine\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(n)\r\n",
+    "\r\n",
+    "# Plot the distribution\r\n",
+    "theme_set(theme_light())\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"cuisine\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FRsBVy5eGrrv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Obstaja končno število kuhinj, vendar je porazdelitev podatkov neenakomerna. To lahko popravite! Preden to storite, raziščite še malo več.\n",
+    "\n",
+    "Nato dodelimo vsako kuhinjo v njen lasten tibble in ugotovimo, koliko podatkov je na voljo (vrstice, stolpci) za posamezno kuhinjo.\n",
+    "\n",
+    "> [Tibble](https://tibble.tidyverse.org/) je sodoben podatkovni okvir.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_filter.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Umetniško delo @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "vVvyDb1kG2in"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create individual tibble for the cuisines\r\n",
+    "thai_df <- df %>% \r\n",
+    "  filter(cuisine == \"thai\")\r\n",
+    "japanese_df <- df %>% \r\n",
+    "  filter(cuisine == \"japanese\")\r\n",
+    "chinese_df <- df %>% \r\n",
+    "  filter(cuisine == \"chinese\")\r\n",
+    "indian_df <- df %>% \r\n",
+    "  filter(cuisine == \"indian\")\r\n",
+    "korean_df <- df %>% \r\n",
+    "  filter(cuisine == \"korean\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Find out how much data is available per cuisine\r\n",
+    "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
+    "    \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
+    "    \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
+    "    \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
+    "    \"korean_df:\", dim(korean_df))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "0TvXUxD3G8Bk"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Vaja - Odkrijte glavne sestavine po kuhinji z uporabo dplyr**\n",
+    "\n",
+    "Zdaj lahko podrobneje raziščete podatke in ugotovite, katere so značilne sestavine za posamezno kuhinjo. Odstraniti morate ponavljajoče se podatke, ki povzročajo zmedo med kuhinjami, zato se lotimo tega problema.\n",
+    "\n",
+    "Ustvarite funkcijo `create_ingredient()` v R, ki vrne podatkovni okvir sestavin. Ta funkcija bo začela z odstranitvijo neuporabnega stolpca in razvrstila sestavine glede na njihovo število.\n",
+    "\n",
+    "Osnovna struktura funkcije v R je:\n",
+    "\n",
+    "`myFunction <- function(arglist){`\n",
+    "\n",
+    "**`...`**\n",
+    "\n",
+    "**`return`**`(value)`\n",
+    "\n",
+    "`}`\n",
+    "\n",
+    "Uvod v funkcije v R najdete [tukaj](https://skirmer.github.io/presentations/functions_with_r.html#1).\n",
+    "\n",
+    "Pojdimo naravnost k stvari! Uporabili bomo [dplyr glagole](https://dplyr.tidyverse.org/), ki smo jih spoznali v prejšnjih lekcijah. Za osvežitev spomina:\n",
+    "\n",
+    "-   `dplyr::select()`: vam pomaga izbrati, katere **stolpce** obdržati ali izključiti.\n",
+    "\n",
+    "-   `dplyr::pivot_longer()`: vam pomaga \"podaljšati\" podatke, s čimer povečate število vrstic in zmanjšate število stolpcev.\n",
+    "\n",
+    "-   `dplyr::group_by()` in `dplyr::summarise()`: vam pomagata najti povzetke statistike za različne skupine in jih predstaviti v pregledni tabeli.\n",
+    "\n",
+    "-   `dplyr::filter()`: ustvari podmnožico podatkov, ki vsebuje samo vrstice, ki ustrezajo vašim pogojem.\n",
+    "\n",
+    "-   `dplyr::mutate()`: vam pomaga ustvariti ali spremeniti stolpce.\n",
+    "\n",
+    "Oglejte si ta [*umetniško* obarvan učni vodič](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) avtorice Allison Horst, ki predstavlja nekaj uporabnih funkcij za obdelavo podatkov v dplyr *(del Tidyverse)*.\n"
+   ],
+   "metadata": {
+    "id": "K3RF5bSCHC76"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Creates a functions that returns the top ingredients by class\r\n",
+    "\r\n",
+    "create_ingredient <- function(df){\r\n",
+    "  \r\n",
+    "  # Drop the id column which is the first colum\r\n",
+    "  ingredient_df = df %>% select(-1) %>% \r\n",
+    "  # Transpose data to a long format\r\n",
+    "    pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
+    "  # Find the top most ingredients for a particular cuisine\r\n",
+    "    group_by(ingredients) %>% \r\n",
+    "    summarise(n_instances = sum(count)) %>% \r\n",
+    "    filter(n_instances != 0) %>% \r\n",
+    "  # Arrange by descending order\r\n",
+    "    arrange(desc(n_instances)) %>% \r\n",
+    "    mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
+    "  \r\n",
+    "  \r\n",
+    "  return(ingredient_df)\r\n",
+    "} # End of function"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "uB_0JR82HTPa"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdaj lahko uporabimo funkcijo, da dobimo vpogled v deset najbolj priljubljenih sestavin po kuhinji. Preizkusimo jo s `thai_df`.\n"
+   ],
+   "metadata": {
+    "id": "h9794WF8HWmc"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Call create_ingredient and display popular ingredients\r\n",
+    "thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
+    "\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "agQ-1HrcHaEA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "V prejšnjem razdelku smo uporabili `geom_col()`, poglejmo, kako lahko uporabite tudi `geom_bar` za ustvarjanje stolpčnih grafikonov. Uporabite `?geom_bar` za nadaljnje branje.\n"
+   ],
+   "metadata": {
+    "id": "kHu9ffGjHdcX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a bar chart for popular thai cuisines\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10) %>% \r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "fb3Bx_3DHj6e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Naredimo enako za japonske podatke\n"
+   ],
+   "metadata": {
+    "id": "RHP_xgdkHnvM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
+    "create_ingredient(df = japanese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "019v8F0XHrRU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kaj pa kitajska kuhinja?\n"
+   ],
+   "metadata": {
+    "id": "iIGM7vO8Hu3v"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
+    "create_ingredient(df = chinese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lHd9_gd2HyzU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ir8qyQbNH1c7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Indian cuisines and make bar chart\r\n",
+    "create_ingredient(df = indian_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ApukQtKjH5FO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Na koncu narišite korejske sestavine.\n"
+   ],
+   "metadata": {
+    "id": "qv30cwY1H-FM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Korean cuisines and make bar chart\r\n",
+    "create_ingredient(df = korean_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lumgk9cHIBie"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Iz vizualizacij podatkov lahko zdaj odstranimo najpogostejše sestavine, ki povzročajo zmedo med različnimi kuhinjami, z uporabo `dplyr::select()`.\n",
+    "\n",
+    "Vsi obožujemo riž, česen in ingver!\n"
+   ],
+   "metadata": {
+    "id": "iO4veMXuIEta"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "iHJPiG6rIUcK"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Predobdelava podatkov z recepti 👩‍🍳👨‍🍳 - Obvladovanje neuravnoteženih podatkov ⚖️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Umetniško delo @allison_horst</figcaption>\n",
+    "\n",
+    "Ker je ta lekcija o kulinariki, moramo postaviti `recepte` v kontekst.\n",
+    "\n",
+    "Tidymodels ponuja še en odličen paket: `recipes` - paket za predobdelavo podatkov.\n"
+   ],
+   "metadata": {
+    "id": "kkFd-JxdIaL6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Poglejmo si ponovno porazdelitev naših kuhinj.\n"
+   ],
+   "metadata": {
+    "id": "6l2ubtTPJAhY"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "old_label_count <- df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "old_label_count"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "1e-E9cb7JDVi"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kot lahko vidite, je število kuhinj precej neenakomerno porazdeljeno. Korejske kuhinje so skoraj trikrat bolj številčne kot tajske kuhinje. Neuravnoteženi podatki pogosto negativno vplivajo na delovanje modela. Pomislite na binarno klasifikacijo. Če večina vaših podatkov pripada enemu razredu, bo model strojnega učenja pogosteje napovedoval ta razred, preprosto zato, ker je zanj na voljo več podatkov. Uravnoteženje podatkov odpravlja to neuravnoteženost in pomaga odstraniti pristranskost. Veliko modelov najbolje deluje, ko je število opazovanj enako, zato se pogosto soočajo s težavami pri delu z neuravnoteženimi podatki.\n",
+    "\n",
+    "Obstajata dva glavna načina za obravnavo neuravnoteženih podatkovnih nizov:\n",
+    "\n",
+    "-   dodajanje opazovanj v manjšinski razred: `Over-sampling`, npr. uporaba algoritma SMOTE\n",
+    "\n",
+    "-   odstranjevanje opazovanj iz večinskega razreda: `Under-sampling`\n",
+    "\n",
+    "Zdaj bomo prikazali, kako obravnavati neuravnotežene podatkovne nize z uporabo `recipe`. Recipe lahko razumemo kot načrt, ki opisuje, katere korake je treba uporabiti na podatkovnem nizu, da ga pripravimo za analizo podatkov.\n"
+   ],
+   "metadata": {
+    "id": "soAw6826JKx9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HS41brUIJVJy"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Razčlenimo naše korake predobdelave.\n",
+    "\n",
+    "-   Klic funkcije `recipe()` s formulo pove receptu *vloge* spremenljivk, pri čemer uporablja podatke `df_select` kot referenco. Na primer, stolpec `cuisine` je bil dodeljen vlogi `outcome`, medtem ko so ostali stolpci dodeljeni vlogi `predictor`.\n",
+    "\n",
+    "-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html) ustvari *specifikacijo* koraka recepta, ki sintetično generira nove primere manjšinske skupine z uporabo najbližjih sosedov teh primerov.\n",
+    "\n",
+    "Če bi želeli videti predobdelane podatke, bi morali [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) in [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) uporabiti na našem receptu.\n",
+    "\n",
+    "`prep()`: oceni potrebne parametre iz učnega nabora, ki jih je mogoče kasneje uporabiti na drugih podatkovnih nizih.\n",
+    "\n",
+    "`bake()`: uporabi pripravljen recept in operacije na katerem koli podatkovnem nizu.\n"
+   ],
+   "metadata": {
+    "id": "Yb-7t7XcJaC8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep and bake the recipe\r\n",
+    "preprocessed_df <- cuisines_recipe %>% \r\n",
+    "  prep() %>% \r\n",
+    "  bake(new_data = NULL) %>% \r\n",
+    "  relocate(cuisine)\r\n",
+    "\r\n",
+    "# Display data\r\n",
+    "preprocessed_df %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Quick summary stats\r\n",
+    "preprocessed_df %>% \r\n",
+    "  introduce()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "9QhSgdpxJl44"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdaj preverimo porazdelitev naših kuhinj in jih primerjajmo z neuravnoteženimi podatki.\n"
+   ],
+   "metadata": {
+    "id": "dmidELh_LdV7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "new_label_count <- preprocessed_df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "list(new_label_count = new_label_count,\r\n",
+    "     old_label_count = old_label_count)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "aSh23klBLwDz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mmm! Podatki so lepi in čisti, uravnoteženi in zelo okusni 😋!\n",
+    "\n",
+    "> Običajno se recept uporablja kot predprocesor za modeliranje, kjer določa, katere korake je treba uporabiti na podatkovnem naboru, da ga pripravimo za modeliranje. V tem primeru se običajno uporablja `workflow()` (kot smo že videli v prejšnjih lekcijah) namesto ročnega ocenjevanja recepta.\n",
+    ">\n",
+    "> Zato običajno ni treba uporabljati **`prep()`** in **`bake()`** receptov, ko uporabljate tidymodels, vendar so to koristne funkcije, ki jih imate v svojem orodju za preverjanje, ali recepti delujejo, kot pričakujete, kot v našem primeru.\n",
+    ">\n",
+    "> Ko z **`new_data = NULL`** **`bake()`** pripravljen recept, dobite nazaj podatke, ki ste jih podali pri definiranju recepta, vendar so že prestali korake predprocesiranja.\n",
+    "\n",
+    "Zdaj shranimo kopijo teh podatkov za uporabo v prihodnjih lekcijah:\n"
+   ],
+   "metadata": {
+    "id": "HEu80HZ8L7ae"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Save preprocessed data\r\n",
+    "write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "cBmCbIgrMOI6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ta svež CSV je zdaj na voljo v korenski mapi podatkov.\n",
+    "\n",
+    "**🚀Izziv**\n",
+    "\n",
+    "Ta učni načrt vsebuje več zanimivih podatkovnih zbirk. Prebrskajte mape `data` in preverite, ali katera vsebuje podatkovne zbirke, ki bi bile primerne za binarno ali večrazredno klasifikacijo? Kakšna vprašanja bi zastavili tej podatkovni zbirki?\n",
+    "\n",
+    "## [**Kvizi po predavanju**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)\n",
+    "\n",
+    "## **Pregled & Samostojno učenje**\n",
+    "\n",
+    "-   Oglejte si [paket themis](https://github.com/tidymodels/themis). Katere druge tehnike bi lahko uporabili za obravnavo neuravnoteženih podatkov?\n",
+    "\n",
+    "-   Referenčna spletna stran za Tidy models [tukaj](https://www.tidymodels.org/start/).\n",
+    "\n",
+    "-   H. Wickham in G. Grolemund, [*R za podatkovno znanost: Vizualizacija, modeliranje, transformacija, urejanje in uvoz podatkov*](https://r4ds.had.co.nz/).\n",
+    "\n",
+    "#### HVALA:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) za ustvarjanje čudovitih ilustracij, ki naredijo R bolj prijazen in privlačen. Več ilustracij najdete v njeni [galeriji](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) in [Jen Looper](https://www.twitter.com/jenlooper) za ustvarjanje izvirne Python različice tega modula ♥️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Umetniško delo @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "WQs5621pMGwf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/1-Introduction/solution/notebook.ipynb b/translations/sl/4-Classification/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..4c42c6a48
--- /dev/null
+++ b/translations/sl/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,699 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Okusne azijske in indijske jedi\n",
+    "\n",
+    "## Uvod\n",
+    "Azijska in indijska kuhinja sta znani po svojih bogatih okusih, raznolikih sestavinah in edinstvenih tehnikah priprave. V tem vodiču bomo raziskali nekaj najbolj priljubljenih jedi iz teh regij.\n",
+    "\n",
+    "## Azijska kuhinja\n",
+    "### Sushi\n",
+    "Sushi je japonska jed, ki vključuje surovo ribe, riž in različne dodatke. Priprava zahteva natančnost in spretnost.\n",
+    "\n",
+    "### Pad Thai\n",
+    "Pad Thai je priljubljena tajska jed iz riževih rezancev, jajc, tofuja, kozic in arašidov. Pogosto se postreže z limeto in čilijem.\n",
+    "\n",
+    "### Dim Sum\n",
+    "Dim Sum je kitajska jed, ki vključuje majhne porcijske prigrizke, kot so cmoki, žemljice in zvitki. Običajno se postreže s čajem.\n",
+    "\n",
+    "## Indijska kuhinja\n",
+    "### Butter Chicken\n",
+    "Butter Chicken je kremasta piščančja jed, pripravljena v paradižnikovi omaki z maslom in začimbami. Pogosto se postreže z naanom ali basmati rižem.\n",
+    "\n",
+    "### Biryani\n",
+    "Biryani je aromatična jed iz riža, mesa, začimb in zelišč. Obstaja veliko različic, odvisno od regije.\n",
+    "\n",
+    "### Samosa\n",
+    "Samosa je ocvrta ali pečena jed, polnjena z začinjenim krompirjem, grahom ali mesom. Pogosto se postreže kot prigrizek.\n",
+    "\n",
+    "## Zaključek\n",
+    "Azijska in indijska kuhinja ponujata širok spekter okusov in tekstur, ki zadovoljijo vsak okus. Poskusite te jedi in odkrijte bogastvo teh kulinaričnih tradicij!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Namestite Imblearn, ki bo omogočil SMOTE. To je paket Scikit-learn, ki pomaga pri obravnavi neuravnoteženih podatkov pri izvajanju klasifikacije. (https://imbalanced-learn.org/stable/)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
+      "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df  = pd.read_csv('../../data/cuisines.csv')"
+   ]
+  },
+  {
+   "source": [
+    "Ta podatkovni niz vključuje 385 stolpcev, ki označujejo vse vrste sestavin v različnih kuhinjah iz danega nabora kuhinj.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0          65  indian       0         0      0           0      0   \n",
+       "1          66  indian       1         0      0           0      0   \n",
+       "2          67  indian       0         0      0           0      0   \n",
+       "3          68  indian       0         0      0           0      0   \n",
+       "4          69  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 385 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>67</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>69</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 385 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2448 entries, 0 to 2447\nColumns: 385 entries, Unnamed: 0 to zucchini\ndtypes: int64(384), object(1)\nmemory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "korean      799\n",
+       "indian      598\n",
+       "chinese     442\n",
+       "japanese    320\n",
+       "thai        289\n",
+       "Name: cuisine, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts()"
+   ]
+  },
+  {
+   "source": [
+    "Prikaži kuhinje v stolpčnem grafikonu\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18799710>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "thai df: (289, 385)\njapanese df: (320, 385)\nchinese df: (442, 385)\nindian df: (598, 385)\nkorean df: (799, 385)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "thai_df = df[(df.cuisine == \"thai\")]\n",
+    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
+    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
+    "indian_df = df[(df.cuisine == \"indian\")]\n",
+    "korean_df = df[(df.cuisine == \"korean\")]\n",
+    "\n",
+    "print(f'thai df: {thai_df.shape}')\n",
+    "print(f'japanese df: {japanese_df.shape}')\n",
+    "print(f'chinese df: {chinese_df.shape}')\n",
+    "print(f'indian df: {indian_df.shape}')\n",
+    "print(f'korean df: {korean_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ingredient_df(df):\n",
+    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    # drop ingredients that have a 0 sum\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    # sort df\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
+    "    return ingredient_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d5917db00>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
+    "thai_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d389634a8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+   ],
+   "source": [
+    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
+    "japanese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18817f98>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
+    "chinese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1880a550>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
+    "indian_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1896ce80>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
+    "korean_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\n",
+    "labels_df = df.cuisine #.unique()\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "source": [
+    "Uravnotežite podatke z uporabo SMOTE nadvzorčenja na najvišji razred. Preberite več tukaj: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oversample = SMOTE()\n",
+    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "new label count: korean      799\nchinese     799\njapanese    799\nindian      799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
+    "print(f'old label count: {df.cuisine.value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "source": [
+    "transformed_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "     cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  \\\n",
+       "0     indian       0         0      0           0      0             0   \n",
+       "1     indian       1         0      0           0      0             0   \n",
+       "2     indian       0         0      0           0      0             0   \n",
+       "3     indian       0         0      0           0      0             0   \n",
+       "4     indian       0         0      0           0      0             0   \n",
+       "...      ...     ...       ...    ...         ...    ...           ...   \n",
+       "3990    thai       0         0      0           0      0             0   \n",
+       "3991    thai       0         0      0           0      0             0   \n",
+       "3992    thai       0         0      0           0      0             0   \n",
+       "3993    thai       0         0      0           0      0             0   \n",
+       "3994    thai       0         0      0           0      0             0   \n",
+       "\n",
+       "      apricot  armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
+       "0           0         0          0  ...        0            0           0   \n",
+       "1           0         0          0  ...        0            0           0   \n",
+       "2           0         0          0  ...        0            0           0   \n",
+       "3           0         0          0  ...        0            0           0   \n",
+       "4           0         0          0  ...        0            0           0   \n",
+       "...       ...       ...        ...  ...      ...          ...         ...   \n",
+       "3990        0         0          0  ...        0            0           0   \n",
+       "3991        0         0          0  ...        0            0           0   \n",
+       "3992        0         0          0  ...        0            0           0   \n",
+       "3993        0         0          0  ...        0            0           0   \n",
+       "3994        0         0          0  ...        0            0           0   \n",
+       "\n",
+       "      whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                           0     0     0    0      0       0         0  \n",
+       "1                           0     0     0    0      0       0         0  \n",
+       "2                           0     0     0    0      0       0         0  \n",
+       "3                           0     0     0    0      0       0         0  \n",
+       "4                           0     0     0    0      0       1         0  \n",
+       "...                       ...   ...   ...  ...    ...     ...       ...  \n",
+       "3990                        0     0     0    0      0       0         0  \n",
+       "3991                        0     0     0    0      0       0         0  \n",
+       "3992                        0     0     0    0      0       0         0  \n",
+       "3993                        0     0     0    0      0       0         0  \n",
+       "3994                        0     0     0    0      0       0         0  \n",
+       "\n",
+       "[3995 rows x 381 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": [
+    "# export transformed data to new df for classification\n",
+    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
+    "transformed_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df.info()"
+   ]
+  },
+  {
+   "source": [
+    "Shrani datoteko za prihodnjo uporabo\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "1da12ed6d238756959b8de9cac2a35a2",
+   "translation_date": "2025-09-06T14:51:29+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/2-Classifiers-1/notebook.ipynb b/translations/sl/4-Classification/2-Classifiers-1/notebook.ipynb
new file mode 100644
index 000000000..1e679052a
--- /dev/null
+++ b/translations/sl/4-Classification/2-Classifiers-1/notebook.ipynb
@@ -0,0 +1,41 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "68829b06b4dcd512d3327849191f4d7f",
+   "translation_date": "2025-09-06T14:32:32+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Izdelava modelov za klasifikacijo\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/sl/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
new file mode 100644
index 000000000..277a1ad6d
--- /dev/null
+++ b/translations/sl/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -0,0 +1,1302 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_11-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "6ea6a5171b1b99b7b5a55f7469c048d2",
+   "translation_date": "2025-09-06T14:35:18+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Zgradite klasifikacijski model: Slastne azijske in indijske kuhinje\n"
+   ],
+   "metadata": {
+    "id": "zs2woWv_HoE8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Razvrščevalniki kuhinj 1\n",
+    "\n",
+    "V tej lekciji bomo raziskali različne razvrščevalnike za *napovedovanje določene nacionalne kuhinje na podlagi skupine sestavin.* Pri tem se bomo naučili več o načinih, kako lahko algoritme uporabimo za naloge razvrščanja.\n",
+    "\n",
+    "### [**Predhodni kviz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
+    "\n",
+    "### **Priprava**\n",
+    "\n",
+    "Ta lekcija temelji na naši [prejšnji lekciji](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb), kjer smo:\n",
+    "\n",
+    "-   Naredili uvod v razvrščanje z uporabo nabora podatkov o vseh čudovitih kuhinjah Azije in Indije 😋.\n",
+    "\n",
+    "-   Raziskali nekaj [glagolov dplyr](https://dplyr.tidyverse.org/) za pripravo in čiščenje podatkov.\n",
+    "\n",
+    "-   Ustvarili čudovite vizualizacije z uporabo ggplot2.\n",
+    "\n",
+    "-   Pokazali, kako obravnavati neuravnotežene podatke z njihovo predobdelavo z uporabo [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html).\n",
+    "\n",
+    "-   Demonstrirali, kako `prep` in `bake` naš recept, da potrdimo, da bo deloval, kot je predvideno.\n",
+    "\n",
+    "#### **Predpogoji**\n",
+    "\n",
+    "Za to lekcijo bomo potrebovali naslednje pakete za čiščenje, pripravo in vizualizacijo podatkov:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [zbirka paketov za R](https://www.tidyverse.org/packages), zasnovana za hitrejše, lažje in bolj zabavno podatkovno znanost!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je okvir [zbirke paketov](https://www.tidymodels.org/packages/) za modeliranje in strojno učenje.\n",
+    "\n",
+    "-   `themis`: Paket [themis](https://themis.tidymodels.org/) ponuja dodatne korake za obdelavo neuravnoteženih podatkov.\n",
+    "\n",
+    "-   `nnet`: Paket [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) ponuja funkcije za ocenjevanje nevronskih mrež s povratnim napajanjem z eno skrito plastjo in za modele multinomialne logistične regresije.\n",
+    "\n",
+    "Namestite jih lahko tako:\n"
+   ],
+   "metadata": {
+    "id": "iDFOb3ebHwQC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Alternativno spodnji skript preveri, ali imate potrebne pakete za dokončanje tega modula, in jih namesti, če manjkajo.\n"
+   ],
+   "metadata": {
+    "id": "4V85BGCjII7F"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, here)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "an5NPyyKIKNR",
+    "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdaj pa zavihajmo rokave!\n",
+    "\n",
+    "## 1. Razdelite podatke na učne in testne sklope.\n",
+    "\n",
+    "Začeli bomo z izbiro nekaj korakov iz naše prejšnje lekcije.\n",
+    "\n",
+    "### Odstranite najpogostejše sestavine, ki povzročajo zmedo med različnimi kuhinjami, z uporabo `dplyr::select()`.\n",
+    "\n",
+    "Vsi obožujemo riž, česen in ingver!\n"
+   ],
+   "metadata": {
+    "id": "0ax9GQLBINVv"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# Load the original cuisines data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
+    "  # Encode cuisine column as categorical\r\n",
+    "  mutate(cuisine = factor(cuisine))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Display distribution of cuisines\r\n",
+    "df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "New names:\n",
+      "* `` -> ...1\n",
+      "\n",
+      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
+      "\n",
+      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+      "\u001b[31mchr\u001b[39m   (1): cuisine\n",
+      "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
+      "\n",
+      "\n",
+      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
+      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 indian  0      0        0     0          0     0            0       0       \n",
+       "2 indian  1      0        0     0          0     0            0       0       \n",
+       "3 indian  0      0        0     0          0     0            0       0       \n",
+       "4 indian  0      0        0     0          0     0            0       0       \n",
+       "5 indian  0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       0    0   \n",
+       "2 0         ⋯ 0       0           0          0                       0    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     1      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   799\n",
+       "2 indian   598\n",
+       "3 chinese  442\n",
+       "4 japanese 320\n",
+       "5 thai     289"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 799 |\n",
+       "| indian   | 598 |\n",
+       "| chinese  | 442 |\n",
+       "| japanese | 320 |\n",
+       "| thai     | 289 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 799\\\\\n",
+       "\t indian   & 598\\\\\n",
+       "\t chinese  & 442\\\\\n",
+       "\t japanese & 320\\\\\n",
+       "\t thai     & 289\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>799</td></tr>\n",
+       "\t<tr><td>indian  </td><td>598</td></tr>\n",
+       "\t<tr><td>chinese </td><td>442</td></tr>\n",
+       "\t<tr><td>japanese</td><td>320</td></tr>\n",
+       "\t<tr><td>thai    </td><td>289</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 735
+    },
+    "id": "jhCrrH22IWVR",
+    "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično! Zdaj je čas, da podatke razdelimo tako, da gre 70 % podatkov za učenje in 30 % za testiranje. Pri razdelitvi bomo uporabili tudi tehniko `stratifikacije`, da `ohranimo razmerje posameznih vrst kuhinj` v učnih in validacijskih naborih podatkov.\n",
+    "\n",
+    "[rsample](https://rsample.tidymodels.org/), paket v Tidymodels, zagotavlja infrastrukturo za učinkovito razdeljevanje in ponovno vzorčenje podatkov:\n"
+   ],
+   "metadata": {
+    "id": "AYTjVyajIdny"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# Load the core Tidymodels packages into R session\r\n",
+    "library(tidymodels)\r\n",
+    "\r\n",
+    "# Create split specification\r\n",
+    "set.seed(2056)\r\n",
+    "cuisines_split <- initial_split(data = df_select,\r\n",
+    "                                strata = cuisine,\r\n",
+    "                                prop = 0.7)\r\n",
+    "\r\n",
+    "# Extract the data in each split\r\n",
+    "cuisines_train <- training(cuisines_split)\r\n",
+    "cuisines_test <- testing(cuisines_split)\r\n",
+    "\r\n",
+    "# Print the number of cases in each split\r\n",
+    "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
+    "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
+    "\r\n",
+    "# Display the first few rows of the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Display distribution of cuisines in the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training cases: 1712\n",
+      "Test cases: 736"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 chinese 0      0        0     0          0     0            0       0       \n",
+       "2 chinese 0      0        0     0          0     0            0       0       \n",
+       "3 chinese 0      0        0     0          0     0            0       0       \n",
+       "4 chinese 0      0        0     0          0     0            0       0       \n",
+       "5 chinese 0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       1    0   \n",
+       "2 0         ⋯ 0       0           0          0                       1    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     0      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   559\n",
+       "2 indian   418\n",
+       "3 chinese  309\n",
+       "4 japanese 224\n",
+       "5 thai     202"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 559 |\n",
+       "| indian   | 418 |\n",
+       "| chinese  | 309 |\n",
+       "| japanese | 224 |\n",
+       "| thai     | 202 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 559\\\\\n",
+       "\t indian   & 418\\\\\n",
+       "\t chinese  & 309\\\\\n",
+       "\t japanese & 224\\\\\n",
+       "\t thai     & 202\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>559</td></tr>\n",
+       "\t<tr><td>indian  </td><td>418</td></tr>\n",
+       "\t<tr><td>chinese </td><td>309</td></tr>\n",
+       "\t<tr><td>japanese</td><td>224</td></tr>\n",
+       "\t<tr><td>thai    </td><td>202</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 535
+    },
+    "id": "w5FWIkEiIjdN",
+    "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Obdelava neuravnoteženih podatkov\n",
+    "\n",
+    "Kot ste morda opazili v izvirnem naboru podatkov in našem učnem naboru, je porazdelitev števila kuhinj precej neenakomerna. Korejske kuhinje so *skoraj* 3-krat pogostejše od tajskih kuhinj. Neuravnoteženi podatki pogosto negativno vplivajo na delovanje modela. Veliko modelov najbolje deluje, ko je število opazovanj enako, zato se pogosto spopadajo z izzivi pri obdelavi neuravnoteženih podatkov.\n",
+    "\n",
+    "Obstajata dva glavna načina za obdelavo neuravnoteženih naborov podatkov:\n",
+    "\n",
+    "-   dodajanje opazovanj v manjšinsko skupino: `Prekomerno vzorčenje` (Over-sampling), na primer z uporabo algoritma SMOTE, ki sintetično generira nove primere manjšinske skupine z uporabo najbližjih sosedov teh primerov.\n",
+    "\n",
+    "-   odstranjevanje opazovanj iz večinske skupine: `Podvzorečenje` (Under-sampling)\n",
+    "\n",
+    "V naši prejšnji lekciji smo prikazali, kako obdelati neuravnotežene nabore podatkov z uporabo `recepta`. Recept si lahko predstavljamo kot načrt, ki opisuje, katere korake je treba uporabiti na naboru podatkov, da ga pripravimo za analizo. V našem primeru želimo doseči enakomerno porazdelitev števila kuhinj v našem `učnem naboru`. Pojdimo kar k stvari.\n"
+   ],
+   "metadata": {
+    "id": "daBi9qJNIwqW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing training data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "# Print recipe\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Data Recipe\n",
+       "\n",
+       "Inputs:\n",
+       "\n",
+       "      role #variables\n",
+       "   outcome          1\n",
+       " predictor        380\n",
+       "\n",
+       "Operations:\n",
+       "\n",
+       "SMOTE based on cuisine"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 200
+    },
+    "id": "Az6LFBGxI1X0",
+    "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Lahko seveda potrdite (z uporabo prep+bake), da bo recept deloval, kot pričakujete - vse oznake kuhinj imajo `559` opazovanj.\n",
+    "\n",
+    "Ker bomo ta recept uporabljali kot predprocesor za modeliranje, bo `workflow()` opravil vse priprave in peko namesto nas, tako da recepta ne bomo morali ročno ocenjevati.\n",
+    "\n",
+    "Zdaj smo pripravljeni na treniranje modela 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Izbira vašega klasifikatorja\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/parsnip.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Umetniško delo @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "NBL3PqIWJBBB"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdaj moramo odločiti, kateri algoritem uporabiti za nalogo 🤔.\n",
+    "\n",
+    "V Tidymodels [`parsnip package`](https://parsnip.tidymodels.org/index.html) zagotavlja dosleden vmesnik za delo z modeli prek različnih pogonov (paketov). Prosimo, preglejte dokumentacijo parsnip za raziskovanje [vrst modelov in pogonov](https://www.tidymodels.org/find/parsnip/#models) ter njihovih ustreznih [argumentov modelov](https://www.tidymodels.org/find/parsnip/#model-args). Raznolikost je na prvi pogled precej osupljiva. Na primer, naslednje metode vključujejo tehnike klasifikacije:\n",
+    "\n",
+    "-   C5.0 modeli klasifikacije na osnovi pravil\n",
+    "\n",
+    "-   Prilagodljivi diskriminantni modeli\n",
+    "\n",
+    "-   Linearni diskriminantni modeli\n",
+    "\n",
+    "-   Regularizirani diskriminantni modeli\n",
+    "\n",
+    "-   Logistični regresijski modeli\n",
+    "\n",
+    "-   Multinomialni regresijski modeli\n",
+    "\n",
+    "-   Naivni Bayesovi modeli\n",
+    "\n",
+    "-   Podporni vektorski stroji\n",
+    "\n",
+    "-   Najbližji sosedje\n",
+    "\n",
+    "-   Odločitvena drevesa\n",
+    "\n",
+    "-   Metode ansambla\n",
+    "\n",
+    "-   Nevronske mreže\n",
+    "\n",
+    "Seznam se nadaljuje!\n",
+    "\n",
+    "### **Kateri klasifikator izbrati?**\n",
+    "\n",
+    "Torej, kateri klasifikator bi morali izbrati? Pogosto je preizkušanje več klasifikatorjev in iskanje dobrega rezultata način testiranja.\n",
+    "\n",
+    "> AutoML to težavo elegantno reši z izvajanjem teh primerjav v oblaku, kar vam omogoča izbiro najboljšega algoritma za vaše podatke. Preizkusite ga [tukaj](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "Izbira klasifikatorja je odvisna tudi od našega problema. Na primer, kadar je rezultat mogoče razvrstiti v `več kot dva razreda`, kot v našem primeru, morate uporabiti `algoritem za večrazredno klasifikacijo` namesto `binarne klasifikacije.`\n",
+    "\n",
+    "### **Boljši pristop**\n",
+    "\n",
+    "Boljši način kot naključno ugibanje je, da sledite idejam iz te prenosljive [ML Cheat Sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Tukaj odkrijemo, da imamo za naš večrazredni problem nekaj možnosti:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/cheatsheet.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Del Microsoftovega algoritmičnega priročnika, ki podrobno opisuje možnosti večrazredne klasifikacije</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "a6DLAZ3vJZ14"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Razmišljanje**\n",
+    "\n",
+    "Poglejmo, ali lahko z razmišljanjem najdemo različne pristope glede na omejitve, ki jih imamo:\n",
+    "\n",
+    "-   **Globoke nevronske mreže so pretežke**. Glede na naš čist, a minimalen nabor podatkov ter dejstvo, da izvajamo učenje lokalno prek beležk, so globoke nevronske mreže za to nalogo preveč zahtevne.\n",
+    "\n",
+    "-   **Brez klasifikatorja z dvema razredoma**. Ne uporabljamo klasifikatorja z dvema razredoma, kar izključuje pristop \"ena proti vsem\".\n",
+    "\n",
+    "-   **Odločilno drevo ali logistična regresija bi lahko delovala**. Odločilno drevo bi lahko delovalo, prav tako multinomna regresija/multirazredna logistična regresija za podatke z več razredi.\n",
+    "\n",
+    "-   **Multirazredna izboljšana odločilna drevesa rešujejo drugačen problem**. Multirazredna izboljšana odločilna drevesa so najbolj primerna za neparametrične naloge, npr. naloge, namenjene gradnji razvrstitev, zato za nas niso uporabna.\n",
+    "\n",
+    "Poleg tega je običajno, preden se lotimo bolj zapletenih modelov strojnega učenja, kot so metode ansambla, dobro zgraditi čim bolj preprost model, da dobimo občutek, kaj se dogaja. Zato bomo v tej lekciji začeli z modelom `multinomne regresije`.\n",
+    "\n",
+    "> Logistična regresija je tehnika, ki se uporablja, kadar je odvisna spremenljivka kategorična (ali nominalna). Pri binarni logistični regresiji je število izhodnih spremenljivk dve, medtem ko je število izhodnih spremenljivk pri multinomni logistični regresiji več kot dve. Za več informacij glejte [Napredne metode regresije](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html).\n",
+    "\n",
+    "## 4. Učenje in ocenjevanje modela multinomne logistične regresije\n",
+    "\n",
+    "V Tidymodels `parsnip::multinom_reg()` definira model, ki uporablja linearne napovedovalce za napovedovanje podatkov z več razredi z uporabo multinomne porazdelitve. Glejte `?multinom_reg()` za različne načine/motorje, ki jih lahko uporabite za prilagoditev tega modela.\n",
+    "\n",
+    "Za ta primer bomo prilagodili model multinomne regresije prek privzetega motorja [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf).\n",
+    "\n",
+    "> Vrednost za `penalty` sem izbral bolj naključno. Obstajajo boljši načini za izbiro te vrednosti, in sicer z uporabo `resampling` in `tuning` modela, o čemer bomo razpravljali kasneje.\n",
+    ">\n",
+    "> Glejte [Tidymodels: Začetek](https://www.tidymodels.org/start/tuning/), če želite izvedeti več o tem, kako nastaviti hiperparametre modela.\n"
+   ],
+   "metadata": {
+    "id": "gWMsVcbBJemu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# Create a multinomial regression model specification\r\n",
+    "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
+    "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
+    "  set_mode(\"classification\")\r\n",
+    "\r\n",
+    "# Print model specification\r\n",
+    "mr_spec"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 166
+    },
+    "id": "Wq_fcyQiJvfG",
+    "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično delo 🥳! Zdaj, ko imamo recept in specifikacijo modela, moramo najti način, kako ju združiti v objekt, ki bo najprej predprocesiral podatke, nato prilagodil model na predprocesirane podatke in omogočil tudi morebitne aktivnosti po obdelavi. V Tidymodels se ta priročen objekt imenuje [`workflow`](https://workflows.tidymodels.org/) in priročno združuje vaše modelne komponente! To je tisto, kar bi v *Pythonu* imenovali *pipelines*.\n",
+    "\n",
+    "Torej, združimo vse v workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "NlSbzDfgJ0zh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# Bundle recipe and model specification\r\n",
+    "mr_wf <- workflow() %>% \r\n",
+    "  add_recipe(cuisines_recipe) %>% \r\n",
+    "  add_model(mr_spec)\r\n",
+    "\r\n",
+    "# Print out workflow\r\n",
+    "mr_wf"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow ════════════════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 333
+    },
+    "id": "Sc1TfPA4Ke3_",
+    "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Poteki dela 👌👌! **`workflow()`** se lahko prilagodi na skoraj enak način kot model. Torej, čas je za treniranje modela!\n"
+   ],
+   "metadata": {
+    "id": "TNQ8i85aKf9L"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# Train a multinomial regression model\n",
+    "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
+    "\n",
+    "mr_fit"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Call:\n",
+       "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
+       "    trace = FALSE)\n",
+       "\n",
+       "Coefficients:\n",
+       "         (Intercept)     almond angelica         anise anise_seed       apple\n",
+       "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
+       "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
+       "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
+       "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
+       "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
+       "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
+       "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
+       "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
+       "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
+       "             avocado       bacon baked_potato balm     banana     barley\n",
+       "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
+       "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
+       "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
+       "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
+       "         bartlett_pear      basil        bay       bean         beech\n",
+       "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
+       "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
+       "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
+       "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
+       "               beef beef_broth   beef_liver         beer        beet\n",
+       "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
+       "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
+       "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
+       "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
+       "         bell_pepper bergamot       berry bitter_orange black_bean\n",
+       "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
+       "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
+       "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
+       "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
+       "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
+       "indian               0             0.38935801   -0.4453495               0\n",
+       "japanese             0            -0.05452887   -0.5440869               0\n",
+       "korean               0            -0.03929970    0.8025454               0\n",
+       "thai                 0            -0.21498372   -0.9854806               0\n",
+       "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
+       "indian          -0.2759246  0.3079977  0.191256164                 0\n",
+       "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
+       "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
+       "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
+       "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
+       "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
+       "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
+       "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
+       "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
+       "\n",
+       "...\n",
+       "and 308 more lines."
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GMbdfVmTKkJI",
+    "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Izhod prikazuje koeficiente, ki jih je model naučil med usposabljanjem.\n",
+    "\n",
+    "### Ovrednotite usposobljeni model\n",
+    "\n",
+    "Čas je, da preverimo, kako se je model odrezal 📏, tako da ga ovrednotimo na testnem naboru podatkov! Začnimo z napovedovanjem na testnem naboru.\n"
+   ],
+   "metadata": {
+    "id": "tt2BfOxrKmcJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# Make predictions on the test set\n",
+    "results <- cuisines_test %>% select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
+    "\n",
+    "# Print out results\n",
+    "results %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class\n",
+       "1 indian  thai       \n",
+       "2 indian  indian     \n",
+       "3 indian  indian     \n",
+       "4 indian  indian     \n",
+       "5 indian  indian     "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
+       "|---|---|\n",
+       "| indian | thai   |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & .pred\\_class\\\\\n",
+       " <fct> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai  \\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "CqtckvtsKqax",
+    "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično delo! V Tidymodels lahko ocenjujemo uspešnost modela z uporabo [yardstick](https://yardstick.tidymodels.org/) - paketa, ki se uporablja za merjenje učinkovitosti modelov z uporabo metrik uspešnosti. Kot smo storili v naši lekciji o logistični regresiji, začnimo z izračunom matrike zmede.\n"
+   ],
+   "metadata": {
+    "id": "8w5N6XsBKss7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "# Confusion matrix for categorical data\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "          Truth\n",
+       "Prediction chinese indian japanese korean thai\n",
+       "  chinese       83      1        8     15   10\n",
+       "  indian         4    163        1      2    6\n",
+       "  japanese      21      5       73     25    1\n",
+       "  korean        15      0       11    191    0\n",
+       "  thai          10     11        3      7   70"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 133
+    },
+    "id": "YvODvsLkK0iG",
+    "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ko se ukvarjamo z več razredi, je na splošno bolj intuitivno to vizualizirati kot toplotni zemljevid, takole:\n"
+   ],
+   "metadata": {
+    "id": "c0HfPL16Lr6U"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
+    "# Visualize confusion matrix\n",
+    "results %>% \n",
+    "  conf_mat(cuisine, .pred_class) %>% \n",
+    "  autoplot(type = \"heatmap\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "plot without title"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {
+      "image/png": {
+       "width": 420,
+       "height": 420
+      }
+     }
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 436
+    },
+    "id": "HsAtwukyLsvt",
+    "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Temnejši kvadrati v grafu matrike zmede označujejo veliko število primerov, in upamo, da lahko vidite diagonalno črto temnejših kvadratov, ki označuje primere, kjer sta napovedana in dejanska oznaka enaka.\n",
+    "\n",
+    "Zdaj pa izračunajmo povzetne statistike za matriko zmede.\n"
+   ],
+   "metadata": {
+    "id": "oOJC87dkLwPr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# Summary stats for confusion matrix\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
+    "summary()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "   .metric              .estimator .estimate\n",
+       "1  accuracy             multiclass 0.7880435\n",
+       "2  kap                  multiclass 0.7276583\n",
+       "3  sens                 macro      0.7780927\n",
+       "4  spec                 macro      0.9477598\n",
+       "5  ppv                  macro      0.7585583\n",
+       "6  npv                  macro      0.9460080\n",
+       "7  mcc                  multiclass 0.7292724\n",
+       "8  j_index              macro      0.7258524\n",
+       "9  bal_accuracy         macro      0.8629262\n",
+       "10 detection_prevalence macro      0.2000000\n",
+       "11 precision            macro      0.7585583\n",
+       "12 recall               macro      0.7780927\n",
+       "13 f_meas               macro      0.7641862"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 13 × 3\n",
+       "\n",
+       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| accuracy             | multiclass | 0.7880435 |\n",
+       "| kap                  | multiclass | 0.7276583 |\n",
+       "| sens                 | macro      | 0.7780927 |\n",
+       "| spec                 | macro      | 0.9477598 |\n",
+       "| ppv                  | macro      | 0.7585583 |\n",
+       "| npv                  | macro      | 0.9460080 |\n",
+       "| mcc                  | multiclass | 0.7292724 |\n",
+       "| j_index              | macro      | 0.7258524 |\n",
+       "| bal_accuracy         | macro      | 0.8629262 |\n",
+       "| detection_prevalence | macro      | 0.2000000 |\n",
+       "| precision            | macro      | 0.7585583 |\n",
+       "| recall               | macro      | 0.7780927 |\n",
+       "| f_meas               | macro      | 0.7641862 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 13 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " .metric & .estimator & .estimate\\\\\n",
+       " <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t accuracy             & multiclass & 0.7880435\\\\\n",
+       "\t kap                  & multiclass & 0.7276583\\\\\n",
+       "\t sens                 & macro      & 0.7780927\\\\\n",
+       "\t spec                 & macro      & 0.9477598\\\\\n",
+       "\t ppv                  & macro      & 0.7585583\\\\\n",
+       "\t npv                  & macro      & 0.9460080\\\\\n",
+       "\t mcc                  & multiclass & 0.7292724\\\\\n",
+       "\t j\\_index              & macro      & 0.7258524\\\\\n",
+       "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
+       "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
+       "\t precision            & macro      & 0.7585583\\\\\n",
+       "\t recall               & macro      & 0.7780927\\\\\n",
+       "\t f\\_meas               & macro      & 0.7641862\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 13 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
+       "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
+       "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
+       "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
+       "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
+       "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
+       "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
+       "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
+       "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 494
+    },
+    "id": "OYqetUyzL5Wz",
+    "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Če se osredotočimo na nekatere metrike, kot so natančnost, občutljivost, ppv, smo za začetek kar dobro na poti 🥳!\n",
+    "\n",
+    "## 4. Poglabljanje\n",
+    "\n",
+    "Postavimo si eno subtilno vprašanje: Katera merila se uporabljajo za izbiro določene vrste kuhinje kot napovedanega rezultata?\n",
+    "\n",
+    "Statistični algoritmi strojnega učenja, kot je logistična regresija, temeljijo na `verjetnosti`; torej, kar dejansko napove klasifikator, je porazdelitev verjetnosti med naborom možnih rezultatov. Razred z najvišjo verjetnostjo je nato izbran kot najbolj verjeten rezultat za dane opazovanja.\n",
+    "\n",
+    "Poglejmo to v praksi, tako da naredimo trde napovedi razredov in verjetnosti.\n"
+   ],
+   "metadata": {
+    "id": "43t7vz8vMJtW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "# Make hard class prediction and probabilities\n",
+    "results_prob <- cuisines_test %>%\n",
+    "  select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
+    "\n",
+    "# Print out results\n",
+    "results_prob %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
+       "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
+       "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
+       "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
+       "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
+       "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
+       "  .pred_thai  \n",
+       "1 5.388194e-01\n",
+       "2 1.577948e-06\n",
+       "3 6.874989e-03\n",
+       "4 3.863391e-03\n",
+       "5 5.653283e-03"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 7\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
+       "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
+       "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
+       "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
+       "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 7\n",
+       "\\begin{tabular}{lllllll}\n",
+       " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
+       " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
+       "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
+       "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
+       "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
+       "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "xdKNs-ZPMTJL",
+    "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Veliko bolje!\n",
+    "\n",
+    "✅ Ali lahko razložiš, zakaj je model precej prepričan, da je prva opazka tajska?\n",
+    "\n",
+    "## **🚀Izziv**\n",
+    "\n",
+    "V tej lekciji si uporabil_a očiščene podatke za izdelavo modela strojnega učenja, ki lahko napove nacionalno kuhinjo na podlagi serije sestavin. Vzemi si nekaj časa in preglej [številne možnosti](https://www.tidymodels.org/find/parsnip/#models), ki jih Tidymodels ponuja za klasifikacijo podatkov, ter [druge načine](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) za prilagoditev multinomnih regresijskih modelov.\n",
+    "\n",
+    "#### HVALA:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) za ustvarjanje neverjetnih ilustracij, ki naredijo R bolj prijazen in privlačen. Več ilustracij najdeš v njeni [galeriji](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) in [Jen Looper](https://www.twitter.com/jenlooper) za ustvarjanje izvirne Python različice tega modula ♥️\n",
+    "\n",
+    "<br>\n",
+    "Dodal_a bi nekaj šal, ampak ne razumem hrane-punov 😅.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Veselo učenje,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), zlati Microsoft Learn študentski ambasador.\n"
+   ],
+   "metadata": {
+    "id": "2tWVHMeLMYdM"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/translations/sl/4-Classification/2-Classifiers-1/solution/notebook.ipynb
new file mode 100644
index 000000000..f7c078105
--- /dev/null
+++ b/translations/sl/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Ustvari modele za klasifikacijo\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy is 0.8181818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print (\"Accuracy is {}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n       'vegetable_oil'],\n      dtype='object')\ncuisine: korean\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test an item\n",
+    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[50]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                 0\n",
+       "korean    0.392231\n",
+       "chinese   0.372872\n",
+       "japanese  0.218825\n",
+       "thai      0.013427\n",
+       "indian    0.002645"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>korean</th>\n      <td>0.392231</td>\n    </tr>\n    <tr>\n      <th>chinese</th>\n      <td>0.372872</td>\n    </tr>\n    <tr>\n      <th>japanese</th>\n      <td>0.218825</td>\n    </tr>\n    <tr>\n      <th>thai</th>\n      <td>0.013427</td>\n    </tr>\n    <tr>\n      <th>indian</th>\n      <td>0.002645</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "#rehsape to 2d array and transpose\n",
+    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
+    "# predict with score\n",
+    "proba = model.predict_proba(test)\n",
+    "classes = model.classes_\n",
+    "# create df with classes and scores\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
+    "\n",
+    "# create df to show results\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.75      0.73      0.74       223\n      indian       0.93      0.88      0.90       255\n    japanese       0.78      0.78      0.78       253\n      korean       0.87      0.86      0.86       236\n        thai       0.76      0.84      0.80       232\n\n    accuracy                           0.82      1199\n   macro avg       0.82      0.82      0.82      1199\nweighted avg       0.82      0.82      0.82      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\r\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "9408506dd864f2b6e334c62f80c0cfcc",
+   "translation_date": "2025-09-06T14:33:00+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/3-Classifiers-2/notebook.ipynb b/translations/sl/4-Classification/3-Classifiers-2/notebook.ipynb
new file mode 100644
index 000000000..7b53b776b
--- /dev/null
+++ b/translations/sl/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ustvari Model za Klasifikacijo\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
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diff --git a/translations/sl/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/translations/sl/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
new file mode 100644
index 000000000..d45e9c81c
--- /dev/null
+++ b/translations/sl/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -0,0 +1,647 @@
+{
+ "nbformat": 4,
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+ "metadata": {
+  "colab": {
+   "name": "lesson_12-R.ipynb",
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+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
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+   "source_file": "4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jsFutf_ygqSx"
+   },
+   "source": [
+    "# Zgradite klasifikacijski model: Slastne azijske in indijske kuhinje\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HD54bEefgtNO"
+   },
+   "source": [
+    "## Razvrščevalniki kuhinj 2\n",
+    "\n",
+    "V tej drugi lekciji o razvrščanju bomo raziskali `več načinov` za razvrščanje kategorijskih podatkov. Prav tako se bomo naučili o posledicah izbire enega razvrščevalnika namesto drugega.\n",
+    "\n",
+    "### [**Predhodni kviz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
+    "\n",
+    "### **Predpogoji**\n",
+    "\n",
+    "Predvidevamo, da ste zaključili prejšnje lekcije, saj bomo nadaljevali z nekaterimi koncepti, ki smo jih že obravnavali.\n",
+    "\n",
+    "Za to lekcijo bomo potrebovali naslednje pakete:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) je [zbirka paketov za R](https://www.tidyverse.org/packages), zasnovana za hitrejše, enostavnejše in bolj zabavno podatkovno znanost!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) je okvir [zbirke paketov](https://www.tidymodels.org/packages/) za modeliranje in strojno učenje.\n",
+    "\n",
+    "-   `themis`: [paket themis](https://themis.tidymodels.org/) ponuja dodatne korake receptov za obravnavo neuravnoteženih podatkov.\n",
+    "\n",
+    "Pakete lahko namestite z naslednjim ukazom:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\n",
+    "\n",
+    "Alternativno, spodnji skript preveri, ali imate nameščene potrebne pakete za dokončanje tega modula, in jih namesti, če manjkajo.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vZ57IuUxgyQt"
+   },
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z22M-pj4g07x"
+   },
+   "source": [
+    "## **1. Zemljevid klasifikacije**\n",
+    "\n",
+    "V naši [prejšnji lekciji](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1) smo poskušali odgovoriti na vprašanje: kako izbrati med več modeli? V veliki meri je to odvisno od značilnosti podatkov in vrste problema, ki ga želimo rešiti (na primer klasifikacija ali regresija?).\n",
+    "\n",
+    "Prej smo se naučili o različnih možnostih, ki jih imate pri klasifikaciji podatkov, z uporabo Microsoftovega pripomočka. Pythonov okvir za strojno učenje, Scikit-learn, ponuja podoben, vendar bolj podroben pripomoček, ki vam lahko dodatno pomaga zožiti izbiro vaših ocenjevalnikov (drugi izraz za klasifikatorje):\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/map.png\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption></figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u1i3xRIVg7vG"
+   },
+   "source": [
+    "> Nasvet: [obiščite ta zemljevid na spletu](https://scikit-learn.org/stable/tutorial/machine_learning_map/) in kliknite po poti, da preberete dokumentacijo.\n",
+    ">\n",
+    "> [Referenčna stran Tidymodels](https://www.tidymodels.org/find/parsnip/#models) prav tako ponuja odlično dokumentacijo o različnih vrstah modelov.\n",
+    "\n",
+    "### **Načrt** 🗺️\n",
+    "\n",
+    "Ta zemljevid je zelo koristen, ko imate jasno predstavo o svojih podatkih, saj lahko 'hodite' po njegovih poteh do odločitve:\n",
+    "\n",
+    "-   Imamo \\>50 vzorcev\n",
+    "\n",
+    "-   Želimo napovedati kategorijo\n",
+    "\n",
+    "-   Imamo označene podatke\n",
+    "\n",
+    "-   Imamo manj kot 100K vzorcev\n",
+    "\n",
+    "-   ✨ Lahko izberemo Linear SVC\n",
+    "\n",
+    "-   Če to ne deluje, ker imamo numerične podatke\n",
+    "\n",
+    "    -   Lahko poskusimo ✨ KNeighbors Classifier\n",
+    "\n",
+    "        -   Če to ne deluje, poskusite ✨ SVC in ✨ Ensemble Classifiers\n",
+    "\n",
+    "To je zelo koristna pot, ki ji lahko sledite. Zdaj pa se lotimo dela z uporabo [tidymodels](https://www.tidymodels.org/) okvira za modeliranje: dosledne in prilagodljive zbirke paketov za R, razvitih za spodbujanje dobre statistične prakse 😊.\n",
+    "\n",
+    "## 2. Razdelite podatke in obravnavajte neuravnotežen nabor podatkov.\n",
+    "\n",
+    "Iz naših prejšnjih lekcij smo se naučili, da obstaja niz skupnih sestavin med našimi kuhinjami. Prav tako je bila precej neenakomerna porazdelitev števila kuhinj.\n",
+    "\n",
+    "To bomo obravnavali tako, da:\n",
+    "\n",
+    "-   Odstranimo najpogostejše sestavine, ki povzročajo zmedo med različnimi kuhinjami, z uporabo `dplyr::select()`.\n",
+    "\n",
+    "-   Uporabimo `recipe`, ki predhodno obdela podatke, da jih pripravi za modeliranje z uporabo algoritma za `over-sampling`.\n",
+    "\n",
+    "To smo že obravnavali v prejšnji lekciji, zato bo to enostavno 🥳!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "6tj_rN00hClA"
+   },
+   "source": [
+    "# Load the core Tidyverse and Tidymodels packages\n",
+    "library(tidyverse)\n",
+    "library(tidymodels)\n",
+    "\n",
+    "# Load the original cuisines data\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
+    "\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\n",
+    "df_select <- df %>% \n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\n",
+    "  # Encode cuisine column as categorical\n",
+    "  mutate(cuisine = factor(cuisine))\n",
+    "\n",
+    "\n",
+    "# Create data split specification\n",
+    "set.seed(2056)\n",
+    "cuisines_split <- initial_split(data = df_select,\n",
+    "                                strata = cuisine,\n",
+    "                                prop = 0.7)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "cuisines_train <- training(cuisines_split)\n",
+    "cuisines_test <- testing(cuisines_split)\n",
+    "\n",
+    "# Display distribution of cuisines in the training set\n",
+    "cuisines_train %>% \n",
+    "  count(cuisine) %>% \n",
+    "  arrange(desc(n))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFin5yw3hHb1"
+   },
+   "source": [
+    "### Obdelava neuravnoteženih podatkov\n",
+    "\n",
+    "Neuravnoteženi podatki pogosto negativno vplivajo na delovanje modela. Veliko modelov deluje najbolje, ko je število opazovanj enako, zato imajo težave z neuravnoteženimi podatki.\n",
+    "\n",
+    "Obstajata predvsem dva načina za obravnavo neuravnoteženih podatkovnih nizov:\n",
+    "\n",
+    "-   dodajanje opazovanj v manjšinsko skupino: `Prevzorčenje` (ang. Over-sampling), npr. z uporabo algoritma SMOTE, ki sintetično ustvari nove primere manjšinske skupine z uporabo najbližjih sosedov teh primerov.\n",
+    "\n",
+    "-   odstranjevanje opazovanj iz večinske skupine: `Podvzorčenje` (ang. Under-sampling)\n",
+    "\n",
+    "V prejšnji lekciji smo prikazali, kako obravnavati neuravnotežene podatkovne nize z uporabo `recepta`. Recept si lahko predstavljamo kot načrt, ki opisuje, kateri koraki naj se uporabijo na podatkovnem nizu, da bo pripravljen za analizo podatkov. V našem primeru želimo doseči enakomerno porazdelitev števila naših kulinarik v `učnem naboru`. Pa začnimo!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "cRzTnHolhLWd"
+   },
+   "source": [
+    "# Load themis package for dealing with imbalanced data\n",
+    "library(themis)\n",
+    "\n",
+    "# Create a recipe for preprocessing training data\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
+    "  step_smote(cuisine) \n",
+    "\n",
+    "# Print recipe\n",
+    "cuisines_recipe"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KxOQ2ORhhO81"
+   },
+   "source": [
+    "Zdaj smo pripravljeni na treniranje modelov 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Onkraj multinomnih regresijskih modelov\n",
+    "\n",
+    "V prejšnji lekciji smo obravnavali multinomne regresijske modele. Raziskali bomo nekaj bolj prilagodljivih modelov za klasifikacijo.\n",
+    "\n",
+    "### Podporni vektorski stroji\n",
+    "\n",
+    "V kontekstu klasifikacije so `Podporni vektorski stroji` tehnika strojnega učenja, ki poskuša najti *hiperploskev*, ki \"najbolje\" ločuje razrede. Poglejmo preprost primer:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/svm.png\"\n",
+    "   width=\"300\"/>\n",
+    "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C4Wsd0vZhXYu"
+   },
+   "source": [
+    "H1~ ne ločuje razredov. H2~ jih ločuje, vendar le z majhno razdaljo. H3~ jih ločuje z največjo razdaljo.\n",
+    "\n",
+    "#### Linearni klasifikator podpornih vektorjev\n",
+    "\n",
+    "Podporni vektorski razvrščanje (SVC) je del družine tehnik strojnega učenja, imenovanih podporni vektorski stroji. Pri SVC je hiperploskev izbrana tako, da pravilno loči `večino` opazovanj v učnem naboru, vendar `lahko napačno razvrsti` nekaj opazovanj. Z dovoljenjem, da so nekateri podatkovni točki na napačni strani, postane SVM bolj odporen na odstopanja, kar omogoča boljšo posplošitev na nove podatke. Parameter, ki uravnava to kršitev, se imenuje `cost` in ima privzeto vrednost 1 (glej `help(\"svm_poly\")`).\n",
+    "\n",
+    "Ustvarimo linearni SVC tako, da nastavimo `degree = 1` v polinomskem modelu SVM.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vJpp6nuChlBz"
+   },
+   "source": [
+    "# Make a linear SVC specification\n",
+    "svc_linear_spec <- svm_poly(degree = 1) %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svc_linear_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svc_linear_spec)\n",
+    "\n",
+    "# Print out workflow\n",
+    "svc_linear_wf"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rDs8cWNkhoqu"
+   },
+   "source": [
+    "Zdaj, ko smo zajeli korake predobdelave in specifikacijo modela v *workflow*, lahko nadaljujemo s treniranjem linearnega SVC in hkrati ocenimo rezultate. Za merjenje učinkovitosti ustvarimo nabor metrik, ki bo ocenjeval: `natančnost`, `občutljivost`, `pozitivno napovedno vrednost` in `F-mero`.\n",
+    "\n",
+    "> `augment()` bo dodal stolpec(-ce) za napovedi k danim podatkom.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "81wiqcwuhrnq"
+   },
+   "source": [
+    "# Train a linear SVC model\n",
+    "svc_linear_fit <- svc_linear_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svc_linear_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0UFQvHf-huo3"
+   },
+   "source": [
+    "#### Podporni vektorski stroj\n",
+    "\n",
+    "Podporni vektorski stroj (SVM) je razširitev podpornega vektorskega klasifikatorja, ki omogoča uporabo nelinearne meje med razredi. V bistvu SVM-ji uporabljajo *trik s kernelom*, da razširijo prostor značilnosti in se prilagodijo nelinearnim odnosom med razredi. Ena izmed priljubljenih in izjemno prilagodljivih funkcij jedra, ki jih uporabljajo SVM-ji, je *funkcija radialne baze.* Poglejmo, kako se bo obnesla na naših podatkih.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-KX4S8mzhzmp"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Make an RBF SVM specification\n",
+    "svm_rbf_spec <- svm_rbf() %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svm_rbf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svm_rbf_spec)\n",
+    "\n",
+    "\n",
+    "# Train an RBF model\n",
+    "svm_rbf_fit <- svm_rbf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svm_rbf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QBFSa7WSh4HQ"
+   },
+   "source": [
+    "Veliko bolje 🤩!\n",
+    "\n",
+    "> ✅ Prosimo, poglejte:\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://bradleyboehmke.github.io/HOML/svm.html), Hands-on Machine Learning with R\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://www.statlearning.com/), An Introduction to Statistical Learning with Applications in R\n",
+    ">\n",
+    "> za dodatno branje.\n",
+    "\n",
+    "### Razvrščevalniki najbližjih sosedov\n",
+    "\n",
+    "Algoritem *K*-najbližjih sosedov (KNN) je metoda, pri kateri se vsaka opazovana vrednost napove na podlagi njene *podobnosti* z drugimi opazovanji.\n",
+    "\n",
+    "Poglejmo, kako ga lahko uporabimo na naših podatkih.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "k4BxxBcdh9Ka"
+   },
+   "source": [
+    "# Make a KNN specification\n",
+    "knn_spec <- nearest_neighbor() %>% \n",
+    "  set_engine(\"kknn\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "knn_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(knn_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "knn_wf_fit <- knn_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "knn_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HaegQseriAcj"
+   },
+   "source": [
+    "Zdi se, da ta model ne deluje najbolje. Verjetno bo izboljšanje delovanja modela mogoče z spremembo argumentov modela (glejte `help(\"nearest_neighbor\")`). Vsekakor poskusite to možnost.\n",
+    "\n",
+    "> ✅ Prosimo, poglejte:\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> za več informacij o klasifikatorjih *K*-Najbližjih Sosedov.\n",
+    "\n",
+    "### Ensemble klasifikatorji\n",
+    "\n",
+    "Ensemble algoritmi delujejo tako, da kombinirajo več osnovnih ocenjevalnikov za izdelavo optimalnega modela bodisi z:\n",
+    "\n",
+    "`bagging`: uporabo *povprečne funkcije* na zbirki osnovnih modelov\n",
+    "\n",
+    "`boosting`: gradnjo zaporedja modelov, ki se medsebojno nadgrajujejo za izboljšanje napovedne zmogljivosti.\n",
+    "\n",
+    "Začnimo z uporabo modela Random Forest, ki gradi veliko zbirko odločitvenih dreves in nato uporabi povprečno funkcijo za boljši celotni model.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "49DPoVs6iK1M"
+   },
+   "source": [
+    "# Make a random forest specification\n",
+    "rf_spec <- rand_forest() %>% \n",
+    "  set_engine(\"ranger\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "rf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(rf_spec)\n",
+    "\n",
+    "# Train a random forest model\n",
+    "rf_wf_fit <- rf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "rf_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RGVYwC_aiUWc"
+   },
+   "source": [
+    "Odlično delo 👏!\n",
+    "\n",
+    "Poskusimo tudi z modelom Boosted Tree.\n",
+    "\n",
+    "Boosted Tree opredeljuje metodo ansambla, ki ustvari serijo zaporednih odločitvenih dreves, kjer vsako drevo temelji na rezultatih prejšnjih dreves, da postopoma zmanjša napako. Osredotoča se na uteži napačno razvrščenih elementov in prilagodi prileganje za naslednji klasifikator, da jih popravi.\n",
+    "\n",
+    "Obstajajo različni načini za prileganje tega modela (glejte `help(\"boost_tree\")`). V tem primeru bomo prilegali Boosted drevesa prek pogona `xgboost`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Py1YWo-micWs"
+   },
+   "source": [
+    "# Make a boosted tree specification\n",
+    "boost_spec <- boost_tree(trees = 200) %>% \n",
+    "  set_engine(\"xgboost\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "boost_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(boost_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "boost_wf_fit <- boost_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "boost_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zNQnbuejigZM"
+   },
+   "source": [
+    "✅ Prosimo, da si ogledate:\n",
+    "\n",
+    "-   [Strojno učenje za družboslovce](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
+    "-   [Praktično strojno učenje z R](https://bradleyboehmke.github.io/HOML/)\n",
+    "-   [Uvod v statistično učenje z aplikacijami v R](https://www.statlearning.com/)\n",
+    "-   <https://algotech.netlify.app/blog/xgboost/> - Raziskuje model AdaBoost, ki je dobra alternativa xgboost.\n",
+    "\n",
+    "za več informacij o Ensemble klasifikatorjih.\n",
+    "\n",
+    "## 4. Dodatno - primerjava več modelov\n",
+    "\n",
+    "V tem laboratoriju smo prilagodili kar nekaj modelov 🙌. Ustvarjanje številnih delovnih tokov iz različnih naborov predprocesorjev in/ali specifikacij modelov ter nato izračunavanje metrik zmogljivosti enega za drugim lahko postane zamudno ali naporno.\n",
+    "\n",
+    "Poglejmo, ali lahko to rešimo z ustvarjanjem funkcije, ki prilagodi seznam delovnih tokov na učnem naboru podatkov in nato vrne metrike zmogljivosti na podlagi testnega nabora. Uporabili bomo `map()` in `map_dfr()` iz paketa [purrr](https://purrr.tidyverse.org/), da funkcije uporabimo na vsakem elementu seznama.\n",
+    "\n",
+    "> Funkcije [`map()`](https://purrr.tidyverse.org/reference/map.html) vam omogočajo, da nadomestite številne for zanke s kodo, ki je bolj jedrnata in lažja za branje. Najboljše mesto za učenje o funkcijah [`map()`](https://purrr.tidyverse.org/reference/map.html) je [poglavje o iteraciji](http://r4ds.had.co.nz/iteration.html) v R za podatkovno znanost.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Qzb7LyZnimd2"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "# Define a function that returns performance metrics\n",
+    "compare_models <- function(workflow_list, train_set, test_set){\n",
+    "  \n",
+    "  suppressWarnings(\n",
+    "    # Fit each model to the train_set\n",
+    "    map(workflow_list, fit, data = train_set) %>% \n",
+    "    # Make predictions on the test set\n",
+    "      map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
+    "    # Select desired columns\n",
+    "      select(model, cuisine, .pred_class) %>% \n",
+    "    # Evaluate model performance\n",
+    "      group_by(model) %>% \n",
+    "      eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
+    "      ungroup()\n",
+    "  )\n",
+    "  \n",
+    "} # End of function"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fwa712sNisDA"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "3i4VJOi2iu-a"
+   },
+   "source": [
+    "# Make a list of workflows\n",
+    "workflow_list <- list(\n",
+    "  \"svc\" = svc_linear_wf,\n",
+    "  \"svm\" = svm_rbf_wf,\n",
+    "  \"knn\" = knn_wf,\n",
+    "  \"random_forest\" = rf_wf,\n",
+    "  \"xgboost\" = boost_wf)\n",
+    "\n",
+    "# Call the function\n",
+    "set.seed(2056)\n",
+    "perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
+    "\n",
+    "# Print out performance metrics\n",
+    "perf_metrics %>% \n",
+    "  group_by(.metric) %>% \n",
+    "  arrange(desc(.estimate)) %>% \n",
+    "  slice_head(n=7)\n",
+    "\n",
+    "# Compare accuracy\n",
+    "perf_metrics %>% \n",
+    "  filter(.metric == \"accuracy\") %>% \n",
+    "  arrange(desc(.estimate))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KuWK_lEli4nW"
+   },
+   "source": [
+    "[**workflowset**](https://workflowsets.tidymodels.org/) paket omogoča uporabnikom, da ustvarijo in enostavno prilegajo veliko število modelov, vendar je večinoma zasnovan za delo s tehnikami ponovnega vzorčenja, kot je `križno preverjanje`, pristop, ki ga bomo še obravnavali.\n",
+    "\n",
+    "## **🚀Izziv**\n",
+    "\n",
+    "Vsaka od teh tehnik ima veliko število parametrov, ki jih lahko prilagodite, na primer `cost` pri SVM, `neighbors` pri KNN, `mtry` (naključno izbrani prediktorji) pri Random Forest.\n",
+    "\n",
+    "Raziskujte privzete parametre za vsako od teh tehnik in razmislite, kaj bi prilagajanje teh parametrov pomenilo za kakovost modela.\n",
+    "\n",
+    "Če želite izvedeti več o določenem modelu in njegovih parametrih, uporabite: `help(\"model\")`, npr. `help(\"rand_forest\")`.\n",
+    "\n",
+    "> V praksi običajno *ocenimo* *najboljše vrednosti* teh parametrov tako, da treniramo veliko modelov na `simuliranem naboru podatkov` in merimo, kako dobro se vsi ti modeli obnesejo. Ta proces imenujemo **uglaševanje**.\n",
+    "\n",
+    "### [**Kvizi po predavanju**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
+    "\n",
+    "### **Pregled in samostojno učenje**\n",
+    "\n",
+    "V teh lekcijah je veliko strokovnih izrazov, zato si vzemite trenutek za pregled [tega seznama](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) uporabne terminologije!\n",
+    "\n",
+    "#### HVALA:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) za ustvarjanje neverjetnih ilustracij, ki naredijo R bolj prijazen in privlačen. Več ilustracij najdete v njeni [galeriji](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) in [Jen Looper](https://www.twitter.com/jenlooper) za ustvarjanje izvirne različice tega modula v Pythonu ♥️\n",
+    "\n",
+    "Veselo učenje,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), zlati Microsoft Learn študentski ambasador.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Ilustracija @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/sl/4-Classification/3-Classifiers-2/solution/notebook.ipynb
new file mode 100644
index 000000000..10d71d4dd
--- /dev/null
+++ b/translations/sl/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -0,0 +1,304 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Zgradite več klasifikacijskih modelov\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Poskusite različne klasifikatorje\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "C = 10\n",
+    "# Create different classifiers.\n",
+    "classifiers = {\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
+    "    'KNN classifier': KNeighborsClassifier(C),\n",
+    "    'SVC': SVC(),\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "    \n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy (train) for Linear SVC: 76.4% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.64      0.66      0.65       242\n",
+      "      indian       0.91      0.86      0.89       236\n",
+      "    japanese       0.72      0.73      0.73       245\n",
+      "      korean       0.83      0.75      0.79       234\n",
+      "        thai       0.75      0.82      0.78       242\n",
+      "\n",
+      "    accuracy                           0.76      1199\n",
+      "   macro avg       0.77      0.76      0.77      1199\n",
+      "weighted avg       0.77      0.76      0.77      1199\n",
+      "\n",
+      "Accuracy (train) for KNN classifier: 70.7% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.65      0.63      0.64       242\n",
+      "      indian       0.84      0.81      0.82       236\n",
+      "    japanese       0.60      0.81      0.69       245\n",
+      "      korean       0.89      0.53      0.67       234\n",
+      "        thai       0.69      0.75      0.72       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.73      0.71      0.71      1199\n",
+      "weighted avg       0.73      0.71      0.71      1199\n",
+      "\n",
+      "Accuracy (train) for SVC: 80.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.71      0.69      0.70       242\n",
+      "      indian       0.92      0.92      0.92       236\n",
+      "    japanese       0.77      0.78      0.77       245\n",
+      "      korean       0.87      0.77      0.82       234\n",
+      "        thai       0.75      0.86      0.80       242\n",
+      "\n",
+      "    accuracy                           0.80      1199\n",
+      "   macro avg       0.80      0.80      0.80      1199\n",
+      "weighted avg       0.80      0.80      0.80      1199\n",
+      "\n",
+      "Accuracy (train) for RFST: 82.8% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.80      0.75      0.77       242\n",
+      "      indian       0.90      0.91      0.90       236\n",
+      "    japanese       0.82      0.78      0.80       245\n",
+      "      korean       0.85      0.82      0.83       234\n",
+      "        thai       0.78      0.89      0.83       242\n",
+      "\n",
+      "    accuracy                           0.83      1199\n",
+      "   macro avg       0.83      0.83      0.83      1199\n",
+      "weighted avg       0.83      0.83      0.83      1199\n",
+      "\n",
+      "Accuracy (train) for ADA: 71.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.60      0.57      0.58       242\n",
+      "      indian       0.87      0.84      0.86       236\n",
+      "    japanese       0.71      0.60      0.65       245\n",
+      "      korean       0.68      0.78      0.72       234\n",
+      "        thai       0.70      0.78      0.74       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.71      0.71      0.71      1199\n",
+      "weighted avg       0.71      0.71      0.71      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "    y_pred = classifier.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
+    "    print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "7ea2b714669c823a596d986ba2d5739f",
+   "translation_date": "2025-09-06T14:42:43+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/4-Applied/notebook.ipynb b/translations/sl/4-Classification/4-Applied/notebook.ipynb
new file mode 100644
index 000000000..2b5774bd9
--- /dev/null
+++ b/translations/sl/4-Classification/4-Applied/notebook.ipynb
@@ -0,0 +1,41 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2f3e0d9e9ac5c301558fb8bf733ac0cb",
+   "translation_date": "2025-09-06T14:41:26+00:00",
+   "source_file": "4-Classification/4-Applied/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Ustvari priporočilnik za kulinariko\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/4-Classification/4-Applied/solution/notebook.ipynb b/translations/sl/4-Classification/4-Applied/solution/notebook.ipynb
new file mode 100644
index 000000000..2b77ff4e4
--- /dev/null
+++ b/translations/sl/4-Classification/4-Applied/solution/notebook.ipynb
@@ -0,0 +1,292 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "49325d6dd12a3628fc64fa7ccb1a80ff",
+   "translation_date": "2025-09-06T14:41:51+00:00",
+   "source_file": "4-Classification/4-Applied/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Ustvari priporočilnik za kulinariko\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: skl2onnx in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.8.0)\n",
+      "Requirement already satisfied: protobuf in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (3.8.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.19.2)\n",
+      "Requirement already satisfied: onnx>=1.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.9.0)\n",
+      "Requirement already satisfied: six in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from skl2onnx) (1.12.0)\n",
+      "Requirement already satisfied: onnxconverter-common<1.9,>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.8.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.19 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (0.24.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.4.1)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->skl2onnx) (45.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnx>=1.2.1->skl2onnx) (3.10.0.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (0.16.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install skl2onnx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 60
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 61
+    }
+   ],
+   "source": [
+    "X = data.iloc[:,2:]\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "  cuisine\n",
+       "0  indian\n",
+       "1  indian\n",
+       "2  indian\n",
+       "3  indian\n",
+       "4  indian"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 62
+    }
+   ],
+   "source": [
+    "y = data[['cuisine']]\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 65
+    }
+   ],
+   "source": [
+    "model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
+    "model.fit(X_train,y_train.values.ravel())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.72      0.70      0.71       236\n      indian       0.91      0.88      0.89       243\n    japanese       0.80      0.75      0.77       240\n      korean       0.80      0.81      0.81       230\n        thai       0.76      0.85      0.80       250\n\n    accuracy                           0.80      1199\n   macro avg       0.80      0.80      0.80      1199\nweighted avg       0.80      0.80      0.80      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}\n",
+    "onx = convert_sklearn(model, initial_types=initial_type, options=options)\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
+    "    f.write(onx.SerializeToString())\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/5-Clustering/1-Visualize/notebook.ipynb b/translations/sl/5-Clustering/1-Visualize/notebook.ipynb
new file mode 100644
index 000000000..41000511d
--- /dev/null
+++ b/translations/sl/5-Clustering/1-Visualize/notebook.ipynb
@@ -0,0 +1,50 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python383jvsc74a57bd0e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95",
+   "display_name": "Python 3.8.3 64-bit (conda)"
+  },
+  "coopTranslator": {
+   "original_hash": "40e0707e96b3e1899a912776006264f9",
+   "translation_date": "2025-09-06T14:07:56+00:00",
+   "source_file": "5-Clustering/1-Visualize/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
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@@ -0,0 +1,488 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Nigerijska glasba pridobljena s Spotifyja - analiza**\n",
+    "\n",
+    "Grozdanje je vrsta [nenadzorovanega učenja](https://wikipedia.org/wiki/Unsupervised_learning), ki predpostavlja, da je podatkovni niz neoznačen ali da njegovi vnosi niso povezani z vnaprej določenimi izhodi. Uporablja različne algoritme za razvrščanje neoznačenih podatkov in zagotavljanje skupin glede na vzorce, ki jih zazna v podatkih.\n",
+    "\n",
+    "[**Predhodni kviz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)\n",
+    "\n",
+    "### **Uvod**\n",
+    "\n",
+    "[Grozdanje](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) je zelo uporabno za raziskovanje podatkov. Poglejmo, ali lahko pomaga odkriti trende in vzorce v načinu, kako nigerijsko občinstvo posluša glasbo.\n",
+    "\n",
+    "> ✅ Vzemite si trenutek in razmislite o uporabi grozdanja. V vsakdanjem življenju se grozdanje zgodi, kadar imate kup perila in morate razvrstiti oblačila družinskih članov 🧦👕👖🩲. V podatkovni znanosti se grozdanje zgodi, ko poskušate analizirati uporabnikove preference ali določiti značilnosti katerega koli neoznačenega podatkovnega niza. Grozdanje na nek način pomaga ustvariti red iz kaosa, kot je predal za nogavice.\n",
+    "\n",
+    "V profesionalnem okolju se grozdanje lahko uporablja za določanje stvari, kot je segmentacija trga, na primer za ugotavljanje, katere starostne skupine kupujejo določene izdelke. Druga uporaba bi bila odkrivanje anomalij, morda za zaznavanje goljufij v podatkovnem nizu transakcij s kreditnimi karticami. Lahko pa uporabite grozdanje za določanje tumorjev v seriji medicinskih skenov.\n",
+    "\n",
+    "✅ Razmislite za trenutek, kako ste morda naleteli na grozdanje 'v naravi', v bančništvu, e-trgovini ali poslovnem okolju.\n",
+    "\n",
+    "> 🎓 Zanimivo je, da analiza grozdov izvira iz področij antropologije in psihologije v 1930-ih. Si lahko predstavljate, kako bi jo takrat uporabljali?\n",
+    "\n",
+    "Alternativno bi jo lahko uporabili za razvrščanje rezultatov iskanja - na primer po nakupovalnih povezavah, slikah ali ocenah. Grozdanje je uporabno, kadar imate velik podatkovni niz, ki ga želite zmanjšati in na katerem želite opraviti bolj podrobno analizo, zato se tehnika lahko uporablja za spoznavanje podatkov, preden se zgradijo drugi modeli.\n",
+    "\n",
+    "✅ Ko so vaši podatki organizirani v grozde, jim dodelite ID grozda, kar je lahko uporabno pri ohranjanju zasebnosti podatkovnega niza; namesto bolj razkrivajočih identifikacijskih podatkov lahko uporabite ID grozda za sklicevanje na podatkovno točko. Ali lahko pomislite na druge razloge, zakaj bi za identifikacijo raje uporabili ID grozda kot druge elemente grozda?\n",
+    "\n",
+    "### Začetek z grozdanjem\n",
+    "\n",
+    "> 🎓 Način, kako ustvarimo grozde, je močno povezan s tem, kako združimo podatkovne točke v skupine. Razčistimo nekaj terminologije:\n",
+    ">\n",
+    "> 🎓 ['Transduktivno' vs. 'induktivno'](https://wikipedia.org/wiki/Transduction_(machine_learning))\n",
+    ">\n",
+    "> Transduktivno sklepanje izhaja iz opazovanih primerov usposabljanja, ki se preslikajo na specifične testne primere. Induktivno sklepanje izhaja iz primerov usposabljanja, ki se preslikajo na splošna pravila, ki se nato uporabijo na testnih primerih.\n",
+    ">\n",
+    "> Primer: Predstavljajte si, da imate podatkovni niz, ki je le delno označen. Nekatere stvari so 'plošče', nekatere 'CD-ji', nekatere pa so prazne. Vaša naloga je dodeliti oznake praznim. Če izberete induktivni pristop, bi usposobili model za iskanje 'plošč' in 'CD-jev' ter te oznake uporabili na neoznačenih podatkih. Ta pristop bo imel težave pri razvrščanju stvari, ki so dejansko 'kasete'. Transduktivni pristop pa bo to neznano podatkovno točko obravnaval bolj učinkovito, saj deluje tako, da združi podobne predmete in nato skupini dodeli oznako. V tem primeru bi grozdi lahko odražali 'okrogle glasbene stvari' in 'kvadratne glasbene stvari'.\n",
+    ">\n",
+    "> 🎓 ['Neploskovna' vs. 'ploskovna' geometrija](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)\n",
+    ">\n",
+    "> Izpeljano iz matematične terminologije, neploskovna vs. ploskovna geometrija se nanaša na merjenje razdalj med točkami bodisi s 'ploskovnimi' ([Evklidskimi](https://wikipedia.org/wiki/Euclidean_geometry)) bodisi z neploskovnimi (neevklidskimi) geometrijskimi metodami.\n",
+    ">\n",
+    "> 'Ploskovna' v tem kontekstu se nanaša na evklidsko geometrijo (deli katere se učijo kot 'ravninska' geometrija), medtem ko se neploskovna nanaša na neevklidsko geometrijo. Kaj ima geometrija skupnega z strojno učenje? Kot dve področji, ki temeljita na matematiki, mora obstajati skupen način za merjenje razdalj med točkami v grozdih, kar se lahko izvede na 'ploskovni' ali 'neploskovni' način, odvisno od narave podatkov. [Evklidske razdalje](https://wikipedia.org/wiki/Euclidean_distance) se merijo kot dolžina odseka med dvema točkama. [Neevklidske razdalje](https://wikipedia.org/wiki/Non-Euclidean_geometry) se merijo vzdolž krivulje. Če se vaši podatki, vizualizirani, ne nahajajo na ravnini, boste morda morali uporabiti specializiran algoritem za obdelavo.\n",
+    ">\n",
+    "> 🎓 ['Razdalje'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)\n",
+    ">\n",
+    "> Grozdi so opredeljeni z matriko razdalj, npr. razdaljami med točkami. Te razdalje je mogoče meriti na več načinov. Evklidski grozdi so opredeljeni z povprečjem vrednosti točk in vsebujejo 'centroid' ali osrednjo točko. Razdalje se tako merijo glede na razdaljo do tega centroida. Neevklidske razdalje se nanašajo na 'clustroid', točko, ki je najbližja drugim točkam. Clustroidi so lahko opredeljeni na različne načine.\n",
+    ">\n",
+    "> 🎓 ['Omejeno'](https://wikipedia.org/wiki/Constrained_clustering)\n",
+    ">\n",
+    "> [Omejeno grozdanje](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) uvaja 'polnadzorovano' učenje v to nenadzorovano metodo. Razmerja med točkami so označena kot 'ne smejo se povezati' ali 'morajo se povezati', tako da se na podatkovni niz uvedejo določena pravila.\n",
+    ">\n",
+    "> Primer: Če je algoritem sproščen na serijo neoznačenih ali delno označenih podatkov, so lahko grozdi, ki jih ustvari, slabe kakovosti. V zgornjem primeru bi grozdi lahko združevali 'okrogle glasbene stvari', 'kvadratne glasbene stvari', 'trikotne stvari' in 'piškote'. Če se uvedejo določene omejitve ali pravila (\"predmet mora biti iz plastike\", \"predmet mora biti sposoben proizvajati glasbo\"), to lahko pomaga 'omejiti' algoritem, da sprejema boljše odločitve.\n",
+    ">\n",
+    "> 🎓 'Gostota'\n",
+    ">\n",
+    "> Podatki, ki so 'hrupni', se štejejo za 'goste'. Razdalje med točkami v vsakem od njegovih grozdov se lahko ob pregledu izkažejo za bolj ali manj goste ali 'natrpane', zato je treba te podatke analizirati z ustrezno metodo grozdanja. [Ta članek](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) prikazuje razliko med uporabo algoritmov K-Means grozdanja in HDBSCAN za raziskovanje hrupnega podatkovnega niza z neenakomerno gostoto grozdov.\n",
+    "\n",
+    "Poglobite svoje razumevanje tehnik grozdanja v tem [učnem modulu](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "### **Algoritmi grozdanja**\n",
+    "\n",
+    "Obstaja več kot 100 algoritmov za grozdanje, njihova uporaba pa je odvisna od narave podatkov. Oglejmo si nekatere glavne:\n",
+    "\n",
+    "-   **Hierarhično grozdanje**. Če je predmet razvrščen glede na svojo bližino bližnjemu predmetu, namesto bolj oddaljenemu, se grozdi oblikujejo na podlagi razdalje med člani. Hierarhično grozdanje je značilno po tem, da se dva grozda večkrat združita.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/hierarchical.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografika: Dasani Madipalli</figcaption>\n",
+    "\n",
+    "-   **Centroidno grozdanje**. Ta priljubljeni algoritem zahteva izbiro 'k', ali število grozdov, ki jih je treba oblikovati, nato pa algoritem določi osrednjo točko grozda in zbira podatke okoli te točke. [K-means grozdanje](https://wikipedia.org/wiki/K-means_clustering) je priljubljena različica centroidnega grozdanja, ki razdeli podatkovni niz v vnaprej določene K skupine. Center je določen z najbližjim povprečjem, od tod tudi ime. Kvadratna razdalja od grozda je minimizirana.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/centroid.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografika: Dasani Madipalli</figcaption>\n",
+    "\n",
+    "-   **Grozdanje na podlagi porazdelitve**. Temelji na statističnem modeliranju, grozdanje na podlagi porazdelitve se osredotoča na določanje verjetnosti, da podatkovna točka pripada grozdu, in ji ustrezno dodeli mesto. Metode Gaussove mešanice spadajo v to vrsto.\n",
+    "\n",
+    "-   **Grozdanje na podlagi gostote**. Podatkovne točke so dodeljene grozdom glede na njihovo gostoto ali njihovo združevanje okoli drugih točk. Podatkovne točke, ki so daleč od skupine, se štejejo za odstopanja ali hrup. DBSCAN, Mean-shift in OPTICS spadajo v to vrsto grozdanja.\n",
+    "\n",
+    "-   **Grozdanje na podlagi mreže**. Za večdimenzionalne podatkovne nize se ustvari mreža, podatki pa se razdelijo med celice mreže, s čimer se ustvarijo grozdi.\n",
+    "\n",
+    "Najboljši način za učenje o grozdanju je, da ga preizkusite sami, kar boste storili v tej vaji.\n",
+    "\n",
+    "Za dokončanje tega modula bomo potrebovali nekaj paketov. Namestite jih lahko z ukazom: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`\n",
+    "\n",
+    "Alternativno spodnji skript preveri, ali imate potrebne pakete za dokončanje tega modula, in jih namesti, če manjkajo.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Naloga - razvrščanje podatkov v skupine\n",
+    "\n",
+    "Razvrščanje v skupine kot tehnika je močno podprto z ustrezno vizualizacijo, zato začnimo z vizualizacijo naših glasbenih podatkov. Ta naloga nam bo pomagala odločiti, katero metodo razvrščanja v skupine bi morali najučinkoviteje uporabiti glede na naravo teh podatkov.\n",
+    "\n",
+    "Začnimo z uvozom podatkov.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# Import the data into a tibble\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows of the data set\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Včasih si želimo pridobiti malo več informacij o naših podatkih. Podatke in njihovo strukturo si lahko ogledamo z uporabo funkcije [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Glimpse into the data set\r\n",
+    "df %>% \r\n",
+    "  glimpse()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično delo!💪\n",
+    "\n",
+    "Opazimo lahko, da `glimpse()` prikaže skupno število vrstic (opazovanj) in stolpcev (spremenljivk), nato pa prvih nekaj vnosov vsake spremenljivke v vrstici za imenom spremenljivke. Poleg tega je *tip podatkov* spremenljivke prikazan takoj za imenom spremenljivke znotraj `< >`.\n",
+    "\n",
+    "`DataExplorer::introduce()` lahko to informacijo povzame na urejen način:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe basic information for our data\r\n",
+    "df %>% \r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# A visual display of the same\r\n",
+    "df %>% \r\n",
+    "  plot_intro()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Odlično! Pravkar smo ugotovili, da naši podatki nimajo manjkajočih vrednosti.\n",
+    "\n",
+    "Medtem lahko raziščemo pogoste statistike centralne tendence (npr. [povprečje](https://en.wikipedia.org/wiki/Arithmetic_mean) in [mediano](https://en.wikipedia.org/wiki/Median)) ter mere razpršenosti (npr. [standardni odklon](https://en.wikipedia.org/wiki/Standard_deviation)) z uporabo `summarytools::descr()`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe common statistics\r\n",
+    "df %>% \r\n",
+    "  descr(stats = \"common\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Poglejmo splošne vrednosti podatkov. Upoštevajte, da lahko priljubljenost znaša `0`, kar pomeni pesmi, ki nimajo uvrstitve. Te bomo kmalu odstranili.\n",
+    "\n",
+    "> 🤔 Če delamo s gručenjem, nenadzorovano metodo, ki ne zahteva označenih podatkov, zakaj prikazujemo te podatke z oznakami? V fazi raziskovanja podatkov so koristni, vendar niso nujni za delovanje algoritmov za gručenje.\n",
+    "\n",
+    "### 1. Raziskovanje priljubljenih žanrov\n",
+    "\n",
+    "Pojdimo naprej in ugotovimo najbolj priljubljene žanre 🎶 tako, da preštejemo, kolikokrat se pojavijo.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Popular genres\r\n",
+    "top_genres <- df %>% \r\n",
+    "  count(artist_top_genre, sort = TRUE) %>% \r\n",
+    "# Encode to categorical and reorder the according to count\r\n",
+    "  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())\r\n",
+    "\r\n",
+    "# Print the top genres\r\n",
+    "top_genres\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "To je šlo dobro! Pravijo, da slika pove več kot tisoč vrstic podatkovnega okvira (pravzaprav tega nihče nikoli ne reče 😅). Ampak razumete bistvo, kajne?\n",
+    "\n",
+    "Eden od načinov za vizualizacijo kategorijskih podatkov (znakovnih ali faktorskih spremenljivk) je uporaba stolpčnih grafov. Naredimo stolpčni graf za 10 najbolj priljubljenih žanrov:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Change the default gray theme\r\n",
+    "theme_set(theme_light())\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zdaj je veliko lažje prepoznati, da imamo `manjkajoče` žanre 🧐!\n",
+    "\n",
+    "> Dobra vizualizacija vam bo pokazala stvari, ki jih niste pričakovali, ali pa sprožila nova vprašanja o podatkih - Hadley Wickham in Garrett Grolemund, [R For Data Science](https://r4ds.had.co.nz/introduction.html)\n",
+    "\n",
+    "Opomba: ko je glavni žanr opisan kot `Manjkajoč`, to pomeni, da ga Spotify ni klasificiral, zato ga odstranimo.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  filter(artist_top_genre != \"Missing\") %>% \r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Iz osnovnega raziskovanja podatkov ugotovimo, da tri najbolj priljubljene zvrsti prevladujejo v tem naboru podatkov. Osredotočimo se na `afro dancehall`, `afropop` in `nigerian pop`, poleg tega pa filtrirajmo nabor podatkov, da odstranimo vse, kar ima vrednost priljubljenosti 0 (kar pomeni, da ni bilo razvrščeno glede na priljubljenost v naboru podatkov in se lahko za naše namene šteje kot šum):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "nigerian_songs <- df %>% \r\n",
+    "  # Concentrate on top 3 genres\r\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \r\n",
+    "  # Remove unclassified observations\r\n",
+    "  filter(popularity != 0)\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "nigerian_songs %>%\r\n",
+    "  count(artist_top_genre) %>%\r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Poglejmo, ali obstaja kakšna očitna linearna povezava med številskimi spremenljivkami v našem naboru podatkov. To povezavo matematično kvantificira [statistika korelacije](https://en.wikipedia.org/wiki/Correlation).\n",
+    "\n",
+    "Statistika korelacije je vrednost med -1 in 1, ki kaže na moč povezave. Vrednosti nad 0 kažejo na *pozitivno* korelacijo (visoke vrednosti ene spremenljivke običajno sovpadajo z visokimi vrednostmi druge), medtem ko vrednosti pod 0 kažejo na *negativno* korelacijo (visoke vrednosti ene spremenljivke običajno sovpadajo z nizkimi vrednostmi druge).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Narrow down to numeric variables and fid correlation\r\n",
+    "corr_mat <- nigerian_songs %>% \r\n",
+    "  select(where(is.numeric)) %>% \r\n",
+    "  cor()\r\n",
+    "\r\n",
+    "# Visualize correlation matrix\r\n",
+    "corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  \r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Podatki niso močno povezani, razen med `energy` in `loudness`, kar je smiselno, saj je glasna glasba običajno precej energična. `Popularity` ima povezavo z `release date`, kar prav tako ni presenetljivo, saj so novejše pesmi verjetno bolj priljubljene. Dolžina in energija se zdita tudi povezani.\n",
+    "\n",
+    "Zanimivo bo videti, kaj lahko algoritem za razvrščanje naredi s temi podatki!\n",
+    "\n",
+    "> 🎓 Upoštevajte, da korelacija ne pomeni vzročne povezanosti! Imamo dokaz o korelaciji, vendar nimamo dokaza o vzročnosti. [Zabavna spletna stran](https://tylervigen.com/spurious-correlations) ponuja nekaj vizualizacij, ki poudarjajo to točko.\n",
+    "\n",
+    "### 2. Raziskovanje porazdelitve podatkov\n",
+    "\n",
+    "Postavimo si nekaj bolj subtilnih vprašanj. Ali se žanri bistveno razlikujejo v zaznavanju njihove plesnosti glede na njihovo priljubljenost? Preučimo porazdelitev podatkov naših treh najbolj priljubljenih žanrov glede priljubljenosti in plesnosti vzdolž določene osi x in y z uporabo [gostotnih grafov](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Perform 2D kernel density estimation\r\n",
+    "density_estimate_2d <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +\r\n",
+    "  geom_density_2d(bins = 5, size = 1) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  xlim(-20, 80) +\r\n",
+    "  ylim(0, 1.2)\r\n",
+    "\r\n",
+    "# Density plot based on the popularity\r\n",
+    "density_estimate_pop <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  theme(legend.position = \"none\")\r\n",
+    "\r\n",
+    "# Density plot based on the danceability\r\n",
+    "density_estimate_dance <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Patch everything together\r\n",
+    "library(patchwork)\r\n",
+    "density_estimate_2d / (density_estimate_pop + density_estimate_dance)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vidimo, da so tu koncentrični krogi, ki se ujemajo, ne glede na žanr. Ali je mogoče, da se nigerijski okusi pri tem žanru zbližajo na določeni ravni plesnosti?\n",
+    "\n",
+    "Na splošno se trije žanri ujemajo glede na svojo priljubljenost in plesnost. Določanje skupkov v teh ohlapno poravnanih podatkih bo izziv. Poglejmo, ali lahko raztreseni diagram to podpre.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# A scatter plot of popularity and danceability\r\n",
+    "scatter_plot <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +\r\n",
+    "  geom_point(size = 2, alpha = 0.8) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"futurevisions::mars\")\r\n",
+    "\r\n",
+    "# Add a touch of interactivity\r\n",
+    "ggplotly(scatter_plot)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Razpršeni diagram istih osi prikazuje podoben vzorec konvergence.\n",
+    "\n",
+    "Na splošno lahko za razvrščanje uporabite razpršene diagrame, da prikažete skupine podatkov, zato je obvladovanje te vrste vizualizacije zelo koristno. V naslednji lekciji bomo uporabili te filtrirane podatke in s k-means razvrščanjem odkrili skupine v teh podatkih, ki se na zanimive načine prekrivajo.\n",
+    "\n",
+    "## **🚀 Izziv**\n",
+    "\n",
+    "V pripravi na naslednjo lekcijo naredite diagram o različnih algoritmih za razvrščanje, ki jih morda odkrijete in uporabite v produkcijskem okolju. Kakšne vrste težav poskuša razvrščanje rešiti?\n",
+    "\n",
+    "## [**Kvizi po predavanju**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)\n",
+    "\n",
+    "## **Pregled & Samostojno učenje**\n",
+    "\n",
+    "Preden uporabite algoritme za razvrščanje, kot smo se naučili, je dobro razumeti naravo vašega nabora podatkov. Več o tej temi preberite [tukaj](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html).\n",
+    "\n",
+    "Poglobite svoje razumevanje tehnik razvrščanja:\n",
+    "\n",
+    "-   [Usposabljanje in ocenjevanje modelov za razvrščanje z uporabo Tidymodels in prijateljev](https://rpubs.com/eR_ic/clustering)\n",
+    "\n",
+    "-   Bradley Boehmke & Brandon Greenwell, [*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/)*.*\n",
+    "\n",
+    "## **Naloga**\n",
+    "\n",
+    "[Raziskujte druge vizualizacije za razvrščanje](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)\n",
+    "\n",
+    "## HVALA:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) za ustvarjanje izvirne Python različice tega modula ♥️\n",
+    "\n",
+    "[`Dasani Madipalli`](https://twitter.com/dasani_decoded) za ustvarjanje neverjetnih ilustracij, ki omogočajo boljše razumevanje konceptov strojnega učenja in jih naredijo bolj dostopne.\n",
+    "\n",
+    "Veselo učenje,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "99c36449cad3708a435f6798cfa39972",
+   "translation_date": "2025-09-06T14:12:22+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: seaborn in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (0.11.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (3.5.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.21.4)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.3.4)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.7.2)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (4.28.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (21.2)\n",
+      "Requirement already satisfied: setuptools-scm>=4 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (6.3.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
+      "Requirement already satisfied: tomli>=1.0.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (1.2.2)\n",
+      "Requirement already satisfied: setuptools in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (59.1.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dobite informacije o podatkovnem okviru\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 530 entries, 0 to 529\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   name              530 non-null    object \n",
+      " 1   album             530 non-null    object \n",
+      " 2   artist            530 non-null    object \n",
+      " 3   artist_top_genre  530 non-null    object \n",
+      " 4   release_date      530 non-null    int64  \n",
+      " 5   length            530 non-null    int64  \n",
+      " 6   popularity        530 non-null    int64  \n",
+      " 7   danceability      530 non-null    float64\n",
+      " 8   acousticness      530 non-null    float64\n",
+      " 9   energy            530 non-null    float64\n",
+      " 10  instrumentalness  530 non-null    float64\n",
+      " 11  liveness          530 non-null    float64\n",
+      " 12  loudness          530 non-null    float64\n",
+      " 13  speechiness       530 non-null    float64\n",
+      " 14  tempo             530 non-null    float64\n",
+      " 15  time_signature    530 non-null    int64  \n",
+      "dtypes: float64(8), int64(4), object(4)\n",
+      "memory usage: 66.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name                0\n",
+       "album               0\n",
+       "artist              0\n",
+       "artist_top_genre    0\n",
+       "release_date        0\n",
+       "length              0\n",
+       "popularity          0\n",
+       "danceability        0\n",
+       "acousticness        0\n",
+       "energy              0\n",
+       "instrumentalness    0\n",
+       "liveness            0\n",
+       "loudness            0\n",
+       "speechiness         0\n",
+       "tempo               0\n",
+       "time_signature      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Oglejte si splošne vrednosti podatkov. Upoštevajte, da je priljubljenost lahko '0' - in obstaja veliko vrstic s to vrednostjo\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2015.390566</td>\n",
+       "      <td>222298.169811</td>\n",
+       "      <td>17.507547</td>\n",
+       "      <td>0.741619</td>\n",
+       "      <td>0.265412</td>\n",
+       "      <td>0.760623</td>\n",
+       "      <td>0.016305</td>\n",
+       "      <td>0.147308</td>\n",
+       "      <td>-4.953011</td>\n",
+       "      <td>0.130748</td>\n",
+       "      <td>116.487864</td>\n",
+       "      <td>3.986792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.131688</td>\n",
+       "      <td>39696.822259</td>\n",
+       "      <td>18.992212</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.208342</td>\n",
+       "      <td>0.148533</td>\n",
+       "      <td>0.090321</td>\n",
+       "      <td>0.123588</td>\n",
+       "      <td>2.464186</td>\n",
+       "      <td>0.092939</td>\n",
+       "      <td>23.518601</td>\n",
+       "      <td>0.333701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>89488.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>0.111000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028300</td>\n",
+       "      <td>-19.362000</td>\n",
+       "      <td>0.027800</td>\n",
+       "      <td>61.695000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>199305.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681000</td>\n",
+       "      <td>0.089525</td>\n",
+       "      <td>0.669000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.075650</td>\n",
+       "      <td>-6.298750</td>\n",
+       "      <td>0.059100</td>\n",
+       "      <td>102.961250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>218509.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.761000</td>\n",
+       "      <td>0.220500</td>\n",
+       "      <td>0.784500</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.103500</td>\n",
+       "      <td>-4.558500</td>\n",
+       "      <td>0.097950</td>\n",
+       "      <td>112.714500</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>242098.500000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.829500</td>\n",
+       "      <td>0.403000</td>\n",
+       "      <td>0.875750</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.164000</td>\n",
+       "      <td>-3.331000</td>\n",
+       "      <td>0.177000</td>\n",
+       "      <td>125.039250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>511738.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>0.966000</td>\n",
+       "      <td>0.954000</td>\n",
+       "      <td>0.995000</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.811000</td>\n",
+       "      <td>0.582000</td>\n",
+       "      <td>0.514000</td>\n",
+       "      <td>206.007000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       release_date         length  popularity  danceability  acousticness  \\\n",
+       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
+       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
+       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
+       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
+       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
+       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
+       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
+       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
+       "\n",
+       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
+       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
+       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
+       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
+       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
+       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
+       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
+       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
+       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
+       "\n",
+       "            tempo  time_signature  \n",
+       "count  530.000000      530.000000  \n",
+       "mean   116.487864        3.986792  \n",
+       "std     23.518601        0.333701  \n",
+       "min     61.695000        3.000000  \n",
+       "25%    102.961250        4.000000  \n",
+       "50%    112.714500        4.000000  \n",
+       "75%    125.039250        4.000000  \n",
+       "max    206.007000        5.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Poglejmo si žanre. Kar nekaj jih je označenih kot 'Manjkajoči', kar pomeni, da v podatkovnem naboru niso kategorizirani z žanrom\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Uvod\n",
+    "\n",
+    "Ta dokument opisuje, kako upravljati z glasbenimi žanri v aplikaciji. Žanri so ključni za organizacijo glasbe in omogočajo uporabnikom, da hitro najdejo skladbe, ki ustrezajo njihovemu okusu.\n",
+    "\n",
+    "## Podprti žanri\n",
+    "\n",
+    "Spodaj je seznam podprtih žanrov:\n",
+    "\n",
+    "- Pop\n",
+    "- Rock\n",
+    "- Jazz\n",
+    "- Hip Hop\n",
+    "- Klasična glasba\n",
+    "- Elektronska glasba\n",
+    "- Country\n",
+    "- Reggae\n",
+    "- Blues\n",
+    "- Funk\n",
+    "- Soul\n",
+    "- R&B\n",
+    "\n",
+    "## Dodajanje novega žanra\n",
+    "\n",
+    "Če želite dodati nov žanr, sledite tem korakom:\n",
+    "\n",
+    "1. Odprite nastavitve aplikacije.\n",
+    "2. Kliknite na \"Upravljanje žanrov\".\n",
+    "3. Vnesite ime novega žanra.\n",
+    "4. Kliknite \"Shrani\".\n",
+    "\n",
+    "[!TIP] Prepričajte se, da ime žanra ni podvojeno.\n",
+    "\n",
+    "## Urejanje obstoječega žanra\n",
+    "\n",
+    "Če želite urediti obstoječi žanr:\n",
+    "\n",
+    "1. Pojdite na seznam žanrov.\n",
+    "2. Kliknite na žanr, ki ga želite urediti.\n",
+    "3. Spremenite ime ali druge lastnosti.\n",
+    "4. Kliknite \"Shrani spremembe\".\n",
+    "\n",
+    "[!IMPORTANT] Urejanje žanrov lahko vpliva na obstoječe sezname predvajanja.\n",
+    "\n",
+    "## Brisanje žanra\n",
+    "\n",
+    "Če želite izbrisati žanr:\n",
+    "\n",
+    "1. Pojdite na seznam žanrov.\n",
+    "2. Izberite žanr, ki ga želite izbrisati.\n",
+    "3. Kliknite \"Izbriši\".\n",
+    "\n",
+    "[!WARNING] Brisanje žanra bo odstranilo vse povezave do skladb, ki so bile razvrščene pod tem žanrom.\n",
+    "\n",
+    "## Pogosta vprašanja\n",
+    "\n",
+    "### Kaj se zgodi, če izbrišem žanr?\n",
+    "\n",
+    "Ko izbrišete žanr, se skladbe, ki so bile razvrščene pod tem žanrom, ne bodo več prikazovale v kategoriji žanrov. Vendar pa bodo še vedno dostopne prek drugih filtrov ali iskanja.\n",
+    "\n",
+    "### Ali lahko obnovim izbrisan žanr?\n",
+    "\n",
+    "Ne, izbrisani žanri ni mogoče obnoviti. Priporočamo, da pred brisanjem ustvarite varnostno kopijo.\n",
+    "\n",
+    "### Ali lahko dodam več žanrov hkrati?\n",
+    "\n",
+    "Trenutno aplikacija podpira dodajanje žanrov enega za drugim. Funkcionalnost za množično dodajanje je v načrtu za prihodnje posodobitve.\n",
+    "\n",
+    "## Zaključek\n",
+    "\n",
+    "Upravljanje žanrov je pomemben del organizacije glasbe v aplikaciji. S pravilnim razvrščanjem skladb po žanrih lahko uporabniki uživajo v boljši izkušnji iskanja in poslušanja glasbe.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[df['artist_top_genre'] != 'Missing']\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Podatki niso močno povezani, razen med energijo in glasnostjo, kar je smiselno. Priljubljenost ima povezavo z datumom izdaje, kar prav tako ima smisel, saj so novejše pesmi verjetno bolj priljubljene. Dolžina in energija se zdita povezani – morda so krajše pesmi bolj energične?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corrmat = df.corr()\n",
+    "f, ax = plt.subplots(figsize=(12, 9))\n",
+    "sns.heatmap(corrmat, vmax=.8, square=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "\n",
+    "# Show the joint distribution using kernel density estimation\n",
+    "g = sns.jointplot(\n",
+    "    data=df,\n",
+    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
+    "    kind=\"kde\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Na splošno se trije žanri ujemajo glede na svojo priljubljenost in plesnost. Raztreseni diagram z istimi osmi prikazuje podoben vzorec konvergence. Poskusite raztreseni diagram, da preverite porazdelitev podatkov po žanrih.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1350b1a90>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 468.975x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
+    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
+    "   .add_legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "c61deff2839902ac8cb4ed411eb10fee",
+   "translation_date": "2025-09-06T14:08:48+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sl/5-Clustering/2-K-Means/notebook.ipynb b/translations/sl/5-Clustering/2-K-Means/notebook.ipynb
new file mode 100644
index 000000000..465876a31
--- /dev/null
+++ b/translations/sl/5-Clustering/2-K-Means/notebook.ipynb
@@ -0,0 +1,231 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "3e5c8ab363e8d88f566d4365efc7e0bd",
+   "translation_date": "2025-09-06T14:19:28+00:00",
+   "source_file": "5-Clustering/2-K-Means/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Začnite tam, kjer smo končali pri zadnji lekciji, z uvoženimi in filtriranimi podatki.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Osredotočili se bomo le na 3 žanre. Morda lahko ustvarimo 3 gruče!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna napačna razumevanja ali napačne interpretacije, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/translations/sl/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
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--- /dev/null
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@@ -0,0 +1,640 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "colab": {
+   "name": "lesson_14.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "coopTranslator": {
+   "original_hash": "ad65fb4aad0a156b42216e4929f490fc",
+   "translation_date": "2025-09-06T14:25:26+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GULATlQXLXyR"
+   },
+   "source": [
+    "## Raziskovanje gručenja K-Means z uporabo R in načel urejenih podatkov.\n",
+    "\n",
+    "### [**Predhodni kviz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)\n",
+    "\n",
+    "V tej lekciji se boste naučili, kako ustvariti gruče z uporabo paketa Tidymodels in drugih paketov v ekosistemu R (imenovali jih bomo prijatelji 🧑‍🤝‍🧑) ter nigerijskega glasbenega nabora podatkov, ki ste ga uvozili prej. Pokrili bomo osnove K-Means za gručenje. Upoštevajte, da, kot ste se naučili v prejšnji lekciji, obstaja veliko načinov za delo z gručenjem, metoda, ki jo uporabite, pa je odvisna od vaših podatkov. Poskusili bomo K-Means, saj je to najpogostejša tehnika gručenja. Začnimo!\n",
+    "\n",
+    "Pojmi, o katerih se boste učili:\n",
+    "\n",
+    "-   Silhuetno ocenjevanje\n",
+    "\n",
+    "-   Metoda komolca\n",
+    "\n",
+    "-   Inercija\n",
+    "\n",
+    "-   Varianca\n",
+    "\n",
+    "### **Uvod**\n",
+    "\n",
+    "[K-Means gručenje](https://wikipedia.org/wiki/K-means_clustering) je metoda, ki izhaja iz področja obdelave signalov. Uporablja se za razdelitev in razvrščanje skupin podatkov v `k gruče` na podlagi podobnosti njihovih značilnosti.\n",
+    "\n",
+    "Gruče je mogoče vizualizirati kot [Voronoijeve diagrame](https://wikipedia.org/wiki/Voronoi_diagram), ki vključujejo točko (ali 'seme') in njeno ustrezno regijo.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/voronoi.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Infografika avtorice Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "Postopek K-Means gručenja vključuje naslednje korake:\n",
+    "\n",
+    "1.  Podatkovni znanstvenik najprej določi želeno število gruč, ki jih želi ustvariti.\n",
+    "\n",
+    "2.  Nato algoritem naključno izbere K opazovanj iz nabora podatkov, ki služijo kot začetna središča gruč (tj. centroidi).\n",
+    "\n",
+    "3.  Nato se vsako preostalo opazovanje dodeli najbližjemu centroidu.\n",
+    "\n",
+    "4.  Nato se izračunajo nove povprečne vrednosti vsake grupe, centroid pa se premakne na povprečje.\n",
+    "\n",
+    "5.  Ko so središča ponovno izračunana, se vsako opazovanje ponovno preveri, ali bi lahko bilo bližje drugi gruč. Vsa opazovanja se ponovno prerazporedijo z uporabo posodobljenih povprečnih vrednosti gruč. Koraki dodeljevanja gruč in posodabljanja centroidov se ponavljajo, dokler se dodelitve gruč ne prenehajo spreminjati (tj. ko je dosežena konvergenca). Algoritem se običajno ustavi, ko vsaka nova iteracija povzroči zanemarljivo premikanje centroidov in gruče postanejo statične.\n",
+    "\n",
+    "<div>\n",
+    "\n",
+    "> Upoštevajte, da zaradi naključnosti začetnih k opazovanj, ki se uporabljajo kot začetni centroidi, lahko dobimo nekoliko različne rezultate vsakič, ko uporabimo postopek. Zaradi tega večina algoritmov uporablja več *naključnih začetkov* in izbere iteracijo z najnižjim WCSS. Zato je močno priporočljivo, da K-Means vedno izvajate z več vrednostmi *nstart*, da se izognete *nezaželenemu lokalnemu optimumu.*\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Ta kratka animacija z uporabo [ilustracij](https://github.com/allisonhorst/stats-illustrations) Allison Horst pojasnjuje postopek gručenja:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/kmeans.gif\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Ilustracija avtorice @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Osnovno vprašanje, ki se pojavi pri gručenju, je naslednje: kako veste, na koliko gruč razdeliti svoje podatke? Ena od pomanjkljivosti uporabe K-Means je dejstvo, da morate določiti `k`, torej število `centroidov`. Na srečo metoda `komolca` pomaga oceniti dobro začetno vrednost za `k`. Kmalu jo boste preizkusili.\n",
+    "\n",
+    "### \n",
+    "\n",
+    "**Predpogoj**\n",
+    "\n",
+    "Nadaljevali bomo tam, kjer smo končali v [prejšnji lekciji](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb), kjer smo analizirali nabor podatkov, ustvarili veliko vizualizacij in filtrirali nabor podatkov na zanimiva opazovanja. Prepričajte se, da si jo ogledate!\n",
+    "\n",
+    "Za dokončanje tega modula bomo potrebovali nekaj paketov. Namestite jih lahko z ukazom: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`\n",
+    "\n",
+    "Alternativno spodnji skript preveri, ali imate potrebne pakete za dokončanje tega modula, in jih namesti, če manjkajo.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ah_tBi58LXyi"
+   },
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7e--UCUTLXym"
+   },
+   "source": [
+    "Začnimo!\n",
+    "\n",
+    "## 1. Ples s podatki: Omejimo se na 3 najbolj priljubljene glasbene zvrsti\n",
+    "\n",
+    "To je povzetek tega, kar smo naredili v prejšnji lekciji. Razčlenimo in analizirajmo nekaj podatkov!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Ycamx7GGLXyn"
+   },
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data into a tibble\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\", show_col_types = FALSE)\n",
+    "\n",
+    "# Narrow down to top 3 popular genres\n",
+    "nigerian_songs <- df %>% \n",
+    "  # Concentrate on top 3 genres\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \n",
+    "  # Remove unclassified observations\n",
+    "  filter(popularity != 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Visualize popular genres using bar plots\n",
+    "theme_set(theme_light())\n",
+    "nigerian_songs %>%\n",
+    "  count(artist_top_genre) %>%\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\n",
+    "                       fill = artist_top_genre)) +\n",
+    "  geom_col(alpha = 0.8) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\n",
+    "  ggtitle(\"Top genres\") +\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b5h5zmkPLXyp"
+   },
+   "source": [
+    "🤩 To je šlo odlično!\n",
+    "\n",
+    "## 2. Več raziskovanja podatkov.\n",
+    "\n",
+    "Kako čisti so ti podatki? Preverimo izstopajoče vrednosti z uporabo škatelnih diagramov. Osredotočili se bomo na številske stolpce z manj izstopajočimi vrednostmi (čeprav bi lahko odstranili izstopajoče vrednosti). Škatelni diagrami lahko pokažejo razpon podatkov in pomagajo pri izbiri, katere stolpce uporabiti. Upoštevajte, da škatelni diagrami ne prikazujejo variance, kar je pomemben element za dobro združljive podatke. Za več informacij si oglejte [to razpravo](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot).\n",
+    "\n",
+    "[Škatelni diagrami](https://en.wikipedia.org/wiki/Box_plot) se uporabljajo za grafično prikazovanje porazdelitve `številskih` podatkov, zato začnimo z *izbiro* vseh številskih stolpcev skupaj s priljubljenimi glasbenimi žanri.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HhNreJKLLXyq"
+   },
+   "source": [
+    "# Select top genre column and all other numeric columns\n",
+    "df_numeric <- nigerian_songs %>% \n",
+    "  select(artist_top_genre, where(is.numeric)) \n",
+    "\n",
+    "# Display the data\n",
+    "df_numeric %>% \n",
+    "  slice_head(n = 5)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uYXrwJRaLXyq"
+   },
+   "source": [
+    "Opazite, kako funkcija za izbiro `where` to olajša 💁? Raziščite še druge podobne funkcije [tukaj](https://tidyselect.r-lib.org/).\n",
+    "\n",
+    "Ker bomo izdelali škatlaste diagrame za vsako številsko značilnost in se želimo izogniti uporabi zank, bomo preoblikovali naše podatke v *daljšo* obliko, ki nam bo omogočila uporabo `facets` - podgrafov, ki vsak prikazujejo en podnabor podatkov.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "gd5bR3f8LXys"
+   },
+   "source": [
+    "# Pivot data from wide to long\n",
+    "df_numeric_long <- df_numeric %>% \n",
+    "  pivot_longer(!artist_top_genre, names_to = \"feature_names\", values_to = \"values\") \n",
+    "\n",
+    "# Print out data\n",
+    "df_numeric_long %>% \n",
+    "  slice_head(n = 15)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-7tE1swnLXyv"
+   },
+   "source": [
+    "Zdaj pa nekaj daljšega! Čas je za nekaj `ggplotov`! Kateri `geom` bomo uporabili?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "r88bIsyuLXyy"
+   },
+   "source": [
+    "# Make a box plot\n",
+    "df_numeric_long %>% \n",
+    "  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_wrap(~ feature_names, ncol = 4, scales = \"free\") +\n",
+    "  theme(legend.position = \"none\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EYVyKIUELXyz"
+   },
+   "source": [
+    "Zdaj lahko vidimo, da so ti podatki nekoliko hrupni: če opazujemo vsak stolpec kot škatelni diagram, lahko vidimo odstopajoče vrednosti. Lahko bi pregledali podatkovni niz in odstranili te odstopajoče vrednosti, vendar bi to podatke precej zmanjšalo.\n",
+    "\n",
+    "Zaenkrat izberimo, katere stolpce bomo uporabili za našo nalogo gručenja. Izberimo številske stolpce s podobnimi razponi. Stolpec `artist_top_genre` bi lahko kodirali kot številskega, vendar ga bomo za zdaj izpustili.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-wkpINyZLXy0"
+   },
+   "source": [
+    "# Select variables with similar ranges\n",
+    "df_numeric_select <- df_numeric %>% \n",
+    "  select(popularity, danceability, acousticness, loudness, energy) \n",
+    "\n",
+    "# Normalize data\n",
+    "# df_numeric_select <- scale(df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D7dLzgpqLXy1"
+   },
+   "source": [
+    "## 3. Izračun k-means gručenja v R\n",
+    "\n",
+    "K-means lahko izračunamo v R z vgrajeno funkcijo `kmeans`, glejte `help(\"kmeans()\")`. Funkcija `kmeans()` sprejme podatkovni okvir z vsemi številsko izraženimi stolpci kot svoj primarni argument.\n",
+    "\n",
+    "Prvi korak pri uporabi k-means gručenja je določitev števila gručenj (k), ki bodo ustvarjene v končni rešitvi. Vemo, da obstajajo 3 glasbeni žanri, ki smo jih izluščili iz nabora podatkov, zato poskusimo s 3:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "uC4EQ5w7LXy5"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Kmeans clustering for 3 clusters\n",
+    "kclust <- kmeans(\n",
+    "  df_numeric_select,\n",
+    "  # Specify the number of clusters\n",
+    "  centers = 3,\n",
+    "  # How many random initial configurations\n",
+    "  nstart = 25\n",
+    ")\n",
+    "\n",
+    "# Display clustering object\n",
+    "kclust\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hzfhscWrLXy-"
+   },
+   "source": [
+    "Kmeans objekt vsebuje več informacij, ki so dobro razložene v `help(\"kmeans()\")`. Za zdaj se osredotočimo na nekaj ključnih točk. Vidimo, da so podatki razdeljeni v 3 skupine velikosti 65, 110, 111. Rezultat prav tako vsebuje središča skupin (povprečja) za 3 skupine glede na 5 spremenljivk.\n",
+    "\n",
+    "Vektor razvrščanja predstavlja dodelitev skupine za vsako opazovanje. Uporabimo funkcijo `augment`, da dodamo dodelitev skupine v originalni nabor podatkov.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "0XwwpFGQLXy_"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "augment(kclust, df_numeric_select) %>% \n",
+    "  relocate(.cluster) %>% \n",
+    "  slice_head(n = 10)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NXIVXXACLXzA"
+   },
+   "source": [
+    "Odlično, pravkar smo razdelili naš nabor podatkov v 3 skupine. Kako dobra je torej naša razvrstitev 🤷? Poglejmo si `Silhouette score`.\n",
+    "\n",
+    "### **Silhouette score**\n",
+    "\n",
+    "[Silhouette analiza](https://en.wikipedia.org/wiki/Silhouette_(clustering)) se lahko uporabi za preučevanje razdalje med nastalimi grozdi. Ta ocena se giblje od -1 do 1, pri čemer vrednost blizu 1 pomeni, da je grozd gost in dobro ločen od drugih grozdov. Vrednost blizu 0 predstavlja prekrivajoče se grozde, kjer so vzorci zelo blizu odločitveni meji sosednjih grozdov. [vir](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).\n",
+    "\n",
+    "Metoda povprečnega silhouette izračuna povprečno silhouette opazovanj za različne vrednosti *k*. Visoka povprečna silhouette ocena kaže na dobro razvrstitev.\n",
+    "\n",
+    "Funkcija `silhouette` v paketu za razvrščanje omogoča izračun povprečne širine silhouette.\n",
+    "\n",
+    "> Silhouette se lahko izračuna z uporabo katere koli [razdalje](https://en.wikipedia.org/wiki/Distance \"Distance\"), kot sta [Evklidska razdalja](https://en.wikipedia.org/wiki/Euclidean_distance \"Euclidean distance\") ali [Manhattanska razdalja](https://en.wikipedia.org/wiki/Manhattan_distance \"Manhattan distance\"), ki smo ju obravnavali v [prejšnji lekciji](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Jn0McL28LXzB"
+   },
+   "source": [
+    "# Load cluster package\n",
+    "library(cluster)\n",
+    "\n",
+    "# Compute average silhouette score\n",
+    "ss <- silhouette(kclust$cluster,\n",
+    "                 # Compute euclidean distance\n",
+    "                 dist = dist(df_numeric_select))\n",
+    "mean(ss[, 3])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QyQRn97nLXzC"
+   },
+   "source": [
+    "Naš rezultat je **.549**, kar pomeni, da smo nekje na sredini. To kaže, da naši podatki niso posebej primerni za tovrstno razvrščanje v skupine. Poglejmo, ali lahko to domnevo vizualno potrdimo. Paket [factoextra](https://rpkgs.datanovia.com/factoextra/index.html) ponuja funkcije (`fviz_cluster()`), ki omogočajo vizualizacijo razvrščanja v skupine.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "7a6Km1_FLXzD"
+   },
+   "source": [
+    "library(factoextra)\n",
+    "\n",
+    "# Visualize clustering results\n",
+    "fviz_cluster(kclust, df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IBwCWt-0LXzD"
+   },
+   "source": [
+    "Prekrivanje med grozdi kaže, da naši podatki niso posebej primerni za to vrsto grozdenja, vendar nadaljujmo.\n",
+    "\n",
+    "## 4. Določanje optimalnega števila grozdov\n",
+    "\n",
+    "Osnovno vprašanje, ki se pogosto pojavi pri K-Means grozdenju, je naslednje - brez znanih oznak razredov, kako veste, na koliko grozdov razdeliti svoje podatke?\n",
+    "\n",
+    "Eden od načinov, kako to ugotoviti, je uporaba vzorca podatkov za `ustvarjanje serije modelov grozdenja` z naraščajočim številom grozdov (npr. od 1 do 10) in ocenjevanje metrik grozdenja, kot je **Silhouette score.**\n",
+    "\n",
+    "Določimo optimalno število grozdov tako, da izvedemo algoritem grozdenja za različne vrednosti *k* in ocenimo **vsoto kvadratov znotraj grozda** (WCSS). Skupna vsota kvadratov znotraj grozda (WCSS) meri kompaktnost grozdenja, pri čemer želimo, da je čim manjša, saj nižje vrednosti pomenijo, da so podatkovne točke bližje skupaj.\n",
+    "\n",
+    "Raziskujmo učinek različnih izbir `k`, od 1 do 10, na to grozdenje.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "hSeIiylDLXzE"
+   },
+   "source": [
+    "# Create a series of clustering models\n",
+    "kclusts <- tibble(k = 1:10) %>% \n",
+    "  # Perform kmeans clustering for 1,2,3 ... ,10 clusters\n",
+    "  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),\n",
+    "  # Farm out clustering metrics eg WCSS\n",
+    "         glanced = map(model, ~ glance(.x))) %>% \n",
+    "  unnest(cols = glanced)\n",
+    "  \n",
+    "\n",
+    "# View clustering rsulsts\n",
+    "kclusts\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m7rS2U1eLXzE"
+   },
+   "source": [
+    "Zdaj, ko imamo skupno vsoto kvadratov znotraj grozdov (tot.withinss) za vsak algoritem grozdenja s središčem *k*, uporabimo [metodo komolca](https://en.wikipedia.org/wiki/Elbow_method_(clustering)), da najdemo optimalno število grozdov. Metoda vključuje risanje WCSS kot funkcije števila grozdov in izbiro [komolca krivulje](https://en.wikipedia.org/wiki/Elbow_of_the_curve \"Elbow of the curve\") kot števila grozdov, ki jih uporabimo.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "o_DjHGItLXzF"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Use elbow method to determine optimum number of clusters\n",
+    "kclusts %>% \n",
+    "  ggplot(mapping = aes(x = k, y = tot.withinss)) +\n",
+    "  geom_line(size = 1.2, alpha = 0.8, color = \"#FF7F0EFF\") +\n",
+    "  geom_point(size = 2, color = \"#FF7F0EFF\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLYyt5XSLXzG"
+   },
+   "source": [
+    "Graf prikazuje veliko zmanjšanje WCSS (torej večjo *kompaktnost*), ko se število grozdov poveča z enega na dva, in nadaljnje opazno zmanjšanje z dveh na tri grozde. Po tem je zmanjšanje manj izrazito, kar povzroči `komolec` 💪 na grafu pri približno treh grozdih. To je dober pokazatelj, da obstajata dva do trije razmeroma dobro ločeni grozdi podatkovnih točk.\n",
+    "\n",
+    "Zdaj lahko nadaljujemo in izluščimo model grozdenja, kjer je `k = 3`:\n",
+    "\n",
+    "> `pull()`: uporablja se za izvlečenje ene same kolone\n",
+    ">\n",
+    "> `pluck()`: uporablja se za indeksiranje podatkovnih struktur, kot so seznami\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JP_JPKBILXzG"
+   },
+   "source": [
+    "# Extract k = 3 clustering\n",
+    "final_kmeans <- kclusts %>% \n",
+    "  filter(k == 3) %>% \n",
+    "  pull(model) %>% \n",
+    "  pluck(1)\n",
+    "\n",
+    "\n",
+    "final_kmeans\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l_PDTu8tLXzI"
+   },
+   "source": [
+    "Odlično! Poglejmo si pridobljene grozde. Vas zanima nekaj interaktivnosti z uporabo `plotly`?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dNcleFe-LXzJ"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "results <-  augment(final_kmeans, df_numeric_select) %>% \n",
+    "  bind_cols(df_numeric %>% select(artist_top_genre)) \n",
+    "\n",
+    "# Plot cluster assignments\n",
+    "clust_plt <- results %>% \n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +\n",
+    "  geom_point(size = 2, alpha = 0.8) +\n",
+    "  paletteer::scale_color_paletteer_d(\"ggthemes::Tableau_10\")\n",
+    "\n",
+    "ggplotly(clust_plt)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6JUM_51VLXzK"
+   },
+   "source": [
+    "Morda bi pričakovali, da bo imel vsak grozd (predstavljen z različnimi barvami) različne žanre (predstavljene z različnimi oblikami).\n",
+    "\n",
+    "Poglejmo natančnost modela.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HdIMUGq7LXzL"
+   },
+   "source": [
+    "# Assign genres to predefined integers\n",
+    "label_count <- results %>% \n",
+    "  group_by(artist_top_genre) %>% \n",
+    "  mutate(id = cur_group_id()) %>% \n",
+    "  ungroup() %>% \n",
+    "  summarise(correct_labels = sum(.cluster == id))\n",
+    "\n",
+    "\n",
+    "# Print results  \n",
+    "cat(\"Result:\", label_count$correct_labels, \"out of\", nrow(results), \"samples were correctly labeled.\")\n",
+    "\n",
+    "cat(\"\\nAccuracy score:\", label_count$correct_labels/nrow(results))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C50wvaAOLXzM"
+   },
+   "source": [
+    "Točnost tega modela ni slaba, vendar ni odlična. Morda podatki niso primerni za K-Means razvrščanje. Ti podatki so preveč neuravnoteženi, premalo povezani in med vrednostmi stolpcev je preveč variance, da bi jih lahko dobro razvrstili. Pravzaprav so skupine, ki se oblikujejo, verjetno močno vplivane ali izkrivljene zaradi treh kategorij žanrov, ki smo jih opredelili zgoraj.\n",
+    "\n",
+    "Kljub temu je bil to precej poučen proces!\n",
+    "\n",
+    "V dokumentaciji Scikit-learn lahko vidite, da ima model, kot je ta, pri katerem skupine niso dobro opredeljene, težavo z 'varianco':\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/problems.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Infografika iz Scikit-learn</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "## **Varianca**\n",
+    "\n",
+    "Varianca je opredeljena kot \"povprečje kvadratov razlik od povprečja\" [vir](https://www.mathsisfun.com/data/standard-deviation.html). V kontekstu te težave z razvrščanjem se nanaša na podatke, pri katerih se vrednosti našega nabora podatkov preveč oddaljujejo od povprečja.\n",
+    "\n",
+    "✅ To je odličen trenutek, da razmislite o vseh načinih, kako bi lahko odpravili to težavo. Bi lahko podatke še malo prilagodili? Uporabili različne stolpce? Uporabili drugačen algoritem? Namig: Poskusite [normalizirati podatke](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) in preizkusiti druge stolpce.\n",
+    "\n",
+    "> Poskusite '[kalkulator variance](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)', da bolje razumete koncept.\n",
+    "\n",
+    "------------------------------------------------------------------------\n",
+    "\n",
+    "## **🚀Izziv**\n",
+    "\n",
+    "Preživite nekaj časa s tem zvezkom in prilagodite parametre. Ali lahko izboljšate natančnost modela z dodatnim čiščenjem podatkov (na primer odstranjevanjem odstopanj)? Lahko uporabite uteži, da nekaterim vzorcem podatkov dodelite večjo težo. Kaj še lahko storite, da ustvarite boljše skupine?\n",
+    "\n",
+    "Namig: Poskusite normalizirati podatke. V zvezku je komentirana koda, ki dodaja standardno normalizacijo, da se stolpci podatkov bolj približajo drug drugemu glede na obseg. Ugotovili boste, da se medtem ko se silhuetni rezultat zniža, 'pregib' na grafu komolca zgladi. To je zato, ker nenormalizirani podatki omogočajo, da podatki z manjšo varianco nosijo večjo težo. Preberite več o tej težavi [tukaj](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
+    "\n",
+    "## [**Kvizi po predavanju**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)\n",
+    "\n",
+    "## **Pregled in samostojno učenje**\n",
+    "\n",
+    "-   Oglejte si simulator K-Means [kot je ta](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). S tem orodjem lahko vizualizirate vzorčne podatkovne točke in določite njihove centroidne točke. Lahko urejate naključnost podatkov, število skupin in število centroidov. Ali vam to pomaga pridobiti idejo, kako se podatki lahko razvrstijo?\n",
+    "\n",
+    "-   Prav tako si oglejte [ta priročnik o K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) iz Stanforda.\n",
+    "\n",
+    "Želite preizkusiti svoje novo pridobljene veščine razvrščanja na naborih podatkov, ki so primerni za K-Means razvrščanje? Oglejte si:\n",
+    "\n",
+    "-   [Usposabljanje in ocenjevanje modelov razvrščanja](https://rpubs.com/eR_ic/clustering) z uporabo Tidymodels in podobnih orodij\n",
+    "\n",
+    "-   [Analiza skupin K-Means](https://uc-r.github.io/kmeans_clustering), UC Business Analytics R Programming Guide\n",
+    "\n",
+    "- [K-Means razvrščanje z načeli urejenih podatkov](https://www.tidymodels.org/learn/statistics/k-means/)\n",
+    "\n",
+    "## **Naloga**\n",
+    "\n",
+    "[Preizkusite različne metode razvrščanja](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)\n",
+    "\n",
+    "## HVALA:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) za ustvarjanje izvirne Python različice tega modula ♥️\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) za ustvarjanje čudovitih ilustracij, ki naredijo R bolj prijazen in privlačen. Več ilustracij najdete v njeni [galeriji](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "Veselo učenje,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Umetniško delo @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
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@@ -0,0 +1,550 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "e867e87e3129c8875423a82945f4ad5e",
+   "translation_date": "2025-09-06T14:20:36+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Začnite tam, kjer smo končali pri zadnji lekciji, z uvoženimi in filtriranimi podatki.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Osredotočili se bomo le na 3 žanre. Morda lahko ustvarimo 3 grozde!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Kako čisti so ti podatki? Preverite odstopajoče vrednosti z uporabo škatlastih diagramov. Osredotočili se bomo na stolpce z manj odstopajočimi vrednostmi (čeprav bi lahko odstranili odstopajoče vrednosti). Škatlasti diagrami lahko pokažejo razpon podatkov in pomagajo izbrati, katere stolpce uporabiti. Upoštevajte, da škatlasti diagrami ne prikazujejo variance, kar je pomemben element za dobro združljive podatke (https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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JbpHuRO0qmd7jVgEmoQ2Cgbmet7PlaMdhYMvsG1pr56R7csXP0gUq//2082BV2sIY2DL7ht7L090dMEluuUl5MJq2RGDhVdUeSd6R5Ib9W89rra10rDrLNhLtMZukvxZw737yO+menjQrLxsad+wzgdbah5N8qZ+8eVVN0u9M/doEVXVgknem68D403RtQl+eUfbq1nj+u7aH4UC/184w339NMnhKk7qzTq2187MURJZM9huqN5tkgmP9aVq0uuWa9ZTsNu8VmKbW2jlV9YMkl89SVNNIVXW5LG20/11nVt8eGl/tbl6/MjS+3nxgluV61PLul+Ql/eQpSX6vtXbGRpcLMyzXT+xf35/kzlU1Ks1g2Zesqvv249/baEQsi2eG5Xo4/bfHprpo2iusMx9IMtOyfbskg7tGvLC1dvqY9fliVb0+3R0nblhV11+ARzQCW5A2CAbmed7O1qMdh4Gttm9orZ1RVR9J8ntJ7lpVu690Jy+mS1sYA1tw3/C9fn32ydI5OTOgLRHYwq4zhWV8Z7UEVbVbkrckuVX/1hGttSesMtupQ+Ob3UayvD3mk5uY10bMZHtN2Z2TXK4ff+OoJydtoi8Njc/j2Gc7bq9RvpTkgCR7pDuW+f46519ev1ZquxjUr5bVj2WmaVttq+qe6P7eJJdOcm6SP2itHT+r/KNurcR/12Rmtk2r6mJJ7tNPfjcXDjTfVK2186vqxCQHZvuek8+7/m10/7No1+C28rH+1OyQurVmrllPz44KqOh9KckhSa5VVbv1kWij/MbQ+Hojcod3xL8xNtXG84GBWZR7s5/AAAAgAElEQVTrC6mqu6SLut0l3R/hrVtrszxBZeebRbkePF7qwf2wkn3S3Rk9ST6UxEVkJjGLcv3FofFdV0k7/Pm4dYG1mEXZHj55//QqaT+VLqBikKeACmBetEEwMPPzdrY07TgMbLV9w6CTyyXStYNshY44i0RbGANbbd/QVk/CJtGWCGw5rbWvbHYe/R3tX5euY32SvDnJw1ebr7X2k6r633Sdcza7jWSS9pjzk3xtgrwmNovttQmG7wI+66dQz/W4Z5tur1E2+jsur1+fWSHtoH797yyfwLmdtlVV7Z/uSS+XT7cfuk9r7QMzXg11awz/XZOZ8Ta9U5K9+/E3zDjQL9nm5+RboP5t9Pc7Mckv050P7/hrcFv5WH8TbOu6NQHXrKdgkkevbXXH9a+XzNLjYkYZfpTLR9aZxzeTnDZiOaPcon89NcnJ68wHBmZRrv9PVd06XZTgbkl+kO6Ohl+fdHkwxkzLNczIppfr1topSb7VT+5XY2432dt/aPzUsalgdbPYZw+f0K0W+L37mPkAZk0bBAPObximHYeBrbZvGL4b17Z5zPYOstXKA/OzZcpCVV0hXXBNsnTMyexoSwQW1cuSDJ6UdXSSB7TWLljjvIN9569X1b4rpNvo/+gJSc4bsawL6e9qfdPBPJ4Ct7L+2ON2/eRnWmufn/EqHDA07thncoPf8dx07RDrddzQ+Er1a98k1+4nnRuNUFVXS/eEwisnuSDJg1pr75jDqqhbK/PftbUNB/q9dpYZ93fxH+zn1J3JbGj/01o7L8kn+smD+voxzqBenZuVnwCz6DZyrD8VC1q3XLOegp0YUPH2ofGRd2Dqo6AGf4ZnJvngejJorbUkgwPQ36iqm45K178/iLR5Rz8fTGLTy/XQcg5OV74vnuSsJL/fWvviynPBRGaxv67VhiSn9MlPGXr/0HV+FxiY1f763/vXvZLceoV0dx8aP25sKljdLMr2N4fGD1kl7fDJ2TfHpgLYfNogGJjZeTvbgnYcBrbMvqHvZHFQP3lKa+0nm5EPK9IWxsCW2TckeViSQQf7D21SHoynLRFYOFX1j1l6+vAHktxrhbunjjK87zxsTB6XSHLvfvJLrbUT17ue/fHy4A7vt+mPp0e5e7r9a5Ictd58FtD9snTDpFk/nSK58N2RHftMoKpuluQ3+8njJukg2dfJwZ2K793X2VEOGxpXv5apqiumC6bYr3/rT1trb5zT6qhbK/PftUVV1eWT3KGf/Gxr7bMzXoX7JLlMP67urFPfaf6Ph9767wkXNaije+XC58XDeV0tyW36yQ9oWx1tCsf607KIdcs16ynYcQEVrbVPJPlwP/knVXXQiGR/keQ6/fg/LY+0rKpDq6r1w5FjsnpBusf9JMkLq2rPZcvYM8kL+8nz+/QwkVmV66r67STvThepdnaSO7bWPjWN7wDLzXB/DTMz4+OQc/rxf6yqvZYnqKoHJDm0n3x3a+1/1/5N4MJmVLY/kORn/fgjqup6o9alqm6f5G795KlZ+VHQAJtKGwQDzm8Yph2HgVmUhaq6dlX97krrUVWXSfLGJIM7rM30bnt0/FcwMKN9w35VdYOV1qOq7pTkqf3kz5O8eh1fgynQlggsmqo6PMnj+smPJrlra+3cdS7mqCTf6Mf/sqr2H5HmuUkuNzQ+al0OG9p/Hj4mr+f1r7sleXFV7bpsGfskeU4/eWaSI9b2FRbaoAPX+enOUdakqg4f2l6Hjfj8plV15RXmr6p6RpY6Qn42nnhwEVX1B1Xjn2ZVVdfKhbfbS8akW3F79Qb1a+8kfz9iGfsn+ct+8qQseKfv5arqskmOSfLr/VuPa629YoLlqFuz4b9r6xoO9Ftze1l/zj3YFseO+PxyVXXoKsu4cZIX9ZMtyb+sNf9FUFW36vd14z7fPV35HZwvHz3qPHa1bdU7It3NipLk2dUF2gwvY9d0/3mD+jSyji66aRzrq1uTc816Onab9wpsksemO0DbM8l/VtUz00XT7JnucTIP69OdmOQfJsmgtXZiVT03yZOSHJjkI1X1nCRfT/dI3CcmGTRWP7e19rUJvwsMbGq57g/Yj0kyOBj56yRnVdV1V5jte6217603Lxiy6ftrmINZHId8q6qemq6B8XpJPtEfh3wuS1Hzj+iT/zhLJy2wEZtatltrZ1bVs5P8bZJLJ/loVb0wyfuS/CjJlZLcNclDsxQY/qRZPx4SYARtEAzM5PxmxMXN3x4av11V7Tc0fVJrzd2F50M7DgObvW+4SpIPVNVn092F6lNJTk93sWLfJDdL8if9eJJ8IcmzJ/omTIO2MAY2uyzsl+SDVfWxJEen69g0+A+4ZpJ79sOgw9zjW2unTpAPG6ctEVgIVfWYJE/rJ09N8v+SXGOFvttJ8tXlnXxaa7/ol3V0un3YR/rOvJ9I1xH1oUnu0Sc/LsnrJl3n1tp/VdW/ptsf3yXJ+6rqBUlOS7c/fXKSX+2TP7G19qNJ89qK+s7zN1/29qUGryPaJ97bWjt9heUdkOSGQ2mneX56uyRPqqr3prum8KV0HYUvnuS30t29+iZ92p8leeh2ulPuWkxpex2V5KSqelu6OvXtJOcmuXKS3093bjlY5ltaa2/bwCq/Jt12uVmSR1XVvkleke560I2TPCVdHb8gyZ/N6e7Wm2Kj26qqLp7uBhuDNsE3JHn/Km1CZ7fWJnniu7o1hbrlv2tLGw70e8MUl3uZdOfkn8tSe9130nVC/tUkd0ryR1m6+cnz3CjnIh6U5J1V9c4kxyb5arrz1UulO554WJID+rTfS3duPZHW2g+r6olJXprk6kmOr6q/S/L5dG2vf57kVn3yN7XWjp00r51qWsf6a6Burcw1641qre3IIcmd00WOtTHDV5Nca8y8hw6lO3KFPHZJ8soV8mjpIth2mffvYdgZw2aW63SPlVupLI8aDp/3b2LY/sMs9ter5H9yP//J8/4tDDtnmFW5TvKsdA2J4/L5bpKD5v17GHbOsNllO11HjuevUq5bkvPSdfaY+29iMBgMrWmDMMy8LKznvH3scgzbuzxEO862Gja5LBy6jjLwriRXmPfvsejDLP4rVsn/5GgL2xLDFtk3nJ3kYfP+LRZ9mNV+IdoSDQbDHId0HeHWew6z3wrLe2i6jt7j5j0+yT4rzH/YUNrDV0i3Z7qOy+Py+eVK82/nIes/7zx0leU9eyjtvda5LocPzXvYKp+vNJyS5Gbz/m236vZax7wvSXLxSbfXULp90nUoH5fPOUkeMu/fdqttq3TB0+vdnx47ybZSt6a7L4z/ri01JPmNod/k3eucd7geHrvK5ysN56frhF7z/j222pDkyDX+hp9LcsCk22pZ2r/JyufM706yx7x/m604ZErH+urWVLaFa9YbGHbqEyrSWju6qn4rXdTNHZNcLV3Hq5OS/FuSF7XWfrbBPC5I93iUf08XvXOjdAf8ZyQ5IcnLWmv/sZE8YNgsyjXMmnLNTjSrct1a+8s+Iv8RSQ5Jd4eYc9JFE78zyQtba2etsAhYl80u2607+3pcVb0+yUPS3fHl6kkukeSnfT4fSnecfeJGvgvANGmDYMD5DcOUBwY2uSx8JN2dQm+T7o5QV0v3dLdLpLtr2zeTfDzd3dM+spHvwXTYNzCwyWXhU0kekOSgdPuGK6c7dtwt3V1/v5jkA0mOaJ5eNHfaEgHWr7X2iv5JTH+W5Nbp7h58dpIvp7vD9BFtCne0b639PMkdq+r+6TqyXj/dkwK/m+TD6fbRH9toPjtdVe2S5A/7yTPT/e9M06vTBwamu2v+FZNcPl1nujOSfDrdneHf2Fo7Z8p57yR3Sfcb3iTdtZl9klwy3bnlN9KV+Ve11r4wjcxaa2dU1cHpOpnfP8l1+vxOS3es+k+ttS9OIy8mpm5Nkf+uLeePhsZfO+Vln5bkXunqzo2TXDXdPnWPdB2dv5quA/oRrbWTp5z3TvGcJJ9J9xsekOQKSfZOF5T03SSfTPLWJEe11n45jQxba0+rqmOSPCrdOfOV0h23fDbJq1trb5pGPmyIurUK16w3pvqIEQAAAAAAAAAAAAAAgIWxy7xXAAAAAAAAAAAAAAAAYNYEVAAAAAAAAAAAAAAAAAtHQAUAAAAAAAAAAAAAALBwBFQAAAAAAAAAAAAAAAALR0AFAAAAAAAAAAAAAACwcARUAAAAAAA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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "source": [
+    "Izberite več stolpcev s podobnimi razponi. Prepričajte se, da vključite stolpec artist_top_genre, da ohranimo naše žanre urejene.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "# scaler = StandardScaler()\n",
+    "\n",
+    "X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
+    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "y = le.transform(y)\n",
+    "\n"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 0, 2, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0,\n",
+       "       2, 0, 0, 1, 1, 2, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2,\n",
+       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 0,\n",
+       "       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 2,\n",
+       "       2, 1, 1, 0, 1, 2, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0,\n",
+       "       1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       1, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 1, 1, 2, 1],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "source": [
+    "Te številke nam ne pomenijo veliko, zato si oglejmo 'silhouette score', da preverimo natančnost. Naš rezultat je nekje v sredini.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "source": [
+    "Uvozi KMeans in zgradi model\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "source": [
+    "Uporabite ta model za odločitev, z uporabo metode komolca, koliko grozdov je najbolje ustvariti\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Looks like 3 is a good number after all. Fit the model again and create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Natančnost tega modela ni slaba, a tudi ne odlična. Morda podatki niso primerni za gručenje s K-sredinami. Poskusite lahko drugo metodo.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 811,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitne nesporazume ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/5-Clustering/2-K-Means/solution/tester.ipynb b/translations/sl/5-Clustering/2-K-Means/solution/tester.ipynb
new file mode 100644
index 000000000..d71ed4d7a
--- /dev/null
+++ b/translations/sl/5-Clustering/2-K-Means/solution/tester.ipynb
@@ -0,0 +1,343 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
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+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
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+  },
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+   "source_file": "5-Clustering/2-K-Means/solution/tester.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Začnite tam, kjer smo končali pri zadnji lekciji, z uvoženimi in filtriranimi podatki.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 105
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Osredotočili se bomo le na 3 žanre. Morda lahko ustvarimo 3 grozde!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 106
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 107
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# X = df.loc[:, ('danceability','energy')]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Unknown label type: 'continuous'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-110-3756ba7b0bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# we create an instance of SVM and fit out data. We do not scale our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# data since we want to plot the support vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mls30\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 30% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mls50\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 50% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mls100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 100% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m# actual graph construction (implementations should override this)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    181\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    182\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.semi_supervised import LabelSpreading\n",
+    "from sklearn.semi_supervised import SelfTrainingClassifier\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "X = df[['danceability','acousticness']].values\n",
+    "y = df['energy'].values\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "# step size in the mesh\n",
+    "h = .02\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "y_rand = rng.rand(y.shape[0])\n",
+    "y_30 = np.copy(y)\n",
+    "y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled\n",
+    "y_50 = np.copy(y)\n",
+    "y_50[y_rand < 0.5] = -1\n",
+    "# we create an instance of SVM and fit out data. We do not scale our\n",
+    "# data since we want to plot the support vectors\n",
+    "ls30 = (LabelSpreading().fit(X, y_30), y_30, 'Label Spreading 30% data')\n",
+    "ls50 = (LabelSpreading().fit(X, y_50), y_50, 'Label Spreading 50% data')\n",
+    "ls100 = (LabelSpreading().fit(X, y), y, 'Label Spreading 100% data')\n",
+    "\n",
+    "# the base classifier for self-training is identical to the SVC\n",
+    "base_classifier = SVC(kernel='rbf', gamma=.5, probability=True)\n",
+    "st30 = (SelfTrainingClassifier(base_classifier).fit(X, y_30),\n",
+    "        y_30, 'Self-training 30% data')\n",
+    "st50 = (SelfTrainingClassifier(base_classifier).fit(X, y_50),\n",
+    "        y_50, 'Self-training 50% data')\n",
+    "\n",
+    "rbf_svc = (SVC(kernel='rbf', gamma=.5).fit(X, y), y, 'SVC with rbf kernel')\n",
+    "\n",
+    "# create a mesh to plot in\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                     np.arange(y_min, y_max, h))\n",
+    "\n",
+    "color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}\n",
+    "\n",
+    "classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)\n",
+    "for i, (clf, y_train, title) in enumerate(classifiers):\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    colors = [color_map[y] for y in y_train]\n",
+    "    plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')\n",
+    "\n",
+    "    plt.title(title)\n",
+    "\n",
+    "plt.suptitle(\"Unlabeled points are colored white\", y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb b/translations/sl/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
new file mode 100644
index 000000000..e17088bc3
--- /dev/null
+++ b/translations/sl/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
@@ -0,0 +1,100 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "27de2abc0235ebd22080fc8f1107454d",
+   "translation_date": "2025-09-06T15:22:07+00:00",
+   "source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You should download the book text, clean it, and import it here\n",
+    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
+    "    file_contents = f.read()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "book_pride = TextBlob(file_contents)\n",
+    "positive_sentiment_sentences = []\n",
+    "negative_sentiment_sentences = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sentence in book_pride.sentences:\n",
+    "    if sentence.sentiment.polarity == 1:\n",
+    "        positive_sentiment_sentences.append(sentence)\n",
+    "    if sentence.sentiment.polarity == -1:\n",
+    "        negative_sentiment_sentences.append(sentence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
+    "for sentence in positive_sentiment_sentences:\n",
+    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
+    "for sentence in negative_sentiment_sentences:\n",
+    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/6-NLP/4-Hotel-Reviews-1/notebook.ipynb b/translations/sl/6-NLP/4-Hotel-Reviews-1/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sl/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb b/translations/sl/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
new file mode 100644
index 000000000..29c50d414
--- /dev/null
+++ b/translations/sl/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
@@ -0,0 +1,174 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2d05e7db439376aa824f4b387f8324ca",
+   "translation_date": "2025-09-06T15:21:47+00:00",
+   "source_file": "6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EDA\n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_difference_review_avg(row):\n",
+    "    return row[\"Average_Score\"] - row[\"Calc_Average_Score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "print(\"Loading data file now, this could take a while depending on file size\")\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n",
+    "end = time.time()\n",
+    "print(\"Loading took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What shape is the data (rows, columns)?\n",
+    "print(\"The shape of the data (rows, cols) is \" + str(df.shape))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# value_counts() creates a Series object that has index and values\n",
+    "#                in this case, the country and the frequency they occur in reviewer nationality\n",
+    "nationality_freq = df[\"Reviewer_Nationality\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What reviewer nationality is the most common in the dataset?\n",
+    "print(\"The highest frequency reviewer nationality is \" + str(nationality_freq.index[0]).strip() + \" with \" + str(nationality_freq[0]) + \" reviews.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the top 10 most common nationalities and their frequencies?\n",
+    "print(\"The top 10 highest frequency reviewer nationalities are:\")\n",
+    "print(nationality_freq[0:10].to_string())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many unique nationalities are there?\n",
+    "print(\"There are \" + str(nationality_freq.index.size) + \" unique nationalities in the dataset\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What was the most frequently reviewed hotel for the top 10 nationalities - print the hotel and number of reviews\n",
+    "for nat in nationality_freq[:10].index:\n",
+    "   # First, extract all the rows that match the criteria into a new dataframe\n",
+    "   nat_df = df[df[\"Reviewer_Nationality\"] == nat]   \n",
+    "   # Now get the hotel freq\n",
+    "   freq = nat_df[\"Hotel_Name\"].value_counts()\n",
+    "   print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many reviews are there per hotel (frequency count of hotel) and do the results match the value in `Total_Number_of_Reviews`?\n",
+    "# First create a new dataframe based on the old one, removing the uneeded columns\n",
+    "hotel_freq_df = df.drop([\"Hotel_Address\", \"Additional_Number_of_Scoring\", \"Review_Date\", \"Average_Score\", \"Reviewer_Nationality\", \"Negative_Review\", \"Review_Total_Negative_Word_Counts\", \"Positive_Review\", \"Review_Total_Positive_Word_Counts\", \"Total_Number_of_Reviews_Reviewer_Has_Given\", \"Reviewer_Score\", \"Tags\", \"days_since_review\", \"lat\", \"lng\"], axis = 1)\n",
+    "# Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found\n",
+    "hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')\n",
+    "# Get rid of all the duplicated rows\n",
+    "hotel_freq_df = hotel_freq_df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "print()\n",
+    "print(hotel_freq_df.to_string())\n",
+    "print(str(hotel_freq_df.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# While there is an `Average_Score` for each hotel according to the dataset, \n",
+    "# you can also calculate an average score (getting the average of all reviewer scores in the dataset for each hotel)\n",
+    "# Add a new column to your dataframe with the column header `Calc_Average_Score` that contains that calculated average. \n",
+    "df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n",
+    "# Add a new column with the difference between the two average scores\n",
+    "df[\"Average_Score_Difference\"] = df.apply(get_difference_review_avg, axis = 1)\n",
+    "# Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)\n",
+    "review_scores_df = df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "# Sort the dataframe to find the lowest and highest average score difference\n",
+    "review_scores_df = review_scores_df.sort_values(by=[\"Average_Score_Difference\"])\n",
+    "print(review_scores_df[[\"Average_Score_Difference\", \"Average_Score\", \"Calc_Average_Score\", \"Hotel_Name\"]])\n",
+    "# Do any hotels have the same (rounded to 1 decimal place) `Average_Score` and `Calc_Average_Score`?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/6-NLP/5-Hotel-Reviews-2/notebook.ipynb b/translations/sl/6-NLP/5-Hotel-Reviews-2/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sl/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb b/translations/sl/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
new file mode 100644
index 000000000..c67b82b5b
--- /dev/null
+++ b/translations/sl/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
@@ -0,0 +1,172 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "033cb89c85500224b3c63fd04f49b4aa",
+   "translation_date": "2025-09-06T15:22:29+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import time\n",
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replace_address(row):\n",
+    "    if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Amsterdam, Netherlands\"\n",
+    "    elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Barcelona, Spain\"\n",
+    "    elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
+    "        return \"London, United Kingdom\"\n",
+    "    elif \"Milan\" in row[\"Hotel_Address\"]:        \n",
+    "        return \"Milan, Italy\"\n",
+    "    elif \"France\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Paris, France\"\n",
+    "    elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Vienna, Austria\" \n",
+    "    else:\n",
+    "        return row.Hotel_Address\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dropping columns we will not use:\n",
+    "df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace all the addresses with a shortened, more useful form\n",
+    "df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop `Additional_Number_of_Scoring`\n",
+    "df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
+    "# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
+    "df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
+    "df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process the Tags into new columns\n",
+    "# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
+    "# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
+    "# Family with young children, Family with older children, With a pet\n",
+    "df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
+    "df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
+    "df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
+    "df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
+    "df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
+    "df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
+    "df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
+    "df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No longer need any of these columns\n",
+    "df.drop([\"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_Filtered.csv\n",
+      "Filtering took 23.74 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Saving new data file with calculated columns\n",
+    "print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
+    "df.to_csv(r'../../data/Hotel_Reviews_Filtered.csv', index = False)\n",
+    "end = time.time()\n",
+    "print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb b/translations/sl/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
new file mode 100644
index 000000000..11cfd08ec
--- /dev/null
+++ b/translations/sl/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
@@ -0,0 +1,137 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "341efc86325ec2a214f682f57a189dfd",
+   "translation_date": "2025-09-06T15:22:50+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV (you can )\n",
+    "import pandas as pd \n",
+    "\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We want to find the most useful tags to keep\n",
+    "# Remove opening and closing brackets\n",
+    "df.Tags = df.Tags.str.strip(\"[']\")\n",
+    "# remove all quotes too\n",
+    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
+    "# Now split the strings into a list\n",
+    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove leading and trailing spaces\n",
+    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
+    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
+    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
+    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
+    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
+    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the 6 columns into one with melt\n",
+    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "The shape of the tags with no filtering: (2514684, 2)\n",
+      "                        index   count\n",
+      "0                Leisure trip  338423\n",
+      "1                      Couple  205305\n",
+      "2               Solo traveler   89779\n",
+      "3               Business trip   68176\n",
+      "4                       Group   51593\n",
+      "5  Family with young children   49318\n",
+      "6  Family with older children   21509\n",
+      "7      Travelers with friends    1610\n",
+      "8                  With a pet    1078\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the value counts\n",
+    "tag_vc = df_tags.value.value_counts()\n",
+    "# print(tag_vc)\n",
+    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
+    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
+    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
+    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
+    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
+    "print(tag_vc[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna napačna razumevanja ali napačne interpretacije, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb b/translations/sl/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
new file mode 100644
index 000000000..c764fbdf4
--- /dev/null
+++ b/translations/sl/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
@@ -0,0 +1,260 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "705bf02633759f689abc37b19749a16d",
+   "translation_date": "2025-09-06T15:23:09+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[nltk_data] Downloading package vader_lexicon to\n[nltk_data]     /Users/jenlooper/nltk_data...\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import pandas as pd\n",
+    "import nltk as nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    "nltk.download('vader_lexicon')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vader_sentiment = SentimentIntensityAnalyzer()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are 3 possibilities of input for a review:\n",
+    "# It could be \"No Negative\", in which case, return 0\n",
+    "# It could be \"No Positive\", in which case, return 0\n",
+    "# It could be a review, in which case calculate the sentiment\n",
+    "def calc_sentiment(review):    \n",
+    "    if review == \"No Negative\" or review == \"No Positive\":\n",
+    "        return 0\n",
+    "    return vader_sentiment.polarity_scores(review)[\"compound\"]    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "df = pd.read_csv(\"../../data/Hotel_Reviews_Filtered.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove stop words - can be slow for a lot of text!\n",
+    "# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches\n",
+    "# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends\n",
+    "start = time.time()\n",
+    "cache = set(stopwords.words(\"english\"))\n",
+    "def remove_stopwords(review):\n",
+    "    text = \" \".join([word for word in review.split() if word not in cache])\n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the stop words from both columns\n",
+    "df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   \n",
+    "df.Positive_Review = df.Positive_Review.apply(remove_stopwords)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Removing stop words took 5.77 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "end = time.time()\n",
+    "print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Calculating sentiment columns for both positive and negative reviews\n",
+      "Calculating sentiment took 201.07 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a negative sentiment and positive sentiment column\n",
+    "print(\"Calculating sentiment columns for both positive and negative reviews\")\n",
+    "start = time.time()\n",
+    "df[\"Negative_Sentiment\"] = df.Negative_Review.apply(calc_sentiment)\n",
+    "df[\"Positive_Sentiment\"] = df.Positive_Review.apply(calc_sentiment)\n",
+    "end = time.time()\n",
+    "print(\"Calculating sentiment took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                          Negative_Review  Negative_Sentiment\n",
+      "186584  So bad experience memories I hotel The first n...             -0.9920\n",
+      "129503  First charged twice room booked booking second...             -0.9896\n",
+      "307286  The staff Had bad experience even booking Janu...             -0.9889\n",
+      "452092  No WLAN room Incredibly rude restaurant staff ...             -0.9884\n",
+      "201293  We usually traveling Paris 2 3 times year busi...             -0.9873\n",
+      "...                                                   ...                 ...\n",
+      "26899   I would say however one night expensive even d...              0.9933\n",
+      "138365  Wifi terribly slow I speed test network upload...              0.9938\n",
+      "79215   I find anything hotel first I walked past hote...              0.9938\n",
+      "278506  The property great location There bakery next ...              0.9945\n",
+      "339189  Guys I like hotel I wish return next year Howe...              0.9948\n",
+      "\n",
+      "[515738 rows x 2 columns]\n",
+      "                                          Positive_Review  Positive_Sentiment\n",
+      "137893  Bathroom Shower We going stay twice hotel 2 ni...             -0.9820\n",
+      "5839    I completely disappointed mad since reception ...             -0.9780\n",
+      "64158   get everything extra internet parking breakfas...             -0.9751\n",
+      "124178  I didnt like anythig Room small Asked upgrade ...             -0.9721\n",
+      "489137  Very rude manager abusive staff reception Dirt...             -0.9703\n",
+      "...                                                   ...                 ...\n",
+      "331570  Everything This recently renovated hotel class...              0.9984\n",
+      "322920  From moment stepped doors Guesthouse Hotel sta...              0.9985\n",
+      "293710  This place surprise expected good actually gre...              0.9985\n",
+      "417442  We celebrated wedding night Langham I commend ...              0.9985\n",
+      "132492  We arrived super cute boutique hotel area expl...              0.9987\n",
+      "\n",
+      "[515738 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=[\"Negative_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Negative_Review\", \"Negative_Sentiment\"]])\n",
+    "df = df.sort_values(by=[\"Positive_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Positive_Review\", \"Positive_Sentiment\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)\n",
+    "df = df.reindex([\"Hotel_Name\", \"Hotel_Address\", \"Total_Number_of_Reviews\", \"Average_Score\", \"Reviewer_Score\", \"Negative_Sentiment\", \"Positive_Sentiment\", \"Reviewer_Nationality\", \"Leisure_trip\", \"Couple\", \"Solo_traveler\", \"Business_trip\", \"Group\", \"Family_with_young_children\", \"Family_with_older_children\", \"With_a_pet\", \"Negative_Review\", \"Positive_Review\"], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_NLP.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
+    "df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitne nesporazume ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/7-TimeSeries/1-Introduction/solution/notebook.ipynb b/translations/sl/7-TimeSeries/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..86238287a
--- /dev/null
+++ b/translations/sl/7-TimeSeries/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli in Rob J. Hyndman, \"Probabilistično napovedovanje energije: Globalno tekmovanje v napovedovanju energije 2014 in naprej\", International Journal of Forecasting, vol.32, št.3, str. 896-913, julij-september, 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "from common.utils import load_data\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Naložite podatke iz csv v Pandas dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2698.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2558.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2444.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2402.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2403.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "data_dir = './data'\n",
+    "energy = load_data(data_dir)[['load']]\n",
+    "energy.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prikaži vse razpoložljive podatke o obremenitvi (januar 2012 do december 2014)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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231.185656 \nL 91.475143 185.318661 \nL 91.506964 197.827841 \nL 91.761536 330.381263 \nL 91.825179 313.153708 \nL 91.984286 214.726208 \nL 92.047929 223.065662 \nL 92.143394 231.953764 \nL 92.238858 182.026771 \nL 92.302501 208.252159 \nL 92.525251 302.290472 \nL 92.557073 297.901286 \nL 92.652537 247.645104 \nL 92.779823 197.169464 \nL 92.811645 201.229461 \nL 92.875287 210.446752 \nL 92.907109 207.81324 \nL 93.002573 138.13491 \nL 93.066216 169.84678 \nL 93.257145 279.905623 \nL 93.288967 277.820759 \nL 93.352609 237.440247 \nL 93.416252 165.896512 \nL 93.479895 170.175969 \nL 93.670824 198.925138 \nL 93.702646 189.488388 \nL 93.766288 144.3895 \nL 93.79811 156.679221 \nL 94.052682 299.65696 \nL 94.084503 288.245076 \nL 94.179968 188.500821 \nL 94.275432 193.109466 \nL 94.434539 214.506749 \nL 94.466361 204.850539 \nL 94.530004 162.714352 \nL 94.561825 175.881911 \nL 94.816397 318.969379 \nL 94.848218 308.545062 \nL 95.039147 197.169464 \nL 95.134611 205.947836 \nL 95.198254 212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 197.217872 308.106144 \nL 197.440622 167.981376 \nL 197.59973 160.410029 \nL 197.727015 185.318661 \nL 197.917944 322.480728 \nL 197.981587 297.352638 \nL 198.236159 139.122477 \nL 198.331623 119.919787 \nL 198.363445 120.578165 \nL 198.490731 168.420294 \nL 198.713481 322.261269 \nL 198.745302 321.383432 \nL 199.063517 158.325166 \nL 199.12716 147.791119 \nL 199.158981 152.290035 \nL 199.286267 213.519182 \nL 199.477196 317.542894 \nL 199.509017 319.847217 \nL 199.604482 229.539711 \nL 199.763589 153.82625 \nL 199.827232 152.180305 \nL 199.890875 142.194907 \nL 199.922697 149.437064 \nL 200.049982 220.212691 \nL 200.20909 311.398033 \nL 200.272733 292.305074 \nL 200.495483 131.441401 \nL 200.527304 126.064648 \nL 200.590947 133.965183 \nL 200.622769 133.087346 \nL 200.65459 133.306805 \nL 200.781876 176.2111 \nL 200.972805 319.627757 \nL 201.036448 302.94885 \nL 201.227377 151.851116 \nL 201.259198 152.399765 \nL 201.29102 152.180305 \nL 201.322841 152.619224 \nL 201.354662 152.948413 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 218.983754 162.055974 \nL 219.015576 162.385163 \nL 219.11104 187.622984 \nL 219.301969 310.300737 \nL 219.365612 281.880756 \nL 219.556541 157.995977 \nL 219.683826 161.726785 \nL 219.620183 157.666788 \nL 219.715648 160.410029 \nL 219.747469 161.178137 \nL 219.874755 183.562986 \nL 220.065684 319.737487 \nL 220.097505 319.079109 \nL 220.161148 297.462367 \nL 220.352077 172.80948 \nL 220.383899 175.442992 \nL 220.63847 211.763507 \nL 220.829399 325.22397 \nL 220.861221 324.016944 \nL 220.924864 310.191007 \nL 221.115792 208.471618 \nL 221.147614 210.117563 \nL 221.179435 211.214859 \nL 221.211257 210.88567 \nL 221.2749 200.461353 \nL 221.306721 201.44892 \nL 221.434007 256.533206 \nL 221.593114 330.271534 \nL 221.656757 303.168309 \nL 221.911329 131.55113 \nL 222.006793 125.40627 \nL 222.038615 127.052215 \nL 222.165901 168.420294 \nL 222.356829 309.861818 \nL 222.420472 285.392105 \nL 222.675044 125.845188 \nL 222.738687 129.246808 \nL 222.865973 155.252736 \nL 222.897794 153.058143 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227.702836 315.238571 \nL 227.766479 291.427236 \nL 228.02105 135.391668 \nL 228.116515 126.503566 \nL 228.148336 128.039781 \nL 228.275622 168.200835 \nL 228.466551 311.727222 \nL 228.530194 285.831024 \nL 228.784765 105.764662 \nL 228.848408 100.278179 \nL 228.912051 102.143583 \nL 229.039337 142.085177 \nL 229.230266 295.377504 \nL 229.262087 291.536966 \nL 229.548481 84.696568 \nL 229.643945 97.864127 \nL 229.803052 143.182474 \nL 229.993981 278.369407 \nL 230.057624 251.156453 \nL 230.280374 103.789528 \nL 230.344017 117.396005 \nL 230.598589 209.239725 \nL 230.757696 299.108312 \nL 230.789518 298.559664 \nL 230.853161 280.564001 \nL 231.075911 157.666788 \nL 231.26684 168.310565 \nL 231.298661 167.761916 \nL 231.553233 301.632094 \nL 231.585054 296.4748 \nL 231.648697 262.239148 \nL 231.839626 164.908945 \nL 231.935091 146.254904 \nL 231.966912 138.573828 \nL 232.030555 148.120308 \nL 232.062376 146.035445 \nL 232.285127 300.863987 \nL 232.316948 295.048315 \nL 232.635163 98.961423 \nL 232.698806 100.168449 \nL 232.730627 101.704665 \nL 232.857913 151.302468 \nL 233.048842 295.487234 \nL 233.080663 291.207777 \nL 233.367056 101.046287 \nL 233.430699 107.191147 \nL 233.557985 130.014915 \nL 233.589807 127.710593 \nL 233.812557 294.280207 \nL 233.844378 289.561832 \nL 234.194415 111.690063 \nL 234.258057 119.480868 \nL 234.385343 155.801384 \nL 234.576272 292.414803 \nL 234.608094 288.245076 \nL 234.894487 125.625729 \nL 234.926308 127.491133 \nL 235.085416 148.559227 \nL 235.117237 147.68139 \nL 235.339987 312.166141 \nL 235.371809 306.569929 \nL 235.721845 120.687895 \nL 235.753666 121.346273 \nL 235.880952 144.608959 \nL 236.135524 304.046147 \nL 236.167345 296.4748 \nL 236.549203 193.877574 \nL 236.676489 220.322421 \nL 236.899239 337.513691 \nL 236.962882 327.528292 \nL 237.185632 225.589444 \nL 237.217454 226.79647 \nL 237.249275 224.272688 \nL 237.408383 203.314324 \nL 237.631133 332.466127 \nL 237.662954 326.211537 \nL 237.949347 160.958678 \nL 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345.414226 \nL 247.654894 330.600723 \nL 247.845823 238.208354 \nL 247.877645 242.048892 \nL 247.941288 246.438078 \nL 247.973109 242.59754 \nL 248.068573 234.916464 \nL 248.100395 239.41538 \nL 248.323145 354.192598 \nL 248.354967 351.120168 \nL 248.482252 275.296977 \nL 248.577717 229.539711 \nL 248.64136 231.075927 \nL 248.673181 230.966197 \nL 248.736824 220.651609 \nL 248.832289 201.010001 \nL 248.86411 206.496484 \nL 249.08686 340.586121 \nL 249.150503 302.94885 \nL 249.309611 191.573251 \nL 249.405075 187.513254 \nL 249.468718 192.01217 \nL 249.500539 189.378658 \nL 249.596004 178.844611 \nL 249.691468 261.6905 \nL 249.850575 324.785051 \nL 249.914218 283.526701 \nL 250.073326 161.507326 \nL 250.16879 158.764085 \nL 250.264255 163.043541 \nL 250.296076 161.946245 \nL 250.327897 161.507326 \nL 250.359719 153.387332 \nL 250.39154 165.567323 \nL 250.614291 321.602891 \nL 250.677934 284.953187 \nL 250.868862 168.420294 \nL 250.996148 160.958678 \nL 251.02797 161.946245 \nL 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216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 315.75283 171.382995 \nL 315.880116 189.488388 \nL 315.911937 182.026771 \nL 315.97558 135.281939 \nL 316.039223 155.252736 \nL 316.134688 222.955932 \nL 316.293795 295.487234 \nL 316.357438 280.125082 \nL 316.516545 184.221364 \nL 316.548367 185.977039 \nL 316.643831 197.279193 \nL 316.675652 191.682981 \nL 316.739295 154.704087 \nL 316.802938 177.198666 \nL 317.05751 323.578025 \nL 317.152974 296.4748 \nL 317.312082 212.531615 \nL 317.343903 215.165127 \nL 317.407546 223.065662 \nL 317.439368 216.262423 \nL 317.503011 165.238134 \nL 317.566653 190.256495 \nL 317.789404 338.610988 \nL 317.853047 324.785051 \nL 318.043975 214.28729 \nL 318.171261 228.332685 \nL 318.203083 224.272688 \nL 318.266726 161.397596 \nL 318.330369 174.674884 \nL 318.553119 330.381263 \nL 318.616762 319.518028 \nL 318.807691 200.790542 \nL 318.839512 203.424054 \nL 318.934976 209.349455 \nL 318.966798 204.411621 \nL 319.030441 154.923547 \nL 319.094084 172.480291 \nL 319.348656 336.745583 \nL 319.380477 327.089374 \nL 319.475941 219.993232 \nL 319.603227 220.212691 \nL 319.635049 220.871069 \nL 319.66687 219.225124 \nL 319.698692 219.334854 \nL 319.825978 144.27977 \nL 319.88962 169.956509 \nL 320.080549 296.145611 \nL 320.112371 295.158045 \nL 320.176014 248.852131 \nL 320.239657 173.906777 \nL 320.303299 176.979207 \nL 320.462407 199.583516 \nL 320.494228 195.413789 \nL 320.557871 129.246808 \nL 320.621514 138.13491 \nL 320.844264 294.719126 \nL 320.907907 283.087783 \nL 321.003372 177.308396 \nL 321.098836 183.233797 \nL 321.226122 204.74081 \nL 321.257943 199.144597 \nL 321.321586 157.227869 \nL 321.385229 179.722449 \nL 321.639801 331.039641 \nL 321.703444 314.360734 \nL 321.862551 207.264592 \nL 321.926194 212.641345 \nL 321.989837 222.297554 \nL 322.021659 217.46945 \nL 322.085302 175.881911 \nL 322.148944 200.900272 \nL 322.403516 336.196935 \nL 322.467159 323.248836 \nL 322.658088 232.392682 \nL 322.689909 235.245653 \nL 322.753552 243.475378 \nL 322.785374 235.026194 \nL 322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 340.923609 150.973279 \nL 340.95543 151.083009 \nL 341.082716 162.933811 \nL 341.114538 159.312733 \nL 341.178181 98.741964 \nL 341.241824 109.38574 \nL 341.464574 253.460776 \nL 341.528217 240.293218 \nL 341.623681 133.087346 \nL 341.719146 138.464099 \nL 341.846431 158.434896 \nL 341.878253 156.350032 \nL 341.941896 100.05872 \nL 342.005539 111.141415 \nL 342.26011 265.201849 \nL 342.291932 254.448343 \nL 342.451039 145.377067 \nL 342.482861 145.596526 \nL 342.641968 174.674884 \nL 342.705611 132.209508 \nL 342.769254 155.362465 \nL 343.023826 302.619661 \nL 343.087469 286.489402 \nL 343.246576 199.802975 \nL 343.310219 208.910536 \nL 343.373862 219.225124 \nL 343.405683 214.616478 \nL 343.469326 153.716521 \nL 343.532969 171.053806 \nL 343.787541 298.011016 \nL 343.851184 287.476969 \nL 344.010291 198.705679 \nL 344.073934 203.862972 \nL 344.137577 216.372153 \nL 344.169398 214.17756 \nL 344.233041 148.668956 \nL 344.296684 173.138669 \nL 344.519435 307.228306 \nL 344.551256 304.375335 \nL 344.614899 260.593204 \nL 344.774006 175.662451 \nL 344.901292 193.658115 \nL 344.933114 188.83001 \nL 345.028578 123.650595 \nL 345.060399 137.695991 \nL 345.28315 282.868323 \nL 345.346793 271.456439 \nL 345.474078 169.298131 \nL 345.537721 177.418126 \nL 345.665007 199.473786 \nL 345.696829 196.291626 \nL 345.760472 135.940317 \nL 345.824115 150.973279 \nL 346.046865 299.87642 \nL 346.110508 287.03805 \nL 346.205972 176.10137 \nL 346.301437 181.148934 \nL 346.428722 187.952173 \nL 346.460544 184.550553 \nL 346.524187 138.354369 \nL 346.58783 157.557058 \nL 346.842401 309.532629 \nL 346.874223 297.681827 \nL 347.065152 190.146766 \nL 347.192438 207.264592 \nL 347.224259 204.960269 \nL 347.287902 148.339767 \nL 347.351545 162.494893 \nL 347.574295 305.801821 \nL 347.637938 294.170478 \nL 347.797045 186.196498 \nL 347.828867 187.513254 \nL 347.956153 199.583516 \nL 347.987974 199.473786 \nL 348.051617 159.641922 \nL 348.11526 183.014338 \nL 348.369832 324.565592 \nL 348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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159.203003 \nL 389.3877 188.72028 \nL 389.578629 316.884516 \nL 389.610451 316.555327 \nL 389.674094 271.12725 \nL 389.833201 188.61055 \nL 389.865022 189.488388 \nL 389.992308 201.55865 \nL 390.087773 157.337599 \nL 390.151416 185.099202 \nL 390.342344 310.959115 \nL 390.374166 310.520196 \nL 390.437809 269.152117 \nL 390.596916 186.745146 \nL 390.660559 196.181897 \nL 390.756024 215.494316 \nL 390.787845 210.666211 \nL 390.851488 177.747315 \nL 390.883309 188.939739 \nL 391.10606 317.981813 \nL 391.201524 297.462367 \nL 391.32881 229.320252 \nL 391.392453 237.111058 \nL 391.519739 263.007256 \nL 391.55156 256.094288 \nL 391.615203 206.606214 \nL 391.678846 227.015929 \nL 391.901596 326.211537 \nL 391.933418 325.22397 \nL 392.028882 286.92832 \nL 392.156168 237.440247 \nL 392.187989 242.158622 \nL 392.283454 261.141852 \nL 392.315275 256.423477 \nL 392.41074 198.486219 \nL 392.442561 210.446752 \nL 392.63349 331.47856 \nL 392.665311 330.161804 \nL 392.728954 288.793725 \nL 392.888062 207.045132 \nL 392.983526 220.43215 \nL 393.047169 227.345118 \nL 393.07899 222.078095 \nL 393.174455 181.807312 \nL 393.206276 196.511086 \nL 393.429027 329.283967 \nL 393.49267 289.232643 \nL 393.651777 198.37649 \nL 393.683598 194.097033 \nL 393.747241 199.254327 \nL 393.810884 210.117563 \nL 393.842706 202.875406 \nL 393.93817 178.625152 \nL 393.969991 193.548385 \nL 394.192742 330.929912 \nL 394.256385 290.11048 \nL 394.415492 207.154862 \nL 394.542778 223.394851 \nL 394.574599 229.8689 \nL 394.606421 226.9062 \nL 394.701885 188.391091 \nL 394.733707 197.059734 \nL 394.924635 317.542894 \nL 394.956457 316.006679 \nL 395.0201 274.748329 \nL 395.147386 195.633248 \nL 395.179207 195.19433 \nL 395.338314 210.446752 \nL 395.370136 207.045132 \nL 395.4656 164.908945 \nL 395.497422 172.041373 \nL 395.688351 294.170478 \nL 395.720172 293.621829 \nL 395.783815 253.460776 \nL 395.879279 177.966774 \nL 395.911101 178.734882 \nL 395.974744 187.622984 \nL 396.10203 221.309987 \nL 396.133851 219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 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214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 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319.737487 \nL 413.540193 227.564578 \nL 413.603836 244.024026 \nL 413.667478 254.119154 \nL 413.6993 249.400779 \nL 413.794764 219.883502 \nL 413.826586 221.748906 \nL 414.017515 342.561255 \nL 414.049336 341.463959 \nL 414.112979 295.926152 \nL 414.272086 212.860804 \nL 414.463015 243.255918 \nL 414.494837 237.659706 \nL 414.558479 213.409452 \nL 414.590301 214.06783 \nL 414.78123 341.244499 \nL 414.813051 340.91531 \nL 414.876694 297.572097 \nL 415.035801 207.264592 \nL 415.099444 211.982967 \nL 415.22673 231.075927 \nL 415.290373 222.846203 \nL 415.322195 208.471618 \nL 415.354016 210.007833 \nL 415.544945 343.658552 \nL 415.576766 343.329363 \nL 415.640409 301.741824 \nL 415.767695 215.823505 \nL 415.799517 216.701342 \nL 415.958624 234.367816 \nL 415.990445 231.734305 \nL 416.08591 204.850539 \nL 416.117731 213.738641 \nL 416.30866 345.194767 \nL 416.340482 343.768281 \nL 416.404124 299.327771 \nL 416.53141 221.858636 \nL 416.563232 221.968365 \nL 416.595053 222.517014 \nL 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331.58829 \nL 498.089826 330.381263 \nL 498.153469 308.435333 \nL 498.344397 190.914873 \nL 498.376219 191.024603 \nL 498.439862 185.099202 \nL 498.503505 173.797047 \nL 498.535326 177.418126 \nL 498.662612 228.661874 \nL 498.853541 322.151539 \nL 498.917184 307.886684 \nL 499.171756 188.171632 \nL 499.203577 190.037036 \nL 499.26722 177.637585 \nL 499.330863 181.039204 \nL 499.362684 183.233797 \nL 499.585435 312.824519 \nL 499.617256 303.716958 \nL 499.935471 127.381404 \nL 499.967292 128.039781 \nL 499.999114 127.600863 \nL 500.1264 151.192738 \nL 500.34915 310.849385 \nL 500.412793 287.696428 \nL 500.635543 147.791119 \nL 500.79465 143.072744 \nL 500.921936 184.331094 \nL 501.112865 318.530461 \nL 501.176508 292.743992 \nL 501.399258 149.875983 \nL 501.526544 138.573828 \nL 501.558365 140.439232 \nL 501.685651 179.832178 \nL 501.87658 315.896949 \nL 501.940223 291.317507 \nL 502.162973 136.269506 \nL 502.194795 131.66086 \nL 502.226616 135.830587 \nL 502.449366 185.977039 \nL 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168.639753 \nL 528.288396 168.639753 \nL 528.352039 166.66462 \nL 528.383861 176.2111 \nL 528.606611 328.625589 \nL 528.670254 294.389937 \nL 528.829361 206.057565 \nL 528.861183 203.972702 \nL 528.924826 205.508917 \nL 528.988468 210.666211 \nL 529.083933 210.007833 \nL 529.115754 200.790542 \nL 529.147576 207.374321 \nL 529.370326 349.913142 \nL 529.433969 315.56776 \nL 529.593076 224.382418 \nL 529.688541 222.187825 \nL 529.784005 230.966197 \nL 529.847648 229.97863 \nL 529.879469 222.517014 \nL 529.911291 228.442415 \nL 530.134041 351.449357 \nL 530.165863 348.376927 \nL 530.261327 296.58453 \nL 530.388613 240.841866 \nL 530.420434 241.061325 \nL 530.515899 245.560241 \nL 530.643185 222.078095 \nL 530.675006 231.734305 \nL 530.897756 350.46179 \nL 530.929578 348.486656 \nL 531.025042 307.776955 \nL 531.18415 239.195921 \nL 531.279614 254.228884 \nL 531.311435 247.315915 \nL 531.4069 224.272688 \nL 531.438721 234.477546 \nL 531.62965 357.484488 \nL 531.661472 357.045569 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 569.910873 275.626166 \nL 569.974516 190.037036 \nL 570.038159 197.827841 \nL 570.197266 223.61431 \nL 570.229087 227.125659 \nL 570.260909 219.444583 \nL 570.324552 164.689486 \nL 570.388195 190.037036 \nL 570.610945 322.04181 \nL 570.674588 280.234812 \nL 570.738231 193.109466 \nL 570.801874 195.633248 \nL 570.833695 195.523519 \nL 570.992803 226.68674 \nL 571.024624 222.407284 \nL 571.088267 172.480291 \nL 571.15191 197.498652 \nL 571.342839 325.333699 \nL 571.37466 325.00451 \nL 571.438303 282.539134 \nL 571.59741 195.962437 \nL 571.629232 195.852708 \nL 571.756518 203.094865 \nL 571.820161 186.086768 \nL 571.883804 197.388923 \nL 571.979268 275.845625 \nL 572.138375 340.476392 \nL 572.202018 305.143443 \nL 572.361126 204.192161 \nL 572.392947 204.850539 \nL 572.552054 230.966197 \nL 572.615697 199.254327 \nL 572.647519 214.06783 \nL 572.90209 352.436924 \nL 572.965733 333.782883 \nL 573.124841 247.535375 \nL 573.156662 250.498075 \nL 573.283948 267.835361 \nL 573.31577 258.947259 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199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 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150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 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693.537265 325.443429 \nL 693.569087 321.493162 \nL 693.63273 277.71103 \nL 693.760015 206.386754 \nL 693.791837 206.606214 \nL 693.919123 221.858636 \nL 693.950944 226.357551 \nL 693.982766 225.369985 \nL 694.110052 198.26676 \nL 694.30098 312.934248 \nL 694.332802 310.849385 \nL 694.396445 266.628335 \nL 694.460088 201.229461 \nL 694.523731 207.92297 \nL 694.714659 233.819168 \nL 694.778302 226.9062 \nL 694.873767 204.960269 \nL 695.096517 328.625589 \nL 695.16016 286.92832 \nL 695.255624 214.616478 \nL 695.287446 217.798638 \nL 695.351089 223.72404 \nL 695.478375 242.487811 \nL 695.542017 237.549976 \nL 695.60566 226.79647 \nL 695.637482 228.661874 \nL 695.828411 340.476392 \nL 695.892054 335.648287 \nL 696.051161 243.804566 \nL 696.146625 257.081855 \nL 696.24209 271.23698 \nL 696.273911 268.384009 \nL 696.401197 244.024026 \nL 696.592126 346.511523 \nL 696.655769 344.975307 \nL 696.751233 293.182911 \nL 696.878519 221.968365 \nL 696.910341 223.285121 \nL 697.005805 239.964029 \nL 697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 \nL 707.729639 249.839697 \nL 707.856925 227.893767 \nL 708.047853 338.062339 \nL 708.079675 334.66072 \nL 708.143318 293.621829 \nL 708.302425 218.566746 \nL 708.461532 233.489979 \nL 708.493354 229.649441 \nL 708.588818 213.738641 \nL 708.62064 214.616478 \nL 708.84339 336.635854 \nL 708.907033 300.425068 \nL 709.034319 224.272688 \nL 709.06614 225.589444 \nL 709.225248 245.99916 \nL 709.28889 238.866732 \nL 709.384355 216.701342 \nL 709.607105 343.878011 \nL 709.670748 303.497498 \nL 709.829855 209.568914 \nL 709.92532 215.933234 \nL 709.988963 221.419717 \nL 710.020784 216.701342 \nL 710.116249 201.33919 \nL 710.14807 205.728377 \nL 710.338999 329.722886 \nL 710.37082 328.515859 \nL 710.434463 290.000751 \nL 710.593571 208.910536 \nL 710.657213 216.481883 \nL 710.752678 232.502412 \nL 710.848142 231.514845 \nL 710.911785 218.127827 \nL 711.102714 340.91531 \nL 711.166357 331.259101 \nL 711.357286 232.941331 \nL 711.389107 233.819168 \nL 711.611857 256.752666 \nL 711.6755 241.171055 \nL 711.707322 255.106721 \nL 711.898251 345.962874 \nL 711.961894 333.343964 \nL 712.121001 255.984558 \nL 712.184644 263.775363 \nL 712.248287 274.089951 \nL 712.31193 270.578602 \nL 712.439216 250.607805 \nL 712.661966 354.302328 \nL 712.693787 352.327194 \nL 712.789252 308.764522 \nL 712.916538 252.473209 \nL 712.948359 253.460776 \nL 713.043823 260.702933 \nL 713.202931 237.879165 \nL 713.393859 344.31693 \nL 713.425681 342.341796 \nL 713.489324 302.290472 \nL 713.648431 219.773772 \nL 713.680253 220.102961 \nL 713.807539 240.402947 \nL 713.871181 235.245653 \nL 713.934824 229.97863 \nL 713.966646 220.322421 \nL 714.157575 340.805581 \nL 714.189396 338.391528 \nL 714.253039 301.522364 \nL 714.412146 231.514845 \nL 714.443968 229.649441 \nL 714.475789 234.148357 \nL 714.571254 240.841866 \nL 714.603075 239.63484 \nL 714.730361 218.566746 \nL 714.92129 339.927743 \nL 714.984933 325.772618 \nL 715.112219 234.367816 \nL 715.175862 237.001328 \nL 715.36679 245.121322 \nL 715.462255 236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 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158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 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339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 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\nL 834.569999 204.850539 \nL 834.633642 221.090528 \nL 834.856392 341.13477 \nL 834.888214 337.73315 \nL 835.0155 250.607805 \nL 835.079143 228.332685 \nL 835.142785 239.41538 \nL 835.23825 262.348878 \nL 835.270071 256.423477 \nL 835.333714 219.554313 \nL 835.397357 235.684572 \nL 835.588286 339.598554 \nL 835.651929 335.977476 \nL 835.747393 285.392105 \nL 835.811036 245.88943 \nL 835.874679 254.777532 \nL 835.970144 270.578602 \nL 836.001965 269.920224 \nL 836.097429 215.494316 \nL 836.161072 235.026194 \nL 836.383823 340.366662 \nL 836.447466 308.654792 \nL 836.606573 211.324589 \nL 836.76568 228.113226 \nL 836.797502 223.61431 \nL 836.861145 186.415957 \nL 836.892966 193.767844 \nL 837.147538 345.523956 \nL 837.179359 334.880179 \nL 837.40211 216.262423 \nL 837.497574 219.225124 \nL 837.529395 221.858636 \nL 837.561217 219.554313 \nL 837.62486 186.854876 \nL 837.688503 213.189993 \nL 837.879431 339.379095 \nL 837.911253 339.269365 \nL 837.974896 301.193176 \nL 838.134003 204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 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222.626743 \nL 852.103626 191.682981 \nL 852.135448 200.461353 \nL 852.39002 332.356397 \nL 852.421841 330.052075 \nL 852.485484 283.85589 \nL 852.61277 217.908368 \nL 852.644591 218.127827 \nL 852.676413 219.444583 \nL 852.771877 228.881334 \nL 852.83552 202.217028 \nL 852.867342 172.480291 \nL 852.930985 188.939739 \nL 853.153735 324.016944 \nL 853.217378 310.191007 \nL 853.408307 208.032699 \nL 853.535592 220.761339 \nL 853.567414 217.140261 \nL 853.631057 177.308396 \nL 853.6947 192.451088 \nL 853.91745 332.356397 \nL 853.981093 320.725054 \nL 854.108379 216.481883 \nL 854.203843 220.980798 \nL 854.299308 232.831601 \nL 854.331129 231.624575 \nL 854.394772 181.258664 \nL 854.458415 197.169464 \nL 854.681165 338.940176 \nL 854.744808 325.882348 \nL 854.872094 210.9954 \nL 854.935737 211.214859 \nL 854.967558 210.775941 \nL 854.99938 215.274856 \nL 855.063023 214.06783 \nL 855.126666 188.281362 \nL 855.158487 172.041373 \nL 855.22213 193.109466 \nL 855.44488 330.052075 \nL 855.508523 320.834784 \nL 855.699452 202.875406 \nL 855.731273 204.850539 \nL 855.826738 214.506749 \nL 855.858559 211.653778 \nL 855.922202 171.931643 \nL 855.985845 192.341359 \nL 856.240417 316.555327 \nL 856.30406 296.365071 \nL 856.431346 226.028363 \nL 856.494989 233.489979 \nL 856.590453 250.498075 \nL 856.622275 243.914296 \nL 856.685917 189.268928 \nL 856.74956 213.738641 \nL 857.004132 334.002342 \nL 857.067775 323.468295 \nL 857.258704 241.500244 \nL 857.290525 247.645104 \nL 857.354168 254.996991 \nL 857.38599 247.535375 \nL 857.449633 187.952173 \nL 857.513276 210.117563 \nL 857.736026 333.782883 \nL 857.799669 320.615324 \nL 857.990598 211.434319 \nL 858.117883 227.015929 \nL 858.149705 226.028363 \nL 858.213348 175.772181 \nL 858.276991 193.877574 \nL 858.499741 328.625589 \nL 858.563384 316.116408 \nL 858.754313 219.554313 \nL 858.786134 224.492147 \nL 858.881599 231.734305 \nL 858.91342 228.661874 \nL 858.977063 177.308396 \nL 859.040706 198.486219 \nL 859.263456 338.610988 \nL 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni prevod s strani človeka. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "dddca9ad9e34435494e0933c218e1579",
+   "translation_date": "2025-09-06T14:01:11+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sl/7-TimeSeries/1-Introduction/working/notebook.ipynb b/translations/sl/7-TimeSeries/1-Introduction/working/notebook.ipynb
new file mode 100644
index 000000000..7d93b28d2
--- /dev/null
+++ b/translations/sl/7-TimeSeries/1-Introduction/working/notebook.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Nastavitev podatkov\n",
+    "\n",
+    "V tem zvezku prikazujemo, kako:\n",
+    "\n",
+    "nastaviti podatke časovnih vrst za ta modul\n",
+    "vizualizirati podatke\n",
+    "Podatki v tem primeru so vzeti iz tekmovanja GEFCom2014 za napovedovanje. Vključujejo 3 leta urnih vrednosti porabe električne energije in temperature med letoma 2012 in 2014.\n",
+    "\n",
+    "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli in Rob J. Hyndman, \"Probabilistično napovedovanje energije: Globalno tekmovanje za napovedovanje energije 2014 in naprej\", International Journal of Forecasting, vol.32, št.3, str. 896-913, julij-september, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna napačna razumevanja ali napačne interpretacije, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5e2bbe594906dce3aaaa736d6dac6683",
+   "translation_date": "2025-09-06T14:02:14+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/working/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sl/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/translations/sl/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
new file mode 100644
index 000000000..73a13a939
--- /dev/null
+++ b/translations/sl/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -0,0 +1,1149 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Napovedovanje časovnih vrst z ARIMA\n",
+    "\n",
+    "V tem zvezku bomo prikazali, kako:\n",
+    "- pripraviti podatke časovnih vrst za učenje napovednega modela ARIMA,\n",
+    "- implementirati preprost model ARIMA za napovedovanje naslednjih korakov HORIZON (od časa *t+1* do *t+HORIZON*) v časovni vrsti,\n",
+    "- oceniti model.\n",
+    "\n",
+    "Podatki v tem primeru so vzeti iz tekmovanja za napovedovanje GEFCom2014. Sestavljeni so iz 3 let urnih vrednosti porabe električne energije in temperature med letoma 2012 in 2014. Naloga je napovedati prihodnje vrednosti porabe električne energije. V tem primeru pokažemo, kako napovedati eno časovno točko naprej, pri čemer uporabimo samo zgodovinske podatke o porabi.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli in Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, št.3, str. 896-913, julij-september, 2016.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Namestitev odvisnosti\n",
+    "Začnite z namestitvijo nekaterih potrebnih odvisnosti. Te knjižnice z ustreznimi različicami so preverjeno delujoče za rešitev:\n",
+    "\n",
+    "* `statsmodels == 0.12.2`\n",
+    "* `matplotlib == 3.4.2`\n",
+    "* `scikit-learn == 0.24.2`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "!pip install statsmodels"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "/bin/sh: pip: command not found\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from pandas.plotting import autocorrelation_plot\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "%matplotlib inline\n",
+    "pd.options.display.float_format = '{:,.2f}'.format\n",
+    "np.set_printoptions(precision=2)\n",
+    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load\n",
+       "2012-01-01 00:00:00 2,698.00\n",
+       "2012-01-01 01:00:00 2,558.00\n",
+       "2012-01-01 02:00:00 2,444.00\n",
+       "2012-01-01 03:00:00 2,402.00\n",
+       "2012-01-01 04:00:00 2,403.00\n",
+       "2012-01-01 05:00:00 2,453.00\n",
+       "2012-01-01 06:00:00 2,560.00\n",
+       "2012-01-01 07:00:00 2,719.00\n",
+       "2012-01-01 08:00:00 2,916.00\n",
+       "2012-01-01 09:00:00 3,105.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Prikaži vse razpoložljive podatke o obremenitvi (januar 2012 do december 2014)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Ustvarjanje učnih in testnih podatkovnih nizov\n",
+    "\n",
+    "### Razdelitev podatkov\n",
+    "Razdelitev podatkov na učni in testni niz je ključni korak pri gradnji modela strojnega učenja. Učni niz se uporablja za treniranje modela, medtem ko testni niz služi za ocenjevanje njegove zmogljivosti.\n",
+    "\n",
+    "[!NOTE] Pravilna razdelitev podatkov pomaga preprečiti pristranskost in zagotavlja bolj realistične ocene zmogljivosti modela.\n",
+    "\n",
+    "### Priporočena razmerja\n",
+    "Običajno se podatki razdelijo v naslednjih razmerjih:\n",
+    "- **Učni niz**: 70–80 % celotnih podatkov\n",
+    "- **Testni niz**: 20–30 % celotnih podatkov\n",
+    "\n",
+    "Razmerje je odvisno od velikosti podatkovnega nabora. Pri večjih naborih podatkov lahko uporabite manjši delež za testiranje.\n",
+    "\n",
+    "### Primer razdelitve\n",
+    "Spodaj je primer, kako razdeliti podatke z uporabo knjižnice Python `scikit-learn`:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Razdelitev podatkov\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "```\n",
+    "\n",
+    "V zgornjem primeru:\n",
+    "- `X` predstavlja vhodne značilnosti.\n",
+    "- `y` predstavlja ciljne oznake.\n",
+    "- `test_size=0.2` pomeni, da bo 20 % podatkov uporabljenih za testiranje.\n",
+    "- `random_state=42` zagotavlja ponovljivost razdelitve.\n",
+    "\n",
+    "[!TIP] Vedno uporabite enak `random_state`, če želite zagotoviti, da bo razdelitev podatkov dosledna pri večkratnem zagonu.\n",
+    "\n",
+    "### Preverjanje uravnoteženosti\n",
+    "Po razdelitvi podatkov preverite, ali so razredi enakomerno porazdeljeni v učnem in testnem nizu. To je še posebej pomembno pri neuravnoteženih podatkovnih nizih.\n",
+    "\n",
+    "```python\n",
+    "# Preverjanje porazdelitve razredov\n",
+    "print(\"Porazdelitev v učnem nizu:\", y_train.value_counts())\n",
+    "print(\"Porazdelitev v testnem nizu:\", y_test.value_counts())\n",
+    "```\n",
+    "\n",
+    "[!WARNING] Če so razredi v testnem nizu močno neuravnoteženi, lahko to vpliva na oceno zmogljivosti modela. Razmislite o uporabi stratificirane razdelitve, če je to potrebno.\n",
+    "\n",
+    "### Stratificirana razdelitev\n",
+    "Za ohranitev razmerja razredov v učnem in testnem nizu uporabite parameter `stratify`:\n",
+    "\n",
+    "```python\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n",
+    "```\n",
+    "\n",
+    "[!IMPORTANT] Stratificirana razdelitev je še posebej pomembna pri delu z redkimi razredi ali neuravnoteženimi podatki.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-11-01 00:00:00  0.10\n",
+       "2014-11-01 01:00:00  0.07\n",
+       "2014-11-01 02:00:00  0.05\n",
+       "2014-11-01 03:00:00  0.04\n",
+       "2014-11-01 04:00:00  0.06\n",
+       "2014-11-01 05:00:00  0.10\n",
+       "2014-11-01 06:00:00  0.19\n",
+       "2014-11-01 07:00:00  0.31\n",
+       "2014-11-01 08:00:00  0.40\n",
+       "2014-11-01 09:00:00  0.48"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Izvirni podatki proti skaliranim podatkom:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Povečajmo tudi testne podatke\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-12-30 00:00:00  0.33\n",
+       "2014-12-30 01:00:00  0.29\n",
+       "2014-12-30 02:00:00  0.27\n",
+       "2014-12-30 03:00:00  0.27\n",
+       "2014-12-30 04:00:00  0.30"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Forecasting horizon: 3 hours\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "order = (4, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
+    "results = model.fit()\n",
+    "\n",
+    "print(results.summary())\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Thu, 30 Sep 2021   AIC                          -6942.477\n",
+      "Time:                                    14:36:28   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Ocenite model\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ustvari testno podatkovno točko za vsak korak HORIZON.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load  load+1  load+2\n",
+       "2014-12-30 00:00:00  0.33    0.29    0.27\n",
+       "2014-12-30 01:00:00  0.29    0.27    0.27\n",
+       "2014-12-30 02:00:00  0.27    0.27    0.30\n",
+       "2014-12-30 03:00:00  0.27    0.30    0.41\n",
+       "2014-12-30 04:00:00  0.30    0.41    0.57"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Naredite napovedi na testnih podatkih\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2014-12-30 00:00:00\n",
+      "1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
+      "2014-12-30 01:00:00\n",
+      "2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
+      "2014-12-30 02:00:00\n",
+      "3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
+      "2014-12-30 03:00:00\n",
+      "4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
+      "2014-12-30 04:00:00\n",
+      "5 : predicted = [0.3  0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
+      "2014-12-30 05:00:00\n",
+      "6 : predicted = [0.4  0.55 0.66] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
+      "2014-12-30 06:00:00\n",
+      "7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
+      "2014-12-30 07:00:00\n",
+      "8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
+      "2014-12-30 08:00:00\n",
+      "9 : predicted = [0.75 0.8  0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
+      "2014-12-30 09:00:00\n",
+      "10 : predicted = [0.77 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
+      "2014-12-30 10:00:00\n",
+      "11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
+      "2014-12-30 11:00:00\n",
+      "12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
+      "2014-12-30 12:00:00\n",
+      "13 : predicted = [0.7  0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
+      "2014-12-30 13:00:00\n",
+      "14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
+      "2014-12-30 14:00:00\n",
+      "15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
+      "2014-12-30 15:00:00\n",
+      "16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
+      "2014-12-30 16:00:00\n",
+      "17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
+      "2014-12-30 17:00:00\n",
+      "18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
+      "2014-12-30 18:00:00\n",
+      "19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
+      "2014-12-30 19:00:00\n",
+      "20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
+      "2014-12-30 20:00:00\n",
+      "21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
+      "2014-12-30 21:00:00\n",
+      "22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
+      "2014-12-30 22:00:00\n",
+      "23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
+      "2014-12-30 23:00:00\n",
+      "24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
+      "2014-12-31 00:00:00\n",
+      "25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
+      "2014-12-31 01:00:00\n",
+      "26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
+      "2014-12-31 02:00:00\n",
+      "27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
+      "2014-12-31 03:00:00\n",
+      "28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
+      "2014-12-31 04:00:00\n",
+      "29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
+      "2014-12-31 05:00:00\n",
+      "30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
+      "2014-12-31 06:00:00\n",
+      "31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
+      "2014-12-31 07:00:00\n",
+      "32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
+      "2014-12-31 08:00:00\n",
+      "33 : predicted = [0.79 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
+      "2014-12-31 09:00:00\n",
+      "34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
+      "2014-12-31 10:00:00\n",
+      "35 : predicted = [0.8  0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
+      "2014-12-31 11:00:00\n",
+      "36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
+      "2014-12-31 12:00:00\n",
+      "37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
+      "2014-12-31 13:00:00\n",
+      "38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
+      "2014-12-31 14:00:00\n",
+      "39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
+      "2014-12-31 15:00:00\n",
+      "40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
+      "2014-12-31 16:00:00\n",
+      "41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
+      "2014-12-31 17:00:00\n",
+      "42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
+      "2014-12-31 18:00:00\n",
+      "43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
+      "2014-12-31 19:00:00\n",
+      "44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
+      "2014-12-31 20:00:00\n",
+      "45 : predicted = [0.7  0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
+      "2014-12-31 21:00:00\n",
+      "46 : predicted = [0.6  0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
+      "CPU times: user 12min 15s, sys: 2min 39s, total: 14min 54s\n",
+      "Wall time: 2min 36s\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": true
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Primerjajte napovedi z dejansko obremenitvijo\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp    h  prediction   actual\n",
+       "0 2014-12-30 00:00:00  t+1    3,008.74 3,023.00\n",
+       "1 2014-12-30 01:00:00  t+1    2,955.53 2,935.00\n",
+       "2 2014-12-30 02:00:00  t+1    2,900.17 2,899.00\n",
+       "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
+       "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Izračunajte **povprečno absolutno odstotno napako (MAPE)** za vse napovedi\n",
+    "\n",
+    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "One step forecast MAPE:  0.5570581332313952 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Multi-step forecast MAPE:  1.1460048657704118 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Prikaži napovedi v primerjavi z dejanskimi vrednostmi za prvi teden testnega nabora\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "if(HORIZON == 1):\n",
+    "    ## Plotting single step forecast\n",
+    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
+    "\n",
+    "else:\n",
+    "    ## Plotting multi step forecast\n",
+    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
+    "\n",
+    "    fig = plt.figure(figsize=(15, 8))\n",
+    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        x = plot_df['timestamp'][(t-1):]\n",
+    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
+    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
+    "    \n",
+    "    ax.legend(loc='best')\n",
+    "    \n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "c193140200b9684da27e3890211391b6",
+   "translation_date": "2025-09-06T13:57:24+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sl/7-TimeSeries/2-ARIMA/working/notebook.ipynb b/translations/sl/7-TimeSeries/2-ARIMA/working/notebook.ipynb
new file mode 100644
index 000000000..f5c9e9867
--- /dev/null
+++ b/translations/sl/7-TimeSeries/2-ARIMA/working/notebook.ipynb
@@ -0,0 +1,59 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "523ec472196307b3c4235337353c9ceb",
+   "translation_date": "2025-09-06T14:00:17+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/working/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Napovedovanje časovnih vrst z ARIMA\n",
+    "\n",
+    "V tem zvezku bomo prikazali, kako:\n",
+    "- pripraviti podatke časovnih vrst za učenje napovednega modela ARIMA,\n",
+    "- implementirati preprost model ARIMA za napovedovanje naslednjih korakov HORIZON (od časa *t+1* do *t+HORIZON*) v časovni vrsti,\n",
+    "- oceniti model.\n",
+    "\n",
+    "Podatki v tem primeru so vzeti iz tekmovanja za napovedovanje GEFCom2014. Sestavljeni so iz 3 let urnih vrednosti porabe električne energije in temperature med letoma 2012 in 2014. Naloga je napovedati prihodnje vrednosti porabe električne energije. V tem primeru pokažemo, kako napovedati eno časovno točko naprej, pri čemer uporabimo samo zgodovinske podatke o porabi.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli in Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, št.3, str. 896-913, julij-september, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install statsmodels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/7-TimeSeries/3-SVR/solution/notebook.ipynb b/translations/sl/7-TimeSeries/3-SVR/solution/notebook.ipynb
new file mode 100644
index 000000000..490980636
--- /dev/null
+++ b/translations/sl/7-TimeSeries/3-SVR/solution/notebook.ipynb
@@ -0,0 +1,1029 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": [
+    "# Napovedovanje časovnih vrst z uporabo regresorja podpornih vektorjev\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "V tem zvezku bomo prikazali, kako:\n",
+    "\n",
+    "- pripraviti 2D časovne vrste podatkov za učenje modela regresorja SVM\n",
+    "- implementirati SVR z uporabo RBF jedra\n",
+    "- oceniti model s pomočjo grafov in MAPE\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Uvažanje modulov\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": [
+    "## Priprava podatkov\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Naloži podatke\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zdaj morate pripraviti podatke za učenje z izvajanjem filtriranja in skaliranja vaših podatkov.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prilagodite podatke, da bodo v razponu (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.101611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.065801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.046106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.042525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.059087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-11-01 00:00:00  0.101611\n",
+       "2014-11-01 01:00:00  0.065801\n",
+       "2014-11-01 02:00:00  0.046106\n",
+       "2014-11-01 03:00:00  0.042525\n",
+       "2014-11-01 04:00:00  0.059087"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.329454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.290063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.273948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.268129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.302596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-12-30 00:00:00  0.329454\n",
+       "2014-12-30 01:00:00  0.290063\n",
+       "2014-12-30 02:00:00  0.273948\n",
+       "2014-12-30 03:00:00  0.268129\n",
+       "2014-12-30 04:00:00  0.302596"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": [
+    "### Ustvarjanje podatkov s časovnimi koraki\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "Za naš SVR preoblikujemo vhodne podatke v obliko `[batch, timesteps]`. Tako preoblikujemo obstoječe `train_data` in `test_data`, da dodamo novo dimenzijo, ki se nanaša na časovne korake. V našem primeru vzamemo `timesteps = 5`. Tako so vhodi v model podatki za prve 4 časovne korake, izhod pa bodo podatki za 5. časovni korak.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1412, 5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=np.array([[j for j in train_data[i:i+timesteps]] for i in range(0,len(train_data)-timesteps+1)])[:,:,0]\n",
+    "train_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(44, 5)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=np.array([[j for j in test_data[i:i+timesteps]] for i in range(0,len(test_data)-timesteps+1)])[:,:,0]\n",
+    "test_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 4) (1412, 1)\n",
+      "(44, 4) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train, y_train = train_data_timesteps[:,:timesteps-1],train_data_timesteps[:,[timesteps-1]]\n",
+    "x_test, y_test = test_data_timesteps[:,:timesteps-1],test_data_timesteps[:,[timesteps-1]]\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": [
+    "## Ustvarjanje SVR modela\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = SVR(kernel='rbf',gamma=0.5, C=10, epsilon = 0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVR(C=10, cache_size=200, coef0=0.0, degree=3, epsilon=0.05, gamma=0.5,\n",
+       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit model on training data\n",
+    "\n",
+    "model.fit(x_train, y_train[:,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Naredi napoved modela\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 1) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = model.predict(x_train).reshape(-1,1)\n",
+    "y_test_pred = model.predict(x_test).reshape(-1,1)\n",
+    "\n",
+    "print(y_train_pred.shape, y_test_pred.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": [
+    "## Analiza učinkovitosti modela\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
+    "\n",
+    "print(len(y_train_pred), len(y_test_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)\n",
+    "\n",
+    "print(len(y_train), len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)].index[timesteps-1:]\n",
+    "test_timestamps = energy[test_start_dt:].index[timesteps-1:]\n",
+    "\n",
+    "print(len(train_timestamps), len(test_timestamps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "plt.plot(train_timestamps, y_train, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(train_timestamps, y_train_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for training data:  1.7195710200875551 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "plt.plot(test_timestamps, y_test, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(test_timestamps, y_test_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for testing data:  1.2623790187854018 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Napoved celotnega nabora podatkov\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor shape:  (26300, 5)\n",
+      "X shape:  (26300, 4) \n",
+      "Y shape:  (26300, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = energy.copy().values\n",
+    "\n",
+    "# Scaling\n",
+    "data = scaler.transform(data)\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=np.array([[j for j in data[i:i+timesteps]] for i in range(0,len(data)-timesteps+1)])[:,:,0]\n",
+    "print(\"Tensor shape: \", data_timesteps.shape)\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = data_timesteps[:,:timesteps-1],data_timesteps[:,[timesteps-1]]\n",
+    "print(\"X shape: \", X.shape,\"\\nY shape: \", Y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "Y_pred = model.predict(X).reshape(-1,1)\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = scaler.inverse_transform(Y_pred)\n",
+    "Y = scaler.inverse_transform(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "plt.plot(Y, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(Y_pred, color = 'blue', linewidth=1)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE:  2.0572089029888656 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "f8f3967282314d3995245835bdaa8418",
+   "translation_date": "2025-09-06T14:03:30+00:00",
+   "source_file": "7-TimeSeries/3-SVR/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sl/7-TimeSeries/3-SVR/working/notebook.ipynb b/translations/sl/7-TimeSeries/3-SVR/working/notebook.ipynb
new file mode 100644
index 000000000..e46fe7af6
--- /dev/null
+++ b/translations/sl/7-TimeSeries/3-SVR/working/notebook.ipynb
@@ -0,0 +1,705 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": [
+    "# Napovedovanje časovnih vrst z uporabo regresorja podpornih vektorjev\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "V tem zvezku bomo prikazali, kako:\n",
+    "\n",
+    "- pripraviti 2D časovne vrste podatkov za učenje modela SVM regresorja\n",
+    "- implementirati SVR z uporabo RBF jedra\n",
+    "- oceniti model s pomočjo grafov in MAPE\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Uvažanje modulov\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": [
+    "## Priprava podatkov\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Naloži podatke\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zdaj morate pripraviti podatke za učenje z izvajanjem filtriranja in skaliranja vaših podatkov.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Prilagodite podatke, da bodo v razponu (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": [
+    "### Ustvarjanje podatkov s časovnimi koraki\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "Za naš SVR preoblikujemo vhodne podatke v obliko `[batch, timesteps]`. Tako preoblikujemo obstoječe `train_data` in `test_data`, da dodamo novo dimenzijo, ki se nanaša na časovne korake. V našem primeru vzamemo `timesteps = 5`. Tako so vhodi v model podatki za prve 4 časovne korake, izhod pa bodo podatki za 5. časovni korak.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [],
+   "source": [
+    "x_train, y_train = None\n",
+    "x_test, y_test = None\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": [
+    "## Ustvarjanje SVR modela\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit model on training data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Ustvari napoved modela\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = None\n",
+    "y_test_pred = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": [
+    "## Analiza učinkovitosti modela\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = None\n",
+    "test_timestamps = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Napoved celotnega nabora podatkov\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = None\n",
+    "\n",
+    "# Scaling\n",
+    "data = None\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=None\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = None, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = None\n",
+    "Y = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "e86ce102239a14c44585623b9b924a74",
+   "translation_date": "2025-09-06T14:06:00+00:00",
+   "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sl/8-Reinforcement/1-QLearning/notebook.ipynb b/translations/sl/8-Reinforcement/1-QLearning/notebook.ipynb
new file mode 100644
index 000000000..d3147c5c5
--- /dev/null
+++ b/translations/sl/8-Reinforcement/1-QLearning/notebook.ipynb
@@ -0,0 +1,411 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
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+   "source_file": "8-Reinforcement/1-QLearning/notebook.ipynb",
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+  }
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+ "cells": [
+  {
+   "source": [
+    "# Peter in volk: Uvod v okrepljeno učenje\n",
+    "\n",
+    "V tem vodiču se bomo naučili, kako uporabiti okrepljeno učenje za reševanje problema iskanja poti. Zgodba je navdihnjena z glasbeno pravljico [Peter in volk](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) ruskega skladatelja [Sergeja Prokofjeva](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Gre za zgodbo o mladem pionirju Petru, ki pogumno zapusti svojo hišo in se odpravi na gozdno jaso, da bi lovil volka. Naučili bomo algoritme strojnega učenja, ki bodo Petru pomagali raziskati okolico in zgraditi optimalen navigacijski zemljevid.\n",
+    "\n",
+    "Najprej uvozimo nekaj uporabnih knjižnic:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Pregled učenja z okrepitvijo\n",
+    "\n",
+    "**Učenje z okrepitvijo** (RL) je tehnika učenja, ki nam omogoča, da se naučimo optimalnega vedenja **agenta** v nekem **okolju** z izvajanjem številnih poskusov. Agent v tem okolju mora imeti določen **cilj**, ki ga opredeljuje **funkcija nagrajevanja**.\n",
+    "\n",
+    "## Okolje\n",
+    "\n",
+    "Za enostavnost si predstavljajmo Peterjev svet kot kvadratno ploščo velikosti `width` x `height`. Vsaka celica na tej plošči je lahko:\n",
+    "* **zemlja**, po kateri lahko Peter in druga bitja hodijo\n",
+    "* **voda**, po kateri se seveda ne more hoditi\n",
+    "* **drevo** ali **trava** - mesto, kjer se lahko spočiješ\n",
+    "* **jabolko**, ki predstavlja nekaj, kar bi Peter z veseljem našel, da se nahrani\n",
+    "* **volk**, ki je nevaren in se mu je treba izogniti\n",
+    "\n",
+    "Za delo z okoljem bomo definirali razred `Board`. Da ne bi preveč obremenili tega zvezka, smo vso kodo za delo s ploščo premaknili v ločen modul `rlboard`, ki ga bomo zdaj uvozili. Več podrobnosti o notranji implementaciji si lahko ogledate znotraj tega modula.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Zdaj ustvarimo naključno ploščo in si oglejmo, kako izgleda:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Dejanja in Pravila\n",
+    "\n",
+    "V našem primeru bi bil Peterjev cilj najti jabolko, medtem ko se izogiba volku in drugim oviram. Določite ta dejanja kot slovar in jih povežite s pari ustreznih sprememb koordinat.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 2"
+   ]
+  },
+  {
+   "source": [
+    "Strategija našega agenta (Peter) je določena z tako imenovano **politiko**. Oglejmo si najpreprostejšo politiko, imenovano **naključna hoja**.\n",
+    "\n",
+    "## Naključna hoja\n",
+    "\n",
+    "Najprej rešimo naš problem z implementacijo strategije naključne hoje.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Let's run a random walk experiment several times and see the average number of steps taken: code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 4"
+   ]
+  },
+  {
+   "source": [
+    "## Funkcija nagrajevanja\n",
+    "\n",
+    "Da bi naša politika postala bolj inteligentna, moramo razumeti, kateri premiki so \"boljši\" od drugih.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Učenje\n",
+    "\n",
+    "Ustvarite Q-tabelo ali večdimenzionalno matriko. Ker ima naša plošča dimenzije `width` x `height`, lahko Q-tabelo predstavimo z numpy matriko oblike `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Podajte Q-tabelo funkciji `plot`, da vizualizirate tabelo na plošči:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'm' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8b8a9e4cb2b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Bistvo Q-Učenja: Bellmanova enačba in učni algoritem\n",
+    "\n",
+    "Napišite psevdokodo za naš učni algoritem:\n",
+    "\n",
+    "* Inicializirajte Q-tabelo Q z enakimi vrednostmi za vsa stanja in akcije\n",
+    "* Nastavite hitrost učenja $\\alpha\\leftarrow 1$\n",
+    "* Večkrat ponovite simulacijo\n",
+    "   1. Začnite na naključni poziciji\n",
+    "   1. Ponavljajte\n",
+    "        1. Izberite akcijo $a$ v stanju $s$\n",
+    "        2. Izvedite akcijo z premikom v novo stanje $s'$\n",
+    "        3. Če naletimo na pogoj konca igre ali je skupna nagrada premajhna - zaključite simulacijo  \n",
+    "        4. Izračunajte nagrado $r$ v novem stanju\n",
+    "        5. Posodobite Q-funkcijo v skladu z Bellmanovo enačbo: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Posodobite skupno nagrado in zmanjšajte $\\alpha$.\n",
+    "\n",
+    "## Izkoriščanje vs. Raziskovanje\n",
+    "\n",
+    "Najboljši pristop je uravnotežiti med raziskovanjem in izkoriščanjem. Ko se več naučimo o našem okolju, bomo bolj verjetno sledili optimalni poti, vendar se občasno odločimo za nepreizkušeno pot.\n",
+    "\n",
+    "## Python Implementacija\n",
+    "\n",
+    "Sedaj smo pripravljeni implementirati učni algoritem. Pred tem potrebujemo tudi funkcijo, ki bo poljubne številke v Q-tabeli pretvorila v vektor verjetnosti za ustrezne akcije:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Dodamo majhno količino `eps` k prvotnemu vektorju, da se izognemo deljenju z 0 v začetnem primeru, ko so vse komponente vektorja enake.\n",
+    "\n",
+    "Dejanski učni algoritem bomo izvedli za 5000 poskusov, imenovanih tudi **epohi**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "# code block 8"
+   ]
+  },
+  {
+   "source": [
+    "Po izvedbi tega algoritma bi morala biti Q-tabela posodobljena z vrednostmi, ki določajo privlačnost različnih dejanj na vsakem koraku. Vizualizirajte tabelo tukaj:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Preverjanje politike\n",
+    "\n",
+    "Ker Q-Table navaja \"privlačnost\" vsakega dejanja v vsakem stanju, je zelo enostavno uporabiti to tabelo za določanje učinkovite navigacije v našem svetu. V najpreprostejšem primeru lahko preprosto izberemo dejanje, ki ustreza najvišji vrednosti v Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "# code block 9"
+   ]
+  },
+  {
+   "source": [
+    "Če večkrat preizkusite zgornjo kodo, boste opazili, da se včasih preprosto \"zatakne\" in morate pritisniti gumb STOP v zvezku, da jo prekinete.\n",
+    "\n",
+    "> **Naloga 1:** Spremenite funkcijo `walk`, da omejite največjo dolžino poti na določeno število korakov (recimo 100), in opazujte, kako zgornja koda občasno vrne to vrednost.\n",
+    "\n",
+    "> **Naloga 2:** Spremenite funkcijo `walk`, da se ne vrača na mesta, kjer je že bila prej. To bo preprečilo, da bi se `walk` zanka, vendar se agent še vedno lahko \"ujame\" na lokaciji, iz katere ne more pobegniti.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 5.31, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# code block 10"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26dab295a48>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 57
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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109.232964 214.756364 \r\nL 109.263403 214.085931 \r\nL 109.293843 201.703888 \r\nL 109.385161 202.919046 \r\nL 109.44604 214.756364 \r\nL 109.476479 192.192132 \r\nL 109.537358 214.735413 \r\nL 109.598237 214.756364 \r\nL 109.719994 198.100315 \r\nL 109.811312 214.756364 \r\nL 109.841752 210.230947 \r\nL 109.872191 214.735413 \r\nL 109.93307 208.63867 \r\nL 110.054828 214.756364 \r\nL 110.085267 200.509681 \r\nL 110.146146 208.052042 \r\nL 110.207025 214.756364 \r\nL 110.267904 213.268842 \r\nL 110.298343 193.470143 \r\nL 110.328782 214.756364 \r\nL 110.359222 209.811927 \r\nL 110.420101 214.756364 \r\nL 110.480979 214.044029 \r\nL 110.541858 214.756364 \r\nL 110.572298 212.032733 \r\nL 110.633176 214.756364 \r\nL 110.663616 214.756364 \r\nL 110.694055 199.441179 \r\nL 110.785374 202.981899 \r\nL 110.907131 214.756364 \r\nL 110.937571 211.110889 \r\nL 110.998449 214.756364 \r\nL 111.089768 209.916682 \r\nL 111.181086 214.756364 \r\nL 111.211525 203.882792 \r\nL 111.272404 207.842532 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115.016451 213.43645 \r\nL 115.046891 214.46305 \r\nL 115.07733 207.339708 \r\nL 115.168648 209.66527 \r\nL 115.229527 213.268842 \r\nL 115.290406 211.215644 \r\nL 115.320845 205.642677 \r\nL 115.351285 214.756364 \r\nL 115.381724 209.749074 \r\nL 115.533921 214.756364 \r\nL 115.564361 213.687862 \r\nL 115.655679 206.606423 \r\nL 115.746997 214.756364 \r\nL 115.716558 193.700604 \r\nL 115.777436 214.735413 \r\nL 115.838315 193.763457 \r\nL 115.868755 193.910114 \r\nL 115.899194 214.756364 \r\nL 115.990512 212.346998 \r\nL 116.051391 210.754722 \r\nL 116.11227 214.756364 \r\nL 116.142709 211.76037 \r\nL 116.203588 214.756364 \r\nL 116.234027 214.756364 \r\nL 116.264467 213.478352 \r\nL 116.294906 214.756364 \r\nL 116.325346 184.670721 \r\nL 116.386225 214.756364 \r\nL 116.416664 214.044029 \r\nL 116.447103 214.756364 \r\nL 116.477543 196.047117 \r\nL 116.538422 212.786969 \r\nL 116.5993 210.964232 \r\nL 116.690619 214.756364 \r\nL 116.812376 200.404926 \r\nL 116.903694 214.756364 \r\nL 116.934134 196.403284 \r\nL 116.995013 214.756364 \r\nL 117.055891 214.756364 \r\nL 117.086331 213.939274 \r\nL 117.11677 199.650689 \r\nL 117.177649 212.158439 \r\nL 117.208089 214.756364 \r\nL 117.238528 213.939274 \r\nL 117.268967 205.684579 \r\nL 117.329846 214.756364 \r\nL 117.360286 214.484001 \r\nL 117.421164 195.774754 \r\nL 117.451604 210.000486 \r\nL 117.512483 214.756364 \r\nL 117.542922 176.709339 \r\nL 117.63424 184.64977 \r\nL 117.725558 183.874583 \r\nL 117.755998 214.756364 \r\nL 117.786437 214.756364 \r\nL 117.877756 198.959306 \r\nL 117.908195 210.566163 \r\nL 117.938634 212.766018 \r\nL 117.969074 206.522619 \r\nL 118.029953 214.756364 \r\nL 118.060392 214.756364 \r\nL 118.18215 199.399277 \r\nL 118.273468 214.756364 \r\nL 118.303907 205.035098 \r\nL 118.364786 214.756364 \r\nL 118.456104 214.232589 \r\nL 118.486544 214.756364 \r\nL 118.516983 209.476711 \r\nL 118.577862 214.756364 \r\nL 118.608301 213.352646 \r\nL 118.66918 214.735413 \r\nL 118.69962 186.786773 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200.090661 \r\nL 122.595864 211.927978 \r\nL 122.687182 211.383252 \r\nL 122.717621 214.756364 \r\nL 122.7785 214.756364 \r\nL 122.80894 210.335702 \r\nL 122.900258 212.493655 \r\nL 122.930697 214.756364 \r\nL 123.022015 183.958387 \r\nL 123.052455 208.533915 \r\nL 123.082894 214.756364 \r\nL 123.113334 201.703888 \r\nL 123.143773 210.482359 \r\nL 123.174212 210.419506 \r\nL 123.265531 200.48873 \r\nL 123.29597 214.756364 \r\nL 123.326409 214.735413 \r\nL 123.356849 187.017234 \r\nL 123.417728 214.756364 \r\nL 123.569925 191.291239 \r\nL 123.691682 214.756364 \r\nL 123.722122 214.735413 \r\nL 123.783001 205.579824 \r\nL 123.81344 189.279942 \r\nL 123.874319 214.735413 \r\nL 123.935198 214.735413 \r\nL 123.996076 214.756364 \r\nL 124.026516 206.669276 \r\nL 124.148273 214.756364 \r\nL 124.178713 214.756364 \r\nL 124.239592 209.581466 \r\nL 124.270031 211.96988 \r\nL 124.300471 214.023078 \r\nL 124.33091 201.201064 \r\nL 124.422228 208.471062 \r\nL 124.513546 214.756364 \r\nL 124.543986 212.326047 \r\nL 124.604865 199.818297 \r\nL 124.635304 207.360659 \r\nL 124.726622 214.756364 \r\nL 124.787501 174.467582 \r\nL 124.81794 214.756364 \r\nL 124.909259 214.735413 \r\nL 124.939698 211.173742 \r\nL 124.970137 186.283949 \r\nL 125.031016 211.907027 \r\nL 125.091895 214.756364 \r\nL 125.152774 214.735413 \r\nL 125.274532 173.252424 \r\nL 125.36585 214.756364 \r\nL 125.396289 214.127834 \r\nL 125.426729 214.735413 \r\nL 125.457168 198.267923 \r\nL 125.518047 214.106882 \r\nL 125.548486 210.775673 \r\nL 125.609365 214.756364 \r\nL 125.639804 211.006134 \r\nL 125.731123 214.735413 \r\nL 125.761562 214.421148 \r\nL 125.822441 214.735413 \r\nL 125.913759 173.399081 \r\nL 125.974638 214.756364 \r\nL 126.035517 206.648325 \r\nL 126.065956 206.585472 \r\nL 126.187714 214.756364 \r\nL 126.248593 214.756364 \r\nL 126.309471 213.562156 \r\nL 126.339911 205.852187 \r\nL 126.37035 214.735413 \r\nL 126.40079 212.996479 \r\nL 126.431229 214.756364 \r\nL 126.461668 214.588756 \r\nL 126.522547 198.41458 \r\nL 126.552987 213.059332 \r\nL 126.705184 214.756364 \r\nL 126.735623 192.192132 \r\nL 126.796502 213.750715 \r\nL 126.91826 214.756364 \r\nL 126.979138 210.021437 \r\nL 127.009578 214.756364 \r\nL 127.040017 214.756364 \r\nL 127.131335 210.461408 \r\nL 127.192214 207.402561 \r\nL 127.253093 214.756364 \r\nL 127.283532 210.377604 \r\nL 127.344411 214.735413 \r\nL 127.374851 214.756364 \r\nL 127.40529 214.46305 \r\nL 127.466169 214.756364 \r\nL 127.527048 199.524983 \r\nL 127.557487 214.735413 \r\nL 127.587927 211.404203 \r\nL 127.618366 177.673086 \r\nL 127.648805 214.756364 \r\nL 127.679245 184.251701 \r\nL 127.801002 214.756364 \r\nL 127.861881 214.756364 \r\nL 127.953199 207.612071 \r\nL 128.014078 214.756364 \r\nL 128.044518 204.532273 \r\nL 128.074957 198.309825 \r\nL 128.105396 214.756364 \r\nL 128.166275 214.756364 \r\nL 128.257593 210.105241 \r\nL 128.348912 214.756364 \r\nL 128.501109 186.430606 \r\nL 128.531548 210.314751 \r\nL 128.592427 206.459766 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214.693511 \r\nL 130.236155 214.735413 \r\nL 130.297034 204.972245 \r\nL 130.388352 214.756364 \r\nL 130.449231 190.620806 \r\nL 130.47967 213.331695 \r\nL 130.540549 214.756364 \r\nL 130.601428 214.358295 \r\nL 130.692746 203.547576 \r\nL 130.753625 197.953658 \r\nL 130.814504 214.756364 \r\nL 130.844943 210.649967 \r\nL 130.905822 211.425154 \r\nL 130.936261 214.756364 \r\nL 130.99714 214.546854 \r\nL 131.058019 203.945645 \r\nL 131.088458 213.939274 \r\nL 131.149337 214.756364 \r\nL 131.179777 207.653973 \r\nL 131.240655 214.756364 \r\nL 131.51461 214.756364 \r\nL 131.54505 182.827033 \r\nL 131.605928 214.106882 \r\nL 131.636368 214.756364 \r\nL 131.758125 205.998844 \r\nL 131.788565 214.756364 \r\nL 131.819004 200.111612 \r\nL 131.849444 213.813568 \r\nL 131.879883 194.643399 \r\nL 131.910322 214.756364 \r\nL 131.940762 212.849822 \r\nL 131.971201 214.756364 \r\nL 132.092959 153.16041 \r\nL 132.184277 214.756364 \r\nL 132.214716 191.752161 \r\nL 132.306035 214.756364 \r\nL 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154.952954 214.756364 \r\nL 155.074712 200.823946 \r\nL 155.16603 214.756364 \r\nL 155.196469 214.735413 \r\nL 155.257348 214.735413 \r\nL 155.379106 197.869854 \r\nL 155.500863 214.756364 \r\nL 155.531303 209.141494 \r\nL 155.622621 211.634664 \r\nL 155.65306 209.204347 \r\nL 155.6835 214.756364 \r\nL 155.713939 211.383252 \r\nL 155.744379 213.813568 \r\nL 155.774818 195.167174 \r\nL 155.805257 214.756364 \r\nL 155.835697 213.729764 \r\nL 155.866136 214.756364 \r\nL 156.018333 198.770747 \r\nL 156.048773 210.398555 \r\nL 156.079212 135.854881 \r\nL 156.109651 214.756364 \r\nL 156.140091 195.984264 \r\nL 156.20097 214.756364 \r\nL 156.231409 210.754722 \r\nL 156.261848 183.706975 \r\nL 156.322727 214.756364 \r\nL 156.353167 214.756364 \r\nL 156.474924 196.466137 \r\nL 156.627121 214.756364 \r\nL 156.657561 214.756364 \r\nL 156.688 192.485446 \r\nL 156.748879 214.735413 \r\nL 156.779318 213.520254 \r\nL 156.840197 214.735413 \r\nL 156.870637 214.735413 \r\nL 156.901076 213.289793 \r\nL 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162.83676 214.756364 \r\nL 162.897639 198.016511 \r\nL 162.988958 214.756364 \r\nL 163.019397 209.392906 \r\nL 163.049836 209.351004 \r\nL 163.080276 211.508958 \r\nL 163.141155 204.951294 \r\nL 163.171594 214.735413 \r\nL 163.232473 214.756364 \r\nL 163.262912 211.299448 \r\nL 163.323791 210.649967 \r\nL 163.35423 214.756364 \r\nL 163.415109 201.641035 \r\nL 163.445549 214.735413 \r\nL 163.506427 214.756364 \r\nL 163.536867 213.729764 \r\nL 163.597746 214.756364 \r\nL 163.628185 214.756364 \r\nL 163.719503 191.815014 \r\nL 163.780382 214.756364 \r\nL 163.841261 214.023078 \r\nL 163.8717 202.688585 \r\nL 163.932579 208.743425 \r\nL 163.963019 214.756364 \r\nL 164.023897 212.912675 \r\nL 164.054337 202.939997 \r\nL 164.084776 214.756364 \r\nL 164.145655 205.097951 \r\nL 164.206534 214.756364 \r\nL 164.236973 202.541928 \r\nL 164.267413 189.971325 \r\nL 164.297852 214.756364 \r\nL 164.328291 205.852187 \r\nL 164.38917 214.756364 \r\nL 164.480489 212.870773 \r\nL 164.510928 212.59841 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168.589809 199.88115 \r\nL 168.650687 214.756364 \r\nL 168.772445 205.056049 \r\nL 168.833324 207.255904 \r\nL 168.955081 214.756364 \r\nL 169.076839 204.720832 \r\nL 169.168157 214.756364 \r\nL 169.198597 212.912675 \r\nL 169.289915 214.756364 \r\nL 169.320354 214.735413 \r\nL 169.442112 194.287232 \r\nL 169.56387 214.756364 \r\nL 169.685627 181.884238 \r\nL 169.807385 214.756364 \r\nL 169.837824 200.048758 \r\nL 169.898703 208.072993 \r\nL 169.959582 214.756364 \r\nL 169.990021 207.695875 \r\nL 170.020461 210.189045 \r\nL 170.111779 214.756364 \r\nL 170.142218 187.059136 \r\nL 170.203097 213.059332 \r\nL 170.233537 210.796624 \r\nL 170.263976 213.331695 \r\nL 170.324855 211.404203 \r\nL 170.385734 214.756364 \r\nL 170.446612 214.756364 \r\nL 170.477052 210.733771 \r\nL 170.537931 214.735413 \r\nL 170.629249 214.756364 \r\nL 170.659688 204.406567 \r\nL 170.720567 208.21965 \r\nL 170.842325 214.756364 \r\nL 170.933643 196.298529 \r\nL 170.964082 211.446105 \r\nL 170.994522 214.756364 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178.695692 214.756364 \r\nL 178.726131 205.077 \r\nL 178.78701 214.693511 \r\nL 178.847889 214.756364 \r\nL 178.878328 214.421148 \r\nL 178.908768 214.714462 \r\nL 178.939207 211.844174 \r\nL 178.969647 193.051123 \r\nL 179.030525 214.756364 \r\nL 179.060965 207.255904 \r\nL 179.121844 214.756364 \r\nL 179.213162 211.027085 \r\nL 179.274041 192.548299 \r\nL 179.30448 213.750715 \r\nL 179.334919 214.756364 \r\nL 179.426238 214.358295 \r\nL 179.456677 214.756364 \r\nL 179.487116 214.609707 \r\nL 179.547995 189.887521 \r\nL 179.578435 214.756364 \r\nL 179.639313 214.756364 \r\nL 179.669753 214.504952 \r\nL 179.700192 205.349363 \r\nL 179.730632 214.756364 \r\nL 179.761071 214.756364 \r\nL 179.882829 200.237318 \r\nL 179.913268 214.756364 \r\nL 180.004586 206.564521 \r\nL 180.065465 205.097951 \r\nL 180.095905 195.062419 \r\nL 180.126344 208.994837 \r\nL 180.156783 203.715184 \r\nL 180.278541 214.756364 \r\nL 180.400299 206.145501 \r\nL 180.430738 214.756364 \r\nL 180.461177 208.114895 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200.551583 \r\nL 184.266103 214.756364 \r\nL 184.296543 214.756364 \r\nL 184.387861 203.191409 \r\nL 184.509619 214.756364 \r\nL 184.540058 208.994837 \r\nL 184.600937 214.756364 \r\nL 184.631376 214.756364 \r\nL 184.661816 195.523342 \r\nL 184.722695 214.756364 \r\nL 184.783573 214.756364 \r\nL 184.93577 200.404926 \r\nL 184.96621 214.735413 \r\nL 185.057528 213.541205 \r\nL 185.118407 214.044029 \r\nL 185.240164 192.275936 \r\nL 185.331483 187.373401 \r\nL 185.361922 214.756364 \r\nL 185.392362 203.882792 \r\nL 185.45324 213.981176 \r\nL 185.48368 214.756364 \r\nL 185.544559 180.103402 \r\nL 185.574998 214.756364 \r\nL 185.605437 214.756364 \r\nL 185.757634 190.599855 \r\nL 185.788074 192.171181 \r\nL 185.818513 181.695679 \r\nL 185.909831 214.756364 \r\nL 185.940271 203.149507 \r\nL 185.97071 214.756364 \r\nL 186.031589 202.16481 \r\nL 186.062028 213.22694 \r\nL 186.092468 195.271929 \r\nL 186.153347 207.130198 \r\nL 186.214226 214.756364 \r\nL 186.244665 207.402561 \r\nL 186.305544 213.981176 \r\nL 186.396862 211.236595 \r\nL 186.427301 207.758728 \r\nL 186.457741 214.756364 \r\nL 186.48818 212.661263 \r\nL 186.51862 210.733771 \r\nL 186.609938 214.756364 \r\nL 186.640377 168.035624 \r\nL 186.701256 210.461408 \r\nL 186.762135 208.303454 \r\nL 186.792574 195.942362 \r\nL 186.823014 214.756364 \r\nL 186.853453 198.184119 \r\nL 186.975211 214.756364 \r\nL 187.00565 191.668357 \r\nL 187.066529 209.476711 \r\nL 187.096968 214.756364 \r\nL 187.188287 170.486891 \r\nL 187.310044 214.756364 \r\nL 187.340484 214.756364 \r\nL 187.431802 200.928701 \r\nL 187.462241 214.316393 \r\nL 187.492681 199.294522 \r\nL 187.553559 208.84818 \r\nL 187.614438 210.901379 \r\nL 187.644878 194.978615 \r\nL 187.705756 212.80792 \r\nL 187.736196 210.042388 \r\nL 187.857954 183.853632 \r\nL 187.827514 210.92233 \r\nL 187.888393 186.996283 \r\nL 187.949272 214.756364 \r\nL 187.979711 214.735413 \r\nL 188.010151 192.024524 \r\nL 188.04059 214.756364 \r\nL 188.071029 214.756364 \r\nL 188.162348 206.648325 \r\nL 188.192787 164.432051 \r\nL 188.253666 208.701523 \r\nL 188.284105 207.653973 \r\nL 188.314545 213.897372 \r\nL 188.344984 202.332418 \r\nL 188.405863 210.000486 \r\nL 188.436302 214.756364 \r\nL 188.466742 190.055129 \r\nL 188.52762 214.756364 \r\nL 188.55806 214.044029 \r\nL 188.588499 214.756364 \r\nL 188.710257 204.993196 \r\nL 188.801575 214.756364 \r\nL 188.832015 214.735413 \r\nL 188.862454 214.567805 \r\nL 188.892893 199.105963 \r\nL 188.984212 206.585472 \r\nL 189.136409 214.756364 \r\nL 189.166848 164.369198 \r\nL 189.227727 214.756364 \r\nL 189.319045 200.048758 \r\nL 189.440803 214.756364 \r\nL 189.471242 214.023078 \r\nL 189.501682 204.322763 \r\nL 189.56256 212.724116 \r\nL 189.623439 214.756364 \r\nL 189.653879 194.475791 \r\nL 189.714757 214.756364 \r\nL 189.836515 194.203428 \r\nL 189.866954 205.894089 \r\nL 189.927833 194.894811 \r\nL 189.958273 214.756364 \r\nL 189.988712 193.931065 \r\nL 190.049591 214.274491 \r\nL 190.08003 214.756364 \r\nL 190.11047 202.772389 \r\nL 190.201788 207.59112 \r\nL 190.323546 214.756364 \r\nL 190.353985 214.421148 \r\nL 190.384424 214.756364 \r\nL 190.414864 197.953658 \r\nL 190.475743 209.183396 \r\nL 190.62794 214.756364 \r\nL 190.658379 214.756364 \r\nL 190.688818 189.824668 \r\nL 190.749697 214.714462 \r\nL 190.810576 214.756364 \r\nL 190.932334 201.578182 \r\nL 191.023652 214.756364 \r\nL 191.084531 211.802272 \r\nL 191.175849 214.756364 \r\nL 191.236728 206.836884 \r\nL 191.267167 213.771666 \r\nL 191.328046 214.756364 \r\nL 191.419364 208.680572 \r\nL 191.480243 214.756364 \r\nL 191.541122 214.735413 \r\nL 191.571561 214.756364 \r\nL 191.63244 180.962393 \r\nL 191.662879 212.3889 \r\nL 191.693319 214.756364 \r\nL 191.815077 180.627177 \r\nL 191.936834 214.756364 \r\nL 191.967274 214.756364 \r\nL 191.997713 214.190687 \r\nL 192.028152 179.831039 \r\nL 192.058592 214.756364 \r\nL 192.089031 214.756364 \r\nL 192.14991 210.2938 \r\nL 192.180349 213.331695 \r\nL 192.271668 214.756364 \r\nL 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194.311108 208.931984 \r\nL 194.341547 208.177748 \r\nL 194.463305 214.756364 \r\nL 194.493744 194.203428 \r\nL 194.585063 195.586195 \r\nL 194.73726 214.756364 \r\nL 194.767699 212.053684 \r\nL 194.828578 214.756364 \r\nL 194.859017 214.756364 \r\nL 194.919896 191.144582 \r\nL 194.950335 195.020517 \r\nL 194.980775 214.756364 \r\nL 195.072093 202.479075 \r\nL 195.193851 214.756364 \r\nL 195.315608 201.515329 \r\nL 195.376487 201.431525 \r\nL 195.467805 214.756364 \r\nL 195.589563 203.966596 \r\nL 195.650442 196.382333 \r\nL 195.74176 214.756364 \r\nL 195.833078 206.606423 \r\nL 195.863518 213.562156 \r\nL 195.893957 214.756364 \r\nL 195.924397 213.43645 \r\nL 195.954836 211.425154 \r\nL 195.985275 212.640312 \r\nL 196.015715 194.685301 \r\nL 196.076594 214.756364 \r\nL 196.167912 214.756364 \r\nL 196.25923 192.736858 \r\nL 196.289669 206.711178 \r\nL 196.380988 202.79334 \r\nL 196.411427 214.756364 \r\nL 196.502745 214.756364 \r\nL 196.624503 197.932707 \r\nL 196.715821 214.756364 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200.520747 199.441179 \r\nL 200.612065 203.023801 \r\nL 200.703384 214.67256 \r\nL 200.672944 201.368672 \r\nL 200.733823 208.533915 \r\nL 200.764262 205.077 \r\nL 200.794702 214.756364 \r\nL 200.825141 195.104321 \r\nL 200.855581 160.87038 \r\nL 200.88602 214.756364 \r\nL 200.916459 207.423512 \r\nL 200.977338 214.735413 \r\nL 201.038217 177.777841 \r\nL 201.129535 212.17939 \r\nL 201.159975 210.126192 \r\nL 201.251293 214.232589 \r\nL 201.281732 184.545015 \r\nL 201.40349 213.604058 \r\nL 201.464369 208.911033 \r\nL 201.494808 198.393629 \r\nL 201.525248 214.756364 \r\nL 201.555687 214.756364 \r\nL 201.586126 214.421148 \r\nL 201.738323 146.89606 \r\nL 201.829642 214.756364 \r\nL 201.860081 208.63867 \r\nL 201.89052 206.040746 \r\nL 201.92096 212.053684 \r\nL 201.951399 212.493655 \r\nL 201.981839 214.756364 \r\nL 202.012278 214.735413 \r\nL 202.042717 142.140182 \r\nL 202.103596 159.843781 \r\nL 202.164475 214.756364 \r\nL 202.225354 211.844174 \r\nL 202.255793 157.664876 \r\nL 202.286233 214.756364 \r\nL 202.316672 213.85547 \r\nL 202.377551 205.642677 \r\nL 202.40799 214.756364 \r\nL 202.43843 214.484001 \r\nL 202.468869 188.588559 \r\nL 202.499309 214.756364 \r\nL 202.529748 214.484001 \r\nL 202.651506 198.330776 \r\nL 202.681945 214.756364 \r\nL 202.742824 212.849822 \r\nL 202.773263 182.994641 \r\nL 202.834142 190.055129 \r\nL 202.92546 214.756364 \r\nL 202.9559 214.735413 \r\nL 202.986339 170.067871 \r\nL 203.047218 214.756364 \r\nL 203.077657 208.869131 \r\nL 203.168976 211.089938 \r\nL 203.229854 214.337344 \r\nL 203.260294 211.739419 \r\nL 203.321173 211.948929 \r\nL 203.351612 193.449192 \r\nL 203.382051 214.756364 \r\nL 203.47337 202.311467 \r\nL 203.503809 203.547576 \r\nL 203.534248 214.735413 \r\nL 203.564688 165.66816 \r\nL 203.656006 192.715907 \r\nL 203.686445 193.009221 \r\nL 203.716885 214.400197 \r\nL 203.747324 206.627374 \r\nL 203.777764 170.549744 \r\nL 203.808203 214.756364 \r\nL 203.838643 214.756364 \r\nL 203.869082 208.198699 \r\nL 203.929961 209.288151 \r\nL 203.99084 214.756364 \r\nL 204.021279 210.964232 \r\nL 204.082158 176.353172 \r\nL 204.112597 214.756364 \r\nL 204.173476 213.918323 \r\nL 204.203915 214.756364 \r\nL 204.234355 210.482359 \r\nL 204.295234 214.567805 \r\nL 204.356112 211.96988 \r\nL 204.386552 214.756364 \r\nL 204.447431 186.975332 \r\nL 204.47787 211.446105 \r\nL 204.508309 212.870773 \r\nL 204.538749 209.2672 \r\nL 204.569188 207.989189 \r\nL 204.599628 190.159884 \r\nL 204.660507 214.756364 \r\nL 204.690946 214.756364 \r\nL 204.721385 211.907027 \r\nL 204.782264 214.379246 \r\nL 204.812704 192.213083 \r\nL 204.843143 214.756364 \r\nL 204.904022 205.91504 \r\nL 204.934461 205.244608 \r\nL 204.99534 214.756364 \r\nL 205.056219 211.488007 \r\nL 205.086658 213.771666 \r\nL 205.117098 213.352646 \r\nL 205.147537 171.052568 \r\nL 205.208416 207.549218 \r\nL 205.238855 213.394548 \r\nL 205.299734 204.86749 \r\nL 205.360613 214.442099 \r\nL 205.391052 157.74868 \r\nL 205.421492 214.756364 \r\nL 205.451931 214.756364 \r\nL 205.51281 202.688585 \r\nL 205.543249 213.373597 \r\nL 205.604128 211.865125 \r\nL 205.634568 214.756364 \r\nL 205.665007 199.734493 \r\nL 205.725886 214.756364 \r\nL 205.756325 213.310744 \r\nL 205.786765 193.428241 \r\nL 205.847643 214.756364 \r\nL 205.878083 212.849822 \r\nL 205.908522 212.975528 \r\nL 205.969401 188.777118 \r\nL 206.03028 199.650689 \r\nL 206.091159 214.756364 \r\nL 206.152037 205.077 \r\nL 206.212916 214.756364 \r\nL 206.243356 182.76418 \r\nL 206.304235 214.756364 \r\nL 206.425992 191.354092 \r\nL 206.456432 214.756364 \r\nL 206.51731 204.427518 \r\nL 206.54775 198.142217 \r\nL 206.578189 214.756364 \r\nL 206.669507 197.031814 \r\nL 206.760826 214.756364 \r\nL 206.791265 196.256627 \r\nL 206.852144 210.943281 \r\nL 206.882583 214.756364 \r\nL 206.913023 188.588559 \r\nL 207.004341 205.28651 \r\nL 207.03478 214.756364 \r\nL 207.06522 213.373597 \r\nL 207.095659 198.435531 \r\nL 207.156538 205.768383 \r\nL 207.278296 214.756364 \r\nL 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "## Vaja\n",
+    "## Bolj realističen svet Petra in volka\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/translations/sl/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
new file mode 100644
index 000000000..378b5b57f
--- /dev/null
+++ b/translations/sl/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
@@ -0,0 +1,466 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "eadbd20d2a075efb602615ad90b1e97a",
+   "translation_date": "2025-09-06T15:13:57+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter in volk: Realistično okolje\n",
+    "\n",
+    "V naši situaciji se je Peter lahko premikal skoraj brez utrujenosti ali lakote. V bolj realističnem svetu se mora občasno usesti in spočiti, pa tudi nahraniti. Naredimo naš svet bolj realističen z uvedbo naslednjih pravil:\n",
+    "\n",
+    "1. Z gibanjem iz enega kraja v drugega Peter izgublja **energijo** in pridobiva **utrujenost**.\n",
+    "2. Peter lahko pridobi več energije z uživanjem jabolk.\n",
+    "3. Peter se lahko znebi utrujenosti z počitkom pod drevesom ali na travi (tj. ko stopi na polje z drevesom ali travo - zeleno polje).\n",
+    "4. Peter mora najti in ubiti volka.\n",
+    "5. Da bi ubil volka, mora Peter imeti določene ravni energije in utrujenosti, sicer izgubi boj.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "from rlboard import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "## Določanje stanja\n",
+    "\n",
+    "V naših novih pravilih igre moramo spremljati energijo in utrujenost v vsakem stanju plošče. Zato bomo ustvarili objekt `state`, ki bo nosil vse potrebne informacije o trenutnem stanju problema, vključno s stanjem plošče, trenutnimi ravnmi energije in utrujenosti ter ali lahko premagamo volka v končnem stanju:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class state:\n",
+    "    def __init__(self,board,energy=10,fatigue=0,init=True):\n",
+    "        self.board = board\n",
+    "        self.energy = energy\n",
+    "        self.fatigue = fatigue\n",
+    "        self.dead = False\n",
+    "        if init:\n",
+    "            self.board.random_start()\n",
+    "        self.update()\n",
+    "\n",
+    "    def at(self):\n",
+    "        return self.board.at()\n",
+    "\n",
+    "    def update(self):\n",
+    "        if self.at() == Board.Cell.water:\n",
+    "            self.dead = True\n",
+    "            return\n",
+    "        if self.at() == Board.Cell.tree:\n",
+    "            self.fatigue = 0\n",
+    "        if self.at() == Board.Cell.apple:\n",
+    "            self.energy = 10\n",
+    "\n",
+    "    def move(self,a):\n",
+    "        self.board.move(a)\n",
+    "        self.energy -= 1\n",
+    "        self.fatigue += 1\n",
+    "        self.update()\n",
+    "\n",
+    "    def is_winning(self):\n",
+    "        return self.energy > self.fatigue"
+   ]
+  },
+  {
+   "source": [
+    "Poskusimo rešiti težavo z naključnim sprehodom in preverimo, ali nam uspe:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(state):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(board,policy):\n",
+    "    n = 0 # number of steps\n",
+    "    s = state(board)\n",
+    "    while True:\n",
+    "        if s.at() == Board.Cell.wolf:\n",
+    "            if s.is_winning():\n",
+    "                return n # success!\n",
+    "            else:\n",
+    "                return -n # failure!\n",
+    "        if s.at() == Board.Cell.water:\n",
+    "            return 0 # died\n",
+    "        a = actions[policy(m)]\n",
+    "        s.move(a)\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 5, won: 1 times, drown: 94 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        elif z==0:\n",
+    "            n+=1\n",
+    "        else:\n",
+    "            s+=1\n",
+    "    print(f\"Killed by wolf = {w}, won: {s} times, drown: {n} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Funkcija nagrajevanja\n",
+    "\n",
+    "### Uvod\n",
+    "Funkcija nagrajevanja je ključni del sistema za kreiranje inteligentnih agentov. Omogoča agentu, da oceni svoje delovanje in se prilagodi za dosego optimalnih rezultatov.\n",
+    "\n",
+    "### Cilji\n",
+    "- Definirati jasne kriterije za uspešnost.\n",
+    "- Spodbujati želeno vedenje.\n",
+    "- Preprečiti neželene stranske učinke.\n",
+    "\n",
+    "### Struktura funkcije nagrajevanja\n",
+    "Funkcija nagrajevanja je običajno sestavljena iz več komponent, ki se združijo v eno samo vrednost. Te komponente lahko vključujejo:\n",
+    "- **Osnovne nagrade**: Nagrade za dosego osnovnih ciljev.\n",
+    "- **Kazni**: Kazni za neželena dejanja ali vedenje.\n",
+    "- **Bonusne nagrade**: Dodatne nagrade za preseganje pričakovanj.\n",
+    "\n",
+    "### Primer\n",
+    "Spodaj je primer funkcije nagrajevanja:\n",
+    "\n",
+    "```python\n",
+    "def reward_function(state, action):\n",
+    "    if state == \"goal_reached\":\n",
+    "        return 100  # Osnovna nagrada\n",
+    "    elif action == \"undesired_action\":\n",
+    "        return -10  # Kazen\n",
+    "    else:\n",
+    "        return 0  # Nevtralna vrednost\n",
+    "```\n",
+    "\n",
+    "### Najboljše prakse\n",
+    "- **Jasnost**: Poskrbite, da je funkcija nagrajevanja enostavna za razumevanje in implementacijo.\n",
+    "- **Ravnovesje**: Zagotovite, da nagrade in kazni niso preveč ekstremne, saj lahko to vodi do nepredvidenega vedenja.\n",
+    "- **Testiranje**: Funkcijo nagrajevanja je treba temeljito testirati v različnih scenarijih.\n",
+    "\n",
+    "### Pogoste napake\n",
+    "- **Preveč kompleksna funkcija**: Kompleksne funkcije nagrajevanja lahko otežijo učenje.\n",
+    "- **Nejasni cilji**: Če cilji niso jasno definirani, agent morda ne bo deloval optimalno.\n",
+    "- **Neupoštevanje stranskih učinkov**: Funkcija nagrajevanja mora upoštevati morebitne neželene posledice.\n",
+    "\n",
+    "### Zaključek\n",
+    "Dobro zasnovana funkcija nagrajevanja je bistvenega pomena za uspeh inteligentnih agentov. Z upoštevanjem najboljših praks in izogibanjem pogostim napakam lahko ustvarite sistem, ki učinkovito dosega svoje cilje.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reward(s):\n",
+    "    r = s.energy-s.fatigue\n",
+    "    if s.at()==Board.Cell.wolf:\n",
+    "        return 100 if s.is_winning() else -100\n",
+    "    if s.at()==Board.Cell.water:\n",
+    "        return -100\n",
+    "    return r"
+   ]
+  },
+  {
+   "source": [
+    "## Algoritem Q-Learning\n",
+    "\n",
+    "Dejanski učni algoritem ostaja skoraj nespremenjen, le da namesto samo položaja na plošči uporabljamo `state`.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    s = state(m)\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = s.board.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        while True:\n",
+    "            a = random.choices(list(actions),weights=v)[0]\n",
+    "            dpos = actions[a]\n",
+    "            if s.board.is_valid(s.board.move_pos(s.board.human,dpos)):\n",
+    "                break \n",
+    "        s.move(dpos)\n",
+    "        r = reward(s)\n",
+    "        if abs(r)==100: # end of game\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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l8be//Y3a2tpW86RzI92aptHc3Myrr77KmDFjOP/885k4cSILFy5k0aJFQHb2za9+9SueeuopPvvsMy9XMpPJfGfZhryL46OPPsrll1/eIbqmuVy0bxOLxRg3bpz3OJcl/9e//pXf/va3dO7cmaeffpoLLrhgp9m6JfF4nFdffbVNYZf897z00kucfvrpbSb47giJRMIbIc6VLIs21KMqEB4wEDNchKFqLSk4ZO8VFQ01m/QtBIrrgisI9tsX0xH4XUFauKQcSDsuaVeQdrKPXVVHSafB50dxBaoj0FzQXIfe+++f12P7NoqicNlll/Hoo48yduzYvLefG2EOBoNcdtllrWKLuf1PnDgRn8+HaZqUlpZy7733EolEeOGFF3j33XfJZDIMHz6csWPHcvzxx3uft67rTJo0iVNPPdVrb8OGDTzxxBOMHj2a0tLS79WpvIvj7Nmz+e1vf9shxLF3795tfqCu63L00Ud7sZrKykpqamr41a9+RTAY5J///CePPPLIzjZXsouQSyTeciaG32+hBWyszt3QVNBULdulbrkZWnbE2RECXBCuQHddcATCdREuCMcFAWRcFCFQXIFwXBTbxnBcMq6L6ipojkBzXTTHxWc3055XmqIoHHvssUyePDnv4pirwAPZH5mjjjrqO+K4dOlSRo4cyezZs9E0jaOPPtorjnvzzTfzl7/8Bcg6Qj/72c84/PDDvTJmiUSCM844g08//fQ7+x46dKiX5rO1quK7dbd6a6iqSv/+/b3HxcXFxONx3nzzTRYsWEBJSUney+pLdh9yF1PrJQtcFFVDtWx0TUVRVVRVQVEBFRRVy94LwM0Kn+oKVNdFdQS6C8J1PeHEFQjhIhyB6pLdruV9muOiuQJNgNXYXOjTsd3kqnonEgnGjh3LP//5z1avl5SUsM8+++A4Dr/5zW+YNm2aVwU8V/Q2R1NTE1VVVYTDYfx+P9FolFtuuQXLsjj00ENZvXo1mzZt8ra/6qqreO6553jxxRe9Oo/fJq/imJsTvKvRuXNn5syZw8svv8x5551HR16qVlJ4csnFkO2FmKZJOhHH6rMfqZpqrN57oakKqGo2k1gBR82ORguUrAgKUFxQXDcrlAI018V1QW8RR1zX20YVoDguqhBZYXQFatql8qs1dN9334Kej+0lVxBYVVUefPBBKioqWr3etWtXpkyZwoEHHgjgeYO6rvPxxx/z1ltvedu+9dZbzJw5k3POOceLH95+++0AfPnll/ziF79oJY5CCP74xz9689Lbrdit67qcddZZjB49moULFzJ+/Ph2nZPZXpxxxhlSGCX/kVzuXTKZ9KpSB4siRI4+CTeewAgVg2Wj+G2wbFwrgOuzyfgCOD4bYdngs71tNDuA4rdR/QFUv41q2+i2jW4HMewAZu4+EMAMBDEDQXyBIIZlko42c8xPztspx53vlKHcSHMmk6Fz587fyeldsmQJl112GbW1tfh8PlzXJRwOU1VVxcSJE1m+fLm3bTKZ5O6772b58uX4fD6ampowDINNmzYxduzY79SlnDhxIoFAoFU9x28fX148x+rqaurr6/nZz36Gruu88MIL1NTUUF5eno/mJZIORS4/L5dKUlRURH1DA10OGMySV6Zj19cQ7NoTRAaBiqsIUBQvNugiEDnfwc36k6oQ2dxHV6AIgStAcQUZQUv3W6DhojqgC4jHYrz96DQ6DxhIqFOndj3evn37csQRR/D0009z4YUX5q3dUChEfX29V4D20ksvJZPJcMcdd3i5i59//jlDhgzx5kPnphPW1dV9p72lS5dy3HHHeUtV5OZlb7mtpmlceuml/PrXv6aoqMhb7sK27e/EHfMijlOnTuXaa6/11mTo06cP999/P7feems+mt/jMAxDrrHcgcmVLANalSzTu/Sg84gLWfvQHfQdNY5ARV9EKgEoCAQofNO1Ft94YmYghAsosRhKKoXrQia7QXYfrkBxQRPZ7nVDdRVzpj9FjwEDufSxx9vsEub7eE3TzPuSCY2NjZSVlVFfX09xcTGZTIaJEyeSyWSYMmWKd47bEsKt0dDQsNXXFEVh3Lhx3HDDDQSDQerq6lBVFcuyiMVi+Hy+VgKZl271pEmTGDduHDU1NVRWVnLppZfKaW87gG3b3HzzzYU2Q7IVchVjvl2yTAhB+TEnsfeEm/hq+kM0rvoSX/cKfD16I6wAwgqAFQB/AH/fAdh774seirB5/jy+nvMuDWtX4y/vQqB7T6zSMvRAAN1vY9g2vlAQK5z1dN598gnCPXsy7k8PYJpmh8gM2R5s2/ZWa4xGo94o88SJE/OWfjVx4kTvb1VVmTRpkucx5iqBK4ryHWGEPHmOqqry0ksvMW/ePO/vXfUDk0j+E7mSZbkagbmS/kVFRTQ0NFB21Ik4oQjK+tUsmXIDhj/A/jffB6qBoqk41ZV8NulqHEXFTbi8svQzugwawvFHHsOCaVOINTUS7LkX/Uech684gptJ8/k7b/Pxm//ALCrmrFsmE+zSjeJOnWhoaGjXRbbak3Q67Y1W52KKuTVjcvzyl79k5MiRnHDCCds8jnHPPfcwatQofD4ft912G67rMnXqVK6++mosy/JWPsxVB/o2eRFHRVEYOnQogwcP9uqmSSS7K4FAwJuXm1tUKxwOE4vFiEQiRKNROh00jIaKfdjvuNOI19ewaNoUEiuWYnXtzoBfTaLvHY96BVfVu26hWlWoOPundD1lFMFgkFQ6zbJXXuCj555EMSwGnH4uFz7+V4pLS3Fa8itz+90ZVeIjkQhr1qwhmUx+ZybJ9vJNGtQ3qVEAI0aM8Ao89+7d26uEv611K/v164dlWfTr18/b30svveSVABRCeKGNtgab8npWZSl/yZ5ArtQWZEdccwMLubL/xcXFvLnwTSqbKkklUoSsIPHB/UkO2AuEwqy3n6VX554cN+w4yrp0pcvgbqx7/1PWrV1Bv/4HUFNTQyAQoN9pZzJg5LneFLtcFRvLsqiurqasrIzq6mpCoRChUKjdjrehoYHKykr+9re/UVdXx4EHHsiIESN2uN3cqL+u68TjcW+2zJaxTdM0SSaTOI6zze2//PLL/OAHP2jlieamfOY81Jw32tZSE1LNJJJtJBfAz/WSotEotm2TTCZ4Z8UMXvvsH8xb8j6VNZso95cS0sMEtSBuyoUMLP3ic3409ETmzJ9Dp8MU/jHrNfpXabz94h+JjP09kZJOrYqy5mJxDQ0N+P1+HMchEomQSCQIh8N5nzv+bTZt2sRTTz3lDbjefffdeRHH3EqAufVcMpkMqVSqVc8zHo9vd/zxscceI5FIcNxxx3nPOY5DY2MjkUiEpqYmFEXBsixvWYYtw4FSHCWSbSSX+J2bu28YBpUNlVz41MUsX/8F9Q0NiJSLmlawiwKUBkoIGWFs1Y/fsDmo4kAszWR29fMYf7d48OpnqF62gDefvZMvPxrK/oeNwOcPessF5C7YnAe5Zam03P2W5DPe77ou1113HU8//TRTpkzh7LPPZsCAAUydOpUJEybsUNu5EWPbtqmtrfUqc285W8UwDEaOHLldniNkl0s4cYuiHLquU1ZWRiwWa5XKkxuY2RIpjhLJNrJlaX5XuKyqXckZj4wg2hwjbIXp7O9MULHJJB0qqzay5quvOGXoqfQq683mzZsI9zZ5a+4bxI0M08ZMY69u/bAzCQYccDDvvXI7X3z0AidedA+RThXeWie5tU9ygwi5WSC5i3tL8pmsrSgKd955J5dccgmPPfYYy5cv5/7772f27Nk73HZTU5O3EmBJSYnnOW4p9ldffXWr9+y///4EAgEWLFjwneNWVZWDDjoIIYRXnHfTpk1cfPHF3jaZTIaqqioikQgNDQ2e59hWKo8UR4lkG7Ftm6qqKlxcljQu4Zd/+yXN8Wb27bQvmtBwUw62ahMIB9i3ywC+rvya6rpqAmGDSlbyWfMGSvr15LMvvyTpZpcISHTqxzm/mMZHb97Hovce5b2/Xc3eB53A3kN/Skb3oWkaNTU1lJWVEY1GKS0tpaqq6j9WltlRFEWhtLSUoUOH8sgjj+A4DqNHj87LPv1+P7FYDF3XiUajXpvnn38+77//fqsBmG7dunHRRRcxcuRIKioquO+++xBC4DiOJ6aGYfDzn/+c5uZmpk+fzjPPPMOKFStaHcsll1ziVQDPZRwAbaZESXGUSLaRXGn+ZZu/4PIXL2P5ppUYGZ0VmeWIFLgpl9vPu4MPPptHr/JefLV+Dd2HOSxbuwBfpyBavIRq0cB5J47isP0Po7m5mZKSEpLJJMNOHM+aj58gvnkBi97+kH4HnYsVyK6vXFJSQjQaRdd1amtrW3VL23ORrdLSUiZNmsSSJUvo3bt33tawzmQyXkpNLkzhOA5jxozhmmuu8cTRNE2mT5/O4MGDMU2TeDzOzTff7MUncwtxua6L67qUlpYyYcIETjnlFE466SQvMVzTNC655BJvSdhc+7k1x7+NFEeJZBuxLIvP1y3j3L+dS7FZzLH9jsFWbOLROLbuI+wL8pe3/8yyyiUcNLw7JV1K+DpaR3HncnTVpHdpGYlUiut/eANuwiVQEqC+vj6b86dYXDjpC6LRKIauIRTLE4H6+npKSkq+mbLYMrNkZ+UUDxw4MK/tpdNpgsGgt450rmhtzpvz+/3cdNNNjB492ltOIZeTqOu6V4Riy0rfqVTKW/d60KBBfPjhh8yYMYNrrrnGG/XOrRmTi23mlmz9dukyKY4SyTaSdGL89sNf0L93GT3CPehT3Ae/FuD/3nmSjGXSpXdXyhSXfcs7MaDPAZQM6ETacamN16C5OgPLhlDu60yRHSGTyXhr0gghMAwDoZiYVnaNmkwmg9pywfp8Ps9LSqfT6LpOMpnEMIxdMhE8EAjQ3Nzs5Yrm1uVpbGzkxz/+MYcccog33zqZTHqv5RbKyuU+5rrVuUW0UqkUkUiEVCpF9+7dufDCC3EchxkzZniv5YQ5J4rtNkNGItmTcIXLVcOuoDnTwHqxhJrEWlQ1xPmn/pCgGsFQg3we+ZADtQPpGeyLKjQyjmDJivmc2Pscju99Ep2t7q0SkHPilhsBz91vWRA29/qWFe5z2+6K5DzgXDJ77oeiqKiI//3f/6W4uJjm5mZvjZjcQlx1dXVEIhFisRiWZRGPx71udW4UOjdjqbm5GV3XufDCCzn77LMJh8PU1dURDoe9OGeueO63R6ylOEp2W37xi1/kLT62JX49yAH+Y1oe/JjGaD1FRRE+3TyTubHXaIrXgU8hozaxuGkusWSUsX1uZvgPjqNrsAfFgVJv7eREIkEwGMyuQxMOk0gkKCoqIhqNevG03Eh1bttcXmWuuOuuSjAY9HI36+rqMAzDi6eGQiHq6uq8mUCKomAYBtXV1d6CXLkBndzSq7k2m5ubKS0tJRqNUlRURCaT8WK2TU1NlJaWEovFCIezsdxMJtN2Kk8ymWw1otPRWL9+PQ0NDR3axpqaGoQQHdrGaDRKKPQVllXznzcuEK6byvs5jEajrYqc7giNjY1Eo1FWr169xZMAPhqa4/RmOL2MoQhdkCtQJshetGpaQ0kr1Mcaqc++qZWNW95/X2WZ3DbV1dWeTVuyadMm6uvrO/R3saGhgfXr13uP26q6kzsH9fX133ktd8zfd562XKTr29TW1v5HG5PJJHpTU1OHXsgpdyI7so2rVq1i1iybWGxzoU3ZKp06VfG7372Xt3mx7cGbbzYzcmTH/Zw1LUHp0A94ZcYrhTZlq9gbbY5NHNtm2f+Owrp163iWZ0l9sW1zpXcm3Zq6wfjx40VHZvny5eLhhx8utBnfy0svvSS6dHm/pQBfx7wdcsgkUVtbW+hTtVVc1xVnn31lwc/T9918vhpx8OSDBR34X5c5XcTf//73Qn+c38uDDz4oipYXFfxcfd+/o646Sux6Q1wSiUSyE5DiKJFIJG0gxVEikUjaQIqjRCKRtIEUR4lEImkDKY4SiUTSBlIcJRKJpA2kOEokEkkbSHGUSCSSNtgjxfGJJ57YZSuZSCSSncMeJY7//Ode3/q/AAAgAElEQVQ/OeOMM8hkMowcOZKnn3660CZJJJIOSruKYzQaZdy4cW1W3djZZDIZVqxYwWmnncYpp5zCuHHjWLZsGclkstCmSSSSDki7iuPUqVM566yzKC4ubs/d/Fds2LCBNWvWcNJJJ3HSSScxfPhwDMNgyZIlhTZNIpF0QNpNHFetWkU6naZ///7ttYttonfv3vTr14/LLruMr7/+mquuuopUKsWQIUMKbZpEIumAtEslcCEE69atI5PJ0KdPn/bYxXZx+umnM3DgQH72s59xxRVX0LNnz0KbtFsiWkr574rrmkj+O3JLOOysxb0KQbuIYzQa5dZbb+X1119vj+a3my5dutClSxcCgQCHH364vHjbgdWrV7N06VLmzp3LRRddRN++fVst0i7ZtXFdly+//JJXX32V4cOHc9RRRxXapHajXdThtdde44QTTsA0zfZoXtKBOfPMM5k/fz7HHHMMJ598Mps3d9zq6JJtY86cOTz66KPcddddVFRU7NbCCO3kOd5555189NFH7dG0pADcdNNNrddN+R7WrVvHqlWr2HvvvXnssce49957ufvuu3fr7tfuTmVlJddddx0DBgygZ8+ePPLII3tEryvv4njdddfxhz/8oUNfDE8//TSjR4/mqaeeKrQpuwRjxowhkUj8V9suXLiQ8ePH07VrVyZNmsSYMWPa2TpJPnAch3g8zu9+9zvGjx9PWVkZAFdeeSWbN29m6tSpdO7cmaKiogJbuvPIqzhu3LiRRCLBXnvt1aHFsWfPnmzYsKHQZuwy9OrV67/e9pBDDuGee+7hwAMP5PPPP+/w3wVJlk8++YQRI0bw5JNPcsQRR5BIJNh///2ZOnUqgwYN2iM8xW+TV3F8/fXXGTJkCD169Mhns5JdiMcff5z169fzySefcN111xXaHMl/QSaT4a233uKBBx6gtraW0aNHo2kaP/7xjznooIMKbV7ByMvPQS51Z9myZRx//PH5aFKyC9OjRw9OPfXUQpsh+S9RVZXu3buzYcMGKioqmDBhAtXV1XTq1KnQphWUHRLHRCLBhg0buOCCC1i6dCn19fXSa5RIdjFUVeWggw5iypQprFixgnPPPZfm5mb69u1baNMKyg6J41tvvcVJJ53Eb3/7W0aMGJEvmyQSyU5m0KBBrFy5krVr1/Liiy/y5JNPFtqkgrPd4tjU1MQHH3zA448/zttvv83kyZOpqKhg/vz5+bSvXfD5fFxxxRWFNkMi6XBcc801lJSUFNqMDsF2i6NlWfTt25fFixfzox/9iFNOOYWqqqoONV1wa5imydlnn11oMyQSSQdmu8XRMAz69u3LH/7wBz7//HN++tOfUlxc7OVHSSQSya7MDqXyHHPMMSxcuJB77rmHWbNmyemCEolktyEveY4TJ07MRzMSiUTSYdjz0t4lEonkv0CKo0QikbSBFEeJRCJpAymOEolE0gZSHCUSiaQNpDhKJBJJG0hxlEgkkjaQ4iiRSCRtIMVRIpFI2kBvaGjglVdeKbQdW6W6uppVq1Z1aBuXLFlCp05r8furCm3KVgkEKnnrrbfw+/2FNmWrOE4je+3VcT9nXY8RqAyw1yt7FdqUrRJeHWZJdEmHXppi9erVdJvdjZLPO271H7PBRNd1ndLS0kLbslXS6TR+v79D2xgIBJgwIcQ++3RcG194waS4uJhAIFBoU7ZKcbHOk0923HPY3Oxj0qShxJ+7rdCmbBWr+AsClzZ16OvFsixujdxK59LOhTZlqzyuP44eCAT4wQ9+UGhbtsqKFSuora3t0DZWVVXRuXNnDjvssEKbslVmzpzJ0KFDKS4uLrQpbSKE4JlnnunQn3NtbS2JxEdUVnZcGwEqKqo79HlcvHgxgwYNol+/foU2Zav87W9/kzFHiUQiaQspjhKJRNIGUhwlEomkDaQ4SiQSSRtIcZRIJJI2kOIokUgkbSDFUSKRSNpAiqNEIpG0gRRHyW7Ns88+SzKZLLQZkl0QKY67AEKIQpvQCiEEn376KX/96187nG05Pv74Y04//XTq6uq44IILuOeeewptkmQXQ4pjB0cIwfHHH19oM1oxbNgw7r33XtavX0/v3r1paGgotEmtcF2XNWvWcMghhzBy5Eiuv/56lixZQiwWK7Rpkl0IKY4dnHnz5jFgwIBCm+GxYMECBg8ezF133cVpp53GT3/6U+bMmVNos1oRi8V46623GDVqFOeffz7dunVj//3357333iu0aZJdCL3QBki+n5tuuolnn3220GZ4VFZW0q1bNxYtWsTcuXPp3r0769evL7RZrQgGg4wYMYKxY8eydu1afvOb35DJZLj66qsLbZpkF2K7xVEIgeu6aJqWT3skW+C6LqrasZz7U089lauvvprnn3+exsZGotEoK1euLLRZ32HYsGHcfffdXHnllYwePZrevXsX2iTJLsZ2XXnr1q3jgw8+4LLLLuPzzz+XsZx24o477uCqq64iEokU2pRWLFq0iClTpjB69GjWr19PUVFRoU36DpFIhEMPPZSioiIOOeSQDl0eS9Ix2S5xvPLKK3niiSe44oorOO+88/jkk0/ybVfeqaur26ViTl9//TWpVIqKiooOV9XZsixM08QwDPx+f4ezT7JtOI7Do48+yrx58wptyn/FCy+8sFOyJLZZHN944w2OOuoobrrpJu69914cx2Hy5MkdOpfs5ptv5sYbb2TOnDmMHj2aTZs2Fdqk/8iyZctwXZf99tuv0KZI9gBM02TGjBmMHj2a0aNHd8jUpzfffNO7fi+++GJee+21dt3fNsccDzvsMKZPn86IESO44YYbuP3223n++ecZNmyYFx+bPn06PXr08N4TCAQKFjtLJBK8++67PPDAAwSDQS699FJWrVrFySef7P36zJo1q0N1DYUQJJNJfD5foU3ZKrqe/epkMhnv747Gfffdxy9+8QtCoVChTenQaJrGRRddRH19PdXV1UA2T3Tw4MEA9OjRg+nTp3vbB4NBFEUhFotRVVXF3XffzW233YZt2+02BuE4DosXL+bII4/krLPOwnEcPvroI44++mgA/vGPf3DHHXd8533vvPPOdle/3+ZvdSQSoVevXowfP57TTz+dNWvW8MEHHzBw4EBvm9GjR7Nhwwbv8a9//Wts26Zv376tRHNn8PjjjzN27Fh0XefCCy9k6dKl3HDDDXz44Yded7CjdQsbGxu55557ePvttwttylY5+uijmTlzJu+++26Hy8PMUVtby0EHHdThBrU6KpFIxItv9+3bl7POOguA9evXc/rppwNgGAbXX389qqpyww03UFJSwnXXXcfAgQN57rnnGD58eLvY9uWXX1JTU8PFF1/MJZdcwpdffkk0GmXWrFkAnHTSSXz00Uffed+OXNvb9ZM/efJkmpubeemllzzjtuSpp57y/hZCcNtttxGLxTjllFN2ujhefvnlHH744dTX13PvvfcyZswYHnnkkQ4/yi6E6HCivSU52zrqDBnJjqEoivcZ9+rVi5kzZwKQSqW45ZZbeP/99+nevTuDBg1i48aNPPTQQ7z99tsMGTIEwzDybs++++5LeXk5EyZM4I9//CPXXnst++23H7/5zW/yvq8c290fCgaDXHTRRf9xO0VRuPHGG7d3N3nh3nvvZeXKlUyfPp3bb7+dbt26FdSe/8S1117L7bffXmgzJJLvYJomt956K6tWreLuu+9mv/32o6KiggULFtCzZ8929dLPPPNM9ttvPyZOnMiFF17IPvvs0277gj0kCfzQQw/lkEMO4YwzziAYDBbanO+lpqaGlStXMnTo0EKbIpFslT59+hCPx5k6dSpXXXUVd911F88++2y79sgqKiqoqKjwUrTau2e1R4gjZAcQOrowrlq1iosvvpi//OUvHbpLLZFANp5fX1/PQw89xPLly3fafndW3q+MVHcAHMdh4sSJPP7448TjcaZMmSJjeZJdgkgkwnXXXVdoM9oFKY4dgFQqxYwZMygpKWHMmDHMnTuXTCZTaLP+I5deeinDhg0rtBlb5fLLL+eQQw4ptBmSXRQpjh2AK664gscff5yePXtSUVHBn//8ZyZMmFBos/4j3bp1IxwOF9qMrdKtWzeZ4yjZbvaYmGNH5qGHHqJ///489thjpNNpzjvvvA5ZzEEi2ZOQ4tgB0DSNO++8kw8++ABd17nrrrsKbZJEsscjxbEDoKoq55xzDs3NzSiKQiAQKLRJEskejxTHDkRHTzWSSPYk5ICMRCKRtIEUR4lEImkDKY4SiUTSBlIcJRKJpA2kOEokEkkbSHGUSCSSNpDiKJFIJG0gxVEikUjaQBk1apQYNGhQoe3YKtFolKqqKioqKgptylbZuHEjPp+PkpKSQpuyVb744gv69OnTLiXs88Wnn37KgQceWGgztko6nWbOnFXU1fUvtClbxbJqGTw4SdeuXQttylZZvXo1nTp16tAzwRYtWoS+cWM5f//7ZYW2ZauEw1/xu9/N5YILLii0KVvlzTffpLy8nIMPPrjQpmyV+++/n9GjR3eoVRa/zaWX/pZbbum430Wfr54BV5/H4l8+W2hTtkrnDztzRe0dnHjiiYU2Zas8+eSTHHnkkR3a4fnd736HLoRGMtlxPZ5UqhbLsjq0V2bbNsFgsEPbaBgGkUhku5epbG+yxX079ncRwDEckiUdd432VDCFnbA79HfRsizC4XCHtlHXdRlzlEgkkraQhSfyiOu61NbWYts26XQaAJ/PhxACn89HU1MTuq4Tj8eJRCLU1tZStWwZbz8wjcbqKgAEcNj5F/De008hBLiuQPfb9DzgAJbOm4crQKBQ0rULF9xwAyU9e6IbBrque/HERCJBIBBotbymRCLZNqQ45hHHcchkMt5NVVVvuYPcawCZTIZUMsm8p/+PN+6YTCaVynYrs/9Z+/ECUqk0ritwXIELfP7uTFLpNBkXhGHgDxfx6ax3+eW0PzLo+GO9da5z+8xkMui6LsVRItlOZLc6j8TjccLhMIqiYFkWuq7jui6u6xKNRrEsC0VRCIfDvPvYQ7x1603oThpLU/DrKn5Dw6+rmMLBv8VzlqZiKi6WruHTFXwIbL9Fl4pePHnz/7D83/9GVVWSySSJRAJd14lGo3KRLolkB5DimEds26axsRFFUUgkEp73llsWNpFIoCgK8196jtn33YltaPj1b26WrmLpWTHM/q1iaQqWrmBpGj5dxdJUdEWQaqyDVJKue1Xw1M038cFrr+P3+wkEAmQyGUKhULsusC6R7O7IbnUeaWxspKSkhEQigW3bOI7jxR5jsRiBQIBkPM7at/+Bno5j6iqipS+tKEo2xojAFeAIJXvvCjLCJeOA7kIKFcUVZBIJNq9cQUmXbtjhIjYsW0ZjQwOKqmJZFrW1tZSUlKDr8iOWSLYH6VrkkXA4TFVVFaqqEovFyGQyGIaBYRgUFxcTjcVY8sxfWD/zLfxq1jMMGBoBU8t6kYaKrWv4DY2AoeLXVSxDw9J1fIaGz2jxLHUVU1PRnDRfzHmXkrJSFv7rbTYsXkwkEiGdTlNWVoamaYU+JRLJLosUxzySSCQIhUIIITBNE1VVcRwHx3GyYllfy9fz38enK9n4opEVOr+uZO+Nlpuu4tO0bJdao+Wm4NNUfJqCT1XwaWBoKqrIsOSdf+E3Td5/5e80NTaiaZqMOUokO4gUxzySSqWwLAshBJqmoaoqrusihCCVSpHcvInKue9kY4ktImjrGn5dz3qJelb8sjFH5Zv4o9YSe9SyHqO5hVAaqkaysQG/ZfLvf/yDyjVr0DSNdDotxVEi2QGkOOaRYDBIQ0ODNyCTTqc9kbQNg3fG/8wTPFtTsVu8Rdto+VtrGZwxtG8GZAwNU9eyoqhmRdHUFXRVxVQ1fCromsqy9+dw8LHHMu3SS0ilUgSDQTkgI5HsAPLqySNNTU2UlZUBWaH0+XzeoMzqf89FjTVj6Sq2oWFpGn5dwd/iGfp1FdtQWkRS8UavfZqKpSr4NAVTA59KVhQ1BUMjK5KagpJJM++5ZyguK2XN4sVUVVV5eZUSyY6yZs0avvrqq0KbsVOR4phHLMuisbERwEvlUVUVTdP46okH8GkiG1u0TC+26OUy5tJ4coMumuJ1sc0WkTR1FV1XMFUwNAVdVTA0BdPQsSwLJ5HETaZ58+GHCIfDckBGkjdmzZrFv/71r0KbsVPZbcVRCEEmk+H+++9n0aJFO8WLSqVS+P1+hBDZiestMUdFuLj1VehaNo5oB+2sOGpqS45jy8i0rmwRY2wRRLXlPtetVlUMVcVQwFQVNDWbj6UBmqqwbO5sHFxiiTiu67b7MUt2f4QQCCH2uDDNbnu0mzdvplu3bhQVFXH33XczfPjwnbLf3HS9LaftrXrqT7jVldiahm1bBIqKWkarW2KOLV1tu2Xk2qeDT1fx6SqmnhVBQ1OzN1VBa4k56qqCqamoCALhAH6/H11X+fT1N5n99PSdcrxCCFasWLFT9iUpDCtXruTdd99l9OjRhTZlp7JbiqMQgtdee42bbrqJ/v37c+edd3Lccccxe/bsdt2vaZpEo1EURSGVSuE4DqqqEt+wAd1JY+oq/lCIQFGEQGk5gdJybNvvjVRbLQMyPs9rVDBVFUNTMFQwFBVdy3qNugqaoqChoKkKPsvCHwqgCkGyuRHL59spv/Svv/46f/nLX9p9P5LCkZsCu6dNKNgtjzY3t7mxsRHTNJk8eTKZTAbTNNt1v7FYjLKyMpqamrAsi0wmQzqdxu8z0VUFnx0g1Lkbhm1jhIvQFIGzaT2K7gPTJNXUQLJ6M6CiKAKXrPfpCBVHCBwVMkKgqQqaqlHUrTPJdIaiQBBMi4BQMANhvlq+nFWfLSLe3EwgHG7XY546dSrPP/98u+5DItkehBDceOON3Hbbbdv1/t1SHAFOOOEEDjroIAKBAIsXL2bx4sV88MEH3HjjjZx44omEQqG8V6wpKipi8+bN2LZNNBrNeo2L5lMz7x2CZZ0p6rs3ps+XjRlqCroiQOmO4rPBMHAyXUn37EcyEaN+2RLIZBCKwFQFbkbFUV0MB0r23h/HNEk6Do6ik1EU0o4g6bj4i4oRhs57f53OmeMntKs4Tp06lcsvv5xQKNRu+5BsO7tzJaZYLOZNyd0S0zTx+/3e4wcffJBp06bxv//7vwwcOJCJEycyZsyYbdrXbiuOnTp1YsOGDTz00EP86U9/YsCAAQDceeedPPbYY1x77bVUVFTQp0+fvO3TcRyAVuXDktVVkE4RPmAIPr/fix3qKhiqgh6KoJCdU512XVQXVDeCVt6VtCNIu4KUcElnIOU4ZASkHJeUA81rVmN36kJa1VAdUFyB7kLPAQNpao4jWuxpD2pra6mvr+eoo46So+IdjHwm/wshWLJkCfvuu2/e2twR7rjjDubOnfud5w888EBOO+007/HChQs5//zzMU2TBx54gLlz59LY2Eh4G5yF3VYcc4VeL7/88lbPX3/99Vx99dVMnjzZS7O55pprWv3qbC+O42DbNoAXn8k4aUJ774uvpCw7sqxq6CroWnZQxVAVVBQyQoArwHVxXYHhfPNYuAJcwHFRs9UoUFzoPGAgGddFcwWaA5qbFVjNcdt1Ia2HH36YPn36oCgKQ4YMabf9SAqPEIJbbrmFjz/+uNCmAHDLLbe0+fzChQt56aWXvMcrV670ikufdtppJJNJKY7/DT6fj1tuuYWVK1eyYsUKfvKTnzBs2DBuuOGGHeqS+P1+Nm3aRCgUIplMomkalmWh+ixUfwBNBVVVUVVQVSV701QUBVQ36/mprkBzBa7joLktlcBdt0UcBQiBmy3bg9LiaSqui+oINNdFdV00V0FrB3F89dVXmTZtGhdddBHXX389p556at73IZFsD0OGDGn1Q33HHXfw3HPPMXnyZM466yxGjx5Njx49tqnNPVIcc/Tt25c+ffowfPhwZs6cyTPPPMN555233e01NDRQXl5OPB4nEAjgui5VyTROykE1LVRTR1FVUEDRFFDAVZVst1q0dIdcUFxQW7xETQhc4SAcpeV10eJBguq6KAIUx0ETkHZBdRxSGYGqG5DH2FMqlWLVqlVcfPHFDB48mM2bNyOEIJFIYFlW3vYjkeSDCRMmcOmllzJ27Fjeeeed7eoZ7tHiCNnudyQSYeTIkTvcVlFRERs3bqSoqIjm5mZM06TrMSdSP+ctVM1A89soqgKKglDBVbICCQoCEK6LIhQU10URAlUIVIesQLpZz1BxW7ZzyW7TIpIZkRVS3YG1Cz4iVFJCMI8rDW7cuJHKykpGjhzJtGnT+Pe//82DDz7I8uXLOeCAA/K2H0nHYubMmbvkNFTLsrAsixdffHG729gt8xwLRSwWIxKJANkutqqq4PMT2mcQ9csWo5d1AcsGy0b4Arg+G8e0cXw2rulH+GyEzwZ/AMWyUawAim2j+ANofhvdDqAHAhiBEEYgiGkH0QMBzGAAMxDCZ4dwga8++5ThZ5xBUXl53o6td+/e9OzZk3HjxnHVVVdx2WWXoSiKFMbdlCVLlnDllVdy0UUXsW7dOp588slCm7TT2eM9x3ySSCS8PEfTNHEch1QqRflp5/LZry5C/PMleo28CDfaiFAUXAVUsiOLguyqgiLXvRZZp1ITgOOgtKxEmBECXWR/1XIxyoxw0dzswl3vP/IQnffqy8Bjj8t7ybJzzz2Xww47jPHjx/M///M/dO3aNa/tSzoGQghWrVqFYRh069aNa6+9ljfffJPzzz9/j8pMkOKYR8LhMNXV1ViWRSwWQ1VVDMNALy5l+J//zieXncPGN16g6ylno2o6CAeRzmRVUAgUTUc1fSgiuwyCqps4jksm1ojpD+IIF9IZFMfBFVlxFChoQtBQU8Mr999LUbeeXPl/T2AHA3n/IpeVlVFWVsYzzzwjVzbcjWloaODFF1/k1ltvJRqN8sMf/pBNmzbx+uuvc/rppxfavJ2GFMc80tjYSGlpKc3Nzdi2jeu6JJNJFEUhnnY4YMojrH76Qeo++wjNb2OVdyHcf3+Ekh2Uia9fQ3TFMlw3w8aPP+WDOe+w38FDKR+wP4qqk06n8HfqTqTP3jgIhOPyxdzZNNTUkkpn6DPsMEbc8Dss2099fT2lpaXtMuWrPdOEJIUnEolwzjnncPnll/PAAw/wxz/+kRUrVnDVVVcV2rSdihTHPBIIBKivr8cwDBKJBPBNvmMwGCRlmlRc8VuqF7yPmY7TsHkTtc8/Qaq+jsgBgyk66FCU5maS8TjRvfbh/Q8/YODxp9KtX3+qli8j6PejBIv5csabrF38KcHyrth99qHL/gfSfeBAOu/d36sCXlRUtMdVUZHkj8GDB3PBBRdw2223cfjhh/OjH/2o0CbtdKQ45pFEIkE4HCYWi3mFbjOZDEIIkskklt+ivrme4JAhNDc1EzhAYcOqLykvKiUuQAlHsA45gs6RYkob6zhg40r8RUUY3XvRt98AmpubCYfDmF26sv95F5N2XEq7dQdVJZPJkEqlgOxIXUNDA6WlpVIgJdtF165dGTVqFMceeyxlZWV75PdIimMeUVtESlEUb+2YbFxOMG/VPDZFN3Hb87exofprwmqQEqsUE5Oa6mqSsTSJ5jiXj7ic4w8/jgZ7PfNW/5Nl//4HEyc9woADDkPTNFzXJVhWjs/ny6ZYtNSMzO0f8NawkTFByY7SqVOnQptQMKQ45hHLsojH4+i6TjqdRlEUvqj6klc/e5UH3nmA6tpqSAlCeog+nftiKia2atOvU19010Bx4PMVn/Ovyul0K+nBqB//ghXvvsiM5++mc5eplHbuTSqV8oRR0zRSqZQ3VTJXdSg3jVGKo0Sy/ex5vnI7Eo1GCYfDCCGwLItNsUrG/HUMf3r3T5iqQUVpBQO6DsBUTZZv+JLq2mo6RTpRHCyhe3k3bMumqeQr4o2C/zljKmeeNo599h1EfeXnvProeBbP/4c3Em6aJul0Gtu2PVGMx+PEYjE0TaOpqUlWApdIdgDpOeYRr2RZ0GZp5ef86KET8QmLrqEumBhY+AmoNv1K+6FlVN7/9H00RaNzeRnPznyCg0/pQ8OHGTY3xOnbcx8UReHokddwxA/P5OWHLmHGU1egqtBnwMHEYiq2bVNbW5tNF9J1gsEgmqbR3Ny8x8aJJJJ8Ia+ePJJOpzFMg3nr53HW9LOobq7BdRwiVhHpVAafZrJfj4GUh8rp32sARx5yGGtii3ll05MceHx/Ply2jljI4ZyTz8HQs+kyoXCETr2HctQpvyRiJ/nw+bG8/dgFGJqO4ziYpumJYDqdJpVKZetIxuNy3WqJZAeQnmMecV2XL2tXcOs7k3Ezgr6lffG5Js2JKLZpYxsB1m1ex9fR1fg67cVXjZ/TY/8SNlWqOPjo1akbB3Q+kF8ffSOAVxMSRaP7ficSKCqhqbGJkk7dEC3hRFVVvZhjzoYtH0skku1DimMeiRPjktfHUGKVcMp+JzOg0wBWff0Vz8z5K/32KcIXyLB80Ua07hm0SF+OOOIwFF2nKNCZrnYvnJTglH1HYKnZARfDMGhubqZTp074Ql3Z59DR1NbUUFRURENDg7dmjWEYGIbheZHRaJRAICAFUiLZAaQ45pGq2Bp+cvxgFFWhMvkZq8RyMhWCiyuOoMzXHRWLHhXLKLO6UeLrgq4YLNw4n5JQMT7F4CcHjaWb3ROfz+ctLVteXk40GsU0TWprarBtm6amJvx+P//f3n3HR1Hnfxx/zcz2TS8k9H4oIB4EARGQIKgc6gmiCApWVE6qggUPCx6cnAoiCNI8iqAceCAIPywIIlIPkQ4BIiXBkLJJSHaTze7O/P7YJAfHhrrJLPJ98tgHSXYy33c2ySffmfnO9+t2u8svAPl8PpxOJ+CfMj43N5eoqKjrblEkQQgW8ZsTRI2ibuZx9wQs4Sa25X5FNUtdvHIxRwt38VvJcfKKs1BsXqxWI46SNKoZawEn+OYAACAASURBVNG9YS9+zTnC4LavUeL2D/8pKChAkiTMZjM5OTnExcWVX2TJzMwkJiaGvLy8c+7IMZlM2Gw2DAZD+W2MgiBcOVEcg0hRFGJjYzEajXS1Pur/oKxSw96IXO9pNFXDgAmQKPK4sCkRNIxohlRfwaAYkcz+84dlA7hlWcZkMpVfiQbKbwuMiIhAURRiYmLKty0711i2rSAIV07q3r2X5nTW0DtHhRSlhMjIQkymGL2jVMjrPUNUlKF8/ZhQlJmZSWxsbEhPOZWWdgqDIXR/FsGHRz6FsVroTryhulTCvGGXtVZKVXM4HISFhVX6UslX49SpUxgKC2vz44+T9M5SocjIo9SqtY59+57WO0qFGjT4kk8/TaBdu3Z6R6nQuHHjeP7558sn4w1FQ4cO5cMPP9Q7RoVyc3P5+OOPefXVV/WOUqFNmzaRnZ0d0lOLzZw5kzvuuIOGDRvqHaVCw4cPLzusvhauaoZ+xmvh6nCoZiwbkxmq+c4mMgZHqGcUg8AFQRACEMVREAQhAFEcBUEQAhDFURAEIQBRHAVBEAIQxVEQBCEAURwFQRACEMXxGqZpmpiz8QKuldfnWsl5vRHF8RpVWFjI4MGDOX36tN5RQo6qqqSnpzNhwgTWrFkT0q9RdnY2O3bs4MUXXyQ9PR2Px6N3pHNomkZmZqbeMS7K5XLhcrmCuk8x8cQ1KDc3l+nTp/Poo4+SmJiod5yQk5+fT1JSElOmTGHLli0MGzaMlJQUvWMF1Lt3b5o0aULfvn3p0KEDq1atomnTpnrHAmDv3r0cPHiQr7/+mrvuuoukpCTq16+vd6xzaJrGl19+yeHDhwFo3Lgxf/7zn4Ny943oOV5jvF4v//jHP2jfvj233nqr3nFC0tSpU5k2bRp2u51HHnmEwYMHs2DBAr1jnWf16tV0796dN954g/T0dGbNmsWnn34aMgujLVy4kA0bNjBu3DiWLVvG2rVr9Y50HlVVGTFiBH/84x+5+eabGTFiRNBeP1EcryE+n4+BAwfSr18/OnfurHccZs6cybZt2/SOcZ6uXbuydu1afvrpJ44dO8YPP/zAbbfdpnes89x8883s3r2bjIwM1qxZw+bNm2nfvn1I3HO8adMm7HY7Y8eOZcSIEUyaNIm9e/dy7NgxvaOdY/DgwSxevJiioiLcbjeLFy9m8ODBQdm3OKy+RuTl5fH3v/+dF198kWbNmukdB4D09HQaNGigd4zzNGvWjE2bNnH69Gm++OILGjRoEJI5a9asidlspn379oSFhbF371769esXEsWxXbt2rFy5kmXLlrFjxw5mz55Ns2bNqFOnjt7RzjFp0iTatWtHr169kCSJL774gi1btgRl36LneA0oKChg+vTpdO/enebNm4fEL08oi4iIYOfOnTz11FNMnz6d1atX6x2pQrNnzyYuLo7333+fnTt3hsw0XrIs07ZtW7Zt20ZOTg4nT56kadOmIbfcr8lkonPnzqxbt45169bRuXPnoM0TKXqOIU7TNF577TX69OkTkoeGwtWLj4/nscce0zvGee6//37uu+8+tmzZwvTp0/WOE5Asy3zwwQd88sknSJLEE088EbR9i+IYwoqLixk8eDAvvvgiN954o95xBOG6IopjiHI4HEyZMoXnnnuOG264Qe84gnDdCa0TCALg7zFOmTKFTp060bp165A8x5iamookSSF5oaNMcnIyGzZsoLi4WO8oFZo/fz6PP/643jEqJEkSvXv3Zvny5XpHqVB2djYnTpzgj3/8Y1D3K4pjiNE0jeeff5577rmH5ORkveNU6Foojl26dGHDhg243W69o1Ro3rx5PPnkk3rHqJAkSTz44IMsW7ZM7ygVKiuOLVu2DOp+RXEMIQUFBQwbNoxRo0bRqlUrveMIwjWhso6sxDnHEJGXl8fHH3/MQw89JM4xCsJlqKxJO0RxDAE+n4/x48fzpz/9iQ4dOugd56LGjBnD7t27+e2338jMzOTdd98N6TW7BeFKiMNqnXm9Xp5++mkGDBgQErcEXozH4+Grr77iT3/6E0lJSezevRun06l3rGvS1KlTee6558QflhAliqMODh8+jMfjITc3l9dff50RI0aEzC2BFzN27FjeeecdWrZsScuWLZkxYwbDhg3TO9Y1KSMjg8TERBRF0TvKBVkslpCbjedsFouFunXrBn2/4rC6Cp06dYolS5aQkZFBfHw8xcXF3HnnnbRo0ULvaJfs7bff5sYbb2TcuHG0aNGCnj17snXrVr1jVahnz55Bu50s2G699VZq1qypd4yLqlOnDm+++abeMSpUr1493njjjaDvVxTHKpSamsr69euZNm0aLVq0oGPHjowePVrvWJdt4sSJpKamsnv3bsaPHx/Sh4XBmqGlMvTo0UPvCMIFiOJYRYqKivjwww+ZNm0a48aNY926dWzdupUFCxbQv39/veNdlu7du1NcXExJSQkRERF6xxGESiGKYxWxWCwMHTqUYcOGMXXqVH744QdWr17N0qVL9Y52RSwWCxaLRe8YglBpRHGsIpIkUbduXW6++WamTp2KyWSib9++IXlroCAIojhWqdq1a/Paa6+xd+9emjRpgtFo1DuSIAgVEMVRB82bN9c7giAIFyHGOQqCIAQgiqMgCEIAojgKgiAEIIqjIAhCAKI4CoIgBCCKoyAIQgAGSfJhMuXqnaNCRmMBiuIO6YyKUoTT6SQ3N3Qzejwe8vPz9Y5xQT6fL6Rfw/z8/PLZlEKVy+WiqKgopDO63W4KCwtDOqPP50O6664+Wk5O6I67MxqdNGuWXSlTEgVLRkYGZrOZ6OhovaNUKCUlhfr164f0wPM9e/Zw00036R2jQh6Ph2PHjtG4cWO9o1TI4XBQUlJCYmKi3lEqdOzYMfbH78dj9+gdpUJxe+KgY8dhGmgh+4iMPKzNnDlTC2XLli3TNm3apHeMC3r77bc1h8Ohd4wKqaqqDR48WO8YF5STk6ONGzdO7xgXtHHjRm358uV6x7igjz/+WIs8HKkRwv86De2kiXOOgiAIAYjbB3WmaRonTpzA6XQiSRI1atQgLCyMw4cPo6oqiqLQsGFDDIbgfKtUVcXn86EoCqqqlk98Icty+fNa6YJFsizj8/lwFxaSfzqjNC9IsoQtKopCh6N8vwaTGSSJkrPWiDZZLFSrUwdV08rbUxSlfP9lbYvJN4RQJIqjzjRN4/HHH2f9+vUoisKzzz5LixYtGDZsGG63G7vdzvHjx4mNjQ1KeyUlJeTk5BAXF4fD4cBut+Pz+QgLC0NVVdxuN263G5/PR2RkJLvWfccvXyxlyxdLUFUVNA3ZZKJR+w7s/X4tqgY+VSU8IRFkhdMnT+JTJSSDgYQG9Xlg6FBa33MvEdFRuN1uYmJiKCoqAvzTnpUVZUEINaI46iwvL6/8qp3P52PGjBnIsozH4z9Z7XQ6ycjICEpx1DQNt9tNVFQUxcXFRERE4PP5MBgMOJ1OZFlGkqTyonVk2xa+ev1Vso4exSwBigRIaD4vv/64HotBRtPAq0oUZ2fiVTUsioJHUpHNRkpcLpZNmUJuZha9XhiBxWIhLy8Pg8GApmkUFBQQGRl51V+XIFQGURx15na7/T2yUj6fD5/PV/6+JEnlPa2rJUkSRqOxvCgVFBRgtVrx+XxYrVZUVcXr9VJSUoIj7SSf/+VJCrOysBrLencSmgYqmv9/VcMH+HwaXsCrqnhVDVmVkCQNi8lIREw0O7/7lojoKDr160dkVBQlJSVomobZbA7K1yUIlUEc0+isKs+3aZqGpmmYTCa00vOAmqYhSRKqqpYXaVmWWTVmJGpuDjaDjNUgYzUo2AwyNoOE3aBgMchYjGXPyVgVsCqy/+OKguz1cCbtON7CAiJjYvj35A85tG0bPp+vPEdZ71gQQpHoOV5nyi7GeDwejEZjeXH0er3lb5/4/mtcB/dhMUilfz2l0oe/wKrgP9eoSfg08MoaiiqhqJr/IWnIPg1ZgswjKdgiIqnXrCl71q3jhrZtMZYW57ILM4IQikTP8TpSdlhdWFiI2WwuP8/o8/mwWCyYTCZUTePAiqUonmKsioLFoGA1KliNMhaD/31b6f/+HqOExSBhUWTMBhmz4n+YFAmTImFA5eTunRjR+HHpFyieEgwGAwaDgbCwML1fEkGokCiO1xFVVSkuLqZatWo4nU5iY2Px+XwYjUby8vIoKioiZfF8Tm/50V/8TAo2Y9nhtIzNKGMzSZgN/y2IlvICCVZFwmyQMcqyv1gqEkZFQfJ62Pv9t9xyR2de7Xk/7uJiPB4POTk5er8kglAhURyvI5IkoSgKLpcLo9FIUVERkiT994KMs5Ccvb9gQsVqkLAqElaDjK20t2g1yFgMBqyKfNb7/l6kRVH8BVGWsBhkjAqYZAWzLGGUZWRNJTc9nfDwCPZs2ICiKNjt9qB/jVu2bMHlcgV9v8L1RxTH60xZgdQ0rXyMYdk5x9wjh0j7ZkVpwfNfhPEXwNIeYmkv0Wr0P28u3c5/GC1jVCTMioRJljApCsayt2UwKDL7N6xH0bxs+mIpXq+3Ui7ITJo0iezs7KDvV7j+iOJ4HdE0DZ/Ph81mw+PxYLFYyq9aFxUUkLJoDhaD5O81GhQsiv9ty9nnGEsf/kNqCbMslZ5nlEoPoyVMioZJxn/OUZEwlPYejZLE6cOHiYiJZvPKFUEbonS2v//97wwfPlxc7BGumiiO15GyCzLZ2dnYbDby8/PLB5yH220UbN+AWfEfKoeFh2ErHapjM8qlb0tnFUgFa+lFGItBwWxQMCsKJoO/12hSZIyy5H8YZOxhNkxmEwVZWXz98Qx8xUXExMQE/Wts0KABAKmpqUHfd2XQNI1Dhw7pHUMIQBRHnZWN+7vYNsFQdodMXFwcTqeTqKgoVFXFaDRy6qdvMchgNshYzUas4bb/FsGzCqL/cNp/QabsyrRR9h8+l/1vUiQMMhglf6/RIEloxcVYLGb/NiYDv6xfz4lKKmBz5sxh0KBBlbLvYNM0jYcffljvGEIAojjqyOv18u9//5sTJ05UuI2mabz//vu43e6rbk+SJEwmE3l5eVitVgoKCpBlGa/Xy7GFs8sPke0xMdjCwv29SEX2X4BRzj7vKGNSOGfIjrH0YZBllLKiWFowDbKMLEFETLT/FkVVZfsX/0YrDv5hNYDdbufee+/l3//+d6Xs//coNTWVefPm6R0jpIjiWKqkpITc3Fz69u1LZmZm0K54+nw+8vLyyM7OPq+HqCgKx48fp6Cg4IL7+PnnnwNOUnvmzBmysrIu+cKGpmmUlJQQHh6O2+0uv2VQlmUUdxEmWcJiVLBFRGKLjCb6hmZEN2mKNcyK1WrEajZhNRrKD6H/e8GltDjK/mE8ZecZFRkMRiOKImM0m7CGhRFTPQFZAjSVokLnJb+Ol8NkMtGkSRP27dtXKfv/vbnrrrsYNWoUBQUFNGrUiIyMDL0jhQRRHEstX76c9u3b8/rrr9O7d29eeumlq96nx+Nh3bp1JCcnU79+fX766Seczv8WBEmSLmlWGlmWz9nO6/Wyf/9+7r//furVq8fcuXMveQmEskHfsiyX3xHjPnUC2VuC2W4jvH5j7AnVsYZHYIuKwRYdQ1hEJFEt2hDzxzbYa9XFHh2DNTzcP6axrMcoySiSf24Ko0HBHhVDWFwCtVq2onbzFjRq247ohERqNG5C/RY3YbGa+Wr69Eq7cBIbG4uqqjjOmlZNON+BAweoXr06s2bN4g9/+APVq1fnp59+Ehe0+J3fPnjw4EEWLVp0Sdvu37+fevXq8a9//YvPPvuMWbNmsXfvXpo3v7IlJDRNIzMzkzFjxrBr1y40TeOBBx5g7NixPProo1c8xk/TNDZv3szIkSPZtm0bAG+99RaxsbH07Nnzgvdqy7KMyWQiKyuL+Ph4cnNzCQ8P58S/5+N1ZFOtRRLWqCh/D1ApPSSWQGrQBMlsRUXDYGuMVwWTswBDdhYmn4rbq2JWNdw+FbdXwycbMMQl4EEi6+QJIqvXpERVUXwg+XwYbXY0xcgv36zxTxBZCZKSkli+fDm7du0iOTm5Utr4PUhNTaVBgwa4XC62bNmCw+Ggf//+7Nq1q3yb2NhYhg0bpmNKffyui2NsbCydO3e+5O1zc3Pp1KkTTqcTl8tFfHz8VbUvy3L5/csAmZmZjB07Frvdzv3333/ZBVLTNA4cOMDgwYPZs2fPOc+ZTKaLfr6qqpSUlBAbG4vL5SKqdIac4jP5RDVviTWxRvlhsVGWSv+XMRAJ+GfekVUVSVXRLFbk6Hg8qorFp1GiaXh8pQ9Vo8SroqgQWaMWis3uv9da9d9z7VE1EhraSTt2/LK+/sv1xBNPMG7cONq0aVMpA86DYeTIkUyaNEm39nv06MFf//pXTp06Rbdu3cjOzmbu3LnExcWVb+NwOLjjjjvO+bw+ffowcOBA4Pc7WfHvujjGx8fTpUuXS9q2qKiIkSNHcs8999CnTx/uvfdeEhISrrhtSZJISEjgL3/5C7t37y4/7D116hQDBw7kjTfe4Mcff7zk/RUWFrJnzx6efvpp9u/fX/5xRVHo2rUrd99990V/SGVZxmw2k5eXR0xMDPn5+dhs/iE2UmINFHsYSukFFEUGw1mFEvDPNqGCpqqoPg1NVUEDzaeiqRqSDyRVRVI1UDUkVSPCasWr+d+WfRKKz4eiqhhUKn2i2wYNGlBUVERaWhpNmjSp1Lau1M6dO3nrrbd0zbBhwwYOHDjAN998Q0pKCuHh4ed8b3w+H926dTvncxYtWkTLli2JjY1l7dq1VR25Svyui+Pl6NGjBz169OCNN95g+/btQVmWQJZlHn74YVwuFyNHjiyf1La4uJjU1FR69ep1SavEFRYWMmbMGD744IPz9v/YY48xe/bsS/rrXTYI3GKx4PV6y6cu8/lUjAYTitWOLPvPg0qyfzkEZAnt7OLon9CxtCeoIqtgUP3FEVVDKy2eWmlxLNtGUlX/jD0+DUVV8UlKlcwC/umnn9KqVSt++eWXSm/rWhUeHk6bNm1o06ZNwOcVRTlvUuJBgwZdM8OlrpQojv+jMv6KP/nkk5hMJgYMGHDOie7t27eXXxi5kNOnT/PRRx+d9/GBAwfy3nvvXdZhTVlbZ6/jEt6qPY4VnxFZqw6yLCEppUVLAU2S8Elln+v/PEmVkHwqsqYia/gPl1UNRfUfunNWz1FSNSTNXxxlVUPRQPGqHN61B8VsueTcvzf79u1jypQppKSkMHz4cDp27Mjjjz+udyzhLOJqdRXp168f8+fPR1GU8o+Vzbx9MaqqnjNcx2g08pe//IUJEyZc1rRfkiRhMBgoKirCaDSWj52M7dCVEmchlriayFY7WKxgtaGZ7fjOeqhmG5rZ5n/eZkO22pFKH4rVjmyzYrDZMFjtGG1hmOxhGO12jHY7Jrsdsz0cs82O2W4nJ+04f5n8IVIl9x4lSeKDDz7g5ZdfrtR2LoemaRw7dgyr1UrNmjX505/+xPr16y/pZ0GoOqI4VpGyQ+yPPvroqtZNkSSJBx54gPfff/+y96OqKkVFRVSvXp3CwkLi4uJQVRWzzU5M1/s4vPAjNFsYkj0cLGGoFjuqxYbP7H+oFjuaJQzNEgZW/0O22v0XXGxhGGzhGGzhGMP++zDZIzDbwzCHhWMOC8NotXFg00/kZWZhPeukf2WRJInGjRtTWFjItm3bQmLGnoKCApYuXcqLL75IUlISnTt3JikpidWrV+sdTTiLOKyuQoqiMHDgQFRV5YUXXsDtdl/2eLJ+/frxySefBBwUfjFlPUen03nelGU1ej6K+9gh0r/8jLp9nkSTZUoPwMsmAcf/Aak8s2KxYbTa8JV4KcrLQdIoPfYGRdOQNA1J9b8tqxo+TWPniuVkpqby3PQZ1Grc+LK/hisRFhZGfn4+o0eP5s4776R27dr07du3StoOJCIigj59+vDMM88wY8YMpk6dytGjRxkyZIhumYTziZ5jFZIkCUmSeOaZZ5g/fz69e/c+5zD7Qm6++WY++ugjJk+ejNFovKL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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Rezultati\n",
+    "\n",
+    "Poglejmo, ali smo bili uspešni pri treniranju Petra, da se bori proti volku!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 1, won: 9 times, drown: 90 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [
+    "Zdaj vidimo veliko manj primerov utopitve, vendar Peter še vedno ne more vedno ubiti volka. Poskusite eksperimentirati in preveriti, ali lahko izboljšate ta rezultat z igranjem s hiperparametri.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0987e7dd8>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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UsBjoSO07gCVAu6QqYDZwpKg+pHidsepmZjZBSj6iiIg7I2JxRCwluxj9eET8AfAEcEtqtgF4NE1vT/Ok5Y9HRKT6+nRX1DJgBfAr4GlgRbqLqiY9x/ZS+2tmZqUp54hiLF8Htkn6NvAs8ECqPwD8SFIb0EX2xk9E7JH0MPASMADcERGDAJK+AuwEKoEtEbHnAvTXzMxyjEtQRMQvgF+k6f1kdyyNbNMLfH6M9e8G7h6lvgPYMR59NDOz0vg3s83MLJeDwszMcjkozMwsl4PCzMxyOSjMzCyXg8LMzHI5KMzMLJeDwszMcjkoxpD9dREzM3NQmJlZLgeFmZnlclCYmVkuB4WZmeVyUJiZWS4HhZmZ5XJQmJlZLgeFmZnlKjkoJC2R9ISklyTtkfTVVJ8jaZekfelrU6pL0mZJbZKel3Rt0bY2pPb7JG0oql8n6YW0zmZJKmewZmb2/pVzRDEA/HlErARWAXdIWglsAnZHxApgd5oHuBFYkR4bgfsgCxbgLuB6so9QvWsoXFKbLxWtt7aM/pqZWQlKDoqIOBQRv07T7wIvA4uAdcDW1GwrcHOaXgc8GJkngUZJC4EbgF0R0RUR3cAuYG1a1hART0b29zQeLNqWmZlNkHG5RiFpKXAN8BQwPyIOpUVvAfPT9CLgYNFq7amWV28fpT7a82+U1CqptbOzs6yxmJnZmcoOCkmzgH8C/jQijhcvS0cCF/yv60XE/RHREhEtzc3NF/rpzMwuKWUFhaRqspD4cUT8NJXfTqeNSF8Pp3oHsKRo9cWplldfPErdzMwmUDl3PQl4AHg5Iv6qaNF2YOjOpQ3Ao0X129LdT6uAY+kU1U5gjaSmdBF7DbAzLTsuaVV6rtuKtmVmZhOkqox1Pwn8IfCCpOdS7S+A7wAPS7odOAB8IS3bAdwEtAEngS8CRESXpG8BT6d234yIrjT9ZeCHQB3wWHqYmdkEKjkoIuL/AmP9XsPqUdoHcMcY29oCbBml3gpcXWofzcysfP7NbDMzy+WgMDOzXA4KMzPL5aAwM7NcDgozM8vloDAzs1wOCjMzy+WgGMMF/wNVZmYXCQeFmZnlclCYmVkuB4WZmeVyUJiZWS4HhZmZ5XJQmJlZLgfFGArhG2TNzMBBMab/+vPfTnYXzMymBAfFGJ450D3ZXTAzmxKmfFBIWitpr6Q2SZsmuz9mZpeaKR0UkiqBe4EbgZXArZJWTmQfBgtT+1rFwGCBvoHC+1pnsBDEJXQNJuLSGu9oLobxXwx9vFSV/JnZE+TjQFtE7AeQtA1YB7w03k+0efc+Hn2u44zaVf/hMXr7z34TvuLyWWfVRn6THzrWS0//IALmN8xgRnUlFcr+hlQEvPbOCQCWzatHQHt3D32DBRY11tFxtIfFTXVUV1Yg4GTfIDNrK6nU2R9Rvu/wewDMm1XL7Lqq4b9RNdQy0j9Kz10oBK8fOQnA7zTXD2+nkNoMtR3p1c4Tw9M1lRX0DRZYPi9bv79QoL27h4UNM6itrkSCrhN9HD3Zz4KGGdRWV9A3UODQsV4AFjTMQILKCtHe3TO83eWpP719g7yZ2i6fV4+Uhduxnn7m1NecHleaCLKbDw6kcS2dOxOACgnpdN+XN2evdSGgQiCJUwODHOw63Ye59TUcOdE3vJ3uk/3MrqumQtA3UKDrZN/w98TyefVUVJz5YrWl/TE0lo7uHhrqqmmYUdqPmkbbGTkKEezvPEF1pVjUWDe8rwEuq62iqb6GqgpxaqBA57un6BssUFNZweKmOoJsnxQiOPJeH8d6+gFY1FhHbXXF8Is+UAj6BgrU11ZSCHi3d4BZtZWcGijQ2z9IVWUFM2sqqa48+/+hEcGrnSe4bEYV82bV0j9YoLJCw/uuvqaSGdWVw/sAsp+RUh3v6efIiT5m1lTSN1BgoBDMrqumb6BAT//gcLs59TXMrqtGguM9A0DwzntZHySYXVdNY101kqgs2ucDg9l2evsLzK2vQYI3j/ayYPYM3u0d4J33TgHZz5okIrLv41MDBebNqh3e1pH3TvFu7wAfnDuT4z39w889ZOHsGcysqSTIfrbf6DrJ5ZdlP1uQ/Vyf7Btk863XsGr53JJfr7FM9aBYBBwsmm8Hrh/ZSNJGYCPABz/4wZKeaH5DLVctaOCNrpP0DwZz62uoqhS9/aeQsh+W9u4eFjXW8eH5sxCj/AAXlWZUV7LnzeME2ZvGrNoqqioqUHqDmlVbxQsdx1j5gQYEvPNe9kP7sSWNdBztYW59DUvmZG94zxzoZs7MGi5vqD3rKdu7e+jpH6SupoKrFjScmRA63a1I8xUSrx85ycqFDSxrrh/9rx/mBMW/XNpE32Dwm4NH+UjquyQOdvXwobn1zJ2VvZGf7Bvk8VcO87EljdRWVzBYCH72/CEAPrakkRnVFQwGw0Exb1YNH1nYMPxDMBQUH1l4ekyvdZ5gyZw6qtIb0FA3h95MD6RxrZg/iwjoHyxQIfFq5wmuuHwWVy64DAIqKkQhHSkOFuKMoLjuQ038/KW3uX7ZHN463svcWTU0zaxhUWMW3Md7+3nu4FE63z3F/IYZw8E1ZOiNeuXCBgI4drKfmsoKrlrYMMoLfQ4l/gd7f+cJls+bxfLm+uGgqKmqYFFTHR+efxmDEVRI/M/fvAlA32CBD8+/jKpKMfT/nc53T/Gr17uA7GfjA411w13qGyiw66W3uemfLUASz7cf5YrLZ6XgLXDkvVO8ffwU1y+bPWr/Xu08wbu9A3zqysuprhADheDNoz3MqKpkUVMdr3a+N9x2xeWzsu+BEh3v7ecXezupTM8DWRj29A8yt76GhY0zeLHjONcsaaS+torBQnC0p4/X3zkdsBFZP4719PPh+ZdRiDjj5//p17s41tPP714xDwQLZs/g7eOnuHLBLN5py4LiqgWnx/D28V5aD3RzxeWz+MDs7HVt7e1noBCsXNjAqfT6DrnuQ010dPec/h5K4VyIGH5tKiXqqitpnFld8muVR1P5cE/SLcDaiPiTNP+HwPUR8ZWx1mlpaYnW1taJ6qKZ2bQg6ZmIaBlt2ZS+RgF0AEuK5henmpmZTZCpHhRPAyskLZNUA6wHtk9yn8zMLilT+hpFRAxI+gqwE6gEtkTEnknulpnZJWVKBwVAROwAdkx2P8zMLlVT/dSTmZlNMgeFmZnlclCYmVkuB4WZmeWa0r9wVwpJncCBElefB7wzjt25GHjMlwaP+dJQzpg/FBHNoy2YdkFRDkmtY/1m4nTlMV8aPOZLw4Uas089mZlZLgeFmZnlclCc6f7J7sAk8JgvDR7zpeGCjNnXKMzMLJePKMzMLJeDwszMcjkoEklrJe2V1CZp02T3p1SSlkh6QtJLkvZI+mqqz5G0S9K+9LUp1SVpcxr385KuLdrWhtR+n6QNkzWm8yWpUtKzkn6W5pdJeiqN7SfpT9UjqTbNt6XlS4u2cWeq75V0w+SM5PxIapT0iKRXJL0s6RPTfT9L+rP0ff2ipIckzZhu+1nSFkmHJb1YVBu3/SrpOkkvpHU2S+fxebtDHzx/KT/I/oT5q8ByoAb4DbBysvtV4lgWAtem6cuA3wIrgf8MbEr1TcB30/RNwGNknyy6Cngq1ecA+9PXpjTdNNnjO8fY/z3wD8DP0vzDwPo0/bfAv0nTXwb+Nk2vB36SplemfV8LLEvfE5WTPa6c8W4F/iRN1wCN03k/k3008mtAXdH+/aPptp+B3wOuBV4sqo3bfgV+ldoqrXvjOfs02S/KVHgAnwB2Fs3fCdw52f0ap7E9Cvw+sBdYmGoLgb1p+u+AW4va703LbwX+rqh+Rrup9iD79MPdwKeBn6UfgneAqpH7mOzzTT6RpqtSO43c78XtptoDmJ3eNDWiPm33cwqKg+nNryrt5xum434Glo4IinHZr2nZK0X1M9qN9fCpp8zQN+CQ9lS7qKVD7WuAp4D5EXEoLXoLmJ+mxxr7xfaa/DXwNaCQ5ucCRyNiIM0X9394bGn5sdT+YhrzMqAT+Pt0uu0HkuqZxvs5IjqAvwTeAA6R7bdnmN77ech47ddFaXpkPZeDYpqSNAv4J+BPI+J48bLI/isxbe6LlvRZ4HBEPDPZfZlAVWSnJ+6LiGuAE2SnJIZNw/3cBKwjC8kPAPXA2knt1CSYjP3qoMh0AEuK5hen2kVJUjVZSPw4In6aym9LWpiWLwQOp/pYY7+YXpNPAp+T9Dqwjez00/eARklDn+JY3P/hsaXls4EjXFxjbgfaI+KpNP8IWXBM5/38GeC1iOiMiH7gp2T7fjrv5yHjtV870vTIei4HReZpYEW6e6KG7MLX9knuU0nSHQwPAC9HxF8VLdoODN35sIHs2sVQ/bZ098Qq4Fg6xN0JrJHUlP4ntybVppyIuDMiFkfEUrJ993hE/AHwBHBLajZyzEOvxS2pfaT6+nS3zDJgBdmFvyknIt4CDkq6MpVWAy8xjfcz2SmnVZJmpu/zoTFP2/1cZFz2a1p2XNKq9BreVrStsU32RZup8iC7e+C3ZHdAfGOy+1PGOH6X7LD0eeC59LiJ7NzsbmAf8H+AOam9gHvTuF8AWoq29cdAW3p8cbLHdp7j/xSn73paTvYG0Ab8I1Cb6jPSfFtavrxo/W+k12Iv53E3yCSP9WNAa9rX/4Ps7pZpvZ+B/wS8ArwI/IjszqVptZ+Bh8iuwfSTHTnePp77FWhJr9+rwN8w4oaI0R7+Ex5mZpbLp57MzCyXg8LMzHI5KMzMLJeDwszMcjkozMwsl4PCzMxyOSjMzCzX/wfjiuCHCiJzlAAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem maternem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna napačna razumevanja ali napačne interpretacije, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/translations/sl/8-Reinforcement/1-QLearning/solution/notebook.ipynb
new file mode 100644
index 000000000..4e40d2109
--- /dev/null
+++ b/translations/sl/8-Reinforcement/1-QLearning/solution/notebook.ipynb
@@ -0,0 +1,577 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
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+  },
+  "coopTranslator": {
+   "original_hash": "488431336543f71f14d4aaf0399e3381",
+   "translation_date": "2025-09-06T15:08:38+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter in volk: Uvod v okrepljeno učenje\n",
+    "\n",
+    "V tem vodiču se bomo naučili, kako uporabiti okrepljeno učenje za reševanje problema iskanja poti. Zgodba je navdihnjena z glasbeno pravljico [Peter in volk](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) ruskega skladatelja [Sergeja Prokofjeva](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Gre za zgodbo o mladem pionirju Petru, ki pogumno zapusti svojo hišo in se odpravi na gozdno jaso, da bi lovil volka. Naučili bomo algoritme strojnega učenja, ki bodo Petru pomagali raziskati okolico in zgraditi optimalen navigacijski zemljevid.\n",
+    "\n",
+    "Najprej uvozimo nekaj uporabnih knjižnic:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Pregled učenja z okrepitvijo\n",
+    "\n",
+    "**Učenje z okrepitvijo** (RL) je tehnika učenja, ki nam omogoča, da se naučimo optimalnega vedenja **agenta** v nekem **okolju** z izvajanjem številnih poskusov. Agent v tem okolju mora imeti določen **cilj**, ki ga opredeljuje **funkcija nagrajevanja**.\n",
+    "\n",
+    "## Okolje\n",
+    "\n",
+    "Za enostavnost si predstavljajmo Peterjev svet kot kvadratno ploščo velikosti `width` x `height`. Vsaka celica na tej plošči je lahko:\n",
+    "* **zemlja**, po kateri lahko Peter in druga bitja hodijo\n",
+    "* **voda**, po kateri se seveda ne more hoditi\n",
+    "* **drevo** ali **trava** - mesto, kjer se lahko spočiješ\n",
+    "* **jabolko**, ki predstavlja nekaj, kar bi Peter z veseljem našel, da se nahrani\n",
+    "* **volk**, ki je nevaren in se mu je treba izogniti\n",
+    "\n",
+    "Za delo z okoljem bomo definirali razred `Board`. Da ne bi preveč obremenili tega zvezka, smo ves kodo za delo s ploščo premaknili v ločen modul `rlboard`, ki ga bomo zdaj uvozili. Več podrobnosti o notranji implementaciji si lahko ogledate znotraj tega modula.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rlboard import *"
+   ]
+  },
+  {
+   "source": [
+    "Zdaj ustvarimo naključno ploščo in si oglejmo, kako izgleda:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "source": [
+    "## Dejanja in Pravila\n",
+    "\n",
+    "V našem primeru je Peterjev cilj najti jabolko, medtem ko se izogiba volku in drugim oviram. Da bi to dosegel, se lahko preprosto sprehaja, dokler ne najde jabolka. Tako lahko na katerem koli položaju izbere eno od naslednjih dejanj: gor, dol, levo in desno. Ta dejanja bomo definirali kot slovar in jih preslikali v pare ustreznih sprememb koordinat. Na primer, premik v desno (`R`) bi ustrezal paru `(1,0)`.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "Strategija našega agenta (Peter) je določena z tako imenovano **politiko**. Poglejmo najpreprostejšo politiko, imenovano **naključna hoja**.\n",
+    "\n",
+    "## Naključna hoja\n",
+    "\n",
+    "Najprej rešimo naš problem z implementacijo strategije naključne hoje.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(m):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(m,policy,start_position=None):\n",
+    "    n = 0 # number of steps\n",
+    "    # set initial position\n",
+    "    if start_position:\n",
+    "        m.human = start_position \n",
+    "    else:\n",
+    "        m.random_start()\n",
+    "    while True:\n",
+    "        if m.at() == Board.Cell.apple:\n",
+    "            return n # success!\n",
+    "        if m.at() in [Board.Cell.wolf, Board.Cell.water]:\n",
+    "            return -1 # eaten by wolf or drowned\n",
+    "        while True:\n",
+    "            a = actions[policy(m)]\n",
+    "            new_pos = m.move_pos(m.human,a)\n",
+    "            if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:\n",
+    "                m.move(a) # do the actual move\n",
+    "                break\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "Pojdimo večkrat izvesti poskus naključnega sprehoda in si oglejmo povprečno število opravljenih korakov:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 32.87096774193548, eaten by wolf: 7 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        else:\n",
+    "            s += z\n",
+    "            n += 1\n",
+    "    print(f\"Average path length = {s/n}, eaten by wolf: {w} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Funkcija nagrajevanja\n",
+    "\n",
+    "Da bi naša politika postala bolj inteligentna, moramo razumeti, kateri premiki so \"boljši\" od drugih.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "move_reward = -0.1\n",
+    "goal_reward = 10\n",
+    "end_reward = -10\n",
+    "\n",
+    "def reward(m,pos=None):\n",
+    "    pos = pos or m.human\n",
+    "    if not m.is_valid(pos):\n",
+    "        return end_reward\n",
+    "    x = m.at(pos)\n",
+    "    if x==Board.Cell.water or x == Board.Cell.wolf:\n",
+    "        return end_reward\n",
+    "    if x==Board.Cell.apple:\n",
+    "        return goal_reward\n",
+    "    return move_reward"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Učenje\n",
+    "\n",
+    "Ustvarite Q-tabelo ali večdimenzionalno matriko. Ker ima naša plošča dimenzije `width` x `height`, lahko Q-tabelo predstavimo z numpy matriko oblike `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "source": [
+    "Podajte Q-tabelo funkciji za risanje, da vizualizirate tabelo na plošči:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p267d475764)\">\n    <image height=\"327\" id=\"image00b8757863\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Bistvo Q-Učenja: Bellmanova enačba in učni algoritem\n",
+    "\n",
+    "Napišite psevdokodo za naš učni algoritem:\n",
+    "\n",
+    "* Inicializirajte Q-tabelo Q z enakimi vrednostmi za vsa stanja in akcije\n",
+    "* Nastavite stopnjo učenja $\\alpha\\leftarrow 1$\n",
+    "* Večkrat ponovite simulacijo\n",
+    "   1. Začnite na naključni poziciji\n",
+    "   1. Ponavljajte\n",
+    "        1. Izberite akcijo $a$ v stanju $s$\n",
+    "        2. Izvedite akcijo z premikom v novo stanje $s'$\n",
+    "        3. Če naletimo na pogoj konca igre ali je skupna nagrada premajhna - zaključite simulacijo  \n",
+    "        4. Izračunajte nagrado $r$ v novem stanju\n",
+    "        5. Posodobite Q-funkcijo v skladu z Bellmanovo enačbo: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Posodobite skupno nagrado in zmanjšajte $\\alpha$.\n",
+    "\n",
+    "## Izkoriščanje vs. Raziskovanje\n",
+    "\n",
+    "Najboljši pristop je uravnotežiti med raziskovanjem in izkoriščanjem. Ko se več naučimo o našem okolju, bomo bolj verjetno sledili optimalni poti, vendar se občasno odločimo za nepreizkušeno pot.\n",
+    "\n",
+    "## Python Implementacija\n",
+    "\n",
+    "Zdaj smo pripravljeni implementirati učni algoritem. Pred tem potrebujemo tudi funkcijo, ki bo poljubne številke v Q-tabeli pretvorila v vektor verjetnosti za ustrezne akcije:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "source": [
+    "Dodamo majhno količino `eps` k prvotnemu vektorju, da se izognemo deljenju z 0 v začetnem primeru, ko so vse komponente vektorja enake.\n",
+    "\n",
+    "Dejanski učni algoritem bomo izvedli za 5000 poskusov, imenovanih tudi **epoh**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    m.random_start()\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        dpos = actions[a]\n",
+    "        m.move(dpos,check_correctness=False) # we allow player to move outside the board, which terminates episode\n",
+    "        r = reward(m)\n",
+    "        cum_reward += r\n",
+    "        if r==end_reward or cum_reward < -1000:\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "source": [
+    "Po izvedbi tega algoritma bi morala biti Q-tabela posodobljena z vrednostmi, ki določajo privlačnost različnih dejanj na vsakem koraku. Vizualizirajte tabelo tukaj:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p31952a86ca)\">\n    <image height=\"327\" id=\"image3fd90cd4cb\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Preverjanje politike\n",
+    "\n",
+    "Ker Q-Table prikazuje \"privlačnost\" vsakega dejanja v vsakem stanju, ga je zelo enostavno uporabiti za določanje učinkovite navigacije v našem svetu. V najpreprostejšem primeru lahko preprosto izberemo dejanje, ki ustreza najvišji vrednosti v Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "def qpolicy_strict(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = list(actions)[np.argmax(v)]\n",
+    "        return a\n",
+    "\n",
+    "walk(m,qpolicy_strict)"
+   ]
+  },
+  {
+   "source": [
+    "Če večkrat preizkusite zgornjo kodo, boste opazili, da se včasih preprosto \"zatakne\" in morate pritisniti gumb STOP v beležnici, da jo prekinete.\n",
+    "\n",
+    "> **Naloga 1:** Spremenite funkcijo `walk`, da omejite največjo dolžino poti na določeno število korakov (recimo 100), in opazujte, kako zgornja koda občasno vrne to vrednost.\n",
+    "\n",
+    "> **Naloga 2:** Spremenite funkcijo `walk`, da se ne vrača na mesta, kjer je že bila prej. To bo preprečilo, da bi se `walk` zanka, vendar se agent še vedno lahko \"ujame\" na lokaciji, iz katere ne more pobegniti.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 3.45, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbab852fd68>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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133.949763 214.756364 \nL 133.980202 214.756364 \nL 134.132399 206.226264 \nL 134.254157 214.756364 \nL 134.284596 214.685867 \nL 134.315036 198.00165 \nL 134.345475 214.756364 \nL 134.375914 199.106098 \nL 134.436793 214.756364 \nL 134.528111 214.497876 \nL 134.558551 214.309885 \nL 134.58899 214.756364 \nL 134.649869 214.685867 \nL 134.680308 213.252434 \nL 134.710748 214.756364 \nL 134.741187 214.756364 \nL 134.802066 212.946949 \nL 134.832506 214.756364 \nL 134.954263 193.184376 \nL 135.076021 214.756364 \nL 135.10646 212.594465 \nL 135.167339 214.756364 \nL 135.289097 214.756364 \nL 135.502172 214.756364 \nL 135.532612 206.766738 \nL 135.593491 214.756364 \nL 135.62393 214.662368 \nL 135.65437 214.756364 \nL 135.745688 195.722257 \nL 135.806567 214.756364 \nL 135.867445 203.923372 \nL 135.989203 214.756364 \nL 136.050082 213.111441 \nL 136.080521 214.756364 \nL 136.110961 214.756364 \nL 136.202279 206.837235 \nL 136.293597 214.756364 \nL 136.324036 213.510922 \nL 136.384915 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138.150401 209.375116 \nL 138.21128 214.756364 \nL 138.363477 206.672743 \nL 138.454795 214.756364 \nL 138.485234 205.967776 \nL 138.546113 210.338571 \nL 138.576553 214.732865 \nL 138.637431 211.41952 \nL 138.667871 207.659697 \nL 138.69831 213.604918 \nL 138.789629 213.299432 \nL 138.820068 214.756364 \nL 138.911386 201.737974 \nL 139.002704 214.756364 \nL 139.033144 213.322931 \nL 139.094023 214.756364 \nL 139.124462 210.033085 \nL 139.21578 210.503063 \nL 139.276659 209.7276 \nL 139.337538 214.756364 \nL 139.398417 212.92345 \nL 139.428856 214.756364 \nL 139.459295 214.756364 \nL 139.520174 204.863328 \nL 139.611493 211.114034 \nL 139.641932 211.255028 \nL 139.672371 210.738052 \nL 139.702811 214.756364 \nL 139.794129 213.957401 \nL 139.824568 214.756364 \nL 139.855008 213.581419 \nL 139.885447 207.119222 \nL 139.915887 214.756364 \nL 139.946326 214.004399 \nL 140.037644 211.325525 \nL 140.128962 214.756364 \nL 140.159402 207.260215 \nL 140.220281 214.380381 \nL 140.25072 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147.221344 214.591871 \nL 147.312663 210.57356 \nL 147.373541 211.231529 \nL 147.403981 214.756364 \nL 147.495299 214.19239 \nL 147.525738 214.732865 \nL 147.556178 208.317666 \nL 147.617057 214.262887 \nL 147.677936 213.816408 \nL 147.799693 214.756364 \nL 147.95189 201.502985 \nL 148.043208 214.756364 \nL 148.073648 211.513516 \nL 148.104087 210.78505 \nL 148.134527 197.79016 \nL 148.164966 214.756364 \nL 148.195405 214.568372 \nL 148.225845 214.756364 \nL 148.347602 211.8425 \nL 148.438921 214.756364 \nL 148.46936 209.093129 \nL 148.530239 212.617964 \nL 148.651997 214.756364 \nL 148.682436 201.549983 \nL 148.743315 214.756364 \nL 148.773754 213.698913 \nL 148.804194 214.756364 \nL 148.834633 202.724928 \nL 148.895512 212.335977 \nL 149.017269 214.756364 \nL 149.047709 214.662368 \nL 149.078148 213.698913 \nL 149.108588 207.213217 \nL 149.169466 214.027898 \nL 149.199906 211.137533 \nL 149.291224 208.787644 \nL 149.321664 214.756364 \nL 149.352103 214.756364 \nL 149.382542 188.484596 \nL 149.443421 206.860734 \nL 149.534739 214.756364 \nL 149.565179 207.401209 \nL 149.626058 214.756364 \nL 149.656497 212.547467 \nL 149.717376 214.756364 \nL 149.778255 214.756364 \nL 149.869573 212.312478 \nL 149.900012 214.262887 \nL 149.930452 207.518703 \nL 149.99133 214.685867 \nL 150.02177 208.740646 \nL 150.052209 206.226264 \nL 150.082649 209.7276 \nL 150.234846 214.756364 \nL 150.265285 201.573482 \nL 150.326164 213.9809 \nL 150.356603 213.792909 \nL 150.387043 214.756364 \nL 150.417482 209.539608 \nL 150.478361 213.651915 \nL 150.5088 213.487423 \nL 150.53924 214.756364 \nL 150.569679 208.811143 \nL 150.630558 214.051397 \nL 150.660997 214.756364 \nL 150.691437 207.659697 \nL 150.752316 214.756364 \nL 150.843634 212.852953 \nL 150.904513 213.628417 \nL 150.934952 214.756364 \nL 151.02627 208.388162 \nL 151.05671 209.116628 \nL 151.117589 214.756364 \nL 151.148028 213.463924 \nL 151.178467 204.275855 \nL 151.239346 214.732865 \nL 151.269786 208.01218 \nL 151.300225 211.889498 \nL 151.330664 190.435005 \nL 151.361104 214.756364 \nL 151.391543 211.748505 \nL 151.421983 212.429973 \nL 151.452422 204.322853 \nL 151.482861 214.756364 \nL 151.513301 207.80069 \nL 151.665498 214.756364 \nL 151.726377 208.975635 \nL 151.756816 210.15058 \nL 151.787256 214.756364 \nL 151.817695 211.748505 \nL 151.848134 206.155767 \nL 151.909013 214.521375 \nL 151.969892 214.756364 \nL 152.000331 214.145392 \nL 152.06121 214.756364 \nL 152.182968 197.625667 \nL 152.243847 214.756364 \nL 152.304725 213.181937 \nL 152.335165 212.71196 \nL 152.365604 214.756364 \nL 152.426483 205.897279 \nL 152.456923 214.756364 \nL 152.548241 208.458659 \nL 152.57868 213.111441 \nL 152.60912 214.756364 \nL 152.700438 214.19239 \nL 152.730877 214.215889 \nL 152.822195 214.756364 \nL 152.852635 208.364663 \nL 152.913514 214.121893 \nL 152.943953 184.654276 \nL 153.065711 214.756364 \nL 153.09615 210.926043 \nL 153.157029 214.756364 \nL 153.187468 214.756364 \nL 153.248347 206.320259 \nL 153.309226 207.706694 \nL 153.370105 214.756364 \nL 153.430984 213.158439 \nL 153.461423 213.816408 \nL 153.491862 207.518703 \nL 153.522302 214.756364 \nL 153.552741 212.617964 \nL 153.644059 214.756364 \nL 153.674499 214.356882 \nL 153.704938 214.756364 \nL 153.735378 213.839907 \nL 153.765817 207.542202 \nL 153.796256 214.756364 \nL 153.826696 214.239388 \nL 153.887575 214.756364 \nL 153.918014 204.839829 \nL 153.978893 213.792909 \nL 154.039772 214.756364 \nL 154.070211 213.322931 \nL 154.100651 188.790082 \nL 154.161529 214.215889 \nL 154.191969 214.756364 \nL 154.222408 200.11655 \nL 154.283287 202.630932 \nL 154.405045 214.756364 \nL 154.435484 214.756364 \nL 154.465923 214.591871 \nL 154.496363 208.975635 \nL 154.526802 214.756364 \nL 154.557242 214.756364 \nL 154.587681 214.756364 \nL 154.678999 213.698913 \nL 154.739878 214.756364 \nL 154.770317 214.145392 \nL 154.800757 213.13494 \nL 154.831196 214.756364 \nL 154.861636 214.756364 \nL 154.892075 205.02782 \nL 154.952954 214.756364 \nL 155.013833 213.534421 \nL 155.044272 206.461253 \nL 155.105151 214.756364 \nL 155.16603 214.756364 \nL 155.196469 204.487345 \nL 155.257348 214.756364 \nL 155.379106 213.228935 \nL 155.439984 211.959995 \nL 155.561742 214.756364 \nL 155.622621 214.756364 \nL 155.713939 205.215811 \nL 155.805257 214.756364 \nL 155.835697 202.865921 \nL 155.896576 212.641463 \nL 156.018333 214.756364 \nL 156.048773 213.322931 \nL 156.109651 214.756364 \nL 156.140091 196.356727 \nL 156.261848 214.756364 \nL 156.322727 207.777191 \nL 156.353167 213.040944 \nL 156.414045 214.756364 \nL 156.566243 204.017367 \nL 156.657561 214.756364 \nL 156.688 214.239388 \nL 156.748879 214.756364 \nL 156.779318 208.035679 \nL 156.840197 207.80069 \nL 156.870637 214.756364 \nL 156.961955 202.912919 \nL 157.022834 214.756364 \nL 157.083712 214.356882 \nL 157.175031 200.539531 \nL 157.23591 214.756364 \nL 157.296788 207.847688 \nL 157.357667 214.756364 \nL 157.388107 204.463846 \nL 157.418546 211.278527 \nL 157.448985 210.103582 \nL 157.479425 213.816408 \nL 157.570743 213.534421 \nL 157.631622 214.756364 \nL 157.692501 207.988681 \nL 157.753379 214.756364 \nL 157.814258 214.521375 \nL 157.844698 214.756364 \nL 157.996895 205.403802 \nL 158.027334 205.521297 \nL 158.149092 214.732865 \nL 158.179531 214.756364 \nL 158.209971 204.510844 \nL 158.270849 211.983494 \nL 158.392607 214.756364 \nL 158.453486 214.756364 \nL 158.483925 212.970447 \nL 158.544804 213.416926 \nL 158.605683 214.756364 \nL 158.666562 210.503063 \nL 158.697001 214.756364 \nL 158.72744 213.816408 \nL 158.788319 214.756364 \nL 158.818759 214.756364 \nL 158.940516 210.832048 \nL 158.970956 211.372522 \nL 159.092713 192.620402 \nL 159.031835 213.76941 \nL 159.123153 200.32804 \nL 159.24491 214.756364 \nL 159.366668 212.194984 \nL 159.397107 212.805955 \nL 159.427547 212.335977 \nL 159.457986 214.756364 \nL 159.518865 212.852953 \nL 159.549304 212.429973 \nL 159.640623 214.756364 \nL 159.671062 214.709366 \nL 159.701502 213.675414 \nL 159.731941 214.756364 \nL 159.76238 214.685867 \nL 159.79282 202.25495 \nL 159.853699 210.855547 \nL 159.975456 214.756364 \nL 160.066774 211.865999 \nL 160.158093 214.756364 \nL 160.27985 211.325525 \nL 160.340729 207.565701 \nL 160.371168 214.756364 \nL 160.492926 200.657025 \nL 160.584244 214.168891 \nL 160.614684 209.28112 \nL 160.736441 214.732865 \nL 160.675563 208.670149 \nL 160.766881 214.568372 \nL 160.858199 214.756364 \nL 160.888638 197.155689 \nL 161.010396 214.756364 \nL 161.071275 214.756364 \nL 161.101714 208.905138 \nL 161.162593 214.756364 \nL 161.284351 211.678008 \nL 161.375669 211.231529 \nL 161.406108 214.756364 \nL 161.527866 209.892092 \nL 161.588745 208.411661 \nL 161.649624 214.756364 \nL 161.740942 213.416926 \nL 161.771381 214.756364 \nL 161.801821 213.440425 \nL 161.83226 207.142721 \nL 161.893139 214.756364 \nL 162.045336 214.756364 \nL 162.167094 208.153173 \nL 162.319291 214.662368 \nL 162.410609 214.756364 \nL 162.441048 205.192312 \nL 162.593245 214.756364 \nL 162.745442 205.591794 \nL 162.775882 214.756364 \nL 162.8672 212.899951 \nL 162.958518 214.756364 \nL 162.928079 212.171485 \nL 162.988958 213.675414 \nL 163.049836 214.756364 \nL 163.080276 213.55792 \nL 163.110715 210.949542 \nL 163.171594 214.756364 \nL 163.202033 214.662368 \nL 163.232473 213.040944 \nL 163.262912 197.696164 \nL 163.323791 214.756364 \nL 163.35423 208.693648 \nL 163.415109 213.910403 \nL 163.445549 214.756364 \nL 163.567306 207.119222 \nL 163.719503 214.756364 \nL 163.749943 214.638869 \nL 163.780382 208.059178 \nL 163.841261 214.756364 \nL 163.8717 212.852953 \nL 163.90214 214.756364 \nL 163.932579 199.670071 \nL 163.993458 209.234123 \nL 164.023897 213.76941 \nL 164.115216 212.476971 \nL 164.145655 214.61537 \nL 164.176094 211.63101 \nL 164.206534 212.735458 \nL 164.236973 212.641463 \nL 164.328291 214.756364 \nL 164.358731 213.510922 \nL 164.38917 214.40388 \nL 164.480489 209.915591 \nL 164.510928 211.725006 \nL 164.541367 214.40388 \nL 164.571807 207.424708 \nL 164.602246 212.735458 \nL 164.663125 209.915591 \nL 164.693564 212.71196 \nL 164.724004 212.970447 \nL 164.784883 211.255028 \nL 164.845761 214.756364 \nL 164.876201 214.756364 \nL 165.028398 193.983338 \nL 165.119716 214.756364 \nL 165.150155 213.393428 \nL 165.180595 211.889498 \nL 165.211034 213.487423 \nL 165.241474 214.756364 \nL 165.271913 207.683195 \nL 165.363231 212.147986 \nL 165.393671 214.756364 \nL 165.42411 200.915513 \nL 165.484989 214.756364 \nL 165.515428 214.756364 \nL 165.576307 210.291573 \nL 165.637186 212.382975 \nL 165.758944 214.756364 \nL 165.819822 212.241982 \nL 165.850262 214.756364 \nL 165.880701 214.756364 \nL 165.911141 213.581419 \nL 165.972019 214.662368 \nL 166.032898 214.756364 \nL 166.063338 214.309885 \nL 166.124217 214.756364 \nL 166.154656 214.61537 \nL 166.185095 214.756364 \nL 166.215535 212.006993 \nL 166.276414 213.487423 \nL 166.306853 214.756364 \nL 166.337292 211.912997 \nL 166.367732 214.239388 \nL 166.428611 214.709366 \nL 166.45905 206.273262 \nL 166.580808 214.756364 \nL 166.611247 212.288979 \nL 166.672126 213.55792 \nL 166.733005 214.756364 \nL 166.885202 209.445613 \nL 167.037399 214.756364 \nL 167.159156 212.500469 \nL 167.220035 214.756364 \nL 167.250475 204.957323 \nL 167.311353 214.756364 \nL 167.433111 212.547467 \nL 167.46355 214.40388 \nL 167.49399 207.80069 \nL 167.585308 207.965182 \nL 167.615747 214.756364 \nL 167.646187 195.22878 \nL 167.707066 214.756364 \nL 167.767945 214.756364 \nL 167.798384 211.865999 \nL 167.859263 214.098395 \nL 167.920142 214.756364 \nL 167.950581 213.299432 \nL 168.01146 214.756364 \nL 168.041899 214.732865 \nL 168.072339 214.333383 \nL 168.163657 193.818846 \nL 168.285414 214.756364 \nL 168.315854 208.787644 \nL 168.376733 211.184531 \nL 168.407172 214.756364 \nL 168.437611 214.450878 \nL 168.468051 203.429895 \nL 168.52893 214.756364 \nL 168.589809 212.100988 \nL 168.620248 212.147986 \nL 168.650687 214.756364 \nL 168.681127 211.513516 \nL 168.742006 213.463924 \nL 168.772445 210.526562 \nL 168.802884 184.325291 \nL 168.863763 214.685867 \nL 168.924642 214.756364 \nL 168.955081 214.027898 \nL 168.985521 209.351617 \nL 169.0464 214.756364 \nL 169.076839 214.756364 \nL 169.107278 207.612699 \nL 169.168157 214.591871 \nL 169.198597 213.628417 \nL 169.229036 214.756364 \nL 169.259475 214.756364 \nL 169.320354 214.027898 \nL 169.442112 200.398537 \nL 169.381233 214.756364 \nL 169.472551 206.132268 \nL 169.56387 214.756364 \nL 169.685627 200.586528 \nL 169.807385 214.756364 \nL 169.868264 214.756364 \nL 169.898703 214.568372 \nL 169.929142 207.824189 \nL 169.959582 214.756364 \nL 169.990021 214.756364 \nL 170.0509 211.396021 \nL 170.081339 214.756364 \nL 170.233537 195.886749 \nL 170.263976 214.756364 \nL 170.355294 213.228935 \nL 170.416173 212.26548 \nL 170.446612 213.76941 \nL 170.477052 213.510922 \nL 170.56837 200.563029 \nL 170.629249 203.782378 \nL 170.751006 214.756364 \nL 170.842325 206.907732 \nL 170.872764 207.824189 \nL 170.994522 214.756364 \nL 171.024961 207.753692 \nL 171.08584 214.662368 \nL 171.116279 214.756364 \nL 171.268476 212.71196 \nL 171.298916 214.638869 \nL 171.329355 210.738052 \nL 171.359795 209.351617 \nL 171.481552 214.709366 \nL 171.511992 214.756364 \nL 171.542431 214.286386 \nL 171.57287 206.884233 \nL 171.60331 214.756364 \nL 171.664189 209.116628 \nL 171.755507 214.756364 \nL 171.785946 214.286386 \nL 171.877265 214.756364 \nL 171.907704 212.124487 \nL 171.938143 214.756364 \nL 171.968583 206.437754 \nL 172.059901 211.396021 \nL 172.09034 213.651915 \nL 172.12078 213.534421 \nL 172.151219 205.756286 \nL 172.181659 214.756364 \nL 172.212098 214.756364 \nL 172.272977 214.756364 \nL 172.303416 213.252434 \nL 172.364295 214.333383 \nL 172.394734 214.756364 \nL 172.486053 208.43516 \nL 172.577371 206.343758 \nL 172.60781 214.756364 \nL 172.63825 191.680446 \nL 172.699129 214.756364 \nL 172.729568 214.333383 \nL 172.760007 214.756364 \nL 172.790447 211.466518 \nL 172.851326 214.756364 \nL 172.912204 214.756364 \nL 172.942644 208.623151 \nL 173.033962 211.772004 \nL 173.12528 214.756364 \nL 173.15572 212.030492 \nL 173.216598 214.756364 \nL 173.338356 214.709366 \nL 173.368796 202.724928 \nL 173.429674 214.756364 \nL 173.460114 214.756364 \nL 173.581871 205.709288 \nL 173.703629 214.756364 \nL 173.734068 214.756364 \nL 173.764508 214.544874 \nL 173.855826 214.662368 \nL 173.977584 214.756364 \nL 174.129781 210.855547 \nL 174.16022 204.134862 \nL 174.19066 214.638869 \nL 174.221099 214.215889 \nL 174.251538 210.620558 \nL 174.281978 214.756364 \nL 174.312417 214.756364 \nL 174.342857 214.756364 \nL 174.464614 204.745833 \nL 174.495054 207.871187 \nL 174.616811 214.756364 \nL 174.647251 214.709366 \nL 174.67769 214.145392 \nL 174.708129 214.709366 \nL 174.769008 203.429895 \nL 174.799448 214.756364 \nL 174.890766 214.756364 \nL 174.982084 213.416926 \nL 175.012524 214.756364 \nL 175.042963 211.560514 \nL 175.073402 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177.112843 214.756364 \nL 177.143282 214.027898 \nL 177.173721 208.270668 \nL 177.2346 208.811143 \nL 177.295479 214.756364 \nL 177.356358 212.241982 \nL 177.417237 214.756364 \nL 177.447676 214.568372 \nL 177.508555 208.106176 \nL 177.538994 214.756364 \nL 177.569434 212.359476 \nL 177.599873 212.335977 \nL 177.660752 214.756364 \nL 177.691191 200.022555 \nL 177.78251 214.756364 \nL 177.812949 213.087942 \nL 177.843388 214.756364 \nL 177.873828 214.638869 \nL 177.904267 196.427223 \nL 177.965146 214.756364 \nL 177.995585 208.599652 \nL 178.056464 209.892092 \nL 178.178222 214.756364 \nL 178.208661 214.756364 \nL 178.29998 208.482158 \nL 178.421737 214.756364 \nL 178.482616 211.443019 \nL 178.513055 214.756364 \nL 178.573934 212.030492 \nL 178.634813 213.158439 \nL 178.665252 214.756364 \nL 178.756571 205.380303 \nL 178.847889 214.756364 \nL 178.878328 207.683195 \nL 178.939207 214.756364 \nL 178.969647 214.756364 \nL 179.030525 210.879046 \nL 179.060965 214.19239 \nL 179.121844 212.993946 \nL 179.182722 210.78505 \nL 179.213162 214.756364 \nL 179.243601 210.432566 \nL 179.274041 202.395943 \nL 179.30448 214.756364 \nL 179.334919 214.756364 \nL 179.365359 214.756364 \nL 179.395798 208.129674 \nL 179.456677 214.215889 \nL 179.487116 214.756364 \nL 179.517556 211.114034 \nL 179.578435 214.756364 \nL 179.608874 214.756364 \nL 179.669753 196.544718 \nL 179.730632 212.05399 \nL 179.791511 214.732865 \nL 179.82195 214.168891 \nL 179.852389 200.821517 \nL 179.913268 214.756364 \nL 180.035026 208.576154 \nL 180.126344 214.756364 \nL 180.156783 214.732865 \nL 180.187223 214.427379 \nL 180.217662 214.756364 \nL 180.278541 203.735381 \nL 180.30898 213.158439 \nL 180.369859 214.756364 \nL 180.400299 214.239388 \nL 180.430738 204.769332 \nL 180.491617 213.933902 \nL 180.552496 214.756364 \nL 180.674253 205.920778 \nL 180.735132 214.756364 \nL 180.765572 211.748505 \nL 180.796011 204.275855 \nL 180.82645 214.756364 \nL 180.85689 211.278527 \nL 180.887329 213.910403 \nL 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204.299354 \nL 187.066529 210.879046 \nL 187.218726 214.756364 \nL 187.340484 211.748505 \nL 187.370923 214.380381 \nL 187.431802 201.808471 \nL 187.462241 214.756364 \nL 187.492681 211.137533 \nL 187.52312 214.756364 \nL 187.553559 209.06963 \nL 187.614438 214.709366 \nL 187.644878 195.369773 \nL 187.736196 196.403725 \nL 187.827514 214.756364 \nL 187.857954 213.675414 \nL 187.888393 214.756364 \nL 188.010151 209.892092 \nL 188.04059 214.544874 \nL 188.101469 187.732631 \nL 188.131908 214.756364 \nL 188.162348 213.017445 \nL 188.192787 202.88942 \nL 188.223226 214.427379 \nL 188.253666 213.158439 \nL 188.344984 214.756364 \nL 188.405863 200.069553 \nL 188.466742 212.359476 \nL 188.52762 214.756364 \nL 188.588499 187.591638 \nL 188.679818 214.756364 \nL 188.710257 214.380381 \nL 188.771136 211.63101 \nL 188.801575 214.286386 \nL 188.832015 214.756364 \nL 188.862454 214.732865 \nL 188.923333 214.756364 \nL 188.984212 202.771926 \nL 189.07553 214.756364 \nL 189.105969 211.278527 \nL 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214.756364 \nL 191.023652 214.356882 \nL 191.054091 201.385491 \nL 191.084531 214.568372 \nL 191.14541 208.64665 \nL 191.267167 214.732865 \nL 191.328046 211.959995 \nL 191.358485 214.756364 \nL 191.388925 214.427379 \nL 191.449804 214.638869 \nL 191.541122 203.594387 \nL 191.571561 213.675414 \nL 191.63244 202.701429 \nL 191.662879 198.283636 \nL 191.784637 214.732865 \nL 191.815077 208.200171 \nL 191.845516 214.756364 \nL 191.906395 210.291573 \nL 191.936834 214.756364 \nL 191.967274 210.174079 \nL 191.997713 199.294089 \nL 192.058592 214.756364 \nL 192.119471 213.205436 \nL 192.14991 214.756364 \nL 192.210789 214.309885 \nL 192.241228 214.756364 \nL 192.271668 203.805877 \nL 192.332546 210.197577 \nL 192.454304 214.756364 \nL 192.606501 208.928637 \nL 192.728259 214.756364 \nL 192.789138 214.286386 \nL 192.819577 201.1975 \nL 192.850016 214.756364 \nL 192.880456 213.651915 \nL 192.941335 211.231529 \nL 192.971774 212.26548 \nL 193.032653 214.756364 \nL 193.15441 190.622996 \nL 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209.774597 \nL 195.467805 208.881639 \nL 195.498245 214.756364 \nL 195.528684 205.944277 \nL 195.589563 214.638869 \nL 195.680881 206.508251 \nL 195.711321 214.756364 \nL 195.802639 211.490017 \nL 195.833078 214.756364 \nL 195.863518 188.837079 \nL 195.924397 214.544874 \nL 196.046154 181.411428 \nL 196.167912 214.756364 \nL 196.289669 200.281043 \nL 196.380988 214.756364 \nL 196.350548 179.696008 \nL 196.411427 213.181937 \nL 196.441866 211.114034 \nL 196.472306 214.756364 \nL 196.502745 212.641463 \nL 196.533185 212.547467 \nL 196.563624 213.76941 \nL 196.594063 211.983494 \nL 196.624503 210.385569 \nL 196.685382 214.756364 \nL 196.746261 213.393428 \nL 196.807139 214.756364 \nL 196.837579 214.568372 \nL 196.898458 214.756364 \nL 196.928897 209.304619 \nL 197.020215 214.756364 \nL 197.050655 212.171485 \nL 197.141973 214.756364 \nL 197.172412 183.526329 \nL 197.29417 214.756364 \nL 197.415928 213.675414 \nL 197.476806 214.756364 \nL 197.507246 214.333383 \nL 197.568125 205.051319 \nL 197.598564 206.555248 \nL 197.629003 214.756364 \nL 197.689882 207.260215 \nL 197.720322 196.803206 \nL 197.750761 214.756364 \nL 197.7812 214.756364 \nL 197.81164 211.302026 \nL 197.872519 214.756364 \nL 197.902958 214.756364 \nL 197.933397 175.842189 \nL 197.994276 213.487423 \nL 198.055155 212.382975 \nL 198.116034 214.756364 \nL 198.176913 187.168658 \nL 198.237792 204.910326 \nL 198.268231 214.756364 \nL 198.32911 206.061771 \nL 198.359549 203.594387 \nL 198.389989 186.111207 \nL 198.450867 214.756364 \nL 198.542186 202.654431 \nL 198.572625 213.55792 \nL 198.603064 213.463924 \nL 198.633504 214.756364 \nL 198.663943 207.542202 \nL 198.724822 214.568372 \nL 198.81614 214.756364 \nL 198.877019 206.320259 \nL 198.907458 214.521375 \nL 198.998777 213.886904 \nL 199.029216 200.516032 \nL 199.090095 201.291495 \nL 199.181413 214.756364 \nL 199.211853 213.087942 \nL 199.242292 214.756364 \nL 199.272731 213.863406 \nL 199.303171 189.753537 \nL 199.394489 205.004321 \nL 199.485807 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205.02782 \nL 204.325673 202.536937 \nL 204.447431 214.756364 \nL 204.47787 214.756364 \nL 204.508309 211.114034 \nL 204.569188 212.664962 \nL 204.660507 214.756364 \nL 204.751825 206.484752 \nL 204.843143 214.756364 \nL 204.873582 213.322931 \nL 204.904022 211.20803 \nL 204.934461 214.756364 \nL 204.964901 183.338337 \nL 205.025779 214.756364 \nL 205.056219 214.521375 \nL 205.117098 214.756364 \nL 205.147537 201.455988 \nL 205.208416 214.756364 \nL 205.360613 214.756364 \nL 205.391052 199.0591 \nL 205.451931 214.756364 \nL 205.482371 213.722412 \nL 205.543249 214.709366 \nL 205.573689 214.756364 \nL 205.665007 207.448206 \nL 205.695446 189.988525 \nL 205.756325 212.617964 \nL 205.786765 214.756364 \nL 205.817204 203.852875 \nL 205.878083 211.443019 \nL 205.938962 214.756364 \nL 205.99984 214.662368 \nL 206.03028 214.756364 \nL 206.152037 191.492455 \nL 206.273795 214.756364 \nL 206.304235 197.625667 \nL 206.365113 214.685867 \nL 206.395553 214.544874 \nL 206.425992 208.411661 \nL 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209.657103 \nL 208.404554 214.756364 \nL 208.434993 210.268074 \nL 208.465432 207.941683 \nL 208.495872 213.416926 \nL 208.556751 214.756364 \nL 208.58719 203.500392 \nL 208.678508 207.354211 \nL 208.708948 206.29676 \nL 208.800266 213.463924 \nL 208.830705 212.429973 \nL 208.861145 203.946871 \nL 208.891584 214.732865 \nL 208.952463 208.317666 \nL 208.982902 213.792909 \nL 209.013342 207.777191 \nL 209.043781 212.899951 \nL 209.135099 214.756364 \nL 209.165539 194.688305 \nL 209.226418 214.756364 \nL 209.287296 212.899951 \nL 209.317736 214.756364 \nL 209.348175 209.586606 \nL 209.409054 213.463924 \nL 209.439493 214.756364 \nL 209.469933 201.573482 \nL 209.530812 213.534421 \nL 209.561251 214.286386 \nL 209.591691 204.064365 \nL 209.652569 209.774597 \nL 209.713448 207.424708 \nL 209.774327 214.756364 \nL 209.804766 206.578747 \nL 209.865645 214.450878 \nL 209.896085 214.756364 \nL 209.987403 208.670149 \nL 210.017842 214.756364 \nL 210.10916 214.215889 \nL 210.1396 208.082677 \nL 210.200479 212.312478 \nL 210.261357 214.756364 \nL 210.291797 213.628417 \nL 210.322236 197.625667 \nL 210.352676 214.756364 \nL 210.383115 214.756364 \nL 210.413555 208.22367 \nL 210.474433 214.544874 \nL 210.504873 214.756364 \nL 210.535312 214.121893 \nL 210.596191 208.85814 \nL 210.62663 210.244575 \nL 210.717949 214.756364 \nL 210.748388 193.066881 \nL 210.809267 214.756364 \nL 210.931024 206.108769 \nL 211.052782 214.756364 \nL 211.113661 214.756364 \nL 211.204979 209.328118 \nL 211.296297 214.756364 \nL 211.326737 209.022633 \nL 211.418055 210.244575 \nL 211.448494 211.701507 \nL 211.509373 213.275933 \nL 211.539813 189.965027 \nL 211.570252 214.756364 \nL 211.66157 201.573482 \nL 211.69201 214.732865 \nL 211.722449 191.915435 \nL 211.752888 211.396021 \nL 211.813767 195.722257 \nL 211.844207 214.756364 \nL 211.874646 214.40388 \nL 211.905086 206.014774 \nL 211.935525 214.756364 \nL 211.965964 214.027898 \nL 211.996404 213.252434 \nL 212.026843 214.756364 \nL 212.057283 198.84761 \nL 212.118161 212.899951 \nL 212.148601 211.701507 \nL 212.17904 199.411583 \nL 212.20948 214.756364 \nL 212.239919 214.333383 \nL 212.300798 214.756364 \nL 212.331237 199.952058 \nL 212.392116 208.85814 \nL 212.422555 211.983494 \nL 212.452995 182.468878 \nL 212.483434 213.158439 \nL 212.513874 203.194906 \nL 212.544313 214.756364 \nL 212.635631 212.735458 \nL 212.666071 206.437754 \nL 212.72695 210.409068 \nL 212.757389 214.756364 \nL 212.818268 213.322931 \nL 212.848707 211.725006 \nL 212.879147 198.236639 \nL 212.909586 214.756364 \nL 212.940025 199.223592 \nL 213.000904 214.756364 \nL 213.061783 212.171485 \nL 213.092222 207.307213 \nL 213.122662 214.756364 \nL 213.153101 210.009586 \nL 213.183541 214.756364 \nL 213.244419 208.341165 \nL 213.274859 214.568372 \nL 213.335738 214.756364 \nL 213.396616 213.510922 \nL 213.427056 214.756364 \nL 213.457495 212.735458 \nL 213.487935 213.9809 \nL 213.518374 209.234123 \nL 213.579253 214.756364 \nL 213.609692 214.756364 \nL 213.761889 197.155689 \nL 213.883647 214.756364 \nL 213.914086 214.756364 \nL 213.944526 214.497876 \nL 214.005405 214.756364 \nL 214.035844 214.756364 \nL 214.066283 214.61537 \nL 214.127162 214.756364 \nL 214.188041 209.422114 \nL 214.309799 214.756364 \nL 214.370678 209.539608 \nL 214.401117 214.662368 \nL 214.431556 214.756364 \nL 214.522875 194.218327 \nL 214.553314 210.244575 \nL 214.614193 209.539608 \nL 214.675072 214.756364 \nL 214.76639 213.275933 \nL 214.857708 214.756364 \nL 214.827269 212.92345 \nL 214.888147 214.450878 \nL 214.918587 204.087864 \nL 215.009905 210.197577 \nL 215.101223 214.756364 \nL 215.131663 214.732865 \nL 215.192542 214.756364 \nL 215.25342 206.484752 \nL 215.28386 212.05399 \nL 215.314299 195.745755 \nL 215.375178 203.006915 \nL 215.496936 214.756364 \nL 215.527375 206.531749 \nL 215.588254 214.756364 \nL 215.618693 214.756364 \nL 215.649133 206.790237 \nL 215.710011 214.732865 \nL 215.77089 214.756364 \nL 215.831769 213.534421 \nL 215.862208 199.90506 \nL 215.923087 208.717147 \nL 216.014406 214.756364 \nL 216.044845 212.92345 \nL 216.105724 202.842423 \nL 216.136163 214.756364 \nL 216.227481 206.978229 \nL 216.257921 214.756364 \nL 216.349239 214.709366 \nL 216.440557 202.372445 \nL 216.410118 214.756364 \nL 216.470997 211.889498 \nL 216.531875 209.868593 \nL 216.592754 214.215889 \nL 216.684072 205.544796 \nL 216.775391 214.756364 \nL 216.80583 206.790237 \nL 216.866709 214.756364 \nL 216.927588 211.396021 \nL 216.958027 186.628183 \nL 216.988467 214.591871 \nL 217.018906 212.758957 \nL 217.049345 209.398615 \nL 217.110224 213.863406 \nL 217.262421 214.756364 \nL 217.292861 201.549983 \nL 217.353739 213.463924 \nL 217.384179 213.675414 \nL 217.414618 200.398537 \nL 217.445058 214.521375 \nL 217.475497 211.560514 \nL 217.505937 214.756364 \nL 217.597255 212.735458 \nL 217.658134 214.756364 \nL 217.719012 202.489939 \nL 217.779891 212.077489 \nL 217.84077 210.832048 \nL 217.932088 214.756364 \nL 217.962528 211.349023 \nL 218.023406 212.876452 \nL 218.114725 214.756364 \nL 218.145164 214.732865 \nL 218.206043 214.756364 \nL 218.297361 200.610027 \nL 218.388679 214.756364 \nL 218.419119 207.095723 \nL 218.540876 214.756364 \nL 218.571316 214.591871 \nL 218.632195 203.077411 \nL 218.662634 211.678008 \nL 218.693073 214.756364 \nL 218.723513 201.385491 \nL 218.784392 211.043538 \nL 218.87571 214.756364 \nL 218.936589 201.878968 \nL 218.967028 214.756364 \nL 218.997467 214.756364 \nL 219.027907 201.737974 \nL 219.088786 214.756364 \nL 219.149665 214.756364 \nL 219.180104 197.696164 \nL 219.240983 214.756364 \nL 219.271422 213.087942 \nL 219.301862 214.450878 \nL 219.332301 195.581263 \nL 219.39318 210.503063 \nL 219.423619 210.080083 \nL 219.454059 212.030492 \nL 219.514937 202.771926 \nL 219.545377 214.756364 \nL 219.575816 212.547467 \nL 219.636695 214.756364 \nL 219.697574 214.756364 \nL 219.819331 213.487423 \nL 219.91065 214.756364 \nL 219.971529 195.722257 \nL 220.001968 214.732865 \nL 220.032407 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222.102287 214.756364 \nL 222.132726 213.228935 \nL 222.193605 214.756364 \nL 222.224045 197.437676 \nL 222.284923 211.889498 \nL 222.345802 210.78505 \nL 222.406681 214.756364 \nL 222.437121 214.497876 \nL 222.46756 212.406474 \nL 222.497999 214.756364 \nL 222.528439 213.722412 \nL 222.650196 214.756364 \nL 222.680636 214.004399 \nL 222.802393 203.147908 \nL 222.832833 206.08527 \nL 222.863272 214.685867 \nL 222.924151 208.64665 \nL 222.95459 206.437754 \nL 223.076348 214.756364 \nL 223.106787 214.732865 \nL 223.137227 213.205436 \nL 223.167666 194.241826 \nL 223.228545 211.278527 \nL 223.289424 210.597059 \nL 223.350303 214.756364 \nL 223.380742 189.236561 \nL 223.47206 204.722334 \nL 223.532939 211.114034 \nL 223.563379 189.941528 \nL 223.593818 214.732865 \nL 223.624257 211.63101 \nL 223.715576 214.756364 \nL 223.746015 203.735381 \nL 223.806894 211.584012 \nL 223.837333 214.756364 \nL 223.867773 206.08527 \nL 223.928652 209.798096 \nL 223.98953 214.756364 \nL 224.01997 197.672665 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228.068411 214.756364 \nL 228.09885 210.291573 \nL 228.159729 214.756364 \nL 228.190169 214.756364 \nL 228.220608 214.380381 \nL 228.251047 199.505579 \nL 228.281487 214.756364 \nL 228.311926 211.560514 \nL 228.342366 212.852953 \nL 228.372805 190.998978 \nL 228.433684 214.521375 \nL 228.464123 214.61537 \nL 228.494563 214.239388 \nL 228.555441 214.756364 \nL 228.61632 203.335899 \nL 228.677199 192.808393 \nL 228.738078 214.756364 \nL 228.890275 207.330712 \nL 228.920714 213.087942 \nL 228.951154 197.437676 \nL 228.981593 214.756364 \nL 229.012033 208.458659 \nL 229.042472 214.756364 \nL 229.13379 213.322931 \nL 229.225108 214.756364 \nL 229.255548 213.440425 \nL 229.285987 214.756364 \nL 229.346866 203.335899 \nL 229.377305 208.43516 \nL 229.468624 214.756364 \nL 229.529502 191.844938 \nL 229.559942 196.192234 \nL 229.6817 214.756364 \nL 229.712139 213.181937 \nL 229.773018 214.732865 \nL 229.864336 213.228935 \nL 229.894775 214.40388 \nL 229.925215 214.732865 \nL 229.955654 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231.72114 210.221076 \nL 231.782019 214.756364 \nL 231.812458 213.76941 \nL 231.903776 200.892014 \nL 231.934216 212.382975 \nL 231.964655 214.756364 \nL 232.055973 213.910403 \nL 232.086413 214.168891 \nL 232.177731 214.239388 \nL 232.20817 196.450722 \nL 232.23861 214.756364 \nL 232.329928 214.286386 \nL 232.360367 214.732865 \nL 232.451686 200.633526 \nL 232.421246 214.756364 \nL 232.482125 208.43516 \nL 232.543004 214.756364 \nL 232.603883 213.745911 \nL 232.664761 213.275933 \nL 232.72564 197.155689 \nL 232.786519 206.132268 \nL 232.877837 214.756364 \nL 232.847398 198.518625 \nL 232.908277 213.252434 \nL 232.999595 214.756364 \nL 233.030034 211.278527 \nL 233.121353 213.275933 \nL 233.24311 200.563029 \nL 233.27355 210.479564 \nL 233.303989 214.756364 \nL 233.395307 212.570966 \nL 233.425747 208.200171 \nL 233.456186 186.581185 \nL 233.517065 211.20803 \nL 233.547504 214.756364 \nL 233.608383 209.140127 \nL 233.638823 207.871187 \nL 233.669262 188.249607 \nL 233.730141 214.756364 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237.839461 199.881561 \nL 237.8699 214.756364 \nL 237.90034 214.685867 \nL 238.052537 203.382897 \nL 238.143855 214.756364 \nL 238.174294 205.615292 \nL 238.235173 211.137533 \nL 238.296052 214.756364 \nL 238.326491 212.547467 \nL 238.356931 198.84761 \nL 238.38737 214.756364 \nL 238.41781 205.967776 \nL 238.539567 214.756364 \nL 238.661325 198.072146 \nL 238.691764 214.756364 \nL 238.783082 212.241982 \nL 238.813522 200.093051 \nL 238.874401 214.756364 \nL 238.90484 203.547389 \nL 239.057037 214.756364 \nL 239.087476 207.213217 \nL 239.148355 211.090536 \nL 239.270113 214.756364 \nL 239.361431 191.539453 \nL 239.42231 214.756364 \nL 239.483189 205.87378 \nL 239.544068 214.756364 \nL 239.604946 210.009586 \nL 239.635386 214.427379 \nL 239.665825 201.385491 \nL 239.696265 214.756364 \nL 239.726704 211.41952 \nL 239.787583 214.450878 \nL 239.818022 209.445613 \nL 239.878901 214.756364 \nL 240.000659 209.375116 \nL 240.031098 191.492455 \nL 240.091977 214.662368 \nL 240.122416 214.61537 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254.733332 214.756364 \nL 254.763771 214.756364 \nL 254.794211 198.730115 \nL 254.885529 206.813736 \nL 254.946408 214.756364 \nL 254.976847 213.487423 \nL 255.007287 141.345807 \nL 255.068165 214.756364 \nL 255.098605 213.886904 \nL 255.159484 196.239232 \nL 255.189923 212.664962 \nL 255.220362 210.808549 \nL 255.250802 212.735458 \nL 255.281241 212.030492 \nL 255.311681 214.662368 \nL 255.34212 201.925966 \nL 255.402999 214.756364 \nL 255.463878 211.513516 \nL 255.494317 199.599575 \nL 255.555196 214.756364 \nL 255.616075 214.756364 \nL 255.646514 213.275933 \nL 255.676954 213.698913 \nL 255.707393 201.033007 \nL 255.737832 214.732865 \nL 255.768272 211.067037 \nL 255.798711 214.145392 \nL 255.829151 194.194828 \nL 255.85959 214.756364 \nL 255.890029 214.756364 \nL 255.920469 214.756364 \nL 255.950908 191.210468 \nL 256.011787 212.71196 \nL 256.042226 208.811143 \nL 256.103105 212.946949 \nL 256.133545 214.756364 \nL 256.163984 214.638869 \nL 256.224863 167.923061 \nL 256.255302 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288.947226 213.064443 \nL 288.977665 214.756364 \nL 289.038544 214.215889 \nL 289.099423 204.111363 \nL 289.129863 208.811143 \nL 289.190741 214.756364 \nL 289.25162 212.876452 \nL 289.28206 214.756364 \nL 289.434257 196.521219 \nL 289.556014 214.756364 \nL 289.586454 212.218483 \nL 289.677772 212.735458 \nL 289.708211 189.04857 \nL 289.76909 212.852953 \nL 289.829969 214.756364 \nL 289.860408 205.051319 \nL 289.951727 211.231529 \nL 289.982166 210.526562 \nL 290.012605 211.043538 \nL 290.043045 212.735458 \nL 290.073484 208.059178 \nL 290.103924 214.732865 \nL 290.134363 212.030492 \nL 290.195242 214.286386 \nL 290.225681 207.213217 \nL 290.28656 214.756364 \nL 290.408318 199.552577 \nL 290.469196 198.283636 \nL 290.530075 214.756364 \nL 290.621394 214.685867 \nL 290.651833 203.523891 \nL 290.712712 214.756364 \nL 290.743151 214.756364 \nL 290.864909 204.557842 \nL 290.925788 214.756364 \nL 291.017106 214.427379 \nL 291.047545 214.356882 \nL 291.077985 190.834486 \nL 291.108424 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297.561578 209.351617 \nL 297.592018 214.756364 \nL 297.622457 201.174001 \nL 297.652897 213.322931 \nL 297.805094 203.899873 \nL 297.896412 214.756364 \nL 297.926851 203.194906 \nL 298.01817 209.962589 \nL 298.139927 214.756364 \nL 298.170367 205.192312 \nL 298.231245 214.756364 \nL 298.261685 214.756364 \nL 298.292124 205.779785 \nL 298.353003 206.696242 \nL 298.383442 214.121893 \nL 298.474761 213.181937 \nL 298.535639 214.756364 \nL 298.566079 211.325525 \nL 298.626958 214.544874 \nL 298.687837 214.756364 \nL 298.748715 213.205436 \nL 298.809594 214.756364 \nL 298.840034 213.886904 \nL 298.870473 203.194906 \nL 298.931352 208.176672 \nL 299.02267 214.756364 \nL 299.053109 208.035679 \nL 299.113988 214.756364 \nL 299.144428 214.662368 \nL 299.235746 207.424708 \nL 299.327064 214.756364 \nL 299.357503 211.772004 \nL 299.387943 211.41952 \nL 299.418382 206.531749 \nL 299.448822 207.612699 \nL 299.479261 214.756364 \nL 299.509701 202.019961 \nL 299.54014 212.641463 \nL 299.601019 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301.366504 207.777191 \nL 301.396944 214.756364 \nL 301.427383 211.090536 \nL 301.457823 202.583935 \nL 301.488262 212.218483 \nL 301.518701 203.923372 \nL 301.640459 214.756364 \nL 301.670898 214.098395 \nL 301.701338 214.756364 \nL 301.731777 196.779707 \nL 301.823095 204.534343 \nL 301.883974 214.756364 \nL 301.914414 211.161032 \nL 301.944853 199.247091 \nL 302.005732 214.732865 \nL 302.036171 209.892092 \nL 302.066611 214.756364 \nL 302.12749 211.137533 \nL 302.157929 210.926043 \nL 302.188368 197.061694 \nL 302.249247 211.748505 \nL 302.279687 208.975635 \nL 302.310126 214.756364 \nL 302.340565 213.910403 \nL 302.401444 214.756364 \nL 302.431884 202.25495 \nL 302.492762 207.918184 \nL 302.553641 214.756364 \nL 302.584081 208.811143 \nL 302.61452 196.004243 \nL 302.644959 214.756364 \nL 302.705838 202.513438 \nL 302.736278 202.536937 \nL 302.888475 214.756364 \nL 303.010232 203.453394 \nL 303.13199 214.756364 \nL 303.192869 206.061771 \nL 303.223308 211.278527 \nL 303.314626 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "Kar vidimo tukaj, je, da se je povprečna dolžina poti sprva povečala. To je verjetno posledica dejstva, da ko o okolju ne vemo ničesar, se zlahka ujamemo v slaba stanja, kot so voda ali volk. Ko se naučimo več in začnemo uporabljati to znanje, lahko okolje raziskujemo dlje, vendar še vedno ne vemo dobro, kje so jabolka.\n",
+    "\n",
+    "Ko se dovolj naučimo, postane agentu lažje doseči cilj, dolžina poti pa začne upadati. Vendar smo še vedno odprti za raziskovanje, zato pogosto odstopamo od najboljše poti in raziskujemo nove možnosti, kar podaljša pot nad optimalno dolžino.\n",
+    "\n",
+    "Na tem grafu opazimo tudi, da se je dolžina na neki točki nenadoma povečala. To kaže na stohastično naravo procesa in na to, da lahko na neki točki \"pokvarimo\" koeficiente Q-tabele, tako da jih prepišemo z novimi vrednostmi. To bi morali idealno zmanjšati z znižanjem učne stopnje (tj. proti koncu učenja prilagajamo vrednosti Q-tabele le za majhno vrednost).\n",
+    "\n",
+    "Na splošno je pomembno vedeti, da uspeh in kakovost učnega procesa močno odvisna od parametrov, kot so učna stopnja, zmanjševanje učne stopnje in faktor diskonta. Ti se pogosto imenujejo **hiperparametri**, da jih ločimo od **parametrov**, ki jih optimiziramo med učenjem (npr. koeficienti Q-tabele). Proces iskanja najboljših vrednosti hiperparametrov se imenuje **optimizacija hiperparametrov**, in si zasluži ločeno obravnavo.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## Vaja\n",
+    "#### Bolj realističen svet Petra in volka\n",
+    "\n",
+    "V naši situaciji se je Peter lahko premikal skoraj brez utrujenosti ali lakote. V bolj realističnem svetu se mora občasno ustaviti, da si odpočije, in se tudi nahraniti. Naredimo naš svet bolj realističen z uvedbo naslednjih pravil:\n",
+    "\n",
+    "1. Ko se Peter premika iz enega kraja v drugega, izgublja **energijo** in pridobiva **utrujenost**.\n",
+    "2. Peter lahko pridobi več energije z uživanjem jabolk.\n",
+    "3. Peter se lahko znebi utrujenosti tako, da počiva pod drevesom ali na travi (tj. ko stopi na polje z drevesom ali travo - zeleno polje).\n",
+    "4. Peter mora najti in ubiti volka.\n",
+    "5. Da bi ubil volka, mora Peter imeti določene ravni energije in utrujenosti, sicer izgubi boj.\n",
+    "\n",
+    "Spremenite zgornjo funkcijo nagrajevanja v skladu s pravili igre, zaženite algoritem za okrepitev učenja, da se naučite najboljše strategije za zmago v igri, in primerjajte rezultate naključnega premikanja z vašim algoritmom glede na število zmag in porazov.\n",
+    "\n",
+    "> **Opomba**: Morda boste morali prilagoditi hiperparametre, da bo delovalo, še posebej število epoh. Ker je uspeh igre (boj z volkom) redek dogodek, lahko pričakujete precej daljši čas učenja.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/8-Reinforcement/2-Gym/notebook.ipynb b/translations/sl/8-Reinforcement/2-Gym/notebook.ipynb
new file mode 100644
index 000000000..c04a3c13b
--- /dev/null
+++ b/translations/sl/8-Reinforcement/2-Gym/notebook.ipynb
@@ -0,0 +1,394 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.4 64-bit ('base': conda)"
+  },
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "coopTranslator": {
+   "original_hash": "f22f8f3daed4b6d34648d1254763105b",
+   "translation_date": "2025-09-06T15:16:34+00:00",
+   "source_file": "8-Reinforcement/2-Gym/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Drsanje na CartPole\n",
+    "\n",
+    "> **Problem**: Če želi Peter pobegniti volku, se mora premikati hitreje od njega. Videli bomo, kako se Peter lahko nauči drsati, predvsem pa ohranjati ravnotežje, z uporabo Q-Learninga.\n",
+    "\n",
+    "Najprej namestimo knjižnico gym in uvozimo potrebne knjižnice:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Ustvari okolje cartpole\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 2"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Da vidimo, kako deluje okolje, izvedimo kratko simulacijo za 100 korakov.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Med simulacijo moramo pridobiti opazovanja, da se lahko odločimo, kako ukrepati. Pravzaprav nam funkcija `step` vrne trenutna opazovanja, funkcijo nagrajevanja in zastavico `done`, ki označuje, ali ima smisel nadaljevati simulacijo ali ne:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 4"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Lahko dobimo najmanjšo in največjo vrednost teh števil:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Raziskujmo tudi druge metode diskretizacije z uporabo binov:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Zdaj izvedimo kratko simulacijo in opazujmo te diskretne vrednosti okolja.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -2, -2)\n(0, 1, -2, -5)\n(0, 2, -3, -8)\n(0, 3, -5, -11)\n(0, 3, -7, -14)\n(0, 4, -10, -17)\n(0, 3, -14, -15)\n(0, 3, -17, -12)\n(0, 3, -20, -16)\n(0, 4, -23, -19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 8"
+   ]
+  },
+  {
+   "source": [
+    "## Struktura Q-tabele\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 9"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 22.0, alpha=0.3, epsilon=0.9\n",
+      "5000: 70.1384, alpha=0.3, epsilon=0.9\n",
+      "10000: 121.8586, alpha=0.3, epsilon=0.9\n",
+      "15000: 149.6368, alpha=0.3, epsilon=0.9\n",
+      "20000: 168.2782, alpha=0.3, epsilon=0.9\n",
+      "25000: 196.7356, alpha=0.3, epsilon=0.9\n",
+      "30000: 220.7614, alpha=0.3, epsilon=0.9\n",
+      "35000: 233.2138, alpha=0.3, epsilon=0.9\n",
+      "40000: 248.22, alpha=0.3, epsilon=0.9\n",
+      "45000: 264.636, alpha=0.3, epsilon=0.9\n",
+      "50000: 276.926, alpha=0.3, epsilon=0.9\n",
+      "55000: 277.9438, alpha=0.3, epsilon=0.9\n",
+      "60000: 248.881, alpha=0.3, epsilon=0.9\n",
+      "65000: 272.529, alpha=0.3, epsilon=0.9\n",
+      "70000: 281.7972, alpha=0.3, epsilon=0.9\n",
+      "75000: 284.2844, alpha=0.3, epsilon=0.9\n",
+      "80000: 269.667, alpha=0.3, epsilon=0.9\n",
+      "85000: 273.8652, alpha=0.3, epsilon=0.9\n",
+      "90000: 278.2466, alpha=0.3, epsilon=0.9\n",
+      "95000: 269.1736, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 11"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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125.656548 212.345721 \r\nL 125.738727 132.392727 \r\nL 125.811775 195.872993 \r\nL 125.805688 47.216674 \r\nL 125.817862 130.383858 \r\nL 125.820906 33.958137 \r\nL 125.924391 194.667672 \r\nL 125.991351 17.083636 \r\nL 125.970046 202.301375 \r\nL 126.046137 122.750155 \r\nL 126.137447 186.230421 \r\nL 126.122229 70.519557 \r\nL 126.155709 132.794501 \r\nL 126.16484 85.786962 \r\nL 126.177015 195.069446 \r\nL 126.259194 131.187406 \r\nL 126.271369 203.104922 \r\nL 126.353548 85.786962 \r\nL 126.37181 178.998492 \r\nL 126.453989 86.992284 \r\nL 126.472251 209.533304 \r\nL 126.55443 81.769224 \r\nL 126.584866 133.196275 \r\nL 126.633565 199.087184 \r\nL 126.603128 63.689401 \r\nL 126.694438 104.268559 \r\nL 126.706613 209.13153 \r\nL 126.7127 70.117783 \r\nL 126.807054 158.9098 \r\nL 126.886189 78.153259 \r\nL 126.840534 201.497827 \r\nL 126.916626 132.392727 \r\nL 127.001849 201.497827 \r\nL 126.931844 82.974545 \r\nL 127.023154 138.017561 \r\nL 127.074897 102.661463 \r\nL 127.111421 205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 128.423241 194.265898 \r\nL 128.514551 142.035299 \r\nL 128.56325 208.729756 \r\nL 128.566293 51.234412 \r\nL 128.624123 150.47255 \r\nL 128.730651 46.011353 \r\nL 128.715433 208.327982 \r\nL 128.736739 103.465011 \r\nL 128.751957 172.971885 \r\nL 128.74587 80.162129 \r\nL 128.803699 140.428204 \r\nL 128.858485 33.958137 \r\nL 128.864573 204.712018 \r\nL 128.913271 151.276098 \r\nL 128.919359 192.658803 \r\nL 128.998494 63.287627 \r\nL 129.016756 110.295166 \r\nL 129.077629 60.876984 \r\nL 129.025887 186.230421 \r\nL 129.114153 117.928869 \r\nL 129.117197 166.945277 \r\nL 129.175027 50.832639 \r\nL 129.223725 161.320443 \r\nL 129.321123 63.287627 \r\nL 129.308948 199.890732 \r\nL 129.333297 84.983415 \r\nL 129.357647 198.283636 \r\nL 129.409389 53.243282 \r\nL 129.442869 145.24949 \r\nL 129.518961 20.297827 \r\nL 129.491568 197.078315 \r\nL 129.549398 161.722217 \r\nL 129.640708 202.301375 \r\nL 129.610271 33.556364 \r\nL 129.655926 179.80204 \r\nL 129.716799 19.092506 \r\nL 129.668101 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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51.636186 \r\nL 330.930568 164.13286 \r\nL 330.994485 63.689401 \r\nL 330.970136 197.881863 \r\nL 331.043184 113.911131 \r\nL 331.101013 59.269889 \r\nL 331.13145 192.257029 \r\nL 331.143625 140.02643 \r\nL 331.213629 200.292506 \r\nL 331.22276 49.627317 \r\nL 331.234935 174.980754 \r\nL 331.317114 38.779424 \r\nL 331.262328 199.488958 \r\nL 331.344507 81.36745 \r\nL 331.435817 199.890732 \r\nL 331.444948 50.832639 \r\nL 331.454079 185.0251 \r\nL 331.551476 43.198936 \r\nL 331.536258 208.729756 \r\nL 331.566694 101.456142 \r\nL 331.578869 193.864124 \r\nL 331.633655 76.14439 \r\nL 331.64583 93.018891 \r\nL 331.648873 21.503149 \r\nL 331.664092 202.703149 \r\nL 331.755402 46.8149 \r\nL 331.816275 210.336851 \r\nL 331.831493 46.011353 \r\nL 331.868017 153.284967 \r\nL 331.962371 17.083636 \r\nL 331.910629 201.096053 \r\nL 331.977589 82.974545 \r\nL 332.059768 194.265898 \r\nL 331.989764 58.064568 \r\nL 332.090205 170.963016 \r\nL 332.117598 23.512018 \r\nL 332.141947 188.23929 \r\nL 332.202821 102.661463 \r\nL 332.236301 199.488958 \r\nL 332.306305 31.547494 \r\nL 332.358048 17.083636 \r\nL 332.415877 204.712018 \r\nL 332.522406 17.083636 \r\nL 332.491969 209.533304 \r\nL 332.531537 43.60071 \r\nL 332.635021 209.533304 \r\nL 332.543711 17.083636 \r\nL 332.644152 87.795831 \r\nL 332.711113 44.002483 \r\nL 332.662414 199.890732 \r\nL 332.735462 117.928869 \r\nL 332.738506 196.274767 \r\nL 332.750681 17.083636 \r\nL 332.841991 81.769224 \r\nL 332.863296 192.257029 \r\nL 332.930257 17.083636 \r\nL 332.954606 193.864124 \r\nL 333.039829 80.965676 \r\nL 333.042873 17.083636 \r\nL 333.061135 206.720887 \r\nL 333.149401 39.582971 \r\nL 333.161576 180.605588 \r\nL 333.240711 17.083636 \r\nL 333.258973 87.795831 \r\nL 333.262017 17.083636 \r\nL 333.344196 193.46235 \r\nL 333.365501 76.546164 \r\nL 333.462899 206.319113 \r\nL 333.371589 17.083636 \r\nL 333.475073 121.14306 \r\nL 333.578558 47.618448 \r\nL 333.560296 195.872993 \r\nL 333.587689 55.653925 \r\nL 333.685086 199.087184 \r\nL 333.639431 26.726208 \r\nL 333.697261 81.36745 \r\nL 333.72161 193.060576 \r\nL 333.712479 17.083636 \r\nL 333.785527 147.660133 \r\nL 333.87075 17.083636 \r\nL 333.809877 187.033969 \r\nL 333.895099 185.828647 \r\nL 333.898143 187.033969 \r\nL 333.949885 17.083636 \r\nL 333.955973 197.480089 \r\nL 334.010759 78.956807 \r\nL 334.062501 17.083636 \r\nL 334.065545 203.90847 \r\nL 334.095981 86.188736 \r\nL 334.175117 195.069446 \r\nL 334.105112 17.083636 \r\nL 334.205553 70.92133 \r\nL 334.275558 197.881863 \r\nL 334.23599 17.083636 \r\nL 334.302951 47.618448 \r\nL 334.354693 197.078315 \r\nL 334.403392 17.083636 \r\nL 334.47644 189.042838 \r\nL 334.512964 66.501818 \r\nL 334.592099 189.042838 \r\nL 334.573837 17.083636 \r\nL 334.619492 83.376319 \r\nL 334.689496 17.083636 \r\nL 334.69254 199.087184 \r\nL 334.719933 97.438404 \r\nL 334.722977 202.301375 \r\nL 334.738195 17.083636 \r\nL 334.826461 126.36612 \r\nL 334.893422 17.083636 \r\nL 334.917771 193.864124 \r\nL 334.93299 84.581641 \r\nL 334.990819 192.658803 \r\nL 334.996907 23.913792 \r\nL 335.042562 86.188736 \r\nL 335.127784 198.68541 \r\nL 335.082129 17.083636 \r\nL 335.155177 144.044169 \r\nL 335.228225 17.083636 \r\nL 335.203876 202.301375 \r\nL 335.261706 65.698271 \r\nL 335.362147 196.274767 \r\nL 335.307361 17.083636 \r\nL 335.371278 166.141729 \r\nL 335.453457 17.083636 \r\nL 335.380409 208.729756 \r\nL 335.483893 78.153259 \r\nL 335.587378 195.069446 \r\nL 335.520417 40.386519 \r\nL 335.593465 81.36745 \r\nL 335.617815 192.658803 \r\nL 335.690863 17.083636 \r\nL 335.794347 198.283636 \r\nL 335.803478 147.258359 \r\nL 335.84609 195.069446 \r\nL 335.818697 33.15459 \r\nL 335.891745 115.116452 \r\nL 335.967836 17.083636 \r\nL 335.995229 209.13153 \r\nL 335.998273 163.731086 \r\nL 336.077408 32.752816 \r\nL 336.007404 205.515565 \r\nL 336.123063 69.314235 \r\nL 336.199155 191.051707 \r\nL 336.156544 25.520887 \r\nL 336.235679 101.456142 \r\nL 336.269159 33.15459 \r\nL 336.314814 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Iz tega grafa ni mogoče ničesar razbrati, saj se zaradi narave stohastičnega procesa učenja dolžina učnih sej močno razlikuje. Da bi ta graf imel več smisla, lahko izračunamo **tekoče povprečje** preko serije poskusov, recimo 100. To lahko priročno izvedemo z uporabo `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "#code block 12"
+   ]
+  },
+  {
+   "source": [
+    "## Spreminjanje hiperparametrov in opazovanje rezultatov v praksi\n",
+    "\n",
+    "Zdaj bi bilo zanimivo dejansko videti, kako se obnaša trenirani model. Zaženimo simulacijo in sledili bomo isti strategiji izbire akcij kot med treningom: vzorčenje glede na porazdelitev verjetnosti v Q-tabeli:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 13"
+   ]
+  },
+  {
+   "source": [
+    "## Shranjevanje rezultata v animiran GIF\n",
+    "\n",
+    "Če želite navdušiti svoje prijatelje, jim lahko pošljete animiran GIF slike ravnotežne palice. Za to lahko uporabimo `env.render` za ustvarjanje slikovnega okvirja in nato te okvirje shranimo v animiran GIF z uporabo knjižnice PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve AI za prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna napačna razumevanja ali napačne interpretacije, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/8-Reinforcement/2-Gym/solution/notebook.ipynb b/translations/sl/8-Reinforcement/2-Gym/solution/notebook.ipynb
new file mode 100644
index 000000000..dfd958fc6
--- /dev/null
+++ b/translations/sl/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -0,0 +1,526 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "5c0e485e58d63c506f1791c4dbf990ce",
+   "translation_date": "2025-09-06T15:19:25+00:00",
+   "source_file": "8-Reinforcement/2-Gym/solution/notebook.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Drsanje na CartPole\n",
+    "\n",
+    "> **Problem**: Če želi Peter pobegniti volku, mora biti sposoben premikati se hitreje od njega. Videli bomo, kako se lahko Peter nauči drsati, predvsem ohranjati ravnotežje, z uporabo Q-Learninga.\n",
+    "\n",
+    "Najprej namestimo knjižnico gym in uvozimo potrebne knjižnice:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: gym in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.18.3)\n",
+      "Requirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
+      "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
+      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.6.0)\n",
+      "Requirement already satisfied: pyglet<=1.5.15,>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.5.15)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!pip install gym \n",
+    "\n",
+    "import gym\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "source": [
+    "## Ustvari okolje cartpole\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Da vidimo, kako deluje okolje, izvedimo kratko simulacijo za 100 korakov.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Med simulacijo moramo pridobiti opazovanja, da se lahko odločimo, kako ukrepati. Pravzaprav nam funkcija `step` vrne trenutna opazovanja, funkcijo nagrajevanja in zastavico `done`, ki označuje, ali ima smisel nadaljevati simulacijo ali ne:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[ 0.03044442 -0.19543914 -0.04496216  0.28125618] -> 1.0\n",
+      "[ 0.02653564 -0.38989186 -0.03933704  0.55942606] -> 1.0\n",
+      "[ 0.0187378  -0.19424049 -0.02814852  0.25461393] -> 1.0\n",
+      "[ 0.01485299 -0.38894946 -0.02305624  0.53828712] -> 1.0\n",
+      "[ 0.007074   -0.19351108 -0.0122905   0.23842953] -> 1.0\n",
+      "[ 0.00320378  0.00178427 -0.00752191 -0.05810469] -> 1.0\n",
+      "[ 0.00323946  0.19701326 -0.008684   -0.35315131] -> 1.0\n",
+      "[ 0.00717973  0.00201587 -0.01574703 -0.06321931] -> 1.0\n",
+      "[ 0.00722005  0.19736001 -0.01701141 -0.36082863] -> 1.0\n",
+      "[ 0.01116725  0.39271958 -0.02422798 -0.65882671] -> 1.0\n",
+      "[ 0.01902164  0.19794307 -0.03740452 -0.37387001] -> 1.0\n",
+      "[ 0.0229805   0.39357584 -0.04488192 -0.67810827] -> 1.0\n",
+      "[ 0.03085202  0.58929164 -0.05844408 -0.98457719] -> 1.0\n",
+      "[ 0.04263785  0.78514572 -0.07813563 -1.2950295 ] -> 1.0\n",
+      "[ 0.05834076  0.98116859 -0.10403622 -1.61111521] -> 1.0\n",
+      "[ 0.07796413  0.78741784 -0.13625852 -1.35259196] -> 1.0\n",
+      "[ 0.09371249  0.98396202 -0.16331036 -1.68461179] -> 1.0\n",
+      "[ 0.11339173  0.79106371 -0.1970026  -1.44691436] -> 1.0\n",
+      "[ 0.12921301  0.59883361 -0.22594088 -1.22169133] -> 1.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Lahko dobimo najmanjšo in največjo vrednost teh števil:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(env.observation_space.low)\n",
+    "print(env.observation_space.high)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretize(x):\n",
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+   ]
+  },
+  {
+   "source": [
+    "Raziskujmo tudi druge metode diskretizacije z uporabo binov:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_bins(i,num):\n",
+    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
+    "\n",
+    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
+    "\n",
+    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
+    "nbins = [20,20,10,10] # number of bins for each parameter\n",
+    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
+    "\n",
+    "def discretize_bins(x):\n",
+    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+   ]
+  },
+  {
+   "source": [
+    "Zdaj izvedimo kratko simulacijo in opazujmo te diskretne vrednosti okolja.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 2, -4, -9)\n(0, 3, -6, -12)\n(0, 2, -8, -9)\n(0, 3, -10, -13)\n(0, 4, -13, -16)\n(0, 4, -16, -19)\n(0, 4, -20, -17)\n(0, 4, -24, -20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   #env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   #print(discretize_bins(obs))\n",
+    "   print(discretize(obs))\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Struktura Q-tabele\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = {}\n",
+    "actions = (0,1)\n",
+    "\n",
+    "def qvalues(state):\n",
+    "    return [Q.get((state,a),0) for a in actions]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hyperparameters\n",
+    "alpha = 0.3\n",
+    "gamma = 0.9\n",
+    "epsilon = 0.90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 108.0, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v\n",
+    "\n",
+    "Qmax = 0\n",
+    "cum_rewards = []\n",
+    "rewards = []\n",
+    "for epoch in range(100000):\n",
+    "    obs = env.reset()\n",
+    "    done = False\n",
+    "    cum_reward=0\n",
+    "    # == do the simulation ==\n",
+    "    while not done:\n",
+    "        s = discretize(obs)\n",
+    "        if random.random()<epsilon:\n",
+    "            # exploitation - chose the action according to Q-Table probabilities\n",
+    "            v = probs(np.array(qvalues(s)))\n",
+    "            a = random.choices(actions,weights=v)[0]\n",
+    "        else:\n",
+    "            # exploration - randomly chose the action\n",
+    "            a = np.random.randint(env.action_space.n)\n",
+    "\n",
+    "        obs, rew, done, info = env.step(a)\n",
+    "        cum_reward+=rew\n",
+    "        ns = discretize(obs)\n",
+    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
+    "    cum_rewards.append(cum_reward)\n",
+    "    rewards.append(cum_reward)\n",
+    "    # == Periodically print results and calculate average reward ==\n",
+    "    if epoch%5000==0:\n",
+    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
+    "        if np.average(cum_rewards) > Qmax:\n",
+    "            Qmax = np.average(cum_rewards)\n",
+    "            Qbest = Q\n",
+    "        cum_rewards=[]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Iz tega grafa ni mogoče ničesar razbrati, saj se zaradi narave stohastičnega procesa učenja dolžina učnih sej močno razlikuje. Da bi ta graf bolje razumeli, lahko izračunamo **tekoče povprečje** preko serije poskusov, recimo 100. To lahko enostavno izvedemo z uporabo `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "def running_average(x,window):\n",
+    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
+    "\n",
+    "plt.plot(running_average(rewards,100))"
+   ]
+  },
+  {
+   "source": [
+    "## Spreminjanje hiperparametrov in opazovanje rezultatov v praksi\n",
+    "\n",
+    "Zdaj bi bilo zanimivo dejansko videti, kako se obnaša trenirani model. Zaženimo simulacijo, pri čemer bomo sledili isti strategiji izbire akcij kot med treningom: vzorčenje glede na porazdelitev verjetnosti v Q-tabeli:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset()\n",
+    "done = False\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   env.render()\n",
+    "   v = probs(np.array(qvalues(s)))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Shranjevanje rezultata v animiran GIF\n",
+    "\n",
+    "Če želite navdušiti svoje prijatelje, jim lahko pošljete animiran GIF slike ravnotežne palice. Za to lahko uporabimo `env.render` za ustvarjanje slikovnega okvirja in nato te okvirje shranimo v animiran GIF z uporabo knjižnice PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za prevajanje z umetno inteligenco [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo profesionalni človeški prevod. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki bi nastale zaradi uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sl/PyTorch_Fundamentals.ipynb b/translations/sl/PyTorch_Fundamentals.ipynb
new file mode 100644
index 000000000..04eb2331b
--- /dev/null
+++ b/translations/sl/PyTorch_Fundamentals.ipynb
@@ -0,0 +1,2830 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4",
+   "authorship_tag": "ABX9TyOgv0AozH1FKQBD+RkgT2bV",
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "coopTranslator": {
+   "original_hash": "0ca21b6ee62904d616f2e36dc1cf0da7",
+   "translation_date": "2025-09-06T13:07:55+00:00",
+   "source_file": "PyTorch_Fundamentals.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "view-in-github",
+    "colab_type": "text"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EHh5JllMh1rG",
+    "outputId": "f55755ad-c369-414c-85ec-6e9d4f061a02",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'2.2.1+cu121'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(\"I am excited to run this\")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UPlb-duwXAfz",
+    "outputId": "cfd687e4-1238-49f4-ab6b-ee1305b740d2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "I am excited to run this\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(torch.__version__)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "byWVlJ9wXDSk",
+    "outputId": "fd74a5c4-4d4a-41b2-ef3c-562ea3e4811f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2.2.1+cu121\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "Osm80zoEYklS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# scalar\n",
+    "scalar = torch.tensor(7)\n",
+    "scalar"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-o8wvJ-VXZmI",
+    "outputId": "558816f5-1205-4de1-fe1f-2f96e9bd79e6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(7)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mCZ2tXC4Y_Sg",
+    "outputId": "2d86dbdc-56e1-45c6-d3dd-14515f2a457a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ssN00By0ZQgS",
+    "outputId": "490f40d1-5135-4969-a6d3-c8c902cdc473"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# vector\n",
+    "vector = torch.tensor([7, 7])\n",
+    "vector\n",
+    "#vector.ndim\n",
+    "#vector.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Bws__5wlZnmF",
+    "outputId": "944e38f9-5ba1-4ddc-a9c6-cfb6a19bb488"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([7, 7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "vector.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9pjCvnsZZzNG",
+    "outputId": "e030a4da-8f81-4858-fbce-86da2aaafe52"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([2])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Matrix\n",
+    "MATRIX = torch.tensor([[7, 8],[9, 10]])\n",
+    "MATRIX"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a747hI9SaBGW",
+    "outputId": "af835ddb-81ff-4981-badb-441567194d15"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 7,  8],\n",
+       "        [ 9, 10]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XdTfFa7vaRUj",
+    "outputId": "0fbbab9c-8263-4cad-a380-0d2a16ca499e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX[0]\n",
+    "MATRIX[1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TFeD3jSDafm7",
+    "outputId": "69b44ab3-5ba7-451a-c6b2-f019a03d0c96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Tensor\n",
+    "TENSOR = torch.tensor([[[1, 2, 3],[3,6,9], [2,4,5]]])\n",
+    "TENSOR"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ic3cE47tah42",
+    "outputId": "f250e295-91de-43ec-9d80-588a6fe0abde"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 2, 3],\n",
+       "         [3, 6, 9],\n",
+       "         [2, 4, 5]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wvjf5fczbAM1",
+    "outputId": "9c72b5b8-bafe-4ae7-9883-b051e209eada"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mwtXZwiMbN3m",
+    "outputId": "331a5e36-b1b0-4a5f-a9b8-e7049cbaa8f9"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vzdZu_IfbP3J",
+    "outputId": "e24e7e71-e365-412d-ff50-fc094b56d2f3"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [3, 6, 9],\n",
+       "        [2, 4, 5]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "A8OL9eWfcRrJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor = torch.rand(3,4)\n",
+    "random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hAqSDE1EcVS_",
+    "outputId": "946171c3-d054-400c-f893-79110356888c"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.4414, 0.7681, 0.8385, 0.3166],\n",
+       "        [0.0468, 0.5812, 0.0670, 0.9173],\n",
+       "        [0.2959, 0.3276, 0.7411, 0.4643]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "g4fvPE5GcwzP",
+    "outputId": "8737f36b-6864-4059-eaed-6f9156c22306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XsAg99QmdAU6",
+    "outputId": "35467c11-257c-4f16-99aa-eca930bcbc36"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.size()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cii1pNdVdB68",
+    "outputId": "fc8d2de6-9215-43de-99f7-7b0d7f7d20fa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_image_tensor = torch.rand(size=(3, 224, 224)) #color channels, height, width\n",
+    "random_image_tensor.ndim, random_image_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aTKq2j0cdDjb",
+    "outputId": "6be42057-20b9-4faf-d79d-8b65c42cc27e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([3, 224, 224]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor_ofownsize = torch.rand(size=(5,10,10))\n",
+    "random_tensor_ofownsize.ndim, random_tensor_ofownsize.shape\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IyhDdj-Pd6nC",
+    "outputId": "43e5e334-6d4d-4b67-f87d-7d364c6d8c67"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([5, 10, 10]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "UOJW08uOert_"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero = torch.zeros(size=(3, 4))\n",
+    "zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGvXtaXyefie",
+    "outputId": "d40d3e28-8667-4d2f-8b62-f0829c6162ad"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero*random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OyUkUPkDe0uH",
+    "outputId": "26c2e4be-36ba-4c6c-9a90-2704ec135828"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones = torch.ones(size=(3, 4))\n",
+    "ones\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "y_Ac62Aqe82G",
+    "outputId": "291de5d9-b9df-49de-c9d1-d098e3e9f4d8"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TvGOA9odfIEO",
+    "outputId": "45949ef4-6649-4b6c-d6af-2d4bfb8de832"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones*zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "--pTyge-fI-8",
+    "outputId": "c4d9bb7e-829b-43db-e2db-b1a2d64e61f0"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qDcc7Z36fSJF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "one_to_ten = torch.arange(start = 1, end = 11, step = 1)\n",
+    "one_to_ten"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w3CZB4zUfR1s",
+    "outputId": "197fcba1-da0a-4b4a-ed11-3974bd6c01aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ten_zeros = torch.zeros_like(one_to_ten)\n",
+    "ten_zeros"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WZh99BwVfRy8",
+    "outputId": "51ef8bfb-6fa0-4099-ff66-b97d65b2ddea"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vrste podatkov Tensor\n"
+   ],
+   "metadata": {
+    "id": "pGGhgsbUgqbW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor = torch.tensor([3.0, 6.0,9.0], dtype = None, device = None, requires_grad = False)\n",
+    "float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JORJl4XkfRsx",
+    "outputId": "71114171-0f49-481f-b6fc-6cb48e2fb895"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3., 6., 9.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6wOPPwGyfRLn",
+    "outputId": "f23776a1-b682-404a-9f67-d5bcb0402666"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor = float_32_tensor.type(torch.float16)\n",
+    "float_16_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tFsHCvmZfOYe",
+    "outputId": "d3aa305a-7591-47f5-97fd-61bff60b44bd"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float16"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 31
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TQiCGTPuwq0q",
+    "outputId": "98750fce-1ca3-4889-e269-8b753efdea96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor = torch.tensor([3, 6, 9], dtype = torch.int32)\n",
+    "int_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5hlrLvGUw5D_",
+    "outputId": "41d890a0-9aee-446c-d906-631ce2ab0995"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9], dtype=torch.int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ihApD9u3xTNW",
+    "outputId": "d295eed0-6996-4e0f-8502-ff4b55cd1373"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(0,100,10)"
+   ],
+   "metadata": {
+    "id": "utKhlb_KxWDQ"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p78D74E9Rj7Y",
+    "outputId": "781a1614-a900-41f5-9e5d-358f0b2390aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.min()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4BcSs5NeRkcj",
+    "outputId": "3f24a8dc-58e9-4a5f-9834-e85856a34f9d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.max()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hinqvXVLRm4q",
+    "outputId": "5c7d8a53-3913-4ac1-bba3-5ba8ff68250a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 38
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mean(x.type(torch.float32))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k7okc0_vRpnB",
+    "outputId": "91e5494f-dc57-417c-ea4d-25dbc547c893"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.type(torch.float32).mean()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "29QcDTjHRq10",
+    "outputId": "62937c6c-78e0-49f2-dde3-1543ee8f7907"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wlpY_G_sbdKF",
+    "outputId": "475d8258-af65-4011-a258-b93d4d8142d4"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(450)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmax()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GT6HJzwhbk4n",
+    "outputId": "2e455c20-c322-4bcf-d07c-1259d3ccefc6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmin()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "egL3oi2Mb19P",
+    "outputId": "f71fb32f-6338-44a3-b377-75bea0a3ab54"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p2U8DZKib3DP",
+    "outputId": "b9f613b9-74e9-45f4-ed01-05babb6a6793"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[9]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24qBFlGYcABe",
+    "outputId": "5813cfcb-7f63-4bd7-ee46-f95ccbfda939"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(1, 10)\n",
+    "x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0GPOxEzkcBHO",
+    "outputId": "aefbd903-4f4c-4d2c-c90f-eccd682fe018"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped = x.reshape(1,9)\n",
+    "x_reshaped, x_reshaped.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "spmRgQjwddgp",
+    "outputId": "85a7c55c-2909-4ea2-fc68-386dddc65742"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), torch.Size([1, 9]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped.view(1,9)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tH2ahWGydqqP",
+    "outputId": "65d92263-4fc4-434a-c06d-c5e08436f7fe"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked = torch.stack([x, x, x, x], dim = 1)\n",
+    "x_stacked"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jgCeJcaud_-1",
+    "outputId": "7f293a37-6ef1-43b6-aee5-9d6d91c94f9e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XhJHIK6cfPse",
+    "outputId": "06c47b89-3a9e-453e-bcc3-00cbcb0b8b49"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=1)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ej2c3Xxzf0tq",
+    "outputId": "94024061-eb37-446d-c4a8-e4d16cb6de81"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 52
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4DJYo1a0f5M0",
+    "outputId": "efca2b47-1b14-44de-9a9a-2c83629d153f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 53
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=-2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J4iEjn2ah2HL",
+    "outputId": "22395593-7c16-4162-beae-dd2bbe7bda35"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 55
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "tensor = torch.tensor([1, 2, 3])\n",
+    "tensor = tensor - 10\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cFfiD7Nth7Z_",
+    "outputId": "1139e1f8-fc1a-46ca-d636-f2bc4fd2eef6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-9, -8, -7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mul(tensor, 10)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dyA7BM_GHhqE",
+    "outputId": "0e3b9671-d9e8-4a32-87bb-59bc05986142"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-90, -80, -70])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.sub(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "owtUsZ1KNegI",
+    "outputId": "189b7b23-0041-4e09-b991-cd209a48506a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-109, -108, -107])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.add(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K5STXlQONsyc",
+    "outputId": "00cbb79a-0a1d-4e21-86ec-5c91c37a2d01"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([91, 92, 93])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.divide(tensor, 2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xqMGnzIUNvp0",
+    "outputId": "c894cf3e-f148-45f8-cfc8-d78740735306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-4.5000, -4.0000, -3.5000])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(tensor, tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ruGzKpV8NyBc",
+    "outputId": "fddb63bf-006f-48b6-ae28-287fbcda8bc5"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8GS3r9yTeGfD",
+    "outputId": "c80b12ac-30b5-4f3d-c38c-9e41ba511b0e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QmuYHqXTemC0",
+    "outputId": "402fe3ba-70b5-4bb2-c83b-254db84ff810"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 622 µs, sys: 0 ns, total: 622 µs\n",
+      "Wall time: 516 µs\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "torch.matmul(tensor,tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dGr1fzdNepd8",
+    "outputId": "97bd6c91-bc25-4b38-cdf5-f22dcdef243e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 424 µs, sys: 998 µs, total: 1.42 ms\n",
+      "Wall time: 1.43 ms\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.rand(3,2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pGYDoK2gevfo",
+    "outputId": "2c8783d5-0453-47c5-c7ed-af10d25d6989"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5999, 0.0073],\n",
+       "        [0.9321, 0.3026],\n",
+       "        [0.3463, 0.3872]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(torch.rand(3,2), torch.rand(2,3))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KGBGQoB8e2DP",
+    "outputId": "4c2ef361-a2d0-41ee-c328-3992cbbc138d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.3528, 0.1893, 0.0714],\n",
+       "        [1.2791, 0.7110, 0.2563],\n",
+       "        [0.8812, 0.4553, 0.1803]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch"
+   ],
+   "metadata": {
+    "id": "ib8DMtkBe_LJ"
+   },
+   "execution_count": 1,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.rand(2,9)"
+   ],
+   "metadata": {
+    "id": "nJo8ZBdrQY1b"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wi6oRv4MQfgf",
+    "outputId": "55c99f55-31f6-4cf5-ba4e-19a47c3a0167"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5894, 0.4391, 0.2018, 0.5417, 0.3844, 0.3592, 0.9209, 0.9269, 0.0681],\n",
+       "        [0.0746, 0.1740, 0.6821, 0.6890, 0.0999, 0.7444, 0.2391, 0.4625, 0.8302]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "y=torch.randn(2,3,5)\n",
+    "y"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zpx8myAUQgoc",
+    "outputId": "07756d70-56bd-437c-c74e-9aecc1a77311"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[ 1.5552, -0.4877,  0.5175, -1.7958, -0.6187],\n",
+       "         [-0.3359, -1.9710,  0.0112, -1.7578, -1.5295],\n",
+       "         [ 0.0932,  1.4079,  0.9108,  0.3328, -0.6978]],\n",
+       "\n",
+       "        [[-0.9406, -1.0809, -0.2595,  0.1282,  1.6605],\n",
+       "         [ 1.1624,  1.0902,  1.7092, -0.2842, -1.3780],\n",
+       "         [-0.1534, -1.2795, -0.5495,  0.9902,  0.1822]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original = torch.rand(size=(224,224,3))\n",
+    "x_original"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s4U-X9bJQnWe",
+    "outputId": "657a7a76-962c-4b41-a76b-902d0482266c"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[0.4549, 0.6809, 0.2118],\n",
+       "         [0.4824, 0.9008, 0.8741],\n",
+       "         [0.1715, 0.1757, 0.1845],\n",
+       "         ...,\n",
+       "         [0.8741, 0.6594, 0.2610],\n",
+       "         [0.0092, 0.1984, 0.1955],\n",
+       "         [0.4236, 0.4182, 0.0251]],\n",
+       "\n",
+       "        [[0.9174, 0.1661, 0.5852],\n",
+       "         [0.1837, 0.2351, 0.3810],\n",
+       "         [0.3726, 0.4808, 0.8732],\n",
+       "         ...,\n",
+       "         [0.6794, 0.0554, 0.9202],\n",
+       "         [0.0864, 0.8750, 0.3558],\n",
+       "         [0.8445, 0.9759, 0.4934]],\n",
+       "\n",
+       "        [[0.1600, 0.2635, 0.7194],\n",
+       "         [0.9488, 0.3405, 0.3647],\n",
+       "         [0.6683, 0.5168, 0.9592],\n",
+       "         ...,\n",
+       "         [0.0521, 0.0140, 0.2445],\n",
+       "         [0.3596, 0.3999, 0.2730],\n",
+       "         [0.5926, 0.9877, 0.7784]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[0.4794, 0.5635, 0.3764],\n",
+       "         [0.9124, 0.6094, 0.5059],\n",
+       "         [0.4528, 0.4447, 0.5021],\n",
+       "         ...,\n",
+       "         [0.0089, 0.4816, 0.8727],\n",
+       "         [0.2173, 0.6296, 0.2347],\n",
+       "         [0.2028, 0.9931, 0.7201]],\n",
+       "\n",
+       "        [[0.3116, 0.6459, 0.4703],\n",
+       "         [0.0148, 0.2345, 0.7149],\n",
+       "         [0.8393, 0.5804, 0.6691],\n",
+       "         ...,\n",
+       "         [0.2105, 0.9460, 0.2696],\n",
+       "         [0.5918, 0.9295, 0.2616],\n",
+       "         [0.2537, 0.7819, 0.4700]],\n",
+       "\n",
+       "        [[0.6654, 0.1200, 0.5841],\n",
+       "         [0.9147, 0.5522, 0.6529],\n",
+       "         [0.1799, 0.5276, 0.5415],\n",
+       "         ...,\n",
+       "         [0.7536, 0.4346, 0.8793],\n",
+       "         [0.3793, 0.1750, 0.7792],\n",
+       "         [0.9266, 0.8325, 0.9974]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted=x_original.permute(2, 0, 1)\n",
+    "print(x_original.shape)\n",
+    "print(x_permuted.shape)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DD19_zvbQzHo",
+    "outputId": "1d64ce1b-eb48-47e3-90b6-7f1340e7f2b2"
+   },
+   "execution_count": 9,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "torch.Size([224, 224, 3])\n",
+      "torch.Size([3, 224, 224])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NnPmMk4ZRF7w",
+    "outputId": "2cd5da7f-4a23-4a76-8c4a-bb982113f2a4"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z0ylNoAARgTo",
+    "outputId": "ddca0298-cddf-4048-9b71-a791655e5bed"
+   },
+   "execution_count": 11,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]=0.989"
+   ],
+   "metadata": {
+    "id": "RXw0xXsDRi4L"
+   },
+   "execution_count": 13,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1sFdV6wzRo3f",
+    "outputId": "1cf87d2c-6d88-453a-d136-0f625a2800f1"
+   },
+   "execution_count": 14,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xTX-hx2SR1wp",
+    "outputId": "0d4908c4-c3bc-44e3-8ec6-1487104cc209"
+   },
+   "execution_count": 15,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x=torch.arange(1,10).reshape(1,3,3)\n",
+    "x, x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mZomOe7gR4Q8",
+    "outputId": "0b3c922f-ec11-46de-b8a5-9f9533d866ad"
+   },
+   "execution_count": 18,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[[1, 2, 3],\n",
+       "          [4, 5, 6],\n",
+       "          [7, 8, 9]]]),\n",
+       " torch.Size([1, 3, 3]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3y7v4SQvSBs1",
+    "outputId": "8c53307d-e628-404d-db66-56c6bdffab7c"
+   },
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [4, 5, 6],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hf9uG4xLSNya",
+    "outputId": "3075bc42-9ffa-426b-8a86-95628ffcd824"
+   },
+   "execution_count": 21,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zA4G2Se4SRB3",
+    "outputId": "324312d2-ed0a-49eb-f81f-e904e53992fe"
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(1)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][2][2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mwy3zmKKSdbk",
+    "outputId": "d35172c3-b099-40a6-ddf1-a453c2adfa44"
+   },
+   "execution_count": 23,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[:,1,1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fE3nCM1KS7XT",
+    "outputId": "01f5d755-9737-4235-9f73-dce89ff6ba16"
+   },
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([5])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,0,:]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "luNDINKNTTxp",
+    "outputId": "091195ef-2f71-4602-e95f-529a69193150"
+   },
+   "execution_count": 25,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,:,2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KG8A4xbfThCL",
+    "outputId": "5866bc41-9241-4619-be7b-e9206b3f80ab"
+   },
+   "execution_count": 26,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import numpy as np"
+   ],
+   "metadata": {
+    "id": "CZ3PX0qlTwHJ"
+   },
+   "execution_count": 27,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array = np.arange(1.0, 8.0)"
+   ],
+   "metadata": {
+    "id": "UOBeTumiT3Lf"
+   },
+   "execution_count": 28,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RzcO32E9UCQl",
+    "outputId": "430def24-c42c-461f-e5e7-398544c695d3"
+   },
+   "execution_count": 29,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 4., 5., 6., 7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.from_numpy(array)\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JJIL0q1DUC6O",
+    "outputId": "8a3b1d7c-4482-4d32-f34f-9212d9d3a177"
+   },
+   "execution_count": 32,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1., 2., 3., 4., 5., 6., 7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array[3]=11.0"
+   ],
+   "metadata": {
+    "id": "j3Ce6q3DUIEK"
+   },
+   "execution_count": 33,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dc_BCVdjUsCc",
+    "outputId": "65537325-8b11-4f36-fc73-e56f30d6a036"
+   },
+   "execution_count": 34,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3., 11.,  5.,  6.,  7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VG1e_eITUta2",
+    "outputId": "a26c5198-23b6-4a6d-d73a-ba20cd9782b8"
+   },
+   "execution_count": 35,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1.,  2.,  3., 11.,  5.,  6.,  7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.ones(7)\n",
+    "tensor, tensor.dtype\n",
+    "numpy_tensor = tensor.numpy()\n",
+    "numpy_tensor, numpy_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Swt8JF8vUuev",
+    "outputId": "c9e5bf6a-6d2c-41d6-8327-366867ffdd2d"
+   },
+   "execution_count": 37,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(array([1., 1., 1., 1., 1., 1., 1.], dtype=float32), dtype('float32'))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "random_tensor_A = torch.rand(3,4)\n",
+    "random_tensor_B = torch.rand(3,4)\n",
+    "print(random_tensor_A)\n",
+    "print(random_tensor_B)\n",
+    "print(random_tensor_A == random_tensor_B)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGcagTteVFTD",
+    "outputId": "49405790-08e7-4210-b7f1-f00b904c7eb9"
+   },
+   "execution_count": 38,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.9870, 0.6636, 0.6873, 0.8863],\n",
+      "        [0.8386, 0.4169, 0.3587, 0.0265],\n",
+      "        [0.2981, 0.6025, 0.5652, 0.5840]])\n",
+      "tensor([[0.9821, 0.3481, 0.0913, 0.4940],\n",
+      "        [0.7495, 0.4387, 0.9582, 0.8659],\n",
+      "        [0.5064, 0.6919, 0.0809, 0.9771]])\n",
+      "tensor([[False, False, False, False],\n",
+      "        [False, False, False, False],\n",
+      "        [False, False, False, False]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "RANDOM_SEED = 42\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_C = torch.rand(3,4)\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_D = torch.rand(3,4)\n",
+    "print(random_tensor_C)\n",
+    "print(random_tensor_D)\n",
+    "print(random_tensor_C == random_tensor_D)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HznyXyEaWjLM",
+    "outputId": "25956434-01b6-4059-9054-c9978884ddc1"
+   },
+   "execution_count": 46,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[True, True, True, True],\n",
+      "        [True, True, True, True],\n",
+      "        [True, True, True, True]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!nvidia-smi"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vltPTh0YXJSt",
+    "outputId": "807af6dc-a9ca-4301-ec32-b688dbde8be8"
+   },
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Thu May 23 02:57:59 2024       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   60C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|  No running processes found                                                           |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "torch.cuda.is_available()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L6mMyPDyYh1j",
+    "outputId": "279c5dd8-c2a8-4fbd-f321-2f5d7c6e90e6"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "oOdiYa7ZYytx",
+    "outputId": "d73b04fc-8963-4826-9722-08d118d5ab91"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.cuda.device_count()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vOdsazLqZFM5",
+    "outputId": "8189cd6a-9017-4663-a652-3e15c517d9c3"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.tensor([1,2,3], device = \"cpu\")\n",
+    "print(tensor, tensor.device)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cdik9Vw3ZMv0",
+    "outputId": "044a68fd-83a1-409d-8e3b-655142ca0270"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([1, 2, 3]) cpu\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu = tensor.to(device)\n",
+    "tensor_on_gpu"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zmp835rrZp-z",
+    "outputId": "37fa3413-18a3-47bf-ae51-5b36ff85a3ef"
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3], device='cuda:0')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu.numpy()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 159
+    },
+    "id": "jhriaa8uZ1yM",
+    "outputId": "bc5a3226-1a12-4fea-8769-a44f21cdc323"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "TypeError",
+     "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-e0c96c7436fd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtensor_on_gpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_cpu = tensor_on_gpu.cpu().numpy()"
+   ],
+   "metadata": {
+    "id": "LHGXK3GgaOzL"
+   },
+   "execution_count": 12,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "id": "j-El4LlCajfq"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/1-Tools/notebook.ipynb b/translations/sr/2-Regression/1-Tools/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sr/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/sr/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
new file mode 100644
index 000000000..620ab39c4
--- /dev/null
+++ b/translations/sr/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -0,0 +1,447 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_1-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "c18d3bd0bd8ae3878597e89dcd1fa5c1",
+   "translation_date": "2025-09-06T13:42:12+00:00",
+   "source_file": "2-Regression/1-Tools/solution/R/lesson_1-R.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "YJUHCXqK57yz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Увод у регресију - Лекција 1\n",
+    "\n",
+    "#### Стављање у перспективу\n",
+    "\n",
+    "✅ Постоји много врста метода регресије, а коју ћете изабрати зависи од одговора који тражите. Ако желите да предвидите вероватну висину особе одређеног узраста, користили бисте `линеарну регресију`, јер тражите **нумеричку вредност**. Ако вас занима да ли одређена врста кухиње треба да се сматра веганском или не, тражите **категоријску класификацију**, па бисте користили `логистичку регресију`. О логистичкој регресији ћете више научити касније. Размислите мало о питањима која можете поставити подацима и који од ових метода би био прикладнији.\n",
+    "\n",
+    "У овом делу, радићете са [малим сетом података о дијабетесу](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Замислите да желите да тестирате третман за пацијенте са дијабетесом. Модели машинског учења могу вам помоћи да одредите који пацијенти би боље реаговали на третман, на основу комбинација променљивих. Чак и веома основни модел регресије, када се визуализује, може показати информације о променљивим које би вам помогле да организујете теоријске клиничке студије.\n",
+    "\n",
+    "С тим речима, хајде да започнемо овај задатак!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/encouRage.jpg\"\n",
+    "   width=\"630\"/>\n",
+    "   <figcaption>Илустрација: @allison_horst</figcaption>\n",
+    "\n",
+    "<!--![Илустрација: \\@allison_horst](../../../../../../2-Regression/1-Tools/images/encouRage.jpg)<br>Илустрација: @allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "LWNNzfqd6feZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Учитавање нашег алата\n",
+    "\n",
+    "За овај задатак биће нам потребни следећи пакети:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) је [збирка R пакета](https://www.tidyverse.org/packages) осмишљена да учини науку о подацима бржом, лакшом и забавнијом!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) је [оквир пакета](https://www.tidymodels.org/packages/) за моделирање и машинско учење.\n",
+    "\n",
+    "Можете их инсталирати на следећи начин:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+    "\n",
+    "Скрипта испод проверава да ли имате инсталиране пакете потребне за завршетак овог модула и инсталира их уколико неки недостају.\n"
+   ],
+   "metadata": {
+    "id": "FIo2YhO26wI9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse, tidymodels)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "id": "cIA9fz9v7Dss",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сада, хајде да учитамо ове сјајне пакете и учинимо их доступним у нашој тренутној R сесији. (Ово је само за илустрацију, `pacman::p_load()` је то већ урадио за вас)\n"
+   ],
+   "metadata": {
+    "id": "gpO_P_6f9WUG"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# load the core Tidyverse packages\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# load the core Tidymodels packages\r\n",
+    "library(tidymodels)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NLMycgG-9ezO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Дијабетес скуп података\n",
+    "\n",
+    "У овом задатку, применићемо наше вештине регресије правећи предвиђања на скупу података о дијабетесу. [Скуп података о дијабетесу](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) садржи `442 узорка` података о дијабетесу, са 10 предикторских променљивих: `године`, `пол`, `индекс телесне масе`, `просечан крвни притисак` и `шест мерења крвног серума`, као и излазну променљиву `y`: квантитативну меру напретка болести годину дана након почетног стања.\n",
+    "\n",
+    "|Број опсервација|442|\n",
+    "|----------------|:---|\n",
+    "|Број предиктора|Првих 10 колона су нумерички предиктори|\n",
+    "|Излаз/Циљ|Колона 11 је квантитативна мера напретка болести годину дана након почетног стања|\n",
+    "|Информације о предикторима|- године живота\n",
+    "||- пол\n",
+    "||- bmi индекс телесне масе\n",
+    "||- bp просечан крвни притисак\n",
+    "||- s1 tc, укупни серумски холестерол\n",
+    "||- s2 ldl, липопротеини ниске густине\n",
+    "||- s3 hdl, липопротеини високе густине\n",
+    "||- s4 tch, укупни холестерол / HDL\n",
+    "||- s5 ltg, могуће логаритам нивоа триглицерида у серуму\n",
+    "||- s6 glu, ниво шећера у крви|\n",
+    "\n",
+    "> 🎓 Запамтите, ово је надгледано учење, и потребан нам је именовани циљ 'y'.\n",
+    "\n",
+    "Пре него што можете манипулисати подацима у R-у, потребно је да увезете податке у меморију R-а или успоставите везу са подацима коју R може користити за приступ подацима на даљину.\n",
+    "\n",
+    "> Пакет [readr](https://readr.tidyverse.org/), који је део Tidyverse-а, пружа брз и једноставан начин за читање правоугаоних података у R.\n",
+    "\n",
+    "Сада, учитајмо скуп података о дијабетесу који је доступан на овом URL-у: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+    "\n",
+    "Такође, извршићемо проверу исправности наших података користећи `glimpse()` и приказати првих 5 редова користећи `slice()`.\n",
+    "\n",
+    "Пре него што наставимо даље, представићемо нешто што ћете често сусретати у R коду 🥁🥁: оператор цеви `%>%`\n",
+    "\n",
+    "Оператор цеви (`%>%`) извршава операције у логичком низу тако што прослеђује објекат функцији или изразу. Можете замислити оператор цеви као да у коду каже \"и онда\".\n"
+   ],
+   "metadata": {
+    "id": "KM6iXLH996Cl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import the data set\r\n",
+    "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Get a glimpse and dimensions of the data\r\n",
+    "glimpse(diabetes)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Select the first 5 rows of the data\r\n",
+    "diabetes %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Z1geAMhM-bSP"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`glimpse()` нам показује да овај податак има 442 реда и 11 колона, при чему су све колоне типа податка `double`.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "> glimpse() и slice() су функције у [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, део Tidyverse-а, је граматика за манипулацију подацима која пружа конзистентан сет глагола који вам помажу да решите најчешће изазове у манипулацији подацима.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Сада када имамо податке, усмерићемо се на једну карактеристику (`bmi`) коју ћемо циљати за ову вежбу. Ово ће захтевати да изаберемо жељене колоне. Па, како то можемо урадити?\n",
+    "\n",
+    "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) нам омогућава да *изаберемо* (и опционално преименујемо) колоне у оквиру података.\n"
+   ],
+   "metadata": {
+    "id": "UwjVT1Hz-c3Z"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select predictor feature `bmi` and outcome `y`\r\n",
+    "diabetes_select <- diabetes %>% \r\n",
+    "  select(c(bmi, y))\r\n",
+    "\r\n",
+    "# Print the first 5 rows\r\n",
+    "diabetes_select %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RDY1oAKI-m80"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Тренинг и тестирање података\n",
+    "\n",
+    "У надгледаном учењу је уобичајена пракса да се *подели* подаци на два подскупа; (обично већи) скуп за тренирање модела и мањи \"резервни\" скуп за проверу како је модел функционисао.\n",
+    "\n",
+    "Сада када имамо припремљене податке, можемо видети да ли машина може помоћи у одређивању логичне поделе између бројева у овом скупу података. Можемо користити пакет [rsample](https://tidymodels.github.io/rsample/), који је део оквира Tidymodels, да креирамо објекат који садржи информације о *начину* поделе података, а затим још две rsample функције за издвајање креираних скупова за тренирање и тестирање:\n"
+   ],
+   "metadata": {
+    "id": "SDk668xK-tc3"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\r\n",
+    "# Split 67% of the data for training and the rest for tesing\r\n",
+    "diabetes_split <- diabetes_select %>% \r\n",
+    "  initial_split(prop = 0.67)\r\n",
+    "\r\n",
+    "# Extract the resulting train and test sets\r\n",
+    "diabetes_train <- training(diabetes_split)\r\n",
+    "diabetes_test <- testing(diabetes_split)\r\n",
+    "\r\n",
+    "# Print the first 3 rows of the training set\r\n",
+    "diabetes_train %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "EqtHx129-1h-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Обучите модел линеарне регресије помоћу Tidymodels\n",
+    "\n",
+    "Сада смо спремни да обучимо наш модел!\n",
+    "\n",
+    "У Tidymodels-у, модели се специфицирају помоћу `parsnip()` кроз дефинисање три концепта:\n",
+    "\n",
+    "-   **Тип модела** разликује моделе као што су линеарна регресија, логистичка регресија, модели одлуке стабла и тако даље.\n",
+    "\n",
+    "-   **Режим модела** укључује уобичајене опције као што су регресија и класификација; неки типови модела подржавају обе опције, док неки имају само један режим.\n",
+    "\n",
+    "-   **Енџин модела** је рачунарски алат који ће се користити за прилагођавање модела. Често су то R пакети, као што су **`\"lm\"`** или **`\"ranger\"`**\n",
+    "\n",
+    "Ове информације о моделу се чувају у спецификацији модела, па хајде да направимо једну!\n"
+   ],
+   "metadata": {
+    "id": "sBOS-XhB-6v7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- \r\n",
+    "  # Type\r\n",
+    "  linear_reg() %>% \r\n",
+    "  # Engine\r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  # Mode\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the model specification\r\n",
+    "lm_spec"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "20OwEw20--t3"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Након што је модел *одређен*, модел може бити `процењен` или `трениран` коришћењем функције [`fit()`](https://parsnip.tidymodels.org/reference/fit.html), обично уз помоћ формуле и неких података.\n",
+    "\n",
+    "`y ~ .` значи да ћемо прилагодити `y` као предвиђену вредност/циљ, објашњену свим предикторима/карактеристикама, односно `.` (у овом случају, имамо само један предиктор: `bmi`).\n"
+   ],
+   "metadata": {
+    "id": "_oDHs89k_CJj"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>%\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Train a linear regression model\r\n",
+    "lm_mod <- lm_spec %>% \r\n",
+    "  fit(y ~ ., data = diabetes_train)\r\n",
+    "\r\n",
+    "# Print the model\r\n",
+    "lm_mod"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YlsHqd-q_GJQ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Из модела можемо видети коефицијенте који су научени током тренинга. Они представљају коефицијенте линије најбољег прилагођавања која нам даје најмању укупну грешку између стварне и предвиђене променљиве.\n",
+    "<br>\n",
+    "\n",
+    "## 5. Направите предвиђања на тест сету\n",
+    "\n",
+    "Сада када смо обучили модел, можемо га користити за предвиђање прогресије болести y за тестни скуп података користећи [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Ово ће се користити за цртање линије између група података.\n"
+   ],
+   "metadata": {
+    "id": "kGZ22RQj_Olu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\r\n",
+    "predictions <- lm_mod %>% \r\n",
+    "  predict(new_data = diabetes_test)\r\n",
+    "\r\n",
+    "# Print out some of the predictions\r\n",
+    "predictions %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "nXHbY7M2_aao"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ура! 💃🕺 Управо смо обучили модел и користили га за прављење предвиђања!\n",
+    "\n",
+    "Када правимо предвиђања, конвенција tidymodels-а је да увек производимо tibble/data frame резултата са стандардизованим именима колона. Ово олакшава комбиновање оригиналних података и предвиђања у употребљивом формату за наредне операције као што је креирање графикона.\n",
+    "\n",
+    "`dplyr::bind_cols()` ефикасно спаја више data frame-ова по колонама.\n"
+   ],
+   "metadata": {
+    "id": "R_JstwUY_bIs"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Combine the predictions and the original test set\r\n",
+    "results <- diabetes_test %>% \r\n",
+    "  bind_cols(predictions)\r\n",
+    "\r\n",
+    "\r\n",
+    "results %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RybsMJR7_iI8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 6. Приказивање резултата модела\n",
+    "\n",
+    "Сада је време да ово видимо визуелно 📈. Направићемо расејани графикон свих `y` и `bmi` вредности из тест скупа, а затим ћемо користити предвиђања да нацртамо линију на најприкладнијем месту, између група података модела.\n",
+    "\n",
+    "R има неколико система за прављење графикона, али `ggplot2` је један од најелегантнијих и најсвестранијих. Овај пакет вам омогућава да креирате графиконе **комбиновањем независних компоненти**.\n"
+   ],
+   "metadata": {
+    "id": "XJbYbMZW_n_s"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plot\r\n",
+    "theme_set(theme_light())\r\n",
+    "# Create a scatter plot\r\n",
+    "results %>% \r\n",
+    "  ggplot(aes(x = bmi)) +\r\n",
+    "  # Add a scatter plot\r\n",
+    "  geom_point(aes(y = y), size = 1.6) +\r\n",
+    "  # Add a line plot\r\n",
+    "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R9tYp3VW_sTn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ Размислите мало о томе шта се овде дешава. Права линија пролази кроз много малих тачака података, али шта она заправо ради? Можете ли да видите како би требало да будете у могућности да користите ову линију за предвиђање где би нова, непозната тачка података требало да се уклопи у односу на y осу графикона? Покушајте да речима опишете практичну употребу овог модела.\n",
+    "\n",
+    "Честитамо, направили сте свој први модел линеарне регресије, креирали предвиђање са њим и приказали га на графикону!\n"
+   ],
+   "metadata": {
+    "id": "zrPtHIxx_tNI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/1-Tools/solution/notebook.ipynb b/translations/sr/2-Regression/1-Tools/solution/notebook.ipynb
new file mode 100644
index 000000000..e2a00d691
--- /dev/null
+++ b/translations/sr/2-Regression/1-Tools/solution/notebook.ipynb
@@ -0,0 +1,677 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Линеарна регресија за дијабетес скуп података - Лекција 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Увези потребне библиотеке\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model, model_selection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Учитајте скуп података о дијабетесу, подељен на `X` податке и `y` карактеристике\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "print(X.shape)\n",
+    "print(X[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Изаберите само једну функцију за циљ ове вежбе\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selecting the 3rd feature\n",
+    "X = X[:, 2]\n",
+    "print(X.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 1)\n",
+      "[[ 0.06169621]\n",
+      " [-0.05147406]\n",
+      " [ 0.04445121]\n",
+      " [-0.01159501]\n",
+      " [-0.03638469]\n",
+      " [-0.04069594]\n",
+      " [-0.04716281]\n",
+      " [-0.00189471]\n",
+      " [ 0.06169621]\n",
+      " [ 0.03906215]\n",
+      " [-0.08380842]\n",
+      " [ 0.01750591]\n",
+      " [-0.02884001]\n",
+      " [-0.00189471]\n",
+      " [-0.02560657]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [ 0.01211685]\n",
+      " [-0.0105172 ]\n",
+      " [-0.01806189]\n",
+      " [-0.05686312]\n",
+      " [-0.02237314]\n",
+      " [-0.00405033]\n",
+      " [ 0.06061839]\n",
+      " [ 0.03582872]\n",
+      " [-0.01267283]\n",
+      " [-0.07734155]\n",
+      " [ 0.05954058]\n",
+      " [-0.02129532]\n",
+      " [-0.00620595]\n",
+      " [ 0.04445121]\n",
+      " [-0.06548562]\n",
+      " [ 0.12528712]\n",
+      " [-0.05039625]\n",
+      " [-0.06332999]\n",
+      " [-0.03099563]\n",
+      " [ 0.02289497]\n",
+      " [ 0.01103904]\n",
+      " [ 0.07139652]\n",
+      " [ 0.01427248]\n",
+      " [-0.00836158]\n",
+      " [-0.06764124]\n",
+      " [-0.0105172 ]\n",
+      " [-0.02345095]\n",
+      " [ 0.06816308]\n",
+      " [-0.03530688]\n",
+      " [-0.01159501]\n",
+      " [-0.0730303 ]\n",
+      " [-0.04177375]\n",
+      " [ 0.01427248]\n",
+      " [-0.00728377]\n",
+      " [ 0.0164281 ]\n",
+      " [-0.00943939]\n",
+      " [-0.01590626]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.04931844]\n",
+      " [ 0.04121778]\n",
+      " [-0.06332999]\n",
+      " [-0.06440781]\n",
+      " [-0.02560657]\n",
+      " [-0.00405033]\n",
+      " [ 0.00457217]\n",
+      " [-0.00728377]\n",
+      " [-0.0374625 ]\n",
+      " [-0.02560657]\n",
+      " [-0.02452876]\n",
+      " [-0.01806189]\n",
+      " [-0.01482845]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [-0.06979687]\n",
+      " [ 0.03367309]\n",
+      " [-0.00405033]\n",
+      " [-0.02021751]\n",
+      " [ 0.00241654]\n",
+      " [-0.03099563]\n",
+      " [ 0.02828403]\n",
+      " [-0.03638469]\n",
+      " [-0.05794093]\n",
+      " [-0.0374625 ]\n",
+      " [ 0.01211685]\n",
+      " [-0.02237314]\n",
+      " [-0.03530688]\n",
+      " [ 0.00996123]\n",
+      " [-0.03961813]\n",
+      " [ 0.07139652]\n",
+      " [-0.07518593]\n",
+      " [-0.00620595]\n",
+      " [-0.04069594]\n",
+      " [-0.04824063]\n",
+      " [-0.02560657]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.00457217]\n",
+      " [-0.06440781]\n",
+      " [-0.01698407]\n",
+      " [-0.05794093]\n",
+      " [ 0.00996123]\n",
+      " [ 0.08864151]\n",
+      " [-0.00512814]\n",
+      " [-0.06440781]\n",
+      " [ 0.01750591]\n",
+      " [-0.04500719]\n",
+      " [ 0.02828403]\n",
+      " [ 0.04121778]\n",
+      " [ 0.06492964]\n",
+      " [-0.03207344]\n",
+      " [-0.07626374]\n",
+      " [ 0.04984027]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03207344]\n",
+      " [ 0.00457217]\n",
+      " [ 0.02073935]\n",
+      " [ 0.01427248]\n",
+      " [ 0.11019775]\n",
+      " [ 0.00133873]\n",
+      " [ 0.05846277]\n",
+      " [-0.02129532]\n",
+      " [-0.0105172 ]\n",
+      " [-0.04716281]\n",
+      " [ 0.00457217]\n",
+      " [ 0.01750591]\n",
+      " [ 0.08109682]\n",
+      " [ 0.0347509 ]\n",
+      " [ 0.02397278]\n",
+      " [-0.00836158]\n",
+      " [-0.06117437]\n",
+      " [-0.00189471]\n",
+      " [-0.06225218]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.09618619]\n",
+      " [-0.06979687]\n",
+      " [-0.02129532]\n",
+      " [-0.05362969]\n",
+      " [ 0.0433734 ]\n",
+      " [ 0.05630715]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04984027]\n",
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+      " [ 0.0703187 ]\n",
+      " [-0.00405033]\n",
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+      " [ 0.02073935]\n",
+      " [ 0.06061839]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03315126]\n",
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+      " [ 0.0433734 ]\n",
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+      " [ 0.06385183]\n",
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+      " [ 0.07247433]\n",
+      " [-0.0191397 ]\n",
+      " [-0.06656343]\n",
+      " [-0.06009656]\n",
+      " [ 0.06924089]\n",
+      " [ 0.05954058]\n",
+      " [-0.02668438]\n",
+      " [-0.02021751]\n",
+      " [-0.046085  ]\n",
+      " [ 0.07139652]\n",
+      " [-0.07949718]\n",
+      " [ 0.00996123]\n",
+      " [-0.03854032]\n",
+      " [ 0.01966154]\n",
+      " [ 0.02720622]\n",
+      " [-0.00836158]\n",
+      " [-0.01590626]\n",
+      " [ 0.00457217]\n",
+      " [-0.04285156]\n",
+      " [ 0.00564998]\n",
+      " [-0.03530688]\n",
+      " [ 0.02397278]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00297252]\n",
+      " [-0.06656343]\n",
+      " [-0.01267283]\n",
+      " [-0.04177375]\n",
+      " [-0.03099563]\n",
+      " [-0.00512814]\n",
+      " [-0.05901875]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00349435]\n",
+      " [ 0.05415152]\n",
+      " [-0.04500719]\n",
+      " [-0.05794093]\n",
+      " [-0.05578531]\n",
+      " [ 0.00133873]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00672779]\n",
+      " [ 0.04660684]\n",
+      " [ 0.02612841]\n",
+      " [ 0.04552903]\n",
+      " [ 0.04013997]\n",
+      " [-0.01806189]\n",
+      " [ 0.01427248]\n",
+      " [ 0.03690653]\n",
+      " [ 0.00349435]\n",
+      " [-0.07087468]\n",
+      " [-0.03315126]\n",
+      " [ 0.09403057]\n",
+      " [ 0.03582872]\n",
+      " [ 0.03151747]\n",
+      " [-0.06548562]\n",
+      " [-0.04177375]\n",
+      " [-0.03961813]\n",
+      " [-0.03854032]\n",
+      " [-0.02560657]\n",
+      " [-0.02345095]\n",
+      " [-0.06656343]\n",
+      " [ 0.03259528]\n",
+      " [-0.046085  ]\n",
+      " [-0.02991782]\n",
+      " [-0.01267283]\n",
+      " [-0.01590626]\n",
+      " [ 0.07139652]\n",
+      " [-0.03099563]\n",
+      " [ 0.00026092]\n",
+      " [ 0.03690653]\n",
+      " [ 0.03906215]\n",
+      " [-0.01482845]\n",
+      " [ 0.00672779]\n",
+      " [-0.06871905]\n",
+      " [-0.00943939]\n",
+      " [ 0.01966154]\n",
+      " [ 0.07462995]\n",
+      " [-0.00836158]\n",
+      " [-0.02345095]\n",
+      " [-0.046085  ]\n",
+      " [ 0.05415152]\n",
+      " [-0.03530688]\n",
+      " [-0.03207344]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04768465]\n",
+      " [ 0.06061839]\n",
+      " [ 0.05630715]\n",
+      " [ 0.09834182]\n",
+      " [ 0.05954058]\n",
+      " [ 0.03367309]\n",
+      " [ 0.05630715]\n",
+      " [-0.06548562]\n",
+      " [ 0.16085492]\n",
+      " [-0.05578531]\n",
+      " [-0.02452876]\n",
+      " [-0.03638469]\n",
+      " [-0.00836158]\n",
+      " [-0.04177375]\n",
+      " [ 0.12744274]\n",
+      " [-0.07734155]\n",
+      " [ 0.02828403]\n",
+      " [-0.02560657]\n",
+      " [-0.06225218]\n",
+      " [-0.00081689]\n",
+      " [ 0.08864151]\n",
+      " [-0.03207344]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00888341]\n",
+      " [ 0.00672779]\n",
+      " [-0.02021751]\n",
+      " [-0.02452876]\n",
+      " [-0.01159501]\n",
+      " [ 0.02612841]\n",
+      " [-0.05901875]\n",
+      " [-0.03638469]\n",
+      " [-0.02452876]\n",
+      " [ 0.01858372]\n",
+      " [-0.0902753 ]\n",
+      " [-0.00512814]\n",
+      " [-0.05255187]\n",
+      " [-0.02237314]\n",
+      " [-0.02021751]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00620595]\n",
+      " [-0.01698407]\n",
+      " [ 0.05522933]\n",
+      " [ 0.07678558]\n",
+      " [ 0.01858372]\n",
+      " [-0.02237314]\n",
+      " [ 0.09295276]\n",
+      " [-0.03099563]\n",
+      " [ 0.03906215]\n",
+      " [-0.06117437]\n",
+      " [-0.00836158]\n",
+      " [-0.0374625 ]\n",
+      " [-0.01375064]\n",
+      " [ 0.07355214]\n",
+      " [-0.02452876]\n",
+      " [ 0.03367309]\n",
+      " [ 0.0347509 ]\n",
+      " [-0.03854032]\n",
+      " [-0.03961813]\n",
+      " [-0.00189471]\n",
+      " [-0.03099563]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06492964]\n",
+      " [ 0.04013997]\n",
+      " [-0.02345095]\n",
+      " [ 0.05307371]\n",
+      " [ 0.04013997]\n",
+      " [-0.02021751]\n",
+      " [ 0.01427248]\n",
+      " [-0.03422907]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00457217]\n",
+      " [ 0.03043966]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.06169621]\n",
+      " [-0.00728377]\n",
+      " [ 0.00564998]\n",
+      " [ 0.05415152]\n",
+      " [-0.00836158]\n",
+      " [ 0.114509  ]\n",
+      " [ 0.06708527]\n",
+      " [-0.05578531]\n",
+      " [ 0.03043966]\n",
+      " [-0.02560657]\n",
+      " [ 0.10480869]\n",
+      " [-0.00620595]\n",
+      " [-0.04716281]\n",
+      " [-0.04824063]\n",
+      " [ 0.08540807]\n",
+      " [-0.01267283]\n",
+      " [-0.03315126]\n",
+      " [-0.00728377]\n",
+      " [-0.01375064]\n",
+      " [ 0.05954058]\n",
+      " [ 0.02181716]\n",
+      " [ 0.01858372]\n",
+      " [-0.01159501]\n",
+      " [-0.00297252]\n",
+      " [ 0.01750591]\n",
+      " [-0.02991782]\n",
+      " [-0.02021751]\n",
+      " [-0.05794093]\n",
+      " [ 0.06061839]\n",
+      " [-0.04069594]\n",
+      " [-0.07195249]\n",
+      " [-0.05578531]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03315126]\n",
+      " [ 0.04984027]\n",
+      " [-0.08488624]\n",
+      " [ 0.00564998]\n",
+      " [ 0.02073935]\n",
+      " [-0.00728377]\n",
+      " [ 0.10480869]\n",
+      " [-0.02452876]\n",
+      " [-0.00620595]\n",
+      " [-0.03854032]\n",
+      " [ 0.13714305]\n",
+      " [ 0.17055523]\n",
+      " [ 0.00241654]\n",
+      " [ 0.03798434]\n",
+      " [-0.05794093]\n",
+      " [-0.00943939]\n",
+      " [-0.02345095]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03422907]\n",
+      " [-0.00297252]\n",
+      " [ 0.06816308]\n",
+      " [ 0.00996123]\n",
+      " [ 0.00241654]\n",
+      " [-0.03854032]\n",
+      " [ 0.02612841]\n",
+      " [-0.08919748]\n",
+      " [ 0.06061839]\n",
+      " [-0.02884001]\n",
+      " [-0.02991782]\n",
+      " [-0.0191397 ]\n",
+      " [-0.04069594]\n",
+      " [ 0.01535029]\n",
+      " [-0.02452876]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06924089]\n",
+      " [-0.06979687]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [ 0.01858372]\n",
+      " [ 0.00133873]\n",
+      " [-0.03099563]\n",
+      " [-0.00405033]\n",
+      " [ 0.01535029]\n",
+      " [ 0.02289497]\n",
+      " [ 0.04552903]\n",
+      " [-0.04500719]\n",
+      " [-0.03315126]\n",
+      " [ 0.097264  ]\n",
+      " [ 0.05415152]\n",
+      " [ 0.12313149]\n",
+      " [-0.08057499]\n",
+      " [ 0.09295276]\n",
+      " [-0.05039625]\n",
+      " [-0.01159501]\n",
+      " [-0.0277622 ]\n",
+      " [ 0.05846277]\n",
+      " [ 0.08540807]\n",
+      " [-0.00081689]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00888341]\n",
+      " [ 0.08001901]\n",
+      " [ 0.07139652]\n",
+      " [-0.02452876]\n",
+      " [-0.0547075 ]\n",
+      " [-0.03638469]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.07786339]\n",
+      " [-0.03961813]\n",
+      " [ 0.01103904]\n",
+      " [-0.04069594]\n",
+      " [-0.03422907]\n",
+      " [ 0.00564998]\n",
+      " [ 0.08864151]\n",
+      " [-0.03315126]\n",
+      " [-0.05686312]\n",
+      " [-0.03099563]\n",
+      " [ 0.05522933]\n",
+      " [-0.06009656]\n",
+      " [ 0.00133873]\n",
+      " [-0.02345095]\n",
+      " [-0.07410811]\n",
+      " [ 0.01966154]\n",
+      " [-0.01590626]\n",
+      " [-0.01590626]\n",
+      " [ 0.03906215]\n",
+      " [-0.0730303 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Reshaping to get a 2D array\n",
+    "X = X.reshape(-1, 1)\n",
+    "print(X.shape)\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Раздвојите податке за обуку и тестирање за `X` и `y`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Изаберите модел и обучите га са подацима за тренирање\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Користите тест податке да предвидите линију\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Прикажи резултате на графикону\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test,  color='black')\n",
+    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "16ff1a974f6e4348e869e4a7d366b86a",
+   "translation_date": "2025-09-06T13:38:54+00:00",
+   "source_file": "2-Regression/1-Tools/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/2-Data/notebook.ipynb b/translations/sr/2-Regression/2-Data/notebook.ipynb
new file mode 100644
index 000000000..1b5138e30
--- /dev/null
+++ b/translations/sr/2-Regression/2-Data/notebook.ipynb
@@ -0,0 +1,46 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  },
+  "coopTranslator": {
+   "original_hash": "1b2ab303ac6c604a34c6ca7a49077fc7",
+   "translation_date": "2025-09-06T13:45:55+00:00",
+   "source_file": "2-Regression/2-Data/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/sr/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
new file mode 100644
index 000000000..042aa1010
--- /dev/null
+++ b/translations/sr/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -0,0 +1,672 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_2-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "f3c335f9940cfd76528b3ef918b9b342",
+   "translation_date": "2025-09-06T13:50:30+00:00",
+   "source_file": "2-Regression/2-Data/solution/R/lesson_2-R.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Изградња регресионог модела: припрема и визуализација података\n",
+    "\n",
+    "## **Линеарна регресија за бундеве - Лекција 2**\n",
+    "#### Увод\n",
+    "\n",
+    "Сада када сте опремљени алатима који су вам потребни за почетак изградње модела машинског учења уз помоћ Tidymodels и Tidyverse-а, спремни сте да почнете постављати питања о вашим подацима. Док радите са подацима и примењујете решења машинског учења, веома је важно разумети како поставити право питање како бисте на прави начин искористили потенцијале вашег скупа података.\n",
+    "\n",
+    "У овој лекцији ћете научити:\n",
+    "\n",
+    "- Како припремити податке за изградњу модела.\n",
+    "\n",
+    "- Како користити `ggplot2` за визуализацију података.\n",
+    "\n",
+    "Питање на које желите одговор одредиће који тип алгоритама машинског учења ћете користити. Квалитет одговора који добијете у великој мери ће зависити од природе ваших података.\n",
+    "\n",
+    "Хајде да ово видимо кроз практичну вежбу.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/unruly_data.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Илустрација: @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Илустрација: \\@allison_horst](../../../../../../2-Regression/2-Data/images/unruly_data.jpg)<br>Илустрација: \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "Pg5aexcOPqAZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Увоз података о бундевама и позивање Tidyverse-а\n",
+    "\n",
+    "Биће нам потребни следећи пакети за обраду и анализу података у овој лекцији:\n",
+    "\n",
+    "-   `tidyverse`: [Tidyverse](https://www.tidyverse.org/) је [збирка R пакета](https://www.tidyverse.org/packages) осмишљена да учини науку о подацима бржом, једноставнијом и забавнијом!\n",
+    "\n",
+    "Можете их инсталирати на следећи начин:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\"))`\n",
+    "\n",
+    "Скрипта испод проверава да ли имате инсталиране пакете потребне за завршетак овог модула и инсталира их уколико неки недостају.\n"
+   ],
+   "metadata": {
+    "id": "dc5WhyVdXAjR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "GqPYUZgfXOBt"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сада, хајде да покренемо неке пакете и учитамо [подаци](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) који су обезбеђени за ову лекцију!\n"
+   ],
+   "metadata": {
+    "id": "kvjDTPDSXRr2"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n =50)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VMri-t2zXqgD"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Брзи `glimpse()` одмах показује да постоје празнине и мешавина стрингова (`chr`) и нумеричких података (`dbl`). `Date` је типа карактер, а ту је и чудна колона названа `Package` где су подаци мешавина између `sacks`, `bins` и других вредности. Подаци су, у ствари, прилично неуредни 😤.\n",
+    "\n",
+    "Заправо, није баш уобичајено добити скуп података који је потпуно спреман за употребу и креирање ML модела одмах. Али не брините, у овој лекцији ћете научити како да припремите необрађени скуп података користећи стандардне R библиотеке 🧑‍🔧. Такође ћете научити различите технике за визуализацију података.📈📊\n",
+    "<br>\n",
+    "\n",
+    "> Подсетник: Оператор цеви (`%>%`) извршава операције у логичком низу тако што прослеђује објекат функцији или изразу. Можете замислити оператор цеви као да у вашем коду каже \"и онда\".\n"
+   ],
+   "metadata": {
+    "id": "REWcIv9yX29v"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Провера недостајућих података\n",
+    "\n",
+    "Један од најчешћих проблема са којима се научници за податке сусрећу јесте непотпуни или недостајући подаци. У R-у се недостајуће или непознате вредности представљају посебном вредношћу: `NA` (Not Available).\n",
+    "\n",
+    "Како бисмо знали да ли оквир података садржи недостајуће вредности?  \n",
+    "<br>\n",
+    "- Један једноставан начин био би коришћење основне R функције `anyNA`, која враћа логичке вредности `TRUE` или `FALSE`.\n"
+   ],
+   "metadata": {
+    "id": "Zxfb3AM5YbUe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  anyNA()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "G--DQutAYltj"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Одлично, чини се да недостају неки подаци! То је добро место за почетак.\n",
+    "\n",
+    "-   Други начин би био да се користи функција `is.na()` која указује на то који појединачни елементи у колони недостају логичком вредношћу `TRUE`.\n"
+   ],
+   "metadata": {
+    "id": "mU-7-SB6YokF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "W-DxDOR4YxSW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "У реду, завршен посао, али са овако великим оквиром података, било би неефикасно и практично немогуће прегледати све редове и колоне појединачно😴.\n",
+    "\n",
+    "-   Интуитивнији начин био би да израчунате збир недостајућих вредности за сваку колону:\n"
+   ],
+   "metadata": {
+    "id": "xUWxipKYY0o7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  colSums()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZRBWV6P9ZArL"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Много боље! Недостају неки подаци, али можда то неће бити важно за задатак који је пред нама. Да видимо шта ће даља анализа донети.\n",
+    "\n",
+    "> Поред сјајних сетова пакета и функција, R има веома добру документацију. На пример, користите `help(colSums)` или `?colSums` да бисте сазнали више о функцији.\n"
+   ],
+   "metadata": {
+    "id": "9gv-crB6ZD1Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Dplyr: Граматика за манипулацију подацима\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_wrangling.png\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Илустрација: @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Илустрација: \\@allison_horst](../../../../../../2-Regression/2-Data/images/dplyr_wrangling.png)<br/>Илустрација: \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "o4jLY5-VZO2C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`dplyr`](https://dplyr.tidyverse.org/), пакет у оквиру Tidyverse-а, представља граматику за манипулацију подацима која пружа конзистентан сет глагола који вам помажу да решите најчешће изазове у манипулацији подацима. У овом делу, истражићемо неке од глагола из dplyr-а!\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "i5o33MQBZWWw"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::select()\n",
+    "\n",
+    "`select()` је функција у пакету `dplyr` која вам помаже да изаберете колоне које желите да задржите или изузмете.\n",
+    "\n",
+    "Да бисте свој рад са подацима учинили једноставнијим, уклоните неколико колона из вашег оквира података користећи `select()`, задржавајући само оне колоне које су вам потребне.\n",
+    "\n",
+    "На пример, у овој вежби, наша анализа ће обухватати колоне `Package`, `Low Price`, `High Price` и `Date`. Хајде да изаберемо те колоне.\n"
+   ],
+   "metadata": {
+    "id": "x3VGMAGBZiUr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(Package, `Low Price`, `High Price`, Date)\n",
+    "\n",
+    "\n",
+    "# Print data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "F_FgxQnVZnM0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::mutate()\n",
+    "\n",
+    "`mutate()` је функција у пакету `dplyr` која вам помаже да креирате или модификујете колоне, а да притом задржите постојеће колоне.\n",
+    "\n",
+    "Општа структура функције `mutate` је:\n",
+    "\n",
+    "`data %>%   mutate(new_column_name = what_it_contains)`\n",
+    "\n",
+    "Хајде да испробамо `mutate` користећи колону `Date` и извршимо следеће операције:\n",
+    "\n",
+    "1. Претворимо датуме (који су тренутно типа карактер) у формат месеца (ово су датуми у америчком формату, дакле `MM/DD/YYYY`).\n",
+    "\n",
+    "2. Извуцимо месец из датума у нову колону.\n",
+    "\n",
+    "У програмском језику R, пакет [lubridate](https://lubridate.tidyverse.org/) олакшава рад са подацима типа датум-време. Дакле, хајде да користимо `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` и видимо како да постигнемо горе наведене циљеве. Можемо избацити колону `Date` јер нам више неће бити потребна у наредним операцијама.\n"
+   ],
+   "metadata": {
+    "id": "2KKo0Ed9Z1VB"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load lubridate\n",
+    "library(lubridate)\n",
+    "\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  # Convert the Date column to a date object\n",
+    "  mutate(Date = mdy(Date)) %>% \n",
+    "  # Extract month from Date\n",
+    "  mutate(Month = month(Date)) %>% \n",
+    "  # Drop Date column\n",
+    "  select(-Date)\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5joszIVSZ6xe"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ура! 🤩\n",
+    "\n",
+    "Следеће, хајде да направимо нову колону `Price`, која представља просечну цену бундеве. Сада, хајде да узмемо просек из колона `Low Price` и `High Price` како бисмо попунили нову колону Price.\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "nIgLjNMCZ-6Y"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new column Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Zo0BsqqtaJw2"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Дааа!💪\n",
+    "\n",
+    "\"Али чекај!\", рећи ћеш након што прелетиш цео скуп података помоћу `View(pumpkins)`, \"Овде нешто није у реду!\"🤔\n",
+    "\n",
+    "Ако погледаш колону `Package`, бундеве се продају у различитим конфигурацијама. Неке се продају у мерама `1 1/9 бушел`, неке у мерама `1/2 бушел`, неке по бундеви, неке по фунти, а неке у великим кутијама различитих ширина.\n",
+    "\n",
+    "Хајде да ово проверимо:\n"
+   ],
+   "metadata": {
+    "id": "p77WZr-9aQAR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Verify the distinct observations in Package column\n",
+    "pumpkins %>% \n",
+    "  distinct(Package)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "XISGfh0IaUy6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Невероватно!👏\n",
+    "\n",
+    "Чини се да је веома тешко доследно измерити бундеве, па хајде да их филтрирамо тако што ћемо изабрати само бундеве које у колони `Package` садрже реч *bushel* и ставимо их у нови податак оквира `new_pumpkins`.\n"
+   ],
+   "metadata": {
+    "id": "7sMjiVujaZxY"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::filter() и stringr::str_detect()\n",
+    "\n",
+    "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): креира подскуп података који садржи само **редове** који испуњавају ваше услове, у овом случају, тикве са низом *bushel* у колони `Package`.\n",
+    "\n",
+    "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): открива присуство или одсуство шаблона у низу.\n",
+    "\n",
+    "Пакет [`stringr`](https://github.com/tidyverse/stringr) пружа једноставне функције за уобичајене операције са низовима.\n"
+   ],
+   "metadata": {
+    "id": "L8Qfcs92ageF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Retain only pumpkins with \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "       filter(str_detect(Package, \"bushel\"))\n",
+    "\n",
+    "# Get the dimensions of the new data\n",
+    "dim(new_pumpkins)\n",
+    "\n",
+    "# View a few rows of the new data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "hy_SGYREampd"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Можете видети да смо сузили избор на око 415 редова података који садрже бундеве по бушелу.🤩  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "VrDwF031avlR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::case_when()\n",
+    "\n",
+    "**Али сачекајте! Постоји још нешто што треба урадити**\n",
+    "\n",
+    "Да ли сте приметили да количина у бушелима варира по реду? Потребно је нормализовати цене тако да приказујете цену по бушелу, а не по 1 1/9 или 1/2 бушела. Време је за мало математике како бисмо то стандардизовали.\n",
+    "\n",
+    "Користићемо функцију [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) да *изменимо* колону Price у зависности од одређених услова. `case_when` нам омогућава да векторизујемо више `if_else()` изјава.\n"
+   ],
+   "metadata": {
+    "id": "mLpw2jH4a0tx"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(Price = case_when(\n",
+    "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+    "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+    "    TRUE ~ Price))\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 30)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "P68kLVQmbM6I"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сада можемо анализирати цену по јединици на основу мерења по бушелу. Све ово проучавање бушела бундева, међутим, показује колико је `важно` да `разумете природу ваших података`!\n",
+    "\n",
+    "> ✅ Према [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), тежина бушела зависи од врсте производа, јер је то мера запремине. \"Бушел парадајза, на пример, треба да тежи 56 фунти... Листови и зелено поврће заузимају више простора са мање тежине, па бушел спанаћа тежи само 20 фунти.\" Све је то прилично компликовано! Нећемо се мучити са конверзијом бушела у фунте, већ ћемо одредити цену по бушелу. Све ово проучавање бушела бундева, међутим, показује колико је важно да разумете природу ваших података!\n",
+    ">\n",
+    "> ✅ Да ли сте приметили да су бундеве које се продају по пола бушела веома скупе? Можете ли схватити зашто? Савет: мале бундеве су много скупље од великих, вероватно зато што их има много више по бушелу, с обзиром на неискоришћени простор који заузима једна велика шупља бундева за питу.\n"
+   ],
+   "metadata": {
+    "id": "pS2GNPagbSdb"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сада, зарад авантуре 💁‍♀️, хајде да преместимо колону \"Месец\" на прву позицију, односно `пре` колоне `Пакет`.\n",
+    "\n",
+    "`dplyr::relocate()` се користи за промену позиција колона.\n"
+   ],
+   "metadata": {
+    "id": "qql1SowfbdnP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new data frame new_pumpkins\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(Month, .before = Package)\n",
+    "\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "JJ1x6kw8bixF"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сјајно!👌 Сада имате чист и уредан скуп података на коме можете изградити свој нови регресиони модел!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "y8TJ0Za_bn5Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Визуелизација података са ggplot2\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/data-visualization.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Инфографика од Дасани Мадипали</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Инфографика од Дасани Мадипали](../../../../../../2-Regression/2-Data/images/data-visualization.png){width=\"600\"}-->\n",
+    "\n",
+    "Постоји једна *мудра* изрека која гласи:\n",
+    "\n",
+    "> \"Једноставан графикон је донео више информација у ум аналитичара података него било који други уређај.\" --- Џон Туки\n",
+    "\n",
+    "Део улоге научника за податке је да демонстрира квалитет и природу података са којима ради. Да би то урадили, често креирају занимљиве визуелизације, или графиконе, дијаграме и табеле, које приказују различите аспекте података. На овај начин могу визуелно приказати односе и празнине које је иначе тешко открити.\n",
+    "\n",
+    "Визуелизације такође могу помоћи у одређивању технике машинског учења која је најприкладнија за податке. На пример, расејани графикон који изгледа као да прати линију указује на то да су подаци добар кандидат за вежбу линеарне регресије.\n",
+    "\n",
+    "R нуди неколико система за креирање графикона, али [`ggplot2`](https://ggplot2.tidyverse.org/index.html) је један од најелегантнијих и најсвестранијих. `ggplot2` вам омогућава да саставите графиконе **комбинујући независне компоненте**.\n",
+    "\n",
+    "Хајде да почнемо са једноставним расејаним графиконом за колоне Price и Month.\n",
+    "\n",
+    "У овом случају, почећемо са [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), додаћемо скуп података и естетско мапирање (са [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)), а затим додати слојеве (као [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) за расејане графиконе.\n"
+   ],
+   "metadata": {
+    "id": "mYSH6-EtbvNa"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plots\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+    "p + geom_point()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "g2YjnGeOcLo4"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Да ли је ово користан графикон 🤷? Да ли вас нешто на њему изненађује?\n",
+    "\n",
+    "Није нарочито користан јер све што ради јесте да приказује ваше податке као распоред тачака у датом месецу.  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "Ml7SDCLQcPvE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Како да учинимо да буде корисно?**\n",
+    "\n",
+    "Да би графикони приказивали корисне податке, обично је потребно некако груписати податке. На пример, у нашем случају, проналажење просечне цене бундева за сваки месец би пружило више увида у основне обрасце у нашим подацима. Ово нас доводи до још једног брзог прегледа функција пакета **dplyr**:\n",
+    "\n",
+    "#### `dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "Груписана агрегација у R-у може се лако израчунати помоћу\n",
+    "\n",
+    "`dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "-   `dplyr::group_by()` мења јединицу анализе са целокупног скупа података на појединачне групе, као што су групе по месецима.\n",
+    "\n",
+    "-   `dplyr::summarize()` креира нови скуп података са једном колоном за сваку променљиву груписања и једном колоном за сваку од статистика које сте навели.\n",
+    "\n",
+    "На пример, можемо користити `dplyr::group_by() %>% summarize()` да групишемо бундеве у групе на основу колоне **Month** и затим израчунамо **просечну цену** за сваки месец.\n"
+   ],
+   "metadata": {
+    "id": "jMakvJZIcVkh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "6kVSUa2Bcilf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сажето!✨\n",
+    "\n",
+    "Категоријске карактеристике, као што су месеци, боље се представљају помоћу стубичастог графикона 📊. Слојеви који су одговорни за стубичасте графиконе су `geom_bar()` и `geom_col()`. Погледајте `?geom_bar` за више информација.\n",
+    "\n",
+    "Хајде да направимо један!\n"
+   ],
+   "metadata": {
+    "id": "Kds48GUBcj3W"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price)) %>% \r\n",
+    "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"Pumpkin Price\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VNbU1S3BcrxO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩Ово је кориснија визуализација података! Чини се да указује на то да се највиша цена бундева јавља у септембру и октобру. Да ли то одговара вашим очекивањима? Зашто или зашто не?\n",
+    "\n",
+    "Честитамо на завршетку друге лекције 👏! Припремили сте своје податке за изградњу модела, а затим открили више увида користећи визуализације!\n"
+   ],
+   "metadata": {
+    "id": "zDm0VOzzcuzR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/2-Data/solution/notebook.ipynb b/translations/sr/2-Regression/2-Data/solution/notebook.ipynb
new file mode 100644
index 000000000..c2fae168d
--- /dev/null
+++ b/translations/sr/2-Regression/2-Data/solution/notebook.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/8/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    City Name Type               Package   Variety Sub Variety  Grade  \\\n",
+       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "\n",
+       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
+       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
+       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "\n",
+       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
+       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "\n",
+       "    Unnamed: 25  \n",
+       "70          NaN  \n",
+       "71          NaN  \n",
+       "72          NaN  \n",
+       "73          NaN  \n",
+       "74          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Month               Package  Low Price  High Price  Price\n",
+      "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
+      "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "...     ...                   ...        ...         ...    ...\n",
+      "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
+      "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
+      "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
+      "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Get an average between low and high price for the base pumpkin price\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "# Convert the date to its month only\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "\n",
+    "# Create a new dataframe with this basic data\n",
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
+    "\n",
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
+    "\n",
+    "print(new_pumpkins)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "price = new_pumpkins.Price\n",
+    "month = new_pumpkins.Month\n",
+    "plt.scatter(price, month)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "95726f0b8283628d5356a4f8eb8b4b76",
+   "translation_date": "2025-09-06T13:46:19+00:00",
+   "source_file": "2-Regression/2-Data/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/3-Linear/notebook.ipynb b/translations/sr/2-Regression/3-Linear/notebook.ipynb
new file mode 100644
index 000000000..b765139e7
--- /dev/null
+++ b/translations/sr/2-Regression/3-Linear/notebook.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Цене за тикве\n",
+    "\n",
+    "Учитајте потребне библиотеке и скуп података. Претворите податке у датафрејм који садржи подскуп података:\n",
+    "\n",
+    "- Узмите само тикве чија је цена дата по бушелу\n",
+    "- Претворите датум у месец\n",
+    "- Израчунајте цену као просек високих и ниских цена\n",
+    "- Претворите цену тако да одражава цену по количини бушела\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Основни дијаграм расејања нас подсећа да имамо податке само за месеце од августа до децембра. Вероватно нам је потребно више података како бисмо могли да извучемо закључке на линеаран начин.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter('Month','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "b032d371c75279373507f003439a577e",
+   "translation_date": "2025-09-06T13:08:47+00:00",
+   "source_file": "2-Regression/3-Linear/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/sr/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
new file mode 100644
index 000000000..47935c975
--- /dev/null
+++ b/translations/sr/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -0,0 +1,1084 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_3-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "5015d65d61ba75a223bfc56c273aa174",
+   "translation_date": "2025-09-06T13:18:01+00:00",
+   "source_file": "2-Regression/3-Linear/solution/R/lesson_3-R.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "EgQw8osnsUV-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Линеарна и полиномијална регресија за одређивање цене бундева - Лекција 3\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Инфографика: Дасани Мадипали</figcaption>\n",
+    "\n",
+    "\n",
+    "#### Увод\n",
+    "\n",
+    "До сада сте истраживали шта је регресија користећи пример података из скупа података о ценама бундева који ћемо користити током ове лекције. Такође сте је визуализовали помоћу `ggplot2`. 💪\n",
+    "\n",
+    "Сада сте спремни да дубље уђете у регресију за машинско учење. У овој лекцији ћете научити више о две врсте регресије: *основна линеарна регресија* и *полиномијална регресија*, као и нешто од математике која стоји иза ових техника.\n",
+    "\n",
+    "> Кроз овај курикулум претпостављамо минимално знање математике и настојимо да га учинимо доступним студентима из других области, па обратите пажњу на напомене, 🧮 дијаграме и друге алате за учење који ће вам помоћи у разумевању.\n",
+    "\n",
+    "#### Припрема\n",
+    "\n",
+    "Подсећамо, учитавате ове податке како бисте постављали питања о њима.\n",
+    "\n",
+    "-   Када је најбоље време за куповину бундева?\n",
+    "\n",
+    "-   Коју цену могу да очекујем за кутију минијатурних бундева?\n",
+    "\n",
+    "-   Да ли да их купим у корпама од пола бушела или у кутијама од 1 1/9 бушела? Хајде да наставимо са истраживањем ових података.\n",
+    "\n",
+    "У претходној лекцији, креирали сте `tibble` (модерно преобликовање оквира података) и попунили га делом оригиналног скупа података, стандардизујући цене по бушелу. Међутим, на тај начин сте успели да сакупите само око 400 података и то само за јесење месеце. Можда можемо добити мало више детаља о природи података ако их боље очистимо? Видећемо... 🕵️‍♀️\n",
+    "\n",
+    "За овај задатак биће нам потребни следећи пакети:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) је [збирка R пакета](https://www.tidyverse.org/packages) дизајнирана да учини науку о подацима бржом, лакшом и забавнијом!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) је оквир који представља [збирку пакета](https://www.tidymodels.org/packages/) за моделирање и машинско учење.\n",
+    "\n",
+    "-   `janitor`: [janitor пакет](https://github.com/sfirke/janitor) пружа једноставне алате за испитивање и чишћење \"прљавих\" података.\n",
+    "\n",
+    "-   `corrplot`: [corrplot пакет](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) пружа визуелни алат за истраживање корелационе матрице који подржава аутоматско ређање променљивих како би се открили скривени обрасци међу променљивима.\n",
+    "\n",
+    "Можете их инсталирати на следећи начин:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\n",
+    "\n",
+    "Скрипта испод проверава да ли имате потребне пакете за завршетак овог модула и инсталира их за вас у случају да недостају.\n"
+   ],
+   "metadata": {
+    "id": "WqQPS1OAsg3H"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "tA4C2WN3skCf",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Учитаћемо ове сјајне пакете и учинити их доступним у нашој тренутној R сесији. (Ово је само за илустрацију, `pacman::p_load()` је то већ урадио за вас)\n",
+    "\n",
+    "## 1. Линија линеарне регресије\n",
+    "\n",
+    "Као што сте научили у Лекцији 1, циљ вежбе линеарне регресије је да се нацрта *линија* *најбољег* *прилагођавања* како би се:\n",
+    "\n",
+    "-   **Приказали односи између променљивих**. Приказао однос између променљивих.\n",
+    "\n",
+    "-   **Направиле предикције**. Направиле тачне предикције о томе где би нова тачка података могла пасти у односу на ту линију.\n",
+    "\n",
+    "Да бисмо нацртали ову врсту линије, користимо статистичку технику која се зове **Регресија најмањих квадрата**. Термин `најмањи квадрати` значи да су све тачке података које окружују регресиону линију подигнуте на квадрат и затим сабране. Идеално, та коначна сума је што је могуће мања, јер желимо низак број грешака, односно `најмање квадрате`. Као таква, линија најбољег прилагођавања је линија која нам даје најнижу вредност за збир квадрата грешака - отуда назив *регресија најмањих квадрата*.\n",
+    "\n",
+    "Ово радимо јер желимо да моделирамо линију која има најмању кумулативну удаљеност од свих наших тачака података. Такође подижемо термине на квадрат пре него што их саберемо, јер нас занима њихова величина, а не правац.\n",
+    "\n",
+    "> **🧮 Покажите ми математику**\n",
+    ">\n",
+    "> Ова линија, названа *линија најбољег прилагођавања*, може се изразити [једначином](https://en.wikipedia.org/wiki/Simple_linear_regression):\n",
+    ">\n",
+    ">     Y = a + bX\n",
+    ">\n",
+    "> `X` је '`објашњавајућа променљива` или `предиктор`'. `Y` је '`зависна променљива` или `исход`'. Нагиб линије је `b`, а `a` је пресек са Y-осом, што се односи на вредност `Y` када је `X = 0`.\n",
+    ">\n",
+    "\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/slope.png \"нагиб = $y/x$\")\n",
+    "    Инфографика: Џен Лупер\n",
+    ">\n",
+    "> Прво, израчунајте нагиб `b`.\n",
+    ">\n",
+    "> Другим речима, и позивајући се на оригинално питање о подацима о бундевама: \"предвидите цену бундеве по бушелу по месецу\", `X` би се односио на цену, а `Y` на месец продаје.\n",
+    ">\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/calculation.png)\n",
+    "    Инфографика: Џен Лупер\n",
+    "> \n",
+    "> Израчунајте вредност Y. Ако плаћате око 4 долара, мора да је април!\n",
+    ">\n",
+    "> Математика која израчунава линију мора показати нагиб линије, који такође зависи од пресека, односно где се `Y` налази када је `X = 0`.\n",
+    ">\n",
+    "> Можете видети метод израчунавања ових вредности на веб сајту [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html). Такође посетите [овај калкулатор најмањих квадрата](https://www.mathsisfun.com/data/least-squares-calculator.html) да бисте видели како вредности бројева утичу на линију.\n",
+    "\n",
+    "Није тако страшно, зар не? 🤓\n",
+    "\n",
+    "#### Корелација\n",
+    "\n",
+    "Још један термин који треба разумети је **Коефицијент корелације** између датих X и Y променљивих. Користећи дијаграм расејања, можете брзо визуализовати овај коефицијент. Дијаграм са тачкама података распоређеним у уредну линију има високу корелацију, али дијаграм са тачкама података распршеним свуда између X и Y има ниску корелацију.\n",
+    "\n",
+    "Добар модел линеарне регресије биће онај који има висок (ближи 1 него 0) Коефицијент корелације користећи метод Регресије најмањих квадрата са линијом регресије.\n"
+   ],
+   "metadata": {
+    "id": "cdX5FRpvsoP5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **2. Плес са подацима: креирање дата фрејма који ће се користити за моделирање**\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/janitor.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Илустрација: @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Илустрација: \\@allison_horst](../../../../../../2-Regression/3-Linear/images/janitor.jpg){width=\"700\"}-->\n"
+   ],
+   "metadata": {
+    "id": "WdUKXk7Bs8-V"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Учитајте потребне библиотеке и скуп података. Претворите податке у оквир података који садржи подскуп података:\n",
+    "\n",
+    "-   Узмите само тикве чија је цена одређена по бушелу\n",
+    "\n",
+    "-   Претворите датум у месец\n",
+    "\n",
+    "-   Израчунајте цену као просек високих и ниских цена\n",
+    "\n",
+    "-   Претворите цену тако да одражава цену по количини бушела\n",
+    "\n",
+    "> Ове кораке смо обрадили у [претходној лекцији](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb).\n"
+   ],
+   "metadata": {
+    "id": "fMCtu2G2s-p8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "library(lubridate)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ryMVZEEPtERn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "У духу чисте авантуре, хајде да истражимо [`janitor package`](../../../../../../2-Regression/3-Linear/solution/R/github.com/sfirke/janitor) који пружа једноставне функције за испитивање и чишћење неуређених података. На пример, хајде да погледамо називе колона за наше податке:\n"
+   ],
+   "metadata": {
+    "id": "xcNxM70EtJjb"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5XtpaIigtPfW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤔 Можемо боље. Хајде да направимо ова имена колона `friendR` тако што ћемо их претворити у [snake_case](https://en.wikipedia.org/wiki/Snake_case) конвенцију користећи `janitor::clean_names`. Да бисте сазнали више о овој функцији: `?clean_names`\n"
+   ],
+   "metadata": {
+    "id": "IbIqrMINtSHe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Clean names to the snake_case convention\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  clean_names(case = \"snake\")\n",
+    "\n",
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "a2uYvclYtWvX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Толико tidyR 🧹! Сада, плес са подацима користећи `dplyr`, као у претходној лекцији! 💃\n"
+   ],
+   "metadata": {
+    "id": "HfhnuzDDtaDd"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(variety, city_name, package, low_price, high_price, date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Extract the month from the dates to a new column\n",
+    "pumpkins <- pumpkins %>%\n",
+    "  mutate(date = mdy(date),\n",
+    "         month = month(date)) %>% \n",
+    "  select(-date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a new column for average Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(price = (low_price + high_price)/2)\n",
+    "\n",
+    "\n",
+    "# Retain only pumpkins with the string \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "  filter(str_detect(string = package, pattern = \"bushel\"))\n",
+    "\n",
+    "\n",
+    "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(price = case_when(\n",
+    "    str_detect(package, \"1 1/9\") ~ price/(1.1),\n",
+    "    str_detect(package, \"1/2\") ~ price*2,\n",
+    "    TRUE ~ price))\n",
+    "\n",
+    "# Relocate column positions\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(month, .before = variety)\n",
+    "\n",
+    "\n",
+    "# Display the first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "X0wU3gQvtd9f"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Одличан посао!👌 Сада имате чист, уредан скуп података на којем можете изградити свој нови регресиони модел!\n",
+    "\n",
+    "Шта кажете на расејани графикон?\n"
+   ],
+   "metadata": {
+    "id": "UpaIwaxqth82"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set theme\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Make a scatter plot of month and price\n",
+    "new_pumpkins %>% \n",
+    "  ggplot(mapping = aes(x = month, y = price)) +\n",
+    "  geom_point(size = 1.6)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "DXgU-j37tl5K"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Дијаграм распршивања нас подсећа да имамо податке о месецима само од августа до децембра. Вероватно нам је потребно више података како бисмо могли да извучемо закључке на линеаран начин.\n",
+    "\n",
+    "Хајде да поново погледамо наше податке за моделирање:\n"
+   ],
+   "metadata": {
+    "id": "Ve64wVbwtobI"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Display first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HFQX2ng1tuSJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Шта ако бисмо желели да предвидимо `price` тикве на основу колона `city` или `package`, које су типа карактер? Или још једноставније, како бисмо могли да пронађемо корелацију (која захтева да оба уноса буду нумеричка) између, рецимо, `package` и `price`? 🤷🤷\n",
+    "\n",
+    "Модели машинског учења најбоље функционишу са нумеричким карактеристикама уместо текстуалних вредности, тако да је генерално потребно претворити категоријалне карактеристике у нумеричке репрезентације.\n",
+    "\n",
+    "То значи да морамо пронаћи начин да преобликујемо наше предикторе како бисмо их учинили погоднијим за ефикасно коришћење у моделу, процес познат као `feature engineering`.\n"
+   ],
+   "metadata": {
+    "id": "7hsHoxsStyjJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Предобрада података за моделирање са рецептурама 👩‍🍳👨‍🍳\n",
+    "\n",
+    "Активности које преобликују вредности предиктора како би их учиниле лакшим за ефикасно коришћење модела називају се `инжењеринг карактеристика`.\n",
+    "\n",
+    "Различити модели имају различите захтеве за предобраду. На пример, метода најмањих квадрата захтева `кодирање категоријалних променљивих` као што су месец, сорта и назив града. Ово једноставно подразумева `превод` колоне са `категоријалним вредностима` у једну или више `нумеричких колона` које замењују оригиналну.\n",
+    "\n",
+    "На пример, претпоставимо да ваши подаци укључују следећу категоријалну карактеристику:\n",
+    "\n",
+    "|  град   |\n",
+    "|:-------:|\n",
+    "| Денвер  |\n",
+    "| Најроби |\n",
+    "|  Токио  |\n",
+    "\n",
+    "Можете применити *ордирално кодирање* да замените јединствену целобројну вредност за сваку категорију, овако:\n",
+    "\n",
+    "| град |\n",
+    "|:----:|\n",
+    "|  0   |\n",
+    "|  1   |\n",
+    "|  2   |\n",
+    "\n",
+    "И то је оно што ћемо урадити са нашим подацима!\n",
+    "\n",
+    "У овом одељку, истражићемо још један невероватан пакет из Tidymodels-а: [recipes](https://tidymodels.github.io/recipes/) - који је осмишљен да вам помогне да предобрадите своје податке **пре** тренинга модела. У својој суштини, рецепт је објекат који дефинише које кораке треба применити на скуп података како би био спреман за моделирање.\n",
+    "\n",
+    "Сада, хајде да направимо рецепт који припрема наше податке за моделирање заменом јединственог целобројног броја за све опсервације у колонама предиктора:\n"
+   ],
+   "metadata": {
+    "id": "AD5kQbcvt3Xl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\n",
+    "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "# Print out the recipe\n",
+    "pumpkins_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "BNaFKXfRt9TU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сјајно! 👏 Управо смо направили наш први рецепт који одређује исход (цену) и његове одговарајуће предикторе, а такође и да све колоне предиктора треба да буду кодиране у скуп целих бројева 🙌! Хајде да брзо разложимо шта смо урадили:\n",
+    "\n",
+    "-   Позив функције `recipe()` са формулом говори рецепту *улоге* променљивих користећи податке `new_pumpkins` као референцу. На пример, колона `price` је добила улогу `outcome`, док су остале колоне добиле улогу `predictor`.\n",
+    "\n",
+    "-   `step_integer(all_predictors(), zero_based = TRUE)` одређује да сви предиктори треба да буду претворени у скуп целих бројева, при чему нумерација почиње од 0.\n",
+    "\n",
+    "Сигурни смо да вам можда падају на памет мисли попут: \"Ово је тако кул!! Али шта ако треба да проверим да ли рецепти раде тачно оно што очекујем? 🤔\"\n",
+    "\n",
+    "То је сјајна мисао! Видите, када једном дефинишете рецепт, можете проценити параметре који су потребни за стварну претобраду података, а затим извући обрађене податке. Обично ово није потребно када користите Tidymodels (ускоро ћемо видети уобичајену конвенцију -\\> `workflows`), али може бити корисно када желите да урадите неку врсту провере како бисте потврдили да рецепти раде оно што очекујете.\n",
+    "\n",
+    "За то ће вам бити потребна још два глагола: `prep()` и `bake()`, а као и увек, наши мали пријатељи из R-а од стране [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) помоћи ће вам да ово боље разумете!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Илустрација од @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Илустрација од \\@allison_horst](../../../../../../2-Regression/3-Linear/images/recipes.png){width=\"550\"}-->\n"
+   ],
+   "metadata": {
+    "id": "KEiO0v7kuC9O"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): процењује потребне параметре из тренинг скупа који касније могу бити примењени на друге скупове података. На пример, за дату колону предиктора, који посматрач ће бити додељен као цео број 0, 1, 2 итд.\n",
+    "\n",
+    "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): узима припремљен рецепт и примењује операције на било који скуп података.\n",
+    "\n",
+    "С тим речено, хајде да припремимо и применимо наше рецепте како бисмо заиста потврдили да ће, у позадини, колоне предиктора прво бити кодиране пре него што се модел прилагоди.\n"
+   ],
+   "metadata": {
+    "id": "Q1xtzebuuTCP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep the recipe\n",
+    "pumpkins_prep <- prep(pumpkins_recipe)\n",
+    "\n",
+    "# Bake the recipe to extract a preprocessed new_pumpkins data\n",
+    "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\n",
+    "\n",
+    "# Print out the baked data set\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FGBbJbP_uUUn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ура! 🥳 Обрађени подаци `baked_pumpkins` имају све своје предикторе кодиране, што потврђује да ће кораци предобраде дефинисани као наш рецепт функционисати како је очекивано. Ово чини податке теже читљивим за вас, али много разумљивијим за Tidymodels! Одвојите мало времена да откријете која је опсервација мапирана на одговарајући целобројни вредност.\n",
+    "\n",
+    "Такође је вредно напоменути да је `baked_pumpkins` оквир података на којем можемо изводити прорачуне.\n",
+    "\n",
+    "На пример, хајде да покушамо да пронађемо добру корелацију између две тачке ваших података како бисмо потенцијално изградили добар предиктивни модел. Користићемо функцију `cor()` да то урадимо. Укуцајте `?cor()` да бисте сазнали више о функцији.\n"
+   ],
+   "metadata": {
+    "id": "1dvP0LBUueAW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the correlation between the city_name and the price\n",
+    "cor(baked_pumpkins$city_name, baked_pumpkins$price)\n",
+    "\n",
+    "# Find the correlation between the package and the price\n",
+    "cor(baked_pumpkins$package, baked_pumpkins$price)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "3bQzXCjFuiSV"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Испоставило се да постоји само слаба корелација између Града и Цене. Међутим, постоји нешто боља корелација између Пакета и његове Цене. То има смисла, зар не? Обично, што је већа кутија са производима, то је виша цена.\n",
+    "\n",
+    "Док смо већ код тога, хајде да покушамо и да визуализујемо матрицу корелације свих колона користећи пакет `corrplot`.\n"
+   ],
+   "metadata": {
+    "id": "BToPWbgjuoZw"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the corrplot package\n",
+    "library(corrplot)\n",
+    "\n",
+    "# Obtain correlation matrix\n",
+    "corr_mat <- cor(baked_pumpkins %>% \n",
+    "                  # Drop columns that are not really informative\n",
+    "                  select(-c(low_price, high_price)))\n",
+    "\n",
+    "# Make a correlation plot between the variables\n",
+    "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZwAL3ksmutVR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Много боље.\n",
+    "\n",
+    "Добро питање које сада можемо поставити о овим подацима је: '`Коју цену могу очекивати за одређени пакет бундева?`' Хајде да одмах пређемо на то!\n",
+    "\n",
+    "> Напомена: Када **`bake()`** припремљени рецепт **`pumpkins_prep`** са **`new_data = NULL`**, добијате обрађене (тј. кодиране) податке за обуку. Ако имате други скуп података, на пример тест сет, и желите да видите како би рецепт обрадио те податке, једноставно бисте испекли **`pumpkins_prep`** са **`new_data = test_set`**.\n",
+    "\n",
+    "## 4. Изградња модела линеарне регресије\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Инфографика: Дасани Мадипали</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "YqXjLuWavNxW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сада када смо направили рецепт и заправо потврдили да ће подаци бити одговарајуће претходно обрађени, хајде сада да направимо регресиони модел како бисмо одговорили на питање: `Коју цену могу очекивати за одређени пакет бундеве?`\n",
+    "\n",
+    "#### Обучавање линеарног регресионог модела користећи тренинг сет\n",
+    "\n",
+    "Као што сте вероватно већ закључили, колона *price* је `излазна` променљива, док је колона *package* `предикторска` променљива.\n",
+    "\n",
+    "Да бисмо то урадили, прво ћемо поделити податке тако да 80% иде у тренинг сет, а 20% у тест сет, затим ћемо дефинисати рецепт који ће кодирати предикторску колону у скуп целих бројева, а потом ћемо направити спецификацију модела. Нећемо припремати и обрађивати наш рецепт јер већ знамо да ће он претходно обрадити податке како је очекивано.\n"
+   ],
+   "metadata": {
+    "id": "Pq0bSzCevW-h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\n",
+    "# Split the data into training and test sets\n",
+    "pumpkins_split <- new_pumpkins %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "\n",
+    "# Extract training and test data\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a recipe for preprocessing the data\n",
+    "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a linear model specification\n",
+    "lm_spec <- linear_reg() %>% \n",
+    "  set_engine(\"lm\") %>% \n",
+    "  set_mode(\"regression\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "CyoEh_wuvcLv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Одлично! Сада када имамо рецепт и спецификацију модела, потребно је да пронађемо начин да их спојимо у један објекат који ће прво обрадити податке (припрема + обрада у позадини), затим обучити модел на обрађеним подацима, а такође омогућити потенцијалне активности пост-обраде. Како ти се то чини за мир у души!🤩\n",
+    "\n",
+    "У оквиру Tidymodels-а, овај практични објекат се зове [`workflow`](https://workflows.tidymodels.org/) и згодно чува све компоненте твог модела! Ово је оно што бисмо у *Python*-у назвали *pipelines*.\n",
+    "\n",
+    "Хајде да све спакујемо у један workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "G3zF_3DqviFJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Hold modelling components in a workflow\n",
+    "lm_wf <- workflow() %>% \n",
+    "  add_recipe(lm_pumpkins_recipe) %>% \n",
+    "  add_model(lm_spec)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "lm_wf"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "T3olroU3v-WX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Узгред, ток рада може бити прилагођен/трениран на сличан начин као што се тренира модел.\n"
+   ],
+   "metadata": {
+    "id": "zd1A5tgOwEPX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Train the model\n",
+    "lm_wf_fit <- lm_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the model coefficients learned \n",
+    "lm_wf_fit"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NhJagFumwFHf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Из излаза модела можемо видети коефицијенте који су научени током тренинга. Они представљају коефицијенте праве најбољег прилагођавања која нам даје најмању укупну грешку између стварне и предвиђене променљиве.\n",
+    "\n",
+    "#### Процена перформанси модела коришћењем тест скупа\n",
+    "\n",
+    "Време је да видимо како се модел показао 📏! Како то радимо?\n",
+    "\n",
+    "Сада када смо обучили модел, можемо га користити за прављење предвиђања за test_set користећи `parsnip::predict()`. Затим можемо упоредити ова предвиђања са стварним вредностима ознака како бисмо проценили колико добро (или не!) модел функционише.\n",
+    "\n",
+    "Хајде да почнемо са прављењем предвиђања за тест скуп, а затим да повежемо колоне са тест скупом.\n"
+   ],
+   "metadata": {
+    "id": "_4QkGtBTwItF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\n",
+    "predictions <- lm_wf_fit %>% \n",
+    "  predict(new_data = pumpkins_test)\n",
+    "\n",
+    "\n",
+    "# Bind predictions to the test set\n",
+    "lm_results <- pumpkins_test %>% \n",
+    "  select(c(package, price)) %>% \n",
+    "  bind_cols(predictions)\n",
+    "\n",
+    "\n",
+    "# Print the first ten rows of the tibble\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "UFZzTG0gwTs9"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Да, управо сте обучили модел и користили га за прављење предикција! 🔮 Да ли је добар? Хајде да проценимо перформансе модела!\n",
+    "\n",
+    "У оквиру Tidymodels-а, ово радимо помоћу `yardstick::metrics()`! За линеарну регресију, фокусираћемо се на следеће метрике:\n",
+    "\n",
+    "-   `Root Mean Square Error (RMSE)`: Квадратни корен од [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). Ова метрика даје апсолутну вредност у истој јединици као и ознака (у овом случају, цена бундеве). Што је вредност мања, то је модел бољи (у поједностављеном смислу, представља просечну цену за коју су предикције погрешне!).\n",
+    "\n",
+    "-   `Coefficient of Determination (обично познат као R-squared или R2)`: Релативна метрика код које је већа вредност боља за модел. У суштини, ова метрика представља колико варијансе између предвиђених и стварних вредности ознака модел може да објасни.\n"
+   ],
+   "metadata": {
+    "id": "0A5MjzM7wW9M"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Evaluate performance of linear regression\n",
+    "metrics(data = lm_results,\n",
+    "        truth = price,\n",
+    "        estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "reJ0UIhQwcEH"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ту иде перформанс модела. Хајде да видимо да ли можемо добити бољу индикацију визуелизацијом расејаног графикона пакета и цене, а затим користити предвиђања за преклапање линије најбољег уклапања.\n",
+    "\n",
+    "То значи да ћемо морати припремити и обрадити тестни сет како бисмо кодирали колону пакета, а затим је повезати са предвиђањима која је наш модел направио.\n"
+   ],
+   "metadata": {
+    "id": "fdgjzjkBwfWt"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Encode package column\n",
+    "package_encode <- lm_pumpkins_recipe %>% \n",
+    "  prep() %>% \n",
+    "  bake(new_data = pumpkins_test) %>% \n",
+    "  select(package)\n",
+    "\n",
+    "\n",
+    "# Bind encoded package column to the results\n",
+    "lm_results <- lm_results %>% \n",
+    "  bind_cols(package_encode %>% \n",
+    "              rename(package_integer = package)) %>% \n",
+    "  relocate(package_integer, .after = package)\n",
+    "\n",
+    "\n",
+    "# Print new results data frame\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 5)\n",
+    "\n",
+    "\n",
+    "# Make a scatter plot\n",
+    "lm_results %>% \n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\n",
+    "  geom_point(size = 1.6) +\n",
+    "  # Overlay a line of best fit\n",
+    "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\n",
+    "  xlab(\"package\")\n",
+    "  \n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R0nw719lwkHE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Одлично! Као што можете видети, линеарни регресиони модел не успева добро да генерализује однос између пакета и његове одговарајуће цене.\n",
+    "\n",
+    "🎃 Честитамо, управо сте направили модел који може помоћи у предвиђању цене неколико врста бундева. Ваш празнични бундева врт ће бити прелеп. Али вероватно можете направити бољи модел!\n",
+    "\n",
+    "## 5. Направите полиномијални регресиони модел\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Инфографик од Дасани Мадипали</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Инфографик од Дасани Мадипали](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "HOCqJXLTwtWI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Понекад наши подаци можда немају линеарну везу, али и даље желимо да предвидимо исход. Полиномијална регресија може нам помоћи да направимо предвиђања за сложеније нелинеарне односе.\n",
+    "\n",
+    "Узмимо, на пример, однос између паковања и цене у нашем скупу података о бундевама. Иако понекад постоји линеарна веза између променљивих - што је већа бундева по запремини, то је већа цена - понекад се ти односи не могу приказати као раван или права линија.\n",
+    "\n",
+    "> ✅ Ево [још неких примера](https://online.stat.psu.edu/stat501/lesson/9/9.8) података који би могли користити полиномијалну регресију\n",
+    ">\n",
+    "> Поново погледајте однос између сорте и цене на претходном графикону. Да ли овај распршени графикон изгледа као да би нужно требало да се анализира правом линијом? Можда не. У овом случају, можете пробати полиномијалну регресију.\n",
+    ">\n",
+    "> ✅ Полиноми су математички изрази који могу садржати једну или више променљивих и коефицијената\n",
+    "\n",
+    "#### Тренирајте модел полиномијалне регресије користећи скуп за тренирање\n",
+    "\n",
+    "Полиномијална регресија креира *закривљену линију* како би боље одговарала нелинеарним подацима.\n",
+    "\n",
+    "Хајде да видимо да ли ће полиномијални модел боље функционисати у прављењу предвиђања. Следићемо донекле сличан поступак као што смо радили раније:\n",
+    "\n",
+    "-   Направите рецепт који одређује кораке предобраде који треба да се спроведу на нашим подацима како би били спремни за моделирање, тј. кодирање предиктора и израчунавање полинома степена *n*\n",
+    "\n",
+    "-   Направите спецификацију модела\n",
+    "\n",
+    "-   Спојите рецепт и спецификацију модела у радни ток\n",
+    "\n",
+    "-   Креирајте модел тако што ћете прилагодити радни ток\n",
+    "\n",
+    "-   Процените колико добро модел функционише на тест подацима\n",
+    "\n",
+    "Хајде да почнемо!\n"
+   ],
+   "metadata": {
+    "id": "VcEIpRV9wzYr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\r\n",
+    "poly_pumpkins_recipe <-\r\n",
+    "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+    "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+    "  step_poly(all_predictors(), degree = 4)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model specification\r\n",
+    "poly_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Bundle recipe and model spec into a workflow\r\n",
+    "poly_wf <- workflow() %>% \r\n",
+    "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+    "  add_model(poly_spec)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model\r\n",
+    "poly_wf_fit <- poly_wf %>% \r\n",
+    "  fit(data = pumpkins_train)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print learned model coefficients\r\n",
+    "poly_wf_fit\r\n",
+    "\r\n",
+    "  "
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "63n_YyRXw3CC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Оцена перформанси модела\n",
+    "\n",
+    "👏👏 Направили сте полиномни модел, хајде да направимо предвиђања на тест скупу!\n"
+   ],
+   "metadata": {
+    "id": "-LHZtztSxDP0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make price predictions on test data\r\n",
+    "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+    "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+    "  relocate(.pred, .after = last_col())\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the results\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YUFpQ_dKxJGx"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ву-ху, хајде да проценимо како је модел извршио на test_set користећи `yardstick::metrics()`.\n"
+   ],
+   "metadata": {
+    "id": "qxdyj86bxNGZ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "metrics(data = poly_results, truth = price, estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "8AW5ltkBxXDm"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Много бољи учинак.\n",
+    "\n",
+    "`rmse` се смањио са отприлике 7 на отприлике 3, што указује на смањење грешке између стварне цене и предвиђене цене. Ово можете *слободно* тумачити као да су у просеку нетачне прогнозе погрешне за око \\$3. `rsq` се повећао са отприлике 0.4 на 0.8.\n",
+    "\n",
+    "Сви ови показатељи указују на то да полиномски модел ради много боље од линеарног модела. Одличан посао!\n",
+    "\n",
+    "Хајде да видимо да ли можемо ово да визуализујемо!\n"
+   ],
+   "metadata": {
+    "id": "6gLHNZDwxYaS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Bind encoded package column to the results\r\n",
+    "poly_results <- poly_results %>% \r\n",
+    "  bind_cols(package_encode %>% \r\n",
+    "              rename(package_integer = package)) %>% \r\n",
+    "  relocate(package_integer, .after = package)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print new results data frame\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+    "  xlab(\"package\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "A83U16frxdF1"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Можете видети закривљену линију која боље одговара вашим подацима! 🤩\n",
+    "\n",
+    "Можете је учинити још глаткијом тако што ћете проследити полиномску формулу функцији `geom_smooth` на следећи начин:\n"
+   ],
+   "metadata": {
+    "id": "4U-7aHOVxlGU"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+    "  xlab(\"package\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5vzNT0Uexm-w"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Баш као глатка крива!🤩\n",
+    "\n",
+    "Ево како можете направити нову прогнозу:\n"
+   ],
+   "metadata": {
+    "id": "v9u-wwyLxq4G"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a hypothetical data frame\r\n",
+    "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+    "\r\n",
+    "# Make predictions using linear model\r\n",
+    "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Make predictions using polynomial model\r\n",
+    "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Return predictions in a list\r\n",
+    "list(\"linear model prediction\" = lm_pred, \r\n",
+    "     \"polynomial model prediction\" = poly_pred)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "jRPSyfQGxuQv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Предвиђање помоћу `полиномског модела` има смисла, с обзиром на распршене графике `цена` и `пакета`! И, ако је ово бољи модел од претходног, гледајући исте податке, потребно је да планирате буџет за ове скупље тикве!\n",
+    "\n",
+    "🏆 Браво! Направили сте два модела регресије у једном часу. У завршном делу о регресији, научићете о логистичкој регресији за одређивање категорија.\n",
+    "\n",
+    "## **🚀Изазов**\n",
+    "\n",
+    "Тестирајте неколико различитих променљивих у овом нотебуку да видите како корелација утиче на тачност модела.\n",
+    "\n",
+    "## [**Квиз након предавања**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)\n",
+    "\n",
+    "## **Преглед и Самостално учење**\n",
+    "\n",
+    "У овом часу смо научили о Линеарној регресији. Постоје и други важни типови регресије. Прочитајте о техникама Stepwise, Ridge, Lasso и Elasticnet. Добар курс за даље учење је [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning).\n",
+    "\n",
+    "Ако желите да научите више о коришћењу невероватног Tidymodels оквира, погледајте следеће ресурсе:\n",
+    "\n",
+    "-   Веб-сајт Tidymodels: [Почните са Tidymodels](https://www.tidymodels.org/start/)\n",
+    "\n",
+    "-   Мак Кун и Џулија Силџ, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+    "\n",
+    "###### **ХВАЛА:**\n",
+    "\n",
+    "[Елисон Хорст](https://twitter.com/allison_horst?lang=en) за креирање невероватних илустрација које чине R приступачнијим и занимљивијим. Пронађите више илустрација у њеној [галерији](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n"
+   ],
+   "metadata": {
+    "id": "8zOLOWqMxzk5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/3-Linear/solution/notebook.ipynb b/translations/sr/2-Regression/3-Linear/solution/notebook.ipynb
new file mode 100644
index 000000000..8ccddf415
--- /dev/null
+++ b/translations/sr/2-Regression/3-Linear/solution/notebook.ipynb
@@ -0,0 +1,1113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Линеарна и полиномијална регресија за одређивање цене бундева - Лекција 3\n",
+    "\n",
+    "Учитајте потребне библиотеке и скуп података. Претворите податке у датафрејм који садржи подскуп података:\n",
+    "\n",
+    "- Узмите само бундеве чија је цена дата по бушелу  \n",
+    "- Претворите датум у месец  \n",
+    "- Израчунајте цену као просек између највише и најниже цене  \n",
+    "- Претворите цену тако да одражава цену по количини у бушелима  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Скатерплот нас подсећа да имамо податке само за месеце од августа до децембра. Вероватно нам је потребно више података како бисмо могли да извучемо закључке на линеаран начин.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Month', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('Month','Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Изгледа да је корелација прилично мала, али постоји нека друга важнија веза - јер цене на графикону изнад изгледају као да имају неколико различитих кластера. Хајде да направимо графикон који ће приказати различите врсте бундева:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax=None\n",
+    "colors = ['red','blue','green','yellow']\n",
+    "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    ax = new_pumpkins[new_pumpkins['Variety']==var].plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.2669192282197318\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n",
+    "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))\n",
+    "pie_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Линеарна регресија\n",
+    "\n",
+    "Користићемо Scikit Learn за тренирање модела линеарне регресије:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a3232f8dc0>]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(X_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Нагиб праве може се одредити из коефицијената линеарне регресије:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-0.01751876]), 21.133734359909326)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.coef_, lin_reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Можемо користити обучени модел да предвидимо цену:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.64893156])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pumpkin price on programmer's day\n",
+    "\n",
+    "lin_reg.predict([[256]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Полиномна регресија\n",
+    "\n",
+    "Понекад је однос између карактеристика и резултата природно нелинеаран. На пример, цене бундева могу бити високе током зиме (месеци=1,2), затим падати током лета (месеци=5-7), а потом поново расти. Линеарна регресија није у стању да тачно ухвати овај однос.\n",
+    "\n",
+    "У овом случају можемо размотрити додавање додатних карактеристика. Једноставан начин је коришћење полинома из улазних карактеристика, што би довело до **полиномне регресије**. У Scikit Learn-у, можемо аутоматски претходно израчунати полиномне карактеристике користећи пайплайне:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.73 (17.0%)\n",
+      "Model determination:  0.07639977655280217\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a323368ac0>]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)\n",
+    "\n",
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Кодирање сорти\n",
+    "\n",
+    "У идеалном свету, желимо да будемо у могућности да предвидимо цене за различите сорте бундева користећи исти модел. Да бисмо узели у обзир сорту, прво је потребно да је претворимо у нумерички облик, или **кодирање**. Постоји неколико начина на које то можемо урадити:\n",
+    "\n",
+    "* Једноставно нумеричко кодирање које ће направити табелу различитих сорти, а затим заменити име сорте индексом у тој табели. Ово није најбоља идеја за линеарну регресију, јер линеарна регресија узима у обзир нумеричку вредност индекса, а нумеричка вредност вероватно неће нумерички корелирати са ценом.\n",
+    "* One-hot кодирање, које ће заменити колону `Variety` са 4 различите колоне, по једну за сваку сорту, које ће садржати 1 ако одговарајући ред припада датој сорти, а 0 у супротном.\n",
+    "\n",
+    "Код испод показује како можемо да применимо one-hot кодирање на сорту:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FAIRYTALE</th>\n",
+       "      <th>MINIATURE</th>\n",
+       "      <th>MIXED HEIRLOOM VARIETIES</th>\n",
+       "      <th>PIE TYPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE\n",
+       "70            0          0                         0         1\n",
+       "71            0          0                         0         1\n",
+       "72            0          0                         0         1\n",
+       "73            0          0                         0         1\n",
+       "74            0          0                         0         1\n",
+       "...         ...        ...                       ...       ...\n",
+       "1738          0          1                         0         0\n",
+       "1739          0          1                         0         0\n",
+       "1740          0          1                         0         0\n",
+       "1741          0          1                         0         0\n",
+       "1742          0          1                         0         0\n",
+       "\n",
+       "[415 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(new_pumpkins['Variety'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Линеарна регресија на сорти\n",
+    "\n",
+    "Сада ћемо користити исти код као горе, али уместо `DayOfYear` користићемо нашу једнохот-енкодовану сорту као улаз:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety'])\n",
+    "y = new_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 5.24 (19.7%)\n",
+      "Model determination:  0.774085281105197\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_linear_regression(X,y):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "    lin_reg = LinearRegression()\n",
+    "    lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "    pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "    mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "    print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "    score = lin_reg.score(X_train,y_train)\n",
+    "    print('Model determination: ', score)\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Можемо такође покушати да користимо друге функције на исти начин и комбинујемо их са нумеричким функцијама, као што су `Month` или `DayOfYear`:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.84 (10.5%)\n",
+      "Model determination:  0.9401096672643048\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['Package']))\n",
+    "y = new_pumpkins['Price']\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Полиномна регресија\n",
+    "\n",
+    "Полиномна регресија може се користити и са категоријалним карактеристикама које су једнохот-кодиране. Код за тренирање полиномне регресије би у суштини био исти као што смо видели горе.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.23 (8.25%)\n",
+      "Model determination:  0.9652870784724543\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d77bd89ae7e79780c68c58bab91f13f8",
+   "translation_date": "2025-09-06T13:10:36+00:00",
+   "source_file": "2-Regression/3-Linear/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/4-Logistic/notebook.ipynb b/translations/sr/2-Regression/4-Logistic/notebook.ipynb
new file mode 100644
index 000000000..a5db6bb9b
--- /dev/null
+++ b/translations/sr/2-Regression/4-Logistic/notebook.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Врсте бундева и боја\n",
+    "\n",
+    "Учитајте потребне библиотеке и скуп података. Претворите податке у датафрејм који садржи подскуп података:\n",
+    "\n",
+    "Хајде да погледамо однос између боје и врсте\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
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@@ -0,0 +1,685 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Изградња модела логистичке регресије - Лекција 4\n",
+    "\n",
+    "![Инфографика: Логистичка vs. линеарна регресија](../../../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)\n",
+    "\n",
+    "#### **[Квиз пре предавања](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
+    "\n",
+    "#### Увод\n",
+    "\n",
+    "У овој последњој лекцији о регресији, једној од основних *класичних* техника машинског учења, погледаћемо логистичку регресију. Ову технику користите да бисте открили обрасце за предвиђање бинарних категорија. Да ли је овај слаткиш чоколада или не? Да ли је ова болест заразна или не? Да ли ће овај купац изабрати овај производ или не?\n",
+    "\n",
+    "У овој лекцији ћете научити:\n",
+    "\n",
+    "-   Технике за логистичку регресију\n",
+    "\n",
+    "✅ Продубите своје разумевање рада са овом врстом регресије у овом [модулу за учење](https://learn.microsoft.com/training/modules/introduction-classification-models/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "## Предуслов\n",
+    "\n",
+    "Радећи са подацима о бундевама, сада смо довољно упознати са њима да схватимо да постоји једна бинарна категорија са којом можемо радити: `Color`.\n",
+    "\n",
+    "Хајде да изградимо модел логистичке регресије да бисмо предвидели, на основу неких променљивих, *која је вероватно боја дате бундеве* (наранџаста 🎃 или бела 👻).\n",
+    "\n",
+    "> Зашто говоримо о бинарној класификацији у лекцији која се бави регресијом? Само ради језичке погодности, јер је логистичка регресија [заправо метода класификације](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), иако заснована на линеарним принципима. Сазнајте више о другим начинима класификације података у следећој групи лекција.\n",
+    "\n",
+    "За ову лекцију биће нам потребни следећи пакети:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) је [збирка R пакета](https://www.tidyverse.org/packages) дизајнирана да учини науку о подацима бржом, лакшом и забавнијом!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) оквир је [збирка пакета](https://www.tidymodels.org/packages/) за моделирање и машинско учење.\n",
+    "\n",
+    "-   `janitor`: [janitor пакет](https://github.com/sfirke/janitor) пружа једноставне алате за испитивање и чишћење \"прљавих\" података.\n",
+    "\n",
+    "-   `ggbeeswarm`: [ggbeeswarm пакет](https://github.com/eclarke/ggbeeswarm) пружа методе за креирање графикона у стилу \"роја пчела\" користећи ggplot2.\n",
+    "\n",
+    "Можете их инсталирати помоћу:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+    "\n",
+    "Алтернативно, скрипта испод проверава да ли имате потребне пакете за завршетак овог модула и инсталира их ако недостају.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## **Дефинишите питање**\n",
+    "\n",
+    "За наше потребе, изразићемо ово као бинарно: 'Бело' или 'Није бело'. У нашем сету података постоји и категорија 'пругасто', али има мало примерака, па је нећемо користити. Ионако нестаје када уклонимо вредности null из сета података.\n",
+    "\n",
+    "> 🎃 Забавна чињеница, понекад беле тикве називамо 'духовима' тиквама. Нису баш лаке за резбарење, па нису толико популарне као наранџасте, али изгледају занимљиво! Тако да бисмо могли да преобликујемо наше питање као: 'Дух' или 'Није дух'. 👻\n",
+    "\n",
+    "## **О логистичкој регресији**\n",
+    "\n",
+    "Логистичка регресија се разликује од линеарне регресије, коју сте раније учили, на неколико важних начина.\n",
+    "\n",
+    "#### **Бинарна класификација**\n",
+    "\n",
+    "Логистичка регресија не нуди исте могућности као линеарна регресија. Прва нуди предвиђање о `бинарној категорији` (\"наранџасто или није наранџасто\"), док је друга способна да предвиђа `континуалне вредности`, на пример, на основу порекла тикве и времена жетве, *колико ће њена цена порасти*.\n",
+    "\n",
+    "![Инфографик од Дасани Мадипали](../../../../../../2-Regression/4-Logistic/images/pumpkin-classifier.png)\n",
+    "\n",
+    "### Друге класификације\n",
+    "\n",
+    "Постоје и други типови логистичке регресије, укључујући мултиномијалну и ординалну:\n",
+    "\n",
+    "- **Мултиномијална**, која укључује више од једне категорије - \"Наранџасто, Бело и Пругасто\".\n",
+    "\n",
+    "- **Ординална**, која укључује уређене категорије, корисна ако желимо да логички поређамо наше исходе, као тикве које су поређане по ограниченом броју величина (мини, мало, средње, велико, веома велико, екстра велико).\n",
+    "\n",
+    "![Мултиномијална vs ординална регресија](../../../../../../2-Regression/4-Logistic/images/multinomial-vs-ordinal.png)\n",
+    "\n",
+    "#### **Променљиве НЕ морају да буду корелисане**\n",
+    "\n",
+    "Сећате се како је линеарна регресија боље функционисала са више корелисаних променљивих? Логистичка регресија је супротна - променљиве не морају да буду усклађене. То одговара овим подацима који имају релативно слабе корелације.\n",
+    "\n",
+    "#### **Потребно је много чистих података**\n",
+    "\n",
+    "Логистичка регресија ће дати прецизније резултате ако користите више података; наш мали сет података није оптималан за овај задатак, па то имајте на уму.\n",
+    "\n",
+    "✅ Размислите о типовима података који би били погодни за логистичку регресију\n",
+    "\n",
+    "## Вежба - уредите податке\n",
+    "\n",
+    "Прво, мало очистите податке, уклањајући null вредности и бирајући само неке од колона:\n",
+    "\n",
+    "1. Додајте следећи код:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the core tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data and clean column names\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \n",
+    "  clean_names()\n",
+    "\n",
+    "# Select desired columns\n",
+    "pumpkins_select <- pumpkins %>% \n",
+    "  select(c(city_name, package, variety, origin, item_size, color)) \n",
+    "\n",
+    "# Drop rows containing missing values and encode color as factor (category)\n",
+    "pumpkins_select <- pumpkins_select %>% \n",
+    "  drop_na() %>% \n",
+    "  mutate(color = factor(color))\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins_select %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Увек можете бацити поглед на ваш нови датафрејм користећи функцију [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) као што је приказано испод:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pumpkins_select %>% \n",
+    "  glimpse()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Хајде да потврдимо да ћемо заправо радити на проблему бинарне класификације:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Subset distinct observations in outcome column\n",
+    "pumpkins_select %>% \n",
+    "  distinct(color)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Визуелизација - категоријални графикон\n",
+    "До сада сте поново учитали податке о бундевама и очистили их тако да задржите скуп података који садржи неколико променљивих, укључујући боју. Хајде да визуелизујемо dataframe у бележници користећи библиотеку ggplot.\n",
+    "\n",
+    "Библиотека ggplot нуди неке сјајне начине за визуелизацију ваших података. На пример, можете упоредити расподеле података за сваку сорту и боју у категоријалном графикону.\n",
+    "\n",
+    "1. Направите такав графикон користећи функцију geombar, користећи наше податке о бундевама, и одредите мапирање боја за сваку категорију бундеве (наранџаста или бела):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Specify colors for each value of the hue variable\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# Create the bar plot\n",
+    "ggplot(pumpkins_select, aes(y = variety, fill = color)) +\n",
+    "  geom_bar(position = \"dodge\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(y = \"Variety\", fill = \"Color\") +\n",
+    "  theme_minimal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Посматрајући податке, можете видети како се подаци о Боји односе на Врсту.\n",
+    "\n",
+    "✅ С обзиром на овај категоријални графикон, која занимљива истраживања можете замислити?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Предобрада података: кодирање карактеристика\n",
+    "\n",
+    "Наш скуп података о бундевама садржи стринг вредности за све своје колоне. Рад са категоријским подацима је интуитиван за људе, али не и за машине. Алгоритми машинског учења добро функционишу са бројевима. Зато је кодирање веома важан корак у фази предобраде података, јер нам омогућава да категоријске податке претворимо у нумеричке, без губитка информација. Добро кодирање води ка изградњи доброг модела.\n",
+    "\n",
+    "За кодирање карактеристика постоје два главна типа кодера:\n",
+    "\n",
+    "1. Ординални кодер: добро одговара ординалним променљивама, које су категоријске променљиве где њихови подаци следе логички редослед, као што је колона `item_size` у нашем скупу података. Он креира мапирање тако да је свака категорија представљена бројем, који је редослед категорије у колони.\n",
+    "\n",
+    "2. Категоријски кодер: добро одговара номиналним променљивама, које су категоријске променљиве где њихови подаци не следе логички редослед, као што су све карактеристике различите од `item_size` у нашем скупу података. То је једно-хот кодирање, што значи да је свака категорија представљена бинарном колоном: кодирана променљива је једнака 1 ако бундева припада тој врсти, а 0 у супротном.\n",
+    "\n",
+    "Tidymodels пружа још један користан пакет: [recipes](https://recipes.tidymodels.org/) - пакет за предобраду података. Дефинисаћемо `recipe` који спецификује да све колоне предиктора треба да буду кодиране у сет целих бројева, `prep` да процени потребне количине и статистике за било коју операцију и на крају `bake` да примени израчунавања на нове податке.\n",
+    "\n",
+    "> Уобичајено је да се recipes користи као предобрада за моделирање, где дефинише које кораке треба применити на скуп података како би био спреман за моделирање. У том случају је **топло препоручено** да користите `workflow()` уместо ручног процењивања рецепта помоћу prep и bake. Све ово ћемо видети ускоро.\n",
+    ">\n",
+    "> Међутим, за сада користимо recipes + prep + bake да спецификујемо које кораке треба применити на скуп података како би био спреман за анализу података и затим извукли предобрађене податке са примењеним корацима.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Preprocess and extract data to allow some data analysis\n",
+    "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>%\n",
+    "  # Define ordering for item_size column\n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  # Convert factors to numbers using the order defined above (Ordinal encoding)\n",
+    "  step_integer(item_size, zero_based = F) %>%\n",
+    "  # Encode all other predictors using one hot encoding\n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE) %>%\n",
+    "  prep(data = pumpkin_select) %>%\n",
+    "  bake(new_data = NULL)\n",
+    "\n",
+    "# Display the first few rows of preprocessed data\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "✅ Које су предности коришћења ординалног енкодера за колону величине артикла?\n",
+    "\n",
+    "### Анализа односа између променљивих\n",
+    "\n",
+    "Сада када смо претходно обрадили наше податке, можемо анализирати односе између карактеристика и ознаке како бисмо стекли идеју о томе колико добро модел може предвидети ознаку на основу карактеристика. Најбољи начин за спровођење овакве анализе је кроз визуализацију података. \n",
+    "Поново ћемо користити функцију ggplot geom_boxplot_, како бисмо визуализовали односе између величине артикла, сорте и боје у категоријалном графику. За бољу визуализацију података користићемо енкодовану колону величине артикла и неенкодовану колону сорте.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define the color palette\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins_select_plot<-pumpkins_select\n",
+    "pumpkins_select_plot$item_size <- baked_pumpkins$item_size\n",
+    "\n",
+    "# Create the grouped box plot\n",
+    "ggplot(pumpkins_select_plot, aes(x = `item_size`, y = color, fill = color)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_grid(variety ~ ., scales = \"free_x\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(x = \"Item Size\", y = \"\") +\n",
+    "  theme_minimal() +\n",
+    "  theme(strip.text = element_text(size = 12)) +\n",
+    "  theme(axis.text.x = element_text(size = 10)) +\n",
+    "  theme(axis.title.x = element_text(size = 12)) +\n",
+    "  theme(axis.title.y = element_blank()) +\n",
+    "  theme(legend.position = \"bottom\") +\n",
+    "  guides(fill = guide_legend(title = \"Color\")) +\n",
+    "  theme(panel.spacing = unit(0.5, \"lines\"))+\n",
+    "  theme(strip.text.y = element_text(size = 4, hjust = 0)) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Користите swarm plot\n",
+    "\n",
+    "Пошто је Color бинарна категорија (Бела или Не), потребан је 'специјализовани приступ [визуализацији](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf)'.\n",
+    "\n",
+    "Пробајте `swarm plot` да бисте приказали расподелу боје у односу на item_size.\n",
+    "\n",
+    "Користићемо [ggbeeswarm пакет](https://github.com/eclarke/ggbeeswarm) који пружа методе за креирање beeswarm-стил графикона користећи ggplot2. Beeswarm графикони су начин приказивања тачака које би се иначе преклапале тако да падају једна поред друге.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create beeswarm plots of color and item_size\n",
+    "baked_pumpkins %>% \n",
+    "  mutate(color = factor(color)) %>% \n",
+    "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\n",
+    "  geom_quasirandom() +\n",
+    "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\n",
+    "  theme(legend.position = \"none\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Сада када имамо идеју о односу између бинарних категорија боје и веће групе величина, хајде да истражимо логистичку регресију како бисмо одредили вероватну боју одређене тикве.\n",
+    "\n",
+    "## Направите свој модел\n",
+    "\n",
+    "Изаберите променљиве које желите да користите у свом класификационом моделу и поделите податке на сетове за обуку и тестирање. [rsample](https://rsample.tidymodels.org/), пакет у оквиру Tidymodels, пружа инфраструктуру за ефикасно раздвајање и ресемплинг података:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Split data into 80% for training and 20% for testing\n",
+    "set.seed(2056)\n",
+    "pumpkins_split <- pumpkins_select %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "# Print out the first 5 rows of the training set\n",
+    "pumpkins_train %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🙌 Сада смо спремни да обучимо модел тако што ћемо прилагодити карактеристике за обуку ознаци за обуку (боји).\n",
+    "\n",
+    "Почећемо креирањем рецепта који одређује кораке предобраде који треба да се спроведу на нашим подацима како би били спремни за моделирање, тј. кодирање категоријских променљивих у скуп целих бројева. Баш као `baked_pumpkins`, креирамо `pumpkins_recipe`, али не `prep` и `bake`, јер ће бити укључено у радни ток, што ћете видети за само неколико корака.\n",
+    "\n",
+    "Постоји прилично велики број начина за спецификацију модела логистичке регресије у Tidymodels. Погледајте `?logistic_reg()`. За сада ћемо специфицирати модел логистичке регресије преко подразумеваног `stats::glm()` механизма.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a recipe that specifies preprocessing steps for modelling\n",
+    "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  step_integer(item_size, zero_based = F) %>%  \n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)\n",
+    "\n",
+    "# Create a logistic model specification\n",
+    "log_reg <- logistic_reg() %>% \n",
+    "  set_engine(\"glm\") %>% \n",
+    "  set_mode(\"classification\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Сада када имамо рецепт и спецификацију модела, потребно је да пронађемо начин да их спојимо у један објекат који ће прво обрадити податке (припрема+печење у позадини), затим обучити модел на обрађеним подацима, а такође омогућити потенцијалне активности пост-обраде.\n",
+    "\n",
+    "У Tidymodels-у, овај практични објекат се назива [`workflow`](https://workflows.tidymodels.org/) и згодно чува ваше компоненте за моделирање.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Bundle modelling components in a workflow\n",
+    "log_reg_wf <- workflow() %>% \n",
+    "  add_recipe(pumpkins_recipe) %>% \n",
+    "  add_model(log_reg)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "log_reg_wf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Након што је радни ток *одређен*, модел може бити `обучен` коришћењем функције [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html). Радни ток ће проценити рецепт и претходно обрадити податке пре обуке, тако да нећемо морати то ручно да радимо користећи prep и bake.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "wf_fit <- log_reg_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the trained workflow\n",
+    "wf_fit\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Модел приказује коефицијенте научене током тренинга.\n",
+    "\n",
+    "Сада када смо обучили модел користећи податке за тренинг, можемо направити предвиђања на тест подацима користећи [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Хајде да започнемо тако што ћемо користити модел за предвиђање ознака за наш тест сет и вероватноћа за сваку ознаку. Када је вероватноћа већа од 0.5, предвиђена класа је `WHITE`, иначе је `ORANGE`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions for color and corresponding probabilities\n",
+    "results <- pumpkins_test %>% select(color) %>% \n",
+    "  bind_cols(wf_fit %>% \n",
+    "              predict(new_data = pumpkins_test)) %>%\n",
+    "  bind_cols(wf_fit %>%\n",
+    "              predict(new_data = pumpkins_test, type = \"prob\"))\n",
+    "\n",
+    "# Compare predictions\n",
+    "results %>% \n",
+    "  slice_head(n = 10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Врло лепо! Ово пружа додатне увиде у то како логистичка регресија функционише.\n",
+    "\n",
+    "### Боље разумевање кроз матрицу конфузије\n",
+    "\n",
+    "Поређење сваке предикције са њеном одговарајућом \"истинском\" вредношћу није баш ефикасан начин да се утврди колико добро модел предвиђа. Срећом, Tidymodels има још неке трикове у рукаву: [`yardstick`](https://yardstick.tidymodels.org/) - пакет који се користи за мерење ефикасности модела помоћу метрика перформанси.\n",
+    "\n",
+    "Једна од метрика перформанси која се повезује са проблемима класификације је [`матрица конфузије`](https://wikipedia.org/wiki/Confusion_matrix). Матрица конфузије описује колико добро модел класификације функционише. Матрица конфузије приказује колико примера у свакој класи је модел исправно класификовао. У нашем случају, показаће вам колико наранџастих бундева је класификовано као наранџасте и колико белих бундева је класификовано као беле; матрица конфузије такође показује колико је њих класификовано у **погрешне** категорије.\n",
+    "\n",
+    "Функција [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) из yardstick-а израчунава ову крос-табелацију посматраних и предвиђених класа.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Confusion matrix for prediction results\n",
+    "conf_mat(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Хајде да интерпретирамо матрицу конфузије. Наш модел треба да класификује бундеве у две бинарне категорије, категорију `бела` и категорију `није бела`.\n",
+    "\n",
+    "-   Ако ваш модел предвиди да је бундева бела и она заиста припада категорији „бела“, то називамо `истински позитивно`, што је приказано горњим левим бројем.\n",
+    "\n",
+    "-   Ако ваш модел предвиди да бундева није бела, а она заиста припада категорији „бела“, то називамо `лажно негативно`, што је приказано доњим левим бројем.\n",
+    "\n",
+    "-   Ако ваш модел предвиди да је бундева бела, а она заиста припада категорији „није бела“, то називамо `лажно позитивно`, што је приказано горњим десним бројем.\n",
+    "\n",
+    "-   Ако ваш модел предвиди да бундева није бела, а она заиста припада категорији „није бела“, то називамо `истински негативно`, што је приказано доњим десним бројем.\n",
+    "\n",
+    "| Истина |\n",
+    "|:-----:|\n",
+    "\n",
+    "|               |        |       |\n",
+    "|---------------|--------|-------|\n",
+    "| **Предвиђено** | БЕЛА  | НАРАНЏАСТА |\n",
+    "| БЕЛА          | TP     | FP    |\n",
+    "| НАРАНЏАСТА    | FN     | TN    |\n",
+    "\n",
+    "Као што сте можда претпоставили, пожељно је имати већи број истински позитивних и истински негативних резултата, а мањи број лажно позитивних и лажно негативних резултата, што имплицира да модел боље ради.\n",
+    "\n",
+    "Матрица конфузије је корисна јер омогућава израчунавање других метрика које нам могу помоћи да боље проценимо перформансе класификационог модела. Хајде да их размотримо:\n",
+    "\n",
+    "🎓 Прецизност: `TP/(TP + FP)` дефинисана као однос предвиђених позитивних резултата који су заиста позитивни. Такође се назива [позитивна предиктивна вредност](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\").\n",
+    "\n",
+    "🎓 Осетљивост: `TP/(TP + FN)` дефинисана као однос позитивних резултата у односу на број узорака који су заиста позитивни. Такође позната као `осетљивост`.\n",
+    "\n",
+    "🎓 Специфичност: `TN/(TN + FP)` дефинисана као однос негативних резултата у односу на број узорака који су заиста негативни.\n",
+    "\n",
+    "🎓 Тачност: `TP + TN/(TP + TN + FP + FN)` Проценат етикета које су тачно предвиђене за узорак.\n",
+    "\n",
+    "🎓 F мера: Тежински просек прецизности и осетљивости, где је најбољи резултат 1, а најгори 0.\n",
+    "\n",
+    "Хајде да израчунамо ове метрике!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Combine metric functions and calculate them all at once\n",
+    "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\n",
+    "eval_metrics(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Прикажи РОЦ криву за овај модел\n",
+    "\n",
+    "Хајде да направимо још једну визуализацију како бисмо видели такозвану [`РОЦ криву`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make a roc_curve\n",
+    "results %>% \n",
+    "  roc_curve(color, .pred_ORANGE) %>% \n",
+    "  autoplot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ROC криве се често користе за добијање увида у резултате класификатора у смислу његових истинских и лажних позитивних резултата. ROC криве обично приказују `True Positive Rate`/Сензитивност на Y оси и `False Positive Rate`/1-Специфичност на X оси. Због тога су стрмина криве и простор између средишње линије и криве важни: желите криву која брзо иде нагоре и прелази линију. У нашем случају, постоје лажни позитивни резултати на почетку, а затим линија правилно иде нагоре и прелази.\n",
+    "\n",
+    "На крају, хајде да користимо `yardstick::roc_auc()` за израчунавање стварне Површине испод криве (Area Under the Curve). Један од начина тумачења AUC је као вероватноћа да модел рангира насумични позитиван пример више него насумични негативан пример.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate area under curve\n",
+    "results %>% \n",
+    "  roc_auc(color, .pred_ORANGE)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rezultat je oko `0.975`. S obzirom na to da AUC varira od 0 do 1, želite visok rezultat, jer model koji je 100% tačan u svojim predikcijama ima AUC od 1; u ovom slučaju, model je *prilično dobar*.\n",
+    "\n",
+    "U budućim lekcijama o klasifikacijama, naučićete kako da poboljšate rezultate svog modela (na primer, kako da se nosite sa neuravnoteženim podacima u ovom slučaju).\n",
+    "\n",
+    "## 🚀Изазов\n",
+    "\n",
+    "Postoji mnogo toga što treba istražiti u vezi sa logističkom regresijom! Ali najbolji način da naučite jeste da eksperimentišete. Pronađite skup podataka koji se može analizirati ovim tipom modela i napravite model koristeći ga. Шта сте научили? савет: пробајте [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) за занимљиве скупове података.\n",
+    "\n",
+    "## Преглед и Самостално Учење\n",
+    "\n",
+    "Прочитајте првих неколико страница [овог рада са Стенфорда](https://web.stanford.edu/~jurafsky/slp3/5.pdf) о неким практичним применама логистичке регресије. Размислите о задацима који су боље прилагођени једном или другом типу регресије које смо до сада проучавали. Шта би најбоље функционисало?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "langauge": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "feaf125f481a89c468fa115bf2aed580",
+   "translation_date": "2025-09-06T13:32:16+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sr/2-Regression/4-Logistic/solution/notebook.ipynb b/translations/sr/2-Regression/4-Logistic/solution/notebook.ipynb
new file mode 100644
index 000000000..7da6ba756
--- /dev/null
+++ b/translations/sr/2-Regression/4-Logistic/solution/notebook.ipynb
@@ -0,0 +1,1255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Логистичка регресија - Лекција 4\n",
+    "\n",
+    "Учитајте потребне библиотеке и скуп података. Претворите податке у датафрејм који садржи подскуп података:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date   \n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17  \\\n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition   \n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN  \\\n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Origin</th>\n",
+       "      <th>Item Size</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>DELAWARE</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>VIRGINIA</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>36 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name       Package      Variety    Origin Item Size   Color\n",
+       "2  BALTIMORE  24 inch bins  HOWDEN TYPE  DELAWARE       med  ORANGE\n",
+       "3  BALTIMORE  24 inch bins  HOWDEN TYPE  VIRGINIA       med  ORANGE\n",
+       "4  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "5  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "6  BALTIMORE  36 inch bins  HOWDEN TYPE  MARYLAND       med  ORANGE"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select the columns we want to use\n",
+    "columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Drop rows with missing values\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Хајде да погледамо наше податке!\n",
+    "\n",
+    "Кроз визуализацију са Seaborn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56d0c650>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 609.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# Specify colors for each values of the hue variable\n",
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# Plot a bar plot to visualize how many pumpkins of each variety are orange or white\n",
+    "sns.catplot(\n",
+    "    data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
+    "    palette=palette, \n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Предобрада података\n",
+    "\n",
+    "Хајде да кодирујемо карактеристике и ознаке како бисмо боље приказали податке и обучили модел.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the different values of the 'Item Size' column\n",
+    "pumpkins['Item Size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "# Encode the 'Item Size' column using ordinal encoding\n",
+    "item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
+    "ordinal_features = ['Item Size']\n",
+    "ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "# Encode all the other features using one-hot encoding\n",
+    "categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
+    "categorical_encoder = OneHotEncoder(sparse_output=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_MICHIGAN</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_MICHIGAN  cat__Origin_NEW JERSEY   \n",
+       "2                   0.0  ...                   0.0                     0.0  \\\n",
+       "3                   0.0  ...                   0.0                     0.0   \n",
+       "4                   0.0  ...                   0.0                     0.0   \n",
+       "5                   0.0  ...                   0.0                     0.0   \n",
+       "6                   0.0  ...                   0.0                     0.0   \n",
+       "\n",
+       "   cat__Origin_NEW YORK  cat__Origin_NORTH CAROLINA  cat__Origin_OHIO   \n",
+       "2                   0.0                         0.0               0.0  \\\n",
+       "3                   0.0                         0.0               0.0   \n",
+       "4                   0.0                         0.0               0.0   \n",
+       "5                   0.0                         0.0               0.0   \n",
+       "6                   0.0                         0.0               0.0   \n",
+       "\n",
+       "   cat__Origin_PENNSYLVANIA  cat__Origin_TENNESSEE  cat__Origin_TEXAS   \n",
+       "2                       0.0                    0.0                0.0  \\\n",
+       "3                       0.0                    0.0                0.0   \n",
+       "4                       0.0                    0.0                0.0   \n",
+       "5                       0.0                    0.0                0.0   \n",
+       "6                       0.0                    0.0                0.0   \n",
+       "\n",
+       "   cat__Origin_VERMONT  cat__Origin_VIRGINIA  \n",
+       "2                  0.0                   0.0  \n",
+       "3                  0.0                   1.0  \n",
+       "4                  0.0                   0.0  \n",
+       "5                  0.0                   0.0  \n",
+       "6                  0.0                   0.0  \n",
+       "\n",
+       "[5 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "ct = ColumnTransformer(transformers=[\n",
+    "     ('ord', ordinal_encoder, ordinal_features),\n",
+    "     ('cat', categorical_encoder, categorical_features)\n",
+    "     ])\n",
+    "# Get the encoded features as a pandas DataFrame\n",
+    "ct.set_output(transform='pandas')\n",
+    "encoded_features = ct.fit_transform(pumpkins)\n",
+    "encoded_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_NEW JERSEY  cat__Origin_NEW YORK   \n",
+       "2                   0.0  ...                     0.0                   0.0  \\\n",
+       "3                   0.0  ...                     0.0                   0.0   \n",
+       "4                   0.0  ...                     0.0                   0.0   \n",
+       "5                   0.0  ...                     0.0                   0.0   \n",
+       "6                   0.0  ...                     0.0                   0.0   \n",
+       "\n",
+       "   cat__Origin_NORTH CAROLINA  cat__Origin_OHIO  cat__Origin_PENNSYLVANIA   \n",
+       "2                         0.0               0.0                       0.0  \\\n",
+       "3                         0.0               0.0                       0.0   \n",
+       "4                         0.0               0.0                       0.0   \n",
+       "5                         0.0               0.0                       0.0   \n",
+       "6                         0.0               0.0                       0.0   \n",
+       "\n",
+       "   cat__Origin_TENNESSEE  cat__Origin_TEXAS  cat__Origin_VERMONT   \n",
+       "2                    0.0                0.0                  0.0  \\\n",
+       "3                    0.0                0.0                  0.0   \n",
+       "4                    0.0                0.0                  0.0   \n",
+       "5                    0.0                0.0                  0.0   \n",
+       "6                    0.0                0.0                  0.0   \n",
+       "\n",
+       "   cat__Origin_VIRGINIA  Color  \n",
+       "2                   0.0      0  \n",
+       "3                   1.0      0  \n",
+       "4                   0.0      0  \n",
+       "5                   0.0      0  \n",
+       "6                   0.0      0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "# Encode the 'Color' column using label encoding\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n",
+    "encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
+    "encoded_pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ORANGE', 'WHITE']"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the mapping between the encoded values and the original values\n",
+    "list(label_encoder.inverse_transform([0, 1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56322210>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1620 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=pumpkins,\n",
+    "    x=\"Item Size\", y=\"Color\", row='Variety',\n",
+    "    kind=\"box\", orient=\"h\",\n",
+    "    sharex=False, margin_titles=True,\n",
+    "    height=1.8, aspect=4, palette=palette,\n",
+    ")\n",
+    "# Defining axis labels \n",
+    "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
+    "g.set_titles(row_template=\"{row_name}\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Color', ylabel='ord__Item Size'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Suppressing warning message claiming that a portion of points cannot be placed into the plot due to the high number of data points\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')\n",
+    "\n",
+    "palette = {\n",
+    "    0: 'orange',\n",
+    "    1: 'wheat'\n",
+    "}\n",
+    "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Пазите**: Игнорисање упозорења НИЈЕ најбоља пракса и треба га избегавати кад год је то могуће. Упозорења често садрже корисне поруке које нам омогућавају да побољшамо наш код и решимо проблем.  \n",
+    "Разлог због којег игноришемо ово конкретно упозорење је да обезбедимо читљивост графикона. Приказивање свих тачака података са смањеном величином маркера, уз задржавање конзистентности са бојама палете, ствара нејасну визуализацију.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# X is the encoded features\n",
+    "X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
+    "# y is the encoded label\n",
+    "y = encoded_pumpkins['Color']\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.94      0.98      0.96       166\n",
+      "           1       0.85      0.67      0.75        33\n",
+      "\n",
+      "    accuracy                           0.92       199\n",
+      "   macro avg       0.89      0.82      0.85       199\n",
+      "weighted avg       0.92      0.92      0.92       199\n",
+      "\n",
+      "Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
+      " 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
+      " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+      " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
+      "F1-score:  0.7457627118644068\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Train a logistic regression model on the pumpkin dataset\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model and print the results\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('F1-score: ', f1_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[162,   4],\n",
+       "       [ 11,  22]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "y_scores = model.predict_proba(X_test)\n",
+    "# calculate ROC curve\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "# Plot the diagonal 50% line\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "# Plot the FPR and TPR achieved by our model\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9749908725812341\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate AUC score\n",
+    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+    "print(auc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "vscode": {
+   "interpreter": {
+    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "ef50cc584e0b79412610cc7da15e1f86",
+   "translation_date": "2025-09-06T13:27:28+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sr/3-Web-App/1-Web-App/notebook.ipynb b/translations/sr/3-Web-App/1-Web-App/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sr/3-Web-App/1-Web-App/solution/notebook.ipynb b/translations/sr/3-Web-App/1-Web-App/solution/notebook.ipynb
new file mode 100644
index 000000000..b53d90bf7
--- /dev/null
+++ b/translations/sr/3-Web-App/1-Web-App/solution/notebook.ipynb
@@ -0,0 +1,267 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5fa2e8f4584c78250ca9729b46562ceb",
+   "translation_date": "2025-09-06T14:32:08+00:00",
+   "source_file": "3-Web-App/1-Web-App/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           datetime                  city state country     shape  \\\n",
+       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
+       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
+       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
+       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
+       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
+       "\n",
+       "   duration (seconds) duration (hours/min)  \\\n",
+       "0              2700.0           45 minutes   \n",
+       "1              7200.0              1-2 hrs   \n",
+       "2                20.0           20 seconds   \n",
+       "3                20.0             1/2 hour   \n",
+       "4               900.0           15 minutes   \n",
+       "\n",
+       "                                            comments date posted   latitude  \\\n",
+       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
+       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
+       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
+       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
+       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
+       "\n",
+       "    longitude  \n",
+       "0  -97.941111  \n",
+       "1  -98.581082  \n",
+       "2   -2.916667  \n",
+       "3  -96.645833  \n",
+       "4 -157.803611  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ufos = pd.read_csv('../data/ufos.csv')\n",
+    "ufos.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "\n",
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()\n",
+    "\n",
+    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufos.dropna(inplace=True)\n",
+    "\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "\n",
+    "X = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       1.00      0.02      0.05       250\n",
+      "           2       0.00      0.00      0.00         8\n",
+      "           3       0.94      1.00      0.97       131\n",
+      "           4       0.95      1.00      0.97      4743\n",
+      "\n",
+      "    accuracy                           0.95      5173\n",
+      "   macro avg       0.78      0.60      0.60      5173\n",
+      "weighted avg       0.95      0.95      0.93      5173\n",
+      "\n",
+      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
+      "Accuracy:  0.9512855209742895\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "model_filename = 'ufo-model.pkl'\n",
+    "pickle.dump(model, open(model_filename,'wb'))\n",
+    "\n",
+    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
+    "print(model.predict([[50,44,-12]]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/1-Introduction/notebook.ipynb b/translations/sr/4-Classification/1-Introduction/notebook.ipynb
new file mode 100644
index 000000000..5fb76844f
--- /dev/null
+++ b/translations/sr/4-Classification/1-Introduction/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d544ef384b7ba73757d830a72372a7f2",
+   "translation_date": "2025-09-06T14:50:46+00:00",
+   "source_file": "4-Classification/1-Introduction/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/sr/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
new file mode 100644
index 000000000..ad1bfd5e3
--- /dev/null
+++ b/translations/sr/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -0,0 +1,725 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_10-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "2621e24705e8100893c9bf84e0fc8aef",
+   "translation_date": "2025-09-06T14:56:46+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Направите класификациони модел: Укусна азијска и индијска јела\n"
+   ],
+   "metadata": {
+    "id": "ItETB4tSFprR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Увод у класификацију: Чишћење, припрема и визуализација података\n",
+    "\n",
+    "У ове четири лекције истражићете један од основних аспеката класичног машинског учења - *класификацију*. Проћи ћемо кроз употребу различитих алгоритама класификације са скупом података о свим сјајним кухињама Азије и Индије. Надамо се да сте гладни!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/pinch.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Прославите паназијске кухиње у овим лекцијама! Слика: Џен Лупер</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Прославите паназијске кухиње у овим лекцијама! Слика: Џен Лупер](../../../../../../4-Classification/1-Introduction/solution/R/images/pinch.png)-->\n",
+    "\n",
+    "Класификација је облик [надгледаног учења](https://wikipedia.org/wiki/Supervised_learning) који има доста сличности са техникама регресије. У класификацији, обучавате модел да предвиди којој `категорији` неки елемент припада. Ако је машинско учење усмерено на предвиђање вредности или имена ствари коришћењем скупова података, онда се класификација углавном дели на две групе: *бинарна класификација* и *вишекласна класификација*.\n",
+    "\n",
+    "Запамтите:\n",
+    "\n",
+    "-   **Линеарна регресија** вам је помогла да предвидите односе између променљивих и направите тачна предвиђања о томе где би нова тачка података пала у односу на ту линију. На пример, могли сте да предвидите нумеричке вредности као што је *која би цена бундеве била у септембру у односу на децембар*.\n",
+    "\n",
+    "-   **Логистичка регресија** вам је помогла да откријете \"бинарне категорије\": на овој цени, *да ли је ова бундева наранџаста или није-наранџаста*?\n",
+    "\n",
+    "Класификација користи различите алгоритме за одређивање других начина за идентификацију ознаке или класе неке тачке података. Хајде да радимо са овим подацима о кухињама како бисмо видели да ли можемо, посматрајући групу састојака, одредити њихово порекло.\n",
+    "\n",
+    "### [**Квиз пре предавања**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)\n",
+    "\n",
+    "### **Увод**\n",
+    "\n",
+    "Класификација је једна од основних активности истраживача машинског учења и научника за податке. Од основне класификације бинарне вредности (\"да ли је овај имејл спам или није?\"), до сложене класификације слика и сегментације коришћењем рачунарског вида, увек је корисно бити у могућности да сортирате податке у класе и постављате питања о њима.\n",
+    "\n",
+    "Научно речено, ваш метод класификације креира предиктивни модел који вам омогућава да мапирате однос између улазних променљивих и излазних променљивих.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/binary-multiclass.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Бинарни наспрам вишекласних проблема за алгоритме класификације. Инфографика: Џен Лупер</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Пре него што започнемо процес чишћења наших података, њихове визуализације и припреме за задатке машинског учења, хајде да научимо мало више о различитим начинима на које се машинско учење може користити за класификацију података.\n",
+    "\n",
+    "Изведена из [статистике](https://wikipedia.org/wiki/Statistical_classification), класификација коришћењем класичног машинског учења користи карактеристике, као што су `пушач`, `тежина` и `године`, како би одредила *вероватноћу развоја X болести*. Као техника надгледаног учења слична вежбама регресије које сте раније радили, ваши подаци су означени, а алгоритми машинског учења користе те ознаке да класификују и предвиђају класе (или 'карактеристике') скупа података и додељују их групи или исходу.\n",
+    "\n",
+    "✅ Одвојите тренутак да замислите скуп података о кухињама. На шта би вишекласни модел могао да одговори? На шта би бинарни модел могао да одговори? Шта ако желите да утврдите да ли је одређена кухиња вероватно користила пискавац? Шта ако желите да видите да ли, уз поклон у виду кесе са намирницама пуне звездастог аниса, артичока, карфиола и рена, можете направити типично индијско јело?\n",
+    "\n",
+    "### **Здраво, 'класификаторе'**\n",
+    "\n",
+    "Питање које желимо да поставимо овом скупу података о кухињама је заправо **вишекласно питање**, јер имамо неколико потенцијалних националних кухиња са којима радимо. С обзиром на групу састојака, којој од ових многих класа ће подаци припадати?\n",
+    "\n",
+    "Tidymodels нуди неколико различитих алгоритама за класификацију података, у зависности од врсте проблема који желите да решите. У наредне две лекције, научићете о неколико ових алгоритама.\n",
+    "\n",
+    "#### **Предуслови**\n",
+    "\n",
+    "За ову лекцију, биће нам потребни следећи пакети за чишћење, припрему и визуализацију наших података:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) је [збирка R пакета](https://www.tidyverse.org/packages) дизајнирана да учини науку о подацима бржом, лакшом и забавнијом!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) је оквир који представља [збирку пакета](https://www.tidymodels.org/packages/) за моделирање и машинско учење.\n",
+    "\n",
+    "-   `DataExplorer`: [DataExplorer пакет](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) је намењен поједностављењу и аутоматизацији процеса истраживачке анализе података и генерисању извештаја.\n",
+    "\n",
+    "-   `themis`: [themis пакет](https://themis.tidymodels.org/) пружа додатне кораке за рецепте за рад са неуравнотеженим подацима.\n",
+    "\n",
+    "Можете их инсталирати помоћу:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Алтернативно, скрипта испод проверава да ли имате пакете потребне за завршетак овог модула и инсталира их у случају да недостају.\n"
+   ],
+   "metadata": {
+    "id": "ri5bQxZ-Fz_0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "KIPxa4elGAPI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Касније ћемо учитати ове сјајне пакете и учинити их доступним у нашој тренутној R сесији. (Ово је само за илустрацију, `pacman::p_load()` је то већ урадио за вас)\n"
+   ],
+   "metadata": {
+    "id": "YkKAxOJvGD4C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Вежба - очистите и избалансирајте своје податке\n",
+    "\n",
+    "Први задатак који треба обавити пре почетка овог пројекта је да очистите и **избалансирате** своје податке како бисте добили боље резултате.\n",
+    "\n",
+    "Хајде да упознамо податке! 🕵️\n"
+   ],
+   "metadata": {
+    "id": "PFkQDlk0GN5O"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Qccw7okxGT0S"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Интересантно! По изгледу, прва колона је нека врста `id` колоне. Хајде да добијемо мало више информација о подацима.\n"
+   ],
+   "metadata": {
+    "id": "XrWnlgSrGVmR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Basic information about the data\r\n",
+    "df %>%\r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# Visualize basic information above\r\n",
+    "df %>% \r\n",
+    "  plot_intro(ggtheme = theme_light())"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "4UcGmxRxGieA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Iz rezultata odmah možemo videti da imamo `2448` redova i `385` kolona, kao i `0` nedostajućih vrednosti. Takođe imamo 1 diskretnu kolonu, *cuisine*.\n",
+    "\n",
+    "## Vežba - upoznavanje sa kuhinjama\n",
+    "\n",
+    "Sada posao postaje zanimljiviji. Hajde da otkrijemo raspodelu podataka po kuhinjama.\n"
+   ],
+   "metadata": {
+    "id": "AaPubl__GmH5"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Count observations per cuisine\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(n)\r\n",
+    "\r\n",
+    "# Plot the distribution\r\n",
+    "theme_set(theme_light())\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"cuisine\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FRsBVy5eGrrv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Постоји коначан број кухиња, али је расподела података неравномерна. Можете то поправити! Пре него што то урадите, истражите мало више.\n",
+    "\n",
+    "Следеће, хајде да сваку кухињу доделимо њеној појединачној табели (tibble) и сазнамо колико је података доступно (редови, колоне) по кухињи.\n",
+    "\n",
+    "> [Tibble](https://tibble.tidyverse.org/) је модеран облик података (data frame).\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_filter.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Илустрација: @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "vVvyDb1kG2in"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create individual tibble for the cuisines\r\n",
+    "thai_df <- df %>% \r\n",
+    "  filter(cuisine == \"thai\")\r\n",
+    "japanese_df <- df %>% \r\n",
+    "  filter(cuisine == \"japanese\")\r\n",
+    "chinese_df <- df %>% \r\n",
+    "  filter(cuisine == \"chinese\")\r\n",
+    "indian_df <- df %>% \r\n",
+    "  filter(cuisine == \"indian\")\r\n",
+    "korean_df <- df %>% \r\n",
+    "  filter(cuisine == \"korean\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Find out how much data is available per cuisine\r\n",
+    "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
+    "    \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
+    "    \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
+    "    \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
+    "    \"korean_df:\", dim(korean_df))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "0TvXUxD3G8Bk"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Вежба - Откривање најбољих састојака по кухињи уз помоћ dplyr**\n",
+    "\n",
+    "Сада можете дубље истражити податке и сазнати који су типични састојци за сваку кухињу. Треба да очистите поновљене податке који стварају забуну између кухиња, па хајде да научимо више о овом проблему.\n",
+    "\n",
+    "Направите функцију `create_ingredient()` у R-у која враћа датафрејм са састојцима. Ова функција ће започети уклањањем некорисне колоне и сортирањем састојака према њиховом броју.\n",
+    "\n",
+    "Основна структура функције у R-у је:\n",
+    "\n",
+    "`myFunction <- function(arglist){`\n",
+    "\n",
+    "**`...`**\n",
+    "\n",
+    "**`return`**`(value)`\n",
+    "\n",
+    "`}`\n",
+    "\n",
+    "Уредан увод у функције у R-у можете пронаћи [овде](https://skirmer.github.io/presentations/functions_with_r.html#1).\n",
+    "\n",
+    "Хајде да кренемо! Користићемо [глаголе из dplyr-а](https://dplyr.tidyverse.org/) које смо учили у претходним лекцијама. Као подсетник:\n",
+    "\n",
+    "-   `dplyr::select()`: помаже вам да изаберете које **колоне** желите да задржите или искључите.\n",
+    "\n",
+    "-   `dplyr::pivot_longer()`: помаже вам да \"продужите\" податке, повећавајући број редова и смањујући број колона.\n",
+    "\n",
+    "-   `dplyr::group_by()` и `dplyr::summarise()`: помажу вам да пронађете статистику за различите групе и представите је у лепој табели.\n",
+    "\n",
+    "-   `dplyr::filter()`: креира подскуп података који садржи само редове који задовољавају ваше услове.\n",
+    "\n",
+    "-   `dplyr::mutate()`: помаже вам да креирате или измените колоне.\n",
+    "\n",
+    "Погледајте овај [*уметнички*-испуњен learnr туторијал](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) од Алисон Хорст, који представља неке корисне функције за обраду података у dplyr-у *(део Tidyverse-а)*.\n"
+   ],
+   "metadata": {
+    "id": "K3RF5bSCHC76"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Creates a functions that returns the top ingredients by class\r\n",
+    "\r\n",
+    "create_ingredient <- function(df){\r\n",
+    "  \r\n",
+    "  # Drop the id column which is the first colum\r\n",
+    "  ingredient_df = df %>% select(-1) %>% \r\n",
+    "  # Transpose data to a long format\r\n",
+    "    pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
+    "  # Find the top most ingredients for a particular cuisine\r\n",
+    "    group_by(ingredients) %>% \r\n",
+    "    summarise(n_instances = sum(count)) %>% \r\n",
+    "    filter(n_instances != 0) %>% \r\n",
+    "  # Arrange by descending order\r\n",
+    "    arrange(desc(n_instances)) %>% \r\n",
+    "    mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
+    "  \r\n",
+    "  \r\n",
+    "  return(ingredient_df)\r\n",
+    "} # End of function"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "uB_0JR82HTPa"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сада можемо користити функцију да добијемо идеју о десет најпопуларнијих састојака по кухињи. Хајде да је испробамо са `thai_df`.\n"
+   ],
+   "metadata": {
+    "id": "h9794WF8HWmc"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Call create_ingredient and display popular ingredients\r\n",
+    "thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
+    "\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "agQ-1HrcHaEA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "У претходном одељку користили смо `geom_col()`, хајде да видимо како можете користити и `geom_bar` за креирање стубичастих графикона. Користите `?geom_bar` за додатно читање.\n"
+   ],
+   "metadata": {
+    "id": "kHu9ffGjHdcX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a bar chart for popular thai cuisines\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10) %>% \r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "fb3Bx_3DHj6e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Хајде да урадимо исто за јапанске податке\n"
+   ],
+   "metadata": {
+    "id": "RHP_xgdkHnvM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
+    "create_ingredient(df = japanese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "019v8F0XHrRU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Шта је са кинеском кухињом?\n"
+   ],
+   "metadata": {
+    "id": "iIGM7vO8Hu3v"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
+    "create_ingredient(df = chinese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lHd9_gd2HyzU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ir8qyQbNH1c7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Indian cuisines and make bar chart\r\n",
+    "create_ingredient(df = indian_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ApukQtKjH5FO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qv30cwY1H-FM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Korean cuisines and make bar chart\r\n",
+    "create_ingredient(df = korean_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lumgk9cHIBie"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Из визуализација података, сада можемо изоставити најчешће састојке који стварају забуну између различитих кухиња, користећи `dplyr::select()`.\n",
+    "\n",
+    "Сви воле пиринач, бели лук и ђумбир!\n"
+   ],
+   "metadata": {
+    "id": "iO4veMXuIEta"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "iHJPiG6rIUcK"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Предобрада података уз помоћ рецепата 👩‍🍳👨‍🍳 - Рад са неуравнотеженим подацима ⚖️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Илустрација: @allison_horst</figcaption>\n",
+    "\n",
+    "С обзиром на то да је ова лекција о кухињама, морамо ставити `recipes` у контекст.\n",
+    "\n",
+    "Tidymodels пружа још један користан пакет: `recipes` - пакет за предобраду података.\n"
+   ],
+   "metadata": {
+    "id": "kkFd-JxdIaL6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Хајде да поново погледамо расподелу наших кухиња.\n"
+   ],
+   "metadata": {
+    "id": "6l2ubtTPJAhY"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "old_label_count <- df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "old_label_count"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "1e-E9cb7JDVi"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Као што можете видети, постоји прилично неједнака расподела у броју кухиња. Корејске кухиње су скоро три пута бројније од тајландских. Неуравнотежени подаци често имају негативан утицај на перформансе модела. Размислите о бинарној класификацији. Ако већина ваших података припада једној класи, модел машинског учења ће чешће предвиђати ту класу, једноставно зато што за њу има више података. Уравнотежавање података узима било какве искривљене податке и помаже у уклањању те неравнотеже. Многи модели најбоље функционишу када је број посматрања једнак и, самим тим, имају потешкоћа са неуравнотеженим подацима.\n",
+    "\n",
+    "Постоје углавном два начина за решавање проблема са неуравнотеженим скуповима података:\n",
+    "\n",
+    "-   додавање посматрања мањинској класи: `Over-sampling`, на пример, коришћењем SMOTE алгоритма\n",
+    "\n",
+    "-   уклањање посматрања из већинске класе: `Under-sampling`\n",
+    "\n",
+    "Хајде сада да покажемо како се носити са неуравнотеженим скуповима података користећи `recipe`. Рецепт се може посматрати као план који описује које кораке треба применити на скуп података како би био спреман за анализу података.\n"
+   ],
+   "metadata": {
+    "id": "soAw6826JKx9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HS41brUIJVJy"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Хајде да разложимо кораке претпроцесирања.\n",
+    "\n",
+    "-   Позив функције `recipe()` са формулом говори рецепту *улоге* променљивих користећи `df_select` податке као референцу. На пример, колона `cuisine` је додељена улога `outcome`, док су остале колоне добиле улогу `predictor`.\n",
+    "\n",
+    "-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html) креира *спецификацију* корака рецепта који синтетички генерише нове примере мањинске класе користећи најближе суседе тих случајева.\n",
+    "\n",
+    "Сада, ако желимо да видимо претпроцесиране податке, морамо [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) и [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) наш рецепт.\n",
+    "\n",
+    "`prep()`: процењује потребне параметре из тренинг скупа који се касније могу применити на друге скупове података.\n",
+    "\n",
+    "`bake()`: узима припремљен рецепт и примењује операције на било који скуп података.\n"
+   ],
+   "metadata": {
+    "id": "Yb-7t7XcJaC8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep and bake the recipe\r\n",
+    "preprocessed_df <- cuisines_recipe %>% \r\n",
+    "  prep() %>% \r\n",
+    "  bake(new_data = NULL) %>% \r\n",
+    "  relocate(cuisine)\r\n",
+    "\r\n",
+    "# Display data\r\n",
+    "preprocessed_df %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Quick summary stats\r\n",
+    "preprocessed_df %>% \r\n",
+    "  introduce()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "9QhSgdpxJl44"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Хајде сада да проверимо расподелу наших кухиња и упоредимо их са неуравнотеженим подацима.\n"
+   ],
+   "metadata": {
+    "id": "dmidELh_LdV7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "new_label_count <- preprocessed_df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "list(new_label_count = new_label_count,\r\n",
+    "     old_label_count = old_label_count)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "aSh23klBLwDz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ммм! Подаци су лепо уређени, избалансирани и веома укусни 😋!\n",
+    "\n",
+    "> Обично се рецепт користи као претпроцесор за моделирање, где дефинише које кораке треба применити на скуп података како би био спреман за моделирање. У том случају, `workflow()` се обично користи (као што смо већ видели у претходним лекцијама) уместо ручног процењивања рецепта.\n",
+    ">\n",
+    "> Сходно томе, обично не морате да користите **`prep()`** и **`bake()`** рецепте када користите tidymodels, али то су корисне функције које можете имати у свом алату за потврду да рецепти раде оно што очекујете, као у нашем случају.\n",
+    ">\n",
+    "> Када користите **`bake()`** на припремљеном рецепту са **`new_data = NULL`**, добијате назад податке које сте дали приликом дефинисања рецепта, али који су прошли кроз кораке претпроцесирања.\n",
+    "\n",
+    "Хајде сада да сачувамо копију ових података за употребу у будућим лекцијама:\n"
+   ],
+   "metadata": {
+    "id": "HEu80HZ8L7ae"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Save preprocessed data\r\n",
+    "write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "cBmCbIgrMOI6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Овај нови CSV сада се налази у главном фолдеру за податке.\n",
+    "\n",
+    "**🚀Изазов**\n",
+    "\n",
+    "Овај курикулум садржи неколико занимљивих скупова података. Претражите `data` фолдере и видите да ли неки садрже скупове података који би били погодни за бинарну или мултикласну класификацију? Која питања бисте поставили о овом скупу података?\n",
+    "\n",
+    "## [**Квиз након предавања**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)\n",
+    "\n",
+    "## **Преглед и Самостално учење**\n",
+    "\n",
+    "-   Погледајте [пакет themis](https://github.com/tidymodels/themis). Које друге технике можемо користити за решавање проблема са неуравнотеженим подацима?\n",
+    "\n",
+    "-   Референтни сајт за Tidy models [вебсајт](https://www.tidymodels.org/start/).\n",
+    "\n",
+    "-   Х. Викам и Г. Гролемунд, [*R за науку о подацима: Визуализација, Моделовање, Трансформација, Уређивање и Увоз података*](https://r4ds.had.co.nz/).\n",
+    "\n",
+    "#### ХВАЛА:\n",
+    "\n",
+    "[`Елисон Хорст`](https://twitter.com/allison_horst/) за креирање невероватних илустрација које чине R приступачнијим и занимљивијим. Пронађите више илустрација у њеној [галерији](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Кеси Бревиу](https://www.twitter.com/cassieview) и [Џен Лупер](https://www.twitter.com/jenlooper) за креирање оригиналне Python верзије овог модула ♥️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Илустрација од @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "WQs5621pMGwf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/1-Introduction/solution/notebook.ipynb b/translations/sr/4-Classification/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..2059f5a87
--- /dev/null
+++ b/translations/sr/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,737 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Укусна азијска и индијска јела\n",
+    "\n",
+    "## Увод\n",
+    "\n",
+    "Азијска и индијска кухиња су познате по својим богатим укусима, разноврсним зачинима и јединственим техникама кувања. У овом водичу, истражићемо неке од најпопуларнијих јела и савете за њихову припрему.\n",
+    "\n",
+    "## Популарна јела\n",
+    "\n",
+    "### 1. Пилећи кари\n",
+    "\n",
+    "Пилећи кари је класично јело које комбинује нежно месо са ароматичним сосом од зачина. \n",
+    "\n",
+    "#### Састојци:\n",
+    "- 500 г пилећег меса\n",
+    "- 2 кашике уља\n",
+    "- 1 главица црног лука, ситно сецкана\n",
+    "- 2 чена белог лука, уситњена\n",
+    "- 1 кашичица куркуме\n",
+    "- 1 кашичица куминa\n",
+    "- 1 кашичица гарам масале\n",
+    "- 200 мл кокосовог млека\n",
+    "\n",
+    "#### Упутство:\n",
+    "1. Загрејте уље у тигању и пропржите црни лук док не постане стакласт.\n",
+    "2. Додајте бели лук и зачине, па мешајте док не осетите њихов мирис.\n",
+    "3. Додајте пилеће месо и пржите док не порумени.\n",
+    "4. Сипајте кокосово млеко и кувајте на лаганој ватри 20 минута.\n",
+    "\n",
+    "[!TIP] Послужите са куваним пиринчем или нааном за потпуни оброк.\n",
+    "\n",
+    "### 2. Пад Таи\n",
+    "\n",
+    "Пад Таи је популарно тајландско јело од резанаца које је савршено за брз и укусан оброк.\n",
+    "\n",
+    "#### Састојци:\n",
+    "- 200 г пиринчаних резанаца\n",
+    "- 2 кашике тамаринд пасте\n",
+    "- 1 кашика рибљег соса\n",
+    "- 1 кашика шећера\n",
+    "- 2 јаја\n",
+    "- 100 г шкампа или тофуа\n",
+    "- 2 чена белог лука, уситњена\n",
+    "- 50 г кикирикија, уситњених\n",
+    "\n",
+    "#### Упутство:\n",
+    "1. Скувајте резанце према упутству на паковању и оставите их са стране.\n",
+    "2. У тигању загрејте мало уља и пропржите бели лук.\n",
+    "3. Додајте јаја и мешајте док се не испрже.\n",
+    "4. Додајте шкампе или тофу, а затим резанце.\n",
+    "5. Умешајте тамаринд пасту, рибљи сос и шећер.\n",
+    "6. Послужите са кикирикијем и лиметом.\n",
+    "\n",
+    "[!NOTE] Ово јело можете прилагодити додавањем поврћа по вашем избору.\n",
+    "\n",
+    "## Савети за кување\n",
+    "\n",
+    "- Увек користите свеже зачине за најбољи укус.\n",
+    "- Не плашите се да експериментишете са различитим комбинацијама зачина.\n",
+    "- За аутентичан укус, користите традиционалне састојке као што су гарам масала, тамаринд или кокосово млеко.\n",
+    "\n",
+    "[!IMPORTANT] Увек пробајте јело током кувања како бисте прилагодили зачине по укусу.\n",
+    "\n",
+    "## Закључак\n",
+    "\n",
+    "Азијска и индијска јела нуде невероватну разноврсност укуса и текстура. Уз мало праксе, можете припремити ова укусна јела у удобности свог дома. Испробајте рецепте из овог водича и уживајте у гастрономском путовању!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Инсталирајте Imblearn који ће омогућити SMOTE. Ово је Scikit-learn пакет који помаже у руковању неуравнотеженим подацима приликом извођења класификације. (https://imbalanced-learn.org/stable/)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
+      "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df  = pd.read_csv('../../data/cuisines.csv')"
+   ]
+  },
+  {
+   "source": [
+    "Овај скуп података укључује 385 колона које указују на све врсте састојака у разним кухињама из датог скупа кухиња.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0          65  indian       0         0      0           0      0   \n",
+       "1          66  indian       1         0      0           0      0   \n",
+       "2          67  indian       0         0      0           0      0   \n",
+       "3          68  indian       0         0      0           0      0   \n",
+       "4          69  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 385 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>67</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>69</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 385 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2448 entries, 0 to 2447\nColumns: 385 entries, Unnamed: 0 to zucchini\ndtypes: int64(384), object(1)\nmemory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "korean      799\n",
+       "indian      598\n",
+       "chinese     442\n",
+       "japanese    320\n",
+       "thai        289\n",
+       "Name: cuisine, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts()"
+   ]
+  },
+  {
+   "source": [
+    "Прикажи кухиње у стубичастом графикону\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18799710>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "thai df: (289, 385)\njapanese df: (320, 385)\nchinese df: (442, 385)\nindian df: (598, 385)\nkorean df: (799, 385)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "thai_df = df[(df.cuisine == \"thai\")]\n",
+    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
+    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
+    "indian_df = df[(df.cuisine == \"indian\")]\n",
+    "korean_df = df[(df.cuisine == \"korean\")]\n",
+    "\n",
+    "print(f'thai df: {thai_df.shape}')\n",
+    "print(f'japanese df: {japanese_df.shape}')\n",
+    "print(f'chinese df: {chinese_df.shape}')\n",
+    "print(f'indian df: {indian_df.shape}')\n",
+    "print(f'korean df: {korean_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ingredient_df(df):\n",
+    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    # drop ingredients that have a 0 sum\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    # sort df\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
+    "    return ingredient_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d5917db00>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
+    "thai_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d389634a8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+   ],
+   "source": [
+    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
+    "japanese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18817f98>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
+    "chinese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1880a550>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
+    "indian_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1896ce80>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
+    "korean_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\n",
+    "labels_df = df.cuisine #.unique()\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "source": [
+    "Уравнотежите податке са SMOTE прекомерним узорковањем до највеће класе. Прочитајте више овде: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oversample = SMOTE()\n",
+    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "new label count: korean      799\nchinese     799\njapanese    799\nindian      799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
+    "print(f'old label count: {df.cuisine.value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "source": [
+    "transformed_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "     cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  \\\n",
+       "0     indian       0         0      0           0      0             0   \n",
+       "1     indian       1         0      0           0      0             0   \n",
+       "2     indian       0         0      0           0      0             0   \n",
+       "3     indian       0         0      0           0      0             0   \n",
+       "4     indian       0         0      0           0      0             0   \n",
+       "...      ...     ...       ...    ...         ...    ...           ...   \n",
+       "3990    thai       0         0      0           0      0             0   \n",
+       "3991    thai       0         0      0           0      0             0   \n",
+       "3992    thai       0         0      0           0      0             0   \n",
+       "3993    thai       0         0      0           0      0             0   \n",
+       "3994    thai       0         0      0           0      0             0   \n",
+       "\n",
+       "      apricot  armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
+       "0           0         0          0  ...        0            0           0   \n",
+       "1           0         0          0  ...        0            0           0   \n",
+       "2           0         0          0  ...        0            0           0   \n",
+       "3           0         0          0  ...        0            0           0   \n",
+       "4           0         0          0  ...        0            0           0   \n",
+       "...       ...       ...        ...  ...      ...          ...         ...   \n",
+       "3990        0         0          0  ...        0            0           0   \n",
+       "3991        0         0          0  ...        0            0           0   \n",
+       "3992        0         0          0  ...        0            0           0   \n",
+       "3993        0         0          0  ...        0            0           0   \n",
+       "3994        0         0          0  ...        0            0           0   \n",
+       "\n",
+       "      whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                           0     0     0    0      0       0         0  \n",
+       "1                           0     0     0    0      0       0         0  \n",
+       "2                           0     0     0    0      0       0         0  \n",
+       "3                           0     0     0    0      0       0         0  \n",
+       "4                           0     0     0    0      0       1         0  \n",
+       "...                       ...   ...   ...  ...    ...     ...       ...  \n",
+       "3990                        0     0     0    0      0       0         0  \n",
+       "3991                        0     0     0    0      0       0         0  \n",
+       "3992                        0     0     0    0      0       0         0  \n",
+       "3993                        0     0     0    0      0       0         0  \n",
+       "3994                        0     0     0    0      0       0         0  \n",
+       "\n",
+       "[3995 rows x 381 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": [
+    "# export transformed data to new df for classification\n",
+    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
+    "transformed_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df.info()"
+   ]
+  },
+  {
+   "source": [
+    "Сачувај датотеку за будућу употребу\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен помоћу услуге за превођење уз помоћ вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "1da12ed6d238756959b8de9cac2a35a2",
+   "translation_date": "2025-09-06T14:51:50+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/2-Classifiers-1/notebook.ipynb b/translations/sr/4-Classification/2-Classifiers-1/notebook.ipynb
new file mode 100644
index 000000000..803ecb1bf
--- /dev/null
+++ b/translations/sr/4-Classification/2-Classifiers-1/notebook.ipynb
@@ -0,0 +1,41 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "68829b06b4dcd512d3327849191f4d7f",
+   "translation_date": "2025-09-06T14:32:36+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Изградња модела класификације\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/sr/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
new file mode 100644
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--- /dev/null
+++ b/translations/sr/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -0,0 +1,1298 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_11-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "6ea6a5171b1b99b7b5a55f7469c048d2",
+   "translation_date": "2025-09-06T14:36:23+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Направите модел класификације: Укусна азијска и индијска јела\n"
+   ],
+   "metadata": {
+    "id": "zs2woWv_HoE8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Класификатори кухиња 1\n",
+    "\n",
+    "У овом часу, истражићемо различите класификаторе како бисмо *предвидели одређену националну кухињу на основу групе састојака.* Док то радимо, научићемо више о начинима на које алгоритми могу бити коришћени за задатке класификације.\n",
+    "\n",
+    "### [**Квиз пре предавања**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
+    "\n",
+    "### **Припрема**\n",
+    "\n",
+    "Овај час се надовезује на наш [претходни час](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb) где смо:\n",
+    "\n",
+    "-   Направили лаган увод у класификације користећи скуп података о свим сјајним кухињама Азије и Индије 😋.\n",
+    "\n",
+    "-   Истражили неке [dplyr глаголе](https://dplyr.tidyverse.org/) за припрему и чишћење наших података.\n",
+    "\n",
+    "-   Направили прелепе визуализације користећи ggplot2.\n",
+    "\n",
+    "-   Демонстрирали како се носити са неуравнотеженим подацима кроз њихову претходну обраду користећи [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html).\n",
+    "\n",
+    "-   Показали како да `prep` и `bake` наш рецепт како бисмо потврдили да ће функционисати како је предвиђено.\n",
+    "\n",
+    "#### **Предуслов**\n",
+    "\n",
+    "За овај час, биће нам потребни следећи пакети за чишћење, припрему и визуализацију наших података:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) је [колекција R пакета](https://www.tidyverse.org/packages) дизајнирана да учини науку о подацима бржом, лакшом и забавнијом!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) оквир је [колекција пакета](https://www.tidymodels.org/packages/) за моделирање и машинско учење.\n",
+    "\n",
+    "-   `themis`: [themis пакет](https://themis.tidymodels.org/) пружа додатне кораке за рецепте за рад са неуравнотеженим подацима.\n",
+    "\n",
+    "-   `nnet`: [nnet пакет](https://cran.r-project.org/web/packages/nnet/nnet.pdf) пружа функције за процену неуронских мрежа са једним скривеним слојем, као и за моделе мултиномијалне логистичке регресије.\n",
+    "\n",
+    "Можете их инсталирати као:\n"
+   ],
+   "metadata": {
+    "id": "iDFOb3ebHwQC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Алтернативно, следећи скрипт проверава да ли имате пакете који су потребни за завршетак овог модула и инсталира их уколико недостају.\n"
+   ],
+   "metadata": {
+    "id": "4V85BGCjII7F"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, here)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "an5NPyyKIKNR",
+    "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Поделите податке на сетове за обуку и тестирање.\n",
+    "\n",
+    "Почећемо са неколико корака из нашег претходног часа.\n",
+    "\n",
+    "### Избаците најчешће састојке који стварају забуну између различитих кухиња, користећи `dplyr::select()`.\n",
+    "\n",
+    "Сви воле пиринач, бели лук и ђумбир!\n"
+   ],
+   "metadata": {
+    "id": "0ax9GQLBINVv"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# Load the original cuisines data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
+    "  # Encode cuisine column as categorical\r\n",
+    "  mutate(cuisine = factor(cuisine))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Display distribution of cuisines\r\n",
+    "df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "New names:\n",
+      "* `` -> ...1\n",
+      "\n",
+      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
+      "\n",
+      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+      "\u001b[31mchr\u001b[39m   (1): cuisine\n",
+      "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
+      "\n",
+      "\n",
+      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
+      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 indian  0      0        0     0          0     0            0       0       \n",
+       "2 indian  1      0        0     0          0     0            0       0       \n",
+       "3 indian  0      0        0     0          0     0            0       0       \n",
+       "4 indian  0      0        0     0          0     0            0       0       \n",
+       "5 indian  0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       0    0   \n",
+       "2 0         ⋯ 0       0           0          0                       0    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     1      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   799\n",
+       "2 indian   598\n",
+       "3 chinese  442\n",
+       "4 japanese 320\n",
+       "5 thai     289"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 799 |\n",
+       "| indian   | 598 |\n",
+       "| chinese  | 442 |\n",
+       "| japanese | 320 |\n",
+       "| thai     | 289 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 799\\\\\n",
+       "\t indian   & 598\\\\\n",
+       "\t chinese  & 442\\\\\n",
+       "\t japanese & 320\\\\\n",
+       "\t thai     & 289\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>799</td></tr>\n",
+       "\t<tr><td>indian  </td><td>598</td></tr>\n",
+       "\t<tr><td>chinese </td><td>442</td></tr>\n",
+       "\t<tr><td>japanese</td><td>320</td></tr>\n",
+       "\t<tr><td>thai    </td><td>289</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 735
+    },
+    "id": "jhCrrH22IWVR",
+    "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Савршено! Сада је време да поделимо податке тако да 70% података иде за тренинг, а 30% за тестирање. Такође ћемо применити технику `стратификације` приликом дељења података како бисмо `задржали пропорцију сваке кухиње` у тренинг и валидационим сетовима података.\n",
+    "\n",
+    "[rsample](https://rsample.tidymodels.org/), пакет у оквиру Tidymodels-а, пружа инфраструктуру за ефикасно дељење и ресемплинг података:\n"
+   ],
+   "metadata": {
+    "id": "AYTjVyajIdny"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# Load the core Tidymodels packages into R session\r\n",
+    "library(tidymodels)\r\n",
+    "\r\n",
+    "# Create split specification\r\n",
+    "set.seed(2056)\r\n",
+    "cuisines_split <- initial_split(data = df_select,\r\n",
+    "                                strata = cuisine,\r\n",
+    "                                prop = 0.7)\r\n",
+    "\r\n",
+    "# Extract the data in each split\r\n",
+    "cuisines_train <- training(cuisines_split)\r\n",
+    "cuisines_test <- testing(cuisines_split)\r\n",
+    "\r\n",
+    "# Print the number of cases in each split\r\n",
+    "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
+    "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
+    "\r\n",
+    "# Display the first few rows of the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Display distribution of cuisines in the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training cases: 1712\n",
+      "Test cases: 736"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 chinese 0      0        0     0          0     0            0       0       \n",
+       "2 chinese 0      0        0     0          0     0            0       0       \n",
+       "3 chinese 0      0        0     0          0     0            0       0       \n",
+       "4 chinese 0      0        0     0          0     0            0       0       \n",
+       "5 chinese 0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       1    0   \n",
+       "2 0         ⋯ 0       0           0          0                       1    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     0      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   559\n",
+       "2 indian   418\n",
+       "3 chinese  309\n",
+       "4 japanese 224\n",
+       "5 thai     202"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 559 |\n",
+       "| indian   | 418 |\n",
+       "| chinese  | 309 |\n",
+       "| japanese | 224 |\n",
+       "| thai     | 202 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 559\\\\\n",
+       "\t indian   & 418\\\\\n",
+       "\t chinese  & 309\\\\\n",
+       "\t japanese & 224\\\\\n",
+       "\t thai     & 202\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>559</td></tr>\n",
+       "\t<tr><td>indian  </td><td>418</td></tr>\n",
+       "\t<tr><td>chinese </td><td>309</td></tr>\n",
+       "\t<tr><td>japanese</td><td>224</td></tr>\n",
+       "\t<tr><td>thai    </td><td>202</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 535
+    },
+    "id": "w5FWIkEiIjdN",
+    "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Рад са неуравнотеженим подацима\n",
+    "\n",
+    "Као што сте можда приметили у оригиналном скупу података, као и у нашем скупу за обуку, постоји прилично неједнака расподела броја кухиња. Корејске кухиње су *готово* три пута бројније од тајландских кухиња. Неуравнотежени подаци често имају негативан утицај на перформансе модела. Многи модели најбоље функционишу када је број посматрања једнак и, самим тим, имају потешкоћа са неуравнотеженим подацима.\n",
+    "\n",
+    "Постоје два главна начина за рад са неуравнотеженим скуповима података:\n",
+    "\n",
+    "-   додавање посматрања мањинској класи: `Прекомерно узорковање` (Over-sampling), на пример, коришћењем SMOTE алгоритма који синтетички генерише нове примере мањинске класе користећи најближе суседе тих случајева.\n",
+    "\n",
+    "-   уклањање посматрања из већинске класе: `Потцењивање узорка` (Under-sampling)\n",
+    "\n",
+    "У нашем претходном часу, демонстрирали смо како се ради са неуравнотеженим скуповима података користећи `recipe`. Рецепт се може сматрати као план који описује које кораке треба применити на скуп података како би био спреман за анализу. У нашем случају, желимо да имамо једнаку расподелу броја наших кухиња за наш `скуп за обуку`. Хајде да почнемо.\n"
+   ],
+   "metadata": {
+    "id": "daBi9qJNIwqW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing training data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "# Print recipe\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Data Recipe\n",
+       "\n",
+       "Inputs:\n",
+       "\n",
+       "      role #variables\n",
+       "   outcome          1\n",
+       " predictor        380\n",
+       "\n",
+       "Operations:\n",
+       "\n",
+       "SMOTE based on cuisine"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 200
+    },
+    "id": "Az6LFBGxI1X0",
+    "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Можете, наравно, да потврдите (коришћењем prep+bake) да ће рецепт функционисати како очекујете - сви ознаке кухиње имају `559` посматрања.\n",
+    "\n",
+    "Пошто ћемо користити овај рецепт као претпроцесор за моделирање, `workflow()` ће обавити сав припремни и завршни посао за нас, тако да нећемо морати ручно да процењујемо рецепт.\n",
+    "\n",
+    "Сада смо спремни да обучимо модел 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Избор класификатора\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/parsnip.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Илустрација од @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "NBL3PqIWJBBB"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сада морамо да одлучимо који алгоритам да користимо за овај задатак 🤔.\n",
+    "\n",
+    "У оквиру Tidymodels-а, [`parsnip пакет`](https://parsnip.tidymodels.org/index.html) пружа конзистентан интерфејс за рад са моделима кроз различите механизме (пакете). Погледајте документацију за parsnip како бисте истражили [типове модела и механизме](https://www.tidymodels.org/find/parsnip/#models) и њихове одговарајуће [аргументе модела](https://www.tidymodels.org/find/parsnip/#model-args). Разноврсност може изгледати прилично збуњујуће на први поглед. На пример, следеће методе укључују технике класификације:\n",
+    "\n",
+    "-   C5.0 модели класификације засновани на правилима\n",
+    "\n",
+    "-   Флексибилни дискриминантни модели\n",
+    "\n",
+    "-   Линеарни дискриминантни модели\n",
+    "\n",
+    "-   Регуларизовани дискриминантни модели\n",
+    "\n",
+    "-   Модели логистичке регресије\n",
+    "\n",
+    "-   Модели мултиномијалне регресије\n",
+    "\n",
+    "-   Модели наивног Бајеса\n",
+    "\n",
+    "-   Машине за подршку векторима\n",
+    "\n",
+    "-   Најближи суседи\n",
+    "\n",
+    "-   Дрвеће одлуке\n",
+    "\n",
+    "-   Методе ансамбла\n",
+    "\n",
+    "-   Неуронске мреже\n",
+    "\n",
+    "Списак се наставља!\n",
+    "\n",
+    "### **Који класификатор одабрати?**\n",
+    "\n",
+    "Па, који класификатор треба да изаберете? Често је тестирање неколико њих и тражење доброг резултата начин да се испроба.\n",
+    "\n",
+    "> AutoML решава овај проблем елегантно тако што врши ове поређења у облаку, омогућавајући вам да изаберете најбољи алгоритам за ваше податке. Пробајте [овде](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "Такође, избор класификатора зависи од нашег проблема. На пример, када се исход може категоризовати у `више од две класе`, као у нашем случају, морамо користити `алгоритам за мултикласну класификацију` уместо `бинарне класификације.`\n",
+    "\n",
+    "### **Бољи приступ**\n",
+    "\n",
+    "Бољи начин од насумичног погађања је да следите идеје из овог преузимљивог [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Овде откривамо да, за наш мултикласни проблем, имамо неке опције:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/cheatsheet.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Део Microsoft-овог Cheat Sheet-а за алгоритме, који приказује опције за мултикласну класификацију</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "a6DLAZ3vJZ14"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Размишљање**\n",
+    "\n",
+    "Хајде да видимо како можемо да приступимо проблему узимајући у обзир ограничења која имамо:\n",
+    "\n",
+    "-   **Дубоке неуронске мреже су превише захтевне**. С обзиром на наш чист, али минималан скуп података, и чињеницу да обуку изводимо локално преко нотебука, дубоке неуронске мреже су превише захтевне за овај задатак.\n",
+    "\n",
+    "-   **Нема класификатора са две класе**. Не користимо класификатор са две класе, што искључује приступ један-наспрам-свих.\n",
+    "\n",
+    "-   **Дрво одлуке или логистичка регресија би могли да функционишу**. Дрво одлуке би могло да ради, или мултиномијална регресија/логистичка регресија за више класа.\n",
+    "\n",
+    "-   **Дрво одлуке са више класа и побољшањем решава другачији проблем**. Дрво одлуке са више класа и побољшањем је најпогодније за непараметарске задатке, нпр. задатке који су дизајнирани за креирање рангирања, тако да нам није корисно.\n",
+    "\n",
+    "Такође, обично је добра идеја да се пре него што се крене са сложенијим моделима машинског учења, као што су ансамбл методе, изгради најједноставнији могући модел како би се стекла идеја о томе шта се дешава. Зато ћемо у овој лекцији почети са моделом `мултиномијалне регресије`.\n",
+    "\n",
+    "> Логистичка регресија је техника која се користи када је излазна променљива категоријална (или номинална). За бинарну логистичку регресију број излазних променљивих је два, док је број излазних променљивих за мултиномијалну логистичку регресију више од два. Погледајте [Напредне методе регресије](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html) за додатно читање.\n",
+    "\n",
+    "## 4. Обучите и процените модел мултиномијалне логистичке регресије.\n",
+    "\n",
+    "У Tidymodels-у, `parsnip::multinom_reg()`, дефинише модел који користи линеарне предикторе за предвиђање података са више класа користећи мултиномијалну дистрибуцију. Погледајте `?multinom_reg()` за различите начине/моторе које можете користити за обуку овог модела.\n",
+    "\n",
+    "За овај пример, обучићемо модел мултиномијалне регресије преко подразумеваног [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) мотора.\n",
+    "\n",
+    "> Изабрао сам вредност за `penalty` прилично насумично. Постоје бољи начини за избор ове вредности, као што је коришћење `ресемплинг`-а и `тјунинг`-а модела, о чему ћемо говорити касније.\n",
+    ">\n",
+    "> Погледајте [Tidymodels: Почетак](https://www.tidymodels.org/start/tuning/) ако желите да научите више о томе како да подесите хиперпараметре модела.\n"
+   ],
+   "metadata": {
+    "id": "gWMsVcbBJemu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# Create a multinomial regression model specification\r\n",
+    "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
+    "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
+    "  set_mode(\"classification\")\r\n",
+    "\r\n",
+    "# Print model specification\r\n",
+    "mr_spec"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 166
+    },
+    "id": "Wq_fcyQiJvfG",
+    "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Одличан посао 🥳! Сада када имамо рецепт и спецификацију модела, потребно је да пронађемо начин да их спојимо у један објекат који ће прво обрадити податке, затим применити модел на обрађене податке, а такође омогућити потенцијалне активности након обраде. У Tidymodels-у, овај практични објекат се назива [`workflow`](https://workflows.tidymodels.org/) и згодно чува ваше компоненте за моделирање! Ово је оно што бисмо у *Python*-у назвали *pipelines*.\n",
+    "\n",
+    "Хајде да све спакујемо у workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "NlSbzDfgJ0zh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# Bundle recipe and model specification\r\n",
+    "mr_wf <- workflow() %>% \r\n",
+    "  add_recipe(cuisines_recipe) %>% \r\n",
+    "  add_model(mr_spec)\r\n",
+    "\r\n",
+    "# Print out workflow\r\n",
+    "mr_wf"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow ════════════════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 333
+    },
+    "id": "Sc1TfPA4Ke3_",
+    "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Воркфлови 👌👌! **`workflow()`** може се прилагодити на сличан начин као и модел. Дакле, време је да обучимо модел!\n"
+   ],
+   "metadata": {
+    "id": "TNQ8i85aKf9L"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# Train a multinomial regression model\n",
+    "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
+    "\n",
+    "mr_fit"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Call:\n",
+       "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
+       "    trace = FALSE)\n",
+       "\n",
+       "Coefficients:\n",
+       "         (Intercept)     almond angelica         anise anise_seed       apple\n",
+       "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
+       "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
+       "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
+       "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
+       "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
+       "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
+       "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
+       "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
+       "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
+       "             avocado       bacon baked_potato balm     banana     barley\n",
+       "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
+       "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
+       "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
+       "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
+       "         bartlett_pear      basil        bay       bean         beech\n",
+       "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
+       "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
+       "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
+       "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
+       "               beef beef_broth   beef_liver         beer        beet\n",
+       "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
+       "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
+       "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
+       "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
+       "         bell_pepper bergamot       berry bitter_orange black_bean\n",
+       "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
+       "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
+       "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
+       "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
+       "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
+       "indian               0             0.38935801   -0.4453495               0\n",
+       "japanese             0            -0.05452887   -0.5440869               0\n",
+       "korean               0            -0.03929970    0.8025454               0\n",
+       "thai                 0            -0.21498372   -0.9854806               0\n",
+       "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
+       "indian          -0.2759246  0.3079977  0.191256164                 0\n",
+       "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
+       "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
+       "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
+       "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
+       "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
+       "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
+       "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
+       "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
+       "\n",
+       "...\n",
+       "and 308 more lines."
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GMbdfVmTKkJI",
+    "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Излаз приказује коефицијенте које је модел научио током обуке.\n",
+    "\n",
+    "### Процена обученог модела\n",
+    "\n",
+    "Време је да видимо како се модел показао 📏 процењујући га на тестном скупу! Хајде да почнемо са прављењем предвиђања на тестном скупу.\n"
+   ],
+   "metadata": {
+    "id": "tt2BfOxrKmcJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# Make predictions on the test set\n",
+    "results <- cuisines_test %>% select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
+    "\n",
+    "# Print out results\n",
+    "results %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class\n",
+       "1 indian  thai       \n",
+       "2 indian  indian     \n",
+       "3 indian  indian     \n",
+       "4 indian  indian     \n",
+       "5 indian  indian     "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
+       "|---|---|\n",
+       "| indian | thai   |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & .pred\\_class\\\\\n",
+       " <fct> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai  \\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "CqtckvtsKqax",
+    "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Одличан посао! У Tidymodels-у, процена перформанси модела може се обавити коришћењем [yardstick](https://yardstick.tidymodels.org/) - пакета који се користи за мерење ефикасности модела помоћу метрика перформанси. Као што смо радили у нашој лекцији о логистичкој регресији, хајде да почнемо израчунавањем матрице конфузије.\n"
+   ],
+   "metadata": {
+    "id": "8w5N6XsBKss7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "# Confusion matrix for categorical data\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "          Truth\n",
+       "Prediction chinese indian japanese korean thai\n",
+       "  chinese       83      1        8     15   10\n",
+       "  indian         4    163        1      2    6\n",
+       "  japanese      21      5       73     25    1\n",
+       "  korean        15      0       11    191    0\n",
+       "  thai          10     11        3      7   70"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 133
+    },
+    "id": "YvODvsLkK0iG",
+    "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Када се ради са више класа, генерално је интуитивније ово визуализовати као топлотну мапу, овако:\n"
+   ],
+   "metadata": {
+    "id": "c0HfPL16Lr6U"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
+    "# Visualize confusion matrix\n",
+    "results %>% \n",
+    "  conf_mat(cuisine, .pred_class) %>% \n",
+    "  autoplot(type = \"heatmap\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "plot without title"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {
+      "image/png": {
+       "width": 420,
+       "height": 420
+      }
+     }
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 436
+    },
+    "id": "HsAtwukyLsvt",
+    "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Тамнији квадрати на графикону матрице конфузије указују на велики број случајева, и надамо се да можете видети дијагоналну линију тамнијих квадрата која указује на случајеве где су предвиђена и стварна ознака исте.\n",
+    "\n",
+    "Сада ћемо израчунати резиме статистике за матрицу конфузије.\n"
+   ],
+   "metadata": {
+    "id": "oOJC87dkLwPr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# Summary stats for confusion matrix\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
+    "summary()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "   .metric              .estimator .estimate\n",
+       "1  accuracy             multiclass 0.7880435\n",
+       "2  kap                  multiclass 0.7276583\n",
+       "3  sens                 macro      0.7780927\n",
+       "4  spec                 macro      0.9477598\n",
+       "5  ppv                  macro      0.7585583\n",
+       "6  npv                  macro      0.9460080\n",
+       "7  mcc                  multiclass 0.7292724\n",
+       "8  j_index              macro      0.7258524\n",
+       "9  bal_accuracy         macro      0.8629262\n",
+       "10 detection_prevalence macro      0.2000000\n",
+       "11 precision            macro      0.7585583\n",
+       "12 recall               macro      0.7780927\n",
+       "13 f_meas               macro      0.7641862"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 13 × 3\n",
+       "\n",
+       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| accuracy             | multiclass | 0.7880435 |\n",
+       "| kap                  | multiclass | 0.7276583 |\n",
+       "| sens                 | macro      | 0.7780927 |\n",
+       "| spec                 | macro      | 0.9477598 |\n",
+       "| ppv                  | macro      | 0.7585583 |\n",
+       "| npv                  | macro      | 0.9460080 |\n",
+       "| mcc                  | multiclass | 0.7292724 |\n",
+       "| j_index              | macro      | 0.7258524 |\n",
+       "| bal_accuracy         | macro      | 0.8629262 |\n",
+       "| detection_prevalence | macro      | 0.2000000 |\n",
+       "| precision            | macro      | 0.7585583 |\n",
+       "| recall               | macro      | 0.7780927 |\n",
+       "| f_meas               | macro      | 0.7641862 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 13 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " .metric & .estimator & .estimate\\\\\n",
+       " <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t accuracy             & multiclass & 0.7880435\\\\\n",
+       "\t kap                  & multiclass & 0.7276583\\\\\n",
+       "\t sens                 & macro      & 0.7780927\\\\\n",
+       "\t spec                 & macro      & 0.9477598\\\\\n",
+       "\t ppv                  & macro      & 0.7585583\\\\\n",
+       "\t npv                  & macro      & 0.9460080\\\\\n",
+       "\t mcc                  & multiclass & 0.7292724\\\\\n",
+       "\t j\\_index              & macro      & 0.7258524\\\\\n",
+       "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
+       "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
+       "\t precision            & macro      & 0.7585583\\\\\n",
+       "\t recall               & macro      & 0.7780927\\\\\n",
+       "\t f\\_meas               & macro      & 0.7641862\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 13 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
+       "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
+       "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
+       "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
+       "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
+       "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
+       "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
+       "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
+       "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 494
+    },
+    "id": "OYqetUyzL5Wz",
+    "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ако се фокусирамо на неке метрике као што су тачност, сензитивност, ppv, нисмо лоше започели 🥳!\n",
+    "\n",
+    "## 4. Дубље истраживање\n",
+    "\n",
+    "Хајде да поставимо једно суптилно питање: Који критеријум се користи да се одреди одређени тип кухиње као предвиђени исход?\n",
+    "\n",
+    "Па, статистички алгоритми машинског учења, као што је логистичка регресија, засновани су на `вероватноћи`; дакле, оно што класификатор заправо предвиђа је расподела вероватноће за скуп могућих исхода. Класа са највишом вероватноћом се затим бира као највероватнији исход за дате опсервације.\n",
+    "\n",
+    "Хајде да видимо како ово функционише тако што ћемо направити и тврде предвиђања класе и вероватноће.\n"
+   ],
+   "metadata": {
+    "id": "43t7vz8vMJtW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "# Make hard class prediction and probabilities\n",
+    "results_prob <- cuisines_test %>%\n",
+    "  select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
+    "\n",
+    "# Print out results\n",
+    "results_prob %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
+       "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
+       "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
+       "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
+       "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
+       "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
+       "  .pred_thai  \n",
+       "1 5.388194e-01\n",
+       "2 1.577948e-06\n",
+       "3 6.874989e-03\n",
+       "4 3.863391e-03\n",
+       "5 5.653283e-03"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 7\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
+       "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
+       "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
+       "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
+       "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 7\n",
+       "\\begin{tabular}{lllllll}\n",
+       " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
+       " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
+       "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
+       "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
+       "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
+       "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "xdKNs-ZPMTJL",
+    "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ Можете ли да објасните зашто је модел прилично сигуран да је прва опсервација тајландска?\n",
+    "\n",
+    "## **🚀Изазов**\n",
+    "\n",
+    "У овом лекцији, користили сте своје очишћене податке да изградите модел машинског учења који може да предвиди националну кухињу на основу серије састојака. Одвојите мало времена да прочитате [многе опције](https://www.tidymodels.org/find/parsnip/#models) које Tidymodels пружа за класификацију података и [друге начине](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) за примену мултинационалне регресије.\n",
+    "\n",
+    "#### ХВАЛА:\n",
+    "\n",
+    "[`Елисон Хорст`](https://twitter.com/allison_horst/) за креирање невероватних илустрација које чине R приступачнијим и занимљивијим. Пронађите више илустрација у њеној [галерији](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Каси Бревиу](https://www.twitter.com/cassieview) и [Џен Лупер](https://www.twitter.com/jenlooper) за креирање оригиналне Python верзије овог модула ♥️\n",
+    "\n",
+    "<br>\n",
+    "Додао бих неке шале, али не разумем баш добро игре речи о храни 😅.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Срећно учење,\n",
+    "\n",
+    "[Ерик](https://twitter.com/ericntay), Златни амбасадор за Microsoft Learn студенте.\n"
+   ],
+   "metadata": {
+    "id": "2tWVHMeLMYdM"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/translations/sr/4-Classification/2-Classifiers-1/solution/notebook.ipynb
new file mode 100644
index 000000000..fb91d4078
--- /dev/null
+++ b/translations/sr/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Изградња модела класификације\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy is 0.8181818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print (\"Accuracy is {}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n       'vegetable_oil'],\n      dtype='object')\ncuisine: korean\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test an item\n",
+    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[50]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                 0\n",
+       "korean    0.392231\n",
+       "chinese   0.372872\n",
+       "japanese  0.218825\n",
+       "thai      0.013427\n",
+       "indian    0.002645"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>korean</th>\n      <td>0.392231</td>\n    </tr>\n    <tr>\n      <th>chinese</th>\n      <td>0.372872</td>\n    </tr>\n    <tr>\n      <th>japanese</th>\n      <td>0.218825</td>\n    </tr>\n    <tr>\n      <th>thai</th>\n      <td>0.013427</td>\n    </tr>\n    <tr>\n      <th>indian</th>\n      <td>0.002645</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "#rehsape to 2d array and transpose\n",
+    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
+    "# predict with score\n",
+    "proba = model.predict_proba(test)\n",
+    "classes = model.classes_\n",
+    "# create df with classes and scores\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
+    "\n",
+    "# create df to show results\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.75      0.73      0.74       223\n      indian       0.93      0.88      0.90       255\n    japanese       0.78      0.78      0.78       253\n      korean       0.87      0.86      0.86       236\n        thai       0.76      0.84      0.80       232\n\n    accuracy                           0.82      1199\n   macro avg       0.82      0.82      0.82      1199\nweighted avg       0.82      0.82      0.82      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\r\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "9408506dd864f2b6e334c62f80c0cfcc",
+   "translation_date": "2025-09-06T14:33:05+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/3-Classifiers-2/notebook.ipynb b/translations/sr/4-Classification/3-Classifiers-2/notebook.ipynb
new file mode 100644
index 000000000..6df7eafbf
--- /dev/null
+++ b/translations/sr/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
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+  "language_info": {
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+    "name": "ipython",
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+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
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+   "original_hash": "15a83277036572e0773229b5f21c1e12",
+   "translation_date": "2025-09-06T14:42:19+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/translations/sr/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,650 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "name": "lesson_12-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
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+   "original_hash": "fab50046ca413a38939d579f8432274f",
+   "translation_date": "2025-09-06T14:46:04+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jsFutf_ygqSx"
+   },
+   "source": [
+    "# Направите класификациони модел: Укусна азијска и индијска јела\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HD54bEefgtNO"
+   },
+   "source": [
+    "## Класификатори кухиња 2\n",
+    "\n",
+    "У овом другом часу о класификацији, истражићемо `више начина` за класификацију категоријских података. Такође ћемо научити о последицама избора једног класификатора уместо другог.\n",
+    "\n",
+    "### [**Квиз пре предавања**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
+    "\n",
+    "### **Предуслови**\n",
+    "\n",
+    "Претпостављамо да сте завршили претходне лекције, јер ћемо наставити са неким концептима које смо раније научили.\n",
+    "\n",
+    "За ову лекцију биће нам потребни следећи пакети:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) је [збирка R пакета](https://www.tidyverse.org/packages) дизајнирана да учини науку о подацима бржом, лакшом и забавнијом!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) је [оквир](https://www.tidymodels.org/packages/) који обухвата пакете за моделирање и машинско учење.\n",
+    "\n",
+    "-   `themis`: [themis пакет](https://themis.tidymodels.org/) пружа додатне кораке за рецепте који се баве небалансираним подацима.\n",
+    "\n",
+    "Можете их инсталирати на следећи начин:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\n",
+    "\n",
+    "Алтернативно, скрипта испод проверава да ли имате потребне пакете за завршетак овог модула и инсталира их ако недостају.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vZ57IuUxgyQt"
+   },
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z22M-pj4g07x"
+   },
+   "source": [
+    "## **1. Мапа класификације**\n",
+    "\n",
+    "У нашој [претходној лекцији](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1), покушали смо да одговоримо на питање: како да изаберемо између више модела? У великој мери, то зависи од карактеристика података и типа проблема који желимо да решимо (на пример, класификација или регресија?).\n",
+    "\n",
+    "Раније смо научили о различитим опцијама које имате када класификујете податке користећи Microsoft-ов подсетник. Python-ов оквир за машинско учење, Scikit-learn, нуди сличан, али детаљнији подсетник који може додатно помоћи у сужењу избора проценитеља (други термин за класификаторе):\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/map.png\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption></figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u1i3xRIVg7vG"
+   },
+   "source": [
+    "> Савет: [погледајте ову мапу онлајн](https://scikit-learn.org/stable/tutorial/machine_learning_map/) и кликните дуж путање да бисте прочитали документацију.  \n",
+    ">  \n",
+    "> [Референтни сајт за Tidymodels](https://www.tidymodels.org/find/parsnip/#models) такође пружа одличну документацију о различитим типовима модела.\n",
+    "\n",
+    "### **План** 🗺️\n",
+    "\n",
+    "Ова мапа је веома корисна када имате јасно разумевање ваших података, јер можете „шетати“ дуж њених путања до доношења одлуке:\n",
+    "\n",
+    "-   Имамо више од 50 узорака\n",
+    "\n",
+    "-   Желимо да предвидимо категорију\n",
+    "\n",
+    "-   Имамо означене податке\n",
+    "\n",
+    "-   Имамо мање од 100.000 узорака\n",
+    "\n",
+    "-   ✨ Можемо изабрати Linear SVC\n",
+    "\n",
+    "-   Ако то не функционише, пошто имамо нумеричке податке\n",
+    "\n",
+    "    -   Можемо пробати ✨ KNeighbors Classifier\n",
+    "\n",
+    "        -   Ако ни то не функционише, пробајте ✨ SVC и ✨ Ensemble Classifiers\n",
+    "\n",
+    "Ово је веома корисна путања коју треба пратити. Сада, хајде да одмах пређемо на то користећи [tidymodels](https://www.tidymodels.org/) оквир за моделирање: конзистентну и флексибилну колекцију R пакета развијених да подстакну добру статистичку праксу 😊.\n",
+    "\n",
+    "## 2. Поделите податке и решите проблем неуравнотеженог скупа података.\n",
+    "\n",
+    "Из претходних лекција смо научили да постоји скуп заједничких састојака у нашим кухињама. Такође, постојала је прилично неравномерна расподела у броју кухиња.\n",
+    "\n",
+    "Ово ћемо решити на следећи начин:\n",
+    "\n",
+    "-   Избацивањем најчешћих састојака који стварају конфузију између различитих кухиња, користећи `dplyr::select()`.\n",
+    "\n",
+    "-   Коришћењем `recipe` који претходно обрађује податке како би их припремио за моделирање применом алгоритма `over-sampling`.\n",
+    "\n",
+    "Ово смо већ обрадили у претходној лекцији, тако да би ово требало да буде лако 🥳!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "6tj_rN00hClA"
+   },
+   "source": [
+    "# Load the core Tidyverse and Tidymodels packages\n",
+    "library(tidyverse)\n",
+    "library(tidymodels)\n",
+    "\n",
+    "# Load the original cuisines data\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
+    "\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\n",
+    "df_select <- df %>% \n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\n",
+    "  # Encode cuisine column as categorical\n",
+    "  mutate(cuisine = factor(cuisine))\n",
+    "\n",
+    "\n",
+    "# Create data split specification\n",
+    "set.seed(2056)\n",
+    "cuisines_split <- initial_split(data = df_select,\n",
+    "                                strata = cuisine,\n",
+    "                                prop = 0.7)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "cuisines_train <- training(cuisines_split)\n",
+    "cuisines_test <- testing(cuisines_split)\n",
+    "\n",
+    "# Display distribution of cuisines in the training set\n",
+    "cuisines_train %>% \n",
+    "  count(cuisine) %>% \n",
+    "  arrange(desc(n))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFin5yw3hHb1"
+   },
+   "source": [
+    "### Рад са неуравнотеженим подацима\n",
+    "\n",
+    "Неуравнотежени подаци често имају негативан утицај на перформансе модела. Многи модели најбоље функционишу када је број опсервација једнак и, самим тим, имају потешкоћа са неуравнотеженим подацима.\n",
+    "\n",
+    "Постоје два главна начина за рад са неуравнотеженим скуповима података:\n",
+    "\n",
+    "-   додавање опсервација мањинској класи: `Прекомерно узорковање` (Over-sampling), на пример, коришћењем SMOTE алгоритма који синтетички генерише нове примере мањинске класе користећи најближе суседе тих случајева.\n",
+    "\n",
+    "-   уклањање опсервација из већинске класе: `Потцењено узорковање` (Under-sampling)\n",
+    "\n",
+    "У претходном часу, демонстрирали смо како да радимо са неуравнотеженим скуповима података користећи `рецепт`. Рецепт се може сматрати планом који описује које кораке треба применити на скуп података како би био спреман за анализу података. У нашем случају, желимо да имамо једнаку дистрибуцију броја наших кухиња за наш `тренинг скуп`. Хајде да почнемо.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "cRzTnHolhLWd"
+   },
+   "source": [
+    "# Load themis package for dealing with imbalanced data\n",
+    "library(themis)\n",
+    "\n",
+    "# Create a recipe for preprocessing training data\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
+    "  step_smote(cuisine) \n",
+    "\n",
+    "# Print recipe\n",
+    "cuisines_recipe"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KxOQ2ORhhO81"
+   },
+   "source": [
+    "Сада смо спремни да обучавамо моделе 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Изван модела мултиномијалне регресије\n",
+    "\n",
+    "У претходној лекцији, разматрали смо моделе мултиномијалне регресије. Хајде да истражимо неке флексибилније моделе за класификацију.\n",
+    "\n",
+    "### Машине за подршку векторима\n",
+    "\n",
+    "У контексту класификације, `Машине за подршку векторима` су техника машинског учења која настоји да пронађе *хиперплан* који \"најбоље\" раздваја класе. Хајде да погледамо једноставан пример:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/svm.png\"\n",
+    "   width=\"300\"/>\n",
+    "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C4Wsd0vZhXYu"
+   },
+   "source": [
+    "H1~ не раздваја класе. H2~ их раздваја, али само са малом маргином. H3~ их раздваја са максималном маргином.\n",
+    "\n",
+    "#### Линеарни класификатор подршке векторима\n",
+    "\n",
+    "Кластерисање подршке векторима (SVC) је део породице техника машинског учења заснованих на машинама подршке векторима. У SVC-у, хиперплан се бира тако да правилно раздвоји `већину` посматрања из тренинг скупа, али `може погрешно класификовати` нека посматрања. Дозвољавањем да неке тачке буду на погрешној страни, SVM постаје отпорнији на изузетке, чиме се побољшава генерализација на нове податке. Параметар који регулише ово кршење назива се `cost`, који има подразумевану вредност 1 (погледајте `help(\"svm_poly\")`).\n",
+    "\n",
+    "Хајде да направимо линеарни SVC тако што ћемо поставити `degree = 1` у полиномском SVM моделу.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vJpp6nuChlBz"
+   },
+   "source": [
+    "# Make a linear SVC specification\n",
+    "svc_linear_spec <- svm_poly(degree = 1) %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svc_linear_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svc_linear_spec)\n",
+    "\n",
+    "# Print out workflow\n",
+    "svc_linear_wf"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rDs8cWNkhoqu"
+   },
+   "source": [
+    "Сада када смо обухватили кораке предобраде и спецификацију модела у *workflow*-у, можемо наставити са тренингом линеарног SVC-а и проценом резултата у истом процесу. За метрике перформанси, хајде да направимо сет метрика који ће процењивати: `тачност`, `осетљивост`, `позитивну предиктивну вредност` и `F меру`.\n",
+    "\n",
+    "> `augment()` ће додати колону(е) за предикције у дате податке.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "81wiqcwuhrnq"
+   },
+   "source": [
+    "# Train a linear SVC model\n",
+    "svc_linear_fit <- svc_linear_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svc_linear_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0UFQvHf-huo3"
+   },
+   "source": [
+    "#### Машина за подршку вектора\n",
+    "\n",
+    "Машина за подршку вектора (SVM) је проширење класификатора за подршку вектора како би се омогућила нелинеарна граница између класа. У суштини, SVM користи *трик са језгром* да прошири простор карактеристика и прилагоди се нелинеарним односима између класа. Једна популарна и изузетно флексибилна функција језгра коју SVM користи је *функција радијалне основе.* Хајде да видимо како ће се она показати на нашим подацима.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-KX4S8mzhzmp"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Make an RBF SVM specification\n",
+    "svm_rbf_spec <- svm_rbf() %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svm_rbf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svm_rbf_spec)\n",
+    "\n",
+    "\n",
+    "# Train an RBF model\n",
+    "svm_rbf_fit <- svm_rbf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svm_rbf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QBFSa7WSh4HQ"
+   },
+   "source": [
+    "Много боље 🤩!\n",
+    "\n",
+    "> ✅ Молимо вас да погледате:\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://bradleyboehmke.github.io/HOML/svm.html), Практично машинско учење са R\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://www.statlearning.com/), Увод у статистичко учење са апликацијама у R\n",
+    ">\n",
+    "> за додатно читање.\n",
+    "\n",
+    "### Класификатори најближих суседа\n",
+    "\n",
+    "*K*-најближи сусед (KNN) је алгоритам у којем се свака опсервација предвиђа на основу њене *сличности* са другим опсервацијама.\n",
+    "\n",
+    "Хајде да га применимо на наше податке.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "k4BxxBcdh9Ka"
+   },
+   "source": [
+    "# Make a KNN specification\n",
+    "knn_spec <- nearest_neighbor() %>% \n",
+    "  set_engine(\"kknn\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "knn_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(knn_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "knn_wf_fit <- knn_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "knn_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HaegQseriAcj"
+   },
+   "source": [
+    "Чини се да овај модел не ради баш најбоље. Вероватно ће промена аргумената модела (погледајте `help(\"nearest_neighbor\")`) побољшати перформансе модела. Обавезно пробајте.\n",
+    "\n",
+    "> ✅ Погледајте:\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> да бисте сазнали више о класификаторима *K*-најближих суседа.\n",
+    "\n",
+    "### Енсембл класификатори\n",
+    "\n",
+    "Енсембл алгоритми функционишу тако што комбинују више основних проценитеља како би произвели оптималан модел, било кроз:\n",
+    "\n",
+    "`bagging`: примену *функције просека* на колекцију основних модела\n",
+    "\n",
+    "`boosting`: изградњу секвенце модела који се надовезују један на други ради побољшања предиктивних перформанси.\n",
+    "\n",
+    "Хајде да започнемо са испробавањем модела Случајне шуме (Random Forest), који гради велику колекцију одлуковних стабала, а затим примењује функцију просека ради добијања бољег укупног модела.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "49DPoVs6iK1M"
+   },
+   "source": [
+    "# Make a random forest specification\n",
+    "rf_spec <- rand_forest() %>% \n",
+    "  set_engine(\"ranger\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "rf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(rf_spec)\n",
+    "\n",
+    "# Train a random forest model\n",
+    "rf_wf_fit <- rf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "rf_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RGVYwC_aiUWc"
+   },
+   "source": [
+    "Добар посао 👏!\n",
+    "\n",
+    "Хајде да експериментишемо са моделом Boosted Tree.\n",
+    "\n",
+    "Boosted Tree представља метод ансамбла који креира серију секвенцијалних одлука стабала где свако стабло зависи од резултата претходних стабала у настојању да постепено смањи грешку. Фокусира се на тежине погрешно класификованих ставки и прилагођава модел за следећи класификатор како би исправио грешке.\n",
+    "\n",
+    "Постоје различити начини за подешавање овог модела (погледајте `help(\"boost_tree\")`). У овом примеру, подешаваћемо Boosted стабла преко `xgboost` механизма.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Py1YWo-micWs"
+   },
+   "source": [
+    "# Make a boosted tree specification\n",
+    "boost_spec <- boost_tree(trees = 200) %>% \n",
+    "  set_engine(\"xgboost\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "boost_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(boost_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "boost_wf_fit <- boost_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "boost_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zNQnbuejigZM"
+   },
+   "source": [
+    "> ✅ Молимо вас да погледате:\n",
+    ">\n",
+    "> -   [Машинско учење за друштвене научнике](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
+    ">\n",
+    "> -   [Практично машинско учење са R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [Увод у статистичко учење са апликацијама у R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> -   <https://algotech.netlify.app/blog/xgboost/> - Истражује модел AdaBoost који је добра алтернатива за xgboost.\n",
+    ">\n",
+    "> за више информација о класификаторима ансамбла.\n",
+    "\n",
+    "## 4. Додатно - поређење више модела\n",
+    "\n",
+    "У овом лабораторијском раду смо применили прилично велики број модела 🙌. Може постати заморно или напорно креирати много радних токова из различитих сетова претпроцесора и/или спецификација модела, а затим израчунати метрике перформанси једну по једну.\n",
+    "\n",
+    "Хајде да видимо да ли можемо да решимо ово креирањем функције која примењује листу радних токова на сет за обуку, а затим враћа метрике перформанси на основу тест сета. Користићемо `map()` и `map_dfr()` из пакета [purrr](https://purrr.tidyverse.org/) да применимо функције на сваки елемент у листи.\n",
+    "\n",
+    "> [`map()`](https://purrr.tidyverse.org/reference/map.html) функције вам омогућавају да замените многе for петље кодом који је и сажетији и лакши за читање. Најбоље место за учење о [`map()`](https://purrr.tidyverse.org/reference/map.html) функцијама је [поглавље о итерацији](http://r4ds.had.co.nz/iteration.html) у R за науку о подацима.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Qzb7LyZnimd2"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "# Define a function that returns performance metrics\n",
+    "compare_models <- function(workflow_list, train_set, test_set){\n",
+    "  \n",
+    "  suppressWarnings(\n",
+    "    # Fit each model to the train_set\n",
+    "    map(workflow_list, fit, data = train_set) %>% \n",
+    "    # Make predictions on the test set\n",
+    "      map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
+    "    # Select desired columns\n",
+    "      select(model, cuisine, .pred_class) %>% \n",
+    "    # Evaluate model performance\n",
+    "      group_by(model) %>% \n",
+    "      eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
+    "      ungroup()\n",
+    "  )\n",
+    "  \n",
+    "} # End of function"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fwa712sNisDA"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "3i4VJOi2iu-a"
+   },
+   "source": [
+    "# Make a list of workflows\n",
+    "workflow_list <- list(\n",
+    "  \"svc\" = svc_linear_wf,\n",
+    "  \"svm\" = svm_rbf_wf,\n",
+    "  \"knn\" = knn_wf,\n",
+    "  \"random_forest\" = rf_wf,\n",
+    "  \"xgboost\" = boost_wf)\n",
+    "\n",
+    "# Call the function\n",
+    "set.seed(2056)\n",
+    "perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
+    "\n",
+    "# Print out performance metrics\n",
+    "perf_metrics %>% \n",
+    "  group_by(.metric) %>% \n",
+    "  arrange(desc(.estimate)) %>% \n",
+    "  slice_head(n=7)\n",
+    "\n",
+    "# Compare accuracy\n",
+    "perf_metrics %>% \n",
+    "  filter(.metric == \"accuracy\") %>% \n",
+    "  arrange(desc(.estimate))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KuWK_lEli4nW"
+   },
+   "source": [
+    "Пакет [**workflowset**](https://workflowsets.tidymodels.org/) омогућава корисницима да креирају и лако прилагоде велики број модела, али је углавном дизајниран за рад са техникама ресемплирања као што је `крстална валидација`, приступ који тек треба да обрадимо.\n",
+    "\n",
+    "## **🚀Изазов**\n",
+    "\n",
+    "Свака од ових техника има велики број параметара које можете прилагодити, на пример `cost` у SVM-у, `neighbors` у KNN-у, `mtry` (случајно изабрани предиктори) у Random Forest-у.\n",
+    "\n",
+    "Истражите подразумеване вредности параметара за сваки модел и размислите шта би значило прилагођавање ових параметара за квалитет модела.\n",
+    "\n",
+    "Да бисте сазнали више о одређеном моделу и његовим параметрима, користите: `help(\"model\")`, на пример `help(\"rand_forest\")`.\n",
+    "\n",
+    "> У пракси, обично *процењујемо* *најбоље вредности* за ове параметре тако што тренирамо многе моделе на `симулираном скупу података` и меримо колико добро ти модели раде. Овај процес се назива **тјунинг**.\n",
+    "\n",
+    "### [**Квиз након предавања**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
+    "\n",
+    "### **Преглед и самостално учење**\n",
+    "\n",
+    "У овим лекцијама има доста стручних термина, па одвојите минут да прегледате [ову листу](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) корисне терминологије!\n",
+    "\n",
+    "#### ХВАЛА:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) за креирање невероватних илустрација које чине R приступачнијим и занимљивијим. Пронађите више илустрација у њеној [галерији](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) и [Jen Looper](https://www.twitter.com/jenlooper) за креирање оригиналне Python верзије овог модула ♥️\n",
+    "\n",
+    "Срећно учење,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Златни амбасадор студената Microsoft Learn.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Илустрација од @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/sr/4-Classification/3-Classifiers-2/solution/notebook.ipynb
new file mode 100644
index 000000000..72550a064
--- /dev/null
+++ b/translations/sr/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Пробајте различите класификаторе\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "C = 10\n",
+    "# Create different classifiers.\n",
+    "classifiers = {\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
+    "    'KNN classifier': KNeighborsClassifier(C),\n",
+    "    'SVC': SVC(),\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "    \n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy (train) for Linear SVC: 76.4% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.64      0.66      0.65       242\n",
+      "      indian       0.91      0.86      0.89       236\n",
+      "    japanese       0.72      0.73      0.73       245\n",
+      "      korean       0.83      0.75      0.79       234\n",
+      "        thai       0.75      0.82      0.78       242\n",
+      "\n",
+      "    accuracy                           0.76      1199\n",
+      "   macro avg       0.77      0.76      0.77      1199\n",
+      "weighted avg       0.77      0.76      0.77      1199\n",
+      "\n",
+      "Accuracy (train) for KNN classifier: 70.7% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.65      0.63      0.64       242\n",
+      "      indian       0.84      0.81      0.82       236\n",
+      "    japanese       0.60      0.81      0.69       245\n",
+      "      korean       0.89      0.53      0.67       234\n",
+      "        thai       0.69      0.75      0.72       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.73      0.71      0.71      1199\n",
+      "weighted avg       0.73      0.71      0.71      1199\n",
+      "\n",
+      "Accuracy (train) for SVC: 80.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.71      0.69      0.70       242\n",
+      "      indian       0.92      0.92      0.92       236\n",
+      "    japanese       0.77      0.78      0.77       245\n",
+      "      korean       0.87      0.77      0.82       234\n",
+      "        thai       0.75      0.86      0.80       242\n",
+      "\n",
+      "    accuracy                           0.80      1199\n",
+      "   macro avg       0.80      0.80      0.80      1199\n",
+      "weighted avg       0.80      0.80      0.80      1199\n",
+      "\n",
+      "Accuracy (train) for RFST: 82.8% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.80      0.75      0.77       242\n",
+      "      indian       0.90      0.91      0.90       236\n",
+      "    japanese       0.82      0.78      0.80       245\n",
+      "      korean       0.85      0.82      0.83       234\n",
+      "        thai       0.78      0.89      0.83       242\n",
+      "\n",
+      "    accuracy                           0.83      1199\n",
+      "   macro avg       0.83      0.83      0.83      1199\n",
+      "weighted avg       0.83      0.83      0.83      1199\n",
+      "\n",
+      "Accuracy (train) for ADA: 71.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.60      0.57      0.58       242\n",
+      "      indian       0.87      0.84      0.86       236\n",
+      "    japanese       0.71      0.60      0.65       245\n",
+      "      korean       0.68      0.78      0.72       234\n",
+      "        thai       0.70      0.78      0.74       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.71      0.71      0.71      1199\n",
+      "weighted avg       0.71      0.71      0.71      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "    y_pred = classifier.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
+    "    print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "7ea2b714669c823a596d986ba2d5739f",
+   "translation_date": "2025-09-06T14:42:48+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/4-Applied/notebook.ipynb b/translations/sr/4-Classification/4-Applied/notebook.ipynb
new file mode 100644
index 000000000..aac5cbed1
--- /dev/null
+++ b/translations/sr/4-Classification/4-Applied/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2f3e0d9e9ac5c301558fb8bf733ac0cb",
+   "translation_date": "2025-09-06T14:41:29+00:00",
+   "source_file": "4-Classification/4-Applied/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да превод буде тачан, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/4-Classification/4-Applied/solution/notebook.ipynb b/translations/sr/4-Classification/4-Applied/solution/notebook.ipynb
new file mode 100644
index 000000000..206efb042
--- /dev/null
+++ b/translations/sr/4-Classification/4-Applied/solution/notebook.ipynb
@@ -0,0 +1,290 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "49325d6dd12a3628fc64fa7ccb1a80ff",
+   "translation_date": "2025-09-06T14:41:54+00:00",
+   "source_file": "4-Classification/4-Applied/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: skl2onnx in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.8.0)\n",
+      "Requirement already satisfied: protobuf in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (3.8.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.19.2)\n",
+      "Requirement already satisfied: onnx>=1.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.9.0)\n",
+      "Requirement already satisfied: six in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from skl2onnx) (1.12.0)\n",
+      "Requirement already satisfied: onnxconverter-common<1.9,>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.8.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.19 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (0.24.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.4.1)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->skl2onnx) (45.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnx>=1.2.1->skl2onnx) (3.10.0.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (0.16.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install skl2onnx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 60
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 61
+    }
+   ],
+   "source": [
+    "X = data.iloc[:,2:]\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "  cuisine\n",
+       "0  indian\n",
+       "1  indian\n",
+       "2  indian\n",
+       "3  indian\n",
+       "4  indian"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 62
+    }
+   ],
+   "source": [
+    "y = data[['cuisine']]\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 65
+    }
+   ],
+   "source": [
+    "model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
+    "model.fit(X_train,y_train.values.ravel())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.72      0.70      0.71       236\n      indian       0.91      0.88      0.89       243\n    japanese       0.80      0.75      0.77       240\n      korean       0.80      0.81      0.81       230\n        thai       0.76      0.85      0.80       250\n\n    accuracy                           0.80      1199\n   macro avg       0.80      0.80      0.80      1199\nweighted avg       0.80      0.80      0.80      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}\n",
+    "onx = convert_sklearn(model, initial_types=initial_type, options=options)\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
+    "    f.write(onx.SerializeToString())\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/5-Clustering/1-Visualize/notebook.ipynb b/translations/sr/5-Clustering/1-Visualize/notebook.ipynb
new file mode 100644
index 000000000..e699f99ca
--- /dev/null
+++ b/translations/sr/5-Clustering/1-Visualize/notebook.ipynb
@@ -0,0 +1,50 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+  "kernelspec": {
+   "name": "python383jvsc74a57bd0e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95",
+   "display_name": "Python 3.8.3 64-bit (conda)"
+  },
+  "coopTranslator": {
+   "original_hash": "40e0707e96b3e1899a912776006264f9",
+   "translation_date": "2025-09-06T14:07:59+00:00",
+   "source_file": "5-Clustering/1-Visualize/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/translations/sr/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
new file mode 100644
index 000000000..b0576f099
--- /dev/null
+++ b/translations/sr/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
@@ -0,0 +1,500 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Нигеријска музика преузета са Spotify - анализа**\n",
+    "\n",
+    "Кластеризација је врста [ненаџираног учења](https://wikipedia.org/wiki/Unsupervised_learning) која претпоставља да је скуп података необележен или да његови уноси нису повезани са унапред дефинисаним излазима. Користи различите алгоритме за сортирање необележених података и пружа груписања на основу образаца које препознаје у подацима.\n",
+    "\n",
+    "[**Квиз пре предавања**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)\n",
+    "\n",
+    "### **Увод**\n",
+    "\n",
+    "[Кластеризација](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) је веома корисна за истраживање података. Хајде да видимо да ли може помоћи у откривању трендова и образаца у начину на који нигеријска публика конзумира музику.\n",
+    "\n",
+    "> ✅ Одвојите минут да размислите о употреби кластеризације. У стварном животу, кластеризација се дешава кад год имате гомилу веша и треба да сортирате одећу чланова породице 🧦👕👖🩲. У науци о подацима, кластеризација се дешава када покушавате да анализирате корисничке преференције или одредите карактеристике било ког необележеног скупа података. Кластеризација, на неки начин, помаже да се уведе ред у хаос, као у фиоци за чарапе.\n",
+    "\n",
+    "У професионалном окружењу, кластеризација се може користити за одређивање сегментације тржишта, на пример, за утврђивање које старосне групе купују које производе. Друга употреба би била откривање аномалија, можда за откривање преваре из скупа података о трансакцијама кредитним картицама. Или бисте могли да користите кластеризацију за одређивање тумора у серији медицинских снимака.\n",
+    "\n",
+    "✅ Одвојите минут да размислите о томе како сте можда наишли на кластеризацију „у природи“, у банкарству, е-трговини или пословном окружењу.\n",
+    "\n",
+    "> 🎓 Занимљиво је да је анализа кластера настала у областима антропологије и психологије 1930-их. Можете ли замислити како је могла бити коришћена?\n",
+    "\n",
+    "Алтернативно, могли бисте је користити за груписање резултата претраге - на пример, по куповним линковима, сликама или рецензијама. Кластеризација је корисна када имате велики скуп података који желите да смањите и на којем желите да извршите детаљнију анализу, па се техника може користити за учење о подацима пре него што се изграде други модели.\n",
+    "\n",
+    "✅ Када су ваши подаци организовани у кластере, додељујете им идентификатор кластера, а ова техника може бити корисна када желите да сачувате приватност скупа података; можете се уместо тога позивати на тачку података преко њеног идентификатора кластера, а не преко откривенијих идентификационих података. Можете ли смислити друге разлоге зашто бисте се позивали на идентификатор кластера уместо на друге елементе кластера да бисте га идентификовали?\n",
+    "\n",
+    "### Почетак рада са кластеризацијом\n",
+    "\n",
+    "> 🎓 Начин на који креирамо кластере има много везе са начином на који групишемо тачке података у групе. Хајде да разјаснимо неке термине:\n",
+    ">\n",
+    "> 🎓 ['Трансдуктивно' наспрам 'индуктивно'](https://wikipedia.org/wiki/Transduction_(machine_learning))\n",
+    ">\n",
+    "> Трансдуктивно закључивање се изводи из посматраних случајева обуке који се мапирају на одређене тест случајеве. Индуктивно закључивање се изводи из случајева обуке који се мапирају на општа правила која се тек онда примењују на тест случајеве.\n",
+    ">\n",
+    "> Пример: Замислите да имате скуп података који је само делимично обележен. Неке ствари су „плоче“, неке „ЦД-ови“, а неке су празне. Ваш задатак је да обезбедите ознаке за празнине. Ако изаберете индуктивни приступ, обучили бисте модел тражећи „плоче“ и „ЦД-ове“ и применили те ознаке на необележене податке. Овај приступ ће имати потешкоћа у класификовању ствари које су заправо „касете“. Трансдуктивни приступ, с друге стране, ефикасније обрађује ове непознате податке јер ради на груписању сличних ставки и затим примењује ознаку на групу. У овом случају, кластери би могли одражавати „округле музичке ствари“ и „квадратне музичке ствари“.\n",
+    ">\n",
+    "> 🎓 ['Нефлатна' наспрам 'флатна' геометрија](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)\n",
+    ">\n",
+    "> Изведено из математичке терминологије, нефлатна наспрам флатна геометрија односи се на мерење удаљености између тачака било „флатним“ ([Еуклидским](https://wikipedia.org/wiki/Euclidean_geometry)) или „нефлатним“ (не-Еуклидским) геометријским методама.\n",
+    ">\n",
+    "> „Флатна“ у овом контексту се односи на Еуклидску геометрију (делови које се уче као „планарна“ геометрија), а нефлатна се односи на не-Еуклидску геометрију. Шта геометрија има са машинским учењем? Па, као две области које су укорењене у математици, мора постојати заједнички начин мерења удаљености између тачака у кластерима, а то се може урадити на „флатан“ или „нефлатан“ начин, у зависности од природе података. [Еуклидске удаљености](https://wikipedia.org/wiki/Euclidean_distance) се мере као дужина сегмента линије између две тачке. [Не-Еуклидске удаљености](https://wikipedia.org/wiki/Non-Euclidean_geometry) се мере дуж криве. Ако ваши подаци, визуализовани, изгледају као да не постоје на равни, можда ћете морати да користите специјализовани алгоритам за њихово обрађивање.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/flat-nonflat.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Инфографик од Дасани Мадипалли</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "> 🎓 ['Удаљености'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)\n",
+    ">\n",
+    "> Кластери се дефинишу њиховом матрицом удаљености, нпр. удаљеностима између тачака. Ова удаљеност се може мерити на неколико начина. Еуклидски кластери се дефинишу просеком вредности тачака и садрже „центроид“ или централну тачку. Удаљености се стога мере удаљеношћу до тог центроида. Не-Еуклидске удаљености се односе на „кластроиде“, тачку најближу другим тачкама. Кластроиди се могу дефинисати на различите начине.\n",
+    ">\n",
+    "> 🎓 ['Ограничена'](https://wikipedia.org/wiki/Constrained_clustering)\n",
+    ">\n",
+    "> [Ограничена кластеризација](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) уводи „полу-наџирано“ учење у овај ненаџирани метод. Односи између тачака су означени као „не могу се повезати“ или „морају се повезати“, тако да се нека правила намећу на скуп података.\n",
+    ">\n",
+    "> Пример: Ако је алгоритам пуштен на серију необележених или полу-обележених података, кластери које производи могу бити лошег квалитета. У горњем примеру, кластери би могли груписати „округле музичке ствари“, „квадратне музичке ствари“, „троугласте ствари“ и „колачиће“. Ако се дају нека ограничења или правила која треба да се прате („ставка мора бити направљена од пластике“, „ставка мора бити у стању да производи музику“), то може помоћи да се „ограничи“ алгоритам да прави боље изборе.\n",
+    ">\n",
+    "> 🎓 'Густина'\n",
+    ">\n",
+    "> Подаци који су „шумни“ сматрају се „густим“. Удаљености између тачака у сваком од њихових кластера могу се показати, при испитивању, као више или мање густе, или „претрпане“, и стога ови подаци треба да се анализирају одговарајућим методом кластеризације. [Овај чланак](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) демонстрира разлику између коришћења К-Меанс кластеризације и ХДБСЦАН алгоритама за истраживање шумног скупа података са неравномерном густином кластера.\n",
+    "\n",
+    "Продубите своје разумевање техника кластеризације у овом [Learn модулу](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "### **Алгоритми кластеризације**\n",
+    "\n",
+    "Постоји преко 100 алгоритама кластеризације, а њихова употреба зависи од природе података. Хајде да разговарамо о неким од главних:\n",
+    "\n",
+    "-   **Хијерархијска кластеризација**. Ако се објекат класификује према његовој близини другом објекту, а не оном који је удаљенији, кластери се формирају на основу удаљености њихових чланова од других објеката. Хијерархијска кластеризација се карактерише поновним комбиновањем два кластера.\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/hierarchical.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Инфографик од Дасани Мадипалли</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "-   **Центроидна кластеризација**. Овај популарни алгоритам захтева избор „к“, или броја кластера који треба формирати, након чега алгоритам одређује централну тачку кластера и окупља податке око те тачке. [К-Меанс кластеризација](https://wikipedia.org/wiki/K-means_clustering) је популарна верзија центроидне кластеризације која раздваја скуп података у унапред дефинисане К групе. Центар се одређује најближим просеком, отуда и назив. Квадратна удаљеност од кластера се минимизира.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/centroid.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Инфографик од Дасани Мадипалли</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "-   **Кластеризација заснована на дистрибуцији**. Заснована на статистичком моделовању, кластеризација заснована на дистрибуцији се фокусира на одређивање вероватноће да тачка података припада кластеру и њено додељивање у складу с тим. Методи Гауссове мешавине припадају овом типу.\n",
+    "\n",
+    "-   **Кластеризација заснована на густини**. Тачке података се додељују кластерима на основу њихове густине, или њиховог груписања једна око друге. Тачке података далеко од групе се сматрају изузецима или шумом. ДБСЦАН, Mean-shift и OPTICS припадају овом типу кластеризације.\n",
+    "\n",
+    "-   **Кластеризација заснована на мрежи**. За мултидимензионалне скупове података, креира се мрежа и подаци се деле међу ћелијама мреже, чиме се стварају кластери.\n",
+    "\n",
+    "Најбољи начин да научите о кластеризацији је да је сами испробате, па ћете то урадити у овом задатку.\n",
+    "\n",
+    "Биће нам потребни неки пакети за завршетак овог модула. Можете их инсталирати као: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`\n",
+    "\n",
+    "Алтернативно, скрипта испод проверава да ли имате потребне пакете за завршетак овог модула и инсталира их за вас у случају да неки недостају.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Вежба - кластерисање ваших података\n",
+    "\n",
+    "Кластерисање као техника је знатно олакшано правилном визуализацијом, па хајде да почнемо са визуализацијом наших музичких података. Ова вежба ће нам помоћи да одлучимо који метод кластерисања би био најделотворнији за природу ових података.\n",
+    "\n",
+    "Хајде да одмах кренемо тако што ћемо увезти податке.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# Import the data into a tibble\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows of the data set\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ponekad želimo da saznamo malo više informacija o našim podacima. Možemo pogledati `podatke` i `njihovu strukturu` koristeći funkciju [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Glimpse into the data set\r\n",
+    "df %>% \r\n",
+    "  glimpse()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Браво!💪\n",
+    "\n",
+    "Можемо приметити да `glimpse()` приказује укупан број редова (посматрања) и колона (променљивих), затим првих неколико уноса сваке променљиве у реду након имена променљиве. Поред тога, *тип података* променљиве је наведен одмах након имена променљиве унутар `< >`.\n",
+    "\n",
+    "`DataExplorer::introduce()` може сажети ове информације на уредан начин:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe basic information for our data\r\n",
+    "df %>% \r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# A visual display of the same\r\n",
+    "df %>% \r\n",
+    "  plot_intro()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сјајно! Управо смо сазнали да наши подаци немају недостајуће вредности.\n",
+    "\n",
+    "Док смо ту, можемо истражити уобичајене статистике централне тенденције (нпр. [аритметичка средина](https://en.wikipedia.org/wiki/Arithmetic_mean) и [медијана](https://en.wikipedia.org/wiki/Median)) и мере распршености (нпр. [стандардна девијација](https://en.wikipedia.org/wiki/Standard_deviation)) користећи `summarytools::descr()`.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe common statistics\r\n",
+    "df %>% \r\n",
+    "  descr(stats = \"common\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Хајде да погледамо опште вредности података. Имајте на уму да популарност може бити `0`, што указује на песме које немају ранг. Ускоро ћемо их уклонити.\n",
+    "\n",
+    "> 🤔 Ако радимо са кластерисањем, ненадзираним методом која не захтева означене податке, зашто приказујемо ове податке са ознакама? У фази истраживања података, оне су корисне, али нису неопходне за рад алгоритама кластерисања.\n",
+    "\n",
+    "### 1. Истражите популарне жанрове\n",
+    "\n",
+    "Хајде да сазнамо који су најпопуларнији жанрови 🎶 тако што ћемо пребројати колико се пута појављују.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Popular genres\r\n",
+    "top_genres <- df %>% \r\n",
+    "  count(artist_top_genre, sort = TRUE) %>% \r\n",
+    "# Encode to categorical and reorder the according to count\r\n",
+    "  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())\r\n",
+    "\r\n",
+    "# Print the top genres\r\n",
+    "top_genres\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "То је прошло добро! Кажу да слика вреди хиљаду редова података у оквиру (заправо нико то никада не каже 😅). Али разумеш суштину, зар не?\n",
+    "\n",
+    "Један од начина да визуализујеш категоријске податке (карактери или факторске променљиве) је коришћењем стубичастих графикона. Хајде да направимо стубичасти графикон за топ 10 жанрова:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Change the default gray theme\r\n",
+    "theme_set(theme_light())\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Сада је много лакше уочити да имамо `недостајуће` жанрове 🧐!\n",
+    "\n",
+    "> Добра визуализација ће вам показати ствари које нисте очекивали, или ће покренути нова питања о подацима - Хадли Викхем и Гарет Гролемунд, [R For Data Science](https://r4ds.had.co.nz/introduction.html)\n",
+    "\n",
+    "Имајте на уму, када је главни жанр описан као `Недостаје`, то значи да га Spotify није класификовао, па хајде да га уклонимо.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  filter(artist_top_genre != \"Missing\") %>% \r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Из малог истраживања података, сазнајемо да три најпопуларнија жанра доминирају овим скупом података. Хајде да се усредсредимо на `afro dancehall`, `afropop` и `nigerian pop`, и додатно филтрирамо скуп података како бисмо уклонили све што има вредност популарности 0 (што значи да није класификовано са популарношћу у скупу података и може се сматрати шумом за наше потребе):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "nigerian_songs <- df %>% \r\n",
+    "  # Concentrate on top 3 genres\r\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \r\n",
+    "  # Remove unclassified observations\r\n",
+    "  filter(popularity != 0)\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "nigerian_songs %>%\r\n",
+    "  count(artist_top_genre) %>%\r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Хајде да видимо да ли постоји очигледна линеарна веза између нумеричких променљивих у нашем скупу података. Ова веза се математички квантитативно изражава помоћу [статистике корелације](https://en.wikipedia.org/wiki/Correlation).\n",
+    "\n",
+    "Статистика корелације је вредност између -1 и 1 која указује на јачину везе. Вредности изнад 0 указују на *позитивну* корелацију (високе вредности једне променљиве обично се поклапају са високим вредностима друге), док вредности испод 0 указују на *негативну* корелацију (високе вредности једне променљиве обично се поклапају са ниским вредностима друге).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Narrow down to numeric variables and fid correlation\r\n",
+    "corr_mat <- nigerian_songs %>% \r\n",
+    "  select(where(is.numeric)) %>% \r\n",
+    "  cor()\r\n",
+    "\r\n",
+    "# Visualize correlation matrix\r\n",
+    "corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  \r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Подаци нису јако повезани, осим између `energy` и `loudness`, што има смисла, с обзиром на то да је гласна музика обично прилично енергична. `Popularity` има везу са `release date`, што такође има смисла, јер су новије песме вероватно популарније. Дужина и енергија такође изгледају као да имају корелацију.\n",
+    "\n",
+    "Биће занимљиво видети шта алгоритам за кластеризацију може да уради са овим подацима!\n",
+    "\n",
+    "> 🎓 Имајте на уму да корелација не подразумева узрочност! Имамо доказ корелације, али немамо доказ узрочности. Један [забаван веб сајт](https://tylervigen.com/spurious-correlations) има неке визуализације које наглашавају ову тачку.\n",
+    "\n",
+    "### 2. Истражите расподелу података\n",
+    "\n",
+    "Хајде да поставимо нека суптилнија питања. Да ли се жанрови значајно разликују у перцепцији њихове плесности, на основу њихове популарности? Хајде да испитамо расподелу података за наша три најпопуларнија жанра у погледу популарности и плесности дуж задате x и y осе користећи [графиконе густине](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Perform 2D kernel density estimation\r\n",
+    "density_estimate_2d <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +\r\n",
+    "  geom_density_2d(bins = 5, size = 1) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  xlim(-20, 80) +\r\n",
+    "  ylim(0, 1.2)\r\n",
+    "\r\n",
+    "# Density plot based on the popularity\r\n",
+    "density_estimate_pop <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  theme(legend.position = \"none\")\r\n",
+    "\r\n",
+    "# Density plot based on the danceability\r\n",
+    "density_estimate_dance <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Patch everything together\r\n",
+    "library(patchwork)\r\n",
+    "density_estimate_2d / (density_estimate_pop + density_estimate_dance)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Видимо да постоје концентрични кругови који се поклапају, без обзира на жанр. Да ли је могуће да се укуси у Нигерији конвергирају на одређеном нивоу плесности за овај жанр?\n",
+    "\n",
+    "Уопштено, три жанра се поклапају у смислу популарности и плесности. Одређивање кластера у овим лабаво поравнатим подацима биће изазов. Хајде да видимо да ли расејани дијаграм може да подржи ово.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# A scatter plot of popularity and danceability\r\n",
+    "scatter_plot <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +\r\n",
+    "  geom_point(size = 2, alpha = 0.8) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"futurevisions::mars\")\r\n",
+    "\r\n",
+    "# Add a touch of interactivity\r\n",
+    "ggplotly(scatter_plot)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Дијаграм расејања истих оса показује сличан образац конвергенције.\n",
+    "\n",
+    "Уопштено, за кластеризацију можете користити дијаграме расејања да бисте приказали кластере података, па је овладавање овом врстом визуализације веома корисно. У наредној лекцији, узет ћемо ове филтриране податке и користити k-means кластеризацију да бисмо открили групе у овим подацима које се на занимљив начин преклапају.\n",
+    "\n",
+    "## **🚀 Изазов**\n",
+    "\n",
+    "У припреми за наредну лекцију, направите графикон о различитим алгоритмима кластеризације које можете открити и користити у производном окружењу. Које врсте проблема кластеризација покушава да реши?\n",
+    "\n",
+    "## [**Квиз након предавања**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)\n",
+    "\n",
+    "## **Преглед и самостално учење**\n",
+    "\n",
+    "Пре него што примените алгоритме кластеризације, као што смо научили, добро је разумети природу вашег скупа података. Прочитајте више о овој теми [овде](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html).\n",
+    "\n",
+    "Продубите своје разумевање техника кластеризације:\n",
+    "\n",
+    "-   [Тренирање и евалуација модела кластеризације користећи Tidymodels и пријатеље](https://rpubs.com/eR_ic/clustering)\n",
+    "\n",
+    "-   Бредли Бемке и Брендон Гринвел, [*Практично машинско учење са R*](https://bradleyboehmke.github.io/HOML/)*.*\n",
+    "\n",
+    "## **Задатак**\n",
+    "\n",
+    "[Истражите друге визуализације за кластеризацију](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)\n",
+    "\n",
+    "## ХВАЛА:\n",
+    "\n",
+    "[Џен Лупер](https://www.twitter.com/jenlooper) за креирање оригиналне Python верзије овог модула ♥️\n",
+    "\n",
+    "[`Дасани Мадипали`](https://twitter.com/dasani_decoded) за креирање невероватних илустрација које чине концепте машинског учења интерпретативнијим и лакшим за разумевање.\n",
+    "\n",
+    "Срећно учење,\n",
+    "\n",
+    "[Ерик](https://twitter.com/ericntay), Златни амбасадор Microsoft Learn програма.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
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+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
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+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "99c36449cad3708a435f6798cfa39972",
+   "translation_date": "2025-09-06T14:13:36+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb",
+   "language_code": "sr"
+  }
+ },
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+ "nbformat_minor": 1
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\ No newline at end of file
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+++ b/translations/sr/5-Clustering/1-Visualize/solution/notebook.ipynb
@@ -0,0 +1,882 @@
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+  {
+   "cell_type": "markdown",
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+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: seaborn in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (0.11.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (3.5.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.21.4)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.3.4)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.7.2)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (4.28.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (21.2)\n",
+      "Requirement already satisfied: setuptools-scm>=4 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (6.3.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
+      "Requirement already satisfied: tomli>=1.0.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (1.2.2)\n",
+      "Requirement already satisfied: setuptools in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (59.1.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Добиј информације о датафрејму\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 530 entries, 0 to 529\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   name              530 non-null    object \n",
+      " 1   album             530 non-null    object \n",
+      " 2   artist            530 non-null    object \n",
+      " 3   artist_top_genre  530 non-null    object \n",
+      " 4   release_date      530 non-null    int64  \n",
+      " 5   length            530 non-null    int64  \n",
+      " 6   popularity        530 non-null    int64  \n",
+      " 7   danceability      530 non-null    float64\n",
+      " 8   acousticness      530 non-null    float64\n",
+      " 9   energy            530 non-null    float64\n",
+      " 10  instrumentalness  530 non-null    float64\n",
+      " 11  liveness          530 non-null    float64\n",
+      " 12  loudness          530 non-null    float64\n",
+      " 13  speechiness       530 non-null    float64\n",
+      " 14  tempo             530 non-null    float64\n",
+      " 15  time_signature    530 non-null    int64  \n",
+      "dtypes: float64(8), int64(4), object(4)\n",
+      "memory usage: 66.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name                0\n",
+       "album               0\n",
+       "artist              0\n",
+       "artist_top_genre    0\n",
+       "release_date        0\n",
+       "length              0\n",
+       "popularity          0\n",
+       "danceability        0\n",
+       "acousticness        0\n",
+       "energy              0\n",
+       "instrumentalness    0\n",
+       "liveness            0\n",
+       "loudness            0\n",
+       "speechiness         0\n",
+       "tempo               0\n",
+       "time_signature      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Погледајте опште вредности података. Имајте на уму да популарност може бити „0“ - и постоји много редова са том вредношћу\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2015.390566</td>\n",
+       "      <td>222298.169811</td>\n",
+       "      <td>17.507547</td>\n",
+       "      <td>0.741619</td>\n",
+       "      <td>0.265412</td>\n",
+       "      <td>0.760623</td>\n",
+       "      <td>0.016305</td>\n",
+       "      <td>0.147308</td>\n",
+       "      <td>-4.953011</td>\n",
+       "      <td>0.130748</td>\n",
+       "      <td>116.487864</td>\n",
+       "      <td>3.986792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.131688</td>\n",
+       "      <td>39696.822259</td>\n",
+       "      <td>18.992212</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.208342</td>\n",
+       "      <td>0.148533</td>\n",
+       "      <td>0.090321</td>\n",
+       "      <td>0.123588</td>\n",
+       "      <td>2.464186</td>\n",
+       "      <td>0.092939</td>\n",
+       "      <td>23.518601</td>\n",
+       "      <td>0.333701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>89488.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>0.111000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028300</td>\n",
+       "      <td>-19.362000</td>\n",
+       "      <td>0.027800</td>\n",
+       "      <td>61.695000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>199305.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681000</td>\n",
+       "      <td>0.089525</td>\n",
+       "      <td>0.669000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.075650</td>\n",
+       "      <td>-6.298750</td>\n",
+       "      <td>0.059100</td>\n",
+       "      <td>102.961250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>218509.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.761000</td>\n",
+       "      <td>0.220500</td>\n",
+       "      <td>0.784500</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.103500</td>\n",
+       "      <td>-4.558500</td>\n",
+       "      <td>0.097950</td>\n",
+       "      <td>112.714500</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>242098.500000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.829500</td>\n",
+       "      <td>0.403000</td>\n",
+       "      <td>0.875750</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.164000</td>\n",
+       "      <td>-3.331000</td>\n",
+       "      <td>0.177000</td>\n",
+       "      <td>125.039250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>511738.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>0.966000</td>\n",
+       "      <td>0.954000</td>\n",
+       "      <td>0.995000</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.811000</td>\n",
+       "      <td>0.582000</td>\n",
+       "      <td>0.514000</td>\n",
+       "      <td>206.007000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       release_date         length  popularity  danceability  acousticness  \\\n",
+       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
+       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
+       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
+       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
+       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
+       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
+       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
+       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
+       "\n",
+       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
+       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
+       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
+       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
+       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
+       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
+       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
+       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
+       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
+       "\n",
+       "            tempo  time_signature  \n",
+       "count  530.000000      530.000000  \n",
+       "mean   116.487864        3.986792  \n",
+       "std     23.518601        0.333701  \n",
+       "min     61.695000        3.000000  \n",
+       "25%    102.961250        4.000000  \n",
+       "50%    112.714500        4.000000  \n",
+       "75%    125.039250        4.000000  \n",
+       "max    206.007000        5.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Хајде да испитамо жанрове. Прилично много њих је наведено као „Недостаје“, што значи да нису категорисани у скупу података са жанром.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Увод\n",
+    "\n",
+    "Овај документ објашњава како да анализирате жанрове музике користећи податке са Spotify-а. Циљ је да се идентификују жанрови који су најзаступљенији у одређеном скупу песама.\n",
+    "\n",
+    "## Захтеви\n",
+    "\n",
+    "Пре него што започнете, уверите се да имате следеће:\n",
+    "\n",
+    "- Активан Spotify API кључ\n",
+    "- Основно знање о Python-у и библиотекама као што су pandas и requests\n",
+    "\n",
+    "## Припрема података\n",
+    "\n",
+    "1. Преузмите податке о песмама користећи Spotify API.\n",
+    "2. Уверите се да су подаци форматирани у табелу која садржи следеће колоне:\n",
+    "   - Назив песме\n",
+    "   - Извођач\n",
+    "   - Жанр\n",
+    "   - Популарност\n",
+    "\n",
+    "[!NOTE] Ако колона \"Жанр\" садржи вредност \"Missing\", уклоните те редове, јер Spotify не класификује те жанрове.\n",
+    "\n",
+    "## Анализа жанрова\n",
+    "\n",
+    "Када су подаци припремљени, можете започети анализу. Ево корака:\n",
+    "\n",
+    "1. Групишите песме по жанру.\n",
+    "2. Израчунајте просечну популарност за сваки жанр.\n",
+    "3. Сортирајте жанрове по просечној популарности.\n",
+    "\n",
+    "[!TIP] Користите pandas функцију `groupby` за груписање података.\n",
+    "\n",
+    "## Пример кода\n",
+    "\n",
+    "Ево примера Python кода за анализу жанрова:\n",
+    "\n",
+    "```python\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Учитавање података\n",
+    "data = pd.read_csv('spotify_data.csv')\n",
+    "\n",
+    "# Филтрирање жанрова\n",
+    "filtered_data = data[data['genre'] != 'Missing']\n",
+    "\n",
+    "# Груписање и анализа\n",
+    "genre_popularity = filtered_data.groupby('genre')['popularity'].mean().sort_values(ascending=False)\n",
+    "\n",
+    "print(genre_popularity)\n",
+    "```\n",
+    "\n",
+    "[!WARNING] Уверите се да сте правилно конфигурисали API кључ пре покретања кода.\n",
+    "\n",
+    "## Резултати\n",
+    "\n",
+    "Након анализе, добићете листу жанрова сортираних по њиховој просечној популарности. Ово вам може помоћи да разумете који жанрови су најпопуларнији у вашем скупу података.\n",
+    "\n",
+    "[!IMPORTANT] Ова анализа је заснована на подацима са Spotify-а и може се разликовати у зависности од региона и временског периода.\n",
+    "\n",
+    "## Закључак\n",
+    "\n",
+    "Анализа жанрова је користан начин да се стекне увид у музичке трендове. Користећи Spotify API и Python, можете лако да обрадите и анализирате податке. Уклоните \"Missing\" жанрове како бисте осигурали тачност резултата.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[df['artist_top_genre'] != 'Missing']\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corrmat = df.corr()\n",
+    "f, ax = plt.subplots(figsize=(12, 9))\n",
+    "sns.heatmap(corrmat, vmax=.8, square=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "\n",
+    "# Show the joint distribution using kernel density estimation\n",
+    "g = sns.jointplot(\n",
+    "    data=df,\n",
+    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
+    "    kind=\"kde\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Уопштено, три жанра се поклапају у погледу њихове популарности и плесности. Дијаграм распршивања истих оса показује сличан образац конвергенције. Пробајте дијаграм распршивања да проверите расподелу података по жанру.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1350b1a90>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 468.975x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
+    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
+    "   .add_legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "c61deff2839902ac8cb4ed411eb10fee",
+   "translation_date": "2025-09-06T14:09:08+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sr/5-Clustering/2-K-Means/notebook.ipynb b/translations/sr/5-Clustering/2-K-Means/notebook.ipynb
new file mode 100644
index 000000000..d05924372
--- /dev/null
+++ b/translations/sr/5-Clustering/2-K-Means/notebook.ipynb
@@ -0,0 +1,231 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "3e5c8ab363e8d88f566d4365efc7e0bd",
+   "translation_date": "2025-09-06T14:19:34+00:00",
+   "source_file": "5-Clustering/2-K-Means/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Почните там где смо завршили у претходној лекцији, са увезеним и филтрираним подацима.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Фокусираћемо се само на 3 жанра. Можда можемо направити 3 кластера!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/translations/sr/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
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--- /dev/null
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@@ -0,0 +1,642 @@
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+   "display_name": "R",
+   "language": "R",
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+   "file_extension": ".r",
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+   "source_file": "5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GULATlQXLXyR"
+   },
+   "source": [
+    "## Истражите K-Means кластерисање користећи R и принципе уређених података.\n",
+    "\n",
+    "### [**Квиз пре предавања**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)\n",
+    "\n",
+    "У овом часу ћете научити како да креирате кластере користећи пакет Tidymodels и друге пакете из R екосистема (назваћемо их пријатељи 🧑‍🤝‍🧑), као и нигеријски музички скуп података који сте раније увезли. Покрићемо основе K-Means кластерисања. Имајте на уму да, као што сте научили у претходном часу, постоји много начина за рад са кластерима, а метод који користите зависи од ваших података. Пробаћемо K-Means јер је то најчешћа техника кластерисања. Хајде да почнемо!\n",
+    "\n",
+    "Термини које ћете научити:\n",
+    "\n",
+    "-   Силуетно оцењивање\n",
+    "\n",
+    "-   Метода лакта\n",
+    "\n",
+    "-   Инерција\n",
+    "\n",
+    "-   Варијанса\n",
+    "\n",
+    "### **Увод**\n",
+    "\n",
+    "[K-Means кластерисање](https://wikipedia.org/wiki/K-means_clustering) је метода која потиче из области обраде сигнала. Користи се за поделу и груписање података у `k кластера` на основу сличности њихових карактеристика.\n",
+    "\n",
+    "Кластери се могу визуализовати као [Воронојеви дијаграми](https://wikipedia.org/wiki/Voronoi_diagram), који укључују тачку (или 'семе') и њен одговарајући регион.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/voronoi.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Инфографика од Џен Лупер</figcaption>\n",
+    "\n",
+    "\n",
+    "K-Means кластерисање има следеће кораке:\n",
+    "\n",
+    "1.  Научник за податке започиње одређивањем жељеног броја кластера који ће бити креирани.\n",
+    "\n",
+    "2.  Затим алгоритам насумично бира K опсервација из скупа података које ће служити као почетни центри за кластере (тј. центроиде).\n",
+    "\n",
+    "3.  Затим се свака од преосталих опсервација додељује најближем центроиду.\n",
+    "\n",
+    "4.  Затим се рачуна нова средња вредност сваког кластера и центроид се помера на ту средњу вредност.\n",
+    "\n",
+    "5.  Сада када су центри поново израчунати, свака опсервација се поново проверава да ли би могла бити ближа другом кластеру. Сви објекти се поново додељују користећи ажуриране средње вредности кластера. Кораци доделе кластера и ажурирања центроида се итеративно понављају док се доделе кластера не престану мењати (тј. када се постигне конвергенција). Типично, алгоритам се завршава када свака нова итерација резултира занемарљивим померањем центроида и кластери постану статични.\n",
+    "\n",
+    "<div>\n",
+    "\n",
+    "> Имајте на уму да због насумичности почетних K опсервација које се користе као почетни центроиди, можемо добити благо различите резултате сваки пут када применимо процедуру. Из тог разлога, већина алгоритама користи неколико *насумичних почетака* и бира итерацију са најнижом WCSS. Због тога се снажно препоручује да увек покрећете K-Means са неколико вредности *nstart* како бисте избегли *непожељан локални оптимум.*\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Ова кратка анимација користећи [уметничке радове](https://github.com/allisonhorst/stats-illustrations) Алисон Хорст објашњава процес кластерисања:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/kmeans.gif\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Уметнички рад од @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Основно питање које се јавља у кластерисању је следеће: како знате на колико кластера да поделите своје податке? Један недостатак коришћења K-Means укључује чињеницу да ћете морати да одредите `k`, односно број `центроида`. Срећом, `метода лакта` помаже да се процени добра почетна вредност за `k`. Ускоро ћете је испробати.\n",
+    "\n",
+    "### \n",
+    "\n",
+    "**Предуслов**\n",
+    "\n",
+    "Наставићемо тачно тамо где смо стали у [претходном часу](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb), где смо анализирали скуп података, направили много визуализација и филтрирали скуп података на опсервације од интереса. Обавезно погледајте!\n",
+    "\n",
+    "Биће нам потребни неки пакети да завршимо овај модул. Можете их инсталирати као: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`\n",
+    "\n",
+    "Алтернативно, скрипта испод проверава да ли имате пакете потребне за завршетак овог модула и инсталира их за вас у случају да неки недостају.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ah_tBi58LXyi"
+   },
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7e--UCUTLXym"
+   },
+   "source": [
+    "Хајде да кренемо одмах!\n",
+    "\n",
+    "## 1. Плес са подацима: Сузимо избор на 3 најпопуларнија музичка жанра\n",
+    "\n",
+    "Ово је преглед онога што смо радили у претходној лекцији. Хајде да мало анализирамо податке!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Ycamx7GGLXyn"
+   },
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data into a tibble\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\", show_col_types = FALSE)\n",
+    "\n",
+    "# Narrow down to top 3 popular genres\n",
+    "nigerian_songs <- df %>% \n",
+    "  # Concentrate on top 3 genres\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \n",
+    "  # Remove unclassified observations\n",
+    "  filter(popularity != 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Visualize popular genres using bar plots\n",
+    "theme_set(theme_light())\n",
+    "nigerian_songs %>%\n",
+    "  count(artist_top_genre) %>%\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\n",
+    "                       fill = artist_top_genre)) +\n",
+    "  geom_col(alpha = 0.8) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\n",
+    "  ggtitle(\"Top genres\") +\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b5h5zmkPLXyp"
+   },
+   "source": [
+    "🤩 То је прошло добро!\n",
+    "\n",
+    "## 2. Више истраживања података.\n",
+    "\n",
+    "Колико су ови подаци чисти? Хајде да проверимо постојање екстремних вредности користећи box plot графиконе. Фокусираћемо се на нумеричке колоне са мање екстремних вредности (иако можете уклонити екстремне вредности). Box plot графикони могу показати опсег података и помоћи у избору колона које ћемо користити. Напомена: Box plot графикони не приказују варијансу, што је важан елемент за добро кластерисане податке. Молимо вас да погледате [ову дискусију](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot) за више информација.\n",
+    "\n",
+    "[Box plot графикони](https://en.wikipedia.org/wiki/Box_plot) се користе за графичко приказивање расподеле `нумеричких` података, па хајде да почнемо са *избором* свих нумеричких колона заједно са популарним музичким жанровима.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HhNreJKLLXyq"
+   },
+   "source": [
+    "# Select top genre column and all other numeric columns\n",
+    "df_numeric <- nigerian_songs %>% \n",
+    "  select(artist_top_genre, where(is.numeric)) \n",
+    "\n",
+    "# Display the data\n",
+    "df_numeric %>% \n",
+    "  slice_head(n = 5)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uYXrwJRaLXyq"
+   },
+   "source": [
+    "Погледајте како селектор `where` олакшава овај процес 💁? Истражите и друге сличне функције [овде](https://tidyselect.r-lib.org/).\n",
+    "\n",
+    "Пошто ћемо правити boxplot за сваку нумеричку карактеристику и желимо да избегнемо коришћење петљи, хајде да преобликујемо наше податке у *дужи* формат који ће нам омогућити да искористимо `facets` - подграфиконе који приказују појединачне подскупове података.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "gd5bR3f8LXys"
+   },
+   "source": [
+    "# Pivot data from wide to long\n",
+    "df_numeric_long <- df_numeric %>% \n",
+    "  pivot_longer(!artist_top_genre, names_to = \"feature_names\", values_to = \"values\") \n",
+    "\n",
+    "# Print out data\n",
+    "df_numeric_long %>% \n",
+    "  slice_head(n = 15)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-7tE1swnLXyv"
+   },
+   "source": [
+    "Сада много дужи! Време је за мало `ggplots`! Па који `geom` ћемо користити?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "r88bIsyuLXyy"
+   },
+   "source": [
+    "# Make a box plot\n",
+    "df_numeric_long %>% \n",
+    "  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_wrap(~ feature_names, ncol = 4, scales = \"free\") +\n",
+    "  theme(legend.position = \"none\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EYVyKIUELXyz"
+   },
+   "source": [
+    "Лако-gg!\n",
+    "\n",
+    "Сада можемо видети да су подаци мало \"бучни\": посматрајући сваку колону као boxplot, можете уочити екстремне вредности. Могли бисте проћи кроз скуп података и уклонити те екстремне вредности, али то би учинило податке прилично минималним.\n",
+    "\n",
+    "За сада, хајде да изаберемо које ћемо колоне користити за наш кластеринг задатак. Изабраћемо нумеричке колоне са сличним опсезима. Могли бисмо да енкодирамо `artist_top_genre` као нумеричку вредност, али ћемо је за сада изоставити.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-wkpINyZLXy0"
+   },
+   "source": [
+    "# Select variables with similar ranges\n",
+    "df_numeric_select <- df_numeric %>% \n",
+    "  select(popularity, danceability, acousticness, loudness, energy) \n",
+    "\n",
+    "# Normalize data\n",
+    "# df_numeric_select <- scale(df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D7dLzgpqLXy1"
+   },
+   "source": [
+    "## 3. Израчунавање k-means кластеризације у R-у\n",
+    "\n",
+    "Можемо израчунати k-means кластеризацију у R-у помоћу уграђене функције `kmeans`, погледајте `help(\"kmeans()\")`. Функција `kmeans()` прихвата податке у облику табеле са свим нумеричким колонама као свој примарни аргумент.\n",
+    "\n",
+    "Први корак при коришћењу k-means кластеризације је да се одреди број кластера (k) који ће бити генерисани у коначном решењу. Знамо да постоје 3 жанра песама које смо издвојили из скупа података, па хајде да пробамо са 3:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "uC4EQ5w7LXy5"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Kmeans clustering for 3 clusters\n",
+    "kclust <- kmeans(\n",
+    "  df_numeric_select,\n",
+    "  # Specify the number of clusters\n",
+    "  centers = 3,\n",
+    "  # How many random initial configurations\n",
+    "  nstart = 25\n",
+    ")\n",
+    "\n",
+    "# Display clustering object\n",
+    "kclust\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hzfhscWrLXy-"
+   },
+   "source": [
+    "Објекат kmeans садржи неколико информација које су добро објашњене у `help(\"kmeans()\")`. За сада, хајде да се фокусирамо на неколико њих. Видимо да су подаци груписани у 3 кластера величина 65, 110, 111. Излаз такође садржи центаре кластера (средине) за 3 групе кроз 5 променљивих.\n",
+    "\n",
+    "Вектор кластерисања представља доделу кластера за сваку опсервацију. Хајде да користимо функцију `augment` како бисмо додали доделу кластера оригиналном скупу података.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "0XwwpFGQLXy_"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "augment(kclust, df_numeric_select) %>% \n",
+    "  relocate(.cluster) %>% \n",
+    "  slice_head(n = 10)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NXIVXXACLXzA"
+   },
+   "source": [
+    "Одлично, управо смо поделили наш скуп података у 3 групе. Па, колико је добро наше кластерисање 🤷? Хајде да погледамо `Silhouette score`.\n",
+    "\n",
+    "### **Silhouette score**\n",
+    "\n",
+    "[Silhouette анализа](https://en.wikipedia.org/wiki/Silhouette_(clustering)) може се користити за проучавање удаљености раздвајања између добијених кластера. Овај скор варира од -1 до 1, и ако је скор близу 1, кластер је густ и добро одвојен од других кластера. Вредност близу 0 представља преклапајуће кластере са узорцима који су веома близу граници одлуке суседних кластера. [извор](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).\n",
+    "\n",
+    "Метод просечног silhouette-а израчунава просечан silhouette за различите вредности *k*. Висок просечан silhouette скор указује на добро кластерисање.\n",
+    "\n",
+    "Функција `silhouette` у пакету за кластерисање користи се за израчунавање просечне ширине silhouette-а.\n",
+    "\n",
+    "> Silhouette се може израчунати помоћу било које [метрике удаљености](https://en.wikipedia.org/wiki/Distance \"Distance\"), као што су [Еуклидска удаљеност](https://en.wikipedia.org/wiki/Euclidean_distance \"Euclidean distance\") или [Манхатен удаљеност](https://en.wikipedia.org/wiki/Manhattan_distance \"Manhattan distance\") коју смо разматрали у [претходном часу](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Jn0McL28LXzB"
+   },
+   "source": [
+    "# Load cluster package\n",
+    "library(cluster)\n",
+    "\n",
+    "# Compute average silhouette score\n",
+    "ss <- silhouette(kclust$cluster,\n",
+    "                 # Compute euclidean distance\n",
+    "                 dist = dist(df_numeric_select))\n",
+    "mean(ss[, 3])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QyQRn97nLXzC"
+   },
+   "source": [
+    "Наш резултат је **0.549**, што је тачно на средини. Ово указује да наши подаци нису нарочито погодни за ову врсту кластерисања. Хајде да видимо да ли можемо визуелно да потврдимо ову претпоставку. [factoextra пакет](https://rpkgs.datanovia.com/factoextra/index.html) пружа функције (`fviz_cluster()`) за визуелизацију кластерисања.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "7a6Km1_FLXzD"
+   },
+   "source": [
+    "library(factoextra)\n",
+    "\n",
+    "# Visualize clustering results\n",
+    "fviz_cluster(kclust, df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IBwCWt-0LXzD"
+   },
+   "source": [
+    "Преклапање у кластерима указује на то да наши подаци нису нарочито погодни за ову врсту кластерисања, али хајде да наставимо.\n",
+    "\n",
+    "## 4. Одређивање оптималног броја кластера\n",
+    "\n",
+    "Основно питање које се често јавља код K-Means кластерисања је следеће - без познатих ознака класа, како знати на колико кластера треба поделити податке?\n",
+    "\n",
+    "Један од начина да то сазнамо је да користимо узорак података за `креирање серије модела кластерисања` са повећавајућим бројем кластера (нпр. од 1 до 10), и да проценимо метрике кластерисања као што је **Silhouette score.**\n",
+    "\n",
+    "Хајде да одредимо оптималан број кластера тако што ћемо израчунати алгоритам кластерисања за различите вредности *k* и проценити **Within Cluster Sum of Squares** (WCSS). Укупна сума квадрата унутар кластера (WCSS) мери компактност кластерисања, и желимо да она буде што мања, јер ниже вредности значе да су тачке података ближе једна другој.\n",
+    "\n",
+    "Хајде да истражимо ефекат различитих избора `k`, од 1 до 10, на ово кластерисање.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "hSeIiylDLXzE"
+   },
+   "source": [
+    "# Create a series of clustering models\n",
+    "kclusts <- tibble(k = 1:10) %>% \n",
+    "  # Perform kmeans clustering for 1,2,3 ... ,10 clusters\n",
+    "  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),\n",
+    "  # Farm out clustering metrics eg WCSS\n",
+    "         glanced = map(model, ~ glance(.x))) %>% \n",
+    "  unnest(cols = glanced)\n",
+    "  \n",
+    "\n",
+    "# View clustering rsulsts\n",
+    "kclusts\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m7rS2U1eLXzE"
+   },
+   "source": [
+    "Сада када имамо укупну суму квадрата унутар кластера (tot.withinss) за сваки алгоритам кластерисања са центром *k*, користимо [метод лакта](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) да бисмо пронашли оптималан број кластера. Овај метод се састоји од графичког приказивања WCSS као функције броја кластера и одабирања [лакта криве](https://en.wikipedia.org/wiki/Elbow_of_the_curve \"Elbow of the curve\") као броја кластера који ће се користити.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "o_DjHGItLXzF"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Use elbow method to determine optimum number of clusters\n",
+    "kclusts %>% \n",
+    "  ggplot(mapping = aes(x = k, y = tot.withinss)) +\n",
+    "  geom_line(size = 1.2, alpha = 0.8, color = \"#FF7F0EFF\") +\n",
+    "  geom_point(size = 2, color = \"#FF7F0EFF\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLYyt5XSLXzG"
+   },
+   "source": [
+    "График приказује значајно смањење WCSS (што значи већу *збијеност*) како се број кластера повећава са један на два, и још једно приметно смањење са два на три кластера. Након тога, смањење је мање изражено, што резултира `елбоу` 💪 на графику око три кластера. Ово је добар показатељ да постоје два до три разумно добро одвојена кластера тачака података.\n",
+    "\n",
+    "Сада можемо наставити и издвојити модел кластерисања где је `k = 3`:\n",
+    "\n",
+    "> `pull()`: користи се за издвајање једне колоне\n",
+    ">\n",
+    "> `pluck()`: користи се за индексирање структура података као што су листе\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JP_JPKBILXzG"
+   },
+   "source": [
+    "# Extract k = 3 clustering\n",
+    "final_kmeans <- kclusts %>% \n",
+    "  filter(k == 3) %>% \n",
+    "  pull(model) %>% \n",
+    "  pluck(1)\n",
+    "\n",
+    "\n",
+    "final_kmeans\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l_PDTu8tLXzI"
+   },
+   "source": [
+    "Одлично! Хајде да визуализујемо добијене кластере. Желите ли мало интерактивности уз помоћ `plotly`?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dNcleFe-LXzJ"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "results <-  augment(final_kmeans, df_numeric_select) %>% \n",
+    "  bind_cols(df_numeric %>% select(artist_top_genre)) \n",
+    "\n",
+    "# Plot cluster assignments\n",
+    "clust_plt <- results %>% \n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +\n",
+    "  geom_point(size = 2, alpha = 0.8) +\n",
+    "  paletteer::scale_color_paletteer_d(\"ggthemes::Tableau_10\")\n",
+    "\n",
+    "ggplotly(clust_plt)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6JUM_51VLXzK"
+   },
+   "source": [
+    "Можда бисмо очекивали да сваки кластер (представљен различитим бојама) има јасно одвојене жанрове (представљене различитим облицима).\n",
+    "\n",
+    "Хајде да погледамо тачност модела.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HdIMUGq7LXzL"
+   },
+   "source": [
+    "# Assign genres to predefined integers\n",
+    "label_count <- results %>% \n",
+    "  group_by(artist_top_genre) %>% \n",
+    "  mutate(id = cur_group_id()) %>% \n",
+    "  ungroup() %>% \n",
+    "  summarise(correct_labels = sum(.cluster == id))\n",
+    "\n",
+    "\n",
+    "# Print results  \n",
+    "cat(\"Result:\", label_count$correct_labels, \"out of\", nrow(results), \"samples were correctly labeled.\")\n",
+    "\n",
+    "cat(\"\\nAccuracy score:\", label_count$correct_labels/nrow(results))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C50wvaAOLXzM"
+   },
+   "source": [
+    "Tačnost ovog modela nije loša, ali nije ni sjajna. Moguće je da podaci nisu pogodni za K-Means klasterizaciju. Ovi podaci su previše neuravnoteženi, slabo povezani i postoji prevelika varijansa između vrednosti kolona da bi se dobro grupisali. Zapravo, klasteri koji se formiraju verovatno su značajno pod uticajem ili iskrivljeni zbog tri kategorije žanrova koje smo gore definisali.\n",
+    "\n",
+    "Ipak, ovo je bio prilično poučan proces!\n",
+    "\n",
+    "U dokumentaciji Scikit-learn-a možete videti da model poput ovog, sa klasterima koji nisu dobro definisani, ima problem sa 'varijansom':\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/problems.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Infografika iz Scikit-learn-a</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "## **Varijansa**\n",
+    "\n",
+    "Varijansa se definiše kao \"prosek kvadrata razlika od srednje vrednosti\" [izvor](https://www.mathsisfun.com/data/standard-deviation.html). U kontekstu ovog problema klasterizacije, odnosi se na podatke kod kojih vrednosti našeg skupa podataka imaju tendenciju da se previše udalje od srednje vrednosti.\n",
+    "\n",
+    "✅ Ovo je odličan trenutak da razmislite o svim načinima na koje biste mogli da rešite ovaj problem. Da li da malo prilagodite podatke? Koristite različite kolone? Primenite drugačiji algoritam? Savet: Probajte [skaliranje podataka](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) kako biste ih normalizovali i testirali druge kolone.\n",
+    "\n",
+    "> Probajte ovaj '[kalkulator varijanse](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)' da biste bolje razumeli koncept.\n",
+    "\n",
+    "------------------------------------------------------------------------\n",
+    "\n",
+    "## **🚀Izazov**\n",
+    "\n",
+    "Provedite neko vreme sa ovim beležnicom, podešavajući parametre. Možete li poboljšati tačnost modela čišćenjem podataka (na primer, uklanjanjem ekstremnih vrednosti)? Možete koristiti težine kako biste određenim uzorcima podataka dali veću važnost. Šta još možete učiniti da kreirate bolje klastere?\n",
+    "\n",
+    "Savet: Probajte da skalirate podatke. U beležnici postoji komentarisani kod koji dodaje standardno skaliranje kako bi kolone podataka bile sličnije u smislu opsega. Videćete da, iako se silueta skora smanjuje, 'prelom' u grafiku lakta postaje glađi. To je zato što ostavljanje podataka neskaliranih omogućava podacima sa manjom varijansom da imaju veću težinu. Pročitajte više o ovom problemu [ovde](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
+    "\n",
+    "## [**Kviz nakon predavanja**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)\n",
+    "\n",
+    "## **Pregled i samostalno učenje**\n",
+    "\n",
+    "-   Pogledajte simulator za K-Means [kao što je ovaj](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). Možete koristiti ovaj alat za vizualizaciju uzoraka podataka i određivanje njihovih centara. Možete uređivati nasumičnost podataka, broj klastera i broj centara. Da li vam ovo pomaže da steknete ideju o tome kako se podaci mogu grupisati?\n",
+    "\n",
+    "-   Takođe, pogledajte [ovaj materijal o K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) sa Stanforda.\n",
+    "\n",
+    "Želite da testirate svoje novo stečene veštine klasterizacije na skupovima podataka koji su pogodni za K-Means klasterizaciju? Pogledajte:\n",
+    "\n",
+    "-   [Treniranje i evaluacija modela klasterizacije](https://rpubs.com/eR_ic/clustering) koristeći Tidymodels i slične alate\n",
+    "\n",
+    "-   [K-Means analiza klastera](https://uc-r.github.io/kmeans_clustering), UC Business Analytics R Programming Guide\n",
+    "\n",
+    "- [K-Means klasterizacija sa principima urednih podataka](https://www.tidymodels.org/learn/statistics/k-means/)\n",
+    "\n",
+    "## **Zadatak**\n",
+    "\n",
+    "[Probajte različite metode klasterizacije](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)\n",
+    "\n",
+    "## HVALA:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) za kreiranje originalne Python verzije ovog modula ♥️\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) za kreiranje neverovatnih ilustracija koje čine R pristupačnijim i zanimljivijim. Pronađite više ilustracija u njenoj [galeriji](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "Srećno učenje,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Ilustracija @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/5-Clustering/2-K-Means/solution/notebook.ipynb b/translations/sr/5-Clustering/2-K-Means/solution/notebook.ipynb
new file mode 100644
index 000000000..b90573479
--- /dev/null
+++ b/translations/sr/5-Clustering/2-K-Means/solution/notebook.ipynb
@@ -0,0 +1,548 @@
+{
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+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
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+  "orig_nbformat": 2,
+  "kernelspec": {
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+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
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+  },
+  "coopTranslator": {
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+   "translation_date": "2025-09-06T14:20:53+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Почните там где смо завршили у последњем часу, са увезеним и филтрираним подацима.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Фокусираћемо се само на 3 жанра. Можда можемо направити 3 кластера!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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3gAMmUpQkSdJU5M7rkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDUyc2VfmGQT4MsDTQ8F3g6sBbwEGOvb31pVJ610hZIkSVPESgerqroQ2BwgyQxgIXA88ELgw1X1gSYVSpIkTRGthgK3By6uqssaXU+SJGnKaRWs9gSOGXh+YJJzkhyVZO3xXpBkXpL5SeaPjY2Nd4okSdKUMuFgleQewC7AV/qmw4GH0Q0TLgI+ON7rquqIqppbVXNnzZo10TIkSZKGrkWP1dOBs6rqKoCquqqqbquq24EjgS0bvIckSdLIaxGs9mJgGDDJBgPHdgfOa/AekiRJI2+lVwUCJLk3sAPw0oHm9yXZHCjg0qWOSZIkTVsTClZV9Sfg/ku17TOhiiRJkqYod16XJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKmRmcMuQNJd95vDHj3sEjTNPOjt5w67BGlasMdKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWpk5kQvkORS4AbgNmBxVc1Nsg7wZWA2cCmwR1X9bqLvJUmSNMpa9Vg9uao2r6q5/fM3A6dU1RzglP65JEnStDZZQ4G7Akf3j48Gdpuk95EkSRoZLYJVAd9NcmaSeX3belW1qH98JbDe0i9KMi/J/CTzx8bGGpQhSZI0XBOeYwU8saoWJnkAcHKSXw4erKpKUku/qKqOAI4AmDt37h2OS5IkTTUT7rGqqoX996uB44EtgauSbADQf796ou8jSZI06iYUrJLcO8l9ljwGngacB5wI7Nufti9wwkTeR5IkaSqY6FDgesDxSZZc64tV9e0kZwDHJtkfuAzYY4LvI0mSNPImFKyq6hLgseO0XwtsP5FrS5IkTTXuvC5JktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIysdrJJsnOQHSS5Icn6SV/Xt70iyMMnZ/dfO7cqVJEkaXTMn8NrFwOuq6qwk9wHOTHJyf+zDVfWBiZcnSZI0dax0sKqqRcCi/vENSX4BbNiqMEmSpKmmyRyrJLOBxwE/7ZsOTHJOkqOSrL2M18xLMj/J/LGxsRZlSJIkDdWEg1WSNYHjgFdX1R+Aw4GHAZvT9Wh9cLzXVdURVTW3qubOmjVromVIkiQN3YSCVZLV6ULVF6rqawBVdVVV3VZVtwNHAltOvExJkqTRN5FVgQE+C/yiqj400L7BwGm7A+etfHmSJElTx0RWBW4N7AOcm+Tsvu2twF5JNgcKuBR46YQqlCRJmiImsirwx0DGOXTSypcjSZI0dbnzuiRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDUyke0WJEmaNFt/bOthl6Bp5rRXnjbp72GPlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDUyacEqyU5JLkyyIMmbJ+t9JEmSRsWkBKskM4BPAE8HNgP2SrLZZLyXJEnSqJisHqstgQVVdUlV3QJ8Cdh1kt5LkiRpJKSq2l80+Rdgp6p6cf98H2Crqjpw4Jx5wLz+6SbAhc0L0Z1ZF7hm2EVIk8yfc60K/Dm/+z24qmaNd2Dm3V3JElV1BHDEsN5/VZdkflXNHXYd0mTy51yrAn/OR8tkDQUuBDYeeL5R3yZJkjRtTVawOgOYk+QhSe4B7AmcOEnvJUmSNBImZSiwqhYnORD4DjADOKqqzp+M99JKcxhWqwJ/zrUq8Od8hEzK5HVJkqRVkTuvS5IkNWKwkiRJasRgpSaSzE1yn2HXIUnSMBms1MpLgO8ariRp6kmSYdcwXRisNCFJtgCoqpcCZwLHG640VYz3x8Q/MFrVJElVVZKtk+yfZPt+qyStBFcFakKSnA7cWFVP6Z8fDswBdq+qG4ZanLQCkmxDt6HxH4Bv9n9gVquq24dcmnS3SfJk4LPAl4FnAEcDX6+qBUMtbAqyx0oTUlWPB2Yk+Ub//OXARdhzpRG2pFcqyVzgKGBrYG/g60tClT1XWlUk2QR4GfDqqnoLsC/dB+QdhlrYFGWw0l028EdpJkBVbQvMWipc/RL4fpI1h1aotAx9r9T2wFuAF1fVK4D9gKuBjyw5Z3gVSpMvPWAb4GHAjknuXVVnAccA85KsPdQipyCDle6SJWPx/dMNk8yBv/Rc3T/JN/vnBwKnAusMp1JpudYCdgf+sX9+C/BpwLklmtYGemPXBWZW1ZHAu4DQ3YIO4Erghr5Nd4FzrLRSkrwO2Bm4J/D9qjq4bz8VoKq2GWJ50h0MTNBdD7ihqm5M8s/A14Gdq+rkJDsA76MbArnWXitNV0l2Bg4DFgJ/AvYHnk03DLga3S3v3l9V3xxakVPUpNwrUNNbkhcBu1TVtkk+Brw2yd9V1euqapsk30mycVVdPuxapSX6UPVM4JVAJTmNrodqN+A7SY6l+4R+WFVdM8RSpUmV5JHAO4EDgbOBLwL/r6r2TPJnYEfg3CWhaqmRCi2HQ4FarnEm8S4A9knySmBD4DHA3kk+BVBVOxqqNGqSPIyuN+oNwAfoQtShwLfohgSfCfxPVR2/ZP6gNE3dDFwAnFVVN1bVbsAGSQ6g68H9KfDYJHsaqu46f3louZb8o+onot9cVacmuR+wLfC+qrq4/7S/VZJ1quq6YdYrDRr4w7A2cFlV/W/f/htgK+CpVXVCkn2BY5P8uqp+OLyKpbYGhsFn0HWoXAdsAMwFftyf9iW6X/eLkxwN3Ar8wFB119ljpWVK8rAkm/WPXwt8nm45+gOq6nrg18Czk7yZrufq2YYqjYqBntZ79d/PAxYnORCgqi4ELgc2659/FfgXYNHdXKo0qfpQtStwLN0+VY8EPgF8LMmBSV5MNyy4oD//1qo6uqquGlrRU5iT1zWuJPcCPgZcRddlPA94Od2ta3YHtqALU7sBTwYOqqrzhlOtNL4kO9H9zF4CnA4U3Z5Va9J9Qv80sF9V/Y9DHpqukmwKfAb4d7qVgO8A9qHrldoR2Aj4alV9d1g1TicGKy1Tv5XCa4H7AudX1bv79g8DOwFPqqprktyzqv48xFKlO0jyeOC9dB8QHkO3jcKtdJ/aX0230/r3q+obQytSmmRJHgV8ELiwqg7q23YEPkf3O9yd1RtzKFB/Y3CielVdBLwbuB54TJLH9O2vAf4b+EE/Zn/LMGqVliXJhnQT1H/aD/G9D/gh3bySRVW1P/CGqvqGO6xrmvsV3Z5Uj0wyJ8kaVfUd4Dhg1nBLm54MVvqLwaGQJM9NshuwKV2v1fXA7gPhah7dpN/bvKeaRtBNdJNy90yyVVX9saq+DTyIrveKqlrcf7fbXtNSkhlVdQvwYrq5g68HdkmyLfAsYPEw65uuDFb6i4FQdSDdXj8A36D7Q/ReYH26bRb+vj929d1epDSOgdssPSrJdnRzqN5D11N1WJKn90PbGwO/H1qh0t2k/6B8W5KZVXUrXbhaDfhXulC1X1WdYY9tewYr/UWS1ZJsQDcZfXvgocApwM+r6hK6YcGZdBPa/aSvkdGvetoZOAF4Id1ePM+kG/47jW4DxE8AL6qqs/xjoulm4MPFnCTrL2nvt0+Y2fdcvQKYD/wdcJYLNiaHwWoVt9QfmBl0+5tcS7cr7zbAc6rq1iQv7895vbtSa9QkuTfdH419qmpfuo0/twXWo/tZPhj4I93PtzStDOxTtSNwIt0HiwOSPBz+JlzdSvfv5AF0NyB3L8tJYLBahS01p2pvYF5V3Uy3JP0gun2pbkzyPLr7SFVV3Ta8iqW/SrJa//0f6XaSvgbYBKCqTqDbt+oN/enH0n1SPyTJPe/+aqXJ04equXTDfc8EXgf8PbDbUuFqyZyr5wAf7IOWGjOtrsIGQtUBwIvo9jWhql6aZC3g1CQ/p9uder+qumJoxUq9JPeqqpuq6vYkTwQOp7tx7M+AjZPMrar5dCtXtwBmVNXVSY4AbndrEE03Se5DNwS+Rb99woL+g8dewHOTfKWqftXPuVqtD1e/HWbN05n7WK3ikqwNHAG8qaou6Zfi3twf24muJ+DSqvr1MOuU4C978vwH8Ay6rRMOp9vY8DNJHgocQLfIYjHwD8DBVXX8sOqVJsvS86OSbAJ8lG739Ff2Hzy2A54PvNvf4Xcfg9UqZrzJikm+Rrf673MDvVhbAedU1U1DKFO6gySr0wWpn9L9vD6NbthjbeAFVfXbJOvS7SK9KbCgquY7QVfTzcCcqh3othBJ/+HiEcCb6Ta/fW0frtauqt8NteBVjHOsViFLzama03/CAfgO8GDgn/pjzwXeRrdkXRolC+kmpX+Fbs7UYcDZwEFJ1q+qa6rq7Kr6Uj8c6OpVTSv9UF4l+We6HdWvoNtS5ANV9Su61dvrAx/vX3L9kEpdZdljtYpYKlS9lm5O1U3AfwH/RnfvqMfRDaE8DHheVZ07nGqlv7XUJ/T/BH5YVXv2x7amGxq8F92Qh/uradpJ8hBgtaq6uO+Z/U/gNcCSXqoNge9V1f79h+Y1quqc4VW86nLy+ipiIFQ9HngC8ERgDeAMYHFVHdzvYfVwuiGURUMrVhowEKoeSncLjmcBr07yTrqVTaf1E3V3oxsWNFhpOnoCcFGSK/p7tM6j+3k/lG4+4Wy6Ses3VdWBQ6xzlWewWoUkeSRwCF2v1GpVdVW/VP0nSR5YVa+gu+2BNDL6ULUL3bDfAuAS4NN0S8oPSvLRqvrvJOdWlbuqa1qqqi8kWRM4I8neVXVOkgcCZ/ZzqdYHPkQ3tUND5ByraWzp3aWr6hfAkXTBarsk61bVVcDWwBOTrOeO1Bo1fS/rwcCOwPF0Gxw+je4my9sCr+s3PzRUadoZ2FF9R+BRdEOAR/YrZC8F7pfkk3Q3VT6hqk729/hwOcdqmhpn88916O5y/h3g2XTDJl8HTu33+Jnh5p8aRUk2ottaYW26XdSfB3yKbhf1zwFjVXXG0AqUJlmSLYGPAK+pqtP7ebLPo/s9Dt39XP9UVT8aVo36K4cCp6mBUPUaYFe6VVRvotvs893AbcB+wK1JvgHcPpxKpTvXb0x7RZJ3AV+oqgVJPk93d4Dzq+qy4VYoTZ4kGwNvBM6tqtMBqupDfafUyXS3HTtpiCVqKQaraaa/fcE6VfWzfk7VFnQ3VX4D3f/f69NtpXAY3ZDgmVVlqNJUcC7w0n4/q2cBrzJUaRWwGDgH2DXJTlX1bfhLuJoBrDXU6nQHBqtppL+twf7A6kluBf6Xbhnu04Gdge3ptlk4iO7WHocOq1ZpJZxEt5J1F+BdVXXakOuRmh/RBFIAAAVUSURBVBtYBftPdKtgf0N3t4HfA7snubWqTgGoqvcPsVQtg5PXp4l+07gb6HamXgzsCTyiqhYC9wN+1t8f6hbgW3SrqqQpo6r+UFVHA8+tqv9ygq6moz5UPQ04ClgPOJNugdGJdD1X+/XHNaLssZomBobzdgQeC2wC3DPJZ4CfAJ/t96naDtihqq4cSqHSxN0G7qiu6affj20t4KXA7nSLji4Azuq3x/kKXa+t2+KMMFcFTiNJngR8DNgSeDywE7A63XyqNek2kbugqi4ZWpGSpDuV5E10Iw1PAZ7f77a+H3AqcKnzYkebQ4HTy5rAtVV1S1WdSrevyVPo7hm1TlV901AlSaMnyeZJDumf3hvYB9i7D1WPpVvV/UBD1ehzKHB6+RmwMMmewFeq6swkp9EF6KuGW5okadDARPUnAc8BdkxydVW9PcmmwCFJFgObA2+qqh8PtWCtEIPV9HI98GO6vaqelmQ+3T0Bn11V1wy1MkkS8NdA1YeqbYAvAAcCC4EnJ1mjqvZI8kS6jXE/3n9QjnMLR59zrKaYfvXfMruCk9wL2JRu4uOawGer6vy7qz5J0rL19/d7JPDDqrqtvzPGhlX13v5egJsD7wWOraqPDLNWrRyD1RTV/2N8EHADcMx4PVL9/dMW3+3FSZLGlWRX4CLgCrqtcbYHPky3WvvX/crAo+nmWX21qr44tGK1Upy8PgUleRHdxp8X093376Akj+6PZcn+PoYqSRotVXUCcCXwSbp7/X2X7t6XH+nnVT2G7t6YFwEbDqtOrTznWE0B44yrbwe8vqq+neRU4GC6DUHPdfxdkkbP4O/xqrouyY+Ap9Ft2nw8EOA/6Xqx9qe7HdkO/S2cFvu7fepwKHAKGFg58jLgDLpb1NwT+FD/D/QhdLv07l5Vvx9mrZKk8SXZFng08P2quiDJXnS/z79eVV9Lcu/+1C3p7o6xu3Nkpx6HAkdYkk3gL7c4eBawB/BbunC1Ft3Kv7WAR9F9yrllWLVKku5oydSMJFvRDf9tC7wxyUuq6hjgm8DeSfYA/kz3ofkJwK6GqqnJocARlWRH4PAkW9CNt78YOK+qFgGLkmwMbNO33wN4ZVXdOLSCJUl30H8w3hI4FNirqs7p9xp8Qh+ujkwyA7iwqm4Drk3y/v7erpqCDFYjKMlMuq7gg4HN6Jbf/gDYNckz+h3UP5PkfnR7nPypqsaGV7Ek6U6sBTwV2IHuRspfBW6nn0NVVZ+Ev9nfylA1hRmsRlBVLU5yMfA2uhvOPpmui/gmYJcki6vq21V1Pd2moJKkEVVV3+2nc/x7kt9W1TFJvgrMAP534DwnPU8DBqvRdQ5wI/AH4H5VdU2Sr9F9ytk3ya1VdcpQK5QkrZCqOrG/Pc2/JblHVR0NHDPsutSeqwJHxOBS3CT3AG7rd+V9Pd2NlA+pqjOSbES3iuSb/XwrSdIUkWQX4D10Q4NXelPl6cdgNQKWClUH0s2r+gPwjqr6c5K30t3/7z1V9ZMkM/pJjpKkKSbJLOfFTl8GqxGS5BXAc4HnAWcB3wPeXlUXJ3kn8HBgv6r68xDLlCRJy2CwGhFJ7gt8iG4l4HOAnYGr6bZaeHlVLUhy/6q6dohlSpKkO2GwGiFJ1gA2Bf6jqp7cbyw3RrcD7zuq6tahFihJku6UqwJHSFXdnORGYGZ/U+UHA6cAnzZUSZI0+uyxGjF9r9Wr6VaMPBB4TlVdMNyqJEnSijBYjaD+bubrA7dX1cJh1yNJklaMwUqSJKmR1YZdgCRJ0nRhsJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmN/B/Djeb5PsBsCgAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Колико су чисти ови подаци? Проверите за одступања користећи боксплотове. Фокусираћемо се на колоне са мање одступања (иако можете уклонити одступања). Боксплотови могу показати опсег података и помоћи ће у одабиру колона за употребу. Имајте на уму, боксплотови не показују варијансу, важан елемент добрих података за кластеровање (https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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JbpHuRO0qmd7jVgEmoQ2Cgbmet7PlaMdhYMvsG1pr56R7csXP0gUq//2082BV2sIY2DL7ht7L090dMEluuUl5MJq2RGDhVdUeSd6R5Ib9W89rra10rDrLNhLtMZukvxZw737yO+menjQrLxsad+wzgdbah5N8qZ+8eVVN0u9M/doEVXVgknem68D403RtQl+eUfbq1nj+u7aH4UC/184w339NMnhKk7qzTq2187MURJZM9huqN5tkgmP9aVq0uuWa9ZTsNu8VmKbW2jlV9YMkl89SVNNIVXW5LG20/11nVt8eGl/tbl6/MjS+3nxgluV61PLul+Ql/eQpSX6vtXbGRpcLMyzXT+xf35/kzlU1Ks1g2Zesqvv249/baEQsi2eG5Xo4/bfHprpo2iusMx9IMtOyfbskg7tGvLC1dvqY9fliVb0+3R0nblhV11+ARzQCW5A2CAbmed7O1qMdh4Gttm9orZ1RVR9J8ntJ7lpVu690Jy+mS1sYA1tw3/C9fn32ydI5OTOgLRHYwq4zhWV8Z7UEVbVbkrckuVX/1hGttSesMtupQ+Ob3UayvD3mk5uY10bMZHtN2Z2TXK4ff+OoJydtoi8Njc/j2Gc7bq9RvpTkgCR7pDuW+f46519ev1ZquxjUr5bVj2WmaVttq+qe6P7eJJdOcm6SP2itHT+r/KNurcR/12Rmtk2r6mJJ7tNPfjcXDjTfVK2186vqxCQHZvuek8+7/m10/7No1+C28rH+1OyQurVmrllPz44KqOh9KckhSa5VVbv1kWij/MbQ+Hojcod3xL8xNtXG84GBWZR7s5/AAAAgAElEQVTrC6mqu6SLut0l3R/hrVtrszxBZeebRbkePF7qwf2wkn3S3Rk9ST6UxEVkJjGLcv3FofFdV0k7/Pm4dYG1mEXZHj55//QqaT+VLqBikKeACmBetEEwMPPzdrY07TgMbLV9w6CTyyXStYNshY44i0RbGANbbd/QVk/CJtGWCGw5rbWvbHYe/R3tX5euY32SvDnJw1ebr7X2k6r633Sdcza7jWSS9pjzk3xtgrwmNovttQmG7wI+66dQz/W4Z5tur1E2+jsur1+fWSHtoH797yyfwLmdtlVV7Z/uSS+XT7cfuk9r7QMzXg11awz/XZOZ8Ta9U5K9+/E3zDjQL9nm5+RboP5t9Pc7Mckv050P7/hrcFv5WH8TbOu6NQHXrKdgkkevbXXH9a+XzNLjYkYZfpTLR9aZxzeTnDZiOaPcon89NcnJ68wHBmZRrv9PVd06XZTgbkl+kO6Ohl+fdHkwxkzLNczIppfr1topSb7VT+5XY2432dt/aPzUsalgdbPYZw+f0K0W+L37mPkAZk0bBAPObximHYeBrbZvGL4b17Z5zPYOstXKA/OzZcpCVV0hXXBNsnTMyexoSwQW1cuSDJ6UdXSSB7TWLljjvIN9569X1b4rpNvo/+gJSc4bsawL6e9qfdPBPJ4Ct7L+2ON2/eRnWmufn/EqHDA07thncoPf8dx07RDrddzQ+Er1a98k1+4nnRuNUFVXS/eEwisnuSDJg1pr75jDqqhbK/PftbUNB/q9dpYZ93fxH+zn1J3JbGj/01o7L8kn+smD+voxzqBenZuVnwCz6DZyrD8VC1q3XLOegp0YUPH2ofGRd2Dqo6AGf4ZnJvngejJorbUkgwPQ36iqm45K178/iLR5Rz8fTGLTy/XQcg5OV74vnuSsJL/fWvviynPBRGaxv67VhiSn9MlPGXr/0HV+FxiY1f763/vXvZLceoV0dx8aP25sKljdLMr2N4fGD1kl7fDJ2TfHpgLYfNogGJjZeTvbgnYcBrbMvqHvZHFQP3lKa+0nm5EPK9IWxsCW2TckeViSQQf7D21SHoynLRFYOFX1j1l6+vAHktxrhbunjjK87zxsTB6XSHLvfvJLrbUT17ue/fHy4A7vt+mPp0e5e7r9a5Ictd58FtD9snTDpFk/nSK58N2RHftMoKpuluQ3+8njJukg2dfJwZ2K793X2VEOGxpXv5apqiumC6bYr3/rT1trb5zT6qhbK/PftUVV1eWT3KGf/Gxr7bMzXoX7JLlMP67urFPfaf6Ph9767wkXNaije+XC58XDeV0tyW36yQ9oWx1tCsf607KIdcs16ynYcQEVrbVPJPlwP/knVXXQiGR/keQ6/fg/LY+0rKpDq6r1w5FjsnpBusf9JMkLq2rPZcvYM8kL+8nz+/QwkVmV66r67STvThepdnaSO7bWPjWN7wDLzXB/DTMz4+OQc/rxf6yqvZYnqKoHJDm0n3x3a+1/1/5N4MJmVLY/kORn/fgjqup6o9alqm6f5G795KlZ+VHQAJtKGwQDzm8Yph2HgVmUhaq6dlX97krrUVWXSfLGJIM7rM30bnt0/FcwMKN9w35VdYOV1qOq7pTkqf3kz5O8eh1fgynQlggsmqo6PMnj+smPJrlra+3cdS7mqCTf6Mf/sqr2H5HmuUkuNzQ+al0OG9p/Hj4mr+f1r7sleXFV7bpsGfskeU4/eWaSI9b2FRbaoAPX+enOUdakqg4f2l6Hjfj8plV15RXmr6p6RpY6Qn42nnhwEVX1B1Xjn2ZVVdfKhbfbS8akW3F79Qb1a+8kfz9iGfsn+ct+8qQseKfv5arqskmOSfLr/VuPa629YoLlqFuz4b9r6xoO9Ftze1l/zj3YFseO+PxyVXXoKsu4cZIX9ZMtyb+sNf9FUFW36vd14z7fPV35HZwvHz3qPHa1bdU7It3NipLk2dUF2gwvY9d0/3mD+jSyji66aRzrq1uTc816Onab9wpsksemO0DbM8l/VtUz00XT7JnucTIP69OdmOQfJsmgtXZiVT03yZOSHJjkI1X1nCRfT/dI3CcmGTRWP7e19rUJvwsMbGq57g/Yj0kyOBj56yRnVdV1V5jte6217603Lxiy6ftrmINZHId8q6qemq6B8XpJPtEfh3wuS1Hzj+iT/zhLJy2wEZtatltrZ1bVs5P8bZJLJ/loVb0wyfuS/CjJlZLcNclDsxQY/qRZPx4SYARtEAzM5PxmxMXN3x4av11V7Tc0fVJrzd2F50M7DgObvW+4SpIPVNVn092F6lNJTk93sWLfJDdL8if9eJJ8IcmzJ/omTIO2MAY2uyzsl+SDVfWxJEen69g0+A+4ZpJ79sOgw9zjW2unTpAPG6ctEVgIVfWYJE/rJ09N8v+SXGOFvttJ8tXlnXxaa7/ol3V0un3YR/rOvJ9I1xH1oUnu0Sc/LsnrJl3n1tp/VdW/ptsf3yXJ+6rqBUlOS7c/fXKSX+2TP7G19qNJ89qK+s7zN1/29qUGryPaJ97bWjt9heUdkOSGQ2mneX56uyRPqqr3prum8KV0HYUvnuS30t29+iZ92p8leeh2ulPuWkxpex2V5KSqelu6OvXtJOcmuXKS3093bjlY5ltaa2/bwCq/Jt12uVmSR1XVvkleke560I2TPCVdHb8gyZ/N6e7Wm2Kj26qqLp7uBhuDNsE3JHn/Km1CZ7fWJnniu7o1hbrlv2tLGw70e8MUl3uZdOfkn8tSe9130nVC/tUkd0ryR1m6+cnz3CjnIh6U5J1V9c4kxyb5arrz1UulO554WJID+rTfS3duPZHW2g+r6olJXprk6kmOr6q/S/L5dG2vf57kVn3yN7XWjp00r51qWsf6a6Burcw1641qre3IIcmd00WOtTHDV5Nca8y8hw6lO3KFPHZJ8soV8mjpIth2mffvYdgZw2aW63SPlVupLI8aDp/3b2LY/sMs9ter5H9yP//J8/4tDDtnmFW5TvKsdA2J4/L5bpKD5v17GHbOsNllO11HjuevUq5bkvPSdfaY+29iMBgMrWmDMMy8LKznvH3scgzbuzxEO862Gja5LBy6jjLwriRXmPfvsejDLP4rVsn/5GgL2xLDFtk3nJ3kYfP+LRZ9mNV+IdoSDQbDHId0HeHWew6z3wrLe2i6jt7j5j0+yT4rzH/YUNrDV0i3Z7qOy+Py+eVK82/nIes/7zx0leU9eyjtvda5LocPzXvYKp+vNJyS5Gbz/m236vZax7wvSXLxSbfXULp90nUoH5fPOUkeMu/fdqttq3TB0+vdnx47ybZSt6a7L4z/ri01JPmNod/k3eucd7geHrvK5ysN56frhF7z/j222pDkyDX+hp9LcsCk22pZ2r/JyufM706yx7x/m604ZErH+urWVLaFa9YbGHbqEyrSWju6qn4rXdTNHZNcLV3Hq5OS/FuSF7XWfrbBPC5I93iUf08XvXOjdAf8ZyQ5IcnLWmv/sZE8YNgsyjXMmnLNTjSrct1a+8s+Iv8RSQ5Jd4eYc9JFE78zyQtba2etsAhYl80u2607+3pcVb0+yUPS3fHl6kkukeSnfT4fSnecfeJGvgvANGmDYMD5DcOUBwY2uSx8JN2dQm+T7o5QV0v3dLdLpLtr2zeTfDzd3dM+spHvwXTYNzCwyWXhU0kekOSgdPuGK6c7dtwt3V1/v5jkA0mOaJ5eNHfaEgHWr7X2iv5JTH+W5Nbp7h58dpIvp7vD9BFtCne0b639PMkdq+r+6TqyXj/dkwK/m+TD6fbRH9toPjtdVe2S5A/7yTPT/e9M06vTBwamu2v+FZNcPl1nujOSfDrdneHf2Fo7Z8p57yR3Sfcb3iTdtZl9klwy3bnlN9KV+Ve11r4wjcxaa2dU1cHpOpnfP8l1+vxOS3es+k+ttS9OIy8mpm5Nkf+uLeePhsZfO+Vln5bkXunqzo2TXDXdPnWPdB2dv5quA/oRrbWTp5z3TvGcJJ9J9xsekOQKSfZOF5T03SSfTPLWJEe11n45jQxba0+rqmOSPCrdOfOV0h23fDbJq1trb5pGPmyIurUK16w3pvqIEQAAAAAAAAAAAAAAgIWxy7xXAAAAAAAAAAAAAAAAYNYEVAAAAAAAAAAAAAAAAAtHQAUAAAAAAAAAAAAAALBwBFQAAAAAAAAAAAAAAAALR0AFAAAAAAAAAAAAAACwcARUAAAAAAA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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "source": [
+    "Изаберите неколико колона са сличним опсезима. Обавезно укључите колону artist_top_genre како бисмо задржали жанрове исправним.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "# scaler = StandardScaler()\n",
+    "\n",
+    "X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
+    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "y = le.transform(y)\n",
+    "\n"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 0, 2, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0,\n",
+       "       2, 0, 0, 1, 1, 2, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2,\n",
+       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 0,\n",
+       "       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 2,\n",
+       "       2, 1, 1, 0, 1, 2, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0,\n",
+       "       1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       1, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 1, 1, 2, 1],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "source": [
+    "Ти бројеви нам не значе много, па хајде да добијемо „силуетни скор“ како бисмо видели тачност. Наш скор је у средини.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "source": [
+    "Користите тај модел да одлучите, користећи Елбо метод, најбољи број кластера за израду\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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NMANcuzrqqOKBBp9GlSSp7Rjg2tXmm8PBBxcBLrPqaiRJ0gAywLWzWg2eeAIeeKDqSiRJ0gAywLWzmTOho8NhVEmS2sx6A1xE7BMRE+v2PxkRcyLi3IgY1/jytFG22gqmT3c6EUmS2kxvPXD/DiwHiIgDgK8DFwJLgfMaW5oGRFcXPPJI8ZIkSW2htwA3LDOXlNsfA87LzB9m5l8COza2NA2IWbOKd4dRJUlqG70GuIgYXm4fBNxUd2x4D+er2UyaBPvtZ4CTJKmN9BbgLgZujYg5wJvA7QARsSPFMKpaQa0G990HTz5ZdSWSJGkArDfAZeb/C3wB+D6wf+ZvJhTrAD7T2NI0YGq14t1eOEmS2kJvT6GOBu7NzCsy8/WI2CUizgR2z8z7BqdEbbQddoA99zTASZLUJnobQr0WmAq/GTb9ObADcFpE/F1jS9OAqtXgZz8r1keVJEktrbcANzYzHyu3TwIuzszPAEcARzW0Mg2srq5iSa3Zs6uuRJIkbaTeAlz9IpozgBsAMnM5sLJRRakB3vte2GUXJ/WVJKkN9BbgHoyIfyjve9sRuB4gIrZseGUaWBFFL9wtt8CLL1ZdjSRJ2gi9BbhPA7+muA/u0Mx8o2zfDfiHBtalRqjV4J134Ec/qroSSZK0EXoLcGOAH2XmGZn5i7r2pRQPOKiV7L03TJniMKokSS2utwD3z8D4HtrHAd8a+HLUUBFFL9z118Orr1ZdjSRJ2kC9BbgdM/O2NRsz83bgtxtTkhqqVoPly+Hqq6uuRJIkbaDeAty71nNsxEAWokHyoQ/BxIlO6itJUgvrLcDNj4iPrtkYEUcAjzemJDVURwfMmgXXXANvvll1NZIkaQMM7+X454CrI+J/AfeWbZ3AfjiRb+vq6oJ/+7fiXriZM6uuRpIk9VNvPXBHAr8H3AFMKV+3Ar+dmY82uDY1yvTpMHasw6iSJLWo3nrgtgPOAd4LPEgR5BYBo4G3GluaGmbECDjmGJgzp3igYeTIqiuSJEn9sN4euMw8KzM/BGwDfAlYAvw+8FBE/HIQ6lOjdHXByy8XKzNIkqSW0tsQardNgc2BLcrXs8BdjSpKg+CQQ2DMGCf1lSSpBa03wEXEeRFxB/ADigcXfgYcn5mdmfn7g1GgGmSTTeDII2H27GJ5LUmS1DJ664F7NzAKeB54BlgIvNzoojRIajVYtAjuuKPqSiRJUj/0dg/c4cA+rFq4/gvAPRFxfUT8daOLU4N99KMwapRPo0qS1GJ6vQcuCw8B1wA/pngS9T3AGQ2uTY02ZgwcdlgR4DKrrkaSJPVRb/fAfTYiLomIpyjmfzsK+G+gRrGgvVpdrQZPPw1z51ZdiSRJ6qPe5oGbCvx/wJmZ+Vzjy9GgO/poGD686IXbZ5+qq5EkSX3Q2z1wn8/MH25MeIuIYRFxf0RcVe5vHxF3RcT8iPhBRIws20eV+/PL41PrvuNLZfuvIuKwuvbDy7b5EfHFDa1xSBs3Dg48sJhOxGFUSZJaQl/ngdsYZwCP1O2fDXwzM3cEXgJOLttPBl4q279ZnkdE7AacALwPOBz41zIUDgP+BTgC2A04sTxX/dXVBY89Bg8/XHUlkiSpDxoa4CJiO4r1VL9b7gcwA7isPOUCYFa5PbPcpzx+UHn+TOCSzFyWmU8A84F9y9f8zHw8M5cDl5Tnqr9mzoQIJ/WVJKlFNLoH7hzgfwMry/3xwMuZuaLcXwhMKrcnAU8DlMeXluf/pn2Nz6yrXf01cSLsv7/TiUiS1CIaFuAi4ihgUWbe26if0Y9aTomIuRExd/HixVWX05xqNXjwQZg/v+pKJElSLxrZA/dh4JiIeJJieHMG8C1gy4jofvp1O4oVHijfJwOUx7cAXqxvX+Mz62pfS2aeVy7/1TlhwoSNv7J2dOyxxbu9cJIkNb2GBbjM/FJmbpeZUykeQrgpMz8O3AwcV552EjCn3L6y3Kc8flNmZtl+QvmU6vbATsDdwD3ATuVTrSPLn3Flo66n7U2ZAp2dBjhJklrAYDyFuqY/BT4fEfMp7nE7v2w/Hxhftn8e+CJAZj4MXAr8ErgWOC0z3ynvkzsduI7iKddLy3O1oWo1uOsuWLiw6kokSdJ6RA6xub86OztzrqsO9OzRR2GXXeDcc+Ezn6m6GkmShryIuDczO9dsr6IHTs1q553hfe9zOhFJkpqcAU6r6+qC22+HRYuqrkSSJK2DAU6rq9Vg5Uq40udBJElqVgY4re63fxt22MFhVEmSmpgBTquLKIZRb7wRXn656mokSVIPDHBaW60Gb78NV11VdSWSJKkHBjitbd99YdIkJ/WVJKlJGeC0to6OYmmta6+F11+vuhpJkrQGA5x61tUFb75ZhDhJktRUDHDq2f77w1ZbOYwqSVITMsCpZ8OHw8yZxYMMy5ZVXY0kSapjgNO6dXXBK68UU4pIkqSmYYDTus2YAZtv7qS+kiQ1GQOc1m3UKDj6aJgzB1asqLoaSZJUMsBp/Wo1ePFFuO22qiuRJEklA5zW7/DDYdNNfRpVkqQmYoDT+o0eDUccAVdcAStXVl2NJEnCAKe+qNXg2WfhrruqrkSSJGGAU18cdRSMGOEwqiRJTcIAp95tsQUcfHAxnUhm1dVIkjTkGeDUN11d8MQT8ItfVF2JJElDngFOfXPMMdDR4aS+kiQ1AQOc+mbCBJg+3fvgJElqAsOrLkAt5NRTi4cZ5s+HMWNg662LXjlJkjSo/Ourvlm5EnbaCc48s3ifNg3mzXNuOEmSKmCAU98sWgTHHgsLFhT7CxbAzJlFuyRJGlQGOPXNsmWrwlu3BQuKdkmSNKgMcOqbUaNgypTV26ZMcV44SZIqYIBT32y9NcyZsyrETZkC3/senHyyT6ZKkjTIDHDqm44O2GMPuPNOePLJ4n3XXeHVV4tJfs86C95+u+oqJUkaEgxw6ruODpg4seh9mzgRtt0Wbr8dTjsN/vEfYcaMYtF7SZLUUAY4bZxRo+Db34aLLoL77oO99oKbb666KkmS2poBTgPjd38X7rkHxo0rFr7/u79zjjhJkhrEAKeBs9tucPfdcPzx8Gd/VswT99JLVVclSVLbMcBpYL3rXXDxxfDP/wzXXQcf+EAxtCpJkgaMAU4DLwJOPx1uuw1WrIAPfQjOO8854yRJGiAGODXOtGlF79v06fCHfwif+hS88UbVVUmS1PIMcGqsrbaCa66Br3wF/s//KULdo49WXZUkSS3NAKfGGzYM/uqv4Mc/LuaJ6+yEH/6w6qokSWpZBjgNnsMOK4ZUd9sNjjsOPv95V2+QJGkDGOA0uN797uLhhtNPh29+Ew48EJ55puqqJElqKQY4Db6RI4tpRi6+GB54APbeG266qeqqJElqGQY4VeeEE4rVG8aPh0MOgb/9W1dvkCSpDwxwqtZ731us3vCxj8Gf/zkccwwsWVJ1VZIkNTUDnKo3ZgxcdBF8+9tw/fXF6g1z51ZdlSRJTcsAp+YQAaedBrffDu+8Ax/+MPz7v7t6gyRJPTDAqbl88INw//0wYwaceiqcdBK8/nrVVUmS1FQaFuAiYnJE3BwRv4yIhyPijLJ9XETcEBGPle9jy/aIiHMjYn5EPBgRe9d910nl+Y9FxEl17R+IiHnlZ86NiGjU9WgQjR8PV18Nf/3X8F//Vaze8KtfVV2VJElNo5E9cCuAL2TmbsA04LSI2A34InBjZu4E3FjuAxwB7FS+TgG+A0XgA74CfBDYF/hKd+grz/l03ecOb+D1aDB1dMCXvwzXXgvPPQf77AOXXVZ1VZIkNYWGBbjMfC4z7yu3XwUeASYBM4ELytMuAGaV2zOBC7NwJ7BlRPwWcBhwQ2YuycyXgBuAw8tjm2fmnZmZwIV136V2ceihxZDq+94Hxx8PZ57p6g2SpCFvUO6Bi4ipwF7AXcA2mflceeh5YJtyexLwdN3HFpZt62tf2EN7Tz//lIiYGxFzFy9evFHXogpMngy33gqf/Syccw78zu/AwoW9fkySpHbV8AAXEWOAHwKfy8xX6o+VPWcNf8wwM8/LzM7M7JwwYUKjf5waYeRI+Na34JJL4MEHi9UbfvKTqquSJKkSDQ1wETGCIrxdlJmXl80vlMOflO+LyvZngMl1H9+ubFtf+3Y9tKudfexjxeoNEyYUw6tf+5qrN0iShpxGPoUawPnAI5n5T3WHrgS6nyQ9CZhT1/7J8mnUacDScqj1OuDQiBhbPrxwKHBdeeyViJhW/qxP1n2X2tmuu8Jdd8GJJ8Jf/iUcfbSrN0iShpRG9sB9GPgEMCMiHihfHwW+DhwSEY8BB5f7ANcAjwPzgf8A/hggM5cAXwXuKV9/U7ZRnvPd8jP/A/y4gdejZjJmTDHFyL/+azGUuvfert4gSRoyIofYTPednZ051z/07eWee+C44+D554uHHE49tVjZQZKkFhcR92Zm55rtrsSg1rfPPnDffXDQQfDHfwyf+ISrN0iS2poBTu1h/Hi46ir46lfh//7fYkkuV2+QJLUpA5zaR0cH/MVfwPXXwwsvQGcnXHpp1VVJkjTgDHBqPwcfXKzesMcexbQjZ5wBy5dXXZUkSQPGAKf2tN12cMstRXg791xXb5AktRUDnNrXyJHFU6mXXgrz5sFee8ENN1RdlSRJG80Ap/Z3/PHFHHHbbAOHHVY86ODqDZKkFmaA09Cwyy7F6g0f/zh8+ctw5JHw4otVVyVJ0gYxwGno2GwzuPBC+M534KabitUb7r676qokSeo3A5yGlohipYY77ii299+/WI5riK1IIklqbQY4DU2dncXqDYccAqedBr/3e/Daa1VXJUlSnxjgNHSNGwc/+hF87WtwySXF6g2PP16sqbpgQfHuww6SpCZkgNPQ1tEBf/7nxeoNkyfDk0/CtGkwdWrxPm+eIU6S1HSGV12A1BQOOgh22gkOOKDofYPifeZMuPHGItANG1ZpiZIkdTPASd0yV4W3bgsWFCs4vP/9sO++sN9+xWvaNNhqq2rqlCQNeQY4qduoUTBlyuohbsoUGD8ePvUp+PnP4eyz4Z13imM77bQq0O23H+y+u710kqRBETnEpk/o7OzMuXPnVl2GmtHKlcU9bzNnFiFuyhSYMwf22KO4Vw7g9deLVR1+/vNVr8WLi2ObbWYvnSRpQEXEvZnZuVa7AU6qs3IlLFoEy5YVPXJbb70qvPUkE554YvVA94tf2EsnSRoQBriSAU4NZy+dJGmArCvAeQ+cNNA22wymTy9e0HMvnffSSZI2gj1wUhXqe+nuvLN4X7SoOGYvnSSpZA+c1Ew2ppdu2rSil264//eVpKHKHjipWdlLJ0lDnj1wUqtpRC9df5+ylSQ1JXvgpFbWn166D30Inn56/fPcSZKaitOIlAxwamvrm5fu8svhzDPXXmnijjtg0qTqapYkrZNDqNJQEAE77FC8Pv7xoq27l27s2J7Xep0/v1jrdfvtV7122GHV9pQpMHLk4F+LJGmdDHBSu+u+l+7553te63XMGDjuOHj8cbj/fpg9G95+e9U5EUUP3ZrBrvu17bYOwUrSIDPASUPF1lsX97z1dA/cv/3bqvPeeQeefbYYin3iiSLYdW//5CfFsfpbL0aOhKlT1w523a9x44oQKEkaMN4DJw0lA/EU6rJlRQCsD3b1ryVLVj9/8817DnY77FAEv9GjB+zyJKndeA+cpCKsTZy4cd8xahTsvHPx6snSpT0Hu1/9Cq67Dt58c/Xzt9lm3fffTZ7shMWS1AP/ZZQ0sLbYAvbcs3itKRNeeGH1YNfdk/fzn8Oll66a1w6KNWEnT+453G2/fRH+6odnnedO0hBhgJM0eCKKHsCJE4u56db09tuwcOHa99498QRcfXUR/uptuumq++8OPBD23x9OOGHVPX6zZxf3+A0bNiiXJ0mDxQAnqXmMGLGqd23GjLWPv/EGPPlkz/ff7bzzqvAGxfusWXDOOXDqqcXDFP15bb65vXeSmpYBTlLrGD0adtuteK1pwYKe57mbOhWOOaZ4uGLJEnjqqWJy4yVL4LXX1v2zOjpgyy37H/zGjvW+PUkN578yktrDqFE9z3M3cSKcd17Pn1m+HF56aVW4W9/r17+GRx8ttl9+ef21bL75hgW/TTbp/Tq9z08SBlFgrMMAAApnSURBVDhJ7WJd89xtvfW6PzNyZPEgxDbb9O9nvfNOEeLWF/jqg+HTT6/arn9IY02jR68e6NYMeTvvXDzUcfzxq67x8suLkDpsWBHoNtmkeG/VufcMqFKfGOAktYeOjuKBhTvvbPwf/2HDYPz44tUfmfDqq33r8VuyZFWP34svFr2Fl1++KrxB8V6rwTe/WbzX6w5z9a+e2vpzvD/fsSHDyCtXwrx5PU82bYiTVuNEvpLU7DKL+fOeew523HHt4w89BLfdBm+9tfZr2bKe23s7vrGGDet/APyDP4ATT1x7GHz2bLjnnqLHdOTI4ju6t9fcX9exESOaIwTaw6h+ciJfSWpVEcXw6mab9Xyf3/jx8Ed/NHA/L7Po8duYANiX40uXrn78U5/q+UGUpUvhlFM2/rpGjOh/8Ovvues7tuWWxZPUtdqqHsYrrijmNxw2rOi17A6arToEDu0fUpvk+gxwktQqNuQ+vw0RUfxhGjVqYL+3N88/33NA3WGHYn7AZcuKYNn9qt9f37H+nLtsWdHbuXTp+j+3bNnqawL3xeWXw5lnrj4EfuyxPQ+BjxixKtDVb6/53te2RhzrqW2zzYr7P+tD6uzZxe+wo6M4b/jwIrC2YkhtomF+h1AlqZU0yX/9N0QT/XHskxUr+hcSp07teQqcBx8slpl7++3iO99+e/XtNd/7e6y38wcyB6wZUqH4PfYUUrsDXXfv48ZsD8R39GV7zz3hqKPWvr4779z4ZQrXwSFUSWoHA7GebbMazAdRBkL3H/fRo/t2/rp6GCdMgLPOakyNfbFy5cAFxV126XkYfMcd4e//vngKe8WK4jVQ28uXb9hn69/76pZber6+Zcs2+tfQXwY4SVLzaOeAOlhD4P3V0TFwQ+brC6l/8icb//2NkFmE2L4Ev2HDer6+wb7dAAOcJEmDo9V6GDdEs4bU9Ykogln3XIrrs3Jl01yfAU6SpMHSzj2M0P4htYmur+UDXEQcDnwLGAZ8NzO/XnFJkiQNXUMhpDbB9bV0JI6IYcC/AEcAuwEnRkQPj/hIkiS1j5YOcMC+wPzMfDwzlwOXADMrrkmSJKmhWj3ATQKerttfWLZJkiS1rVYPcH0SEadExNyImLt48eKqy5EkSdoorR7gngEm1+1vV7atJjPPy8zOzOycMGHCoBUnSZLUCK0e4O4BdoqI7SNiJHACcGXFNUmSJDVUS08jkpkrIuJ04DqKaUS+l5kPV1yWJElSQ7V0gAPIzGuAa6quQ5IkabBEZlZdw6CKiMXAgl5P1PpsBfy66iK0Ufwdtj5/h63N31/rG6zf4ZTMXOsG/iEX4LTxImJuZnZWXYc2nL/D1ufvsLX5+2t9Vf8OW/0hBkmSpCHHACdJktRiDHDaEOdVXYA2mr/D1ufvsLX5+2t9lf4OvQdOkiSpxdgDJ0mS1GIMcOqziJgcETdHxC8j4uGIOKPqmtR/ETEsIu6PiKuqrkX9FxFbRsRlEfHfEfFIROxXdU3qn4g4s/w39KGIuDgiNqm6Jq1fRHwvIhZFxEN1beMi4oaIeKx8HzuYNRng1B8rgC9k5m7ANOC0iNit4prUf2cAj1RdhDbYt4BrM3NX4P34u2wpETEJ+CzQmZm7U6widEK1VakPvg8cvkbbF4EbM3Mn4MZyf9AY4NRnmflcZt5Xbr9K8YdjUrVVqT8iYjvgSOC7Vdei/ouILYADgPMBMnN5Zr5cbVXaAMOBTSNiODAaeLbietSLzLwNWLJG80zggnL7AmDWYNZkgNMGiYipwF7AXdVWon46B/jfwMqqC9EG2R5YDPxnOQz+3YjYrOqi1HeZ+QzwD8BTwHPA0sy8vtqqtIG2ycznyu3ngW0G84cb4NRvETEG+CHwucx8pep61DcRcRSwKDPvrboWbbDhwN7AdzJzL+B1BnnYRhunvE9qJkUY3xbYLCJ+r9qqtLGymNJjUKf1MMCpXyJiBEV4uygzL6+6HvXLh4FjIuJJ4BJgRkT8V7UlqZ8WAgszs7vn+zKKQKfWcTDwRGYuzsy3gcuBD1VckzbMCxHxWwDl+6LB/OEGOPVZRATFvTePZOY/VV2P+iczv5SZ22XmVIqbpm/KTP/Lv4Vk5vPA0xGxS9l0EPDLCktS/z0FTIuI0eW/qQfhgyit6krgpHL7JGDOYP5wA5z648PAJyh6bh4oXx+tuihpiPkMcFFEPAjsCfxtxfWoH8re08uA+4B5FH+HXZWhyUXExcDPgV0iYmFEnAx8HTgkIh6j6Fn9+qDW5EoMkiRJrcUeOEmSpBZjgJMkSWoxBjhJkqQWY4CTJElqMQY4SZKkFmOAk9Q0IiIj4h/r9s+KiL8aoO/+fkQcNxDf1cvPOT4iHomImxtZV0RMjYjf7X+FktqBAU5SM1kG1CJiq6oLqVcuOt5XJwOfzswDG1VPaSrQrwDXz+uQ1MQMcJKayQqKSU3PXPPAmj1VEfFa+f47EXFrRMyJiMcj4usR8fGIuDsi5kXEe+q+5uCImBsRj5ZrwxIRwyLiGxFxT0Q8GBF/WPe9t0fElfSw2kFEnFh+/0MRcXbZ9mVgf+D8iPhGD5/50/Izv4iItSb9jIgnu8NrRHRGxC3l9vS6ybPvj4h3UUwa+pGy7cy+XkdEbBYRV5c1PBQRH+vLL0ZSc/G/xiQ1m38BHoyIv+/HZ94PvBdYAjwOfDcz942IMyhWLvhced5UYF/gPcDNEbEj8ElgaWbuExGjgDsi4vry/L2B3TPzifofFhHbAmcDHwBeAq6PiFmZ+TcRMQM4KzPnrvGZIygWMf9gZr4REeP6cX1nAadl5h0RMQZ4i2IR+7MyszuIntKX64iILuDZzDyy/NwW/ahDUpOwB05SU8nMV4ALgc/242P3ZOZzmbkM+B+gO7jMowht3S7NzJWZ+RhF0NsVOBT4ZEQ8ANwFjAd2Ks+/e83wVtoHuKVckHwFcBFwQC81Hgz8Z2a+UV7nkn5c3x3AP0XEZ4Ety5+5pr5exzyK5X/OjoiPZObSftQhqUkY4CQ1o3Mo7iXbrK5tBeW/WRHRAYysO7asbntl3f5KVh9pWHPtwAQC+Exm7lm+ts/M7gD4+kZdRf/95hqBTX5TZObXgT8ANqXoWdu1h8/26Toy81GKHrl5wNfKYV9JLcYAJ6nplL1Tl1KEuG5PUgxZAhwDjNiArz4+IjrK++J2AH4FXAf8UUSMAIiInSNis/V9CXA3MD0itoqIYcCJwK29fOYG4PcjYnT5c3oaQn2SVdfY1d0YEe/JzHmZeTZwD0XP4avAu+o+26frKId/38jM/wK+QRHmJLUY74GT1Kz+ETi9bv8/gDkR8QvgWjasd+wpivC1OXBqZr4VEd+lGGa9LyICWAzMWt+XZOZzEfFF4GaKnq+rM3NOL5+5NiL2BOZGxHLgGuDP1jjtrykegPgqcEtd++ci4kCKHsWHgR+X2++U/3t8H/hWH69jD+AbEbESeBv4o/XVLak5ReaaIwqSJElqZg6hSpIktRgDnCRJUosxwEmSJLUYA5wkSVKLMcBJkiS1GAOcJElSizHASZIktRgDnCRJUov5/wEEXUm8vjXJ1AAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Looks like 3 is a good number after all. Fit the model again and create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Тачност овог модела није лоша, али није ни сјајна. Могуће је да подаци нису погодни за К-Меанс кластерисање. Можете покушати са другом методом.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 811,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/5-Clustering/2-K-Means/solution/tester.ipynb b/translations/sr/5-Clustering/2-K-Means/solution/tester.ipynb
new file mode 100644
index 000000000..8d79b7eef
--- /dev/null
+++ b/translations/sr/5-Clustering/2-K-Means/solution/tester.ipynb
@@ -0,0 +1,343 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "6f92868513e59d321245137c1c4c5311",
+   "translation_date": "2025-09-06T14:22:27+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/tester.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Почните там где смо завршили у последњем часу, са увезеним и филтрираним подацима.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 105
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Фокусираћемо се само на 3 жанра. Можда можемо направити 3 кластера!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 106
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"474.468454pt\" version=\"1.1\" viewBox=\"0 0 598.4875 474.468454\" width=\"598.4875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 474.468454 \nL 598.4875 474.468454 \nL 598.4875 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 402.838125 \nL 591.2875 402.838125 \nL 591.2875 22.318125 \nL 33.2875 22.318125 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path clip-path=\"url(#p0704da7660)\" d=\"M 51.8875 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 107
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# X = df.loc[:, ('danceability','energy')]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Unknown label type: 'continuous'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-110-3756ba7b0bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# we create an instance of SVM and fit out data. We do not scale our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# data since we want to plot the support vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mls30\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 30% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mls50\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 50% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mls100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 100% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m# actual graph construction (implementations should override this)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    181\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    182\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.semi_supervised import LabelSpreading\n",
+    "from sklearn.semi_supervised import SelfTrainingClassifier\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "X = df[['danceability','acousticness']].values\n",
+    "y = df['energy'].values\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "# step size in the mesh\n",
+    "h = .02\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "y_rand = rng.rand(y.shape[0])\n",
+    "y_30 = np.copy(y)\n",
+    "y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled\n",
+    "y_50 = np.copy(y)\n",
+    "y_50[y_rand < 0.5] = -1\n",
+    "# we create an instance of SVM and fit out data. We do not scale our\n",
+    "# data since we want to plot the support vectors\n",
+    "ls30 = (LabelSpreading().fit(X, y_30), y_30, 'Label Spreading 30% data')\n",
+    "ls50 = (LabelSpreading().fit(X, y_50), y_50, 'Label Spreading 50% data')\n",
+    "ls100 = (LabelSpreading().fit(X, y), y, 'Label Spreading 100% data')\n",
+    "\n",
+    "# the base classifier for self-training is identical to the SVC\n",
+    "base_classifier = SVC(kernel='rbf', gamma=.5, probability=True)\n",
+    "st30 = (SelfTrainingClassifier(base_classifier).fit(X, y_30),\n",
+    "        y_30, 'Self-training 30% data')\n",
+    "st50 = (SelfTrainingClassifier(base_classifier).fit(X, y_50),\n",
+    "        y_50, 'Self-training 50% data')\n",
+    "\n",
+    "rbf_svc = (SVC(kernel='rbf', gamma=.5).fit(X, y), y, 'SVC with rbf kernel')\n",
+    "\n",
+    "# create a mesh to plot in\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                     np.arange(y_min, y_max, h))\n",
+    "\n",
+    "color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}\n",
+    "\n",
+    "classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)\n",
+    "for i, (clf, y_train, title) in enumerate(classifiers):\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    colors = [color_map[y] for y in y_train]\n",
+    "    plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')\n",
+    "\n",
+    "    plt.title(title)\n",
+    "\n",
+    "plt.suptitle(\"Unlabeled points are colored white\", y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb b/translations/sr/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
new file mode 100644
index 000000000..b2d02248b
--- /dev/null
+++ b/translations/sr/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
@@ -0,0 +1,100 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "27de2abc0235ebd22080fc8f1107454d",
+   "translation_date": "2025-09-06T15:22:10+00:00",
+   "source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You should download the book text, clean it, and import it here\n",
+    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
+    "    file_contents = f.read()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "book_pride = TextBlob(file_contents)\n",
+    "positive_sentiment_sentences = []\n",
+    "negative_sentiment_sentences = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sentence in book_pride.sentences:\n",
+    "    if sentence.sentiment.polarity == 1:\n",
+    "        positive_sentiment_sentences.append(sentence)\n",
+    "    if sentence.sentiment.polarity == -1:\n",
+    "        negative_sentiment_sentences.append(sentence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
+    "for sentence in positive_sentiment_sentences:\n",
+    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
+    "for sentence in negative_sentiment_sentences:\n",
+    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/6-NLP/4-Hotel-Reviews-1/notebook.ipynb b/translations/sr/6-NLP/4-Hotel-Reviews-1/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sr/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb b/translations/sr/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
new file mode 100644
index 000000000..c4caec531
--- /dev/null
+++ b/translations/sr/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
@@ -0,0 +1,174 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2d05e7db439376aa824f4b387f8324ca",
+   "translation_date": "2025-09-06T15:21:49+00:00",
+   "source_file": "6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EDA\n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_difference_review_avg(row):\n",
+    "    return row[\"Average_Score\"] - row[\"Calc_Average_Score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "print(\"Loading data file now, this could take a while depending on file size\")\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n",
+    "end = time.time()\n",
+    "print(\"Loading took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What shape is the data (rows, columns)?\n",
+    "print(\"The shape of the data (rows, cols) is \" + str(df.shape))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# value_counts() creates a Series object that has index and values\n",
+    "#                in this case, the country and the frequency they occur in reviewer nationality\n",
+    "nationality_freq = df[\"Reviewer_Nationality\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What reviewer nationality is the most common in the dataset?\n",
+    "print(\"The highest frequency reviewer nationality is \" + str(nationality_freq.index[0]).strip() + \" with \" + str(nationality_freq[0]) + \" reviews.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the top 10 most common nationalities and their frequencies?\n",
+    "print(\"The top 10 highest frequency reviewer nationalities are:\")\n",
+    "print(nationality_freq[0:10].to_string())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many unique nationalities are there?\n",
+    "print(\"There are \" + str(nationality_freq.index.size) + \" unique nationalities in the dataset\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What was the most frequently reviewed hotel for the top 10 nationalities - print the hotel and number of reviews\n",
+    "for nat in nationality_freq[:10].index:\n",
+    "   # First, extract all the rows that match the criteria into a new dataframe\n",
+    "   nat_df = df[df[\"Reviewer_Nationality\"] == nat]   \n",
+    "   # Now get the hotel freq\n",
+    "   freq = nat_df[\"Hotel_Name\"].value_counts()\n",
+    "   print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many reviews are there per hotel (frequency count of hotel) and do the results match the value in `Total_Number_of_Reviews`?\n",
+    "# First create a new dataframe based on the old one, removing the uneeded columns\n",
+    "hotel_freq_df = df.drop([\"Hotel_Address\", \"Additional_Number_of_Scoring\", \"Review_Date\", \"Average_Score\", \"Reviewer_Nationality\", \"Negative_Review\", \"Review_Total_Negative_Word_Counts\", \"Positive_Review\", \"Review_Total_Positive_Word_Counts\", \"Total_Number_of_Reviews_Reviewer_Has_Given\", \"Reviewer_Score\", \"Tags\", \"days_since_review\", \"lat\", \"lng\"], axis = 1)\n",
+    "# Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found\n",
+    "hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')\n",
+    "# Get rid of all the duplicated rows\n",
+    "hotel_freq_df = hotel_freq_df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "print()\n",
+    "print(hotel_freq_df.to_string())\n",
+    "print(str(hotel_freq_df.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# While there is an `Average_Score` for each hotel according to the dataset, \n",
+    "# you can also calculate an average score (getting the average of all reviewer scores in the dataset for each hotel)\n",
+    "# Add a new column to your dataframe with the column header `Calc_Average_Score` that contains that calculated average. \n",
+    "df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n",
+    "# Add a new column with the difference between the two average scores\n",
+    "df[\"Average_Score_Difference\"] = df.apply(get_difference_review_avg, axis = 1)\n",
+    "# Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)\n",
+    "review_scores_df = df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "# Sort the dataframe to find the lowest and highest average score difference\n",
+    "review_scores_df = review_scores_df.sort_values(by=[\"Average_Score_Difference\"])\n",
+    "print(review_scores_df[[\"Average_Score_Difference\", \"Average_Score\", \"Calc_Average_Score\", \"Hotel_Name\"]])\n",
+    "# Do any hotels have the same (rounded to 1 decimal place) `Average_Score` and `Calc_Average_Score`?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да превод буде тачан, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/6-NLP/5-Hotel-Reviews-2/notebook.ipynb b/translations/sr/6-NLP/5-Hotel-Reviews-2/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sr/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb b/translations/sr/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
new file mode 100644
index 000000000..d7189cfa6
--- /dev/null
+++ b/translations/sr/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
@@ -0,0 +1,172 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "033cb89c85500224b3c63fd04f49b4aa",
+   "translation_date": "2025-09-06T15:22:32+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import time\n",
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replace_address(row):\n",
+    "    if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Amsterdam, Netherlands\"\n",
+    "    elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Barcelona, Spain\"\n",
+    "    elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
+    "        return \"London, United Kingdom\"\n",
+    "    elif \"Milan\" in row[\"Hotel_Address\"]:        \n",
+    "        return \"Milan, Italy\"\n",
+    "    elif \"France\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Paris, France\"\n",
+    "    elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Vienna, Austria\" \n",
+    "    else:\n",
+    "        return row.Hotel_Address\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dropping columns we will not use:\n",
+    "df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace all the addresses with a shortened, more useful form\n",
+    "df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop `Additional_Number_of_Scoring`\n",
+    "df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
+    "# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
+    "df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
+    "df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process the Tags into new columns\n",
+    "# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
+    "# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
+    "# Family with young children, Family with older children, With a pet\n",
+    "df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
+    "df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
+    "df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
+    "df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
+    "df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
+    "df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
+    "df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
+    "df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No longer need any of these columns\n",
+    "df.drop([\"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_Filtered.csv\n",
+      "Filtering took 23.74 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Saving new data file with calculated columns\n",
+    "print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
+    "df.to_csv(r'../../data/Hotel_Reviews_Filtered.csv', index = False)\n",
+    "end = time.time()\n",
+    "print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb b/translations/sr/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
new file mode 100644
index 000000000..901808791
--- /dev/null
+++ b/translations/sr/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
@@ -0,0 +1,137 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "341efc86325ec2a214f682f57a189dfd",
+   "translation_date": "2025-09-06T15:22:52+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV (you can )\n",
+    "import pandas as pd \n",
+    "\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We want to find the most useful tags to keep\n",
+    "# Remove opening and closing brackets\n",
+    "df.Tags = df.Tags.str.strip(\"[']\")\n",
+    "# remove all quotes too\n",
+    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
+    "# Now split the strings into a list\n",
+    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove leading and trailing spaces\n",
+    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
+    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
+    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
+    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
+    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
+    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the 6 columns into one with melt\n",
+    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "The shape of the tags with no filtering: (2514684, 2)\n",
+      "                        index   count\n",
+      "0                Leisure trip  338423\n",
+      "1                      Couple  205305\n",
+      "2               Solo traveler   89779\n",
+      "3               Business trip   68176\n",
+      "4                       Group   51593\n",
+      "5  Family with young children   49318\n",
+      "6  Family with older children   21509\n",
+      "7      Travelers with friends    1610\n",
+      "8                  With a pet    1078\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the value counts\n",
+    "tag_vc = df_tags.value.value_counts()\n",
+    "# print(tag_vc)\n",
+    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
+    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
+    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
+    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
+    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
+    "print(tag_vc[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да превод буде тачан, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb b/translations/sr/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
new file mode 100644
index 000000000..28518ef28
--- /dev/null
+++ b/translations/sr/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
@@ -0,0 +1,260 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "705bf02633759f689abc37b19749a16d",
+   "translation_date": "2025-09-06T15:23:12+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[nltk_data] Downloading package vader_lexicon to\n[nltk_data]     /Users/jenlooper/nltk_data...\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import pandas as pd\n",
+    "import nltk as nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    "nltk.download('vader_lexicon')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vader_sentiment = SentimentIntensityAnalyzer()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are 3 possibilities of input for a review:\n",
+    "# It could be \"No Negative\", in which case, return 0\n",
+    "# It could be \"No Positive\", in which case, return 0\n",
+    "# It could be a review, in which case calculate the sentiment\n",
+    "def calc_sentiment(review):    \n",
+    "    if review == \"No Negative\" or review == \"No Positive\":\n",
+    "        return 0\n",
+    "    return vader_sentiment.polarity_scores(review)[\"compound\"]    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "df = pd.read_csv(\"../../data/Hotel_Reviews_Filtered.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove stop words - can be slow for a lot of text!\n",
+    "# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches\n",
+    "# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends\n",
+    "start = time.time()\n",
+    "cache = set(stopwords.words(\"english\"))\n",
+    "def remove_stopwords(review):\n",
+    "    text = \" \".join([word for word in review.split() if word not in cache])\n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the stop words from both columns\n",
+    "df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   \n",
+    "df.Positive_Review = df.Positive_Review.apply(remove_stopwords)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Removing stop words took 5.77 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "end = time.time()\n",
+    "print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Calculating sentiment columns for both positive and negative reviews\n",
+      "Calculating sentiment took 201.07 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a negative sentiment and positive sentiment column\n",
+    "print(\"Calculating sentiment columns for both positive and negative reviews\")\n",
+    "start = time.time()\n",
+    "df[\"Negative_Sentiment\"] = df.Negative_Review.apply(calc_sentiment)\n",
+    "df[\"Positive_Sentiment\"] = df.Positive_Review.apply(calc_sentiment)\n",
+    "end = time.time()\n",
+    "print(\"Calculating sentiment took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                          Negative_Review  Negative_Sentiment\n",
+      "186584  So bad experience memories I hotel The first n...             -0.9920\n",
+      "129503  First charged twice room booked booking second...             -0.9896\n",
+      "307286  The staff Had bad experience even booking Janu...             -0.9889\n",
+      "452092  No WLAN room Incredibly rude restaurant staff ...             -0.9884\n",
+      "201293  We usually traveling Paris 2 3 times year busi...             -0.9873\n",
+      "...                                                   ...                 ...\n",
+      "26899   I would say however one night expensive even d...              0.9933\n",
+      "138365  Wifi terribly slow I speed test network upload...              0.9938\n",
+      "79215   I find anything hotel first I walked past hote...              0.9938\n",
+      "278506  The property great location There bakery next ...              0.9945\n",
+      "339189  Guys I like hotel I wish return next year Howe...              0.9948\n",
+      "\n",
+      "[515738 rows x 2 columns]\n",
+      "                                          Positive_Review  Positive_Sentiment\n",
+      "137893  Bathroom Shower We going stay twice hotel 2 ni...             -0.9820\n",
+      "5839    I completely disappointed mad since reception ...             -0.9780\n",
+      "64158   get everything extra internet parking breakfas...             -0.9751\n",
+      "124178  I didnt like anythig Room small Asked upgrade ...             -0.9721\n",
+      "489137  Very rude manager abusive staff reception Dirt...             -0.9703\n",
+      "...                                                   ...                 ...\n",
+      "331570  Everything This recently renovated hotel class...              0.9984\n",
+      "322920  From moment stepped doors Guesthouse Hotel sta...              0.9985\n",
+      "293710  This place surprise expected good actually gre...              0.9985\n",
+      "417442  We celebrated wedding night Langham I commend ...              0.9985\n",
+      "132492  We arrived super cute boutique hotel area expl...              0.9987\n",
+      "\n",
+      "[515738 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=[\"Negative_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Negative_Review\", \"Negative_Sentiment\"]])\n",
+    "df = df.sort_values(by=[\"Positive_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Positive_Review\", \"Positive_Sentiment\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)\n",
+    "df = df.reindex([\"Hotel_Name\", \"Hotel_Address\", \"Total_Number_of_Reviews\", \"Average_Score\", \"Reviewer_Score\", \"Negative_Sentiment\", \"Positive_Sentiment\", \"Reviewer_Nationality\", \"Leisure_trip\", \"Couple\", \"Solo_traveler\", \"Business_trip\", \"Group\", \"Family_with_young_children\", \"Family_with_older_children\", \"With_a_pet\", \"Negative_Review\", \"Positive_Review\"], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_NLP.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
+    "df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације, препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/7-TimeSeries/1-Introduction/solution/notebook.ipynb b/translations/sr/7-TimeSeries/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..9848c7909
--- /dev/null
+++ b/translations/sr/7-TimeSeries/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Три године података о часовном електричном оптерећењу и температури између 2012. и 2014. године.\n",
+    "\n",
+    "Тао Хонг, Пјер Пинсон, Шу Фан, Хамидреза Зареипур, Алберто Троколи и Роб Џ. Хајндман, \"Прогностичко предвиђање енергије: Глобално такмичење у предвиђању енергије 2014 и даље\", International Journal of Forecasting, vol.32, no.3, стр. 896-913, јул-септембар, 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "from common.utils import load_data\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Учитај податке из csv у Pandas dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2698.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2558.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2444.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2402.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2403.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "data_dir = './data'\n",
+    "energy = load_data(data_dir)[['load']]\n",
+    "energy.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Прикажи све доступне податке о оптерећењу (јануар 2012. до децембар 2014.)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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87.688389 203.753243 \nL 87.94296 312.49533 \nL 87.974782 303.497498 \nL 88.102068 192.670548 \nL 88.165711 195.742978 \nL 88.324818 223.72404 \nL 88.356639 210.666211 \nL 88.420282 165.457594 \nL 88.452104 179.832178 \nL 88.674854 324.565592 \nL 88.738497 313.373167 \nL 88.929426 202.875406 \nL 88.961247 205.508917 \nL 89.056712 216.372153 \nL 89.088533 214.397019 \nL 89.183998 154.155439 \nL 89.24764 180.490556 \nL 89.438569 296.035882 \nL 89.470391 295.048315 \nL 89.534034 251.485642 \nL 89.597677 175.333262 \nL 89.661319 181.148934 \nL 89.820427 213.080263 \nL 89.852248 211.10513 \nL 89.947713 153.716521 \nL 90.011356 182.46569 \nL 90.202284 308.435333 \nL 90.234106 308.215873 \nL 90.297749 265.640768 \nL 90.361392 191.682981 \nL 90.425035 198.815408 \nL 90.615963 224.711607 \nL 90.711428 166.884079 \nL 90.775071 192.560818 \nL 90.966 312.60506 \nL 90.997821 311.398033 \nL 91.061464 268.384009 \nL 91.125107 195.962437 \nL 91.18875 202.875406 \nL 91.284214 219.005665 \nL 91.379679 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212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 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253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 197.217872 308.106144 \nL 197.440622 167.981376 \nL 197.59973 160.410029 \nL 197.727015 185.318661 \nL 197.917944 322.480728 \nL 197.981587 297.352638 \nL 198.236159 139.122477 \nL 198.331623 119.919787 \nL 198.363445 120.578165 \nL 198.490731 168.420294 \nL 198.713481 322.261269 \nL 198.745302 321.383432 \nL 199.063517 158.325166 \nL 199.12716 147.791119 \nL 199.158981 152.290035 \nL 199.286267 213.519182 \nL 199.477196 317.542894 \nL 199.509017 319.847217 \nL 199.604482 229.539711 \nL 199.763589 153.82625 \nL 199.827232 152.180305 \nL 199.890875 142.194907 \nL 199.922697 149.437064 \nL 200.049982 220.212691 \nL 200.20909 311.398033 \nL 200.272733 292.305074 \nL 200.495483 131.441401 \nL 200.527304 126.064648 \nL 200.590947 133.965183 \nL 200.622769 133.087346 \nL 200.65459 133.306805 \nL 200.781876 176.2111 \nL 200.972805 319.627757 \nL 201.036448 302.94885 \nL 201.227377 151.851116 \nL 201.259198 152.399765 \nL 201.29102 152.180305 \nL 201.322841 152.619224 \nL 201.354662 152.948413 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 209.819172 114.104115 \nL 209.946458 145.377067 \nL 210.169208 297.352638 \nL 210.20103 298.998582 \nL 210.264673 252.363479 \nL 210.42378 123.540866 \nL 210.455601 124.089514 \nL 210.519244 127.052215 \nL 210.551066 124.857622 \nL 210.582887 122.663028 \nL 210.614709 126.613296 \nL 210.710173 158.215436 \nL 210.901102 282.429405 \nL 210.964745 257.630503 \nL 211.219317 61.8728 \nL 211.251138 59.678207 \nL 211.28296 63.079826 \nL 211.442067 94.901426 \nL 211.664817 273.870492 \nL 211.72846 250.717535 \nL 211.95121 91.828996 \nL 212.078496 63.847934 \nL 212.110318 65.274419 \nL 212.237604 119.590598 \nL 212.428532 259.825096 \nL 212.492175 235.026194 \nL 212.683104 85.684135 \nL 212.714926 86.013324 \nL 212.746747 91.060888 \nL 212.81039 83.928461 \nL 212.842211 84.03819 \nL 212.905854 100.497638 \nL 213.064962 236.123491 \nL 213.192248 300.205609 \nL 213.224069 295.158045 \nL 213.319533 209.898103 \nL 213.510462 147.900849 \nL 213.828677 273.431573 \nL 213.955963 325.662888 \nL 213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 218.983754 162.055974 \nL 219.015576 162.385163 \nL 219.11104 187.622984 \nL 219.301969 310.300737 \nL 219.365612 281.880756 \nL 219.556541 157.995977 \nL 219.683826 161.726785 \nL 219.620183 157.666788 \nL 219.715648 160.410029 \nL 219.747469 161.178137 \nL 219.874755 183.562986 \nL 220.065684 319.737487 \nL 220.097505 319.079109 \nL 220.161148 297.462367 \nL 220.352077 172.80948 \nL 220.383899 175.442992 \nL 220.63847 211.763507 \nL 220.829399 325.22397 \nL 220.861221 324.016944 \nL 220.924864 310.191007 \nL 221.115792 208.471618 \nL 221.147614 210.117563 \nL 221.179435 211.214859 \nL 221.211257 210.88567 \nL 221.2749 200.461353 \nL 221.306721 201.44892 \nL 221.434007 256.533206 \nL 221.593114 330.271534 \nL 221.656757 303.168309 \nL 221.911329 131.55113 \nL 222.006793 125.40627 \nL 222.038615 127.052215 \nL 222.165901 168.420294 \nL 222.356829 309.861818 \nL 222.420472 285.392105 \nL 222.675044 125.845188 \nL 222.738687 129.246808 \nL 222.865973 155.252736 \nL 222.897794 153.058143 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227.702836 315.238571 \nL 227.766479 291.427236 \nL 228.02105 135.391668 \nL 228.116515 126.503566 \nL 228.148336 128.039781 \nL 228.275622 168.200835 \nL 228.466551 311.727222 \nL 228.530194 285.831024 \nL 228.784765 105.764662 \nL 228.848408 100.278179 \nL 228.912051 102.143583 \nL 229.039337 142.085177 \nL 229.230266 295.377504 \nL 229.262087 291.536966 \nL 229.548481 84.696568 \nL 229.643945 97.864127 \nL 229.803052 143.182474 \nL 229.993981 278.369407 \nL 230.057624 251.156453 \nL 230.280374 103.789528 \nL 230.344017 117.396005 \nL 230.598589 209.239725 \nL 230.757696 299.108312 \nL 230.789518 298.559664 \nL 230.853161 280.564001 \nL 231.075911 157.666788 \nL 231.26684 168.310565 \nL 231.298661 167.761916 \nL 231.553233 301.632094 \nL 231.585054 296.4748 \nL 231.648697 262.239148 \nL 231.839626 164.908945 \nL 231.935091 146.254904 \nL 231.966912 138.573828 \nL 232.030555 148.120308 \nL 232.062376 146.035445 \nL 232.285127 300.863987 \nL 232.316948 295.048315 \nL 232.635163 98.961423 \nL 232.698806 100.168449 \nL 232.730627 101.704665 \nL 232.857913 151.302468 \nL 233.048842 295.487234 \nL 233.080663 291.207777 \nL 233.367056 101.046287 \nL 233.430699 107.191147 \nL 233.557985 130.014915 \nL 233.589807 127.710593 \nL 233.812557 294.280207 \nL 233.844378 289.561832 \nL 234.194415 111.690063 \nL 234.258057 119.480868 \nL 234.385343 155.801384 \nL 234.576272 292.414803 \nL 234.608094 288.245076 \nL 234.894487 125.625729 \nL 234.926308 127.491133 \nL 235.085416 148.559227 \nL 235.117237 147.68139 \nL 235.339987 312.166141 \nL 235.371809 306.569929 \nL 235.721845 120.687895 \nL 235.753666 121.346273 \nL 235.880952 144.608959 \nL 236.135524 304.046147 \nL 236.167345 296.4748 \nL 236.549203 193.877574 \nL 236.676489 220.322421 \nL 236.899239 337.513691 \nL 236.962882 327.528292 \nL 237.185632 225.589444 \nL 237.217454 226.79647 \nL 237.249275 224.272688 \nL 237.408383 203.314324 \nL 237.631133 332.466127 \nL 237.662954 326.211537 \nL 237.949347 160.958678 \nL 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258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 315.75283 171.382995 \nL 315.880116 189.488388 \nL 315.911937 182.026771 \nL 315.97558 135.281939 \nL 316.039223 155.252736 \nL 316.134688 222.955932 \nL 316.293795 295.487234 \nL 316.357438 280.125082 \nL 316.516545 184.221364 \nL 316.548367 185.977039 \nL 316.643831 197.279193 \nL 316.675652 191.682981 \nL 316.739295 154.704087 \nL 316.802938 177.198666 \nL 317.05751 323.578025 \nL 317.152974 296.4748 \nL 317.312082 212.531615 \nL 317.343903 215.165127 \nL 317.407546 223.065662 \nL 317.439368 216.262423 \nL 317.503011 165.238134 \nL 317.566653 190.256495 \nL 317.789404 338.610988 \nL 317.853047 324.785051 \nL 318.043975 214.28729 \nL 318.171261 228.332685 \nL 318.203083 224.272688 \nL 318.266726 161.397596 \nL 318.330369 174.674884 \nL 318.553119 330.381263 \nL 318.616762 319.518028 \nL 318.807691 200.790542 \nL 318.839512 203.424054 \nL 318.934976 209.349455 \nL 318.966798 204.411621 \nL 319.030441 154.923547 \nL 319.094084 172.480291 \nL 319.348656 336.745583 \nL 319.380477 327.089374 \nL 319.475941 219.993232 \nL 319.603227 220.212691 \nL 319.635049 220.871069 \nL 319.66687 219.225124 \nL 319.698692 219.334854 \nL 319.825978 144.27977 \nL 319.88962 169.956509 \nL 320.080549 296.145611 \nL 320.112371 295.158045 \nL 320.176014 248.852131 \nL 320.239657 173.906777 \nL 320.303299 176.979207 \nL 320.462407 199.583516 \nL 320.494228 195.413789 \nL 320.557871 129.246808 \nL 320.621514 138.13491 \nL 320.844264 294.719126 \nL 320.907907 283.087783 \nL 321.003372 177.308396 \nL 321.098836 183.233797 \nL 321.226122 204.74081 \nL 321.257943 199.144597 \nL 321.321586 157.227869 \nL 321.385229 179.722449 \nL 321.639801 331.039641 \nL 321.703444 314.360734 \nL 321.862551 207.264592 \nL 321.926194 212.641345 \nL 321.989837 222.297554 \nL 322.021659 217.46945 \nL 322.085302 175.881911 \nL 322.148944 200.900272 \nL 322.403516 336.196935 \nL 322.467159 323.248836 \nL 322.658088 232.392682 \nL 322.689909 235.245653 \nL 322.753552 243.475378 \nL 322.785374 235.026194 \nL 322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 340.923609 150.973279 \nL 340.95543 151.083009 \nL 341.082716 162.933811 \nL 341.114538 159.312733 \nL 341.178181 98.741964 \nL 341.241824 109.38574 \nL 341.464574 253.460776 \nL 341.528217 240.293218 \nL 341.623681 133.087346 \nL 341.719146 138.464099 \nL 341.846431 158.434896 \nL 341.878253 156.350032 \nL 341.941896 100.05872 \nL 342.005539 111.141415 \nL 342.26011 265.201849 \nL 342.291932 254.448343 \nL 342.451039 145.377067 \nL 342.482861 145.596526 \nL 342.641968 174.674884 \nL 342.705611 132.209508 \nL 342.769254 155.362465 \nL 343.023826 302.619661 \nL 343.087469 286.489402 \nL 343.246576 199.802975 \nL 343.310219 208.910536 \nL 343.373862 219.225124 \nL 343.405683 214.616478 \nL 343.469326 153.716521 \nL 343.532969 171.053806 \nL 343.787541 298.011016 \nL 343.851184 287.476969 \nL 344.010291 198.705679 \nL 344.073934 203.862972 \nL 344.137577 216.372153 \nL 344.169398 214.17756 \nL 344.233041 148.668956 \nL 344.296684 173.138669 \nL 344.519435 307.228306 \nL 344.551256 304.375335 \nL 344.614899 260.593204 \nL 344.774006 175.662451 \nL 344.901292 193.658115 \nL 344.933114 188.83001 \nL 345.028578 123.650595 \nL 345.060399 137.695991 \nL 345.28315 282.868323 \nL 345.346793 271.456439 \nL 345.474078 169.298131 \nL 345.537721 177.418126 \nL 345.665007 199.473786 \nL 345.696829 196.291626 \nL 345.760472 135.940317 \nL 345.824115 150.973279 \nL 346.046865 299.87642 \nL 346.110508 287.03805 \nL 346.205972 176.10137 \nL 346.301437 181.148934 \nL 346.428722 187.952173 \nL 346.460544 184.550553 \nL 346.524187 138.354369 \nL 346.58783 157.557058 \nL 346.842401 309.532629 \nL 346.874223 297.681827 \nL 347.065152 190.146766 \nL 347.192438 207.264592 \nL 347.224259 204.960269 \nL 347.287902 148.339767 \nL 347.351545 162.494893 \nL 347.574295 305.801821 \nL 347.637938 294.170478 \nL 347.797045 186.196498 \nL 347.828867 187.513254 \nL 347.956153 199.583516 \nL 347.987974 199.473786 \nL 348.051617 159.641922 \nL 348.11526 183.014338 \nL 348.369832 324.565592 \nL 348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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162.385163 \nL 356.35702 164.140838 \nL 356.388841 162.604622 \nL 356.452484 109.056551 \nL 356.516127 120.468435 \nL 356.738877 256.094288 \nL 356.80252 242.158622 \nL 356.993449 134.184642 \nL 357.088913 145.047878 \nL 357.120735 148.998145 \nL 357.152556 146.474363 \nL 357.248021 88.098187 \nL 357.279842 100.607368 \nL 357.502592 239.195921 \nL 357.566235 225.918633 \nL 357.6617 116.408438 \nL 357.757164 121.126813 \nL 357.916271 139.012747 \nL 358.011736 87.988458 \nL 358.043557 100.497638 \nL 358.298129 244.572674 \nL 358.32995 232.941331 \nL 358.457236 127.052215 \nL 358.520879 130.344104 \nL 358.679987 181.148934 \nL 358.711808 162.165704 \nL 358.743629 123.101947 \nL 358.807272 138.903017 \nL 359.061844 272.334277 \nL 359.125487 252.034291 \nL 359.252773 166.993809 \nL 359.316416 171.492724 \nL 359.41188 192.231629 \nL 359.443702 186.854876 \nL 359.507345 129.795456 \nL 359.570988 146.693823 \nL 359.825559 270.029954 \nL 359.889202 259.166718 \nL 360.04831 178.295963 \nL 360.111952 184.660283 \nL 360.175595 198.37649 \nL 360.207417 194.97487 \nL 360.302881 126.942485 \nL 360.334703 142.633825 \nL 360.557453 272.992654 \nL 360.621096 260.044555 \nL 360.780203 149.546794 \nL 360.812025 150.314901 \nL 360.939311 163.921378 \nL 360.971132 160.848948 \nL 361.034775 119.261409 \nL 361.098418 149.107875 \nL 361.35299 288.683995 \nL 361.416633 247.206186 \nL 361.480275 172.91921 \nL 361.543918 174.016507 \nL 361.57574 174.235966 \nL 361.703026 187.184065 \nL 361.734847 185.757579 \nL 361.830312 137.695991 \nL 361.862133 152.180305 \nL 362.116705 306.899118 \nL 362.148526 299.437501 \nL 362.339455 182.136501 \nL 362.371277 183.014338 \nL 362.466741 195.413789 \nL 362.498562 194.865141 \nL 362.562205 153.497061 \nL 362.625848 173.138669 \nL 362.88042 323.907214 \nL 362.912241 314.799653 \nL 363.10317 206.715943 \nL 363.134992 209.568914 \nL 363.230456 219.993232 \nL 363.294099 194.97487 \nL 363.357742 150.534361 \nL 363.389563 159.861381 \nL 363.612314 292.853722 \nL 363.675957 281.002919 \nL 363.866885 170.505158 \nL 363.96235 181.478123 \nL 364.025993 187.952173 \nL 364.121457 139.451665 \nL 364.153279 150.095442 \nL 364.40785 275.187248 \nL 364.471493 258.61807 \nL 364.630601 179.064071 \nL 364.662422 182.026771 \nL 364.757886 197.827841 \nL 364.789708 195.633248 \nL 364.853351 150.534361 \nL 364.916994 166.774349 \nL 365.171565 292.524533 \nL 365.235208 280.125082 \nL 365.426137 192.560818 \nL 365.48978 199.802975 \nL 365.521602 202.436487 \nL 365.553423 196.511086 \nL 365.617066 144.060311 \nL 365.680709 175.772181 \nL 365.903459 291.427236 \nL 365.967102 276.613733 \nL 366.158031 168.420294 \nL 366.189852 168.091105 \nL 366.285317 178.954341 \nL 366.317138 176.979207 \nL 366.412603 119.480868 \nL 366.476246 149.107875 \nL 366.667174 268.493739 \nL 366.698996 267.835361 \nL 366.762639 222.626743 \nL 366.826282 150.973279 \nL 366.889925 159.971111 \nL 367.01721 180.161367 \nL 367.049032 186.635417 \nL 367.080853 185.318661 \nL 367.176318 124.199244 \nL 367.239961 155.362465 \nL 367.462711 281.771027 \nL 367.526354 239.63484 \nL 367.621818 165.786782 \nL 367.65364 166.55489 \nL 367.812747 188.83001 \nL 367.844569 188.61055 \nL 367.940033 130.563564 \nL 368.003676 155.691654 \nL 368.194605 271.34671 \nL 368.226426 270.139683 \nL 368.290069 219.993232 \nL 368.385533 144.938148 \nL 368.417355 150.64409 \nL 368.608284 179.722449 \nL 368.703748 123.321406 \nL 368.767391 146.803552 \nL 368.990141 271.785628 \nL 369.053784 232.612142 \nL 369.244713 127.710593 \nL 369.371999 152.399765 \nL 369.40382 143.950581 \nL 369.435642 115.85979 \nL 369.499285 146.254904 \nL 369.753856 282.209945 \nL 369.817499 270.139683 \nL 370.008428 178.625152 \nL 370.04025 181.478123 \nL 370.135714 192.231629 \nL 370.231178 138.244639 \nL 370.263 154.594358 \nL 370.517572 282.209945 \nL 370.549393 278.369407 \nL 370.7085 200.241894 \nL 370.803965 214.397019 \nL 370.867608 230.746738 \nL 370.899429 227.125659 \nL 370.994894 154.155439 \nL 371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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159.203003 \nL 389.3877 188.72028 \nL 389.578629 316.884516 \nL 389.610451 316.555327 \nL 389.674094 271.12725 \nL 389.833201 188.61055 \nL 389.865022 189.488388 \nL 389.992308 201.55865 \nL 390.087773 157.337599 \nL 390.151416 185.099202 \nL 390.342344 310.959115 \nL 390.374166 310.520196 \nL 390.437809 269.152117 \nL 390.596916 186.745146 \nL 390.660559 196.181897 \nL 390.756024 215.494316 \nL 390.787845 210.666211 \nL 390.851488 177.747315 \nL 390.883309 188.939739 \nL 391.10606 317.981813 \nL 391.201524 297.462367 \nL 391.32881 229.320252 \nL 391.392453 237.111058 \nL 391.519739 263.007256 \nL 391.55156 256.094288 \nL 391.615203 206.606214 \nL 391.678846 227.015929 \nL 391.901596 326.211537 \nL 391.933418 325.22397 \nL 392.028882 286.92832 \nL 392.156168 237.440247 \nL 392.187989 242.158622 \nL 392.283454 261.141852 \nL 392.315275 256.423477 \nL 392.41074 198.486219 \nL 392.442561 210.446752 \nL 392.63349 331.47856 \nL 392.665311 330.161804 \nL 392.728954 288.793725 \nL 392.888062 207.045132 \nL 392.983526 220.43215 \nL 393.047169 227.345118 \nL 393.07899 222.078095 \nL 393.174455 181.807312 \nL 393.206276 196.511086 \nL 393.429027 329.283967 \nL 393.49267 289.232643 \nL 393.651777 198.37649 \nL 393.683598 194.097033 \nL 393.747241 199.254327 \nL 393.810884 210.117563 \nL 393.842706 202.875406 \nL 393.93817 178.625152 \nL 393.969991 193.548385 \nL 394.192742 330.929912 \nL 394.256385 290.11048 \nL 394.415492 207.154862 \nL 394.542778 223.394851 \nL 394.574599 229.8689 \nL 394.606421 226.9062 \nL 394.701885 188.391091 \nL 394.733707 197.059734 \nL 394.924635 317.542894 \nL 394.956457 316.006679 \nL 395.0201 274.748329 \nL 395.147386 195.633248 \nL 395.179207 195.19433 \nL 395.338314 210.446752 \nL 395.370136 207.045132 \nL 395.4656 164.908945 \nL 395.497422 172.041373 \nL 395.688351 294.170478 \nL 395.720172 293.621829 \nL 395.783815 253.460776 \nL 395.879279 177.966774 \nL 395.911101 178.734882 \nL 395.974744 187.622984 \nL 396.10203 221.309987 \nL 396.133851 219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 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214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 406.475827 286.708861 \nL 406.571292 209.459185 \nL 406.634935 213.628912 \nL 406.825863 237.549976 \nL 406.857685 233.489979 \nL 406.921328 218.237557 \nL 406.953149 221.858636 \nL 407.1759 334.66072 \nL 407.207721 329.613156 \nL 407.303185 269.152117 \nL 407.39865 234.367816 \nL 407.430471 239.086191 \nL 407.557757 268.822928 \nL 407.589579 267.067253 \nL 407.716864 232.612142 \nL 407.748686 252.802398 \nL 407.907793 339.269365 \nL 407.939615 337.84288 \nL 408.003258 323.139106 \nL 408.130544 242.378081 \nL 408.194186 248.742401 \nL 408.321472 283.526701 \nL 408.353294 278.149948 \nL 408.448758 234.258087 \nL 408.48058 239.195921 \nL 408.671508 342.890444 \nL 408.70333 340.91531 \nL 408.766973 294.499667 \nL 408.92608 203.314324 \nL 408.989723 209.788374 \nL 409.085187 219.664043 \nL 409.117009 217.24999 \nL 409.212473 185.53812 \nL 409.244295 191.353792 \nL 409.467045 316.555327 \nL 409.530688 271.456439 \nL 409.689795 193.877574 \nL 409.848903 211.544048 \nL 409.976189 173.906777 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319.737487 \nL 413.540193 227.564578 \nL 413.603836 244.024026 \nL 413.667478 254.119154 \nL 413.6993 249.400779 \nL 413.794764 219.883502 \nL 413.826586 221.748906 \nL 414.017515 342.561255 \nL 414.049336 341.463959 \nL 414.112979 295.926152 \nL 414.272086 212.860804 \nL 414.463015 243.255918 \nL 414.494837 237.659706 \nL 414.558479 213.409452 \nL 414.590301 214.06783 \nL 414.78123 341.244499 \nL 414.813051 340.91531 \nL 414.876694 297.572097 \nL 415.035801 207.264592 \nL 415.099444 211.982967 \nL 415.22673 231.075927 \nL 415.290373 222.846203 \nL 415.322195 208.471618 \nL 415.354016 210.007833 \nL 415.544945 343.658552 \nL 415.576766 343.329363 \nL 415.640409 301.741824 \nL 415.767695 215.823505 \nL 415.799517 216.701342 \nL 415.958624 234.367816 \nL 415.990445 231.734305 \nL 416.08591 204.850539 \nL 416.117731 213.738641 \nL 416.30866 345.194767 \nL 416.340482 343.768281 \nL 416.404124 299.327771 \nL 416.53141 221.858636 \nL 416.563232 221.968365 \nL 416.595053 222.517014 \nL 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331.58829 \nL 498.089826 330.381263 \nL 498.153469 308.435333 \nL 498.344397 190.914873 \nL 498.376219 191.024603 \nL 498.439862 185.099202 \nL 498.503505 173.797047 \nL 498.535326 177.418126 \nL 498.662612 228.661874 \nL 498.853541 322.151539 \nL 498.917184 307.886684 \nL 499.171756 188.171632 \nL 499.203577 190.037036 \nL 499.26722 177.637585 \nL 499.330863 181.039204 \nL 499.362684 183.233797 \nL 499.585435 312.824519 \nL 499.617256 303.716958 \nL 499.935471 127.381404 \nL 499.967292 128.039781 \nL 499.999114 127.600863 \nL 500.1264 151.192738 \nL 500.34915 310.849385 \nL 500.412793 287.696428 \nL 500.635543 147.791119 \nL 500.79465 143.072744 \nL 500.921936 184.331094 \nL 501.112865 318.530461 \nL 501.176508 292.743992 \nL 501.399258 149.875983 \nL 501.526544 138.573828 \nL 501.558365 140.439232 \nL 501.685651 179.832178 \nL 501.87658 315.896949 \nL 501.940223 291.317507 \nL 502.162973 136.269506 \nL 502.194795 131.66086 \nL 502.226616 135.830587 \nL 502.449366 185.977039 \nL 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531.725114 315.128842 \nL 531.884222 221.639176 \nL 531.979686 219.334854 \nL 532.043329 225.260255 \nL 532.075151 224.272688 \nL 532.170615 199.802975 \nL 532.202436 213.189993 \nL 532.425187 347.828278 \nL 532.48883 312.056411 \nL 532.647937 202.985135 \nL 532.743401 198.595949 \nL 532.807044 202.546217 \nL 532.838866 197.279193 \nL 532.93433 173.138669 \nL 532.966152 186.196498 \nL 533.188902 320.725054 \nL 533.252545 281.002919 \nL 533.443474 120.797624 \nL 533.602581 79.319815 \nL 533.634402 82.831164 \nL 533.698045 82.063056 \nL 533.729867 103.789528 \nL 533.952617 287.03805 \nL 533.984439 281.880756 \nL 534.207189 129.795456 \nL 534.270832 134.95275 \nL 534.334475 148.230038 \nL 534.398118 141.426799 \nL 534.429939 141.31707 \nL 534.46176 142.633825 \nL 534.589046 263.116986 \nL 534.716332 309.532629 \nL 534.748154 302.290472 \nL 534.970904 168.420294 \nL 535.002725 170.066239 \nL 535.034547 171.163536 \nL 535.32094 269.042387 \nL 535.480047 344.755848 \nL 535.54369 332.466127 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228.991063 \nL 545.853845 225.479714 \nL 545.917488 211.434319 \nL 545.949309 227.015929 \nL 546.17206 353.973139 \nL 546.203881 351.668816 \nL 546.39481 248.193753 \nL 546.522096 261.471041 \nL 546.553917 261.141852 \nL 546.681203 229.100793 \nL 546.713025 245.88943 \nL 546.935775 361.325026 \nL 546.967596 358.910974 \nL 547.063061 308.215873 \nL 547.190347 255.545639 \nL 547.222168 257.740233 \nL 547.285811 265.201849 \nL 547.317632 262.458608 \nL 547.444918 213.189993 \nL 547.47674 231.844034 \nL 547.667669 352.985572 \nL 547.69949 352.327194 \nL 547.763133 311.398033 \nL 547.92224 218.676476 \nL 548.017705 215.933234 \nL 548.081348 223.72404 \nL 548.113169 220.761339 \nL 548.208633 186.086768 \nL 548.240455 207.593781 \nL 548.463205 347.169901 \nL 548.526848 306.679658 \nL 548.685955 214.616478 \nL 548.78142 209.129996 \nL 548.813241 210.9954 \nL 548.845063 211.982967 \nL 548.876884 209.678644 \nL 548.972349 183.782446 \nL 549.22692 344.865578 \nL 549.258742 335.867746 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 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199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 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150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 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697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 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\nL 711.707322 255.106721 \nL 711.898251 345.962874 \nL 711.961894 333.343964 \nL 712.121001 255.984558 \nL 712.184644 263.775363 \nL 712.248287 274.089951 \nL 712.31193 270.578602 \nL 712.439216 250.607805 \nL 712.661966 354.302328 \nL 712.693787 352.327194 \nL 712.789252 308.764522 \nL 712.916538 252.473209 \nL 712.948359 253.460776 \nL 713.043823 260.702933 \nL 713.202931 237.879165 \nL 713.393859 344.31693 \nL 713.425681 342.341796 \nL 713.489324 302.290472 \nL 713.648431 219.773772 \nL 713.680253 220.102961 \nL 713.807539 240.402947 \nL 713.871181 235.245653 \nL 713.934824 229.97863 \nL 713.966646 220.322421 \nL 714.157575 340.805581 \nL 714.189396 338.391528 \nL 714.253039 301.522364 \nL 714.412146 231.514845 \nL 714.443968 229.649441 \nL 714.475789 234.148357 \nL 714.571254 240.841866 \nL 714.603075 239.63484 \nL 714.730361 218.566746 \nL 714.92129 339.927743 \nL 714.984933 325.772618 \nL 715.112219 234.367816 \nL 715.175862 237.001328 \nL 715.36679 245.121322 \nL 715.462255 236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 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158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 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339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 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204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 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222.626743 \nL 852.103626 191.682981 \nL 852.135448 200.461353 \nL 852.39002 332.356397 \nL 852.421841 330.052075 \nL 852.485484 283.85589 \nL 852.61277 217.908368 \nL 852.644591 218.127827 \nL 852.676413 219.444583 \nL 852.771877 228.881334 \nL 852.83552 202.217028 \nL 852.867342 172.480291 \nL 852.930985 188.939739 \nL 853.153735 324.016944 \nL 853.217378 310.191007 \nL 853.408307 208.032699 \nL 853.535592 220.761339 \nL 853.567414 217.140261 \nL 853.631057 177.308396 \nL 853.6947 192.451088 \nL 853.91745 332.356397 \nL 853.981093 320.725054 \nL 854.108379 216.481883 \nL 854.203843 220.980798 \nL 854.299308 232.831601 \nL 854.331129 231.624575 \nL 854.394772 181.258664 \nL 854.458415 197.169464 \nL 854.681165 338.940176 \nL 854.744808 325.882348 \nL 854.872094 210.9954 \nL 854.935737 211.214859 \nL 854.967558 210.775941 \nL 854.99938 215.274856 \nL 855.063023 214.06783 \nL 855.126666 188.281362 \nL 855.158487 172.041373 \nL 855.22213 193.109466 \nL 855.44488 330.052075 \nL 855.508523 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "dddca9ad9e34435494e0933c218e1579",
+   "translation_date": "2025-09-06T14:01:21+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sr/7-TimeSeries/1-Introduction/working/notebook.ipynb b/translations/sr/7-TimeSeries/1-Introduction/working/notebook.ipynb
new file mode 100644
index 000000000..ff63ca998
--- /dev/null
+++ b/translations/sr/7-TimeSeries/1-Introduction/working/notebook.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Подешавање података\n",
+    "\n",
+    "У овом нотебуку, показујемо како да:\n",
+    "\n",
+    "припремите временске серије података за овај модул  \n",
+    "визуелизујете податке  \n",
+    "Подаци у овом примеру преузети су из такмичења за прогнозирање GEFCom2014. Они обухватају 3 године података о сатној потрошњи електричне енергије и вредностима температуре у периоду од 2012. до 2014. године.\n",
+    "\n",
+    "1Таао Хонг, Пјер Пинсон, Шу Фан, Хамидреза Зареипур, Алберто Троколи и Роб Џ. Хајндман, \"Прогнозирање енергије са вероватноћом: Глобално такмичење за прогнозирање енергије 2014 и даље\", International Journal of Forecasting, вол.32, бр.3, стр. 896-913, јул-септембар, 2016.  \n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5e2bbe594906dce3aaaa736d6dac6683",
+   "translation_date": "2025-09-06T14:02:20+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/working/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sr/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/translations/sr/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
new file mode 100644
index 000000000..54c4c4f46
--- /dev/null
+++ b/translations/sr/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -0,0 +1,1140 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Тао Хонг, Пјер Пинсон, Шу Фан, Хамидреза Зареипур, Алберто Троколи и Роб Џ. Хајндман, \"Прогностичко предвиђање енергије: Глобално такмичење у предвиђању енергије 2014 и даље\", International Journal of Forecasting, вол.32, бр.3, стр. 896-913, јул-септембар, 2016.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Инсталирајте зависности\n",
+    "Започните инсталирањем неких потребних зависности. Познато је да ове библиотеке са одговарајућим верзијама функционишу за решење:\n",
+    "\n",
+    "* `statsmodels == 0.12.2`\n",
+    "* `matplotlib == 3.4.2`\n",
+    "* `scikit-learn == 0.24.2`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "!pip install statsmodels"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "/bin/sh: pip: command not found\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from pandas.plotting import autocorrelation_plot\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "%matplotlib inline\n",
+    "pd.options.display.float_format = '{:,.2f}'.format\n",
+    "np.set_printoptions(precision=2)\n",
+    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load\n",
+       "2012-01-01 00:00:00 2,698.00\n",
+       "2012-01-01 01:00:00 2,558.00\n",
+       "2012-01-01 02:00:00 2,444.00\n",
+       "2012-01-01 03:00:00 2,402.00\n",
+       "2012-01-01 04:00:00 2,403.00\n",
+       "2012-01-01 05:00:00 2,453.00\n",
+       "2012-01-01 06:00:00 2,560.00\n",
+       "2012-01-01 07:00:00 2,719.00\n",
+       "2012-01-01 08:00:00 2,916.00\n",
+       "2012-01-01 09:00:00 3,105.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Прикажи све доступне податке о оптерећењу (јануар 2012. до децембар 2014.)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Креирајте скупове података за обуку и тестирање\n",
+    "\n",
+    "### Подела података на скупове за обуку и тестирање\n",
+    "\n",
+    "Када радите са машинским учењем, важно је да поделите своје податке на два дела: скуп за обуку и скуп за тестирање. Ова подела омогућава да се модел обучи на једном делу података, а затим процени његова тачност на другом, независном скупу.\n",
+    "\n",
+    "### Зашто је ова подела важна?\n",
+    "\n",
+    "Подела података помаже у процени како ће модел функционисати на новим, невидљивим подацима. Ако модел тестирате на истим подацима на којима је обучен, резултати могу бити пристрасни и неће одражавати стварну перформансу модела.\n",
+    "\n",
+    "### Како поделити податке?\n",
+    "\n",
+    "Ево неколико корака које можете пратити:\n",
+    "\n",
+    "1. **Случајно мешање података**  \n",
+    "   Пре него што поделите податке, уверите се да су добро измешани. Ово осигурава да подаци буду равномерно распоређени између скупа за обуку и скупа за тестирање.\n",
+    "\n",
+    "2. **Одређивање пропорције**  \n",
+    "   Уобичајена пракса је да се 70-80% података користи за обуку, а преосталих 20-30% за тестирање. На пример, ако имате 1000 узорака, можете користити 800 за обуку и 200 за тестирање.\n",
+    "\n",
+    "3. **Коришћење библиотека**  \n",
+    "   Многе библиотеке за машинско учење, као што је scikit-learn, нуде уграђене функције за поделу података. На пример, функција `train_test_split` може аутоматски поделити ваше податке.\n",
+    "\n",
+    "### Пример кода\n",
+    "\n",
+    "Ево примера како можете поделити податке користећи Python и scikit-learn:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Претпоставимо да имате податке X и ознаке y\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(\"Број узорака у скупу за обуку:\", len(X_train))\n",
+    "print(\"Број узорака у скупу за тестирање:\", len(X_test))\n",
+    "```\n",
+    "\n",
+    "### Савети\n",
+    "\n",
+    "- **Чување репродуктивности**  \n",
+    "  Користите параметар `random_state` у функцији `train_test_split` како бисте осигурали да је подела података увек иста сваки пут када покренете код.\n",
+    "\n",
+    "- **Проверите расподелу класа**  \n",
+    "  Ако радите са класификационим проблемом, уверите се да је расподела класа слична у оба скупа. Ово можете постићи коришћењем параметра `stratify` у функцији `train_test_split`.\n",
+    "\n",
+    "- **Избегавајте цурење података**  \n",
+    "  Уверите се да подаци из скупа за тестирање не утичу на процес обуке. На пример, ако радите са временским серијама, подаци треба да буду подељени хронолошки, а не случајно.\n",
+    "\n",
+    "### Закључак\n",
+    "\n",
+    "Подела података на скуп за обуку и скуп за тестирање је кључни корак у машинском учењу. Правилна подела осигурава да ваш модел буде добро обучен и да се његова перформанса реално процени.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-11-01 00:00:00  0.10\n",
+       "2014-11-01 01:00:00  0.07\n",
+       "2014-11-01 02:00:00  0.05\n",
+       "2014-11-01 03:00:00  0.04\n",
+       "2014-11-01 04:00:00  0.06\n",
+       "2014-11-01 05:00:00  0.10\n",
+       "2014-11-01 06:00:00  0.19\n",
+       "2014-11-01 07:00:00  0.31\n",
+       "2014-11-01 08:00:00  0.40\n",
+       "2014-11-01 09:00:00  0.48"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Оригинални наспрам скалираних података:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Хајде да такође скалирамо тест податке\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-12-30 00:00:00  0.33\n",
+       "2014-12-30 01:00:00  0.29\n",
+       "2014-12-30 02:00:00  0.27\n",
+       "2014-12-30 03:00:00  0.27\n",
+       "2014-12-30 04:00:00  0.30"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Forecasting horizon: 3 hours\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "order = (4, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
+    "results = model.fit()\n",
+    "\n",
+    "print(results.summary())\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Thu, 30 Sep 2021   AIC                          -6942.477\n",
+      "Time:                                    14:36:28   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Процените модел\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Креирајте тестну тачку података за сваки корак HORIZON.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load  load+1  load+2\n",
+       "2014-12-30 00:00:00  0.33    0.29    0.27\n",
+       "2014-12-30 01:00:00  0.29    0.27    0.27\n",
+       "2014-12-30 02:00:00  0.27    0.27    0.30\n",
+       "2014-12-30 03:00:00  0.27    0.30    0.41\n",
+       "2014-12-30 04:00:00  0.30    0.41    0.57"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Направите предвиђања на тест подацима\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2014-12-30 00:00:00\n",
+      "1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
+      "2014-12-30 01:00:00\n",
+      "2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
+      "2014-12-30 02:00:00\n",
+      "3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
+      "2014-12-30 03:00:00\n",
+      "4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
+      "2014-12-30 04:00:00\n",
+      "5 : predicted = [0.3  0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
+      "2014-12-30 05:00:00\n",
+      "6 : predicted = [0.4  0.55 0.66] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
+      "2014-12-30 06:00:00\n",
+      "7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
+      "2014-12-30 07:00:00\n",
+      "8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
+      "2014-12-30 08:00:00\n",
+      "9 : predicted = [0.75 0.8  0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
+      "2014-12-30 09:00:00\n",
+      "10 : predicted = [0.77 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
+      "2014-12-30 10:00:00\n",
+      "11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
+      "2014-12-30 11:00:00\n",
+      "12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
+      "2014-12-30 12:00:00\n",
+      "13 : predicted = [0.7  0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
+      "2014-12-30 13:00:00\n",
+      "14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
+      "2014-12-30 14:00:00\n",
+      "15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
+      "2014-12-30 15:00:00\n",
+      "16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
+      "2014-12-30 16:00:00\n",
+      "17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
+      "2014-12-30 17:00:00\n",
+      "18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
+      "2014-12-30 18:00:00\n",
+      "19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
+      "2014-12-30 19:00:00\n",
+      "20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
+      "2014-12-30 20:00:00\n",
+      "21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
+      "2014-12-30 21:00:00\n",
+      "22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
+      "2014-12-30 22:00:00\n",
+      "23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
+      "2014-12-30 23:00:00\n",
+      "24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
+      "2014-12-31 00:00:00\n",
+      "25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
+      "2014-12-31 01:00:00\n",
+      "26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
+      "2014-12-31 02:00:00\n",
+      "27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
+      "2014-12-31 03:00:00\n",
+      "28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
+      "2014-12-31 04:00:00\n",
+      "29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
+      "2014-12-31 05:00:00\n",
+      "30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
+      "2014-12-31 06:00:00\n",
+      "31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
+      "2014-12-31 07:00:00\n",
+      "32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
+      "2014-12-31 08:00:00\n",
+      "33 : predicted = [0.79 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
+      "2014-12-31 09:00:00\n",
+      "34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
+      "2014-12-31 10:00:00\n",
+      "35 : predicted = [0.8  0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
+      "2014-12-31 11:00:00\n",
+      "36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
+      "2014-12-31 12:00:00\n",
+      "37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
+      "2014-12-31 13:00:00\n",
+      "38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
+      "2014-12-31 14:00:00\n",
+      "39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
+      "2014-12-31 15:00:00\n",
+      "40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
+      "2014-12-31 16:00:00\n",
+      "41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
+      "2014-12-31 17:00:00\n",
+      "42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
+      "2014-12-31 18:00:00\n",
+      "43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
+      "2014-12-31 19:00:00\n",
+      "44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
+      "2014-12-31 20:00:00\n",
+      "45 : predicted = [0.7  0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
+      "2014-12-31 21:00:00\n",
+      "46 : predicted = [0.6  0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
+      "CPU times: user 12min 15s, sys: 2min 39s, total: 14min 54s\n",
+      "Wall time: 2min 36s\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": true
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Упоредите предвиђања са стварним оптерећењем\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp    h  prediction   actual\n",
+       "0 2014-12-30 00:00:00  t+1    3,008.74 3,023.00\n",
+       "1 2014-12-30 01:00:00  t+1    2,955.53 2,935.00\n",
+       "2 2014-12-30 02:00:00  t+1    2,900.17 2,899.00\n",
+       "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
+       "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Израчунајте **средњу апсолутну процентуалну грешку (MAPE)** за све предвиђања\n",
+    "\n",
+    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "One step forecast MAPE:  0.5570581332313952 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Multi-step forecast MAPE:  1.1460048657704118 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Прикажи предвиђања у односу на стварне вредности за прву недељу тестног скупа\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "if(HORIZON == 1):\n",
+    "    ## Plotting single step forecast\n",
+    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
+    "\n",
+    "else:\n",
+    "    ## Plotting multi step forecast\n",
+    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
+    "\n",
+    "    fig = plt.figure(figsize=(15, 8))\n",
+    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        x = plot_df['timestamp'][(t-1):]\n",
+    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
+    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
+    "    \n",
+    "    ax.legend(loc='best')\n",
+    "    \n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "c193140200b9684da27e3890211391b6",
+   "translation_date": "2025-09-06T13:57:54+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sr/7-TimeSeries/2-ARIMA/working/notebook.ipynb b/translations/sr/7-TimeSeries/2-ARIMA/working/notebook.ipynb
new file mode 100644
index 000000000..b1e27df85
--- /dev/null
+++ b/translations/sr/7-TimeSeries/2-ARIMA/working/notebook.ipynb
@@ -0,0 +1,50 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "523ec472196307b3c4235337353c9ceb",
+   "translation_date": "2025-09-06T14:00:22+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/working/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "Тао Хонг, Пјер Пинсон, Шу Фан, Хамидреза Зареипур, Алберто Троколи и Роб Џ. Хиндман, \"Прогностичко предвиђање енергије: Глобално такмичење у предвиђању енергије 2014 и даље\", International Journal of Forecasting, вол.32, бр.3, стр. 896-913, јул-септембар, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install statsmodels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако настојимо да обезбедимо тачност, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/7-TimeSeries/3-SVR/solution/notebook.ipynb b/translations/sr/7-TimeSeries/3-SVR/solution/notebook.ipynb
new file mode 100644
index 000000000..10b1990bd
--- /dev/null
+++ b/translations/sr/7-TimeSeries/3-SVR/solution/notebook.ipynb
@@ -0,0 +1,1023 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "У овом бележнику демонстрирамо како да:\n",
+    "\n",
+    "- припремите 2Д временске серије за тренирање модела СВМ регресора  \n",
+    "- имплементирате СВР користећи РБФ језгро  \n",
+    "- евалуирате модел користећи графиконе и МАПЕ  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Увоз модула\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": [
+    "## Припрема података\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Учитај податке\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Сада треба да припремите податке за обуку извршавањем филтрирања и скалирања ваших података.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Скалирајте податке да буду у опсегу (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.101611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.065801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.046106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.042525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.059087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-11-01 00:00:00  0.101611\n",
+       "2014-11-01 01:00:00  0.065801\n",
+       "2014-11-01 02:00:00  0.046106\n",
+       "2014-11-01 03:00:00  0.042525\n",
+       "2014-11-01 04:00:00  0.059087"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.329454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.290063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.273948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.268129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.302596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-12-30 00:00:00  0.329454\n",
+       "2014-12-30 01:00:00  0.290063\n",
+       "2014-12-30 02:00:00  0.273948\n",
+       "2014-12-30 03:00:00  0.268129\n",
+       "2014-12-30 04:00:00  0.302596"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": [
+    "### Креирање података са временским корацима\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "За наш СВР, трансформишемо улазне податке у облик `[batch, timesteps]`. Дакле, преобликујемо постојеће `train_data` и `test_data` тако да постоји нова димензија која се односи на временске кораке. У нашем примеру, узимамо `timesteps = 5`. Дакле, улази у модел су подаци за прва 4 временска корака, а излаз ће бити подаци за 5.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1412, 5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=np.array([[j for j in train_data[i:i+timesteps]] for i in range(0,len(train_data)-timesteps+1)])[:,:,0]\n",
+    "train_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(44, 5)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=np.array([[j for j in test_data[i:i+timesteps]] for i in range(0,len(test_data)-timesteps+1)])[:,:,0]\n",
+    "test_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 4) (1412, 1)\n",
+      "(44, 4) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train, y_train = train_data_timesteps[:,:timesteps-1],train_data_timesteps[:,[timesteps-1]]\n",
+    "x_test, y_test = test_data_timesteps[:,:timesteps-1],test_data_timesteps[:,[timesteps-1]]\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = SVR(kernel='rbf',gamma=0.5, C=10, epsilon = 0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVR(C=10, cache_size=200, coef0=0.0, degree=3, epsilon=0.05, gamma=0.5,\n",
+       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit model on training data\n",
+    "\n",
+    "model.fit(x_train, y_train[:,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Направи предвиђање модела\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 1) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = model.predict(x_train).reshape(-1,1)\n",
+    "y_test_pred = model.predict(x_test).reshape(-1,1)\n",
+    "\n",
+    "print(y_train_pred.shape, y_test_pred.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
+    "\n",
+    "print(len(y_train_pred), len(y_test_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)\n",
+    "\n",
+    "print(len(y_train), len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)].index[timesteps-1:]\n",
+    "test_timestamps = energy[test_start_dt:].index[timesteps-1:]\n",
+    "\n",
+    "print(len(train_timestamps), len(test_timestamps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "plt.plot(train_timestamps, y_train, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(train_timestamps, y_train_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for training data:  1.7195710200875551 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "plt.plot(test_timestamps, y_test, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(test_timestamps, y_test_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for testing data:  1.2623790187854018 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Предвиђање целокупног скупа података\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor shape:  (26300, 5)\n",
+      "X shape:  (26300, 4) \n",
+      "Y shape:  (26300, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = energy.copy().values\n",
+    "\n",
+    "# Scaling\n",
+    "data = scaler.transform(data)\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=np.array([[j for j in data[i:i+timesteps]] for i in range(0,len(data)-timesteps+1)])[:,:,0]\n",
+    "print(\"Tensor shape: \", data_timesteps.shape)\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = data_timesteps[:,:timesteps-1],data_timesteps[:,[timesteps-1]]\n",
+    "print(\"X shape: \", X.shape,\"\\nY shape: \", Y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "Y_pred = model.predict(X).reshape(-1,1)\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = scaler.inverse_transform(Y_pred)\n",
+    "Y = scaler.inverse_transform(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "plt.plot(Y, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(Y_pred, color = 'blue', linewidth=1)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE:  2.0572089029888656 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "f8f3967282314d3995245835bdaa8418",
+   "translation_date": "2025-09-06T14:03:51+00:00",
+   "source_file": "7-TimeSeries/3-SVR/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sr/7-TimeSeries/3-SVR/working/notebook.ipynb b/translations/sr/7-TimeSeries/3-SVR/working/notebook.ipynb
new file mode 100644
index 000000000..934543ee6
--- /dev/null
+++ b/translations/sr/7-TimeSeries/3-SVR/working/notebook.ipynb
@@ -0,0 +1,699 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "У овом бележнику демонстрирамо како да:\n",
+    "\n",
+    "- припремите 2Д временске серије за тренирање модела СВМ регресора  \n",
+    "- имплементирате СВР користећи РБФ језгро  \n",
+    "- евалуирате модел користећи графике и МАПЕ  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Увоз модула\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": [
+    "## Припрема података\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Учитај податке\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Сада треба да припремите податке за обуку извршавањем филтрирања и скалирања ваших података.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Скалирајте податке да буду у опсегу (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": [
+    "### Креирање података са временским корацима\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "За наш SVR, трансформишемо улазне податке у облик `[batch, timesteps]`. Дакле, преобликујемо постојеће `train_data` и `test_data` тако да постоји нова димензија која се односи на временске кораке. У нашем примеру, узимамо `timesteps = 5`. Дакле, улази у модел су подаци за прва 4 временска корака, а излаз ће бити подаци за 5. временски корак.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [],
+   "source": [
+    "x_train, y_train = None\n",
+    "x_test, y_test = None\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit model on training data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Направи предвиђање модела\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = None\n",
+    "y_test_pred = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = None\n",
+    "test_timestamps = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Предвиђање целокупног скупа података\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = None\n",
+    "\n",
+    "# Scaling\n",
+    "data = None\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=None\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = None, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = None\n",
+    "Y = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "e86ce102239a14c44585623b9b924a74",
+   "translation_date": "2025-09-06T14:06:21+00:00",
+   "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sr/8-Reinforcement/1-QLearning/notebook.ipynb b/translations/sr/8-Reinforcement/1-QLearning/notebook.ipynb
new file mode 100644
index 000000000..aaf55ee51
--- /dev/null
+++ b/translations/sr/8-Reinforcement/1-QLearning/notebook.ipynb
@@ -0,0 +1,411 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+   "display_name": "Python 3.7.0 64-bit ('3.7')"
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+   "original_hash": "17e5a668646eabf5aabd0e9bfcf17876",
+   "translation_date": "2025-09-06T15:04:04+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Петар и вук: Увод у учење путем појачања\n",
+    "\n",
+    "У овом туторијалу ћемо научити како да применимо учење путем појачања на проблем проналажења пута. Овај сценарио је инспирисан музичком бајком [Петар и вук](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) руског композитора [Сергеја Прокофјева](https://en.wikipedia.org/wiki/Sergei_Prokofiev). То је прича о младом пиониру Петру, који храбро излази из своје куће на шумску чистину да би јурио вука. Тренираћемо алгоритме машинског учења који ће помоћи Петру да истражи околину и направи оптималну навигациону мапу.\n",
+    "\n",
+    "Прво, хајде да увеземо неколико корисних библиотека:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Преглед учења путем појачања\n",
+    "\n",
+    "**Учење путем појачања** (RL) је техника учења која нам омогућава да научимо оптимално понашање **агента** у неком **окружењу** кроз извођење многих експеримената. Агент у овом окружењу треба да има неки **циљ**, који је дефинисан **функцијом награде**.\n",
+    "\n",
+    "## Окружење\n",
+    "\n",
+    "Ради једноставности, замислимо да је Петеров свет квадратна табла димензија `width` x `height`. Свака ћелија на овој табли може бити:\n",
+    "* **тло**, по коме Петер и друга створења могу ходати\n",
+    "* **вода**, по којој, наравно, не можете ходати\n",
+    "* **дрво** или **трава** - место где можете одморити\n",
+    "* **јабука**, која представља нешто што би Петер радо пронашао како би се прехранио\n",
+    "* **вук**, који је опасан и треба га избегавати\n",
+    "\n",
+    "Да бисмо радили са окружењем, дефинисаћемо класу под називом `Board`. Да не бисмо превише оптеретили овај бележник, сав код за рад са таблом преместили смо у посебан модул `rlboard`, који ћемо сада увозити. Можете погледати унутар овог модула за више детаља о унутрашњости имплементације.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Хајде сада да направимо насумичну таблу и видимо како изгледа:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Акције и Политика\n",
+    "\n",
+    "У нашем примеру, циљ Петра би био да пронађе јабуку, избегавајући вука и друге препреке. Дефинишите те акције као речник и повежите их са паровима одговарајућих промена координата.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 2"
+   ]
+  },
+  {
+   "source": [
+    "Стратегија нашег агента (Петар) дефинисана је такозваном **политиком**. Хајде да размотримо најједноставнију политику која се зове **случајна шетња**.\n",
+    "\n",
+    "## Случајна шетња\n",
+    "\n",
+    "Прво ћемо решити наш проблем применом стратегије случајне шетње.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Let's run a random walk experiment several times and see the average number of steps taken: code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 4"
+   ]
+  },
+  {
+   "source": [
+    "## Функција награде\n",
+    "\n",
+    "Да бисмо нашу политику учинили интелигентнијом, потребно је да разумемо који потези су \"бољи\" од других.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Лернинг\n",
+    "\n",
+    "Направите Q-Табелу, или вишедимензионални низ. Пошто наша табла има димензије `width` x `height`, можемо представити Q-Табелу помоћу numpy низа са обликом `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Проследите Q-табелу функцији `plot` како бисте визуализовали табелу на табли:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'm' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8b8a9e4cb2b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Суштина Q-Learning-а: Белманова једначина и алгоритам учења\n",
+    "\n",
+    "Напишите псеудо-код за наш алгоритам учења:\n",
+    "\n",
+    "* Иницијализујте Q-табелу Q са истим вредностима за сва стања и акције\n",
+    "* Поставите стопу учења $\\alpha\\leftarrow 1$\n",
+    "* Поновите симулацију много пута\n",
+    "   1. Почните са случајне позиције\n",
+    "   1. Понављајте\n",
+    "        1. Изаберите акцију $a$ у стању $s$\n",
+    "        2. Извршите акцију преласком у ново стање $s'$\n",
+    "        3. Ако наиђемо на услов краја игре или је укупна награда сувише мала - изађите из симулације  \n",
+    "        4. Израчунајте награду $r$ у новом стању\n",
+    "        5. Ажурирајте Q-функцију према Белмановој једначини: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Ажурирајте укупну награду и смањите $\\alpha$.\n",
+    "\n",
+    "## Експлоатација vs. Експлорација\n",
+    "\n",
+    "Најбољи приступ је пронаћи равнотежу између истраживања и искоришћавања. Како више учимо о нашем окружењу, вероватније је да ћемо следити оптималну путању, али је важно повремено изабрати неистражену путању.\n",
+    "\n",
+    "## Python имплементација\n",
+    "\n",
+    "Сада смо спремни да имплементирамо алгоритам учења. Пре тога, потребна нам је и функција која ће произвољне бројеве из Q-табеле претворити у вектор вероватноћа за одговарајуће акције:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Мало количину `eps` додајемо оригиналном вектору како бисмо избегли дељење са 0 у почетном случају, када су све компоненте вектора идентичне.\n",
+    "\n",
+    "Стварни алгоритам учења ћемо покренути за 5000 експеримената, који се такође називају **епохе**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "# code block 8"
+   ]
+  },
+  {
+   "source": [
+    "Након извршавања овог алгоритма, Q-табела би требало да буде ажурирана вредностима које дефинишу привлачност различитих акција у сваком кораку. Визуализујте табелу овде:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Провера политике\n",
+    "\n",
+    "Пошто Q-табела наводи „атрактивност“ сваке акције у сваком стању, прилично је једноставно користити је за дефинисање ефикасне навигације у нашем свету. У најједноставнијем случају, можемо једноставно изабрати акцију која одговара највишој вредности у Q-табели:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "# code block 9"
+   ]
+  },
+  {
+   "source": [
+    "Ако пробате код изнад неколико пута, можда ћете приметити да понекад само \"заглави\", и потребно је да притиснете дугме STOP у нотебоок-у како бисте га прекинули.\n",
+    "\n",
+    "> **Задатак 1:** Измените функцију `walk` тако да ограничи максималну дужину пута на одређени број корака (рецимо, 100), и посматрајте како код изнад повремено враћа ову вредност.\n",
+    "\n",
+    "> **Задатак 2:** Измените функцију `walk` тако да не иде назад на места на којима је већ био раније. Ово ће спречити `walk` да уђе у петљу, али агент и даље може завршити \"заробљен\" на локацији са које не може да побегне.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 5.31, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# code block 10"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26dab295a48>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 57
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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109.232964 214.756364 \r\nL 109.263403 214.085931 \r\nL 109.293843 201.703888 \r\nL 109.385161 202.919046 \r\nL 109.44604 214.756364 \r\nL 109.476479 192.192132 \r\nL 109.537358 214.735413 \r\nL 109.598237 214.756364 \r\nL 109.719994 198.100315 \r\nL 109.811312 214.756364 \r\nL 109.841752 210.230947 \r\nL 109.872191 214.735413 \r\nL 109.93307 208.63867 \r\nL 110.054828 214.756364 \r\nL 110.085267 200.509681 \r\nL 110.146146 208.052042 \r\nL 110.207025 214.756364 \r\nL 110.267904 213.268842 \r\nL 110.298343 193.470143 \r\nL 110.328782 214.756364 \r\nL 110.359222 209.811927 \r\nL 110.420101 214.756364 \r\nL 110.480979 214.044029 \r\nL 110.541858 214.756364 \r\nL 110.572298 212.032733 \r\nL 110.633176 214.756364 \r\nL 110.663616 214.756364 \r\nL 110.694055 199.441179 \r\nL 110.785374 202.981899 \r\nL 110.907131 214.756364 \r\nL 110.937571 211.110889 \r\nL 110.998449 214.756364 \r\nL 111.089768 209.916682 \r\nL 111.181086 214.756364 \r\nL 111.211525 203.882792 \r\nL 111.272404 207.842532 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115.016451 213.43645 \r\nL 115.046891 214.46305 \r\nL 115.07733 207.339708 \r\nL 115.168648 209.66527 \r\nL 115.229527 213.268842 \r\nL 115.290406 211.215644 \r\nL 115.320845 205.642677 \r\nL 115.351285 214.756364 \r\nL 115.381724 209.749074 \r\nL 115.533921 214.756364 \r\nL 115.564361 213.687862 \r\nL 115.655679 206.606423 \r\nL 115.746997 214.756364 \r\nL 115.716558 193.700604 \r\nL 115.777436 214.735413 \r\nL 115.838315 193.763457 \r\nL 115.868755 193.910114 \r\nL 115.899194 214.756364 \r\nL 115.990512 212.346998 \r\nL 116.051391 210.754722 \r\nL 116.11227 214.756364 \r\nL 116.142709 211.76037 \r\nL 116.203588 214.756364 \r\nL 116.234027 214.756364 \r\nL 116.264467 213.478352 \r\nL 116.294906 214.756364 \r\nL 116.325346 184.670721 \r\nL 116.386225 214.756364 \r\nL 116.416664 214.044029 \r\nL 116.447103 214.756364 \r\nL 116.477543 196.047117 \r\nL 116.538422 212.786969 \r\nL 116.5993 210.964232 \r\nL 116.690619 214.756364 \r\nL 116.812376 200.404926 \r\nL 116.903694 214.756364 \r\nL 116.934134 196.403284 \r\nL 116.995013 214.756364 \r\nL 117.055891 214.756364 \r\nL 117.086331 213.939274 \r\nL 117.11677 199.650689 \r\nL 117.177649 212.158439 \r\nL 117.208089 214.756364 \r\nL 117.238528 213.939274 \r\nL 117.268967 205.684579 \r\nL 117.329846 214.756364 \r\nL 117.360286 214.484001 \r\nL 117.421164 195.774754 \r\nL 117.451604 210.000486 \r\nL 117.512483 214.756364 \r\nL 117.542922 176.709339 \r\nL 117.63424 184.64977 \r\nL 117.725558 183.874583 \r\nL 117.755998 214.756364 \r\nL 117.786437 214.756364 \r\nL 117.877756 198.959306 \r\nL 117.908195 210.566163 \r\nL 117.938634 212.766018 \r\nL 117.969074 206.522619 \r\nL 118.029953 214.756364 \r\nL 118.060392 214.756364 \r\nL 118.18215 199.399277 \r\nL 118.273468 214.756364 \r\nL 118.303907 205.035098 \r\nL 118.364786 214.756364 \r\nL 118.456104 214.232589 \r\nL 118.486544 214.756364 \r\nL 118.516983 209.476711 \r\nL 118.577862 214.756364 \r\nL 118.608301 213.352646 \r\nL 118.66918 214.735413 \r\nL 118.69962 186.786773 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200.090661 \r\nL 122.595864 211.927978 \r\nL 122.687182 211.383252 \r\nL 122.717621 214.756364 \r\nL 122.7785 214.756364 \r\nL 122.80894 210.335702 \r\nL 122.900258 212.493655 \r\nL 122.930697 214.756364 \r\nL 123.022015 183.958387 \r\nL 123.052455 208.533915 \r\nL 123.082894 214.756364 \r\nL 123.113334 201.703888 \r\nL 123.143773 210.482359 \r\nL 123.174212 210.419506 \r\nL 123.265531 200.48873 \r\nL 123.29597 214.756364 \r\nL 123.326409 214.735413 \r\nL 123.356849 187.017234 \r\nL 123.417728 214.756364 \r\nL 123.569925 191.291239 \r\nL 123.691682 214.756364 \r\nL 123.722122 214.735413 \r\nL 123.783001 205.579824 \r\nL 123.81344 189.279942 \r\nL 123.874319 214.735413 \r\nL 123.935198 214.735413 \r\nL 123.996076 214.756364 \r\nL 124.026516 206.669276 \r\nL 124.148273 214.756364 \r\nL 124.178713 214.756364 \r\nL 124.239592 209.581466 \r\nL 124.270031 211.96988 \r\nL 124.300471 214.023078 \r\nL 124.33091 201.201064 \r\nL 124.422228 208.471062 \r\nL 124.513546 214.756364 \r\nL 124.543986 212.326047 \r\nL 124.604865 199.818297 \r\nL 124.635304 207.360659 \r\nL 124.726622 214.756364 \r\nL 124.787501 174.467582 \r\nL 124.81794 214.756364 \r\nL 124.909259 214.735413 \r\nL 124.939698 211.173742 \r\nL 124.970137 186.283949 \r\nL 125.031016 211.907027 \r\nL 125.091895 214.756364 \r\nL 125.152774 214.735413 \r\nL 125.274532 173.252424 \r\nL 125.36585 214.756364 \r\nL 125.396289 214.127834 \r\nL 125.426729 214.735413 \r\nL 125.457168 198.267923 \r\nL 125.518047 214.106882 \r\nL 125.548486 210.775673 \r\nL 125.609365 214.756364 \r\nL 125.639804 211.006134 \r\nL 125.731123 214.735413 \r\nL 125.761562 214.421148 \r\nL 125.822441 214.735413 \r\nL 125.913759 173.399081 \r\nL 125.974638 214.756364 \r\nL 126.035517 206.648325 \r\nL 126.065956 206.585472 \r\nL 126.187714 214.756364 \r\nL 126.248593 214.756364 \r\nL 126.309471 213.562156 \r\nL 126.339911 205.852187 \r\nL 126.37035 214.735413 \r\nL 126.40079 212.996479 \r\nL 126.431229 214.756364 \r\nL 126.461668 214.588756 \r\nL 126.522547 198.41458 \r\nL 126.552987 213.059332 \r\nL 126.705184 214.756364 \r\nL 126.735623 192.192132 \r\nL 126.796502 213.750715 \r\nL 126.91826 214.756364 \r\nL 126.979138 210.021437 \r\nL 127.009578 214.756364 \r\nL 127.040017 214.756364 \r\nL 127.131335 210.461408 \r\nL 127.192214 207.402561 \r\nL 127.253093 214.756364 \r\nL 127.283532 210.377604 \r\nL 127.344411 214.735413 \r\nL 127.374851 214.756364 \r\nL 127.40529 214.46305 \r\nL 127.466169 214.756364 \r\nL 127.527048 199.524983 \r\nL 127.557487 214.735413 \r\nL 127.587927 211.404203 \r\nL 127.618366 177.673086 \r\nL 127.648805 214.756364 \r\nL 127.679245 184.251701 \r\nL 127.801002 214.756364 \r\nL 127.861881 214.756364 \r\nL 127.953199 207.612071 \r\nL 128.014078 214.756364 \r\nL 128.044518 204.532273 \r\nL 128.074957 198.309825 \r\nL 128.105396 214.756364 \r\nL 128.166275 214.756364 \r\nL 128.257593 210.105241 \r\nL 128.348912 214.756364 \r\nL 128.501109 186.430606 \r\nL 128.531548 210.314751 \r\nL 128.592427 206.459766 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214.693511 \r\nL 130.236155 214.735413 \r\nL 130.297034 204.972245 \r\nL 130.388352 214.756364 \r\nL 130.449231 190.620806 \r\nL 130.47967 213.331695 \r\nL 130.540549 214.756364 \r\nL 130.601428 214.358295 \r\nL 130.692746 203.547576 \r\nL 130.753625 197.953658 \r\nL 130.814504 214.756364 \r\nL 130.844943 210.649967 \r\nL 130.905822 211.425154 \r\nL 130.936261 214.756364 \r\nL 130.99714 214.546854 \r\nL 131.058019 203.945645 \r\nL 131.088458 213.939274 \r\nL 131.149337 214.756364 \r\nL 131.179777 207.653973 \r\nL 131.240655 214.756364 \r\nL 131.51461 214.756364 \r\nL 131.54505 182.827033 \r\nL 131.605928 214.106882 \r\nL 131.636368 214.756364 \r\nL 131.758125 205.998844 \r\nL 131.788565 214.756364 \r\nL 131.819004 200.111612 \r\nL 131.849444 213.813568 \r\nL 131.879883 194.643399 \r\nL 131.910322 214.756364 \r\nL 131.940762 212.849822 \r\nL 131.971201 214.756364 \r\nL 132.092959 153.16041 \r\nL 132.184277 214.756364 \r\nL 132.214716 191.752161 \r\nL 132.306035 214.756364 \r\nL 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154.952954 214.756364 \r\nL 155.074712 200.823946 \r\nL 155.16603 214.756364 \r\nL 155.196469 214.735413 \r\nL 155.257348 214.735413 \r\nL 155.379106 197.869854 \r\nL 155.500863 214.756364 \r\nL 155.531303 209.141494 \r\nL 155.622621 211.634664 \r\nL 155.65306 209.204347 \r\nL 155.6835 214.756364 \r\nL 155.713939 211.383252 \r\nL 155.744379 213.813568 \r\nL 155.774818 195.167174 \r\nL 155.805257 214.756364 \r\nL 155.835697 213.729764 \r\nL 155.866136 214.756364 \r\nL 156.018333 198.770747 \r\nL 156.048773 210.398555 \r\nL 156.079212 135.854881 \r\nL 156.109651 214.756364 \r\nL 156.140091 195.984264 \r\nL 156.20097 214.756364 \r\nL 156.231409 210.754722 \r\nL 156.261848 183.706975 \r\nL 156.322727 214.756364 \r\nL 156.353167 214.756364 \r\nL 156.474924 196.466137 \r\nL 156.627121 214.756364 \r\nL 156.657561 214.756364 \r\nL 156.688 192.485446 \r\nL 156.748879 214.735413 \r\nL 156.779318 213.520254 \r\nL 156.840197 214.735413 \r\nL 156.870637 214.735413 \r\nL 156.901076 213.289793 \r\nL 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162.83676 214.756364 \r\nL 162.897639 198.016511 \r\nL 162.988958 214.756364 \r\nL 163.019397 209.392906 \r\nL 163.049836 209.351004 \r\nL 163.080276 211.508958 \r\nL 163.141155 204.951294 \r\nL 163.171594 214.735413 \r\nL 163.232473 214.756364 \r\nL 163.262912 211.299448 \r\nL 163.323791 210.649967 \r\nL 163.35423 214.756364 \r\nL 163.415109 201.641035 \r\nL 163.445549 214.735413 \r\nL 163.506427 214.756364 \r\nL 163.536867 213.729764 \r\nL 163.597746 214.756364 \r\nL 163.628185 214.756364 \r\nL 163.719503 191.815014 \r\nL 163.780382 214.756364 \r\nL 163.841261 214.023078 \r\nL 163.8717 202.688585 \r\nL 163.932579 208.743425 \r\nL 163.963019 214.756364 \r\nL 164.023897 212.912675 \r\nL 164.054337 202.939997 \r\nL 164.084776 214.756364 \r\nL 164.145655 205.097951 \r\nL 164.206534 214.756364 \r\nL 164.236973 202.541928 \r\nL 164.267413 189.971325 \r\nL 164.297852 214.756364 \r\nL 164.328291 205.852187 \r\nL 164.38917 214.756364 \r\nL 164.480489 212.870773 \r\nL 164.510928 212.59841 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168.589809 199.88115 \r\nL 168.650687 214.756364 \r\nL 168.772445 205.056049 \r\nL 168.833324 207.255904 \r\nL 168.955081 214.756364 \r\nL 169.076839 204.720832 \r\nL 169.168157 214.756364 \r\nL 169.198597 212.912675 \r\nL 169.289915 214.756364 \r\nL 169.320354 214.735413 \r\nL 169.442112 194.287232 \r\nL 169.56387 214.756364 \r\nL 169.685627 181.884238 \r\nL 169.807385 214.756364 \r\nL 169.837824 200.048758 \r\nL 169.898703 208.072993 \r\nL 169.959582 214.756364 \r\nL 169.990021 207.695875 \r\nL 170.020461 210.189045 \r\nL 170.111779 214.756364 \r\nL 170.142218 187.059136 \r\nL 170.203097 213.059332 \r\nL 170.233537 210.796624 \r\nL 170.263976 213.331695 \r\nL 170.324855 211.404203 \r\nL 170.385734 214.756364 \r\nL 170.446612 214.756364 \r\nL 170.477052 210.733771 \r\nL 170.537931 214.735413 \r\nL 170.629249 214.756364 \r\nL 170.659688 204.406567 \r\nL 170.720567 208.21965 \r\nL 170.842325 214.756364 \r\nL 170.933643 196.298529 \r\nL 170.964082 211.446105 \r\nL 170.994522 214.756364 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178.695692 214.756364 \r\nL 178.726131 205.077 \r\nL 178.78701 214.693511 \r\nL 178.847889 214.756364 \r\nL 178.878328 214.421148 \r\nL 178.908768 214.714462 \r\nL 178.939207 211.844174 \r\nL 178.969647 193.051123 \r\nL 179.030525 214.756364 \r\nL 179.060965 207.255904 \r\nL 179.121844 214.756364 \r\nL 179.213162 211.027085 \r\nL 179.274041 192.548299 \r\nL 179.30448 213.750715 \r\nL 179.334919 214.756364 \r\nL 179.426238 214.358295 \r\nL 179.456677 214.756364 \r\nL 179.487116 214.609707 \r\nL 179.547995 189.887521 \r\nL 179.578435 214.756364 \r\nL 179.639313 214.756364 \r\nL 179.669753 214.504952 \r\nL 179.700192 205.349363 \r\nL 179.730632 214.756364 \r\nL 179.761071 214.756364 \r\nL 179.882829 200.237318 \r\nL 179.913268 214.756364 \r\nL 180.004586 206.564521 \r\nL 180.065465 205.097951 \r\nL 180.095905 195.062419 \r\nL 180.126344 208.994837 \r\nL 180.156783 203.715184 \r\nL 180.278541 214.756364 \r\nL 180.400299 206.145501 \r\nL 180.430738 214.756364 \r\nL 180.461177 208.114895 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200.551583 \r\nL 184.266103 214.756364 \r\nL 184.296543 214.756364 \r\nL 184.387861 203.191409 \r\nL 184.509619 214.756364 \r\nL 184.540058 208.994837 \r\nL 184.600937 214.756364 \r\nL 184.631376 214.756364 \r\nL 184.661816 195.523342 \r\nL 184.722695 214.756364 \r\nL 184.783573 214.756364 \r\nL 184.93577 200.404926 \r\nL 184.96621 214.735413 \r\nL 185.057528 213.541205 \r\nL 185.118407 214.044029 \r\nL 185.240164 192.275936 \r\nL 185.331483 187.373401 \r\nL 185.361922 214.756364 \r\nL 185.392362 203.882792 \r\nL 185.45324 213.981176 \r\nL 185.48368 214.756364 \r\nL 185.544559 180.103402 \r\nL 185.574998 214.756364 \r\nL 185.605437 214.756364 \r\nL 185.757634 190.599855 \r\nL 185.788074 192.171181 \r\nL 185.818513 181.695679 \r\nL 185.909831 214.756364 \r\nL 185.940271 203.149507 \r\nL 185.97071 214.756364 \r\nL 186.031589 202.16481 \r\nL 186.062028 213.22694 \r\nL 186.092468 195.271929 \r\nL 186.153347 207.130198 \r\nL 186.214226 214.756364 \r\nL 186.244665 207.402561 \r\nL 186.305544 213.981176 \r\nL 186.396862 211.236595 \r\nL 186.427301 207.758728 \r\nL 186.457741 214.756364 \r\nL 186.48818 212.661263 \r\nL 186.51862 210.733771 \r\nL 186.609938 214.756364 \r\nL 186.640377 168.035624 \r\nL 186.701256 210.461408 \r\nL 186.762135 208.303454 \r\nL 186.792574 195.942362 \r\nL 186.823014 214.756364 \r\nL 186.853453 198.184119 \r\nL 186.975211 214.756364 \r\nL 187.00565 191.668357 \r\nL 187.066529 209.476711 \r\nL 187.096968 214.756364 \r\nL 187.188287 170.486891 \r\nL 187.310044 214.756364 \r\nL 187.340484 214.756364 \r\nL 187.431802 200.928701 \r\nL 187.462241 214.316393 \r\nL 187.492681 199.294522 \r\nL 187.553559 208.84818 \r\nL 187.614438 210.901379 \r\nL 187.644878 194.978615 \r\nL 187.705756 212.80792 \r\nL 187.736196 210.042388 \r\nL 187.857954 183.853632 \r\nL 187.827514 210.92233 \r\nL 187.888393 186.996283 \r\nL 187.949272 214.756364 \r\nL 187.979711 214.735413 \r\nL 188.010151 192.024524 \r\nL 188.04059 214.756364 \r\nL 188.071029 214.756364 \r\nL 188.162348 206.648325 \r\nL 188.192787 164.432051 \r\nL 188.253666 208.701523 \r\nL 188.284105 207.653973 \r\nL 188.314545 213.897372 \r\nL 188.344984 202.332418 \r\nL 188.405863 210.000486 \r\nL 188.436302 214.756364 \r\nL 188.466742 190.055129 \r\nL 188.52762 214.756364 \r\nL 188.55806 214.044029 \r\nL 188.588499 214.756364 \r\nL 188.710257 204.993196 \r\nL 188.801575 214.756364 \r\nL 188.832015 214.735413 \r\nL 188.862454 214.567805 \r\nL 188.892893 199.105963 \r\nL 188.984212 206.585472 \r\nL 189.136409 214.756364 \r\nL 189.166848 164.369198 \r\nL 189.227727 214.756364 \r\nL 189.319045 200.048758 \r\nL 189.440803 214.756364 \r\nL 189.471242 214.023078 \r\nL 189.501682 204.322763 \r\nL 189.56256 212.724116 \r\nL 189.623439 214.756364 \r\nL 189.653879 194.475791 \r\nL 189.714757 214.756364 \r\nL 189.836515 194.203428 \r\nL 189.866954 205.894089 \r\nL 189.927833 194.894811 \r\nL 189.958273 214.756364 \r\nL 189.988712 193.931065 \r\nL 190.049591 214.274491 \r\nL 190.08003 214.756364 \r\nL 190.11047 202.772389 \r\nL 190.201788 207.59112 \r\nL 190.323546 214.756364 \r\nL 190.353985 214.421148 \r\nL 190.384424 214.756364 \r\nL 190.414864 197.953658 \r\nL 190.475743 209.183396 \r\nL 190.62794 214.756364 \r\nL 190.658379 214.756364 \r\nL 190.688818 189.824668 \r\nL 190.749697 214.714462 \r\nL 190.810576 214.756364 \r\nL 190.932334 201.578182 \r\nL 191.023652 214.756364 \r\nL 191.084531 211.802272 \r\nL 191.175849 214.756364 \r\nL 191.236728 206.836884 \r\nL 191.267167 213.771666 \r\nL 191.328046 214.756364 \r\nL 191.419364 208.680572 \r\nL 191.480243 214.756364 \r\nL 191.541122 214.735413 \r\nL 191.571561 214.756364 \r\nL 191.63244 180.962393 \r\nL 191.662879 212.3889 \r\nL 191.693319 214.756364 \r\nL 191.815077 180.627177 \r\nL 191.936834 214.756364 \r\nL 191.967274 214.756364 \r\nL 191.997713 214.190687 \r\nL 192.028152 179.831039 \r\nL 192.058592 214.756364 \r\nL 192.089031 214.756364 \r\nL 192.14991 210.2938 \r\nL 192.180349 213.331695 \r\nL 192.271668 214.756364 \r\nL 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194.311108 208.931984 \r\nL 194.341547 208.177748 \r\nL 194.463305 214.756364 \r\nL 194.493744 194.203428 \r\nL 194.585063 195.586195 \r\nL 194.73726 214.756364 \r\nL 194.767699 212.053684 \r\nL 194.828578 214.756364 \r\nL 194.859017 214.756364 \r\nL 194.919896 191.144582 \r\nL 194.950335 195.020517 \r\nL 194.980775 214.756364 \r\nL 195.072093 202.479075 \r\nL 195.193851 214.756364 \r\nL 195.315608 201.515329 \r\nL 195.376487 201.431525 \r\nL 195.467805 214.756364 \r\nL 195.589563 203.966596 \r\nL 195.650442 196.382333 \r\nL 195.74176 214.756364 \r\nL 195.833078 206.606423 \r\nL 195.863518 213.562156 \r\nL 195.893957 214.756364 \r\nL 195.924397 213.43645 \r\nL 195.954836 211.425154 \r\nL 195.985275 212.640312 \r\nL 196.015715 194.685301 \r\nL 196.076594 214.756364 \r\nL 196.167912 214.756364 \r\nL 196.25923 192.736858 \r\nL 196.289669 206.711178 \r\nL 196.380988 202.79334 \r\nL 196.411427 214.756364 \r\nL 196.502745 214.756364 \r\nL 196.624503 197.932707 \r\nL 196.715821 214.756364 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200.520747 199.441179 \r\nL 200.612065 203.023801 \r\nL 200.703384 214.67256 \r\nL 200.672944 201.368672 \r\nL 200.733823 208.533915 \r\nL 200.764262 205.077 \r\nL 200.794702 214.756364 \r\nL 200.825141 195.104321 \r\nL 200.855581 160.87038 \r\nL 200.88602 214.756364 \r\nL 200.916459 207.423512 \r\nL 200.977338 214.735413 \r\nL 201.038217 177.777841 \r\nL 201.129535 212.17939 \r\nL 201.159975 210.126192 \r\nL 201.251293 214.232589 \r\nL 201.281732 184.545015 \r\nL 201.40349 213.604058 \r\nL 201.464369 208.911033 \r\nL 201.494808 198.393629 \r\nL 201.525248 214.756364 \r\nL 201.555687 214.756364 \r\nL 201.586126 214.421148 \r\nL 201.738323 146.89606 \r\nL 201.829642 214.756364 \r\nL 201.860081 208.63867 \r\nL 201.89052 206.040746 \r\nL 201.92096 212.053684 \r\nL 201.951399 212.493655 \r\nL 201.981839 214.756364 \r\nL 202.012278 214.735413 \r\nL 202.042717 142.140182 \r\nL 202.103596 159.843781 \r\nL 202.164475 214.756364 \r\nL 202.225354 211.844174 \r\nL 202.255793 157.664876 \r\nL 202.286233 214.756364 \r\nL 202.316672 213.85547 \r\nL 202.377551 205.642677 \r\nL 202.40799 214.756364 \r\nL 202.43843 214.484001 \r\nL 202.468869 188.588559 \r\nL 202.499309 214.756364 \r\nL 202.529748 214.484001 \r\nL 202.651506 198.330776 \r\nL 202.681945 214.756364 \r\nL 202.742824 212.849822 \r\nL 202.773263 182.994641 \r\nL 202.834142 190.055129 \r\nL 202.92546 214.756364 \r\nL 202.9559 214.735413 \r\nL 202.986339 170.067871 \r\nL 203.047218 214.756364 \r\nL 203.077657 208.869131 \r\nL 203.168976 211.089938 \r\nL 203.229854 214.337344 \r\nL 203.260294 211.739419 \r\nL 203.321173 211.948929 \r\nL 203.351612 193.449192 \r\nL 203.382051 214.756364 \r\nL 203.47337 202.311467 \r\nL 203.503809 203.547576 \r\nL 203.534248 214.735413 \r\nL 203.564688 165.66816 \r\nL 203.656006 192.715907 \r\nL 203.686445 193.009221 \r\nL 203.716885 214.400197 \r\nL 203.747324 206.627374 \r\nL 203.777764 170.549744 \r\nL 203.808203 214.756364 \r\nL 203.838643 214.756364 \r\nL 203.869082 208.198699 \r\nL 203.929961 209.288151 \r\nL 203.99084 214.756364 \r\nL 204.021279 210.964232 \r\nL 204.082158 176.353172 \r\nL 204.112597 214.756364 \r\nL 204.173476 213.918323 \r\nL 204.203915 214.756364 \r\nL 204.234355 210.482359 \r\nL 204.295234 214.567805 \r\nL 204.356112 211.96988 \r\nL 204.386552 214.756364 \r\nL 204.447431 186.975332 \r\nL 204.47787 211.446105 \r\nL 204.508309 212.870773 \r\nL 204.538749 209.2672 \r\nL 204.569188 207.989189 \r\nL 204.599628 190.159884 \r\nL 204.660507 214.756364 \r\nL 204.690946 214.756364 \r\nL 204.721385 211.907027 \r\nL 204.782264 214.379246 \r\nL 204.812704 192.213083 \r\nL 204.843143 214.756364 \r\nL 204.904022 205.91504 \r\nL 204.934461 205.244608 \r\nL 204.99534 214.756364 \r\nL 205.056219 211.488007 \r\nL 205.086658 213.771666 \r\nL 205.117098 213.352646 \r\nL 205.147537 171.052568 \r\nL 205.208416 207.549218 \r\nL 205.238855 213.394548 \r\nL 205.299734 204.86749 \r\nL 205.360613 214.442099 \r\nL 205.391052 157.74868 \r\nL 205.421492 214.756364 \r\nL 205.451931 214.756364 \r\nL 205.51281 202.688585 \r\nL 205.543249 213.373597 \r\nL 205.604128 211.865125 \r\nL 205.634568 214.756364 \r\nL 205.665007 199.734493 \r\nL 205.725886 214.756364 \r\nL 205.756325 213.310744 \r\nL 205.786765 193.428241 \r\nL 205.847643 214.756364 \r\nL 205.878083 212.849822 \r\nL 205.908522 212.975528 \r\nL 205.969401 188.777118 \r\nL 206.03028 199.650689 \r\nL 206.091159 214.756364 \r\nL 206.152037 205.077 \r\nL 206.212916 214.756364 \r\nL 206.243356 182.76418 \r\nL 206.304235 214.756364 \r\nL 206.425992 191.354092 \r\nL 206.456432 214.756364 \r\nL 206.51731 204.427518 \r\nL 206.54775 198.142217 \r\nL 206.578189 214.756364 \r\nL 206.669507 197.031814 \r\nL 206.760826 214.756364 \r\nL 206.791265 196.256627 \r\nL 206.852144 210.943281 \r\nL 206.882583 214.756364 \r\nL 206.913023 188.588559 \r\nL 207.004341 205.28651 \r\nL 207.03478 214.756364 \r\nL 207.06522 213.373597 \r\nL 207.095659 198.435531 \r\nL 207.156538 205.768383 \r\nL 207.278296 214.756364 \r\nL 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "## Вежба\n",
+    "## Реалистичнији свет Петра и вука\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/translations/sr/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
new file mode 100644
index 000000000..849658320
--- /dev/null
+++ b/translations/sr/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
@@ -0,0 +1,469 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "eadbd20d2a075efb602615ad90b1e97a",
+   "translation_date": "2025-09-06T15:14:18+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Петар и вук: Реалистично окружење\n",
+    "\n",
+    "У нашој ситуацији, Петар је могао да се креће готово без умора или глади. У реалистичнијем свету, морао би с времена на време да седне и одмори се, као и да се нахрани. Хајде да учинимо наш свет реалистичнијим, применом следећих правила:\n",
+    "\n",
+    "1. Крећући се са једног места на друго, Петар губи **енергију** и добија одређени ниво **умора**.\n",
+    "2. Петар може да повећа енергију једући јабуке.\n",
+    "3. Петар може да се ослободи умора одмарајући се испод дрвета или на трави (тј. уласком на локацију табле са дрветом или травом - зелено поље).\n",
+    "4. Петар мора да пронађе и убије вука.\n",
+    "5. Да би убио вука, Петар мора да има одређене нивое енергије и умора, иначе губи битку.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "from rlboard import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "## Дефинисање стања\n",
+    "\n",
+    "У нашим новим правилима игре, потребно је да пратимо енергију и умор у сваком стању табле. Због тога ћемо креирати објекат `state` који ће носити све потребне информације о тренутном стању проблема, укључујући стање табле, тренутне нивое енергије и умора, и да ли можемо победити вука у терминалном стању:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class state:\n",
+    "    def __init__(self,board,energy=10,fatigue=0,init=True):\n",
+    "        self.board = board\n",
+    "        self.energy = energy\n",
+    "        self.fatigue = fatigue\n",
+    "        self.dead = False\n",
+    "        if init:\n",
+    "            self.board.random_start()\n",
+    "        self.update()\n",
+    "\n",
+    "    def at(self):\n",
+    "        return self.board.at()\n",
+    "\n",
+    "    def update(self):\n",
+    "        if self.at() == Board.Cell.water:\n",
+    "            self.dead = True\n",
+    "            return\n",
+    "        if self.at() == Board.Cell.tree:\n",
+    "            self.fatigue = 0\n",
+    "        if self.at() == Board.Cell.apple:\n",
+    "            self.energy = 10\n",
+    "\n",
+    "    def move(self,a):\n",
+    "        self.board.move(a)\n",
+    "        self.energy -= 1\n",
+    "        self.fatigue += 1\n",
+    "        self.update()\n",
+    "\n",
+    "    def is_winning(self):\n",
+    "        return self.energy > self.fatigue"
+   ]
+  },
+  {
+   "source": [
+    "Хајде да покушамо да решимо проблем користећи случајну шетњу и видимо да ли ћемо успети:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(state):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(board,policy):\n",
+    "    n = 0 # number of steps\n",
+    "    s = state(board)\n",
+    "    while True:\n",
+    "        if s.at() == Board.Cell.wolf:\n",
+    "            if s.is_winning():\n",
+    "                return n # success!\n",
+    "            else:\n",
+    "                return -n # failure!\n",
+    "        if s.at() == Board.Cell.water:\n",
+    "            return 0 # died\n",
+    "        a = actions[policy(m)]\n",
+    "        s.move(a)\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 5, won: 1 times, drown: 94 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        elif z==0:\n",
+    "            n+=1\n",
+    "        else:\n",
+    "            s+=1\n",
+    "    print(f\"Killed by wolf = {w}, won: {s} times, drown: {n} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Функција награде\n",
+    "\n",
+    "Функција награде је кључни део система за учење заснованог на награђивању. Она дефинише како агент добија повратне информације за своје акције у окружењу.\n",
+    "\n",
+    "### Основни принципи\n",
+    "\n",
+    "- Функција награде треба да буде једноставна и интуитивна.\n",
+    "- Треба да подстиче жељено понашање агента.\n",
+    "- Избегавајте сложене математичке изразе који могу довести до неочекиваних резултата.\n",
+    "\n",
+    "[!NOTE] Увек тестирајте функцију награде у симулацији пре него што је примените у стварном окружењу.\n",
+    "\n",
+    "### Пример\n",
+    "\n",
+    "Ево једноставног примера функције награде:\n",
+    "\n",
+    "```python\n",
+    "def reward_function(params):\n",
+    "    if params['is_off_track']:\n",
+    "        return -10  # Казна за излазак са стазе\n",
+    "    elif params['is_near_center']:\n",
+    "        return 5  # Награда за кретање близу центра\n",
+    "    else:\n",
+    "        return 1  # Мала награда за кретање по стази\n",
+    "```\n",
+    "\n",
+    "### Савети за креирање функције награде\n",
+    "\n",
+    "[!TIP] Почните са основним правилима и постепено додајте сложеност.\n",
+    "\n",
+    "- Користите параметре као што су брзина, позиција и оријентација за израчунавање награде.\n",
+    "- Увек узмите у обзир крајњи циљ агента.\n",
+    "\n",
+    "[!WARNING] Превише сложена функција награде може довести до проблема са учењем.\n",
+    "\n",
+    "### Честа питања\n",
+    "\n",
+    "#### Шта ако агент не учи како треба?\n",
+    "\n",
+    "Проверите да ли функција награде правилно одражава жељено понашање. Можда је потребно да прилагодите вредности награда и казни.\n",
+    "\n",
+    "#### Могу ли користити више функција награде?\n",
+    "\n",
+    "Да, можете комбиновати више функција награде како бисте постигли сложеније циљеве. Међутим, будите опрезни да не створите конфликте између различитих функција.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reward(s):\n",
+    "    r = s.energy-s.fatigue\n",
+    "    if s.at()==Board.Cell.wolf:\n",
+    "        return 100 if s.is_winning() else -100\n",
+    "    if s.at()==Board.Cell.water:\n",
+    "        return -100\n",
+    "    return r"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning алгоритам\n",
+    "\n",
+    "Сам алгоритам учења остаје углавном непромењен, само користимо `state` уместо само позиције на табли.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    s = state(m)\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = s.board.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        while True:\n",
+    "            a = random.choices(list(actions),weights=v)[0]\n",
+    "            dpos = actions[a]\n",
+    "            if s.board.is_valid(s.board.move_pos(s.board.human,dpos)):\n",
+    "                break \n",
+    "        s.move(dpos)\n",
+    "        r = reward(s)\n",
+    "        if abs(r)==100: # end of game\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Резултати\n",
+    "\n",
+    "Хајде да видимо да ли смо успешно обучили Петра да се бори против вука!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 1, won: 9 times, drown: 90 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [
+    "Сада видимо много мање случајева дављења, али Петар још увек није увек у стању да убије вука. Покушајте да експериментишете и видите да ли можете побољшати овај резултат играјући се са хиперпараметрима.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0987e7dd8>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/translations/sr/8-Reinforcement/1-QLearning/solution/notebook.ipynb
new file mode 100644
index 000000000..f27461799
--- /dev/null
+++ b/translations/sr/8-Reinforcement/1-QLearning/solution/notebook.ipynb
@@ -0,0 +1,577 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "488431336543f71f14d4aaf0399e3381",
+   "translation_date": "2025-09-06T15:09:26+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Петар и вук: Увод у учење путем појачања\n",
+    "\n",
+    "У овом туторијалу ћемо научити како применити учење путем појачања на проблем проналажења пута. Овај сценарио је инспирисан музичком бајком [Петар и вук](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) руског композитора [Сергеја Прокофјева](https://en.wikipedia.org/wiki/Sergei_Prokofiev). То је прича о младом пиониру Петру, који храбро излази из своје куће на шумску чистину да би јурио вука. Тренираћемо алгоритме машинског учења који ће помоћи Петру да истражи околину и направи оптималну навигациону мапу.\n",
+    "\n",
+    "Прво, хајде да увеземо неколико корисних библиотека:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Преглед учења путем појачања\n",
+    "\n",
+    "**Учење путем појачања** (RL) је техника учења која нам омогућава да научимо оптимално понашање **агента** у неком **окружењу** кроз извођење многих експеримената. Агент у овом окружењу треба да има неки **циљ**, који је дефинисан помоћу **функције награде**.\n",
+    "\n",
+    "## Окружење\n",
+    "\n",
+    "За једноставност, хајде да замислимо Петеров свет као квадратну таблу величине `width` x `height`. Свака ћелија на овој табли може бити:\n",
+    "* **земља**, по којој Петер и друга створења могу ходати\n",
+    "* **вода**, по којој, очигледно, не можете ходати\n",
+    "* **дрво** или **трава** - место где можете мало одморити\n",
+    "* **јабука**, која представља нешто што би Петер радо пронашао да би се нахранио\n",
+    "* **вук**, који је опасан и треба га избегавати\n",
+    "\n",
+    "Да бисмо радили са окружењем, дефинисаћемо класу под називом `Board`. Да не бисмо превише оптеретили овај нотебук, сав код за рад са таблом преместили смо у посебан модул `rlboard`, који ћемо сада увозити. Можете погледати унутар овог модула за више детаља о интерним аспектима имплементације.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rlboard import *"
+   ]
+  },
+  {
+   "source": [
+    "Хајде сада да направимо насумичну таблу и видимо како изгледа:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "source": [
+    "## Акције и Политика\n",
+    "\n",
+    "У нашем примеру, циљ Петра би био да пронађе јабуку, избегавајући вука и друге препреке. Да би то урадио, он може суштински да се креће док не пронађе јабуку. Због тога, на било којој позицији може да изабере једну од следећих акција: горе, доле, лево и десно. Те акције ћемо дефинисати као речник и повезати их са паровима одговарајућих промена координата. На пример, кретање десно (`R`) би одговарало пару `(1,0)`.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "Стратегија нашег агента (Петар) дефинисана је такозваном **политиком**. Хајде да размотримо најједноставнију политику која се зове **случајно кретање**.\n",
+    "\n",
+    "## Случајно кретање\n",
+    "\n",
+    "Хајде прво да решимо наш проблем применом стратегије случајног кретања.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(m):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(m,policy,start_position=None):\n",
+    "    n = 0 # number of steps\n",
+    "    # set initial position\n",
+    "    if start_position:\n",
+    "        m.human = start_position \n",
+    "    else:\n",
+    "        m.random_start()\n",
+    "    while True:\n",
+    "        if m.at() == Board.Cell.apple:\n",
+    "            return n # success!\n",
+    "        if m.at() in [Board.Cell.wolf, Board.Cell.water]:\n",
+    "            return -1 # eaten by wolf or drowned\n",
+    "        while True:\n",
+    "            a = actions[policy(m)]\n",
+    "            new_pos = m.move_pos(m.human,a)\n",
+    "            if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:\n",
+    "                m.move(a) # do the actual move\n",
+    "                break\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "Хајде да спроведемо експеримент случајног хода неколико пута и видимо просечан број корака:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 32.87096774193548, eaten by wolf: 7 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        else:\n",
+    "            s += z\n",
+    "            n += 1\n",
+    "    print(f\"Average path length = {s/n}, eaten by wolf: {w} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Функција награђивања\n",
+    "\n",
+    "Да бисмо нашу политику учинили интелигентнијом, потребно је да разумемо који потези су \"бољи\" од других.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "move_reward = -0.1\n",
+    "goal_reward = 10\n",
+    "end_reward = -10\n",
+    "\n",
+    "def reward(m,pos=None):\n",
+    "    pos = pos or m.human\n",
+    "    if not m.is_valid(pos):\n",
+    "        return end_reward\n",
+    "    x = m.at(pos)\n",
+    "    if x==Board.Cell.water or x == Board.Cell.wolf:\n",
+    "        return end_reward\n",
+    "    if x==Board.Cell.apple:\n",
+    "        return goal_reward\n",
+    "    return move_reward"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Лернинг\n",
+    "\n",
+    "Направите Q-Табелу, или вишедимензионални низ. Пошто наша табла има димензије `width` x `height`, можемо представити Q-Табелу помоћу numpy низа са обликом `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "source": [
+    "Проследите Q-табелу функцији за цртање како бисте визуализовали табелу на табли:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Суштина Q-Learning-а: Белманова једначина и алгоритам учења\n",
+    "\n",
+    "Напишите псеудо-код за наш алгоритам учења:\n",
+    "\n",
+    "* Иницијализујте Q-табелу Q са једнаким вредностима за сва стања и акције\n",
+    "* Поставите стопу учења $\\alpha\\leftarrow 1$\n",
+    "* Поновите симулацију више пута\n",
+    "   1. Почните са случајне позиције\n",
+    "   1. Понављајте\n",
+    "        1. Изаберите акцију $a$ у стању $s$\n",
+    "        2. Извршите акцију преласком у ново стање $s'$\n",
+    "        3. Ако наиђемо на услов за крај игре, или је укупна награда сувише мала - изађите из симулације  \n",
+    "        4. Израчунајте награду $r$ у новом стању\n",
+    "        5. Ажурирајте Q-функцију према Белмановој једначини: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Ажурирајте укупну награду и смањите $\\alpha$.\n",
+    "\n",
+    "## Експлоатација vs. Експлорација\n",
+    "\n",
+    "Најбољи приступ је пронаћи баланс између експлорације и експлоатације. Како више учимо о нашем окружењу, вероватније је да ћемо следити оптималну путању, али је важно да повремено изаберемо непознат пут.\n",
+    "\n",
+    "## Python имплементација\n",
+    "\n",
+    "Сада смо спремни да имплементирамо алгоритам учења. Пре тога, потребна нам је и функција која ће произвољне бројеве у Q-табели претворити у вектор вероватноћа за одговарајуће акције:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "source": [
+    "Малој количини `eps` додајемо оригиналном вектору како бисмо избегли дељење са 0 у почетном случају, када су све компоненте вектора идентичне.\n",
+    "\n",
+    "Стварни алгоритам учења покренућемо за 5000 експеримената, који се такође називају **епохе**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    m.random_start()\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        dpos = actions[a]\n",
+    "        m.move(dpos,check_correctness=False) # we allow player to move outside the board, which terminates episode\n",
+    "        r = reward(m)\n",
+    "        cum_reward += r\n",
+    "        if r==end_reward or cum_reward < -1000:\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "source": [
+    "Након извршавања овог алгоритма, Q-табела би требало да буде ажурирана вредностима које дефинишу привлачност различитих акција у сваком кораку. Визуелизујте табелу овде:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p31952a86ca)\">\n    <image height=\"327\" id=\"image3fd90cd4cb\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Провера политике\n",
+    "\n",
+    "Пошто Q-табела приказује „атрактивност“ сваке акције у сваком стању, веома је једноставно користити је за дефинисање ефикасне навигације у нашем свету. У најједноставнијем случају, можемо једноставно изабрати акцију која одговара највишој вредности у Q-табели:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "def qpolicy_strict(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = list(actions)[np.argmax(v)]\n",
+    "        return a\n",
+    "\n",
+    "walk(m,qpolicy_strict)"
+   ]
+  },
+  {
+   "source": [
+    "Ако покренете горњи код неколико пута, можда ћете приметити да понекад само \"заглави\", и потребно је да притиснете дугме STOP у бележници како бисте га прекинули.\n",
+    "\n",
+    "> **Задатак 1:** Измените функцију `walk` тако да ограничи максималну дужину пута на одређени број корака (на пример, 100), и посматрајте како горњи код повремено враћа ову вредност.\n",
+    "\n",
+    "> **Задатак 2:** Измените функцију `walk` тако да се не враћа на места на којима је већ била раније. Ово ће спречити да `walk` упадне у петљу, али агент и даље може завршити \"заробљен\" на локацији са које не може да побегне.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 3.45, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbab852fd68>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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133.949763 214.756364 \nL 133.980202 214.756364 \nL 134.132399 206.226264 \nL 134.254157 214.756364 \nL 134.284596 214.685867 \nL 134.315036 198.00165 \nL 134.345475 214.756364 \nL 134.375914 199.106098 \nL 134.436793 214.756364 \nL 134.528111 214.497876 \nL 134.558551 214.309885 \nL 134.58899 214.756364 \nL 134.649869 214.685867 \nL 134.680308 213.252434 \nL 134.710748 214.756364 \nL 134.741187 214.756364 \nL 134.802066 212.946949 \nL 134.832506 214.756364 \nL 134.954263 193.184376 \nL 135.076021 214.756364 \nL 135.10646 212.594465 \nL 135.167339 214.756364 \nL 135.289097 214.756364 \nL 135.502172 214.756364 \nL 135.532612 206.766738 \nL 135.593491 214.756364 \nL 135.62393 214.662368 \nL 135.65437 214.756364 \nL 135.745688 195.722257 \nL 135.806567 214.756364 \nL 135.867445 203.923372 \nL 135.989203 214.756364 \nL 136.050082 213.111441 \nL 136.080521 214.756364 \nL 136.110961 214.756364 \nL 136.202279 206.837235 \nL 136.293597 214.756364 \nL 136.324036 213.510922 \nL 136.384915 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138.150401 209.375116 \nL 138.21128 214.756364 \nL 138.363477 206.672743 \nL 138.454795 214.756364 \nL 138.485234 205.967776 \nL 138.546113 210.338571 \nL 138.576553 214.732865 \nL 138.637431 211.41952 \nL 138.667871 207.659697 \nL 138.69831 213.604918 \nL 138.789629 213.299432 \nL 138.820068 214.756364 \nL 138.911386 201.737974 \nL 139.002704 214.756364 \nL 139.033144 213.322931 \nL 139.094023 214.756364 \nL 139.124462 210.033085 \nL 139.21578 210.503063 \nL 139.276659 209.7276 \nL 139.337538 214.756364 \nL 139.398417 212.92345 \nL 139.428856 214.756364 \nL 139.459295 214.756364 \nL 139.520174 204.863328 \nL 139.611493 211.114034 \nL 139.641932 211.255028 \nL 139.672371 210.738052 \nL 139.702811 214.756364 \nL 139.794129 213.957401 \nL 139.824568 214.756364 \nL 139.855008 213.581419 \nL 139.885447 207.119222 \nL 139.915887 214.756364 \nL 139.946326 214.004399 \nL 140.037644 211.325525 \nL 140.128962 214.756364 \nL 140.159402 207.260215 \nL 140.220281 214.380381 \nL 140.25072 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147.221344 214.591871 \nL 147.312663 210.57356 \nL 147.373541 211.231529 \nL 147.403981 214.756364 \nL 147.495299 214.19239 \nL 147.525738 214.732865 \nL 147.556178 208.317666 \nL 147.617057 214.262887 \nL 147.677936 213.816408 \nL 147.799693 214.756364 \nL 147.95189 201.502985 \nL 148.043208 214.756364 \nL 148.073648 211.513516 \nL 148.104087 210.78505 \nL 148.134527 197.79016 \nL 148.164966 214.756364 \nL 148.195405 214.568372 \nL 148.225845 214.756364 \nL 148.347602 211.8425 \nL 148.438921 214.756364 \nL 148.46936 209.093129 \nL 148.530239 212.617964 \nL 148.651997 214.756364 \nL 148.682436 201.549983 \nL 148.743315 214.756364 \nL 148.773754 213.698913 \nL 148.804194 214.756364 \nL 148.834633 202.724928 \nL 148.895512 212.335977 \nL 149.017269 214.756364 \nL 149.047709 214.662368 \nL 149.078148 213.698913 \nL 149.108588 207.213217 \nL 149.169466 214.027898 \nL 149.199906 211.137533 \nL 149.291224 208.787644 \nL 149.321664 214.756364 \nL 149.352103 214.756364 \nL 149.382542 188.484596 \nL 149.443421 206.860734 \nL 149.534739 214.756364 \nL 149.565179 207.401209 \nL 149.626058 214.756364 \nL 149.656497 212.547467 \nL 149.717376 214.756364 \nL 149.778255 214.756364 \nL 149.869573 212.312478 \nL 149.900012 214.262887 \nL 149.930452 207.518703 \nL 149.99133 214.685867 \nL 150.02177 208.740646 \nL 150.052209 206.226264 \nL 150.082649 209.7276 \nL 150.234846 214.756364 \nL 150.265285 201.573482 \nL 150.326164 213.9809 \nL 150.356603 213.792909 \nL 150.387043 214.756364 \nL 150.417482 209.539608 \nL 150.478361 213.651915 \nL 150.5088 213.487423 \nL 150.53924 214.756364 \nL 150.569679 208.811143 \nL 150.630558 214.051397 \nL 150.660997 214.756364 \nL 150.691437 207.659697 \nL 150.752316 214.756364 \nL 150.843634 212.852953 \nL 150.904513 213.628417 \nL 150.934952 214.756364 \nL 151.02627 208.388162 \nL 151.05671 209.116628 \nL 151.117589 214.756364 \nL 151.148028 213.463924 \nL 151.178467 204.275855 \nL 151.239346 214.732865 \nL 151.269786 208.01218 \nL 151.300225 211.889498 \nL 151.330664 190.435005 \nL 151.361104 214.756364 \nL 151.391543 211.748505 \nL 151.421983 212.429973 \nL 151.452422 204.322853 \nL 151.482861 214.756364 \nL 151.513301 207.80069 \nL 151.665498 214.756364 \nL 151.726377 208.975635 \nL 151.756816 210.15058 \nL 151.787256 214.756364 \nL 151.817695 211.748505 \nL 151.848134 206.155767 \nL 151.909013 214.521375 \nL 151.969892 214.756364 \nL 152.000331 214.145392 \nL 152.06121 214.756364 \nL 152.182968 197.625667 \nL 152.243847 214.756364 \nL 152.304725 213.181937 \nL 152.335165 212.71196 \nL 152.365604 214.756364 \nL 152.426483 205.897279 \nL 152.456923 214.756364 \nL 152.548241 208.458659 \nL 152.57868 213.111441 \nL 152.60912 214.756364 \nL 152.700438 214.19239 \nL 152.730877 214.215889 \nL 152.822195 214.756364 \nL 152.852635 208.364663 \nL 152.913514 214.121893 \nL 152.943953 184.654276 \nL 153.065711 214.756364 \nL 153.09615 210.926043 \nL 153.157029 214.756364 \nL 153.187468 214.756364 \nL 153.248347 206.320259 \nL 153.309226 207.706694 \nL 153.370105 214.756364 \nL 153.430984 213.158439 \nL 153.461423 213.816408 \nL 153.491862 207.518703 \nL 153.522302 214.756364 \nL 153.552741 212.617964 \nL 153.644059 214.756364 \nL 153.674499 214.356882 \nL 153.704938 214.756364 \nL 153.735378 213.839907 \nL 153.765817 207.542202 \nL 153.796256 214.756364 \nL 153.826696 214.239388 \nL 153.887575 214.756364 \nL 153.918014 204.839829 \nL 153.978893 213.792909 \nL 154.039772 214.756364 \nL 154.070211 213.322931 \nL 154.100651 188.790082 \nL 154.161529 214.215889 \nL 154.191969 214.756364 \nL 154.222408 200.11655 \nL 154.283287 202.630932 \nL 154.405045 214.756364 \nL 154.435484 214.756364 \nL 154.465923 214.591871 \nL 154.496363 208.975635 \nL 154.526802 214.756364 \nL 154.557242 214.756364 \nL 154.587681 214.756364 \nL 154.678999 213.698913 \nL 154.739878 214.756364 \nL 154.770317 214.145392 \nL 154.800757 213.13494 \nL 154.831196 214.756364 \nL 154.861636 214.756364 \nL 154.892075 205.02782 \nL 154.952954 214.756364 \nL 155.013833 213.534421 \nL 155.044272 206.461253 \nL 155.105151 214.756364 \nL 155.16603 214.756364 \nL 155.196469 204.487345 \nL 155.257348 214.756364 \nL 155.379106 213.228935 \nL 155.439984 211.959995 \nL 155.561742 214.756364 \nL 155.622621 214.756364 \nL 155.713939 205.215811 \nL 155.805257 214.756364 \nL 155.835697 202.865921 \nL 155.896576 212.641463 \nL 156.018333 214.756364 \nL 156.048773 213.322931 \nL 156.109651 214.756364 \nL 156.140091 196.356727 \nL 156.261848 214.756364 \nL 156.322727 207.777191 \nL 156.353167 213.040944 \nL 156.414045 214.756364 \nL 156.566243 204.017367 \nL 156.657561 214.756364 \nL 156.688 214.239388 \nL 156.748879 214.756364 \nL 156.779318 208.035679 \nL 156.840197 207.80069 \nL 156.870637 214.756364 \nL 156.961955 202.912919 \nL 157.022834 214.756364 \nL 157.083712 214.356882 \nL 157.175031 200.539531 \nL 157.23591 214.756364 \nL 157.296788 207.847688 \nL 157.357667 214.756364 \nL 157.388107 204.463846 \nL 157.418546 211.278527 \nL 157.448985 210.103582 \nL 157.479425 213.816408 \nL 157.570743 213.534421 \nL 157.631622 214.756364 \nL 157.692501 207.988681 \nL 157.753379 214.756364 \nL 157.814258 214.521375 \nL 157.844698 214.756364 \nL 157.996895 205.403802 \nL 158.027334 205.521297 \nL 158.149092 214.732865 \nL 158.179531 214.756364 \nL 158.209971 204.510844 \nL 158.270849 211.983494 \nL 158.392607 214.756364 \nL 158.453486 214.756364 \nL 158.483925 212.970447 \nL 158.544804 213.416926 \nL 158.605683 214.756364 \nL 158.666562 210.503063 \nL 158.697001 214.756364 \nL 158.72744 213.816408 \nL 158.788319 214.756364 \nL 158.818759 214.756364 \nL 158.940516 210.832048 \nL 158.970956 211.372522 \nL 159.092713 192.620402 \nL 159.031835 213.76941 \nL 159.123153 200.32804 \nL 159.24491 214.756364 \nL 159.366668 212.194984 \nL 159.397107 212.805955 \nL 159.427547 212.335977 \nL 159.457986 214.756364 \nL 159.518865 212.852953 \nL 159.549304 212.429973 \nL 159.640623 214.756364 \nL 159.671062 214.709366 \nL 159.701502 213.675414 \nL 159.731941 214.756364 \nL 159.76238 214.685867 \nL 159.79282 202.25495 \nL 159.853699 210.855547 \nL 159.975456 214.756364 \nL 160.066774 211.865999 \nL 160.158093 214.756364 \nL 160.27985 211.325525 \nL 160.340729 207.565701 \nL 160.371168 214.756364 \nL 160.492926 200.657025 \nL 160.584244 214.168891 \nL 160.614684 209.28112 \nL 160.736441 214.732865 \nL 160.675563 208.670149 \nL 160.766881 214.568372 \nL 160.858199 214.756364 \nL 160.888638 197.155689 \nL 161.010396 214.756364 \nL 161.071275 214.756364 \nL 161.101714 208.905138 \nL 161.162593 214.756364 \nL 161.284351 211.678008 \nL 161.375669 211.231529 \nL 161.406108 214.756364 \nL 161.527866 209.892092 \nL 161.588745 208.411661 \nL 161.649624 214.756364 \nL 161.740942 213.416926 \nL 161.771381 214.756364 \nL 161.801821 213.440425 \nL 161.83226 207.142721 \nL 161.893139 214.756364 \nL 162.045336 214.756364 \nL 162.167094 208.153173 \nL 162.319291 214.662368 \nL 162.410609 214.756364 \nL 162.441048 205.192312 \nL 162.593245 214.756364 \nL 162.745442 205.591794 \nL 162.775882 214.756364 \nL 162.8672 212.899951 \nL 162.958518 214.756364 \nL 162.928079 212.171485 \nL 162.988958 213.675414 \nL 163.049836 214.756364 \nL 163.080276 213.55792 \nL 163.110715 210.949542 \nL 163.171594 214.756364 \nL 163.202033 214.662368 \nL 163.232473 213.040944 \nL 163.262912 197.696164 \nL 163.323791 214.756364 \nL 163.35423 208.693648 \nL 163.415109 213.910403 \nL 163.445549 214.756364 \nL 163.567306 207.119222 \nL 163.719503 214.756364 \nL 163.749943 214.638869 \nL 163.780382 208.059178 \nL 163.841261 214.756364 \nL 163.8717 212.852953 \nL 163.90214 214.756364 \nL 163.932579 199.670071 \nL 163.993458 209.234123 \nL 164.023897 213.76941 \nL 164.115216 212.476971 \nL 164.145655 214.61537 \nL 164.176094 211.63101 \nL 164.206534 212.735458 \nL 164.236973 212.641463 \nL 164.328291 214.756364 \nL 164.358731 213.510922 \nL 164.38917 214.40388 \nL 164.480489 209.915591 \nL 164.510928 211.725006 \nL 164.541367 214.40388 \nL 164.571807 207.424708 \nL 164.602246 212.735458 \nL 164.663125 209.915591 \nL 164.693564 212.71196 \nL 164.724004 212.970447 \nL 164.784883 211.255028 \nL 164.845761 214.756364 \nL 164.876201 214.756364 \nL 165.028398 193.983338 \nL 165.119716 214.756364 \nL 165.150155 213.393428 \nL 165.180595 211.889498 \nL 165.211034 213.487423 \nL 165.241474 214.756364 \nL 165.271913 207.683195 \nL 165.363231 212.147986 \nL 165.393671 214.756364 \nL 165.42411 200.915513 \nL 165.484989 214.756364 \nL 165.515428 214.756364 \nL 165.576307 210.291573 \nL 165.637186 212.382975 \nL 165.758944 214.756364 \nL 165.819822 212.241982 \nL 165.850262 214.756364 \nL 165.880701 214.756364 \nL 165.911141 213.581419 \nL 165.972019 214.662368 \nL 166.032898 214.756364 \nL 166.063338 214.309885 \nL 166.124217 214.756364 \nL 166.154656 214.61537 \nL 166.185095 214.756364 \nL 166.215535 212.006993 \nL 166.276414 213.487423 \nL 166.306853 214.756364 \nL 166.337292 211.912997 \nL 166.367732 214.239388 \nL 166.428611 214.709366 \nL 166.45905 206.273262 \nL 166.580808 214.756364 \nL 166.611247 212.288979 \nL 166.672126 213.55792 \nL 166.733005 214.756364 \nL 166.885202 209.445613 \nL 167.037399 214.756364 \nL 167.159156 212.500469 \nL 167.220035 214.756364 \nL 167.250475 204.957323 \nL 167.311353 214.756364 \nL 167.433111 212.547467 \nL 167.46355 214.40388 \nL 167.49399 207.80069 \nL 167.585308 207.965182 \nL 167.615747 214.756364 \nL 167.646187 195.22878 \nL 167.707066 214.756364 \nL 167.767945 214.756364 \nL 167.798384 211.865999 \nL 167.859263 214.098395 \nL 167.920142 214.756364 \nL 167.950581 213.299432 \nL 168.01146 214.756364 \nL 168.041899 214.732865 \nL 168.072339 214.333383 \nL 168.163657 193.818846 \nL 168.285414 214.756364 \nL 168.315854 208.787644 \nL 168.376733 211.184531 \nL 168.407172 214.756364 \nL 168.437611 214.450878 \nL 168.468051 203.429895 \nL 168.52893 214.756364 \nL 168.589809 212.100988 \nL 168.620248 212.147986 \nL 168.650687 214.756364 \nL 168.681127 211.513516 \nL 168.742006 213.463924 \nL 168.772445 210.526562 \nL 168.802884 184.325291 \nL 168.863763 214.685867 \nL 168.924642 214.756364 \nL 168.955081 214.027898 \nL 168.985521 209.351617 \nL 169.0464 214.756364 \nL 169.076839 214.756364 \nL 169.107278 207.612699 \nL 169.168157 214.591871 \nL 169.198597 213.628417 \nL 169.229036 214.756364 \nL 169.259475 214.756364 \nL 169.320354 214.027898 \nL 169.442112 200.398537 \nL 169.381233 214.756364 \nL 169.472551 206.132268 \nL 169.56387 214.756364 \nL 169.685627 200.586528 \nL 169.807385 214.756364 \nL 169.868264 214.756364 \nL 169.898703 214.568372 \nL 169.929142 207.824189 \nL 169.959582 214.756364 \nL 169.990021 214.756364 \nL 170.0509 211.396021 \nL 170.081339 214.756364 \nL 170.233537 195.886749 \nL 170.263976 214.756364 \nL 170.355294 213.228935 \nL 170.416173 212.26548 \nL 170.446612 213.76941 \nL 170.477052 213.510922 \nL 170.56837 200.563029 \nL 170.629249 203.782378 \nL 170.751006 214.756364 \nL 170.842325 206.907732 \nL 170.872764 207.824189 \nL 170.994522 214.756364 \nL 171.024961 207.753692 \nL 171.08584 214.662368 \nL 171.116279 214.756364 \nL 171.268476 212.71196 \nL 171.298916 214.638869 \nL 171.329355 210.738052 \nL 171.359795 209.351617 \nL 171.481552 214.709366 \nL 171.511992 214.756364 \nL 171.542431 214.286386 \nL 171.57287 206.884233 \nL 171.60331 214.756364 \nL 171.664189 209.116628 \nL 171.755507 214.756364 \nL 171.785946 214.286386 \nL 171.877265 214.756364 \nL 171.907704 212.124487 \nL 171.938143 214.756364 \nL 171.968583 206.437754 \nL 172.059901 211.396021 \nL 172.09034 213.651915 \nL 172.12078 213.534421 \nL 172.151219 205.756286 \nL 172.181659 214.756364 \nL 172.212098 214.756364 \nL 172.272977 214.756364 \nL 172.303416 213.252434 \nL 172.364295 214.333383 \nL 172.394734 214.756364 \nL 172.486053 208.43516 \nL 172.577371 206.343758 \nL 172.60781 214.756364 \nL 172.63825 191.680446 \nL 172.699129 214.756364 \nL 172.729568 214.333383 \nL 172.760007 214.756364 \nL 172.790447 211.466518 \nL 172.851326 214.756364 \nL 172.912204 214.756364 \nL 172.942644 208.623151 \nL 173.033962 211.772004 \nL 173.12528 214.756364 \nL 173.15572 212.030492 \nL 173.216598 214.756364 \nL 173.338356 214.709366 \nL 173.368796 202.724928 \nL 173.429674 214.756364 \nL 173.460114 214.756364 \nL 173.581871 205.709288 \nL 173.703629 214.756364 \nL 173.734068 214.756364 \nL 173.764508 214.544874 \nL 173.855826 214.662368 \nL 173.977584 214.756364 \nL 174.129781 210.855547 \nL 174.16022 204.134862 \nL 174.19066 214.638869 \nL 174.221099 214.215889 \nL 174.251538 210.620558 \nL 174.281978 214.756364 \nL 174.312417 214.756364 \nL 174.342857 214.756364 \nL 174.464614 204.745833 \nL 174.495054 207.871187 \nL 174.616811 214.756364 \nL 174.647251 214.709366 \nL 174.67769 214.145392 \nL 174.708129 214.709366 \nL 174.769008 203.429895 \nL 174.799448 214.756364 \nL 174.890766 214.756364 \nL 174.982084 213.416926 \nL 175.012524 214.756364 \nL 175.042963 211.560514 \nL 175.073402 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177.112843 214.756364 \nL 177.143282 214.027898 \nL 177.173721 208.270668 \nL 177.2346 208.811143 \nL 177.295479 214.756364 \nL 177.356358 212.241982 \nL 177.417237 214.756364 \nL 177.447676 214.568372 \nL 177.508555 208.106176 \nL 177.538994 214.756364 \nL 177.569434 212.359476 \nL 177.599873 212.335977 \nL 177.660752 214.756364 \nL 177.691191 200.022555 \nL 177.78251 214.756364 \nL 177.812949 213.087942 \nL 177.843388 214.756364 \nL 177.873828 214.638869 \nL 177.904267 196.427223 \nL 177.965146 214.756364 \nL 177.995585 208.599652 \nL 178.056464 209.892092 \nL 178.178222 214.756364 \nL 178.208661 214.756364 \nL 178.29998 208.482158 \nL 178.421737 214.756364 \nL 178.482616 211.443019 \nL 178.513055 214.756364 \nL 178.573934 212.030492 \nL 178.634813 213.158439 \nL 178.665252 214.756364 \nL 178.756571 205.380303 \nL 178.847889 214.756364 \nL 178.878328 207.683195 \nL 178.939207 214.756364 \nL 178.969647 214.756364 \nL 179.030525 210.879046 \nL 179.060965 214.19239 \nL 179.121844 212.993946 \nL 179.182722 210.78505 \nL 179.213162 214.756364 \nL 179.243601 210.432566 \nL 179.274041 202.395943 \nL 179.30448 214.756364 \nL 179.334919 214.756364 \nL 179.365359 214.756364 \nL 179.395798 208.129674 \nL 179.456677 214.215889 \nL 179.487116 214.756364 \nL 179.517556 211.114034 \nL 179.578435 214.756364 \nL 179.608874 214.756364 \nL 179.669753 196.544718 \nL 179.730632 212.05399 \nL 179.791511 214.732865 \nL 179.82195 214.168891 \nL 179.852389 200.821517 \nL 179.913268 214.756364 \nL 180.035026 208.576154 \nL 180.126344 214.756364 \nL 180.156783 214.732865 \nL 180.187223 214.427379 \nL 180.217662 214.756364 \nL 180.278541 203.735381 \nL 180.30898 213.158439 \nL 180.369859 214.756364 \nL 180.400299 214.239388 \nL 180.430738 204.769332 \nL 180.491617 213.933902 \nL 180.552496 214.756364 \nL 180.674253 205.920778 \nL 180.735132 214.756364 \nL 180.765572 211.748505 \nL 180.796011 204.275855 \nL 180.82645 214.756364 \nL 180.85689 211.278527 \nL 180.887329 213.910403 \nL 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204.299354 \nL 187.066529 210.879046 \nL 187.218726 214.756364 \nL 187.340484 211.748505 \nL 187.370923 214.380381 \nL 187.431802 201.808471 \nL 187.462241 214.756364 \nL 187.492681 211.137533 \nL 187.52312 214.756364 \nL 187.553559 209.06963 \nL 187.614438 214.709366 \nL 187.644878 195.369773 \nL 187.736196 196.403725 \nL 187.827514 214.756364 \nL 187.857954 213.675414 \nL 187.888393 214.756364 \nL 188.010151 209.892092 \nL 188.04059 214.544874 \nL 188.101469 187.732631 \nL 188.131908 214.756364 \nL 188.162348 213.017445 \nL 188.192787 202.88942 \nL 188.223226 214.427379 \nL 188.253666 213.158439 \nL 188.344984 214.756364 \nL 188.405863 200.069553 \nL 188.466742 212.359476 \nL 188.52762 214.756364 \nL 188.588499 187.591638 \nL 188.679818 214.756364 \nL 188.710257 214.380381 \nL 188.771136 211.63101 \nL 188.801575 214.286386 \nL 188.832015 214.756364 \nL 188.862454 214.732865 \nL 188.923333 214.756364 \nL 188.984212 202.771926 \nL 189.07553 214.756364 \nL 189.105969 211.278527 \nL 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214.756364 \nL 191.023652 214.356882 \nL 191.054091 201.385491 \nL 191.084531 214.568372 \nL 191.14541 208.64665 \nL 191.267167 214.732865 \nL 191.328046 211.959995 \nL 191.358485 214.756364 \nL 191.388925 214.427379 \nL 191.449804 214.638869 \nL 191.541122 203.594387 \nL 191.571561 213.675414 \nL 191.63244 202.701429 \nL 191.662879 198.283636 \nL 191.784637 214.732865 \nL 191.815077 208.200171 \nL 191.845516 214.756364 \nL 191.906395 210.291573 \nL 191.936834 214.756364 \nL 191.967274 210.174079 \nL 191.997713 199.294089 \nL 192.058592 214.756364 \nL 192.119471 213.205436 \nL 192.14991 214.756364 \nL 192.210789 214.309885 \nL 192.241228 214.756364 \nL 192.271668 203.805877 \nL 192.332546 210.197577 \nL 192.454304 214.756364 \nL 192.606501 208.928637 \nL 192.728259 214.756364 \nL 192.789138 214.286386 \nL 192.819577 201.1975 \nL 192.850016 214.756364 \nL 192.880456 213.651915 \nL 192.941335 211.231529 \nL 192.971774 212.26548 \nL 193.032653 214.756364 \nL 193.15441 190.622996 \nL 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209.774597 \nL 195.467805 208.881639 \nL 195.498245 214.756364 \nL 195.528684 205.944277 \nL 195.589563 214.638869 \nL 195.680881 206.508251 \nL 195.711321 214.756364 \nL 195.802639 211.490017 \nL 195.833078 214.756364 \nL 195.863518 188.837079 \nL 195.924397 214.544874 \nL 196.046154 181.411428 \nL 196.167912 214.756364 \nL 196.289669 200.281043 \nL 196.380988 214.756364 \nL 196.350548 179.696008 \nL 196.411427 213.181937 \nL 196.441866 211.114034 \nL 196.472306 214.756364 \nL 196.502745 212.641463 \nL 196.533185 212.547467 \nL 196.563624 213.76941 \nL 196.594063 211.983494 \nL 196.624503 210.385569 \nL 196.685382 214.756364 \nL 196.746261 213.393428 \nL 196.807139 214.756364 \nL 196.837579 214.568372 \nL 196.898458 214.756364 \nL 196.928897 209.304619 \nL 197.020215 214.756364 \nL 197.050655 212.171485 \nL 197.141973 214.756364 \nL 197.172412 183.526329 \nL 197.29417 214.756364 \nL 197.415928 213.675414 \nL 197.476806 214.756364 \nL 197.507246 214.333383 \nL 197.568125 205.051319 \nL 197.598564 206.555248 \nL 197.629003 214.756364 \nL 197.689882 207.260215 \nL 197.720322 196.803206 \nL 197.750761 214.756364 \nL 197.7812 214.756364 \nL 197.81164 211.302026 \nL 197.872519 214.756364 \nL 197.902958 214.756364 \nL 197.933397 175.842189 \nL 197.994276 213.487423 \nL 198.055155 212.382975 \nL 198.116034 214.756364 \nL 198.176913 187.168658 \nL 198.237792 204.910326 \nL 198.268231 214.756364 \nL 198.32911 206.061771 \nL 198.359549 203.594387 \nL 198.389989 186.111207 \nL 198.450867 214.756364 \nL 198.542186 202.654431 \nL 198.572625 213.55792 \nL 198.603064 213.463924 \nL 198.633504 214.756364 \nL 198.663943 207.542202 \nL 198.724822 214.568372 \nL 198.81614 214.756364 \nL 198.877019 206.320259 \nL 198.907458 214.521375 \nL 198.998777 213.886904 \nL 199.029216 200.516032 \nL 199.090095 201.291495 \nL 199.181413 214.756364 \nL 199.211853 213.087942 \nL 199.242292 214.756364 \nL 199.272731 213.863406 \nL 199.303171 189.753537 \nL 199.394489 205.004321 \nL 199.485807 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205.02782 \nL 204.325673 202.536937 \nL 204.447431 214.756364 \nL 204.47787 214.756364 \nL 204.508309 211.114034 \nL 204.569188 212.664962 \nL 204.660507 214.756364 \nL 204.751825 206.484752 \nL 204.843143 214.756364 \nL 204.873582 213.322931 \nL 204.904022 211.20803 \nL 204.934461 214.756364 \nL 204.964901 183.338337 \nL 205.025779 214.756364 \nL 205.056219 214.521375 \nL 205.117098 214.756364 \nL 205.147537 201.455988 \nL 205.208416 214.756364 \nL 205.360613 214.756364 \nL 205.391052 199.0591 \nL 205.451931 214.756364 \nL 205.482371 213.722412 \nL 205.543249 214.709366 \nL 205.573689 214.756364 \nL 205.665007 207.448206 \nL 205.695446 189.988525 \nL 205.756325 212.617964 \nL 205.786765 214.756364 \nL 205.817204 203.852875 \nL 205.878083 211.443019 \nL 205.938962 214.756364 \nL 205.99984 214.662368 \nL 206.03028 214.756364 \nL 206.152037 191.492455 \nL 206.273795 214.756364 \nL 206.304235 197.625667 \nL 206.365113 214.685867 \nL 206.395553 214.544874 \nL 206.425992 208.411661 \nL 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209.657103 \nL 208.404554 214.756364 \nL 208.434993 210.268074 \nL 208.465432 207.941683 \nL 208.495872 213.416926 \nL 208.556751 214.756364 \nL 208.58719 203.500392 \nL 208.678508 207.354211 \nL 208.708948 206.29676 \nL 208.800266 213.463924 \nL 208.830705 212.429973 \nL 208.861145 203.946871 \nL 208.891584 214.732865 \nL 208.952463 208.317666 \nL 208.982902 213.792909 \nL 209.013342 207.777191 \nL 209.043781 212.899951 \nL 209.135099 214.756364 \nL 209.165539 194.688305 \nL 209.226418 214.756364 \nL 209.287296 212.899951 \nL 209.317736 214.756364 \nL 209.348175 209.586606 \nL 209.409054 213.463924 \nL 209.439493 214.756364 \nL 209.469933 201.573482 \nL 209.530812 213.534421 \nL 209.561251 214.286386 \nL 209.591691 204.064365 \nL 209.652569 209.774597 \nL 209.713448 207.424708 \nL 209.774327 214.756364 \nL 209.804766 206.578747 \nL 209.865645 214.450878 \nL 209.896085 214.756364 \nL 209.987403 208.670149 \nL 210.017842 214.756364 \nL 210.10916 214.215889 \nL 210.1396 208.082677 \nL 210.200479 212.312478 \nL 210.261357 214.756364 \nL 210.291797 213.628417 \nL 210.322236 197.625667 \nL 210.352676 214.756364 \nL 210.383115 214.756364 \nL 210.413555 208.22367 \nL 210.474433 214.544874 \nL 210.504873 214.756364 \nL 210.535312 214.121893 \nL 210.596191 208.85814 \nL 210.62663 210.244575 \nL 210.717949 214.756364 \nL 210.748388 193.066881 \nL 210.809267 214.756364 \nL 210.931024 206.108769 \nL 211.052782 214.756364 \nL 211.113661 214.756364 \nL 211.204979 209.328118 \nL 211.296297 214.756364 \nL 211.326737 209.022633 \nL 211.418055 210.244575 \nL 211.448494 211.701507 \nL 211.509373 213.275933 \nL 211.539813 189.965027 \nL 211.570252 214.756364 \nL 211.66157 201.573482 \nL 211.69201 214.732865 \nL 211.722449 191.915435 \nL 211.752888 211.396021 \nL 211.813767 195.722257 \nL 211.844207 214.756364 \nL 211.874646 214.40388 \nL 211.905086 206.014774 \nL 211.935525 214.756364 \nL 211.965964 214.027898 \nL 211.996404 213.252434 \nL 212.026843 214.756364 \nL 212.057283 198.84761 \nL 212.118161 212.899951 \nL 212.148601 211.701507 \nL 212.17904 199.411583 \nL 212.20948 214.756364 \nL 212.239919 214.333383 \nL 212.300798 214.756364 \nL 212.331237 199.952058 \nL 212.392116 208.85814 \nL 212.422555 211.983494 \nL 212.452995 182.468878 \nL 212.483434 213.158439 \nL 212.513874 203.194906 \nL 212.544313 214.756364 \nL 212.635631 212.735458 \nL 212.666071 206.437754 \nL 212.72695 210.409068 \nL 212.757389 214.756364 \nL 212.818268 213.322931 \nL 212.848707 211.725006 \nL 212.879147 198.236639 \nL 212.909586 214.756364 \nL 212.940025 199.223592 \nL 213.000904 214.756364 \nL 213.061783 212.171485 \nL 213.092222 207.307213 \nL 213.122662 214.756364 \nL 213.153101 210.009586 \nL 213.183541 214.756364 \nL 213.244419 208.341165 \nL 213.274859 214.568372 \nL 213.335738 214.756364 \nL 213.396616 213.510922 \nL 213.427056 214.756364 \nL 213.457495 212.735458 \nL 213.487935 213.9809 \nL 213.518374 209.234123 \nL 213.579253 214.756364 \nL 213.609692 214.756364 \nL 213.761889 197.155689 \nL 213.883647 214.756364 \nL 213.914086 214.756364 \nL 213.944526 214.497876 \nL 214.005405 214.756364 \nL 214.035844 214.756364 \nL 214.066283 214.61537 \nL 214.127162 214.756364 \nL 214.188041 209.422114 \nL 214.309799 214.756364 \nL 214.370678 209.539608 \nL 214.401117 214.662368 \nL 214.431556 214.756364 \nL 214.522875 194.218327 \nL 214.553314 210.244575 \nL 214.614193 209.539608 \nL 214.675072 214.756364 \nL 214.76639 213.275933 \nL 214.857708 214.756364 \nL 214.827269 212.92345 \nL 214.888147 214.450878 \nL 214.918587 204.087864 \nL 215.009905 210.197577 \nL 215.101223 214.756364 \nL 215.131663 214.732865 \nL 215.192542 214.756364 \nL 215.25342 206.484752 \nL 215.28386 212.05399 \nL 215.314299 195.745755 \nL 215.375178 203.006915 \nL 215.496936 214.756364 \nL 215.527375 206.531749 \nL 215.588254 214.756364 \nL 215.618693 214.756364 \nL 215.649133 206.790237 \nL 215.710011 214.732865 \nL 215.77089 214.756364 \nL 215.831769 213.534421 \nL 215.862208 199.90506 \nL 215.923087 208.717147 \nL 216.014406 214.756364 \nL 216.044845 212.92345 \nL 216.105724 202.842423 \nL 216.136163 214.756364 \nL 216.227481 206.978229 \nL 216.257921 214.756364 \nL 216.349239 214.709366 \nL 216.440557 202.372445 \nL 216.410118 214.756364 \nL 216.470997 211.889498 \nL 216.531875 209.868593 \nL 216.592754 214.215889 \nL 216.684072 205.544796 \nL 216.775391 214.756364 \nL 216.80583 206.790237 \nL 216.866709 214.756364 \nL 216.927588 211.396021 \nL 216.958027 186.628183 \nL 216.988467 214.591871 \nL 217.018906 212.758957 \nL 217.049345 209.398615 \nL 217.110224 213.863406 \nL 217.262421 214.756364 \nL 217.292861 201.549983 \nL 217.353739 213.463924 \nL 217.384179 213.675414 \nL 217.414618 200.398537 \nL 217.445058 214.521375 \nL 217.475497 211.560514 \nL 217.505937 214.756364 \nL 217.597255 212.735458 \nL 217.658134 214.756364 \nL 217.719012 202.489939 \nL 217.779891 212.077489 \nL 217.84077 210.832048 \nL 217.932088 214.756364 \nL 217.962528 211.349023 \nL 218.023406 212.876452 \nL 218.114725 214.756364 \nL 218.145164 214.732865 \nL 218.206043 214.756364 \nL 218.297361 200.610027 \nL 218.388679 214.756364 \nL 218.419119 207.095723 \nL 218.540876 214.756364 \nL 218.571316 214.591871 \nL 218.632195 203.077411 \nL 218.662634 211.678008 \nL 218.693073 214.756364 \nL 218.723513 201.385491 \nL 218.784392 211.043538 \nL 218.87571 214.756364 \nL 218.936589 201.878968 \nL 218.967028 214.756364 \nL 218.997467 214.756364 \nL 219.027907 201.737974 \nL 219.088786 214.756364 \nL 219.149665 214.756364 \nL 219.180104 197.696164 \nL 219.240983 214.756364 \nL 219.271422 213.087942 \nL 219.301862 214.450878 \nL 219.332301 195.581263 \nL 219.39318 210.503063 \nL 219.423619 210.080083 \nL 219.454059 212.030492 \nL 219.514937 202.771926 \nL 219.545377 214.756364 \nL 219.575816 212.547467 \nL 219.636695 214.756364 \nL 219.697574 214.756364 \nL 219.819331 213.487423 \nL 219.91065 214.756364 \nL 219.971529 195.722257 \nL 220.001968 214.732865 \nL 220.032407 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222.102287 214.756364 \nL 222.132726 213.228935 \nL 222.193605 214.756364 \nL 222.224045 197.437676 \nL 222.284923 211.889498 \nL 222.345802 210.78505 \nL 222.406681 214.756364 \nL 222.437121 214.497876 \nL 222.46756 212.406474 \nL 222.497999 214.756364 \nL 222.528439 213.722412 \nL 222.650196 214.756364 \nL 222.680636 214.004399 \nL 222.802393 203.147908 \nL 222.832833 206.08527 \nL 222.863272 214.685867 \nL 222.924151 208.64665 \nL 222.95459 206.437754 \nL 223.076348 214.756364 \nL 223.106787 214.732865 \nL 223.137227 213.205436 \nL 223.167666 194.241826 \nL 223.228545 211.278527 \nL 223.289424 210.597059 \nL 223.350303 214.756364 \nL 223.380742 189.236561 \nL 223.47206 204.722334 \nL 223.532939 211.114034 \nL 223.563379 189.941528 \nL 223.593818 214.732865 \nL 223.624257 211.63101 \nL 223.715576 214.756364 \nL 223.746015 203.735381 \nL 223.806894 211.584012 \nL 223.837333 214.756364 \nL 223.867773 206.08527 \nL 223.928652 209.798096 \nL 223.98953 214.756364 \nL 224.01997 197.672665 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228.068411 214.756364 \nL 228.09885 210.291573 \nL 228.159729 214.756364 \nL 228.190169 214.756364 \nL 228.220608 214.380381 \nL 228.251047 199.505579 \nL 228.281487 214.756364 \nL 228.311926 211.560514 \nL 228.342366 212.852953 \nL 228.372805 190.998978 \nL 228.433684 214.521375 \nL 228.464123 214.61537 \nL 228.494563 214.239388 \nL 228.555441 214.756364 \nL 228.61632 203.335899 \nL 228.677199 192.808393 \nL 228.738078 214.756364 \nL 228.890275 207.330712 \nL 228.920714 213.087942 \nL 228.951154 197.437676 \nL 228.981593 214.756364 \nL 229.012033 208.458659 \nL 229.042472 214.756364 \nL 229.13379 213.322931 \nL 229.225108 214.756364 \nL 229.255548 213.440425 \nL 229.285987 214.756364 \nL 229.346866 203.335899 \nL 229.377305 208.43516 \nL 229.468624 214.756364 \nL 229.529502 191.844938 \nL 229.559942 196.192234 \nL 229.6817 214.756364 \nL 229.712139 213.181937 \nL 229.773018 214.732865 \nL 229.864336 213.228935 \nL 229.894775 214.40388 \nL 229.925215 214.732865 \nL 229.955654 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231.72114 210.221076 \nL 231.782019 214.756364 \nL 231.812458 213.76941 \nL 231.903776 200.892014 \nL 231.934216 212.382975 \nL 231.964655 214.756364 \nL 232.055973 213.910403 \nL 232.086413 214.168891 \nL 232.177731 214.239388 \nL 232.20817 196.450722 \nL 232.23861 214.756364 \nL 232.329928 214.286386 \nL 232.360367 214.732865 \nL 232.451686 200.633526 \nL 232.421246 214.756364 \nL 232.482125 208.43516 \nL 232.543004 214.756364 \nL 232.603883 213.745911 \nL 232.664761 213.275933 \nL 232.72564 197.155689 \nL 232.786519 206.132268 \nL 232.877837 214.756364 \nL 232.847398 198.518625 \nL 232.908277 213.252434 \nL 232.999595 214.756364 \nL 233.030034 211.278527 \nL 233.121353 213.275933 \nL 233.24311 200.563029 \nL 233.27355 210.479564 \nL 233.303989 214.756364 \nL 233.395307 212.570966 \nL 233.425747 208.200171 \nL 233.456186 186.581185 \nL 233.517065 211.20803 \nL 233.547504 214.756364 \nL 233.608383 209.140127 \nL 233.638823 207.871187 \nL 233.669262 188.249607 \nL 233.730141 214.756364 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237.839461 199.881561 \nL 237.8699 214.756364 \nL 237.90034 214.685867 \nL 238.052537 203.382897 \nL 238.143855 214.756364 \nL 238.174294 205.615292 \nL 238.235173 211.137533 \nL 238.296052 214.756364 \nL 238.326491 212.547467 \nL 238.356931 198.84761 \nL 238.38737 214.756364 \nL 238.41781 205.967776 \nL 238.539567 214.756364 \nL 238.661325 198.072146 \nL 238.691764 214.756364 \nL 238.783082 212.241982 \nL 238.813522 200.093051 \nL 238.874401 214.756364 \nL 238.90484 203.547389 \nL 239.057037 214.756364 \nL 239.087476 207.213217 \nL 239.148355 211.090536 \nL 239.270113 214.756364 \nL 239.361431 191.539453 \nL 239.42231 214.756364 \nL 239.483189 205.87378 \nL 239.544068 214.756364 \nL 239.604946 210.009586 \nL 239.635386 214.427379 \nL 239.665825 201.385491 \nL 239.696265 214.756364 \nL 239.726704 211.41952 \nL 239.787583 214.450878 \nL 239.818022 209.445613 \nL 239.878901 214.756364 \nL 240.000659 209.375116 \nL 240.031098 191.492455 \nL 240.091977 214.662368 \nL 240.122416 214.61537 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254.733332 214.756364 \nL 254.763771 214.756364 \nL 254.794211 198.730115 \nL 254.885529 206.813736 \nL 254.946408 214.756364 \nL 254.976847 213.487423 \nL 255.007287 141.345807 \nL 255.068165 214.756364 \nL 255.098605 213.886904 \nL 255.159484 196.239232 \nL 255.189923 212.664962 \nL 255.220362 210.808549 \nL 255.250802 212.735458 \nL 255.281241 212.030492 \nL 255.311681 214.662368 \nL 255.34212 201.925966 \nL 255.402999 214.756364 \nL 255.463878 211.513516 \nL 255.494317 199.599575 \nL 255.555196 214.756364 \nL 255.616075 214.756364 \nL 255.646514 213.275933 \nL 255.676954 213.698913 \nL 255.707393 201.033007 \nL 255.737832 214.732865 \nL 255.768272 211.067037 \nL 255.798711 214.145392 \nL 255.829151 194.194828 \nL 255.85959 214.756364 \nL 255.890029 214.756364 \nL 255.920469 214.756364 \nL 255.950908 191.210468 \nL 256.011787 212.71196 \nL 256.042226 208.811143 \nL 256.103105 212.946949 \nL 256.133545 214.756364 \nL 256.163984 214.638869 \nL 256.224863 167.923061 \nL 256.255302 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288.947226 213.064443 \nL 288.977665 214.756364 \nL 289.038544 214.215889 \nL 289.099423 204.111363 \nL 289.129863 208.811143 \nL 289.190741 214.756364 \nL 289.25162 212.876452 \nL 289.28206 214.756364 \nL 289.434257 196.521219 \nL 289.556014 214.756364 \nL 289.586454 212.218483 \nL 289.677772 212.735458 \nL 289.708211 189.04857 \nL 289.76909 212.852953 \nL 289.829969 214.756364 \nL 289.860408 205.051319 \nL 289.951727 211.231529 \nL 289.982166 210.526562 \nL 290.012605 211.043538 \nL 290.043045 212.735458 \nL 290.073484 208.059178 \nL 290.103924 214.732865 \nL 290.134363 212.030492 \nL 290.195242 214.286386 \nL 290.225681 207.213217 \nL 290.28656 214.756364 \nL 290.408318 199.552577 \nL 290.469196 198.283636 \nL 290.530075 214.756364 \nL 290.621394 214.685867 \nL 290.651833 203.523891 \nL 290.712712 214.756364 \nL 290.743151 214.756364 \nL 290.864909 204.557842 \nL 290.925788 214.756364 \nL 291.017106 214.427379 \nL 291.047545 214.356882 \nL 291.077985 190.834486 \nL 291.108424 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297.561578 209.351617 \nL 297.592018 214.756364 \nL 297.622457 201.174001 \nL 297.652897 213.322931 \nL 297.805094 203.899873 \nL 297.896412 214.756364 \nL 297.926851 203.194906 \nL 298.01817 209.962589 \nL 298.139927 214.756364 \nL 298.170367 205.192312 \nL 298.231245 214.756364 \nL 298.261685 214.756364 \nL 298.292124 205.779785 \nL 298.353003 206.696242 \nL 298.383442 214.121893 \nL 298.474761 213.181937 \nL 298.535639 214.756364 \nL 298.566079 211.325525 \nL 298.626958 214.544874 \nL 298.687837 214.756364 \nL 298.748715 213.205436 \nL 298.809594 214.756364 \nL 298.840034 213.886904 \nL 298.870473 203.194906 \nL 298.931352 208.176672 \nL 299.02267 214.756364 \nL 299.053109 208.035679 \nL 299.113988 214.756364 \nL 299.144428 214.662368 \nL 299.235746 207.424708 \nL 299.327064 214.756364 \nL 299.357503 211.772004 \nL 299.387943 211.41952 \nL 299.418382 206.531749 \nL 299.448822 207.612699 \nL 299.479261 214.756364 \nL 299.509701 202.019961 \nL 299.54014 212.641463 \nL 299.601019 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301.366504 207.777191 \nL 301.396944 214.756364 \nL 301.427383 211.090536 \nL 301.457823 202.583935 \nL 301.488262 212.218483 \nL 301.518701 203.923372 \nL 301.640459 214.756364 \nL 301.670898 214.098395 \nL 301.701338 214.756364 \nL 301.731777 196.779707 \nL 301.823095 204.534343 \nL 301.883974 214.756364 \nL 301.914414 211.161032 \nL 301.944853 199.247091 \nL 302.005732 214.732865 \nL 302.036171 209.892092 \nL 302.066611 214.756364 \nL 302.12749 211.137533 \nL 302.157929 210.926043 \nL 302.188368 197.061694 \nL 302.249247 211.748505 \nL 302.279687 208.975635 \nL 302.310126 214.756364 \nL 302.340565 213.910403 \nL 302.401444 214.756364 \nL 302.431884 202.25495 \nL 302.492762 207.918184 \nL 302.553641 214.756364 \nL 302.584081 208.811143 \nL 302.61452 196.004243 \nL 302.644959 214.756364 \nL 302.705838 202.513438 \nL 302.736278 202.536937 \nL 302.888475 214.756364 \nL 303.010232 203.453394 \nL 303.13199 214.756364 \nL 303.192869 206.061771 \nL 303.223308 211.278527 \nL 303.314626 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "Оно што овде видимо је да је на почетку просечна дужина пута порасла. Ово је вероватно због чињенице да када ништа не знамо о окружењу – вероватно ћемо се заглавити у лошим стањима, као што су вода или вук. Како учимо више и почнемо да користимо то знање, можемо истраживати окружење дуже, али и даље не знамо добро где се налазе јабуке.\n",
+    "\n",
+    "Када научимо довољно, агенту постаје лакше да постигне циљ, и дужина пута почиње да се смањује. Међутим, и даље смо отворени за истраживање, па често одступамо од најбољег пута и истражујемо нове опције, што чини пут дужим од оптималног.\n",
+    "\n",
+    "Оно што такође примећујемо на овом графику је да је у једном тренутку дужина нагло порасла. Ово указује на стохастичку природу процеса и на то да у неком тренутку можемо „покварити“ коефицијенте Q-табеле, тако што ћемо их преписати новим вредностима. Ово би идеално требало минимизовати смањењем стопе учења (тј. пред крај обуке подешавамо вредности Q-табеле само за малу вредност).\n",
+    "\n",
+    "Уопштено, важно је запамтити да успех и квалитет процеса учења значајно зависе од параметара, као што су стопа учења, смањење стопе учења и фактор дисконтовања. Ови параметри се често називају **хиперпараметри**, како би се разликовали од **параметара** које оптимизујемо током обуке (нпр. коефицијенти Q-табеле). Процес проналажења најбољих вредности хиперпараметара назива се **оптимизација хиперпараметара**, и заслужује посебну тему.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## Вежба\n",
+    "#### Реалнији свет Петра и вука\n",
+    "\n",
+    "У нашој ситуацији, Петар је могао да се креће готово без умарања или глади. У реалнијем свету, он мора с времена на време да седне и одмори се, као и да се храни. Хајде да наш свет учинимо реалнијим применом следећих правила:\n",
+    "\n",
+    "1. Крећући се са једног места на друго, Петар губи **енергију** и добија одређени ниво **умора**.\n",
+    "2. Петар може да повећа енергију једући јабуке.\n",
+    "3. Петар може да се ослободи умора одмарајући се испод дрвета или на трави (тј. уласком на локацију табле са дрветом или травом - зелено поље).\n",
+    "4. Петар мора да пронађе и убије вука.\n",
+    "5. Да би убио вука, Петар мора да има одређене нивое енергије и умора, иначе губи битку.\n",
+    "\n",
+    "Измените функцију награде изнад у складу са правилима игре, покрените алгоритам за учење појачања како бисте научили најбољу стратегију за победу у игри, и упоредите резултате насумичног кретања са вашим алгоритмом у смислу броја добијених и изгубљених игара.\n",
+    "\n",
+    "> **Напомена**: Можда ћете морати да прилагодите хиперпараметре да би све функционисало, посебно број епоха. Пошто је успех у игри (борба са вуком) редак догађај, можете очекивати много дуже време тренинга.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква неспоразумевања или погрешна тумачења која могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/8-Reinforcement/2-Gym/notebook.ipynb b/translations/sr/8-Reinforcement/2-Gym/notebook.ipynb
new file mode 100644
index 000000000..96ad81053
--- /dev/null
+++ b/translations/sr/8-Reinforcement/2-Gym/notebook.ipynb
@@ -0,0 +1,394 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.4 64-bit ('base': conda)"
+  },
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "coopTranslator": {
+   "original_hash": "f22f8f3daed4b6d34648d1254763105b",
+   "translation_date": "2025-09-06T15:16:57+00:00",
+   "source_file": "8-Reinforcement/2-Gym/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Скејтовање на CartPole-у\n",
+    "\n",
+    "> **Проблем**: Ако Петар жели да побегне од вука, мора да се креће брже од њега. Видећемо како Петар може да научи да скејтује, конкретно, да одржава равнотежу, користећи Q-Learning.\n",
+    "\n",
+    "Прво, хајде да инсталирамо gym и увеземо потребне библиотеке:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Направите окружење за колица и шипку\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 2"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Да бисмо видели како окружење функционише, хајде да покренемо кратку симулацију од 100 корака.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Током симулације, потребно је добити опсервације како бисмо одлучили како да поступимо. У ствари, функција `step` нам враћа тренутне опсервације, функцију награде и заставицу `done` која указује да ли има смисла наставити симулацију или не:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 4"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Можемо добити минималну и максималну вредност тих бројева:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Хајде да истражимо и друге методе дискретизације користећи бинове:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Хајде сада да покренемо кратку симулацију и посматрамо те дискретне вредности окружења.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -2, -2)\n(0, 1, -2, -5)\n(0, 2, -3, -8)\n(0, 3, -5, -11)\n(0, 3, -7, -14)\n(0, 4, -10, -17)\n(0, 3, -14, -15)\n(0, 3, -17, -12)\n(0, 3, -20, -16)\n(0, 4, -23, -19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 8"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Table Структура\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 9"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 22.0, alpha=0.3, epsilon=0.9\n",
+      "5000: 70.1384, alpha=0.3, epsilon=0.9\n",
+      "10000: 121.8586, alpha=0.3, epsilon=0.9\n",
+      "15000: 149.6368, alpha=0.3, epsilon=0.9\n",
+      "20000: 168.2782, alpha=0.3, epsilon=0.9\n",
+      "25000: 196.7356, alpha=0.3, epsilon=0.9\n",
+      "30000: 220.7614, alpha=0.3, epsilon=0.9\n",
+      "35000: 233.2138, alpha=0.3, epsilon=0.9\n",
+      "40000: 248.22, alpha=0.3, epsilon=0.9\n",
+      "45000: 264.636, alpha=0.3, epsilon=0.9\n",
+      "50000: 276.926, alpha=0.3, epsilon=0.9\n",
+      "55000: 277.9438, alpha=0.3, epsilon=0.9\n",
+      "60000: 248.881, alpha=0.3, epsilon=0.9\n",
+      "65000: 272.529, alpha=0.3, epsilon=0.9\n",
+      "70000: 281.7972, alpha=0.3, epsilon=0.9\n",
+      "75000: 284.2844, alpha=0.3, epsilon=0.9\n",
+      "80000: 269.667, alpha=0.3, epsilon=0.9\n",
+      "85000: 273.8652, alpha=0.3, epsilon=0.9\n",
+      "90000: 278.2466, alpha=0.3, epsilon=0.9\n",
+      "95000: 269.1736, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 11"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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108.563315 90.206475 \r\nL 108.618101 199.087184 \r\nL 108.645494 123.955477 \r\nL 108.675931 111.098714 \r\nL 108.75811 203.90847 \r\nL 108.855507 101.456142 \r\nL 108.870725 152.883193 \r\nL 108.8829 201.096053 \r\nL 108.885944 102.25969 \r\nL 108.931599 110.69694 \r\nL 108.965079 67.70714 \r\nL 108.998559 209.533304 \r\nL 109.016821 157.302705 \r\nL 109.086826 203.104922 \r\nL 109.083782 85.786962 \r\nL 109.117262 140.829978 \r\nL 109.19031 17.083636 \r\nL 109.181179 201.096053 \r\nL 109.226834 167.347051 \r\nL 109.333363 211.542173 \r\nL 109.239009 110.69694 \r\nL 109.342494 210.738625 \r\nL 109.406411 121.946608 \r\nL 109.455109 139.222882 \r\nL 109.564681 208.729756 \r\nL 109.540332 117.527095 \r\nL 109.567725 166.141729 \r\nL 109.659035 74.537295 \r\nL 109.640773 211.542173 \r\nL 109.680341 152.883193 \r\nL 109.76252 211.140399 \r\nL 109.741214 110.69694 \r\nL 109.789913 152.883193 \r\nL 109.856874 212.747494 \r\nL 109.890354 91.81357 \r\nL 109.981664 210.336851 \r\nL 110.00297 190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 191.855255 \r\nL 111.330008 138.821109 \r\nL 111.339139 143.642395 \r\nL 111.336096 115.518226 \r\nL 111.34827 139.222882 \r\nL 111.357401 98.241951 \r\nL 111.366532 197.881863 \r\nL 111.457842 138.017561 \r\nL 111.466973 212.747494 \r\nL 111.479148 95.027761 \r\nL 111.570458 170.159468 \r\nL 111.655681 98.241951 \r\nL 111.582633 182.212683 \r\nL 111.683074 159.713348 \r\nL 111.743947 107.080976 \r\nL 111.71351 191.051707 \r\nL 111.750034 176.989623 \r\nL 111.774384 195.069446 \r\nL 111.765253 126.36612 \r\nL 111.789602 135.205144 \r\nL 111.850475 102.25969 \r\nL 111.823082 210.336851 \r\nL 111.899174 123.955477 \r\nL 111.978309 195.47122 \r\nL 111.923523 103.465011 \r\nL 112.008746 119.535965 \r\nL 112.030052 207.122661 \r\nL 112.066576 56.457472 \r\nL 112.118318 171.364789 \r\nL 112.191366 76.947938 \r\nL 112.185279 197.881863 \r\nL 112.230934 115.92 \r\nL 112.276589 207.524435 \r\nL 112.337462 101.054368 \r\nL 112.349637 153.284967 \r\nL 112.370942 74.135521 \r\nL 112.367899 203.506696 \r\nL 112.459209 153.284967 \r\nL 112.489645 199.488958 \r\nL 112.556606 78.555033 \r\nL 112.562693 130.383858 \r\nL 112.599217 78.153259 \r\nL 112.571824 191.453481 \r\nL 112.666178 102.25969 \r\nL 112.772706 209.533304 \r\nL 112.769663 17.083636 \r\nL 112.778794 159.713348 \r\nL 112.812274 79.358581 \r\nL 112.803143 211.140399 \r\nL 112.894453 127.169667 \r\nL 112.976632 211.943947 \r\nL 112.912715 82.974545 \r\nL 113.004025 154.088514 \r\nL 113.070986 90.206475 \r\nL 113.067942 212.345721 \r\nL 113.110553 168.552373 \r\nL 113.171427 204.310244 \r\nL 113.159252 93.822439 \r\nL 113.210994 126.36612 \r\nL 113.226213 46.8149 \r\nL 113.256649 203.506696 \r\nL 113.311435 139.222882 \r\nL 113.408833 207.926208 \r\nL 113.390571 44.404257 \r\nL 113.421007 168.552373 \r\nL 113.436226 94.224213 \r\nL 113.497099 192.257029 \r\nL 113.530579 136.812239 \r\nL 113.603627 205.917339 \r\nL 113.588409 32.351042 \r\nL 113.634064 158.106253 \r\nL 113.637108 72.528426 \r\nL 113.734505 190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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117.676054 183.418004 \r\nL 117.779538 123.955477 \r\nL 117.785626 176.587849 \r\nL 117.803888 17.083636 \r\nL 117.870848 77.349712 \r\nL 117.873892 27.93153 \r\nL 117.943896 178.998492 \r\nL 117.974333 86.188736 \r\nL 117.977377 193.864124 \r\nL 117.989551 17.083636 \r\nL 118.086949 163.329313 \r\nL 118.120429 21.904922 \r\nL 118.175215 197.078315 \r\nL 118.190433 188.641064 \r\nL 118.233045 205.515565 \r\nL 118.223914 34.359911 \r\nL 118.284787 130.785632 \r\nL 118.318267 78.956807 \r\nL 118.324355 190.649933 \r\nL 118.394359 100.25082 \r\nL 118.497844 200.292506 \r\nL 118.415665 99.447273 \r\nL 118.503931 107.482749 \r\nL 118.506975 108.688071 \r\nL 118.531324 211.943947 \r\nL 118.589154 34.761685 \r\nL 118.616547 100.25082 \r\nL 118.680464 197.078315 \r\nL 118.665245 21.503149 \r\nL 118.726119 141.231752 \r\nL 118.796123 64.091175 \r\nL 118.805254 195.47122 \r\nL 118.835691 132.392727 \r\nL 118.939175 163.731086 \r\nL 118.89352 80.965676 \r\nL 118.945263 149.267228 \r\nL 118.966568 21.503149 \r\nL 119.033529 212.747494 \r\nL 119.051791 169.35592 \r\nL 119.143101 203.90847 \r\nL 119.127883 45.609579 \r\nL 119.155276 163.329313 \r\nL 119.252673 17.083636 \r\nL 119.161363 209.13153 \r\nL 119.270935 82.170998 \r\nL 119.307459 194.265898 \r\nL 119.334852 74.537295 \r\nL 119.383551 133.196275 \r\nL 119.392682 31.949268 \r\nL 119.435293 190.649933 \r\nL 119.487035 123.553703 \r\nL 119.59052 200.292506 \r\nL 119.55704 75.340843 \r\nL 119.596607 134.80337 \r\nL 119.630088 189.444612 \r\nL 119.605738 93.822439 \r\nL 119.703136 122.750155 \r\nL 119.706179 122.348381 \r\nL 119.71531 133.999823 \r\nL 119.748791 201.096053 \r\nL 119.727485 67.70714 \r\nL 119.824882 143.642395 \r\nL 119.919236 106.277428 \r\nL 119.858363 203.104922 \r\nL 119.934454 146.856585 \r\nL 120.034895 204.310244 \r\nL 119.958804 84.179867 \r\nL 120.037939 125.160798 \r\nL 120.089681 86.59051 \r\nL 120.044026 207.524435 \r\nL 120.114031 119.937738 \r\nL 120.153598 202.703149 \r\nL 120.168817 61.278758 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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125.656548 212.345721 \r\nL 125.738727 132.392727 \r\nL 125.811775 195.872993 \r\nL 125.805688 47.216674 \r\nL 125.817862 130.383858 \r\nL 125.820906 33.958137 \r\nL 125.924391 194.667672 \r\nL 125.991351 17.083636 \r\nL 125.970046 202.301375 \r\nL 126.046137 122.750155 \r\nL 126.137447 186.230421 \r\nL 126.122229 70.519557 \r\nL 126.155709 132.794501 \r\nL 126.16484 85.786962 \r\nL 126.177015 195.069446 \r\nL 126.259194 131.187406 \r\nL 126.271369 203.104922 \r\nL 126.353548 85.786962 \r\nL 126.37181 178.998492 \r\nL 126.453989 86.992284 \r\nL 126.472251 209.533304 \r\nL 126.55443 81.769224 \r\nL 126.584866 133.196275 \r\nL 126.633565 199.087184 \r\nL 126.603128 63.689401 \r\nL 126.694438 104.268559 \r\nL 126.706613 209.13153 \r\nL 126.7127 70.117783 \r\nL 126.807054 158.9098 \r\nL 126.886189 78.153259 \r\nL 126.840534 201.497827 \r\nL 126.916626 132.392727 \r\nL 127.001849 201.497827 \r\nL 126.931844 82.974545 \r\nL 127.023154 138.017561 \r\nL 127.074897 102.661463 \r\nL 127.111421 205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 182.212683 \r\nL 140.649651 195.069446 \r\nL 140.613127 74.135521 \r\nL 140.652694 174.57898 \r\nL 140.704437 51.636186 \r\nL 140.674 201.096053 \r\nL 140.76531 133.598049 \r\nL 140.832271 195.872993 \r\nL 140.847489 76.14439 \r\nL 140.874882 188.23929 \r\nL 140.887057 55.252151 \r\nL 140.963148 203.90847 \r\nL 140.987498 135.205144 \r\nL 141.087939 206.720887 \r\nL 141.002716 22.70847 \r\nL 141.09707 189.042838 \r\nL 141.197511 62.885854 \r\nL 141.194467 194.265898 \r\nL 141.206642 74.537295 \r\nL 141.218816 183.819778 \r\nL 141.319257 136.812239 \r\nL 141.352738 17.083636 \r\nL 141.419698 199.087184 \r\nL 141.498834 17.083636 \r\nL 141.489703 211.140399 \r\nL 141.532314 80.965676 \r\nL 141.608406 199.890732 \r\nL 141.611449 17.887184 \r\nL 141.641886 78.956807 \r\nL 141.675366 210.336851 \r\nL 141.672323 17.083636 \r\nL 141.751458 115.92 \r\nL 141.787982 17.083636 \r\nL 141.839724 205.917339 \r\nL 141.864074 99.849047 \r\nL 141.964515 189.846386 \r\nL 141.897554 48.020222 \r\nL 141.973646 169.757694 \r\nL 141.98582 37.574102 \r\nL 142.064956 184.221552 \r\nL 142.089305 38.779424 \r\nL 142.159309 194.265898 \r\nL 142.119742 31.949268 \r\nL 142.201921 134.80337 \r\nL 142.241488 46.413126 \r\nL 142.232357 203.506696 \r\nL 142.265838 93.420665 \r\nL 142.320624 199.087184 \r\nL 142.332798 48.823769 \r\nL 142.37541 111.098714 \r\nL 142.478894 199.890732 \r\nL 142.396715 48.020222 \r\nL 142.484982 120.741286 \r\nL 142.488025 21.503149 \r\nL 142.503244 201.497827 \r\nL 142.59151 74.939069 \r\nL 142.597597 203.506696 \r\nL 142.701082 53.645055 \r\nL 142.704126 146.454812 \r\nL 142.743693 44.002483 \r\nL 142.801523 186.230421 \r\nL 142.813698 87.394058 \r\nL 142.868484 199.890732 \r\nL 142.850222 52.841508 \r\nL 142.926313 193.46235 \r\nL 142.944575 65.698271 \r\nL 142.965881 193.864124 \r\nL 143.035885 142.437073 \r\nL 143.066322 194.265898 \r\nL 143.072409 66.501818 \r\nL 143.142414 93.822439 \r\nL 143.215462 202.301375 \r\nL 143.233724 80.965676 \r\nL 143.255029 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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198.68541 \r\nL 167.330434 17.083636 \r\nL 167.406525 97.03663 \r\nL 167.479573 17.083636 \r\nL 167.513054 202.703149 \r\nL 167.516097 70.92133 \r\nL 167.549578 209.935078 \r\nL 167.625669 125.160798 \r\nL 167.689586 203.104922 \r\nL 167.637844 44.002483 \r\nL 167.735241 160.91867 \r\nL 167.850901 29.538625 \r\nL 167.771765 197.881863 \r\nL 167.853944 87.394058 \r\nL 167.860032 195.069446 \r\nL 167.863075 42.395388 \r\nL 167.963516 84.983415 \r\nL 167.972647 190.24816 \r\nL 168.030477 54.448603 \r\nL 168.070045 87.795831 \r\nL 168.076132 209.935078 \r\nL 168.179617 17.083636 \r\nL 168.210053 185.426874 \r\nL 168.292232 103.866785 \r\nL 168.316582 206.720887 \r\nL 168.347018 34.359911 \r\nL 168.410935 171.364789 \r\nL 168.496158 17.083636 \r\nL 168.490071 201.899601 \r\nL 168.532682 112.705809 \r\nL 168.544857 200.694279 \r\nL 168.623992 33.15459 \r\nL 168.63921 108.286297 \r\nL 168.733564 59.269889 \r\nL 168.721389 200.694279 \r\nL 168.748782 74.939069 \r\nL 168.815743 193.46235 \r\nL 168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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17.083636 \r\nL 174.148247 207.122661 \r\nL 174.178684 195.872993 \r\nL 174.23347 40.788293 \r\nL 174.19999 200.694279 \r\nL 174.303474 55.653925 \r\nL 174.306518 208.729756 \r\nL 174.364348 17.083636 \r\nL 174.413046 103.063237 \r\nL 174.443483 190.649933 \r\nL 174.431308 49.225543 \r\nL 174.525662 122.348381 \r\nL 174.574361 17.083636 \r\nL 174.562186 181.409135 \r\nL 174.63219 144.847716 \r\nL 174.711326 194.265898 \r\nL 174.708282 56.055698 \r\nL 174.7235 102.25969 \r\nL 174.753937 28.333304 \r\nL 174.763068 161.320443 \r\nL 174.833072 110.69694 \r\nL 174.912208 50.832639 \r\nL 174.857422 207.524435 \r\nL 174.936557 115.518226 \r\nL 174.988299 203.104922 \r\nL 175.024823 40.788293 \r\nL 175.043085 181.810909 \r\nL 175.046129 47.216674 \r\nL 175.155701 75.742616 \r\nL 175.240924 201.497827 \r\nL 175.189181 39.181197 \r\nL 175.268317 130.785632 \r\nL 175.36267 29.136851 \r\nL 175.280491 166.543503 \r\nL 175.377889 125.562572 \r\nL 175.426587 24.315565 \r\nL 175.396151 199.890732 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 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188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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17.083636 \r\nL 193.238126 211.542173 \r\nL 193.283781 119.535965 \r\nL 193.286824 194.265898 \r\nL 193.320305 17.083636 \r\nL 193.390309 174.57898 \r\nL 193.45727 39.582971 \r\nL 193.408571 197.881863 \r\nL 193.502925 56.859246 \r\nL 193.52423 188.641064 \r\nL 193.554667 17.083636 \r\nL 193.618584 144.847716 \r\nL 193.712938 17.083636 \r\nL 193.685545 192.257029 \r\nL 193.7312 62.082306 \r\nL 193.743374 19.494279 \r\nL 193.749462 138.821109 \r\nL 193.758593 25.119113 \r\nL 193.807291 205.113792 \r\nL 193.789029 17.083636 \r\nL 193.868165 46.8149 \r\nL 193.968606 178.194945 \r\nL 193.883383 17.083636 \r\nL 193.977737 59.269889 \r\nL 194.011217 191.051707 \r\nL 194.062959 34.359911 \r\nL 194.081221 120.741286 \r\nL 194.1634 17.083636 \r\nL 194.154269 162.123991 \r\nL 194.190793 129.178537 \r\nL 194.291234 184.623326 \r\nL 194.22123 17.083636 \r\nL 194.297322 178.596718 \r\nL 194.379501 17.083636 \r\nL 194.306453 209.533304 \r\nL 194.406894 111.500488 \r\nL 194.482985 184.221552 \r\nL 194.449505 24.315565 \r\nL 194.49516 102.661463 \r\nL 194.556033 17.083636 \r\nL 194.537771 203.506696 \r\nL 194.601688 176.989623 \r\nL 194.604732 185.0251 \r\nL 194.632125 17.083636 \r\nL 194.665605 116.723548 \r\nL 194.686911 17.083636 \r\nL 194.67778 207.122661 \r\nL 194.775177 41.190067 \r\nL 194.872575 181.409135 \r\nL 194.793439 17.083636 \r\nL 194.884749 123.955477 \r\nL 194.988234 48.823769 \r\nL 194.976059 196.274767 \r\nL 194.997365 75.742616 \r\nL 195.067369 209.935078 \r\nL 195.036933 17.083636 \r\nL 195.106937 208.729756 \r\nL 195.201291 23.110244 \r\nL 195.222596 60.475211 \r\nL 195.295644 17.083636 \r\nL 195.313906 192.658803 \r\nL 195.329125 80.563902 \r\nL 195.383911 198.283636 \r\nL 195.420435 17.083636 \r\nL 195.438697 140.02643 \r\nL 195.542181 17.083636 \r\nL 195.469133 193.060576 \r\nL 195.548269 133.999823 \r\nL 195.551312 202.301375 \r\nL 195.645666 17.083636 \r\nL 195.654797 62.48408 \r\nL 195.71567 17.083636 \r\nL 195.700452 192.257029 \r\nL 195.74002 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51.636186 \r\nL 330.930568 164.13286 \r\nL 330.994485 63.689401 \r\nL 330.970136 197.881863 \r\nL 331.043184 113.911131 \r\nL 331.101013 59.269889 \r\nL 331.13145 192.257029 \r\nL 331.143625 140.02643 \r\nL 331.213629 200.292506 \r\nL 331.22276 49.627317 \r\nL 331.234935 174.980754 \r\nL 331.317114 38.779424 \r\nL 331.262328 199.488958 \r\nL 331.344507 81.36745 \r\nL 331.435817 199.890732 \r\nL 331.444948 50.832639 \r\nL 331.454079 185.0251 \r\nL 331.551476 43.198936 \r\nL 331.536258 208.729756 \r\nL 331.566694 101.456142 \r\nL 331.578869 193.864124 \r\nL 331.633655 76.14439 \r\nL 331.64583 93.018891 \r\nL 331.648873 21.503149 \r\nL 331.664092 202.703149 \r\nL 331.755402 46.8149 \r\nL 331.816275 210.336851 \r\nL 331.831493 46.011353 \r\nL 331.868017 153.284967 \r\nL 331.962371 17.083636 \r\nL 331.910629 201.096053 \r\nL 331.977589 82.974545 \r\nL 332.059768 194.265898 \r\nL 331.989764 58.064568 \r\nL 332.090205 170.963016 \r\nL 332.117598 23.512018 \r\nL 332.141947 188.23929 \r\nL 332.202821 102.661463 \r\nL 332.236301 199.488958 \r\nL 332.306305 31.547494 \r\nL 332.358048 17.083636 \r\nL 332.415877 204.712018 \r\nL 332.522406 17.083636 \r\nL 332.491969 209.533304 \r\nL 332.531537 43.60071 \r\nL 332.635021 209.533304 \r\nL 332.543711 17.083636 \r\nL 332.644152 87.795831 \r\nL 332.711113 44.002483 \r\nL 332.662414 199.890732 \r\nL 332.735462 117.928869 \r\nL 332.738506 196.274767 \r\nL 332.750681 17.083636 \r\nL 332.841991 81.769224 \r\nL 332.863296 192.257029 \r\nL 332.930257 17.083636 \r\nL 332.954606 193.864124 \r\nL 333.039829 80.965676 \r\nL 333.042873 17.083636 \r\nL 333.061135 206.720887 \r\nL 333.149401 39.582971 \r\nL 333.161576 180.605588 \r\nL 333.240711 17.083636 \r\nL 333.258973 87.795831 \r\nL 333.262017 17.083636 \r\nL 333.344196 193.46235 \r\nL 333.365501 76.546164 \r\nL 333.462899 206.319113 \r\nL 333.371589 17.083636 \r\nL 333.475073 121.14306 \r\nL 333.578558 47.618448 \r\nL 333.560296 195.872993 \r\nL 333.587689 55.653925 \r\nL 333.685086 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Са овог графикона није могуће ништа закључити, јер због природе стохастичког процеса тренинга дужина сесија тренинга веома варира. Да би овај графикон имао више смисла, можемо израчунати **покретни просек** преко серије експеримената, рецимо 100. Ово се може лако урадити користећи `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "#code block 12"
+   ]
+  },
+  {
+   "source": [
+    "## Променљиви хиперпараметри и посматрање резултата у пракси\n",
+    "\n",
+    "Сада би било занимљиво видети како се обучени модел заправо понаша. Покренимо симулацију, и пратићемо исту стратегију избора акција као током обуке: узорковање према расподели вероватноће у Q-табели:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 13"
+   ]
+  },
+  {
+   "source": [
+    "## Чување резултата у анимираном GIF-у\n",
+    "\n",
+    "Ако желите да импресионирате своје пријатеље, можда ћете желети да им пошаљете анимирану GIF слику балансирајуће шипке. Да бисте то урадили, можемо позвати `env.render` да произведемо слику кадра, а затим их сачувати као анимиран GIF користећи PIL библиотеку:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да превод буде тачан, молимо вас да имате у виду да аутоматизовани преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/8-Reinforcement/2-Gym/solution/notebook.ipynb b/translations/sr/8-Reinforcement/2-Gym/solution/notebook.ipynb
new file mode 100644
index 000000000..82d6e622b
--- /dev/null
+++ b/translations/sr/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -0,0 +1,526 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "5c0e485e58d63c506f1791c4dbf990ce",
+   "translation_date": "2025-09-06T15:19:48+00:00",
+   "source_file": "8-Reinforcement/2-Gym/solution/notebook.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Карпул клизање\n",
+    "\n",
+    "> **Проблем**: Ако Петар жели да побегне од вука, мора бити бржи од њега. Видећемо како Петар може научити да клиза, посебно како да одржи равнотежу, користећи Q-Learning.\n",
+    "\n",
+    "Прво, хајде да инсталирамо gym и увеземо потребне библиотеке:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: gym in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.18.3)\n",
+      "Requirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
+      "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
+      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.6.0)\n",
+      "Requirement already satisfied: pyglet<=1.5.15,>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.5.15)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!pip install gym \n",
+    "\n",
+    "import gym\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "source": [
+    "## Направите окружење за колица и шипку\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Да бисмо видели како окружење функционише, хајде да покренемо кратку симулацију од 100 корака.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Током симулације, потребно је добити опсервације како бисмо одлучили како да поступимо. У ствари, функција `step` нам враћа тренутне опсервације, функцију награде и заставицу `done` која указује да ли има смисла наставити симулацију или не:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[ 0.03044442 -0.19543914 -0.04496216  0.28125618] -> 1.0\n",
+      "[ 0.02653564 -0.38989186 -0.03933704  0.55942606] -> 1.0\n",
+      "[ 0.0187378  -0.19424049 -0.02814852  0.25461393] -> 1.0\n",
+      "[ 0.01485299 -0.38894946 -0.02305624  0.53828712] -> 1.0\n",
+      "[ 0.007074   -0.19351108 -0.0122905   0.23842953] -> 1.0\n",
+      "[ 0.00320378  0.00178427 -0.00752191 -0.05810469] -> 1.0\n",
+      "[ 0.00323946  0.19701326 -0.008684   -0.35315131] -> 1.0\n",
+      "[ 0.00717973  0.00201587 -0.01574703 -0.06321931] -> 1.0\n",
+      "[ 0.00722005  0.19736001 -0.01701141 -0.36082863] -> 1.0\n",
+      "[ 0.01116725  0.39271958 -0.02422798 -0.65882671] -> 1.0\n",
+      "[ 0.01902164  0.19794307 -0.03740452 -0.37387001] -> 1.0\n",
+      "[ 0.0229805   0.39357584 -0.04488192 -0.67810827] -> 1.0\n",
+      "[ 0.03085202  0.58929164 -0.05844408 -0.98457719] -> 1.0\n",
+      "[ 0.04263785  0.78514572 -0.07813563 -1.2950295 ] -> 1.0\n",
+      "[ 0.05834076  0.98116859 -0.10403622 -1.61111521] -> 1.0\n",
+      "[ 0.07796413  0.78741784 -0.13625852 -1.35259196] -> 1.0\n",
+      "[ 0.09371249  0.98396202 -0.16331036 -1.68461179] -> 1.0\n",
+      "[ 0.11339173  0.79106371 -0.1970026  -1.44691436] -> 1.0\n",
+      "[ 0.12921301  0.59883361 -0.22594088 -1.22169133] -> 1.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Можемо добити минималну и максималну вредност тих бројева:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(env.observation_space.low)\n",
+    "print(env.observation_space.high)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretize(x):\n",
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+   ]
+  },
+  {
+   "source": [
+    "Хајде да истражимо и друге методе дискретизације користећи бинове:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_bins(i,num):\n",
+    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
+    "\n",
+    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
+    "\n",
+    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
+    "nbins = [20,20,10,10] # number of bins for each parameter\n",
+    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
+    "\n",
+    "def discretize_bins(x):\n",
+    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+   ]
+  },
+  {
+   "source": [
+    "Хајде сада да покренемо кратку симулацију и посматрамо те дискретне вредности окружења.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 2, -4, -9)\n(0, 3, -6, -12)\n(0, 2, -8, -9)\n(0, 3, -10, -13)\n(0, 4, -13, -16)\n(0, 4, -16, -19)\n(0, 4, -20, -17)\n(0, 4, -24, -20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   #env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   #print(discretize_bins(obs))\n",
+    "   print(discretize(obs))\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Структура Q-Табеле\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = {}\n",
+    "actions = (0,1)\n",
+    "\n",
+    "def qvalues(state):\n",
+    "    return [Q.get((state,a),0) for a in actions]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hyperparameters\n",
+    "alpha = 0.3\n",
+    "gamma = 0.9\n",
+    "epsilon = 0.90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 108.0, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v\n",
+    "\n",
+    "Qmax = 0\n",
+    "cum_rewards = []\n",
+    "rewards = []\n",
+    "for epoch in range(100000):\n",
+    "    obs = env.reset()\n",
+    "    done = False\n",
+    "    cum_reward=0\n",
+    "    # == do the simulation ==\n",
+    "    while not done:\n",
+    "        s = discretize(obs)\n",
+    "        if random.random()<epsilon:\n",
+    "            # exploitation - chose the action according to Q-Table probabilities\n",
+    "            v = probs(np.array(qvalues(s)))\n",
+    "            a = random.choices(actions,weights=v)[0]\n",
+    "        else:\n",
+    "            # exploration - randomly chose the action\n",
+    "            a = np.random.randint(env.action_space.n)\n",
+    "\n",
+    "        obs, rew, done, info = env.step(a)\n",
+    "        cum_reward+=rew\n",
+    "        ns = discretize(obs)\n",
+    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
+    "    cum_rewards.append(cum_reward)\n",
+    "    rewards.append(cum_reward)\n",
+    "    # == Periodically print results and calculate average reward ==\n",
+    "    if epoch%5000==0:\n",
+    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
+    "        if np.average(cum_rewards) > Qmax:\n",
+    "            Qmax = np.average(cum_rewards)\n",
+    "            Qbest = Q\n",
+    "        cum_rewards=[]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Са овог графикона није могуће рећи ништа, јер због природе стохастичког процеса тренинга дужина сесија тренинга веома варира. Да би овај графикон имао више смисла, можемо израчунати **покретни просек** преко серије експеримената, рецимо 100. Ово се може лако урадити користећи `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "def running_average(x,window):\n",
+    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
+    "\n",
+    "plt.plot(running_average(rewards,100))"
+   ]
+  },
+  {
+   "source": [
+    "## Променљиви хиперпараметри и посматрање резултата у пракси\n",
+    "\n",
+    "Сада би било занимљиво видети како се обучени модел заправо понаша. Покренимо симулацију, и пратићемо исту стратегију избора акција као током обуке: узорковање према расподели вероватноће у Q-табели:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset()\n",
+    "done = False\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   env.render()\n",
+    "   v = probs(np.array(qvalues(s)))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Чување резултата у анимираном GIF-у\n",
+    "\n",
+    "Ако желите да импресионирате своје пријатеље, можда ћете желети да им пошаљете анимирану GIF слику балансирајуће шипке. Да бисте то урадили, можемо позвати `env.render` да произведемо слику кадра, а затим их сачувати као анимирани GIF користећи PIL библиотеку:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако се трудимо да обезбедимо тачност, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на његовом изворном језику треба сматрати меродавним извором. За критичне информације, препоручује се професионални превод од стране људи. Не преузимамо одговорност за било каква погрешна тумачења или неспоразуме који могу настати услед коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sr/PyTorch_Fundamentals.ipynb b/translations/sr/PyTorch_Fundamentals.ipynb
new file mode 100644
index 000000000..15b8c22c9
--- /dev/null
+++ b/translations/sr/PyTorch_Fundamentals.ipynb
@@ -0,0 +1,2830 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4",
+   "authorship_tag": "ABX9TyOgv0AozH1FKQBD+RkgT2bV",
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "coopTranslator": {
+   "original_hash": "0ca21b6ee62904d616f2e36dc1cf0da7",
+   "translation_date": "2025-09-06T13:08:01+00:00",
+   "source_file": "PyTorch_Fundamentals.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "view-in-github",
+    "colab_type": "text"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EHh5JllMh1rG",
+    "outputId": "f55755ad-c369-414c-85ec-6e9d4f061a02",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'2.2.1+cu121'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(\"I am excited to run this\")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UPlb-duwXAfz",
+    "outputId": "cfd687e4-1238-49f4-ab6b-ee1305b740d2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "I am excited to run this\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(torch.__version__)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "byWVlJ9wXDSk",
+    "outputId": "fd74a5c4-4d4a-41b2-ef3c-562ea3e4811f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2.2.1+cu121\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "Osm80zoEYklS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# scalar\n",
+    "scalar = torch.tensor(7)\n",
+    "scalar"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-o8wvJ-VXZmI",
+    "outputId": "558816f5-1205-4de1-fe1f-2f96e9bd79e6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(7)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mCZ2tXC4Y_Sg",
+    "outputId": "2d86dbdc-56e1-45c6-d3dd-14515f2a457a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ssN00By0ZQgS",
+    "outputId": "490f40d1-5135-4969-a6d3-c8c902cdc473"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# vector\n",
+    "vector = torch.tensor([7, 7])\n",
+    "vector\n",
+    "#vector.ndim\n",
+    "#vector.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Bws__5wlZnmF",
+    "outputId": "944e38f9-5ba1-4ddc-a9c6-cfb6a19bb488"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([7, 7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "vector.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9pjCvnsZZzNG",
+    "outputId": "e030a4da-8f81-4858-fbce-86da2aaafe52"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([2])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Matrix\n",
+    "MATRIX = torch.tensor([[7, 8],[9, 10]])\n",
+    "MATRIX"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a747hI9SaBGW",
+    "outputId": "af835ddb-81ff-4981-badb-441567194d15"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 7,  8],\n",
+       "        [ 9, 10]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XdTfFa7vaRUj",
+    "outputId": "0fbbab9c-8263-4cad-a380-0d2a16ca499e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX[0]\n",
+    "MATRIX[1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TFeD3jSDafm7",
+    "outputId": "69b44ab3-5ba7-451a-c6b2-f019a03d0c96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Tensor\n",
+    "TENSOR = torch.tensor([[[1, 2, 3],[3,6,9], [2,4,5]]])\n",
+    "TENSOR"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ic3cE47tah42",
+    "outputId": "f250e295-91de-43ec-9d80-588a6fe0abde"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 2, 3],\n",
+       "         [3, 6, 9],\n",
+       "         [2, 4, 5]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wvjf5fczbAM1",
+    "outputId": "9c72b5b8-bafe-4ae7-9883-b051e209eada"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mwtXZwiMbN3m",
+    "outputId": "331a5e36-b1b0-4a5f-a9b8-e7049cbaa8f9"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vzdZu_IfbP3J",
+    "outputId": "e24e7e71-e365-412d-ff50-fc094b56d2f3"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [3, 6, 9],\n",
+       "        [2, 4, 5]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "A8OL9eWfcRrJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor = torch.rand(3,4)\n",
+    "random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hAqSDE1EcVS_",
+    "outputId": "946171c3-d054-400c-f893-79110356888c"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.4414, 0.7681, 0.8385, 0.3166],\n",
+       "        [0.0468, 0.5812, 0.0670, 0.9173],\n",
+       "        [0.2959, 0.3276, 0.7411, 0.4643]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "g4fvPE5GcwzP",
+    "outputId": "8737f36b-6864-4059-eaed-6f9156c22306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XsAg99QmdAU6",
+    "outputId": "35467c11-257c-4f16-99aa-eca930bcbc36"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.size()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cii1pNdVdB68",
+    "outputId": "fc8d2de6-9215-43de-99f7-7b0d7f7d20fa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_image_tensor = torch.rand(size=(3, 224, 224)) #color channels, height, width\n",
+    "random_image_tensor.ndim, random_image_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aTKq2j0cdDjb",
+    "outputId": "6be42057-20b9-4faf-d79d-8b65c42cc27e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([3, 224, 224]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor_ofownsize = torch.rand(size=(5,10,10))\n",
+    "random_tensor_ofownsize.ndim, random_tensor_ofownsize.shape\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IyhDdj-Pd6nC",
+    "outputId": "43e5e334-6d4d-4b67-f87d-7d364c6d8c67"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([5, 10, 10]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "UOJW08uOert_"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero = torch.zeros(size=(3, 4))\n",
+    "zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGvXtaXyefie",
+    "outputId": "d40d3e28-8667-4d2f-8b62-f0829c6162ad"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero*random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OyUkUPkDe0uH",
+    "outputId": "26c2e4be-36ba-4c6c-9a90-2704ec135828"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones = torch.ones(size=(3, 4))\n",
+    "ones\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "y_Ac62Aqe82G",
+    "outputId": "291de5d9-b9df-49de-c9d1-d098e3e9f4d8"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TvGOA9odfIEO",
+    "outputId": "45949ef4-6649-4b6c-d6af-2d4bfb8de832"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones*zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "--pTyge-fI-8",
+    "outputId": "c4d9bb7e-829b-43db-e2db-b1a2d64e61f0"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qDcc7Z36fSJF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "one_to_ten = torch.arange(start = 1, end = 11, step = 1)\n",
+    "one_to_ten"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w3CZB4zUfR1s",
+    "outputId": "197fcba1-da0a-4b4a-ed11-3974bd6c01aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ten_zeros = torch.zeros_like(one_to_ten)\n",
+    "ten_zeros"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WZh99BwVfRy8",
+    "outputId": "51ef8bfb-6fa0-4099-ff66-b97d65b2ddea"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Тензорски типови података\n"
+   ],
+   "metadata": {
+    "id": "pGGhgsbUgqbW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor = torch.tensor([3.0, 6.0,9.0], dtype = None, device = None, requires_grad = False)\n",
+    "float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JORJl4XkfRsx",
+    "outputId": "71114171-0f49-481f-b6fc-6cb48e2fb895"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3., 6., 9.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6wOPPwGyfRLn",
+    "outputId": "f23776a1-b682-404a-9f67-d5bcb0402666"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor = float_32_tensor.type(torch.float16)\n",
+    "float_16_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tFsHCvmZfOYe",
+    "outputId": "d3aa305a-7591-47f5-97fd-61bff60b44bd"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float16"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 31
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TQiCGTPuwq0q",
+    "outputId": "98750fce-1ca3-4889-e269-8b753efdea96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor = torch.tensor([3, 6, 9], dtype = torch.int32)\n",
+    "int_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5hlrLvGUw5D_",
+    "outputId": "41d890a0-9aee-446c-d906-631ce2ab0995"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9], dtype=torch.int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ihApD9u3xTNW",
+    "outputId": "d295eed0-6996-4e0f-8502-ff4b55cd1373"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(0,100,10)"
+   ],
+   "metadata": {
+    "id": "utKhlb_KxWDQ"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p78D74E9Rj7Y",
+    "outputId": "781a1614-a900-41f5-9e5d-358f0b2390aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.min()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4BcSs5NeRkcj",
+    "outputId": "3f24a8dc-58e9-4a5f-9834-e85856a34f9d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.max()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hinqvXVLRm4q",
+    "outputId": "5c7d8a53-3913-4ac1-bba3-5ba8ff68250a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 38
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mean(x.type(torch.float32))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k7okc0_vRpnB",
+    "outputId": "91e5494f-dc57-417c-ea4d-25dbc547c893"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.type(torch.float32).mean()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "29QcDTjHRq10",
+    "outputId": "62937c6c-78e0-49f2-dde3-1543ee8f7907"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wlpY_G_sbdKF",
+    "outputId": "475d8258-af65-4011-a258-b93d4d8142d4"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(450)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmax()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GT6HJzwhbk4n",
+    "outputId": "2e455c20-c322-4bcf-d07c-1259d3ccefc6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmin()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "egL3oi2Mb19P",
+    "outputId": "f71fb32f-6338-44a3-b377-75bea0a3ab54"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p2U8DZKib3DP",
+    "outputId": "b9f613b9-74e9-45f4-ed01-05babb6a6793"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[9]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24qBFlGYcABe",
+    "outputId": "5813cfcb-7f63-4bd7-ee46-f95ccbfda939"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(1, 10)\n",
+    "x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0GPOxEzkcBHO",
+    "outputId": "aefbd903-4f4c-4d2c-c90f-eccd682fe018"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped = x.reshape(1,9)\n",
+    "x_reshaped, x_reshaped.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "spmRgQjwddgp",
+    "outputId": "85a7c55c-2909-4ea2-fc68-386dddc65742"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), torch.Size([1, 9]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped.view(1,9)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tH2ahWGydqqP",
+    "outputId": "65d92263-4fc4-434a-c06d-c5e08436f7fe"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked = torch.stack([x, x, x, x], dim = 1)\n",
+    "x_stacked"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jgCeJcaud_-1",
+    "outputId": "7f293a37-6ef1-43b6-aee5-9d6d91c94f9e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XhJHIK6cfPse",
+    "outputId": "06c47b89-3a9e-453e-bcc3-00cbcb0b8b49"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=1)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ej2c3Xxzf0tq",
+    "outputId": "94024061-eb37-446d-c4a8-e4d16cb6de81"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 52
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4DJYo1a0f5M0",
+    "outputId": "efca2b47-1b14-44de-9a9a-2c83629d153f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 53
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=-2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J4iEjn2ah2HL",
+    "outputId": "22395593-7c16-4162-beae-dd2bbe7bda35"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 55
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "tensor = torch.tensor([1, 2, 3])\n",
+    "tensor = tensor - 10\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cFfiD7Nth7Z_",
+    "outputId": "1139e1f8-fc1a-46ca-d636-f2bc4fd2eef6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-9, -8, -7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mul(tensor, 10)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dyA7BM_GHhqE",
+    "outputId": "0e3b9671-d9e8-4a32-87bb-59bc05986142"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-90, -80, -70])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.sub(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "owtUsZ1KNegI",
+    "outputId": "189b7b23-0041-4e09-b991-cd209a48506a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-109, -108, -107])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.add(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K5STXlQONsyc",
+    "outputId": "00cbb79a-0a1d-4e21-86ec-5c91c37a2d01"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([91, 92, 93])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.divide(tensor, 2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xqMGnzIUNvp0",
+    "outputId": "c894cf3e-f148-45f8-cfc8-d78740735306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-4.5000, -4.0000, -3.5000])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(tensor, tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ruGzKpV8NyBc",
+    "outputId": "fddb63bf-006f-48b6-ae28-287fbcda8bc5"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8GS3r9yTeGfD",
+    "outputId": "c80b12ac-30b5-4f3d-c38c-9e41ba511b0e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QmuYHqXTemC0",
+    "outputId": "402fe3ba-70b5-4bb2-c83b-254db84ff810"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 622 µs, sys: 0 ns, total: 622 µs\n",
+      "Wall time: 516 µs\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "torch.matmul(tensor,tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dGr1fzdNepd8",
+    "outputId": "97bd6c91-bc25-4b38-cdf5-f22dcdef243e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 424 µs, sys: 998 µs, total: 1.42 ms\n",
+      "Wall time: 1.43 ms\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.rand(3,2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pGYDoK2gevfo",
+    "outputId": "2c8783d5-0453-47c5-c7ed-af10d25d6989"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5999, 0.0073],\n",
+       "        [0.9321, 0.3026],\n",
+       "        [0.3463, 0.3872]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(torch.rand(3,2), torch.rand(2,3))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KGBGQoB8e2DP",
+    "outputId": "4c2ef361-a2d0-41ee-c328-3992cbbc138d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.3528, 0.1893, 0.0714],\n",
+       "        [1.2791, 0.7110, 0.2563],\n",
+       "        [0.8812, 0.4553, 0.1803]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch"
+   ],
+   "metadata": {
+    "id": "ib8DMtkBe_LJ"
+   },
+   "execution_count": 1,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.rand(2,9)"
+   ],
+   "metadata": {
+    "id": "nJo8ZBdrQY1b"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wi6oRv4MQfgf",
+    "outputId": "55c99f55-31f6-4cf5-ba4e-19a47c3a0167"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5894, 0.4391, 0.2018, 0.5417, 0.3844, 0.3592, 0.9209, 0.9269, 0.0681],\n",
+       "        [0.0746, 0.1740, 0.6821, 0.6890, 0.0999, 0.7444, 0.2391, 0.4625, 0.8302]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "y=torch.randn(2,3,5)\n",
+    "y"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zpx8myAUQgoc",
+    "outputId": "07756d70-56bd-437c-c74e-9aecc1a77311"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[ 1.5552, -0.4877,  0.5175, -1.7958, -0.6187],\n",
+       "         [-0.3359, -1.9710,  0.0112, -1.7578, -1.5295],\n",
+       "         [ 0.0932,  1.4079,  0.9108,  0.3328, -0.6978]],\n",
+       "\n",
+       "        [[-0.9406, -1.0809, -0.2595,  0.1282,  1.6605],\n",
+       "         [ 1.1624,  1.0902,  1.7092, -0.2842, -1.3780],\n",
+       "         [-0.1534, -1.2795, -0.5495,  0.9902,  0.1822]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original = torch.rand(size=(224,224,3))\n",
+    "x_original"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s4U-X9bJQnWe",
+    "outputId": "657a7a76-962c-4b41-a76b-902d0482266c"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[0.4549, 0.6809, 0.2118],\n",
+       "         [0.4824, 0.9008, 0.8741],\n",
+       "         [0.1715, 0.1757, 0.1845],\n",
+       "         ...,\n",
+       "         [0.8741, 0.6594, 0.2610],\n",
+       "         [0.0092, 0.1984, 0.1955],\n",
+       "         [0.4236, 0.4182, 0.0251]],\n",
+       "\n",
+       "        [[0.9174, 0.1661, 0.5852],\n",
+       "         [0.1837, 0.2351, 0.3810],\n",
+       "         [0.3726, 0.4808, 0.8732],\n",
+       "         ...,\n",
+       "         [0.6794, 0.0554, 0.9202],\n",
+       "         [0.0864, 0.8750, 0.3558],\n",
+       "         [0.8445, 0.9759, 0.4934]],\n",
+       "\n",
+       "        [[0.1600, 0.2635, 0.7194],\n",
+       "         [0.9488, 0.3405, 0.3647],\n",
+       "         [0.6683, 0.5168, 0.9592],\n",
+       "         ...,\n",
+       "         [0.0521, 0.0140, 0.2445],\n",
+       "         [0.3596, 0.3999, 0.2730],\n",
+       "         [0.5926, 0.9877, 0.7784]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[0.4794, 0.5635, 0.3764],\n",
+       "         [0.9124, 0.6094, 0.5059],\n",
+       "         [0.4528, 0.4447, 0.5021],\n",
+       "         ...,\n",
+       "         [0.0089, 0.4816, 0.8727],\n",
+       "         [0.2173, 0.6296, 0.2347],\n",
+       "         [0.2028, 0.9931, 0.7201]],\n",
+       "\n",
+       "        [[0.3116, 0.6459, 0.4703],\n",
+       "         [0.0148, 0.2345, 0.7149],\n",
+       "         [0.8393, 0.5804, 0.6691],\n",
+       "         ...,\n",
+       "         [0.2105, 0.9460, 0.2696],\n",
+       "         [0.5918, 0.9295, 0.2616],\n",
+       "         [0.2537, 0.7819, 0.4700]],\n",
+       "\n",
+       "        [[0.6654, 0.1200, 0.5841],\n",
+       "         [0.9147, 0.5522, 0.6529],\n",
+       "         [0.1799, 0.5276, 0.5415],\n",
+       "         ...,\n",
+       "         [0.7536, 0.4346, 0.8793],\n",
+       "         [0.3793, 0.1750, 0.7792],\n",
+       "         [0.9266, 0.8325, 0.9974]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted=x_original.permute(2, 0, 1)\n",
+    "print(x_original.shape)\n",
+    "print(x_permuted.shape)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DD19_zvbQzHo",
+    "outputId": "1d64ce1b-eb48-47e3-90b6-7f1340e7f2b2"
+   },
+   "execution_count": 9,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "torch.Size([224, 224, 3])\n",
+      "torch.Size([3, 224, 224])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NnPmMk4ZRF7w",
+    "outputId": "2cd5da7f-4a23-4a76-8c4a-bb982113f2a4"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z0ylNoAARgTo",
+    "outputId": "ddca0298-cddf-4048-9b71-a791655e5bed"
+   },
+   "execution_count": 11,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]=0.989"
+   ],
+   "metadata": {
+    "id": "RXw0xXsDRi4L"
+   },
+   "execution_count": 13,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1sFdV6wzRo3f",
+    "outputId": "1cf87d2c-6d88-453a-d136-0f625a2800f1"
+   },
+   "execution_count": 14,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xTX-hx2SR1wp",
+    "outputId": "0d4908c4-c3bc-44e3-8ec6-1487104cc209"
+   },
+   "execution_count": 15,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x=torch.arange(1,10).reshape(1,3,3)\n",
+    "x, x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mZomOe7gR4Q8",
+    "outputId": "0b3c922f-ec11-46de-b8a5-9f9533d866ad"
+   },
+   "execution_count": 18,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[[1, 2, 3],\n",
+       "          [4, 5, 6],\n",
+       "          [7, 8, 9]]]),\n",
+       " torch.Size([1, 3, 3]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3y7v4SQvSBs1",
+    "outputId": "8c53307d-e628-404d-db66-56c6bdffab7c"
+   },
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [4, 5, 6],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hf9uG4xLSNya",
+    "outputId": "3075bc42-9ffa-426b-8a86-95628ffcd824"
+   },
+   "execution_count": 21,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zA4G2Se4SRB3",
+    "outputId": "324312d2-ed0a-49eb-f81f-e904e53992fe"
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(1)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][2][2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mwy3zmKKSdbk",
+    "outputId": "d35172c3-b099-40a6-ddf1-a453c2adfa44"
+   },
+   "execution_count": 23,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[:,1,1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fE3nCM1KS7XT",
+    "outputId": "01f5d755-9737-4235-9f73-dce89ff6ba16"
+   },
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([5])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,0,:]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "luNDINKNTTxp",
+    "outputId": "091195ef-2f71-4602-e95f-529a69193150"
+   },
+   "execution_count": 25,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,:,2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KG8A4xbfThCL",
+    "outputId": "5866bc41-9241-4619-be7b-e9206b3f80ab"
+   },
+   "execution_count": 26,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import numpy as np"
+   ],
+   "metadata": {
+    "id": "CZ3PX0qlTwHJ"
+   },
+   "execution_count": 27,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array = np.arange(1.0, 8.0)"
+   ],
+   "metadata": {
+    "id": "UOBeTumiT3Lf"
+   },
+   "execution_count": 28,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RzcO32E9UCQl",
+    "outputId": "430def24-c42c-461f-e5e7-398544c695d3"
+   },
+   "execution_count": 29,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 4., 5., 6., 7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.from_numpy(array)\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JJIL0q1DUC6O",
+    "outputId": "8a3b1d7c-4482-4d32-f34f-9212d9d3a177"
+   },
+   "execution_count": 32,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1., 2., 3., 4., 5., 6., 7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array[3]=11.0"
+   ],
+   "metadata": {
+    "id": "j3Ce6q3DUIEK"
+   },
+   "execution_count": 33,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dc_BCVdjUsCc",
+    "outputId": "65537325-8b11-4f36-fc73-e56f30d6a036"
+   },
+   "execution_count": 34,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3., 11.,  5.,  6.,  7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VG1e_eITUta2",
+    "outputId": "a26c5198-23b6-4a6d-d73a-ba20cd9782b8"
+   },
+   "execution_count": 35,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1.,  2.,  3., 11.,  5.,  6.,  7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.ones(7)\n",
+    "tensor, tensor.dtype\n",
+    "numpy_tensor = tensor.numpy()\n",
+    "numpy_tensor, numpy_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Swt8JF8vUuev",
+    "outputId": "c9e5bf6a-6d2c-41d6-8327-366867ffdd2d"
+   },
+   "execution_count": 37,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(array([1., 1., 1., 1., 1., 1., 1.], dtype=float32), dtype('float32'))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "random_tensor_A = torch.rand(3,4)\n",
+    "random_tensor_B = torch.rand(3,4)\n",
+    "print(random_tensor_A)\n",
+    "print(random_tensor_B)\n",
+    "print(random_tensor_A == random_tensor_B)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGcagTteVFTD",
+    "outputId": "49405790-08e7-4210-b7f1-f00b904c7eb9"
+   },
+   "execution_count": 38,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.9870, 0.6636, 0.6873, 0.8863],\n",
+      "        [0.8386, 0.4169, 0.3587, 0.0265],\n",
+      "        [0.2981, 0.6025, 0.5652, 0.5840]])\n",
+      "tensor([[0.9821, 0.3481, 0.0913, 0.4940],\n",
+      "        [0.7495, 0.4387, 0.9582, 0.8659],\n",
+      "        [0.5064, 0.6919, 0.0809, 0.9771]])\n",
+      "tensor([[False, False, False, False],\n",
+      "        [False, False, False, False],\n",
+      "        [False, False, False, False]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "RANDOM_SEED = 42\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_C = torch.rand(3,4)\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_D = torch.rand(3,4)\n",
+    "print(random_tensor_C)\n",
+    "print(random_tensor_D)\n",
+    "print(random_tensor_C == random_tensor_D)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HznyXyEaWjLM",
+    "outputId": "25956434-01b6-4059-9054-c9978884ddc1"
+   },
+   "execution_count": 46,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[True, True, True, True],\n",
+      "        [True, True, True, True],\n",
+      "        [True, True, True, True]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!nvidia-smi"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vltPTh0YXJSt",
+    "outputId": "807af6dc-a9ca-4301-ec32-b688dbde8be8"
+   },
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Thu May 23 02:57:59 2024       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   60C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|  No running processes found                                                           |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "torch.cuda.is_available()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L6mMyPDyYh1j",
+    "outputId": "279c5dd8-c2a8-4fbd-f321-2f5d7c6e90e6"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "oOdiYa7ZYytx",
+    "outputId": "d73b04fc-8963-4826-9722-08d118d5ab91"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.cuda.device_count()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vOdsazLqZFM5",
+    "outputId": "8189cd6a-9017-4663-a652-3e15c517d9c3"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.tensor([1,2,3], device = \"cpu\")\n",
+    "print(tensor, tensor.device)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cdik9Vw3ZMv0",
+    "outputId": "044a68fd-83a1-409d-8e3b-655142ca0270"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([1, 2, 3]) cpu\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu = tensor.to(device)\n",
+    "tensor_on_gpu"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zmp835rrZp-z",
+    "outputId": "37fa3413-18a3-47bf-ae51-5b36ff85a3ef"
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3], device='cuda:0')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu.numpy()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 159
+    },
+    "id": "jhriaa8uZ1yM",
+    "outputId": "bc5a3226-1a12-4fea-8769-a44f21cdc323"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "TypeError",
+     "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-e0c96c7436fd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtensor_on_gpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_cpu = tensor_on_gpu.cpu().numpy()"
+   ],
+   "metadata": {
+    "id": "LHGXK3GgaOzL"
+   },
+   "execution_count": 12,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "id": "j-El4LlCajfq"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/2-Regression/1-Tools/notebook.ipynb b/translations/sv/2-Regression/1-Tools/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sv/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/sv/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
new file mode 100644
index 000000000..fc88b9af9
--- /dev/null
+++ b/translations/sv/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -0,0 +1,447 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_1-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "c18d3bd0bd8ae3878597e89dcd1fa5c1",
+   "translation_date": "2025-09-06T13:42:52+00:00",
+   "source_file": "2-Regression/1-Tools/solution/R/lesson_1-R.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "YJUHCXqK57yz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Introduktion till regression - Lektion 1\n",
+    "\n",
+    "#### Sätta det i perspektiv\n",
+    "\n",
+    "✅ Det finns många typer av regressionsmetoder, och vilken du väljer beror på vilken typ av svar du söker. Om du vill förutsäga den sannolika längden för en person i en viss ålder, skulle du använda `linjär regression`, eftersom du söker ett **numeriskt värde**. Om du är intresserad av att avgöra om en viss typ av mat ska betraktas som vegansk eller inte, söker du en **kategoriindelning** och skulle använda `logistisk regression`. Du kommer att lära dig mer om logistisk regression senare. Fundera lite på några frågor du kan ställa till data, och vilken av dessa metoder som skulle vara mest lämplig.\n",
+    "\n",
+    "I det här avsnittet kommer du att arbeta med en [liten dataset om diabetes](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Föreställ dig att du ville testa en behandling för diabetiker. Maskininlärningsmodeller kan hjälpa dig att avgöra vilka patienter som skulle svara bättre på behandlingen, baserat på kombinationer av variabler. Även en mycket enkel regressionsmodell, när den visualiseras, kan visa information om variabler som kan hjälpa dig att organisera dina teoretiska kliniska studier.\n",
+    "\n",
+    "Med det sagt, låt oss sätta igång med denna uppgift!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/encouRage.jpg\"\n",
+    "   width=\"630\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n",
+    "\n",
+    "<!--![Konstverk av \\@allison_horst](../../../../../../2-Regression/1-Tools/images/encouRage.jpg)<br>Konstverk av @allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "LWNNzfqd6feZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Ladda upp vårt verktygspaket\n",
+    "\n",
+    "För den här uppgiften behöver vi följande paket:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) är en [samling av R-paket](https://www.tidyverse.org/packages) som är utformade för att göra dataanalys snabbare, enklare och roligare!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) är ett ramverk som består av en [samling paket](https://www.tidymodels.org/packages/) för modellering och maskininlärning.\n",
+    "\n",
+    "Du kan installera dem med följande kommando:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+    "\n",
+    "Skriptet nedan kontrollerar om du har de paket som krävs för att slutföra denna modul och installerar dem åt dig om några saknas.\n"
+   ],
+   "metadata": {
+    "id": "FIo2YhO26wI9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse, tidymodels)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "id": "cIA9fz9v7Dss",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nu, låt oss ladda dessa fantastiska paket och göra dem tillgängliga i vår nuvarande R-session. (Detta är bara för illustration, `pacman::p_load()` har redan gjort det åt dig)\n"
+   ],
+   "metadata": {
+    "id": "gpO_P_6f9WUG"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# load the core Tidyverse packages\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# load the core Tidymodels packages\r\n",
+    "library(tidymodels)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NLMycgG-9ezO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Diabetes-datasetet\n",
+    "\n",
+    "I den här övningen ska vi använda våra regressionskunskaper för att göra förutsägelser på ett diabetes-dataset. [Diabetes-datasetet](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) innehåller `442 prover` med data relaterad till diabetes, med 10 prediktorvariabler: `ålder`, `kön`, `kroppsmasseindex`, `genomsnittligt blodtryck` och `sex blodserummätningar` samt en utfallsvariabel `y`: ett kvantitativt mått på sjukdomsprogression ett år efter baslinjen.\n",
+    "\n",
+    "|Antal observationer|442|\n",
+    "|-------------------|:---|\n",
+    "|Antal prediktorer|De första 10 kolumnerna är numeriska prediktorer|\n",
+    "|Utfall/Mål|Kolumn 11 är ett kvantitativt mått på sjukdomsprogression ett år efter baslinjen|\n",
+    "|Information om prediktorer|- ålder i år\n",
+    "||- kön\n",
+    "||- bmi kroppsmasseindex\n",
+    "||- bp genomsnittligt blodtryck\n",
+    "||- s1 tc, totalt serumkolesterol\n",
+    "||- s2 ldl, lågdensitetslipoproteiner\n",
+    "||- s3 hdl, högdensitetslipoproteiner\n",
+    "||- s4 tch, totalt kolesterol / HDL\n",
+    "||- s5 ltg, möjligen logaritmen av serumtriglyceridnivå\n",
+    "||- s6 glu, blodsockernivå|\n",
+    "\n",
+    "> 🎓 Kom ihåg, detta är övervakad inlärning, och vi behöver ett namngivet mål 'y'.\n",
+    "\n",
+    "Innan du kan manipulera data med R, måste du importera data till R:s minne eller skapa en anslutning till data som R kan använda för att komma åt den på distans.\n",
+    "\n",
+    "> Paketet [readr](https://readr.tidyverse.org/), som är en del av Tidyverse, erbjuder ett snabbt och användarvänligt sätt att läsa in rektangulära data i R.\n",
+    "\n",
+    "Nu ska vi ladda diabetes-datasetet från denna käll-URL: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+    "\n",
+    "Vi ska också göra en snabb kontroll av vår data med hjälp av `glimpse()` och visa de första 5 raderna med `slice()`.\n",
+    "\n",
+    "Innan vi går vidare, låt oss introducera något du ofta kommer att stöta på i R-kod 🥁🥁: pipe-operatorn `%>%`\n",
+    "\n",
+    "Pipe-operatorn (`%>%`) utför operationer i logisk sekvens genom att skicka ett objekt vidare till en funktion eller ett uttryck. Du kan tänka på pipe-operatorn som att säga \"och sedan\" i din kod.\n"
+   ],
+   "metadata": {
+    "id": "KM6iXLH996Cl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import the data set\r\n",
+    "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Get a glimpse and dimensions of the data\r\n",
+    "glimpse(diabetes)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Select the first 5 rows of the data\r\n",
+    "diabetes %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Z1geAMhM-bSP"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`glimpse()` visar oss att denna data har 442 rader och 11 kolumner, där alla kolumner är av datatypen `double`.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "> glimpse() och slice() är funktioner i [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, som är en del av Tidyverse, är en grammatik för datamanipulation som erbjuder en konsekvent uppsättning verb för att lösa de vanligaste utmaningarna inom datamanipulation.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Nu när vi har datan, låt oss fokusera på en specifik variabel (`bmi`) som mål för denna övning. Detta kräver att vi väljer ut de önskade kolumnerna. Så, hur gör vi detta?\n",
+    "\n",
+    "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) låter oss *välja* (och eventuellt byta namn på) kolumner i en data frame.\n"
+   ],
+   "metadata": {
+    "id": "UwjVT1Hz-c3Z"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select predictor feature `bmi` and outcome `y`\r\n",
+    "diabetes_select <- diabetes %>% \r\n",
+    "  select(c(bmi, y))\r\n",
+    "\r\n",
+    "# Print the first 5 rows\r\n",
+    "diabetes_select %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RDY1oAKI-m80"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Tränings- och testdata\n",
+    "\n",
+    "Det är vanligt inom övervakad inlärning att *dela upp* data i två delmängder; en (vanligtvis större) uppsättning för att träna modellen, och en mindre \"håll-ut\" uppsättning för att se hur modellen presterade.\n",
+    "\n",
+    "Nu när vi har data redo kan vi se om en maskin kan hjälpa till att avgöra en logisk uppdelning mellan siffrorna i detta dataset. Vi kan använda paketet [rsample](https://tidymodels.github.io/rsample/), som är en del av Tidymodels-ramverket, för att skapa ett objekt som innehåller information om *hur* man delar upp data, och sedan två ytterligare rsample-funktioner för att extrahera de skapade tränings- och testuppsättningarna:\n"
+   ],
+   "metadata": {
+    "id": "SDk668xK-tc3"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\r\n",
+    "# Split 67% of the data for training and the rest for tesing\r\n",
+    "diabetes_split <- diabetes_select %>% \r\n",
+    "  initial_split(prop = 0.67)\r\n",
+    "\r\n",
+    "# Extract the resulting train and test sets\r\n",
+    "diabetes_train <- training(diabetes_split)\r\n",
+    "diabetes_test <- testing(diabetes_split)\r\n",
+    "\r\n",
+    "# Print the first 3 rows of the training set\r\n",
+    "diabetes_train %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "EqtHx129-1h-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Träna en linjär regressionsmodell med Tidymodels\n",
+    "\n",
+    "Nu är vi redo att träna vår modell!\n",
+    "\n",
+    "I Tidymodels specificerar du modeller med `parsnip()` genom att ange tre koncept:\n",
+    "\n",
+    "-   Modellens **typ** skiljer mellan olika modeller som linjär regression, logistisk regression, beslutsträd och så vidare.\n",
+    "\n",
+    "-   Modellens **läge** inkluderar vanliga alternativ som regression och klassificering; vissa modelltyper stödjer båda dessa medan andra bara har ett läge.\n",
+    "\n",
+    "-   Modellens **motor** är det beräkningsverktyg som kommer att användas för att anpassa modellen. Ofta är dessa R-paket, såsom **`\"lm\"`** eller **`\"ranger\"`**\n",
+    "\n",
+    "Denna modellinformation fångas i en modelspecifikation, så låt oss skapa en!\n"
+   ],
+   "metadata": {
+    "id": "sBOS-XhB-6v7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- \r\n",
+    "  # Type\r\n",
+    "  linear_reg() %>% \r\n",
+    "  # Engine\r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  # Mode\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the model specification\r\n",
+    "lm_spec"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "20OwEw20--t3"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Efter att en modell har *specificerats* kan modellen `estimeras` eller `tränas` med hjälp av funktionen [`fit()`](https://parsnip.tidymodels.org/reference/fit.html), vanligtvis med en formel och lite data.\n",
+    "\n",
+    "`y ~ .` betyder att vi kommer att anpassa `y` som den förutsagda kvantiteten/målet, förklarad av alla prediktorer/funktioner, dvs. `.` (i det här fallet har vi bara en prediktor: `bmi`).\n"
+   ],
+   "metadata": {
+    "id": "_oDHs89k_CJj"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>%\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Train a linear regression model\r\n",
+    "lm_mod <- lm_spec %>% \r\n",
+    "  fit(y ~ ., data = diabetes_train)\r\n",
+    "\r\n",
+    "# Print the model\r\n",
+    "lm_mod"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YlsHqd-q_GJQ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Från modellens output kan vi se de koefficienter som lärdes in under träningen. Dessa representerar koefficienterna för den bästa anpassade linjen som ger oss det lägsta totala felet mellan den faktiska och den förutsagda variabeln.\n",
+    "<br>\n",
+    "\n",
+    "## 5. Gör förutsägelser på testuppsättningen\n",
+    "\n",
+    "Nu när vi har tränat en modell kan vi använda den för att förutsäga sjukdomsprogressionen y för testdatamängden med hjälp av [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Detta kommer att användas för att dra linjen mellan datagrupper.\n"
+   ],
+   "metadata": {
+    "id": "kGZ22RQj_Olu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\r\n",
+    "predictions <- lm_mod %>% \r\n",
+    "  predict(new_data = diabetes_test)\r\n",
+    "\r\n",
+    "# Print out some of the predictions\r\n",
+    "predictions %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "nXHbY7M2_aao"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woohoo! 💃🕺 Vi har precis tränat en modell och använt den för att göra förutsägelser!\n",
+    "\n",
+    "När man gör förutsägelser är tidymodels-konventionen att alltid skapa en tibble/data frame med resultat och standardiserade kolumnnamn. Detta gör det enkelt att kombinera den ursprungliga datan med förutsägelserna i ett användbart format för efterföljande operationer, såsom att skapa diagram.\n",
+    "\n",
+    "`dplyr::bind_cols()` binder effektivt flera data frames kolumnvis.\n"
+   ],
+   "metadata": {
+    "id": "R_JstwUY_bIs"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Combine the predictions and the original test set\r\n",
+    "results <- diabetes_test %>% \r\n",
+    "  bind_cols(predictions)\r\n",
+    "\r\n",
+    "\r\n",
+    "results %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RybsMJR7_iI8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 6. Visa modellresultat\n",
+    "\n",
+    "Nu är det dags att se detta visuellt 📈. Vi ska skapa ett spridningsdiagram med alla `y`- och `bmi`-värden från testuppsättningen och sedan använda förutsägelserna för att rita en linje på den mest lämpliga platsen, mellan modellens datagrupperingar.\n",
+    "\n",
+    "R har flera system för att skapa grafer, men `ggplot2` är ett av de mest eleganta och mångsidiga. Det gör det möjligt att komponera grafer genom att **kombinera oberoende komponenter**.\n"
+   ],
+   "metadata": {
+    "id": "XJbYbMZW_n_s"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plot\r\n",
+    "theme_set(theme_light())\r\n",
+    "# Create a scatter plot\r\n",
+    "results %>% \r\n",
+    "  ggplot(aes(x = bmi)) +\r\n",
+    "  # Add a scatter plot\r\n",
+    "  geom_point(aes(y = y), size = 1.6) +\r\n",
+    "  # Add a line plot\r\n",
+    "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R9tYp3VW_sTn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ Fundera lite på vad som händer här. En rak linje går genom många små datapunkter, men vad gör den egentligen? Kan du se hur du borde kunna använda denna linje för att förutsäga var en ny, osedd datapunkt borde passa i förhållande till diagrammets y-axel? Försök att formulera den praktiska användningen av denna modell.\n",
+    "\n",
+    "Grattis, du har byggt din första linjära regressionsmodell, gjort en förutsägelse med den och visat den i ett diagram!\n"
+   ],
+   "metadata": {
+    "id": "zrPtHIxx_tNI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/2-Regression/1-Tools/solution/notebook.ipynb b/translations/sv/2-Regression/1-Tools/solution/notebook.ipynb
new file mode 100644
index 000000000..53be42a79
--- /dev/null
+++ b/translations/sv/2-Regression/1-Tools/solution/notebook.ipynb
@@ -0,0 +1,677 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linjär regression för Diabetes-datasetet - Lektion 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Importera nödvändiga bibliotek\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model, model_selection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ladda diabetesdatasetet, uppdelat i `X`-data och `y`-funktioner\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "print(X.shape)\n",
+    "print(X[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Välj bara en funktion att rikta in dig på för denna övning\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selecting the 3rd feature\n",
+    "X = X[:, 2]\n",
+    "print(X.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 1)\n",
+      "[[ 0.06169621]\n",
+      " [-0.05147406]\n",
+      " [ 0.04445121]\n",
+      " [-0.01159501]\n",
+      " [-0.03638469]\n",
+      " [-0.04069594]\n",
+      " [-0.04716281]\n",
+      " [-0.00189471]\n",
+      " [ 0.06169621]\n",
+      " [ 0.03906215]\n",
+      " [-0.08380842]\n",
+      " [ 0.01750591]\n",
+      " [-0.02884001]\n",
+      " [-0.00189471]\n",
+      " [-0.02560657]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [ 0.01211685]\n",
+      " [-0.0105172 ]\n",
+      " [-0.01806189]\n",
+      " [-0.05686312]\n",
+      " [-0.02237314]\n",
+      " [-0.00405033]\n",
+      " [ 0.06061839]\n",
+      " [ 0.03582872]\n",
+      " [-0.01267283]\n",
+      " [-0.07734155]\n",
+      " [ 0.05954058]\n",
+      " [-0.02129532]\n",
+      " [-0.00620595]\n",
+      " [ 0.04445121]\n",
+      " [-0.06548562]\n",
+      " [ 0.12528712]\n",
+      " [-0.05039625]\n",
+      " [-0.06332999]\n",
+      " [-0.03099563]\n",
+      " [ 0.02289497]\n",
+      " [ 0.01103904]\n",
+      " [ 0.07139652]\n",
+      " [ 0.01427248]\n",
+      " [-0.00836158]\n",
+      " [-0.06764124]\n",
+      " [-0.0105172 ]\n",
+      " [-0.02345095]\n",
+      " [ 0.06816308]\n",
+      " [-0.03530688]\n",
+      " [-0.01159501]\n",
+      " [-0.0730303 ]\n",
+      " [-0.04177375]\n",
+      " [ 0.01427248]\n",
+      " [-0.00728377]\n",
+      " [ 0.0164281 ]\n",
+      " [-0.00943939]\n",
+      " [-0.01590626]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.04931844]\n",
+      " [ 0.04121778]\n",
+      " [-0.06332999]\n",
+      " [-0.06440781]\n",
+      " [-0.02560657]\n",
+      " [-0.00405033]\n",
+      " [ 0.00457217]\n",
+      " [-0.00728377]\n",
+      " [-0.0374625 ]\n",
+      " [-0.02560657]\n",
+      " [-0.02452876]\n",
+      " [-0.01806189]\n",
+      " [-0.01482845]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [-0.06979687]\n",
+      " [ 0.03367309]\n",
+      " [-0.00405033]\n",
+      " [-0.02021751]\n",
+      " [ 0.00241654]\n",
+      " [-0.03099563]\n",
+      " [ 0.02828403]\n",
+      " [-0.03638469]\n",
+      " [-0.05794093]\n",
+      " [-0.0374625 ]\n",
+      " [ 0.01211685]\n",
+      " [-0.02237314]\n",
+      " [-0.03530688]\n",
+      " [ 0.00996123]\n",
+      " [-0.03961813]\n",
+      " [ 0.07139652]\n",
+      " [-0.07518593]\n",
+      " [-0.00620595]\n",
+      " [-0.04069594]\n",
+      " [-0.04824063]\n",
+      " [-0.02560657]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.00457217]\n",
+      " [-0.06440781]\n",
+      " [-0.01698407]\n",
+      " [-0.05794093]\n",
+      " [ 0.00996123]\n",
+      " [ 0.08864151]\n",
+      " [-0.00512814]\n",
+      " [-0.06440781]\n",
+      " [ 0.01750591]\n",
+      " [-0.04500719]\n",
+      " [ 0.02828403]\n",
+      " [ 0.04121778]\n",
+      " [ 0.06492964]\n",
+      " [-0.03207344]\n",
+      " [-0.07626374]\n",
+      " [ 0.04984027]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03207344]\n",
+      " [ 0.00457217]\n",
+      " [ 0.02073935]\n",
+      " [ 0.01427248]\n",
+      " [ 0.11019775]\n",
+      " [ 0.00133873]\n",
+      " [ 0.05846277]\n",
+      " [-0.02129532]\n",
+      " [-0.0105172 ]\n",
+      " [-0.04716281]\n",
+      " [ 0.00457217]\n",
+      " [ 0.01750591]\n",
+      " [ 0.08109682]\n",
+      " [ 0.0347509 ]\n",
+      " [ 0.02397278]\n",
+      " [-0.00836158]\n",
+      " [-0.06117437]\n",
+      " [-0.00189471]\n",
+      " [-0.06225218]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.09618619]\n",
+      " [-0.06979687]\n",
+      " [-0.02129532]\n",
+      " [-0.05362969]\n",
+      " [ 0.0433734 ]\n",
+      " [ 0.05630715]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04984027]\n",
+      " [ 0.11127556]\n",
+      " [ 0.06169621]\n",
+      " [ 0.01427248]\n",
+      " [ 0.04768465]\n",
+      " [ 0.01211685]\n",
+      " [ 0.00564998]\n",
+      " [ 0.04660684]\n",
+      " [ 0.12852056]\n",
+      " [ 0.05954058]\n",
+      " [ 0.09295276]\n",
+      " [ 0.01535029]\n",
+      " [-0.00512814]\n",
+      " [ 0.0703187 ]\n",
+      " [-0.00405033]\n",
+      " [-0.00081689]\n",
+      " [-0.04392938]\n",
+      " [ 0.02073935]\n",
+      " [ 0.06061839]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03315126]\n",
+      " [-0.06548562]\n",
+      " [ 0.0433734 ]\n",
+      " [-0.06225218]\n",
+      " [ 0.06385183]\n",
+      " [ 0.03043966]\n",
+      " [ 0.07247433]\n",
+      " [-0.0191397 ]\n",
+      " [-0.06656343]\n",
+      " [-0.06009656]\n",
+      " [ 0.06924089]\n",
+      " [ 0.05954058]\n",
+      " [-0.02668438]\n",
+      " [-0.02021751]\n",
+      " [-0.046085  ]\n",
+      " [ 0.07139652]\n",
+      " [-0.07949718]\n",
+      " [ 0.00996123]\n",
+      " [-0.03854032]\n",
+      " [ 0.01966154]\n",
+      " [ 0.02720622]\n",
+      " [-0.00836158]\n",
+      " [-0.01590626]\n",
+      " [ 0.00457217]\n",
+      " [-0.04285156]\n",
+      " [ 0.00564998]\n",
+      " [-0.03530688]\n",
+      " [ 0.02397278]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00297252]\n",
+      " [-0.06656343]\n",
+      " [-0.01267283]\n",
+      " [-0.04177375]\n",
+      " [-0.03099563]\n",
+      " [-0.00512814]\n",
+      " [-0.05901875]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00349435]\n",
+      " [ 0.05415152]\n",
+      " [-0.04500719]\n",
+      " [-0.05794093]\n",
+      " [-0.05578531]\n",
+      " [ 0.00133873]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00672779]\n",
+      " [ 0.04660684]\n",
+      " [ 0.02612841]\n",
+      " [ 0.04552903]\n",
+      " [ 0.04013997]\n",
+      " [-0.01806189]\n",
+      " [ 0.01427248]\n",
+      " [ 0.03690653]\n",
+      " [ 0.00349435]\n",
+      " [-0.07087468]\n",
+      " [-0.03315126]\n",
+      " [ 0.09403057]\n",
+      " [ 0.03582872]\n",
+      " [ 0.03151747]\n",
+      " [-0.06548562]\n",
+      " [-0.04177375]\n",
+      " [-0.03961813]\n",
+      " [-0.03854032]\n",
+      " [-0.02560657]\n",
+      " [-0.02345095]\n",
+      " [-0.06656343]\n",
+      " [ 0.03259528]\n",
+      " [-0.046085  ]\n",
+      " [-0.02991782]\n",
+      " [-0.01267283]\n",
+      " [-0.01590626]\n",
+      " [ 0.07139652]\n",
+      " [-0.03099563]\n",
+      " [ 0.00026092]\n",
+      " [ 0.03690653]\n",
+      " [ 0.03906215]\n",
+      " [-0.01482845]\n",
+      " [ 0.00672779]\n",
+      " [-0.06871905]\n",
+      " [-0.00943939]\n",
+      " [ 0.01966154]\n",
+      " [ 0.07462995]\n",
+      " [-0.00836158]\n",
+      " [-0.02345095]\n",
+      " [-0.046085  ]\n",
+      " [ 0.05415152]\n",
+      " [-0.03530688]\n",
+      " [-0.03207344]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04768465]\n",
+      " [ 0.06061839]\n",
+      " [ 0.05630715]\n",
+      " [ 0.09834182]\n",
+      " [ 0.05954058]\n",
+      " [ 0.03367309]\n",
+      " [ 0.05630715]\n",
+      " [-0.06548562]\n",
+      " [ 0.16085492]\n",
+      " [-0.05578531]\n",
+      " [-0.02452876]\n",
+      " [-0.03638469]\n",
+      " [-0.00836158]\n",
+      " [-0.04177375]\n",
+      " [ 0.12744274]\n",
+      " [-0.07734155]\n",
+      " [ 0.02828403]\n",
+      " [-0.02560657]\n",
+      " [-0.06225218]\n",
+      " [-0.00081689]\n",
+      " [ 0.08864151]\n",
+      " [-0.03207344]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00888341]\n",
+      " [ 0.00672779]\n",
+      " [-0.02021751]\n",
+      " [-0.02452876]\n",
+      " [-0.01159501]\n",
+      " [ 0.02612841]\n",
+      " [-0.05901875]\n",
+      " [-0.03638469]\n",
+      " [-0.02452876]\n",
+      " [ 0.01858372]\n",
+      " [-0.0902753 ]\n",
+      " [-0.00512814]\n",
+      " [-0.05255187]\n",
+      " [-0.02237314]\n",
+      " [-0.02021751]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00620595]\n",
+      " [-0.01698407]\n",
+      " [ 0.05522933]\n",
+      " [ 0.07678558]\n",
+      " [ 0.01858372]\n",
+      " [-0.02237314]\n",
+      " [ 0.09295276]\n",
+      " [-0.03099563]\n",
+      " [ 0.03906215]\n",
+      " [-0.06117437]\n",
+      " [-0.00836158]\n",
+      " [-0.0374625 ]\n",
+      " [-0.01375064]\n",
+      " [ 0.07355214]\n",
+      " [-0.02452876]\n",
+      " [ 0.03367309]\n",
+      " [ 0.0347509 ]\n",
+      " [-0.03854032]\n",
+      " [-0.03961813]\n",
+      " [-0.00189471]\n",
+      " [-0.03099563]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06492964]\n",
+      " [ 0.04013997]\n",
+      " [-0.02345095]\n",
+      " [ 0.05307371]\n",
+      " [ 0.04013997]\n",
+      " [-0.02021751]\n",
+      " [ 0.01427248]\n",
+      " [-0.03422907]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00457217]\n",
+      " [ 0.03043966]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.06169621]\n",
+      " [-0.00728377]\n",
+      " [ 0.00564998]\n",
+      " [ 0.05415152]\n",
+      " [-0.00836158]\n",
+      " [ 0.114509  ]\n",
+      " [ 0.06708527]\n",
+      " [-0.05578531]\n",
+      " [ 0.03043966]\n",
+      " [-0.02560657]\n",
+      " [ 0.10480869]\n",
+      " [-0.00620595]\n",
+      " [-0.04716281]\n",
+      " [-0.04824063]\n",
+      " [ 0.08540807]\n",
+      " [-0.01267283]\n",
+      " [-0.03315126]\n",
+      " [-0.00728377]\n",
+      " [-0.01375064]\n",
+      " [ 0.05954058]\n",
+      " [ 0.02181716]\n",
+      " [ 0.01858372]\n",
+      " [-0.01159501]\n",
+      " [-0.00297252]\n",
+      " [ 0.01750591]\n",
+      " [-0.02991782]\n",
+      " [-0.02021751]\n",
+      " [-0.05794093]\n",
+      " [ 0.06061839]\n",
+      " [-0.04069594]\n",
+      " [-0.07195249]\n",
+      " [-0.05578531]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03315126]\n",
+      " [ 0.04984027]\n",
+      " [-0.08488624]\n",
+      " [ 0.00564998]\n",
+      " [ 0.02073935]\n",
+      " [-0.00728377]\n",
+      " [ 0.10480869]\n",
+      " [-0.02452876]\n",
+      " [-0.00620595]\n",
+      " [-0.03854032]\n",
+      " [ 0.13714305]\n",
+      " [ 0.17055523]\n",
+      " [ 0.00241654]\n",
+      " [ 0.03798434]\n",
+      " [-0.05794093]\n",
+      " [-0.00943939]\n",
+      " [-0.02345095]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03422907]\n",
+      " [-0.00297252]\n",
+      " [ 0.06816308]\n",
+      " [ 0.00996123]\n",
+      " [ 0.00241654]\n",
+      " [-0.03854032]\n",
+      " [ 0.02612841]\n",
+      " [-0.08919748]\n",
+      " [ 0.06061839]\n",
+      " [-0.02884001]\n",
+      " [-0.02991782]\n",
+      " [-0.0191397 ]\n",
+      " [-0.04069594]\n",
+      " [ 0.01535029]\n",
+      " [-0.02452876]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06924089]\n",
+      " [-0.06979687]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [ 0.01858372]\n",
+      " [ 0.00133873]\n",
+      " [-0.03099563]\n",
+      " [-0.00405033]\n",
+      " [ 0.01535029]\n",
+      " [ 0.02289497]\n",
+      " [ 0.04552903]\n",
+      " [-0.04500719]\n",
+      " [-0.03315126]\n",
+      " [ 0.097264  ]\n",
+      " [ 0.05415152]\n",
+      " [ 0.12313149]\n",
+      " [-0.08057499]\n",
+      " [ 0.09295276]\n",
+      " [-0.05039625]\n",
+      " [-0.01159501]\n",
+      " [-0.0277622 ]\n",
+      " [ 0.05846277]\n",
+      " [ 0.08540807]\n",
+      " [-0.00081689]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00888341]\n",
+      " [ 0.08001901]\n",
+      " [ 0.07139652]\n",
+      " [-0.02452876]\n",
+      " [-0.0547075 ]\n",
+      " [-0.03638469]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.07786339]\n",
+      " [-0.03961813]\n",
+      " [ 0.01103904]\n",
+      " [-0.04069594]\n",
+      " [-0.03422907]\n",
+      " [ 0.00564998]\n",
+      " [ 0.08864151]\n",
+      " [-0.03315126]\n",
+      " [-0.05686312]\n",
+      " [-0.03099563]\n",
+      " [ 0.05522933]\n",
+      " [-0.06009656]\n",
+      " [ 0.00133873]\n",
+      " [-0.02345095]\n",
+      " [-0.07410811]\n",
+      " [ 0.01966154]\n",
+      " [-0.01590626]\n",
+      " [-0.01590626]\n",
+      " [ 0.03906215]\n",
+      " [-0.0730303 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Reshaping to get a 2D array\n",
+    "X = X.reshape(-1, 1)\n",
+    "print(X.shape)\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dela upp tränings- och testdata för både `X` och `y`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Välj modellen och anpassa den med träningsdatan\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Använd testdata för att förutsäga en linje\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Visa resultaten i ett diagram\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test,  color='black')\n",
+    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "16ff1a974f6e4348e869e4a7d366b86a",
+   "translation_date": "2025-09-06T13:39:08+00:00",
+   "source_file": "2-Regression/1-Tools/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sv/2-Regression/2-Data/notebook.ipynb b/translations/sv/2-Regression/2-Data/notebook.ipynb
new file mode 100644
index 000000000..b923d18b6
--- /dev/null
+++ b/translations/sv/2-Regression/2-Data/notebook.ipynb
@@ -0,0 +1,46 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  },
+  "coopTranslator": {
+   "original_hash": "1b2ab303ac6c604a34c6ca7a49077fc7",
+   "translation_date": "2025-09-06T13:45:57+00:00",
+   "source_file": "2-Regression/2-Data/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/sv/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
new file mode 100644
index 000000000..2c5ba848d
--- /dev/null
+++ b/translations/sv/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -0,0 +1,671 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_2-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "f3c335f9940cfd76528b3ef918b9b342",
+   "translation_date": "2025-09-06T13:51:44+00:00",
+   "source_file": "2-Regression/2-Data/solution/R/lesson_2-R.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Bygg en regressionsmodell: förbered och visualisera data\n",
+    "\n",
+    "## **Linjär regression för pumpor - Lektion 2**\n",
+    "#### Introduktion\n",
+    "\n",
+    "Nu när du har verktygen du behöver för att börja bygga maskininlärningsmodeller med Tidymodels och Tidyverse, är du redo att börja ställa frågor till dina data. När du arbetar med data och tillämpar ML-lösningar är det mycket viktigt att förstå hur man ställer rätt fråga för att verkligen utnyttja potentialen i din dataset.\n",
+    "\n",
+    "I den här lektionen kommer du att lära dig:\n",
+    "\n",
+    "- Hur du förbereder dina data för modellbyggande.\n",
+    "\n",
+    "- Hur du använder `ggplot2` för datavisualisering.\n",
+    "\n",
+    "Frågan du behöver svar på avgör vilken typ av ML-algoritmer du kommer att använda. Och kvaliteten på svaret du får tillbaka kommer att vara starkt beroende av datans natur.\n",
+    "\n",
+    "Låt oss se detta genom att arbeta igenom en praktisk övning.\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/unruly_data.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Konstverk av \\@allison_horst](../../../../../../2-Regression/2-Data/images/unruly_data.jpg)<br>Konstverk av \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "Pg5aexcOPqAZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Importera pumpadata och kalla på Tidyverse\n",
+    "\n",
+    "Vi kommer att behöva följande paket för att hantera denna lektion:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) är en [samling av R-paket](https://www.tidyverse.org/packages) som är utformade för att göra datavetenskap snabbare, enklare och roligare!\n",
+    "\n",
+    "Du kan installera dem med:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\"))`\n",
+    "\n",
+    "Skriptet nedan kontrollerar om du har de paket som krävs för att slutföra denna modul och installerar dem åt dig om några saknas.\n"
+   ],
+   "metadata": {
+    "id": "dc5WhyVdXAjR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "GqPYUZgfXOBt"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nu sätter vi igång några paket och laddar [data](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) som tillhandahålls för denna lektion!\n"
+   ],
+   "metadata": {
+    "id": "kvjDTPDSXRr2"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n =50)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VMri-t2zXqgD"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "En snabb `glimpse()` visar direkt att det finns tomma värden och en blandning av strängar (`chr`) och numeriska data (`dbl`). `Date` är av typen tecken och det finns också en märklig kolumn kallad `Package` där datan är en blandning av `sacks`, `bins` och andra värden. Datan är faktiskt lite av en röra 😤.\n",
+    "\n",
+    "Det är faktiskt inte särskilt vanligt att få en dataset som är helt redo att användas för att skapa en ML-modell direkt. Men oroa dig inte, i denna lektion kommer du att lära dig hur man förbereder en rå dataset med hjälp av standardbibliotek i R 🧑‍🔧. Du kommer också att lära dig olika tekniker för att visualisera datan.📈📊\n",
+    "<br>\n",
+    "\n",
+    "> En påminnelse: Pipe-operatorn (`%>%`) utför operationer i logisk sekvens genom att skicka ett objekt framåt in i en funktion eller ett uttryck. Du kan tänka på pipe-operatorn som att säga \"och sedan\" i din kod.\n"
+   ],
+   "metadata": {
+    "id": "REWcIv9yX29v"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Kontrollera för saknade data\n",
+    "\n",
+    "Ett av de vanligaste problemen som dataanalytiker måste hantera är ofullständig eller saknad data. R representerar saknade eller okända värden med ett speciellt sentinelvärde: `NA` (Not Available).\n",
+    "\n",
+    "Så hur kan vi veta att dataframen innehåller saknade värden?\n",
+    "<br>\n",
+    "-   Ett direkt sätt skulle vara att använda R:s inbyggda funktion `anyNA`, som returnerar de logiska objekten `TRUE` eller `FALSE`.\n"
+   ],
+   "metadata": {
+    "id": "Zxfb3AM5YbUe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  anyNA()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "G--DQutAYltj"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bra, det verkar som att det saknas viss data! Det är en bra utgångspunkt.\n",
+    "\n",
+    "-   Ett annat sätt skulle vara att använda funktionen `is.na()` som visar vilka enskilda kolumnelement som saknas med ett logiskt värde `TRUE`.\n"
+   ],
+   "metadata": {
+    "id": "mU-7-SB6YokF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "W-DxDOR4YxSW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Okej, jobbet är klart men med en så stor dataram som denna skulle det vara ineffektivt och praktiskt taget omöjligt att granska alla rader och kolumner individuellt😴.\n",
+    "\n",
+    "-   Ett mer intuitivt sätt skulle vara att beräkna summan av de saknade värdena för varje kolumn:\n"
+   ],
+   "metadata": {
+    "id": "xUWxipKYY0o7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  colSums()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZRBWV6P9ZArL"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mycket bättre! Det saknas data, men kanske spelar det ingen roll för uppgiften. Låt oss se vad vidare analys ger.\n",
+    "\n",
+    "> Tillsammans med de fantastiska uppsättningarna av paket och funktioner har R en mycket bra dokumentation. Till exempel, använd `help(colSums)` eller `?colSums` för att ta reda på mer om funktionen.\n"
+   ],
+   "metadata": {
+    "id": "9gv-crB6ZD1Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Dplyr: En grammatik för datamanipulation\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_wrangling.png\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Konstverk av \\@allison_horst](../../../../../../2-Regression/2-Data/images/dplyr_wrangling.png)<br/>Konstverk av \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "o4jLY5-VZO2C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`dplyr`](https://dplyr.tidyverse.org/), ett paket i Tidyverse, är en grammatik för datamanipulation som erbjuder en konsekvent uppsättning verb som hjälper dig att lösa de vanligaste utmaningarna inom datamanipulation. I det här avsnittet ska vi utforska några av dplyrs verb!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "i5o33MQBZWWw"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::select()\n",
+    "\n",
+    "`select()` är en funktion i paketet `dplyr` som hjälper dig att välja vilka kolumner du vill behålla eller exkludera.\n",
+    "\n",
+    "För att göra din data frame enklare att arbeta med, ta bort flera av dess kolumner med hjälp av `select()` och behåll endast de kolumner du behöver.\n",
+    "\n",
+    "Till exempel, i denna övning kommer vår analys att involvera kolumnerna `Package`, `Low Price`, `High Price` och `Date`. Låt oss välja dessa kolumner.\n"
+   ],
+   "metadata": {
+    "id": "x3VGMAGBZiUr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(Package, `Low Price`, `High Price`, Date)\n",
+    "\n",
+    "\n",
+    "# Print data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "F_FgxQnVZnM0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::mutate()\n",
+    "\n",
+    "`mutate()` är en funktion i paketet `dplyr` som hjälper dig att skapa eller ändra kolumner, samtidigt som de befintliga kolumnerna behålls.\n",
+    "\n",
+    "Den generella strukturen för mutate är:\n",
+    "\n",
+    "`data %>%   mutate(new_column_name = what_it_contains)`\n",
+    "\n",
+    "Låt oss testa `mutate` med hjälp av kolumnen `Date` genom att utföra följande operationer:\n",
+    "\n",
+    "1.  Konvertera datumen (som för närvarande är av typen tecken) till ett månadsformat (det här är amerikanska datum, så formatet är `MM/DD/YYYY`).\n",
+    "\n",
+    "2.  Extrahera månaden från datumen till en ny kolumn.\n",
+    "\n",
+    "I R gör paketet [lubridate](https://lubridate.tidyverse.org/) det enklare att arbeta med datum- och tidsdata. Så låt oss använda `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` och se hur vi kan uppnå ovanstående mål. Vi kan ta bort kolumnen Date eftersom vi inte kommer att behöva den igen i efterföljande operationer.\n"
+   ],
+   "metadata": {
+    "id": "2KKo0Ed9Z1VB"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load lubridate\n",
+    "library(lubridate)\n",
+    "\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  # Convert the Date column to a date object\n",
+    "  mutate(Date = mdy(Date)) %>% \n",
+    "  # Extract month from Date\n",
+    "  mutate(Month = month(Date)) %>% \n",
+    "  # Drop Date column\n",
+    "  select(-Date)\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5joszIVSZ6xe"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woohoo! 🤩\n",
+    "\n",
+    "Nu ska vi skapa en ny kolumn `Price`, som representerar det genomsnittliga priset på en pumpa. Låt oss nu ta genomsnittet av kolumnerna `Low Price` och `High Price` för att fylla i den nya kolumnen Price.\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "nIgLjNMCZ-6Y"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new column Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Zo0BsqqtaJw2"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Jaaa!💪\n",
+    "\n",
+    "\"Men vänta lite!\", säger du efter att ha skummat igenom hela datasettet med `View(pumpkins)`, \"Det är något konstigt här!\"🤔\n",
+    "\n",
+    "Om du tittar på kolumnen `Package`, säljs pumpor i många olika konfigurationer. Vissa säljs i mått som `1 1/9 bushel`, andra i `1/2 bushel`, några per pumpa, några per pund, och vissa i stora lådor med varierande bredder.\n",
+    "\n",
+    "Låt oss kontrollera detta:\n"
+   ],
+   "metadata": {
+    "id": "p77WZr-9aQAR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Verify the distinct observations in Package column\n",
+    "pumpkins %>% \n",
+    "  distinct(Package)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "XISGfh0IaUy6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Fantastiskt!👏\n",
+    "\n",
+    "Pumpor verkar vara väldigt svåra att väga konsekvent, så låt oss filtrera dem genom att välja endast pumpor med strängen *bushel* i kolumnen `Package` och lägga detta i en ny data frame `new_pumpkins`.\n"
+   ],
+   "metadata": {
+    "id": "7sMjiVujaZxY"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::filter() och stringr::str_detect()\n",
+    "\n",
+    "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): skapar en delmängd av data som endast innehåller **rader** som uppfyller dina villkor, i detta fall pumpor med strängen *bushel* i kolumnen `Package`.\n",
+    "\n",
+    "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): identifierar förekomsten eller frånvaron av ett mönster i en sträng.\n",
+    "\n",
+    "Paketet [`stringr`](https://github.com/tidyverse/stringr) erbjuder enkla funktioner för vanliga strängoperationer.\n"
+   ],
+   "metadata": {
+    "id": "L8Qfcs92ageF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Retain only pumpkins with \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "       filter(str_detect(Package, \"bushel\"))\n",
+    "\n",
+    "# Get the dimensions of the new data\n",
+    "dim(new_pumpkins)\n",
+    "\n",
+    "# View a few rows of the new data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "hy_SGYREampd"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Du kan se att vi har begränsat oss till ungefär 415 rader med data som innehåller pumpor i skäppor.🤩  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "VrDwF031avlR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::case_when()\n",
+    "\n",
+    "**Men vänta! Det finns en sak till att göra**\n",
+    "\n",
+    "Märkte du att mängden per skäppa varierar per rad? Du behöver normalisera prissättningen så att du visar priset per skäppa, inte per 1 1/9 eller 1/2 skäppa. Dags att göra lite matematik för att standardisera det.\n",
+    "\n",
+    "Vi kommer att använda funktionen [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) för att *mutera* kolumnen Price beroende på vissa villkor. `case_when` gör det möjligt att vektorisera flera `if_else()`-satser.\n"
+   ],
+   "metadata": {
+    "id": "mLpw2jH4a0tx"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(Price = case_when(\n",
+    "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+    "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+    "    TRUE ~ Price))\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 30)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "P68kLVQmbM6I"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nu kan vi analysera priset per enhet baserat på deras mått i bushels. All denna undersökning av pumpors bushels visar dock hur `viktigt` det är att `förstå din datas natur`!\n",
+    "\n",
+    "> ✅ Enligt [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308) beror en bushels vikt på typen av gröda, eftersom det är en volymmätning. \"En bushel tomater, till exempel, ska väga 56 pund... Blad och gröna tar upp mer plats med mindre vikt, så en bushel spenat väger bara 20 pund.\" Det är ganska komplicerat! Låt oss inte bry oss om att göra en omvandling från bushel till pund, utan istället prissätta per bushel. All denna undersökning av pumpors bushels visar dock hur viktigt det är att förstå din datas natur!\n",
+    ">\n",
+    "> ✅ Lade du märke till att pumpor som säljs per halv-bushel är väldigt dyra? Kan du lista ut varför? Tips: små pumpor är mycket dyrare än stora, antagligen eftersom det finns så många fler av dem per bushel, med tanke på det outnyttjade utrymmet som en stor ihålig pajpumpa tar upp.\n"
+   ],
+   "metadata": {
+    "id": "pS2GNPagbSdb"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nu slutligen, för äventyrets skull 💁‍♀️, låt oss också flytta kolumnen Månad till första positionen, dvs `före` kolumnen `Paket`.\n",
+    "\n",
+    "`dplyr::relocate()` används för att ändra kolumnpositioner.\n"
+   ],
+   "metadata": {
+    "id": "qql1SowfbdnP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new data frame new_pumpkins\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(Month, .before = Package)\n",
+    "\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "JJ1x6kw8bixF"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bra jobbat! 👌 Du har nu ett rent och prydligt dataset som du kan använda för att bygga din nya regressionsmodell!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "y8TJ0Za_bn5Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Datavisualisering med ggplot2\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/data-visualization.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografik av Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografik av Dasani Madipalli](../../../../../../2-Regression/2-Data/images/data-visualization.png){width=\"600\"}-->\n",
+    "\n",
+    "Det finns ett *klokt* ordspråk som lyder så här:\n",
+    "\n",
+    "> \"Den enkla grafen har fört mer information till dataanalytikerns sinne än någon annan metod.\" --- John Tukey\n",
+    "\n",
+    "En del av dataforskarens roll är att visa kvaliteten och karaktären hos de data de arbetar med. För att göra detta skapar de ofta intressanta visualiseringar, eller diagram, grafer och tabeller, som visar olika aspekter av data. På så sätt kan de visuellt visa relationer och luckor som annars är svåra att upptäcka.\n",
+    "\n",
+    "Visualiseringar kan också hjälpa till att avgöra vilken maskininlärningsteknik som är mest lämplig för datan. Ett spridningsdiagram som verkar följa en linje, till exempel, indikerar att datan är en bra kandidat för en linjär regressionsanalys.\n",
+    "\n",
+    "R erbjuder flera system för att skapa grafer, men [`ggplot2`](https://ggplot2.tidyverse.org/index.html) är ett av de mest eleganta och mångsidiga. `ggplot2` gör det möjligt att skapa grafer genom att **kombinera oberoende komponenter**.\n",
+    "\n",
+    "Låt oss börja med ett enkelt spridningsdiagram för kolumnerna Price och Month.\n",
+    "\n",
+    "I det här fallet börjar vi med [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), tillhandahåller en dataset och estetisk mappning (med [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) och lägger sedan till lager (som [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) för spridningsdiagram.\n"
+   ],
+   "metadata": {
+    "id": "mYSH6-EtbvNa"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plots\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+    "p + geom_point()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "g2YjnGeOcLo4"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Är detta en användbar graf 🤷? Är det något med den som förvånar dig?\n",
+    "\n",
+    "Den är inte särskilt användbar eftersom allt den gör är att visa dina data som en spridning av punkter under en viss månad.\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "Ml7SDCLQcPvE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Hur gör vi det användbart?**\n",
+    "\n",
+    "För att få diagram att visa användbar data behöver du oftast gruppera datan på något sätt. Till exempel, i vårt fall skulle det ge mer insikt i de underliggande mönstren i vår data om vi hittar det genomsnittliga priset på pumpor för varje månad. Detta leder oss till ännu en snabbgenomgång av **dplyr**:\n",
+    "\n",
+    "#### `dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "Grupperad aggregering i R kan enkelt beräknas med\n",
+    "\n",
+    "`dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "-   `dplyr::group_by()` ändrar analysenheten från hela datasetet till individuella grupper, som per månad.\n",
+    "\n",
+    "-   `dplyr::summarize()` skapar en ny dataram med en kolumn för varje grupperingsvariabel och en kolumn för varje sammanfattningsstatistik som du har specificerat.\n",
+    "\n",
+    "Till exempel kan vi använda `dplyr::group_by() %>% summarize()` för att gruppera pumporna i grupper baserade på **Month**-kolumnen och sedan hitta **medelpriset** för varje månad.\n"
+   ],
+   "metadata": {
+    "id": "jMakvJZIcVkh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "6kVSUa2Bcilf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kategoriska funktioner, såsom månader, representeras bäst med ett stapeldiagram 📊. De lager som används för stapeldiagram är `geom_bar()` och `geom_col()`. Konsultera `?geom_bar` för att lära dig mer.\n",
+    "\n",
+    "Låt oss skapa ett!\n"
+   ],
+   "metadata": {
+    "id": "Kds48GUBcj3W"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price)) %>% \r\n",
+    "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"Pumpkin Price\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VNbU1S3BcrxO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩Det här är en mer användbar datavisualisering! Det verkar indikera att det högsta priset för pumpor inträffar i september och oktober. Stämmer det med dina förväntningar? Varför eller varför inte?\n",
+    "\n",
+    "Grattis till att ha avslutat den andra lektionen 👏! Du förberedde dina data för modellbyggande och upptäckte sedan fler insikter med hjälp av visualiseringar!\n"
+   ],
+   "metadata": {
+    "id": "zDm0VOzzcuzR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/2-Regression/2-Data/solution/notebook.ipynb b/translations/sv/2-Regression/2-Data/solution/notebook.ipynb
new file mode 100644
index 000000000..41c6128d6
--- /dev/null
+++ b/translations/sv/2-Regression/2-Data/solution/notebook.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/8/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    City Name Type               Package   Variety Sub Variety  Grade  \\\n",
+       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "\n",
+       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
+       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
+       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "\n",
+       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
+       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "\n",
+       "    Unnamed: 25  \n",
+       "70          NaN  \n",
+       "71          NaN  \n",
+       "72          NaN  \n",
+       "73          NaN  \n",
+       "74          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Month               Package  Low Price  High Price  Price\n",
+      "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
+      "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "...     ...                   ...        ...         ...    ...\n",
+      "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
+      "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
+      "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
+      "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Get an average between low and high price for the base pumpkin price\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "# Convert the date to its month only\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "\n",
+    "# Create a new dataframe with this basic data\n",
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
+    "\n",
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
+    "\n",
+    "print(new_pumpkins)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "price = new_pumpkins.Price\n",
+    "month = new_pumpkins.Month\n",
+    "plt.scatter(price, month)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "95726f0b8283628d5356a4f8eb8b4b76",
+   "translation_date": "2025-09-06T13:46:22+00:00",
+   "source_file": "2-Regression/2-Data/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sv/2-Regression/3-Linear/notebook.ipynb b/translations/sv/2-Regression/3-Linear/notebook.ipynb
new file mode 100644
index 000000000..dc64f79ad
--- /dev/null
+++ b/translations/sv/2-Regression/3-Linear/notebook.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pumpaprisering\n",
+    "\n",
+    "Ladda in nödvändiga bibliotek och dataset. Konvertera data till en dataframe som innehåller ett urval av data:\n",
+    "\n",
+    "- Ta endast med pumpor som är prissatta per skäppa\n",
+    "- Konvertera datumet till en månad\n",
+    "- Beräkna priset som ett genomsnitt av högsta och lägsta priser\n",
+    "- Konvertera priset för att återspegla prissättningen per skäppmängd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ett grundläggande spridningsdiagram påminner oss om att vi bara har månadsdata från augusti till december. Vi behöver förmodligen mer data för att kunna dra slutsatser på ett linjärt sätt.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter('Month','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "b032d371c75279373507f003439a577e",
+   "translation_date": "2025-09-06T13:08:53+00:00",
+   "source_file": "2-Regression/3-Linear/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sv/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/sv/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
new file mode 100644
index 000000000..14d4df1e7
--- /dev/null
+++ b/translations/sv/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -0,0 +1,1089 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_3-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "5015d65d61ba75a223bfc56c273aa174",
+   "translation_date": "2025-09-06T13:19:40+00:00",
+   "source_file": "2-Regression/3-Linear/solution/R/lesson_3-R.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "EgQw8osnsUV-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Linjär och Polynomisk Regression för Pumpapris - Lektion 3\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Infografik av Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografik av Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n",
+    "\n",
+    "#### Introduktion\n",
+    "\n",
+    "Hittills har du utforskat vad regression är med hjälp av exempeldata från pumpapris-datasetet som vi kommer att använda genom hela denna lektion. Du har också visualiserat det med hjälp av `ggplot2`.💪\n",
+    "\n",
+    "Nu är du redo att fördjupa dig i regression för maskininlärning. I denna lektion kommer du att lära dig mer om två typer av regression: *grundläggande linjär regression* och *polynomisk regression*, tillsammans med lite av matematiken bakom dessa tekniker.\n",
+    "\n",
+    "> Genom hela denna kurs antar vi minimal kunskap om matematik och strävar efter att göra det tillgängligt för studenter från andra områden. Håll utkik efter anteckningar, 🧮 matematiska inslag, diagram och andra lärverktyg som hjälper till att förstå.\n",
+    "\n",
+    "#### Förberedelse\n",
+    "\n",
+    "Som en påminnelse, du laddar denna data för att kunna ställa frågor om den.\n",
+    "\n",
+    "-   När är den bästa tiden att köpa pumpor?\n",
+    "\n",
+    "-   Vilket pris kan jag förvänta mig för en låda med miniatyrpumpor?\n",
+    "\n",
+    "-   Bör jag köpa dem i halv-bushelkorgar eller i 1 1/9 bushel-lådor? Låt oss gräva djupare i denna data.\n",
+    "\n",
+    "I föregående lektion skapade du en `tibble` (en modern omarbetning av data frame) och fyllde den med en del av den ursprungliga datasetet, där du standardiserade prissättningen per bushel. Genom att göra det kunde du dock bara samla in cirka 400 datapunkter och endast för höstmånaderna. Kanske kan vi få lite mer detaljer om datans natur genom att städa upp den mer? Vi får se... 🕵️‍♀️\n",
+    "\n",
+    "För denna uppgift behöver vi följande paket:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) är en [samling av R-paket](https://www.tidyverse.org/packages) designade för att göra dataanalys snabbare, enklare och roligare!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) är ett [ramverk av paket](https://www.tidymodels.org/packages/) för modellering och maskininlärning.\n",
+    "\n",
+    "-   `janitor`: [janitor-paketet](https://github.com/sfirke/janitor) erbjuder enkla verktyg för att undersöka och städa smutsig data.\n",
+    "\n",
+    "-   `corrplot`: [corrplot-paketet](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) erbjuder ett visuellt utforskande verktyg för korrelationsmatriser som stödjer automatisk omordning av variabler för att hjälpa till att upptäcka dolda mönster bland variabler.\n",
+    "\n",
+    "Du kan installera dem med:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\n",
+    "\n",
+    "Skriptet nedan kontrollerar om du har de paket som krävs för att slutföra denna modul och installerar dem åt dig om de saknas.\n"
+   ],
+   "metadata": {
+    "id": "WqQPS1OAsg3H"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "tA4C2WN3skCf",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vi kommer senare att ladda dessa fantastiska paket och göra dem tillgängliga i vår nuvarande R-session. (Detta är bara för illustration, `pacman::p_load()` har redan gjort det åt dig)\n",
+    "\n",
+    "## 1. En linjär regressionslinje\n",
+    "\n",
+    "Som du lärde dig i Lektion 1 är målet med en linjär regressionsövning att kunna rita en *linje* *som* *passar bäst* för att:\n",
+    "\n",
+    "-   **Visa samband mellan variabler**. Visa relationen mellan variabler.\n",
+    "\n",
+    "-   **Göra förutsägelser**. Göra exakta förutsägelser om var en ny datapunkt skulle hamna i förhållande till den linjen.\n",
+    "\n",
+    "För att rita denna typ av linje använder vi en statistisk teknik som kallas **Minsta kvadratmetoden**. Termen `minsta kvadrat` betyder att alla datapunkter runt regressionslinjen kvadreras och sedan summeras. Idealiskt sett är den slutliga summan så liten som möjligt, eftersom vi vill ha ett lågt antal fel, eller `minsta kvadrat`. Därför är den linje som passar bäst den linje som ger oss det lägsta värdet för summan av de kvadrerade felen - därav namnet *minsta kvadratmetoden*.\n",
+    "\n",
+    "Vi gör detta eftersom vi vill modellera en linje som har den minsta kumulativa avvikelsen från alla våra datapunkter. Vi kvadrerar också termerna innan vi summerar dem eftersom vi är intresserade av deras storlek snarare än deras riktning.\n",
+    "\n",
+    "> **🧮 Visa mig matematiken**\n",
+    ">\n",
+    "> Denna linje, kallad *linjen som passar bäst*, kan uttryckas med [en ekvation](https://en.wikipedia.org/wiki/Simple_linear_regression):\n",
+    ">\n",
+    ">     Y = a + bX\n",
+    ">\n",
+    "> `X` är den '`förklarande variabeln` eller `prediktorn`'. `Y` är den '`beroende variabeln` eller `utfallet`'. Lutningen på linjen är `b` och `a` är skärningspunkten med y-axeln, vilket hänvisar till värdet av `Y` när `X = 0`.\n",
+    ">\n",
+    "\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/slope.png \"lutning = $y/x$\")\n",
+    "    Infografik av Jen Looper\n",
+    ">\n",
+    "> Först, beräkna lutningen `b`.\n",
+    ">\n",
+    "> Med andra ord, och med hänvisning till vår pumpadata och den ursprungliga frågan: \"förutsäg priset på en pumpa per skäppa beroende på månad\", skulle `X` hänvisa till priset och `Y` hänvisa till försäljningsmånaden.\n",
+    ">\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/calculation.png)\n",
+    "    Infografik av Jen Looper\n",
+    "> \n",
+    "> Beräkna värdet av Y. Om du betalar runt \\$4, måste det vara april!\n",
+    ">\n",
+    "> Matematiken som beräknar linjen måste visa lutningen på linjen, som också beror på skärningspunkten, eller var `Y` befinner sig när `X = 0`.\n",
+    ">\n",
+    "> Du kan se metoden för att beräkna dessa värden på webbplatsen [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html). Besök också [denna Minsta kvadrat-kalkylator](https://www.mathsisfun.com/data/least-squares-calculator.html) för att se hur värdena påverkar linjen.\n",
+    "\n",
+    "Inte så skrämmande, eller hur? 🤓\n",
+    "\n",
+    "#### Korrelation\n",
+    "\n",
+    "Ett annat begrepp att förstå är **Korrelationskoefficienten** mellan givna X- och Y-variabler. Med hjälp av ett spridningsdiagram kan du snabbt visualisera denna koefficient. Ett diagram med datapunkter som är snyggt uppradade har hög korrelation, men ett diagram med datapunkter spridda överallt mellan X och Y har låg korrelation.\n",
+    "\n",
+    "En bra linjär regressionsmodell är en som har en hög (närmare 1 än 0) korrelationskoefficient med hjälp av Minsta kvadratmetoden och en regressionslinje.\n"
+   ],
+   "metadata": {
+    "id": "cdX5FRpvsoP5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **2. En dans med data: skapa en data frame som ska användas för modellering**\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/janitor.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Konstverk av \\@allison_horst](../../../../../../2-Regression/3-Linear/images/janitor.jpg){width=\"700\"}-->\n"
+   ],
+   "metadata": {
+    "id": "WdUKXk7Bs8-V"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ladda upp nödvändiga bibliotek och dataset. Konvertera data till en data frame som innehåller ett urval av data:\n",
+    "\n",
+    "-   Ta endast med pumpor som är prissatta per skäppa\n",
+    "\n",
+    "-   Konvertera datumet till en månad\n",
+    "\n",
+    "-   Beräkna priset som ett genomsnitt av högsta och lägsta pris\n",
+    "\n",
+    "-   Konvertera priset för att återspegla prissättningen per skäppmängd\n",
+    "\n",
+    "> Vi gick igenom dessa steg i [föregående lektion](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb).\n"
+   ],
+   "metadata": {
+    "id": "fMCtu2G2s-p8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "library(lubridate)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ryMVZEEPtERn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "I äkta äventyrsanda, låt oss utforska [`janitor-paketet`](../../../../../../2-Regression/3-Linear/solution/R/github.com/sfirke/janitor) som erbjuder enkla funktioner för att undersöka och rengöra smutsiga data. Till exempel, låt oss titta på kolumnnamnen för våra data:\n"
+   ],
+   "metadata": {
+    "id": "xcNxM70EtJjb"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5XtpaIigtPfW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤔 Vi kan göra bättre. Låt oss göra dessa kolumnnamn `friendR` genom att konvertera dem till [snake_case](https://en.wikipedia.org/wiki/Snake_case)-konventionen med hjälp av `janitor::clean_names`. För att ta reda på mer om denna funktion: `?clean_names`\n"
+   ],
+   "metadata": {
+    "id": "IbIqrMINtSHe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Clean names to the snake_case convention\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  clean_names(case = \"snake\")\n",
+    "\n",
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "a2uYvclYtWvX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mycket tidyR 🧹! Nu, en dans med datan med hjälp av `dplyr`, precis som i den förra lektionen! 💃\n"
+   ],
+   "metadata": {
+    "id": "HfhnuzDDtaDd"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(variety, city_name, package, low_price, high_price, date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Extract the month from the dates to a new column\n",
+    "pumpkins <- pumpkins %>%\n",
+    "  mutate(date = mdy(date),\n",
+    "         month = month(date)) %>% \n",
+    "  select(-date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a new column for average Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(price = (low_price + high_price)/2)\n",
+    "\n",
+    "\n",
+    "# Retain only pumpkins with the string \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "  filter(str_detect(string = package, pattern = \"bushel\"))\n",
+    "\n",
+    "\n",
+    "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(price = case_when(\n",
+    "    str_detect(package, \"1 1/9\") ~ price/(1.1),\n",
+    "    str_detect(package, \"1/2\") ~ price*2,\n",
+    "    TRUE ~ price))\n",
+    "\n",
+    "# Relocate column positions\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(month, .before = variety)\n",
+    "\n",
+    "\n",
+    "# Display the first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "X0wU3gQvtd9f"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bra jobbat! 👌 Du har nu en ren och prydlig datamängd som du kan använda för att bygga din nya regressionsmodell!\n",
+    "\n",
+    "Vad sägs om ett spridningsdiagram?\n"
+   ],
+   "metadata": {
+    "id": "UpaIwaxqth82"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set theme\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Make a scatter plot of month and price\n",
+    "new_pumpkins %>% \n",
+    "  ggplot(mapping = aes(x = month, y = price)) +\n",
+    "  geom_point(size = 1.6)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "DXgU-j37tl5K"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ett spridningsdiagram påminner oss om att vi bara har månadsdata från augusti till december. Vi behöver troligen mer data för att kunna dra slutsatser på ett linjärt sätt.\n",
+    "\n",
+    "Låt oss titta på våra modelleringsdata igen:\n"
+   ],
+   "metadata": {
+    "id": "Ve64wVbwtobI"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Display first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HFQX2ng1tuSJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vad händer om vi ville förutsäga `price` för en pumpa baserat på kolumnerna `city` eller `package`, som är av typen text? Eller ännu enklare, hur skulle vi kunna hitta korrelationen (som kräver att båda dess indata är numeriska) mellan till exempel `package` och `price`? 🤷🤷\n",
+    "\n",
+    "Maskininlärningsmodeller fungerar bäst med numeriska egenskaper snarare än textvärden, så du behöver vanligtvis konvertera kategoriska egenskaper till numeriska representationer.\n",
+    "\n",
+    "Detta innebär att vi måste hitta ett sätt att omforma våra prediktorer för att göra dem enklare för en modell att använda effektivt, en process som kallas `feature engineering`.\n"
+   ],
+   "metadata": {
+    "id": "7hsHoxsStyjJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Förbereda data för modellering med recipes 👩‍🍳👨‍🍳\n",
+    "\n",
+    "Aktiviteter som omformar prediktorvärden för att göra dem enklare för en modell att använda effektivt kallas `feature engineering`.\n",
+    "\n",
+    "Olika modeller har olika krav på förbehandling. Till exempel kräver minsta kvadratmetoden `kodning av kategoriska variabler` såsom månad, sort och stad_namn. Detta innebär helt enkelt att `översätta` en kolumn med `kategoriska värden` till en eller flera `numeriska kolumner` som ersätter den ursprungliga.\n",
+    "\n",
+    "Till exempel, anta att din data innehåller följande kategoriska variabel:\n",
+    "\n",
+    "|  stad   |\n",
+    "|:-------:|\n",
+    "| Denver  |\n",
+    "| Nairobi |\n",
+    "|  Tokyo  |\n",
+    "\n",
+    "Du kan använda *ordinal kodning* för att ersätta varje kategori med ett unikt heltalsvärde, som detta:\n",
+    "\n",
+    "| stad |\n",
+    "|:----:|\n",
+    "|  0   |\n",
+    "|  1   |\n",
+    "|  2   |\n",
+    "\n",
+    "Och det är precis vad vi ska göra med vår data!\n",
+    "\n",
+    "I det här avsnittet ska vi utforska ett annat fantastiskt Tidymodels-paket: [recipes](https://tidymodels.github.io/recipes/) - som är utformat för att hjälpa dig att förbehandla din data **innan** du tränar din modell. Kärnan i en recipe är ett objekt som definierar vilka steg som ska tillämpas på en dataset för att göra den redo för modellering.\n",
+    "\n",
+    "Nu ska vi skapa en recipe som förbereder vår data för modellering genom att ersätta varje observation i prediktorkolumnerna med ett unikt heltal:\n"
+   ],
+   "metadata": {
+    "id": "AD5kQbcvt3Xl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\n",
+    "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "# Print out the recipe\n",
+    "pumpkins_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "BNaFKXfRt9TU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Fantastiskt! 👏 Vi har precis skapat vårt första recept som specificerar ett utfall (pris) och dess motsvarande prediktorer, och att alla prediktorkolumner ska kodas om till en uppsättning heltal 🙌! Låt oss snabbt bryta ner det:\n",
+    "\n",
+    "-   Anropet till `recipe()` med en formel berättar för receptet vilka *roller* variablerna har, med hjälp av `new_pumpkins`-data som referens. Till exempel har kolumnen `price` tilldelats rollen `outcome`, medan resten av kolumnerna har tilldelats rollen `predictor`.\n",
+    "\n",
+    "-   `step_integer(all_predictors(), zero_based = TRUE)` specificerar att alla prediktorer ska konverteras till en uppsättning heltal, där numreringen börjar på 0.\n",
+    "\n",
+    "Vi är säkra på att du kanske tänker något i stil med: \"Det här är så häftigt!! Men vad händer om jag behöver bekräfta att recepten gör exakt det jag förväntar mig? 🤔\"\n",
+    "\n",
+    "Det är en fantastisk tanke! Du förstår, när ditt recept är definierat kan du uppskatta de parametrar som krävs för att faktiskt förbehandla datan och sedan extrahera den bearbetade datan. Du behöver vanligtvis inte göra detta när du använder Tidymodels (vi kommer att se den normala konventionen om en liten stund -> `workflows`), men det kan vara användbart när du vill göra någon form av rimlighetskontroll för att bekräfta att recepten gör det du förväntar dig.\n",
+    "\n",
+    "För detta behöver du två ytterligare verb: `prep()` och `bake()`, och som alltid hjälper våra små R-vänner från [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) dig att förstå detta bättre!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Konstverk av \\@allison_horst](../../../../../../2-Regression/3-Linear/images/recipes.png){width=\"550\"}-->\n"
+   ],
+   "metadata": {
+    "id": "KEiO0v7kuC9O"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): beräknar de nödvändiga parametrarna från en träningsuppsättning som senare kan tillämpas på andra datamängder. Till exempel, för en given prediktorkolumn, vilken observation kommer att tilldelas heltalet 0, 1, 2 osv.\n",
+    "\n",
+    "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): tar ett förberett recept och tillämpar operationerna på vilken datamängd som helst.\n",
+    "\n",
+    "Med det sagt, låt oss förbereda och tillämpa våra recept för att verkligen bekräfta att bakom kulisserna kommer prediktorkolumnerna först att kodas innan en modell anpassas.\n"
+   ],
+   "metadata": {
+    "id": "Q1xtzebuuTCP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep the recipe\n",
+    "pumpkins_prep <- prep(pumpkins_recipe)\n",
+    "\n",
+    "# Bake the recipe to extract a preprocessed new_pumpkins data\n",
+    "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\n",
+    "\n",
+    "# Print out the baked data set\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FGBbJbP_uUUn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woo-hoo!🥳 Den bearbetade datan `baked_pumpkins` har alla sina prediktorer kodade, vilket bekräftar att de förbehandlingssteg som definierats som vårt recept fungerar som förväntat. Detta gör det svårare för dig att läsa men mycket mer begripligt för Tidymodels! Ta lite tid att ta reda på vilken observation som har mappats till ett motsvarande heltal.\n",
+    "\n",
+    "Det är också värt att nämna att `baked_pumpkins` är en data frame som vi kan utföra beräkningar på.\n",
+    "\n",
+    "Till exempel, låt oss försöka hitta en bra korrelation mellan två punkter i din data för att potentiellt bygga en bra prediktiv modell. Vi kommer att använda funktionen `cor()` för detta. Skriv `?cor()` för att ta reda på mer om funktionen.\n"
+   ],
+   "metadata": {
+    "id": "1dvP0LBUueAW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the correlation between the city_name and the price\n",
+    "cor(baked_pumpkins$city_name, baked_pumpkins$price)\n",
+    "\n",
+    "# Find the correlation between the package and the price\n",
+    "cor(baked_pumpkins$package, baked_pumpkins$price)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "3bQzXCjFuiSV"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Som det visar sig finns det bara en svag korrelation mellan Stad och Pris. Däremot finns det en något bättre korrelation mellan Paket och dess Pris. Det är väl logiskt, eller hur? Vanligtvis, ju större lådan med varor, desto högre pris.\n",
+    "\n",
+    "När vi ändå håller på, låt oss också försöka visualisera en korrelationsmatris för alla kolumner med hjälp av paketet `corrplot`.\n"
+   ],
+   "metadata": {
+    "id": "BToPWbgjuoZw"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the corrplot package\n",
+    "library(corrplot)\n",
+    "\n",
+    "# Obtain correlation matrix\n",
+    "corr_mat <- cor(baked_pumpkins %>% \n",
+    "                  # Drop columns that are not really informative\n",
+    "                  select(-c(low_price, high_price)))\n",
+    "\n",
+    "# Make a correlation plot between the variables\n",
+    "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZwAL3ksmutVR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Mycket bättre.\n",
+    "\n",
+    "En bra fråga att ställa om dessa data är: '`Vilket pris kan jag förvänta mig för ett visst pumpapaket?`' Låt oss sätta igång!\n",
+    "\n",
+    "> Note: När du **`bake()`** det förberedda receptet **`pumpkins_prep`** med **`new_data = NULL`**, extraherar du den bearbetade (dvs. kodade) träningsdatan. Om du hade en annan dataset, till exempel en testuppsättning, och ville se hur ett recept skulle förbehandla den, skulle du helt enkelt baka **`pumpkins_prep`** med **`new_data = test_set`**\n",
+    "\n",
+    "## 4. Bygg en linjär regressionsmodell\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Infografik av Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografik av Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "YqXjLuWavNxW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nu när vi har skapat ett recept och faktiskt bekräftat att datan kommer att förbehandlas korrekt, låt oss nu bygga en regressionsmodell för att besvara frågan: `Vilket pris kan jag förvänta mig för ett givet pumpapaket?`\n",
+    "\n",
+    "#### Träna en linjär regressionsmodell med träningsuppsättningen\n",
+    "\n",
+    "Som du kanske redan har räknat ut är kolumnen *price* den `beroende` variabeln medan kolumnen *package* är den `oberoende` variabeln.\n",
+    "\n",
+    "För att göra detta kommer vi först att dela upp datan så att 80% går till träningsuppsättningen och 20% till testuppsättningen, sedan definiera ett recept som kommer att koda den oberoende kolumnen till en uppsättning heltal, och därefter bygga en modellspecifikation. Vi kommer inte att förbereda och baka vårt recept eftersom vi redan vet att det kommer att förbehandla datan som förväntat.\n"
+   ],
+   "metadata": {
+    "id": "Pq0bSzCevW-h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\n",
+    "# Split the data into training and test sets\n",
+    "pumpkins_split <- new_pumpkins %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "\n",
+    "# Extract training and test data\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a recipe for preprocessing the data\n",
+    "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a linear model specification\n",
+    "lm_spec <- linear_reg() %>% \n",
+    "  set_engine(\"lm\") %>% \n",
+    "  set_mode(\"regression\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "CyoEh_wuvcLv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bra jobbat! Nu när vi har ett recept och en modellspecifikation behöver vi hitta ett sätt att kombinera dem till ett objekt som först förbehandlar data (prep+bake bakom kulisserna), tränar modellen på den förbehandlade datan och även möjliggör eventuella efterbehandlingsaktiviteter. Hur låter det för din sinnesro!🤩\n",
+    "\n",
+    "I Tidymodels kallas detta praktiska objekt för en [`workflow`](https://workflows.tidymodels.org/) och innehåller smidigt dina modelleringskomponenter! Det är vad vi skulle kalla *pipelines* i *Python*.\n",
+    "\n",
+    "Så låt oss paketera allt i ett workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "G3zF_3DqviFJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Hold modelling components in a workflow\n",
+    "lm_wf <- workflow() %>% \n",
+    "  add_recipe(lm_pumpkins_recipe) %>% \n",
+    "  add_model(lm_spec)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "lm_wf"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "T3olroU3v-WX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Dessutom kan ett arbetsflöde anpassas/tränas på ungefär samma sätt som en modell kan.\n"
+   ],
+   "metadata": {
+    "id": "zd1A5tgOwEPX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Train the model\n",
+    "lm_wf_fit <- lm_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the model coefficients learned \n",
+    "lm_wf_fit"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NhJagFumwFHf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Från modellens output kan vi se de koefficienter som lärdes under träningen. De representerar koefficienterna för den bästa linjen som ger oss det lägsta totala felet mellan den faktiska och den förutspådda variabeln.\n",
+    "\n",
+    "#### Utvärdera modellens prestanda med hjälp av testuppsättningen\n",
+    "\n",
+    "Det är dags att se hur modellen presterade 📏! Hur gör vi detta?\n",
+    "\n",
+    "Nu när vi har tränat modellen kan vi använda den för att göra förutsägelser för test_set med `parsnip::predict()`. Därefter kan vi jämföra dessa förutsägelser med de faktiska etikettvärdena för att utvärdera hur bra (eller inte!) modellen fungerar.\n",
+    "\n",
+    "Låt oss börja med att göra förutsägelser för testuppsättningen och sedan binda kolumnerna till testuppsättningen.\n"
+   ],
+   "metadata": {
+    "id": "_4QkGtBTwItF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\n",
+    "predictions <- lm_wf_fit %>% \n",
+    "  predict(new_data = pumpkins_test)\n",
+    "\n",
+    "\n",
+    "# Bind predictions to the test set\n",
+    "lm_results <- pumpkins_test %>% \n",
+    "  select(c(package, price)) %>% \n",
+    "  bind_cols(predictions)\n",
+    "\n",
+    "\n",
+    "# Print the first ten rows of the tibble\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "UFZzTG0gwTs9"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ja, du har precis tränat en modell och använt den för att göra förutsägelser! 🔮 Är den bra? Låt oss utvärdera modellens prestanda!\n",
+    "\n",
+    "I Tidymodels gör vi detta med `yardstick::metrics()`! För linjär regression fokuserar vi på följande mått:\n",
+    "\n",
+    "-   `Root Mean Square Error (RMSE)`: Kvadratroten av [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). Detta ger ett absolut mått i samma enhet som etiketten (i detta fall priset på en pumpa). Ju mindre värde, desto bättre modell (förenklat sett representerar det det genomsnittliga priset med vilket förutsägelserna är fel!)\n",
+    "\n",
+    "-   `Coefficient of Determination (vanligtvis känt som R-squared eller R2)`: Ett relativt mått där ett högre värde innebär en bättre passform för modellen. I grund och botten representerar detta mått hur mycket av variansen mellan förutsagda och faktiska etikettvärden modellen kan förklara.\n"
+   ],
+   "metadata": {
+    "id": "0A5MjzM7wW9M"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Evaluate performance of linear regression\n",
+    "metrics(data = lm_results,\n",
+    "        truth = price,\n",
+    "        estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "reJ0UIhQwcEH"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Där går modellens prestanda. Låt oss se om vi kan få en bättre indikation genom att visualisera ett spridningsdiagram av paketet och priset, och sedan använda de gjorda förutsägelserna för att lägga till en linje som bäst passar.\n",
+    "\n",
+    "Detta innebär att vi måste förbereda och bearbeta testuppsättningen för att koda paketkolumnen och sedan binda detta till de förutsägelser som vår modell har gjort.\n"
+   ],
+   "metadata": {
+    "id": "fdgjzjkBwfWt"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Encode package column\n",
+    "package_encode <- lm_pumpkins_recipe %>% \n",
+    "  prep() %>% \n",
+    "  bake(new_data = pumpkins_test) %>% \n",
+    "  select(package)\n",
+    "\n",
+    "\n",
+    "# Bind encoded package column to the results\n",
+    "lm_results <- lm_results %>% \n",
+    "  bind_cols(package_encode %>% \n",
+    "              rename(package_integer = package)) %>% \n",
+    "  relocate(package_integer, .after = package)\n",
+    "\n",
+    "\n",
+    "# Print new results data frame\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 5)\n",
+    "\n",
+    "\n",
+    "# Make a scatter plot\n",
+    "lm_results %>% \n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\n",
+    "  geom_point(size = 1.6) +\n",
+    "  # Overlay a line of best fit\n",
+    "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\n",
+    "  xlab(\"package\")\n",
+    "  \n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R0nw719lwkHE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bra! Som du kan se, generaliserar inte linjär regressionsmodellen särskilt väl relationen mellan ett paket och dess motsvarande pris.\n",
+    "\n",
+    "🎃 Grattis, du har precis skapat en modell som kan hjälpa till att förutsäga priset på några olika sorters pumpor. Din pumpaplantage inför högtiden kommer att bli fantastisk. Men du kan förmodligen skapa en ännu bättre modell!\n",
+    "\n",
+    "## 5. Bygg en polynomregressionsmodell\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Infografik av Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infografik av Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "HOCqJXLTwtWI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ibland kanske våra data inte har en linjär relation, men vi vill ändå förutsäga ett resultat. Polynomregression kan hjälpa oss att göra förutsägelser för mer komplexa icke-linjära samband.\n",
+    "\n",
+    "Ta till exempel sambandet mellan förpackning och pris i vår dataset med pumpor. Även om det ibland finns en linjär relation mellan variabler - ju större pumpan är i volym, desto högre pris - kan dessa samband ibland inte plottas som ett plan eller en rak linje.\n",
+    "\n",
+    "> ✅ Här är [några fler exempel](https://online.stat.psu.edu/stat501/lesson/9/9.8) på data som kan använda polynomregression\n",
+    ">\n",
+    "> Titta en gång till på sambandet mellan Sort och Pris i det tidigare diagrammet. Verkar det som att detta spridningsdiagram nödvändigtvis bör analyseras med en rak linje? Kanske inte. I detta fall kan du prova polynomregression.\n",
+    ">\n",
+    "> ✅ Polynom är matematiska uttryck som kan bestå av en eller flera variabler och koefficienter\n",
+    "\n",
+    "#### Träna en polynomregressionsmodell med hjälp av träningsdata\n",
+    "\n",
+    "Polynomregression skapar en *kurvad linje* för att bättre passa icke-linjär data.\n",
+    "\n",
+    "Låt oss se om en polynommodell presterar bättre när det gäller att göra förutsägelser. Vi kommer att följa en liknande procedur som vi gjorde tidigare:\n",
+    "\n",
+    "-   Skapa ett recept som specificerar de förbehandlingssteg som ska utföras på våra data för att göra dem redo för modellering, dvs: kodning av prediktorer och beräkning av polynom av grad *n*\n",
+    "\n",
+    "-   Bygga en modellspecifikation\n",
+    "\n",
+    "-   Paketera receptet och modellspecifikationen i ett arbetsflöde\n",
+    "\n",
+    "-   Skapa en modell genom att passa arbetsflödet\n",
+    "\n",
+    "-   Utvärdera hur väl modellen presterar på testdata\n",
+    "\n",
+    "Nu kör vi igång!\n"
+   ],
+   "metadata": {
+    "id": "VcEIpRV9wzYr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\r\n",
+    "poly_pumpkins_recipe <-\r\n",
+    "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+    "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+    "  step_poly(all_predictors(), degree = 4)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model specification\r\n",
+    "poly_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Bundle recipe and model spec into a workflow\r\n",
+    "poly_wf <- workflow() %>% \r\n",
+    "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+    "  add_model(poly_spec)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model\r\n",
+    "poly_wf_fit <- poly_wf %>% \r\n",
+    "  fit(data = pumpkins_train)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print learned model coefficients\r\n",
+    "poly_wf_fit\r\n",
+    "\r\n",
+    "  "
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "63n_YyRXw3CC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Utvärdera modellens prestanda\n",
+    "\n",
+    "👏👏Du har skapat en polynommodell, låt oss göra förutsägelser på testuppsättningen!\n"
+   ],
+   "metadata": {
+    "id": "-LHZtztSxDP0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make price predictions on test data\r\n",
+    "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+    "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+    "  relocate(.pred, .after = last_col())\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the results\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YUFpQ_dKxJGx"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woo-hoo, låt oss utvärdera hur modellen presterade på test_set med hjälp av `yardstick::metrics()`.\n"
+   ],
+   "metadata": {
+    "id": "qxdyj86bxNGZ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "metrics(data = poly_results, truth = price, estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "8AW5ltkBxXDm"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Mycket bättre prestanda.\n",
+    "\n",
+    "`rmse` minskade från cirka 7 till cirka 3, vilket indikerar en minskad felmarginal mellan det faktiska priset och det förutspådda priset. Du kan *ungefärligt* tolka detta som att felaktiga förutsägelser i genomsnitt är fel med cirka 3 dollar. `rsq` ökade från cirka 0,4 till 0,8.\n",
+    "\n",
+    "Alla dessa mätvärden visar att den polynomiska modellen presterar mycket bättre än den linjära modellen. Bra jobbat!\n",
+    "\n",
+    "Låt oss se om vi kan visualisera detta!\n"
+   ],
+   "metadata": {
+    "id": "6gLHNZDwxYaS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Bind encoded package column to the results\r\n",
+    "poly_results <- poly_results %>% \r\n",
+    "  bind_cols(package_encode %>% \r\n",
+    "              rename(package_integer = package)) %>% \r\n",
+    "  relocate(package_integer, .after = package)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print new results data frame\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+    "  xlab(\"package\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "A83U16frxdF1"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Du kan se en kurva som passar dina data bättre! 🤩\n",
+    "\n",
+    "Du kan göra detta ännu mjukare genom att skicka en polynomformel till `geom_smooth` så här:\n"
+   ],
+   "metadata": {
+    "id": "4U-7aHOVxlGU"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+    "  xlab(\"package\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5vzNT0Uexm-w"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mycket som en jämn kurva!🤩\n",
+    "\n",
+    "Så här gör du en ny förutsägelse:\n"
+   ],
+   "metadata": {
+    "id": "v9u-wwyLxq4G"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a hypothetical data frame\r\n",
+    "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+    "\r\n",
+    "# Make predictions using linear model\r\n",
+    "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Make predictions using polynomial model\r\n",
+    "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Return predictions in a list\r\n",
+    "list(\"linear model prediction\" = lm_pred, \r\n",
+    "     \"polynomial model prediction\" = poly_pred)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "jRPSyfQGxuQv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Prediktionen med `polynomial model` är rimlig, med tanke på spridningsdiagrammen för `price` och `package`! Och om detta är en bättre modell än den tidigare, baserat på samma data, behöver du planera för dessa dyrare pumpor!\n",
+    "\n",
+    "🏆 Bra jobbat! Du skapade två regressionsmodeller under en lektion. I den sista delen om regression kommer du att lära dig om logistisk regression för att bestämma kategorier.\n",
+    "\n",
+    "## **🚀Utmaning**\n",
+    "\n",
+    "Testa flera olika variabler i denna notebook för att se hur korrelationen påverkar modellens noggrannhet.\n",
+    "\n",
+    "## [**Quiz efter föreläsningen**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)\n",
+    "\n",
+    "## **Granskning & Självstudier**\n",
+    "\n",
+    "I denna lektion lärde vi oss om linjär regression. Det finns andra viktiga typer av regression. Läs om Stepwise, Ridge, Lasso och Elasticnet-tekniker. En bra kurs att studera för att lära dig mer är [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning).\n",
+    "\n",
+    "Om du vill lära dig mer om hur du använder det fantastiska Tidymodels-ramverket, kolla in följande resurser:\n",
+    "\n",
+    "-   Tidymodels webbplats: [Kom igång med Tidymodels](https://www.tidymodels.org/start/)\n",
+    "\n",
+    "-   Max Kuhn och Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+    "\n",
+    "###### **TACK TILL:**\n",
+    "\n",
+    "[Allison Horst](https://twitter.com/allison_horst?lang=en) för att ha skapat de fantastiska illustrationerna som gör R mer välkomnande och engagerande. Hitta fler illustrationer i hennes [galleri](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n"
+   ],
+   "metadata": {
+    "id": "8zOLOWqMxzk5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/2-Regression/3-Linear/solution/notebook.ipynb b/translations/sv/2-Regression/3-Linear/solution/notebook.ipynb
new file mode 100644
index 000000000..073f488b4
--- /dev/null
+++ b/translations/sv/2-Regression/3-Linear/solution/notebook.ipynb
@@ -0,0 +1,1113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linjär och Polynomisk Regression för Pumpapris - Lektion 3\n",
+    "\n",
+    "Ladda in nödvändiga bibliotek och dataset. Konvertera data till en dataram som innehåller ett urval av data:\n",
+    "\n",
+    "- Ta endast med pumpor som är prissatta per skäppa\n",
+    "- Konvertera datumet till en månad\n",
+    "- Beräkna priset som ett genomsnitt av högsta och lägsta priser\n",
+    "- Konvertera priset för att återspegla prissättningen per skäppmängd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ett spridningsdiagram påminner oss om att vi bara har månadsdata från augusti till december. Vi behöver förmodligen mer data för att kunna dra slutsatser på ett linjärt sätt.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Month', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('Month','Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Det verkar som att korrelationen är ganska liten, men det finns någon annan viktigare relation - eftersom prisnivåerna i diagrammet ovan verkar ha flera distinkta kluster. Låt oss skapa ett diagram som visar olika pumpasorter:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax=None\n",
+    "colors = ['red','blue','green','yellow']\n",
+    "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    ax = new_pumpkins[new_pumpkins['Variety']==var].plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAGKCAYAAAAVEBpAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcxklEQVR4nO3df5h3dV3n8ecrwEBEARm4bhW8UxEDfwDekqztpiiFmiGGCW5GZhduLRVpJaX5a7e9bP25musurAiRgqaopGEiF+TiKnrzQ4SQIEMSEG615BbzB/DeP8538sswv+c7c85n5vm4rrnm/PjOzIvx/r488znnfE6qCklSe36s7wCSpOWxwCWpURa4JDXKApekRlngktSoHdfyh+211161efPmtfyRktS8yy677BtVNTVz+5oW+ObNm9m6deta/khJal6Sr862fcEhlCQ7J/l8ki8muSbJ60bbX5vk5iRXjj6eNenQkqS5LeYI/PvAEVX1nSQ7AZckOX+0761V9abViydJmsuCBV7drZrfGa3uNPrw9k1J6tmirkJJskOSK4HbgQuq6tLRrpOSXJXk9CR7rFZISdJ9LarAq+ruqjoYeBhwWJLHAu8CHgkcDNwKvHm2r01yYpKtSbZu27ZtIqElSUu8Dryq/gW4GDiqqm4bFfs9wGnAYXN8zalVtaWqtkxN3ecqGEnSMi3mKpSpJLuPlncBngF8OcmmsZcdA1y9KgklSbNazFUom4Azk+xAV/gfqKqPJTkrycF0JzRvBF66aiklSfexmKtQrgIOmWX7i1YlkSRpUdb0TkwN3+ZTPt53hAXd+IZn9x1BGgQns5KkRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1KjmH6nWwiPAwMeASZo8j8AlqVEWuCQ1asECT7Jzks8n+WKSa5K8brR9zyQXJLl+9HmP1Y8rSZq2mCPw7wNHVNUTgIOBo5I8GTgFuLCq9gcuHK1LktbIggVene+MVncafRRwNHDmaPuZwHNXI6AkaXaLGgNPskOSK4HbgQuq6lJgn6q6FWD0ee85vvbEJFuTbN22bduEYkuSFlXgVXV3VR0MPAw4LMljF/sDqurUqtpSVVumpqaWGVOSNNOSrkKpqn8BLgaOAm5Lsglg9Pn2SYeTJM1tMVehTCXZfbS8C/AM4MvAecAJo5edAHx0lTJKkmaxmDsxNwFnJtmBrvA/UFUfS/JZ4ANJXgLcBDx/FXNKkmZYsMCr6irgkFm2fxN4+mqEkiQtzDsxJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhq1YIEn2TfJRUmuTXJNkt8ZbX9tkpuTXDn6eNbqx5UkTdtxEa+5C3h5VV2eZDfgsiQXjPa9taretHrxJElzWbDAq+pW4NbR8vYk1wIPXe1gkqT5LWkMPMlm4BDg0tGmk5JcleT0JHvM8TUnJtmaZOu2bdtWllaS9G8WXeBJHgB8CDi5qu4A3gU8EjiY7gj9zbN9XVWdWlVbqmrL1NTUyhNLkoBFFniSnejK+71VdS5AVd1WVXdX1T3AacBhqxdTkjTTYq5CCfBu4NqqesvY9k1jLzsGuHry8SRJc1nMVShPAV4EfCnJlaNtfwQcn+RgoIAbgZeuQj5J0hwWcxXKJUBm2fXXk48jSVos78SUpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJatSCBZ5k3yQXJbk2yTVJfme0fc8kFyS5fvR5j9WPK0matpgj8LuAl1fVTwJPBv5zkgOBU4ALq2p/4MLRuiRpjSxY4FV1a1VdPlreDlwLPBQ4Gjhz9LIzgeeuUkZJ0iyWNAaeZDNwCHApsE9V3QpdyQN7TzydJGlOiy7wJA8APgScXFV3LOHrTkyyNcnWbdu2LSejJGkWiyrwJDvRlfd7q+rc0ebbkmwa7d8E3D7b11bVqVW1paq2TE1NTSKzJInFXYUS4N3AtVX1lrFd5wEnjJZPAD46+XiSpLnsuIjXPAV4EfClJFeOtv0R8AbgA0leAtwEPH9VEkqSZrVggVfVJUDm2P30ycaRJC2Wd2JKUqMscElqlAUuSY2ywCWpURa4JDXKApekRi3mOnBJy7D5lI/3HWFRbnzDs/uOoGXyCFySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjXIyK0lNcHKw+/IIXJIaZYFLUqMWLPAkpye5PcnVY9tem+TmJFeOPp61ujElSTMt5gj8DOCoWba/taoOHn389WRjSZIWsmCBV9WngW+tQRZJ0hKsZAz8pCRXjYZY9pjrRUlOTLI1ydZt27at4MdJksYtt8DfBTwSOBi4FXjzXC+sqlOraktVbZmamlrmj5MkzbSsAq+q26rq7qq6BzgNOGyysSRJC1lWgSfZNLZ6DHD1XK+VJK2OBe/ETHI28FRgryRfA14DPDXJwUABNwIvXb2IkqTZLFjgVXX8LJvfvQpZJElL4J2YktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY1asMCTnJ7k9iRXj23bM8kFSa4ffd5jdWNKkmZazBH4GcBRM7adAlxYVfsDF47WJUlraMECr6pPA9+asflo4MzR8pnAcycbS5K0kOWOge9TVbcCjD7vPdcLk5yYZGuSrdu2bVvmj5MkzbTqJzGr6tSq2lJVW6amplb7x0nShrHcAr8tySaA0efbJxdJkrQYyy3w84ATRssnAB+dTBxJ0mIt5jLCs4HPAgck+VqSlwBvAI5Mcj1w5GhdkrSGdlzoBVV1/By7nj7hLJKkJfBOTElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGLfhU+vkkuRHYDtwN3FVVWyYRSpK0sBUV+MjTquobE/g+kqQlcAhFkhq10gIv4JNJLkty4iQCSZIWZ6VDKE+pqluS7A1ckOTLVfXp8ReMiv1EgP3222+FP06SNG1FR+BVdcvo8+3Ah4HDZnnNqVW1paq2TE1NreTHSZLGLLvAk+yaZLfpZeBngasnFUySNL+VDKHsA3w4yfT3eV9VfWIiqSRJC1p2gVfVV4AnTDCLJGkJvIxQkhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1akUFnuSoJNcluSHJKZMKJUla2LILPMkOwDuBZwIHAscnOXBSwSRJ81vJEfhhwA1V9ZWq+gFwDnD0ZGJJkhaSqlreFybHAkdV1a+P1l8E/FRVnTTjdScCJ45WDwCuW37cNbMX8I2+Q6wj/j4nx9/lZLXy+3x4VU3N3LjjCr5hZtl2n/83qKpTgVNX8HPWXJKtVbWl7xzrhb/PyfF3OVmt/z5XMoTyNWDfsfWHAbesLI4kabFWUuBfAPZP8hNJ7gccB5w3mViSpIUsewilqu5KchLwN8AOwOlVdc3EkvWrqSGfBvj7nBx/l5PV9O9z2ScxJUn98k5MSWqUBS5JjbLAJalRG77Akzxwnn37rWUWaS5JHpzkmCRP7DtLi5J8YGz5T2fs++TaJ5qMDV/gwMXTC0kunLHvI2uapHHr9U3ShyQfS/LY0fIm4Grg14CzkpzcZ7ZG7T+2fOSMffe5w7EVFvi97yjdc559Wti6fJP05Ceq6urR8ouBC6rqOcBP0RW5lma+y+2avRRvJbfSrxc1x/Js65rfunyT9OSHY8tPB04DqKrtSe7pJ1LT7p/kELqD1l1Gyxl97NJrshWwwGHvJC+j+x9yepnRukeNS7Mu3yQ9+ackv0U3ZcWhwCcAkuwC7NRnsEZ9HXjLLMvT603a8DfyJHnNfPur6nVrlaV1SS6ab39VPW2tsrQuyd7A64FNwDur6pOj7U8DnlhVb+ozn4Zhwxf4fJKcXFVv6zuHNC7JjlV1V985WpJkf+CNwKOALwG/V1U395tq5TyJOb+XLfwSLSTJkUku6DtHS5JcMrZ81ozdn1/jOOvB6cDHgV8ELgfe0W+cybDA5+dVKEuQ5Igkf5/kO0n+IsmBSbYCbwDe1Xe+xuw6tnzQjH3+u1y63arqtKq6rqreCGzuO9AkeBJzfo4vLc2b6Z6+9Fm6Z6V+DvjjqvofvaZqk1f0TNbOYyfV4d4n2amqy3tLtgIbvsCTbGf2N0SA+69xnNZVVV08Wv5Ikm2W97LtnuQYur+Sd0/yvNH2AA/qL1azZrvyZHq9gCPWPNEEeBJTE5PkK8DvjW160/h6VZ275qEaleQ98+2vqhevVZb1IMkDq+qOvnNMmgU+iyS7As8FXlhVz+45TjMWKJ2qKu8gVC+S/APwyqo6p+8sk2SBj4weC/cs4IXAUcCHgHOr6q96DaYNaeyGsllV1Vvm2697S/Jw4G3AA4DfqKob+k00GY6BJ0cCxwM/B1wEnAUc5p+oSzdL6RTwDeCSqvrHHiK1bLe+A6wnVfVV4JgkRwGfSfIF4J6x/b/QW7gV2PAFTvdMz/8L/PR0ySTxxNvyzFY6m4FXJnntevvzdZV9s6r+rO8Q60mSA4A/oHu/v5OxAm/Vhh9CGV1KdBxwLPAV4Bzg1VX18F6DrSNJ9gQ+VVWH9p2lFUku9/c1OUneAPwC8PKqOr/vPJOy4W/kqaorquoVVfVI4LXAIcD9kpyf5MR+060PVfUtvPlE/doCHLqeyhss8Hs9aKCqPlNVJwEPpTvhcXhfudaTJEcA/9x3jsY8Pskds3xsT7LuLodbA3tW1ff6DjFpjoHPMmVsVd1DNzb+N2sfp11JvsR9b4raE7gF+JW1T9S0L1XVIX2H0LBZ4PCgsbvc7sObT5bkecAPxtaL7mTcnT3lkaY9Isl5c+30KpR2PQj4eWYfoy3AAl+893vibWL+su8A68w2url61hULHG7yDsGJ8UTl5Bw8vZDkT6vqFWPrn6yqn+0lVbu2V9Xf9h1i0ixwZ3abpKn57iD07sEledTY8pHAK8bWfdTf0t3Yd4DVYIHDt9brRDc92IHuVmWPxFeXBx1LVFVznudqmQUOnwQuS/Kaqnpf32Ead2tVvb7vEOuED4jWgjb8nZgASR5KNzfwXnRPjhmfI8GTmIuU5IrZLn1Lsi9w3OhJKFqEJBczz5G2D4gWeAQOQFXdnOTjwJ8Az+FHBe5VKEvz9OmFJHsBz6ebKOxh+Htckqp6at8Z1pMkv1xVfzFafkpVfWZs30mtzjuz4Y/AkxxEd9R9C/C7VXVrz5GalWQ34Bi6KXkfDXwYeEFVPazXYA2a794E8C/DpRqfW2bmPDMtzzvjETh8EDi5qu5z12WSXb0JZUlup3ti+qvoppCt0WPBtHTPmWeffxkuXeZYnm29GRZ4d73tXkm2AFdV1Q+S7A2cDPwq8JD+ojXnj+hmdnwX8L4k7+85T7Ocj37iao7l2dabseEnswL+E3Al8A7gc0lOAK6lO9P/xB5zNaeq3lpVP0U3bWeAjwAPSfKKJI/uNVyDkuwwOpcwvX6/JCcmubbPXI16TJKrRvP1TC9Prx/Qd7jlcgw8+Tu6hzl8K8l+wA3Af6iqz/UcbV1I8ji6MfFfGk3Zq0VIchzwv4E7gevppjo+C/gC8F+q6vL+0rVn9Ei1OY2e2NMcC/y+JzSurqrH9plJSnI18NyquiHJocBn6S7F/HDP0TQgFnhyO91TeKYdN75eVb+95qEalWQ7s48nhu6p9A9c40jNmuXA4stV9Zg+M7Vs7N/m9AnL6X+nTf/b9CQm/P6M9ct6SbEOVJUP4p2cvWfMK/OA8XXnlVma9fpvc8MfgUtDlOQ18+wupyxYmiQ7012w8CjgKuD0qrqr31Qrt+ELPMlfMf8ty01O9N6HWf5MZbS+I3C/qvIvvglI8qSq+kLfOVoyuqT1h3RPpH8m8NWq+p1+U62cbyh4U98B1ouZf6aO7sz8TeCldHdlapmSHEh3fuZ44Nt0D+nV4h1YVY8DSPJuuhvOmrfhC3yuSd6nJ2AC1t0k8Kstye50N0L9CvA+4ElV9c0+M7VodOnb8aOPu4CHA1uq6sY+czXqh9MLVXVX0uzNl/ey4Qt83IwJmB6KR41LMvr9vRx4AXA6cEhVfbvfVG1K8v/oHvd3DnBsVV2f5B8t72V7QpLpOf9DN0XvHXgVStvmmIDpEU7AtCxfpXv24HuA7wIvGT/S8cqJJdlGN4vjPnRP4Lmehm/57ltV7dB3htWw4QscJ2CapDfyo5JZl5dtrZWqOjrJg4BfBF6X5FHA7kkOq6p1MX6rlfMqlOR36ca6d6Ubr30/cEFVPaLXYNrQkuxUVT8cW9+bbmjqeGDfqtq3t3AajA1f4NOSPILuzXEcsD/wGuDDVfX3vQZrSJK3z7ffu1oXb3SH8EeBs4GLauyNmuThrc7docna8AWeZL+qumnGtsfRlfkLnIBp8UYzOc6pqs5cqyytS/Jg4Fh+dEDxQeDsqrq012AaFAv83k/q+FBV/WLfmaRxSR5Cd3XUccDewDlV9cp+U2kILPCxB/HO9VBeLU6S8+bb712ty5fkAcDzgJcBm6pqn54jaQC8CmX+J3VoaQ4H/olu3PZSGn5U1RCM5u94Dt1w3lOATwB/CHyyz1waDo/Ak7vpJs0P3VN4vju9i4Yv8O9Dkh2AI+kK5/HAx+nGba/pNViDkrwPeAbwabqbeT5WVd/rN5WGZsMXuFZHkh+nK/I3Aq+vqnf0HKkpoxPC51bV9r6zaLgscE3UqLifTVfem4Hz6KbuvLnPXNJ6ZIFrYpKcCTwWOJ/uSomre44krWsWuCYmyT105xPg3ieEPZ8grQILXBqgJM+bb39VnbtWWTRcFrg0QKO/Zq4cfcCMpxxV1a+tdSYNjwUuDdBoRswX0D3D8aN0l2Pe0G8qDY0FLg1Ykl2Bo+nK/MHAK+d6ipQ2nh/rO4CkeX2P7hmYd9BNebxzv3E0JB6BSwOU5Gl019IfBnyK7rLMrf2m0tBY4NIAjU5iXgVcQndJ5r3eqM6tLnAyK2moXtx3AA2fR+DSwI2mkq2qunPBF2tD8SSmNFBJfiPJTcBXgZuSfDXJb/adS8NhgUsDlORVdHOBP7WqHlxVDwaeBjxztE9yCEUaoiTXAU+YOQd4kl2AL1bVo/tJpiHxCFwaqNke4FBV/wrc00McDZAFLg3T15I8febGJEcAt/aQRwPkEIo0QEkOopsD5RLgMrrrwJ9E92zMo31MncAClwZr9FDjFwIH0c1GeA3wXp+NqWneyCMNVFV9L8lFwO10R+DXWt4a5xG4NEBJHgj8H+CJdHOC/xjwBLrhlJdU1R39pdNQWODSACU5A7gReH1V3TPaFuCPgUdV1a/0l05DYYFLA5Tk+qraf6n7tLF4GaE0TFn4JdroLHBpmD6T5NWjYZN/k+SPgc/1lEkD4xCKNECjk5jvBg6lO4lZwCHAFXQnMb/dXzoNhQUuDViSRwIHMroOvKr+IcnJVfW2fpNpCCxwqTFJbqqq/frOof45Bi61xxOcAixwqUX+2SzAW+mlQUqyndmLOsAuaxxHA+UYuCQ1yiEUqSFJdk/yyr5zaBgscGmAkuyb5NQkH0vy60nun+TNwPXA3n3n0zA4Bi4N058Dfwt8CDiK7u7La4DHVdXX+wym4XAMXBqgJF+sqieMrd8G7FdV3+8xlgbGI3BpoJLswY+u+f46cP8kuwJU1bd6C6bB8AhcGqAkN9I9fX62m3aqqh6xtok0RBa4JDXKIRRpgJIcOt/+qrp8rbJouDwClwZo9DDjuVRVHbFmYTRYFrgkNcobeaQBSvIHY8vPn7Hvv619Ig2RBS4N03Fjy384Y99RaxlEw2WBS8OUOZZnW9cGZYFLw1RzLM+2rg3Kk5jSACW5G7iTH83//d3pXcDOVbVTX9k0HBa4JDXKG3mkAUqy53z7nQtF4BG4NEhJ/pFurDvAJuAWfnTy0rlQBFjg0uAluaKqDuk7h4bHq1Ck4fMoS7OywCWpUZ7ElAYoycvGVveesU5VvWWNI2mALHBpmHYbWz5txroEeBJTkprlGLgkNcoCl6RGWeCS1ChPYkoDleQA4ETgMaNN1wKnVdV1/aXSkHgELg1QksOBi4HtwKl0V6LcCVyU5Mk9RtOAeBWKNEBJzgf+tKounrH9Z4BTquqZvQTToFjg0gAl+fuqevQc+66rqgPWOpOGxyEUaZi2z7PvzjVLoUHzJKY0TPsmefss2wM8dK3DaJgscGmYfn+efVvXLIUGzTFwqTFJdqyqu/rOof45Bi4NUJJLxpbPmrH782scRwNlgUvDtOvY8kEz9gUJC1waqvnGNh33FOBJTGmodk9yDN1B1u5JnjfaHuBB/cXSkHgSUxqgJGcwz5F2Vb147dJoqCxwSWqUY+DSACV5R5L7PEYtyWOSfKqPTBoeC1wapq8DVyZ5IUCS+yf578B5wDt7TabBcAhFGqgkPwH8Gd0DjR8CfAD4r1X13V6DaTA8ApeGa/roake69+q1lrfGWeDSACV5FfAp4M+r6t8B/x44OsnfJjmw33QaCq8Dl4ZpCjikqrYDVNXNwLFJngl8CPjJPsNpGBwDlxqT5Mer6vt951D/PAKXBmiOucDH/faaBNGgWeDSMF3WdwANn0MoktQoj8ClAUpy3nz7q+oX1iqLhssCl4bpcOCfgLOBS3EOcM3CIRRpgJLsABwJHA88Hvg4cHZVXdNrMA2KN/JIA1RVd1fVJ6rqBODJwA3AxUl+q+doGhCHUKSBSvLjwLPpjsI3A28Hzu0zk4bFIRRpgJKcCTwWOB84p6qu7jmSBsgClwYoyT3AnaPV8TdpgKqqB659Kg2NBS5JjfIkpiQ1ygKXpEZZ4JLUKAtczUtycZKfm7Ht5CT/c5Ff//okz1jgNb+a5CErySlNmgWu9eBs4LgZ244bbZ9Xkh2q6tVVtdCT3n+V7rmU0mBY4FoPPgj8/OjGF5JspivbFybZmuSaJK+bfnGSG5O8OsklwPOTnJHk2NG+J44eW3ZZkr9Jsmm0bwvw3iRXJnl2kg+Pfb8jk3iDjdacBa7mVdU3gc8DR402HQe8H3hlVW2hm0vkZ5I8fuzLvldVP11V50xvSLIT8A7g2Kp6InA68CdV9UFgK/Afq+pg4K+Bn0wyNfrSFwPvWbX/QGkOFrjWi/FhlOnhk19KcjlwBXAQMP4w4PfP8j0OoLv78YIkVwKvAh4280XV3TxxFvDLSXanmznw/In8V0hL4FwoWi8+ArwlyaHALsA/A78HPKmq/jnJGcDOY6+/8z7fobvL8ZqqOnwRP+89wF8B3wP+sqruWkF2aVk8Ate6UFXfAS6mG/Y4G3ggXUl/O8k+wDMX8W2uA6aSHA7dkEqSg0b7tgO7jf28W4Bb6I7Sz5jMf4W0NB6Baz05m262vuOq6stJrgCuAb4CfGahL66qH4xOWL49yYPo3h9vG32PM4D/leRfgcOr6l+B9wJTVfV3q/EfIy3EuVCkZUryZ8AVVfXuvrNoY7LApWVIchndEM2RVfX9vvNoY7LAJalRnsSUpEZZ4JLUKAtckhplgUtSoyxwSWrU/wdO32Yxjk19aAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.2669192282197318\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n",
+    "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))\n",
+    "pie_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linjär regression\n",
+    "\n",
+    "Vi kommer att använda Scikit Learn för att träna en linjär regressionsmodell:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a3232f8dc0>]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(X_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Linjens lutning kan bestämmas från linjär regressionskoefficienter:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-0.01751876]), 21.133734359909326)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.coef_, lin_reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vi kan använda den tränade modellen för att förutsäga pris:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.64893156])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pumpkin price on programmer's day\n",
+    "\n",
+    "lin_reg.predict([[256]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Polynomregression\n",
+    "\n",
+    "Ibland är förhållandet mellan funktioner och resultatet i grunden icke-linjärt. Till exempel kan pumpapriser vara höga på vintern (månader=1,2), sedan sjunka under sommaren (månader=5-7) och sedan stiga igen. Linjär regression kan inte fånga detta förhållande korrekt.\n",
+    "\n",
+    "I sådana fall kan vi överväga att lägga till extra funktioner. Ett enkelt sätt är att använda polynom från indatafunktionerna, vilket resulterar i **polynomregression**. I Scikit Learn kan vi automatiskt förberäkna polynomfunktioner med hjälp av pipelines:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.73 (17.0%)\n",
+      "Model determination:  0.07639977655280217\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a323368ac0>]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)\n",
+    "\n",
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kodning av sorter\n",
+    "\n",
+    "I en idealisk värld vill vi kunna förutsäga priser för olika pumpasorter med samma modell. För att ta hänsyn till sorten måste vi först konvertera den till numerisk form, eller **koda**. Det finns flera sätt att göra detta:\n",
+    "\n",
+    "* Enkel numerisk kodning som skapar en tabell över olika sorter och sedan ersätter sortnamnet med ett index i den tabellen. Detta är inte den bästa idén för linjär regression, eftersom linjär regression tar hänsyn till det numeriska värdet av indexet, och det numeriska värdet sannolikt inte korrelerar numeriskt med priset.\n",
+    "* One-hot-kodning, som ersätter `Variety`-kolumnen med 4 olika kolumner, en för varje sort, som innehåller 1 om den motsvarande raden är av en viss sort, och 0 annars.\n",
+    "\n",
+    "Koden nedan visar hur vi kan one-hot-koda en sort:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FAIRYTALE</th>\n",
+       "      <th>MINIATURE</th>\n",
+       "      <th>MIXED HEIRLOOM VARIETIES</th>\n",
+       "      <th>PIE TYPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE\n",
+       "70            0          0                         0         1\n",
+       "71            0          0                         0         1\n",
+       "72            0          0                         0         1\n",
+       "73            0          0                         0         1\n",
+       "74            0          0                         0         1\n",
+       "...         ...        ...                       ...       ...\n",
+       "1738          0          1                         0         0\n",
+       "1739          0          1                         0         0\n",
+       "1740          0          1                         0         0\n",
+       "1741          0          1                         0         0\n",
+       "1742          0          1                         0         0\n",
+       "\n",
+       "[415 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(new_pumpkins['Variety'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Linjär regression på sort\n",
+    "\n",
+    "Vi kommer nu att använda samma kod som ovan, men istället för `DayOfYear` kommer vi att använda vår one-hot-kodade sort som indata:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety'])\n",
+    "y = new_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 5.24 (19.7%)\n",
+      "Model determination:  0.774085281105197\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_linear_regression(X,y):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "    lin_reg = LinearRegression()\n",
+    "    lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "    pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "    mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "    print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "    score = lin_reg.score(X_train,y_train)\n",
+    "    print('Model determination: ', score)\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vi kan också försöka använda andra funktioner på samma sätt och kombinera dem med numeriska funktioner, såsom `Month` eller `DayOfYear`:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.84 (10.5%)\n",
+      "Model determination:  0.9401096672643048\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['Package']))\n",
+    "y = new_pumpkins['Price']\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Polynomregression\n",
+    "\n",
+    "Polynomregression kan också användas med kategoriska funktioner som är one-hot-kodade. Koden för att träna polynomregression skulle i princip vara densamma som vi har sett ovan.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.23 (8.25%)\n",
+      "Model determination:  0.9652870784724543\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d77bd89ae7e79780c68c58bab91f13f8",
+   "translation_date": "2025-09-06T13:11:01+00:00",
+   "source_file": "2-Regression/3-Linear/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sv/2-Regression/4-Logistic/notebook.ipynb b/translations/sv/2-Regression/4-Logistic/notebook.ipynb
new file mode 100644
index 000000000..150d4345d
--- /dev/null
+++ b/translations/sv/2-Regression/4-Logistic/notebook.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pumpasorter och färg\n",
+    "\n",
+    "Ladda in nödvändiga bibliotek och dataset. Konvertera data till en dataframe som innehåller ett urval av data:\n",
+    "\n",
+    "Låt oss titta på sambandet mellan färg och sort\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bygg en logistisk regressionsmodell - Lektion 4\n",
+    "\n",
+    "![Infografik om logistisk vs. linjär regression](../../../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)\n",
+    "\n",
+    "#### **[Quiz före föreläsningen](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
+    "\n",
+    "#### Introduktion\n",
+    "\n",
+    "I denna sista lektion om regression, en av de grundläggande *klassiska* ML-teknikerna, ska vi titta på logistisk regression. Du kan använda denna teknik för att upptäcka mönster och förutsäga binära kategorier. Är detta godis choklad eller inte? Är denna sjukdom smittsam eller inte? Kommer denna kund att välja denna produkt eller inte?\n",
+    "\n",
+    "I denna lektion kommer du att lära dig:\n",
+    "\n",
+    "-   Tekniker för logistisk regression\n",
+    "\n",
+    "✅ Fördjupa din förståelse för att arbeta med denna typ av regression i detta [Learn-modul](https://learn.microsoft.com/training/modules/introduction-classification-models/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "## Förkunskaper\n",
+    "\n",
+    "Efter att ha arbetat med pumpadatan är vi nu tillräckligt bekanta med den för att inse att det finns en binär kategori som vi kan arbeta med: `Color`.\n",
+    "\n",
+    "Låt oss bygga en logistisk regressionsmodell för att förutsäga, baserat på vissa variabler, *vilken färg en given pumpa sannolikt har* (orange 🎃 eller vit 👻).\n",
+    "\n",
+    "> Varför pratar vi om binär klassificering i en lektion som handlar om regression? Endast av språklig bekvämlighet, eftersom logistisk regression [egentligen är en klassificeringsmetod](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), om än en linjärbaserad sådan. Lär dig om andra sätt att klassificera data i nästa lektionsgrupp.\n",
+    "\n",
+    "För denna lektion behöver vi följande paket:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) är en [samling av R-paket](https://www.tidyverse.org/packages) som är utformade för att göra datavetenskap snabbare, enklare och roligare!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) är ett [ramverk av paket](https://www.tidymodels.org/packages/) för modellering och maskininlärning.\n",
+    "\n",
+    "-   `janitor`: [janitor-paketet](https://github.com/sfirke/janitor) erbjuder enkla verktyg för att undersöka och rengöra smutsiga data.\n",
+    "\n",
+    "-   `ggbeeswarm`: [ggbeeswarm-paketet](https://github.com/eclarke/ggbeeswarm) tillhandahåller metoder för att skapa beeswarm-stil diagram med ggplot2.\n",
+    "\n",
+    "Du kan installera dem med:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+    "\n",
+    "Alternativt kan skriptet nedan kontrollera om du har de paket som krävs för att slutföra denna modul och installera dem åt dig om de saknas.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## **Definiera frågan**\n",
+    "\n",
+    "För våra ändamål kommer vi att uttrycka detta som en binär: 'Vit' eller 'Inte Vit'. Det finns också en kategori 'randig' i vår dataset, men det finns få exempel på den, så vi kommer inte att använda den. Den försvinner ändå när vi tar bort nullvärden från datasetet.\n",
+    "\n",
+    "> 🎃 Rolig fakta, vi kallar ibland vita pumpor för 'spök'-pumpor. De är inte så lätta att skära i, så de är inte lika populära som de orangea, men de ser häftiga ut! Så vi skulle också kunna omformulera vår fråga som: 'Spök' eller 'Inte Spök'. 👻\n",
+    "\n",
+    "## **Om logistisk regression**\n",
+    "\n",
+    "Logistisk regression skiljer sig från linjär regression, som du lärde dig om tidigare, på några viktiga sätt.\n",
+    "\n",
+    "#### **Binär klassificering**\n",
+    "\n",
+    "Logistisk regression erbjuder inte samma funktioner som linjär regression. Den förstnämnda ger en förutsägelse om en `binär kategori` (\"orange eller inte orange\") medan den senare kan förutsäga `kontinuerliga värden`, till exempel givet ursprunget av en pumpa och skördetiden, *hur mycket priset kommer att stiga*.\n",
+    "\n",
+    "![Infografik av Dasani Madipalli](../../../../../../2-Regression/4-Logistic/images/pumpkin-classifier.png)\n",
+    "\n",
+    "### Andra klassificeringar\n",
+    "\n",
+    "Det finns andra typer av logistisk regression, inklusive multinomial och ordinal:\n",
+    "\n",
+    "- **Multinomial**, som innebär att ha mer än en kategori - \"Orange, Vit och Randig\".\n",
+    "\n",
+    "- **Ordinal**, som innebär ordnade kategorier, användbart om vi vill ordna våra resultat logiskt, som våra pumpor som är ordnade efter ett begränsat antal storlekar (mini,sm,med,lg,xl,xxl).\n",
+    "\n",
+    "![Multinomial vs ordinal regression](../../../../../../2-Regression/4-Logistic/images/multinomial-vs-ordinal.png)\n",
+    "\n",
+    "#### **Variabler BEHÖVER INTE korrelera**\n",
+    "\n",
+    "Kommer du ihåg hur linjär regression fungerade bättre med mer korrelerade variabler? Logistisk regression är motsatsen - variablerna behöver inte stämma överens. Det fungerar för denna data som har ganska svaga korrelationer.\n",
+    "\n",
+    "#### **Du behöver mycket ren data**\n",
+    "\n",
+    "Logistisk regression ger mer exakta resultat om du använder mer data; vår lilla dataset är inte optimal för denna uppgift, så ha det i åtanke.\n",
+    "\n",
+    "✅ Fundera på vilka typer av data som skulle passa bra för logistisk regression\n",
+    "\n",
+    "## Övning - städa upp datan\n",
+    "\n",
+    "Först, städa upp datan lite, ta bort nullvärden och välj endast några av kolumnerna:\n",
+    "\n",
+    "1. Lägg till följande kod:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the core tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data and clean column names\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \n",
+    "  clean_names()\n",
+    "\n",
+    "# Select desired columns\n",
+    "pumpkins_select <- pumpkins %>% \n",
+    "  select(c(city_name, package, variety, origin, item_size, color)) \n",
+    "\n",
+    "# Drop rows containing missing values and encode color as factor (category)\n",
+    "pumpkins_select <- pumpkins_select %>% \n",
+    "  drop_na() %>% \n",
+    "  mutate(color = factor(color))\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins_select %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Du kan alltid ta en titt på din nya dataframe genom att använda funktionen [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) enligt nedan:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pumpkins_select %>% \n",
+    "  glimpse()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Låt oss bekräfta att vi faktiskt kommer att arbeta med ett binärt klassificeringsproblem:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Subset distinct observations in outcome column\n",
+    "pumpkins_select %>% \n",
+    "  distinct(color)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualisering - kategoriskt diagram\n",
+    "Nu har du laddat upp pumpadatan igen och rensat den för att bevara en dataset som innehåller några variabler, inklusive Färg. Låt oss visualisera dataframen i notebooken med hjälp av ggplot-biblioteket.\n",
+    "\n",
+    "Biblioteket ggplot erbjuder några smarta sätt att visualisera din data. Till exempel kan du jämföra distributionerna av data för varje Sort och Färg i ett kategoriskt diagram.\n",
+    "\n",
+    "1. Skapa ett sådant diagram genom att använda funktionen geombar, med vår pumpadata, och specificera en färgkartläggning för varje pumpakategori (orange eller vit):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Specify colors for each value of the hue variable\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# Create the bar plot\n",
+    "ggplot(pumpkins_select, aes(y = variety, fill = color)) +\n",
+    "  geom_bar(position = \"dodge\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(y = \"Variety\", fill = \"Color\") +\n",
+    "  theme_minimal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Genom att observera data kan du se hur färgdata relaterar till sort.\n",
+    "\n",
+    "✅ Givet detta kategoriska diagram, vilka intressanta undersökningar kan du föreställa dig?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Datapreparation: funktionskodning\n",
+    "\n",
+    "Vår pumpadataset innehåller strängvärden för alla sina kolumner. Att arbeta med kategoriska data är intuitivt för människor men inte för maskiner. Maskininlärningsalgoritmer fungerar bra med siffror. Därför är kodning ett mycket viktigt steg i datapreparationsfasen, eftersom det gör det möjligt för oss att omvandla kategoriska data till numeriska data utan att förlora någon information. Bra kodning leder till att bygga en bra modell.\n",
+    "\n",
+    "För funktionskodning finns det två huvudsakliga typer av kodare:\n",
+    "\n",
+    "1. Ordinal kodare: den passar bra för ordnade variabler, som är kategoriska variabler där deras data följer en logisk ordning, som kolumnen `item_size` i vårt dataset. Den skapar en mappning där varje kategori representeras av ett nummer, vilket är ordningen för kategorin i kolumnen.\n",
+    "\n",
+    "2. Kategorisk kodare: den passar bra för nominella variabler, som är kategoriska variabler där deras data inte följer en logisk ordning, som alla funktioner utom `item_size` i vårt dataset. Det är en one-hot-kodning, vilket innebär att varje kategori representeras av en binär kolumn: den kodade variabeln är lika med 1 om pumpan tillhör den sorten och 0 annars.\n",
+    "\n",
+    "Tidymodels erbjuder ytterligare ett smidigt paket: [recipes](https://recipes.tidymodels.org/) - ett paket för datapreparation. Vi kommer att definiera en `recipe` som specificerar att alla prediktorkolumner ska kodas till en uppsättning heltal, `prep` för att uppskatta de nödvändiga mängderna och statistiken som behövs för alla operationer och slutligen `bake` för att tillämpa beräkningarna på ny data.\n",
+    "\n",
+    "> Vanligtvis används recipes som en förprocessor för modellering där den definierar vilka steg som ska tillämpas på en dataset för att göra den redo för modellering. I det fallet är det **starkt rekommenderat** att du använder en `workflow()` istället för att manuellt uppskatta en recipe med prep och bake. Vi kommer att se allt detta om en liten stund.\n",
+    ">\n",
+    "> Men för tillfället använder vi recipes + prep + bake för att specificera vilka steg som ska tillämpas på en dataset för att göra den redo för dataanalys och sedan extrahera den förprocessade datan med de tillämpade stegen.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Preprocess and extract data to allow some data analysis\n",
+    "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>%\n",
+    "  # Define ordering for item_size column\n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  # Convert factors to numbers using the order defined above (Ordinal encoding)\n",
+    "  step_integer(item_size, zero_based = F) %>%\n",
+    "  # Encode all other predictors using one hot encoding\n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE) %>%\n",
+    "  prep(data = pumpkin_select) %>%\n",
+    "  bake(new_data = NULL)\n",
+    "\n",
+    "# Display the first few rows of preprocessed data\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "✅ Vilka är fördelarna med att använda en ordinal encoder för kolumnen Item Size?\n",
+    "\n",
+    "### Analysera relationer mellan variabler\n",
+    "\n",
+    "Nu när vi har förbehandlat vår data kan vi analysera relationerna mellan funktionerna och etiketten för att få en uppfattning om hur väl modellen kommer att kunna förutsäga etiketten baserat på funktionerna. Det bästa sättet att utföra denna typ av analys är att visualisera datan.  \n",
+    "Vi kommer återigen att använda ggplot-funktionen geom_boxplot_ för att visualisera relationerna mellan Item Size, Variety och Color i ett kategoriskt diagram. För att bättre kunna plotta datan kommer vi att använda den kodade kolumnen Item Size och den okodade kolumnen Variety.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define the color palette\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins_select_plot<-pumpkins_select\n",
+    "pumpkins_select_plot$item_size <- baked_pumpkins$item_size\n",
+    "\n",
+    "# Create the grouped box plot\n",
+    "ggplot(pumpkins_select_plot, aes(x = `item_size`, y = color, fill = color)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_grid(variety ~ ., scales = \"free_x\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(x = \"Item Size\", y = \"\") +\n",
+    "  theme_minimal() +\n",
+    "  theme(strip.text = element_text(size = 12)) +\n",
+    "  theme(axis.text.x = element_text(size = 10)) +\n",
+    "  theme(axis.title.x = element_text(size = 12)) +\n",
+    "  theme(axis.title.y = element_blank()) +\n",
+    "  theme(legend.position = \"bottom\") +\n",
+    "  guides(fill = guide_legend(title = \"Color\")) +\n",
+    "  theme(panel.spacing = unit(0.5, \"lines\"))+\n",
+    "  theme(strip.text.y = element_text(size = 4, hjust = 0)) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Använd ett swarm-diagram\n",
+    "\n",
+    "Eftersom Color är en binär kategori (Vit eller Inte), krävs 'ett [specialiserat tillvägagångssätt](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf) för visualisering'.\n",
+    "\n",
+    "Prova ett `swarm-diagram` för att visa fördelningen av färg i förhållande till item_size.\n",
+    "\n",
+    "Vi kommer att använda [ggbeeswarm-paketet](https://github.com/eclarke/ggbeeswarm) som erbjuder metoder för att skapa beeswarm-stil diagram med ggplot2. Beeswarm-diagram är ett sätt att plotta punkter som normalt skulle överlappa varandra så att de istället placeras bredvid varandra.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create beeswarm plots of color and item_size\n",
+    "baked_pumpkins %>% \n",
+    "  mutate(color = factor(color)) %>% \n",
+    "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\n",
+    "  geom_quasirandom() +\n",
+    "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\n",
+    "  theme(legend.position = \"none\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nu när vi har en uppfattning om sambandet mellan de binära färgkategorierna och den större gruppen av storlekar, låt oss utforska logistisk regression för att avgöra en pumpas sannolika färg.\n",
+    "\n",
+    "## Bygg din modell\n",
+    "\n",
+    "Välj de variabler du vill använda i din klassificeringsmodell och dela upp data i tränings- och testuppsättningar. [rsample](https://rsample.tidymodels.org/), ett paket i Tidymodels, erbjuder infrastruktur för effektiv datadelning och återprovtagning:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Split data into 80% for training and 20% for testing\n",
+    "set.seed(2056)\n",
+    "pumpkins_split <- pumpkins_select %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "# Print out the first 5 rows of the training set\n",
+    "pumpkins_train %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🙌 Vi är nu redo att träna en modell genom att passa träningsfunktionerna till träningsetiketten (färg).\n",
+    "\n",
+    "Vi börjar med att skapa ett recept som anger de förbehandlingssteg som ska utföras på vår data för att göra den redo för modellering, dvs: koda kategoriska variabler till en uppsättning heltal. Precis som `baked_pumpkins` skapar vi ett `pumpkins_recipe` men vi `prep` och `bake` inte eftersom det kommer att paketeras i ett arbetsflöde, vilket du kommer att se om bara några steg.\n",
+    "\n",
+    "Det finns ganska många sätt att specificera en logistisk regressionsmodell i Tidymodels. Se `?logistic_reg()` För tillfället kommer vi att specificera en logistisk regressionsmodell via den förvalda `stats::glm()`-motorn.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a recipe that specifies preprocessing steps for modelling\n",
+    "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  step_integer(item_size, zero_based = F) %>%  \n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)\n",
+    "\n",
+    "# Create a logistic model specification\n",
+    "log_reg <- logistic_reg() %>% \n",
+    "  set_engine(\"glm\") %>% \n",
+    "  set_mode(\"classification\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nu när vi har ett recept och en modellspecifikation behöver vi hitta ett sätt att kombinera dem i ett objekt som först förbehandlar data (prep+bake i bakgrunden), anpassar modellen på den förbehandlade datan och även möjliggör eventuella efterbehandlingsaktiviteter.\n",
+    "\n",
+    "I Tidymodels kallas detta praktiska objekt för en [`workflow`](https://workflows.tidymodels.org/) och det håller smidigt dina modellkomponenter.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Bundle modelling components in a workflow\n",
+    "log_reg_wf <- workflow() %>% \n",
+    "  add_recipe(pumpkins_recipe) %>% \n",
+    "  add_model(log_reg)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "log_reg_wf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Efter att ett arbetsflöde har *specificerats* kan en modell `tränas` med hjälp av [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html)-funktionen. Arbetsflödet kommer att uppskatta ett recept och förbehandla data innan träningen, så vi behöver inte göra det manuellt med prep och bake.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "wf_fit <- log_reg_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the trained workflow\n",
+    "wf_fit\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modellen visar de koefficienter som lärts in under träningen.\n",
+    "\n",
+    "Nu när vi har tränat modellen med träningsdata kan vi göra förutsägelser på testdata med [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Låt oss börja med att använda modellen för att förutsäga etiketter för vårt testset och sannolikheterna för varje etikett. När sannolikheten är mer än 0.5 är den förutsagda klassen `WHITE`, annars `ORANGE`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions for color and corresponding probabilities\n",
+    "results <- pumpkins_test %>% select(color) %>% \n",
+    "  bind_cols(wf_fit %>% \n",
+    "              predict(new_data = pumpkins_test)) %>%\n",
+    "  bind_cols(wf_fit %>%\n",
+    "              predict(new_data = pumpkins_test, type = \"prob\"))\n",
+    "\n",
+    "# Compare predictions\n",
+    "results %>% \n",
+    "  slice_head(n = 10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Väldigt bra! Detta ger några fler insikter i hur logistisk regression fungerar.\n",
+    "\n",
+    "### Bättre förståelse via en förvirringsmatris\n",
+    "\n",
+    "Att jämföra varje förutsägelse med dess motsvarande \"ground truth\"-värde är inte ett särskilt effektivt sätt att avgöra hur väl modellen förutspår. Lyckligtvis har Tidymodels några fler knep i rockärmen: [`yardstick`](https://yardstick.tidymodels.org/) - ett paket som används för att mäta modellers effektivitet med hjälp av prestationsmått.\n",
+    "\n",
+    "Ett prestationsmått som är kopplat till klassificeringsproblem är [`förvirringsmatrisen`](https://wikipedia.org/wiki/Confusion_matrix). En förvirringsmatris beskriver hur väl en klassificeringsmodell presterar. En förvirringsmatris sammanställer hur många exempel i varje klass som korrekt klassificerades av en modell. I vårt fall kommer den att visa hur många orangea pumpor som klassificerades som orangea och hur många vita pumpor som klassificerades som vita; förvirringsmatrisen visar också hur många som klassificerades i de **felaktiga** kategorierna.\n",
+    "\n",
+    "Funktionen [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) från yardstick beräknar denna kors-tabell över observerade och förutspådda klasser.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Confusion matrix for prediction results\n",
+    "conf_mat(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Låt oss tolka förvirringsmatrisen. Vår modell ska klassificera pumpor mellan två binära kategorier, kategori `vit` och kategori `inte-vit`.\n",
+    "\n",
+    "-   Om din modell förutspår att en pumpa är vit och den faktiskt tillhör kategorin 'vit' kallar vi det en `sann positiv`, vilket visas av siffran längst upp till vänster.\n",
+    "\n",
+    "-   Om din modell förutspår att en pumpa inte är vit och den faktiskt tillhör kategorin 'vit' kallar vi det en `falsk negativ`, vilket visas av siffran längst ner till vänster.\n",
+    "\n",
+    "-   Om din modell förutspår att en pumpa är vit och den faktiskt tillhör kategorin 'inte-vit' kallar vi det en `falsk positiv`, vilket visas av siffran längst upp till höger.\n",
+    "\n",
+    "-   Om din modell förutspår att en pumpa inte är vit och den faktiskt tillhör kategorin 'inte-vit' kallar vi det en `sann negativ`, vilket visas av siffran längst ner till höger.\n",
+    "\n",
+    "| Sanning |\n",
+    "|:-----:|\n",
+    "\n",
+    "\n",
+    "|               |        |       |\n",
+    "|---------------|--------|-------|\n",
+    "| **Förutspådd** | VIT | ORANGE |\n",
+    "| VIT           | TP     | FP    |\n",
+    "| ORANGE        | FN     | TN    |\n",
+    "\n",
+    "Som du kanske har gissat är det att föredra att ha ett större antal sanna positiva och sanna negativa samt ett lägre antal falska positiva och falska negativa, vilket innebär att modellen presterar bättre.\n",
+    "\n",
+    "Förvirringsmatrisen är användbar eftersom den ger upphov till andra mått som kan hjälpa oss att bättre utvärdera prestandan hos en klassificeringsmodell. Låt oss gå igenom några av dem:\n",
+    "\n",
+    "🎓 Precision: `TP/(TP + FP)` definieras som andelen förutspådda positiva som faktiskt är positiva. Kallas också [positivt prediktivt värde](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\").\n",
+    "\n",
+    "🎓 Recall: `TP/(TP + FN)` definieras som andelen positiva resultat av antalet prover som faktiskt var positiva. Kallas också `sensitivitet`.\n",
+    "\n",
+    "🎓 Specificitet: `TN/(TN + FP)` definieras som andelen negativa resultat av antalet prover som faktiskt var negativa.\n",
+    "\n",
+    "🎓 Noggrannhet: `TP + TN/(TP + TN + FP + FN)` Den procentandel av etiketter som förutspåtts korrekt för ett prov.\n",
+    "\n",
+    "🎓 F-mått: Ett viktat genomsnitt av precision och recall, där det bästa är 1 och det sämsta är 0.\n",
+    "\n",
+    "Låt oss beräkna dessa mått!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Combine metric functions and calculate them all at once\n",
+    "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\n",
+    "eval_metrics(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualisera ROC-kurvan för den här modellen\n",
+    "\n",
+    "Låt oss göra en ytterligare visualisering för att se den så kallade [`ROC-kurvan`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make a roc_curve\n",
+    "results %>% \n",
+    "  roc_curve(color, .pred_ORANGE) %>% \n",
+    "  autoplot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ROC-kurvor används ofta för att få en överblick över en klassificerares resultat i termer av dess sanna respektive falska positiva. ROC-kurvor visar vanligtvis `True Positive Rate`/Sensitivitet på Y-axeln och `False Positive Rate`/1-Specificitet på X-axeln. Därför spelar kurvans branthet och avståndet mellan mittlinjen och kurvan roll: du vill ha en kurva som snabbt går upp och över linjen. I vårt fall finns det falska positiva i början, och sedan går linjen upp och över på rätt sätt.\n",
+    "\n",
+    "Slutligen, låt oss använda `yardstick::roc_auc()` för att beräkna det faktiska Area Under the Curve. Ett sätt att tolka AUC är som sannolikheten att modellen rankar ett slumpmässigt positivt exempel högre än ett slumpmässigt negativt exempel.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate area under curve\n",
+    "results %>% \n",
+    "  roc_auc(color, .pred_ORANGE)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Resultatet är cirka `0.975`. Eftersom AUC sträcker sig från 0 till 1 vill du ha ett högt värde, eftersom en modell som är 100 % korrekt i sina förutsägelser kommer att ha en AUC på 1; i det här fallet är modellen *ganska bra*.\n",
+    "\n",
+    "I framtida lektioner om klassificering kommer du att lära dig hur du kan förbättra modellens resultat (till exempel hantering av obalanserad data i detta fall).\n",
+    "\n",
+    "## 🚀Utmaning\n",
+    "\n",
+    "Det finns mycket mer att utforska kring logistisk regression! Men det bästa sättet att lära sig är att experimentera. Hitta en dataset som lämpar sig för denna typ av analys och bygg en modell med den. Vad lär du dig? tips: prova [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) för intressanta dataset.\n",
+    "\n",
+    "## Granskning & Självstudier\n",
+    "\n",
+    "Läs de första sidorna av [denna artikel från Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) om några praktiska användningsområden för logistisk regression. Fundera på uppgifter som passar bättre för den ena eller andra typen av regressionsuppgifter som vi har studerat hittills. Vad skulle fungera bäst?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "langauge": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "feaf125f481a89c468fa115bf2aed580",
+   "translation_date": "2025-09-06T13:33:24+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
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diff --git a/translations/sv/2-Regression/4-Logistic/solution/notebook.ipynb b/translations/sv/2-Regression/4-Logistic/solution/notebook.ipynb
new file mode 100644
index 000000000..26f3545f3
--- /dev/null
+++ b/translations/sv/2-Regression/4-Logistic/solution/notebook.ipynb
@@ -0,0 +1,1257 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logistisk regression - Lektion 4\n",
+    "\n",
+    "Ladda in nödvändiga bibliotek och dataset. Konvertera data till en dataframe som innehåller ett urval av datan:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date   \n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17  \\\n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition   \n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN  \\\n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Origin</th>\n",
+       "      <th>Item Size</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>DELAWARE</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>VIRGINIA</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>36 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name       Package      Variety    Origin Item Size   Color\n",
+       "2  BALTIMORE  24 inch bins  HOWDEN TYPE  DELAWARE       med  ORANGE\n",
+       "3  BALTIMORE  24 inch bins  HOWDEN TYPE  VIRGINIA       med  ORANGE\n",
+       "4  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "5  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "6  BALTIMORE  36 inch bins  HOWDEN TYPE  MARYLAND       med  ORANGE"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select the columns we want to use\n",
+    "columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Drop rows with missing values\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Låt oss ta en titt på vår data!\n",
+    "\n",
+    "Genom att visualisera den med Seaborn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56d0c650>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 609.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# Specify colors for each values of the hue variable\n",
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# Plot a bar plot to visualize how many pumpkins of each variety are orange or white\n",
+    "sns.catplot(\n",
+    "    data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
+    "    palette=palette, \n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Databehandling\n",
+    "\n",
+    "Låt oss koda funktioner och etiketter för att bättre visualisera data och träna modellen\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the different values of the 'Item Size' column\n",
+    "pumpkins['Item Size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "# Encode the 'Item Size' column using ordinal encoding\n",
+    "item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
+    "ordinal_features = ['Item Size']\n",
+    "ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "# Encode all the other features using one-hot encoding\n",
+    "categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
+    "categorical_encoder = OneHotEncoder(sparse_output=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_MICHIGAN</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_MICHIGAN  cat__Origin_NEW JERSEY   \n",
+       "2                   0.0  ...                   0.0                     0.0  \\\n",
+       "3                   0.0  ...                   0.0                     0.0   \n",
+       "4                   0.0  ...                   0.0                     0.0   \n",
+       "5                   0.0  ...                   0.0                     0.0   \n",
+       "6                   0.0  ...                   0.0                     0.0   \n",
+       "\n",
+       "   cat__Origin_NEW YORK  cat__Origin_NORTH CAROLINA  cat__Origin_OHIO   \n",
+       "2                   0.0                         0.0               0.0  \\\n",
+       "3                   0.0                         0.0               0.0   \n",
+       "4                   0.0                         0.0               0.0   \n",
+       "5                   0.0                         0.0               0.0   \n",
+       "6                   0.0                         0.0               0.0   \n",
+       "\n",
+       "   cat__Origin_PENNSYLVANIA  cat__Origin_TENNESSEE  cat__Origin_TEXAS   \n",
+       "2                       0.0                    0.0                0.0  \\\n",
+       "3                       0.0                    0.0                0.0   \n",
+       "4                       0.0                    0.0                0.0   \n",
+       "5                       0.0                    0.0                0.0   \n",
+       "6                       0.0                    0.0                0.0   \n",
+       "\n",
+       "   cat__Origin_VERMONT  cat__Origin_VIRGINIA  \n",
+       "2                  0.0                   0.0  \n",
+       "3                  0.0                   1.0  \n",
+       "4                  0.0                   0.0  \n",
+       "5                  0.0                   0.0  \n",
+       "6                  0.0                   0.0  \n",
+       "\n",
+       "[5 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "ct = ColumnTransformer(transformers=[\n",
+    "     ('ord', ordinal_encoder, ordinal_features),\n",
+    "     ('cat', categorical_encoder, categorical_features)\n",
+    "     ])\n",
+    "# Get the encoded features as a pandas DataFrame\n",
+    "ct.set_output(transform='pandas')\n",
+    "encoded_features = ct.fit_transform(pumpkins)\n",
+    "encoded_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_NEW JERSEY  cat__Origin_NEW YORK   \n",
+       "2                   0.0  ...                     0.0                   0.0  \\\n",
+       "3                   0.0  ...                     0.0                   0.0   \n",
+       "4                   0.0  ...                     0.0                   0.0   \n",
+       "5                   0.0  ...                     0.0                   0.0   \n",
+       "6                   0.0  ...                     0.0                   0.0   \n",
+       "\n",
+       "   cat__Origin_NORTH CAROLINA  cat__Origin_OHIO  cat__Origin_PENNSYLVANIA   \n",
+       "2                         0.0               0.0                       0.0  \\\n",
+       "3                         0.0               0.0                       0.0   \n",
+       "4                         0.0               0.0                       0.0   \n",
+       "5                         0.0               0.0                       0.0   \n",
+       "6                         0.0               0.0                       0.0   \n",
+       "\n",
+       "   cat__Origin_TENNESSEE  cat__Origin_TEXAS  cat__Origin_VERMONT   \n",
+       "2                    0.0                0.0                  0.0  \\\n",
+       "3                    0.0                0.0                  0.0   \n",
+       "4                    0.0                0.0                  0.0   \n",
+       "5                    0.0                0.0                  0.0   \n",
+       "6                    0.0                0.0                  0.0   \n",
+       "\n",
+       "   cat__Origin_VIRGINIA  Color  \n",
+       "2                   0.0      0  \n",
+       "3                   1.0      0  \n",
+       "4                   0.0      0  \n",
+       "5                   0.0      0  \n",
+       "6                   0.0      0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "# Encode the 'Color' column using label encoding\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n",
+    "encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
+    "encoded_pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ORANGE', 'WHITE']"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the mapping between the encoded values and the original values\n",
+    "list(label_encoder.inverse_transform([0, 1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56322210>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1620 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=pumpkins,\n",
+    "    x=\"Item Size\", y=\"Color\", row='Variety',\n",
+    "    kind=\"box\", orient=\"h\",\n",
+    "    sharex=False, margin_titles=True,\n",
+    "    height=1.8, aspect=4, palette=palette,\n",
+    ")\n",
+    "# Defining axis labels \n",
+    "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
+    "g.set_titles(row_template=\"{row_name}\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Color', ylabel='ord__Item Size'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Suppressing warning message claiming that a portion of points cannot be placed into the plot due to the high number of data points\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')\n",
+    "\n",
+    "palette = {\n",
+    "    0: 'orange',\n",
+    "    1: 'wheat'\n",
+    "}\n",
+    "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Var uppmärksam**: Att ignorera varningar är INTE en bra praxis och bör undvikas när det är möjligt. Varningar innehåller ofta användbara meddelanden som hjälper oss att förbättra vår kod och lösa ett problem.  \n",
+    "Anledningen till att vi ignorerar just denna varning är för att säkerställa läsbarheten av diagrammet. Att plotta alla datapunkter med en reducerad markörstorlek, samtidigt som vi behåller konsekvensen i palettens färger, skapar en otydlig visualisering.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bygg din modell\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# X is the encoded features\n",
+    "X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
+    "# y is the encoded label\n",
+    "y = encoded_pumpkins['Color']\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.94      0.98      0.96       166\n",
+      "           1       0.85      0.67      0.75        33\n",
+      "\n",
+      "    accuracy                           0.92       199\n",
+      "   macro avg       0.89      0.82      0.85       199\n",
+      "weighted avg       0.92      0.92      0.92       199\n",
+      "\n",
+      "Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
+      " 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
+      " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+      " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
+      "F1-score:  0.7457627118644068\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Train a logistic regression model on the pumpkin dataset\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model and print the results\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('F1-score: ', f1_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[162,   4],\n",
+       "       [ 11,  22]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "y_scores = model.predict_proba(X_test)\n",
+    "# calculate ROC curve\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "# Plot the diagonal 50% line\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "# Plot the FPR and TPR achieved by our model\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9749908725812341\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate AUC score\n",
+    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+    "print(auc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "vscode": {
+   "interpreter": {
+    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "ef50cc584e0b79412610cc7da15e1f86",
+   "translation_date": "2025-09-06T13:27:40+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sv/3-Web-App/1-Web-App/notebook.ipynb b/translations/sv/3-Web-App/1-Web-App/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sv/3-Web-App/1-Web-App/solution/notebook.ipynb b/translations/sv/3-Web-App/1-Web-App/solution/notebook.ipynb
new file mode 100644
index 000000000..92963b2a4
--- /dev/null
+++ b/translations/sv/3-Web-App/1-Web-App/solution/notebook.ipynb
@@ -0,0 +1,267 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5fa2e8f4584c78250ca9729b46562ceb",
+   "translation_date": "2025-09-06T14:32:11+00:00",
+   "source_file": "3-Web-App/1-Web-App/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           datetime                  city state country     shape  \\\n",
+       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
+       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
+       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
+       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
+       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
+       "\n",
+       "   duration (seconds) duration (hours/min)  \\\n",
+       "0              2700.0           45 minutes   \n",
+       "1              7200.0              1-2 hrs   \n",
+       "2                20.0           20 seconds   \n",
+       "3                20.0             1/2 hour   \n",
+       "4               900.0           15 minutes   \n",
+       "\n",
+       "                                            comments date posted   latitude  \\\n",
+       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
+       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
+       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
+       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
+       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
+       "\n",
+       "    longitude  \n",
+       "0  -97.941111  \n",
+       "1  -98.581082  \n",
+       "2   -2.916667  \n",
+       "3  -96.645833  \n",
+       "4 -157.803611  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ufos = pd.read_csv('../data/ufos.csv')\n",
+    "ufos.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "\n",
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()\n",
+    "\n",
+    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufos.dropna(inplace=True)\n",
+    "\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "\n",
+    "X = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       1.00      0.02      0.05       250\n",
+      "           2       0.00      0.00      0.00         8\n",
+      "           3       0.94      1.00      0.97       131\n",
+      "           4       0.95      1.00      0.97      4743\n",
+      "\n",
+      "    accuracy                           0.95      5173\n",
+      "   macro avg       0.78      0.60      0.60      5173\n",
+      "weighted avg       0.95      0.95      0.93      5173\n",
+      "\n",
+      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
+      "Accuracy:  0.9512855209742895\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "model_filename = 'ufo-model.pkl'\n",
+    "pickle.dump(model, open(model_filename,'wb'))\n",
+    "\n",
+    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
+    "print(model.predict([[50,44,-12]]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/1-Introduction/notebook.ipynb b/translations/sv/4-Classification/1-Introduction/notebook.ipynb
new file mode 100644
index 000000000..79c3c6aff
--- /dev/null
+++ b/translations/sv/4-Classification/1-Introduction/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d544ef384b7ba73757d830a72372a7f2",
+   "translation_date": "2025-09-06T14:50:49+00:00",
+   "source_file": "4-Classification/1-Introduction/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiserade översättningar kan innehålla fel eller inexaktheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/sv/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
new file mode 100644
index 000000000..ebe6f38c1
--- /dev/null
+++ b/translations/sv/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -0,0 +1,727 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_10-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "2621e24705e8100893c9bf84e0fc8aef",
+   "translation_date": "2025-09-06T14:57:53+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Bygg en klassificeringsmodell: Utsökta asiatiska och indiska rätter\n"
+   ],
+   "metadata": {
+    "id": "ItETB4tSFprR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Introduktion till klassificering: Rensa, förbered och visualisera din data\n",
+    "\n",
+    "I dessa fyra lektioner kommer du att utforska ett grundläggande fokus inom klassisk maskininlärning - *klassificering*. Vi kommer att gå igenom hur man använder olika klassificeringsalgoritmer med en dataset om alla de fantastiska köken i Asien och Indien. Hoppas du är hungrig!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/pinch.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Fira pan-asiatiska kök i dessa lektioner! Bild av Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Fira pan-asiatiska kök i dessa lektioner! Bild av Jen Looper](../../../../../../4-Classification/1-Introduction/solution/R/images/pinch.png)-->\n",
+    "\n",
+    "Klassificering är en form av [övervakad inlärning](https://wikipedia.org/wiki/Supervised_learning) som har mycket gemensamt med regressionstekniker. Vid klassificering tränar du en modell för att förutsäga vilken `kategori` ett objekt tillhör. Om maskininlärning handlar om att förutsäga värden eller namn på saker med hjälp av datasets, så faller klassificering generellt sett in i två grupper: *binär klassificering* och *multiklassklassificering*.\n",
+    "\n",
+    "Kom ihåg:\n",
+    "\n",
+    "-   **Linjär regression** hjälpte dig att förutsäga relationer mellan variabler och göra exakta förutsägelser om var en ny datapunkt skulle hamna i förhållande till den linjen. Så du kunde förutsäga numeriska värden, som *vad priset på en pumpa skulle vara i september jämfört med december*, till exempel.\n",
+    "\n",
+    "-   **Logistisk regression** hjälpte dig att upptäcka \"binära kategorier\": vid denna prisnivå, *är denna pumpa orange eller inte-orange*?\n",
+    "\n",
+    "Klassificering använder olika algoritmer för att avgöra andra sätt att bestämma en datapunkts etikett eller klass. Låt oss arbeta med denna matlagningsdata för att se om vi, genom att observera en grupp ingredienser, kan avgöra dess ursprungskök.\n",
+    "\n",
+    "### [**Quiz före föreläsningen**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)\n",
+    "\n",
+    "### **Introduktion**\n",
+    "\n",
+    "Klassificering är en av de grundläggande aktiviteterna för forskare inom maskininlärning och dataanalytiker. Från grundläggande klassificering av ett binärt värde (\"är detta e-postmeddelande skräppost eller inte?\") till komplex bildklassificering och segmentering med hjälp av datorseende, är det alltid användbart att kunna sortera data i klasser och ställa frågor om den.\n",
+    "\n",
+    "För att uttrycka processen på ett mer vetenskapligt sätt skapar din klassificeringsmetod en prediktiv modell som gör det möjligt att kartlägga relationen mellan indata och utdata.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/binary-multiclass.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Binära vs. multiklassproblem för klassificeringsalgoritmer att hantera. Infografik av Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Innan vi börjar processen med att rensa vår data, visualisera den och förbereda den för våra ML-uppgifter, låt oss lära oss lite om de olika sätt som maskininlärning kan användas för att klassificera data.\n",
+    "\n",
+    "Härledd från [statistik](https://wikipedia.org/wiki/Statistical_classification), klassificering med klassisk maskininlärning använder egenskaper, såsom `rökare`, `vikt` och `ålder` för att avgöra *sannolikheten att utveckla X sjukdom*. Som en övervakad inlärningsteknik liknande de regressionsexempel du utförde tidigare, är din data märkt och ML-algoritmerna använder dessa etiketter för att klassificera och förutsäga klasser (eller 'egenskaper') i en dataset och tilldela dem till en grupp eller ett resultat.\n",
+    "\n",
+    "✅ Ta en stund och föreställ dig en dataset om matlagning. Vad skulle en multiklassmodell kunna svara på? Vad skulle en binär modell kunna svara på? Vad händer om du ville avgöra om ett visst kök sannolikt använder bockhornsklöver? Vad händer om du ville se om du, med en present av en matkasse full av stjärnanis, kronärtskockor, blomkål och pepparrot, kunde skapa en typisk indisk maträtt?\n",
+    "\n",
+    "### **Hej 'klassificerare'**\n",
+    "\n",
+    "Frågan vi vill ställa om denna matlagningsdataset är faktiskt en **multiklassfråga**, eftersom vi har flera potentiella nationella kök att arbeta med. Givet en grupp ingredienser, vilken av dessa många klasser passar datan in i?\n",
+    "\n",
+    "Tidymodels erbjuder flera olika algoritmer att använda för att klassificera data, beroende på vilken typ av problem du vill lösa. Under de kommande två lektionerna kommer du att lära dig om flera av dessa algoritmer.\n",
+    "\n",
+    "#### **Förkunskaper**\n",
+    "\n",
+    "För denna lektion behöver vi följande paket för att rensa, förbereda och visualisera vår data:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) är en [samling av R-paket](https://www.tidyverse.org/packages) designade för att göra dataanalys snabbare, enklare och roligare!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) är ett [ramverk av paket](https://www.tidymodels.org/packages/) för modellering och maskininlärning.\n",
+    "\n",
+    "-   `DataExplorer`: [DataExplorer-paketet](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) är avsett att förenkla och automatisera EDA-processen och rapportgenerering.\n",
+    "\n",
+    "-   `themis`: [themis-paketet](https://themis.tidymodels.org/) erbjuder extra receptsteg för att hantera obalanserad data.\n",
+    "\n",
+    "Du kan installera dem med:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Alternativt kontrollerar skriptet nedan om du har de paket som krävs för att slutföra denna modul och installerar dem åt dig om de saknas.\n"
+   ],
+   "metadata": {
+    "id": "ri5bQxZ-Fz_0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "KIPxa4elGAPI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vi kommer senare att ladda dessa fantastiska paket och göra dem tillgängliga i vår nuvarande R-session. (Detta är bara för illustration, `pacman::p_load()` har redan gjort det åt dig)\n"
+   ],
+   "metadata": {
+    "id": "YkKAxOJvGD4C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Övning - rensa och balansera din data\n",
+    "\n",
+    "Den första uppgiften, innan du börjar med detta projekt, är att rensa och **balansera** din data för att få bättre resultat.\n",
+    "\n",
+    "Låt oss bekanta oss med datan!🕵️\n"
+   ],
+   "metadata": {
+    "id": "PFkQDlk0GN5O"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Qccw7okxGT0S"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Intressant! Det verkar som att den första kolumnen är en slags `id`-kolumn. Låt oss ta reda på lite mer information om data.\n"
+   ],
+   "metadata": {
+    "id": "XrWnlgSrGVmR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Basic information about the data\r\n",
+    "df %>%\r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# Visualize basic information above\r\n",
+    "df %>% \r\n",
+    "  plot_intro(ggtheme = theme_light())"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "4UcGmxRxGieA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Från resultatet kan vi direkt se att vi har `2448` rader och `385` kolumner samt `0` saknade värden. Vi har också 1 diskret kolumn, *cuisine*.\n",
+    "\n",
+    "## Övning - lära känna kökstyper\n",
+    "\n",
+    "Nu börjar arbetet bli mer intressant. Låt oss utforska datafördelningen per kökstyp.\n"
+   ],
+   "metadata": {
+    "id": "AaPubl__GmH5"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Count observations per cuisine\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(n)\r\n",
+    "\r\n",
+    "# Plot the distribution\r\n",
+    "theme_set(theme_light())\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"cuisine\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FRsBVy5eGrrv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Det finns ett begränsat antal kök, men fördelningen av data är ojämn. Du kan fixa det! Utforska lite mer innan du gör det.\n",
+    "\n",
+    "Låt oss nu tilldela varje kök till sin egen tibble och ta reda på hur mycket data som finns tillgängligt (rader, kolumner) per kök.\n",
+    "\n",
+    "> En [tibble](https://tibble.tidyverse.org/) är en modern dataram.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_filter.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "vVvyDb1kG2in"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create individual tibble for the cuisines\r\n",
+    "thai_df <- df %>% \r\n",
+    "  filter(cuisine == \"thai\")\r\n",
+    "japanese_df <- df %>% \r\n",
+    "  filter(cuisine == \"japanese\")\r\n",
+    "chinese_df <- df %>% \r\n",
+    "  filter(cuisine == \"chinese\")\r\n",
+    "indian_df <- df %>% \r\n",
+    "  filter(cuisine == \"indian\")\r\n",
+    "korean_df <- df %>% \r\n",
+    "  filter(cuisine == \"korean\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Find out how much data is available per cuisine\r\n",
+    "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
+    "    \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
+    "    \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
+    "    \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
+    "    \"korean_df:\", dim(korean_df))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "0TvXUxD3G8Bk"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Övning - Upptäcka toppingredienser per kök med hjälp av dplyr**\n",
+    "\n",
+    "Nu kan du gräva djupare i datan och ta reda på vilka som är de typiska ingredienserna för varje kök. Du bör rensa bort återkommande data som skapar förvirring mellan köken, så låt oss lära oss mer om detta problem.\n",
+    "\n",
+    "Skapa en funktion `create_ingredient()` i R som returnerar en ingrediens-dataram. Denna funktion börjar med att ta bort en oanvändbar kolumn och sortera ingredienser baserat på deras antal.\n",
+    "\n",
+    "Den grundläggande strukturen för en funktion i R är:\n",
+    "\n",
+    "`myFunction <- function(arglist){`\n",
+    "\n",
+    "**`...`**\n",
+    "\n",
+    "**`return`**`(value)`\n",
+    "\n",
+    "`}`\n",
+    "\n",
+    "En enkel introduktion till R-funktioner finns [här](https://skirmer.github.io/presentations/functions_with_r.html#1).\n",
+    "\n",
+    "Låt oss sätta igång! Vi kommer att använda [dplyr-verb](https://dplyr.tidyverse.org/) som vi har lärt oss i våra tidigare lektioner. Som en sammanfattning:\n",
+    "\n",
+    "-   `dplyr::select()`: hjälper dig att välja vilka **kolumner** du vill behålla eller utesluta.\n",
+    "\n",
+    "-   `dplyr::pivot_longer()`: hjälper dig att \"förlänga\" data, vilket ökar antalet rader och minskar antalet kolumner.\n",
+    "\n",
+    "-   `dplyr::group_by()` och `dplyr::summarise()`: hjälper dig att hitta sammanfattande statistik för olika grupper och presentera dem i en snygg tabell.\n",
+    "\n",
+    "-   `dplyr::filter()`: skapar en delmängd av datan som endast innehåller rader som uppfyller dina villkor.\n",
+    "\n",
+    "-   `dplyr::mutate()`: hjälper dig att skapa eller ändra kolumner.\n",
+    "\n",
+    "Kolla in denna [*konst*-fyllda learnr-tutorial](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) av Allison Horst, som introducerar några användbara datahanteringsfunktioner i dplyr *(en del av Tidyverse)*.\n"
+   ],
+   "metadata": {
+    "id": "K3RF5bSCHC76"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Creates a functions that returns the top ingredients by class\r\n",
+    "\r\n",
+    "create_ingredient <- function(df){\r\n",
+    "  \r\n",
+    "  # Drop the id column which is the first colum\r\n",
+    "  ingredient_df = df %>% select(-1) %>% \r\n",
+    "  # Transpose data to a long format\r\n",
+    "    pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
+    "  # Find the top most ingredients for a particular cuisine\r\n",
+    "    group_by(ingredients) %>% \r\n",
+    "    summarise(n_instances = sum(count)) %>% \r\n",
+    "    filter(n_instances != 0) %>% \r\n",
+    "  # Arrange by descending order\r\n",
+    "    arrange(desc(n_instances)) %>% \r\n",
+    "    mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
+    "  \r\n",
+    "  \r\n",
+    "  return(ingredient_df)\r\n",
+    "} # End of function"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "uB_0JR82HTPa"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nu kan vi använda funktionen för att få en uppfattning om de tio mest populära ingredienserna per kök. Låt oss testa den med `thai_df`.\n"
+   ],
+   "metadata": {
+    "id": "h9794WF8HWmc"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Call create_ingredient and display popular ingredients\r\n",
+    "thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
+    "\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "agQ-1HrcHaEA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "I föregående avsnitt använde vi `geom_col()`, låt oss se hur du också kan använda `geom_bar` för att skapa stapeldiagram. Använd `?geom_bar` för vidare läsning.\n"
+   ],
+   "metadata": {
+    "id": "kHu9ffGjHdcX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a bar chart for popular thai cuisines\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10) %>% \r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "fb3Bx_3DHj6e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Låt oss göra samma sak för de japanska uppgifterna\n"
+   ],
+   "metadata": {
+    "id": "RHP_xgdkHnvM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
+    "create_ingredient(df = japanese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "019v8F0XHrRU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vad sägs om de kinesiska köken?\n"
+   ],
+   "metadata": {
+    "id": "iIGM7vO8Hu3v"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
+    "create_ingredient(df = chinese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lHd9_gd2HyzU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ir8qyQbNH1c7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Indian cuisines and make bar chart\r\n",
+    "create_ingredient(df = indian_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ApukQtKjH5FO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Slutligen, plotta de koreanska ingredienserna.\n"
+   ],
+   "metadata": {
+    "id": "qv30cwY1H-FM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Korean cuisines and make bar chart\r\n",
+    "create_ingredient(df = korean_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lumgk9cHIBie"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Från datavisualiseringarna kan vi nu ta bort de vanligaste ingredienserna som skapar förvirring mellan olika kök, med hjälp av `dplyr::select()`.\n",
+    "\n",
+    "Alla älskar ris, vitlök och ingefära!\n"
+   ],
+   "metadata": {
+    "id": "iO4veMXuIEta"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "iHJPiG6rIUcK"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Förbehandling av data med recept 👩‍🍳👨‍🍳 - Hantera obalanserad data ⚖️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n",
+    "\n",
+    "Eftersom den här lektionen handlar om kök, måste vi sätta `recept` i rätt sammanhang.\n",
+    "\n",
+    "Tidymodels erbjuder ännu ett smidigt paket: `recipes` - ett paket för förbehandling av data.\n"
+   ],
+   "metadata": {
+    "id": "kkFd-JxdIaL6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Låt oss titta på fördelningen av våra kök igen.\n"
+   ],
+   "metadata": {
+    "id": "6l2ubtTPJAhY"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "old_label_count <- df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "old_label_count"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "1e-E9cb7JDVi"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Som du kan se, finns det en ganska ojämn fördelning i antalet kök. Koreanska kök är nästan tre gånger fler än thailändska kök. Obalanserad data har ofta negativa effekter på modellens prestanda. Tänk på en binär klassificering. Om majoriteten av din data tillhör en klass, kommer en ML-modell att förutsäga den klassen oftare, bara för att det finns mer data för den. Att balansera data tar bort snedvridningar och hjälper till att eliminera denna obalans. Många modeller presterar bäst när antalet observationer är lika och har därför svårt med obalanserad data.\n",
+    "\n",
+    "Det finns huvudsakligen två sätt att hantera obalanserade datasätt:\n",
+    "\n",
+    "-   lägga till observationer till minoritetsklassen: `Över-sampling`, t.ex. med hjälp av en SMOTE-algoritm\n",
+    "\n",
+    "-   ta bort observationer från majoritetsklassen: `Under-sampling`\n",
+    "\n",
+    "Låt oss nu demonstrera hur man hanterar obalanserade datasätt med hjälp av ett `recept`. Ett recept kan ses som en ritning som beskriver vilka steg som ska tillämpas på ett datasätt för att göra det redo för dataanalys.\n"
+   ],
+   "metadata": {
+    "id": "soAw6826JKx9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HS41brUIJVJy"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Låt oss gå igenom våra förbehandlingssteg.\n",
+    "\n",
+    "-   Anropet till `recipe()` med en formel talar om för receptet vilka *roller* variablerna har, med hjälp av `df_select`-data som referens. Till exempel har kolumnen `cuisine` tilldelats rollen `outcome`, medan resten av kolumnerna har tilldelats rollen `predictor`.\n",
+    "\n",
+    "-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html) skapar en *specifikation* för ett receptsteg som syntetiskt genererar nya exempel för minoritetsklassen med hjälp av närmaste grannar till dessa fall.\n",
+    "\n",
+    "Nu, om vi vill se den förbehandlade datan, måste vi [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) och [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) vårt recept.\n",
+    "\n",
+    "`prep()`: uppskattar de nödvändiga parametrarna från en träningsuppsättning som senare kan tillämpas på andra dataset.\n",
+    "\n",
+    "`bake()`: tar ett förberett recept och tillämpar operationerna på valfritt dataset.\n"
+   ],
+   "metadata": {
+    "id": "Yb-7t7XcJaC8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep and bake the recipe\r\n",
+    "preprocessed_df <- cuisines_recipe %>% \r\n",
+    "  prep() %>% \r\n",
+    "  bake(new_data = NULL) %>% \r\n",
+    "  relocate(cuisine)\r\n",
+    "\r\n",
+    "# Display data\r\n",
+    "preprocessed_df %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Quick summary stats\r\n",
+    "preprocessed_df %>% \r\n",
+    "  introduce()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "9QhSgdpxJl44"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Låt oss nu kontrollera fördelningen av våra kök och jämföra dem med den obalanserade datan.\n"
+   ],
+   "metadata": {
+    "id": "dmidELh_LdV7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "new_label_count <- preprocessed_df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "list(new_label_count = new_label_count,\r\n",
+    "     old_label_count = old_label_count)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "aSh23klBLwDz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mums! Datan är ren, balanserad och väldigt läcker 😋!\n",
+    "\n",
+    "> Vanligtvis används ett recept som en förprocessor för modellering där det definierar vilka steg som ska tillämpas på en dataset för att göra den redo för modellering. I sådana fall används vanligtvis en `workflow()` (som vi redan har sett i våra tidigare lektioner) istället för att manuellt uppskatta ett recept.\n",
+    ">\n",
+    "> Därför behöver du vanligtvis inte **`prep()`** och **`bake()`** recept när du använder tidymodels, men de är användbara funktioner att ha i din verktygslåda för att bekräfta att recepten gör det du förväntar dig, som i vårt fall.\n",
+    ">\n",
+    "> När du **`bake()`** ett förberett recept med **`new_data = NULL`**, får du tillbaka den data som du angav när du definierade receptet, men som har genomgått förbearbetningsstegen.\n",
+    "\n",
+    "Låt oss nu spara en kopia av denna data för användning i framtida lektioner:\n"
+   ],
+   "metadata": {
+    "id": "HEu80HZ8L7ae"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Save preprocessed data\r\n",
+    "write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "cBmCbIgrMOI6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Denna nya CSV-fil finns nu i rotmappen för data.\n",
+    "\n",
+    "**🚀Utmaning**\n",
+    "\n",
+    "Detta kursmaterial innehåller flera intressanta dataset. Gå igenom `data`-mapparna och se om någon innehåller dataset som skulle passa för binär eller multi-klassklassificering? Vilka frågor skulle du ställa till detta dataset?\n",
+    "\n",
+    "## [**Quiz efter föreläsningen**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)\n",
+    "\n",
+    "## **Granskning & Självstudier**\n",
+    "\n",
+    "-   Kolla in [paketet themis](https://github.com/tidymodels/themis). Vilka andra tekniker kan vi använda för att hantera obalanserad data?\n",
+    "\n",
+    "-   Tidy models [referenswebbplats](https://www.tidymodels.org/start/).\n",
+    "\n",
+    "-   H. Wickham och G. Grolemund, [*R för Data Science: Visualisera, Modellera, Transformera, Städa och Importera Data*](https://r4ds.had.co.nz/).\n",
+    "\n",
+    "#### TACK TILL:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) för att ha skapat de fantastiska illustrationerna som gör R mer välkomnande och engagerande. Hitta fler illustrationer i hennes [galleri](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) och [Jen Looper](https://www.twitter.com/jenlooper) för att ha skapat den ursprungliga Python-versionen av denna modul ♥️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "WQs5621pMGwf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/1-Introduction/solution/notebook.ipynb b/translations/sv/4-Classification/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..5b1cf058b
--- /dev/null
+++ b/translations/sv/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,711 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Utsökta asiatiska och indiska maträtter\n",
+    "\n",
+    "## Introduktion\n",
+    "\n",
+    "Asiatisk och indisk mat är känd för sina rika smaker, aromatiska kryddor och mångsidiga ingredienser. Den här guiden ger dig en inblick i några av de mest populära rätterna och deras unika egenskaper.\n",
+    "\n",
+    "## Populära asiatiska rätter\n",
+    "\n",
+    "### Sushi\n",
+    "Sushi är en japansk rätt som består av vinägersmaksatt ris kombinerat med olika ingredienser som rå fisk, grönsaker och ibland frukt. Det finns flera olika typer av sushi, inklusive maki, nigiri och sashimi.\n",
+    "\n",
+    "### Pad Thai\n",
+    "Pad Thai är en klassisk thailändsk nudelrätt som vanligtvis tillagas med risnudlar, ägg, tofu, räkor eller kyckling, och smaksätts med tamarind, fisksås och lime. Den toppas ofta med hackade jordnötter och färska örter.\n",
+    "\n",
+    "### Pekinganka\n",
+    "Pekinganka är en kinesisk specialitet där ankan tillagas tills skinnet blir krispigt. Den serveras vanligtvis med tunna pannkakor, hoisinsås och skivade grönsaker.\n",
+    "\n",
+    "## Populära indiska rätter\n",
+    "\n",
+    "### Butter Chicken\n",
+    "Butter Chicken, eller smörkyckling, är en krämig och smakrik curry som görs med kyckling tillagad i en tomatbaserad sås med smör och grädde. Den serveras ofta med naanbröd eller basmatiris.\n",
+    "\n",
+    "### Biryani\n",
+    "Biryani är en aromatisk risrätt som tillagas med kryddor, kött (som kyckling, lamm eller get) och ibland grönsaker. Den är populär i hela Indien och finns i många regionala variationer.\n",
+    "\n",
+    "### Samosa\n",
+    "Samosa är ett friterat eller bakat bakverk fyllt med kryddad potatis, ärtor och ibland kött. Det är en populär indisk snacksrätt som ofta serveras med chutney.\n",
+    "\n",
+    "## Tips för att laga mat hemma\n",
+    "\n",
+    "[!TIP] Börja med att samla alla ingredienser innan du börjar laga mat. Många asiatiska och indiska rätter kräver specifika kryddor och såser som kan vara svåra att hitta i vanliga mataffärer.\n",
+    "\n",
+    "[!NOTE] Om du är nybörjare, börja med enklare recept som inte kräver för många steg eller ovanliga ingredienser.\n",
+    "\n",
+    "[!WARNING] Var försiktig med mängden chili och kryddor om du inte är van vid stark mat.\n",
+    "\n",
+    "## Avslutning\n",
+    "\n",
+    "Att laga asiatisk och indisk mat hemma kan vara både roligt och givande. Med rätt ingredienser och lite övning kan du skapa autentiska smaker som tar dig på en kulinarisk resa utan att lämna ditt kök.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Installera Imblearn som möjliggör SMOTE. Detta är ett Scikit-learn-paket som hjälper till att hantera obalanserad data vid klassificering. (https://imbalanced-learn.org/stable/)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
+      "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df  = pd.read_csv('../../data/cuisines.csv')"
+   ]
+  },
+  {
+   "source": [
+    "Denna datamängd inkluderar 385 kolumner som anger alla typer av ingredienser i olika kök från en given uppsättning kök.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0          65  indian       0         0      0           0      0   \n",
+       "1          66  indian       1         0      0           0      0   \n",
+       "2          67  indian       0         0      0           0      0   \n",
+       "3          68  indian       0         0      0           0      0   \n",
+       "4          69  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 385 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>67</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>69</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 385 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2448 entries, 0 to 2447\nColumns: 385 entries, Unnamed: 0 to zucchini\ndtypes: int64(384), object(1)\nmemory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "korean      799\n",
+       "indian      598\n",
+       "chinese     442\n",
+       "japanese    320\n",
+       "thai        289\n",
+       "Name: cuisine, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts()"
+   ]
+  },
+  {
+   "source": [
+    "Visa köken i ett stapeldiagram\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18799710>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "thai df: (289, 385)\njapanese df: (320, 385)\nchinese df: (442, 385)\nindian df: (598, 385)\nkorean df: (799, 385)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "thai_df = df[(df.cuisine == \"thai\")]\n",
+    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
+    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
+    "indian_df = df[(df.cuisine == \"indian\")]\n",
+    "korean_df = df[(df.cuisine == \"korean\")]\n",
+    "\n",
+    "print(f'thai df: {thai_df.shape}')\n",
+    "print(f'japanese df: {japanese_df.shape}')\n",
+    "print(f'chinese df: {chinese_df.shape}')\n",
+    "print(f'indian df: {indian_df.shape}')\n",
+    "print(f'korean df: {korean_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ingredient_df(df):\n",
+    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    # drop ingredients that have a 0 sum\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    # sort df\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
+    "    return ingredient_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d5917db00>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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8P/V/MSIWpuk6oGZHCo2IyRFRiIhC99377siqzMysRN6CqTOVPjq9kXduld/MO8ehV5dWZGZmW8lbMD0EjJHUD0DSXsATwAVp/jjg0TT9IPDZ1K+7pL5p3jmSdpe0B/Cxkv6tWQ4ck6bPK2l/A+izQ3tjZmbbLFfBFBFLgRuBuZIWATcDVwCfkbQY+DTF606knydLqqd4au7I9Cj3KcAfgXnATyLiqXY2+x3gs5KeAvqXtM8BjvTND2ZmXUsRkXUNFa/ngMEx4KLvZV2GWUXwNz9YE0l1EVFo3u6vJCqDoQP7Uut/bGZmZZGrU3lmZmYOJjMzyxUHk5mZ5YqDyczMcsXBZGZmueJgMjOzXHEwmZlZrjiYzMwsVxxMZmaWKw4mMzPLFX8lURnUr2ygZuLsrMswq2j+Dj1r4hGTmZnlSsUHk6RvSDo16zrMzKw8Kv5UXkRc39nbkNQ9Iho7eztmZlZhIyZJX5W0TNJjkn4u6RpJUySdl+Yvl/R1SQsk1Us6PLXvLel3kpZK+omklyT1T/M+JemP6YGAP5bUPbWvk/Qf6YGFx2e202ZmVaZigknSscC5wDDgDGCrh0slayPiaOA24JrU9jXgoYgYAswEDkzrPAIYC5wYEcOBRoqPbwfYA5gXEcMi4rEW6hkvqVZSbeP6hrLso5mZVdapvBOB+yJiA7BB0q9a6XdP+lkHfDxNnwR8DCAifivpb6n9FOAYYL4kgN2A1WleI/DL1oqJiMnAZCg+wXZ7dsjMzLZWScHUURvTz0ba3z8BUyPi31qYt8HXlczMul7FnMoDHgdGS+olqTcwahuXPR9A0keAd6f2B4HzJO2T5u0l6aAy1mxmZtuoYkZMETFf0ixgMfAKUA909OLO14GfS/o08CTwMvBGRKyVdB3wgKRuwCbg88BLZd8BMzPrEEVUzuURSb0jYp2k3YFHgPERsaADy/UEGiNis6TjgdvSzQ5lUSgUora2tlyrMzOrCpLqImKrG9kqZsSUTJZ0JNCL4rWhdkMpORCYnkZF/wQu66wCzcxsx1RUMEXEJ7dzueeBEWUux8zMOkEl3fxgZmZVwMFkZma54mAyM7NccTCZmVmuOJjMzCxXHExmZpYrDiYzM8sVB5OZmeVKRf2BbV7Vr2ygZuLsrMswqzrLbzoz6xKsE3jEZGZmueJgMjOzXKmIYJJ0uaQL0/QUSedt53qGS/poeaszM7NyqohrTBFxe5lWNRwoAP/dfIakXSJic5m2Y2Zm2ymXwZRGR9cAQfHBgH8C1kXEd5r1ux4YDewGPAH8a0SEpIeBecDJwJ7Apen9N4DdJJ0E/DtwBHAIcDDwP5I+A9xGMbw2A1dHxJzO3VszMyuVu1N5koYA1wEjI2IYcGUb3W+JiGMj4iiK4VT6uPVdIuJ9wFXA1yLin8D1wLSIGB4R01K/I4FTI+ITFJ9eGxExFPgEMFVSr1bqHC+pVlJt4/qOPkjXzMzak7tgAkYCMyJiLUBEvNZG35MlzZNUn5YbUjLvnvSzDqhpYx2zIuLNNH0S8J9pu89SfMT6oS0tFBGTI6IQEYXuu/dtZ5fMzKyjcnkqryPSSOZHQCEi/iJpEsUn2zbZmH420vZ+/qNzKjQzs+2RxxHTQ8AYSf0AJO3VSr+mEForqTfQkTv13gD6tDH/UWBc2u6hFB/JvqwjRZuZWXnkLpgiYilwIzBX0iLg5lb6vQ7cASwB7gfmd2D1c4AjJS2UNLaF+T8CuqVTg9OAiyNiYwv9zMyskygisq6h4hUKhaitrc26DDOziiKpLiIKzdtzN2IyM7Pq5mAyM7NccTCZmVmuOJjMzCxXHExmZpYrDiYzM8sVB5OZmeWKg8nMzHLFwWRmZrniYDIzs1yp2G8Xz5P6lQ3UTJyddRlm1omW33Rm1iVUDY+YzMwsVxxMZmaWKw4mMzPLlVwEk6QLJS2WtEjSXZJGp0emPyXp95L2ldRN0vOS9k7LdJP0gqS90+uXkuan14mpzyRJd0p6WNKfJU1I7TWSnpF0h6Slkh6QtFuad4ik30qqk/SopMOzOzJmZtUn82CSNAS4DhgZEcOAK4HHgOMiYgTwC+ArEfEW8J+kJ8wCpwKLImIN8H3guxFxLHAu8JOSTRwOnAa8D/iapB6pfTBwa0QMAV5PywFMBq6IiGOAayg+PLClusdLqpVU27i+YYePg5mZFeXhrryRwIyIWAsQEa9JGgpMkzQA2BV4MfW9E7gP+B5wCfDT1H4qxSfTNq3zXelx6wCz01NoN0paDeyb2l+MiIVpug6oScucAMwoWVfPloqOiMkUQ4yeAwb7aYtmZmWSh2BqyQ+BmyNilqQPAZMAIuIvkl6RNJLiCKhp9NSN4ghrQ+lKUriUPhq9kXf2uXn7bmk9r0fE8LLujZmZdVjmp/KAh4AxkvoBSNoL6AusTPMvatb/JxRP6c2IiMbU9gBwRVMHSdsVLBHxd+BFSWPSeiRp2Pasy8zMtk/mwRQRS4EbgbmSFgE3UxwhzZBUB6xttsgsoDfvnMYDmAAU0g0UTwOX70BJ44BLUy1LgbN3YF1mZraNFFFZl0ckFSje6PD+rGtp0nPA4Bhw0feyLsPMOpG/+aH8JNVFRKF5e16vMbVI0kTgs7xzbSkXhg7sS60/tGZmZZH5qbxtERE3RcRBEfFY1rWYmVnnqKhgMjOznZ+DyczMcsXBZGZmueJgMjOzXHEwmZlZrjiYzMwsVxxMZmaWKw4mMzPLFQeTmZnlSkV9JVFe1a9soGbi7KzLMLOdWDV9V59HTGZmlitVEUySJkh6RtLf0hfBttbvYkm3dGVtZma2pWo5lfc54NSIWJF1IWZm1radfsQk6XbgYOA3kr7YNCKSNEbSEkmLJD1Sssh7JP1W0vOSvpVJ0WZmVWynD6aIuBz4K3Ay8LeSWdcDp0XEMOCskvbhwFhgKDBW0gEtrVfSeEm1kmob1zd0TvFmZlVopw+mNjwOTJF0GdC9pP3BiGiIiA3A08BBLS0cEZMjohARhe679+2Ccs3MqkPVBlMaSV0HHADUSeqXZm0s6dZI9VyHMzPLhar9pSvpkIiYB8yTdAbFgDIzs4xV7YgJ+LakeklLgCeARVkXZGZmVTJiioiaNDklvYiIj7fQ9e35qc+oTi3MzMy2UhXB1NmGDuxLbRV9XYiZWWeq5lN5ZmaWQw4mMzPLFQeTmZnlioPJzMxyxcFkZma54mAyM7NccTCZmVmuOJjMzCxXHExmZpYr/uaHMqhf2UDNxNlZl2Fm1qWWd9I33njEZGZmueJgMjOzXKnaYJJ0saRb0vTlki7MuiYzM6vSa0ySttjviLg9q1rMzGxLFR1Mkr4KfApYA/wFqAMagPHArsALwKcjYr2kKcAGYATwOLC4ZD2TgHUR8R1J/wu4Hdib4qPVx0TEn7pqn8zMql3FnsqTdCxwLjAMOAMopFn3RMSxETEMeAa4tGSx/YETIuLqNlZ9N3BrWv4EYFUr2x8vqVZSbeP6hh3cGzMza1LJI6YTgfsiYgOwQdKvUvtRkm4A9gR6A/eXLDMjIhpbW6GkPsDAiLgXIK27RRExGZgM0HPA4NihPTEzs7dV7IipDVOAL0TEUODrQK+Sef/IpCIzM+uwSg6mx4HRknpJ6g2MSu19gFWSegDjtmWFEfEGsELSOQCSekravZxFm5lZ2yo2mCJiPjCL4k0MvwHqKd748FVgHsXgenY7Vv1pYIKkxcATwH5lKdjMzDpEEZV7eURS74hYl0Y1jwDjI2JBV9dRKBSitra2qzdrZlbRJNVFRKF5eyXf/AAwWdKRFK8jTc0ilMzMrLwqOpgi4pNZ12BmZuVVsdeYzMxs5+RgMjOzXHEwmZlZrlT0XXl5IekNYFnWdWyH/sDarIvYDq67a7nurlUtda8FiIjTm8+o6JsfcmRZS7c85p2kWtfddVx313LdXaucdftUnpmZ5YqDyczMcsXBVB6Tsy5gO7nuruW6u5br7lplq9s3P5iZWa54xGRmZrniYDIzs1xxMO0ASadLWibpBUkTs66nNZIOkDRH0tOSlkq6MrVPkrRS0sL0+mjWtTYnabmk+lRfbWrbS9LvJD2ffr476zpLSTqs5JgulPR3SVfl9XhLulPSaklLStpaPMYq+kH6zC+WdHTO6v62pGdTbfdK2jO110h6s+TY356zulv9bEj6t3S8l0k6LZuqW617WknNyyUtTO07drwjwq/teAHdgT8BBwO7AouAI7Ouq5VaBwBHp+k+wHPAkcAk4Jqs62un9uVA/2Zt3wImpumJwDezrrOdz8nLwEF5Pd7AB4CjgSXtHWPgoxSffybgOGBezur+CLBLmv5mSd01pf1yeLxb/Gykf6eLgJ7AoPQ7p3te6m42/z+A68txvD1i2n7vA16IiD9HxD+BXwBnZ1xTiyJiVaRHgkTxKb3PAAOzrWqHnA1MTdNTgXMyrKU9pwB/ioiXsi6kNRHxCPBas+bWjvHZwM+i6A/AnpIGdE2lW2qp7oh4ICI2p7d/APbv8sLa0crxbs3ZwC8iYmNEvAi8QPF3T5drq25JAs4Hfl6ObTmYtt9A4C8l71dQAb/sJdUAIyg+5RfgC+m0x515OyWWBPCApDpJ41PbvhGxKk2/DOybTWkdcgFb/mPN+/Fu0toxrqTP/SUUR3dNBkl6StJcSe/Pqqg2tPTZqJTj/X7glYh4vqRtu4+3g6mKSOoN/BK4KiL+DtwGHAIMB1ZRHIrnzUkRcTRwBvB5SR8onRnF8wa5/JsHSbsCZwEzUlMlHO+t5PkYt0bStcBm4O7UtAo4MCJGAFcD/yXpXVnV14KK/GyU+ARb/gdsh463g2n7rQQOKHm/f2rLJUk9KIbS3RFxD0BEvBIRjRHxFnAHGZ0iaEtErEw/VwP3UqzxlabTR+nn6uwqbNMZwIKIeAUq43iXaO0Y5/5zL+liYBQwLoUq6VTYq2m6juK1mkMzK7KZNj4blXC8dwE+DkxratvR4+1g2n7zgcGSBqX/GV8AzMq4phal87//F3gmIm4uaS+9NvAxYEnzZbMkaQ9JfZqmKV7YXkLxOF+Uul0E3JdNhe3a4n+ReT/ezbR2jGcBF6a7844DGkpO+WVO0unAV4CzImJ9Sfvekrqn6YOBwcCfs6lya218NmYBF0jqKWkQxbr/2NX1teNU4NmIWNHUsMPHO4u7O3aWF8U7lJ6j+L+Ba7Oup406T6J4KmYxsDC9PgrcBdSn9lnAgKxrbVb3wRTvSFoELG06xkA/4EHgeeD3wF5Z19pC7XsArwJ9S9pyebwphucqYBPFaxiXtnaMKd6Nd2v6zNcDhZzV/QLFazJNn/PbU99z02doIbAAGJ2zulv9bADXpuO9DDgjT3Wn9inA5c367tDx9lcSmZlZrvhUnpmZ5YqDyczMcsXBZGZmueJgMjOzXHEwmZlZrjiYzMwsVxxMZmaWK/8fnSxrKwF+wYgAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
+    "thai_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d389634a8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+   ],
+   "source": [
+    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
+    "japanese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18817f98>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
+    "chinese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1880a550>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
+    "indian_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1896ce80>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
+    "korean_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\n",
+    "labels_df = df.cuisine #.unique()\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "source": [
+    "Balansera data med SMOTE-översampling till den högsta klassen. Läs mer här: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oversample = SMOTE()\n",
+    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "new label count: korean      799\nchinese     799\njapanese    799\nindian      799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
+    "print(f'old label count: {df.cuisine.value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "source": [
+    "transformed_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "     cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  \\\n",
+       "0     indian       0         0      0           0      0             0   \n",
+       "1     indian       1         0      0           0      0             0   \n",
+       "2     indian       0         0      0           0      0             0   \n",
+       "3     indian       0         0      0           0      0             0   \n",
+       "4     indian       0         0      0           0      0             0   \n",
+       "...      ...     ...       ...    ...         ...    ...           ...   \n",
+       "3990    thai       0         0      0           0      0             0   \n",
+       "3991    thai       0         0      0           0      0             0   \n",
+       "3992    thai       0         0      0           0      0             0   \n",
+       "3993    thai       0         0      0           0      0             0   \n",
+       "3994    thai       0         0      0           0      0             0   \n",
+       "\n",
+       "      apricot  armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
+       "0           0         0          0  ...        0            0           0   \n",
+       "1           0         0          0  ...        0            0           0   \n",
+       "2           0         0          0  ...        0            0           0   \n",
+       "3           0         0          0  ...        0            0           0   \n",
+       "4           0         0          0  ...        0            0           0   \n",
+       "...       ...       ...        ...  ...      ...          ...         ...   \n",
+       "3990        0         0          0  ...        0            0           0   \n",
+       "3991        0         0          0  ...        0            0           0   \n",
+       "3992        0         0          0  ...        0            0           0   \n",
+       "3993        0         0          0  ...        0            0           0   \n",
+       "3994        0         0          0  ...        0            0           0   \n",
+       "\n",
+       "      whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                           0     0     0    0      0       0         0  \n",
+       "1                           0     0     0    0      0       0         0  \n",
+       "2                           0     0     0    0      0       0         0  \n",
+       "3                           0     0     0    0      0       0         0  \n",
+       "4                           0     0     0    0      0       1         0  \n",
+       "...                       ...   ...   ...  ...    ...     ...       ...  \n",
+       "3990                        0     0     0    0      0       0         0  \n",
+       "3991                        0     0     0    0      0       0         0  \n",
+       "3992                        0     0     0    0      0       0         0  \n",
+       "3993                        0     0     0    0      0       0         0  \n",
+       "3994                        0     0     0    0      0       0         0  \n",
+       "\n",
+       "[3995 rows x 381 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": [
+    "# export transformed data to new df for classification\n",
+    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
+    "transformed_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df.info()"
+   ]
+  },
+  {
+   "source": [
+    "Spara filen för framtida användning\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "1da12ed6d238756959b8de9cac2a35a2",
+   "translation_date": "2025-09-06T14:52:08+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/2-Classifiers-1/notebook.ipynb b/translations/sv/4-Classification/2-Classifiers-1/notebook.ipynb
new file mode 100644
index 000000000..8119e7f54
--- /dev/null
+++ b/translations/sv/4-Classification/2-Classifiers-1/notebook.ipynb
@@ -0,0 +1,41 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "68829b06b4dcd512d3327849191f4d7f",
+   "translation_date": "2025-09-06T14:32:40+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Bygg klassificeringsmodeller\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/sv/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
new file mode 100644
index 000000000..521b277d9
--- /dev/null
+++ b/translations/sv/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -0,0 +1,1298 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_11-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "6ea6a5171b1b99b7b5a55f7469c048d2",
+   "translation_date": "2025-09-06T14:37:24+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Bygg en klassificeringsmodell: Utsökta asiatiska och indiska rätter\n"
+   ],
+   "metadata": {
+    "id": "zs2woWv_HoE8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Klassificering av kök 1\n",
+    "\n",
+    "I den här lektionen ska vi utforska olika klassificeringsmetoder för att *förutsäga ett visst nationellt kök baserat på en grupp ingredienser.* Samtidigt kommer vi att lära oss mer om hur algoritmer kan användas för klassificeringsuppgifter.\n",
+    "\n",
+    "### [**Quiz före lektionen**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
+    "\n",
+    "### **Förberedelse**\n",
+    "\n",
+    "Den här lektionen bygger vidare på vår [föregående lektion](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb) där vi:\n",
+    "\n",
+    "-   Gjorde en mjuk introduktion till klassificeringar med hjälp av en dataset om alla fantastiska kök från Asien och Indien 😋.\n",
+    "\n",
+    "-   Utforskade några [dplyr-verb](https://dplyr.tidyverse.org/) för att förbereda och städa vår data.\n",
+    "\n",
+    "-   Skapade vackra visualiseringar med ggplot2.\n",
+    "\n",
+    "-   Visade hur man hanterar obalanserad data genom att förbehandla den med [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html).\n",
+    "\n",
+    "-   Demonstrerade hur man `prep` och `bake` vår recipe för att säkerställa att den fungerar som den ska.\n",
+    "\n",
+    "#### **Förkunskaper**\n",
+    "\n",
+    "För den här lektionen behöver vi följande paket för att städa, förbereda och visualisera vår data:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) är en [samling av R-paket](https://www.tidyverse.org/packages) som är utformade för att göra datavetenskap snabbare, enklare och roligare!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) är ett [ramverk av paket](https://www.tidymodels.org/packages/) för modellering och maskininlärning.\n",
+    "\n",
+    "-   `themis`: [themis-paketet](https://themis.tidymodels.org/) erbjuder extra steg för att hantera obalanserad data.\n",
+    "\n",
+    "-   `nnet`: [nnet-paketet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) tillhandahåller funktioner för att uppskatta feed-forward neurala nätverk med ett enda dolt lager, samt för multinomiala logistiska regressionsmodeller.\n",
+    "\n",
+    "Du kan installera dem som:\n"
+   ],
+   "metadata": {
+    "id": "iDFOb3ebHwQC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Alternativt kontrollerar skriptet nedan om du har de paket som krävs för att slutföra denna modul och installerar dem åt dig om de saknas.\n"
+   ],
+   "metadata": {
+    "id": "4V85BGCjII7F"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, here)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "an5NPyyKIKNR",
+    "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Dela upp data i tränings- och testuppsättningar.\n",
+    "\n",
+    "Vi börjar med att välja några steg från vår tidigare lektion.\n",
+    "\n",
+    "### Ta bort de vanligaste ingredienserna som skapar förvirring mellan olika kök, med hjälp av `dplyr::select()`.\n",
+    "\n",
+    "Alla älskar ris, vitlök och ingefära!\n"
+   ],
+   "metadata": {
+    "id": "0ax9GQLBINVv"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# Load the original cuisines data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
+    "  # Encode cuisine column as categorical\r\n",
+    "  mutate(cuisine = factor(cuisine))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Display distribution of cuisines\r\n",
+    "df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "New names:\n",
+      "* `` -> ...1\n",
+      "\n",
+      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
+      "\n",
+      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+      "\u001b[31mchr\u001b[39m   (1): cuisine\n",
+      "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
+      "\n",
+      "\n",
+      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
+      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 indian  0      0        0     0          0     0            0       0       \n",
+       "2 indian  1      0        0     0          0     0            0       0       \n",
+       "3 indian  0      0        0     0          0     0            0       0       \n",
+       "4 indian  0      0        0     0          0     0            0       0       \n",
+       "5 indian  0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       0    0   \n",
+       "2 0         ⋯ 0       0           0          0                       0    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     1      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   799\n",
+       "2 indian   598\n",
+       "3 chinese  442\n",
+       "4 japanese 320\n",
+       "5 thai     289"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 799 |\n",
+       "| indian   | 598 |\n",
+       "| chinese  | 442 |\n",
+       "| japanese | 320 |\n",
+       "| thai     | 289 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 799\\\\\n",
+       "\t indian   & 598\\\\\n",
+       "\t chinese  & 442\\\\\n",
+       "\t japanese & 320\\\\\n",
+       "\t thai     & 289\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>799</td></tr>\n",
+       "\t<tr><td>indian  </td><td>598</td></tr>\n",
+       "\t<tr><td>chinese </td><td>442</td></tr>\n",
+       "\t<tr><td>japanese</td><td>320</td></tr>\n",
+       "\t<tr><td>thai    </td><td>289</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 735
+    },
+    "id": "jhCrrH22IWVR",
+    "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Perfekt! Nu är det dags att dela upp datan så att 70 % går till träning och 30 % till testning. Vi kommer också att använda en `stratifieringsteknik` vid uppdelningen för att `behålla proportionen av varje kök` i tränings- och valideringsdatan.\n",
+    "\n",
+    "[rsample](https://rsample.tidymodels.org/), ett paket i Tidymodels, erbjuder infrastruktur för effektiv datauppdelning och resampling:\n"
+   ],
+   "metadata": {
+    "id": "AYTjVyajIdny"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# Load the core Tidymodels packages into R session\r\n",
+    "library(tidymodels)\r\n",
+    "\r\n",
+    "# Create split specification\r\n",
+    "set.seed(2056)\r\n",
+    "cuisines_split <- initial_split(data = df_select,\r\n",
+    "                                strata = cuisine,\r\n",
+    "                                prop = 0.7)\r\n",
+    "\r\n",
+    "# Extract the data in each split\r\n",
+    "cuisines_train <- training(cuisines_split)\r\n",
+    "cuisines_test <- testing(cuisines_split)\r\n",
+    "\r\n",
+    "# Print the number of cases in each split\r\n",
+    "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
+    "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
+    "\r\n",
+    "# Display the first few rows of the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Display distribution of cuisines in the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training cases: 1712\n",
+      "Test cases: 736"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 chinese 0      0        0     0          0     0            0       0       \n",
+       "2 chinese 0      0        0     0          0     0            0       0       \n",
+       "3 chinese 0      0        0     0          0     0            0       0       \n",
+       "4 chinese 0      0        0     0          0     0            0       0       \n",
+       "5 chinese 0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       1    0   \n",
+       "2 0         ⋯ 0       0           0          0                       1    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     0      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   559\n",
+       "2 indian   418\n",
+       "3 chinese  309\n",
+       "4 japanese 224\n",
+       "5 thai     202"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 559 |\n",
+       "| indian   | 418 |\n",
+       "| chinese  | 309 |\n",
+       "| japanese | 224 |\n",
+       "| thai     | 202 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 559\\\\\n",
+       "\t indian   & 418\\\\\n",
+       "\t chinese  & 309\\\\\n",
+       "\t japanese & 224\\\\\n",
+       "\t thai     & 202\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>559</td></tr>\n",
+       "\t<tr><td>indian  </td><td>418</td></tr>\n",
+       "\t<tr><td>chinese </td><td>309</td></tr>\n",
+       "\t<tr><td>japanese</td><td>224</td></tr>\n",
+       "\t<tr><td>thai    </td><td>202</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 535
+    },
+    "id": "w5FWIkEiIjdN",
+    "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Hantera obalanserad data\n",
+    "\n",
+    "Som du kanske har märkt i den ursprungliga datamängden såväl som i vår träningsuppsättning, finns det en ganska ojämn fördelning i antalet kök. Koreanska kök är *nästan* tre gånger fler än thailändska kök. Obalanserad data har ofta negativa effekter på modellens prestanda. Många modeller fungerar bäst när antalet observationer är lika och har därför svårt att hantera obalanserad data.\n",
+    "\n",
+    "Det finns huvudsakligen två sätt att hantera obalanserade datamängder:\n",
+    "\n",
+    "-   lägga till observationer till minoritetsklassen: `Över-sampling`, t.ex. med en SMOTE-algoritm som syntetiskt genererar nya exempel för minoritetsklassen med hjälp av närmaste grannar till dessa fall.\n",
+    "\n",
+    "-   ta bort observationer från majoritetsklassen: `Under-sampling`\n",
+    "\n",
+    "I vår tidigare lektion visade vi hur man hanterar obalanserade datamängder med hjälp av ett `recept`. Ett recept kan ses som en ritning som beskriver vilka steg som ska tillämpas på en datamängd för att göra den redo för dataanalys. I vårt fall vill vi ha en jämn fördelning av antalet kök i vår `träningsuppsättning`. Låt oss sätta igång direkt.\n"
+   ],
+   "metadata": {
+    "id": "daBi9qJNIwqW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing training data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "# Print recipe\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Data Recipe\n",
+       "\n",
+       "Inputs:\n",
+       "\n",
+       "      role #variables\n",
+       "   outcome          1\n",
+       " predictor        380\n",
+       "\n",
+       "Operations:\n",
+       "\n",
+       "SMOTE based on cuisine"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 200
+    },
+    "id": "Az6LFBGxI1X0",
+    "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Du kan självklart gå vidare och bekräfta (genom att förbereda och baka) att receptet fungerar som du förväntar dig - alla kökskategorier har `559` observationer.\n",
+    "\n",
+    "Eftersom vi kommer att använda detta recept som en förprocessor för modellering, kommer en `workflow()` att hantera all förberedelse och bakning åt oss, så vi behöver inte manuellt uppskatta receptet.\n",
+    "\n",
+    "Nu är vi redo att träna en modell 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Välja din klassificerare\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/parsnip.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "NBL3PqIWJBBB"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nu måste vi bestämma vilken algoritm vi ska använda för uppgiften 🤔.\n",
+    "\n",
+    "I Tidymodels erbjuder [`parsnip-paketet`](https://parsnip.tidymodels.org/index.html) ett konsekvent gränssnitt för att arbeta med modeller över olika motorer (paket). Se gärna dokumentationen för parsnip för att utforska [modelltyper och motorer](https://www.tidymodels.org/find/parsnip/#models) samt deras motsvarande [modellargument](https://www.tidymodels.org/find/parsnip/#model-args). Utbudet kan verka överväldigande vid första anblicken. Till exempel inkluderar följande metoder alla klassificeringstekniker:\n",
+    "\n",
+    "-   C5.0 Regelbaserade klassificeringsmodeller\n",
+    "\n",
+    "-   Flexibla diskriminantmodeller\n",
+    "\n",
+    "-   Linjära diskriminantmodeller\n",
+    "\n",
+    "-   Regulariserade diskriminantmodeller\n",
+    "\n",
+    "-   Logistiska regressionsmodeller\n",
+    "\n",
+    "-   Multinomiala regressionsmodeller\n",
+    "\n",
+    "-   Naiva Bayes-modeller\n",
+    "\n",
+    "-   Supportvektormaskiner\n",
+    "\n",
+    "-   Närmaste grannar\n",
+    "\n",
+    "-   Beslutsträd\n",
+    "\n",
+    "-   Ensemblemetoder\n",
+    "\n",
+    "-   Neurala nätverk\n",
+    "\n",
+    "Listan fortsätter!\n",
+    "\n",
+    "### **Vilken klassificerare ska man välja?**\n",
+    "\n",
+    "Så, vilken klassificerare ska du välja? Ofta är det en bra idé att testa flera och leta efter ett bra resultat.\n",
+    "\n",
+    "> AutoML löser detta problem smidigt genom att köra dessa jämförelser i molnet, vilket gör att du kan välja den bästa algoritmen för dina data. Prova det [här](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "Valet av klassificerare beror också på vårt problem. Till exempel, när resultatet kan kategoriseras i `fler än två klasser`, som i vårt fall, måste du använda en `multiklassklassificeringsalgoritm` istället för `binär klassificering.`\n",
+    "\n",
+    "### **En bättre metod**\n",
+    "\n",
+    "En bättre metod än att gissa vilt är att följa idéerna i detta nedladdningsbara [ML Cheat Sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott). Här upptäcker vi att, för vårt multiklassproblem, har vi några alternativ:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/cheatsheet.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>En del av Microsofts algoritm-översikt, som beskriver alternativ för multiklassklassificering</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "a6DLAZ3vJZ14"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Resonemang**\n",
+    "\n",
+    "Låt oss se om vi kan resonera oss fram till olika tillvägagångssätt med de begränsningar vi har:\n",
+    "\n",
+    "-   **Djupa neurala nätverk är för tunga**. Med tanke på vårt rena, men minimala dataset, och det faktum att vi kör träningen lokalt via notebooks, är djupa neurala nätverk för resurskrävande för denna uppgift.\n",
+    "\n",
+    "-   **Ingen tvåklassklassificerare**. Vi använder inte en tvåklassklassificerare, vilket utesluter one-vs-all.\n",
+    "\n",
+    "-   **Beslutsträd eller logistisk regression kan fungera**. Ett beslutsträd kan fungera, eller multinomial regression/multiklass logistisk regression för multiklassdata.\n",
+    "\n",
+    "-   **Multiklass Boosted Decision Trees löser ett annat problem**. Multiklass Boosted Decision Trees är mest lämpliga för icke-parametriska uppgifter, t.ex. uppgifter som är utformade för att skapa rankningar, så de är inte användbara för oss.\n",
+    "\n",
+    "Dessutom, innan man vanligtvis ger sig in på mer komplexa maskininlärningsmodeller, t.ex. ensemblemetoder, är det en bra idé att bygga den enklaste möjliga modellen för att få en uppfattning om vad som händer. Så för denna lektion börjar vi med en `multinomial regression`-modell.\n",
+    "\n",
+    "> Logistisk regression är en teknik som används när utfallsvariabeln är kategorisk (eller nominell). För binär logistisk regression är antalet utfallsvariabler två, medan antalet utfallsvariabler för multinomial logistisk regression är fler än två. Se [Avancerade regressionsmetoder](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html) för vidare läsning.\n",
+    "\n",
+    "## 4. Träna och utvärdera en multinomial logistisk regressionsmodell.\n",
+    "\n",
+    "I Tidymodels definierar `parsnip::multinom_reg()` en modell som använder linjära prediktorer för att förutsäga multiklassdata med hjälp av multinomialfördelningen. Se `?multinom_reg()` för de olika sätt/engines du kan använda för att passa denna modell.\n",
+    "\n",
+    "För detta exempel kommer vi att passa en multinomial regressionsmodell via den förvalda [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf)-motorn.\n",
+    "\n",
+    "> Jag valde ett värde för `penalty` lite slumpmässigt. Det finns bättre sätt att välja detta värde, nämligen genom att använda `resampling` och `tuning` av modellen, vilket vi kommer att diskutera senare.\n",
+    ">\n",
+    "> Se [Tidymodels: Kom igång](https://www.tidymodels.org/start/tuning/) om du vill lära dig mer om hur man finjusterar modellens hyperparametrar.\n"
+   ],
+   "metadata": {
+    "id": "gWMsVcbBJemu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# Create a multinomial regression model specification\r\n",
+    "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
+    "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
+    "  set_mode(\"classification\")\r\n",
+    "\r\n",
+    "# Print model specification\r\n",
+    "mr_spec"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 166
+    },
+    "id": "Wq_fcyQiJvfG",
+    "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bra jobbat 🥳! Nu när vi har ett recept och en modellspecifikation behöver vi hitta ett sätt att kombinera dem till ett objekt som först förbehandlar data, sedan anpassar modellen på den förbehandlade datan och även möjliggör potentiella efterbehandlingsaktiviteter. I Tidymodels kallas detta praktiska objekt för en [`workflow`](https://workflows.tidymodels.org/) och håller smidigt dina modellkomponenter! Detta är vad vi skulle kalla *pipelines* i *Python*.\n",
+    "\n",
+    "Så låt oss samla allt i en workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "NlSbzDfgJ0zh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# Bundle recipe and model specification\r\n",
+    "mr_wf <- workflow() %>% \r\n",
+    "  add_recipe(cuisines_recipe) %>% \r\n",
+    "  add_model(mr_spec)\r\n",
+    "\r\n",
+    "# Print out workflow\r\n",
+    "mr_wf"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow ════════════════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 333
+    },
+    "id": "Sc1TfPA4Ke3_",
+    "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Arbetsflöden 👌👌! En **`workflow()`** kan anpassas på ungefär samma sätt som en modell kan. Så, dags att träna en modell!\n"
+   ],
+   "metadata": {
+    "id": "TNQ8i85aKf9L"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# Train a multinomial regression model\n",
+    "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
+    "\n",
+    "mr_fit"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Call:\n",
+       "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
+       "    trace = FALSE)\n",
+       "\n",
+       "Coefficients:\n",
+       "         (Intercept)     almond angelica         anise anise_seed       apple\n",
+       "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
+       "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
+       "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
+       "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
+       "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
+       "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
+       "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
+       "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
+       "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
+       "             avocado       bacon baked_potato balm     banana     barley\n",
+       "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
+       "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
+       "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
+       "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
+       "         bartlett_pear      basil        bay       bean         beech\n",
+       "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
+       "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
+       "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
+       "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
+       "               beef beef_broth   beef_liver         beer        beet\n",
+       "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
+       "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
+       "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
+       "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
+       "         bell_pepper bergamot       berry bitter_orange black_bean\n",
+       "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
+       "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
+       "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
+       "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
+       "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
+       "indian               0             0.38935801   -0.4453495               0\n",
+       "japanese             0            -0.05452887   -0.5440869               0\n",
+       "korean               0            -0.03929970    0.8025454               0\n",
+       "thai                 0            -0.21498372   -0.9854806               0\n",
+       "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
+       "indian          -0.2759246  0.3079977  0.191256164                 0\n",
+       "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
+       "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
+       "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
+       "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
+       "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
+       "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
+       "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
+       "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
+       "\n",
+       "...\n",
+       "and 308 more lines."
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GMbdfVmTKkJI",
+    "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Utdata visar de koefficienter som modellen lärde sig under träningen.\n",
+    "\n",
+    "### Utvärdera den tränade modellen\n",
+    "\n",
+    "Det är dags att se hur modellen presterade 📏 genom att utvärdera den på en testuppsättning! Låt oss börja med att göra förutsägelser på testuppsättningen.\n"
+   ],
+   "metadata": {
+    "id": "tt2BfOxrKmcJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# Make predictions on the test set\n",
+    "results <- cuisines_test %>% select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
+    "\n",
+    "# Print out results\n",
+    "results %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class\n",
+       "1 indian  thai       \n",
+       "2 indian  indian     \n",
+       "3 indian  indian     \n",
+       "4 indian  indian     \n",
+       "5 indian  indian     "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
+       "|---|---|\n",
+       "| indian | thai   |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & .pred\\_class\\\\\n",
+       " <fct> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai  \\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "CqtckvtsKqax",
+    "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bra jobbat! I Tidymodels kan utvärdering av modellprestanda göras med [yardstick](https://yardstick.tidymodels.org/) - ett paket som används för att mäta modellers effektivitet med hjälp av prestandamått. Som vi gjorde i vår lektion om logistisk regression, låt oss börja med att beräkna en förväxlingsmatris.\n"
+   ],
+   "metadata": {
+    "id": "8w5N6XsBKss7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "# Confusion matrix for categorical data\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "          Truth\n",
+       "Prediction chinese indian japanese korean thai\n",
+       "  chinese       83      1        8     15   10\n",
+       "  indian         4    163        1      2    6\n",
+       "  japanese      21      5       73     25    1\n",
+       "  korean        15      0       11    191    0\n",
+       "  thai          10     11        3      7   70"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 133
+    },
+    "id": "YvODvsLkK0iG",
+    "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "När man arbetar med flera klasser är det generellt mer intuitivt att visualisera detta som en värmekarta, så här:\n"
+   ],
+   "metadata": {
+    "id": "c0HfPL16Lr6U"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
+    "# Visualize confusion matrix\n",
+    "results %>% \n",
+    "  conf_mat(cuisine, .pred_class) %>% \n",
+    "  autoplot(type = \"heatmap\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "plot without title"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {
+      "image/png": {
+       "width": 420,
+       "height": 420
+      }
+     }
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 436
+    },
+    "id": "HsAtwukyLsvt",
+    "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "De mörkare rutorna i förvirringsmatrisens diagram indikerar ett högt antal fall, och förhoppningsvis kan du se en diagonal linje av mörkare rutor som visar fall där den förutspådda och faktiska etiketten är densamma.\n",
+    "\n",
+    "Låt oss nu beräkna sammanfattande statistik för förvirringsmatrisen.\n"
+   ],
+   "metadata": {
+    "id": "oOJC87dkLwPr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# Summary stats for confusion matrix\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
+    "summary()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "   .metric              .estimator .estimate\n",
+       "1  accuracy             multiclass 0.7880435\n",
+       "2  kap                  multiclass 0.7276583\n",
+       "3  sens                 macro      0.7780927\n",
+       "4  spec                 macro      0.9477598\n",
+       "5  ppv                  macro      0.7585583\n",
+       "6  npv                  macro      0.9460080\n",
+       "7  mcc                  multiclass 0.7292724\n",
+       "8  j_index              macro      0.7258524\n",
+       "9  bal_accuracy         macro      0.8629262\n",
+       "10 detection_prevalence macro      0.2000000\n",
+       "11 precision            macro      0.7585583\n",
+       "12 recall               macro      0.7780927\n",
+       "13 f_meas               macro      0.7641862"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 13 × 3\n",
+       "\n",
+       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| accuracy             | multiclass | 0.7880435 |\n",
+       "| kap                  | multiclass | 0.7276583 |\n",
+       "| sens                 | macro      | 0.7780927 |\n",
+       "| spec                 | macro      | 0.9477598 |\n",
+       "| ppv                  | macro      | 0.7585583 |\n",
+       "| npv                  | macro      | 0.9460080 |\n",
+       "| mcc                  | multiclass | 0.7292724 |\n",
+       "| j_index              | macro      | 0.7258524 |\n",
+       "| bal_accuracy         | macro      | 0.8629262 |\n",
+       "| detection_prevalence | macro      | 0.2000000 |\n",
+       "| precision            | macro      | 0.7585583 |\n",
+       "| recall               | macro      | 0.7780927 |\n",
+       "| f_meas               | macro      | 0.7641862 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 13 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " .metric & .estimator & .estimate\\\\\n",
+       " <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t accuracy             & multiclass & 0.7880435\\\\\n",
+       "\t kap                  & multiclass & 0.7276583\\\\\n",
+       "\t sens                 & macro      & 0.7780927\\\\\n",
+       "\t spec                 & macro      & 0.9477598\\\\\n",
+       "\t ppv                  & macro      & 0.7585583\\\\\n",
+       "\t npv                  & macro      & 0.9460080\\\\\n",
+       "\t mcc                  & multiclass & 0.7292724\\\\\n",
+       "\t j\\_index              & macro      & 0.7258524\\\\\n",
+       "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
+       "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
+       "\t precision            & macro      & 0.7585583\\\\\n",
+       "\t recall               & macro      & 0.7780927\\\\\n",
+       "\t f\\_meas               & macro      & 0.7641862\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 13 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
+       "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
+       "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
+       "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
+       "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
+       "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
+       "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
+       "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
+       "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 494
+    },
+    "id": "OYqetUyzL5Wz",
+    "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Om vi fokuserar på några mått som noggrannhet, sensitivitet, ppv, så är vi inte helt fel ute för en början 🥳!\n",
+    "\n",
+    "## 4. Gå Djupare\n",
+    "\n",
+    "Låt oss ställa en subtil fråga: Vilka kriterier används för att välja en viss typ av kök som det förutspådda resultatet?\n",
+    "\n",
+    "Statistiska maskininlärningsalgoritmer, som logistisk regression, baseras på `sannolikhet`; så det som faktiskt förutspås av en klassificerare är en sannolikhetsfördelning över en uppsättning möjliga utfall. Klassen med högst sannolikhet väljs sedan som det mest sannolika resultatet för de givna observationerna.\n",
+    "\n",
+    "Låt oss se detta i praktiken genom att göra både hårda klassförutsägelser och sannolikheter.\n"
+   ],
+   "metadata": {
+    "id": "43t7vz8vMJtW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "# Make hard class prediction and probabilities\n",
+    "results_prob <- cuisines_test %>%\n",
+    "  select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
+    "\n",
+    "# Print out results\n",
+    "results_prob %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
+       "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
+       "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
+       "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
+       "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
+       "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
+       "  .pred_thai  \n",
+       "1 5.388194e-01\n",
+       "2 1.577948e-06\n",
+       "3 6.874989e-03\n",
+       "4 3.863391e-03\n",
+       "5 5.653283e-03"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 7\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
+       "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
+       "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
+       "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
+       "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 7\n",
+       "\\begin{tabular}{lllllll}\n",
+       " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
+       " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
+       "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
+       "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
+       "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
+       "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "xdKNs-ZPMTJL",
+    "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ Kan du förklara varför modellen är ganska säker på att den första observationen är thailändsk?\n",
+    "\n",
+    "## **🚀Utmaning**\n",
+    "\n",
+    "I den här lektionen använde du dina rensade data för att bygga en maskininlärningsmodell som kan förutsäga ett nationellt kök baserat på en serie ingredienser. Ta dig tid att läsa igenom de [många alternativen](https://www.tidymodels.org/find/parsnip/#models) som Tidymodels erbjuder för att klassificera data och [andra sätt](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) att anpassa multinomial regression.\n",
+    "\n",
+    "#### TACK TILL:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) för att ha skapat de fantastiska illustrationerna som gör R mer välkomnande och engagerande. Hitta fler illustrationer i hennes [galleri](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) och [Jen Looper](https://www.twitter.com/jenlooper) för att ha skapat den ursprungliga Python-versionen av denna modul ♥️\n",
+    "\n",
+    "<br>\n",
+    "Skulle ha slängt in några skämt, men jag fattar inte matvitsar 😅.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Lycka till med lärandet,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n"
+   ],
+   "metadata": {
+    "id": "2tWVHMeLMYdM"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/translations/sv/4-Classification/2-Classifiers-1/solution/notebook.ipynb
new file mode 100644
index 000000000..8e884cc43
--- /dev/null
+++ b/translations/sv/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Bygg klassificeringsmodeller\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy is 0.8181818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print (\"Accuracy is {}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n       'vegetable_oil'],\n      dtype='object')\ncuisine: korean\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test an item\n",
+    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[50]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                 0\n",
+       "korean    0.392231\n",
+       "chinese   0.372872\n",
+       "japanese  0.218825\n",
+       "thai      0.013427\n",
+       "indian    0.002645"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>korean</th>\n      <td>0.392231</td>\n    </tr>\n    <tr>\n      <th>chinese</th>\n      <td>0.372872</td>\n    </tr>\n    <tr>\n      <th>japanese</th>\n      <td>0.218825</td>\n    </tr>\n    <tr>\n      <th>thai</th>\n      <td>0.013427</td>\n    </tr>\n    <tr>\n      <th>indian</th>\n      <td>0.002645</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "#rehsape to 2d array and transpose\n",
+    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
+    "# predict with score\n",
+    "proba = model.predict_proba(test)\n",
+    "classes = model.classes_\n",
+    "# create df with classes and scores\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
+    "\n",
+    "# create df to show results\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.75      0.73      0.74       223\n      indian       0.93      0.88      0.90       255\n    japanese       0.78      0.78      0.78       253\n      korean       0.87      0.86      0.86       236\n        thai       0.76      0.84      0.80       232\n\n    accuracy                           0.82      1199\n   macro avg       0.82      0.82      0.82      1199\nweighted avg       0.82      0.82      0.82      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\r\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "9408506dd864f2b6e334c62f80c0cfcc",
+   "translation_date": "2025-09-06T14:33:09+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/3-Classifiers-2/notebook.ipynb b/translations/sv/4-Classification/3-Classifiers-2/notebook.ipynb
new file mode 100644
index 000000000..67a191a8f
--- /dev/null
+++ b/translations/sv/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
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+   "source_file": "4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jsFutf_ygqSx"
+   },
+   "source": [
+    "# Bygg en klassificeringsmodell: Utsökta asiatiska och indiska rätter\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HD54bEefgtNO"
+   },
+   "source": [
+    "## Klassificerare för kök 2\n",
+    "\n",
+    "I denna andra lektion om klassificering kommer vi att utforska `fler sätt` att klassificera kategoriska data. Vi kommer också att lära oss om konsekvenserna av att välja en klassificerare framför en annan.\n",
+    "\n",
+    "### [**Quiz före föreläsningen**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
+    "\n",
+    "### **Förkunskaper**\n",
+    "\n",
+    "Vi antar att du har slutfört de tidigare lektionerna eftersom vi kommer att bygga vidare på några koncept vi lärde oss tidigare.\n",
+    "\n",
+    "För denna lektion behöver vi följande paket:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) är en [samling av R-paket](https://www.tidyverse.org/packages) som är utformade för att göra datavetenskap snabbare, enklare och roligare!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) är ett [ramverk av paket](https://www.tidymodels.org/packages/) för modellering och maskininlärning.\n",
+    "\n",
+    "-   `themis`: [themis-paketet](https://themis.tidymodels.org/) tillhandahåller extra receptsteg för att hantera obalanserad data.\n",
+    "\n",
+    "Du kan installera dem med:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\n",
+    "\n",
+    "Alternativt kan skriptet nedan kontrollera om du har de paket som krävs för att slutföra denna modul och installera dem åt dig om de saknas.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vZ57IuUxgyQt"
+   },
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z22M-pj4g07x"
+   },
+   "source": [
+    "## **1. En klassificeringskarta**\n",
+    "\n",
+    "I vår [föregående lektion](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1) försökte vi besvara frågan: hur väljer vi mellan flera modeller? Till stor del beror det på egenskaperna hos datan och typen av problem vi vill lösa (till exempel klassificering eller regression).\n",
+    "\n",
+    "Tidigare lärde vi oss om de olika alternativen du har när du klassificerar data med hjälp av Microsofts fusklapp. Python's Machine Learning-ramverk, Scikit-learn, erbjuder en liknande men mer detaljerad fusklapp som kan hjälpa dig att ytterligare begränsa dina estimatorer (ett annat ord för klassificerare):\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/map.png\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption></figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u1i3xRIVg7vG"
+   },
+   "source": [
+    "> Tips: [besök den här kartan online](https://scikit-learn.org/stable/tutorial/machine_learning_map/) och klicka längs vägen för att läsa dokumentationen.\n",
+    ">\n",
+    "> [Tidymodels referenssida](https://www.tidymodels.org/find/parsnip/#models) erbjuder också utmärkt dokumentation om olika typer av modeller.\n",
+    "\n",
+    "### **Planen** 🗺️\n",
+    "\n",
+    "Den här kartan är väldigt användbar när du har en tydlig förståelse för din data, eftersom du kan \"vandra\" längs dess vägar mot ett beslut:\n",
+    "\n",
+    "-   Vi har \\>50 prover\n",
+    "\n",
+    "-   Vi vill förutsäga en kategori\n",
+    "\n",
+    "-   Vi har märkt data\n",
+    "\n",
+    "-   Vi har färre än 100K prover\n",
+    "\n",
+    "-   ✨ Vi kan välja en Linear SVC\n",
+    "\n",
+    "-   Om det inte fungerar, eftersom vi har numerisk data\n",
+    "\n",
+    "    -   Kan vi prova en ✨ KNeighbors Classifier\n",
+    "\n",
+    "        -   Om det inte fungerar, prova ✨ SVC och ✨ Ensemble Classifiers\n",
+    "\n",
+    "Det här är en väldigt användbar väg att följa. Nu ska vi dyka rakt in i det med [tidymodels](https://www.tidymodels.org/) modelleringsramverket: en konsekvent och flexibel samling av R-paket utvecklade för att främja god statistisk praxis 😊.\n",
+    "\n",
+    "## 2. Dela upp data och hantera obalanserade dataset.\n",
+    "\n",
+    "Från våra tidigare lektioner lärde vi oss att det fanns en uppsättning vanliga ingredienser över våra kök. Dessutom fanns det en ganska ojämn fördelning i antalet kök.\n",
+    "\n",
+    "Vi kommer att hantera detta genom att\n",
+    "\n",
+    "-   Ta bort de vanligaste ingredienserna som skapar förvirring mellan olika kök, med hjälp av `dplyr::select()`.\n",
+    "\n",
+    "-   Använda ett `recipe` som förbehandlar data för att göra den redo för modellering genom att tillämpa en `over-sampling`-algoritm.\n",
+    "\n",
+    "Vi tittade redan på detta i den tidigare lektionen, så det här borde gå som en dans 🥳!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "6tj_rN00hClA"
+   },
+   "source": [
+    "# Load the core Tidyverse and Tidymodels packages\n",
+    "library(tidyverse)\n",
+    "library(tidymodels)\n",
+    "\n",
+    "# Load the original cuisines data\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
+    "\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\n",
+    "df_select <- df %>% \n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\n",
+    "  # Encode cuisine column as categorical\n",
+    "  mutate(cuisine = factor(cuisine))\n",
+    "\n",
+    "\n",
+    "# Create data split specification\n",
+    "set.seed(2056)\n",
+    "cuisines_split <- initial_split(data = df_select,\n",
+    "                                strata = cuisine,\n",
+    "                                prop = 0.7)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "cuisines_train <- training(cuisines_split)\n",
+    "cuisines_test <- testing(cuisines_split)\n",
+    "\n",
+    "# Display distribution of cuisines in the training set\n",
+    "cuisines_train %>% \n",
+    "  count(cuisine) %>% \n",
+    "  arrange(desc(n))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFin5yw3hHb1"
+   },
+   "source": [
+    "### Hantera obalanserad data\n",
+    "\n",
+    "Obalanserad data påverkar ofta modellens prestanda negativt. Många modeller presterar bäst när antalet observationer är lika, och har därför en tendens att ha svårt med obalanserad data.\n",
+    "\n",
+    "Det finns huvudsakligen två sätt att hantera obalanserade datasätt:\n",
+    "\n",
+    "-   lägga till observationer till minoritetsklassen: `Över-sampling`, t.ex. med hjälp av en SMOTE-algoritm som syntetiskt genererar nya exempel av minoritetsklassen genom att använda närmaste grannar till dessa fall.\n",
+    "\n",
+    "-   ta bort observationer från majoritetsklassen: `Under-sampling`\n",
+    "\n",
+    "I vår tidigare lektion visade vi hur man hanterar obalanserade datasätt med hjälp av ett `recept`. Ett recept kan ses som en ritning som beskriver vilka steg som ska tillämpas på ett datasätt för att göra det redo för dataanalys. I vårt fall vill vi ha en jämn fördelning av antalet kök i vår `träningsuppsättning`. Låt oss sätta igång!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "cRzTnHolhLWd"
+   },
+   "source": [
+    "# Load themis package for dealing with imbalanced data\n",
+    "library(themis)\n",
+    "\n",
+    "# Create a recipe for preprocessing training data\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
+    "  step_smote(cuisine) \n",
+    "\n",
+    "# Print recipe\n",
+    "cuisines_recipe"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KxOQ2ORhhO81"
+   },
+   "source": [
+    "Nu är vi redo att träna modeller 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Utöver multinomiala regressionsmodeller\n",
+    "\n",
+    "I vår tidigare lektion tittade vi på multinomiala regressionsmodeller. Låt oss utforska några mer flexibla modeller för klassificering.\n",
+    "\n",
+    "### Support Vector Machines\n",
+    "\n",
+    "I klassificeringssammanhang är `Support Vector Machines` en maskininlärningsteknik som försöker hitta ett *hyperplan* som \"bäst\" separerar klasserna. Låt oss titta på ett enkelt exempel:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/svm.png\"\n",
+    "   width=\"300\"/>\n",
+    "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C4Wsd0vZhXYu"
+   },
+   "source": [
+    "H1~ separerar inte klasserna. H2~ gör det, men endast med en liten marginal. H3~ separerar dem med maximal marginal.\n",
+    "\n",
+    "#### Linjär Support Vector Classifier\n",
+    "\n",
+    "Support-Vector clustering (SVC) är en del av Support-Vector-maskinerna inom ML-tekniker. I SVC väljs hyperplanet för att korrekt separera `de flesta` av träningsobservationerna, men `kan felklassificera` några observationer. Genom att tillåta vissa punkter att vara på fel sida blir SVM mer robust mot avvikelser och därmed bättre på att generalisera till ny data. Parametern som reglerar denna överträdelse kallas `cost` och har ett standardvärde på 1 (se `help(\"svm_poly\")`).\n",
+    "\n",
+    "Låt oss skapa en linjär SVC genom att sätta `degree = 1` i en polynomisk SVM-modell.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vJpp6nuChlBz"
+   },
+   "source": [
+    "# Make a linear SVC specification\n",
+    "svc_linear_spec <- svm_poly(degree = 1) %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svc_linear_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svc_linear_spec)\n",
+    "\n",
+    "# Print out workflow\n",
+    "svc_linear_wf"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rDs8cWNkhoqu"
+   },
+   "source": [
+    "Nu när vi har sammanställt förbehandlingsstegen och modellspecifikationen i ett *arbetsflöde*, kan vi gå vidare och träna den linjära SVC och samtidigt utvärdera resultaten. För prestandamått, låt oss skapa en uppsättning mått som kommer att utvärdera: `accuracy`, `sensitivity`, `Positive Predicted Value` och `F Measure`.\n",
+    "\n",
+    "> `augment()` kommer att lägga till kolumn(er) för förutsägelser till den angivna datan.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "81wiqcwuhrnq"
+   },
+   "source": [
+    "# Train a linear SVC model\n",
+    "svc_linear_fit <- svc_linear_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svc_linear_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0UFQvHf-huo3"
+   },
+   "source": [
+    "#### Support Vector Machine\n",
+    "\n",
+    "Support Vector Machine (SVM) är en vidareutveckling av support vector classifier för att hantera en icke-linjär gräns mellan klasserna. I grund och botten använder SVMs *kernel-tricket* för att utöka funktionsutrymmet och anpassa sig till icke-linjära relationer mellan klasser. En populär och mycket flexibel kernel-funktion som används av SVMs är *Radial basis function.* Låt oss se hur den presterar på vår data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-KX4S8mzhzmp"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Make an RBF SVM specification\n",
+    "svm_rbf_spec <- svm_rbf() %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svm_rbf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svm_rbf_spec)\n",
+    "\n",
+    "\n",
+    "# Train an RBF model\n",
+    "svm_rbf_fit <- svm_rbf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svm_rbf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QBFSa7WSh4HQ"
+   },
+   "source": [
+    "Mycket bättre 🤩!\n",
+    "\n",
+    "> ✅ Vänligen se:\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://bradleyboehmke.github.io/HOML/svm.html), Hands-on Machine Learning with R\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://www.statlearning.com/), An Introduction to Statistical Learning with Applications in R\n",
+    ">\n",
+    "> för vidare läsning.\n",
+    "\n",
+    "### Närmaste granne-klassificerare\n",
+    "\n",
+    "*K*-närmsta granne (KNN) är en algoritm där varje observation förutsägs baserat på dess *likhet* med andra observationer.\n",
+    "\n",
+    "Låt oss anpassa en till vår data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "k4BxxBcdh9Ka"
+   },
+   "source": [
+    "# Make a KNN specification\n",
+    "knn_spec <- nearest_neighbor() %>% \n",
+    "  set_engine(\"kknn\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "knn_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(knn_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "knn_wf_fit <- knn_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "knn_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HaegQseriAcj"
+   },
+   "source": [
+    "Det verkar som att den här modellen inte presterar särskilt bra. Förmodligen kan modellens prestanda förbättras genom att ändra argumenten (se `help(\"nearest_neighbor\")`). Se till att testa detta.\n",
+    "\n",
+    "> ✅ Vänligen se:\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> för att lära dig mer om *K*-Närmaste Grannar-klassificerare.\n",
+    "\n",
+    "### Ensembleklassificerare\n",
+    "\n",
+    "Ensemblealgoritmer fungerar genom att kombinera flera basmodeller för att skapa en optimal modell antingen genom:\n",
+    "\n",
+    "`bagging`: att använda en *medelvärdesfunktion* på en samling av basmodeller\n",
+    "\n",
+    "`boosting`: att bygga en sekvens av modeller som bygger på varandra för att förbättra den prediktiva prestandan.\n",
+    "\n",
+    "Låt oss börja med att testa en Random Forest-modell, som bygger en stor samling beslutsträd och sedan använder en medelvärdesfunktion för att skapa en bättre övergripande modell.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "49DPoVs6iK1M"
+   },
+   "source": [
+    "# Make a random forest specification\n",
+    "rf_spec <- rand_forest() %>% \n",
+    "  set_engine(\"ranger\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "rf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(rf_spec)\n",
+    "\n",
+    "# Train a random forest model\n",
+    "rf_wf_fit <- rf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "rf_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RGVYwC_aiUWc"
+   },
+   "source": [
+    "Bra jobbat 👏!\n",
+    "\n",
+    "Låt oss också experimentera med en Boosted Tree-modell.\n",
+    "\n",
+    "Boosted Tree definierar en ensemblemetod som skapar en serie sekventiella beslutsträd där varje träd beror på resultaten från tidigare träd i ett försök att gradvis minska felet. Den fokuserar på vikterna för felklassificerade objekt och justerar passformen för nästa klassificerare för att korrigera.\n",
+    "\n",
+    "Det finns olika sätt att passa denna modell (se `help(\"boost_tree\")`). I detta exempel kommer vi att passa Boosted trees via `xgboost`-motorn.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Py1YWo-micWs"
+   },
+   "source": [
+    "# Make a boosted tree specification\n",
+    "boost_spec <- boost_tree(trees = 200) %>% \n",
+    "  set_engine(\"xgboost\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "boost_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(boost_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "boost_wf_fit <- boost_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "boost_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zNQnbuejigZM"
+   },
+   "source": [
+    "> ✅ Vänligen se:\n",
+    ">\n",
+    "> -   [Machine Learning för samhällsvetare](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning med R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [En introduktion till statistisk inlärning med applikationer i R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> -   <https://algotech.netlify.app/blog/xgboost/> - Utforskar AdaBoost-modellen som är ett bra alternativ till xgboost.\n",
+    ">\n",
+    "> för att lära dig mer om ensembleklassificerare.\n",
+    "\n",
+    "## 4. Extra - jämföra flera modeller\n",
+    "\n",
+    "Vi har anpassat ganska många modeller i denna labb 🙌. Det kan bli tröttsamt eller jobbigt att skapa många arbetsflöden från olika uppsättningar av förbehandlingsmetoder och/eller modellspecifikationer och sedan beräkna prestandamåtten en efter en.\n",
+    "\n",
+    "Låt oss se om vi kan lösa detta genom att skapa en funktion som anpassar en lista med arbetsflöden på träningsuppsättningen och sedan returnerar prestandamåtten baserat på testuppsättningen. Vi kommer att använda `map()` och `map_dfr()` från paketet [purrr](https://purrr.tidyverse.org/) för att tillämpa funktioner på varje element i en lista.\n",
+    "\n",
+    "> [`map()`](https://purrr.tidyverse.org/reference/map.html)-funktioner låter dig ersätta många for-loopar med kod som både är mer kortfattad och lättare att läsa. Det bästa stället att lära sig om [`map()`](https://purrr.tidyverse.org/reference/map.html)-funktionerna är [kapitlet om iteration](http://r4ds.had.co.nz/iteration.html) i R för data science.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Qzb7LyZnimd2"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "# Define a function that returns performance metrics\n",
+    "compare_models <- function(workflow_list, train_set, test_set){\n",
+    "  \n",
+    "  suppressWarnings(\n",
+    "    # Fit each model to the train_set\n",
+    "    map(workflow_list, fit, data = train_set) %>% \n",
+    "    # Make predictions on the test set\n",
+    "      map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
+    "    # Select desired columns\n",
+    "      select(model, cuisine, .pred_class) %>% \n",
+    "    # Evaluate model performance\n",
+    "      group_by(model) %>% \n",
+    "      eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
+    "      ungroup()\n",
+    "  )\n",
+    "  \n",
+    "} # End of function"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fwa712sNisDA"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "3i4VJOi2iu-a"
+   },
+   "source": [
+    "# Make a list of workflows\n",
+    "workflow_list <- list(\n",
+    "  \"svc\" = svc_linear_wf,\n",
+    "  \"svm\" = svm_rbf_wf,\n",
+    "  \"knn\" = knn_wf,\n",
+    "  \"random_forest\" = rf_wf,\n",
+    "  \"xgboost\" = boost_wf)\n",
+    "\n",
+    "# Call the function\n",
+    "set.seed(2056)\n",
+    "perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
+    "\n",
+    "# Print out performance metrics\n",
+    "perf_metrics %>% \n",
+    "  group_by(.metric) %>% \n",
+    "  arrange(desc(.estimate)) %>% \n",
+    "  slice_head(n=7)\n",
+    "\n",
+    "# Compare accuracy\n",
+    "perf_metrics %>% \n",
+    "  filter(.metric == \"accuracy\") %>% \n",
+    "  arrange(desc(.estimate))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KuWK_lEli4nW"
+   },
+   "source": [
+    "[**workflowset**](https://workflowsets.tidymodels.org/) paketet gör det möjligt för användare att skapa och enkelt anpassa ett stort antal modeller, men är främst utformat för att fungera med omprovningstekniker som `cross-validation`, en metod vi ännu inte har täckt.\n",
+    "\n",
+    "## **🚀Utmaning**\n",
+    "\n",
+    "Var och en av dessa tekniker har ett stort antal parametrar som du kan justera, till exempel `cost` i SVMs, `neighbors` i KNN, `mtry` (Slumpmässigt Valda Prediktorer) i Random Forest.\n",
+    "\n",
+    "Undersök standardparametrarna för var och en och fundera på vad justering av dessa parametrar skulle innebära för modellens kvalitet.\n",
+    "\n",
+    "För att ta reda på mer om en specifik modell och dess parametrar, använd: `help(\"model\")` t.ex. `help(\"rand_forest\")`\n",
+    "\n",
+    "> I praktiken brukar vi *estimera* de *bästa värdena* för dessa genom att träna många modeller på en `simulerad datamängd` och mäta hur bra alla dessa modeller presterar. Denna process kallas **tuning**.\n",
+    "\n",
+    "### [**Quiz efter föreläsningen**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
+    "\n",
+    "### **Granskning & Självstudier**\n",
+    "\n",
+    "Det finns mycket facktermer i dessa lektioner, så ta en stund att gå igenom [denna lista](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) med användbar terminologi!\n",
+    "\n",
+    "#### TACK TILL:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) för att ha skapat de fantastiska illustrationerna som gör R mer välkomnande och engagerande. Hitta fler illustrationer i hennes [galleri](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) och [Jen Looper](https://www.twitter.com/jenlooper) för att ha skapat den ursprungliga Python-versionen av denna modul ♥️\n",
+    "\n",
+    "Lycka till med lärandet,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör det noteras att automatiserade översättningar kan innehålla fel eller brister. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som kan uppstå vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/sv/4-Classification/3-Classifiers-2/solution/notebook.ipynb
new file mode 100644
index 000000000..c0a0c0c42
--- /dev/null
+++ b/translations/sv/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Prova olika klassificerare\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "C = 10\n",
+    "# Create different classifiers.\n",
+    "classifiers = {\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
+    "    'KNN classifier': KNeighborsClassifier(C),\n",
+    "    'SVC': SVC(),\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "    \n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy (train) for Linear SVC: 76.4% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.64      0.66      0.65       242\n",
+      "      indian       0.91      0.86      0.89       236\n",
+      "    japanese       0.72      0.73      0.73       245\n",
+      "      korean       0.83      0.75      0.79       234\n",
+      "        thai       0.75      0.82      0.78       242\n",
+      "\n",
+      "    accuracy                           0.76      1199\n",
+      "   macro avg       0.77      0.76      0.77      1199\n",
+      "weighted avg       0.77      0.76      0.77      1199\n",
+      "\n",
+      "Accuracy (train) for KNN classifier: 70.7% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.65      0.63      0.64       242\n",
+      "      indian       0.84      0.81      0.82       236\n",
+      "    japanese       0.60      0.81      0.69       245\n",
+      "      korean       0.89      0.53      0.67       234\n",
+      "        thai       0.69      0.75      0.72       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.73      0.71      0.71      1199\n",
+      "weighted avg       0.73      0.71      0.71      1199\n",
+      "\n",
+      "Accuracy (train) for SVC: 80.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.71      0.69      0.70       242\n",
+      "      indian       0.92      0.92      0.92       236\n",
+      "    japanese       0.77      0.78      0.77       245\n",
+      "      korean       0.87      0.77      0.82       234\n",
+      "        thai       0.75      0.86      0.80       242\n",
+      "\n",
+      "    accuracy                           0.80      1199\n",
+      "   macro avg       0.80      0.80      0.80      1199\n",
+      "weighted avg       0.80      0.80      0.80      1199\n",
+      "\n",
+      "Accuracy (train) for RFST: 82.8% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.80      0.75      0.77       242\n",
+      "      indian       0.90      0.91      0.90       236\n",
+      "    japanese       0.82      0.78      0.80       245\n",
+      "      korean       0.85      0.82      0.83       234\n",
+      "        thai       0.78      0.89      0.83       242\n",
+      "\n",
+      "    accuracy                           0.83      1199\n",
+      "   macro avg       0.83      0.83      0.83      1199\n",
+      "weighted avg       0.83      0.83      0.83      1199\n",
+      "\n",
+      "Accuracy (train) for ADA: 71.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.60      0.57      0.58       242\n",
+      "      indian       0.87      0.84      0.86       236\n",
+      "    japanese       0.71      0.60      0.65       245\n",
+      "      korean       0.68      0.78      0.72       234\n",
+      "        thai       0.70      0.78      0.74       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.71      0.71      0.71      1199\n",
+      "weighted avg       0.71      0.71      0.71      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "    y_pred = classifier.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
+    "    print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "7ea2b714669c823a596d986ba2d5739f",
+   "translation_date": "2025-09-06T14:42:52+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/4-Applied/notebook.ipynb b/translations/sv/4-Classification/4-Applied/notebook.ipynb
new file mode 100644
index 000000000..2490b7253
--- /dev/null
+++ b/translations/sv/4-Classification/4-Applied/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2f3e0d9e9ac5c301558fb8bf733ac0cb",
+   "translation_date": "2025-09-06T14:41:32+00:00",
+   "source_file": "4-Classification/4-Applied/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/4-Classification/4-Applied/solution/notebook.ipynb b/translations/sv/4-Classification/4-Applied/solution/notebook.ipynb
new file mode 100644
index 000000000..7241b9530
--- /dev/null
+++ b/translations/sv/4-Classification/4-Applied/solution/notebook.ipynb
@@ -0,0 +1,290 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "49325d6dd12a3628fc64fa7ccb1a80ff",
+   "translation_date": "2025-09-06T14:41:57+00:00",
+   "source_file": "4-Classification/4-Applied/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: skl2onnx in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.8.0)\n",
+      "Requirement already satisfied: protobuf in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (3.8.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.19.2)\n",
+      "Requirement already satisfied: onnx>=1.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.9.0)\n",
+      "Requirement already satisfied: six in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from skl2onnx) (1.12.0)\n",
+      "Requirement already satisfied: onnxconverter-common<1.9,>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.8.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.19 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (0.24.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.4.1)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->skl2onnx) (45.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnx>=1.2.1->skl2onnx) (3.10.0.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (0.16.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install skl2onnx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 60
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 61
+    }
+   ],
+   "source": [
+    "X = data.iloc[:,2:]\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "  cuisine\n",
+       "0  indian\n",
+       "1  indian\n",
+       "2  indian\n",
+       "3  indian\n",
+       "4  indian"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 62
+    }
+   ],
+   "source": [
+    "y = data[['cuisine']]\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 65
+    }
+   ],
+   "source": [
+    "model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
+    "model.fit(X_train,y_train.values.ravel())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.72      0.70      0.71       236\n      indian       0.91      0.88      0.89       243\n    japanese       0.80      0.75      0.77       240\n      korean       0.80      0.81      0.81       230\n        thai       0.76      0.85      0.80       250\n\n    accuracy                           0.80      1199\n   macro avg       0.80      0.80      0.80      1199\nweighted avg       0.80      0.80      0.80      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}\n",
+    "onx = convert_sklearn(model, initial_types=initial_type, options=options)\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
+    "    f.write(onx.SerializeToString())\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/5-Clustering/1-Visualize/notebook.ipynb b/translations/sv/5-Clustering/1-Visualize/notebook.ipynb
new file mode 100644
index 000000000..1ce436d9e
--- /dev/null
+++ b/translations/sv/5-Clustering/1-Visualize/notebook.ipynb
@@ -0,0 +1,50 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python383jvsc74a57bd0e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95",
+   "display_name": "Python 3.8.3 64-bit (conda)"
+  },
+  "coopTranslator": {
+   "original_hash": "40e0707e96b3e1899a912776006264f9",
+   "translation_date": "2025-09-06T14:08:02+00:00",
+   "source_file": "5-Clustering/1-Visualize/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/translations/sv/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
new file mode 100644
index 000000000..08e3bc095
--- /dev/null
+++ b/translations/sv/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
@@ -0,0 +1,500 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Nigeriansk musik hämtad från Spotify - en analys**\n",
+    "\n",
+    "Klustring är en typ av [Oövervakad inlärning](https://wikipedia.org/wiki/Unsupervised_learning) som förutsätter att en dataset är oetiketterad eller att dess indata inte matchas med fördefinierade utdata. Den använder olika algoritmer för att sortera igenom oetiketterad data och skapa grupper baserat på mönster den identifierar i datan.\n",
+    "\n",
+    "[**Quiz före föreläsningen**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)\n",
+    "\n",
+    "### **Introduktion**\n",
+    "\n",
+    "[Klustring](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) är mycket användbart för datautforskning. Låt oss se om det kan hjälpa oss att upptäcka trender och mönster i hur nigerianska lyssnare konsumerar musik.\n",
+    "\n",
+    "> ✅ Ta en minut och fundera över användningsområden för klustring. I vardagen sker klustring när du har en hög med tvätt och behöver sortera ut familjemedlemmarnas kläder 🧦👕👖🩲. Inom datavetenskap sker klustring när man försöker analysera en användares preferenser eller bestämma egenskaperna hos en oetiketterad dataset. Klustring hjälper på sätt och vis att skapa ordning i kaos, som en strumplåda.\n",
+    "\n",
+    "I en professionell miljö kan klustring användas för att bestämma saker som marknadssegmentering, till exempel vilka åldersgrupper som köper vilka produkter. Ett annat användningsområde kan vara att upptäcka avvikelser, kanske för att identifiera bedrägerier i en dataset med kreditkortstransaktioner. Eller så kan du använda klustring för att identifiera tumörer i en samling medicinska skanningar.\n",
+    "\n",
+    "✅ Fundera en minut på hur du kan ha stött på klustring \"i det vilda\", inom bank, e-handel eller affärsverksamhet.\n",
+    "\n",
+    "> 🎓 Intressant nog har klusteranalys sitt ursprung inom antropologi och psykologi på 1930-talet. Kan du föreställa dig hur det kan ha använts?\n",
+    "\n",
+    "Alternativt kan du använda det för att gruppera sökresultat - till exempel shoppinglänkar, bilder eller recensioner. Klustring är användbart när du har en stor dataset som du vill reducera och analysera mer detaljerat, så tekniken kan användas för att förstå data innan andra modeller konstrueras.\n",
+    "\n",
+    "✅ När din data är organiserad i kluster tilldelar du den ett kluster-ID, och denna teknik kan vara användbar för att bevara en datasets integritet; du kan istället referera till en datapunkt med dess kluster-ID, snarare än med mer avslöjande identifierbar data. Kan du tänka dig andra anledningar till varför du skulle referera till ett kluster-ID istället för andra element i klustret för att identifiera det?\n",
+    "\n",
+    "### Kom igång med klustring\n",
+    "\n",
+    "> 🎓 Hur vi skapar kluster har mycket att göra med hur vi samlar datapunkterna i grupper. Låt oss packa upp lite terminologi:\n",
+    ">\n",
+    "> 🎓 ['Transduktiv' vs. 'induktiv'](https://wikipedia.org/wiki/Transduction_(machine_learning))\n",
+    ">\n",
+    "> Transduktiv inferens härleds från observerade träningsfall som kartläggs till specifika testfall. Induktiv inferens härleds från träningsfall som kartläggs till generella regler som sedan tillämpas på testfall.\n",
+    ">\n",
+    "> Ett exempel: Föreställ dig att du har en dataset som bara är delvis etiketterad. Vissa saker är \"skivor\", vissa \"cd-skivor\" och vissa är tomma. Din uppgift är att tilldela etiketter till de tomma. Om du väljer en induktiv metod skulle du träna en modell som letar efter \"skivor\" och \"cd-skivor\" och tillämpa dessa etiketter på din oetiketterade data. Denna metod skulle ha svårt att klassificera saker som faktiskt är \"kassetter\". En transduktiv metod, å andra sidan, hanterar denna okända data mer effektivt eftersom den arbetar för att gruppera liknande objekt och sedan tilldelar en etikett till en grupp. I detta fall kan kluster reflektera \"runda musikföremål\" och \"fyrkantiga musikföremål\".\n",
+    ">\n",
+    "> 🎓 ['Icke-platt' vs. 'platt' geometri](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)\n",
+    ">\n",
+    "> Härstammar från matematisk terminologi, icke-platt vs. platt geometri hänvisar till mätningen av avstånd mellan punkter antingen med \"platt\" ([Euklidisk](https://wikipedia.org/wiki/Euclidean_geometry)) eller \"icke-platt\" (icke-Euklidisk) geometriska metoder.\n",
+    ">\n",
+    "> \"Platt\" i detta sammanhang hänvisar till Euklidisk geometri (delar av vilken lärs ut som \"plan\" geometri), och icke-platt hänvisar till icke-Euklidisk geometri. Vad har geometri med maskininlärning att göra? Tja, som två områden som är rotade i matematik måste det finnas ett gemensamt sätt att mäta avstånd mellan punkter i kluster, och det kan göras på ett \"platt\" eller \"icke-platt\" sätt, beroende på datans natur. [Euklidiska avstånd](https://wikipedia.org/wiki/Euclidean_distance) mäts som längden på en linjesegment mellan två punkter. [Icke-Euklidiska avstånd](https://wikipedia.org/wiki/Non-Euclidean_geometry) mäts längs en kurva. Om din data, visualiserad, verkar inte existera på en plan, kan du behöva använda en specialiserad algoritm för att hantera den.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/flat-nonflat.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografik av Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "> 🎓 ['Avstånd'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)\n",
+    ">\n",
+    "> Kluster definieras av deras avståndsmatris, t.ex. avstånden mellan punkter. Detta avstånd kan mätas på några sätt. Euklidiska kluster definieras av medelvärdet av punktvärdena och innehåller en \"centroid\" eller mittpunkt. Avstånd mäts således genom avståndet till den centroiden. Icke-Euklidiska avstånd hänvisar till \"clustroids\", den punkt som är närmast andra punkter. Clustroids kan i sin tur definieras på olika sätt.\n",
+    ">\n",
+    "> 🎓 ['Begränsad'](https://wikipedia.org/wiki/Constrained_clustering)\n",
+    ">\n",
+    "> [Begränsad klustring](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) introducerar \"semi-övervakad\" inlärning i denna oövervakade metod. Relationerna mellan punkter flaggas som \"kan inte länka\" eller \"måste länka\" så att vissa regler tvingas på datasetet.\n",
+    ">\n",
+    "> Ett exempel: Om en algoritm släpps fri på en samling oetiketterad eller semi-etiketterad data kan klustren den producerar vara av dålig kvalitet. I exemplet ovan kan klustren gruppera \"runda musikföremål\" och \"fyrkantiga musikföremål\" och \"triangulära föremål\" och \"kakor\". Om algoritmen ges vissa begränsningar, eller regler att följa (\"föremålet måste vara gjort av plast\", \"föremålet måste kunna producera musik\") kan detta hjälpa till att \"begränsa\" algoritmen att göra bättre val.\n",
+    ">\n",
+    "> 🎓 'Densitet'\n",
+    ">\n",
+    "> Data som är \"brusig\" anses vara \"tät\". Avstånden mellan punkter i varje kluster kan vid undersökning visa sig vara mer eller mindre täta, eller \"trånga\", och denna data behöver analyseras med lämplig klustringsmetod. [Denna artikel](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) demonstrerar skillnaden mellan att använda K-Means klustring och HDBSCAN-algoritmer för att utforska en brusig dataset med ojämn klusterdensitet.\n",
+    "\n",
+    "Fördjupa din förståelse av klustringstekniker i detta [Learn-modul](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "### **Klustringsalgoritmer**\n",
+    "\n",
+    "Det finns över 100 klustringsalgoritmer, och deras användning beror på datans natur. Låt oss diskutera några av de viktigaste:\n",
+    "\n",
+    "-   **Hierarkisk klustring**. Om ett objekt klassificeras baserat på dess närhet till ett närliggande objekt, snarare än till ett längre bort, bildas kluster baserat på medlemmarnas avstånd till och från andra objekt. Hierarkisk klustring kännetecknas av att två kluster upprepade gånger kombineras.\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/hierarchical.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografik av Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "-   **Centroid-klustring**. Denna populära algoritm kräver valet av \"k\", eller antalet kluster som ska bildas, varefter algoritmen bestämmer mittpunkten för ett kluster och samlar data runt den punkten. [K-means klustring](https://wikipedia.org/wiki/K-means_clustering) är en populär version av centroid-klustring som separerar en dataset i fördefinierade K-grupper. Centret bestäms av det närmaste medelvärdet, därav namnet. Det kvadrerade avståndet från klustret minimeras.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/centroid.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infografik av Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "-   **Fördelningsbaserad klustring**. Baserad på statistisk modellering fokuserar fördelningsbaserad klustring på att bestämma sannolikheten att en datapunkt tillhör ett kluster och tilldelar den därefter. Gaussiska blandningsmetoder tillhör denna typ.\n",
+    "\n",
+    "-   **Densitetsbaserad klustring**. Datapunkter tilldelas kluster baserat på deras densitet, eller deras gruppering runt varandra. Datapunkter långt från gruppen anses vara avvikelser eller brus. DBSCAN, Mean-shift och OPTICS tillhör denna typ av klustring.\n",
+    "\n",
+    "-   **Rutbaserad klustring**. För multidimensionella datasets skapas ett rutnät och datan delas upp mellan rutnätets celler, vilket skapar kluster.\n",
+    "\n",
+    "Det bästa sättet att lära sig om klustring är att prova det själv, så det är vad du kommer att göra i denna övning.\n",
+    "\n",
+    "Vi behöver några paket för att genomföra denna modul. Du kan installera dem med: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`\n",
+    "\n",
+    "Alternativt kontrollerar skriptet nedan om du har de paket som krävs för att slutföra denna modul och installerar dem åt dig om några saknas.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Övning - klustra din data\n",
+    "\n",
+    "Klustring som teknik underlättas mycket av korrekt visualisering, så låt oss börja med att visualisera vår musikdata. Denna övning kommer att hjälpa oss att avgöra vilken av klustringsmetoderna vi bör använda mest effektivt för denna datas natur.\n",
+    "\n",
+    "Låt oss sätta igång genom att importera datan.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# Import the data into a tibble\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows of the data set\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ibland kan vi vilja ha lite mer information om vår data. Vi kan titta på `data` och `dess struktur` genom att använda funktionen [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Glimpse into the data set\r\n",
+    "df %>% \r\n",
+    "  glimpse()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bra jobbat!💪\n",
+    "\n",
+    "Vi kan se att `glimpse()` visar det totala antalet rader (observationer) och kolumner (variabler), samt de första få värdena för varje variabel i en rad efter variabelns namn. Dessutom anges *datatypen* för variabeln direkt efter variabelns namn inom `< >`.\n",
+    "\n",
+    "`DataExplorer::introduce()` kan sammanfatta denna information på ett snyggt sätt:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe basic information for our data\r\n",
+    "df %>% \r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# A visual display of the same\r\n",
+    "df %>% \r\n",
+    "  plot_intro()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Fantastiskt! Vi har precis fått veta att vår data inte har några saknade värden.\n",
+    "\n",
+    "När vi ändå håller på kan vi utforska vanliga mått för central tendens (t.ex. [medelvärde](https://en.wikipedia.org/wiki/Arithmetic_mean) och [median](https://en.wikipedia.org/wiki/Median)) samt spridningsmått (t.ex. [standardavvikelse](https://en.wikipedia.org/wiki/Standard_deviation)) med hjälp av `summarytools::descr()`.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe common statistics\r\n",
+    "df %>% \r\n",
+    "  descr(stats = \"common\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Låt oss titta på de generella värdena i datan. Observera att popularitet kan vara `0`, vilket visar låtar som inte har någon ranking. Vi kommer att ta bort dessa snart.\n",
+    "\n",
+    "> 🤔 Om vi arbetar med klustring, en osuperviserad metod som inte kräver märkt data, varför visar vi då denna data med etiketter? Under datautforskningsfasen är de användbara, men de är inte nödvändiga för att klustringsalgoritmerna ska fungera.\n",
+    "\n",
+    "### 1. Utforska populära genrer\n",
+    "\n",
+    "Låt oss gå vidare och ta reda på de mest populära genrerna 🎶 genom att räkna antalet förekomster.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Popular genres\r\n",
+    "top_genres <- df %>% \r\n",
+    "  count(artist_top_genre, sort = TRUE) %>% \r\n",
+    "# Encode to categorical and reorder the according to count\r\n",
+    "  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())\r\n",
+    "\r\n",
+    "# Print the top genres\r\n",
+    "top_genres\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Det gick bra! De säger att en bild säger mer än tusen rader i en data frame (fast egentligen säger ingen det 😅). Men du förstår poängen, eller hur?\n",
+    "\n",
+    "Ett sätt att visualisera kategoriska data (tecken- eller faktorvariabler) är att använda stapeldiagram. Låt oss skapa ett stapeldiagram över de 10 populäraste genrerna:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Change the default gray theme\r\n",
+    "theme_set(theme_light())\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nu är det mycket enklare att identifiera att vi har `saknade` genrer 🧐!\n",
+    "\n",
+    "> En bra visualisering visar dig saker som du inte förväntade dig, eller väcker nya frågor om datan - Hadley Wickham och Garrett Grolemund, [R For Data Science](https://r4ds.had.co.nz/introduction.html)\n",
+    "\n",
+    "Observera, när den främsta genren beskrivs som `Saknad`, betyder det att Spotify inte har klassificerat den, så låt oss ta bort den.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  filter(artist_top_genre != \"Missing\") %>% \r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Från den lilla datautforskningen lär vi oss att de tre främsta genrerna dominerar denna dataset. Låt oss fokusera på `afro dancehall`, `afropop` och `nigerian pop`, och dessutom filtrera datasetet för att ta bort allt med ett popularitetsvärde på 0 (vilket innebär att det inte klassificerades med en popularitet i datasetet och kan betraktas som brus för våra syften):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "nigerian_songs <- df %>% \r\n",
+    "  # Concentrate on top 3 genres\r\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \r\n",
+    "  # Remove unclassified observations\r\n",
+    "  filter(popularity != 0)\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "nigerian_songs %>%\r\n",
+    "  count(artist_top_genre) %>%\r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Låt oss undersöka om det finns någon tydlig linjär relation mellan de numeriska variablerna i vår datamängd. Denna relation kvantifieras matematiskt med [korrelationsstatistiken](https://en.wikipedia.org/wiki/Correlation).\n",
+    "\n",
+    "Korrelationsstatistiken är ett värde mellan -1 och 1 som anger styrkan i en relation. Värden över 0 indikerar en *positiv* korrelation (höga värden för en variabel tenderar att sammanfalla med höga värden för den andra), medan värden under 0 indikerar en *negativ* korrelation (höga värden för en variabel tenderar att sammanfalla med låga värden för den andra).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Narrow down to numeric variables and fid correlation\r\n",
+    "corr_mat <- nigerian_songs %>% \r\n",
+    "  select(where(is.numeric)) %>% \r\n",
+    "  cor()\r\n",
+    "\r\n",
+    "# Visualize correlation matrix\r\n",
+    "corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  \r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Data är inte starkt korrelerad förutom mellan `energy` och `loudness`, vilket är logiskt eftersom hög musik oftast är ganska energisk. `Popularity` har en koppling till `release date`, vilket också är rimligt, eftersom nyare låtar förmodligen är mer populära. Längd och energi verkar också ha en korrelation.\n",
+    "\n",
+    "Det ska bli intressant att se vad en klustringsalgoritm kan göra med denna data!\n",
+    "\n",
+    "> 🎓 Observera att korrelation inte innebär kausalitet! Vi har bevis på korrelation men inget bevis på kausalitet. En [underhållande webbplats](https://tylervigen.com/spurious-correlations) har några visuella exempel som betonar denna poäng.\n",
+    "\n",
+    "### 2. Utforska datadistribution\n",
+    "\n",
+    "Låt oss ställa några mer subtila frågor. Är genrerna signifikant olika i uppfattningen av deras dansvänlighet, baserat på deras popularitet? Låt oss undersöka datadistributionen för våra tre främsta genrer när det gäller popularitet och dansvänlighet längs en given x- och y-axel med hjälp av [täthetsdiagram](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Perform 2D kernel density estimation\r\n",
+    "density_estimate_2d <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +\r\n",
+    "  geom_density_2d(bins = 5, size = 1) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  xlim(-20, 80) +\r\n",
+    "  ylim(0, 1.2)\r\n",
+    "\r\n",
+    "# Density plot based on the popularity\r\n",
+    "density_estimate_pop <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  theme(legend.position = \"none\")\r\n",
+    "\r\n",
+    "# Density plot based on the danceability\r\n",
+    "density_estimate_dance <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Patch everything together\r\n",
+    "library(patchwork)\r\n",
+    "density_estimate_2d / (density_estimate_pop + density_estimate_dance)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vi ser att det finns koncentriska cirklar som stämmer överens, oavsett genre. Kan det vara så att nigerianska smakpreferenser möts vid en viss nivå av dansvänlighet för denna genre?\n",
+    "\n",
+    "Generellt sett är de tre genrerna i linje när det gäller deras popularitet och dansvänlighet. Att identifiera kluster i dessa löst sammanhängande data kommer att vara en utmaning. Låt oss se om ett spridningsdiagram kan ge stöd för detta.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# A scatter plot of popularity and danceability\r\n",
+    "scatter_plot <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +\r\n",
+    "  geom_point(size = 2, alpha = 0.8) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"futurevisions::mars\")\r\n",
+    "\r\n",
+    "# Add a touch of interactivity\r\n",
+    "ggplotly(scatter_plot)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ett spridningsdiagram med samma axlar visar ett liknande mönster av konvergens.\n",
+    "\n",
+    "Generellt sett, för klustring, kan du använda spridningsdiagram för att visa datakluster, så att bemästra denna typ av visualisering är mycket användbart. I nästa lektion kommer vi att ta denna filtrerade data och använda k-means klustring för att upptäcka grupper i denna data som verkar överlappa på intressanta sätt.\n",
+    "\n",
+    "## **🚀 Utmaning**\n",
+    "\n",
+    "Som förberedelse inför nästa lektion, skapa ett diagram över de olika klustringsalgoritmer du kan upptäcka och använda i en produktionsmiljö. Vilka typer av problem försöker klustringen lösa?\n",
+    "\n",
+    "## [**Quiz efter föreläsningen**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)\n",
+    "\n",
+    "## **Granskning & Självstudier**\n",
+    "\n",
+    "Innan du tillämpar klustringsalgoritmer, som vi har lärt oss, är det en bra idé att förstå naturen av din dataset. Läs mer om detta ämne [här](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)\n",
+    "\n",
+    "Fördjupa din förståelse för klustringstekniker:\n",
+    "\n",
+    "-   [Träna och utvärdera klustringsmodeller med Tidymodels och vänner](https://rpubs.com/eR_ic/clustering)\n",
+    "\n",
+    "-   Bradley Boehmke & Brandon Greenwell, [*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/)*.*\n",
+    "\n",
+    "## **Uppgift**\n",
+    "\n",
+    "[Utforska andra visualiseringar för klustring](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)\n",
+    "\n",
+    "## TACK TILL:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) för att ha skapat den ursprungliga Python-versionen av denna modul ♥️\n",
+    "\n",
+    "[`Dasani Madipalli`](https://twitter.com/dasani_decoded) för att ha skapat de fantastiska illustrationerna som gör maskininlärningskoncept mer begripliga och lättare att förstå.\n",
+    "\n",
+    "Lycka till med lärandet,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "99c36449cad3708a435f6798cfa39972",
+   "translation_date": "2025-09-06T14:14:37+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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@@ -0,0 +1,821 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: seaborn in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (0.11.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (3.5.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.21.4)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.3.4)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.7.2)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (4.28.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (21.2)\n",
+      "Requirement already satisfied: setuptools-scm>=4 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (6.3.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
+      "Requirement already satisfied: tomli>=1.0.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (1.2.2)\n",
+      "Requirement already satisfied: setuptools in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (59.1.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Få information om dataframen\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 530 entries, 0 to 529\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   name              530 non-null    object \n",
+      " 1   album             530 non-null    object \n",
+      " 2   artist            530 non-null    object \n",
+      " 3   artist_top_genre  530 non-null    object \n",
+      " 4   release_date      530 non-null    int64  \n",
+      " 5   length            530 non-null    int64  \n",
+      " 6   popularity        530 non-null    int64  \n",
+      " 7   danceability      530 non-null    float64\n",
+      " 8   acousticness      530 non-null    float64\n",
+      " 9   energy            530 non-null    float64\n",
+      " 10  instrumentalness  530 non-null    float64\n",
+      " 11  liveness          530 non-null    float64\n",
+      " 12  loudness          530 non-null    float64\n",
+      " 13  speechiness       530 non-null    float64\n",
+      " 14  tempo             530 non-null    float64\n",
+      " 15  time_signature    530 non-null    int64  \n",
+      "dtypes: float64(8), int64(4), object(4)\n",
+      "memory usage: 66.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name                0\n",
+       "album               0\n",
+       "artist              0\n",
+       "artist_top_genre    0\n",
+       "release_date        0\n",
+       "length              0\n",
+       "popularity          0\n",
+       "danceability        0\n",
+       "acousticness        0\n",
+       "energy              0\n",
+       "instrumentalness    0\n",
+       "liveness            0\n",
+       "loudness            0\n",
+       "speechiness         0\n",
+       "tempo               0\n",
+       "time_signature      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Titta på de allmänna värdena för data. Observera att popularitet kan vara '0' - och det finns många rader med det värdet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
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+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2015.390566</td>\n",
+       "      <td>222298.169811</td>\n",
+       "      <td>17.507547</td>\n",
+       "      <td>0.741619</td>\n",
+       "      <td>0.265412</td>\n",
+       "      <td>0.760623</td>\n",
+       "      <td>0.016305</td>\n",
+       "      <td>0.147308</td>\n",
+       "      <td>-4.953011</td>\n",
+       "      <td>0.130748</td>\n",
+       "      <td>116.487864</td>\n",
+       "      <td>3.986792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.131688</td>\n",
+       "      <td>39696.822259</td>\n",
+       "      <td>18.992212</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.208342</td>\n",
+       "      <td>0.148533</td>\n",
+       "      <td>0.090321</td>\n",
+       "      <td>0.123588</td>\n",
+       "      <td>2.464186</td>\n",
+       "      <td>0.092939</td>\n",
+       "      <td>23.518601</td>\n",
+       "      <td>0.333701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>89488.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>0.111000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028300</td>\n",
+       "      <td>-19.362000</td>\n",
+       "      <td>0.027800</td>\n",
+       "      <td>61.695000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>199305.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681000</td>\n",
+       "      <td>0.089525</td>\n",
+       "      <td>0.669000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.075650</td>\n",
+       "      <td>-6.298750</td>\n",
+       "      <td>0.059100</td>\n",
+       "      <td>102.961250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>218509.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.761000</td>\n",
+       "      <td>0.220500</td>\n",
+       "      <td>0.784500</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.103500</td>\n",
+       "      <td>-4.558500</td>\n",
+       "      <td>0.097950</td>\n",
+       "      <td>112.714500</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>242098.500000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.829500</td>\n",
+       "      <td>0.403000</td>\n",
+       "      <td>0.875750</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.164000</td>\n",
+       "      <td>-3.331000</td>\n",
+       "      <td>0.177000</td>\n",
+       "      <td>125.039250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>511738.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>0.966000</td>\n",
+       "      <td>0.954000</td>\n",
+       "      <td>0.995000</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.811000</td>\n",
+       "      <td>0.582000</td>\n",
+       "      <td>0.514000</td>\n",
+       "      <td>206.007000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       release_date         length  popularity  danceability  acousticness  \\\n",
+       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
+       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
+       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
+       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
+       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
+       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
+       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
+       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
+       "\n",
+       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
+       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
+       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
+       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
+       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
+       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
+       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
+       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
+       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
+       "\n",
+       "            tempo  time_signature  \n",
+       "count  530.000000      530.000000  \n",
+       "mean   116.487864        3.986792  \n",
+       "std     23.518601        0.333701  \n",
+       "min     61.695000        3.000000  \n",
+       "25%    102.961250        4.000000  \n",
+       "50%    112.714500        4.000000  \n",
+       "75%    125.039250        4.000000  \n",
+       "max    206.007000        5.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Låt oss undersöka genrerna. Ganska många är listade som 'Saknas', vilket betyder att de inte är kategoriserade i datasetet med en genre.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I'm sorry, but I need the content of the markdown file to proceed with the translation. Could you please provide the text you'd like me to translate?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[df['artist_top_genre'] != 'Missing']\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corrmat = df.corr()\n",
+    "f, ax = plt.subplots(figsize=(12, 9))\n",
+    "sns.heatmap(corrmat, vmax=.8, square=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "\n",
+    "# Show the joint distribution using kernel density estimation\n",
+    "g = sns.jointplot(\n",
+    "    data=df,\n",
+    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
+    "    kind=\"kde\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generellt sett överensstämmer de tre genrerna när det gäller deras popularitet och dansbarhet. Ett spridningsdiagram med samma axlar visar ett liknande mönster av konvergens. Prova ett spridningsdiagram för att kontrollera fördelningen av data per genre.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1350b1a90>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 468.975x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
+    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
+    "   .add_legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör det noteras att automatiserade översättningar kan innehålla fel eller brister. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "c61deff2839902ac8cb4ed411eb10fee",
+   "translation_date": "2025-09-06T14:09:22+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sv/5-Clustering/2-K-Means/notebook.ipynb b/translations/sv/5-Clustering/2-K-Means/notebook.ipynb
new file mode 100644
index 000000000..5eea38f2b
--- /dev/null
+++ b/translations/sv/5-Clustering/2-K-Means/notebook.ipynb
@@ -0,0 +1,231 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "3e5c8ab363e8d88f566d4365efc7e0bd",
+   "translation_date": "2025-09-06T14:19:41+00:00",
+   "source_file": "5-Clustering/2-K-Means/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Börja där vi avslutade i förra lektionen, med data importerad och filtrerad.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Vi kommer att fokusera på endast 3 genrer. Kanske kan vi få 3 kluster skapade!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
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+   "source_file": "5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GULATlQXLXyR"
+   },
+   "source": [
+    "## Utforska K-Means-klustring med R och principer för Tidy-data.\n",
+    "\n",
+    "### [**Quiz före föreläsningen**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)\n",
+    "\n",
+    "I den här lektionen kommer du att lära dig hur man skapar kluster med hjälp av Tidymodels-paketet och andra paket i R-ekosystemet (vi kallar dem vänner 🧑‍🤝‍🧑) samt den nigerianska musikdatamängden som du importerade tidigare. Vi kommer att gå igenom grunderna i K-Means för klustring. Kom ihåg att, som du lärde dig i den tidigare lektionen, finns det många sätt att arbeta med kluster, och metoden du använder beror på din data. Vi kommer att prova K-Means eftersom det är den vanligaste klustringstekniken. Nu kör vi!\n",
+    "\n",
+    "Begrepp du kommer att lära dig om:\n",
+    "\n",
+    "-   Silhuettvärdering\n",
+    "\n",
+    "-   Armbågmetoden\n",
+    "\n",
+    "-   Tröghet\n",
+    "\n",
+    "-   Varians\n",
+    "\n",
+    "### **Introduktion**\n",
+    "\n",
+    "[K-Means-klustring](https://wikipedia.org/wiki/K-means_clustering) är en metod som härstammar från signalbehandlingsområdet. Den används för att dela upp och gruppera data i `k kluster` baserat på likheter i deras egenskaper.\n",
+    "\n",
+    "Klustrerna kan visualiseras som [Voronoi-diagram](https://wikipedia.org/wiki/Voronoi_diagram), som inkluderar en punkt (eller 'frö') och dess motsvarande område.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/voronoi.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Infografik av Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "K-Means-klustring har följande steg:\n",
+    "\n",
+    "1.  Dataanalytikern börjar med att specificera det önskade antalet kluster som ska skapas.\n",
+    "\n",
+    "2.  Därefter väljer algoritmen slumpmässigt K observationer från datamängden som ska fungera som de initiala centren för klustren (dvs. centroiderna).\n",
+    "\n",
+    "3.  Sedan tilldelas varje återstående observation till sin närmaste centroid.\n",
+    "\n",
+    "4.  Därefter beräknas de nya medelvärdena för varje kluster och centroiden flyttas till medelvärdet.\n",
+    "\n",
+    "5.  Nu när centren har räknats om kontrolleras varje observation igen för att se om den kanske är närmare ett annat kluster. Alla objekt tilldelas på nytt med hjälp av de uppdaterade klustermedlen. Stegen för klustertilldelning och centroiduppdatering upprepas iterativt tills klustertilldelningarna slutar förändras (dvs. när konvergens uppnås). Vanligtvis avslutas algoritmen när varje ny iteration resulterar i försumbar rörelse av centroiderna och klustren blir statiska.\n",
+    "\n",
+    "<div>\n",
+    "\n",
+    "> Observera att på grund av slumpmässigheten i de initiala k observationerna som används som startcentroider kan vi få något olika resultat varje gång vi tillämpar proceduren. Av denna anledning använder de flesta algoritmer flera *slumpmässiga starter* och väljer iterationen med lägst WCSS. Därför rekommenderas det starkt att alltid köra K-Means med flera värden på *nstart* för att undvika ett *oönskat lokalt optimum.*\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Den här korta animationen med [illustrationer](https://github.com/allisonhorst/stats-illustrations) av Allison Horst förklarar klustringsprocessen:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/kmeans.gif\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Illustration av @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "En grundläggande fråga som uppstår vid klustring är denna: hur vet du hur många kluster du ska dela upp din data i? En nackdel med att använda K-Means är att du måste fastställa `k`, det vill säga antalet `centroider`. Lyckligtvis hjälper `armbågmetoden` till att uppskatta ett bra startvärde för `k`. Du kommer att prova det om en stund.\n",
+    "\n",
+    "### \n",
+    "\n",
+    "**Förkunskaper**\n",
+    "\n",
+    "Vi fortsätter precis där vi slutade i [föregående lektion](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb), där vi analyserade datamängden, skapade många visualiseringar och filtrerade datamängden till intressanta observationer. Se till att kolla in den!\n",
+    "\n",
+    "Vi kommer att behöva några paket för att klara av den här modulen. Du kan installera dem med: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`\n",
+    "\n",
+    "Alternativt kontrollerar skriptet nedan om du har de paket som krävs för att slutföra den här modulen och installerar dem åt dig om några saknas.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ah_tBi58LXyi"
+   },
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7e--UCUTLXym"
+   },
+   "source": [
+    "## 1. En dans med data: Begränsa till de 3 mest populära musikgenrerna\n",
+    "\n",
+    "Det här är en sammanfattning av vad vi gjorde i föregående lektion. Låt oss analysera och bearbeta lite data!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Ycamx7GGLXyn"
+   },
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data into a tibble\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\", show_col_types = FALSE)\n",
+    "\n",
+    "# Narrow down to top 3 popular genres\n",
+    "nigerian_songs <- df %>% \n",
+    "  # Concentrate on top 3 genres\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \n",
+    "  # Remove unclassified observations\n",
+    "  filter(popularity != 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Visualize popular genres using bar plots\n",
+    "theme_set(theme_light())\n",
+    "nigerian_songs %>%\n",
+    "  count(artist_top_genre) %>%\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\n",
+    "                       fill = artist_top_genre)) +\n",
+    "  geom_col(alpha = 0.8) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\n",
+    "  ggtitle(\"Top genres\") +\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b5h5zmkPLXyp"
+   },
+   "source": [
+    "🤩 Det gick bra!\n",
+    "\n",
+    "## 2. Mer datautforskning.\n",
+    "\n",
+    "Hur ren är denna data? Låt oss kontrollera för avvikare med hjälp av låddiagram. Vi kommer att fokusera på numeriska kolumner med färre avvikare (även om du skulle kunna rensa bort avvikarna). Låddiagram kan visa datans intervall och hjälpa till att välja vilka kolumner som ska användas. Observera att låddiagram inte visar varians, en viktig aspekt av bra data som kan klustras. Se gärna [denna diskussion](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot) för mer information.\n",
+    "\n",
+    "[Låddiagram](https://en.wikipedia.org/wiki/Box_plot) används för att grafiskt visa fördelningen av `numerisk` data, så låt oss börja med att *välja* alla numeriska kolumner tillsammans med de populära musikgenrerna.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HhNreJKLLXyq"
+   },
+   "source": [
+    "# Select top genre column and all other numeric columns\n",
+    "df_numeric <- nigerian_songs %>% \n",
+    "  select(artist_top_genre, where(is.numeric)) \n",
+    "\n",
+    "# Display the data\n",
+    "df_numeric %>% \n",
+    "  slice_head(n = 5)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uYXrwJRaLXyq"
+   },
+   "source": [
+    "Se hur urvalshjälparen `where` gör detta enkelt 💁? Utforska fler sådana funktioner [här](https://tidyselect.r-lib.org/).\n",
+    "\n",
+    "Eftersom vi ska skapa ett lådagram för varje numerisk egenskap och vill undvika att använda loopar, låt oss omformatera våra data till ett *längre* format som gör det möjligt för oss att dra nytta av `facets` - delgrafer som var och en visar en delmängd av data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "gd5bR3f8LXys"
+   },
+   "source": [
+    "# Pivot data from wide to long\n",
+    "df_numeric_long <- df_numeric %>% \n",
+    "  pivot_longer(!artist_top_genre, names_to = \"feature_names\", values_to = \"values\") \n",
+    "\n",
+    "# Print out data\n",
+    "df_numeric_long %>% \n",
+    "  slice_head(n = 15)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-7tE1swnLXyv"
+   },
+   "source": [
+    "Mycket längre! Nu är det dags för några `ggplots`! Så vilken `geom` ska vi använda?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "r88bIsyuLXyy"
+   },
+   "source": [
+    "# Make a box plot\n",
+    "df_numeric_long %>% \n",
+    "  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_wrap(~ feature_names, ncol = 4, scales = \"free\") +\n",
+    "  theme(legend.position = \"none\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EYVyKIUELXyz"
+   },
+   "source": [
+    "Easy-gg!\n",
+    "\n",
+    "Nu kan vi se att dessa data är lite brusiga: genom att observera varje kolumn som ett låddiagram kan du se avvikare. Du skulle kunna gå igenom datasetet och ta bort dessa avvikare, men det skulle göra datan ganska minimal.\n",
+    "\n",
+    "För tillfället, låt oss välja vilka kolumner vi ska använda för vår klusterövning. Låt oss välja de numeriska kolumnerna med liknande intervall. Vi skulle kunna koda `artist_top_genre` som numerisk, men vi hoppar över det för tillfället.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-wkpINyZLXy0"
+   },
+   "source": [
+    "# Select variables with similar ranges\n",
+    "df_numeric_select <- df_numeric %>% \n",
+    "  select(popularity, danceability, acousticness, loudness, energy) \n",
+    "\n",
+    "# Normalize data\n",
+    "# df_numeric_select <- scale(df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D7dLzgpqLXy1"
+   },
+   "source": [
+    "## 3. Beräkning av k-means-klustring i R\n",
+    "\n",
+    "Vi kan beräkna k-means i R med den inbyggda funktionen `kmeans`, se `help(\"kmeans()\")`. Funktionen `kmeans()` accepterar en data frame med enbart numeriska kolumner som sitt primära argument.\n",
+    "\n",
+    "Det första steget när man använder k-means-klustring är att ange antalet kluster (k) som ska genereras i den slutliga lösningen. Vi vet att det finns 3 musikgenrer som vi har identifierat från datasetet, så låt oss prova med 3:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "uC4EQ5w7LXy5"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Kmeans clustering for 3 clusters\n",
+    "kclust <- kmeans(\n",
+    "  df_numeric_select,\n",
+    "  # Specify the number of clusters\n",
+    "  centers = 3,\n",
+    "  # How many random initial configurations\n",
+    "  nstart = 25\n",
+    ")\n",
+    "\n",
+    "# Display clustering object\n",
+    "kclust\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hzfhscWrLXy-"
+   },
+   "source": [
+    "Kmeans-objektet innehåller flera delar av information som förklaras väl i `help(\"kmeans()\")`. För tillfället fokuserar vi på några få. Vi ser att data har delats in i 3 kluster med storlekarna 65, 110, 111. Utdata innehåller också klustercentra (medelvärden) för de 3 grupperna över de 5 variablerna.\n",
+    "\n",
+    "Klustervektorn är klustertilldelningen för varje observation. Låt oss använda funktionen `augment` för att lägga till klustertilldelningen till den ursprungliga datamängden.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "0XwwpFGQLXy_"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "augment(kclust, df_numeric_select) %>% \n",
+    "  relocate(.cluster) %>% \n",
+    "  slice_head(n = 10)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NXIVXXACLXzA"
+   },
+   "source": [
+    "Perfekt, vi har precis delat upp vår dataset i en uppsättning av 3 grupper. Så, hur bra är vår klustring 🤷? Låt oss ta en titt på `Silhouette score`.\n",
+    "\n",
+    "### **Silhouette score**\n",
+    "\n",
+    "[Silhouette-analys](https://en.wikipedia.org/wiki/Silhouette_(clustering)) kan användas för att studera separationsavståndet mellan de resulterande klustren. Denna poäng varierar från -1 till 1, och om poängen är nära 1 är klustret tätt och väl separerat från andra kluster. Ett värde nära 0 representerar överlappande kluster med prover som ligger mycket nära beslutsgränsen för de närliggande klustren. [källa](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).\n",
+    "\n",
+    "Metoden för genomsnittlig silhouette beräknar den genomsnittliga silhouette för observationer för olika värden av *k*. En hög genomsnittlig silhouette-poäng indikerar en bra klustring.\n",
+    "\n",
+    "Funktionen `silhouette` i klusterpaketet används för att beräkna den genomsnittliga silhouette-bredden.\n",
+    "\n",
+    "> Silhouetten kan beräknas med vilken [avståndsmetrik](https://en.wikipedia.org/wiki/Distance \"Distance\") som helst, såsom [Euklidiskt avstånd](https://en.wikipedia.org/wiki/Euclidean_distance \"Euclidean distance\") eller [Manhattan-avstånd](https://en.wikipedia.org/wiki/Manhattan_distance \"Manhattan distance\") som vi diskuterade i [föregående lektion](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Jn0McL28LXzB"
+   },
+   "source": [
+    "# Load cluster package\n",
+    "library(cluster)\n",
+    "\n",
+    "# Compute average silhouette score\n",
+    "ss <- silhouette(kclust$cluster,\n",
+    "                 # Compute euclidean distance\n",
+    "                 dist = dist(df_numeric_select))\n",
+    "mean(ss[, 3])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QyQRn97nLXzC"
+   },
+   "source": [
+    "Vårt resultat är **0,549**, alltså precis i mitten. Detta tyder på att våra data inte är särskilt väl lämpade för den här typen av klustring. Låt oss se om vi kan bekräfta denna misstanke visuellt. Paketet [factoextra](https://rpkgs.datanovia.com/factoextra/index.html) erbjuder funktioner (`fviz_cluster()`) för att visualisera klustring.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "7a6Km1_FLXzD"
+   },
+   "source": [
+    "library(factoextra)\n",
+    "\n",
+    "# Visualize clustering results\n",
+    "fviz_cluster(kclust, df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IBwCWt-0LXzD"
+   },
+   "source": [
+    "Överlappningen i kluster indikerar att vår data inte är särskilt väl lämpad för denna typ av klustring, men låt oss fortsätta.\n",
+    "\n",
+    "## 4. Bestämma optimalt antal kluster\n",
+    "\n",
+    "En grundläggande fråga som ofta uppstår vid K-Means-klustring är denna - utan kända klassetiketter, hur vet du hur många kluster du ska dela upp din data i?\n",
+    "\n",
+    "Ett sätt att försöka ta reda på det är att använda ett dataprovs för att `skapa en serie klustringsmodeller` med ett ökande antal kluster (t.ex. från 1-10) och utvärdera klustringsmått som **Silhouette-poängen.**\n",
+    "\n",
+    "Låt oss bestämma det optimala antalet kluster genom att beräkna klustringsalgoritmen för olika värden av *k* och utvärdera **Within Cluster Sum of Squares** (WCSS). Den totala inom-kluster-summan av kvadrater (WCSS) mäter klustringens kompakthet, och vi vill att den ska vara så liten som möjligt, där lägre värden innebär att datapunkterna är närmare varandra.\n",
+    "\n",
+    "Låt oss undersöka effekten av olika val av `k`, från 1 till 10, på denna klustring.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "hSeIiylDLXzE"
+   },
+   "source": [
+    "# Create a series of clustering models\n",
+    "kclusts <- tibble(k = 1:10) %>% \n",
+    "  # Perform kmeans clustering for 1,2,3 ... ,10 clusters\n",
+    "  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),\n",
+    "  # Farm out clustering metrics eg WCSS\n",
+    "         glanced = map(model, ~ glance(.x))) %>% \n",
+    "  unnest(cols = glanced)\n",
+    "  \n",
+    "\n",
+    "# View clustering rsulsts\n",
+    "kclusts\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m7rS2U1eLXzE"
+   },
+   "source": [
+    "Nu när vi har den totala inomklustersumman av kvadrater (tot.withinss) för varje klustringsalgoritm med centrum *k*, använder vi [elbow-metoden](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) för att hitta det optimala antalet kluster. Metoden innebär att man plottar WCSS som en funktion av antalet kluster och väljer [kurvans \"armbåge\"](https://en.wikipedia.org/wiki/Elbow_of_the_curve \"Elbow of the curve\") som det antal kluster som ska användas.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "o_DjHGItLXzF"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Use elbow method to determine optimum number of clusters\n",
+    "kclusts %>% \n",
+    "  ggplot(mapping = aes(x = k, y = tot.withinss)) +\n",
+    "  geom_line(size = 1.2, alpha = 0.8, color = \"#FF7F0EFF\") +\n",
+    "  geom_point(size = 2, color = \"#FF7F0EFF\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLYyt5XSLXzG"
+   },
+   "source": [
+    "Diagrammet visar en stor minskning i WCSS (alltså större *sammanhållning*) när antalet kluster ökar från ett till två, och ytterligare en märkbar minskning från två till tre kluster. Därefter blir minskningen mindre framträdande, vilket resulterar i en `armbåge` 💪 i diagrammet vid ungefär tre kluster. Detta är en bra indikation på att det finns två till tre rimligt väl separerade kluster av datapunkter.\n",
+    "\n",
+    "Vi kan nu gå vidare och extrahera klustermodellen där `k = 3`:\n",
+    "\n",
+    "> `pull()`: används för att extrahera en enskild kolumn\n",
+    ">\n",
+    "> `pluck()`: används för att indexera datastrukturer såsom listor\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JP_JPKBILXzG"
+   },
+   "source": [
+    "# Extract k = 3 clustering\n",
+    "final_kmeans <- kclusts %>% \n",
+    "  filter(k == 3) %>% \n",
+    "  pull(model) %>% \n",
+    "  pluck(1)\n",
+    "\n",
+    "\n",
+    "final_kmeans\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l_PDTu8tLXzI"
+   },
+   "source": [
+    "Bra! Låt oss visualisera de kluster vi fått fram. Är du sugen på lite interaktivitet med hjälp av `plotly`?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dNcleFe-LXzJ"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "results <-  augment(final_kmeans, df_numeric_select) %>% \n",
+    "  bind_cols(df_numeric %>% select(artist_top_genre)) \n",
+    "\n",
+    "# Plot cluster assignments\n",
+    "clust_plt <- results %>% \n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +\n",
+    "  geom_point(size = 2, alpha = 0.8) +\n",
+    "  paletteer::scale_color_paletteer_d(\"ggthemes::Tableau_10\")\n",
+    "\n",
+    "ggplotly(clust_plt)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6JUM_51VLXzK"
+   },
+   "source": [
+    "Kanske hade vi förväntat oss att varje kluster (representerat av olika färger) skulle ha distinkta genrer (representerat av olika former).\n",
+    "\n",
+    "Låt oss ta en titt på modellens noggrannhet.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HdIMUGq7LXzL"
+   },
+   "source": [
+    "# Assign genres to predefined integers\n",
+    "label_count <- results %>% \n",
+    "  group_by(artist_top_genre) %>% \n",
+    "  mutate(id = cur_group_id()) %>% \n",
+    "  ungroup() %>% \n",
+    "  summarise(correct_labels = sum(.cluster == id))\n",
+    "\n",
+    "\n",
+    "# Print results  \n",
+    "cat(\"Result:\", label_count$correct_labels, \"out of\", nrow(results), \"samples were correctly labeled.\")\n",
+    "\n",
+    "cat(\"\\nAccuracy score:\", label_count$correct_labels/nrow(results))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C50wvaAOLXzM"
+   },
+   "source": [
+    "Den här modellens noggrannhet är inte dålig, men inte heller fantastisk. Det kan vara så att datan inte lämpar sig särskilt väl för K-Means-klustring. Datan är för obalanserad, har för låg korrelation och det finns för mycket variation mellan kolumnvärdena för att klustringen ska fungera bra. Faktum är att de kluster som bildas troligen är starkt påverkade eller snedvridna av de tre genrekategorier vi definierade ovan.\n",
+    "\n",
+    "Trots det var det en lärorik process!\n",
+    "\n",
+    "I Scikit-learns dokumentation kan du se att en modell som denna, med kluster som inte är särskilt väl avgränsade, har ett \"varians\"-problem:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/problems.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Infografik från Scikit-learn</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "## **Varians**\n",
+    "\n",
+    "Varians definieras som \"medelvärdet av de kvadrerade avvikelserna från medelvärdet\" [källa](https://www.mathsisfun.com/data/standard-deviation.html). I kontexten av detta klustringsproblem syftar det på att siffrorna i vår dataset tenderar att avvika lite för mycket från medelvärdet.\n",
+    "\n",
+    "✅ Det här är ett bra tillfälle att fundera över alla sätt du kan lösa detta problem. Justera datan lite mer? Använda andra kolumner? Testa en annan algoritm? Tips: Prova att [skala din data](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) för att normalisera den och testa andra kolumner.\n",
+    "\n",
+    "> Prova denna '[varianskalkylator](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)' för att förstå konceptet lite bättre.\n",
+    "\n",
+    "------------------------------------------------------------------------\n",
+    "\n",
+    "## **🚀Utmaning**\n",
+    "\n",
+    "Tillbringa lite tid med denna notebook och justera parametrarna. Kan du förbättra modellens noggrannhet genom att rensa datan mer (till exempel ta bort outliers)? Du kan använda vikter för att ge större vikt åt vissa dataprover. Vad mer kan du göra för att skapa bättre kluster?\n",
+    "\n",
+    "Tips: Försök att skala din data. Det finns kommenterad kod i notebooken som lägger till standardisering för att få datakolumnerna att likna varandra mer i termer av intervall. Du kommer att märka att även om silhuettpoängen sjunker, så jämnas \"knäet\" i armbågsgrafen ut. Detta beror på att om datan lämnas oskalad, får data med mindre varians större vikt. Läs mer om detta problem [här](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
+    "\n",
+    "## [**Efterföreläsningsquiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)\n",
+    "\n",
+    "## **Granskning & Självstudier**\n",
+    "\n",
+    "-   Ta en titt på en K-Means-simulator [som denna](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). Du kan använda detta verktyg för att visualisera exempeldata och bestämma dess centroid. Du kan redigera datans slumpmässighet, antal kluster och antal centroid. Hjälper detta dig att få en bättre förståelse för hur datan kan grupperas?\n",
+    "\n",
+    "-   Ta också en titt på [detta handout om K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) från Stanford.\n",
+    "\n",
+    "Vill du testa dina nyförvärvade klustringskunskaper på dataset som lämpar sig väl för K-Means-klustring? Se:\n",
+    "\n",
+    "-   [Träna och utvärdera klustringsmodeller](https://rpubs.com/eR_ic/clustering) med hjälp av Tidymodels och vänner\n",
+    "\n",
+    "-   [K-means Cluster Analysis](https://uc-r.github.io/kmeans_clustering), UC Business Analytics R Programming Guide\n",
+    "\n",
+    "- [K-means-klustring med principer för \"tidy data\"](https://www.tidymodels.org/learn/statistics/k-means/)\n",
+    "\n",
+    "## **Uppgift**\n",
+    "\n",
+    "[Prova olika klustringsmetoder](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)\n",
+    "\n",
+    "## TACK TILL:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) för att ha skapat den ursprungliga Python-versionen av denna modul ♥️\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) för att ha skapat de fantastiska illustrationerna som gör R mer välkomnande och engagerande. Hitta fler illustrationer i hennes [galleri](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "Lycka till med lärandet,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Konstverk av @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
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@@ -0,0 +1,550 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "e867e87e3129c8875423a82945f4ad5e",
+   "translation_date": "2025-09-06T14:21:11+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Börja där vi slutade i förra lektionen, med data importerad och filtrerad.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Vi kommer att fokusera på endast 3 genrer. Kanske kan vi få 3 kluster skapade!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Hur ren är denna data? Kontrollera efter avvikare med hjälp av lådagram. Vi kommer att fokusera på kolumner med färre avvikare (även om du skulle kunna rensa bort avvikelserna). Lådagram kan visa datans omfång och hjälpa till att välja vilka kolumner som ska användas. Observera att lådagram inte visar varians, en viktig komponent för bra klusterbar data (https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "source": [
+    "Välj flera kolumner med liknande intervall. Se till att inkludera kolumnen artist_top_genre för att hålla våra genrer tydliga.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "# scaler = StandardScaler()\n",
+    "\n",
+    "X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
+    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "y = le.transform(y)\n",
+    "\n"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 0, 2, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0,\n",
+       "       2, 0, 0, 1, 1, 2, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2,\n",
+       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 0,\n",
+       "       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 2,\n",
+       "       2, 1, 1, 0, 1, 2, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0,\n",
+       "       1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       1, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 1, 1, 2, 1],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "source": [
+    "De där siffrorna betyder inte mycket för oss, så låt oss ta ett 'silhuettvärde' för att se noggrannheten. Vårt värde är i mitten.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "source": [
+    "Importera KMeans och bygg en modell\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "source": [
+    "Använd den modellen för att bestämma, med hjälp av armbågmetoden, det bästa antalet kluster att skapa\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Looks like 3 is a good number after all. Fit the model again and create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Den här modellens noggrannhet är inte dålig, men inte heller fantastisk. Det kan vara så att datan inte lämpar sig väl för K-Means-klustring. Du kanske kan prova en annan metod.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 811,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/5-Clustering/2-K-Means/solution/tester.ipynb b/translations/sv/5-Clustering/2-K-Means/solution/tester.ipynb
new file mode 100644
index 000000000..5e5d45d59
--- /dev/null
+++ b/translations/sv/5-Clustering/2-K-Means/solution/tester.ipynb
@@ -0,0 +1,343 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "6f92868513e59d321245137c1c4c5311",
+   "translation_date": "2025-09-06T14:22:33+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/tester.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Börja där vi slutade i förra lektionen, med data importerad och filtrerad.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 105
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Vi kommer att fokusera på endast 3 genrer. Kanske kan vi få 3 kluster byggda!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 106
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 107
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# X = df.loc[:, ('danceability','energy')]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Unknown label type: 'continuous'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-110-3756ba7b0bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# we create an instance of SVM and fit out data. We do not scale our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# data since we want to plot the support vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mls30\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 30% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mls50\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 50% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mls100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 100% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m# actual graph construction (implementations should override this)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    181\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    182\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.semi_supervised import LabelSpreading\n",
+    "from sklearn.semi_supervised import SelfTrainingClassifier\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "X = df[['danceability','acousticness']].values\n",
+    "y = df['energy'].values\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "# step size in the mesh\n",
+    "h = .02\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "y_rand = rng.rand(y.shape[0])\n",
+    "y_30 = np.copy(y)\n",
+    "y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled\n",
+    "y_50 = np.copy(y)\n",
+    "y_50[y_rand < 0.5] = -1\n",
+    "# we create an instance of SVM and fit out data. We do not scale our\n",
+    "# data since we want to plot the support vectors\n",
+    "ls30 = (LabelSpreading().fit(X, y_30), y_30, 'Label Spreading 30% data')\n",
+    "ls50 = (LabelSpreading().fit(X, y_50), y_50, 'Label Spreading 50% data')\n",
+    "ls100 = (LabelSpreading().fit(X, y), y, 'Label Spreading 100% data')\n",
+    "\n",
+    "# the base classifier for self-training is identical to the SVC\n",
+    "base_classifier = SVC(kernel='rbf', gamma=.5, probability=True)\n",
+    "st30 = (SelfTrainingClassifier(base_classifier).fit(X, y_30),\n",
+    "        y_30, 'Self-training 30% data')\n",
+    "st50 = (SelfTrainingClassifier(base_classifier).fit(X, y_50),\n",
+    "        y_50, 'Self-training 50% data')\n",
+    "\n",
+    "rbf_svc = (SVC(kernel='rbf', gamma=.5).fit(X, y), y, 'SVC with rbf kernel')\n",
+    "\n",
+    "# create a mesh to plot in\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                     np.arange(y_min, y_max, h))\n",
+    "\n",
+    "color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}\n",
+    "\n",
+    "classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)\n",
+    "for i, (clf, y_train, title) in enumerate(classifiers):\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    colors = [color_map[y] for y in y_train]\n",
+    "    plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')\n",
+    "\n",
+    "    plt.title(title)\n",
+    "\n",
+    "plt.suptitle(\"Unlabeled points are colored white\", y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb b/translations/sv/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
new file mode 100644
index 000000000..7e2f77e7b
--- /dev/null
+++ b/translations/sv/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
@@ -0,0 +1,100 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "27de2abc0235ebd22080fc8f1107454d",
+   "translation_date": "2025-09-06T15:22:13+00:00",
+   "source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You should download the book text, clean it, and import it here\n",
+    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
+    "    file_contents = f.read()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "book_pride = TextBlob(file_contents)\n",
+    "positive_sentiment_sentences = []\n",
+    "negative_sentiment_sentences = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sentence in book_pride.sentences:\n",
+    "    if sentence.sentiment.polarity == 1:\n",
+    "        positive_sentiment_sentences.append(sentence)\n",
+    "    if sentence.sentiment.polarity == -1:\n",
+    "        negative_sentiment_sentences.append(sentence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
+    "for sentence in positive_sentiment_sentences:\n",
+    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
+    "for sentence in negative_sentiment_sentences:\n",
+    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/6-NLP/4-Hotel-Reviews-1/notebook.ipynb b/translations/sv/6-NLP/4-Hotel-Reviews-1/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sv/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb b/translations/sv/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
new file mode 100644
index 000000000..2f7cf9051
--- /dev/null
+++ b/translations/sv/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
@@ -0,0 +1,174 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2d05e7db439376aa824f4b387f8324ca",
+   "translation_date": "2025-09-06T15:21:52+00:00",
+   "source_file": "6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EDA\n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_difference_review_avg(row):\n",
+    "    return row[\"Average_Score\"] - row[\"Calc_Average_Score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "print(\"Loading data file now, this could take a while depending on file size\")\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n",
+    "end = time.time()\n",
+    "print(\"Loading took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What shape is the data (rows, columns)?\n",
+    "print(\"The shape of the data (rows, cols) is \" + str(df.shape))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# value_counts() creates a Series object that has index and values\n",
+    "#                in this case, the country and the frequency they occur in reviewer nationality\n",
+    "nationality_freq = df[\"Reviewer_Nationality\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What reviewer nationality is the most common in the dataset?\n",
+    "print(\"The highest frequency reviewer nationality is \" + str(nationality_freq.index[0]).strip() + \" with \" + str(nationality_freq[0]) + \" reviews.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the top 10 most common nationalities and their frequencies?\n",
+    "print(\"The top 10 highest frequency reviewer nationalities are:\")\n",
+    "print(nationality_freq[0:10].to_string())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many unique nationalities are there?\n",
+    "print(\"There are \" + str(nationality_freq.index.size) + \" unique nationalities in the dataset\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What was the most frequently reviewed hotel for the top 10 nationalities - print the hotel and number of reviews\n",
+    "for nat in nationality_freq[:10].index:\n",
+    "   # First, extract all the rows that match the criteria into a new dataframe\n",
+    "   nat_df = df[df[\"Reviewer_Nationality\"] == nat]   \n",
+    "   # Now get the hotel freq\n",
+    "   freq = nat_df[\"Hotel_Name\"].value_counts()\n",
+    "   print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many reviews are there per hotel (frequency count of hotel) and do the results match the value in `Total_Number_of_Reviews`?\n",
+    "# First create a new dataframe based on the old one, removing the uneeded columns\n",
+    "hotel_freq_df = df.drop([\"Hotel_Address\", \"Additional_Number_of_Scoring\", \"Review_Date\", \"Average_Score\", \"Reviewer_Nationality\", \"Negative_Review\", \"Review_Total_Negative_Word_Counts\", \"Positive_Review\", \"Review_Total_Positive_Word_Counts\", \"Total_Number_of_Reviews_Reviewer_Has_Given\", \"Reviewer_Score\", \"Tags\", \"days_since_review\", \"lat\", \"lng\"], axis = 1)\n",
+    "# Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found\n",
+    "hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')\n",
+    "# Get rid of all the duplicated rows\n",
+    "hotel_freq_df = hotel_freq_df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "print()\n",
+    "print(hotel_freq_df.to_string())\n",
+    "print(str(hotel_freq_df.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# While there is an `Average_Score` for each hotel according to the dataset, \n",
+    "# you can also calculate an average score (getting the average of all reviewer scores in the dataset for each hotel)\n",
+    "# Add a new column to your dataframe with the column header `Calc_Average_Score` that contains that calculated average. \n",
+    "df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n",
+    "# Add a new column with the difference between the two average scores\n",
+    "df[\"Average_Score_Difference\"] = df.apply(get_difference_review_avg, axis = 1)\n",
+    "# Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)\n",
+    "review_scores_df = df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "# Sort the dataframe to find the lowest and highest average score difference\n",
+    "review_scores_df = review_scores_df.sort_values(by=[\"Average_Score_Difference\"])\n",
+    "print(review_scores_df[[\"Average_Score_Difference\", \"Average_Score\", \"Calc_Average_Score\", \"Hotel_Name\"]])\n",
+    "# Do any hotels have the same (rounded to 1 decimal place) `Average_Score` and `Calc_Average_Score`?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör det noteras att automatiserade översättningar kan innehålla fel eller brister. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som kan uppstå vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/6-NLP/5-Hotel-Reviews-2/notebook.ipynb b/translations/sv/6-NLP/5-Hotel-Reviews-2/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sv/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb b/translations/sv/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
new file mode 100644
index 000000000..14fbcdff1
--- /dev/null
+++ b/translations/sv/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
@@ -0,0 +1,172 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "033cb89c85500224b3c63fd04f49b4aa",
+   "translation_date": "2025-09-06T15:22:34+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import time\n",
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replace_address(row):\n",
+    "    if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Amsterdam, Netherlands\"\n",
+    "    elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Barcelona, Spain\"\n",
+    "    elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
+    "        return \"London, United Kingdom\"\n",
+    "    elif \"Milan\" in row[\"Hotel_Address\"]:        \n",
+    "        return \"Milan, Italy\"\n",
+    "    elif \"France\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Paris, France\"\n",
+    "    elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Vienna, Austria\" \n",
+    "    else:\n",
+    "        return row.Hotel_Address\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dropping columns we will not use:\n",
+    "df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace all the addresses with a shortened, more useful form\n",
+    "df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop `Additional_Number_of_Scoring`\n",
+    "df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
+    "# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
+    "df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
+    "df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process the Tags into new columns\n",
+    "# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
+    "# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
+    "# Family with young children, Family with older children, With a pet\n",
+    "df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
+    "df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
+    "df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
+    "df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
+    "df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
+    "df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
+    "df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
+    "df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No longer need any of these columns\n",
+    "df.drop([\"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_Filtered.csv\n",
+      "Filtering took 23.74 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Saving new data file with calculated columns\n",
+    "print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
+    "df.to_csv(r'../../data/Hotel_Reviews_Filtered.csv', index = False)\n",
+    "end = time.time()\n",
+    "print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiserade översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb b/translations/sv/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
new file mode 100644
index 000000000..57f9f688f
--- /dev/null
+++ b/translations/sv/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
@@ -0,0 +1,137 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "341efc86325ec2a214f682f57a189dfd",
+   "translation_date": "2025-09-06T15:22:55+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV (you can )\n",
+    "import pandas as pd \n",
+    "\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We want to find the most useful tags to keep\n",
+    "# Remove opening and closing brackets\n",
+    "df.Tags = df.Tags.str.strip(\"[']\")\n",
+    "# remove all quotes too\n",
+    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
+    "# Now split the strings into a list\n",
+    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove leading and trailing spaces\n",
+    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
+    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
+    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
+    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
+    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
+    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the 6 columns into one with melt\n",
+    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "The shape of the tags with no filtering: (2514684, 2)\n",
+      "                        index   count\n",
+      "0                Leisure trip  338423\n",
+      "1                      Couple  205305\n",
+      "2               Solo traveler   89779\n",
+      "3               Business trip   68176\n",
+      "4                       Group   51593\n",
+      "5  Family with young children   49318\n",
+      "6  Family with older children   21509\n",
+      "7      Travelers with friends    1610\n",
+      "8                  With a pet    1078\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the value counts\n",
+    "tag_vc = df_tags.value.value_counts()\n",
+    "# print(tag_vc)\n",
+    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
+    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
+    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
+    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
+    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
+    "print(tag_vc[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör det noteras att automatiserade översättningar kan innehålla fel eller brister. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som kan uppstå vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb b/translations/sv/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
new file mode 100644
index 000000000..f837e1619
--- /dev/null
+++ b/translations/sv/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
@@ -0,0 +1,260 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "705bf02633759f689abc37b19749a16d",
+   "translation_date": "2025-09-06T15:23:15+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[nltk_data] Downloading package vader_lexicon to\n[nltk_data]     /Users/jenlooper/nltk_data...\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import pandas as pd\n",
+    "import nltk as nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    "nltk.download('vader_lexicon')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vader_sentiment = SentimentIntensityAnalyzer()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are 3 possibilities of input for a review:\n",
+    "# It could be \"No Negative\", in which case, return 0\n",
+    "# It could be \"No Positive\", in which case, return 0\n",
+    "# It could be a review, in which case calculate the sentiment\n",
+    "def calc_sentiment(review):    \n",
+    "    if review == \"No Negative\" or review == \"No Positive\":\n",
+    "        return 0\n",
+    "    return vader_sentiment.polarity_scores(review)[\"compound\"]    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "df = pd.read_csv(\"../../data/Hotel_Reviews_Filtered.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove stop words - can be slow for a lot of text!\n",
+    "# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches\n",
+    "# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends\n",
+    "start = time.time()\n",
+    "cache = set(stopwords.words(\"english\"))\n",
+    "def remove_stopwords(review):\n",
+    "    text = \" \".join([word for word in review.split() if word not in cache])\n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the stop words from both columns\n",
+    "df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   \n",
+    "df.Positive_Review = df.Positive_Review.apply(remove_stopwords)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Removing stop words took 5.77 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "end = time.time()\n",
+    "print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Calculating sentiment columns for both positive and negative reviews\n",
+      "Calculating sentiment took 201.07 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a negative sentiment and positive sentiment column\n",
+    "print(\"Calculating sentiment columns for both positive and negative reviews\")\n",
+    "start = time.time()\n",
+    "df[\"Negative_Sentiment\"] = df.Negative_Review.apply(calc_sentiment)\n",
+    "df[\"Positive_Sentiment\"] = df.Positive_Review.apply(calc_sentiment)\n",
+    "end = time.time()\n",
+    "print(\"Calculating sentiment took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                          Negative_Review  Negative_Sentiment\n",
+      "186584  So bad experience memories I hotel The first n...             -0.9920\n",
+      "129503  First charged twice room booked booking second...             -0.9896\n",
+      "307286  The staff Had bad experience even booking Janu...             -0.9889\n",
+      "452092  No WLAN room Incredibly rude restaurant staff ...             -0.9884\n",
+      "201293  We usually traveling Paris 2 3 times year busi...             -0.9873\n",
+      "...                                                   ...                 ...\n",
+      "26899   I would say however one night expensive even d...              0.9933\n",
+      "138365  Wifi terribly slow I speed test network upload...              0.9938\n",
+      "79215   I find anything hotel first I walked past hote...              0.9938\n",
+      "278506  The property great location There bakery next ...              0.9945\n",
+      "339189  Guys I like hotel I wish return next year Howe...              0.9948\n",
+      "\n",
+      "[515738 rows x 2 columns]\n",
+      "                                          Positive_Review  Positive_Sentiment\n",
+      "137893  Bathroom Shower We going stay twice hotel 2 ni...             -0.9820\n",
+      "5839    I completely disappointed mad since reception ...             -0.9780\n",
+      "64158   get everything extra internet parking breakfas...             -0.9751\n",
+      "124178  I didnt like anythig Room small Asked upgrade ...             -0.9721\n",
+      "489137  Very rude manager abusive staff reception Dirt...             -0.9703\n",
+      "...                                                   ...                 ...\n",
+      "331570  Everything This recently renovated hotel class...              0.9984\n",
+      "322920  From moment stepped doors Guesthouse Hotel sta...              0.9985\n",
+      "293710  This place surprise expected good actually gre...              0.9985\n",
+      "417442  We celebrated wedding night Langham I commend ...              0.9985\n",
+      "132492  We arrived super cute boutique hotel area expl...              0.9987\n",
+      "\n",
+      "[515738 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=[\"Negative_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Negative_Review\", \"Negative_Sentiment\"]])\n",
+    "df = df.sort_values(by=[\"Positive_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Positive_Review\", \"Positive_Sentiment\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)\n",
+    "df = df.reindex([\"Hotel_Name\", \"Hotel_Address\", \"Total_Number_of_Reviews\", \"Average_Score\", \"Reviewer_Score\", \"Negative_Sentiment\", \"Positive_Sentiment\", \"Reviewer_Nationality\", \"Leisure_trip\", \"Couple\", \"Solo_traveler\", \"Business_trip\", \"Group\", \"Family_with_young_children\", \"Family_with_older_children\", \"With_a_pet\", \"Negative_Review\", \"Positive_Review\"], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_NLP.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
+    "df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiserade översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/7-TimeSeries/1-Introduction/solution/notebook.ipynb b/translations/sv/7-TimeSeries/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..12ba423ea
--- /dev/null
+++ b/translations/sv/7-TimeSeries/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,170 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Datainställning\n",
+    "\n",
+    "I den här notebooken visar vi hur man:\n",
+    "- ställer in tidsseriedata för den här modulen\n",
+    "- visualiserar data\n",
+    "\n",
+    "Datan i det här exemplet är hämtad från GEFCom2014 prognostävling. Den består av 3 års timvisa elförbruknings- och temperaturvärden mellan 2012 och 2014.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli och Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, s. 896-913, juli-september, 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "from common.utils import load_data\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ladda data från csv till en Pandas-dataram\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2698.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2558.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2444.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2402.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2403.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "data_dir = './data'\n",
+    "energy = load_data(data_dir)[['load']]\n",
+    "energy.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotta all tillgänglig lastdata (januari 2012 till december 2014)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 1080x576 with 1 Axes>",
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231.185656 \nL 91.475143 185.318661 \nL 91.506964 197.827841 \nL 91.761536 330.381263 \nL 91.825179 313.153708 \nL 91.984286 214.726208 \nL 92.047929 223.065662 \nL 92.143394 231.953764 \nL 92.238858 182.026771 \nL 92.302501 208.252159 \nL 92.525251 302.290472 \nL 92.557073 297.901286 \nL 92.652537 247.645104 \nL 92.779823 197.169464 \nL 92.811645 201.229461 \nL 92.875287 210.446752 \nL 92.907109 207.81324 \nL 93.002573 138.13491 \nL 93.066216 169.84678 \nL 93.257145 279.905623 \nL 93.288967 277.820759 \nL 93.352609 237.440247 \nL 93.416252 165.896512 \nL 93.479895 170.175969 \nL 93.670824 198.925138 \nL 93.702646 189.488388 \nL 93.766288 144.3895 \nL 93.79811 156.679221 \nL 94.052682 299.65696 \nL 94.084503 288.245076 \nL 94.179968 188.500821 \nL 94.275432 193.109466 \nL 94.434539 214.506749 \nL 94.466361 204.850539 \nL 94.530004 162.714352 \nL 94.561825 175.881911 \nL 94.816397 318.969379 \nL 94.848218 308.545062 \nL 95.039147 197.169464 \nL 95.134611 205.947836 \nL 95.198254 212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 197.217872 308.106144 \nL 197.440622 167.981376 \nL 197.59973 160.410029 \nL 197.727015 185.318661 \nL 197.917944 322.480728 \nL 197.981587 297.352638 \nL 198.236159 139.122477 \nL 198.331623 119.919787 \nL 198.363445 120.578165 \nL 198.490731 168.420294 \nL 198.713481 322.261269 \nL 198.745302 321.383432 \nL 199.063517 158.325166 \nL 199.12716 147.791119 \nL 199.158981 152.290035 \nL 199.286267 213.519182 \nL 199.477196 317.542894 \nL 199.509017 319.847217 \nL 199.604482 229.539711 \nL 199.763589 153.82625 \nL 199.827232 152.180305 \nL 199.890875 142.194907 \nL 199.922697 149.437064 \nL 200.049982 220.212691 \nL 200.20909 311.398033 \nL 200.272733 292.305074 \nL 200.495483 131.441401 \nL 200.527304 126.064648 \nL 200.590947 133.965183 \nL 200.622769 133.087346 \nL 200.65459 133.306805 \nL 200.781876 176.2111 \nL 200.972805 319.627757 \nL 201.036448 302.94885 \nL 201.227377 151.851116 \nL 201.259198 152.399765 \nL 201.29102 152.180305 \nL 201.322841 152.619224 \nL 201.354662 152.948413 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 218.983754 162.055974 \nL 219.015576 162.385163 \nL 219.11104 187.622984 \nL 219.301969 310.300737 \nL 219.365612 281.880756 \nL 219.556541 157.995977 \nL 219.683826 161.726785 \nL 219.620183 157.666788 \nL 219.715648 160.410029 \nL 219.747469 161.178137 \nL 219.874755 183.562986 \nL 220.065684 319.737487 \nL 220.097505 319.079109 \nL 220.161148 297.462367 \nL 220.352077 172.80948 \nL 220.383899 175.442992 \nL 220.63847 211.763507 \nL 220.829399 325.22397 \nL 220.861221 324.016944 \nL 220.924864 310.191007 \nL 221.115792 208.471618 \nL 221.147614 210.117563 \nL 221.179435 211.214859 \nL 221.211257 210.88567 \nL 221.2749 200.461353 \nL 221.306721 201.44892 \nL 221.434007 256.533206 \nL 221.593114 330.271534 \nL 221.656757 303.168309 \nL 221.911329 131.55113 \nL 222.006793 125.40627 \nL 222.038615 127.052215 \nL 222.165901 168.420294 \nL 222.356829 309.861818 \nL 222.420472 285.392105 \nL 222.675044 125.845188 \nL 222.738687 129.246808 \nL 222.865973 155.252736 \nL 222.897794 153.058143 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227.702836 315.238571 \nL 227.766479 291.427236 \nL 228.02105 135.391668 \nL 228.116515 126.503566 \nL 228.148336 128.039781 \nL 228.275622 168.200835 \nL 228.466551 311.727222 \nL 228.530194 285.831024 \nL 228.784765 105.764662 \nL 228.848408 100.278179 \nL 228.912051 102.143583 \nL 229.039337 142.085177 \nL 229.230266 295.377504 \nL 229.262087 291.536966 \nL 229.548481 84.696568 \nL 229.643945 97.864127 \nL 229.803052 143.182474 \nL 229.993981 278.369407 \nL 230.057624 251.156453 \nL 230.280374 103.789528 \nL 230.344017 117.396005 \nL 230.598589 209.239725 \nL 230.757696 299.108312 \nL 230.789518 298.559664 \nL 230.853161 280.564001 \nL 231.075911 157.666788 \nL 231.26684 168.310565 \nL 231.298661 167.761916 \nL 231.553233 301.632094 \nL 231.585054 296.4748 \nL 231.648697 262.239148 \nL 231.839626 164.908945 \nL 231.935091 146.254904 \nL 231.966912 138.573828 \nL 232.030555 148.120308 \nL 232.062376 146.035445 \nL 232.285127 300.863987 \nL 232.316948 295.048315 \nL 232.635163 98.961423 \nL 232.698806 100.168449 \nL 232.730627 101.704665 \nL 232.857913 151.302468 \nL 233.048842 295.487234 \nL 233.080663 291.207777 \nL 233.367056 101.046287 \nL 233.430699 107.191147 \nL 233.557985 130.014915 \nL 233.589807 127.710593 \nL 233.812557 294.280207 \nL 233.844378 289.561832 \nL 234.194415 111.690063 \nL 234.258057 119.480868 \nL 234.385343 155.801384 \nL 234.576272 292.414803 \nL 234.608094 288.245076 \nL 234.894487 125.625729 \nL 234.926308 127.491133 \nL 235.085416 148.559227 \nL 235.117237 147.68139 \nL 235.339987 312.166141 \nL 235.371809 306.569929 \nL 235.721845 120.687895 \nL 235.753666 121.346273 \nL 235.880952 144.608959 \nL 236.135524 304.046147 \nL 236.167345 296.4748 \nL 236.549203 193.877574 \nL 236.676489 220.322421 \nL 236.899239 337.513691 \nL 236.962882 327.528292 \nL 237.185632 225.589444 \nL 237.217454 226.79647 \nL 237.249275 224.272688 \nL 237.408383 203.314324 \nL 237.631133 332.466127 \nL 237.662954 326.211537 \nL 237.949347 160.958678 \nL 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345.414226 \nL 247.654894 330.600723 \nL 247.845823 238.208354 \nL 247.877645 242.048892 \nL 247.941288 246.438078 \nL 247.973109 242.59754 \nL 248.068573 234.916464 \nL 248.100395 239.41538 \nL 248.323145 354.192598 \nL 248.354967 351.120168 \nL 248.482252 275.296977 \nL 248.577717 229.539711 \nL 248.64136 231.075927 \nL 248.673181 230.966197 \nL 248.736824 220.651609 \nL 248.832289 201.010001 \nL 248.86411 206.496484 \nL 249.08686 340.586121 \nL 249.150503 302.94885 \nL 249.309611 191.573251 \nL 249.405075 187.513254 \nL 249.468718 192.01217 \nL 249.500539 189.378658 \nL 249.596004 178.844611 \nL 249.691468 261.6905 \nL 249.850575 324.785051 \nL 249.914218 283.526701 \nL 250.073326 161.507326 \nL 250.16879 158.764085 \nL 250.264255 163.043541 \nL 250.296076 161.946245 \nL 250.327897 161.507326 \nL 250.359719 153.387332 \nL 250.39154 165.567323 \nL 250.614291 321.602891 \nL 250.677934 284.953187 \nL 250.868862 168.420294 \nL 250.996148 160.958678 \nL 251.02797 161.946245 \nL 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216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 315.75283 171.382995 \nL 315.880116 189.488388 \nL 315.911937 182.026771 \nL 315.97558 135.281939 \nL 316.039223 155.252736 \nL 316.134688 222.955932 \nL 316.293795 295.487234 \nL 316.357438 280.125082 \nL 316.516545 184.221364 \nL 316.548367 185.977039 \nL 316.643831 197.279193 \nL 316.675652 191.682981 \nL 316.739295 154.704087 \nL 316.802938 177.198666 \nL 317.05751 323.578025 \nL 317.152974 296.4748 \nL 317.312082 212.531615 \nL 317.343903 215.165127 \nL 317.407546 223.065662 \nL 317.439368 216.262423 \nL 317.503011 165.238134 \nL 317.566653 190.256495 \nL 317.789404 338.610988 \nL 317.853047 324.785051 \nL 318.043975 214.28729 \nL 318.171261 228.332685 \nL 318.203083 224.272688 \nL 318.266726 161.397596 \nL 318.330369 174.674884 \nL 318.553119 330.381263 \nL 318.616762 319.518028 \nL 318.807691 200.790542 \nL 318.839512 203.424054 \nL 318.934976 209.349455 \nL 318.966798 204.411621 \nL 319.030441 154.923547 \nL 319.094084 172.480291 \nL 319.348656 336.745583 \nL 319.380477 327.089374 \nL 319.475941 219.993232 \nL 319.603227 220.212691 \nL 319.635049 220.871069 \nL 319.66687 219.225124 \nL 319.698692 219.334854 \nL 319.825978 144.27977 \nL 319.88962 169.956509 \nL 320.080549 296.145611 \nL 320.112371 295.158045 \nL 320.176014 248.852131 \nL 320.239657 173.906777 \nL 320.303299 176.979207 \nL 320.462407 199.583516 \nL 320.494228 195.413789 \nL 320.557871 129.246808 \nL 320.621514 138.13491 \nL 320.844264 294.719126 \nL 320.907907 283.087783 \nL 321.003372 177.308396 \nL 321.098836 183.233797 \nL 321.226122 204.74081 \nL 321.257943 199.144597 \nL 321.321586 157.227869 \nL 321.385229 179.722449 \nL 321.639801 331.039641 \nL 321.703444 314.360734 \nL 321.862551 207.264592 \nL 321.926194 212.641345 \nL 321.989837 222.297554 \nL 322.021659 217.46945 \nL 322.085302 175.881911 \nL 322.148944 200.900272 \nL 322.403516 336.196935 \nL 322.467159 323.248836 \nL 322.658088 232.392682 \nL 322.689909 235.245653 \nL 322.753552 243.475378 \nL 322.785374 235.026194 \nL 322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 340.923609 150.973279 \nL 340.95543 151.083009 \nL 341.082716 162.933811 \nL 341.114538 159.312733 \nL 341.178181 98.741964 \nL 341.241824 109.38574 \nL 341.464574 253.460776 \nL 341.528217 240.293218 \nL 341.623681 133.087346 \nL 341.719146 138.464099 \nL 341.846431 158.434896 \nL 341.878253 156.350032 \nL 341.941896 100.05872 \nL 342.005539 111.141415 \nL 342.26011 265.201849 \nL 342.291932 254.448343 \nL 342.451039 145.377067 \nL 342.482861 145.596526 \nL 342.641968 174.674884 \nL 342.705611 132.209508 \nL 342.769254 155.362465 \nL 343.023826 302.619661 \nL 343.087469 286.489402 \nL 343.246576 199.802975 \nL 343.310219 208.910536 \nL 343.373862 219.225124 \nL 343.405683 214.616478 \nL 343.469326 153.716521 \nL 343.532969 171.053806 \nL 343.787541 298.011016 \nL 343.851184 287.476969 \nL 344.010291 198.705679 \nL 344.073934 203.862972 \nL 344.137577 216.372153 \nL 344.169398 214.17756 \nL 344.233041 148.668956 \nL 344.296684 173.138669 \nL 344.519435 307.228306 \nL 344.551256 304.375335 \nL 344.614899 260.593204 \nL 344.774006 175.662451 \nL 344.901292 193.658115 \nL 344.933114 188.83001 \nL 345.028578 123.650595 \nL 345.060399 137.695991 \nL 345.28315 282.868323 \nL 345.346793 271.456439 \nL 345.474078 169.298131 \nL 345.537721 177.418126 \nL 345.665007 199.473786 \nL 345.696829 196.291626 \nL 345.760472 135.940317 \nL 345.824115 150.973279 \nL 346.046865 299.87642 \nL 346.110508 287.03805 \nL 346.205972 176.10137 \nL 346.301437 181.148934 \nL 346.428722 187.952173 \nL 346.460544 184.550553 \nL 346.524187 138.354369 \nL 346.58783 157.557058 \nL 346.842401 309.532629 \nL 346.874223 297.681827 \nL 347.065152 190.146766 \nL 347.192438 207.264592 \nL 347.224259 204.960269 \nL 347.287902 148.339767 \nL 347.351545 162.494893 \nL 347.574295 305.801821 \nL 347.637938 294.170478 \nL 347.797045 186.196498 \nL 347.828867 187.513254 \nL 347.956153 199.583516 \nL 347.987974 199.473786 \nL 348.051617 159.641922 \nL 348.11526 183.014338 \nL 348.369832 324.565592 \nL 348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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159.203003 \nL 389.3877 188.72028 \nL 389.578629 316.884516 \nL 389.610451 316.555327 \nL 389.674094 271.12725 \nL 389.833201 188.61055 \nL 389.865022 189.488388 \nL 389.992308 201.55865 \nL 390.087773 157.337599 \nL 390.151416 185.099202 \nL 390.342344 310.959115 \nL 390.374166 310.520196 \nL 390.437809 269.152117 \nL 390.596916 186.745146 \nL 390.660559 196.181897 \nL 390.756024 215.494316 \nL 390.787845 210.666211 \nL 390.851488 177.747315 \nL 390.883309 188.939739 \nL 391.10606 317.981813 \nL 391.201524 297.462367 \nL 391.32881 229.320252 \nL 391.392453 237.111058 \nL 391.519739 263.007256 \nL 391.55156 256.094288 \nL 391.615203 206.606214 \nL 391.678846 227.015929 \nL 391.901596 326.211537 \nL 391.933418 325.22397 \nL 392.028882 286.92832 \nL 392.156168 237.440247 \nL 392.187989 242.158622 \nL 392.283454 261.141852 \nL 392.315275 256.423477 \nL 392.41074 198.486219 \nL 392.442561 210.446752 \nL 392.63349 331.47856 \nL 392.665311 330.161804 \nL 392.728954 288.793725 \nL 392.888062 207.045132 \nL 392.983526 220.43215 \nL 393.047169 227.345118 \nL 393.07899 222.078095 \nL 393.174455 181.807312 \nL 393.206276 196.511086 \nL 393.429027 329.283967 \nL 393.49267 289.232643 \nL 393.651777 198.37649 \nL 393.683598 194.097033 \nL 393.747241 199.254327 \nL 393.810884 210.117563 \nL 393.842706 202.875406 \nL 393.93817 178.625152 \nL 393.969991 193.548385 \nL 394.192742 330.929912 \nL 394.256385 290.11048 \nL 394.415492 207.154862 \nL 394.542778 223.394851 \nL 394.574599 229.8689 \nL 394.606421 226.9062 \nL 394.701885 188.391091 \nL 394.733707 197.059734 \nL 394.924635 317.542894 \nL 394.956457 316.006679 \nL 395.0201 274.748329 \nL 395.147386 195.633248 \nL 395.179207 195.19433 \nL 395.338314 210.446752 \nL 395.370136 207.045132 \nL 395.4656 164.908945 \nL 395.497422 172.041373 \nL 395.688351 294.170478 \nL 395.720172 293.621829 \nL 395.783815 253.460776 \nL 395.879279 177.966774 \nL 395.911101 178.734882 \nL 395.974744 187.622984 \nL 396.10203 221.309987 \nL 396.133851 219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 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214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 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319.737487 \nL 413.540193 227.564578 \nL 413.603836 244.024026 \nL 413.667478 254.119154 \nL 413.6993 249.400779 \nL 413.794764 219.883502 \nL 413.826586 221.748906 \nL 414.017515 342.561255 \nL 414.049336 341.463959 \nL 414.112979 295.926152 \nL 414.272086 212.860804 \nL 414.463015 243.255918 \nL 414.494837 237.659706 \nL 414.558479 213.409452 \nL 414.590301 214.06783 \nL 414.78123 341.244499 \nL 414.813051 340.91531 \nL 414.876694 297.572097 \nL 415.035801 207.264592 \nL 415.099444 211.982967 \nL 415.22673 231.075927 \nL 415.290373 222.846203 \nL 415.322195 208.471618 \nL 415.354016 210.007833 \nL 415.544945 343.658552 \nL 415.576766 343.329363 \nL 415.640409 301.741824 \nL 415.767695 215.823505 \nL 415.799517 216.701342 \nL 415.958624 234.367816 \nL 415.990445 231.734305 \nL 416.08591 204.850539 \nL 416.117731 213.738641 \nL 416.30866 345.194767 \nL 416.340482 343.768281 \nL 416.404124 299.327771 \nL 416.53141 221.858636 \nL 416.563232 221.968365 \nL 416.595053 222.517014 \nL 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331.58829 \nL 498.089826 330.381263 \nL 498.153469 308.435333 \nL 498.344397 190.914873 \nL 498.376219 191.024603 \nL 498.439862 185.099202 \nL 498.503505 173.797047 \nL 498.535326 177.418126 \nL 498.662612 228.661874 \nL 498.853541 322.151539 \nL 498.917184 307.886684 \nL 499.171756 188.171632 \nL 499.203577 190.037036 \nL 499.26722 177.637585 \nL 499.330863 181.039204 \nL 499.362684 183.233797 \nL 499.585435 312.824519 \nL 499.617256 303.716958 \nL 499.935471 127.381404 \nL 499.967292 128.039781 \nL 499.999114 127.600863 \nL 500.1264 151.192738 \nL 500.34915 310.849385 \nL 500.412793 287.696428 \nL 500.635543 147.791119 \nL 500.79465 143.072744 \nL 500.921936 184.331094 \nL 501.112865 318.530461 \nL 501.176508 292.743992 \nL 501.399258 149.875983 \nL 501.526544 138.573828 \nL 501.558365 140.439232 \nL 501.685651 179.832178 \nL 501.87658 315.896949 \nL 501.940223 291.317507 \nL 502.162973 136.269506 \nL 502.194795 131.66086 \nL 502.226616 135.830587 \nL 502.449366 185.977039 \nL 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168.639753 \nL 528.288396 168.639753 \nL 528.352039 166.66462 \nL 528.383861 176.2111 \nL 528.606611 328.625589 \nL 528.670254 294.389937 \nL 528.829361 206.057565 \nL 528.861183 203.972702 \nL 528.924826 205.508917 \nL 528.988468 210.666211 \nL 529.083933 210.007833 \nL 529.115754 200.790542 \nL 529.147576 207.374321 \nL 529.370326 349.913142 \nL 529.433969 315.56776 \nL 529.593076 224.382418 \nL 529.688541 222.187825 \nL 529.784005 230.966197 \nL 529.847648 229.97863 \nL 529.879469 222.517014 \nL 529.911291 228.442415 \nL 530.134041 351.449357 \nL 530.165863 348.376927 \nL 530.261327 296.58453 \nL 530.388613 240.841866 \nL 530.420434 241.061325 \nL 530.515899 245.560241 \nL 530.643185 222.078095 \nL 530.675006 231.734305 \nL 530.897756 350.46179 \nL 530.929578 348.486656 \nL 531.025042 307.776955 \nL 531.18415 239.195921 \nL 531.279614 254.228884 \nL 531.311435 247.315915 \nL 531.4069 224.272688 \nL 531.438721 234.477546 \nL 531.62965 357.484488 \nL 531.661472 357.045569 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 569.910873 275.626166 \nL 569.974516 190.037036 \nL 570.038159 197.827841 \nL 570.197266 223.61431 \nL 570.229087 227.125659 \nL 570.260909 219.444583 \nL 570.324552 164.689486 \nL 570.388195 190.037036 \nL 570.610945 322.04181 \nL 570.674588 280.234812 \nL 570.738231 193.109466 \nL 570.801874 195.633248 \nL 570.833695 195.523519 \nL 570.992803 226.68674 \nL 571.024624 222.407284 \nL 571.088267 172.480291 \nL 571.15191 197.498652 \nL 571.342839 325.333699 \nL 571.37466 325.00451 \nL 571.438303 282.539134 \nL 571.59741 195.962437 \nL 571.629232 195.852708 \nL 571.756518 203.094865 \nL 571.820161 186.086768 \nL 571.883804 197.388923 \nL 571.979268 275.845625 \nL 572.138375 340.476392 \nL 572.202018 305.143443 \nL 572.361126 204.192161 \nL 572.392947 204.850539 \nL 572.552054 230.966197 \nL 572.615697 199.254327 \nL 572.647519 214.06783 \nL 572.90209 352.436924 \nL 572.965733 333.782883 \nL 573.124841 247.535375 \nL 573.156662 250.498075 \nL 573.283948 267.835361 \nL 573.31577 258.947259 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199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 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150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 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693.537265 325.443429 \nL 693.569087 321.493162 \nL 693.63273 277.71103 \nL 693.760015 206.386754 \nL 693.791837 206.606214 \nL 693.919123 221.858636 \nL 693.950944 226.357551 \nL 693.982766 225.369985 \nL 694.110052 198.26676 \nL 694.30098 312.934248 \nL 694.332802 310.849385 \nL 694.396445 266.628335 \nL 694.460088 201.229461 \nL 694.523731 207.92297 \nL 694.714659 233.819168 \nL 694.778302 226.9062 \nL 694.873767 204.960269 \nL 695.096517 328.625589 \nL 695.16016 286.92832 \nL 695.255624 214.616478 \nL 695.287446 217.798638 \nL 695.351089 223.72404 \nL 695.478375 242.487811 \nL 695.542017 237.549976 \nL 695.60566 226.79647 \nL 695.637482 228.661874 \nL 695.828411 340.476392 \nL 695.892054 335.648287 \nL 696.051161 243.804566 \nL 696.146625 257.081855 \nL 696.24209 271.23698 \nL 696.273911 268.384009 \nL 696.401197 244.024026 \nL 696.592126 346.511523 \nL 696.655769 344.975307 \nL 696.751233 293.182911 \nL 696.878519 221.968365 \nL 696.910341 223.285121 \nL 697.005805 239.964029 \nL 697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 \nL 707.729639 249.839697 \nL 707.856925 227.893767 \nL 708.047853 338.062339 \nL 708.079675 334.66072 \nL 708.143318 293.621829 \nL 708.302425 218.566746 \nL 708.461532 233.489979 \nL 708.493354 229.649441 \nL 708.588818 213.738641 \nL 708.62064 214.616478 \nL 708.84339 336.635854 \nL 708.907033 300.425068 \nL 709.034319 224.272688 \nL 709.06614 225.589444 \nL 709.225248 245.99916 \nL 709.28889 238.866732 \nL 709.384355 216.701342 \nL 709.607105 343.878011 \nL 709.670748 303.497498 \nL 709.829855 209.568914 \nL 709.92532 215.933234 \nL 709.988963 221.419717 \nL 710.020784 216.701342 \nL 710.116249 201.33919 \nL 710.14807 205.728377 \nL 710.338999 329.722886 \nL 710.37082 328.515859 \nL 710.434463 290.000751 \nL 710.593571 208.910536 \nL 710.657213 216.481883 \nL 710.752678 232.502412 \nL 710.848142 231.514845 \nL 710.911785 218.127827 \nL 711.102714 340.91531 \nL 711.166357 331.259101 \nL 711.357286 232.941331 \nL 711.389107 233.819168 \nL 711.611857 256.752666 \nL 711.6755 241.171055 \nL 711.707322 255.106721 \nL 711.898251 345.962874 \nL 711.961894 333.343964 \nL 712.121001 255.984558 \nL 712.184644 263.775363 \nL 712.248287 274.089951 \nL 712.31193 270.578602 \nL 712.439216 250.607805 \nL 712.661966 354.302328 \nL 712.693787 352.327194 \nL 712.789252 308.764522 \nL 712.916538 252.473209 \nL 712.948359 253.460776 \nL 713.043823 260.702933 \nL 713.202931 237.879165 \nL 713.393859 344.31693 \nL 713.425681 342.341796 \nL 713.489324 302.290472 \nL 713.648431 219.773772 \nL 713.680253 220.102961 \nL 713.807539 240.402947 \nL 713.871181 235.245653 \nL 713.934824 229.97863 \nL 713.966646 220.322421 \nL 714.157575 340.805581 \nL 714.189396 338.391528 \nL 714.253039 301.522364 \nL 714.412146 231.514845 \nL 714.443968 229.649441 \nL 714.475789 234.148357 \nL 714.571254 240.841866 \nL 714.603075 239.63484 \nL 714.730361 218.566746 \nL 714.92129 339.927743 \nL 714.984933 325.772618 \nL 715.112219 234.367816 \nL 715.175862 237.001328 \nL 715.36679 245.121322 \nL 715.462255 236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 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158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 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339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 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\nL 834.569999 204.850539 \nL 834.633642 221.090528 \nL 834.856392 341.13477 \nL 834.888214 337.73315 \nL 835.0155 250.607805 \nL 835.079143 228.332685 \nL 835.142785 239.41538 \nL 835.23825 262.348878 \nL 835.270071 256.423477 \nL 835.333714 219.554313 \nL 835.397357 235.684572 \nL 835.588286 339.598554 \nL 835.651929 335.977476 \nL 835.747393 285.392105 \nL 835.811036 245.88943 \nL 835.874679 254.777532 \nL 835.970144 270.578602 \nL 836.001965 269.920224 \nL 836.097429 215.494316 \nL 836.161072 235.026194 \nL 836.383823 340.366662 \nL 836.447466 308.654792 \nL 836.606573 211.324589 \nL 836.76568 228.113226 \nL 836.797502 223.61431 \nL 836.861145 186.415957 \nL 836.892966 193.767844 \nL 837.147538 345.523956 \nL 837.179359 334.880179 \nL 837.40211 216.262423 \nL 837.497574 219.225124 \nL 837.529395 221.858636 \nL 837.561217 219.554313 \nL 837.62486 186.854876 \nL 837.688503 213.189993 \nL 837.879431 339.379095 \nL 837.911253 339.269365 \nL 837.974896 301.193176 \nL 838.134003 204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 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222.626743 \nL 852.103626 191.682981 \nL 852.135448 200.461353 \nL 852.39002 332.356397 \nL 852.421841 330.052075 \nL 852.485484 283.85589 \nL 852.61277 217.908368 \nL 852.644591 218.127827 \nL 852.676413 219.444583 \nL 852.771877 228.881334 \nL 852.83552 202.217028 \nL 852.867342 172.480291 \nL 852.930985 188.939739 \nL 853.153735 324.016944 \nL 853.217378 310.191007 \nL 853.408307 208.032699 \nL 853.535592 220.761339 \nL 853.567414 217.140261 \nL 853.631057 177.308396 \nL 853.6947 192.451088 \nL 853.91745 332.356397 \nL 853.981093 320.725054 \nL 854.108379 216.481883 \nL 854.203843 220.980798 \nL 854.299308 232.831601 \nL 854.331129 231.624575 \nL 854.394772 181.258664 \nL 854.458415 197.169464 \nL 854.681165 338.940176 \nL 854.744808 325.882348 \nL 854.872094 210.9954 \nL 854.935737 211.214859 \nL 854.967558 210.775941 \nL 854.99938 215.274856 \nL 855.063023 214.06783 \nL 855.126666 188.281362 \nL 855.158487 172.041373 \nL 855.22213 193.109466 \nL 855.44488 330.052075 \nL 855.508523 320.834784 \nL 855.699452 202.875406 \nL 855.731273 204.850539 \nL 855.826738 214.506749 \nL 855.858559 211.653778 \nL 855.922202 171.931643 \nL 855.985845 192.341359 \nL 856.240417 316.555327 \nL 856.30406 296.365071 \nL 856.431346 226.028363 \nL 856.494989 233.489979 \nL 856.590453 250.498075 \nL 856.622275 243.914296 \nL 856.685917 189.268928 \nL 856.74956 213.738641 \nL 857.004132 334.002342 \nL 857.067775 323.468295 \nL 857.258704 241.500244 \nL 857.290525 247.645104 \nL 857.354168 254.996991 \nL 857.38599 247.535375 \nL 857.449633 187.952173 \nL 857.513276 210.117563 \nL 857.736026 333.782883 \nL 857.799669 320.615324 \nL 857.990598 211.434319 \nL 858.117883 227.015929 \nL 858.149705 226.028363 \nL 858.213348 175.772181 \nL 858.276991 193.877574 \nL 858.499741 328.625589 \nL 858.563384 316.116408 \nL 858.754313 219.554313 \nL 858.786134 224.492147 \nL 858.881599 231.734305 \nL 858.91342 228.661874 \nL 858.977063 177.308396 \nL 859.040706 198.486219 \nL 859.263456 338.610988 \nL 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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/M4S/eddS5FlNGb329WvKYTMb2MBmlpq6PVhTmQ8hhCLXNxkNuP2ShfjTKTfOuv2KxEBEREREymCSqHGxmMQ3XziO6sIc3HbJwoxf32Ez47rV5Xj2UDeC4WjGr69GoUgUJ/t8ipWaJn3okmqYDAKPvM7VRCIiIiI9YZKocS8296K524vPXrMMFpMyL/et66vgDUbw0rE+Ra6vNicSTWvWVCqbJJY4bLi2vgy/PtDJBJ+IiIhIR5gkapiUEv/7ymnUFufhprWVisVxWV0RKvJtLDmdoaYuLwAoniQCwB2bFsIzFsbvDvcoHQoRERERZQiTRA3bcXIAzd1e3HVVHYwGZfa2AfEB7e9bX4WdJwc4oH0GjnR54LSZUF2oTNOayS6rLUKdKw8Ps+SUiIiISDeYJGrY/a+cRmVBDm5Zp9wqYtKtF3Nm4kw1dXmwWsGmNZMJIfCRTYvQ2DGCpsRYDiIiIiLSNiaJGrX3zCDeaB3GJ7fWwmxU/mVeXJyHDYsW4DcHOjkz8TzGIzGc6PVlRalp0vvWV8FmNuCxfe1Kh0JEREREGaB89kBp8b0dLSi2W/ChjdVKhzLh/RdX4XT/KB7Z285E8RxO9vkwHo2hPouSxPwcM66tL8NzR3owHokpHQ4RERERpRmTRA063DmCnScH8FeX18JmNiodzoSbGiqwqbYQX3mqCZ986ADcoyGlQ8o6yZLObFpJBICbGyoxEgjj1ZMDSodC0+gcDuDh19vw6/0dCEeZyBMREdH8ZHayOmXE/a+0wGkz4SObMj8X8XxyLSY8+vFN+Omus/jmiydw7bd34uu3rMF7V5cpHVrWaOr2wGE1YVFhrtKhvM3lS4tRmGfBU41duGZVqdLh6F4kGsP+tmG8crwfr5zox8m+0YnHfvBqC7503Uq8e2VJVuxrJSIiIvXhSqLGnOrz4YXmXnxscw0cNrPS4byDwSDw8Stq8bu/uRxl+Tbc9fABfO5XhxCKcA4fABzp8mJVhRMGBbvRTsdsNODPLirHS0f74AuGlQ5H18YjMXzkJ3vx4Qdex093nYXLYcVXbliJlz57JX505wZICXz8wf247Uev40gnmw0RERHR7DFJ1Jjv72hBjtmIj21ZrHQo57Ws1IEn79mCe66qw2/f7MTTjd1Kh6S4cDSGYz3erCs1Tbp5XSVCkRheaOpVOhTdklLin55uwutnhvDPN67CwX96Dx75+CZ8/IpaLCmx45pVpXjxM1tx3/Z6nOwbxY3/+xq+8fxxpcMmIiIilWGSqCEdQwE8fagbd1y6EIV5FqXDuSCLyYAvXLscRXkW7GkZVDocxZ3uH8V4JIY1VdmZJK6rLsCiolwm9Ap6cE8bfvlGB/56Wx3+Ysti2K3v3DFgNhpw52U12PGFq7C9oQI/3NmCloHRac5GREREND0miRryfFMPojGJj22pUTqUGRNCYFNtEfa0DOq+4+mRRNOa+orsTBKFENi+tgK7Wtzo8waVDkd3dp12477fHcXVK0vxuWuWX/D5TpsZX/2zVbAYDXjg1TMZiJCIiIi0gkmihuw4MYDlpQ5ULciupicXsqmuCL3eIFoHA0qHoqjmLg/yLEbUFucpHco5bV9XCSmBZw9xNTGTWt1+3PPIm6hz5eG/P9ww4z2rxXYrPrihGk8c7ESvh4k9ERERzQyTRI0YDUXwRusQrlruUjqUWbustggAdF9yeqTLg/qK/KxrWjNZncuOi6ry8VRjl9Kh6IYvGMbHH9wPIYAf37lx2hLT8/nk1lrEJPDTXWfTFCERERFpDZNEjdh92o1wVOLKZepLEutceXA5rNhzRr9JYiQaw9EeL+ornUqHckHbGyrR1OXF6X6f0qFonpQSn/3VIZx1+3H/HeuxsGj2VQLVhbm4YU05Hnm9DZ4AO9MSERHRhTFJ1IhXTw4gz2LEhppCpUOZNSEELtP5vsQzbj+C4VjWdjad7Ma15TAI4KmDLDlNt5/tasX/He3Dl69fic11xXM+z11X1sE/HsXDe9tSGB0RERFpFZNEDZBSYseJAWxeUgyLSZ0v6WV1RXCPhnTbhTE5z04NSWKJw4YtS4rx9KEu3Sb1mXC4cwT//vwxXL2yFH85z2ZUqyqcuHKZCz/bdRbBMGeSEhER0fmpM6Ogt2kZGEXXyJgq9yMmTexLPDOkcCTKONLlQY7ZiFqXXelQZuTmhkp0DI3hzfZhpUPRJG8wjE8/ehAuuxXf+sBFEGL++1TvvqoO7tFx/PpAZwoiJCIiIi1jkqgBO04MAIAq9yMmLSrKRXm+Da/rtHlNc7cHqyqcMGZx05rJrl1dBpvZgGc4MzHlpJT40hNH0DUyhu/ctg4FuamZeXrp4kI0VBfgRzvPIBKNpeScREREpE1MEjXg1ZMDWFJiV93oi8mS+xJfP6O/fYmxmERztxerK7K/aU2S3WrClrpivHJiQHevV7o9tq8Dzx3uwefesyyle4yFELj7qjq0DwXwfFNvys5LRERE2sMkUeUC4xHsPTOEq1S8ipi0qa4Ig/5xnOzT177ErpExBMajWF6mniQRAK5aUYL2oQDOuP1Kh6IZx3q8+NqzzbhiaTHu2lqX8vNfs7IUda483L+jBbEYk3siIiKaHpNEldvTMojxaAxXLS9ROpR5e2teolvhSDKrdTCeZC0uzlM4ktlJ3ph45Xi/wpFox33PHoXDZsa3P9SQlnmZBoPAX29bgmM9XrzYzNVEIiIiml7Gk0QhxFIhRFAI8XDi71cJIWJCiNFJH38+6fmFQognhRB+IUSbEOL2Kee7PfF5vxDiKSGE+mZAzMOOEwPIMRuxcfECpUOZt+rCXFQtyNHdvMRWtzqTxOrCXCwtsU/siaX5cY+GsPfsIG6/pBrFdmvarrO9oRJ1rjx8+6WTiHI1kYiIiKahxEri9wC8MeVz3VJK+6SPX0x5/jiAUgB3APi+EKIeABL//SGAjyYeDwC4P91fQLaQUmLHyX5sriuC1WRUOpyUuKy2CHvPDumqFO6M248csxGlzvQlBuly1XIX9p0dgj8UUToU1XvpaB9iMt4UKJ2MBoG/v3oZTvaN4rkjPWm9FhEREalTRpNEIcSHAYwAeHmGz88DcCuAr0opR6WUrwF4BvGkEIgnjc9KKXdKKUcBfBXA+4QQjtRHn33Ouv3oGFL36IupLqsrwkggjGO9XqVDyZhWtx81xXkpGXOQaduWl2A8GsNunXalTaUXmntRXZiDVeXp35t6w5pyLC914L9fOslOp0RERPQOGUsShRBOAPcB+Ow0D5cIIfqEEGeFEN9OJIcAsAxAREp5ctJzDwGoT/y5PvF3AICUsgXxVcdlKf8CslCyzE8L+xGTLqtL7kvUT9LROhjA4mJ1dqbdUFOIPIsRr5zgvsT58AbD2HXajffWl2XkZoHBIPCZa5bizIAfT3OMCREREU2RyZXEfwHwEynl1EnOxwE0ACgH8C4AFwP4r8RjdgBTl5Q8AByTHvec5/EJQohPCiH2CyH2DwxoYw/VjpMDqHXlobpQnQnGdMrzc1BTlIvXdbIvMRyNoX0ogJoide1HTLKYDLh8aTF2HO/nKIx5eOV4P8JRifemudR0smvry1Bf4cR3/ngKYa4mEhER0SQZSRKFEA0Argbw7amPSSl7pZRHpZQxKeVZAP8P8RJTABgFMLX2ygnAN8PHJ1/nASnlBinlBpdL/eWZwXAUe88M4koNjL6Y6rK6+L5EPTTV6BweQzQmVde0ZrJty0vQ7QnqbnRJKr3Q1AuXw4p11ZlrQCWEwGeuXoa2wQCeeHPqvTsiIiLSs0ytJF4FoAZAuxCiF8DnAdwqhHhzmufKSXGdBGASQiyd9PhaAM2JPzcn/g4AEELUArAmjtO0vWeHEIpoY/TFVJtqi+ALRtDcPXWRWHvU2tl0suS/QZaczs3YeBQ7Tgzg2vrStIy9OJ93ryzB2qp8fOfl0xiPcDWRiIiI4jKVJD4AoA7xstIGAD8A8ByAa4UQ24QQi0RcNYBvAHgaAKSUfgBPALhPCJEnhNgCYDuAhxLnfQTAjUKIKxL7GO8D8ISU8h0riVpzsH0YQgAXL1L/6IupLqoqAACc0sHK1BkNJIll+TasLHdyXuIc7Tw1gLFwFO+tL8/4tYUQ+Mw1y9A1MobH93dk/PpERESUnTKSJEopA4my0l4pZS/iZaJBKeUAgHUAdgPwJ/57BMDfTjr8HgA5APoBPAbgbillc+K8zQDuQjxZ7Ed8L+I9mfialHa404OlJXbYrSalQ0m5YrsFADDoDykcSfq1uv1w2EwozLMoHcq8bFvuwv62YXiDYaVDUZ0Xm3qRn2PGpbXKjHi9cpkLGxYtwHdePgXPGF8/IiIiUmZOIqSU90opP5L4839JKSullLlSymop5d9OXgmUUg5JKW+WUuZJKRdKKR+dcq5HE5/Pk1Jul1IOZfrryTQpJQ51jGBtYsVNa+xWE6wmA9yj40qHknatg34sVun4i8m2rShBNCax65Rb6VBUZTwSw0vH+nD1ylKYjYr8OIYQAvfeVI8h/zi+8fwxRWIgIiKi7KLMuxKal87hMQz6x3FRtTaTRCEEiu1WuH3aX0k8M+BXdalp0rrqAjhtJu5LnKXXzwzCG4xktKvpdFZX5uPjly/GY/s6dDV+hoiIiKbHJFGFDnWOAAAaNLqSCMRLTgdGtZ0kBsNRdHvGVDv+YjKT0YArlrnwyokBjsKYhReae5FrMeKKpcVKh4K/v3oZFhbm4stPHkEwHFU6HCIiIlIQk0QVOtzpgcVkwPKyd4yD1IxiuxWDGi83bR8KQEp1N62ZbNvyEgz4QmjunjralKYTjUn8obkP25aXwGY2Kh0OcixG/Pv71uCs24/vvHxK6XCIiIhIQUwSVaixYwSryp2wmLT78hXbrXBrfCXxrAY6m06WnNn5R3Y5nZE324fhHg3hWoVLTSfbsqQYH7i4Cj/ceQZHmewTERHplvZaY2pcJBrDkU4PPrSxWulQ0qrYYcGgfxyxmMz47LhMSc5IrNFIkuhyWLGpthC/3NeOu6+qU6wRi1q80NQLi9GAbctdSofyNv94w0q8cqIf//DEYTxx92aY+DoSERGd15mBUfzxeD9CkRhCkRjC0RjGIzFYTAasKHNgVbkTi4vzVPU7lUmiypweGMVYOIq11flKh5JWRXlWRGMSnrEwFqh8PMS5nHX7UZhnQX6OWelQUuYTV9Tir36xH78/0oPtDZVKh5PVdp1249LaQjhs2fX6F+RacO9N9fj0owfxs12t+MTWWqVDIiIiykrjkRh+8GoL/vePpzEejU183mI0wGIyIBSJIhyN92qwJraKbawpxKe3Lcn697dMElXmcIcHADQ7/iKp2GEFALhHQ1n/TTRXZ93a6Gw62bblJahz5eGHr57BTWsrVD/aI11GQxGc6PPh2vrsKTWd7IY15fjVsk78cGcLPn7FYr6OREREUxxoG8I//PYITvWP4sa1FfjSdStQZLfAYjRM/N4MR2NoGRjFsR4vjnZ7cbTHi1/sbsVTB7vwTzeuyur3SupZ8yQAQGPnCBw2kyY6Yp5PsT2eGGq5w2nroF9zr6PBIPDJrbU42uPFrtMcpXAuhztHICXQsDA7b/YIIXDd6jK4R8cn9s4SERFR/EbvV546glu/vweB8Sh+9rGN+O5t61BRkAOryfi2pM9sNGBFmRO3rKvCP96wCo98fBOe/ZvLUVWYi7/7ZSM+9rM30DEUUPCrOTcmiSpzqGMEa6sKNLtPL6nYnlxJ1GaHU38ogj5vCIuLc5UOJeW2N1Si2G7FA386o3QoWauxI/vH2GysWQAA2N86rHAkRERE2ePeZ5rx6N52/OWWxfjDZ7Zi24qSWR2/styJJ+7ejH++cRXeaB3Ce769Ez/fdTZN0c4dk0QVCYajON7r0/x+ROCtJHFQoyuJrYPJzqZ2hSNJPZvZiL/YUoOdJwdwrIcdMqfT2D6CmqLcrC6lrnPZsSDXjP1tQ0qHQkRElBU6hwN46mAXPrZ5Mf7pxlXIs85t557RIPAXWxbj/z57JTbVFuLeZ4/iV290pDja+WGSqCLN3V5EYxIXZfHqQ6oU5JhhNAjNjsFodcdLC2o0uJIIAHdcuhC5FiN+xNXEd5BS4mDHCNYtXKB0KMnIF7IAACAASURBVOclhMDFixZwJZGIiCjhRzvPQAjgE1sXp+R8lQU5+NGdG3DF0mJ85akmHGzPnt+5TBJV5FCyRK1a+0miwSBQmGeB26fNctOz7lEA0NyexKSCXAs+uKEazzR2o8czpnQ4WaXbE8SAL6SK7+MNNYU44/Zr9mYNERHRTLlHQ/jlGx24ZV0lyvNzUnZek9GA7962DmX5Ntz18AH0e4MpO/d8MElUkUOdIyhz2lDqtCkdSkYU260Y9GvzzelZdwClTuucyxTU4K8uXwwJ4Oe7WpUOJas0tqvnZk9yX+KBtuy5s0lERKSEn+06i/FoDJ+6si7l5y7IteCBOy+GdyyCux4+gFAkmvJrzBaTRBU53OnRxX7EpGK7BQMabVyjxc6mU1UX5uL6NeV4dG87fMGw0uFkjYPtw7CYDFhZ7lQ6lAtaXZkPi8mA/a3cl0hERPrlC4bx4J42XLe6DHWu9PSTWFHmxH9+cC3ebB/Bvc8cTcs1ZoNJokqMBOKt6PWwHzGp2G6F26fVlUQ/al3aThIB4JNX1MIXiuDxLNuMraTGjhGsrnDCYsr+H79WkxFrq/LxBvclEhGRjj38ejt8wQjuuWpJWq9z/Zpy/PW2Ojy2rx2P7G1L67UuJPvfpRCA+CoioI4StVQptlsw6A9BSql0KCnlGQtjyD+u+ZVEAFhTlY/lpQ7sOu1WOpSsEI7GcKTLg4bq7G5aM9mGmkI0dXkwNq586QsREVGmBcNR/OS1s7hiaTFWV6a/ou+z1yzHtuUu3PtMM9oGlZtVzCRRJZJNa9ZU6anc1IpgOAa/xt6ctiaGk9cUaz9JBID6CieOchQGAOBErw+hSAwNC9Vzs2djzQJEYhKHOkeUDoWIiCjjfn2gE+7REO6+KvV7EadjNAj8x60XwWQw4JsvnMjINafDJFElDnV6UOfKg9NmVjqUjEnOStRayenZRJJYq5MkcWW5E33ekGZnXs5GsrX1OhVVBKxPjOrgvkQiItKbSDSGB3a2oKG6AJfVFmXsuiVOGz51ZS2eO9KjWPM4JokqIKVEY8cI1upoPyIAFNnjg8a11n7/rNsPIeKNXfRgVUW8QcuxHp/CkSjvYMcIiu0WVC1IXevsdCvItWBZqZ37EomISHd+d7gHHUNjuOeqOgghMnrtT26tRYnDin997qgiW6+YJKpAjycI92gIa1W0+pAKEyuJGutwetbtR0V+Dmxmo9KhZESyi+cxlpyisWMEDdUFGf9FM18bagrxZvswojFt7Q8mIiI6l3A0hv9+6SRWlDlw9crSjF8/12LC59+zHAfbR/D7I70Zvz6TRBVINq25SEf7EQHA5UgmidpaSWwd1Edn06TCPAvKnDbdJ4meQBhnBvxYt1A9TWuSNtYsgC8Ywck+rgYTEZE+/Gp/B1oHA/jCtcthMChzc/fWi6uwosyB/3jheMZnJzJJVIFTiTdmy0odCkeSWYV52is3lVLirFv7MxKnWlnu0H3zmsZE4xc1dijesKgQAPclEhGRPoyNR/E/L53ChkUL8K4VJYrFYTQIfPn6lWgfCuChPZkdicEkUQVO9o+iakEO8qwmpUPJKLPRgIJcMwY1VG46HAjDF4xgUZE+9iMmrSx34nT/aMbvgmWTxvYRCKHOioCqBTkodVqxX6HN80RERJn0892t6PeF8MXrVii+RWTrMhe2LnPhu388jZFA5t4TM0lUgVN9PiwtsSsdhiKK7VZNrSR2DAUAAAt10rQmaVWFE5GYxKm+UaVDUUxjxzCWltjhUGGHYiEENtQUYj+b1xARkcZ5AmF8f8dpvGtFCTbWFCodDgDgy9evgC8Yxnf/eDpj12SSmOUi0RjODPh1V2qaVJRn0VaSOBxPEvXS2TRJ781rkh2K1VhqmrRx0QJ0jYyhe2RM6VCIiIjS5vuvtsAXiuAL1y5XOpQJK8qc+OCGavxidyt2t7gzck0miVmufSiA8WgMS/S6kuiwaqq7afuQPpPEmqI85JiNuh2D0TYYwHAgjIZq9TWtSdqQuJvKklMiItKqXk8QP9t1Fjc3VE7c4M4WX7p+JRYX5+Guhw7gdH/6K7OYJGa5U4l/BEt1upLo0ly56RgW5Jph19n+UqNBYHmZA0d7PEqHoojGDvU2rUlaUeZAnsXI5jVERKRZ3/njKcSkxGevWaZ0KO+Qn2PGTz+2ERaTAX/x830YTPP7YyaJWS7Z2VS3K4l2C3zBCIJhbTQ86RwO6G4VMWlluRPHenyKDIRVWmPHCHItRiwrVe/3sclowPpFC7DrtBsxzkskIiKNOTMwisff6MAdly7K2vdq1YW5+PGfb0S/N4RPPLg/re+PmSRmuVP9o6gsyNHdylNSkT0+K3HQr42S046hAKoXZOcPnnRbVeGEZyyMbk9Q6VAyrrFjBKsr82EyqvtH7vaGSrQM+PHs4W6lQyEiIkqpH/3pLCxGA/562xKlQzmvhuoC/PeHGnCwYwSf+/WhtN24Vfc7Fh042TeKpSpefZiv4mSSqIGS02hMomtkDFWFOUqHoohV5fGS6WPd+mpeE4nGcKzHizWV6ht9MdX71lWivsKJ/3j+uGZW94mIiCLRGF5s7sU1q0rhcliVDueCrltTji9dtwLPHe7Bt/5wIi3XYJKYxaIxiZaBUd2OvwDi5aYANLEvsc8bRDgqdbuSuLzMCSH01+G0ZcCPUCSG1ZXZtQF+LgwGga/csArdniB+8tpZpcMhIiJKiX2tQxjyj+O61WVKhzJjn7iiFu+/uArff7UFQ2mouGOSmMXahwIYj8R027QGeGsl0e1Tf7lph047mybZrSYsKszFUZ0lic3d8WY9qyvUv5IIAJfVFeE9q0px/yun0e/TX+kwERFpz/NHemEzG3DlcpfSocyYEAI3N1RCyvTcgGeSmMWSTWv0vZIYTxIHNLCS2DEcny9XvUCf5aZAfF+i3lYSm7q8sJkNqHVp5/v4S9evxHg0hv/6w0mlQyEiIpqXWEziheZebFteglyLunqArExu5WGSqC96H38BADkWI/IsRgxqYFZix1AAQgCVOk4SV5Y50ToYwGgoonQoGdPU7cGqcieMBqF0KCmzuDgPd15Wg8f3d+CozvaYEhGRthxoH8aAL4T3qqjUNKnIbkWJw5qWKi0miVnsVJ8PFfk23XY2TSrSyKzEjuEASh02WE1GpUNRTHIw7YlefSQWsZjE0W4vVmugac1Uf/uupcjPMeNfnzuqy7EmRESkDc8f6YXFZMC7VpQoHcqcrKpwpuWGLZPELHaqfxRLdLyKmFRst2giSewcGkO1TjubJq2qiCeJR3t8CkeSGW1D8VVTrexHnCw/14y/f/dS7G4ZxMvH+pUOh4iIaNaklHihqQdblxbDYTMrHc6crCx3omVgFOORWErPyyQxS0VjEqf7R7FMx/sRk4rtVm2Umw7rd0ZiUnm+Dfk5Zt2UKDZ1xZvW1Gugs+l07ti0CLWuvLS13yYiIkqnQ50edHuCeO/qcqVDmbNV5U6EoxKn+lN7A55JYpbqHA4gFInpekZiUrFD/eWmoUgUvd4gqnTa2TRJCIFV5fppXtPU7YHZKLC0RJsVAWajAe+/uArHe30YCaj/Rg4REenL8009MBkErllZqnQoc5bcynMsxVVaTBKz1Kk+Nq1JKs6zYCgwjkg0tcvomdQ9EoSU+u5smrSy3InjvV5EY9rfx3a024vlZQ5YTNr9Ubu2qgAAcLjTo3AkREREMyelxPNHerF5STHyc9VZagrEm8nZzIaUV2lp952Lyp1MLBkvYbkpih1WSAkMqXilQu8zEidbWe5AMBxD66Bf6VDSSkqJpi6PJvcjTpZsynO4c0ThSIiIiGbuaI8X7UMBXK/CrqaTGQ0Cy8tSX6XFJDFLne4bRXm+DU6VbqJNpeSsRDXvS+wYZpKYlGxeo/WS025PEMOBMOo12Nl0svwcM2pdeWjs4EoiERGpxwtNvTAI4JpV6i01TVpV7sCxXm9Ku40zScxSJ/t9XEVMKMqzAICq9yV2DI3BbBQoc9qUDkVxS0rsMBmE5pvXJJvWrK7QZtOaydZWFeBQ5whHYRARkWr8/kgPNtUWoSixGKFmq8qdGAmE0eMJpuycTBKzUCzZ2ZT7EQHEy00BlSeJwwFUFORoaqD6XFlNRiwszEVbogRXq5q7PDAaxMSGci1bW5WPAV8Ivd7U/XIiIiJKl1N9PrQM+HGdyktNk95qXpO6G/BMErNQ5/AYguEYlnIlEYA2yk07hzj+YrISpxX9Gk8omrq9WOKyw2Y2Kh1K2l1UHW9ec4glp0REpAIvNPVCCODaem0kiSsSSWIqq7SYJGah5JwTjr+Ic9pMsBgNGFD1SuIYqgvZ2TSp1GlDn1e9r+dMNHV5NDsfcapV5U6YDAKH2LyGiIhUYF/rEFaWOVGikW1AdqsJi4pycayXSaKmneqPj79YotHZarMlhECR3QK3T50rif5QBEP+cTatmaTUaUO/L6jZPWz9viD6fSHNdzZNspmNWFHuYIdTIiLKerGYRGPHCBoWFigdSkqtKndyJVHrTvb5UOq0Ij+HnU2Tiu1W1e5JnOhsynLTCSUOK4LhGLzBiNKhpEVz4od0vQ6a1iStrSrA4Q4PYjqYf0lEROp1xu2HLxhBQ7W2ksSV5U60DQXgD6XmvRWTxCzEpjXvVGy3YNCv0iRxaAwAx19Mlizv0Oq+xOZEZ9NVOksSfaEIzmp8/iUREalbY0e86mWdBpNEKYHjvb6UnI9JYpaJxSRO9Y1y/MUURXarastNO4aSK4nck5hUmuhYq9V9iU1dXiwuzoNDR3NO1yZ+2bLklIiIslljxzAcVhPqXNp6r528MX00RR1OmSRmma6RMYyFo1xJnKLYbsWgP6TKPWwdwwHkWowoTMx7pPieRCC+d0+Lmro9uio1BeLzL3MtRnY4JSKirNbYMYKLqvNh0NhYsop8G5w2U8rGYDBJzDKnJ5rWaOvuxnwV2y0IRyW8Y+rbw9YxNIbqBbkQQls/jOajxKndlcSRwDg6h8ewulIfTWuSjAaB1RX57HBKRERZKxiO4niPT3P7EYF4o8dVFalrXsMkMcu0J0oTa4ryFI4kuyRnJapxDEbncIDjL6bItZjgsJrQp8E9icmmNXrpbDrZ2up8NHd7MR6JKR0KERHROzR1eRCJSTRUL1A6lLRYWe7EiV4foiloIsckMct0DgdgNRlQbGdp4mTJJFFtHU6llOgYCqCKnU3focRp1WS5aXN3vNxSb+WmAHBRVQHGIzGc7EvNpnkiIqJUSjat0eJKIhAfgzEWjqItBU3kmCRmma6RMVQuyGFp4hTFjnjSrLYkcTgQhn88ys6m0yh12jRZbtrU5UVlQQ4W6HAPavKXbvKXMBERUTY52DGCyoIcuBIN9LRmZXnqmtcwScwyncNjXHWahiu5kuhTV1LBzqbnVuq0aXIl8WSfDyvK9Nl4qmpBDhbkmtnhlIiIslJj+wgaFmpzFREAlpbaYTKIlDSvYZKYZbqGx1BZwIRiqgW5FhgNQnV7EjuGE0kiVxLfocRhRZ9XnR1rzyUWk2gd9GNxsT73FAshsLa6gB1OiYgo6wz4QugaGdPcfMTJrCYjlpTYU9K8JuNJohBiqRAiKIR4eNLnbhdCtAkh/EKIp4QQhZMeKxRCPJl4rE0IcfuU853zWLUJjEcw6B9HFVed3sFgECi2WzCgupXEMQBMEqdT4rRhPBKDZyysdCgp0+sNIhiOYbFLn0kiEN+XeKrfB39IfZ2IiYhIu7S+HzFpZbkTx3rm3xtAiZXE7wF4I/kXIUQ9gB8C+CiAUgABAPdPef544rE7AHw/ccxMjlWV7pF4QsEkcXouh1V9SeJwAAtyzbBbTUqHknVKNTgGo9Ud3yi+WMfdiRuq8xGT8Q5yRERE2aKxYxgmg9D8iKrlZQ70eoPwBud3Ez6jSaIQ4sMARgC8POnTdwB4Vkq5U0o5CuCrAN4nhHAIIfIA3Argq1LKUSnlawCeQTwpPO+xmfqaUqljmEni+bjsVvWVmw4FuIp4DqVOGwBoal/i2UQ3sRqdlpsC8ZVEADjcySSRiIiyR2PHCFaUO2AzG5UOJa3K8+Pvr+a7sJKxJFEI4QRwH4DPTnmoHsCh5F+klC2IrxwuS3xEpJQnJz3/UOKYCx2rOl2JJLGygEnFdFS5kjgUQDUbEU2rxKG9lcSzA37YzAaUJRJgPSq2W1FZkINGNq8hIqIsEYtJHO7waL7UFHir2aNqkkQA/wLgJ1LKzimftwOYesvZA8CReGzqzsvkYxc69m2EEJ8UQuwXQuwfGBiYQ/jp1zk8BrNRTLx5prdzOaxwj44jloIBoZkQjUl0jYyhqpArw9MpccQTqT6vdlYSWwf9qCnKg8Gg7xE2a6vzcYQriURElCVaBkbhC0XQUL1A6VDSLjneQxVJohCiAcDVAL49zcOjAKZOnXYC8F3gsQsd+zZSygeklBuklBtcLtfsvoAM6RoZQ0VBju7fYJ5Lsd2KaExiODCudCgzMjgaQjgq2a32HHIsRjhtJvRrKEk8444niXq3qCgPPZ4x1dzQISIibTuok6Y1QOqSxEx107gKQA2A9sSQeDsAoxBiFYAXAKxNPlEIUQvACuAkgBgAkxBiqZTyVOIpawE0J/7cfJ5jVadzOMD9iOcx8Y9+NIQie/avtvYnvjmTK2b0TvFZidooN41EY+gYCuDa+jKlQ1FcqcOKcDR+Q0cN36tERKRtjR0jcNhMqNVBz4D8HDPMxvmPjctUuekDAOoANCQ+fgDgOQDXAngEwI1CiCsSjWruA/CElNInpfQDeALAfUKIPCHEFgDbATyUOO85j83Q15VSnJF4fqmqsc6UZEOWEiffJJ9LidOqmXLT7pEgwlGp686mScmmRL0aeW2JiEjdGttH0FBdoItqPSFEvNmjGspNpZQBKWVv8gPxMtGglHJAStkM4C7EE75+xPcT3jPp8HsA5CQeewzA3YljMINjVSMYjqLfF0IVm5ycU3Il0a2SDqf93uRKIpPEcyl12DTTuOaMexQAdD0jMak00VmtXyOvLRERqdfYeBQn+ny6KDVNSkWzR0WGt0kp753y90cBPHqO5w4BuPk85zrnsWrS44nfcedK4rmlqsY6U5JllC4miedU4rSh3xeElBKJUnTVSs5I5J7Et1YStbJKTERE6nWky4NoTOouSewemd/v4IzOSaRz6xwOAOCMxPOxW02wmQ0qShKDKMg1w2rS9jye+Sh1JveuzW/gazZoHQzAbjWh2G5ROhTFJUvDtbJKTERE6tXYMQxAH01rklyO+c8WZ5KYJToTMxKrOHj9nIQQqpqV2O8NTbxZpuklV5yS+zfV7Izbj8XFeapfEU0Fi8mAojwL+jTwuhIRkbod6vSgakGOrhqpuexWDI6GEJ1Hl3EmiVmia3gMRoNAKUsTz8tln/+dkUwZGA2xac0FJPdramHFqdXtR40OuqbNVInTpqnxJkREpE5Hu71YXZGvdBgZ5XJYEZPAkH/uY+OYJGaJzuEAyvNtMBn5kpyP2lYSOf7i/LSyd208EkPncACLi1gJkFTqtLK7KRERKcoXDOOs24/VlVPHqmtbKvp4MCPJEl0jHH8xE2pJEqWUGPCF2Nn0ApI/xNS+4tQ+FEBMsrPpZFrqXEtEROp0tNsLAKjX4UoigHlV3zFJzBKdw2McfzEDLrsNw4EwxiMxpUM5L89YGOPRGDubXoDNbERBrnmiE6xasbPpO5Xm2+AeDSESze7vVSIi0q7mZJKot5VEe7xSiyuJKjceiaHPG0QlO5teUDLpGvRnd1KRTHpKnCw3vZASh1X15aZnE0niYu5JnFDqtEJKwD069/0QRERE89HU7UGJw6q77T/FjnindSaJKtfrCSImOf5iJtQyKzE5RJzlphdW6lR/WeLZQT8W5JpRkMvxF0mlDm3sNyUiIvU62u1FfYW+VhEBINdigt1qYpKodp0jiRmJ3JN4QapJEhOt/5kkXliJQ/1dMNnZ9J200pSIiIjUKRiO4lT/KFZX6ms/YtJ8ZyUyScwCEzMSuSfxgtSTJLLcdKZKnVb0+0KIzWOWj9LOuv1YzP2Ib1OaGP/Sl+Xfq0REpE3He32IxqQuVxKBxNi4ecwrZpKYBbqGxyAEUJbPhOJCiu3zr7HOhH5vCLkWI+xWk9KhZL0ShxWRmMRwQJ1718bGo+jxBLkfcYoiuxVGg0CfhyuJRESUec3dHgD662yaNN+JAEwSs0Dn8BjKnDZYTHw5LsRqMiI/xzyv5fNM6PcFWWo6Q2+VJWb3a3ourYOJzqZMEt/GaBBw2dXflIiIiNSpqcuL/Byzbnt+MEnUgK6RAGckzoIaZiX2+0K666Q1V8mS3L55lEQoqZWdTc+p1GlluSkRESniaLcH9RVOCCGUDkURLocV3mAEwXB0TsczScwC8RmJTBJnKl5jnd1vPAd8IbicXEmcieTeNbU2rznLlcRzKnGqvykRERGpTzgaw7Fen26b1gDx98sA4J5j9R2TRIVFojH0ejgjcTZcDuuc/8FnSr+X5aYzlWxG1K/SctOzA364HFbuP51GqZPlpkRElHmn+0cxHonptmkNMP9mj0wSFdbnCyESk+xsOgvZXm7qD0XgH4+y3HSGrCYjFuSa1VtuOuhnqek5lDpsGA6EEYrMrdSFiIhoLpq7vQD027QGYJKoel0T4y+4kjhTLocV/vEo/KGI0qFMK/nNyJXEmSt12lTbuIbjL84t2ZRIravERESkTk1dHuSYjbq+iTuRJLLcVJ06hwMAwMY1szDfGut0e2tGIpPEmVLr3jVfMAz36Dj3I55D8nuAJadERJRJR7u9WFXhhNGgz6Y1AFCYZ4EQXElUreRKYgWTxBmb7/J5uvUnyiZdXEmcsVKHdSK5VpNWd/wmj57vVJ5PcvarWleJiYhIfWIxieZuD1breD8iAJiNBhTmWpgkqlXn8BhcDitsZqPSoahGsT3Lk0RvstyUexJnqsQZTxJjMal0KLNyxj0KgEniuZQ6kkkiVxKJiCgzWgf98I9Hdb0fMWk+fTyYJCqsa4TjL2ZrvjXW6dbvC8FsFFiQa1Y6FNUoddoQjUkM+seVDmVWkiuJi4rYeGo6BblmWIwG1TYlIiIi9ZloWlOp75VEIJEkck+iOnUOB7gfcZYK8ywwzKPGOt36fUG47FbdDm+dixKVrji1DvpRWZDDSoBzEELEV4lZbkpERBnS1O2B2SiwtMShdCiKm89scSaJCorFJLpHghx/MUtGg0DRPP7Rp9uALwSXk6Wms1GaaHDSr7IVp9ZBP1cRLyDeuVZdrysREanX0W4vlpc5YDExzUmWm0o5++08/L+noIHREMajMVSy3HTW5nNnJN36vSGOv5ilEpWOSujzBCeas9D0Sp1WJokAxiMx/Pvvj+GKb/4RX3riMHafdiOqsj24RJQddre48b77d+Hzvz6kdChZR0qJpi4PVnM/IoB4khiKxOCbw9g4UxrioRnq5IzEOZtPjXW69fuC2FCzQOkwVCU51kRNXTBjMYl+XwhlXDU+rxKHDTtPupUOQ1EdQwF8+rGDONQxgksWF+Lpxm48tq8DLocVN6wpx63rq7Cmim9oiOj8Tvf78I3nj+OlY/3IsxjxZvsIrl5ZiveuLlM6tKzR7QliOBBGvc47myZNngjgtM2uVwaTRAV1jcSTRO5JnD2Xw4qTfT6lw3iH8UgMw4EwO5vOksVkQFGeRVUNTtz+ECIxyZXECyjLt2E0FMFoKAK7VX+/cp4/0oP/99vDgATuv2M9rl9TjrHxKF450Y9nD3Xj0X3teHBPK35792asW8ibS0T0ToOjIXz7pZN4bF8Hcs1GfPG9K/DRyxbh/d/fjXufacaWJUVwzDIB0KrmLg8AoL6SN96At27CD/hCqHPZZ3XseX9jCyEeAnDBehgp5Z2zuioBiJeqAeCbzDlwOaxwj8ZHJhiyaFBqcnUzOUScZq7EaUO/isoS+zzx17qUK4nnNbHf1BuEfZa/oNQsFIni688dw4N72rC2ugD/e9s6VBfG96/mWIy4fk05rl9TjmH/ON77Pzvxlaea8MynL9f14GcieicpJT72szdwtMeLOy5diL9791IUJd74f+PWi3DL/bvwrRdP4GvbVyscaXZo6vbCIICVZVxJBOY3W/xCexJPA2hJfHgA3AzACKAzcex2ACOzvioBiHdyzDEb4dDh3fX5ctmtCEclPGNhpUN5m2SSwz2Js1fiiM9KVIvexGvNctPze2tWonpe21S4/5UWPLinDZ+4YjF+/anLJhLEqRbkWfDVP1uF5m4vHn69LcNRElG2e7N9BEe6PLj3pnrct331RIIIAA3VBbhz0yI8+HobDrYPKxhl9jjd78OiojzkWNh1HEhjkiil/FryA8AyADdIKe+QUn5ZSvkRADcAWD77kAkA+nwhlDo5KmEusnVWYjLJYbnp7KmtwclEkshKgPOaaEqkolLi+RqPxPDI3na8e0UJ/vGGVRfssHfDmnJcsbQY33rxhK7+PxHRhT2ytw12qwm3rKuc9vHPX7scpQ4bvvTEEYSjsQxHl33ahwJYeI6bcnqUn2OG2Sjm9H55Nt1NNwF4fcrn9gK4bNZXJQDxlcQSrkLMSTJJdGfZytNEkshy01krddow4AuppuNjnycIo0Gg2M7X+nyS5aZqugEwX8839cA9GsKdm2tm9HwhBL52Uz1CkRj+7blj6Q2OiFRjJDCO3x3uwc3rKs65p9thM+Nr2+txvNeHH//pbIYjzD7tg0wSJxNCzHkiwGySxIMA/k0IkZO4aA6ArwNonPVVCUC8NJGlanOTrSuJA94ghACK8ixKh6I6JU4bYjK+QV8Ner1BuOxW7iG7ALvVhFyLEb0edbyuqfDQnjbUFOXiiiXFMz6m1mXHXVfW4qnGbuxu0Xc3WCKK+82BToxHYrj9kkXnfd619WV4z6pS/M/LJ9E+AeO7GwAAIABJREFUGMhQdNnHEwjDG4xwfvEUyVmJszWbJPFjALYA8Agh+hDfo3g5ADatmQMpJfq8oYm77DQ786mxTqeB0RCK8qwwGTmCdLZKHeoag9HnDaKUpaYXJIRAqdOmqs6189Hc7cH+tmF8ZNOiWTfVumfbElQX5uCrTzVhPMKyMSI9k1Li0b3tWL+wAKtmMM7ha9vrYTIY8Fe/eAMH2vS5P7FtyA8A59wDrldpTxKllK1Sys0AlgC4CcASKeVmKWXrrK9K8IUiGAtH2RlxjhxWE6wmQ9Ylif3eEJvWzJHa9q71eoIo402eGSl1WlXVuXY+HtrThhyzER+4uHrWx9rMRtx302q0DPjx49fOpCE6IlKLPWcGccbtxx2Xnn8VMak8Pwffu2M9fMEIbv3+bnzxN4cx5B9Pc5TZpX0ovorKctO3m+ts8Vkvd0gp2wHsA9AphDAIIbhkMgcTXTCZJM6JEGLOd0bSqd8X4n7EOXpr71p2vabn0sty8RkrddpU87rOhycQxlONXbh5XQXyc+c2s2zbihJcW1+K77x8SjeJNRG90yN721GQa8YNF5XP+Jgrl7nw8ueuxKe21uK3b3biXf+5A7/c146YSvb6zxeTxOm57FYMjs6+58OMEzwhRIUQ4kkhxCCACIDwpA+apeQbplKuOs3ZXO+MpFO/LzgxuJRmp9huhRDqaHASGI/AF4yw3HSG4kliEFJq+43Krw90IBiO4aObauZ1ni9fvxLjkRh+9CeuJhLp0YAvhBebevH+9VWwmWc3yiHPasKXrl+J5/72CiwrceAfnjiCL/72cJoizS7tgwEU2y3I42i5t3E5rIhJzHpleTargD8EMA7g3QBGAawH8AyAu2Z1RQIQL1UDOIh7PubarSldojEJ9+g4VxLnyGw0oCjPqopy0+T3L1cSZ6bEYUUoEoN3LKJ0KGkTi0k89HobNtYsmNH+ofNZVJSH7Q2VePj1dt2VixER8Kv9HYjEJG67dOGcz7G8zIHHP7UJt1+6EE8e7NLFz5L2oQD3I05jrn08ZpMkbgbwl1LKRgBSSnkIwF8B+NysrkgAMNHEgQnF3GVbuemQfxzRmOSMxHkocVjRr4KyxIkZiUwSZyR5M0zLzWtePTWAtsEA7rysJiXnu+eqOgQjUfz0Nba0J9KTaEzisX3t2FxXhDqXfV7nEkLgo5sWIRKT+N3h7hRFmL3ahwJYxCTxHeY6EWA2SWIU8TJTABgRQrgA+AFMP92TzqvfG4LDZkKuhUvic+VyWDEUGM+a4bHJFTA2rpm7UqdVFYlEsiSW5aYzk0wSkyuwWvTg7la4HFZcW1+WkvMtLXXgutVl+MXuVnjGuKuDSC92nhpA5/DYjBvWXMjKcidWlDnwxJtdKTlfthqPxNA9Msb9iNNw2eO/g9O5krgXwPWJP78I4HEATwDYP6srEoBE+3yuQsyLy2GFnEONdbr0J775uDo8d2ppcJKc+cfv4Zl5qymRNpPEtkE/dpwcwO2XLITFlLpebn+9bQl8oQge3N2asnMSUXZ75PU2FNutuGZVacrO+b71lWjsGMGZgdGUnTPbdI+MISY5/mI6xY747O50JokfBfBq4s9/D+AVAE0Abp/VFQlAMklkMjEfyQYx2VJyOpBIblhuOnclThvcoyFEsmR1+Fz6vEHYrSbYuTl+Rkonxptkx/dqqv3ucA+kBG6fx/6h6dRX5OPdK0rwk11n4Q9pdz8nEcV1DAXw8vF+fHhjdUpvOG1vqIRBAE8d1O5qYrKz6aKiPIUjyT65lvj7lbQliVLKESnlUOLPY1LKf5FS/n/27jy8zbPKG//31m7ttmXZlvclq90szdq00JbSBei0tEBLYYBSBjoF5jcsA7zDDMsLww868w4wzFtm2AtlK2UoOwUKbWmzJ03TJI0d77skW7K1Wvv9/iHJMSGJJevZZJ3PdeW6Umt57lSW9JznnPucj3DOZ4pcK0G2uyllIUqz2o24YsmXm9ZRuemqOXPZYZ9CssOXQhd5imPQqmGr0q7ZTOLZmSBaaqpE+Ux/zyu6sRBN4nuHxwR/bkKIsnz30BhUjOHNe4W94FRvNeDqbgcef2FqzXaZHqPxF5e1mokAxYzA0DLG/jdjbIQxFmOMDef+W1f0Sisc5xzeEJWblqohtx9sOrAo80qyvKE4rAZN0e2qyXlLDU4UHky4g7Gl3z9SmHqrXvGv62r1uUPY2FBaR9NLubK1Gtd0O/DVP40glkyLcgxCiPwWE2n88OgEbulpQKOtSvDnv2N7Eyb8izg2Ni/4cyvBhD8KnUZFfSEuITsRoLjv4GJy2f8K4JUA7gewFdnRF68A8GBRRySYjyaRTHOakViieosBOo0K476o3EsBkG1G5KTAvyTn964pIzt8KZ4AXeQpVrnsNy1WLJnG8GwYmxosoh3jva/oxlw4jkePToh2DEKIvH72whQCi0m89SphGtZc6OaeBlRp1fjJ85OiPL/cxn1RtFRXQaVici9FkVYzEaCYIPENAG7jnP+Oc97POf8dgDsA3FXUEcn5zoh0klkSlYqhtcaIUV9E7qUAyJab0hWs0pRDJjGT4fCG4jT+okhOi0HRr+tqDXrDyHBgY6M4mUQA2NNRg13t1fjvZ4Yom0jIGsQ5x8MHRrGxwYLdHTWiHMOk1+CW3gb88sWZNfk5MuaP0n7EyxA7SLxUaE4he5HyM9Yo61S69lojxpSSSQzFKUgsUa1JB8YAr4KDiblIHKkMp3LTIjXY9PCG4khn1tZ+mLMzQQDARhEziYwxfODGDZgJxPC53/SJdhxCiDyOjs6jzx3CvfvawZh4p9V3bG9CKJbCH/u8oh1DDpxzTPijtB/xMuosegRjqaIuEBQTJD4G4BeMsZsZY5sYY7cA+Gnu56QI3qVMIgUUpWqtMWHcH5V9IzbnHLMhKjctlUatgsOsV3QXTA+Nv1iVBqsB6QyHr8iN80rX5w7BoFWJfgX7qq5a3LuvHQ8fGMWzA7OiHosQIq1vHxiFrUqL27eJO3r86m4HnBb9mpuZOB9NIhxP0fiLy8gnMYqp6CkmSPwwgCcBPATgOID/RHYMxoeKeA6C8/utqAtm6dpqjYgm0kV3bBJaMJZCPJWhTKIAlN7gJF8JQOWmxWnINWKYCSj3tV2NPncQG+otUEuwD+Z/vWoj1jnN+IfHTmIhquwOwISQwswEFvHEGTfu3tWCKp24je/UKobbt7nwdL9XMTOmhTCW23bURkHiJTXZs9/BUwuFN3u8bJDIGHtF/g+AawA8DeBdAP4K2QY2T+V+TorgCcZQY9JBr6EumKVqq81+IMhdcuqhEmLB1FuU3eBkKUikctOi5INqt4IvAKxGv4idTS9k0Krxhbu3wR9J4J8eP11QBUUmw/HQU4PY+///Afd89RA+95s+PHF6BjMK6QpNSKX7/uFxZDjHW/aK07DmQndsb0Yqw/HLF6clOZ4U8jMSW2spSLyUxlyQOLNQ+HfwSpOgv3GJn+e/mVju750FH5HAE6S9a0LJl3iN+aLY1S7OZu9CuAOUXRKK02rAyckFuZdxSZ5ADGoVg8NM7+Fi5INq9xrKJM6G4pgLJ7BBxP2IF+ptsuH9N67Hvz7RjxtOOHHnlc2XvO9CNIEP/Ogk/tjnxVWdtYgkUvjGc8NIprNf4V11JvzwXVdRVQshMomn0vjBkXHcsNEpWankZpcV6+vNeOK0G2+9ql2SY4ptIhcktlRTkHgpjfmxcUVkEi8bJHLOO0pbErkYmpEonCZ7FdQqhnGZO5x6qARRME6LHr5IAsl0Blp1MRXx0nAHY6gz6yUpL1xLak06aNVsTWUS+9y5pjWN0gWJAHD/y7vwdN8sPvGzM9jdUYPmi5wYvTi5gHd/73l4gjF86vYevGVvGxhjiCXTODsTxPGxeXzm12fxyMFRfOCmDZKunxCS9asXZzAXTuBt+9olPW6Py4YjI35JjymmMV8UTote9HLdcmbQqlFr0mG6iAu1K2USiQg8wRg2SVSetNbpNCq47AaMKqbclK7Il6reagDnwFw4LspA4VJ5gjHUU6lp0VQqBqfFsKYyiX0zIQCQrNw0T61i+Pe7tuJV//Es3vO953HH9iZUm3SoNelRbdLi+bF5fPqXZ1Fn0eOxv92HbS32pccatGpsb63G9tZqHBr24buHx/Hu67th0NLJFSFSymQ4vv7sCDrrTLim2yHpsV12A9zBGNIZviYueI5TZ9OCNNoNwmUSifDSmWwXTOpsKpz2WhPG/PIGie5gDHajlk60BJB/b3iCygwS3YEYOutoFtNqNNjWVpB41h1EvVWPGpNO8mO31Bjx2TuvwAcfO4mTv3jpL26/bkMdvnDXNlRfZm33Xd2BJ88exs9emMLdu1rFXC4h5AI/fn4SL80E8cW7t4k69uJimuxGpDMcnmAMLrvyvmeLNeGPYm9nrdzLUDyXraqo2eIUJEpsLhxHhlODEyG11hjxq1Mzsq7BHaDh6kLJl2IrtcOpOxjDvi76MlqNBpsBL00H5V6GYPpmpGtaczF/tdWFV1/RiIVoAvPRBPyRJPyRBNQqhhs2OqFaIUNwVVctNjZY8M3nRnHXzhbJT1QJqVTheAr/9tt+bG+14/ZtLsmP77Kf359W7kFiPJXGTDBGTWsK4LJX4eCQr+D7K2/DzxrnWZqRSAGFUNprTViIJhGIJmVbA+0zFU6+qZNXgUFiNJFCKJaictNVarBmM4lyzzUVQjKdwaA3LPl+xAupVQy1Zj26nRbs7qjBLb0NuHFz/YoBIgAwxnDfNR3o94Swf7DwEwdCSGm+/NQgZkNxfPzWzbJcnFnNOASlmpxfBOegctMCuOwGhOIpBGOFnS9TkCixfGt/KjcVTv7q0ZhfvuY17kCMXlOB1Jr1UDHAG1LeGAzqYluaRpsBi8k0gospuZdSstG5CBLpTNnvL79tqwsOsw7f3D8i91IIqQgT/ii+/twI7tjehO2t1bKswbWGgsSl8RcUJK4ov4Wn0DEYFCRKjDKJwpN7VmIqncFcmMpNhaJWMdRZ9IosN3XT+7ck9WtoVuJZd7ZpjZTjL8Rg0Krx5j1t+GOfF0OzYbmXQ8ia99nfnIWaMXz4Fvm6Cpv0GtiN2qKamCjVuI+CxEItLzMuBAWJEvMGY1CxbDt4Ioz8B8O4TM1r5sIJZDioBFFA9VbDUtZdSbxLlQD0Wq9Gfk7TWggS+2aC0KgYuurMci+lZH+9tw06tQoP7x+VeymErGmHhn349Sk3/vbaLtkbs7lsVZiaXwNBoj8Kg1ZF814LkM8gTwcUFiQyxr7LGJthjAUZY+cYY3+T+3k7Y4wzxsLL/nxs2eP0jLFv5h7nZox94ILnvYEx1scYizLGnmKMtUn1b1oNTzAOh1kPjQLnv5Uro04Dp0WP0Tl5yk2XsksWChyE4lR4JrGBLgisylImscAvKCXrc4fQ7TRDpyn/z/I6ix63bXPhx8cnsRBNyL0cQtakdIbj0798CS6bAe96eafcy0FTdRWmCyw7VLL8+AtqvLUyp8UAtYopstz0swDaOedWALcB+BfG2I5lt9s55+bcn08v+/knAawD0AbgegAfZozdAgCMMQeAnwD4GIAaAMcAPCr6v6QEHmpwIgo5x2As7VOjwEEwTqsBswrdk2jWa2DWU2Po1TgfJCrvtS1W30wQG8u81HS5+67uwGIyjR8enZB7KYSsST8+PoEz00F85FUbFTH0vcletWbKTanUtDBqFUODtfBZiZIFiZzzM5zz/JkBz/3pKuChbwPwac75POf8LICvAbg3d9udAM5wzh/jnMeQDSi3MsY2Crp4AXmCcQoSRdBaa8RYEbNfhET7TIVXbzHAF0kgkcrIvZQ/4wlSg6JS6DQqOMw6uIPlfWISiCYxHYhhY2N5N61ZbrPLiqs6a/HtA6NIppX1viOknKUzHF9+ehD//NPT2NlWjdu2Sj/y4mKa7FUIxVMILMrXGb5UnPNcJpFmFxeq0WZQXrkpADDGvswYiwLoAzAD4NfLbh5jjE0yxr6VyxCCMVYNoBHAyWX3OwmgJ/f3nuW3cc4jAIaW3b782O9ijB1jjB2bnZ0V8p9VFC+dZIqivdYITzCOxURa8mN7gjFoVIz2mQoo/x6ZDSsr4+QOxihjXKIGm2Ep+16u+tzZWY9rKZMIAO96eSdmAjE8cnBM7qUQsiaMzkVw11cO4l+f6MeNm+vxtbfuVExZ5NL+tDLOJs6FE1hMptFaU96zHqXUaC+8zFjSIJFz/m4AFgAvQ7ZMNA5gDsAuZMtJd+Ru/17uIfmOAIFlTxPI3Sd/+/LbLrx9+bG/yjnfyTnfWVdXV/o/ZhXiqTR8kQRlnETQWpu9iiRH8xp3MAanRV/QXDJSmPx7RGn7Ej0BKhcvVYPVgJmyDxKznU03raFMIgBct6EO166vwxd+fw7eUHm/RoTIiXOO7x4aw6v+41kMeEL4jzduw0NvuhLVCrqYnO90Wc7Na8Zzo8/yo9DIylz27IXaTGblecWS77jnnKc5588BaAbwAOc8zDk/xjlPcc49AN4L4CbGmAVAvh/38m9iK4BQ7u/hC2678HZFye+xokyi8NqXxmBIX3LqCcaos6nA8l3KvArqcJrJcHhDNOqkVA02g+KC/2L1uUOwG7VwrrFueowxfPK2HsRTGXzu131yL4eQssM5x9P9Xrzxq4fwzz89jR1t1fjt+1+O27c1KSaDmNdUXVynSyU6PyORyk0L5bJVIZHOwBdZuUmZnG3ZNLj4nsR8aKvinM8jW5a6ddntWwGcyf39zPLbGGOm3HOegQLlW/o76SRTcG25Dwg5ZiV6ghQ4CC2frVNSNmMuEkcqw6nctEQNVgPmo0nEktKXhgulz51tWqO0kz4hdDhMeNfLO/GTE1M4POyTezmElIV4Ko0fHZvALV98Fvd+6yhG5iL49Gt78Z37dss+6uJSHCY9dGoVpsq43HTcl117c7Uy/x8rUTFlxpIEiYwxJ2PsjYwxM2NMzRi7GcA9AP7AGNvDGNvAGFMxxmoBfAnA05zzfBnpdwD8M2OsOteQ5p0AHs7d9jiAXsbY6xhjBgAfB/Ai51yRl0C9NCpBNDajFnajFmN+GTKJVIIouFqTDmoVU1TGyROgGYlCaMidMCnptS1GJsPR7w5hY8PaKjVd7j3Xd6PJXoVP/PwMUtTEhpBL4pzj2wdGcc2DT+HDP34RjAH//oateO4jr8Bb9rYpehuKSsXgshvKutx0Yj6KeqseBq383WLLRX5e8UwBGWSpMokcwAMAJgHMA/g/AN7HOf85gE4ATyBbInoa2X2K9yx77CeQbUYzBuAZAP/GOX8CADjnswBeB+AzuefdA+CNEvx7VuV8F8y1VaKkFG01RskziZF4CqF4igIHgalULDcrUTnlpkszEum1Lkn+/1+57kucmI8imkhjU+PaalqzXJVOjY/duhl97hC+Q01sCLmodIbjYz87jU/8/AzWOc145B278Zu/fxlet6O5bOanusp8DIY7EFNsplap8pnEqQKa10gy7CsXzF17idt+AOAHl3lsHMB9uT8Xu/1JAIodebGcJxSHVs1QbVTOxuW1pLXWhJMTC5Ie8/xwdQr8hZYNEpUTSJx/rSlILEX+vaKk17YYZ2eyW97XciYRAG7uqV9qYnPr1kY4qQKGkCWxZBrv++ELeOKMG/df24mP3LxR0VnDS3HZq/DcwJzcy1g1dzCG7jrzynckS6qNWhi0KswopdyUZHmCMTgthrL8ICkH7bVGTC0sSjrji2YkisdpNSw1e1ICTyAGtYrBYaYLAqXIl5uWaybxnCcbJK6rX9snJtTEhpCLC0STeOs3juCJM2587NbN+MdXbSrb87omexU8oZjiZhIXyhOgsVTFYozBZasqqGERBYkS8gbjcFKpqWhaa4xIZ7ik9fUUJIqn3qq8TGKdWQ91mZ4MKIVZr4FZrynbWYmjvggabQYYdZIU4siqw2HCO1/egZ+cmMKgV5FNwwmR1ExgEXd95SBOTMzjS/dsxzuu6ZB7SSVpsleB8/Ks7AjTdp9VcxU4K5GCRAm5gzHazySidkeuw6mEsxLduWYm9LoKr96S7YIZTymjCyaNOhFOg81QtkHimC+KtgqayfWmPW0AgGfOlW9JGiFCWEyk8eavHcbUwiIefvtu3LbVJfeSSpbfnzZZhs1r8t8htN2neI02g6Ia1xDkTjIpmBBNW430sxI9wRgseg1M+rWfVZBaPuuulFmJnmAM9WtsLp5cGqyGpT2e5WbMF0F7beXM5GqyV6HDYcKBQQoSSWX7wpPnMDwXwVfesgNXdzvkXo4glmYllmHzGqrkWr1GexW8ofiKZcYUJEokmkghFEtRuamI6ix6VGnVknY4peySeJxLsxKVEiTGae+DQMo1kxiOpzAXTqCtgoJEALi6uxaHhn2S7vcmRElOTizg688O457drWsmQATOj0MoxyBxKZNIQWLRmuyGgsqMKUiUSD4bQjMSxcMYQ1utUdJMojsYo5EmIsm/V7wKyDjFkmkEFpN0xVIgDVYDZsPxspvBl/9saa+gclMAuLrLgUgiLXn3aEKUIJHK4MM/fhFOiwH/+OqyaKZfMINWDYdZj6lyDBKp4/iq5ceGrHRxgIJEiVBaXBrZIFG6TKI3GKfXVCT54FsJG+rzF3mcVG4qiAabAekMx1w4IfdSipL/bKm0TOJVXbVgDNg/6JN7KYRI7qGnBtHvCeEzd/TCatDKvRzBNdkNZRkkeoIxWAyaimgiJrT8XtSVuoxTkCgRT65kjrJO4mqrNWHcH0Umw0U/VibD4aFmRKKpNuqgUbGl946cPCG6yCOk/Hum3PYljuYyiZXUuAYA7EYdrmiyYT/tSyQVps8dxENPDeL2bS7csKle7uWIItvpsvyCRHeAzr9Wy2XPlRmv0LyGgkSJ5EvmnPQLLarWGiPiqczSSb2YfJEEUhlOpQ4iUakYnBa9IvauUSWAsPLvGSW8tsUYm4uizqKvyEZV+7oceH58HpF4Su6lECKJVDpbZmqr0uITf9Uj93JE02SvwtTCIjgX/+K6kDxBmpG4WkadBrYqLZWbKoUnGINBq4LVUHknF1LKdx2UouQ0Hzg4aZ+paDrqTBiek26P6aV4glQJIKTzQWJ5Xb0e9UUqbj9i3jXdDqQyHEdG/XIvhRBJfHP/CF6cDOCTt/WgxqSTezmicdmrEEtmMB9Nyr2UorhpYkBJXPYqzKwwK5GCRIl4cnvXGKNB3GLqrMsGiX0zQdGP5aFN06LrrjNjyBuW/QqnNxiDTqOCrWrt7UeRQ41RB51aBbdCxpsUKjsjsbL2I+btbK+GTqPC/gEqOSVrXybD8V9PD+G6DXW4dUuj3MsRVX4MxlQZzUpMpTOYDcWp3LQELtvKe1EpSJSIOxijzqYScNmr0FxdhYPD4jdYWOqsRR9Soul2mhGOp2Tfu+bJdbGlizzCUKkYnFZ9WWUSFxNpuIOxis0kGrRq7GyrxnO0L5FUgLPuIOajSdy21bXmP/ebck1Myql5zVw4gQwHjSArgcteRY1rlMJL8/Qkc3WXAweHfEiL3LzGE4hBxQCHee2Wocit22kBAAx6w7KuwxOM00UegTXaDLIH/8UY91dmZ9Plru52oM8dwly4vDLAhBTr4FD2QvNVXbUyr0R8+U6X5dS8hi7Sl67RbkBg8fIlxhQkSoBznjvJpP1MUtjXXYtgLIUz0wFRj+MJxuEw66FR09tILN1OMwBgwCNzkBiivQ9Cq7cayqpxzejSjMTKDhIB4MAQjcIga9vBIR86HaaleXJrWbVRiyqtuqwyifnvDgoSV89VwO82nd1KIBRPYTGZppNMieSv/Ik908tNnbVE5zDrYKvSYnBW3iDRG4zDSU1rBJXPJMq937RQY7kgsbVCy00B4IomGywGDe1LJGtaKp3B4RE/9lZAFhEAGGNw2Q1llUlc6jhuo+/l1cpnkC+HgkQJnB9/Qb/MUnBaDFhfb8aBIXFPZDzUWUt0jDGsc5plLTcNx1MIx1P0Wgus3mpALJlZsdxFKUZ9UdSYdBXdvEitYriqsxbPDc6VTXBfaYKxJE6Mz+OxYxP4/O/6MeEXv9P3WnNqKoBwPIV9FRIkAuU3K9EdjEGjYnCY6Lx6tRoLSHLQPAYJnG+fTyeZUtnX5cAPj44jnkpDr1GLcgx3MIad7dWiPDc5r9tpxu9e8sh2fO/SjET6MhJSvozLHYzBblT+vt4xXwRtFZxFzLtmnQO/e8mDcX/ldnpVmlgyjQ/9+EUcHvbBG/rz/aJP9c/ifx7YB52GcgKFypdT7+2snCCxuboKv5egK7xQPIEYnBY9VKq13VRITA02A1bqyUSfGhKgQdzS29dVi1gygxPjC6I8fyyZxkI0SfXwEuh2muGPJOCPJGQ5/tJFHmpcI6iGXJnQSt3VlGJ0LlrR+xHz8vsSqcupcjxycAy/ODmNvZ21+MgtG/G1t+7EHz94LR5605U4NRXAl/4wIPcSy8rBIR82NljgMFfOhUGXrQpz4QRiybTcSymIm5pBlkyrVsG5Qq8UChIlkD/JXOnFIMLZ01kLFQMOiHQi46XssGTyzWvkKjn1hvLl4vRaCyn/3vGUQZAYT6UxHVikTCKATocJDVYDDoi855sUJhhL4qGnB/GydQ586Z7teOC6Lty4uR6ddWa8ZksjXr+jGV9+ehDHx/xyL7UsxFNpHBvzV0RX0+XysxLLpeTUHYzRRXoBrNSYiYJECXiCMVj0Gpj0VN0rFVuVFlc027FfpC58bsoOS2apw6k3JMvxPVRuKgqnJVvqUg6ZxAn/Ijiv7M6meYwxXN3twP6hOWREHjNEVvaVZ4awEE3iI7dsvOjtn/irzXDZq/D+R08iHE9JvLry88L4AmLJDPZ1OeReiqTOj8FQ/ucxkL24SOdfpWtaoXkNBYkS8ARj1LRGBld31eLkxIIoX4z5wIG6m4rPZathi8JhAAAgAElEQVRClVYtWybRE4zDqFPDTBd5BKXTqFBr0i+9l5Qs39mUMolZV3fXYiGaRL9Hngs3JMsbjOEbz43gtq0u9DbZLnofi0GLL9y9DZPzUXzqF2ckXmH5OTDkg4oBuztq5F6KpJrKaFZiKJZEJJGm8y8BrNS8hs56JOChUQmy2NflwJefHsLRET+u3+gU9Llpn6l0VCqGbhk7nOa72LKVdniTojXaDGWRSRz1ZTtEUiYxq8eVDUj63SFsarTKvBp59LmDeNs3j8AX/vO90nqNCp9+bS/uvLJZ9DV88Q8DSKU5PnjT+sveb1d7DR64rgsPPTWEV2x04pbeRtHXVq4ODvnQ22SruC7G+SYmk2UQJC5dpKfzr5KtNAaDgkQJeIJx7Kmwq1JKsLO9GjqNCvsH5wQPEt2BGKq0algN9BaSQrfTjEPD8uyB8gbjtJ9YJPVWAybnld+if8wXgdWggd1YWSeOl9LhMEGjYjhXoZnExUQaf/f9E0hngPuv7fyz2w4O+fDhH7+IOoseL1tXJ9oahmfDePToBN68p7WgLrN/f8N6PHNuFv/4k1Mw6TXoddlQbVJ+V2EpLSbSODExj/uu6ZB7KZLTqlWot5THrER3gHpCCOWW3obL3k5nuCLjnMMbilHTCxkYtGrsaK0WZV+iOxhDvVVP2SWJdDvNePzEFMLxlORln55QDFub7ZIes1K47AYcHvGBc67o99KoL4p2h0nRa5SSTqNCu8OEARnnl8rp0796CQPeMB55x+6/CASDsSTu+u+DeOC7z+NH91+FzS5xMq3//rtz0GtU+LtXrCvo/jqNCl+8ezte+9B+vOUbRwBkM/mbGq3ocVlx77521FZQN8+LOTbmRzLNK24/Yl5TdXnMSnTTdh/BrJRJpD2JIpuPJpFMc2p6IZN9XbU4OxOELxxf+c5F8AbjdBVLQvnmNUMSn5RyznPlpvT+FUNrjRGhWArz0aTcS7ms7IxEKjVdbp3TjIEKzCT+5tQMvn94HPdf23nRTKHVoMW33r4LZr0Gb3/4iCgn3ScnFvCrUzP4m5d1oq6IKodupxnPfeR6PPKO3fjHV23Eno4aTM5H8dBTg/jgYyfBeWU3Ijow5INGxbCrQucfu+xVmCqDIJHKTaVDQaLIaO+avPblZnodGha2/beb9plK6nyHU2mDxGAshVgyQ+9fkXQ4soHXyFxE5pVcWjKdweT8ItpqqGnNcuvqLRjzR8tmrpoQphYW8ZH/eRFbm2344I0bLnm/RlsVvvX2XYjG07j3W0cQWBTuIgjnHA8+0Ycakw7vfFnxZZF2ow4vW1eH+6/twhffuB2/e/+1+OirN+Hp/ln84axXsHWWowNDPmxrscOoq8wiO5fdgJmFmOK7FrsDMVgNGlTp1HIvZc2jIFFk1D5fXlubbTDrNdg/JNy8RM45zeiRWFuNEVo1k7x5jTdIMxLFlA8SRxUcJE7NLyKd4dTZ9ALr683gXL75pVJLpTN43w9PIMOBL92zHTrN5U+fNjVa8d9v2YHh2Qj+9pHjiKeECaafHZjDgSEf3nt9NywGYfbIvm1fO7qdZnzqly9VVNC/XDCWxKnJBeyrsPmIyzXbq5BIZzAncOWV0OgivXQoSBRZfui600K/0HLQqFXY01GDA4PCBYmz4TgSKcouSUmjVqHDYZL8hNSTe//WU+MaUbTUGKFWMUVnEkdz4y/aHVRuutz6egsA+eaXSu0//ziIo6Pz+JfX9hZcenx1twP/+votODjsw+d/f67kNWQy2Sxic3UV3ry3teTny9OqVfjkX/Vg3B/F158dFux5y8mRYT8yHLiqQvcjAuf3pym95DTfcZyIj4JEkbmXMhF0kimXq7pqMeqLCvbBd3x0HgCwteXic6mIOLqdZgzNSh0kUrm4mLRqFZqrqzDiU26QOJYbf0GZxD/XXpvtcDrgWfuZxAl/FP/5xwHceWUTXru9qajH3nllM964qwVf+9MwXphYKGkdvzw1gzPTQXzgxvXQa4QttbtmnQOv6m3AQ08NlUXzEqEdHPZBr1Fhe2vlNilzLc1KVPZYIneAKrmkQkGiyDzBGKqNWsE/0EnhrlmXvTL4TP+sIM93eMSPKq0aVzRV7peJHLqdFoz5IpKWQ3lCdJFHbO21JkWXm475ojDq1Kir8M6PF9Jpstn9cxUQJD4/Po8MB975ss6V73wRH33NJtRbDfjQYydX/fmVSGXw77/rx8YGC27fVlygWqh/es0mcHB85tdnRXl+JTs45MPO9moYtJV7rtZUnc8kKncsUSpXDkvlptKgIFFkHuqCKbsN9RZ0OEz4+ckpQZ7v0LAPO9qqV9yTQoTV7TQjw8+X/0nBG4zDYtBUbCMDKXQ4skGiUjsr5jub0viLv7Su3lwR5aZnpoPQaVRLDbSKZTVo8dk7r8CAN4wv/WFgVc/xw6PjGPNF8ZFbNkKtEud3sbnaiAeu7cavXpwRdIuG0iXTGZzzhLClwkcdWQ1aWPQaRWcSZ8NxZDiNv5AKneWKzBui2mm5McZw+zYXDo/4MRMorYxmIZpAvyeEPR01Aq2OFKq7LnuCJuW+RNr7IL4OhwmRRBqzIWU2Sxj1RdBOpaYXtc5pwbg/isXE2m52cmoygE2NVmjVqz9lum6DE3ftbMZ/PzOEk0WWnUbiKXzpDwPY3VGD6zb85dgNId1/bSeaq6vwyV+cQTKdEfVYSjHujyKV4UvfMZWsqVrZYzDcARp/ISUKEkVGM9aU4bXbmsA58PMXpkt6nsMjfnAO7K3gDmhy6awzQcUg6R4oev+Kr13BYzDSGY4J/yLNSLyE9fUWcA7J9wpLiXOO09MB9LqsJT/XP71mM+osenzoxyeL6nb6jedGMBdO4H+9aqPoGW2DVo2P3boZ5zxh/OT5SVGPpRT5C49dq8wUryUuexWm5pUbJFKfAGlRkCiidIZjNkTlpkrQ7jBha4sdj58oreT08LAfeo0KW5qpaY3UDFo1WmqMGJTwhNQTjKOeOhOLqiMXgElZRlyomcAiEukMZRIvYX19fn7p2i05HfdHEYql0NtU+me+rSpbdnrOE8b//eNgQY/xheP46p+GcXNPPa5slWbI+02b69FoM2D/oE+S48ltKUiso4tBLrsB0yVWXIlpKZNI5aaSoCBRRL5c7TTNWFOGO7a50OcOod+9+hOawyM+XNlaTY2IZNJdZ8aQROWmnHN4QzF6/4rMZTdAq2YYmVNes4TznU3p5PFi2h0maNVsTTevOT0VBABcIUCQCACv2FiP113ZjC8/PYQ/nPWseP+HnhpCNJHCh27eIMjxC8EYw/ZWO05MzEt2TDkNzYbRYDUINneynDXZjViIJhGJp+ReykW5g3Fo1Qw1Rp3cS6kIFCSKiGasKcutW11Qqxh++sLqsomBxSRemgliTyftR5RLd70Zw7MRpCTYKzMfTSKZ5lRuKjKNWoXWGqMiO5yen5FImcSL0ebmlw541m4m8fR0AFo1w7p64UoRP37rZqxzmvGObx/DRx8/ddET8mAsiQef6MN3Do7iDTta0O20CHb8QmxvqcaEf1Gxe4WFNOQNo8tJF4KA7EU7AIodg+IJxuC0GKASqXkT+XMUJIqIaqeVxWHW45puB37+wjQymeI7KR7N70fspP2IcumuMyORzmBCgj0T9P6VTofDpMg9icOzERi0Kio5vox1TssazyQGsKHBImj1iM2oxU/fczXuf3knfnBkHK/6j2dxbNQPINtp85GDo7ju357Gfz09hNu2uvDRV28S7NiFys8LLHW2o9JxzjE0G6GmNTnNS2MwlBkkugMxKjWVEAWJInLTSabi3LG9CVMLizia+0IuxuERH3QaFba1VHabbDnlW9BL0eH0fJBImUSxtdeaMOqLrOrijZgGvGF0O8101foy1tWbMTG/Njuccs5xeiqAXpfwe9ANWjX+8dWb8Oi7rgIHx11fOYiPPn4KN3/xT/jYz85gfb0Zv3jvNfj83dtgM0pfBtnbZINGxXBifG2XnHqCcYTjqVWPN1lrXHZlB4meYIw6m0qIgkQReYMxMAY4zFQ7rRQ3bq5HlVaNn66iy+nhET+2tdgretiu3PJf5FI0yvDmysWdlEUSXbvDhHgqs3RhTSmGvGHKMKxgLXc4nVpYxHw0iR6B9iNezO6OGvzm71+Ou3e14PuHxwEAX3/rTvzgnXtxhYwN0gxaNTa7rDgxvrYzieeb1tD7HMh+32lUTJHlppxzuGkslaQqMkiMJaW54ukJxuEw66EpYbYSEZZJr8FNPfX49akZJFKF72sLxZI4PRWgUlOZWQxaNNoMOFdC86FC5TOJTsokiq4jNwZDSfsSI/EUphYWKcOwgnyH03NrcF+i0E1rLsWs1+Czd27BU/9wHX77vpfjlZvrRR91UYjtLXacnFxAWmEZfiHlL27Q+zxLrWJosBkwvaCsC3YAEIqnEE2k0WCj72SpVGT0MuANYz6SEP04nhClxZXotdubEFhM4ul+b8GPOTY6jwwH9nZQ0xq5XdlWnZtXKe6JiycUQ7VRS51sJZAPEocVFCSeP3mUtmFIuWmrXbsdTs9MB6BWMWxskOZ3oMNhglZBF5W3t1YjmkivyQsAeYPeMCx6DeqoweASpc5K9ARoC5fUlPNpJLHnJaiz9wTjtJ9JgV7W7UCtSVdUl9NDIz5o1QzbJZpTRS5tX1ctZgIx0RudZN+/9GUkhQarAXqNSlGZxHwZGmUYLk+rVqHTYV6THU5PTQWwzmmu2C0G+eY1a7nkdNAbRpfTrIjMrVI02asUuScxvx2Bki/SqcggkQE4NiZ+kOgN0ow1JdKoVbh1SyOePOtFMJYs6DGHh/3Y2mxHla4yTxaU5OouBwBg/5C4g57p/SsdlYotNa9RikFvGBoVQ1stjb9YSXe9Geck2CcspaWmNSKXmipZa40RNSbdmm5eMzQbpgtBF2iyV8EdjEkyaqoY7lwmkbqbSqcig0SDVo3jo+J+6CVSGfgiCWqdrlCv3d6ERCqDx59fOZsYiadwivYjKkZbrRFN9iocGJwT9TieYJxmnEqo3WFU1BiMAW9YceV/SrXeacHk/CKiCWUO4F4NbyiOuXACvS6r3EuRDWMM21vsa3YMRjCWhDcUp6Y1F3DZq5DOcHgVNiOTxlJJryK//Ux6DU5OLhTVuKRYs+Hsm4vKTZVpW4sduztq8J9/HED4IoOMlzs2No90hmNPJ+1HVALGGPZ11eLgsE+0kQnpDMdsmMpNpdThMGPcH1XM1eshL2UYCrW+3pztcOpVTpBfqlOTAQCQtcOoEmxrsWPAG0ZgsbCqm3JCJeUX15Sblai0DqfuYAx2o7Ziy7/lUJFBolGnRjyVwZnpgGjHcNMGW0VjjOGjr96EuXACX/3T8GXve3jYB42KYUcb7UdUin3dtViIJvHSTFCU5/dF4khnOF3kkVCHw4hkmiuiq148lcaoL0InjwVaV59t7LKWGpycng5AxYBNjZWbSQSwtA//xcm1l00coiDxoprs2fNWpe1LnFmgZpBSq8gg0aTTAACOi7gv0Uvt8xVvW4sdr9nSiK/9aXjp9bqYwyN+bGm2wZj7vSHy25fbl3hgSJyS06UZifSFJJn22myH0xEF7EscnYsiw+nksVDttUbo1Ko1tS/x9FQAXXXmiv/c39JiA2Nrs3nN4GwYOrUKLbnMGcly2bP/P5QWJE4HYktrI9KoyCBRo2ZoqakSNUik2uny8OGbNyCVyeALTw5c9PZxXxQvTi5gD+1HVJR6qwHdTjP2D4rTvIbev9JT0qxEKkMrjkatQmedCQNraAzG6algRTetybMatFjnNK/J5jVD3gjaHUaaZX0Bo06DaqNWceWmM4FFNFLTGklV7DtjZ1sNjo3NizZrzROKQ6NiqDHqRHl+Ioy2WhP+em8bHj06/hct3EfmIrjrKwdh1mvwhh3NMq2QXMq+rlocHfWLsrfYE6Q9xVKrs+hh0qkV0bxmwBsCY6CGFkXodpoxsEYyibOhONzBGHoquGnNcttbqnFiYkH02bRSo86ml6a0WYnRRAoL0SRlEiVWsUHilW3VmA3FMSnSm8ATjMFp0UOlotk7Svd3r1gHk06DB5/oW/rZoDeMu79yEMl0Bj9411500smi4uzrciCaSOOkCHtlPMEYGAMcZgoSpcIYQ7vDpIggcdAbRku1kRokFGF9vQUT/rXR4fR0rl/BFZRJBJCdl7gQTWLUF5V7KYKJp9IY80XoQtAlNNmrFLE/PC+/FpedMolSqtggcWeuCcmxMb8oz+8NxlFPafGyUGPS4YHru/DkWS8ODftwzhPCG796EBkO/PBde7Gxga4mK9FVnbVQMWC/CKMwvKEYak16Gn8gsXaHMmYlDlJn06Ktr8/+/8qX6pazM1PZIHEzZRIBnG9es5ZKTsd8tO/4clz2KkwtLComezwTyCZ0Gm2USZRSxZ4Bra+3wKLX4JhI8xI9wRjNSCwj913dgUabAR//2Wm88auHoFYxPHr/3qWufUR5bEYteptsODAk/L5ETzBOpaYy6Kg1YXJ+EUkZx2CkMxzDc9TZtFj5jMzwrPxBfqlOTQXQ6TDBYtDKvRRF6HaaYdZr1lTzmvzFDMokXlyTvQrheArBmDIqA2bymUQKEiVVsUGiWsWwrdUuWvMaTzBGJ5llxKBV44M3bcA5TxgGjQqPvusq+vIoA1d11eLE+LzgJW6jcxHa+yCDdocJ6QzHhF++srYJfxSJVIaCxCK11hqhYsCwAsqFS3V6KogeKjVdolYxbG2x4cTE2skk5oPEzjqTzCtRJqXNSpwOLIIxoN5G59VSqtggEcg2r+n3hBCMCTskdjGRRjCWovb5ZeaO7U34l9f24kd/exXaHfTFUQ6u7nIgmeY4KmBFgDcUw/BcZKkknUgn3+FUzn2J1Nl0dfQaNZqrjRieLe9y0/lIAlMLi+ilUtM/s72lGmdnQlhMpOVeiiCGZsNosldV/IiTS1kag6GQ5jUzCzE4zHroNbRPXEoVHSTuaKsG58LP/6H2+eVJrWL4671taK42yr0UUqBd7TXQqVU4IOC+xCMj2X3KNPZEekoIEgcoSFy1zjpT2Zeb5l//jY0UJC63vdWOdIbjVG6/Zrkb9IbRRe/xS8o3iJkOKCNInA4swkV9PiRX0UHitlY7VAyCl5yeDxIpLU6ImKp0amxvtQu6L/HQsA8mnZoyCTKoNmphNWhkbV4z6A2j3qqHlfajFa3TYcbIXASZjDKaXaxG/nevvZYuFi63rcUOAHhhDZScZjI8O/6CtpRcksOkh06jwpRSyk0XFqlpjQwkCxIZY99ljM0wxoKMsXOMsb9ZdtsNjLE+xliUMfYUY6xt2W16xtg3c49zM8Y+cMHzXvKxKzHrNdjUaMVxgTuczgQok0iIVPZ1OXB6OoCFaEKQ5zs07MfO9hoasCwDxhg6HCaMzsm3J3HQG6Is4ip11pmwmEzDHVRO6/xijfui0KgYmmhP8p+pNetRZ9FjwFPe5cRANisVS9K+48tRqRhcNoMiyk0555gJxKhPgAykPAv6LIB2zrkVwG0A/oUxtoMx5gDwEwAfA1AD4BiAR5c97pMA1gFoA3A9gA8zxm4BgAIeu6IdbdU4Mb6AlIDd9I6N+WHSqZdKpwgh4rm6uxacZzOApZoLxzHoDWMvlZrKpt1hkm1fG+ccQ7MRyjCsUr4JSDmXnI76ImiqrqKLRBfRVWfCUJnvOQWWdzalc7TLaaquUkTjmuBiCtFEmmYkykCyT0HO+RnOeTz/n7k/XQDuBHCGc/4Y5zyGbFC4lTG2MXfftwH4NOd8nnN+FsDXANybu22lx65oR1s1ook0+tyh0v6ByxwY9GFPZy3NWCNEAltb7DDp1Hh2oPR9iYeHs1UFeztrSn4usjqbG62YDsTgDUmfjXIHYwjHU+im0Ter0unIjcGYK99AYtwfRVstBQ8X01VnxtBsRDGz81aLmlMVxmWrUkS5aX4NVG4qPUmjGMbYlxljUQB9AGYA/BpAD4CT+ftwziMAhgD0MMaqATQuvz33957c3y/52ELXtLM9ezIo1L7EqYVFDM9FsK+LMhGESEGrVuH6jU788sUZxJKldd47NOyDUadGL7W/l00+i5sP2KWUL6WjTOLq1Fv1MOnU5Z1JnIugrYb2I15MV50ZgcUkfBFhSvvlMjQbQbVRi1oz9Y24HJe9Ct5QHImUfHNrAWAm1zynkTKJkpM0SOScvxuABcDLkC0TjQMwA7iwXVYgdz/zsv++8Das8Ng/wxh7F2PsGGPs2Ozs7NLPXTYDGqwGHBMoSNyf67J4zTqHIM9HCFnZm3a3IrCYxK9PzZT0PIdHfNjZXkNVADLqcVlh1msEKR8uFmUYSsMYQ0edqWxnJS5EEwjGUmijpjUXtRbKiQFgyBumOcgFaKquAufnmzHKZTrX58NFmUTJSX4mxDlPc86fA9AM4AEAYQAXthG0AgjlbsMFt+dvwwqPvfC4X+Wc7+Sc76yrq1v6OWMMO9qr8bxAQeKBwTk4zDpsoHIlQiRzVVctOhwmfP/w+KqfYy4cxzlPmEpNZaZRq7CzvRqHR6TPJA7OhmE3auEw6yQ/9lrR6TCX7azEUV+2YRKVm15cPrAq932Jw3MUJBYi37xpUubmNTMLi9CoGOoslPmVmpyXyzXI7kk8A2Br/oeMMVP+55zzeWTLUrcue9zW3GNwuccWs5BdbdWYWlgs+YOPc479Qz7s63KAMVbScxFCCscYwz27W3BsbB7nPKvbX7w0H7GDSsXltrezFoPeMGZD8ZXvLKBBT7YtPn1+r15nnQlTC4sll37LYSw3/oIyiRfXZK+CXqPCkLd8g8RwPIW5cAJtDnqNV5IPEuVuXjO9sIh6qwFqFX0uS02SIJEx5mSMvZExZmaMqRljNwO4B8AfADwOoJcx9jrGmAHAxwG8yDnvyz38OwD+mTFWnWtI804AD+duW+mxBXn1lkaoVQw/OjZR0r9zIHdSc003lZoSIrXX72iBTq1adTbx8LAPVVo1tjTTfkS57enIZnMPj0hbcjo4G6ZS0xJ11pnBOWSddbla47lMYivtSbwolYqhs85c1pnEpQsBNZQtXklDbni97EFiIEadTWUiVSaRI1taOglgHsD/AfA+zvnPOeezAF4H4DO52/YAeOOyx34C2WY0YwCeAfBvnPMnAKCAxxbEaTHgho1O/M/xSSRLGIXxXK674tW0H5EQydWYdLi5twH/8/wkFhPFZzGy8xGraT+iAvQ22WDSqSVtXuMLx+GPJChILFGno3z3rY36omiwGmDQquVeimJlx2CU32ubN75UUkwXAlZi0KrhMOtl73A6E1ikzqYykeRsiHM+yzm/lnNu55xbOedXcM6/tuz2JznnGznnVZzz6zjno8tui3PO78s9rp5z/vkLnvuSjy3G3btaMBdO4A9nvav9Z2L/4Bzaa400hJcQmbxpdytCsRR++eJ0UY/zRxLo94RoPqJCaNUq7GyvkbR5DTWtEUbHUpBYftmmcX+EgocVdNaZMTkfLctyYgAY8+eyxfQ6F6SpWt4xGJkMhzsQg4vOq2VBl8xzrl1fh3qrftUlp8l0BodH/LiaSk0Jkc3ezhp01pnw/SPFlZweyZU1UtMa5djTWYMBbxhzYWn2JQ7OUpAoBJNegwaroWwziRQkXl5XnQkZDozlMnLlZswXRbVRC6tBK/dSykJ7rVHWPahz4TiSaU7lpjKhIDFHo1bhDTta8HS/d2kmSzFenFxAOJ6i/YiEyIgxhjftbsWJ8QWcnQkW/LhDw35UadW4osku4upIMfJZ3SMSdTk9NRmA1aChNusC6CzDMRjRRAqzoTh1Nl1BuXc4HfdH0EqvccF6XTZMB2LwyzQbMz/+gspN5UFB4jJ37WxBhgM/PjZZ9GOfG/CBsWwrfkKIfF53ZTN0muIa2Bwa9mFHWzV0GvpIVIormmww6tSSlZweGfVjZ3sNVNRBr2SddSYMz4bBOZd7KQUbo71qBcnPSizXDqdjvijaqDFRwXpc2SlzZ6YvHEkujZlcqWujjTKJcqAzomVaa43Y11WLR49NIJMp7stt/9Acel022I00X4sQOVWbdHh1bwN+emIK0URqxfvPRxLoc4eo1FRhtGoVdrRVSxIkzoXjGJ6NYHcH/Q4IodNhRjCWgk+m7MNqLAWJ1PXysow6DZrsVWWZSUykMpheWEQ7XQgoWI8r2+379FThlTlCymcSaU+iPChIvMDdu1owOb+Ig0WcmETiKZwYn6f9iIQoxJv2tCEUT+EXJ1duYJMf2k5Na5Rnb2ctznnC8Im8L/Fo7ndgVzsFiULIZ5vKaV9ifjQCNTRZWTmWEwPA1MIiMhxUbloEm1GLlpoqnJYxk2jQqlBtpD2kcqAg8QI39zTAVqXFD48W3sDmyKgfyTTH1d10kkmIEuxqr8bGBgsefKJ/xSveh4Z9MGhV2NJM+xGVRqp9iUdG/TBoVbiiiWZkCqHTkd23Vk4dTsf82YYmtio6GV1JV50ZQ97yKicGls1IpAsBRelptOGlabkyiYtw2arAGG0DkAMFiRcwaNW4Y3sTfnvajfkCS2UODM5Bp1HRVWhCFIIxhv/66x1gAN7y9cOXbEZ1bNSPn74whV3tNbQfUYG2NNtQpRV/X+LRUT+2tdjpd0AgTdVV0GlUZZVtGvNFqGlNgbrqTIgk0vAEpek8LJRxf76kmILEYvQ2WTEyF0EolpT82NMLMTRSZ1PZ0DfiRdy9qwWJdAaPn5gq6P7PDfqws62aBvASoiAdDhO+fd9uhGIpvOUbR/7ios9PT0zhTV87jGqjDp+6vVemVZLLyc5LrF4qCRZDKJbES9NB7O6gShChqFUM7bXG8sok0viLgpVrh9MxXxRVWjXqLHq5l1JW8vsS5cgmzgQWqbOpjChIvIhNjVZsbbbh0aMTK5ZTzIXjODsTpP2IhChQb5MNX3vbToz7o2+OHqYAACAASURBVLj34aOIxFPgnOPzv+vH+x59Adtb7Xj83fuWBoAT5dnbWYs+d0i0FuzHx+aR4cBuqgQRVKfDXDaZxHxDE8owFabLWb5BYmuNkUoXi9TTlO9wKm2QmExn4A3FqWmNjChIvIQ372lDvyeEb+0fvez9fvJ8dlzGPhp9QYgi7e2sxUNvuhKnpwL42+8ex//3wxfwpT8O4g07mvHIO/ZQR2KF25PrOHpkRJyS06OjfqhVDNtbaU+qkDrrTBj3RZFMZ+Reyoom56PIcFC5aYGcFj3Mek1ZNSYC8jMS6UJAsZwWA5wWveTNa9yBGDgHXDT+QjYUJF7C63c046bN9fjMr8/iwNDcRe/zVL8Xn/tNH27Y6MS2FjrBIESpbtxcjwdftwXPDszhFyen8ZFbNuJfX7+F9qCVgS3Ndhi0KhwaFqfk9OjIPHpdVpj0GlGev1J11pmRynBM5PaBKdmYn2YkFoMxhq46U1llEjnnGPfTjMTV6nFZJS83ncmNv2ikTKJs6AzpElQqhs/fvQ2dDhPe873n/+KL7qXpIN77veexqdGKL92zncoXCFG41+9oxn+9+Up8577deOC6LnrPlgmdRoWdbTWiNK+Jp9J4YXKB5iOKIF/CXQ7ZprG5fNdLyiQWqjPX4bRceENxxJIZuhCwSr1NNgx4w4gl05IdM99wjjKJ8qEg8TLMeg2++tadSGU47n/kOBYT2TeHJxjDO759FBaDFt942y66Ak1ImXjVFY14+fo6uZdBinTt+jr0uUPocwt7JfvFyQASqQx1phZBV35W4pzyA4kxfxRGnRoOM5WeF6qrzoTpQAyReErupRRkdC4/B5MuBKxGj8uKdIajzx2S7JjTC5RJlBsFiSvocJjwpXu246w7iI/8z4uIxFO47+GjCC4m8c17d6GBrnAQQoioXr+jGXqNCt8+MCbo8+bnL1KQKDy7UYcak648Mom+KNpqTVRdUIR8h9ORMmlONEbjL0qS73B6RsJ9idMLi7AaNDBTIkY2FCQW4PoNTvzDTRvw85PTeNV/PIuzM0H83zddic0uq9xLI4SQNa/apMPt21z46YkpBKLCzeo6MuLHOqcZ1SbKIImh02EqkyAxQsFDkcqtw+m4Lwq1iqGpmrJSq9FcXQVblRanp6TblzgTWKTOpjKjILFA776uC6++ogHj/ij+9209uH6jU+4lEUJIxXjrVe1YTKbx2PEJQZ4vneF4fmye9iOKqLPOpPgxGOkMx4R/kfaqFamt1ggVA4bK4CIAkM0kuuwGaNV02rsajLFc8xopM4kxNFK1nqzo3VIgxhi+cPc2/Py9V+MtV7XLvRxCCKkovU027GyrxncOjiGdufz82kKcnQkiFE9RkCiizjoz5sJxBGPCZX+F5g7GkEhnqGlNkfQaNVpqjGWUSYygnV7jkvQ22XDWHZJsrM1MYJH2I8qMgsQi6DVqbGmmUReEECKHt+1rx7g/iqf7vSU/F+1HFF9nrsPpoIK7YJ7vbEqZxGJ1lVGH0zF/FK1UUlySHpcViVRGkvfzYiKN+WgSTRQkyoqCREIIIWXhlt4GOC16fPtg6Q1sjo760WSvoj0vItrUmN23L/V8tWLQjMTV66ozYWQugowAmX0xBRaTWIgm6TUu0fnmNeK/n6dz4y+o3FReFCQSQggpC1q1Cm/e04Y/nZstqcyNc46jo37soVJTUeWbXUhxUrlao74ItGqGRhtdLChWV50Z8VQGUwuLci/lssZ92QsBrTVUblqKDocJRp0ap6fE35c4kx9/Qe9LWVGQSAghpGzcs6cFWjXDIyVkE0fmIpgLJ7CLgkRR5ZtdSNk2v1jjvihaqo1Qq2j8RbHKpcPpmJ9KioWgVjFsapTm/ZzPJLrslEmUEwWJhBBCyobTYsBrrmjEj49PIrzKQd77B+cA0H5EKfS4rOiTsNlFsUZ9UQoeVik/K1HJe06B7BxMALQnUQC9Litemg6KXmKczyTSLHJ5UZBICCGkrLx1XzvC8RR+8vxk0Y89NRnA537Th94mK7rqqPxMbD0uGxKpjCKzTZxzjPsi1Nl0lWpMOjjMevS5Q3Iv5bLGfVE4zHqYaCh7yXpcNkQSaYz6xB19MrWQfc30GrWoxyGXR0EiIYSQsrK9xY4tzTZ8a/8oYsl0wY8b80Xw9oePwG7U4Rtv2wXGqMRQbL1N2eY1Ug7hLpQvkkAkkaZMYgl6m6yS7FErxZg/Qq+xQHpy72ex9xmfngpiQ4NZ1GOQlVGQSAghpKwwxvD+G9djZC6Cj//sNDhfufRpNhTHW795BOkMx3fesRv1VipjkkKHw4wqrVqR+xJHcuMvaH7e6vW6bBjwhou6WCO1cV8UbVRqKoh1Tgt0ahVOi/h+DsdT6HMHsaONtgPIjYJEQgghZef6DU689/pu/OjYJH5wZOKy9w3HU7jv4aPwBGP4xr27lvZSEfFlm11YFNnhNF8mub7BIvNKyldvkxXpDEe/QktO46k0ZoIxtFImURA6jQrrG8yiZo9PjM8jw4GdbdWiHYMUhoJEQgghZen9N67Hy9fX4RM/P40T4/MXvU8ilcED3z2Ol2aC+PKbr8SVrXTiIbUel02SZhfFOucOwaLXwEXNMVYtPztPzMxSKSb8i+CcOpsKaXd7LY6OziOaWF3jsJUcG52HigHbW+2iPD8pHAWJhBBCypJaxfClN25Dg82AB777PGZD8aXbOOd4qs+L2x/aj2cH5vDZO6/AKzbWy7jaytXjsiIcT2E8N7heKfo9IaxvsNDe1BLkZ2Eqcc8pAIznxl/QjEThvHKzE4lUBs8OzIny/MfH5rGhwQqLQSvK85PCUZBICCGkbNmNOvzXm3dgPprA3/3geaTSGRwb9ePurxzC2x8+ikg8hYfedCXu2tki91IrVm9TNtukpJJTzrMlkuvrqdS0FIwxXNFkU2zzmvz4C8okCmdXew2sBg2efMkj+HOn0hmcGJ+nUlOFoCCREEJIWettsuGzd16BQ8N+3PTFP+H1/30QI74IPv3aXjz5gWvxmi2Nci+xoq2rN0OjYooqSfSG4ggsJrGR9iOWrKfJin53CImU8mZhjvmiMOs1qDXp5F7KmqFVq3D9Rif+2OdFWuAS8j53CJFEGjvbKUhUAgoSCSGElL07r2zG31zTAV84gQ/fsgHPfOg6vGVvG3Qa+pqTm16jxrp6ZTWvyTdaoUxi6XpdNiTSGQx4lde8ZswXQWuNkUqKBfbKTfXwRRJ4YeLie8FX6/hY9vl2UCZREejbkxBCyJrwz7duxomP3Yh3X9cNo44GZytJr8uKM1OBgsaVSCEfJG6gTGLJlsqJFbgvccwfpVJTEVy7oQ4aFcPvX/IK+rzHxubRYDWgyV4l6POS1aEgkRBCyJqhUlHGQIl6XFb4Igl4gvGV7yyBfk8IdRY9aqgMsWRtNUaY9RpFlRMD2c7G474oOhzUtEZoVoMWeztr8eRZYfclHh/1Y0d7NWV+FYKCREIIIYSIqmepeY0yAol+dwgbqNRUECoVw2aXVXHNa0Z9EaQynEqKRfLKTU4MesMYmYsI8nzTC4uYDsSoaY2CUJBICCGEEFFtarSCMWV0OE1nOAa8ISo1FVCvy4aXZoKCNzIpxTkP7TsV0w2bsiOF/iBQNvFYbj/izrYaQZ6PlI6CREIIIYSIyqzXoKPWpIhs04Q/ilgyQ5lEAV3RbEUsmcHwbFjupSw55w5BxYDOOio3FUNLjREbGyz4vUCjMI6P+mHUqbGpkd6XSkFBIiGEEEJE19NkU0QmsS/f2ZQyiYLpdWXLiZW0L/GcJ4z2WhMMWrXcS1mzbtxcj6OjfsxHEiU/17GxeWxrsUOjptBEKeiVIIQQQojoelxWTC0sYiFa+gllKc6XIZplXcda0llnhkGrwqlJ+S8C5J3zhqjUVGSv3FSPDAee6i+ty2k4nsLZmSDtR1QYChIJIYQQIroelxWA/PsS+z0htNYYaUyKgNQqhs2NVsVkEmPJNEbnInQhQGRXNNngtOhL7nL6wvgCMhzY0U77EZWEgkRCCCGEiK7HpYwOp/1ualojht4mG16aDiKjgOY1w7MRZDiwjjKJolKpGG7YVI9n+mcRT6VX/TzHxvxgDNjeahdwdaRUFCQSQgghRHQ1Jh1cNoOsmcR4Ko2RuQg1rRFBr8uGcDyFMX9U7qVgwJstKaaLAeK7cbMTkUQah4b9q36O42Pz2FBvgdWgFXBlpFQUJBJCCCFEEptdNlk7nA7PRpDOcGpaI4Kepmw5sRI62Pa7Q9CoGNprqbOp2PZ1OVClVePJVXY5TWc4TowvYGc77UdUGgoSCSGEECKJ3iYrhuciiCZSshy/P9fZdCMFiYJbX2+BTq1SxL7Ec54wOutM0GnoNFdsBq0a12+sw09fmMJcOF704/vcQYTjKZqPqED07iGEEEKIJHpdNnAOnJqUJ5Do94SgVTN0OCjDJDStWoWNjRacmZK/w+k5T4j2I0roAzeux2IijQd/01f0Y4+PzQMAdlBnU8WhIJEQQgghktjdWQONiuGZc7OyHL/fHUJXnRlamsUmih6XDaenA+BcvuY1i4k0JuajWO+kIFEq3U4L3nFNBx47PrkU9BWCc45fn5pBg9WA5uoqEVdIVoM+JQkhhBAiCatBix1t1XiqX74gkWbniae3yYqFaBKT84uyrWHQGwbnwIYGGn8hpb+7YR0arAZ8/GenkS6ww+33j4zj0LAf73lFNxhjIq+QFIuCREIIIYRI5vqNTpydCcIdiEl63FAsiamFRep4KaJeBYw56fdk951Suam0zHoN/uk1m3BmOojvHx5b8f4T/ig+86uzuKbbgb/e0yrBCkmxKEgkhBBCiGSu3+AEADxzzivpcc95wgBA4y9EtKHBAq2a4fnxBdnWMOAJQadWoa3GKNsaKtWtWxqxr6sW//bbfvgu08Qmk+H4h8dOQsUYHnz9FsoiKhQFiYQQQgiRzPp6M1w2A57qk7bk9JyHZueJzaBVY29nLZ58ySPbvsR+TwhdTjM0tO9UcowxfOr2HkQTaTz4xKWb2Hz74CgOj/jxsVs3oclOexGVit5BhBBCCJEMYwzXbXTiucE5JFIZyY7b7w7BqFPTSanIbuppwPBcBIPesCzHH/CEsb6e9iPKJd/E5kfHLt7EZmQuggef6MP1G+pw184WGVZICkVBIiGEEEIkdf0GJ8LxFI6N+SU7Zr5pjUpFpW1iumlzPQDgd6scrl6K/L5Tak4kr3wTm3u/eQT3P3IMjxwcxfBsGOlcmalOrcLnXkdlpkqnkXsBhBBCCKks+7pqoVOr8HT/LPZ1OSQ55jlPCK/cVC/JsSpZvdWAbS12/PaMG++5vlvSYw/kspcUJMrLrNfgm/fuwncOjuLZgTn89kz2gkG1UYv5aBJfuHsr6q0GeRdJVkRBIiGEEEIkZdJrsLujBk/1efHRV28S/Xhz4Th8kQTtR5TIzT0NePCJPkwvLMIlYXnvQG7fKZWbym+zy4rPvW4LOOcY90fx3OAc9g/OocFahddua5J7eaQAVG5KCCGEEMldt6EOA94wJvxR0Y91bDS7N6rHZRX9WAS4qSebsf29xCWn5zxhGLQqtFRTZ1OlYIyhrdaEN+9p+3/t3XmU3VWV6PHvTmUkEwkZCIGEKQMQmiGAIE8IIDg1zVOUp9KIz25A1O7WFlkuZwXn7nZoWxREBARFUZ8KikpAUEAlqCEMSRiSAGYmY2WqpGq/P+6viktIVZLKrXtvVX0/a9VK1W84v31z1q/q7nvObx++fv50Pnb24U4z7SZMEiVJUtWdNrW0FMZv53d9ldOZjy9j6MC+HDtxRJdfS3DI6CEcOmYIv3p0aVWvO3/ZeiaN8blTqRJMEiVJUtUdPGowE0buxT3zuna9xJaW5O55y5kxZQz9XBahal51xFj+uGAVqzc0Ve2a85etZ5JTTaWKqMpvy4gYEBHXRsSiiFgfEX+NiNcU+w6MiIyIxrKvj2537rcjYl1ELI2If9+u7TMiYm5EbIyIuyNiYjVekyRJ6ryI4LQpo7nvyefZvLW5y64z+7k1rGxs4pWHjemya+ilzjp8X5pbkrvmdu2HAK3WbtzKsnVbmGLRGqkiqvWRWl/gWeBUYDjwEeAHEXFg2TF7Z+aQ4uuKsu2fACYBE4HTgMsj4tUAETEK+DHwUWAkMAu4pUtfiSRJqogZU8ewaWszf1rQdUthzHx8OQ19glMnj+6ya+il/m7/4YwbPrBqU07nL28tWmOSKFVCVZLEzNyQmZ/IzIWZ2ZKZtwELgOm7cPqFwBWZuTozHweuAd5e7HsD8Ghm/jAzN1NKKI+KiKmVfxWSJKmSTjp4Hwb07cPdXTjl9M7HlzF94gj23qt/l11DLxURnHX4WO59YgWbmrpupLjV/KKyqdNNpcqoyeT8iBgLTAYeLdu8KCKei4jrihFCImIEMA6YXXbcbOCI4vsjyvdl5gbgqbL9kiSpTg3s18BJh+zDb+d1TfGa51ZvZO7S9U41rZGzjtiXzVtbuPeJri9ONH/pegb3b2B8FZfckHqyqieJEdEPuAm4PjPnAiuB4ylNJ50ODC32A7R+HLS2rIm1xTGt+8v3bb+//LoXR8SsiJi1YkXX/7KSJEk7d9qUMSxYuYEni+mCldT6PNwZh42teNvauRMOGsnwQf2qMuV0/rJGJo0d6vIKUoVUNUmMiD7AjUAT8B6AzGzMzFmZuS0zlxXbz4qIoUBjcWr5wkbDgNa/JI3b7dt+f5vMvDozj8vM40aP9rkESZLqwWuO3JdB/Rr48p1PVLztmY8v56BRgzlktFMQa6FfQx/OmDqGmY8vZ2tzS5ddZ1tzC48tWWfRGqmCqpYkRumjnWuBscC5mbm1nUOz+LdPZq4GlgBHle0/ihemqT5avi8iBgOH8OJprJIkqU6NGTqQi085mNseXsKfn1ldsXY3bNnGA089z+lTnWpaS2cdsS9rN23lwS4sTvSnBatYu2krM6Y4CCBVSjVHEq8CDgPOzsxNrRsj4mURMSUi+kTEPsBXgd9mZus00huAj0TEiKIgzUXAd4p9PwGmRcS5ETEQ+BjwcDGNVZIkdQMXn3Iwo4cO4NO3P05m7vyEXfC7J1bS1NzCGT6PWFOnTh7NoH4N3Prn57rsGrfPWcKgfg3MmGJfS5VSrXUSJwKXAEcDS8vWQzwfOBi4g9IU0UeALcBbyk7/OKViNIuAe4AvZuYdAJm5AjgX+DSwGngZ8OZqvCZJklQZgwf05f1nTuahRau545HKPL828/FlDB3Yl+MPHFmR9tQ5g/o38JYTJvDTvy7m2VUbK95+c0vyq0eXcvrUMQzq31Dx9qXeqlpLYCzKzMjMgWVrIQ7JzJsy83uZeVBmDs7McZn5tsxcWnbulsx8R2YOy8yxmflf27V9Z2ZOzcxBmTkjMxdW4zVJkqTKedNxBzBl7FA+d8dcmrbt2fNrLS3J3fOWM2PKGPo11KSQu8pcdMpB9Am4+t6nK972Hxc8z8rGJl575LiKty31Zv7mlCRJNdfQJ/jQ6w5j0fMbufEPi/aordnPrWFlYxNn+DxiXRg3fBBvnL4/t8x6luXrNle07V/MWcLAfn04barPI0qVZJIoSZLqwqmTR/OKSaP46swnWLOxqdPtzHx8OQ19wkImdeSSUw5hW3ML1/5+QcXabG5J7nhkGadPHcNe/ftWrF1JJomSJKmOfOi1h7Fu81a+dteTnW7jzseXMX3iCPbeq38FI9OeOHDUYM4+aj+++4dFe/QBQLk/LVjFysYtTjWVuoBJoiRJqhuHjRvGm6bvz/UPLOSJZS9Z9ninnl7RyNyl63mlVU3rzrtmHMqGpma+c//CirT3y0dKU01d5kSqPJNESZJUVy47awrDBvbjkhsfYt3m9pZVfqltzS184NaHGTqgL/9w1PgujFCdMWXfoZx5+Fiuu28hjVu27VFbzS3JLx9ZymlTnGoqdQWTREmSVFfGDBvI188/lmdWbeS93/8rLS27tnbi1+5+kocWrebK109j3+EDuzhKdca7ZhzC2k1bufmPe1acaNbCVaxY71RTqauYJEqSpLrzsoP34WNnH85dc5fzpTvn7/T4hxat4qszn+D1x4znnKMdRaxXx0wYwcmH7sM1v1vA5q3NnW7nF3OWMKCvU02lrmKSKEmS6tIFJ07kvOP257/vepI7HlnS7nHrN2/lvbf8lfEjBvGpc46oYoTqjHefdigr1m/p9LOJLcVU0xlTRjN4gFNNpa5gkihJkupSRPCpc6Zx9AF78+8/mM28pTsuZPPxnz7K4jWb+fL/OZqhA/tVOUrtrpMO3oczDx/LF+6Yy52PLdvt82ctWs1yp5pKXcokUZIk1a2B/Rr4xj9OZ/CAvlx0wyyuv38h9z25kmXrNpOZ/PSvf+PHf/kb/3L6oUyfOLLW4WoXRARfefPRTBs/nH/53l+Y/eya3Tr/F3OW0L9vH844bGwXRSgpMnftYfCe5LjjjstZs2bVOgxJkrSLHlq0mktunMXKxhfW2Bs6oC9NzS1MGz+cWy4+kb4NfvbdnaxYv4U3XHUfm5qa+fGlJzNhn706PH7Dlm3c8MAivnbXE5x86CiufttxVYpU6pki4qHM3OGNZJIoSZK6hcxk+fotPLW8kSdXNPLU8kaWrdvCh193GAeM7DjBUH16akUj5151PyP36s+PLn05Iwb3f8kxG5u2ceMDi/jmvU+zakMTp04ezRXnTNtpUimpYyaJ2zFJlCRJqg+zFq7ird/6I0eOH84V50xjzcYmVm1sYvWGJhav3cwPZz3LysYmXjFpFO995WSmTxxR65ClHsEkcTsmiZIkSfXjF3OW8O6b/8yO3paWksNJPnMqVVhHSaJ1gyVJklRTrz1yHLe+8+UsW7eZkYP7M3Jwf0bs1Z8Re/XzWVOpBkwSJUmSVHNOI5Xqhx/NSJIkSZLamCRKkiRJktqYJEqSJEmS2pgkSpIkSZLamCRKkiRJktqYJEqSJEmS2pgkSpIkSZLamCRKkiRJktqYJEqSJEmS2pgkSpIkSZLamCRKkiRJktqYJEqSJEmS2pgkSpIkSZLamCRKkiRJktqYJEqSJEmS2kRm1jqGqouI9cC8WsehLjUcWFvrINRl7N+ezf7t2ezfns8+7tns355jSmYO3dGOvtWOpE7My8zjah2Euk5EXJ2ZF9c6DnUN+7dns397Nvu357OPezb7t+eIiFnt7XO6qXqqn9c6AHUp+7dns397Nvu357OPezb7txfordNNZzmSKEmSJKm36ign6q0jiVfXOgBJkiRJqqF2c6JeOZIoSZIkSdqx3jqSKEmSJEnaAZNEdWsRMTIifhIRGyJiUUS8tdj+uoj4fUSsiYilEfGtiNhhiV/Vrw7697SImFP07/PFMeNrHa92T3v9u90x346IjIhDaxGj9kwH9/CMiGiJiMayrwtrHa92T0f3cESMjoibI2JtRKyOiJtqGat2Xwf374e2u3c3FffzqFrHrMrprUtgqOf4H6AJGAscDdweEbMpreFzJXAvMAC4Gfgi8M4axanOaa9/HwNelZmLI2IAcAVwFfAPNYtUnbHD/s3MRwEi4n8Bh9QwPu259u5hgMWZuX/NIlMldHQP/xh4EJgAbASm1SxKdVZ7/fsZ4DOtB0XEJ4BTMnNlTaJUl/CZRHVbETEYWA1My8z5xbYbgb9l5ge3O/YNwCcz88jqR6rO2NX+LZLETwDnZObhtYhVu29n/RsRfSm9wbwQmA1MyswnaxawdltHfQzcAXzXJLH72kn/3kWpIMYhmdlcuyjVWbvxNziApyi9x7q+JsGqS9TddNOIGF7rGNRtTAa2tf7yKswGjtjBsacAj1YlKlVKh/0bERMiYg2wCbgM+EL1Q9Qe2Nn9+z7g3sx8uOqRqVJ21sdjImJZRCyIiC8Vb0rVfXTUvycC84Dri0cCHoyIU2sRpDptV99jvQIYA/yoWoGpOuomSYyIQRFxLfB0REyodTzqFoYA67bbthZ40bOHEXEmpdGIj1UpLlVGh/2bmc9k5t7AKOAjwNzqhqc91G7/RsQBwCV4z3Z3Hd3DcylNXxsHnA5MB/6rqtFpT3XUv/sDZwF3A/sC/wn81GfWupVdeo9F6f3VrZnZWJWoVDV1kSRGxBBKzxONApZTer5I2plGYNh224YB61t/iIgTKT2P+MbtPg1T/dtp/wJk5irgekpvQHzOuvvoqH+/DHwqM9dWPSpVUrt9nJlLM/OxzGzJzAXA5cC5VY9Qe6Kje3gTsDAzr83MrZn5feBZ4OQqx6jO25X3WHsBb6L0N1g9TE2TxIgYFRH9ik8ffgJ8GDgHuCAiXl7L2NQtzAf6RsSksm1HUUwrjYhjgJ8B78jMmTWIT3umw/7dTl9K0122/4Om+tVR/54BfLGoTLy02PfAjqqfqq7tzj2c1MkH19plHfXvw5T6tJxFMLqXXbl/Xw+sAn5bxbhUJTUpXBMRBwKtpZDXAR8E5mXm5mL/NcBRmXlC1YNTtxIR36f0h+efKU1d+gXwciCAmcC/ZuYttYtQe6KD/p1C6Q/VE8A+lCqwHZqZx9YoVHVCB/27ghcnDEuAk4DZmbmp2nGq8zro4zHA08AzlKYm3kBp5On/1ihUdUIH/buEUjGT9wLfpZRMXA1MtgJm99Fe/5ZVoP418IfM9NGAHqjqn9pFxCDgW8BDwBsoDWd/AnhL2WHvBo4oXzMpIvyEUTvyLmAQpWnK3wMuLX55vR8YDVxbto6PhWu6n/b6dzyl6ojrgTlAC6U3Iepedti/mbm8mI64NDNbRxJXmiB2S+3dw8cA9wMbin/nAP9aqyDVae3dw6soLUl0GaXn2D5IqQK1CWL30t79S7E28emUPuBRD1T1kcRi2Po64J8zc25EDAP+DTgNuLi1xHlEvA+4PDPHFT8Pzcz1ERHpuh2SJEmS1CVqMToXlBZUXQuQmesoLbi6GLgUSqOGQZwyKgAACatJREFUmfklYFVE/CwiNgKfLY43QZQkSZKkLlL1JLGoMDmHF5c2n0tpusmBEXFwZrZExBhKCeWxwPsz8z3VjlWSJEmSeptaPef3OeD1ETEZIDObgceASbywJsts4M+ZuX9mXlWbMCVJkiSpd6nVmmJ3Fl83ACcW2x4p/h0OrAQmuTCnJEmSJFVXTZbAgLYFOGdTWofld8AFwCzgosxs6uC8ccBFwN2Z+TsL2UiSJElS5dRqJJHM3BgRZ1Na++o1wLeKYjU7szdwMhARMcuS6JIkSZJUOTUbSXxRELs4Gth6XERcCpwBfCczb+v6CCVJkiSpd6iLBep3J0EsfrwFaATOjIh9W/d3YYiSJEmS1CvURZJYrr1krxhBnBwRZ2TmKuBnwIHAq1v3Vy9KSZIkSeqZ6i5JLJLB9uI6D7g9IvoDPwEWAqdExOHgaKIkSZIk7am6SxIj4tXAlRGxX/HzKa37MvNKYDHw0WLk8BZgBKXCN44mSpIkSdIeqrskEWgAzgJOjojXAddExKll+/8N+EBETMjM+yktm3FsRJxWg1glSZIkqUepuyQxM28H/gS8EmihNK30PWX7f17s/2yx6fvAaGB6RDRUN1pJkiRJ6lnqKkkse6bwK8BhwETgAWDviHhb2aH3AG8uitg8BVyWmf+Rmc3VjViSJEmSepa6ShKLojWRmfOAX1NaC3Fr8f3FETG8OHQt8CBwcnHewwAdFLyRJEmSJO2CqNdaLxExFPgxcBfwG+AKYBylQjWzgLdn5vraRShJkiRJPU/fWgewIxHRJzPXR8QNwNspjRqeB/w90JyZP9ju2JbaRCpJkiRJPUvdjiS2iojvA88Dn8zM5WXbG3wGUZIkSZIqq26f4SsrYvPfwHTgwPLtJoiSJEmSVHl1myQWRWz6ZOZ9lOJ8Vev2nZ0bEQdHxLDi+9jZ8ZIkSZKkkrpNEgEysyUi9gI2AfN25ZyIeDfwCHBW0UZ9z6eVJEmSpDpS10li4X8Df6FU6XRXHAWsBk6IiEldFpUkSZIk9UDdoXBN7OIU04bMbI6ID1BaKmM68B3g5szc0sVhSpIkSVKPUPcjie0liBExoPi3oTiutZDNScB1wG3AOcBBVQhTkiRJknqEuk8StxcRIyLi28A34IXkMCJaX8uzwAHAtcBA4C0RcWVE/F0t4pUkSZKk7qRbJYkRcSTwE+B4YHJEvKHY3iczW4rDjgHmZeYqYCvwYeBI4OkahCxJkiRJ3Uq3ShKB/sCNwNuBmcBFEdG/qILavzjmj8AnI2IOMAz4PbAQGFz9cCVJkiSpe6nrJDEipkbEqRExptg0B7g1Mx8CfgUk8B6AzGwqppyOA44AvpyZpwKfB0ZWP3pJkiRJ6n7qsrppUYzmG8B5wEOUEr/LM/PnZccMAf4JOBe4IDMXFdsPApZl5saqBy5JkiRJ3Vy9jiQeARwKHAKcRWkpi69ExCmtB2RmI6Upp4uB95Wd+2xmbmwtZBMRUa2gJUmSJKm7q5skMSKGl1UoPRGYmJkrgZbM/DylZw0vjIiDy06bD3wPmBYRn4mI+4AzAFoL2ezKGouSJEmSpJKaJ4kRMSkifgXcBPwoIiYCjwHPRMTRZVVLPwscBbQtZZGZTUAzpaTyQuCazPxVVV+AJEmSJPUgNU0SI+KfgLuAvwCXUyow81GgL7CM0lRTADLzYUqFay4ozm2IiDOBW4GvZ+b4zPxOVV+AJEmSJPUwNS1cExFXAosy85ri5/2BucBkSsngscA3M/OuYv/ZwOeA44vnDscDGzJzTU1egCRJkiT1MH1rfP1vAFsAImIAsBF4ChgE/JBS4Zr3RsRTRfXS44Fft1Yuzcy/1SRqSZIkSeqhapokZuZzUKpAmplbIuJwSlNgny3WPfwqcCVwe0SsAaYA59cuYkmSJEnq2Wo9kgi8qALpDGBeUZCGzHwkIs4FjgGOyMzraxSiJEmSJPUKdZEkRkRDZjYDJwB3FNsupTRy+OnMnAXMqmGIkiRJktQr1EWSmJnNEdGXUnXTMRFxL3Ag8I7MXFHT4CRJkiSpF6lpddNyEXEkMJvS0hf/mZn/UeOQJEmSJKnXqacksT/wHkprHm6udTySJEmS1BvVTZIoSZIkSaq9PrUOQJIkSZJUP0wSJUmSJEltTBIlSZIkSW1MEiVJkiRJbUwSJUmSJEltTBIlSQIiYkJENEZEQ61jkSSplkwSJUm9VkQsjIhXAmTmM5k5JDObq3j9GRHxXLWuJ0nSrjBJlCRJkiS1MUmUJPVKEXEjMAH4eTHN9PKIyIjoW+z/bURcGRH3F/t/HhH7RMRNEbEuIh6MiAPL2psaEb+JiFURMS8izivb99qIeCwi1kfE3yLisogYDPwS2K9ovzEi9ouIEyLigYhYExFLIuJrEdG/rK2MiHdFxBNFe1dExCFFnOsi4getx7eOVEbEhyJiZTFyen51/oclSd2VSaIkqVfKzAuAZ4CzM3MI8IMdHPZm4AJgPHAI8ABwHTASeBz4OECR8P0GuBkYU5z39Yg4vGjnWuCSzBwKTAPuyswNwGuAxcU01yGZuRhoBt4HjAJOAs4A3rVdXK8CpgMnApcDVwP/CBxQtP+WsmP3LdoaD1wIXB0RU3brP0uS1KuYJEqS1L7rMvOpzFxLadTvqcy8MzO3AT8EjimO+3tgYWZel5nbMvMvwI+ANxX7twKHR8SwzFydmX9u74KZ+VBm/qFoZyHwTeDU7Q77Qmauy8xHgUeAX2fm02VxHrPd8R/NzC2ZeQ9wO3AekiS1wyRRkqT2LSv7ftMOfh5SfD8ReFkxRXRNRKwBzqc0igdwLvBaYFFE3BMRJ7V3wYiYHBG3RcTSiFgHfIbSSGBn4gJYXYxatloE7Nfe9SVJMkmUJPVmWaF2ngXuycy9y76GZOalAJn5YGaeQ2kq6v/jhamtO7r+VcBcYFJmDgM+BMQexDaimA7bagKweA/akyT1cCaJkqTebBlwcAXauQ2YHBEXRES/4uv4iDgsIvpHxPkRMTwztwLrgJay6+8TEcPL2hpaHNMYEVOBSysQ3yeLOF5BaWrsDyvQpiSphzJJlCT1Zp8FPlJMD31jZxvJzPXAWZQK1iwGlgKfBwYUh1wALCymj76T0lRUMnMu8D3g6WKa6n7AZcBbgfXANcAtnY2rsBRYXcR1E/DO4rqSJO1QZFZqpo0kSaonETED+G5m7l/rWCRJ3YcjiZIkSZKkNiaJkiRJkqQ2TjeVJEmSJLVxJFGSJEmS1MYkUZIkSZLUxiRRkiRJktTGJFGSJEmS1MYkUZIkSZLUxiRRkiRJktTm/wOL0q2viKFO9gAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiserade översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "dddca9ad9e34435494e0933c218e1579",
+   "translation_date": "2025-09-06T14:01:29+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sv/7-TimeSeries/1-Introduction/working/notebook.ipynb b/translations/sv/7-TimeSeries/1-Introduction/working/notebook.ipynb
new file mode 100644
index 000000000..8ac017716
--- /dev/null
+++ b/translations/sv/7-TimeSeries/1-Introduction/working/notebook.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Datainställning\n",
+    "\n",
+    "I den här notebooken visar vi hur man:\n",
+    "\n",
+    "ställer in tidsseriedata för denna modul  \n",
+    "visualiserar data  \n",
+    "Datan i detta exempel är hämtad från GEFCom2014 prognostävling1. Den består av 3 års timvisa värden för elförbrukning och temperatur mellan 2012 och 2014.\n",
+    "\n",
+    "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli och Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, s. 896-913, juli-september, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på sitt ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5e2bbe594906dce3aaaa736d6dac6683",
+   "translation_date": "2025-09-06T14:02:25+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/working/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sv/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/translations/sv/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
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+++ b/translations/sv/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -0,0 +1,1132 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Tidsserieprognos med ARIMA\n",
+    "\n",
+    "I denna notebook demonstrerar vi hur man:\n",
+    "- förbereder tidsseriedata för att träna en ARIMA-modell för tidsserieprognos\n",
+    "- implementerar en enkel ARIMA-modell för att prognostisera de kommande HORIZON-stegen framåt (tid *t+1* till *t+HORIZON*) i tidsserien\n",
+    "- utvärderar modellen\n",
+    "\n",
+    "Data i detta exempel är hämtad från GEFCom2014 prognostävling. Det består av 3 års timvisa värden för elförbrukning och temperatur mellan 2012 och 2014. Uppgiften är att prognostisera framtida värden för elförbrukning. I detta exempel visar vi hur man prognostiserar ett tidssteg framåt, med endast historiska data för elförbrukning.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli och Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, s. 896-913, juli-september, 2016.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Installera beroenden\n",
+    "Kom igång genom att installera några av de nödvändiga beroendena. Dessa bibliotek med sina motsvarande versioner är kända för att fungera för lösningen:\n",
+    "\n",
+    "* `statsmodels == 0.12.2`\n",
+    "* `matplotlib == 3.4.2`\n",
+    "* `scikit-learn == 0.24.2`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "!pip install statsmodels"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "/bin/sh: pip: command not found\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from pandas.plotting import autocorrelation_plot\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "%matplotlib inline\n",
+    "pd.options.display.float_format = '{:,.2f}'.format\n",
+    "np.set_printoptions(precision=2)\n",
+    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load\n",
+       "2012-01-01 00:00:00 2,698.00\n",
+       "2012-01-01 01:00:00 2,558.00\n",
+       "2012-01-01 02:00:00 2,444.00\n",
+       "2012-01-01 03:00:00 2,402.00\n",
+       "2012-01-01 04:00:00 2,403.00\n",
+       "2012-01-01 05:00:00 2,453.00\n",
+       "2012-01-01 06:00:00 2,560.00\n",
+       "2012-01-01 07:00:00 2,719.00\n",
+       "2012-01-01 08:00:00 2,916.00\n",
+       "2012-01-01 09:00:00 3,105.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Plotta all tillgänglig belastningsdata (januari 2012 till december 2014)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Skapa tränings- och testdatauppsättningar\n",
+    "\n",
+    "### Introduktion\n",
+    "\n",
+    "Att dela upp data i tränings- och testuppsättningar är en viktig del av maskininlärningsprocessen. Träningsuppsättningen används för att lära modellen, medan testuppsättningen används för att utvärdera modellens prestanda på ny, osedd data.\n",
+    "\n",
+    "### Steg för att skapa datauppsättningar\n",
+    "\n",
+    "1. **Samla in och förbered data**  \n",
+    "   Se till att din data är ren och välstrukturerad innan du delar upp den. Detta inkluderar att hantera saknade värden, normalisera data och ta bort irrelevanta funktioner.\n",
+    "\n",
+    "2. **Dela upp data**  \n",
+    "   Använd en metod som @@INLINE_CODE_1@@ för att dela upp data i tränings- och testuppsättningar. En vanlig fördelning är 80 % för träning och 20 % för testning, men detta kan variera beroende på datasetets storlek och problemets natur.\n",
+    "\n",
+    "   ```python\n",
+    "   # Exempel på att dela upp data\n",
+    "   from sklearn.model_selection import train_test_split\n",
+    "   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "   ```\n",
+    "\n",
+    "3. **Verifiera fördelningen**  \n",
+    "   Kontrollera att både tränings- och testuppsättningarna representerar hela datasetet på ett rättvist sätt. Detta är särskilt viktigt för obalanserade dataset.\n",
+    "\n",
+    "### Vanliga misstag att undvika\n",
+    "\n",
+    "- **Överträning**  \n",
+    "  Om testdata används under träningen kan modellen överanpassas och prestera dåligt på ny data. Håll testdata strikt åtskild från träningsdata.\n",
+    "\n",
+    "- **För liten testuppsättning**  \n",
+    "  En för liten testuppsättning kan leda till opålitliga utvärderingar. Se till att testuppsättningen är tillräckligt stor för att ge en rättvis bedömning av modellens prestanda.\n",
+    "\n",
+    "### Slutsats\n",
+    "\n",
+    "Att korrekt dela upp data i tränings- och testuppsättningar är avgörande för att bygga robusta maskininlärningsmodeller. Följ bästa praxis och undvik vanliga fallgropar för att säkerställa att din modell presterar bra på verkliga data.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-11-01 00:00:00  0.10\n",
+       "2014-11-01 01:00:00  0.07\n",
+       "2014-11-01 02:00:00  0.05\n",
+       "2014-11-01 03:00:00  0.04\n",
+       "2014-11-01 04:00:00  0.06\n",
+       "2014-11-01 05:00:00  0.10\n",
+       "2014-11-01 06:00:00  0.19\n",
+       "2014-11-01 07:00:00  0.31\n",
+       "2014-11-01 08:00:00  0.40\n",
+       "2014-11-01 09:00:00  0.48"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Original vs skalad data:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Låt oss också skala testdatan\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-12-30 00:00:00  0.33\n",
+       "2014-12-30 01:00:00  0.29\n",
+       "2014-12-30 02:00:00  0.27\n",
+       "2014-12-30 03:00:00  0.27\n",
+       "2014-12-30 04:00:00  0.30"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Forecasting horizon: 3 hours\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "order = (4, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
+    "results = model.fit()\n",
+    "\n",
+    "print(results.summary())\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Thu, 30 Sep 2021   AIC                          -6942.477\n",
+      "Time:                                    14:36:28   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Utvärdera modellen\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Skapa en testdatapunkt för varje HORIZON-steg.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load  load+1  load+2\n",
+       "2014-12-30 00:00:00  0.33    0.29    0.27\n",
+       "2014-12-30 01:00:00  0.29    0.27    0.27\n",
+       "2014-12-30 02:00:00  0.27    0.27    0.30\n",
+       "2014-12-30 03:00:00  0.27    0.30    0.41\n",
+       "2014-12-30 04:00:00  0.30    0.41    0.57"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Gör förutsägelser på testdatan\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2014-12-30 00:00:00\n",
+      "1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
+      "2014-12-30 01:00:00\n",
+      "2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
+      "2014-12-30 02:00:00\n",
+      "3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
+      "2014-12-30 03:00:00\n",
+      "4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
+      "2014-12-30 04:00:00\n",
+      "5 : predicted = [0.3  0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
+      "2014-12-30 05:00:00\n",
+      "6 : predicted = [0.4  0.55 0.66] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
+      "2014-12-30 06:00:00\n",
+      "7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
+      "2014-12-30 07:00:00\n",
+      "8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
+      "2014-12-30 08:00:00\n",
+      "9 : predicted = [0.75 0.8  0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
+      "2014-12-30 09:00:00\n",
+      "10 : predicted = [0.77 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
+      "2014-12-30 10:00:00\n",
+      "11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
+      "2014-12-30 11:00:00\n",
+      "12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
+      "2014-12-30 12:00:00\n",
+      "13 : predicted = [0.7  0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
+      "2014-12-30 13:00:00\n",
+      "14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
+      "2014-12-30 14:00:00\n",
+      "15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
+      "2014-12-30 15:00:00\n",
+      "16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
+      "2014-12-30 16:00:00\n",
+      "17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
+      "2014-12-30 17:00:00\n",
+      "18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
+      "2014-12-30 18:00:00\n",
+      "19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
+      "2014-12-30 19:00:00\n",
+      "20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
+      "2014-12-30 20:00:00\n",
+      "21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
+      "2014-12-30 21:00:00\n",
+      "22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
+      "2014-12-30 22:00:00\n",
+      "23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
+      "2014-12-30 23:00:00\n",
+      "24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
+      "2014-12-31 00:00:00\n",
+      "25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
+      "2014-12-31 01:00:00\n",
+      "26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
+      "2014-12-31 02:00:00\n",
+      "27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
+      "2014-12-31 03:00:00\n",
+      "28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
+      "2014-12-31 04:00:00\n",
+      "29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
+      "2014-12-31 05:00:00\n",
+      "30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
+      "2014-12-31 06:00:00\n",
+      "31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
+      "2014-12-31 07:00:00\n",
+      "32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
+      "2014-12-31 08:00:00\n",
+      "33 : predicted = [0.79 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
+      "2014-12-31 09:00:00\n",
+      "34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
+      "2014-12-31 10:00:00\n",
+      "35 : predicted = [0.8  0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
+      "2014-12-31 11:00:00\n",
+      "36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
+      "2014-12-31 12:00:00\n",
+      "37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
+      "2014-12-31 13:00:00\n",
+      "38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
+      "2014-12-31 14:00:00\n",
+      "39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
+      "2014-12-31 15:00:00\n",
+      "40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
+      "2014-12-31 16:00:00\n",
+      "41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
+      "2014-12-31 17:00:00\n",
+      "42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
+      "2014-12-31 18:00:00\n",
+      "43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
+      "2014-12-31 19:00:00\n",
+      "44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
+      "2014-12-31 20:00:00\n",
+      "45 : predicted = [0.7  0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
+      "2014-12-31 21:00:00\n",
+      "46 : predicted = [0.6  0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
+      "CPU times: user 12min 15s, sys: 2min 39s, total: 14min 54s\n",
+      "Wall time: 2min 36s\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": true
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Jämför prognoser med faktisk belastning\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp    h  prediction   actual\n",
+       "0 2014-12-30 00:00:00  t+1    3,008.74 3,023.00\n",
+       "1 2014-12-30 01:00:00  t+1    2,955.53 2,935.00\n",
+       "2 2014-12-30 02:00:00  t+1    2,900.17 2,899.00\n",
+       "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
+       "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Beräkna **medelprocentuellt absolutfel (MAPE)** för alla förutsägelser\n",
+    "\n",
+    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "One step forecast MAPE:  0.5570581332313952 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Multi-step forecast MAPE:  1.1460048657704118 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Plotta förutsägelserna mot de faktiska värdena för den första veckan av testuppsättningen\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "if(HORIZON == 1):\n",
+    "    ## Plotting single step forecast\n",
+    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
+    "\n",
+    "else:\n",
+    "    ## Plotting multi step forecast\n",
+    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
+    "\n",
+    "    fig = plt.figure(figsize=(15, 8))\n",
+    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        x = plot_df['timestamp'][(t-1):]\n",
+    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
+    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
+    "    \n",
+    "    ax.legend(loc='best')\n",
+    "    \n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiserade översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "c193140200b9684da27e3890211391b6",
+   "translation_date": "2025-09-06T13:58:21+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sv/7-TimeSeries/2-ARIMA/working/notebook.ipynb b/translations/sv/7-TimeSeries/2-ARIMA/working/notebook.ipynb
new file mode 100644
index 000000000..4b24d6429
--- /dev/null
+++ b/translations/sv/7-TimeSeries/2-ARIMA/working/notebook.ipynb
@@ -0,0 +1,59 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "523ec472196307b3c4235337353c9ceb",
+   "translation_date": "2025-09-06T14:00:29+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/working/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Tidsserieprognos med ARIMA\n",
+    "\n",
+    "I denna notebook demonstrerar vi hur man:\n",
+    "- förbereder tidsseriedata för att träna en ARIMA-modell för tidsserieprognos\n",
+    "- implementerar en enkel ARIMA-modell för att prognostisera de kommande HORIZON stegen framåt (tid *t+1* till *t+HORIZON*) i tidsserien\n",
+    "- utvärderar modellen\n",
+    "\n",
+    "Data i detta exempel är hämtad från GEFCom2014 prognostävling. Det består av 3 års timvisa värden för elförbrukning och temperatur mellan 2012 och 2014. Uppgiften är att prognostisera framtida värden för elförbrukning. I detta exempel visar vi hur man prognostiserar ett tidssteg framåt, med hjälp av historiska data för elförbrukning.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli och Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, s. 896-913, juli-september, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install statsmodels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/7-TimeSeries/3-SVR/solution/notebook.ipynb b/translations/sv/7-TimeSeries/3-SVR/solution/notebook.ipynb
new file mode 100644
index 000000000..8399e3128
--- /dev/null
+++ b/translations/sv/7-TimeSeries/3-SVR/solution/notebook.ipynb
@@ -0,0 +1,1025 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I den här anteckningsboken demonstrerar vi hur man:\n",
+    "\n",
+    "- förbereder 2D-tidsseriedata för att träna en SVM-regressormodell\n",
+    "- implementerar SVR med hjälp av RBF-kärna\n",
+    "- utvärderar modellen med hjälp av diagram och MAPE\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importera moduler\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": [
+    "## Förbereder data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Ladda data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nu behöver du förbereda data för träning genom att utföra filtrering och skalning av din data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Skala data för att vara inom intervallet (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.101611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.065801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.046106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.042525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.059087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-11-01 00:00:00  0.101611\n",
+       "2014-11-01 01:00:00  0.065801\n",
+       "2014-11-01 02:00:00  0.046106\n",
+       "2014-11-01 03:00:00  0.042525\n",
+       "2014-11-01 04:00:00  0.059087"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.329454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.290063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.273948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.268129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.302596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-12-30 00:00:00  0.329454\n",
+       "2014-12-30 01:00:00  0.290063\n",
+       "2014-12-30 02:00:00  0.273948\n",
+       "2014-12-30 03:00:00  0.268129\n",
+       "2014-12-30 04:00:00  0.302596"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": [
+    "### Skapa data med tidssteg\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "För vår SVR transformerar vi indatadata till formen `[batch, timesteps]`. Så vi omformar den befintliga `train_data` och `test_data` så att det finns en ny dimension som hänvisar till tidsstegen. För vårt exempel tar vi `timesteps = 5`. Så indata till modellen är data för de första 4 tidsstegen, och utdata kommer att vara data för det 5:e tidssteget.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1412, 5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=np.array([[j for j in train_data[i:i+timesteps]] for i in range(0,len(train_data)-timesteps+1)])[:,:,0]\n",
+    "train_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(44, 5)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=np.array([[j for j in test_data[i:i+timesteps]] for i in range(0,len(test_data)-timesteps+1)])[:,:,0]\n",
+    "test_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 4) (1412, 1)\n",
+      "(44, 4) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train, y_train = train_data_timesteps[:,:timesteps-1],train_data_timesteps[:,[timesteps-1]]\n",
+    "x_test, y_test = test_data_timesteps[:,:timesteps-1],test_data_timesteps[:,[timesteps-1]]\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": [
+    "## Skapa SVR-modell\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = SVR(kernel='rbf',gamma=0.5, C=10, epsilon = 0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVR(C=10, cache_size=200, coef0=0.0, degree=3, epsilon=0.05, gamma=0.5,\n",
+       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit model on training data\n",
+    "\n",
+    "model.fit(x_train, y_train[:,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Gör modellförutsägelse\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 1) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = model.predict(x_train).reshape(-1,1)\n",
+    "y_test_pred = model.predict(x_test).reshape(-1,1)\n",
+    "\n",
+    "print(y_train_pred.shape, y_test_pred.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
+    "\n",
+    "print(len(y_train_pred), len(y_test_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)\n",
+    "\n",
+    "print(len(y_train), len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)].index[timesteps-1:]\n",
+    "test_timestamps = energy[test_start_dt:].index[timesteps-1:]\n",
+    "\n",
+    "print(len(train_timestamps), len(test_timestamps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "plt.plot(train_timestamps, y_train, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(train_timestamps, y_train_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for training data:  1.7195710200875551 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "plt.plot(test_timestamps, y_test, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(test_timestamps, y_test_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for testing data:  1.2623790187854018 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Full dataset prediction\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor shape:  (26300, 5)\n",
+      "X shape:  (26300, 4) \n",
+      "Y shape:  (26300, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = energy.copy().values\n",
+    "\n",
+    "# Scaling\n",
+    "data = scaler.transform(data)\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=np.array([[j for j in data[i:i+timesteps]] for i in range(0,len(data)-timesteps+1)])[:,:,0]\n",
+    "print(\"Tensor shape: \", data_timesteps.shape)\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = data_timesteps[:,:timesteps-1],data_timesteps[:,[timesteps-1]]\n",
+    "print(\"X shape: \", X.shape,\"\\nY shape: \", Y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "Y_pred = model.predict(X).reshape(-1,1)\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = scaler.inverse_transform(Y_pred)\n",
+    "Y = scaler.inverse_transform(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "plt.plot(Y, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(Y_pred, color = 'blue', linewidth=1)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE:  2.0572089029888656 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiserade översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "f8f3967282314d3995245835bdaa8418",
+   "translation_date": "2025-09-06T14:04:11+00:00",
+   "source_file": "7-TimeSeries/3-SVR/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sv/7-TimeSeries/3-SVR/working/notebook.ipynb b/translations/sv/7-TimeSeries/3-SVR/working/notebook.ipynb
new file mode 100644
index 000000000..57cf84aef
--- /dev/null
+++ b/translations/sv/7-TimeSeries/3-SVR/working/notebook.ipynb
@@ -0,0 +1,701 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I den här anteckningsboken demonstrerar vi hur man:\n",
+    "\n",
+    "- förbereder 2D-tidsseriedata för att träna en SVM-regressormodell\n",
+    "- implementerar SVR med hjälp av RBF-kärna\n",
+    "- utvärderar modellen med hjälp av diagram och MAPE\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importera moduler\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": [
+    "## Förbereder data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Ladda data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nu behöver du förbereda data för träning genom att utföra filtrering och skalning av din data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Skala data för att vara inom intervallet (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": [
+    "### Skapa data med tidssteg\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "För vår SVR transformerar vi indata till formen `[batch, timesteps]`. Så vi omformar den befintliga `train_data` och `test_data` så att det finns en ny dimension som hänvisar till tidsstegen. I vårt exempel tar vi `timesteps = 5`. Så indata till modellen är data för de första 4 tidsstegen, och utdata kommer att vara data för det 5:e tidssteget.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [],
+   "source": [
+    "x_train, y_train = None\n",
+    "x_test, y_test = None\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": [
+    "## Skapa SVR-modell\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit model on training data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Gör modellförutsägelse\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = None\n",
+    "y_test_pred = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = None\n",
+    "test_timestamps = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Fullständig datasetförutsägelse\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = None\n",
+    "\n",
+    "# Scaling\n",
+    "data = None\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=None\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = None, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = None\n",
+    "Y = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
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+   "original_hash": "e86ce102239a14c44585623b9b924a74",
+   "translation_date": "2025-09-06T14:06:41+00:00",
+   "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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diff --git a/translations/sv/8-Reinforcement/1-QLearning/notebook.ipynb b/translations/sv/8-Reinforcement/1-QLearning/notebook.ipynb
new file mode 100644
index 000000000..5849bf6cb
--- /dev/null
+++ b/translations/sv/8-Reinforcement/1-QLearning/notebook.ipynb
@@ -0,0 +1,411 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "17e5a668646eabf5aabd0e9bfcf17876",
+   "translation_date": "2025-09-06T15:04:39+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter och vargen: Introduktion till förstärkningsinlärning\n",
+    "\n",
+    "I denna handledning kommer vi att lära oss hur man tillämpar förstärkningsinlärning på ett problem med att hitta vägar. Miljön är inspirerad av den musikaliska sagan [Peter och vargen](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) av den ryske kompositören [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Det är en berättelse om den unge pionjären Peter, som modigt lämnar sitt hus och går till skogsgläntan för att jaga en varg. Vi kommer att träna maskininlärningsalgoritmer som hjälper Peter att utforska området och skapa en optimal navigeringskarta.\n",
+    "\n",
+    "Först, låt oss importera en mängd användbara bibliotek:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Översikt av Förstärkningsinlärning\n",
+    "\n",
+    "**Förstärkningsinlärning** (RL) är en inlärningsteknik som låter oss lära oss ett optimalt beteende för en **agent** i en viss **miljö** genom att utföra många experiment. En agent i denna miljö bör ha ett **mål**, definierat av en **belöningsfunktion**.\n",
+    "\n",
+    "## Miljön\n",
+    "\n",
+    "För enkelhetens skull, låt oss betrakta Peters värld som en kvadratisk spelplan med storleken `width` x `height`. Varje ruta på denna spelplan kan vara:\n",
+    "* **mark**, där Peter och andra varelser kan gå\n",
+    "* **vatten**, där man uppenbarligen inte kan gå\n",
+    "* **ett träd** eller **gräs** – en plats där man kan vila\n",
+    "* **ett äpple**, som representerar något Peter gärna skulle vilja hitta för att mätta sig\n",
+    "* **en varg**, som är farlig och bör undvikas\n",
+    "\n",
+    "För att arbeta med miljön kommer vi att definiera en klass som heter `Board`. För att inte göra denna anteckningsbok för rörig har vi flyttat all kod för att arbeta med spelplanen till en separat modul som heter `rlboard`, vilken vi nu kommer att importera. Du kan titta in i denna modul för att få mer information om implementationens detaljer.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Låt oss nu skapa ett slumpmässigt bräde och se hur det ser ut:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Åtgärder och Policy\n",
+    "\n",
+    "I vårt exempel är Peters mål att hitta ett äpple, samtidigt som han undviker vargen och andra hinder. Definiera dessa åtgärder som en ordbok och koppla dem till par av motsvarande koordinatförändringar.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 2"
+   ]
+  },
+  {
+   "source": [
+    "Strategin för vår agent (Peter) definieras av en så kallad **policy**. Låt oss titta på den enklaste policyn som kallas **slumpvandring**.\n",
+    "\n",
+    "## Slumpvandring\n",
+    "\n",
+    "Låt oss först lösa vårt problem genom att implementera en strategi för slumpvandring.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Let's run a random walk experiment several times and see the average number of steps taken: code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 4"
+   ]
+  },
+  {
+   "source": [
+    "## Belöningsfunktion\n",
+    "\n",
+    "För att göra vår policy mer intelligent behöver vi förstå vilka drag som är \"bättre\" än andra.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning\n",
+    "\n",
+    "Bygg en Q-Tabell, eller en flerdimensionell matris. Eftersom vår spelplan har dimensionerna `width` x `height`, kan vi representera Q-Tabellen med en numpy-array med formen `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Passera Q-tabellen till `plot`-funktionen för att visualisera tabellen på brädet:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'm' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8b8a9e4cb2b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Essensen av Q-Learning: Bellman-ekvationen och inlärningsalgoritmen\n",
+    "\n",
+    "Skriv en pseudokod för vår inlärningsalgoritm:\n",
+    "\n",
+    "* Initiera Q-Tabell Q med lika värden för alla tillstånd och handlingar\n",
+    "* Sätt inlärningshastighet $\\alpha\\leftarrow 1$\n",
+    "* Upprepa simuleringen många gånger\n",
+    "   1. Börja på en slumpmässig position\n",
+    "   1. Upprepa\n",
+    "        1. Välj en handling $a$ vid tillstånd $s$\n",
+    "        2. Utför handlingen genom att flytta till ett nytt tillstånd $s'$\n",
+    "        3. Om vi stöter på ett slutspelsvillkor, eller den totala belöningen är för liten - avsluta simuleringen  \n",
+    "        4. Beräkna belöningen $r$ vid det nya tillståndet\n",
+    "        5. Uppdatera Q-Funktionen enligt Bellman-ekvationen: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Uppdatera total belöning och minska $\\alpha$.\n",
+    "\n",
+    "## Exploatera vs. Utforska\n",
+    "\n",
+    "Den bästa metoden är att balansera mellan utforskning och exploatering. När vi lär oss mer om vår miljö, kommer vi vara mer benägna att följa den optimala vägen, men ändå välja den outforskade vägen då och då.\n",
+    "\n",
+    "## Python-implementation\n",
+    "\n",
+    "Nu är vi redo att implementera inlärningsalgoritmen. Innan dess behöver vi också en funktion som kan konvertera godtyckliga värden i Q-Tabellen till en sannolikhetsvektor för motsvarande handlingar:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Vi lägger till en liten mängd `eps` till den ursprungliga vektorn för att undvika division med 0 i det initiala fallet, när alla komponenter i vektorn är identiska.\n",
+    "\n",
+    "Den faktiska inlärningsalgoritmen kommer vi att köra i 5000 experiment, även kallade **epoker**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "# code block 8"
+   ]
+  },
+  {
+   "source": [
+    "Efter att ha kört denna algoritm bör Q-tabellen uppdateras med värden som definierar attraktiviteten hos olika handlingar vid varje steg. Visualisera tabellen här:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kontrollera policyn\n",
+    "\n",
+    "Eftersom Q-Tabellen listar \"attraktiviteten\" för varje handling i varje tillstånd, är det ganska enkelt att använda den för att definiera den effektiva navigeringen i vår värld. I det enklaste fallet kan vi helt enkelt välja den handling som motsvarar det högsta värdet i Q-Tabellen:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "# code block 9"
+   ]
+  },
+  {
+   "source": [
+    "Om du testar koden ovan flera gånger, kanske du märker att den ibland bara \"fastnar\", och du måste trycka på STOP-knappen i notebooken för att avbryta den.\n",
+    "\n",
+    "> **Uppgift 1:** Ändra `walk`-funktionen så att den begränsar den maximala längden på vägen till ett visst antal steg (säg, 100), och observera hur koden ovan returnerar detta värde då och då.\n",
+    "\n",
+    "> **Uppgift 2:** Ändra `walk`-funktionen så att den inte återvänder till platser där den redan har varit tidigare. Detta kommer att förhindra att `walk` hamnar i en loop, men agenten kan fortfarande bli \"fast\" på en plats där den inte kan ta sig vidare.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 5.31, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# code block 10"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26dab295a48>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 57
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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109.232964 214.756364 \r\nL 109.263403 214.085931 \r\nL 109.293843 201.703888 \r\nL 109.385161 202.919046 \r\nL 109.44604 214.756364 \r\nL 109.476479 192.192132 \r\nL 109.537358 214.735413 \r\nL 109.598237 214.756364 \r\nL 109.719994 198.100315 \r\nL 109.811312 214.756364 \r\nL 109.841752 210.230947 \r\nL 109.872191 214.735413 \r\nL 109.93307 208.63867 \r\nL 110.054828 214.756364 \r\nL 110.085267 200.509681 \r\nL 110.146146 208.052042 \r\nL 110.207025 214.756364 \r\nL 110.267904 213.268842 \r\nL 110.298343 193.470143 \r\nL 110.328782 214.756364 \r\nL 110.359222 209.811927 \r\nL 110.420101 214.756364 \r\nL 110.480979 214.044029 \r\nL 110.541858 214.756364 \r\nL 110.572298 212.032733 \r\nL 110.633176 214.756364 \r\nL 110.663616 214.756364 \r\nL 110.694055 199.441179 \r\nL 110.785374 202.981899 \r\nL 110.907131 214.756364 \r\nL 110.937571 211.110889 \r\nL 110.998449 214.756364 \r\nL 111.089768 209.916682 \r\nL 111.181086 214.756364 \r\nL 111.211525 203.882792 \r\nL 111.272404 207.842532 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115.016451 213.43645 \r\nL 115.046891 214.46305 \r\nL 115.07733 207.339708 \r\nL 115.168648 209.66527 \r\nL 115.229527 213.268842 \r\nL 115.290406 211.215644 \r\nL 115.320845 205.642677 \r\nL 115.351285 214.756364 \r\nL 115.381724 209.749074 \r\nL 115.533921 214.756364 \r\nL 115.564361 213.687862 \r\nL 115.655679 206.606423 \r\nL 115.746997 214.756364 \r\nL 115.716558 193.700604 \r\nL 115.777436 214.735413 \r\nL 115.838315 193.763457 \r\nL 115.868755 193.910114 \r\nL 115.899194 214.756364 \r\nL 115.990512 212.346998 \r\nL 116.051391 210.754722 \r\nL 116.11227 214.756364 \r\nL 116.142709 211.76037 \r\nL 116.203588 214.756364 \r\nL 116.234027 214.756364 \r\nL 116.264467 213.478352 \r\nL 116.294906 214.756364 \r\nL 116.325346 184.670721 \r\nL 116.386225 214.756364 \r\nL 116.416664 214.044029 \r\nL 116.447103 214.756364 \r\nL 116.477543 196.047117 \r\nL 116.538422 212.786969 \r\nL 116.5993 210.964232 \r\nL 116.690619 214.756364 \r\nL 116.812376 200.404926 \r\nL 116.903694 214.756364 \r\nL 116.934134 196.403284 \r\nL 116.995013 214.756364 \r\nL 117.055891 214.756364 \r\nL 117.086331 213.939274 \r\nL 117.11677 199.650689 \r\nL 117.177649 212.158439 \r\nL 117.208089 214.756364 \r\nL 117.238528 213.939274 \r\nL 117.268967 205.684579 \r\nL 117.329846 214.756364 \r\nL 117.360286 214.484001 \r\nL 117.421164 195.774754 \r\nL 117.451604 210.000486 \r\nL 117.512483 214.756364 \r\nL 117.542922 176.709339 \r\nL 117.63424 184.64977 \r\nL 117.725558 183.874583 \r\nL 117.755998 214.756364 \r\nL 117.786437 214.756364 \r\nL 117.877756 198.959306 \r\nL 117.908195 210.566163 \r\nL 117.938634 212.766018 \r\nL 117.969074 206.522619 \r\nL 118.029953 214.756364 \r\nL 118.060392 214.756364 \r\nL 118.18215 199.399277 \r\nL 118.273468 214.756364 \r\nL 118.303907 205.035098 \r\nL 118.364786 214.756364 \r\nL 118.456104 214.232589 \r\nL 118.486544 214.756364 \r\nL 118.516983 209.476711 \r\nL 118.577862 214.756364 \r\nL 118.608301 213.352646 \r\nL 118.66918 214.735413 \r\nL 118.69962 186.786773 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200.090661 \r\nL 122.595864 211.927978 \r\nL 122.687182 211.383252 \r\nL 122.717621 214.756364 \r\nL 122.7785 214.756364 \r\nL 122.80894 210.335702 \r\nL 122.900258 212.493655 \r\nL 122.930697 214.756364 \r\nL 123.022015 183.958387 \r\nL 123.052455 208.533915 \r\nL 123.082894 214.756364 \r\nL 123.113334 201.703888 \r\nL 123.143773 210.482359 \r\nL 123.174212 210.419506 \r\nL 123.265531 200.48873 \r\nL 123.29597 214.756364 \r\nL 123.326409 214.735413 \r\nL 123.356849 187.017234 \r\nL 123.417728 214.756364 \r\nL 123.569925 191.291239 \r\nL 123.691682 214.756364 \r\nL 123.722122 214.735413 \r\nL 123.783001 205.579824 \r\nL 123.81344 189.279942 \r\nL 123.874319 214.735413 \r\nL 123.935198 214.735413 \r\nL 123.996076 214.756364 \r\nL 124.026516 206.669276 \r\nL 124.148273 214.756364 \r\nL 124.178713 214.756364 \r\nL 124.239592 209.581466 \r\nL 124.270031 211.96988 \r\nL 124.300471 214.023078 \r\nL 124.33091 201.201064 \r\nL 124.422228 208.471062 \r\nL 124.513546 214.756364 \r\nL 124.543986 212.326047 \r\nL 124.604865 199.818297 \r\nL 124.635304 207.360659 \r\nL 124.726622 214.756364 \r\nL 124.787501 174.467582 \r\nL 124.81794 214.756364 \r\nL 124.909259 214.735413 \r\nL 124.939698 211.173742 \r\nL 124.970137 186.283949 \r\nL 125.031016 211.907027 \r\nL 125.091895 214.756364 \r\nL 125.152774 214.735413 \r\nL 125.274532 173.252424 \r\nL 125.36585 214.756364 \r\nL 125.396289 214.127834 \r\nL 125.426729 214.735413 \r\nL 125.457168 198.267923 \r\nL 125.518047 214.106882 \r\nL 125.548486 210.775673 \r\nL 125.609365 214.756364 \r\nL 125.639804 211.006134 \r\nL 125.731123 214.735413 \r\nL 125.761562 214.421148 \r\nL 125.822441 214.735413 \r\nL 125.913759 173.399081 \r\nL 125.974638 214.756364 \r\nL 126.035517 206.648325 \r\nL 126.065956 206.585472 \r\nL 126.187714 214.756364 \r\nL 126.248593 214.756364 \r\nL 126.309471 213.562156 \r\nL 126.339911 205.852187 \r\nL 126.37035 214.735413 \r\nL 126.40079 212.996479 \r\nL 126.431229 214.756364 \r\nL 126.461668 214.588756 \r\nL 126.522547 198.41458 \r\nL 126.552987 213.059332 \r\nL 126.705184 214.756364 \r\nL 126.735623 192.192132 \r\nL 126.796502 213.750715 \r\nL 126.91826 214.756364 \r\nL 126.979138 210.021437 \r\nL 127.009578 214.756364 \r\nL 127.040017 214.756364 \r\nL 127.131335 210.461408 \r\nL 127.192214 207.402561 \r\nL 127.253093 214.756364 \r\nL 127.283532 210.377604 \r\nL 127.344411 214.735413 \r\nL 127.374851 214.756364 \r\nL 127.40529 214.46305 \r\nL 127.466169 214.756364 \r\nL 127.527048 199.524983 \r\nL 127.557487 214.735413 \r\nL 127.587927 211.404203 \r\nL 127.618366 177.673086 \r\nL 127.648805 214.756364 \r\nL 127.679245 184.251701 \r\nL 127.801002 214.756364 \r\nL 127.861881 214.756364 \r\nL 127.953199 207.612071 \r\nL 128.014078 214.756364 \r\nL 128.044518 204.532273 \r\nL 128.074957 198.309825 \r\nL 128.105396 214.756364 \r\nL 128.166275 214.756364 \r\nL 128.257593 210.105241 \r\nL 128.348912 214.756364 \r\nL 128.501109 186.430606 \r\nL 128.531548 210.314751 \r\nL 128.592427 206.459766 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214.693511 \r\nL 130.236155 214.735413 \r\nL 130.297034 204.972245 \r\nL 130.388352 214.756364 \r\nL 130.449231 190.620806 \r\nL 130.47967 213.331695 \r\nL 130.540549 214.756364 \r\nL 130.601428 214.358295 \r\nL 130.692746 203.547576 \r\nL 130.753625 197.953658 \r\nL 130.814504 214.756364 \r\nL 130.844943 210.649967 \r\nL 130.905822 211.425154 \r\nL 130.936261 214.756364 \r\nL 130.99714 214.546854 \r\nL 131.058019 203.945645 \r\nL 131.088458 213.939274 \r\nL 131.149337 214.756364 \r\nL 131.179777 207.653973 \r\nL 131.240655 214.756364 \r\nL 131.51461 214.756364 \r\nL 131.54505 182.827033 \r\nL 131.605928 214.106882 \r\nL 131.636368 214.756364 \r\nL 131.758125 205.998844 \r\nL 131.788565 214.756364 \r\nL 131.819004 200.111612 \r\nL 131.849444 213.813568 \r\nL 131.879883 194.643399 \r\nL 131.910322 214.756364 \r\nL 131.940762 212.849822 \r\nL 131.971201 214.756364 \r\nL 132.092959 153.16041 \r\nL 132.184277 214.756364 \r\nL 132.214716 191.752161 \r\nL 132.306035 214.756364 \r\nL 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154.952954 214.756364 \r\nL 155.074712 200.823946 \r\nL 155.16603 214.756364 \r\nL 155.196469 214.735413 \r\nL 155.257348 214.735413 \r\nL 155.379106 197.869854 \r\nL 155.500863 214.756364 \r\nL 155.531303 209.141494 \r\nL 155.622621 211.634664 \r\nL 155.65306 209.204347 \r\nL 155.6835 214.756364 \r\nL 155.713939 211.383252 \r\nL 155.744379 213.813568 \r\nL 155.774818 195.167174 \r\nL 155.805257 214.756364 \r\nL 155.835697 213.729764 \r\nL 155.866136 214.756364 \r\nL 156.018333 198.770747 \r\nL 156.048773 210.398555 \r\nL 156.079212 135.854881 \r\nL 156.109651 214.756364 \r\nL 156.140091 195.984264 \r\nL 156.20097 214.756364 \r\nL 156.231409 210.754722 \r\nL 156.261848 183.706975 \r\nL 156.322727 214.756364 \r\nL 156.353167 214.756364 \r\nL 156.474924 196.466137 \r\nL 156.627121 214.756364 \r\nL 156.657561 214.756364 \r\nL 156.688 192.485446 \r\nL 156.748879 214.735413 \r\nL 156.779318 213.520254 \r\nL 156.840197 214.735413 \r\nL 156.870637 214.735413 \r\nL 156.901076 213.289793 \r\nL 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162.83676 214.756364 \r\nL 162.897639 198.016511 \r\nL 162.988958 214.756364 \r\nL 163.019397 209.392906 \r\nL 163.049836 209.351004 \r\nL 163.080276 211.508958 \r\nL 163.141155 204.951294 \r\nL 163.171594 214.735413 \r\nL 163.232473 214.756364 \r\nL 163.262912 211.299448 \r\nL 163.323791 210.649967 \r\nL 163.35423 214.756364 \r\nL 163.415109 201.641035 \r\nL 163.445549 214.735413 \r\nL 163.506427 214.756364 \r\nL 163.536867 213.729764 \r\nL 163.597746 214.756364 \r\nL 163.628185 214.756364 \r\nL 163.719503 191.815014 \r\nL 163.780382 214.756364 \r\nL 163.841261 214.023078 \r\nL 163.8717 202.688585 \r\nL 163.932579 208.743425 \r\nL 163.963019 214.756364 \r\nL 164.023897 212.912675 \r\nL 164.054337 202.939997 \r\nL 164.084776 214.756364 \r\nL 164.145655 205.097951 \r\nL 164.206534 214.756364 \r\nL 164.236973 202.541928 \r\nL 164.267413 189.971325 \r\nL 164.297852 214.756364 \r\nL 164.328291 205.852187 \r\nL 164.38917 214.756364 \r\nL 164.480489 212.870773 \r\nL 164.510928 212.59841 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168.589809 199.88115 \r\nL 168.650687 214.756364 \r\nL 168.772445 205.056049 \r\nL 168.833324 207.255904 \r\nL 168.955081 214.756364 \r\nL 169.076839 204.720832 \r\nL 169.168157 214.756364 \r\nL 169.198597 212.912675 \r\nL 169.289915 214.756364 \r\nL 169.320354 214.735413 \r\nL 169.442112 194.287232 \r\nL 169.56387 214.756364 \r\nL 169.685627 181.884238 \r\nL 169.807385 214.756364 \r\nL 169.837824 200.048758 \r\nL 169.898703 208.072993 \r\nL 169.959582 214.756364 \r\nL 169.990021 207.695875 \r\nL 170.020461 210.189045 \r\nL 170.111779 214.756364 \r\nL 170.142218 187.059136 \r\nL 170.203097 213.059332 \r\nL 170.233537 210.796624 \r\nL 170.263976 213.331695 \r\nL 170.324855 211.404203 \r\nL 170.385734 214.756364 \r\nL 170.446612 214.756364 \r\nL 170.477052 210.733771 \r\nL 170.537931 214.735413 \r\nL 170.629249 214.756364 \r\nL 170.659688 204.406567 \r\nL 170.720567 208.21965 \r\nL 170.842325 214.756364 \r\nL 170.933643 196.298529 \r\nL 170.964082 211.446105 \r\nL 170.994522 214.756364 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178.695692 214.756364 \r\nL 178.726131 205.077 \r\nL 178.78701 214.693511 \r\nL 178.847889 214.756364 \r\nL 178.878328 214.421148 \r\nL 178.908768 214.714462 \r\nL 178.939207 211.844174 \r\nL 178.969647 193.051123 \r\nL 179.030525 214.756364 \r\nL 179.060965 207.255904 \r\nL 179.121844 214.756364 \r\nL 179.213162 211.027085 \r\nL 179.274041 192.548299 \r\nL 179.30448 213.750715 \r\nL 179.334919 214.756364 \r\nL 179.426238 214.358295 \r\nL 179.456677 214.756364 \r\nL 179.487116 214.609707 \r\nL 179.547995 189.887521 \r\nL 179.578435 214.756364 \r\nL 179.639313 214.756364 \r\nL 179.669753 214.504952 \r\nL 179.700192 205.349363 \r\nL 179.730632 214.756364 \r\nL 179.761071 214.756364 \r\nL 179.882829 200.237318 \r\nL 179.913268 214.756364 \r\nL 180.004586 206.564521 \r\nL 180.065465 205.097951 \r\nL 180.095905 195.062419 \r\nL 180.126344 208.994837 \r\nL 180.156783 203.715184 \r\nL 180.278541 214.756364 \r\nL 180.400299 206.145501 \r\nL 180.430738 214.756364 \r\nL 180.461177 208.114895 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200.551583 \r\nL 184.266103 214.756364 \r\nL 184.296543 214.756364 \r\nL 184.387861 203.191409 \r\nL 184.509619 214.756364 \r\nL 184.540058 208.994837 \r\nL 184.600937 214.756364 \r\nL 184.631376 214.756364 \r\nL 184.661816 195.523342 \r\nL 184.722695 214.756364 \r\nL 184.783573 214.756364 \r\nL 184.93577 200.404926 \r\nL 184.96621 214.735413 \r\nL 185.057528 213.541205 \r\nL 185.118407 214.044029 \r\nL 185.240164 192.275936 \r\nL 185.331483 187.373401 \r\nL 185.361922 214.756364 \r\nL 185.392362 203.882792 \r\nL 185.45324 213.981176 \r\nL 185.48368 214.756364 \r\nL 185.544559 180.103402 \r\nL 185.574998 214.756364 \r\nL 185.605437 214.756364 \r\nL 185.757634 190.599855 \r\nL 185.788074 192.171181 \r\nL 185.818513 181.695679 \r\nL 185.909831 214.756364 \r\nL 185.940271 203.149507 \r\nL 185.97071 214.756364 \r\nL 186.031589 202.16481 \r\nL 186.062028 213.22694 \r\nL 186.092468 195.271929 \r\nL 186.153347 207.130198 \r\nL 186.214226 214.756364 \r\nL 186.244665 207.402561 \r\nL 186.305544 213.981176 \r\nL 186.396862 211.236595 \r\nL 186.427301 207.758728 \r\nL 186.457741 214.756364 \r\nL 186.48818 212.661263 \r\nL 186.51862 210.733771 \r\nL 186.609938 214.756364 \r\nL 186.640377 168.035624 \r\nL 186.701256 210.461408 \r\nL 186.762135 208.303454 \r\nL 186.792574 195.942362 \r\nL 186.823014 214.756364 \r\nL 186.853453 198.184119 \r\nL 186.975211 214.756364 \r\nL 187.00565 191.668357 \r\nL 187.066529 209.476711 \r\nL 187.096968 214.756364 \r\nL 187.188287 170.486891 \r\nL 187.310044 214.756364 \r\nL 187.340484 214.756364 \r\nL 187.431802 200.928701 \r\nL 187.462241 214.316393 \r\nL 187.492681 199.294522 \r\nL 187.553559 208.84818 \r\nL 187.614438 210.901379 \r\nL 187.644878 194.978615 \r\nL 187.705756 212.80792 \r\nL 187.736196 210.042388 \r\nL 187.857954 183.853632 \r\nL 187.827514 210.92233 \r\nL 187.888393 186.996283 \r\nL 187.949272 214.756364 \r\nL 187.979711 214.735413 \r\nL 188.010151 192.024524 \r\nL 188.04059 214.756364 \r\nL 188.071029 214.756364 \r\nL 188.162348 206.648325 \r\nL 188.192787 164.432051 \r\nL 188.253666 208.701523 \r\nL 188.284105 207.653973 \r\nL 188.314545 213.897372 \r\nL 188.344984 202.332418 \r\nL 188.405863 210.000486 \r\nL 188.436302 214.756364 \r\nL 188.466742 190.055129 \r\nL 188.52762 214.756364 \r\nL 188.55806 214.044029 \r\nL 188.588499 214.756364 \r\nL 188.710257 204.993196 \r\nL 188.801575 214.756364 \r\nL 188.832015 214.735413 \r\nL 188.862454 214.567805 \r\nL 188.892893 199.105963 \r\nL 188.984212 206.585472 \r\nL 189.136409 214.756364 \r\nL 189.166848 164.369198 \r\nL 189.227727 214.756364 \r\nL 189.319045 200.048758 \r\nL 189.440803 214.756364 \r\nL 189.471242 214.023078 \r\nL 189.501682 204.322763 \r\nL 189.56256 212.724116 \r\nL 189.623439 214.756364 \r\nL 189.653879 194.475791 \r\nL 189.714757 214.756364 \r\nL 189.836515 194.203428 \r\nL 189.866954 205.894089 \r\nL 189.927833 194.894811 \r\nL 189.958273 214.756364 \r\nL 189.988712 193.931065 \r\nL 190.049591 214.274491 \r\nL 190.08003 214.756364 \r\nL 190.11047 202.772389 \r\nL 190.201788 207.59112 \r\nL 190.323546 214.756364 \r\nL 190.353985 214.421148 \r\nL 190.384424 214.756364 \r\nL 190.414864 197.953658 \r\nL 190.475743 209.183396 \r\nL 190.62794 214.756364 \r\nL 190.658379 214.756364 \r\nL 190.688818 189.824668 \r\nL 190.749697 214.714462 \r\nL 190.810576 214.756364 \r\nL 190.932334 201.578182 \r\nL 191.023652 214.756364 \r\nL 191.084531 211.802272 \r\nL 191.175849 214.756364 \r\nL 191.236728 206.836884 \r\nL 191.267167 213.771666 \r\nL 191.328046 214.756364 \r\nL 191.419364 208.680572 \r\nL 191.480243 214.756364 \r\nL 191.541122 214.735413 \r\nL 191.571561 214.756364 \r\nL 191.63244 180.962393 \r\nL 191.662879 212.3889 \r\nL 191.693319 214.756364 \r\nL 191.815077 180.627177 \r\nL 191.936834 214.756364 \r\nL 191.967274 214.756364 \r\nL 191.997713 214.190687 \r\nL 192.028152 179.831039 \r\nL 192.058592 214.756364 \r\nL 192.089031 214.756364 \r\nL 192.14991 210.2938 \r\nL 192.180349 213.331695 \r\nL 192.271668 214.756364 \r\nL 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194.311108 208.931984 \r\nL 194.341547 208.177748 \r\nL 194.463305 214.756364 \r\nL 194.493744 194.203428 \r\nL 194.585063 195.586195 \r\nL 194.73726 214.756364 \r\nL 194.767699 212.053684 \r\nL 194.828578 214.756364 \r\nL 194.859017 214.756364 \r\nL 194.919896 191.144582 \r\nL 194.950335 195.020517 \r\nL 194.980775 214.756364 \r\nL 195.072093 202.479075 \r\nL 195.193851 214.756364 \r\nL 195.315608 201.515329 \r\nL 195.376487 201.431525 \r\nL 195.467805 214.756364 \r\nL 195.589563 203.966596 \r\nL 195.650442 196.382333 \r\nL 195.74176 214.756364 \r\nL 195.833078 206.606423 \r\nL 195.863518 213.562156 \r\nL 195.893957 214.756364 \r\nL 195.924397 213.43645 \r\nL 195.954836 211.425154 \r\nL 195.985275 212.640312 \r\nL 196.015715 194.685301 \r\nL 196.076594 214.756364 \r\nL 196.167912 214.756364 \r\nL 196.25923 192.736858 \r\nL 196.289669 206.711178 \r\nL 196.380988 202.79334 \r\nL 196.411427 214.756364 \r\nL 196.502745 214.756364 \r\nL 196.624503 197.932707 \r\nL 196.715821 214.756364 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200.520747 199.441179 \r\nL 200.612065 203.023801 \r\nL 200.703384 214.67256 \r\nL 200.672944 201.368672 \r\nL 200.733823 208.533915 \r\nL 200.764262 205.077 \r\nL 200.794702 214.756364 \r\nL 200.825141 195.104321 \r\nL 200.855581 160.87038 \r\nL 200.88602 214.756364 \r\nL 200.916459 207.423512 \r\nL 200.977338 214.735413 \r\nL 201.038217 177.777841 \r\nL 201.129535 212.17939 \r\nL 201.159975 210.126192 \r\nL 201.251293 214.232589 \r\nL 201.281732 184.545015 \r\nL 201.40349 213.604058 \r\nL 201.464369 208.911033 \r\nL 201.494808 198.393629 \r\nL 201.525248 214.756364 \r\nL 201.555687 214.756364 \r\nL 201.586126 214.421148 \r\nL 201.738323 146.89606 \r\nL 201.829642 214.756364 \r\nL 201.860081 208.63867 \r\nL 201.89052 206.040746 \r\nL 201.92096 212.053684 \r\nL 201.951399 212.493655 \r\nL 201.981839 214.756364 \r\nL 202.012278 214.735413 \r\nL 202.042717 142.140182 \r\nL 202.103596 159.843781 \r\nL 202.164475 214.756364 \r\nL 202.225354 211.844174 \r\nL 202.255793 157.664876 \r\nL 202.286233 214.756364 \r\nL 202.316672 213.85547 \r\nL 202.377551 205.642677 \r\nL 202.40799 214.756364 \r\nL 202.43843 214.484001 \r\nL 202.468869 188.588559 \r\nL 202.499309 214.756364 \r\nL 202.529748 214.484001 \r\nL 202.651506 198.330776 \r\nL 202.681945 214.756364 \r\nL 202.742824 212.849822 \r\nL 202.773263 182.994641 \r\nL 202.834142 190.055129 \r\nL 202.92546 214.756364 \r\nL 202.9559 214.735413 \r\nL 202.986339 170.067871 \r\nL 203.047218 214.756364 \r\nL 203.077657 208.869131 \r\nL 203.168976 211.089938 \r\nL 203.229854 214.337344 \r\nL 203.260294 211.739419 \r\nL 203.321173 211.948929 \r\nL 203.351612 193.449192 \r\nL 203.382051 214.756364 \r\nL 203.47337 202.311467 \r\nL 203.503809 203.547576 \r\nL 203.534248 214.735413 \r\nL 203.564688 165.66816 \r\nL 203.656006 192.715907 \r\nL 203.686445 193.009221 \r\nL 203.716885 214.400197 \r\nL 203.747324 206.627374 \r\nL 203.777764 170.549744 \r\nL 203.808203 214.756364 \r\nL 203.838643 214.756364 \r\nL 203.869082 208.198699 \r\nL 203.929961 209.288151 \r\nL 203.99084 214.756364 \r\nL 204.021279 210.964232 \r\nL 204.082158 176.353172 \r\nL 204.112597 214.756364 \r\nL 204.173476 213.918323 \r\nL 204.203915 214.756364 \r\nL 204.234355 210.482359 \r\nL 204.295234 214.567805 \r\nL 204.356112 211.96988 \r\nL 204.386552 214.756364 \r\nL 204.447431 186.975332 \r\nL 204.47787 211.446105 \r\nL 204.508309 212.870773 \r\nL 204.538749 209.2672 \r\nL 204.569188 207.989189 \r\nL 204.599628 190.159884 \r\nL 204.660507 214.756364 \r\nL 204.690946 214.756364 \r\nL 204.721385 211.907027 \r\nL 204.782264 214.379246 \r\nL 204.812704 192.213083 \r\nL 204.843143 214.756364 \r\nL 204.904022 205.91504 \r\nL 204.934461 205.244608 \r\nL 204.99534 214.756364 \r\nL 205.056219 211.488007 \r\nL 205.086658 213.771666 \r\nL 205.117098 213.352646 \r\nL 205.147537 171.052568 \r\nL 205.208416 207.549218 \r\nL 205.238855 213.394548 \r\nL 205.299734 204.86749 \r\nL 205.360613 214.442099 \r\nL 205.391052 157.74868 \r\nL 205.421492 214.756364 \r\nL 205.451931 214.756364 \r\nL 205.51281 202.688585 \r\nL 205.543249 213.373597 \r\nL 205.604128 211.865125 \r\nL 205.634568 214.756364 \r\nL 205.665007 199.734493 \r\nL 205.725886 214.756364 \r\nL 205.756325 213.310744 \r\nL 205.786765 193.428241 \r\nL 205.847643 214.756364 \r\nL 205.878083 212.849822 \r\nL 205.908522 212.975528 \r\nL 205.969401 188.777118 \r\nL 206.03028 199.650689 \r\nL 206.091159 214.756364 \r\nL 206.152037 205.077 \r\nL 206.212916 214.756364 \r\nL 206.243356 182.76418 \r\nL 206.304235 214.756364 \r\nL 206.425992 191.354092 \r\nL 206.456432 214.756364 \r\nL 206.51731 204.427518 \r\nL 206.54775 198.142217 \r\nL 206.578189 214.756364 \r\nL 206.669507 197.031814 \r\nL 206.760826 214.756364 \r\nL 206.791265 196.256627 \r\nL 206.852144 210.943281 \r\nL 206.882583 214.756364 \r\nL 206.913023 188.588559 \r\nL 207.004341 205.28651 \r\nL 207.03478 214.756364 \r\nL 207.06522 213.373597 \r\nL 207.095659 198.435531 \r\nL 207.156538 205.768383 \r\nL 207.278296 214.756364 \r\nL 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "## Övning\n",
+    "## En mer realistisk värld för Peter och vargen\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/translations/sv/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
new file mode 100644
index 000000000..1a5c41e60
--- /dev/null
+++ b/translations/sv/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
@@ -0,0 +1,469 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "eadbd20d2a075efb602615ad90b1e97a",
+   "translation_date": "2025-09-06T15:14:38+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter och vargen: Realistisk miljö\n",
+    "\n",
+    "I vår situation kunde Peter röra sig nästan utan att bli trött eller hungrig. I en mer realistisk värld måste han sätta sig ner och vila då och då, och även äta för att hålla sig mätt. Låt oss göra vår värld mer realistisk genom att implementera följande regler:\n",
+    "\n",
+    "1. När Peter rör sig från en plats till en annan förlorar han **energi** och får en viss **trötthet**.\n",
+    "2. Peter kan få mer energi genom att äta äpplen.\n",
+    "3. Peter kan bli av med trötthet genom att vila under ett träd eller på gräset (dvs. gå in på en plats på spelplanen med ett träd eller gräs - grönt fält).\n",
+    "4. Peter måste hitta och döda vargen.\n",
+    "5. För att kunna döda vargen måste Peter ha vissa nivåer av energi och trötthet, annars förlorar han striden.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "from rlboard import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "## Definiera tillstånd\n",
+    "\n",
+    "I våra nya spelregler behöver vi hålla koll på energi och trötthet vid varje brädposition. Därför kommer vi att skapa ett objekt `state` som innehåller all nödvändig information om det aktuella problemtillståndet, inklusive brädets tillstånd, aktuella nivåer av energi och trötthet, samt om vi kan besegra vargen vid slutläget:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class state:\n",
+    "    def __init__(self,board,energy=10,fatigue=0,init=True):\n",
+    "        self.board = board\n",
+    "        self.energy = energy\n",
+    "        self.fatigue = fatigue\n",
+    "        self.dead = False\n",
+    "        if init:\n",
+    "            self.board.random_start()\n",
+    "        self.update()\n",
+    "\n",
+    "    def at(self):\n",
+    "        return self.board.at()\n",
+    "\n",
+    "    def update(self):\n",
+    "        if self.at() == Board.Cell.water:\n",
+    "            self.dead = True\n",
+    "            return\n",
+    "        if self.at() == Board.Cell.tree:\n",
+    "            self.fatigue = 0\n",
+    "        if self.at() == Board.Cell.apple:\n",
+    "            self.energy = 10\n",
+    "\n",
+    "    def move(self,a):\n",
+    "        self.board.move(a)\n",
+    "        self.energy -= 1\n",
+    "        self.fatigue += 1\n",
+    "        self.update()\n",
+    "\n",
+    "    def is_winning(self):\n",
+    "        return self.energy > self.fatigue"
+   ]
+  },
+  {
+   "source": [
+    "Låt oss försöka lösa problemet med hjälp av slumpvandring och se om vi lyckas:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(state):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(board,policy):\n",
+    "    n = 0 # number of steps\n",
+    "    s = state(board)\n",
+    "    while True:\n",
+    "        if s.at() == Board.Cell.wolf:\n",
+    "            if s.is_winning():\n",
+    "                return n # success!\n",
+    "            else:\n",
+    "                return -n # failure!\n",
+    "        if s.at() == Board.Cell.water:\n",
+    "            return 0 # died\n",
+    "        a = actions[policy(m)]\n",
+    "        s.move(a)\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 5, won: 1 times, drown: 94 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        elif z==0:\n",
+    "            n+=1\n",
+    "        else:\n",
+    "            s+=1\n",
+    "    print(f\"Killed by wolf = {w}, won: {s} times, drown: {n} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Belöningsfunktion\n",
+    "\n",
+    "### Introduktion\n",
+    "Belöningsfunktionen är en viktig komponent i att definiera hur en agent ska bete sig i en given miljö. Den hjälper till att styra agentens beslut genom att tilldela poäng baserat på dess handlingar.\n",
+    "\n",
+    "### Grundläggande principer\n",
+    "- Belöningar bör vara utformade för att uppmuntra önskade beteenden.\n",
+    "- Straff kan användas för att avskräcka oönskade beteenden.\n",
+    "- En välbalanserad belöningsfunktion är avgörande för att uppnå optimala resultat.\n",
+    "\n",
+    "### Exempel på belöningsfunktion\n",
+    "Nedan följer ett exempel på hur en belöningsfunktion kan implementeras:\n",
+    "\n",
+    "```python\n",
+    "def reward_function(params):\n",
+    "    # Extrahera relevanta parametrar\n",
+    "    speed = params['speed']\n",
+    "    distance_from_center = params['distance_from_center']\n",
+    "    track_width = params['track_width']\n",
+    "\n",
+    "    # Beräkna belöning baserat på position på banan\n",
+    "    if distance_from_center < 0.1 * track_width:\n",
+    "        reward = 1.0  # Hög belöning för att hålla sig nära mitten\n",
+    "    else:\n",
+    "        reward = 0.5  # Lägre belöning för att vara längre från mitten\n",
+    "\n",
+    "    # Justera belöning baserat på hastighet\n",
+    "    reward *= speed\n",
+    "\n",
+    "    return reward\n",
+    "```\n",
+    "\n",
+    "### Vanliga misstag\n",
+    "- **Överkomplicerade belöningsfunktioner**: Försök att hålla belöningsfunktionen enkel och lätt att förstå.\n",
+    "- **Felaktiga parametrar**: Se till att använda rätt parametrar för att undvika oväntade beteenden.\n",
+    "- **Obalanserade belöningar**: Om belöningarna är för höga eller för låga kan det leda till suboptimala resultat.\n",
+    "\n",
+    "### Tips för att designa en effektiv belöningsfunktion\n",
+    "- Testa belöningsfunktionen i olika scenarier för att säkerställa att den fungerar som avsett.\n",
+    "- Analysera agentens beteende och justera belöningsfunktionen vid behov.\n",
+    "- Dokumentera tydligt hur belöningsfunktionen är utformad och vilka parametrar den använder.\n",
+    "\n",
+    "### Slutsats\n",
+    "En välutformad belöningsfunktion är avgörande för att styra agentens beteende och uppnå önskade mål. Genom att följa bästa praxis och undvika vanliga misstag kan du skapa en belöningsfunktion som är både effektiv och robust.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reward(s):\n",
+    "    r = s.energy-s.fatigue\n",
+    "    if s.at()==Board.Cell.wolf:\n",
+    "        return 100 if s.is_winning() else -100\n",
+    "    if s.at()==Board.Cell.water:\n",
+    "        return -100\n",
+    "    return r"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning-algoritm\n",
+    "\n",
+    "Själva inlärningsalgoritmen förblir i stort sett oförändrad, vi använder bara `state` istället för enbart brädposition.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    s = state(m)\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = s.board.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        while True:\n",
+    "            a = random.choices(list(actions),weights=v)[0]\n",
+    "            dpos = actions[a]\n",
+    "            if s.board.is_valid(s.board.move_pos(s.board.human,dpos)):\n",
+    "                break \n",
+    "        s.move(dpos)\n",
+    "        r = reward(s)\n",
+    "        if abs(r)==100: # end of game\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Resultat\n",
+    "\n",
+    "Låt oss se om vi lyckades träna Peter att bekämpa vargen!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 1, won: 9 times, drown: 90 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [
+    "Vi ser nu mycket färre fall av drunkning, men Peter kan fortfarande inte alltid döda vargen. Försök att experimentera och se om du kan förbättra detta resultat genom att justera hyperparametrar.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0987e7dd8>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiserade översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/translations/sv/8-Reinforcement/1-QLearning/solution/notebook.ipynb
new file mode 100644
index 000000000..acef3ec70
--- /dev/null
+++ b/translations/sv/8-Reinforcement/1-QLearning/solution/notebook.ipynb
@@ -0,0 +1,577 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+  },
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+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
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+  "coopTranslator": {
+   "original_hash": "488431336543f71f14d4aaf0399e3381",
+   "translation_date": "2025-09-06T15:10:12+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter och vargen: Introduktion till förstärkningsinlärning\n",
+    "\n",
+    "I denna handledning kommer vi att lära oss hur man tillämpar förstärkningsinlärning på ett problem med att hitta vägar. Miljön är inspirerad av den musikaliska sagan [Peter och vargen](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) av den ryske kompositören [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Det är en berättelse om den unge pionjären Peter, som modigt lämnar sitt hus och går till skogsgläntan för att jaga vargen. Vi kommer att träna maskininlärningsalgoritmer som hjälper Peter att utforska området och skapa en optimal navigeringskarta.\n",
+    "\n",
+    "Först, låt oss importera några användbara bibliotek:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Översikt av Förstärkningsinlärning\n",
+    "\n",
+    "**Förstärkningsinlärning** (RL) är en inlärningsteknik som låter oss lära oss ett optimalt beteende hos en **agent** i en viss **miljö** genom att utföra många experiment. En agent i denna miljö bör ha ett **mål**, definierat av en **belöningsfunktion**.\n",
+    "\n",
+    "## Miljön\n",
+    "\n",
+    "För enkelhetens skull, låt oss anta att Peters värld är en kvadratisk spelbräda med storleken `width` x `height`. Varje ruta på denna bräda kan vara:\n",
+    "* **mark**, där Peter och andra varelser kan gå\n",
+    "* **vatten**, där man uppenbarligen inte kan gå\n",
+    "* **ett träd** eller **gräs** - en plats där man kan vila\n",
+    "* **ett äpple**, som representerar något Peter gärna vill hitta för att äta\n",
+    "* **en varg**, som är farlig och bör undvikas\n",
+    "\n",
+    "För att arbeta med miljön kommer vi att definiera en klass som heter `Board`. För att undvika att överbelasta denna notebook med kod har vi flyttat all kod för att arbeta med brädan till en separat modul som heter `rlboard`, vilken vi nu kommer att importera. Du kan titta inuti denna modul för att få mer detaljer om implementeringens interna funktioner.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rlboard import *"
+   ]
+  },
+  {
+   "source": [
+    "Låt oss nu skapa ett slumpmässigt bräde och se hur det ser ut:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "source": [
+    "## Åtgärder och Policy\n",
+    "\n",
+    "I vårt exempel är Peters mål att hitta ett äpple, samtidigt som han undviker vargen och andra hinder. För att göra detta kan han i princip gå runt tills han hittar ett äpple. Därför kan han vid varje position välja mellan en av följande åtgärder: upp, ner, vänster och höger. Vi kommer att definiera dessa åtgärder som en ordbok och koppla dem till par av motsvarande koordinatförändringar. Till exempel skulle att röra sig åt höger (`R`) motsvara paret `(1,0)`.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "Strategin för vår agent (Peter) definieras av en så kallad **policy**. Låt oss titta på den enklaste policyn som kallas **slumpmässig promenad**.\n",
+    "\n",
+    "## Slumpmässig promenad\n",
+    "\n",
+    "Låt oss först lösa vårt problem genom att implementera en strategi för slumpmässig promenad.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(m):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(m,policy,start_position=None):\n",
+    "    n = 0 # number of steps\n",
+    "    # set initial position\n",
+    "    if start_position:\n",
+    "        m.human = start_position \n",
+    "    else:\n",
+    "        m.random_start()\n",
+    "    while True:\n",
+    "        if m.at() == Board.Cell.apple:\n",
+    "            return n # success!\n",
+    "        if m.at() in [Board.Cell.wolf, Board.Cell.water]:\n",
+    "            return -1 # eaten by wolf or drowned\n",
+    "        while True:\n",
+    "            a = actions[policy(m)]\n",
+    "            new_pos = m.move_pos(m.human,a)\n",
+    "            if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:\n",
+    "                m.move(a) # do the actual move\n",
+    "                break\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "Låt oss köra slumpvandringsexperimentet flera gånger och se det genomsnittliga antalet steg som tas:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 32.87096774193548, eaten by wolf: 7 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        else:\n",
+    "            s += z\n",
+    "            n += 1\n",
+    "    print(f\"Average path length = {s/n}, eaten by wolf: {w} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Belöningsfunktion\n",
+    "\n",
+    "För att göra vår policy mer intelligent behöver vi förstå vilka drag som är \"bättre\" än andra.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "move_reward = -0.1\n",
+    "goal_reward = 10\n",
+    "end_reward = -10\n",
+    "\n",
+    "def reward(m,pos=None):\n",
+    "    pos = pos or m.human\n",
+    "    if not m.is_valid(pos):\n",
+    "        return end_reward\n",
+    "    x = m.at(pos)\n",
+    "    if x==Board.Cell.water or x == Board.Cell.wolf:\n",
+    "        return end_reward\n",
+    "    if x==Board.Cell.apple:\n",
+    "        return goal_reward\n",
+    "    return move_reward"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning\n",
+    "\n",
+    "Bygg en Q-Tabell, eller en flerdimensionell matris. Eftersom vår spelplan har dimensionerna `width` x `height`, kan vi representera Q-Tabellen med en numpy-matris med formen `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "source": [
+    "Skicka Q-tabellen till plotfunktionen för att visualisera tabellen på brädet:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p267d475764)\">\n    <image height=\"327\" id=\"image00b8757863\" 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5eG2HjGOTtiBj2WTs3LzHtAW2rCJlMuDxItkOsuWg2KDYFn2GDi3Kz1RI+/dLfupLsX8X85fpfr+fn/70px36FiHXapw+fToejwdd16murubuu++moqKC5557jiVLlpDNZjn00EO56KKLOOaYY9wJ3aqqMnPmTE466ST3eNu3b2fevHlMnjyZ6urqr8yporUcKyoq6NGjB3PnzmX48OGMHz8eSZLo1atXsV7iGxsyZAgNDQ3MmzePUCjEpZdeyvDhw8tqo5+jjjqKRYsWsWLFClasWMFvfvMbTjjhhFKX1al4PB5GjBjBww8/zKBBg5gwYQK7d+9myJAhRTm+ruuk02lSqZQ7IGN4DRSfidG1B4rPl/swfaimH9XrR/P50Pwmqs9ENX0oph+17XHN50P15Z6n+/xoZu6z7g+g+X2Y1dX4a7tg+H0Y/kDbhw/D58djmnxbzZBRo0axbt06XnzxRXbv3s0NN9zA0UcfXbS+2/b/YFmWxRFHHPGl56xfv57x48cTjUbJZrMceeSRnHPOORx33HGsXr2aRx99lCeeeIJ//etfHHroofzkJz/hxBNPdJdBO/XUU1m9evWXjjt69OgO03wKhWRRW47nnXce5557LlOnTmXNmjVlNxgD8OCDDxKLxbjxxhvZvHlzWdb43nvvuQMyO3bsKMsay92YMWPYvHkz11xzDa+88gp+v79ox863qDpuWWAjyQqyYaIqMpIsI8sSkgzIIMlK7rMD2E6uBWg7yLaNbDmoNji2DTY4tgO2g+PYOJaDbJN7Xtv3KZaNYjsoDhit0aL9XIW8/PLLfPrppzz++ON8+umnRf1dzK/qnUwmueiii/jnP//Z4fGqqir23XdfLMvi+uuvZ/bs2e4q4PlFb/MikQj19fUEg0G8Xi+xWIxbbrkFwzD43ve+x5YtW9i1a5f7/CuuuIJnnnmG559/3l3n8YuKGo75WegPP/xwMQ9bVLIsEwgEuP/++0tdyh4pisLo0aMZPXp0qUvptPK/i/fdd1/Rj52fXAy5y3Zd18kkExj99yfd2IDRpx+KLIEs567NJLDk3Gi0g5QLQQckGyTbzgWlA4ptY9ugtoUjtu0+R3ZAsmxkx8kFo+0gZ2x2bv2YnvvtV/SfMU+WZfr06cONN95Y9GPnFwSWZZk5c+bQt2/fDo93796dO+64gxEjRgC4rUFVVXn33Xd55ZVX3Oe+8sorLFq0iIkTJ7q3At52220AfPjhh/zsZz/rEI6O4/C73/3OvS9dLHYrCEWQn3uXv6xOJBL4QxVUHHk8diKJFqgEw0TymmCY2IYP22OS9fiwPCaOYYLHdJ+jmD4kr4ns9SF7TWTTRDXN3CW36UPPf/b5cpfbPj8enx/N0MnEohz147NLfUq+lvx+Ltlslq5du3LLLbd0eHzdunX89Kc/pampCY/Hg23bBINB6uvrmT59Ohs3bnSfm0ql+O1vf8vGjRvxeDxEIhE0TWPXrl1cdNFFvP322x2OPX36dHw+X4f1HL/YnyomgQvCXsrPz8tPJQmFQrSEw3QbdiDrXnwSs6URf/fe4GRxkLElByTJ7Ru0cXDyA752rj0pO05u7qPtIDkOtgOS7ZB1aLv8dlCwkS1QHUjE47w6dzZdhxxAoEuXEp2JbyYQCNDS0uIuQHvxxReTzWa5/fbb3bmLH3zwAQcddJB7P3T+dsLm5uYvHW/9+vWMGTPG3aoiP5jU/rmKonDxxRdz3XXXEQqF3JFu0zS/1O8owlEQ9lJ+yTKgw5JlardedD3tHD75w+0MOGsKvr4DcNJJQMLBAYnPL62dz0fUdV8AG5DicaR0GtuGbO4JudewHSQbFCd3eR1uqGfZk4/Ta8gBXPzwIwUvCTuD1tZWampqaGlpobKykmw2y/Tp08lms9xxxx3uOS4UhHsSDof3+JgkSUyZMoUbbrgBv99Pc3MzsixjGAbxeByPx9MhIMVltSDspfyKMe1bJ/lpLrVHHc+gq37J1if/QOvmD/H07IunVx8cw4dj+MDwgdeHd8AQzEH7oQYq2P32Cj5btoTwJ1vw1nbD17M3RnUNqs+H6jXRTBNPwI8RzLV0ljw2j2Dv3kz5/f+g63pZTpv7/2GaprtbYywWczfLmj59esH1Fb+O6dOnu3+WZZmZM2e6Lcb8SuCSJH0pGEG0HAVhr+WXLMuvEZhf0j8UChEOh6k5YixWoAJp2xbW3XEDmtfH0F/dA7KGpMhYDTt5f+Y1WJKMnbR5cf37dBt+EMf88ChWzb6DeKQVf+9+DD7tbDyVFdjZDB+8/irvvvwP9FAlp98yC3+3HlR26UI4HO60sxkymYw7Wp3vU8zvGZN3+eWXM378eI499ti9nnx+1113cdZZZ+HxePj1r3+Nbdvce++9XHPNNRiG4e58mF8d6ItEOArCXvL5fO59uflNtYLBIPF4nIqKCmKxGF1GHkK4777sP+ZkEi2NrJl9B8lN6zG692TI1TMZcPtcd8FV+c5baJAl+p5xPt1PPAu/3086k6Huxed455nHkDSDIaecyTmP/JnK6mqstpWu86/bWbdmbX+TQ/sdAE877TQikQgAffr04aCDDnL3m94bAwcOxDAMBg4c6L7e/PnzufLKK93/zndtFJrc3jnPqiCUUH6pLciNuOYHFvLL/ldWVvLyv19mZ2Qn6WSagOEnceBgUkP6gSOx+NWn2adrb8YcMoaabt3pdmAPPl2+mk8/2cTAwcNobGzE5/Mx8OQJDBl/pnuLXX4VG8MwaGhooKamhoaGBgKBAIFAoMRnZe/lR/1VVSWRSLh3y+RXPILcPz6pVArLsvb6+H/96185/PDDO7REJUnC5/O5LdR8a7TQVhMiHAVhL+U78PNzKWOxGKZpkkoleX3TQv72/j9YsW45Oxt3UeutJqAG8St+7LQNWVi/4QN+NHosy95eRpfDJP6x+G8Mrld49fnfUXHRf1FR1aXDoqz5vrhwOIzX68WyLCoqKkgmkwSDwS+tJtNZ5HcCzO/nks1mSafTHQaYEonE1+5/fPjhh0kmk4wZM8b9mmVZtLa2UlFRQSQSQZIkDMNwt2Vo3+8owlEQ9lJ+4nd+WTRN09gZ3sk5j5/Hxm0baAmHcdI2ckbCDPmo9lUR0IKYshevZjKy7wgMReeNhmfRXjCYc81TNNSt4uWnf8OH74xm6GGn4fH63e0C8m/YfAuy/VJp+c/tdZYBmvyIsWmaNDU1uStzt79bRdM0xo8f/7VajpDbLmHs2M/Xu1RVlZqaGuLxeIepPO33sHGf+/V+LEH47mq/NL/t2Gxu+ohTHzqNWDRO0AjS1dsVv2SSTVnsrN/Bx1u3cuLok9inpg+7d+8i2EfnlTcXkNCyzL5wNv16DMTMJhky7GCWvngbG955jrHn3kVFl77ugg/5vU/ygwj5u0Dyb+72OstCJZFIxN0JsKqqym05tg/7a665psP3DB06FJ/Px6pVq770c8uyzMiRI3Ech3fffReAXbt2cd5557nPyWaz1NfXU1FRQTgcdluOhabyiHAUhL1kmib19fXY2KxrXcflf7mcaCLKfl32Q3EU7LSFKZv4gj726zaEz3Z+RkNzA76gxk4+4v3odqoG9ub9Dz8kZee2CEh2GcjEn83mnZfvYc3SuSz9yzUMGnksg0afT1b1oCgKjY2N1NTUEIvFqK6upr6+/j+uLFPOvF4v8XgcVVWJxWLuz/GTn/yE5cuXdxiA6dGjB+eeey7jx4+nb9++3HPPPTiOg2VZbphqmsbUqVOJRqM8+eSTPPXUU2zatMk9hiRJTJs2zV0BPD/jACg4JUqEoyDspfzS/HW7N3DJ8z9l466P0LIqm7IbcdJgp21uO/t2/vX+Cvap3Yet2z6m5yEWdZ+swtPFj5KoosEJc/bYszhs6GFEo1GqqqpIpVIcMvZKPn53Hondq1jz6koGjjwTw5fbX7mqqopYLIaqqjQ1NXW4LO2Mm2xls1l3Sk2+m8KyLC688EJmzJjhhqOu6zz55JMceOCB6LpOIpHgV7/6lds/md+IK78UYXV1NVdddRUnnngixx9/vDsxXFEUpk2b5m4Jmz9+fs/xLxLhKAh7yTAMPvi0jjP/ciaVeiVHDzwKUzJJxBKYqoegx8+jr/6Rup3rGHloT6q6VfFZrJnKrrWosk6f6hqS6TS/OO4G7KSNr8pHS0tLbs6fZHDOzA3EYjE0VcGRDDcEWlpaqKqq+vyWxbY7SzpryzGTyeD3+919pPPbs+Zbc16vl1/+8pdMnjzZ3U4hPydRVVV3EYr8iHN+8Cq/7/Xw4cNZuXIlCxcuZMaMGe6od37PmHzfZn7L1vZ7YIMIR0HYaykrzk0rf8bgPjX0Cvaif2V/vIqPP73+GFlDp1uf7tRINvvVdmFI/2FUDelCxrJpSjSi2CoH1BxEracrIbOCbDbr7knjOA6apuFIOrqR24Yhm80it71hPR6P20rKZDKoqkoqlULTtE45Edzn8xGNRt25ovmtJ1pbWznhhBMYNWqUe791KpVyH8tvlJWf+5i/rM5vopVOp6moqCCdTtOzZ0/OOeccLMti4cKF7mP5YM6HorhDRhCKwHZsrjjkUqLZMNucdTQmP0GWA/zkpOPwyxVosp8PKlYyQhlBb/8AZEchazms2/Q2Y/tM5Jg+x9PV6NlhAnI+3PIj4PnPX1zBPN+yat9a6iwDMF+UbwHnJ7Pn/6EIhUI88MADVFZWEo1GMU0Tx3Hcjbiam5upqKggHo9jGAaJRMK9rM6PQufvWIpGo6iqyjnnnMMZZ5xBMBikubmZYDDo9nN6PB73rifRchSEb8Cr+hnmPartP06gNdZCKFTB6t2LeDP+NyKJZvBIZOUIayNvEk/FuKj/rzj08DF09/ei0lft7p2cTCbx+/1EIhGCwSDJZJJQKEQsFnP70/Ij1fnn5udV5hd37azyG2x5vV6am5vRNM3tTw0EAjQ3N7t3AkmShKZpNDQ0EAwGaWpqcgd08luv5o8ZjUaprq4mFosRCoXIZrNun20kEqG6upp4PE4wmOvLzWazhafypFKpDiM65Wbbtm2Ew+GyrrGxsRHHccq6xlgsRiCwFcNo/M9PLhHbTpf1OWxtbSUWi7Fly5Z2XwTwEI4m6MOh7KONxlEd8guUOeTetHJGQcpItMRbacl9kysWi3X4/FUry+Sf09DQ4NbU3q5du2hpaSnr8xgOhztsvFdo1Z38OSi082b+Z/6q89R+k64vampq+o81plIp1EgkwsKFC//jk0slfyLLucbNmzezeLFJPL671KXsUZcu9dx889Ky2i/ni15+Ocr48eX796woSapH/4sXF75Y6lL2yNxhcnTy6ILL/peLTz/9lKd5mvSGvbtX+n9Tj0gPuPLKK51ytnHjRufBBx8sdRlfaf78+U63bsvbFuArz49Ro2Y6TU1NpT5Ve2TbtnPGGZeV/Dx91YfH0+gcPOtghzL+X7dl3ZwXXnih1H+dX2nOnDlOaGOo5Ofqq/53xBVHOJ1viEsQBOF/gQhHQRCEAkQ4CoIgFCDCURAEoQARjoIgCAWIcBQEQShAhKMgCEIBIhwFQRAKEOEoCIJQgAhHQRCEAkQ4CoIgFCDCURAEoQARjoIgCAWIcBQEQSjgWwnH/EY55cppt8y88PV15iX6hf9/39X3S1HDsaWlhbq6OiZOnMj69eupr68v5uGL4rPPPuO9995j8uTJ1NXVEY1GS11Sp2NZFnV1ddx2220899xzZb3qtPDNfPzxxyxZsoRrrrmGuro6UqlUqUv6X1PUcHz22WeZNm0a9957L9dddx333ntvMQ9fFD//+c+5++67uemmm5g0aRLLli0rdUmdTiQS4ZhjjmHo0KFs3bqVE044odQlCd+SyZMn8/e//52JEydy6qmndtwi4v+4ooXjZ599xkcffcT3v/99Zs2axUMPPYSu66xZs6ZYL/GNLV68mKFDh+Lz+Zg/fz4PP/wwr7zyimg97qXbbruNGTNm8PzzzzNu3Diuv/565syZU+qyhCJ75plnGD9+PI2NjXzwwQc88sgjzJkz5ztziV0fPtO8AAAgAElEQVS0cKypqaFnz55069aN6dOns3HjRjKZDAMGDCjWS3xjBx54IJs3b+a0007jJz/5CYsWLeKQQw7B6/WWurRO5dxzz2XZsmVcd911dOvWjb/85S+ceuqppS5LKLKjjz6aN954g4svvphx48bxwgsvcNppp31pl77/q4oWjrqu07dvX+bPn8+7777Lf/3Xf9GnTx98Pl+xXuIbC4VC7LPPPtx9990sXryY+fPn069fPxRFKXVpnUrfvn1paGjg2Wef5dprryUYDNK9e/dSlyUUWb7Bc8cdd/Dcc8/x1ltv0a9fv+9MOBZ13+qTTjqJk046ifvvv59FixaV5Um88cYbmTFjBvPmzWPJkiWlLqdT8vv9LFmyhNdee41JkyYxcODAUpckfEseeOABGhoaWLRo0Xfu/VLUcMy74oorvo3DFo3H42Hq1KmlLqPTO/bYY0tdgvC/oKamhokTJ5a6jP91YhK4IAhCASIcBUEQChDhKAiCUIAIR0EQhAJEOAqCIBQgwlEQBKEAEY6CIAgFiHAUBEEoQISjIAhCASIcBUEQChDhKAiCUIAIR0EQhAJEOAqCIBQgwlEQBKEAEY6CIAgFiHAUBEEoQA2Hw7z44oulrmOPGhoa2Lx5c1nXuG7dOrp0+QSvt/y2os3z+XbyyiuvlPV+OZbVSr9+5fv3rKpxfDt99HuxX6lL2aPgliDrYuvKchX+vC1bttDjjR5UfVBV6lL2SA/rqKqqUl1dXepa9iiTyeD1esu6Rp/Px1VXBdh33/Kt8bnndCorK8tqT58vqqxUeeyx8j2H0aiHmTNHk3jm16UuZY+Myg34Lo6U9fvFMAxurbiVrtVdS13KHj2iPoLq8/k4/PDDS13LHm3atImmpqayrrG+vp6uXbty2GGHlbqUPVq0aBGjR4+msrKy1KUU5DgOTz31VFn/PTc1NZFMvsPOneVbI0Dfvg1lfR7Xrl3L8OHDy3rvob/85S+iz1EQBKEQEY6CIAgFiHAUBEEoQISjIAhCASIcBUEQChDhKAiCUIAIR0EQhAJEOAqCIBQgwlEQBKEAEY6CIAgFiHAUBEEoQISjIAhCASIcBUEQChDhKAiCUMC3Eo6bN2/GcZxv49BFYVkWW7duLXUZXykWi7F79+5Sl/GVdu/eTTQaLXUZX2nr1q1YllXqMjq1VCrF9u3bS13GV2ppaaGlpaWox1SLebC6ujqWLl3K2rVrGTp0KCNHjmT06NHFfIlvbPHixdTV1bFx40aGDBnC8ccfT+/evUtdVgdPPfUU27dvJxKJ0LNnTyZPnozH4yl1Wa5UKsW8efPYvn07gUCAHj16cPbZZ5e6rA62bdvGggULqKurY9CgQQwePJijjz661GV1Oi+99BJbt27ls88+o3///kycOJGKiopSl+VyHIc//elP7NixA0mS6NatG+eff35RVkIvastx2bJlrFy5kv/+7/9m69atvPDCC8U8fFHMnTuXZDLJtddeyz//+U/WrVtX6pK+ZObMmQwdOpTx48dz//33E4/HS11SB4lEgvvuu4/x48czbNgwZs6cWeqSvuSDDz7g5ZdfZsaMGaTTaf74xz+WuqRO6be//S21tbVMmTKFxx9/nJ07d5a6pA5s22bWrFkceeSRHHHEEcyaNQvbtoty7KKF45YtW1izZg26rnPyySfz85//HMMwePPNN4v1Et/Yc889x2GHHcaCBQu46aabmD17Nk888QThcLjUpbluuOEGbrvtNq6//nqWL1/O/PnzmTJlSqnL6mDKlCn84he/4PzzzycUCvG73/2OX/ziF6Uuy9Xa2sq8efM44IADOOGEEzjnnHP4wQ9+wLPPPlvq0jqV+++/n0suuYR7772XRx99lMcee4wbbrihrLoppk6dyl133cVll11GY2Mjzz77LFOnTi3KsYt2Wd23b1+GDx/ORx99xIIFC3j00UdJJpN8//vfL9ZLfGMTJkzgvPPO44ILLmDs2LFce+21TJo0iWAwWOrSXLNmzWL48OHMmzePyspKzjzzTBYsWFDqsjp46KGH+NGPfsTzzz9POBxmypQprFmzptRluQKBAJMnT+bpp59m4cKFLFy4kDfffJPHHnus1KV1KpdffjlHH300t99+O4MHD2bq1KnceeedyHL5jOM++OCDDBs2jOeffx5JkpgwYULRfheLFo6SJDFy5Ei2bNnCfffdRzKZ5KijjiqrXdAkSeLkk09m9erVfPjhh3Tt2pX+/fuXXY3nnXce8+fPR5ZljjvuOEzTLHVZHRiGwY9+9CMeffRRbNtm8uTJZXcO+/fvT7du3Zg9ezbpdJpTTjmlrGrsDCRJYtKkSbz22mssWbKEESNGUFNTU1bnUZZlzjrrLJ566ikAzjzzzKKFd1EHZEaNGsWoUaNYvnw5hx12WFmdxLyJEycyfvx4/v3vf3PIIYeUupyCZsyYQWNjI01NTQwaNKjU5XyJ1+tl1qxZbNq0icrKyrLc6W7gwIHMmjWLlStXcuCBB6KqRf1V/86YOnUqsViMzZs3M2zYsFKX8yWyLPPLX/6Sbdu2IUkSPXv2LNqxv5XfmHK6lC5EVdWyDca86urqsgyd9sp597i8cpst0Rn5fL6yDMb2evXqVfRjlk/ngSAIQhkR4SgIglCACEdBEIQCRDgKgiAUIMJREAShABGOgiAIBYhwFARBKECEoyAIQgEiHAVBEAoQ4SgIglCACEdBEIQCRDgKgiAUIMJREAShABGOgiAIBYhwFARBKECEoyAIQgHSWWed5QwfPrzUdexRLBajvr6evn37lrqUPdqxYwcej4eqqqpSl7JHGzZsoH///miaVupS9mj16tWMGDGi1GXsUSaTYdmyzTQ3Dy51KXtkGE0ceGCK7t27l7qUPdqyZQtdunTB5/OVupQ9WrNmDeqOHbW88MJPS13LHgWDW7n55jeZNGlSqUvZo5dffpna2loOPvjgUpeyR/fddx+TJ08mFAqVupQ9uvjim7jllvL9XfR4WhhyzdmsvfzpUpeyR11XduXSptsZO3ZsqUvZo8cee4wf/vCHZd3gufnmm1EdRyGVKt8WTzrdhGEYZd0qM00Tv99f1jVqmkZFRQWVlZWlLqUgx3GA8v5dBLA0i1RVqtRl7FHan8ZMmmX9u2gYBsFgsKxrVFVV9DkKgiAUIrZkKyLbtmlqasI0TTKZDAAejwfHcfB4PEQiEVRVJZFIUFFRQVNTE/V1dbz6P7NpbagHwAEO+8kklj7xOI4Dtu2gek16DxvG+hUrsB1wkKjq3o1JN9xAVe/eqJqGqqpuf2IymcTn8yFJUlnuACkInYEIxyKyLItsNut+yLJMNpvt8BhANpslnUqx4ok/seD2WWTT6dxlZe7/fPLuKtLpDLbtYNkONvDBkkWkMxmyNjiahjcYYvXiJVw++3cMP+ZoHMdBkiT3NbPZLKqqinAUhK9JXFYXUSKRIBgMIkkShmGgqiq2bWPbNrFYDMMwkCSJYDDIkof/wCu3/hLVymAoEl5VxqspeFUZ3bHwtvuaocjoko2hKnhUCQ8OptegW999eOxX/83Gt95ClmVSqRTJZBJVVYnFYm39eIIgfB0iHIvINE1aW1uRJIlkMum23lRVxe/3k0wmkSSJt+c/wxv3/AZTU/Cqn38Yqoyh5sIw92cZQ5EwVAlDUfCoMoYio0oO6dZmSKfo3q8vj//ql/zrb3/H6/Xi8/nIZrMEAgFkWfz1CsLXJS6ri6i1tZWqqiqSySSmaWJZltv3GI/H8fl8pBIJPnn1H6iZBLoq47RdS0uSlOtjxMF2wHKk3GfbIevYZC1QbUgjI9kO2WSS3R9toqpbD8xgiO11dbSGw0iyjGEYNDU1UVVVhaqKv2JB+DpE06KIgsEg9fX1yLJMPB4nm82iaRqaplFZWUksHmfdU4+ybdEreOVcy9CnKfh0JdeK1GRMVcGrKfg0Ga8qY2gKhqri0RQ8WlvLUpXRFRnFyrBh2RKqaqr592uvsn3tWioqKshkMtTU1KAoSqlPiSB0WiIciyiZTBIIBHAcB13XkWUZy7KwLCsXli1NfPb2cjyqlOtf1HJB51Wl3Get7UOV8ShK7pJaoe1DwqPIeBQJjyzhUUBTZGQny7rXX8Or6yx/8QUira0oiiL6HAXhGxLhWETpdBrDMHAcB0VRkGUZ27ZxHId0Ok1q9y52vvl6ri+xLQRNVcGrqrlWopoLv1yfo/R5/6PS1veo5FqMerug1GSFVGsYr6Hz1j/+wc6PP0ZRFDKZjAhHQfgGRDgWkd/vJxwOuwMymUzGDUlT03j9ygvcwDMVGbOttWhqbX9W2gZnNOXzARlNQVeVXCjKuVDUVQlVltFlBY8MqiJTt3wZBx99NLMvnkY6ncbv94sBGUH4BsS7p4gikQg1NTVALig9Ho87KLPlrTeR41EMVcbUFAxFwatKeNtahl5VxtSktpCU3NFrjyJjyBIeRUJXwCOTC0VFQlPIhaQiIWUzrHjmKSprqvl47Vrq6+vdeZWCIOw9EY5FZBgGra2tAO5UHlmWURSFrfP+B4/i5PoWDd3tW3TnMuan8eQHXRTJvcTW20JSV2VUVUKXQVMkVFlCUyR0TcUwDKxkCjuV4eUH/0AwGBQDMoLwDYhwLKJ0Oo3X68VxnNyN6219jpJjY7fUoyq5fkTTb+bCUZHb5ji2jUyrUrs+xrZAlNs+5y+rZRlNltEk0GUJRc7Nx1IARZaoe/MNLGziyQS2bZf6lAhCpyXCscjyt+u1v21v8+O/x27YiakomKaBLxRqG61u63Nsu9Q220auPSp4VBmPKqOruRDUFDn3IUsobX2OqiyhKzIyDr6gD6/Xi6rKrP77y7zxxJOlOgWC8H/CdzIc0+k0L730UtGPq+s6sVgMSZJIp9NYloUsyyS2b0e1MuiqjDcQwBeqwFddi6+6FtP0uiPVRtuAjMdtNUrosoymSGgyaJKMquRajaoMiiShIKHIEh7DwBvwITsOqWgrhsfzrQ/IrFq1iu3bt3+rryGUXjgcZtGiRaUu4yvV1dWxYcOGoh7zO3f7xJw5c/jXv/7FQQcdxDnnnMN1111HsVZCj8fj1NTUEIlEMAyDbDZLJpPB69FRZQmP6SPQtQeaaaIFQyiSg7VrG5LqAV0nHQmTatgNyEiSg02u9Wk5MpbjYMmQdRwUWUKRFUI9upLKZAn5/KAb+BwJ3Rdk68aNbH5/DYloFF8wWJSf7Ys/58UXX8yAAQNobm5GkiTuvffeor+OUHq33norn376Kf3792fu3Lnccccd9OjRo9RluWzbZurUqe5AaENDAw899FBRGgbfqXBMp9O89dZbnHXWWYwePZqdO3eydetWhg0bVpTVa0KhELt378Y0TWKxWK7VuOZtGle8jr+mK6EBg9A9nlyfoSKhSg5IPZE8JmgaVrY7md4DSSXjtNStg2wWR3LQZQc7K2PJNpoFVYOGYuk6KcvCklSykkTGckhZNt5QJY6msvTPTzLhyqu+lXBsbm5m+/bt3HrrrciyzBlnnEE8Hsc0zaK/lrD3irUSUzKZZOnSpcyaNYt99tmH6667jp07d9K9e/eyWe0pFovx/vvv8+c//xnDMDj99NOJxWIEAoFvfOzv1GX1K6+8wsiRI/njH//IrFmzmDZtGi+99BKRSKQox7csC6DD8mGphnrIpAnuNwxPqBLd50f3+9F8PnR/ALNbb8zKagx/AD0YwlNVjbdbD6p/eCw1R4yl61Fj6Xb0j+h59Dh6H308fY85nkDPHphVNWTicQyficfvx+MPYASCeIMheg85gFBtLU5bPcV20003MWnSJC688ELq6+u58cYbueeee76V1xL2XrEm/z/++OP8+Mc/5qabbuKRRx7h5z//Offcc09ZDfTdfPPNXHXVVUybNo3333+fO++8k5tvvrkox/5OtRxPOukkzj33XAYOHMgll1zClClTmDFjBsEita4sy3JbT/kFH7JWhsCg/fBU1eRGlmUFVQZVyQ2qaLKEjETWccB2wLaxbQfN+vy/HdsBG7Bs5NxqFEg2dB1yAFnbRrEdFAsU2yFj2yiW/a1upHXPPfcwatQo5s6dy7p165g1axZ1dXXf2usJpTFlyhSOPPJITjvtNMaMGcM555zDE088UVZTxO666y769+/PAw88QDqd5uKLL2bz5s1FOfZ3KhwBrrvuOrZu3cqtt97KtddeW9RNsbxeL7t27SIQCJBKpVAUBcMwkD0GsteHIoMsy8gyyLKU+1BkJAlkGyTbQbYdFNvBtiwUu20lcNtuC0cHHAc7t2wPkm23fZ+NbDkoto1s2yi2hPIthqPP5+N3v/sdixYtomfPnjz44IPf2msJpXXnnXeyZcsWHn30UX7961/Tq1evUpfUgSzLzJ07lzVr1gAwd+7cog1EfufCcdiwYQwdOpSjjjqqKP0S7YXDYWpra0kkEvh8Pmzbpj6VwUpbyLqBrKtIsgwSSIoEEtiyhESukejk1ixDskFuayUqjoPtWDiW1Pa409aCBNm2kRyQLAvFgYwNsmWRzjrIqgbfUr+QpmmMHTuWI444osP2DML/PYcccggHH3wwJ510UllupSpJEsceeyw//OEPgdy2JMXynQtHyJ3QYgcj5AZkduzYQSgUIhqNous63Y8aS8uyV5AVDcVrIskSSBKODLaUC0iQcADHtpEcCcm2kRwH2XGQLXIBaedahpLd9jyb3HPaQjLr5IJUteCTVe8QqKrC/y3vNOj1er/V4wvlQVGUsgzG9ooZinnfyXD8tsTjcSoqKnAcx71TBo+XwL7DaalbS4/jT8GOR3LhKEnYUm7PGCQJHAfHcXAcCXDaAjIXgJJNLhid3EK4jg1y2yV4xnGQHBvZlrBth3Qkwtb3V3PcJZcSqq0t8RkRhM5LhGMRJZNJd56jrutYlkU6nab25DN5/+pzcf45n33Gn4sda3XDUc7FY67lSG418PxmWxKgOIBlIbXtRJh1HFQnN80g30eZdWwUO7dx1/KH/kDXfgM44OgxYskyQfgGRDgWUTAYpKGhAcMwiMfjyLKMpmmoldUc+scXeO+nE9mx4Dm6n3gGsqKCY+FksrkUdBwkRUXWPUhObhsEWdWxLJtsvBXd68dybMhkkSwL28mFo4OE4jiEGxt58b67CfXozWV/mofp95XVqKIgdDYiHIuotbWV6upqotEopmli2zapVApJkkhkLIbd8RBbnphD8/vvoHhNjNpuBAcPxZFygzKJbR8T21SHbWfZ8e5q/rXsdfY/eDS1Q4YiySqZTBpvl55U9B+EhYNj2Wx48w3CjU2kM1n6H3IYp91wM4bppaWlherqarGHjCB8TeKdU0Q+n4+WlhY0TSOZTAKfz3f0+/2kdZ2+l95Ew6rl6JkE4d27aHp2HumWZiqGHUho5PeQolFSiQSxfvuyfOW/OOCYk+gxcDD1G+vwe71I/ko+XPgyn6xdjb+2O2b/fek2dAQ9DziAroMGu6uAh0IhsditIHwDIhyLKJlMEgwGicfj7kK32WwWx3FIpVIYXoOWaAv+gw4iGoniGyaxffOH1IaqSTggBSswRv2ArhWVVLc2M2zHR3hDIbSe+zBg4BCi0SjBYBC9W3eGnn0eGcumukdPkGWy2SzpdBrIrSsZDoeprq4WASkIX5MIxyKS20JKkiR375jcPagOKzavYFdsF79+9tdsb/iMoOynyqhGR6exoYFUPEMymuCS0y7hmO+PIWxuY8WWf1L31j+YPvMhhgw7DEVRsG0bf00tHo8nt9J325qR+dcH3D1syuX+V0HojEQ4FpFhGCQSCVRVJZPJIEkSG+o/5KX3X+J/Xv8fGpoaIO0QUAP07zoAXdIxZZOBXQag2hqSBR9s+oDXdj5Jj6penHXCz9i05HkWPvtbuna7l+qufUin024wKopCOp1GkiQkSULXdeDz2xhFOArC1yeuuYooFosRDAZxHAfDMNgV38mFf76Q3y/5Pbqs0be6L0O6D0GXdTZu/5CGpga6VHSh0l9Fz9oemIZJpGoriVaH/z71XiacPIV99xtOy84PeGnulax9+x/uSLiu62QyGUzTdEMxkUgQj8dRFIVIJFJWCwQIQmcjWo5F5C5Z5jdZv/MDfvSHsXgcg+6BbuhoGHjxySYDqweiZGWWr16OIil0ra3h6UXzOPjE/oRXZtkdTjCg975IksSR42fwg+Mm8Nc/TGPh45ciy9B/yMHE4zKmadLU1JSbLqSq+P1+FEUhGo1SU1Mj+hsF4RsQ754iymQyaLrGim0rOP3J02mINmJbFhVGiEw6i0fR2b/XAdQGahm8zxB+OOowPo6v5cVdjzHimMGsrPuUeMBi4riJaGrufuVAsIIufUZzxImXU2GmWPnsRbz68CQ0RcWyLHRdd0Mwk8mQTqdz60gmEmISuCB8A6LlWES2bfNh0yZufX0WdtZhQPUAPLZONBnD1E1Mzcenuz/ls9gWPF36sbX1A3oNrWLXThkLD/t06cGwriO47sgbAdw1IZEUeu4/Fl+oikhrhKouPXDauhNlWXb7HPM1tP9vQRC+HhGORZQgzrS/X0iVUcWJ+49jSJchbP5sK08t+zMD9w3h8WXZuGYHSs8sSsUAfvCDw5BUlZCvK93NfbDSDifudxqGnBtw0TSNaDRKly5d8AS6s+/3JtPU2EgoFCIcDrt71miahqZpbisyFovh8/lEQArCNyDCsYjq4x/z42MORJIldqbeZ7OzkWxfh/P6/oAaT09kDHr1raPG6EGVpxuqpPHvHW9TFajEI2n8eORF9DB74/F4cByHbDZLbW0tsVgMXddpamzENE0ikQher5dUKuUOAFmWRSwWA3IbfTU3N1NRUSHukBGEr0m8c4poYMUIzk/9BiOg83bz3+hi9CErJ/koupod6Y9pSdajmFm8Xo2m9Da6aL0YN2ACWxo3cdmhN5JO5ab/RCIRJEnC4/HQ2NhITU2NO8iye/duqqqqaGlp6XB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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Essensen av Q-Learning: Bellman-ekvationen och inlärningsalgoritmen\n",
+    "\n",
+    "Skriv en pseudokod för vår inlärningsalgoritm:\n",
+    "\n",
+    "* Initiera Q-Tabell Q med lika värden för alla tillstånd och handlingar\n",
+    "* Sätt inlärningshastighet $\\alpha\\leftarrow 1$\n",
+    "* Upprepa simuleringen många gånger\n",
+    "   1. Börja på en slumpmässig position\n",
+    "   1. Upprepa\n",
+    "        1. Välj en handling $a$ vid tillstånd $s$\n",
+    "        2. Utför handlingen genom att flytta till ett nytt tillstånd $s'$\n",
+    "        3. Om vi stöter på ett slutspelsvillkor, eller den totala belöningen är för liten - avsluta simuleringen  \n",
+    "        4. Beräkna belöningen $r$ vid det nya tillståndet\n",
+    "        5. Uppdatera Q-Funktionen enligt Bellman-ekvationen: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Uppdatera total belöning och minska $\\alpha$.\n",
+    "\n",
+    "## Exploatera vs. Utforska\n",
+    "\n",
+    "Det bästa tillvägagångssättet är att balansera mellan utforskning och exploatering. När vi lär oss mer om vår miljö, kommer vi vara mer benägna att följa den optimala vägen, men det är också viktigt att välja den outforskade vägen då och då.\n",
+    "\n",
+    "## Python-implementation\n",
+    "\n",
+    "Nu är vi redo att implementera inlärningsalgoritmen. Innan dess behöver vi också en funktion som kan konvertera godtyckliga värden i Q-Tabellen till en sannolikhetsvektor för motsvarande handlingar:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "source": [
+    "Vi lägger till en liten mängd `eps` till den ursprungliga vektorn för att undvika division med 0 i det initiala fallet, när alla komponenter i vektorn är identiska.\n",
+    "\n",
+    "Den faktiska inlärningsalgoritmen kommer vi att köra i 5000 experiment, även kallade **epoker**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    m.random_start()\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        dpos = actions[a]\n",
+    "        m.move(dpos,check_correctness=False) # we allow player to move outside the board, which terminates episode\n",
+    "        r = reward(m)\n",
+    "        cum_reward += r\n",
+    "        if r==end_reward or cum_reward < -1000:\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "source": [
+    "Efter att ha kört denna algoritm bör Q-Tabellen uppdateras med värden som definierar attraktiviteten hos olika åtgärder vid varje steg. Visualisera tabellen här:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p31952a86ca)\">\n    <image height=\"327\" id=\"image3fd90cd4cb\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kontrollera policyn\n",
+    "\n",
+    "Eftersom Q-Tabellen listar \"attraktiviteten\" för varje handling i varje tillstånd, är det ganska enkelt att använda den för att definiera den effektiva navigeringen i vår värld. I det enklaste fallet kan vi helt enkelt välja den handling som motsvarar det högsta värdet i Q-Tabellen:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "def qpolicy_strict(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = list(actions)[np.argmax(v)]\n",
+    "        return a\n",
+    "\n",
+    "walk(m,qpolicy_strict)"
+   ]
+  },
+  {
+   "source": [
+    "Om du testar koden ovan flera gånger, kanske du märker att den ibland bara \"fastnar\", och du måste trycka på STOP-knappen i notebooken för att avbryta den.\n",
+    "\n",
+    "> **Uppgift 1:** Ändra `walk`-funktionen så att den begränsar den maximala längden på vägen till ett visst antal steg (till exempel 100), och observera hur koden ovan returnerar detta värde då och då.\n",
+    "\n",
+    "> **Uppgift 2:** Ändra `walk`-funktionen så att den inte går tillbaka till platser där den redan har varit tidigare. Detta kommer att förhindra att `walk` hamnar i en loop, men agenten kan fortfarande bli \"fast\" på en plats där den inte kan ta sig vidare.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 3.45, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbab852fd68>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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133.949763 214.756364 \nL 133.980202 214.756364 \nL 134.132399 206.226264 \nL 134.254157 214.756364 \nL 134.284596 214.685867 \nL 134.315036 198.00165 \nL 134.345475 214.756364 \nL 134.375914 199.106098 \nL 134.436793 214.756364 \nL 134.528111 214.497876 \nL 134.558551 214.309885 \nL 134.58899 214.756364 \nL 134.649869 214.685867 \nL 134.680308 213.252434 \nL 134.710748 214.756364 \nL 134.741187 214.756364 \nL 134.802066 212.946949 \nL 134.832506 214.756364 \nL 134.954263 193.184376 \nL 135.076021 214.756364 \nL 135.10646 212.594465 \nL 135.167339 214.756364 \nL 135.289097 214.756364 \nL 135.502172 214.756364 \nL 135.532612 206.766738 \nL 135.593491 214.756364 \nL 135.62393 214.662368 \nL 135.65437 214.756364 \nL 135.745688 195.722257 \nL 135.806567 214.756364 \nL 135.867445 203.923372 \nL 135.989203 214.756364 \nL 136.050082 213.111441 \nL 136.080521 214.756364 \nL 136.110961 214.756364 \nL 136.202279 206.837235 \nL 136.293597 214.756364 \nL 136.324036 213.510922 \nL 136.384915 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138.150401 209.375116 \nL 138.21128 214.756364 \nL 138.363477 206.672743 \nL 138.454795 214.756364 \nL 138.485234 205.967776 \nL 138.546113 210.338571 \nL 138.576553 214.732865 \nL 138.637431 211.41952 \nL 138.667871 207.659697 \nL 138.69831 213.604918 \nL 138.789629 213.299432 \nL 138.820068 214.756364 \nL 138.911386 201.737974 \nL 139.002704 214.756364 \nL 139.033144 213.322931 \nL 139.094023 214.756364 \nL 139.124462 210.033085 \nL 139.21578 210.503063 \nL 139.276659 209.7276 \nL 139.337538 214.756364 \nL 139.398417 212.92345 \nL 139.428856 214.756364 \nL 139.459295 214.756364 \nL 139.520174 204.863328 \nL 139.611493 211.114034 \nL 139.641932 211.255028 \nL 139.672371 210.738052 \nL 139.702811 214.756364 \nL 139.794129 213.957401 \nL 139.824568 214.756364 \nL 139.855008 213.581419 \nL 139.885447 207.119222 \nL 139.915887 214.756364 \nL 139.946326 214.004399 \nL 140.037644 211.325525 \nL 140.128962 214.756364 \nL 140.159402 207.260215 \nL 140.220281 214.380381 \nL 140.25072 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147.221344 214.591871 \nL 147.312663 210.57356 \nL 147.373541 211.231529 \nL 147.403981 214.756364 \nL 147.495299 214.19239 \nL 147.525738 214.732865 \nL 147.556178 208.317666 \nL 147.617057 214.262887 \nL 147.677936 213.816408 \nL 147.799693 214.756364 \nL 147.95189 201.502985 \nL 148.043208 214.756364 \nL 148.073648 211.513516 \nL 148.104087 210.78505 \nL 148.134527 197.79016 \nL 148.164966 214.756364 \nL 148.195405 214.568372 \nL 148.225845 214.756364 \nL 148.347602 211.8425 \nL 148.438921 214.756364 \nL 148.46936 209.093129 \nL 148.530239 212.617964 \nL 148.651997 214.756364 \nL 148.682436 201.549983 \nL 148.743315 214.756364 \nL 148.773754 213.698913 \nL 148.804194 214.756364 \nL 148.834633 202.724928 \nL 148.895512 212.335977 \nL 149.017269 214.756364 \nL 149.047709 214.662368 \nL 149.078148 213.698913 \nL 149.108588 207.213217 \nL 149.169466 214.027898 \nL 149.199906 211.137533 \nL 149.291224 208.787644 \nL 149.321664 214.756364 \nL 149.352103 214.756364 \nL 149.382542 188.484596 \nL 149.443421 206.860734 \nL 149.534739 214.756364 \nL 149.565179 207.401209 \nL 149.626058 214.756364 \nL 149.656497 212.547467 \nL 149.717376 214.756364 \nL 149.778255 214.756364 \nL 149.869573 212.312478 \nL 149.900012 214.262887 \nL 149.930452 207.518703 \nL 149.99133 214.685867 \nL 150.02177 208.740646 \nL 150.052209 206.226264 \nL 150.082649 209.7276 \nL 150.234846 214.756364 \nL 150.265285 201.573482 \nL 150.326164 213.9809 \nL 150.356603 213.792909 \nL 150.387043 214.756364 \nL 150.417482 209.539608 \nL 150.478361 213.651915 \nL 150.5088 213.487423 \nL 150.53924 214.756364 \nL 150.569679 208.811143 \nL 150.630558 214.051397 \nL 150.660997 214.756364 \nL 150.691437 207.659697 \nL 150.752316 214.756364 \nL 150.843634 212.852953 \nL 150.904513 213.628417 \nL 150.934952 214.756364 \nL 151.02627 208.388162 \nL 151.05671 209.116628 \nL 151.117589 214.756364 \nL 151.148028 213.463924 \nL 151.178467 204.275855 \nL 151.239346 214.732865 \nL 151.269786 208.01218 \nL 151.300225 211.889498 \nL 151.330664 190.435005 \nL 151.361104 214.756364 \nL 151.391543 211.748505 \nL 151.421983 212.429973 \nL 151.452422 204.322853 \nL 151.482861 214.756364 \nL 151.513301 207.80069 \nL 151.665498 214.756364 \nL 151.726377 208.975635 \nL 151.756816 210.15058 \nL 151.787256 214.756364 \nL 151.817695 211.748505 \nL 151.848134 206.155767 \nL 151.909013 214.521375 \nL 151.969892 214.756364 \nL 152.000331 214.145392 \nL 152.06121 214.756364 \nL 152.182968 197.625667 \nL 152.243847 214.756364 \nL 152.304725 213.181937 \nL 152.335165 212.71196 \nL 152.365604 214.756364 \nL 152.426483 205.897279 \nL 152.456923 214.756364 \nL 152.548241 208.458659 \nL 152.57868 213.111441 \nL 152.60912 214.756364 \nL 152.700438 214.19239 \nL 152.730877 214.215889 \nL 152.822195 214.756364 \nL 152.852635 208.364663 \nL 152.913514 214.121893 \nL 152.943953 184.654276 \nL 153.065711 214.756364 \nL 153.09615 210.926043 \nL 153.157029 214.756364 \nL 153.187468 214.756364 \nL 153.248347 206.320259 \nL 153.309226 207.706694 \nL 153.370105 214.756364 \nL 153.430984 213.158439 \nL 153.461423 213.816408 \nL 153.491862 207.518703 \nL 153.522302 214.756364 \nL 153.552741 212.617964 \nL 153.644059 214.756364 \nL 153.674499 214.356882 \nL 153.704938 214.756364 \nL 153.735378 213.839907 \nL 153.765817 207.542202 \nL 153.796256 214.756364 \nL 153.826696 214.239388 \nL 153.887575 214.756364 \nL 153.918014 204.839829 \nL 153.978893 213.792909 \nL 154.039772 214.756364 \nL 154.070211 213.322931 \nL 154.100651 188.790082 \nL 154.161529 214.215889 \nL 154.191969 214.756364 \nL 154.222408 200.11655 \nL 154.283287 202.630932 \nL 154.405045 214.756364 \nL 154.435484 214.756364 \nL 154.465923 214.591871 \nL 154.496363 208.975635 \nL 154.526802 214.756364 \nL 154.557242 214.756364 \nL 154.587681 214.756364 \nL 154.678999 213.698913 \nL 154.739878 214.756364 \nL 154.770317 214.145392 \nL 154.800757 213.13494 \nL 154.831196 214.756364 \nL 154.861636 214.756364 \nL 154.892075 205.02782 \nL 154.952954 214.756364 \nL 155.013833 213.534421 \nL 155.044272 206.461253 \nL 155.105151 214.756364 \nL 155.16603 214.756364 \nL 155.196469 204.487345 \nL 155.257348 214.756364 \nL 155.379106 213.228935 \nL 155.439984 211.959995 \nL 155.561742 214.756364 \nL 155.622621 214.756364 \nL 155.713939 205.215811 \nL 155.805257 214.756364 \nL 155.835697 202.865921 \nL 155.896576 212.641463 \nL 156.018333 214.756364 \nL 156.048773 213.322931 \nL 156.109651 214.756364 \nL 156.140091 196.356727 \nL 156.261848 214.756364 \nL 156.322727 207.777191 \nL 156.353167 213.040944 \nL 156.414045 214.756364 \nL 156.566243 204.017367 \nL 156.657561 214.756364 \nL 156.688 214.239388 \nL 156.748879 214.756364 \nL 156.779318 208.035679 \nL 156.840197 207.80069 \nL 156.870637 214.756364 \nL 156.961955 202.912919 \nL 157.022834 214.756364 \nL 157.083712 214.356882 \nL 157.175031 200.539531 \nL 157.23591 214.756364 \nL 157.296788 207.847688 \nL 157.357667 214.756364 \nL 157.388107 204.463846 \nL 157.418546 211.278527 \nL 157.448985 210.103582 \nL 157.479425 213.816408 \nL 157.570743 213.534421 \nL 157.631622 214.756364 \nL 157.692501 207.988681 \nL 157.753379 214.756364 \nL 157.814258 214.521375 \nL 157.844698 214.756364 \nL 157.996895 205.403802 \nL 158.027334 205.521297 \nL 158.149092 214.732865 \nL 158.179531 214.756364 \nL 158.209971 204.510844 \nL 158.270849 211.983494 \nL 158.392607 214.756364 \nL 158.453486 214.756364 \nL 158.483925 212.970447 \nL 158.544804 213.416926 \nL 158.605683 214.756364 \nL 158.666562 210.503063 \nL 158.697001 214.756364 \nL 158.72744 213.816408 \nL 158.788319 214.756364 \nL 158.818759 214.756364 \nL 158.940516 210.832048 \nL 158.970956 211.372522 \nL 159.092713 192.620402 \nL 159.031835 213.76941 \nL 159.123153 200.32804 \nL 159.24491 214.756364 \nL 159.366668 212.194984 \nL 159.397107 212.805955 \nL 159.427547 212.335977 \nL 159.457986 214.756364 \nL 159.518865 212.852953 \nL 159.549304 212.429973 \nL 159.640623 214.756364 \nL 159.671062 214.709366 \nL 159.701502 213.675414 \nL 159.731941 214.756364 \nL 159.76238 214.685867 \nL 159.79282 202.25495 \nL 159.853699 210.855547 \nL 159.975456 214.756364 \nL 160.066774 211.865999 \nL 160.158093 214.756364 \nL 160.27985 211.325525 \nL 160.340729 207.565701 \nL 160.371168 214.756364 \nL 160.492926 200.657025 \nL 160.584244 214.168891 \nL 160.614684 209.28112 \nL 160.736441 214.732865 \nL 160.675563 208.670149 \nL 160.766881 214.568372 \nL 160.858199 214.756364 \nL 160.888638 197.155689 \nL 161.010396 214.756364 \nL 161.071275 214.756364 \nL 161.101714 208.905138 \nL 161.162593 214.756364 \nL 161.284351 211.678008 \nL 161.375669 211.231529 \nL 161.406108 214.756364 \nL 161.527866 209.892092 \nL 161.588745 208.411661 \nL 161.649624 214.756364 \nL 161.740942 213.416926 \nL 161.771381 214.756364 \nL 161.801821 213.440425 \nL 161.83226 207.142721 \nL 161.893139 214.756364 \nL 162.045336 214.756364 \nL 162.167094 208.153173 \nL 162.319291 214.662368 \nL 162.410609 214.756364 \nL 162.441048 205.192312 \nL 162.593245 214.756364 \nL 162.745442 205.591794 \nL 162.775882 214.756364 \nL 162.8672 212.899951 \nL 162.958518 214.756364 \nL 162.928079 212.171485 \nL 162.988958 213.675414 \nL 163.049836 214.756364 \nL 163.080276 213.55792 \nL 163.110715 210.949542 \nL 163.171594 214.756364 \nL 163.202033 214.662368 \nL 163.232473 213.040944 \nL 163.262912 197.696164 \nL 163.323791 214.756364 \nL 163.35423 208.693648 \nL 163.415109 213.910403 \nL 163.445549 214.756364 \nL 163.567306 207.119222 \nL 163.719503 214.756364 \nL 163.749943 214.638869 \nL 163.780382 208.059178 \nL 163.841261 214.756364 \nL 163.8717 212.852953 \nL 163.90214 214.756364 \nL 163.932579 199.670071 \nL 163.993458 209.234123 \nL 164.023897 213.76941 \nL 164.115216 212.476971 \nL 164.145655 214.61537 \nL 164.176094 211.63101 \nL 164.206534 212.735458 \nL 164.236973 212.641463 \nL 164.328291 214.756364 \nL 164.358731 213.510922 \nL 164.38917 214.40388 \nL 164.480489 209.915591 \nL 164.510928 211.725006 \nL 164.541367 214.40388 \nL 164.571807 207.424708 \nL 164.602246 212.735458 \nL 164.663125 209.915591 \nL 164.693564 212.71196 \nL 164.724004 212.970447 \nL 164.784883 211.255028 \nL 164.845761 214.756364 \nL 164.876201 214.756364 \nL 165.028398 193.983338 \nL 165.119716 214.756364 \nL 165.150155 213.393428 \nL 165.180595 211.889498 \nL 165.211034 213.487423 \nL 165.241474 214.756364 \nL 165.271913 207.683195 \nL 165.363231 212.147986 \nL 165.393671 214.756364 \nL 165.42411 200.915513 \nL 165.484989 214.756364 \nL 165.515428 214.756364 \nL 165.576307 210.291573 \nL 165.637186 212.382975 \nL 165.758944 214.756364 \nL 165.819822 212.241982 \nL 165.850262 214.756364 \nL 165.880701 214.756364 \nL 165.911141 213.581419 \nL 165.972019 214.662368 \nL 166.032898 214.756364 \nL 166.063338 214.309885 \nL 166.124217 214.756364 \nL 166.154656 214.61537 \nL 166.185095 214.756364 \nL 166.215535 212.006993 \nL 166.276414 213.487423 \nL 166.306853 214.756364 \nL 166.337292 211.912997 \nL 166.367732 214.239388 \nL 166.428611 214.709366 \nL 166.45905 206.273262 \nL 166.580808 214.756364 \nL 166.611247 212.288979 \nL 166.672126 213.55792 \nL 166.733005 214.756364 \nL 166.885202 209.445613 \nL 167.037399 214.756364 \nL 167.159156 212.500469 \nL 167.220035 214.756364 \nL 167.250475 204.957323 \nL 167.311353 214.756364 \nL 167.433111 212.547467 \nL 167.46355 214.40388 \nL 167.49399 207.80069 \nL 167.585308 207.965182 \nL 167.615747 214.756364 \nL 167.646187 195.22878 \nL 167.707066 214.756364 \nL 167.767945 214.756364 \nL 167.798384 211.865999 \nL 167.859263 214.098395 \nL 167.920142 214.756364 \nL 167.950581 213.299432 \nL 168.01146 214.756364 \nL 168.041899 214.732865 \nL 168.072339 214.333383 \nL 168.163657 193.818846 \nL 168.285414 214.756364 \nL 168.315854 208.787644 \nL 168.376733 211.184531 \nL 168.407172 214.756364 \nL 168.437611 214.450878 \nL 168.468051 203.429895 \nL 168.52893 214.756364 \nL 168.589809 212.100988 \nL 168.620248 212.147986 \nL 168.650687 214.756364 \nL 168.681127 211.513516 \nL 168.742006 213.463924 \nL 168.772445 210.526562 \nL 168.802884 184.325291 \nL 168.863763 214.685867 \nL 168.924642 214.756364 \nL 168.955081 214.027898 \nL 168.985521 209.351617 \nL 169.0464 214.756364 \nL 169.076839 214.756364 \nL 169.107278 207.612699 \nL 169.168157 214.591871 \nL 169.198597 213.628417 \nL 169.229036 214.756364 \nL 169.259475 214.756364 \nL 169.320354 214.027898 \nL 169.442112 200.398537 \nL 169.381233 214.756364 \nL 169.472551 206.132268 \nL 169.56387 214.756364 \nL 169.685627 200.586528 \nL 169.807385 214.756364 \nL 169.868264 214.756364 \nL 169.898703 214.568372 \nL 169.929142 207.824189 \nL 169.959582 214.756364 \nL 169.990021 214.756364 \nL 170.0509 211.396021 \nL 170.081339 214.756364 \nL 170.233537 195.886749 \nL 170.263976 214.756364 \nL 170.355294 213.228935 \nL 170.416173 212.26548 \nL 170.446612 213.76941 \nL 170.477052 213.510922 \nL 170.56837 200.563029 \nL 170.629249 203.782378 \nL 170.751006 214.756364 \nL 170.842325 206.907732 \nL 170.872764 207.824189 \nL 170.994522 214.756364 \nL 171.024961 207.753692 \nL 171.08584 214.662368 \nL 171.116279 214.756364 \nL 171.268476 212.71196 \nL 171.298916 214.638869 \nL 171.329355 210.738052 \nL 171.359795 209.351617 \nL 171.481552 214.709366 \nL 171.511992 214.756364 \nL 171.542431 214.286386 \nL 171.57287 206.884233 \nL 171.60331 214.756364 \nL 171.664189 209.116628 \nL 171.755507 214.756364 \nL 171.785946 214.286386 \nL 171.877265 214.756364 \nL 171.907704 212.124487 \nL 171.938143 214.756364 \nL 171.968583 206.437754 \nL 172.059901 211.396021 \nL 172.09034 213.651915 \nL 172.12078 213.534421 \nL 172.151219 205.756286 \nL 172.181659 214.756364 \nL 172.212098 214.756364 \nL 172.272977 214.756364 \nL 172.303416 213.252434 \nL 172.364295 214.333383 \nL 172.394734 214.756364 \nL 172.486053 208.43516 \nL 172.577371 206.343758 \nL 172.60781 214.756364 \nL 172.63825 191.680446 \nL 172.699129 214.756364 \nL 172.729568 214.333383 \nL 172.760007 214.756364 \nL 172.790447 211.466518 \nL 172.851326 214.756364 \nL 172.912204 214.756364 \nL 172.942644 208.623151 \nL 173.033962 211.772004 \nL 173.12528 214.756364 \nL 173.15572 212.030492 \nL 173.216598 214.756364 \nL 173.338356 214.709366 \nL 173.368796 202.724928 \nL 173.429674 214.756364 \nL 173.460114 214.756364 \nL 173.581871 205.709288 \nL 173.703629 214.756364 \nL 173.734068 214.756364 \nL 173.764508 214.544874 \nL 173.855826 214.662368 \nL 173.977584 214.756364 \nL 174.129781 210.855547 \nL 174.16022 204.134862 \nL 174.19066 214.638869 \nL 174.221099 214.215889 \nL 174.251538 210.620558 \nL 174.281978 214.756364 \nL 174.312417 214.756364 \nL 174.342857 214.756364 \nL 174.464614 204.745833 \nL 174.495054 207.871187 \nL 174.616811 214.756364 \nL 174.647251 214.709366 \nL 174.67769 214.145392 \nL 174.708129 214.709366 \nL 174.769008 203.429895 \nL 174.799448 214.756364 \nL 174.890766 214.756364 \nL 174.982084 213.416926 \nL 175.012524 214.756364 \nL 175.042963 211.560514 \nL 175.073402 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177.112843 214.756364 \nL 177.143282 214.027898 \nL 177.173721 208.270668 \nL 177.2346 208.811143 \nL 177.295479 214.756364 \nL 177.356358 212.241982 \nL 177.417237 214.756364 \nL 177.447676 214.568372 \nL 177.508555 208.106176 \nL 177.538994 214.756364 \nL 177.569434 212.359476 \nL 177.599873 212.335977 \nL 177.660752 214.756364 \nL 177.691191 200.022555 \nL 177.78251 214.756364 \nL 177.812949 213.087942 \nL 177.843388 214.756364 \nL 177.873828 214.638869 \nL 177.904267 196.427223 \nL 177.965146 214.756364 \nL 177.995585 208.599652 \nL 178.056464 209.892092 \nL 178.178222 214.756364 \nL 178.208661 214.756364 \nL 178.29998 208.482158 \nL 178.421737 214.756364 \nL 178.482616 211.443019 \nL 178.513055 214.756364 \nL 178.573934 212.030492 \nL 178.634813 213.158439 \nL 178.665252 214.756364 \nL 178.756571 205.380303 \nL 178.847889 214.756364 \nL 178.878328 207.683195 \nL 178.939207 214.756364 \nL 178.969647 214.756364 \nL 179.030525 210.879046 \nL 179.060965 214.19239 \nL 179.121844 212.993946 \nL 179.182722 210.78505 \nL 179.213162 214.756364 \nL 179.243601 210.432566 \nL 179.274041 202.395943 \nL 179.30448 214.756364 \nL 179.334919 214.756364 \nL 179.365359 214.756364 \nL 179.395798 208.129674 \nL 179.456677 214.215889 \nL 179.487116 214.756364 \nL 179.517556 211.114034 \nL 179.578435 214.756364 \nL 179.608874 214.756364 \nL 179.669753 196.544718 \nL 179.730632 212.05399 \nL 179.791511 214.732865 \nL 179.82195 214.168891 \nL 179.852389 200.821517 \nL 179.913268 214.756364 \nL 180.035026 208.576154 \nL 180.126344 214.756364 \nL 180.156783 214.732865 \nL 180.187223 214.427379 \nL 180.217662 214.756364 \nL 180.278541 203.735381 \nL 180.30898 213.158439 \nL 180.369859 214.756364 \nL 180.400299 214.239388 \nL 180.430738 204.769332 \nL 180.491617 213.933902 \nL 180.552496 214.756364 \nL 180.674253 205.920778 \nL 180.735132 214.756364 \nL 180.765572 211.748505 \nL 180.796011 204.275855 \nL 180.82645 214.756364 \nL 180.85689 211.278527 \nL 180.887329 213.910403 \nL 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204.299354 \nL 187.066529 210.879046 \nL 187.218726 214.756364 \nL 187.340484 211.748505 \nL 187.370923 214.380381 \nL 187.431802 201.808471 \nL 187.462241 214.756364 \nL 187.492681 211.137533 \nL 187.52312 214.756364 \nL 187.553559 209.06963 \nL 187.614438 214.709366 \nL 187.644878 195.369773 \nL 187.736196 196.403725 \nL 187.827514 214.756364 \nL 187.857954 213.675414 \nL 187.888393 214.756364 \nL 188.010151 209.892092 \nL 188.04059 214.544874 \nL 188.101469 187.732631 \nL 188.131908 214.756364 \nL 188.162348 213.017445 \nL 188.192787 202.88942 \nL 188.223226 214.427379 \nL 188.253666 213.158439 \nL 188.344984 214.756364 \nL 188.405863 200.069553 \nL 188.466742 212.359476 \nL 188.52762 214.756364 \nL 188.588499 187.591638 \nL 188.679818 214.756364 \nL 188.710257 214.380381 \nL 188.771136 211.63101 \nL 188.801575 214.286386 \nL 188.832015 214.756364 \nL 188.862454 214.732865 \nL 188.923333 214.756364 \nL 188.984212 202.771926 \nL 189.07553 214.756364 \nL 189.105969 211.278527 \nL 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214.756364 \nL 191.023652 214.356882 \nL 191.054091 201.385491 \nL 191.084531 214.568372 \nL 191.14541 208.64665 \nL 191.267167 214.732865 \nL 191.328046 211.959995 \nL 191.358485 214.756364 \nL 191.388925 214.427379 \nL 191.449804 214.638869 \nL 191.541122 203.594387 \nL 191.571561 213.675414 \nL 191.63244 202.701429 \nL 191.662879 198.283636 \nL 191.784637 214.732865 \nL 191.815077 208.200171 \nL 191.845516 214.756364 \nL 191.906395 210.291573 \nL 191.936834 214.756364 \nL 191.967274 210.174079 \nL 191.997713 199.294089 \nL 192.058592 214.756364 \nL 192.119471 213.205436 \nL 192.14991 214.756364 \nL 192.210789 214.309885 \nL 192.241228 214.756364 \nL 192.271668 203.805877 \nL 192.332546 210.197577 \nL 192.454304 214.756364 \nL 192.606501 208.928637 \nL 192.728259 214.756364 \nL 192.789138 214.286386 \nL 192.819577 201.1975 \nL 192.850016 214.756364 \nL 192.880456 213.651915 \nL 192.941335 211.231529 \nL 192.971774 212.26548 \nL 193.032653 214.756364 \nL 193.15441 190.622996 \nL 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209.774597 \nL 195.467805 208.881639 \nL 195.498245 214.756364 \nL 195.528684 205.944277 \nL 195.589563 214.638869 \nL 195.680881 206.508251 \nL 195.711321 214.756364 \nL 195.802639 211.490017 \nL 195.833078 214.756364 \nL 195.863518 188.837079 \nL 195.924397 214.544874 \nL 196.046154 181.411428 \nL 196.167912 214.756364 \nL 196.289669 200.281043 \nL 196.380988 214.756364 \nL 196.350548 179.696008 \nL 196.411427 213.181937 \nL 196.441866 211.114034 \nL 196.472306 214.756364 \nL 196.502745 212.641463 \nL 196.533185 212.547467 \nL 196.563624 213.76941 \nL 196.594063 211.983494 \nL 196.624503 210.385569 \nL 196.685382 214.756364 \nL 196.746261 213.393428 \nL 196.807139 214.756364 \nL 196.837579 214.568372 \nL 196.898458 214.756364 \nL 196.928897 209.304619 \nL 197.020215 214.756364 \nL 197.050655 212.171485 \nL 197.141973 214.756364 \nL 197.172412 183.526329 \nL 197.29417 214.756364 \nL 197.415928 213.675414 \nL 197.476806 214.756364 \nL 197.507246 214.333383 \nL 197.568125 205.051319 \nL 197.598564 206.555248 \nL 197.629003 214.756364 \nL 197.689882 207.260215 \nL 197.720322 196.803206 \nL 197.750761 214.756364 \nL 197.7812 214.756364 \nL 197.81164 211.302026 \nL 197.872519 214.756364 \nL 197.902958 214.756364 \nL 197.933397 175.842189 \nL 197.994276 213.487423 \nL 198.055155 212.382975 \nL 198.116034 214.756364 \nL 198.176913 187.168658 \nL 198.237792 204.910326 \nL 198.268231 214.756364 \nL 198.32911 206.061771 \nL 198.359549 203.594387 \nL 198.389989 186.111207 \nL 198.450867 214.756364 \nL 198.542186 202.654431 \nL 198.572625 213.55792 \nL 198.603064 213.463924 \nL 198.633504 214.756364 \nL 198.663943 207.542202 \nL 198.724822 214.568372 \nL 198.81614 214.756364 \nL 198.877019 206.320259 \nL 198.907458 214.521375 \nL 198.998777 213.886904 \nL 199.029216 200.516032 \nL 199.090095 201.291495 \nL 199.181413 214.756364 \nL 199.211853 213.087942 \nL 199.242292 214.756364 \nL 199.272731 213.863406 \nL 199.303171 189.753537 \nL 199.394489 205.004321 \nL 199.485807 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205.02782 \nL 204.325673 202.536937 \nL 204.447431 214.756364 \nL 204.47787 214.756364 \nL 204.508309 211.114034 \nL 204.569188 212.664962 \nL 204.660507 214.756364 \nL 204.751825 206.484752 \nL 204.843143 214.756364 \nL 204.873582 213.322931 \nL 204.904022 211.20803 \nL 204.934461 214.756364 \nL 204.964901 183.338337 \nL 205.025779 214.756364 \nL 205.056219 214.521375 \nL 205.117098 214.756364 \nL 205.147537 201.455988 \nL 205.208416 214.756364 \nL 205.360613 214.756364 \nL 205.391052 199.0591 \nL 205.451931 214.756364 \nL 205.482371 213.722412 \nL 205.543249 214.709366 \nL 205.573689 214.756364 \nL 205.665007 207.448206 \nL 205.695446 189.988525 \nL 205.756325 212.617964 \nL 205.786765 214.756364 \nL 205.817204 203.852875 \nL 205.878083 211.443019 \nL 205.938962 214.756364 \nL 205.99984 214.662368 \nL 206.03028 214.756364 \nL 206.152037 191.492455 \nL 206.273795 214.756364 \nL 206.304235 197.625667 \nL 206.365113 214.685867 \nL 206.395553 214.544874 \nL 206.425992 208.411661 \nL 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209.657103 \nL 208.404554 214.756364 \nL 208.434993 210.268074 \nL 208.465432 207.941683 \nL 208.495872 213.416926 \nL 208.556751 214.756364 \nL 208.58719 203.500392 \nL 208.678508 207.354211 \nL 208.708948 206.29676 \nL 208.800266 213.463924 \nL 208.830705 212.429973 \nL 208.861145 203.946871 \nL 208.891584 214.732865 \nL 208.952463 208.317666 \nL 208.982902 213.792909 \nL 209.013342 207.777191 \nL 209.043781 212.899951 \nL 209.135099 214.756364 \nL 209.165539 194.688305 \nL 209.226418 214.756364 \nL 209.287296 212.899951 \nL 209.317736 214.756364 \nL 209.348175 209.586606 \nL 209.409054 213.463924 \nL 209.439493 214.756364 \nL 209.469933 201.573482 \nL 209.530812 213.534421 \nL 209.561251 214.286386 \nL 209.591691 204.064365 \nL 209.652569 209.774597 \nL 209.713448 207.424708 \nL 209.774327 214.756364 \nL 209.804766 206.578747 \nL 209.865645 214.450878 \nL 209.896085 214.756364 \nL 209.987403 208.670149 \nL 210.017842 214.756364 \nL 210.10916 214.215889 \nL 210.1396 208.082677 \nL 210.200479 212.312478 \nL 210.261357 214.756364 \nL 210.291797 213.628417 \nL 210.322236 197.625667 \nL 210.352676 214.756364 \nL 210.383115 214.756364 \nL 210.413555 208.22367 \nL 210.474433 214.544874 \nL 210.504873 214.756364 \nL 210.535312 214.121893 \nL 210.596191 208.85814 \nL 210.62663 210.244575 \nL 210.717949 214.756364 \nL 210.748388 193.066881 \nL 210.809267 214.756364 \nL 210.931024 206.108769 \nL 211.052782 214.756364 \nL 211.113661 214.756364 \nL 211.204979 209.328118 \nL 211.296297 214.756364 \nL 211.326737 209.022633 \nL 211.418055 210.244575 \nL 211.448494 211.701507 \nL 211.509373 213.275933 \nL 211.539813 189.965027 \nL 211.570252 214.756364 \nL 211.66157 201.573482 \nL 211.69201 214.732865 \nL 211.722449 191.915435 \nL 211.752888 211.396021 \nL 211.813767 195.722257 \nL 211.844207 214.756364 \nL 211.874646 214.40388 \nL 211.905086 206.014774 \nL 211.935525 214.756364 \nL 211.965964 214.027898 \nL 211.996404 213.252434 \nL 212.026843 214.756364 \nL 212.057283 198.84761 \nL 212.118161 212.899951 \nL 212.148601 211.701507 \nL 212.17904 199.411583 \nL 212.20948 214.756364 \nL 212.239919 214.333383 \nL 212.300798 214.756364 \nL 212.331237 199.952058 \nL 212.392116 208.85814 \nL 212.422555 211.983494 \nL 212.452995 182.468878 \nL 212.483434 213.158439 \nL 212.513874 203.194906 \nL 212.544313 214.756364 \nL 212.635631 212.735458 \nL 212.666071 206.437754 \nL 212.72695 210.409068 \nL 212.757389 214.756364 \nL 212.818268 213.322931 \nL 212.848707 211.725006 \nL 212.879147 198.236639 \nL 212.909586 214.756364 \nL 212.940025 199.223592 \nL 213.000904 214.756364 \nL 213.061783 212.171485 \nL 213.092222 207.307213 \nL 213.122662 214.756364 \nL 213.153101 210.009586 \nL 213.183541 214.756364 \nL 213.244419 208.341165 \nL 213.274859 214.568372 \nL 213.335738 214.756364 \nL 213.396616 213.510922 \nL 213.427056 214.756364 \nL 213.457495 212.735458 \nL 213.487935 213.9809 \nL 213.518374 209.234123 \nL 213.579253 214.756364 \nL 213.609692 214.756364 \nL 213.761889 197.155689 \nL 213.883647 214.756364 \nL 213.914086 214.756364 \nL 213.944526 214.497876 \nL 214.005405 214.756364 \nL 214.035844 214.756364 \nL 214.066283 214.61537 \nL 214.127162 214.756364 \nL 214.188041 209.422114 \nL 214.309799 214.756364 \nL 214.370678 209.539608 \nL 214.401117 214.662368 \nL 214.431556 214.756364 \nL 214.522875 194.218327 \nL 214.553314 210.244575 \nL 214.614193 209.539608 \nL 214.675072 214.756364 \nL 214.76639 213.275933 \nL 214.857708 214.756364 \nL 214.827269 212.92345 \nL 214.888147 214.450878 \nL 214.918587 204.087864 \nL 215.009905 210.197577 \nL 215.101223 214.756364 \nL 215.131663 214.732865 \nL 215.192542 214.756364 \nL 215.25342 206.484752 \nL 215.28386 212.05399 \nL 215.314299 195.745755 \nL 215.375178 203.006915 \nL 215.496936 214.756364 \nL 215.527375 206.531749 \nL 215.588254 214.756364 \nL 215.618693 214.756364 \nL 215.649133 206.790237 \nL 215.710011 214.732865 \nL 215.77089 214.756364 \nL 215.831769 213.534421 \nL 215.862208 199.90506 \nL 215.923087 208.717147 \nL 216.014406 214.756364 \nL 216.044845 212.92345 \nL 216.105724 202.842423 \nL 216.136163 214.756364 \nL 216.227481 206.978229 \nL 216.257921 214.756364 \nL 216.349239 214.709366 \nL 216.440557 202.372445 \nL 216.410118 214.756364 \nL 216.470997 211.889498 \nL 216.531875 209.868593 \nL 216.592754 214.215889 \nL 216.684072 205.544796 \nL 216.775391 214.756364 \nL 216.80583 206.790237 \nL 216.866709 214.756364 \nL 216.927588 211.396021 \nL 216.958027 186.628183 \nL 216.988467 214.591871 \nL 217.018906 212.758957 \nL 217.049345 209.398615 \nL 217.110224 213.863406 \nL 217.262421 214.756364 \nL 217.292861 201.549983 \nL 217.353739 213.463924 \nL 217.384179 213.675414 \nL 217.414618 200.398537 \nL 217.445058 214.521375 \nL 217.475497 211.560514 \nL 217.505937 214.756364 \nL 217.597255 212.735458 \nL 217.658134 214.756364 \nL 217.719012 202.489939 \nL 217.779891 212.077489 \nL 217.84077 210.832048 \nL 217.932088 214.756364 \nL 217.962528 211.349023 \nL 218.023406 212.876452 \nL 218.114725 214.756364 \nL 218.145164 214.732865 \nL 218.206043 214.756364 \nL 218.297361 200.610027 \nL 218.388679 214.756364 \nL 218.419119 207.095723 \nL 218.540876 214.756364 \nL 218.571316 214.591871 \nL 218.632195 203.077411 \nL 218.662634 211.678008 \nL 218.693073 214.756364 \nL 218.723513 201.385491 \nL 218.784392 211.043538 \nL 218.87571 214.756364 \nL 218.936589 201.878968 \nL 218.967028 214.756364 \nL 218.997467 214.756364 \nL 219.027907 201.737974 \nL 219.088786 214.756364 \nL 219.149665 214.756364 \nL 219.180104 197.696164 \nL 219.240983 214.756364 \nL 219.271422 213.087942 \nL 219.301862 214.450878 \nL 219.332301 195.581263 \nL 219.39318 210.503063 \nL 219.423619 210.080083 \nL 219.454059 212.030492 \nL 219.514937 202.771926 \nL 219.545377 214.756364 \nL 219.575816 212.547467 \nL 219.636695 214.756364 \nL 219.697574 214.756364 \nL 219.819331 213.487423 \nL 219.91065 214.756364 \nL 219.971529 195.722257 \nL 220.001968 214.732865 \nL 220.032407 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222.102287 214.756364 \nL 222.132726 213.228935 \nL 222.193605 214.756364 \nL 222.224045 197.437676 \nL 222.284923 211.889498 \nL 222.345802 210.78505 \nL 222.406681 214.756364 \nL 222.437121 214.497876 \nL 222.46756 212.406474 \nL 222.497999 214.756364 \nL 222.528439 213.722412 \nL 222.650196 214.756364 \nL 222.680636 214.004399 \nL 222.802393 203.147908 \nL 222.832833 206.08527 \nL 222.863272 214.685867 \nL 222.924151 208.64665 \nL 222.95459 206.437754 \nL 223.076348 214.756364 \nL 223.106787 214.732865 \nL 223.137227 213.205436 \nL 223.167666 194.241826 \nL 223.228545 211.278527 \nL 223.289424 210.597059 \nL 223.350303 214.756364 \nL 223.380742 189.236561 \nL 223.47206 204.722334 \nL 223.532939 211.114034 \nL 223.563379 189.941528 \nL 223.593818 214.732865 \nL 223.624257 211.63101 \nL 223.715576 214.756364 \nL 223.746015 203.735381 \nL 223.806894 211.584012 \nL 223.837333 214.756364 \nL 223.867773 206.08527 \nL 223.928652 209.798096 \nL 223.98953 214.756364 \nL 224.01997 197.672665 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228.068411 214.756364 \nL 228.09885 210.291573 \nL 228.159729 214.756364 \nL 228.190169 214.756364 \nL 228.220608 214.380381 \nL 228.251047 199.505579 \nL 228.281487 214.756364 \nL 228.311926 211.560514 \nL 228.342366 212.852953 \nL 228.372805 190.998978 \nL 228.433684 214.521375 \nL 228.464123 214.61537 \nL 228.494563 214.239388 \nL 228.555441 214.756364 \nL 228.61632 203.335899 \nL 228.677199 192.808393 \nL 228.738078 214.756364 \nL 228.890275 207.330712 \nL 228.920714 213.087942 \nL 228.951154 197.437676 \nL 228.981593 214.756364 \nL 229.012033 208.458659 \nL 229.042472 214.756364 \nL 229.13379 213.322931 \nL 229.225108 214.756364 \nL 229.255548 213.440425 \nL 229.285987 214.756364 \nL 229.346866 203.335899 \nL 229.377305 208.43516 \nL 229.468624 214.756364 \nL 229.529502 191.844938 \nL 229.559942 196.192234 \nL 229.6817 214.756364 \nL 229.712139 213.181937 \nL 229.773018 214.732865 \nL 229.864336 213.228935 \nL 229.894775 214.40388 \nL 229.925215 214.732865 \nL 229.955654 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231.72114 210.221076 \nL 231.782019 214.756364 \nL 231.812458 213.76941 \nL 231.903776 200.892014 \nL 231.934216 212.382975 \nL 231.964655 214.756364 \nL 232.055973 213.910403 \nL 232.086413 214.168891 \nL 232.177731 214.239388 \nL 232.20817 196.450722 \nL 232.23861 214.756364 \nL 232.329928 214.286386 \nL 232.360367 214.732865 \nL 232.451686 200.633526 \nL 232.421246 214.756364 \nL 232.482125 208.43516 \nL 232.543004 214.756364 \nL 232.603883 213.745911 \nL 232.664761 213.275933 \nL 232.72564 197.155689 \nL 232.786519 206.132268 \nL 232.877837 214.756364 \nL 232.847398 198.518625 \nL 232.908277 213.252434 \nL 232.999595 214.756364 \nL 233.030034 211.278527 \nL 233.121353 213.275933 \nL 233.24311 200.563029 \nL 233.27355 210.479564 \nL 233.303989 214.756364 \nL 233.395307 212.570966 \nL 233.425747 208.200171 \nL 233.456186 186.581185 \nL 233.517065 211.20803 \nL 233.547504 214.756364 \nL 233.608383 209.140127 \nL 233.638823 207.871187 \nL 233.669262 188.249607 \nL 233.730141 214.756364 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237.839461 199.881561 \nL 237.8699 214.756364 \nL 237.90034 214.685867 \nL 238.052537 203.382897 \nL 238.143855 214.756364 \nL 238.174294 205.615292 \nL 238.235173 211.137533 \nL 238.296052 214.756364 \nL 238.326491 212.547467 \nL 238.356931 198.84761 \nL 238.38737 214.756364 \nL 238.41781 205.967776 \nL 238.539567 214.756364 \nL 238.661325 198.072146 \nL 238.691764 214.756364 \nL 238.783082 212.241982 \nL 238.813522 200.093051 \nL 238.874401 214.756364 \nL 238.90484 203.547389 \nL 239.057037 214.756364 \nL 239.087476 207.213217 \nL 239.148355 211.090536 \nL 239.270113 214.756364 \nL 239.361431 191.539453 \nL 239.42231 214.756364 \nL 239.483189 205.87378 \nL 239.544068 214.756364 \nL 239.604946 210.009586 \nL 239.635386 214.427379 \nL 239.665825 201.385491 \nL 239.696265 214.756364 \nL 239.726704 211.41952 \nL 239.787583 214.450878 \nL 239.818022 209.445613 \nL 239.878901 214.756364 \nL 240.000659 209.375116 \nL 240.031098 191.492455 \nL 240.091977 214.662368 \nL 240.122416 214.61537 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254.733332 214.756364 \nL 254.763771 214.756364 \nL 254.794211 198.730115 \nL 254.885529 206.813736 \nL 254.946408 214.756364 \nL 254.976847 213.487423 \nL 255.007287 141.345807 \nL 255.068165 214.756364 \nL 255.098605 213.886904 \nL 255.159484 196.239232 \nL 255.189923 212.664962 \nL 255.220362 210.808549 \nL 255.250802 212.735458 \nL 255.281241 212.030492 \nL 255.311681 214.662368 \nL 255.34212 201.925966 \nL 255.402999 214.756364 \nL 255.463878 211.513516 \nL 255.494317 199.599575 \nL 255.555196 214.756364 \nL 255.616075 214.756364 \nL 255.646514 213.275933 \nL 255.676954 213.698913 \nL 255.707393 201.033007 \nL 255.737832 214.732865 \nL 255.768272 211.067037 \nL 255.798711 214.145392 \nL 255.829151 194.194828 \nL 255.85959 214.756364 \nL 255.890029 214.756364 \nL 255.920469 214.756364 \nL 255.950908 191.210468 \nL 256.011787 212.71196 \nL 256.042226 208.811143 \nL 256.103105 212.946949 \nL 256.133545 214.756364 \nL 256.163984 214.638869 \nL 256.224863 167.923061 \nL 256.255302 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288.947226 213.064443 \nL 288.977665 214.756364 \nL 289.038544 214.215889 \nL 289.099423 204.111363 \nL 289.129863 208.811143 \nL 289.190741 214.756364 \nL 289.25162 212.876452 \nL 289.28206 214.756364 \nL 289.434257 196.521219 \nL 289.556014 214.756364 \nL 289.586454 212.218483 \nL 289.677772 212.735458 \nL 289.708211 189.04857 \nL 289.76909 212.852953 \nL 289.829969 214.756364 \nL 289.860408 205.051319 \nL 289.951727 211.231529 \nL 289.982166 210.526562 \nL 290.012605 211.043538 \nL 290.043045 212.735458 \nL 290.073484 208.059178 \nL 290.103924 214.732865 \nL 290.134363 212.030492 \nL 290.195242 214.286386 \nL 290.225681 207.213217 \nL 290.28656 214.756364 \nL 290.408318 199.552577 \nL 290.469196 198.283636 \nL 290.530075 214.756364 \nL 290.621394 214.685867 \nL 290.651833 203.523891 \nL 290.712712 214.756364 \nL 290.743151 214.756364 \nL 290.864909 204.557842 \nL 290.925788 214.756364 \nL 291.017106 214.427379 \nL 291.047545 214.356882 \nL 291.077985 190.834486 \nL 291.108424 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297.561578 209.351617 \nL 297.592018 214.756364 \nL 297.622457 201.174001 \nL 297.652897 213.322931 \nL 297.805094 203.899873 \nL 297.896412 214.756364 \nL 297.926851 203.194906 \nL 298.01817 209.962589 \nL 298.139927 214.756364 \nL 298.170367 205.192312 \nL 298.231245 214.756364 \nL 298.261685 214.756364 \nL 298.292124 205.779785 \nL 298.353003 206.696242 \nL 298.383442 214.121893 \nL 298.474761 213.181937 \nL 298.535639 214.756364 \nL 298.566079 211.325525 \nL 298.626958 214.544874 \nL 298.687837 214.756364 \nL 298.748715 213.205436 \nL 298.809594 214.756364 \nL 298.840034 213.886904 \nL 298.870473 203.194906 \nL 298.931352 208.176672 \nL 299.02267 214.756364 \nL 299.053109 208.035679 \nL 299.113988 214.756364 \nL 299.144428 214.662368 \nL 299.235746 207.424708 \nL 299.327064 214.756364 \nL 299.357503 211.772004 \nL 299.387943 211.41952 \nL 299.418382 206.531749 \nL 299.448822 207.612699 \nL 299.479261 214.756364 \nL 299.509701 202.019961 \nL 299.54014 212.641463 \nL 299.601019 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301.366504 207.777191 \nL 301.396944 214.756364 \nL 301.427383 211.090536 \nL 301.457823 202.583935 \nL 301.488262 212.218483 \nL 301.518701 203.923372 \nL 301.640459 214.756364 \nL 301.670898 214.098395 \nL 301.701338 214.756364 \nL 301.731777 196.779707 \nL 301.823095 204.534343 \nL 301.883974 214.756364 \nL 301.914414 211.161032 \nL 301.944853 199.247091 \nL 302.005732 214.732865 \nL 302.036171 209.892092 \nL 302.066611 214.756364 \nL 302.12749 211.137533 \nL 302.157929 210.926043 \nL 302.188368 197.061694 \nL 302.249247 211.748505 \nL 302.279687 208.975635 \nL 302.310126 214.756364 \nL 302.340565 213.910403 \nL 302.401444 214.756364 \nL 302.431884 202.25495 \nL 302.492762 207.918184 \nL 302.553641 214.756364 \nL 302.584081 208.811143 \nL 302.61452 196.004243 \nL 302.644959 214.756364 \nL 302.705838 202.513438 \nL 302.736278 202.536937 \nL 302.888475 214.756364 \nL 303.010232 203.453394 \nL 303.13199 214.756364 \nL 303.192869 206.061771 \nL 303.223308 211.278527 \nL 303.314626 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "Vad vi ser här är att den genomsnittliga längden på vägen först ökade. Detta beror troligen på att när vi inte vet något om miljön – är det troligt att vi fastnar i dåliga tillstånd, vatten eller varg. När vi lär oss mer och börjar använda denna kunskap kan vi utforska miljön längre, men vi vet fortfarande inte riktigt var äpplena finns.\n",
+    "\n",
+    "När vi har lärt oss tillräckligt blir det lättare för agenten att nå målet, och vägens längd börjar minska. Dock är vi fortfarande öppna för utforskning, så vi avviker ofta från den bästa vägen och utforskar nya alternativ, vilket gör vägen längre än optimal.\n",
+    "\n",
+    "Vad vi också observerar på denna graf är att längden vid något tillfälle ökade abrupt. Detta indikerar den stokastiska naturen hos processen, och att vi vid något tillfälle kan \"förstöra\" Q-Tabellens koefficienter genom att skriva över dem med nya värden. Detta bör idealt minimeras genom att minska inlärningshastigheten (dvs. mot slutet av träningen justerar vi endast Q-Tabellens värden med ett litet värde).\n",
+    "\n",
+    "Överlag är det viktigt att komma ihåg att framgången och kvaliteten på inlärningsprocessen beror avsevärt på parametrar, såsom inlärningshastighet, minskning av inlärningshastighet och diskonteringsfaktor. Dessa kallas ofta **hyperparametrar**, för att skilja dem från **parametrar** som vi optimerar under träningen (t.ex. Q-Tabellens koefficienter). Processen att hitta de bästa värdena för hyperparametrar kallas **hyperparameteroptimering**, och det förtjänar ett eget ämne.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## Övning\n",
+    "#### En Mer Realistisk Värld för Peter och Vargen\n",
+    "\n",
+    "I vår situation kunde Peter röra sig nästan utan att bli trött eller hungrig. I en mer realistisk värld måste han sätta sig ner och vila då och då, samt äta för att hålla sig vid liv. Låt oss göra vår värld mer realistisk genom att implementera följande regler:\n",
+    "\n",
+    "1. När Peter rör sig från en plats till en annan förlorar han **energi** och blir mer **trött**.\n",
+    "2. Peter kan få mer energi genom att äta äpplen.\n",
+    "3. Peter kan bli av med trötthet genom att vila under trädet eller på gräset (dvs. gå till en plats på spelplanen med ett träd eller gräs - grönt fält).\n",
+    "4. Peter måste hitta och döda vargen.\n",
+    "5. För att kunna döda vargen behöver Peter ha vissa nivåer av energi och trötthet, annars förlorar han striden.\n",
+    "\n",
+    "Modifiera belöningsfunktionen ovan enligt spelets regler, kör förstärkningsinlärningsalgoritmen för att lära dig den bästa strategin för att vinna spelet, och jämför resultaten av slumpmässiga rörelser med din algoritm i termer av antal vunna och förlorade spel.\n",
+    "\n",
+    "> **Note**: Du kan behöva justera hyperparametrar för att få det att fungera, särskilt antalet epoker. Eftersom spelets framgång (att slåss mot vargen) är en sällsynt händelse, kan du förvänta dig mycket längre träningstid.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiserade översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/8-Reinforcement/2-Gym/notebook.ipynb b/translations/sv/8-Reinforcement/2-Gym/notebook.ipynb
new file mode 100644
index 000000000..ec8c9323d
--- /dev/null
+++ b/translations/sv/8-Reinforcement/2-Gym/notebook.ipynb
@@ -0,0 +1,394 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.4 64-bit ('base': conda)"
+  },
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "coopTranslator": {
+   "original_hash": "f22f8f3daed4b6d34648d1254763105b",
+   "translation_date": "2025-09-06T15:17:18+00:00",
+   "source_file": "8-Reinforcement/2-Gym/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## CartPole Skating\n",
+    "\n",
+    "> **Problem**: Om Peter vill fly från vargen måste han kunna röra sig snabbare än den. Vi ska se hur Peter kan lära sig att åka skridskor, särskilt att hålla balansen, med hjälp av Q-Learning.\n",
+    "\n",
+    "Först, låt oss installera gym och importera nödvändiga bibliotek:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Skapa en cartpole-miljö\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 2"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "För att se hur miljön fungerar, låt oss köra en kort simulering i 100 steg.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Under simulering behöver vi få observationer för att kunna bestämma hur vi ska agera. Faktum är att `step`-funktionen ger oss aktuella observationer, belöningsfunktionen och `done`-flaggan som indikerar om det är meningsfullt att fortsätta simuleringen eller inte:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 4"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Vi kan få min- och maxvärde för de där siffrorna:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Låt oss också utforska en annan diskretiseringsmetod med hjälp av bin:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Låt oss nu köra en kort simulering och observera dessa diskreta miljövärden.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -2, -2)\n(0, 1, -2, -5)\n(0, 2, -3, -8)\n(0, 3, -5, -11)\n(0, 3, -7, -14)\n(0, 4, -10, -17)\n(0, 3, -14, -15)\n(0, 3, -17, -12)\n(0, 3, -20, -16)\n(0, 4, -23, -19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 8"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Tabellstruktur\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 9"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 22.0, alpha=0.3, epsilon=0.9\n",
+      "5000: 70.1384, alpha=0.3, epsilon=0.9\n",
+      "10000: 121.8586, alpha=0.3, epsilon=0.9\n",
+      "15000: 149.6368, alpha=0.3, epsilon=0.9\n",
+      "20000: 168.2782, alpha=0.3, epsilon=0.9\n",
+      "25000: 196.7356, alpha=0.3, epsilon=0.9\n",
+      "30000: 220.7614, alpha=0.3, epsilon=0.9\n",
+      "35000: 233.2138, alpha=0.3, epsilon=0.9\n",
+      "40000: 248.22, alpha=0.3, epsilon=0.9\n",
+      "45000: 264.636, alpha=0.3, epsilon=0.9\n",
+      "50000: 276.926, alpha=0.3, epsilon=0.9\n",
+      "55000: 277.9438, alpha=0.3, epsilon=0.9\n",
+      "60000: 248.881, alpha=0.3, epsilon=0.9\n",
+      "65000: 272.529, alpha=0.3, epsilon=0.9\n",
+      "70000: 281.7972, alpha=0.3, epsilon=0.9\n",
+      "75000: 284.2844, alpha=0.3, epsilon=0.9\n",
+      "80000: 269.667, alpha=0.3, epsilon=0.9\n",
+      "85000: 273.8652, alpha=0.3, epsilon=0.9\n",
+      "90000: 278.2466, alpha=0.3, epsilon=0.9\n",
+      "95000: 269.1736, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 11"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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125.656548 212.345721 \r\nL 125.738727 132.392727 \r\nL 125.811775 195.872993 \r\nL 125.805688 47.216674 \r\nL 125.817862 130.383858 \r\nL 125.820906 33.958137 \r\nL 125.924391 194.667672 \r\nL 125.991351 17.083636 \r\nL 125.970046 202.301375 \r\nL 126.046137 122.750155 \r\nL 126.137447 186.230421 \r\nL 126.122229 70.519557 \r\nL 126.155709 132.794501 \r\nL 126.16484 85.786962 \r\nL 126.177015 195.069446 \r\nL 126.259194 131.187406 \r\nL 126.271369 203.104922 \r\nL 126.353548 85.786962 \r\nL 126.37181 178.998492 \r\nL 126.453989 86.992284 \r\nL 126.472251 209.533304 \r\nL 126.55443 81.769224 \r\nL 126.584866 133.196275 \r\nL 126.633565 199.087184 \r\nL 126.603128 63.689401 \r\nL 126.694438 104.268559 \r\nL 126.706613 209.13153 \r\nL 126.7127 70.117783 \r\nL 126.807054 158.9098 \r\nL 126.886189 78.153259 \r\nL 126.840534 201.497827 \r\nL 126.916626 132.392727 \r\nL 127.001849 201.497827 \r\nL 126.931844 82.974545 \r\nL 127.023154 138.017561 \r\nL 127.074897 102.661463 \r\nL 127.111421 205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 128.423241 194.265898 \r\nL 128.514551 142.035299 \r\nL 128.56325 208.729756 \r\nL 128.566293 51.234412 \r\nL 128.624123 150.47255 \r\nL 128.730651 46.011353 \r\nL 128.715433 208.327982 \r\nL 128.736739 103.465011 \r\nL 128.751957 172.971885 \r\nL 128.74587 80.162129 \r\nL 128.803699 140.428204 \r\nL 128.858485 33.958137 \r\nL 128.864573 204.712018 \r\nL 128.913271 151.276098 \r\nL 128.919359 192.658803 \r\nL 128.998494 63.287627 \r\nL 129.016756 110.295166 \r\nL 129.077629 60.876984 \r\nL 129.025887 186.230421 \r\nL 129.114153 117.928869 \r\nL 129.117197 166.945277 \r\nL 129.175027 50.832639 \r\nL 129.223725 161.320443 \r\nL 129.321123 63.287627 \r\nL 129.308948 199.890732 \r\nL 129.333297 84.983415 \r\nL 129.357647 198.283636 \r\nL 129.409389 53.243282 \r\nL 129.442869 145.24949 \r\nL 129.518961 20.297827 \r\nL 129.491568 197.078315 \r\nL 129.549398 161.722217 \r\nL 129.640708 202.301375 \r\nL 129.610271 33.556364 \r\nL 129.655926 179.80204 \r\nL 129.716799 19.092506 \r\nL 129.668101 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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51.636186 \r\nL 330.930568 164.13286 \r\nL 330.994485 63.689401 \r\nL 330.970136 197.881863 \r\nL 331.043184 113.911131 \r\nL 331.101013 59.269889 \r\nL 331.13145 192.257029 \r\nL 331.143625 140.02643 \r\nL 331.213629 200.292506 \r\nL 331.22276 49.627317 \r\nL 331.234935 174.980754 \r\nL 331.317114 38.779424 \r\nL 331.262328 199.488958 \r\nL 331.344507 81.36745 \r\nL 331.435817 199.890732 \r\nL 331.444948 50.832639 \r\nL 331.454079 185.0251 \r\nL 331.551476 43.198936 \r\nL 331.536258 208.729756 \r\nL 331.566694 101.456142 \r\nL 331.578869 193.864124 \r\nL 331.633655 76.14439 \r\nL 331.64583 93.018891 \r\nL 331.648873 21.503149 \r\nL 331.664092 202.703149 \r\nL 331.755402 46.8149 \r\nL 331.816275 210.336851 \r\nL 331.831493 46.011353 \r\nL 331.868017 153.284967 \r\nL 331.962371 17.083636 \r\nL 331.910629 201.096053 \r\nL 331.977589 82.974545 \r\nL 332.059768 194.265898 \r\nL 331.989764 58.064568 \r\nL 332.090205 170.963016 \r\nL 332.117598 23.512018 \r\nL 332.141947 188.23929 \r\nL 332.202821 102.661463 \r\nL 332.236301 199.488958 \r\nL 332.306305 31.547494 \r\nL 332.358048 17.083636 \r\nL 332.415877 204.712018 \r\nL 332.522406 17.083636 \r\nL 332.491969 209.533304 \r\nL 332.531537 43.60071 \r\nL 332.635021 209.533304 \r\nL 332.543711 17.083636 \r\nL 332.644152 87.795831 \r\nL 332.711113 44.002483 \r\nL 332.662414 199.890732 \r\nL 332.735462 117.928869 \r\nL 332.738506 196.274767 \r\nL 332.750681 17.083636 \r\nL 332.841991 81.769224 \r\nL 332.863296 192.257029 \r\nL 332.930257 17.083636 \r\nL 332.954606 193.864124 \r\nL 333.039829 80.965676 \r\nL 333.042873 17.083636 \r\nL 333.061135 206.720887 \r\nL 333.149401 39.582971 \r\nL 333.161576 180.605588 \r\nL 333.240711 17.083636 \r\nL 333.258973 87.795831 \r\nL 333.262017 17.083636 \r\nL 333.344196 193.46235 \r\nL 333.365501 76.546164 \r\nL 333.462899 206.319113 \r\nL 333.371589 17.083636 \r\nL 333.475073 121.14306 \r\nL 333.578558 47.618448 \r\nL 333.560296 195.872993 \r\nL 333.587689 55.653925 \r\nL 333.685086 199.087184 \r\nL 333.639431 26.726208 \r\nL 333.697261 81.36745 \r\nL 333.72161 193.060576 \r\nL 333.712479 17.083636 \r\nL 333.785527 147.660133 \r\nL 333.87075 17.083636 \r\nL 333.809877 187.033969 \r\nL 333.895099 185.828647 \r\nL 333.898143 187.033969 \r\nL 333.949885 17.083636 \r\nL 333.955973 197.480089 \r\nL 334.010759 78.956807 \r\nL 334.062501 17.083636 \r\nL 334.065545 203.90847 \r\nL 334.095981 86.188736 \r\nL 334.175117 195.069446 \r\nL 334.105112 17.083636 \r\nL 334.205553 70.92133 \r\nL 334.275558 197.881863 \r\nL 334.23599 17.083636 \r\nL 334.302951 47.618448 \r\nL 334.354693 197.078315 \r\nL 334.403392 17.083636 \r\nL 334.47644 189.042838 \r\nL 334.512964 66.501818 \r\nL 334.592099 189.042838 \r\nL 334.573837 17.083636 \r\nL 334.619492 83.376319 \r\nL 334.689496 17.083636 \r\nL 334.69254 199.087184 \r\nL 334.719933 97.438404 \r\nL 334.722977 202.301375 \r\nL 334.738195 17.083636 \r\nL 334.826461 126.36612 \r\nL 334.893422 17.083636 \r\nL 334.917771 193.864124 \r\nL 334.93299 84.581641 \r\nL 334.990819 192.658803 \r\nL 334.996907 23.913792 \r\nL 335.042562 86.188736 \r\nL 335.127784 198.68541 \r\nL 335.082129 17.083636 \r\nL 335.155177 144.044169 \r\nL 335.228225 17.083636 \r\nL 335.203876 202.301375 \r\nL 335.261706 65.698271 \r\nL 335.362147 196.274767 \r\nL 335.307361 17.083636 \r\nL 335.371278 166.141729 \r\nL 335.453457 17.083636 \r\nL 335.380409 208.729756 \r\nL 335.483893 78.153259 \r\nL 335.587378 195.069446 \r\nL 335.520417 40.386519 \r\nL 335.593465 81.36745 \r\nL 335.617815 192.658803 \r\nL 335.690863 17.083636 \r\nL 335.794347 198.283636 \r\nL 335.803478 147.258359 \r\nL 335.84609 195.069446 \r\nL 335.818697 33.15459 \r\nL 335.891745 115.116452 \r\nL 335.967836 17.083636 \r\nL 335.995229 209.13153 \r\nL 335.998273 163.731086 \r\nL 336.077408 32.752816 \r\nL 336.007404 205.515565 \r\nL 336.123063 69.314235 \r\nL 336.199155 191.051707 \r\nL 336.156544 25.520887 \r\nL 336.235679 101.456142 \r\nL 336.269159 33.15459 \r\nL 336.314814 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Från denna graf är det inte möjligt att säga något, eftersom längden på träningssessionerna varierar kraftigt på grund av den stokastiska träningsprocessens natur. För att göra denna graf mer meningsfull kan vi beräkna **glidande medelvärde** över en serie experiment, låt oss säga 100. Detta kan göras enkelt med hjälp av `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "#code block 12"
+   ]
+  },
+  {
+   "source": [
+    "## Variera hyperparametrar och se resultatet i praktiken\n",
+    "\n",
+    "Nu skulle det vara intressant att faktiskt se hur den tränade modellen beter sig. Låt oss köra simuleringen, och vi kommer att följa samma strategi för val av åtgärder som under träningen: sampling enligt sannolikhetsfördelningen i Q-Tabellen:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 13"
+   ]
+  },
+  {
+   "source": [
+    "## Spara resultatet som en animerad GIF\n",
+    "\n",
+    "Om du vill imponera på dina vänner kanske du vill skicka den animerade GIF-bilden av balansstången till dem. För att göra detta kan vi anropa `env.render` för att skapa en bildruta och sedan spara dessa som en animerad GIF med hjälp av PIL-biblioteket:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör det noteras att automatiserade översättningar kan innehålla fel eller brister. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som kan uppstå vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/8-Reinforcement/2-Gym/solution/notebook.ipynb b/translations/sv/8-Reinforcement/2-Gym/solution/notebook.ipynb
new file mode 100644
index 000000000..e43d44e50
--- /dev/null
+++ b/translations/sv/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -0,0 +1,526 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "5c0e485e58d63c506f1791c4dbf990ce",
+   "translation_date": "2025-09-06T15:20:10+00:00",
+   "source_file": "8-Reinforcement/2-Gym/solution/notebook.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## CartPole Skridskoåkning\n",
+    "\n",
+    "> **Problem**: Om Peter vill fly från vargen måste han kunna röra sig snabbare än den. Vi ska se hur Peter kan lära sig att åka skridskor, särskilt att hålla balansen, med hjälp av Q-Learning.\n",
+    "\n",
+    "Först, låt oss installera gym och importera nödvändiga bibliotek:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: gym in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.18.3)\n",
+      "Requirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
+      "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
+      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.6.0)\n",
+      "Requirement already satisfied: pyglet<=1.5.15,>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.5.15)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!pip install gym \n",
+    "\n",
+    "import gym\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "source": [
+    "## Skapa en cartpole-miljö\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "För att se hur miljön fungerar, låt oss köra en kort simulering i 100 steg.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Under simulering behöver vi få observationer för att kunna bestämma hur vi ska agera. Faktum är att `step`-funktionen ger oss aktuella observationer, belöningsfunktionen och `done`-flaggan som indikerar om det är meningsfullt att fortsätta simuleringen eller inte:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[ 0.03044442 -0.19543914 -0.04496216  0.28125618] -> 1.0\n",
+      "[ 0.02653564 -0.38989186 -0.03933704  0.55942606] -> 1.0\n",
+      "[ 0.0187378  -0.19424049 -0.02814852  0.25461393] -> 1.0\n",
+      "[ 0.01485299 -0.38894946 -0.02305624  0.53828712] -> 1.0\n",
+      "[ 0.007074   -0.19351108 -0.0122905   0.23842953] -> 1.0\n",
+      "[ 0.00320378  0.00178427 -0.00752191 -0.05810469] -> 1.0\n",
+      "[ 0.00323946  0.19701326 -0.008684   -0.35315131] -> 1.0\n",
+      "[ 0.00717973  0.00201587 -0.01574703 -0.06321931] -> 1.0\n",
+      "[ 0.00722005  0.19736001 -0.01701141 -0.36082863] -> 1.0\n",
+      "[ 0.01116725  0.39271958 -0.02422798 -0.65882671] -> 1.0\n",
+      "[ 0.01902164  0.19794307 -0.03740452 -0.37387001] -> 1.0\n",
+      "[ 0.0229805   0.39357584 -0.04488192 -0.67810827] -> 1.0\n",
+      "[ 0.03085202  0.58929164 -0.05844408 -0.98457719] -> 1.0\n",
+      "[ 0.04263785  0.78514572 -0.07813563 -1.2950295 ] -> 1.0\n",
+      "[ 0.05834076  0.98116859 -0.10403622 -1.61111521] -> 1.0\n",
+      "[ 0.07796413  0.78741784 -0.13625852 -1.35259196] -> 1.0\n",
+      "[ 0.09371249  0.98396202 -0.16331036 -1.68461179] -> 1.0\n",
+      "[ 0.11339173  0.79106371 -0.1970026  -1.44691436] -> 1.0\n",
+      "[ 0.12921301  0.59883361 -0.22594088 -1.22169133] -> 1.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Vi kan få min- och maxvärde för dessa nummer:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(env.observation_space.low)\n",
+    "print(env.observation_space.high)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretize(x):\n",
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+   ]
+  },
+  {
+   "source": [
+    "Låt oss också utforska en annan diskretiseringsmetod med hjälp av bin:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_bins(i,num):\n",
+    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
+    "\n",
+    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
+    "\n",
+    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
+    "nbins = [20,20,10,10] # number of bins for each parameter\n",
+    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
+    "\n",
+    "def discretize_bins(x):\n",
+    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+   ]
+  },
+  {
+   "source": [
+    "Låt oss nu köra en kort simulering och observera dessa diskreta miljövärden.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 2, -4, -9)\n(0, 3, -6, -12)\n(0, 2, -8, -9)\n(0, 3, -10, -13)\n(0, 4, -13, -16)\n(0, 4, -16, -19)\n(0, 4, -20, -17)\n(0, 4, -24, -20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   #env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   #print(discretize_bins(obs))\n",
+    "   print(discretize(obs))\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Tabellstruktur\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = {}\n",
+    "actions = (0,1)\n",
+    "\n",
+    "def qvalues(state):\n",
+    "    return [Q.get((state,a),0) for a in actions]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hyperparameters\n",
+    "alpha = 0.3\n",
+    "gamma = 0.9\n",
+    "epsilon = 0.90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 108.0, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v\n",
+    "\n",
+    "Qmax = 0\n",
+    "cum_rewards = []\n",
+    "rewards = []\n",
+    "for epoch in range(100000):\n",
+    "    obs = env.reset()\n",
+    "    done = False\n",
+    "    cum_reward=0\n",
+    "    # == do the simulation ==\n",
+    "    while not done:\n",
+    "        s = discretize(obs)\n",
+    "        if random.random()<epsilon:\n",
+    "            # exploitation - chose the action according to Q-Table probabilities\n",
+    "            v = probs(np.array(qvalues(s)))\n",
+    "            a = random.choices(actions,weights=v)[0]\n",
+    "        else:\n",
+    "            # exploration - randomly chose the action\n",
+    "            a = np.random.randint(env.action_space.n)\n",
+    "\n",
+    "        obs, rew, done, info = env.step(a)\n",
+    "        cum_reward+=rew\n",
+    "        ns = discretize(obs)\n",
+    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
+    "    cum_rewards.append(cum_reward)\n",
+    "    rewards.append(cum_reward)\n",
+    "    # == Periodically print results and calculate average reward ==\n",
+    "    if epoch%5000==0:\n",
+    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
+    "        if np.average(cum_rewards) > Qmax:\n",
+    "            Qmax = np.average(cum_rewards)\n",
+    "            Qbest = Q\n",
+    "        cum_rewards=[]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 191.855255 \r\nL 111.330008 138.821109 \r\nL 111.339139 143.642395 \r\nL 111.336096 115.518226 \r\nL 111.34827 139.222882 \r\nL 111.357401 98.241951 \r\nL 111.366532 197.881863 \r\nL 111.457842 138.017561 \r\nL 111.466973 212.747494 \r\nL 111.479148 95.027761 \r\nL 111.570458 170.159468 \r\nL 111.655681 98.241951 \r\nL 111.582633 182.212683 \r\nL 111.683074 159.713348 \r\nL 111.743947 107.080976 \r\nL 111.71351 191.051707 \r\nL 111.750034 176.989623 \r\nL 111.774384 195.069446 \r\nL 111.765253 126.36612 \r\nL 111.789602 135.205144 \r\nL 111.850475 102.25969 \r\nL 111.823082 210.336851 \r\nL 111.899174 123.955477 \r\nL 111.978309 195.47122 \r\nL 111.923523 103.465011 \r\nL 112.008746 119.535965 \r\nL 112.030052 207.122661 \r\nL 112.066576 56.457472 \r\nL 112.118318 171.364789 \r\nL 112.191366 76.947938 \r\nL 112.185279 197.881863 \r\nL 112.230934 115.92 \r\nL 112.276589 207.524435 \r\nL 112.337462 101.054368 \r\nL 112.349637 153.284967 \r\nL 112.370942 74.135521 \r\nL 112.367899 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 182.212683 \r\nL 140.649651 195.069446 \r\nL 140.613127 74.135521 \r\nL 140.652694 174.57898 \r\nL 140.704437 51.636186 \r\nL 140.674 201.096053 \r\nL 140.76531 133.598049 \r\nL 140.832271 195.872993 \r\nL 140.847489 76.14439 \r\nL 140.874882 188.23929 \r\nL 140.887057 55.252151 \r\nL 140.963148 203.90847 \r\nL 140.987498 135.205144 \r\nL 141.087939 206.720887 \r\nL 141.002716 22.70847 \r\nL 141.09707 189.042838 \r\nL 141.197511 62.885854 \r\nL 141.194467 194.265898 \r\nL 141.206642 74.537295 \r\nL 141.218816 183.819778 \r\nL 141.319257 136.812239 \r\nL 141.352738 17.083636 \r\nL 141.419698 199.087184 \r\nL 141.498834 17.083636 \r\nL 141.489703 211.140399 \r\nL 141.532314 80.965676 \r\nL 141.608406 199.890732 \r\nL 141.611449 17.887184 \r\nL 141.641886 78.956807 \r\nL 141.675366 210.336851 \r\nL 141.672323 17.083636 \r\nL 141.751458 115.92 \r\nL 141.787982 17.083636 \r\nL 141.839724 205.917339 \r\nL 141.864074 99.849047 \r\nL 141.964515 189.846386 \r\nL 141.897554 48.020222 \r\nL 141.973646 169.757694 \r\nL 141.98582 37.574102 \r\nL 142.064956 184.221552 \r\nL 142.089305 38.779424 \r\nL 142.159309 194.265898 \r\nL 142.119742 31.949268 \r\nL 142.201921 134.80337 \r\nL 142.241488 46.413126 \r\nL 142.232357 203.506696 \r\nL 142.265838 93.420665 \r\nL 142.320624 199.087184 \r\nL 142.332798 48.823769 \r\nL 142.37541 111.098714 \r\nL 142.478894 199.890732 \r\nL 142.396715 48.020222 \r\nL 142.484982 120.741286 \r\nL 142.488025 21.503149 \r\nL 142.503244 201.497827 \r\nL 142.59151 74.939069 \r\nL 142.597597 203.506696 \r\nL 142.701082 53.645055 \r\nL 142.704126 146.454812 \r\nL 142.743693 44.002483 \r\nL 142.801523 186.230421 \r\nL 142.813698 87.394058 \r\nL 142.868484 199.890732 \r\nL 142.850222 52.841508 \r\nL 142.926313 193.46235 \r\nL 142.944575 65.698271 \r\nL 142.965881 193.864124 \r\nL 143.035885 142.437073 \r\nL 143.066322 194.265898 \r\nL 143.072409 66.501818 \r\nL 143.142414 93.822439 \r\nL 143.215462 202.301375 \r\nL 143.233724 80.965676 \r\nL 143.255029 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 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188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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17.083636 \r\nL 193.238126 211.542173 \r\nL 193.283781 119.535965 \r\nL 193.286824 194.265898 \r\nL 193.320305 17.083636 \r\nL 193.390309 174.57898 \r\nL 193.45727 39.582971 \r\nL 193.408571 197.881863 \r\nL 193.502925 56.859246 \r\nL 193.52423 188.641064 \r\nL 193.554667 17.083636 \r\nL 193.618584 144.847716 \r\nL 193.712938 17.083636 \r\nL 193.685545 192.257029 \r\nL 193.7312 62.082306 \r\nL 193.743374 19.494279 \r\nL 193.749462 138.821109 \r\nL 193.758593 25.119113 \r\nL 193.807291 205.113792 \r\nL 193.789029 17.083636 \r\nL 193.868165 46.8149 \r\nL 193.968606 178.194945 \r\nL 193.883383 17.083636 \r\nL 193.977737 59.269889 \r\nL 194.011217 191.051707 \r\nL 194.062959 34.359911 \r\nL 194.081221 120.741286 \r\nL 194.1634 17.083636 \r\nL 194.154269 162.123991 \r\nL 194.190793 129.178537 \r\nL 194.291234 184.623326 \r\nL 194.22123 17.083636 \r\nL 194.297322 178.596718 \r\nL 194.379501 17.083636 \r\nL 194.306453 209.533304 \r\nL 194.406894 111.500488 \r\nL 194.482985 184.221552 \r\nL 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Från denna graf är det inte möjligt att säga något, eftersom längden på träningssessionerna varierar kraftigt på grund av den stokastiska träningsprocessens natur. För att göra denna graf mer meningsfull kan vi beräkna **rullande medelvärde** över en serie experiment, låt oss säga 100. Detta kan enkelt göras med `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "def running_average(x,window):\n",
+    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
+    "\n",
+    "plt.plot(running_average(rewards,100))"
+   ]
+  },
+  {
+   "source": [
+    "## Variera hyperparametrar och se resultatet i praktiken\n",
+    "\n",
+    "Nu skulle det vara intressant att faktiskt se hur den tränade modellen beter sig. Låt oss köra simuleringen, och vi kommer att följa samma strategi för val av åtgärder som under träningen: sampling enligt sannolikhetsfördelningen i Q-Tabellen:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset()\n",
+    "done = False\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   env.render()\n",
+    "   v = probs(np.array(qvalues(s)))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Spara resultatet som en animerad GIF\n",
+    "\n",
+    "Om du vill imponera på dina vänner kanske du vill skicka den animerade GIF-bilden av balansstången till dem. För att göra detta kan vi anropa `env.render` för att skapa en bildruta och sedan spara dessa som en animerad GIF med hjälp av PIL-biblioteket:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, bör du vara medveten om att automatiserade översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess ursprungliga språk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sv/PyTorch_Fundamentals.ipynb b/translations/sv/PyTorch_Fundamentals.ipynb
new file mode 100644
index 000000000..57bf9dd39
--- /dev/null
+++ b/translations/sv/PyTorch_Fundamentals.ipynb
@@ -0,0 +1,2830 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4",
+   "authorship_tag": "ABX9TyOgv0AozH1FKQBD+RkgT2bV",
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "coopTranslator": {
+   "original_hash": "0ca21b6ee62904d616f2e36dc1cf0da7",
+   "translation_date": "2025-09-06T13:08:06+00:00",
+   "source_file": "PyTorch_Fundamentals.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "view-in-github",
+    "colab_type": "text"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EHh5JllMh1rG",
+    "outputId": "f55755ad-c369-414c-85ec-6e9d4f061a02",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'2.2.1+cu121'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(\"I am excited to run this\")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UPlb-duwXAfz",
+    "outputId": "cfd687e4-1238-49f4-ab6b-ee1305b740d2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "I am excited to run this\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(torch.__version__)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "byWVlJ9wXDSk",
+    "outputId": "fd74a5c4-4d4a-41b2-ef3c-562ea3e4811f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2.2.1+cu121\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "Osm80zoEYklS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# scalar\n",
+    "scalar = torch.tensor(7)\n",
+    "scalar"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-o8wvJ-VXZmI",
+    "outputId": "558816f5-1205-4de1-fe1f-2f96e9bd79e6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(7)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mCZ2tXC4Y_Sg",
+    "outputId": "2d86dbdc-56e1-45c6-d3dd-14515f2a457a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ssN00By0ZQgS",
+    "outputId": "490f40d1-5135-4969-a6d3-c8c902cdc473"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# vector\n",
+    "vector = torch.tensor([7, 7])\n",
+    "vector\n",
+    "#vector.ndim\n",
+    "#vector.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Bws__5wlZnmF",
+    "outputId": "944e38f9-5ba1-4ddc-a9c6-cfb6a19bb488"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([7, 7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "vector.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9pjCvnsZZzNG",
+    "outputId": "e030a4da-8f81-4858-fbce-86da2aaafe52"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([2])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Matrix\n",
+    "MATRIX = torch.tensor([[7, 8],[9, 10]])\n",
+    "MATRIX"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a747hI9SaBGW",
+    "outputId": "af835ddb-81ff-4981-badb-441567194d15"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 7,  8],\n",
+       "        [ 9, 10]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XdTfFa7vaRUj",
+    "outputId": "0fbbab9c-8263-4cad-a380-0d2a16ca499e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX[0]\n",
+    "MATRIX[1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TFeD3jSDafm7",
+    "outputId": "69b44ab3-5ba7-451a-c6b2-f019a03d0c96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Tensor\n",
+    "TENSOR = torch.tensor([[[1, 2, 3],[3,6,9], [2,4,5]]])\n",
+    "TENSOR"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ic3cE47tah42",
+    "outputId": "f250e295-91de-43ec-9d80-588a6fe0abde"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 2, 3],\n",
+       "         [3, 6, 9],\n",
+       "         [2, 4, 5]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wvjf5fczbAM1",
+    "outputId": "9c72b5b8-bafe-4ae7-9883-b051e209eada"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mwtXZwiMbN3m",
+    "outputId": "331a5e36-b1b0-4a5f-a9b8-e7049cbaa8f9"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vzdZu_IfbP3J",
+    "outputId": "e24e7e71-e365-412d-ff50-fc094b56d2f3"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [3, 6, 9],\n",
+       "        [2, 4, 5]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "A8OL9eWfcRrJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor = torch.rand(3,4)\n",
+    "random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hAqSDE1EcVS_",
+    "outputId": "946171c3-d054-400c-f893-79110356888c"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.4414, 0.7681, 0.8385, 0.3166],\n",
+       "        [0.0468, 0.5812, 0.0670, 0.9173],\n",
+       "        [0.2959, 0.3276, 0.7411, 0.4643]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "g4fvPE5GcwzP",
+    "outputId": "8737f36b-6864-4059-eaed-6f9156c22306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XsAg99QmdAU6",
+    "outputId": "35467c11-257c-4f16-99aa-eca930bcbc36"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.size()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cii1pNdVdB68",
+    "outputId": "fc8d2de6-9215-43de-99f7-7b0d7f7d20fa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_image_tensor = torch.rand(size=(3, 224, 224)) #color channels, height, width\n",
+    "random_image_tensor.ndim, random_image_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aTKq2j0cdDjb",
+    "outputId": "6be42057-20b9-4faf-d79d-8b65c42cc27e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([3, 224, 224]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor_ofownsize = torch.rand(size=(5,10,10))\n",
+    "random_tensor_ofownsize.ndim, random_tensor_ofownsize.shape\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IyhDdj-Pd6nC",
+    "outputId": "43e5e334-6d4d-4b67-f87d-7d364c6d8c67"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([5, 10, 10]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "UOJW08uOert_"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero = torch.zeros(size=(3, 4))\n",
+    "zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGvXtaXyefie",
+    "outputId": "d40d3e28-8667-4d2f-8b62-f0829c6162ad"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero*random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OyUkUPkDe0uH",
+    "outputId": "26c2e4be-36ba-4c6c-9a90-2704ec135828"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones = torch.ones(size=(3, 4))\n",
+    "ones\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "y_Ac62Aqe82G",
+    "outputId": "291de5d9-b9df-49de-c9d1-d098e3e9f4d8"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TvGOA9odfIEO",
+    "outputId": "45949ef4-6649-4b6c-d6af-2d4bfb8de832"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones*zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "--pTyge-fI-8",
+    "outputId": "c4d9bb7e-829b-43db-e2db-b1a2d64e61f0"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qDcc7Z36fSJF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "one_to_ten = torch.arange(start = 1, end = 11, step = 1)\n",
+    "one_to_ten"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w3CZB4zUfR1s",
+    "outputId": "197fcba1-da0a-4b4a-ed11-3974bd6c01aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ten_zeros = torch.zeros_like(one_to_ten)\n",
+    "ten_zeros"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WZh99BwVfRy8",
+    "outputId": "51ef8bfb-6fa0-4099-ff66-b97d65b2ddea"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tensor Datatyper\n"
+   ],
+   "metadata": {
+    "id": "pGGhgsbUgqbW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor = torch.tensor([3.0, 6.0,9.0], dtype = None, device = None, requires_grad = False)\n",
+    "float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JORJl4XkfRsx",
+    "outputId": "71114171-0f49-481f-b6fc-6cb48e2fb895"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3., 6., 9.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6wOPPwGyfRLn",
+    "outputId": "f23776a1-b682-404a-9f67-d5bcb0402666"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor = float_32_tensor.type(torch.float16)\n",
+    "float_16_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tFsHCvmZfOYe",
+    "outputId": "d3aa305a-7591-47f5-97fd-61bff60b44bd"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float16"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 31
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TQiCGTPuwq0q",
+    "outputId": "98750fce-1ca3-4889-e269-8b753efdea96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor = torch.tensor([3, 6, 9], dtype = torch.int32)\n",
+    "int_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5hlrLvGUw5D_",
+    "outputId": "41d890a0-9aee-446c-d906-631ce2ab0995"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9], dtype=torch.int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ihApD9u3xTNW",
+    "outputId": "d295eed0-6996-4e0f-8502-ff4b55cd1373"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(0,100,10)"
+   ],
+   "metadata": {
+    "id": "utKhlb_KxWDQ"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p78D74E9Rj7Y",
+    "outputId": "781a1614-a900-41f5-9e5d-358f0b2390aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.min()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4BcSs5NeRkcj",
+    "outputId": "3f24a8dc-58e9-4a5f-9834-e85856a34f9d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.max()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hinqvXVLRm4q",
+    "outputId": "5c7d8a53-3913-4ac1-bba3-5ba8ff68250a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 38
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mean(x.type(torch.float32))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k7okc0_vRpnB",
+    "outputId": "91e5494f-dc57-417c-ea4d-25dbc547c893"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.type(torch.float32).mean()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "29QcDTjHRq10",
+    "outputId": "62937c6c-78e0-49f2-dde3-1543ee8f7907"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wlpY_G_sbdKF",
+    "outputId": "475d8258-af65-4011-a258-b93d4d8142d4"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(450)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmax()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GT6HJzwhbk4n",
+    "outputId": "2e455c20-c322-4bcf-d07c-1259d3ccefc6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmin()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "egL3oi2Mb19P",
+    "outputId": "f71fb32f-6338-44a3-b377-75bea0a3ab54"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p2U8DZKib3DP",
+    "outputId": "b9f613b9-74e9-45f4-ed01-05babb6a6793"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[9]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24qBFlGYcABe",
+    "outputId": "5813cfcb-7f63-4bd7-ee46-f95ccbfda939"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(1, 10)\n",
+    "x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0GPOxEzkcBHO",
+    "outputId": "aefbd903-4f4c-4d2c-c90f-eccd682fe018"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped = x.reshape(1,9)\n",
+    "x_reshaped, x_reshaped.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "spmRgQjwddgp",
+    "outputId": "85a7c55c-2909-4ea2-fc68-386dddc65742"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), torch.Size([1, 9]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped.view(1,9)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tH2ahWGydqqP",
+    "outputId": "65d92263-4fc4-434a-c06d-c5e08436f7fe"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked = torch.stack([x, x, x, x], dim = 1)\n",
+    "x_stacked"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jgCeJcaud_-1",
+    "outputId": "7f293a37-6ef1-43b6-aee5-9d6d91c94f9e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XhJHIK6cfPse",
+    "outputId": "06c47b89-3a9e-453e-bcc3-00cbcb0b8b49"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=1)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ej2c3Xxzf0tq",
+    "outputId": "94024061-eb37-446d-c4a8-e4d16cb6de81"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 52
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4DJYo1a0f5M0",
+    "outputId": "efca2b47-1b14-44de-9a9a-2c83629d153f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 53
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=-2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J4iEjn2ah2HL",
+    "outputId": "22395593-7c16-4162-beae-dd2bbe7bda35"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 55
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "tensor = torch.tensor([1, 2, 3])\n",
+    "tensor = tensor - 10\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cFfiD7Nth7Z_",
+    "outputId": "1139e1f8-fc1a-46ca-d636-f2bc4fd2eef6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-9, -8, -7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mul(tensor, 10)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dyA7BM_GHhqE",
+    "outputId": "0e3b9671-d9e8-4a32-87bb-59bc05986142"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-90, -80, -70])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.sub(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "owtUsZ1KNegI",
+    "outputId": "189b7b23-0041-4e09-b991-cd209a48506a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-109, -108, -107])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.add(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K5STXlQONsyc",
+    "outputId": "00cbb79a-0a1d-4e21-86ec-5c91c37a2d01"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([91, 92, 93])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.divide(tensor, 2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xqMGnzIUNvp0",
+    "outputId": "c894cf3e-f148-45f8-cfc8-d78740735306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-4.5000, -4.0000, -3.5000])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(tensor, tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ruGzKpV8NyBc",
+    "outputId": "fddb63bf-006f-48b6-ae28-287fbcda8bc5"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8GS3r9yTeGfD",
+    "outputId": "c80b12ac-30b5-4f3d-c38c-9e41ba511b0e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QmuYHqXTemC0",
+    "outputId": "402fe3ba-70b5-4bb2-c83b-254db84ff810"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 622 µs, sys: 0 ns, total: 622 µs\n",
+      "Wall time: 516 µs\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "torch.matmul(tensor,tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dGr1fzdNepd8",
+    "outputId": "97bd6c91-bc25-4b38-cdf5-f22dcdef243e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 424 µs, sys: 998 µs, total: 1.42 ms\n",
+      "Wall time: 1.43 ms\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.rand(3,2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pGYDoK2gevfo",
+    "outputId": "2c8783d5-0453-47c5-c7ed-af10d25d6989"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5999, 0.0073],\n",
+       "        [0.9321, 0.3026],\n",
+       "        [0.3463, 0.3872]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(torch.rand(3,2), torch.rand(2,3))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KGBGQoB8e2DP",
+    "outputId": "4c2ef361-a2d0-41ee-c328-3992cbbc138d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.3528, 0.1893, 0.0714],\n",
+       "        [1.2791, 0.7110, 0.2563],\n",
+       "        [0.8812, 0.4553, 0.1803]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch"
+   ],
+   "metadata": {
+    "id": "ib8DMtkBe_LJ"
+   },
+   "execution_count": 1,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.rand(2,9)"
+   ],
+   "metadata": {
+    "id": "nJo8ZBdrQY1b"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wi6oRv4MQfgf",
+    "outputId": "55c99f55-31f6-4cf5-ba4e-19a47c3a0167"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5894, 0.4391, 0.2018, 0.5417, 0.3844, 0.3592, 0.9209, 0.9269, 0.0681],\n",
+       "        [0.0746, 0.1740, 0.6821, 0.6890, 0.0999, 0.7444, 0.2391, 0.4625, 0.8302]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "y=torch.randn(2,3,5)\n",
+    "y"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zpx8myAUQgoc",
+    "outputId": "07756d70-56bd-437c-c74e-9aecc1a77311"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[ 1.5552, -0.4877,  0.5175, -1.7958, -0.6187],\n",
+       "         [-0.3359, -1.9710,  0.0112, -1.7578, -1.5295],\n",
+       "         [ 0.0932,  1.4079,  0.9108,  0.3328, -0.6978]],\n",
+       "\n",
+       "        [[-0.9406, -1.0809, -0.2595,  0.1282,  1.6605],\n",
+       "         [ 1.1624,  1.0902,  1.7092, -0.2842, -1.3780],\n",
+       "         [-0.1534, -1.2795, -0.5495,  0.9902,  0.1822]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original = torch.rand(size=(224,224,3))\n",
+    "x_original"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s4U-X9bJQnWe",
+    "outputId": "657a7a76-962c-4b41-a76b-902d0482266c"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[0.4549, 0.6809, 0.2118],\n",
+       "         [0.4824, 0.9008, 0.8741],\n",
+       "         [0.1715, 0.1757, 0.1845],\n",
+       "         ...,\n",
+       "         [0.8741, 0.6594, 0.2610],\n",
+       "         [0.0092, 0.1984, 0.1955],\n",
+       "         [0.4236, 0.4182, 0.0251]],\n",
+       "\n",
+       "        [[0.9174, 0.1661, 0.5852],\n",
+       "         [0.1837, 0.2351, 0.3810],\n",
+       "         [0.3726, 0.4808, 0.8732],\n",
+       "         ...,\n",
+       "         [0.6794, 0.0554, 0.9202],\n",
+       "         [0.0864, 0.8750, 0.3558],\n",
+       "         [0.8445, 0.9759, 0.4934]],\n",
+       "\n",
+       "        [[0.1600, 0.2635, 0.7194],\n",
+       "         [0.9488, 0.3405, 0.3647],\n",
+       "         [0.6683, 0.5168, 0.9592],\n",
+       "         ...,\n",
+       "         [0.0521, 0.0140, 0.2445],\n",
+       "         [0.3596, 0.3999, 0.2730],\n",
+       "         [0.5926, 0.9877, 0.7784]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[0.4794, 0.5635, 0.3764],\n",
+       "         [0.9124, 0.6094, 0.5059],\n",
+       "         [0.4528, 0.4447, 0.5021],\n",
+       "         ...,\n",
+       "         [0.0089, 0.4816, 0.8727],\n",
+       "         [0.2173, 0.6296, 0.2347],\n",
+       "         [0.2028, 0.9931, 0.7201]],\n",
+       "\n",
+       "        [[0.3116, 0.6459, 0.4703],\n",
+       "         [0.0148, 0.2345, 0.7149],\n",
+       "         [0.8393, 0.5804, 0.6691],\n",
+       "         ...,\n",
+       "         [0.2105, 0.9460, 0.2696],\n",
+       "         [0.5918, 0.9295, 0.2616],\n",
+       "         [0.2537, 0.7819, 0.4700]],\n",
+       "\n",
+       "        [[0.6654, 0.1200, 0.5841],\n",
+       "         [0.9147, 0.5522, 0.6529],\n",
+       "         [0.1799, 0.5276, 0.5415],\n",
+       "         ...,\n",
+       "         [0.7536, 0.4346, 0.8793],\n",
+       "         [0.3793, 0.1750, 0.7792],\n",
+       "         [0.9266, 0.8325, 0.9974]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted=x_original.permute(2, 0, 1)\n",
+    "print(x_original.shape)\n",
+    "print(x_permuted.shape)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DD19_zvbQzHo",
+    "outputId": "1d64ce1b-eb48-47e3-90b6-7f1340e7f2b2"
+   },
+   "execution_count": 9,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "torch.Size([224, 224, 3])\n",
+      "torch.Size([3, 224, 224])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NnPmMk4ZRF7w",
+    "outputId": "2cd5da7f-4a23-4a76-8c4a-bb982113f2a4"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z0ylNoAARgTo",
+    "outputId": "ddca0298-cddf-4048-9b71-a791655e5bed"
+   },
+   "execution_count": 11,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]=0.989"
+   ],
+   "metadata": {
+    "id": "RXw0xXsDRi4L"
+   },
+   "execution_count": 13,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1sFdV6wzRo3f",
+    "outputId": "1cf87d2c-6d88-453a-d136-0f625a2800f1"
+   },
+   "execution_count": 14,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xTX-hx2SR1wp",
+    "outputId": "0d4908c4-c3bc-44e3-8ec6-1487104cc209"
+   },
+   "execution_count": 15,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x=torch.arange(1,10).reshape(1,3,3)\n",
+    "x, x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mZomOe7gR4Q8",
+    "outputId": "0b3c922f-ec11-46de-b8a5-9f9533d866ad"
+   },
+   "execution_count": 18,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[[1, 2, 3],\n",
+       "          [4, 5, 6],\n",
+       "          [7, 8, 9]]]),\n",
+       " torch.Size([1, 3, 3]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3y7v4SQvSBs1",
+    "outputId": "8c53307d-e628-404d-db66-56c6bdffab7c"
+   },
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [4, 5, 6],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hf9uG4xLSNya",
+    "outputId": "3075bc42-9ffa-426b-8a86-95628ffcd824"
+   },
+   "execution_count": 21,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zA4G2Se4SRB3",
+    "outputId": "324312d2-ed0a-49eb-f81f-e904e53992fe"
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(1)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][2][2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mwy3zmKKSdbk",
+    "outputId": "d35172c3-b099-40a6-ddf1-a453c2adfa44"
+   },
+   "execution_count": 23,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[:,1,1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fE3nCM1KS7XT",
+    "outputId": "01f5d755-9737-4235-9f73-dce89ff6ba16"
+   },
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([5])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,0,:]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "luNDINKNTTxp",
+    "outputId": "091195ef-2f71-4602-e95f-529a69193150"
+   },
+   "execution_count": 25,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,:,2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KG8A4xbfThCL",
+    "outputId": "5866bc41-9241-4619-be7b-e9206b3f80ab"
+   },
+   "execution_count": 26,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import numpy as np"
+   ],
+   "metadata": {
+    "id": "CZ3PX0qlTwHJ"
+   },
+   "execution_count": 27,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array = np.arange(1.0, 8.0)"
+   ],
+   "metadata": {
+    "id": "UOBeTumiT3Lf"
+   },
+   "execution_count": 28,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RzcO32E9UCQl",
+    "outputId": "430def24-c42c-461f-e5e7-398544c695d3"
+   },
+   "execution_count": 29,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 4., 5., 6., 7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.from_numpy(array)\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JJIL0q1DUC6O",
+    "outputId": "8a3b1d7c-4482-4d32-f34f-9212d9d3a177"
+   },
+   "execution_count": 32,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1., 2., 3., 4., 5., 6., 7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array[3]=11.0"
+   ],
+   "metadata": {
+    "id": "j3Ce6q3DUIEK"
+   },
+   "execution_count": 33,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dc_BCVdjUsCc",
+    "outputId": "65537325-8b11-4f36-fc73-e56f30d6a036"
+   },
+   "execution_count": 34,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3., 11.,  5.,  6.,  7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VG1e_eITUta2",
+    "outputId": "a26c5198-23b6-4a6d-d73a-ba20cd9782b8"
+   },
+   "execution_count": 35,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1.,  2.,  3., 11.,  5.,  6.,  7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.ones(7)\n",
+    "tensor, tensor.dtype\n",
+    "numpy_tensor = tensor.numpy()\n",
+    "numpy_tensor, numpy_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Swt8JF8vUuev",
+    "outputId": "c9e5bf6a-6d2c-41d6-8327-366867ffdd2d"
+   },
+   "execution_count": 37,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(array([1., 1., 1., 1., 1., 1., 1.], dtype=float32), dtype('float32'))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "random_tensor_A = torch.rand(3,4)\n",
+    "random_tensor_B = torch.rand(3,4)\n",
+    "print(random_tensor_A)\n",
+    "print(random_tensor_B)\n",
+    "print(random_tensor_A == random_tensor_B)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGcagTteVFTD",
+    "outputId": "49405790-08e7-4210-b7f1-f00b904c7eb9"
+   },
+   "execution_count": 38,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.9870, 0.6636, 0.6873, 0.8863],\n",
+      "        [0.8386, 0.4169, 0.3587, 0.0265],\n",
+      "        [0.2981, 0.6025, 0.5652, 0.5840]])\n",
+      "tensor([[0.9821, 0.3481, 0.0913, 0.4940],\n",
+      "        [0.7495, 0.4387, 0.9582, 0.8659],\n",
+      "        [0.5064, 0.6919, 0.0809, 0.9771]])\n",
+      "tensor([[False, False, False, False],\n",
+      "        [False, False, False, False],\n",
+      "        [False, False, False, False]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "RANDOM_SEED = 42\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_C = torch.rand(3,4)\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_D = torch.rand(3,4)\n",
+    "print(random_tensor_C)\n",
+    "print(random_tensor_D)\n",
+    "print(random_tensor_C == random_tensor_D)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HznyXyEaWjLM",
+    "outputId": "25956434-01b6-4059-9054-c9978884ddc1"
+   },
+   "execution_count": 46,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[True, True, True, True],\n",
+      "        [True, True, True, True],\n",
+      "        [True, True, True, True]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!nvidia-smi"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vltPTh0YXJSt",
+    "outputId": "807af6dc-a9ca-4301-ec32-b688dbde8be8"
+   },
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Thu May 23 02:57:59 2024       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   60C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|  No running processes found                                                           |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "torch.cuda.is_available()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L6mMyPDyYh1j",
+    "outputId": "279c5dd8-c2a8-4fbd-f321-2f5d7c6e90e6"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "oOdiYa7ZYytx",
+    "outputId": "d73b04fc-8963-4826-9722-08d118d5ab91"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.cuda.device_count()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vOdsazLqZFM5",
+    "outputId": "8189cd6a-9017-4663-a652-3e15c517d9c3"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.tensor([1,2,3], device = \"cpu\")\n",
+    "print(tensor, tensor.device)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cdik9Vw3ZMv0",
+    "outputId": "044a68fd-83a1-409d-8e3b-655142ca0270"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([1, 2, 3]) cpu\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu = tensor.to(device)\n",
+    "tensor_on_gpu"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zmp835rrZp-z",
+    "outputId": "37fa3413-18a3-47bf-ae51-5b36ff85a3ef"
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3], device='cuda:0')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu.numpy()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 159
+    },
+    "id": "jhriaa8uZ1yM",
+    "outputId": "bc5a3226-1a12-4fea-8769-a44f21cdc323"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "TypeError",
+     "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-e0c96c7436fd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtensor_on_gpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_cpu = tensor_on_gpu.cpu().numpy()"
+   ],
+   "metadata": {
+    "id": "LHGXK3GgaOzL"
+   },
+   "execution_count": 12,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "id": "j-El4LlCajfq"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/1-Tools/notebook.ipynb b/translations/sw/2-Regression/1-Tools/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sw/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/sw/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
new file mode 100644
index 000000000..75632799f
--- /dev/null
+++ b/translations/sw/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -0,0 +1,448 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_1-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "c18d3bd0bd8ae3878597e89dcd1fa5c1",
+   "translation_date": "2025-09-06T13:43:37+00:00",
+   "source_file": "2-Regression/1-Tools/solution/R/lesson_1-R.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "YJUHCXqK57yz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Utangulizi wa Urejeleaji - Somo la 1\n",
+    "\n",
+    "#### Kuweka katika muktadha\n",
+    "\n",
+    "✅ Kuna aina nyingi za mbinu za urejeleaji, na chaguo lako linategemea jibu unalotafuta. Ikiwa unataka kutabiri urefu unaowezekana wa mtu wa umri fulani, ungetumia `urejeleaji wa mstari`, kwa kuwa unatafuta **thamani ya nambari**. Ikiwa unavutiwa na kugundua kama aina fulani ya chakula inapaswa kuzingatiwa kuwa cha mboga au la, unatafuta **ugawaji wa kategoria**, kwa hivyo ungetumia `urejeleaji wa kimantiki`. Utajifunza zaidi kuhusu urejeleaji wa kimantiki baadaye. Fikiria kidogo kuhusu maswali unayoweza kuuliza kutoka kwa data, na ni mbinu ipi kati ya hizi ingefaa zaidi.\n",
+    "\n",
+    "Katika sehemu hii, utatumia [seti ndogo ya data kuhusu kisukari](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Fikiria kwamba unataka kujaribu matibabu kwa wagonjwa wa kisukari. Miundo ya Kujifunza kwa Mashine inaweza kukusaidia kubaini ni wagonjwa gani wangepokea matibabu vizuri zaidi, kulingana na mchanganyiko wa vigezo. Hata mfano wa urejeleaji wa msingi kabisa, unapowekwa kwenye taswira, unaweza kuonyesha taarifa kuhusu vigezo ambavyo vingekusaidia kupanga majaribio yako ya kinadharia ya kliniki.\n",
+    "\n",
+    "Kwa hayo, hebu tuanze kazi hii!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/encouRage.jpg\"\n",
+    "   width=\"630\"/>\n",
+    "   <figcaption>Uchoraji na @allison_horst</figcaption>\n",
+    "\n",
+    "<!--![Uchoraji na \\@allison_horst](../../../../../../2-Regression/1-Tools/images/encouRage.jpg)<br>Uchoraji na @allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "LWNNzfqd6feZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Kuandaa zana zetu\n",
+    "\n",
+    "Kwa kazi hii, tutahitaji vifurushi vifuatavyo:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) ni [mkusanyiko wa vifurushi vya R](https://www.tidyverse.org/packages) ulioundwa kufanya sayansi ya data kuwa ya haraka, rahisi, na ya kufurahisha!\n",
+    "\n",
+    "-   `tidymodels`: Mfumo wa [tidymodels](https://www.tidymodels.org/) ni [mkusanyiko wa vifurushi](https://www.tidymodels.org/packages/) kwa ajili ya uundaji wa mifano na ujifunzaji wa mashine.\n",
+    "\n",
+    "Unaweza kuvifunga kwa kutumia:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+    "\n",
+    "Skripti iliyo hapa chini inakagua kama una vifurushi vinavyohitajika kukamilisha moduli hii na inavifunga kwa ajili yako endapo baadhi havipo.\n"
+   ],
+   "metadata": {
+    "id": "FIo2YhO26wI9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse, tidymodels)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "id": "cIA9fz9v7Dss",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sasa, hebu tupakie vifurushi hivi vya kushangaza na kuvifanya vipatikane katika kikao chetu cha sasa cha R. (Hii ni kwa maelezo tu, `pacman::p_load()` tayari ilifanya hivyo kwa ajili yako)\n"
+   ],
+   "metadata": {
+    "id": "gpO_P_6f9WUG"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# load the core Tidyverse packages\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# load the core Tidymodels packages\r\n",
+    "library(tidymodels)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NLMycgG-9ezO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Seti ya data ya kisukari\n",
+    "\n",
+    "Katika zoezi hili, tutatumia ujuzi wetu wa regression kufanya utabiri kwenye seti ya data ya kisukari. [Seti ya data ya kisukari](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) ina `442 sampuli` za data zinazohusiana na kisukari, ikiwa na vipengele 10 vya utabiri, `umri`, `jinsia`, `kiwango cha uzito wa mwili`, `shinikizo la damu la wastani`, na `vipimo sita vya damu` pamoja na kipengele cha matokeo `y`: kipimo cha kiasi cha maendeleo ya ugonjwa mwaka mmoja baada ya msingi.\n",
+    "\n",
+    "|Idadi ya uchunguzi|442|\n",
+    "|------------------|:---|\n",
+    "|Idadi ya vipengele vya utabiri|Safu 10 za kwanza ni za nambari za utabiri|\n",
+    "|Matokeo/Lengo|Safu ya 11 ni kipimo cha kiasi cha maendeleo ya ugonjwa mwaka mmoja baada ya msingi|\n",
+    "|Maelezo ya vipengele vya utabiri|- umri kwa miaka\n",
+    "||- jinsia\n",
+    "||- bmi kiwango cha uzito wa mwili\n",
+    "||- bp shinikizo la damu la wastani\n",
+    "||- s1 tc, jumla ya cholesterol ya damu\n",
+    "||- s2 ldl, lipoproteini zenye msongamano mdogo\n",
+    "||- s3 hdl, lipoproteini zenye msongamano mkubwa\n",
+    "||- s4 tch, jumla ya cholesterol / HDL\n",
+    "||- s5 ltg, labda logi ya kiwango cha triglycerides ya damu\n",
+    "||- s6 glu, kiwango cha sukari kwenye damu|\n",
+    "\n",
+    "> 🎓 Kumbuka, huu ni ujifunzaji unaosimamiwa, na tunahitaji lengo lililopewa jina 'y'.\n",
+    "\n",
+    "Kabla ya kuweza kushughulikia data na R, unahitaji kuingiza data kwenye kumbukumbu ya R, au kujenga muunganisho na data ambayo R inaweza kutumia kufikia data hiyo kwa mbali.\n",
+    "\n",
+    "> Kifurushi cha [readr](https://readr.tidyverse.org/), ambacho ni sehemu ya Tidyverse, kinatoa njia ya haraka na rafiki ya kusoma data ya mstatili kwenye R.\n",
+    "\n",
+    "Sasa, hebu tuingize seti ya data ya kisukari iliyotolewa kwenye URL hii ya chanzo: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+    "\n",
+    "Pia, tutafanya ukaguzi wa hali ya data yetu kwa kutumia `glimpse()` na kuonyesha safu 5 za kwanza kwa kutumia `slice()`.\n",
+    "\n",
+    "Kabla ya kuendelea zaidi, hebu pia tuanzishe kitu ambacho utakutana nacho mara nyingi katika msimbo wa R 🥁🥁: opereta wa bomba `%>%`\n",
+    "\n",
+    "Opereta wa bomba (`%>%`) hufanya operesheni kwa mpangilio wa kimantiki kwa kupitisha kitu mbele kwenye kazi au usemi wa wito. Unaweza kufikiria opereta wa bomba kama kusema \"na kisha\" katika msimbo wako.\n"
+   ],
+   "metadata": {
+    "id": "KM6iXLH996Cl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import the data set\r\n",
+    "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Get a glimpse and dimensions of the data\r\n",
+    "glimpse(diabetes)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Select the first 5 rows of the data\r\n",
+    "diabetes %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Z1geAMhM-bSP"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`glimpse()` inaonyesha kuwa data hii ina safu 442 na nguzo 11, ambapo nguzo zote zina aina ya data `double`\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "> glimpse() na slice() ni kazi katika [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, sehemu ya Tidyverse, ni sarufi ya uendeshaji wa data inayotoa seti thabiti ya vitenzi vinavyokusaidia kutatua changamoto za kawaida za uendeshaji wa data.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Sasa kwa kuwa tuna data, hebu tuzingatie kipengele kimoja (`bmi`) kwa lengo la zoezi hili. Hii itahitaji kuchagua nguzo zinazohitajika. Kwa hivyo, tunafanyaje hili?\n",
+    "\n",
+    "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) inatuwezesha *kuchagua* (na kwa hiari kubadilisha jina) nguzo katika fremu ya data.\n"
+   ],
+   "metadata": {
+    "id": "UwjVT1Hz-c3Z"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select predictor feature `bmi` and outcome `y`\r\n",
+    "diabetes_select <- diabetes %>% \r\n",
+    "  select(c(bmi, y))\r\n",
+    "\r\n",
+    "# Print the first 5 rows\r\n",
+    "diabetes_select %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RDY1oAKI-m80"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Mafunzo na Data ya Kupima\n",
+    "\n",
+    "Ni jambo la kawaida katika kujifunza kwa usimamizi *kugawanya* data katika sehemu mbili; seti moja (ambayo kwa kawaida ni kubwa zaidi) ya kufundishia modeli, na seti ndogo ya \"kuhifadhi\" ili kuona jinsi modeli ilivyofanya kazi.\n",
+    "\n",
+    "Sasa kwa kuwa tuna data tayari, tunaweza kuona kama mashine inaweza kusaidia kuamua mgawanyo wa kimantiki kati ya nambari katika seti hii ya data. Tunaweza kutumia kifurushi cha [rsample](https://tidymodels.github.io/rsample/), ambacho ni sehemu ya mfumo wa Tidymodels, kuunda kitu kinachobeba taarifa juu ya *jinsi* ya kugawanya data, na kisha kutumia kazi mbili zaidi za rsample kutoa seti za mafunzo na kupima zilizoundwa:\n"
+   ],
+   "metadata": {
+    "id": "SDk668xK-tc3"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\r\n",
+    "# Split 67% of the data for training and the rest for tesing\r\n",
+    "diabetes_split <- diabetes_select %>% \r\n",
+    "  initial_split(prop = 0.67)\r\n",
+    "\r\n",
+    "# Extract the resulting train and test sets\r\n",
+    "diabetes_train <- training(diabetes_split)\r\n",
+    "diabetes_test <- testing(diabetes_split)\r\n",
+    "\r\n",
+    "# Print the first 3 rows of the training set\r\n",
+    "diabetes_train %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "EqtHx129-1h-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Fundisha mfano wa regression ya mstari kwa kutumia Tidymodels\n",
+    "\n",
+    "Sasa tuko tayari kufundisha mfano wetu!\n",
+    "\n",
+    "Katika Tidymodels, unataja mifano kwa kutumia `parsnip()` kwa kufafanua dhana tatu:\n",
+    "\n",
+    "-   **Aina ya mfano** inatofautisha mifano kama regression ya mstari, regression ya logistic, mifano ya mti wa maamuzi, na kadhalika.\n",
+    "\n",
+    "-   **Hali ya mfano** inajumuisha chaguo za kawaida kama regression na uainishaji; baadhi ya aina za mifano zinaunga mkono mojawapo ya hizi au zote mbili, wakati nyingine zina hali moja tu.\n",
+    "\n",
+    "-   **Injini ya mfano** ni zana ya kihesabu ambayo itatumika kufanikisha mfano. Mara nyingi hizi ni pakiti za R, kama **`\"lm\"`** au **`\"ranger\"`**\n",
+    "\n",
+    "Taarifa hii ya uundaji wa mfano inahifadhiwa katika maelezo ya mfano, kwa hivyo hebu tuunde moja!\n"
+   ],
+   "metadata": {
+    "id": "sBOS-XhB-6v7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- \r\n",
+    "  # Type\r\n",
+    "  linear_reg() %>% \r\n",
+    "  # Engine\r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  # Mode\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the model specification\r\n",
+    "lm_spec"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "20OwEw20--t3"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Baada ya mfano *kuelezwa*, mfano unaweza `kukadiriwa` au `kufunzwa` kwa kutumia kazi ya [`fit()`](https://parsnip.tidymodels.org/reference/fit.html), kwa kawaida kwa kutumia fomula na data fulani.\n",
+    "\n",
+    "`y ~ .` inamaanisha tutafitisha `y` kama kiasi kinachotabiriwa/lengo, kinachoelezewa na vihisishi/vipengele vyote yaani, `.` (katika kesi hii, tuna kihisishi kimoja tu: `bmi`)\n"
+   ],
+   "metadata": {
+    "id": "_oDHs89k_CJj"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>%\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Train a linear regression model\r\n",
+    "lm_mod <- lm_spec %>% \r\n",
+    "  fit(y ~ ., data = diabetes_train)\r\n",
+    "\r\n",
+    "# Print the model\r\n",
+    "lm_mod"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YlsHqd-q_GJQ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kutoka kwenye matokeo ya modeli, tunaweza kuona vigezo vilivyojifunzwa wakati wa mafunzo. Vigezo hivi vinawakilisha vigezo vya mstari wa kufaa bora ambao hutupatia makosa ya chini kabisa kati ya thamani halisi na iliyotabiriwa.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "## 5. Fanya utabiri kwenye seti ya majaribio\n",
+    "\n",
+    "Sasa kwa kuwa tumefundisha modeli, tunaweza kuitumia kutabiri maendeleo ya ugonjwa y kwa kutumia seti ya data ya majaribio kwa kutumia [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Hii itatumika kuchora mstari kati ya makundi ya data.\n"
+   ],
+   "metadata": {
+    "id": "kGZ22RQj_Olu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\r\n",
+    "predictions <- lm_mod %>% \r\n",
+    "  predict(new_data = diabetes_test)\r\n",
+    "\r\n",
+    "# Print out some of the predictions\r\n",
+    "predictions %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "nXHbY7M2_aao"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woohoo! 💃🕺 Tumefanikiwa kufundisha modeli na kuitumia kufanya utabiri!\n",
+    "\n",
+    "Wakati wa kufanya utabiri, utaratibu wa tidymodels ni daima kutoa tibble/data frame ya matokeo yenye majina ya safu yaliyosanifishwa. Hii hufanya iwe rahisi kuunganisha data ya awali na utabiri katika muundo unaoweza kutumika kwa shughuli zinazofuata kama vile kuchora grafu.\n",
+    "\n",
+    "`dplyr::bind_cols()` inaunganisha kwa ufanisi data frame nyingi kwa safu.\n"
+   ],
+   "metadata": {
+    "id": "R_JstwUY_bIs"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Combine the predictions and the original test set\r\n",
+    "results <- diabetes_test %>% \r\n",
+    "  bind_cols(predictions)\r\n",
+    "\r\n",
+    "\r\n",
+    "results %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RybsMJR7_iI8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 6. Onyesha matokeo ya uundaji wa modeli\n",
+    "\n",
+    "Sasa ni wakati wa kuona hili kwa njia ya picha 📈. Tutatengeneza mchoro wa alama za kutawanyika wa thamani zote za `y` na `bmi` kutoka kwenye seti ya majaribio, kisha tutatumia utabiri kuonyesha mstari mahali panapofaa zaidi, kati ya makundi ya data ya modeli.\n",
+    "\n",
+    "R ina mifumo kadhaa ya kutengeneza grafu, lakini `ggplot2` ni mojawapo ya mifumo maridadi na yenye matumizi mengi zaidi. Hii inakuwezesha kuunda grafu kwa **kuunganisha vipengele huru**.\n"
+   ],
+   "metadata": {
+    "id": "XJbYbMZW_n_s"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plot\r\n",
+    "theme_set(theme_light())\r\n",
+    "# Create a scatter plot\r\n",
+    "results %>% \r\n",
+    "  ggplot(aes(x = bmi)) +\r\n",
+    "  # Add a scatter plot\r\n",
+    "  geom_point(aes(y = y), size = 1.6) +\r\n",
+    "  # Add a line plot\r\n",
+    "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R9tYp3VW_sTn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "> ✅ Fikiria kidogo kuhusu kinachoendelea hapa. Mstari wa moja kwa moja unapita katikati ya nukta nyingi ndogo za data, lakini unafanya nini hasa? Je, unaweza kuona jinsi unavyoweza kutumia mstari huu kutabiri mahali ambapo nukta mpya ya data isiyoonekana inapaswa kuwekwa kuhusiana na mhimili wa y wa mchoro? Jaribu kuelezea kwa maneno matumizi ya vitendo ya mfano huu.\n",
+    "\n",
+    "Hongera, umeunda mfano wako wa kwanza wa regression ya mstari, ukatengeneza utabiri kwa kutumia huo, na ukaonyesha katika mchoro!\n"
+   ],
+   "metadata": {
+    "id": "zrPtHIxx_tNI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/1-Tools/solution/notebook.ipynb b/translations/sw/2-Regression/1-Tools/solution/notebook.ipynb
new file mode 100644
index 000000000..bfd389c43
--- /dev/null
+++ b/translations/sw/2-Regression/1-Tools/solution/notebook.ipynb
@@ -0,0 +1,671 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model, model_selection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pakia seti ya data ya kisukari, gawanya kuwa data za `X` na sifa za `y`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "print(X.shape)\n",
+    "print(X[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chagua kipengele kimoja tu kulenga kwa zoezi hili\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selecting the 3rd feature\n",
+    "X = X[:, 2]\n",
+    "print(X.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 1)\n",
+      "[[ 0.06169621]\n",
+      " [-0.05147406]\n",
+      " [ 0.04445121]\n",
+      " [-0.01159501]\n",
+      " [-0.03638469]\n",
+      " [-0.04069594]\n",
+      " [-0.04716281]\n",
+      " [-0.00189471]\n",
+      " [ 0.06169621]\n",
+      " [ 0.03906215]\n",
+      " [-0.08380842]\n",
+      " [ 0.01750591]\n",
+      " [-0.02884001]\n",
+      " [-0.00189471]\n",
+      " [-0.02560657]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [ 0.01211685]\n",
+      " [-0.0105172 ]\n",
+      " [-0.01806189]\n",
+      " [-0.05686312]\n",
+      " [-0.02237314]\n",
+      " [-0.00405033]\n",
+      " [ 0.06061839]\n",
+      " [ 0.03582872]\n",
+      " [-0.01267283]\n",
+      " [-0.07734155]\n",
+      " [ 0.05954058]\n",
+      " [-0.02129532]\n",
+      " [-0.00620595]\n",
+      " [ 0.04445121]\n",
+      " [-0.06548562]\n",
+      " [ 0.12528712]\n",
+      " [-0.05039625]\n",
+      " [-0.06332999]\n",
+      " [-0.03099563]\n",
+      " [ 0.02289497]\n",
+      " [ 0.01103904]\n",
+      " [ 0.07139652]\n",
+      " [ 0.01427248]\n",
+      " [-0.00836158]\n",
+      " [-0.06764124]\n",
+      " [-0.0105172 ]\n",
+      " [-0.02345095]\n",
+      " [ 0.06816308]\n",
+      " [-0.03530688]\n",
+      " [-0.01159501]\n",
+      " [-0.0730303 ]\n",
+      " [-0.04177375]\n",
+      " [ 0.01427248]\n",
+      " [-0.00728377]\n",
+      " [ 0.0164281 ]\n",
+      " [-0.00943939]\n",
+      " [-0.01590626]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.04931844]\n",
+      " [ 0.04121778]\n",
+      " [-0.06332999]\n",
+      " [-0.06440781]\n",
+      " [-0.02560657]\n",
+      " [-0.00405033]\n",
+      " [ 0.00457217]\n",
+      " [-0.00728377]\n",
+      " [-0.0374625 ]\n",
+      " [-0.02560657]\n",
+      " [-0.02452876]\n",
+      " [-0.01806189]\n",
+      " [-0.01482845]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [-0.06979687]\n",
+      " [ 0.03367309]\n",
+      " [-0.00405033]\n",
+      " [-0.02021751]\n",
+      " [ 0.00241654]\n",
+      " [-0.03099563]\n",
+      " [ 0.02828403]\n",
+      " [-0.03638469]\n",
+      " [-0.05794093]\n",
+      " [-0.0374625 ]\n",
+      " [ 0.01211685]\n",
+      " [-0.02237314]\n",
+      " [-0.03530688]\n",
+      " [ 0.00996123]\n",
+      " [-0.03961813]\n",
+      " [ 0.07139652]\n",
+      " [-0.07518593]\n",
+      " [-0.00620595]\n",
+      " [-0.04069594]\n",
+      " [-0.04824063]\n",
+      " [-0.02560657]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.00457217]\n",
+      " [-0.06440781]\n",
+      " [-0.01698407]\n",
+      " [-0.05794093]\n",
+      " [ 0.00996123]\n",
+      " [ 0.08864151]\n",
+      " [-0.00512814]\n",
+      " [-0.06440781]\n",
+      " [ 0.01750591]\n",
+      " [-0.04500719]\n",
+      " [ 0.02828403]\n",
+      " [ 0.04121778]\n",
+      " [ 0.06492964]\n",
+      " [-0.03207344]\n",
+      " [-0.07626374]\n",
+      " [ 0.04984027]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03207344]\n",
+      " [ 0.00457217]\n",
+      " [ 0.02073935]\n",
+      " [ 0.01427248]\n",
+      " [ 0.11019775]\n",
+      " [ 0.00133873]\n",
+      " [ 0.05846277]\n",
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+      " [-0.0105172 ]\n",
+      " [-0.04716281]\n",
+      " [ 0.00457217]\n",
+      " [ 0.01750591]\n",
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+      " [ 0.0347509 ]\n",
+      " [ 0.02397278]\n",
+      " [-0.00836158]\n",
+      " [-0.06117437]\n",
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+      " [-0.06225218]\n",
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+      " [ 0.09618619]\n",
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+      " [-0.02129532]\n",
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+      " [ 0.05630715]\n",
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+      " [ 0.04768465]\n",
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+      " [ 0.00564998]\n",
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+      " [ 0.05954058]\n",
+      " [ 0.09295276]\n",
+      " [ 0.01535029]\n",
+      " [-0.00512814]\n",
+      " [ 0.0703187 ]\n",
+      " [-0.00405033]\n",
+      " [-0.00081689]\n",
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+      " [ 0.06061839]\n",
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+      " [-0.03315126]\n",
+      " [-0.06548562]\n",
+      " [ 0.0433734 ]\n",
+      " [-0.06225218]\n",
+      " [ 0.06385183]\n",
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+      " [ 0.07247433]\n",
+      " [-0.0191397 ]\n",
+      " [-0.06656343]\n",
+      " [-0.06009656]\n",
+      " [ 0.06924089]\n",
+      " [ 0.05954058]\n",
+      " [-0.02668438]\n",
+      " [-0.02021751]\n",
+      " [-0.046085  ]\n",
+      " [ 0.07139652]\n",
+      " [-0.07949718]\n",
+      " [ 0.00996123]\n",
+      " [-0.03854032]\n",
+      " [ 0.01966154]\n",
+      " [ 0.02720622]\n",
+      " [-0.00836158]\n",
+      " [-0.01590626]\n",
+      " [ 0.00457217]\n",
+      " [-0.04285156]\n",
+      " [ 0.00564998]\n",
+      " [-0.03530688]\n",
+      " [ 0.02397278]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
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+      " [-0.06656343]\n",
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+      " [-0.046085  ]\n",
+      " [ 0.00349435]\n",
+      " [ 0.05415152]\n",
+      " [-0.04500719]\n",
+      " [-0.05794093]\n",
+      " [-0.05578531]\n",
+      " [ 0.00133873]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00672779]\n",
+      " [ 0.04660684]\n",
+      " [ 0.02612841]\n",
+      " [ 0.04552903]\n",
+      " [ 0.04013997]\n",
+      " [-0.01806189]\n",
+      " [ 0.01427248]\n",
+      " [ 0.03690653]\n",
+      " [ 0.00349435]\n",
+      " [-0.07087468]\n",
+      " [-0.03315126]\n",
+      " [ 0.09403057]\n",
+      " [ 0.03582872]\n",
+      " [ 0.03151747]\n",
+      " [-0.06548562]\n",
+      " [-0.04177375]\n",
+      " [-0.03961813]\n",
+      " [-0.03854032]\n",
+      " [-0.02560657]\n",
+      " [-0.02345095]\n",
+      " [-0.06656343]\n",
+      " [ 0.03259528]\n",
+      " [-0.046085  ]\n",
+      " [-0.02991782]\n",
+      " [-0.01267283]\n",
+      " [-0.01590626]\n",
+      " [ 0.07139652]\n",
+      " [-0.03099563]\n",
+      " [ 0.00026092]\n",
+      " [ 0.03690653]\n",
+      " [ 0.03906215]\n",
+      " [-0.01482845]\n",
+      " [ 0.00672779]\n",
+      " [-0.06871905]\n",
+      " [-0.00943939]\n",
+      " [ 0.01966154]\n",
+      " [ 0.07462995]\n",
+      " [-0.00836158]\n",
+      " [-0.02345095]\n",
+      " [-0.046085  ]\n",
+      " [ 0.05415152]\n",
+      " [-0.03530688]\n",
+      " [-0.03207344]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04768465]\n",
+      " [ 0.06061839]\n",
+      " [ 0.05630715]\n",
+      " [ 0.09834182]\n",
+      " [ 0.05954058]\n",
+      " [ 0.03367309]\n",
+      " [ 0.05630715]\n",
+      " [-0.06548562]\n",
+      " [ 0.16085492]\n",
+      " [-0.05578531]\n",
+      " [-0.02452876]\n",
+      " [-0.03638469]\n",
+      " [-0.00836158]\n",
+      " [-0.04177375]\n",
+      " [ 0.12744274]\n",
+      " [-0.07734155]\n",
+      " [ 0.02828403]\n",
+      " [-0.02560657]\n",
+      " [-0.06225218]\n",
+      " [-0.00081689]\n",
+      " [ 0.08864151]\n",
+      " [-0.03207344]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00888341]\n",
+      " [ 0.00672779]\n",
+      " [-0.02021751]\n",
+      " [-0.02452876]\n",
+      " [-0.01159501]\n",
+      " [ 0.02612841]\n",
+      " [-0.05901875]\n",
+      " [-0.03638469]\n",
+      " [-0.02452876]\n",
+      " [ 0.01858372]\n",
+      " [-0.0902753 ]\n",
+      " [-0.00512814]\n",
+      " [-0.05255187]\n",
+      " [-0.02237314]\n",
+      " [-0.02021751]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00620595]\n",
+      " [-0.01698407]\n",
+      " [ 0.05522933]\n",
+      " [ 0.07678558]\n",
+      " [ 0.01858372]\n",
+      " [-0.02237314]\n",
+      " [ 0.09295276]\n",
+      " [-0.03099563]\n",
+      " [ 0.03906215]\n",
+      " [-0.06117437]\n",
+      " [-0.00836158]\n",
+      " [-0.0374625 ]\n",
+      " [-0.01375064]\n",
+      " [ 0.07355214]\n",
+      " [-0.02452876]\n",
+      " [ 0.03367309]\n",
+      " [ 0.0347509 ]\n",
+      " [-0.03854032]\n",
+      " [-0.03961813]\n",
+      " [-0.00189471]\n",
+      " [-0.03099563]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06492964]\n",
+      " [ 0.04013997]\n",
+      " [-0.02345095]\n",
+      " [ 0.05307371]\n",
+      " [ 0.04013997]\n",
+      " [-0.02021751]\n",
+      " [ 0.01427248]\n",
+      " [-0.03422907]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00457217]\n",
+      " [ 0.03043966]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.06169621]\n",
+      " [-0.00728377]\n",
+      " [ 0.00564998]\n",
+      " [ 0.05415152]\n",
+      " [-0.00836158]\n",
+      " [ 0.114509  ]\n",
+      " [ 0.06708527]\n",
+      " [-0.05578531]\n",
+      " [ 0.03043966]\n",
+      " [-0.02560657]\n",
+      " [ 0.10480869]\n",
+      " [-0.00620595]\n",
+      " [-0.04716281]\n",
+      " [-0.04824063]\n",
+      " [ 0.08540807]\n",
+      " [-0.01267283]\n",
+      " [-0.03315126]\n",
+      " [-0.00728377]\n",
+      " [-0.01375064]\n",
+      " [ 0.05954058]\n",
+      " [ 0.02181716]\n",
+      " [ 0.01858372]\n",
+      " [-0.01159501]\n",
+      " [-0.00297252]\n",
+      " [ 0.01750591]\n",
+      " [-0.02991782]\n",
+      " [-0.02021751]\n",
+      " [-0.05794093]\n",
+      " [ 0.06061839]\n",
+      " [-0.04069594]\n",
+      " [-0.07195249]\n",
+      " [-0.05578531]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03315126]\n",
+      " [ 0.04984027]\n",
+      " [-0.08488624]\n",
+      " [ 0.00564998]\n",
+      " [ 0.02073935]\n",
+      " [-0.00728377]\n",
+      " [ 0.10480869]\n",
+      " [-0.02452876]\n",
+      " [-0.00620595]\n",
+      " [-0.03854032]\n",
+      " [ 0.13714305]\n",
+      " [ 0.17055523]\n",
+      " [ 0.00241654]\n",
+      " [ 0.03798434]\n",
+      " [-0.05794093]\n",
+      " [-0.00943939]\n",
+      " [-0.02345095]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03422907]\n",
+      " [-0.00297252]\n",
+      " [ 0.06816308]\n",
+      " [ 0.00996123]\n",
+      " [ 0.00241654]\n",
+      " [-0.03854032]\n",
+      " [ 0.02612841]\n",
+      " [-0.08919748]\n",
+      " [ 0.06061839]\n",
+      " [-0.02884001]\n",
+      " [-0.02991782]\n",
+      " [-0.0191397 ]\n",
+      " [-0.04069594]\n",
+      " [ 0.01535029]\n",
+      " [-0.02452876]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06924089]\n",
+      " [-0.06979687]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [ 0.01858372]\n",
+      " [ 0.00133873]\n",
+      " [-0.03099563]\n",
+      " [-0.00405033]\n",
+      " [ 0.01535029]\n",
+      " [ 0.02289497]\n",
+      " [ 0.04552903]\n",
+      " [-0.04500719]\n",
+      " [-0.03315126]\n",
+      " [ 0.097264  ]\n",
+      " [ 0.05415152]\n",
+      " [ 0.12313149]\n",
+      " [-0.08057499]\n",
+      " [ 0.09295276]\n",
+      " [-0.05039625]\n",
+      " [-0.01159501]\n",
+      " [-0.0277622 ]\n",
+      " [ 0.05846277]\n",
+      " [ 0.08540807]\n",
+      " [-0.00081689]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00888341]\n",
+      " [ 0.08001901]\n",
+      " [ 0.07139652]\n",
+      " [-0.02452876]\n",
+      " [-0.0547075 ]\n",
+      " [-0.03638469]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.07786339]\n",
+      " [-0.03961813]\n",
+      " [ 0.01103904]\n",
+      " [-0.04069594]\n",
+      " [-0.03422907]\n",
+      " [ 0.00564998]\n",
+      " [ 0.08864151]\n",
+      " [-0.03315126]\n",
+      " [-0.05686312]\n",
+      " [-0.03099563]\n",
+      " [ 0.05522933]\n",
+      " [-0.06009656]\n",
+      " [ 0.00133873]\n",
+      " [-0.02345095]\n",
+      " [-0.07410811]\n",
+      " [ 0.01966154]\n",
+      " [-0.01590626]\n",
+      " [-0.01590626]\n",
+      " [ 0.03906215]\n",
+      " [-0.0730303 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Reshaping to get a 2D array\n",
+    "X = X.reshape(-1, 1)\n",
+    "print(X.shape)\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chagua mfano na uufanyie mazoezi kwa data ya mafunzo\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tumia data ya majaribio kutabiri mstari\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Onyesha matokeo katika mchoro\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test,  color='black')\n",
+    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "16ff1a974f6e4348e869e4a7d366b86a",
+   "translation_date": "2025-09-06T13:39:19+00:00",
+   "source_file": "2-Regression/1-Tools/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/2-Data/notebook.ipynb b/translations/sw/2-Regression/2-Data/notebook.ipynb
new file mode 100644
index 000000000..8f35fe4af
--- /dev/null
+++ b/translations/sw/2-Regression/2-Data/notebook.ipynb
@@ -0,0 +1,46 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  },
+  "coopTranslator": {
+   "original_hash": "1b2ab303ac6c604a34c6ca7a49077fc7",
+   "translation_date": "2025-09-06T13:46:00+00:00",
+   "source_file": "2-Regression/2-Data/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/sw/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
new file mode 100644
index 000000000..e96c6cf16
--- /dev/null
+++ b/translations/sw/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -0,0 +1,671 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_2-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "f3c335f9940cfd76528b3ef918b9b342",
+   "translation_date": "2025-09-06T13:52:52+00:00",
+   "source_file": "2-Regression/2-Data/solution/R/lesson_2-R.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Jenga mfano wa regression: andaa na onyesha data\n",
+    "\n",
+    "## **Regression ya Mstari kwa Malenge - Somo la 2**\n",
+    "#### Utangulizi\n",
+    "\n",
+    "Sasa kwa kuwa umejiandaa na zana unazohitaji kuanza kujenga mifano ya kujifunza mashine kwa kutumia Tidymodels na Tidyverse, uko tayari kuanza kuuliza maswali kuhusu data yako. Unapofanya kazi na data na kutumia suluhisho za ML, ni muhimu sana kuelewa jinsi ya kuuliza swali sahihi ili kufungua uwezo wa dataset yako ipasavyo.\n",
+    "\n",
+    "Katika somo hili, utajifunza:\n",
+    "\n",
+    "- Jinsi ya kuandaa data yako kwa ajili ya kujenga mifano.\n",
+    "\n",
+    "- Jinsi ya kutumia `ggplot2` kwa uonyeshaji wa data.\n",
+    "\n",
+    "Swali unalotaka kujibiwa litaamua ni aina gani ya algorithmi za ML utatumia. Na ubora wa jibu unalopata utategemea sana asili ya data yako.\n",
+    "\n",
+    "Hebu tuone hili kwa kufanya zoezi la vitendo.\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/unruly_data.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Sanaa na @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Sanaa na \\@allison_horst](../../../../../../2-Regression/2-Data/images/unruly_data.jpg)<br>Sanaa na \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "Pg5aexcOPqAZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Kuleta data za malenge na kuitumia Tidyverse\n",
+    "\n",
+    "Tutahitaji vifurushi vifuatavyo ili kuchambua somo hili:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) ni [mkusanyiko wa vifurushi vya R](https://www.tidyverse.org/packages) vilivyoundwa ili kufanya sayansi ya data kuwa ya haraka, rahisi, na ya kufurahisha!\n",
+    "\n",
+    "Unaweza kuvifunga kwa kutumia:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\"))`\n",
+    "\n",
+    "Skripti iliyo hapa chini inakagua kama una vifurushi vinavyohitajika kukamilisha moduli hii na kuvifunga kwako endapo baadhi havipo.\n"
+   ],
+   "metadata": {
+    "id": "dc5WhyVdXAjR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "GqPYUZgfXOBt"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sasa, wacha tuwashe baadhi ya vifurushi na kupakia [data](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) iliyotolewa kwa somo hili!\n"
+   ],
+   "metadata": {
+    "id": "kvjDTPDSXRr2"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n =50)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VMri-t2zXqgD"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`glimpse()` ya haraka inaonyesha mara moja kwamba kuna nafasi tupu na mchanganyiko wa mistari (`chr`) na data ya nambari (`dbl`). `Date` ni aina ya herufi, na pia kuna safu ya ajabu inayoitwa `Package` ambapo data ni mchanganyiko wa `sacks`, `bins`, na thamani nyinginezo. Kwa kweli, data hii ni fujo kidogo 😤.\n",
+    "\n",
+    "Kwa kweli, si jambo la kawaida kupewa seti ya data ambayo iko tayari kabisa kutumika kuunda mfano wa ML moja kwa moja. Lakini usiwe na wasiwasi, katika somo hili, utajifunza jinsi ya kuandaa seti ya data mbichi kwa kutumia maktaba za kawaida za R 🧑‍🔧. Pia utajifunza mbinu mbalimbali za kuona data. 📈📊\n",
+    "<br>\n",
+    "\n",
+    "> Kumbusho: Opereta wa bomba (`%>%`) hufanya shughuli kwa mpangilio wa kimantiki kwa kupitisha kitu mbele kwenye kazi au usemi wa simu. Unaweza kufikiria opereta wa bomba kama kusema \"na kisha\" katika msimbo wako.\n"
+   ],
+   "metadata": {
+    "id": "REWcIv9yX29v"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Angalia data iliyokosekana\n",
+    "\n",
+    "Moja ya changamoto za kawaida ambazo wanasayansi wa data hukutana nazo ni data isiyokamilika au iliyokosekana. R inawakilisha thamani zilizokosekana, au zisizojulikana, kwa thamani maalum: `NA` (Not Available).\n",
+    "\n",
+    "Kwa hivyo, tunawezaje kujua kwamba fremu ya data ina thamani zilizokosekana?\n",
+    "<br>\n",
+    "-   Njia moja rahisi ni kutumia kazi ya msingi ya R `anyNA` ambayo inarejesha vitu vya kimantiki `TRUE` au `FALSE`.\n"
+   ],
+   "metadata": {
+    "id": "Zxfb3AM5YbUe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  anyNA()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "G--DQutAYltj"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kunaonekana kuna baadhi ya data zinazokosekana! Hapo ndipo mahali pazuri pa kuanzia.\n",
+    "\n",
+    "-   Njia nyingine ni kutumia kazi `is.na()` ambayo inaonyesha ni vipengele vipi vya safu wima vinavyokosekana kwa kutumia mantiki ya `TRUE`.\n"
+   ],
+   "metadata": {
+    "id": "mU-7-SB6YokF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "W-DxDOR4YxSW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sawa, kazi imekamilika lakini kwa fremu kubwa ya data kama hii, itakuwa isiyo na ufanisi na karibu haiwezekani kupitia safu zote na nguzo moja baada ya nyingine😴.\n",
+    "\n",
+    "-   Njia ya kueleweka zaidi itakuwa ni kuhesabu jumla ya thamani zinazokosekana kwa kila nguzo:\n"
+   ],
+   "metadata": {
+    "id": "xUWxipKYY0o7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  colSums()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZRBWV6P9ZArL"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ni bora zaidi! Kuna data inayokosekana, lakini labda haitakuwa na umuhimu kwa kazi inayofanyika. Hebu tuone uchambuzi zaidi utakavyoleta matokeo.\n",
+    "\n",
+    "> Pamoja na seti nzuri za pakiti na kazi, R ina nyaraka bora sana. Kwa mfano, tumia `help(colSums)` au `?colSums` ili kujifunza zaidi kuhusu kazi hiyo.\n"
+   ],
+   "metadata": {
+    "id": "9gv-crB6ZD1Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Dplyr: Sarufi ya Uendeshaji wa Takwimu\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_wrangling.png\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Sanaa na @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Sanaa na \\@allison_horst](../../../../../../2-Regression/2-Data/images/dplyr_wrangling.png)<br/>Sanaa na \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "o4jLY5-VZO2C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`dplyr`](https://dplyr.tidyverse.org/), kifurushi katika Tidyverse, ni sarufi ya uendeshaji wa data inayotoa seti thabiti ya vitenzi vinavyokusaidia kutatua changamoto za kawaida za uendeshaji wa data. Katika sehemu hii, tutachunguza baadhi ya vitenzi vya dplyr!\n"
+   ],
+   "metadata": {
+    "id": "i5o33MQBZWWw"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::select()\n",
+    "\n",
+    "`select()` ni kazi katika kifurushi cha `dplyr` ambayo hukusaidia kuchagua safu za kuweka au kuondoa.\n",
+    "\n",
+    "Ili kufanya fremu yako ya data iwe rahisi kufanya kazi nayo, ondoa safu kadhaa zake, ukitumia `select()`, ukihifadhi tu safu unazohitaji.\n",
+    "\n",
+    "Kwa mfano, katika zoezi hili, uchambuzi wetu utahusisha safu za `Package`, `Low Price`, `High Price` na `Date`. Hebu tuchague safu hizi.\n"
+   ],
+   "metadata": {
+    "id": "x3VGMAGBZiUr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(Package, `Low Price`, `High Price`, Date)\n",
+    "\n",
+    "\n",
+    "# Print data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "F_FgxQnVZnM0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::mutate()\n",
+    "\n",
+    "`mutate()` ni kazi katika kifurushi cha `dplyr` ambayo husaidia kuunda au kurekebisha safu, huku ukihifadhi safu zilizopo.\n",
+    "\n",
+    "Muundo wa jumla wa `mutate` ni:\n",
+    "\n",
+    "`data %>%   mutate(new_column_name = what_it_contains)`\n",
+    "\n",
+    "Hebu tujaribu `mutate` kwa kutumia safu ya `Date` kwa kufanya shughuli zifuatazo:\n",
+    "\n",
+    "1. Badilisha tarehe (ambazo kwa sasa ni aina ya herufi) kuwa muundo wa mwezi (hizi ni tarehe za Marekani, kwa hivyo muundo ni `MM/DD/YYYY`).\n",
+    "\n",
+    "2. Toa mwezi kutoka kwa tarehe na uweke kwenye safu mpya.\n",
+    "\n",
+    "Katika R, kifurushi [lubridate](https://lubridate.tidyverse.org/) hufanya iwe rahisi kufanya kazi na data ya tarehe na muda. Kwa hivyo, hebu tutumie `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` na tuone jinsi ya kufanikisha malengo haya. Tunaweza kuondoa safu ya Date kwa kuwa hatutaihitaji tena katika shughuli zinazofuata.\n"
+   ],
+   "metadata": {
+    "id": "2KKo0Ed9Z1VB"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load lubridate\n",
+    "library(lubridate)\n",
+    "\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  # Convert the Date column to a date object\n",
+    "  mutate(Date = mdy(Date)) %>% \n",
+    "  # Extract month from Date\n",
+    "  mutate(Month = month(Date)) %>% \n",
+    "  # Drop Date column\n",
+    "  select(-Date)\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5joszIVSZ6xe"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woohoo! 🤩\n",
+    "\n",
+    "Sasa, hebu tuunde safu mpya `Price`, inayowakilisha bei ya wastani ya malenge. Sasa, chukua wastani wa safu za `Low Price` na `High Price` ili kujaza safu mpya ya Price.\n"
+   ],
+   "metadata": {
+    "id": "nIgLjNMCZ-6Y"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new column Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Zo0BsqqtaJw2"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ndio!💪\n",
+    "\n",
+    "\"Lakini subiri kidogo!\", utasema baada ya kuangalia haraka seti nzima ya data kwa kutumia `View(pumpkins)`, \"Kuna kitu cha ajabu hapa!\"🤔\n",
+    "\n",
+    "Ukichunguza safu ya `Package`, malenge yanauzwa katika mipangilio mbalimbali. Baadhi yanauzwa kwa kipimo cha `1 1/9 bushel`, mengine kwa kipimo cha `1/2 bushel`, mengine kwa kila malenge, mengine kwa paundi, na mengine katika masanduku makubwa yenye upana tofauti.\n",
+    "\n",
+    "Hebu tuhakikishe hili:\n"
+   ],
+   "metadata": {
+    "id": "p77WZr-9aQAR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Verify the distinct observations in Package column\n",
+    "pumpkins %>% \n",
+    "  distinct(Package)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "XISGfh0IaUy6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ajabu!👏\n",
+    "\n",
+    "Malenge yanaonekana kuwa magumu sana kupima kwa uthabiti, kwa hivyo hebu tuyachuje kwa kuchagua malenge tu yenye neno *bushel* katika safu ya `Package` na kuweka haya kwenye fremu mpya ya data `new_pumpkins`.\n"
+   ],
+   "metadata": {
+    "id": "7sMjiVujaZxY"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::filter() na stringr::str_detect()\n",
+    "\n",
+    "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): huunda sehemu ndogo ya data inayojumuisha tu **mistari** inayokidhi masharti yako, katika hali hii, maboga yenye neno *bushel* katika safu ya `Package`.\n",
+    "\n",
+    "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): hutambua uwepo au kutokuwepo kwa muundo fulani ndani ya maandishi.\n",
+    "\n",
+    "Kifurushi cha [`stringr`](https://github.com/tidyverse/stringr) kinatoa kazi rahisi kwa operesheni za kawaida za maandishi.\n"
+   ],
+   "metadata": {
+    "id": "L8Qfcs92ageF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Retain only pumpkins with \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "       filter(str_detect(Package, \"bushel\"))\n",
+    "\n",
+    "# Get the dimensions of the new data\n",
+    "dim(new_pumpkins)\n",
+    "\n",
+    "# View a few rows of the new data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "hy_SGYREampd"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Unaweza kuona kwamba tumepunguza hadi takriban safu 415 za data zinazohusiana na maboga kwa gunia.🤩\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "VrDwF031avlR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::case_when()\n",
+    "\n",
+    "**Lakini subiri! Kuna jambo moja zaidi la kufanya**\n",
+    "\n",
+    "Je, uliona kwamba kiasi cha bushel kinatofautiana kwa kila safu? Unahitaji kuweka bei sawa ili kuonyesha bei kwa bushel moja, si kwa 1 1/9 au 1/2 bushel. Ni wakati wa kufanya hesabu ili kuifanya iwe ya kawaida.\n",
+    "\n",
+    "Tutatumia kazi [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) kubadilisha safu ya Bei kulingana na masharti fulani. `case_when` inakuruhusu kuunganisha taarifa nyingi za `if_else()` kwa urahisi.\n"
+   ],
+   "metadata": {
+    "id": "mLpw2jH4a0tx"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(Price = case_when(\n",
+    "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+    "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+    "    TRUE ~ Price))\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 30)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "P68kLVQmbM6I"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sasa, tunaweza kuchambua bei kwa kila kipimo kulingana na kipimo chao cha busheli. Hata hivyo, utafiti huu wote wa busheli za maboga unaonyesha jinsi ilivyo `muhimu` sana `kuelewa asili ya data yako`!\n",
+    "\n",
+    "> ✅ Kulingana na [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), uzito wa busheli hutegemea aina ya mazao, kwa kuwa ni kipimo cha ujazo. \"Busheli ya nyanya, kwa mfano, inapaswa kuwa na uzito wa pauni 56... Majani na mboga za majani huchukua nafasi zaidi na uzito mdogo, hivyo busheli ya mchicha ni pauni 20 tu.\" Ni jambo gumu kidogo! Tusijisumbue na kufanya ubadilishaji wa busheli hadi pauni, badala yake tuweke bei kwa busheli. Hata hivyo, utafiti huu wote wa busheli za maboga unaonyesha jinsi ilivyo muhimu sana kuelewa asili ya data yako!\n",
+    "\n",
+    "> ✅ Je, uliona kwamba maboga yanayouzwa kwa nusu busheli ni ghali sana? Je, unaweza kubaini kwa nini? Dokezo: maboga madogo ni ghali zaidi kuliko makubwa, labda kwa sababu kuna mengi zaidi yao kwa busheli, ikizingatiwa nafasi isiyotumika inayochukuliwa na boga moja kubwa la pie lenye uwazi.\n"
+   ],
+   "metadata": {
+    "id": "pS2GNPagbSdb"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sasa mwisho, kwa ajili ya kujifurahisha tu 💁‍♀️, hebu pia tuhamishe safu ya Mwezi kwenye nafasi ya kwanza yaani `kabla` ya safu ya `Package`.\n",
+    "\n",
+    "`dplyr::relocate()` inatumika kubadilisha nafasi za safu.\n"
+   ],
+   "metadata": {
+    "id": "qql1SowfbdnP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new data frame new_pumpkins\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(Month, .before = Package)\n",
+    "\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "JJ1x6kw8bixF"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kazi nzuri!👌 Sasa una seti safi na nadhifu ya data ambayo unaweza kutumia kujenga mfano wako mpya wa regression!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "y8TJ0Za_bn5Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Uonyeshaji wa data kwa ggplot2\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/data-visualization.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infographic na Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Infographic na Dasani Madipalli](../../../../../../2-Regression/2-Data/images/data-visualization.png){width=\"600\"}-->\n",
+    "\n",
+    "Kuna msemo *mwenye busara* unaosema hivi:\n",
+    "\n",
+    "> \"Grafu rahisi imeleta taarifa zaidi kwa akili ya mchambuzi wa data kuliko kifaa kingine chochote.\" --- John Tukey\n",
+    "\n",
+    "Sehemu ya jukumu la mwanasayansi wa data ni kuonyesha ubora na asili ya data wanayofanyia kazi. Ili kufanya hivyo, mara nyingi huunda uonyeshaji wa kuvutia, au michoro, grafu, na chati, zinazoonyesha vipengele tofauti vya data. Kwa njia hii, wanaweza kuonyesha kwa taswira mahusiano na mapungufu ambayo vinginevyo ni vigumu kugundua.\n",
+    "\n",
+    "Uonyeshaji pia unaweza kusaidia kuamua mbinu ya kujifunza kwa mashine inayofaa zaidi kwa data. Kwa mfano, grafu ya alama inayofuata mstari inaweza kuonyesha kuwa data ni mgombea mzuri kwa zoezi la regression ya mstari.\n",
+    "\n",
+    "R inatoa mifumo kadhaa ya kutengeneza grafu, lakini [`ggplot2`](https://ggplot2.tidyverse.org/index.html) ni mojawapo ya mifumo maridadi na yenye uwezo mkubwa. `ggplot2` hukuruhusu kuunda grafu kwa **kuunganisha vipengele huru**.\n",
+    "\n",
+    "Tuanzie na grafu rahisi ya alama kwa safu za Price na Month.\n",
+    "\n",
+    "Kwa hivyo, katika hali hii, tutaanza na [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), tutaweka dataset na ramani ya esthetiki (kwa [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) kisha tutaongeza tabaka (kama [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) kwa grafu za alama.\n"
+   ],
+   "metadata": {
+    "id": "mYSH6-EtbvNa"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plots\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+    "p + geom_point()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "g2YjnGeOcLo4"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Je, huu ni mchoro wa maana 🤷? Kuna chochote kinachokushangaza kuhusu huu mchoro?\n",
+    "\n",
+    "Sio wa maana sana kwani unachofanya tu ni kuonyesha data yako kama usambazaji wa alama katika mwezi fulani.  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "Ml7SDCLQcPvE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Je, tunafanyaje iwe ya manufaa?**\n",
+    "\n",
+    "Ili kupata chati zinazoonyesha data ya manufaa, mara nyingi unahitaji kuunganisha data kwa namna fulani. Kwa mfano, katika hali yetu, kupata wastani wa bei ya maboga kwa kila mwezi kungeweza kutoa ufahamu zaidi kuhusu mifumo ya msingi katika data yetu. Hii inatupeleka kwenye kipengele kingine cha **dplyr**:\n",
+    "\n",
+    "#### `dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "Uchanganuzi wa vikundi katika R unaweza kufanywa kwa urahisi kwa kutumia\n",
+    "\n",
+    "`dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "-   `dplyr::group_by()` hubadilisha kitengo cha uchambuzi kutoka seti nzima ya data hadi vikundi vya mtu binafsi kama vile kwa kila mwezi.\n",
+    "\n",
+    "-   `dplyr::summarize()` huunda fremu mpya ya data yenye safu moja kwa kila kigezo cha kikundi na safu moja kwa kila takwimu ya muhtasari uliyoainisha.\n",
+    "\n",
+    "Kwa mfano, tunaweza kutumia `dplyr::group_by() %>% summarize()` kuunganisha maboga katika vikundi kulingana na safu ya **Month** na kisha kupata **wastani wa bei** kwa kila mwezi.\n"
+   ],
+   "metadata": {
+    "id": "jMakvJZIcVkh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "6kVSUa2Bcilf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Fupi!✨\n",
+    "\n",
+    "Vipengele vya kategoria kama miezi vinaonyeshwa vyema kwa kutumia mchoro wa mistari 📊. Tabaka zinazohusika na michoro ya mistari ni `geom_bar()` na `geom_col()`. Tazama `?geom_bar` ili kujifunza zaidi.\n",
+    "\n",
+    "Hebu tuunde moja!\n"
+   ],
+   "metadata": {
+    "id": "Kds48GUBcj3W"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price)) %>% \r\n",
+    "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"Pumpkin Price\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VNbU1S3BcrxO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩Hii ni uwasilishaji wa data unaofaa zaidi! Inaonekana inaonyesha kwamba bei ya juu zaidi ya maboga hutokea mwezi wa Septemba na Oktoba. Je, hilo linakubaliana na matarajio yako? Kwa nini au kwa nini siyo?\n",
+    "\n",
+    "Hongera kwa kumaliza somo la pili 👏! Uliandaa data yako kwa ajili ya kujenga modeli, kisha ukagundua maarifa zaidi kwa kutumia uwasilishaji wa data!\n"
+   ],
+   "metadata": {
+    "id": "zDm0VOzzcuzR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/2-Data/solution/notebook.ipynb b/translations/sw/2-Regression/2-Data/solution/notebook.ipynb
new file mode 100644
index 000000000..36f1d0a54
--- /dev/null
+++ b/translations/sw/2-Regression/2-Data/solution/notebook.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/8/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    City Name Type               Package   Variety Sub Variety  Grade  \\\n",
+       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "\n",
+       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
+       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
+       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "\n",
+       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
+       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "\n",
+       "    Unnamed: 25  \n",
+       "70          NaN  \n",
+       "71          NaN  \n",
+       "72          NaN  \n",
+       "73          NaN  \n",
+       "74          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Month               Package  Low Price  High Price  Price\n",
+      "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
+      "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "...     ...                   ...        ...         ...    ...\n",
+      "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
+      "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
+      "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
+      "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Get an average between low and high price for the base pumpkin price\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "# Convert the date to its month only\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "\n",
+    "# Create a new dataframe with this basic data\n",
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
+    "\n",
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
+    "\n",
+    "print(new_pumpkins)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "price = new_pumpkins.Price\n",
+    "month = new_pumpkins.Month\n",
+    "plt.scatter(price, month)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "95726f0b8283628d5356a4f8eb8b4b76",
+   "translation_date": "2025-09-06T13:46:25+00:00",
+   "source_file": "2-Regression/2-Data/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/3-Linear/notebook.ipynb b/translations/sw/2-Regression/3-Linear/notebook.ipynb
new file mode 100644
index 000000000..0b8c37fc5
--- /dev/null
+++ b/translations/sw/2-Regression/3-Linear/notebook.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bei za Maboga\n",
+    "\n",
+    "Pakia maktaba zinazohitajika na seti ya data. Badilisha data kuwa dataframe inayoonyesha sehemu ndogo ya data:\n",
+    "\n",
+    "- Chagua tu maboga yaliyo na bei kwa kipimo cha bushel\n",
+    "- Badilisha tarehe kuwa mwezi\n",
+    "- Hesabu bei kuwa wastani wa bei ya juu na ya chini\n",
+    "- Badilisha bei ili kuonyesha bei kulingana na idadi ya bushel\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mchoro wa msingi wa kutawanya unatukumbusha kwamba tuna data ya miezi kutoka Agosti hadi Desemba tu. Huenda tunahitaji data zaidi ili kuweza kutoa hitimisho kwa mtindo wa mstari.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter('Month','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "b032d371c75279373507f003439a577e",
+   "translation_date": "2025-09-06T13:09:00+00:00",
+   "source_file": "2-Regression/3-Linear/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/sw/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
new file mode 100644
index 000000000..bb7e99d76
--- /dev/null
+++ b/translations/sw/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -0,0 +1,1084 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_3-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "5015d65d61ba75a223bfc56c273aa174",
+   "translation_date": "2025-09-06T13:21:17+00:00",
+   "source_file": "2-Regression/3-Linear/solution/R/lesson_3-R.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "EgQw8osnsUV-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Urejeleaji wa Linear na Polynomial kwa Bei ya Maboga - Somo la 3\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Picha ya Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "#### Utangulizi\n",
+    "\n",
+    "Hadi sasa umechunguza maana ya urejeleaji (regression) kwa kutumia data ya mfano iliyokusanywa kutoka kwenye seti ya data ya bei ya maboga ambayo tutatumia katika somo hili. Pia umeweza kuiona kwa kutumia `ggplot2`.💪\n",
+    "\n",
+    "Sasa uko tayari kuingia kwa undani zaidi katika urejeleaji kwa ML. Katika somo hili, utajifunza zaidi kuhusu aina mbili za urejeleaji: *urejeleaji wa msingi wa linear* na *urejeleaji wa polynomial*, pamoja na baadhi ya hesabu zinazohusiana na mbinu hizi.\n",
+    "\n",
+    "> Katika mtaala huu, tunadhani ujuzi mdogo wa hesabu, na tunalenga kuifanya iwe rahisi kwa wanafunzi kutoka nyanja nyingine, kwa hivyo angalia maelezo, 🧮 vidokezo, michoro, na zana nyingine za kujifunza ili kusaidia kuelewa.\n",
+    "\n",
+    "#### Maandalizi\n",
+    "\n",
+    "Kama ukumbusho, unachukua data hii ili kuuliza maswali kuhusu data hiyo.\n",
+    "\n",
+    "-   Ni wakati gani bora wa kununua maboga?\n",
+    "\n",
+    "-   Ni bei gani ninayoweza kutarajia kwa sanduku la maboga madogo?\n",
+    "\n",
+    "-   Je, ninunue maboga kwa vikapu vya nusu-bushel au kwa sanduku la bushel 1 1/9? Hebu tuendelee kuchimba data hii.\n",
+    "\n",
+    "Katika somo lililopita, uliunda `tibble` (mabadiliko ya kisasa ya fremu ya data) na kuijaza na sehemu ya seti ya data ya awali, ukistandardisha bei kwa bushel. Kwa kufanya hivyo, hata hivyo, uliweza tu kukusanya takriban alama 400 za data na tu kwa miezi ya vuli. Labda tunaweza kupata maelezo zaidi kuhusu asili ya data kwa kuisafisha zaidi? Tutaona... 🕵️‍♀️\n",
+    "\n",
+    "Kwa kazi hii, tutahitaji vifurushi vifuatavyo:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) ni [mkusanyiko wa vifurushi vya R](https://www.tidyverse.org/packages) iliyoundwa kufanya sayansi ya data kuwa ya haraka, rahisi na ya kufurahisha!\n",
+    "\n",
+    "-   `tidymodels`: Mfumo wa [tidymodels](https://www.tidymodels.org/) ni [mkusanyiko wa vifurushi](https://www.tidymodels.org/packages/) kwa ajili ya uundaji wa mifano na ujifunzaji wa mashine.\n",
+    "\n",
+    "-   `janitor`: Kifurushi cha [janitor](https://github.com/sfirke/janitor) kinatoa zana rahisi za kuchunguza na kusafisha data chafu.\n",
+    "\n",
+    "-   `corrplot`: Kifurushi cha [corrplot](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) kinatoa zana ya kuona kwa uchunguzi wa matriki ya uhusiano ambayo inaunga mkono upangaji upya wa kiotomatiki wa vigezo ili kusaidia kugundua mifumo iliyofichwa kati ya vigezo.\n",
+    "\n",
+    "Unaweza kuvifunga kwa kutumia:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\n",
+    "\n",
+    "Skripti iliyo hapa chini inakagua kama una vifurushi vinavyohitajika kukamilisha moduli hii na kuvifunga kwako endapo havipo.\n"
+   ],
+   "metadata": {
+    "id": "WqQPS1OAsg3H"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "tA4C2WN3skCf",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tutatumia baadaye vifurushi hivi vya ajabu na kuvifanya viweze kupatikana katika kikao chetu cha sasa cha R. (Hii ni kwa madhumuni ya maelezo tu, `pacman::p_load()` tayari imefanya hivyo kwako)\n",
+    "\n",
+    "## 1. Mstari wa regression ya mstari\n",
+    "\n",
+    "Kama ulivyojifunza katika Somo la 1, lengo la zoezi la regression ya mstari ni kuweza kuchora *mstari* *wa* *ufanisi bora* ili:\n",
+    "\n",
+    "-   **Kuonyesha uhusiano wa vigezo**. Kuonyesha uhusiano kati ya vigezo\n",
+    "\n",
+    "-   **Kutabiri**. Kufanya utabiri sahihi kuhusu mahali ambapo data mpya itakuwa ikilinganishwa na mstari huo.\n",
+    "\n",
+    "Ili kuchora aina hii ya mstari, tunatumia mbinu ya takwimu inayoitwa **Regression ya Least-Squares**. Neno `least-squares` linamaanisha kwamba alama zote za data zinazozunguka mstari wa regression zinapigwa mraba na kisha kuongezwa. Kwa kawaida, jumla ya mwisho inapaswa kuwa ndogo iwezekanavyo, kwa sababu tunataka idadi ndogo ya makosa, au `least-squares`. Kwa hivyo, mstari wa ufanisi bora ni mstari unaotupa thamani ya chini zaidi kwa jumla ya makosa yaliyopigwa mraba - hivyo jina *least squares regression*.\n",
+    "\n",
+    "Tunafanya hivyo kwa sababu tunataka kuunda mstari ambao una umbali wa chini zaidi wa jumla kutoka kwa alama zote za data. Pia tunapiga mraba maneno kabla ya kuyaongeza kwa sababu tunajali ukubwa wake badala ya mwelekeo wake.\n",
+    "\n",
+    "> **🧮 Nionyeshe hesabu**\n",
+    ">\n",
+    "> Mstari huu, unaoitwa *mstari wa ufanisi bora* unaweza kuonyeshwa na [mchoro wa hesabu](https://en.wikipedia.org/wiki/Simple_linear_regression):\n",
+    ">\n",
+    ">     Y = a + bX\n",
+    ">\n",
+    "> `X` ni '`kigezo cha kuelezea` au `kigezo cha kutabiri`'. `Y` ni '`kigezo kinachotegemea` au `matokeo`'. Mwelekeo wa mstari ni `b` na `a` ni y-intercept, ambayo inahusu thamani ya `Y` wakati `X = 0`.\n",
+    ">\n",
+    "\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/slope.png \"mwelekeo = $y/x$\")\n",
+    "    Picha ya maelezo na Jen Looper\n",
+    ">\n",
+    "> Kwanza, hesabu mwelekeo `b`.\n",
+    ">\n",
+    "> Kwa maneno mengine, na tukirejelea swali la awali la data ya malenge: \"tabiri bei ya malenge kwa gunia kwa mwezi\", `X` ingekuwa inahusu bei na `Y` ingekuwa inahusu mwezi wa mauzo.\n",
+    ">\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/calculation.png)\n",
+    "    Picha ya maelezo na Jen Looper\n",
+    "> \n",
+    "> Hesabu thamani ya Y. Ikiwa unalipa karibu \\$4, lazima iwe Aprili!\n",
+    ">\n",
+    "> Hesabu inayochora mstari lazima ionyeshe mwelekeo wa mstari, ambao pia unategemea intercept, au mahali ambapo `Y` iko wakati `X = 0`.\n",
+    ">\n",
+    "> Unaweza kuona mbinu ya hesabu ya thamani hizi kwenye tovuti ya [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html). Pia tembelea [hii Calculator ya Least-Squares](https://www.mathsisfun.com/data/least-squares-calculator.html) ili kuona jinsi thamani za namba zinavyoathiri mstari.\n",
+    "\n",
+    "Si ya kutisha sana, sivyo? 🤓\n",
+    "\n",
+    "#### Uhusiano\n",
+    "\n",
+    "Neno moja zaidi la kuelewa ni **Coefficient ya Uhusiano** kati ya vigezo X na Y vilivyotolewa. Kwa kutumia scatterplot, unaweza kuona haraka coefficient hii. Mchoro wenye alama za data zilizopangwa kwa mstari mzuri una uhusiano wa juu, lakini mchoro wenye alama za data zilizotawanyika kila mahali kati ya X na Y una uhusiano wa chini.\n",
+    "\n",
+    "Mfano mzuri wa regression ya mstari utakuwa ule ambao una Coefficient ya Uhusiano ya juu (karibu na 1 kuliko 0) kwa kutumia mbinu ya Regression ya Least-Squares na mstari wa regression.\n"
+   ],
+   "metadata": {
+    "id": "cdX5FRpvsoP5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **2. Mchezo na data: kuunda fremu ya data itakayotumika kwa uundaji wa mifano**\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/janitor.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Sanaa na @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Sanaa na \\@allison_horst](../../../../../../2-Regression/3-Linear/images/janitor.jpg){width=\"700\"}-->\n"
+   ],
+   "metadata": {
+    "id": "WdUKXk7Bs8-V"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Pakia maktaba zinazohitajika na seti ya data. Badilisha data kuwa fremu ya data inayojumuisha sehemu ndogo ya data:\n",
+    "\n",
+    "-   Chagua tu maboga yanayouzwa kwa bei ya bushel\n",
+    "\n",
+    "-   Badilisha tarehe kuwa mwezi\n",
+    "\n",
+    "-   Hesabu bei kuwa wastani wa bei ya juu na ya chini\n",
+    "\n",
+    "-   Badilisha bei ili kuonyesha bei kulingana na idadi ya bushel\n",
+    "\n",
+    "> Tulifunika hatua hizi katika [somo la awali](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb).\n"
+   ],
+   "metadata": {
+    "id": "fMCtu2G2s-p8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "library(lubridate)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ryMVZEEPtERn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kwa roho ya ujasiri wa kweli, hebu tuchunguze [`janitor package`](../../../../../../2-Regression/3-Linear/solution/R/github.com/sfirke/janitor) ambayo inatoa kazi rahisi za kuchunguza na kusafisha data chafu. Kwa mfano, hebu tuangalie majina ya safu kwa data yetu:\n"
+   ],
+   "metadata": {
+    "id": "xcNxM70EtJjb"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5XtpaIigtPfW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤔 Tunaweza kufanya vizuri zaidi. Hebu tufanye majina haya ya safu `friendR` kwa kuyabadilisha kuwa muundo wa [snake_case](https://en.wikipedia.org/wiki/Snake_case) kwa kutumia `janitor::clean_names`. Ili kujifunza zaidi kuhusu kazi hii: `?clean_names`\n"
+   ],
+   "metadata": {
+    "id": "IbIqrMINtSHe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Clean names to the snake_case convention\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  clean_names(case = \"snake\")\n",
+    "\n",
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "a2uYvclYtWvX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Safi sana tidyR 🧹! Sasa, dansi na data ukitumia `dplyr` kama kwenye somo lililopita! 💃\n"
+   ],
+   "metadata": {
+    "id": "HfhnuzDDtaDd"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(variety, city_name, package, low_price, high_price, date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Extract the month from the dates to a new column\n",
+    "pumpkins <- pumpkins %>%\n",
+    "  mutate(date = mdy(date),\n",
+    "         month = month(date)) %>% \n",
+    "  select(-date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a new column for average Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(price = (low_price + high_price)/2)\n",
+    "\n",
+    "\n",
+    "# Retain only pumpkins with the string \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "  filter(str_detect(string = package, pattern = \"bushel\"))\n",
+    "\n",
+    "\n",
+    "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(price = case_when(\n",
+    "    str_detect(package, \"1 1/9\") ~ price/(1.1),\n",
+    "    str_detect(package, \"1/2\") ~ price*2,\n",
+    "    TRUE ~ price))\n",
+    "\n",
+    "# Relocate column positions\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(month, .before = variety)\n",
+    "\n",
+    "\n",
+    "# Display the first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "X0wU3gQvtd9f"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kazi nzuri!👌 Sasa una seti ya data safi na nadhifu ambayo unaweza kutumia kujenga mfano wako mpya wa regression!\n",
+    "\n",
+    "Ungependa mchoro wa kutawanyika?\n"
+   ],
+   "metadata": {
+    "id": "UpaIwaxqth82"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set theme\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Make a scatter plot of month and price\n",
+    "new_pumpkins %>% \n",
+    "  ggplot(mapping = aes(x = month, y = price)) +\n",
+    "  geom_point(size = 1.6)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "DXgU-j37tl5K"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mchoro wa kutawanyika unatukumbusha kwamba tuna data ya miezi kuanzia Agosti hadi Desemba tu. Huenda tunahitaji data zaidi ili kuweza kutoa hitimisho kwa mtindo wa mstari.\n",
+    "\n",
+    "Hebu tuangalie tena data yetu ya uundaji modeli:\n"
+   ],
+   "metadata": {
+    "id": "Ve64wVbwtobI"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Display first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HFQX2ng1tuSJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Je, tungependa kutabiri `bei` ya boga kwa kuzingatia safu za `jiji` au `kifurushi` ambazo ni za aina ya herufi? Au hata kwa urahisi zaidi, tunawezaje kupata uhusiano (ambao unahitaji viingizo vyake vyote viwe vya namba) kati ya, kwa mfano, `kifurushi` na `bei`? 🤷🤷\n",
+    "\n",
+    "Mifano ya kujifunza kwa mashine hufanya kazi vizuri zaidi na vipengele vya namba badala ya thamani za maandishi, kwa hivyo kwa kawaida unahitaji kubadilisha vipengele vya kategoria kuwa uwakilishi wa namba.\n",
+    "\n",
+    "Hii inamaanisha kuwa tunapaswa kupata njia ya kurekebisha vigezo vyetu ili kuzifanya ziwe rahisi kwa mfano kutumia kwa ufanisi, mchakato unaojulikana kama `ufundi wa vipengele` (feature engineering).\n"
+   ],
+   "metadata": {
+    "id": "7hsHoxsStyjJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Kuchakata data kwa ajili ya modeli kwa kutumia recipes 👩‍🍳👨‍🍳\n",
+    "\n",
+    "Shughuli zinazobadilisha maadili ya utabiri ili kuyafanya rahisi kwa modeli kutumia kwa ufanisi zimepewa jina `feature engineering`.\n",
+    "\n",
+    "Modeli tofauti zina mahitaji tofauti ya uchakataji wa awali. Kwa mfano, least squares inahitaji `encoding categorical variables` kama mwezi, aina, na city_name. Hii inahusisha tu `kutafsiri` safu yenye `categorical values` kuwa moja au zaidi ya `numeric columns` zinazochukua nafasi ya ile ya awali.\n",
+    "\n",
+    "Kwa mfano, fikiria data yako ina kipengele cha kategoria kama ifuatavyo:\n",
+    "\n",
+    "|  city   |\n",
+    "|:-------:|\n",
+    "| Denver  |\n",
+    "| Nairobi |\n",
+    "|  Tokyo  |\n",
+    "\n",
+    "Unaweza kutumia *ordinal encoding* kubadilisha kila kategoria kuwa thamani ya kipekee ya nambari, kama hivi:\n",
+    "\n",
+    "| city |\n",
+    "|:----:|\n",
+    "|  0   |\n",
+    "|  1   |\n",
+    "|  2   |\n",
+    "\n",
+    "Na hivyo ndivyo tutakavyofanya kwa data yetu!\n",
+    "\n",
+    "Katika sehemu hii, tutachunguza kifurushi kingine cha ajabu cha Tidymodels: [recipes](https://tidymodels.github.io/recipes/) - ambacho kimeundwa kusaidia kuchakata data yako **kabla** ya kufundisha modeli yako. Kwa msingi wake, recipe ni kitu kinachofafanua hatua gani zinapaswa kutumika kwenye seti ya data ili kuifanya iwe tayari kwa modeli.\n",
+    "\n",
+    "Sasa, hebu tuunde recipe inayotayarisha data yetu kwa modeli kwa kubadilisha nambari ya kipekee kwa maoni yote katika safu za utabiri:\n"
+   ],
+   "metadata": {
+    "id": "AD5kQbcvt3Xl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\n",
+    "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "# Print out the recipe\n",
+    "pumpkins_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "BNaFKXfRt9TU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hongera! 👏 Tumetengeneza mapishi yetu ya kwanza yanayobainisha matokeo (bei) na viashiria vyake vinavyolingana, na kwamba safu zote za viashiria zinapaswa kubadilishwa kuwa seti ya nambari za mzima 🙌! Hebu tuichambue haraka:\n",
+    "\n",
+    "-   Wito kwa `recipe()` na fomula unaeleza mapishi kuhusu *majukumu* ya vigezo kwa kutumia data ya `new_pumpkins` kama rejeleo. Kwa mfano, safu ya `price` imepewa jukumu la `outcome` huku safu nyingine zote zikipewa jukumu la `predictor`.\n",
+    "\n",
+    "-   `step_integer(all_predictors(), zero_based = TRUE)` inaeleza kwamba viashiria vyote vinapaswa kubadilishwa kuwa seti ya nambari za mzima, na kuhesabu kuanzia 0.\n",
+    "\n",
+    "Tuna hakika unaweza kuwa na mawazo kama: \"Hii ni ya kuvutia sana!! Lakini vipi kama ningehitaji kuthibitisha kwamba mapishi yanatekeleza kile ninachotarajia? 🤔\"\n",
+    "\n",
+    "Hilo ni wazo zuri sana! Unaona, mara mapishi yako yanapobainishwa, unaweza kukadiria vigezo vinavyohitajika ili kuchakata data, kisha kutoa data iliyochakatwa. Kwa kawaida huhitaji kufanya hivi unapotumia Tidymodels (tutaona utaratibu wa kawaida muda si muda-\\> `workflows`) lakini inaweza kuwa muhimu unapohitaji kufanya ukaguzi wa haraka ili kuthibitisha kwamba mapishi yanatekeleza kile unachotarajia.\n",
+    "\n",
+    "Kwa hilo, utahitaji vitenzi viwili zaidi: `prep()` na `bake()` na kama kawaida, marafiki wetu wadogo wa R kutoka kwa [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) wanakusaidia kuelewa hili vyema zaidi!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Uchoraji na @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "KEiO0v7kuC9O"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): inakadiria vigezo vinavyohitajika kutoka kwenye seti ya mafunzo ambayo inaweza kutumika baadaye kwenye seti nyingine za data. Kwa mfano, kwa safu fulani ya utabiri, ni uchunguzi gani utakaotolewa namba ya mzima 0 au 1 au 2 na kadhalika.\n",
+    "\n",
+    "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): inachukua mapishi yaliyotayarishwa na kutekeleza operesheni kwenye seti yoyote ya data.\n",
+    "\n",
+    "Kwa kusema hivyo, hebu tuandae na kutekeleza mapishi yetu ili kuthibitisha kweli kwamba ndani ya mfumo, safu za utabiri zitakuwa zimekodishwa kwanza kabla ya modeli kufanyiwa kazi.\n"
+   ],
+   "metadata": {
+    "id": "Q1xtzebuuTCP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep the recipe\n",
+    "pumpkins_prep <- prep(pumpkins_recipe)\n",
+    "\n",
+    "# Bake the recipe to extract a preprocessed new_pumpkins data\n",
+    "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\n",
+    "\n",
+    "# Print out the baked data set\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FGBbJbP_uUUn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woo-hoo!🥳 Data iliyochakatwa `baked_pumpkins` ina vigezo vyake vyote vimekodishwa, ikithibitisha kwamba hatua za awali za uchakataji zilizofafanuliwa kama mapishi yetu zitafanya kazi kama ilivyotarajiwa. Hii inafanya iwe ngumu kwako kusoma lakini rahisi zaidi kueleweka kwa Tidymodels! Chukua muda kidogo kugundua ni uchunguzi gani umebadilishwa kuwa nambari inayolingana.\n",
+    "\n",
+    "Pia ni muhimu kutaja kwamba `baked_pumpkins` ni fremu ya data ambayo tunaweza kufanya mahesabu juu yake.\n",
+    "\n",
+    "Kwa mfano, hebu jaribu kutafuta uhusiano mzuri kati ya alama mbili za data yako ili kujenga mfano mzuri wa utabiri. Tutatumia kazi `cor()` kufanya hivyo. Andika `?cor()` ili kujifunza zaidi kuhusu kazi hiyo.\n"
+   ],
+   "metadata": {
+    "id": "1dvP0LBUueAW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the correlation between the city_name and the price\n",
+    "cor(baked_pumpkins$city_name, baked_pumpkins$price)\n",
+    "\n",
+    "# Find the correlation between the package and the price\n",
+    "cor(baked_pumpkins$package, baked_pumpkins$price)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "3bQzXCjFuiSV"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kama inavyotokea, kuna uhusiano dhaifu tu kati ya Jiji na Bei. Hata hivyo, kuna uhusiano bora kidogo kati ya Kifurushi na Bei yake. Hilo lina mantiki, sivyo? Kwa kawaida, kadri sanduku la mazao linavyokuwa kubwa, ndivyo bei inavyokuwa juu.\n",
+    "\n",
+    "Wakati tuko hapa, hebu pia tujaribu kuonyesha matriki ya uhusiano wa safu zote kwa kutumia pakiti ya `corrplot`.\n"
+   ],
+   "metadata": {
+    "id": "BToPWbgjuoZw"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the corrplot package\n",
+    "library(corrplot)\n",
+    "\n",
+    "# Obtain correlation matrix\n",
+    "corr_mat <- cor(baked_pumpkins %>% \n",
+    "                  # Drop columns that are not really informative\n",
+    "                  select(-c(low_price, high_price)))\n",
+    "\n",
+    "# Make a correlation plot between the variables\n",
+    "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZwAL3ksmutVR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Bora zaidi.\n",
+    "\n",
+    "Swali zuri la kuuliza kuhusu data hii sasa ni: '`Ni bei gani ninayoweza kutarajia kwa kifurushi fulani cha malenge?`' Hebu tuanze moja kwa moja!\n",
+    "\n",
+    "> Note: Unapobake **`bake()`** mapishi yaliyoandaliwa **`pumpkins_prep`** na **`new_data = NULL`**, unatoa data ya mafunzo iliyosindikwa (yaani, iliyosimbwa). Ikiwa ulikuwa na seti nyingine ya data, kwa mfano seti ya majaribio, na ungependa kuona jinsi mapishi yanavyoweza kuisindika, ungebake tu **`pumpkins_prep`** na **`new_data = test_set`**\n",
+    "\n",
+    "## 4. Tengeneza modeli ya regression ya mstari\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Picha ya Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Picha ya Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "YqXjLuWavNxW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sasa kwa kuwa tumetengeneza mapishi, na tumethibitisha kuwa data itachakatwa ipasavyo, hebu sasa tujenge mfano wa regression ili kujibu swali: `Ni bei gani ninayoweza kutarajia kwa kifurushi fulani cha malenge?`\n",
+    "\n",
+    "#### Fundisha mfano wa regression ya mstari ukitumia seti ya mafunzo\n",
+    "\n",
+    "Kama unavyoweza kuwa umeshagundua, safu ya *price* ni `kigezo cha matokeo` wakati safu ya *package* ni `kigezo cha utabiri`.\n",
+    "\n",
+    "Ili kufanya hivi, tutagawanya data kwanza ili asilimia 80 iingie kwenye seti ya mafunzo na asilimia 20 kwenye seti ya majaribio, kisha tutaelezea mapishi ambayo yataweka safu ya utabiri katika seti ya namba nzima, kisha tujenge maelezo ya mfano. Hatutapika na kuandaa mapishi yetu kwa sababu tayari tunajua yatachakata data kama inavyotarajiwa.\n"
+   ],
+   "metadata": {
+    "id": "Pq0bSzCevW-h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\n",
+    "# Split the data into training and test sets\n",
+    "pumpkins_split <- new_pumpkins %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "\n",
+    "# Extract training and test data\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a recipe for preprocessing the data\n",
+    "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a linear model specification\n",
+    "lm_spec <- linear_reg() %>% \n",
+    "  set_engine(\"lm\") %>% \n",
+    "  set_mode(\"regression\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "CyoEh_wuvcLv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kazi nzuri! Sasa kwa kuwa tuna mapishi na maelezo ya mfano, tunahitaji kupata njia ya kuyafungamanisha pamoja katika kitu ambacho kitafanya kazi ya kwanza ya kuchakata data (prep+bake nyuma ya pazia), kufundisha mfano kwenye data iliyochakatwa, na pia kuruhusu shughuli za baada ya uchakataji. Hiyo inakupa utulivu wa akili, sivyo!🤩\n",
+    "\n",
+    "Katika Tidymodels, kitu hiki rahisi kinaitwa [`workflow`](https://workflows.tidymodels.org/) na kwa urahisi kinashikilia vipengele vyako vya uundaji wa mifano! Hiki ndicho tunachokiita *pipelines* katika *Python*.\n",
+    "\n",
+    "Sasa hebu tufungamanishe kila kitu katika workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "G3zF_3DqviFJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Hold modelling components in a workflow\n",
+    "lm_wf <- workflow() %>% \n",
+    "  add_recipe(lm_pumpkins_recipe) %>% \n",
+    "  add_model(lm_spec)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "lm_wf"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "T3olroU3v-WX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Pia, mchakato wa kazi unaweza kufaa/kufunzwa kwa njia sawa na jinsi mfano unavyoweza kufanywa.\n"
+   ],
+   "metadata": {
+    "id": "zd1A5tgOwEPX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Train the model\n",
+    "lm_wf_fit <- lm_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the model coefficients learned \n",
+    "lm_wf_fit"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NhJagFumwFHf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kutoka kwenye matokeo ya modeli, tunaweza kuona vigezo vilivyojifunzwa wakati wa mafunzo. Vigezo hivi vinawakilisha vigezo vya mstari wa kufaa bora ambao hutupatia makosa ya chini kabisa kati ya thamani halisi na ile iliyotabiriwa.\n",
+    "\n",
+    "#### Kutathmini utendaji wa modeli kwa kutumia seti ya majaribio\n",
+    "\n",
+    "Ni wakati wa kuona jinsi modeli ilivyofanya kazi 📏! Tunafanyaje hivi?\n",
+    "\n",
+    "Sasa kwa kuwa tumefundisha modeli, tunaweza kuitumia kutabiri kwa seti ya majaribio (`test_set`) kwa kutumia `parsnip::predict()`. Kisha tunaweza kulinganisha utabiri huu na thamani halisi za lebo ili kutathmini jinsi modeli inavyofanya kazi (au haifanyi kazi!).\n",
+    "\n",
+    "Hebu tuanze kwa kufanya utabiri kwa seti ya majaribio kisha tuunganishe safu kwenye seti ya majaribio.\n"
+   ],
+   "metadata": {
+    "id": "_4QkGtBTwItF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\n",
+    "predictions <- lm_wf_fit %>% \n",
+    "  predict(new_data = pumpkins_test)\n",
+    "\n",
+    "\n",
+    "# Bind predictions to the test set\n",
+    "lm_results <- pumpkins_test %>% \n",
+    "  select(c(package, price)) %>% \n",
+    "  bind_cols(predictions)\n",
+    "\n",
+    "\n",
+    "# Print the first ten rows of the tibble\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "UFZzTG0gwTs9"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ndio, umeshafundisha modeli na kuitumia kufanya utabiri!🔮 Je, ni nzuri? Hebu tathmini utendaji wa modeli!\n",
+    "\n",
+    "Katika Tidymodels, tunafanya hivi kwa kutumia `yardstick::metrics()`! Kwa regression ya mstari, hebu tuzingatie vipimo vifuatavyo:\n",
+    "\n",
+    "-   `Root Mean Square Error (RMSE)`: Mizizi ya mraba ya [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). Hii inatoa kipimo cha moja kwa moja katika kipimo sawa na lebo (katika kesi hii, bei ya malenge). Thamani ndogo zaidi, ndivyo modeli inavyokuwa bora (kwa mtazamo rahisi, inawakilisha wastani wa bei ambayo utabiri uko makosa!)\n",
+    "\n",
+    "-   `Coefficient of Determination (maarufu kama R-squared au R2)`: Kipimo cha kulinganisha ambapo thamani ya juu zaidi inaonyesha modeli inayofaa zaidi. Kimsingi, kipimo hiki kinaonyesha ni kiasi gani cha tofauti kati ya thamani za lebo zilizotabiriwa na halisi ambacho modeli inaweza kuelezea.\n"
+   ],
+   "metadata": {
+    "id": "0A5MjzM7wW9M"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Evaluate performance of linear regression\n",
+    "metrics(data = lm_results,\n",
+    "        truth = price,\n",
+    "        estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "reJ0UIhQwcEH"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hapo ndipo utendaji wa modeli unaporudi chini. Hebu tuone kama tunaweza kupata dalili bora kwa kuonyesha mchoro wa kutawanyika wa kifurushi na bei kisha kutumia utabiri uliofanywa kuweka mstari wa kufaa bora.\n",
+    "\n",
+    "Hii inamaanisha tutalazimika kuandaa na kuchakata seti ya majaribio ili kusimba safu ya kifurushi kisha kuunganisha hii na utabiri uliofanywa na modeli yetu.\n"
+   ],
+   "metadata": {
+    "id": "fdgjzjkBwfWt"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Encode package column\n",
+    "package_encode <- lm_pumpkins_recipe %>% \n",
+    "  prep() %>% \n",
+    "  bake(new_data = pumpkins_test) %>% \n",
+    "  select(package)\n",
+    "\n",
+    "\n",
+    "# Bind encoded package column to the results\n",
+    "lm_results <- lm_results %>% \n",
+    "  bind_cols(package_encode %>% \n",
+    "              rename(package_integer = package)) %>% \n",
+    "  relocate(package_integer, .after = package)\n",
+    "\n",
+    "\n",
+    "# Print new results data frame\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 5)\n",
+    "\n",
+    "\n",
+    "# Make a scatter plot\n",
+    "lm_results %>% \n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\n",
+    "  geom_point(size = 1.6) +\n",
+    "  # Overlay a line of best fit\n",
+    "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\n",
+    "  xlab(\"package\")\n",
+    "  \n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R0nw719lwkHE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nzuri! Kama unavyoona, mfano wa linear regression hauwezi kwa kweli kujumlisha uhusiano kati ya kifurushi na bei yake inayolingana.\n",
+    "\n",
+    "🎃 Hongera, umeunda mfano ambao unaweza kusaidia kutabiri bei ya aina chache za maboga. Shamba lako la maboga kwa ajili ya sikukuu litakuwa zuri. Lakini pengine unaweza kuunda mfano bora zaidi!\n",
+    "\n",
+    "## 5. Jenga mfano wa polynomial regression\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Picha ya maelezo na Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Picha ya maelezo na Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "HOCqJXLTwtWI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Wakati mwingine data zetu zinaweza kuwa hazina uhusiano wa moja kwa moja, lakini bado tunataka kutabiri matokeo. Urejeleaji wa polinomu unaweza kutusaidia kufanya utabiri kwa uhusiano mgumu zaidi usio wa moja kwa moja.\n",
+    "\n",
+    "Chukua kwa mfano uhusiano kati ya kifurushi na bei katika seti yetu ya data ya maboga. Ingawa wakati mwingine kuna uhusiano wa moja kwa moja kati ya vigezo - boga kubwa zaidi kwa ujazo, bei ya juu zaidi - wakati mwingine uhusiano huu hauwezi kuchorwa kama ndege au mstari wa moja kwa moja.\n",
+    "\n",
+    "> ✅ Hapa kuna [mifano zaidi](https://online.stat.psu.edu/stat501/lesson/9/9.8) ya data ambayo inaweza kutumia urejeleaji wa polinomu  \n",
+    ">  \n",
+    "> Angalia tena uhusiano kati ya Aina na Bei katika mchoro wa awali. Je, mchoro huu wa alama unaonekana kama unapaswa kuchambuliwa kwa mstari wa moja kwa moja? Labda hapana. Katika hali hii, unaweza kujaribu urejeleaji wa polinomu.  \n",
+    ">  \n",
+    "> ✅ Polinomu ni maelezo ya kihisabati ambayo yanaweza kuwa na moja au zaidi ya vigezo na viwango  \n",
+    "\n",
+    "#### Fundisha mfano wa urejeleaji wa polinomu kwa kutumia seti ya mafunzo\n",
+    "\n",
+    "Urejeleaji wa polinomu huunda *mstari uliopinda* ili kuendana vyema na data isiyo ya moja kwa moja.\n",
+    "\n",
+    "Hebu tuone kama mfano wa polinomu utaonyesha utendaji bora katika kufanya utabiri. Tutafuata utaratibu unaofanana kidogo na tulivyofanya awali:\n",
+    "\n",
+    "-   Unda mapishi yanayobainisha hatua za awali za uchakataji ambazo zinapaswa kufanywa kwenye data yetu ili kuifanya iwe tayari kwa uundaji wa mifano, yaani: kuweka vigezo na kuhesabu polinomu za kiwango *n*\n",
+    "\n",
+    "-   Tengeneza maelezo ya mfano\n",
+    "\n",
+    "-   Unganisha mapishi na maelezo ya mfano katika mtiririko wa kazi\n",
+    "\n",
+    "-   Unda mfano kwa kufanikisha mtiririko wa kazi\n",
+    "\n",
+    "-   Tathmini jinsi mfano unavyofanya kazi kwenye data ya majaribio\n",
+    "\n",
+    "Hebu tuanze moja kwa moja!\n"
+   ],
+   "metadata": {
+    "id": "VcEIpRV9wzYr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\r\n",
+    "poly_pumpkins_recipe <-\r\n",
+    "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+    "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+    "  step_poly(all_predictors(), degree = 4)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model specification\r\n",
+    "poly_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Bundle recipe and model spec into a workflow\r\n",
+    "poly_wf <- workflow() %>% \r\n",
+    "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+    "  add_model(poly_spec)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model\r\n",
+    "poly_wf_fit <- poly_wf %>% \r\n",
+    "  fit(data = pumpkins_train)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print learned model coefficients\r\n",
+    "poly_wf_fit\r\n",
+    "\r\n",
+    "  "
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "63n_YyRXw3CC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Tathmini Utendaji wa Modeli\n",
+    "\n",
+    "👏👏Umeunda modeli ya polinomial, hebu tufanye utabiri kwenye seti ya majaribio!\n"
+   ],
+   "metadata": {
+    "id": "-LHZtztSxDP0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make price predictions on test data\r\n",
+    "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+    "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+    "  relocate(.pred, .after = last_col())\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the results\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YUFpQ_dKxJGx"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woo-hoo, wacha tupime jinsi mfano ulivyofanya kazi kwenye test_set kwa kutumia `yardstick::metrics()`.\n"
+   ],
+   "metadata": {
+    "id": "qxdyj86bxNGZ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "metrics(data = poly_results, truth = price, estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "8AW5ltkBxXDm"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Utendaji bora zaidi.\n",
+    "\n",
+    "`rmse` ilipungua kutoka takriban 7 hadi takriban 3, ikionyesha kupungua kwa makosa kati ya bei halisi na bei iliyotabiriwa. Unaweza *kwa urahisi* kufasiri hili kama kwamba kwa wastani, utabiri usio sahihi unakosea kwa takriban \\$3. `rsq` iliongezeka kutoka takriban 0.4 hadi 0.8.\n",
+    "\n",
+    "Vipimo vyote hivi vinaonyesha kwamba modeli ya polynomial inafanya kazi vizuri zaidi kuliko modeli ya mstari. Kazi nzuri!\n",
+    "\n",
+    "Hebu tuone kama tunaweza kuonyesha hili!\n"
+   ],
+   "metadata": {
+    "id": "6gLHNZDwxYaS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Bind encoded package column to the results\r\n",
+    "poly_results <- poly_results %>% \r\n",
+    "  bind_cols(package_encode %>% \r\n",
+    "              rename(package_integer = package)) %>% \r\n",
+    "  relocate(package_integer, .after = package)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print new results data frame\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+    "  xlab(\"package\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "A83U16frxdF1"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Unaweza kuona mstari uliopinda unaofaa data yako vizuri zaidi! 🤩\n",
+    "\n",
+    "Unaweza kufanya hii kuwa laini zaidi kwa kupitisha fomula ya polinomial kwa `geom_smooth` kama hivi:\n"
+   ],
+   "metadata": {
+    "id": "4U-7aHOVxlGU"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+    "  xlab(\"package\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5vzNT0Uexm-w"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kama vile mwelekeo laini!🤩\n",
+    "\n",
+    "Hivi ndivyo unavyoweza kufanya utabiri mpya:\n"
+   ],
+   "metadata": {
+    "id": "v9u-wwyLxq4G"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a hypothetical data frame\r\n",
+    "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+    "\r\n",
+    "# Make predictions using linear model\r\n",
+    "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Make predictions using polynomial model\r\n",
+    "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Return predictions in a list\r\n",
+    "list(\"linear model prediction\" = lm_pred, \r\n",
+    "     \"polynomial model prediction\" = poly_pred)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "jRPSyfQGxuQv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Utabiri wa `polynomial model` una mantiki, ukizingatia grafu za kutawanyika za `price` na `package`! Na, ikiwa huu ni mfano bora kuliko ule wa awali, ukitazama data hiyo hiyo, unahitaji kupanga bajeti kwa ajili ya malenge haya ya gharama kubwa zaidi!\n",
+    "\n",
+    "🏆 Hongera! Umeunda mifano miwili ya regression katika somo moja. Katika sehemu ya mwisho ya regression, utajifunza kuhusu logistic regression ili kubaini kategoria.\n",
+    "\n",
+    "## **🚀Changamoto**\n",
+    "\n",
+    "Jaribu kutofautisha vigezo kadhaa katika daftari hili ili kuona jinsi uhusiano unavyolingana na usahihi wa mfano.\n",
+    "\n",
+    "## [**Jaribio baada ya somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)\n",
+    "\n",
+    "## **Mapitio na Kujisomea**\n",
+    "\n",
+    "Katika somo hili tulijifunza kuhusu Linear Regression. Kuna aina nyingine muhimu za Regression. Soma kuhusu mbinu za Stepwise, Ridge, Lasso na Elasticnet. Kozi nzuri ya kusoma ili kujifunza zaidi ni [Kozi ya Stanford ya Statistical Learning](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning).\n",
+    "\n",
+    "Ikiwa unataka kujifunza zaidi kuhusu jinsi ya kutumia mfumo wa ajabu wa Tidymodels, tafadhali angalia rasilimali zifuatazo:\n",
+    "\n",
+    "-   Tovuti ya Tidymodels: [Anza na Tidymodels](https://www.tidymodels.org/start/)\n",
+    "\n",
+    "-   Max Kuhn na Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+    "\n",
+    "###### **ASANTE KWA:**\n",
+    "\n",
+    "[Allison Horst](https://twitter.com/allison_horst?lang=en) kwa kuunda michoro ya ajabu inayofanya R kuwa ya kuvutia na ya kupendeza zaidi. Pata michoro zaidi kwenye [galeria yake](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n"
+   ],
+   "metadata": {
+    "id": "8zOLOWqMxzk5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/3-Linear/solution/notebook.ipynb b/translations/sw/2-Regression/3-Linear/solution/notebook.ipynb
new file mode 100644
index 000000000..adcdc9a55
--- /dev/null
+++ b/translations/sw/2-Regression/3-Linear/solution/notebook.ipynb
@@ -0,0 +1,1111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usawazishaji wa Mstari na Usawazishaji wa Polynomial kwa Bei ya Maboga - Somo la 3\n",
+    "\n",
+    "Pakia maktaba zinazohitajika na seti ya data. Badilisha data kuwa fremu ya data inayoonyesha sehemu ndogo ya data:\n",
+    "\n",
+    "- Chagua tu maboga yaliyo na bei kwa kipimo cha bushel\n",
+    "- Badilisha tarehe kuwa mwezi\n",
+    "- Hesabu bei kuwa wastani wa bei ya juu na ya chini\n",
+    "- Badilisha bei ili kuonyesha upimaji kwa idadi ya bushel\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mchoro wa kutawanyika unatukumbusha kwamba tuna data ya miezi kutoka Agosti hadi Desemba tu. Huenda tunahitaji data zaidi ili tuweze kutoa hitimisho kwa mtindo wa mstari.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Month', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('Month','Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Inaonekana kama uhusiano ni mdogo sana, lakini kuna uhusiano mwingine muhimu zaidi - kwa sababu bei katika mchoro hapo juu zinaonekana kuwa na makundi tofauti tofauti. Hebu tufanye mchoro ambao utaonyesha aina mbalimbali za maboga:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax=None\n",
+    "colors = ['red','blue','green','yellow']\n",
+    "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    ax = new_pumpkins[new_pumpkins['Variety']==var].plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAGKCAYAAAAVEBpAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcxklEQVR4nO3df5h3dV3n8ecrwEBEARm4bhW8UxEDfwDekqztpiiFmiGGCW5GZhduLRVpJaX5a7e9bP25musurAiRgqaopGEiF+TiKnrzQ4SQIEMSEG615BbzB/DeP8538sswv+c7c85n5vm4rrnm/PjOzIvx/r488znnfE6qCklSe36s7wCSpOWxwCWpURa4JDXKApekRlngktSoHdfyh+211161efPmtfyRktS8yy677BtVNTVz+5oW+ObNm9m6deta/khJal6Sr862fcEhlCQ7J/l8ki8muSbJ60bbX5vk5iRXjj6eNenQkqS5LeYI/PvAEVX1nSQ7AZckOX+0761V9abViydJmsuCBV7drZrfGa3uNPrw9k1J6tmirkJJskOSK4HbgQuq6tLRrpOSXJXk9CR7rFZISdJ9LarAq+ruqjoYeBhwWJLHAu8CHgkcDNwKvHm2r01yYpKtSbZu27ZtIqElSUu8Dryq/gW4GDiqqm4bFfs9wGnAYXN8zalVtaWqtkxN3ecqGEnSMi3mKpSpJLuPlncBngF8OcmmsZcdA1y9KgklSbNazFUom4Azk+xAV/gfqKqPJTkrycF0JzRvBF66aiklSfexmKtQrgIOmWX7i1YlkSRpUdb0TkwN3+ZTPt53hAXd+IZn9x1BGgQns5KkRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1KjmH6nWwiPAwMeASZo8j8AlqVEWuCQ1asECT7Jzks8n+WKSa5K8brR9zyQXJLl+9HmP1Y8rSZq2mCPw7wNHVNUTgIOBo5I8GTgFuLCq9gcuHK1LktbIggVene+MVncafRRwNHDmaPuZwHNXI6AkaXaLGgNPskOSK4HbgQuq6lJgn6q6FWD0ee85vvbEJFuTbN22bduEYkuSFlXgVXV3VR0MPAw4LMljF/sDqurUqtpSVVumpqaWGVOSNNOSrkKpqn8BLgaOAm5Lsglg9Pn2SYeTJM1tMVehTCXZfbS8C/AM4MvAecAJo5edAHx0lTJKkmaxmDsxNwFnJtmBrvA/UFUfS/JZ4ANJXgLcBDx/FXNKkmZYsMCr6irgkFm2fxN4+mqEkiQtzDsxJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhq1YIEn2TfJRUmuTXJNkt8ZbX9tkpuTXDn6eNbqx5UkTdtxEa+5C3h5VV2eZDfgsiQXjPa9taretHrxJElzWbDAq+pW4NbR8vYk1wIPXe1gkqT5LWkMPMlm4BDg0tGmk5JcleT0JHvM8TUnJtmaZOu2bdtWllaS9G8WXeBJHgB8CDi5qu4A3gU8EjiY7gj9zbN9XVWdWlVbqmrL1NTUyhNLkoBFFniSnejK+71VdS5AVd1WVXdX1T3AacBhqxdTkjTTYq5CCfBu4NqqesvY9k1jLzsGuHry8SRJc1nMVShPAV4EfCnJlaNtfwQcn+RgoIAbgZeuQj5J0hwWcxXKJUBm2fXXk48jSVos78SUpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJatSCBZ5k3yQXJbk2yTVJfme0fc8kFyS5fvR5j9WPK0matpgj8LuAl1fVTwJPBv5zkgOBU4ALq2p/4MLRuiRpjSxY4FV1a1VdPlreDlwLPBQ4Gjhz9LIzgeeuUkZJ0iyWNAaeZDNwCHApsE9V3QpdyQN7TzydJGlOiy7wJA8APgScXFV3LOHrTkyyNcnWbdu2LSejJGkWiyrwJDvRlfd7q+rc0ebbkmwa7d8E3D7b11bVqVW1paq2TE1NTSKzJInFXYUS4N3AtVX1lrFd5wEnjJZPAD46+XiSpLnsuIjXPAV4EfClJFeOtv0R8AbgA0leAtwEPH9VEkqSZrVggVfVJUDm2P30ycaRJC2Wd2JKUqMscElqlAUuSY2ywCWpURa4JDXKApekRi3mOnBJy7D5lI/3HWFRbnzDs/uOoGXyCFySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjXIyK0lNcHKw+/IIXJIaZYFLUqMWLPAkpye5PcnVY9tem+TmJFeOPp61ujElSTMt5gj8DOCoWba/taoOHn389WRjSZIWsmCBV9WngW+tQRZJ0hKsZAz8pCRXjYZY9pjrRUlOTLI1ydZt27at4MdJksYtt8DfBTwSOBi4FXjzXC+sqlOraktVbZmamlrmj5MkzbSsAq+q26rq7qq6BzgNOGyysSRJC1lWgSfZNLZ6DHD1XK+VJK2OBe/ETHI28FRgryRfA14DPDXJwUABNwIvXb2IkqTZLFjgVXX8LJvfvQpZJElL4J2YktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY1asMCTnJ7k9iRXj23bM8kFSa4ffd5jdWNKkmZazBH4GcBRM7adAlxYVfsDF47WJUlraMECr6pPA9+asflo4MzR8pnAcycbS5K0kOWOge9TVbcCjD7vPdcLk5yYZGuSrdu2bVvmj5MkzbTqJzGr6tSq2lJVW6amplb7x0nShrHcAr8tySaA0efbJxdJkrQYyy3w84ATRssnAB+dTBxJ0mIt5jLCs4HPAgck+VqSlwBvAI5Mcj1w5GhdkrSGdlzoBVV1/By7nj7hLJKkJfBOTElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGLfhU+vkkuRHYDtwN3FVVWyYRSpK0sBUV+MjTquobE/g+kqQlcAhFkhq10gIv4JNJLkty4iQCSZIWZ6VDKE+pqluS7A1ckOTLVfXp8ReMiv1EgP3222+FP06SNG1FR+BVdcvo8+3Ah4HDZnnNqVW1paq2TE1NreTHSZLGLLvAk+yaZLfpZeBngasnFUySNL+VDKHsA3w4yfT3eV9VfWIiqSRJC1p2gVfVV4AnTDCLJGkJvIxQkhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1akUFnuSoJNcluSHJKZMKJUla2LILPMkOwDuBZwIHAscnOXBSwSRJ81vJEfhhwA1V9ZWq+gFwDnD0ZGJJkhaSqlreFybHAkdV1a+P1l8E/FRVnTTjdScCJ45WDwCuW37cNbMX8I2+Q6wj/j4nx9/lZLXy+3x4VU3N3LjjCr5hZtl2n/83qKpTgVNX8HPWXJKtVbWl7xzrhb/PyfF3OVmt/z5XMoTyNWDfsfWHAbesLI4kabFWUuBfAPZP8hNJ7gccB5w3mViSpIUsewilqu5KchLwN8AOwOlVdc3EkvWrqSGfBvj7nBx/l5PV9O9z2ScxJUn98k5MSWqUBS5JjbLAJalRG77Akzxwnn37rWUWaS5JHpzkmCRP7DtLi5J8YGz5T2fs++TaJ5qMDV/gwMXTC0kunLHvI2uapHHr9U3ShyQfS/LY0fIm4Grg14CzkpzcZ7ZG7T+2fOSMffe5w7EVFvi97yjdc559Wti6fJP05Ceq6urR8ouBC6rqOcBP0RW5lma+y+2avRRvJbfSrxc1x/Js65rfunyT9OSHY8tPB04DqKrtSe7pJ1LT7p/kELqD1l1Gyxl97NJrshWwwGHvJC+j+x9yepnRukeNS7Mu3yQ9+ackv0U3ZcWhwCcAkuwC7NRnsEZ9HXjLLMvT603a8DfyJHnNfPur6nVrlaV1SS6ab39VPW2tsrQuyd7A64FNwDur6pOj7U8DnlhVb+ozn4Zhwxf4fJKcXFVv6zuHNC7JjlV1V985WpJkf+CNwKOALwG/V1U395tq5TyJOb+XLfwSLSTJkUku6DtHS5JcMrZ81ozdn1/jOOvB6cDHgV8ELgfe0W+cybDA5+dVKEuQ5Igkf5/kO0n+IsmBSbYCbwDe1Xe+xuw6tnzQjH3+u1y63arqtKq6rqreCGzuO9AkeBJzfo4vLc2b6Z6+9Fm6Z6V+DvjjqvofvaZqk1f0TNbOYyfV4d4n2amqy3tLtgIbvsCTbGf2N0SA+69xnNZVVV08Wv5Ikm2W97LtnuQYur+Sd0/yvNH2AA/qL1azZrvyZHq9gCPWPNEEeBJTE5PkK8DvjW160/h6VZ275qEaleQ98+2vqhevVZb1IMkDq+qOvnNMmgU+iyS7As8FXlhVz+45TjMWKJ2qKu8gVC+S/APwyqo6p+8sk2SBj4weC/cs4IXAUcCHgHOr6q96DaYNaeyGsllV1Vvm2697S/Jw4G3AA4DfqKob+k00GY6BJ0cCxwM/B1wEnAUc5p+oSzdL6RTwDeCSqvrHHiK1bLe+A6wnVfVV4JgkRwGfSfIF4J6x/b/QW7gV2PAFTvdMz/8L/PR0ySTxxNvyzFY6m4FXJnntevvzdZV9s6r+rO8Q60mSA4A/oHu/v5OxAm/Vhh9CGV1KdBxwLPAV4Bzg1VX18F6DrSNJ9gQ+VVWH9p2lFUku9/c1OUneAPwC8PKqOr/vPJOy4W/kqaorquoVVfVI4LXAIcD9kpyf5MR+060PVfUtvPlE/doCHLqeyhss8Hs9aKCqPlNVJwEPpTvhcXhfudaTJEcA/9x3jsY8Pskds3xsT7LuLodbA3tW1ff6DjFpjoHPMmVsVd1DNzb+N2sfp11JvsR9b4raE7gF+JW1T9S0L1XVIX2H0LBZ4PCgsbvc7sObT5bkecAPxtaL7mTcnT3lkaY9Isl5c+30KpR2PQj4eWYfoy3AAl+893vibWL+su8A68w2url61hULHG7yDsGJ8UTl5Bw8vZDkT6vqFWPrn6yqn+0lVbu2V9Xf9h1i0ixwZ3abpKn57iD07sEledTY8pHAK8bWfdTf0t3Yd4DVYIHDt9brRDc92IHuVmWPxFeXBx1LVFVznudqmQUOnwQuS/Kaqnpf32Ead2tVvb7vEOuED4jWgjb8nZgASR5KNzfwXnRPjhmfI8GTmIuU5IrZLn1Lsi9w3OhJKFqEJBczz5G2D4gWeAQOQFXdnOTjwJ8Az+FHBe5VKEvz9OmFJHsBz6ebKOxh+Htckqp6at8Z1pMkv1xVfzFafkpVfWZs30mtzjuz4Y/AkxxEd9R9C/C7VXVrz5GalWQ34Bi6KXkfDXwYeEFVPazXYA2a794E8C/DpRqfW2bmPDMtzzvjETh8EDi5qu5z12WSXb0JZUlup3ti+qvoppCt0WPBtHTPmWeffxkuXeZYnm29GRZ4d73tXkm2AFdV1Q+S7A2cDPwq8JD+ojXnj+hmdnwX8L4k7+85T7Ocj37iao7l2dabseEnswL+E3Al8A7gc0lOAK6lO9P/xB5zNaeq3lpVP0U3bWeAjwAPSfKKJI/uNVyDkuwwOpcwvX6/JCcmubbPXI16TJKrRvP1TC9Prx/Qd7jlcgw8+Tu6hzl8K8l+wA3Af6iqz/UcbV1I8ji6MfFfGk3Zq0VIchzwv4E7gevppjo+C/gC8F+q6vL+0rVn9Ei1OY2e2NMcC/y+JzSurqrH9plJSnI18NyquiHJocBn6S7F/HDP0TQgFnhyO91TeKYdN75eVb+95qEalWQ7s48nhu6p9A9c40jNmuXA4stV9Zg+M7Vs7N/m9AnL6X+nTf/b9CQm/P6M9ct6SbEOVJUP4p2cvWfMK/OA8XXnlVma9fpvc8MfgUtDlOQ18+wupyxYmiQ7012w8CjgKuD0qrqr31Qrt+ELPMlfMf8ty01O9N6HWf5MZbS+I3C/qvIvvglI8qSq+kLfOVoyuqT1h3RPpH8m8NWq+p1+U62cbyh4U98B1ouZf6aO7sz8TeCldHdlapmSHEh3fuZ44Nt0D+nV4h1YVY8DSPJuuhvOmrfhC3yuSd6nJ2AC1t0k8Kstye50N0L9CvA+4ElV9c0+M7VodOnb8aOPu4CHA1uq6sY+czXqh9MLVXVX0uzNl/ey4Qt83IwJmB6KR41LMvr9vRx4AXA6cEhVfbvfVG1K8v/oHvd3DnBsVV2f5B8t72V7QpLpOf9DN0XvHXgVStvmmIDpEU7AtCxfpXv24HuA7wIvGT/S8cqJJdlGN4vjPnRP4Lmehm/57ltV7dB3htWw4QscJ2CapDfyo5JZl5dtrZWqOjrJg4BfBF6X5FHA7kkOq6p1MX6rlfMqlOR36ca6d6Ubr30/cEFVPaLXYNrQkuxUVT8cW9+bbmjqeGDfqtq3t3AajA1f4NOSPILuzXEcsD/wGuDDVfX3vQZrSJK3z7ffu1oXb3SH8EeBs4GLauyNmuThrc7docna8AWeZL+qumnGtsfRlfkLnIBp8UYzOc6pqs5cqyytS/Jg4Fh+dEDxQeDsqrq012AaFAv83k/q+FBV/WLfmaRxSR5Cd3XUccDewDlV9cp+U2kILPCxB/HO9VBeLU6S8+bb712ty5fkAcDzgJcBm6pqn54jaQC8CmX+J3VoaQ4H/olu3PZSGn5U1RCM5u94Dt1w3lOATwB/CHyyz1waDo/Ak7vpJs0P3VN4vju9i4Yv8O9Dkh2AI+kK5/HAx+nGba/pNViDkrwPeAbwabqbeT5WVd/rN5WGZsMXuFZHkh+nK/I3Aq+vqnf0HKkpoxPC51bV9r6zaLgscE3UqLifTVfem4Hz6KbuvLnPXNJ6ZIFrYpKcCTwWOJ/uSomre44krWsWuCYmyT105xPg3ieEPZ8grQILXBqgJM+bb39VnbtWWTRcFrg0QKO/Zq4cfcCMpxxV1a+tdSYNjwUuDdBoRswX0D3D8aN0l2Pe0G8qDY0FLg1Ykl2Bo+nK/MHAK+d6ipQ2nh/rO4CkeX2P7hmYd9BNebxzv3E0JB6BSwOU5Gl019IfBnyK7rLMrf2m0tBY4NIAjU5iXgVcQndJ5r3eqM6tLnAyK2moXtx3AA2fR+DSwI2mkq2qunPBF2tD8SSmNFBJfiPJTcBXgZuSfDXJb/adS8NhgUsDlORVdHOBP7WqHlxVDwaeBjxztE9yCEUaoiTXAU+YOQd4kl2AL1bVo/tJpiHxCFwaqNke4FBV/wrc00McDZAFLg3T15I8febGJEcAt/aQRwPkEIo0QEkOopsD5RLgMrrrwJ9E92zMo31MncAClwZr9FDjFwIH0c1GeA3wXp+NqWneyCMNVFV9L8lFwO10R+DXWt4a5xG4NEBJHgj8H+CJdHOC/xjwBLrhlJdU1R39pdNQWODSACU5A7gReH1V3TPaFuCPgUdV1a/0l05DYYFLA5Tk+qraf6n7tLF4GaE0TFn4JdroLHBpmD6T5NWjYZN/k+SPgc/1lEkD4xCKNECjk5jvBg6lO4lZwCHAFXQnMb/dXzoNhQUuDViSRwIHMroOvKr+IcnJVfW2fpNpCCxwqTFJbqqq/frOof45Bi61xxOcAixwqUX+2SzAW+mlQUqyndmLOsAuaxxHA+UYuCQ1yiEUqSFJdk/yyr5zaBgscGmAkuyb5NQkH0vy60nun+TNwPXA3n3n0zA4Bi4N058Dfwt8CDiK7u7La4DHVdXX+wym4XAMXBqgJF+sqieMrd8G7FdV3+8xlgbGI3BpoJLswY+u+f46cP8kuwJU1bd6C6bB8AhcGqAkN9I9fX62m3aqqh6xtok0RBa4JDXKIRRpgJIcOt/+qrp8rbJouDwClwZo9DDjuVRVHbFmYTRYFrgkNcobeaQBSvIHY8vPn7Hvv619Ig2RBS4N03Fjy384Y99RaxlEw2WBS8OUOZZnW9cGZYFLw1RzLM+2rg3Kk5jSACW5G7iTH83//d3pXcDOVbVTX9k0HBa4JDXKG3mkAUqy53z7nQtF4BG4NEhJ/pFurDvAJuAWfnTy0rlQBFjg0uAluaKqDuk7h4bHq1Ck4fMoS7OywCWpUZ7ElAYoycvGVveesU5VvWWNI2mALHBpmHYbWz5txroEeBJTkprlGLgkNcoCl6RGWeCS1ChPYkoDleQA4ETgMaNN1wKnVdV1/aXSkHgELg1QksOBi4HtwKl0V6LcCVyU5Mk9RtOAeBWKNEBJzgf+tKounrH9Z4BTquqZvQTToFjg0gAl+fuqevQc+66rqgPWOpOGxyEUaZi2z7PvzjVLoUHzJKY0TPsmefss2wM8dK3DaJgscGmYfn+efVvXLIUGzTFwqTFJdqyqu/rOof45Bi4NUJJLxpbPmrH782scRwNlgUvDtOvY8kEz9gUJC1waqvnGNh33FOBJTGmodk9yDN1B1u5JnjfaHuBB/cXSkHgSUxqgJGcwz5F2Vb147dJoqCxwSWqUY+DSACV5R5L7PEYtyWOSfKqPTBoeC1wapq8DVyZ5IUCS+yf578B5wDt7TabBcAhFGqgkPwH8Gd0DjR8CfAD4r1X13V6DaTA8ApeGa/roake69+q1lrfGWeDSACV5FfAp4M+r6t8B/x44OsnfJjmw33QaCq8Dl4ZpCjikqrYDVNXNwLFJngl8CPjJPsNpGBwDlxqT5Mer6vt951D/PAKXBmiOucDH/faaBNGgWeDSMF3WdwANn0MoktQoj8ClAUpy3nz7q+oX1iqLhssCl4bpcOCfgLOBS3EOcM3CIRRpgJLsABwJHA88Hvg4cHZVXdNrMA2KN/JIA1RVd1fVJ6rqBODJwA3AxUl+q+doGhCHUKSBSvLjwLPpjsI3A28Hzu0zk4bFIRRpgJKcCTwWOB84p6qu7jmSBsgClwYoyT3AnaPV8TdpgKqqB659Kg2NBS5JjfIkpiQ1ygKXpEZZ4JLUKAtczUtycZKfm7Ht5CT/c5Ff//okz1jgNb+a5CErySlNmgWu9eBs4LgZ244bbZ9Xkh2q6tVVtdCT3n+V7rmU0mBY4FoPPgj8/OjGF5JspivbFybZmuSaJK+bfnGSG5O8OsklwPOTnJHk2NG+J44eW3ZZkr9Jsmm0bwvw3iRXJnl2kg+Pfb8jk3iDjdacBa7mVdU3gc8DR402HQe8H3hlVW2hm0vkZ5I8fuzLvldVP11V50xvSLIT8A7g2Kp6InA68CdV9UFgK/Afq+pg4K+Bn0wyNfrSFwPvWbX/QGkOFrjWi/FhlOnhk19KcjlwBXAQMP4w4PfP8j0OoLv78YIkVwKvAh4280XV3TxxFvDLSXanmznw/In8V0hL4FwoWi8+ArwlyaHALsA/A78HPKmq/jnJGcDOY6+/8z7fobvL8ZqqOnwRP+89wF8B3wP+sqruWkF2aVk8Ate6UFXfAS6mG/Y4G3ggXUl/O8k+wDMX8W2uA6aSHA7dkEqSg0b7tgO7jf28W4Bb6I7Sz5jMf4W0NB6Baz05m262vuOq6stJrgCuAb4CfGahL66qH4xOWL49yYPo3h9vG32PM4D/leRfgcOr6l+B9wJTVfV3q/EfIy3EuVCkZUryZ8AVVfXuvrNoY7LApWVIchndEM2RVfX9vvNoY7LAJalRnsSUpEZZ4JLUKAtckhplgUtSoyxwSWrU/wdO32Yxjk19aAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.2669192282197318\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n",
+    "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))\n",
+    "pie_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Urejeleaji wa Mstari\n",
+    "\n",
+    "Tutatumia Scikit Learn kufundisha mfano wa urejeleaji wa mstari:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a3232f8dc0>]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(X_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mwelekeo wa mstari unaweza kuamuliwa kutoka kwa vigezo vya usawa wa mstari:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-0.01751876]), 21.133734359909326)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.coef_, lin_reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.64893156])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pumpkin price on programmer's day\n",
+    "\n",
+    "lin_reg.predict([[256]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Usawazishaji wa Polynomial\n",
+    "\n",
+    "Wakati mwingine uhusiano kati ya vipengele na matokeo ni wa asili usio wa mstari. Kwa mfano, bei za maboga zinaweza kuwa juu wakati wa baridi (miezi=1,2), kisha zishuke wakati wa kiangazi (miezi=5-7), na kisha zipande tena. Usawazishaji wa mstari hauwezi kupata uhusiano huu kwa usahihi.\n",
+    "\n",
+    "Katika hali hii, tunaweza kufikiria kuongeza vipengele vya ziada. Njia rahisi ni kutumia polinomiali kutoka kwa vipengele vya ingizo, ambayo itasababisha **usawazishaji wa polynomial**. Katika Scikit Learn, tunaweza kuhesabu kiotomatiki vipengele vya polynomial kwa kutumia pipelines:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.73 (17.0%)\n",
+      "Model determination:  0.07639977655280217\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a323368ac0>]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)\n",
+    "\n",
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Aina za usimbaji\n",
+    "\n",
+    "Katika ulimwengu bora, tunataka kuwa na uwezo wa kutabiri bei za aina tofauti za maboga kwa kutumia modeli moja. Ili kuzingatia aina, tunahitaji kwanza kuibadilisha kuwa fomu ya nambari, au **kusimba**. Kuna njia kadhaa tunazoweza kutumia:\n",
+    "\n",
+    "* Usimbaji rahisi wa nambari ambao utajenga jedwali la aina tofauti, kisha kubadilisha jina la aina kwa kiashiria katika jedwali hilo. Hii si wazo bora kwa urarukaji wa mstari, kwa sababu urarukaji wa mstari unazingatia thamani ya nambari ya kiashiria, na thamani ya nambari huenda isiwe na uhusiano wa moja kwa moja na bei.\n",
+    "* Usimbaji wa one-hot, ambao utabadilisha safu ya `Variety` kuwa safu 4 tofauti, moja kwa kila aina, ambayo itakuwa na 1 ikiwa safu husika ni ya aina fulani, na 0 vinginevyo.\n",
+    "\n",
+    "Nambari iliyo hapa chini inaonyesha jinsi tunavyoweza kusimba aina kwa one-hot:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FAIRYTALE</th>\n",
+       "      <th>MINIATURE</th>\n",
+       "      <th>MIXED HEIRLOOM VARIETIES</th>\n",
+       "      <th>PIE TYPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE\n",
+       "70            0          0                         0         1\n",
+       "71            0          0                         0         1\n",
+       "72            0          0                         0         1\n",
+       "73            0          0                         0         1\n",
+       "74            0          0                         0         1\n",
+       "...         ...        ...                       ...       ...\n",
+       "1738          0          1                         0         0\n",
+       "1739          0          1                         0         0\n",
+       "1740          0          1                         0         0\n",
+       "1741          0          1                         0         0\n",
+       "1742          0          1                         0         0\n",
+       "\n",
+       "[415 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(new_pumpkins['Variety'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Urejeleaji wa Mstari kwenye Aina\n",
+    "\n",
+    "Sasa tutatumia msimbo ule ule kama hapo juu, lakini badala ya `DayOfYear` tutatumia aina yetu iliyowekwa kwa njia ya one-hot-encoding kama ingizo:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety'])\n",
+    "y = new_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 5.24 (19.7%)\n",
+      "Model determination:  0.774085281105197\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_linear_regression(X,y):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "    lin_reg = LinearRegression()\n",
+    "    lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "    pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "    mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "    print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "    score = lin_reg.score(X_train,y_train)\n",
+    "    print('Model determination: ', score)\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tunaweza pia kujaribu kutumia vipengele vingine kwa njia hiyo hiyo, na kuviunganisha na vipengele vya nambari, kama vile `Month` au `DayOfYear`:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.84 (10.5%)\n",
+      "Model determination:  0.9401096672643048\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['Package']))\n",
+    "y = new_pumpkins['Price']\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Usajili wa Polynomial\n",
+    "\n",
+    "Usajili wa polynomial unaweza pia kutumika na vipengele vya kategoria ambavyo vimekodishwa kwa njia ya one-hot. Msimbo wa kufundisha usajili wa polynomial kimsingi utakuwa sawa na tulivyoona hapo juu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.23 (8.25%)\n",
+      "Model determination:  0.9652870784724543\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d77bd89ae7e79780c68c58bab91f13f8",
+   "translation_date": "2025-09-06T13:11:26+00:00",
+   "source_file": "2-Regression/3-Linear/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/4-Logistic/notebook.ipynb b/translations/sw/2-Regression/4-Logistic/notebook.ipynb
new file mode 100644
index 000000000..4862612b7
--- /dev/null
+++ b/translations/sw/2-Regression/4-Logistic/notebook.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Aina za Malenge na Rangi\n",
+    "\n",
+    "Pakia maktaba zinazohitajika na seti ya data. Badilisha data kuwa dataframe inayojumuisha sehemu ya data:\n",
+    "\n",
+    "Hebu tuangalie uhusiano kati ya rangi na aina\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
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@@ -0,0 +1,686 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Jenga mfano wa logistic regression - Somo la 4\n",
+    "\n",
+    "![Picha ya kulinganisha logistic na linear regression](../../../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)\n",
+    "\n",
+    "#### **[Jaribio la kabla ya somo](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
+    "\n",
+    "#### Utangulizi\n",
+    "\n",
+    "Katika somo hili la mwisho kuhusu Regression, mojawapo ya mbinu za msingi za *klasiki* za ML, tutachunguza Logistic Regression. Ungetumia mbinu hii kugundua mifumo ya kutabiri makundi mawili. Je, pipi hii ni ya chokoleti au la? Je, ugonjwa huu unaambukiza au la? Je, mteja huyu atachagua bidhaa hii au la?\n",
+    "\n",
+    "Katika somo hili, utajifunza:\n",
+    "\n",
+    "-   Mbinu za logistic regression\n",
+    "\n",
+    "✅ Kuimarisha uelewa wako wa kufanya kazi na aina hii ya regression katika [moduli ya kujifunza](https://learn.microsoft.com/training/modules/introduction-classification-models/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "## Mahitaji ya awali\n",
+    "\n",
+    "Baada ya kufanya kazi na data ya malenge, sasa tunajua vya kutosha kutambua kwamba kuna kundi moja la binary ambalo tunaweza kufanya kazi nalo: `Color`.\n",
+    "\n",
+    "Hebu tujenge mfano wa logistic regression ili kutabiri, kwa kuzingatia baadhi ya vigezo, *rangi ya malenge fulani itakuwa nini* (machungwa 🎃 au nyeupe 👻).\n",
+    "\n",
+    "> Kwa nini tunazungumzia binary classification katika somo lililojumuishwa kuhusu regression? Ni kwa urahisi wa lugha tu, kwani logistic regression ni [kweli ni mbinu ya classification](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), ingawa inategemea linear. Jifunze kuhusu njia nyingine za kuainisha data katika kundi la somo linalofuata.\n",
+    "\n",
+    "Kwa somo hili, tutahitaji vifurushi vifuatavyo:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) ni [mkusanyiko wa vifurushi vya R](https://www.tidyverse.org/packages) vilivyoundwa kufanya sayansi ya data kuwa ya haraka, rahisi na ya kufurahisha!\n",
+    "\n",
+    "-   `tidymodels`: Mfumo wa [tidymodels](https://www.tidymodels.org/) ni [mkusanyiko wa vifurushi](https://www.tidymodels.org/packages/) kwa ajili ya uundaji wa mifano na machine learning.\n",
+    "\n",
+    "-   `janitor`: Kifurushi cha [janitor](https://github.com/sfirke/janitor) kinatoa zana rahisi za kuchunguza na kusafisha data chafu.\n",
+    "\n",
+    "-   `ggbeeswarm`: Kifurushi cha [ggbeeswarm](https://github.com/eclarke/ggbeeswarm) kinatoa mbinu za kuunda michoro ya mtindo wa beeswarm kwa kutumia ggplot2.\n",
+    "\n",
+    "Unaweza kuvifunga kwa kutumia:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+    "\n",
+    "Vinginevyo, script hapa chini itakagua kama una vifurushi vinavyohitajika kukamilisha moduli hii na kuvifunga kwako endapo havipo.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## **Tafsiri swali**\n",
+    "\n",
+    "Kwa madhumuni yetu, tutalielezea kama binary: 'Nyeupe' au 'Sio Nyeupe'. Pia kuna kategoria ya 'mistari' katika seti yetu ya data lakini kuna mifano michache sana ya hiyo, kwa hivyo hatutaitumia. Inatoweka mara tu tunapoondoa thamani za null kutoka kwenye seti ya data, hata hivyo.\n",
+    "\n",
+    "> 🎃 Ukweli wa kufurahisha, wakati mwingine tunaita maboga meupe 'maboga ya roho'. Hayachongwi kwa urahisi, kwa hivyo si maarufu kama yale ya rangi ya machungwa lakini yanaonekana ya kuvutia! Kwa hivyo tunaweza pia kuunda upya swali letu kama: 'Roho' au 'Sio Roho'. 👻\n",
+    "\n",
+    "## **Kuhusu regression ya logistic**\n",
+    "\n",
+    "Regression ya logistic inatofautiana na regression ya linear, ambayo ulijifunza hapo awali, kwa njia kadhaa muhimu.\n",
+    "\n",
+    "#### **Uainishaji wa binary**\n",
+    "\n",
+    "Regression ya logistic haitoi vipengele sawa na regression ya linear. Ya kwanza inatoa utabiri kuhusu `kategoria ya binary` (\"machungwa au sio machungwa\") ilhali ya pili ina uwezo wa kutabiri `thamani zinazoendelea`, kwa mfano ukizingatia asili ya boga na wakati wa kuvuna, *bei yake itaongezeka kwa kiasi gani*.\n",
+    "\n",
+    "![Infographic na Dasani Madipalli](../../../../../../2-Regression/4-Logistic/images/pumpkin-classifier.png)\n",
+    "\n",
+    "### Uainishaji mwingine\n",
+    "\n",
+    "Kuna aina nyingine za regression ya logistic, ikiwa ni pamoja na multinomial na ordinal:\n",
+    "\n",
+    "- **Multinomial**, ambayo inahusisha kuwa na zaidi ya kategoria moja - \"Machungwa, Nyeupe, na Mistari\".\n",
+    "\n",
+    "- **Ordinal**, ambayo inahusisha kategoria zilizo na mpangilio, muhimu ikiwa tungependa kupanga matokeo yetu kwa mantiki, kama maboga yetu ambayo yamepangwa kwa idadi finyu ya ukubwa (mini,sm,med,lg,xl,xxl).\n",
+    "\n",
+    "![Multinomial vs ordinal regression](../../../../../../2-Regression/4-Logistic/images/multinomial-vs-ordinal.png)\n",
+    "\n",
+    "#### **Vigezo HAVIHITAJI kuhusiana**\n",
+    "\n",
+    "Unakumbuka jinsi regression ya linear ilivyofanya kazi vizuri zaidi na vigezo vilivyohusiana? Regression ya logistic ni kinyume - vigezo havihitaji kuhusiana. Hii inafaa kwa data hii ambayo ina uhusiano dhaifu kiasi.\n",
+    "\n",
+    "#### **Unahitaji data safi nyingi**\n",
+    "\n",
+    "Regression ya logistic itatoa matokeo sahihi zaidi ikiwa utatumia data nyingi; seti yetu ndogo ya data si bora kwa kazi hii, kwa hivyo kumbuka hilo.\n",
+    "\n",
+    "✅ Fikiria aina za data ambazo zinaweza kufaa kwa regression ya logistic\n",
+    "\n",
+    "## Zoezi - safisha data\n",
+    "\n",
+    "Kwanza, safisha data kidogo, ukiondoa thamani za null na kuchagua baadhi tu ya safu:\n",
+    "\n",
+    "1. Ongeza msimbo ufuatao:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the core tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data and clean column names\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \n",
+    "  clean_names()\n",
+    "\n",
+    "# Select desired columns\n",
+    "pumpkins_select <- pumpkins %>% \n",
+    "  select(c(city_name, package, variety, origin, item_size, color)) \n",
+    "\n",
+    "# Drop rows containing missing values and encode color as factor (category)\n",
+    "pumpkins_select <- pumpkins_select %>% \n",
+    "  drop_na() %>% \n",
+    "  mutate(color = factor(color))\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins_select %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unaweza kila wakati kutazama kwa haraka dataframe yako mpya, kwa kutumia [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) kama ilivyo hapa chini:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pumpkins_select %>% \n",
+    "  glimpse()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tuthibitishe kwamba tutakuwa tunashughulikia tatizo la uainishaji wa binary:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Subset distinct observations in outcome column\n",
+    "pumpkins_select %>% \n",
+    "  distinct(color)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Uonyeshaji - mchoro wa kategoria\n",
+    "Hadi sasa umepakia tena data ya malenge na kuisafisha ili kuhifadhi seti ya data inayojumuisha vigezo kadhaa, ikiwemo Rangi. Hebu tuonyeshe dataframe kwenye daftari kwa kutumia maktaba ya ggplot.\n",
+    "\n",
+    "Maktaba ya ggplot inatoa njia nzuri za kuonyesha data yako. Kwa mfano, unaweza kulinganisha usambazaji wa data kwa kila Aina na Rangi katika mchoro wa kategoria.\n",
+    "\n",
+    "1. Tengeneza mchoro kama huo kwa kutumia kazi ya geombar, ukitumia data yetu ya malenge, na kubainisha ramani ya rangi kwa kila kategoria ya malenge (machungwa au nyeupe):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Specify colors for each value of the hue variable\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# Create the bar plot\n",
+    "ggplot(pumpkins_select, aes(y = variety, fill = color)) +\n",
+    "  geom_bar(position = \"dodge\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(y = \"Variety\", fill = \"Color\") +\n",
+    "  theme_minimal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kwa kuangalia data, unaweza kuona jinsi data ya Rangi inavyohusiana na Aina.\n",
+    "\n",
+    "✅ Kwa kuzingatia mchoro huu wa kategoria, ni uchunguzi gani wa kuvutia unaweza kufikiria?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Usindikaji wa data: usimbaji wa vipengele\n",
+    "\n",
+    "Seti yetu ya data ya malenge ina thamani za maandishi kwa safu zake zote. Kufanya kazi na data ya kategoria ni rahisi kwa binadamu lakini si kwa mashine. Algorithimu za kujifunza kwa mashine hufanya kazi vizuri na nambari. Ndiyo sababu usimbaji ni hatua muhimu sana katika awamu ya usindikaji wa data, kwani inatuwezesha kubadilisha data ya kategoria kuwa data ya nambari bila kupoteza taarifa yoyote. Usimbaji mzuri huchangia kujenga modeli nzuri.\n",
+    "\n",
+    "Kwa usimbaji wa vipengele kuna aina mbili kuu za usimbaji:\n",
+    "\n",
+    "1. **Ordinal encoder**: Inafaa vizuri kwa vigezo vya ordinal, ambavyo ni vigezo vya kategoria ambapo data yake inafuata mpangilio wa kimantiki, kama safu ya `item_size` katika seti yetu ya data. Inaunda ramani ambapo kila kategoria inawakilishwa na nambari, ambayo ni mpangilio wa kategoria katika safu.\n",
+    "\n",
+    "2. **Categorical encoder**: Inafaa vizuri kwa vigezo vya nominal, ambavyo ni vigezo vya kategoria ambapo data yake haifuati mpangilio wa kimantiki, kama vipengele vyote tofauti na `item_size` katika seti yetu ya data. Hii ni usimbaji wa one-hot, ambayo inamaanisha kwamba kila kategoria inawakilishwa na safu ya binary: kigezo kilichosimbwa ni sawa na 1 ikiwa malenge yanahusiana na Aina hiyo na 0 vinginevyo.\n",
+    "\n",
+    "Tidymodels inatoa kifurushi kingine kizuri: [recipes](https://recipes.tidymodels.org/) - kifurushi cha kusindika data. Tutafafanua `recipe` inayobainisha kwamba safu zote za utabiri zinapaswa kusimbwa kuwa seti ya nambari, `prep` ili kukadiria kiasi na takwimu zinazohitajika kwa operesheni yoyote, na hatimaye `bake` ili kutumia hesabu kwa data mpya.\n",
+    "\n",
+    "> Kwa kawaida, recipes hutumika kama usindikaji wa awali kwa uundaji wa modeli ambapo inabainisha hatua gani zinapaswa kutumika kwa seti ya data ili kuifanya iwe tayari kwa uundaji wa modeli. Katika hali hiyo, **inapendekezwa sana** kwamba utumie `workflow()` badala ya kukadiria recipe kwa mikono ukitumia prep na bake. Tutaliona hili kwa undani muda si mrefu.\n",
+    ">\n",
+    "> Hata hivyo, kwa sasa tunatumia recipes + prep + bake kubainisha hatua gani zinapaswa kutumika kwa seti ya data ili kuifanya iwe tayari kwa uchambuzi wa data na kisha kutoa data iliyosindikwa na hatua zilizotumika.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Preprocess and extract data to allow some data analysis\n",
+    "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>%\n",
+    "  # Define ordering for item_size column\n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  # Convert factors to numbers using the order defined above (Ordinal encoding)\n",
+    "  step_integer(item_size, zero_based = F) %>%\n",
+    "  # Encode all other predictors using one hot encoding\n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE) %>%\n",
+    "  prep(data = pumpkin_select) %>%\n",
+    "  bake(new_data = NULL)\n",
+    "\n",
+    "# Display the first few rows of preprocessed data\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "✅ Je, ni faida gani za kutumia ordinal encoder kwa safu ya Item Size?\n",
+    "\n",
+    "### Kuchambua uhusiano kati ya vigezo\n",
+    "\n",
+    "Sasa kwa kuwa tumeshughulikia data yetu, tunaweza kuchambua uhusiano kati ya vipengele na lebo ili kupata wazo la jinsi ambavyo modeli itaweza kutabiri lebo kwa kuzingatia vipengele. Njia bora ya kufanya uchambuzi wa aina hii ni kwa kuchora data.  \n",
+    "Tutatumia tena kipengele cha ggplot geom_boxplot_ ili kuonyesha uhusiano kati ya Item Size, Variety, na Color katika mchoro wa kategoria. Ili kuchora data vizuri zaidi, tutatumia safu ya Item Size iliyosimbwa na safu ya Variety ambayo haijasimbwa.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define the color palette\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins_select_plot<-pumpkins_select\n",
+    "pumpkins_select_plot$item_size <- baked_pumpkins$item_size\n",
+    "\n",
+    "# Create the grouped box plot\n",
+    "ggplot(pumpkins_select_plot, aes(x = `item_size`, y = color, fill = color)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_grid(variety ~ ., scales = \"free_x\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(x = \"Item Size\", y = \"\") +\n",
+    "  theme_minimal() +\n",
+    "  theme(strip.text = element_text(size = 12)) +\n",
+    "  theme(axis.text.x = element_text(size = 10)) +\n",
+    "  theme(axis.title.x = element_text(size = 12)) +\n",
+    "  theme(axis.title.y = element_blank()) +\n",
+    "  theme(legend.position = \"bottom\") +\n",
+    "  guides(fill = guide_legend(title = \"Color\")) +\n",
+    "  theme(panel.spacing = unit(0.5, \"lines\"))+\n",
+    "  theme(strip.text.y = element_text(size = 4, hjust = 0)) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tumia mchoro wa kundi\n",
+    "\n",
+    "Kwa kuwa Rangi ni kategoria ya binary (Nyeupe au Sio Nyeupe), inahitaji '[mbinu maalum](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf)' kwa ajili ya uonyeshaji.\n",
+    "\n",
+    "Jaribu `mchoro wa kundi` kuonyesha usambazaji wa rangi kulingana na ukubwa wa kipengee.\n",
+    "\n",
+    "Tutatumia [pakiti ya ggbeeswarm](https://github.com/eclarke/ggbeeswarm) ambayo inatoa mbinu za kuunda michoro ya mtindo wa nyuki kwa kutumia ggplot2. Michoro ya nyuki ni njia ya kuchora alama ambazo kwa kawaida zingekuwa zinagongana ili ziweze kuangukia karibu na kila moja badala yake.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create beeswarm plots of color and item_size\n",
+    "baked_pumpkins %>% \n",
+    "  mutate(color = factor(color)) %>% \n",
+    "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\n",
+    "  geom_quasirandom() +\n",
+    "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\n",
+    "  theme(legend.position = \"none\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sasa kwa kuwa tuna wazo la uhusiano kati ya makundi mawili ya rangi na kundi kubwa la ukubwa, hebu tuchunguze logistic regression ili kubaini rangi inayowezekana ya malenge fulani.\n",
+    "\n",
+    "## Tengeneza modeli yako\n",
+    "\n",
+    "Chagua vigezo unavyotaka kutumia katika modeli yako ya uainishaji na gawanya data katika seti za mafunzo na majaribio. [rsample](https://rsample.tidymodels.org/), kifurushi katika Tidymodels, kinatoa miundombinu ya kugawanya data kwa ufanisi na kufanya resampling:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Split data into 80% for training and 20% for testing\n",
+    "set.seed(2056)\n",
+    "pumpkins_split <- pumpkins_select %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "# Print out the first 5 rows of the training set\n",
+    "pumpkins_train %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🙌 Sasa tuko tayari kufundisha modeli kwa kuoanisha vipengele vya mafunzo na lebo ya mafunzo (rangi).\n",
+    "\n",
+    "Tutaanza kwa kuunda mapishi yanayobainisha hatua za awali za uchakataji ambazo zinapaswa kufanywa kwenye data yetu ili kujiandaa kwa uundaji wa modeli, yaani: kubadilisha vigezo vya kategoria kuwa seti ya nambari. Kama vile `baked_pumpkins`, tunaunda `pumpkins_recipe` lakini hatufanyi `prep` na `bake` kwa sababu itajumuishwa katika mtiririko wa kazi, ambao utaona katika hatua chache zijazo.\n",
+    "\n",
+    "Kuna njia kadhaa za kubainisha modeli ya regression ya logistic katika Tidymodels. Tazama `?logistic_reg()` Kwa sasa, tutabainisha modeli ya regression ya logistic kupitia injini ya msingi `stats::glm()`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a recipe that specifies preprocessing steps for modelling\n",
+    "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  step_integer(item_size, zero_based = F) %>%  \n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)\n",
+    "\n",
+    "# Create a logistic model specification\n",
+    "log_reg <- logistic_reg() %>% \n",
+    "  set_engine(\"glm\") %>% \n",
+    "  set_mode(\"classification\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sasa kwa kuwa tuna mapishi na maelezo ya mfano, tunahitaji kupata njia ya kuyafungasha pamoja kuwa kitu kimoja ambacho kwanza kitachakata data (prep+bake kwa nyuma ya pazia), kufundisha mfano kwa data iliyochakatwa, na pia kuruhusu shughuli za baada ya uchakataji ikiwa zitahitajika.\n",
+    "\n",
+    "Katika Tidymodels, kitu hiki rahisi kinaitwa [`workflow`](https://workflows.tidymodels.org/) na kwa urahisi kinashikilia vipengele vyako vya uundaji wa mifano.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Bundle modelling components in a workflow\n",
+    "log_reg_wf <- workflow() %>% \n",
+    "  add_recipe(pumpkins_recipe) %>% \n",
+    "  add_model(log_reg)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "log_reg_wf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Baada ya mtiririko wa kazi kuwa *umeainishwa*, modeli inaweza `kufundishwa` kwa kutumia [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) kazi. Mtiririko wa kazi utatathmini mapishi na kuchakata data kabla ya mafunzo, kwa hivyo hatutalazimika kufanya hivyo kwa mikono kwa kutumia prep na bake.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "wf_fit <- log_reg_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the trained workflow\n",
+    "wf_fit\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mfano unatoa viwango vilivyojifunzwa wakati wa mafunzo.\n",
+    "\n",
+    "Sasa tumefundisha mfano kwa kutumia data ya mafunzo, tunaweza kufanya utabiri kwenye data ya majaribio kwa kutumia [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Hebu tuanze kwa kutumia mfano kutabiri lebo za seti yetu ya majaribio na uwezekano wa kila lebo. Wakati uwezekano ni zaidi ya 0.5, darasa linalotabiriwa ni `WHITE` vinginevyo ni `ORANGE`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions for color and corresponding probabilities\n",
+    "results <- pumpkins_test %>% select(color) %>% \n",
+    "  bind_cols(wf_fit %>% \n",
+    "              predict(new_data = pumpkins_test)) %>%\n",
+    "  bind_cols(wf_fit %>%\n",
+    "              predict(new_data = pumpkins_test, type = \"prob\"))\n",
+    "\n",
+    "# Compare predictions\n",
+    "results %>% \n",
+    "  slice_head(n = 10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hii ni nzuri sana! Inatoa ufahamu zaidi kuhusu jinsi logistic regression inavyofanya kazi.\n",
+    "\n",
+    "### Uelewa bora kupitia matriki ya kuchanganya\n",
+    "\n",
+    "Kulinganisha kila utabiri na thamani yake halisi ya \"ground truth\" si njia bora sana ya kuamua jinsi mfano unavyotabiri kwa usahihi. Kwa bahati nzuri, Tidymodels ina mbinu nyingine chache za kusaidia: [`yardstick`](https://yardstick.tidymodels.org/) - kifurushi kinachotumika kupima ufanisi wa mifano kwa kutumia vipimo vya utendaji.\n",
+    "\n",
+    "Kipimo kimoja cha utendaji kinachohusiana na matatizo ya uainishaji ni [`confusion matrix`](https://wikipedia.org/wiki/Confusion_matrix). Matriki ya kuchanganya inaelezea jinsi mfano wa uainishaji unavyofanya kazi. Matriki ya kuchanganya huonyesha ni mifano mingapi katika kila darasa iliyoainishwa kwa usahihi na mfano. Katika hali yetu, itakuonyesha ni maboga ya rangi ya machungwa mangapi yaliyoainishwa kama machungwa na ni maboga meupe mangapi yaliyoainishwa kama meupe; matriki ya kuchanganya pia inaonyesha ni mangapi yaliyoainishwa katika makundi **yasiyo sahihi**.\n",
+    "\n",
+    "Kazi ya [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) kutoka yardstick huhesabu msalaba huu wa tabulation wa madarasa yaliyotazamwa na yaliyotabiriwa.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Confusion matrix for prediction results\n",
+    "conf_mat(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tuchambue matriki ya mkanganyiko. Modeli yetu imepewa jukumu la kuainisha maboga kati ya makundi mawili ya binary, kundi `nyeupe` na kundi `sio-nyeupe`.\n",
+    "\n",
+    "-   Ikiwa modeli yako inatabiri boga kuwa nyeupe na kwa kweli linahusiana na kundi 'nyeupe', tunaliita `chanya halisi`, linaonyeshwa na namba ya juu kushoto.\n",
+    "\n",
+    "-   Ikiwa modeli yako inatabiri boga kuwa sio nyeupe na kwa kweli linahusiana na kundi 'nyeupe', tunaliita `hasi ya uongo`, linaonyeshwa na namba ya chini kushoto.\n",
+    "\n",
+    "-   Ikiwa modeli yako inatabiri boga kuwa nyeupe na kwa kweli linahusiana na kundi 'sio-nyeupe', tunaliita `chanya ya uongo`, linaonyeshwa na namba ya juu kulia.\n",
+    "\n",
+    "-   Ikiwa modeli yako inatabiri boga kuwa sio nyeupe na kwa kweli linahusiana na kundi 'sio-nyeupe', tunaliita `hasi halisi`, linaonyeshwa na namba ya chini kulia.\n",
+    "\n",
+    "| Ukweli |\n",
+    "|:-----:|\n",
+    "\n",
+    "\n",
+    "|               |        |       |\n",
+    "|---------------|--------|-------|\n",
+    "| **Iliyotabiriwa** | NYEUPE | MACHUNGWA |\n",
+    "| NYEUPE        | TP     | FP    |\n",
+    "| MACHUNGWA         | FN     | TN    |\n",
+    "\n",
+    "Kama ulivyotambua, ni bora kuwa na idadi kubwa ya chanya halisi na hasi halisi, na idadi ndogo ya chanya ya uongo na hasi ya uongo, ambayo inaonyesha kuwa modeli inafanya kazi vizuri zaidi.\n",
+    "\n",
+    "Matriki ya mkanganyiko ni muhimu kwa sababu inazalisha vipimo vingine ambavyo vinaweza kutusaidia kutathmini utendaji wa modeli ya uainishaji kwa usahihi zaidi. Hebu tuzipitie:\n",
+    "\n",
+    "🎓 Usahihi: `TP/(TP + FP)` inafafanuliwa kama uwiano wa chanya zilizotabiriwa ambazo kwa kweli ni chanya. Pia huitwa [thamani ya utabiri chanya](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\").\n",
+    "\n",
+    "🎓 Urejeshaji: `TP/(TP + FN)` inafafanuliwa kama uwiano wa matokeo chanya kati ya idadi ya sampuli ambazo kwa kweli ni chanya. Pia inajulikana kama `hisia`.\n",
+    "\n",
+    "🎓 Umaalumu: `TN/(TN + FP)` inafafanuliwa kama uwiano wa matokeo hasi kati ya idadi ya sampuli ambazo kwa kweli ni hasi.\n",
+    "\n",
+    "🎓 Usahihi wa jumla: `TP + TN/(TP + TN + FP + FN)` Asilimia ya lebo zilizotabiriwa kwa usahihi kwa sampuli.\n",
+    "\n",
+    "🎓 Kipimo cha F: Wastani wa uzito wa usahihi na urejeshaji, bora ikiwa ni 1 na mbaya ikiwa ni 0.\n",
+    "\n",
+    "Hebu tuhisi vipimo hivi!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Combine metric functions and calculate them all at once\n",
+    "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\n",
+    "eval_metrics(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kuonyesha Mchoro wa ROC wa mfano huu\n",
+    "\n",
+    "Hebu tufanye uonyeshaji mwingine ili kuona kinachoitwa [`Mchoro wa ROC`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make a roc_curve\n",
+    "results %>% \n",
+    "  roc_curve(color, .pred_ORANGE) %>% \n",
+    "  autoplot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mikondo ya ROC mara nyingi hutumika kupata mtazamo wa matokeo ya kiondoaji katika muktadha wa kweli dhidi ya chanya za uongo. Mikondo ya ROC kwa kawaida huonyesha `True Positive Rate`/Unyeti kwenye mhimili wa Y, na `False Positive Rate`/1-Specifisiti kwenye mhimili wa X. Kwa hivyo, mwinuko wa mkondo na nafasi kati ya mstari wa katikati na mkondo ni muhimu: unataka mkondo unaopanda haraka na kuvuka mstari. Katika hali yetu, kuna chanya za uongo mwanzoni, kisha mstari unapanda na kuvuka vizuri.\n",
+    "\n",
+    "Hatimaye, hebu tutumie `yardstick::roc_auc()` kuhesabu eneo halisi chini ya mkondo. Njia moja ya kufasiri AUC ni kama uwezekano kwamba modeli itaweka mfano chanya wa nasibu juu zaidi kuliko mfano hasi wa nasibu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate area under curve\n",
+    "results %>% \n",
+    "  roc_auc(color, .pred_ORANGE)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matokeo ni karibu `0.975`. Kwa kuwa AUC inatoka 0 hadi 1, unataka alama kubwa, kwani modeli ambayo ni sahihi kwa 100% katika utabiri wake itakuwa na AUC ya 1; katika hali hii, modeli ni *nzuri sana*.\n",
+    "\n",
+    "Katika masomo ya baadaye kuhusu uainishaji, utajifunza jinsi ya kuboresha alama za modeli yako (kama kushughulikia data isiyo na uwiano katika hali hii).\n",
+    "\n",
+    "## 🚀Changamoto\n",
+    "\n",
+    "Kuna mengi zaidi ya kuchunguza kuhusu regression ya logistic! Lakini njia bora ya kujifunza ni kujaribu. Tafuta seti ya data inayofaa kwa aina hii ya uchambuzi na tengeneza modeli nayo. Unajifunza nini? kidokezo: jaribu [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) kwa seti za data za kuvutia.\n",
+    "\n",
+    "## Mapitio na Kujisomea\n",
+    "\n",
+    "Soma kurasa chache za mwanzo za [karatasi hii kutoka Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) kuhusu matumizi ya vitendo ya regression ya logistic. Fikiria kuhusu kazi ambazo zinafaa zaidi kwa aina moja au nyingine ya kazi za regression ambazo tumejifunza hadi sasa. Nini kingefanya kazi vizuri zaidi?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "langauge": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "feaf125f481a89c468fa115bf2aed580",
+   "translation_date": "2025-09-06T13:34:32+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sw/2-Regression/4-Logistic/solution/notebook.ipynb b/translations/sw/2-Regression/4-Logistic/solution/notebook.ipynb
new file mode 100644
index 000000000..343d3a9ab
--- /dev/null
+++ b/translations/sw/2-Regression/4-Logistic/solution/notebook.ipynb
@@ -0,0 +1,1255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usajili wa Kihesabu - Somo la 4\n",
+    "\n",
+    "Pakia maktaba zinazohitajika na seti ya data. Badilisha data kuwa fremu ya data inayojumuisha sehemu ndogo ya data:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date   \n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17  \\\n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition   \n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN  \\\n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Origin</th>\n",
+       "      <th>Item Size</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>DELAWARE</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>VIRGINIA</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>36 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name       Package      Variety    Origin Item Size   Color\n",
+       "2  BALTIMORE  24 inch bins  HOWDEN TYPE  DELAWARE       med  ORANGE\n",
+       "3  BALTIMORE  24 inch bins  HOWDEN TYPE  VIRGINIA       med  ORANGE\n",
+       "4  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "5  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "6  BALTIMORE  36 inch bins  HOWDEN TYPE  MARYLAND       med  ORANGE"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select the columns we want to use\n",
+    "columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Drop rows with missing values\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hebu tuangalie data yetu!\n",
+    "\n",
+    "Kwa kuitazama kwa kutumia Seaborn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56d0c650>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 609.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# Specify colors for each values of the hue variable\n",
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# Plot a bar plot to visualize how many pumpkins of each variety are orange or white\n",
+    "sns.catplot(\n",
+    "    data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
+    "    palette=palette, \n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Usindikaji wa Awali wa Data\n",
+    "\n",
+    "Hebu tusimbue sifa na lebo ili kuweza kuchora data vizuri na kufundisha modeli.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the different values of the 'Item Size' column\n",
+    "pumpkins['Item Size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "# Encode the 'Item Size' column using ordinal encoding\n",
+    "item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
+    "ordinal_features = ['Item Size']\n",
+    "ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "# Encode all the other features using one-hot encoding\n",
+    "categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
+    "categorical_encoder = OneHotEncoder(sparse_output=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_MICHIGAN</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_MICHIGAN  cat__Origin_NEW JERSEY   \n",
+       "2                   0.0  ...                   0.0                     0.0  \\\n",
+       "3                   0.0  ...                   0.0                     0.0   \n",
+       "4                   0.0  ...                   0.0                     0.0   \n",
+       "5                   0.0  ...                   0.0                     0.0   \n",
+       "6                   0.0  ...                   0.0                     0.0   \n",
+       "\n",
+       "   cat__Origin_NEW YORK  cat__Origin_NORTH CAROLINA  cat__Origin_OHIO   \n",
+       "2                   0.0                         0.0               0.0  \\\n",
+       "3                   0.0                         0.0               0.0   \n",
+       "4                   0.0                         0.0               0.0   \n",
+       "5                   0.0                         0.0               0.0   \n",
+       "6                   0.0                         0.0               0.0   \n",
+       "\n",
+       "   cat__Origin_PENNSYLVANIA  cat__Origin_TENNESSEE  cat__Origin_TEXAS   \n",
+       "2                       0.0                    0.0                0.0  \\\n",
+       "3                       0.0                    0.0                0.0   \n",
+       "4                       0.0                    0.0                0.0   \n",
+       "5                       0.0                    0.0                0.0   \n",
+       "6                       0.0                    0.0                0.0   \n",
+       "\n",
+       "   cat__Origin_VERMONT  cat__Origin_VIRGINIA  \n",
+       "2                  0.0                   0.0  \n",
+       "3                  0.0                   1.0  \n",
+       "4                  0.0                   0.0  \n",
+       "5                  0.0                   0.0  \n",
+       "6                  0.0                   0.0  \n",
+       "\n",
+       "[5 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "ct = ColumnTransformer(transformers=[\n",
+    "     ('ord', ordinal_encoder, ordinal_features),\n",
+    "     ('cat', categorical_encoder, categorical_features)\n",
+    "     ])\n",
+    "# Get the encoded features as a pandas DataFrame\n",
+    "ct.set_output(transform='pandas')\n",
+    "encoded_features = ct.fit_transform(pumpkins)\n",
+    "encoded_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_NEW JERSEY  cat__Origin_NEW YORK   \n",
+       "2                   0.0  ...                     0.0                   0.0  \\\n",
+       "3                   0.0  ...                     0.0                   0.0   \n",
+       "4                   0.0  ...                     0.0                   0.0   \n",
+       "5                   0.0  ...                     0.0                   0.0   \n",
+       "6                   0.0  ...                     0.0                   0.0   \n",
+       "\n",
+       "   cat__Origin_NORTH CAROLINA  cat__Origin_OHIO  cat__Origin_PENNSYLVANIA   \n",
+       "2                         0.0               0.0                       0.0  \\\n",
+       "3                         0.0               0.0                       0.0   \n",
+       "4                         0.0               0.0                       0.0   \n",
+       "5                         0.0               0.0                       0.0   \n",
+       "6                         0.0               0.0                       0.0   \n",
+       "\n",
+       "   cat__Origin_TENNESSEE  cat__Origin_TEXAS  cat__Origin_VERMONT   \n",
+       "2                    0.0                0.0                  0.0  \\\n",
+       "3                    0.0                0.0                  0.0   \n",
+       "4                    0.0                0.0                  0.0   \n",
+       "5                    0.0                0.0                  0.0   \n",
+       "6                    0.0                0.0                  0.0   \n",
+       "\n",
+       "   cat__Origin_VIRGINIA  Color  \n",
+       "2                   0.0      0  \n",
+       "3                   1.0      0  \n",
+       "4                   0.0      0  \n",
+       "5                   0.0      0  \n",
+       "6                   0.0      0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "# Encode the 'Color' column using label encoding\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n",
+    "encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
+    "encoded_pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ORANGE', 'WHITE']"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the mapping between the encoded values and the original values\n",
+    "list(label_encoder.inverse_transform([0, 1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56322210>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1620 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=pumpkins,\n",
+    "    x=\"Item Size\", y=\"Color\", row='Variety',\n",
+    "    kind=\"box\", orient=\"h\",\n",
+    "    sharex=False, margin_titles=True,\n",
+    "    height=1.8, aspect=4, palette=palette,\n",
+    ")\n",
+    "# Defining axis labels \n",
+    "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
+    "g.set_titles(row_template=\"{row_name}\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Color', ylabel='ord__Item Size'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Suppressing warning message claiming that a portion of points cannot be placed into the plot due to the high number of data points\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')\n",
+    "\n",
+    "palette = {\n",
+    "    0: 'orange',\n",
+    "    1: 'wheat'\n",
+    "}\n",
+    "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Angalia**: Kupuuza maonyo SIYO mbinu bora na inapaswa kuepukwa, inapowezekana. Maonyo mara nyingi huwa na ujumbe muhimu unaotusaidia kuboresha msimbo wetu na kutatua tatizo.  \n",
+    "Sababu ya kupuuza onyo hili maalum ni kuhakikisha usomaji wa mchoro. Kuchora alama zote za data kwa ukubwa mdogo wa alama, huku tukidumisha uthabiti na rangi ya palette, kunasababisha taswira isiyo wazi.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# X is the encoded features\n",
+    "X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
+    "# y is the encoded label\n",
+    "y = encoded_pumpkins['Color']\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.94      0.98      0.96       166\n",
+      "           1       0.85      0.67      0.75        33\n",
+      "\n",
+      "    accuracy                           0.92       199\n",
+      "   macro avg       0.89      0.82      0.85       199\n",
+      "weighted avg       0.92      0.92      0.92       199\n",
+      "\n",
+      "Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
+      " 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
+      " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+      " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
+      "F1-score:  0.7457627118644068\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Train a logistic regression model on the pumpkin dataset\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model and print the results\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('F1-score: ', f1_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[162,   4],\n",
+       "       [ 11,  22]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "y_scores = model.predict_proba(X_test)\n",
+    "# calculate ROC curve\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "# Plot the diagonal 50% line\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "# Plot the FPR and TPR achieved by our model\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9749908725812341\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate AUC score\n",
+    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+    "print(auc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "vscode": {
+   "interpreter": {
+    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "ef50cc584e0b79412610cc7da15e1f86",
+   "translation_date": "2025-09-06T13:27:50+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sw/3-Web-App/1-Web-App/notebook.ipynb b/translations/sw/3-Web-App/1-Web-App/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sw/3-Web-App/1-Web-App/solution/notebook.ipynb b/translations/sw/3-Web-App/1-Web-App/solution/notebook.ipynb
new file mode 100644
index 000000000..03b48afaf
--- /dev/null
+++ b/translations/sw/3-Web-App/1-Web-App/solution/notebook.ipynb
@@ -0,0 +1,267 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5fa2e8f4584c78250ca9729b46562ceb",
+   "translation_date": "2025-09-06T14:32:14+00:00",
+   "source_file": "3-Web-App/1-Web-App/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           datetime                  city state country     shape  \\\n",
+       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
+       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
+       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
+       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
+       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
+       "\n",
+       "   duration (seconds) duration (hours/min)  \\\n",
+       "0              2700.0           45 minutes   \n",
+       "1              7200.0              1-2 hrs   \n",
+       "2                20.0           20 seconds   \n",
+       "3                20.0             1/2 hour   \n",
+       "4               900.0           15 minutes   \n",
+       "\n",
+       "                                            comments date posted   latitude  \\\n",
+       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
+       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
+       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
+       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
+       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
+       "\n",
+       "    longitude  \n",
+       "0  -97.941111  \n",
+       "1  -98.581082  \n",
+       "2   -2.916667  \n",
+       "3  -96.645833  \n",
+       "4 -157.803611  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ufos = pd.read_csv('../data/ufos.csv')\n",
+    "ufos.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "\n",
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()\n",
+    "\n",
+    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufos.dropna(inplace=True)\n",
+    "\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "\n",
+    "X = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       1.00      0.02      0.05       250\n",
+      "           2       0.00      0.00      0.00         8\n",
+      "           3       0.94      1.00      0.97       131\n",
+      "           4       0.95      1.00      0.97      4743\n",
+      "\n",
+      "    accuracy                           0.95      5173\n",
+      "   macro avg       0.78      0.60      0.60      5173\n",
+      "weighted avg       0.95      0.95      0.93      5173\n",
+      "\n",
+      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
+      "Accuracy:  0.9512855209742895\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "model_filename = 'ufo-model.pkl'\n",
+    "pickle.dump(model, open(model_filename,'wb'))\n",
+    "\n",
+    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
+    "print(model.predict([[50,44,-12]]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/1-Introduction/notebook.ipynb b/translations/sw/4-Classification/1-Introduction/notebook.ipynb
new file mode 100644
index 000000000..c5095a128
--- /dev/null
+++ b/translations/sw/4-Classification/1-Introduction/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d544ef384b7ba73757d830a72372a7f2",
+   "translation_date": "2025-09-06T14:50:53+00:00",
+   "source_file": "4-Classification/1-Introduction/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/sw/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
new file mode 100644
index 000000000..4afca5939
--- /dev/null
+++ b/translations/sw/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -0,0 +1,721 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_10-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "2621e24705e8100893c9bf84e0fc8aef",
+   "translation_date": "2025-09-06T14:58:56+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ItETB4tSFprR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Utangulizi wa uainishaji: Kusafisha, kuandaa, na kuonyesha data yako\n",
+    "\n",
+    "Katika masomo haya manne, utachunguza kipengele muhimu cha ujifunzaji wa mashine wa kawaida - *uainishaji*. Tutapitia matumizi ya algoriti mbalimbali za uainishaji kwa kutumia seti ya data kuhusu vyakula vya ajabu vya Asia na India. Tumaini una njaa!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/pinch.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Sherehekea vyakula vya pan-Asia katika masomo haya! Picha na Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Sherehekea vyakula vya pan-Asia katika masomo haya! Picha na Jen Looper](../../../../../../4-Classification/1-Introduction/solution/R/images/pinch.png)-->\n",
+    "\n",
+    "Uainishaji ni aina ya [ujifunzaji unaosimamiwa](https://wikipedia.org/wiki/Supervised_learning) ambao una mfanano mkubwa na mbinu za regression. Katika uainishaji, unafundisha modeli kutabiri ni `kategoria` gani kipengele fulani kinachohusiana nacho. Ikiwa ujifunzaji wa mashine unahusu kutabiri thamani au majina ya vitu kwa kutumia seti za data, basi uainishaji kwa ujumla unagawanyika katika makundi mawili: *uainishaji wa binary* na *uainishaji wa multiclass*.\n",
+    "\n",
+    "Kumbuka:\n",
+    "\n",
+    "-   **Linear regression** ilikusaidia kutabiri uhusiano kati ya vigezo na kufanya utabiri sahihi kuhusu mahali ambapo kipengele kipya kingeangukia kwa uhusiano na mstari huo. Kwa mfano, ungeweza kutabiri thamani ya nambari kama vile *bei ya malenge itakuwa kiasi gani mwezi wa Septemba dhidi ya Desemba*.\n",
+    "\n",
+    "-   **Logistic regression** ilikusaidia kugundua \"kategoria za binary\": kwa kiwango hiki cha bei, *je, malenge hili ni la rangi ya machungwa au si la machungwa*?\n",
+    "\n",
+    "Uainishaji hutumia algoriti mbalimbali kuamua njia nyingine za kutambua lebo au darasa la kipengele cha data. Hebu tufanye kazi na data hii ya vyakula ili kuona kama, kwa kuangalia kikundi cha viungo, tunaweza kuamua asili ya vyakula hivyo.\n",
+    "\n",
+    "### [**Jaribio la awali la somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)\n",
+    "\n",
+    "### **Utangulizi**\n",
+    "\n",
+    "Uainishaji ni moja ya shughuli za msingi za mtafiti wa ujifunzaji wa mashine na mwanasayansi wa data. Kutoka uainishaji wa msingi wa thamani ya binary (\"je, barua pepe hii ni spam au si spam?\"), hadi uainishaji wa picha ngumu na kugawanya kwa kutumia maono ya kompyuta, daima ni muhimu kuwa na uwezo wa kupanga data katika madarasa na kuuliza maswali kuhusu data hiyo.\n",
+    "\n",
+    "Kwa kusema mchakato kwa njia ya kisayansi zaidi, mbinu yako ya uainishaji inaunda modeli ya utabiri inayokuwezesha kuonyesha uhusiano kati ya vigezo vya ingizo na vigezo vya matokeo.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/binary-multiclass.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Masuala ya binary dhidi ya multiclass kwa algoriti za uainishaji kushughulikia. Infographic na Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Kabla ya kuanza mchakato wa kusafisha data yetu, kuionyesha, na kuandaa kwa kazi zetu za ML, hebu tujifunze kidogo kuhusu njia mbalimbali ambazo ujifunzaji wa mashine unaweza kutumika kuainisha data.\n",
+    "\n",
+    "Iliyotokana na [takwimu](https://wikipedia.org/wiki/Statistical_classification), uainishaji kwa kutumia ujifunzaji wa mashine wa kawaida hutumia vipengele, kama vile `smoker`, `weight`, na `age` kuamua *uwezekano wa kupata ugonjwa X*. Kama mbinu ya ujifunzaji unaosimamiwa inayofanana na mazoezi ya regression uliyofanya awali, data yako ina lebo na algoriti za ML hutumia lebo hizo kuainisha na kutabiri madarasa (au 'vipengele') vya seti ya data na kuyapanga katika kikundi au matokeo.\n",
+    "\n",
+    "✅ Chukua muda kufikiria seti ya data kuhusu vyakula. Je, modeli ya multiclass ingeweza kujibu nini? Je, modeli ya binary ingeweza kujibu nini? Je, ungependa kuamua kama chakula fulani kina uwezekano wa kutumia fenugreek? Je, ungependa kuona kama, ukipewa zawadi ya mfuko wa mboga uliojaa star anise, artichokes, cauliflower, na horseradish, ungeweza kuunda chakula cha kawaida cha Kihindi?\n",
+    "\n",
+    "### **Habari 'classifier'**\n",
+    "\n",
+    "Swali tunalotaka kuuliza kuhusu seti hii ya data ya vyakula ni swali la **multiclass**, kwa kuwa tuna vyakula vya kitaifa kadhaa vya kufanya kazi navyo. Ukipewa kundi la viungo, ni darasa gani kati ya haya mengi data itafaa?\n",
+    "\n",
+    "Tidymodels inatoa algoriti mbalimbali za kutumia kuainisha data, kulingana na aina ya tatizo unalotaka kutatua. Katika masomo mawili yajayo, utajifunza kuhusu baadhi ya algoriti hizi.\n",
+    "\n",
+    "#### **Mahitaji ya awali**\n",
+    "\n",
+    "Kwa somo hili, tutahitaji vifurushi vifuatavyo kusafisha, kuandaa na kuonyesha data yetu:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) ni [mkusanyiko wa vifurushi vya R](https://www.tidyverse.org/packages) iliyoundwa kufanya sayansi ya data kuwa ya haraka, rahisi na ya kufurahisha!\n",
+    "\n",
+    "-   `tidymodels`: Mfumo wa [tidymodels](https://www.tidymodels.org/) ni [mkusanyiko wa vifurushi](https://www.tidymodels.org/packages/) kwa ajili ya uundaji wa modeli na ujifunzaji wa mashine.\n",
+    "\n",
+    "-   `DataExplorer`: Kifurushi cha [DataExplorer](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) kinalenga kurahisisha na kuendesha mchakato wa EDA na uzalishaji wa ripoti.\n",
+    "\n",
+    "-   `themis`: Kifurushi cha [themis](https://themis.tidymodels.org/) kinatoa Hatua za Ziada za Mapishi kwa Kushughulikia Data Isiyosawazishwa.\n",
+    "\n",
+    "Unaweza kuvifunga kwa:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Vinginevyo, script iliyo hapa chini inakagua kama una vifurushi vinavyohitajika kukamilisha moduli hii na kuvifunga kwako endapo vinakosekana.\n"
+   ],
+   "metadata": {
+    "id": "ri5bQxZ-Fz_0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "KIPxa4elGAPI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tutapakia baadaye vifurushi hivi vya ajabu na kuvifanya viweze kupatikana katika kikao chetu cha sasa cha R. (Hii ni kwa madhumuni ya maelezo tu, `pacman::p_load()` tayari ilifanya hivyo kwako)\n"
+   ],
+   "metadata": {
+    "id": "YkKAxOJvGD4C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Zoezi - safisha na linganisha data yako\n",
+    "\n",
+    "Kazi ya kwanza kabla ya kuanza mradi huu ni kusafisha na **kulinganisha** data yako ili kupata matokeo bora.\n",
+    "\n",
+    "Hebu tukutane na data!🕵️\n"
+   ],
+   "metadata": {
+    "id": "PFkQDlk0GN5O"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Qccw7okxGT0S"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Inavutia! Kwa kuangalia, safu ya kwanza ni aina ya safu ya `id`. Hebu tupate maelezo zaidi kuhusu data.\n"
+   ],
+   "metadata": {
+    "id": "XrWnlgSrGVmR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Basic information about the data\r\n",
+    "df %>%\r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# Visualize basic information above\r\n",
+    "df %>% \r\n",
+    "  plot_intro(ggtheme = theme_light())"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "4UcGmxRxGieA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kutoka kwa matokeo, tunaweza kuona mara moja kwamba tuna `2448` safu na `385` nguzo na `0` thamani zilizokosekana. Pia tuna safu moja ya kidhahania, *cuisine*.\n",
+    "\n",
+    "## Zoezi - kujifunza kuhusu vyakula\n",
+    "\n",
+    "Sasa kazi inaanza kuwa ya kuvutia zaidi. Hebu tugundue usambazaji wa data, kwa kila aina ya chakula.\n"
+   ],
+   "metadata": {
+    "id": "AaPubl__GmH5"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Count observations per cuisine\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(n)\r\n",
+    "\r\n",
+    "# Plot the distribution\r\n",
+    "theme_set(theme_light())\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"cuisine\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FRsBVy5eGrrv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kuna idadi ndogo ya vyakula, lakini usambazaji wa data hauko sawa. Unaweza kurekebisha hilo! Kabla ya kufanya hivyo, chunguza kidogo zaidi.\n",
+    "\n",
+    "Sasa, wacha tugawanye kila aina ya chakula kwenye tibble yake binafsi na tujue ni kiasi gani cha data kinapatikana (safu, nguzo) kwa kila aina ya chakula.\n",
+    "\n",
+    "> [Tibble](https://tibble.tidyverse.org/) ni fremu ya data ya kisasa.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_filter.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Sanaa na @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "vVvyDb1kG2in"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create individual tibble for the cuisines\r\n",
+    "thai_df <- df %>% \r\n",
+    "  filter(cuisine == \"thai\")\r\n",
+    "japanese_df <- df %>% \r\n",
+    "  filter(cuisine == \"japanese\")\r\n",
+    "chinese_df <- df %>% \r\n",
+    "  filter(cuisine == \"chinese\")\r\n",
+    "indian_df <- df %>% \r\n",
+    "  filter(cuisine == \"indian\")\r\n",
+    "korean_df <- df %>% \r\n",
+    "  filter(cuisine == \"korean\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Find out how much data is available per cuisine\r\n",
+    "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
+    "    \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
+    "    \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
+    "    \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
+    "    \"korean_df:\", dim(korean_df))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "0TvXUxD3G8Bk"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Zoezi - Kugundua viungo bora kwa kila aina ya vyakula kwa kutumia dplyr**\n",
+    "\n",
+    "Sasa unaweza kuchunguza zaidi data na kujifunza viungo vya kawaida kwa kila aina ya vyakula. Unapaswa kuondoa data inayojirudia ambayo inasababisha mkanganyiko kati ya aina za vyakula, kwa hivyo hebu tujifunze kuhusu tatizo hili.\n",
+    "\n",
+    "Tengeneza kazi `create_ingredient()` katika R ambayo inarudisha dataframe ya viungo. Kazi hii itaanza kwa kuondoa safu isiyo ya msaada na kuchambua viungo kulingana na idadi yao.\n",
+    "\n",
+    "Muundo wa msingi wa kazi katika R ni:\n",
+    "\n",
+    "`myFunction <- function(arglist){`\n",
+    "\n",
+    "**`...`**\n",
+    "\n",
+    "**`return`**`(value)`\n",
+    "\n",
+    "`}`\n",
+    "\n",
+    "Utangulizi mzuri wa kazi za R unaweza kupatikana [hapa](https://skirmer.github.io/presentations/functions_with_r.html#1).\n",
+    "\n",
+    "Hebu tuanze moja kwa moja! Tutatumia [vitenzi vya dplyr](https://dplyr.tidyverse.org/) ambavyo tumekuwa tukijifunza katika masomo yetu ya awali. Kama ukumbusho:\n",
+    "\n",
+    "-   `dplyr::select()`: hukusaidia kuchagua ni **safu** zipi za kuweka au kuondoa.\n",
+    "\n",
+    "-   `dplyr::pivot_longer()`: hukusaidia \"kurefusha\" data, kuongeza idadi ya safu na kupunguza idadi ya safu wima.\n",
+    "\n",
+    "-   `dplyr::group_by()` na `dplyr::summarise()`: hukusaidia kupata takwimu za muhtasari kwa vikundi tofauti, na kuziweka katika jedwali zuri.\n",
+    "\n",
+    "-   `dplyr::filter()`: huunda subset ya data inayojumuisha tu safu zinazokidhi masharti yako.\n",
+    "\n",
+    "-   `dplyr::mutate()`: hukusaidia kuunda au kurekebisha safu wima.\n",
+    "\n",
+    "Angalia [mafunzo ya learnr yaliyojaa *sanaa*](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) na Allison Horst, ambayo yanatambulisha baadhi ya kazi muhimu za kushughulikia data katika dplyr *(sehemu ya Tidyverse)*\n"
+   ],
+   "metadata": {
+    "id": "K3RF5bSCHC76"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Creates a functions that returns the top ingredients by class\r\n",
+    "\r\n",
+    "create_ingredient <- function(df){\r\n",
+    "  \r\n",
+    "  # Drop the id column which is the first colum\r\n",
+    "  ingredient_df = df %>% select(-1) %>% \r\n",
+    "  # Transpose data to a long format\r\n",
+    "    pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
+    "  # Find the top most ingredients for a particular cuisine\r\n",
+    "    group_by(ingredients) %>% \r\n",
+    "    summarise(n_instances = sum(count)) %>% \r\n",
+    "    filter(n_instances != 0) %>% \r\n",
+    "  # Arrange by descending order\r\n",
+    "    arrange(desc(n_instances)) %>% \r\n",
+    "    mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
+    "  \r\n",
+    "  \r\n",
+    "  return(ingredient_df)\r\n",
+    "} # End of function"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "uB_0JR82HTPa"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sasa tunaweza kutumia kazi hii kupata wazo la viungo kumi maarufu zaidi kwa kila aina ya vyakula. Hebu tuijaribu na `thai_df`.\n"
+   ],
+   "metadata": {
+    "id": "h9794WF8HWmc"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Call create_ingredient and display popular ingredients\r\n",
+    "thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
+    "\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "agQ-1HrcHaEA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Katika sehemu iliyopita, tulitumia `geom_col()`, hebu tuone jinsi unavyoweza kutumia `geom_bar` pia, kuunda chati za mstari. Tumia `?geom_bar` kwa kusoma zaidi.\n"
+   ],
+   "metadata": {
+    "id": "kHu9ffGjHdcX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a bar chart for popular thai cuisines\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10) %>% \r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "fb3Bx_3DHj6e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "RHP_xgdkHnvM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
+    "create_ingredient(df = japanese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "019v8F0XHrRU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vipi kuhusu vyakula vya Kichina?\n"
+   ],
+   "metadata": {
+    "id": "iIGM7vO8Hu3v"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
+    "create_ingredient(df = chinese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lHd9_gd2HyzU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ir8qyQbNH1c7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Indian cuisines and make bar chart\r\n",
+    "create_ingredient(df = indian_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ApukQtKjH5FO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qv30cwY1H-FM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Korean cuisines and make bar chart\r\n",
+    "create_ingredient(df = korean_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lumgk9cHIBie"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kutokana na taswira za data, sasa tunaweza kuondoa viungo vya kawaida zaidi vinavyosababisha mkanganyiko kati ya vyakula tofauti, kwa kutumia `dplyr::select()`.\n",
+    "\n",
+    "Kila mtu anapenda mchele, vitunguu saumu na tangawizi!\n"
+   ],
+   "metadata": {
+    "id": "iO4veMXuIEta"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "iHJPiG6rIUcK"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Kuchakata data kwa kutumia mapishi 👩‍🍳👨‍🍳 - Kushughulikia data isiyo na uwiano ⚖️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Uchoraji na @allison_horst</figcaption>\n",
+    "\n",
+    "Kwa kuwa somo hili linahusu vyakula, tunapaswa kuweka `mapishi` katika muktadha.\n",
+    "\n",
+    "Tidymodels inatoa kifurushi kingine kizuri: `recipes` - kifurushi cha kuchakata data.\n"
+   ],
+   "metadata": {
+    "id": "kkFd-JxdIaL6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tuchunguze tena usambazaji wa vyakula vyetu.\n"
+   ],
+   "metadata": {
+    "id": "6l2ubtTPJAhY"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "old_label_count <- df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "old_label_count"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "1e-E9cb7JDVi"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kama unavyoona, kuna usambazaji usio sawa kabisa katika idadi ya vyakula. Vyakula vya Kikorea ni karibu mara tatu ya vyakula vya Kithai. Data isiyo na uwiano mara nyingi ina athari mbaya kwenye utendaji wa modeli. Fikiria kuhusu uainishaji wa binary. Ikiwa data yako nyingi ni ya darasa moja, modeli ya ML itaelekea kutabiri darasa hilo mara kwa mara, kwa sababu kuna data zaidi kwa ajili yake. Kusawazisha data huchukua data iliyopotoshwa na husaidia kuondoa uwiano huu. Modeli nyingi hufanya kazi vizuri zaidi wakati idadi ya uchunguzi ni sawa na, kwa hivyo, huwa na changamoto na data isiyo na uwiano.\n",
+    "\n",
+    "Kuna njia kuu mbili za kushughulikia seti za data zisizo na uwiano:\n",
+    "\n",
+    "-   kuongeza uchunguzi kwenye darasa la wachache: `Over-sampling` mfano kutumia algorithimu ya SMOTE\n",
+    "\n",
+    "-   kuondoa uchunguzi kutoka darasa la walio wengi: `Under-sampling`\n",
+    "\n",
+    "Sasa wacha tuonyeshe jinsi ya kushughulikia seti za data zisizo na uwiano kwa kutumia `recipe`. Recipe inaweza kufikiriwa kama ramani inayoelezea hatua gani zinapaswa kutumika kwenye seti ya data ili kuifanya iwe tayari kwa uchambuzi wa data.\n"
+   ],
+   "metadata": {
+    "id": "soAw6826JKx9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HS41brUIJVJy"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tuchambue hatua zetu za awali za usindikaji.\n",
+    "\n",
+    "-   Simu ya `recipe()` na fomula inaambia recipe *majukumu* ya vigezo kwa kutumia data ya `df_select` kama rejeleo. Kwa mfano, safu ya `cuisine` imepewa jukumu la `outcome` huku safu nyingine zimepewa jukumu la `predictor`.\n",
+    "\n",
+    "-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html) inaunda *maelezo maalum* ya hatua ya recipe ambayo huzalisha mifano mipya ya darasa la wachache kwa kutumia majirani wa karibu wa kesi hizi.\n",
+    "\n",
+    "Sasa, ikiwa tungependa kuona data iliyosindikwa, tungehitaji [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) na [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) recipe yetu.\n",
+    "\n",
+    "`prep()`: inakadiria vigezo vinavyohitajika kutoka kwenye seti ya mafunzo ambavyo vinaweza kutumika baadaye kwenye seti nyingine za data.\n",
+    "\n",
+    "`bake()`: inachukua recipe iliyosindikwa na kutumia operesheni kwenye seti yoyote ya data.\n"
+   ],
+   "metadata": {
+    "id": "Yb-7t7XcJaC8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep and bake the recipe\r\n",
+    "preprocessed_df <- cuisines_recipe %>% \r\n",
+    "  prep() %>% \r\n",
+    "  bake(new_data = NULL) %>% \r\n",
+    "  relocate(cuisine)\r\n",
+    "\r\n",
+    "# Display data\r\n",
+    "preprocessed_df %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Quick summary stats\r\n",
+    "preprocessed_df %>% \r\n",
+    "  introduce()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "9QhSgdpxJl44"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hebu sasa tukague usambazaji wa vyakula vyetu na tuvilinganishe na data isiyo na uwiano.\n"
+   ],
+   "metadata": {
+    "id": "dmidELh_LdV7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "new_label_count <- preprocessed_df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "list(new_label_count = new_label_count,\r\n",
+    "     old_label_count = old_label_count)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "aSh23klBLwDz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Yum! Takwimu ni safi, zenye uwiano mzuri, na ladha tamu sana 😋!\n",
+    "\n",
+    "> Kwa kawaida, mapishi hutumika kama mchakato wa awali wa kuandaa modeli ambapo hufafanua hatua gani zinapaswa kutekelezwa kwenye seti ya data ili kuifanya iwe tayari kwa modeli. Katika hali hiyo, `workflow()` kwa kawaida hutumika (kama tulivyoona katika masomo yetu ya awali) badala ya kukadiria mapishi kwa mikono.\n",
+    ">\n",
+    "> Kwa hivyo, kwa kawaida huhitaji kutumia **`prep()`** na **`bake()`** mapishi unapotumia tidymodels, lakini ni kazi muhimu kuwa nazo kwenye zana zako ili kuthibitisha kwamba mapishi yanafanya kile unachotarajia kama ilivyo katika hali yetu.\n",
+    ">\n",
+    "> Unapofanya **`bake()`** kwenye mapishi yaliyotayarishwa kwa **`new_data = NULL`**, unapata data uliyotoa wakati wa kufafanua mapishi, lakini ikiwa imepitia hatua za usindikaji wa awali.\n",
+    "\n",
+    "Sasa tuweke nakala ya data hii kwa matumizi ya masomo ya baadaye:\n"
+   ],
+   "metadata": {
+    "id": "HEu80HZ8L7ae"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Save preprocessed data\r\n",
+    "write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "cBmCbIgrMOI6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "CSV mpya sasa inaweza kupatikana katika folda kuu ya data.\n",
+    "\n",
+    "**🚀Changamoto**\n",
+    "\n",
+    "Mtaala huu una seti kadhaa za data za kuvutia. Chunguza folda za `data` na uone kama kuna seti za data zinazofaa kwa uainishaji wa binary au wa darasa nyingi? Ni maswali gani ungeuliza kuhusu seti hii ya data?\n",
+    "\n",
+    "## [**Jaribio baada ya somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)\n",
+    "\n",
+    "## **Mapitio na Kujisomea**\n",
+    "\n",
+    "-   Angalia [pakiti ya themis](https://github.com/tidymodels/themis). Ni mbinu gani nyingine tunaweza kutumia kushughulikia data isiyo na uwiano?\n",
+    "\n",
+    "-   Tovuti ya marejeleo ya [Tidy models](https://www.tidymodels.org/start/).\n",
+    "\n",
+    "-   H. Wickham na G. Grolemund, [*R kwa Sayansi ya Data: Kuonyesha, Kuelekeza, Kubadilisha, Kupanga, na Kuingiza Data*](https://r4ds.had.co.nz/).\n",
+    "\n",
+    "#### ASANTE KWA:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) kwa kuunda michoro ya kushangaza inayofanya R kuwa ya kuvutia na ya kupendeza zaidi. Pata michoro zaidi katika [galeria yake](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) na [Jen Looper](https://www.twitter.com/jenlooper) kwa kuunda toleo la awali la moduli hii kwa Python ♥️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Sanaa na @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "WQs5621pMGwf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuchukuliwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutokuelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/1-Introduction/solution/notebook.ipynb b/translations/sw/4-Classification/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..2259f301d
--- /dev/null
+++ b/translations/sw/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,739 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Mapishi Matamu ya Kiasia na Kihindi\n",
+    "\n",
+    "## Utangulizi\n",
+    "\n",
+    "Chakula cha Kiasia na Kihindi ni maarufu kwa ladha zake za kipekee, matumizi ya viungo mbalimbali, na mbinu za kupika zinazovutia. Katika mwongozo huu, tutachunguza baadhi ya mapishi maarufu na vidokezo vya jinsi ya kuandaa vyakula hivi nyumbani.\n",
+    "\n",
+    "[!NOTE] Mapishi haya yanaweza kubadilishwa kulingana na ladha yako au upatikanaji wa viungo.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Mapishi Maarufu\n",
+    "\n",
+    "### 1. **Chakula cha Kiasia: Noodles za Mboga**\n",
+    "Noodles za mboga ni rahisi kuandaa na zinaweza kubadilishwa kulingana na mboga unazopenda.\n",
+    "\n",
+    "#### Viungo:\n",
+    "- Noodles zilizopikwa tayari\n",
+    "- Mafuta ya ufuta\n",
+    "- Vitunguu saumu, iliyokatwa vizuri\n",
+    "- Karoti, zilizokatwa nyembamba\n",
+    "- Pilipili hoho, zilizokatwa vipande vidogo\n",
+    "- Mchuzi wa soya\n",
+    "- Mchuzi wa oyster (hiari)\n",
+    "\n",
+    "#### Maelekezo:\n",
+    "1. Katika sufuria kubwa, ongeza mafuta ya ufuta na kaanga vitunguu saumu hadi viwe vya dhahabu.\n",
+    "2. Ongeza karoti na pilipili hoho, pika kwa dakika chache.\n",
+    "3. Changanya noodles zilizopikwa tayari na ongeza mchuzi wa soya na mchuzi wa oyster.\n",
+    "4. Koroga vizuri na pika kwa dakika 2-3 zaidi.\n",
+    "5. Tumikia moto na ufurahie!\n",
+    "\n",
+    "---\n",
+    "\n",
+    "### 2. **Chakula cha Kihindi: Kuku wa Butter**\n",
+    "Kuku wa butter ni moja ya vyakula maarufu vya Kihindi, maarufu kwa ladha yake laini na mchuzi wake wa krimu.\n",
+    "\n",
+    "#### Viungo:\n",
+    "- Vipande vya kuku, vilivyopikwa\n",
+    "- Siagi\n",
+    "- Vitunguu, vilivyokatwa vizuri\n",
+    "- Nyanya, zilizopondwa\n",
+    "- Cream nzito\n",
+    "- Viungo: Garam masala, unga wa coriander, unga wa manjano, na pilipili nyekundu\n",
+    "\n",
+    "#### Maelekezo:\n",
+    "1. Katika sufuria, yayusha siagi na kaanga vitunguu hadi viwe laini.\n",
+    "2. Ongeza nyanya zilizopondwa na pika hadi mafuta yatengane.\n",
+    "3. Changanya viungo na pika kwa dakika chache.\n",
+    "4. Ongeza vipande vya kuku na cream nzito, koroga vizuri.\n",
+    "5. Pika kwa moto mdogo kwa dakika 10-15.\n",
+    "6. Tumikia na wali au chapati.\n",
+    "\n",
+    "[!TIP] Unaweza kuongeza kiasi cha pilipili kulingana na upendeleo wako wa viwango vya ukali.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Vidokezo vya Jumla\n",
+    "\n",
+    "- **Tumia Viungo Safi:** Ladha ya chakula chako itakuwa bora zaidi unapochagua viungo safi na vya hali ya juu.\n",
+    "- **Usiogope Kucheza na Viungo:** Mapishi haya ni mwongozo tu; unaweza kubadilisha viungo ili kufanikisha ladha unayoipenda.\n",
+    "- **Andaa Mapema:** Hakikisha viungo vyote vimeandaliwa kabla ya kuanza kupika ili kurahisisha mchakato.\n",
+    "\n",
+    "[!IMPORTANT] Hakikisha unafuata hatua za usalama wa chakula, kama vile kuosha mikono na kuhifadhi vyakula kwa njia sahihi.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Hitimisho\n",
+    "\n",
+    "Kupika vyakula vya Kiasia na Kihindi nyumbani ni njia nzuri ya kufurahia ladha za kipekee na za kitamaduni. Kwa kufuata mapishi haya na vidokezo, utaweza kuandaa chakula kitamu ambacho familia na marafiki watafurahia. Jaribu leo na ugundue ladha mpya!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Sakinisha Imblearn ambayo itawezesha SMOTE. Hii ni kifurushi cha Scikit-learn kinachosaidia kushughulikia data isiyo na uwiano wakati wa kufanya uainishaji. (https://imbalanced-learn.org/stable/)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
+      "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df  = pd.read_csv('../../data/cuisines.csv')"
+   ]
+  },
+  {
+   "source": [
+    "Seti hii ya data inajumuisha safu 385 zinazoonyesha aina zote za viungo katika vyakula mbalimbali kutoka kwa seti fulani ya vyakula.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0          65  indian       0         0      0           0      0   \n",
+       "1          66  indian       1         0      0           0      0   \n",
+       "2          67  indian       0         0      0           0      0   \n",
+       "3          68  indian       0         0      0           0      0   \n",
+       "4          69  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 385 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>67</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>69</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 385 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2448 entries, 0 to 2447\nColumns: 385 entries, Unnamed: 0 to zucchini\ndtypes: int64(384), object(1)\nmemory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "korean      799\n",
+       "indian      598\n",
+       "chinese     442\n",
+       "japanese    320\n",
+       "thai        289\n",
+       "Name: cuisine, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts()"
+   ]
+  },
+  {
+   "source": [
+    "Onyesha vyakula katika grafu ya mstari.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18799710>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "thai df: (289, 385)\njapanese df: (320, 385)\nchinese df: (442, 385)\nindian df: (598, 385)\nkorean df: (799, 385)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "thai_df = df[(df.cuisine == \"thai\")]\n",
+    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
+    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
+    "indian_df = df[(df.cuisine == \"indian\")]\n",
+    "korean_df = df[(df.cuisine == \"korean\")]\n",
+    "\n",
+    "print(f'thai df: {thai_df.shape}')\n",
+    "print(f'japanese df: {japanese_df.shape}')\n",
+    "print(f'chinese df: {chinese_df.shape}')\n",
+    "print(f'indian df: {indian_df.shape}')\n",
+    "print(f'korean df: {korean_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ingredient_df(df):\n",
+    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    # drop ingredients that have a 0 sum\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    # sort df\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
+    "    return ingredient_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d5917db00>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
+    "thai_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d389634a8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+   ],
+   "source": [
+    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
+    "japanese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18817f98>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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bq8lGWo16Aysl1QDjmsQxRlI3SfsB+wJNE9B9wASlYZmk4a306ZHG9iV9jGxk5YRmZpYDm0xMEbGILGEsj4iVEfFb4L+AOZLqgZlA74h4iuye0QLgHqAeaHwqYBzwdUnzgUVA44MJtwIXpAcQ9gO+CzxBluSeaxLK/weeTG2fnaYMS30fqAEWSFqUPrfk50C3FP9twBkRsW5TfwszM+t4img6I7YFjUm9IuLt9DTdw8D4iJhbthPkVKFQiGKxWOkwzMyqiqS6iCg0LS/3u/KmpC/M9gSmdYWkZGZm5VXWxBQRXylne1tK0lHAZU2KX4yIL1UiHjMz27St+u3iEXEff/vIuZmZ5VylX0lkZmb2N5yYzMwsV5yYzMwsV5yYzMwsV5yYzMwsV5yYzMwsV5yYzMwsV7bq7zF1lvrlDdROmlXpMMw6xNJLj6t0CNbFeMRkZma54sRkZma54sTUhKSzJZ2WtqdKGl3pmMzMuhLfY2oiIq6tdAxmZl1ZWUdMkraXNEvSfEkLJY2VNFLSQ5LqJN0nqV+qe56kZyQtkHRrKjtI0py0cODjkj6eys+Q9CtJ90taKulcSd9K9X4vaadUbz9J96ZzPSJpYCux1kp6MJ3/AUl7pfLJkia2oa/jJRUlFTesadhUdTMza6NyT+UdDayIiKERMRi4F7gKGB0RI4HrgR+kupOA4RFxAHB2KnsO+HREDAe+B/xrSduDgb8DDkxtrEn15gCnpTpTgAnpXBPJVqptyVVka0YdANwMXNmejkbElIgoRESh+3Z92nOomZm1otxTefXAjyRdBvwGeIMsodwvCaA7sDLVXQDcLOlXwK9SWR9gmqQBQJAtld5odkSsBlZLagB+XXLOAyT1Ag4GZqRzAfRoJdZRZIkO4Ebg8vZ318zMyq3cCwUukTQCOBa4BHgQWBQRo5qpfhzwGeAE4EJJQ4DvkyWgL0mqBX5XUn9dyfbGks8byfrRDXgzIoaVrUNmZtbpyn2PaXeyKbabgCuATwJ9JY1K+2skDZLUDdgzImYD/0Q2UuqVfi9PzZ3RnnNHxFvAi5LGpHNJ0tBWDnkcOCVtjwMeac/5zMysY5R7Km8IcIWkjcB64BzgXeBKSX3S+X4KLAFuSmUCroyINyVdTjaVdxGwOa9SGAdck46vAW4F5rdQdwJwg6QLgFXA1zbjfAAM6d+Hor8db2ZWFoqISsdQ9QqFQhSLxUqHYWZWVSTVRUShabm/YGtmZrmy1X/BVtKFwJgmxTMi4gfN1Tczs8ra6hNTSkBOQmZmVcJTeWZmlitOTGZmlitOTGZmlitOTGZmlitOTGZmlitOTGZmlitb/ePinaF+eQO1kzbnDUpmtqWW+nVgWx2PmMzMLFecmMzMLFe2usSUlkxfmLY/J+k3afuLkiZVNjozM9uULnOPKSLuBu6udBxmZta63I2YJG0vaZak+ZIWShor6UBJj6eyJyX1TiOjRyTNTT8Hb6LdMyT9LG3XSnpQ0gJJD0jaK5VPlXRlOtcfJY3ujD6bmdn78jhiOhpYERHHAaTFBOcBYyPiKUk7AO8ArwBHRsRaSQOAW4APrOvRgquAaRExTdKZwJXASWlfP+BQYCDZCGtmcw1IGg+MB+i+Q9/299LMzJqVuxETUA8cKekySZ8G9gJWRsRTkC2hHhHvkq1Q+wtJ9cAMYP92nGMU8F9p+0ayRNToVxGxMSKeAT7SUgMRMSUiChFR6L5dn3ac2szMWpO7EVNELJE0AjgWuAR4sIWq3wReBoaSJdi1ZQphXcm2ytSmmZm1Ue5GTJJ2B9ZExE3AFcAngX6SDkz7e0vaBuhDNpLaCJwKdG/HaR4HTknb44BHyhW/mZltmdyNmIAhwBWSNgLrgXPIRi5XSfoQ2f2lI4CfA7dLOg24F/hLO84xAbhB0gXAKuBrZYzfzMy2gCKi0jFUvUKhEMVisdJhmJlVFUl1EfGBh9ZyN5VnZmZdmxOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlihOTmZnlSh7flVd16pc3UDtpVqXDMLMyWnrpcZUOocvyiMnMzHKlyyamJkutn53eUm5mZhXWJafy0npO74mIaysVi5mZ/a2qTkySvgt8lWxNpT8BdUADMB7YFngBODUi1kiaSrbK7XDgMWBBSTuTgbcj4oeSPgpcC/QFNgBjIuIPndUnM7Ourmqn8tKKtl8mW1r9GKBxTY87IuLAiBgKPAt8veSwPYCDI+JbrTR9M3B1Ov5gYGUL5x8vqSipuGFNwxb2xszMGlXziOkQ4K6IWAuslfTrVD5Y0iXAjkAv4L6SY2ZExIaWGpTUG+gfEXcCpLabFRFTgCkAPfoN8GqLZmZlUrUjplZMBc6NiCHAxUDPkn3tWX7dzMwqoJoT02PACZJ6SuoFHJ/KewMrJdUA49rTYESsBpZJOglAUg9J25UzaDMza13VJqaIeAq4m+whhnuAerIHH74LPEGWuJ7bjKZPBc6TtAB4HNitLAGbmVmbKKJ6b49I6hURb6dRzcPA+IiY29lx9Og3IPqd/tPOPq2ZdSC/+aHjSaqLiELT8mp++AFgiqT9ye4jTatEUgIY0r8PRf8jNjMri6pOTBHxlUrHYGZm5VW195jMzGzr5MRkZma54sRkZma54sRkZma54sRkZma54sRkZma54sRkZma54sRkZma54sRkZma5UtVvfsiL+uUN1E6aVekwzKwD+J15nc8jJjMzy5WqT0yS/kXSEZWOw8zMyqPqp/Ii4nsdfQ5J3Vtbkt3MzMqnqkZMkr4rabGkRyXdImmipKmSRqf9SyVdLGmupHpJA1N5X0n3S1ok6ZeSXpK0S9r3VUlPSnpa0nWSuqfytyX9SNJ8YFTFOm1m1sVUTWKSdCDwZWAocAzwgcWlklcjYgRwDTAxlf0z8GBEDAJmAnulNj8BjAUOiYhhwAbeX459e+CJiBgaEY82E894SUVJxQ1rGsrSRzMzq66pvEOAuyJiLbBW0q9bqHdH+l0H/F3aPhT4EkBE3CvpjVR+ODASeEoSwIeAV9K+DcDtLQUTEVOAKZCtYLs5HTIzsw+qpsTUVuvS7w1sun8iW/n2O83sW+v7SmZmna9qpvKAx4ATJPWU1As4vp3Hngwg6QvAh1P5A8BoSbumfTtJ2ruMMZuZWTtVzYgpIp6SdDewAHgZqAfaenPnYuAWSacCc4A/A6sj4lVJFwG/ldQNWA98A3ip7B0wM7M2qZrElPwwIiZL2g54GKiLiF807oyI2pLtIvC59LEBOCoi3pU0CjgwItalercBtzU9UUT06rBemJlZi6otMU2RtD/Qk+ze0Nw2HrcXMD2Niv4KnFXOoIb070PRry0xMyuLqkpMEfGVzTzueWB4mcMxM7MOUE0PP5iZWRfgxGRmZrnixGRmZrnixGRmZrnixGRmZrnixGRmZrnixGRmZrnixGRmZrlSVV+wzav65Q3UTppV6TDMzDrV0g56441HTGZmlitOTGZmlitOTGZmlitOTGZmlisdmpgkbS9plqT5khZKGivpcEnzJNVLul5SD0mHSfpVyXFHSrqzhTa7S5qa2quX9M1Ufpakp9K5bk9rNpHqji45/u2S7X9KbcyXdGkq20/SvZLqJD0iaWBH/X3MzOyDOnrEdDSwIiKGRsRg4F5gKjA2IoaQPRV4DjAbGCipbzrua8D1LbQ5DOgfEYNTGzek8jsi4sCIGAo8C3y9tcAkHQOcCHwyHXN52jUFmBARI4GJwM9bOH68pKKk4oY1bV1I18zMNqWjE1M9cKSkyyR9GqgFXoyIJWn/NOAzERHAjcBXJe0IjALuaaHNPwL7SrpK0tHAW6l8cBrh1APjgEGbiO0I4IaIWAMQEa9L6gUcDMyQ9DRwHdCvuYMjYkpEFCKi0H27Ppv6O5iZWRt16PeYImKJpBHAscAlwIOtVL8B+DWwFpgREe+20OYbkoYCRwFnAycDZ5KNxE6KiPmSzuD9ZdXfJSXgtILttq3E0A14MyKGtaV/ZmZWfh19j2l3YE1E3ARcQTYSqpX00VTlVOAhgIhYAawALuL96bnm2twF6BYRt6e6I9Ku3sBKSTVkI6ZGS4GRafuLQE3avh/4Wsm9qJ0i4i3gRUljUplSEjQzs07S0W9+GAJcIWkjsJ7sflIfsqmybYCngGtL6t8M9I2IZ1tpsz9wQxr9AHwn/f4u8ASwKv3uncp/AdwlaT7ZPa6/AETEvZKGAUVJfwX+G/h/ZEntGkkXkSWxW4H5m9l/MzNrJ2W3d/JB0s+AeRHxH5WOpT0KhUIUi8VKh2FmVlUk1UVEoWl5bt6VJ6mObDTz7UrHYmZmlZObxJQez/4bkp4AejQpPjUi6jsnKjMz62y5SUzNiYhPVjoGMzPrXH4lkZmZ5YoTk5mZ5UqunsqrVpJWA4srHUcZ7QK8Wukgysx9yr+trT+w9fWp3P3ZOyL6Ni3M9T2mKrK4uUceq5Wk4tbUH3CfqsHW1h/Y+vrUWf3xVJ6ZmeWKE5OZmeWKE1N5TKl0AGW2tfUH3KdqsLX1B7a+PnVKf/zwg5mZ5YpHTGZmlitOTGZmlitOTFtA0tGSFkt6QdKkSsezuSQtlVQv6WlJxVS2k6T7JT2ffn+40nG2RtL1kl6RtLCkrNk+pHW2rkzXbUFazDJXWujPZEnL03V6WtKxJfu+k/qzWNJRlYm6ZZL2lDRb0jOSFkn6x1RezdeopT5V83XqKelJSfNTny5O5ftIeiLFfpukbVN5j/T5hbS/tiyBRIR/NuMH6A78AdiXbFXc+cD+lY5rM/uyFNilSdnlwKS0PQm4rNJxbqIPnyFbNHLhpvpAtqLyPYCATwFPVDr+NvZnMjCxmbr7p39/PYB90r/L7pXuQ5MY+wEj0nZvYEmKu5qvUUt9qubrJKBX2q4hW9vuU8B04JRUfi1wTtr+B+DatH0KcFs54vCIafMdBLwQEX+MiL+SLSh4YoVjKqcTgWlpexpwUgVj2aSIeBh4vUlxS304EfjPyPwe2FFSv86JtG1a6E9LTgRujYh1EfEi8ALZv8/ciIiVETE3ba8GniVb9LOar1FLfWpJNVyniIi308ea9BPAYcDMVN70OjVev5nA4ZK0pXE4MW2+/sCfSj4vo/V/lHkWwG8l1Ukan8o+EhEr0/afgY9UJrQt0lIfqvnanZumtq4vmV6tqv6k6Z7hZP9vfKu4Rk36BFV8nSR1l/Q08ApwP9nI7s2IeDdVKY37vT6l/Q3AzlsagxOTARwaESOAY4BvSPpM6c7IxulV/b2CraEPwDXAfsAwYCXwo8qG036SegG3A+dHxFul+6r1GjXTp6q+ThGxISKGAXuQjegGdnYMTkybbzmwZ8nnPVJZ1YmI5en3K8CdZP8YX26cOkm/X6lchJutpT5U5bWLiJfTfzQ2Ar/g/WmgquiPpBqy/4DfHBF3pOKqvkbN9anar1OjiHgTmA2MIptKbXy3amnc7/Up7e8DvLal53Zi2nxPAQPS0yrbkt34u7vCMbWbpO0l9W7cBr4ALCTry+mp2unAXZWJcIu01Ie7gdPSk1+fAhpKppNyq8k9li+RXSfI+nNKekJqH2AA8GRnx9eadN/hP4BnI+LHJbuq9hq11Kcqv059Je2Ytj8EHEl272w2MDpVa3qdGq/faODBNPLdMpV+CqSaf8ieHFpCNgd7YaXj2cw+7Ev2pNB8YFFjP8jmiR8Angf+B9ip0rFuoh+3kE2brCebA/96S30ge/Lo6nTd6oFCpeNvY39uTPEuSP9B6FdS/8LUn8XAMZWOv5n+HEo2TbcAeDr9HFvl16ilPlXzdToAmJdiXwh8L5XvS5ZEXwBmAD1Sec/0+YW0f99yxOFXEpmZWa54Ks/MzHLFicnMzHLFicnMzHLFicnMzHLFicnMzHLFicnMzHLFicnMzHLlfwHH5sUVMquziAAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
+    "chinese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1880a550>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
+    "indian_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1896ce80>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
+    "korean_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\n",
+    "labels_df = df.cuisine #.unique()\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "source": [
+    "Sawazisha data kwa kutumia SMOTE oversampling hadi darasa la juu zaidi. Soma zaidi hapa: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oversample = SMOTE()\n",
+    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "new label count: korean      799\nchinese     799\njapanese    799\nindian      799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
+    "print(f'old label count: {df.cuisine.value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "source": [
+    "transformed_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "     cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  \\\n",
+       "0     indian       0         0      0           0      0             0   \n",
+       "1     indian       1         0      0           0      0             0   \n",
+       "2     indian       0         0      0           0      0             0   \n",
+       "3     indian       0         0      0           0      0             0   \n",
+       "4     indian       0         0      0           0      0             0   \n",
+       "...      ...     ...       ...    ...         ...    ...           ...   \n",
+       "3990    thai       0         0      0           0      0             0   \n",
+       "3991    thai       0         0      0           0      0             0   \n",
+       "3992    thai       0         0      0           0      0             0   \n",
+       "3993    thai       0         0      0           0      0             0   \n",
+       "3994    thai       0         0      0           0      0             0   \n",
+       "\n",
+       "      apricot  armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
+       "0           0         0          0  ...        0            0           0   \n",
+       "1           0         0          0  ...        0            0           0   \n",
+       "2           0         0          0  ...        0            0           0   \n",
+       "3           0         0          0  ...        0            0           0   \n",
+       "4           0         0          0  ...        0            0           0   \n",
+       "...       ...       ...        ...  ...      ...          ...         ...   \n",
+       "3990        0         0          0  ...        0            0           0   \n",
+       "3991        0         0          0  ...        0            0           0   \n",
+       "3992        0         0          0  ...        0            0           0   \n",
+       "3993        0         0          0  ...        0            0           0   \n",
+       "3994        0         0          0  ...        0            0           0   \n",
+       "\n",
+       "      whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                           0     0     0    0      0       0         0  \n",
+       "1                           0     0     0    0      0       0         0  \n",
+       "2                           0     0     0    0      0       0         0  \n",
+       "3                           0     0     0    0      0       0         0  \n",
+       "4                           0     0     0    0      0       1         0  \n",
+       "...                       ...   ...   ...  ...    ...     ...       ...  \n",
+       "3990                        0     0     0    0      0       0         0  \n",
+       "3991                        0     0     0    0      0       0         0  \n",
+       "3992                        0     0     0    0      0       0         0  \n",
+       "3993                        0     0     0    0      0       0         0  \n",
+       "3994                        0     0     0    0      0       0         0  \n",
+       "\n",
+       "[3995 rows x 381 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": [
+    "# export transformed data to new df for classification\n",
+    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
+    "transformed_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df.info()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "1da12ed6d238756959b8de9cac2a35a2",
+   "translation_date": "2025-09-06T14:52:26+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/2-Classifiers-1/notebook.ipynb b/translations/sw/4-Classification/2-Classifiers-1/notebook.ipynb
new file mode 100644
index 000000000..9435cd6ce
--- /dev/null
+++ b/translations/sw/4-Classification/2-Classifiers-1/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "68829b06b4dcd512d3327849191f4d7f",
+   "translation_date": "2025-09-06T14:32:43+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/sw/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
new file mode 100644
index 000000000..def29434d
--- /dev/null
+++ b/translations/sw/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -0,0 +1,1294 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_11-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "6ea6a5171b1b99b7b5a55f7469c048d2",
+   "translation_date": "2025-09-06T14:38:21+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "zs2woWv_HoE8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Vainishi vya vyakula 1\n",
+    "\n",
+    "Katika somo hili, tutachunguza aina mbalimbali za vainishi ili *kutabiri aina ya chakula cha kitaifa kulingana na kikundi cha viungo.* Wakati wa kufanya hivyo, tutajifunza zaidi kuhusu baadhi ya njia ambazo algorithimu zinaweza kutumika kwa kazi za uainishaji.\n",
+    "\n",
+    "### [**Jaribio la kabla ya somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
+    "\n",
+    "### **Maandalizi**\n",
+    "\n",
+    "Somo hili linajengwa juu ya [somo letu la awali](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb) ambapo tulifanya yafuatayo:\n",
+    "\n",
+    "-   Tulifanya utangulizi rahisi wa uainishaji kwa kutumia seti ya data kuhusu vyakula vyote vya kuvutia vya Asia na India 😋.\n",
+    "\n",
+    "-   Tulichunguza baadhi ya [vitenzi vya dplyr](https://dplyr.tidyverse.org/) ili kuandaa na kusafisha data yetu.\n",
+    "\n",
+    "-   Tulitengeneza taswira nzuri kwa kutumia ggplot2.\n",
+    "\n",
+    "-   Tulionyesha jinsi ya kushughulikia data isiyo na uwiano kwa kuisafisha kwa kutumia [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html).\n",
+    "\n",
+    "-   Tulionyesha jinsi ya `prep` na `bake` mapishi yetu ili kuthibitisha kuwa yatafanya kazi kama inavyotarajiwa.\n",
+    "\n",
+    "#### **Mahitaji ya awali**\n",
+    "\n",
+    "Kwa somo hili, tutahitaji vifurushi vifuatavyo ili kusafisha, kuandaa, na kutazama data yetu:\n",
+    "\n",
+    "-   `tidyverse`: [Tidyverse](https://www.tidyverse.org/) ni [mkusanyiko wa vifurushi vya R](https://www.tidyverse.org/packages) vilivyoundwa kufanya sayansi ya data kuwa ya haraka, rahisi, na ya kufurahisha!\n",
+    "\n",
+    "-   `tidymodels`: Mfumo wa [tidymodels](https://www.tidymodels.org/) ni [mkusanyiko wa vifurushi](https://www.tidymodels.org/packages/) kwa ajili ya uundaji wa mifano na ujifunzaji wa mashine.\n",
+    "\n",
+    "-   `themis`: [Themis package](https://themis.tidymodels.org/) hutoa Hatua za Ziada za Mapishi kwa Kushughulikia Data Isiyo na Uwiano.\n",
+    "\n",
+    "-   `nnet`: [Nnet package](https://cran.r-project.org/web/packages/nnet/nnet.pdf) hutoa kazi za kukadiria mitandao ya neva ya kulisha mbele yenye safu moja ya siri, na kwa mifano ya uratibu wa kimantiki wa multinomial.\n",
+    "\n",
+    "Unaweza kuvisakinisha kama:\n"
+   ],
+   "metadata": {
+    "id": "iDFOb3ebHwQC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Vinginevyo, maandishi yaliyo hapa chini yanakagua kama una pakiti zinazohitajika kukamilisha moduli hii na kuyasakinisha kwako endapo hazipo.\n"
+   ],
+   "metadata": {
+    "id": "4V85BGCjII7F"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, here)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "an5NPyyKIKNR",
+    "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Gawanya data kuwa seti za mafunzo na majaribio.\n",
+    "\n",
+    "Tutaanza kwa kuchagua hatua chache kutoka somo letu la awali.\n",
+    "\n",
+    "### Ondoa viungo vya kawaida zaidi vinavyosababisha mkanganyiko kati ya vyakula tofauti, kwa kutumia `dplyr::select()`.\n",
+    "\n",
+    "Kila mtu anapenda mchele, vitunguu saumu na tangawizi!\n"
+   ],
+   "metadata": {
+    "id": "0ax9GQLBINVv"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# Load the original cuisines data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
+    "  # Encode cuisine column as categorical\r\n",
+    "  mutate(cuisine = factor(cuisine))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Display distribution of cuisines\r\n",
+    "df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "New names:\n",
+      "* `` -> ...1\n",
+      "\n",
+      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
+      "\n",
+      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+      "\u001b[31mchr\u001b[39m   (1): cuisine\n",
+      "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
+      "\n",
+      "\n",
+      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
+      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 indian  0      0        0     0          0     0            0       0       \n",
+       "2 indian  1      0        0     0          0     0            0       0       \n",
+       "3 indian  0      0        0     0          0     0            0       0       \n",
+       "4 indian  0      0        0     0          0     0            0       0       \n",
+       "5 indian  0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       0    0   \n",
+       "2 0         ⋯ 0       0           0          0                       0    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     1      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   799\n",
+       "2 indian   598\n",
+       "3 chinese  442\n",
+       "4 japanese 320\n",
+       "5 thai     289"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 799 |\n",
+       "| indian   | 598 |\n",
+       "| chinese  | 442 |\n",
+       "| japanese | 320 |\n",
+       "| thai     | 289 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 799\\\\\n",
+       "\t indian   & 598\\\\\n",
+       "\t chinese  & 442\\\\\n",
+       "\t japanese & 320\\\\\n",
+       "\t thai     & 289\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>799</td></tr>\n",
+       "\t<tr><td>indian  </td><td>598</td></tr>\n",
+       "\t<tr><td>chinese </td><td>442</td></tr>\n",
+       "\t<tr><td>japanese</td><td>320</td></tr>\n",
+       "\t<tr><td>thai    </td><td>289</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 735
+    },
+    "id": "jhCrrH22IWVR",
+    "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sawa! Sasa, ni wakati wa kugawanya data ambapo 70% ya data itaenda kwa mafunzo na 30% itaenda kwa majaribio. Pia tutatumia mbinu ya `stratification` wakati wa kugawanya data ili `kuweka uwiano wa kila aina ya chakula` katika seti za mafunzo na uthibitisho.\n",
+    "\n",
+    "[rsample](https://rsample.tidymodels.org/), kifurushi katika Tidymodels, kinatoa miundombinu kwa ajili ya kugawanya data na kurudia kwa ufanisi:\n"
+   ],
+   "metadata": {
+    "id": "AYTjVyajIdny"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# Load the core Tidymodels packages into R session\r\n",
+    "library(tidymodels)\r\n",
+    "\r\n",
+    "# Create split specification\r\n",
+    "set.seed(2056)\r\n",
+    "cuisines_split <- initial_split(data = df_select,\r\n",
+    "                                strata = cuisine,\r\n",
+    "                                prop = 0.7)\r\n",
+    "\r\n",
+    "# Extract the data in each split\r\n",
+    "cuisines_train <- training(cuisines_split)\r\n",
+    "cuisines_test <- testing(cuisines_split)\r\n",
+    "\r\n",
+    "# Print the number of cases in each split\r\n",
+    "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
+    "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
+    "\r\n",
+    "# Display the first few rows of the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Display distribution of cuisines in the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training cases: 1712\n",
+      "Test cases: 736"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 chinese 0      0        0     0          0     0            0       0       \n",
+       "2 chinese 0      0        0     0          0     0            0       0       \n",
+       "3 chinese 0      0        0     0          0     0            0       0       \n",
+       "4 chinese 0      0        0     0          0     0            0       0       \n",
+       "5 chinese 0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       1    0   \n",
+       "2 0         ⋯ 0       0           0          0                       1    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     0      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   559\n",
+       "2 indian   418\n",
+       "3 chinese  309\n",
+       "4 japanese 224\n",
+       "5 thai     202"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 559 |\n",
+       "| indian   | 418 |\n",
+       "| chinese  | 309 |\n",
+       "| japanese | 224 |\n",
+       "| thai     | 202 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 559\\\\\n",
+       "\t indian   & 418\\\\\n",
+       "\t chinese  & 309\\\\\n",
+       "\t japanese & 224\\\\\n",
+       "\t thai     & 202\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>559</td></tr>\n",
+       "\t<tr><td>indian  </td><td>418</td></tr>\n",
+       "\t<tr><td>chinese </td><td>309</td></tr>\n",
+       "\t<tr><td>japanese</td><td>224</td></tr>\n",
+       "\t<tr><td>thai    </td><td>202</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 535
+    },
+    "id": "w5FWIkEiIjdN",
+    "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Kushughulikia data isiyo na uwiano\n",
+    "\n",
+    "Kama ulivyogundua kwenye seti ya data ya awali pamoja na seti yetu ya mafunzo, kuna usambazaji usio sawa kabisa katika idadi ya vyakula. Vyakula vya Kikorea ni *karibu* mara 3 ya vyakula vya Kithai. Data isiyo na uwiano mara nyingi ina athari mbaya kwenye utendaji wa modeli. Modeli nyingi hufanya kazi vizuri zaidi pale ambapo idadi ya uchunguzi ni sawa, na kwa hivyo huwa na changamoto wanapokutana na data isiyo na uwiano.\n",
+    "\n",
+    "Kuna njia kuu mbili za kushughulikia seti za data zisizo na uwiano:\n",
+    "\n",
+    "-   kuongeza uchunguzi kwenye darasa lenye idadi ndogo: `Over-sampling` kwa mfano kutumia algoriti ya SMOTE ambayo huzalisha mifano mipya ya darasa lenye idadi ndogo kwa kutumia majirani wa karibu wa kesi hizo.\n",
+    "\n",
+    "-   kuondoa uchunguzi kutoka darasa lenye idadi kubwa: `Under-sampling`\n",
+    "\n",
+    "Katika somo letu la awali, tulionyesha jinsi ya kushughulikia seti za data zisizo na uwiano kwa kutumia `recipe`. Recipe inaweza kufikiriwa kama mpango unaoelezea hatua gani zinapaswa kutumika kwenye seti ya data ili kuifanya iwe tayari kwa uchambuzi wa data. Katika hali yetu, tunataka kuwa na usambazaji sawa wa idadi ya vyakula vyetu kwa `training set` yetu. Hebu tuingie moja kwa moja.\n"
+   ],
+   "metadata": {
+    "id": "daBi9qJNIwqW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing training data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "# Print recipe\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Data Recipe\n",
+       "\n",
+       "Inputs:\n",
+       "\n",
+       "      role #variables\n",
+       "   outcome          1\n",
+       " predictor        380\n",
+       "\n",
+       "Operations:\n",
+       "\n",
+       "SMOTE based on cuisine"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 200
+    },
+    "id": "Az6LFBGxI1X0",
+    "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Unaweza kuthibitisha (kwa kutumia prep+bake) kwamba mapishi yatafanya kazi kama unavyotarajia - lebo zote za vyakula zikiwa na uchunguzi `559`.\n",
+    "\n",
+    "Kwa kuwa tutatumia mapishi haya kama sehemu ya maandalizi ya uundaji wa modeli, `workflow()` itafanya maandalizi na kuoka kwa ajili yetu, kwa hivyo hatutalazimika kukadiria mapishi kwa mikono.\n",
+    "\n",
+    "Sasa tuko tayari kufundisha modeli 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Kuchagua kiondoaji wako\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/parsnip.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Sanaa na @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "NBL3PqIWJBBB"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sasa tunapaswa kuamua ni algoriti gani ya kutumia kwa kazi hii 🤔.\n",
+    "\n",
+    "Katika Tidymodels, [`parsnip package`](https://parsnip.tidymodels.org/index.html) hutoa kiolesura thabiti cha kufanya kazi na mifano kupitia injini tofauti (pakiti). Tafadhali angalia nyaraka za parsnip ili kuchunguza [aina za mifano na injini](https://www.tidymodels.org/find/parsnip/#models) pamoja na [hoja za mifano](https://www.tidymodels.org/find/parsnip/#model-args) zinazohusiana. Aina mbalimbali zinaweza kuonekana kuwa nyingi mwanzoni. Kwa mfano, mbinu zifuatazo zote zinajumuisha mbinu za uainishaji:\n",
+    "\n",
+    "-   C5.0 Mifano ya Uainishaji Inayotegemea Kanuni\n",
+    "\n",
+    "-   Mifano ya Uainishaji Inayobadilika\n",
+    "\n",
+    "-   Mifano ya Uainishaji wa Mstari\n",
+    "\n",
+    "-   Mifano ya Uainishaji wa Kawaida\n",
+    "\n",
+    "-   Mifano ya Usawazishaji wa Kilogistiki\n",
+    "\n",
+    "-   Mifano ya Usawazishaji wa Multinomial\n",
+    "\n",
+    "-   Mifano ya Naive Bayes\n",
+    "\n",
+    "-   Mashine za Msaada wa Vector\n",
+    "\n",
+    "-   Majirani wa Karibu\n",
+    "\n",
+    "-   Miti ya Maamuzi\n",
+    "\n",
+    "-   Mbinu za Ensemble\n",
+    "\n",
+    "-   Mitandao ya Neva\n",
+    "\n",
+    "Orodha inaendelea!\n",
+    "\n",
+    "### **Ni uainishaji gani wa kuchagua?**\n",
+    "\n",
+    "Kwa hivyo, ni uainishaji gani unapaswa kuchagua? Mara nyingi, kujaribu kadhaa na kutafuta matokeo mazuri ni njia ya kupima.\n",
+    "\n",
+    "> AutoML hutatua tatizo hili kwa urahisi kwa kuendesha kulinganisha hizi mtandaoni, ikikuruhusu kuchagua algoriti bora kwa data yako. Jaribu hapa [hapa](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "Pia, chaguo la uainishaji linategemea tatizo letu. Kwa mfano, wakati matokeo yanaweza kugawanywa katika `madaraja zaidi ya mawili`, kama ilivyo katika kesi yetu, lazima utumie `algoriti ya uainishaji wa madaraja mengi` badala ya `uainishaji wa binary.`\n",
+    "\n",
+    "### **Njia bora zaidi**\n",
+    "\n",
+    "Njia bora zaidi kuliko kubahatisha kiholela, hata hivyo, ni kufuata mawazo yaliyo kwenye [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott) inayoweza kupakuliwa. Hapa, tunagundua kwamba, kwa tatizo letu la madaraja mengi, tuna chaguo kadhaa:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/cheatsheet.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Sehemu ya Karatasi ya Udanganyifu ya Microsoft, ikionyesha chaguo za uainishaji wa madaraja mengi</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "a6DLAZ3vJZ14"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Mantiki**\n",
+    "\n",
+    "Tujaribu kufikiria njia tofauti za kutatua tatizo hili tukizingatia vikwazo tulivyo navyo:\n",
+    "\n",
+    "-   **Mitandao ya neva yenye kina ni nzito sana**. Kwa dataset yetu safi lakini ndogo, na kwa kuwa tunafanya mafunzo kwa ndani kupitia notebooks, mitandao ya neva yenye kina ni nzito sana kwa kazi hii.\n",
+    "\n",
+    "-   **Hakuna classifier ya darasa mbili**. Hatutumii classifier ya darasa mbili, kwa hivyo hiyo inatupilia mbali mbinu ya one-vs-all.\n",
+    "\n",
+    "-   **Mti wa maamuzi au regression ya logistic inaweza kufanya kazi**. Mti wa maamuzi unaweza kufanya kazi, au regression ya multinomial/regression ya logistic ya darasa nyingi kwa data ya darasa nyingi.\n",
+    "\n",
+    "-   **Multiclass Boosted Decision Trees hutatua tatizo tofauti**. Multiclass boosted decision tree inafaa zaidi kwa kazi zisizo za parametric, kwa mfano kazi zinazolenga kujenga rankings, kwa hivyo siyo muhimu kwetu.\n",
+    "\n",
+    "Pia, kwa kawaida kabla ya kuanza kutumia mifano ya machine learning yenye ugumu zaidi kama ensemble methods, ni wazo zuri kujenga mfano rahisi iwezekanavyo ili kupata wazo la kinachoendelea. Kwa somo hili, tutaanza na mfano wa `multinomial regression`.\n",
+    "\n",
+    "> Regression ya logistic ni mbinu inayotumika pale ambapo variable ya matokeo ni ya kikundi (au nominal). Kwa Binary logistic regression idadi ya variable za matokeo ni mbili, ilhali idadi ya variable za matokeo kwa multinomial logistic regression ni zaidi ya mbili. Tazama [Advanced Regression Methods](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html) kwa maelezo zaidi.\n",
+    "\n",
+    "## 4. Kufundisha na kutathmini mfano wa Multinomial logistic regression.\n",
+    "\n",
+    "Katika Tidymodels, `parsnip::multinom_reg()`, inafafanua mfano unaotumia predictors za linear kutabiri data ya darasa nyingi kwa kutumia usambazaji wa multinomial. Tazama `?multinom_reg()` kwa njia tofauti/engines unazoweza kutumia kufit mfano huu.\n",
+    "\n",
+    "Kwa mfano huu, tutafit mfano wa Multinomial regression kupitia engine ya default [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf).\n",
+    "\n",
+    "> Nilichagua thamani ya `penalty` kwa njia ya nasibu. Kuna njia bora za kuchagua thamani hii, yaani, kwa kutumia `resampling` na `tuning` ya mfano ambayo tutajadili baadaye.\n",
+    ">\n",
+    "> Tazama [Tidymodels: Get Started](https://www.tidymodels.org/start/tuning/) ikiwa unataka kujifunza zaidi kuhusu jinsi ya kurekebisha hyperparameters za mfano.\n"
+   ],
+   "metadata": {
+    "id": "gWMsVcbBJemu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# Create a multinomial regression model specification\r\n",
+    "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
+    "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
+    "  set_mode(\"classification\")\r\n",
+    "\r\n",
+    "# Print model specification\r\n",
+    "mr_spec"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 166
+    },
+    "id": "Wq_fcyQiJvfG",
+    "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kazi nzuri 🥳! Sasa kwa kuwa tuna mapishi na maelezo ya mfano, tunahitaji kupata njia ya kuyafunga pamoja katika kitu ambacho kwanza kitaandaa data, kisha kufitisha mfano kwenye data iliyotayarishwa, na pia kuruhusu shughuli za baada ya usindikaji. Katika Tidymodels, kitu hiki rahisi kinaitwa [`workflow`](https://workflows.tidymodels.org/) na kwa urahisi huhifadhi vipengele vyako vya uundaji wa mifano! Hiki ndicho tungeita *pipelines* katika *Python*.\n",
+    "\n",
+    "Sasa hebu tufunge kila kitu kwenye workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "NlSbzDfgJ0zh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# Bundle recipe and model specification\r\n",
+    "mr_wf <- workflow() %>% \r\n",
+    "  add_recipe(cuisines_recipe) %>% \r\n",
+    "  add_model(mr_spec)\r\n",
+    "\r\n",
+    "# Print out workflow\r\n",
+    "mr_wf"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow ════════════════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 333
+    },
+    "id": "Sc1TfPA4Ke3_",
+    "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mtiririko wa kazi 👌👌! **`workflow()`** inaweza kutumika kwa njia sawa na jinsi mfano unavyoweza kutumika. Kwa hivyo, ni wakati wa kufundisha mfano!\n"
+   ],
+   "metadata": {
+    "id": "TNQ8i85aKf9L"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# Train a multinomial regression model\n",
+    "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
+    "\n",
+    "mr_fit"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Call:\n",
+       "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
+       "    trace = FALSE)\n",
+       "\n",
+       "Coefficients:\n",
+       "         (Intercept)     almond angelica         anise anise_seed       apple\n",
+       "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
+       "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
+       "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
+       "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
+       "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
+       "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
+       "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
+       "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
+       "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
+       "             avocado       bacon baked_potato balm     banana     barley\n",
+       "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
+       "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
+       "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
+       "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
+       "         bartlett_pear      basil        bay       bean         beech\n",
+       "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
+       "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
+       "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
+       "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
+       "               beef beef_broth   beef_liver         beer        beet\n",
+       "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
+       "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
+       "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
+       "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
+       "         bell_pepper bergamot       berry bitter_orange black_bean\n",
+       "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
+       "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
+       "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
+       "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
+       "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
+       "indian               0             0.38935801   -0.4453495               0\n",
+       "japanese             0            -0.05452887   -0.5440869               0\n",
+       "korean               0            -0.03929970    0.8025454               0\n",
+       "thai                 0            -0.21498372   -0.9854806               0\n",
+       "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
+       "indian          -0.2759246  0.3079977  0.191256164                 0\n",
+       "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
+       "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
+       "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
+       "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
+       "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
+       "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
+       "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
+       "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
+       "\n",
+       "...\n",
+       "and 308 more lines."
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GMbdfVmTKkJI",
+    "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Matokeo yanaonyesha vigezo ambavyo modeli ilijifunza wakati wa mafunzo.\n",
+    "\n",
+    "### Tathmini Modeli Iliyofunzwa\n",
+    "\n",
+    "Ni wakati wa kuona jinsi modeli ilivyofanya kazi 📏 kwa kuitathmini kwenye seti ya majaribio! Hebu tuanze kwa kufanya utabiri kwenye seti ya majaribio.\n"
+   ],
+   "metadata": {
+    "id": "tt2BfOxrKmcJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# Make predictions on the test set\n",
+    "results <- cuisines_test %>% select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
+    "\n",
+    "# Print out results\n",
+    "results %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class\n",
+       "1 indian  thai       \n",
+       "2 indian  indian     \n",
+       "3 indian  indian     \n",
+       "4 indian  indian     \n",
+       "5 indian  indian     "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
+       "|---|---|\n",
+       "| indian | thai   |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & .pred\\_class\\\\\n",
+       " <fct> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai  \\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "CqtckvtsKqax",
+    "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kazi nzuri! Katika Tidymodels, kutathmini utendaji wa modeli kunaweza kufanywa kwa kutumia [yardstick](https://yardstick.tidymodels.org/) - kifurushi kinachotumika kupima ufanisi wa modeli kwa kutumia vipimo vya utendaji. Kama tulivyofanya katika somo letu la usanjari wa kimantiki, hebu tuanze kwa kuhesabu matriki ya kuchanganya.\n"
+   ],
+   "metadata": {
+    "id": "8w5N6XsBKss7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "# Confusion matrix for categorical data\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "          Truth\n",
+       "Prediction chinese indian japanese korean thai\n",
+       "  chinese       83      1        8     15   10\n",
+       "  indian         4    163        1      2    6\n",
+       "  japanese      21      5       73     25    1\n",
+       "  korean        15      0       11    191    0\n",
+       "  thai          10     11        3      7   70"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 133
+    },
+    "id": "YvODvsLkK0iG",
+    "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "c0HfPL16Lr6U"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
+    "# Visualize confusion matrix\n",
+    "results %>% \n",
+    "  conf_mat(cuisine, .pred_class) %>% \n",
+    "  autoplot(type = \"heatmap\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "plot without title"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {
+      "image/png": {
+       "width": 420,
+       "height": 420
+      }
+     }
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 436
+    },
+    "id": "HsAtwukyLsvt",
+    "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mraba za giza kwenye mchoro wa matriki ya mkanganyiko zinaonyesha idadi kubwa ya kesi, na unaweza kuona mstari wa diagonal wa mraba za giza unaoonyesha kesi ambapo lebo iliyotabiriwa na lebo halisi ni sawa.\n",
+    "\n",
+    "Sasa hebu tuhifadhi takwimu za muhtasari kwa matriki ya mkanganyiko.\n"
+   ],
+   "metadata": {
+    "id": "oOJC87dkLwPr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# Summary stats for confusion matrix\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
+    "summary()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "   .metric              .estimator .estimate\n",
+       "1  accuracy             multiclass 0.7880435\n",
+       "2  kap                  multiclass 0.7276583\n",
+       "3  sens                 macro      0.7780927\n",
+       "4  spec                 macro      0.9477598\n",
+       "5  ppv                  macro      0.7585583\n",
+       "6  npv                  macro      0.9460080\n",
+       "7  mcc                  multiclass 0.7292724\n",
+       "8  j_index              macro      0.7258524\n",
+       "9  bal_accuracy         macro      0.8629262\n",
+       "10 detection_prevalence macro      0.2000000\n",
+       "11 precision            macro      0.7585583\n",
+       "12 recall               macro      0.7780927\n",
+       "13 f_meas               macro      0.7641862"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 13 × 3\n",
+       "\n",
+       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| accuracy             | multiclass | 0.7880435 |\n",
+       "| kap                  | multiclass | 0.7276583 |\n",
+       "| sens                 | macro      | 0.7780927 |\n",
+       "| spec                 | macro      | 0.9477598 |\n",
+       "| ppv                  | macro      | 0.7585583 |\n",
+       "| npv                  | macro      | 0.9460080 |\n",
+       "| mcc                  | multiclass | 0.7292724 |\n",
+       "| j_index              | macro      | 0.7258524 |\n",
+       "| bal_accuracy         | macro      | 0.8629262 |\n",
+       "| detection_prevalence | macro      | 0.2000000 |\n",
+       "| precision            | macro      | 0.7585583 |\n",
+       "| recall               | macro      | 0.7780927 |\n",
+       "| f_meas               | macro      | 0.7641862 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 13 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " .metric & .estimator & .estimate\\\\\n",
+       " <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t accuracy             & multiclass & 0.7880435\\\\\n",
+       "\t kap                  & multiclass & 0.7276583\\\\\n",
+       "\t sens                 & macro      & 0.7780927\\\\\n",
+       "\t spec                 & macro      & 0.9477598\\\\\n",
+       "\t ppv                  & macro      & 0.7585583\\\\\n",
+       "\t npv                  & macro      & 0.9460080\\\\\n",
+       "\t mcc                  & multiclass & 0.7292724\\\\\n",
+       "\t j\\_index              & macro      & 0.7258524\\\\\n",
+       "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
+       "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
+       "\t precision            & macro      & 0.7585583\\\\\n",
+       "\t recall               & macro      & 0.7780927\\\\\n",
+       "\t f\\_meas               & macro      & 0.7641862\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 13 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
+       "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
+       "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
+       "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
+       "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
+       "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
+       "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
+       "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
+       "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 494
+    },
+    "id": "OYqetUyzL5Wz",
+    "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ikiwa tutazingatia vipimo kama vile usahihi, unyeti, na ppv, hatuko mbali sana kwa mwanzo mzuri 🥳!\n",
+    "\n",
+    "## 4. Kuchunguza Kwa Undani\n",
+    "\n",
+    "Hebu tujiulize swali moja la kina: Ni vigezo gani vinavyotumika kuamua aina fulani ya chakula kama matokeo yaliyotabiriwa?\n",
+    "\n",
+    "Kweli, algoriti za kujifunza kwa mashine za takwimu, kama vile logistic regression, zinategemea `uwezekano`; kwa hivyo kinachotabiriwa na classifier kwa kweli ni mgawanyo wa uwezekano juu ya seti ya matokeo yanayowezekana. Darasa lenye uwezekano wa juu zaidi ndilo huchaguliwa kama matokeo yanayowezekana zaidi kwa uchunguzi uliotolewa.\n",
+    "\n",
+    "Hebu tuone hili likifanyika kwa kufanya utabiri wa madarasa madhubuti na uwezekano.\n"
+   ],
+   "metadata": {
+    "id": "43t7vz8vMJtW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "# Make hard class prediction and probabilities\n",
+    "results_prob <- cuisines_test %>%\n",
+    "  select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
+    "\n",
+    "# Print out results\n",
+    "results_prob %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
+       "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
+       "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
+       "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
+       "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
+       "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
+       "  .pred_thai  \n",
+       "1 5.388194e-01\n",
+       "2 1.577948e-06\n",
+       "3 6.874989e-03\n",
+       "4 3.863391e-03\n",
+       "5 5.653283e-03"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 7\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
+       "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
+       "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
+       "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
+       "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 7\n",
+       "\\begin{tabular}{lllllll}\n",
+       " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
+       " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
+       "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
+       "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
+       "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
+       "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "xdKNs-ZPMTJL",
+    "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kwa nini mfano una uhakika kwamba uchunguzi wa kwanza ni wa Kithai?\n",
+    "\n",
+    "## **🚀Changamoto**\n",
+    "\n",
+    "Katika somo hili, ulitumia data yako iliyosafishwa kujenga mfano wa mashine unaoweza kutabiri aina ya chakula cha kitaifa kulingana na mfululizo wa viungo. Chukua muda kusoma [chaguzi nyingi](https://www.tidymodels.org/find/parsnip/#models) zinazotolewa na Tidymodels ili kuainisha data na [njia nyingine](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) za kufanikisha multinomial regression.\n",
+    "\n",
+    "#### SHUKRANI KWA:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) kwa kuunda michoro ya kushangaza inayofanya R kuwa ya kuvutia na ya kirafiki zaidi. Tafuta michoro zaidi kwenye [galeria yake](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) na [Jen Looper](https://www.twitter.com/jenlooper) kwa kuunda toleo la awali la Python la moduli hii ♥️\n",
+    "\n",
+    "<br>\n",
+    "Ningetupa vichekesho lakini sijaelewa utani wa chakula 😅.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Jifunze kwa furaha,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Balozi wa Dhahabu wa Wanafunzi wa Microsoft Learn.\n"
+   ],
+   "metadata": {
+    "id": "2tWVHMeLMYdM"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/translations/sw/4-Classification/2-Classifiers-1/solution/notebook.ipynb
new file mode 100644
index 000000000..b5232ef1b
--- /dev/null
+++ b/translations/sw/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy is 0.8181818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print (\"Accuracy is {}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n       'vegetable_oil'],\n      dtype='object')\ncuisine: korean\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test an item\n",
+    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[50]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                 0\n",
+       "korean    0.392231\n",
+       "chinese   0.372872\n",
+       "japanese  0.218825\n",
+       "thai      0.013427\n",
+       "indian    0.002645"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>korean</th>\n      <td>0.392231</td>\n    </tr>\n    <tr>\n      <th>chinese</th>\n      <td>0.372872</td>\n    </tr>\n    <tr>\n      <th>japanese</th>\n      <td>0.218825</td>\n    </tr>\n    <tr>\n      <th>thai</th>\n      <td>0.013427</td>\n    </tr>\n    <tr>\n      <th>indian</th>\n      <td>0.002645</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "#rehsape to 2d array and transpose\n",
+    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
+    "# predict with score\n",
+    "proba = model.predict_proba(test)\n",
+    "classes = model.classes_\n",
+    "# create df with classes and scores\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
+    "\n",
+    "# create df to show results\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.75      0.73      0.74       223\n      indian       0.93      0.88      0.90       255\n    japanese       0.78      0.78      0.78       253\n      korean       0.87      0.86      0.86       236\n        thai       0.76      0.84      0.80       232\n\n    accuracy                           0.82      1199\n   macro avg       0.82      0.82      0.82      1199\nweighted avg       0.82      0.82      0.82      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\r\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "9408506dd864f2b6e334c62f80c0cfcc",
+   "translation_date": "2025-09-06T14:33:11+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/3-Classifiers-2/notebook.ipynb b/translations/sw/4-Classification/3-Classifiers-2/notebook.ipynb
new file mode 100644
index 000000000..e4fa9d14a
--- /dev/null
+++ b/translations/sw/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
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+   "pygments_lexer": "ipython3",
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+   "source_file": "4-Classification/3-Classifiers-2/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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new file mode 100644
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--- /dev/null
+++ b/translations/sw/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -0,0 +1,648 @@
+{
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+ "nbformat_minor": 0,
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+  "colab": {
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+  },
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+   "display_name": "R"
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+   "name": "R"
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+   "source_file": "4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb",
+   "language_code": "sw"
+  }
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+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jsFutf_ygqSx"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HD54bEefgtNO"
+   },
+   "source": [
+    "## Wainishaji wa vyakula 2\n",
+    "\n",
+    "Katika somo hili la pili la uainishaji, tutachunguza `njia zaidi` za kuainisha data ya kategoria. Pia tutajifunza kuhusu athari za kuchagua mainishaji mmoja badala ya mwingine.\n",
+    "\n",
+    "### [**Jaribio la awali la somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
+    "\n",
+    "### **Mahitaji ya awali**\n",
+    "\n",
+    "Tunadhani kuwa umekamilisha masomo ya awali kwa kuwa tutatumia baadhi ya dhana tulizojifunza hapo kabla.\n",
+    "\n",
+    "Kwa somo hili, tutahitaji vifurushi vifuatavyo:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) ni [mkusanyiko wa vifurushi vya R](https://www.tidyverse.org/packages) vilivyoundwa ili kufanya sayansi ya data kuwa ya haraka, rahisi, na ya kufurahisha!\n",
+    "\n",
+    "-   `tidymodels`: Mfumo wa [tidymodels](https://www.tidymodels.org/) ni [mkusanyiko wa vifurushi](https://www.tidymodels.org/packages/) kwa ajili ya uundaji wa mifano na ujifunzaji wa mashine.\n",
+    "\n",
+    "-   `themis`: [Kifurushi cha themis](https://themis.tidymodels.org/) kinatoa Hatua za Ziada za Mapishi kwa Kushughulikia Data Isiyosawazishwa.\n",
+    "\n",
+    "Unaweza kuvifunga kwa kutumia:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\n",
+    "\n",
+    "Vinginevyo, script iliyo hapa chini hukagua kama una vifurushi vinavyohitajika kukamilisha moduli hii na kuvifunga kwako endapo havipo.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vZ57IuUxgyQt"
+   },
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z22M-pj4g07x"
+   },
+   "source": [
+    "## **1. Ramani ya uainishaji**\n",
+    "\n",
+    "Katika [somo letu la awali](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1), tulijaribu kujibu swali: tunachaguaje kati ya mifano mbalimbali? Kwa kiasi kikubwa, inategemea sifa za data na aina ya tatizo tunalotaka kutatua (kwa mfano, uainishaji au regression?)\n",
+    "\n",
+    "Hapo awali, tulijifunza kuhusu chaguo mbalimbali unazoweza kutumia unapouainisha data kwa kutumia karatasi ya msaada ya Microsoft. Mfumo wa Kujifunza kwa Mashine wa Python, Scikit-learn, unatoa karatasi ya msaada inayofanana lakini ya kina zaidi ambayo inaweza kusaidia zaidi kupunguza chaguo zako za estimators (neno lingine kwa classifiers):\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/map.png\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption></figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u1i3xRIVg7vG"
+   },
+   "source": [
+    "> Kidokezo: [tembelea ramani hii mtandaoni](https://scikit-learn.org/stable/tutorial/machine_learning_map/) na bonyeza kwenye njia ili kusoma nyaraka.\n",
+    ">\n",
+    "> Tovuti ya [Tidymodels reference](https://www.tidymodels.org/find/parsnip/#models) pia inatoa nyaraka bora kuhusu aina tofauti za modeli.\n",
+    "\n",
+    "### **Mpango** 🗺️\n",
+    "\n",
+    "Ramani hii ni muhimu sana mara tu unapokuwa na uelewa mzuri wa data yako, kwani unaweza 'kutembea' kwenye njia zake kuelekea uamuzi:\n",
+    "\n",
+    "-   Tuna sampuli \\>50\n",
+    "\n",
+    "-   Tunataka kutabiri kategoria\n",
+    "\n",
+    "-   Tuna data yenye lebo\n",
+    "\n",
+    "-   Tuna sampuli chini ya 100K\n",
+    "\n",
+    "-   ✨ Tunaweza kuchagua Linear SVC\n",
+    "\n",
+    "-   Ikiwa hiyo haifanyi kazi, kwa kuwa tuna data ya nambari\n",
+    "\n",
+    "    -   Tunaweza kujaribu ✨ KNeighbors Classifier\n",
+    "\n",
+    "        -   Ikiwa hiyo haifanyi kazi, jaribu ✨ SVC na ✨ Ensemble Classifiers\n",
+    "\n",
+    "Hii ni njia muhimu sana ya kufuata. Sasa, hebu tuanze moja kwa moja kwa kutumia mfumo wa modeli wa [tidymodels](https://www.tidymodels.org/): mkusanyiko thabiti na rahisi wa pakiti za R zilizotengenezwa ili kuhimiza mazoea mazuri ya takwimu 😊.\n",
+    "\n",
+    "## 2. Gawanya data na kushughulikia seti ya data isiyo na uwiano.\n",
+    "\n",
+    "Kutoka kwenye masomo yetu ya awali, tulijifunza kuwa kulikuwa na seti ya viungo vya kawaida katika vyakula vyetu. Pia, kulikuwa na usambazaji usio sawa katika idadi ya vyakula.\n",
+    "\n",
+    "Tutashughulikia haya kwa:\n",
+    "\n",
+    "-   Kuondoa viungo vya kawaida zaidi vinavyosababisha mkanganyiko kati ya vyakula tofauti, kwa kutumia `dplyr::select()`.\n",
+    "\n",
+    "-   Kutumia `recipe` inayosindika data ili kuifanya iwe tayari kwa modeli kwa kutumia algoriti ya `over-sampling`.\n",
+    "\n",
+    "Tayari tulitazama haya katika somo la awali kwa hivyo hili linapaswa kuwa rahisi 🥳!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "6tj_rN00hClA"
+   },
+   "source": [
+    "# Load the core Tidyverse and Tidymodels packages\n",
+    "library(tidyverse)\n",
+    "library(tidymodels)\n",
+    "\n",
+    "# Load the original cuisines data\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
+    "\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\n",
+    "df_select <- df %>% \n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\n",
+    "  # Encode cuisine column as categorical\n",
+    "  mutate(cuisine = factor(cuisine))\n",
+    "\n",
+    "\n",
+    "# Create data split specification\n",
+    "set.seed(2056)\n",
+    "cuisines_split <- initial_split(data = df_select,\n",
+    "                                strata = cuisine,\n",
+    "                                prop = 0.7)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "cuisines_train <- training(cuisines_split)\n",
+    "cuisines_test <- testing(cuisines_split)\n",
+    "\n",
+    "# Display distribution of cuisines in the training set\n",
+    "cuisines_train %>% \n",
+    "  count(cuisine) %>% \n",
+    "  arrange(desc(n))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFin5yw3hHb1"
+   },
+   "source": [
+    "### Kushughulikia Data Isiyosawazishwa\n",
+    "\n",
+    "Data isiyosawazishwa mara nyingi ina athari mbaya kwenye utendaji wa modeli. Modeli nyingi hufanya kazi vizuri zaidi pale idadi ya uchunguzi ni sawa, na kwa hivyo huwa zinapata changamoto na data isiyosawazishwa.\n",
+    "\n",
+    "Kuna njia kuu mbili za kushughulikia seti za data isiyosawazishwa:\n",
+    "\n",
+    "-   kuongeza uchunguzi kwenye darasa lenye idadi ndogo: `Over-sampling` kwa mfano kutumia algoriti ya SMOTE ambayo huzalisha mifano mipya ya darasa lenye idadi ndogo kwa kutumia majirani wa karibu wa kesi hizo.\n",
+    "\n",
+    "-   kuondoa uchunguzi kutoka darasa lenye idadi kubwa: `Under-sampling`\n",
+    "\n",
+    "Katika somo letu la awali, tulionyesha jinsi ya kushughulikia seti za data isiyosawazishwa kwa kutumia `recipe`. Recipe inaweza kufikiriwa kama mpango unaoelezea hatua gani zinapaswa kutumika kwenye seti ya data ili kuifanya iwe tayari kwa uchambuzi wa data. Katika hali yetu, tunataka kuwa na usambazaji sawa wa idadi ya vyakula vyetu kwa `training set` yetu. Hebu tuingie moja kwa moja.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "cRzTnHolhLWd"
+   },
+   "source": [
+    "# Load themis package for dealing with imbalanced data\n",
+    "library(themis)\n",
+    "\n",
+    "# Create a recipe for preprocessing training data\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
+    "  step_smote(cuisine) \n",
+    "\n",
+    "# Print recipe\n",
+    "cuisines_recipe"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KxOQ2ORhhO81"
+   },
+   "source": [
+    "Sasa tuko tayari kufundisha mifano 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Zaidi ya mifano ya regression ya multinomial\n",
+    "\n",
+    "Katika somo letu la awali, tulichunguza mifano ya regression ya multinomial. Hebu tuangalie mifano mingine yenye kubadilika zaidi kwa ajili ya uainishaji.\n",
+    "\n",
+    "### Support Vector Machines\n",
+    "\n",
+    "Katika muktadha wa uainishaji, `Support Vector Machines` ni mbinu ya kujifunza kwa mashine inayojaribu kutafuta *hyperplane* inayotenganisha darasa kwa \"ubora\" zaidi. Hebu tuangalie mfano rahisi:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/svm.png\"\n",
+    "   width=\"300\"/>\n",
+    "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C4Wsd0vZhXYu"
+   },
+   "source": [
+    "H1~ haigawanyi madarasa. H2~ inagawanya, lakini kwa pengo dogo tu. H3~ inagawanya kwa pengo kubwa zaidi.\n",
+    "\n",
+    "#### Klasifaya ya Msaada wa Vector ya Mstari\n",
+    "\n",
+    "Kuweka vikundi kwa kutumia Support-Vector (SVC) ni sehemu ya familia ya mbinu za ML za Support-Vector machines. Katika SVC, hyperplane huchaguliwa ili kutenganisha kwa usahihi `sehemu kubwa` ya uchunguzi wa mafunzo, lakini `inaweza kukosea` uchunguzi kadhaa. Kwa kuruhusu baadhi ya alama kuwa upande usio sahihi, SVM inakuwa thabiti zaidi kwa data isiyo ya kawaida na hivyo kuboresha uwezo wa kujumlisha data mpya. Kigezo kinachosimamia ukiukaji huu kinaitwa `gharama` ambayo ina thamani ya msingi ya 1 (tazama `help(\"svm_poly\")`).\n",
+    "\n",
+    "Hebu tuunde SVC ya mstari kwa kuweka `degree = 1` katika mfano wa polynomial SVM.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vJpp6nuChlBz"
+   },
+   "source": [
+    "# Make a linear SVC specification\n",
+    "svc_linear_spec <- svm_poly(degree = 1) %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svc_linear_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svc_linear_spec)\n",
+    "\n",
+    "# Print out workflow\n",
+    "svc_linear_wf"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rDs8cWNkhoqu"
+   },
+   "source": [
+    "Sasa kwa kuwa tumeshakamata hatua za awali za uchakataji na maelezo ya modeli ndani ya *workflow*, tunaweza kuendelea na kufundisha SVC ya mstari na kutathmini matokeo wakati huo huo. Kwa vipimo vya utendaji, hebu tuunde seti ya vipimo ambayo itatathmini: `accuracy`, `sensitivity`, `Positive Predicted Value` na `F Measure`.\n",
+    "\n",
+    "> `augment()` itaongeza safu(safu) za utabiri kwenye data iliyotolewa.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "81wiqcwuhrnq"
+   },
+   "source": [
+    "# Train a linear SVC model\n",
+    "svc_linear_fit <- svc_linear_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svc_linear_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0UFQvHf-huo3"
+   },
+   "source": [
+    "#### Mashine ya Msaada wa Vector\n",
+    "\n",
+    "Mashine ya msaada wa vector (SVM) ni upanuzi wa mclasifia wa msaada wa vector ili kuweza kushughulikia mpaka usio wa mstari kati ya madarasa. Kimsingi, SVM hutumia *mbinu ya kernel* kupanua nafasi ya sifa ili kuendana na uhusiano usio wa mstari kati ya madarasa. Mojawapo ya kazi maarufu na yenye kubadilika sana ya kernel inayotumiwa na SVM ni *Radial basis function.* Hebu tuone jinsi itakavyofanya kazi kwenye data yetu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-KX4S8mzhzmp"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Make an RBF SVM specification\n",
+    "svm_rbf_spec <- svm_rbf() %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svm_rbf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svm_rbf_spec)\n",
+    "\n",
+    "\n",
+    "# Train an RBF model\n",
+    "svm_rbf_fit <- svm_rbf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svm_rbf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QBFSa7WSh4HQ"
+   },
+   "source": [
+    "Bora zaidi 🤩!\n",
+    "\n",
+    "> ✅ Tafadhali angalia:\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://bradleyboehmke.github.io/HOML/svm.html), Hands-on Machine Learning with R\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://www.statlearning.com/), An Introduction to Statistical Learning with Applications in R\n",
+    ">\n",
+    "> kwa kusoma zaidi.\n",
+    "\n",
+    "### Vainishi vya Jirani wa Karibu\n",
+    "\n",
+    "*K*-jirani wa karibu (KNN) ni algorithimu ambapo kila uchunguzi unatabiriwa kulingana na *ufanano* wake na uchunguzi mwingine.\n",
+    "\n",
+    "Hebu tuifanyie data yetu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "k4BxxBcdh9Ka"
+   },
+   "source": [
+    "# Make a KNN specification\n",
+    "knn_spec <- nearest_neighbor() %>% \n",
+    "  set_engine(\"kknn\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "knn_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(knn_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "knn_wf_fit <- knn_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "knn_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HaegQseriAcj"
+   },
+   "source": [
+    "Inaonekana kwamba modeli hii haifanyi kazi vizuri sana. Huenda kubadilisha vigezo vya modeli (tazama `help(\"nearest_neighbor\")`) kutaboresha utendaji wa modeli. Hakikisha kujaribu.\n",
+    "\n",
+    "> ✅ Tafadhali angalia:\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> ili kujifunza zaidi kuhusu *K*-Nearest Neighbors classifiers.\n",
+    "\n",
+    "### Wahesabuji wa Ensemble\n",
+    "\n",
+    "Algoriti za ensemble hufanya kazi kwa kuunganisha makadirio kadhaa ya msingi ili kuzalisha modeli bora kwa kutumia:\n",
+    "\n",
+    "`bagging`: kutumia *kazi ya wastani* kwa mkusanyiko wa modeli za msingi\n",
+    "\n",
+    "`boosting`: kujenga mfululizo wa modeli zinazojenga juu ya kila moja ili kuboresha utendaji wa utabiri.\n",
+    "\n",
+    "Hebu tuanze kwa kujaribu modeli ya Random Forest, ambayo hujenga mkusanyiko mkubwa wa miti ya maamuzi kisha hutumia kazi ya wastani ili kupata modeli bora zaidi kwa ujumla.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "49DPoVs6iK1M"
+   },
+   "source": [
+    "# Make a random forest specification\n",
+    "rf_spec <- rand_forest() %>% \n",
+    "  set_engine(\"ranger\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "rf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(rf_spec)\n",
+    "\n",
+    "# Train a random forest model\n",
+    "rf_wf_fit <- rf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "rf_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RGVYwC_aiUWc"
+   },
+   "source": [
+    "Kazi nzuri 👏!\n",
+    "\n",
+    "Hebu pia tujaribu na mfano wa Boosted Tree.\n",
+    "\n",
+    "Boosted Tree hufafanua mbinu ya ensemble inayounda mfululizo wa miti ya maamuzi ya mfululizo ambapo kila mti unategemea matokeo ya miti ya awali kwa lengo la kupunguza makosa hatua kwa hatua. Inalenga uzito wa vitu vilivyokosewa kuainishwa na kurekebisha mwelekeo wa classifier inayofuata ili kusahihisha.\n",
+    "\n",
+    "Kuna njia tofauti za kufanikisha mfano huu (tazama `help(\"boost_tree\")`). Katika mfano huu, tutafanikisha Boosted trees kupitia injini ya `xgboost`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Py1YWo-micWs"
+   },
+   "source": [
+    "# Make a boosted tree specification\n",
+    "boost_spec <- boost_tree(trees = 200) %>% \n",
+    "  set_engine(\"xgboost\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "boost_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(boost_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "boost_wf_fit <- boost_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "boost_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zNQnbuejigZM"
+   },
+   "source": [
+    "✅ Tafadhali angalia:\n",
+    "\n",
+    "-   [Machine Learning for Social Scientists](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
+    "\n",
+    "-   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    "\n",
+    "-   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    "\n",
+    "-   <https://algotech.netlify.app/blog/xgboost/> - Inachunguza mfano wa AdaBoost ambao ni mbadala mzuri kwa xgboost.\n",
+    "\n",
+    "kujifunza zaidi kuhusu waainishaji wa Ensemble.\n",
+    "\n",
+    "## 4. Ziada - kulinganisha mifano mingi\n",
+    "\n",
+    "Tumetengeneza idadi kubwa ya mifano katika maabara hii 🙌. Inaweza kuwa kazi ngumu au ya kuchosha kuunda mtiririko wa kazi nyingi kutoka kwa seti tofauti za preprocessors na/au maelezo ya mifano kisha kuhesabu vipimo vya utendaji moja baada ya nyingine.\n",
+    "\n",
+    "Hebu tuone kama tunaweza kushughulikia hili kwa kuunda kazi ambayo inafaa orodha ya mtiririko wa kazi kwenye seti ya mafunzo kisha inarudisha vipimo vya utendaji kulingana na seti ya majaribio. Tutatumia `map()` na `map_dfr()` kutoka kwenye kifurushi cha [purrr](https://purrr.tidyverse.org/) ili kutumia kazi kwa kila kipengele katika orodha.\n",
+    "\n",
+    "> [`map()`](https://purrr.tidyverse.org/reference/map.html) kazi zinakuruhusu kubadilisha mikondo mingi ya for na msimbo ambao ni mfupi zaidi na rahisi kusoma. Sehemu bora ya kujifunza kuhusu [`map()`](https://purrr.tidyverse.org/reference/map.html) kazi ni sura ya [iteration](http://r4ds.had.co.nz/iteration.html) katika R kwa data science.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Qzb7LyZnimd2"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "# Define a function that returns performance metrics\n",
+    "compare_models <- function(workflow_list, train_set, test_set){\n",
+    "  \n",
+    "  suppressWarnings(\n",
+    "    # Fit each model to the train_set\n",
+    "    map(workflow_list, fit, data = train_set) %>% \n",
+    "    # Make predictions on the test set\n",
+    "      map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
+    "    # Select desired columns\n",
+    "      select(model, cuisine, .pred_class) %>% \n",
+    "    # Evaluate model performance\n",
+    "      group_by(model) %>% \n",
+    "      eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
+    "      ungroup()\n",
+    "  )\n",
+    "  \n",
+    "} # End of function"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fwa712sNisDA"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "3i4VJOi2iu-a"
+   },
+   "source": [
+    "# Make a list of workflows\n",
+    "workflow_list <- list(\n",
+    "  \"svc\" = svc_linear_wf,\n",
+    "  \"svm\" = svm_rbf_wf,\n",
+    "  \"knn\" = knn_wf,\n",
+    "  \"random_forest\" = rf_wf,\n",
+    "  \"xgboost\" = boost_wf)\n",
+    "\n",
+    "# Call the function\n",
+    "set.seed(2056)\n",
+    "perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
+    "\n",
+    "# Print out performance metrics\n",
+    "perf_metrics %>% \n",
+    "  group_by(.metric) %>% \n",
+    "  arrange(desc(.estimate)) %>% \n",
+    "  slice_head(n=7)\n",
+    "\n",
+    "# Compare accuracy\n",
+    "perf_metrics %>% \n",
+    "  filter(.metric == \"accuracy\") %>% \n",
+    "  arrange(desc(.estimate))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KuWK_lEli4nW"
+   },
+   "source": [
+    "Kifurushi cha [**workflowset**](https://workflowsets.tidymodels.org/) kinawawezesha watumiaji kuunda na kufanikisha urahisi idadi kubwa ya mifano ya modeli, lakini kimeundwa hasa kufanya kazi na mbinu za sampuli kama `cross-validation`, mbinu ambayo bado hatujafikia.\n",
+    "\n",
+    "## **🚀Changamoto**\n",
+    "\n",
+    "Kila moja ya mbinu hizi ina idadi kubwa ya vigezo ambavyo unaweza kurekebisha, kwa mfano `cost` katika SVMs, `neighbors` katika KNN, `mtry` (Vitabiri Vilivyochaguliwa kwa Nasibu) katika Random Forest.\n",
+    "\n",
+    "Fanya utafiti kuhusu vigezo vya msingi vya kila moja na fikiria maana ya kurekebisha vigezo hivi kwa ubora wa modeli.\n",
+    "\n",
+    "Ili kupata maelezo zaidi kuhusu modeli fulani na vigezo vyake, tumia: `help(\"model\")` mfano `help(\"rand_forest\")`\n",
+    "\n",
+    "> Kwa vitendo, mara nyingi tunafanya *makadirio* ya *thamani bora* kwa kufundisha modeli nyingi kwenye `seti ya data iliyosimuliwa` na kupima jinsi modeli hizi zinavyofanya kazi. Mchakato huu unaitwa **tuning**.\n",
+    "\n",
+    "### [**Jaribio la baada ya somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
+    "\n",
+    "### **Mapitio na Kujifunza Binafsi**\n",
+    "\n",
+    "Kuna maneno mengi ya kitaalamu katika masomo haya, kwa hivyo chukua muda kupitia [orodha hii](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) ya istilahi muhimu!\n",
+    "\n",
+    "#### SHUKRANI KWA:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) kwa kuunda michoro ya kuvutia inayofanya R kuwa ya kupendeza na ya kuvutia zaidi. Pata michoro zaidi kwenye [galeria yake](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) na [Jen Looper](https://www.twitter.com/jenlooper) kwa kuunda toleo la awali la moduli hii kwa Python ♥️\n",
+    "\n",
+    "Jifunze kwa furaha,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Balozi wa Wanafunzi wa Microsoft Learn wa Dhahabu.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Michoro na @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/sw/4-Classification/3-Classifiers-2/solution/notebook.ipynb
new file mode 100644
index 000000000..7341987c3
--- /dev/null
+++ b/translations/sw/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Jaribu aina tofauti za viainishi\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "C = 10\n",
+    "# Create different classifiers.\n",
+    "classifiers = {\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
+    "    'KNN classifier': KNeighborsClassifier(C),\n",
+    "    'SVC': SVC(),\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "    \n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy (train) for Linear SVC: 76.4% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.64      0.66      0.65       242\n",
+      "      indian       0.91      0.86      0.89       236\n",
+      "    japanese       0.72      0.73      0.73       245\n",
+      "      korean       0.83      0.75      0.79       234\n",
+      "        thai       0.75      0.82      0.78       242\n",
+      "\n",
+      "    accuracy                           0.76      1199\n",
+      "   macro avg       0.77      0.76      0.77      1199\n",
+      "weighted avg       0.77      0.76      0.77      1199\n",
+      "\n",
+      "Accuracy (train) for KNN classifier: 70.7% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.65      0.63      0.64       242\n",
+      "      indian       0.84      0.81      0.82       236\n",
+      "    japanese       0.60      0.81      0.69       245\n",
+      "      korean       0.89      0.53      0.67       234\n",
+      "        thai       0.69      0.75      0.72       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.73      0.71      0.71      1199\n",
+      "weighted avg       0.73      0.71      0.71      1199\n",
+      "\n",
+      "Accuracy (train) for SVC: 80.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.71      0.69      0.70       242\n",
+      "      indian       0.92      0.92      0.92       236\n",
+      "    japanese       0.77      0.78      0.77       245\n",
+      "      korean       0.87      0.77      0.82       234\n",
+      "        thai       0.75      0.86      0.80       242\n",
+      "\n",
+      "    accuracy                           0.80      1199\n",
+      "   macro avg       0.80      0.80      0.80      1199\n",
+      "weighted avg       0.80      0.80      0.80      1199\n",
+      "\n",
+      "Accuracy (train) for RFST: 82.8% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.80      0.75      0.77       242\n",
+      "      indian       0.90      0.91      0.90       236\n",
+      "    japanese       0.82      0.78      0.80       245\n",
+      "      korean       0.85      0.82      0.83       234\n",
+      "        thai       0.78      0.89      0.83       242\n",
+      "\n",
+      "    accuracy                           0.83      1199\n",
+      "   macro avg       0.83      0.83      0.83      1199\n",
+      "weighted avg       0.83      0.83      0.83      1199\n",
+      "\n",
+      "Accuracy (train) for ADA: 71.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.60      0.57      0.58       242\n",
+      "      indian       0.87      0.84      0.86       236\n",
+      "    japanese       0.71      0.60      0.65       245\n",
+      "      korean       0.68      0.78      0.72       234\n",
+      "        thai       0.70      0.78      0.74       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.71      0.71      0.71      1199\n",
+      "weighted avg       0.71      0.71      0.71      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "    y_pred = classifier.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
+    "    print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "7ea2b714669c823a596d986ba2d5739f",
+   "translation_date": "2025-09-06T14:42:56+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/4-Applied/notebook.ipynb b/translations/sw/4-Classification/4-Applied/notebook.ipynb
new file mode 100644
index 000000000..ef586c536
--- /dev/null
+++ b/translations/sw/4-Classification/4-Applied/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2f3e0d9e9ac5c301558fb8bf733ac0cb",
+   "translation_date": "2025-09-06T14:41:35+00:00",
+   "source_file": "4-Classification/4-Applied/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/4-Classification/4-Applied/solution/notebook.ipynb b/translations/sw/4-Classification/4-Applied/solution/notebook.ipynb
new file mode 100644
index 000000000..697253ac9
--- /dev/null
+++ b/translations/sw/4-Classification/4-Applied/solution/notebook.ipynb
@@ -0,0 +1,290 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "49325d6dd12a3628fc64fa7ccb1a80ff",
+   "translation_date": "2025-09-06T14:42:00+00:00",
+   "source_file": "4-Classification/4-Applied/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: skl2onnx in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.8.0)\n",
+      "Requirement already satisfied: protobuf in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (3.8.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.19.2)\n",
+      "Requirement already satisfied: onnx>=1.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.9.0)\n",
+      "Requirement already satisfied: six in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from skl2onnx) (1.12.0)\n",
+      "Requirement already satisfied: onnxconverter-common<1.9,>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.8.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.19 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (0.24.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.4.1)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->skl2onnx) (45.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnx>=1.2.1->skl2onnx) (3.10.0.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (0.16.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install skl2onnx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 60
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 61
+    }
+   ],
+   "source": [
+    "X = data.iloc[:,2:]\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "  cuisine\n",
+       "0  indian\n",
+       "1  indian\n",
+       "2  indian\n",
+       "3  indian\n",
+       "4  indian"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 62
+    }
+   ],
+   "source": [
+    "y = data[['cuisine']]\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 65
+    }
+   ],
+   "source": [
+    "model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
+    "model.fit(X_train,y_train.values.ravel())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.72      0.70      0.71       236\n      indian       0.91      0.88      0.89       243\n    japanese       0.80      0.75      0.77       240\n      korean       0.80      0.81      0.81       230\n        thai       0.76      0.85      0.80       250\n\n    accuracy                           0.80      1199\n   macro avg       0.80      0.80      0.80      1199\nweighted avg       0.80      0.80      0.80      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}\n",
+    "onx = convert_sklearn(model, initial_types=initial_type, options=options)\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
+    "    f.write(onx.SerializeToString())\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/5-Clustering/1-Visualize/notebook.ipynb b/translations/sw/5-Clustering/1-Visualize/notebook.ipynb
new file mode 100644
index 000000000..0a62ecdb9
--- /dev/null
+++ b/translations/sw/5-Clustering/1-Visualize/notebook.ipynb
@@ -0,0 +1,50 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python383jvsc74a57bd0e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95",
+   "display_name": "Python 3.8.3 64-bit (conda)"
+  },
+  "coopTranslator": {
+   "original_hash": "40e0707e96b3e1899a912776006264f9",
+   "translation_date": "2025-09-06T14:08:05+00:00",
+   "source_file": "5-Clustering/1-Visualize/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/translations/sw/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
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@@ -0,0 +1,500 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Muziki wa Nigeria uliokusanywa kutoka Spotify - uchambuzi**\n",
+    "\n",
+    "Clustering ni aina ya [Unsupervised Learning](https://wikipedia.org/wiki/Unsupervised_learning) inayodhani kuwa seti ya data haina lebo au kwamba maingizo yake hayajafungamanishwa na matokeo yaliyoainishwa. Inatumia algorithmi mbalimbali kuchambua data isiyo na lebo na kutoa makundi kulingana na mifumo inayotambua kwenye data.\n",
+    "\n",
+    "[**Maswali ya awali ya somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)\n",
+    "\n",
+    "### **Utangulizi**\n",
+    "\n",
+    "[Clustering](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) ni muhimu sana kwa uchunguzi wa data. Hebu tuone kama inaweza kusaidia kugundua mitindo na mifumo katika jinsi hadhira ya Nigeria inavyotumia muziki.\n",
+    "\n",
+    "> ✅ Chukua dakika moja kufikiria matumizi ya clustering. Katika maisha ya kila siku, clustering hutokea unapokuwa na rundo la nguo na unahitaji kupanga nguo za wanafamilia wako 🧦👕👖🩲. Katika sayansi ya data, clustering hutokea unapojaribu kuchambua mapendeleo ya mtumiaji, au kubaini sifa za seti yoyote ya data isiyo na lebo. Kwa namna fulani, clustering husaidia kuleta mpangilio katika hali ya fujo, kama droo ya soksi.\n",
+    "\n",
+    "Katika mazingira ya kitaalamu, clustering inaweza kutumika kubaini mambo kama mgawanyiko wa soko, kubaini ni makundi ya umri gani yanayonunua bidhaa fulani, kwa mfano. Matumizi mengine yanaweza kuwa kugundua hali zisizo za kawaida, labda kugundua udanganyifu kutoka kwa seti ya data ya miamala ya kadi za mkopo. Au unaweza kutumia clustering kubaini uvimbe katika kundi la skani za matibabu.\n",
+    "\n",
+    "✅ Fikiria kwa dakika moja jinsi unavyoweza kuwa umekutana na clustering 'katika mazingira halisi', katika benki, biashara ya mtandaoni, au mazingira ya kibiashara.\n",
+    "\n",
+    "> 🎓 Kwa kushangaza, uchambuzi wa makundi ulianzia katika nyanja za Anthropolojia na Saikolojia katika miaka ya 1930. Je, unaweza kufikiria jinsi ulivyotumika?\n",
+    "\n",
+    "Vinginevyo, unaweza kuitumia kwa kupanga matokeo ya utafutaji - kwa viungo vya ununuzi, picha, au hakiki, kwa mfano. Clustering ni muhimu unapokuwa na seti kubwa ya data unayotaka kupunguza na ambayo unataka kufanya uchambuzi wa kina zaidi, hivyo mbinu hii inaweza kutumika kujifunza kuhusu data kabla ya kujenga mifano mingine.\n",
+    "\n",
+    "✅ Mara data yako inapopangwa katika makundi, unaiwekea kitambulisho cha kundi, na mbinu hii inaweza kuwa muhimu katika kuhifadhi faragha ya seti ya data; badala yake unaweza kurejelea kipengele cha data kwa kitambulisho cha kundi, badala ya data inayofichua zaidi. Je, unaweza kufikiria sababu nyingine za kurejelea kitambulisho cha kundi badala ya vipengele vingine vya kundi ili kukitambua?\n",
+    "\n",
+    "### Kuanza na clustering\n",
+    "\n",
+    "> 🎓 Jinsi tunavyounda makundi inahusiana sana na jinsi tunavyokusanya vipengele vya data katika vikundi. Hebu tuchambue baadhi ya istilahi:\n",
+    ">\n",
+    "> 🎓 ['Transductive' vs. 'inductive'](https://wikipedia.org/wiki/Transduction_(machine_learning))\n",
+    ">\n",
+    "> Utoaji wa hitimisho wa transductive hutokana na kesi za mafunzo zilizotazamwa ambazo zinahusiana na kesi maalum za majaribio. Utoaji wa hitimisho wa inductive hutokana na kesi za mafunzo ambazo zinahusiana na sheria za jumla ambazo baadaye tu zinatumika kwa kesi za majaribio.\n",
+    ">\n",
+    "> Mfano: Fikiria una seti ya data ambayo imewekwa lebo kwa sehemu tu. Baadhi ya vitu ni 'rekodi', baadhi ni 'cds', na baadhi havina lebo. Kazi yako ni kutoa lebo kwa vile visivyo na lebo. Ukichagua mbinu ya inductive, ungefundisha mfano kutafuta 'rekodi' na 'cds', na kutumia lebo hizo kwa data isiyo na lebo. Mbinu hii itakuwa na shida kuainisha vitu ambavyo kwa kweli ni 'kanda za kaseti'. Mbinu ya transductive, kwa upande mwingine, hushughulikia data isiyojulikana kwa ufanisi zaidi kwani inafanya kazi ya kuunda vikundi vya vitu vinavyofanana na kisha kutumia lebo kwa kundi. Katika kesi hii, makundi yanaweza kuonyesha 'vitu vya muziki vya mviringo' na 'vitu vya muziki vya mraba'.\n",
+    ">\n",
+    "> 🎓 ['Non-flat' vs. 'flat' geometry](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)\n",
+    ">\n",
+    "> Imetokana na istilahi za hisabati, 'non-flat' vs. 'flat' geometry inahusu kipimo cha umbali kati ya vipengele kwa kutumia mbinu za kijiometri za 'flat' ([Euclidean](https://wikipedia.org/wiki/Euclidean_geometry)) au 'non-flat' (non-Euclidean).\n",
+    ">\n",
+    "> 'Flat' katika muktadha huu inahusu jiometri ya Euclidean (sehemu zake hufundishwa kama jiometri ya 'plane'), na 'non-flat' inahusu jiometri isiyo ya Euclidean. Jiometri inahusiana vipi na ujifunzaji wa mashine? Kweli, kama nyanja mbili zinazotokana na hisabati, lazima kuwe na njia ya kawaida ya kupima umbali kati ya vipengele katika makundi, na hiyo inaweza kufanywa kwa njia ya 'flat' au 'non-flat', kulingana na asili ya data. [Umbali wa Euclidean](https://wikipedia.org/wiki/Euclidean_distance) hupimwa kama urefu wa sehemu ya mstari kati ya vipengele viwili. [Umbali usio wa Euclidean](https://wikipedia.org/wiki/Non-Euclidean_geometry) hupimwa kando ya mkurva. Ikiwa data yako, ikionyeshwa, inaonekana haipo kwenye ndege, unaweza kuhitaji kutumia algorithmi maalum kuishughulikia.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/flat-nonflat.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infographic na Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "> 🎓 ['Umbali'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)\n",
+    ">\n",
+    "> Makundi yanafafanuliwa na matrix ya umbali, yaani umbali kati ya vipengele. Umbali huu unaweza kupimwa kwa njia kadhaa. Makundi ya Euclidean yanafafanuliwa na wastani wa thamani za vipengele, na yana 'centroid' au kipengele cha katikati. Umbali hupimwa kwa umbali hadi centroid hiyo. Umbali usio wa Euclidean unahusu 'clustroids', kipengele kilicho karibu zaidi na vipengele vingine. Clustroids kwa upande wake vinaweza kufafanuliwa kwa njia mbalimbali.\n",
+    ">\n",
+    "> 🎓 ['Constrained'](https://wikipedia.org/wiki/Constrained_clustering)\n",
+    ">\n",
+    "> [Constrained Clustering](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) huanzisha 'semi-supervised' learning katika mbinu hii isiyo na usimamizi. Mahusiano kati ya vipengele yanawekwa alama kama 'cannot link' au 'must-link' ili sheria fulani zifuatwe kwenye seti ya data.\n",
+    ">\n",
+    "> Mfano: Ikiwa algorithmi inaruhusiwa kuchambua kundi la data isiyo na lebo au yenye lebo kwa sehemu, makundi inayozalisha yanaweza kuwa ya ubora duni. Katika mfano hapo juu, makundi yanaweza kuunda 'vitu vya muziki vya mviringo' na 'vitu vya muziki vya mraba' na 'vitu vya pembetatu' na 'biskuti'. Ikiwa algorithmi inapewa vikwazo, au sheria za kufuata (\"kipengele lazima kiwe cha plastiki\", \"kipengele kinahitaji kuwa na uwezo wa kutoa muziki\") hii inaweza kusaidia 'kuzuia' algorithmi kufanya chaguo bora.\n",
+    ">\n",
+    "> 🎓 'Density'\n",
+    ">\n",
+    "> Data iliyo na 'kelele' inachukuliwa kuwa 'dense'. Umbali kati ya vipengele katika kila moja ya makundi yake unaweza kuonyesha, kwa uchunguzi, kuwa ni zaidi au chini ya 'dense', au 'imejaa' na hivyo data hii inahitaji kuchambuliwa kwa mbinu sahihi ya clustering. [Makala hii](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) inaonyesha tofauti kati ya kutumia algorithmi za K-Means clustering vs. HDBSCAN kuchunguza seti ya data yenye kelele na density isiyo sawa.\n",
+    "\n",
+    "Panua uelewa wako wa mbinu za clustering katika [Learn module](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "### **Algorithmi za clustering**\n",
+    "\n",
+    "Kuna zaidi ya algorithmi 100 za clustering, na matumizi yake yanategemea asili ya data inayoshughulikiwa. Hebu tujadili baadhi ya zile kuu:\n",
+    "\n",
+    "-   **Hierarchical clustering**. Ikiwa kipengele kinaainishwa kwa ukaribu wake na kipengele kilicho karibu, badala ya kile kilicho mbali zaidi, makundi yanaundwa kulingana na umbali wa wanachama wake kutoka na kwenda kwa vipengele vingine. Hierarchical clustering inajulikana kwa kuunganisha makundi mawili mara kwa mara.\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/hierarchical.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infographic na Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "-   **Centroid clustering**. Algorithmi hii maarufu inahitaji kuchagua 'k', au idadi ya makundi ya kuunda, baada ya hapo algorithmi huamua kipengele cha katikati cha kundi na kukusanya data karibu na kipengele hicho. [K-means clustering](https://wikipedia.org/wiki/K-means_clustering) ni toleo maarufu la centroid clustering ambalo linatenganisha seti ya data katika makundi ya K yaliyoainishwa awali. Kipengele cha katikati kinaamuliwa na wastani wa karibu zaidi, hivyo jina hilo. Umbali wa mraba kutoka kwa kundi hupunguzwa.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/centroid.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infographic na Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "-   **Distribution-based clustering**. Ikitokana na uundaji wa takwimu, distribution-based clustering inazingatia kubaini uwezekano wa kipengele cha data kuwa sehemu ya kundi, na kukipa kundi ipasavyo. Mbinu za Gaussian mixture zinahusiana na aina hii.\n",
+    "\n",
+    "-   **Density-based clustering**. Vipengele vya data vinapewa makundi kulingana na density yao, au jinsi vinavyokusanyika karibu na kila kimoja. Vipengele vya data vilivyo mbali na kundi vinachukuliwa kuwa outliers au kelele. DBSCAN, Mean-shift na OPTICS vinahusiana na aina hii ya clustering.\n",
+    "\n",
+    "-   **Grid-based clustering**. Kwa seti za data za vipimo vingi, gridi huundwa na data hugawanywa kati ya seli za gridi hiyo, hivyo kuunda makundi.\n",
+    "\n",
+    "Njia bora ya kujifunza kuhusu clustering ni kuijaribu mwenyewe, hivyo ndivyo utakavyofanya katika zoezi hili.\n",
+    "\n",
+    "Tutahitaji baadhi ya pakiti ili kukamilisha moduli hii. Unaweza kuzisakinisha kama: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`\n",
+    "\n",
+    "Vinginevyo, script hapa chini hukagua ikiwa una pakiti zinazohitajika kukamilisha moduli hii na kuzisakinisha kwako ikiwa baadhi zinakosekana.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Zoezi - pangisha data yako katika makundi\n",
+    "\n",
+    "Upangishaji katika makundi kama mbinu husaidiwa sana na uonyeshaji sahihi wa data, kwa hivyo hebu tuanze kwa kuonyesha data yetu ya muziki. Zoezi hili litatusaidia kuamua ni mbinu gani ya upangishaji katika makundi tunapaswa kutumia kwa ufanisi zaidi kulingana na asili ya data hii.\n",
+    "\n",
+    "Hebu tuanze mara moja kwa kuingiza data.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# Import the data into a tibble\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows of the data set\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Wakati mwingine, tunaweza kutaka maelezo zaidi kuhusu data yetu. Tunaweza kuangalia `data` na `muundo wake` kwa kutumia [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) kazi:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Glimpse into the data set\r\n",
+    "df %>% \r\n",
+    "  glimpse()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kazi nzuri!💪\n",
+    "\n",
+    "Tunaweza kuona kwamba `glimpse()` itakupa jumla ya idadi ya safu (uchunguzi) na safu wima (vigezo), kisha, maingizo machache ya kwanza ya kila kigezo katika safu baada ya jina la kigezo. Zaidi ya hayo, *aina ya data* ya kigezo inatolewa mara moja baada ya jina la kila kigezo ndani ya `< >`.\n",
+    "\n",
+    "`DataExplorer::introduce()` inaweza kufupisha taarifa hii kwa urahisi:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe basic information for our data\r\n",
+    "df %>% \r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# A visual display of the same\r\n",
+    "df %>% \r\n",
+    "  plot_intro()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nzuri sana! Tumegundua kuwa data yetu haina thamani zilizokosekana.\n",
+    "\n",
+    "Wakati tukiendelea, tunaweza kuchunguza takwimu za kawaida za mwelekeo wa kati (mfano [wastani](https://en.wikipedia.org/wiki/Arithmetic_mean) na [median](https://en.wikipedia.org/wiki/Median)) na vipimo vya mtawanyiko (mfano [mkengeuko wa kawaida](https://en.wikipedia.org/wiki/Standard_deviation)) kwa kutumia `summarytools::descr()`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe common statistics\r\n",
+    "df %>% \r\n",
+    "  descr(stats = \"common\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hebu tuangalie maadili ya jumla ya data. Kumbuka kuwa umaarufu unaweza kuwa `0`, ambayo inaonyesha nyimbo ambazo hazina daraja. Tutaziondoa hivi karibuni.\n",
+    "\n",
+    "> 🤔 Ikiwa tunafanya kazi na clustering, mbinu isiyo ya kusimamiwa ambayo haihitaji data yenye lebo, kwa nini tunaonyesha data hii ikiwa na lebo? Katika awamu ya uchunguzi wa data, zinaweza kuwa muhimu, lakini hazihitajiki kwa algorithimu za clustering kufanya kazi.\n",
+    "\n",
+    "### 1. Chunguza aina maarufu za muziki\n",
+    "\n",
+    "Twende mbele na tujue aina za muziki maarufu 🎶 kwa kuhesabu idadi ya mara zinavyoonekana.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Popular genres\r\n",
+    "top_genres <- df %>% \r\n",
+    "  count(artist_top_genre, sort = TRUE) %>% \r\n",
+    "# Encode to categorical and reorder the according to count\r\n",
+    "  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())\r\n",
+    "\r\n",
+    "# Print the top genres\r\n",
+    "top_genres\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hiyo imeenda vizuri! Wanasema picha ina thamani ya mistari elfu moja ya fremu ya data (kwa kweli hakuna mtu anayesema hivyo 😅). Lakini unaelewa maana yake, sivyo?\n",
+    "\n",
+    "Njia moja ya kuonyesha data ya kategoria (vigezo vya herufi au sababu) ni kutumia chati za mistari. Hebu tufanye chati ya mistari ya aina 10 bora za muziki:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Change the default gray theme\r\n",
+    "theme_set(theme_light())\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sasa ni rahisi zaidi kutambua kwamba tuna `missing` aina za muziki 🧐!\n",
+    "\n",
+    "> Uwasilishaji mzuri wa data utaonyesha mambo ambayo hukutarajia, au kuibua maswali mapya kuhusu data - Hadley Wickham na Garrett Grolemund, [R For Data Science](https://r4ds.had.co.nz/introduction.html)\n",
+    "\n",
+    "Kumbuka, pale ambapo aina kuu ya muziki imeelezwa kama `Missing`, inamaanisha kwamba Spotify haikuigawa, kwa hivyo tuiondoe.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  filter(artist_top_genre != \"Missing\") %>% \r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kutokana na uchunguzi mdogo wa data, tunajifunza kwamba aina tatu kuu za muziki zinatawala dataset hii. Hebu tuzingatie `afro dancehall`, `afropop`, na `nigerian pop`, na pia tuchuje dataset ili kuondoa chochote chenye thamani ya umaarufu ya 0 (ikimaanisha hakikuainishwa na umaarufu katika dataset na kinaweza kuchukuliwa kama kelele kwa madhumuni yetu):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "nigerian_songs <- df %>% \r\n",
+    "  # Concentrate on top 3 genres\r\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \r\n",
+    "  # Remove unclassified observations\r\n",
+    "  filter(popularity != 0)\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "nigerian_songs %>%\r\n",
+    "  count(artist_top_genre) %>%\r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hebu tuone kama kuna uhusiano wa moja kwa moja kati ya vigezo vya namba katika seti yetu ya data. Uhusiano huu hupimwa kihisabati kwa kutumia [takwimu ya uhusiano](https://en.wikipedia.org/wiki/Correlation).\n",
+    "\n",
+    "Takwimu ya uhusiano ni thamani kati ya -1 na 1 inayonyesha nguvu ya uhusiano. Thamani zilizo juu ya 0 zinaonyesha uhusiano *chanya* (thamani za juu za kigezo kimoja huwa sambamba na thamani za juu za kigezo kingine), wakati thamani zilizo chini ya 0 zinaonyesha uhusiano *hasi* (thamani za juu za kigezo kimoja huwa sambamba na thamani za chini za kigezo kingine).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Narrow down to numeric variables and fid correlation\r\n",
+    "corr_mat <- nigerian_songs %>% \r\n",
+    "  select(where(is.numeric)) %>% \r\n",
+    "  cor()\r\n",
+    "\r\n",
+    "# Visualize correlation matrix\r\n",
+    "corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  \r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Data haijaonyesha uhusiano mkubwa isipokuwa kati ya `energy` na `loudness`, jambo ambalo linaeleweka, kwa kuwa muziki wenye sauti kubwa mara nyingi huwa na nguvu nyingi. `Popularity` ina uhusiano na `release date`, jambo ambalo pia lina mantiki, kwa kuwa nyimbo za hivi karibuni huenda zikawa maarufu zaidi. Urefu na nguvu pia vinaonekana kuwa na uhusiano.\n",
+    "\n",
+    "Itakuwa ya kuvutia kuona kile ambacho algorithimu ya kugawanya (clustering algorithm) inaweza kufanya na data hii!\n",
+    "\n",
+    "> 🎓 Kumbuka kwamba uhusiano hauimaanishi sababu! Tuna ushahidi wa uhusiano lakini hatuna ushahidi wa sababu. [Tovuti ya kufurahisha](https://tylervigen.com/spurious-correlations) ina michoro inayoangazia hoja hii.\n",
+    "\n",
+    "### 2. Chunguza usambazaji wa data\n",
+    "\n",
+    "Hebu tujiulize maswali ya kina zaidi. Je, aina za muziki (genres) zinatofautiana sana katika mtazamo wa uwezo wa kuchezeka (danceability), kulingana na umaarufu wao? Hebu tuchunguze usambazaji wa data wa aina zetu tatu kuu za muziki kwa umaarufu na uwezo wa kuchezeka kwenye mhimili wa x na y kwa kutumia [density plots](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Perform 2D kernel density estimation\r\n",
+    "density_estimate_2d <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +\r\n",
+    "  geom_density_2d(bins = 5, size = 1) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  xlim(-20, 80) +\r\n",
+    "  ylim(0, 1.2)\r\n",
+    "\r\n",
+    "# Density plot based on the popularity\r\n",
+    "density_estimate_pop <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  theme(legend.position = \"none\")\r\n",
+    "\r\n",
+    "# Density plot based on the danceability\r\n",
+    "density_estimate_dance <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Patch everything together\r\n",
+    "library(patchwork)\r\n",
+    "density_estimate_2d / (density_estimate_pop + density_estimate_dance)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tunaona kwamba kuna miduara inayozunguka kwa mduara mmoja ndani ya mwingine ambayo inalingana, bila kujali aina ya muziki. Inawezekana kwamba ladha za Wanigeria zinakubaliana kwa kiwango fulani cha uwezo wa kuchezeka kwa aina hii ya muziki?\n",
+    "\n",
+    "Kwa ujumla, aina hizi tatu za muziki zinaendana kwa umaarufu na uwezo wa kuchezeka. Kuamua makundi katika data hii isiyo na mpangilio wa moja kwa moja itakuwa changamoto. Hebu tuone kama mchoro wa kutawanyika unaweza kusaidia katika hili.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# A scatter plot of popularity and danceability\r\n",
+    "scatter_plot <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +\r\n",
+    "  geom_point(size = 2, alpha = 0.8) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"futurevisions::mars\")\r\n",
+    "\r\n",
+    "# Add a touch of interactivity\r\n",
+    "ggplotly(scatter_plot)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Grafu ya kutawanyika ya mhimili sawa inaonyesha mtindo unaofanana wa muunganiko.\n",
+    "\n",
+    "Kwa ujumla, kwa ajili ya kugawanya data katika makundi, unaweza kutumia grafu za kutawanyika kuonyesha makundi ya data, hivyo kujifunza aina hii ya uwasilishaji ni muhimu sana. Katika somo lijalo, tutachukua data hii iliyochujwa na kutumia k-means clustering kugundua makundi katika data hii ambayo yanaonekana kuingiliana kwa njia za kuvutia.\n",
+    "\n",
+    "## **🚀 Changamoto**\n",
+    "\n",
+    "Kwa maandalizi ya somo lijalo, tengeneza chati kuhusu mbinu mbalimbali za kugawanya data katika makundi ambazo unaweza kugundua na kutumia katika mazingira ya uzalishaji. Ni aina gani za matatizo mbinu za kugawanya data zinajaribu kutatua?\n",
+    "\n",
+    "## [**Jaribio la baada ya somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)\n",
+    "\n",
+    "## **Mapitio na Kujisomea**\n",
+    "\n",
+    "Kabla ya kutumia mbinu za kugawanya data, kama tulivyojifunza, ni wazo zuri kuelewa asili ya seti yako ya data. Soma zaidi kuhusu mada hii [hapa](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)\n",
+    "\n",
+    "Kuimarisha uelewa wako wa mbinu za kugawanya data:\n",
+    "\n",
+    "-   [Fanya mafunzo na tathmini ya mifano ya kugawanya data kwa kutumia Tidymodels na marafiki](https://rpubs.com/eR_ic/clustering)\n",
+    "\n",
+    "-   Bradley Boehmke & Brandon Greenwell, [*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/)*.*\n",
+    "\n",
+    "## **Kazi ya Nyumbani**\n",
+    "\n",
+    "[Chunguza uwasilishaji mwingine wa kugawanya data katika makundi](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)\n",
+    "\n",
+    "## ASANTE KWA:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) kwa kuunda toleo la awali la moduli hii kwa Python ♥️\n",
+    "\n",
+    "[`Dasani Madipalli`](https://twitter.com/dasani_decoded) kwa kuunda michoro ya kuvutia ambayo hufanya dhana za kujifunza kwa mashine kueleweka zaidi na rahisi kufuatilia.\n",
+    "\n",
+    "Jifunze kwa furaha,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Balozi wa Dhahabu wa Wanafunzi wa Microsoft Learn.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "99c36449cad3708a435f6798cfa39972",
+   "translation_date": "2025-09-06T14:15:48+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: seaborn in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (0.11.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (3.5.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.21.4)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.3.4)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.7.2)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (4.28.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (21.2)\n",
+      "Requirement already satisfied: setuptools-scm>=4 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (6.3.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
+      "Requirement already satisfied: tomli>=1.0.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (1.2.2)\n",
+      "Requirement already satisfied: setuptools in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (59.1.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pata taarifa kuhusu dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 530 entries, 0 to 529\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   name              530 non-null    object \n",
+      " 1   album             530 non-null    object \n",
+      " 2   artist            530 non-null    object \n",
+      " 3   artist_top_genre  530 non-null    object \n",
+      " 4   release_date      530 non-null    int64  \n",
+      " 5   length            530 non-null    int64  \n",
+      " 6   popularity        530 non-null    int64  \n",
+      " 7   danceability      530 non-null    float64\n",
+      " 8   acousticness      530 non-null    float64\n",
+      " 9   energy            530 non-null    float64\n",
+      " 10  instrumentalness  530 non-null    float64\n",
+      " 11  liveness          530 non-null    float64\n",
+      " 12  loudness          530 non-null    float64\n",
+      " 13  speechiness       530 non-null    float64\n",
+      " 14  tempo             530 non-null    float64\n",
+      " 15  time_signature    530 non-null    int64  \n",
+      "dtypes: float64(8), int64(4), object(4)\n",
+      "memory usage: 66.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name                0\n",
+       "album               0\n",
+       "artist              0\n",
+       "artist_top_genre    0\n",
+       "release_date        0\n",
+       "length              0\n",
+       "popularity          0\n",
+       "danceability        0\n",
+       "acousticness        0\n",
+       "energy              0\n",
+       "instrumentalness    0\n",
+       "liveness            0\n",
+       "loudness            0\n",
+       "speechiness         0\n",
+       "tempo               0\n",
+       "time_signature      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Angalia thamani za jumla za data. Kumbuka kuwa umaarufu unaweza kuwa '0' - na kuna safu nyingi zenye thamani hiyo\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2015.390566</td>\n",
+       "      <td>222298.169811</td>\n",
+       "      <td>17.507547</td>\n",
+       "      <td>0.741619</td>\n",
+       "      <td>0.265412</td>\n",
+       "      <td>0.760623</td>\n",
+       "      <td>0.016305</td>\n",
+       "      <td>0.147308</td>\n",
+       "      <td>-4.953011</td>\n",
+       "      <td>0.130748</td>\n",
+       "      <td>116.487864</td>\n",
+       "      <td>3.986792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.131688</td>\n",
+       "      <td>39696.822259</td>\n",
+       "      <td>18.992212</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.208342</td>\n",
+       "      <td>0.148533</td>\n",
+       "      <td>0.090321</td>\n",
+       "      <td>0.123588</td>\n",
+       "      <td>2.464186</td>\n",
+       "      <td>0.092939</td>\n",
+       "      <td>23.518601</td>\n",
+       "      <td>0.333701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>89488.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>0.111000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028300</td>\n",
+       "      <td>-19.362000</td>\n",
+       "      <td>0.027800</td>\n",
+       "      <td>61.695000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>199305.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681000</td>\n",
+       "      <td>0.089525</td>\n",
+       "      <td>0.669000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.075650</td>\n",
+       "      <td>-6.298750</td>\n",
+       "      <td>0.059100</td>\n",
+       "      <td>102.961250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>218509.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.761000</td>\n",
+       "      <td>0.220500</td>\n",
+       "      <td>0.784500</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.103500</td>\n",
+       "      <td>-4.558500</td>\n",
+       "      <td>0.097950</td>\n",
+       "      <td>112.714500</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>242098.500000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.829500</td>\n",
+       "      <td>0.403000</td>\n",
+       "      <td>0.875750</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.164000</td>\n",
+       "      <td>-3.331000</td>\n",
+       "      <td>0.177000</td>\n",
+       "      <td>125.039250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>511738.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>0.966000</td>\n",
+       "      <td>0.954000</td>\n",
+       "      <td>0.995000</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.811000</td>\n",
+       "      <td>0.582000</td>\n",
+       "      <td>0.514000</td>\n",
+       "      <td>206.007000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       release_date         length  popularity  danceability  acousticness  \\\n",
+       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
+       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
+       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
+       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
+       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
+       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
+       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
+       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
+       "\n",
+       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
+       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
+       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
+       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
+       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
+       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
+       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
+       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
+       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
+       "\n",
+       "            tempo  time_signature  \n",
+       "count  530.000000      530.000000  \n",
+       "mean   116.487864        3.986792  \n",
+       "std     23.518601        0.333701  \n",
+       "min     61.695000        3.000000  \n",
+       "25%    102.961250        4.000000  \n",
+       "50%    112.714500        4.000000  \n",
+       "75%    125.039250        4.000000  \n",
+       "max    206.007000        5.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Maudhui ya faili ya Markdown hayakutolewa. Tafadhali weka maudhui ya faili ili niweze kutafsiri kwa Kiswahili kulingana na sheria ulizotoa.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[df['artist_top_genre'] != 'Missing']\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corrmat = df.corr()\n",
+    "f, ax = plt.subplots(figsize=(12, 9))\n",
+    "sns.heatmap(corrmat, vmax=.8, square=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "\n",
+    "# Show the joint distribution using kernel density estimation\n",
+    "g = sns.jointplot(\n",
+    "    data=df,\n",
+    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
+    "    kind=\"kde\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1350b1a90>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 468.975x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
+    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
+    "   .add_legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "c61deff2839902ac8cb4ed411eb10fee",
+   "translation_date": "2025-09-06T14:09:34+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sw/5-Clustering/2-K-Means/notebook.ipynb b/translations/sw/5-Clustering/2-K-Means/notebook.ipynb
new file mode 100644
index 000000000..4ca300902
--- /dev/null
+++ b/translations/sw/5-Clustering/2-K-Means/notebook.ipynb
@@ -0,0 +1,229 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "3e5c8ab363e8d88f566d4365efc7e0bd",
+   "translation_date": "2025-09-06T14:19:46+00:00",
+   "source_file": "5-Clustering/2-K-Means/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Tutazingatia tu aina 3. Labda tunaweza kupata makundi 3 yaliyoundwa!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/translations/sw/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
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@@ -0,0 +1,642 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
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+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "colab": {
+   "name": "lesson_14.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "coopTranslator": {
+   "original_hash": "ad65fb4aad0a156b42216e4929f490fc",
+   "translation_date": "2025-09-06T14:28:43+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GULATlQXLXyR"
+   },
+   "source": [
+    "## Chunguza K-Means clustering kwa kutumia R na kanuni za data safi.\n",
+    "\n",
+    "### [**Jaribio la kabla ya somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)\n",
+    "\n",
+    "Katika somo hili, utajifunza jinsi ya kuunda makundi kwa kutumia kifurushi cha Tidymodels na vifurushi vingine katika mfumo wa R (tutaviita marafiki 🧑‍🤝‍🧑), pamoja na seti ya data ya muziki wa Nigeria uliyoingiza awali. Tutashughulikia misingi ya K-Means kwa Clustering. Kumbuka kwamba, kama ulivyojifunza katika somo la awali, kuna njia nyingi za kufanya kazi na makundi, na mbinu unayotumia inategemea data yako. Tutajaribu K-Means kwa kuwa ni mbinu ya kawaida zaidi ya clustering. Twende kazi!\n",
+    "\n",
+    "Maneno utakayojifunza:\n",
+    "\n",
+    "-   Alama ya Silhouette\n",
+    "\n",
+    "-   Njia ya Elbow\n",
+    "\n",
+    "-   Inertia\n",
+    "\n",
+    "-   Variance\n",
+    "\n",
+    "### **Utangulizi**\n",
+    "\n",
+    "[K-Means Clustering](https://wikipedia.org/wiki/K-means_clustering) ni mbinu inayotokana na uwanja wa usindikaji wa ishara. Inatumika kugawanya na kupanga vikundi vya data katika `k clusters` kulingana na kufanana kwa sifa zao.\n",
+    "\n",
+    "Makundi yanaweza kuonyeshwa kama [Voronoi diagrams](https://wikipedia.org/wiki/Voronoi_diagram), ambayo yanajumuisha nukta (au 'mbegu') na eneo lake linalohusiana.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/voronoi.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Infographic na Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "Hatua za K-Means clustering ni kama ifuatavyo:\n",
+    "\n",
+    "1.  Mwanasayansi wa data huanza kwa kutaja idadi ya makundi yanayotakiwa kuundwa.\n",
+    "\n",
+    "2.  Kisha, algoriti huchagua kwa nasibu K uchunguzi kutoka seti ya data ili kutumika kama vituo vya awali vya makundi (yaani, centroids).\n",
+    "\n",
+    "3.  Kisha, kila uchunguzi uliobaki unagawiwa kwa centroid yake ya karibu zaidi.\n",
+    "\n",
+    "4.  Kisha, wastani mpya wa kila kundi unahesabiwa na centroid inahamishwa hadi wastani huo.\n",
+    "\n",
+    "5.  Sasa kwamba vituo vimehesabiwa upya, kila uchunguzi unakaguliwa tena ili kuona kama unaweza kuwa karibu na kundi tofauti. Vitu vyote vinagawiwa tena kwa kutumia wastani wa makundi uliosasishwa. Hatua za kugawa makundi na kusasisha centroid hurudiwa mara kwa mara hadi mgawanyo wa makundi usibadilike tena (yaani, wakati mchakato unafikia muafaka). Kwa kawaida, algoriti hukoma wakati kila mzunguko mpya husababisha harakati ndogo za centroids na makundi yanakuwa thabiti.\n",
+    "\n",
+    "<div>\n",
+    "\n",
+    "> Kumbuka kwamba kutokana na nasibu ya uchunguzi wa awali wa k uliotumika kama centroids za kuanzia, tunaweza kupata matokeo tofauti kidogo kila tunapotekeleza utaratibu. Kwa sababu hii, algoriti nyingi hutumia *mianzo ya nasibu* kadhaa na kuchagua mzunguko wenye WCSS ya chini zaidi. Kwa hivyo, inashauriwa sana kila mara kuendesha K-Means na thamani kadhaa za *nstart* ili kuepuka *muafaka usiofaa wa ndani.*\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Uhuishaji huu mfupi ukitumia [mchoro](https://github.com/allisonhorst/stats-illustrations) wa Allison Horst unaelezea mchakato wa clustering:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/kmeans.gif\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Mchoro na @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Swali la msingi linalojitokeza katika clustering ni hili: unajuaje ni makundi mangapi ya kugawanya data yako? Changamoto moja ya kutumia K-Means ni kwamba utahitaji kuanzisha `k`, yaani idadi ya `centroids`. Kwa bahati nzuri, `njia ya elbow` husaidia kukadiria thamani nzuri ya kuanzia kwa `k`. Utajaribu muda si mrefu.\n",
+    "\n",
+    "### \n",
+    "\n",
+    "**Mahitaji ya awali**\n",
+    "\n",
+    "Tutaanza moja kwa moja kutoka pale tulipoishia katika [somo la awali](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb), ambapo tulichambua seti ya data, tukafanya visualizations nyingi na kuchuja seti ya data kwa uchunguzi wa kuvutia. Hakikisha umeangalia!\n",
+    "\n",
+    "Tutahitaji vifurushi kadhaa ili kukamilisha moduli hii. Unaweza kuviweka kwa: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`\n",
+    "\n",
+    "Vinginevyo, script hapa chini hukagua kama una vifurushi vinavyohitajika kukamilisha moduli hii na kuvifunga kwako endapo vingine vinakosekana.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ah_tBi58LXyi"
+   },
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7e--UCUTLXym"
+   },
+   "source": [
+    "Tuanzie kazi mara moja!\n",
+    "\n",
+    "## 1. Mdundo na data: Punguza hadi aina 3 maarufu zaidi za muziki\n",
+    "\n",
+    "Hii ni muhtasari wa kile tulichofanya katika somo lililopita. Hebu tukate na kuchambua data!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Ycamx7GGLXyn"
+   },
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data into a tibble\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\", show_col_types = FALSE)\n",
+    "\n",
+    "# Narrow down to top 3 popular genres\n",
+    "nigerian_songs <- df %>% \n",
+    "  # Concentrate on top 3 genres\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \n",
+    "  # Remove unclassified observations\n",
+    "  filter(popularity != 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Visualize popular genres using bar plots\n",
+    "theme_set(theme_light())\n",
+    "nigerian_songs %>%\n",
+    "  count(artist_top_genre) %>%\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\n",
+    "                       fill = artist_top_genre)) +\n",
+    "  geom_col(alpha = 0.8) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\n",
+    "  ggtitle(\"Top genres\") +\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b5h5zmkPLXyp"
+   },
+   "source": [
+    "🤩 Hilo lilifanikiwa vizuri!\n",
+    "\n",
+    "## 2. Uchunguzi zaidi wa data.\n",
+    "\n",
+    "Je, data hii ni safi kiasi gani? Hebu tuangalie data zisizo za kawaida kwa kutumia grafu za sanduku (box plots). Tutazingatia safu za nambari zenye data chache zisizo za kawaida (ingawa unaweza kusafisha data zisizo za kawaida). Grafu za sanduku zinaweza kuonyesha wigo wa data na zitasaidia kuchagua ni safu zipi za kutumia. Kumbuka, grafu za sanduku hazionyeshi tofauti (variance), kipengele muhimu cha data nzuri inayoweza kugawanyika katika makundi. Tafadhali angalia [mjadala huu](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot) kwa maelezo zaidi.\n",
+    "\n",
+    "[Grafu za sanduku](https://en.wikipedia.org/wiki/Box_plot) hutumika kuonyesha kwa picha usambazaji wa data ya `nambari`, kwa hivyo hebu tuanze kwa *kuchagua* safu zote za nambari pamoja na aina maarufu za muziki.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HhNreJKLLXyq"
+   },
+   "source": [
+    "# Select top genre column and all other numeric columns\n",
+    "df_numeric <- nigerian_songs %>% \n",
+    "  select(artist_top_genre, where(is.numeric)) \n",
+    "\n",
+    "# Display the data\n",
+    "df_numeric %>% \n",
+    "  slice_head(n = 5)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uYXrwJRaLXyq"
+   },
+   "source": [
+    "Tazama jinsi msaidizi wa kuchagua `where` unavyorahisisha hili 💁? Chunguza kazi nyingine kama hizi [hapa](https://tidyselect.r-lib.org/).\n",
+    "\n",
+    "Kwa kuwa tutakuwa tunatengeneza boxplot kwa kila kipengele cha nambari na tunataka kuepuka kutumia loops, hebu tuweke data yetu katika muundo *mrefu zaidi* ambao utatuwezesha kutumia `facets` - chati ndogo ambazo kila moja inaonyesha sehemu moja ya data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "gd5bR3f8LXys"
+   },
+   "source": [
+    "# Pivot data from wide to long\n",
+    "df_numeric_long <- df_numeric %>% \n",
+    "  pivot_longer(!artist_top_genre, names_to = \"feature_names\", values_to = \"values\") \n",
+    "\n",
+    "# Print out data\n",
+    "df_numeric_long %>% \n",
+    "  slice_head(n = 15)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-7tE1swnLXyv"
+   },
+   "source": [
+    "Sasa ni muda wa `ggplots` zaidi! Kwa hivyo tutatumia `geom` gani?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "r88bIsyuLXyy"
+   },
+   "source": [
+    "# Make a box plot\n",
+    "df_numeric_long %>% \n",
+    "  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_wrap(~ feature_names, ncol = 4, scales = \"free\") +\n",
+    "  theme(legend.position = \"none\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EYVyKIUELXyz"
+   },
+   "source": [
+    "Rahisi-gg!\n",
+    "\n",
+    "Sasa tunaweza kuona kuwa data hii ina kelele kidogo: kwa kuchunguza kila safu kama boxplot, unaweza kuona data zilizotengwa (outliers). Unaweza kupitia seti ya data na kuondoa data hizi zilizotengwa, lakini kufanya hivyo kutafanya data kuwa ndogo sana.\n",
+    "\n",
+    "Kwa sasa, hebu tuchague safu ambazo tutatumia kwa zoezi letu la kugawanya makundi. Hebu tuchague safu za nambari zenye viwango vinavyofanana. Tunaweza kubadilisha `artist_top_genre` kuwa nambari lakini kwa sasa tutaiacha.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-wkpINyZLXy0"
+   },
+   "source": [
+    "# Select variables with similar ranges\n",
+    "df_numeric_select <- df_numeric %>% \n",
+    "  select(popularity, danceability, acousticness, loudness, energy) \n",
+    "\n",
+    "# Normalize data\n",
+    "# df_numeric_select <- scale(df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D7dLzgpqLXy1"
+   },
+   "source": [
+    "## 3. Kuhesabu k-means clustering katika R\n",
+    "\n",
+    "Tunaweza kuhesabu k-means katika R kwa kutumia kazi ya ndani `kmeans`, angalia `help(\"kmeans()\")`. Kazi ya `kmeans()` inakubali fremu ya data yenye safu zote za nambari kama hoja yake kuu.\n",
+    "\n",
+    "Hatua ya kwanza wakati wa kutumia k-means clustering ni kubainisha idadi ya makundi (k) ambayo yatatengenezwa katika suluhisho la mwisho. Tunajua kuna aina 3 za muziki ambazo tumechambua kutoka kwenye seti ya data, kwa hivyo hebu tujaribu 3:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "uC4EQ5w7LXy5"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Kmeans clustering for 3 clusters\n",
+    "kclust <- kmeans(\n",
+    "  df_numeric_select,\n",
+    "  # Specify the number of clusters\n",
+    "  centers = 3,\n",
+    "  # How many random initial configurations\n",
+    "  nstart = 25\n",
+    ")\n",
+    "\n",
+    "# Display clustering object\n",
+    "kclust\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hzfhscWrLXy-"
+   },
+   "source": [
+    "Kitu cha kmeans kina taarifa kadhaa ambazo zimeelezewa vizuri katika `help(\"kmeans()\")`. Kwa sasa, hebu tuzingatie chache. Tunaona kwamba data imegawanywa katika makundi 3 yenye ukubwa wa 65, 110, 111. Matokeo pia yanajumuisha vituo vya makundi (wastani) kwa makundi 3 katika vigezo 5.\n",
+    "\n",
+    "Vector ya clustering ni mgawanyo wa kundi kwa kila uchunguzi. Hebu tutumie kazi ya `augment` kuongeza mgawanyo wa kundi kwenye seti ya data ya awali.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "0XwwpFGQLXy_"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "augment(kclust, df_numeric_select) %>% \n",
+    "  relocate(.cluster) %>% \n",
+    "  slice_head(n = 10)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NXIVXXACLXzA"
+   },
+   "source": [
+    "Perfect, tumegawanya seti yetu ya data katika vikundi 3. Sasa, je, makundi yetu ni mazuri kiasi gani 🤷? Hebu tuangalie `Silhouette score`\n",
+    "\n",
+    "### **Silhouette score**\n",
+    "\n",
+    "[Uchambuzi wa Silhouette](https://en.wikipedia.org/wiki/Silhouette_(clustering)) unaweza kutumika kuchunguza umbali wa kutenganisha kati ya makundi yaliyopatikana. Alama hii inatofautiana kutoka -1 hadi 1, na ikiwa alama iko karibu na 1, kundi ni lenye msongamano na limetenganishwa vizuri na makundi mengine. Thamani karibu na 0 inaonyesha makundi yanayofungamana na sampuli zikiwa karibu sana na mpaka wa maamuzi wa makundi jirani. [chanzo](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).\n",
+    "\n",
+    "Njia ya wastani ya silhouette inahesabu wastani wa silhouette wa uchunguzi kwa thamani tofauti za *k*. Alama ya wastani ya silhouette ya juu inaonyesha upangaji mzuri wa makundi.\n",
+    "\n",
+    "`silhouette` ni kazi katika kifurushi cha cluster inayotumika kuhesabu upana wa wastani wa silhouette.\n",
+    "\n",
+    "> Silhouette inaweza kuhesabiwa kwa kutumia kipimo chochote cha [umbali](https://en.wikipedia.org/wiki/Distance \"Distance\"), kama vile [umbali wa Euclidean](https://en.wikipedia.org/wiki/Euclidean_distance \"Euclidean distance\") au [umbali wa Manhattan](https://en.wikipedia.org/wiki/Manhattan_distance \"Manhattan distance\") ambao tulijadili katika [somo lililopita](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Jn0McL28LXzB"
+   },
+   "source": [
+    "# Load cluster package\n",
+    "library(cluster)\n",
+    "\n",
+    "# Compute average silhouette score\n",
+    "ss <- silhouette(kclust$cluster,\n",
+    "                 # Compute euclidean distance\n",
+    "                 dist = dist(df_numeric_select))\n",
+    "mean(ss[, 3])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QyQRn97nLXzC"
+   },
+   "source": [
+    "Alama yetu ni **.549**, kwa hivyo iko katikati. Hii inaonyesha kuwa data yetu haifai sana kwa aina hii ya ugawanyaji. Hebu tuone kama tunaweza kuthibitisha hisia hii kwa njia ya kuona. [Kifurushi cha factoextra](https://rpkgs.datanovia.com/factoextra/index.html) kinatoa kazi (`fviz_cluster()`) za kuonyesha ugawanyaji.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "7a6Km1_FLXzD"
+   },
+   "source": [
+    "library(factoextra)\n",
+    "\n",
+    "# Visualize clustering results\n",
+    "fviz_cluster(kclust, df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IBwCWt-0LXzD"
+   },
+   "source": [
+    "Kuwepo kwa mwingiliano katika makundi kunaonyesha kuwa data yetu haifai sana kwa aina hii ya ugawaji, lakini tuendelee.\n",
+    "\n",
+    "## 4. Kuamua idadi bora ya makundi\n",
+    "\n",
+    "Swali la msingi ambalo mara nyingi hujitokeza katika ugawaji wa K-Means ni hili - bila lebo za darasa zinazojulikana, unajuaje ni makundi mangapi ya kugawa data yako?\n",
+    "\n",
+    "Njia moja ya kujaribu kujua ni kutumia sampuli ya data `kuunda mfululizo wa mifano ya ugawaji` na idadi inayoongezeka ya makundi (kwa mfano kutoka 1-10), na kutathmini vipimo vya ugawaji kama vile **Silhouette score.**\n",
+    "\n",
+    "Hebu tuamue idadi bora ya makundi kwa kuhesabu algoriti ya ugawaji kwa thamani tofauti za *k* na kutathmini **Within Cluster Sum of Squares** (WCSS). Jumla ya WCSS inapima ukaribu wa ugawaji, na tunataka iwe ndogo iwezekanavyo, ambapo thamani za chini zinaonyesha kuwa vidokezo vya data viko karibu zaidi.\n",
+    "\n",
+    "Hebu tuchunguze athari za chaguo tofauti za `k`, kutoka 1 hadi 10, kwenye ugawaji huu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "hSeIiylDLXzE"
+   },
+   "source": [
+    "# Create a series of clustering models\n",
+    "kclusts <- tibble(k = 1:10) %>% \n",
+    "  # Perform kmeans clustering for 1,2,3 ... ,10 clusters\n",
+    "  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),\n",
+    "  # Farm out clustering metrics eg WCSS\n",
+    "         glanced = map(model, ~ glance(.x))) %>% \n",
+    "  unnest(cols = glanced)\n",
+    "  \n",
+    "\n",
+    "# View clustering rsulsts\n",
+    "kclusts\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m7rS2U1eLXzE"
+   },
+   "source": [
+    "Sasa kwa kuwa tuna jumla ya ndani ya mraba wa makundi (tot.withinss) kwa kila algoriti ya kugawanya na kituo *k*, tunatumia [mbinu ya kiwiko](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) kutafuta idadi bora ya makundi. Mbinu hii inahusisha kuchora WCSS kama kazi ya idadi ya makundi, na kuchagua [kiwiko cha mchepuko](https://en.wikipedia.org/wiki/Elbow_of_the_curve \"Elbow of the curve\") kama idadi ya makundi ya kutumia.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "o_DjHGItLXzF"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Use elbow method to determine optimum number of clusters\n",
+    "kclusts %>% \n",
+    "  ggplot(mapping = aes(x = k, y = tot.withinss)) +\n",
+    "  geom_line(size = 1.2, alpha = 0.8, color = \"#FF7F0EFF\") +\n",
+    "  geom_point(size = 2, color = \"#FF7F0EFF\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLYyt5XSLXzG"
+   },
+   "source": [
+    "Grafu inaonyesha kupungua kubwa kwa WCSS (kwa hivyo *ukamilifu zaidi*) kadri idadi ya makundi inavyoongezeka kutoka moja hadi mawili, na kupungua zaidi kunakoonekana kutoka makundi mawili hadi matatu. Baada ya hapo, kupungua hakutambuliki sana, na kusababisha `kiwiko` 💪 kwenye grafu karibu na makundi matatu. Hii ni ishara nzuri kwamba kuna makundi mawili hadi matatu ya alama za data ambazo zimetenganishwa vyema.\n",
+    "\n",
+    "Sasa tunaweza kuendelea na kutoa modeli ya makundi ambapo `k = 3`:\n",
+    "\n",
+    "> `pull()`: hutumika kutoa safu moja\n",
+    ">\n",
+    "> `pluck()`: hutumika kuorodhesha miundo ya data kama vile orodha\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JP_JPKBILXzG"
+   },
+   "source": [
+    "# Extract k = 3 clustering\n",
+    "final_kmeans <- kclusts %>% \n",
+    "  filter(k == 3) %>% \n",
+    "  pull(model) %>% \n",
+    "  pluck(1)\n",
+    "\n",
+    "\n",
+    "final_kmeans\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l_PDTu8tLXzI"
+   },
+   "source": [
+    "Hebu tuendelee na tuone makundi tuliyopata. Unapenda kuwa na mwingiliano kwa kutumia `plotly`?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dNcleFe-LXzJ"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "results <-  augment(final_kmeans, df_numeric_select) %>% \n",
+    "  bind_cols(df_numeric %>% select(artist_top_genre)) \n",
+    "\n",
+    "# Plot cluster assignments\n",
+    "clust_plt <- results %>% \n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +\n",
+    "  geom_point(size = 2, alpha = 0.8) +\n",
+    "  paletteer::scale_color_paletteer_d(\"ggthemes::Tableau_10\")\n",
+    "\n",
+    "ggplotly(clust_plt)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6JUM_51VLXzK"
+   },
+   "source": [
+    "Labda tungetarajia kwamba kila kundi (linalowakilishwa na rangi tofauti) lingekuwa na aina tofauti za muziki (zinazowakilishwa na maumbo tofauti).\n",
+    "\n",
+    "Hebu tuangalie usahihi wa mfano.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HdIMUGq7LXzL"
+   },
+   "source": [
+    "# Assign genres to predefined integers\n",
+    "label_count <- results %>% \n",
+    "  group_by(artist_top_genre) %>% \n",
+    "  mutate(id = cur_group_id()) %>% \n",
+    "  ungroup() %>% \n",
+    "  summarise(correct_labels = sum(.cluster == id))\n",
+    "\n",
+    "\n",
+    "# Print results  \n",
+    "cat(\"Result:\", label_count$correct_labels, \"out of\", nrow(results), \"samples were correctly labeled.\")\n",
+    "\n",
+    "cat(\"\\nAccuracy score:\", label_count$correct_labels/nrow(results))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C50wvaAOLXzM"
+   },
+   "source": [
+    "Usahihi wa modeli hii si mbaya, lakini si mzuri sana. Inawezekana kuwa data haifai vizuri kwa K-Means Clustering. Data hii haina uwiano mzuri, haina uhusiano wa kutosha, na kuna tofauti kubwa sana kati ya thamani za safu ili kuunda makundi vizuri. Kwa kweli, makundi yanayoundwa huenda yameathiriwa sana au kupotoshwa na zile kategoria tatu za muziki tulizotaja hapo juu.\n",
+    "\n",
+    "Hata hivyo, hilo lilikuwa somo la kujifunza!\n",
+    "\n",
+    "Katika nyaraka za Scikit-learn, unaweza kuona kuwa modeli kama hii, yenye makundi yasiyo na mipaka dhahiri, ina tatizo la 'tofauti':\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/problems.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Picha kutoka Scikit-learn</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "## **Tofauti**\n",
+    "\n",
+    "Tofauti inafafanuliwa kama \"wastani wa tofauti za mraba kutoka kwa wastani\" [chanzo](https://www.mathsisfun.com/data/standard-deviation.html). Katika muktadha wa tatizo hili la clustering, inahusu data ambapo namba za seti yetu ya data zina mwelekeo wa kutofautiana sana kutoka kwa wastani.\n",
+    "\n",
+    "✅ Huu ni wakati mzuri wa kufikiria njia zote unazoweza kutumia kurekebisha tatizo hili. Je, urekebishe data kidogo zaidi? Utumie safu tofauti? Utumie algorithimu tofauti? Kidokezo: Jaribu [kupanua data yako](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) ili kuifanya iwe ya kawaida na ujaribu safu nyingine.\n",
+    "\n",
+    "> Jaribu '[kikokotoo cha tofauti](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)' ili kuelewa dhana hii zaidi.\n",
+    "\n",
+    "------------------------------------------------------------------------\n",
+    "\n",
+    "## **🚀Changamoto**\n",
+    "\n",
+    "Tumia muda na daftari hili, ukibadilisha vigezo. Je, unaweza kuboresha usahihi wa modeli kwa kusafisha data zaidi (kwa mfano, kuondoa data zisizo za kawaida)? Unaweza kutumia uzito ili kutoa uzito zaidi kwa sampuli fulani za data. Je, ni nini kingine unaweza kufanya ili kuunda makundi bora?\n",
+    "\n",
+    "Kidokezo: Jaribu kupanua data yako. Kuna msimbo uliotolewa maoni katika daftari unaoongeza upanuzi wa kawaida ili kufanya safu za data zifanane zaidi kwa karibu katika suala la masafa. Utagundua kuwa ingawa alama ya silhouette inashuka, 'kink' katika grafu ya kiwiko inakuwa laini. Hii ni kwa sababu kuacha data bila kupanuliwa kunaruhusu data yenye tofauti ndogo kuwa na uzito zaidi. Soma zaidi kuhusu tatizo hili [hapa](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
+    "\n",
+    "## [**Maswali ya baada ya somo**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)\n",
+    "\n",
+    "## **Mapitio na Kujisomea**\n",
+    "\n",
+    "-   Angalia Simulator ya K-Means [kama hii](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). Unaweza kutumia zana hii kuona alama za data za sampuli na kubaini centroids zake. Unaweza kuhariri nasibu ya data, idadi ya makundi na idadi ya centroids. Je, hii inakusaidia kupata wazo la jinsi data inaweza kugawanywa?\n",
+    "\n",
+    "-   Pia, angalia [karatasi hii kuhusu K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) kutoka Stanford.\n",
+    "\n",
+    "Unataka kujaribu ujuzi wako mpya wa clustering kwenye seti za data zinazofaa kwa K-Means clustering? Tafadhali angalia:\n",
+    "\n",
+    "-   [Fanya mafunzo na tathmini modeli za clustering](https://rpubs.com/eR_ic/clustering) ukitumia Tidymodels na marafiki\n",
+    "\n",
+    "-   [Uchambuzi wa K-Means Cluster](https://uc-r.github.io/kmeans_clustering), Mwongozo wa Programu ya R ya UC Business Analytics\n",
+    "\n",
+    "- [K-Means clustering kwa kanuni za data iliyopangwa](https://www.tidymodels.org/learn/statistics/k-means/)\n",
+    "\n",
+    "## **Kazi**\n",
+    "\n",
+    "[Jaribu mbinu tofauti za clustering](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)\n",
+    "\n",
+    "## ASANTE KWA:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) kwa kuunda toleo la awali la Python la moduli hii ♥️\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) kwa kuunda michoro ya kushangaza inayofanya R kuwa ya kuvutia na ya kupendeza zaidi. Pata michoro zaidi kwenye [galeria yake](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "Jifunze kwa furaha,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Balozi wa Wanafunzi wa Microsoft Learn wa Dhahabu.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Sanaa na @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/5-Clustering/2-K-Means/solution/notebook.ipynb b/translations/sw/5-Clustering/2-K-Means/solution/notebook.ipynb
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+++ b/translations/sw/5-Clustering/2-K-Means/solution/notebook.ipynb
@@ -0,0 +1,544 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "e867e87e3129c8875423a82945f4ad5e",
+   "translation_date": "2025-09-06T14:21:24+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Tutazingatia tu aina 3. Labda tunaweza kupata makundi 3 yaliyoundwa!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Je, data hii ni safi kiasi gani? Angalia data isiyo ya kawaida kwa kutumia boxplots. Tutazingatia safu zenye data isiyo ya kawaida kidogo (ingawa unaweza kuondoa data isiyo ya kawaida). Boxplots zinaweza kuonyesha wigo wa data na zitasaidia kuchagua safu za kutumia. Kumbuka, Boxplots haziwezi kuonyesha tofauti (variance), kipengele muhimu cha data nzuri inayoweza kugawanyika katika makundi (https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 4000x4000 with 12 Axes>",
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LjoZkfWCdut6vc3Br/0MoU9potJZvrHeaBcGynCci4tHAE+vmRW37lgO2qJtn9an0t8pxeWCzgWZS9xERL6RM7XU9U9Gi/fh3uXBYluNlt/qYlOkfAYiINSLikRGxRp/jLc/54aWN9WM67LccJQ3DKNpC/PzSOBqndsB+7R/SoMzFff9JYGXgC5l5xhKeS5qtod/7tY2zFUD6/cy8vT6/dEQ8NCLWi4hOA81IwzKqz/xWQPT63RJExKrAmm3pJUkLl/VtTQrr2Jok1qs1KaxLS4O1OVMBQV1/I679RVsD8m0+oFm9RmIhB1Q8tj7+vs+ov83OwI/tmkrjaqPG+kVdU913v2U9ets21n/TYb9lOcYiYqXayfctlH+Iy9RdH21L+ihg6bpuOY6JiLg/cGjd3K9OrzUd/l2Op90i4tcRcVtE3BwRv4uIoyOiV6S8ZTlenlIfL83MmyPiRRFxAXAdcDFwXUT8NiLeGhHLdzje8hxzEXE/piLtLwPO6JDMcpQ0DKNoC/HzS+NonNoB+7V/SIMy0vs+IvYAdgBuAP59tueRBmAU9/4TmJqZ94KIWDUiPkoZSfBPlBEHb4qI70fEohmeW5qNUX3mf7o+PiAiXtslzQEd0kuSFi7r25oU1rE1SaxXa1JYl5YGaza/ES8DPHI42Rm8BRlQUSMYWxFd3aYVASAzb6BEoQE8dJj50lCs21jvWdbA5Y11y3qEImIp4O2Np77aIZllOWYiYu+IyIhIyufkxcAhwINqkvcDX2o7zHIcTx8E1gbOBI6YwXGW53jaiFKJWxG4H/AIykj4P4iIEyNitQ7HWJZjov5PfEzdvDYiDgW+CGzclvRRwIco5Xr/tn2W5/j7F8qoQgDHZmZ2SGM5ShqoEbaF+PmlsTJO7YAR8QRgx7p5QWbawUNDMer7PiJWZ2pQjbdn5jWzOY+0pEZ47zd/HFwKOBd4I9Csny8HPINSb99vhueXpm3En/mfZ2qWzcMi4nMRsVNEbBYRu0bEiUzNfvyezDx1FteQJM0T1rc1Kaxja5JYr9aksC4tDcWC/414QQZUAKs01m+ZRvrWB+L9hpAXDddMyvrWxrplPVpvBrao6ydkZqdptCzL+eN8YIvM3L9DB1HLccxExDbAq4C7gNd26dTbjeU5Xm4DvgK8GtgG2BR4FvAeyswGAM8HTuowXZplOT5WY+o7+CbAvwFXAnsBawArUUZZak1/txWlAt5keY6/lzbWj+mSxnKUNGijagvx80vjZizaAevMYoczNWvjOwZ5fqnNqO/7D1EG2DgL+NwszyENwqju/TUa6/tRRlH7DqWNewXggcDrgJuAAN4fEc+b4TWk6RrZZ35m3p2ZLwN2A35BaVf+JnAO8HVK2+PpwDMz850zPb8kad6xvq1JYR1bk8R6tSaFdWlp8Bb8b8QLNaBihcb64mmkv6M+rjiEvGi4ZlLWdzTWLesRiYhtKTMZAFxN+ULciWU5fr5B6ey7CaVSsydwIvBE4MsR8dwOx1iOYyQilgM+S6mAfiQzfzXDU1ie4+UhmblnZh6emT/JzPMz8/u1wvU44Lyablvu+1lrWY6PlRvrK1ACZbbLzC9m5g2Z+ffM/BHwdEplG2CXiPintuNaLM8xExHrAovq5v9m5sVdklqOkgZtVG0hfn5p3IxLO+AngM3q+tGZ+a0Bn19qGtl9HxFPA17B7AZqkAZtVPd+e939+8BzM/OczLwjM6/JzE8DzwXuqeneFxExw+tI0zHS7zoR8VjKQBGbdEmyJfDKiHjIbM4vSZpXrG9rUljH1iSxXq1JYV1aGrwF/xvxQg2ouL2xvtw00i9fH/8+hLxouGZS1ss31i3rEYiIx1E64C9DKavdMvPqLsktyzGTmTdm5q/qck5mfiUzd6V8AdyAMgr+3m2HWY7j5T+BxwB/Ag6exfGW5xjJzBt77Psr8ALgzvrUvm1JLMvxcXvb9uGZ+dv2RJn5d+49ytLuXc5heY6fvZiqZx3dI53lKGnQRtUW4ueXxs2ctwNGxP6UUaegjDr1r4M6t9TFSO77OhJsa6CGQzPzguXcewAAIABJREFUlzM5XhqCufi+A7BfZt7dnigzfwKcUDcfS/cfzaUlMbLvOnW247OAnYA/Ay8B1q7XfSjlO85twB7A2fU3IEnSwmV9W5PCOrYmifVqTQrr0tLgLfjfiBdqQMXNjfXpTBfSioqczvQ+Gi8zKetm9KtlPWQRsT7wPWB14G5gjzridjeW5TyRmV8Avkb5H/KJiGhO1Wc5jomIeAywf93cNzNv7ZW+C8tzHsnMP1JGdgB4RESs09htWY6Pm9u2v9cj7WmUEWoANu9yDstz/LykPt4BHNcjneUoadBG1Rbi55fGzZy2A0bEa4D31s2LgB1mWf+SZmJU9/07gEcDlwMHzvBYaRjm4vvONZl5XteU8N3G+uZdU0mzN5L7vnbw+zKwGnAV8JTMPDYz/5qZd2bmFZn5SeBplB/Q16H3QBKSpPnP+rYmhXVsTRLr1ZoU1qWlwVvwvxEvM9cZGIbMvD0irgMeAKzbK21ErM5U4V0+7Lxp4K5orPcsa0rEX4tlPUS1E++plC8BCbwiM0/qc5hlOb+cBLyQ8vn5HOBL9XnLcXy8mRIN+kdgpYjYo0OajRvrT4+Itev6t2qDpOU5//wa2KGuPwT4S123LMdEZt4REdcAa9Wnur7H9TvttZTRC9Zq7LI8x1REbAZsVDdPzswbeiS3HCUN1AjbQvz80liZy3bAiNgT+GTdvAx4ZmZeu6TnlfoZ4X2/X308FdgpIjqlaZ175Ubbw9WZ+YMZXkvqa4T3fjP9FV1T3TftWl1TSbM0wvv+OZT2RICPZ+ZVXfJzYUQcSxkt/MkR8YTM/MUMryVJmgesb2tSWMfWJLFerUlhXVoaivbfiM/tkXZe/ka8IAMqql8D21BGaF4mM+/qku4xjfXfDD9bGrBfN9Yf0zXVffdb1kMSEWtSRkjfoD61b2YeM41DLcv55ZrG+sMb6xdTZiRZGstxrrWmztqAEg3dzwGN9fWBW/Hvcj7KLs9bluPlQmBRXV+6T9rW/uZ3WctzfL20sd5vZAnLUdIwjKItxM8vjaORtwNGxM7AMZTZG68Ets/Mfj8OSoM0ivu+NW33y+vSy5pMtT/8ELCzh4ZlFPf+hY316dbb4d51d2mQRnHfP7ax/vM+aX9G6QTSuqadQCRp4bK+rUlhHVuTxHq1JoV1aWmwZvMb8V3A74aTncFbaq4zMEQ/qY8rA0/ukW7bxvqZw8uOhuQSpkbf3rZXQsrUSQB/Bi4dVoYmWUSsRpmKrTUy89sz87BpHm5Zzi8Paaz/Y1qmzFwMnF03t4yI5eiuVc530DtiUXPHv8v5Z6PG+l8a65blePlRY32DbokiYlVKgymU8mixPMdQRCwLtEYLugb4dp9DLEdJwzCKthA/vzSORtoOGBHbA1+lDFZzHWWkzD/M9nzSLNn+rUk19Hs/My8D/lQ314suQ8dWGzbW/9w1lbRkRvGZ3+xY0m9AvmW7HCdJWnisb2tSWMfWJLFerUlhXVoarHOAxXW962/Etb/oU1rHZOadw87YoCzkgIpvNNY7RvZGxFJMjSJ7I3D6sDOlwcrMBE6qm4+JiKd0Slefb0U9nVSP0wBFxErAKcCT6lPvycwPTPd4y3Le2a2xfkHbvtbn76rArp0Ojoh1gWfUzdMy8+bBZk+ZuXdmRq8FOLhxyHaNfZfWc/h3OY9ExPrAM+vmHzLzH40NluXY+XpjfZce6XYBWo1LP249aXmOrX9mahraL/UY4QKwHCUNzdDbQvz80pgaWTtgRGxF+RtYHrgJeHZmXtj7KGkoRvGZ37NdobYtXFaTX9Z4ftEMX4s0E6P6zG/V3VcFtu+Rrtn++ZOuqaQlM4r7/pLG+jZ90jZ/NL+kaypJ0kJgfVuTwjq2Jon1ak0K69LSANX+nafVzWfU/p+d7Er57Ac4cegZG6AFG1CRmWcz1fHslRGxZYdk/87UtDuHzqdIGN3LR4G76/rHI2LF5s66/fG6eVdNrwGqUWUnAlvXpw7NzHfO4lSW5RyLiL0jYoU+ad4M7FA3L6HRybc6nNLIBfD+iHhA2/FLA59katq+Dy1RpjVs/l2OgYjYKSK6RrNHxIMoDRKtWWE+2SGZZTkmMvOXTM1esGcdceleImJt4N11czFwZFsSy3P8vLSxfsw0j7EcJQ3UINpCImJRRGRdjupyKT+/NFZGde9HxBMpg0msDNwK7JiZPxvEa5BmaoSf+dJYGfH3ndvr+v/UWSTvJSL2AhbVzVMy8/LpvxJp+kZ0358G3FbXXxcRm3TKS0T8M1MDhPwZOH/6r0SSNN9Y39aksI6tSWK9WpPCurQ0M7XfaOt+P6hLsg/Xx2WAw2o/0OY51gRag7DfSOlHOm/0m2ZmvnsjZRqeFYHvRcR7KVFkKwJ7APvUdBcDh8xJDidcRDwVeETjqTUb64+IiL2b6TPzqPZzZObFEfEh4O3AZsCZEfEB4A+UacH2AzatyT+Umb8b2AtQy5eBZ9X1HwBHRMTGPdIvzsyL25+0LMfCQcAhEfF1SuT3H4BbgFWATYAXMxU4sxjYJzPvbp4gM6+PiP2ATwMPB/4vIt5DmcliHeBNwHY1+Zcz84xhviAtGf8ux8bHgWXr3+ZZwKXA3yn/NxcBr2Hqf+hPgMPaT2BZjp03AVsC9wdOjoiPAv+PUq5bAPsDrWjuA5ozjoDlOW4iYnXguXXzV5n58+kcZzlKGpKht4X4+aUxNdR7PyI2BL5L+f4G8E7gpj7tH1dn5tUzvZY0A7Z/a1KN4vvOnyLiv4APUtpFz67fd37J1My8r6vJ/wa8eXYvRZq2od73mXljRLwfeBfl94CfRsTHge8DNwAPAp4HvJqpAfvenpn3zPoVSZLmC+vbmhTWsTVJrFdrUliX1kQYRD/s6cjMH0TEVyh/PzsD36/9nf5C+ax/B/Cwmny/zLxhNteZK5GZc52HoYqInYBjmZpCpN3FlOj2348uV2qpkXsvm276OsVdp/MsBXwOeEWPw4+gdP72H9KARcRMP0guy8z1upzLspxDEXEpJQiinyuAV2Tm93uc62DgAKDj3y2l4/C/ZObtXfZryGo06YF1c7tuwS3+Xc69Gfxtfh14VWbe2OU8luUYqRWa4ymV6E4SeE9mHtDleMtzTETEa4FP1c3/yMxpz75kOUoahiVpC4mIRUxNaXx0Zu7d5Rp+fmnsDPPer43N7bOG9XNwZh40w2OkGRnFZ36f619Kqa92be+ThmFU935EvI8SLNqtjfNq4PmZedb0ci7N3rDv+4gI4H8oHU663fMAdwL/mZkf7pFGkrSAWN/WpLCOrUlivVqTwrq0JsEg+mG3fS/v+n07Ilak9HXaocvp7wH+ez5+X1+qf5L5LTO/BTwe+Ajlw+82ylQi51JHTDSYYv7LzHsy85XAjsBJlIinxfXxJGCHzHyVnTjGn2U5555Nmc7sBEpU+F+Bu4CbKaPNfh14OfDoXsEUAJl5IPBU4EvA5ZRyvJoShfuizNzRYIr5wb/LsfAySvDLdyjfZ66n/G3eSJn95TPAVpn5gm7BFGBZjpvM/AnwOOBg4BeUkTduBy6hVFKe3C2Yoh5veY6Pl9THu4EvzuRAy1HSMIyiLcTPL40j2wE1ibzvNalGde9n5v6UGXu/QJkx9A7gJuAcymAyj7LTh0Zl2Pd9Fm8GNqfMQP0rym8Dd1Pu+59ROolsbAcQSZos1js0KbzXNUmsV2tSWJeWBisz/56ZOwIvpvQDvZryG/HllH6iT52PwRQwATNUSJIkSZIkSZIkSZIkSZIkSZIktVvwM1RIkiRJkiRJkiRJkiRJkiRJkiS1M6BCkiRJkiRJkiRJkiRJkiRJkiRNHAMqJEmSJEmSJEmSJEmSJEmSJEnSxDGgQpIkSZIkSZIkSZIkSZIkSZIkTRwDKiRJkiRJkiRJkiRJkiRJkiRJ0sQxoEKSJEmSJEmSJEmSJEmSJEmSJE0cAyokSZIkSZIkSZIkSZIkSZIkSdLEMaBCkiRJkiRJkiRJkiRJkiRJkiRNHAMqJEmSJEmSJEmSJEmSJEmSJEnSxDGgQpIkSZIkSZIkSZIkSZIkSZIkTRwDKiRJkiRJkiRJkiRJkiRJkiRJ0sQxoEKSJEmSJEmSJEmSJEmSJEmSJE0cAyokSZIkSZIkSZIkSZIkSZIkSdLEMaBCkiRJkiRJkiRJkiRJkiRJkiRNHAMqJEmSJEmSJEmSJEmSJEmSJEnSxDGgQpIkSZIkSZIkSZIkSZIkSZIkTRwDKiRJGjMRcVREZERcOtd5AYiIvWt+MiLWm+v8SJIkSZKkyRARixptEovmOj+SJEmSJEmSJGnhMaBCkiRJkiRJkiRJkiRJkiRJkiRNHAMqJEnSEhm3GTUkSZIkSdL4ioj1GrNO7D3X+ZEkSZIkSZIkSZNtmbnOgCRJGm+ZeRRw1BxnQ5IkSZIkTZjMPAOIuc6HJEmSJEmSJElauJyhQpIkSZIkSZIkSZIkSZIkSZIkTRwDKiRJkiRJkiRJkiRJkiRJkiRJ0sQxoEKSNKci4qCIyIjIun3/iDg4Ii6MiFsi4vqIOD0i9pzGudaLiI/UY2+OiNsi4ncR8ZmI2KTPsVmXg+r2MyLimxFxZUTcHhF/jIhPRMRDpvtaeqRb1Ljeon6vq8PxK0fE7hFxeEScHxE3RcSdEXFNRPwwIt4aEfeb4et9ekR8LSIur+e6tJF270b69dpfL/Cy+tTDG+n+sdS0Ozee22Mar/GQmvauiFhnpu+RJEmSJEnDFBEbR8Q7I+K7EXFFRNxR2zF+FxFHR8RTpnmerWv9/rcR8beIWFzPd3JE/GtE3L/HsTtFxPGN618XEWdFxNt7tQsMsv0iIp4cEUdExMURcWttQ7k8In4WEYfV9oBopE/gksYpjuzQlnDQTPLQSLtDRBxb23BaebkkIr5e2zZWakt/r/aOiFgqIvaJiJ9GxA31HL+MiHe0H9vl+ktHxMtq2f2lUSY/iYi3RMSKfY6f0XvZOG6FiPi3iDgjStvQnVHa034bEd+u116vX/4lSZIkSVqIImK72lbzxyh9SP4WERdExIe69UVobzupde+3RcTPo/RFuTkizo6IN0TEMtPIwwo17WkRcVVt/7k6Ik6NiFf2OkdEXFrzclTdfnJEHFXbPO7o1L4TEStFxAG1XePWRvvEK6Lo2N4SEY9vPP/2abyufRvpt+iXXpKkcdb3H7okSaMSEesD3wc2bDy9MrAIWBQRzwdenJl3dTj2pcBngeXbdj2iLq+MiAMy833TyMeBwEFtT68P/CuwV0TslJk/ntaLGo5TgG07PL8m8LS6vD4idsjMi/qdLCLeA/znYLN4H6cAVwIPBvYGvtIjP8sAe9XN72TmX4acN0mSJEmSpq3+0Hx6h13LMdUO8dKIeH9m7t/lHCsCRwCdBpB4SF12BNairY0iIlYAvgTs0nbcGsBT6rJvROyYmedP71XNXES8Gfgw9x24ad26PAl4PbAKcMsQ8/EA4Dhg+w6716vLrnX7qC6nWQn4XodzbFKXnSPi6Zl5a5c8PAz4JvCEtl1rAFvX5XW1TC7ucPys3suIeDBwKrBR23Gr1+VRwHOAdYC3dsq7JEmSJEkLUW0/ORLoNODjxnV5XUTsmZnf6nGeBwHfAZ7YtmvzujwrIp6fmfd0Of4JwEnAw9t2rUVph9geeE3th/LXPq/ptcDH6dHnMyLWBX4APLLx9EpMtU/sAnys07GZ+cuIOKe+rpcB7++VH+Dl9fHCzDy7T1pJksaaARWSpHFyHCVw4dPA8cBNwOOB/Sg/AL8Q+Avw5uZBEbEj5QfxoPyofAjlx+S7gK2A/SnBBu+NiBsz81M98rAjsBnwW+CDwC+B1YDdgFfX9ZMjYuPMvHyJX/HsLANcQPmh/lzKexKUCvgulPdpfeAbEfHEzLy9x7l2pXQMuAD4CPArYEXu2xjQyScp5fRu4Hk1H8/ulDAz764jJuwPPDMi1s3MK7qcd0fggXX989PIhyRJkiRJo7QMcCtl8IAfABcBf6PUZR8H/Buljv72iLg4M49sHhwRS1F+SH9mfep3lDr2ucBtlMEItqLU7zs5mqlgil9Q2kF+Q+m8vwdlIIN1gNMi4vGZ+ecle7n3FRGPZyoA4BLgE8D5wPWUTv+PBrajtBc0bVLz9t26/U7Ke9F09QzysRIluKU1M+nPKANu/Aq4A3goZeCJ3fuc6nOUQJSjga8CVwEPA/4D2BLYoub1PgEyNaDjJ/Vad9Rz/RC4FLgf8CzgjZRAm29HxJMy86bG8bN9L6F0omgFUxwLnEBpn7mbch9t1uU4SZIkSZIWrDrD4/GUvgcA36LU9/8I3EOp5/87pe5/fERsnZnndjndCZS698fqea6n1NUPAB4L7ETpS/KZDvl4BKWNYDVK29FhwNnA5cADgJ2B11ACGE6KiG0y884u+dicMjDl5ZR2hHMpbVTbNK63LKW9qhVMcQqlneIKyoAN+wDPpQRzdHN4vdZjImLLzDyrU6IaKLJp3bRfhyRp3ovMnrN6S5I0VBFxEHBg46kXZeaX29KsAvyYMsrfPcATMvNXdd+ylB+o16EEU2zTPvpiRDwcOIvyQ/JtwMMz89q2NM1/iD8Hts3MW9rSvAQ4pm5+LTNf2Lb/H68lM6PHa17E1EiW22XmGW37j6JE+1+Wmet1OP6Rmfm7Hud/BqVjwlLAqzLziA5pmq/3NGDHzLyjy/n2pozcALB+Zl46k/w20m1I6SQSwDsy871d0p1EaTi4BnhIjwYDSZIkSZJGLiLWBO7KzBu77F8OOJkSMHEZsGFm3t3Y/2/AoXXzRGDPTnXyGnjx4GZARB1U4uS6eRqwQ2Yubjvu1ZSgAoCvZububfsPYgnbLyLiXZSOA7fW19dxBMWIWA24uTlKY0SsRwkcAHh5Zh41mzzU/f/D1MAbhwH7ZocfPWqZrN7MZ1t7B8BLMvPYtuOWp3RQ2Bi4Dli7febUiPgi8CJKWW+XmZfQJiI2pbRtrQy8NzPf0dg3q/eyjrT5N2BZ4JDM7DoDRUSskZnXd9svSZIkSdJC0mgbuRPYOTO/0yHN6pS6+uOAMzPzqY19BzHVj+VO4Fkd2iTWAH4NPAj4ZWa2z1pJRJxJGTTjvHqOazukeQ4l8GEpYJ/M/Fzb/kuZmt3iAuBpPdqk3gh8tG5+NDPf3CHNx4E3NJ5qb/NZBbiS0obxuczcp8u1DqUMKnInpV/HNZ3SSZI0X7RPHy1J0lw6uT2YAiAzb6ZEykP53/Xaxu5dKMEUAO9uD6aox18GvK1ursTUtIPd7NMeTFHP8wXg263rRsTafc4zFL2CKer+UymzVwA8v8/p7qEEXXQMphikzPwDcEbd3LtTmjpd5g5181iDKSRJkiRJ4yYzr+32w3Xdv5ipdoiH05gFsgZJtPZdAby0W508M+/pMLvEv9bHOynBCIvb9lN/eD+1bu4aEQ/u85Jmo9UmcnG3AICal5uawRSDFBH3p4ziCGVmijd2Cqao+VjcK5/ACe3BFPW4OygzRkAZOXKj5v4aHNIKWHlDp2CKep7zKAEfcN82kdm+l2tQgikAftTtuHqswRSSJEmSpIlQZ6fYr25+rFMwBUBm3sBUG83WEfHITumAj7cHU9Tjr2dqoIZN6kAIzXxsQwmmAHhZp2CKep7vUGbTgC79KBr+tVebFFN9aa4A3t4lzX9QZrfsqPbP+Wrd3D0iVmxPUweueHHdPNlgCknSQmBAhSRpnBzZbUdmng1cWDef0djVWk96TyP4NeCmDse3uyAzf9Zjf+saywCLeqQbmYhYKyIeGREbtxbK7A5QZvXo5cz2GSeG7PD6+MiIeGqH/XtR3ltwWkhJkiRJ0jwQEctHxMMiYqNGvbw580Ozbv5EYN26/rlOAzr0uM4ywLZ183uZeXmP5K3RDIfVfnFlfdwoIrYYwvmn4+mUgTOgdJC4u1fiPr7YY1+znWiDtn07AktTZkT9Nr2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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "# scaler = StandardScaler()\n",
+    "\n",
+    "X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
+    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "y = le.transform(y)\n",
+    "\n"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 0, 2, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0,\n",
+       "       2, 0, 0, 1, 1, 2, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2,\n",
+       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 0,\n",
+       "       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 2,\n",
+       "       2, 1, 1, 0, 1, 2, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0,\n",
+       "       1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       1, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 1, 1, 2, 1],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "source": [
+    "Nambari hizo hazimaanishi mengi kwetu, kwa hivyo wacha tupate 'alama ya silhouette' ili kuona usahihi. Alama yetu iko katikati.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "source": [
+    "Tumia mfano huo kuamua, kwa kutumia Mbinu ya Elbow, idadi bora ya vikundi vya kujenga\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Looks like 3 is a good number after all. Fit the model again and create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Usahihi wa mfano huu si mbaya, lakini si mzuri. Inawezekana kuwa data haiendani vizuri na K-Means Clustering. Unaweza kujaribu mbinu tofauti.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 811,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/5-Clustering/2-K-Means/solution/tester.ipynb b/translations/sw/5-Clustering/2-K-Means/solution/tester.ipynb
new file mode 100644
index 000000000..3c5dc9d72
--- /dev/null
+++ b/translations/sw/5-Clustering/2-K-Means/solution/tester.ipynb
@@ -0,0 +1,341 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
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+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
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+  "kernelspec": {
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+   "display_name": "Python 3.7.0 64-bit ('3.7')"
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+  "metadata": {
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+   "original_hash": "6f92868513e59d321245137c1c4c5311",
+   "translation_date": "2025-09-06T14:22:38+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/tester.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 105
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Tutazingatia tu aina 3. Labda tunaweza kupata makundi 3 yaliyoundwa!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 106
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 107
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# X = df.loc[:, ('danceability','energy')]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Unknown label type: 'continuous'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-110-3756ba7b0bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# we create an instance of SVM and fit out data. We do not scale our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# data since we want to plot the support vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mls30\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 30% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mls50\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 50% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mls100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 100% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m# actual graph construction (implementations should override this)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    181\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    182\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.semi_supervised import LabelSpreading\n",
+    "from sklearn.semi_supervised import SelfTrainingClassifier\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "X = df[['danceability','acousticness']].values\n",
+    "y = df['energy'].values\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "# step size in the mesh\n",
+    "h = .02\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "y_rand = rng.rand(y.shape[0])\n",
+    "y_30 = np.copy(y)\n",
+    "y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled\n",
+    "y_50 = np.copy(y)\n",
+    "y_50[y_rand < 0.5] = -1\n",
+    "# we create an instance of SVM and fit out data. We do not scale our\n",
+    "# data since we want to plot the support vectors\n",
+    "ls30 = (LabelSpreading().fit(X, y_30), y_30, 'Label Spreading 30% data')\n",
+    "ls50 = (LabelSpreading().fit(X, y_50), y_50, 'Label Spreading 50% data')\n",
+    "ls100 = (LabelSpreading().fit(X, y), y, 'Label Spreading 100% data')\n",
+    "\n",
+    "# the base classifier for self-training is identical to the SVC\n",
+    "base_classifier = SVC(kernel='rbf', gamma=.5, probability=True)\n",
+    "st30 = (SelfTrainingClassifier(base_classifier).fit(X, y_30),\n",
+    "        y_30, 'Self-training 30% data')\n",
+    "st50 = (SelfTrainingClassifier(base_classifier).fit(X, y_50),\n",
+    "        y_50, 'Self-training 50% data')\n",
+    "\n",
+    "rbf_svc = (SVC(kernel='rbf', gamma=.5).fit(X, y), y, 'SVC with rbf kernel')\n",
+    "\n",
+    "# create a mesh to plot in\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                     np.arange(y_min, y_max, h))\n",
+    "\n",
+    "color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}\n",
+    "\n",
+    "classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)\n",
+    "for i, (clf, y_train, title) in enumerate(classifiers):\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    colors = [color_map[y] for y in y_train]\n",
+    "    plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')\n",
+    "\n",
+    "    plt.title(title)\n",
+    "\n",
+    "plt.suptitle(\"Unlabeled points are colored white\", y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb b/translations/sw/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
new file mode 100644
index 000000000..2514041e3
--- /dev/null
+++ b/translations/sw/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
@@ -0,0 +1,100 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "27de2abc0235ebd22080fc8f1107454d",
+   "translation_date": "2025-09-06T15:22:16+00:00",
+   "source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You should download the book text, clean it, and import it here\n",
+    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
+    "    file_contents = f.read()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "book_pride = TextBlob(file_contents)\n",
+    "positive_sentiment_sentences = []\n",
+    "negative_sentiment_sentences = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sentence in book_pride.sentences:\n",
+    "    if sentence.sentiment.polarity == 1:\n",
+    "        positive_sentiment_sentences.append(sentence)\n",
+    "    if sentence.sentiment.polarity == -1:\n",
+    "        negative_sentiment_sentences.append(sentence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
+    "for sentence in positive_sentiment_sentences:\n",
+    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
+    "for sentence in negative_sentiment_sentences:\n",
+    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/6-NLP/4-Hotel-Reviews-1/notebook.ipynb b/translations/sw/6-NLP/4-Hotel-Reviews-1/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sw/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb b/translations/sw/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
new file mode 100644
index 000000000..543ae01a3
--- /dev/null
+++ b/translations/sw/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
@@ -0,0 +1,174 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2d05e7db439376aa824f4b387f8324ca",
+   "translation_date": "2025-09-06T15:21:54+00:00",
+   "source_file": "6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EDA\n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_difference_review_avg(row):\n",
+    "    return row[\"Average_Score\"] - row[\"Calc_Average_Score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "print(\"Loading data file now, this could take a while depending on file size\")\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n",
+    "end = time.time()\n",
+    "print(\"Loading took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What shape is the data (rows, columns)?\n",
+    "print(\"The shape of the data (rows, cols) is \" + str(df.shape))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# value_counts() creates a Series object that has index and values\n",
+    "#                in this case, the country and the frequency they occur in reviewer nationality\n",
+    "nationality_freq = df[\"Reviewer_Nationality\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What reviewer nationality is the most common in the dataset?\n",
+    "print(\"The highest frequency reviewer nationality is \" + str(nationality_freq.index[0]).strip() + \" with \" + str(nationality_freq[0]) + \" reviews.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the top 10 most common nationalities and their frequencies?\n",
+    "print(\"The top 10 highest frequency reviewer nationalities are:\")\n",
+    "print(nationality_freq[0:10].to_string())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many unique nationalities are there?\n",
+    "print(\"There are \" + str(nationality_freq.index.size) + \" unique nationalities in the dataset\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What was the most frequently reviewed hotel for the top 10 nationalities - print the hotel and number of reviews\n",
+    "for nat in nationality_freq[:10].index:\n",
+    "   # First, extract all the rows that match the criteria into a new dataframe\n",
+    "   nat_df = df[df[\"Reviewer_Nationality\"] == nat]   \n",
+    "   # Now get the hotel freq\n",
+    "   freq = nat_df[\"Hotel_Name\"].value_counts()\n",
+    "   print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many reviews are there per hotel (frequency count of hotel) and do the results match the value in `Total_Number_of_Reviews`?\n",
+    "# First create a new dataframe based on the old one, removing the uneeded columns\n",
+    "hotel_freq_df = df.drop([\"Hotel_Address\", \"Additional_Number_of_Scoring\", \"Review_Date\", \"Average_Score\", \"Reviewer_Nationality\", \"Negative_Review\", \"Review_Total_Negative_Word_Counts\", \"Positive_Review\", \"Review_Total_Positive_Word_Counts\", \"Total_Number_of_Reviews_Reviewer_Has_Given\", \"Reviewer_Score\", \"Tags\", \"days_since_review\", \"lat\", \"lng\"], axis = 1)\n",
+    "# Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found\n",
+    "hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')\n",
+    "# Get rid of all the duplicated rows\n",
+    "hotel_freq_df = hotel_freq_df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "print()\n",
+    "print(hotel_freq_df.to_string())\n",
+    "print(str(hotel_freq_df.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# While there is an `Average_Score` for each hotel according to the dataset, \n",
+    "# you can also calculate an average score (getting the average of all reviewer scores in the dataset for each hotel)\n",
+    "# Add a new column to your dataframe with the column header `Calc_Average_Score` that contains that calculated average. \n",
+    "df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n",
+    "# Add a new column with the difference between the two average scores\n",
+    "df[\"Average_Score_Difference\"] = df.apply(get_difference_review_avg, axis = 1)\n",
+    "# Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)\n",
+    "review_scores_df = df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "# Sort the dataframe to find the lowest and highest average score difference\n",
+    "review_scores_df = review_scores_df.sort_values(by=[\"Average_Score_Difference\"])\n",
+    "print(review_scores_df[[\"Average_Score_Difference\", \"Average_Score\", \"Calc_Average_Score\", \"Hotel_Name\"]])\n",
+    "# Do any hotels have the same (rounded to 1 decimal place) `Average_Score` and `Calc_Average_Score`?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/6-NLP/5-Hotel-Reviews-2/notebook.ipynb b/translations/sw/6-NLP/5-Hotel-Reviews-2/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/sw/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb b/translations/sw/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
new file mode 100644
index 000000000..e17c2e6de
--- /dev/null
+++ b/translations/sw/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
@@ -0,0 +1,172 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "033cb89c85500224b3c63fd04f49b4aa",
+   "translation_date": "2025-09-06T15:22:37+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import time\n",
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replace_address(row):\n",
+    "    if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Amsterdam, Netherlands\"\n",
+    "    elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Barcelona, Spain\"\n",
+    "    elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
+    "        return \"London, United Kingdom\"\n",
+    "    elif \"Milan\" in row[\"Hotel_Address\"]:        \n",
+    "        return \"Milan, Italy\"\n",
+    "    elif \"France\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Paris, France\"\n",
+    "    elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Vienna, Austria\" \n",
+    "    else:\n",
+    "        return row.Hotel_Address\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dropping columns we will not use:\n",
+    "df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace all the addresses with a shortened, more useful form\n",
+    "df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop `Additional_Number_of_Scoring`\n",
+    "df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
+    "# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
+    "df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
+    "df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process the Tags into new columns\n",
+    "# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
+    "# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
+    "# Family with young children, Family with older children, With a pet\n",
+    "df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
+    "df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
+    "df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
+    "df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
+    "df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
+    "df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
+    "df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
+    "df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No longer need any of these columns\n",
+    "df.drop([\"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_Filtered.csv\n",
+      "Filtering took 23.74 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Saving new data file with calculated columns\n",
+    "print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
+    "df.to_csv(r'../../data/Hotel_Reviews_Filtered.csv', index = False)\n",
+    "end = time.time()\n",
+    "print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb b/translations/sw/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
new file mode 100644
index 000000000..5146db551
--- /dev/null
+++ b/translations/sw/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
@@ -0,0 +1,137 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "341efc86325ec2a214f682f57a189dfd",
+   "translation_date": "2025-09-06T15:22:57+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV (you can )\n",
+    "import pandas as pd \n",
+    "\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We want to find the most useful tags to keep\n",
+    "# Remove opening and closing brackets\n",
+    "df.Tags = df.Tags.str.strip(\"[']\")\n",
+    "# remove all quotes too\n",
+    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
+    "# Now split the strings into a list\n",
+    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove leading and trailing spaces\n",
+    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
+    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
+    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
+    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
+    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
+    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the 6 columns into one with melt\n",
+    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "The shape of the tags with no filtering: (2514684, 2)\n",
+      "                        index   count\n",
+      "0                Leisure trip  338423\n",
+      "1                      Couple  205305\n",
+      "2               Solo traveler   89779\n",
+      "3               Business trip   68176\n",
+      "4                       Group   51593\n",
+      "5  Family with young children   49318\n",
+      "6  Family with older children   21509\n",
+      "7      Travelers with friends    1610\n",
+      "8                  With a pet    1078\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the value counts\n",
+    "tag_vc = df_tags.value.value_counts()\n",
+    "# print(tag_vc)\n",
+    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
+    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
+    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
+    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
+    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
+    "print(tag_vc[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb b/translations/sw/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
new file mode 100644
index 000000000..737f1c0e4
--- /dev/null
+++ b/translations/sw/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
@@ -0,0 +1,260 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "705bf02633759f689abc37b19749a16d",
+   "translation_date": "2025-09-06T15:23:17+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[nltk_data] Downloading package vader_lexicon to\n[nltk_data]     /Users/jenlooper/nltk_data...\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import pandas as pd\n",
+    "import nltk as nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    "nltk.download('vader_lexicon')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vader_sentiment = SentimentIntensityAnalyzer()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are 3 possibilities of input for a review:\n",
+    "# It could be \"No Negative\", in which case, return 0\n",
+    "# It could be \"No Positive\", in which case, return 0\n",
+    "# It could be a review, in which case calculate the sentiment\n",
+    "def calc_sentiment(review):    \n",
+    "    if review == \"No Negative\" or review == \"No Positive\":\n",
+    "        return 0\n",
+    "    return vader_sentiment.polarity_scores(review)[\"compound\"]    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "df = pd.read_csv(\"../../data/Hotel_Reviews_Filtered.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove stop words - can be slow for a lot of text!\n",
+    "# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches\n",
+    "# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends\n",
+    "start = time.time()\n",
+    "cache = set(stopwords.words(\"english\"))\n",
+    "def remove_stopwords(review):\n",
+    "    text = \" \".join([word for word in review.split() if word not in cache])\n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the stop words from both columns\n",
+    "df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   \n",
+    "df.Positive_Review = df.Positive_Review.apply(remove_stopwords)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Removing stop words took 5.77 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "end = time.time()\n",
+    "print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Calculating sentiment columns for both positive and negative reviews\n",
+      "Calculating sentiment took 201.07 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a negative sentiment and positive sentiment column\n",
+    "print(\"Calculating sentiment columns for both positive and negative reviews\")\n",
+    "start = time.time()\n",
+    "df[\"Negative_Sentiment\"] = df.Negative_Review.apply(calc_sentiment)\n",
+    "df[\"Positive_Sentiment\"] = df.Positive_Review.apply(calc_sentiment)\n",
+    "end = time.time()\n",
+    "print(\"Calculating sentiment took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                          Negative_Review  Negative_Sentiment\n",
+      "186584  So bad experience memories I hotel The first n...             -0.9920\n",
+      "129503  First charged twice room booked booking second...             -0.9896\n",
+      "307286  The staff Had bad experience even booking Janu...             -0.9889\n",
+      "452092  No WLAN room Incredibly rude restaurant staff ...             -0.9884\n",
+      "201293  We usually traveling Paris 2 3 times year busi...             -0.9873\n",
+      "...                                                   ...                 ...\n",
+      "26899   I would say however one night expensive even d...              0.9933\n",
+      "138365  Wifi terribly slow I speed test network upload...              0.9938\n",
+      "79215   I find anything hotel first I walked past hote...              0.9938\n",
+      "278506  The property great location There bakery next ...              0.9945\n",
+      "339189  Guys I like hotel I wish return next year Howe...              0.9948\n",
+      "\n",
+      "[515738 rows x 2 columns]\n",
+      "                                          Positive_Review  Positive_Sentiment\n",
+      "137893  Bathroom Shower We going stay twice hotel 2 ni...             -0.9820\n",
+      "5839    I completely disappointed mad since reception ...             -0.9780\n",
+      "64158   get everything extra internet parking breakfas...             -0.9751\n",
+      "124178  I didnt like anythig Room small Asked upgrade ...             -0.9721\n",
+      "489137  Very rude manager abusive staff reception Dirt...             -0.9703\n",
+      "...                                                   ...                 ...\n",
+      "331570  Everything This recently renovated hotel class...              0.9984\n",
+      "322920  From moment stepped doors Guesthouse Hotel sta...              0.9985\n",
+      "293710  This place surprise expected good actually gre...              0.9985\n",
+      "417442  We celebrated wedding night Langham I commend ...              0.9985\n",
+      "132492  We arrived super cute boutique hotel area expl...              0.9987\n",
+      "\n",
+      "[515738 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=[\"Negative_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Negative_Review\", \"Negative_Sentiment\"]])\n",
+    "df = df.sort_values(by=[\"Positive_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Positive_Review\", \"Positive_Sentiment\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)\n",
+    "df = df.reindex([\"Hotel_Name\", \"Hotel_Address\", \"Total_Number_of_Reviews\", \"Average_Score\", \"Reviewer_Score\", \"Negative_Sentiment\", \"Positive_Sentiment\", \"Reviewer_Nationality\", \"Leisure_trip\", \"Couple\", \"Solo_traveler\", \"Business_trip\", \"Group\", \"Family_with_young_children\", \"Family_with_older_children\", \"With_a_pet\", \"Negative_Review\", \"Positive_Review\"], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_NLP.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
+    "df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati asilia katika lugha yake ya awali inapaswa kuchukuliwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/7-TimeSeries/1-Introduction/solution/notebook.ipynb b/translations/sw/7-TimeSeries/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..51b16c9e0
--- /dev/null
+++ b/translations/sw/7-TimeSeries/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli na Rob J. Hyndman, \"Utabiri wa nishati wa kiuhalisia: Mashindano ya Utabiri wa Nishati ya Kimataifa 2014 na zaidi\", Jarida la Kimataifa la Utabiri, vol.32, no.3, uk. 896-913, Julai-Septemba, 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "from common.utils import load_data\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pakia data kutoka csv kwenye dataframe ya Pandas\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2698.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2558.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2444.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2402.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2403.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "data_dir = './data'\n",
+    "energy = load_data(data_dir)[['load']]\n",
+    "energy.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Panga data zote za mzigo zinazopatikana (Januari 2012 hadi Desemba 2014)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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231.185656 \nL 91.475143 185.318661 \nL 91.506964 197.827841 \nL 91.761536 330.381263 \nL 91.825179 313.153708 \nL 91.984286 214.726208 \nL 92.047929 223.065662 \nL 92.143394 231.953764 \nL 92.238858 182.026771 \nL 92.302501 208.252159 \nL 92.525251 302.290472 \nL 92.557073 297.901286 \nL 92.652537 247.645104 \nL 92.779823 197.169464 \nL 92.811645 201.229461 \nL 92.875287 210.446752 \nL 92.907109 207.81324 \nL 93.002573 138.13491 \nL 93.066216 169.84678 \nL 93.257145 279.905623 \nL 93.288967 277.820759 \nL 93.352609 237.440247 \nL 93.416252 165.896512 \nL 93.479895 170.175969 \nL 93.670824 198.925138 \nL 93.702646 189.488388 \nL 93.766288 144.3895 \nL 93.79811 156.679221 \nL 94.052682 299.65696 \nL 94.084503 288.245076 \nL 94.179968 188.500821 \nL 94.275432 193.109466 \nL 94.434539 214.506749 \nL 94.466361 204.850539 \nL 94.530004 162.714352 \nL 94.561825 175.881911 \nL 94.816397 318.969379 \nL 94.848218 308.545062 \nL 95.039147 197.169464 \nL 95.134611 205.947836 \nL 95.198254 212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 197.217872 308.106144 \nL 197.440622 167.981376 \nL 197.59973 160.410029 \nL 197.727015 185.318661 \nL 197.917944 322.480728 \nL 197.981587 297.352638 \nL 198.236159 139.122477 \nL 198.331623 119.919787 \nL 198.363445 120.578165 \nL 198.490731 168.420294 \nL 198.713481 322.261269 \nL 198.745302 321.383432 \nL 199.063517 158.325166 \nL 199.12716 147.791119 \nL 199.158981 152.290035 \nL 199.286267 213.519182 \nL 199.477196 317.542894 \nL 199.509017 319.847217 \nL 199.604482 229.539711 \nL 199.763589 153.82625 \nL 199.827232 152.180305 \nL 199.890875 142.194907 \nL 199.922697 149.437064 \nL 200.049982 220.212691 \nL 200.20909 311.398033 \nL 200.272733 292.305074 \nL 200.495483 131.441401 \nL 200.527304 126.064648 \nL 200.590947 133.965183 \nL 200.622769 133.087346 \nL 200.65459 133.306805 \nL 200.781876 176.2111 \nL 200.972805 319.627757 \nL 201.036448 302.94885 \nL 201.227377 151.851116 \nL 201.259198 152.399765 \nL 201.29102 152.180305 \nL 201.322841 152.619224 \nL 201.354662 152.948413 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 218.983754 162.055974 \nL 219.015576 162.385163 \nL 219.11104 187.622984 \nL 219.301969 310.300737 \nL 219.365612 281.880756 \nL 219.556541 157.995977 \nL 219.683826 161.726785 \nL 219.620183 157.666788 \nL 219.715648 160.410029 \nL 219.747469 161.178137 \nL 219.874755 183.562986 \nL 220.065684 319.737487 \nL 220.097505 319.079109 \nL 220.161148 297.462367 \nL 220.352077 172.80948 \nL 220.383899 175.442992 \nL 220.63847 211.763507 \nL 220.829399 325.22397 \nL 220.861221 324.016944 \nL 220.924864 310.191007 \nL 221.115792 208.471618 \nL 221.147614 210.117563 \nL 221.179435 211.214859 \nL 221.211257 210.88567 \nL 221.2749 200.461353 \nL 221.306721 201.44892 \nL 221.434007 256.533206 \nL 221.593114 330.271534 \nL 221.656757 303.168309 \nL 221.911329 131.55113 \nL 222.006793 125.40627 \nL 222.038615 127.052215 \nL 222.165901 168.420294 \nL 222.356829 309.861818 \nL 222.420472 285.392105 \nL 222.675044 125.845188 \nL 222.738687 129.246808 \nL 222.865973 155.252736 \nL 222.897794 153.058143 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227.702836 315.238571 \nL 227.766479 291.427236 \nL 228.02105 135.391668 \nL 228.116515 126.503566 \nL 228.148336 128.039781 \nL 228.275622 168.200835 \nL 228.466551 311.727222 \nL 228.530194 285.831024 \nL 228.784765 105.764662 \nL 228.848408 100.278179 \nL 228.912051 102.143583 \nL 229.039337 142.085177 \nL 229.230266 295.377504 \nL 229.262087 291.536966 \nL 229.548481 84.696568 \nL 229.643945 97.864127 \nL 229.803052 143.182474 \nL 229.993981 278.369407 \nL 230.057624 251.156453 \nL 230.280374 103.789528 \nL 230.344017 117.396005 \nL 230.598589 209.239725 \nL 230.757696 299.108312 \nL 230.789518 298.559664 \nL 230.853161 280.564001 \nL 231.075911 157.666788 \nL 231.26684 168.310565 \nL 231.298661 167.761916 \nL 231.553233 301.632094 \nL 231.585054 296.4748 \nL 231.648697 262.239148 \nL 231.839626 164.908945 \nL 231.935091 146.254904 \nL 231.966912 138.573828 \nL 232.030555 148.120308 \nL 232.062376 146.035445 \nL 232.285127 300.863987 \nL 232.316948 295.048315 \nL 232.635163 98.961423 \nL 232.698806 100.168449 \nL 232.730627 101.704665 \nL 232.857913 151.302468 \nL 233.048842 295.487234 \nL 233.080663 291.207777 \nL 233.367056 101.046287 \nL 233.430699 107.191147 \nL 233.557985 130.014915 \nL 233.589807 127.710593 \nL 233.812557 294.280207 \nL 233.844378 289.561832 \nL 234.194415 111.690063 \nL 234.258057 119.480868 \nL 234.385343 155.801384 \nL 234.576272 292.414803 \nL 234.608094 288.245076 \nL 234.894487 125.625729 \nL 234.926308 127.491133 \nL 235.085416 148.559227 \nL 235.117237 147.68139 \nL 235.339987 312.166141 \nL 235.371809 306.569929 \nL 235.721845 120.687895 \nL 235.753666 121.346273 \nL 235.880952 144.608959 \nL 236.135524 304.046147 \nL 236.167345 296.4748 \nL 236.549203 193.877574 \nL 236.676489 220.322421 \nL 236.899239 337.513691 \nL 236.962882 327.528292 \nL 237.185632 225.589444 \nL 237.217454 226.79647 \nL 237.249275 224.272688 \nL 237.408383 203.314324 \nL 237.631133 332.466127 \nL 237.662954 326.211537 \nL 237.949347 160.958678 \nL 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345.414226 \nL 247.654894 330.600723 \nL 247.845823 238.208354 \nL 247.877645 242.048892 \nL 247.941288 246.438078 \nL 247.973109 242.59754 \nL 248.068573 234.916464 \nL 248.100395 239.41538 \nL 248.323145 354.192598 \nL 248.354967 351.120168 \nL 248.482252 275.296977 \nL 248.577717 229.539711 \nL 248.64136 231.075927 \nL 248.673181 230.966197 \nL 248.736824 220.651609 \nL 248.832289 201.010001 \nL 248.86411 206.496484 \nL 249.08686 340.586121 \nL 249.150503 302.94885 \nL 249.309611 191.573251 \nL 249.405075 187.513254 \nL 249.468718 192.01217 \nL 249.500539 189.378658 \nL 249.596004 178.844611 \nL 249.691468 261.6905 \nL 249.850575 324.785051 \nL 249.914218 283.526701 \nL 250.073326 161.507326 \nL 250.16879 158.764085 \nL 250.264255 163.043541 \nL 250.296076 161.946245 \nL 250.327897 161.507326 \nL 250.359719 153.387332 \nL 250.39154 165.567323 \nL 250.614291 321.602891 \nL 250.677934 284.953187 \nL 250.868862 168.420294 \nL 250.996148 160.958678 \nL 251.02797 161.946245 \nL 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216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 315.75283 171.382995 \nL 315.880116 189.488388 \nL 315.911937 182.026771 \nL 315.97558 135.281939 \nL 316.039223 155.252736 \nL 316.134688 222.955932 \nL 316.293795 295.487234 \nL 316.357438 280.125082 \nL 316.516545 184.221364 \nL 316.548367 185.977039 \nL 316.643831 197.279193 \nL 316.675652 191.682981 \nL 316.739295 154.704087 \nL 316.802938 177.198666 \nL 317.05751 323.578025 \nL 317.152974 296.4748 \nL 317.312082 212.531615 \nL 317.343903 215.165127 \nL 317.407546 223.065662 \nL 317.439368 216.262423 \nL 317.503011 165.238134 \nL 317.566653 190.256495 \nL 317.789404 338.610988 \nL 317.853047 324.785051 \nL 318.043975 214.28729 \nL 318.171261 228.332685 \nL 318.203083 224.272688 \nL 318.266726 161.397596 \nL 318.330369 174.674884 \nL 318.553119 330.381263 \nL 318.616762 319.518028 \nL 318.807691 200.790542 \nL 318.839512 203.424054 \nL 318.934976 209.349455 \nL 318.966798 204.411621 \nL 319.030441 154.923547 \nL 319.094084 172.480291 \nL 319.348656 336.745583 \nL 319.380477 327.089374 \nL 319.475941 219.993232 \nL 319.603227 220.212691 \nL 319.635049 220.871069 \nL 319.66687 219.225124 \nL 319.698692 219.334854 \nL 319.825978 144.27977 \nL 319.88962 169.956509 \nL 320.080549 296.145611 \nL 320.112371 295.158045 \nL 320.176014 248.852131 \nL 320.239657 173.906777 \nL 320.303299 176.979207 \nL 320.462407 199.583516 \nL 320.494228 195.413789 \nL 320.557871 129.246808 \nL 320.621514 138.13491 \nL 320.844264 294.719126 \nL 320.907907 283.087783 \nL 321.003372 177.308396 \nL 321.098836 183.233797 \nL 321.226122 204.74081 \nL 321.257943 199.144597 \nL 321.321586 157.227869 \nL 321.385229 179.722449 \nL 321.639801 331.039641 \nL 321.703444 314.360734 \nL 321.862551 207.264592 \nL 321.926194 212.641345 \nL 321.989837 222.297554 \nL 322.021659 217.46945 \nL 322.085302 175.881911 \nL 322.148944 200.900272 \nL 322.403516 336.196935 \nL 322.467159 323.248836 \nL 322.658088 232.392682 \nL 322.689909 235.245653 \nL 322.753552 243.475378 \nL 322.785374 235.026194 \nL 322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 340.923609 150.973279 \nL 340.95543 151.083009 \nL 341.082716 162.933811 \nL 341.114538 159.312733 \nL 341.178181 98.741964 \nL 341.241824 109.38574 \nL 341.464574 253.460776 \nL 341.528217 240.293218 \nL 341.623681 133.087346 \nL 341.719146 138.464099 \nL 341.846431 158.434896 \nL 341.878253 156.350032 \nL 341.941896 100.05872 \nL 342.005539 111.141415 \nL 342.26011 265.201849 \nL 342.291932 254.448343 \nL 342.451039 145.377067 \nL 342.482861 145.596526 \nL 342.641968 174.674884 \nL 342.705611 132.209508 \nL 342.769254 155.362465 \nL 343.023826 302.619661 \nL 343.087469 286.489402 \nL 343.246576 199.802975 \nL 343.310219 208.910536 \nL 343.373862 219.225124 \nL 343.405683 214.616478 \nL 343.469326 153.716521 \nL 343.532969 171.053806 \nL 343.787541 298.011016 \nL 343.851184 287.476969 \nL 344.010291 198.705679 \nL 344.073934 203.862972 \nL 344.137577 216.372153 \nL 344.169398 214.17756 \nL 344.233041 148.668956 \nL 344.296684 173.138669 \nL 344.519435 307.228306 \nL 344.551256 304.375335 \nL 344.614899 260.593204 \nL 344.774006 175.662451 \nL 344.901292 193.658115 \nL 344.933114 188.83001 \nL 345.028578 123.650595 \nL 345.060399 137.695991 \nL 345.28315 282.868323 \nL 345.346793 271.456439 \nL 345.474078 169.298131 \nL 345.537721 177.418126 \nL 345.665007 199.473786 \nL 345.696829 196.291626 \nL 345.760472 135.940317 \nL 345.824115 150.973279 \nL 346.046865 299.87642 \nL 346.110508 287.03805 \nL 346.205972 176.10137 \nL 346.301437 181.148934 \nL 346.428722 187.952173 \nL 346.460544 184.550553 \nL 346.524187 138.354369 \nL 346.58783 157.557058 \nL 346.842401 309.532629 \nL 346.874223 297.681827 \nL 347.065152 190.146766 \nL 347.192438 207.264592 \nL 347.224259 204.960269 \nL 347.287902 148.339767 \nL 347.351545 162.494893 \nL 347.574295 305.801821 \nL 347.637938 294.170478 \nL 347.797045 186.196498 \nL 347.828867 187.513254 \nL 347.956153 199.583516 \nL 347.987974 199.473786 \nL 348.051617 159.641922 \nL 348.11526 183.014338 \nL 348.369832 324.565592 \nL 348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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159.203003 \nL 389.3877 188.72028 \nL 389.578629 316.884516 \nL 389.610451 316.555327 \nL 389.674094 271.12725 \nL 389.833201 188.61055 \nL 389.865022 189.488388 \nL 389.992308 201.55865 \nL 390.087773 157.337599 \nL 390.151416 185.099202 \nL 390.342344 310.959115 \nL 390.374166 310.520196 \nL 390.437809 269.152117 \nL 390.596916 186.745146 \nL 390.660559 196.181897 \nL 390.756024 215.494316 \nL 390.787845 210.666211 \nL 390.851488 177.747315 \nL 390.883309 188.939739 \nL 391.10606 317.981813 \nL 391.201524 297.462367 \nL 391.32881 229.320252 \nL 391.392453 237.111058 \nL 391.519739 263.007256 \nL 391.55156 256.094288 \nL 391.615203 206.606214 \nL 391.678846 227.015929 \nL 391.901596 326.211537 \nL 391.933418 325.22397 \nL 392.028882 286.92832 \nL 392.156168 237.440247 \nL 392.187989 242.158622 \nL 392.283454 261.141852 \nL 392.315275 256.423477 \nL 392.41074 198.486219 \nL 392.442561 210.446752 \nL 392.63349 331.47856 \nL 392.665311 330.161804 \nL 392.728954 288.793725 \nL 392.888062 207.045132 \nL 392.983526 220.43215 \nL 393.047169 227.345118 \nL 393.07899 222.078095 \nL 393.174455 181.807312 \nL 393.206276 196.511086 \nL 393.429027 329.283967 \nL 393.49267 289.232643 \nL 393.651777 198.37649 \nL 393.683598 194.097033 \nL 393.747241 199.254327 \nL 393.810884 210.117563 \nL 393.842706 202.875406 \nL 393.93817 178.625152 \nL 393.969991 193.548385 \nL 394.192742 330.929912 \nL 394.256385 290.11048 \nL 394.415492 207.154862 \nL 394.542778 223.394851 \nL 394.574599 229.8689 \nL 394.606421 226.9062 \nL 394.701885 188.391091 \nL 394.733707 197.059734 \nL 394.924635 317.542894 \nL 394.956457 316.006679 \nL 395.0201 274.748329 \nL 395.147386 195.633248 \nL 395.179207 195.19433 \nL 395.338314 210.446752 \nL 395.370136 207.045132 \nL 395.4656 164.908945 \nL 395.497422 172.041373 \nL 395.688351 294.170478 \nL 395.720172 293.621829 \nL 395.783815 253.460776 \nL 395.879279 177.966774 \nL 395.911101 178.734882 \nL 395.974744 187.622984 \nL 396.10203 221.309987 \nL 396.133851 219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 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214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 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319.737487 \nL 413.540193 227.564578 \nL 413.603836 244.024026 \nL 413.667478 254.119154 \nL 413.6993 249.400779 \nL 413.794764 219.883502 \nL 413.826586 221.748906 \nL 414.017515 342.561255 \nL 414.049336 341.463959 \nL 414.112979 295.926152 \nL 414.272086 212.860804 \nL 414.463015 243.255918 \nL 414.494837 237.659706 \nL 414.558479 213.409452 \nL 414.590301 214.06783 \nL 414.78123 341.244499 \nL 414.813051 340.91531 \nL 414.876694 297.572097 \nL 415.035801 207.264592 \nL 415.099444 211.982967 \nL 415.22673 231.075927 \nL 415.290373 222.846203 \nL 415.322195 208.471618 \nL 415.354016 210.007833 \nL 415.544945 343.658552 \nL 415.576766 343.329363 \nL 415.640409 301.741824 \nL 415.767695 215.823505 \nL 415.799517 216.701342 \nL 415.958624 234.367816 \nL 415.990445 231.734305 \nL 416.08591 204.850539 \nL 416.117731 213.738641 \nL 416.30866 345.194767 \nL 416.340482 343.768281 \nL 416.404124 299.327771 \nL 416.53141 221.858636 \nL 416.563232 221.968365 \nL 416.595053 222.517014 \nL 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331.58829 \nL 498.089826 330.381263 \nL 498.153469 308.435333 \nL 498.344397 190.914873 \nL 498.376219 191.024603 \nL 498.439862 185.099202 \nL 498.503505 173.797047 \nL 498.535326 177.418126 \nL 498.662612 228.661874 \nL 498.853541 322.151539 \nL 498.917184 307.886684 \nL 499.171756 188.171632 \nL 499.203577 190.037036 \nL 499.26722 177.637585 \nL 499.330863 181.039204 \nL 499.362684 183.233797 \nL 499.585435 312.824519 \nL 499.617256 303.716958 \nL 499.935471 127.381404 \nL 499.967292 128.039781 \nL 499.999114 127.600863 \nL 500.1264 151.192738 \nL 500.34915 310.849385 \nL 500.412793 287.696428 \nL 500.635543 147.791119 \nL 500.79465 143.072744 \nL 500.921936 184.331094 \nL 501.112865 318.530461 \nL 501.176508 292.743992 \nL 501.399258 149.875983 \nL 501.526544 138.573828 \nL 501.558365 140.439232 \nL 501.685651 179.832178 \nL 501.87658 315.896949 \nL 501.940223 291.317507 \nL 502.162973 136.269506 \nL 502.194795 131.66086 \nL 502.226616 135.830587 \nL 502.449366 185.977039 \nL 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168.639753 \nL 528.288396 168.639753 \nL 528.352039 166.66462 \nL 528.383861 176.2111 \nL 528.606611 328.625589 \nL 528.670254 294.389937 \nL 528.829361 206.057565 \nL 528.861183 203.972702 \nL 528.924826 205.508917 \nL 528.988468 210.666211 \nL 529.083933 210.007833 \nL 529.115754 200.790542 \nL 529.147576 207.374321 \nL 529.370326 349.913142 \nL 529.433969 315.56776 \nL 529.593076 224.382418 \nL 529.688541 222.187825 \nL 529.784005 230.966197 \nL 529.847648 229.97863 \nL 529.879469 222.517014 \nL 529.911291 228.442415 \nL 530.134041 351.449357 \nL 530.165863 348.376927 \nL 530.261327 296.58453 \nL 530.388613 240.841866 \nL 530.420434 241.061325 \nL 530.515899 245.560241 \nL 530.643185 222.078095 \nL 530.675006 231.734305 \nL 530.897756 350.46179 \nL 530.929578 348.486656 \nL 531.025042 307.776955 \nL 531.18415 239.195921 \nL 531.279614 254.228884 \nL 531.311435 247.315915 \nL 531.4069 224.272688 \nL 531.438721 234.477546 \nL 531.62965 357.484488 \nL 531.661472 357.045569 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 569.910873 275.626166 \nL 569.974516 190.037036 \nL 570.038159 197.827841 \nL 570.197266 223.61431 \nL 570.229087 227.125659 \nL 570.260909 219.444583 \nL 570.324552 164.689486 \nL 570.388195 190.037036 \nL 570.610945 322.04181 \nL 570.674588 280.234812 \nL 570.738231 193.109466 \nL 570.801874 195.633248 \nL 570.833695 195.523519 \nL 570.992803 226.68674 \nL 571.024624 222.407284 \nL 571.088267 172.480291 \nL 571.15191 197.498652 \nL 571.342839 325.333699 \nL 571.37466 325.00451 \nL 571.438303 282.539134 \nL 571.59741 195.962437 \nL 571.629232 195.852708 \nL 571.756518 203.094865 \nL 571.820161 186.086768 \nL 571.883804 197.388923 \nL 571.979268 275.845625 \nL 572.138375 340.476392 \nL 572.202018 305.143443 \nL 572.361126 204.192161 \nL 572.392947 204.850539 \nL 572.552054 230.966197 \nL 572.615697 199.254327 \nL 572.647519 214.06783 \nL 572.90209 352.436924 \nL 572.965733 333.782883 \nL 573.124841 247.535375 \nL 573.156662 250.498075 \nL 573.283948 267.835361 \nL 573.31577 258.947259 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199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 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150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 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693.537265 325.443429 \nL 693.569087 321.493162 \nL 693.63273 277.71103 \nL 693.760015 206.386754 \nL 693.791837 206.606214 \nL 693.919123 221.858636 \nL 693.950944 226.357551 \nL 693.982766 225.369985 \nL 694.110052 198.26676 \nL 694.30098 312.934248 \nL 694.332802 310.849385 \nL 694.396445 266.628335 \nL 694.460088 201.229461 \nL 694.523731 207.92297 \nL 694.714659 233.819168 \nL 694.778302 226.9062 \nL 694.873767 204.960269 \nL 695.096517 328.625589 \nL 695.16016 286.92832 \nL 695.255624 214.616478 \nL 695.287446 217.798638 \nL 695.351089 223.72404 \nL 695.478375 242.487811 \nL 695.542017 237.549976 \nL 695.60566 226.79647 \nL 695.637482 228.661874 \nL 695.828411 340.476392 \nL 695.892054 335.648287 \nL 696.051161 243.804566 \nL 696.146625 257.081855 \nL 696.24209 271.23698 \nL 696.273911 268.384009 \nL 696.401197 244.024026 \nL 696.592126 346.511523 \nL 696.655769 344.975307 \nL 696.751233 293.182911 \nL 696.878519 221.968365 \nL 696.910341 223.285121 \nL 697.005805 239.964029 \nL 697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 \nL 707.729639 249.839697 \nL 707.856925 227.893767 \nL 708.047853 338.062339 \nL 708.079675 334.66072 \nL 708.143318 293.621829 \nL 708.302425 218.566746 \nL 708.461532 233.489979 \nL 708.493354 229.649441 \nL 708.588818 213.738641 \nL 708.62064 214.616478 \nL 708.84339 336.635854 \nL 708.907033 300.425068 \nL 709.034319 224.272688 \nL 709.06614 225.589444 \nL 709.225248 245.99916 \nL 709.28889 238.866732 \nL 709.384355 216.701342 \nL 709.607105 343.878011 \nL 709.670748 303.497498 \nL 709.829855 209.568914 \nL 709.92532 215.933234 \nL 709.988963 221.419717 \nL 710.020784 216.701342 \nL 710.116249 201.33919 \nL 710.14807 205.728377 \nL 710.338999 329.722886 \nL 710.37082 328.515859 \nL 710.434463 290.000751 \nL 710.593571 208.910536 \nL 710.657213 216.481883 \nL 710.752678 232.502412 \nL 710.848142 231.514845 \nL 710.911785 218.127827 \nL 711.102714 340.91531 \nL 711.166357 331.259101 \nL 711.357286 232.941331 \nL 711.389107 233.819168 \nL 711.611857 256.752666 \nL 711.6755 241.171055 \nL 711.707322 255.106721 \nL 711.898251 345.962874 \nL 711.961894 333.343964 \nL 712.121001 255.984558 \nL 712.184644 263.775363 \nL 712.248287 274.089951 \nL 712.31193 270.578602 \nL 712.439216 250.607805 \nL 712.661966 354.302328 \nL 712.693787 352.327194 \nL 712.789252 308.764522 \nL 712.916538 252.473209 \nL 712.948359 253.460776 \nL 713.043823 260.702933 \nL 713.202931 237.879165 \nL 713.393859 344.31693 \nL 713.425681 342.341796 \nL 713.489324 302.290472 \nL 713.648431 219.773772 \nL 713.680253 220.102961 \nL 713.807539 240.402947 \nL 713.871181 235.245653 \nL 713.934824 229.97863 \nL 713.966646 220.322421 \nL 714.157575 340.805581 \nL 714.189396 338.391528 \nL 714.253039 301.522364 \nL 714.412146 231.514845 \nL 714.443968 229.649441 \nL 714.475789 234.148357 \nL 714.571254 240.841866 \nL 714.603075 239.63484 \nL 714.730361 218.566746 \nL 714.92129 339.927743 \nL 714.984933 325.772618 \nL 715.112219 234.367816 \nL 715.175862 237.001328 \nL 715.36679 245.121322 \nL 715.462255 236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 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158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 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339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 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\nL 834.569999 204.850539 \nL 834.633642 221.090528 \nL 834.856392 341.13477 \nL 834.888214 337.73315 \nL 835.0155 250.607805 \nL 835.079143 228.332685 \nL 835.142785 239.41538 \nL 835.23825 262.348878 \nL 835.270071 256.423477 \nL 835.333714 219.554313 \nL 835.397357 235.684572 \nL 835.588286 339.598554 \nL 835.651929 335.977476 \nL 835.747393 285.392105 \nL 835.811036 245.88943 \nL 835.874679 254.777532 \nL 835.970144 270.578602 \nL 836.001965 269.920224 \nL 836.097429 215.494316 \nL 836.161072 235.026194 \nL 836.383823 340.366662 \nL 836.447466 308.654792 \nL 836.606573 211.324589 \nL 836.76568 228.113226 \nL 836.797502 223.61431 \nL 836.861145 186.415957 \nL 836.892966 193.767844 \nL 837.147538 345.523956 \nL 837.179359 334.880179 \nL 837.40211 216.262423 \nL 837.497574 219.225124 \nL 837.529395 221.858636 \nL 837.561217 219.554313 \nL 837.62486 186.854876 \nL 837.688503 213.189993 \nL 837.879431 339.379095 \nL 837.911253 339.269365 \nL 837.974896 301.193176 \nL 838.134003 204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 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222.626743 \nL 852.103626 191.682981 \nL 852.135448 200.461353 \nL 852.39002 332.356397 \nL 852.421841 330.052075 \nL 852.485484 283.85589 \nL 852.61277 217.908368 \nL 852.644591 218.127827 \nL 852.676413 219.444583 \nL 852.771877 228.881334 \nL 852.83552 202.217028 \nL 852.867342 172.480291 \nL 852.930985 188.939739 \nL 853.153735 324.016944 \nL 853.217378 310.191007 \nL 853.408307 208.032699 \nL 853.535592 220.761339 \nL 853.567414 217.140261 \nL 853.631057 177.308396 \nL 853.6947 192.451088 \nL 853.91745 332.356397 \nL 853.981093 320.725054 \nL 854.108379 216.481883 \nL 854.203843 220.980798 \nL 854.299308 232.831601 \nL 854.331129 231.624575 \nL 854.394772 181.258664 \nL 854.458415 197.169464 \nL 854.681165 338.940176 \nL 854.744808 325.882348 \nL 854.872094 210.9954 \nL 854.935737 211.214859 \nL 854.967558 210.775941 \nL 854.99938 215.274856 \nL 855.063023 214.06783 \nL 855.126666 188.281362 \nL 855.158487 172.041373 \nL 855.22213 193.109466 \nL 855.44488 330.052075 \nL 855.508523 320.834784 \nL 855.699452 202.875406 \nL 855.731273 204.850539 \nL 855.826738 214.506749 \nL 855.858559 211.653778 \nL 855.922202 171.931643 \nL 855.985845 192.341359 \nL 856.240417 316.555327 \nL 856.30406 296.365071 \nL 856.431346 226.028363 \nL 856.494989 233.489979 \nL 856.590453 250.498075 \nL 856.622275 243.914296 \nL 856.685917 189.268928 \nL 856.74956 213.738641 \nL 857.004132 334.002342 \nL 857.067775 323.468295 \nL 857.258704 241.500244 \nL 857.290525 247.645104 \nL 857.354168 254.996991 \nL 857.38599 247.535375 \nL 857.449633 187.952173 \nL 857.513276 210.117563 \nL 857.736026 333.782883 \nL 857.799669 320.615324 \nL 857.990598 211.434319 \nL 858.117883 227.015929 \nL 858.149705 226.028363 \nL 858.213348 175.772181 \nL 858.276991 193.877574 \nL 858.499741 328.625589 \nL 858.563384 316.116408 \nL 858.754313 219.554313 \nL 858.786134 224.492147 \nL 858.881599 231.734305 \nL 858.91342 228.661874 \nL 858.977063 177.308396 \nL 859.040706 198.486219 \nL 859.263456 338.610988 \nL 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "dddca9ad9e34435494e0933c218e1579",
+   "translation_date": "2025-09-06T14:01:37+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sw/7-TimeSeries/1-Introduction/working/notebook.ipynb b/translations/sw/7-TimeSeries/1-Introduction/working/notebook.ipynb
new file mode 100644
index 000000000..edeac1f30
--- /dev/null
+++ b/translations/sw/7-TimeSeries/1-Introduction/working/notebook.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Kuandaa Data\n",
+    "\n",
+    "Katika daftari hili, tunaonyesha jinsi ya:\n",
+    "\n",
+    "kuandaa data ya mfululizo wa muda kwa ajili ya moduli hii  \n",
+    "kuonyesha data kwa njia ya picha  \n",
+    "Data katika mfano huu imetolewa kutoka kwenye mashindano ya utabiri ya GEFCom2014. Inajumuisha miaka 3 ya mzigo wa umeme wa kila saa na thamani za joto kati ya mwaka 2012 na 2014.\n",
+    "\n",
+    "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli na Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, uk. 896-913, Julai-Septemba, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutokuelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5e2bbe594906dce3aaaa736d6dac6683",
+   "translation_date": "2025-09-06T14:02:32+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/working/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sw/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/translations/sw/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
new file mode 100644
index 000000000..4eb9ea259
--- /dev/null
+++ b/translations/sw/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -0,0 +1,1131 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Utabiri wa mfululizo wa muda kwa kutumia ARIMA\n",
+    "\n",
+    "Katika daftari hili, tunaonyesha jinsi ya:\n",
+    "- kuandaa data ya mfululizo wa muda kwa ajili ya kufundisha mfano wa utabiri wa mfululizo wa muda wa ARIMA\n",
+    "- kutekeleza mfano rahisi wa ARIMA ili kutabiri hatua za HORIZON zijazo (muda *t+1* hadi *t+HORIZON*) katika mfululizo wa muda\n",
+    "- kutathmini mfano\n",
+    "\n",
+    "Data katika mfano huu imetolewa kutoka mashindano ya utabiri ya GEFCom2014. \n",
+    "\n",
+    "Inajumuisha miaka 3 ya mzigo wa umeme wa kila saa na thamani za joto kati ya mwaka 2012 na 2014. Kazi ni kutabiri thamani za baadaye za mzigo wa umeme. Katika mfano huu, tunaonyesha jinsi ya kutabiri hatua moja ya muda mbele, kwa kutumia data ya kihistoria ya mzigo pekee.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli na Rob J. Hyndman, \"Utabiri wa nishati wa uwezekano: Mashindano ya Utabiri wa Nishati ya Kimataifa 2014 na zaidi\", Jarida la Kimataifa la Utabiri, vol.32, no.3, uk. 896-913, Julai-Septemba, 2016.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Sakinisha Vitegemezi  \n",
+    "Anza kwa kusakinisha baadhi ya vitegemezi vinavyohitajika. Maktaba hizi pamoja na matoleo yao yanajulikana kufanya kazi kwa suluhisho:  \n",
+    "\n",
+    "* `statsmodels == 0.12.2`  \n",
+    "* `matplotlib == 3.4.2`  \n",
+    "* `scikit-learn == 0.24.2`  \n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "!pip install statsmodels"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "/bin/sh: pip: command not found\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from pandas.plotting import autocorrelation_plot\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "%matplotlib inline\n",
+    "pd.options.display.float_format = '{:,.2f}'.format\n",
+    "np.set_printoptions(precision=2)\n",
+    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load\n",
+       "2012-01-01 00:00:00 2,698.00\n",
+       "2012-01-01 01:00:00 2,558.00\n",
+       "2012-01-01 02:00:00 2,444.00\n",
+       "2012-01-01 03:00:00 2,402.00\n",
+       "2012-01-01 04:00:00 2,403.00\n",
+       "2012-01-01 05:00:00 2,453.00\n",
+       "2012-01-01 06:00:00 2,560.00\n",
+       "2012-01-01 07:00:00 2,719.00\n",
+       "2012-01-01 08:00:00 2,916.00\n",
+       "2012-01-01 09:00:00 3,105.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Panga data zote za mzigo zinazopatikana (Januari 2012 hadi Desemba 2014)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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JJOklaEOiTUyiU+9RzfrI+kWEpAcv9fLCwq50j6M2AupLufKZu1YC8K9MO3lvqlt7/V2EJAa999LMTXUR9oSUA1QSSWoJehKjjkl0f5VxnF0RkhrioxoGA9e23OHV3VTDyeJj4KEVJLY4zW5qBnMhED1UEklqCTpFu50l0SnjaEkkJHXE8XP30iXt72AJDGd4uU36d8VRMXQ+CuKQB1ccLun1nttSj81H2kt6TeIcKomEBIR+HHaTVKConfy5VBIJIaXmeNcJDGe0Oone26FeUsz2ug7UtPYF0pbezXRhJeOYiT1VLX22LuSVJSqvo/HrF3bhXx/ZUNJrEudQSSSpJWi3Ti+ZTJXtBNAGISQZxO07f2WnTuFgncRAueyh9fh0PhbRD8Y4xB31nZbHU2FPDyrLtF7GLNzdmD9QfT69TYkeKokktQQfkxhQO3kpT1lNSHqIy/c+4nOWqFkq6G0aHm4XFuj6SzRaewct9/tJVkXKDyqJhMSMIDILEkKSxdbaDgyOeC+LEBQZXXHEOGVcJYTYY5ekaLyNC5X+i+cchFBJJKml5KurDuWtlriG8pmQ8keviBW4ekZEUG7zJDykLHw2do9JP9L98tntYXSJxBi9jBmf/77NZj/6XAj8/gmVRJJa4uqAo/VLE9b7G7uZljpBVLf0YtXBlqi7QYhv/EmduErY9KFfD31xx7HoOkJCx27te5ydJdHF4kMYSCnR0TcUwZWJCiqJhMQMvZA/2NSDL923BvctOxhdh4grPnv3Knz/8c0YGI7edZDEn3k66+G2uo4IexIMXM4iJBqklNjf2GN5zAQbJbHQklj6r/mhlVX44E1LcbzrRMmvTYqhkkjSSwAL3UMjWfuDXPKbFyoB5Fb0tBW1ebuid0Mj7vhsAFkMSfkza3Pd6M8zNtUV7JNSomdwpKT9GVfgbup+kjhWJzGoHhG/8FGkA7N6g/rP2C4mseA8vx3ywJK9TQCA410DEVydGCm5kiiEeKcQYkAI8Zf8758WQmSFEL26f9/XHf9GIcRcIUSfEKJWCHGlob0r89v7hBAvCiHeWOq/iSQTuwBvJ/x81jbHx7pNAiEBnDQh94keae1Dcw+FZpI4xkGOeGDvse7Rn5ftay759fXKnZ9JIhUTZ7T0DNoq47saOjGSKVyQdJNghNlN00HfkP2CkqYkmr0SUddnZgkw5wxngjdSGInCkvgggC2GbceklKfq/j1lOH4IwJsBXAXgYSHEuQCQ/+8jAL6b398P4KGw/wBCNBbvaQq1ff2qX2sP/fQJKXe+MnXN6M/9DiZ9QeNfneDsTsX6w62m+zTriYp9x7tx6QPr8IX7Vpse8/zWBl99I+lhvM2CQTZ8vcMRYXhplROHmnrwzt8txILKcL3MSqokCiG+DaATwHKHx78WwOUArpNS9kop1wJ4GTmlEMgpjfOklKullL0ArgPwTSHEaU7a39/YHfoNJvGl5MlNbeZOBwyxBKsOthRYO6Ne4SOElD8FlkQPIofupmqunL5p9Offza0s2NfWa74A2NKTq2tX3dJnekxn/zC6B4ZN9/NRpINymCLsqO8EADxXUY+B4QyT9plQebQLALBkT2Oo1ymZkiiEOB3AjQB+pdh9lhCiSQhxRAhxb145BIB3ARiRUuqzduwEcG7+53PzvwMApJRVyFkd3+WkT1+6bw2unuHcXZCUF3EbOP/fn9cX/K5NDjSoJBJCwka/MKV3kd9Q1Ya6tv4oulR2GGNPrWS7mbJtdDG1nEybtLHnWBc6++mhUi4EMUXwu0gUFFUtvXjPdYtw28J90XUixpRqEa6UlsSbADwmpTT6RewH8AEAfwPgswA+DOCe/L5TAXQbju8CcJpuf5fF/lGEED8WQlQIISpaWpienpQeO3mrcq/QT9K4oEZIuohiklYw+chff2gkiyse3YiL7lxhe77W5SBivklhIqGguWTqWnzzofX2B5JEkAlAYMTFA0CbDz27uT7insSbsIeIkiiJQogPALgYwL3GfVLKRinlXillVkp5BMCvkXMxBYBeAKcbTjkdQI/D/frrTJNSXiClvODMM8/EvuNG3ZOkjVIIQzfJagZHsrjg5mWF5+tOpyWREBIF33x4neNj6W7qHqNkP9Z5ApOnzMeO+k7HqrbV8GClsFe3mruxkmRhlsBItdnsnYjL4s6E8bl+cN6jplTPqVSWxE8DmAygTgjRCOB/AVwuhFD5ekpdvw4CmCCEeKdu//kA9uR/3pP/HQAghHg7gJPz51ly+8L97v4CUnaUWhiqBLhxItXaW+hiqj+DvvmEkLDRZ8LUJM7uo+4XVakkusAwNqw5lPN2mrGxNorekITiJNml3SyiMLtxdHOOscUmCpIoKZWSOA3AO5BzK/0AgD8DmA/gi0KIzwgh3iZy/B2A2wG8BABSyj4AcwDcKIR4rRDiEwC+DuCZfLszAHxNCHFhPo7xRgBzpJTW1UTBAYwkD6qI5U8UxYsJ0TPOb+IaSirXGNf/tAVMCfNJsvEu+7nrPRZJb0hyCMLqVkqlLOykK2kg7ClDSZREKWV/3q20UUrZiJyb6ICUsgXABwGsB9CX/28lgP/SnX41gNcAaAYwC8BPpZR78u3uAfAT5JTFZuRiEa920ifqiMQLe451eU7N7OVb1isN1B/Kn1mMvyA6olC4CkMS3V9/1ALAUdY7ultXijn7HYsOhH8REjpmSqLqOw7ivWrqHkCjj3rAP35mq+k+znesKZUuP6E0lylESnm97ud7MJaoRnVsO4BvWOyfCWCm2z44XS3ZXteB889+PcaN44CXdura+nHJ1LX4/sfehhu+/r6i/SqZZjdREibnaehXmGllKn/WV5nXUiPpI8ykJWYEZkngkOkYq1gy1W082nki0OsPDGcCbY9EQ9Dxe3bN/fOtuWp2NbdfEuh1AX0CLGJFWSSuSSrrDrfisofW4zuPbbI/mJQ9rX25eMGdDcaEuuboV/C8ye+xkxbutnfN+OFTFfjq/WtsjyPxhPEXRE8U74PvOonBdaUsGM5k8Z3p1nOIYnfTHBLS8TvARUSSNXFy4qtBvJJaJdGJ2G3oyNWEWl/V5sukTsoDLXGMmVE5jOmcXrg/ub7G9vhl+5o8JZkg8YAqItET9fvgx0U+6r7HhcV7GrH2sLWHgPE+6xVDMx1xQ1WbZRuEqEiKwjj5jFNyP1CQWBL24lB6lUQHq3N6V0G6Y5CRvJI4YZz6sxmxyT6qjguwfg8TIs9JQNCQmC6qW3ot90fx/RdkN/UxAaFVPEfGR1bqxbsbTRcljxlcTt3c7U3VbfYHkbKhpWewaFtcv85vXfB3AID3vfV1AOLbz6gJQr5mshK1bdYlcFKsJDo5aOxHTtaJNtiPNxm1T4SwkJCUVT8SDFHEoJHo+Ozdqyz3R+FCyDcwWJxM5ozPWTujbyiD4yF4MV09Yxv2HHMeNkGSzQ+froi6C47RPheW/HKGn7t037KD+NSdKy2PSa2SSIhbtHHcxJDooAEv16SgTBNeJujDmSzWHmLCm3Li2c11uOb5nZFcuz4fZgF4jEkczW5KvKLXKweGvWXTNmsPyA1Fl0xdW/C7FRU17Zg8ZT52H6VimXSSUqKGOqI1o5+0j/u00YFHQWqVRA5gxCtB6m1276GbS/11a4OfrpCIGRzJYM72o67Pu2vxAXznsU3YWtseQq9IFEyZU4nntzbYypq23sHA4+UfWVUdSDs0iud44NVDvs63ynzqvA1fXcDSfU0AgDVcjCobnFm4S9ARs2vnZz90W1cTxG1xUqYovUqigxtcUC+KFh3iE29JIJwfe/vC/R6uQKyYuakOP3zK2lVn99GuQFxjqpqtYwNMz2vJndfWO+S7DyRe2K36f/jmZfjobcvDu76n7KYcK/UcbLKOO1XhZH7ip9yB1/kMny0pFZxylwAHcia9SqKDu2NcwWjqHqCySEqKm0GZpTyD59q5lViWX0VXsam6DV+9fy0eX3ekhL1SQ8lUfpiltI8zaXE3zWZlYHFT7X2FCzz6+UkQlhRjE72DIwW/23mhmM2XZm6qw476Tl99I+lkJGMt3EbrJJa7IIk5qVUS3XKktQ//fOtyTFsdjCsOiTcnhjJFgf1ehJV+TcG4vjCcydpmRHWDWUIdEh71HbkMg3uP+y87YvZ+XTFtI/71zxsKth1p7cNP/7IVgyPMukzCw4vlaFRJLPPZ3duvXYArp28MpK2HVlYV/F5YqzL45Z/hTHGbRw3ZUp1w7dxKfOPBdUF0icSQMC3Hdyw+YH1tltJxxPzK456rLzi5t6lVEt2OX/+Rdzmzq3dEkoPVO/CbF3bhkqlri1Z4g+S3cypdnzNzUx2au9UxSMyMGSEhmvE2VLdhc01hvOHv5lZi4e5GVNR0hHdhEjhuB/OorcN+9JM0SKON1dHFAQetOw6P2Jut6UhVPkT9fe40sUBr7xjfNWv01v0GXbIxV23Q3dQctzGJGnxxywerZ7mtLjf57h0YMT/IAQUrwoYp32tPGm/fgKGP186txPce32x7LVIagrjlmaxEfXu/Z0s1n3s4DAxnMGzjEuWWG+btdXV81OENnuKoA+9F+iioVemzraGRrO+kZpQxJGjs3mvGv8aD1CqJTlAJRr646UL5DgT0Ckwcb//5qS7VrCiMC9CSGCbXv7wntLbvW3YQF96xAnVtxauBbmKeuIAVLO+5bhH+xeDm6xe3K76JfqQURyXl57O2K7cfaOxx3dblD6/HPUus3QGHHFgeSfwoZVZc68bVm0frJCZa+MWfjr4hR54QVBIBLNtrnpjCCCdi6UD1nP3OeYxtOtHpVP0wsy5QRwyPJ9fXWO63EgurD7Zg9cEW0/3rq3K1ilTK/83z91led2A4g6Uu5BdxR+RJOSIeb3xZMjlWeqZAlJvcR+OCtSZHitryMC5sre3A1FcPWx7z2btXum+YEBs4xy4N2+udhaqkVknU+/P+8GnrFPd6+AITN1i9L04SO6jSnJs1SUtiPPne45tNXYQB68WHF7ZZu4m5kV0ketyOH5kEupsS/zi576WytAxnsnjYkFgHABo63Ce6IeGSpCkAvfLc8ak7V+CL964e/d3v/XNS4QFIsZLo5AbT3ZQAuYLVQaB/c25dsC/wTLlJGiDKkfr2fnSdGHZ9ntVzc2fJoWyKO27HDz+18ILA1+Upj2KB03HB7FGf0CVb2ljdhhaTcAeSMJx4MrlsUkqJW+bvdTQOmsmW2RX1lvvTSm1bPw40uXcdN8WhXEitkugEp5o2KT+0yfnqQy348M3LsPJAc6DtO1UQVXLSKDx7B0fQNzhCS2IEaLdcSokL71iBz9+zytX5O+o7sSWfoVT1rAeG1XE/bh61lDLyBCgkR2FJHPtnkui4nCT3PQL074P+847TwvSaQ634yC3LXJ93y/y92FobXSZYMkaYb9OcbUfx6JojOP+GJZ76cWKoOPtzfN7+ePGzmeoYZKc4nUKkVkn0qgByrpUuttflYpL0sUleB20vE3Wlu6lh2/v+sBjn37AELJMYDJuq2xxnA/zVczsLfjdLKmRGQY0xxbMeCiC75qUPrMM5v13gux3iH+0Rrz7Y4uiZuC3W/u7/W4hvPhRk3ToOeCrCKI3kdkGg26HXgp/F7t5Bf9m9NR5dcwSXPxxsEigSP5p61OW5VDidD3X2u/fOIcGRWiXRK1QS04GTx1zZ0BV6P1Qd0W9aWHkcADCSlbR8B8S3pm3E/z6/0/7AhFB5tATvKXGEtsC0wqFnglt308GRLLbVeU+2Y5y40d1UzYV/fDXwNt0uIj6kiBNU4cfB5Fezd3g/mcQaJ6+F23fy3W8+zfGxSi8p3Vb9z6rM38QfTnJiAFQSC1h3uBVbdEWrGZOYXjTZOE4U/q5n3q5j4ffDRku85q+7Rn+mt6l7DjX1YPKU+aN1MZMMF7Dij/aMJjg0+0f9TP1cflOEhebDpk/hFueXqD9flUJQ3dpXzrp+Kgkz9ODUkycAAD4y+Q0O+uFsGwC09TEONmjobuqBq6ZvCrwuFkk2mnVO5n4pwK2w9SKblYJU97N+rsmYRPesypemmL8rZ5Ft9ZikKIhhN+pJIgkf7Rk7XcUtdeKaIC6n/WVHO5n90g1mz9rvM4liWPjUnSssMz7OJVwAACAASURBVDqT4NhW14GB4eAXLexQXXNcfkLiNZba7LSoE3iVI07lQnqVRIsb1NIziGV7m9RBtHxXU0GRBU8ZG+ikHZ/9sKmTOGH82Cc8Lr1fsy09A8N4aOXhohgvbbKuDUK/eNZfMLgfKFtSQP4ZO8446fGd2He8Gy/tOOrtZJ/X52scLH7vp5swBCklegb8xYDVt/ejtq3fsjYsCYZjnSfwzYfW49o5laG0b/Xu+b2mOimfVB4QQGg+8ciEqDsQR66avhEHm3qV+zgApgNpMZnT9pUi86Dab3+M8TpTIi2J5tz0yl48V9GAd551Gj7/j28e3W50Jw46SP7pDTWBtkeSjbb45HTi7jW84ct/WgMA+PoH3urqPOPV0hhesXxfE045aQI+9o4zou5KILgpgfHEuhrc+MresW0eVgk+c9dK1+cQb/TlEwvt8hB37tSbwYw5230uQrmoAZ1JdJrnYOk6MYzXvWai73ZYJ9EHNa3mQbJMJZ8uavMB089srMWqA4Uro05cIPy+L3bn6z9zv0K/nNGy9A2OFHoHaHdMe5b6W/jY2iOO2zd7TE+uq3Hchhu8JCkayWRxuDnAOkvENVaLTyqinht5EV9Jl0L/8VQFrnh0Yyonpov2NPpuY8Tivmlu/SQYRt07Y/Kuuvn2nZT30qC76Rjn37AEK/b7L8lGd1M/WNw8TsTTgSaSNEvd4EgWjzisbWjapgc5p5L9Zu2wBIY5o7GlhnunDbKjk3fdx3+TbkXdK1EPbXO3j5XyuHPxAVx8z2rUtPZF2KN0o70PTr/VoCdHaw61KMMoNIJYBI36nQ+KP7y8u6TXC2seHPWwoH+n/nPmNkeF1okzNO+hTEgvj1Wzp08K3hFR/67oL53GBRsrNh0xTwq2/nAr9h3vtm2DiWtCYmttB5q61bVgjrT2YX+j/cMh8UeTVef+7ekAgG9+qNhty8mEKowpl94FTL9mEfVkINbkb87PZ23Hz2ZuM25WWhLd8PJO+0y3VpPzIFC9a3ctPjj6c0VtLoNri8fkPGmlti04pVqTGY7dTQOcGx1q6sF3H9uM9/5+kfn1Qrx+0nhpR/jZq8Oie2AYn75zBXYf7Yo067VqjOSEPzjGa0qiz3s6ogv6U8WSZrOy6FmecpK5kuhkbqSuAa0+NiwlOGqe3VyHyVPmY2gkuKDLK6dvwpf/tAZTlx/CuRay3ilUEj1Q3aKeNHzmrpX40n1rStwb4hU3g6dKRrmVy17ie9ykiSZqVhxoLnBzekX3s+YZMJp1MuBr6wfLq2dstTzWr9WILjnh8P0AszS6tSQGGd7gxYLjRWaVzWJVgj+njVVtqGnrx33LDtofnMfPq9beN6Tcft+yQ94bJbZMGJ/72rTYRK9ocahVLepcHG+/dgHO+e2Cgm16GTbsIbNMVnGK/hXUy75MJsEfowV3LD4AwH0maCdy+Z6lB61L9dDd1Dt2985vMP9IJsvVtBjgd/7l5D3wfw1n24g5P3hii+k+zV1ndEAKcdl9Q3Wb5X67K1ebDOAamazEZ+9eiVdKUL8zTQwGuMqrvWaHmq2fpfH4IHAy5Biv5ze7aZJj+PWLLjvrO/HLZ7eXLPZLL4b+uGi/h/P1bvTOZZrxSKd/7e9fUrvmzthUy0XNENFurd/XcuneJgBAt3EhyaJdfejV/zy30/U1bbOb6ijXBVDtDrpO9hTA7WDiGh/YTQpae9WrZk459w+L8ck/vuqrDRI2Uvf/homP9l+3lkQPH/acbQ3FG8tTXkaCNs5pq5pWFp7fzqn0tbgzMJxFXdtYUiy3qeY/d8+q0Z9Vuuwvnt2B6pY+XPP8LuX5ZWPhKTHjAwz21d4eM8uLkSB1kigUtnKZ2/3w6Qq8uOOYrat298Cwo3ggO/T3zUvG5VJ/60G6yxHnaN/0qBu7xwc/brQUlLfz51ceL7j+trpOW+umqqtmly9bJVF3EyZPmY+VB/wnpDFy1fSNvs5PrZLoR4j+1yx/tdQGR7I43qWOayTxwIlMciZQ/Qm3ZfuKhUZBTCKn/r4w3j2ruzlrcx3ece0CiyOKMT79F3W16z5+m7uFIilzMQy3Ldzn6jxVO8Q5DR0n0D/kz51rlPzNd1quJsjJkSNLYsArUEl+1fR9156W3eO46tFcPJCUEhuq2iK3pPq9enVLH4Z9uPpJqYpzLdzSOziCP6+qik2GziTiZuFadcz4cQaPGgfoRZhKTtnNcdWWxLGf9xwbW2yxypibbArHgdlb6h2d5eZurDvchsGRTJFLK7Obhsx3pm/C9rqOqLtBIsTtBCAoMedn3rG1tsN3/EI5cyTkzJ/6Z9fj4TlMmVOJR1b5y7JL3BPUe+E6jtniY3frVlyQOdChEPHtLp/gFQkvXa/M16ubX3kcVzy6EbM2O5v0GfHr9V6Q0MxnWwsq7ctWmF2jrW/INl7t9oX7cPvC/VgcQPmN1OLzM9OURDfySb/QpfpWvHjd6Bep1leNhWcwPKuQaS4z7f9i1g584vZXC+4js5uGzNrDrfj1X9VuXST5GAe9AiEoFdtKiNfLdvUP4/KH1+PnPi3hUdDeN4SHVh4ObdIpIbGroRMdHly7koKW3ZS4R5VkwQ+fe+9Zjo6zett/NtPdd6yfZ5lNuurbC2sEW1kWpZTosvleegaSuyClyiLd3OPMA6i+Pbdq7yYzbpCiTeuvO3kZTjzY8xXWirL2jgQZ+5sWjI/G64KA9p4Yn/VDKw+bnqO+1thGO8VO6W5qckq5lp4L6s+yKokBjNU/9aJsU0n0Adc2yp+xFZvip+02cU1QCo7XdobyK7o76zsD6UcpmfLCLtyx6AA22whDI8e7nGcNqzNMkIOgKBEIpUYiCcrtU3v+kyaOD6Q9N+j/BrO5wsX3rHbc3rTV1Tj/xiU4ZpGZL8kLE6pHfukD64q2zdpcV7RtxqbaXBserz047F1hqm/vx6GmXGKkOBhgBgx/S1N3YVxngo3NscHNLZy+NjenUc0jjJusakPbuczbyUw7d9PC7eX5khQlivL4Z/5urrOarl7mH1QSCfFIVANwQaxMeS6wFdGbd810Gx/zMYdxfyL/v7AJaqzzmzyLWGOclASlJAZtkbRDX/OsUEn0726quQcalUT9V5RkN7HCmERz2TBzU7GS2NCRuyeVDV2Or6d3ab5+3h7H5xm58I4VuG2h+4yoZqjeAWMtPav7Y5yYfmWqukxYWsayMHCjRO0+WpxYqSafUM1VTKLjI53DRVTvuFkQdwOVRB/4Xd1YtNve159ERTAiUJUVNSo0AZzEwTisPk+ZUwkgvMEprHaDyKBInBOUruO2meWKxFVu+J6uxqN+uAozW6C+5QATw5Ye3R/iVf7Ylb3Ro1eevLrp7j1WKBdcOZu6sOJc81d1yYPO/uLFK6evWpkai0pCELcum5Xu5JzNN+FFxqTtHSgKa/LxJD9+u/2CuJf7m1olUV9QOyr+a9aOqLtAHBIn4WU6mNueqP2Q5Jlb9Axnsnhs7RFHad/bXFj8SvWKlavrTpgE7SrutL29AS4G6CcgbiaDxvfcTeHsJMcSFWaRTgY/eHKz/UEmBPGorn/ZuwWUeCcI8TRrS13kpSbSNjIF6b0U1qNLpZLYF1Q6c5+4XTV4vqI+NJMyscbrB1gYkxhMX/R4ETEJnreF6o7i9L78ZWMtbnplLx5be8T22P6hTMHvVr0P6rHY/R36iftLO45ixf7gazMlHeO3GrTX5HUvlX4yrf+bnLqBSgC/eq5wMfOdv1uIGotsr8Lk56Th2AIWo6mtcdIZxOKGmxaM8Yduzyfu8D4vKT7xWOeJAJREbwtRGoPDGfuDiClVLb2W+6evcZ8ZPZVKokpqLd3b5LqZqpY+/HlVVQAdsqdnYBjX/HUXrpq+qSTXI+Y4nRSEbbFZsqcR3Tq3JLsJWZIH61LECzq9guYK1j2Q/Eyov3h2B37w5JaouxF7AktcE+FHmC1YsHLeEZXXzeFm88lIkuWMnsLYb2vpIKXEPUsP+r7m5CnzXcUxGlF1043kVJ2velXCyESa5MXLUvDo6mpc//IedcIZl1+d6pmOE8JVK3aPy63MrG/vVy4ylDNGd3y/48O1+fAZM+5aMiajnHp5pFNJVHDTK3s9nXe7IkD8Bh9B52ZoCQ9aegatDySB4MRX3O4TW7i70SDQg5s+tfcN4cfPbB1N6OKkdU1ocyz2h/ZIvQj0qcsPmU7Q6zvoJRBXgs5uGgVOsps6Zfx4MZrd0Kqtkycmd4rhRpFu6R3E1OWHArnu0r3e6wX6yZZoduyrCk+DThelgujd7p/Dzb24ZcE+PLm+RpmF23X9VcU2gVxcop82Cva7fPDzdh3DSROSKy+8kAR3/HQ9EQuCHLyfWFeDHgdWBgrP5ODlWRlrjq077DyJgR1erJSa/E+AXDIlrG9GSuf3ZWeDvxIiZq5+TtxXSTQE9d5FKfP1MsOpu6lV3O2Ykmje1htfe5LD3pWe3/x1F+ZsazDdr5/A2ckG1WKxVxoCXCwKYl6z9nBrAD0xh9Mge07owhYKPAJGiza7a085fxDCkbJ5uLnHtOxNYXIsd30aGMog7W+D378+jLkdlcSQOO/6JWjvY5r6cqHzhH/Xwj8EGNTvxRKgrRKWwnUzaOKk2Gor614nYOkeBpOB8RkFZ0mMB04Xmf71kQ2m+8blZw9G64O+7TgvhM6uqMevnlNn6QSc3yMpgTnbjgbVLczZ7r0to2Vi3eE2fPbuVY7O/da0jZ6va0WcYjaTiv6xKmWRy/HRzJLo5J2/+J7VoWTSHE5wuZy4EIa8TaeSqPig6tuDd/UK1DU0RpPkNKKKWXWiuIQh9oSwF+ZWhXLjpHC5JaxhxNM9CSCZURj0D2WYwTRgAiuBEelzGXvJM377IYHx+YAa320lACv5UC5/vp9FbavySnb3Z97OY46OIzmUr6JrS6KiXeFfzulPb+oecN2ntL0DR00sskbCMDg5nfOkUkmMyxzZ0/eQP2nR7uNo6Cj2TSfO8aosaUkborTIeXl30jCZ80ru1rh7nl6tS6VYWX/Bo2XjPdctxKOr3WdAK3fcxOroOdTUU/B7pCpigTXCf3vHu3KTwC01Hf4bizlJ8b5wOukMmiDuT5IXL0vJxPFj03bP2U0hC5LehcHPZ2237oOh82mzOKuyips9z//38PqQe2NOKpXEUiOlxOqDLdh91DprWW1bn+0xGj/5yzZcMnVtEN1LLVYC1mq86ugfRr/DMiphxtC5Yf3hVnz+npzbUZLH4p31/uIB40ApdPXjHieLA8NZ3LJgX8C9SR5BWfwW7TYkIYlwHqT/7odGsvjSfaux6mCL7Xn/cNapRdskJKpbcmUwnt1cZ9hXfugVmIESpOm/8J1vCv0aSWPR7uOYPGU+ugII/Uga+vcvCGVaNc+MfA056uuXGLtyFXqqLUoO6QnjFlJJLAE/enorvvf4Znz1fmul7lN3rrQ9Rk8ahWVcGBrJYjhrna45Tiuj//fSbozkzQdJlsVBpJkHcq4wxpqjbp9X5IMqiT3FWZKjQx+vdrzzBPY39timTAeASQnOUBoGpShD9f6zXxf6NUpFUAsuD6/KeThUu5hclyNB3M4DjcX3MIin5KZvxvjZrEyXLVGd2dTfHTB6rgTBhMBbTCGNXQN4y+smFW3XXvll+9Q1GN0IT+19StNHFGcGhrOOkhWUUuzpRY5Vts4kKjdBp4r+3mObccCnQPV6G5N4/9OO1+9YNRGKA4fzE22v7onTdC7Jp5w0PpA+xRn9U9xaW+heG48nmnxsZXxMvp2o0d8G4x1x6varkmdm81Enmfq9UORuysfrmw4XpWmcUvIlQiHEO4UQA0KIv+i2XSmEqBVC9AkhXhRCvFG3741CiLn5fbVCiCsN7ZmeWyo+etvyUl+ShIzdeNXn0N00LJRCXvdzc88gntlYW7oOJYzmHndB9SriHJN43GXSAFJIOc5X9CJt77FuX21trG4f/fmnn36Hr7bihL7UgFPKKUmU19hCt4l9NJfdsBSQcqNQMfT/vqmeidnCsmlsobIN875lshLTVleZhupkLRLXlNEnZon2dx7vOoFbF+zzHAsfJFH4kTwIYIv2ixDiXACPAPgugDcD6AfwkOH4ofy+qwA8nD/HybmREuSL3d7LchpxwmmdMaMgnfLCLnSXYGD88TMVuO7F3WPJjQIeZErJdS/uxmoHsVN+eH5rA4Yz1u7DQSFlcQ3NoJm5qc7XAPOHl3bjh09VBNijdFLkbhplTKKuLx/4u9cDcOZKatfnSRMNlsRkiZcCrpoeThkIt5TTpFj1pzxXUQ8A2N9Y6M1x37KD+PKf1qgbilP8RoQU1En0vFCp2iaVSwRaoj6/vLLrGG5dsB93L8mFjBgtx0mbl/jF6m3+n+d2Ytrqamytiz4pWEmVRCHEtwF0AtCb3q4CME9KuVpK2QvgOgDfFEKcJoR4LYDLAVwnpeyVUq4F8DJySqHluVb9iPuruKGquOj6p+9aWfqOEFNUmamc8OyWerz/+iW+r283NrTlFxW0sMkjbc4Cn+NIqSyifYPurMNeJ3JZKfGzmdu8newCu+5ZxTQ/taHW1E0+jXh91kbLTJQTIf2cbEI+Q+I7zixOShMkSVN2ttWNJcbSdz1od3c7EnbbsNCYoEmH6h0YyeRLZhi237fsEPYd7w4s9rxckA4VQ8fyRdHGgyuqQn3vevPjq2ZJVLmbpklRtPpLtQVrKYEn1x0J5fpOJVrJlEQhxOkAbgTwK8OucwGMVrSVUlYhZzl8V/7fiJRSLzF25s+xOzexXPFoPFYz04yd281tC/c7aiMqkdfWl6vROTiSc+tJ2mQtCnoHw89aCACv7m/GcKb4gZRyHvp8RT3Ov8H/YkW5EtT3EitLok6maa7STvpDAw5xwv5GtQuzXWiEhv41m7r8UDCdShiZrLT1mlHdu6FMFq29zutym332QyP+vGmsxIlmAR1nIlDiEq8dJ5q6B3D9vL2R9qGUlsSbADwmpWwwbD8VgDEfbxeA0/L7jJJH22d3bgFCiB8LISqEEBVdnc7KTPgljHfeuPri1vpB7OnsH0JjADFdYa6KaTXKzBgYzgn7Odu91ctLKm29g45ccFRH3PRKaYTxL57dodxuNniGwcqQ3XfLjR6PNcWMTzQu8yCtX04yZNv12bi/XKwBUo5ZP6K4dtLodxHPubG6DdvrOvDSjmOj26zEX317P3pTEL84bXU1vvf4Ziw3eHHovyn9+KZ/TY609vmuVxmmoqb122ycS+I77werJ6U9R8dhTSFSEiVRCPEBABcDuFexuxfA6YZtpwPosdlnd24BUsppUsoLpJQXvO71waeXVrlHhDHnM7q9/CmlK25hsnRvcG52aw61BtaWhgBwucPiquUueDdWt6Ern9Fr77FufPjmZaPxLlZ0hpAFzC9BDwjllFAjan45W63Y26EaA0ZKFPtqpGCimf/v0c4TofYn6Qrj0xtqbI8JZTE4ofdN9b6r7s/SvU247KH1jkMJLrxjBapakhsy4RQth8AxQ9bhQndT9bluxg+zNlQeLm6w+ha0/o0zmRdnpSz7+QqQy458m8NaxHHw4iiVJfHTACYDqBNCNAL4XwCXCyG2AdgD4HztQCHE2wGcDOBg/t8EIcQ7dW2dnz8HNueWlCfWuvcbzkrYJsuQUqLJwqpVisK+aSOoybqAwJ9XVQXSllcyNrUck8zAcAbfnrYR59+4BFJKHGrOrQ2tPVwc00vGMC+N4u69v/mVvZg8ZX5ZKqNBTdKLYhKlRCYO90vXhSGbMchuohKHiYwfVhxoxs9nbceDKw4X7YvDoyolu4+VxsvKD+X8SDQrm9UUxGyfGyug2RxH1UZQ3/eokmiiJUoA23UxwYX7yuepX/7wejyiKyEUFU6fa6mUxGkA3gHgA/l/fwYwH8AXAcwA8DUhxIX5RDU3ApgjpeyRUvYBmAPgRiHEa4UQnwDwdQDP5Ns1PbdEf5clTpSNp9bXWO5/dks9/vlWltgoJSMhm/g/+vbSVWl56+tfU7JrlRr99/V8xZgXezkqLV74rUmh9KBuz3QPC2NpwzgQW6V594Ofd96v9cBI0j6/J9bVYN7OY7hz8QHTY6zmU37rrSqJ6B56dau24qkNNY6OW+bQgyfhaxKWaPpTUVKXgp/1rqdj292sB9eaJLKzkyOP+5D5WtPjTd1NJa6dqx6zygXH7usxeslLoiRKKfullI3aP+TcRAeklC1Syj0AfoKcwteMXDzh1brTrwbwmvy+WQB+mj8HDs4tGapP66v3r7U9z04ob6ymVaTUhO0H7ncSZXa6fhXuda+ZCAA4+w2n+LtYjNGPNU3dAyXPQOiHUnT1+a3G8G9r7N7LbFZia2279UEkEpzIFLNaa3GIewmbxq4BzN5Sp9znNYY5TMrpiTh17X9RF58IjLleGmtXJknOu0WYWBL176heGXx+61hohRtL4lMbvGUMv9Embt/K4qf1z9SSWE4vvQl6bwUnf24c3vUo6iRCSnm9lPI7ut9nSin/Xkr5Winl16WU7bp97VLKb+T3/b2UcqahLdNz44BdjZk/LT/ka5BOw4dVasK2JJbikWkJKVTXKpd3pjBbY+mvn1RXb6/jzrQ11bj84Q1Yf7gwzrZc3qdSIBFO3I2fJkdsTBB++huXd+Mzd63Eb16oRHufu3rDUXklmFl60sQn/7gCQK52op7op83hoclm41un/10fc//IqjG3xSDc2FVNOG12tCazCVr/zMafNGQ31cd/P7zSPBRp85H2ouOjIhIlMU3cMG9Pwe/GVTHAfpAmpSX0OD6fstDNIJkGwQtEE7Pw7Bb7BDlJob1vyPYOHsy71R01JFUoR7x+Np+7eyUes3DJCmsxw5E1zCT5hV9L4pM2IRNRs+ZQC07kF3TcjrXarSm1YrJ4T/JqlLb2DPrOrqmix8RFr769H+sOB58YLkq0+6eqIahh9r1lAxAuflr45B9XWHrG2bub+rh4QtBbBp2ULOlzkTHYQ28cHUUlscT8z/PusuSV86pZXAnfklg6aVjOglc/1uj/zld2HY/FClyS+METm22PMZsAlvEr5pqqlj7LUiotPYOobrX2LvGCW5GVdaEk2lmet9d1FiwcxO19ONBoHTNYzjKylPz4ma2htGtUKlp6cpPrT9+1EldN3xTKNaNC88Q0Lu46WQQKwm1cdRnTRGeKbVZKota/uSZluVLg9R4znN3wdCqJESYOqDxanD1sYMj7hLacsj6VGjPhlwkokYN5FslAmic6JAoXVJp6nBcWThuq96+2vd+xDEvD69sS4vuztbYj8DbdjgP640ds5J2T18LMOvDI6mizOzvB6t5RVkfPeEMM2w+frgBQnrG02t9q/NOc/KlBeA35baN/yFxJ1No2q/FsNf6Uy3doZ/SpajEsIMbgD0+nkhgCZmZhu1VMADj/xiVBd4c4wPj97TvejfVVraEnFYn+sy8PCupZelh5jZIYxKO7ZrTP8b61gXDhHSuKtsX9nbJD33v9pHPqq/5r7U4cr36hF1Q2+m47UFzGXHERNnqSKCs948OSmJXe67kGRd+guXuknaIbty9ta20HXjSxenrG5l2uaesv8MoIdyGE7qaxoNnjivT/Pr/TtoYiCZYv/2kNrnx0U+gZpdISJxg2P5+1ffTnokB/3mJThADm7zpesE1K+0Ha7KtIuvLklKAG7CBih7zwo7wFBih8ZnO2+Z8I6f+icnodyulvSSrjUqQlan+r8b1zIjICcTdVbXPR7Kknj/d87bjNiy5/eH0kSndlw1iW+oCrExXQPeAs6zCVxJjy160NSrckozk/Zt9Voohq7In6mZXjKxO3ASbOvLq/2df5ErJA0fngTUv9dilVjB8f/LDr9vXXP78L3vaGkl+/lNgt+llbErVGAusOcYnR3bScGXXW8GRJVB/jZhEvzAU/u6cYZxnihRNDGVQ2dKFNl6DGSWKnn/xl2+jPVvHtfvnBE1scHUclMcaoYkXK0A0/MsyEUtjK4476TvuDSsSm6jZMnjIfdW3W6atJ+WCWettukB5Nzy6BGZvG6myFUYC7nInDnFf/qP/jk+dYHutWHoaeHdoHD68qfvc3WNUiLreZawKhJdHZwm4QlkSVl4Ob2++0CypltFwWem9buA9L9zbhq/evwdceWIuP3f7q6L4kvspUEkuMm+/gO4/ZZ+4qj88qXoSRxjuuaAXXNx6xmCglACnNs52SYNDuqRDA7qPdrs6ta+tHo0nCgqQQ1CsVhrtpVkq8vPOY+X7DNfW/zrGJu3HyLelj9657aY/FkdHyxLoa1+dIKVHdwrqFURGHRZVSIUZjEu2P3WRY3AhizHPj3qhS9JxaIlV/X7kM2Y+sqsaPnq5AVV5mDI3Ed9HMCVQSY47KVUb/Ie5qiI9VipCoMA46f90a7xqGpVqIcFrTUAjnSToEBGZXuLu/F925Ah+9bbmrc8oBlfwOwxvksTVH8F+6GF0jxkLb+jGkIAGUV2I2w5NSYmN1G6SUvr40CWCeIX6XlJYkWl+8oskLoyxW6V7fmrbRUZulXDBt6xtydNzVM4rLpcQpu2lYCWOS+CpTSUwg+kFr99FudPY7+zCJM9I0KGnCt7492e6mKwxxdlNfPRxRT0i5EtRERe+qGxRNPdZWWuOkJ2jXrpjpiJixqQ7fnrYRC3f7y656tOME2h0UvSbhUNnQlSp3U+0v9aKjBPEN+o1JvN9i3NU/xsV7ihem4uSlfveSA4G219DRj7WHws+cHwZUEmPOlpr2om3GCf3AcIy+rjIggd+xb6yEexI40NSDhg5nVjOihi66peFgU6/9QS6xs0w/urq64Pegn3Xc3p2a1pyr11GfMmHO9qO4fl54ySOINV97YG2qxuNx+sBvl5gpeFF+mtp3CNjLKCeeLK29g7h2bqVrF84jrX1Yd7jV8fHrqorDbzJZie8+tgnrq5y3o/HZP5TmDwAAIABJREFUu1Y5Ch+LI1QSY4JZnIqTiS9rOQVLUKs9S4Jw4wqZJK5sAWrL5+0L90fQE2/E7Zs1m5OoEho5TZ1N4sPdSw8W/J6WBGhx+86IB5I6SHlAi780esI4eY/nV/p3i3ajmzopFfbpu1Y6bs+JTLpx3l7M3FSHRXvceQh85q6VuGq6PyWtvW8Iaw61Wrr1mzGUL2f34Ap10rg4QyUxJtyxOFjzNvFOUPFim48UW4GDIMw6jltrO3Dn4mCVratnbA3cxe7z964KtL1S4zbxS9h0nRhG32BxltKL7swVlZ+38xg2570abp6/r6R9iwtuFY7GrgG098XTVTFJmQSPdp7AZ+9e6SrxkZ2IZIhGSIQwNKVHRfSnD6880OL7+m5kXNDlMpw0p7nNR5HM6LoXdwMAWnuHEp+Mxg1UEmPCggBWgUgwlNvC5coDhfF6VsL48ofX48EVVYEOAAsqG/G7ubsDaw+gi3UYzNhUZ7rv57O2o5ZlUlzx0duWl2zl+JmN7hZhjN/3h29a6qsMTpgWuxkba1Hd0uc4GdVv51Ti0TVHLI+58I8rgugaKQHlNh5bYbYA7Gc4dlcn0ft1SoG2uBVFnKreeuk2cVuceGRVFSZPme/4eCqJJSaIj7C40Kr/Nkn50txTaM1o7R3EwHAGAHC4WR0fFcY7FWahXkLCppxeX+Pf0tY3hF+/sDOazjjEqQfFrM2Fix2q03oUVnMST9JUkirqMTLMqweh140pif7bcoPxuQwn2JLodkGRSmKJGQzg5SqnyUqUpGWFUhXv+t18EPWO+uBLqPQMDKPrRHHc2jRD8gwSL8zkSkOHvYUp6skN8U9Yad+9sOdYF04MZaLuBokB9y47aH9QmdDZH228t0qMt/YE45p9jyEuen9jj+s2djV0AQg35AYodnH+z5nblMc9vaEGk6fMT5T7qdtbRyWxxLQynXZsMJvXllvKbdXcb0tNh+U5fqaL512/BOffsKRo+1oX2cVI6TFzGfykA9e8WxekM04xqaie9IgPJTHINYKegWFcMnUtfvFsLkGEWdPvuW7haJxQKfpF4kO5Lkq99Q2vUW7389f6vVMnhkuzWGM17fr1C7swecp8HM/HJZd6jragsjBRjnZ5TfFVxfPHFbeWeSqJMUFleSHRUGY6Ymoz/O07Hq/kMHHHz7zLLgaMRMdIpniVW/Ws42JJ3FSdS5C0rc56IWtgOFvkOqWyPpaZOCcAHl1THl4pzT0DoSsYAy6UvLDmCgccWA2txh+jbIoicY2RE0OZUctvPCRnOFBJjAlulETjC/lcgoNoo2TjkeJaOOWH9FaYN8YrtTe/4qxu2bVzK0PuSXnhpo6UGSeGMkqlhESHKnO2ajLox2UqSGnxw6cr8j+5nwl++9GNAfaEuGX+rtIk4Asrc3ip+adbluMrU9eEeo3zri/26jEjrGH/i/etDrQ9r5ZEVd1xr+gztu8MIWwnLOhumgKM6cvvW3Yoop4kl/VVrabZGmOwSBUocVb4vDB9rTOrVbk9x7CpqLW23Djhvb9fhH97YkvBNv2EzsmKclxJ6me0SpEaX/W3BBEv75dhh1ZPPVUtY8m3jJM1pVstFzEST0yM3oHgJGt0uY3hKnpc1N/16u31L3/e4O1EBXrr5g+e3GJxZLKhkphA0pTty4z+oRHcs/Sg59Xvlh7z2NCwg6JLiZTqxDUaH3v7GY7a2dXQiUsfWJuoZBLl9ByThDH29F8fGRuYr1BYevqHRrChKg1W/Wg40ORMMY9D7cR/t5hsmX3On7t7FWpa+xxfwywJBUkOr+5vtj8o4UShGLoqlxHwtTe5sA67sSS6UT6BnHwII6FfXGjvdZeIiEpiAklrjJmeh1dWYeryQ3h2i3ltNz1Pra/BQl0tylaLD6W9r7wKLde1nyjadtqkCQDMJ4bGrde/vAe7Grqw51iX536Uesyjihg/VFac/31+J654dCOOdxW/p3GinOSu6i8pVS02K9YcGltgcLPG4yYh3OI9TW66REgk6Nd2SyV5YrBO5Ag3SqJdcisjpXKZjgq35X+oJCaQID7kHz61Bc9sqPHfUEQMZ3I3oWfA2Qv/h5f34KczxlaQ3/XmU02P/dPy8nHflQAeX1fsnvm9j70tt9/hu6QdZpTNaw+1FqzUOZ0s/s9zOx0VdE2S5ZLYozJqa6nQ+wb5rEtGDGaDgyMZ/PfsHTjaab044EQ5j/6vISRYIrEklvyK3pg43rmSaDQI6I0A7X1D2FTt3otFoHgu1Nw94LqdJEAlMYEEocQs29eM617aE0BvouGkvJB4vqIe020yne09VpzlstzKXLhF+/v1lkS9W6r5+DR235q7B/CdxzbhF8/uGN1228L9jq7/wrYGy/0zNtViR30nPnPXSkftqUj5I44l1o8kKVOU5KO0JJb4/q860IK524/iojvUJVaM74rXMAu6nZMkov8aX9p+tHBfSApkGM2G0dcJLpRE4+d/9YytAHL9+tBNS/GtacEku/pCwMl54kIqlcQjLuIXSEzJf/k1bf24eb51jbaws4clmYxOgM/YbO66q5LzWv0kfXzIk+tqzNuAREffUEHK71f3q12/fjd3N77x4Do0+lidY+xudOxqcB7ToT2lGBi3LIl7/9wQ9N/itL31h1vR3JP7prVTMlmJQw7jJs2YZSK7yumZkXShX8B9ccexCHuixknCHQB4wmJO4JWJ472rLsc6c/JnzrYxxTuImHitHEa5kUolkeNG8mlzEYOiIi3qg90kSe/+19Q1ppCZWRXMFuXXHCrOoGhk3eE2fPCmpQXWwX9/siJRhWiJMy59YB2qdVknrdCs2pTLpUP1fZdCobpy+iZc9uD6ou29VjJA16/9jd3oUkzG9BM+QlQMjmQwb+exxGQKtepmWH9CRW3wpUX2NwZfr9iPJ5h2alPP2HxHlVDNC9ttarsmkVQqieVI/9BIwc8Pr6yKTXHkMOh2GItYYVIXhy5IOZzW97F7kxo6iuOKzFLNNxsyy577h8XhJAviI46UDtXKquKZaJ9iHLJrWhHv3plz5mknF217dI2zMjJhoMUgzts5Zh1xWr/sS/etwbemOU9j/8dF++l2TgAA9yw5iJ/P2o5VB+0XNONAFOJQnzgqKEZKPA8dGM6gqqXXdjEgjPt72UPFC2BJh0pimfBN3ct51+KD+OOi/QWDcFpZvKcx6i4EjhvlX0Li6x/4W+U+Y8IIy5ikvETVVvCue3E3PnXnytHdoxN83YTsR6NFse2ZvaUeT2+oweQp8wsWPPzAuWG0DAw7S0Qzakm0eP3aegfRkaCsw3G3VqhKB5W6y6/osgjeuqA4lrm5ZxAnhjJFSbP2u6y1uSIF5RKIPdp413UiGW6BVuNxvKVLIWEYK6xk1XuuW4TP3b0Kty7Yh4MKN/batn40dDhzlTVDCJGacBYqiWWCfuDsHcwJwcER+0nakj2NsU89DwCzt9Rh0e6xSYXTz/M1E8eH06EywDiJ1wte/c8X37MKOxtypS+0+/7MxtqCc7NZicGRTMHkc4WiiLcZEhLTVucSELX2BKMM0IJQWuoMMSpXTd9UdIwqG7FQJFEy8uGbl+GDNy312cPS8det1omZSkmSP4OZuljDXofeI0bcyCFS/iTFi6hcHMFKbUnUeHTNEXzhXnUymWV7i3Mh+A1hKlcmWO0UQjwDB4sWUsrvBdYj4ovhTBbPVeQmKE5Whn/8TC7TU83tl4TZLd/85oVKAMDqaz6Dvz/jFN/tlVPNMyvM3gHVMGl2Rw43O4ste3xtjaPjzNDG7rQ8m3Ljz6urPJ2XnMQ1zjtYr3C/LmfCenZSytH7/sCKw+FchCSa7XUdmDRxPN77N6dbHhdz8VKElbyJu6eCnqT09av3r8WG334u6m7EDjtL4mEAVfl/XQC+AWA8gIb8uV8H4DyNHQmd+nZ/ZvS4820X8ShA8gaGMHC6bupElpvWNBNiNNupV+rbTzjuhxPS4g4SF/w+NyeeD3FGmpniI8YYAxwVXiaLQog43UoSQy57aD2+/Kc1WHGgGb2DI/jV7B3o7Fd4oxRHRMQa7bXPKixxWp3oJBBKWQ1InBjK4M7F+/Hx25bjE7e/6up8lTX5eJc6k7pKbs2vPJ4aTyVLJVFKeYP2D8C7AFwipbxKSnmtlPI7AC4B8O5SdJS458Udyc/41tg1gF88u33092MmH7IZxu9Yy6T5WISJG0qNmYz2IryvnrHNdJ8fmRnGQJIWIR4XvK4Y7z2ey34XRqr0IHHz1019NXlWL7ui9l7Ze6wbk6fMxyGHHgmEeKGquRd/2ViLOduPmroZAsBShathFEx5YVfRtoJ1pnzkxqAiflir9ZcEwkpI9tDKw3hwRRWOdQ2EJrsAYMam4vI6m48EnwU2rriJSfwoAGOe2E0APhZcd0iQbKxO/ot88/y9eElRI8ix2DFoCuf+YTG6TgxjeUqSGVjdJ+OkXu/mGWVm3OSskRI9ficDh5qSrUSk2eJl5SI+b1dOfj/gUXEul9gsEi76Mau5ZxAfvXV5wX7tHX05Jgn9nt1Sb7lf6+84xSw9SXG2YX2/ThOjqeSymwXktSFkfE0SbpTE7QBuFUK8BgDy/70FwI4wOkYIUFwP5/RJlmG0Rahkwdxt8UkqESWft1htff8NS1y1JaAWvF5qJAUVw+CnlhJxj0nFE8eMGxfv55XJSGwrwzpYduw55vwb3n20y3Sf18n54+vS4/VBvGMsw9TY7c7rKA7oh6zuEzmvp6QvPoURk6hqUuWWa91Gwm9siXCjJP4bgE8A6BJCNCEXo/hJAExaEyPK7bUfb5g4Thife2X/7g2vcXS+Sk+IKttWFEgp0dztMCZJd1usLIl7XUwan/CQzCY9T6e88JtwaHzMc23fvfQAvvnQeuw+2oVN1W2YPGU+qluSbf0Miu11udQEX71/bdE+P3OxeC8bkDhhzLitJ5uVWFCZrHJYF925AusOJ9+KVarp1m/nVJbmQnlunr+vpNeLCsfDspSyRkr5cQD/AOBSAP8gpfy4lLImrM4Rd7y04yh+/ddiP/ckY7QGaS5t737LaY7OVyUvGfJr8kgYG6rbijf6sLJ9Zeoax8d6URz6B5OdwCS1OHzU+lI2esbH3PJbmS8D0943hBfy3gh6l/40L278cjYdikj80MrybHXpAXDrgn34/uObw+iSElXdUiBXRmjfcffeOHEijJhEVYuzK6zdd4k3XK/dSinrAGwG0CCEGCeEiPn6b3r4xbM7sLW2vNyhJhgsiW5dCpSWxARlBislxrtyuNld0eqgsol+7YFia4QXYq5zlB1Ovyoz98Uvvu8twXXGBc09A/j9S7sxbLN4pGUUnDBOjJYZqmnrC71/SYclbUhUVNTmFnHsvm0j01ZXY9XB0sX93bYwZ5VSJUSpbUt2xvpSOm6psvurvD0EnHs4DCQ867ZfHCt4Qoi/FULMFUK0ARgBMKz7RxLAzvpOtPcFU6i8VIwfXzjTdytvVHrCSMosiSrWK9xYjD76VhninOJUEDM+IPk4fYZmuvtZp03y1a4batv68IeXdiOTlfj9i3vw9IZavGqTzEpzwR43TuAT/3AGgMIYab7DznH8rgjgg3//+pB7Q8oVTUHJxnzIP9iUW5BduLvYJTbp4TGllIsX3rGiaJtZRnynvVqZoCRBYeDGCvgIgCEAnwPQC+BDAF4G8JMQ+kVC4OsPrsM3H1oXdTdcMdFgSQxC3gwnXOgGQYXC4my8t25uE612pKXXYeyr7mWpc7BK3pMvWxMk/z17B57aUIvKo12jli67yYzmNjVOiNG43LuWHMSjq6sD71+5M9sms6OerhNchybe0OolhlWGISjGq1KY5nHrPRU3QnE39dumyYQlyqzuccWNkvhxAP8updwBQEopdwL4DwD/E0rPSKDM3a65RyXLdcEoPHsHR9DVr540fPX+NTjv+sUF21SyYNjE/78ccSNL/YjH383dHXibJFmsO6yIfbXhojuLV35LgVZMeTiTHXUrs5sfaPHRmawsOPaWBTlXMb7rznGaMXU4k0V1C116iTe05CJNMc90apXYORNzBdeOuFtx9UxfwwU/I27qCWSQczMFgE4hxJkAugG8NfBekcD579k7bY+RUkLKeKWinzC+uC9rTTJ+7T5aPPEQCi0x6e4bcUWlkNcyZosYiIN00UTc/a8exsBwbhajrXibrVJrclFKWZR12W3MU6pQ3E6nE1/KauKGJ0zKpVwT84R+Kw+0oM/EYyLp30BYVly/zarOr+9IlhGlFLixJG4C8JX8z4sBzAYwB0BF0J0i0fDL2Tvw9msXBN7uusOto5ZMtzQrVgCdTDCyWWkae5imCZ2bpBG+PTgU27bUOEuklPDFUuKB70zfZLrv1gX78H6DV0CQaEreal1yCu0dPOe3ahmoLXhkpCzKunzN8zv5Dpugui1+MzY6LaRN0kWT03JPMaROkXQFSH6scxjdT/YdSRZulMTvAliV//mXAFYA2A3gyqA7RaLhpR3qYsdba9t9Caqrpm/Cf8/eiZ4BZ7ElN87bi6V7mwAAzT3FQt9JX741bQP+4XcLldYt+p3Hj7nbj0bdBVIi5uWLqpt5BAC57ILdA/li0gGt6TR3D2Awn6nOaAkE7Cce/UO5czNZWVTT8UUT2UnUGJVst9y1+EDRtn97onQlC0h5kM1KTJ4yH4+sqjLdXyruXnJQuT3p85U4xoMeanKXuT3NuKmT2CmlbM//fEJKeZOU8jdSSnXRK1IWLN/XhMsf3mBZqNYpxzrt4wKGRrJ4fN0R/OjpnIH69EkTi45xInQ0C5aqLEMMZVYsiDJVfXVr8G6ptDZEz/GuE0Xb3D7r37+sjnd1yz/duhw/n7kdgDpRhNOFMCnVNR27HS6CpQ3V4qDTmphm9ePmKBaV0p6FkLhjz7Gu0djYOxWLDgDwSAmTUi3b16TcTiUxeJ7eUKuc78Swq5HjpgTGRCHEDUKII0KIASFEdf73k8LsIPGPn8myVnfmcHNxrRkjU5cfsgz8dTIveHV/oaBUpT/PZtWxhk6vmaa6XU5iUTXKTUA6dXUl4fHAq4d9t2Hm4aAim5X44VNbsKm6MImOpgAuyXsotCo9FBxeQ0pl3PZft3pzqS93Zm0uzmRqkcyxgPuWHVJuH+QCEPHJJVPXjtbkNfv0Dzq0OO091o3Khq6AelZIHJUsN4Sh4yb8liQKN+6mdwC4GMD/B+B85EpffBbAH52cLIT4ixDiuBCiWwhxUAjxw/z2yUIIKYTo1f27TnfeyUKIx/PnNQohfmVo93NCiP1CiH4hxAohxNtc/E1lTyYr8Z7rFnk+36kyBgD3LD04mk1MhZN8OA0dxZYHI3YxiUn34Y8K1X3bfdT5wMcyGMSIl1Vwo4Lnho7+ISzb14xvTdtYsN34ah9TWDidLh5lJfDevzm9aHuasib7RSWiZ26qc3x+3xCVRBI+ThW0r0xdM6pwBt+HUJotGTvqO0Np1+9tUcVFtygWD9OOGyXxXwBcKqVcIqU8IKVcAuAyAP/q8PzbAEyWUp4O4FIANwshPqzb/3op5an5fzfptl8P4J0A3gbgMwB+LYT4EgAIId6EXPKc6wC8EbkkOrNd/E1lz188uIn+fNZ2TJ4y33JlrH9oZDS+xzn2WoSVkqlhpwTqd6sUU+qQalS3ZYaLiRvvKzFipiRq9cv0SClR1dJbpOC5QX81vZww9kIVE+f0/X1y/RF8ZPIbirYnPVV9KVHdqWvnVpa8H4QA5nIqDp900t1Nw+B41wnfz2bxnmL33g0+FijLFTdKotkM35H9QEq5R0qpqeky/+8dDk79PoCbpJQdUsp9AB4F8G/5fd8EsEdK+byUcgA5hfJ8IcR7nPQpDbT1FU/G7NASS6w+VJz5T+Mff78YX7h3tat27SxNTi2AdslJfzZr29g1VTGJjq6SPhbtboy6C6TMMJvgfOm+NcrtxsLpZhmKzdCLkL26lWKjbFEmrnEoGMzqQXIu5xyzWENCSsHaQ+qkWVLKAot2HFw9S5k8Jyn8bOb2UMKG/CbUKkfcKInPA5gnhPiiEOK9eWvei/ntjhBCPCSE6AewH8BxAPpc47VCiAYhxBN5CyGEEG8A8DcA9IFVOwGcm//5XP0+KWUfgCrdfuKDTFZaKna1be5qyuibymYl7l16EK29Y+Z9NzFBRqpbxmImF1RS2fHCyRPdiANC7DGzrjWaFLc2ihujZ8Gqg86Tk1wydcz9yzjPUnoYOG5ZDSdzzgnLBY0QJzyyWp3NdPGepgKLttUXXdPah2km7aiob+/Hot3u8zzSQ6F0xKhEeGxwMyv8NYBlAB4EsBXA/ciVwbjGaQNSyqsBnAbgQuTcRAcBtAL4CHLupB/O75+RP+XU/H/1fo9d+WO0/UafSP3+UYQQPxZCVAghUl/XUTVAm1nxhjP5AtMBrNoMjmSx8kAzAKCitgN/Wn4I1zzvPLGKhqqvn717leJIk8Q1lLlK/lERZ8WFNeIHv9/aRoP7z5EW6wRaZnJKv31gOKPMrimlxPoq87IcdsTB6kAIsWcko15oPjFcWNDeyrvpykc34tYF+9HVb5/VeGttBy68YwV+8pdttsca4eKTmjDEbYeDZ5k2JljtFEJ81rBpZf6fwNgiyycBvOr0glLKDIC1QojvAPiplHIqcrGEANAkhPgZgONCiNMAaDOC0wEM6H7WUk715n/Xo9+vv+40ANMA4OS/eWeqvzpVxq5pilTPAsBNr+zN/+xfW/jyn3IuZiv/99OjE6q+wbG4RtVDUW3z66OfpuymbhhWDJxu4F0lRtwqTsajjae7/fS7+ofxulMmFrSz6Ui7MjuplLkshV7hXI6Q5PL0hlq8/+zXFWzLWnhF9wzmFMotNe22bd++0D7XghmUKyRKLJVEAI+ZbNdeW01ZfLvHa6tiErW2x0kpO4QQx5HLpro0v/18AHvyP+9BLmYx1xkhXptvU9tPFKj8rlWFrfWHHe20zzrqlN7BEUzIT9LMXCnOOu1k0/PdCE032VnTzrDL+C8j9yxVFwMm6cXt/OZHTxU6ehgXdOza21BVaHn87uOb8MJPP47GrjH31l//dScmTRxfdK6EVMYqOoWWRELih8oaqFooFqJ4UWrEYrLRM5BTEo+Y1H0dGM7g5AnjIIQYPRYAatv68LYzXuuk6wDobkqixdLdVEp5jsm/t+f/nSOltFUQhRBnCSG+LYQ4VQgxXgjxRQBXAFguhPhnIcS7hRDjhBBnAJgKYKWUUnMjfRrA/wkh3pBPSPMjAE/m980F8D4hxOVCiEkAfg9gl5Ryv6e7kRK8ZPx8dX/zaFFkv+4P+jpj2+s6cKAxZ9lcqPPXt9LtslI6tmsqj6PMVaJSEjk+ET/M3+UuBseYaMsoaroUWVH1rD5YuNi1/3gPrp1TiU/ftXJ021mnTcLrTyku7yulwyxsJjALISHxQ/VZqsY1KYu9rLQC91JK3L3kAKoU7u63LCi2EjZ2DeA91y3Cn1flPLROmjA21X6+wl09VbqbqmGps9JQqkwVEsBPATQA6ABwF4BfSilfRs4KuQg5F9HdyMUpXqE79w/IJaOpBbAKwJ1SykUAIKVsAXA5gFvy7f4zgG+X4O9JNMr07w40p968e4U+2YwXsrrJWFYCX7wvlyX1ZzO3OzyfbpFhsLG62G1m8xGmhCbRYfzWp7562NX5GSlHJ3oaEhIXvK24hIWEOuupU2hJJCR+fHvahqJtytAWKfGIIuwGAB5bewT3v3oY33tss6NrNnTkkvr9cdF+1LT24TU6z4UHVhx25dbOxSc1vCulwc7dNBDyytynTPbNAjDL4txBAP+e/6favwwAS164wGsylxX7W/CFc9/s20deSn9RgUFcnzhD7yZDSJioJ27u2nhhW+EqfSZbLGvMvKql9OeezhV/QuLHlpqOom2bjxQviJp9vXuPdY9mWR6xClLUoY95rmrpxYTxhXLl8ofXO2oHoLupGbwtpYE571OI00LSxgnTtXMrcfE9qxwLSjP8zqXcrNirjqVscY7fOEVCnLJEUdw4iAWdTkPGOmnirp6V/mIS0z6Z67RxBSYkzph9vvqMp04T+J00fmxqnZXFcy431sGUixUSMVQSU4iykLRCderoKx70O/uHAxBaEgNDGcsjmrrNXVq5Yl86VKnCCQmD3sHi9ONhfOpmEzQJKEtjOCXtYukDNy61P4iQmGLu3zQmE/5/9u47TK6y7B/4957tve8mm81ms8luyibZ9E3vCamUhIRAKCHEEIo0aUpCkRZpgl2UpqIvivhTRFGxoLzWCK8FRTESQARF6R2S5/fHzJk9M3POzDlnTpuZ7+e6cmV35pwzz0555qn3/dwrb1kaOC1J6CRaC5xjhstNjX3rd/8MuggFgZ3EAtNYVWoYuMZoP9rnH3rC8eM88Kd/4Yd/Tp0ZAKKNqY/96PGM1zDLP5Qu4ljqYxlU0KxzLSv02RHyj9XBqzlX/zDh9888uA/ff/Q5y49j+p5WKqu8oBy8IspdZtVCcp3w1b1Pm17j10+8gEM++lO8895gR9JoNYSdqoLfwcZefpM5Df3ATmKBqa8ocTUthNkG7O1f2IuTksLZa5Qy7pQme9Fk+dKNDzyOf79qLXgO69fsvJFhxpfILUZLuYxWtv9Tl84CAPZ89zHs+OJvs378g4qBa4gK1Q///G/D25NrhG8+Yj6Ddem3HsVf/vVqQhTUAwdTl5va2jLDwScKEDuJhUaAR595OfNxaejrtx//xbhi1fzt36+m3KaUwsRhdQZHJ7o6TQLay7/9p4znAybhry2dSUR+MsrVmqkx9T+/fsr24/z9eeO8ZirLPYlsyxHlrudeecvw9uRB9V/vNx/g1uqPd3RLUg8ohRV9bQnH2RlP4uATBYmdxDz39rupM0F2w8ink2m9/JP/fSPlNqsj9maNOTuMl5uy0iXKBc++bNxw01x4zx88qc2IAAAgAElEQVRceywF46BeVnHEnyj/2KkR/hAbgD//7t/Hbztw8GDCHkW77GyvIXIbO4l5zizvTzb0+4Tefi+1E/pv3YicYdJaWNv749WGbVa5RPnnlbey26OiFFBZWpT5QBPcO0SUf4zaKl+xsYIh2wDhHHyiILGTWGDcmJ3T++SP96XcNvOqwcAShtWbxTrv7/95Pes9haxgifLPC6+/g56LvpNw29MvpK5asEMBCUmv07nqO4+l3Maqhij/GO2V/uyDqe0eMwcCThlGlA12EslTZpG9kqtdsyWgH7k/tTFm6/Et30hEueI3+1/Au0npWbIdUHr0ny9nVTVwQIoo/xjNJNpZNZDtTCJTPVCQ2Ekk2+w0xowOVVBoqSlzrTzpZJujiIjCx6gOeifL1tg9Dz+T1fkMMEFUGOxMDnIZOuUydhLJU0YpMpQChjdUJtz2lV+b5x7Kxo0PpOZjZJ1NlH/qK0qyvkY2dYM+NxoR5QejmUS7KSy++htv2jdEXmMnkWxLrh7TRQu96YcGnTQAn3/oiYTbrrjPWkoLIiKjNQrFkWC/zn74WPp0QESUe4z2JNqJOPreQYW9T77oZpGIfMNOImVt5Ae/k/kgHaNROD+XanEmkSi37TMIwJVF9oo4LkUnIr1/v5qahsdOGi3uVaZcxk4i2fbSG+9kdf57Bww6iVypRUQWXfu9v6Tc9vSL2UU3JSJK9tIbqal17PT7XsyyvUQUJHYSKa2e1uqU2954JzU3oh1/+/drKbf5OpPI2QKivPPcy6kj/nb95okXXCgJEeULo/bCC68bd/xmdTem3Papn1hPl0EUNuwkUlpdzVUptxkluX/TRsfxXYMohH5GAHvrXU5bEuUbN1Z1PW4wgEVEhctO02RIbbl3BSEKADuJlNYb77yXcpvRrN8HvvZ/lq/55V89lXKbn/sEH/zr8/49GBH54s13s1vhAABFERc2NhJ5aMnY1qCLUFD+89rblo/lGiXKN+wkkm1GHbqHn3zJ+vmsSonIZVd823qE5P+aLBdjJ5HCbmBk6pJG8s5V33ks6CIQBYadRErLKPyz0UziS29a35y9bFxbVmUiIkr2to08hf96xXj/IjuJFHZuRPElbxgNoG+bO9L/ghC5hJ1Ess1o74+dfX6cRySiMCpiC5yIHDKKsMxxJ8pl7CRSWg/97T8ptxkFrrGDeQqJKIw4k0hhZ7S6h8Lhkaesb7shygXsJJJtdhLJEhGFzVMvGOdUFM4kUh6rKCkKughElEPYSSTbsg81z04mEQXnHy++aXj7K2+mJs4mCpNsxjG+fcY89wpCRHmPnUSy7Rd/T12CSkSU6+77w7NBF4HIM6NaqoMuQsFxIzUPUVDYSSTbvvTL1DyHRERERDToToO80ES5gp1E8t1jz70adBGIQq2qlHuH0uHzQ4WK+2aJyC/sJJLvGAGMKD02BNOrKisOughEgWDNQER+YSeRiChk2BBM79+vvh10EYgCwfEjIvILO4lERGHDhiARGWDVQER+YSeRiIiIKAdwKToR+YWdRCKikGEzkIiMJPcRT5g9IpiCEFHeYyeRiIiIKAckDyCdtawXE4fVBVIWIspv7CQSEYVMJMK5RCIywOWmROQTdhKJiEKGzUAiMpJcN7DPSEReYSeRiIiIKAewU0hEfmEnkYgoZAZGNgVdBCLy2PDGCtvnRJJ6icJ1B0TkEXYSiYhC5sbNk4MuAhF5bOuckbbPYZeQiPzCTiIRUciUlxQFXQQKme+eOR+XrhuPzsbKoItCAUpZbspeIxF5pDjoAhAREVF644bWYtzQWvzz5bdw80//HnRxyAVO+ndcXkpEfuFMIhERUY5gF6GwdTVXJfxuNZDNl04a8KA0RJTP2EkkIiIi8sENm/rjPzuJVDqls97R484c2ejoPCIqXOwkEhEREflAqcGf7fYRW2rKUm6zeg2zDun1G/uN7yCigsdOIhEREZHPxOZU4uiW6pROod1rpJYhq9OJKI8xcA0RERGRD3QTibY6aLedOANTOxtSbrc8kxj7f+7oJvzv3/5r/YGJqGBxJpGIiIjIB0q33tTOJN7iMa2oqyhJmTm0OxN4x4kz7Z0Q8/4lox2dR0S5y7dOooh8SUSeFZFXROSvIrJdd99SEXlMRN4QkR+LyAjdfWUicmvsvOdE5Jyk65qeS0QUhMpS5jkkj3B5YE5TmQ+xxW5KDKedzLmjm209DhHlPj9nEq8G0KWUqgVwKIArRGSaiDQDuAfAbgCNAPYCuEt33qUAegCMALAYwPkishIALJxLROS7hsrSoItARCFUpO+VOdgQmLon0eJ5sQNTzrfYyZza2YCmKtZrRIXEt06iUupRpdTb2q+xf6MArAfwqFLqa0qptxDtFPaLyNjYsScAuFwp9aJS6s8APgdga+y+TOcSERERhUJnU2X853yfFL7s0L6gi0BEWfB1T6KIfEpE3gDwGIBnAXwHQB+A32nHKKVeB7APQJ+INAAYqr8/9rNW85ie61aZL1473q1LERERpRg3tDboIpBPZnQ1Yl1/u+Pzk2cOLc8kmhxfUmStGcgoqESFx9dOolLqVAA1AOYjukz0bQDVAF5OOvTl2HHVut+T70OGcxOIyA4R2Ssie7P5G4iIiNy0ZGxL0EUgH0zprAcAVJdFA8u70fGyuydRb/LwelSWcf80ERnzPbqpUuqAUuohAB0ATgHwGoDkYdRaAK/G7kPS/dp9yHBu8uPerJSarpSant1fQEREROQ/EcH0EQ263+2fr9E6rZbOs/cwOHtZr80ziChsgkyBUYzonsRHAfRrN4pIlXa7UupFRJel9uvO64+dg3TnelpyIqI0uDSL3HTXjlnxn7OZOaKwyC7G6cLewZlny3kSDQ708r3UVluWkO6DiHKPL51EEWkVkc0iUi0iRSJyCICjAfwQwDcATBCRDSJSDuBiAL9XSj0WO/0LAHaJSEMsIM37ANweuy/TuUREOenrp8y2lJvsisMn+FAaCtJAd1PQRSAXJPeZBIK+9uD2o3Iwi4jS8WsmUSG6tPQfAF4EcB2As5RS31JKPQ9gA4ArY/cNANisO/cSRIPRPAngQQDXKqXuBwAL5xIR5aRpIxrxgRVjUFqcvppmQ48oNxRFoh9WfWfxsMnOg9gAqXkP7Rxnp+qw+jhElD+K/XiQWGduYZr7HwBgmLYiljZjW+yfrXPdwMUSRBSkoXXlePK/bwRdDAoJttVz19C6cgCDnUR3Atc4F4l492Yq8vDaROSPIPck5gRWc0Rkl58Nee5RI8oNV6+fmPC7G5/cbOoaATBvdLPp/WVJqxjsDJozrQtR7mMnkQpeR0NF0EUgIqI8V1Ne4vo1RQTK4ZqnUxePRklRxLSjePKC7sHHsXHd2vJiTBhW56hMRBQe7CRmwOWm+a++0v0vbips2czuPbx7ua3jnTYQKTw4G1xY3P7MOg0iWlfhzXdfQ1WpJ9clIn+xk5gBv7qJyK5sloA1soFFlNeOn92F4ohg0ZjWrK5z+uLM0Y+tMOu06m8VAVNaEBUYXwLX5DJWiUQUZmy3EeWWCcPq8LerVjs+P/kj71UdwLqFqLBxJpGIyGV2JxLXThrq+LHYjissXN2SX9xYapxtHWDUGTxyWofrj0O5I1PqJSoMfBdkwC9kIvKaFhrfidndjS6WhIjCYl2/tRyKXiwDXT6+LWEZqoj4MrNYUVLk/YMQEQDg+o39ae9nJzED5qQiO+7eOTvoIlAI+JV4ev+eNRjdWoMvnTTgy+MRUf4x6vwplXq71c5oNp3JjdNTZzCJyBsbDFYM6BV8J7GlpizoIlAemd7FWR2yvwIh2xH6eT3muc6IKPfcvXO2aT3i9hCU1WiryfXUJhsdutLiCC5YOdZOsYgojZV9Qzx/jILvJH7ztLlp7+dEIhEFiXUQUeFJF+XY7cA1RueLGDxO0u/XHGm8VM1oP9ufLjsEpywalbEsrO+IrJnf6/3gcMF3Etvr0ydST142tnVOl4elIaK84GJLh8EiiArP8MZKy8d6kSs1m+WmNx83LeU2v5bgE+WiGzal3xsYlILvJGair9duOWE6Lj20L7jCEBFRQWNbO78YvZ5rJg5FSVHEctcv65lE09uVpeOSdbdUOy4LO5NE4cFOog2su8gLs7ubgi4CuWyEjVmATKxWO1WljApIVIi8WG0gknrhbDqjbD755wdnLwi6COQDN9LnZMJOIlHAvnjSzKCLQC67cfMU1661a814S8fd+/55rj0mEQXH7vJRJykwDp+sS69hFt009rO2PzKbZa1WB9kPHEx9jIW9LY4ftxD1tNUEXQSyqbgonN2xcJaKqICEtXIg5+oqSly71rLxbZaOy2aJF+UOP0aPKTOrn/Fl41o9K4PW8Tpy2nBb5/31ilW4YdPkjMdtnjEcdRUl8QGobKIpWl1G2j+8PuW2bPLIEoVde105Rof0+5utU6IQuHvnbOxcmDnyGxERBa+h0upAkHedem0CcefCblvnlRZHEIkMlstohrC2vBjdLdX43SUrMCwW4M8smmkms7qtp4ZaO2moo8eg9Ni+CK+ff3ApKhxsF/EiYFUydhKJQmB6VyOOmdkZdDEoh3xt5+ygi0AuiXByMKfVlhf7+nha51B732Qb7KW7OXEW44rDJ2D2qNS98kapLawYY2P5I2M/EFnjx6oSf2s2IiIyNTAy84j70TM70dFQgRld1kfnKdwY0TH36F8zv9PUHNR6iS69by47rA937X06/vuxs0a4cl0KDz9mnci5sH4DsJNoA/eCEJEXtK/vZeMy7z+8ev1EbwtDvouwk5i3vHhptfrCrUuXl3gbGdlO98SonZVtig9i+zUfcbkpEcWVOVzqQ0ThtrZ/KB66YDGKuO604Ew2CNSSUazXFMTgQnssiMz8nuaMxxp1Zu8/a777hSJH1vW3Zz6IfBHWcUK2OpOcsbQn4ff6ytKASkKFRj8q1N9Rl3DfRzZMRHsseAAR5Y+mqlKMaqlGR0MlKi0ELwhrY6LQTBhWl/mgmKKI4LDJxg3y6V2NpjlOzVJbHLS52nSYi98dPz5vER7ZvRy3bp2R8djBVbGDBR07pDbtOXx/++fjR7uXqonCaccCe0GtkrGTmCS5fpoyvB41Pm9KJzpyWkfC74dPGRZQSfLfKYuCjfp24tyu+M9eN5CWW0ynQdTTGs6Q7GFxzYZJlo/dd9Vq3JQmd2pNuXGkVLPFZNqAotXq4mSb0U/TKSsuQkNVKUospG6yuhjOyqwkZc9p4CHynpPlwFaWYW9MakvaxXdMkvVTBxvjwxsrMLyxEtNGNARYIipISb0F7ifwzsimqkAff9UE/0K+V5dxwCvUQrT3aqKNmbJ8YyUap9WQ9V7U3Hbj1gT17aHNhGYqZ5euDjY79NcfWmorSiqlum6jsxQmXrKeSoaSZVpuXlYcQU+Wnxl2EpOM0FVW/R3RvQLcNJ3fDh4MugSpkrcmcQmOd4w2f+tn97zm52trtnyNvPW546e7di1WBd5bOcFm0nivPlYm143v9UtTebTVlg3+4mElk7zqxYgbg5ytteUod5BLjsKNkZ2d8+OpYyeRCt5BFxrOt26djod3LwcAlJc4+1jpi1ESiSTMagvYwHfD/j1rLB33odXjPC7JIMYqyX9uLvMttrDMj7LjZuMrm2uZRS+0MpN47ooxg2VwXoSMtgyY5/d1kqmDnQZ33HfGvKCLQB7L9Elxo8XIb5s0vKyszlrWk/kg8kVrbXnW11gytg2NVdEgRz8+d1HW1wMSlzwxRL6/9Ptt3OjEvX/JaNP7Ohv9W+7KYYbw0X+0qywsBz5hTpd3hdHheyV4ZuOC6/qjS9RXpBl8KNOltQj664PfXv7ra09dLs6B5vzCmcSApTz/Lr4gDYyaGho3HTXZ1esNrXMnktwRU4ahu7kKPzt/MSKcbsppZq/e1M56tNSUcUl7ATp3RW/Kbf+zY1bG8+oqgtnD01xdlvkgcpVZvdDXXof9e9ZgdKv5fqNh9eXxtElB7WmPB9ixM5NodBu//rLG5zD/+PG5ZicxDS/b5cfOGuHdxcmWhqpwdNiT2wOtteX40bmLMLyxMpDyUHasRE31e2kVO6PhYfRadDUHG0SJnPFuS2I2VxYcPjm6ZSGoDoJRCgzD45L+zn4nuSMpJxm9M46eOdz3cuQki5/r5JRqdrCTmIZZxXbByrFZXhdMmkyU5y5YOZbLyslTayb6Fxm3voCiENodoU93dDaj/dkM6ojYT5XhNqvFP6g7UAS43UIORnLHvacHu3fR6D0yqYODBEbWJ6VCy/i5jj25x6TZN5wJO4lpaC9A8ps46LxqVAA4hpBzPnpUanhxqyPpfuFEYn4Z354+Mbmb7tg207fHykUzRzYa3p5d4BrnosHOsi9DNhpj22paMixVTu4Mm63u4di6+yZmMcvkhuSXdOnYVsfB//LdDUdNdjVSthUF/UpkSiwaloYd5ba5o5tM77twVXaz0mTNyQu68f2zFzg6N11DrbGqFFM76/GB5b04YkpqKHgtcm6QgYdmdDHPaxgNvq9y43tmWL07e63z1e0nuj/7ld1MogymygjoPXbktA589Kh+bJs3MuW+Kl06iym65aXp2l1Xr5/obgHJE3ZWOCS/3txek15Hg7/1cEF3En9+4RLD26/ZMAkAN/qSO8YPNR/tXzbOvdD4ZO7khaPQa5JUNpuG2NTOetxz6ly8f6nxstKWmugIemut8Uj64GqF9IXoy2LG6IsnDcR/ZnS78NvqU/TSdO6yEECHdAQoNUlNkk074pgB53uzhtVXxNMoDXQbz3J6LRIRHDGlI2V7zQPnLMCD5y+O/75xeuZciwAwdoh/M+f5yK/q384KB7az09u1JjEd17ihtaiMDbBY3eubzSBRQXcSq03CjZut4w/be3kPR9VygpMZaaMzDrJ978iQ2vJ4ehK3XBTPo5j+tT12YAQ+ccwUHDU9u43438pi30h5CRNQ55JLD+3Dnz58SKBlYDRl+/T1fLrUFHYsGduG+T3Nts9b0NuClpoyzBnVjP171mBEU7gCIo1urUmIlstVW94TZBsIyRt85dMz+mxoqzr8qKYLupNoRhtt0ZaIaaPvQVdkycsWWa/mPv1rmGmWJ4wVfC7w4nNiNbR7JCJYO6k960a31UBXx87qxOqJQ9BeZ5z70+130LD6Cpy8oNvlqxaGdB/3ytJiTLYQ4dGrmeFM7zY/A+bkIv2g1JqJ7Vld644TZ2LfVattnWM2q+mFCcOc72m7+bhpOHtZaioYAPjVh5bijDT5ZSk/MAd0lJ32ndGRRjER3FDQnUSz9+bc0dGRu00zoksg4pu/s3is0a3V8Z+dfq/ftjUxcEBQ+wzIPdpyRCu4UjAYbj3vpy32PuDVFYdPxKe2TMPPP7jU88f6+NFT8LWds/HB1eMyH5yjVk0YEthje/Vx15YgppOp3fZRl3PLho2TATmzp2zNpOw61JGI2I6G7me7u6QogpV9zj4nK/qG4EyTCNBtteUYyn2wee8cg3yxhcisnWH0UR6cuBq87cBB69e0o6A7iWaGN1Zi/541mDYicR1/kAMeNeXFqYF22Ef0nZNGo9nLtH7qMNSWG4eVN5q1ZifRGS8+JlM7o8FgTrSxf+y8Q8ZikkkkOd+ioLr4HlrX3472PG/E9ZjsY3WD1dlot6OK9qRJwK4RkbSzlJmCvgHh2Fvpl5StKTY/xgddrNx7WqtxznI2vCk4dt7/m7LcilGIjGqLBo9SFBV0J9HqTJwbmz+9EK7SFIbrN3kzpQ8kfvCNIqL6Ge4+nzjteH1kw0R88piphvc1V5dh/541mDPa3n6hTG1BfqbDJ+jgUrXlqXvnp3ZGl6J6NXCULthWIbD7XZ/6MmR3/shm53sIf3DOQozL4vVzknibWyFyg9X64mNHT/HlcWiQ2VNm1HzR9iTq80lGRPCjDyzE2CHuDmwWdCdRkykAzMHYNK7dtuaXtw8kjKZuySKhpZGg90gWmqrSIlSWGgc7SsvmyzSyuQpD61JnaG48ajK+tnO2/ccnR46a0Wm6VMzpR8+sMWW0fMQLbMzZIwCWjWv15NoZG1Jp9sJ/afsAfqaLDummJWNbCz7YkZPPYVb5EHXvhY3TOkz36fnhazvnBPbYyTJ9Rm47cQYuXjvel6X8heTQ/uz20R7MIsqeUqogO5l29pd//Ogp+MQxUzCqpRoLe1vit3e3VOP+swZTfcWvmEXdVNCdRK1SP3xK5j0agP3nec7oZhwT6xgKEN+7w74dAfZHq6vKijGjK5hQ5vksyO8jbcR/MJ+Zt5aO9WZW7KbNmfeoHT2Ty4rcUllaHHg+sWNcHvTMZdkuN7143fj4z9du7Eexj4FnkllZSuyFm4+bltI5eS82Ql9i8nwsHtOKbfNGFmSnwi4/2518OdKbOTK1HWfnOauvLMXaSdY78vqYKHYVdifR4nF2Rt9/8cHE3IuDe42yawAancu+pr+cztxa7QxWxEbvRzQxmayRGoNldwBwzZGT0p6X7Zej/vrDG6MzvE6Xnic3ZnatHZdwu1erA+7YNhN71k/EhmnW8pGlM21Eg6Pzupudf1GRsSAbY1cdkb8pmATAeoPB43vTpKLJ5rOb7cxNPljRNyRlmeM770U7ifqO64PnLUo5t8oknRkl8qszfYD5utK640R395lnMrWzId6+tKugO4lW2WnAJS8T1Dak6xuVTr5KjB6bM5K5QQT47HHTsC5DQ6C9vgKfP3561vsB8tWK8cZBg7L9GGT64tRvrHf7S7asOFpxWw1i4tTC3hZsnunOzM+XThrAry/yPnpqGIh41xHza/aYnJnfm7rfeGLSfr0dTP8CwLvOx8Zpw7GgtyUhzY5Rzsft80d6UwDK6PPHT0+5bblLeUKPsLjKL9dUlKZ22Mw+Q2bBDdOpMRg0cbrPuaA7iVZH/uJf5g6+zRNmEtkayGlmL983T5ub8dxD+obg40mdP6MZ6mXj2xxVCpqTF+Zno+WE2SMsrdm//PAJKbdl+tw5Cndvcs0LVo7FNRvMZzbN/gQ30uzY1ddei08eMxUzbS5hrigtQmuNcR7GdFYGmE4ilDLmRc3OrG4uTXfK6nf1qYsS98LN7o4GHDOahcxnc0YNBlq70cX0KHWVJfjCtplorU1f35QVF6HYj8ziOaw44n5z//6z5mNZUofw8MntmDCsDkMyvGZWeJUHNkweOGdh7CfjvzXTdjijuurhi5dj4rA6S9tAMuEcPSx8IaR5n1aVFuH1dw6kOTW7N/molirse/71rK5B3uq3kPTaL0V5OhKxbHwbvvHwM4b36Qd7jP76yhL3qrlM31mnLHIWQGFwg7l/r9+9p89DJCJYM2koui68L+X+lpoyPP/q2wm3mUV7zaSvvbbgl4Rl+q4w4/QdEbZo3LnE7tJR7fiv7Jjl+DG/edpcvPb2e47Pz9bdO2fj7w7bGifM6cKl9/4JgPUYD+SNK49IHSgFgJ0LR+E7f3jW+oUsNF3HDkmNomsn9zMN7hc0aluUFkUy5kg1Oq+kKIJ732++NF5z3cbM0foLeybR4nHa1HBJkdGST2svoIg4+tI+OmmJmH4Dap72B8LLaURL0ySp7r+A+TzuZhoiOsN5t2xNXQ6TcF0fnzSzhwpiJjGS4cvnp+elRs90mhicdRXw0AVLMh9kwO5z96ktU7FrzThLA5S71owzvc/Kx4Kzle7pH16PuTZT6rhpelcjNs1wFlyKkdbDr6K0KOEzfdysEfGf6w1y7IUhEraImMYioChtS1umzqTRR7S+IvOqtYLuJGq0xrrZc3zdxn6cs7w3nkA78Vyrj6H7OfZqWQnbXBbbsD0v9uVxgi6lhrafibylreXO9CG0Il/X2PvBLOG0vvLraEhNHdLRkD4QkJOvQvfbRP6kwLDDaN+EU7m6asjNgZyGqtKE3y1mwLB9zOqJQ7F9frfhfTO6nAUdMpM8iJlP3HjPHsVE4b4x+37IJ06Dhmn0HS59ELPvnDE/5VinT6cbL8OYWK6/uaOb8zZGw/qpwwyjnNo1ObaSbWhd9st7jfjSSRSRMhG5RUSeFJFXReT/RGRV7L4uEVEi8pru3+6kc28VkVdE5DkROSfp2ktF5DEReUNEfiwiI5If37xcKeU0PK6lpgxnLO1JuL9O64Fn2u8U+8BEIqmPZ2XvWXlJEX587qKUJO4MP+6OxqSGW7K22rJ4bsKICy34j7q4X6OQKAWcsbTH8D7tc3TSvJHO8lja+FbLdo+E2fmDM4nudUru2jELnznW2vLQ3rbEyKOruH8wzuu2p1cDA0bFttKpC9E4Reile2/s37MGH8kQeZncUwgBNVf2ZVcvrzAJKGP01Dl9OjPVZ5m+WxSAvvY6PLJ7OY50IRp3WN2waTK+evJg3utRLc6if5+1rBc/OHsBetpq3CpaAr9mEosBPA1gIYA6ALsAfFVEunTH1CulqmP/LtfdfimAHgAjACwGcL6IrAQAEWkGcA+A3QAaAewFcJfdwmnT6nYmih7ZvRwLe1vw6S3TLF3bKLqp1Q/hyOaqgk9u7BWjcNp6I5urUFdRgpqyYly8dnzaY3PB4Cbp3GNWiS4d14rrNvbjvEPGOLqus5lEd5vSWs4jN0YWNQPdTVg5wdry0C0D0bG19VOG4Y+XHZISZInc58bKhOQlYfr9QAMuvpfsujXDEu9cUAATU67aPm+kK+9pp4y2A+Ubo68dLdeutfMF/UkRes2cvni05esmP4ZVPzs/dUuDJnnlRb6LRATLxrXaPq8oIpY6iEYvi5UqzpdOolLqdaXUpUqp/Uqpg0qpbwN4AkD6HlbUCQAuV0q9qJT6M4DPAdgau289gEeVUl9TSr2FaIeyX0TGWimX9mbWvgzsLN+MRAR3bJuJeT3p9xDoo5ua3UfBSVehbRnoxGeOnYaSogj+cNkh8Q356fbxhF02SVXDqKasGCKCI6d1+DKQ4hN5q48AACAASURBVNVHdl5PM/bvWRP461NdXozqsuJAk3kXirWx/Z0Le1sM7zcaYMzk/jMHl42dtaw34/HpUivlf5PbXJiWfeeKXWvH429Xrgrs8f/faXNxxhJnHZtc1p2U2sCt1ShnL+/F/j1rXLmWnr7da7REMqX0bCe7wml/I5CWgIi0AegF8Kju5idF5B8icltshhAi0gBgKIDf6Y77HYC+2M99+vuUUq8D2Ke7X/+YO0Rkr4jsTb6vvKQIZy/rxddPmePo79FPGQPApI46fGh1tJ86mCdRX5bo/5uTNomfv3KMq+GjKTtXHjER9ZW5NZpVaAMPRyV9hqwsB71odWIn38lz5vRrONvXZ55HgS2yXUZbCPuB3DR2SA1Gt9bgoQsW44rDjZPSX7ByLNpqyzCq1Xp+q6bqwZlE57M6scFTh2eHVXeLszxhVoShU/nDDyx03IZxi18BbPasn4gvbEtMSN7XXoczLQyMkH2HT06f41kvc7KAwZpFe7/cduIM3f2Fy8uv0ZzpJIpICYA7AdyhlHoMwH8AzEB0Oek0ADWx+wFAG1Z/WXeJl2PHaPfr70u+P04pdbNSarpSynAdzJnLeuKbZe3Slog1xCJEfev0edixIBqUpq+9DivGt+EjR05K+SJpqCqNnwMApy4anRI+Ol2lG4LvpZznx3NoFiXMre/Tn5y7yPCx9qxPbHyeNC//Ew5bqQe3JT0PdjpI2VbiNxyVOeS0mb9esQp3JDWM3KLt53G67/bgwcGfL1hpaSFHTih2eQnb8bMTt8x3NFSitNj4a3h+Twt+9aFlaffZZvt+9LL+C0PHSa/NRm5Pq7MxYWrQjmqpzjqwSa7YPLMTC0xm4POZG50Iu5f4yxUrcf2mwcmL5DoshWiPk/mRtHGsuaOadbclBwuxVMyc8f2zF5jeF6b6RONrJ1FEIgC+COAdAKcDgFLqNaXUXqXUe0qpf8VuXyEiNQBei52qX3RdC+DV2M+vJd2XfL9v7toxC/eflfrilxZHcPPx0xPyyei/gML4pigkXjVk9MmFk22Y6u5m7C79chPdGyr5b9udB3sqg9JWW5YwoOP0fdPXbm0/iJHS4sw5k6xYMzF1n6I2E+i0k7hwzGCDrbEqc0CuXLFtrrsDK8kz336z8vKmO+Z7Zy3A9wy+51KuEcKWnZ3E0iLm383fOn1ufGBOy0s71uEAM7krfO867xmlr3BTWXFRwvfOhw8zzsNolUpoo0js/3QnZPVwoTOsPjUCuybdgHXyykO7DIMTWRh18K2TKNF3wy0A2gBsUEq9a3KoVuqIUupFAM8C0A+/92Nwmeqj+vtEpArAKCQuY/XFQHcT2mrTj1QavR4HY0P4i8dkNypWCJu2c8kn0iQdH/Awt1ie1aeeSP6kWHnOfnHhUvx21/JQ5I7K1ieOmYJ9V61OuG3x2OiG+cOnJC4rshoMqLm6DLecMB29bdU5t0TbTETcTQMSRnbHBMYMqXG84iZorUnfz/2x0PFmmqqN38eTOurjA3N1lSX44kkz8dnjcj9QTz4ZbyOYS667yIUYCVY6C2ct67G07FQbIDK7ZKZHciOKfK46NPn5jT0V+65ajavXG29LsMrplhI/ZxI/DWAcgHVKqTe1G0VkQETGiEhERJoAfAzAT5RS2jLSLwDYJSINsYA07wNwe+y+bwCYICIbRKQcwMUAfh9bxhpeus/AjZsno7+jDp8/YUamQ9PiLJEz2Y54/+aiZY7P9WL9ub4iCONovteSN/G7JRIRRCKCoXXRUcDiSO4GdhGRlBnJUS3V2L9nDSZ1JDac+zvSN6T1lo5rw/fPXohig9lOblmMUlku6025nitXSTTSo89Q2Bi9TzWlRREssriccX5Py2BKLApUJCK4a8cs3Ll9wPQYp1E7w6DNINCL3bRPg6tGBm+zUj+ftawXN262HvXa6JKXH9aX8bHyvY+Yru4/YorxKrOiiGS933dGl7PJCb/yJI4AcDKAyQCe0+VD3AKgG8D9iC4R/SOAtwEcrTv9EkSD0TwJ4EEA1yql7gcApdTzADYAuBLAiwAGAGz2429yy5Kxbfjm6fNMl5Elvy+0TkCRCOZmiKxK3lBplnTGb0/42b9az6/AAWHVWluORRlm5bN5ij5/wnTctHlyQqoBsua9AwczHxRCbs4eV5VFG3R2wtZ7yeijcO6K1NnjNZOMU6ks6rUfsj0XbJnVCRFBT55Fgy4EA91NaKgqNVwFccXhE3Cuw1RJYbDO5HNoR6aBqh/rYhw4oV3WqDM4vr0OUzpTBx71JQkwi4ovgmqiOZ1I8isFxpNKKVFKletyIVYrpe5USn1FKTVSKVWllBqqlDpeKfWc7ty3lVLblFK1Sqk2pdQNSdd+QCk1VilVoZRapJTa78fflI16G6OOyW8obXlqRKJJxPkllh0nH9jyEnsfm+RGppb8PduORmdjZcqI6Wn6UdI8qmztNNPtzlrZOb65ugyHTR6W+cACdsAkq3U2y1DzZaZmZHMVvvy+AVx5RHb7ejTZblMweqWMAuncsMk44FJdZUne7cerKSuOp8PiBHjuOs1gxvDYWRmCroSAWfChjdM6ICJp97RZoVXPRm2fYfUVjlYS7Fk/EX3t0YGvdBE3RIBTFo5Ke618D1xji4sVkFG9Hpo8iRRVXlKEyw+fgLt3Wg9TnTwLNSm2j2KuC6HwV00YkvU1CtHmmZ3xn53UX4f0teHaIyfh7OU9WZVjVEtVyvsgXxrTfhrWkN2Xbj474GCd6OKxrTh2VmfK7aXFEWx3GGG3KoC9gW7Nyn/lfbMSfp8zqtm1nJ5TOhts5TIrxCXo6WgRoLuaKgdvTFiGN/j+T7c8lcgtZkHvpnS6E7l2WH10yWpN2WBbQUtcv3Nht6VrXLpuPKbqZgQ3z+yMtye1anOhwSoDQXRJcMrturo2pd7N4ZEaoxWCubbn0t5iZsracTZHsuqSIldN7WzAHy87BNVl2b90c0Y14bt/fC7zgZSgpCiChsoSvPjGu6YNyXT1gIhg4/TsoxzqE553NlbiqRfeyPqahSD5Nettq0F7XTn++fJb2DqnC4fZyAmV7+aOasLWOV3YsaAbjVXWZgJLiiK44vCJ+NIvn/K4dLmhtdb7pcm71ozD628fsHRsQp4yi9fP187ljFj6qoRGqsmx2S7DI8pGpqjDh9z4UwCZVzpdv3Eyfr7vP+jUDYxUlxXbGmzaOncktmaI/Hz1+on4+sP/SLjNysDbIX1tlssRdqNbqvGXfyUmW8ixPiI7iWF00ryRGDOkBuUlRYab593oIJK3/AjUoV+y9s3T5uLfr76dVIYcHoLzWWdTJf758ltYMb7NtRHbfFBcFMGlh/a5dr21/e34/ENPuHY9ito+39oMwJC6cjzzUjxuHIY3VqY5Ojthr34Eg2U0a7fp/wQvnysK1pi2mpTGfC4ZM6QGj12+Erf9734c2p9+kLOusgSrDNIgZWvwsxT9NJnlfzWi//zlw/fv2ct68dEH/mrYIcyxPiKXm4bR7rXjsWn6cBza355x5CXk38Oh53RUJ9Pz7sfr0qpLDt1QVZqzoendZve5F9NfyC3aazJ5eD0uXDXW9vlBBmQa3pBd5yBMy4tmdQ8uZYtINCKtGSsBunJfai8xIe0Jv2ALwsbp7uYuzobTj1p5SRFOWTQqYYWRn7SgXDXl5pMYblQjWwZStzKEyTdOnYPl483rVVvfBx7Xu1YG8thJpILmdBnVsQPRZcOVJnulDoZgGD0ERXBNkY2K1ckMavIoKGVvl0n+rp0GgQs2TE3fSAsy48gCi6kQzITlHaUFOdM+H9rrYCVQRZg6um4ymknU7yFtZhRj8tjpi0dbXsofZsfNHoFda8bhxAzLUPPdlM6GhCX9v7tkBf5w6Yr477lWlbKTmONy7P2WNz6wohf7rlptGoAinzpoYaAtXZlqED7bDd0tseTYDPzjGv0SyEz1lFmwBs2WAf+jEuoHG4Y3Og9uFLYOVrxjFCvWHSfOzHiOk78g7MvdRQYnCvUz1d0tgxHDP71lqs+lIj+EqZ4/95Ax+MTRmfMPhqsWSVVSFMH2+d1pl5mapgxL88fpO1ybpnfkxOT+YB0rqKsoQU354Pst19KUsZOY47Tolk4qvdoQVZRBMfu8plsuED0vNSG5np31+F7JhcrUqumxsOBfPMk8SbJTbbXluGRdH24/cQbGt4cjf12+yfa9ePICa3vu3OTWl3mQs6DfOn0uzlneC2AwSEu8YxRrdnY2VeLB8xalnKuPbJtj7Zq4IbWpycf1Mu1JbKrmTGI++tbpc4Mugm3aZzBXP4sA0BFbum+2cmSHQT2vb9tedmh2qYO2zunK6vxMlo1LjOjq5KWa0eXnnszM38yMgJLjPrh6LLYMdOL5197OfHCSKcMbcMe2mTioFE687TcelC533HfGPLz61nvYfPMvAUSXgGSjrqIEG6Z2pET3csNFq8ehp815fswtA52481e5E3ly/NDaeNjsKgtBm46c1oGfPf4fS9fWR3RbNMbfxODdDvJRFaogRl/1j5jNpFimjoqXJnXUY1JHPVZPHILOxuj7LXkmEQBGNKW+Fy9eOx73/f7Z2LH2n/9cGKTSZilyueFN9iWncg160ltfHu3H8UNr8adnX0k5NuiyOvXE1avj9cj1m/px3cZJ8ftExDS66rQRjbj9xBmYO7oZJVnst9y/Zw2eeelN3P7z/Sn3nbm0Bzf98HFL1ykpErx7IPVF0P99RnWsVV/aPoCX3ngXA1f90P7JNlmp14Of7qCslBUXoaetJmXZ4/GzrS3PWtjbgsU+N47DRPuI9LXXYVZ3E/buWoYLVo7FpI66rK+tjQg1ubzf4H0LutN2aOb3RGeXzb5M1kxyP7JZmFhJdr9kbCtuPm6aD6VJ9fMLl+DWrdPxzRwczfbLeYeMCboIrtg6pyuwQBJ6o1tr4qsb4h2jDOe0Zdm5zYXGLPciF6Z0KS9Ptpgr0E3KoyGV31y0zJPrWvXI7uXxn5M7JHYGnhaNac2qg6hpM9hjPKWzHmfHVltY8YEVid9Nd2ybib9ftTrh72mriz7Oal0U2ZuPm4YlYzO3tcuKiwaD/3hch1rJ1xv8txe5YmpS2ODKUk4SO9FcXYZTFo1yZeZi0/ThuPbISdjm80buzx0/HT87f7Gvj5lrbt06Ayv6hgTy2O31FVgyti1hn0K+2r12vKPzTtPN5A+tC2YmLmx7CV3n5d8n3jV8nTh/pfGgg/YUFBfl+WtNCUY0VSXUTfr3aksAS4z1M4na8kqzrQ92guJpA9SdAaVvaQhZQJ7iokjKIMAd24z3ZH/zNGuDuAt7W+IrnTStNeX4/aUrcOqiwSBtK/qG4NatMyxd049Bq/NXjsH82Ha1dNiTyCPze5rjy+zOXNqDzzy4L+AShccRU4bhG488k3K7l8vYIhHBxunDPbu+mfKSIgxvrEz44ls/NfPsGpHbBmL74KyqKS/GtUf2J9z24HkBDXjkab8h3T68Y9wKLx+e/iEA4PDY6oIvbJuJ42/9dfz23tYa7FjQjeNmjcB7B1Uo9pKTe6Z01uORp14yvO+keSOxasIQ7Hv+NfzZYFmnn/Qdv46GSnz9lNnoa6/D3b8d3K6idRzszNBHIoJbTpiOiS6sjMqGlRk0v3QlLa2v1Q3W6peS9g9PDJJ3z6lz8JsnXrBctdW6MQjs4XfQqYusbalijZinKkxSM1h12uLUMPVhdPYya8sESgp8pDiXl+/l+4ROPtMaP8Xp1nfpTB5ej5UTEmd4c7nhPqolfPtOByN6Jt6+f88aXHXERPceJ2QdRSA1nUkkIvjQ6nEY3liJkc1VGFbvPIothc/JC9K3Y9rrKzC/pyXhvaqtIFg9MbEeuuzQPtfLF5f0WZk2otF0KaDd9FpLx7Ul5FT222OXr8Tnjp/u2vWybQ5snmE8cP/9sxfgVx8yX547tbMBJy8cFcp6zUu5++1LnogI8MljpqIoyJB8HvjQauOcbfnc/yiEysysAzmr294MFnljaF200Z08k33JuvFZpZXwg34WtNnhErRjZ/mfuiMTLTWF10uagq5+Lna41DnZA+csxL2nz3PlWhROm2cMzqBrA8rJbSAvByutdPy0aOpBf67sKi8pShsJ3ksVBh1tETHck9rbVmMpX+XI5sGlu17tXw3TwHh+9QTIsYtinajRrdV5GdikvrIUzdWpFUCYPoxuC9uXybr+dleuU2ZhZunL22dlPIa811JThsevXJUSevzEuSNx98458d+tjEnduX0Ad+2Y5Uu00K+fMhuf1QU2+tzx03HNhklpzjAWxpxYI2NRdXuziJCsZ9a+DXKQateacdg2z5294KNbqwNfrkfZqS4rxg2b+k3vr6sswS8/uBTXbezHnNg+rZVJe9anDPcuNYHZZ+XnFy6J/3zo5PbYsYMHX36Yh7ObOeTYWYOd/PVTBgckT5o3Etdt7MeX35eYOkt7Br9zxnzbj7VywmD7+CSX6hi/tdvY589OIgEA5oyOJrPOtxlEve+cmVoh5PVsW8B/3EyL+9EqbS6N/rWFiG3JG8kpOCVFEcPOUltteXy2rrw483tg7uhmDHQ3OSyD4I+XHWL5+GkjGhOCf7XUlGGTyTIlfaMkFxwxZRi+edpcrJro7WDgxunGudDCIowdeHJb9DtwzqgmrDfJzacZUleOI6d1YFRLNfZdtTplsHxiR517e3aTNFQl7ovTaEFsSosj8eie+q/142Z3eVKeXGO20uOsZT04cloH5oxKDNCiPYdOB8qcriyxSptZ9qoZ890zF1g+Nn97BJSRUUSv5NGzsLMTQS95Xf6iMalRqfJJkKH35/c044wlPQm3TRqWOhp/2aF9+OCqsbauXV4y+HexoZfbbj5+Ov5nxyyUxwYKvHo9BYJqCzk27br5uGm44ajJCbctDVGQBiMikhKUwQvJEbeDkG4/ax5X/RTz9nsHAdiP0ZC8PLLf45nkaSMacd4hY9DVVGk4uKovznVpZkQLgVGLb5NJgMBMbSCj75tbTpgeb5MEtcxci3Zb7NGkTV2l9aA6BdlJtBpEoZC01pbjkd3L8f4l0YhH6/JwyWmyGV35vW9tQ4aRU80mD0b8ZyY9t3+5YqVrSzPKiotwyqLcCKxE6dVVlGBWd1N8CZVnVbNL101uwBmlUbnFYpjzvGbx+d46pwtfOmkg84ExOxZY2wO0bFwbAOCzx03DtrkjDdOo5H2KE8IhfUOwcVoHdq1xtj81+b2Z7eKcdN9bpy0ejZ+ctzghNZL2cPr3aiHntQYSl9sCwK8+tBTt9RU4Y+ngoPQ5y3vxPzsybzkx+r5ZOq4NJy+Mvk7my8y9XaVVVVqEHQu6cdfJwW+bKchOYk9bTV5uRE9umNvVUFUan1nraatBVZYRUt20MBaV7oKVibNO+d7Ry0ZpcSS+fyvdl9s1SSkH3Hjdd+q+DOeMakJZcZH5rG2GxprR3doyHMoPVbGlnVqgG7ele4fZCXK0M4BE20FqNUg+bcXYITWWjtuxoBvzejLn6gKiS8fOWtaT8bhh9RXoiu27HDe0FhevG284Y8BOYv4rLynCtRv70eLwfVxdnrj6YJLLM4q3bk0f9VOb0DjKZKl7IUpuy7TF2jj6XJBnLO3BrDRbE7SPvtOVK1p6i1KPVmuJRKMu97W7+377wdkL8JX32et4FmQnsTgiebkR/dTFo3HXjlm4/yz7m3HD7qI14zB9RAPW9SfOcM4d3Zwx+Izd5YyFbrHN5XKfOXZaym0lJpXncUnRHkVg2oPVZg38SCxLwZo5shEfPao/ISLl/WfNTxtsIp2+pETUWh2RbZQ9fcMjUwNPvyw6V33nzPm47wx7A6rzRjdn1dkfP9Q4ifhZy3oT9olqTpjtLIIs+4hkl1n6BKeWjG1Le395SRH+cOkK7HY4E5qPzAa8tY+zlTm+e0+fhzOXZh5wMnPHtpm4/LA+1FdmjoYaJj1tNZg9yt6+/tz/FqO4oohgoLsJY4dEv2QzzQjZ2bPmViQ8p3rbanD3KXMMGwnJe43uOXVOQr6rIbqlRv938XLvCpknrt/Uj4cusJ7APDmvXTqXHz4h4XcRMa3UtdnjdI05tvPyg4jgiCkdCXuHxg6pzRhswsz0EYn74bSBhhuT9g/aVVlajEVjWlJuT+5IXbex31ZwgLBqri7LOJp92BTrUYu17QyZ3LCpH+NMOot6zdWluOywCRmPM2IlSjKRXhB74GvKS/I6doJdh002rm8OxLcsZH6uJgyrw9nLreXYNjK8sbJgggaxlsxj92UI72snlPztJ87E1evdS7TsqqRextTOBlxvMgOhD2SwJOQBJrzSkGH0q6y4CB0NlWmPSUeLAKcFFUpXZxeJ4ODB1G7iSfNGoipNoJG8jkpLWUt+e2jvQa/eNlpaCc2R0zpSbstXpyx0f3/w+qkdWD4uc/18zylzHT/GVWH9PqOcc9URfC/5RUtRkkzrHJblwQqOMOGzmce6khop+un1VROG2Fp61V5fgaNnZhf++eNHT7F87Ke3TDW8XV/iyw6N5giy0/DTL120MlKd67RQyvrX2s2/+55T56TcdkVsZF9bcqbNCBpZ1z/U8PU7d8WY+M9p36UcYCUDyYMI8aVIHF1wnYjgy9sHLOVBtbPH0WDsKEVnU+pgltWAgMnRronMZHorDmswXl59RdLKmfkmHRzK3qH97Thp3siUuBXZ+ul5i/HrDy119Zq5hJ3EAuLHRv2fX7gEf7tyVfz3c1dEp/Tn9zRbTqZ+0+bJlpZ1xGcHdA0/bc+b6br1AutUaJ3E5L/7W6fbG4FfPXFIQnJxjRbmXp/MVlsaM7q1Gr/44BK8b75xsI9h9RWoKS9BV1PqjEtFaVHaBv0xA504pK8NJy9glNNCkyntzdY5XViaNAvl5jIxoysV+r7ZOaObM0ZJvnTdeGwZGIG/XbkKbbXmncUVfdF9WgczdOgXmAw+zexKv+fmmg2T8NWTZ6c9hgjQ56tL//meM6rJsG2R/A6eM7oZj+vaR06t629P2XddSJqro6uh9AGySosj2L12vOtB7TqbKtFqY9VdvnE/cRSF1oLeZnz0gb9aPl6r4H5lYxSlXbcXMCLA6Ut6sGVgBCrLrEfMPGzyMNz/x2fjv+vzjmWqiD98WHR2savZ+XLJfDJmSA3+87f/oiwpWfmkjnqMaKrEk/99w9J1zlnei9GtNVgxvg3f/9O/ACRGE7v8sAn40i+fSjkvXQAL7bU0C5Sj7SVdOi51c39dRQk+e1z6wCEAMKXT+3xw5K9MM0xLxramdCAGZxJTj180phVD6yowqqUK133fev1IiTJN0m6dG02BE4Ggt60G/3rl7XhU20kddfj9P14GgHh+VX2exU8ek7qypMik3X7V+vR7FDcxUiRZpEWvzLQ1JyKC/o56/N/TLyXeYfChMAvqZoedVVn56P+dNhePPPUS1hZAqragsZNYIJ64erXt0XRtFK2m3P7b5MOH9cUjATZUWY8AVRL75tc3BHea5BYyavhpf+PQugqsnTQU3/79s6knFpBPHzsNjz7ziuHo2r3vn4eXXn/X1vVuPn46ui68L+V2JzM1+lOG1VfgmZfeTLh/aF0FfrtrWcY9lGb271nj6DwKt+QZ5qOmD8dde59Of1J8T2LiuXt3LUNTVWn8/eu0k1hoKxSy9cktU6P1Uiyp81dPno2xu+8HMLgSYdn4wcEho/3jbbqG+7ffPw9rP/4QAKQMiBE5NWFYHa7ZMAkrJ5oHZ9sy0ImiiHEANv0gqdFKHHKmo6Eyq7gJZB2XmxYIJ414rS3mZJnq8bO70NtmLVeWnrZsy2xUOuH2WLnYQDNXW15iGvK4trzEcE+PVdk+7+cdMrh3wGxpWVN1GSO7UYIbj0ocRR/RXJkxIfsRU4YBSK1XmqvLEupGK1GcuavRWLr64Le7liX8nlwvlZcYd+y0QGP6az9x9Wpcs2ESLl43mBZgwrD8S2lF4bBpxvB4XjwjjWkGwStLi/DY5SuxZ/1ErNANeowdUoPhjd7khCVyEzuJZGrxmOjobba5xdJJaViYjPhr9JGrtFPv3pkaPCV6bXYu7Hp492CKkE8c482SlpMXduPCVWNxqG6P6gErUSqIgJTk6ycvGIV5Pc3xVQiaS3SdiEvWRZehz80QOOKeU+fify9ckvYY7b1apIuQUlYcQWVpEW7anF2KjVymDfAZfV80VTtLZm70NSAi2DRjuGE6JCKvrUla4rhlIJan02Cgs6GqFOUlRdg8szOhPXL/WQvws/PT1zNEYcBOYoGy0n+6cfNk/Oz8xaZr6I+c1oHbT5yRVTn+b/eKhA3Y6fYOAdE8Zdo6dO1vGG+ygdso8InWgJk8nHvVjDRUOt/0fe/p8/Dt92dOvP3BVeOwMyls/ieOmYrFBvnniDLRPtMzuhoBDNYLJ8b2wOmPaastx6QO81mn6rLihByrRuKdRF0lKiL404dX4rDJw+z/AXliVncjjps1AtccOcm1a9bH6iM39nERueEmXa7V4ogk5GHWa6wqLYgI6pTfWPOSqfKSIgxvNF+OuGWgE4vG2Ms1OD9pFqCusgRf3j4r/ruVfGbaHslMEQWrY3n29HsqS4oi+POHV+KeU4xnHwvVhGHRLzP9aGemQBQXrR6X8PvEjjrHy75mjmzEbSfOdHQuERBNibN4TEu8s2hG6+Q5HZQYnEnkSgW94qIILj98QsJewWzdvXMOrtkwic81hUaxxQGLgZHp6yGiXMD1GgUqqHRhH1w1Dj97/GcJt9XpZq+0jt/UNFEptVHl4gwNh4vWjMPo1ur4sllNRSkDGyS7c/ss/DMpcIy2H8jMij7zzfxEfutpq7E00KCtbP6ALhenHWsnDcWvnniBEZR90NlUmdW+aSIvlem+I5ObVEcxii7lAXYS89zHjp6CKoNOkdOciXduH8CWz//KcXky5b7Slm50NFRi2ogGT3xSTgAAE95JREFU/PbJF1OO+cCKMSgtiuDwKYNLuxb0tuDlN95JOK6mvATbTXL0UaK6ipKUCKjLDVJPEOU6ZTH3mZljZ43AphnDGUWTqMB947TUfMNLxrbi1q3ZbcMhCgt2EvPcoSYJ7IOK6ZJpBvPL7xuMUqg15pKLWldRgl1rxyfc9oVtXKrotkhEMKqlCvuefz3oohAZOmtZT9r7P3PstJQVB/EE2Q43W4gIO4gWdTRU4B8vvpn5QAB3bJuJJhvpkpLduX0gvsWAyA9GEdzPWJq+TiLKJdyTWKCanUabM7BhaoflY/VRSy87tC/lfn1eIe79CZ72had/XYjC4qxlvWnvXzlhSEK+PQCoiEXFLHbaSyTLvnHqXHxt52xLxy7sbckqlcXc0c3oZ0AyCsjuteMxurUaYxyk/iIKKw67FZjm6jIURwQXrhqb+WCLrt/Uj68//A9Lx45srgIQHeFfOSH9nrayWO4s7iEMzmGTh6VEbLx03XgMYaeRctRnjp2Kex5+BqNaqoIuSt5rqSlDS417A5JEYTWjqxEPnLMw6GIQuYqdxAKzNympcTYaKu0vDaopL8H+PWssHfvxo6fgnoef4chcyGzVpRYgCkJfey1eeuNdR+cOravAaYtHu1wiIiKi/MJOIjlSHBF0NQ+OxP/momUZo43a1VZbjlMWjcp8IBEVlPvOmB90EYiIiPIaO4nkyMykHEBcUkRERERElB/YSSRHgsqzSERERBSUR3YvDyxCPJGf2EkkW7INH29m15pxENa6REREFGINWaRqIcol7CSSLbGsFI4TUZth0nsiIiIionBgoiiypSjWOawoYVoKIiIiIqJ8xJlEsmXOqCacsWQ0TpjTFXRRiIiIiIjIA+wkki2RiOCcFWOCLgYREREREXmEy02JiIiIiIgojp1EIiIiIiIiivOlkygiZSJyi4g8KSKvisj/icgq3f1LReQxEXlDRH4sIiOSzr1VRF4RkedE5Jyka5ueS0RERERERPb4NZNYDOBpAAsB1AHYBeCrItIlIs0A7gGwG0AjgL0A7tKdeymAHgAjACwGcL6IrAQAC+cSERERERGRDb4ErlFKvY5oZ0/zbRF5AsA0AE0AHlVKfQ0ARORSAP8RkbFKqccAnABgq1LqRQAvisjnAGwFcD+A9RnOJSIiIiIiIhsC2ZMoIm0AegE8CqAPwO+0+2Idyn0A+kSkAcBQ/f2xn/tiP5uea/CYO0Rkr4jsff755939g4iIiIiIiPKE751EESkBcCeAO2KzfdUAXk467GUANbH7kHS/dh8ynJtAKXWzUmq6Ump6S0tLdn8EERERERFRnvK1kygiEQBfBPAOgNNjN78GoDbp0FoAr8buQ9L92n2ZziWiHDSqpSroIhAREREVNF/2JAKAiAiAWwC0AVitlHo3dtejiO471I6rAjAK0b2GL4rIswD6Afwgdkh/7Jy053r4pxCRR352/mLUVZYEXQwiIiKigubnTOKnAYwDsE4p9abu9m8AmCAiG0SkHMDFAH6vCzzzBQC7RKRBRMYCeB+A2y2eS0Q5ZHhjJWrL2UkkIiIiCpJfeRJHADgZwGQAz4nIa7F/W5RSzwPYAOBKAC8CGACwWXf6JYgGo3kSwIMArlVK3Q8AFs4lIiIiIiIiG0QpFXQZfDd9+nS1d+/eoItBREREREQUCBH5rVJqutF9gaTAICIiIiIionBiJ5GIiIiIiIji2EkkIiIiIiKiOHYSiYiIiIiIKI6dRCIiIiIiIopjJ5GIiIiIiIji2EkkIiIiIiKiOHYSiYiIiIiIKI6dRCIiIiIiIopjJ5GIiIiIiIji2EkkIiIiIiKiOHYSiYiIiIiIKI6dRCIiIiIiIooTpVTQZfCdiLwK4C8ePkQdgJdz6Lp+XJ/Xzr/r89rGmgH8x8Pre1H+XH0f5mq5vb6219fP1Wt7ff1crlty8TOaq9f2+vosu//X9vr6Xl57jFKqxvAepVTB/QOw1+Pr35xL1/Xj+rx2/l2f1za9fs7VL7n6PszVcrPsfF4cXt+zuiUXP6O5em2WPf+unctlT1evcLmpN+7Nsev6cX1eO/+uz2sHw4vy5+r7MFfL7fW1vb5+rl7b6+vnct2Si5/RXL2219dn2f2/ttfXD6RuKdTlpnuVUtODLgcR5R/WL0TkBdYtROS2dPVKoc4k3hx0AYgob7F+ISIvsG4hIreZ1isFOZNIRERERERExgp1JpHIERG5XUSuCLocRJRfWLcQkRdYt5BT7CQSARCRn4jI9qDLQUT5hXULEXmBdQt5jZ1EIiIiIiIiimMnkUhHRLaKyENJtykRGR1UmYgo97FuISIvsG4hr+RcJ1FEcq7MRJQbWL8QkdtYrxBRLsqpiktEipRSB4MuBxHlH9YvROQ21itElKtyopMoIkUAoJQ6ICLNIvIxETlbRPqCLhsR5TbWL0TkNtYrRJTrcqKTqJQ6AAAiMhfAgwDaABwK4FoRmRy7Lyf+Fgq91wFUar+IyJAAy0I+YP1CPmHdUkBYr5CPWLeQJ0JZQYmIJP1eJiJfBnAJgI8rpY4CcDqAfQDOBwAu5yCX/A5An4hMFpFyAJcGXB5yGesXCgjrljzGeoUCxLqFPBGqTqJEFSmllP52pdTbAH4KYCKAmthtjwL4LoDhInJk7PxQ/T2Uc5RS6q8APgzgAQCPA3go/SmUK1i/UIBYt+Qp1isUMNYt5BlJqteCKYRIRD+iJiLVAC4C8CqA3yqlvhcbpfsmgMcA3KSUekZEWgCcCmABgDVKqbcCKD7lARF5GMCHlVL/L+iykLtYv1CQWLfkJ9YrFDTWLeS1wEewRGQlgCtFpDP2+3YAfwcwDkA/gI+LyHGxUbpbAMyK/YNS6nkAPwYgAOYFUHzKA7FAAuMAPBJ0WchdrF8oSKxb8hPrFQoa6xbyQ+CdRADFAJYBmCkilQCmA3i/Uurw2Br+HwG4EgCUUt8E8FcAK0VkfOz8XwPYoJR6wP+iU64TkY8A+D6AC5RSTwZdHnId6xcKBOuWvMZ6hQLDuoX8Epblpp8EUAvgCgCvKqX+KSI9AD4PoAPR9fxfUUqdKSJTAXwF0c3gd2n7ALRN48n7AoiosLF+ISK3sV4honwX6EyiLhrYTQC6ACwB8IKIdAP4KoBfKKVGAbgZwOkiMlIp9TCA7Uqp/9FXrCrG37+AiMKK9QsRuY31ChEVikA7iUopJSISi8z0XQBrEF1jPQrAC0qpC2OHliG68XtD7LyfAakhp4mINKxfiMhtrFeIqFCEYrkpEI8M9g1E1/K/BWA9ohXsAgB7AZyqlHo5uBISUa5i/UJEbmO9QkT5LAyBa7RQ0q8B+CKAuQCeQ3SdfwmA65RSW5RSL8fyEaUts4iU6q/rZbmJKPxcrl+q9df1stxEFF5u1iux63WLSG3sZ842ElHgQjOTqBGRuwA8D+ASpdR/dbcXKaUOpDmvE8AeAO8A+IdSapfnhSWinJJl/XITgPcQzYO2Qyn1ntflJaLwc1qv6I47DcC1AI5XSt3tXUmJiKwLzUi4buTsYwBmILq+HyJSBAAZGnA7EV3a8Syiyz6OEpFbY/eF5m8komBkWb9cBOBhAE8D+DCA5QA+mXRdIiow2dQrSfoBvIhoSo0et8tJROREaDpQsc3gEaXU/yKaZPaQ2O1pK1kRqQfQA+B0pdQHlFJfALARwHoRqVVKHfS67EQUblnULwLgIICVSqkzlFJ/APAQgNpY8IpwLcUgIt84rVc0WmcSwOMA7gIwAGCeiJR5UV4iIjtC00kEAKXUwVhi2jcB/MXsOK0CjTXg3kY0/9D9sdsiAOoB/BnRSpuIyEn9UhzrBN6glNorItNE5C8ADgXwdwCH6/dAE1HhsVqvAAl1S/JM42wAtwH4NoDDAIz0rMBERBaFqpMYcziARwDck3yHiDTElpF+BojnGHpTKbVXKfVKbGT/IKKhp18F8JqfBSei0LNTv7wX+//t2CHtAD6hlKoCcAOiibEvEpEaPwpORKFlWq8AhnXLgdjtWhvsaQDDAdwCoBzA0SJyhYhM8rrgRERmwhi4xnAJl4hMBPBxAE0AXgFwvVLqHqON4SLyaQDvKqXO8KXQRJQTXKpfJLbM7EgA1wMYr5R63Y/yE1H4pFt6nqZuiWjbYUTkpwBOVErtE5F7AawCcB+ALbEIqkREvgvdTGKaPT6liIaa3grghwDeJyKlSqkD2uZxEYnElnFMQ3R9P0Rku4ic4n3JiSjssqlfdIpj/7+KaLCJWi/KSkS5IcPeZLO65aBuufqvAFwmIn9AtD55CMB+AFWeFZqIKIPQdRI1IjJWRBaKSGvspj8AuFsp9VsA3wOgAJyuHR77XyFawf4bQIeI/AjAlYgu5SAiAuC4ftFC2r8rIuMQzYn2XaXUs36WnYjCy07dopR6J7bkdCiAPgA3KqUWAvgIgEb/S09ENCiMy02LEF23vwnAbxGtPM9XSt2rO6YawEkANgA4Tin1pLZ0Q0SWI1oRvwDgY0qpD/v+RxBRKGVRvwiAakQDTJwGYAGAa5VSV/n8JxBRCDmtW2K3jwTwL6XUG74XnIjIRBhnEvsAjEY039AKALcDuElEFmgHxNbo/xDAPwGcHbvtYKySfgnApQC62EEkoiRO6xeFaCCsxxHdK9TJDiIR6TiqW2KeVkq9oQWyYf5VIgqDUHQSRaROF+VrFoARSqn/ADiolPoIouv1TxCRbt1pf0U09cUEEblKRH4BYKFS6jdKqQ9zszcRAa7WL8uUUk8opW5WSr3q6x9BRKHjUt3yvwCWAtHB7tj/4VriRUQFKdBOooj0iMj3ANwJ4OsiMgLAnwA8JSKTtQoTwNUA+gHEw0Erpd4BcADRivkEAJ9VSv3I1z+AiELLg/rlB77+AUQUSi7XLZ9TSn3P1z+AiMiCwDqJInISgB8hmlvofEQ3ae9GNHLgvxBdrgEAUEr9HtHN38fFzi2K7T28G8CnlFLDlFK3+/oHEFFosX4hIi+wbiGiQhFY4BoRuQLAk0qpz8V+7wDwGIBeRCvUqdDNDorIOgB7AMyIrd0fBuB1pdRLgfwBRBRarF+IyAusW4ioUBRnPsQznwHwNgCISBmANwDsA1AB4GuIbv4+S0T2xSKAzQDwfS36l1LqmUBKTUS5gPULEXmBdQsRFYTAOolKqX8A0SheSqm3RWQ8ostfn47lDvoYonnI7hORlwCMAbAlqPISUe5g/UJEXmDdQkSFIsiZRAAJUbwWAfhLbFM3lFJ/FJENAKYA6FNK3RFQEYkoR7F+ISIvsG4honwXeCdRRIqUUgcAzARwf+y2UxAdfbtSKbUXwN4Ai0hEOYr1CxF5gXULEeW7wDuJSqkDIlKMaISwVhH5KYAuANuUUs8HWjgiymmsX4jIC6xbiCjfBRbdNKEQIhMB/A7R8NHXK6WuC7hIRJQnWL8QkRdYtxBRPgtLJ7EUwOmI5g16K+jyEFH+YP1CRF5g3UJE+SwUnUQiIiIiIiIKh0jQBSAiIiIiIqLwYCeRiIiIiIiI4thJJCIiIiIiojh2EomIiIiIiCiOnUQiIiIiIiKKYyeRiIgIgIh0ishrIlIUdFmIiIiCxE4iEREVLBHZLyLLAEAp9ZRSqlopdcDHx18kIv/w6/GIiIisYCeRiIiIiIiI4thJJCKigiQiXwTQCeDe2DLT80VEiUhx7P6fiMgVIvLz2P33ikiTiNwpIq+IyG9EpEt3vbEi8gMReUFE/iIim3T3rRaRP4nIqyLyjIicKyJVAL4LoD12/ddEpF1EZorIL0TkJRF5VkQ+ISKlumspETlVRB6PXe9yERkVK+crIvJV7XhtplJEPiQi/4nNnG7x5xkmIqJcxU4iEREVJKXUcQCeArBOKVUN4KsGh20GcByAYQBGAfgFgNsANAL4M4BLACDW4fsBgC8DaI2d9ykRGR+7zi0ATlZK1QCYAOBHSqnXAawC8M/YMtdqpdQ/ARwAcDaAZgCzASwFcGpSuQ4BMA3ALADnA7gZwLEAhseuf7Tu2CGxaw0DcAKAm0VkjK0ni4iICgo7iUREROZuU0rtU0q9jOis3z6l1ANKqfcAfA3AlNhxawHsV0rdppR6Tyn1CICvA9gYu/9dAONFpFYp9aJS6mGzB1RK/VYp9cvYdfYD+CyAhUmHXaOUekUp9SiAPwL4vlLq77pyTkk6frdS6m2l1IMA7gOwCURERCbYSSQiIjL3L93Pbxr8Xh37eQSAgdgS0ZdE5CUAWxCdxQOA/9/OHbJmGUZhHP9fwVnUKbYhBsExP4DBIJgMFoMmZX3rJllZUfwEBqsiYjHsCyz7BZbEIYzXNNhsgsfw3Lt9w1bePaDu/f/gbg/nnHo4F88j4AGwm2Q7yZ2TGiZZTrKVZJLkAHjBcAmcZS6A/Xa1PLILLJ3UX5Ikl0RJ0jyrkep8A7ar6vLUu1BV6wBV9bmqHjJEUT/xJ9p6XP/XwA5ws6ouAc+BnGK2Ky0Oe+Q6sHeKepKkM84lUZI0z74DN0aoswUsJ1lNcq6920luJVlI8jTJYlX9BA6AX1P9ryZZnKp1sX3zI8kKsD7CfJttjrsM0diPI9SUJJ1RLomSpHn2Etho8dDHsxapqkPgPsMPa/aACfAKON8+WQW+tvjoGkMUlaraAd4DX1pMdQl4BjwBDoE3wIdZ52omwH6b6x2w1vpKknSsVI2VtJEkSf+SJPeAt1V17W/PIkn6f3hJlCRJkiR1LomSJEmSpM64qSRJkiSp85IoSZIkSepcEiVJkiRJnUuiJEmSJKlzSZQkSZIkdS6JkiRJkqTOJVGSJEmS1P0G8XdW8bXHiqkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Unda seti za data za mafunzo na majaribio\n",
+    "\n",
+    "Kabla ya kuanza kufundisha mfano wako, ni muhimu kugawanya data yako katika seti mbili: moja ya mafunzo na nyingine ya majaribio. Hii inahakikisha kuwa unaweza kupima utendaji wa mfano wako kwa data ambayo haujafunzwa nayo.\n",
+    "\n",
+    "### Kwa nini tunahitaji kugawanya data?\n",
+    "\n",
+    "Wakati wa kujenga mifano ya kujifunza kwa mashine, tunataka kuhakikisha kuwa mfano haujajifunza tu data ya mafunzo kwa kukariri, bali pia unaweza kufanya utabiri sahihi kwa data mpya. Kugawanya data katika seti za mafunzo na majaribio husaidia kupima uwezo huu wa jumla.\n",
+    "\n",
+    "[!NOTE] Ni mazoea mazuri kutumia takriban 70-80% ya data yako kwa mafunzo na 20-30% kwa majaribio.\n",
+    "\n",
+    "### Jinsi ya kugawanya data\n",
+    "\n",
+    "Unaweza kutumia zana au maktaba kama @@INLINE_CODE_1@@ kugawanya data yako kwa urahisi. Hapa kuna mfano wa jinsi ya kufanya hivyo:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Kugawanya data katika seti za mafunzo na majaribio\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "```\n",
+    "\n",
+    "Katika mfano huu:\n",
+    "\n",
+    "- @@INLINE_CODE_2@@ ni data ya vipengele (features).\n",
+    "- @@INLINE_CODE_3@@ ni lebo (labels) zinazohusiana na data hiyo.\n",
+    "- @@INLINE_CODE_4@@ inawakilisha asilimia ya data inayotengwa kwa majaribio.\n",
+    "- @@INLINE_CODE_5@@ inahakikisha mgawanyo wa data ni wa nasibu lakini unaweza kurudiwa.\n",
+    "\n",
+    "[!TIP] Hakikisha data yako imechanganywa vizuri kabla ya kugawanya, hasa ikiwa data yako imepangwa kwa utaratibu fulani.\n",
+    "\n",
+    "### Kumbuka\n",
+    "\n",
+    "- Usitumie data ya majaribio wakati wa kufundisha mfano wako.\n",
+    "- Data ya majaribio inapaswa kubaki \"safi\" hadi wakati wa kutathmini utendaji wa mfano.\n",
+    "- Ikiwa una seti kubwa ya data, unaweza pia kuunda seti ya tatu inayoitwa \"data ya uthibitisho\" kwa ajili ya kurekebisha vigezo vya mfano.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-11-01 00:00:00  0.10\n",
+       "2014-11-01 01:00:00  0.07\n",
+       "2014-11-01 02:00:00  0.05\n",
+       "2014-11-01 03:00:00  0.04\n",
+       "2014-11-01 04:00:00  0.06\n",
+       "2014-11-01 05:00:00  0.10\n",
+       "2014-11-01 06:00:00  0.19\n",
+       "2014-11-01 07:00:00  0.31\n",
+       "2014-11-01 08:00:00  0.40\n",
+       "2014-11-01 09:00:00  0.48"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Data asilia dhidi ya data iliyopimwa:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hebu pia tupime data ya majaribio\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-12-30 00:00:00  0.33\n",
+       "2014-12-30 01:00:00  0.29\n",
+       "2014-12-30 02:00:00  0.27\n",
+       "2014-12-30 03:00:00  0.27\n",
+       "2014-12-30 04:00:00  0.30"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Forecasting horizon: 3 hours\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "order = (4, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
+    "results = model.fit()\n",
+    "\n",
+    "print(results.summary())\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Thu, 30 Sep 2021   AIC                          -6942.477\n",
+      "Time:                                    14:36:28   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Unda data ya majaribio kwa kila hatua ya HORIZON.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load  load+1  load+2\n",
+       "2014-12-30 00:00:00  0.33    0.29    0.27\n",
+       "2014-12-30 01:00:00  0.29    0.27    0.27\n",
+       "2014-12-30 02:00:00  0.27    0.27    0.30\n",
+       "2014-12-30 03:00:00  0.27    0.30    0.41\n",
+       "2014-12-30 04:00:00  0.30    0.41    0.57"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2014-12-30 00:00:00\n",
+      "1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
+      "2014-12-30 01:00:00\n",
+      "2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
+      "2014-12-30 02:00:00\n",
+      "3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
+      "2014-12-30 03:00:00\n",
+      "4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
+      "2014-12-30 04:00:00\n",
+      "5 : predicted = [0.3  0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
+      "2014-12-30 05:00:00\n",
+      "6 : predicted = [0.4  0.55 0.66] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
+      "2014-12-30 06:00:00\n",
+      "7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
+      "2014-12-30 07:00:00\n",
+      "8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
+      "2014-12-30 08:00:00\n",
+      "9 : predicted = [0.75 0.8  0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
+      "2014-12-30 09:00:00\n",
+      "10 : predicted = [0.77 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
+      "2014-12-30 10:00:00\n",
+      "11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
+      "2014-12-30 11:00:00\n",
+      "12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
+      "2014-12-30 12:00:00\n",
+      "13 : predicted = [0.7  0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
+      "2014-12-30 13:00:00\n",
+      "14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
+      "2014-12-30 14:00:00\n",
+      "15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
+      "2014-12-30 15:00:00\n",
+      "16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
+      "2014-12-30 16:00:00\n",
+      "17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
+      "2014-12-30 17:00:00\n",
+      "18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
+      "2014-12-30 18:00:00\n",
+      "19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
+      "2014-12-30 19:00:00\n",
+      "20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
+      "2014-12-30 20:00:00\n",
+      "21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
+      "2014-12-30 21:00:00\n",
+      "22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
+      "2014-12-30 22:00:00\n",
+      "23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
+      "2014-12-30 23:00:00\n",
+      "24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
+      "2014-12-31 00:00:00\n",
+      "25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
+      "2014-12-31 01:00:00\n",
+      "26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
+      "2014-12-31 02:00:00\n",
+      "27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
+      "2014-12-31 03:00:00\n",
+      "28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
+      "2014-12-31 04:00:00\n",
+      "29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
+      "2014-12-31 05:00:00\n",
+      "30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
+      "2014-12-31 06:00:00\n",
+      "31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
+      "2014-12-31 07:00:00\n",
+      "32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
+      "2014-12-31 08:00:00\n",
+      "33 : predicted = [0.79 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
+      "2014-12-31 09:00:00\n",
+      "34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
+      "2014-12-31 10:00:00\n",
+      "35 : predicted = [0.8  0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
+      "2014-12-31 11:00:00\n",
+      "36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
+      "2014-12-31 12:00:00\n",
+      "37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
+      "2014-12-31 13:00:00\n",
+      "38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
+      "2014-12-31 14:00:00\n",
+      "39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
+      "2014-12-31 15:00:00\n",
+      "40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
+      "2014-12-31 16:00:00\n",
+      "41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
+      "2014-12-31 17:00:00\n",
+      "42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
+      "2014-12-31 18:00:00\n",
+      "43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
+      "2014-12-31 19:00:00\n",
+      "44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
+      "2014-12-31 20:00:00\n",
+      "45 : predicted = [0.7  0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
+      "2014-12-31 21:00:00\n",
+      "46 : predicted = [0.6  0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
+      "CPU times: user 12min 15s, sys: 2min 39s, total: 14min 54s\n",
+      "Wall time: 2min 36s\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": true
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Linganisha utabiri na mzigo halisi\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp    h  prediction   actual\n",
+       "0 2014-12-30 00:00:00  t+1    3,008.74 3,023.00\n",
+       "1 2014-12-30 01:00:00  t+1    2,955.53 2,935.00\n",
+       "2 2014-12-30 02:00:00  t+1    2,900.17 2,899.00\n",
+       "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
+       "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hesabu **kosa la wastani wa asilimia ya thamani kamili (MAPE)** kwa utabiri wote\n",
+    "\n",
+    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "One step forecast MAPE:  0.5570581332313952 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Multi-step forecast MAPE:  1.1460048657704118 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Panga utabiri dhidi ya halisi kwa wiki ya kwanza ya seti ya majaribio\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "if(HORIZON == 1):\n",
+    "    ## Plotting single step forecast\n",
+    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
+    "\n",
+    "else:\n",
+    "    ## Plotting multi step forecast\n",
+    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
+    "\n",
+    "    fig = plt.figure(figsize=(15, 8))\n",
+    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        x = plot_df['timestamp'][(t-1):]\n",
+    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
+    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
+    "    \n",
+    "    ax.legend(loc='best')\n",
+    "    \n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "c193140200b9684da27e3890211391b6",
+   "translation_date": "2025-09-06T13:58:46+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/sw/7-TimeSeries/2-ARIMA/working/notebook.ipynb b/translations/sw/7-TimeSeries/2-ARIMA/working/notebook.ipynb
new file mode 100644
index 000000000..07e9f27b8
--- /dev/null
+++ b/translations/sw/7-TimeSeries/2-ARIMA/working/notebook.ipynb
@@ -0,0 +1,61 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "523ec472196307b3c4235337353c9ceb",
+   "translation_date": "2025-09-06T14:00:36+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/working/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Utabiri wa mfululizo wa muda kwa kutumia ARIMA\n",
+    "\n",
+    "Katika daftari hili, tunadhihirisha jinsi ya:\n",
+    "- kuandaa data ya mfululizo wa muda kwa ajili ya kufundisha mfano wa utabiri wa ARIMA\n",
+    "- kutekeleza mfano rahisi wa ARIMA ili kutabiri hatua za HORIZON zijazo (wakati *t+1* hadi *t+HORIZON*) katika mfululizo wa muda\n",
+    "- kutathmini mfano\n",
+    "\n",
+    "Data katika mfano huu imetolewa kutoka mashindano ya utabiri ya GEFCom2014. \n",
+    "\n",
+    "Inajumuisha miaka 3 ya data ya saa kwa saa ya mzigo wa umeme na thamani za joto kati ya mwaka 2012 na 2014. Kazi ni kutabiri thamani za baadaye za mzigo wa umeme. Katika mfano huu, tunaonyesha jinsi ya kutabiri hatua moja mbele, kwa kutumia data ya kihistoria ya mzigo pekee.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli na Rob J. Hyndman, \"Utabiri wa nishati wa kiuhalisia: Mashindano ya Utabiri wa Nishati ya Kimataifa 2014 na zaidi\", Jarida la Kimataifa la Utabiri, vol.32, no.3, uk. 896-913, Julai-Septemba, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install statsmodels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuchukuliwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/7-TimeSeries/3-SVR/solution/notebook.ipynb b/translations/sw/7-TimeSeries/3-SVR/solution/notebook.ipynb
new file mode 100644
index 000000000..e85955e39
--- /dev/null
+++ b/translations/sw/7-TimeSeries/3-SVR/solution/notebook.ipynb
@@ -0,0 +1,1017 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Katika daftari hili, tunaonyesha jinsi ya:\n",
+    "\n",
+    "- kuandaa data ya mfululizo wa muda wa 2D kwa ajili ya kufundisha mfano wa SVM regressor  \n",
+    "- kutekeleza SVR kwa kutumia kernel ya RBF  \n",
+    "- kutathmini mfano kwa kutumia michoro na MAPE  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kuingiza moduli\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Pakia data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sasa, unahitaji kuandaa data kwa mafunzo kwa kufanya uchujaji na kupima data yako.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pima data kuwa katika kiwango (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.101611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.065801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.046106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.042525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.059087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-11-01 00:00:00  0.101611\n",
+       "2014-11-01 01:00:00  0.065801\n",
+       "2014-11-01 02:00:00  0.046106\n",
+       "2014-11-01 03:00:00  0.042525\n",
+       "2014-11-01 04:00:00  0.059087"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.329454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.290063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.273948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.268129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.302596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-12-30 00:00:00  0.329454\n",
+       "2014-12-30 01:00:00  0.290063\n",
+       "2014-12-30 02:00:00  0.273948\n",
+       "2014-12-30 03:00:00  0.268129\n",
+       "2014-12-30 04:00:00  0.302596"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "Kwa SVR yetu, tunabadilisha data ya ingizo kuwa katika umbo la `[batch, timesteps]`. Kwa hivyo, tunabadilisha umbo la `train_data` na `test_data` zilizopo ili kuwe na kipimo kipya kinachorejelea timesteps. Kwa mfano wetu, tunachukua `timesteps = 5`. Kwa hivyo, viingizo kwa mfano ni data za timesteps 4 za kwanza, na matokeo yatakuwa data ya timestep ya 5.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1412, 5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=np.array([[j for j in train_data[i:i+timesteps]] for i in range(0,len(train_data)-timesteps+1)])[:,:,0]\n",
+    "train_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(44, 5)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=np.array([[j for j in test_data[i:i+timesteps]] for i in range(0,len(test_data)-timesteps+1)])[:,:,0]\n",
+    "test_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 4) (1412, 1)\n",
+      "(44, 4) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train, y_train = train_data_timesteps[:,:timesteps-1],train_data_timesteps[:,[timesteps-1]]\n",
+    "x_test, y_test = test_data_timesteps[:,:timesteps-1],test_data_timesteps[:,[timesteps-1]]\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = SVR(kernel='rbf',gamma=0.5, C=10, epsilon = 0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVR(C=10, cache_size=200, coef0=0.0, degree=3, epsilon=0.05, gamma=0.5,\n",
+       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit model on training data\n",
+    "\n",
+    "model.fit(x_train, y_train[:,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Fanya utabiri wa modeli\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 1) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = model.predict(x_train).reshape(-1,1)\n",
+    "y_test_pred = model.predict(x_test).reshape(-1,1)\n",
+    "\n",
+    "print(y_train_pred.shape, y_test_pred.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
+    "\n",
+    "print(len(y_train_pred), len(y_test_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)\n",
+    "\n",
+    "print(len(y_train), len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)].index[timesteps-1:]\n",
+    "test_timestamps = energy[test_start_dt:].index[timesteps-1:]\n",
+    "\n",
+    "print(len(train_timestamps), len(test_timestamps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "plt.plot(train_timestamps, y_train, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(train_timestamps, y_train_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for training data:  1.7195710200875551 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "plt.plot(test_timestamps, y_test, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(test_timestamps, y_test_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for testing data:  1.2623790187854018 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor shape:  (26300, 5)\n",
+      "X shape:  (26300, 4) \n",
+      "Y shape:  (26300, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = energy.copy().values\n",
+    "\n",
+    "# Scaling\n",
+    "data = scaler.transform(data)\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=np.array([[j for j in data[i:i+timesteps]] for i in range(0,len(data)-timesteps+1)])[:,:,0]\n",
+    "print(\"Tensor shape: \", data_timesteps.shape)\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = data_timesteps[:,:timesteps-1],data_timesteps[:,[timesteps-1]]\n",
+    "print(\"X shape: \", X.shape,\"\\nY shape: \", Y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "Y_pred = model.predict(X).reshape(-1,1)\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = scaler.inverse_transform(Y_pred)\n",
+    "Y = scaler.inverse_transform(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "plt.plot(Y, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(Y_pred, color = 'blue', linewidth=1)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE:  2.0572089029888656 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "f8f3967282314d3995245835bdaa8418",
+   "translation_date": "2025-09-06T14:04:27+00:00",
+   "source_file": "7-TimeSeries/3-SVR/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sw/7-TimeSeries/3-SVR/working/notebook.ipynb b/translations/sw/7-TimeSeries/3-SVR/working/notebook.ipynb
new file mode 100644
index 000000000..5612d7071
--- /dev/null
+++ b/translations/sw/7-TimeSeries/3-SVR/working/notebook.ipynb
@@ -0,0 +1,693 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Katika daftari hili, tunaonyesha jinsi ya:\n",
+    "\n",
+    "- kuandaa data ya mfululizo wa muda wa 2D kwa ajili ya kufundisha mfano wa SVM regressor  \n",
+    "- kutekeleza SVR kwa kutumia kernel ya RBF  \n",
+    "- kutathmini mfano kwa kutumia michoro na MAPE  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kuingiza moduli\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Pakia data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sasa, unahitaji kuandaa data kwa mafunzo kwa kufanya uchujaji na kupima data yako.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pima data kuwa katika kiwango (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "Kwa SVR yetu, tunabadilisha data ya ingizo kuwa katika umbo la `[batch, timesteps]`. Kwa hivyo, tunabadilisha umbo la `train_data` na `test_data` zilizopo ili kuwe na kipimo kipya kinachorejelea timesteps. Kwa mfano wetu, tunachukua `timesteps = 5`. Kwa hivyo, viingizo kwa mfano ni data ya timesteps 4 za kwanza, na matokeo yatakuwa data ya timestep ya 5.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [],
+   "source": [
+    "x_train, y_train = None\n",
+    "x_test, y_test = None\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit model on training data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Fanya utabiri wa mfano\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = None\n",
+    "y_test_pred = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = None\n",
+    "test_timestamps = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = None\n",
+    "\n",
+    "# Scaling\n",
+    "data = None\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=None\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = None, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = None\n",
+    "Y = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "e86ce102239a14c44585623b9b924a74",
+   "translation_date": "2025-09-06T14:06:58+00:00",
+   "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/sw/8-Reinforcement/1-QLearning/notebook.ipynb b/translations/sw/8-Reinforcement/1-QLearning/notebook.ipynb
new file mode 100644
index 000000000..417cf3e17
--- /dev/null
+++ b/translations/sw/8-Reinforcement/1-QLearning/notebook.ipynb
@@ -0,0 +1,411 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "17e5a668646eabf5aabd0e9bfcf17876",
+   "translation_date": "2025-09-06T15:05:14+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter na Mbwa Mwitu: Utangulizi wa Kujifunza kwa Kuimarisha\n",
+    "\n",
+    "Katika mafunzo haya, tutajifunza jinsi ya kutumia kujifunza kwa kuimarisha kutatua tatizo la kutafuta njia. Mazingira haya yamechochewa na hadithi ya muziki [Peter na Mbwa Mwitu](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) iliyoandikwa na mtunzi wa Kirusi [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Ni hadithi kuhusu mvumbuzi kijana Peter, ambaye kwa ujasiri anatoka nyumbani kwake kwenda kwenye uwazi wa msitu kumfuatilia mbwa mwitu. Tutafundisha algoriti za kujifunza kwa mashine ambazo zitamsaidia Peter kuchunguza eneo linalomzunguka na kujenga ramani bora ya urambazaji.\n",
+    "\n",
+    "Kwanza, hebu tuagize maktaba kadhaa muhimu:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Muhtasari wa Kujifunza kwa Kuimarisha\n",
+    "\n",
+    "**Kujifunza kwa Kuimarisha** (RL) ni mbinu ya kujifunza inayotuwezesha kujifunza tabia bora ya **wakala** katika **mazingira** fulani kwa kufanya majaribio mengi. Wakala katika mazingira haya anapaswa kuwa na **lengo**, linalofafanuliwa na **kazi ya zawadi**.\n",
+    "\n",
+    "## Mazingira\n",
+    "\n",
+    "Kwa urahisi, hebu tuchukulie ulimwengu wa Peter kuwa ubao wa mraba wa ukubwa `width` x `height`. Kila seli katika ubao huu inaweza kuwa:\n",
+    "* **ardhi**, ambapo Peter na viumbe wengine wanaweza kutembea\n",
+    "* **maji**, ambapo ni wazi huwezi kutembea\n",
+    "* **mti** au **nyasi** - mahali ambapo unaweza kupumzika\n",
+    "* **tufaha**, ambayo inawakilisha kitu ambacho Peter angefurahia kukipata ili kujilisha\n",
+    "* **mbwa mwitu**, ambaye ni hatari na anapaswa kuepukwa\n",
+    "\n",
+    "Ili kufanya kazi na mazingira haya, tutafafanua darasa linaloitwa `Board`. Ili kuepuka kujaa sana katika daftari hili, tumepitisha msimbo wote wa kufanya kazi na ubao kwenye moduli tofauti inayoitwa `rlboard`, ambayo sasa tutaiingiza. Unaweza kuangalia ndani ya moduli hii ili kupata maelezo zaidi kuhusu mambo ya ndani ya utekelezaji.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Hebu sasa tuunde ubao wa nasibu na tuone jinsi unavyoonekana:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Hatua na Sera\n",
+    "\n",
+    "Katika mfano wetu, lengo la Peter litakuwa kupata tofaa, huku akiepuka mbwa mwitu na vizuizi vingine. Fafanua hatua hizo kama kamusi, na uzihusishe na jozi za mabadiliko ya kuratibu yanayolingana.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 2"
+   ]
+  },
+  {
+   "source": [
+    "Mkakati wa wakala wetu (Peter) unafafanuliwa na kile kinachoitwa **sera**. Hebu tuchukulie sera rahisi zaidi inayoitwa **kutembea kwa bahati nasibu**.\n",
+    "\n",
+    "## Kutembea kwa bahati nasibu\n",
+    "\n",
+    "Hebu kwanza tutatue tatizo letu kwa kutekeleza mkakati wa kutembea kwa bahati nasibu.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Let's run a random walk experiment several times and see the average number of steps taken: code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 4"
+   ]
+  },
+  {
+   "source": [
+    "## Kazi ya Tuzo\n",
+    "\n",
+    "Ili kufanya sera yetu iwe na akili zaidi, tunahitaji kuelewa ni hatua zipi ni \"bora\" kuliko nyingine.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning\n",
+    "\n",
+    "Jenga Q-Table, au safu yenye vipimo vingi. Kwa kuwa ubao wetu una vipimo vya `width` x `height`, tunaweza kuwakilisha Q-Table kwa safu ya numpy yenye umbo la `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Pitisha Jedwali la Q kwa kazi ya `plot` ili kuonyesha jedwali kwenye ubao:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'm' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8b8a9e4cb2b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kiini cha Q-Learning: Mlinganyo wa Bellman na Algorithimu ya Kujifunza\n",
+    "\n",
+    "Andika pseudo-code kwa algorithimu yetu ya kujifunza:\n",
+    "\n",
+    "* Anzisha Q-Table Q na namba sawa kwa hali zote na vitendo vyote\n",
+    "* Weka kiwango cha kujifunza $\\alpha\\leftarrow 1$\n",
+    "* Rudia simulizi mara nyingi\n",
+    "   1. Anza katika nafasi ya bahati nasibu\n",
+    "   1. Rudia\n",
+    "        1. Chagua kitendo $a$ katika hali $s$\n",
+    "        2. Tekeleza kitendo kwa kuhamia hali mpya $s'$\n",
+    "        3. Ikiwa tunakutana na hali ya mwisho wa mchezo, au jumla ya zawadi ni ndogo sana - toka kwenye simulizi  \n",
+    "        4. Hesabu zawadi $r$ katika hali mpya\n",
+    "        5. Sasisha Q-Function kulingana na mlinganyo wa Bellman: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Sasisha jumla ya zawadi na punguza $\\alpha$.\n",
+    "\n",
+    "## Kutumia vs. Kuchunguza\n",
+    "\n",
+    "Njia bora ni kusawazisha kati ya kuchunguza na kutumia. Tunapojifunza zaidi kuhusu mazingira yetu, tutakuwa na uwezekano mkubwa wa kufuata njia bora, hata hivyo, kuchagua njia ambayo haijachunguzwa mara moja moja.\n",
+    "\n",
+    "## Utekelezaji wa Python\n",
+    "\n",
+    "Sasa tuko tayari kutekeleza algorithimu ya kujifunza. Kabla ya hapo, tunahitaji pia kazi fulani ambayo itabadilisha namba za bahati nasibu katika Q-Table kuwa vector ya uwezekano kwa vitendo vinavyolingana:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Tunaongeza kiasi kidogo cha `eps` kwenye vector ya awali ili kuepuka kugawanya kwa 0 katika hali ya mwanzo, ambapo vipengele vyote vya vector vinafanana.\n",
+    "\n",
+    "Algoriti halisi ya kujifunza tutakayoendesha kwa majaribio 5000, pia inaitwa **epochs**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "# code block 8"
+   ]
+  },
+  {
+   "source": [
+    "Baada ya kutekeleza algoriti hii, Jedwali la Q linapaswa kusasishwa na maadili yanayofafanua mvuto wa vitendo tofauti katika kila hatua. Onyesha jedwali hapa:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kukagua Sera\n",
+    "\n",
+    "Kwa kuwa Q-Table inaorodhesha \"mvuto\" wa kila kitendo katika kila hali, ni rahisi kuitumia kufafanua urambazaji bora katika ulimwengu wetu. Katika hali rahisi zaidi, tunaweza tu kuchagua kitendo kinacholingana na thamani ya juu zaidi ya Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "# code block 9"
+   ]
+  },
+  {
+   "source": [
+    "Ikiwa unajaribu msimbo hapo juu mara kadhaa, unaweza kugundua kwamba wakati mwingine unakwama tu, na unahitaji kubonyeza kitufe cha STOP kwenye daftari ili kuukatiza. \n",
+    "\n",
+    "> **Kazi ya 1:** Badilisha kazi ya `walk` ili kuweka kikomo cha urefu wa njia kwa idadi fulani ya hatua (sema, 100), na angalia msimbo hapo juu ukirudisha thamani hii mara kwa mara.\n",
+    "\n",
+    "> **Kazi ya 2:** Badilisha kazi ya `walk` ili isirudi kwenye maeneo ambayo tayari imekuwa hapo awali. Hii itazuia `walk` kuzunguka, hata hivyo, wakala bado anaweza kujikuta \"amekwama\" mahali ambapo hawezi kutoroka.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 5.31, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# code block 10"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26dab295a48>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 57
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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109.232964 214.756364 \r\nL 109.263403 214.085931 \r\nL 109.293843 201.703888 \r\nL 109.385161 202.919046 \r\nL 109.44604 214.756364 \r\nL 109.476479 192.192132 \r\nL 109.537358 214.735413 \r\nL 109.598237 214.756364 \r\nL 109.719994 198.100315 \r\nL 109.811312 214.756364 \r\nL 109.841752 210.230947 \r\nL 109.872191 214.735413 \r\nL 109.93307 208.63867 \r\nL 110.054828 214.756364 \r\nL 110.085267 200.509681 \r\nL 110.146146 208.052042 \r\nL 110.207025 214.756364 \r\nL 110.267904 213.268842 \r\nL 110.298343 193.470143 \r\nL 110.328782 214.756364 \r\nL 110.359222 209.811927 \r\nL 110.420101 214.756364 \r\nL 110.480979 214.044029 \r\nL 110.541858 214.756364 \r\nL 110.572298 212.032733 \r\nL 110.633176 214.756364 \r\nL 110.663616 214.756364 \r\nL 110.694055 199.441179 \r\nL 110.785374 202.981899 \r\nL 110.907131 214.756364 \r\nL 110.937571 211.110889 \r\nL 110.998449 214.756364 \r\nL 111.089768 209.916682 \r\nL 111.181086 214.756364 \r\nL 111.211525 203.882792 \r\nL 111.272404 207.842532 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115.016451 213.43645 \r\nL 115.046891 214.46305 \r\nL 115.07733 207.339708 \r\nL 115.168648 209.66527 \r\nL 115.229527 213.268842 \r\nL 115.290406 211.215644 \r\nL 115.320845 205.642677 \r\nL 115.351285 214.756364 \r\nL 115.381724 209.749074 \r\nL 115.533921 214.756364 \r\nL 115.564361 213.687862 \r\nL 115.655679 206.606423 \r\nL 115.746997 214.756364 \r\nL 115.716558 193.700604 \r\nL 115.777436 214.735413 \r\nL 115.838315 193.763457 \r\nL 115.868755 193.910114 \r\nL 115.899194 214.756364 \r\nL 115.990512 212.346998 \r\nL 116.051391 210.754722 \r\nL 116.11227 214.756364 \r\nL 116.142709 211.76037 \r\nL 116.203588 214.756364 \r\nL 116.234027 214.756364 \r\nL 116.264467 213.478352 \r\nL 116.294906 214.756364 \r\nL 116.325346 184.670721 \r\nL 116.386225 214.756364 \r\nL 116.416664 214.044029 \r\nL 116.447103 214.756364 \r\nL 116.477543 196.047117 \r\nL 116.538422 212.786969 \r\nL 116.5993 210.964232 \r\nL 116.690619 214.756364 \r\nL 116.812376 200.404926 \r\nL 116.903694 214.756364 \r\nL 116.934134 196.403284 \r\nL 116.995013 214.756364 \r\nL 117.055891 214.756364 \r\nL 117.086331 213.939274 \r\nL 117.11677 199.650689 \r\nL 117.177649 212.158439 \r\nL 117.208089 214.756364 \r\nL 117.238528 213.939274 \r\nL 117.268967 205.684579 \r\nL 117.329846 214.756364 \r\nL 117.360286 214.484001 \r\nL 117.421164 195.774754 \r\nL 117.451604 210.000486 \r\nL 117.512483 214.756364 \r\nL 117.542922 176.709339 \r\nL 117.63424 184.64977 \r\nL 117.725558 183.874583 \r\nL 117.755998 214.756364 \r\nL 117.786437 214.756364 \r\nL 117.877756 198.959306 \r\nL 117.908195 210.566163 \r\nL 117.938634 212.766018 \r\nL 117.969074 206.522619 \r\nL 118.029953 214.756364 \r\nL 118.060392 214.756364 \r\nL 118.18215 199.399277 \r\nL 118.273468 214.756364 \r\nL 118.303907 205.035098 \r\nL 118.364786 214.756364 \r\nL 118.456104 214.232589 \r\nL 118.486544 214.756364 \r\nL 118.516983 209.476711 \r\nL 118.577862 214.756364 \r\nL 118.608301 213.352646 \r\nL 118.66918 214.735413 \r\nL 118.69962 186.786773 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200.090661 \r\nL 122.595864 211.927978 \r\nL 122.687182 211.383252 \r\nL 122.717621 214.756364 \r\nL 122.7785 214.756364 \r\nL 122.80894 210.335702 \r\nL 122.900258 212.493655 \r\nL 122.930697 214.756364 \r\nL 123.022015 183.958387 \r\nL 123.052455 208.533915 \r\nL 123.082894 214.756364 \r\nL 123.113334 201.703888 \r\nL 123.143773 210.482359 \r\nL 123.174212 210.419506 \r\nL 123.265531 200.48873 \r\nL 123.29597 214.756364 \r\nL 123.326409 214.735413 \r\nL 123.356849 187.017234 \r\nL 123.417728 214.756364 \r\nL 123.569925 191.291239 \r\nL 123.691682 214.756364 \r\nL 123.722122 214.735413 \r\nL 123.783001 205.579824 \r\nL 123.81344 189.279942 \r\nL 123.874319 214.735413 \r\nL 123.935198 214.735413 \r\nL 123.996076 214.756364 \r\nL 124.026516 206.669276 \r\nL 124.148273 214.756364 \r\nL 124.178713 214.756364 \r\nL 124.239592 209.581466 \r\nL 124.270031 211.96988 \r\nL 124.300471 214.023078 \r\nL 124.33091 201.201064 \r\nL 124.422228 208.471062 \r\nL 124.513546 214.756364 \r\nL 124.543986 212.326047 \r\nL 124.604865 199.818297 \r\nL 124.635304 207.360659 \r\nL 124.726622 214.756364 \r\nL 124.787501 174.467582 \r\nL 124.81794 214.756364 \r\nL 124.909259 214.735413 \r\nL 124.939698 211.173742 \r\nL 124.970137 186.283949 \r\nL 125.031016 211.907027 \r\nL 125.091895 214.756364 \r\nL 125.152774 214.735413 \r\nL 125.274532 173.252424 \r\nL 125.36585 214.756364 \r\nL 125.396289 214.127834 \r\nL 125.426729 214.735413 \r\nL 125.457168 198.267923 \r\nL 125.518047 214.106882 \r\nL 125.548486 210.775673 \r\nL 125.609365 214.756364 \r\nL 125.639804 211.006134 \r\nL 125.731123 214.735413 \r\nL 125.761562 214.421148 \r\nL 125.822441 214.735413 \r\nL 125.913759 173.399081 \r\nL 125.974638 214.756364 \r\nL 126.035517 206.648325 \r\nL 126.065956 206.585472 \r\nL 126.187714 214.756364 \r\nL 126.248593 214.756364 \r\nL 126.309471 213.562156 \r\nL 126.339911 205.852187 \r\nL 126.37035 214.735413 \r\nL 126.40079 212.996479 \r\nL 126.431229 214.756364 \r\nL 126.461668 214.588756 \r\nL 126.522547 198.41458 \r\nL 126.552987 213.059332 \r\nL 126.705184 214.756364 \r\nL 126.735623 192.192132 \r\nL 126.796502 213.750715 \r\nL 126.91826 214.756364 \r\nL 126.979138 210.021437 \r\nL 127.009578 214.756364 \r\nL 127.040017 214.756364 \r\nL 127.131335 210.461408 \r\nL 127.192214 207.402561 \r\nL 127.253093 214.756364 \r\nL 127.283532 210.377604 \r\nL 127.344411 214.735413 \r\nL 127.374851 214.756364 \r\nL 127.40529 214.46305 \r\nL 127.466169 214.756364 \r\nL 127.527048 199.524983 \r\nL 127.557487 214.735413 \r\nL 127.587927 211.404203 \r\nL 127.618366 177.673086 \r\nL 127.648805 214.756364 \r\nL 127.679245 184.251701 \r\nL 127.801002 214.756364 \r\nL 127.861881 214.756364 \r\nL 127.953199 207.612071 \r\nL 128.014078 214.756364 \r\nL 128.044518 204.532273 \r\nL 128.074957 198.309825 \r\nL 128.105396 214.756364 \r\nL 128.166275 214.756364 \r\nL 128.257593 210.105241 \r\nL 128.348912 214.756364 \r\nL 128.501109 186.430606 \r\nL 128.531548 210.314751 \r\nL 128.592427 206.459766 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214.693511 \r\nL 130.236155 214.735413 \r\nL 130.297034 204.972245 \r\nL 130.388352 214.756364 \r\nL 130.449231 190.620806 \r\nL 130.47967 213.331695 \r\nL 130.540549 214.756364 \r\nL 130.601428 214.358295 \r\nL 130.692746 203.547576 \r\nL 130.753625 197.953658 \r\nL 130.814504 214.756364 \r\nL 130.844943 210.649967 \r\nL 130.905822 211.425154 \r\nL 130.936261 214.756364 \r\nL 130.99714 214.546854 \r\nL 131.058019 203.945645 \r\nL 131.088458 213.939274 \r\nL 131.149337 214.756364 \r\nL 131.179777 207.653973 \r\nL 131.240655 214.756364 \r\nL 131.51461 214.756364 \r\nL 131.54505 182.827033 \r\nL 131.605928 214.106882 \r\nL 131.636368 214.756364 \r\nL 131.758125 205.998844 \r\nL 131.788565 214.756364 \r\nL 131.819004 200.111612 \r\nL 131.849444 213.813568 \r\nL 131.879883 194.643399 \r\nL 131.910322 214.756364 \r\nL 131.940762 212.849822 \r\nL 131.971201 214.756364 \r\nL 132.092959 153.16041 \r\nL 132.184277 214.756364 \r\nL 132.214716 191.752161 \r\nL 132.306035 214.756364 \r\nL 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154.952954 214.756364 \r\nL 155.074712 200.823946 \r\nL 155.16603 214.756364 \r\nL 155.196469 214.735413 \r\nL 155.257348 214.735413 \r\nL 155.379106 197.869854 \r\nL 155.500863 214.756364 \r\nL 155.531303 209.141494 \r\nL 155.622621 211.634664 \r\nL 155.65306 209.204347 \r\nL 155.6835 214.756364 \r\nL 155.713939 211.383252 \r\nL 155.744379 213.813568 \r\nL 155.774818 195.167174 \r\nL 155.805257 214.756364 \r\nL 155.835697 213.729764 \r\nL 155.866136 214.756364 \r\nL 156.018333 198.770747 \r\nL 156.048773 210.398555 \r\nL 156.079212 135.854881 \r\nL 156.109651 214.756364 \r\nL 156.140091 195.984264 \r\nL 156.20097 214.756364 \r\nL 156.231409 210.754722 \r\nL 156.261848 183.706975 \r\nL 156.322727 214.756364 \r\nL 156.353167 214.756364 \r\nL 156.474924 196.466137 \r\nL 156.627121 214.756364 \r\nL 156.657561 214.756364 \r\nL 156.688 192.485446 \r\nL 156.748879 214.735413 \r\nL 156.779318 213.520254 \r\nL 156.840197 214.735413 \r\nL 156.870637 214.735413 \r\nL 156.901076 213.289793 \r\nL 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162.83676 214.756364 \r\nL 162.897639 198.016511 \r\nL 162.988958 214.756364 \r\nL 163.019397 209.392906 \r\nL 163.049836 209.351004 \r\nL 163.080276 211.508958 \r\nL 163.141155 204.951294 \r\nL 163.171594 214.735413 \r\nL 163.232473 214.756364 \r\nL 163.262912 211.299448 \r\nL 163.323791 210.649967 \r\nL 163.35423 214.756364 \r\nL 163.415109 201.641035 \r\nL 163.445549 214.735413 \r\nL 163.506427 214.756364 \r\nL 163.536867 213.729764 \r\nL 163.597746 214.756364 \r\nL 163.628185 214.756364 \r\nL 163.719503 191.815014 \r\nL 163.780382 214.756364 \r\nL 163.841261 214.023078 \r\nL 163.8717 202.688585 \r\nL 163.932579 208.743425 \r\nL 163.963019 214.756364 \r\nL 164.023897 212.912675 \r\nL 164.054337 202.939997 \r\nL 164.084776 214.756364 \r\nL 164.145655 205.097951 \r\nL 164.206534 214.756364 \r\nL 164.236973 202.541928 \r\nL 164.267413 189.971325 \r\nL 164.297852 214.756364 \r\nL 164.328291 205.852187 \r\nL 164.38917 214.756364 \r\nL 164.480489 212.870773 \r\nL 164.510928 212.59841 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168.589809 199.88115 \r\nL 168.650687 214.756364 \r\nL 168.772445 205.056049 \r\nL 168.833324 207.255904 \r\nL 168.955081 214.756364 \r\nL 169.076839 204.720832 \r\nL 169.168157 214.756364 \r\nL 169.198597 212.912675 \r\nL 169.289915 214.756364 \r\nL 169.320354 214.735413 \r\nL 169.442112 194.287232 \r\nL 169.56387 214.756364 \r\nL 169.685627 181.884238 \r\nL 169.807385 214.756364 \r\nL 169.837824 200.048758 \r\nL 169.898703 208.072993 \r\nL 169.959582 214.756364 \r\nL 169.990021 207.695875 \r\nL 170.020461 210.189045 \r\nL 170.111779 214.756364 \r\nL 170.142218 187.059136 \r\nL 170.203097 213.059332 \r\nL 170.233537 210.796624 \r\nL 170.263976 213.331695 \r\nL 170.324855 211.404203 \r\nL 170.385734 214.756364 \r\nL 170.446612 214.756364 \r\nL 170.477052 210.733771 \r\nL 170.537931 214.735413 \r\nL 170.629249 214.756364 \r\nL 170.659688 204.406567 \r\nL 170.720567 208.21965 \r\nL 170.842325 214.756364 \r\nL 170.933643 196.298529 \r\nL 170.964082 211.446105 \r\nL 170.994522 214.756364 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178.695692 214.756364 \r\nL 178.726131 205.077 \r\nL 178.78701 214.693511 \r\nL 178.847889 214.756364 \r\nL 178.878328 214.421148 \r\nL 178.908768 214.714462 \r\nL 178.939207 211.844174 \r\nL 178.969647 193.051123 \r\nL 179.030525 214.756364 \r\nL 179.060965 207.255904 \r\nL 179.121844 214.756364 \r\nL 179.213162 211.027085 \r\nL 179.274041 192.548299 \r\nL 179.30448 213.750715 \r\nL 179.334919 214.756364 \r\nL 179.426238 214.358295 \r\nL 179.456677 214.756364 \r\nL 179.487116 214.609707 \r\nL 179.547995 189.887521 \r\nL 179.578435 214.756364 \r\nL 179.639313 214.756364 \r\nL 179.669753 214.504952 \r\nL 179.700192 205.349363 \r\nL 179.730632 214.756364 \r\nL 179.761071 214.756364 \r\nL 179.882829 200.237318 \r\nL 179.913268 214.756364 \r\nL 180.004586 206.564521 \r\nL 180.065465 205.097951 \r\nL 180.095905 195.062419 \r\nL 180.126344 208.994837 \r\nL 180.156783 203.715184 \r\nL 180.278541 214.756364 \r\nL 180.400299 206.145501 \r\nL 180.430738 214.756364 \r\nL 180.461177 208.114895 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200.551583 \r\nL 184.266103 214.756364 \r\nL 184.296543 214.756364 \r\nL 184.387861 203.191409 \r\nL 184.509619 214.756364 \r\nL 184.540058 208.994837 \r\nL 184.600937 214.756364 \r\nL 184.631376 214.756364 \r\nL 184.661816 195.523342 \r\nL 184.722695 214.756364 \r\nL 184.783573 214.756364 \r\nL 184.93577 200.404926 \r\nL 184.96621 214.735413 \r\nL 185.057528 213.541205 \r\nL 185.118407 214.044029 \r\nL 185.240164 192.275936 \r\nL 185.331483 187.373401 \r\nL 185.361922 214.756364 \r\nL 185.392362 203.882792 \r\nL 185.45324 213.981176 \r\nL 185.48368 214.756364 \r\nL 185.544559 180.103402 \r\nL 185.574998 214.756364 \r\nL 185.605437 214.756364 \r\nL 185.757634 190.599855 \r\nL 185.788074 192.171181 \r\nL 185.818513 181.695679 \r\nL 185.909831 214.756364 \r\nL 185.940271 203.149507 \r\nL 185.97071 214.756364 \r\nL 186.031589 202.16481 \r\nL 186.062028 213.22694 \r\nL 186.092468 195.271929 \r\nL 186.153347 207.130198 \r\nL 186.214226 214.756364 \r\nL 186.244665 207.402561 \r\nL 186.305544 213.981176 \r\nL 186.396862 211.236595 \r\nL 186.427301 207.758728 \r\nL 186.457741 214.756364 \r\nL 186.48818 212.661263 \r\nL 186.51862 210.733771 \r\nL 186.609938 214.756364 \r\nL 186.640377 168.035624 \r\nL 186.701256 210.461408 \r\nL 186.762135 208.303454 \r\nL 186.792574 195.942362 \r\nL 186.823014 214.756364 \r\nL 186.853453 198.184119 \r\nL 186.975211 214.756364 \r\nL 187.00565 191.668357 \r\nL 187.066529 209.476711 \r\nL 187.096968 214.756364 \r\nL 187.188287 170.486891 \r\nL 187.310044 214.756364 \r\nL 187.340484 214.756364 \r\nL 187.431802 200.928701 \r\nL 187.462241 214.316393 \r\nL 187.492681 199.294522 \r\nL 187.553559 208.84818 \r\nL 187.614438 210.901379 \r\nL 187.644878 194.978615 \r\nL 187.705756 212.80792 \r\nL 187.736196 210.042388 \r\nL 187.857954 183.853632 \r\nL 187.827514 210.92233 \r\nL 187.888393 186.996283 \r\nL 187.949272 214.756364 \r\nL 187.979711 214.735413 \r\nL 188.010151 192.024524 \r\nL 188.04059 214.756364 \r\nL 188.071029 214.756364 \r\nL 188.162348 206.648325 \r\nL 188.192787 164.432051 \r\nL 188.253666 208.701523 \r\nL 188.284105 207.653973 \r\nL 188.314545 213.897372 \r\nL 188.344984 202.332418 \r\nL 188.405863 210.000486 \r\nL 188.436302 214.756364 \r\nL 188.466742 190.055129 \r\nL 188.52762 214.756364 \r\nL 188.55806 214.044029 \r\nL 188.588499 214.756364 \r\nL 188.710257 204.993196 \r\nL 188.801575 214.756364 \r\nL 188.832015 214.735413 \r\nL 188.862454 214.567805 \r\nL 188.892893 199.105963 \r\nL 188.984212 206.585472 \r\nL 189.136409 214.756364 \r\nL 189.166848 164.369198 \r\nL 189.227727 214.756364 \r\nL 189.319045 200.048758 \r\nL 189.440803 214.756364 \r\nL 189.471242 214.023078 \r\nL 189.501682 204.322763 \r\nL 189.56256 212.724116 \r\nL 189.623439 214.756364 \r\nL 189.653879 194.475791 \r\nL 189.714757 214.756364 \r\nL 189.836515 194.203428 \r\nL 189.866954 205.894089 \r\nL 189.927833 194.894811 \r\nL 189.958273 214.756364 \r\nL 189.988712 193.931065 \r\nL 190.049591 214.274491 \r\nL 190.08003 214.756364 \r\nL 190.11047 202.772389 \r\nL 190.201788 207.59112 \r\nL 190.323546 214.756364 \r\nL 190.353985 214.421148 \r\nL 190.384424 214.756364 \r\nL 190.414864 197.953658 \r\nL 190.475743 209.183396 \r\nL 190.62794 214.756364 \r\nL 190.658379 214.756364 \r\nL 190.688818 189.824668 \r\nL 190.749697 214.714462 \r\nL 190.810576 214.756364 \r\nL 190.932334 201.578182 \r\nL 191.023652 214.756364 \r\nL 191.084531 211.802272 \r\nL 191.175849 214.756364 \r\nL 191.236728 206.836884 \r\nL 191.267167 213.771666 \r\nL 191.328046 214.756364 \r\nL 191.419364 208.680572 \r\nL 191.480243 214.756364 \r\nL 191.541122 214.735413 \r\nL 191.571561 214.756364 \r\nL 191.63244 180.962393 \r\nL 191.662879 212.3889 \r\nL 191.693319 214.756364 \r\nL 191.815077 180.627177 \r\nL 191.936834 214.756364 \r\nL 191.967274 214.756364 \r\nL 191.997713 214.190687 \r\nL 192.028152 179.831039 \r\nL 192.058592 214.756364 \r\nL 192.089031 214.756364 \r\nL 192.14991 210.2938 \r\nL 192.180349 213.331695 \r\nL 192.271668 214.756364 \r\nL 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194.311108 208.931984 \r\nL 194.341547 208.177748 \r\nL 194.463305 214.756364 \r\nL 194.493744 194.203428 \r\nL 194.585063 195.586195 \r\nL 194.73726 214.756364 \r\nL 194.767699 212.053684 \r\nL 194.828578 214.756364 \r\nL 194.859017 214.756364 \r\nL 194.919896 191.144582 \r\nL 194.950335 195.020517 \r\nL 194.980775 214.756364 \r\nL 195.072093 202.479075 \r\nL 195.193851 214.756364 \r\nL 195.315608 201.515329 \r\nL 195.376487 201.431525 \r\nL 195.467805 214.756364 \r\nL 195.589563 203.966596 \r\nL 195.650442 196.382333 \r\nL 195.74176 214.756364 \r\nL 195.833078 206.606423 \r\nL 195.863518 213.562156 \r\nL 195.893957 214.756364 \r\nL 195.924397 213.43645 \r\nL 195.954836 211.425154 \r\nL 195.985275 212.640312 \r\nL 196.015715 194.685301 \r\nL 196.076594 214.756364 \r\nL 196.167912 214.756364 \r\nL 196.25923 192.736858 \r\nL 196.289669 206.711178 \r\nL 196.380988 202.79334 \r\nL 196.411427 214.756364 \r\nL 196.502745 214.756364 \r\nL 196.624503 197.932707 \r\nL 196.715821 214.756364 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200.520747 199.441179 \r\nL 200.612065 203.023801 \r\nL 200.703384 214.67256 \r\nL 200.672944 201.368672 \r\nL 200.733823 208.533915 \r\nL 200.764262 205.077 \r\nL 200.794702 214.756364 \r\nL 200.825141 195.104321 \r\nL 200.855581 160.87038 \r\nL 200.88602 214.756364 \r\nL 200.916459 207.423512 \r\nL 200.977338 214.735413 \r\nL 201.038217 177.777841 \r\nL 201.129535 212.17939 \r\nL 201.159975 210.126192 \r\nL 201.251293 214.232589 \r\nL 201.281732 184.545015 \r\nL 201.40349 213.604058 \r\nL 201.464369 208.911033 \r\nL 201.494808 198.393629 \r\nL 201.525248 214.756364 \r\nL 201.555687 214.756364 \r\nL 201.586126 214.421148 \r\nL 201.738323 146.89606 \r\nL 201.829642 214.756364 \r\nL 201.860081 208.63867 \r\nL 201.89052 206.040746 \r\nL 201.92096 212.053684 \r\nL 201.951399 212.493655 \r\nL 201.981839 214.756364 \r\nL 202.012278 214.735413 \r\nL 202.042717 142.140182 \r\nL 202.103596 159.843781 \r\nL 202.164475 214.756364 \r\nL 202.225354 211.844174 \r\nL 202.255793 157.664876 \r\nL 202.286233 214.756364 \r\nL 202.316672 213.85547 \r\nL 202.377551 205.642677 \r\nL 202.40799 214.756364 \r\nL 202.43843 214.484001 \r\nL 202.468869 188.588559 \r\nL 202.499309 214.756364 \r\nL 202.529748 214.484001 \r\nL 202.651506 198.330776 \r\nL 202.681945 214.756364 \r\nL 202.742824 212.849822 \r\nL 202.773263 182.994641 \r\nL 202.834142 190.055129 \r\nL 202.92546 214.756364 \r\nL 202.9559 214.735413 \r\nL 202.986339 170.067871 \r\nL 203.047218 214.756364 \r\nL 203.077657 208.869131 \r\nL 203.168976 211.089938 \r\nL 203.229854 214.337344 \r\nL 203.260294 211.739419 \r\nL 203.321173 211.948929 \r\nL 203.351612 193.449192 \r\nL 203.382051 214.756364 \r\nL 203.47337 202.311467 \r\nL 203.503809 203.547576 \r\nL 203.534248 214.735413 \r\nL 203.564688 165.66816 \r\nL 203.656006 192.715907 \r\nL 203.686445 193.009221 \r\nL 203.716885 214.400197 \r\nL 203.747324 206.627374 \r\nL 203.777764 170.549744 \r\nL 203.808203 214.756364 \r\nL 203.838643 214.756364 \r\nL 203.869082 208.198699 \r\nL 203.929961 209.288151 \r\nL 203.99084 214.756364 \r\nL 204.021279 210.964232 \r\nL 204.082158 176.353172 \r\nL 204.112597 214.756364 \r\nL 204.173476 213.918323 \r\nL 204.203915 214.756364 \r\nL 204.234355 210.482359 \r\nL 204.295234 214.567805 \r\nL 204.356112 211.96988 \r\nL 204.386552 214.756364 \r\nL 204.447431 186.975332 \r\nL 204.47787 211.446105 \r\nL 204.508309 212.870773 \r\nL 204.538749 209.2672 \r\nL 204.569188 207.989189 \r\nL 204.599628 190.159884 \r\nL 204.660507 214.756364 \r\nL 204.690946 214.756364 \r\nL 204.721385 211.907027 \r\nL 204.782264 214.379246 \r\nL 204.812704 192.213083 \r\nL 204.843143 214.756364 \r\nL 204.904022 205.91504 \r\nL 204.934461 205.244608 \r\nL 204.99534 214.756364 \r\nL 205.056219 211.488007 \r\nL 205.086658 213.771666 \r\nL 205.117098 213.352646 \r\nL 205.147537 171.052568 \r\nL 205.208416 207.549218 \r\nL 205.238855 213.394548 \r\nL 205.299734 204.86749 \r\nL 205.360613 214.442099 \r\nL 205.391052 157.74868 \r\nL 205.421492 214.756364 \r\nL 205.451931 214.756364 \r\nL 205.51281 202.688585 \r\nL 205.543249 213.373597 \r\nL 205.604128 211.865125 \r\nL 205.634568 214.756364 \r\nL 205.665007 199.734493 \r\nL 205.725886 214.756364 \r\nL 205.756325 213.310744 \r\nL 205.786765 193.428241 \r\nL 205.847643 214.756364 \r\nL 205.878083 212.849822 \r\nL 205.908522 212.975528 \r\nL 205.969401 188.777118 \r\nL 206.03028 199.650689 \r\nL 206.091159 214.756364 \r\nL 206.152037 205.077 \r\nL 206.212916 214.756364 \r\nL 206.243356 182.76418 \r\nL 206.304235 214.756364 \r\nL 206.425992 191.354092 \r\nL 206.456432 214.756364 \r\nL 206.51731 204.427518 \r\nL 206.54775 198.142217 \r\nL 206.578189 214.756364 \r\nL 206.669507 197.031814 \r\nL 206.760826 214.756364 \r\nL 206.791265 196.256627 \r\nL 206.852144 210.943281 \r\nL 206.882583 214.756364 \r\nL 206.913023 188.588559 \r\nL 207.004341 205.28651 \r\nL 207.03478 214.756364 \r\nL 207.06522 213.373597 \r\nL 207.095659 198.435531 \r\nL 207.156538 205.768383 \r\nL 207.278296 214.756364 \r\nL 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "## Zoezi\n",
+    "## Ulimwengu halisi wa Peter na Mbwa Mwitu\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/translations/sw/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
new file mode 100644
index 000000000..954db2d76
--- /dev/null
+++ b/translations/sw/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
@@ -0,0 +1,425 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "eadbd20d2a075efb602615ad90b1e97a",
+   "translation_date": "2025-09-06T15:14:53+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter na Mbwa Mwitu: Mazingira Halisi\n",
+    "\n",
+    "Katika hali yetu, Peter aliweza kuzunguka karibu bila kuchoka au kuhisi njaa. Katika dunia halisi zaidi, anapaswa kukaa chini na kupumzika mara kwa mara, na pia kujilisha. Hebu tufanye dunia yetu iwe halisi zaidi kwa kutekeleza sheria zifuatazo:\n",
+    "\n",
+    "1. Kwa kusafiri kutoka sehemu moja hadi nyingine, Peter hupoteza **nguvu** na kupata **uchovu**.\n",
+    "2. Peter anaweza kupata nguvu zaidi kwa kula matufaha.\n",
+    "3. Peter anaweza kuondoa uchovu kwa kupumzika chini ya mti au kwenye nyasi (yaani, kutembea hadi eneo lenye mti au nyasi - uwanja wa kijani).\n",
+    "4. Peter anahitaji kumtafuta na kumuua mbwa mwitu.\n",
+    "5. Ili kumuua mbwa mwitu, Peter anahitaji kuwa na viwango fulani vya nguvu na uchovu, vinginevyo atashindwa katika mapambano.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "from rlboard import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "## Kufafanua hali\n",
+    "\n",
+    "Katika sheria zetu mpya za mchezo, tunahitaji kufuatilia nishati na uchovu katika kila hali ya ubao. Kwa hivyo tutaunda kitu `state` ambacho kitabeba taarifa zote zinazohitajika kuhusu hali ya sasa ya tatizo, ikijumuisha hali ya ubao, viwango vya sasa vya nishati na uchovu, na kama tunaweza kumshinda mbwa mwitu tukiwa katika hali ya mwisho:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class state:\n",
+    "    def __init__(self,board,energy=10,fatigue=0,init=True):\n",
+    "        self.board = board\n",
+    "        self.energy = energy\n",
+    "        self.fatigue = fatigue\n",
+    "        self.dead = False\n",
+    "        if init:\n",
+    "            self.board.random_start()\n",
+    "        self.update()\n",
+    "\n",
+    "    def at(self):\n",
+    "        return self.board.at()\n",
+    "\n",
+    "    def update(self):\n",
+    "        if self.at() == Board.Cell.water:\n",
+    "            self.dead = True\n",
+    "            return\n",
+    "        if self.at() == Board.Cell.tree:\n",
+    "            self.fatigue = 0\n",
+    "        if self.at() == Board.Cell.apple:\n",
+    "            self.energy = 10\n",
+    "\n",
+    "    def move(self,a):\n",
+    "        self.board.move(a)\n",
+    "        self.energy -= 1\n",
+    "        self.fatigue += 1\n",
+    "        self.update()\n",
+    "\n",
+    "    def is_winning(self):\n",
+    "        return self.energy > self.fatigue"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(state):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(board,policy):\n",
+    "    n = 0 # number of steps\n",
+    "    s = state(board)\n",
+    "    while True:\n",
+    "        if s.at() == Board.Cell.wolf:\n",
+    "            if s.is_winning():\n",
+    "                return n # success!\n",
+    "            else:\n",
+    "                return -n # failure!\n",
+    "        if s.at() == Board.Cell.water:\n",
+    "            return 0 # died\n",
+    "        a = actions[policy(m)]\n",
+    "        s.move(a)\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 5, won: 1 times, drown: 94 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        elif z==0:\n",
+    "            n+=1\n",
+    "        else:\n",
+    "            s+=1\n",
+    "    print(f\"Killed by wolf = {w}, won: {s} times, drown: {n} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Kazi ya Malipo\n",
+    "\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reward(s):\n",
+    "    r = s.energy-s.fatigue\n",
+    "    if s.at()==Board.Cell.wolf:\n",
+    "        return 100 if s.is_winning() else -100\n",
+    "    if s.at()==Board.Cell.water:\n",
+    "        return -100\n",
+    "    return r"
+   ]
+  },
+  {
+   "source": [
+    "## Algorithimu ya Q-Learning\n",
+    "\n",
+    "Algorithimu halisi ya kujifunza haibadiliki sana, tunatumia tu `state` badala ya nafasi ya bodi pekee.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    s = state(m)\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = s.board.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        while True:\n",
+    "            a = random.choices(list(actions),weights=v)[0]\n",
+    "            dpos = actions[a]\n",
+    "            if s.board.is_valid(s.board.move_pos(s.board.human,dpos)):\n",
+    "                break \n",
+    "        s.move(dpos)\n",
+    "        r = reward(s)\n",
+    "        if abs(r)==100: # end of game\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p3155e0caf7)\">\n    <image height=\"327\" id=\"image3956b3e364\" 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eMSutPPXUUzz22GM888wzjBkzhg8++MDoSGfk+uuvp2/fvqxcuZJ69eoxaNAg5s+fb2imd955h5tuuomXX36ZjRs30r9/f0P/UGuaxiuvvMI333xjWIaaYNOmTRF/HuXzzz/H5XIZmqHGdJVEqszMTJKSkpgzZw6PPPIIl156KZs3byYnJ4eMjMhfR3nFihXs3buXuXPncvjwYb766isef/xxLBZj/+uUnYSuVasW69evZ+/evWzfvt2wPEIIoqOj+eqrr5g7dy6xsbHMmTOH0EIjyikEAgHuuece6tevj9frxe128+KLL0bU67dz504ee+wx2rdvz7Jly2jdujUPPPCAIVlqZOGOjo7G6/UipTT8B9+qVSuWLVtGVlYWkydP5r333uOpp55i9erVrFy5MvwuwehC+L+klHz11Vds3bqVSZMmERMTg8PhQNd1EhMTjY5HfHw8q1ev5sUXX6Rbt25ceumlhuYxmUwMGzaM4uJicnNzcblcdOzYEYCZM2fSsWNHLBYL0dHRuFwuNm/ezJdffsm4ceOIjY3FZKpRb37PmNPpZMeOHTz55JO4XC4GDBiAw+EI//7u37+fYcOGnfB7V61aRUpKSqXmk1Jy8OBB4uPjueOOO9i6dSvTp09n1KhR4X0+/vhjZsyYcdz3pqWlsXLlygrNE1nVooLMmTOHTp06sWHDBsMLd2xsLI0aNWLKlCmMHDmSjz/+mEWLFnHZZZcxYMAApJQMHTqUtm3b0r17d0OzHm3t2rV88MEHPP300+HXMD4+3uBUfzCbzRw4cIDU1FRuvfVWw3/OZRITE0lMTERKSWZmJlD67mDq1Km0a9eOwYMHc/fdd9OnTx969epF06ZN+fnnn6lbt67ByY01efJknn32Wb799ltefPFF9u/ff8x1GPXq1Qu/nv+rKn72gUCAV199lX/84x/Mnj2bL7/8km3btjFw4MDwPtdee225GStajSzcQoiIumry3nvv5S9/+Qsvv/wyq1evDm9ftWoVAG+88Qbr1q2LiML9008/8fHHHwMcU7QjUdnPOBIzCiHCuZ599lkAfvnlF0aMGEHv3r2pV68eycnJzJw5k6VLl3L//fdH5POoKrNnz6Zly5bMmzePe++9lz179rBq1aqIeU1sNhv33nsvd955J6+++ipRUVEkJiby0UcfGZKnRhbuSGQymRgzZswJ77vjjjuqOM3xpJTs2bOHpUuXcvXVV3P55ZdHzC9NeSI93/+6+OKLWbx4MTNmzKBTp040aNCA9evX06lTJ6OjGc5qtTJ//nw2bNhAfHw8r776qtGRjtOqVSumTJnC888/T+/evenbt69hWVThVoDSUREPPfQQr732GrGxsUbHOS09e/akR48eRsc4I23atCE7O5sFCxbQs2dPFi5cyF/+8pdq90eooplMJvr06cPll1+OyWTCZrMZHek4iYmJXHvttfTo0cPw8xKqcCsAWCwWli1bZnSMM2K1WrFarUbHOGMrV64kKyuL9evXs3PnTqPjRJSoqCijI5xSJJzrUYVbUQzQrFkzmjVrZnQMpZqqsWOQlixZct6//VQUpWaqsS3u1q1bGx1BURSlUtTYFreiKEpNpQq3oihKNaMKt6IoSjWjCreiKEo1owq3oihKNXPKwi2EWCCEOCKE2HzUthQhxEohxI7Q5+TQdiGEmC2EyBJC/CqE6FiZ4RVFUc5Hp9Pifh3434vyJwOrpJTNgVWh2wDXAc1DH6OAORUTU1GU6kRdQ1G5Tlm4pZRrgYL/2TwQeCP09RvAjUdtXyhLfQckCSHqVFRYRVGqh0ianbMmOts+7gwpZXbo68NA2VIu9YD9R+13ILRNURRFqSDnfHJSlv5pPeM/r0KIUUKIDUKIDR6P51xjKIqinDfOtnDnlHWBhD4fCW0/CDQ4ar/6oW3HkVLOk1J2llJ2jo6OPssYiqIo55+znavkfeAOYEbo83+P2j5OCPEW0BUoPqpLpVyapvHee++dZZTKl5eXx86dOyM64+bNm9m7dy85OTlGRynX4cOH+fTTT4nkP9QlJSUR/XN2u93EZsfS5L0mRkcpV/yeeDa7Nkd0P/euXbuwWCxs3rz51DsbRNO0cu87ZeEWQiwFrgTShBAHgEcpLdjLhBAjgb3An0O7fwxcD2QBbmDE6QT0+wVjxkTuiucxMTp33BET0auy7927l8TExIjOaLfbqVWrVkQv1GCxWCL6NXQ6nXSxd2FGxvGL0kaKbYXbcJgcEf06xsTE8GTKk7gz3EZHKZdf+Mu975SFW0p5azl39T7BvhIYe9rJwt9n4vBh49dbLE9iYhZ16uRHxJqQ5cnJySEjI+OsM0op+f777xk8ePAx20ePHs3DDz9cISuSrFq1ik6dOmGz2XA4HCSnJJFTeIj42ERKAkf4vHAhu9xbMAUs2EUcQjeT7ThEt+S+XHPBLfjdPurXakhJSQmxsbEUFhYSExNDIBBA0zRiY2ORUhIdHU1BQQFxcXE4HA4SExPDt30+H4mJifh8PqSUREVFYTKZwuuULlmyJKJ/zgUFBfz4448RnVHXdfLy8iI646+//kr+hfkUNys2Okq54kxx5d5XY6d1VU5fMBhk/fr1XHPNNfh8vmPue+yxx7BarUycOJGYmJhzPpaUOvmBQ+xybcGEzvvZL9EstiN+3Y+NaFrYunLIt49iTxGtkjrQKPUiEqzJ/G31MOKtqYzt8A9q2epgC9gwmUzoug6ULn2laRpSSnw+H0IINE1DCEEgEAjfL4TA7/eH34YGg8GIXCZLUU5GFe7znKZprFixgokTJx5XtMs8+uijFBcX88QTT5zzUmESycYj3zNr43QyYjNomNiI4mCAX3ZvZc+h/bRp1gBrwMb2XVnktSjigsTWCA5glwlEiwSW/ryAlikXcm2z/kTZohFCYDab0XU93KcaCASwWq1omobFYkHTNOx2O0IILBYLwWCwNIuUBAIBVbiVakfNVXKeE0Lw3XffkZ1d/jnkYDDIO++8UyGLo5qEmc5pV1En0Iktvxfw65ZcNv6aTckhG3Z3bVz7Yzi43c+Wjbl8v3EjW3b9yNqf1uBxBVm/81uOOPKZu/5FCnx5OBwOoPStucfjwWKxYDIJYmKi8Xo9WK1WfD4fUVFRuFyucGs7NjY2XMQr4l2EolQ11eI+z2VnZ/P777+fcgRASUkJGzZsoGvXrud0PF3XiTXHMLv/bO5aMYJPNn+M7oNoGYVN2vgpS+NPl9zEyD5dKHYVYfPYOOD+BG9JPnkFhezQdhIMmBk4pz8rx68GwGazERUVhdfjZvOqGWT9uJhgUKN19zvodMPjOBwOUlNT8Xq9REdHk5eXh91uJxgM4na7SU1NPafnpChVTbW4z3Nms/m0ugpOd79TMZlM2O12vE4PL980l+tb9cNiNtOkVhO6NevGRY3bsTd3L1sObibfUUB2fjax+Y1w/Z7IhQmt8RTnge5FKxbcPftuhBB4vV4KCvJx5Gxh55Z1FJZ4qdduAEl12+MoKSEuLo7c3FyEELhcLtLS0rBYLFgsFpKSks75OSlKVVMt7vNcrVq1aNCgwSn3s9vttG3b9pyPJ6XE7/eTnJxMIBBgzk0v8Y/of/Ju5rsUOYuINccSI6LxCT9H8rdRXFhMvDWBgd0H4nQ4iSaF/NwjmJIP4c8JoGlBrFYrq1fM5MiebyjM3k+HqyZx+YBJBIOl93k8HpKTk9E0jZiYGIqLizGbzUgpcTqdJCYmnvPzUpSqpFrc5zmTycSIESNo1qzZSfd76qmnsFgq5u+8yWTCZDIhpSQ5OoXHr32cIZ1vxRlwsSt3N5sPbuXH3T+yr3A/Teo3pWHdhuzK3oXD6yBepHJJw57kbfBhb32Y1957hYDfy49r/oPXZ2Hg6AV06TMq/Phlw/zMZjNA+HYZNYudUh2pFvd5TghBu3btuOyyyzCZTGzfvv2Y+zMyMmjUqBG9e/eukJOTUFq4nU4nsbGxuFwuEuwJzOj3JI9f9yiDXhxMYUkhWft3kR6fRoEznzhrPF63FwKS3Nx84qyx9Ok0gAMHtvO1XMF3Y14jWZP07XUbjVp3x2q14na7sdvt4ZOTTqcTm82G3+8nJiYGTdPQdf2cR8mcqaysLOrUqRPRFyEpkU8VbgWz2cycOXO4++67ycrKCo+NBmjYsCHz588nJSWlQo5VNs46NTWVgoICkpKScLlc2Kw2/E4/H479kD0Fe/gg8wNcXhemoIlYWwwlRSUgBR63F7vZxpCrh9D54s6s/fVz5q+fwhX9hnBxtxvQNA2n00lKSgolJSUkJiZSVFREWloaDoeD6Oho8vPziYmJQUqJy+Wqkiv8ioqKmDt3bvgPSoMGDRg+fHilH1epmVThPs9JKZFSMnnyZJYuXXpM0Qb48ccfGTVqFCtXriQuLu6cuxaEENjtdgoKCoiOjqa4uBir1UowGCQuLg4pJc3SmzG+z3iklNgsZg6v+4LDP7xLjD2K1F7XkdS9N1a7ncLCQgKHg3iKBJdefRM2mw0pJUlJSeTt2cOPr75AwYF9JDdtTac77iEpvVa4v1vXdXRdr7J5UwoKCvjss894/fXX2bFjB//85z+5/fbbVVeNclZU4T5PlRXs/fv388gjj7B8+fLjinaZ77//ni5duvD666/TuXNnzGbzWRecshZ3YmIixcXFJCQk4Ha7sVgs4bHY+L2YfF62TRmP9HupP2gYnR/+P3Rhwmo2sXvev8j/JZOgppOVV4Q99wi+zT+y4Zu1HPn1JwKaRushd9Fh8C34fV40r4+lo27HWeJkwJSpJFzQlIwGDTGZTLhcLux2+7m8lKf1nCdNmsQbb7zBrFmzGDlyJA899BDPPPMMf/3rXyv12KcjPz+f5OTkCusKUyqfKtznISkluq7z7rvvsnz5ct59992TzkQG8Pvvv/OXv/yFUaNGMWTIEFJSUs66eJvNZgKBQPgqxrITiWazGc1RzKF5T+Hal0Xr+x/HGp9AoKgQ764dIMAnod7g22g0fCxBl4N6X62i8/bfyP9mLY0vv4oLh95NMOjHVViI31GMJkFHMuDvjxHUdL5+cyG/rlvH6Fdep0nHTuGTlpVJCMFzzz3HbbfdRvfu3Vm7di1vvPEG3377baUf+2RycnL45ptvWLlyJVdeeSVNmzalc+fOhmZSTo8q3OeZspb2vHnzuP/++8OTLZ2OX375hbFjx7J+/XoWLFiA1Wo94+IthDhmHpGyPxhSSggG2Tvn/9ByDtFk2F/w5x4mmHsYgaTsMEKCf99uvFKiAwktW5PUvhOaP4inKJ+SvTvRpESToEmJLiWaDrqUBHVJxxsGENB13vzr/dzyf/+m+TleUHS6UlNTadiwIZmZmaSmpjJhwoQqOe7JbNy4kVdeeYWXX36ZBQsW8NFHH7Fw4UKjYymnQRXu84ymabz66qs8/PDDeL3es3qMJUuWoGkar732GlFRUWf0vVJKgsEgycnJx5yctFgs7F+xGE/Wb1xw218g4EXoIETo45jHKC3gINHcLvxSlhbrUIHWdIkuCRfvoCbRpE4wtE+7nr3wef3MHTOaSW8vp3XHjmf1OpyJsvHiEyZMoF27doZfal9SUsLbb7/NvHnzmD59Ok888QRLlizh/fffZ8CAAYZmU05NFe7ziK7rvPXWW4wdO/aUXSMnI6XkP//5DykpKTz55JNndAGLyWQiKiqK7OxsUlNTycvLIzY2Fp/bRcEX79Ny2Fg0dzHSBAiBKdRCN4k/ji2lLF0sT0ooK9K6RNclQamj6RJNg2CocAd0naCEoK6j6QJN12nd41KOHDiAJy/vrF+H0yWlZMeOHcTFxXHJJZdU+vFOR3x8PDfffDPPPPMMmZmZrFmzhszMTMaMGWN0NOU0qMJ9HlmyZAnDhw8/pmuk7GKYshnzymMymcJ901A6A99LL72Epmk8/fTTxMWVP3fw0cpa3NHR0QQCgfCJwfx1X2CLjcObdxCzSWAyl54oE2YwH1W4dVnaqpa6AE1HlzpSgtRDLW29rEBLAnpp90hQlwQlpQVcL+1GCQR1Uus34qX7JjB/y1ZEJfZ1SymZOHEiP//8c6Ud40wJIWjcuDEej4eDBw/y8ccfc+WVV1bYRVZK5VKnkSPQo48+espCehHdQoQAACAASURBVKYWLFjAhAkTjuvP7tKlC/369TtlX3VGRgZjxx6/Rsb8+fO57777zmiZqrJjlX2WUuL4aT0xjZuheVzoHhfS7QKvCzxuhNeN2efB7PMgvKW3pdeF9LrRPW50txvd7UJ3u9DcTjS3m4DbddSHE7/rjw+vw4HX5aBu86ZovrPrLqoJ2rZty9y5c2nRogUzZ87kzjvvNDqScppU4Y4gH330Ea1bt6ZHjx506dKFKVOmnPNjlnWP3H///RQWFoa3R0VF0aRJE959911atGhxyseJi4tj2rRprF+/njZt2hzz+G+88QYjRow4rT82ZfNne71eLBYLfr8/tM2E1Pzhwq17XEiPC+lxQ6hYC2/p13g8cNR+utdF0BP6cLsJup0EQ0Xb73bhczrxuxz4XE68TjdepxOv04mnuLjcIZAV6bbbbuPtt9+u9ONUZw6Hgx9++IEnnniC4hP8XDRNo7i4+JiPOXPm0L59e3r3Pm4xrhpPvS+qZLm5uWzatOm09v3++++5+uqrsdlsvP3227zyyivhJcnOhpSSnJwcXnrpJYqL/1iiqW7duvzrX//ixhtvPKNLr+Pi4ujWrRvLly/n1ltvZdOmTUgp0TSNL774gk8//fSUrXdd1/H5fCQlJeF2u0lISMDv9+P3+ZH5OdhDXTfCLDCZBMIsECYTpW0MSRDQdJ2grhPUSrtBAqGvA1IS0EIfusQf1AnqUFJSjDkmFr8m8etH3R+6CKcy7dq1i+joaOrXr1+pxzkXHTp0IDMzkyuvvNKwDD179qRr16706dOHFi1a8Pzzz5OWlha+v6CggDlz5hzzPUOGDGHjxo1VHTUiqMJdyfLz81mzZs1p7bt161ZcLhdr167l7rvvJiYmhtzc3HO6JFvXdQKBQHhypfT0dKZMmcKgQYPOar4MIQStW7fmhRde4K9//Ss//PBD+D6/v/zFTcuYTCZsNhv5+fnUqlWLwsJC4uPjiUpIJPurT7GZTJCUBKHijal0SEnQ70PYo9Ep67cGn8uBOy8Xv6bjC+r4dYlP0/EFJZrJgiUtgwCC4kMHiKldD7+uE9DAp2kEdcjNPoz/LEfWnK7XXnuN2267LaLnJnn66afp1KmTYUXwo48+YtCgQdx1110sWLCAtLQ07rzzzmMuTkpNTWXVqlWG5ItEqnBXslatWvH444+f1r7Lli3j0Ucf5dlnn+XWW2/loosuol27dmd9bCEE6enpTJs2jb/97W9kZWXxn//8hw4dOpxTIRFC0L17d1577TXGjRvH999/z6OPPkrv3r1P2Veu6zp+v59atUovP09KSsLv91Nn8HByv1lF0e+b0Oo1JDYtHd0k0E2CoIDg/p1YGzRFAp6cQwRKivH6fKXdHkENvybxBCW+oIZX0/Ej0Pfvw4+Z6AYNKc7ORsTGEtDAq+kUFxSwa8tW2t9wI1TSZeeZmZlYLBYuvvjiSnn8mqJJkyYsX76cmJgYunXrxsqVK3n88ccZPHiwmhKgHKpwR5Abb7yRPn36cO+99/LOO++c9kiNk7FarfTq1YvVq1cTDAZJTU095peh7CrKUymb26PssmiLxUKbNm147733wl0fpzvTnq7r4XUiy94J2Os2RLfYCLjcsHsHaBq2uDgCUsMM+EuKEb/+UDpWW9MIaDp+Tcev/dE9EpR6aOw2BDQNb1EBvqBOfl4enoCGH0FCg8YUFhZy5OBhvP4gN4wZU2nFIT8/H5PJVGETdNVUrVu3Jjs7m3vuuYdevXqRnZ3NpZdeqor2SajCHUFsNhs2m42lS5dW6OOazeZyV3rRNI1GjRoRHx9PSUlJuY/RsWPHY4bvlUlISDijLEIIbDYbDocDu92Ox+MJF3HNHo1fl8iAhrmkmKAWQDu0PzQcUCAADRm+yMav6wQ1gV8/uu9aD/d5B/XSC26CWgBNg0BQw+N0UpCdgy4BYSI6rnK6MPx+P7///nuFLD5xPvjss8/YtWsXX3/9NVlZWUbHiXhqVMl5zmKxMHjwYBo2bFjuPkIIHnjggQqZjKlsBZykpCQ8Hg/x8fHouo7FYqHxsLvxhfqpXQUFuJ0OfJqOV9PxaDpuTccb1PEES2/7NfCFWt3HtLx1vfSKSb3s5GXpNl1CSUFh6YrwJhNdbhqMiKqc2QFdLhcffPABgwcPrpTHr4maNGnCHXfcYXSMakG1uJXTmu2voiZjKpvWNS8vj7i4OIqKirDZbAQCAepe2oeNOuhSR5cBdIcbgnrp+UlR2saQUg9dhAPB0MU2/tDJSr9eNlpE4tdK7w+UFXApEVFReD2+0n20IO2vvJKGTZpUyPP6XyNHjjxuFESkEkLw1ltvGR1DOQOqxa1UKSklgUCAtLQ03G43iYmJ4ZVoHC438V16lraygxpOhxN3oLSF7Q7ooa9laYs7qOMJanhCI0q8QQ1fUMOnafiDEr+m4dd0AqFiHgjquJxu/D4/8bVqce1fRmOOiqagoKDCn+OuXbuA0hZkdSCEoGXLlkbHUM6AKtxKlSq7AMftdmO1WvF6veFZAqPj42kxdCTeoAwVaA1vaLSIN6jhDWpHFe3SLhRvUIa7V3yaxBfqLvFrAr8Ofk0eM947ICUZzZtTUlBI9/4DKmUhhYcffpiZM2eqk2tKpVGFW6lyZRftCCHCI1qklFgsFpKbtaT+NQNChTrUqg6W9m3/0b8t8QRK7/eF9vOFRpkEQsW7tLtEKy3iusSvQ1DTadPzSjRhocdNN2OxWCplzclJkyYdc/GIolQ0VbiVKlVWtGNiYggEAkRHR4cXUfB4PJhi40ht1x4/ptJWt1baNeIOarjDRTxYerIyfLu0Ne7VSsdw+3SJN1h6sY1f1/CFWtu6MJFcrx4ORwkX9uyJpmm4XK4Kf47dunUzfNpWpWZTJyeVKlU2reuRI0dITU0lPz+fuLg4AoEASUlJaJpGiyHD2bluDXvXrkIgwnNyA0gpwhNaBeUfQwMDUhLUQicjQ5e0+8r6uDUdabHRrmcvfly1hhe//QZbVBRSyjMezqgokUC1uJUqVXZyMi4uDp/PR2xsbPiCHK/Xi9/vxyQErQfcjGaNwqOF+rYDGp7AH61r99F93prEG5Slre1Qt8nRwwSDmGhwUQcCCC6/+SY0q41gMEgwGMTpdBr9kijKGTtl4RZCLBBCHBFCbD5q22NCiINCiJ9DH9cfdd/DQogsIcTvQohrKyu4Un2ZzWY0TcNqtR4zj4rFYgkPO2x41bXEtGqLNyhxByXuoI776BOToe1l/d++QGl/ty980vKPfu/0Zi2ISU5hz5atXNirF7FxceF5yNX800p1dDot7teBvifY/pyUsn3o42MAIUQb4Bagbeh7XhJCVP5qrMo5OZO5tM9V2ZqTZdO5lp2klFKGiymUXhbfb9rTmJJTjyrYWqiAS1yhk5LewB/F3KOBJ1S0vZqGbrGSUL8Rlrh4igsKGHzfBFpeckl43LoQolJOTipKZTtl4ZZSrgVOd7DrQOAtKaVPSrkbyAIiY60mpVx2uz1cMKG0RXx0QZNSVtiwuf/tKomJiQnPgeLxeMIr7NhsNuo2a84tLy0gvmFjPAE99FHaReIrG99ddjWlpodHoviCEl9Q4pcCrz9ASUEhHa7uw9UjRhAVHY3D4UDTtEo7Oakole1c+rjHCSF+DXWlJIe21QP2H7XPgdC24wghRgkhNgghNgQCnnOIoZyrpKQkkpNLf4Rms5nRo0fz/PPPhy9xj42NpXbt2hVyrLIrJ4uKioiKigrPjxIMBomNjcVutyOlxOv14nA4aHZJN254/P/oMPjP+KQIjzLxmy1ccPmV4SGC3qBGVFo6cbXr4tW00svhfQFsMTEMGj+ePnfdhRACr9dLUlISZrMZi8VCfHx8hTwvRalKZ9vBNweYRumSrdOAZ4C7zuQBpJTzgHkA8fEZ0uc7yyTKORNC8Prrr+NyuRBCULduXeLi4rjiiivCJw7PZEHgU7HZbKSnp2M2m6lVq1b4QpWjZx4sG05nMpno1Kcv7bpfRv+/TQZCq7ybBDFJSTiPuvLRYrODEMfMsW2LiiK9YUP00JDD6OhohBDhdxDqIhmlOjqrwi2lzCn7WggxH/gwdPMg0OCoXeuHtikRTAhBo0aNjtveqlWrSjne0X3ZR3fRlPnfeVFMJhPW5GTikpOP2zc54/TeCZQ9YtnxVMFWqrOz6ioRQtQ56uYgoGzEyfvALUIIuxDiAqA58MP/fr+iKIpy9sSpRhQIIZYCVwJpQA7waOh2e0q7SvYAo6WU2aH9/05pt0kQmCil/ORUIRITU2SLFvef7XOodFari7Zt807YKo0Uhw8fxm63h/uqI9H27du54IILInokx6ZNm7jwwguNjlGuQCDAnj17aN68udFRylVQUIDf76+w8yKVYc+ePWyttZVAbMDoKOXa/ux2iguKT/jW8JSFuyrEx6dLv/93o2OUKyFhD3XrfsO2bcOMjlKuRo0+5aWXatGpUyejo5Rr5syZjBgxokL7yyva3//+d6ZPn250jHIVFRWxcOFCJkyYYHSUcm3YsIH8/HyuvTZyL+NYtGgRPXv2jOjGWMuWLTly5MgJC3eEXH0g8Psjt6UYCOSjafaIzqhp0cTGxkZ0i9tqtZKYmBixGcvmTInUfFCa0Wq1RnTGmJgY3G53RGe02+3ExcVFdMaTnYdRl7wriqJUM6pwK4qiVDOqcCuKolQzqnAriqJUM6pwK4qiVDOqcCuKolQzqnCfpzZv3hyeiU9RlOolQsZxK1Vl//79LFy4EJ/Ph81mo1WrVtx8881Gx1IU5QyoFvd5RErJ3r17+eWXXxg3bhwtW7Zk6dKlVbqQgqIo504V7vOI1+tl9uzZzJo1i8cff5zWrVtz/fXXs3jxYqOjnRWv1xuez1tRzieqq+Q8Eh0dzYQJE7j33nt56aWXuOiii7j88st59913jY52xj755BN27dpFbm4uF154If3798dmsxkdS1GqhGpxn2eaNGnClVdeyezZs3n44Yfp3Lkza9asMTrWGbv//vupU6cOffv25ZFHHsHtdhsdqVwvvPACHk9krvL00UcfkZWVZXQM5Qypwn2eqVu3Lvfddx933XUX48ePZ8yYMXz++ef8+uuv1aav+5///CczZ86kfv36/Prrr6xYsYJ7773X6FjlWrFiBX6/3+gYJ/Ttt99y8GDkr3Wyb98+HnvsMaNjlGvPnj1MnTq1yo6nCvd5qnnz5uFZ5h5//HGee+45tmzZYnSs0zJlyhQmT57Mxo0b2bhxI6NHj2bWrFlGx6qWateuzeHDh9E0zegoJ+X1etm9e7fRMcrl9XrZu3dvlR1PFW4Fi8XCK6+8wsKFC6tFt4nVauWGG27g448/JjMzk4suuojY2FijY1VL48aNY+7cuRHd1aQcTxVuBShd5/GRRx7hu+++Y926dUbHOaVp06Yxfvx4+vXrx4svvhheXFhRzgeqcCthSUlJjB07lmXLlrFt27Zq0+etKEar6sWnVeFWjhEfH8+sWbN46qmn+Omnn4yOoyjVQlU3clThVo4jhODFF1/kww8/ZPXq1UbHKVeTJk2QUrJr1y6jo5Tryy+/pGfPntjtdqOjlOuOO+5gwYIFRscol5SS5cuXM2jQIKOjlCstLY2GDRuycePGKjmeKtzKCUVFRTF+/HjWrl3Lhg0bIrLbpDoU7tWrV9OzZ0+ioqKMjlKu4cOH8/rrrxsdo1xSSt555x1uvPFGo6OUq6xw//zzz1VyPFW4lXKlpKTw0EMPMXfuXLZt22Z0HEVRQlThVk4qKiqK+fPnM2fOHL755huj4yiKgircymkQQjB9+nTWrl1bLcZ5K2cuNzeXN954w+gYx3nvvfcYO3YsBw4cYMyYMRHZeNB1nYkTJ7Jo0SIWLVrExIkT0XW9Uo+pCrdyWuLj4xkzZgyffPIJmzdvjsg+70hSUlJChw4dePXVVxkzZgzXX3+90ZHKdffdd5OXl8cDDzxAhw4d2Llzp9GRgNKC+P3333PJJZeQmppKgwYN2Lp1a6UXxTPl9/tZs2YNvXr1olevXqxZs6bSpzhQhVs5bUlJSTz55JM888wzbN682eg4ANSrV4+EhASjYxxny5Yt9OjRgxEjRjB79myio6Mj8iTqwYMH8fl8rF+/nuuuu47Bgwfz22+/RcQf5u+++47Y2FgGDRpEp06duPvuu9myZQv79u0zOtoxJk2axLx582jfvj3t27dn3rx5TJo0qVKPqQq3ckbMZjPz589nyZIlEdFtMmrUKC655BKjYxzniy++oHfv3lx66aU0btyYK664IiLf5v/yyy9cdNFF1K5dm759+9K9e3fWr18fEYW7R48euFwupkyZwnPPPcekSZNo164djRs3NjraMV544QWGDBlCdHQ0drudIUOG8MILL1TqMdV83MoZs1gsPPjgg8yZMwe73U737t2NjhRxxo0bR+vWrXn++ed58803Wbp0Kdu3bzc61nGuv/56/v3vf7Nr1y5uvfVWRowYwUcffYTJFBltumHDhrFt2zb+/ve/h1vekcZkMvHcc8+FhwI+99xzlf76qcKtnJXk5GQmTJjAQw89xAUXXEDt2rWNjhRREhMTyczMZNGiRXTr1i2ip51955132LNnD0uWLGHdunWkp6cbHSmsXbt2tG3blp49e0ZUrqMJIbjxxhvDE3VVxbw5qnArZy0uLq7S3xJWVyaTiXr16vHQQw8BVT+XxZlIS0sjNTWVTp06RWROIUTEFu2jVeVEZ6dszwshGgghVgshtgohtggh7gttTxFCrBRC7Ah9Tg5tF0KI2UKILCHEr0KIjpX9JBTjCCEi8pc9UlSX16e65FRKnU5HTBB4QErZBugGjBVCtAEmA6uklM2BVaHbANcBzUMfo4A5FZ5aURTlPHbKwi2lzJZS/hT62gH8BtQDBgJlI/bfAMomEhgILJSlvgOShBB1Kjy5oijKeeqMTn0KIRoDHYDvgQwpZXborsNARujresD+o77tQGjb/z7WKCHEBiHEhkAgMhdSVRRFiUSnXbiFEHHAf4CJUsqSo++TpYM+z2jgp5RynpSys5Sys9UafSbfqiiKcl47rcIthLBSWrTflFK+G9qcU9YFEvp8JLT9INDgqG+vH9qmKIqiVIDTGVUigFeB36SUzx511/vAHaGv7wD+e9T24aHRJd2A4qO6VBRFUZRzdDrjuC8Fbgc2CSHKZgl/BJgBLBNCjAT2An8O3fcxcD2QBbiBERWaWFEU5Tx3ysItpVwHlDfAs/cJ9pfA2DOPYvzcCKcW+RkjYY6JU4n0jJGeD1TGilIdMp6IiITgiYnJsn3724yOUS6z2U9iohObLcXoKOUKBktISrJU6dVbZ+rIkSOkpqZiNpuNjlKuAwcOYbHUNTrGSWgETIewpluNDlIu3a0TF4yLyFkbyxQUFBAXF4fNZjM6SrkWL15MYWHhCRvNEVG44+MzpNOZY3SMciUmZvHUU6u55557jI5Srvfee4+MjAy6du2Kz+fDarX+MW+xSeewby+FwRykLrFgAwSegJsYcwJNE9oidDM2mxVN0xBCEAwGEUJgMpkIBoPYbLbw57LHDwaDmM3mY/YtuwIvGAxitZYWl7Ir8p544gnGjh1LcnKyQa/SyUkp+fOfJ/DOO88bHaVcdnsB7aZcQ+YjmUZHKVftb2ozN28uAwcONDpKuV5++WV69+5Ns2bNjI5SroyMDHJyck5YuNVcJTWMpmnk5+cTFW/jh8IPSY9qRNDkZafzF7L9e3F4nTi8xdSNborH7yHdWp8dUb+xOz+LcV3/jt8XQAiB0+lECIHdbsfpdJKWlobT6SQlJYXi4mJSUlIoKSkhNjaWoqIirFYrNpsNm82GxWLB6XRGbIFWlOpOFe4aJqvoF/5T+ByiWHDYtxerjCIYlMSSTJq9HkkkU+R24dEDpNjrg27lk53vEm2JZ9qXD3JLu5HUjWlAfHw8UkqCwSCpqam4XC7sdjt5eXnExcVRUlJCdHQ0Pp+PpKQkpJRomhaeIc1ms5Gfn09SUhIWi/pvpigVSf1G1TC1Yhrx1qqNpESlcFGti2iS3opdh/bwxrqlNGuRSK3YOHb8mo25XpBL2/TEHIwi2pJEgSMPe0w8C36YQ7/WN9I2+WIsFitWq5Xc3FzS09NxuVykpKZSkJ9PYmIixcXFxMbGUlJSgtVaum9sbCwmkwmXy0VycnLEzOusKDWJKtw1TDQxzOu3gAc//xsfbf2EzzZ/gV23kZFcG3+uHZ8jjebpjThUtButSOfbn7+lfrsUsg4folmqnyJ3MV6fRtMrWpFkiUYIQVxcHH6/H58jm+3b3sdR4iAlvS5pTXqjaRpRUVHhfuyytfZMJhNer5fo6Gg165yiVDDVHKphTCYTLVKa8Y+r/o7JItiZv5NCTyFxUbG4/W7cARcN0hvQOq09CZ5mNE5og2O7RPh1zPjYd+QQn21axfQPnwBKT9jpug5S4+DWz1jz1kQyP/4HmZ8/gwid19Z1HV3Xw0OrTCYTUspqO9RKUSKdKtw1jNVqJeAP0L1+d/4z9D+kxaViMpsp8hZjtVnwaX62HthCriOX3/dt4+sN39Ioph0DMm7nl1W/06VVA2IcZpZ/spxAMACAo6SII3t/ZO1Hz1PkttPl5lfpc9ebBLTSUSV+vz88gqXsJKWu66q1rSiVRHWV1DDFxcXh/ujWtdvwzYR1DH7lZrLzs7FLGzZpJwo7ufm5SL9ORnJtNKmRcySPAR2HUPRbEYn2InyJ0ezcv51WF7TlqxVPsy3zQxpc0JrLrh5Fu0tuoKSkhLiYGLxeLykpKWiaRiAQwOl0IqUkJiaGvLw8UlNT1clJRalg6jeqhik7WWixWPB6vWTE1GbBrQv4YNMHzPlyDocKssEvibfE06ZeG2zCxpGiI8RYonGUOBAaxBc3xpFQxNT/TuRPTYeQ9duvJNVuQ/+RM0nNaITX6yUmJga/34/VasXtdofHb0dHl870qGka8fHx6uSkolQCVbhrmLITgoFAIHwRTstaLWjRaxKX1OtCjiuHJ995koN5h9iVs5OUqFRs2MjPy8PnDuB1ehhz4xjG9xhHccwBXn/uXyQf0Xhg2nySazXA7XYTHR2N1+vFbreHL8op6+cuOzlZVtDtdrvBr4ii1DyqcNcwuq5jsVjw+/3HnCSUEro36U5UdBR92/TFarPidDixmQUHd22nVmIqPgkxKbWIskWRnJRMSUkhv1/wM73u6kfj5u0RQqBpGiaTCWdeLgGLmYCmk1q3HiaTKVy8gfC+6gSlcq6OHDlCWlqaevd2FFW4a5ioqKjwuGqfzwcQnhvEbrfj9/uJj4onb8N6ogIeHEdyiD+0l5KiQpIu7EBC+24492Sx2+Nh/+EjbPr6G7p1vIzAwX0c2rGNqOhoSuKS2fv1KvZt/oW4WnWIadKCuNQ06rVtS0bzluHL4BMTE9Uvm3LWsrOzWbt2LWvXrqVHjx40a9aMrl27Gh0rIqjCXcO4XC5SU1NxOp1ERUWh6zo+nw8hBB6PhyiPg91vziU2ORV/dAyJtWqT0OMKpBAIwHNgL7K4ALseJHb3dnr43MhVH3Lo4B6EyUJhwE90ej1a9O5L097XIjWd379Zy+HNv7BvYyYOj5cbH/knyWlpFBcXk5qaqoq3clY2btzIm2++yZw5c1iwYAGff/65KtwhqnDXMAkJCaVzlURF4Xa7MZlMWK1WpJTEWs38PP4eEps0J7nnNZjMFpAa/oP7SifulRKz2UJis1boUhLboCnNBt+Cpun43CVYouPQpE4gEMRTXIAuQdMl9dtdTB0pKc7P5/1Zz/LqvaMZ9/pikpKSKm0mwEAggMViUcMNa6iioiKWL1/OnDlzmDp1KjNmzGDx4sW8//77DBgwwOh4hlNNoRqmpKSEtLS08JA8q9VKIBDAW5jP93ffSEzdetS57iZ0RzF6cQHSUYzwOhEeJ3hdSFcJWkEuwYJcdJeDYHE+mqMQ4ffjLyogUFhI0FFC0OUi6HYRcLvwOx34nKXdMwMnPoDzcDYv3Dmc/Tt3omlahT6/vLw8Nm7cyC233MLPP//M4cOHK/TxlciQmJjI4MGDefrpp/npp59YuXIlGzdupF+/fkZHiwiqcNcwUVFRuFwuhBAEAgE0TcNsNpP7wTJSGjSl3rWDCORlg9eN8Loxed0Irwfh82LyehAeF8JTeh8eJ9LtRHM7CHrcBN1Ogh4nuidUtJ1Ogk4nPpcTv8uJz+Ui4PHS45ah5OzeyZbVX1Z4i3jZsmU89NBDzJo1i6lTp/Lyyy9X6OMrkUEIQZMmTQgEAhw6dIjRo0dz1VVXRfRc7lVJFe4aJiYmhqKiIgA8Hk/pKA+fB8f2X0lq1Y5g3mHwuksLt8+FyefG7Hdj9rkx+T0Inxvhc4PHhfS6kV4X0u1GelxoHjdBt4ugy0XA5SDgcuJ3Owm6XPidLvwuBz63AxPQ+MKL+f6//6U4N7fCntvevXvZv38/r7zyCrNnz2bu3LlIKdm0aVOFHUOJHG3btuWFF15g0aJFNGjQgNtvv93oSBFDFe4IIKWkqKiIFStWsHTp0nN6rOLiYjIyMpBSEhcXh8ViIXvNZ+Dzo2sBNI8L6SktzKUtbhdmnxuLz4XJ60L4QsXa60G63eguN7rHheZxoLtLi3fA80c3ScDlxOd24nM58LuceJ0uPM4SajdrhqOgAGdhYQW9SlCnTh1q167NunXrGDFiRPhEVfPmzSvsGErk6dWrV7W8+tbr9VJYWMjgwYMpLCzE6/VW2GNXv1ejhtm5cydZWVm89NJLXHLJJTzyyCPn9HiJiYnk5OQQHx+Py+XCbDYTY7fisJnR/V70IEiTCUwgTQJMApPZhBAgdRC6BF0idYmuaeh66QlITdfRdAhqkoCU+HVJUJMEdZ2ADgFdJxC67dd1grpADwagAsdx22w2mjRpwgsvvICu66SnpyOEICoqqsKOUNx5nAAAIABJREFUoSgVZebMmSxfvpzly5dz1VVXMXz4cCZNmlQhj60Kt0F8Ph/Tp0/HZDJh/n/2zjxMiur63++t3qene1b2fTMoRECWQNxQIqIRlyRuuH0JKjHiL0YFJLgnGjdcokYkiiARxYhbNCFxjcEFRVAEkQAyyLDNMHvvtdzfH91dzigDA0zTPXjf5+mnq6uqqz59u/vUrXPPPcfh4Nlnn7Wnix8I0WiUQCAAYM9ajMViWPFYsuesgUNzYGlgOQSWpmFpAg2BJVMG27IwLYllSttoG5ZMGmgzuWyYSYOdMK2UsZboJuiWTBlxC1PXD/jzfJvx48czfvx45syZwx//+Efee++9Vj+HQrE/rFixghdffNF+/f7773PYYYfx/PPPs3DhQhYtWkR5eTldu3Y94HMpw51B0rMWH3vsMY4//nj69+8PwF133cW7777L1KlT6dmzJ7179261czocDrs6TXpg0ulw0bB+Lb5AAcLnw3BoCEey1y00AcKBACySRtewwLRMdFMmH5ZElxa6AQnTxJBJg50woWLzJvLad0TXHOgmyZ64BQkjmXQqU1x++eVUV1ezdOlSVqxYwVFHHZWxcymyixCCm266iTvvvJPrr78+23K46aabdtthGDRoEOPHj7df79y5k549e3L00UdTWVmJx+NptQLKynBnkIqKCgYPHsytt97Kr3/9a1avXk3Xrl2ZOXMmV155JYFAoNWjLtKj7kIIO5e2p7QduNzUr/0c0acf0uNBahrSIZBCkgg3IDx54HJhGgZ6wiAei1D75RoShkHMkMQtScwwiZkWcRMC/QZiut248vKIhSMYQqCbkriZdJls+3ozdZWViAxGARQXF1NYWMimTZsYNGiQijjIIVrzdy2EYMCAAbz00kutdswD4frrr99tp8Ttdje5a163bh0PP/wwo0aNYsqUKVx77bXKcLcF3njjDaZNm8bQoUNZtWoV/fv35//+7/8YOXJkxs6ZTuva0NCA3+/HMAw4cgQlo05k5z+fx4yGKezZBzMvD1MTOITE3LkV4fSA202ioY74rgoSZtKPHTctDFOSMCS6aWIYEt202LrqY+IGOEs7ENcN8OeD20tCCmp3VbN5/XpG//Iyijt1ythnBbj66qv5yU9+wpgxYygsLMzouRQt51DOUZOXl9ei/X71q18xefJkZs6cyZo1a1pVg4oqyRBSSjuDXiKRYObMmQQCATt7XqbIy8ujrq4OIQSxWAzDSBY7iMYTGJYkHgnTsHMbsVA99V9vor7sK8I1tYS2fk39pg2EK5JGO91z1k1JIjXoaFgSw5KYMj1gaVK3bSt1O3aw43//o2b7dio2l7H9q41YFvT+4ZH48vMz+nkhabxnzZqV8fMoFPuKEII77rij1Y+retwZQgjBaaedxg9/+EPuvvtuHn30UdasWcM999yT0fMmEgny8/OJRqO43W5M08Q0TXxdumA4XGDoiIYGpNuNrKrEIS2E0JIz3gFTJgcm9bSv2pIkUhEjugW6tFKRJSR94VJikhzEjMdiRENRLCHw5AeJxeNYlpXxXCU//elP7fEDxaGJpmlomoZhGG0yNLC1UT3uDNK+fXu2bdtGXV0d1113HcuWLTso503fpja+Xe194a/RSjsSMU0ikRjhujqiuklUt4jqFhHDIqKbRAyLqCGJGxA3LOKGRcIgFTWSjBbRLYlpfNMLT5gWFoJwfZhoNIphWAz66TiOu2DCQfm8Qgj69u17UM6lyA59+vTh+OOP56mnnsq2lJxAXboyiBACp9PJb37zm4N2TrfbTTQatXsn8E3xXq2wHcbXm5DSxAxF0EwLh5AIJKQHMwFLymTMtmXZPe94ymgnrORApW5Z6DJp0E0LDMAk6ULpf/RxONDI8/pUZkBFq5CusJTO9/59RxnuQ4x0Dch0WlfDMNB1Hcuy6HnxFXz824/RLAvDSqAhcGiSZELXJBYyOelGSgxJKn5bohvJiTUJ08IwIWGRmnCT8oNbJnHDwuH1oHlcjLt8MvX19Xi9XmW8Fa3C6NGjD+lBz31BGe5DjEAgwK5du/B6vYRCIYQQuFwuHA4HvX50NMvy8kk01KEJcGoCzRIIIdNZXTFlssdtkexxmxYYqZmSycHKpNFOWCZxE3QzuV/ClEinix+ffR7rVn5Kj4ED8fv9yh+paDV69OiRbQk5w167QkKIbkKIt4UQXwgh1gghfpNaf4sQYqsQ4tPU49RG75khhNgghFgnhDg5kx9A0ZRQKERBQQFSSrxeLy6XC9M0sSyLiK5z4oNP2vHYETPp247qFpGUnztqmkQNk6huEjOs5EM3SRhmctJNKkQwYaSnt5vELTBMi/4/PoZP3n6bKY/Nwe12EwqF1K2tQpEBWtIdMoBrpZQrhBAB4BMhxOupbfdLKe9tvLMQ4gjgPGAA0Bl4QwhxmJSydRMzK3aL2+0mFos1qfmYdlW43W487TvQ8egT+fq/b6Kl/IaCpJ9boiGRqZ530ndtWhaGlN9Mebe+CRFMWBZxM+nv9gQLiMYS/OjUU+nYowemaeJyuVShA4UiA+y1xy2l3C6lXJFabgDWAl328JYzgGellHEp5SZgAzCiNcQq9o7X66WhoQEhBIlEAsuycDgcyWRTeXk4C4vpPOLHxA2ZiipJ9qyjhkw+p6JMooZF3DSJmZKYSeqR7G3HzeQAZdJVYmEJJwNO/AnRRIIfn34mgWAQ0zTx+/3KcCsUGWCfRo2EED2BIUA6rm2KEGKVEGKuEKIota4LsKXR28rZs6FXtCL19fW0a9cOy7KShtrpRNd1dF2npqYGf14eA867hK4njCVqJV0hYd0knDCJpMIDIylXSThlwGO6ScwwiOsmcd1KulqM5ECl6XDxg2OOp3pXFUf95CS6DBxIbW0tLpeLXbt2tXoFHIVCsQ+GWwiRDywGrpZS1gOPAn2AwcB2YJ+mrgkhLhdCLBdCLNf16L68VbEHgsEg1dXVaJpGJBJB13VcLhcul4vCwkIikQgOl4vuJ52K4fLZcdtRUyZjuc3Ua0MSNSz7ETMkMVMSTfu4LQleL+379EU6HUTq6+jSvz/BggIKCwvRdZ3i4mKVP0ShyAAtGvIXQrhIGu2npZQvAEgpdzba/hfg1dTLrUC3Rm/vmlrXBCnlHGAOQCDQQcbj+yNf8W0ikQjBlKsiXeU9Hc+dSCTwer2YpsmIs84mWl3Fq7fcQFNvxjfx3KYlkwWBU1PcDZnMHKhbFlI4yA8WgdvD9k1lXH7PPQw49lii0agdv97Q0EAwGFTGW6FoZVoSVSKAJ4C1Usr7Gq1vnD3oLGB1avkV4DwhhEcI0QvoB3zUepIVe8Ln81FfX2/nSjEMw54u7Pf7icViSCmpr6/n+F9OZuwNt2A4XMnetGEl/d6GRUI4iDZaFzMtElIjZpjEDUkcQSQaY0fZ11x08630+9GPkpkIPR47flz5uBWKzNCSHvfRwEXA50KIT1PrfgecL4QYTDLFRRkwGUBKuUYI8RzwBcmIlCtVRMnBw+Fw4HQ6cTqd9mSF9HLjbU6nE7fHw6gL/o++Q0fy+qMPU78rWR9SAqMmXMB/n/4rUoJlSZy+PLr98Ies/eADLAkSQXGnjlzwu99R3K0bTpfLPm76nE6nUxluhSID7NVwSymXArv79/1jD++5Hbj9AHQp9hNN0ygtLW12e0FBAQB+vx9I5lNp3749A4477jv7jp146X7rcLlc+/1ehUKxZ9RcZIVCoWhj5Mh8ZInHU51tEc3idtcTi8Wors5djZFIhFAolNMadV2ntrY2x/NNmDn9W/R4anHoDjzVnmxLaRZ3yE0kEsnp32IsFqO+vj6nNe7pfyJy4U9UXFwsr7vuumzLaJZwOExlZSU9e/bMtpRm2b59Ox6Ph+Li4mxLaZZ169bRu3fvnHajfPbZZwwaNCjbMppF13WWLv2KmpofZFtKs3i91QwZEqdThqsfHQibNm2iffv2tsswF7n33nuprq7e/SBRuqBtNh/t27eXucz69evlnDlzsi1jj7z44ovy/fffz7aMPfL73/9eVldXZ1tGs1iWJadMmZJtGXukqqpKDh16u0ymBMvNR8eOS+VLL72U7abaI7Nnz5br16/Ptow9krKLu7WZysetUCgUbQxluBUKhaKNoQy3QqFQtDGU4VYoFIo2hjLcCoVC0cZQhluhUCjaGMpwKxQKRRtDGW6FQqFoYyjDrWhCKBQiHA5nW4ZCodgDOZKrRJFtLMti8eLFbNy4EafTSa9evfjZz36m0rIqFDmI6nErADBNk+nTpzNy5EgGDRrE1KlTsy1JoVA0gzLcCgAmT57M4sWL2bVrF7qu88wzzzBlypRsy1IoFLtBuUoUADzyyCOMGjWKSy65BI/Hw/Tp01mxYkW2Ze2Vbdu2kZ+fTzAYzLaU3bJt2zYCgQCBQCDbUhSHEKrHrQDA7XYzZswYqqurmTt3LkcffbRdhiyXeeyxx/joo9wtafroo4+yfPnybMtQHGIow60AkrUqZ82axcSJE/H5fEybNk0NTCraBLW1tdx1113ZlnFQUYb7IGIYBqFQKNsy9kjv3r156aWXuOyyy3K8Uo1CARMnTuT000+nT58+9OvX76C597JdyUkZ7oPEhx9+yKJFi7j11ltZsmQJkUgk25KapaSkhD59+vDxxx9nW4pC0SxfffUVPp+Pq6++mt69ezN16lQ+//xzTNPM2DnLyspYsmQJU6ZM4V//+hdfffVVxs61J9q04Q6FQixYsGCv+0kpuf3225k5cybvv//+QVD2Xa655hpqa2uZMGECM2bMYNu2bVnR0VLuueceZsyYkW0ZCsV3SCQS3HDDDVx66aXs2rWLL774go0bN9KtWze2bNmCZVkZO/cLL7zAggULmDVrFgsXLuS5557L2Ln2RO6PPjXDzJkz+eyzzzj99NMZPXo0Dz30EAMGDLC3X3zxxWzdutV+PW3aNPLy8ujevftB1/roo49y5ZVXMnLkSC699FK2b9/OZZddxhtvvGH7kYUQOedTFkIgpcw5XWnSt6q5qk9xYKTLdAGUl5dzySWXAOByuZgxYwYnnXQSv/vd74hGo4wePZoJEybwt7/9LWM1TdeuXUtlZSUPPPAAV1xxBf/73/947733+Ne//gXAuHHjdjv/IRP/7TZpuGtra/n666958MEH0XWd9957j5EjR9K3b180LXkTsXDhQrp27Wq/x+/329sONhMnTuTUU09l2LBh/PWvf2Xy5MnMnDmT4cOH2z/Md955h4KCgqzo2x3BYJBrr72WO+64g5kzZ2Zbzm75z3/+g6ZpHH/88dmW0izFxcVUV1djWVbWfn9tidraWnbt2gXAypUrueOOOwDo2rUrr7zyir1ffn4+Qghef/11Kisruffee1mzZg15eXkZ03bYYYdRUlLCSy+9xJw5c3j66aeprq7mmmuuAeAf//gHQ4cO/c773nrrLYqKilpVS5s03B988AGDBw8mPz+fqVOnsmLFCkaPHs0LL7yAx+PJtrzv4PV6Of7443nwwQfp1q0bxcXF9O7dO6fjpIUQeDwe4vF4tqU0i2EYADkdtvjb3/6WMWPG8JOf/CSnLsy5hmmaLFy4kM2bN/O///0PgEGDBrFy5co9vi8vL48ePXrw0EMPZVyjw+Fg4MCBLFy4EF3X+fjjjznnnHPsGP1zzz2Xc889N+M6oI0a7lNOOYXZs2ezYcMGrrnmGi655BJmzpyZk0Y7zc0330xNTQ2rV69uM77j/v378+677/LFF19wxBFHZFuO4hAnkUgwZswYbrjhhmxLaZZx48Yxbtw4Fi9ezPz587PmpmuThhvg4Ycfpry8nEceeYRnnnmGnj17ZlvSXikqKuLYY4/NtowW07lzZ9xuN2VlZRx++OE55UuOxWIkEgl0XScajeL1enNKn2LfcDgcTJo0KdsyWszPf/7zrJ6/zRrubt260bVrV0aMGIHD4ci2nEOW66+/nlNPPZVRo0a1up/uQDjyyCNxu93U19fz6KOPsnHjRgoLC7Mtqwm1tbV8+eWX1NXVsXz5cnr06EHfvn2zLUtxCNBmDTck/bDKaGcWTdMyGl61P7z66qtMmDCBY445hvfee4+OHTuyaNEiJk+enG1pTfjoo4+47bbbqKio4KmnnsIwDJ5++ulsy1IcAqhhbsVeueWWW3IqsqRjx45s27aNI488knPPPZetW7c2iSDKBUKhEC+99BJPPPEE/fr1484772To0KF26JhCcSDs1XALIbxCiI+EEJ8JIdYIIW5Nre8lhFgmhNgghFgkhHCn1ntSrzektvfM7EdQZJpRo0bx5ZdfZluGzbBhw1i5ciVTp07l73//O/PmzeOYY47Jtqwm5OXlMXbsWBYtWsTChQvZtm0bq1evblNjHIrcpSWukjhwopQyJIRwAUuFEP8ErgHul1I+K4SYDUwCHk0910gp+wohzgPuAg5OjIwiIwghePPNN7MtowkfffQRq1at4osvvmDz5s05NzCpaRo9evRg7ty5tG/fnjfeeINRo0ZlNM5Y8f1hr4ZbJmeIpDMjuVIPCZwITEitnw/cQtJwn5FaBngeeFgIIaTKWNSmyTXDKIRg0KBBDBo0KNtSmmXIkCG88sorLFq0iKeffjqnw1UVbYsW+biFEA4hxKdABfA6sBGolVIaqV3KgS6p5S7AFoDU9jqgpDVFKxRtiXPPPVcZbUWr0iLDLaU0pZSDga7ACKD/gZ5YCHG5EGK5EGJ5NBo90MMpFArF94Z9iiqRUtYCbwOjgEIhRNrV0hVIZ3TaCnQDSG0vAKp2c6w5UsphUsphPp9vP+UrFArF94+WRJW0E0IUppZ9wEnAWpIG/Bep3S4BXk4tv5J6TWr7W8q/rVAoFK1HS6JKOgHzhRAOkob+OSnlq0KIL4BnhRB/AFYCT6T2fwJYIITYAFQD52VAt0KhUHxvaUlUySpgyG7Wf0XS3/3t9THg7FZRp1AoFIrvoGZOKhQKRRtDGW6FQqFoYyjDrVAoFG2MnMgOaFkW7733XrZlNMuOHTvYvn17TmssKyujpqYm5zL5Naa6upqPP/4Yv9+fbSnNEolEcvp7DoVCeL3VdOyYuxqLitZRVtaQ0+24fft2Vq1axc6dO7MtpVn29F/OCcMtpaSq6juh3jlDXV0d0Wg0pzWGw2GefFKjoSF3NXbvnuBHP6ohFotlW0qz1NQYXHRR7rah0xmh07iP8U17IdtSmsW9KUg4fE5O/19isRg31N5AzJm7v8W4bL5sYE4YbofDwemnn55tGc2yYcMGTNPMaY2WZVFR0YEdO0ZlW0qzlJSsYuzYsTlVkKExUkoWLHidTZty93v2eKoJdryXTadvyraUZun4XkcG7BqQ0/+X7du3s+24bdT1rcu2lGbJd+Q3u035uBUKhaKNoQy3QqFQtDGU4VYoFIo2hjLcCoVC0cZQhluhUCjaGMpwKxQKRRtDGW6FQqFoYyjDrVAoFG0MZbgVCoWijXHIGO5Zs2aRSCSyLUOhUCgyTps33O+88w5HHXUUPXv2ZPTo0dxyyy3ZlqRQKBQZpU0bbl3X2bhxI//v//0/jjjiCObNm0dNTQ27du3KtjSFQqHIGG3acMdiMTZu3MjAgQP597//zWuvvUa7du346quvsi1tryQSCZ5//vlsy1AoFG2QNm24A4EAI0eOZOLEiZx00knMnDmTsrIyRoz4TinMnCMej/PII49kW4ZCkXPce++9VFdXZ1tGTtOmDTfA2LFjWbJkCX/4wx946aWXsi1HoVDsJ6tWraJPnz50796dn/3sZ1x00UXZlpSztHnD7fV66dKlC08//TSHH344hYWFlJeXZ1uWQqHYByzL4tNPP2XatGn07duX5557jvz8fDZu3JhtaTlJmzfcaYQQdOvWjf79+/Pmm29mW44iy5SXl/Pqq69mW4aihViWxdatW+nSpQtlZWU88MADlJaWUlFRkW1pOckhY7jT/PSnP2XFihWq1/09ZuLEiUybNo01a9Zw/PHHqyijNoDT6WTs2LFcccUVFBcX89RTT/Hoo48yY8YMPv3005yupZoNcqJ0WWvSqVMnvF4vmzZtokuXLgghsi1pt2zZsoUuXbpkW0ab4euvv25xrcrly5czb948OnXqRFlZGZs2baKkpCRnfwuKJIMHD2bt2rXceOONLF26lNLSUgCmTJlCRUUFDzzwAB06dKCgoCDLSrPPIWe4Ae666y6GDBnCJ598krN/1gsuuIBPPvkk2zLaDHPnzmXTppbVWdy+fTsPPvggJ598Mueccw7PPvssw4YNy7BCxYHicDjIz8/n/vvvb7J+3rx57Nixg+nTp9O/f3+6devGhAkT0LRDzmHQYg5Jww0wffp07rnnHqZPn55tKYpWYF9mxA4ZMoTevXvTvn17fvnLX7J06dKcvYArWkbHjh2ZP38+S5cuZe3atVx22WWMGzeOs88+O9vSssIhe8k67bTTeOONN1T+ku8hL7zwAiNGjOCdd97hn//8J+3bt8+2JEUrccwxxzBp0iSmTp1KWVkZ7777brYlZYVDtsft9/u54YYbuO222/jDH/6QbTk2O3bsYOPGjYTDYd5//326detGjx49si3rkKJXr1707NmTcePGfa9vpw9VNE2jf//+HHbYYd/bO6lD9ledDg90Op05NQX+lVde4Y9//CN1dXU88sgj/OUvf8m2pEMSIYQy2oc4mqYpw90cQgivEOIjIcRnQog1QohbU+vnCSE2CSE+TT0Gp9YLIcSfhBAbhBCrhBBHZfpDNEfv3r1xuVysW7cuWxKasHnzZjZs2MDs2bPp3Lkzf/rTn3C73axYsSLb0hQKRRuiJV2SOHCilHIQMBgYJ4QYmdo2VUo5OPX4NLXuFKBf6nE58Ghri94Xrr76ahYvXkxNTU02ZQDQpUsXevTowZIlS1iyZAnLli1D13UGDBiQbWkKhaINsVcft5RSAqHUS1fqIffwljOAp1Lv+1AIUSiE6CSl3H7AavcDv9/P448/no1Tfwen00nfvn3585//jKZpvPLKK5x99tl4PJ5sS1MoFG2IFjkBhRAOIcSnQAXwupRyWWrT7Sl3yP1CiLT16QJsafT28tQ6BXDyySfz8ssv43Q6efHFF7nggguyLUmhULQxWmS4pZSmlHIw0BUYIYQYCMwA+gPDgWJgnwKmhRCXCyGWCyGWR6PRfZTd9rn44ou/twMrCoXiwNinYXcpZS3wNjBOSrldJokDTwLpJNhbgW6N3tY1te7bx5ojpRwmpRzm8/n2T71CoVB8D2lJVEk7IURhatkHnAR8KYTolFongDOB1am3vAJcnIouGQnUZcu/rVAoFIciLZmA0wmYL4RwkDT0z0kpXxVCvCWEaAcI4FPgV6n9/wGcCmwAIsDE1petUCgU319aElWyChiym/UnNrO/BK48cGkKhUKh2B1qaplCoVC0MZThVigUijaGMtwKhULRxlCGW6FQKNoYynArFApFGyMn8nEbhsFjjz2WbRnNUldXR3l5eU5r/Oqrr+jePY/S0lXZltIswWAZCxYsyOncLIZRzcCBufs9OxwxCjYVMPCxgdmW0ix52/P4IPYBO3bsyLaUZlm9ejV96vqQKMjdQitfG183uy0nDLfD4WDMmDHZltEs5eXlaJqW0xqdTicjRxbzwx/+MNtSmuWJJ8r4/e+PRdcD2ZbSLCedtIIXX8zd77m+vp7FiyuYOGb30yMkEomFlBKBsNcBaMJhr8skq1atora2luOOOy7j59pf6urqmDViFl27ds22lGYZpY1qdltOGG4hBH379s22jD2yfv36nNa4evVqOnTokNMa/X4/DQ09iceLsi2lGSSa5m7VNty+fTv5+fkEAq1zsaqursbv99OrVy+qqqqSK3069eFaCgoK+azibd6LvEpDrAbLEPi1YsLxMJF4mEm9b8Xr8tEpvytF/hLq6upwuVyEQiFKS0vZtWsXwWCQSCRCaWkp4XAYh8OBruuYponD4SAcDtvbCgoKqKystKuxpwtX7Ny5E4fDkdO/xYKCArp27Uq3bt0IhUL4fD7C4TAulwun00k0GiUQCNjb4vE4QghcLheRSIRgMEhDQwM+nw9d1/F4PCSnsIDb7SYUCpGfn084HCYvLw/DMLAsC4/HQ0NDA4FAgEgkgtfrxbIsDMPA6XTi9XrtHEZ7KgSSE4ZboThU+fOf/8yJJ57ICSec0KrHjRohPo++Q8ioo7x+DVWxHXirAwjLSXutF118P+SLXR/jdAQYGBiMlu/gs+oPeHXDIk7ucTZjepxGB28XpJR4vV7i8bhtRNLGybIs2xiljUh6XyEEkUgEt9ttP7vd7lb9jAeDUChEQUEBoVCIoqIiDMNA13WKi4upqamhqKjINsJSSuLxOKWlpdTU1FBcXEwkEiEvL49oNIoQAsuy7GNWVVVRUFBAXV0dTqcTTdOorq6msLCQqqoqgsEg9fX1CCHweDxEo1E8Hk+Lks8pw61QtEE0ofGnjx5BN+N0DXald1FvPA4/895aQDDg5rAenajaHKYqvoZBA2spdrdHNy06+fqwZscqMJy083Tg5MNOB7CNTnpZ0zQsy0LTNAzDaHJuIUST0nBtuYSYz+cjFArhdDqpr6/H4XCgaRp1dXVcddVVDBs2jMmTJxOJROzPXFtbi9frpb6+HqfTSSwWw+lMmlJN0+yLW0FBAYlEAr/fj2VZzJ8/nzfffJPHHnuMgoICdF23t0kpW2y0QRluhaJN4nHk8Yfhf+bMRWdQ4TbZ4KwmT+RRLHqQF/MQKctn19YoX+6owJP3Od6qYmqKd+F3FuPU3NTVx4glEozsehxO6cLv9xMOhxFCJG/9XZJELIzL6QDhxZISh8NBPB7H7/djGAYul4twOEwgEGizhjscDlNUVER9fT35+fmYpomu6wSDQf7xj3/w8ssvY5omF198MYWFhcTjcYLBoN3jDoVCuN2O+SXYAAAgAElEQVRuYrEYgN3jLiwspLa2loKCArZu3cqbb77J9OnTicfjPPnkk9TW1hIMBgmFkjVq0sbe5/O1qC1VOKBC0QaJxWL0bteT5855jnq9lrc3vMO/1/6bL3as4eOvVvD6Z+9wyUmXcsbgczg2eD7VO6Czv4ianZXUh+r4onwdX5Sv54+v34Hm1QiHwwSDQUzTxCVj/PXGH7D4D0fw7K2HoYercLvdCCEoLCwkHA7bvdK8vDxqampsw5Vp1qxZYxu71sDlcmEYBg6HA9M0k4O6qTsKgGg0yvTp0+nRowfLli1DCGH7ow3DQNM0pJRomobD4cDhcNj+brfbzapVqxg+fDhXXHEF4XAYSAZjpN1KLpcLl8tl9+ZVj1uhOITJy8ujsrKSLv7OPPqz2Vz13FVU1FTQt6QfDunASpj87b1F+B1+orEIbqeLnR856d9jGNsqNlJfUkGp3o1n/rWIsT3HceqPTqWyshKvGz7514PUhXTadx9Gv8E/QbjyiMfjOBwOqqur7cHJ4uJiKisrKSkpyXiPu6qqigceeACn04lpmnTr1o3LLrvsgI/rdDrRdR1N09B13f4cc+fObXIxSiQSTJgwgYsuuoizzjqLnj17ctdddyGlTF7sXC4gaYgvu+wydu7cycKFC3n22Wepq6uzj2OaJnPmzOGyyy7DsiycTqc9juBwOFqu+4A/uUKhOOhEIhHy8/MBGOYdxjMXLeSMv5zJlxXrCDgD+ISPuIhTGd/FjsrtVO+q5qfDT6PU3RkLB0fmD+Pfn/2TYo8Tj+aioaGBuooN/P2VB6jYvJz2XY7i2HNmUdi+J5oQOBwOLMuipKSEcDiM0+mkqqqKQCBATU0NeXl55OXlZeSzSimpqqri448/Zu7cuaxfv54bbriBSy+99IAvGNFolOLiYurr6wkGgxiGQSKRYOHChSQSTWO8t23bxl133cVrr72G3+9n+fLlmKbZZB9N03jttdeQUrJy5crdfpY5c+Zw3nnnUVhYSCgUQgiB1+slkUjYPf69oVwlCkUbJN07k1KiCY2+xf1481dv0rfjYdTH6lm3438s37yCVVtWEcgPMnzAcKJ6lK93bkY4Neq3Jhjd5xTy85zc+NcpbNq2ga83rObLzz/h2NNn8PMpCyjp2BtBcjAybVDSYYFCCJxOJ5Zl2S6CxrRmD1xKyfTp05kzZw533HEHHTp04De/+Q0PPvjgAR87feHxeDxUV1cTiUQA0HXd3ue+++5rModj9erVLFu27DtGG5I+7hUrVjQx2h06dGD+/Pn2a6fTSbt27dB1nYKCAvx+P5C8i1KuEoXiEEbTNGKxGCLVG9Z1nY4FHVky+VVe+/w1Xv38H3yw5n12VO0kkghTZTmIOxJYCQsMWLvuC8YOP5njSn9B+1GCq+47nx9UOhg8bAyHDT2FvPwC20inox6EECQSCVwuF6Zp4na77UHKbxuc9O1/a33Wu+66iwsuuACHw8Hzzz/PkiVLWLp06QEfOx0GWF9fT3Fxsd3jTrs+IGnEX3zxRYqKinZrrPfGmDFjmlwIDMNg165dFBYWUldXZ/e4VTigQnGIE4vFbNdENBrF7/dTW1tLIBDgxL5j+PnwX7BkxRJ2NOwgEUsQ8OYTjUSJRxMgBcYJBt07dOPEESdSXFRMcEcxW97/jJN+diWl7TtTVVWF3+9H13WcTqdtpNPxyV6vl9raWnviTiAQyGgcd4cOHbjwwgtZsGABpmly3XXXtcpx0+GALlfSXZQeIGxsoH0+H/tb0PyXv/wld999N//+97/tdQ6Hg2Aw2CQcELAHgFvCIWe4DcOweyEKxaFKXl4e9fX1QPIPn56Nl/bZhsNhTh5yMnW1teS53URrq/h6/sPENqzF26kL/X/7exIuFw5g147t7Fi5DY+/Pd2696W+upqiQICErrPh7y/wyd8WIFxe+p9+Dn1Gn0hRSQmmaVJaWkooFKKkpMSOY84UBQUFdOzYkXPOOYfJkye3Wr6beDxOfn4+kUgEn89nz2L0er32PolEAo/HY0ee7AtnnHEGQJOBTikl4XAYv99vr3e73U165XvjkDHcUkqWL1/OBx98gKZpjBw5kqFDh7bZ+FKFYk+Ew2F7Nl80GiU/P9+OG04/71y5DFG+ibLXnsPl83PkrfeD5kI4NMxdO1h74/WYQsOKWVhrP6f9kUdR9vw8trz7NpGGevK79eIHZ57P+NtmYRk6X7z1On+deD7ugiJO/H/XkN+xMz369aOurg6fz2cPlmaK2tpa8vLyWjVJWWP/vZTSdvG89NJLdOzYkYaGBjZv3syKFSu+MxGpJWzYsIGhQ4eyYcMG+3xnnXWW3bFsHHq4L7bqkDHclmVx1llnMWvWLHt58+bNynArDkk8Hk8TH3cikcDr9aLrOl6vl13v/ovNs26k23mXMmDaHQgB4XVrSf8dpBAMvPE+pIDYju0UfbiURCKBQ2gMmzINnC7i0QiJaIRIVQWWlPQYOpzuQ0dQV13N4ptmEuzWnUvufQBfMJjxHnemcLlcxONxNE2zp/ILIZr0kB966CEeeuih/Tr+tddey7Zt25g1axaQ9NdfffXVeDweLMvC7XbbF4t9acNDJqrkxhtv5PHHH6ekpISOHTvy2GOPcdNNN2VbVpslEolw8803Z1uGohnS0RyNJ4BYloUQgsp3lrD+gVvoOWEywd6HEd9aRrx8MyIWRsTCEAtDNEx045dE1q/FaKil/YhRdD7meAq69yJauYPw1i3EqnZhhMMY0Qh6JEK8IUSsvg6Hw8HxF11M/ZYtPP7rK+wwtrZIOqwy7W9OG9JZs2btt1/726SNNiS/txtvvJG6umQ7hkIhotGonQelpe3YNi+Tu+Hqq6/m/PPPZ/z48TidThYvXsxzzz2XbVltFl3XW2XUXpEZ0lEdjWfyRSIRRNVOdr70V7qfeQGe4lKsuio0NIRIzQgEBGAhwUouY0kSkRCmlBgWmJbEkhJLJpeN9LMlMbHQTXB7fBwz4UJefvB+Hv7lRK5b+EzGP28ikcDn87XqcdPT171eLzU1NUgpeeSRR7j33nubuEaKiopwOBxNwiJramp2e8yCggJcLpd9IbUsy95XSsnjjz+Ow+Hg5ptvtiNVTNPcp3DAQ6bHXVpaSmFhIU8++SRXXXUVPp+PkpKSbMtSKDJC2qedzjxXV1dHYUEBOz5fSbC0I/7CEqxQLcQiiHgILR7BEQ+jxSPJR7r3HQ1DLATRMFYkjIyEMCMhjEgII9xAIhxCDzWQCDWQCDcQb0g+x0L1WIbOSZMupaa8nIaKiox+3o0bN7J06VIuuOCCVj1uQ0MDhYWFJBIJAoEAjz32GLfddluTyTdHHHEEK1asoLy8nI0bN1JRUcHy5csZPnz4d453+OGH89Zbb1FeXs7nn39OeXk5H330EYMGDbL3MU2TP//5z9x9991s27bNngofiURa3OM+ZAy3pmksXryYp556iqOOOooHH3xwj/lsc5WXX36ZrVu3ZluGIsdJJyTyeDyYppkMa6urpfY/S9B8XvSGGohFkNEIxJKGWotHcMbDOOIRRCwC8Yi9jxkJI6MRrGgYKxrBikQwIhGMSAg9EiaRfg6HSYRDJMIh4uEQeiyBy5/PO89mtsedprXHrHw+H5FIBKfTyc6dO7/jXh0wYACzZ8+muLjY9oXX19fTrl07Zs2aRb9+/ex9PR4P1113Hf369SMejxMIBNB1nQ4dOvDEE08wYsSIJseeNWsW4XDYHmz9XocDDho0iIEDc7esU3Ps3LmTn//855x55pk888wzeL1e5s2bl21Zihwl7RqB5B8+kUjg0QSxr76gZMxpWNEwpqbh0ESye6aBQ3OgaWBJEJYESyItibQspCmxLDAtC8sCw5LolkSXFrqZdKEYlpVcZ0kMM7UsoWPPHuit5A8+2Oi6Tl5eHrFYjF/96ld2dEma7du3M23aNEzTpH///jz88MN4vV4ikQhDhgxh7NixrF+/HoCxY8dywgkn2C6dSCTCLbfcwsqVK7Esi82bNzc5txCCK6+8khdeeAG3271PoYaHnOFuK1iWxfr16+0fyY4dO/D5fIwbN45LLrmESZMmsXPnTjp06JBlpYpcpHH4mh3SpgmkZWLFIhgaaJoDSxNITYAmkA4BacNkgbQklmVhmclnwwLDtDAk6IaFIZN+7YRpJQ25aWFYFglLoJsS3bLQTYtYuPWy9R1s0gUMnE4nTzzxBP/5z3+YMGGCvb26upoPP/yQPn36cOedd+JwOIhEIng8HuLxeJNIkEAgQLt27ewoH7/fz0033cQpp5zCihUrvnPuP/3pT5x//vlNCli0lEPScB933HG8/fbb9O3bN+vhgJs3b+aNN974znrTNFm2bJn9OhwOs2HDBu6//35uuOEGTj75ZN54441W9+kpDg0SiYQ9U9E0TbxeL7G6WsxwhNjObfiCBZiaA80hEBoIhwChYaFhITGkxLSSBtkw071qiSEtEibo6R61mRyMjEajxHUdPD4SlkwZbtAtk3gkQiZjSqSUvP322xmpYdk4qZPD4eDdd9/9zj6HH344ixYtIj8/H6fTyeuvv05FRQWFhYUMGjSISy65BMMw+NGPfsSyZcsoKyvD5/Nx5pln4vV6efnllznttNP47LPPmhz3448/5uyzz7Y7b/sSmXNIGu5JkyYxZMiQVskedqA0rhTSGI/Hw+OPP27r27JlC8cddxznn38+Tz75JK+99hqffPLJwZZr4/P5GD9+PC+++CJnnXVW1nS0dc466ywWLFjAyJEjWzUiwuv1UlFRgRACv9+frIMYyMeSUP/lGhz9+iN8XtC0VE87FUmiGwiPF1NaScNrGIS3bSEWDhMzLRKmJG5I4pZJ3ABXSQcIBIlFosQTCYRhkkjtp1uShGGyefVq+g4fsXfR+4mUktmzZ+82215rkK70EwqFmD17Nqeffjrr1q1j3bp19vlnzZrFPffcgxCCqqoqrrnmGn784x/z/PPPc9ZZZ9npWSdPnszzzz/PfffdByRnct94441NjHKXLl0YM2YMf/3rX5k+fTp5eXktzgqY5pA03LlE9+7dmThx9xW5G9OxY0eWLFnCvHnzGD16NJMmTToI6prH7XZz5JFH8s477yjDfQAcddRRTJ06tdVD2dLFetOTRQKBAA2hBo6Yfjtrbr0a8/MwpT8YiPS4MTWBKUDEI1i1NTg6dMYyTBo2rME0JLF4nLiuEzct4gZEDZO4YREzLfQd29BxIP0FOAoKkZEYhsOJbkLCtNjw+So0dx5HHHNsq322g0m6sK/X68Xr9fLRRx9RWlrKhRdeaO/z5Zdfsm7dOt59913OPfdcJk2aRHFxsR3uZ5qmXTzBNE3y8/MZP348c+fO5f7776esrMzORwJQWFjI/fffz1VXXUWvXr3sqkP7MgFHGe4cweVy8YMf/IDbb7+9yTRYhaI5TNO07+aSvUYHIlCEblho4TDVX3xKQd/+aKaBwzIRehy9citsL0/GalugWxYJK9mDThjJXrRJKnZbQiKeIKabxOoaiG/ZQsy0MFwe/B07s61sMw0NEXqOOIyBGXBjHAzShX3j8TjFxcUUFRWxZcsWYrGYPakJkr3uTZs2ceedd7JmzRpeeeUVnnzySaSU+Hw+O3xw4MCBXHfddVx//fUsWrToO+4PTdOIRqNs376dww8/3J7k43K5iMViLZ7O32LDLYRwAMuBrVLK04QQvYBngRLgE+AiKWVCCOEBngKGAlXAuVLKspaep7W44IILeOaZZ9qcj7gthjAqDj7pqdpp451OrxoCLK+XRDwGukG4tgbC9YhQA5om0BBIJKa0sGTScBsWKZ/1N75rI+3/tpL+cMuSmFJiWmDqOqGaWmKRKA6PFylbP0zvYJGfn29XY6+trcXtdrNx40Z+/OMfc/LJJ1NfX28PYM6ePRspJX//+98ZNWoU06dPt6vd+/1+pJRce+21LFiwoInRnjJlit0jTycH27BhA507d7bLxe3rHdm+9Lh/A6wFgqnXdwH3SymfFULMBiYBj6aea6SUfYUQ56X2O3cfztMqTJ48mfHjx7c5w50rTJo0iTVr1lBVVcUnn3xiD84ocoN4PG5nsItEIuTl5SXTrB7+Q4qOGcvOf72EhYGsqsIpLDTDQmgCkTLclmxkiKVM+rZN2cSAG40GLw2ZHLA0pcTQJfGaOiwJDq+X8dOm2jlSMsGMGTO4++67M3LstMspkUhQUFCAlJJjjz2WE088kVgsZlem0TSNfv36cc011wDwwAMP8Nvf/tYOJ0wkEvYsyfvuu8822jfffDNXXHEFXq/XnuXq9XqJxWJ2VkfArhbf0tS4LereCSG6Aj8FHk+9FsCJwPOpXeYDZ6aWz0i9JrV9jMjC5VgIoWZO7ic1NTVs3LiRadOmccYZZ+D1etmxY0e2ZSka4ff7CYVCTXJJFxQUEBcOgj36YlgQ1y2ikSjRaIKIaRE1LCJG8jlqWMSMpLGO6jI5MGlZJFLhf7qUxC2JYUoMKUikety6ZaH585OuBLcP3TAYddLJGStbBrBs2TJGjRqVkWPn5eU1acO0y6O+vh6fz0d9fb1d3f7www+332cYhl1LMhaL4XK5mhQBTtOvXz+KiopwuVxomkYwGCQajVJQUGDnR0n3tPcln3lLe9wPANOAQOp1CVArpUxP5i8HuqSWuwBbAKSUhhCiLrX/rharagXy8/NZvHjxwTzlIcP8+fO5/PLL6du3L4lEgjPPPJMHH3xwvzOkKVqfSCRCIBBoslxXV0cgEEDr2Q+tXWdiO8rRZQIHAodGKjNgsq8mZdNed3pyjR0tYproZtJ4J6x0PLfEMCFWU4sl4MgxJ+AtLqGyspLCwkJbT1sineclHUeddlWmixK7XC6klDgcjiaDh0IIO+46ncOk8SNNuhp8ep2u63acd9rFlfajNx7A3Bt77XELIU4DKqSUrRqbJoS4XAixXAixvLWycClah6uvvprbbruN//73vxQVFXHhhRdy2223ZVuWohFpv2s0GrUHvNK39T2OHo23S3eipkUsFR2S7GFbxAyDmGEQNUyihvnNdttIpwYqTZmM504b81Sct24lXSilPXvx1eo1nPbrKQSDwYxWv8kk6VDAtHFuHNOdzsCYzr7Yq1evJoUR0vMz0i6StP+7qqoKSJYsGzhwoL0tHXWiaRqmaTZ5H7R+HPfRwOlCiFMBL0kf94NAoRDCmep1dwXSCTa2At2AciGEEyggOUjZBCnlHGAOQIcOHdpmTshDmEWLFrF69Wo+/PBDnnvuuTbZmzqUSf/x03/+dARE2uAMm3obf79wPNFoCIcQyYFJmex1S8ACrHQWQCSGkYwkSRpnC8OEhJU05rplpaJPkgbcEwjSvu8PaNe3L8WdOtnlvjL1OTM5YJ8uEhwMBqmrq8PtduNyuexKQtXV1QQCASKRCIWFhRx77LG8/PLLhMNhpkyZQrdu3WzDDlBeXm5nAhw6dCidOnWy86Snc8rU1NTYleXTpcsSiUTrhgNKKWcAMwCEEKOB66SUFwgh/gb8gmRkySXAy6m3vJJ6/UFq+1uyrSbr/R6Tzvmyr1NxFd8lEz9/0zTtP3r6lj4SieB2u4lGoxT27kNe915UrPkUTWg47JSuFhINKVI9wNTgpGnJVArXdD4SYfe0dcsiZiZdJgnLJBAsRHO76TVoEIHCQurr69E0LSO97ltuuYUbbrjBroTe2qSzA8ZiMQoLC7EsC9M0KS4utsuyRaNRAoEAUko7PwxAZWUllZWVzR47fReUzr2taRo1NTX4/X6qq6ttH3ra7ZIuFtwSDuRSNh24RgixgaQP+4nU+ieAktT6a4DrD+AciizicDiU0W4FMtEb9fv9NDQ0EAqFcDqddjxyJBKhpKSESCTCKY88SVy3iBsmUd1MuUdk8jlhEdWT7pN42o1iSqImxAxBzLBImBZxM7leNy0ShklRl+70O/pYvHl+xp53Hg0NDZSWlmZscDLtg85Ujz4QCFBTU4Pb7aampsaOq04XQN61axcOh4P6+noikQjDhw+nW7duez1ux44dOeGEE+wLgsfjQdM0ux5oaWmpHcmSvijtSxvuk+GWUr4jpTwttfyVlHKElLKvlPJsKWU8tT6Wet03tf2rfTmHQqHYO9FolLy8PHw+n52EPz0DsK6uDq/Xi3S6GXTRpUlDbSYNd0T/xredjC4xk/5vUzYy4slp7XHDIm77uyXBjl3oPWwE28rK+MnEidQ1hPD5fNTW1jYp9dWWiEQidsX1YDBohzQWFhba7hHTNPH7/Xi9Xo4++mjmz59PYWFhs8d0u908/vjjjB49Go/HQ0NDA7quI6W0o1VqamqScfepCjjAPrWhmu2hULRBPB4Puq7bUQrRaNSewZefn58sDFBUTOmo49DadSJqSCKGRcRMhgR+ExYov1k2LWK6mexlG8kQwbhpkrAk7mAB7fv2o6piJ5GGEL0HDyYQCBCPx/H7/Rm7M5s6dep38li3Jl6vl3A4jNPpJBwO2+GA6YtgQ0MDDoeDWCxm16Q8/PDDWblyJfPmzSMYDBIIBAgGgwSDQe6//37WrVvHqFGjCAQCJBIJ8vLy7LuGdGX3QCCAYRhNih9nIhxQoVDkEI2nYqcjIhrnzkgPWvYaMYphF1/KW/ffgx4J2++XqYk4UiYHKU3S/m6S6VztCTgW3uJS8jt0IhKN4vF4uev1f9saGg+KZoLi4uKMHDdN4/JiaRqXJ2u8LZ0+V9M02rdvzymnnMLXX3+NYRj2zEjAHm9I59e2LMuOHmn8HUFyfKJx1ElLUYZboWiDmKZph6qlDadhGGiahq7r9rPb7ebYSb/ClJJX/3ArsomBSkaYmJJkTHd6Wrv8Ji+3IQWaKamrqaFnp05ces89aKlMePF43I5JFkK0yUrvjY1uenYjJHvi6XS50LQ3nN7WeOJM45A+XddxuVx2pIiu6/Z7E4mEvS39nTW+ULQU5SpRKNog6ZjtWCxmJ/dPr0tXLU/f6muaxogJF/OLe/9E1yHDk/7s1KPLsBF4O3QkZlqph6TfcaOJWySnwFsQi0Q56qSfMPGPfySvqAiPx4NlWeTn5xOPx8nPz2+zcdxpw5qeDJM2no2NbnqqeroHns7kl3arpEMW0ymcXS6XXczZsiycTqe93eVyYRhGk23pC96+3LW0vUukQtFGiEajVFZWEovFKC8vR9d1SktLW+34aTeCEAKfz4cQwl5XVFSEEILOnTvb20+8+P849uxzMRv1AB0uF5ZlYpnf9MSdbjd6o2K5AG6vF7fXa/cOg8GgnVairSaYguQF0OPxNGlD+MZdkt7WmHQ19t1tS7Mnv/X++LS/jTLcCkWG+O9//8u1115LRUUF1157LSUlJTz99NOtdvzGE1PSBmRvz44WJgrzNhM33dxx2yqNUyg3/ix7+ny58NmVq0ShyACRSIQ333yTuXPnMnDgQP7yl78wYMAAli5dmm1pikMAkQuTGouKiuRFF12UbRnNEo/H7VlUuUpdXR1OpzNjM8xag507d7JzZylSZiYCoTUoLNxKjx5d9r7jXjBNk82bN9O7d282btxIz549qa+vx7KsA/odmaZJVVUV7du3P2CNmSIcDmOaJsFgcO87Z4mqqiry8/NbPFMxGyxYsICamprddutzwnALISqBMAc5g+A+UIrStj8obfuH0rZ/HGraekgp2+1uQ04YbgAhxHIp5bBs69gdStv+obTtH0rb/vF90qZ83AqFQtHGUIZboVAo2hi5ZLjnZFvAHlDa9g+lbf9Q2vaP7422nPFxKxQKhaJl5FKPW6FQKBQtIOuGWwgxTgixTgixQQiR9aILQogyIcTnQohPhRDLU+uKhRCvCyHWp56LDpKWuUKICiHE6kbrdqtFJPlTqh1XCSGOypK+W4QQW1Pt92mq5F1624yUvnVCiJMzqKubEOJtIcQXQog1QojfpNZnve32oC3r7ZY6l1cI8ZEQ4rOUvltT63sJIZaldCwSQrhT6z2p1xtS23tmQds8IcSmRm03OLU+G/8JhxBipRDi1dTrzLTbt6sTH8wH4AA2Ar0BN/AZcESWNZUBpd9adzdwfWr5euCug6TlOOAoYPXetACnAv8EBDASWJYlfbeQLG/37X2PSH2/HqBX6nt3ZEhXJ+Co1HIA+F/q/Flvuz1oy3q7pc4ngPzUsgtYlmqT54DzUutnA1ekln8NzE4tnwcsyoK2ecAvdrN/Nv4T1wALgVdTrzPSbtnucY8ANshkNZ0EyfqVZ2RZ0+44A5ifWp4PnHkwTiqlfBeobqGWM4CnZJIPSRZz7pQFfc1xBvCslDIupdwEbCD5/WdC13Yp5YrUcgOwFuhCDrTdHrQ1x0Frt5QmKaUMpV66Ug8JnAg8n1r/7bZLt+nzwBghMpPEYw/amuOg/ieEEF2BnwKPp14LMtRu2TbcXYAtjV6Xs+cf8cFAAv8WQnwihLg8ta6DlHJ7ankH0CE70vaoJZfackrq1nRuI7dSVvSlbkGHkOyd5VTbfUsb5Ei7pW73PwUqgNdJ9vJrpZTGbjTY+lLb60jWoD0o2qSU6ba7PdV29wsh0vPYD3bbPQBMA9KpFkvIULtl23DnIsdIKY8CTgGuFEIc13ijTN7b5EQoTi5pacSjQB9gMLAdmJUtIUKIfGAxcLWUsr7xtmy33W605Uy7SSlNKeVgoCvJ3n3/bGn5Nt/WJoQYCMwgqXE4UEyykPlBRQhxGlAhpfzkYJwv24Z7K9C4ZHLX1LqsIaXcmnquAF4k+cPdmb7FSj1XZE9hs1pyoi2llDtTfy4L+Avf3NYfVH1CCBdJw/i0lPKF1OqcaLvdacuVdmuMlLIWeBsYRdLNkE4D3ViDrS+1vQCoOojaxqXcT1ImC5Y/SXba7mjgdCFEGUmX74nAg2So3bJtuD8G+qVGXt0knfSvZEuMEMIvhAikl4GxwOqUpktSu10CvJwdhR0Bo5UAAAF0SURBVLAHLa8AF6dG0kcCdY3cAgeNb/kQzyLZfml956VG03sB/YCPMqRBAE8Aa6WU9zXalPW2a05bLrRbSkc7IURhatkHnETSD/828IvUbt9uu3Sb/gJ4K3U3c7C0fdnoYixI+pAbt91B+V6llDOklF2llD1J2rG3pJQXkKl2+//t2z1uwkAQhuG3g5qOlgNQpUxBC9fIMZByi5wgkVJwBeAANBAgRX5ukibFDIIGJBf2stL7SC7ASPtphEfyjt3GZLXJQUx+v4l9tHnhLCNigv8BfJ7yEHtPK+AHWAKDjvK8E7fNf8T+2NO1LMTk/CXreAAeCuV7zfX3+eccXvx+nvm+gGmLuR6JbZA9sMtjdg+1u5GteN1yrTGwzRxH4Pni2tgQw9EF0Mvv+/n5N8+PCmRbZ+2OwBvnJ086vyZy3Qnnp0paqZtvTkpSZUpvlUiSGrJxS1JlbNySVBkbtyRVxsYtSZWxcUtSZWzcklQZG7ckVeYf2tkbinO+r1AAAAAASUVORK5CYII=\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Matokeo\n",
+    "\n",
+    "Tuweke kuona kama tulifanikiwa kumfundisha Peter kupambana na mbwa mwitu!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 1, won: 9 times, drown: 90 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [
+    "Sasa tunaona visa vichache sana vya kuzama, lakini Peter bado hawezi kila wakati kumuua mbwa mwitu. Jaribu kujaribu na uone kama unaweza kuboresha matokeo haya kwa kucheza na hyperparameters.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0987e7dd8>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/translations/sw/8-Reinforcement/1-QLearning/solution/notebook.ipynb
new file mode 100644
index 000000000..6e06f6f5e
--- /dev/null
+++ b/translations/sw/8-Reinforcement/1-QLearning/solution/notebook.ipynb
@@ -0,0 +1,577 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "488431336543f71f14d4aaf0399e3381",
+   "translation_date": "2025-09-06T15:11:00+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter na Mbwa Mwitu: Utangulizi wa Kujifunza kwa Kuimarisha\n",
+    "\n",
+    "Katika mafunzo haya, tutajifunza jinsi ya kutumia kujifunza kwa kuimarisha kutatua tatizo la kutafuta njia. Mazingira haya yamechochewa na hadithi ya muziki [Peter na Mbwa Mwitu](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) iliyoandikwa na mtunzi wa Kirusi [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Ni hadithi kuhusu mvumbuzi kijana Peter, ambaye kwa ujasiri anatoka nyumbani kwake kwenda kwenye uwazi wa msitu kumfuatilia mbwa mwitu. Tutafundisha algoriti za kujifunza kwa mashine ambazo zitamsaidia Peter kuchunguza eneo linalomzunguka na kujenga ramani bora ya urambazaji.\n",
+    "\n",
+    "Kwanza, hebu tuagize maktaba kadhaa muhimu:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Muhtasari wa Kujifunza kwa Kuimarisha\n",
+    "\n",
+    "**Kujifunza kwa Kuimarisha** (RL) ni mbinu ya kujifunza inayotuwezesha kujifunza tabia bora ya **wakala** katika **mazingira** fulani kwa kufanya majaribio mengi. Wakala katika mazingira haya anapaswa kuwa na **lengo**, linalofafanuliwa na **kazi ya zawadi**.\n",
+    "\n",
+    "## Mazingira\n",
+    "\n",
+    "Kwa urahisi, hebu tuchukulie ulimwengu wa Peter kuwa ubao wa mraba wa ukubwa `width` x `height`. Kila seli katika ubao huu inaweza kuwa:\n",
+    "* **ardhi**, ambapo Peter na viumbe wengine wanaweza kutembea\n",
+    "* **maji**, ambapo ni wazi huwezi kutembea\n",
+    "* **mti** au **nyasi** - mahali ambapo unaweza kupumzika kidogo\n",
+    "* **tufaha**, ambayo inawakilisha kitu ambacho Peter angefurahia kukipata ili kujilisha\n",
+    "* **mbwa mwitu**, ambaye ni hatari na anapaswa kuepukwa\n",
+    "\n",
+    "Ili kufanya kazi na mazingira haya, tutafafanua darasa linaloitwa `Board`. Ili kuepuka kujaa sana katika daftari hili, tumetenganisha msimbo wote wa kufanya kazi na ubao katika moduli tofauti inayoitwa `rlboard`, ambayo sasa tutaiingiza. Unaweza kuangalia ndani ya moduli hii ili kupata maelezo zaidi kuhusu mambo ya ndani ya utekelezaji.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rlboard import *"
+   ]
+  },
+  {
+   "source": [
+    "Hebu sasa tuunde ubao wa nasibu na tuone jinsi unavyoonekana:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "source": [
+    "## Hatua na Sera\n",
+    "\n",
+    "Katika mfano wetu, lengo la Peter litakuwa kupata tofaa, huku akiepuka mbwa mwitu na vikwazo vingine. Ili kufanya hivyo, anaweza kimsingi kutembea huku na huku hadi apate tofaa. Kwa hivyo, katika nafasi yoyote anaweza kuchagua mojawapo ya hatua zifuatazo: juu, chini, kushoto, na kulia. Tutafafanua hatua hizo kama kamusi, na kuzihusisha na jozi za mabadiliko ya kuratibu yanayolingana. Kwa mfano, kusonga kulia (`R`) kungefanana na jozi `(1,0)`.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "Mkakati wa wakala wetu (Peter) unafafanuliwa na kile kinachoitwa **sera**. Hebu tuzingatie sera rahisi zaidi inayoitwa **kutembea bila mpangilio**.\n",
+    "\n",
+    "## Kutembea bila mpangilio\n",
+    "\n",
+    "Hebu kwanza tutatue tatizo letu kwa kutekeleza mkakati wa kutembea bila mpangilio.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(m):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(m,policy,start_position=None):\n",
+    "    n = 0 # number of steps\n",
+    "    # set initial position\n",
+    "    if start_position:\n",
+    "        m.human = start_position \n",
+    "    else:\n",
+    "        m.random_start()\n",
+    "    while True:\n",
+    "        if m.at() == Board.Cell.apple:\n",
+    "            return n # success!\n",
+    "        if m.at() in [Board.Cell.wolf, Board.Cell.water]:\n",
+    "            return -1 # eaten by wolf or drowned\n",
+    "        while True:\n",
+    "            a = actions[policy(m)]\n",
+    "            new_pos = m.move_pos(m.human,a)\n",
+    "            if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:\n",
+    "                m.move(a) # do the actual move\n",
+    "                break\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "Wacha tufanye jaribio la matembezi ya bahati nasibu mara kadhaa na tuone wastani wa idadi ya hatua zilizochukuliwa:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 32.87096774193548, eaten by wolf: 7 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        else:\n",
+    "            s += z\n",
+    "            n += 1\n",
+    "    print(f\"Average path length = {s/n}, eaten by wolf: {w} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Kazi ya Zawadi\n",
+    "\n",
+    "Ili kufanya sera yetu iwe na akili zaidi, tunahitaji kuelewa ni hatua zipi ni \"bora\" kuliko nyingine.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "move_reward = -0.1\n",
+    "goal_reward = 10\n",
+    "end_reward = -10\n",
+    "\n",
+    "def reward(m,pos=None):\n",
+    "    pos = pos or m.human\n",
+    "    if not m.is_valid(pos):\n",
+    "        return end_reward\n",
+    "    x = m.at(pos)\n",
+    "    if x==Board.Cell.water or x == Board.Cell.wolf:\n",
+    "        return end_reward\n",
+    "    if x==Board.Cell.apple:\n",
+    "        return goal_reward\n",
+    "    return move_reward"
+   ]
+  },
+  {
+   "source": [
+    "## Kujifunza kwa Q\n",
+    "\n",
+    "Jenga Jedwali la Q, au safu yenye vipimo vingi. Kwa kuwa ubao wetu una vipimo `width` x `height`, tunaweza kuwakilisha Jedwali la Q kwa safu ya numpy yenye umbo `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "source": [
+    "Pitisha Jedwali-Q kwenye kazi ya mchoro ili kuonyesha jedwali kwenye ubao:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p267d475764)\">\n    <image height=\"327\" id=\"image00b8757863\" 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I0KdPH3r16sVVV10VdhsQAEopLr30Uvr37w9ATExMo6+zyRV3tZSUFKsj1EspFbalXc3hcIRlIR4uHP+n8lPh/nOOBHa7PSxL+3Ano7CrNamdk0IIcSqQ4hZCiAgjxS2EEBFGilsIISKMFLcQQkQYKW4hhIgwUtxCCBFhpLiFECLCHLO4lVKvKqUOKKW2HLYsXSn1iVIqN/Q5LbRcKaWeV0rtUEptUkqd25jhhRDiVHQ8W9yvAVf+ZNlDwAqtdSdgRehrgF8BnUIf44EXGyamECKSyOn9jeuYxa21/hIo/cni64B5odvzgOsPWz5fV/k/IFUp1aqhwgohIkM4XS+7KfqlY9wttNaFodv7gRah21nAj4c9bm9omRBCiAZywjsnddX/Wn/2/16VUuOVUuuVUuvDaeYSIYQId7+0uIuqh0BCnw+ElhcApx/2uNahZUfQWs/WWvfWWveOhCu8CSFEuPill3X9EBgNPBn6vOSw5ROUUm8B5wPlhw2p1MkwDBYvXvwLozS+4uJidu7cGdYZt2zZwu7duykqKrI6Sp3279/PRx99FNaXYq2oqAjrn7Pb7SahMIH2i9tbHaVOSflJbHFtCetx7l27dhEVFcWWLVuO/WCLGIZR533HLG6l1JvAxUAzpdRe4P9RVdjvKKXGAruBYaGHLwN+DewA3MCY4wno9yvuuqvFsR9okfh4k9Gj42nRInwz7t69m1mzUigrC9+MHTvGcP31zcNqtvifioqKCuufs9Pp5LyY83iyxZNWR6nT1kNbqbRVhvX3MT4+nifSn8DdonEn9T0RflX3xNLHLG6t9c113DX4KI/VwO+PO1nN82zs39/35z7tpElJ2UGrViX07Ru+GYuKiigraxHW38fWrVfQq1cv0tLSftHzg8Egs2bN4oknnqi1fPbs2fz6178+4emitNa88cYbYf1zLi0t5ZtvvgnrjKZpUlxcHNYZN23aREmPEso7llsdpU6JtronjmiyM+CIpsXv9/Pqq68yceLEI/4Ev/baa/nwww+58soriYqSX2nR9Mkp7yLsBQIBZsyYwaRJk446bqq1ZtSoUSxYsIBgMGhBQiFOLiluEfZsNhuLFy/G5/PV+ZhDhw6xcuXKRp9dW4hwIL/lIuxt2bKFgwcPHvNx+fn55OXlnYREQlhLiluEPYfDcVxj18f7OCEinRS3CHudOnUiKSnpmI9r0aIFWVlyhQXR9Elxi7Bnt9vJycnB4XDU+ZjmzZszadIk7Hb7SUwmhDWkuEXYs9ls9O/fn/PPP/+oW9Tt27fnwgsv5Nxzz5XLiYpTghS3iAhxcXHMnz+fDh061CpnpRTdu3dn3rx5Mr4tThlS3CLsaa0JBoOMGzeOL7/8stax3FprPvzwQ+6++2601mF9fQwhGooUtwhbWmsMw2DDhg1cdNFFrFixos7HvvHGG1x33XXk5uZimqYUuGjS5G9LEZa01rhcLt544w1ee+011q9fX+/jDcNg2bJlaK256aabuPnmm7Hb7TLmLZokKW4RdrTWmKbJww8/zAsvvHDczzNNk2XLlvHRRx9RUFDA5MmTsdlsUt6iyZGhEhF2/H4/d999N7NmzfrZz60eXnn88ceZPn26XLtENElS3CKsuFwuHn74YV566aUTKl23280TTzzBnDlzCAQCDZhQCOtJcYuwEQgE+POf/8zMmTMxTbNmeVRU1HFdPCoqKqrWCThOp5O77rqLWbNmyc5K0aRIcVvE4/GQk5NjdYywMmXKFJ599tkjlo8YMYIzzzzzmM/v378/gwcPPmJM+6GHHuK5555rsJw/1xNPPIHbHb4zrWitmTJlitUx6rV///5fNHTWVElxW+Dee+/l4osvJjs7m7POOosvvvjC6kiWCgaD3H///Tz//PO1trTj4+O5/vrrmTFjBunp6fW+hlKKNm3asHDhQpYtW0Zi4v9mD3G73TzyyCP8/e9/r/X6je2rr76iS5cudO/enUsuuYRJkyadtHUfr2effZbs7GwuvfRSunbtyltvvWV1pCPcdNNNjBkzhujoaDp37szOnTutjmQ5Ke6TrKCgAMMwmDx5MllZWUyZMoXt27efsuOwWmvWrFnDhx9+iN9fNceeUorOnTuzcuVK3nrrLVJTU4/79Zo1a8Zll13GG2+8Qdu2bWu2vl0uF6+99hq5ubknZdgkEAiwbds2br75ZhITE3nnnXcwTZOCgoJGX/fxKikpoby8nHvvvZfY2FhmzpxJQUEBLpfL6mg1du7cSXx8PBMnTuSCCy5g3LhxbNiw4ZQf+pLiPskKCwtJS0tj8+bNbNiwgbZt27J3795T+uiHQCBQa0u4R48e/PWvf6V3797ExMT87MP57HY7l112GTk5ObRp06ZmeTAYrHfm7IYUDAb58ccf0VrzxRdfEB0dTXp6OoWFhSdl/cejtLQUm81Gfn4+69ato2XLllRWVoZVce/atYt27dqxZs0atm7dSufOnfnhhx+kuK0OcKrp3bs3u3btYs2aNZx77rmMHTuWvn37EhcXZ3U0Syil6NOnD48++igZGRmcc845LFiwgEsuueSErvQXGxvLjTfeyDvvvEOLFi3o1KkTjz32GO3btz8px3XHxcVx0UUX8frrr3P99dczevRocnNz6d27d6Ov+3h16tSJYDDI8uXLufrqqxkxYgRZWVlkZmZaHa3GZZddxsqVK8nPzycxMZG77rqLIUOGnPIzHckJOBZ48cUXKSsrY8qUKaxbt67WeOypKDExkZtuuqlmst+fDo2YpnnMsenqk3a01jXFHBcXR58+ffjuu+9QSpGcnHxSL0Q1aNAgvvnmG+69917mzJnzi2e3b0xTp07lnnvuYfz48Xz55ZfEx8dbHekIS5cuJT8/nwULFrB582aSk5OtjmQ5KW4LJCQkkJCQwLx586yOEjYcDgfNmjU76n3BYJCzzz6bdevW1VngsbGxNVuQP71ud0ZGRoPnPR4Oh4O0tDTmzp1ryfqPR1xcHHFxcSxatMjqKHVKSkqiR48eTJ8+3eooYePU/ntDRITo6GgmTpxY79Zyeno6o0aNqneyBSGaCiluERGONcShlJLZb8QpQ4pbCCEijBS3EEJEGCluIYSIMFLcQggRYaS4hRAiwkhxCyFEhDlmcSulXlVKHVBKbTls2aNKqQKl1IbQx68Pu+9hpdQOpdQ2pdQVjRVcCCFOVcezxf0acOVRls/UWvcMfSwDUEp1BYYD3ULP+YdSSg6uFSfsWBcVOtUvOiROLccsbq31l0Dpcb7edcBbWmuf1joP2AH0OYF8QgAccQ2NqKioWifl2Gw2YmJiTnYsISxxImPcE5RSm0JDKdVXz8kCfjzsMXtDy46glBqvlFqvlFofCHhOIIY4FWRmZtZcjMvhcPDUU09x//3315R3SkqKZdckEeJk+6UXmXoReBzQoc/PArf9nBfQWs8GZgMkJbXQPt8vTCJOCQ6HgzVr1hAMBlFK0bFjR/x+P6NGjUJrTWxs7Em5XKsQ4eAXFbfWuqj6tlLqZWBp6MsC4PTDHto6tEyIE2Kz2Y6Yd9LhcHDWWWdZlEgI6/yioRKlVKvDvrwBqD7i5ENguFIqRil1BtAJWHdiEYUQQhxOHWtvvFLqTeBioBlQBPy/0Nc9qRoqyQfu0FoXhh4/laphkyAwSWu9/FghUlLS9Zln3vtL/w2NzuFw0a1bMW3btrU6Sp3279/Pxo0xeL3hd7H+amlp2+nb94ywvvTq5s2b6dGjh9Ux6hQIBMjPz6dTp05WR6lTaWkpfr+fli1bWh2lTvn5+Xzf/HsCCeE71+v2GdspLy0/6vjfMYv7ZEhKytR+/zarY9QpOTmf005bxdatt1odpU5t237EP/7RnF69elkdpU5//etfGTNmDCkpKVZHqdPUqVPJycmxOkadysrKmD9/PhMnTrQ6Sp3Wr19PSUkJV1wRvqdxLFiwgAEDBoT1xljnzp05cODAUYs7TGbAUfj94bulGAiUYBgxYZ3RMOJISEgIy+mxqjkcDlJSUsI2o9Yau90etvmgKmP1zDrhKj4+HrfbHdYZY2JiSExMDOuM9e1sl1PehRAiwkhxCyFEhJHiFkKICCPFLYQQEUaKWwghIowUtxBCRBgpbiGEiDBS3EIIEWGkuIUQIsJIcQshRISR4hZCiAgjxS2EEBFGilsIISKMFLcQQkQYKW4hhIgwUtxCCBFhpLjFUVVUVLBy5UpmzJhBWVkZpmlaHakWrTVlZWXMnDmTFStWUFFRYXWkIwQCAcrKyhgzZgwFBQW4XC6rIx3B6/Vy6NAhhgwZQllZGT6fz+pIR3A6nWzZsoUHH3yQsrIyDMOwOlItWmvKy8t58803efPNNykvL6exZxaT4hZH1bt3b5YtW0bz5s3p2LEj5eXlVkeqpby8nI4dO5KRkcFHH30UllO2ff755/Tu3Zu7776bMWPGcMcdd1gd6Qg5OTlcfvnlPPnkk/Tr14/58+dbHekIV111FU8++SSXXHIJ3bp1Izc31+pItZimSadOndi7dy979+6lU6dOjb6hI8UtjrBo0SJuueUWEhISaN26NTNnzmTu3LlWx6pl7ty5TJgwgZ07d3LnnXcyduxY3nvvPatj1fB4PKxatYoRI0bw4YcfMn/+fDp27Mj69eutjlZjx44d2O12rrrqKv71r3+xcOFCCgoKOHDggNXRanz66acMGjSIDh064Ha7efHFF1m0aFFY/QX42muv8bvf/Q6n08mll17Kn/70J1577bVGXacUtzhC586d2b59O/369aNNmzZs2bKF7Oxsq2PVkp2dzY8//kj//v1JTU3l+++/p0uXLlbHqhEVFUXr1q1RStG/f38CgQCHDh2iVatWVkerkZaWhmmatGnThvPOO4+ioiKSkpKIj4+3OlqNdu3asWfPHs4//3zOPPNMtm/fTufOneudj/Fky87OJjc3l/79+9OiRQs2btzY6O8XKW5xhO7du1NQUMD8+fN57733eP/99znvvPOsjlXLeeedx5dffsn69eu55557yMvLo3v37lbHquFwOOjcuTNvvvkmLpeLoUOHopQiKyvL6mg1MjIySElJYebMmXi9XiZNmkRWVhaJiYlWR6vRsWNHXC4Xf/vb31i7di0vv/wy55xzTlgVd69evdi4cSPLly/nmWeeYc2aNY0+dBcms7yLcPPFF1/w3Xff8cMPP4TdmCJAcnIyubm5vPfee1x99dVhVdrV+vfvz9atW8nJyWHlypVhtSVb7b777uPee+9lypQpfP/991bHOaq3336bwsJClixZwrZt26yOcwSbzcaWLVv4/PPPUUoxY8aMRl+nFLeoU7du3ejWrZvVMeo1ZMgQqyMc09SpU62OUC+lFH/5y1+sjlGvVq1aceedd1odo14XX3zxSVuXDJUIIUSEkeIWQogII8UthBARRopbCCEijBS3EEJEGCluIYSIMMcsbqXU6Uqpz5RS3yulvlNK/SG0PF0p9YlSKjf0OS20XCmlnldK7VBKbVJKndvY/wghhDiVHM8WdxC4T2vdFbgA+L1SqivwELBCa90JWBH6GuBXQKfQx3jgxQZPLYQQp7BjFrfWulBr/Z/Q7UrgByALuA6YF3rYPOD60O3rgPm6yv8BqUqp8LlAgxBCRLifNcatlGoHnAOsBVporQtDd+0HWoRuZwE/Hva0vaFlP32t8Uqp9Uqp9YGA52fGFkKIU9dxF7dSKhFYBEzSWte6ar2uumr4z7pyuNZ6tta6t9a6t8MR93OeKoQQp7TjKm6llIOq0l6otX4/tLioeggk9Ln6Ir4FwOmHPb11aJkQQogGcDxHlShgDvCD1vrwy159CIwO3R4NLDls+ajQ0SUXAOWHDakIIYQ4QcdzdcCLgJHAZqXUhtCyKcCTwDtKqbHAbmBY6L5lwK+BHYAbGNOgiYUQ4hR3zOLWWn8N1HXV8sFHebwGfv/zozTu5JoNI/wzNvYkpQ0h3DOGez6QjA0lEjIejQqH4CkpabpnzxFWx6iT3e4nJcVJdHS61VHqFAxWkJoaFZYX66924MABMjIysNvtVkep0969+4iKOs3qGPUwCNj24ch0WB2kTqbbJDGYSHJystVR6lRaWkpiYiLR0dFWR6nT66+/zqFDh4660RDZX9YAACAASURBVBwWxZ2U1EI7nUVWx6hTSsoOnn76M8aNG2d1lDotXryYFi1acP755+Pz+XA4HP+bUNVmst+3m0PBIrSpiSIaUHgCbuLtyXRI7oYy7URHOzAMA6UUwWAQpRQ2m41gMEh0dHTN5+rXDwaD2O32Wo9VStU83+GoKpfqaaamTZvG73//e9LS0iz6LtVPa82wYRN5772/WR2lTjExpXT/0+V8O+Vbq6PUqeWqlswqnsV1111ndZQ6vfTSSwwePJiOHTtaHaVOLVq0oKio6KjFLTPgNDGGYVBSUkJsUjTrDi0lM7YtQZuXnc6NFPp3U+l1Uukt57S4Dnj8HjIdrcmN/YG8kh1MOH8qfl8ApRROpxOlFDExMTidTpo1a4bT6SQ9PZ3y8nLS09OpqKggISGBsrIyHA4H0dHRREdHExUVhdPpDNuCFiLSSXE3MTvKNrLo0ExUuWK/bzcOHUswqEkgjWYxWaSSRpnbhccMkB7TGkwHy3e+T1xUEo+vfIDh3cdyWvzpJCUlobUmGAySkZGBy+UiJiaG4uJiEhMTqaioIC4uDp/PR2pqKlprDMPA7XYDEB0dTUlJCampqURFya+ZEA1J3lFNTPP4try14r+kx6aT3Tyb9pld2LUvn3lfv0nHM1NonpBI7qZC7FlBLuo6AHswlrioVEori4mJT+LVdS9y1VnX0y3tbKKiHDgcDg4ePEhmZiYul4v0jAxKS0pISUmhvLychIQEKioqcDiqHpuQkIDNZsPlcpGWlobNJhegFKKhSXE3MXHEM/uqV3ng35P51/fL+XjLp8SY0bRIa4n/YAy+ymZ0ymzLvrI8jDKTNRvW0Lp7Ojv276Njhp8ydzlen0GHgV1IjYpDKUViYiJ+vx9fZSHbt35IZUUl6Zmn0az9YAzDIDY2tmYc2+/3A1UzX3u9XuLi4mruE0I0DNkcamJsNhtnpnfkkUumYotS7CzZySHPIRJjE3D73bgDLk7PPJ2zmvUk2dORdsldqdyuUX4TOz72HNjHx5tXkLN0GlC1w840TdAGBd9/zOdvTeLbZY/w7b+fRYX2a5umiWmaNYdW2Ww2tNYRe6iVEOFOiruJcTgcBPwB+rbuy6JbFtEsMQOb3U6ZtxxHdBQ+w8/3e7/jYOVBtu3Zylfr19A2vjvXthjJxhXbOK/L6cRX2nl3+bsEggEAKivKOLD7G778198oc8dw3pA5XHbbQgJG1VElfr+/5giW6p2UpmnK1rYQjUSGSpqY8vLymvHos1p2ZdXEr7nxlSEUlhQSo6OJ1jHEEsPBkoNov0mLtJYY2qDoQDHXnnsTZT+UkRJThi8ljp0/bqfLGd344oNn2PrtUk4/4yz6XTqe7n2upqKigsT4eLxeL+np6RiGQSAQwOl0orUmPj6e4uJiMjIyZOekEA1M3lFNTPXOwqioKLxeLy3iW/Lqza/yz83/5MWVL7KvtBD8mqSoJLpmdSVaRXOg7ADxUXFUVlSiDEgqb0dlchl/XjKJoR1uYscPm0ht2ZVrxv6VjBZt8Xq9xMfH4/f7cTgcuN3umuO34+KqrvRoGAZJSUmyc1KIRiDF3cRU7xAMBAI1J+F0bn4mZw66hz5Z51HkKuKJ956goHgfu4p2kh6bQTTRlBQX43MH8Do93HX9Xdx94QTK4/fy2synSDtgcN/jL5PW/HTcbjdxcXF4vV5iYmJqTsqpHueu3jlZXegxMTEWf0eEaHqkuJsY0zSJiorC7/fX2kmoNfRt35fYuFiu7HoljmgHzkon0XZFwa7tNE/JwKchPr05sdGxpKWmUVFxiG1nbGDQbVfRrlNPlFIYhoHNZsNZfJBAlJ2AYZJxWhY2m62mvIGax8oOSiEanhR3ExMbG1tzXLXP5wOouTZITEwMfr+fpNgkitevJjbgofJAEUn7dlNRdojUHueQ3PMCnPk7yPN4+HH/ATZ/tYoLzu1HoGAP+3K3EhsXR0ViGru/WsGeLRtJbN6K+PZnkpjRjKxu3WjRqXPNafApKSkyVCJEI5DibmJcLhcZGRk4nU5iY2MxTROfz4dSCo/HQ6ynkryFs0hIy8AfF09K85YkXzgQrRQK8OzdjS4vJcYMkpC3nQt9bvSKpewryEfZojgU8BOXmcWZg6+kw+Ar0IbJtlVfsn/LRvb891sqPV6un/JH0po1o7y8nIyMDClvIRqYFHcTk5ycXHWtkthY3G43NpsNh8OB1poEh50Nd48jpX0n0gZcjs0eBdrAX7Cn6sK9WmO3R5HSsQum1iSc3oGONw7HMEx87gqi4hIxtEkgEMRTXoqpwTA1rbufTSutKS8p4cPnZjDnd3cw4bXXSU1NDesrAQoRqWRTqImpqKigWbNmNYfkORwOAoEA3kMlrL39euJPy6LVr36DWVmOWV6KrixHeZ0ojxO8LrSrAqP0IMHSg5iuSoLlJRiVh1B+P/6yUgKHDhGsrCDochF0uwi4XfidlficVcMz1026D+f+Ql747Sh+3LkTwzCs/pYI0eTIFncTExsbi8vlQilFIBBAa43dbqfwn++QfnoHTrviWgLFhdhDh+/ZVGiWDKVQWmNqDVqh0GCaaA2G1gRNMEwTU2tMTehrjWFqAlpjaJOgqTBNzYXDb+GTua/y3WcrOaNzZ6u/JUI0OVLcTUx8fDyFhYWkpKTg8XiIjo7GFvBRuX0TLc7qSbB4PzabqipqG9hC5U1VVaNNE7QKlXboiBSj6tT3qqI2MU0ImCaGCUGtMUJfB7XG0Bob0K7H2axdsoT+vxlCesuW1n5ThGhipLgtorXG6XSSlJTUoK9bXl5OixYt8Hg8JCYmYpomBZ98CD4/phHA8LhQNhsoUPaq0rbbqnZMGpqqLWoTtAnaMDHNqq1wQxuYhgptfWuChknQhKBpEtAQMAwMDQGz6nbLjh3ZnZuL89ChRi1uj8dDVFRUzaQNomkyDAOv10tCQoLVUepUfRTXyTh3QYrbAps3byY/P58lS5YwdOhQevXqRbNmzRrktVNSUigqKiIpKQmXy4Xdbic+xkFltB3T78UMgrbZwAbapsCmsNltKFVV1srUYGq0qTENA7NmSCS0hW1UDY34TU3Q0FXFHdriDoS+9puhYZNgABrpOO5AIMDKlStZs2YNWVlZdO7cmQEDBjTKuoS11q1bR15eHmvXruWKK67goosuIjEx0epYNbTWrFixgk2bNgGQnZ3N4MGDG/U6PbJz0gLTp0/n66+/5pFHHuHpp59m/fr1DfbaHo+nZis+Jiam5tR30+fF9LgwPC5Mj7vqw+vG9HowPW60O/TZ4z7scR4MjxvD4yLocRPwuAl4qnZKBl1OAm4XPpcLv6sSn8uJz+XC63Ljc7nxOisxAoEG+3f9lMvl4ve//z2DBg0iNjaW8ePHN9q6hLUmT57Mvn37GD16NFOmTGHv3r1WR6rFNE3Gjh1Lx44d6dixI2PHjv3ftIGNRIr7JFu6dCm9e/dmx44dPPfcc7zyyissWrSIioqKBnl9u92O2+2umb1Ga02U3UFl7g/4SosxXC6CbidBj7uqgN1OAi43/pqjRJwE3W4Mt5OA20nA5STgqloecDrxOyvxu5z4XU58TidFW7/DU3YIr7MSr7MSj7MSr9OFp9JJoBGL+5577uHhhx/mscceo1u3bkyfPp2cnJxGW5+wxiuvvMJvf/tbPv74Yz766CNef/11cnJywupopfvuu4+//OUvPPfcc0RHR/P6669z3333Neo6ZajkJLv88su54447uP322znvvPOYMWMG11xzTYONdVcfN62UqrmWdkyz5uCIpuKHzagOndAxMWibDW1XaKXxuypRMfHgcGAEgwT8QXxeN2Vbv8MfDOINanymxhs08BomPgOSOnXHiI7GER+P1+UmqBQBQ+MzqoZM9u3ZTfnBg6hGOo572rRpjBw5krlz52Kz2bjrrrv47LPPGmVd4udrqGGCESNGcN1115GTk0ObNm144IEHuOeee8LqpK7HH3+cwYMH8+abbxIbG8tvfvMbPvnkk0ZdpxT3SRYdHc3555/P22+/TW5uLnv27GH48OEN9otefVnXyspKEhISCAaDkN2HjL6XULT8PQyPi9R2HTDi4zFsCrvSGEUFqKgYiI7GX1mOr/gAfqNqHNtnmAQNjT+oCRgGwaAmYJgUbPoGXxCimrXAFwhCQiJEx+LXirLiUnbn5nLxbeNIb9WqQf5dP5WWlkZWVhZz587l0KFD9O3bl/j4+EZZl/j5GuoaNbGxsfTv35+XX36Z9u3bEwwGadmyZVhd5z0hIYEePXowe/ZsALp169boO1GluC1w5513ctttt/Hxxx8zceLEBn3t+Ph4ysvLsdvteL1eoGor3OPzEzQ1PreLyqJ9xDfPxFNWil2b4HWD34dJ1Y5IU4cK24SAofGHdjoGzaojSgz9vx2Wrn0F+AyNxzCJyWiOy+enpOggpgnte2QT10g7keLj41mwYAHr16+nVatWZGVlNcp6hPUeeeQRysvL+c9//sODDz5odZwj2Gw25syZw9atW1FK0fkknLsgxW2R6OhorrnmmgZ/Xb/fT2JiYs0x3IZhYBgGcVlZBO0OCAZQlZXo6Gh0yUHs2kQpW9UZ74ChzaqTasyqk278psYfOmIkYEJAm6EjS0In4WiNQdUx3j6vF4/Tg6kUMYnJeH0+TNNs1D9re/fu3WivLcJHSkoKgwYNsjpGvbp06XLS1hU+A0WiwVT/mXr4n6vtR/wOW7OWuA0Dt9uLq7wcT8DAEzDxBEzcQRN3wMAdNPEENb4g+IImvqCJP1hV4AHDrPowNUbwf1vhfsPEROGqcOHxeAgGTc6+6koG3HqLVd8CIZo02eJuYqKjo/F4PNhstqrxbf43ea8ttTnBPXlobWA43dgME7vSVedMVu/MpOokHKP65JrQlrcvVNp+s2pHZSB04o3fDD0WMKgaQuly0QDs2IiPjQurnUhCNBXyrmpivF4vycnJQNWOnaioKEzTxDAM2o26C5+h8AZNPF5/1dZ2MPQRMPAGzaojRwKhz4bGZ2i8hok/aOILfQ4GNf7Q+HfQ1FXj4IEgXq8Xe2wMthgHV46/g4qKirA6bEuIpkK2uJuYpKQkiouLiY2Nxel0opTC4XBgt9s54/yLWBufiL+yHJuCKJvCZiqU0tVXdf3fae9UbXFXX4/EHyrogAF+E/ymgc+AgFH1OL+h0VEOLhw6nG3/3UDb7t1JSEiQiYKFaATH3OJWSp2ulPpMKfW9Uuo7pdQfQssfVUoVKKU2hD5+fdhzHlZK7VBKbVNKXdGY/wBRm9PpJCUlBa01sbGxOBwODMPANE3cgQCXPDe35nhst1E1tu0JmLhD49wew8ATNA7bAjfxBgz8QQN/9VCJYeIPVp/ebuAzIWiYdLmwH99+9hkTXppNdHQ0Tqez0c8gE+JUdDybQ0HgPq31f5RSScC3Sqnqo8tnaq2fOfzBSqmuwHCgG3Aa8KlS6kyttfzNfBJER0fj9XprzflYPc4cHR1NTGYLWl50CXu+WoEtdGlXRdU4t8aGRtdcytUIXco1GLqwVNU1SXTNIYJ+08RnVI13xySn4PH6Of/Xv6Zl27YYhoHD4Qir422FaCqOucWttS7UWv8ndLsS+AGo76DZ64C3tNY+rXUesAPo0xBhxbHFxsZSWVmJUgq/349pmtjt9qqLTcXHE5Wazml9LsQX1KGjSqq2rD1BXfU5dJSJJ2jiM6rGub0GoY+qrW2fUbWDsmqoxMRUUXS75FI8fj8XXns9ScnJGIZBQkKCFLcQjeBn7ZxUSrUDzgHWhhZNUEptUkq9qpRKCy3LAn487Gl7qb/oRQOqqKigefPmmKZZVdRRUQQCAQKBAIcOHSIhPp5uw0fTetDleMyqoRBXwMDlN3CHDg90h4ZKXKEC9wYMvMEgvoCBr3rHZdDEb5gYdged+w2ktLiEcy+9jKzu3SkrK8PhcFBcXCw7J4VoBMdd3EqpRGARMElrXQG8CHQAegKFwLM/Z8VKqfFKqfVKqfWBgOfnPFXUIzk5mdLSUmw2G263m0AggMPhwOFwkJqaitvtxu5w0OayXxN0xNUct+0xdNWx3Ebo66D+3xEnQRNvUOM1NJ7qMW5TQ2wsmR06oqPsuCvKyerSheSUFFJTUwkEAqSnp8uck0I0guPa5a+UclBV2gu11u8DaK2LDrv/ZWBp6MsC4PTDnt46tKwWrfVsYDZAUlILHboGuThBbreb5NBQRfUs79XHc/v9fmJjYzEMgz43DMVTWsLSRx+h9mjG/47nrjr9nZpT3IM6dBq8aaKVncTkNIiOoTAvn/FPP023/v3xeDwopYiKiqKyspLk5GQpbyEa2PEcVaKAOcAPWusZhy0//OpBNwBbQrc/BIYrpWKUUmcAnYB1DRdZ1CcuLo6Kigq01ni9XoLBIDabDZvNRkJCAl6vF601FRUVDLztDi5/5FGCdkfV1nToeG5P0MSv7HgOW+Y1TPzahjdo4AtqfCjcHi/78/cw8v/9mU7nn191JcKYGGJjYwkGgzLGLUQjOZ4t7ouAkcBmpdSG0LIpwM1KqZ5UXeIiH7gDQGv9nVLqHeB7qo5I+b0cUXLy2O12oqKiiIqKqjnlvfr24fdFRUURHRND31t/S8deF/DJiy9QUXwQqPqB9r3lVr5a+Dpag2lqouLiOb1HD35YswZTg0aR3qolt06ZQvrppxPlcNS8bvU6o6KipLiFaATHLG6t9deEJgL/iWX1PCcHkKvaW8Bms9U7DVpKSgpAzWUnMzMzyczMpNtRpv26fMztvziHzAEpROORU96FECLChMn5yJqYmFKrQ9QpOroCr9dLaWn4ZnS73TidzrDOGAgEKCsra7CL7DcOI6x/F2NiyrAH7MSUNv5M4r9UtDMat9sd1r+LXq+XioqKsM5Y3/tEhcObKD09Xd9///1Wx6iTy+Xi4MGDtGvXzuoodSosLCQmJob09HSro9Rp27ZttG/fPqyHUTZu3MjZZ59tdYw6BQIBvv56F4cONf7F+n+p2NhSzjnHR6tGmv2oIeTl5ZGZmdnoM9WciGeeeYbS0tKj7yTSWlv+kZmZqcNZbm6unj17ttUx6vXBBx/o1atXWx2jXo8//rguLS21OkadTNPUEyZMsDpGvUpKSnSvXjm66pJg4fnRsuXXevHixVZ/q+o1a9YsnZuba3WMeoV68aidKWPcQggRYaS4hRAiwkhxCyFEhJHiFkKICCPFLYQQEUaKWwghIowUtxBCRBgpbiGEiDBS3EIIEWGkuIUQIsJIcQshRISR4hZCiAgjxS2EEBFGilsIISKMFLcQQkQYKW4hhIgwTba4V69eHdZTZAWDQdatW2d1jHqVlJSQm5trdYx67dixg5KSEqtj1Oubb74hGAxaHSOiuVwuNm/ebHWMeu3du5eCgoKTsq4wmXOy4axfv54PPviA2NhY/vWvf3HxxRdz2WWXWR2rlnfffZeNGzcSHR3NkiVLGD16NGeeeabVsWp55plnqKiowGazEQgEeOSRR4iLi7M6Vg2Px8O0adNwOByYpklSUhKTJ0+2OlYtO3bsYO7cucTExLBkyRKys7MZNmyY1bEizssvv8zu3btxOBy89dZbTJo0iebNm1sdq4Zpmjz22GM1G4pKKf70pz9hszXednGT2uLWWrNhwwZM0+QPf/gDLVu25PPPPw+rLW+tNf/85z/p3r07d999N0VFRezatSvsMs6bN48bbriB3/72t3zyySe43W6rY9Xi9Xr597//zejRo7nxxhuZP39+2H0Pd+3axf79+5kwYQJnn302H374YVhljARaaxYuXMill17K+PHj2bhxI8XFxWH1fTRNk7fffpvhw4dz8803884772CaZqOus0kVd35+Pps2baKyspJf/epXjB49mtjYWFavXm11tBrvv/8+F1xwAXPnzmXy5MlMmzaNhQsXUlFRYXW0GlOnTiUnJ4fbbruN5cuX88YbbzB+/HirY9Uybtw4Jk2axI033ojP5+P5559nypQpVseqUVlZyfz588nKymLw4MEMHjyYiy66iEWLFlkdLaL87W9/48477+Shhx7ixRdf5B//+AdTp05t9GL8OcaPH8+TTz7JLbfcwvbt20/K+6VJDZWcccYZZGdns27dOv75z3/y1FNPAXDRRRdZnOx/fvOb3zBy5Eh+9atfMXz4cCZMmMDtt99OSkqK1dFqPPHEE3Tr1o0ZM2bQsmVLbrjhBr788kurY9Xyyiuv0K9fPxYuXEhRURGTJk3i+++/tzpWjeTkZEaNGsVLL73EsmXLeP3111m7di0LFy60OlpEmThxIgMHDmTSpEmcd955jBw5kpdeegm73W51tBovv/wynTt3Zt68eQAMGTKEbdu2Neo6m1RxA/Tr1w+tNX/+85/p3r07PXv2tDrSEcaOHcvWrVt5+umnueKKK+jWrZvVkY7wxz/+kS1btrBmzRomTpxIfHy81ZFqiYuL4w9/+AMffPABSUlJ/PGPf7Q60hG6du3KlVdeyTPPPEOnTp24/fbbrY4Uke6//37y8/N55ZVXGDFiBC1btrQ6Ui02m42pU6fyxRdfoJRi6tSpjTq+DU2wuLt06UKXLl3YtWsXZ5xxBkopqyMd4eKLL6Z///78+OOPtGvXzuo4RzV8+HBcLhcul4vMzEyr4xwhJiaGcePGceDAAeLj40lMTLQ60hFat27NuHHjyM/P5/TTTw+rrcRIcs011+Dz+SguLiYrK8vqOEdQSjFmzBjKysoASE1NbfR1Nrnirta+fXurI9TLbreHbWlXS0hIICEhweoY9QrH/6n8VLj/nCNBTExMWJb24U5GYVdrUjsnhRDiVCDFLYQQEeaYxa2UilVKrVNKbVRKfaeU+nNo+RlKqbVKqR1KqbeVUtGh5TGhr3eE7m/XuP8EIYQ4tRzPFrcPuERrfTbQE7hSKXUB8BQwU2vdETgEjA09fixwKLR8ZuhxQgghGsgxi1tXcYa+dIQ+NHAJ8F5o+Tzg+tDt60JfE7p/sArHQzuEECJCHdcYt1LKrpTaABwAPgF2AmVa6+or5+wFqnf5ZgE/AoTuLwcyGjK0EEKcyo6ruLXWhta6J9Aa6AN0OdEVK6XGK6XWK6XWezyeE305IYQ4Zfyso0q01mXAZ0BfIFUpVX0ceGug+nqGBcDpAKH7U4AjrruptZ6tte6tte4dTledE0KIcHc8R5U0V0qlhm7HAZcBP1BV4ENCDxsNLAnd/jD0NaH7V+pwupSXEEJEuOM5c7IVME8pZaeq6N/RWi9VSn0PvKWUmgb8F5gTevwcYIFSagdQCgxvhNxCCHHKOmZxa603AeccZfkuqsa7f7rcCwxtkHRCCCGOIGdOCiFEhJHiFkKICCPFLYQQESYsLutqmiarVq2yOkad9u/fT2FhYVhnzM/P59ChQ2E1pdNPlZaW8s0334T1pWLdbndY/5ydTiexsaW0bBm+GdPStpGfXxnW38fCwkI2bdpEUVGR1VHqVN97OSyKW2tNSckRh3qHjfLycjweT1hndLlczJ1ro7IyfDO2aePn/PMP4fV6rY5Sp0OHgowcGb7fw6goN62u/Ia4B963OkqdovOScbmGhfX7xev18kjZI3ijwvd30ad9dd4XFsVtt9u59tprrY5Rpx07dmAYRlhnNE2TAwdasH9/X6uj1CkjYxOXX345aWlpVkc5Kq01CxZ8Ql5e+P6cY2JKSW75DHnX5lkdpU4tV7WkW3G3sH6/FBYWsm/APso7llsdpU6J9rpndZIxbiGEiDBS3EIIEWGkuIUQIsJIcQshRISR4hZCiAgjxS2EEBFGilsIISKMFLcQQkQYKW4hhIgwUtxCCBFhpLiFECLCSHELIUSEkeIWQogII8UthBARRopbCCEijBS3EEJEmCZb3M8//zxaa6tj1Mnn8/Hyyy9bHSPiffrpp+zYscPqGKKRFRcX8+6771odI2w0ueJeunQpAwcOpEWLFgwaNCgsyzEnJ4drrrmG6OhoBg4cyNq1a62OFHGcTicDBw5k1apVPPHEEwwbNszqSKKR3H333dxxxx3s27ePgQMHsnv3bqsjWS4spi5rKH6/n/z8fG644QbOOeccHnvsMf7973/jcrnCZoLa8vJy9uzZw7333stZZ53FoUOHyMvLo3fv3tjtdqvjRYz8/HyaNWvGkCFDaNmyJbfeeiuFhYW0atXK6miiARUXF1NQUMCDDz5IVlYWBQUF5OXl0aZNG5RSVsezTJPa4q7+Ie/fv59nn32WTp064XA42Llzp9XRavz3v/+lffv2LF68mDfeeINBgwaxbt06PB6P1dEiyoIFC+jXrx/Tp09n//79DBs2jCVLllgdSzSwzz77jAEDBvDSSy+xfPlyrr/+ehYvXhzWw6AnQ5Pa4j7ttNPo0KED8+fP5/XXX2fcuHGcffbZZGdnWx2txsUXX8ycOXMAuOGGG7j11lvJyckhMbHuiUHFkR5++GG6devGP/7xD5YvX87s2bPZvn271bFEAxs6dCgDBw6kT58+dO3alTFjxrBkyRJstia1zfmzNaniBhgyZAj9+vVj0qRJTJ8+nebNm1sd6QhPPfUURUVFTJs2jYULF9K6dWurI0WcpKQkVqxYwaJFi+jcuTPLli2zOpJoJPPnzycvL493332XJUuWcMYZZ1gdyXJNrrhTU1NJTU3l3XffxWazheU42GmnnUarVq2YP3/+Kb/l8EvZ7Xa6dOnCww8/jFIqLH/OomG0bduWNm3aMGDAAHm/hDS54q4W7jv6pGwahryRTw3yfqntmL/1SqlYpdQ6pdRGpdR3Sqk/h5a/ppTKU0ptCH30DC1XSqnnlVI7lFKblFLnNvY/QgghTiXHs8XtAy7RWjuVUg7ga6XU8tB9k7XW7/3k8b8COoU+zgdeDH0WQgjRAI65xa2rOENfOkIf9R2Lcx0wP/S8/wNSlVJycK0QQjSQ4xogVErZEnWskwAAIABJREFUlVIbgAPAJ1rr6lP9ckLDITOVUjGhZVnAj4c9fW9omRBCiAZwXMWttTa01j2B1kAfpVR34GGgC3AekA48+HNWrJQar5Rar5RaLyefCCHE8ftZu+S11v+/vTOPs6OqEv/31tvXfr1kIwtJSIyBsCeRiCAkEMBBFmUUdYAfi6BjQAWGwDgBZUYENBBxcADZQhBBkQgCKkhAPsPIEgJkkURCSEhn6e708paq9+rVcn9/1EJ3yNKJSV4/qO/n8z5Vr+7tqtP3vXfq1LnnntMDPA+cLKXc5LpDdOA+YKrbbQMwstefjXCPbX2uu6SUk6WUkxOJxO5JHxAQEPAxpD9RJYOEEDl3PwGcCKz0/NbCidE5A1ju/skTwLludMlRQF5KuWmvSB8QEBDwMaQ/USXDgPlCiBCOov+1lPJJIcQiIcQgQABvAt9w+z8NfA5YDWjA+Xte7ICAgICPLztV3FLKpcDh2zg+fTv9JfCtf1y0gICAgIBtESw7CwgICKgzAsUdEBAQUGcEijsgICCgzggUd0BAQECdESjugICAgDpjQKR1NU2TO++8s9ZibJd8Pk9ra+uAlnHNmjWMGpWkpWVprUXZLtnsWhYsWEAsFtt55xphml1MmjRwP+dQqELDew1MunNSrUXZLslNSf5a+SubN2+utSjbZfny5RyQP4BqQ7XWomyX9833t9s2IBR3KBRixowZtRZju7S2tqIoyoCWMRwOc9RRTRx88MG1FmW73HPPWv7zP4/BMDK1FmW7nHjiEhYuHLifc6FQ4Le/bef8GdteHiGRSGyklAiEfwxAESH/2N5k6dKl9PT0cOyxx+71a+0u+XyeuVPnDujqU9OUadttGxCKWwjBuHHjai3GDnnnnXcGtIzLly9nyJAhA1rGVCpFsTgaXW+stSjbQaIo0QE9hl1dXaRSKcaMGUNnZ6dzMGFQUHtoaMjxVvvzvKQ9SbHSjW0KUkoTqq6i6SoXjv0B8UiCYekRNKaayefzRCIRSqUSLS0tbNmyhWw2i6ZptLS0oKoqoVAIwzCwLItQKISqqn5bQ0MDHR0dtLS0AB8UtWhrayMUCg3ocWxoaGDEiBGMHDmSUqlEIpFAVVUikQjhcJhyuUwmk/HbdF1HCEEkEkHTNLLZLMVikUQigWEYxGIxv4BxNBqlVCqRTqdRVZVkMolpmti2TSwWo1gskslk0DSNeDyObduYpkk4HCYej/sFI3ZUJGRAKO6AgIBdo2yWWFZ+gZKZp7Wwgs7KZuJdGYQdZrAyhuGJg/nbltcIhzJMyhyGkg7xVtdfeXL1I5y0/z8zY/9TGRIfjpSSeDyOruu+EvGUk23bvjLylIjXVwiBpmlEo1F/G41Gazkku0WpVKKhoYFSqURjYyOmaWIYBk1NTXR3d9PY2OgrYSkluq7T0tJCd3c3TU1NaJpGMpmkXC4jhMC2bf+cnZ2dNDQ0kM/nCYfDKIpCV1cXuVyOzs5OstkshUIBIQSxWIxyuUwsFutXpZ9AcQcE1CGKULjt1dsxLJ0R2RGMbRxLLJTi/kULyGaifGL/YXSuU+nUV3DopB6aooMxLJthiQNYsXkpmGEGxYZw0idOA/CVjrevKAq2baMoCqZp9rm2V0bMU+YDtbZrf0gkEpRKJcLhMIVCgVAohKIo5PN5Lr30UiZPnswll1yCpmn+/9zT00M8HqdQKBAOh6lUKoTDjipVFMW/uTU0NFCtVkmlUti2zfz583nuuee48847aWhowDAMv01K2W+lDYHiDgioS2KhJP815eec8cjptEctVoe7SIokTWJ/kpUY2to0WzaUWbm5nVhyGfHOJrqbtpAKNxFWouQLFSrVKkeNOJawjJBKpVBVFSGE8+gfkVQrKpFwCEQcW0pCoRC6rpNKpTBNk0gkgqqqZDKZulXcqqrS2NhIoVAgnU5jWRaGYZDNZnn66ad5/PHHsSyLc889l1wuh67rZLNZ3+IulUpEo1EqlQqAb3Hncjl6enpoaGhgw4YNPPfcc8yePRtd17nvvvvo6ekhm81SKjk1ajxln0gkAos7IOCjSqVSYeyg0fz6S7/mK7/5Mq+vfZ2IGaY52oSsgl21+dFXbuTlZX9lVHYUf1rxJ4aPbGTt+x3EMmk2dXRSqZr86NkbuO7UH6CqKtlsFl3XicgKD845EtusgJB84d/eIJEbim3b5HI5VFUlHA6Tz+dJJpN0d3eTTCZJJpO1HpZdJhKJYJomoVAIy7KcSd1ehYnL5TKzZ89mzpw5PPPMMxx++OG+P9o0TRRFQUrpP3V4bg8pJdFolKVLl3LyySeTz+cBJ4ggFAr5bqVIJAJ88JQTWNwBAR9hkskkHR0dDE/tx/984Q4u/fWltHe3M655PCEZwq5a/OalR0iFUpQrGtFwhLZXw3xy/8lsbH+XQnM7LcZIfvWnR5g5+mQ+96nP0dHRQTwKr//pp+RLBoNHTWb8YScgIkl0XScUCtHV1eVPTjY1NdHR0UFzc3PdWtzhcBjDMFAUBcMw/P/j3nvv9a1ogGq1yle/+lXOOecczjzzTEaPHs1NN92ElBLLsnwFHIlE+PrXv05bWxsPPfQQDz/8sK+0ASzL4q677uLrX/86tm0TDof9eYRQKNR/uffEPx8QELBv0TSNdDoNwOT4ZH51zkOc/oszWNm+ikw4Q0Ik0IVOh76FzR2b6NrSxT9NOZWW6H7YhDgkPZln3voDTbEwMSVCsVgk376a3z8xj/Z1ixk8/AiO+dJccoNHowhBKBTCtm2am5t9i7uzs5NMJlPXFne5XKapqYlCoUA2m8U0TarVKg899BDVat8Y740bN3LTTTfx1FNPkUqlWLx4MZZl9emjKApPPfUUUkreeOOND11PSsldd93F2WefTS6Xo1QqIYQgHo9TrVZ9i39nBCsnAwLqEM86k1KiCIVxTeN57hvPMW7oJyhUCqza/HcWr1vC0vVLyaSzTDloCmWjzPtt6xBhhcKGKscdcArpZJg5D87ivY2reX/1clYue51jTruGL85aQPPQsQicx3hPoXhhgUIIwuEwtm0TCoU+ZC3WiwXu3XhisRhdXV1omgaAYRh+n1tuuaXPGo7ly5fzyiuvfEhpg+PjXrJkSR+lPWTIEObPn++/D4fDDBo0CMMwaGhoIJVKAc5TVOAqCQj4CKMoCpVKBeFaw4ZhMLRhKH+85EmeWvYUTy57mr+u+D82d7ahVVU67RB6qIpdtcGEt1f9jZlTTuLYlrMYPE1w6S1fYUJHiMMmz+ATR55CMt3gK2kv6kEIQbVaJRKJYFkW0WjUn6TcWuF4j/8DHS8MsFAo0NTU5FvcnusDHCW+cOFCGhsbt6msd8aMGTP63AhM02TLli3kcjny+bxvcQfhgAEBH3EqlYrvmiiXy6RSKXp6eshkMkwfN4MvTjmLPy75I5uLm6lWqmTiacpaGb1cBSkwjzcZNWQk06dOp6mxiezmJtb/31uc+IVv0TJ4Pzo7O0mlUhiGQTgc9pW0F58cj8fp6enxF+5kMpm6jOP2wgEjEcdd5E0Q9lbQiUSC3S1ofsEFF3DzzTfzzDPP+MdCoRDZbLZPOCA4C3cCizsg4CNMMpmkUCgAzg/eW43n+WxVVeWkw08i39NDMhql3NPJ+/P/m8rqt4kPG84nv/ufVCMRQsCWzZvY/MZGYqnBjBw1jkJXF42ZDFXDYPXvH+P13yxAROJ88rQvccBx02lsbsayLFpaWiiVSjQ3N/txzPWGruuk02k0TSORSPirGOPxuN+nWq0Si8X8yJNd4fTTTwfoM9EppURVVVKplH88Go32scp3Rn2OdkDAxxxVVf3VfOVymXQ67ccNe9u2N15BtL7H2qd+TSSR4pAf3ApKBBFSsLZs5u05V2MJBbtiY7+9jMGHHMHaR+9n/YvPoxULpEeOYcIZX+Hz18/FNg3+tuhZHjz/K0QbGpl+2eWkh+7H/uPHk8/nSSQS/mRpPdHbfy+l9F08v/vd7xg6dCjFYpF169axZMmSDy1E6g+rV6/myCOPZPXq1f71zjzzTH9OoHfo4a7MCwSKOyCgDonFYn183NVqlXg8jmEYxONxtrz4J9bNncPIsy/ioKtuQAhQV72NpxukEEyacwtSQGXzJhpf/l+q1SohoTB51lUQjqCXNaplDa2zHVtK9j9yCqOOnEq+q4vfXvs9siNHcd5P5pHIZuvW4o5EIui6jqIo/lJ+IUQfC/lnP/sZP/vZz3br/FdccQUbN25k7ty5gDM38Z3vfIdYLIZt20SjUf9msStjGESVBATUIV40R+8FILZtI4Sg44U/8s687zP6q5eQHfsJ9A1r0VvXISoqoqJCRYWySvndlWjvvI1Z7GHw1Gns95nP0jBqDOWOzagb1lPp3IKpqphlDUPT0IslKoU8oVCIz55zLoX167n7X7/ph7HVI15Ypedv9hTp3Llzd9uvvTWe0gbnc5szZw75vDOOpVKJcrns50Hp7zjW520yIOBjjhfVIYTwV/JpmobobKPtdw8y6oyvEWtqwc53oqAghLsiEBCAjQTb2ceWVLUSlpSYNli2xJYSWzr7pre1JRY2hgXRWILPfPVfePynt/LfF5zPlQ/9qrYDspt4y9fj8Tjd3d1IKbn99tv5yU9+0sc10tjYSCgU6hMW2d3dvc1zNjQ0EIlE/Bupbdt+Xykld999N6FQiOuuu86PVLEsa5fCAQOLOyCgDvF82l7muXw+T66hgc3L3iDbMpRUrhm71AMVDaGXUHSNkK6i6Jrz8qzvsgqVEpRVbE1FaiUsrYSplTDVIlW1hFEqUi0VqapF9KKzrZQK2KbBiRdeRHdrK8X29loPyW5RLBbJ5XJUq1UymQx33nkn119/fZ/FNwceeCBLliyhtbWVd999l/b2dhYvXsyUKVM+dL6JEyeyaNEiWltbWbZsGa2trbz66qsceuihfh/Lsvj5z3/OzTffzMaNG1FVFXCs//5a3IHiDgioQ7yERLFYDMuynLC2fA89f/kjSiKOUeyGioYsa1BxFLWia4R1lZCuISoa6Jrfx9JUZFnDLqvYZQ1b0zA1DVMrYWgqVW+rqlTVElW1hK6WMCpVIqk0LzxcnxZ3IpFA0zTC4TBtbW1ce+21fdoPOugg7rjjDpqamnxfeKFQYNCgQcydO5fx48f7fWOxGFdeeSXjx49H13UymQyGYTBkyBDuuecepk6d2ufcc+fORVVVvyLUroQDfuQUt5c74IILLvCTlw80bNumWCxy2WWX+YltBhqWZfHaa69x9913D1gZBzred/Hb3/42hUJhj34XvSRHXqKjarVKRBFU1vyNaHMLdlnFKmuORV12/NqhSplQtYyiawi97Cjtiuq8XIvb0pytqakYmopR9pS25ihsTUVXVfRSiUqphF7RGDp6f4w95A/eFrZts27dOn74wx/u8e+iYRhEo1Fs2+Yb3/jGhxTnpk2buOqqqzjhhBOYNWuWn7/cNE0OP/xwZs6c6fedOXMmxx9/PNVqlXA4jK7rXHPNNZx88snMmjWLdevW9Tm3EIJvfetbfhjgroQafuQU9/z585kwYQKXXXYZhxxyCNdff32tRfoQF198MSeccAJf+tKXGDt2LH/+859rLdKHOOyww7jzzjvRdZ1hw4bR09NTa5HqjkWLFjF27FjOOussZs6cyUUXXbTHzu2Fr3l+VD+kzbawKxpmueQo47JjSVMuIysqlDVk2du6FrbmbM2yo7DNsoqheu4Sz8IuopeKVEsFV2mrVEolKoUCFbW0x/6vbeEpvgkTJjBy5Ej+/ve/77FzewUMQqEQ99xzD7/85S/7tHd1dfHyyy/T1dXFjTfeSCgUQtM0YrGYvzjJI5PJMGjQIJLJpD/Zee2111KpVHj55Zdpa2vrc+7bbruNxx57zI8Z771ac2d8pBR3T08PGzdu5MILL2Tp0qUsXLgQKSWtra21Fs1n5cqVtLS0cO6555LP57n99ttZunQpuq7XWjSfF154geOPP55p06Yxbdo0Zs+ezdNPP11rseoKXdd56623uOCCC3jnnXd47LHHGDx4MCtXrtwj569Wq0SjUd9VEo/HqZQrWKpGpW0jlqo6L011FHC5hKGqGCUNU9UwNdX1ZTvthqpiqk6/qlrC0JxttVTEKKlonZ2UOtpdhV10XyoVtYSuaeyt57HFixdz0EEHcdpppzF48GBuuOEGnn/++T329NI7qVMoFOLFF1/8UJ+JEyeycOFC0uk04XCYv/zlLzz44IM8++yzHHrooZx33nl87Wtf46ijjuKVV17hoYce8hNNxeNxHn/88T4+bo/XXnsN0zT9J4hdeZL4yEWV9K7OMRAf7z3rSFGUD6WSHCj0rn7S25oL2DV61w70FnfsqXGMx+O0t7cjhCCVSjl1EDNpbAmFlSsIjf8kIhEHRUGGBAg3ksQwEbE4lrQxbDBME3XjeiqqSsWyqVoS3ZTotoVuQqR5CGSyVLQyerWKMC2qbj/DllRNi3XLlzNuytSdC70b9P69eBEae/q76H3XS6USd9xxB6eddhqrVq1i1apVAH544I9//GOEEHR2dnL55Zfz6U9/mkcffZQzzzzTT896ySWX8Oijj3LLLbcATl6SOXPm9NFFw4cPZ8aMGTz44IPMnj2bZDK5y9+Nj5TFncvlGDZsGL/4xS846KCD+MIXvoCiKAOqkvOECRPo7OzkvvvuI51Oc+mll3LwwQf7ExQDgWOPPZYXX3yRl156iZdeeombb76ZU045pdZi1RWxWIxDDjmEe++9l7Fjx/LFL36Rjo4OJkyYsEfO7xXrbWhowDRNMpkMRb3KgbN/iNbVwZZlr6Pn875PuqKqaF1bKK1/D62Yp9zTQ/eSl8gveZnSujWom1rRNrWibtxAceN6iq2tFDa8z+YVb7D+5f9ly7ur0QoFSp2daMUi5WIJrVBk5Ssvo0SiHPiZY/bI/7U1Rx55JG+//TYLFy6kra2N//iP/+C4447bYSHdXSEajfo+6Xg8zquvvsq8efNobm72+6xcuZIHHniAT33qU9xwww189rOfpampyb+JeMm4vCXx6XSaz3/+89x7771MmTKFBx54gKVLl/rny+Vy3HrrrbzyyiuMGTPGT9K1KwtwPnIW93nnncc555zDxRdfzNKlS/fYB7wnueuuu9A0jX//939nzZo1A1LGN998kyVLlvDWW2+xadOmASnjQGf69OmsWbOGK664gmeffdZP37mnsCzL/1wcqzGEyDRimDaKqtL1tzdpGPdJFMskZFsIQ8fo2ACbWp1YbRsM26ZqOxZ01XSsaAs3dltCVa9SMSwq+SL6+vVULBszEiM1dD82rl1HsagxeuonmHTssXv0f+vNH/7wB1pbW1mwYAHr16/fo99Fr7Cvrus0NTXR2NjI+vXrqVQq/pMnOFb3e++9x4033siKFSt44oknuO+++5BSkkgk/PDBSZMmceWVV3L11VfzyCOPfOipX1EUyuUymzZtYuLEif4in0gkQqVS6bcB12/FLYQIAYuBDVLKU4UQY4CHgWbgdeAcKWVVCBEDHgCOBDqBL0sp1/b3Ov8o3hLge+65Z19dcpdRFIV0Os1tt91Wa1G2i6IoTJ48mcmTJ9dalLrF+y7Omzdvj5/bW6rtKW8vvWoJsONxqnoFDBO1pxvUAqJURFEECgKJxJI2tnQUt2mD4bo+nC2Yto3pLroxpcS2JZaUWDZYhkGpu4eKViYUiyPl3s2/rSgKo0aN4nvf+94eP3c6nfarsff09BCNRnn33Xf59Kc/zUknnUShUPAnMO+44w6klPz+97/35368avepVAopJVdccQULFizoo7RnzZrllzPzkoOtXr2a/fbbj2w2i2VZVKtVEolEv+XeFYv728DbQNZ9fxNwq5TyYSHEHcCFwP+4224p5TghxNluvy/vwnUCAgJ2gq7rfjSCpmkkk0knzerEg2n8zEza/vQ7bExkZydhYaOYNkIRCFdx27KXIpbS8W1bso8C95W3ZWNKMCzbWV1pSPTuPLaEUDzO56/6Nz9HSr3huZyq1SoNDQ1IKTnmmGOYPn06lUrFr0yjKArjx4/n8ssvB2DevHl897vfxTAMkskk1WrV98HfcsstvtK+7rrr+OY3v0k8HvdXucbjcSqVip/VEfCrxfc3NW6/njmEECOAfwLudt8LYDrwqNtlPnCGu3+6+x63fYYIZrYCAvYoqVSKUqnUJ5d0Q0MDugiR3X8cpg26YVPWypTLVTTLpmzaaKazLZs2FdNR1mVDOhOTtk3VllQtG0NKdFtiWhJTCqquxW3YNkoq7bgSogkM02TaiSfVZdkycNLj9h5Dz+VRKBRIJBIUCgW/uv3EiRP9vzNN068lWalUiEQifYoAe4wfP57GxkYikQiKopDNZimXyzQ0NPghg56lvSv5zPtrcc8DrgIy7vtmoEdK6S3mbwWGu/vDgfUAUkpTCJF3+2/pt1QBAQE7RNM0MplMn/18Pk8mk0EZPR5l0H5UNrdiyCohBCEFNzOgY6tJ2dfqNm3biRLxokUsC8NylHfVdZlULYlpQaW7B1vAITOOJ97UTEdHB7lczpennvDyvNi27StXcCxgrwiwlJJQKNRn8lAI4cddezlMer88vIVS3jHDMPxsjp6Ly/Oj70qI404tbiHEqUC7lPL1fp+1HwghLhZCLBZCLN5TWbgCAj4ueH7XcrnsT3h5j/X7H30c8eGjKFs2FdOmYnkWtk3FNKmYJmXTomxaH7T7StqdqLQkVYsPlLnlKG/DdlwoLaPHsGb5Ck7911lks9m6rH4DH4QCesq5d0y3l4HRC0ccM2ZMn8II3sI5z0Xi+b87OzsBp2TZpEmT/DZvJa2iKFiW1efvYM/HcR8NnCaE+BwQx/Fx/xTICSHCrtU9Atjg9t8AjARahRBhoAFnkrIPUsq7gLsAhgwZMvACrgMCBjDeD9/78XsREJ7Cmfxv1/P7f/k85XKJkBDOxKR0rG4J2IDtZQFEYppOJImjnG1MC6q2o8wN23ajTxwFHstkGTxuAoPGjaNp2DA/xroe8YoEZ7NZ8vk80WiUSCTiVxLq6uoik8mgaRq5XI5jjjmGxx9/HFVVmTVrFiNHjvQVO0Bra6ufCfDII49k2LBhfp50L6dMd3e3X1neK13mhST2l532lFJeA1wDIIQ4DrhSSvk1IcRvgLNwIkvOAx53/+QJ9/1f3fZFciCuhAkIqGMsy/J/6N4jvaZpRKNRyuUyubEHkBw1hvYVb6IIhZCf0tVGoiCFawG6k5OWLd0Uro7LxLCFb2kbtk3FclwmVdsik82hRKOMOfRQMrkchUIBRVHq0ur2sgNWKhVyuRy2bWNZFk1NTX5ZtnK5TCaTQUrpV4EH6OjooKOjY7vn9p6CvNzbiqLQ3d1NKpWiq6vL96F7bhevWHB/+EcCImcDlwshVuP4sL34u3uAZvf45cDV/8A1AgICtkEqlaJYLFIqlQiHw348sqZpNDc3o2kap9x+H7pho5sWZcNy3SPS2VZtyobjPtE9N4olKVtQMQUV06Zq2eiWc9ywbKqmRePwUYw/+hjiyRQzzz6bYrFIS0tL3U5OZjIZuru7iUajdHd3+3HVXgHkLVu2EAqFKBQKaJrGlClTGDly5E7PO3ToUI4//nj/hhCLxVAUxa8H2tLS4keyePH9uzKGu6S4pZQvSClPdffXSCmnSinHSSn/WUqpu8cr7vtxbvuaXblGQEDAzimXyySTSRKJhJ+Ev1Qq+RZePB5HhqMces5FjqK2HMWtGR/4tp3oEsvxf1uylxJ3lrXrpo3u+7sl2aHDGTt5KhvXruWE888nXyyRSCTo6enpU+qrntA0za+4ns1m/ZDGXC7nu0csyyKVShGPxzn66KOZP38+uVxuu+eMRqPcfffdHHfcccRiMYrFIoZhIKX0o1W6u7uduHu3Ag6wS2MYLIcLCKhDvOx0XpRCuVz2V/Cl02mnMEBjEy3TjkUZNIyyKdFMG81yQgI/CAuUH+xbNhXDcqxs0wkR1C2Lqi2JZhsYPG48ne1taMUSYw87jEwmg67rpFKpXcpsN5CIx+Ooqko4HEZVVT8c0LsJFotFQqEQlUrFr0k5ceJE3njjDe6//36y2SyZTIZsNks2m+XWW29l1apVTJs2jUwmQ7VaJZlMEg6H/bwylUqFTCaDaZokk8k++bj7y0duyXtAwMeB3kuxvYiI3rkzvEnLMVOnMfnci1h0648xNNX/e+kuxJHSmaS08PzdYEo3ftu2MW2beFML6SHD0MplYrE4Nz37jC9D70nReqR3eTGP3uXJerf1Tng1ePBgTjnlFN5//31M0/RXRgL+fIOXX9u2bT96pPdnBM78RO+ok/4SKO6AgDrES2zkKYNQKOQXVTAMw99Go1GOufAbWFLy5H/9ANlHQTkRJpbEien2lrVL/NWSphQoliTf3c3oYcO46Mc/RnEz4em67sck72qSpIFCb6XrrW4ExxL30uVCX2vYa+u9cKZ3SJ9hGEQiET9SxCuUAE46Xq/N+8x63yj6S+AqCQioQ7yY7Uql4if39455Vcu9R31FUZj61XM56ye3MeLwKY4/230NnzyV+JChVCzbfUnGH3scuo2zBN6GilbmiBNP4Pwf/YhkYyOxWAzbtkmn0+i6TjqdrsuIEsBXrN5iGE959la63lJ1zwL3Cih4bhUvNttLJx2JRPxizrZtEw6H/fZIJIJpmn3avBverjy11N8tMiAgAICmpibAeYRPJBIIIfxjjY2NCCHYb7/9/Pbp5/4/jvnnL2P1sgBDkQi2bWFbH1ji4WgUo1exXIBoPE40Hvetw2w2ixCC5ubmuo3hBucGGIvF+owhfOAu8dp641Vj31abx4781rvj096aQHEHBNQpvdObegpkZ9tQOt2vc8e3k4J2e+etV7xFTN5+7+NbH+tP274icJUEBAQE1BliICxqbGxslOecc06txdguuq77q6iTozRIAAAFj0lEQVQGKvl8nnA4vMeT9e9J2traaGtrQcqBG4GQy21g//2H77xjjbAsi87OTgYPHlxrUbaLqqpYlkU2m9155xrR2dlJOp0eUJWntmbBggV0d3dv06wfEIpbCNEBqAzcDIItBLLtDoFsu0cg2+7xUZNtfynloG01DAjFDSCEWCylHJDlVgLZdo9Att0jkG33+DjJFvi4AwICAuqMQHEHBAQE1BkDSXHfVWsBdkAg2+4RyLZ7BLLtHh8b2QaMjzsgICAgoH8MJIs7ICAgIKAf1FxxCyFOFkKsEkKsFkLUvOiCEGKtEGKZEOJNIcRi91iTEOJZIcQ77rZxH8lyrxCiXQixvNexbcoiHG5zx3GpEOKIGsn3fSHEBnf83nRL3nlt17jyrRJCnLQX5RophHheCPE3IcQKIcS33eM1H7sdyFbzcXOvFRdCvCqEeMuV7wfu8TFCiFdcOR4RQkTd4zH3/Wq3fXQNZLtfCPFer7E7zD1ei99ESAjxhhDiSff93hm3rasT78sXEALeBcYCUeAt4MAay7QWaNnq2M3A1e7+1cBN+0iWY4EjgOU7kwX4HPAHQABHAa/USL7v45S327rvge7nGwPGuJ97aC/JNQw4wt3PAH93r1/zsduBbDUfN/d6Aki7+xHgFXdMfg2c7R6/A/imu/+vwB3u/tnAIzWQ7X7grG30r8Vv4nLgIeBJ9/1eGbdaW9xTgdXSqaZTxalfeXqNZdoWpwPz3f35wBn74qJSyheBrn7KcjrwgHR4GaeY87AayLc9TgcellLqUsr3gNU4n//ekGuTlHKJu18E3gaGMwDGbgeybY99Nm6uTFJKWXLfRtyXBKYDj7rHtx47b0wfBWYIsXeSeOxAtu2xT38TQogRwD8Bd7vvBXtp3GqtuIcD63u9b2XHX+J9gQSeEUK8LoS42D02REq5yd3fDAypjWg7lGUgjeUs99H03l5upZrI5z6CHo5jnQ2osdtKNhgg4+Y+7r8JtAPP4lj5PVJKcxsy+PK57XmcGrT7RDYppTd2P3TH7lYhhLeOfV+P3TzgKsBLtdjMXhq3WivugchnpJRHAKcA3xJCHNu7UTrPNgMiFGcgydKL/wEOAA4DNgFzayWIECIN/Bb4jpSy0Lut1mO3DdkGzLhJKS0p5WHACBzr/pO1kmVrtpZNCDEJuAZHxilAE04h832KEOJUoF1K+fq+uF6tFfcGoHfJ5BHusZohpdzgbtuBhThf3DbvEcvdttdOwu3KMiDGUkrZ5v64bOAXfPBYv0/lE0JEcBTjL6WUj7mHB8TYbUu2gTJuvZFS9gDPA9Nw3AxeGujeMvjyue0NQOc+lO1k1/0kpVOw/D5qM3ZHA6cJIdbiuHynAz9lL41brRX3a8B4d+Y1iuOkf6JWwgghUkKIjLcPzASWuzKd53Y7D3i8NhLCDmR5AjjXnUk/Csj3cgvsM7byIZ6JM36efGe7s+ljgPHAq3tJBgHcA7wtpbylV1PNx257sg2EcXPlGCSEyLn7CeBEHD/888BZbretx84b07OARe7TzL6SbWWvm7HA8SH3Hrt98rlKKa+RUo6QUo7G0WOLpJRfY2+N296YWd2VF87M799x/Gjfq7EsY3Fm8N8CVnjy4PiengPeAf4MNO0jeX6F89hs4PjHLtyeLDgz57e747gMmFwj+Ra411/qfjmH9er/PVe+VcApe1Guz+C4QZYCb7qvzw2EsduBbDUfN/dahwBvuHIsB67t9dt4FWdy9DdAzD0ed9+vdtvH1kC2Re7YLQce5IPIk33+m3CvexwfRJXslXELVk4GBAQE1Bm1dpUEBAQEBOwigeIOCAgIqDMCxR0QEBBQZwSKOyAgIKDOCBR3QEBAQJ0RKO6AgICAOiNQ3AEBAQF1RqC4AwICAuqM/w9pIihoDh14YgAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kiini cha Q-Learning: Mlinganyo wa Bellman na Algorithimu ya Kujifunza\n",
+    "\n",
+    "Andika pseudo-code kwa algorithimu yetu ya kujifunza:\n",
+    "\n",
+    "* Anzisha Q-Table Q na namba sawa kwa hali zote na vitendo vyote\n",
+    "* Weka kiwango cha kujifunza $\\alpha\\leftarrow 1$\n",
+    "* Rudia simulizi mara nyingi\n",
+    "   1. Anza katika nafasi ya bahati nasibu\n",
+    "   1. Rudia\n",
+    "        1. Chagua kitendo $a$ katika hali $s$\n",
+    "        2. Tekeleza kitendo kwa kuhamia hali mpya $s'$\n",
+    "        3. Ikiwa tunakutana na hali ya mwisho wa mchezo, au jumla ya zawadi ni ndogo sana - toka kwenye simulizi  \n",
+    "        4. Hesabu zawadi $r$ katika hali mpya\n",
+    "        5. Sasisha Q-Function kulingana na mlinganyo wa Bellman: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Sasisha jumla ya zawadi na punguza $\\alpha$.\n",
+    "\n",
+    "## Kutumia vs. Kuchunguza\n",
+    "\n",
+    "Njia bora ni kusawazisha kati ya kuchunguza na kutumia. Tunapojifunza zaidi kuhusu mazingira yetu, tutakuwa na uwezekano mkubwa wa kufuata njia bora, lakini kuchagua njia ambayo haijachunguzwa mara moja kwa wakati.\n",
+    "\n",
+    "## Utekelezaji wa Python\n",
+    "\n",
+    "Sasa tuko tayari kutekeleza algorithimu ya kujifunza. Kabla ya hilo, tunahitaji pia kazi fulani ambayo itabadilisha namba za bahati nasibu katika Q-Table kuwa vector ya uwezekano kwa vitendo vinavyolingana:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "source": [
+    "Tunaongeza kiasi kidogo cha `eps` kwenye vector ya asili ili kuepuka kugawanya kwa 0 katika hali ya awali, ambapo vipengele vyote vya vector ni sawa.\n",
+    "\n",
+    "Algoriti halisi ya kujifunza tutakayoendesha kwa majaribio 5000, pia inaitwa **epochs**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    m.random_start()\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        dpos = actions[a]\n",
+    "        m.move(dpos,check_correctness=False) # we allow player to move outside the board, which terminates episode\n",
+    "        r = reward(m)\n",
+    "        cum_reward += r\n",
+    "        if r==end_reward or cum_reward < -1000:\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "source": [
+    "Baada ya kutekeleza algoriti hii, Jedwali la Q linapaswa kusasishwa na maadili yanayofafanua mvuto wa vitendo tofauti katika kila hatua. Onyesha jedwali hapa:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p31952a86ca)\">\n    <image height=\"327\" id=\"image3fd90cd4cb\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kukagua Sera\n",
+    "\n",
+    "Kwa kuwa Q-Table inaorodhesha \"mvuto\" wa kila kitendo katika kila hali, ni rahisi kuitumia kufafanua urambazaji bora katika ulimwengu wetu. Katika hali rahisi zaidi, tunaweza tu kuchagua kitendo kinacholingana na thamani ya juu zaidi katika Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "def qpolicy_strict(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = list(actions)[np.argmax(v)]\n",
+    "        return a\n",
+    "\n",
+    "walk(m,qpolicy_strict)"
+   ]
+  },
+  {
+   "source": [
+    "Ikiwa unajaribu msimbo hapo juu mara kadhaa, unaweza kugundua kwamba wakati mwingine unakwama tu, na unahitaji kubonyeza kitufe cha STOP kwenye daftari ili kuusimamisha. \n",
+    "\n",
+    "> **Kazi ya 1:** Badilisha kazi ya `walk` ili kuweka kikomo cha urefu wa njia kwa idadi fulani ya hatua (sema, 100), na angalia msimbo hapo juu ukirudisha thamani hii mara kwa mara.\n",
+    "\n",
+    "> **Kazi ya 2:** Badilisha kazi ya `walk` ili isirudi kwenye maeneo ambayo tayari imekuwa hapo awali. Hii itazuia `walk` kurudia, hata hivyo, wakala bado anaweza kujikuta \"amekwama\" katika eneo ambalo hawezi kutoroka.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 3.45, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbab852fd68>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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133.949763 214.756364 \nL 133.980202 214.756364 \nL 134.132399 206.226264 \nL 134.254157 214.756364 \nL 134.284596 214.685867 \nL 134.315036 198.00165 \nL 134.345475 214.756364 \nL 134.375914 199.106098 \nL 134.436793 214.756364 \nL 134.528111 214.497876 \nL 134.558551 214.309885 \nL 134.58899 214.756364 \nL 134.649869 214.685867 \nL 134.680308 213.252434 \nL 134.710748 214.756364 \nL 134.741187 214.756364 \nL 134.802066 212.946949 \nL 134.832506 214.756364 \nL 134.954263 193.184376 \nL 135.076021 214.756364 \nL 135.10646 212.594465 \nL 135.167339 214.756364 \nL 135.289097 214.756364 \nL 135.502172 214.756364 \nL 135.532612 206.766738 \nL 135.593491 214.756364 \nL 135.62393 214.662368 \nL 135.65437 214.756364 \nL 135.745688 195.722257 \nL 135.806567 214.756364 \nL 135.867445 203.923372 \nL 135.989203 214.756364 \nL 136.050082 213.111441 \nL 136.080521 214.756364 \nL 136.110961 214.756364 \nL 136.202279 206.837235 \nL 136.293597 214.756364 \nL 136.324036 213.510922 \nL 136.384915 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138.150401 209.375116 \nL 138.21128 214.756364 \nL 138.363477 206.672743 \nL 138.454795 214.756364 \nL 138.485234 205.967776 \nL 138.546113 210.338571 \nL 138.576553 214.732865 \nL 138.637431 211.41952 \nL 138.667871 207.659697 \nL 138.69831 213.604918 \nL 138.789629 213.299432 \nL 138.820068 214.756364 \nL 138.911386 201.737974 \nL 139.002704 214.756364 \nL 139.033144 213.322931 \nL 139.094023 214.756364 \nL 139.124462 210.033085 \nL 139.21578 210.503063 \nL 139.276659 209.7276 \nL 139.337538 214.756364 \nL 139.398417 212.92345 \nL 139.428856 214.756364 \nL 139.459295 214.756364 \nL 139.520174 204.863328 \nL 139.611493 211.114034 \nL 139.641932 211.255028 \nL 139.672371 210.738052 \nL 139.702811 214.756364 \nL 139.794129 213.957401 \nL 139.824568 214.756364 \nL 139.855008 213.581419 \nL 139.885447 207.119222 \nL 139.915887 214.756364 \nL 139.946326 214.004399 \nL 140.037644 211.325525 \nL 140.128962 214.756364 \nL 140.159402 207.260215 \nL 140.220281 214.380381 \nL 140.25072 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147.221344 214.591871 \nL 147.312663 210.57356 \nL 147.373541 211.231529 \nL 147.403981 214.756364 \nL 147.495299 214.19239 \nL 147.525738 214.732865 \nL 147.556178 208.317666 \nL 147.617057 214.262887 \nL 147.677936 213.816408 \nL 147.799693 214.756364 \nL 147.95189 201.502985 \nL 148.043208 214.756364 \nL 148.073648 211.513516 \nL 148.104087 210.78505 \nL 148.134527 197.79016 \nL 148.164966 214.756364 \nL 148.195405 214.568372 \nL 148.225845 214.756364 \nL 148.347602 211.8425 \nL 148.438921 214.756364 \nL 148.46936 209.093129 \nL 148.530239 212.617964 \nL 148.651997 214.756364 \nL 148.682436 201.549983 \nL 148.743315 214.756364 \nL 148.773754 213.698913 \nL 148.804194 214.756364 \nL 148.834633 202.724928 \nL 148.895512 212.335977 \nL 149.017269 214.756364 \nL 149.047709 214.662368 \nL 149.078148 213.698913 \nL 149.108588 207.213217 \nL 149.169466 214.027898 \nL 149.199906 211.137533 \nL 149.291224 208.787644 \nL 149.321664 214.756364 \nL 149.352103 214.756364 \nL 149.382542 188.484596 \nL 149.443421 206.860734 \nL 149.534739 214.756364 \nL 149.565179 207.401209 \nL 149.626058 214.756364 \nL 149.656497 212.547467 \nL 149.717376 214.756364 \nL 149.778255 214.756364 \nL 149.869573 212.312478 \nL 149.900012 214.262887 \nL 149.930452 207.518703 \nL 149.99133 214.685867 \nL 150.02177 208.740646 \nL 150.052209 206.226264 \nL 150.082649 209.7276 \nL 150.234846 214.756364 \nL 150.265285 201.573482 \nL 150.326164 213.9809 \nL 150.356603 213.792909 \nL 150.387043 214.756364 \nL 150.417482 209.539608 \nL 150.478361 213.651915 \nL 150.5088 213.487423 \nL 150.53924 214.756364 \nL 150.569679 208.811143 \nL 150.630558 214.051397 \nL 150.660997 214.756364 \nL 150.691437 207.659697 \nL 150.752316 214.756364 \nL 150.843634 212.852953 \nL 150.904513 213.628417 \nL 150.934952 214.756364 \nL 151.02627 208.388162 \nL 151.05671 209.116628 \nL 151.117589 214.756364 \nL 151.148028 213.463924 \nL 151.178467 204.275855 \nL 151.239346 214.732865 \nL 151.269786 208.01218 \nL 151.300225 211.889498 \nL 151.330664 190.435005 \nL 151.361104 214.756364 \nL 151.391543 211.748505 \nL 151.421983 212.429973 \nL 151.452422 204.322853 \nL 151.482861 214.756364 \nL 151.513301 207.80069 \nL 151.665498 214.756364 \nL 151.726377 208.975635 \nL 151.756816 210.15058 \nL 151.787256 214.756364 \nL 151.817695 211.748505 \nL 151.848134 206.155767 \nL 151.909013 214.521375 \nL 151.969892 214.756364 \nL 152.000331 214.145392 \nL 152.06121 214.756364 \nL 152.182968 197.625667 \nL 152.243847 214.756364 \nL 152.304725 213.181937 \nL 152.335165 212.71196 \nL 152.365604 214.756364 \nL 152.426483 205.897279 \nL 152.456923 214.756364 \nL 152.548241 208.458659 \nL 152.57868 213.111441 \nL 152.60912 214.756364 \nL 152.700438 214.19239 \nL 152.730877 214.215889 \nL 152.822195 214.756364 \nL 152.852635 208.364663 \nL 152.913514 214.121893 \nL 152.943953 184.654276 \nL 153.065711 214.756364 \nL 153.09615 210.926043 \nL 153.157029 214.756364 \nL 153.187468 214.756364 \nL 153.248347 206.320259 \nL 153.309226 207.706694 \nL 153.370105 214.756364 \nL 153.430984 213.158439 \nL 153.461423 213.816408 \nL 153.491862 207.518703 \nL 153.522302 214.756364 \nL 153.552741 212.617964 \nL 153.644059 214.756364 \nL 153.674499 214.356882 \nL 153.704938 214.756364 \nL 153.735378 213.839907 \nL 153.765817 207.542202 \nL 153.796256 214.756364 \nL 153.826696 214.239388 \nL 153.887575 214.756364 \nL 153.918014 204.839829 \nL 153.978893 213.792909 \nL 154.039772 214.756364 \nL 154.070211 213.322931 \nL 154.100651 188.790082 \nL 154.161529 214.215889 \nL 154.191969 214.756364 \nL 154.222408 200.11655 \nL 154.283287 202.630932 \nL 154.405045 214.756364 \nL 154.435484 214.756364 \nL 154.465923 214.591871 \nL 154.496363 208.975635 \nL 154.526802 214.756364 \nL 154.557242 214.756364 \nL 154.587681 214.756364 \nL 154.678999 213.698913 \nL 154.739878 214.756364 \nL 154.770317 214.145392 \nL 154.800757 213.13494 \nL 154.831196 214.756364 \nL 154.861636 214.756364 \nL 154.892075 205.02782 \nL 154.952954 214.756364 \nL 155.013833 213.534421 \nL 155.044272 206.461253 \nL 155.105151 214.756364 \nL 155.16603 214.756364 \nL 155.196469 204.487345 \nL 155.257348 214.756364 \nL 155.379106 213.228935 \nL 155.439984 211.959995 \nL 155.561742 214.756364 \nL 155.622621 214.756364 \nL 155.713939 205.215811 \nL 155.805257 214.756364 \nL 155.835697 202.865921 \nL 155.896576 212.641463 \nL 156.018333 214.756364 \nL 156.048773 213.322931 \nL 156.109651 214.756364 \nL 156.140091 196.356727 \nL 156.261848 214.756364 \nL 156.322727 207.777191 \nL 156.353167 213.040944 \nL 156.414045 214.756364 \nL 156.566243 204.017367 \nL 156.657561 214.756364 \nL 156.688 214.239388 \nL 156.748879 214.756364 \nL 156.779318 208.035679 \nL 156.840197 207.80069 \nL 156.870637 214.756364 \nL 156.961955 202.912919 \nL 157.022834 214.756364 \nL 157.083712 214.356882 \nL 157.175031 200.539531 \nL 157.23591 214.756364 \nL 157.296788 207.847688 \nL 157.357667 214.756364 \nL 157.388107 204.463846 \nL 157.418546 211.278527 \nL 157.448985 210.103582 \nL 157.479425 213.816408 \nL 157.570743 213.534421 \nL 157.631622 214.756364 \nL 157.692501 207.988681 \nL 157.753379 214.756364 \nL 157.814258 214.521375 \nL 157.844698 214.756364 \nL 157.996895 205.403802 \nL 158.027334 205.521297 \nL 158.149092 214.732865 \nL 158.179531 214.756364 \nL 158.209971 204.510844 \nL 158.270849 211.983494 \nL 158.392607 214.756364 \nL 158.453486 214.756364 \nL 158.483925 212.970447 \nL 158.544804 213.416926 \nL 158.605683 214.756364 \nL 158.666562 210.503063 \nL 158.697001 214.756364 \nL 158.72744 213.816408 \nL 158.788319 214.756364 \nL 158.818759 214.756364 \nL 158.940516 210.832048 \nL 158.970956 211.372522 \nL 159.092713 192.620402 \nL 159.031835 213.76941 \nL 159.123153 200.32804 \nL 159.24491 214.756364 \nL 159.366668 212.194984 \nL 159.397107 212.805955 \nL 159.427547 212.335977 \nL 159.457986 214.756364 \nL 159.518865 212.852953 \nL 159.549304 212.429973 \nL 159.640623 214.756364 \nL 159.671062 214.709366 \nL 159.701502 213.675414 \nL 159.731941 214.756364 \nL 159.76238 214.685867 \nL 159.79282 202.25495 \nL 159.853699 210.855547 \nL 159.975456 214.756364 \nL 160.066774 211.865999 \nL 160.158093 214.756364 \nL 160.27985 211.325525 \nL 160.340729 207.565701 \nL 160.371168 214.756364 \nL 160.492926 200.657025 \nL 160.584244 214.168891 \nL 160.614684 209.28112 \nL 160.736441 214.732865 \nL 160.675563 208.670149 \nL 160.766881 214.568372 \nL 160.858199 214.756364 \nL 160.888638 197.155689 \nL 161.010396 214.756364 \nL 161.071275 214.756364 \nL 161.101714 208.905138 \nL 161.162593 214.756364 \nL 161.284351 211.678008 \nL 161.375669 211.231529 \nL 161.406108 214.756364 \nL 161.527866 209.892092 \nL 161.588745 208.411661 \nL 161.649624 214.756364 \nL 161.740942 213.416926 \nL 161.771381 214.756364 \nL 161.801821 213.440425 \nL 161.83226 207.142721 \nL 161.893139 214.756364 \nL 162.045336 214.756364 \nL 162.167094 208.153173 \nL 162.319291 214.662368 \nL 162.410609 214.756364 \nL 162.441048 205.192312 \nL 162.593245 214.756364 \nL 162.745442 205.591794 \nL 162.775882 214.756364 \nL 162.8672 212.899951 \nL 162.958518 214.756364 \nL 162.928079 212.171485 \nL 162.988958 213.675414 \nL 163.049836 214.756364 \nL 163.080276 213.55792 \nL 163.110715 210.949542 \nL 163.171594 214.756364 \nL 163.202033 214.662368 \nL 163.232473 213.040944 \nL 163.262912 197.696164 \nL 163.323791 214.756364 \nL 163.35423 208.693648 \nL 163.415109 213.910403 \nL 163.445549 214.756364 \nL 163.567306 207.119222 \nL 163.719503 214.756364 \nL 163.749943 214.638869 \nL 163.780382 208.059178 \nL 163.841261 214.756364 \nL 163.8717 212.852953 \nL 163.90214 214.756364 \nL 163.932579 199.670071 \nL 163.993458 209.234123 \nL 164.023897 213.76941 \nL 164.115216 212.476971 \nL 164.145655 214.61537 \nL 164.176094 211.63101 \nL 164.206534 212.735458 \nL 164.236973 212.641463 \nL 164.328291 214.756364 \nL 164.358731 213.510922 \nL 164.38917 214.40388 \nL 164.480489 209.915591 \nL 164.510928 211.725006 \nL 164.541367 214.40388 \nL 164.571807 207.424708 \nL 164.602246 212.735458 \nL 164.663125 209.915591 \nL 164.693564 212.71196 \nL 164.724004 212.970447 \nL 164.784883 211.255028 \nL 164.845761 214.756364 \nL 164.876201 214.756364 \nL 165.028398 193.983338 \nL 165.119716 214.756364 \nL 165.150155 213.393428 \nL 165.180595 211.889498 \nL 165.211034 213.487423 \nL 165.241474 214.756364 \nL 165.271913 207.683195 \nL 165.363231 212.147986 \nL 165.393671 214.756364 \nL 165.42411 200.915513 \nL 165.484989 214.756364 \nL 165.515428 214.756364 \nL 165.576307 210.291573 \nL 165.637186 212.382975 \nL 165.758944 214.756364 \nL 165.819822 212.241982 \nL 165.850262 214.756364 \nL 165.880701 214.756364 \nL 165.911141 213.581419 \nL 165.972019 214.662368 \nL 166.032898 214.756364 \nL 166.063338 214.309885 \nL 166.124217 214.756364 \nL 166.154656 214.61537 \nL 166.185095 214.756364 \nL 166.215535 212.006993 \nL 166.276414 213.487423 \nL 166.306853 214.756364 \nL 166.337292 211.912997 \nL 166.367732 214.239388 \nL 166.428611 214.709366 \nL 166.45905 206.273262 \nL 166.580808 214.756364 \nL 166.611247 212.288979 \nL 166.672126 213.55792 \nL 166.733005 214.756364 \nL 166.885202 209.445613 \nL 167.037399 214.756364 \nL 167.159156 212.500469 \nL 167.220035 214.756364 \nL 167.250475 204.957323 \nL 167.311353 214.756364 \nL 167.433111 212.547467 \nL 167.46355 214.40388 \nL 167.49399 207.80069 \nL 167.585308 207.965182 \nL 167.615747 214.756364 \nL 167.646187 195.22878 \nL 167.707066 214.756364 \nL 167.767945 214.756364 \nL 167.798384 211.865999 \nL 167.859263 214.098395 \nL 167.920142 214.756364 \nL 167.950581 213.299432 \nL 168.01146 214.756364 \nL 168.041899 214.732865 \nL 168.072339 214.333383 \nL 168.163657 193.818846 \nL 168.285414 214.756364 \nL 168.315854 208.787644 \nL 168.376733 211.184531 \nL 168.407172 214.756364 \nL 168.437611 214.450878 \nL 168.468051 203.429895 \nL 168.52893 214.756364 \nL 168.589809 212.100988 \nL 168.620248 212.147986 \nL 168.650687 214.756364 \nL 168.681127 211.513516 \nL 168.742006 213.463924 \nL 168.772445 210.526562 \nL 168.802884 184.325291 \nL 168.863763 214.685867 \nL 168.924642 214.756364 \nL 168.955081 214.027898 \nL 168.985521 209.351617 \nL 169.0464 214.756364 \nL 169.076839 214.756364 \nL 169.107278 207.612699 \nL 169.168157 214.591871 \nL 169.198597 213.628417 \nL 169.229036 214.756364 \nL 169.259475 214.756364 \nL 169.320354 214.027898 \nL 169.442112 200.398537 \nL 169.381233 214.756364 \nL 169.472551 206.132268 \nL 169.56387 214.756364 \nL 169.685627 200.586528 \nL 169.807385 214.756364 \nL 169.868264 214.756364 \nL 169.898703 214.568372 \nL 169.929142 207.824189 \nL 169.959582 214.756364 \nL 169.990021 214.756364 \nL 170.0509 211.396021 \nL 170.081339 214.756364 \nL 170.233537 195.886749 \nL 170.263976 214.756364 \nL 170.355294 213.228935 \nL 170.416173 212.26548 \nL 170.446612 213.76941 \nL 170.477052 213.510922 \nL 170.56837 200.563029 \nL 170.629249 203.782378 \nL 170.751006 214.756364 \nL 170.842325 206.907732 \nL 170.872764 207.824189 \nL 170.994522 214.756364 \nL 171.024961 207.753692 \nL 171.08584 214.662368 \nL 171.116279 214.756364 \nL 171.268476 212.71196 \nL 171.298916 214.638869 \nL 171.329355 210.738052 \nL 171.359795 209.351617 \nL 171.481552 214.709366 \nL 171.511992 214.756364 \nL 171.542431 214.286386 \nL 171.57287 206.884233 \nL 171.60331 214.756364 \nL 171.664189 209.116628 \nL 171.755507 214.756364 \nL 171.785946 214.286386 \nL 171.877265 214.756364 \nL 171.907704 212.124487 \nL 171.938143 214.756364 \nL 171.968583 206.437754 \nL 172.059901 211.396021 \nL 172.09034 213.651915 \nL 172.12078 213.534421 \nL 172.151219 205.756286 \nL 172.181659 214.756364 \nL 172.212098 214.756364 \nL 172.272977 214.756364 \nL 172.303416 213.252434 \nL 172.364295 214.333383 \nL 172.394734 214.756364 \nL 172.486053 208.43516 \nL 172.577371 206.343758 \nL 172.60781 214.756364 \nL 172.63825 191.680446 \nL 172.699129 214.756364 \nL 172.729568 214.333383 \nL 172.760007 214.756364 \nL 172.790447 211.466518 \nL 172.851326 214.756364 \nL 172.912204 214.756364 \nL 172.942644 208.623151 \nL 173.033962 211.772004 \nL 173.12528 214.756364 \nL 173.15572 212.030492 \nL 173.216598 214.756364 \nL 173.338356 214.709366 \nL 173.368796 202.724928 \nL 173.429674 214.756364 \nL 173.460114 214.756364 \nL 173.581871 205.709288 \nL 173.703629 214.756364 \nL 173.734068 214.756364 \nL 173.764508 214.544874 \nL 173.855826 214.662368 \nL 173.977584 214.756364 \nL 174.129781 210.855547 \nL 174.16022 204.134862 \nL 174.19066 214.638869 \nL 174.221099 214.215889 \nL 174.251538 210.620558 \nL 174.281978 214.756364 \nL 174.312417 214.756364 \nL 174.342857 214.756364 \nL 174.464614 204.745833 \nL 174.495054 207.871187 \nL 174.616811 214.756364 \nL 174.647251 214.709366 \nL 174.67769 214.145392 \nL 174.708129 214.709366 \nL 174.769008 203.429895 \nL 174.799448 214.756364 \nL 174.890766 214.756364 \nL 174.982084 213.416926 \nL 175.012524 214.756364 \nL 175.042963 211.560514 \nL 175.073402 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177.112843 214.756364 \nL 177.143282 214.027898 \nL 177.173721 208.270668 \nL 177.2346 208.811143 \nL 177.295479 214.756364 \nL 177.356358 212.241982 \nL 177.417237 214.756364 \nL 177.447676 214.568372 \nL 177.508555 208.106176 \nL 177.538994 214.756364 \nL 177.569434 212.359476 \nL 177.599873 212.335977 \nL 177.660752 214.756364 \nL 177.691191 200.022555 \nL 177.78251 214.756364 \nL 177.812949 213.087942 \nL 177.843388 214.756364 \nL 177.873828 214.638869 \nL 177.904267 196.427223 \nL 177.965146 214.756364 \nL 177.995585 208.599652 \nL 178.056464 209.892092 \nL 178.178222 214.756364 \nL 178.208661 214.756364 \nL 178.29998 208.482158 \nL 178.421737 214.756364 \nL 178.482616 211.443019 \nL 178.513055 214.756364 \nL 178.573934 212.030492 \nL 178.634813 213.158439 \nL 178.665252 214.756364 \nL 178.756571 205.380303 \nL 178.847889 214.756364 \nL 178.878328 207.683195 \nL 178.939207 214.756364 \nL 178.969647 214.756364 \nL 179.030525 210.879046 \nL 179.060965 214.19239 \nL 179.121844 212.993946 \nL 179.182722 210.78505 \nL 179.213162 214.756364 \nL 179.243601 210.432566 \nL 179.274041 202.395943 \nL 179.30448 214.756364 \nL 179.334919 214.756364 \nL 179.365359 214.756364 \nL 179.395798 208.129674 \nL 179.456677 214.215889 \nL 179.487116 214.756364 \nL 179.517556 211.114034 \nL 179.578435 214.756364 \nL 179.608874 214.756364 \nL 179.669753 196.544718 \nL 179.730632 212.05399 \nL 179.791511 214.732865 \nL 179.82195 214.168891 \nL 179.852389 200.821517 \nL 179.913268 214.756364 \nL 180.035026 208.576154 \nL 180.126344 214.756364 \nL 180.156783 214.732865 \nL 180.187223 214.427379 \nL 180.217662 214.756364 \nL 180.278541 203.735381 \nL 180.30898 213.158439 \nL 180.369859 214.756364 \nL 180.400299 214.239388 \nL 180.430738 204.769332 \nL 180.491617 213.933902 \nL 180.552496 214.756364 \nL 180.674253 205.920778 \nL 180.735132 214.756364 \nL 180.765572 211.748505 \nL 180.796011 204.275855 \nL 180.82645 214.756364 \nL 180.85689 211.278527 \nL 180.887329 213.910403 \nL 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204.299354 \nL 187.066529 210.879046 \nL 187.218726 214.756364 \nL 187.340484 211.748505 \nL 187.370923 214.380381 \nL 187.431802 201.808471 \nL 187.462241 214.756364 \nL 187.492681 211.137533 \nL 187.52312 214.756364 \nL 187.553559 209.06963 \nL 187.614438 214.709366 \nL 187.644878 195.369773 \nL 187.736196 196.403725 \nL 187.827514 214.756364 \nL 187.857954 213.675414 \nL 187.888393 214.756364 \nL 188.010151 209.892092 \nL 188.04059 214.544874 \nL 188.101469 187.732631 \nL 188.131908 214.756364 \nL 188.162348 213.017445 \nL 188.192787 202.88942 \nL 188.223226 214.427379 \nL 188.253666 213.158439 \nL 188.344984 214.756364 \nL 188.405863 200.069553 \nL 188.466742 212.359476 \nL 188.52762 214.756364 \nL 188.588499 187.591638 \nL 188.679818 214.756364 \nL 188.710257 214.380381 \nL 188.771136 211.63101 \nL 188.801575 214.286386 \nL 188.832015 214.756364 \nL 188.862454 214.732865 \nL 188.923333 214.756364 \nL 188.984212 202.771926 \nL 189.07553 214.756364 \nL 189.105969 211.278527 \nL 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214.756364 \nL 191.023652 214.356882 \nL 191.054091 201.385491 \nL 191.084531 214.568372 \nL 191.14541 208.64665 \nL 191.267167 214.732865 \nL 191.328046 211.959995 \nL 191.358485 214.756364 \nL 191.388925 214.427379 \nL 191.449804 214.638869 \nL 191.541122 203.594387 \nL 191.571561 213.675414 \nL 191.63244 202.701429 \nL 191.662879 198.283636 \nL 191.784637 214.732865 \nL 191.815077 208.200171 \nL 191.845516 214.756364 \nL 191.906395 210.291573 \nL 191.936834 214.756364 \nL 191.967274 210.174079 \nL 191.997713 199.294089 \nL 192.058592 214.756364 \nL 192.119471 213.205436 \nL 192.14991 214.756364 \nL 192.210789 214.309885 \nL 192.241228 214.756364 \nL 192.271668 203.805877 \nL 192.332546 210.197577 \nL 192.454304 214.756364 \nL 192.606501 208.928637 \nL 192.728259 214.756364 \nL 192.789138 214.286386 \nL 192.819577 201.1975 \nL 192.850016 214.756364 \nL 192.880456 213.651915 \nL 192.941335 211.231529 \nL 192.971774 212.26548 \nL 193.032653 214.756364 \nL 193.15441 190.622996 \nL 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209.774597 \nL 195.467805 208.881639 \nL 195.498245 214.756364 \nL 195.528684 205.944277 \nL 195.589563 214.638869 \nL 195.680881 206.508251 \nL 195.711321 214.756364 \nL 195.802639 211.490017 \nL 195.833078 214.756364 \nL 195.863518 188.837079 \nL 195.924397 214.544874 \nL 196.046154 181.411428 \nL 196.167912 214.756364 \nL 196.289669 200.281043 \nL 196.380988 214.756364 \nL 196.350548 179.696008 \nL 196.411427 213.181937 \nL 196.441866 211.114034 \nL 196.472306 214.756364 \nL 196.502745 212.641463 \nL 196.533185 212.547467 \nL 196.563624 213.76941 \nL 196.594063 211.983494 \nL 196.624503 210.385569 \nL 196.685382 214.756364 \nL 196.746261 213.393428 \nL 196.807139 214.756364 \nL 196.837579 214.568372 \nL 196.898458 214.756364 \nL 196.928897 209.304619 \nL 197.020215 214.756364 \nL 197.050655 212.171485 \nL 197.141973 214.756364 \nL 197.172412 183.526329 \nL 197.29417 214.756364 \nL 197.415928 213.675414 \nL 197.476806 214.756364 \nL 197.507246 214.333383 \nL 197.568125 205.051319 \nL 197.598564 206.555248 \nL 197.629003 214.756364 \nL 197.689882 207.260215 \nL 197.720322 196.803206 \nL 197.750761 214.756364 \nL 197.7812 214.756364 \nL 197.81164 211.302026 \nL 197.872519 214.756364 \nL 197.902958 214.756364 \nL 197.933397 175.842189 \nL 197.994276 213.487423 \nL 198.055155 212.382975 \nL 198.116034 214.756364 \nL 198.176913 187.168658 \nL 198.237792 204.910326 \nL 198.268231 214.756364 \nL 198.32911 206.061771 \nL 198.359549 203.594387 \nL 198.389989 186.111207 \nL 198.450867 214.756364 \nL 198.542186 202.654431 \nL 198.572625 213.55792 \nL 198.603064 213.463924 \nL 198.633504 214.756364 \nL 198.663943 207.542202 \nL 198.724822 214.568372 \nL 198.81614 214.756364 \nL 198.877019 206.320259 \nL 198.907458 214.521375 \nL 198.998777 213.886904 \nL 199.029216 200.516032 \nL 199.090095 201.291495 \nL 199.181413 214.756364 \nL 199.211853 213.087942 \nL 199.242292 214.756364 \nL 199.272731 213.863406 \nL 199.303171 189.753537 \nL 199.394489 205.004321 \nL 199.485807 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205.02782 \nL 204.325673 202.536937 \nL 204.447431 214.756364 \nL 204.47787 214.756364 \nL 204.508309 211.114034 \nL 204.569188 212.664962 \nL 204.660507 214.756364 \nL 204.751825 206.484752 \nL 204.843143 214.756364 \nL 204.873582 213.322931 \nL 204.904022 211.20803 \nL 204.934461 214.756364 \nL 204.964901 183.338337 \nL 205.025779 214.756364 \nL 205.056219 214.521375 \nL 205.117098 214.756364 \nL 205.147537 201.455988 \nL 205.208416 214.756364 \nL 205.360613 214.756364 \nL 205.391052 199.0591 \nL 205.451931 214.756364 \nL 205.482371 213.722412 \nL 205.543249 214.709366 \nL 205.573689 214.756364 \nL 205.665007 207.448206 \nL 205.695446 189.988525 \nL 205.756325 212.617964 \nL 205.786765 214.756364 \nL 205.817204 203.852875 \nL 205.878083 211.443019 \nL 205.938962 214.756364 \nL 205.99984 214.662368 \nL 206.03028 214.756364 \nL 206.152037 191.492455 \nL 206.273795 214.756364 \nL 206.304235 197.625667 \nL 206.365113 214.685867 \nL 206.395553 214.544874 \nL 206.425992 208.411661 \nL 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209.657103 \nL 208.404554 214.756364 \nL 208.434993 210.268074 \nL 208.465432 207.941683 \nL 208.495872 213.416926 \nL 208.556751 214.756364 \nL 208.58719 203.500392 \nL 208.678508 207.354211 \nL 208.708948 206.29676 \nL 208.800266 213.463924 \nL 208.830705 212.429973 \nL 208.861145 203.946871 \nL 208.891584 214.732865 \nL 208.952463 208.317666 \nL 208.982902 213.792909 \nL 209.013342 207.777191 \nL 209.043781 212.899951 \nL 209.135099 214.756364 \nL 209.165539 194.688305 \nL 209.226418 214.756364 \nL 209.287296 212.899951 \nL 209.317736 214.756364 \nL 209.348175 209.586606 \nL 209.409054 213.463924 \nL 209.439493 214.756364 \nL 209.469933 201.573482 \nL 209.530812 213.534421 \nL 209.561251 214.286386 \nL 209.591691 204.064365 \nL 209.652569 209.774597 \nL 209.713448 207.424708 \nL 209.774327 214.756364 \nL 209.804766 206.578747 \nL 209.865645 214.450878 \nL 209.896085 214.756364 \nL 209.987403 208.670149 \nL 210.017842 214.756364 \nL 210.10916 214.215889 \nL 210.1396 208.082677 \nL 210.200479 212.312478 \nL 210.261357 214.756364 \nL 210.291797 213.628417 \nL 210.322236 197.625667 \nL 210.352676 214.756364 \nL 210.383115 214.756364 \nL 210.413555 208.22367 \nL 210.474433 214.544874 \nL 210.504873 214.756364 \nL 210.535312 214.121893 \nL 210.596191 208.85814 \nL 210.62663 210.244575 \nL 210.717949 214.756364 \nL 210.748388 193.066881 \nL 210.809267 214.756364 \nL 210.931024 206.108769 \nL 211.052782 214.756364 \nL 211.113661 214.756364 \nL 211.204979 209.328118 \nL 211.296297 214.756364 \nL 211.326737 209.022633 \nL 211.418055 210.244575 \nL 211.448494 211.701507 \nL 211.509373 213.275933 \nL 211.539813 189.965027 \nL 211.570252 214.756364 \nL 211.66157 201.573482 \nL 211.69201 214.732865 \nL 211.722449 191.915435 \nL 211.752888 211.396021 \nL 211.813767 195.722257 \nL 211.844207 214.756364 \nL 211.874646 214.40388 \nL 211.905086 206.014774 \nL 211.935525 214.756364 \nL 211.965964 214.027898 \nL 211.996404 213.252434 \nL 212.026843 214.756364 \nL 212.057283 198.84761 \nL 212.118161 212.899951 \nL 212.148601 211.701507 \nL 212.17904 199.411583 \nL 212.20948 214.756364 \nL 212.239919 214.333383 \nL 212.300798 214.756364 \nL 212.331237 199.952058 \nL 212.392116 208.85814 \nL 212.422555 211.983494 \nL 212.452995 182.468878 \nL 212.483434 213.158439 \nL 212.513874 203.194906 \nL 212.544313 214.756364 \nL 212.635631 212.735458 \nL 212.666071 206.437754 \nL 212.72695 210.409068 \nL 212.757389 214.756364 \nL 212.818268 213.322931 \nL 212.848707 211.725006 \nL 212.879147 198.236639 \nL 212.909586 214.756364 \nL 212.940025 199.223592 \nL 213.000904 214.756364 \nL 213.061783 212.171485 \nL 213.092222 207.307213 \nL 213.122662 214.756364 \nL 213.153101 210.009586 \nL 213.183541 214.756364 \nL 213.244419 208.341165 \nL 213.274859 214.568372 \nL 213.335738 214.756364 \nL 213.396616 213.510922 \nL 213.427056 214.756364 \nL 213.457495 212.735458 \nL 213.487935 213.9809 \nL 213.518374 209.234123 \nL 213.579253 214.756364 \nL 213.609692 214.756364 \nL 213.761889 197.155689 \nL 213.883647 214.756364 \nL 213.914086 214.756364 \nL 213.944526 214.497876 \nL 214.005405 214.756364 \nL 214.035844 214.756364 \nL 214.066283 214.61537 \nL 214.127162 214.756364 \nL 214.188041 209.422114 \nL 214.309799 214.756364 \nL 214.370678 209.539608 \nL 214.401117 214.662368 \nL 214.431556 214.756364 \nL 214.522875 194.218327 \nL 214.553314 210.244575 \nL 214.614193 209.539608 \nL 214.675072 214.756364 \nL 214.76639 213.275933 \nL 214.857708 214.756364 \nL 214.827269 212.92345 \nL 214.888147 214.450878 \nL 214.918587 204.087864 \nL 215.009905 210.197577 \nL 215.101223 214.756364 \nL 215.131663 214.732865 \nL 215.192542 214.756364 \nL 215.25342 206.484752 \nL 215.28386 212.05399 \nL 215.314299 195.745755 \nL 215.375178 203.006915 \nL 215.496936 214.756364 \nL 215.527375 206.531749 \nL 215.588254 214.756364 \nL 215.618693 214.756364 \nL 215.649133 206.790237 \nL 215.710011 214.732865 \nL 215.77089 214.756364 \nL 215.831769 213.534421 \nL 215.862208 199.90506 \nL 215.923087 208.717147 \nL 216.014406 214.756364 \nL 216.044845 212.92345 \nL 216.105724 202.842423 \nL 216.136163 214.756364 \nL 216.227481 206.978229 \nL 216.257921 214.756364 \nL 216.349239 214.709366 \nL 216.440557 202.372445 \nL 216.410118 214.756364 \nL 216.470997 211.889498 \nL 216.531875 209.868593 \nL 216.592754 214.215889 \nL 216.684072 205.544796 \nL 216.775391 214.756364 \nL 216.80583 206.790237 \nL 216.866709 214.756364 \nL 216.927588 211.396021 \nL 216.958027 186.628183 \nL 216.988467 214.591871 \nL 217.018906 212.758957 \nL 217.049345 209.398615 \nL 217.110224 213.863406 \nL 217.262421 214.756364 \nL 217.292861 201.549983 \nL 217.353739 213.463924 \nL 217.384179 213.675414 \nL 217.414618 200.398537 \nL 217.445058 214.521375 \nL 217.475497 211.560514 \nL 217.505937 214.756364 \nL 217.597255 212.735458 \nL 217.658134 214.756364 \nL 217.719012 202.489939 \nL 217.779891 212.077489 \nL 217.84077 210.832048 \nL 217.932088 214.756364 \nL 217.962528 211.349023 \nL 218.023406 212.876452 \nL 218.114725 214.756364 \nL 218.145164 214.732865 \nL 218.206043 214.756364 \nL 218.297361 200.610027 \nL 218.388679 214.756364 \nL 218.419119 207.095723 \nL 218.540876 214.756364 \nL 218.571316 214.591871 \nL 218.632195 203.077411 \nL 218.662634 211.678008 \nL 218.693073 214.756364 \nL 218.723513 201.385491 \nL 218.784392 211.043538 \nL 218.87571 214.756364 \nL 218.936589 201.878968 \nL 218.967028 214.756364 \nL 218.997467 214.756364 \nL 219.027907 201.737974 \nL 219.088786 214.756364 \nL 219.149665 214.756364 \nL 219.180104 197.696164 \nL 219.240983 214.756364 \nL 219.271422 213.087942 \nL 219.301862 214.450878 \nL 219.332301 195.581263 \nL 219.39318 210.503063 \nL 219.423619 210.080083 \nL 219.454059 212.030492 \nL 219.514937 202.771926 \nL 219.545377 214.756364 \nL 219.575816 212.547467 \nL 219.636695 214.756364 \nL 219.697574 214.756364 \nL 219.819331 213.487423 \nL 219.91065 214.756364 \nL 219.971529 195.722257 \nL 220.001968 214.732865 \nL 220.032407 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222.102287 214.756364 \nL 222.132726 213.228935 \nL 222.193605 214.756364 \nL 222.224045 197.437676 \nL 222.284923 211.889498 \nL 222.345802 210.78505 \nL 222.406681 214.756364 \nL 222.437121 214.497876 \nL 222.46756 212.406474 \nL 222.497999 214.756364 \nL 222.528439 213.722412 \nL 222.650196 214.756364 \nL 222.680636 214.004399 \nL 222.802393 203.147908 \nL 222.832833 206.08527 \nL 222.863272 214.685867 \nL 222.924151 208.64665 \nL 222.95459 206.437754 \nL 223.076348 214.756364 \nL 223.106787 214.732865 \nL 223.137227 213.205436 \nL 223.167666 194.241826 \nL 223.228545 211.278527 \nL 223.289424 210.597059 \nL 223.350303 214.756364 \nL 223.380742 189.236561 \nL 223.47206 204.722334 \nL 223.532939 211.114034 \nL 223.563379 189.941528 \nL 223.593818 214.732865 \nL 223.624257 211.63101 \nL 223.715576 214.756364 \nL 223.746015 203.735381 \nL 223.806894 211.584012 \nL 223.837333 214.756364 \nL 223.867773 206.08527 \nL 223.928652 209.798096 \nL 223.98953 214.756364 \nL 224.01997 197.672665 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228.068411 214.756364 \nL 228.09885 210.291573 \nL 228.159729 214.756364 \nL 228.190169 214.756364 \nL 228.220608 214.380381 \nL 228.251047 199.505579 \nL 228.281487 214.756364 \nL 228.311926 211.560514 \nL 228.342366 212.852953 \nL 228.372805 190.998978 \nL 228.433684 214.521375 \nL 228.464123 214.61537 \nL 228.494563 214.239388 \nL 228.555441 214.756364 \nL 228.61632 203.335899 \nL 228.677199 192.808393 \nL 228.738078 214.756364 \nL 228.890275 207.330712 \nL 228.920714 213.087942 \nL 228.951154 197.437676 \nL 228.981593 214.756364 \nL 229.012033 208.458659 \nL 229.042472 214.756364 \nL 229.13379 213.322931 \nL 229.225108 214.756364 \nL 229.255548 213.440425 \nL 229.285987 214.756364 \nL 229.346866 203.335899 \nL 229.377305 208.43516 \nL 229.468624 214.756364 \nL 229.529502 191.844938 \nL 229.559942 196.192234 \nL 229.6817 214.756364 \nL 229.712139 213.181937 \nL 229.773018 214.732865 \nL 229.864336 213.228935 \nL 229.894775 214.40388 \nL 229.925215 214.732865 \nL 229.955654 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231.72114 210.221076 \nL 231.782019 214.756364 \nL 231.812458 213.76941 \nL 231.903776 200.892014 \nL 231.934216 212.382975 \nL 231.964655 214.756364 \nL 232.055973 213.910403 \nL 232.086413 214.168891 \nL 232.177731 214.239388 \nL 232.20817 196.450722 \nL 232.23861 214.756364 \nL 232.329928 214.286386 \nL 232.360367 214.732865 \nL 232.451686 200.633526 \nL 232.421246 214.756364 \nL 232.482125 208.43516 \nL 232.543004 214.756364 \nL 232.603883 213.745911 \nL 232.664761 213.275933 \nL 232.72564 197.155689 \nL 232.786519 206.132268 \nL 232.877837 214.756364 \nL 232.847398 198.518625 \nL 232.908277 213.252434 \nL 232.999595 214.756364 \nL 233.030034 211.278527 \nL 233.121353 213.275933 \nL 233.24311 200.563029 \nL 233.27355 210.479564 \nL 233.303989 214.756364 \nL 233.395307 212.570966 \nL 233.425747 208.200171 \nL 233.456186 186.581185 \nL 233.517065 211.20803 \nL 233.547504 214.756364 \nL 233.608383 209.140127 \nL 233.638823 207.871187 \nL 233.669262 188.249607 \nL 233.730141 214.756364 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237.839461 199.881561 \nL 237.8699 214.756364 \nL 237.90034 214.685867 \nL 238.052537 203.382897 \nL 238.143855 214.756364 \nL 238.174294 205.615292 \nL 238.235173 211.137533 \nL 238.296052 214.756364 \nL 238.326491 212.547467 \nL 238.356931 198.84761 \nL 238.38737 214.756364 \nL 238.41781 205.967776 \nL 238.539567 214.756364 \nL 238.661325 198.072146 \nL 238.691764 214.756364 \nL 238.783082 212.241982 \nL 238.813522 200.093051 \nL 238.874401 214.756364 \nL 238.90484 203.547389 \nL 239.057037 214.756364 \nL 239.087476 207.213217 \nL 239.148355 211.090536 \nL 239.270113 214.756364 \nL 239.361431 191.539453 \nL 239.42231 214.756364 \nL 239.483189 205.87378 \nL 239.544068 214.756364 \nL 239.604946 210.009586 \nL 239.635386 214.427379 \nL 239.665825 201.385491 \nL 239.696265 214.756364 \nL 239.726704 211.41952 \nL 239.787583 214.450878 \nL 239.818022 209.445613 \nL 239.878901 214.756364 \nL 240.000659 209.375116 \nL 240.031098 191.492455 \nL 240.091977 214.662368 \nL 240.122416 214.61537 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254.733332 214.756364 \nL 254.763771 214.756364 \nL 254.794211 198.730115 \nL 254.885529 206.813736 \nL 254.946408 214.756364 \nL 254.976847 213.487423 \nL 255.007287 141.345807 \nL 255.068165 214.756364 \nL 255.098605 213.886904 \nL 255.159484 196.239232 \nL 255.189923 212.664962 \nL 255.220362 210.808549 \nL 255.250802 212.735458 \nL 255.281241 212.030492 \nL 255.311681 214.662368 \nL 255.34212 201.925966 \nL 255.402999 214.756364 \nL 255.463878 211.513516 \nL 255.494317 199.599575 \nL 255.555196 214.756364 \nL 255.616075 214.756364 \nL 255.646514 213.275933 \nL 255.676954 213.698913 \nL 255.707393 201.033007 \nL 255.737832 214.732865 \nL 255.768272 211.067037 \nL 255.798711 214.145392 \nL 255.829151 194.194828 \nL 255.85959 214.756364 \nL 255.890029 214.756364 \nL 255.920469 214.756364 \nL 255.950908 191.210468 \nL 256.011787 212.71196 \nL 256.042226 208.811143 \nL 256.103105 212.946949 \nL 256.133545 214.756364 \nL 256.163984 214.638869 \nL 256.224863 167.923061 \nL 256.255302 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288.947226 213.064443 \nL 288.977665 214.756364 \nL 289.038544 214.215889 \nL 289.099423 204.111363 \nL 289.129863 208.811143 \nL 289.190741 214.756364 \nL 289.25162 212.876452 \nL 289.28206 214.756364 \nL 289.434257 196.521219 \nL 289.556014 214.756364 \nL 289.586454 212.218483 \nL 289.677772 212.735458 \nL 289.708211 189.04857 \nL 289.76909 212.852953 \nL 289.829969 214.756364 \nL 289.860408 205.051319 \nL 289.951727 211.231529 \nL 289.982166 210.526562 \nL 290.012605 211.043538 \nL 290.043045 212.735458 \nL 290.073484 208.059178 \nL 290.103924 214.732865 \nL 290.134363 212.030492 \nL 290.195242 214.286386 \nL 290.225681 207.213217 \nL 290.28656 214.756364 \nL 290.408318 199.552577 \nL 290.469196 198.283636 \nL 290.530075 214.756364 \nL 290.621394 214.685867 \nL 290.651833 203.523891 \nL 290.712712 214.756364 \nL 290.743151 214.756364 \nL 290.864909 204.557842 \nL 290.925788 214.756364 \nL 291.017106 214.427379 \nL 291.047545 214.356882 \nL 291.077985 190.834486 \nL 291.108424 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297.561578 209.351617 \nL 297.592018 214.756364 \nL 297.622457 201.174001 \nL 297.652897 213.322931 \nL 297.805094 203.899873 \nL 297.896412 214.756364 \nL 297.926851 203.194906 \nL 298.01817 209.962589 \nL 298.139927 214.756364 \nL 298.170367 205.192312 \nL 298.231245 214.756364 \nL 298.261685 214.756364 \nL 298.292124 205.779785 \nL 298.353003 206.696242 \nL 298.383442 214.121893 \nL 298.474761 213.181937 \nL 298.535639 214.756364 \nL 298.566079 211.325525 \nL 298.626958 214.544874 \nL 298.687837 214.756364 \nL 298.748715 213.205436 \nL 298.809594 214.756364 \nL 298.840034 213.886904 \nL 298.870473 203.194906 \nL 298.931352 208.176672 \nL 299.02267 214.756364 \nL 299.053109 208.035679 \nL 299.113988 214.756364 \nL 299.144428 214.662368 \nL 299.235746 207.424708 \nL 299.327064 214.756364 \nL 299.357503 211.772004 \nL 299.387943 211.41952 \nL 299.418382 206.531749 \nL 299.448822 207.612699 \nL 299.479261 214.756364 \nL 299.509701 202.019961 \nL 299.54014 212.641463 \nL 299.601019 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301.366504 207.777191 \nL 301.396944 214.756364 \nL 301.427383 211.090536 \nL 301.457823 202.583935 \nL 301.488262 212.218483 \nL 301.518701 203.923372 \nL 301.640459 214.756364 \nL 301.670898 214.098395 \nL 301.701338 214.756364 \nL 301.731777 196.779707 \nL 301.823095 204.534343 \nL 301.883974 214.756364 \nL 301.914414 211.161032 \nL 301.944853 199.247091 \nL 302.005732 214.732865 \nL 302.036171 209.892092 \nL 302.066611 214.756364 \nL 302.12749 211.137533 \nL 302.157929 210.926043 \nL 302.188368 197.061694 \nL 302.249247 211.748505 \nL 302.279687 208.975635 \nL 302.310126 214.756364 \nL 302.340565 213.910403 \nL 302.401444 214.756364 \nL 302.431884 202.25495 \nL 302.492762 207.918184 \nL 302.553641 214.756364 \nL 302.584081 208.811143 \nL 302.61452 196.004243 \nL 302.644959 214.756364 \nL 302.705838 202.513438 \nL 302.736278 202.536937 \nL 302.888475 214.756364 \nL 303.010232 203.453394 \nL 303.13199 214.756364 \nL 303.192869 206.061771 \nL 303.223308 211.278527 \nL 303.314626 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7rKdRZ5ai9NDZ88KTtveOyFBV03CEdw8fxknHHxto+zte3cDra2vSb1hgIWconKDislUyP9rekZ0XiqCiL+kPpR+EZNf598zh3HuCDzqbsGg7e4bQyOTCOvWkVnuoJeuN34/O3ZrdFywADUfaKCktY/KK3Tl9n6IP+ku2pq5DlOLX6qsV0mkZQMmxd4Gis9PRoYbKotbW0Unj0bYBPW9pP+0ZAFV1zQA8tWD7gPIWVNEH/flb9oedBRkCWgKeHHbXN6dscPw/45fyt//x+oDev2xNDWOmVQzoub2t3FUf+hQR+ZLvsRI3PreCT942I+PnPTBjM98et4RVVQdzkKvMFH3QHyqXwEFtqz3MjPV7eHPz4KedHgp2hDTFQLLAXlXXzBfGzOXBWZuTPuetQVxV3vj8CsbOy06Vxb8+9hbfGPtWVl4r2/oL0YXQE2f5znpKSsvYdyh5Nd+MDXsBOHS0jYbm4CX+LX48wf5DLYPP5CAVfdAvNl9+4E1ueHY5oycsY9Oe4ANTnHM8MGMTW2sP5zB3PTU0t/GRW97gra0Du9qasqqaf7p/XsGc4Pb5H2x8YFCuTHq7ig3vNKbfMI343DyFqqbhCCWlZazYlf2rkpLSMq5/OvOJ6CYsilWtLNnWc1qwlvYO5id8Dz9zx0w+dUfmJf4COK8Vf9AvhA85Vw5lULdYe7iFR+ZUcu1TS3OYo57WVB+ktaOTR+dWDuj5a3c3ALA5g5NbEB2djvumV3DgcOpS1xr/3v3JVcn0ly+v4bKHFwTePlU+CqlRuq2jb5fNBb7q9fmluwb12gcOt9BwpO9vYdbGvUxZVU1JaVngm8nHawZ6X+nd/uoGvjthWdfj9iHcdlP0QV88/x1t7cjfl9X8T2iwwTFZvW1bRydH2wbWbW9h5X4enbuV//hL6lG38SCyac8hLnxgHg3NbRn3QIn3xphbEbvVREt7BzN99UBQN09ew6i7ZiZdly4/zsGsDN8vV+Il55fK+/ZMSfX9aO/opLqf6TDiPnvXLM65M/ln9PNJqwEC30z+mBQf6vo0V15XjX0ra9VzuVb0QX9jzeAvk4tCnho3nHNdpaT472egQb+/oHbxg/P56H9Ny+j1SkrLKCkt66r2SGy8TZXHh+dsYWttE/M27+Prj2VWTx6vfntsXuxKZ8wbm/jBM+Us2x58RvEXllWx/3Dym6QE+VyXpOkxkm+JJfJ0X8k7X9vABffO6feKLP4ZDLTX1KGjbT16bsW/cw/P3pLR65TvrO+3Ib6QxtEVfdCX/Lr0oQV85D/fALp/1Iu3HehRH5oN2wbRwJvs0vxIa/KrhncPHwZAS9vg6sedc131xfXNrextPNrVRS+I3fU9tx1K8+4kSnbV9vKK3Un3J96W03i0fcDvly7WfuK2GVw1tvsqIL791tqe369sxexddc2hF0QV9CWrKvYcoq3D0dreSVtCcE2sDy00zjn+9NaOPul/mLOF494V+4mk68e/Jclsj4mBrLVXo+q5v5nNP/x2Lm+sraEuwO0Oxy/czrZejfDJSo/J6raHghW76lO2v8U/x6q6ZtZV92xraU5xsu56bpK0zk7X4yS6NuE1X1mV+VTN//lK+sn54u58bQOXPhS8vSYXFPSLRF1TKyWlZUxZle62w/kpIZ5z50xGZynQ57JQe8G9c/jof03j8Tf71sfeP2NzmhJed8a++uD8wO+5NKFnyA+fW8H1T7/d9ThVT6U/LtrRI1ik+kxqE7oaPrlgG0OlvfHWqetZVdWzF0/vuYX+4bdzueKRhT3SEk/W2/c3sbhXt9lk1T6Pz9/KF8bMpXJf/72w9h9uCdRu9N9LBtcQvXjrAfb3U4WVbQr6Q1ji1zleCnxm8c5+n7P/cCt3l21Iuu7f/7w6W1njcMvAL8lTmblhL+ffMzvQKNsgU/g6B9UHj/Q7MOtQvGphAJWyiUHLEk4f8WqeuMTGyv5OlIn5vPyRhXz6jljjpXOO7fub+lwxPLN4Z5/3ClviySrx81lX3cg3xqZubA1SnfWl++dxzZNL+p0/6WhbB1NWxkrz6RqJR901i+//6W2f17RvD8SutJLnNfULXPPkEr71xOKuE8xARvxmQkF/CHs2SYAP8uN4MsUw7z8v7+5Z8Vbl/gH3jgni7F9P46kFmd1j9rap66lpOJpy4EyilVka+Tg1fmemJJ9rPCnV55TLevfe9cJfun8eX/ztXAp9OGLQj+QXk7oLII5YQ2lQyX4XcT/87+Vs8lVxQY5PpgPuPnX7DMYmuWpMJd7Yv622iReWVQGwuz59j6XBUNAP2Z/Lq3qMADx0NNbN7y8r00+6NDXhVnHpSiKWQTCo2NPI/35qKXe8lvyKIG5ddQMlpWVU7Mm8Yaq5tYO7yjb2SX9m8Y6uElj85NT7pxkscKTeKNux+N43evbaONjcmlFj3d7G7Fza5+LqKiwvr9jddcP3xiNtVGTweaaavrmj0zF3U3Y7FCQb7Pj62hpeXf0Ozjne3FzLj59fweyKnl1ndx5oYuy8rfwhYQxLR2f3ldwbgWZ7HRgF/ZBNKo+d3edv3k9JaRlPvBkr/T4+L7NScDLlO+oC1PH3ddAPL09X5zlt3R4AZq7PTl/w/Ydb+PWU9fzbhGVZv8roWbebedQ/mqT3jhlcPW5xn0bgrz26iEsfWjDo+e57108Xg4F8JKUvr+XY4cFDVaqj+9XfvZnyOQP9rL/9RN8qqXXVjfzkhZXc/uoGRk9YxmtravoUNK4dv4wx03oOEEzc5IfPrRhQfoJQ0A9ZvAQeH3Y/ZXUsSMd/HLVp5uroXXed+Oiqxxfz0xdXJX3ehnca09ZpLtte1+/8Isf4PAZtLDzgG8ZSXVbHA3PDkTb+/aU1XekT367qdyqGICXcB2Zs6loeSEl/x4G+XUSd6ztcP7ZtrGfI7xPm6Pn62EUZv+c1Ty7J+DmF1B88mR51+gGfU9fcyruGdYeq/u5fHHuT5Mm9u/kmbpbus06V11RjKAC27Es9knxXku66+Tp0kQ76902v6Cqthq3dX9ol/ijmVOzlc3fP6jfg9a6CWbkrWF32ZQ8v4IJ756Td7kfPL2f5zjqfN9czYPsI8+CszYG6HX72rllcO35pj32ctWFv12CpxPTVCXXy2/c3pWzg3LTnEB+/dTpXj1tMSWlZV5fFHz+/ssd2Cyu75/8ZyF2ckp3YZvuRtqkk1gevq85P3+yFBT6rbKdzTCqv4puPv9U1l1E6HZ2OYcd0h8S/T/O9zdbdufIhjJN0pO+cFb8Rw457Lwdi9emfuG0GY77xCb79uQ/mJQ/L/JwgvXvd1DW18vLyWKl/1a6D/ONHRiR9/p/e2sEFHz6Fk49/V1eac65P1UImX67EYLWo8gCLKhez497LueeNCsbN38bW31zGsGOMhN8h5TvquuZS6c/bO+p7/Civf6acn154FjsPNHGMf0Gz5PlNlhZvT4iXuG+auIpLPvbX1DT0bOwdnpDZ219dDxAov73fpxAlhrhbp64PLR9BLN1ex1I/IvntHcEaZ48x65rKIoigIb8+QEFlMIKcexZVdv/W+rZd9f0dZ0OkS/q9xQNFupsYHDjcQklpGTPWd18lHGzu7iff2t5Jc+vgGtX2HWqhLGBjzg+eKSfx4vCuso18NmEukoPNrSlHnCaTagj6hIWxzyVeDdN7npJnl/TfXTSu95e7qr6ZV1a9w+QVvmorgwvd3tVXcyr28cuX1/RIe2T2lh4/nvoMpsSNC3oFJdl3jGU2aCrovQR+Pil7XZSTybTnT++TRK4uWBT0gR8/v4LW9s7AH3KF72b1u5nddbbxngYTFm7n2+MWc/avpw8oLwM90A1Hukst4xdu50BCKebTd8zkn/+wMNnTAnt0bmWfkvZAyyB9Bsz0ehhkIrFMPDAz3SCr3Eo3anSwDg1imoKhIGg1UC7l4yb0fUr6OXqfyAb9xMag19bUUL6zrmtekKDHtyJhyt/EL0W2S4VB7g70/T/1P3f4wRSl29518akGhvxx0Y6u5U/ePp3New91VcfE8hjczyf1LJ33fm5Nw9GkQbrQGylTSTdDoxSG+6YnnzDtD3O2hHInslyN84hs0D/ST5dAw7i7bENXcFr/TgOXPbSAppZ2rnx0Ed9JMid91zzcad73wOEW7nljI3sakg8wStYItauuOdAI04HoPSVtqlvB7T/cQpuflvloWyfP9arKGTc/eBfT19f2bDwP2vAW78Y8kFvkZTK4R6Ip1c3W75+R/E5p2fbq6p5VWLkq6UeqIXfsvK3s2N/EmKs+2e/lfuPRtq6BQb/71qe5/OFY1cijcyt79CpJFHQa4V+9vJZZG/fyxJvbWPirL/VZ37sBEmDyimr+5uTj+fQHT+z/xfMssU5/MCWhZJ9ZssvpeLXQr6cUdmOlSDaoTj8LxkyrYKIfDJVM/ENOFngBHgtwk4T+SqGdnY5ZG7sHMn1hzNy0rxe3dPuBrE1glg2O7M3VHvS7He/WWii3TxTJpVzd9D0SQT9I3VjZmuQ9ZUpKy/p9XlNLO00t7fxrkhts3DRxFfdNr8A5R0enY9r6gY8JGMxNt3NhTsW+rAXfPQ19B9tsTzJf/kBvlCEyFOWqpB+p6p24+Ztr+wSV55buYuQpx2f8WjdNXMU1536wKyAlDsL5y8pYF8QPnPgebvnLuiHbEJlMNieFCtpfeyBdLUWkp0gE/abWDt53XPeuprqhR7IJwNLZuKcxbXfAW/6yDsjtvPAiUlxyNbI4EkH/47dO54pPnpaT166qy+00qCISTWrIHaTXUtTZZ8OOQdyvVUQkmVyV9CMT9HPptlf7n3deRCRTN01MPkPuYCnoi4gUoFkbg08ylwkFfRGRCMl70DezS8xsk5lVmllpvt9fRCTK8hr0zWwY8ChwKXA2cI2ZnZ3PPIiIRFm+S/qfByqdc9ucc63Ai8CV2X6T3fV9b0UmIiL5D/qnA4mT3+z2aV3M7AYzKzez8tragQ3zz/ZNtUVE8u0r/+vUnLxuwQ3Ocs6NA8YBjBo1akAdVT/8P/+q6xaIIiLSLd8l/WrgzITHZ/g0ERHJg3wH/beBs8xspJkdC1wNTM1zHkREIiuv1TvOuXYz+zEwHRgGTHDO6Y4YIiJ5kvc6fefc68Dr+X5fERHRiFwRkUhR0BcRiRAFfRGRCFHQFxGJEAty0/CwmFktsHMQL3EKsD9L2RkKora/oH2OCu1zZv7GOTci2YqCDvqDZWblzrlRYecjX6K2v6B9jgrtc/aoekdEJEIU9EVEIqTYg/64sDOQZ1HbX9A+R4X2OUuKuk5fRER6KvaSvoiIJFDQFxGJkKIM+sV083UzO9PM5prZBjNbb2Y/9eknm9lMM9vi/5/k083MHvb7vsbMzkl4rdF++y1mNjqsfQrCzIaZ2Uoze80/HmlmS/1+TfRTc2Nmx/nHlX59ScJr3OzTN5nZxeHsSTBmdqKZvWRmFWa20czOj8Axvsl/p9eZ2Qtm9u5iO85mNsHM9pnZuoS0rB1XM/usma31z3nYzCxtppxzRfVHbMrmrcCHgGOB1cDZYedrEPtzGnCOX/4rYDOxm8r/Fij16aXAGL98GfAGYMB5wFKffjKwzf8/yS+fFPb+9bPfPweeB17zjycBV/vlx4Ef+uUfAY/75auBiX75bH/sjwNG+u/EsLD3q5/9fRq43i8fC5xYzMeY2G1StwPvSTi+/1Zsxxn4InAOsC4hLWvHFVjmtzX/3EvT5insDyUHH/L5wPSExzcDN4edryzu3xTgq8Am4DSfdhqwyS8/AVyTsP0mv/4a4ImE9B7bFdIfsTuqzQa+DLzmv9D7geG9jzGxezOc75eH++2s93FP3K7Q/oATfAC0XunFfIzj98s+2R+314CLi/E4AyW9gn5WjqtfV5GQ3mO7VH/FWL2T9ubrQ5W/pP0MsBQ41TlX41ftAeJ3UU61/0Ppc/k98Eug0z9+P3DQOdfuHyfmvWu//PoGv/1Q2t+RQC3wR1+l9ZSZHU8RH2PnXDVwP7ALqCF23JZT3Mc5LlvH9XS/3Du9X8UY9IuSmb0PeBn4mXOuMXGdi53mi6LvrZldAexzzi0POy95NJxYFcBY59xngCZil/1diukYA/h67CuJnfA+ABwPXBJqpuR29Y8AAAHSSURBVEIQxnEtxqBfdDdfN7N3EQv4zznnJvvkvWZ2ml9/GrDPp6fa/6HyuVwA/IuZ7QBeJFbF8xBwopnF7/SWmPeu/fLrTwAOMHT2F2IltN3OuaX+8UvETgLFeowBvgJsd87VOufagMnEjn0xH+e4bB3Xar/cO71fxRj0i+rm6741fjyw0Tn3u4RVU4F4K/5oYnX98fTv+p4A5wEN/lJyOnCRmZ3kS1kX+bSC4py72Tl3hnOuhNixm+Oc+w4wF7jKb9Z7f+Ofw1V+e+fTr/a9PkYCZxFr9Co4zrk9QJWZ/Z1PuhDYQJEeY28XcJ6Zvdd/x+P7XLTHOUFWjqtf12hm5/nP8LsJr5Va2I0cOWo4uYxYL5etwC1h52eQ+/IFYpd/a4BV/u8yYvWZs4EtwCzgZL+9AY/6fV8LjEp4re8Dlf7ve2HvW4B9/ye6e+98iNiPuRL4M3CcT3+3f1zp138o4fm3+M9hEwF6NYS8r58Gyv1xfoVYL42iPsbA7UAFsA54llgPnKI6zsALxNos2ohd0V2XzeMKjPKf31bgD/TqDJDsT9MwiIhESDFW74iISAoK+iIiEaKgLyISIQr6IiIRoqAvIhIhCvoiIhGioC8iEiH/H6G+/rPuz7xgAAAAAElFTkSuQmCC\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "Kile tunachokiona hapa ni kwamba mwanzoni urefu wa njia ya wastani uliongezeka. Hii huenda inatokana na ukweli kwamba tunapokuwa hatujui chochote kuhusu mazingira - tuna uwezekano mkubwa wa kujikuta katika hali mbaya, maji au mbwa mwitu. Tunapojifunza zaidi na kuanza kutumia maarifa haya, tunaweza kuchunguza mazingira kwa muda mrefu zaidi, lakini bado hatujui vizuri mahali ambapo tufaha zipo.\n",
+    "\n",
+    "Mara tu tunapojifunza vya kutosha, inakuwa rahisi kwa wakala kufanikisha lengo, na urefu wa njia huanza kupungua. Hata hivyo, bado tunakuwa wazi kwa uchunguzi, kwa hivyo mara nyingi tunatoka nje ya njia bora, na kuchunguza chaguo mpya, jambo ambalo hufanya njia kuwa ndefu zaidi kuliko inavyopaswa.\n",
+    "\n",
+    "Kile tunachokiona pia kwenye grafu hii, ni kwamba wakati fulani urefu uliongezeka ghafla. Hii inaonyesha asili ya mchakato wa nasibu, na kwamba tunaweza wakati fulani \"kuharibu\" viwango vya Q-Table, kwa kuandika upya thamani mpya. Hili linapaswa kupunguzwa kwa kupunguza kiwango cha kujifunza (yaani, kuelekea mwisho wa mafunzo tunarekebisha thamani za Q-Table kwa thamani ndogo).\n",
+    "\n",
+    "Kwa ujumla, ni muhimu kukumbuka kwamba mafanikio na ubora wa mchakato wa kujifunza unategemea sana vigezo, kama vile kiwango cha kujifunza, kupungua kwa kiwango cha kujifunza, na kipengele cha punguzo. Hivi mara nyingi huitwa **vigezo vya juu**, ili kuvitofautisha na **vigezo** ambavyo tunaboresha wakati wa mafunzo (mfano, viwango vya Q-Table). Mchakato wa kutafuta thamani bora za vigezo vya juu huitwa **ubunifu wa vigezo vya juu**, na unastahili mada tofauti kabisa.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## Zoezi\n",
+    "#### Ulimwengu Halisi Zaidi wa Peter na Mbwa Mwitu\n",
+    "\n",
+    "Katika hali yetu, Peter aliweza kusafiri karibu bila kuchoka au kuhisi njaa. Katika ulimwengu halisi zaidi, anapaswa kukaa chini na kupumzika mara kwa mara, na pia kujilisha. Hebu tufanye ulimwengu wetu uwe halisi zaidi kwa kutekeleza sheria zifuatazo:\n",
+    "\n",
+    "1. Kwa kusafiri kutoka sehemu moja hadi nyingine, Peter hupoteza **nguvu** na kupata **uchovu**.\n",
+    "2. Peter anaweza kupata nguvu zaidi kwa kula matufaha.\n",
+    "3. Peter anaweza kuondoa uchovu kwa kupumzika chini ya mti au kwenye nyasi (yaani, kutembea hadi eneo la ubao lenye mti au nyasi - uwanja wa kijani).\n",
+    "4. Peter anahitaji kumtafuta na kumuua mbwa mwitu.\n",
+    "5. Ili kumuua mbwa mwitu, Peter anahitaji kuwa na viwango fulani vya nguvu na uchovu, vinginevyo atashindwa katika vita.\n",
+    "\n",
+    "Badilisha kazi ya malipo hapo juu kulingana na sheria za mchezo, endesha algoriti ya kujifunza kwa kuimarisha ili kujifunza mkakati bora wa kushinda mchezo, na linganisha matokeo ya kutembea bila mpangilio na algoriti yako kwa kuzingatia idadi ya michezo iliyoshinda na kupotezwa.\n",
+    "\n",
+    "> **Note**: Unaweza kuhitaji kurekebisha hyperparameters ili ifanye kazi, hasa idadi ya epochs. Kwa sababu mafanikio ya mchezo (kupigana na mbwa mwitu) ni tukio nadra, unaweza kutarajia muda mrefu zaidi wa mafunzo.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/8-Reinforcement/2-Gym/notebook.ipynb b/translations/sw/8-Reinforcement/2-Gym/notebook.ipynb
new file mode 100644
index 000000000..bb990d2fc
--- /dev/null
+++ b/translations/sw/8-Reinforcement/2-Gym/notebook.ipynb
@@ -0,0 +1,392 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.4 64-bit ('base': conda)"
+  },
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "coopTranslator": {
+   "original_hash": "f22f8f3daed4b6d34648d1254763105b",
+   "translation_date": "2025-09-06T15:17:39+00:00",
+   "source_file": "8-Reinforcement/2-Gym/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Kuteleza kwa CartPole\n",
+    "\n",
+    "> **Tatizo**: Ikiwa Peter anataka kutoroka kutoka kwa mbwa mwitu, anahitaji kuwa na uwezo wa kusonga haraka kuliko yeye. Tutaona jinsi Peter anaweza kujifunza kuteleza, hasa, kudumisha usawa, kwa kutumia Q-Learning.\n",
+    "\n",
+    "Kwanza, wacha tusakinishe gym na kuingiza maktaba zinazohitajika:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Unda mazingira ya cartpole\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 2"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Ili kuona jinsi mazingira yanavyofanya kazi, wacha tuendeshe simulizi fupi kwa hatua 100.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Wakati wa uigaji, tunahitaji kupata uchunguzi ili kuamua jinsi ya kutenda. Kwa kweli, kazi ya `step` inaturudishia uchunguzi wa sasa, kazi ya zawadi, na bendera ya `done` inayonyesha ikiwa ina maana kuendelea na uigaji au la:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 4"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Tunaweza kupata thamani ya chini na ya juu ya nambari hizo:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Hebu pia tuchunguze mbinu nyingine ya kugawanya kwa kutumia mabano:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Hebu sasa tuendeshe uigaji mfupi na tuangalie zile thamani za mazingira zisizoendelea.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -2, -2)\n(0, 1, -2, -5)\n(0, 2, -3, -8)\n(0, 3, -5, -11)\n(0, 3, -7, -14)\n(0, 4, -10, -17)\n(0, 3, -14, -15)\n(0, 3, -17, -12)\n(0, 3, -20, -16)\n(0, 4, -23, -19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 8"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 9"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 22.0, alpha=0.3, epsilon=0.9\n",
+      "5000: 70.1384, alpha=0.3, epsilon=0.9\n",
+      "10000: 121.8586, alpha=0.3, epsilon=0.9\n",
+      "15000: 149.6368, alpha=0.3, epsilon=0.9\n",
+      "20000: 168.2782, alpha=0.3, epsilon=0.9\n",
+      "25000: 196.7356, alpha=0.3, epsilon=0.9\n",
+      "30000: 220.7614, alpha=0.3, epsilon=0.9\n",
+      "35000: 233.2138, alpha=0.3, epsilon=0.9\n",
+      "40000: 248.22, alpha=0.3, epsilon=0.9\n",
+      "45000: 264.636, alpha=0.3, epsilon=0.9\n",
+      "50000: 276.926, alpha=0.3, epsilon=0.9\n",
+      "55000: 277.9438, alpha=0.3, epsilon=0.9\n",
+      "60000: 248.881, alpha=0.3, epsilon=0.9\n",
+      "65000: 272.529, alpha=0.3, epsilon=0.9\n",
+      "70000: 281.7972, alpha=0.3, epsilon=0.9\n",
+      "75000: 284.2844, alpha=0.3, epsilon=0.9\n",
+      "80000: 269.667, alpha=0.3, epsilon=0.9\n",
+      "85000: 273.8652, alpha=0.3, epsilon=0.9\n",
+      "90000: 278.2466, alpha=0.3, epsilon=0.9\n",
+      "95000: 269.1736, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 11"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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125.656548 212.345721 \r\nL 125.738727 132.392727 \r\nL 125.811775 195.872993 \r\nL 125.805688 47.216674 \r\nL 125.817862 130.383858 \r\nL 125.820906 33.958137 \r\nL 125.924391 194.667672 \r\nL 125.991351 17.083636 \r\nL 125.970046 202.301375 \r\nL 126.046137 122.750155 \r\nL 126.137447 186.230421 \r\nL 126.122229 70.519557 \r\nL 126.155709 132.794501 \r\nL 126.16484 85.786962 \r\nL 126.177015 195.069446 \r\nL 126.259194 131.187406 \r\nL 126.271369 203.104922 \r\nL 126.353548 85.786962 \r\nL 126.37181 178.998492 \r\nL 126.453989 86.992284 \r\nL 126.472251 209.533304 \r\nL 126.55443 81.769224 \r\nL 126.584866 133.196275 \r\nL 126.633565 199.087184 \r\nL 126.603128 63.689401 \r\nL 126.694438 104.268559 \r\nL 126.706613 209.13153 \r\nL 126.7127 70.117783 \r\nL 126.807054 158.9098 \r\nL 126.886189 78.153259 \r\nL 126.840534 201.497827 \r\nL 126.916626 132.392727 \r\nL 127.001849 201.497827 \r\nL 126.931844 82.974545 \r\nL 127.023154 138.017561 \r\nL 127.074897 102.661463 \r\nL 127.111421 205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 128.423241 194.265898 \r\nL 128.514551 142.035299 \r\nL 128.56325 208.729756 \r\nL 128.566293 51.234412 \r\nL 128.624123 150.47255 \r\nL 128.730651 46.011353 \r\nL 128.715433 208.327982 \r\nL 128.736739 103.465011 \r\nL 128.751957 172.971885 \r\nL 128.74587 80.162129 \r\nL 128.803699 140.428204 \r\nL 128.858485 33.958137 \r\nL 128.864573 204.712018 \r\nL 128.913271 151.276098 \r\nL 128.919359 192.658803 \r\nL 128.998494 63.287627 \r\nL 129.016756 110.295166 \r\nL 129.077629 60.876984 \r\nL 129.025887 186.230421 \r\nL 129.114153 117.928869 \r\nL 129.117197 166.945277 \r\nL 129.175027 50.832639 \r\nL 129.223725 161.320443 \r\nL 129.321123 63.287627 \r\nL 129.308948 199.890732 \r\nL 129.333297 84.983415 \r\nL 129.357647 198.283636 \r\nL 129.409389 53.243282 \r\nL 129.442869 145.24949 \r\nL 129.518961 20.297827 \r\nL 129.491568 197.078315 \r\nL 129.549398 161.722217 \r\nL 129.640708 202.301375 \r\nL 129.610271 33.556364 \r\nL 129.655926 179.80204 \r\nL 129.716799 19.092506 \r\nL 129.668101 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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51.636186 \r\nL 330.930568 164.13286 \r\nL 330.994485 63.689401 \r\nL 330.970136 197.881863 \r\nL 331.043184 113.911131 \r\nL 331.101013 59.269889 \r\nL 331.13145 192.257029 \r\nL 331.143625 140.02643 \r\nL 331.213629 200.292506 \r\nL 331.22276 49.627317 \r\nL 331.234935 174.980754 \r\nL 331.317114 38.779424 \r\nL 331.262328 199.488958 \r\nL 331.344507 81.36745 \r\nL 331.435817 199.890732 \r\nL 331.444948 50.832639 \r\nL 331.454079 185.0251 \r\nL 331.551476 43.198936 \r\nL 331.536258 208.729756 \r\nL 331.566694 101.456142 \r\nL 331.578869 193.864124 \r\nL 331.633655 76.14439 \r\nL 331.64583 93.018891 \r\nL 331.648873 21.503149 \r\nL 331.664092 202.703149 \r\nL 331.755402 46.8149 \r\nL 331.816275 210.336851 \r\nL 331.831493 46.011353 \r\nL 331.868017 153.284967 \r\nL 331.962371 17.083636 \r\nL 331.910629 201.096053 \r\nL 331.977589 82.974545 \r\nL 332.059768 194.265898 \r\nL 331.989764 58.064568 \r\nL 332.090205 170.963016 \r\nL 332.117598 23.512018 \r\nL 332.141947 188.23929 \r\nL 332.202821 102.661463 \r\nL 332.236301 199.488958 \r\nL 332.306305 31.547494 \r\nL 332.358048 17.083636 \r\nL 332.415877 204.712018 \r\nL 332.522406 17.083636 \r\nL 332.491969 209.533304 \r\nL 332.531537 43.60071 \r\nL 332.635021 209.533304 \r\nL 332.543711 17.083636 \r\nL 332.644152 87.795831 \r\nL 332.711113 44.002483 \r\nL 332.662414 199.890732 \r\nL 332.735462 117.928869 \r\nL 332.738506 196.274767 \r\nL 332.750681 17.083636 \r\nL 332.841991 81.769224 \r\nL 332.863296 192.257029 \r\nL 332.930257 17.083636 \r\nL 332.954606 193.864124 \r\nL 333.039829 80.965676 \r\nL 333.042873 17.083636 \r\nL 333.061135 206.720887 \r\nL 333.149401 39.582971 \r\nL 333.161576 180.605588 \r\nL 333.240711 17.083636 \r\nL 333.258973 87.795831 \r\nL 333.262017 17.083636 \r\nL 333.344196 193.46235 \r\nL 333.365501 76.546164 \r\nL 333.462899 206.319113 \r\nL 333.371589 17.083636 \r\nL 333.475073 121.14306 \r\nL 333.578558 47.618448 \r\nL 333.560296 195.872993 \r\nL 333.587689 55.653925 \r\nL 333.685086 199.087184 \r\nL 333.639431 26.726208 \r\nL 333.697261 81.36745 \r\nL 333.72161 193.060576 \r\nL 333.712479 17.083636 \r\nL 333.785527 147.660133 \r\nL 333.87075 17.083636 \r\nL 333.809877 187.033969 \r\nL 333.895099 185.828647 \r\nL 333.898143 187.033969 \r\nL 333.949885 17.083636 \r\nL 333.955973 197.480089 \r\nL 334.010759 78.956807 \r\nL 334.062501 17.083636 \r\nL 334.065545 203.90847 \r\nL 334.095981 86.188736 \r\nL 334.175117 195.069446 \r\nL 334.105112 17.083636 \r\nL 334.205553 70.92133 \r\nL 334.275558 197.881863 \r\nL 334.23599 17.083636 \r\nL 334.302951 47.618448 \r\nL 334.354693 197.078315 \r\nL 334.403392 17.083636 \r\nL 334.47644 189.042838 \r\nL 334.512964 66.501818 \r\nL 334.592099 189.042838 \r\nL 334.573837 17.083636 \r\nL 334.619492 83.376319 \r\nL 334.689496 17.083636 \r\nL 334.69254 199.087184 \r\nL 334.719933 97.438404 \r\nL 334.722977 202.301375 \r\nL 334.738195 17.083636 \r\nL 334.826461 126.36612 \r\nL 334.893422 17.083636 \r\nL 334.917771 193.864124 \r\nL 334.93299 84.581641 \r\nL 334.990819 192.658803 \r\nL 334.996907 23.913792 \r\nL 335.042562 86.188736 \r\nL 335.127784 198.68541 \r\nL 335.082129 17.083636 \r\nL 335.155177 144.044169 \r\nL 335.228225 17.083636 \r\nL 335.203876 202.301375 \r\nL 335.261706 65.698271 \r\nL 335.362147 196.274767 \r\nL 335.307361 17.083636 \r\nL 335.371278 166.141729 \r\nL 335.453457 17.083636 \r\nL 335.380409 208.729756 \r\nL 335.483893 78.153259 \r\nL 335.587378 195.069446 \r\nL 335.520417 40.386519 \r\nL 335.593465 81.36745 \r\nL 335.617815 192.658803 \r\nL 335.690863 17.083636 \r\nL 335.794347 198.283636 \r\nL 335.803478 147.258359 \r\nL 335.84609 195.069446 \r\nL 335.818697 33.15459 \r\nL 335.891745 115.116452 \r\nL 335.967836 17.083636 \r\nL 335.995229 209.13153 \r\nL 335.998273 163.731086 \r\nL 336.077408 32.752816 \r\nL 336.007404 205.515565 \r\nL 336.123063 69.314235 \r\nL 336.199155 191.051707 \r\nL 336.156544 25.520887 \r\nL 336.235679 101.456142 \r\nL 336.269159 33.15459 \r\nL 336.314814 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Kutoka kwa grafu hii, haiwezekani kusema chochote, kwa sababu kutokana na asili ya mchakato wa mafunzo wa stochastic urefu wa vipindi vya mafunzo hutofautiana sana. Ili kufanya grafu hii iwe na maana zaidi, tunaweza kuhesabu **wastani wa kuendelea** juu ya mfululizo wa majaribio, tuseme 100. Hii inaweza kufanywa kwa urahisi kwa kutumia `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "#code block 12"
+   ]
+  },
+  {
+   "source": [
+    "## Kubadilisha Vigezo na Kuona Matokeo kwa Vitendo\n",
+    "\n",
+    "Sasa itakuwa ya kuvutia kuona jinsi modeli iliyofunzwa inavyofanya kazi. Hebu tuendeshe simulizi, na tutafuata mkakati sawa wa kuchagua hatua kama wakati wa mafunzo: kuchagua kwa kuzingatia usambazaji wa uwezekano katika Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 13"
+   ]
+  },
+  {
+   "source": [
+    "## Kuhifadhi matokeo kwenye GIF inayotembea\n",
+    "\n",
+    "Ikiwa unataka kuwavutia marafiki zako, unaweza kutaka kuwatumia picha ya GIF inayotembea ya fimbo ya kusawazisha. Ili kufanya hivyo, tunaweza kutumia `env.render` kuzalisha fremu ya picha, kisha kuhifadhi hizo kwenye GIF inayotembea kwa kutumia maktaba ya PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya kutafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutokuelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/8-Reinforcement/2-Gym/solution/notebook.ipynb b/translations/sw/8-Reinforcement/2-Gym/solution/notebook.ipynb
new file mode 100644
index 000000000..a3a62fc7c
--- /dev/null
+++ b/translations/sw/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -0,0 +1,524 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "5c0e485e58d63c506f1791c4dbf990ce",
+   "translation_date": "2025-09-06T15:20:31+00:00",
+   "source_file": "8-Reinforcement/2-Gym/solution/notebook.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Kuteleza kwa CartPole\n",
+    "\n",
+    "> **Tatizo**: Ikiwa Peter anataka kutoroka kutoka kwa mbwa mwitu, anahitaji kuwa na uwezo wa kusonga haraka kuliko yeye. Tutaona jinsi Peter anaweza kujifunza kuteleza, hasa, kudumisha usawa, kwa kutumia Q-Learning.\n",
+    "\n",
+    "Kwanza, wacha tusakinishe gym na kuingiza maktaba zinazohitajika:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: gym in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.18.3)\n",
+      "Requirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
+      "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
+      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.6.0)\n",
+      "Requirement already satisfied: pyglet<=1.5.15,>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.5.15)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!pip install gym \n",
+    "\n",
+    "import gym\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "source": [
+    "## Unda mazingira ya cartpole\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Ili kuona jinsi mazingira yanavyofanya kazi, hebu tuendeshe simulizi fupi kwa hatua 100.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Wakati wa uigaji, tunahitaji kupata uchunguzi ili kuamua jinsi ya kutenda. Kwa kweli, kazi ya `step` inaturudishia uchunguzi wa sasa, kazi ya zawadi, na bendera ya `done` inayonyesha ikiwa ina maana kuendelea na uigaji au la:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[ 0.03044442 -0.19543914 -0.04496216  0.28125618] -> 1.0\n",
+      "[ 0.02653564 -0.38989186 -0.03933704  0.55942606] -> 1.0\n",
+      "[ 0.0187378  -0.19424049 -0.02814852  0.25461393] -> 1.0\n",
+      "[ 0.01485299 -0.38894946 -0.02305624  0.53828712] -> 1.0\n",
+      "[ 0.007074   -0.19351108 -0.0122905   0.23842953] -> 1.0\n",
+      "[ 0.00320378  0.00178427 -0.00752191 -0.05810469] -> 1.0\n",
+      "[ 0.00323946  0.19701326 -0.008684   -0.35315131] -> 1.0\n",
+      "[ 0.00717973  0.00201587 -0.01574703 -0.06321931] -> 1.0\n",
+      "[ 0.00722005  0.19736001 -0.01701141 -0.36082863] -> 1.0\n",
+      "[ 0.01116725  0.39271958 -0.02422798 -0.65882671] -> 1.0\n",
+      "[ 0.01902164  0.19794307 -0.03740452 -0.37387001] -> 1.0\n",
+      "[ 0.0229805   0.39357584 -0.04488192 -0.67810827] -> 1.0\n",
+      "[ 0.03085202  0.58929164 -0.05844408 -0.98457719] -> 1.0\n",
+      "[ 0.04263785  0.78514572 -0.07813563 -1.2950295 ] -> 1.0\n",
+      "[ 0.05834076  0.98116859 -0.10403622 -1.61111521] -> 1.0\n",
+      "[ 0.07796413  0.78741784 -0.13625852 -1.35259196] -> 1.0\n",
+      "[ 0.09371249  0.98396202 -0.16331036 -1.68461179] -> 1.0\n",
+      "[ 0.11339173  0.79106371 -0.1970026  -1.44691436] -> 1.0\n",
+      "[ 0.12921301  0.59883361 -0.22594088 -1.22169133] -> 1.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Tunaweza kupata thamani ya chini na ya juu ya nambari hizo:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(env.observation_space.low)\n",
+    "print(env.observation_space.high)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretize(x):\n",
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+   ]
+  },
+  {
+   "source": [
+    "Hebu pia tuchunguze mbinu nyingine ya kugawanya kwa kutumia mabano:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_bins(i,num):\n",
+    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
+    "\n",
+    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
+    "\n",
+    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
+    "nbins = [20,20,10,10] # number of bins for each parameter\n",
+    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
+    "\n",
+    "def discretize_bins(x):\n",
+    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+   ]
+  },
+  {
+   "source": [
+    "Sasa tuendeshe uigaji mfupi na tuangalie zile thamani za mazingira zisizoendelea.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 2, -4, -9)\n(0, 3, -6, -12)\n(0, 2, -8, -9)\n(0, 3, -10, -13)\n(0, 4, -13, -16)\n(0, 4, -16, -19)\n(0, 4, -20, -17)\n(0, 4, -24, -20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   #env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   #print(discretize_bins(obs))\n",
+    "   print(discretize(obs))\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = {}\n",
+    "actions = (0,1)\n",
+    "\n",
+    "def qvalues(state):\n",
+    "    return [Q.get((state,a),0) for a in actions]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hyperparameters\n",
+    "alpha = 0.3\n",
+    "gamma = 0.9\n",
+    "epsilon = 0.90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 108.0, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v\n",
+    "\n",
+    "Qmax = 0\n",
+    "cum_rewards = []\n",
+    "rewards = []\n",
+    "for epoch in range(100000):\n",
+    "    obs = env.reset()\n",
+    "    done = False\n",
+    "    cum_reward=0\n",
+    "    # == do the simulation ==\n",
+    "    while not done:\n",
+    "        s = discretize(obs)\n",
+    "        if random.random()<epsilon:\n",
+    "            # exploitation - chose the action according to Q-Table probabilities\n",
+    "            v = probs(np.array(qvalues(s)))\n",
+    "            a = random.choices(actions,weights=v)[0]\n",
+    "        else:\n",
+    "            # exploration - randomly chose the action\n",
+    "            a = np.random.randint(env.action_space.n)\n",
+    "\n",
+    "        obs, rew, done, info = env.step(a)\n",
+    "        cum_reward+=rew\n",
+    "        ns = discretize(obs)\n",
+    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
+    "    cum_rewards.append(cum_reward)\n",
+    "    rewards.append(cum_reward)\n",
+    "    # == Periodically print results and calculate average reward ==\n",
+    "    if epoch%5000==0:\n",
+    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
+    "        if np.average(cum_rewards) > Qmax:\n",
+    "            Qmax = np.average(cum_rewards)\n",
+    "            Qbest = Q\n",
+    "        cum_rewards=[]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Kutoka kwa grafu hii, haiwezekani kusema chochote, kwa sababu kutokana na asili ya mchakato wa mafunzo wa stochastic urefu wa vipindi vya mafunzo hutofautiana sana. Ili kufanya grafu hii iwe na maana zaidi, tunaweza kuhesabu **wastani unaoendelea** juu ya mfululizo wa majaribio, tuseme 100. Hii inaweza kufanywa kwa urahisi kwa kutumia `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "def running_average(x,window):\n",
+    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
+    "\n",
+    "plt.plot(running_average(rewards,100))"
+   ]
+  },
+  {
+   "source": [
+    "## Kubadilisha Hyperparameta na Kuona Matokeo kwa Vitendo\n",
+    "\n",
+    "Sasa itakuwa ya kuvutia kuona jinsi modeli iliyofunzwa inavyofanya kazi. Hebu tuendeshe simulizi, na tutafuata mkakati sawa wa kuchagua hatua kama wakati wa mafunzo: kuchagua kulingana na usambazaji wa uwezekano katika Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset()\n",
+    "done = False\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   env.render()\n",
+    "   v = probs(np.array(qvalues(s)))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Kuhifadhi matokeo kwenye GIF ya michoro\n",
+    "\n",
+    "Ikiwa unataka kuwavutia marafiki zako, unaweza kutaka kuwatumia picha ya michoro ya fimbo ya kusawazisha. Ili kufanya hivyo, tunaweza kutumia `env.render` kuzalisha fremu ya picha, kisha kuhifadhi hizo kwenye GIF ya michoro kwa kutumia maktaba ya PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kuhakikisha usahihi, tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuchukuliwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/sw/PyTorch_Fundamentals.ipynb b/translations/sw/PyTorch_Fundamentals.ipynb
new file mode 100644
index 000000000..12aec6bb6
--- /dev/null
+++ b/translations/sw/PyTorch_Fundamentals.ipynb
@@ -0,0 +1,2828 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4",
+   "authorship_tag": "ABX9TyOgv0AozH1FKQBD+RkgT2bV",
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "coopTranslator": {
+   "original_hash": "0ca21b6ee62904d616f2e36dc1cf0da7",
+   "translation_date": "2025-09-06T13:08:11+00:00",
+   "source_file": "PyTorch_Fundamentals.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "view-in-github",
+    "colab_type": "text"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EHh5JllMh1rG",
+    "outputId": "f55755ad-c369-414c-85ec-6e9d4f061a02",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'2.2.1+cu121'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(\"I am excited to run this\")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UPlb-duwXAfz",
+    "outputId": "cfd687e4-1238-49f4-ab6b-ee1305b740d2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "I am excited to run this\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(torch.__version__)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "byWVlJ9wXDSk",
+    "outputId": "fd74a5c4-4d4a-41b2-ef3c-562ea3e4811f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2.2.1+cu121\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "Osm80zoEYklS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# scalar\n",
+    "scalar = torch.tensor(7)\n",
+    "scalar"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-o8wvJ-VXZmI",
+    "outputId": "558816f5-1205-4de1-fe1f-2f96e9bd79e6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(7)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mCZ2tXC4Y_Sg",
+    "outputId": "2d86dbdc-56e1-45c6-d3dd-14515f2a457a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ssN00By0ZQgS",
+    "outputId": "490f40d1-5135-4969-a6d3-c8c902cdc473"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# vector\n",
+    "vector = torch.tensor([7, 7])\n",
+    "vector\n",
+    "#vector.ndim\n",
+    "#vector.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Bws__5wlZnmF",
+    "outputId": "944e38f9-5ba1-4ddc-a9c6-cfb6a19bb488"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([7, 7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "vector.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9pjCvnsZZzNG",
+    "outputId": "e030a4da-8f81-4858-fbce-86da2aaafe52"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([2])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Matrix\n",
+    "MATRIX = torch.tensor([[7, 8],[9, 10]])\n",
+    "MATRIX"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a747hI9SaBGW",
+    "outputId": "af835ddb-81ff-4981-badb-441567194d15"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 7,  8],\n",
+       "        [ 9, 10]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XdTfFa7vaRUj",
+    "outputId": "0fbbab9c-8263-4cad-a380-0d2a16ca499e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX[0]\n",
+    "MATRIX[1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TFeD3jSDafm7",
+    "outputId": "69b44ab3-5ba7-451a-c6b2-f019a03d0c96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Tensor\n",
+    "TENSOR = torch.tensor([[[1, 2, 3],[3,6,9], [2,4,5]]])\n",
+    "TENSOR"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ic3cE47tah42",
+    "outputId": "f250e295-91de-43ec-9d80-588a6fe0abde"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 2, 3],\n",
+       "         [3, 6, 9],\n",
+       "         [2, 4, 5]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wvjf5fczbAM1",
+    "outputId": "9c72b5b8-bafe-4ae7-9883-b051e209eada"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mwtXZwiMbN3m",
+    "outputId": "331a5e36-b1b0-4a5f-a9b8-e7049cbaa8f9"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vzdZu_IfbP3J",
+    "outputId": "e24e7e71-e365-412d-ff50-fc094b56d2f3"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [3, 6, 9],\n",
+       "        [2, 4, 5]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "A8OL9eWfcRrJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor = torch.rand(3,4)\n",
+    "random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hAqSDE1EcVS_",
+    "outputId": "946171c3-d054-400c-f893-79110356888c"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.4414, 0.7681, 0.8385, 0.3166],\n",
+       "        [0.0468, 0.5812, 0.0670, 0.9173],\n",
+       "        [0.2959, 0.3276, 0.7411, 0.4643]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "g4fvPE5GcwzP",
+    "outputId": "8737f36b-6864-4059-eaed-6f9156c22306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XsAg99QmdAU6",
+    "outputId": "35467c11-257c-4f16-99aa-eca930bcbc36"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.size()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cii1pNdVdB68",
+    "outputId": "fc8d2de6-9215-43de-99f7-7b0d7f7d20fa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_image_tensor = torch.rand(size=(3, 224, 224)) #color channels, height, width\n",
+    "random_image_tensor.ndim, random_image_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aTKq2j0cdDjb",
+    "outputId": "6be42057-20b9-4faf-d79d-8b65c42cc27e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([3, 224, 224]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor_ofownsize = torch.rand(size=(5,10,10))\n",
+    "random_tensor_ofownsize.ndim, random_tensor_ofownsize.shape\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IyhDdj-Pd6nC",
+    "outputId": "43e5e334-6d4d-4b67-f87d-7d364c6d8c67"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([5, 10, 10]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "UOJW08uOert_"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero = torch.zeros(size=(3, 4))\n",
+    "zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGvXtaXyefie",
+    "outputId": "d40d3e28-8667-4d2f-8b62-f0829c6162ad"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero*random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OyUkUPkDe0uH",
+    "outputId": "26c2e4be-36ba-4c6c-9a90-2704ec135828"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones = torch.ones(size=(3, 4))\n",
+    "ones\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "y_Ac62Aqe82G",
+    "outputId": "291de5d9-b9df-49de-c9d1-d098e3e9f4d8"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TvGOA9odfIEO",
+    "outputId": "45949ef4-6649-4b6c-d6af-2d4bfb8de832"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones*zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "--pTyge-fI-8",
+    "outputId": "c4d9bb7e-829b-43db-e2db-b1a2d64e61f0"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qDcc7Z36fSJF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "one_to_ten = torch.arange(start = 1, end = 11, step = 1)\n",
+    "one_to_ten"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w3CZB4zUfR1s",
+    "outputId": "197fcba1-da0a-4b4a-ed11-3974bd6c01aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ten_zeros = torch.zeros_like(one_to_ten)\n",
+    "ten_zeros"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WZh99BwVfRy8",
+    "outputId": "51ef8bfb-6fa0-4099-ff66-b97d65b2ddea"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "pGGhgsbUgqbW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor = torch.tensor([3.0, 6.0,9.0], dtype = None, device = None, requires_grad = False)\n",
+    "float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JORJl4XkfRsx",
+    "outputId": "71114171-0f49-481f-b6fc-6cb48e2fb895"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3., 6., 9.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6wOPPwGyfRLn",
+    "outputId": "f23776a1-b682-404a-9f67-d5bcb0402666"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor = float_32_tensor.type(torch.float16)\n",
+    "float_16_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tFsHCvmZfOYe",
+    "outputId": "d3aa305a-7591-47f5-97fd-61bff60b44bd"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float16"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 31
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TQiCGTPuwq0q",
+    "outputId": "98750fce-1ca3-4889-e269-8b753efdea96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor = torch.tensor([3, 6, 9], dtype = torch.int32)\n",
+    "int_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5hlrLvGUw5D_",
+    "outputId": "41d890a0-9aee-446c-d906-631ce2ab0995"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9], dtype=torch.int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ihApD9u3xTNW",
+    "outputId": "d295eed0-6996-4e0f-8502-ff4b55cd1373"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(0,100,10)"
+   ],
+   "metadata": {
+    "id": "utKhlb_KxWDQ"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p78D74E9Rj7Y",
+    "outputId": "781a1614-a900-41f5-9e5d-358f0b2390aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.min()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4BcSs5NeRkcj",
+    "outputId": "3f24a8dc-58e9-4a5f-9834-e85856a34f9d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.max()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hinqvXVLRm4q",
+    "outputId": "5c7d8a53-3913-4ac1-bba3-5ba8ff68250a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 38
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mean(x.type(torch.float32))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k7okc0_vRpnB",
+    "outputId": "91e5494f-dc57-417c-ea4d-25dbc547c893"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.type(torch.float32).mean()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "29QcDTjHRq10",
+    "outputId": "62937c6c-78e0-49f2-dde3-1543ee8f7907"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wlpY_G_sbdKF",
+    "outputId": "475d8258-af65-4011-a258-b93d4d8142d4"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(450)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmax()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GT6HJzwhbk4n",
+    "outputId": "2e455c20-c322-4bcf-d07c-1259d3ccefc6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmin()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "egL3oi2Mb19P",
+    "outputId": "f71fb32f-6338-44a3-b377-75bea0a3ab54"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p2U8DZKib3DP",
+    "outputId": "b9f613b9-74e9-45f4-ed01-05babb6a6793"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[9]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24qBFlGYcABe",
+    "outputId": "5813cfcb-7f63-4bd7-ee46-f95ccbfda939"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(1, 10)\n",
+    "x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0GPOxEzkcBHO",
+    "outputId": "aefbd903-4f4c-4d2c-c90f-eccd682fe018"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped = x.reshape(1,9)\n",
+    "x_reshaped, x_reshaped.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "spmRgQjwddgp",
+    "outputId": "85a7c55c-2909-4ea2-fc68-386dddc65742"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), torch.Size([1, 9]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped.view(1,9)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tH2ahWGydqqP",
+    "outputId": "65d92263-4fc4-434a-c06d-c5e08436f7fe"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked = torch.stack([x, x, x, x], dim = 1)\n",
+    "x_stacked"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jgCeJcaud_-1",
+    "outputId": "7f293a37-6ef1-43b6-aee5-9d6d91c94f9e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XhJHIK6cfPse",
+    "outputId": "06c47b89-3a9e-453e-bcc3-00cbcb0b8b49"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=1)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ej2c3Xxzf0tq",
+    "outputId": "94024061-eb37-446d-c4a8-e4d16cb6de81"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 52
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4DJYo1a0f5M0",
+    "outputId": "efca2b47-1b14-44de-9a9a-2c83629d153f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 53
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=-2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J4iEjn2ah2HL",
+    "outputId": "22395593-7c16-4162-beae-dd2bbe7bda35"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 55
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "tensor = torch.tensor([1, 2, 3])\n",
+    "tensor = tensor - 10\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cFfiD7Nth7Z_",
+    "outputId": "1139e1f8-fc1a-46ca-d636-f2bc4fd2eef6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-9, -8, -7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mul(tensor, 10)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dyA7BM_GHhqE",
+    "outputId": "0e3b9671-d9e8-4a32-87bb-59bc05986142"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-90, -80, -70])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.sub(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "owtUsZ1KNegI",
+    "outputId": "189b7b23-0041-4e09-b991-cd209a48506a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-109, -108, -107])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.add(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K5STXlQONsyc",
+    "outputId": "00cbb79a-0a1d-4e21-86ec-5c91c37a2d01"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([91, 92, 93])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.divide(tensor, 2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xqMGnzIUNvp0",
+    "outputId": "c894cf3e-f148-45f8-cfc8-d78740735306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-4.5000, -4.0000, -3.5000])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(tensor, tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ruGzKpV8NyBc",
+    "outputId": "fddb63bf-006f-48b6-ae28-287fbcda8bc5"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8GS3r9yTeGfD",
+    "outputId": "c80b12ac-30b5-4f3d-c38c-9e41ba511b0e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QmuYHqXTemC0",
+    "outputId": "402fe3ba-70b5-4bb2-c83b-254db84ff810"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 622 µs, sys: 0 ns, total: 622 µs\n",
+      "Wall time: 516 µs\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "torch.matmul(tensor,tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dGr1fzdNepd8",
+    "outputId": "97bd6c91-bc25-4b38-cdf5-f22dcdef243e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 424 µs, sys: 998 µs, total: 1.42 ms\n",
+      "Wall time: 1.43 ms\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.rand(3,2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pGYDoK2gevfo",
+    "outputId": "2c8783d5-0453-47c5-c7ed-af10d25d6989"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5999, 0.0073],\n",
+       "        [0.9321, 0.3026],\n",
+       "        [0.3463, 0.3872]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(torch.rand(3,2), torch.rand(2,3))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KGBGQoB8e2DP",
+    "outputId": "4c2ef361-a2d0-41ee-c328-3992cbbc138d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.3528, 0.1893, 0.0714],\n",
+       "        [1.2791, 0.7110, 0.2563],\n",
+       "        [0.8812, 0.4553, 0.1803]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch"
+   ],
+   "metadata": {
+    "id": "ib8DMtkBe_LJ"
+   },
+   "execution_count": 1,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.rand(2,9)"
+   ],
+   "metadata": {
+    "id": "nJo8ZBdrQY1b"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wi6oRv4MQfgf",
+    "outputId": "55c99f55-31f6-4cf5-ba4e-19a47c3a0167"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5894, 0.4391, 0.2018, 0.5417, 0.3844, 0.3592, 0.9209, 0.9269, 0.0681],\n",
+       "        [0.0746, 0.1740, 0.6821, 0.6890, 0.0999, 0.7444, 0.2391, 0.4625, 0.8302]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "y=torch.randn(2,3,5)\n",
+    "y"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zpx8myAUQgoc",
+    "outputId": "07756d70-56bd-437c-c74e-9aecc1a77311"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[ 1.5552, -0.4877,  0.5175, -1.7958, -0.6187],\n",
+       "         [-0.3359, -1.9710,  0.0112, -1.7578, -1.5295],\n",
+       "         [ 0.0932,  1.4079,  0.9108,  0.3328, -0.6978]],\n",
+       "\n",
+       "        [[-0.9406, -1.0809, -0.2595,  0.1282,  1.6605],\n",
+       "         [ 1.1624,  1.0902,  1.7092, -0.2842, -1.3780],\n",
+       "         [-0.1534, -1.2795, -0.5495,  0.9902,  0.1822]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original = torch.rand(size=(224,224,3))\n",
+    "x_original"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s4U-X9bJQnWe",
+    "outputId": "657a7a76-962c-4b41-a76b-902d0482266c"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[0.4549, 0.6809, 0.2118],\n",
+       "         [0.4824, 0.9008, 0.8741],\n",
+       "         [0.1715, 0.1757, 0.1845],\n",
+       "         ...,\n",
+       "         [0.8741, 0.6594, 0.2610],\n",
+       "         [0.0092, 0.1984, 0.1955],\n",
+       "         [0.4236, 0.4182, 0.0251]],\n",
+       "\n",
+       "        [[0.9174, 0.1661, 0.5852],\n",
+       "         [0.1837, 0.2351, 0.3810],\n",
+       "         [0.3726, 0.4808, 0.8732],\n",
+       "         ...,\n",
+       "         [0.6794, 0.0554, 0.9202],\n",
+       "         [0.0864, 0.8750, 0.3558],\n",
+       "         [0.8445, 0.9759, 0.4934]],\n",
+       "\n",
+       "        [[0.1600, 0.2635, 0.7194],\n",
+       "         [0.9488, 0.3405, 0.3647],\n",
+       "         [0.6683, 0.5168, 0.9592],\n",
+       "         ...,\n",
+       "         [0.0521, 0.0140, 0.2445],\n",
+       "         [0.3596, 0.3999, 0.2730],\n",
+       "         [0.5926, 0.9877, 0.7784]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[0.4794, 0.5635, 0.3764],\n",
+       "         [0.9124, 0.6094, 0.5059],\n",
+       "         [0.4528, 0.4447, 0.5021],\n",
+       "         ...,\n",
+       "         [0.0089, 0.4816, 0.8727],\n",
+       "         [0.2173, 0.6296, 0.2347],\n",
+       "         [0.2028, 0.9931, 0.7201]],\n",
+       "\n",
+       "        [[0.3116, 0.6459, 0.4703],\n",
+       "         [0.0148, 0.2345, 0.7149],\n",
+       "         [0.8393, 0.5804, 0.6691],\n",
+       "         ...,\n",
+       "         [0.2105, 0.9460, 0.2696],\n",
+       "         [0.5918, 0.9295, 0.2616],\n",
+       "         [0.2537, 0.7819, 0.4700]],\n",
+       "\n",
+       "        [[0.6654, 0.1200, 0.5841],\n",
+       "         [0.9147, 0.5522, 0.6529],\n",
+       "         [0.1799, 0.5276, 0.5415],\n",
+       "         ...,\n",
+       "         [0.7536, 0.4346, 0.8793],\n",
+       "         [0.3793, 0.1750, 0.7792],\n",
+       "         [0.9266, 0.8325, 0.9974]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted=x_original.permute(2, 0, 1)\n",
+    "print(x_original.shape)\n",
+    "print(x_permuted.shape)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DD19_zvbQzHo",
+    "outputId": "1d64ce1b-eb48-47e3-90b6-7f1340e7f2b2"
+   },
+   "execution_count": 9,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "torch.Size([224, 224, 3])\n",
+      "torch.Size([3, 224, 224])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NnPmMk4ZRF7w",
+    "outputId": "2cd5da7f-4a23-4a76-8c4a-bb982113f2a4"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z0ylNoAARgTo",
+    "outputId": "ddca0298-cddf-4048-9b71-a791655e5bed"
+   },
+   "execution_count": 11,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]=0.989"
+   ],
+   "metadata": {
+    "id": "RXw0xXsDRi4L"
+   },
+   "execution_count": 13,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1sFdV6wzRo3f",
+    "outputId": "1cf87d2c-6d88-453a-d136-0f625a2800f1"
+   },
+   "execution_count": 14,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xTX-hx2SR1wp",
+    "outputId": "0d4908c4-c3bc-44e3-8ec6-1487104cc209"
+   },
+   "execution_count": 15,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x=torch.arange(1,10).reshape(1,3,3)\n",
+    "x, x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mZomOe7gR4Q8",
+    "outputId": "0b3c922f-ec11-46de-b8a5-9f9533d866ad"
+   },
+   "execution_count": 18,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[[1, 2, 3],\n",
+       "          [4, 5, 6],\n",
+       "          [7, 8, 9]]]),\n",
+       " torch.Size([1, 3, 3]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3y7v4SQvSBs1",
+    "outputId": "8c53307d-e628-404d-db66-56c6bdffab7c"
+   },
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [4, 5, 6],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hf9uG4xLSNya",
+    "outputId": "3075bc42-9ffa-426b-8a86-95628ffcd824"
+   },
+   "execution_count": 21,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zA4G2Se4SRB3",
+    "outputId": "324312d2-ed0a-49eb-f81f-e904e53992fe"
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(1)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][2][2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mwy3zmKKSdbk",
+    "outputId": "d35172c3-b099-40a6-ddf1-a453c2adfa44"
+   },
+   "execution_count": 23,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[:,1,1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fE3nCM1KS7XT",
+    "outputId": "01f5d755-9737-4235-9f73-dce89ff6ba16"
+   },
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([5])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,0,:]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "luNDINKNTTxp",
+    "outputId": "091195ef-2f71-4602-e95f-529a69193150"
+   },
+   "execution_count": 25,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,:,2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KG8A4xbfThCL",
+    "outputId": "5866bc41-9241-4619-be7b-e9206b3f80ab"
+   },
+   "execution_count": 26,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import numpy as np"
+   ],
+   "metadata": {
+    "id": "CZ3PX0qlTwHJ"
+   },
+   "execution_count": 27,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array = np.arange(1.0, 8.0)"
+   ],
+   "metadata": {
+    "id": "UOBeTumiT3Lf"
+   },
+   "execution_count": 28,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RzcO32E9UCQl",
+    "outputId": "430def24-c42c-461f-e5e7-398544c695d3"
+   },
+   "execution_count": 29,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 4., 5., 6., 7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.from_numpy(array)\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JJIL0q1DUC6O",
+    "outputId": "8a3b1d7c-4482-4d32-f34f-9212d9d3a177"
+   },
+   "execution_count": 32,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1., 2., 3., 4., 5., 6., 7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array[3]=11.0"
+   ],
+   "metadata": {
+    "id": "j3Ce6q3DUIEK"
+   },
+   "execution_count": 33,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dc_BCVdjUsCc",
+    "outputId": "65537325-8b11-4f36-fc73-e56f30d6a036"
+   },
+   "execution_count": 34,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3., 11.,  5.,  6.,  7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VG1e_eITUta2",
+    "outputId": "a26c5198-23b6-4a6d-d73a-ba20cd9782b8"
+   },
+   "execution_count": 35,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1.,  2.,  3., 11.,  5.,  6.,  7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.ones(7)\n",
+    "tensor, tensor.dtype\n",
+    "numpy_tensor = tensor.numpy()\n",
+    "numpy_tensor, numpy_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Swt8JF8vUuev",
+    "outputId": "c9e5bf6a-6d2c-41d6-8327-366867ffdd2d"
+   },
+   "execution_count": 37,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(array([1., 1., 1., 1., 1., 1., 1.], dtype=float32), dtype('float32'))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "random_tensor_A = torch.rand(3,4)\n",
+    "random_tensor_B = torch.rand(3,4)\n",
+    "print(random_tensor_A)\n",
+    "print(random_tensor_B)\n",
+    "print(random_tensor_A == random_tensor_B)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGcagTteVFTD",
+    "outputId": "49405790-08e7-4210-b7f1-f00b904c7eb9"
+   },
+   "execution_count": 38,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.9870, 0.6636, 0.6873, 0.8863],\n",
+      "        [0.8386, 0.4169, 0.3587, 0.0265],\n",
+      "        [0.2981, 0.6025, 0.5652, 0.5840]])\n",
+      "tensor([[0.9821, 0.3481, 0.0913, 0.4940],\n",
+      "        [0.7495, 0.4387, 0.9582, 0.8659],\n",
+      "        [0.5064, 0.6919, 0.0809, 0.9771]])\n",
+      "tensor([[False, False, False, False],\n",
+      "        [False, False, False, False],\n",
+      "        [False, False, False, False]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "RANDOM_SEED = 42\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_C = torch.rand(3,4)\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_D = torch.rand(3,4)\n",
+    "print(random_tensor_C)\n",
+    "print(random_tensor_D)\n",
+    "print(random_tensor_C == random_tensor_D)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HznyXyEaWjLM",
+    "outputId": "25956434-01b6-4059-9054-c9978884ddc1"
+   },
+   "execution_count": 46,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[True, True, True, True],\n",
+      "        [True, True, True, True],\n",
+      "        [True, True, True, True]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!nvidia-smi"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vltPTh0YXJSt",
+    "outputId": "807af6dc-a9ca-4301-ec32-b688dbde8be8"
+   },
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Thu May 23 02:57:59 2024       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   60C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|  No running processes found                                                           |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "torch.cuda.is_available()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L6mMyPDyYh1j",
+    "outputId": "279c5dd8-c2a8-4fbd-f321-2f5d7c6e90e6"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "oOdiYa7ZYytx",
+    "outputId": "d73b04fc-8963-4826-9722-08d118d5ab91"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.cuda.device_count()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vOdsazLqZFM5",
+    "outputId": "8189cd6a-9017-4663-a652-3e15c517d9c3"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.tensor([1,2,3], device = \"cpu\")\n",
+    "print(tensor, tensor.device)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cdik9Vw3ZMv0",
+    "outputId": "044a68fd-83a1-409d-8e3b-655142ca0270"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([1, 2, 3]) cpu\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu = tensor.to(device)\n",
+    "tensor_on_gpu"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zmp835rrZp-z",
+    "outputId": "37fa3413-18a3-47bf-ae51-5b36ff85a3ef"
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3], device='cuda:0')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu.numpy()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 159
+    },
+    "id": "jhriaa8uZ1yM",
+    "outputId": "bc5a3226-1a12-4fea-8769-a44f21cdc323"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "TypeError",
+     "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-e0c96c7436fd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtensor_on_gpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_cpu = tensor_on_gpu.cpu().numpy()"
+   ],
+   "metadata": {
+    "id": "LHGXK3GgaOzL"
+   },
+   "execution_count": 12,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "id": "j-El4LlCajfq"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/1-Tools/notebook.ipynb b/translations/th/2-Regression/1-Tools/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/th/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/th/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
new file mode 100644
index 000000000..9cf70b748
--- /dev/null
+++ b/translations/th/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -0,0 +1,448 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_1-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "c18d3bd0bd8ae3878597e89dcd1fa5c1",
+   "translation_date": "2025-09-06T13:44:22+00:00",
+   "source_file": "2-Regression/1-Tools/solution/R/lesson_1-R.ipynb",
+   "language_code": "th"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "YJUHCXqK57yz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## บทนำสู่การวิเคราะห์การถดถอย - บทเรียนที่ 1\n",
+    "\n",
+    "#### ทำความเข้าใจในมุมมอง\n",
+    "\n",
+    "✅ มีวิธีการวิเคราะห์การถดถอยหลายประเภท และการเลือกใช้วิธีใดขึ้นอยู่กับคำตอบที่คุณต้องการ หากคุณต้องการทำนายความสูงที่เป็นไปได้ของบุคคลในช่วงอายุหนึ่ง คุณจะใช้ `linear regression` เพราะคุณกำลังมองหาค่า **ตัวเลข** หากคุณสนใจที่จะค้นหาว่าประเภทของอาหารควรถือว่าเป็นมังสวิรัติหรือไม่ คุณกำลังมองหาการ **จัดหมวดหมู่** ดังนั้นคุณจะใช้ `logistic regression` คุณจะได้เรียนรู้เพิ่มเติมเกี่ยวกับ logistic regression ในภายหลัง ลองคิดถึงคำถามบางข้อที่คุณสามารถถามจากข้อมูล และวิธีการใดที่เหมาะสมที่สุด\n",
+    "\n",
+    "ในส่วนนี้ คุณจะได้ทำงานกับ [ชุดข้อมูลขนาดเล็กเกี่ยวกับโรคเบาหวาน](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) ลองจินตนาการว่าคุณต้องการทดสอบการรักษาสำหรับผู้ป่วยโรคเบาหวาน โมเดล Machine Learning อาจช่วยคุณระบุว่าผู้ป่วยคนใดจะตอบสนองต่อการรักษาได้ดีกว่า โดยอิงจากการผสมผสานของตัวแปรต่าง ๆ แม้แต่โมเดลการถดถอยที่พื้นฐานที่สุด เมื่อถูกนำเสนอในรูปแบบภาพ อาจแสดงข้อมูลเกี่ยวกับตัวแปรที่ช่วยให้คุณจัดการทดลองทางคลินิกในเชิงทฤษฎีได้อย่างมีประสิทธิภาพ\n",
+    "\n",
+    "เมื่อกล่าวเช่นนั้น มาเริ่มต้นงานนี้กันเลย!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/encouRage.jpg\"\n",
+    "   width=\"630\"/>\n",
+    "   <figcaption>ผลงานศิลปะโดย @allison_horst</figcaption>\n",
+    "\n",
+    "<!--![ผลงานศิลปะโดย \\@allison_horst](../../../../../../2-Regression/1-Tools/images/encouRage.jpg)<br>ผลงานศิลปะโดย @allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "LWNNzfqd6feZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. การเตรียมเครื่องมือของเรา\n",
+    "\n",
+    "สำหรับงานนี้ เราจะต้องใช้แพ็กเกจดังต่อไปนี้:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) คือ [ชุดของแพ็กเกจใน R](https://www.tidyverse.org/packages) ที่ออกแบบมาเพื่อทำให้การวิเคราะห์ข้อมูลรวดเร็วขึ้น ง่ายขึ้น และสนุกมากขึ้น!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) เป็นกรอบการทำงานที่เป็น [ชุดของแพ็กเกจ](https://www.tidymodels.org/packages/) สำหรับการสร้างโมเดลและการเรียนรู้ของเครื่อง\n",
+    "\n",
+    "คุณสามารถติดตั้งแพ็กเกจเหล่านี้ได้ด้วยคำสั่ง:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+    "\n",
+    "สคริปต์ด้านล่างจะตรวจสอบว่าคุณมีแพ็กเกจที่จำเป็นสำหรับการทำโมดูลนี้หรือไม่ และจะติดตั้งให้ในกรณีที่บางแพ็กเกจขาดหายไป\n"
+   ],
+   "metadata": {
+    "id": "FIo2YhO26wI9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse, tidymodels)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "id": "cIA9fz9v7Dss",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ตอนนี้ มาโหลดแพ็กเกจที่ยอดเยี่ยมเหล่านี้และทำให้พร้อมใช้งานในเซสชัน R ปัจจุบันของเรา (นี่เป็นเพียงตัวอย่าง `pacman::p_load()` ได้ทำสิ่งนี้ให้คุณแล้ว)\n"
+   ],
+   "metadata": {
+    "id": "gpO_P_6f9WUG"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# load the core Tidyverse packages\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# load the core Tidymodels packages\r\n",
+    "library(tidymodels)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NLMycgG-9ezO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. ชุดข้อมูลโรคเบาหวาน\n",
+    "\n",
+    "ในแบบฝึกหัดนี้ เราจะนำทักษะการวิเคราะห์การถดถอยมาใช้โดยการทำนายบนชุดข้อมูลโรคเบาหวาน ชุดข้อมูล [โรคเบาหวาน](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) ประกอบด้วย `442 ตัวอย่าง` ของข้อมูลเกี่ยวกับโรคเบาหวาน โดยมีตัวแปรคุณลักษณะเชิงพยากรณ์ 10 ตัว ได้แก่ `อายุ`, `เพศ`, `ดัชนีมวลกาย`, `ความดันโลหิตเฉลี่ย`, และ `การวัดระดับเซรั่มในเลือดหกตัว` รวมถึงตัวแปรผลลัพธ์ `y`: การวัดเชิงปริมาณของการพัฒนาของโรคในหนึ่งปีหลังจากจุดเริ่มต้น\n",
+    "\n",
+    "|จำนวนการสังเกตการณ์|442|\n",
+    "|----------------------|:---|\n",
+    "|จำนวนตัวแปรพยากรณ์|10 คอลัมน์แรกเป็นตัวแปรเชิงพยากรณ์เชิงตัวเลข|\n",
+    "|ผลลัพธ์/เป้าหมาย|คอลัมน์ที่ 11 เป็นการวัดเชิงปริมาณของการพัฒนาของโรคในหนึ่งปีหลังจากจุดเริ่มต้น|\n",
+    "|ข้อมูลตัวแปรพยากรณ์|- อายุเป็นปี\n",
+    "||- เพศ\n",
+    "||- bmi ดัชนีมวลกาย\n",
+    "||- bp ความดันโลหิตเฉลี่ย\n",
+    "||- s1 tc, คอเลสเตอรอลรวมในเซรั่ม\n",
+    "||- s2 ldl, ไลโปโปรตีนความหนาแน่นต่ำ\n",
+    "||- s3 hdl, ไลโปโปรตีนความหนาแน่นสูง\n",
+    "||- s4 tch, คอเลสเตอรอลรวม / HDL\n",
+    "||- s5 ltg, อาจเป็นค่าลอการิทึมของระดับไตรกลีเซอไรด์ในเซรั่ม\n",
+    "||- s6 glu, ระดับน้ำตาลในเลือด|\n",
+    "\n",
+    "> 🎓 จำไว้ว่า นี่คือการเรียนรู้แบบมีผู้สอน และเราต้องมีเป้าหมายที่ชื่อว่า 'y'\n",
+    "\n",
+    "ก่อนที่คุณจะสามารถจัดการข้อมูลด้วย R ได้ คุณจำเป็นต้องนำเข้าข้อมูลเข้าสู่หน่วยความจำของ R หรือสร้างการเชื่อมต่อกับข้อมูลที่ R สามารถใช้เพื่อเข้าถึงข้อมูลจากระยะไกล\n",
+    "\n",
+    "> แพ็กเกจ [readr](https://readr.tidyverse.org/) ซึ่งเป็นส่วนหนึ่งของ Tidyverse ให้วิธีการที่รวดเร็วและเป็นมิตรในการอ่านข้อมูลแบบสี่เหลี่ยมเข้าสู่ R\n",
+    "\n",
+    "ตอนนี้ เรามาโหลดชุดข้อมูลโรคเบาหวานจาก URL แหล่งข้อมูลนี้: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+    "\n",
+    "นอกจากนี้ เราจะทำการตรวจสอบความสมเหตุสมผลของข้อมูลโดยใช้ `glimpse()` และแสดง 5 แถวแรกโดยใช้ `slice()`\n",
+    "\n",
+    "ก่อนที่จะไปต่อ เรามาแนะนำสิ่งที่คุณจะพบเจอบ่อยในโค้ด R 🥁🥁: ตัวดำเนินการ pipe `%>%`\n",
+    "\n",
+    "ตัวดำเนินการ pipe (`%>%`) ทำการดำเนินการตามลำดับตรรกะโดยส่งวัตถุไปข้างหน้าเข้าสู่ฟังก์ชันหรือคำสั่ง คุณสามารถคิดว่าตัวดำเนินการ pipe เป็นการพูดว่า \"และจากนั้น\" ในโค้ดของคุณ\n"
+   ],
+   "metadata": {
+    "id": "KM6iXLH996Cl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import the data set\r\n",
+    "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Get a glimpse and dimensions of the data\r\n",
+    "glimpse(diabetes)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Select the first 5 rows of the data\r\n",
+    "diabetes %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Z1geAMhM-bSP"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`glimpse()` แสดงให้เห็นว่าข้อมูลนี้มี 442 แถว และ 11 คอลัมน์ โดยทุกคอลัมน์เป็นชนิดข้อมูล `double`\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "> `glimpse()` และ `slice()` เป็นฟังก์ชันใน [`dplyr`](https://dplyr.tidyverse.org/) Dplyr ซึ่งเป็นส่วนหนึ่งของ Tidyverse เป็นไวยากรณ์สำหรับการจัดการข้อมูลที่ให้ชุดคำกริยาที่สอดคล้องกันเพื่อช่วยแก้ปัญหาท้าทายที่พบบ่อยที่สุดในการจัดการข้อมูล\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "ตอนนี้เมื่อเรามีข้อมูลแล้ว เรามาโฟกัสไปที่คุณลักษณะหนึ่ง (`bmi`) เพื่อใช้ในแบบฝึกหัดนี้ ซึ่งจะต้องเลือกคอลัมน์ที่ต้องการ แล้วเราจะทำสิ่งนี้ได้อย่างไร?\n",
+    "\n",
+    "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) ช่วยให้เราสามารถ *เลือก* (และเปลี่ยนชื่อได้ถ้าต้องการ) คอลัมน์ใน data frame\n"
+   ],
+   "metadata": {
+    "id": "UwjVT1Hz-c3Z"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select predictor feature `bmi` and outcome `y`\r\n",
+    "diabetes_select <- diabetes %>% \r\n",
+    "  select(c(bmi, y))\r\n",
+    "\r\n",
+    "# Print the first 5 rows\r\n",
+    "diabetes_select %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RDY1oAKI-m80"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. ข้อมูลการฝึกและการทดสอบ\n",
+    "\n",
+    "ในกระบวนการเรียนรู้แบบมีผู้สอน มักจะมีการ *แบ่ง* ข้อมูลออกเป็นสองชุดย่อย; ชุดที่ใหญ่กว่า (โดยทั่วไป) สำหรับการฝึกโมเดล และชุดที่เล็กกว่า \"สำรองไว้\" เพื่อดูว่าโมเดลทำงานได้ดีเพียงใด\n",
+    "\n",
+    "ตอนนี้เรามีข้อมูลพร้อมแล้ว เราสามารถดูได้ว่ามีวิธีที่เครื่องสามารถช่วยกำหนดการแบ่งที่เหมาะสมระหว่างตัวเลขในชุดข้อมูลนี้หรือไม่ เราสามารถใช้แพ็กเกจ [rsample](https://tidymodels.github.io/rsample/) ซึ่งเป็นส่วนหนึ่งของเฟรมเวิร์ก Tidymodels เพื่อสร้างออบเจ็กต์ที่มีข้อมูลเกี่ยวกับ *วิธี* การแบ่งข้อมูล และใช้ฟังก์ชัน rsample อีกสองตัวเพื่อดึงชุดข้อมูลการฝึกและการทดสอบที่สร้างขึ้น:\n"
+   ],
+   "metadata": {
+    "id": "SDk668xK-tc3"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\r\n",
+    "# Split 67% of the data for training and the rest for tesing\r\n",
+    "diabetes_split <- diabetes_select %>% \r\n",
+    "  initial_split(prop = 0.67)\r\n",
+    "\r\n",
+    "# Extract the resulting train and test sets\r\n",
+    "diabetes_train <- training(diabetes_split)\r\n",
+    "diabetes_test <- testing(diabetes_split)\r\n",
+    "\r\n",
+    "# Print the first 3 rows of the training set\r\n",
+    "diabetes_train %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "EqtHx129-1h-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. ฝึกโมเดลการถดถอยเชิงเส้นด้วย Tidymodels\n",
+    "\n",
+    "ตอนนี้เราพร้อมที่จะฝึกโมเดลของเราแล้ว!\n",
+    "\n",
+    "ใน Tidymodels คุณสามารถกำหนดโมเดลโดยใช้ `parsnip()` โดยระบุแนวคิดสามอย่างดังนี้:\n",
+    "\n",
+    "-   **ประเภทของโมเดล** ใช้เพื่อแยกแยะโมเดล เช่น การถดถอยเชิงเส้น การถดถอยโลจิสติก โมเดลต้นไม้ตัดสินใจ และอื่นๆ\n",
+    "\n",
+    "-   **โหมดของโมเดล** รวมถึงตัวเลือกทั่วไป เช่น การถดถอยและการจำแนกประเภท; โมเดลบางประเภทสามารถรองรับทั้งสองโหมดนี้ ในขณะที่บางประเภทมีเพียงโหมดเดียว\n",
+    "\n",
+    "-   **เครื่องมือของโมเดล** คือเครื่องมือคำนวณที่จะใช้ในการปรับโมเดล โดยมักจะเป็นแพ็กเกจใน R เช่น **`\"lm\"`** หรือ **`\"ranger\"`**\n",
+    "\n",
+    "ข้อมูลเกี่ยวกับการสร้างโมเดลนี้จะถูกบันทึกไว้ในสเปคโมเดล ดังนั้นเรามาสร้างกันเลย!\n"
+   ],
+   "metadata": {
+    "id": "sBOS-XhB-6v7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- \r\n",
+    "  # Type\r\n",
+    "  linear_reg() %>% \r\n",
+    "  # Engine\r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  # Mode\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the model specification\r\n",
+    "lm_spec"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "20OwEw20--t3"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "หลังจากที่โมเดลถูก *กำหนด* แล้ว โมเดลสามารถถูก `ประเมิน` หรือ `ฝึกฝน` ได้โดยใช้ฟังก์ชัน [`fit()`](https://parsnip.tidymodels.org/reference/fit.html) ซึ่งมักจะใช้สูตรและข้อมูลบางส่วน\n",
+    "\n",
+    "`y ~ .` หมายความว่าเราจะปรับ `y` ให้เป็นค่าที่คาดการณ์/เป้าหมาย โดยอธิบายด้วยตัวแปรทำนาย/คุณลักษณะทั้งหมด เช่น `.` (ในกรณีนี้ เรามีตัวแปรทำนายเพียงตัวเดียว: `bmi`)\n"
+   ],
+   "metadata": {
+    "id": "_oDHs89k_CJj"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>%\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Train a linear regression model\r\n",
+    "lm_mod <- lm_spec %>% \r\n",
+    "  fit(y ~ ., data = diabetes_train)\r\n",
+    "\r\n",
+    "# Print the model\r\n",
+    "lm_mod"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YlsHqd-q_GJQ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "จากผลลัพธ์ของโมเดล เราสามารถเห็นค่าสัมประสิทธิ์ที่ได้จากการฝึกฝน ซึ่งค่าสัมประสิทธิ์เหล่านี้แสดงถึงค่าของเส้นที่เหมาะสมที่สุดที่ช่วยลดข้อผิดพลาดโดยรวมระหว่างตัวแปรจริงและตัวแปรที่คาดการณ์\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "## 5. ทำนายผลบนชุดข้อมูลทดสอบ\n",
+    "\n",
+    "เมื่อเราได้ฝึกฝนโมเดลแล้ว เราสามารถใช้มันเพื่อทำนายการพัฒนาของโรค y สำหรับชุดข้อมูลทดสอบโดยใช้ [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html) ซึ่งจะถูกใช้เพื่อวาดเส้นแบ่งระหว่างกลุ่มข้อมูล\n"
+   ],
+   "metadata": {
+    "id": "kGZ22RQj_Olu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\r\n",
+    "predictions <- lm_mod %>% \r\n",
+    "  predict(new_data = diabetes_test)\r\n",
+    "\r\n",
+    "# Print out some of the predictions\r\n",
+    "predictions %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "nXHbY7M2_aao"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เย้! 💃🕺 เราเพิ่งฝึกโมเดลและใช้มันเพื่อทำการพยากรณ์!\n",
+    "\n",
+    "เมื่อทำการพยากรณ์ ตามธรรมเนียมของ tidymodels จะสร้างผลลัพธ์ในรูปแบบ tibble หรือ data frame ที่มีชื่อคอลัมน์มาตรฐานเสมอ วิธีนี้ช่วยให้สามารถรวมข้อมูลต้นฉบับและผลการพยากรณ์ในรูปแบบที่ใช้งานได้ง่ายสำหรับการดำเนินการต่อ เช่น การสร้างกราฟ\n",
+    "\n",
+    "`dplyr::bind_cols()` ช่วยรวมหลาย data frame เข้าด้วยกันอย่างมีประสิทธิภาพในรูปแบบคอลัมน์\n"
+   ],
+   "metadata": {
+    "id": "R_JstwUY_bIs"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Combine the predictions and the original test set\r\n",
+    "results <- diabetes_test %>% \r\n",
+    "  bind_cols(predictions)\r\n",
+    "\r\n",
+    "\r\n",
+    "results %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RybsMJR7_iI8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 6. แสดงผลการสร้างโมเดล\n",
+    "\n",
+    "ถึงเวลาที่จะดูผลลัพธ์ในรูปแบบภาพแล้ว 📈 เราจะสร้างกราฟกระจายของค่าทั้งหมดใน `y` และ `bmi` จากชุดข้อมูลทดสอบ จากนั้นใช้ค่าที่คาดการณ์เพื่อวาดเส้นในตำแหน่งที่เหมาะสมที่สุด ระหว่างกลุ่มข้อมูลของโมเดล\n",
+    "\n",
+    "R มีระบบหลายแบบสำหรับการสร้างกราฟ แต่ `ggplot2` เป็นหนึ่งในระบบที่ดูเรียบง่ายและมีความหลากหลายมากที่สุด ระบบนี้ช่วยให้คุณสามารถสร้างกราฟโดย **การรวมองค์ประกอบที่เป็นอิสระเข้าด้วยกัน**\n"
+   ],
+   "metadata": {
+    "id": "XJbYbMZW_n_s"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plot\r\n",
+    "theme_set(theme_light())\r\n",
+    "# Create a scatter plot\r\n",
+    "results %>% \r\n",
+    "  ggplot(aes(x = bmi)) +\r\n",
+    "  # Add a scatter plot\r\n",
+    "  geom_point(aes(y = y), size = 1.6) +\r\n",
+    "  # Add a line plot\r\n",
+    "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R9tYp3VW_sTn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ ลองคิดดูว่าเกิดอะไรขึ้นที่นี่ เส้นตรงเส้นหนึ่งกำลังผ่านจุดข้อมูลเล็กๆ หลายจุด แต่จริงๆ แล้วมันกำลังทำอะไรอยู่? คุณเห็นไหมว่าคุณควรจะสามารถใช้เส้นนี้เพื่อทำนายว่าจุดข้อมูลใหม่ที่ยังไม่เคยเห็นควรจะอยู่ตรงไหนในความสัมพันธ์กับแกน y ของกราฟ? ลองอธิบายเป็นคำพูดถึงการใช้งานจริงของโมเดลนี้\n",
+    "\n",
+    "ขอแสดงความยินดี! คุณสร้างโมเดลการถดถอยเชิงเส้นตัวแรกของคุณ สร้างการทำนายด้วยมัน และแสดงผลในกราฟ!\n"
+   ],
+   "metadata": {
+    "id": "zrPtHIxx_tNI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/1-Tools/solution/notebook.ipynb b/translations/th/2-Regression/1-Tools/solution/notebook.ipynb
new file mode 100644
index 000000000..04c2ce5ee
--- /dev/null
+++ b/translations/th/2-Regression/1-Tools/solution/notebook.ipynb
@@ -0,0 +1,669 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model, model_selection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "print(X.shape)\n",
+    "print(X[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "เลือกเพียงคุณสมบัติเดียวเพื่อมุ่งเน้นสำหรับการออกกำลังกายนี้\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selecting the 3rd feature\n",
+    "X = X[:, 2]\n",
+    "print(X.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 1)\n",
+      "[[ 0.06169621]\n",
+      " [-0.05147406]\n",
+      " [ 0.04445121]\n",
+      " [-0.01159501]\n",
+      " [-0.03638469]\n",
+      " [-0.04069594]\n",
+      " [-0.04716281]\n",
+      " [-0.00189471]\n",
+      " [ 0.06169621]\n",
+      " [ 0.03906215]\n",
+      " [-0.08380842]\n",
+      " [ 0.01750591]\n",
+      " [-0.02884001]\n",
+      " [-0.00189471]\n",
+      " [-0.02560657]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [ 0.01211685]\n",
+      " [-0.0105172 ]\n",
+      " [-0.01806189]\n",
+      " [-0.05686312]\n",
+      " [-0.02237314]\n",
+      " [-0.00405033]\n",
+      " [ 0.06061839]\n",
+      " [ 0.03582872]\n",
+      " [-0.01267283]\n",
+      " [-0.07734155]\n",
+      " [ 0.05954058]\n",
+      " [-0.02129532]\n",
+      " [-0.00620595]\n",
+      " [ 0.04445121]\n",
+      " [-0.06548562]\n",
+      " [ 0.12528712]\n",
+      " [-0.05039625]\n",
+      " [-0.06332999]\n",
+      " [-0.03099563]\n",
+      " [ 0.02289497]\n",
+      " [ 0.01103904]\n",
+      " [ 0.07139652]\n",
+      " [ 0.01427248]\n",
+      " [-0.00836158]\n",
+      " [-0.06764124]\n",
+      " [-0.0105172 ]\n",
+      " [-0.02345095]\n",
+      " [ 0.06816308]\n",
+      " [-0.03530688]\n",
+      " [-0.01159501]\n",
+      " [-0.0730303 ]\n",
+      " [-0.04177375]\n",
+      " [ 0.01427248]\n",
+      " [-0.00728377]\n",
+      " [ 0.0164281 ]\n",
+      " [-0.00943939]\n",
+      " [-0.01590626]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.04931844]\n",
+      " [ 0.04121778]\n",
+      " [-0.06332999]\n",
+      " [-0.06440781]\n",
+      " [-0.02560657]\n",
+      " [-0.00405033]\n",
+      " [ 0.00457217]\n",
+      " [-0.00728377]\n",
+      " [-0.0374625 ]\n",
+      " [-0.02560657]\n",
+      " [-0.02452876]\n",
+      " [-0.01806189]\n",
+      " [-0.01482845]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [-0.06979687]\n",
+      " [ 0.03367309]\n",
+      " [-0.00405033]\n",
+      " [-0.02021751]\n",
+      " [ 0.00241654]\n",
+      " [-0.03099563]\n",
+      " [ 0.02828403]\n",
+      " [-0.03638469]\n",
+      " [-0.05794093]\n",
+      " [-0.0374625 ]\n",
+      " [ 0.01211685]\n",
+      " [-0.02237314]\n",
+      " [-0.03530688]\n",
+      " [ 0.00996123]\n",
+      " [-0.03961813]\n",
+      " [ 0.07139652]\n",
+      " [-0.07518593]\n",
+      " [-0.00620595]\n",
+      " [-0.04069594]\n",
+      " [-0.04824063]\n",
+      " [-0.02560657]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.00457217]\n",
+      " [-0.06440781]\n",
+      " [-0.01698407]\n",
+      " [-0.05794093]\n",
+      " [ 0.00996123]\n",
+      " [ 0.08864151]\n",
+      " [-0.00512814]\n",
+      " [-0.06440781]\n",
+      " [ 0.01750591]\n",
+      " [-0.04500719]\n",
+      " [ 0.02828403]\n",
+      " [ 0.04121778]\n",
+      " [ 0.06492964]\n",
+      " [-0.03207344]\n",
+      " [-0.07626374]\n",
+      " [ 0.04984027]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03207344]\n",
+      " [ 0.00457217]\n",
+      " [ 0.02073935]\n",
+      " [ 0.01427248]\n",
+      " [ 0.11019775]\n",
+      " [ 0.00133873]\n",
+      " [ 0.05846277]\n",
+      " [-0.02129532]\n",
+      " [-0.0105172 ]\n",
+      " [-0.04716281]\n",
+      " [ 0.00457217]\n",
+      " [ 0.01750591]\n",
+      " [ 0.08109682]\n",
+      " [ 0.0347509 ]\n",
+      " [ 0.02397278]\n",
+      " [-0.00836158]\n",
+      " [-0.06117437]\n",
+      " [-0.00189471]\n",
+      " [-0.06225218]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.09618619]\n",
+      " [-0.06979687]\n",
+      " [-0.02129532]\n",
+      " [-0.05362969]\n",
+      " [ 0.0433734 ]\n",
+      " [ 0.05630715]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04984027]\n",
+      " [ 0.11127556]\n",
+      " [ 0.06169621]\n",
+      " [ 0.01427248]\n",
+      " [ 0.04768465]\n",
+      " [ 0.01211685]\n",
+      " [ 0.00564998]\n",
+      " [ 0.04660684]\n",
+      " [ 0.12852056]\n",
+      " [ 0.05954058]\n",
+      " [ 0.09295276]\n",
+      " [ 0.01535029]\n",
+      " [-0.00512814]\n",
+      " [ 0.0703187 ]\n",
+      " [-0.00405033]\n",
+      " [-0.00081689]\n",
+      " [-0.04392938]\n",
+      " [ 0.02073935]\n",
+      " [ 0.06061839]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03315126]\n",
+      " [-0.06548562]\n",
+      " [ 0.0433734 ]\n",
+      " [-0.06225218]\n",
+      " [ 0.06385183]\n",
+      " [ 0.03043966]\n",
+      " [ 0.07247433]\n",
+      " [-0.0191397 ]\n",
+      " [-0.06656343]\n",
+      " [-0.06009656]\n",
+      " [ 0.06924089]\n",
+      " [ 0.05954058]\n",
+      " [-0.02668438]\n",
+      " [-0.02021751]\n",
+      " [-0.046085  ]\n",
+      " [ 0.07139652]\n",
+      " [-0.07949718]\n",
+      " [ 0.00996123]\n",
+      " [-0.03854032]\n",
+      " [ 0.01966154]\n",
+      " [ 0.02720622]\n",
+      " [-0.00836158]\n",
+      " [-0.01590626]\n",
+      " [ 0.00457217]\n",
+      " [-0.04285156]\n",
+      " [ 0.00564998]\n",
+      " [-0.03530688]\n",
+      " [ 0.02397278]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00297252]\n",
+      " [-0.06656343]\n",
+      " [-0.01267283]\n",
+      " [-0.04177375]\n",
+      " [-0.03099563]\n",
+      " [-0.00512814]\n",
+      " [-0.05901875]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00349435]\n",
+      " [ 0.05415152]\n",
+      " [-0.04500719]\n",
+      " [-0.05794093]\n",
+      " [-0.05578531]\n",
+      " [ 0.00133873]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00672779]\n",
+      " [ 0.04660684]\n",
+      " [ 0.02612841]\n",
+      " [ 0.04552903]\n",
+      " [ 0.04013997]\n",
+      " [-0.01806189]\n",
+      " [ 0.01427248]\n",
+      " [ 0.03690653]\n",
+      " [ 0.00349435]\n",
+      " [-0.07087468]\n",
+      " [-0.03315126]\n",
+      " [ 0.09403057]\n",
+      " [ 0.03582872]\n",
+      " [ 0.03151747]\n",
+      " [-0.06548562]\n",
+      " [-0.04177375]\n",
+      " [-0.03961813]\n",
+      " [-0.03854032]\n",
+      " [-0.02560657]\n",
+      " [-0.02345095]\n",
+      " [-0.06656343]\n",
+      " [ 0.03259528]\n",
+      " [-0.046085  ]\n",
+      " [-0.02991782]\n",
+      " [-0.01267283]\n",
+      " [-0.01590626]\n",
+      " [ 0.07139652]\n",
+      " [-0.03099563]\n",
+      " [ 0.00026092]\n",
+      " [ 0.03690653]\n",
+      " [ 0.03906215]\n",
+      " [-0.01482845]\n",
+      " [ 0.00672779]\n",
+      " [-0.06871905]\n",
+      " [-0.00943939]\n",
+      " [ 0.01966154]\n",
+      " [ 0.07462995]\n",
+      " [-0.00836158]\n",
+      " [-0.02345095]\n",
+      " [-0.046085  ]\n",
+      " [ 0.05415152]\n",
+      " [-0.03530688]\n",
+      " [-0.03207344]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04768465]\n",
+      " [ 0.06061839]\n",
+      " [ 0.05630715]\n",
+      " [ 0.09834182]\n",
+      " [ 0.05954058]\n",
+      " [ 0.03367309]\n",
+      " [ 0.05630715]\n",
+      " [-0.06548562]\n",
+      " [ 0.16085492]\n",
+      " [-0.05578531]\n",
+      " [-0.02452876]\n",
+      " [-0.03638469]\n",
+      " [-0.00836158]\n",
+      " [-0.04177375]\n",
+      " [ 0.12744274]\n",
+      " [-0.07734155]\n",
+      " [ 0.02828403]\n",
+      " [-0.02560657]\n",
+      " [-0.06225218]\n",
+      " [-0.00081689]\n",
+      " [ 0.08864151]\n",
+      " [-0.03207344]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00888341]\n",
+      " [ 0.00672779]\n",
+      " [-0.02021751]\n",
+      " [-0.02452876]\n",
+      " [-0.01159501]\n",
+      " [ 0.02612841]\n",
+      " [-0.05901875]\n",
+      " [-0.03638469]\n",
+      " [-0.02452876]\n",
+      " [ 0.01858372]\n",
+      " [-0.0902753 ]\n",
+      " [-0.00512814]\n",
+      " [-0.05255187]\n",
+      " [-0.02237314]\n",
+      " [-0.02021751]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00620595]\n",
+      " [-0.01698407]\n",
+      " [ 0.05522933]\n",
+      " [ 0.07678558]\n",
+      " [ 0.01858372]\n",
+      " [-0.02237314]\n",
+      " [ 0.09295276]\n",
+      " [-0.03099563]\n",
+      " [ 0.03906215]\n",
+      " [-0.06117437]\n",
+      " [-0.00836158]\n",
+      " [-0.0374625 ]\n",
+      " [-0.01375064]\n",
+      " [ 0.07355214]\n",
+      " [-0.02452876]\n",
+      " [ 0.03367309]\n",
+      " [ 0.0347509 ]\n",
+      " [-0.03854032]\n",
+      " [-0.03961813]\n",
+      " [-0.00189471]\n",
+      " [-0.03099563]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06492964]\n",
+      " [ 0.04013997]\n",
+      " [-0.02345095]\n",
+      " [ 0.05307371]\n",
+      " [ 0.04013997]\n",
+      " [-0.02021751]\n",
+      " [ 0.01427248]\n",
+      " [-0.03422907]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00457217]\n",
+      " [ 0.03043966]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.06169621]\n",
+      " [-0.00728377]\n",
+      " [ 0.00564998]\n",
+      " [ 0.05415152]\n",
+      " [-0.00836158]\n",
+      " [ 0.114509  ]\n",
+      " [ 0.06708527]\n",
+      " [-0.05578531]\n",
+      " [ 0.03043966]\n",
+      " [-0.02560657]\n",
+      " [ 0.10480869]\n",
+      " [-0.00620595]\n",
+      " [-0.04716281]\n",
+      " [-0.04824063]\n",
+      " [ 0.08540807]\n",
+      " [-0.01267283]\n",
+      " [-0.03315126]\n",
+      " [-0.00728377]\n",
+      " [-0.01375064]\n",
+      " [ 0.05954058]\n",
+      " [ 0.02181716]\n",
+      " [ 0.01858372]\n",
+      " [-0.01159501]\n",
+      " [-0.00297252]\n",
+      " [ 0.01750591]\n",
+      " [-0.02991782]\n",
+      " [-0.02021751]\n",
+      " [-0.05794093]\n",
+      " [ 0.06061839]\n",
+      " [-0.04069594]\n",
+      " [-0.07195249]\n",
+      " [-0.05578531]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03315126]\n",
+      " [ 0.04984027]\n",
+      " [-0.08488624]\n",
+      " [ 0.00564998]\n",
+      " [ 0.02073935]\n",
+      " [-0.00728377]\n",
+      " [ 0.10480869]\n",
+      " [-0.02452876]\n",
+      " [-0.00620595]\n",
+      " [-0.03854032]\n",
+      " [ 0.13714305]\n",
+      " [ 0.17055523]\n",
+      " [ 0.00241654]\n",
+      " [ 0.03798434]\n",
+      " [-0.05794093]\n",
+      " [-0.00943939]\n",
+      " [-0.02345095]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03422907]\n",
+      " [-0.00297252]\n",
+      " [ 0.06816308]\n",
+      " [ 0.00996123]\n",
+      " [ 0.00241654]\n",
+      " [-0.03854032]\n",
+      " [ 0.02612841]\n",
+      " [-0.08919748]\n",
+      " [ 0.06061839]\n",
+      " [-0.02884001]\n",
+      " [-0.02991782]\n",
+      " [-0.0191397 ]\n",
+      " [-0.04069594]\n",
+      " [ 0.01535029]\n",
+      " [-0.02452876]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06924089]\n",
+      " [-0.06979687]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [ 0.01858372]\n",
+      " [ 0.00133873]\n",
+      " [-0.03099563]\n",
+      " [-0.00405033]\n",
+      " [ 0.01535029]\n",
+      " [ 0.02289497]\n",
+      " [ 0.04552903]\n",
+      " [-0.04500719]\n",
+      " [-0.03315126]\n",
+      " [ 0.097264  ]\n",
+      " [ 0.05415152]\n",
+      " [ 0.12313149]\n",
+      " [-0.08057499]\n",
+      " [ 0.09295276]\n",
+      " [-0.05039625]\n",
+      " [-0.01159501]\n",
+      " [-0.0277622 ]\n",
+      " [ 0.05846277]\n",
+      " [ 0.08540807]\n",
+      " [-0.00081689]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00888341]\n",
+      " [ 0.08001901]\n",
+      " [ 0.07139652]\n",
+      " [-0.02452876]\n",
+      " [-0.0547075 ]\n",
+      " [-0.03638469]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.07786339]\n",
+      " [-0.03961813]\n",
+      " [ 0.01103904]\n",
+      " [-0.04069594]\n",
+      " [-0.03422907]\n",
+      " [ 0.00564998]\n",
+      " [ 0.08864151]\n",
+      " [-0.03315126]\n",
+      " [-0.05686312]\n",
+      " [-0.03099563]\n",
+      " [ 0.05522933]\n",
+      " [-0.06009656]\n",
+      " [ 0.00133873]\n",
+      " [-0.02345095]\n",
+      " [-0.07410811]\n",
+      " [ 0.01966154]\n",
+      " [-0.01590626]\n",
+      " [-0.01590626]\n",
+      " [ 0.03906215]\n",
+      " [-0.0730303 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Reshaping to get a 2D array\n",
+    "X = X.reshape(-1, 1)\n",
+    "print(X.shape)\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "เลือกโมเดลและปรับให้เหมาะสมกับข้อมูลการฝึกอบรม\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ใช้ข้อมูลทดสอบเพื่อทำนายเส้น\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "แสดงผลลัพธ์ในกราฟ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pMAeFSCesOkvRMK8pOR06BAweDLS2Rm/36KPA739vTJ+MxBwUogSz0+ZvPp8PXq8XixYtgtfrZTE6jTCvKbn4fMCECUC/ftGDk/Hj/YXW7BicqMUAhUhjdqo663a7kZubi8LCQkyePBmFhYXIzc21VIBlZsxrSg7z5wPdu/vrmkSSmuovY//22/YptBYv3uIh0phddu+NVKMj8O2eA6h2zJCrZIY+2M2qVcryRzZuVJaPYge67WZMRPLssDpDbhZIkiTMmjULRUVFHMQ0kJKSktBglflS2tq9G8jNlW+3cCFw001698a6eIuHSGNGrs7QKz+Eew8lDzvlSyXa8ePAiBHywcmttwLt7QxO5DBAIdKYUbv36pkfYodZIJJnp3ypRBICuP12oHdv4IsvIrcbPhw4dgx48cXkKLQWLwYoRBozYnWG3t96WaMjMjutauJMWfxcLn+hteefj95u507g66/9QQwpwwCFSAd6rs4w4luvUbNAVmO3VU1azJTZKWBT49NP/bMgU6ZEb7dypX+GRcnmfxSKAQqRToqLi7Fr1y54PB64XC54PB7U1tbGnXSo97fewGqOwAoe1ujws2OuRrwzZXYL2JQ4fBjo29e/qV80jzziD0wmTDCkW/YkLKipqUkAEE1NTYnuCpHhXC6XACD7cLlcqo9dVVUlHA5HyHFSUlJC/u10OkVVVZUO78y82traulyXjg9JkoTT6RRtbW2J7qoqgfclSZLq91VVVRX25yRJEpIk2e5vpK1NiGuvFcIfdkR+/OQnQpw6lejempea8ZszKEQWo1d+SKQZgsCU/axZszSbBbIau+ZqxJovlWzJtQsW+Aut/fOfkdukpAAHDvj3zunOAh6aYIBCZDF65IdEG3ACx6yqqkra4l12XtUUS76UXQO2zt5/359ncs890dutX+8vT5+dbUy/kgXjPCKLCXzrLSkpgSRJIUFFrPkhagYcM1e/1YvdVzWp3aVbq4DNrNVr9+wBhg6Vb/fXvwK//KX+/UlWnEEhsiCtVwnZeYZAC8mwqilQzba0tBQFBQVRAwUtAjYzJtieOAGMGiUfnNxyi7/QGoMTfTFAIbIoLVcJ2X2GIF7RcjUA/wzTH//4R1N8+zdCvAGb2VZECQH89rdAr17Ali2R2511FnD0KPCXv7DQmhG4WSARwefzITc3F3V1dWHzUCRJgsPhQG1tbdIMwuGE27MmINn2rgkEGQDC3maMNJMX+FuLdEvR6L+1118HSkvl2+3Y4Q9QKD5qxm/OoBCRIdVv7aC4uBhPP/102NesXA8lFrHeZjRLgu2WLf5ZELngZPly/wwLgxPjMUAhIgDfDziDBg0KeX7w4MEhA06yVg4F/O999uzZYV+z4/JaObHcZkx0vtORI0BGhj/XJJp58/yBybXX6tINUoCreIgoRKS8AiD8LY5kurXB1U5dBZJrlUpUvlN7O1BcDCxbFr1dYSFrmZgFZ1CICIB84uI999xjqsTGREj0t38ziHcGLRErop5+2l9ITS44aWz01z5hcGIODFCISLYyqBACTz31VNJUDo0k2Vc7abE02Mh8J6/Xn2dy553R261b57+d079/3KckDTFAISLZWxcAogYfdqkcKicZ6qFEouXSYD13+waAffv8gUlhYfR2L77oD0wuuyyu05FOGKAQkWa3JOx8awNI3tVOeuy9o8du362twA9/CDid0dtNm+bPSbn11phPRQbgnTYiE0lU6W+tbknY9dZGR4Fv/+GShSsqKmyZLKxXcrDaBNto7rzTn2sSzdChwOefA337qj++Wcvy2xkDFCKTCLdCJjMzE2VlZbjvvvt0/TAM3LqQu80TSaC4lh1vbYSjdu8aqzNzcvCSJcD118u3+/prYPjw2M6R7KvXEkZYUFNTkwAgmpqaEt0VIiGEEG1tbcLj8QiXyyU8Ho9oa2tT9fNVVVVCkiQBIOwjKytLVFVV6dT77/sQ6fzRHpIkCUmSdO8fyYv37zASj8ej6G/B4/Focj4lPv9cCH8GSfTHW2/Fd55I/9/k331s1IzfDFCI4lRVVSUcDkfIh5fD4VD8wdXW1tbl5yM99P4wLC8vVx2gOJ1OfkibQLx/h9EE/kYjBdGSJAmn06lZQBTNN98IccYZ8oHJ/ffHfy65/28a+b7tggEKkUG0+Hal9NtpIBjQ88NQyQeyw+EQq1at0vxbOsXOiG/5gXN0Po9RMwk+nxCTJskHJvn5Qpw8qc05zThzZHVqxm+u4iGKkVYrG9Tct9d7KW9glYokSRFXqVRWVmLs2LEoLS1FQUGBbfMurEKPFTbh6L00OJo//clfaK2qKnq7hgbggw+AHj20Oa+Zc2+SAQMUohhptemZ2pUven8YJnIgIvWM3HxPj6XB0VRX++uZlJVFb/fRR/75kwEDtD1/shfmSzSu4iGKkVbfrtSuoDHiwzDZVqlYmdHf8rVcGhxJXR3gcMi3e/55YPp0/foR+P9mXV1d2BmqZFu9ZjTOoBDFSKtvVx1vq0RjdJXSwEDEWznmZqdv+SdPApdeKh+cTJniL7SmZ3ACJG9hPrNggEIUIy3Lngduq2RlZUU8FsAPQ+rKLuX3Z80CUlOBDRsitxk8GGhuBl591X/rxwi85Zk4DFCIYqT1t6vi4mI0NjaivLwcmZmZIa/xw5Aisfq3/D/8wR9sfPcWIvryS/8eO6efbky/OjI694a+o2Z50GOPPSYuueQS0bdvX5GdnS2KiorEl19+GdLmqquu6rIE69e//nVIm927d4trrrlG9OrVS2RnZ4s5c+aIU6dOKe4HlxmTmYSrPxFvbRC9Cm6Rfenxd6inNWuUFVp7881E95S0pGb8loQIk/kTwYQJE/CLX/wCl156Kdra2vD73/8en3/+ObZt24Y+ffoAAAoKCnDOOefg4YcfDv5c7969kZaWBsC/JO7CCy9ETk4OFixYgPr6etx444249dZb8dhjjynqR3NzM9LT09HU1BQ8LlEicZ8OMgMr/B0ePAj07y/fbu5cQOGQQBaiZvxWFaB0dvDgQfTv3x9r1qzBlVdeCcAfoFx44YWoqKgI+zMrV67Eddddh/3792PAd2vCXnjhBdx77704ePAgevbsKXteBihERNbS3u6vT9LeHr3doEFAbS2gYCggC1IzfseVg9LU1AQAXe6Xv/baa+jXrx8uuOACzJ07F99++23wtZqaGowcOTIYnADA+PHj0dzcjK1bt4Y9T2trK5qbm0MeRERkDTfd5C+0JhecfP21f4kxgxMC4qiD0t7ejlmzZuFHP/oRLrjgguDzkydPxtChQzFo0CB89tlnuPfee/HVV1/B7XYDABoaGkKCEwDBfzc0NIQ91/z581FeXh5rV4mIKAFefx0oLVXW7oYb9O8PWUvMAcrMmTPx+eefY+3atSHP33bbbcH/HjlyJAYOHIixY8di586dOOuss2I619y5c3HnnXcG/93c3Ayn0xlbx4mISFdffw2ce658u5tuAhYu1L07ZFExBSh33HEHli9fjg8++AAOmYo6Y8aMAQDs2LEDZ511FnJycvDJJ5+EtGlsbAQA5OTkhD1GamoqUlNTY+kqEZGurJCYapQTJ4BeveTb9ejhb9uNhS4oClV/HkII3HHHHXjjjTfw/vvv48wzz5T9mc2bNwP4vophXl4etmzZggMHDgTbvPfee0hLS8OIESPUdIeIKKHcbjdyc3NRWFiIyZMno7CwEP3798fDDz8c9+Z8VnPZZcqCk4MH/RVjGZyQHFV/IjNnzsSrr74Kl8uF008/HQ0NDWhoaMDx48cBADt37sQjjzyCjRs3YteuXXjrrbdw44034sorr8SoUaMAAFdffTVGjBiBqVOn4tNPP8U777yD+++/HzNnzuQsCRFZhtvtRklJSZc9lA4fPox58+ZhwIABwdw7O3v8cX+htfXro7d75JE1EALo18+YfpENqCmwgk4F2AKPhQsXCiGE2LNnj7jyyitFZmamSE1NFWeffba4++67uxRk2bVrl5g4caLo1auX6Nevn7jrrrtYqI2ILKOtra1LUbRwD0mSTFsoLV5r1yortAb8XkiSJJxOJwsOkn6F2syCdVCIKJG8Xi8KCwsVtXU6naitrbVNXsqhQ0pnQf4F4OKQZzwej+47IZO5GVYHhYgoGdXX1ytuu3fvXlRXV+vYG2O0t/s381MWnPRC5+AEUHfdiBigEBGpFEj6V8rqA/Mtt/gLrZ08KdfyXAASgBNhX1V73Si5MUAhIlIpPz9ftsRCR1YdmBcv9ifAvvxy9HZ//3s7HA4nJGl72NclSYLT6UR+fr4OvSS7YoBCRKRSSkoKKisrZdtZdWDeudMfmMhVd50yxZ8GO3Vqt+D1kCQppE3g3xUVFbbJwyFjMEAhshmfzwev14tFixbB6/UmXT0OoxQXF6OqqgpZWVlhX7fiwNza6g9Mzj5bvq3PB7z66vf/Li4uxtKlSzF48OCQdg6HA0uXLkVxcbHGvSW74yoeIhtxu90oKysLqc3hcDhQWVnJAUInPp8Pjz76KCorK3H48OHg806nExUVFZa57ldcAdTUyLdrbAT694/8OivrUjRqxm8GKEQ2ESgc1vn/0oFv8vwWqy+rDswLFgD33CPfzuMBuEI4dlb9+9AaAxSiJOPz+ZCbm9ulqmmAJElwOBy2qscRDQcDeR9/DOTlybd76CFg3jzdu2NrnNn8HuugECWZ6urqiMEJ4N9Hyy71OOSE2x8nNzc3KcrOK3H4sD/PRC44ueACfwIsg5P4RNoSoa6uDiUlJfy7jIIBCpENKK2zYfV6HHJiGQySJalYCOD004EIOb0hjh0DtmzRv0925/P5UFZW1uW2K4Dgc7NmzbLt31y8GKAQmZDaQVNpnQ2r1uNQIpbBIFlmW6ZP9+8efPRo9HZbt/oDmd69jemX3XFmMz4MUIhMJpZBM1A4rHMNigCr1uNQQ+1gkAxT7263/3bOn/8cvd0rr/gDkxEjDOlW0uDMZnwYoBCZSKyDZsfCYclaKEvNYGD3qffaWn9gMmlS9HbXX+8PTKZNM6ZfyYYzm/FhgEJkEvEOmsleKEvNYGDXqfeTJ/2BybBh8m3b2oD/+z/9+5TMOLMZHwYoRCahxaBZXFyMXbt2wePxwOVywePxoLa21vbBCaBuMLDj1PtVV/l3G5ZTX++fNbHxZJppcGYzPgxQiExCq0EzJSUFBQUFKC0tRUFBQdJ8+KkZDOw09V5R4Z81+eCD6O1Wr/YHJjk5hnSLvpPsM5vxYIBCZBJ2GjQTRelgYIep9/Xr/YHJ7NnR2z3wgD8w+fGPjekXdZXMM5vxYCVZIpMIVIOtq6sLm4eSbNVg46GkkmwgIRlAyPU2+9YAR44AZ5wh3+7cc4Evv9S9O0SqsJIskQXxfrV2lNzmstrUuxD+ImtKgpOjRxmckPVxBoXIZMLt22G1nXGtxAr79vzmN8D//q98uy1b/CXqicyKmwUSWZwVBk0jJev1eOstoKhIvt3LLwM336x/f4jipWb87m5Qn4hIhcAtCkrOnWB37wZyc+XbFRcDVVW6d4coIRigEJFpBRJZO0/0BirrmjFXJB4nTyqrZQL4C60lwSQSJTEmyRKRKdm9HH1nP/mJsuCkro6F1ig5MEAhIlOyazn6zv73f/31TFatit7u3Xf9gcmgQcb0iyjReIuHiEzJKuXoY03g3bgRuOQS+ePPnQs89pgGHSWyGAYoFpSsKxoouVihsm4sCbxNTUBGhvyxhw0Dduzwz64QJSMuM7aYZFzRQMnJ7JV1IyXwRqpEG9gH58AB+WO3tAB9+2raXSJTYCVZmwp8IHa+Lx9Y0eB2uxPUM7Iyn88Hr9eLRYsWwev1mibp1MyVddUm8N55J9Ctm3xw8umn/kCGwQkRAxTLSLYVDWQMt9uN3NxcFBYWYvLkySgsLERubq5pgl2zlqNXmsD75JNbIUnA009HP96LL/oDk1GjNO4okYXxFo9FeL1eFBYWyrbzeDws8EWKqL1FkUhmy7tatGgRJk+eHKWFA8Be2eP89Kf+arFEyYKVZG3IKisaKPGUDOZyM3KSJGHWrFkoKioyRQK22SrrRk7M7Q7glKJjnDoFdOcnMFFEvMVjEVZY0UCJp/SWTbLUGNFLfn4+HA5Hp9yYf0JJcLJ3r/92DoMTougYoFhE+A/E70mSBKfTifz8fIN7RmahJok60TNyZk3MVapjAi8wHYAAcE3Un1m50h+YOBx6947IHhigWISZVzRQ4qlNok7kjJzZE3OVGjasGEK0A3g+ars5c/yByYQJxvSLyC6YJGsx4eqgOJ1OVFRUmCahUQmzJT3GwkzvQW0SdaJqjFgpMTeS5mYgPV2+3ZAhwK5dLLRG1JGq8VtYUFNTkwAgmpqaEt2VhGhraxMej0e4XC7h8XhEW1tborukSlVVlXA4HAL+eXEBQDgcDlFVVZXorilmtvfgcrlC+hLp4XK5Qt6DJElCkqSQNoHntH4vbW1tXa5Z5/M6nU7T/j23twsxeLAQ/vmQ6I8k/WgikqVm/GaAQoYKDIrhBic9BkU9mPE9eDweRQGKx+Pp8l46Bw1Op1OX9xBrH81gzhxlgcm//pXonhKZm5rxm7d4yDCB2wqRVo8kunS5EmZ9D/HcsjHqVpV87RA/l8uF0tJSzc8fi7ffBiZOlG/33HPAjBn694fI6ljqnkzJDktbzfoe4kmiDtQYKS0tRUFBgS7Bic/nQ2Njo6K2ZlgqX1fnzx2RC04mTPDPnTA4IdIeAxQyTKKXtmph2bJlitol4j2YtSx8YNXO7Nmzo7Yzw1L5tjZ/YKJkKfDJk/6lw0SkD5YKIsMYvbRV61sXbrcbFRUVitomahaguLgYRUVFplldFGnVTmdmWCpfVKSs7PyePYDTqX9/zMZMq9YoSahJbnnsscfEJZdcIvr27Suys7NFUVGR+PLLL0PaHD9+XNx+++0iMzNT9OnTRxQXF4uGhoaQNrt37xbXXHON6NWrl8jOzhZz5swRp06dUtwPJslaU2AVR7gEU2i4iqOtrU2Ul5eLzMxMzVbZyK1A0fo92IHSawYdE3OVeOklZQmwy5cnpHumYLZVa2Rduq3iGT9+vFi4cKH4/PPPxebNm8U111wjhgwZIo4ePRpsM336dOF0OsXq1avFhg0bxOWXXy6uuOKK4OttbW3iggsuEOPGjRObNm0SK1asEP369RNz587V5Q2Suei9tLWqqkpkZWVFDB5iPYfSFSgA+KH9HaXX7Omnn1Yd0Gmx1P7TT5UFJrNmqT60rZhx1RpZl2HLjA8cOCAAiDVr1gghhDhy5Ijo0aOHWLJkSbDNF198IQCImpoaIYQQK1asEN26dQuZVXn++edFWlqaaG1tVXReBijWptfS1qqqKt1mOJTWGZmV7KNZB7HUZlEi3m/zLS3KApOcHH/tk2Rm9do1ZD5qxu+4kmSbmpoAAJmZmQCAjRs34tSpUxg3blywzXnnnYchQ4agpqYGAFBTU4ORI0diwIABwTbjx49Hc3Mztm7dGvY8ra2taG5uDnmQdRUXF2PXrl3weDxwuVzweDyora2NK4kzUOpdjohxlY3SnJKioiJVx7UzPXKO1Ow31JkQwLBhwOmny5/nyBGgvp5VYM26ao2SQ8wBSnt7O2bNmoUf/ehHuOCCCwAADQ0N6NmzJzIyMkLaDhgwAA0NDcE2HYOTwOuB18KZP38+0tPTgw9nMmao2YzWS1vlPkg7U7vKhps1qqf1NVO731BHc+cC3boBtbXRz7Fhgz+QUVLKPhnYYeUdWVfMAcrMmTPx+eef4/XXX9eyP2HNnTsXTU1NwcfevXt1PydZi9oPSLWrbLhZo3paXzOl3+afeeaZYJDy7rv+WZDHH49+7Gee8QcmF1+sqCtJI5GbShLFFKDccccdWL58OTweDxwdCgbk5OTg5MmTOHLkSEj7xsZG5OTkBNt0LtgU+HegTWepqalIS0sLeRB1pOYDMtaZDrPWGTEzLa+Z0iB09uzZcDovgyQB48dHbzt2rD8wueMOxd1IKpw5pIRSk9zS3t4uZs6cKQYNGiS+/vrrLq8HkmSXLl0afO7LL78MmyTb2NgYbPPnP/9ZpKWliRMnTijqB5NkqTO5JczokNQXbzKu1TdrTAQtrpmyVUHdFCXAAkIozMlPekZvKkn2ptsqnhkzZoj09HTh9XpFfX198PHtt98G20yfPl0MGTJEvP/++2LDhg0iLy9P5OXlBV8PLDO++uqrxebNm8Xbb78tsrOzucyY4hbpgzTwyMrK4oephckHoUsVBSa7diX6nViPkZtKkr3pFqBE+taycOHCYJtAobYzzjhD9O7dW/zsZz8T9fX1IcfZtWuXmDhxoujVq5fo16+fuOuuu1iojTQR7oM0MzNTlJeX22KmI9lnb8IHoTcrCkyWLUt0760t2f/2SBvczZiSml1LcrvdbpSVlYUkijocDlRWViZV/sv31wEA5BPmf/KTr/Huu+fo3i8ikqdm/GaAQmQBkfa0CSQvJlOSbmsrMGaMwKefyhUpOQSgHzweDwoKCgzoGRHJUTN+czdjIpOLp/6H3cyaBZx2GhQEJxmQpGyuMCGyMAYoRCbHap7A//2fv57Jd2VVorgUgARJ8lebZm0aIutigEJkcslczXPLFn9g8otfRG+XlTUNgARgAwDWpiGyg+6J7gBRR3ZNcI1HMlbzPHIEyM0FvtvuK6J584CHHgJ8vpdRXX0z/26IbIRJsmQaXKUSns/nQ25uLurq6sLmoUiSBIfDgdraWssPyu3twKRJwJtvRm931VXAe+8BPXoY0i0i0giTZMly4tml1u6SZR+gigogJUU+OGlsBLxeBidEdscAhRKOq1TkmXkfIJ/PB6/Xi0WLFsHr9ar+Pa1Z488zmT07ert16/wl1/r3j6OzRGQZvMVDCef1elFYWCjbjvUszJejE89tuX37AKdT/hwvvgjcemu8PSUiM1AzfjNJlhIumVepqJWSkmKaIC1S8bjAbblIMzutrcAVVwD/+lf040+dCvztb/7ZFSJKPrzFQwmXjKtUrC7W23Jz5vgLrUULTpxOoKUF+PvfGZwQJTPOoFDC5efnw+FwyK5S6VwR1Cy3O8zSDyOpKR5XUFCApUuBn/9c/rhffQWcw21ziAicQSETiGWVitvtRm5uLgoLCzF58mQUFhYiNzfX8NU+ZumH0ZTeblu//hgkST44eestfwKslYKTeJODiUiGxjspG0LNds1kHVVVVcLhcAgAwYfT6RRVVVVd2kmSFNIOgJAkSUiS1KW9nv01Qz8SwePxdHnfoY80ARwU/rAj8uO++xL9TmIT7m/V4XDY+ndOpAU14zdX8ZCpyN0uCRQti3R7waiiZWbpR6JELh4nAXgdwPVRfz4/H1i92pq1TLizNFHs1IzfDFDIUsyyJNks/UikwEANBBJj7wDwjOzPNTQAAwbo2ze98oKSPTAlihcryZJtmWVJsln6kUiB4nH9+v0X/Hc5ogcnH33kv7Gjd3CiZ14Qd5YmMg4DFLIUsyxJNks/Emn/fmDSpGIcPBh94H/+eX9gkpenf5/03jKBgSmRcRigkKVcccUV6NevX8TXJUmC0+nssiRZa4Gl0Z1XHRndj0Q4eRK47DKgU9X9LqZM8W/+N326MStejNgygYEpkXEYoJBluN1unHXWWfj3v/8d9vV4N85TM4gmywZ+nd1zD5CaCqxfH7nN4MFAczPw6qv+QmtGLcU24vZLMgemRIbTaymRnrjM2K+trU14PB7hcrmEx+MRbW1tie6SbiIt6e34CCxJjuW6xLpsVOnSaKurqoq+XDjw+PLLzj9n3FJsl8sls/TZ/3C5XHGdJ/CeOr+vZFheThQvNeM3AxSLSqY6DG1tbV3ea+dHdna2aG1tjem6xDuI2jlQ/OILZYHJG290/Vm535skScLpdGp2veRrs/gfHo8n7nMlS2BKpDUGKDaXbAXClA485eXlqq+LFoOoHQOUpiYhBgyQD0x+97vIxzAyYBDi+99lpJk2rQMiO/7eifTGAMXGjP5WagZKp+4zMzNVX5d4B1G7zWS1twtxww3ygUlenhCtrdGPZdQtl454+4XI3NSM30yStZhkrMOgdEXE4cOHI74W6brEs2xU7yWtRnv2WaBbN+D//i96u/37/TVNevaM3i4RK14CtVkGd1pi5HA4WOGVyGIYoFhMMtZhULJyIisrS9GxOl+X/v37K/q5zu2MWNJqlA8/9K+2ueOO6O3WrvXPnyiNJxK14qW4uBi7du2Cx+OBy+WCx+NBbW0tgxMii2GAYjHJWIdByZLe3/72t4qOpdV1scNMVkODPzD5j/+I3u6ZZ/yByY9+pO74iVyKnZKSgoKCApSWlqKgoMB2y72JkgEDFItJ1joMclP39913X0zX5cCBA4rO37mdlWeyTp0CrrhCfibkhhsAn09+ZiUa3nIholh1T3QHSJ3At9KSkhJIkhRyi8HOBcIA/2BXVFQUcRO4WK5LrDNSVp3J+v3vgfnzo7cZMAD4+mtAq3045X5vZqHXBoNEFCM9s3X1ksyreAJiqcOQDMsi1V6XWJemGr2kNV5vvqmsnsm2bYnuaWLYbTUWkVlxmXGSUBNwJNMHsNpALNalqVZY0vrVV8oCk6VLE93TxEm2ukJEicQAhULwA1herJVBzVpRtLlZiMGD5QOTOXMS2s2ES8a6QkSJpGb8loQIs07S5Jqbm5Geno6mpiakaXWj3KZ8Ph9yc3MjrjiRJAkOhwO1tbVJf7891hwEM+UuCAFMnQq89lr0dpde6l82LFfLxO68Xi8KCwtl23k8HhQUFOjfISKbUzN+M0nWpLQa9NQsh032D+DA0lSjfk5rf/4zMH26fLu6OmDQIP37YwVWXo1FZHcMUEzI7XajrKwsJLBwOByorKxUvSyTH8D29/HHQF6efLsPPgBstvo8blZdjUWUDFgHxWS0Lp/OD2D7amz0F1qTC04qKvy3fhicdJWsdYWIrIABionoUT6dH8D2c+oUcOWVQE5O9HaTJvkLrZWVGdMvK0pktVsiio4BionoUT6dH8D28sAD/sTWaH8CWVnAkSPA0qX+zf8oOla7JTIn5qCYiNI8kLq6Oni9XsUJtIEP4HB5LRUVFfwAtoDly4Gf/lS+3eefA+efr39/7MYq1W6JkgmXGZuI0iWP2dnZOHjwYPDfShNozbQcVg92fH/btwPnnCPfbvFi4Oc/178/RETxUDN+M0AxkUDNkrq6urB5KJEEbtUk83S0liufzODoUeCCC4Ddu6O3u/NO4I9/DH3OjoGaFfC6E8lTNX7rVS1OT3auJBupfLrcI5krXtqpUm57uxDTpslXgL34YiFOnOj688m0pYGZ8LoTKaNrqfs1a9aI6667TgwcOFAAEG+88UbI69OmTesyUIwfPz6kzaFDh8TkyZPF6aefLtLT08Uvf/lL0dLSorgPdg5QhAj/YZedna0oUPF4PInuvqGUlCp3OBxi1apVsnvzJHozxZdeUrZvzt694X/eioFaoq+5Fqx43YkSRdcAZcWKFeK+++4Tbrc7YoAyYcIEUV9fH3wcPnw4pM2ECRPE6NGjxccffyyqq6vF2WefLUpLSxX3we4BihBdP7hfffVVRQGKy+VKdNcN5fF4VM00Rfpmm8hvwOvWKQtMvN7Ix7DinjJ2mHWw4nUnSiTDNguMFKAUFRVF/Jlt27YJAGL9+vXB51auXCkkSRJ1dXWKzpsMAUpnSgfiZJtBcblcqgOUzt9sE/UNuLFRWWDy1FPyx7La34ddZh2sdt2JEk3N+K1LlQSv14v+/fvj3HPPxYwZM3Do0KHgazU1NcjIyMAll1wSfG7cuHHo1q0b1q1bp0d3bIEF18KLpQKu6FD07uTJk5oXx5PT1gYUFAADBkRv97Of+QutzZ4tf0wrbWmgR0HCRLHSdSeyGs0DlAkTJuDvf/87Vq9ejSeeeAJr1qzBxIkTgx82DQ0N6N+/f8jPdO/eHZmZmWhoaAh7zNbWVjQ3N4c8kg0LroUnF7hFIr4revfcc89pXhwvmoceAnr0ANasidwmIwP45hvA7VZeaM1KWxroUZAwUax03YmsRvMA5Re/+AX+8z//EyNHjsR//dd/Yfny5Vi/fj28Xm/Mx5w/fz7S09ODD6fTqV2HLYQVL7uKFrgpsXPnTkXt4v0GvGKFf9+c8vLo7T77zB+cZGSoO76VZtjsNOtgpetOZDW6F8IeNmwY+vXrhx07dgAAcnJycODAgZA2bW1tOHz4MHIibC4yd+5cNDU1BR979+7Vu9umVVxcjF27dsHj8cDlcsHj8aC2tjYpg5OASIGbEmeddZaidrF+A9650x+YXHtt9HaLFvkzTkaOjOk0lpphs9Osg5WuO5HlxJPsgjBJsp3t3btXSJIkli1bJoT4Pkl2w4YNwTbvvPMOk2Qpbh1XPq1atSrq6goAwul0itbWVuFwOCLWnYl1FcbRo0KceaZ8AmxZmbbXINLKmPLyctMs5Q2sfNH6midSuOvudDotk+xLZBRdV/G0tLSITZs2iU2bNgkA4qmnnhKbNm0Su3fvFi0tLWLOnDmipqZG1NbWilWrVokf/vCHYvjw4eJEh6pSEyZMEBdddJFYt26dWLt2rRg+fDiXGZPm7r777qgByt133y2EiFwcL5YVJe3tQtx8s3xgMnq0EMeP6/O+OwZq5eXlYvDgwSHva/DgwQkPWLS85mZhh5ouRHrTNUCJtKxu2rRp4ttvvxVXX321yM7OFj169BBDhw4Vt956q2hoaAg5xqFDh0Rpaano27evSEtLEzfffDMLtZGm5OpTBL7hBgYRLb4Bv/yysmXDe/bo9a5DRVrK2/mRqNojesw6MEggMjc14zf34iFTindfE6UbL3o8HhQUFMR1zg0bgEsvle/T6tXAj38s304LgX2doq2WCUjkXk5a7l9jt/2YiOyIe/GQpWlRYVRpAbd4Ku8eOCBESor8jMmCBTGfImZqK+xaMe+jI7sUfiOyu4QXaiP78/l88Hq9WLRoEbxer2ZFtdxuN0pKSrp886+rq0NJSQncbrei4+i5UqStDRg3Dujf319ILZKf/tTfds4c1aeQJXf91S7RFRaqPdKZnQq/EVEHekdLeuAMSmLptYeKlvua6LVS5JFH5GdMTj9diE7bT2lKyfWPZY8ixDmjlCgsN09kHZxBId1oNcMRjpYVRrWuT/HOO/56Jg88EL3d5s1AczNwxhmKDqua0usfa4VdK9Qe6czowm96zR4SUSgGKKSY3lPpWg80WlTera31ByYTJkRv9+qr/vmT0aMVdS0maq6/2gq7Vq54amThN7fbjdzcXBQWFmLy5MkoLCxEbm5uXIE5EUWg72SOPniLRxtql2TqPZWu1/FjWXp67JgQw4fL386ZOdNf+8QIsVyfcLeDOj+snkhqVOE3JuISxU/XOihmwAAlfrHkkei9MsYMFUbb24W47Tb5wOT884X49lvduhFWrNe/c+E2O1Y81bvwm5b5UUTJjAEKRRXrN0EjkhETWWH0b39TVmht1y7duhCVVtffrsXM9Cw3z0RcIm0wQKGI4vkmaORUupHf8v/1L2WBybvv6nJ6xcwww2R2egVfRtTVIUoGasbv7qCkomalTKDCakAg8bKkpASSJIUka2q5c2txcTGKioo0qzDaWaB66ddfH8Idd/wMp05FzxWfPx/43e80OXVcjLr+VpaSktLl71YLRu/ArGWFXSLL0jta0gNnUGKnxTdBK+/cWlVVJQYPHiKAlbIzJhMnCmHGyQgrX3+rMnL2Sq86Q0RmwL14KKJY9qgJx4rf8NxuNyZN2gDgsajtevUC9u4FsrKM6VcsrHj9jaDndQnUoAEQdvZKi72MAufo/LGcyP2SiLTEvXgoomTNY1i5sk1Rnsn69fZ638nEiJkHPWevuFKIkgEryVJEWldYNbtdu/yF1iZOlHs/UwFIOHrUenvRdJSsVU71rHDcUXFxMXbt2gWPxwOXywWPx4Pa2lpNZjW0rKRMZAcMUJKQFhVWze74cWDECODMM+VaPgdAAvAqAO3KoSdCslY5NXqzwEAibmlpKQoKCjQL5o0u2U9kdgxQkpSe3wQTSQjg9tuB3r2BL76I1vJLAL0AzAx51op70QDGzSCYkV1mHoxeKURkdlxmnMT0WpKZKK++CkydqqTlmQB2hTwjSRIcDocl96KRm0GQJAmzZs1CUVGRbW7ddWSXmYfABo91dXVhf5dW/hsligVnUMjyNm/255nIBScPPLAWktQNkrQ75Hmr597YZQYhVnaZeUi2/DAiOQxQyLIOH/bfyrnooujtHn3Uf+vn4Yf/w5a5N4meQUh0Ym5g5iHSrs1W2qk5GfLDiJTiLR6yHJ8PKCoC/vnP6O2uvhpYsQLo+IVT7yq1iZDIGQS3242ysrKQGRyHw4HKykrDBlO7Vdi1498oUSxYqI0sZcEC4J57orfp0QPYvx/o18+YPiWaz+dDbm6ubO5CbW2tpoOc2YqKhQuWnE4nKioqOPNAZBJqxm8GKGQJq1cD48bJt9uwAbj4Yv37YzZGVDntKBAURcp90SsoUtIvzjwQmZea8Zs5KGRqe/b4E2DlgpOFC/15JskYnADG5y6YNTFXrxolRGQ85qCQKZ04AVx6KfD559HbFRbuwAMP7MOVV+YDSO7ByMjchUQn5hKR/TFAIVMRAigrA555Jnq77t1r0dZ2Pjye4/B4jE/MNCujatvYZWkvEZkXc1DINBYtAiZPVtLybAA7Q54x+26vdsuNSFRiLhFZG3NQyFI++8yfZyIXnPzjHz44HE50Dk4AffZc0Yod98hhUTEi0hsDFEqYI0eA9HRg9Ojo7crL/bd++vY1Z2JmNHbeI4dFxYhIT8xBIcO1twPFxcCyZdHbjR0LvP020P27v1KrJWaq2SMHgCVvAbGoGBHphQEKGeqpp4C77oreRpKAhgagf//Q562WmKl0Ke6jjz6Kl156KaHVWONht00nicgceIuHDOH1+gMPueDkk0/8MyydgxPAenuuKJ3JmTdvni1vARERxYMBCulq3z5/YFJYGL3dX/7izzO59NLIbayWmBnPTI6Zk34pvERvmkhkNwxQSBetrcAPfwg4ndHb3Xyzf8bklluUHddKiZlyMz5yzJj0S+HZcaUWUaIxQCHN3XkncNppwKZNkduceSZw9Cjw8sv+GRY1iouLsWvXLng8HrhcLng8HtTW1poqOAGUzfgoYZakXwrPziu1iBKJhdpIM4sXAzfcIN9u+3bg7LP1749ZhNtlNysrC4cOHVL08x6PJ5iEareCb1Zn1k0TicyKhdrIUFu3+mdB5IKTf/zDn2eSTMEJ0HXGZ9WqVejVq5fsz3VO+uVtBPMx66aJRHbAAIViduQIkJkJXHBB9HYPPugPTK67zpBumVLHXXZTUlKiDmoBQohg0i9vI5iT1WrzEFkJAxRSLVBo7YwzgG++idzuqquAkyf9lWDpe0oHq1mzZqG4uFi24FugLVeNGM9qtXmIrISF2jrg/X15FRXA7Nny7RoagAEDdO+OrvT6e1A6WHWsMKv0NgILphkrsFJLbtNEs9TmIbISzqB8h/f3o/vgA3+eiVxw8vHH/ts5geDEqrUh9Px7UFtwjrcRzMtqtXmILEVYUFNTkwAgmpqaNDleVVWVkCRJAAh5SJIkJEkSVVVVmpzHivbtE8IfckR/vPhi15+tqqoSDocj5Jo6HI6w17OtrU14PB7hcrmEx+MRbW1tBry78Iz4ewico/N5wp3D4/F06Uu4h8fjibtfFJtwf+tOpzOpPzuIwlEzfid9gNLW1tblg6XzgOF0OoMDppkGUj2dOCHExRfLByZTpwrR3t7159UM8moCGb2p/XuIh9JBLdCncNdT6z5R7JLls4EoHgxQVFDz7dRMA6me7rpLPjBxOoVoaQn/82oGebPNXhk9W6F0UFMz40JEZFZqxm/VOSgffPABfvrTn2LQoEGQJAlvvvlmyOtCCDz44IMYOHAgevXqhXHjxmH79u0hbQ4fPowpU6YgLS0NGRkZuOWWW3D06FG1XdGE0vv2y5Yts/0yz6VL/Xkmf/xj9HZffQXs2QP07Rv+daVJnV6v13SrU4zO9+i4/LigoCBiroKVSvybiVVzoIgohiTZY8eOYfTo0Xj22WfDvv7kk0/iT3/6E1544QWsW7cOffr0wfjx43HixIlgmylTpmDr1q147733sHz5cnzwwQe47bbbYn8XcVC6ouLVV1811UCqpS++8AcmP/959HbLlvnnT845J3o7pYO31+s1XZErMy8btUqJf7Ng4juRxcUzVQNAvPHGG8F/t7e3i5ycHLFgwYLgc0eOHBGpqali0aJFQgghtm3bJgCI9evXB9usXLlSSJIk6urqFJ1XjxyUaPf3s7OzbZmk2NQkRHa2/O2c++5Td1ylt0nuv/9+Re1cLpc+FyAMub8HfHdbr+OtGOYemI/Zbh0SkZ+ut3iiqa2tRUNDA8aNGxd8Lj09HWPGjEFNTQ0AoKamBhkZGbjkkkuCbcaNG4du3bph3bp1YY/b2tqK5ubmkIdWlCwTnDJliqJjGbXMMzBt/dprr6GiogKvvfaaqunr9nbg+uuB9HTg4MHI7X70I3+htT/8QV3/lC6jVVqzw8jZimh/DwHHjx/HsmXLAFj7W7pdb3+wsB2RTcQTCaHTDMqHH34oAIj9+/eHtPv5z38urr/+eiGEEI8++qg455xzuhwrOztbPPfcc2HPM2/evLDfZLVaZixE9BUVZlrmGa6f6PDNXu6b4TPPKFs2XF8ffz/lkjrNvDqlqqpKZGVlReyXJEni7rvvtuy3dDsnfJvp/69EFMqwVTxGBSgnTpwQTU1NwcfevXs1D1CEiDxVb5aBNNK0dee+hBtk1q5VFph89JG2/ZVbRmvW1SlKViKlpKREfd2sS3/tfvvD5XIpClCMvHVIRH4Ju8WTk5MDAGhsbAx5vrGxMfhaTk4ODhw4EPJ6W1sbDh8+HGzTWWpqKtLS0kIeeoi0osIM1SKjTVt31nH6ur7enwD7H/8R/Weef94fouTladFbv2hJnYHbC62trXjooYdMtzpFyUqkaLcIhEl3sU2G2x9mTnQmIhXiiYSA8Emy//M//xMSLYVLkt2wYUOwzTvvvJOwJFk1ElktUum0deDx7rteMWaM/IzJ5MnhC63pKdx1HDx4sCgvL9cl0TSWJFal38LlHoFv6WZJpE2G2x9mmfEkoq50vcXT0tIiNm3aJDZt2iQAiKeeekps2rRJ7N69WwghxOOPPy4yMjLEsmXLxGeffSaKiorEmWeeKY4fPx48xoQJE8RFF10k1q1bJ9auXSuGDx8uSktLdXmDWkvUQKNuwJwvG5gMHChEc7MhXQ9h9O2FWHMt1AaE0QZ6M+V7JMvtDyNuHZol6CSyEl0DlEgf3NOmTRNC+GdRHnjgATFgwACRmpoqxo4dK7766quQYxw6dEiUlpaKvn37irS0NHHzzTeLlkhlScNIZICSKMoGzP+SDUwAIb74IjHvwcgy8kLEFwwp+RauJAdlyZIlpsr3SIYZlAA9ZzzNFHQSWQlL3dtQ9MH9HEWBSYe7cbr0T+7bpJGDoxbBkNy38MAqnkivL1682NCATM11SZbbH3rMctg9yZhITwxQbKrrB2NfAdTJBia/+53+/VLybdLI2wtaBUNy38Ktsjy983sy48opKzB6FpDIbhig2FhVVZUYPNghgNdkA5MxY4Robf3+ZxP9bVLrATva+9EyGJK7bpFeN3O+RyITvq3MrEEnkVWoGb+7gyylsbEYdXXyS2/r6oBBg77/t9vtRllZWcjSWYfDgcrKypiX8sotWZUkCbNmzUJRURFSUlKCFWbr6urC/owkSXA4HMjPzw97rurqatTX12PgwIE4ePAg7rzzzojvR8ulpoHl52pfV9qHzptpGqG4uBhFRUUh1zQ/P1/XpfJ2YPRmkkRJTe9oSQ/JOIPy0UfKCq2tXdv1ZxcvXhxxOjqeKf1Yvk3GcnshWvXcSMcwQ66Fkn19Ag/OXFgDZ1CI4sNbPDZSX68sMHnmmfA/v2TJEt0qnsZ6C0PN7QUl1XMjvR8z5Foorf7LvAVrMEPgS2RlDFBs4ORJIX70I/nA5IYbhPD5wh+jqqpK8cAeyze+eL5NKsmHkUtIlDtnW1ubKC8vF5mZmYqCIb2Ul5fzW7eNmCHwJbIq5qBY3H33AY89Fr1Ndjawfbt/R+JwAvkhSsVyzzyenBK5vA5Avtx8NMuWLcPUqVNDfj4zMxNlZWW47777DM21GD58uKJ2zFuwhuLiYixdujRsTldFRUXCtmcgshsGKCby1ltAUZF8u61bgREjordRO7jHsi9JYI+ikpISSJIUEqRosUdRPAN2RUVFl+e++eYbPPTQQ7jgggsMHUS4N4z9MMmYSH+abhZIsfn6a/+GfnLBydKl/hs7csEJoG5wdzqdYWc5lAh8m9Rjs79YBmxJkiIOEoEAyujN8AIzTZ03mgyQJCmu3wElRqTNRYlIGwxQEujoUcDhAM49N3q7OXP8gcmkScqPrWZwj3cn5mi7FsdDbmDvLDCLY7Zdhs2wGzYRkdUwQEkAIYD//m/g9NP99UoiufRS4MQJYMEC9edQMrinpKRg8eLFmtzu0OPbZLSBPRyHw4FZs2YpOrbR+R56zjQREdmRJMJlN5pcc3Mz0tPT0dTUhLS0tER3R5UXXwR+/Wv5dvv2AZ3GMtXcbjdKSkoAIGwS65IlS4Kvm1mkInNPP/00+vXrF5IDUF1djcLCQtljejwe2SRdPXQuOMe8BSJKJmrGbybJGmTdOuDyy+XbffABoFUqQqTVBk6n03KrDToHWEIIdOvWrUuQEc/KIiMoWb1EREScQdFdYyOQkyPfrqICULEqWBUrf2sPzAJ1/jMN3PIJd3sk0sxRtJ8hIiL9qRm/GaDo5NQpYOxYQC4Xc9IkYPFioBuzgbrw+XzIzc2NuFw6MBtSW1vbJeAKd1vIijNHRER2wgAlwebNAx5+OHqbrCxg587IhdbsIp7ZG6/XG1c+iZVnjoiI7Ig5KAnyz38C110n3+7zz4Hzz9e/P0aIFgTEu4NyrDvHdu7T9ddfz8CEiMhiGKBoYMcOQEk188WLgZ//XP/+GCVaAAIgbO5IXV0dSkpKFOWBxFKBNd6giIiIzIG3eOJw7Jh/JmT37ujtZs8GnnrKmD51pOctjmjJq0IIZGVl4dChQ2F/NlruSOf+5+bmyq7ICRwnloRaIiIyjprxm6mZMRACuPlmoG/f6MHJRRf5C60lIjhxu93Izc1FYWEhJk+ejMLCQuTm5sLtdsd97MBGhOGChsBzkYKTQBsl1VzVVGCV65MQArfddhtWr15taJl7IiKKDQMUlf76V/+Km1deid5u717gX/8CUlMN6VaIwExC59Uvgdsr8QYp8ewy3JGSHBOlFViV9OnQoUMYN26cZoEa6cfn88Hr9WLRokXwer0MKomSEAMUhdav92/o96tfRW/n8fhnWBwOY/rVmZLZjXg3y9OqTLzSHBMle/2o6ZNWgRrpQ8/ZPyKyDibJyjh4EBgwwB90RPPHPwJ33mlMn6KRm0noeHsl1oqmsewy3FEs1VzlKrCq6ZMQApIkYdasWSgqKjJ0hQ+XPkcXKY9ITXK1Vvi7IkowYUFNTU0CgGhqatLtHKdOCfHjHwvhD00iP4qKhPD5dOuGai6XSwCQfbhcrpjP0dbWJhwOh5AkKeyxJUkSWVlZQpKkLm0Cz1VVVWn4ruX7FOnh8Xg07Uc0VVVVwuFwhJzf4XBofi2sKvA7jPS7kiRJOJ1O0dbWpntf+Lsi0oea8Zu3eMJ45BGgRw/g/fcjt0lPBw4fBt5801xVYGNZmquWkuTVF1980dDde9XufBxg1K7GeucF2YGa2T898XdFZBL6x0va02sGZe1a+RkTQIjPPtP0tJpSMruh1bfQcN8ynU5nyLfMtrY24fF4hMvlEh6PR7S2tob8W+tvw+H6FO1hxAyKmWYGzMyI2T85/F0R6UvN+M0A5Tv79wvRq1f0wGTRIs1Op6uqqqqwt1cCjyVLlmh2rs4BSOCDO9zzRk2bt7W1iVWrVonMzExTDDQej8c0wZKZmeE6maEPRHbGACUGr78eOTD5zW+EaG/X7FSKRBr4lYo2k5CZmSnKy8t1G5zDnTsrKytioKBHTkqgH0bmwURihpkBKzBy9i8S/q6I9MUAJQbbtgmRkhIamIwcKcTx45qdQjGtZhoWL14c9UM2KytL80E6EBQovcWi98Cj5DaU3vitXLlEB5X8XRHpiwFKjN59V4gbbxRi6lQhdu/W9NCKRRrg1X5Ay91L7/jQ6kNfzTmN/NCPdzZKi/MnembAShIZVPJ3RaQvNeM39+IxkcDeM5FWMijdwwYAvF4vCgsLFZ3X6XQqOqYcNecMx+VyobS0NK4+mFVgZQiAkBof3CcovETWIOHvikg/3IvHorRcZqlm+axWSzfjXbIbbwG4RJIrza60ZL9W57O6QGG+0tJSFBQUGFogTevfFRHFhpVkTUTpAF9XVwev1xv126XawV6LeiCxBhixVJY1E7fbjbKyspDg0uFwoLKyMmQwKy4uRlFRUVwzAz6fD48++igqKytx+PDhqOej2GnxuyKiOOl7t0kfRlSSTQSlCXr9+vWTTaBVmw+iRf5HLNVcjV5Ro7WqqipD8nsC50rEaigiIq0wB8WiAjkodXV1YTf7iyTSvfFI+5p0/lmleS1KRLt/L4RAVlYWDh06FHze6XSioqLCkt/8fT4fBgwYEPJ+OsvKykJjY2Pc19btdmPSpElR22j9uyQi0hpzUCxKSQn5cAKBQOddigP30rOyssL+XOCYFRUVmg1o0e7fV1VVobGxMequxFbi9XqjBicAcOjQIXi93rjOE9ihWo4wqBQ8EZERGKCYTKQBPlKQERBpcCouLkZjYyPKy8uRmZkZ8ppeSX/FxcXYtWtX2EAkkcmPWlMaeMQboMglT3dm1P5CRER6YpKsCXVO0Nu+fXtwZkVOuMEpJSUFDz74IO677z7Dkv4CgUi8uOW9+oDDyquhiIgCGKCYVGCAd7vdeOihhxTnpEQbnLQKGowSbnVMZmYmysrKcN999+keqMgFRwUFBfjDH/4ge5x4r7magMPpdFp2NRQRUUdMkjUxucJtHdklQTIQFCxbtgwVFRUR22VlZeHFF1/ULX9FydJho5JklSZPS5KU0DodnO0iIjmqxm99FhLpy0zLjPUso6502TG+W2Zq9SWm0TY4jPTQc5PBcNe483U2apmx3A7VeuyrpLZ/RuxUTUTWxr14DKL3h7LSnVUzMzMtPxDEsskgAM33RZGrHxNuL5aqqioxePBg3QfncH9veu9MrbRfWuwfRUT2l9AAZd68eV0+qM4999zg68ePHxe33367yMzMFH369BHFxcWioaFB1TnMEKAY8aGsdAZl1apVGryjxDHTJoOx7mZr1IaEid74MFx/1AZ0RJS81IzfuiwzPv/881FfXx98rF27Nvja7Nmz8Y9//ANLlizBmjVrsH//fsvVwQjUpRBh8gFEhJokscjPz4fD4YhYA0WSJDidTkslvoajdhltZ1ouq1V6rM7t7LR8Wg0t948iIupIl1U83bt3R05OTpfnm5qa8Ne//hUulws//vGPAQALFy7ED37wA3z88ce4/PLL9eiO5tR8KMcTPAQKt5WUlAQrsQboUWQtFlokRpppk0Glx0rEUl6le/4YKdaAjohIji4zKNu3b8egQYMwbNgwTJkyBXv27AEAbNy4EadOncK4ceOCbc877zwMGTIENTU1EY/X2tqK5ubmkEciGfmhbOadVd1uN3Jzc1FYWIjJkyejsLAQubm5cLvdqo4TzyaDWi+rVTprZfRS3sAWAp0D47q6OpSUlKi+5loxc0BHRBan9f2lFStWiMWLF4tPP/1UvP322yIvL08MGTJENDc3i9dee0307Nmzy89ceuml4p577ol4zHB5LUhgDkqseQrxMFvugZY5OGbbZDDSiplEJX2aOc9D7nfHHBQi6shUq3i++eYbkZaWJv7yl7/EHKCcOHFCNDU1BR979+5NaIBi1g9lIxM1tR4w5ZbR9u3bN+TfTqdT10Ah3IoZvc8ZSSICYjXMFtARkXklPEm2o4yMDJxzzjnYsWMHcnJycPLkSRw5ciSkTWNjY9iclYDU1FSkpaWFPBJJyaZ+RueGaHW7RQk9EiMj3cpyOp2oqqrCkSNHDN1kMNp+QkYze56HmW9DEpGF6R0ttbS0iDPOOENUVlaKI0eOiB49eoilS5cGX//yyy8FAFFTU6P4mGZYZiyEeb5lG12HQml9FpfLpfrYZruVZQZmn0EJ4O+OiOSoGb81L3U/Z84c/PSnP8XQoUOxf/9+zJs3D5s3b8a2bduQnZ2NGTNmYMWKFXjllVeQlpaG3/zmNwCAjz76SPE5zFTqPtHlveXK4Sspga/2PXi9XhQWFsr2zePxBFcxJfo6WZlcqXu7bHNARPaX0FL3N9xwgxg4cKDo2bOnGDx4sLjhhhvEjh07gq8HCrWdccYZonfv3uJnP/uZqK+vV3UOs8ygmEG8365jqYarNgeHZdDjxzwPIrIDUyXJ6oEByvfiud0Sz60hpQMmy6Brxyy3FImIYpXQWzxGMNMtnkSL5XYLoM2toXCFw5xOJyoqKlBcXKzJOSgUb5URkZWpGb8ZoFhcrPkJsQY24c4facDU6hxERGQPasZvXUrdk3FiLYev1dLVwB40sfys2nZERJQ8dK+DQvqLpQ6FESXKWQadiIhixVs8NqImP8GIpatcHktERB2pGb85g2IjgdstpaWlKCgoiDroG1EN14wVd4mIyBoYoCQxI0qUsww6ERHFgrd4yJClq1weS0REXGZMlCAMxIiIIuMyY6IECFe4zuFwoLKykreyiIhUYg4KkQbcbjdKSkq6VM2tq6tDSUkJ3G53gnpGRGRNDFCI4uTz+VBWVhZ2KXXguVmzZsHn8xndNSIiy2KAQhSn6urqiPsNAf4gZe/evaiurjawV0RE1sYAhShOLOlPRKQ9BihEcWJJfyIi7TFAIYpTfn4+HA5Hl2q5AZIkwel0Ij8/3+CeERFZFwMUojixpD8RkfYYoBBpgCX9iYi0xUqyRBpiJVkioshYSZYoQQI7ShMRUXx4i4eIiIhMhwEKERERmQ4DFCIiIjIdBihERERkOgxQiIiIyHQYoBAREZHpMEAhIiIi02GAQkRERKbDAIWIiIhMx5KVZAPV+ZubmxPcEyIiIlIqMG4r2WXHkgFKS0sLAMDpdCa4J0RERKRWS0sL0tPTo7ax5GaB7e3t2L9/P04//XS0tLTA6XRi79693DhQR83NzbzOBuB1Ng6vtTF4nY1hlesshEBLSwsGDRqEbt2iZ5lYcgalW7ducDgcAABJkgAAaWlppv6l2AWvszF4nY3Da20MXmdjWOE6y82cBDBJloiIiEyHAQoRERGZjuUDlNTUVMybNw+pqamJ7oqt8Tobg9fZOLzWxuB1NoYdr7Mlk2SJiIjI3iw/g0JERET2wwCFiIiITIcBChEREZkOAxQiIiIyHdMHKIcPH8aUKVOQlpaGjIwM3HLLLTh69GjUn3nxxRdRUFCAtLQ0SJKEI0eOaHJcu4vlmpw4cQIzZ85EVlYW+vbti0mTJqGxsTGkjSRJXR6vv/66nm/FVJ599lnk5ubitNNOw5gxY/DJJ59Ebb9kyRKcd955OO200zBy5EisWLEi5HUhBB588EEMHDgQvXr1wrhx47B9+3Y934IlaH2db7rppi5/txMmTNDzLViCmuu8detWTJo0Cbm5uZAkCRUVFXEfM1lofZ0feuihLn/P5513no7vQAPC5CZMmCBGjx4tPv74Y1FdXS3OPvtsUVpaGvVnnn76aTF//nwxf/58AUB88803mhzX7mK5JtOnTxdOp1OsXr1abNiwQVx++eXiiiuuCGkDQCxcuFDU19cHH8ePH9fzrZjG66+/Lnr27ClefvllsXXrVnHrrbeKjIwM0djYGLb9hx9+KFJSUsSTTz4ptm3bJu6//37Ro0cPsWXLlmCbxx9/XKSnp4s333xTfPrpp+I///M/xZlnnpk01zQcPa7ztGnTxIQJE0L+bg8fPmzUWzIltdf5k08+EXPmzBGLFi0SOTk54umnn477mMlAj+s8b948cf7554f8PR88eFDndxIfUwco27ZtEwDE+vXrg8+tXLlSSJIk6urqZH/e4/GEDVDiPa4dxXJNjhw5Inr06CGWLFkSfO6LL74QAERNTU3wOQDijTfe0K3vZnbZZZeJmTNnBv/t8/nEoEGDxPz588O2v/7668W1114b8tyYMWPEr3/9ayGEEO3t7SInJ0csWLAg+PqRI0dEamqqWLRokQ7vwBq0vs5C+AOUoqIiXfprVWqvc0dDhw4NO3DGc0y70uM6z5s3T4wePVrDXurP1Ld4ampqkJGRgUsuuST43Lhx49CtWzesW7fOdMe1sliuycaNG3Hq1CmMGzcu+Nx5552HIUOGoKamJqTtzJkz0a9fP1x22WV4+eWXFW21bXUnT57Exo0bQ65Pt27dMG7cuC7XJ6CmpiakPQCMHz8+2L62thYNDQ0hbdLT0zFmzJiIx7Q7Pa5zgNfrRf/+/XHuuedixowZOHTokPZvwCJiuc6JOKbV6XlNtm/fjkGDBmHYsGGYMmUK9uzZE293dWXqAKWhoQH9+/cPea579+7IzMxEQ0OD6Y5rZbFck4aGBvTs2RMZGRkhzw8YMCDkZx5++GEsXrwY7733HiZNmoTbb78dzzzzjObvwWz+/e9/w+fzYcCAASHPd74+HTU0NERtH/hfNce0Oz2uMwBMmDABf//737F69Wo88cQTWLNmDSZOnAifz6f9m7CAWK5zIo5pdXpdkzFjxuCVV17B22+/jeeffx61tbXIz89HS0tLvF3WTUJ2M/7d736HJ554ImqbL774wqDe2JsZrvUDDzwQ/O+LLroIx44dw4IFC/Db3/5W1/MSxeMXv/hF8L9HjhyJUaNG4ayzzoLX68XYsWMT2DMi9SZOnBj871GjRmHMmDEYOnQoFi9ejFtuuSWBPYssIQHKXXfdhZtuuilqm2HDhiEnJwcHDhwIeb6trQ2HDx9GTk5OzOfX67hmpOe1zsnJwcmTJ3HkyJGQWZTGxsao13HMmDF45JFH0Nraaqt9Izrr168fUlJSuqxqinZ9cnJyorYP/G9jYyMGDhwY0ubCCy/UsPfWocd1DmfYsGHo168fduzYkZQBSizXORHHtDqjrklGRgbOOecc7NixQ7Njai0ht3iys7Nx3nnnRX307NkTeXl5OHLkCDZu3Bj82ffffx/t7e0YM2ZMzOfX67hmpOe1vvjii9GjRw+sXr06+NxXX32FPXv2IC8vL2KfNm/ejDPOOMPWwQkA9OzZExdffHHI9Wlvb8fq1asjXp+8vLyQ9gDw3nvvBdufeeaZyMnJCWnT3NyMdevWRb3mdqbHdQ5n3759OHToUEhgmExiuc6JOKbVGXVNjh49ip07d5r77znRWbpyJkyYIC666CKxbt06sXbtWjF8+PCQpa/79u0T5557rli3bl3wufr6erFp0ybx0ksvCQDigw8+EJs2bRKHDh1SfNxkFMu1nj59uhgyZIh4//33xYYNG0ReXp7Iy8sLvv7WW2+Jl156SWzZskVs375dPPfcc6J3797iwQcfNPS9Jcrrr78uUlNTxSuvvCK2bdsmbrvtNpGRkSEaGhqEEEJMnTpV/O53vwu2//DDD0X37t3F//zP/4gvvvhCzJs3L+wy44yMDLFs2TLx2WefiaKiIi4z1vg6t7S0iDlz5oiamhpRW1srVq1aJX74wx+K4cOHixMnTiTkPZqB2uvc2toqNm3aJDZt2iQGDhwo5syZIzZt2iS2b9+u+JjJSI/rfNdddwmv1ytqa2vFhx9+KMaNGyf69esnDhw4YPj7U8r0AcqhQ4dEaWmp6Nu3r0hLSxM333yzaGlpCb5eW1srAAiPxxN8bt68eQJAl8fChQsVHzcZxXKtjx8/Lm6//XZxxhlniN69e4uf/exnor6+Pvj6ypUrxYUXXij69u0r+vTpI0aPHi1eeOEF4fP5jHxrCfXMM8+IIUOGiJ49e4rLLrtMfPzxx8HXrrrqKjFt2rSQ9osXLxbnnHOO6Nmzpzj//PPFP//5z5DX29vbxQMPPCAGDBggUlNTxdixY8VXX31lxFsxNS2v87fffiuuvvpqkZ2dLXr06CGGDh0qbr311qQeNAPUXOfAZ0bnx1VXXaX4mMlK6+t8ww03iIEDB4qePXuKwYMHixtuuEHs2LHDwHekniREEqz3JCIiIksx9TJjIiIiSk4MUIiIiMh0GKAQERGR6TBAISIiItNhgEJERESmwwCFiIiITIcBChEREZkOAxQiIiIyHQYoREREZDoMUIiIiMh0GKAQERGR6TBAISIiItP5/wEp1b8yUKjSwAAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test,  color='black')\n",
+    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "16ff1a974f6e4348e869e4a7d366b86a",
+   "translation_date": "2025-09-06T13:39:29+00:00",
+   "source_file": "2-Regression/1-Tools/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/2-Data/notebook.ipynb b/translations/th/2-Regression/2-Data/notebook.ipynb
new file mode 100644
index 000000000..7705c608e
--- /dev/null
+++ b/translations/th/2-Regression/2-Data/notebook.ipynb
@@ -0,0 +1,46 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  },
+  "coopTranslator": {
+   "original_hash": "1b2ab303ac6c604a34c6ca7a49077fc7",
+   "translation_date": "2025-09-06T13:46:03+00:00",
+   "source_file": "2-Regression/2-Data/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/th/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
new file mode 100644
index 000000000..7c67e3eea
--- /dev/null
+++ b/translations/th/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -0,0 +1,672 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_2-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "f3c335f9940cfd76528b3ef918b9b342",
+   "translation_date": "2025-09-06T13:54:07+00:00",
+   "source_file": "2-Regression/2-Data/solution/R/lesson_2-R.ipynb",
+   "language_code": "th"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# สร้างโมเดลการถดถอย: เตรียมและแสดงข้อมูล\n",
+    "\n",
+    "## **การถดถอยเชิงเส้นสำหรับฟักทอง - บทเรียนที่ 2**\n",
+    "#### บทนำ\n",
+    "\n",
+    "เมื่อคุณมีเครื่องมือที่จำเป็นสำหรับการเริ่มต้นสร้างโมเดลการเรียนรู้ของเครื่องด้วย Tidymodels และ Tidyverse คุณก็พร้อมที่จะเริ่มตั้งคำถามกับข้อมูลของคุณแล้ว การทำงานกับข้อมูลและการนำโซลูชัน ML มาใช้ สิ่งสำคัญคือการเข้าใจวิธีตั้งคำถามที่ถูกต้องเพื่อปลดล็อกศักยภาพของชุดข้อมูลของคุณอย่างเหมาะสม\n",
+    "\n",
+    "ในบทเรียนนี้ คุณจะได้เรียนรู้:\n",
+    "\n",
+    "- วิธีเตรียมข้อมูลของคุณสำหรับการสร้างโมเดล\n",
+    "\n",
+    "- วิธีใช้ `ggplot2` สำหรับการแสดงข้อมูล\n",
+    "\n",
+    "คำถามที่คุณต้องการคำตอบจะกำหนดประเภทของอัลกอริทึม ML ที่คุณจะใช้ และคุณภาพของคำตอบที่คุณได้รับจะขึ้นอยู่กับลักษณะของข้อมูลของคุณอย่างมาก\n",
+    "\n",
+    "มาดูตัวอย่างการทำงานจริงกันเถอะ\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/unruly_data.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>ภาพประกอบโดย @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![ภาพประกอบโดย \\@allison_horst](../../../../../../2-Regression/2-Data/images/unruly_data.jpg)<br>ภาพประกอบโดย \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "Pg5aexcOPqAZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. การนำเข้าข้อมูลฟักทองและเรียกใช้ Tidyverse\n",
+    "\n",
+    "เราจะต้องใช้แพ็กเกจต่อไปนี้เพื่อจัดการบทเรียนนี้:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) คือ [ชุดของแพ็กเกจ R](https://www.tidyverse.org/packages) ที่ออกแบบมาเพื่อทำให้การวิเคราะห์ข้อมูลเร็วขึ้น ง่ายขึ้น และสนุกมากขึ้น!\n",
+    "\n",
+    "คุณสามารถติดตั้งแพ็กเกจเหล่านี้ได้ด้วยคำสั่ง:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\"))`\n",
+    "\n",
+    "สคริปต์ด้านล่างจะตรวจสอบว่าคุณมีแพ็กเกจที่จำเป็นสำหรับการทำโมดูลนี้หรือไม่ และจะติดตั้งให้คุณในกรณีที่บางแพ็กเกจขาดหายไป\n"
+   ],
+   "metadata": {
+    "id": "dc5WhyVdXAjR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "GqPYUZgfXOBt"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ตอนนี้ มาเริ่มต้นใช้งานแพ็กเกจและโหลด [ข้อมูล](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) ที่เตรียมไว้สำหรับบทเรียนนี้!\n"
+   ],
+   "metadata": {
+    "id": "kvjDTPDSXRr2"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n =50)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VMri-t2zXqgD"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "การใช้คำสั่ง `glimpse()` อย่างรวดเร็วจะช่วยให้เห็นได้ทันทีว่ามีช่องว่างในข้อมูล และมีการผสมผสานระหว่างข้อมูลประเภทข้อความ (`chr`) และข้อมูลตัวเลข (`dbl`) นอกจากนี้ `Date` ยังเป็นประเภทตัวอักษร และยังมีคอลัมน์แปลก ๆ ที่ชื่อว่า `Package` ซึ่งข้อมูลในคอลัมน์นี้เป็นการผสมกันระหว่าง `sacks`, `bins` และค่าอื่น ๆ อีกด้วย โดยรวมแล้ว ข้อมูลนี้ค่อนข้างยุ่งเหยิง 😤\n",
+    "\n",
+    "ในความเป็นจริง การได้รับชุดข้อมูลที่พร้อมใช้งานสำหรับสร้างโมเดล Machine Learning โดยตรงนั้นไม่ใช่เรื่องปกติ แต่ไม่ต้องกังวล เพราะในบทเรียนนี้ คุณจะได้เรียนรู้วิธีการเตรียมชุดข้อมูลดิบโดยใช้ไลบรารีมาตรฐานของ R 🧑‍🔧 นอกจากนี้ คุณยังจะได้เรียนรู้เทคนิคต่าง ๆ ในการสร้างภาพข้อมูลอีกด้วย 📈📊\n",
+    "<br>\n",
+    "\n",
+    "> ทบทวนสั้น ๆ: ตัวดำเนินการ pipe (`%>%`) ทำหน้าที่ดำเนินการตามลำดับตรรกะโดยส่งวัตถุไปข้างหน้าเข้าสู่ฟังก์ชันหรือคำสั่ง คุณสามารถคิดว่าตัวดำเนินการ pipe เป็นเหมือนการพูดว่า \"และจากนั้น\" ในโค้ดของคุณ\n"
+   ],
+   "metadata": {
+    "id": "REWcIv9yX29v"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. ตรวจสอบข้อมูลที่หายไป\n",
+    "\n",
+    "หนึ่งในปัญหาที่พบบ่อยที่สุดที่นักวิทยาศาสตร์ข้อมูลต้องจัดการคือข้อมูลที่ไม่สมบูรณ์หรือข้อมูลที่หายไป R แทนค่าที่หายไปหรือค่าที่ไม่ทราบด้วยค่าพิเศษที่เรียกว่า `NA` (Not Available)\n",
+    "\n",
+    "แล้วเราจะรู้ได้อย่างไรว่ามีค่าที่หายไปใน data frame?\n",
+    "<br>\n",
+    "-   วิธีที่ตรงไปตรงมาคือการใช้ฟังก์ชันพื้นฐานของ R `anyNA` ซึ่งจะคืนค่าตรรกะเป็น `TRUE` หรือ `FALSE`\n"
+   ],
+   "metadata": {
+    "id": "Zxfb3AM5YbUe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  anyNA()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "G--DQutAYltj"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมเลย ดูเหมือนว่าจะมีข้อมูลบางส่วนหายไป! นั่นเป็นจุดเริ่มต้นที่ดี\n",
+    "\n",
+    "-   อีกวิธีหนึ่งคือการใช้ฟังก์ชัน `is.na()` ซึ่งจะแสดงว่ามีองค์ประกอบในคอลัมน์ใดที่หายไป โดยจะระบุด้วยค่าตรรกะ `TRUE`\n"
+   ],
+   "metadata": {
+    "id": "mU-7-SB6YokF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "W-DxDOR4YxSW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "โอเค งานเสร็จแล้ว แต่เมื่อเจอกับ Data Frame ขนาดใหญ่แบบนี้ การตรวจสอบแถวและคอลัมน์ทั้งหมดทีละตัวจะไม่มีประสิทธิภาพและแทบจะเป็นไปไม่ได้เลย😴\n",
+    "\n",
+    "-   วิธีที่เข้าใจง่ายกว่าคือการคำนวณผลรวมของค่าที่หายไปในแต่ละคอลัมน์:\n"
+   ],
+   "metadata": {
+    "id": "xUWxipKYY0o7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  colSums()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZRBWV6P9ZArL"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ดีขึ้นมาก! มีข้อมูลที่ขาดหายไป แต่บางทีอาจจะไม่สำคัญสำหรับงานที่กำลังทำอยู่ ลองดูว่าการวิเคราะห์เพิ่มเติมจะนำไปสู่อะไร\n",
+    "\n",
+    "> นอกจากชุดแพ็กเกจและฟังก์ชันที่ยอดเยี่ยมแล้ว R ยังมีเอกสารประกอบที่ดีมากอีกด้วย ตัวอย่างเช่น ใช้ `help(colSums)` หรือ `?colSums` เพื่อค้นหาข้อมูลเพิ่มเติมเกี่ยวกับฟังก์ชันนี้\n"
+   ],
+   "metadata": {
+    "id": "9gv-crB6ZD1Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Dplyr: ไวยากรณ์สำหรับการจัดการข้อมูล\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_wrangling.png\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>ผลงานโดย @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![ผลงานโดย \\@allison_horst](../../../../../../2-Regression/2-Data/images/dplyr_wrangling.png)<br/>ผลงานโดย \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "o4jLY5-VZO2C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`dplyr`](https://dplyr.tidyverse.org/) เป็นแพ็กเกจใน Tidyverse ที่เป็นไวยากรณ์สำหรับการจัดการข้อมูล โดยมีชุดคำกริยาที่สอดคล้องกันซึ่งช่วยให้คุณแก้ปัญหาการจัดการข้อมูลที่พบบ่อยที่สุดได้ ในส่วนนี้ เราจะมาสำรวจคำกริยาบางตัวของ dplyr กัน!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "i5o33MQBZWWw"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::select()\n",
+    "\n",
+    "`select()` เป็นฟังก์ชันในแพ็กเกจ `dplyr` ที่ช่วยให้คุณเลือกคอลัมน์ที่ต้องการเก็บไว้หรือไม่ต้องการ\n",
+    "\n",
+    "เพื่อให้การทำงานกับ data frame ง่ายขึ้น คุณสามารถลบคอลัมน์บางส่วนออกโดยใช้ `select()` และเก็บไว้เฉพาะคอลัมน์ที่คุณต้องการ\n",
+    "\n",
+    "ตัวอย่างเช่น ในการวิเคราะห์ครั้งนี้ เราจะใช้คอลัมน์ `Package`, `Low Price`, `High Price` และ `Date` มาทำการเลือกคอลัมน์เหล่านี้กัน\n"
+   ],
+   "metadata": {
+    "id": "x3VGMAGBZiUr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(Package, `Low Price`, `High Price`, Date)\n",
+    "\n",
+    "\n",
+    "# Print data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "F_FgxQnVZnM0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::mutate()\n",
+    "\n",
+    "`mutate()` เป็นฟังก์ชันในแพ็กเกจ `dplyr` ที่ช่วยให้คุณสร้างหรือแก้ไขคอลัมน์ โดยยังคงคอลัมน์เดิมไว้\n",
+    "\n",
+    "โครงสร้างทั่วไปของ `mutate` คือ:\n",
+    "\n",
+    "`data %>%   mutate(new_column_name = what_it_contains)`\n",
+    "\n",
+    "ลองใช้ `mutate` กับคอลัมน์ `Date` โดยทำตามขั้นตอนต่อไปนี้:\n",
+    "\n",
+    "1. แปลงวันที่ (ซึ่งปัจจุบันเป็นประเภทตัวอักษร) ให้เป็นรูปแบบเดือน (วันที่เหล่านี้เป็นวันที่ในสหรัฐฯ ดังนั้นรูปแบบคือ `MM/DD/YYYY`)\n",
+    "\n",
+    "2. ดึงข้อมูลเดือนจากวันที่ไปยังคอลัมน์ใหม่\n",
+    "\n",
+    "ใน R แพ็กเกจ [lubridate](https://lubridate.tidyverse.org/) ทำให้การทำงานกับข้อมูลวันที่และเวลาเป็นเรื่องง่ายขึ้น ดังนั้นเราจะใช้ `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` เพื่อบรรลุเป้าหมายข้างต้น เราสามารถลบคอลัมน์ Date ได้ เนื่องจากเราไม่จำเป็นต้องใช้มันอีกในขั้นตอนถัดไป\n"
+   ],
+   "metadata": {
+    "id": "2KKo0Ed9Z1VB"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load lubridate\n",
+    "library(lubridate)\n",
+    "\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  # Convert the Date column to a date object\n",
+    "  mutate(Date = mdy(Date)) %>% \n",
+    "  # Extract month from Date\n",
+    "  mutate(Month = month(Date)) %>% \n",
+    "  # Drop Date column\n",
+    "  select(-Date)\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5joszIVSZ6xe"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมไปเลย! 🤩\n",
+    "\n",
+    "ต่อไป มาสร้างคอลัมน์ใหม่ชื่อ `Price` ซึ่งแสดงถึงราคากลางของฟักทองกันเถอะ ตอนนี้เราจะคำนวณค่าเฉลี่ยของคอลัมน์ `Low Price` และ `High Price` เพื่อเติมข้อมูลในคอลัมน์ Price ใหม่นี้\n"
+   ],
+   "metadata": {
+    "id": "nIgLjNMCZ-6Y"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new column Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Zo0BsqqtaJw2"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยส!💪\n",
+    "\n",
+    "\"แต่เดี๋ยวก่อน!\" คุณอาจพูดหลังจากดูข้อมูลทั้งหมดด้วย `View(pumpkins)` \"มีอะไรแปลกๆ อยู่ที่นี่!\"🤔\n",
+    "\n",
+    "ถ้าคุณดูที่คอลัมน์ `Package` จะเห็นว่าฟักทองถูกขายในรูปแบบที่หลากหลาย บางส่วนขายในหน่วย `1 1/9 bushel` บางส่วนในหน่วย `1/2 bushel` บางส่วนขายเป็นลูก บางส่วนขายเป็นปอนด์ และบางส่วนขายในกล่องใหญ่ที่มีขนาดแตกต่างกัน\n",
+    "\n",
+    "ลองตรวจสอบดูสิ:\n"
+   ],
+   "metadata": {
+    "id": "p77WZr-9aQAR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Verify the distinct observations in Package column\n",
+    "pumpkins %>% \n",
+    "  distinct(Package)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "XISGfh0IaUy6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "น่าทึ่งมาก!👏\n",
+    "\n",
+    "ดูเหมือนว่าฟักทองจะมีน้ำหนักที่วัดได้ไม่คงที่ ดังนั้นเรามากรองพวกมันโดยเลือกเฉพาะฟักทองที่มีคำว่า *bushel* อยู่ในคอลัมน์ `Package` และนำสิ่งนี้ไปใส่ในกรอบข้อมูลใหม่ชื่อ `new_pumpkins`\n"
+   ],
+   "metadata": {
+    "id": "7sMjiVujaZxY"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::filter() และ stringr::str_detect()\n",
+    "\n",
+    "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): สร้างชุดข้อมูลย่อยที่มีเฉพาะ **แถว** ที่ตรงตามเงื่อนไขของคุณ ในกรณีนี้คือ ฟักทองที่มีคำว่า *bushel* อยู่ในคอลัมน์ `Package`\n",
+    "\n",
+    "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): ตรวจสอบว่ามีหรือไม่มีรูปแบบที่กำหนดในสตริง\n",
+    "\n",
+    "แพ็กเกจ [`stringr`](https://github.com/tidyverse/stringr) มีฟังก์ชันที่ใช้งานง่ายสำหรับการจัดการสตริงทั่วไป\n"
+   ],
+   "metadata": {
+    "id": "L8Qfcs92ageF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Retain only pumpkins with \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "       filter(str_detect(Package, \"bushel\"))\n",
+    "\n",
+    "# Get the dimensions of the new data\n",
+    "dim(new_pumpkins)\n",
+    "\n",
+    "# View a few rows of the new data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "hy_SGYREampd"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "คุณสามารถเห็นได้ว่าเราจำกัดข้อมูลให้เหลือประมาณ 415 แถวที่เกี่ยวกับฟักทองตามปริมาณเป็นบุชเชล 🤩\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "VrDwF031avlR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::case_when()\n",
+    "\n",
+    "**แต่เดี๋ยวก่อน! ยังมีอีกสิ่งที่ต้องทำ**\n",
+    "\n",
+    "คุณสังเกตไหมว่าปริมาณในหน่วย bushel แตกต่างกันในแต่ละแถว? คุณจำเป็นต้องปรับราคาที่แสดงให้เป็นราคาต่อ bushel ไม่ใช่ต่อ 1 1/9 หรือ 1/2 bushel ถึงเวลาทำคณิตศาสตร์เพื่อทำให้มันเป็นมาตรฐานเดียวกัน\n",
+    "\n",
+    "เราจะใช้ฟังก์ชัน [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) เพื่อ *ปรับเปลี่ยน* คอลัมน์ Price ตามเงื่อนไขบางอย่าง `case_when` ช่วยให้คุณสามารถจัดการคำสั่ง `if_else()` หลายตัวได้ในรูปแบบเวกเตอร์\n"
+   ],
+   "metadata": {
+    "id": "mLpw2jH4a0tx"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(Price = case_when(\n",
+    "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+    "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+    "    TRUE ~ Price))\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 30)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "P68kLVQmbM6I"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ตอนนี้เราสามารถวิเคราะห์ราคาต่อหน่วยโดยอิงจากการวัดผลตามหน่วย bushel ได้แล้ว การศึกษาหน่วย bushel ของฟักทองนี้แสดงให้เห็นว่า `สำคัญมาก` ที่จะต้อง `เข้าใจลักษณะของข้อมูลของคุณ`!\n",
+    "\n",
+    "> ✅ ตามข้อมูลจาก [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308) น้ำหนักของ bushel ขึ้นอยู่กับประเภทของผลผลิต เนื่องจากมันเป็นการวัดตามปริมาตร \"ตัวอย่างเช่น bushel ของมะเขือเทศควรมีน้ำหนัก 56 ปอนด์... ใบและผักใบเขียวใช้พื้นที่มากกว่าแต่น้ำหนักน้อยกว่า ดังนั้น bushel ของผักโขมจึงมีน้ำหนักเพียง 20 ปอนด์\" มันค่อนข้างซับซ้อน! เราไม่ต้องยุ่งยากกับการแปลง bushel เป็นปอนด์ แต่ให้ตั้งราคาตาม bushel แทน การศึกษาหน่วย bushel ของฟักทองนี้แสดงให้เห็นว่า สำคัญมากที่จะต้องเข้าใจลักษณะของข้อมูลของคุณ!\n",
+    "\n",
+    "> ✅ คุณสังเกตไหมว่าฟักทองที่ขายเป็นครึ่ง bushel นั้นมีราคาแพงมาก? คุณสามารถหาสาเหตุได้ไหม? คำใบ้: ฟักทองลูกเล็กมีราคาสูงกว่าฟักทองลูกใหญ่มาก อาจเป็นเพราะมีจำนวนมากกว่าต่อ bushel เนื่องจากพื้นที่ที่ไม่ได้ใช้ซึ่งถูกครอบครองโดยฟักทองพายลูกใหญ่ที่กลวง\n"
+   ],
+   "metadata": {
+    "id": "pS2GNPagbSdb"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "สุดท้ายนี้ เพื่อความสนุกสนานและการผจญภัย 💁‍♀️ เรามาย้ายคอลัมน์ Month ไปอยู่ในตำแหน่งแรกกันดีกว่า นั่นคือ `ก่อน` คอลัมน์ `Package`\n",
+    "\n",
+    "สามารถใช้ `dplyr::relocate()` เพื่อเปลี่ยนตำแหน่งของคอลัมน์ได้\n"
+   ],
+   "metadata": {
+    "id": "qql1SowfbdnP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new data frame new_pumpkins\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(Month, .before = Package)\n",
+    "\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "JJ1x6kw8bixF"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมมาก!👌 ตอนนี้คุณมีชุดข้อมูลที่สะอาดและเป็นระเบียบเรียบร้อย ซึ่งคุณสามารถใช้สร้างโมเดลการถดถอยใหม่ของคุณได้!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "y8TJ0Za_bn5Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. การแสดงข้อมูลด้วย ggplot2\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/data-visualization.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>อินโฟกราฟิกโดย Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![อินโฟกราฟิกโดย Dasani Madipalli](../../../../../../2-Regression/2-Data/images/data-visualization.png){width=\"600\"}-->\n",
+    "\n",
+    "มีคำกล่าวที่ชาญฉลาดว่า:\n",
+    "\n",
+    "> \"กราฟง่าย ๆ ได้นำข้อมูลมาสู่ความคิดของนักวิเคราะห์ข้อมูลมากกว่าวิธีการอื่นใด\" --- John Tukey\n",
+    "\n",
+    "หนึ่งในบทบาทของนักวิทยาศาสตร์ข้อมูลคือการแสดงให้เห็นถึงคุณภาพและลักษณะของข้อมูลที่พวกเขากำลังทำงานด้วย เพื่อทำสิ่งนี้ พวกเขามักสร้างการแสดงผลที่น่าสนใจ เช่น แผนภาพ กราฟ และแผนภูมิ ที่แสดงแง่มุมต่าง ๆ ของข้อมูล ด้วยวิธีนี้ พวกเขาสามารถแสดงความสัมพันธ์และช่องว่างที่อาจยากต่อการค้นพบในรูปแบบอื่น\n",
+    "\n",
+    "การแสดงผลข้อมูลยังช่วยในการเลือกเทคนิคการเรียนรู้ของเครื่องที่เหมาะสมที่สุดสำหรับข้อมูล ตัวอย่างเช่น แผนภาพกระจายที่ดูเหมือนจะมีแนวโน้มตามเส้นตรง อาจบ่งชี้ว่าข้อมูลนั้นเหมาะสำหรับการวิเคราะห์การถดถอยเชิงเส้น\n",
+    "\n",
+    "R มีระบบหลายแบบสำหรับการสร้างกราฟ แต่ [`ggplot2`](https://ggplot2.tidyverse.org/index.html) เป็นหนึ่งในระบบที่มีความสง่างามและหลากหลายที่สุด `ggplot2` ช่วยให้คุณสร้างกราฟโดย **การรวมองค์ประกอบอิสระเข้าด้วยกัน**\n",
+    "\n",
+    "เริ่มต้นด้วยแผนภาพกระจายง่าย ๆ สำหรับคอลัมน์ Price และ Month\n",
+    "\n",
+    "ในกรณีนี้ เราจะเริ่มต้นด้วย [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html) โดยใส่ชุดข้อมูลและการแมปเชิงสุนทรียะ (ด้วย [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) จากนั้นเพิ่มเลเยอร์ (เช่น [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) สำหรับแผนภาพกระจาย\n"
+   ],
+   "metadata": {
+    "id": "mYSH6-EtbvNa"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plots\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+    "p + geom_point()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "g2YjnGeOcLo4"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "นี่เป็นพล็อตที่มีประโยชน์หรือเปล่า 🤷? มีอะไรที่ทำให้คุณแปลกใจไหม?\n",
+    "\n",
+    "มันไม่ได้มีประโยชน์มากนัก เพราะทั้งหมดที่มันทำคือแสดงข้อมูลของคุณเป็นการกระจายของจุดในเดือนที่กำหนด\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "Ml7SDCLQcPvE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **เราจะทำให้มันมีประโยชน์ได้อย่างไร?**\n",
+    "\n",
+    "เพื่อให้กราฟแสดงข้อมูลที่มีประโยชน์ คุณมักจะต้องจัดกลุ่มข้อมูลในบางรูปแบบ ตัวอย่างเช่น ในกรณีของเรา การหาค่าเฉลี่ยของราคาฟักทองในแต่ละเดือนจะช่วยให้เราเข้าใจรูปแบบที่ซ่อนอยู่ในข้อมูลได้มากขึ้น ซึ่งนำเราไปสู่การใช้งาน **dplyr** อีกหนึ่งฟังก์ชัน:\n",
+    "\n",
+    "#### `dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "การคำนวณแบบจัดกลุ่มใน R สามารถทำได้ง่าย ๆ โดยใช้\n",
+    "\n",
+    "`dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "-   `dplyr::group_by()` เปลี่ยนหน่วยการวิเคราะห์จากทั้งชุดข้อมูลไปเป็นกลุ่มย่อย เช่น กลุ่มตามเดือน\n",
+    "\n",
+    "-   `dplyr::summarize()` สร้าง Data Frame ใหม่ที่มีคอลัมน์สำหรับตัวแปรที่ใช้จัดกลุ่ม และคอลัมน์สำหรับสถิติสรุปที่คุณระบุ\n",
+    "\n",
+    "ตัวอย่างเช่น เราสามารถใช้ `dplyr::group_by() %>% summarize()` เพื่อจัดกลุ่มฟักทองตามคอลัมน์ **Month** และหาค่า **ราคาเฉลี่ย** สำหรับแต่ละเดือน\n"
+   ],
+   "metadata": {
+    "id": "jMakvJZIcVkh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "6kVSUa2Bcilf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "กระชับ!✨\n",
+    "\n",
+    "ฟีเจอร์ประเภทหมวดหมู่ เช่น เดือน มักจะแสดงผลได้ดีกว่าด้วยกราฟแท่ง 📊 เลเยอร์ที่ใช้สำหรับสร้างกราฟแท่งคือ `geom_bar()` และ `geom_col()` สามารถดูข้อมูลเพิ่มเติมได้ที่ `?geom_bar`\n",
+    "\n",
+    "มาลองสร้างกันเลย!\n"
+   ],
+   "metadata": {
+    "id": "Kds48GUBcj3W"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price)) %>% \r\n",
+    "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"Pumpkin Price\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VNbU1S3BcrxO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩นี่คือการแสดงข้อมูลที่มีประโยชน์มากขึ้น! ดูเหมือนว่าราคาสูงสุดของฟักทองจะเกิดขึ้นในเดือนกันยายนและตุลาคม ตรงกับที่คุณคาดไว้หรือไม่? เพราะอะไร?\n",
+    "\n",
+    "ขอแสดงความยินดีที่คุณจบบทเรียนที่สอง 👏! คุณได้เตรียมข้อมูลสำหรับการสร้างโมเดล จากนั้นค้นพบข้อมูลเชิงลึกเพิ่มเติมผ่านการแสดงผลข้อมูล!\n"
+   ],
+   "metadata": {
+    "id": "zDm0VOzzcuzR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/2-Data/solution/notebook.ipynb b/translations/th/2-Regression/2-Data/solution/notebook.ipynb
new file mode 100644
index 000000000..149f35adf
--- /dev/null
+++ b/translations/th/2-Regression/2-Data/solution/notebook.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/8/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    City Name Type               Package   Variety Sub Variety  Grade  \\\n",
+       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "\n",
+       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
+       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
+       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "\n",
+       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
+       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "\n",
+       "    Unnamed: 25  \n",
+       "70          NaN  \n",
+       "71          NaN  \n",
+       "72          NaN  \n",
+       "73          NaN  \n",
+       "74          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Month               Package  Low Price  High Price  Price\n",
+      "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
+      "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "...     ...                   ...        ...         ...    ...\n",
+      "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
+      "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
+      "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
+      "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Get an average between low and high price for the base pumpkin price\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "# Convert the date to its month only\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "\n",
+    "# Create a new dataframe with this basic data\n",
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
+    "\n",
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
+    "\n",
+    "print(new_pumpkins)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "price = new_pumpkins.Price\n",
+    "month = new_pumpkins.Month\n",
+    "plt.scatter(price, month)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามนุษย์มืออาชีพ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "95726f0b8283628d5356a4f8eb8b4b76",
+   "translation_date": "2025-09-06T13:46:29+00:00",
+   "source_file": "2-Regression/2-Data/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/3-Linear/notebook.ipynb b/translations/th/2-Regression/3-Linear/notebook.ipynb
new file mode 100644
index 000000000..9750b6f0a
--- /dev/null
+++ b/translations/th/2-Regression/3-Linear/notebook.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## การตั้งราคาฟักทอง\n",
+    "\n",
+    "โหลดไลบรารีและชุดข้อมูลที่จำเป็น แปลงข้อมูลให้เป็น dataframe ที่มีข้อมูลบางส่วนดังนี้:\n",
+    "\n",
+    "- เลือกเฉพาะฟักทองที่ตั้งราคาเป็นหน่วย bushel\n",
+    "- แปลงวันที่ให้เป็นเดือน\n",
+    "- คำนวณราคาโดยเฉลี่ยจากราคาสูงสุดและต่ำสุด\n",
+    "- แปลงราคาให้สะท้อนถึงการตั้งราคาตามปริมาณ bushel\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "แผนภาพกระจายพื้นฐานเตือนเราว่าเรามีข้อมูลรายเดือนเฉพาะตั้งแต่เดือนสิงหาคมถึงเดือนธันวาคม เราอาจต้องการข้อมูลเพิ่มเติมเพื่อที่จะสรุปผลในรูปแบบเชิงเส้น\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter('Month','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "b032d371c75279373507f003439a577e",
+   "translation_date": "2025-09-06T13:09:07+00:00",
+   "source_file": "2-Regression/3-Linear/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/th/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
new file mode 100644
index 000000000..8fbb161ab
--- /dev/null
+++ b/translations/th/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -0,0 +1,1083 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_3-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "5015d65d61ba75a223bfc56c273aa174",
+   "translation_date": "2025-09-06T13:23:02+00:00",
+   "source_file": "2-Regression/3-Linear/solution/R/lesson_3-R.ipynb",
+   "language_code": "th"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "EgQw8osnsUV-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## การวิเคราะห์การถดถอยเชิงเส้นและพหุนามสำหรับการตั้งราคาฟักทอง - บทเรียนที่ 3\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>อินโฟกราฟิกโดย Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![อินโฟกราฟิกโดย Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n",
+    "\n",
+    "#### บทนำ\n",
+    "\n",
+    "จนถึงตอนนี้ คุณได้สำรวจว่าการถดถอยคืออะไรโดยใช้ข้อมูลตัวอย่างจากชุดข้อมูลการตั้งราคาฟักทองที่เราจะใช้ตลอดบทเรียนนี้ คุณยังได้สร้างภาพด้วย `ggplot2` 💪\n",
+    "\n",
+    "ตอนนี้คุณพร้อมที่จะเจาะลึกลงไปในเรื่องการถดถอยสำหรับการเรียนรู้ของเครื่อง (ML) ในบทเรียนนี้ คุณจะได้เรียนรู้เพิ่มเติมเกี่ยวกับการถดถอยสองประเภท: *การถดถอยเชิงเส้นพื้นฐาน* และ *การถดถอยพหุนาม* พร้อมกับคณิตศาสตร์บางส่วนที่อยู่เบื้องหลังเทคนิคเหล่านี้\n",
+    "\n",
+    "> ตลอดหลักสูตรนี้ เราสมมติว่าคุณมีความรู้ทางคณิตศาสตร์ในระดับพื้นฐาน และพยายามทำให้เนื้อหาเข้าถึงได้สำหรับนักเรียนที่มาจากสาขาอื่น ดังนั้นโปรดสังเกตหมายเหตุ 🧮 การเรียกออกมา แผนภาพ และเครื่องมือการเรียนรู้อื่น ๆ เพื่อช่วยในการทำความเข้าใจ\n",
+    "\n",
+    "#### การเตรียมตัว\n",
+    "\n",
+    "เพื่อเป็นการเตือนความจำ คุณกำลังโหลดข้อมูลนี้เพื่อถามคำถามเกี่ยวกับข้อมูลดังกล่าว\n",
+    "\n",
+    "-   ช่วงเวลาใดที่ดีที่สุดในการซื้อฟักทอง?\n",
+    "\n",
+    "-   ราคาที่คาดหวังสำหรับฟักทองขนาดเล็กหนึ่งกล่องคือเท่าไหร่?\n",
+    "\n",
+    "-   ควรซื้อฟักทองในตะกร้าครึ่งบุชเชลหรือในกล่องขนาด 1 1/9 บุชเชล? มาลองเจาะลึกข้อมูลนี้กันต่อไป\n",
+    "\n",
+    "ในบทเรียนก่อนหน้านี้ คุณได้สร้าง `tibble` (การปรับปรุงใหม่ของ data frame) และเติมข้อมูลบางส่วนจากชุดข้อมูลต้นฉบับ โดยการปรับมาตรฐานราคาตามบุชเชล อย่างไรก็ตาม ด้วยวิธีนั้น คุณสามารถรวบรวมข้อมูลได้เพียงประมาณ 400 จุดข้อมูล และเฉพาะในช่วงฤดูใบไม้ร่วงเท่านั้น บางทีเราอาจได้รายละเอียดเพิ่มเติมเกี่ยวกับลักษณะของข้อมูลโดยการทำความสะอาดข้อมูลมากขึ้น? เราจะได้รู้กัน... 🕵️‍♀️\n",
+    "\n",
+    "สำหรับงานนี้ เราจะต้องใช้แพ็กเกจดังต่อไปนี้:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) คือ [ชุดของแพ็กเกจ R](https://www.tidyverse.org/packages) ที่ออกแบบมาเพื่อทำให้การวิเคราะห์ข้อมูลเร็วขึ้น ง่ายขึ้น และสนุกขึ้น!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) เป็นกรอบงาน [ชุดของแพ็กเกจ](https://www.tidymodels.org/packages/) สำหรับการสร้างแบบจำลองและการเรียนรู้ของเครื่อง\n",
+    "\n",
+    "-   `janitor`: [แพ็กเกจ janitor](https://github.com/sfirke/janitor) มีเครื่องมือเล็ก ๆ ที่เรียบง่ายสำหรับการตรวจสอบและทำความสะอาดข้อมูลที่สกปรก\n",
+    "\n",
+    "-   `corrplot`: [แพ็กเกจ corrplot](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) เป็นเครื่องมือสำรวจภาพบนเมทริกซ์ความสัมพันธ์ที่สนับสนุนการจัดเรียงตัวแปรอัตโนมัติเพื่อช่วยตรวจจับรูปแบบที่ซ่อนอยู่ระหว่างตัวแปร\n",
+    "\n",
+    "คุณสามารถติดตั้งแพ็กเกจเหล่านี้ได้ด้วยคำสั่ง:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\n",
+    "\n",
+    "สคริปต์ด้านล่างจะตรวจสอบว่าคุณมีแพ็กเกจที่จำเป็นสำหรับการทำโมดูลนี้หรือไม่ และจะติดตั้งให้คุณในกรณีที่ยังไม่มี\n"
+   ],
+   "metadata": {
+    "id": "WqQPS1OAsg3H"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "tA4C2WN3skCf",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เราจะโหลดแพ็กเกจที่ยอดเยี่ยมเหล่านี้และทำให้พร้อมใช้งานในเซสชัน R ปัจจุบันของเรา (นี่เป็นเพียงตัวอย่าง เพราะ `pacman::p_load()` ได้ทำสิ่งนี้ให้คุณแล้ว)\n",
+    "\n",
+    "## 1. เส้นการถดถอยเชิงเส้น\n",
+    "\n",
+    "ตามที่คุณได้เรียนรู้ในบทเรียนที่ 1 เป้าหมายของการถดถอยเชิงเส้นคือการสามารถวาด *เส้น* *ที่* *เหมาะสมที่สุด* เพื่อ:\n",
+    "\n",
+    "-   **แสดงความสัมพันธ์ระหว่างตัวแปร** แสดงความสัมพันธ์ระหว่างตัวแปรต่าง ๆ\n",
+    "\n",
+    "-   **ทำการพยากรณ์** ทำการพยากรณ์ที่แม่นยำเกี่ยวกับตำแหน่งที่จุดข้อมูลใหม่จะตกอยู่ในความสัมพันธ์กับเส้นนั้น\n",
+    "\n",
+    "ในการวาดเส้นประเภทนี้ เราใช้เทคนิคทางสถิติที่เรียกว่า **Least-Squares Regression** คำว่า `least-squares` หมายถึงการที่จุดข้อมูลทั้งหมดที่อยู่รอบเส้นการถดถอยถูกยกกำลังสองและนำมารวมกัน ผลรวมสุดท้ายควรมีค่าน้อยที่สุดเท่าที่จะเป็นไปได้ เพราะเราต้องการจำนวนข้อผิดพลาดที่ต่ำที่สุด หรือ `least-squares` ดังนั้น เส้นที่เหมาะสมที่สุดคือเส้นที่ให้ค่าผลรวมของข้อผิดพลาดที่ยกกำลังสองต่ำที่สุด - ซึ่งเป็นที่มาของชื่อ *least squares regression*\n",
+    "\n",
+    "เราทำเช่นนี้เพราะเราต้องการสร้างแบบจำลองเส้นที่มีระยะทางสะสมจากจุดข้อมูลทั้งหมดน้อยที่สุด นอกจากนี้ เรายังยกกำลังสองก่อนที่จะรวมกัน เพราะเราสนใจขนาดของมันมากกว่าทิศทาง\n",
+    "\n",
+    "> **🧮 แสดงคณิตศาสตร์ให้ฉันดู**\n",
+    ">\n",
+    "> เส้นนี้ ซึ่งเรียกว่า *เส้นที่เหมาะสมที่สุด* สามารถแสดงได้ด้วย [สมการ](https://en.wikipedia.org/wiki/Simple_linear_regression):\n",
+    ">\n",
+    ">     Y = a + bX\n",
+    ">\n",
+    "> `X` คือ '`ตัวแปรอธิบาย` หรือ `ตัวพยากรณ์`' ส่วน `Y` คือ '`ตัวแปรตาม` หรือ `ผลลัพธ์`' ความชันของเส้นคือ `b` และ `a` คือจุดตัดแกน y ซึ่งหมายถึงค่าของ `Y` เมื่อ `X = 0`\n",
+    ">\n",
+    "\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/slope.png \"slope = $y/x$\")\n",
+    "    อินโฟกราฟิกโดย Jen Looper\n",
+    ">\n",
+    "> ขั้นแรก คำนวณความชัน `b`\n",
+    ">\n",
+    "> กล่าวอีกนัยหนึ่ง และอ้างอิงถึงคำถามดั้งเดิมของข้อมูลฟักทองของเรา: \"พยากรณ์ราคาของฟักทองต่อบุชเชลตามเดือน\" `X` จะหมายถึงราคา และ `Y` จะหมายถึงเดือนที่ขาย\n",
+    ">\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/calculation.png)\n",
+    "    อินโฟกราฟิกโดย Jen Looper\n",
+    "> \n",
+    "> คำนวณค่าของ Y ถ้าคุณจ่ายประมาณ \\$4 นั่นต้องเป็นเดือนเมษายน!\n",
+    ">\n",
+    "> คณิตศาสตร์ที่คำนวณเส้นนี้ต้องแสดงความชันของเส้น ซึ่งยังขึ้นอยู่กับจุดตัดแกน หรือที่ที่ `Y` อยู่เมื่อ `X = 0`\n",
+    ">\n",
+    "> คุณสามารถดูวิธีการคำนวณค่าสำหรับสิ่งเหล่านี้ได้ที่เว็บไซต์ [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) และเยี่ยมชม [เครื่องคำนวณ Least-squares](https://www.mathsisfun.com/data/least-squares-calculator.html) เพื่อดูว่าค่าต่าง ๆ ส่งผลต่อเส้นอย่างไร\n",
+    "\n",
+    "ไม่น่ากลัวเท่าไหร่ใช่ไหม? 🤓\n",
+    "\n",
+    "#### ความสัมพันธ์\n",
+    "\n",
+    "อีกคำหนึ่งที่ควรเข้าใจคือ **ค่าสัมประสิทธิ์ความสัมพันธ์** ระหว่างตัวแปร X และ Y ที่กำหนด โดยใช้แผนภาพกระจาย (scatterplot) คุณสามารถมองเห็นค่าสัมประสิทธิ์นี้ได้อย่างรวดเร็ว แผนภาพที่มีจุดข้อมูลกระจายเป็นเส้นเรียบร้อยจะมีความสัมพันธ์สูง แต่แผนภาพที่มีจุดข้อมูลกระจายไปทั่วระหว่าง X และ Y จะมีความสัมพันธ์ต่ำ\n",
+    "\n",
+    "โมเดลการถดถอยเชิงเส้นที่ดีจะเป็นโมเดลที่มีค่าสัมประสิทธิ์ความสัมพันธ์สูง (ใกล้ 1 มากกว่า 0) โดยใช้วิธี Least-Squares Regression กับเส้นการถดถอย\n"
+   ],
+   "metadata": {
+    "id": "cdX5FRpvsoP5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **2. การเต้นรำกับข้อมูล: สร้าง Data Frame สำหรับการสร้างโมเดล**\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/janitor.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>ผลงานโดย @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![ผลงานโดย \\@allison_horst](../../../../../../2-Regression/3-Linear/images/janitor.jpg){width=\"700\"}-->\n"
+   ],
+   "metadata": {
+    "id": "WdUKXk7Bs8-V"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "โหลดไลบรารีและชุดข้อมูลที่จำเป็น จากนั้นแปลงข้อมูลให้เป็น Data Frame ที่มีเฉพาะส่วนย่อยของข้อมูล:\n",
+    "\n",
+    "-   เลือกเฉพาะฟักทองที่มีการตั้งราคาเป็นหน่วยบุชเชล\n",
+    "\n",
+    "-   แปลงวันที่ให้เป็นเดือน\n",
+    "\n",
+    "-   คำนวณราคาให้เป็นค่าเฉลี่ยระหว่างราคาสูงสุดและราคาต่ำสุด\n",
+    "\n",
+    "-   แปลงราคาให้สะท้อนถึงการตั้งราคาตามปริมาณในหน่วยบุชเชล\n",
+    "\n",
+    "> เราได้ครอบคลุมขั้นตอนเหล่านี้ใน [บทเรียนก่อนหน้า](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb)\n"
+   ],
+   "metadata": {
+    "id": "fMCtu2G2s-p8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "library(lubridate)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ryMVZEEPtERn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ด้วยจิตวิญญาณแห่งการผจญภัย ลองมาสำรวจ [`janitor package`](../../../../../../2-Regression/3-Linear/solution/R/github.com/sfirke/janitor) ที่มีฟังก์ชันง่ายๆ สำหรับตรวจสอบและทำความสะอาดข้อมูลที่ยุ่งเหยิงกัน ตัวอย่างเช่น ลองมาดูชื่อคอลัมน์ของข้อมูลของเรากัน:\n"
+   ],
+   "metadata": {
+    "id": "xcNxM70EtJjb"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5XtpaIigtPfW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤔 เราสามารถทำได้ดีกว่านี้ มาทำให้ชื่อคอลัมน์เหล่านี้เป็น `friendR` โดยการแปลงให้เป็นรูปแบบ [snake_case](https://en.wikipedia.org/wiki/Snake_case) ด้วยการใช้ `janitor::clean_names` หากต้องการทราบข้อมูลเพิ่มเติมเกี่ยวกับฟังก์ชันนี้: `?clean_names`\n"
+   ],
+   "metadata": {
+    "id": "IbIqrMINtSHe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Clean names to the snake_case convention\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  clean_names(case = \"snake\")\n",
+    "\n",
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "a2uYvclYtWvX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ช่างเป็น tidyR 🧹! ตอนนี้ มาลองเต้นรำกับข้อมูลโดยใช้ `dplyr` เหมือนในบทเรียนก่อนหน้านี้! 💃\n"
+   ],
+   "metadata": {
+    "id": "HfhnuzDDtaDd"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(variety, city_name, package, low_price, high_price, date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Extract the month from the dates to a new column\n",
+    "pumpkins <- pumpkins %>%\n",
+    "  mutate(date = mdy(date),\n",
+    "         month = month(date)) %>% \n",
+    "  select(-date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a new column for average Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(price = (low_price + high_price)/2)\n",
+    "\n",
+    "\n",
+    "# Retain only pumpkins with the string \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "  filter(str_detect(string = package, pattern = \"bushel\"))\n",
+    "\n",
+    "\n",
+    "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(price = case_when(\n",
+    "    str_detect(package, \"1 1/9\") ~ price/(1.1),\n",
+    "    str_detect(package, \"1/2\") ~ price*2,\n",
+    "    TRUE ~ price))\n",
+    "\n",
+    "# Relocate column positions\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(month, .before = variety)\n",
+    "\n",
+    "\n",
+    "# Display the first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "X0wU3gQvtd9f"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมมาก!👌 ตอนนี้คุณมีชุดข้อมูลที่สะอาดและเป็นระเบียบเรียบร้อย พร้อมสำหรับการสร้างโมเดลการถดถอยใหม่ของคุณแล้ว!\n",
+    "\n",
+    "สนใจกราฟกระจายไหม?\n"
+   ],
+   "metadata": {
+    "id": "UpaIwaxqth82"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set theme\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Make a scatter plot of month and price\n",
+    "new_pumpkins %>% \n",
+    "  ggplot(mapping = aes(x = month, y = price)) +\n",
+    "  geom_point(size = 1.6)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "DXgU-j37tl5K"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "แผนภาพกระจายช่วยเตือนเราว่าเรามีข้อมูลรายเดือนเพียงตั้งแต่เดือนสิงหาคมถึงเดือนธันวาคมเท่านั้น เราอาจต้องการข้อมูลเพิ่มเติมเพื่อที่จะสามารถสรุปผลในลักษณะเชิงเส้นได้\n",
+    "\n",
+    "ลองกลับมาดูข้อมูลการสร้างแบบจำลองของเราอีกครั้ง:\n"
+   ],
+   "metadata": {
+    "id": "Ve64wVbwtobI"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Display first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HFQX2ng1tuSJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ถ้าเราต้องการทำนาย `price` ของฟักทองโดยใช้คอลัมน์ `city` หรือ `package` ซึ่งเป็นข้อมูลประเภทตัวอักษรล่ะ? หรือถ้าจะง่ายกว่านั้น เราจะหาความสัมพันธ์ (ซึ่งต้องใช้ข้อมูลทั้งสองเป็นตัวเลข) ระหว่าง `package` กับ `price` ได้อย่างไร? 🤷🤷\n",
+    "\n",
+    "โมเดลการเรียนรู้ของเครื่องทำงานได้ดีที่สุดเมื่อใช้คุณลักษณะเป็นตัวเลขแทนที่จะเป็นค่าข้อความ ดังนั้นโดยทั่วไปคุณจำเป็นต้องแปลงคุณลักษณะเชิงหมวดหมู่ให้เป็นตัวแทนในรูปแบบตัวเลข\n",
+    "\n",
+    "นั่นหมายความว่าเราต้องหาวิธีปรับรูปแบบตัวทำนายของเราให้ใช้งานได้ง่ายขึ้นสำหรับโมเดล ซึ่งกระบวนการนี้เรียกว่า `feature engineering`\n"
+   ],
+   "metadata": {
+    "id": "7hsHoxsStyjJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. การเตรียมข้อมูลสำหรับการสร้างโมเดลด้วย recipes 👩‍🍳👨‍🍳\n",
+    "\n",
+    "กิจกรรมที่ปรับเปลี่ยนค่าของตัวแปรพยากรณ์เพื่อให้โมเดลใช้งานได้อย่างมีประสิทธิภาพมากขึ้น เรียกว่า `feature engineering`\n",
+    "\n",
+    "โมเดลแต่ละแบบมีความต้องการการเตรียมข้อมูลที่แตกต่างกัน ตัวอย่างเช่น least squares ต้องการ `การเข้ารหัสตัวแปรเชิงหมวดหมู่` เช่น เดือน, ชนิด และ city_name ซึ่งกระบวนการนี้เกี่ยวข้องกับ `การแปลง` คอลัมน์ที่มี `ค่าหมวดหมู่` ให้กลายเป็นหนึ่งหรือมากกว่า `คอลัมน์ตัวเลข` ที่มาแทนที่คอลัมน์เดิม\n",
+    "\n",
+    "ตัวอย่างเช่น สมมติว่าข้อมูลของคุณมีตัวแปรเชิงหมวดหมู่ดังนี้:\n",
+    "\n",
+    "|  city   |\n",
+    "|:-------:|\n",
+    "| Denver  |\n",
+    "| Nairobi |\n",
+    "|  Tokyo  |\n",
+    "\n",
+    "คุณสามารถใช้ *ordinal encoding* เพื่อแทนค่าหมวดหมู่แต่ละค่าเป็นตัวเลขจำนวนเต็มที่ไม่ซ้ำกัน เช่นนี้:\n",
+    "\n",
+    "| city |\n",
+    "|:----:|\n",
+    "|  0   |\n",
+    "|  1   |\n",
+    "|  2   |\n",
+    "\n",
+    "และนี่คือสิ่งที่เราจะทำกับข้อมูลของเรา!\n",
+    "\n",
+    "ในส่วนนี้ เราจะสำรวจแพ็กเกจ Tidymodels ที่น่าทึ่งอีกตัวหนึ่ง: [recipes](https://tidymodels.github.io/recipes/) - ซึ่งถูกออกแบบมาเพื่อช่วยคุณเตรียมข้อมูลของคุณ **ก่อน** การฝึกโมเดล โดยพื้นฐานแล้ว recipe คือออบเจ็กต์ที่กำหนดว่าควรมีขั้นตอนใดบ้างที่ต้องนำไปใช้กับชุดข้อมูลเพื่อเตรียมให้พร้อมสำหรับการสร้างโมเดล\n",
+    "\n",
+    "ตอนนี้ เรามาสร้าง recipe ที่เตรียมข้อมูลของเราสำหรับการสร้างโมเดลโดยการแทนค่าจำนวนเต็มที่ไม่ซ้ำกันให้กับทุกค่าที่พบในคอลัมน์ตัวแปรพยากรณ์:\n"
+   ],
+   "metadata": {
+    "id": "AD5kQbcvt3Xl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\n",
+    "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "# Print out the recipe\n",
+    "pumpkins_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "BNaFKXfRt9TU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมมาก! 👏 เราเพิ่งสร้างสูตรแรกที่กำหนดผลลัพธ์ (ราคา) และตัวทำนายที่เกี่ยวข้อง พร้อมทั้งกำหนดให้คอลัมน์ตัวทำนายทั้งหมดถูกแปลงเป็นชุดของตัวเลขจำนวนเต็ม 🙌! มาดูรายละเอียดกันอย่างรวดเร็ว:\n",
+    "\n",
+    "-   การเรียกใช้ `recipe()` พร้อมสูตรจะบอกสูตรเกี่ยวกับ *บทบาท* ของตัวแปร โดยใช้ข้อมูล `new_pumpkins` เป็นข้อมูลอ้างอิง ตัวอย่างเช่น คอลัมน์ `price` ถูกกำหนดให้มีบทบาทเป็น `outcome` ในขณะที่คอลัมน์อื่น ๆ ถูกกำหนดให้มีบทบาทเป็น `predictor`\n",
+    "\n",
+    "-   `step_integer(all_predictors(), zero_based = TRUE)` ระบุว่าตัวทำนายทั้งหมดควรถูกแปลงเป็นชุดของตัวเลขจำนวนเต็ม โดยเริ่มต้นการนับที่ 0\n",
+    "\n",
+    "เราเชื่อว่าคุณอาจกำลังคิดว่า: \"นี่มันเจ๋งมาก!! แต่ถ้าฉันต้องการยืนยันว่าสูตรกำลังทำงานตามที่ฉันคาดหวังจริง ๆ ล่ะ? 🤔\"\n",
+    "\n",
+    "นั่นเป็นความคิดที่ยอดเยี่ยม! คุณเห็นไหมว่า เมื่อสูตรของคุณถูกกำหนดแล้ว คุณสามารถประมาณค่าพารามิเตอร์ที่จำเป็นสำหรับการเตรียมข้อมูล และจากนั้นดึงข้อมูลที่ผ่านการประมวลผลออกมาได้ โดยปกติคุณไม่จำเป็นต้องทำเช่นนี้เมื่อใช้ Tidymodels (เราจะเห็นวิธีการทั่วไปในอีกสักครู่-\\> `workflows`) แต่สิ่งนี้อาจมีประโยชน์เมื่อคุณต้องการตรวจสอบความถูกต้องเพื่อยืนยันว่าสูตรกำลังทำงานตามที่คุณคาดหวัง\n",
+    "\n",
+    "สำหรับสิ่งนี้ คุณจะต้องใช้คำกริยาอีกสองคำ: `prep()` และ `bake()` และเช่นเคย เพื่อนตัวน้อยใน R ของเราโดย [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) จะช่วยให้คุณเข้าใจสิ่งนี้ได้ดียิ่งขึ้น!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>ภาพวาดโดย @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "KEiO0v7kuC9O"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): ประเมินค่าพารามิเตอร์ที่จำเป็นจากชุดข้อมูลการฝึกอบรม ซึ่งสามารถนำไปใช้กับชุดข้อมูลอื่นในภายหลังได้ ตัวอย่างเช่น สำหรับคอลัมน์ตัวทำนายที่กำหนด จะมีการกำหนดค่าการสังเกตเป็นเลขจำนวนเต็ม 0 หรือ 1 หรือ 2 เป็นต้น\n",
+    "\n",
+    "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): ใช้สูตรที่เตรียมไว้แล้วและดำเนินการกับชุดข้อมูลใดๆ\n",
+    "\n",
+    "เมื่อกล่าวเช่นนี้ เรามาเตรียมและดำเนินการสูตรของเราเพื่อยืนยันจริงๆ ว่าเบื้องหลังนั้น คอลัมน์ตัวทำนายจะถูกเข้ารหัสก่อนที่จะนำไปปรับใช้กับโมเดล\n"
+   ],
+   "metadata": {
+    "id": "Q1xtzebuuTCP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep the recipe\n",
+    "pumpkins_prep <- prep(pumpkins_recipe)\n",
+    "\n",
+    "# Bake the recipe to extract a preprocessed new_pumpkins data\n",
+    "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\n",
+    "\n",
+    "# Print out the baked data set\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FGBbJbP_uUUn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมไปเลย! 🥳 ข้อมูลที่ผ่านการประมวลผล `baked_pumpkins` มีตัวแปรทำนายทั้งหมดที่ถูกเข้ารหัสแล้ว ซึ่งยืนยันว่าขั้นตอนการเตรียมข้อมูลที่เรากำหนดไว้ในสูตรนั้นทำงานได้ตามที่คาดหวัง แม้ว่ามันอาจจะทำให้คุณอ่านยากขึ้น แต่ก็ทำให้ Tidymodels เข้าใจข้อมูลได้ง่ายขึ้น ลองใช้เวลาสักครู่เพื่อดูว่าแต่ละข้อมูลถูกแปลงเป็นตัวเลขใดบ้าง\n",
+    "\n",
+    "นอกจากนี้ยังควรกล่าวถึงว่า `baked_pumpkins` เป็น data frame ที่เราสามารถทำการคำนวณต่าง ๆ ได้\n",
+    "\n",
+    "ตัวอย่างเช่น ลองหาความสัมพันธ์ที่ดีระหว่างสองจุดในข้อมูลของคุณเพื่อสร้างโมเดลทำนายที่มีประสิทธิภาพ เราจะใช้ฟังก์ชัน `cor()` เพื่อทำสิ่งนี้ พิมพ์ `?cor()` เพื่อดูข้อมูลเพิ่มเติมเกี่ยวกับฟังก์ชันนี้\n"
+   ],
+   "metadata": {
+    "id": "1dvP0LBUueAW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the correlation between the city_name and the price\n",
+    "cor(baked_pumpkins$city_name, baked_pumpkins$price)\n",
+    "\n",
+    "# Find the correlation between the package and the price\n",
+    "cor(baked_pumpkins$package, baked_pumpkins$price)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "3bQzXCjFuiSV"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ปรากฏว่าความสัมพันธ์ระหว่างเมืองและราคานั้นค่อนข้างอ่อนแอ อย่างไรก็ตาม มีความสัมพันธ์ที่ดีกว่าระหว่างแพ็คเกจและราคา ซึ่งก็สมเหตุสมผลใช่ไหม? โดยปกติแล้ว กล่องสินค้ายิ่งใหญ่ ราคาก็ยิ่งสูงขึ้น\n",
+    "\n",
+    "ในขณะเดียวกัน เรามาลองสร้างภาพเมทริกซ์ความสัมพันธ์ของทุกคอลัมน์โดยใช้แพ็คเกจ `corrplot` กันเถอะ\n"
+   ],
+   "metadata": {
+    "id": "BToPWbgjuoZw"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the corrplot package\n",
+    "library(corrplot)\n",
+    "\n",
+    "# Obtain correlation matrix\n",
+    "corr_mat <- cor(baked_pumpkins %>% \n",
+    "                  # Drop columns that are not really informative\n",
+    "                  select(-c(low_price, high_price)))\n",
+    "\n",
+    "# Make a correlation plot between the variables\n",
+    "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZwAL3ksmutVR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 ดีขึ้นมาก\n",
+    "\n",
+    "คำถามที่ดีที่ควรถามเกี่ยวกับข้อมูลนี้คือ: '`ราคาที่คาดหวังสำหรับแพ็คเกจฟักทองคือเท่าไหร่?`' มาเริ่มกันเลย!\n",
+    "\n",
+    "> หมายเหตุ: เมื่อคุณ **`bake()`** สูตรที่เตรียมไว้ **`pumpkins_prep`** โดยใช้ **`new_data = NULL`** คุณจะได้ข้อมูลการฝึกที่ผ่านการประมวลผล (เช่น การเข้ารหัส) หากคุณมีชุดข้อมูลอื่น เช่น ชุดทดสอบ และต้องการดูว่าสูตรจะประมวลผลอย่างไร คุณเพียงแค่ bake **`pumpkins_prep`** โดยใช้ **`new_data = test_set`**\n",
+    "\n",
+    "## 4. สร้างโมเดลการถดถอยเชิงเส้น\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>อินโฟกราฟิกโดย Dasani Madipalli</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "YqXjLuWavNxW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ตอนนี้ที่เราได้สร้างสูตรและยืนยันแล้วว่าข้อมูลจะถูกประมวลผลล่วงหน้าอย่างเหมาะสม มาสร้างโมเดลการถดถอยเพื่อหาคำตอบสำหรับคำถามนี้กัน: `ราคาที่คาดหวังของแพ็คเกจฟักทองที่กำหนดคือเท่าไร?`\n",
+    "\n",
+    "#### ฝึกโมเดลการถดถอยเชิงเส้นโดยใช้ชุดข้อมูลการฝึก\n",
+    "\n",
+    "คุณอาจสังเกตเห็นแล้วว่า คอลัมน์ *price* คือ `ตัวแปรผลลัพธ์` ในขณะที่คอลัมน์ *package* คือ `ตัวแปรพยากรณ์`\n",
+    "\n",
+    "เพื่อทำสิ่งนี้ เราจะเริ่มต้นด้วยการแบ่งข้อมูล โดยให้ 80% เป็นชุดข้อมูลการฝึก และ 20% เป็นชุดข้อมูลทดสอบ จากนั้นกำหนดสูตรที่จะเข้ารหัสคอลัมน์ตัวแปรพยากรณ์ให้เป็นชุดของตัวเลข และสร้างสเปคของโมเดล เราจะไม่เตรียมและอบสูตรของเราอีกครั้ง เพราะเรารู้อยู่แล้วว่ามันจะประมวลผลข้อมูลได้ตามที่คาดไว้\n"
+   ],
+   "metadata": {
+    "id": "Pq0bSzCevW-h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\n",
+    "# Split the data into training and test sets\n",
+    "pumpkins_split <- new_pumpkins %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "\n",
+    "# Extract training and test data\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a recipe for preprocessing the data\n",
+    "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a linear model specification\n",
+    "lm_spec <- linear_reg() %>% \n",
+    "  set_engine(\"lm\") %>% \n",
+    "  set_mode(\"regression\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "CyoEh_wuvcLv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมมาก! ตอนนี้เรามีสูตรและสเปคของโมเดลแล้ว เราต้องหาวิธีรวมสิ่งเหล่านี้เข้าด้วยกันเป็นวัตถุที่สามารถทำการเตรียมข้อมูล (prep+bake เบื้องหลัง) ฝึกโมเดลบนข้อมูลที่ผ่านการเตรียมแล้ว และยังรองรับกิจกรรมหลังการประมวลผลได้อีกด้วย แบบนี้ช่วยให้คุณสบายใจขึ้นใช่ไหม!🤩\n",
+    "\n",
+    "ใน Tidymodels วัตถุที่สะดวกนี้เรียกว่า [`workflow`](https://workflows.tidymodels.org/) ซึ่งช่วยจัดการองค์ประกอบการสร้างโมเดลของคุณได้อย่างสะดวก! สิ่งนี้คือสิ่งที่เราเรียกว่า *pipelines* ใน *Python* นั่นเอง\n",
+    "\n",
+    "ดังนั้น มาเริ่มรวมทุกอย่างเข้าด้วยกันใน workflow กันเถอะ!📦\n"
+   ],
+   "metadata": {
+    "id": "G3zF_3DqviFJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Hold modelling components in a workflow\n",
+    "lm_wf <- workflow() %>% \n",
+    "  add_recipe(lm_pumpkins_recipe) %>% \n",
+    "  add_model(lm_spec)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "lm_wf"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "T3olroU3v-WX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "นอกจากนี้ เวิร์กโฟลว์ยังสามารถปรับแต่ง/ฝึกฝนได้ในลักษณะเดียวกับที่โมเดลสามารถทำได้\n"
+   ],
+   "metadata": {
+    "id": "zd1A5tgOwEPX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Train the model\n",
+    "lm_wf_fit <- lm_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the model coefficients learned \n",
+    "lm_wf_fit"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NhJagFumwFHf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "จากผลลัพธ์ของโมเดล เราสามารถเห็นค่าสัมประสิทธิ์ที่ได้จากการฝึกฝน ซึ่งค่าสัมประสิทธิ์เหล่านี้แสดงถึงค่าของเส้นที่เหมาะสมที่สุดที่ช่วยลดข้อผิดพลาดโดยรวมระหว่างค่าจริงและค่าที่โมเดลทำนายได้\n",
+    "\n",
+    "#### ประเมินประสิทธิภาพของโมเดลด้วยชุดข้อมูลทดสอบ\n",
+    "\n",
+    "ถึงเวลาตรวจสอบว่าโมเดลทำงานได้ดีแค่ไหน 📏! เราจะทำอย่างไร?\n",
+    "\n",
+    "เมื่อเราได้ฝึกฝนโมเดลแล้ว เราสามารถใช้โมเดลนี้เพื่อทำนายค่าจาก `test_set` โดยใช้ `parsnip::predict()` จากนั้นเราสามารถเปรียบเทียบค่าที่ทำนายได้กับค่าป้ายกำกับจริงเพื่อประเมินว่าโมเดลทำงานได้ดีหรือไม่ดีเพียงใด\n",
+    "\n",
+    "เริ่มต้นด้วยการทำนายค่าจากชุดข้อมูลทดสอบ แล้วรวมคอลัมน์เข้ากับชุดข้อมูลทดสอบ\n"
+   ],
+   "metadata": {
+    "id": "_4QkGtBTwItF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\n",
+    "predictions <- lm_wf_fit %>% \n",
+    "  predict(new_data = pumpkins_test)\n",
+    "\n",
+    "\n",
+    "# Bind predictions to the test set\n",
+    "lm_results <- pumpkins_test %>% \n",
+    "  select(c(package, price)) %>% \n",
+    "  bind_cols(predictions)\n",
+    "\n",
+    "\n",
+    "# Print the first ten rows of the tibble\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "UFZzTG0gwTs9"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "คุณเพิ่งฝึกโมเดลและใช้มันเพื่อทำการพยากรณ์!🔮 มันดีแค่ไหน? มาประเมินประสิทธิภาพของโมเดลกันเถอะ!\n",
+    "\n",
+    "ใน Tidymodels เราทำสิ่งนี้โดยใช้ `yardstick::metrics()`! สำหรับการวิเคราะห์การถดถอยเชิงเส้น (linear regression) เรามุ่งเน้นไปที่ตัวชี้วัดต่อไปนี้:\n",
+    "\n",
+    "-   `Root Mean Square Error (RMSE)`: รากที่สองของ [MSE](https://en.wikipedia.org/wiki/Mean_squared_error) ซึ่งให้ค่าตัวชี้วัดแบบสัมบูรณ์ในหน่วยเดียวกับป้ายกำกับ (ในกรณีนี้คือราคาของฟักทอง) ค่ายิ่งเล็กยิ่งดีสำหรับโมเดล (ในความหมายง่ายๆ มันแสดงถึงราคาที่การพยากรณ์ผิดพลาดโดยเฉลี่ย!)\n",
+    "\n",
+    "-   `Coefficient of Determination (มักเรียกว่า R-squared หรือ R2)`: ตัวชี้วัดแบบสัมพัทธ์ที่ค่ายิ่งสูงยิ่งดีสำหรับการปรับให้เข้ากับโมเดล โดยพื้นฐานแล้ว ตัวชี้วัดนี้แสดงถึงว่าระดับความแปรปรวนระหว่างค่าที่พยากรณ์และค่าจริงที่โมเดลสามารถอธิบายได้มากน้อยเพียงใด\n"
+   ],
+   "metadata": {
+    "id": "0A5MjzM7wW9M"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Evaluate performance of linear regression\n",
+    "metrics(data = lm_results,\n",
+    "        truth = price,\n",
+    "        estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "reJ0UIhQwcEH"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ประสิทธิภาพของโมเดลลดลงไปแล้ว ลองมาดูกันว่าเราจะได้ข้อมูลที่ชัดเจนขึ้นหรือไม่โดยการสร้างกราฟกระจายของแพ็กเกจและราคา แล้วใช้การคาดการณ์ที่ได้มาวางเส้นแนวโน้มที่เหมาะสมที่สุดลงไป\n",
+    "\n",
+    "นั่นหมายความว่าเราจะต้องเตรียมและประมวลผลชุดทดสอบเพื่อเข้ารหัสคอลัมน์แพ็กเกจ จากนั้นจึงรวมข้อมูลนี้เข้ากับการคาดการณ์ที่โมเดลของเราสร้างขึ้น\n"
+   ],
+   "metadata": {
+    "id": "fdgjzjkBwfWt"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Encode package column\n",
+    "package_encode <- lm_pumpkins_recipe %>% \n",
+    "  prep() %>% \n",
+    "  bake(new_data = pumpkins_test) %>% \n",
+    "  select(package)\n",
+    "\n",
+    "\n",
+    "# Bind encoded package column to the results\n",
+    "lm_results <- lm_results %>% \n",
+    "  bind_cols(package_encode %>% \n",
+    "              rename(package_integer = package)) %>% \n",
+    "  relocate(package_integer, .after = package)\n",
+    "\n",
+    "\n",
+    "# Print new results data frame\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 5)\n",
+    "\n",
+    "\n",
+    "# Make a scatter plot\n",
+    "lm_results %>% \n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\n",
+    "  geom_point(size = 1.6) +\n",
+    "  # Overlay a line of best fit\n",
+    "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\n",
+    "  xlab(\"package\")\n",
+    "  \n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R0nw719lwkHE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ยอดเยี่ยม! ดังที่คุณเห็น โมเดลการถดถอยเชิงเส้นไม่ได้ทำงานได้ดีนักในการสรุปความสัมพันธ์ระหว่างแพ็กเกจกับราคาที่เกี่ยวข้อง\n",
+    "\n",
+    "🎃 ขอแสดงความยินดี คุณเพิ่งสร้างโมเดลที่สามารถช่วยทำนายราคาของฟักทองหลากหลายชนิดได้ แปลงฟักทองสำหรับวันหยุดของคุณจะสวยงาม แต่คุณอาจสร้างโมเดลที่ดีกว่านี้ได้!\n",
+    "\n",
+    "## 5. สร้างโมเดลการถดถอยเชิงพหุนาม\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>อินโฟกราฟิกโดย Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![อินโฟกราฟิกโดย Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "HOCqJXLTwtWI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "บางครั้งข้อมูลของเราอาจไม่มีความสัมพันธ์แบบเชิงเส้นตรง แต่เรายังคงต้องการทำนายผลลัพธ์ การใช้การถดถอยพหุนาม (Polynomial Regression) สามารถช่วยให้เราทำนายความสัมพันธ์ที่ซับซ้อนและไม่เป็นเชิงเส้นได้\n",
+    "\n",
+    "ลองพิจารณาความสัมพันธ์ระหว่างขนาดของแพ็กเกจกับราคาของชุดข้อมูลฟักทองของเรา แม้ว่าบางครั้งจะมีความสัมพันธ์แบบเชิงเส้นตรงระหว่างตัวแปร เช่น ฟักทองที่มีปริมาตรมากขึ้นมักจะมีราคาสูงขึ้น แต่บางครั้งความสัมพันธ์เหล่านี้ไม่สามารถแสดงออกมาในรูปแบบระนาบหรือเส้นตรงได้\n",
+    "\n",
+    "> ✅ นี่คือตัวอย่างเพิ่มเติม [บางตัวอย่าง](https://online.stat.psu.edu/stat501/lesson/9/9.8) ของข้อมูลที่อาจใช้การถดถอยพหุนาม\n",
+    ">\n",
+    "> ลองพิจารณาความสัมพันธ์ระหว่างชนิดของฟักทอง (Variety) กับราคาในกราฟก่อนหน้านี้อีกครั้ง คุณคิดว่ากราฟกระจายนี้ควรวิเคราะห์ด้วยเส้นตรงหรือไม่? อาจจะไม่ ในกรณีนี้ คุณสามารถลองใช้การถดถอยพหุนามได้\n",
+    ">\n",
+    "> ✅ พหุนาม (Polynomial) คือการแสดงออกทางคณิตศาสตร์ที่อาจประกอบด้วยตัวแปรและสัมประสิทธิ์หนึ่งตัวหรือมากกว่า\n",
+    "\n",
+    "#### ฝึกโมเดลการถดถอยพหุนามโดยใช้ชุดข้อมูลการฝึก\n",
+    "\n",
+    "การถดถอยพหุนามจะสร้าง *เส้นโค้ง* เพื่อให้เหมาะสมกับข้อมูลที่ไม่เป็นเชิงเส้นมากขึ้น\n",
+    "\n",
+    "มาดูกันว่าโมเดลพหุนามจะทำงานได้ดีกว่าในการทำนายหรือไม่ เราจะทำตามขั้นตอนที่คล้ายกับที่เราเคยทำมาก่อนหน้านี้:\n",
+    "\n",
+    "-   สร้างสูตร (recipe) ที่ระบุขั้นตอนการเตรียมข้อมูลที่ควรดำเนินการเพื่อให้ข้อมูลพร้อมสำหรับการสร้างโมเดล เช่น การเข้ารหัสตัวทำนาย (predictors) และการคำนวณพหุนามของระดับ *n*\n",
+    "\n",
+    "-   สร้างสเปคของโมเดล (model specification)\n",
+    "\n",
+    "-   รวมสูตรและสเปคของโมเดลเข้าด้วยกันในเวิร์กโฟลว์ (workflow)\n",
+    "\n",
+    "-   สร้างโมเดลโดยการปรับเวิร์กโฟลว์\n",
+    "\n",
+    "-   ประเมินว่าโมเดลทำงานได้ดีเพียงใดกับข้อมูลทดสอบ\n",
+    "\n",
+    "มาเริ่มกันเลย!\n"
+   ],
+   "metadata": {
+    "id": "VcEIpRV9wzYr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\r\n",
+    "poly_pumpkins_recipe <-\r\n",
+    "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+    "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+    "  step_poly(all_predictors(), degree = 4)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model specification\r\n",
+    "poly_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Bundle recipe and model spec into a workflow\r\n",
+    "poly_wf <- workflow() %>% \r\n",
+    "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+    "  add_model(poly_spec)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model\r\n",
+    "poly_wf_fit <- poly_wf %>% \r\n",
+    "  fit(data = pumpkins_train)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print learned model coefficients\r\n",
+    "poly_wf_fit\r\n",
+    "\r\n",
+    "  "
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "63n_YyRXw3CC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### ประเมินประสิทธิภาพของโมเดล\n",
+    "\n",
+    "👏👏คุณได้สร้างโมเดลพหุนามเสร็จเรียบร้อยแล้ว มาลองทำการพยากรณ์บนชุดทดสอบกันเถอะ!\n"
+   ],
+   "metadata": {
+    "id": "-LHZtztSxDP0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make price predictions on test data\r\n",
+    "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+    "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+    "  relocate(.pred, .after = last_col())\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the results\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YUFpQ_dKxJGx"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "วู้ฮู มาประเมินกันว่ารุ่นทำงานอย่างไรกับ test_set โดยใช้ `yardstick::metrics()`\n"
+   ],
+   "metadata": {
+    "id": "qxdyj86bxNGZ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "metrics(data = poly_results, truth = price, estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "8AW5ltkBxXDm"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 ประสิทธิภาพดีขึ้นมาก\n",
+    "\n",
+    "`rmse` ลดลงจากประมาณ 7 เหลือประมาณ 3 ซึ่งแสดงถึงข้อผิดพลาดที่ลดลงระหว่างราคาจริงและราคาที่คาดการณ์ คุณสามารถ *ตีความแบบคร่าวๆ* ได้ว่าค่าเฉลี่ยของการคาดการณ์ที่ผิดพลาดนั้นผิดไปประมาณ \\$3 `rsq` เพิ่มขึ้นจากประมาณ 0.4 เป็น 0.8\n",
+    "\n",
+    "ตัวชี้วัดทั้งหมดนี้แสดงให้เห็นว่าโมเดลพหุนามทำงานได้ดีกว่าโมเดลเชิงเส้นมาก เยี่ยมมาก!\n",
+    "\n",
+    "ลองมาดูว่าพอจะสร้างภาพให้เห็นได้ไหม!\n"
+   ],
+   "metadata": {
+    "id": "6gLHNZDwxYaS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Bind encoded package column to the results\r\n",
+    "poly_results <- poly_results %>% \r\n",
+    "  bind_cols(package_encode %>% \r\n",
+    "              rename(package_integer = package)) %>% \r\n",
+    "  relocate(package_integer, .after = package)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print new results data frame\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+    "  xlab(\"package\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "A83U16frxdF1"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "คุณสามารถเห็นเส้นโค้งที่เข้ากับข้อมูลของคุณได้ดีขึ้น! 🤩\n",
+    "\n",
+    "คุณสามารถทำให้เส้นนี้ดูเรียบเนียนขึ้นได้โดยการส่งสูตรพหุนามไปที่ `geom_smooth` แบบนี้:\n"
+   ],
+   "metadata": {
+    "id": "4U-7aHOVxlGU"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+    "  xlab(\"package\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5vzNT0Uexm-w"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เหมือนกับเส้นโค้งที่ราบรื่น!🤩\n",
+    "\n",
+    "นี่คือวิธีที่คุณจะสร้างการพยากรณ์ใหม่:\n"
+   ],
+   "metadata": {
+    "id": "v9u-wwyLxq4G"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a hypothetical data frame\r\n",
+    "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+    "\r\n",
+    "# Make predictions using linear model\r\n",
+    "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Make predictions using polynomial model\r\n",
+    "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Return predictions in a list\r\n",
+    "list(\"linear model prediction\" = lm_pred, \r\n",
+    "     \"polynomial model prediction\" = poly_pred)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "jRPSyfQGxuQv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "การทำนายด้วย `polynomial model` ดูสมเหตุสมผลเมื่อพิจารณาจากกราฟกระจายของ `price` และ `package`! และถ้านี่เป็นโมเดลที่ดีกว่าโมเดลก่อนหน้า เมื่อดูจากข้อมูลเดียวกัน คุณจำเป็นต้องวางแผนงบประมาณสำหรับฟักทองที่มีราคาสูงขึ้นเหล่านี้!\n",
+    "\n",
+    "🏆 ยอดเยี่ยมมาก! คุณได้สร้างโมเดลการถดถอยสองแบบในบทเรียนเดียว ในส่วนสุดท้ายเกี่ยวกับการถดถอย คุณจะได้เรียนรู้เกี่ยวกับการถดถอยโลจิสติกเพื่อกำหนดหมวดหมู่\n",
+    "\n",
+    "## **🚀ความท้าทาย**\n",
+    "\n",
+    "ทดลองใช้ตัวแปรที่แตกต่างกันหลายตัวในโน้ตบุ๊กนี้เพื่อดูว่าความสัมพันธ์ส่งผลต่อความแม่นยำของโมเดลอย่างไร\n",
+    "\n",
+    "## [**แบบทดสอบหลังบทเรียน**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)\n",
+    "\n",
+    "## **ทบทวนและศึกษาด้วยตนเอง**\n",
+    "\n",
+    "ในบทเรียนนี้เราได้เรียนรู้เกี่ยวกับการถดถอยเชิงเส้น ยังมีประเภทการถดถอยที่สำคัญอื่น ๆ อีก อ่านเพิ่มเติมเกี่ยวกับเทคนิค Stepwise, Ridge, Lasso และ Elasticnet หลักสูตรที่ดีสำหรับการศึกษาเพิ่มเติมคือ [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning)\n",
+    "\n",
+    "หากคุณต้องการเรียนรู้เพิ่มเติมเกี่ยวกับการใช้เฟรมเวิร์ก Tidymodels ที่น่าทึ่งนี้ โปรดดูแหล่งข้อมูลต่อไปนี้:\n",
+    "\n",
+    "-   เว็บไซต์ Tidymodels: [เริ่มต้นกับ Tidymodels](https://www.tidymodels.org/start/)\n",
+    "\n",
+    "-   Max Kuhn และ Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+    "\n",
+    "###### **ขอขอบคุณ:**\n",
+    "\n",
+    "[Allison Horst](https://twitter.com/allison_horst?lang=en) สำหรับการสร้างภาพประกอบที่น่าทึ่งซึ่งทำให้ R ดูน่าสนใจและเข้าถึงได้มากขึ้น ค้นหาภาพประกอบเพิ่มเติมได้ที่ [แกลเลอรีของเธอ](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM)\n"
+   ],
+   "metadata": {
+    "id": "8zOLOWqMxzk5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/3-Linear/solution/notebook.ipynb b/translations/th/2-Regression/3-Linear/solution/notebook.ipynb
new file mode 100644
index 000000000..1f864da24
--- /dev/null
+++ b/translations/th/2-Regression/3-Linear/solution/notebook.ipynb
@@ -0,0 +1,1109 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## การวิเคราะห์เชิงเส้นและพหุนามสำหรับการตั้งราคาฟักทอง - บทเรียนที่ 3\n",
+    "\n",
+    "โหลดไลบรารีและชุดข้อมูลที่จำเป็น จากนั้นแปลงข้อมูลให้เป็น DataFrame ที่มีเพียงส่วนย่อยของข้อมูล:\n",
+    "\n",
+    "- เลือกเฉพาะฟักทองที่ตั้งราคาเป็นหน่วย bushel\n",
+    "- แปลงวันที่ให้เป็นเดือน\n",
+    "- คำนวณราคาให้เป็นค่าเฉลี่ยระหว่างราคาสูงสุดและต่ำสุด\n",
+    "- แปลงราคาให้สะท้อนถึงการตั้งราคาต่อปริมาณในหน่วย bushel\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "แผนภาพกระจายเตือนเราว่าเรามีข้อมูลรายเดือนเฉพาะตั้งแต่เดือนสิงหาคมถึงเดือนธันวาคม เราอาจต้องการข้อมูลเพิ่มเติมเพื่อที่จะสรุปผลในรูปแบบเชิงเส้น\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Month', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('Month','Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ดูเหมือนว่าความสัมพันธ์จะค่อนข้างน้อย แต่มีความสัมพันธ์อื่นที่สำคัญกว่า - เพราะจุดราคาบนกราฟด้านบนดูเหมือนจะมีการจัดกลุ่มที่แตกต่างกันหลายกลุ่ม ลองสร้างกราฟที่แสดงพันธุ์ฟักทองที่แตกต่างกัน:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax=None\n",
+    "colors = ['red','blue','green','yellow']\n",
+    "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    ax = new_pumpkins[new_pumpkins['Variety']==var].plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.2669192282197318\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcL0lEQVR4nO3df5RcZZ3n8fenSduJJJDQCTEnPwxOhFkWYpSWhYkogsPiHA5wNuiZOSK4OpOVHT3i6iaic3D1zHpMMqszLu4ZGWUJLMMsQxAcZjiQBZTBJUCTyQ9+KdlBSCKE0CRDtyZth/7uH3U7qW6qOl3Vdavurft5ndMnVU9Vfe/zVFe+dfu5z/1eRQRmZlYcHa3ugJmZNZcTv5lZwTjxm5kVjBO/mVnBOPGbmRXMlFZ3YCJmz54dixcvbnU3zMxy5Yknnng1IuaMbc9F4l+8eDG9vb2t7oaZWa5IeqFSu6d6zMwKxonfzKxgnPjNzArGid/MrGCc+M3MCia1xC9poaQHJT0j6SlJn0va10l6VtI2ST+UNDOtPtjk9A0MsnXnfvoGBjMZz8zqk+ZyzkPAFyJis6QZwBOSNgIbgWsi4pCkNcA1wOoU+2F1uGvLblZv2EZnRwdDw8OsXbGUi5fNz0w8M6tfanv8EfFSRGxObvcDzwDzI+K+iDiUPG0TsCCtPlh9+gYGWb1hGweHhukfPMTBoWFWbdhW9556o+OZ2eQ0ZY5f0mLg3cCjYx76JHBPldeslNQrqXfv3r0p99DK7dp3gM6O0R+Nzo4Odu07kIl4ZjY5qSd+SdOBDcDVEfF6WftXKE0H3VLpdRFxfUT0RETPnDlvOuPYUrRg1jSGhodHtQ0ND7Ng1rRMxDOzyUk18UvqpJT0b4mIO8rarwQuAj4WvgRY5nRP72LtiqVM7exgRtcUpnZ2sHbFUrqnd2UinplNjtLKu5IErAdei4iry9ovBL4FfCAiJjSH09PTE67V03x9A4Ps2neABbOmNSRJNzqemY1P0hMR0TO2Pc1VPcuBjwPbJW1J2r4MfAfoAjaWvhvYFBGfTrEfVqfu6V0NTdCNjmdm9Ukt8UfEw4AqPPQPaW3TzMyOzmfumpkVjBO/mVnBOPGbmRWME7+ZWcE48ZuZFYwTv5lZwTjxm5kVjBO/mVnBOPGbmRWME7+ZWcE48ZuZFYwTv5lZwTjxm5kVjBO/mVnBOPGbmRWME7+ZWcE48ZuZFYwTv5lZwTjxm5kVjBO/tZW+gUG27txP38Cg4zle7uOlJbWLrZs1211bdrN6wzY6OzoYGh5m7YqlXLxsvuM5Xi7jpcl7/NYW+gYGWb1hGweHhukfPMTBoWFWbdhW956X4zleK+OlzYnf2sKufQfo7Bj9ce7s6GDXvgOO53i5i5c2J35rCwtmTWNoeHhU29DwMAtmTXM8x8tdvLQ58Vtb6J7exdoVS5na2cGMrilM7exg7YqldE/vcjzHy128tCkiWt2Ho+rp6Yne3t5Wd8NyoG9gkF37DrBg1rSG/KdzPMdrZbzJkvRERPS8qd2J38ysPVVL/KlN9UhaKOlBSc9IekrS55L2EyRtlPRc8u+stPqQdVlfQ5yXNclmVps01/EfAr4QEZslzQCekLQR+ARwf0R8U9KXgC8Bq1PsRyZlfQ1xntYkm1ltUtvjj4iXImJzcrsfeAaYD1wCrE+eth64NK0+ZFXW1xDnbU2ymdWmKat6JC0G3g08CsyNiJeg9OUAnFjlNSsl9Urq3bt3bzO62TRZX0OctzXJZlab1BO/pOnABuDqiHh9oq+LiOsjoicieubMmZNeB1sg62uI87Ym2cxqk2ril9RJKenfEhF3JM17JM1LHp8HvJJmH7Io62uI87Ym2cxqk9pyTkmiNIf/WkRcXda+DugrO7h7QkSsGi9Wuy7nzPoa4qytSTaz2lRbzpnmqp7lwMeB7ZK2JG1fBr4J3CbpU8CLwEdS7EOmdU/vamhCzXo8M8uG1BJ/RDwMqMrD56e1XTMzG59r9ZiZFYwTv5lZwTjxm5kVjBO/mVnBOPGbmRWME7+ZWcE48ZuZFYwTv5lZwTjxm5kVjBN/C2X9ilnV4tW7nUqvy+t74HiWZ2nW6rFxZP2KWdXi1budSq8LyOV74HiWd77Yegv0DQyyfM0DHBw6UvN+amcHP119Xl1F0ZoV7+7PvI+Lrnu45u1Uitc1pQMIBg8d+fzl4T1wPMuTpl9s3arL+hWzqsXbsnN/XdupFO+YDnGM8vceOJ61A0/1tEDWr5hVLd6yhTPr2k6leG8MBzD6r808vAeOZ+3Ae/wtkPUrZlWLt2TujLq2UyneusuWsu6yd+XuPXA8awee42+hrF8xq1q8erdT6XV5fQ8cz/Kg2hy/E7+ZWZvywV0zMwOc+M3MCseJ38ysYJz4zcwKxonfzKxgnPjNzAqmrRN/1isXOp6ZtULblmzIeuVCx3PlR7NWacs9/r6BQVZv2MbBoWH6Bw9xcGiYVRu21b2n6XjZimdmk5Na4pd0g6RXJD1Z1rZM0iZJWyT1SjozjW1nvXKh47nyo1krpbnHfyNw4Zi2tcDXImIZcG1yv+GyXrnQ8Vz50ayVUkv8EfEQ8NrYZuC45PbxwC/T2HbWKxc6nis/mrVSqkXaJC0G7o6I05L7/wq4FxClL53fiYgXjhan3iJtWa9c6HhmlqaWVOeskPi/A/wkIjZI+iiwMiI+VOW1K4GVAIsWLTrjhReO+v1gZmZlslKd80rgjuT23wJVD+5GxPUR0RMRPXPmzGlK58zMiqDZif+XwAeS2+cBzzV5+2ZmhZfaCVySbgXOBWZL2gV8Ffgj4C8kTQEOkkzlmJlZ86SW+CPiD6o8dEZa2zQzs6NryzN3zcysOid+M7OCceI3MysYJ34zs4Jp68RftPrvWa+fX7Tfh1lWuR5/m8h6/fyi/T7Msqwt9/iLVv896/Xzi/b7MMu6tkz8Rav/nvX6+UX7fZhlXVsm/qLVf896/fyi/T7Msq4tE3/R6r9nvX5+nn8fWT/AXbR41hiplmVulKzU48+6rNfPz9vvI+sHuIsWz2rXknr8jVJv4jerV9/AIMvXPMDBoSNTVFM7O/jp6vPq+tJyvMnFs/pkpR6/WS5k/QB30eJZY00o8Us6WdL9kp5M7i+V9Cfpds2sdbJ+gLto8ayxJrrH/1fANcAQQERsA34/rU6ZtVrWD3AXLZ411oTm+CU9HhHvlfRPEfHupG1LRCxLu4PgOX5rnawf4C5aPKtNtTn+iZZseFXSbwGRBLsMeKmB/TPLpO7pXQ1NWI5nWTDRxP/HwPXAb0vaDTwPXJ5ar8zMLDUTSvwR8c/AhyQdC3RERH+63TIzs7RMdFXPNyTNjIhfRUS/pFmS/jTtzpmZWeNNdFXPhyNi/8idiNgH/F4qPTIzs1RNNPEfI+nwERpJ04DCHbHJeh2T3uf7+NZ9P6P3+b6GxNuxp5/be3eyY8/omb16+13pdVl/T83a0UQP7v4v4H5J/5PSyp5PAutT61UGZb2OyeXf38TDO0oJ/zsP7OCcJd3c/Idn1R3v2ju3c9OmFw/fv+LsRXz9ktPr7nel1wVk+j01a1cTrtUj6cPA+YCA+yLi3jQ7Vq7V6/izXsek9/k+Lvvepje13/4fzqLnpO6a4+3Y08+Hvv1QxXiX3/BYzf2uNN6uKR1AMHjoyOcvS++pWTuYdK2eiLgnIr4YEV9oZtLPgqzXMXnouVdraj+aLTv3V41XT78rjfeYDnGMsvuemrWzcRO/pIeTf/slvV720y/p9eZ0sfWyXsfk/e+cXVP70SxbOLNqvHr6XWm8bwwHb0R239MRPgZh7WjcxB8R70v+nRERx5X9zIiI45rTxdbLeh2TnpO6OWfJ6Cmdc5Z01zXNA7Bk7gyuOHvRqLYrzl5Ez0nddfW70njXXbaUdZe9q6Hv6UfPWDCq7aM9CyY1zXPXlt0sX/MAl3//UZaveYAfbdldd6w04pnV66hz/JI6gG0RcVpNgaUbgIuAV8pfK+mzwGeAQ8DfR8Sqo8Vq9Rz/iKzXMel9vo+HnnuV979zdt1Jv9yOPf1s2bmfZQtnsmTujMPt9fa70usa9R5k/TiMj0FYK9RdqycihiVtlbQoIl482vPL3AhcB9xU1okPApcASyNiUNKJNcRruazXMek5qf69/EqWzJ0xKuGPqLfflV7XqPdgZI7/IEcS68gcfz3xsx7PbDImupxzHvCUpMeAX400RsTF1V4QEQ9JWjym+SrgmxExmDznldq6a81UbW+8kX+pNOqvlKwfh3F9esuSiSb+rzVoeycD50j6r8BB4IsR8XilJ0paCawEWLRoUaWnWIqqrYlv5Fr5Rp570D29i0UnTOPnew7vl/D2E+r/Ykoj3toVS1k15r3z3r61wriJX9JU4NPAEmA78IOIODTJ7c0CzgLeC9wm6R1R4UBDRFxPqSIoPT092b8wcBvpGxhk9YZtHBwaPjw1sWrDNk6dd1zF9uVLZtecwHqf7zuc9Ef8444+ep/vq2vPv/f5vlFJGuBne36VmXgAFy+bz/Ils12f3lruaOv41wM9lJL+h4H/Nsnt7QLuiJLHgGGgvjWHlppqa+K37NzfsLXyjT73IOvxRnRP7+JdC2c66VtLHS3xnxoRl0fE94DLgHMmub07gfOgdB1f4C3A5P4nWcNVm49etnBmw+apG33uQdbjmWXJ0RL/0MiNWqd4JN0KPAKcImmXpE8BNwDvSC7a/jfAlZWmeay1qp1nsGTujIadf9Docw+yHs8sS8Zdxy/pDY6s4hEwDfh1cjuadRJXVtbxF02eVvXkJZ5ZM1Vbxz/hIm2t5MRvZla7SRdpMxvRyLr/1Wr+t2u8Ozfv5A/XP86dm3dmMt79T7/M6tu3cv/TLzckXqOvEdGsa060a7wR3uO3mpSvvQcmtfa+Ws3/emU93lnf2MjLr//m8P15x72FR778u5mJd8G3fzxqCespc4/l3s+fW3e8Rn5W0oiX9c9LI+J5j98mbby197Xasad/1Ica4KZHXqx7zybr8e7cvHNUkgZ46fXf1L2n3uh49z/9csXzFurd82/kZyWNeFn/vDQ63lhO/DZhjVzbXq3mf7X2vMe7e3vlBFqtvdnx7nt6T03tR5P18yqy/nlpdLyxnPhtwhq5tr1azf9q7XmPd9Hpb6upvdnxLjh1bk3tR5P18yqy/nlpdLyxnPhtwhq5tr1azf9K1UDbId6l71nIvOPeMqpt3nFv4dL3LMxEvPNPfRunzD12VNspc4/l/FPr+yLJ+nkVWf+8NDreWD64azVr5Nr2ajX/2zXenZt3cvf2l7no9LfVnaTTjHf/0y9z39N7uODUuXUn/XJZP68i65+XycbzOn4zs4Lxqh6zNuRr+Fo9JlqP38wyppHXRrBi8R6/WQ6VXzOhf/AQB4eGWbVhm/f8bUKc+M1yqNo1E+q5NoIVjxO/WQ75Gr42GU78ZjlU7ZoJvrKXTYQP7prllK/ha/Vy4jfLse7pXU74VjNP9VhVeaktbma18R6/VZTF2uJm1hje47c3yVttcTOrjRO/vUneaoubWW2c+O1N8lZb3Mxq48Rvb5K32uJmVhuXZbaqslZb3MxqU60ss1f1WFVL5s5oaIJudDwzq4+neqxmjVyPn/VzBRzP8VoZL63rLXiP32rSyPX4WT9XwPEcr5Xx0rzeQmp7/JJukPSKpCcrPPZFSSFpdlrbt8Zr5Hr8rJ8r4HiO18p4aV9vIc2pnhuBC8c2SloI/C7w4tjHLNsauR4/6+cKOJ7jtTJe2tdbSC3xR8RDwGsVHvo2sArI/nIiG6WR6/Gzfq6A4zleK+Olfb2Fph7clXQxsDsitk7guSsl9Urq3bt3bxN6Z0fTyPX4WT9XwPEcr5Xx0r7eQqrr+CUtBu6OiNMkvRV4ELggIv5F0i+Anoh49WhxvI4/Wxq5Hj/r5wo4nuO1Ml7fwOCkrrdQbR1/MxP/6cD9wK+ThxcAvwTOjIiXx4vjxG9mVrtqib9pUz0RsT0iToyIxRGxGNgFvOdoSd+yp961xWmtSU5zG0WLZ8WQ2jp+SbcC5wKzJe0CvhoRP0hre9Yc9a4tTnNNclrbKFo8Kw7X6rEJ6xsYZPmaBzg4dGS1wdTODn66+rxx5x/rfV0z+uZ41s5aPtVj+Vfv2uK01ySnsY2ixbNiceK3Cat3bXHaa5LT2EbR4o3I+jGIrMfLCyd+m7B61xanvSY5jW0ULR6UjhksX/MAl3//UZaveYAfbdldd6wixssTz/FbzepdWzzZNcmt2EZR4mX9GETW42WV6/Fbw3RP76rrP0e9r2vlNooSb+SYwUGOJMKRYwb1xC9avLzxVI+ZZf4YRNbj5Y0Tv5ll/hhE1uPljef4zeywrB6DyEu8rPEcv5kdVVaPQeQlXl54qsfMrGCc+M3MCsaJ38ysYJz4zcwKxonfzKxgnPjNzArGid/MrGCc+M3MCsaJ38ysYJz4zcwKxonfzKxgnPjNzArGid/MrGCc+M3MCsaJ38ysYJz4zcwKxonfzKxgUkv8km6Q9IqkJ8va1kl6VtI2ST+UNDOt7ZuZWWVp7vHfCFw4pm0jcFpELAV+DlyT4vbNzKyC1BJ/RDwEvDam7b6IOJTc3QQsSGv7ZmZWWSvn+D8J3FPtQUkrJfVK6t27d28Tu2Vm1t5akvglfQU4BNxS7TkRcX1E9EREz5w5c5rXObMyfQODbN25n76BwVZ3xaxhpjR7g5KuBC4Czo+IaPb2zSbqri27Wb1hG50dHQwND7N2xVIuXja/1d0ym7Sm7vFLuhBYDVwcEb9u5rbNatE3MMjqDds4ODRM/+AhDg4Ns2rDNu/5W1tIcznnrcAjwCmSdkn6FHAdMAPYKGmLpL9Ma/tmk7Fr3wE6O0b/9+js6GDXvgMt6pFZ46Q21RMRf1Ch+Qdpbc+skRbMmsbQ8PCotqHhYRbMmtaiHpk1js/cNauge3oXa1csZWpnBzO6pjC1s4O1K5bSPb2r1V0zm7SmH9w1y4uLl81n+ZLZ7Np3gAWzpjnpW9tw4jcbR/f0Lid8azue6jEzKxgnfjOzgnHiNzMrGCd+M7OCceI3MysYJ34zs4Jx4jczKxgnfjOzgnHiN2uiHXv6ub13Jzv29Gcynq8/UAw+c9esSa69czs3bXrx8P0rzl7E1y85PTPxfP2B4vAev1kT7NjTPypJA9z0yIt176k3Op6vP1AsTvxmTbBl5/6a2psdz9cfKBYnfrMmWLZwZk3tzY7n6w8UixO/WRMsmTuDK85eNKrtirMXsWTujEzE8/UHikV5uN55T09P9Pb2trobZpO2Y08/W3buZ9nCmXUn6TTj9Q0M+voDbUTSExHRM7bdq3rMmmjJ3BkNSdBpxfP1B4rBUz1mZgXjxG9mVjBO/GZmBePEb2ZWME78ZmYFk4vlnJL2Ai+kuInZwKspxs+Cdh9ju48PPMZ20cwxvj0i5oxtzEXiT5uk3kprXdtJu4+x3ccHHmO7yMIYPdVjZlYwTvxmZgXjxF9yfas70ATtPsZ2Hx94jO2i5WP0HL+ZWcF4j9/MrGCc+M3MCqbtE7+khZIelPSMpKckfW7M41+UFJJml7VdI2mHpJ9J+rfN73VtxhujpM8m43hK0tqy9rYYo6RlkjZJ2iKpV9KZZa/J2xinSnpM0tZkjF9L2k+QtFHSc8m/s8pek5sxjjO+dZKelbRN0g8lzSx7TW7GB9XHWPZ4NvJNRLT1DzAPeE9yewbwc+DU5P5C4F5KJ4fNTtpOBbYCXcBJwP8Djmn1OOoZI/BB4P8AXcljJ7bhGO8DPpy0/x7w4xyPUcD05HYn8ChwFrAW+FLS/iVgTR7HOM74LgCmJO1r8jq+8caY3M9Mvmn7Pf6IeCkiNie3+4FngPnJw98GVgHlR7gvAf4mIgYj4nlgB3AmGTbOGK8CvhkRg8ljryQvaacxBnBc8rTjgV8mt/M4xoiIgeRuZ/ITlMayPmlfD1ya3M7VGKuNLyLui4hDSfsmYEFyO1fjg3F/h5ChfNP2ib+cpMXAu4FHJV0M7I6IrWOeNh/YWXZ/F0e+KDKvfIzAycA5kh6V9BNJ702e1k5jvBpYJ2kn8GfANcnTcjlGScdI2gK8AmyMiEeBuRHxEpS+AIETk6fnboxVxlfuk8A9ye3cjQ8qjzFr+aYwiV/SdGADpURxCPgKcG2lp1Zoy8Wa1/IxRsTrlK6wNovSn9P/GbhNkmivMV4FfD4iFgKfB34w8tQKL8/8GCPijYhYRmmv90xJp43z9NyNcbzxSfoKpf+bt4w0VQqReicnqcIYl5KxfFOIxC+pk1KyuCUi7gB+i9J82lZJv6D0C9os6W2UvnEXlr18AUemDzKrwhihNJY7kj8/HwOGKRWIaqcxXgmM3P5bjvyZnMsxjoiI/cCPgQuBPZLmAST/jkzZ5XaMY8aHpCuBi4CPRTL5TY7HB6PGeAlZyzetPBDSjB9K36g3AX8+znN+wZGDLf+a0Qdb/pl8HFB60xiBTwNfT26fTOlPSrXZGJ8Bzk1unw88kePf4xxgZnJ7GvCPlJLhOkYf3F2bxzGOM74LgaeBOWOen6vxjTfGMc9peb4pwsXWlwMfB7Yn824AX46If6j05Ih4StJtlD6Ih4A/jog3mtLT+lUcI3ADcIOkJ4HfAFdG6dPWTmP8I+AvJE0BDgIrIbe/x3nAeknHUPpr/LaIuFvSI5Sm6T4FvAh8BHI5xmrj20Ep8W0szUSyKSI+ncPxQZUxVntyq8bokg1mZgVTiDl+MzM7wonfzKxgnPjNzArGid/MrGCc+M3MCsaJ39qWpDeSqp1PJdUS/5Okuj/zkt6XVF58NvlZWfbYnKQ0xj+pVEX0qrLH/k1SebIIy6ctB/xBtHZ2IEqnziPpROCvKRVy+2qtgZKzLP8auDQiNidlde+VtDsi/p7SyWPPRsSVkuYCj0i6HegDrgP+YxwpRFbrtkVp6fVwPa83G8vr+K1tSRqIiOll998BPE6pbMXbgZuBY5OHPxMR/1fSzcDtEXFX8ppbgP8NvJdS8cVry+KdD/wX4LPAjyidqbkbOBv498lrHgfOoHRi2TeBcymdrPTdiPheUnvoLko1lTqBP4mIu5JCdPcADybxLo2IFxr5/lhxOfFb2xqb+JO2fcBvA/3AcEQclPRO4NaI6JH0AUpF3y6VdDywBXgncBuwfuQLIYl1PPB8RJwg6RNAT0R8JnmsA3iEUiXNHmAFpesh/KmkLuCnlM7A3Qm8NSJeT/6K2JRs7+2UTt//nYjYlMobZIXlqR4rmpFqiJ3AdZKWAW9QqmVERPxE0neTqaF/B2yIiEPJdEulvaSKe04RMSzpe5S+DPokXQAslXRZ8pTjKSX4XcA3JL2fUhG9+cDc5DkvOOlbGpz4rTCSqZ43KFW3/CqwB3gXpUUOB8ueejPwMeD3KdWHB3iK0p77j8qedwalGivVDCc/UPrC+WxE3DumT5+gVNjrjIgYSqo3Tk0e/tXER2c2cV7VY4UgaQ7wl8B1SaG644GXkgOmHweOKXv6jZSu20BEPJW0fRf4RPIXApK6KV0mcC0Tcy9wVVJaGkknSzo26ccrSdL/IKUpHrNUeY/f2tm0pJJnJ6XKhzcD30oe+x/ABkkfoXQA9fDedUTskfQMcGdZ20uSLgf+StIMSnvwfx4RfzfBvnwfWEypDruAvZQuoXgL8HeSeikdT3i2noGa1cIHd83GkPRWYDuli7v/S6v7Y9ZonuoxKyPpQ5T2uv+7k761K+/xm5kVjPf4zcwKxonfzKxgnPjNzArGid/MrGCc+M3MCub/A2eoKqxqO9IQAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n",
+    "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))\n",
+    "pie_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### การถดถอยเชิงเส้น\n",
+    "\n",
+    "เราจะใช้ Scikit Learn เพื่อฝึกโมเดลการถดถอยเชิงเส้น:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a3232f8dc0>]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(X_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-0.01751876]), 21.133734359909326)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.coef_, lin_reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.64893156])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pumpkin price on programmer's day\n",
+    "\n",
+    "lin_reg.predict([[256]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### การถดถอยเชิงพหุนาม\n",
+    "\n",
+    "บางครั้งความสัมพันธ์ระหว่างคุณลักษณะและผลลัพธ์อาจไม่เป็นเชิงเส้นโดยธรรมชาติ ตัวอย่างเช่น ราคาฟักทองอาจสูงในฤดูหนาว (เดือน 1, 2) จากนั้นลดลงในฤดูร้อน (เดือน 5-7) และกลับมาสูงขึ้นอีกครั้ง การถดถอยเชิงเส้นไม่สามารถจับความสัมพันธ์นี้ได้อย่างแม่นยำ\n",
+    "\n",
+    "ในกรณีนี้ เราอาจพิจารณาเพิ่มคุณลักษณะเพิ่มเติม วิธีง่ายๆ คือการใช้พหุนามจากคุณลักษณะอินพุต ซึ่งจะนำไปสู่ **การถดถอยเชิงพหุนาม** ใน Scikit Learn เราสามารถคำนวณคุณลักษณะพหุนามล่วงหน้าโดยอัตโนมัติด้วยการใช้ pipelines:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.73 (17.0%)\n",
+      "Model determination:  0.07639977655280217\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a323368ac0>]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)\n",
+    "\n",
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### การเข้ารหัสชนิดต่าง ๆ\n",
+    "\n",
+    "ในโลกที่สมบูรณ์แบบ เราต้องการที่จะสามารถทำนายราคาของฟักทองชนิดต่าง ๆ โดยใช้โมเดลเดียวกัน เพื่อที่จะนำชนิดของฟักทองมาพิจารณา เราจำเป็นต้องแปลงมันให้อยู่ในรูปแบบตัวเลข หรือที่เรียกว่า **การเข้ารหัส** มีหลายวิธีที่เราสามารถทำได้:\n",
+    "\n",
+    "* การเข้ารหัสตัวเลขแบบง่าย ซึ่งจะสร้างตารางของชนิดฟักทองต่าง ๆ แล้วแทนชื่อชนิดด้วยดัชนีในตารางนั้น วิธีนี้ไม่ใช่ตัวเลือกที่ดีที่สุดสำหรับการวิเคราะห์ถดถอยเชิงเส้น (linear regression) เพราะการวิเคราะห์ถดถอยเชิงเส้นจะนำค่าตัวเลขของดัชนีมาพิจารณา และค่าตัวเลขนั้นอาจไม่มีความสัมพันธ์เชิงตัวเลขกับราคา\n",
+    "* การเข้ารหัสแบบ One-hot ซึ่งจะเปลี่ยนคอลัมน์ `Variety` ให้เป็น 4 คอลัมน์ที่แตกต่างกัน โดยแต่ละคอลัมน์จะเป็นตัวแทนของชนิดฟักทองแต่ละชนิด และจะมีค่าเป็น 1 หากแถวที่เกี่ยวข้องเป็นชนิดนั้น และมีค่าเป็น 0 หากไม่ใช่\n",
+    "\n",
+    "โค้ดด้านล่างแสดงวิธีที่เราสามารถเข้ารหัสชนิดฟักทองแบบ One-hot:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FAIRYTALE</th>\n",
+       "      <th>MINIATURE</th>\n",
+       "      <th>MIXED HEIRLOOM VARIETIES</th>\n",
+       "      <th>PIE TYPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE\n",
+       "70            0          0                         0         1\n",
+       "71            0          0                         0         1\n",
+       "72            0          0                         0         1\n",
+       "73            0          0                         0         1\n",
+       "74            0          0                         0         1\n",
+       "...         ...        ...                       ...       ...\n",
+       "1738          0          1                         0         0\n",
+       "1739          0          1                         0         0\n",
+       "1740          0          1                         0         0\n",
+       "1741          0          1                         0         0\n",
+       "1742          0          1                         0         0\n",
+       "\n",
+       "[415 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(new_pumpkins['Variety'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### การถดถอยเชิงเส้นบนชนิดพันธุ์\n",
+    "\n",
+    "ตอนนี้เราจะใช้โค้ดเดิมเหมือนข้างต้น แต่แทนที่จะใช้ `DayOfYear` เราจะใช้ชนิดพันธุ์ที่ผ่านการเข้ารหัสแบบ one-hot เป็นข้อมูลนำเข้า:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety'])\n",
+    "y = new_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 5.24 (19.7%)\n",
+      "Model determination:  0.774085281105197\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_linear_regression(X,y):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "    lin_reg = LinearRegression()\n",
+    "    lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "    pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "    mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "    print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "    score = lin_reg.score(X_train,y_train)\n",
+    "    print('Model determination: ', score)\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "เรายังสามารถลองใช้คุณสมบัติอื่นในลักษณะเดียวกัน และรวมเข้ากับคุณสมบัติทางตัวเลข เช่น `Month` หรือ `DayOfYear`:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.84 (10.5%)\n",
+      "Model determination:  0.9401096672643048\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['Package']))\n",
+    "y = new_pumpkins['Price']\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### การถดถอยเชิงพหุนาม\n",
+    "\n",
+    "การถดถอยเชิงพหุนามสามารถนำมาใช้กับคุณลักษณะเชิงหมวดหมู่ที่ผ่านการเข้ารหัสแบบ one-hot ได้เช่นกัน โค้ดสำหรับการฝึกการถดถอยเชิงพหุนามจะมีลักษณะเหมือนกับที่เราได้เห็นไปก่อนหน้านี้\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.23 (8.25%)\n",
+      "Model determination:  0.9652870784724543\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d77bd89ae7e79780c68c58bab91f13f8",
+   "translation_date": "2025-09-06T13:11:52+00:00",
+   "source_file": "2-Regression/3-Linear/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/4-Logistic/notebook.ipynb b/translations/th/2-Regression/4-Logistic/notebook.ipynb
new file mode 100644
index 000000000..f67ac940f
--- /dev/null
+++ b/translations/th/2-Regression/4-Logistic/notebook.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## สายพันธุ์ฟักทองและสี\n",
+    "\n",
+    "โหลดไลบรารีและชุดข้อมูลที่จำเป็น แปลงข้อมูลให้เป็น dataframe ที่มีเพียงส่วนย่อยของข้อมูล:\n",
+    "\n",
+    "มาดูความสัมพันธ์ระหว่างสีและสายพันธุ์\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "dee08c2b49057b0de8b6752c4dbca368",
+   "translation_date": "2025-09-06T13:26:44+00:00",
+   "source_file": "2-Regression/4-Logistic/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/th/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
new file mode 100644
index 000000000..1c2468329
--- /dev/null
+++ b/translations/th/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -0,0 +1,686 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## สร้างโมเดล Logistic Regression - บทเรียนที่ 4\n",
+    "\n",
+    "![ภาพประกอบ Logistic vs. Linear Regression](../../../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)\n",
+    "\n",
+    "#### **[แบบทดสอบก่อนเรียน](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
+    "\n",
+    "#### บทนำ\n",
+    "\n",
+    "ในบทเรียนสุดท้ายเกี่ยวกับ Regression ซึ่งเป็นหนึ่งในเทคนิคพื้นฐานของ *คลาสสิก* ML เราจะมาดู Logistic Regression กัน คุณสามารถใช้เทคนิคนี้เพื่อค้นหารูปแบบในการทำนายหมวดหมู่แบบไบนารี ตัวอย่างเช่น ลูกอมนี้เป็นช็อกโกแลตหรือไม่? โรคนี้ติดต่อหรือไม่? ลูกค้าคนนี้จะเลือกสินค้านี้หรือไม่?\n",
+    "\n",
+    "ในบทเรียนนี้ คุณจะได้เรียนรู้:\n",
+    "\n",
+    "- เทคนิคสำหรับ Logistic Regression\n",
+    "\n",
+    "✅ เพิ่มความเข้าใจเกี่ยวกับการทำงานกับ Regression ประเภทนี้ใน [โมดูลการเรียนรู้](https://learn.microsoft.com/training/modules/introduction-classification-models/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "## ความต้องการเบื้องต้น\n",
+    "\n",
+    "หลังจากที่เราได้ทำงานกับข้อมูลฟักทองมาแล้ว ตอนนี้เราคุ้นเคยกับมันมากพอที่จะสังเกตเห็นว่ามีหมวดหมู่แบบไบนารีที่เราสามารถทำงานด้วยได้: `Color`\n",
+    "\n",
+    "เรามาสร้างโมเดล Logistic Regression เพื่อทำนายว่า *ฟักทองที่กำหนดมีแนวโน้มที่จะมีสีอะไร* (สีส้ม 🎃 หรือสีขาว 👻)\n",
+    "\n",
+    "> ทำไมเราถึงพูดถึงการจัดหมวดหมู่แบบไบนารีในบทเรียนที่เกี่ยวกับ Regression? ก็เพื่อความสะดวกทางภาษาเท่านั้น เพราะ Logistic Regression นั้น [จริงๆ แล้วเป็นวิธีการจัดหมวดหมู่](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression) แม้ว่าจะอิงตามเส้นตรงก็ตาม เรียนรู้วิธีอื่นๆ ในการจัดหมวดหมู่ข้อมูลในกลุ่มบทเรียนถัดไป\n",
+    "\n",
+    "สำหรับบทเรียนนี้ เราจะต้องใช้แพ็กเกจดังต่อไปนี้:\n",
+    "\n",
+    "- `tidyverse`: [tidyverse](https://www.tidyverse.org/) คือ [ชุดของแพ็กเกจ R](https://www.tidyverse.org/packages) ที่ออกแบบมาเพื่อทำให้วิทยาศาสตร์ข้อมูลเร็วขึ้น ง่ายขึ้น และสนุกขึ้น!\n",
+    "\n",
+    "- `tidymodels`: [tidymodels](https://www.tidymodels.org/) เป็นกรอบงานที่เป็น [ชุดของแพ็กเกจ](https://www.tidymodels.org/packages/) สำหรับการสร้างโมเดลและการเรียนรู้ของเครื่อง\n",
+    "\n",
+    "- `janitor`: [แพ็กเกจ janitor](https://github.com/sfirke/janitor) มีเครื่องมือเล็กๆ ที่เรียบง่ายสำหรับการตรวจสอบและทำความสะอาดข้อมูลที่ไม่สมบูรณ์\n",
+    "\n",
+    "- `ggbeeswarm`: [แพ็กเกจ ggbeeswarm](https://github.com/eclarke/ggbeeswarm) มีวิธีการสร้างกราฟแบบ beeswarm โดยใช้ ggplot2\n",
+    "\n",
+    "คุณสามารถติดตั้งแพ็กเกจเหล่านี้ได้ด้วยคำสั่ง:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+    "\n",
+    "หรือใช้สคริปต์ด้านล่างเพื่อตรวจสอบว่าคุณมีแพ็กเกจที่จำเป็นสำหรับบทเรียนนี้หรือไม่ และติดตั้งให้ในกรณีที่ยังไม่มี\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## **กำหนดคำถาม**\n",
+    "\n",
+    "สำหรับจุดประสงค์ของเรา เราจะกำหนดคำถามในรูปแบบไบนารี: 'สีขาว' หรือ 'ไม่ใช่สีขาว' ในชุดข้อมูลของเรายังมีหมวดหมู่ 'ลาย' อยู่ด้วย แต่มีตัวอย่างในหมวดหมู่นี้น้อยมาก ดังนั้นเราจะไม่ใช้มัน และมันจะหายไปเมื่อเราลบค่าที่เป็น null ออกจากชุดข้อมูลอยู่แล้ว\n",
+    "\n",
+    "> 🎃 ข้อเท็จจริงสนุกๆ บางครั้งเราจะเรียกฟักทองสีขาวว่า 'ฟักทองผี' ฟักทองเหล่านี้แกะสลักได้ยากกว่า จึงไม่ได้รับความนิยมเท่าฟักทองสีส้ม แต่ก็ดูเท่ดี! ดังนั้นเราสามารถปรับคำถามของเราใหม่ได้ว่า: 'ฟักทองผี' หรือ 'ไม่ใช่ฟักทองผี' 👻\n",
+    "\n",
+    "## **เกี่ยวกับการถดถอยโลจิสติก**\n",
+    "\n",
+    "การถดถอยโลจิสติกแตกต่างจากการถดถอยเชิงเส้นที่คุณเคยเรียนรู้มาก่อนในหลายๆ แง่มุมที่สำคัญ\n",
+    "\n",
+    "#### **การจัดประเภทแบบไบนารี**\n",
+    "\n",
+    "การถดถอยโลจิสติกไม่ได้มีคุณสมบัติเหมือนกับการถดถอยเชิงเส้น การถดถอยโลจิสติกให้การคาดการณ์เกี่ยวกับ `หมวดหมู่แบบไบนารี` (\"สีส้มหรือไม่ใช่สีส้ม\") ในขณะที่การถดถอยเชิงเส้นสามารถคาดการณ์ `ค่าต่อเนื่อง` ได้ เช่น จากแหล่งที่มาของฟักทองและเวลาที่เก็บเกี่ยว *ราคาของมันจะเพิ่มขึ้นเท่าไร*\n",
+    "\n",
+    "![อินโฟกราฟิกโดย Dasani Madipalli](../../../../../../2-Regression/4-Logistic/images/pumpkin-classifier.png)\n",
+    "\n",
+    "### การจัดประเภทอื่นๆ\n",
+    "\n",
+    "ยังมีการถดถอยโลจิสติกประเภทอื่นๆ เช่น มัลติโนเมียลและออร์ดินัล:\n",
+    "\n",
+    "- **มัลติโนเมียล** ซึ่งเกี่ยวข้องกับการมีมากกว่าหนึ่งหมวดหมู่ - \"สีส้ม, สีขาว, และลาย\"\n",
+    "\n",
+    "- **ออร์ดินัล** ซึ่งเกี่ยวข้องกับหมวดหมู่ที่มีลำดับ เหมาะสมหากเราต้องการจัดลำดับผลลัพธ์อย่างมีตรรกะ เช่น ฟักทองของเราที่จัดลำดับตามขนาดที่มีจำนวนจำกัด (เล็กมาก, เล็ก, กลาง, ใหญ่, ใหญ่มาก, ใหญ่ที่สุด)\n",
+    "\n",
+    "![การถดถอยมัลติโนเมียล vs ออร์ดินัล](../../../../../../2-Regression/4-Logistic/images/multinomial-vs-ordinal.png)\n",
+    "\n",
+    "#### **ตัวแปรไม่จำเป็นต้องมีความสัมพันธ์กัน**\n",
+    "\n",
+    "จำได้ไหมว่าการถดถอยเชิงเส้นทำงานได้ดีขึ้นเมื่อมีตัวแปรที่มีความสัมพันธ์กันมากขึ้น? การถดถอยโลจิสติกตรงกันข้าม - ตัวแปรไม่จำเป็นต้องสอดคล้องกัน ซึ่งเหมาะกับข้อมูลนี้ที่มีความสัมพันธ์ค่อนข้างอ่อน\n",
+    "\n",
+    "#### **คุณต้องการข้อมูลที่สะอาดและมากพอ**\n",
+    "\n",
+    "การถดถอยโลจิสติกจะให้ผลลัพธ์ที่แม่นยำมากขึ้นหากคุณใช้ข้อมูลจำนวนมาก ชุดข้อมูลขนาดเล็กของเราไม่เหมาะสมที่สุดสำหรับงานนี้ ดังนั้นโปรดคำนึงถึงข้อนี้\n",
+    "\n",
+    "✅ ลองคิดถึงประเภทของข้อมูลที่เหมาะสมกับการถดถอยโลจิสติก\n",
+    "\n",
+    "## แบบฝึกหัด - จัดระเบียบข้อมูล\n",
+    "\n",
+    "ก่อนอื่น ทำความสะอาดข้อมูลเล็กน้อย โดยลบค่าที่เป็น null และเลือกเฉพาะบางคอลัมน์:\n",
+    "\n",
+    "1. เพิ่มโค้ดต่อไปนี้:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the core tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data and clean column names\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \n",
+    "  clean_names()\n",
+    "\n",
+    "# Select desired columns\n",
+    "pumpkins_select <- pumpkins %>% \n",
+    "  select(c(city_name, package, variety, origin, item_size, color)) \n",
+    "\n",
+    "# Drop rows containing missing values and encode color as factor (category)\n",
+    "pumpkins_select <- pumpkins_select %>% \n",
+    "  drop_na() %>% \n",
+    "  mutate(color = factor(color))\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins_select %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "คุณสามารถดูข้อมูลใน dataframe ใหม่ของคุณได้เสมอ โดยใช้ฟังก์ชัน [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) ดังตัวอย่างด้านล่าง:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pumpkins_select %>% \n",
+    "  glimpse()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "เรามายืนยันกันว่าเรากำลังทำปัญหาการจำแนกประเภทแบบไบนารีจริงๆ:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Subset distinct observations in outcome column\n",
+    "pumpkins_select %>% \n",
+    "  distinct(color)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### การแสดงผล - แผนภูมิประเภทหมวดหมู่\n",
+    "ตอนนี้คุณได้โหลดข้อมูลฟักทองขึ้นมาอีกครั้งและทำการทำความสะอาดเพื่อให้ได้ชุดข้อมูลที่มีตัวแปรบางตัว รวมถึงตัวแปร Color มาลองแสดงผล dataframe ในโน้ตบุ๊กโดยใช้ไลบรารี ggplot กัน\n",
+    "\n",
+    "ไลบรารี ggplot มีวิธีที่น่าสนใจในการแสดงผลข้อมูลของคุณ ตัวอย่างเช่น คุณสามารถเปรียบเทียบการกระจายตัวของข้อมูลสำหรับแต่ละ Variety และ Color ในแผนภูมิประเภทหมวดหมู่ได้\n",
+    "\n",
+    "1. สร้างแผนภูมิประเภทนี้โดยใช้ฟังก์ชัน geombar โดยใช้ข้อมูลฟักทองของเรา และกำหนดการจับคู่สีสำหรับแต่ละหมวดหมู่ของฟักทอง (สีส้มหรือสีขาว):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Specify colors for each value of the hue variable\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# Create the bar plot\n",
+    "ggplot(pumpkins_select, aes(y = variety, fill = color)) +\n",
+    "  geom_bar(position = \"dodge\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(y = \"Variety\", fill = \"Color\") +\n",
+    "  theme_minimal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "จากการสังเกตข้อมูล คุณสามารถเห็นได้ว่า ข้อมูลสีมีความสัมพันธ์กับชนิดพันธุ์อย่างไร\n",
+    "\n",
+    "✅ จากกราฟประเภทนี้ มีการสำรวจที่น่าสนใจอะไรบ้างที่คุณสามารถจินตนาการได้?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### การเตรียมข้อมูล: การเข้ารหัสคุณลักษณะ\n",
+    "\n",
+    "ชุดข้อมูลฟักทองของเรามีค่าที่เป็นสตริงในทุกคอลัมน์ การทำงานกับข้อมูลประเภทหมวดหมู่เป็นเรื่องง่ายสำหรับมนุษย์ แต่ไม่ใช่สำหรับเครื่องจักร อัลกอริธึมการเรียนรู้ของเครื่องทำงานได้ดีเมื่อใช้ตัวเลข นั่นเป็นเหตุผลว่าทำไมการเข้ารหัสจึงเป็นขั้นตอนสำคัญในกระบวนการเตรียมข้อมูล เนื่องจากช่วยให้เราสามารถเปลี่ยนข้อมูลประเภทหมวดหมู่ให้เป็นข้อมูลเชิงตัวเลขโดยไม่สูญเสียข้อมูล การเข้ารหัสที่ดีนำไปสู่การสร้างโมเดลที่ดี\n",
+    "\n",
+    "สำหรับการเข้ารหัสคุณลักษณะ มีตัวเข้ารหัสหลักสองประเภท:\n",
+    "\n",
+    "1. **Ordinal encoder**: เหมาะสำหรับตัวแปรประเภทลำดับ (ordinal variables) ซึ่งเป็นตัวแปรหมวดหมู่ที่ข้อมูลมีการเรียงลำดับอย่างมีเหตุผล เช่น คอลัมน์ `item_size` ในชุดข้อมูลของเรา มันจะสร้างการจับคู่ที่แต่ละหมวดหมู่ถูกแทนด้วยตัวเลข ซึ่งตัวเลขนั้นแสดงถึงลำดับของหมวดหมู่ในคอลัมน์\n",
+    "\n",
+    "2. **Categorical encoder**: เหมาะสำหรับตัวแปรประเภทนามธรรม (nominal variables) ซึ่งเป็นตัวแปรหมวดหมู่ที่ข้อมูลไม่มีการเรียงลำดับอย่างมีเหตุผล เช่น คุณลักษณะทั้งหมดที่แตกต่างจาก `item_size` ในชุดข้อมูลของเรา มันใช้การเข้ารหัสแบบ one-hot ซึ่งหมายความว่าแต่ละหมวดหมู่จะถูกแทนด้วยคอลัมน์ไบนารี: ตัวแปรที่ถูกเข้ารหัสจะเท่ากับ 1 หากฟักทองนั้นอยู่ใน Variety นั้น และเท่ากับ 0 หากไม่ใช่\n",
+    "\n",
+    "Tidymodels มีอีกหนึ่งแพ็กเกจที่น่าสนใจ: [recipes](https://recipes.tidymodels.org/) - แพ็กเกจสำหรับการเตรียมข้อมูล เราจะกำหนด `recipe` ที่ระบุว่าคอลัมน์ตัวทำนายทั้งหมดควรถูกเข้ารหัสเป็นชุดของตัวเลข จากนั้น `prep` เพื่อประมาณค่าปริมาณและสถิติที่จำเป็นสำหรับการดำเนินการใด ๆ และสุดท้าย `bake` เพื่อใช้การคำนวณกับข้อมูลใหม่\n",
+    "\n",
+    "> โดยปกติ recipes มักถูกใช้เป็นตัวเตรียมข้อมูลสำหรับการสร้างโมเดล ซึ่งมันจะกำหนดว่าควรมีการดำเนินการใดบ้างกับชุดข้อมูลเพื่อเตรียมให้พร้อมสำหรับการสร้างโมเดล ในกรณีนี้ **แนะนำอย่างยิ่ง** ให้คุณใช้ `workflow()` แทนการประมาณ recipe ด้วย prep และ bake ด้วยตนเอง เราจะเห็นทั้งหมดนี้ในอีกสักครู่\n",
+    ">\n",
+    "> อย่างไรก็ตาม สำหรับตอนนี้ เรากำลังใช้ recipes + prep + bake เพื่อระบุว่าควรมีการดำเนินการใดบ้างกับชุดข้อมูลเพื่อเตรียมให้พร้อมสำหรับการวิเคราะห์ข้อมูล และจากนั้นดึงข้อมูลที่ผ่านการเตรียมพร้อมแล้วพร้อมกับขั้นตอนที่ได้ดำเนินการ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Preprocess and extract data to allow some data analysis\n",
+    "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>%\n",
+    "  # Define ordering for item_size column\n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  # Convert factors to numbers using the order defined above (Ordinal encoding)\n",
+    "  step_integer(item_size, zero_based = F) %>%\n",
+    "  # Encode all other predictors using one hot encoding\n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE) %>%\n",
+    "  prep(data = pumpkin_select) %>%\n",
+    "  bake(new_data = NULL)\n",
+    "\n",
+    "# Display the first few rows of preprocessed data\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "✅ ข้อดีของการใช้ ordinal encoder กับคอลัมน์ Item Size คืออะไร?\n",
+    "\n",
+    "### วิเคราะห์ความสัมพันธ์ระหว่างตัวแปร\n",
+    "\n",
+    "เมื่อเราได้ทำการเตรียมข้อมูลเบื้องต้นแล้ว เราสามารถวิเคราะห์ความสัมพันธ์ระหว่างฟีเจอร์และป้ายกำกับ (label) เพื่อทำความเข้าใจว่าโมเดลจะสามารถทำนายป้ายกำกับจากฟีเจอร์ได้ดีแค่ไหน วิธีที่ดีที่สุดในการวิเคราะห์ประเภทนี้คือการสร้างกราฟ \n",
+    "เราจะใช้ฟังก์ชัน ggplot geom_boxplot_ อีกครั้ง เพื่อแสดงความสัมพันธ์ระหว่าง Item Size, Variety และ Color ในกราฟแบบหมวดหมู่ (categorical plot) เพื่อให้การแสดงผลข้อมูลดียิ่งขึ้น เราจะใช้คอลัมน์ Item Size ที่ถูกเข้ารหัสแล้ว และคอลัมน์ Variety ที่ยังไม่ได้เข้ารหัส\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define the color palette\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins_select_plot<-pumpkins_select\n",
+    "pumpkins_select_plot$item_size <- baked_pumpkins$item_size\n",
+    "\n",
+    "# Create the grouped box plot\n",
+    "ggplot(pumpkins_select_plot, aes(x = `item_size`, y = color, fill = color)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_grid(variety ~ ., scales = \"free_x\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(x = \"Item Size\", y = \"\") +\n",
+    "  theme_minimal() +\n",
+    "  theme(strip.text = element_text(size = 12)) +\n",
+    "  theme(axis.text.x = element_text(size = 10)) +\n",
+    "  theme(axis.title.x = element_text(size = 12)) +\n",
+    "  theme(axis.title.y = element_blank()) +\n",
+    "  theme(legend.position = \"bottom\") +\n",
+    "  guides(fill = guide_legend(title = \"Color\")) +\n",
+    "  theme(panel.spacing = unit(0.5, \"lines\"))+\n",
+    "  theme(strip.text.y = element_text(size = 4, hjust = 0)) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### ใช้ swarm plot\n",
+    "\n",
+    "เนื่องจาก Color เป็นหมวดหมู่แบบไบนารี (สีขาวหรือไม่ใช่สีขาว) จึงต้องใช้ '[วิธีการเฉพาะทาง](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf)' ในการแสดงผลข้อมูล\n",
+    "\n",
+    "ลองใช้ `swarm plot` เพื่อแสดงการกระจายของสีในความสัมพันธ์กับ item_size\n",
+    "\n",
+    "เราจะใช้ [ggbeeswarm package](https://github.com/eclarke/ggbeeswarm) ซึ่งมีวิธีการสร้างกราฟแบบ beeswarm โดยใช้ ggplot2 กราฟ beeswarm เป็นวิธีการแสดงจุดข้อมูลที่ปกติจะทับซ้อนกันให้อยู่ข้างกันแทน\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create beeswarm plots of color and item_size\n",
+    "baked_pumpkins %>% \n",
+    "  mutate(color = factor(color)) %>% \n",
+    "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\n",
+    "  geom_quasirandom() +\n",
+    "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\n",
+    "  theme(legend.position = \"none\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ตอนนี้เรามีความเข้าใจเกี่ยวกับความสัมพันธ์ระหว่างหมวดหมู่แบบทวิภาคของสีและกลุ่มขนาดที่ใหญ่ขึ้นแล้ว ลองมาสำรวจการใช้โลจิสติกรีเกรสชันเพื่อกำหนดสีที่เป็นไปได้ของฟักทองแต่ละลูกกัน\n",
+    "\n",
+    "## สร้างโมเดลของคุณ\n",
+    "\n",
+    "เลือกตัวแปรที่คุณต้องการใช้ในโมเดลการจำแนกประเภท และแบ่งข้อมูลออกเป็นชุดฝึกอบรมและชุดทดสอบ [rsample](https://rsample.tidymodels.org/) ซึ่งเป็นแพ็กเกจใน Tidymodels มีโครงสร้างพื้นฐานสำหรับการแบ่งข้อมูลและการสุ่มตัวอย่างซ้ำอย่างมีประสิทธิภาพ:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Split data into 80% for training and 20% for testing\n",
+    "set.seed(2056)\n",
+    "pumpkins_split <- pumpkins_select %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "# Print out the first 5 rows of the training set\n",
+    "pumpkins_train %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🙌 เราพร้อมแล้วที่จะฝึกโมเดลโดยการปรับคุณสมบัติการฝึกให้เข้ากับป้ายกำกับการฝึก (สี)\n",
+    "\n",
+    "เราจะเริ่มต้นด้วยการสร้างสูตรที่ระบุขั้นตอนการเตรียมข้อมูลที่ควรดำเนินการเพื่อเตรียมข้อมูลให้พร้อมสำหรับการสร้างโมเดล เช่น การเข้ารหัสตัวแปรประเภทให้เป็นชุดของตัวเลข เช่นเดียวกับ `baked_pumpkins` เราสร้าง `pumpkins_recipe` แต่จะไม่ใช้ `prep` และ `bake` เนื่องจากจะถูกรวมไว้ในเวิร์กโฟลว์ ซึ่งคุณจะได้เห็นในอีกไม่กี่ขั้นตอนจากนี้\n",
+    "\n",
+    "มีหลายวิธีในการระบุโมเดลการถดถอยโลจิสติกใน Tidymodels ดูที่ `?logistic_reg()` สำหรับตอนนี้ เราจะระบุโมเดลการถดถอยโลจิสติกผ่านเครื่องยนต์เริ่มต้น `stats::glm()`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a recipe that specifies preprocessing steps for modelling\n",
+    "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  step_integer(item_size, zero_based = F) %>%  \n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)\n",
+    "\n",
+    "# Create a logistic model specification\n",
+    "log_reg <- logistic_reg() %>% \n",
+    "  set_engine(\"glm\") %>% \n",
+    "  set_mode(\"classification\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ตอนนี้เรามีสูตรและสเปคของโมเดลแล้ว เราจำเป็นต้องหาวิธีรวมสิ่งเหล่านี้เข้าด้วยกันเป็นวัตถุหนึ่ง ที่จะช่วยจัดการการเตรียมข้อมูล (prep+bake เบื้องหลัง) ฝึกโมเดลด้วยข้อมูลที่ผ่านการเตรียมแล้ว และยังรองรับกิจกรรมการประมวลผลหลังการฝึกโมเดลได้อีกด้วย\n",
+    "\n",
+    "ใน Tidymodels วัตถุที่สะดวกนี้เรียกว่า [`workflow`](https://workflows.tidymodels.org/) ซึ่งช่วยจัดเก็บองค์ประกอบการสร้างโมเดลของคุณได้อย่างสะดวกสบาย\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Bundle modelling components in a workflow\n",
+    "log_reg_wf <- workflow() %>% \n",
+    "  add_recipe(pumpkins_recipe) %>% \n",
+    "  add_model(log_reg)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "log_reg_wf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "หลังจากที่กำหนด *workflow* แล้ว สามารถ `train` โมเดลได้โดยใช้ฟังก์ชัน [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) ฟังก์ชันนี้จะช่วยประเมินสูตรและเตรียมข้อมูลก่อนการฝึกโมเดล ดังนั้นเราไม่จำเป็นต้องทำการเตรียมข้อมูลด้วย prep และ bake ด้วยตัวเอง\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "wf_fit <- log_reg_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the trained workflow\n",
+    "wf_fit\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "โมเดลจะแสดงค่าสัมประสิทธิ์ที่ได้จากการฝึกสอนระหว่างการเทรน\n",
+    "\n",
+    "ตอนนี้เราได้ฝึกสอนโมเดลด้วยข้อมูลการฝึกสอนเรียบร้อยแล้ว เราสามารถใช้โมเดลนี้เพื่อทำนายผลบนข้อมูลทดสอบได้โดยใช้ [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html) มาเริ่มต้นด้วยการใช้โมเดลเพื่อทำนายป้ายกำกับสำหรับชุดข้อมูลทดสอบ และความน่าจะเป็นของแต่ละป้ายกำกับกัน เมื่อความน่าจะเป็นมากกว่า 0.5 ป้ายกำกับที่ทำนายจะเป็น `WHITE` หากน้อยกว่านั้นจะเป็น `ORANGE`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions for color and corresponding probabilities\n",
+    "results <- pumpkins_test %>% select(color) %>% \n",
+    "  bind_cols(wf_fit %>% \n",
+    "              predict(new_data = pumpkins_test)) %>%\n",
+    "  bind_cols(wf_fit %>%\n",
+    "              predict(new_data = pumpkins_test, type = \"prob\"))\n",
+    "\n",
+    "# Compare predictions\n",
+    "results %>% \n",
+    "  slice_head(n = 10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "เยี่ยมมาก! นี่ช่วยให้เข้าใจการทำงานของโลจิสติกรีเกรสชันได้ลึกซึ้งยิ่งขึ้น\n",
+    "\n",
+    "### เข้าใจได้ดีขึ้นผ่านเมทริกซ์ความสับสน\n",
+    "\n",
+    "การเปรียบเทียบแต่ละการทำนายกับค่าจริงที่สอดคล้องกัน (\"ground truth\") ไม่ใช่วิธีที่มีประสิทธิภาพนักในการประเมินว่ารุ่นทำนายได้ดีแค่ไหน โชคดีที่ Tidymodels มีเครื่องมือเพิ่มเติมที่ช่วยได้: [`yardstick`](https://yardstick.tidymodels.org/) - แพ็กเกจที่ใช้วัดประสิทธิภาพของโมเดลด้วยเมตริกประสิทธิภาพ\n",
+    "\n",
+    "หนึ่งในเมตริกประสิทธิภาพที่เกี่ยวข้องกับปัญหาการจำแนกประเภทคือ [`confusion matrix`](https://wikipedia.org/wiki/Confusion_matrix) เมทริกซ์ความสับสนอธิบายว่ารุ่นการจำแนกประเภททำงานได้ดีเพียงใด โดยเมทริกซ์ความสับสนจะจัดทำตารางว่าตัวอย่างในแต่ละคลาสถูกจำแนกอย่างถูกต้องโดยโมเดลกี่ตัวอย่าง ในกรณีของเรา มันจะแสดงให้คุณเห็นว่าฟักทองสีส้มถูกจำแนกเป็นสีส้มกี่ลูก และฟักทองสีขาวถูกจำแนกเป็นสีขาวกี่ลูก นอกจากนี้ เมทริกซ์ความสับสนยังแสดงให้เห็นว่ามีการจำแนกไปยังหมวดหมู่ที่ **ผิด** กี่ตัวอย่างด้วย\n",
+    "\n",
+    "ฟังก์ชัน [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) จาก yardstick ใช้คำนวณการจัดตารางไขว้ระหว่างคลาสที่สังเกตได้และคลาสที่ทำนาย\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Confusion matrix for prediction results\n",
+    "conf_mat(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "มาทำความเข้าใจเกี่ยวกับ confusion matrix กัน โมเดลของเราถูกขอให้จำแนกฟักทองออกเป็นสองประเภท คือประเภท `white` และประเภท `not-white`\n",
+    "\n",
+    "-   หากโมเดลของคุณทำนายว่าฟักทองเป็นสีขาว และในความเป็นจริงมันอยู่ในประเภท 'white' เราเรียกสิ่งนี้ว่า `true positive` ซึ่งแสดงด้วยตัวเลขด้านบนซ้าย\n",
+    "\n",
+    "-   หากโมเดลของคุณทำนายว่าฟักทองไม่ใช่สีขาว และในความเป็นจริงมันอยู่ในประเภท 'white' เราเรียกสิ่งนี้ว่า `false negative` ซึ่งแสดงด้วยตัวเลขด้านล่างซ้าย\n",
+    "\n",
+    "-   หากโมเดลของคุณทำนายว่าฟักทองเป็นสีขาว และในความเป็นจริงมันอยู่ในประเภท 'not-white' เราเรียกสิ่งนี้ว่า `false positive` ซึ่งแสดงด้วยตัวเลขด้านบนขวา\n",
+    "\n",
+    "-   หากโมเดลของคุณทำนายว่าฟักทองไม่ใช่สีขาว และในความเป็นจริงมันอยู่ในประเภท 'not-white' เราเรียกสิ่งนี้ว่า `true negative` ซึ่งแสดงด้วยตัวเลขด้านล่างขวา\n",
+    "\n",
+    "| ความจริง |\n",
+    "|:-----:|\n",
+    "\n",
+    "\n",
+    "|               |        |       |\n",
+    "|---------------|--------|-------|\n",
+    "| **ทำนาย**    | WHITE | ORANGE |\n",
+    "| WHITE         | TP     | FP    |\n",
+    "| ORANGE        | FN     | TN    |\n",
+    "\n",
+    "อย่างที่คุณอาจเดาได้ว่า เราต้องการให้มีจำนวน true positive และ true negative มากขึ้น และจำนวน false positive และ false negative น้อยลง ซึ่งหมายความว่าโมเดลทำงานได้ดีขึ้น\n",
+    "\n",
+    "confusion matrix มีประโยชน์เพราะมันช่วยให้เราสามารถคำนวณตัวชี้วัดอื่น ๆ ที่ช่วยประเมินประสิทธิภาพของโมเดลการจำแนกประเภทได้ดียิ่งขึ้น มาดูตัวชี้วัดเหล่านี้กัน:\n",
+    "\n",
+    "🎓 Precision: `TP/(TP + FP)` หมายถึงสัดส่วนของผลลัพธ์ที่ทำนายว่าเป็นบวกที่เป็นบวกจริง ๆ หรือที่เรียกว่า [positive predictive value](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\")\n",
+    "\n",
+    "🎓 Recall: `TP/(TP + FN)` หมายถึงสัดส่วนของผลลัพธ์ที่เป็นบวกจากจำนวนตัวอย่างที่เป็นบวกจริง ๆ หรือที่เรียกว่า `sensitivity`\n",
+    "\n",
+    "🎓 Specificity: `TN/(TN + FP)` หมายถึงสัดส่วนของผลลัพธ์ที่เป็นลบจากจำนวนตัวอย่างที่เป็นลบจริง ๆ\n",
+    "\n",
+    "🎓 Accuracy: `TP + TN/(TP + TN + FP + FN)` เปอร์เซ็นต์ของป้ายกำกับที่ทำนายได้อย่างถูกต้องสำหรับตัวอย่าง\n",
+    "\n",
+    "🎓 F Measure: ค่าเฉลี่ยถ่วงน้ำหนักระหว่าง precision และ recall โดยค่าที่ดีที่สุดคือ 1 และค่าที่แย่ที่สุดคือ 0\n",
+    "\n",
+    "มาคำนวณตัวชี้วัดเหล่านี้กัน!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Combine metric functions and calculate them all at once\n",
+    "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\n",
+    "eval_metrics(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## แสดงกราฟ ROC ของโมเดลนี้\n",
+    "\n",
+    "มาทำการแสดงผลอีกครั้งเพื่อดูสิ่งที่เรียกว่า [`กราฟ ROC`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make a roc_curve\n",
+    "results %>% \n",
+    "  roc_curve(color, .pred_ORANGE) %>% \n",
+    "  autoplot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "กราฟ ROC มักถูกใช้เพื่อดูผลลัพธ์ของตัวจำแนกในแง่ของค่าบวกจริง (True Positives) เทียบกับค่าบวกเท็จ (False Positives) กราฟ ROC โดยทั่วไปจะแสดง `True Positive Rate`/Sensitivity บนแกน Y และ `False Positive Rate`/1-Specificity บนแกน X ดังนั้น ความชันของกราฟและพื้นที่ระหว่างเส้นกลางกับกราฟจึงมีความสำคัญ: คุณต้องการกราฟที่พุ่งขึ้นและข้ามเส้นไปอย่างรวดเร็ว ในกรณีของเรา มีค่าบวกเท็จในช่วงเริ่มต้น และจากนั้นเส้นก็พุ่งขึ้นและข้ามเส้นไปอย่างเหมาะสม\n",
+    "\n",
+    "สุดท้ายนี้ เรามาใช้ `yardstick::roc_auc()` เพื่อคำนวณค่าพื้นที่ใต้กราฟ (Area Under the Curve) วิธีหนึ่งในการตีความ AUC คือความน่าจะเป็นที่โมเดลจะจัดอันดับตัวอย่างบวกแบบสุ่มให้สูงกว่าตัวอย่างลบแบบสุ่ม\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate area under curve\n",
+    "results %>% \n",
+    "  roc_auc(color, .pred_ORANGE)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ผลลัพธ์อยู่ที่ประมาณ `0.975` ซึ่งเมื่อพิจารณาว่า AUC มีค่าตั้งแต่ 0 ถึง 1 คุณต้องการคะแนนที่สูง เพราะโมเดลที่ทำนายได้ถูกต้อง 100% จะมีค่า AUC เท่ากับ 1; ในกรณีนี้ โมเดลถือว่า *ค่อนข้างดี*\n",
+    "\n",
+    "ในบทเรียนอนาคตเกี่ยวกับการจำแนกประเภท คุณจะได้เรียนรู้วิธีปรับปรุงคะแนนของโมเดล (เช่น การจัดการกับข้อมูลที่ไม่สมดุลในกรณีนี้)\n",
+    "\n",
+    "## 🚀ความท้าทาย\n",
+    "\n",
+    "ยังมีอีกมากมายเกี่ยวกับการวิเคราะห์ Logistic Regression! แต่วิธีที่ดีที่สุดในการเรียนรู้คือการทดลอง ค้นหาชุดข้อมูลที่เหมาะสมกับการวิเคราะห์ประเภทนี้และสร้างโมเดลด้วยชุดข้อมูลนั้น คุณได้เรียนรู้อะไร? เคล็ดลับ: ลองดู [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) สำหรับชุดข้อมูลที่น่าสนใจ\n",
+    "\n",
+    "## ทบทวนและศึกษาด้วยตนเอง\n",
+    "\n",
+    "อ่านหน้าแรก ๆ ของ [เอกสารนี้จาก Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) เกี่ยวกับการใช้งาน Logistic Regression ในทางปฏิบัติ ลองคิดถึงงานที่เหมาะสมกับการวิเคราะห์แบบ Regression ประเภทต่าง ๆ ที่เราได้ศึกษาไปจนถึงตอนนี้ งานแบบไหนที่เหมาะสมที่สุด?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "langauge": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "feaf125f481a89c468fa115bf2aed580",
+   "translation_date": "2025-09-06T13:35:48+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/th/2-Regression/4-Logistic/solution/notebook.ipynb b/translations/th/2-Regression/4-Logistic/solution/notebook.ipynb
new file mode 100644
index 000000000..0b09ea0d5
--- /dev/null
+++ b/translations/th/2-Regression/4-Logistic/solution/notebook.ipynb
@@ -0,0 +1,1255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## การถดถอยโลจิสติก - บทเรียนที่ 4\n",
+    "\n",
+    "โหลดไลบรารีและชุดข้อมูลที่จำเป็น แปลงข้อมูลให้เป็น DataFrame ที่มีเฉพาะส่วนย่อยของข้อมูล:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date   \n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17  \\\n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition   \n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN  \\\n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Origin</th>\n",
+       "      <th>Item Size</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>DELAWARE</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>VIRGINIA</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>36 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name       Package      Variety    Origin Item Size   Color\n",
+       "2  BALTIMORE  24 inch bins  HOWDEN TYPE  DELAWARE       med  ORANGE\n",
+       "3  BALTIMORE  24 inch bins  HOWDEN TYPE  VIRGINIA       med  ORANGE\n",
+       "4  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "5  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "6  BALTIMORE  36 inch bins  HOWDEN TYPE  MARYLAND       med  ORANGE"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select the columns we want to use\n",
+    "columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Drop rows with missing values\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# มาดูข้อมูลของเรากันเถอะ!\n",
+    "\n",
+    "ด้วยการแสดงผลด้วย Seaborn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56d0c650>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 609.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# Specify colors for each values of the hue variable\n",
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# Plot a bar plot to visualize how many pumpkins of each variety are orange or white\n",
+    "sns.catplot(\n",
+    "    data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
+    "    palette=palette, \n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# การเตรียมข้อมูลล่วงหน้า\n",
+    "\n",
+    "มาเข้ารหัสคุณลักษณะและป้ายกำกับเพื่อให้สามารถแสดงข้อมูลและฝึกโมเดลได้ดียิ่งขึ้น\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the different values of the 'Item Size' column\n",
+    "pumpkins['Item Size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "# Encode the 'Item Size' column using ordinal encoding\n",
+    "item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
+    "ordinal_features = ['Item Size']\n",
+    "ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "# Encode all the other features using one-hot encoding\n",
+    "categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
+    "categorical_encoder = OneHotEncoder(sparse_output=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_MICHIGAN</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_MICHIGAN  cat__Origin_NEW JERSEY   \n",
+       "2                   0.0  ...                   0.0                     0.0  \\\n",
+       "3                   0.0  ...                   0.0                     0.0   \n",
+       "4                   0.0  ...                   0.0                     0.0   \n",
+       "5                   0.0  ...                   0.0                     0.0   \n",
+       "6                   0.0  ...                   0.0                     0.0   \n",
+       "\n",
+       "   cat__Origin_NEW YORK  cat__Origin_NORTH CAROLINA  cat__Origin_OHIO   \n",
+       "2                   0.0                         0.0               0.0  \\\n",
+       "3                   0.0                         0.0               0.0   \n",
+       "4                   0.0                         0.0               0.0   \n",
+       "5                   0.0                         0.0               0.0   \n",
+       "6                   0.0                         0.0               0.0   \n",
+       "\n",
+       "   cat__Origin_PENNSYLVANIA  cat__Origin_TENNESSEE  cat__Origin_TEXAS   \n",
+       "2                       0.0                    0.0                0.0  \\\n",
+       "3                       0.0                    0.0                0.0   \n",
+       "4                       0.0                    0.0                0.0   \n",
+       "5                       0.0                    0.0                0.0   \n",
+       "6                       0.0                    0.0                0.0   \n",
+       "\n",
+       "   cat__Origin_VERMONT  cat__Origin_VIRGINIA  \n",
+       "2                  0.0                   0.0  \n",
+       "3                  0.0                   1.0  \n",
+       "4                  0.0                   0.0  \n",
+       "5                  0.0                   0.0  \n",
+       "6                  0.0                   0.0  \n",
+       "\n",
+       "[5 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "ct = ColumnTransformer(transformers=[\n",
+    "     ('ord', ordinal_encoder, ordinal_features),\n",
+    "     ('cat', categorical_encoder, categorical_features)\n",
+    "     ])\n",
+    "# Get the encoded features as a pandas DataFrame\n",
+    "ct.set_output(transform='pandas')\n",
+    "encoded_features = ct.fit_transform(pumpkins)\n",
+    "encoded_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_NEW JERSEY  cat__Origin_NEW YORK   \n",
+       "2                   0.0  ...                     0.0                   0.0  \\\n",
+       "3                   0.0  ...                     0.0                   0.0   \n",
+       "4                   0.0  ...                     0.0                   0.0   \n",
+       "5                   0.0  ...                     0.0                   0.0   \n",
+       "6                   0.0  ...                     0.0                   0.0   \n",
+       "\n",
+       "   cat__Origin_NORTH CAROLINA  cat__Origin_OHIO  cat__Origin_PENNSYLVANIA   \n",
+       "2                         0.0               0.0                       0.0  \\\n",
+       "3                         0.0               0.0                       0.0   \n",
+       "4                         0.0               0.0                       0.0   \n",
+       "5                         0.0               0.0                       0.0   \n",
+       "6                         0.0               0.0                       0.0   \n",
+       "\n",
+       "   cat__Origin_TENNESSEE  cat__Origin_TEXAS  cat__Origin_VERMONT   \n",
+       "2                    0.0                0.0                  0.0  \\\n",
+       "3                    0.0                0.0                  0.0   \n",
+       "4                    0.0                0.0                  0.0   \n",
+       "5                    0.0                0.0                  0.0   \n",
+       "6                    0.0                0.0                  0.0   \n",
+       "\n",
+       "   cat__Origin_VIRGINIA  Color  \n",
+       "2                   0.0      0  \n",
+       "3                   1.0      0  \n",
+       "4                   0.0      0  \n",
+       "5                   0.0      0  \n",
+       "6                   0.0      0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "# Encode the 'Color' column using label encoding\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n",
+    "encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
+    "encoded_pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ORANGE', 'WHITE']"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the mapping between the encoded values and the original values\n",
+    "list(label_encoder.inverse_transform([0, 1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56322210>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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brs8FAADAtXXo0KHEvi+++ELTp0/XoUOH9Nhjj2nq1Kku91flIbZ169YKDAxUYmKiI8QmJiZq8ODB2rBhg7Zu3eoYkU1MTFSfPn3K/VmNGjVyvK5fv74kyd/fv9RwWrduXQUEBJT7swAAAFA+27dv17Rp0/Tll1/qwQcf1Jo1a9SwYcMy9VEtS2z16dNHCQkJjvcJCQmKiopSZGSkY//58+f19ddfVyjEAgAA4Jfr4MGDGjZsmHr27KnAwEAdOHBAr7zySpkDrFSNIXbz5s0qLCxUbm6udu3apcjISPXu3VuJiYmSpC1btqigoMApxD7++OPy8fFx2p555plKqWn48OEl+j569OhVjy8oKFBOTo7TBgAAANc8/PDDateunbKzs7V9+3b961//UvPmzcvdX7WsThAVFaVz585p+/btysrKUnh4uBo1aqTIyEjFxMQoPz9fiYmJatGihZo1a+Y479FHH3XcjHXZyy+/rI0bN1a4prlz56pfv35O+4KCgq56fFxcnGbNmlXhzwUAAHBHr7/+ury9vZWZmamYmJirHrdr1y6X+quWEBsWFqamTZsqISFBWVlZioyMlHQpNAYHB+urr75SQkKC+vbt63Rew4YNFRYW5rTv8lzXigoICCjR97VMnz5dU6ZMcbzPyclRcHBwpdQCAADwazdjxoxK7a/a1ont06ePEhMTlZWVpUcffdSxv3fv3lq1apW2bdumcePGVVc5Zebl5SUvLy+zywAAALCkv/3tb5XaX7WG2NjYWF28eNExEitJkZGRGj9+vC5cuFCtN3WdPXtWGRkZTvt8fX1Vu3btaqsBAAAA5VMtN3ZJl0Ls+fPnFRYWpsaNGzv2R0ZGKjc317EUV3WJiYlRYGCg0/bKK69U2+cDAACg/GzGlc/5gstycnLk5+en7Oxsx8MUAAAAUD2qbSQWAAAAqCyEWAAAAJju7NmzZbo/ihALAAAA0124cEFJSUkuH0+IBQAAgOVU2xJbvzaX74fj8bMAAMCd+Pr6ymazlfm8H3744Zrtp06dKlN/rE5QTt9//71atmxpdhkAAADVKjMzU40aNSrzeXa7XYZhyGazqbT4eXl/cXGxS/0xEltOlx9/e/ToUfn5+Zlcjfu5/NjfY8eOscSZCbj+5uL6m4drby6uv7kuX39PT89ynb9r165rtp85c0Z9+/Z1uT9CbDnVqHFpOrGfnx//IZmoTp06XH8Tcf3NxfU3D9feXFx/c5VnKoEkdejQ4ZrtmZmZZeqPG7sAAADwi1CWgEyIBQAAgOk8PT3Vtm1bl48nxJaTl5eXZsyYIS8vL7NLcUtcf3Nx/c3F9TcP195cXH9zVfX137Rpk06fPu3y8axOAAAAANPk5uZq0qRJevfddzVjxgxNnz7dpfO4sQsAAACmSEhI0JgxY1S/fn3t2LFD7dq1c/lcQiwAAACqXN++fZ3Why0sLNTWrVv1xBNP6G9/+5vsdnuZ+iPEAgAAoMp16tTJ6X1hYaH27Nmj/fv368yZM2V+gAJzYgEAAGCK48ePKyYmRt9++63mz5+vIUOGuHwuqxOU04IFCxQaGipvb29169ZN27ZtM7skt7Bx40ZFR0crKChINptNH3/8sdkluY24uDjdfPPN8vX1lb+/v+666y6lpqaaXZbbePXVV9WhQwfHIu89evTQqlWrzC7Lbc2ZM0c2m02TJ082uxS3MHPmTNlsNqctIiLC7LLcyo8//qh7771XDRo0UK1atXTDDTcoOTm5wv02bdpUa9eu1d/+9jfFxMRo6NChLp9LiC2H9957T1OmTNGMGTO0c+dOdezYUQMGDCjzkyZQdufOnVPHjh21YMECs0txO0lJSYqNjdXWrVu1du1aXbx4UbfffrvOnTtndmluoWnTppozZ4527Nih5ORk9e3bV4MHD9bevXvNLs3tbN++Xa+99tp1nz6EytWuXTulp6c7ti+//NLsktxGVlaWevXqpZo1a2rVqlXat2+fXnjhBdWrV6/SPiM2NlYpKSk6fvy4y+cwnaAcunXrpptvvlnz58+XJBUXFys4OFgTJkzQtGnTTK7OfdhsNq1cuVJ33XWX2aW4pVOnTsnf319JSUnq3bu32eW4pfr16+u5557T/fffb3YpbiMvL0833nijFi5cqNmzZ6tTp06aN2+e2WX96s2cOVMff/yxUlJSzC7FLU2bNk2bN2/Wpk2bqvyzDMNw+ald3NhVRhcuXNCOHTuc1jCrUaOG+vXrpy1btphYGVC9srOzJV0KUqheRUVF+uCDD3Tu3Dn16NHD7HLcSmxsrO644w7169dPs2fPNrsct3Lw4EEFBQXJ29tbPXr0UFxcnJo1a2Z2WW7h008/1YABAzRkyBAlJSWpSZMm+vOf/6yxY8eWqZ9Zs2Zd9xjDMDRz5kyX+iPEltHp06dVVFSkxo0bO+1v3LixDhw4YFJVQPUqLi7W5MmT1atXL7Vv397sctzG7t271aNHD+Xn58vHx0crV64s0yMaUTHLly/Xzp07tX37drNLcTvdunXTm2++qdatWys9PV2zZs3Srbfeqj179sjX19fs8n71vv/+e7366quaMmWKnnjiCW3fvl0TJ06Up6enRo0a5XI/n3zyieP1hQsXdODAAadpOZdXKyDEAqgysbGx2rNnD3PSqlnr1q2VkpKi7Oxsffjhhxo1apSSkpIIstXg2LFjmjRpktauXStvb2+zy3E7gwYNcrzu0KGDunXrppCQEL3//vtMp6kGxcXF6tKli5555hlJUufOnbVnzx4tWrSoTCF2586djtdpaWnq2LGj075Tp04pICDA5f64sauMGjZsKLvdrpMnTzrtP3nyZJkuPGBV48eP1+eff66EhAQ1bdrU7HLciqenp8LCwnTTTTcpLi5OHTt21EsvvWR2WW5hx44dyszM1I033igPDw95eHgoKSlJL7/8sjw8PFRUVGR2iW6lbt26Cg8P16FDh8wuxS0EBgaW+GW5TZs2Onr0aLn7tNvtKiwsdNp38eJF1ajhejQlxJaRp6enbrrpJq1fv96xr7i4WOvXr2duGn7VDMPQ+PHjtXLlSm3YsEHNmzc3uyS3V1xcrIKCArPLcAu33Xabdu/erZSUFMfWpUsXjRgxQikpKWV+0hAqJi8vT4cPH1ZgYKDZpbiFXr16lVhS8bvvvlNISEi5+wwKCtLFixe1Y8cOx77NmzeXaXCE6QTlMGXKFI0aNUpdunRR165dNW/ePJ07d04xMTFml/arl5eX5/Sbd1pamlJSUlS/fn0m+Fex2NhYvfvuu/rkk0/k6+urjIwMSZKfn59q1aplcnW/ftOnT9egQYPUrFkz5ebm6t1331ViYqLWrFljdmluwdfXt8T879q1a6tBgwbMC68GU6dOVXR0tEJCQnTixAnNmDFDdrtdw4cPN7s0t/DII4+oZ8+eeuaZZzR06FBt27ZNixcv1uLFi8vdp4eHh373u99p0KBBGj58uPLz87V06VI9+OCDrndioFxeeeUVo1mzZoanp6fRtWtXY+vWrWaX5BYSEhIMSSW2UaNGmV3ar15p112SsWTJErNLcwtjxowxQkJCDE9PT6NRo0bGbbfdZnzxxRdml+XWIiMjjUmTJpldhlsYNmyYERgYaHh6ehpNmjQxhg0bZhw6dMjsstzKZ599ZrRv397w8vIyIiIijMWLF1e4z8zMTGPYsGFGw4YNjaCgIOPhhx828vLyXD6fdWIBAABgOUwnAAAAQJX74YcfXDrO1bm2jMQCAACgytntdscTuUqLn5f3FxcXu9QfI7EAAACoFuvWrVPDhg0lScePH9fQoUP11VdfSZLOnDmjvn37utwXIRYAAADVol27do6nnvr4+Mhmszme2pWZmVmmvlgnFgAAAJZDiAUAAECVq+zbsAixAAAAqHI2m+26+0o75moIsQDgBo4cOSKbzaaUlBSzSwHgppYtW6a6des63rdo0UI5OTmO9w0aNNCWLVtc7o8QCwDlMHr0aN11112O91FRUZo8ebJp9aSlpelPf/qTgoKC5O3traZNm2rw4ME6cOCAJCk4OFjp6ek8IhWAaYYOHSovL6+rttvtdnXt2tXl/lidAAAs7uLFi+rfv79at26tFStWKDAwUMePH9eqVat09uxZSZd+OAQEBJhbKABUIkZiAaCCRo8eraSkJL300kuy2Wyy2Ww6cuSIJGnPnj0aNGiQfHx81LhxY40cOVKnT592nBsVFaUJEyZo8uTJqlevnho3bqzXX39d586dU0xMjHx9fRUWFqZVq1Zd9fP37t2rw4cPa+HCherevbtCQkLUq1cvzZ49W927d5dUcjrB6NGjHbVeuSUmJkqSCgoKNHXqVDVp0kS1a9dWt27dHG0A8EtAiAWACnrppZfUo0cPjR07Vunp6UpPT1dwcLDOnj2rvn37qnPnzkpOTtbq1at18uRJDR061On8t956Sw0bNtS2bds0YcIEjRs3TkOGDFHPnj21c+dO3X777Ro5cqT++9//lvr5jRo1Uo0aNfThhx+qqKjI5Zov15qenq5JkybJ399fERERkqTx48dry5YtWr58ub799lsNGTJEAwcO1MGDByt2sQCgkvDYWQAoh9GjR+vs2bP6+OOPJV0aUe3UqZPmzZvnOGb27NnatGmT1qxZ49h3/PhxBQcHKzU1VeHh4YqKilJRUZE2bdokSSoqKpKfn5/uvvtuvf3225KkjIwMBQYGasuWLY6R1Z9bsGCBHnvsMdntdnXp0kV9+vTRiBEj1KJFC0mXRmKbN2+uXbt2qVOnTk7nrlixQiNGjNC6devUq1cvHT16VC1atNDRo0cVFBTkOK5fv37q2rWrnnnmmYpePgCoMEZiAaCKfPPNN0pISJCPj49juzzSefjwYcdxl59WI12au9qgQQPdcMMNjn2Xn25zrafZxMbGKiMjQ0uXLlWPHj30wQcfqF27dlq7du01a9y1a5dGjhyp+fPnq1evXpKk3bt3q6ioSOHh4U61JyUlOdUNAGbixi4AqCJ5eXmKjo7Ws88+W6ItMDDQ8bpmzZpObTabzWnf5XUTi4uLr/l5vr6+io6OVnR0tGbPnq0BAwZo9uzZ6t+/f6nHZ2Rk6M4779QDDzyg+++/36luu92uHTt2yG63O53j4+NzzRoAoLoQYgGgEnh6epaYj3rjjTfqo48+UmhoqDw8qvfbrc1mU0REhL766qtS2/Pz8zV48GBFREToxRdfdGrr3LmzioqKlJmZqVtvvbU6ygWAMmM6AQBUgtDQUH399dc6cuSITp8+reLiYsXGxurMmTMaPny4tm/frsOHD2vNmjWKiYlx+QYsV6SkpGjw4MH68MMPtW/fPh06dEjx8fF64403NHjw4FLPeeihh3Ts2DG9/PLLOnXqlDIyMpSRkaELFy4oPDxcI0aM0H333acVK1YoLS1N27ZtU1xcnP79739XWt0AUBGMxAJAJZg6dapGjRqltm3b6vz580pLS1NoaKg2b96sxx9/XLfffrsKCgoUEhKigQMHqkaNyhtDaNq0qUJDQzVr1izHUlqX3z/yyCOlnpOUlKT09HS1bdvWaX9CQoKioqK0ZMkSzZ49W3/5y1/0448/qmHDhurevbt+97vfVVrdAFARrE4AAAAAy2E6AQAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBxCLAAAACyHEAsAAADLIcQCAADAcgixAAAAsBwPswsAAADAr19MTIxLxy1ZssSl42yGYRgVKQgAAAC4nrvvvtvx+ty5c9qwYYOio6Md+woKCrRq1SoVFxe71B8hFgAAANUqLS1NHTp0UG5urmPfqVOnFBAQoKKiIpf6YE4sAAAAqlXNmjV18eJFp335+fny8HB9pishtpwMw1BOTo4YyAYAACiboKAgGYahdevWOfb95z//UbNmzVzugxu7yik3N1d+fn46efKk6tSpY3Y5bsUwDBUUFEiSvLy8ZLPZTK7IfXH9AQDlUaNGDY0YMULR0dEaMGCAzp8/r3Xr1mnmzJku98Gc2HLKycmRn5+fBgwYoJo1a5pdDmCKDz74QN7e3maXAQCwoPPnz2vWrFlav369PD09deedd+rRRx9VjRquTRRgJBYAAADVrlatWpozZ065zyfEVtCM/t+pvo/ZVbiXgkKbnlzdVpL09MB98vLgjwnV6UJRDT2xqo3ZZQAALCYpKcml4yIjI106jhBbQTVrFMvLgzmBZvHyMAix1c619fuAXxvm4wMV07dvXxmGcc3/dgzDcHmdWEJsBV0o4psYALiDgoICDRkyRBLzwYHyyMrKqtT+CLEAAACocpW9mlO51ok9duyYxowZo6CgIHl6eiokJESTJk3STz/95DgmKipKNptNNptN3t7eCg8PV1xcXKnrqm7ZskV2u1133HFHibYjR47IZrPJ39/f6akOktSpU6cSSzEcOnRIY8aMUbNmzeTl5aUmTZrotttu09KlS1VYWOg47nJtP9+WL19enksCAACAa0hKSnJpc1WZR2K///579ejRQ+Hh4Vq2bJmaN2+uvXv36tFHH9WqVau0detW1a9fX5I0duxYPfXUUyooKNCGDRv04IMPqm7duho3bpxTn/Hx8ZowYYLi4+N14sQJBQUFlfjc3NxcPf/885o1a9ZVa9u2bZv69eundu3aacGCBYqIiJAkJScna8GCBWrfvr06duzoOH7JkiUaOHCgUx9169Yt6yUBAADAdZQ2J7a091U2JzY2Nlaenp764osvVKtWLUlSs2bN1LlzZ7Vs2VJPPvmkXn31VUnSb37zGwUEBEiSYmJiNH/+fK1du9YpxObl5em9995TcnKyMjIy9Oabb+qJJ54o8bkTJkzQiy++qNjYWPn7+5doNwxDo0ePVnh4uDZv3uy0xlirVq00fPjwEqPAdevWddQHwDVX/meUn59vXiFANbvy3ztLrAPlk5qaqsaNG0u69Nf2W265RceOHZPNZtOpU6cUHh7ucl9lCrFnzpzRmjVr9PTTTzsC7GUBAQEaMWKE3nvvPS1cuNCpzTAMffnllzpw4IBatWrl1Pb+++8rIiJCrVu31r333qvJkydr+vTpJe5cGz58uNauXaunnnpK8+fPL1FbSkqK9u/fr2XLll11kdyK3ElaUFDguCtVuvSwA8AdXXkz48iRI02sBDBPQUFBiZ+DAK6vTp06jrmxPj4+MgxDfn5+ki79oliWXxDLNCf24MGDMgxDbdqUvkZkmzZtlJWVpVOnTkmSFi5cKB8fH3l5eal3794qLi7WxIkTnc6Jj4/XvffeK0kaOHCgsrOzS50PYbPZNGfOHC1evFiHDx8u0f7dd99Jklq3bu3Yl5mZKR8fH8f283A9fPhwp3YfHx8dPXq01K8tLi5Ofn5+ji04OPhqlwkAAABVrFyrE7iakkeMGKEnn3xSWVlZmjFjhnr27KmePXs62lNTU7Vt2zatXLnyUjEeHho2bJji4+MVFRVVor8BAwbolltu0V//+le9++671/38Bg0aKCUlRdKlG80uXLjg1D537lz169fPaV9p83Elafr06ZoyZYrjfU5ODkEWbsnT/r///t955x2WGYLbyM/Pd/z1wcvLy+RqAOup7Gk4ZQqxYWFhstls2r9/v37/+9+XaN+/f7/q1aunRo0aSZL8/PwUFhYm6dK0gbCwMHXv3t0RHOPj41VYWOgUHA3DkJeXl+bPn+8YXr7SnDlz1KNHDz366KNO+y9PU0hNTVXnzp0lSXa73fH5Hh4lv9SAgABH+/V4eXnxTQuQdOWsHG9vb0Is3BIPOgDK7uf/3dSsWVOhoaHXPOZayjSdoEGDBurfv78WLlyo8+fPO7VlZGRo6dKlGjZsWKkF+Pj4aNKkSZo6daoMw1BhYaHefvttvfDCC0pJSXFs33zzjYKCgrRs2bJSa+jatavuvvtuTZs2zWl/586dFRERoeeff97lu9oAAABQPbZs2aIGDRo43gcHB2v37t2O9/7+/kpPT3e5vzJPJ5g/f7569uypAQMGaPbs2U5LbDVp0kRPP/30Vc996KGH9Pe//10fffSRPDw8lJWVpfvvv7/EiOs999yj+Ph4Pfzww6X28/TTT6tdu3ZOo6s2m01LlixR//791atXL02fPl1t2rTRxYsXtXHjRp06dUp2u92pn7NnzyojI8Npn6+vr2rXrl3WywIAAIBr6Nq163WPKW0Fqqsp88MOWrVqpeTkZLVo0UJDhw5Vy5Yt9eCDD6pPnz7asmWLY43Y0tSvX1/33XefZs6cqfj4ePXr16/UKQP33HOPkpOT9e2335baT3h4uMaMGVNieZ/u3btrx44dat26tWJjY9W2bVv17NlTy5Yt09y5c0usTxsTE6PAwECn7ZVXXinT9bhyfiAA4NfLy8tLH3zwgT744AOmlwG/ADaDxe7KJScnR35+fkqeG6IGvsyNqk4FhTZN/bydJOn53+2Vlwf/hKvTldef58cDAMxSrtUJ8D8XimqooPD6x6HyFBTaSn2N6nGhqFxPqwYAoFIRYito1tpw1axZ0+wy3NaTq9uaXQIAAKgE//3vf/Xcc89pxowZLh3PkAoAAABMl5eXp1mzZrl8PCOxFfT22287Hp+G6mEYhuMRwF5eXqzXaCJubgEAVKay/EwnxFYQi72bg2eWm+fKXyIAAKhMZVlvgBALoEwKCgo0ZMgQSaxOAABwXYsWLa4ZUouKisrUHyEWQJlcuT5zfn4+IRYA4JLJkydfsz0vL0//93//53J/hFgAAABUuYkTJ16zPTMzs0whltUJAAAAYDmEWABlUlxcXOprAAAqqiyrExBiAZRJbm5uqa8BAKiICxcuqG/fvi4fX+UhdtGiRfL19VVh4f+ezZqXl6eaNWsqKirK6djExETZbDYdPnxYoaGhmjdvXon+Zs6cqU6dOpX6PjQ0VDab7arb6NGjJemq7cuXL6/krx4AAADX8/bbb6tDhw6y2+0un1PlN3b16dNHeXl5Sk5OVvfu3SVJmzZtUkBAgL7++munu5sTEhLUrFkztWzZslyftX37dsfyDF999ZXuuecepaamOh5GcOXaokuWLNHAgQOdzq9bt265PhcAAABld+rUKT300ENat26dXnjhBY0dO9blc6s8xLZu3VqBgYFKTEx0hNjExEQNHjxYGzZs0NatWx0jsomJierTp0+5P6tRo0aO1/Xr15ck+fv7lxpO69atq4CAgHJ/FgAAAFz380fKFhYW6rXXXlP79u21e/duhYSElKm/alliq0+fPkpISNC0adMkXRpxfeyxx1RUVKSEhARFRUXp/Pnz+vrrrzVmzJjqKKnMCgoKnJ5SlJOTY2I1AAAA1vLJJ584vS8sLFRWVpbuvvvuMgdYqRpD7OTJk1VYWKjz589r165dioyM1MWLF7Vo0SJJ0pYtW1RQUOA0Evv444+XWC/swoULatu2bYVrGj58eIl5F/v27VOzZs1KPT4uLq7EbxAAAABwzc6dO0vs++yzzzR27FitWLFC8fHxat68ucv9VcvqBFFRUTp37py2b9+uTZs2KTw8XI0aNVJkZKRjXmxiYqJatGjhFCIfffRRpaSkOG0PP/xwpdQ0d+7cEn0HBQVd9fjp06crOzvbsR07dqxS6gAAAHBX0dHR2rt3rxo0aKAOHTpo4cKFLp9bLSOxYWFhatq0qRISEpSVlaXIyEhJUlBQkIKDg/XVV18pISGhxLIKDRs2VFhYmNO+y3NdKyogIKBE39fi5eUlLy+vSvlsAAAAXNKgQQN98MEHevfddxUbG6s///nPLp1XbevE9unTR4mJiUpMTHRaWqt3795atWqVtm3bVqGbugAAAGBdf/rTn7R3716Xj6+WkVjpUoiNjY3VxYsXHSOxkhQZGanx48frwoUL1Rpiz549q4yMDKd9vr6+ql27drXVAAAA4C6SkpJcOu5a0zuvVK0h9vz584qIiFDjxo0d+yMjI5Wbm+tYiqu6xMTElNgXFxfnWEEBAAAAladv374yDMPxaNkrX19mGIbLjzS3GYZhVHqVbiAnJ0d+fn7Kzs52PEwBcAdnzpzRqFGjJElvvfVWpc1TBwD8ul25POmRI0d0yy236Pjx4459p06dUnh4uOPBVddTbSOxAH4datSoUeprAACu5cpBP19fXxUXFzvtO3/+vMoytspPIAAAAFQrf39/nT9/Xunp6Y59Bw8elL+/v8t9MBILoEy8vb1LfQ0AgKtq166tTp066Q9/+IOmTp2q/Px8PfXUU7rllltc7oM5seXEnFi4K8MwHI9g9vLyKjEpHwAAV2zfvl333HOPfvzxR0lS27Zt9emnn7r81C5CbDllZ2erbt26OnbsGCEWAAC4DV9f30obwCgsLFRqaqo8PT0VFhZWpn4JseX0/fffq2XLlmaXAQAAUK0yMzPVqFEjs8tgTmx5XV5W6OjRo/Lz8zO5GveTk5Oj4OBgRsJNwvU3F9ffPFx7c3H9zXX5+nt6epbrfFemCRiGoSNHjrjUHyG2nC4vLeTn58d/SCaqU6cO199EXH9zcf3Nw7U3F9ffXOWdSnD06FE99dRT8vX1lSSdPn1azz33nJ599llJUl5env7v//7P5f4IsQAAAKgWDzzwgOPJrd9//73mzp2riRMnSro0TaEsIZZ1YgEAAGA5hNhy8vLy0owZM+Tl5WV2KW6J628urr+5uP7m4dqbi+tvrl/a9Wd1AgAAAFQ5u92uEydOOE0n6NSpk3JyciRdmk4QGBiooqIil/pjJBYAAABV7qGHHtJvfvMbx/smTZpo1apVjve+vr6Ki4tzuT9GYgEAAGA5jMQCAADAcgix5bRgwQKFhobK29tb3bp107Zt28wuyS1s3LhR0dHRCgoKks1m08cff2x2SW4jLi5ON998s3x9feXv76+77rpLqampZpflNl599VV16NDBsT5mjx49nP4Mh+o1Z84c2Ww2TZ482exS3MLMmTNls9mctoiICLPLcis//vij7r33XjVo0EC1atXSDTfcoOTkZFNrIsSWw3vvvacpU6ZoxowZ2rlzpzp27KgBAwYoMzPT7NJ+9c6dO6eOHTtqwYIFZpfidpKSkhQbG6utW7dq7dq1unjxom6//XadO3fO7NLcQtOmTTVnzhzt2LFDycnJ6tu3rwYPHqy9e/eaXZrb2b59u1577TV16NDB7FLcSrt27ZSenu7YvvzyS7NLchtZWVnq1auXatasqVWrVmnfvn164YUXVK9ePVPrYk5sOXTr1k0333yz5s+fL0kqLi5WcHCwJkyYoGnTpplcnfuw2WxauXKl7rrrLrNLcUunTp2Sv7+/kpKS1Lt3b7PLcUv169fXc889p/vvv9/sUtxGXl6ebrzxRi1cuFCzZ89Wp06dNG/ePLPL+tWbOXOmPv74Y6WkpJhdiluaNm2aNm/erE2bNpldihNGYsvowoUL2rFjh/r16+fYV6NGDfXr109btmwxsTKgemVnZ0u6FKRQvYqKirR8+XKdO3dOPXr0MLsctxIbG6s77rjD6WcAqsfBgwcVFBSkFi1aaMSIETp69KjZJbmNTz/9VF26dNGQIUPk7++vzp076/XXXze7LEJsWZ0+fVpFRUWONc4ua9y4sTIyMkyqCqhexcXFmjx5snr16qX27dubXY7b2L17t3x8fOTl5aWHH35YK1euVNu2bc0uy20sX75cO3fuLNMSQKgc3bp105tvvqnVq1fr1VdfVVpamm699Vbl5uaaXZpb+P777/Xqq6+qVatWWrNmjcaNG6eJEyfqrbfeMrUuD1M/HYAlxcbGas+ePcxJq2atW7dWSkqKsrOz9eGHH2rUqFFKSkoiyFaDY8eOadKkSVq7dq28vb3NLsftDBo0yPG6Q4cO6tatm0JCQvT+++8znaYaFBcXq0uXLnrmmWckSZ07d9aePXu0aNEijRo1yrS6GIkto4YNG8put+vkyZNO+0+ePKmAgACTqgKqz/jx4/X5558rISFBTZs2Nbsct+Lp6amwsDDddNNNiouLU8eOHfXSSy+ZXZZb2LFjhzIzM3XjjTfKw8NDHh4eSkpK0ssvvywPDw+XnzCEylG3bl2Fh4fr0KFDZpfiFgIDA0v8stymTRvTp3QQYsvI09NTN910k9avX+/YV1xcrPXr1zM3Db9qhmFo/PjxWrlypTZs2KDmzZubXZLbKy4uVkFBgdlluIXbbrtNu3fvVkpKimPr0qWLRowYoZSUFNntdrNLdCt5eXk6fPiwAgMDzS7FLfTq1avEkorfffedQkJCTKroEqYTlMOUKVM0atQodenSRV27dtW8efN07tw5xcTEmF3ar15eXp7Tb95paWlKSUlR/fr11axZMxMr+/WLjY3Vu+++q08++US+vr6OOeB+fn6qVauWydX9+k2fPl2DBg1Ss2bNlJubq3fffVeJiYlas2aN2aW5BV9f3xLzv2vXrq0GDRowL7waTJ06VdHR0QoJCdGJEyc0Y8YM2e12DR8+3OzS3MIjjzyinj176plnntHQoUO1bds2LV68WIsXLza3MAPl8sorrxjNmjUzPD09ja5duxpbt241uyS3kJCQYEgqsY0aNcrs0n71SrvukowlS5aYXZpbGDNmjBESEmJ4enoajRo1Mm677Tbjiy++MLsstxYZGWlMmjTJ7DLcwrBhw4zAwEDD09PTaNKkiTFs2DDj0KFDZpflVj777DOjffv2hpeXlxEREWEsXrzY7JIM1okFAACA5TAnFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgDcwJEjR2Sz2ZSSkmJ2KQBQKQixAFAOo0eP1l133eV4HxUVpcmTJ5tWT1pamv70pz8pKChI3t7eatq0qQYPHqwDBw5IkoKDg5Wenq727dubViMAVCYPswsAAFTMxYsX1b9/f7Vu3VorVqxQYGCgjh8/rlWrVuns2bOSJLvdroCAAHMLBYBKxEgsAFTQ6NGjlZSUpJdeekk2m002m01HjhyRJO3Zs0eDBg2Sj4+PGjdurJEjR+r06dOOc6OiojRhwgRNnjxZ9erVU+PGjfX666/r3LlziomJka+vr8LCwrRq1aqrfv7evXt1+PBhLVy4UN27d1dISIh69eql2bNnq3v37pJKTicYPXq0o9Yrt8TERElSQUGBpk6dqiZNmqh27drq1q2bow0AfgkIsQBQQS+99JJ69OihsWPHKj09Xenp6QoODtbZs2fVt29fde7cWcnJyVq9erVOnjypoUOHOp3/1ltvqWHDhtq2bZsmTJigcePGaciQIerZs6d27typ22+/XSNHjtR///vfUj+/UaNGqlGjhj788EMVFRW5XPPlWtPT0zVp0iT5+/srIiJCkjR+/Hht2bJFy5cv17fffqshQ4Zo4MCBOnjwYMUuFgBUEpthGIbZRQCA1YwePVpnz57Vxx9/LOnSiGqnTp00b948xzGzZ8/Wpk2btGbNGse+48ePKzg4WKmpqQoPD1dUVJSKioq0adMmSVJRUZH8/Px099136+2335YkZWRkKDAwUFu2bHGMrP7cggUL9Nhjj8lut6tLly7q06ePRowYoRYtWki6NBLbvHlz7dq1S506dXI6d8WKFRoxYoTWrVunXr166ejRo2rRooWOHj2qoKAgx3H9+vVT165d9cwzz1T08gFAhTESCwBV5JtvvlFCQoJ8fHwc2+WRzsOHDzuO69Chg+O13W5XgwYNdMMNNzj2NW7cWJKUmZl51c+KjY1VRkaGli5dqh49euiDDz5Qu3bttHbt2mvWuGvXLo0cOVLz589Xr169JEm7d+9WUVGRwsPDnWpPSkpyqhsAzMSNXQBQRfLy8hQdHa1nn322RFtgYKDjdc2aNZ3abDab0z6bzSZJKi4uvubn+fr6Kjo6WtHR0Zo9e7YGDBig2bNnq3///qUen5GRoTvvvFMPPPCA7r//fqe67Xa7duzYIbvd7nSOj4/PNWsAgOpCiAWASuDp6VliPuqNN96ojz76SKGhofLwqN5vtzabTREREfrqq69Kbc/Pz9fgwYMVERGhF1980amtc+fOKioqUmZmpm699dbqKBcAyozpBABQCUJDQ/X111/ryJEjOn36tIqLixUbG6szZ85o+PDh2r59uw4fPqw1a9YoJibG5RuwXJGSkqLBgwfrww8/1L59+3To0CHFx8frjTfe0ODBg0s956GHHtKxY8f08ssv69SpU8rIyFBGRoYuXLig8PBwjRgxQvfdd59WrFihtLQ0bdu2TXFxcfr3v/9daXUDQEUwEgsAlWDq1KkaNWqU2rZtq/PnzystLU2hoaHavHmzHn/8cd1+++0qKChQSEiIBg4cqBo1Km8MoWnTpgoNDdWsWbMcS2ldfv/II4+Uek5SUpLS09PVtm1bp/0JCQmKiorSkiVLNHv2bP3lL3/Rjz/+qIYNG6p79+763e9+V2l1A0BFsDoBAAAALIfpBAAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAyyHEAgAAwHIIsQAAALAcQiwAAAAshxALAAAAy/EwuwAAAAD8+sXExLh03JIlS1w6zmYYhlGRggAAAIDrsdvtGjhwoLy8vCRJ586d04YNGxQdHS1JKigo0KpVq1RcXOxSf4RYAAAAVDm73a4TJ06ocePGkqS0tDR16NBBubm5kqRTp06pcePGLodY5sQCAACg2v18HLWs46qEWAAAAFQ5X19fZWVlOd5nZWXp3LlzysvLkyRlZGSofv36LvdHiAUAAECVi4iI0CuvvKLi4mIVFxdr4cKFCgoK0tSpU7V582Y9+eSTuvnmm13ujzmxAAAAqHIff/yx/vCHP6h27doqLi5W7dq1tXr1av3xj3/UwYMHFRwcrM8++0w33HCDS/0RYgEAAFAtNm7cqM8++0y1atXS2LFjFRwcLEn66aef1KBBgzL1RYgFAACA5TAnFgAAAJbDE7sAAABQ5ex2u0vLaLm6TiwhFgAAAFVu5cqVldofc2IBAABgOYzEAgAAoNocO3ZMH374oQ4ePChJatWqlf7whz84VipwFSOxAAAAqBbz58/XX/7yFxUWFsrPz0+GYSgnJ0ceHh6aO3eu/vznP7vcF6sTAAAAoMpt2LBBkydP1vjx45Wenq4zZ84oKytL6enpmjhxoiZMmKCEhASX+2MkFgAAAFXud7/7nRo2bKg333yz1PYxY8bo1KlT+uyzz1zqj5FYAAAAVLmvv/5ao0ePvmr7fffdp6+//trl/gixAAAAqHI5OTlq3ry54/1///tfrVixwvG+ZcuWys3Ndbk/QiwAAACqXIMGDXTmzBnH+4yMDI0aNcrxPjs7W02bNnW5P5bYAgAAQJXr0aOH3nnnHdWrV082m03Hjx93at+wYYO6dOnicn/c2AUAAIAqt27dOg0YMMDx6Fmbzabf/OY3jikEN998s1588UXdeuutLvVHiAUAAEC12Lt3r4qKihzv7Xa72rVrV66+CLEAAACwHG7sAgAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOILSfDMJSTkyPuiwMAAKh+POygnHJzc+Xn56fs7GzVqVPH7HIAAAB+0Vq0aHHdwT/DMHTkyBGX+iPEAoCFGIahgoICs8twS1de+zp16qhGDf6YCZTF5MmTr9p28uRJLV26VEePHnW5P0IsAFhIQUGBhgwZYnYZbu+dd95R3bp1zS4DsJSJEyc6vS8qKtK///1vLVmyRGvWrFFkZKT+8Y9/uNwfIRYAAADV5sCBA3rjjTf0r3/9S7Vr11ZMTIzmz5+vJk2alKkfQiwAWNQzg/bL015sdhluI6/ArplrI8wuA7C0rKwstWvXTr1799by5cvVu3fvcvdFiAUAC7nypoiaNYrl5cEKKdWloJBfGMx05ZxkLy8v2Ww2kytCefzmN7/RH//4R3388ceaMWOGYmJiNGTIENWqVavMfTErHQAs5Mqbui4U8UMc7uPyfPAhQ4Zwc6OFeXl5aenSpUpPT9fQoUP18ssvKzAwUA899JC+/vrrMvVVrhB77NgxjRkzRkFBQfL09FRISIgmTZqkn376yXFMVFSUbDabbDabvL29FR4erri4uFKXVtiyZYvsdrvuuOOOEm1HjhyRzWaTv7+/cnNzndo6deqkmTNnOu07dOiQxowZo2bNmsnLy0tNmjTRbbfdpqVLl6qwsNBx3OXafr4tX768TNdi7ty5ZToelePZZ59VdHS0nn32WbNLAQAAZVSnTh2NGzdOycnJ2rhxo2rVqqU77rhD7dq1c7mPMofY77//Xl26dNHBgwe1bNkyHTp0SIsWLdL69evVo0cPnTlzxnHs2LFjlZ6ertTUVE2fPl1/+9vftGjRohJ9xsfHa8KECdq4caNOnDhR6ufm5ubq+eefv2Zt27Zt04033qj9+/drwYIF2rNnjxITE/XAAw/o1Vdf1d69e52OX7JkidLT0522u+66q0zXY8uWLcrMzCzTOaiYzMxMffnll5KkL7/8kusPAIAF1K9fX/Xq1SuxRUZG6u2339aFCxe0f/9+l/sr85zY2NhYeXp66osvvnDMX2jWrJk6d+6sli1b6sknn9Srr74q6dK8h4CAAEly3Hm2du1ajRs3ztFfXl6e3nvvPSUnJysjI0NvvvmmnnjiiRKfO2HCBL344ouKjY2Vv79/iXbDMDR69GiFh4dr8+bNTuv3tWrVSsOHDy8xCly3bl1HfRXx+OOPa8mSJRXuB655/PHHS7zn+gMA8Mv20ksvVeqTTssUYs+cOaM1a9bo6aefLjEBNyAgQCNGjNB7772nhQsXOrUZhqEvv/xSBw4cUKtWrZza3n//fUVERKh169a69957NXnyZE2fPr3EhO3hw4dr7dq1euqppzR//vwStaWkpGj//v1atmzZVRegrqpJ4KdPn9b69et12223VUn/+J/169fr9OnTTvu4/gCqxRU/e/Pz85Wfn29eLW7oyuvNI9+t6Y9//KNq1qxZaf2VKcQePHhQhmGoTZs2pba3adNGWVlZOnXqlCRp4cKF+uc//6kLFy7o4sWL8vb2LrHQbXx8vO69915J0sCBA5Wdna2kpCRFRUU5HWez2TRnzhxFR0frkUceUcuWLZ3av/vuO0lS69atHfsyMzPVokULx/t//OMf+vOf/+x4P3z4cNntdqd+9u3bp2bNmpX42goKCpwmkufk5Di1v/LKK4qKiirRHypPUVGRXnnllVLbuP4AqtqVN9KNHTvWxEpQUFBQrrvZYa4mTZroT3/6k+6//37dcMMNFe6vXDd2ufob0IgRI5SSkqLNmzdr0KBBevLJJ9WzZ09He2pqqrZt26bhw4dLkjw8PDRs2DDFx8eX2t+AAQN0yy236K9//atLn9+gQQOlpKQoJSVFdevW1YULF5za586d62i/vAUFBZXaV1xcnPz8/BxbcHCwU3tRUZFWr17tUl0on9WrV6uoqKjUNq4/AAC/bNOmTdO2bdvUuXNndevWTa+99lqJm/bLokwjsWFhYbLZbNq/f79+//vfl2jfv3+/6tWrp0aNGkmS/Pz8FBYWJunStIGwsDB1795d/fr1k3RpFLawsNApOBqGIS8vL82fP19+fn4lPmPOnDnq0aOHHn30Uaf9l6cppKamqnPnzpIku93u+HwPj5JfakBAgKP9eqZPn64pU6Y43ufk5DgFWbvdroEDB7rUF8pn4MCBev3110sNslx/AFXN0/6/AZzXX3+dx85Ws/z8fI0cOVLSpWWaYD1TpkzRlClTtHnzZkVGRio/P19TpkzRH/7wB40ZM0aRkZFl6q9MIbZBgwbq37+/Fi5cqEceecRpKD8jI0NLly7VfffdV+rcUx8fH02aNElTp07Vrl27VFRUpLffflsvvPCCbr/9dqdj77rrLi1btkwPP/xwiX66du2qu+++W9OmTXPa37lzZ0VEROj555/X0KFDrzovtry8vLyu+R/NxIkT+VN2FbPb7ZowYYLmzZtXoo3rD6DKXfGjzdvbW97e3ubV4uZ40IG11atXTzabTd9884327Nmjd955RyNHjpS3t7diYmI0ffp0l/opc9KbP3++CgoKNGDAAG3cuFHHjh3T6tWr1b9/fzVp0kRPP/30Vc996KGH9N133+mjjz7S559/rqysLN1///1q376903bPPfdcdUqBJD399NPasGGDUlNTHftsNpuWLFmi1NRU9erVS59++qkOHjyoffv2adGiRTp16lSJkHP27FllZGQ4befOnSvrJVHDhg3Vt2/fMp+HsrvtttvUsGFDp31cfwAArKl9+/Z69tlnlZaWpiFDhrg8ZVQqR4ht1aqVkpOT1aJFCw0dOlQtW7bUgw8+qD59+mjLli2qX7/+Vc+tX7++7rvvPs2cOVPx8fHq169fqVMG7rnnHiUnJ+vbb78ttZ/w8HCNGTOmxJ2h3bt3144dO9S6dWvFxsaqbdu26tmzp5YtW6a5c+c6Le0lXVr2KzAw0Gm72o1D18KC+9Xr59eb6w8AgDV98803evTRRxUSEqKPP/64TD/Ty7xOrCSFhITozTffvOYxiYmJpe4v7WEHP9e1a1enm8dKu5Hstdde02uvvVZif3h4+HVru1qf5dGjR49S161F1fH399ctt9yiL7/8UrfccgvXH27lymlNV87RBH7tvLy89MEHHzhew7p++uknGYahG264QUePHtWQIUP04Ycfqnv37mXqp1whFv/zyCOPmF2CW3r88cdLPPQAcAdXzgVkWiDcyeXH2MO6nnvuOX300UdKTk5Wjx49NGbMGA0bNky/+c1vytUfIRYALOpCUQ1JxWaX4TYuFlXuDcOAu3nxxRc1cuRIvf322woPD69wf4RYALCoJ1aV/uAZAPglOn78uOMm+6ysLB08eFA2m01hYWGqV69emfvj10oAAABUObvdrrS0NP32t79Vw4YN1b17d3Xr1k0NGzbUb3/7W/3www9l6s9m8ADicsnJyZGfn5+ys7NVp04ds8sB4CYMw3B6BDaqz5XXvk6dOpW+Hjnwa3fy5EndeOONstvtio2NVUREhKRLD6qaP3++ioqKtHPnTjVu3Nil/gix5USIBWAGQizcFb9EWN/EiROVkJCgbdu2OT0wS7r0RLabb75ZUVFRLi93SogtJ0IsADPk5+dryJAhZpcBmOqdd97hsb8W1KJFCz3//PO6++67S23/+OOP9Ze//EWHDx92qT9+jQEAAECVS09PV4cOHa7a3r59e/34448u98fqBABgUa/8fay8PGuaXQZQLXLy/qupf3/T7DJQAY0aNVJhYeFV2y9evOjyfFiJEAsLunJelJeXl9Pi74A78fKsKS8vQizcg2fB/yILMyGt6aabbtIXX3zhuKHr51avXq2OHTu63B/TCWA5BQUFGjJkiIYMGcINLgDgJi5c/N8IHt/7remRRx7Ra6+9puzs7BJtOTk5ev311zV58mSX+2MkFgAAAFWud+/e2rt3b6ltderU0b59+8rUX5WPxC5atEi+vr5OcyDy8vJUs2ZNRUVFOR2bmJgom82mw4cPKzQ0VPPmzSvR38yZM9WpU6dS34eGhspms111Gz16tCRdtX358uWV/NUDAACgKlT5SGyfPn2Ul5en5ORkde/eXZK0adMmBQQE6Ouvv1Z+fr68vb0lSQkJCWrWrJlatmxZrs/avn27ioqKJElfffWV7rnnHqWmpjqWwLpyTbIlS5Zo4MCBTuezXAcAAEDVaNGihUvzmdPS0lzqr8pDbOvWrRUYGKjExERHiE1MTNTgwYO1YcMGbd261TEim5iYqD59+pT7sxo1auR4Xb9+fUmSv79/qeG0bt26CggIKPdnwTxX/geQn59vYiVA9bvy3zw3twCwkp/Pd12/fr3WrFmjv/71r/L19S1zf9UyJ7ZPnz5KSEjQtGnTJF0acX3sscdUVFSkhIQERUVF6fz58/r66681ZsyY6iipzAoKCpwmkufk5JhYjXu78v+HkSNHmlgJYK4LFwvl7e1pdhkA4JKJEyc6Xq9bt07Tp09XnTp1lJiYqH//+9/y9Czb97NqWZ2gT58+2rx5swoLC5Wbm6tdu3YpMjJSvXv3VmJioiRpy5YtKigocBqJffzxx+Xj4+O0PfPMM5VS0/Dhw0v0ffTo0aseHxcXJz8/P8cWHBxcKXUAAAC4k/Xr1+vOO+/UQw89pP379+vHH3/UH//4RxUXF5epn2oZiY2KitK5c+e0fft2ZWVlKTw8XI0aNVJkZKRiYmKUn5+vxMREtWjRQs2aNXOc9+ijjzpuxrrs5Zdf1saNGytc09y5c9WvXz+nfUFBQVc9fvr06ZoyZYrjfU5ODkHWJF5eXo7X77zzjmNONeAO8vPzHX+B8KzJAjMArCUhIUF33nmnHnzwQb344ouSLo3K9uzZUw888IDeeOMNl/uqlu+AYWFhatq0qRISEpSVlaXIyEhJl0JjcHCwvvrqKyUkJKhv375O5zVs2FBhYWFO+y7Pda2ogICAEn1fi5eXl1N4gnmufLiBt7c3IRZuiwd9ALCSpKQkRUdH64EHHnBagSooKEjr1q3TLbfcoqlTp+r55593qb9qe9hBnz59lJiYqMTERKeltXr37q1Vq1Zp27ZtFbqpCwAAAL9c0dHRiomJ0UsvvVSiLSwsTKtXr9Y///lPl/urtr9F9enTR7Gxsbp48aJjJFaSIiMjNX78eF24cKFaQ+zZs2eVkZHhtM/X11e1a9euthoAAADcxahRo/TKK69ctb1Tp0769NNPXe6vWkPs+fPnFRERocaNGzv2R0ZGKjc317EUV3WJiYkpsS8uLs6xggJ+uby8vPTBBx84XgMAfv2unAPO935rulaAvax3794u92czWGiwXHJycuTn56fs7GzHwxQAoKrl5+dryJAhkqTFz/5ZXl41Ta4IqB45uf/VhL++LunSTb08oAjc2goAFlVw4aLZJQDVhn/v+DlCLABY1OVRKQBwR9W2OgEAAABwNT/99JOaN2/u8vHMiS2n7Oxs1a1bV8eOHWNOLIBqYxiG06OXAXdx5b/9OnXqqEYNxuHM4uvrWyXrVGdmZiogIMDlJ3cxnaCcfvrpJ0niqV0AAMCtZGZmqlGjRmaXQYgtr8tPDjt69Kj8/PxMrsb9XH7sLyPh5uD6m4vrbx6uvbm4/ua6fP09PT3LdX5SUtI128+cOVOm/gix5XT5zxh+fn78h2SiOnXqcP1NxPU3F9ffPFx7c3H9zVXeqQR9+/aVYRiVNhWBEAsAAIAql5WVdc32U6dOqVWrVi73R4gFAABAlbve6Hl+fn6Z+uPWvnLy8vLSjBkzePSdSbj+5uL6m4vrbx6uvbm4/uaqjutflqkGLLEFAAAA0+Xm5urhhx/W0qVLXTqekVgAAACY7q233tLHH3/s8vHMiQUAAIBpjh49qjFjxiglJUXx8fEun8dILAAAAEzx1ltvqWPHjqpVq5b27t2rP/7xjy6fy0gsAAAAqlyLFi105a1YhYWFSk9P16JFi/TAAw+UuT9GYstpwYIFCg0Nlbe3t7p166Zt27aZXZJb2Lhxo6KjoxUUFCSbzVamuTOomLi4ON18883y9fWVv7+/7rrrLqWmpppdltt49dVX1aFDB8ci7z169NCqVavMLsttzZkzRzabTZMnTza7FLcwc+ZM2Ww2py0iIsLsstzKjz/+qHvvvVcNGjRQrVq1dMMNNyg5OblMfUyePFmPPPKI0xYcHKz4+HgdOHCgzDUxElsO7733nqZMmaJFixapW7dumjdvngYMGKDU1FT5+/ubXd6v2rlz59SxY0eNGTNGd999t9nluJWkpCTFxsbq5ptvVmFhoZ544gndfvvt2rdvn2rXrm12eb96TZs21Zw5c9SqVSsZhqG33npLgwcP1q5du9SuXTuzy3Mr27dv12uvvaYOHTqYXYpbadeundatW+d47+FBhKkuWVlZ6tWrl/r06aNVq1apUaNGOnjwoOrVq1emfiZOnFhi34MPPqjJkyfrxhtv1KxZszR16lSXl9liia1y6Natm26++WbNnz9fklRcXKzg4GBNmDBB06ZNM7k692Gz2bRy5UrdddddZpfilk6dOiV/f38lJSWpd+/eZpfjlurXr6/nnntO999/v9mluI28vDzdeOONWrhwoWbPnq1OnTpp3rx5Zpf1qzdz5kx9/PHHSklJMbsUtzRt2jRt3rxZmzZtqrLP+Pe//62xY8cqNDRUX331lUvnMJ2gjC5cuKAdO3aoX79+jn01atRQv379tGXLFhMrA6pXdna2pEtBCtWrqKhIy5cv17lz59SjRw+zy3ErsbGxuuOOO5x+BqB6HDx4UEFBQWrRooVGjBiho0ePml2S2/j000/VpUsXDRkyRP7+/urcubNef/31Sv2MO+64Q3v37lXTpk1dPocQW0anT59WUVGRGjdu7LS/cePGysjIMKkqoHoVFxdr8uTJ6tWrl9q3b292OW5j9+7d8vHxkZeXlx5++GGtXLlSbdu2Nbsst7F8+XLt3LlTcXFxZpfidrp166Y333xTq1ev1quvvqq0tDTdeuutys3NNbs0t/D999/r1VdfVatWrbRmzRqNGzdOEydO1FtvvVWpn1OvXj29//77Lh/PhBIAZRYbG6s9e/boyy+/NLsUt9K6dWulpKQoOztbH374oUaNGqWkpCSCbDU4duyYJk2apLVr18rb29vsctzOoEGDHK87dOigbt26KSQkRO+//z7TaapBcXGxunTpomeeeUaS1LlzZ+3Zs0eLFi3SqFGjXO4nJibmuscYhqE333zTpf4YiS2jhg0bym636+TJk077T548qYCAAJOqAqrP+PHj9fnnnyshIaFMf/ZBxXl6eiosLEw33XST4uLi1LFjR7300ktml+UWduzYoczMTN14443y8PCQh4eHkpKS9PLLL8vDw0NFRUVml+hW6tatq/DwcB06dMjsUtxCYGBgiV+W27RpU+YpHdnZ2Y7txIkT+te//uW0LzMzU2+//bbL/TESW0aenp666aabtH79escNRcXFxVq/fr3Gjx9vbnFAFTIMQxMmTNDKlSuVmJio5s2bm12S2ysuLlZBQYHZZbiF2267Tbt373baFxMTo4iICD3++OOy2+0mVeae8vLydPjwYY0cOdLsUtxCr169Siyp+N133ykkJKRM/axYscLxOi0tTR06dHDad+rUqTINCBJiy2HKlCkaNWqUunTpoq5du2revHk6d+6cS8PkqJi8vDyn37zT0tKUkpKi+vXrq1mzZiZW9usXGxurd999V5988ol8fX0dc8D9/PxUq1Ytk6v79Zs+fboGDRqkZs2aKTc3V++++64SExO1Zs0as0tzC76+viXmf9euXVsNGjRgXng1mDp1qqKjoxUSEqITJ05oxowZstvtGj58uNmluYVHHnlEPXv21DPPPKOhQ4dq27ZtWrx4sRYvXlzuPmvWrKmLFy867cvPzy/b0mkGyuWVV14xmjVrZnh6ehpdu3Y1tm7danZJbiEhIcGQVGIbNWqU2aX96pV23SUZS5YsMbs0tzBmzBgjJCTE8PT0NBo1amTcdtttxhdffGF2WW4tMjLSmDRpktlluIVhw4YZgYGBhqenp9GkSRNj2LBhxqFDh8wuy6189tlnRvv27Q0vLy8jIiLCWLx4cYX6KyoqMjw9PY21a9c69i1atMgICwtzuQ/WiQUAAEC1GzNmjJYtW6YBAwbo/PnzWrdunWbOnKm//vWvLp1PiAUAAEC1O3/+vGbNmqX169fL09NTd955px599FHVqOHaugOEWAAAAFgON3YBAACgyiUlJbl0XGRkpEvHMRILAACAKme322UYhmw2m2Nfae+Li4td6o+HHQAAAKBapKamKisrS1lZWdq1a5d8fHx05swZZWVl6bvvvnMKtNfDdAIAAABUizp16qhOnTqSJB8fHxmGIT8/P0mX1oktywQBRmIBAABgOYRYAAAAVLnKvg2LEAsAbuDIkSOy2WxKSUkxuxQAburn811r1qyp0NDQax5zLYRYACiH0aNH66677nK8j4qK0uTJk02rJy0tTX/6058UFBQkb29vNW3aVIMHD9aBAwckScHBwUpPT1f79u1NqxGAe9uyZYsaNGjgeB8cHKzdu3c73vv7+ys9Pd3l/rixCwAs7uLFi+rfv79at26tFStWKDAwUMePH9eqVat09uxZSZeWtgkICDC3UABurWvXrtc9xt/f3+X+GIkFgAoaPXq0kpKS9NJLL8lms8lms+nIkSOSpD179mjQoEHy8fFR48aNNXLkSJ0+fdpxblRUlCZMmKDJkyerXr16aty4sV5//XWdO3dOMTEx8vX1VVhYmFatWnXVz9+7d68OHz6shQsXqnv37goJCVGvXr00e/Zsde/eXVLJ6QSjR4921HrllpiYKEkqKCjQ1KlT1aRJE9WuXVvdunVztAHALwEhFgAq6KWXXlKPHj00duxYpaenKz09XcHBwTp79qz69u2rzp07Kzk5WatXr9bJkyc1dOhQp/PfeustNWzYUNu2bdOECRM0btw4DRkyRD179tTOnTt1++23a+TIkfrvf/9b6uc3atRINWrU0IcffqiioiKXa75ca3p6uiZNmiR/f39FRERIksaPH68tW7Zo+fLl+vbbbzVkyBANHDhQBw8erNjFAoBKwhO7AKAcRo8erbNnz+rjjz+WdGlEtVOnTpo3b57jmNmzZ2vTpk1as2aNY9/x48cVHBys1NRUhYeHKyoqSkVFRdq0aZMkqaioSH5+frr77rv19ttvS5IyMjIUGBioLVu2OEZWf27BggV67LHHZLfb1aVLF/Xp00cjRoxQixYtJF0aiW3evLl27dqlTp06OZ27YsUKjRgxQuvWrVOvXr109OhRtWjRQkePHlVQUJDjuH79+qlr16565plnKnr5AKDCGIkFgCryzTffKCEhQT4+Po7t8kjn4cOHHcd16NDB8dput6tBgwa64YYbHPsaN24sScrMzLzqZ8XGxiojI0NLly5Vjx499MEHH6hdu3Zau3btNWvctWuXRo4cqfnz56tXr16SpN27d6uoqEjh4eFOtSclJTnVDQBm4sYuAKgieXl5io6O1rPPPluiLTAw0PG6Zs2aTm02m81p3+UlZ673PHFfX19FR0crOjpas2fP1oABAzR79mz179+/1OMzMjJ055136oEHHtD999/vVLfdbteOHTtkt9udzvHx8blmDQBQXQixAFAJPD09S8xHvfHGG/XRRx8pNDRUHh7V++3WZrMpIiJCX331Vant+fn5Gjx4sCIiIvTiiy86tXXu3FlFRUXKzMzUrbfeWh3lAkCZMZ0AACpBaGiovv76ax05ckSnT59WcXGxYmNjdebMGQ0fPlzbt2/X4cOHtWbNGsXExLh8A5YrUlJSNHjwYH344Yfat2+fDh06pPj4eL3xxhsaPHhwqec89NBDOnbsmF5++WWdOnVKGRkZysjI0IULFxQeHq4RI0bovvvu04oVK5SWlqZt27YpLi5O//73vyutbgCoCEZiAaASTJ06VaNGjVLbtm11/vx5paWlKTQ0VJs3b9bjjz+u22+/XQUFBQoJCdHAgQNVo0bljSE0bdpUoaGhmjVrlmMprcvvH3nkkVLPSUpKUnp6utq2beu0PyEhQVFRUVqyZIlmz56tv/zlL/rxxx/VsGFDde/eXb/73e8qrW4AqAhWJwAAAIDlMJ0AAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOUQYgEAAGA5hFgAAABYDiEWAAAAlkOIBQAAgOV4mF0AAAAA3EdMTMx1jzEMQ2+++eY1j7EZhmFUUk0AAADANd19991XbTMMQ7t27dLRo0dVXFx8zX4YiQUAAEC1WbFiRYl9p0+f1tKlS/XGG28oJydHDz/88HX7YSQWAAAA1a64uFirV6/WkiVL9Pnnn+uWW27RmDFjdPfdd8vLy+u65xNiAQAAUK3y8vIUEREhT09PjR49WjExMQoODi5TH6xOAAAAgGpnt9tls9lkGMZ157+WhhALAACAauXj46MjR45o4cKF2rt3r9q2bav+/ftr2bJlKigocKkPphMAAADAVGfOnNG//vUvLVmyRD/88IOGDx+uBQsWXPMcQiwAAACqze9///urttlsNu3atUs//PADS2wBAADgl6N+/fq61hhqnz59XOqHkVgAAABYDiOxAAAAqDauPHZWkpYsWXLNdkIsAAAAqk12dnal9MN0gnIyDEO5ubny9fWVzWYzuxwAAAC3wjqx5ZSbmys/Pz/l5OSYXQoAAIDbYTpBBbm6IC8AAABcmxNrGIbefPPNax5DiK2g/Px8s0sAAACwjGvNiS0qKtK6det0/vx5QiwAAAB+OVasWFHq/k8++URPPPGEvL29NWPGjOv2w5xYAAAAmGbTpk3q2bOnhg8frt/97nf6/vvv9dhjj133PEJsBV3vkWgAAAAoac+ePYqOjtZtt92mdu3a6dChQ3r22Wfl5+fn0vmE2ArKy8szuwQAbmbcuHGKjo7WuHHjzC4FAMrshx9+0KhRo9SpUyd5eHho9+7dev311xUUFFSmfsoVYo8dO6YxY8YoKChInp6eCgkJ0aRJk/TTTz85jomKipLNZpPNZpO3t7fCw8MVFxdX6rNyt2zZIrvdrjvuuKNE25EjR2Sz2eTv76/c3Fyntk6dOmnmzJlO+w4dOqQxY8aoWbNm8vLyUpMmTXTbbbdp6dKlKiwsdBx3ubafb8uXLy/PJQGAanH48GEdP35cknT8+HEdPnzY5IoAoGxat26tDz74QFOnTtXo0aN14MABffLJJyW26ynzjV3ff/+9evToofDwcC1btkzNmzfX3r179eijj2rVqlXaunWr6tevL0kaO3asnnrqKRUUFGjDhg168MEHVbdu3RKjB/Hx8ZowYYLi4+N14sSJUpN4bm6unn/+ec2aNeuqtW3btk39+vVTu3bttGDBAkVEREiSkpOTtWDBArVv314dO3Z0HL9kyRINHDjQqY+6deuW9ZIAQLWZOnVqifcrV640qRoAKLvCwkIZhqHnnnvuqscYhnHdKZtlHomNjY2Vp6envvjiC0VGRqpZs2YaNGiQ1q1bpx9//FFPPvmk49jf/OY3CggIUEhIiGJiYtShQwetXbvWqb+8vDy99957GjdunO64446rLqcwYcIEvfjii8rMzCy13TAMjR49WuHh4dq8ebOio6PVqlUrtWrVSsOHD9eXX36pDh06OJ1Tt25dBQQEOG3e3t5lvSQAUC2WLFni9Bcl6dIPg+s9XxwAfkkKCwtVVFR0zc2Ve47KFGLPnDmjNWvW6M9//rNq1arl1BYQEKARI0bovffeKzFlwDAMbdq0SQcOHJCnp6dT2/vvv6+IiAi1bt1a9957r954441SpxwMHz5cYWFheuqpp0qtLSUlRfv379fUqVNVo0bpX1ZFHg9bUFCgnJwcpw0AqsvFixevuizNihUrdPHixWquCADMVabpBAcPHpRhGGrTpk2p7W3atFFWVpZOnTolSVq4cKH++c9/6sKFC7p48aK8vb01ceJEp3Pi4+N17733SpIGDhyo7OxsJSUlKSoqyuk4m82mOXPmKDo6Wo888ohatmzp1P7dd99JujTP4rLMzEy1aNHC8f4f//iH/vznPzveDx8+XHa73amfffv2qVmzZiW+tri4uGtOZQCAqvT6669ft/3K728A8EuVlJTk0nGRkZHXbC/Xww5KGyktzYgRI/Tkk08qKytLM2bMUM+ePdWzZ09He2pqqrZt2+aYz+Xh4aFhw4YpPj6+RIiVpAEDBuiWW27RX//6V7377rvX/fwGDRooJSVF0qUbzS5cuODUPnfuXPXr189p39XujJs+fbqmTJnieJ+Tk6Pg4ODr1gAAlWHs2LFatWrVNdsBwAr69u0rwzCu+RdyV+bElinEhoWFyWazaf/+/fr9739fon3//v2qV6+eGjVqJEny8/NTWFiYpEvTBsLCwtS9e3dHcIyPj1dhYaFTcDQMQ15eXpo/f36p64TNmTNHPXr00KOPPuq0v1WrVpIuBePOnTtLkux2u+PzPTxKfqkBAQGO9uvx8vKSl5eXS8cCQGWrWbOm7r777lKnFNxzzz2qWbOmCVUBQNllZWVVSj9lmhPboEED9e/fXwsXLtT58+ed2jIyMrR06VINGzas1GTt4+OjSZMmaerUqTIMQ4WFhXr77bf1wgsvKCUlxbF98803CgoK0rJly0qtoWvXrrr77rs1bdo0p/2dO3dWRESEnn/+eR5AAOBXKSYmpsQv5B4eHho9erQ5BQFAOaxbt061atVSnTp1rrldT5lXJ5g/f74KCgo0YMAAbdy4UceOHdPq1avVv39/NWnSRE8//fRVz33ooYf03Xff6aOPPtLnn3+urKws3X///Wrfvr3Tds899yg+Pv6q/Tz99NPasGGDUlNTHftsNpuWLFmi1NRU9erVS59++qkOHjyoffv2adGiRTp16lSJ+a9nz55VRkaG03bu3LmyXhIAqDbPP//8Nd8DwC/dsGHD1LRpU02dOlX79+8vdz9lDrGtWrVScnKyWrRooaFDh6ply5Z68MEH1adPH23ZssWxRmxp6tevr/vuu08zZ85UfHy8+vXrV+qUgXvuuUfJycn69ttvS+0nPDxcY8aMUX5+vtP+7t27a8eOHWrdurViY2PVtm1b9ezZU8uWLdPcuXNLrE8bExOjwMBAp+2VV14p6yUBgGrTsmVLNW3aVJLUtGnTEje5AsAv3YkTJzR79mzt2LFD7dq1U8+ePfX666+X+SmoNsPVu7TgJCcnR35+fkpLS1NoaKjZ5QAAAFjK/v371aFDB/3jH//Qv/71L3333XcaMmSI7r//fvXq1eu655frsbP4n6utSQsAAICruzyO+sgjj2jHjh3avn27AgMDde+99zqeunotJDAAAACYLjw8XL1799Ytt9yiH3744brHE2IriMfUAgAAlF9ycrKmTJmiJk2aaNKkSbrhhhtcCrHletgB/oe1YwEAAMrmwIEDeuONN1RcXKyoqCgNHTpUH374oUtzYS8jxFbQtZ42AQAAAGft27fXvn371L17dy1evFh//OMfVbt2bUd7YWGhNm/eXDWPncX/sLgDAACA6wYOHKgPPvhAbdq0KbX9zJkz6tOnT+U+dhYlFRQUmF0CAACAZbjykBZX/tLNjV0V9PMHLgAAAKBiXPlLNyOxAAAAqDYxMTHXbD9//rxL/RBiK+h68zUAAADwP9nZ2ddsd3WqJiG2gsr6nF8AAAB3tmLFimu2nzp1So0bN75uP1U+J3bRokXy9fVVYWGhY19eXp5q1qypqKgop2MTExNls9l0+PBhhYaGat68eSX6mzlzpjp16lTq+9DQUNlstqtuo0ePlqSrti9fvrySv3oAAACUhasrP1X5SGyfPn2Ul5en5ORkde/eXZK0adMmBQQE6Ouvv1Z+fr7jqVcJCQlq1qyZWrZsWa7P2r59u4qKiiRJX331le655x6lpqaqTp06kqRatWo5jl2yZIkGDhzodH7dunXL9bkAAACoPK6sTlDlIbZ169YKDAxUYmKiI8QmJiZq8ODB2rBhg7Zu3eoYkU1MTFSfPn3K/VmNGjVyvK5fv74kyd/fv9RwWrduXQUEBJT7swAAAFD56tevr4SEhOseVy1zYvv06aOEhARNmzZN0qUR18cee0xFRUVKSEhQVFSUzp8/r6+//lpjxoypjpIAAABgkqKiIq1Zs0apqanKyckp9ZjevXtfs49qC7GTJ09WYWGhzp8/r127dikyMlIXL17UokWLJElbtmxRQUGB00js448/rv/7v/9z6uvChQtq27ZthWsaPny47Ha70759+/apWbNmpR5fUFDgdLfc1S44AAAAri4jI0O33367UlNT1bRpU/n5+ZU4xjAMzZgx45r9VEuIjYqK0rlz57R9+3ZlZWUpPDxcjRo1UmRkpGJiYpSfn6/ExES1aNHCKUQ++uijjpuxLnv55Ze1cePGCtc0d+5c9evXz2lfUFDQVY+Pi4vTrFmzKvy5AAAA7uyJJ56Qv7+/1q9f7zQVtKyqJcSGhYWpadOmSkhIUFZWliIjIyVdCo3BwcH66quvlJCQoL59+zqd17BhQ4WFhTntuzzXtaICAgJK9H0t06dP15QpUxzvc3JyFBwcXCm1AAAAuIuEhAS9++67FQqwUjWuE9unTx8lJiYqKytLjz76qGN/7969tWrVKm3btk3jxo2rrnLKzMvLS15eXmaXAQAAYGmnTp2qlJvrqzXExsbG6uLFi46RWEmKjIzU+PHjdeHChQqtTFBWZ8+eVUZGhtM+X19f1a5du9pqAAAAcDfNmzfXjh071Lx58wr1U60h9vz584qIiHB6CkNkZKRyc3MdS3FVl9Ke2xsXF+dYQQEAAACVb9SoUZo0aZJycnJ00003XXWd/pCQkGv2YzNcfSwCnOTk5MjPz09paWkKDQ01uxwAAABLKCoq0t/+9jfNmzdP+fn5JZ7QZbPZZBiGiouLr9kPIbacLofYH3744arLcgEAAKB0hmHo6NGjys7OLrW9Q4cO1zy/2qYTAAAAAJfZbLbrThm4FkIsAAAAqk1SUpJLx125EEBpCLEV5O3tbXYJAAAAltG3b18ZhiGbzebYV9r7682JJcSW0+WpxAUFBTyCFgAAuA1fX1+nwFlWWVlZTu+PHDmiW265RceOHZPNZtOpU6cUHh5+3X4IseX0008/SRI3dQEAALeSmZlZoadt1alTx+l9rVq1ZBiG/Pz8JKnUFQtKQ4gtp8uPvz169KjjoqP6XH7s77Fjx0r8x4Cqx/U3F9ffPFx7c3H9zXX5+nt6elZqv1u3btW5c+eUnZ0tPz8/nThxQg0bNrzueYTYcqpRo4Ykyc/Pj/+QTFSnTh2uv4m4/ubi+puHa28urr+5KjKV4Ernz5/XggULFB8fL0n685//rOHDh2vevHm6+eabr3t+jUqpAgAAAHDB8ePHNW3aNDVt2lSfffaZEhMTddddd2n58uUaPHiwDh06pGefffa6/TASCwAAgGrTsmVLRURE6I033tDgwYMlSStWrNB3332nCxcuKCIiQh4e14+ohNhy8vLy0owZM+Tl5WV2KW6J628urr+5uP7m4dqbi+tvrsq6/kuXLtUf/vCHEvtdWZHgSjx2FgAAAJbDSCwAAACqTUxMjEvHLVmy5JrtjMQCAACg2tjtdg0cONAxLeHcuXPasGGDoqOjJV16kNSqVauu+8QuQiwAAACqjd1u14kTJ9S4cWNJUlpamjp06KDc3FxJ0qlTpxQQEKCioqJr9sMSWwAAADDNz8dTDcNw6YldhNhyWrBggUJDQ+Xt7a1u3bpp27ZtZpfkFjZu3Kjo6GgFBQXJZrPp448/NrsktxEXF6ebb75Zvr6+8vf311133aXU1FSzy3Ibr776qjp06OBY5L1Hjx5atWqV2WW5rTlz5shms2ny5Mlml+IWZs6cKZvN5rRFRESYXZZb+fHHH3XvvfeqQYMGqlWrlm644QYlJyebWhMhthzee+89TZkyRTNmzNDOnTvVsWNHDRgwQJmZmWaX9qt37tw5dezYUQsWLDC7FLeTlJSk2NhYbd26VWvXrtXFixd1++2369y5c2aX5haaNm2qOXPmaMeOHUpOTlbfvn01ePBg7d271+zS3M727dv12muvqUOHDmaX4lbatWun9PR0x/bll1+aXZLbyMrKUq9evVSzZk2tWrVK+/bt0wsvvKB69epV2mf8/ClgLj0VzECZde3a1YiNjXW8LyoqMoKCgoy4uDgTq3I/koyVK1eaXYbbyszMNCQZSUlJZpfiturVq2f885//NLsMt5Kbm2u0atXKWLt2rREZGWlMmjTJ7JLcwowZM4yOHTuaXYbbevzxx41bbrml0vpr06aNcfr0acf7rKwspwx19uxZY9CgQdfth5HYMrpw4YJ27Nihfv36OfbVqFFD/fr105YtW0ysDKhe2dnZkqT69eubXIn7KSoq0vLly3Xu3Dn16NHD7HLcSmxsrO644w6nnwGoHgcPHlRQUJBatGihESNG6OjRo2aX5DY+/fRTdenSRUOGDJG/v786d+6s119/vdz97du3Tw0aNHC8r1u3rqZNm+Z47+fnp//85z/X7YcQW0anT59WUVGR4466yxo3bqyMjAyTqgKqV3FxsSZPnqxevXqpffv2ZpfjNnbv3i0fHx95eXnp4Ycf1sqVK9W2bVuzy3Iby5cv186dOxUXF2d2KW6nW7duevPNN7V69Wq9+uqrSktL06233uq4mx1V6/vvv9err76qVq1aac2aNRo3bpwmTpyot956y9S6eNgBgDKLjY3Vnj17mJNWzVq3bq2UlBRlZ2frww8/1KhRo5SUlESQrQbHjh3TpEmTtHbtWnl7e5tdjtsZNGiQ43WHDh3UrVs3hYSE6P3339f9999vYmXuobi4WF26dNEzzzwjSercubP27NmjRYsWadSoUabVxUhsGTVs2FB2u10nT5502n/y5EkFBASYVBVQfcaPH6/PP/9cCQkJatq0qdnluBVPT0+FhYXppptuUlxcnDp27KiXXnrJ7LLcwo4dO5SZmakbb7xRHh4e8vDwUFJSkl5++WV5eHhcdz1LVK66desqPDxchw4dMrsUtxAYGFjil+U2bdqYPqWDEFtGnp6euummm7R+/XrHvuLiYq1fv565afhVMwxD48eP18qVK7VhwwY1b97c7JLcXnFxsQoKCswuwy3cdttt2r17t1JSUhxbly5dNGLECKWkpMhut5tdolvJy8vT4cOHFRgYaHYpbqFXr14lllT87rvvFBISYlJFlzCdoBymTJmiUaNGqUuXLuratavmzZunc+fOufwsYJRfXl6e02/eaWlpSklJUf369dWsWTMTK/v1i42N1bvvvqtPPvlEvr6+jjngfn5+qlWrlsnV/fpNnz5dgwYNUrNmzZSbm6t3331XiYmJWrNmjdmluQVfX98S879r166tBg0aMC+8GkydOlXR0dEKCQnRiRMnNGPGDNntdg0fPtzs0tzCI488op49e+qZZ57R0KFDtW3bNi1evFiLFy82t7BKWy/BzbzyyitGs2bNDE9PT6Nr167G1q1bzS7JLSQkJBiSSmyjRo0yu7RfvdKuuyRjyZIlZpfmFsaMGWOEhIQYnp6eRqNGjYzbbrvN+OKLL8wuy62xxFb1GTZsmBEYGGh4enoaTZo0MYYNG2YcOnTI7LLcymeffWa0b9/e8PLyMiIiIozFixebXZJhMwwXnusFAAAA/IIwJxYAAACWQ4gFAACA5RBiAQAAYDmEWAAAAFgOIRYAAACWQ4gFAACA5RBiAQAAYDmEWABwA0eOHJHNZlNKSorZpQBApSDEAkA5jB49WnfddZfjfVRUlCZPnmxaPWlpafrTn/6koKAgeXt7q2nTpho8eLAOHDggSQoODlZ6ejqPSAXwq+FhdgEAgIq5ePGi+vfvr9atW2vFihUKDAzU8ePHtWrVKp09e1aSZLfbFRAQYG6hAFCJGIkFgAoaPXq0kpKS9NJLL8lms8lms+nIkSOSpD179mjQoEHy8fFR48aNNXLkSJ0+fdpxblRUlCZMmKDJkyerXr16aty4sV5//XWdO3dOMTEx8vX1VVhYmFatWnXVz9+7d68OHz6shQsXqnv37goJCVGvXr00e/Zsde/eXVLJ6QSjR4921HrllpiYKEkqKCjQ1KlT1aRJE9WuXVvdunVztAHALwEhFgAq6KWXXlKPHj00duxYpaenKz09XcHBwTp79qz69u2rzp07Kzk5WatXr9bJkyc1dOhQp/PfeustNWzYUNu2bdOECRM0btw4DRkyRD179tTOnTt1++23a+TIkfrvf/9b6uc3atRINWrU0IcffqiioiKXa75ca3p6uiZNmiR/f39FRERIksaPH68tW7Zo+fLl+vbbbzVkyBANHDhQBw8erNjFAoBKYjMMwzC7CACwmtGjR+vs2bP6+OOPJV0aUe3UqZPmzZvnOGb27NnatGmT1qxZ49h3/PhxBQcHKzU1VeHh4YqKilJRUZE2bdokSSoqKpKfn5/uvvtuvf3225KkjIwMBQYGasuWLY6R1Z9bsGCBHnvsMdntdnXp0kV9+vTRiBEj1KJFC0mXRmKbN2+uXbt2qVOnTk7nrlixQiNGjNC6devUq1cvHT16VC1atNDRo0cVFBTkOK5fv37q2rWrnnnmmYpePgCoMEZiAaCKfPPNN0pISJCPj49juzzSefjwYcdxHTp0cLy22+1q0KCBbrjhBse+xo0bS5IyMzOv+lmxsbHKyMjQ0qVL1aNHD33wwQdq166d1q5de80ad+3apZEjR2r+/Pnq1auXJGn37t0qKipSeHi4U+1JSUlOdQOAmbixCwCqSF5enqKjo/Xss8+WaAsMDHS8rlmzplObzWZz2mez2SRJxcXF1/w8X19fRUdHKzo6WrNnz9aAAQM0e/Zs9e/fv9TjMzIydOedd+qBBx7Q/fff71S33W7Xjh07ZLfbnc7x8fG5Zg0AUF0IsQBQCTw9PUvMR73xxhv10UcfKTQ0VB4e1fvt1mazKSIiQl999VWp7fn5+Ro8eLAiIiL04osvOrV17txZRUVFyszM1K233lod5QJAmTGdAAAqQWhoqL7++msdOXJEp0+fVnFxsWJjY3XmzBkNHz5c27dv1+HDh7VmzRrFxMS4fAOWK1JSUjR48GB9+OGH2rdvnw4dOqT4+Hi98cYbGjx4cKnnPPTQQzp27JhefvllnTp1ShkZGcrIyNCFCxcUHh6uESNG6L777tOKFSuUlpambdu2KS4uTv/+978rrW4AqAhGYgGgEkydOlWjRo1S27Ztdf78eaWlpSk0NFSbN2/W448/rttvv10FBQUKCQnRwIEDVaNG5Y0hNG3aVKGhoZo1a5ZjKa3L7x955JFSz0lKSlJ6erratm3rtD8hIUFRUVFasmSJZs+erb/85S/68ccf1bBhQ3Xv3l2/+93vKq1uAKgIVicAAACA5TCdAAAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWA4hFgAAAJZDiAUAAIDlEGIBAABgOYRYAAAAWI6H2QUAAADA/WRlZengwYOy2WwKCwtTvXr1ynQ+I7EAAACoNmlpafrtb3+rhg0bqnv37urWrZsaNmyo3/72t/rhhx9c7sdmGIZRhXUCAAAAkqSTJ0/qxhtvlN1uV2xsrCIiIiRJqampmj9/voqKirRz5041btz4un0RYgEAAFAtJk6cqISEBG3btk21atVyasvPz9fNN9+sqKgovfLKK9fti+kEAAAAqBaff/65Zs2aVSLASpK3t7f+/ve/6z//+Y9LfRFiAQAAUC3S09PVoUOHq7a3b99eP/74o0t9EWIBAABQLRo1aqTCwsKrtl+8eNGl+bASIRYAAADV5KabbtIXX3xx1fbVq1erY8eOLvVFiAUAAEC1mDJlil577TVlZ2eXaMvJydHrr7+uyZMnu9QXqxMAAADAcnhiFwAAAKpFixYt5Mr4aVpa2nWPIcQCAACgWrg6VcAVTCcAAACA5XBjFwAAACyH6QQAAACoFq7MiTUMQ0eOHLluX4RYAAAAVItrzYlNS0vT4sWLdf78eZf6Yk4sAAAATHP69Gk9/fTTWrRokbp3765nn31WXbt2ve55jMQCAACg2p07d04vvviinn/+eTVv3lwrV67UwIEDXT6fEAsAAIBqU1hYqMWLF2v27NmqVauWFi5cqBEjRpS5H0IsAAAAqsXy5cv117/+VdnZ2XryyScVGxsrD4/yxVHmxJaTYRjKzc2Vr6+vbDab2eUAAAD84tntdnl5eelPf/qTfH19r3rc3Llzr9sXI7HllJubKz8/P2VnZ6tOnTpmlwMAqGKGYaigoECS5OXlxQAGUA5RUVEyDEPff//9VY9xdXyVkdhyysnJIcQCgBvJz8/XkCFDJEkffPCBvL29Ta4IcG88sQsAABfk5+eX+hqAOZhOAAAAgGoxa9Ysl46bMWPGdY8hxAIA4ILi4uJSXwNw3VNPPaV27dpddUWCwsJC7dmzhxALAEBlyc3NdXpdv359E6sBrGvt2rVq3LhxqW2nTp1SQECAS/2Ua07ssWPHNGbMGAUFBcnT01MhISGaNGmSfvrpJ8cxUVFRstlsstls8vb2Vnh4uOLi4kq942zLli2y2+264447SrQdOXJENptN/v7+Tt9AJKlTp06aOXOm075Dhw5pzJgxatasmby8vNSkSRPddtttWrp0qQoLCx3HXa7t59vy5cvLc0kAAABwHXa7/Zp/ySgqKlKNGq7F0zKH2O+//15dunTRwYMHtWzZMh06dEiLFi3S+vXr1aNHD505c8Zx7NixY5Wenq7U1FRNnz5df/vb37Ro0aISfcbHx2vChAnauHGjTpw4Uern5ubm/j979x5WVZX/cfxzPAiaECpeAC8gIuAlFTMVKQHT1CnTLHMYNEQrc9DUxkprJq1UbKaL5TUbMptKLc2aZgbNFNBMRUzM+y1MTRCvXBxFgf37w8fz6wTqAYEzp/N+Pc9+Ovu2zpf9mH5crr2WXn/99RvWlpaWpk6dOmnv3r2aO3eudu3apZSUFD3++OOaP3++du/ebXX9okWLlJWVZbUNHDiwvI8EAAAANvD09LTq9Py1M2fOqG7duja1Ve7hBPHx8XJ1ddXXX3+t2rVrS5KaN2+u0NBQtWzZUi+++KLmz58vSbrtttssXcJxcXGaM2eO1qxZo9GjR1vaKygo0LJly5Senq7s7Gx98MEHeuGFF0p979ixY/Xmm28qPj5ejRo1KnXeMAwNHz5cQUFB2rhxo1WKb9WqlaKjo0v1AtetW9fmLmsAAADcmjZt2ig5OVnt2rUr83xKSoratGljU1vl6ok9e/asVq9erT/+8Y+WAHuNt7e3YmJitGzZslJh0TAMbdiwQfv27ZOrq6vVuU8//VQhISEKDg7W0KFD9f7775c55CA6OlqBgYF65ZVXyqwtIyNDe/fu1cSJE6/bDc3E1AAAAPbzyCOPaPr06dq3b1+pc/v379err76qQYMG2dRWuULswYMHZRiGWrduXeb51q1b69y5czp16pQkad68eXJ3d5ebm5t69OihkpISPf3001b3JCYmaujQoZKkvn37Kjc3V6mpqaXaNplMmjlzphYuXKjDhw+XOn/gwAFJUnBwsOVYTk6O3N3dLdu8efOs7omOjrY67+7urqNHj5b5sxUWFiovL89qAwAAgO2eeuoptWjRQh06dNCAAQM0adIkTZ48WQ899JA6dOigpk2bWv2L/Y1U6MUuWxf5iomJUUZGhjZu3Kh+/frpxRdfVPfu3S3n9+/fr7S0NEVHR0uSXFxcNGTIECUmJpbZXp8+fXT33XfrL3/5i03f7+XlpYyMDGVkZKhu3bq6fPmy1fm33nrLcv7a5uvrW2ZbCQkJ8vT0tGzNmjWzqQYAAABcVbNmTa1du1YvvviiMjMzNXfuXM2ePVsHDx7Uc889p9TU1FL/an895RoTGxgYKJPJpL179+qhhx4qdX7v3r2qV6+eGjZsKOnq4N3AwEBJV4cNBAYGqlu3burVq5ekq72wRUVFVsHRMAy5ublpzpw58vT0LPUdM2fOVFhYmJ599lmr461atZJ0NRiHhoZKuvoG3LXvL2s+Mm9vb8v5m5k8ebKeeeYZy35eXh5BFgAAoJxuu+02vfTSS3rppZduqZ1y9cR6eXmpd+/emjdvni5evGh1Ljs7Wx9//LGGDBlS5thTd3d3jRs3ThMnTpRhGCoqKtKHH36oN954w6ondMeOHfL19dWSJUvKrKFLly4aNGiQJk2aZHU8NDRUISEhev3116tkEmo3NzfdfvvtVhsAAADso9yzE8yZM0fdu3dXnz59NG3aNLVo0UK7d+/Ws88+qyZNmmj69OnXvXfUqFF69dVXtWLFCrm4uOjcuXMaOXJkqR7Xhx9+WImJiXrqqafKbGf69OmlVnswmUxatGiRevfurfDwcE2ePFmtW7fWlStXtH79ep06dUpms9mqnfPnzys7O9vqmIeHh+rUqVPexwIAAICbiIqKuuk1hmEoJSXlpteVe0xsq1atlJ6eroCAAD366KNq2bKlnnzySUVFRWnTpk03XMGkfv36euyxxzR16lQlJiaqV69eZQ4ZePjhh5Wenq4ffvihzHaCgoI0YsQIXbp0yep4t27dtG3bNgUHBys+Pl5t2rRR9+7dtWTJEr311lulBgrHxcXJx8fHaps9e3Z5HwkAAABssH79egUHBys0NFShoaEKCAjQd999Z9kPDg7W+vXrbWrLZNj6lhas5OXlydPTU7m5uQwtAAAncPbsWcXGxkqSFi9ezLKzQAWYzWadOHHCsuzsjz/+qA4dOlhWZc3JyZG3t7dNQ0MrNDsBAADO5pdzkNu6LCaAqsP/hQAAAHA4hFgAAADYza9ntbJ1hVVCLAAANqhVq1aZnwHYrk+fPnJzc7PsN27cWO+++65l/7bbbtOoUaNsaosXuyqIF7sAwLkYhqHCwkJJV+cOt7W3CEDVoCcWAAAADqfcix0AAOCMCgsLNXjwYEnSZ599xpACoALMZrNsGQRgyxRbhFgAAGzwywV2Ll26RIgFKmDlypVW+ydPntT48eO1ZMkSSVJubq5lPuabIcQCAACgWjz44INW+z/++KNq1KhhOZ6Tk2NzW4yJBQAAgF38+OOPunjxooqLiyVdfXHew8PDpnsJsQAA2OCXY/RsGa8H4Ma2bt2qCRMmqKSkRLNnz1ZBQYHmzp2r4OBgm+4nxAIAYINra7v/+jOA8lm1apX69u2r3/3ud3rnnXcUEBCgZ555Rrfffrvmzp2rP//5zza1U+UhdsGCBfLw8FBRUZHlWEFBgWrWrKnIyEira1NSUmQymXT48GH5+/tr1qxZpdqbOnWqOnbsWOa+v7+/TCbTdbfhw4dL0nXPL126tJJ/egAAAFzTtm1bPfTQQ2rdurX27t2rqKgopaena8GCBXrnnXe0ffv2UuNmr6fKX+yKiopSQUGB0tPT1a1bN0nShg0b5O3trS1btli94ZmcnKzmzZurZcuWFfqurVu3WsZUfPfdd3r44Ye1f/9+y2IEtWvXtly7aNEi9e3b1+r+unXrVuh7AQAAcHNRUVH65ptv5OPjYzlWt25dPfnkk+Vuq8pDbHBwsHx8fJSSkmIJsSkpKRowYIDWrVunzZs3W3pkU1JSFBUVVeHvatiwoeVz/fr1JUmNGjUqM5zWrVtX3t7eFf4uAAAAlM+cOXMqra1qmWIrKipKycnJmjRpkqSrPa7PPfeciouLlZycrMjISF28eFFbtmzRiBEjqqOkcissLLQsNyhdfXsOAAAAtlu8eLFN19kyV2y1hdjx48erqKhIFy9e1Pbt2xUREaErV65owYIFkqRNmzapsLDQqif2+eefLzW49/Lly2rTps0t1xQdHS2z2Wx1bM+ePWrevHmZ1yckJOjll1++5e8FAABwViNGjNDtt98uk8kk6epMH3l5eZZ/NTcMw+YFD6olxEZGRurChQvaunWrzp07p6CgIDVs2FARERGKi4vTpUuXlJKSooCAAKsQ+eyzz1pexrrmnXfe0fr162+5prfeeku9evWyOubr63vd6ydPnqxnnnnGsp+Xl6dmzZrdch0AAADOZN++fWrcuLEkKTMzUx06dNDZs2clSadOnbJ5uGe1hNjAwEA1bdpUycnJOnfunCIiIiRdDY3NmjXTd999p+TkZPXs2dPqvgYNGigwMNDq2LWxrrfK29u7VNs34ubmJjc3t0r5bgAAAFzteTUM47r7N1Jt88RGRUUpJSVFKSkpVlNr9ejRQ0lJSUpLS7ull7oAAADgPKqlJ1a6GmLj4+N15coVS0+sJEVERGjMmDG6fPlytYbY8+fPKzs72+qYh4eH6tSpU201AAAAOBNbe1ltUa09sRcvXlRgYKBlHIR0NcTm5+dbpuKqLnFxcfLx8bHaZs+eXW3fDwAA4GyuvdB1Te3atdWjRw+r89fWD7hpW0ZlRmInkpeXJ09PT+Xm5loWUwAA/HadPXvW8sb04sWLK+0dDcCZ5OTkqFGjRpXSVrX1xAIA4Mhq1KhR5mcAtqusACtV45hYAAAAOLe4uDibrlu0aNFNryHEAgBgg1+O07N1zB4Aax9++KEiIiIsixvcCsbEVlBubq7q1q2rY8eOMSYWAJyAYRiW5cfd3NxKvaACOAsPD48K//o3m836/vvv1aFDh1uug57YCjpz5owksWoXAABwKjk5OWrYsKG9yyDEVtS1t1KPHj0qT09PO1fjfK4t+0tPuH3w/O2L528/PHv74vnb17Xn7+rqau9SJBFiK+zam6menp78j2RHt99+O8/fjnj+9sXztx+evX3x/O3rf2UoDXOEAAAAoFqMHDlSXl5eldIWPbEAAACoFgsXLqy0tgixFeTm5qYpU6bIzc3N3qU4JZ6/ffH87Yvnbz88e/vi+dtXZTz/Fi1a2HRdZmbmTa9hii0AAABUC7PZrIkTJ6pJkyY3vO7pp5++aVuEWAAAAFSLypwnlhe7AAAA4HAIsQAAAHA4hFgAAAA4HEJsBc2dO1f+/v6qVauWunbtqrS0NHuX5BTWr1+v/v37y9fXVyaTSV988YW9S3IaCQkJuuuuu+Th4aFGjRpp4MCB2r9/v73Lchrz589X+/btLZO8h4WFKSkpyd5lOa2ZM2fKZDJp/Pjx9i7FKUydOlUmk8lqCwkJsXdZTuXnn3/W0KFD5eXlpdq1a+uOO+5Qenp6udv5xz/+IT8/v0qpiRBbAcuWLdMzzzyjKVOmWAYn9+nTRzk5OfYu7TfvwoUL6tChg+bOnWvvUpxOamqq4uPjtXnzZq1Zs0ZXrlzRfffdpwsXLti7NKfQtGlTzZw5U9u2bVN6erp69uypAQMGaPfu3fYuzels3bpV7777rtq3b2/vUpxK27ZtlZWVZdm+/fZbe5fkNM6dO6fw8HDVrFlTSUlJ2rNnj9544w3Vq1ev3G399a9/1QcffKCzZ8/eemEGyq1Lly5GfHy8Zb+4uNjw9fU1EhIS7FiV85FkrFy50t5lOK2cnBxDkpGammrvUpxWvXr1jL///e/2LsOp5OfnG61atTLWrFljREREGOPGjbN3SU5hypQpRocOHexdhtN6/vnnjbvvvrtS2ho7dqzRpEkTw83NzRg8eLCxatUqo6SkpEJt0RNbTpcvX9a2bdvUq1cvy7EaNWqoV69e2rRpkx0rA6pXbm6uJKl+/fp2rsT5FBcXa+nSpbpw4YLCwsLsXY5TiY+P1/3332/1ZwCqx8GDB+Xr66uAgADFxMTo6NGj9i7Jafzzn/9U586dNXjwYDVq1EihoaF67733KtTWO++8o+PHj+ujjz7SihUrNHToUPn7+2vKlCk2LXDwS4TYcjp9+rSKi4vVuHFjq+ONGzdWdna2naoCqldJSYnGjx+v8PBwtWvXzt7lOI2dO3fK3d1dbm5ueuqpp7Ry5Uq1adPG3mU5jaVLl+r7779XQkKCvUtxOl27dtUHH3ygVatWaf78+crMzNQ999yj/Px8e5fmFH788UfNnz9frVq10urVqzV69Gg9/fTTWrx4cYXbbNOmjWrUqKHs7GzNnTtX+/bt0x133KHevXtryZIlNrXBsrMAyi0+Pl67du1iTFo1Cw4OVkZGhnJzc7V8+XLFxsYqNTWVIFsNjh07pnHjxmnNmjWqVauWvctxOv369bN8bt++vbp27So/Pz99+umnGjlypB0rcw4lJSXq3LmzZsyYIUkKDQ3Vrl27tGDBAsXGxt5S22azWQ888IAeeOAB5efna+bMmRo2bJiio6Nvei8htpwaNGggs9mskydPWh0/efKkvL297VQVUH3GjBmjf/3rX1q/fr2aNm1q73KciqurqwIDAyVJd955p7Zu3aq3335b7777rp0r++3btm2bcnJy1KlTJ8ux4uJirV+/XnPmzFFhYaHMZrMdK3QudevWVVBQkA4dOmTvUpyCj49Pqb8st27dWitWrKiU9s+cOaOlS5fqo48+0sGDBzV69Gib7mM4QTm5urrqzjvv1Nq1ay3HSkpKtHbtWsam4TfNMAyNGTNGK1eu1Lp169SiRQt7l+T0SkpKVFhYaO8ynMK9996rnTt3KiMjw7J17txZMTExysjIIMBWs4KCAh0+fFg+Pj72LsUphIeHl5pS8cCBA7c0VVZxcbEMw1D//v3VtGlTrVy5UuPGjdOJEyc0e/Zsm9qgJ7YCnnnmGcXGxqpz587q0qWLZs2apQsXLiguLs7epf3mFRQUWP3NOzMzUxkZGapfv76aN29ux8p+++Lj4/XJJ5/oyy+/lIeHh2UMuKenp2rXrm3n6n77Jk+erH79+ql58+bKz8/XJ598opSUFK1evdrepTkFDw+PUuO/69SpIy8vL8aFV4OJEyeqf//+8vPz04kTJzRlyhSZzWab/skZt27ChAnq3r27ZsyYoUcffVRpaWlauHChFi5cWO62PvvsM61YsUJJSUlq2rSpOnXqpDlz5lQsEFfKfAlOaPbs2Ubz5s0NV1dXo0uXLsbmzZvtXZJTSE5ONiSV2mJjY+1d2m9eWc9dkrFo0SJ7l+YURowYYfj5+Rmurq5Gw4YNjXvvvdf4+uuv7V2WU2OKreozZMgQw8fHx3B1dTWaNGliDBkyxDh06JC9y3IqX331ldGuXTvDzc3NCAkJMRYuXFihdmrVqmUMHjzYWL169S3XZDIMwyh/9AUAAADK5+zZs5U2NSMhFgAAAA6HF7sAAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCgBM4cuSITCaTMjIy7F0KAFQKQiwAVMDw4cM1cOBAy35kZKTGjx9vt3oyMzP1hz/8Qb6+vqpVq5aaNm2qAQMGaN++fZKkZs2aKSsrS+3atbNbjQBQmVzsXQAA4NZcuXJFvXv3VnBwsD7//HP5+Pjo+PHjSkpK0vnz5yVJZrNZ3t7e9i0UACoRPbEAcIuGDx+u1NRUvf322zKZTDKZTDpy5IgkadeuXerXr5/c3d3VuHFjDRs2TKdPn7bcGxkZqbFjx2r8+PGqV6+eGjdurPfee08XLlxQXFycPDw8FBgYqKSkpOt+/+7du3X48GHNmzdP3bp1k5+fn8LDwzVt2jR169ZNUunhBMOHD7fU+sstJSVFklRYWKiJEyeqSZMmqlOnjrp27Wo5BwD/CwixAHCL3n77bYWFhemJJ55QVlaWsrKy1KxZM50/f149e/ZUaGio0tPTtWrVKp08eVKPPvqo1f2LFy9WgwYNlJaWprFjx2r06NEaPHiwunfvru+//1733Xefhg0bpv/+979lfn/Dhg1Vo0YNLV++XMXFxTbXfK3WrKwsjRs3To0aNVJISIgkacyYMdq0aZOWLl2qH374QYMHD1bfvn118ODBW3tYAFBJTIZhGPYuAgAczfDhw3X+/Hl98cUXkq72qHbs2FGzZs2yXDNt2jRt2LBBq1evthw7fvy4mjVrpv379ysoKEiRkZEqLi7Whg0bJEnFxcXy9PTUoEGD9OGHH0qSsrOz5ePjo02bNll6Vn9t7ty5eu6552Q2m9W5c2dFRUUpJiZGAQEBkq72xLZo0ULbt29Xx44dre79/PPPFRMTo2+++Ubh4eE6evSoAgICdPToUfn6+lqu69Wrl7p06aIZM2bc6uMDgFtGTywAVJEdO3YoOTlZ7u7ulu1aT+fhw4ct17Vv397y2Ww2y8vLS3fccYflWOPGjSVJOTk51/2u+Ph4ZWdn6+OPP1ZYWJg+++wztW3bVmvWrLlhjdu3b9ewYcM0Z84chYeHS5J27typ4uJiBQUFWdWemppqVTcA2BMvdgFAFSkoKFD//v312muvlTrn4+Nj+VyzZk2rcyaTyeqYyWSSJJWUlNzw+zw8PNS/f3/1799f06ZNU58+fTRt2jT17t27zOuzs7P14IMP6vHHH9fIkSOt6jabzdq2bZvMZrPVPe7u7jesAQCqCyEWACqBq6trqfGonTp10ooVK+Tv7y8Xl+r97dZkMikkJETfffddmecvXbqkAQMGKCQkRG+++abVudDQUBUXFysnJ0f33HNPdZQLAOXGcAIAqAT+/v7asmWLjhw5otOnT6ukpETx8fE6e/asoqOjtXXrVh0+fFirV69WXFyczS9g2SIjI0MDBgzQ8uXLtWfPHh06dEiJiYl6//33NWDAgDLvGTVqlI4dO6Z33nlHp06dUnZ2trKzs3X58mUFBQUpJiZGjz32mD7//HNlZmYqLS1NCQkJ+ve//11pdQPAraAnFgAqwcSJExUbG6s2bdro4sWLyszMlL+/vzZu3Kjnn39e9913nwoLC+Xn56e+ffuqRo3K60No2rSp/P399fLLL1um0rq2P2HChDLvSU1NVVZWltq0aWN1PDk5WZGRkVq0aJGmTZumP/3pT/r555/VoEEDdevWTQ888ECl1Q0At4LZCQAAAOBwGE4AAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADsfF3gUAAADAecTFxdl03aJFi2543mQYhlEZBQEAAAA3M2jQIKv9CxcuaN26derfv78kqbCwUElJSSopKblhO4RYAAAA2E1mZqbat2+v/Px8SdKpU6fk7e2t4uLiG97HmFgAAADYza/7Uw3DKHWsLIRYAAAAVJsrV65USjuEWAAAAFSbJk2aaPz48dq5c6ckqU6dOvrd735ndY3JZLppO4RYAAAAVJtJkyYpLS1NoaGh6tq1q7744gv9/e9/t5xv2LChDh48eNN2eLELAAAA1W7jxo2KiIhQ27ZtdejQIT3yyCMaMWKEIiIibLqfnlgAAABUu3r16slkMmnHjh3asmWLvL29NWzYMAUFBSkhIeGm99MTCwAAgGq3Z88edejQwepFr+LiYr300kt67bXXVFRUdMP7WbELAAAAdrVjxw599NFHWrJkiTw9PfXaa6/d9B5CbAUZhqH8/Hx5eHjY9AYdAAAA/t+ZM2dkGIbuuOMOHT16VIMHD9by5cvVrVs3m+4nxFZQfn6+PD09dfLkSd1+++32LgeoNoZhqLCwUJLk5ubGX+LglPi1D1Tc3/72N61YsULp6ekKCwvTiBEjNGTIEN12223laocxsRWUl5cnT09P9enTRzVr1rR3OQCAavTZZ5+pVq1a9i4DcEg+Pj567LHHNHLkSAUFBVW4HXpiAQAAUG2OHz8us9l8y+0QYm/RlN4HVN/d3lUA1aewyKQXV7WRJE3vu0duLvxjDpzD5eIaeiGptb3LABzeRx99ZNN1sbGxNzxPiL1FruYSubkwLgrOyc3FIMTCiZTYuwDgN2HChAk3vcYwDEJsVWNEMQA4h1/+fs/rJEDFnT17tlLaYcWuW3S5mF5YAHAGv/z9/toMHQDsp0Ih9tixYxoxYoR8fX3l6uoqPz8/jRs3TmfOnLFcExkZKZPJJJPJpFq1almWECvrb6+bNm2S2WzW/fffX+rckSNHZDKZ1KhRI+Xn51ud69ixo6ZOnWp17NChQxoxYoSaN28uNzc3NWnSRPfee68+/vhjq5UfrtX2623p0qUVeSQAAACwweLFi23abqbcwwl+/PFHhYWFKSgoSEuWLFGLFi20e/duPfvss0pKStLmzZtVv359SdITTzyhV155RYWFhVq3bp2efPJJ1a1bV6NHj7ZqMzExUWPHjlViYqJOnDghX1/fUt+bn5+v119/XS+//PJ1a0tLS1OvXr3Utm1bzZ07VyEhIZKk9PR0zZ07V+3atVOHDh0s1y9atEh9+/a1aqNu3brlfSQAAACw0a/HxJaUlCgvL8+SwQzDUG5ubuWPiY2Pj5erq6u+/vpr1a5dW5LUvHlzhYaGqmXLlnrxxRc1f/58SdJtt90mb29vSVJcXJzmzJmjNWvWWIXYgoICLVu2TOnp6crOztYHH3ygF154odT3jh07Vm+++abi4+PVqFGjUucNw9Dw4cMVFBSkjRs3qkaN/+9kbtWqlaKjo0v1AtetW9dSHwAAAKrer8fEZmZmqkOHDpbjp06dsimflWs4wdmzZ7V69Wr98Y9/tATYa7y9vRUTE6Nly5aVCouGYWjDhg3at2+fXF1drc59+umnCgkJUXBwsIYOHar333+/zCEH0dHRCgwM1CuvvFJmbRkZGdq7d68mTpxoFWB/6VZWVyksLFReXp7VBgAAgFtz+fJllZT8/+wfv/x8I+UKsQcPHpRhGGrduux58lq3bq1z587p1KlTkqR58+bJ3d1dbm5u6tGjh0pKSvT0009b3ZOYmKihQ4dKkvr27avc3FylpqaWattkMmnmzJlauHChDh8+XOr8gQMHJEnBwcGWYzk5OXJ3d7ds8+bNs7onOjra6ry7u7uOHj1a5s+WkJAgT09Py9asWbPrPSYAAADYKCkpSf/973+Vk5Mj6WrPbOPGjW96X4Ve7LJ1apGYmBhlZGRo48aN6tevn1588UV1797dcn7//v1KS0tTdHS0JMnFxUVDhgxRYmJime316dNHd999t/7yl7/Y9P1eXl7KyMhQRkaG6tatq8uXL1udf+uttyznr21ljceVpMmTJys3N9eyHTt2zKYaAAAAUNrx48c1ZswYffzxxzKZTPr973+vOXPmaPTo0erRo8dN7y9XiA0MDJTJZNLevXvLPL93717Vq1dPDRs2lCR5enoqMDBQd911lz799FPNmTNH33zzjeX6xMREFRUVydfXVy4uLnJxcdH8+fO1YsUK5ebmlvkdM2fO1LJly7R9+3ar461atZJ0NRhfYzabFRgYqMDAQLm4lB7+6+3tbTl/o+skyc3NTbfffrvVBgAAgPLZsmWLfv/736tly5bKy8tTamqqxo0bp2+//Vbjxo1TrVq19Prrr9+0nXKFWC8vL/Xu3Vvz5s3TxYsXrc5lZ2fr448/1pAhQ8oce+ru7q5x48Zp4sSJMgxDRUVF+vDDD/XGG29Y9YTu2LFDvr6+WrJkSZk1dOnSRYMGDdKkSZOsjoeGhiokJESvv/66zWMpAAAAUL26d++uCxcuaMuWLfrwww9122236c0337T8a/emTZvUtGnTm7ZT7tkJ5syZo+7du6tPnz6aNm2a1RRbTZo00fTp069776hRo/Tqq69qxYoVcnFx0blz5zRy5Eh5enpaXffwww8rMTFRTz31VJntTJ8+XW3btrXqNTWZTFq0aJF69+6t8PBwTZ48Wa1bt9aVK1e0fv16nTp1Smaz2aqd8+fPKzs72+qYh4eH6tSpU97HAgAAABts2bJFnTt3LnX82qQBx48fV1xcnNasWXPDdso9JrZVq1ZKT09XQECAHn30UbVs2VJPPvmkoqKitGnTJsscsWWpX7++HnvsMU2dOlWJiYnq1atXqQArXQ2x6enp+uGHH8psJygoSCNGjNClS5esjnfr1k3btm1TcHCw4uPj1aZNG3Xv3l1LlizRW2+9VWp+2ri4OPn4+Fhts2fPLu8jAQAAgI3KCrDXfPjhh2rfvn2pjseymAwWgK6QvLw8eXp6auubfmpwO0vPwnkUFpk08V9tJUmvP7Bbbi78FgLncOmKSc/+++qv/U8//bTUVJMAKu7UqVMaNWqUvvnmG73xxht64oknbnpPuYcTwNqVkhoqLLr5dcBvRWGRqczPwG/dlZL//8fLW5l3HHB2v159taioSO+++67atWunnTt3ys/Pz6Z2CLG36OU1QapZs6a9ywDs4sVVbexdAgDAwXz55ZdW+0VFRTp37pwGDRpkc4CVCLEAAACoRt9//32pY1999ZWeeOIJff7550pMTFSLFi1u2g5jYivo2pjYkydPMmcsnIphGCosLJR0df5k/lkVzohf+0DlO3v2rEaNGqVVq1bptdde0x//+McbXk+IraBrITY3N5cQCwAAUEk++eQTxcfH69y5cze8rkLLzgIA4GwMw9ClS5d06dIlm5dfB1B+/fv3v+E0XNcQYgEAsEFhYaEGDx6swYMHW4bUAKh8Fy9e1Nq1a296HSEWAAAb/HKBnV8vtgOg+hFiAQAA4HCYYgsAABuUlJSU+RlA+ZjN5koZV06IBQDABvn5+Vaf69evb8dqAMe1cuXKG57Pzc1VbGzsTdup8uEECxYskIeHh4qK/n9t1oKCAtWsWVORkZFW16akpMhkMunw4cPy9/fXrFmzSrU3depUdezYscx9f39/mUym627Dhw+XpOueX7p0aSX/9AAAAPilBx988IZbnz59bGqnyntio6KiVFBQoPT0dHXr1k2StGHDBnl7e2vLli26dOmSatWqJUlKTk5W8+bN1bJlywp919atW1VcXCxJ+u677/Twww9r//79lnlca9eubbl20aJF6tu3r9X9devWrdD3AgAAwDZXrlxRzZo1b7mdKu+JDQ4Olo+Pj1JSUizHUlJSNGDAALVo0UKbN2+2Oh4VFVXh72rYsKG8vb3l7e1t+WeeRo0aWY55enparq1bt67l+LXtWpgGAABA1WjSpInGjx+vnTt3lnnebDbL39//pu1Uy+wEUVFRSk5OtuwnJycrMjJSERERluMXL17Uli1bbinEAgAA4H/bpEmTlJaWptDQUHXt2lXvvvuu1ZhzLy8v/fjjjzdtp9pC7MaNG1VUVKT8/Hxt375dERER6tGjh6WHdtOmTSosLLQKsc8//7zc3d2tthkzZlRKTdHR0aXaPnr06HWvLywsVF5entUGAACA8nnmmWf03XffKTU1Vdu2bdO8efPk7e2t2NhYpaam2txOtYTYyMhIXbhwQVu3btWGDRsUFBSkhg0bKiIiwjIuNiUlRQEBAWrevLnlvmeffVYZGRlW21NPPVUpNb311lul2vb19b3u9QkJCfL09LRszZo1q5Q6AAAAnFG9evVkMpm0Y8cObdmyRd7e3ho2bJiCgoKUkJBw0/urJcQGBgaqadOmSk5OVnJysiIiIiRJvr6+atasmb777jslJyerZ8+eVvc1aNBAgYGBVltlTWni7e1dqm0Xl+u/5zZ58mTl5uZatmPHjlVKHQAAAM6uXbt2eu2115SZmanBgwfrL3/5y03vqbZ5YqOiopSSkqJz587p2WeftRzv0aOHkpKSlJaWptGjR1dXOeXm5uYmNzc3e5cBAADwm7Njxw599NFHWrJkiTw9PfXaa6/d9J5qDbHx8fG6cuWKpSdWkiIiIjRmzBhdvny5Wl/qOn/+vLKzs62OeXh4qE6dOtVWAwAAgLM6c+aMDMPQHXfcoaNHj2rw4MFavny5ZUrWm6nWEHvx4kWFhISocePGluMRERHKz8+3TMVVXeLi4kodS0hI0KRJk6qtBgAAAGfzt7/9TStWrFB6errCwsI0YsQIDRkyRLfddlu52jEZlbF4rRPKy8uTp6encnNzLYspAAB+u86ePWtZCnPx4sUsOwtUkI+Pjx577DGNHDlSQUFBFW6n2npiAQBwZDVq1CjzM4DyOX78uMxm8y23Q4gFAABAtfnoo49suu7av3xcDyEWAAAA1WbEiBGqU6fODac2NQyDEAsAQGWoVatWmZ8BlN+GDRvUoUOHW2qDEFtB196HY/lZAHAOhmEoMTFR0tWlyC9fvmznigD78PDwkMlksncZhNiKOnPmjCSx/CwAAHAqOTk5atiwob3LIMRW1LWpVY4ePSpPT087V+N88vLy1KxZMx07dowpzuyA529fPH/74dnbF8/fvq49f1dXV3uXIokQW2HXplfx9PTkfyQ7uv3223n+dsTzty+ev/3w7O2L529ftzqUoGXLlnJzc7vlOgixAAAAqDYHDhyolHYIsQAAAKg2cXFxNl23aNGiG55nyZEKcnNz05QpUyqlOxzlx/O3L56/ffH87Ydnb188f/uqrOf/4YcfKicnR7m5ucrNzdWJEyf00UcfWfZzcnK0ePHim7ZjMq7NFQUAAABUMbPZrBMnTqhx48aSpMzMTLVv3175+fmSpFOnTsnb21vFxcU3bIeeWAAAANjNr/tTDcModawshFgAAAA4HEIsAAAA7OrX03bZMo0XIRYAAADVJjg4WC4u/z9BVv369fXCCy9Y9t3c3NSnT5+btkOIraC5c+fK399ftWrVUteuXZWWlmbvkpzC+vXr1b9/f/n6+spkMumLL76wd0lOIyEhQXfddZc8PDzUqFEjDRw4UPv377d3WU5j/vz5at++vWWS97CwMCUlJdm7LKc1c+ZMmUwmjR8/3t6lOIWpU6fKZDJZbSEhIfYuy6n8/PPPGjp0qLy8vFS7dm3dcccdSk9Pr1Bbe/bskZeXl2W/bt26mjRpkmXf09NT//nPf27aDiG2ApYtW6ZnnnlGU6ZM0ffff68OHTqoT58+ysnJsXdpv3kXLlxQhw4dNHfuXHuX4nRSU1MVHx+vzZs3a82aNbpy5Yruu+8+Xbhwwd6lOYWmTZtq5syZ2rZtm9LT09WzZ08NGDBAu3fvtndpTmfr1q1699131b59e3uX4lTatm2rrKwsy/btt9/auySnce7cOYWHh6tmzZpKSkrSnj179MYbb6hevXp2rYsptiqga9euuuuuuzRnzhxJUklJiZo1a6axY8da/U0CVctkMmnlypUaOHCgvUtxSqdOnVKjRo2UmpqqHj162Lscp1S/fn397W9/08iRI+1ditMoKChQp06dNG/ePE2bNk0dO3bUrFmz7F3Wb97UqVP1xRdfKCMjw96lOKVJkyZp48aN2rBhg71LsUJPbDldvnxZ27ZtU69evSzHatSooV69emnTpk12rAyoXrm5uZKuBilUr+LiYi1dulQXLlxQWFiYvctxKvHx8br//vut/gxA9Th48KB8fX0VEBCgmJgYHT161N4lOY1//vOf6ty5swYPHqxGjRopNDRU7733nr3LIsSW1+nTp1VcXGyZoPeaxo0bKzs7205VAdWrpKRE48ePV3h4uNq1a2fvcpzGzp075e7uLjc3Nz311FNauXKl2rRpY++ynMbSpUv1/fffKyEhwd6lOJ2uXbvqgw8+0KpVqzR//nxlZmbqnnvusUyOj6r1448/av78+WrVqpVWr16t0aNH6+mnn7ZpVa2q5HLzSwDAWnx8vHbt2sWYtGoWHBysjIwM5ebmavny5YqNjVVqaipBthocO3ZM48aN05o1a1SrVi17l+N0+vXrZ/ncvn17de3aVX5+fvr0008ZTlMNSkpK1LlzZ82YMUOSFBoaql27dmnBggWKjY21W130xJZTgwYNZDabdfLkSavjJ0+elLe3t52qAqrPmDFj9K9//UvJyclq2rSpvctxKq6urgoMDNSdd96phIQEdejQQW+//ba9y3IK27ZtU05Ojjp16iQXFxe5uLgoNTVV77zzjlxcXG66PCYqV926dRUUFKRDhw7ZuxSn4OPjU+ovy61bt7b7kA5CbDm5urrqzjvv1Nq1ay3HSkpKtHbtWsam4TfNMAyNGTNGK1eu1Lp169SiRQt7l+T0SkpKVFhYaO8ynMK9996rnTt3KiMjw7J17txZMTExysjIkNlstneJTqWgoECHDx+Wj4+PvUtxCuHh4aWmVDxw4ID8/PzsVNFVDCeogGeeeUaxsbHq3LmzunTpolmzZunChQuKi4uzd2m/eQUFBVZ/887MzFRGRobq16+v5s2b27Gy3774+Hh98skn+vLLL+Xh4WEZA+7p6anatWvbubrfvsmTJ6tfv35q3ry58vPz9cknnyglJUWrV6+2d2lOwcPDo9T47zp16sjLy4tx4dVg4sSJ6t+/v/z8/HTixAlNmTJFZrNZ0dHR9i7NKUyYMEHdu3fXjBkz9OijjyotLU0LFy7UwoUL7VuYgQqZPXu20bx5c8PV1dXo0qWLsXnzZnuX5BSSk5MNSaW22NhYe5f2m1fWc5dkLFq0yN6lOYURI0YYfn5+hqurq9GwYUPj3nvvNb7++mt7l+XUIiIijHHjxtm7DKcwZMgQw8fHx3B1dTWaNGliDBkyxDh06JC9y3IqX331ldGuXTvDzc3NCAkJMRYuXGjvkgzmiQUAAIDDYUwsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAHACR44ckclkUkZGhr1LAYBKQYgFgAoYPny4Bg4caNmPjIzU+PHj7VZPZmam/vCHP8jX11e1atVS06ZNNWDAAO3bt0+S1KxZM2VlZbFEKoDfDBd7FwAAuDVXrlxR7969FRwcrM8//1w+Pj46fvy4kpKSdP78eUmS2WyWt7e3fQsFgEpETywA3KLhw4crNTVVb7/9tkwmk0wmk44cOSJJ2rVrl/r16yd3d3c1btxYw4YN0+nTpy33RkZGauzYsRo/frzq1aunxo0b67333tOFCxcUFxcnDw8PBQYGKikp6brfv3v3bh0+fFjz5s1Tt27d5Ofnp/DwcE2bNk3dunWTVHo4wfDhwy21/nJLSUmRJBUWFmrixIlq0qSJ6tSpo65du1rOAcD/AkIsANyit99+W2FhYXriiSeUlZWlrKwsNWvWTOfPn1fPnj0VGhqq9PR0rVq1SidPntSjjz5qdf/ixYvVoEEDpaWlaezYsRo9erQGDx6s7t276/vvv9d9992nYcOG6b///W+Z39+wYUPVqFFDy5cvV3Fxsc01X6s1KytL48aNU6NGjRQSEiJJGjNmjDZt2qSlS5fqhx9+0ODBg9W3b18dPHjw1h4WAFQSk2EYhr2LAABHM3z4cJ0/f15ffPGFpKs9qh07dtSsWbMs10ybNk0bNmzQ6tWrLceOHz+uZs2aaf/+/QoKClJkZKSKi4u1YcMGSVJxcbE8PT01aNAgffjhh5Kk7Oxs+fj4aNOmTZae1V+bO3eunnvuOZnNZnXu3FlRUVGKiYlRQECApKs9sS1atND27dvVsWNHq3s///xzxcTE6JtvvlF4eLiOHj2qgIAAHT16VL6+vpbrevXqpS5dumjGjBm3+vgA4JbREwsAVWTHjh1KTk6Wu7u7ZbvW03n48GHLde3bt7d8NpvN8vLy0h133GE51rhxY0lSTk7Odb8rPj5e2dnZ+vjjjxUWFqbPPvtMbdu21Zo1a25Y4/bt2zVs2DDNmTNH4eHhkqSdO3equLhYQUFBVrWnpqZa1Q0A9sSLXQBQRQoKCtS/f3+99tprpc75+PhYPtesWdPqnMlksjpmMpkkSSUlJTf8Pg8PD/Xv31/9+/fXtGnT1KdPH02bNk29e/cu8/rs7Gw9+OCDevzxxzVy5Eirus1ms7Zt2yaz2Wx1j7u7+w1rAIDqQogFgErg6upaajxqp06dtGLFCvn7+8vFpXp/uzWZTAoJCdF3331X5vlLly5pwIABCgkJ0Ztvvml1LjQ0VMXFxcrJydE999xTHeUCQLkxnAAAKoG/v7+2bNmiI0eO6PTp0yopKVF8fLzOnj2r6Ohobd26VYcPH9bq1asVFxdn8wtYtsjIyNCAAQO0fPly7dmzR4cOHVJiYqLef/99DRgwoMx7Ro0apWPHjumdd97RqVOnlJ2drezsbF2+fFlBQUGKiYnRY489ps8//1yZmZlKS0tTQkKC/v3vf1da3QBwK+iJBYBKMHHiRMXGxqpNmza6ePGiMjMz5e/vr40bN+r555/Xfffdp8LCQvn5+alv376qUaPy+hCaNm0qf39/vfzyy5aptK7tT5gwocx7UlNTlZWVpTZt2lgdT05OVmRkpBYtWqRp06bpT3/6k37++Wc1aNBA3bp10wMPPFBpdQPArWB2AgAAADgchhMAAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAw3GxdwEAAAD47YuLi7PpukWLFtl0nckwDONWCgIAAABuxmw2q2/fvnJzc5MkXbhwQevWrVP//v0lSYWFhUpKSlJJSYlN7RFiAQAAUOXMZrNOnDihxo0bS5IyMzPVvn175efnS5JOnTqlxo0b2xxiGRMLAACAavfrftTy9qsSYgEAAFDlPDw8dO7cOcv+uXPndOHCBRUUFEiSsrOzVb9+fZvbI8QCAACgyoWEhGj27NkqKSlRSUmJ5s2bJ19fX02cOFEbN27Uiy++qLvuusvm9hgTCwAAgCr3xRdf6JFHHlGdOnVUUlKiOnXqaNWqVfr973+vgwcPqlmzZvrqq690xx132NQeIRYAAADVYv369frqq69Uu3ZtPfHEE2rWrJkk6cyZM/Ly8ipXW4RYAAAAOBzGxAIAAMDhsGIXAAAAqpzZbLZpGi1b54klxAIAAKBavPXWW2rRokWltMWY2AoyDEP5+fny8PCQyWSydzkAAAD/08xms77//nt16NChUtpjTGwF5efny9PT07JUGgDgt80wDF26dEmXLl0q98pCACofIRYAABsUFhZq8ODBGjx4sAoLC+1dDuD0CLEAANjg0qVLZX4GYB+82AUAAIAqt2HDBrVq1UqSdO7cOeXl5ZV5nZ+fn03tEWIBAABQ5cLCwvTaa6/pzTff1OnTp0udN5lMMgyDKbYAAKhMv/yD1dY/ZAH8v3nz5unNN9/UCy+8oDvvvFOenp631B4hFgAAG/xyNpr8/HzVr1/fjtUAjue9997TrFmz9Ic//KFS2qvQi13Hjh3TiBEj5OvrK1dXV/n5+WncuHE6c+aM5ZrIyEiZTCaZTCbVqlVLQUFBSkhIKHNakk2bNslsNuv+++8vde7IkSMymUxq1KhRqemsOnbsqKlTp1odO3TokEaMGKHmzZvLzc1NTZo00b333quPP/5YRUVFluuu1fbrbenSpRV5JAAAALiBw4cPq1u3bpXWXrlD7I8//qjOnTvr4MGDWrJkiQ4dOqQFCxZo7dq1CgsL09mzZy3XPvHEE8rKytL+/fs1efJkvfTSS1qwYEGpNhMTEzV27FitX79eJ06cKPN78/Pz9frrr9+wtrS0NHXq1El79+7V3LlztWvXLqWkpOjxxx/X/PnztXv3bqvrFy1apKysLKtt4MCB5X0kAAAAuIn69euXORa2osodYuPj4+Xq6qqvv/5aERERat68ufr166dvvvlGP//8s1588UXLtbfddpu8vb3l5+enuLg4tW/fXmvWrLFqr6CgQMuWLdPo0aN1//3364MPPijze8eOHas333xTOTk5ZZ43DEPDhw9XUFCQNm7cqP79+6tVq1Zq1aqVoqOj9e2336p9+/ZW99StW1fe3t5WW61atcr7SAAAAHAT4eHhevXVV687K0F5lSvEnj17VqtXr9Yf//hH1a5d2+qct7e3YmJitGzZslJDBgzD0IYNG7Rv3z65urpanfv0008VEhKi4OBgDR06VO+//36ZQw6io6MVGBioV155pczaMjIytHfvXk2cOFE1apT9Y93K8rCFhYXKy8uz2gAAAGCbmTNnau/evWrSpIlCQ0MVFRVV5marcoXYgwcPyjAMtW7duszzrVu31rlz53Tq1ClJV99Cc3d3l5ubm3r06KGSkhI9/fTTVvckJiZq6NChkqS+ffsqNzdXqamppdo2mUyaOXOmFi5cqMOHD5c6f+DAAUlScHCw5VhOTo7c3d0t27x586zuiY6Otjrv7u6uo0ePlvmzJSQkyNPT07I1a9bseo8JAAAAv9K8eXPt3r1bCxcu1MCBAxUaGlrmZqsKzU5g65rRMTExevHFF3Xu3DlNmTJF3bt3V/fu3S3n9+/fr7S0NK1cufJqMS4uGjJkiBITExUZGVmqvT59+ujuu+/WX/7yF33yySc3/X4vLy9lZGRIuvqi2eXLl63Ov/XWW+rVq5fVMV9f3zLbmjx5sp555hnLfl5eHkEWAACgHNzc3BQdHV0pbZUrxAYGBspkMmnv3r166KGHSp3fu3ev6tWrp4YNG0qSPD09FRgYKOnqsIHAwEB169bNEhwTExNVVFRkFRwNw5Cbm5vmzJlT5vxhM2fOVFhYmJ599lmr49dWgNi/f78lxZvNZsv3u7iU/lG9vb0t52/Gzc1Nbm5uNl0LAACAqlWu4QReXl7q3bu35s2bp4sXL1qdy87O1scff6whQ4aUOfbU3d1d48aN08SJE2UYhoqKivThhx/qjTfeUEZGhmXbsWOHfH19tWTJkjJr6NKliwYNGqRJkyZZHQ8NDVVISIhef/11JqEGAAD4H9OiRYubbv7+/ja3V+7hBHPmzFH37t3Vp08fTZs2TS1atNDu3bv17LPPqkmTJpo+ffp17x01apReffVVrVixQi4uLjp37pxGjhxZqsf14YcfVmJiop566qky25k+fbratm1r1btqMpm0aNEi9e7dW+Hh4Zo8ebJat26tK1euaP369Tp16pTMZrNVO+fPn1d2drbVMQ8PD9WpU6e8jwUAAAA3cPToUb3yyivy8PCQJJ0+fVp/+9vf9Nprr0m6OmPVn//8Z9sbNCrgyJEjRmxsrNG4cWOjZs2aRrNmzYyxY8cap0+ftlwTERFhjBs3rtS9o0aNMtq2bWs88MADxu9+97sy29+yZYshydixY4eRmZlpSDK2b99udc2TTz5pSDKmTJlidXz//v1GbGys0bRpU8PFxcXw9PQ0evToYbz77rvGlStXLNdJKnNLSEiw6Rnk5uYakozc3FybrgcAOLYjR44YDzzwgPHAAw8YR44csXc5gMOpUaOGkZ2dbdk/fPiw4e7ubtk/efKkYTKZbG7PZBg2vqUFK3l5efL09FRubq5uv/12e5cDAKhiZ8+eVWxsrCRp8eLFLDsLlJPZbNaJEyfUuHFjSVcX0OrQoYNlRdacnBx5e3vbPCy0QsvOAgDgbH45B/n15iMHUH34vxAAAAB28evJAMqzMBUhFgAAG/xyWXKWKAfKb9SoUbrtttss+02aNFFSUpJl38PDQwkJCTa3x5jYCmJMLAA4F8MwVFhYKOnq3OG3spQ5gFtXoRW7AABwNiaTiR5Y4H8IwwkAAADgcAixAAAAcDiEWAAAANidYRj66aefbL6eEAsAAAC7O3XqlFq0aGHz9YRYAAAA/E9gnlgAAAA4nPLM/FrlU2wtWLBAzz77rM6dOycXl6tfV1BQoHr16ik8PFwpKSmWa1NSUhQVFaVDhw7p3nvv1fjx4zV+/Hir9qZOnaovvvhCGRkZpfb9/f1vOJYiNjZWH3zwwXVT/pIlS/T73//+ln5eAAAAlPbyyy/f8HxBQUG52qvyEBsVFaWCggKlp6erW7dukqQNGzbI29tbW7Zs0aVLlyzz7iUnJ6t58+Zq2bJlhb5r69atKi4uliR99913evjhh7V//37LYgS1a9e2XLto0SL17dvX6v66detW6HsBAABwY19++eUNzxcVFZWrvSoPscHBwfLx8VFKSoolxKakpGjAgAFat26dNm/erMjISMvxqKioCn9Xw4YNLZ/r168vSWrUqFGZ4bRu3bry9vau8HcBAADAdt9///0Nz586dUqNGze2ub1qGRMbFRWl5ORky35ycrIiIyMVERFhOX7x4kVt2bLllkJsVSosLFReXp7VBgAAgMpRnvGwUjWG2I0bN6qoqEj5+fnavn27IiIi1KNHD8uY2E2bNqmwsNAqxD7//PNyd3e32mbMmFEpNUVHR5dq++jRo9e9PiEhQZ6enpatWbNmlVIHAAAArirP7ARVPpxAkiIjI3XhwgVt3bpV586dU1BQkBo2bKiIiAjFxcXp0qVLSklJUUBAgJo3b26579lnn9Xw4cOt2nrnnXe0fv36W67prbfeUq9evayO+fr6Xvf6yZMn65lnnrHs5+XlEWQBAAAqiaen501f/vqlagmxgYGBatq0qZKTk3Xu3DlFRERIuhoamzVrpu+++07Jycnq2bOn1X0NGjRQYGCg1bFrY11vlbe3d6m2b8TNzU1ubm6V8t0AAAD4f6dPn9ZTTz2lVatW6c9//rNN91TbPLFRUVFKSUlRSkqK5UUuSerRo4eSkpKUlpb2PzseFgAAAFXjyy+/VLt27XTy5En98MMPNt9XLT2x0tUQGx8frytXrlh6YiUpIiJCY8aM0eXLl6s1xJ4/f17Z2dlWxzw8PFSnTp1qqwEAAMBZ/Hou/6KiIr366qv67LPP9Oqrr1oN27RFtYbYixcvKiQkxGr6hIiICOXn51um4qoucXFxpY4lJCRo0qRJ1VYDAACAswgICJBhGDKZTJb/mkwmrVq1qtR7SrYwGeWdzwCSrr7Y5enpqdzcXMtiCgAAACjbr4cKFBUVafr06UpOTtabb75Z6mX+myHEVhAhFgAA4NZ99NFHevrppxUWFqb33nvvhrNF/VK1vdgFAAAA/NrQoUO1a9cuFRUVqV27djbfV21jYgEAAICy+Pr6avXq1Zo3b57N9zCcoIIYTgAAAGA/9MRW0LXsn5eXZ+dKAAAAqo+Hh0e5loe95trsBDdiGIaOHDliU3uE2Ao6c+aMJLH0LAAAcCo5OTlq2LBhue8bP3685fPp06f1t7/9Ta+99prlWEFBgc2rdUkMJ6iw8+fPq169ejp69Kg8PT3tXY7TycvLU7NmzXTs2DGGc9gBz9++eP72w7O3L56/fV17/ufPn7/l7PPjjz+qQ4cOys/PtxzLycmRt7e3SkpKbGqDntgKqlHj6sQOnp6e/I9kR7fffjvP3454/vbF87cfnr198fztqyJDCX7N3d1dly5d0uXLl+Xq6irpaki+7bbbbG6DKbYAAABQrRo1aiQPDw+98cYbkqTi4mK9/vrrCg4OtrkNemIBAABQ7V544QU9//zz+utf/6orV67o4sWLWrZsmc33E2IryM3NTVOmTJGbm5u9S3FKPH/74vnbF8/ffnj29sXzt6/Kfv4TJ05Uu3bttHbtWrm6uqp///7q1q2bzffzYhcAAAAcDj2xAAAAqHIvv/yyTddNmTLFpuvoiQUAAECVM5vNatu2rVxcrvahXr58Wfv27VP79u0lSUVFRdq1a5fNU2wRYgEAAFDlzGazTpw4ocaNG0uSMjMz1b59e8tcsadOnZK3t7eKi4ttao8ptgAAAFDtft2PahjGTZel/SVCbAXNnTtX/v7+qlWrlrp27aq0tDR7l+QU1q9fr/79+8vX11cmk0lffPGFvUtyGgkJCbrrrrvk4eGhRo0aaeDAgdq/f7+9y3Ia8+fPV/v27S2TvIeFhSkpKcneZTmtmTNnymQyWS2jiaozdepUmUwmqy0kJMTeZTmVn3/+WUOHDpWXl5dq166tO+64Q+np6XatiRBbAcuWLdMzzzyjKVOm6Pvvv1eHDh3Up08f5eTk2Lu037wLFy6oQ4cOmjt3rr1LcTqpqamKj4/X5s2btWbNGl25ckX33XefLly4YO/SnELTpk01c+ZMbdu2Tenp6erZs6cGDBig3bt327s0p7N161a9++67lnF8qB5t27ZVVlaWZfv222/tXZLTOHfunMLDw1WzZk0lJSVpz549euONN1SvXr1ytVNWL+strf5loNy6dOlixMfHW/aLi4sNX19fIyEhwY5VOR9JxsqVK+1dhtPKyckxJBmpqan2LsVp1atXz/j73/9u7zKcSn5+vtGqVStjzZo1RkREhDFu3Dh7l+QUpkyZYnTo0MHeZTit559/3rj77rtvuZ3atWsbJ0+etOyfOnXKGD16tGX/zJkzRuvWrW1uj57Ycrp8+bK2bdumXr16WY7VqFFDvXr10qZNm+xYGVC9cnNzJUn169e3cyXOp7i4WEuXLtWFCxcUFhZm73KcSnx8vO6//36rPwNQPQ4ePChfX18FBAQoJiZGR48etXdJTuOf//ynOnfurMGDB6tRo0YKDQ3Ve++9V+52/vvf/6pRo0aW/QYNGmjevHmW/fr162vPnj02t0eILafTp0+ruLjY8mbdNY0bN1Z2dradqgKqV0lJicaPH6/w8HC1a9fO3uU4jZ07d8rd3V1ubm566qmntHLlSrVp08beZTmNpUuX6vvvv1dCQoK9S3E6Xbt21QcffKBVq1Zp/vz5yszM1D333GN5qx1V68cff9T8+fPVqlUrrV69WqNHj9bTTz+txYsX27UuFjsAUG7x8fHatWsXY9KqWXBwsDIyMpSbm6vly5crNjZWqampBNlqcOzYMY0bN05r1qxRrVq17F2O0+nXr5/lc/v27dW1a1f5+fnp008/1ciRI+1YmXMoKSlR586dNWPGDElSaGiodu3apQULFig2NtZuddETW04NGjSQ2WzWyZMnrY6fPHlS3t7edqoKqD5jxozRv/71LyUnJ6tp06b2LsepuLq6KjAwUHfeeacSEhLUoUMHvf322/Yuyyls27ZNOTk56tSpk1xcXOTi4qLU1FS98847cnFxsXleS1SOunXrKigoSIcOHbJ3KU7Bx8en1F+WW7dubfchHYTYcnJ1ddWdd96ptWvXWo6VlJRo7dq1jE3Db5phGBozZoxWrlypdevWqUWLFvYuyemVlJSosLDQ3mU4hXvvvVc7d+5URkaGZevcubNiYmKUkZEhs9ls7xKdSkFBgQ4fPiwfHx97l+IUwsPDS02peODAAfn5+dmpoqsYTlABzzzzjGJjY9W5c2d16dJFs2bN0oULFxQXF2fv0n7zCgoKrP7mnZmZqYyMDNWvX1/Nmze3Y2W/ffHx8frkk0/05ZdfysPDwzIG3NPTU7Vr17Zzdb99kydPVr9+/dS8eXPl5+frk08+UUpKilavXm3v0pyCh4dHqfHfderUkZeXF+PCq8HEiRPVv39/+fn56cSJE5oyZYrMZrOio6PtXZpTmDBhgrp3764ZM2bo0UcfVVpamhYuXKiFCxfat7Bbni/BSc2ePdto3ry54erqanTp0sXYvHmzvUtyCsnJyYakUltsbKy9S/vNK+u5SzIWLVpk79KcwogRIww/Pz/D1dXVaNiwoXHvvfcaX3/9tb3LcmpMsVV9hgwZYvj4+Biurq5GkyZNjCFDhhiHDh2yd1lO5auvvjLatWtnuLm5GSEhIcbChQvtXZJhMoxyrO8FAAAA/A9gTCwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAcAJHjhyRyWRSRkaGvUsBgEpBiAWAChg+fLgGDhxo2Y+MjNT48ePtVk9mZqb+8Ic/yNfXV7Vq1VLTpk01YMAA7du3T5LUrFkzZWVlsUQqgN8MF3sXAAC4NVeuXFHv3r0VHByszz//XD4+Pjp+/LiSkpJ0/vx5SZLZbJa3t7d9CwWASkRPLADcouHDhys1NVVvv/22TCaTTCaTjhw5IknatWuX+vXrJ3d3dzVu3FjDhg3T6dOnLfdGRkZq7NixGj9+vOrVq6fGjRvrvffe04ULFxQXFycPDw8FBgYqKSnput+/e/duHT58WPPmzVO3bt3k5+en8PBwTZs2Td26dZNUejjB8OHDLbX+cktJSZEkFRYWauLEiWrSpInq1Kmjrl27Ws4BwP8CQiwA3KK3335bYWFheuKJJ5SVlaWsrCw1a9ZM58+fV8+ePRUaGqr09HStWrVKJ0+e1KOPPmp1/+LFi9WgQQOlpaVp7NixGj16tAYPHqzu3bvr+++/13333adhw4bpv//9b5nf37BhQ9WoUUPLly9XcXGxzTVfqzUrK0vjxo1To0aNFBISIkkaM2aMNm3apKVLl+qHH37Q4MGD1bdvXx08ePDWHhYAVBKTYRiGvYsAAEczfPhwnT9/Xl988YWkqz2qHTt21KxZsyzXTJs2TRs2bNDq1astx44fP65mzZpp//79CgoKUmRkpIqLi7VhwwZJUnFxsTw9PTVo0CB9+OGHkqTs7Gz5+Pho06ZNlp7VX5s7d66ee+45mc1mde7cWVFRUYqJiVFAQICkqz2xLVq00Pbt29WxY0erez///HPFxMTom2++UXh4uI4ePaqAgAAdPXpUvr6+lut69eqlLl26aMaMGbf6+ADgltETCwBVZMeOHUpOTpa7u7tlu9bTefjwYct17du3t3w2m83y8vLSHXfcYTnWuHFjSVJOTs51vys+Pl7Z2dn6+OOPFRYWps8++0xt27bVmjVrbljj9u3bNWzYMM2ZM0fh4eGSpJ07d6q4uFhBQUFWtaemplrVDQD2xItdAFBFCgoK1L9/f7322mulzvn4+Fg+16xZ0+qcyWSyOmYymSRJJSUlN/w+Dw8P9e/fX/3799e0adPUp08fTZs2Tb179y7z+uzsbD344IN6/PHHNXLkSKu6zWaztm3bJrPZbHWPu7v7DWsAgOpCiAWASuDq6lpqPGqnTp20YsUK+fv7y8Wlen+7NZlMCgkJ0XfffVfm+UuXLmnAgAEKCQnRm2++aXUuNDRUxcXFysnJ0T333FMd5QJAuTGcAAAqgb+/v7Zs2aIjR47o9OnTKikpUXx8vM6ePavo6Ght3bpVhw8f1urVqxUXF2fzC1i2yMjI0IABA7R8+XLt2bNHhw4dUmJiot5//30NGDCgzHtGjRqlY8eO6Z133tGpU6eUnZ2t7OxsXb58WUFBQYqJidFjjz2mzz//XJmZmUpLS1NCQoL+/e9/V1rdAHAr6IkFgEowceJExcbGqk2bNrp48aIyMzPl7++vjRs36vnnn9d9992nwsJC+fn5qW/fvqpRo/L6EJo2bSp/f3+9/PLLlqm0ru1PmDChzHtSU1OVlZWlNm3aWB1PTk5WZGSkFi1apGnTpulPf/qTfv75ZzVo0EDdunXTAw88UGl1A8CtYHYCAAAAOByGEwAAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACH42LvAgAAAPDbFxcXZ9N1ixYtsuk6QiwAAACqXG5urtX+zz//rB07duh3v/tdhdojxAIAAKDKff7555bPx44dU2RkpK5cuaKgoCDNnDmz3O0xJhYAAADV5vjx44qMjFSTJk20du1azZ8/X3/961/L3Q49sQAAAKgWJ06cUFRUlHx9fZWUlKQ6deroyy+/1P3336/69evr8ccft7ktQiwAAACqXFZWliIjI9W4cWOtWrVKderUkSRFRkZqyZIlGjJkiOrWratHHnnEpvZMhmEYVVkwAAAAEBISIi8vL61evVru7u6lzn/44YcaNWqULl68aFN79MQCAACgynl5eWnVqlVlBlhJeuyxx3TmzBmb26MnFgAAAFWuoKDgugG2IgixAAAAqHK2LHZgGIY++OADm9ojxAIAAKDKDRo06LrniouL9c033+jixYsqKSmxqT3GxAIAAKDK/XKxg1/68ssv9cILL6hWrVqaMmWKze2x2AEAAACq3YYNG9S9e3dFR0frgQce0I8//qjnnnvO5vsJsQAAAKg2u3btUv/+/XXvvfeqbdu2OnTokF577TV5enqWqx1CLAAAAKrcTz/9pNjYWHXs2FEuLi7auXOn3nvvPfn6+laoPV7sAgAAQJWrVauWatSooaefflphYWHXvW7AgAE2tUeIBQAAQJVzcXHRzWKnYRg2z05AiAUAAIDDYUwsAAAAHA7zxAIAAKDa7NmzR/v371deXl6Z52NjY21qh+EEAAAAqHJ5eXl69NFHtWbNGrm4uKhOnTqlrjEMQ+fOnbOpPXpiAQAAUOWmTJminJwcbdu2TR07drzl9uiJBQAAQJULCgrSggUL1LNnz0ppjxe7AAAAUOVOnDihgICASmuPEAsAAIAq17RpU+3bt6/S2mNMLAAAAKrcoEGDNGHCBLm6uurOO++Up6fnLbXHmFgAAABUuf/+97964okntHTp0huu3MWKXQAAAPifk5WVpQMHDig3N7fM8w8++KBN7RBiK8gwDOXn58vDw0Mmk8ne5QAAADgVxsRWUH5+vjw9PZWbm6vbb7/d3uUAAAA4hLS0NH3yySc6cOCATCaTAgMDFRMToy5dupSrHWYnAADABoZh6NKlS7p06dINx/MBuL5JkyYpLCxMixYtUlZWlk6cOKEPPvhA3bp104svvliutgixAADYoLCwUIMHD9bgwYNVWFho73IAh7N8+XK9+eabmjVrls6ePavt27dr+/btOnv2rN555x399a9/1YoVK2xujxALAIANLl26VOZnALaZO3euJkyYoLFjx8psNluOm81mjRkzRn/60580Z84cm9sjxAIAAKDKbd++XQ899NB1zw8cOFDbt2+3uT1CLAAANvjl3JW2zmMJ4P+VlJTI19f3uud9fX1VXFxsc3uEWAAAbJCfn1/mZwC2admypQ4ePHjd8wcPHlTLli1tbq9CIfbYsWMaMWKEfH195erqKj8/P40bN05nzpyxXBMZGSmTySSTyaRatWopKChICQkJZb7RuWnTJpnNZt1///2lzh05ckQmk0mNGjUq9ZtGx44dNXXqVKtjhw4d0ogRI9S8eXO5ubmpSZMmuvfee/Xxxx+rqKjIct212n69LV26tCKPBAAAADfwyCOP6N13373u+QULFujhhx+2ub1yh9gff/xRnTt31sGDB7VkyRIdOnRICxYs0Nq1axUWFqazZ89arn3iiSeUlZWl/fv3a/LkyXrppZe0YMGCUm0mJiZq7NixWr9+vU6cOFHm9+bn5+v111+/YW1paWnq1KmT9u7dq7lz52rXrl1KSUnR448/rvnz52v37t1W11+b3uGX28CBA8v7SAAAAHATY8eO1T333FPmSl15eXnq0aOHxowZY3N75V7sID4+Xq6urvr6669Vu3ZtSVLz5s0VGhqqli1b6sUXX9T8+fMlSbfddpu8vb0lSXFxcZozZ47WrFmj0aNHW9orKCjQsmXLlJ6eruzsbH3wwQd64YUXSn3v2LFj9eabbyo+Pl6NGjUqdd4wDA0fPlxBQUHauHGjatT4/3zeqlUrRUdHl+oFrlu3rqU+AAAAVJ3bb79dY8eOLfe56ylXiD179qxWr16t6dOnWwLsNd7e3oqJidGyZcs0b948q3OGYejbb7/Vvn371KpVK6tzn376qUJCQhQcHKyhQ4dq/Pjxmjx5cqmlXKOjo7VmzRq98sorZU6/kJGRob1792rJkiVWAfaXWB4WAADAPhYvXmzTdbGxsTZdV64Qe/DgQRmGodatW5d5vnXr1jp37pxOnTolSZo3b57+/ve/6/Lly7py5Ypq1aqlp59+2uqexMREDR06VJLUt29f5ebmKjU1VZGRkVbXmUwmzZw5U/3799eECRNKDfw9cOCAJCk4ONhyLCcnRwEBAZb9v/71r/rjH/9o2Y+Ojraap0yS9uzZo+bNm5f62QoLC60mt87LyyvzGQAAAKC0CRMmWO0XFRXp4sWL8vDwsBwzDMPmEFuhF7tsXW4vJiZGGRkZ2rhxo/r166cXX3xR3bt3t5zfv3+/0tLSFB0dLUlycXHRkCFDlJiYWGZ7ffr00d13362//OUvNn2/l5eXMjIylJGRobp16+ry5ctW59966y3L+Wvb9aZ+SEhIkKenp2Vr1qyZTTUAAADg6r/oX9tOnjype+65R5L0/vvvW46fO3fO5vbKFWIDAwNlMpm0d+/eMs/v3btX9erVU8OGDSVJnp6eCgwM1F133aVPP/1Uc+bM0TfffGO5PjExUUVFRfL19ZWLi4tcXFw0f/58rVixosxBv5I0c+ZMLVu2rNRkuNeGKezfv99yzGw2KzAwUIGBgXJxKd3p7O3tbTl/o+skafLkycrNzbVsx44du8GTAgAAQFkuX76shx56SBkZGXrxxRf1hz/8QevWrSt3O+UKsV5eXurdu7fmzZunixcvWp3Lzs7Wxx9/rCFDhpQ59tTd3V3jxo3TxIkTZRiGioqK9OGHH+qNN96w6gndsWOHfH19tWTJkjJr6NKliwYNGqRJkyZZHQ8NDVVISIhef/31KpmE2s3NTbfffrvVBgAAANsVFRXpkUce0bZt27R27VpNnTpVr7zyigYOHKitW7eWq61yz04wZ84cde/eXX369NG0adPUokUL7d69W88++6yaNGmi6dOnX/feUaNG6dVXX9WKFSvk4uKic+fOaeTIkfL09LS67uGHH1ZiYqKeeuqpMtuZPn262rZta9VrajKZtGjRIvXu3Vvh4eGaPHmyWrdurStXrmj9+vU6depUqfGv58+fV3Z2ttUxDw8P1alTp7yPBQAAADdQXFysRx55RFu3blVycrJCQkIkSRMnTtTZs2f1u9/9TuvXr7/uu1e/Vu4xsa1atVJ6eroCAgL06KOPqmXLlnryyScVFRWlTZs2qX79+te9t379+nrsscc0depUJSYmqlevXqUCrHQ1xKanp+uHH34os52goCCNGDFCly5dsjrerVs3bdu2TcHBwYqPj1ebNm3UvXt3LVmyRG+99ZbV1F7S1Wm/fHx8rLbZs2eX95EAAADgJgYPHqzNmzdr3bp1lgB7zYwZM/Twww+rT58+NrdnMmx9SwtW8vLy5OnpqdzcXIYWAIATOHv2rOWt6cWLF9+w0wZAad7e3lq3bp3atGlz3WuGDBmiZcuW2dRehWYnAADA2fxyDvLrzUcO4PpuFmAl6eOPP7a5vXKPiQUAAADKa+vWrTd9eevaCqy2IMQCAACgyo0YMUJ16tS57nSmEiEWAIBKV6tWrTI/A7Ddhg0b1KFDh0ppixALAIAN3Nzc9Nlnn1k+A7AvQiwAADYwmUz0wAL/Q3i9EgAAAA6HEAsAAIAq17Jly0odisNwAgAAAFS5AwcOVGp7hFgAAABUubi4OJuuW7RokU3XsexsBbHsLAAAgO3MZrP69u1rGVJw4cIFrVu3Tv3795ckFRYWKikpSSUlJTa1R4itIEIsAACA7cxms06cOKHGjRtLkjIzM9W+fXvl5+dLkk6dOiVvb28VFxfb1F6Vv9i1YMECeXh4qKioyHKsoKBANWvWVGRkpNW1KSkpMplMOnz4sPz9/TVr1qxS7U2dOlUdO3Ysc9/f318mk+m627UVIK53funSpZX80wMAAKAsv+5HNQyj1LEbqfIxsVFRUSooKFB6erq6desm6epqDd7e3tqyZYsuXbpkmXcvOTlZzZs3V8uWLSv0XVu3brWk9++++04PP/yw9u/fb+kprV27tuXaRYsWqW/fvlb3161bt0LfCwAAgOpV5SE2ODhYPj4+SklJsYTYlJQUDRgwQOvWrdPmzZstPbIpKSmKioqq8Hc1bNjQ8rl+/fqSpEaNGpUZTuvWrStvb+8KfxcAAABujclkuuH+jVTLPLFRUVFKTk627CcnJysyMlIRERGW4xcvXtSWLVtuKcRWpcLCQuXl5VltAAAAsE1wcLBcXP6//7R+/fp64YUXLPtubm7q06ePze1VW4jduHGjioqKlJ+fr+3btysiIkI9evRQSkqKJGnTpk0qLCy0CrHPP/+83N3drbYZM2ZUSk3R0dGl2j569Oh1r09ISJCnp6dla9asWaXUAQAA4Az27NkjLy8vy37dunU1adIky76np6f+85//2NxetcwTGxkZqQsXLmjr1q06d+6cgoKC1LBhQ0VERCguLk6XLl1SSkqKAgIC1Lx5c8t9zz77rOVlrGveeecdrV+//pZreuutt9SrVy+rY76+vte9fvLkyXrmmWcs+3l5eQRZAAAAO6mWEBsYGKimTZsqOTlZ586dU0REhKSrobFZs2b67rvvlJycrJ49e1rd16BBAwUGBloduzbW9VZ5e3uXavtG3NzcKnWpNAAAAFRctQwnkK4OKUhJSVFKSorV1Fo9evRQUlKS0tLS/mfHwwIAAOB/S7UtOxsVFaX4+HhduXLF0hMrSRERERozZowuX75crSH2/Pnzys7Otjrm4eGhOnXqVFsNAAAAqJhq7Ym9ePGiAgMDLSs1SFdDbH5+vmUqruoSFxcnHx8fq2327NnV9v0AAACoOJadrSCWnQUAALDdyy+/bNN1U6ZMsek6QmwFEWIBAABs16lTJ6v9y5cva9++fWrfvr3lmGEY2r59u03tEWIriBALAABQcZmZmWrfvr3y8/MrdH+1jYkFAAAArrnVftRqm53gt+bag2f5WQAA4Ew8PDxkMpnsXQYhtqLOnDkjSazaBQAAnEpOTo4aNmxo7zIIsRV1beWwo0ePytPT087VOJ9ry/4eO3aMMcl2wPO3L56//fDs7Yvnb1/Xnr+rq2uF7v/pp5+s9o8fPy7DMHTkyBGrnl0/Pz+b2iPEVlCNGleHE3t6evI/kh3dfvvtPH874vnbF8/ffnj29sXzt6+KDiUICAiwGgd7rZ2AgADLvmEYKikpsak9QiwAAACqnK1TZ9mKEAsAAIAq98v5YCsDIbaC3NzcNGXKFLm5udm7FKfE87cvnr998fzth2dvXzx/+7rV5//rMbHXY+uYWBY7AAAAQJUzm80yDMMy9vXXGBMLAACA/0nffPONGjRoIOnq7ASPPvqovvvuO0nS2bNn1bNnT5vbIsQCAACgWrRt21aNGzeWJLm7u8tkMlnGyubk5JSrLZadBQAAgMMhxAIAAKDKVfZrWITYCpo7d678/f1Vq1Ytde3aVWlpafYuySmsX79e/fv3l6+vr0wmk7744gt7l+Q0EhISdNddd8nDw0ONGjXSwIEDtX//fnuX5TTmz5+v9u3bWyZ5DwsLU1JSkr3LclozZ86UyWTS+PHj7V2KU5g6dapMJpPVFhISYu+ynMrPP/+soUOHysvLS7Vr19Ydd9yh9PT0crVR1iIJvz5WnoUUCLEVsGzZMj3zzDOaMmWKvv/+e3Xo0EF9+vQp91gOlN+FCxfUoUMHzZ07196lOJ3U1FTFx8dr8+bNWrNmja5cuaL77rtPFy5csHdpTqFp06aaOXOmtm3bpvT0dPXs2VMDBgzQ7t277V2a09m6davefffdSp/zEjfWtm1bZWVlWbZvv/3W3iU5jXPnzik8PFw1a9ZUUlKS9uzZozfeeEP16tUrVztLlixR3bp1LfsBAQHKy8uz7Ht5eWnTpk02t8cUWxXQtWtX3XXXXZozZ44kqaSkRM2aNdPYsWM1adIkO1fnPEwmk1auXKmBAwfauxSndOrUKTVq1Eipqanq0aOHvctxSvXr19ff/vY3jRw50t6lOI2CggJ16tRJ8+bN07Rp09SxY0fNmjXL3mX95k2dOlVffPGFMjIy7F2KU5o0aZI2btyoDRs23FI7b775poYNG6aGDRtWSl30xJbT5cuXtW3bNvXq1ctyrEaNGurVq1e5/vYAOLrc3FxJV4MUqldxcbGWLl2qCxcuKCwszN7lOJX4+Hjdf//9Vn8GoHocPHhQvr6+CggIUExMjI4ePWrvkpzGP//5T3Xu3FmDBw9Wo0aNFBoaqvfee6/c7UyfPl1NmzbVI488oqSkJJvng70eQmw5nT59WsXFxZbpIa5p3LixsrOz7VQVUL1KSko0fvx4hYeHq127dvYux2ns3LlT7u7ucnNz01NPPaWVK1eqTZs29i7LaSxdulTff/+9EhIS7F2K0+natas++OADrVq1SvPnz1dmZqbuuece5efn27s0p/Djjz9q/vz5atWqlVavXq3Ro0fr6aef1uLFi8vVTk5Ojv71r3/Jzc1N999/v/z8/PTnP/9Zhw8frlBdzBMLoNzi4+O1a9cuxqRVs+DgYGVkZCg3N1fLly9XbGysUlNTCbLV4NixYxo3bpzWrFmjWrVq2bscp9OvXz/L5/bt26tr167y8/PTp59+ynCaalBSUqLOnTtrxowZkqTQ0FDt2rVLCxYsUGxsrM3tmM1m9e7dW02aNNFnn32md955Rx999JHuuOMOdevWTSNHjtTDDz9s8/9j9MSWU4MGDWQ2m3Xy5Emr4ydPnpS3t7edqgKqz5gxY/Svf/1LycnJatq0qb3LcSqurq4KDAzUnXfeqYSEBHXo0EFvv/22vctyCtu2bVNOTo46deokFxcXubi4KDU1Ve+8845cXFxUXFxs7xKdSt26dRUUFKRDhw7ZuxSn4OPjU+ovy61bt76lIR2GYeihhx7SihUrlJ2drejoaC1cuFC+vr42t0GILSdXV1fdeeedWrt2reVYSUmJ1q5dy9g0/KYZhqExY8Zo5cqVWrdunVq0aGHvkpxeSUmJCgsL7V2GU7j33nu1c+dOZWRkWLbOnTsrJiZGGRkZMpvN9i7RqRQUFOjw4cPy8fGxdylOITw8vNSUigcOHJCfn1+ltF9cXCzDMGQYhlxcbB8kwHCCCnjmmWcUGxurzp07q0uXLpo1a5YuXLiguLg4e5f2m1dQUGD1N+/MzExlZGSofv36at68uR0r++2Lj4/XJ598oi+//FIeHh6WMeCenp6qXbu2nav77Zs8ebL69eun5s2bKz8/X5988olSUlK0evVqe5fmFDw8PEqN/65Tp468vLwYF14NJk6cqP79+8vPz08nTpzQlClTZDabFR0dbe/SnMKECRPUvXt3zZgxQ48++qjS0tK0cOFCLVy48JbaXb58uT755BOtWrVK99xzj8aOHVu+GYcMVMjs2bON5s2bG66urkaXLl2MzZs327skp5CcnGxIKrXFxsbau7TfvLKeuyRj0aJF9i7NKYwYMcLw8/MzXF1djYYNGxr33nuv8fXXX9u7LKcWERFhjBs3zt5lOIUhQ4YYPj4+hqurq9GkSRNjyJAhxqFDh+xdllP56quvjHbt2hlubm5GSEiIsXDhwnK3cfnyZSMpKcn4wx/+YNSoUcPw9/c3Xn75ZePo0aMVqol5YgEAAFDlvLy89N///lcPPfSQRo4cqXvvvfeW2iPEAgAAoMrNmTNHQ4cOtVq161YQYgEAAOBweLELAAAAVc6WWW0Mw9CRI0dsao+eWAAAAFQ5s9msV155RR4eHmWeLygo0J///Gebl6MlxAIAAKDKmc1mnThxQo0bNy7zfE5Ojry9vW0OsSx2AAAAgCrn6uqqK1euXPf85cuXy7WsMyEWAAAAVc7b21uZmZnXPX/kyJHr9tKWhRALAACAKtetWzf94x//uO75f/zjH+rSpYvN7RFiAcAJHDlyRCaTSRkZGfYuBYCTGj16tN5//3299NJLOnv2rOX4uXPnNHXqVP3973/XU089ZXN7hFgAqIDhw4dbrfEdGRmp8ePH262ezMxM/eEPf5Cvr69q1aqlpk2basCAAdq3b58kqVmzZsrKylK7du3sViMA59ajRw/NnDlTM2fOVMOGDeXt7S0fHx81aNBA06dP17Rp0xQVFWVze8wTCwAO7sqVK+rdu7eCg4P1+eefy8fHR8ePH1dSUpLOnz8v6epbwd7e3vYtFIDTmzhxogYNGqQvvvhCmZmZMgxD/v7+GjBggFq1alW+xgwAQLnFxsYaAwYMsHyWZLVlZmYahmEYO3fuNPr27WvUqVPHaNSokTF06FDj1KlTlnYiIiKMMWPGGOPGjTPq1q1rNGrUyFi4cKFRUFBgDB8+3HB3dzdatmxp/Oc//7luLdu3bzckGUeOHLnuNZmZmYYkY/v27detWZKRnJxsGIZhXLp0yfjTn/5k+Pr6GrfddpvRpUsXyzkA+F/AcAIAuEVvv/22wsLC9MQTTygrK0tZWVlq1qyZzp8/r549eyo0NFTp6elatWqVTp48qUcffdTq/sWLF6tBgwZKS0vT2LFjNXr0aA0ePFjdu3fX999/r/vuu0/Dhg3Tf//73zK/v2HDhqpRo4aWL1+u4uJim2u+VmtWVpbGjRunRo0aKSQkRJI0ZswYbdq0SUuXLtUPP/ygwYMHq2/fvjp48OCtPSwAqCQsdgAAFTB8+HCdP39eX3zxhaSrY2I7duyoWbNmWa6ZNm2aNmzYoNWrV1uOHT9+XM2aNdP+/fsVFBSkyMhIFRcXa8OGDZKk4uJieXp6atCgQfrwww8lSdnZ2fLx8dGmTZvUrVu3MuuZO3eunnvuOZnNZnXu3FlRUVGKiYlRQECApKsvdrVo0ULbt29Xx44dre79/PPPFRMTo2+++Ubh4eE6evSoAgICdPToUfn6+lqu69Wrl7p06aIZM2bc6uMDgFtGTywAVJEdO3YoOTlZ7u7ulu1aT+fhw4ct17Vv397y2Ww2y8vLS3fccYfl2LV5E3Nycq77XfHx8crOztbHH3+ssLAwffbZZ2rbtq3WrFlzwxq3b9+uYcOGac6cOQoPD5ck7dy5U8XFxQoKCrKqPTU11apuALAnXuwCgCpSUFCg/v3767XXXit1zsfHx/K5Zs2aVudMJpPVMZPJJEk3XYrRw8ND/fv3V//+/TVt2jT16dNH06ZNU+/evcu8Pjs7Ww8++KAef/xxjRw50qpus9msbdu2yWw2W93j7u5+wxoAoLoQYgGgEri6upYaj9qpUyetWLFC/v7+cnGp3t9uTSaTQkJC9N1335V5/tKlSxowYIBCQkL05ptvWp0LDQ1VcXGxcnJydM8991RHuQBQbgwnAIBK4O/vry1btujIkSM6ffq0SkpKFB8fr7Nnzyo6Olpbt27V4cOHtXr1asXFxdn8ApYtMjIyNGDAAC1fvlx79uzRoUOHlJiYqPfff18DBgwo855Ro0bp2LFjeuedd3Tq1CllZ2crOztbly9fVlBQkGJiYvTYY4/p888/V2ZmptLS0pSQkKB///vflVY3ANwKemIBoBJMnDhRsbGxatOmjS5evKjMzEz5+/tr48aNev7553XfffepsLBQfn5+6tu3r2rUqLw+hKZNm8rf318vv/yyZWWua/sTJkwo857U1FRlZWWpTZs2VseTk5MVGRmpRYsWadq0afrTn/6kn3/+WQ0aNFC3bt30wAMPVFrdAHArmJ0AAAAADofhBAAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcAixAAAAcDiEWAAAADgcQiwAAAAcDiEWAAAADocQCwAAAIdDiAUAAIDDIcQCAADA4RBiAQAA4HAIsQAAAHA4hFgAAAA4HEIsAAAAHA4hFgAAAA6HEAsAAACHQ4gFAACAwyHEAgAAwOEQYgEAAOBwCLEAAABwOIRYAAAAOBxCLAAAABwOIRYAAAAOhxALAAAAh0OIBQAAgMMhxAIAAMDhEGIBAADgcFzsXQAAAAB+++Li4my6btGiRTZdR4gFAABAlcvNzbXav3DhgtatW6f+/ftXqD2TYRhGZRQGAAAA2CozM1Pt27dXfn5+he5nTCwAAACq3a32oxJiAQAA4HAIsQAAAHA4vNgFAACAKpeammq1//PPP6u4uFgpKSkymUyW4xERETa1x4tdAAAAqHJms1mGYVgF1l8zDEMlJSU2tUdPLAAAAKrcuXPnKrU9emIBAADgcHixCwAAANVm6dKlGjhwoNq0aaM2bdpo4MCBWrZsWbnboScWAAAAVa6kpESDBw/WF198oVatWql169YymUzau3ev9u/fr4cffljLli1TjRq29bEyJhYAAABVbtasWUpNTdU///lP3X///Vbn/vOf/2jYsGF6++23NWHCBJvaoycWAAAAVa59+/YaP368RowYUeb5RYsW6a233tIPP/xgU3uEWAAAAFS52rVra9++ffLz8yvz/E8//aSQkBBdvHjRpvZ4sQsAAABVrlatWsrNzb3u+by8PNWuXdvm9gixAAAAqHJhYWGaO3fudc/PmTNH3bp1s7k9XuwCAABAlXvppZcUERGh06dP609/+pPatGkjSdq7d6/eeOMN/fOf/1RKSorN7TEmFgAAANXiq6++0siRI3X69Gmr4w0aNNDf//53Pfjggza3RYgFAABAtbl48aLWrFmjAwcOSJKCgoLUu3fvco2HlQixAAAA+B+xf/9+BQcH23QtY2IBAABgF4cPH1ZycrJly87OVklJiU33EmIBAABQLY4cOWIJrCkpKTp+/Ljc3d119913a/z48YqMjLS5LYYTAAAAoMq1aNFCP/30k+rUqaPw8HBFRUUpMjJSd911l2rUKP+sr4RYAAAAVDkXFxe5u7srLi5OvXv31j333CMPD48Kt0eIBQAAQJXLyclRamqqUlNTlZKSogMHDig0NFSRkZGKiorS3XffLXd3d5vbI8QCAACg2p05c0YpKSmWULt//36FhoZq8+bNNt3Pi10AAACodl5eXgoPD1dJSYlKSkqUm5urHTt22Hw/PbEAAACoFseOHVNqaqrWr1+v9evX66efflLXrl3Vs2dPRUZGqlu3bnJ1dbWpLUIsAAAAqlxAQIBOnDihrl27KjIyUj179lRYWJjNofXXyj+fAQAAAFBOR48elclkkmEYMgzDMoygouiJBQAAQJU7efKkUlJSLIsdHDp0SK6ururSpYuioqIUERGh7t27y83Nzab2CLEAAACodj///LPVkrM//fSTXF1ddfHiRZvuJ8QCAADA7o4ePaq1a9cqLi7OpusJsQAAAHA4zBMLAACAKmdLD6thGPrggw9sao+eWAAAAFQ5s9msvn37XvfFrcLCQiUlJdk8YwEhFgAAAFXObDbrxIkTaty4cZnnT506JW9vbxUXF9vUHvPEAgAAoMq5uLjcMKAWFRXJbDbb3B4hFgAAAFWuXr16Onny5HXPnzx5UvXr17e5PUIsAAAAqlyHDh2UlJR03fOrVq1S+/btbW6PEAsAAIAqFxMTo5kzZ2rdunWlziUnJ2vGjBmKjo62uT1e7AIAAEC1GDhwoP75z3/qjjvuUOvWrWUymbRv3z7t2LFDv/vd7/TVV1/JZDLZ1BYhFgAAANXCMAz94x//0PLly5WZmSnDMOTv769BgwZp+PDhqlHD9kEChFgAAAA4HMbEAgAAwOGw7CwAAACqXIsWLW56jWEYOnLkiE3tMZwAAAAAVc5sNuuVV16Rh4eHJOn06dP629/+ptdee02SVFBQoD//+c8sOwsAAID/Hb9edvbHH39Uhw4dlJ+fL0nKycmRt7e3zSGWMbEAAABwOIRYAAAAOBxCbAUZhqG8vDwxGgMAAKBifr2wga0LHUiE2ArLz8+Xp6enZRwHAAAArm/UqFG67bbbLPtNmjRRUlKSZd/Dw0MJCQk2t8eLXRWUl5cnT09P5ebm6vbbb7d3OQAAAE6FeWIBAABQ5X766SebrvPz87PpOkIsAAAAqlxAQIAMw5DJZLJ6p+jX+7ZOsUWIBQAAQJXbvn17mccNw9CSJUs0e/Zsy0IItiDEAgAAoMq1b9++1LGvv/5akydP1qFDh/Tcc89p4sSJNrdHiAUAAEC12rp1qyZNmqRvv/1WTz75pFavXq0GDRqUq40KTbF17NgxjRgxQr6+vnJ1dZWfn5/GjRunM2fOWK6JjIyUyWSSyWRSrVq1FBQUpISEhDLnVd20aZPMZrPuv//+UueOHDkik8mkRo0alZrOqmPHjpo6darVsUOHDmnEiBFq3ry53Nzc1KRJE9177736+OOPVVRUZLnuWm2/3pYuXVqRRwIAAICbOHjwoIYMGaLu3bvLx8dH+/bt0+zZs8sdYKUKhNgff/xRnTt31sGDB7VkyRIdOnRICxYs0Nq1axUWFqazZ89arn3iiSeUlZWl/fv3a/LkyXrppZe0YMGCUm0mJiZq7NixWr9+vU6cOFHm9+bn5+v111+/YW1paWnq1KmT9u7dq7lz52rXrl1KSUnR448/rvnz52v37t1W1y9atEhZWVlW28CBA8v7SAAAAHATTz31lNq2bavc3Fxt3bpVH330kVq0aFHh9so9T2y/fv20a9cuHThwQLVr17Ycz87OVsuWLfXYY49p/vz5ioyMVMeOHTVr1izLNXfeeaf8/Pz0+eefW44VFBTIx8dH6enpmjJlitq3b68XXnjBcv7IkSNq0aKFnn32Wc2fP1+HDx9Wo0aNJF3tiR04cKCmTp0qwzDUtm1b3XbbbUpLS1ONGqXz+bU34qSrPbErV66scGhlnlgAAADbmc1m1apVS8HBwTdc8fR6L4D9WrnGxJ49e1arV6/W9OnTrQKsJHl7eysmJkbLli3TvHnzrM4ZhqFvv/1W+/btU6tWrazOffrppwoJCVFwcLCGDh2q8ePHa/LkyaWWHYuOjtaaNWv0yiuvaM6cOaVqy8jI0N69e7VkyZIyA6xUvqXMfq2wsFCFhYWW/by8vAq3BQAA4GymTJlSqe2VK8QePHhQhmGodevWZZ5v3bq1zp07p1OnTkmS5s2bp7///e+6fPmyrly5olq1aunpp5+2uicxMVFDhw6VJPXt21e5ublKTU1VZGSk1XUmk0kzZ85U//79NWHCBLVs2dLq/IEDByRJwcHBlmM5OTkKCAiw7P/1r3/VH//4R8t+dHS0zGazVTt79uxR8+bNS/1sCQkJevnll8v8uQEAAHBjL730UqW2V6EXu2wdgRATE6OMjAxt3LhR/fr104svvqju3btbzu/fv19paWmKjo6WJLm4uGjIkCFKTEwss70+ffro7rvv1l/+8hebvt/Ly0sZGRnKyMhQ3bp1dfnyZavzb731luX8tc3X17fMtiZPnqzc3FzLduzYMZtqAAAAQOUrV09sYGCgTCaT9u7dq4ceeqjU+b1796pevXpq2LChJMnT01OBgYGSrg4bCAwMVLdu3dSrVy9JV3thi4qKrIKjYRhyc3PTnDlz5OnpWeo7Zs6cqbCwMD377LNWx68NU9i/f79CQ0MlXR17ce37XVxK/6je3t6W8zfj5uYmNzc3m64FAABA1SpXT6yXl5d69+6tefPm6eLFi1bnsrOz9fHHH2vIkCFljj11d3fXuHHjNHHiRBmGoaKiIn344Yd64403rHpCd+zYIV9fXy1ZsqTMGrp06aJBgwZp0qRJVsdDQ0MVEhKi119/3eblygAAAOCYyr3YwZw5c9S9e3f16dNH06ZNU4sWLbR79249++yzatKkiaZPn37de0eNGqVXX31VK1askIuLi86dO6eRI0eW6nF9+OGHlZiYqKeeeqrMdqZPn662bdta9a6aTCYtWrRIvXv3Vnh4uCZPnqzWrVvrypUrWr9+vU6dOlVq/Ov58+eVnZ1tdczDw0N16tQp72MBAABANSr3mNhWrVopPT1dAQEBevTRR9WyZUs9+eSTioqK0qZNm1S/fv3r3lu/fn099thjmjp1qhITE9WrV68yhww8/PDDSk9P1w8//FBmO0FBQRoxYoQuXbpkdbxbt27atm2bgoODFR8frzZt2qh79+5asmSJ3nrrLY0ePdrq+ri4OPn4+Fhts2fPLu8jAQAAQCXYv3+/zdeWe55YXMU8sQAAALfm8OHDSk5OtmzZ2dk2Dwst93ACAAAAoCKOHDliCawpKSk6fvy43N3ddffdd2v8+PGlpli9EUIsAAAAqlyLFi30008/qU6dOgoPD1d8fLwiIyN11113XXehqhshxAIAAKDKHTt2TLfffrvi4uLUu3dv3XPPPfLw8KhwexVa7AAAAAAojxMnTui9997TlStX9Nxzz8nLy0tdu3bV888/r1WrVqmgoKBc7fFiVwXxYhcAAEDFnTlzRikpKUpNTVVKSoplwarNmzfbdD/DCeBwDMNQYWGhvcsA7OKXv/7d3NzKXFwG+K3j1/5vg5eXl8LDw1VSUqKSkhLl5uZqx44dNt9PiIXDKSws1ODBg+1dBgDATj777DPVqlXL3mWgAo4dO6bU1FStX79e69ev108//aSuXbuqZ8+e+sc//qFu3brZ3BYhFgAAAFUuICBAJ06cUNeuXRUZGal3331XYWFhcnV1rVB7hFg4tNmvPiE315r2LgOoNoWXr2jsX96TxK9/OJdf/tqHYzp69Khq1qwpwzBkGIZlGEFFEWJvEe/F2Zeba025ufGHOJwTv/7hTH755y1/9jqmn3/+WSkpKUpOTtayZcs0ffp0ubq6qkuXLoqKilJERIS6d+8uNzc3m9ojxN4iXjACAKDqXb5SZPlcWFio2rVr27EaVETjxo01ZMgQ/V97dx7U1PX2AfwbAgEUBBcIIEtcitQqoKKojLKIWyvitKOORQxorbVxQes6OhV/MmBra9W64liq4zhWrVq7oLWa4C6KUrepVcBxA8EphkUIGPL+4ZDXFNSwJLeY72cmM7kn9548nkmbJ4fnnjN+/HgAz5Pa2t27duzYgRUrVkAikaCiosKo/ky+TuzmzZvh6OiIZ8/+/8NXVlYGGxubOluLqVQqiEQi5OTkQCaTYc2aNXX6S0xMRGBgYL3HMpkMIpHopY+4uDgAeOnru3fvbuZ/PRERERHVp2PHjpg4cSK2bduG3Nxc5OXlYePGjUZfb/KZ2PDwcJSVleHixYv6O85OnjwJNzc3nD9/HpWVlfo7DJVKJby9vdGlS5dGvdeFCxeg1WoBAGfOnMEHH3yAmzdv6tdxffFXW1paGkaMGGFwvbOzc6Pel4iIiIiaxtvbG/Hx8Uafb/Iktlu3bnB3d4dKpdInsSqVCtHR0Th+/DjOnTunn5FVqVQIDw9v9Hu5uLjon7dr1w4A4OrqWm9y6uzsDDc3t0a/B2svWgAAEfNJREFUFxEREREZz9gENS0tzajzzFITGx4eDqVSiUWLFgF4PuO6YMECaLVaKJVKhIWFoaKiAufPn8fkyZPNEVKzqaysRGVlpdBhWJQXx5vF/URERC2DWq02OC4vL8fx48cRFRXVqP7MlsQmJCTg2bNnqKiowOXLlxEaGorq6mps3rwZAHD27FloNBqDmdiFCxdi6dKlBn1VVVWhe/fuTY5pwoQJEIvFBm03btyAt7d3vedrNBqDm7hKSkoAAB9//DFsbHh3sFCqqp/Bzq5x68sRERGR+ezfv9/gOC8vD/7+/nXajWWWJDYsLAzl5eW4cOECiouL4evrCxcXF4SGhiI+Ph6VlZVQqVTo3LmzQRI5f/58/c1YtdatW4cTJ040OaZvvvkGkZGRBm0eHh4vPT8lJQXLly9v8vsSERERUdP/mmqWJLZr167w9PSEUqlEcXExQkNDATxPGr28vHDmzBkolUpEREQYXNehQwd07drVoK221rWp3Nzc6vT9KosXL8bcuXP1xyUlJfDy8kJqaipcXV2bJSYyTmVlJWJjYwEAEhuuEkdERGSJzJYBhIeHQ6VSobi4GPPnz9e3Dx48GOnp6cjMzMT06dPNFU6D2dra1rv4rp2dHfdvFpBIJBI6BCIiIhKAWZNYhUKB6upq/UwsAISGhmLGjBmoqqpq0soEDfXkyRMUFBQYtDk6OqJ169Zmi4GIiIjIUmRkZBgcP3jwAFqtVr9PQK0X88RXMWsSW1FRAT8/P0ilUn17aGgoSktL9UtxmUt9yzykpKToV1AgIiIiouYTEREBnU5X56+oQ4YM0T/X6XSoqakxqj+zJbEymazeAl4fH5962+/cuVNvP4mJiUhMTHzpca2wsLCXFgw357JMxu7vS0RERI334j0Q/O5tmYqLi5u1P94V00SsySQiIjK9F79v+d3bMtXuoNpcmMRSi6apqhY6BCKzevEzz88/WRJ+3lu+f9fEvoyxNbEiHbc8apSSkhI4OTlBrVY3+y8LerXKykqMHTtW6DCIiEgge/fu5cpALZBYLK63JvZF/8maWCIiIiKyXM1dE8uZ2EZSq9VwdnbGvXv3OBNrZjqdzmALYCJL8uLn39bWlrWBZJH42ReWo6OjSca/tLQUs2bNQlpamlHnM4ltpNzcXHTp0kXoMIiIiIjMqrCwEC4uLibp183NjeUEpla7/e3du3fh5OQkcDSWp3bbX86EC4PjLyyOv3A49sLi+AurdvwlEonJ3qMhM7xMYhvJysoKAODk5MT/kATUpk0bjr+AOP7C4vgLh2MvLI6/sExZytGQAgEmsURERERkcrWrEzQXJrFEREREZHIHDhx45etqtRpyudzo/pjENpKtrS2WLVvGre8EwvEXFsdfWBx/4XDshcXxF1ZTx3/06NGvfL2wsLBB/XF1AiIiIiISXENXJ7AycTxERERERK8lFoshk8mMPp8zsURERETU4rAmloiIiIhMLjw8/LXn6HQ6qFQqo/rjTCwRERERmZxYLMbUqVPRqlUrAM9XI9i5cycUCgUA4OnTp0hNTWVNrKlt2LABMpkMdnZ2CA4ORmZmptAhWYQTJ04gKioKHh4eEIlEOHjwoNAhWYyUlBT07dsXjo6OcHV1xZgxY3Dz5k2hw7IYmzZtgr+/v36R9wEDBiA9PV3osCzWypUrIRKJkJCQIHQoFiExMREikcjg4efnJ3RYFuXBgweYOHEi2rdvD3t7e/Ts2RMXL15scD/Lly/H6tWrsXr1aixZsgQSiUR//L///a9BfTGJbYQffvgBc+fOxbJly3Dp0iUEBARg+PDhDV4aghquvLwcAQEB2LBhg9ChWJyMjAwoFAqcO3cOR48eRXV1NYYNG4by8nKhQ7MInp6eWLlyJbKysnDx4kVEREQgOjoa169fFzo0i3PhwgVs2bIF/v7+QodiUd555x3k5+frH6dOnRI6JItRXFyMkJAQ2NjYID09HTdu3MDXX3+Ntm3bChoXywkaITg4GH379sX69esBADU1NfDy8sLMmTOxaNEigaOzHCKRCAcOHMCYMWOEDsUiFRUVwdXVFRkZGRg8eLDQ4Vikdu3aYdWqVZgyZYrQoViMsrIy9O7dGxs3bkRSUhICAwOxZs0aocN64yUmJuLgwYPIzs4WOhSLtGjRIpw+fRonT55sUj9isRgPHz6EVCoFAOTm5iIgIAClpaUAuMSWyVVVVSErKwuRkZH6NisrK0RGRuLs2bMCRkZkXmq1GsDzRIrMS6vVYvfu3SgvL8eAAQOEDseiKBQKvPfeewbfAWQet27dgoeHBzp37oyYmBjcvXtX6JAsxqFDhxAUFISxY8fC1dUVvXr1wtatW5ulb5FI9MrjV2ES20CPHz+GVqvV/4qoJZVKUVBQIFBUROZVU1ODhIQEhISEoEePHkKHYzGuXr0KBwcH2Nra4pNPPsGBAwfQvXt3ocOyGLt378alS5eQkpIidCgWJzg4GN9//z0OHz6MTZs2IS8vD4MGDdLP4JFp5ebmYtOmTXjrrbdw5MgRTJ8+HbNmzcL27dsb1M/w4cMNdvuSSqXYsmWL/rhVq1aYNm2a0f1xiS0iajCFQoFr166xJs3MunXrhuzsbKjVauzbtw9yuRwZGRlMZM3g3r17mD17No4ePQo7Ozuhw7E4I0eO1D/39/dHcHAwfHx8sGfPHpbTmEFNTQ2CgoKQnJwMAOjVqxeuXbuGzZs3Qy6XG93Pb7/9ZnDcunVrTJgwQX/s4OCAjRs3Gt0fZ2IbqEOHDhCLxXj06JFB+6NHj+Dm5iZQVETmM2PGDPzyyy9QKpXw9PQUOhyLIpFI0LVrV/Tp0wcpKSkICAjA2rVrhQ7LImRlZaGwsBC9e/eGtbU1rK2tkZGRgXXr1sHa2hparVboEC2Ks7MzfH19cfv2baFDsQju7u51fiy//fbbgpd0MIltIIlEgj59+uDYsWP6tpqaGhw7doy1afRG0+l0mDFjBg4cOIDjx4+jU6dOQodk8WpqaqDRaIQOwyIMGTIEV69eRXZ2tv4RFBSEmJgYZGdnQywWCx2iRSkrK0NOTg7c3d2FDsUihISE1FlS8e+//4aPj49AET3HcoJGmDt3LuRyOYKCgtCvXz+sWbMG5eXliI+PFzq0N15ZWZnBL++8vDxkZ2ejXbt28Pb2FjCyN59CocCuXbvw008/wdHRUV8D7uTkBHt7e4Gje/MtXrwYI0eOhLe3N0pLS7Fr1y6oVCocOXJE6NAsgqOjY53679atW6N9+/asCzeDefPmISoqCj4+Pnj48CGWLVsGsVhs8KdoMp05c+Zg4MCBSE5Oxrhx45CZmYnU1FSkpqYKG5iOGuXbb7/VeXt76yQSia5fv366c+fOCR2SRVAqlToAdR5yuVzo0N549Y07AF1aWprQoVmEyZMn63x8fHQSiUTn4uKiGzJkiO73338XOiyLFhoaqps9e7bQYViE8ePH69zd3XUSiUTXsWNH3fjx43W3b98WOiyL8vPPP+t69Oihs7W11fn5+elSU1OFDknHdWKJiIiISHBcJ5aIiIiIWiSuE0tERERELU5DCgR4YxcRERERmdzrNkeo3QnSWKyJJSIiIiKTe9025TqdDmq12uiaWCaxRERERCS4oqIiSKVS3thFRERERC1HQ+dVmcQSERER0X8CVycgIiIDd+7cgUgkQnZ2ttChEBHVy8HBAaGhoUafzySWiKgR4uLiMGbMGP1xWFgYEhISBIsnLy8PH374ITw8PGBnZwdPT09ER0fjr7/+AgB4eXkhPz+fW6QS0X+SRqPB559/jlOnThl9DZfYIiJq4aqrqzF06FB069YN+/fvh7u7O+7fv4/09HQ8efIEACAWi+Hm5iZsoERE9cjKyoJcLodGo4FKpTL6Os7EEhE1UVxcHDIyMrB27VqIRCKIRCLcuXMHAHDt2jWMHDkSDg4OkEqliI2NxePHj/XXhoWFYebMmUhISEDbtm0hlUqxdetWlJeXIz4+Ho6OjujatSvS09Nf+v7Xr19HTk4ONm7ciP79+8PHxwchISFISkpC//79AdQtJ4iLi9PH+uKj9gtEo9Fg3rx56NixI1q3bo3g4OAGfbkQEb2OVqvF8uXLERISgoiICFy5cgUDBw40+nomsURETbR27VoMGDAAU6dORX5+PvLz8+Hl5YUnT54gIiICvXr1wsWLF3H48GE8evQI48aNM7h++/bt6NChAzIzMzFz5kxMnz4dY8eOxcCBA3Hp0iUMGzYMsbGxePr0ab3v7+LiAisrK+zbtw9ardbomGtjzc/Px+zZs+Hq6go/Pz8AwIwZM3D27Fns3r0bV65cwdixYzFixAjcunWraYNFRBZLLBbDyspK/7CxscGKFSuwZ88erFu3Dvb29g3qj+vEEhE1QlxcHJ48eYKDBw8CeD6jGhgYiDVr1ujPSUpKwsmTJ3HkyBF92/379+Hl5YWbN2/C19cXYWFh0Gq1OHnyJIDnMxNOTk54//33sWPHDgBAQUEB3N3dcfbsWf3M6r9t2LABCxYsgFgsRlBQEMLDwxETE4POnTsDeD4T26lTJ1y+fBmBgYEG1+7fvx8xMTH4448/EBISgrt376Jz5864e/cuPDw89OdFRkaiX79+SE5OburwEZEFOnTokMGxVqvFypUrUVRUhG3btiE8PLxB/bEmlojIRP78808olUo4ODjUeS0nJwe+vr4AAH9/f327WCxG+/bt0bNnT32bVCoFABQWFr70vRQKBSZNmgSVSoVz585h7969SE5OxqFDhzB06NCXXnf58mXExsZi/fr1CAkJAQBcvXoVWq1WH18tjUaD9u3bG/EvJyKqa/To0XXaoqOjkZycjHfffReTJ0/GqlWr0KpVK6P6YxJLRGQiZWVliIqKwhdffFHnNXd3d/1zGxsbg9dEIpFBW+26ia/bxcbR0RFRUVGIiopCUlIShg8fjqSkpJcmsQUFBRg9ejQ++ugjTJkyxSBusViMrKwsiMVig2vqS8iJiBrLysoKS5cuxahRoxAbG4sePXogNzfXqGuZxBIRNQOJRFKnHrV379748ccfIZPJYG1t3v/dikQi+Pn54cyZM/W+XllZiejoaPj5+WH16tUGr/Xq1QtarRaFhYUYNGiQOcIlIgsXGBiIS5cuYcmSJUZfwxu7iIiagUwmw/nz53Hnzh08fvwYNTU1UCgU+OeffzBhwgRcuHABOTk5OHLkCOLj442+AcsY2dnZiI6Oxr59+3Djxg3cvn0b27Ztw3fffYfo6Oh6r5k2bRru3buHdevWoaioCAUFBSgoKEBVVRV8fX0RExODSZMmYf/+/cjLy0NmZiZSUlLw66+/NlvcREQvsrGxwZdffmn0+ZyJJSJqBvPmzYNcLkf37t1RUVGBvLw8yGQynD59GgsXLsSwYcOg0Wjg4+ODESNGwMqq+eYQPD09IZPJsHz5cv1SWrXHc+bMqfeajIwM5Ofno3v37gbtSqUSYWFhSEtLQ1JSEj777DM8ePAAHTp0QP/+/TFq1Khmi5uILEtERARet56ATqczejk/rk5ARERERCY3d+5c/XO1Wo2dO3dCoVDo254+fYrU1NTX1v/XYhJLRERERGaVm5uLgIAAlJaW6tuKiooglUqNTmJZE0tEREREZmVvb4+qqiqDhLW8vBy2trZG98EkloiIiIjMyt3dHdbW1ti1a5e+bfv27foNWozBG7uIiIiIyOw+/fRTyOVyfPXVV6ioqMCtW7ewYcMGo69nTSwRERERCWLz5s04duwYJBIJRo8ejfHjxxt9LZNYIiIiImpxWE5ARERERCa3fft2o86Ty+VGnceZWCIiIiIyObFYjDZt2kAkEgEAampqUFJSAmdnZwDPNzpQq9VcJ5aIiIiI/jvEYjEePnwIqVQKAMjLy0NAQABKSkoAPF8n1s3NzehtubnEFhERERGZnU6nM9iG9t/Hr8MkloiIiIhaHCaxRERERGRyzV3ByiSWiIiIiEyu9oauWvb29hg8eLDB63Z2dsb3xxu7iIiIiMjUCgsL4eLiUieZbSwmsURERETU4rCcgIiIiIhaHCaxRERERNTiMIklIiIiohaHSSwRERERtThMYomIiIioxWESS0REREQtDpNYIiIiImpxmMQSERERUYvzf6ZZxmSdoxyiAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 720x1620 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=pumpkins,\n",
+    "    x=\"Item Size\", y=\"Color\", row='Variety',\n",
+    "    kind=\"box\", orient=\"h\",\n",
+    "    sharex=False, margin_titles=True,\n",
+    "    height=1.8, aspect=4, palette=palette,\n",
+    ")\n",
+    "# Defining axis labels \n",
+    "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
+    "g.set_titles(row_template=\"{row_name}\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Color', ylabel='ord__Item Size'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Suppressing warning message claiming that a portion of points cannot be placed into the plot due to the high number of data points\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')\n",
+    "\n",
+    "palette = {\n",
+    "    0: 'orange',\n",
+    "    1: 'wheat'\n",
+    "}\n",
+    "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**ระวัง**: การละเลยคำเตือนไม่ใช่วิธีปฏิบัติที่ดีและควรหลีกเลี่ยงเมื่อเป็นไปได้ คำเตือนมักมีข้อความที่เป็นประโยชน์ซึ่งช่วยให้เราปรับปรุงโค้ดและแก้ไขปัญหา  \n",
+    "เหตุผลที่เราละเลยคำเตือนนี้โดยเฉพาะคือเพื่อรับประกันความชัดเจนของกราฟ การแสดงจุดข้อมูลทั้งหมดด้วยขนาดเครื่องหมายที่ลดลง ในขณะที่ยังคงความสอดคล้องกับสีของพาเลต จะทำให้การแสดงผลไม่ชัดเจน\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# X is the encoded features\n",
+    "X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
+    "# y is the encoded label\n",
+    "y = encoded_pumpkins['Color']\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.94      0.98      0.96       166\n",
+      "           1       0.85      0.67      0.75        33\n",
+      "\n",
+      "    accuracy                           0.92       199\n",
+      "   macro avg       0.89      0.82      0.85       199\n",
+      "weighted avg       0.92      0.92      0.92       199\n",
+      "\n",
+      "Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
+      " 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
+      " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+      " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
+      "F1-score:  0.7457627118644068\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Train a logistic regression model on the pumpkin dataset\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model and print the results\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('F1-score: ', f1_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[162,   4],\n",
+       "       [ 11,  22]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "y_scores = model.predict_proba(X_test)\n",
+    "# calculate ROC curve\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "# Plot the diagonal 50% line\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "# Plot the FPR and TPR achieved by our model\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9749908725812341\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate AUC score\n",
+    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+    "print(auc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "vscode": {
+   "interpreter": {
+    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "ef50cc584e0b79412610cc7da15e1f86",
+   "translation_date": "2025-09-06T13:28:01+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/3-Web-App/1-Web-App/notebook.ipynb b/translations/th/3-Web-App/1-Web-App/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/th/3-Web-App/1-Web-App/solution/notebook.ipynb b/translations/th/3-Web-App/1-Web-App/solution/notebook.ipynb
new file mode 100644
index 000000000..6fbe430c7
--- /dev/null
+++ b/translations/th/3-Web-App/1-Web-App/solution/notebook.ipynb
@@ -0,0 +1,267 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5fa2e8f4584c78250ca9729b46562ceb",
+   "translation_date": "2025-09-06T14:32:18+00:00",
+   "source_file": "3-Web-App/1-Web-App/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           datetime                  city state country     shape  \\\n",
+       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
+       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
+       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
+       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
+       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
+       "\n",
+       "   duration (seconds) duration (hours/min)  \\\n",
+       "0              2700.0           45 minutes   \n",
+       "1              7200.0              1-2 hrs   \n",
+       "2                20.0           20 seconds   \n",
+       "3                20.0             1/2 hour   \n",
+       "4               900.0           15 minutes   \n",
+       "\n",
+       "                                            comments date posted   latitude  \\\n",
+       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
+       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
+       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
+       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
+       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
+       "\n",
+       "    longitude  \n",
+       "0  -97.941111  \n",
+       "1  -98.581082  \n",
+       "2   -2.916667  \n",
+       "3  -96.645833  \n",
+       "4 -157.803611  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ufos = pd.read_csv('../data/ufos.csv')\n",
+    "ufos.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "\n",
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()\n",
+    "\n",
+    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufos.dropna(inplace=True)\n",
+    "\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "\n",
+    "X = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       1.00      0.02      0.05       250\n",
+      "           2       0.00      0.00      0.00         8\n",
+      "           3       0.94      1.00      0.97       131\n",
+      "           4       0.95      1.00      0.97      4743\n",
+      "\n",
+      "    accuracy                           0.95      5173\n",
+      "   macro avg       0.78      0.60      0.60      5173\n",
+      "weighted avg       0.95      0.95      0.93      5173\n",
+      "\n",
+      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
+      "Accuracy:  0.9512855209742895\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "model_filename = 'ufo-model.pkl'\n",
+    "pickle.dump(model, open(model_filename,'wb'))\n",
+    "\n",
+    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
+    "print(model.predict([[50,44,-12]]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามนุษย์มืออาชีพ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/1-Introduction/notebook.ipynb b/translations/th/4-Classification/1-Introduction/notebook.ipynb
new file mode 100644
index 000000000..bcad60b1d
--- /dev/null
+++ b/translations/th/4-Classification/1-Introduction/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d544ef384b7ba73757d830a72372a7f2",
+   "translation_date": "2025-09-06T14:50:56+00:00",
+   "source_file": "4-Classification/1-Introduction/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/th/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
new file mode 100644
index 000000000..34b01cff4
--- /dev/null
+++ b/translations/th/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -0,0 +1,716 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_10-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "2621e24705e8100893c9bf84e0fc8aef",
+   "translation_date": "2025-09-06T15:00:03+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb",
+   "language_code": "th"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ItETB4tSFprR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## บทนำสู่การจำแนกประเภท: ทำความสะอาด เตรียม และแสดงข้อมูลของคุณ\n",
+    "\n",
+    "ในบทเรียนทั้งสี่นี้ คุณจะได้สำรวจหัวข้อพื้นฐานของการเรียนรู้ด้วยเครื่องแบบคลาสสิก - *การจำแนกประเภท* เราจะเดินทางผ่านการใช้อัลกอริธึมการจำแนกประเภทต่าง ๆ กับชุดข้อมูลเกี่ยวกับอาหารที่ยอดเยี่ยมของเอเชียและอินเดีย หวังว่าคุณจะหิวแล้ว!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/pinch.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>เฉลิมฉลองอาหารเอเชียในบทเรียนเหล่านี้! ภาพโดย Jen Looper</figcaption>\n",
+    "\n",
+    "การจำแนกประเภทเป็นรูปแบบหนึ่งของ [การเรียนรู้แบบมีผู้สอน](https://wikipedia.org/wiki/Supervised_learning) ซึ่งมีความคล้ายคลึงกับเทคนิคการถดถอย ในการจำแนกประเภท คุณจะฝึกโมเดลเพื่อทำนายว่า `หมวดหมู่` ใดที่รายการนั้นอยู่ หากการเรียนรู้ด้วยเครื่องเกี่ยวกับการทำนายค่าหรือชื่อของสิ่งต่าง ๆ โดยใช้ชุดข้อมูล การจำแนกประเภทมักจะแบ่งออกเป็นสองกลุ่ม: *การจำแนกประเภทแบบทวิภาค* และ *การจำแนกประเภทแบบหลายคลาส*\n",
+    "\n",
+    "จำไว้ว่า:\n",
+    "\n",
+    "-   **การถดถอยเชิงเส้น** ช่วยให้คุณทำนายความสัมพันธ์ระหว่างตัวแปรและทำการทำนายที่แม่นยำเกี่ยวกับตำแหน่งที่จุดข้อมูลใหม่จะตกอยู่ในความสัมพันธ์กับเส้นนั้น ตัวอย่างเช่น คุณสามารถทำนายค่าตัวเลข เช่น *ราคาของฟักทองในเดือนกันยายนเทียบกับเดือนธันวาคม*\n",
+    "\n",
+    "-   **การถดถอยโลจิสติก** ช่วยให้คุณค้นพบ \"หมวดหมู่ทวิภาค\": ที่จุดราคานี้ *ฟักทองนี้เป็นสีส้มหรือไม่เป็นสีส้ม*?\n",
+    "\n",
+    "การจำแนกประเภทใช้หลากหลายอัลกอริธึมเพื่อกำหนดวิธีอื่น ๆ ในการกำหนดฉลากหรือคลาสของจุดข้อมูล ลองทำงานกับข้อมูลอาหารนี้เพื่อดูว่า โดยการสังเกตกลุ่มของส่วนผสม เราสามารถกำหนดแหล่งกำเนิดของอาหารได้หรือไม่\n",
+    "\n",
+    "### [**แบบทดสอบก่อนการบรรยาย**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)\n",
+    "\n",
+    "### **บทนำ**\n",
+    "\n",
+    "การจำแนกประเภทเป็นหนึ่งในกิจกรรมพื้นฐานของนักวิจัยและนักวิทยาศาสตร์ข้อมูลด้านการเรียนรู้ด้วยเครื่อง ตั้งแต่การจำแนกค่าทวิภาคพื้นฐาน (\"อีเมลนี้เป็นสแปมหรือไม่?\") ไปจนถึงการจำแนกภาพและการแบ่งส่วนที่ซับซ้อนโดยใช้การมองเห็นด้วยคอมพิวเตอร์ การสามารถจัดเรียงข้อมูลเป็นคลาสและตั้งคำถามกับมันเป็นสิ่งที่มีประโยชน์เสมอ\n",
+    "\n",
+    "หากจะกล่าวถึงกระบวนการในเชิงวิทยาศาสตร์ วิธีการจำแนกประเภทของคุณจะสร้างโมเดลการทำนายที่ช่วยให้คุณสามารถจับคู่ความสัมพันธ์ระหว่างตัวแปรอินพุตกับตัวแปรเอาต์พุตได้\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/binary-multiclass.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>ปัญหาแบบทวิภาคและแบบหลายคลาสสำหรับอัลกอริธึมการจำแนกประเภท ภาพประกอบโดย Jen Looper</figcaption>\n",
+    "\n",
+    "ก่อนเริ่มกระบวนการทำความสะอาดข้อมูลของเรา การแสดงภาพ และการเตรียมข้อมูลสำหรับงาน ML ของเรา ลองเรียนรู้เกี่ยวกับวิธีต่าง ๆ ที่การเรียนรู้ด้วยเครื่องสามารถนำมาใช้เพื่อจำแนกข้อมูลได้\n",
+    "\n",
+    "การจำแนกประเภทที่ได้มาจาก [สถิติ](https://wikipedia.org/wiki/Statistical_classification) ใช้คุณลักษณะ เช่น `smoker`, `weight`, และ `age` เพื่อกำหนด *ความน่าจะเป็นในการพัฒนาโรค X* ในฐานะเทคนิคการเรียนรู้แบบมีผู้สอนที่คล้ายกับการฝึกถดถอยที่คุณทำมาก่อนหน้านี้ ข้อมูลของคุณจะถูกติดป้ายกำกับ และอัลกอริธึม ML จะใช้ป้ายกำกับเหล่านั้นเพื่อจำแนกและทำนายคลาส (หรือ 'คุณลักษณะ') ของชุดข้อมูลและกำหนดให้กับกลุ่มหรือผลลัพธ์\n",
+    "\n",
+    "✅ ลองใช้เวลาสักครู่เพื่อจินตนาการถึงชุดข้อมูลเกี่ยวกับอาหาร โมเดลแบบหลายคลาสจะสามารถตอบคำถามอะไรได้บ้าง? โมเดลแบบทวิภาคจะสามารถตอบคำถามอะไรได้บ้าง? ถ้าคุณต้องการกำหนดว่าอาหารที่กำหนดมีแนวโน้มที่จะใช้ลูกซัดหรือไม่? หรือถ้าคุณต้องการดูว่า หากคุณได้รับของขวัญเป็นถุงช้อปปิ้งที่เต็มไปด้วยโป๊ยกั๊ก อาร์ติโชก กะหล่ำดอก และฮอร์สแรดิช คุณจะสามารถสร้างอาหารอินเดียทั่วไปได้หรือไม่?\n",
+    "\n",
+    "### **สวัสดี 'ตัวจำแนก'**\n",
+    "\n",
+    "คำถามที่เราต้องการถามจากชุดข้อมูลอาหารนี้เป็นคำถามแบบ **หลายคลาส** เนื่องจากเรามีอาหารประจำชาติหลายประเภทที่สามารถทำงานได้ เมื่อพิจารณากลุ่มของส่วนผสมแล้ว ข้อมูลจะเข้ากับคลาสใดในหลาย ๆ คลาสนี้?\n",
+    "\n",
+    "Tidymodels มีอัลกอริธึมหลายแบบให้เลือกใช้เพื่อจำแนกข้อมูล ขึ้นอยู่กับประเภทของปัญหาที่คุณต้องการแก้ไข ในสองบทเรียนถัดไป คุณจะได้เรียนรู้เกี่ยวกับอัลกอริธึมเหล่านี้\n",
+    "\n",
+    "#### **ข้อกำหนดเบื้องต้น**\n",
+    "\n",
+    "สำหรับบทเรียนนี้ เราจะต้องใช้แพ็กเกจต่อไปนี้เพื่อทำความสะอาด เตรียม และแสดงข้อมูลของเรา:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) เป็น [ชุดของแพ็กเกจ R](https://www.tidyverse.org/packages) ที่ออกแบบมาเพื่อทำให้วิทยาศาสตร์ข้อมูลเร็วขึ้น ง่ายขึ้น และสนุกขึ้น!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) เป็นกรอบงาน [ชุดของแพ็กเกจ](https://www.tidymodels.org/packages/) สำหรับการสร้างโมเดลและการเรียนรู้ด้วยเครื่อง\n",
+    "\n",
+    "-   `DataExplorer`: [แพ็กเกจ DataExplorer](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) มีไว้เพื่อทำให้กระบวนการ EDA และการสร้างรายงานง่ายขึ้นและอัตโนมัติ\n",
+    "\n",
+    "-   `themis`: [แพ็กเกจ themis](https://themis.tidymodels.org/) ให้ขั้นตอนเพิ่มเติมสำหรับการจัดการข้อมูลที่ไม่สมดุล\n",
+    "\n",
+    "คุณสามารถติดตั้งแพ็กเกจเหล่านี้ได้โดยใช้:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "หรือใช้สคริปต์ด้านล่างเพื่อตรวจสอบว่าคุณมีแพ็กเกจที่จำเป็นสำหรับการทำโมดูลนี้หรือไม่ และติดตั้งให้คุณในกรณีที่ขาดหายไป\n"
+   ],
+   "metadata": {
+    "id": "ri5bQxZ-Fz_0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "KIPxa4elGAPI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เราจะโหลดแพ็กเกจที่ยอดเยี่ยมเหล่านี้ในภายหลังและทำให้พร้อมใช้งานในเซสชัน R ปัจจุบันของเรา (นี่เป็นเพียงการแสดงตัวอย่าง `pacman::p_load()` ได้ทำสิ่งนี้ให้คุณแล้ว)\n"
+   ],
+   "metadata": {
+    "id": "YkKAxOJvGD4C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## แบบฝึกหัด - ทำความสะอาดและปรับสมดุลข้อมูลของคุณ\n",
+    "\n",
+    "งานแรกที่ต้องทำก่อนเริ่มโครงการนี้คือการทำความสะอาดและ **ปรับสมดุล** ข้อมูลของคุณเพื่อให้ได้ผลลัพธ์ที่ดียิ่งขึ้น\n",
+    "\n",
+    "มาทำความรู้จักกับข้อมูลกันเถอะ!🕵️\n"
+   ],
+   "metadata": {
+    "id": "PFkQDlk0GN5O"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Qccw7okxGT0S"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "น่าสนใจ! จากลักษณะของมัน คอลัมน์แรกดูเหมือนจะเป็นคอลัมน์ประเภท `id` ลองมาหาข้อมูลเพิ่มเติมเกี่ยวกับข้อมูลนี้กันเถอะ\n"
+   ],
+   "metadata": {
+    "id": "XrWnlgSrGVmR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Basic information about the data\r\n",
+    "df %>%\r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# Visualize basic information above\r\n",
+    "df %>% \r\n",
+    "  plot_intro(ggtheme = theme_light())"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "4UcGmxRxGieA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "จากผลลัพธ์ เราสามารถเห็นได้ทันทีว่าเรามี `2448` แถว และ `385` คอลัมน์ และไม่มีค่าที่หายไปเลย (`0` missing values) นอกจากนี้ เรายังมีคอลัมน์แบบไม่ต่อเนื่อง 1 คอลัมน์ คือ *cuisine*\n",
+    "\n",
+    "## แบบฝึกหัด - เรียนรู้เกี่ยวกับประเภทอาหาร\n",
+    "\n",
+    "ตอนนี้งานเริ่มน่าสนใจมากขึ้นแล้ว มาค้นพบการกระจายของข้อมูลในแต่ละประเภทอาหารกันเถอะ\n"
+   ],
+   "metadata": {
+    "id": "AaPubl__GmH5"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Count observations per cuisine\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(n)\r\n",
+    "\r\n",
+    "# Plot the distribution\r\n",
+    "theme_set(theme_light())\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"cuisine\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FRsBVy5eGrrv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "มีจำนวนอาหารที่จำกัด แต่การกระจายของข้อมูลไม่เท่ากัน คุณสามารถแก้ไขได้! ก่อนที่จะทำเช่นนั้น ลองสำรวจเพิ่มเติมอีกเล็กน้อย\n",
+    "\n",
+    "ต่อไป เรามาแบ่งอาหารแต่ละประเภทออกเป็น tibble ของตัวเอง และตรวจสอบว่ามีข้อมูลมากน้อยแค่ไหน (จำนวนแถวและคอลัมน์) ต่ออาหารแต่ละประเภท\n",
+    "\n",
+    "> [tibble](https://tibble.tidyverse.org/) คือรูปแบบข้อมูลเฟรมที่ทันสมัย\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_filter.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>ภาพประกอบโดย @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "vVvyDb1kG2in"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create individual tibble for the cuisines\r\n",
+    "thai_df <- df %>% \r\n",
+    "  filter(cuisine == \"thai\")\r\n",
+    "japanese_df <- df %>% \r\n",
+    "  filter(cuisine == \"japanese\")\r\n",
+    "chinese_df <- df %>% \r\n",
+    "  filter(cuisine == \"chinese\")\r\n",
+    "indian_df <- df %>% \r\n",
+    "  filter(cuisine == \"indian\")\r\n",
+    "korean_df <- df %>% \r\n",
+    "  filter(cuisine == \"korean\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Find out how much data is available per cuisine\r\n",
+    "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
+    "    \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
+    "    \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
+    "    \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
+    "    \"korean_df:\", dim(korean_df))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "0TvXUxD3G8Bk"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **แบบฝึกหัด - ค้นหาเครื่องปรุงยอดนิยมตามประเภทอาหารด้วย dplyr**\n",
+    "\n",
+    "ตอนนี้คุณสามารถเจาะลึกลงไปในข้อมูลและเรียนรู้ว่าเครื่องปรุงที่เป็นเอกลักษณ์ของแต่ละประเภทอาหารคืออะไร คุณควรทำความสะอาดข้อมูลที่ซ้ำซ้อนซึ่งอาจสร้างความสับสนระหว่างประเภทอาหาร ดังนั้นมาทำความเข้าใจปัญหานี้กันเถอะ\n",
+    "\n",
+    "สร้างฟังก์ชัน `create_ingredient()` ใน R ที่จะคืนค่าเป็น dataframe ของเครื่องปรุง ฟังก์ชันนี้จะเริ่มต้นด้วยการลบคอลัมน์ที่ไม่เป็นประโยชน์ออก และจัดเรียงเครื่องปรุงตามจำนวนครั้งที่ปรากฏ\n",
+    "\n",
+    "โครงสร้างพื้นฐานของฟังก์ชันใน R คือ:\n",
+    "\n",
+    "`myFunction <- function(arglist){`\n",
+    "\n",
+    "**`...`**\n",
+    "\n",
+    "**`return`**`(value)`\n",
+    "\n",
+    "`}`\n",
+    "\n",
+    "สามารถดูการแนะนำเบื้องต้นเกี่ยวกับฟังก์ชันใน R ได้ [ที่นี่](https://skirmer.github.io/presentations/functions_with_r.html#1)\n",
+    "\n",
+    "มาเริ่มกันเลย! เราจะใช้ [คำกริยาใน dplyr](https://dplyr.tidyverse.org/) ที่เราได้เรียนรู้ในบทเรียนก่อนหน้า เพื่อทบทวน:\n",
+    "\n",
+    "-   `dplyr::select()`: ช่วยให้คุณเลือกว่าจะเก็บหรือไม่เก็บ **คอลัมน์** ใด\n",
+    "\n",
+    "-   `dplyr::pivot_longer()`: ช่วยให้คุณ \"ยืด\" ข้อมูล เพิ่มจำนวนแถวและลดจำนวนคอลัมน์\n",
+    "\n",
+    "-   `dplyr::group_by()` และ `dplyr::summarise()`: ช่วยให้คุณหาสถิติสรุปสำหรับกลุ่มต่าง ๆ และจัดให้อยู่ในตารางที่ดูดี\n",
+    "\n",
+    "-   `dplyr::filter()`: สร้างชุดข้อมูลย่อยที่มีเฉพาะแถวที่ตรงตามเงื่อนไขของคุณ\n",
+    "\n",
+    "-   `dplyr::mutate()`: ช่วยให้คุณสร้างหรือแก้ไขคอลัมน์\n",
+    "\n",
+    "ลองดู [บทเรียน learnr ที่เต็มไปด้วยศิลปะ](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) โดย Allison Horst ที่แนะนำฟังก์ชันการจัดการข้อมูลที่มีประโยชน์ใน dplyr *(ส่วนหนึ่งของ Tidyverse)*\n"
+   ],
+   "metadata": {
+    "id": "K3RF5bSCHC76"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Creates a functions that returns the top ingredients by class\r\n",
+    "\r\n",
+    "create_ingredient <- function(df){\r\n",
+    "  \r\n",
+    "  # Drop the id column which is the first colum\r\n",
+    "  ingredient_df = df %>% select(-1) %>% \r\n",
+    "  # Transpose data to a long format\r\n",
+    "    pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
+    "  # Find the top most ingredients for a particular cuisine\r\n",
+    "    group_by(ingredients) %>% \r\n",
+    "    summarise(n_instances = sum(count)) %>% \r\n",
+    "    filter(n_instances != 0) %>% \r\n",
+    "  # Arrange by descending order\r\n",
+    "    arrange(desc(n_instances)) %>% \r\n",
+    "    mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
+    "  \r\n",
+    "  \r\n",
+    "  return(ingredient_df)\r\n",
+    "} # End of function"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "uB_0JR82HTPa"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ตอนนี้เราสามารถใช้ฟังก์ชันนี้เพื่อดูแนวโน้มของส่วนผสมยอดนิยมสิบอันดับแรกตามประเภทของอาหารได้แล้ว ลองนำไปใช้กับ `thai_df` กันดู\n"
+   ],
+   "metadata": {
+    "id": "h9794WF8HWmc"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Call create_ingredient and display popular ingredients\r\n",
+    "thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
+    "\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "agQ-1HrcHaEA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ในส่วนก่อนหน้านี้ เราได้ใช้ `geom_col()` มาดูกันว่าคุณสามารถใช้ `geom_bar` ได้อย่างไรบ้างในการสร้างแผนภูมิแท่ง ใช้ `?geom_bar` เพื่ออ่านเพิ่มเติม\n"
+   ],
+   "metadata": {
+    "id": "kHu9ffGjHdcX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a bar chart for popular thai cuisines\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10) %>% \r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "fb3Bx_3DHj6e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "RHP_xgdkHnvM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
+    "create_ingredient(df = japanese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "019v8F0XHrRU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "แล้วอาหารจีนล่ะ?\n"
+   ],
+   "metadata": {
+    "id": "iIGM7vO8Hu3v"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
+    "create_ingredient(df = chinese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lHd9_gd2HyzU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ir8qyQbNH1c7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Indian cuisines and make bar chart\r\n",
+    "create_ingredient(df = indian_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ApukQtKjH5FO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qv30cwY1H-FM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Korean cuisines and make bar chart\r\n",
+    "create_ingredient(df = korean_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lumgk9cHIBie"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "จากการวิเคราะห์ข้อมูลด้วยภาพ เราสามารถตัดส่วนผสมที่พบบ่อยที่สุดซึ่งสร้างความสับสนระหว่างอาหารที่แตกต่างกันออกได้ โดยใช้ `dplyr::select()`\n",
+    "\n",
+    "ใครๆ ก็ชอบข้าว กระเทียม และขิง!\n"
+   ],
+   "metadata": {
+    "id": "iO4veMXuIEta"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "iHJPiG6rIUcK"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## การเตรียมข้อมูลด้วย Recipes 👩‍🍳👨‍🍳 - การจัดการข้อมูลที่ไม่สมดุล ⚖️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>ภาพประกอบโดย @allison_horst</figcaption>\n",
+    "\n",
+    "เนื่องจากบทเรียนนี้เกี่ยวกับอาหาร เราจึงต้องนำ `recipes` มาใช้ในบริบทที่เหมาะสม\n",
+    "\n",
+    "Tidymodels มีอีกหนึ่งแพ็กเกจที่น่าสนใจ: `recipes` - แพ็กเกจสำหรับการเตรียมข้อมูลก่อนการวิเคราะห์\n"
+   ],
+   "metadata": {
+    "id": "kkFd-JxdIaL6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "มาดูการกระจายของอาหารของเราอีกครั้ง\n"
+   ],
+   "metadata": {
+    "id": "6l2ubtTPJAhY"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "old_label_count <- df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "old_label_count"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "1e-E9cb7JDVi"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ดังที่คุณเห็น มีการกระจายจำนวนอาหารที่ไม่เท่ากันอย่างชัดเจน อาหารเกาหลีมีจำนวนเกือบ 3 เท่าของอาหารไทย ข้อมูลที่ไม่สมดุลมักส่งผลเสียต่อประสิทธิภาพของโมเดล ลองนึกถึงการจำแนกประเภทแบบสองค่า หากข้อมูลส่วนใหญ่เป็นคลาสเดียว โมเดลการเรียนรู้ของเครื่อง (ML) จะมีแนวโน้มที่จะทำนายคลาสนั้นบ่อยขึ้น เพียงเพราะมีข้อมูลสำหรับคลาสนั้นมากกว่า การปรับสมดุลข้อมูลจะช่วยแก้ไขความไม่สมดุลนี้โดยการปรับข้อมูลที่มีการกระจายไม่เท่ากัน หลายโมเดลทำงานได้ดีที่สุดเมื่อจำนวนการสังเกตเท่ากัน และมักจะประสบปัญหาเมื่อข้อมูลไม่สมดุล\n",
+    "\n",
+    "มีวิธีหลักสองวิธีในการจัดการกับชุดข้อมูลที่ไม่สมดุล:\n",
+    "\n",
+    "-   เพิ่มจำนวนการสังเกตในคลาสที่มีจำนวนน้อย: `Over-sampling` เช่น การใช้ SMOTE algorithm\n",
+    "\n",
+    "-   ลดจำนวนการสังเกตในคลาสที่มีจำนวนมาก: `Under-sampling`\n",
+    "\n",
+    "ตอนนี้เรามาแสดงวิธีจัดการกับชุดข้อมูลที่ไม่สมดุลโดยใช้ `recipe` กัน `Recipe` สามารถมองว่าเป็นแผนงานที่อธิบายขั้นตอนที่ควรนำไปใช้กับชุดข้อมูลเพื่อเตรียมพร้อมสำหรับการวิเคราะห์ข้อมูล\n"
+   ],
+   "metadata": {
+    "id": "soAw6826JKx9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HS41brUIJVJy"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "มาดูขั้นตอนการเตรียมข้อมูลของเรากัน\n",
+    "\n",
+    "-   การเรียกใช้ `recipe()` พร้อมสูตรจะบอกให้ recipe กำหนด *บทบาท* ของตัวแปรโดยใช้ข้อมูล `df_select` เป็นข้อมูลอ้างอิง ตัวอย่างเช่น คอลัมน์ `cuisine` ถูกกำหนดให้มีบทบาทเป็น `outcome` ในขณะที่คอลัมน์อื่นๆ ถูกกำหนดให้มีบทบาทเป็น `predictor`\n",
+    "\n",
+    "-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html) สร้าง *สเปค* ของขั้นตอนใน recipe ที่สร้างตัวอย่างใหม่ของคลาสที่มีจำนวนน้อยโดยใช้เพื่อนบ้านที่ใกล้ที่สุดของกรณีเหล่านี้\n",
+    "\n",
+    "ตอนนี้ หากเราต้องการดูข้อมูลที่ผ่านการเตรียมแล้ว เราจะต้อง [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) และ [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) recipe ของเรา\n",
+    "\n",
+    "`prep()`: ประเมินพารามิเตอร์ที่จำเป็นจากชุดข้อมูลการฝึกที่สามารถนำไปใช้กับชุดข้อมูลอื่นในภายหลัง\n",
+    "\n",
+    "`bake()`: ใช้ recipe ที่ผ่านการเตรียมแล้วและดำเนินการกับชุดข้อมูลใดๆ\n"
+   ],
+   "metadata": {
+    "id": "Yb-7t7XcJaC8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep and bake the recipe\r\n",
+    "preprocessed_df <- cuisines_recipe %>% \r\n",
+    "  prep() %>% \r\n",
+    "  bake(new_data = NULL) %>% \r\n",
+    "  relocate(cuisine)\r\n",
+    "\r\n",
+    "# Display data\r\n",
+    "preprocessed_df %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Quick summary stats\r\n",
+    "preprocessed_df %>% \r\n",
+    "  introduce()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "9QhSgdpxJl44"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ตอนนี้เรามาตรวจสอบการกระจายของอาหารของเราและเปรียบเทียบกับข้อมูลที่ไม่สมดุลกัน\n"
+   ],
+   "metadata": {
+    "id": "dmidELh_LdV7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "new_label_count <- preprocessed_df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "list(new_label_count = new_label_count,\r\n",
+    "     old_label_count = old_label_count)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "aSh23klBLwDz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "อร่อย! ข้อมูลสะอาด สมดุล และน่าทานมาก 😋!\n",
+    "\n",
+    "> โดยปกติแล้ว สูตร (recipe) มักถูกใช้เป็นตัวเตรียมข้อมูลก่อนการสร้างโมเดล ซึ่งจะกำหนดขั้นตอนที่ควรนำไปใช้กับชุดข้อมูลเพื่อเตรียมให้พร้อมสำหรับการสร้างโมเดล ในกรณีนี้ `workflow()` มักจะถูกใช้งาน (อย่างที่เราได้เห็นในบทเรียนก่อนหน้านี้) แทนที่จะประเมินสูตรด้วยตนเอง\n",
+    ">\n",
+    "> ดังนั้น โดยทั่วไปคุณไม่จำเป็นต้องใช้ **`prep()`** และ **`bake()`** กับสูตรเมื่อคุณใช้ tidymodels แต่ฟังก์ชันเหล่านี้มีประโยชน์ในกรณีที่คุณต้องการยืนยันว่าสูตรทำงานตามที่คุณคาดหวังไว้ เช่นในกรณีของเรา\n",
+    ">\n",
+    "> เมื่อคุณใช้ **`bake()`** กับสูตรที่ผ่านการ **`prep()`** แล้ว โดยกำหนด **`new_data = NULL`** คุณจะได้ข้อมูลที่คุณให้ไว้ตอนกำหนดสูตรกลับมา แต่ข้อมูลนั้นจะผ่านขั้นตอนการเตรียมข้อมูลแล้ว\n",
+    "\n",
+    "ตอนนี้เรามาบันทึกสำเนาของข้อมูลนี้ไว้เพื่อใช้ในบทเรียนถัดไป:\n"
+   ],
+   "metadata": {
+    "id": "HEu80HZ8L7ae"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Save preprocessed data\r\n",
+    "write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "cBmCbIgrMOI6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ไฟล์ CSV ใหม่สามารถพบได้ในโฟลเดอร์ข้อมูลหลัก\n",
+    "\n",
+    "**🚀ความท้าทาย**\n",
+    "\n",
+    "หลักสูตรนี้มีชุดข้อมูลที่น่าสนใจหลายชุด ลองค้นหาในโฟลเดอร์ `data` และดูว่ามีชุดข้อมูลใดที่เหมาะสมสำหรับการจัดประเภทแบบไบนารีหรือหลายคลาสหรือไม่? คุณจะตั้งคำถามอะไรกับชุดข้อมูลนี้?\n",
+    "\n",
+    "## [**แบบทดสอบหลังการบรรยาย**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)\n",
+    "\n",
+    "## **ทบทวนและศึกษาด้วยตนเอง**\n",
+    "\n",
+    "-   ลองดู [แพ็กเกจ themis](https://github.com/tidymodels/themis) มีเทคนิคอื่นใดที่เราสามารถใช้เพื่อจัดการกับข้อมูลที่ไม่สมดุลได้บ้าง?\n",
+    "\n",
+    "-   เว็บไซต์อ้างอิงของ Tidy models [เว็บไซต์อ้างอิง](https://www.tidymodels.org/start/)\n",
+    "\n",
+    "-   H. Wickham และ G. Grolemund, [*R for Data Science: Visualize, Model, Transform, Tidy, and Import Data*](https://r4ds.had.co.nz/)\n",
+    "\n",
+    "#### ขอขอบคุณ:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) สำหรับการสร้างภาพประกอบที่ยอดเยี่ยมซึ่งทำให้ R น่าสนใจและเข้าถึงได้มากขึ้น ค้นหาภาพประกอบเพิ่มเติมได้ที่ [แกลเลอรี](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM)\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) และ [Jen Looper](https://www.twitter.com/jenlooper) สำหรับการสร้างเวอร์ชัน Python ดั้งเดิมของโมดูลนี้ ♥️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>ภาพประกอบโดย @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "WQs5621pMGwf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามนุษย์มืออาชีพ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/1-Introduction/solution/notebook.ipynb b/translations/th/4-Classification/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..b82f87655
--- /dev/null
+++ b/translations/th/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,700 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# อาหารเอเชียและอินเดียแสนอร่อย\n",
+    "\n",
+    "## แนะนำ\n",
+    "อาหารเอเชียและอินเดียมีรสชาติที่หลากหลายและเต็มไปด้วยเครื่องเทศที่เป็นเอกลักษณ์ ไม่ว่าคุณจะชอบรสเผ็ด รสหวาน หรือรสเปรี้ยว คุณจะพบเมนูที่ตอบโจทย์ความชอบของคุณได้อย่างแน่นอน\n",
+    "\n",
+    "## อาหารเอเชียยอดนิยม\n",
+    "### ซูชิ\n",
+    "ซูชิเป็นอาหารญี่ปุ่นที่ได้รับความนิยมทั่วโลก ประกอบด้วยข้าวปรุงรสและปลาดิบหรือส่วนผสมอื่น ๆ ที่ห่อด้วยสาหร่ายหรือจัดวางอย่างสวยงาม\n",
+    "\n",
+    "### ผัดไทย\n",
+    "ผัดไทยเป็นอาหารไทยที่มีชื่อเสียง ประกอบด้วยเส้นก๋วยเตี๋ยวผัดกับไข่ เต้าหู้ กุ้ง และซอสที่มีรสชาติกลมกล่อม\n",
+    "\n",
+    "### ติ่มซำ\n",
+    "ติ่มซำเป็นอาหารจีนที่มักเสิร์ฟในรูปแบบของอาหารว่าง มีหลากหลายชนิด เช่น ขนมจีบ ฮะเก๋า และซาลาเปา\n",
+    "\n",
+    "## อาหารอินเดียยอดนิยม\n",
+    "### แกงกะหรี่\n",
+    "แกงกะหรี่เป็นอาหารอินเดียที่มีรสชาติเข้มข้นและหอมเครื่องเทศ มักเสิร์ฟพร้อมข้าวหรือแป้งนาน\n",
+    "\n",
+    "### ไก่ทันดูรี\n",
+    "ไก่ทันดูรีเป็นเมนูที่ปรุงด้วยเครื่องเทศและโยเกิร์ต แล้วนำไปย่างในเตาทันดูร์จนได้รสชาติที่หอมและเข้มข้น\n",
+    "\n",
+    "### ซาโมซ่า\n",
+    "ซาโมซ่าเป็นอาหารว่างที่มีไส้หลากหลาย เช่น มันฝรั่งและถั่ว มักทอดจนกรอบและเสิร์ฟพร้อมซอสจิ้ม\n",
+    "\n",
+    "## เคล็ดลับการทำอาหาร\n",
+    "- [!TIP] ใช้เครื่องเทศสดใหม่เพื่อเพิ่มรสชาติให้กับอาหารของคุณ\n",
+    "- [!NOTE] การปรับรสชาติให้เหมาะกับความชอบของคุณเป็นสิ่งสำคัญ\n",
+    "- [!WARNING] ระวังอย่าใช้เครื่องเทศมากเกินไป เพราะอาจทำให้อาหารมีรสชาติเข้มจนเกินไป\n",
+    "\n",
+    "## สรุป\n",
+    "อาหารเอเชียและอินเดียมีความหลากหลายและเต็มไปด้วยรสชาติที่น่าตื่นเต้น ลองสำรวจเมนูต่าง ๆ และปรุงอาหารด้วยตัวเองเพื่อสัมผัสประสบการณ์ที่ไม่เหมือนใคร!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
+      "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df  = pd.read_csv('../../data/cuisines.csv')"
+   ]
+  },
+  {
+   "source": [
+    "ชุดข้อมูลนี้ประกอบด้วย 385 คอลัมน์ที่แสดงถึงส่วนผสมทุกประเภทในอาหารหลากหลายประเภทจากชุดอาหารที่กำหนด\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0          65  indian       0         0      0           0      0   \n",
+       "1          66  indian       1         0      0           0      0   \n",
+       "2          67  indian       0         0      0           0      0   \n",
+       "3          68  indian       0         0      0           0      0   \n",
+       "4          69  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 385 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>67</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>69</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 385 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2448 entries, 0 to 2447\nColumns: 385 entries, Unnamed: 0 to zucchini\ndtypes: int64(384), object(1)\nmemory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "korean      799\n",
+       "indian      598\n",
+       "chinese     442\n",
+       "japanese    320\n",
+       "thai        289\n",
+       "Name: cuisine, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts()"
+   ]
+  },
+  {
+   "source": [
+    "แสดงอาหารในกราฟแท่ง\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18799710>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "thai df: (289, 385)\njapanese df: (320, 385)\nchinese df: (442, 385)\nindian df: (598, 385)\nkorean df: (799, 385)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "thai_df = df[(df.cuisine == \"thai\")]\n",
+    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
+    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
+    "indian_df = df[(df.cuisine == \"indian\")]\n",
+    "korean_df = df[(df.cuisine == \"korean\")]\n",
+    "\n",
+    "print(f'thai df: {thai_df.shape}')\n",
+    "print(f'japanese df: {japanese_df.shape}')\n",
+    "print(f'chinese df: {chinese_df.shape}')\n",
+    "print(f'indian df: {indian_df.shape}')\n",
+    "print(f'korean df: {korean_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ingredient_df(df):\n",
+    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    # drop ingredients that have a 0 sum\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    # sort df\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
+    "    return ingredient_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d5917db00>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
+    "thai_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d389634a8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+   ],
+   "source": [
+    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
+    "japanese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18817f98>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
+    "chinese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1880a550>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
+    "indian_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1896ce80>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
+    "korean_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\n",
+    "labels_df = df.cuisine #.unique()\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "source": [
+    "ปรับสมดุลข้อมูลด้วยการทำโอเวอร์แซมพลิง SMOTE ให้เท่ากับคลาสที่มีจำนวนสูงสุด อ่านเพิ่มเติมได้ที่นี่: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oversample = SMOTE()\n",
+    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "new label count: korean      799\nchinese     799\njapanese    799\nindian      799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
+    "print(f'old label count: {df.cuisine.value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "source": [
+    "transformed_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "     cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  \\\n",
+       "0     indian       0         0      0           0      0             0   \n",
+       "1     indian       1         0      0           0      0             0   \n",
+       "2     indian       0         0      0           0      0             0   \n",
+       "3     indian       0         0      0           0      0             0   \n",
+       "4     indian       0         0      0           0      0             0   \n",
+       "...      ...     ...       ...    ...         ...    ...           ...   \n",
+       "3990    thai       0         0      0           0      0             0   \n",
+       "3991    thai       0         0      0           0      0             0   \n",
+       "3992    thai       0         0      0           0      0             0   \n",
+       "3993    thai       0         0      0           0      0             0   \n",
+       "3994    thai       0         0      0           0      0             0   \n",
+       "\n",
+       "      apricot  armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
+       "0           0         0          0  ...        0            0           0   \n",
+       "1           0         0          0  ...        0            0           0   \n",
+       "2           0         0          0  ...        0            0           0   \n",
+       "3           0         0          0  ...        0            0           0   \n",
+       "4           0         0          0  ...        0            0           0   \n",
+       "...       ...       ...        ...  ...      ...          ...         ...   \n",
+       "3990        0         0          0  ...        0            0           0   \n",
+       "3991        0         0          0  ...        0            0           0   \n",
+       "3992        0         0          0  ...        0            0           0   \n",
+       "3993        0         0          0  ...        0            0           0   \n",
+       "3994        0         0          0  ...        0            0           0   \n",
+       "\n",
+       "      whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                           0     0     0    0      0       0         0  \n",
+       "1                           0     0     0    0      0       0         0  \n",
+       "2                           0     0     0    0      0       0         0  \n",
+       "3                           0     0     0    0      0       0         0  \n",
+       "4                           0     0     0    0      0       1         0  \n",
+       "...                       ...   ...   ...  ...    ...     ...       ...  \n",
+       "3990                        0     0     0    0      0       0         0  \n",
+       "3991                        0     0     0    0      0       0         0  \n",
+       "3992                        0     0     0    0      0       0         0  \n",
+       "3993                        0     0     0    0      0       0         0  \n",
+       "3994                        0     0     0    0      0       0         0  \n",
+       "\n",
+       "[3995 rows x 381 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": [
+    "# export transformed data to new df for classification\n",
+    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
+    "transformed_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df.info()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "1da12ed6d238756959b8de9cac2a35a2",
+   "translation_date": "2025-09-06T14:52:42+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/2-Classifiers-1/notebook.ipynb b/translations/th/4-Classification/2-Classifiers-1/notebook.ipynb
new file mode 100644
index 000000000..3cf25c6b2
--- /dev/null
+++ b/translations/th/4-Classification/2-Classifiers-1/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "68829b06b4dcd512d3327849191f4d7f",
+   "translation_date": "2025-09-06T14:32:45+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/th/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
new file mode 100644
index 000000000..0c830e42f
--- /dev/null
+++ b/translations/th/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -0,0 +1,1294 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_11-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "6ea6a5171b1b99b7b5a55f7469c048d2",
+   "translation_date": "2025-09-06T14:39:22+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb",
+   "language_code": "th"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "zs2woWv_HoE8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## ตัวจำแนกประเภทอาหาร 1\n",
+    "\n",
+    "ในบทเรียนนี้ เราจะสำรวจตัวจำแนกประเภทหลากหลายชนิดเพื่อ *ทำนายประเภทอาหารประจำชาติจากกลุ่มของส่วนผสมที่ให้มา* ในขณะเดียวกัน เราจะเรียนรู้เพิ่มเติมเกี่ยวกับวิธีที่อัลกอริทึมสามารถนำมาใช้ในงานการจำแนกประเภทได้\n",
+    "\n",
+    "### [**แบบทดสอบก่อนเรียน**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
+    "\n",
+    "### **การเตรียมตัว**\n",
+    "\n",
+    "บทเรียนนี้ต่อยอดจาก [บทเรียนก่อนหน้า](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb) ซึ่งเราได้:\n",
+    "\n",
+    "-   แนะนำเบื้องต้นเกี่ยวกับการจำแนกประเภทโดยใช้ชุดข้อมูลเกี่ยวกับอาหารที่ยอดเยี่ยมของเอเชียและอินเดีย 😋\n",
+    "\n",
+    "-   สำรวจ [dplyr verbs](https://dplyr.tidyverse.org/) เพื่อเตรียมและทำความสะอาดข้อมูลของเรา\n",
+    "\n",
+    "-   สร้างภาพที่สวยงามโดยใช้ ggplot2\n",
+    "\n",
+    "-   แสดงวิธีจัดการกับข้อมูลที่ไม่สมดุลโดยการเตรียมข้อมูลด้วย [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html)\n",
+    "\n",
+    "-   แสดงวิธี `prep` และ `bake` สูตรของเราเพื่อยืนยันว่ามันทำงานตามที่คาดไว้\n",
+    "\n",
+    "#### **ข้อกำหนดเบื้องต้น**\n",
+    "\n",
+    "สำหรับบทเรียนนี้ เราจะต้องใช้แพ็กเกจต่อไปนี้เพื่อทำความสะอาด เตรียม และสร้างภาพข้อมูลของเรา:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) คือ [ชุดของแพ็กเกจ R](https://www.tidyverse.org/packages) ที่ออกแบบมาเพื่อทำให้การวิเคราะห์ข้อมูลเร็วขึ้น ง่ายขึ้น และสนุกขึ้น!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) เป็นกรอบงานที่เป็น [ชุดของแพ็กเกจ](https://www.tidymodels.org/packages/) สำหรับการสร้างแบบจำลองและการเรียนรู้ของเครื่อง\n",
+    "\n",
+    "-   `themis`: [แพ็กเกจ themis](https://themis.tidymodels.org/) ให้ขั้นตอนเพิ่มเติมในสูตรสำหรับจัดการกับข้อมูลที่ไม่สมดุล\n",
+    "\n",
+    "-   `nnet`: [แพ็กเกจ nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) ให้ฟังก์ชันสำหรับการประมาณเครือข่ายประสาทเทียมแบบ feed-forward ที่มีชั้นซ่อนเพียงชั้นเดียว และสำหรับแบบจำลองการถดถอยโลจิสติกแบบหลายตัวแปร\n",
+    "\n",
+    "คุณสามารถติดตั้งแพ็กเกจเหล่านี้ได้ดังนี้:\n"
+   ],
+   "metadata": {
+    "id": "iDFOb3ebHwQC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "หรือคุณสามารถใช้สคริปต์ด้านล่างเพื่อตรวจสอบว่าคุณมีแพ็กเกจที่จำเป็นสำหรับการทำโมดูลนี้หรือไม่ และติดตั้งให้คุณในกรณีที่ยังไม่มี\n"
+   ],
+   "metadata": {
+    "id": "4V85BGCjII7F"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, here)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "an5NPyyKIKNR",
+    "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. แบ่งข้อมูลออกเป็นชุดฝึกอบรมและชุดทดสอบ\n",
+    "\n",
+    "เราจะเริ่มต้นด้วยการเลือกขั้นตอนบางส่วนจากบทเรียนก่อนหน้านี้\n",
+    "\n",
+    "### ลบส่วนผสมที่พบได้บ่อยที่สุดซึ่งสร้างความสับสนระหว่างอาหารที่แตกต่างกัน โดยใช้ `dplyr::select()`\n",
+    "\n",
+    "ใครๆ ก็ชอบข้าว กระเทียม และขิง!\n"
+   ],
+   "metadata": {
+    "id": "0ax9GQLBINVv"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# Load the original cuisines data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
+    "  # Encode cuisine column as categorical\r\n",
+    "  mutate(cuisine = factor(cuisine))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Display distribution of cuisines\r\n",
+    "df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "New names:\n",
+      "* `` -> ...1\n",
+      "\n",
+      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
+      "\n",
+      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+      "\u001b[31mchr\u001b[39m   (1): cuisine\n",
+      "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
+      "\n",
+      "\n",
+      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
+      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 indian  0      0        0     0          0     0            0       0       \n",
+       "2 indian  1      0        0     0          0     0            0       0       \n",
+       "3 indian  0      0        0     0          0     0            0       0       \n",
+       "4 indian  0      0        0     0          0     0            0       0       \n",
+       "5 indian  0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       0    0   \n",
+       "2 0         ⋯ 0       0           0          0                       0    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     1      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   799\n",
+       "2 indian   598\n",
+       "3 chinese  442\n",
+       "4 japanese 320\n",
+       "5 thai     289"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 799 |\n",
+       "| indian   | 598 |\n",
+       "| chinese  | 442 |\n",
+       "| japanese | 320 |\n",
+       "| thai     | 289 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 799\\\\\n",
+       "\t indian   & 598\\\\\n",
+       "\t chinese  & 442\\\\\n",
+       "\t japanese & 320\\\\\n",
+       "\t thai     & 289\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>799</td></tr>\n",
+       "\t<tr><td>indian  </td><td>598</td></tr>\n",
+       "\t<tr><td>chinese </td><td>442</td></tr>\n",
+       "\t<tr><td>japanese</td><td>320</td></tr>\n",
+       "\t<tr><td>thai    </td><td>289</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 735
+    },
+    "id": "jhCrrH22IWVR",
+    "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ยอดเยี่ยม! ตอนนี้ถึงเวลาที่จะแบ่งข้อมูล โดยให้ 70% ของข้อมูลไปที่ชุดการฝึกอบรม และ 30% ไปที่ชุดการทดสอบ เราจะใช้เทคนิค `stratification` ในการแบ่งข้อมูลเพื่อ `รักษาสัดส่วนของแต่ละประเภทอาหาร` ในชุดข้อมูลการฝึกอบรมและการตรวจสอบ\n",
+    "\n",
+    "[rsample](https://rsample.tidymodels.org/), แพ็กเกจใน Tidymodels, ให้โครงสร้างสำหรับการแบ่งข้อมูลและการสุ่มตัวอย่างที่มีประสิทธิภาพ:\n"
+   ],
+   "metadata": {
+    "id": "AYTjVyajIdny"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# Load the core Tidymodels packages into R session\r\n",
+    "library(tidymodels)\r\n",
+    "\r\n",
+    "# Create split specification\r\n",
+    "set.seed(2056)\r\n",
+    "cuisines_split <- initial_split(data = df_select,\r\n",
+    "                                strata = cuisine,\r\n",
+    "                                prop = 0.7)\r\n",
+    "\r\n",
+    "# Extract the data in each split\r\n",
+    "cuisines_train <- training(cuisines_split)\r\n",
+    "cuisines_test <- testing(cuisines_split)\r\n",
+    "\r\n",
+    "# Print the number of cases in each split\r\n",
+    "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
+    "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
+    "\r\n",
+    "# Display the first few rows of the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Display distribution of cuisines in the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training cases: 1712\n",
+      "Test cases: 736"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 chinese 0      0        0     0          0     0            0       0       \n",
+       "2 chinese 0      0        0     0          0     0            0       0       \n",
+       "3 chinese 0      0        0     0          0     0            0       0       \n",
+       "4 chinese 0      0        0     0          0     0            0       0       \n",
+       "5 chinese 0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       1    0   \n",
+       "2 0         ⋯ 0       0           0          0                       1    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     0      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   559\n",
+       "2 indian   418\n",
+       "3 chinese  309\n",
+       "4 japanese 224\n",
+       "5 thai     202"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 559 |\n",
+       "| indian   | 418 |\n",
+       "| chinese  | 309 |\n",
+       "| japanese | 224 |\n",
+       "| thai     | 202 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 559\\\\\n",
+       "\t indian   & 418\\\\\n",
+       "\t chinese  & 309\\\\\n",
+       "\t japanese & 224\\\\\n",
+       "\t thai     & 202\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>559</td></tr>\n",
+       "\t<tr><td>indian  </td><td>418</td></tr>\n",
+       "\t<tr><td>chinese </td><td>309</td></tr>\n",
+       "\t<tr><td>japanese</td><td>224</td></tr>\n",
+       "\t<tr><td>thai    </td><td>202</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 535
+    },
+    "id": "w5FWIkEiIjdN",
+    "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. จัดการกับข้อมูลที่ไม่สมดุล\n",
+    "\n",
+    "คุณอาจสังเกตเห็นในชุดข้อมูลต้นฉบับรวมถึงชุดข้อมูลการฝึกของเรา ว่ามีการกระจายจำนวนของประเภทอาหารที่ไม่เท่ากันอย่างมาก อาหารเกาหลีมีจำนวน *เกือบ* 3 เท่าของอาหารไทย ข้อมูลที่ไม่สมดุลมักส่งผลเสียต่อประสิทธิภาพของโมเดล หลายโมเดลทำงานได้ดีที่สุดเมื่อจำนวนตัวอย่างมีความเท่ากัน และดังนั้นจึงมักมีปัญหาเมื่อข้อมูลไม่สมดุล\n",
+    "\n",
+    "มีวิธีหลัก ๆ สองวิธีในการจัดการกับชุดข้อมูลที่ไม่สมดุล:\n",
+    "\n",
+    "-   เพิ่มตัวอย่างในกลุ่มที่มีจำนวนน้อย: `Over-sampling` เช่น การใช้ SMOTE algorithm ซึ่งสร้างตัวอย่างใหม่ในกลุ่มที่มีจำนวนน้อยโดยใช้เพื่อนบ้านที่ใกล้เคียงของกรณีเหล่านั้น\n",
+    "\n",
+    "-   ลบตัวอย่างในกลุ่มที่มีจำนวนมาก: `Under-sampling`\n",
+    "\n",
+    "ในบทเรียนก่อนหน้านี้ เราได้แสดงวิธีจัดการกับชุดข้อมูลที่ไม่สมดุลโดยใช้ `recipe` ซึ่งสามารถคิดได้ว่าเป็นแผนงานที่อธิบายขั้นตอนที่ควรนำไปใช้กับชุดข้อมูลเพื่อเตรียมให้พร้อมสำหรับการวิเคราะห์ข้อมูล ในกรณีของเรา เราต้องการให้มีการกระจายจำนวนประเภทอาหารที่เท่ากันใน `training set` ของเรา มาเริ่มกันเลย!\n"
+   ],
+   "metadata": {
+    "id": "daBi9qJNIwqW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing training data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "# Print recipe\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Data Recipe\n",
+       "\n",
+       "Inputs:\n",
+       "\n",
+       "      role #variables\n",
+       "   outcome          1\n",
+       " predictor        380\n",
+       "\n",
+       "Operations:\n",
+       "\n",
+       "SMOTE based on cuisine"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 200
+    },
+    "id": "Az6LFBGxI1X0",
+    "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "คุณสามารถยืนยันได้เลย (โดยใช้ prep+bake) ว่าสูตรนี้จะทำงานตามที่คุณคาดหวัง - โดยที่ป้ายกำกับอาหารทั้งหมดมี `559` การสังเกตการณ์\n",
+    "\n",
+    "เนื่องจากเราจะใช้สูตรนี้เป็นตัวเตรียมข้อมูลสำหรับการสร้างแบบจำลอง `workflow()` จะทำหน้าที่เตรียมและประมวลผลข้อมูลทั้งหมดให้เรา ดังนั้นเราจะไม่ต้องคำนวณสูตรด้วยตัวเอง\n",
+    "\n",
+    "ตอนนี้เราพร้อมที่จะฝึกโมเดลแล้ว 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. การเลือกตัวจำแนกประเภทของคุณ\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/parsnip.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>ภาพประกอบโดย @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "NBL3PqIWJBBB"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ตอนนี้เราต้องตัดสินใจว่าจะใช้อัลกอริทึมใดสำหรับงานนี้ 🤔\n",
+    "\n",
+    "ใน Tidymodels, [`parsnip package`](https://parsnip.tidymodels.org/index.html) มีอินเทอร์เฟซที่สม่ำเสมอสำหรับการทำงานกับโมเดลในหลายๆ engine (แพ็กเกจ) โปรดดูเอกสารของ parsnip เพื่อสำรวจ [ประเภทโมเดลและ engine](https://www.tidymodels.org/find/parsnip/#models) และ [อาร์กิวเมนต์ของโมเดล](https://www.tidymodels.org/find/parsnip/#model-args) ที่เกี่ยวข้อง ความหลากหลายอาจทำให้สับสนในตอนแรก ตัวอย่างเช่น วิธีการต่อไปนี้ล้วนเป็นเทคนิคการจัดประเภท:\n",
+    "\n",
+    "-   โมเดลการจัดประเภทแบบใช้กฎ C5.0\n",
+    "\n",
+    "-   โมเดลการจำแนกแบบยืดหยุ่น\n",
+    "\n",
+    "-   โมเดลการจำแนกเชิงเส้น\n",
+    "\n",
+    "-   โมเดลการจำแนกแบบมีการปรับค่า\n",
+    "\n",
+    "-   โมเดลการถดถอยโลจิสติก\n",
+    "\n",
+    "-   โมเดลการถดถอยแบบหลายตัวแปร\n",
+    "\n",
+    "-   โมเดล Naive Bayes\n",
+    "\n",
+    "-   Support Vector Machines\n",
+    "\n",
+    "-   Nearest Neighbors\n",
+    "\n",
+    "-   Decision Trees\n",
+    "\n",
+    "-   Ensemble methods\n",
+    "\n",
+    "-   Neural Networks\n",
+    "\n",
+    "รายการยังคงมีต่อไป!\n",
+    "\n",
+    "### **จะเลือกตัวจัดประเภทตัวไหนดี?**\n",
+    "\n",
+    "แล้วเราควรเลือกตัวจัดประเภทตัวไหน? บ่อยครั้ง การลองใช้หลายๆ ตัวและมองหาผลลัพธ์ที่ดีเป็นวิธีการทดสอบ\n",
+    "\n",
+    "> AutoML แก้ปัญหานี้ได้อย่างลงตัวโดยการเปรียบเทียบในระบบคลาวด์ ทำให้คุณสามารถเลือกอัลกอริทึมที่ดีที่สุดสำหรับข้อมูลของคุณ ลองใช้ [ที่นี่](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "นอกจากนี้ การเลือกตัวจัดประเภทขึ้นอยู่กับปัญหาของเรา ตัวอย่างเช่น เมื่อผลลัพธ์สามารถจัดหมวดหมู่ได้เป็น `มากกว่าสองคลาส` เช่นในกรณีของเรา คุณต้องใช้ `อัลกอริทึมการจัดประเภทแบบหลายคลาส` แทนที่จะเป็น `การจัดประเภทแบบไบนารี`\n",
+    "\n",
+    "### **วิธีที่ดีกว่า**\n",
+    "\n",
+    "วิธีที่ดีกว่าการเดาแบบสุ่มคือการทำตามแนวคิดใน [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott) ที่สามารถดาวน์โหลดได้ ที่นี่เราพบว่า สำหรับปัญหาแบบหลายคลาสของเรา เรามีตัวเลือกบางอย่าง:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/cheatsheet.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>ส่วนหนึ่งของ Algorithm Cheat Sheet ของ Microsoft ที่แสดงตัวเลือกการจัดประเภทแบบหลายคลาส</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "a6DLAZ3vJZ14"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **เหตุผล**\n",
+    "\n",
+    "ลองมาดูว่ามีวิธีการใดบ้างที่เราสามารถใช้ได้ตามข้อจำกัดที่กำหนดไว้:\n",
+    "\n",
+    "-   **โครงข่ายประสาทเทียมเชิงลึกหนักเกินไป** เนื่องจากเรามีชุดข้อมูลที่สะอาดแต่มีขนาดเล็ก และเรากำลังฝึกโมเดลในเครื่องผ่านโน้ตบุ๊ก โครงข่ายประสาทเทียมเชิงลึกจึงไม่เหมาะสมสำหรับงานนี้\n",
+    "\n",
+    "-   **ไม่ใช้ตัวจำแนกแบบสองคลาส** เราไม่ได้ใช้ตัวจำแนกแบบสองคลาส ดังนั้นจึงตัดวิธี one-vs-all ออกไป\n",
+    "\n",
+    "-   **ต้นไม้ตัดสินใจหรือการถดถอยโลจิสติกอาจใช้ได้** ต้นไม้ตัดสินใจอาจเหมาะสม หรือการถดถอยพหุคลาส/การถดถอยโลจิสติกแบบพหุคลาสสำหรับข้อมูลหลายคลาส\n",
+    "\n",
+    "-   **ต้นไม้ตัดสินใจแบบบูสต์สำหรับพหุคลาสแก้ปัญหาคนละแบบ** ต้นไม้ตัดสินใจแบบบูสต์สำหรับพหุคลาสเหมาะสำหรับงานที่ไม่ใช่พารามิเตอร์ เช่น งานที่ออกแบบมาเพื่อสร้างการจัดอันดับ ดังนั้นจึงไม่เหมาะกับเรา\n",
+    "\n",
+    "โดยปกติแล้ว ก่อนที่จะเริ่มใช้โมเดลการเรียนรู้ของเครื่องที่ซับซ้อนขึ้น เช่น วิธีการแบบเอนเซมเบิล ควรเริ่มจากโมเดลที่ง่ายที่สุดเพื่อทำความเข้าใจภาพรวมของข้อมูล ดังนั้นในบทเรียนนี้ เราจะเริ่มต้นด้วยโมเดล `การถดถอยพหุคลาส` \n",
+    "\n",
+    "> การถดถอยโลจิสติกเป็นเทคนิคที่ใช้เมื่อผลลัพธ์เป็นตัวแปรเชิงหมวดหมู่ (หรือเชิงนาม) สำหรับการถดถอยโลจิสติกแบบไบนารี จำนวนตัวแปรผลลัพธ์จะมีสองค่า ในขณะที่การถดถอยโลจิสติกแบบพหุคลาสจะมีจำนวนตัวแปรผลลัพธ์มากกว่าสองค่า ดูเพิ่มเติมได้ที่ [Advanced Regression Methods](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html)\n",
+    "\n",
+    "## 4. ฝึกและประเมินโมเดลการถดถอยโลจิสติกแบบพหุคลาส\n",
+    "\n",
+    "ใน Tidymodels, `parsnip::multinom_reg()` ใช้กำหนดโมเดลที่ใช้ตัวทำนายเชิงเส้นเพื่อทำนายข้อมูลหลายคลาสโดยใช้การแจกแจงแบบพหุคลาส ดู `?multinom_reg()` เพื่อดูวิธี/เอนจินต่าง ๆ ที่คุณสามารถใช้ในการปรับโมเดลนี้\n",
+    "\n",
+    "สำหรับตัวอย่างนี้ เราจะปรับโมเดลการถดถอยพหุคลาสผ่านเอนจินเริ่มต้น [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf)\n",
+    "\n",
+    "> ฉันเลือกค่า `penalty` แบบสุ่ม มีวิธีที่ดีกว่าในการเลือกค่านี้ เช่น การใช้ `resampling` และ `tuning` โมเดล ซึ่งเราจะพูดถึงในภายหลัง\n",
+    ">\n",
+    "> ดูเพิ่มเติมที่ [Tidymodels: Get Started](https://www.tidymodels.org/start/tuning/) หากคุณต้องการเรียนรู้เพิ่มเติมเกี่ยวกับการปรับแต่งพารามิเตอร์ของโมเดล\n"
+   ],
+   "metadata": {
+    "id": "gWMsVcbBJemu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# Create a multinomial regression model specification\r\n",
+    "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
+    "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
+    "  set_mode(\"classification\")\r\n",
+    "\r\n",
+    "# Print model specification\r\n",
+    "mr_spec"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 166
+    },
+    "id": "Wq_fcyQiJvfG",
+    "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมมาก 🥳! ตอนนี้เรามีสูตรและสเปคของโมเดลแล้ว เราต้องหาวิธีรวมสิ่งเหล่านี้เข้าด้วยกันเป็นวัตถุที่จะช่วยในการเตรียมข้อมูลเบื้องต้น จากนั้นจึงปรับโมเดลกับข้อมูลที่ผ่านการเตรียมแล้ว และยังสามารถรองรับกิจกรรมหลังการประมวลผลได้อีกด้วย ใน Tidymodels วัตถุที่สะดวกนี้เรียกว่า [`workflow`](https://workflows.tidymodels.org/) ซึ่งจะเก็บส่วนประกอบของการสร้างโมเดลของคุณไว้อย่างสะดวก! สิ่งนี้คือสิ่งที่เราเรียกว่า *pipelines* ใน *Python* \n",
+    "\n",
+    "ดังนั้น มาเริ่มรวมทุกอย่างเข้าด้วยกันใน workflow กันเถอะ!📦\n"
+   ],
+   "metadata": {
+    "id": "NlSbzDfgJ0zh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# Bundle recipe and model specification\r\n",
+    "mr_wf <- workflow() %>% \r\n",
+    "  add_recipe(cuisines_recipe) %>% \r\n",
+    "  add_model(mr_spec)\r\n",
+    "\r\n",
+    "# Print out workflow\r\n",
+    "mr_wf"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow ════════════════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 333
+    },
+    "id": "Sc1TfPA4Ke3_",
+    "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เวิร์กโฟลว์ 👌👌! **`workflow()`** สามารถปรับให้เหมาะสมได้ในลักษณะเดียวกับที่โมเดลสามารถทำได้ ดังนั้น ถึงเวลาฝึกโมเดลแล้ว!\n"
+   ],
+   "metadata": {
+    "id": "TNQ8i85aKf9L"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# Train a multinomial regression model\n",
+    "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
+    "\n",
+    "mr_fit"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Call:\n",
+       "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
+       "    trace = FALSE)\n",
+       "\n",
+       "Coefficients:\n",
+       "         (Intercept)     almond angelica         anise anise_seed       apple\n",
+       "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
+       "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
+       "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
+       "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
+       "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
+       "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
+       "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
+       "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
+       "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
+       "             avocado       bacon baked_potato balm     banana     barley\n",
+       "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
+       "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
+       "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
+       "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
+       "         bartlett_pear      basil        bay       bean         beech\n",
+       "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
+       "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
+       "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
+       "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
+       "               beef beef_broth   beef_liver         beer        beet\n",
+       "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
+       "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
+       "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
+       "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
+       "         bell_pepper bergamot       berry bitter_orange black_bean\n",
+       "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
+       "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
+       "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
+       "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
+       "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
+       "indian               0             0.38935801   -0.4453495               0\n",
+       "japanese             0            -0.05452887   -0.5440869               0\n",
+       "korean               0            -0.03929970    0.8025454               0\n",
+       "thai                 0            -0.21498372   -0.9854806               0\n",
+       "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
+       "indian          -0.2759246  0.3079977  0.191256164                 0\n",
+       "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
+       "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
+       "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
+       "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
+       "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
+       "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
+       "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
+       "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
+       "\n",
+       "...\n",
+       "and 308 more lines."
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GMbdfVmTKkJI",
+    "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ผลลัพธ์จะแสดงค่าสัมประสิทธิ์ที่โมเดลได้เรียนรู้ระหว่างการฝึก\n",
+    "\n",
+    "### ประเมินผลโมเดลที่ผ่านการฝึก\n",
+    "\n",
+    "ถึงเวลาที่จะดูว่าโมเดลทำงานได้ดีแค่ไหน 📏 โดยการประเมินผลบนชุดทดสอบ! มาเริ่มต้นด้วยการสร้างการคาดการณ์บนชุดทดสอบกันเถอะ!\n"
+   ],
+   "metadata": {
+    "id": "tt2BfOxrKmcJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# Make predictions on the test set\n",
+    "results <- cuisines_test %>% select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
+    "\n",
+    "# Print out results\n",
+    "results %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class\n",
+       "1 indian  thai       \n",
+       "2 indian  indian     \n",
+       "3 indian  indian     \n",
+       "4 indian  indian     \n",
+       "5 indian  indian     "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
+       "|---|---|\n",
+       "| indian | thai   |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & .pred\\_class\\\\\n",
+       " <fct> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai  \\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "CqtckvtsKqax",
+    "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "งานยอดเยี่ยม! ใน Tidymodels การประเมินประสิทธิภาพของโมเดลสามารถทำได้โดยใช้ [yardstick](https://yardstick.tidymodels.org/) - แพ็กเกจที่ใช้วัดประสิทธิภาพของโมเดลด้วยตัวชี้วัดประสิทธิภาพ เช่นเดียวกับที่เราได้ทำในบทเรียนการถดถอยโลจิสติก มาเริ่มต้นด้วยการคำนวณเมทริกซ์ความสับสนกันเถอะ\n"
+   ],
+   "metadata": {
+    "id": "8w5N6XsBKss7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "# Confusion matrix for categorical data\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "          Truth\n",
+       "Prediction chinese indian japanese korean thai\n",
+       "  chinese       83      1        8     15   10\n",
+       "  indian         4    163        1      2    6\n",
+       "  japanese      21      5       73     25    1\n",
+       "  korean        15      0       11    191    0\n",
+       "  thai          10     11        3      7   70"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 133
+    },
+    "id": "YvODvsLkK0iG",
+    "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "c0HfPL16Lr6U"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
+    "# Visualize confusion matrix\n",
+    "results %>% \n",
+    "  conf_mat(cuisine, .pred_class) %>% \n",
+    "  autoplot(type = \"heatmap\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "plot without title"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {
+      "image/png": {
+       "width": 420,
+       "height": 420
+      }
+     }
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 436
+    },
+    "id": "HsAtwukyLsvt",
+    "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "สี่เหลี่ยมที่มีสีเข้มในกราฟเมทริกซ์ความสับสนแสดงถึงจำนวนกรณีที่สูง และคุณน่าจะเห็นเส้นทแยงมุมของสี่เหลี่ยมสีเข้มที่บ่งบอกถึงกรณีที่ป้ายกำกับที่คาดการณ์และป้ายกำกับจริงตรงกัน\n",
+    "\n",
+    "ตอนนี้เรามาคำนวณสถิติสรุปสำหรับเมทริกซ์ความสับสนกัน\n"
+   ],
+   "metadata": {
+    "id": "oOJC87dkLwPr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# Summary stats for confusion matrix\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
+    "summary()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "   .metric              .estimator .estimate\n",
+       "1  accuracy             multiclass 0.7880435\n",
+       "2  kap                  multiclass 0.7276583\n",
+       "3  sens                 macro      0.7780927\n",
+       "4  spec                 macro      0.9477598\n",
+       "5  ppv                  macro      0.7585583\n",
+       "6  npv                  macro      0.9460080\n",
+       "7  mcc                  multiclass 0.7292724\n",
+       "8  j_index              macro      0.7258524\n",
+       "9  bal_accuracy         macro      0.8629262\n",
+       "10 detection_prevalence macro      0.2000000\n",
+       "11 precision            macro      0.7585583\n",
+       "12 recall               macro      0.7780927\n",
+       "13 f_meas               macro      0.7641862"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 13 × 3\n",
+       "\n",
+       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| accuracy             | multiclass | 0.7880435 |\n",
+       "| kap                  | multiclass | 0.7276583 |\n",
+       "| sens                 | macro      | 0.7780927 |\n",
+       "| spec                 | macro      | 0.9477598 |\n",
+       "| ppv                  | macro      | 0.7585583 |\n",
+       "| npv                  | macro      | 0.9460080 |\n",
+       "| mcc                  | multiclass | 0.7292724 |\n",
+       "| j_index              | macro      | 0.7258524 |\n",
+       "| bal_accuracy         | macro      | 0.8629262 |\n",
+       "| detection_prevalence | macro      | 0.2000000 |\n",
+       "| precision            | macro      | 0.7585583 |\n",
+       "| recall               | macro      | 0.7780927 |\n",
+       "| f_meas               | macro      | 0.7641862 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 13 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " .metric & .estimator & .estimate\\\\\n",
+       " <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t accuracy             & multiclass & 0.7880435\\\\\n",
+       "\t kap                  & multiclass & 0.7276583\\\\\n",
+       "\t sens                 & macro      & 0.7780927\\\\\n",
+       "\t spec                 & macro      & 0.9477598\\\\\n",
+       "\t ppv                  & macro      & 0.7585583\\\\\n",
+       "\t npv                  & macro      & 0.9460080\\\\\n",
+       "\t mcc                  & multiclass & 0.7292724\\\\\n",
+       "\t j\\_index              & macro      & 0.7258524\\\\\n",
+       "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
+       "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
+       "\t precision            & macro      & 0.7585583\\\\\n",
+       "\t recall               & macro      & 0.7780927\\\\\n",
+       "\t f\\_meas               & macro      & 0.7641862\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 13 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
+       "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
+       "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
+       "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
+       "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
+       "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
+       "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
+       "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
+       "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 494
+    },
+    "id": "OYqetUyzL5Wz",
+    "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ถ้าเรามุ่งเน้นไปที่ตัวชี้วัดบางอย่าง เช่น ความแม่นยำ, ความไว, ppv เราก็ไม่ได้เริ่มต้นแย่เลย 🥳!\n",
+    "\n",
+    "## 4. เจาะลึกลงไปอีก\n",
+    "\n",
+    "ลองถามคำถามที่ละเอียดอ่อนสักข้อ: เกณฑ์อะไรที่ใช้ในการตัดสินใจเลือกประเภทของอาหารเป็นผลลัพธ์ที่คาดการณ์?\n",
+    "\n",
+    "จริง ๆ แล้ว อัลกอริทึมการเรียนรู้ของเครื่องเชิงสถิติ เช่น logistic regression จะอิงอยู่บน `ความน่าจะเป็น`; ดังนั้นสิ่งที่ตัวจำแนกประเภทคาดการณ์จริง ๆ ก็คือการแจกแจงความน่าจะเป็นในชุดของผลลัพธ์ที่เป็นไปได้ คลาสที่มีความน่าจะเป็นสูงสุดจะถูกเลือกเป็นผลลัพธ์ที่มีแนวโน้มมากที่สุดสำหรับการสังเกตที่กำหนด\n",
+    "\n",
+    "ลองมาดูตัวอย่างนี้ในทางปฏิบัติโดยการทำทั้งการคาดการณ์แบบคลาสที่ชัดเจนและการคาดการณ์แบบความน่าจะเป็น\n"
+   ],
+   "metadata": {
+    "id": "43t7vz8vMJtW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "# Make hard class prediction and probabilities\n",
+    "results_prob <- cuisines_test %>%\n",
+    "  select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
+    "\n",
+    "# Print out results\n",
+    "results_prob %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
+       "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
+       "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
+       "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
+       "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
+       "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
+       "  .pred_thai  \n",
+       "1 5.388194e-01\n",
+       "2 1.577948e-06\n",
+       "3 6.874989e-03\n",
+       "4 3.863391e-03\n",
+       "5 5.653283e-03"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 7\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
+       "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
+       "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
+       "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
+       "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 7\n",
+       "\\begin{tabular}{lllllll}\n",
+       " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
+       " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
+       "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
+       "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
+       "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
+       "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "xdKNs-ZPMTJL",
+    "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ คุณสามารถอธิบายได้ไหมว่าทำไมโมเดลถึงมั่นใจว่าการสังเกตการณ์แรกเป็นภาษาไทย?\n",
+    "\n",
+    "## **🚀ความท้าทาย**\n",
+    "\n",
+    "ในบทเรียนนี้ คุณได้ใช้ข้อมูลที่ทำความสะอาดแล้วเพื่อสร้างโมเดลการเรียนรู้ของเครื่องที่สามารถทำนายอาหารประจำชาติได้จากชุดของส่วนผสม ลองใช้เวลาศึกษา [ตัวเลือกมากมาย](https://www.tidymodels.org/find/parsnip/#models) ที่ Tidymodels มีให้สำหรับการจัดประเภทข้อมูล และ [วิธีอื่นๆ](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) ในการปรับโมเดลการถดถอยแบบหลายตัวแปร\n",
+    "\n",
+    "#### ขอบคุณ:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) สำหรับการสร้างภาพประกอบที่น่าทึ่งซึ่งทำให้ R ดูน่าสนใจและเข้าถึงได้มากขึ้น ค้นหาภาพประกอบเพิ่มเติมได้ที่ [แกลเลอรี](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM) ของเธอ\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) และ [Jen Looper](https://www.twitter.com/jenlooper) สำหรับการสร้างเวอร์ชัน Python ดั้งเดิมของโมดูลนี้ ♥️\n",
+    "\n",
+    "<br>\n",
+    "อยากจะใส่มุกตลกลงไป แต่ฉันไม่เข้าใจมุกเกี่ยวกับอาหารเลย 😅\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "เรียนรู้อย่างมีความสุข,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador\n"
+   ],
+   "metadata": {
+    "id": "2tWVHMeLMYdM"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามนุษย์มืออาชีพ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/translations/th/4-Classification/2-Classifiers-1/solution/notebook.ipynb
new file mode 100644
index 000000000..e26dc114f
--- /dev/null
+++ b/translations/th/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -0,0 +1,279 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy is 0.8181818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print (\"Accuracy is {}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n       'vegetable_oil'],\n      dtype='object')\ncuisine: korean\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test an item\n",
+    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[50]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                 0\n",
+       "korean    0.392231\n",
+       "chinese   0.372872\n",
+       "japanese  0.218825\n",
+       "thai      0.013427\n",
+       "indian    0.002645"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>korean</th>\n      <td>0.392231</td>\n    </tr>\n    <tr>\n      <th>chinese</th>\n      <td>0.372872</td>\n    </tr>\n    <tr>\n      <th>japanese</th>\n      <td>0.218825</td>\n    </tr>\n    <tr>\n      <th>thai</th>\n      <td>0.013427</td>\n    </tr>\n    <tr>\n      <th>indian</th>\n      <td>0.002645</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "#rehsape to 2d array and transpose\n",
+    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
+    "# predict with score\n",
+    "proba = model.predict_proba(test)\n",
+    "classes = model.classes_\n",
+    "# create df with classes and scores\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
+    "\n",
+    "# create df to show results\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.75      0.73      0.74       223\n      indian       0.93      0.88      0.90       255\n    japanese       0.78      0.78      0.78       253\n      korean       0.87      0.86      0.86       236\n        thai       0.76      0.84      0.80       232\n\n    accuracy                           0.82      1199\n   macro avg       0.82      0.82      0.82      1199\nweighted avg       0.82      0.82      0.82      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\r\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาที่เป็นต้นฉบับควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "9408506dd864f2b6e334c62f80c0cfcc",
+   "translation_date": "2025-09-06T14:33:14+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/3-Classifiers-2/notebook.ipynb b/translations/th/4-Classification/3-Classifiers-2/notebook.ipynb
new file mode 100644
index 000000000..32e49fc89
--- /dev/null
+++ b/translations/th/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "15a83277036572e0773229b5f21c1e12",
+   "translation_date": "2025-09-06T14:42:28+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/translations/th/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
new file mode 100644
index 000000000..05bf31910
--- /dev/null
+++ b/translations/th/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -0,0 +1,648 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "name": "lesson_12-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "fab50046ca413a38939d579f8432274f",
+   "translation_date": "2025-09-06T14:48:47+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb",
+   "language_code": "th"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jsFutf_ygqSx"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HD54bEefgtNO"
+   },
+   "source": [
+    "## ตัวจำแนกประเภทอาหาร 2\n",
+    "\n",
+    "ในบทเรียนการจำแนกประเภทครั้งที่สองนี้ เราจะสำรวจ `วิธีเพิ่มเติม` ในการจำแนกข้อมูลเชิงหมวดหมู่ นอกจากนี้เรายังจะเรียนรู้ถึงผลกระทบจากการเลือกตัวจำแนกประเภทหนึ่งแทนอีกตัวหนึ่ง\n",
+    "\n",
+    "### [**แบบทดสอบก่อนเรียน**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
+    "\n",
+    "### **ความรู้พื้นฐานที่ต้องมี**\n",
+    "\n",
+    "เราสมมติว่าคุณได้เรียนจบบทเรียนก่อนหน้านี้แล้ว เนื่องจากเราจะนำแนวคิดบางอย่างที่เราเรียนรู้มาก่อนมาใช้ต่อในบทเรียนนี้\n",
+    "\n",
+    "สำหรับบทเรียนนี้ เราจะต้องใช้แพ็กเกจดังต่อไปนี้:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) คือ [ชุดของแพ็กเกจ R](https://www.tidyverse.org/packages) ที่ออกแบบมาเพื่อทำให้การวิเคราะห์ข้อมูลเร็วขึ้น ง่ายขึ้น และสนุกมากขึ้น!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) เป็นกรอบงานที่เป็น [ชุดของแพ็กเกจ](https://www.tidymodels.org/packages/) สำหรับการสร้างแบบจำลองและการเรียนรู้ของเครื่อง\n",
+    "\n",
+    "-   `themis`: [แพ็กเกจ themis](https://themis.tidymodels.org/) ให้ขั้นตอนเพิ่มเติมสำหรับการจัดการข้อมูลที่ไม่สมดุล\n",
+    "\n",
+    "คุณสามารถติดตั้งแพ็กเกจเหล่านี้ได้โดยใช้คำสั่ง:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\n",
+    "\n",
+    "หรือใช้สคริปต์ด้านล่างเพื่อตรวจสอบว่าคุณมีแพ็กเกจที่จำเป็นสำหรับการเรียนในโมดูลนี้หรือไม่ และติดตั้งให้ในกรณีที่ยังไม่มี\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vZ57IuUxgyQt"
+   },
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z22M-pj4g07x"
+   },
+   "source": [
+    "## **1. แผนที่การจัดประเภท**\n",
+    "\n",
+    "ใน [บทเรียนก่อนหน้านี้](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1) เราได้พยายามตอบคำถามว่า: เราจะเลือกใช้โมเดลใดในหลายๆ โมเดลที่มีอยู่? คำตอบส่วนใหญ่ขึ้นอยู่กับลักษณะของข้อมูลและประเภทของปัญหาที่เราต้องการแก้ไข (เช่น การจัดประเภทหรือการถดถอย)\n",
+    "\n",
+    "ก่อนหน้านี้ เราได้เรียนรู้เกี่ยวกับตัวเลือกต่างๆ ที่คุณมีเมื่อจัดประเภทข้อมูลโดยใช้แผ่นโกงของ Microsoft Python's Machine Learning framework, Scikit-learn มีแผ่นโกงที่คล้ายกันแต่มีรายละเอียดมากขึ้น ซึ่งสามารถช่วยจำกัดตัวเลือกของคุณให้แคบลง (อีกคำหนึ่งที่ใช้เรียกตัวจัดประเภท):\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/map.png\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption></figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u1i3xRIVg7vG"
+   },
+   "source": [
+    "> เคล็ดลับ: [เยี่ยมชมแผนที่นี้ออนไลน์](https://scikit-learn.org/stable/tutorial/machine_learning_map/) และคลิกตามเส้นทางเพื่ออ่านเอกสารประกอบ  \n",
+    ">  \n",
+    "> [เว็บไซต์อ้างอิงของ Tidymodels](https://www.tidymodels.org/find/parsnip/#models) ยังมีเอกสารที่ยอดเยี่ยมเกี่ยวกับประเภทของโมเดลต่าง ๆ ให้ศึกษาเพิ่มเติมด้วย\n",
+    "\n",
+    "### **แผนการ** 🗺️\n",
+    "\n",
+    "แผนที่นี้มีประโยชน์มากเมื่อคุณเข้าใจข้อมูลของคุณอย่างชัดเจน เพราะคุณสามารถ 'เดิน' ไปตามเส้นทางเพื่อหาคำตอบได้:\n",
+    "\n",
+    "-   เรามีตัวอย่างมากกว่า 50 ตัวอย่าง\n",
+    "\n",
+    "-   เราต้องการทำนายประเภท (category)\n",
+    "\n",
+    "-   เรามีข้อมูลที่มีป้ายกำกับ (labeled data)\n",
+    "\n",
+    "-   เรามีตัวอย่างน้อยกว่า 100,000 ตัวอย่าง\n",
+    "\n",
+    "-   ✨ เราสามารถเลือกใช้ Linear SVC ได้\n",
+    "\n",
+    "-   ถ้าไม่ได้ผล เนื่องจากเรามีข้อมูลเชิงตัวเลข\n",
+    "\n",
+    "    -   เราสามารถลองใช้ ✨ KNeighbors Classifier\n",
+    "\n",
+    "        -   ถ้ายังไม่ได้ผลอีก ให้ลองใช้ ✨ SVC และ ✨ Ensemble Classifiers\n",
+    "\n",
+    "นี่เป็นเส้นทางที่มีประโยชน์มากในการปฏิบัติตาม ตอนนี้ มาเริ่มต้นกันเลยโดยใช้ [tidymodels](https://www.tidymodels.org/) ซึ่งเป็นกรอบการทำงานสำหรับการสร้างโมเดล: คอลเลกชันของแพ็กเกจ R ที่สอดคล้องและยืดหยุ่น ซึ่งพัฒนาขึ้นเพื่อส่งเสริมการปฏิบัติทางสถิติที่ดี 😊\n",
+    "\n",
+    "## 2. แบ่งข้อมูลและจัดการกับชุดข้อมูลที่ไม่สมดุล\n",
+    "\n",
+    "จากบทเรียนก่อนหน้า เราได้เรียนรู้ว่ามีส่วนผสมบางอย่างที่พบได้ทั่วไปในอาหารของเรา นอกจากนี้ ยังมีการกระจายตัวของจำนวนอาหารที่ไม่เท่ากันอย่างมาก\n",
+    "\n",
+    "เราจะจัดการกับสิ่งเหล่านี้โดย:\n",
+    "\n",
+    "-   ลบส่วนผสมที่พบได้บ่อยที่สุดซึ่งสร้างความสับสนระหว่างอาหารที่แตกต่างกัน โดยใช้ `dplyr::select()`\n",
+    "\n",
+    "-   ใช้ `recipe` เพื่อเตรียมข้อมูลให้พร้อมสำหรับการสร้างโมเดล โดยการใช้อัลกอริธึม `over-sampling`\n",
+    "\n",
+    "เราได้ดูสิ่งเหล่านี้ไปแล้วในบทเรียนก่อนหน้า ดังนั้นสิ่งนี้น่าจะง่ายมาก 🥳!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "6tj_rN00hClA"
+   },
+   "source": [
+    "# Load the core Tidyverse and Tidymodels packages\n",
+    "library(tidyverse)\n",
+    "library(tidymodels)\n",
+    "\n",
+    "# Load the original cuisines data\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
+    "\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\n",
+    "df_select <- df %>% \n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\n",
+    "  # Encode cuisine column as categorical\n",
+    "  mutate(cuisine = factor(cuisine))\n",
+    "\n",
+    "\n",
+    "# Create data split specification\n",
+    "set.seed(2056)\n",
+    "cuisines_split <- initial_split(data = df_select,\n",
+    "                                strata = cuisine,\n",
+    "                                prop = 0.7)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "cuisines_train <- training(cuisines_split)\n",
+    "cuisines_test <- testing(cuisines_split)\n",
+    "\n",
+    "# Display distribution of cuisines in the training set\n",
+    "cuisines_train %>% \n",
+    "  count(cuisine) %>% \n",
+    "  arrange(desc(n))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFin5yw3hHb1"
+   },
+   "source": [
+    "### จัดการกับข้อมูลที่ไม่สมดุล\n",
+    "\n",
+    "ข้อมูลที่ไม่สมดุลมักส่งผลเสียต่อประสิทธิภาพของโมเดล หลายโมเดลทำงานได้ดีที่สุดเมื่อจำนวนข้อมูลมีความเท่ากัน และดังนั้นจึงมักมีปัญหาเมื่อเจอกับข้อมูลที่ไม่สมดุล\n",
+    "\n",
+    "มีวิธีหลัก ๆ สองวิธีในการจัดการกับชุดข้อมูลที่ไม่สมดุล:\n",
+    "\n",
+    "-   เพิ่มข้อมูลในกลุ่มที่มีจำนวนน้อย: `Over-sampling` เช่น การใช้ SMOTE algorithm ซึ่งสร้างตัวอย่างใหม่ในกลุ่มที่มีจำนวนน้อยโดยใช้ข้อมูลจากเพื่อนบ้านที่ใกล้เคียงที่สุดของกรณีเหล่านั้น\n",
+    "\n",
+    "-   ลบข้อมูลออกจากกลุ่มที่มีจำนวนมาก: `Under-sampling`\n",
+    "\n",
+    "ในบทเรียนก่อนหน้านี้ เราได้แสดงวิธีจัดการกับชุดข้อมูลที่ไม่สมดุลโดยใช้ `recipe` ซึ่งสามารถมองว่าเป็นแผนงานที่อธิบายขั้นตอนที่ควรนำไปใช้กับชุดข้อมูลเพื่อเตรียมให้พร้อมสำหรับการวิเคราะห์ข้อมูล ในกรณีของเรา เราต้องการให้มีการกระจายจำนวนข้อมูลในกลุ่มอาหารของเราอย่างเท่าเทียมกันสำหรับ `training set` มาเริ่มกันเลย!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "cRzTnHolhLWd"
+   },
+   "source": [
+    "# Load themis package for dealing with imbalanced data\n",
+    "library(themis)\n",
+    "\n",
+    "# Create a recipe for preprocessing training data\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
+    "  step_smote(cuisine) \n",
+    "\n",
+    "# Print recipe\n",
+    "cuisines_recipe"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KxOQ2ORhhO81"
+   },
+   "source": [
+    "ตอนนี้เราพร้อมที่จะฝึกโมเดลแล้ว 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. เกินกว่ารุ่นการถดถอยแบบพหุคูณ\n",
+    "\n",
+    "ในบทเรียนก่อนหน้า เราได้ศึกษารุ่นการถดถอยแบบพหุคูณ ลองมาสำรวจโมเดลที่ยืดหยุ่นมากขึ้นสำหรับการจำแนกประเภทกันเถอะ\n",
+    "\n",
+    "### Support Vector Machines\n",
+    "\n",
+    "ในบริบทของการจำแนกประเภท `Support Vector Machines` เป็นเทคนิคการเรียนรู้ของเครื่องที่พยายามค้นหา *ไฮเปอร์เพลน* ที่ \"ดีที่สุด\" ในการแยกประเภท ลองดูตัวอย่างง่าย ๆ:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/svm.png\"\n",
+    "   width=\"300\"/>\n",
+    "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C4Wsd0vZhXYu"
+   },
+   "source": [
+    "H1~ ไม่ได้แยกคลาสออกจากกัน H2~ แยกคลาสออกจากกัน แต่มีระยะห่างเพียงเล็กน้อย H3~ แยกคลาสออกจากกันด้วยระยะห่างสูงสุด\n",
+    "\n",
+    "#### ตัวจำแนกเชิงเส้นแบบ Support Vector\n",
+    "\n",
+    "Support-Vector clustering (SVC) เป็นส่วนหนึ่งของกลุ่มเทคนิคการเรียนรู้ของเครื่อง (ML) ในตระกูล Support-Vector machines ใน SVC จะมีการเลือก hyperplane เพื่อแยก `ส่วนใหญ่` ของข้อมูลการฝึกอบรมออกจากกันอย่างถูกต้อง แต่ `อาจมีการจัดประเภทผิดพลาด` สำหรับบางข้อมูล โดยการอนุญาตให้บางจุดอยู่ในด้านที่ผิด SVM จะมีความทนทานต่อค่าผิดปกติมากขึ้น และสามารถปรับตัวให้เข้ากับข้อมูลใหม่ได้ดีขึ้น พารามิเตอร์ที่ควบคุมการละเมิดนี้เรียกว่า `cost` ซึ่งมีค่าเริ่มต้นเป็น 1 (ดู `help(\"svm_poly\")`)\n",
+    "\n",
+    "มาลองสร้าง SVC เชิงเส้นโดยตั้งค่า `degree = 1` ในโมเดล SVM แบบพหุนาม\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vJpp6nuChlBz"
+   },
+   "source": [
+    "# Make a linear SVC specification\n",
+    "svc_linear_spec <- svm_poly(degree = 1) %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svc_linear_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svc_linear_spec)\n",
+    "\n",
+    "# Print out workflow\n",
+    "svc_linear_wf"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rDs8cWNkhoqu"
+   },
+   "source": [
+    "ตอนนี้ที่เราได้รวบรวมขั้นตอนการเตรียมข้อมูลและการกำหนดโมเดลไว้ใน *workflow* แล้ว เราสามารถเริ่มฝึก Linear SVC และประเมินผลลัพธ์ไปพร้อมกันได้ สำหรับตัวชี้วัดประสิทธิภาพ เรามาสร้างชุดตัวชี้วัดที่จะประเมิน: `accuracy`, `sensitivity`, `Positive Predicted Value` และ `F Measure`\n",
+    "\n",
+    "> `augment()` จะเพิ่มคอลัมน์สำหรับการทำนายลงในข้อมูลที่กำหนด\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "81wiqcwuhrnq"
+   },
+   "source": [
+    "# Train a linear SVC model\n",
+    "svc_linear_fit <- svc_linear_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svc_linear_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0UFQvHf-huo3"
+   },
+   "source": [
+    "#### ซัพพอร์ตเวกเตอร์แมชชีน\n",
+    "\n",
+    "ซัพพอร์ตเวกเตอร์แมชชีน (SVM) เป็นการขยายความสามารถของซัพพอร์ตเวกเตอร์คลาสซิไฟเออร์เพื่อรองรับเส้นแบ่งระหว่างคลาสที่ไม่เป็นเส้นตรง โดยหลักการแล้ว SVM ใช้ *เคอร์เนลทริก* เพื่อขยายพื้นที่ฟีเจอร์ให้สามารถปรับตัวเข้ากับความสัมพันธ์ที่ไม่เป็นเส้นตรงระหว่างคลาสได้ หนึ่งในฟังก์ชันเคอร์เนลที่ได้รับความนิยมและมีความยืดหยุ่นสูงที่ SVM ใช้คือ *Radial basis function* มาดูกันว่า SVM จะทำงานกับข้อมูลของเราได้อย่างไร\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-KX4S8mzhzmp"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Make an RBF SVM specification\n",
+    "svm_rbf_spec <- svm_rbf() %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svm_rbf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svm_rbf_spec)\n",
+    "\n",
+    "\n",
+    "# Train an RBF model\n",
+    "svm_rbf_fit <- svm_rbf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svm_rbf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QBFSa7WSh4HQ"
+   },
+   "source": [
+    "ดีขึ้นมาก 🤩!\n",
+    "\n",
+    "> ✅ โปรดดู:\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://bradleyboehmke.github.io/HOML/svm.html), Hands-on Machine Learning with R\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://www.statlearning.com/), An Introduction to Statistical Learning with Applications in R\n",
+    ">\n",
+    "> สำหรับการอ่านเพิ่มเติม\n",
+    "\n",
+    "### ตัวจำแนกประเภท Nearest Neighbor\n",
+    "\n",
+    "*K*-nearest neighbor (KNN) เป็นอัลกอริทึมที่การคาดการณ์แต่ละค่าจะขึ้นอยู่กับ *ความคล้ายคลึง* กับค่าของการสังเกตอื่น ๆ\n",
+    "\n",
+    "ลองปรับให้เข้ากับข้อมูลของเรากันเถอะ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "k4BxxBcdh9Ka"
+   },
+   "source": [
+    "# Make a KNN specification\n",
+    "knn_spec <- nearest_neighbor() %>% \n",
+    "  set_engine(\"kknn\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "knn_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(knn_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "knn_wf_fit <- knn_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "knn_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HaegQseriAcj"
+   },
+   "source": [
+    "ดูเหมือนว่ารุ่นนี้จะยังทำงานได้ไม่ดีนัก อาจจะลองเปลี่ยนพารามิเตอร์ของรุ่น (ดู `help(\"nearest_neighbor\")`) เพื่อปรับปรุงประสิทธิภาพของรุ่น อย่าลืมทดลองดูนะ\n",
+    "\n",
+    "> ✅ โปรดดู:\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> เพื่อเรียนรู้เพิ่มเติมเกี่ยวกับตัวจัดประเภท *K*-Nearest Neighbors\n",
+    "\n",
+    "### ตัวจัดประเภทแบบ Ensemble\n",
+    "\n",
+    "อัลกอริธึมแบบ Ensemble ทำงานโดยการรวมตัวประมาณค่าพื้นฐานหลายตัวเข้าด้วยกันเพื่อสร้างโมเดลที่เหมาะสมที่สุด โดยใช้วิธีการดังนี้:\n",
+    "\n",
+    "`bagging`: ใช้ *ฟังก์ชันเฉลี่ย* กับชุดของโมเดลพื้นฐาน\n",
+    "\n",
+    "`boosting`: สร้างลำดับของโมเดลที่ต่อยอดจากกันและกันเพื่อปรับปรุงประสิทธิภาพการพยากรณ์\n",
+    "\n",
+    "เริ่มต้นด้วยการลองใช้โมเดล Random Forest ซึ่งสร้างชุดของต้นไม้ตัดสินใจจำนวนมาก จากนั้นใช้ฟังก์ชันเฉลี่ยเพื่อสร้างโมเดลโดยรวมที่ดียิ่งขึ้น\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "49DPoVs6iK1M"
+   },
+   "source": [
+    "# Make a random forest specification\n",
+    "rf_spec <- rand_forest() %>% \n",
+    "  set_engine(\"ranger\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "rf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(rf_spec)\n",
+    "\n",
+    "# Train a random forest model\n",
+    "rf_wf_fit <- rf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "rf_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RGVYwC_aiUWc"
+   },
+   "source": [
+    "ทำได้ดีมาก 👏!\n",
+    "\n",
+    "ลองมาทดลองใช้โมเดล Boosted Tree กันเถอะ\n",
+    "\n",
+    "Boosted Tree เป็นวิธีการแบบกลุ่มที่สร้างชุดของต้นไม้ตัดสินใจที่ต่อเนื่องกัน โดยที่แต่ละต้นไม้จะขึ้นอยู่กับผลลัพธ์ของต้นไม้ก่อนหน้า เพื่อพยายามลดข้อผิดพลาดลงทีละน้อย วิธีนี้เน้นไปที่น้ำหนักของรายการที่ถูกจัดประเภทผิด และปรับการคาดการณ์สำหรับตัวจัดประเภทถัดไปเพื่อแก้ไขข้อผิดพลาด\n",
+    "\n",
+    "มีหลายวิธีในการปรับโมเดลนี้ (ดู `help(\"boost_tree\")`) ในตัวอย่างนี้ เราจะปรับ Boosted trees ผ่านเครื่องมือ `xgboost`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Py1YWo-micWs"
+   },
+   "source": [
+    "# Make a boosted tree specification\n",
+    "boost_spec <- boost_tree(trees = 200) %>% \n",
+    "  set_engine(\"xgboost\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "boost_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(boost_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "boost_wf_fit <- boost_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "boost_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zNQnbuejigZM"
+   },
+   "source": [
+    "> ✅ โปรดดู:\n",
+    ">\n",
+    "> -   [Machine Learning for Social Scientists](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> -   <https://algotech.netlify.app/blog/xgboost/> - สำรวจโมเดล AdaBoost ซึ่งเป็นทางเลือกที่ดีสำหรับ xgboost\n",
+    ">\n",
+    "> เพื่อเรียนรู้เพิ่มเติมเกี่ยวกับ Ensemble classifiers\n",
+    "\n",
+    "## 4. เพิ่มเติม - การเปรียบเทียบโมเดลหลายตัว\n",
+    "\n",
+    "เราได้สร้างโมเดลจำนวนมากในห้องทดลองนี้ 🙌 การสร้าง workflows จำนวนมากจากชุด preprocessors และ/หรือ model specifications ที่แตกต่างกัน แล้วคำนวณค่าประสิทธิภาพทีละตัวอาจกลายเป็นงานที่น่าเบื่อหรือยุ่งยาก\n",
+    "\n",
+    "ลองมาดูกันว่าเราสามารถแก้ไขปัญหานี้ได้หรือไม่โดยการสร้างฟังก์ชันที่สามารถปรับ workflows หลายตัวในชุดข้อมูลการฝึกอบรม แล้วคืนค่าประสิทธิภาพตามชุดข้อมูลทดสอบ เราจะใช้ `map()` และ `map_dfr()` จากแพ็กเกจ [purrr](https://purrr.tidyverse.org/) เพื่อใช้ฟังก์ชันกับแต่ละองค์ประกอบในลิสต์\n",
+    "\n",
+    "> ฟังก์ชัน [`map()`](https://purrr.tidyverse.org/reference/map.html) ช่วยให้คุณแทนที่ for loops จำนวนมากด้วยโค้ดที่กระชับและอ่านง่ายขึ้น จุดเริ่มต้นที่ดีที่สุดในการเรียนรู้เกี่ยวกับฟังก์ชัน [`map()`](https://purrr.tidyverse.org/reference/map.html) คือบท [iteration chapter](http://r4ds.had.co.nz/iteration.html) ใน R for data science\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Qzb7LyZnimd2"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "# Define a function that returns performance metrics\n",
+    "compare_models <- function(workflow_list, train_set, test_set){\n",
+    "  \n",
+    "  suppressWarnings(\n",
+    "    # Fit each model to the train_set\n",
+    "    map(workflow_list, fit, data = train_set) %>% \n",
+    "    # Make predictions on the test set\n",
+    "      map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
+    "    # Select desired columns\n",
+    "      select(model, cuisine, .pred_class) %>% \n",
+    "    # Evaluate model performance\n",
+    "      group_by(model) %>% \n",
+    "      eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
+    "      ungroup()\n",
+    "  )\n",
+    "  \n",
+    "} # End of function"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fwa712sNisDA"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "3i4VJOi2iu-a"
+   },
+   "source": [
+    "# Make a list of workflows\n",
+    "workflow_list <- list(\n",
+    "  \"svc\" = svc_linear_wf,\n",
+    "  \"svm\" = svm_rbf_wf,\n",
+    "  \"knn\" = knn_wf,\n",
+    "  \"random_forest\" = rf_wf,\n",
+    "  \"xgboost\" = boost_wf)\n",
+    "\n",
+    "# Call the function\n",
+    "set.seed(2056)\n",
+    "perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
+    "\n",
+    "# Print out performance metrics\n",
+    "perf_metrics %>% \n",
+    "  group_by(.metric) %>% \n",
+    "  arrange(desc(.estimate)) %>% \n",
+    "  slice_head(n=7)\n",
+    "\n",
+    "# Compare accuracy\n",
+    "perf_metrics %>% \n",
+    "  filter(.metric == \"accuracy\") %>% \n",
+    "  arrange(desc(.estimate))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KuWK_lEli4nW"
+   },
+   "source": [
+    "แพ็กเกจ [**workflowset**](https://workflowsets.tidymodels.org/) ช่วยให้ผู้ใช้สามารถสร้างและปรับใช้โมเดลจำนวนมากได้อย่างง่ายดาย แต่ส่วนใหญ่ถูกออกแบบมาเพื่อใช้งานร่วมกับเทคนิคการสุ่มตัวอย่าง เช่น `cross-validation` ซึ่งเป็นวิธีที่เรายังไม่ได้กล่าวถึง\n",
+    "\n",
+    "## **🚀ความท้าทาย**\n",
+    "\n",
+    "แต่ละเทคนิคเหล่านี้มีพารามิเตอร์จำนวนมากที่คุณสามารถปรับแต่งได้ เช่น `cost` ใน SVMs, `neighbors` ใน KNN, `mtry` (ตัวทำนายที่ถูกเลือกแบบสุ่ม) ใน Random Forest\n",
+    "\n",
+    "ค้นคว้าพารามิเตอร์เริ่มต้นของแต่ละโมเดล และลองคิดดูว่าการปรับแต่งพารามิเตอร์เหล่านี้จะส่งผลต่อคุณภาพของโมเดลอย่างไร\n",
+    "\n",
+    "หากต้องการทราบข้อมูลเพิ่มเติมเกี่ยวกับโมเดลและพารามิเตอร์ของมัน ให้ใช้: `help(\"model\")` เช่น `help(\"rand_forest\")`\n",
+    "\n",
+    "> ในการใช้งานจริง เรามักจะ *ประมาณค่า* *ค่าที่ดีที่สุด* โดยการฝึกโมเดลหลายตัวบน `ชุดข้อมูลจำลอง` และวัดผลว่าโมเดลเหล่านี้ทำงานได้ดีเพียงใด กระบวนการนี้เรียกว่า **การปรับแต่ง**\n",
+    "\n",
+    "### [**แบบทดสอบหลังการบรรยาย**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
+    "\n",
+    "### **ทบทวนและศึกษาด้วยตนเอง**\n",
+    "\n",
+    "มีคำศัพท์เฉพาะมากมายในบทเรียนเหล่านี้ ใช้เวลาสักครู่เพื่อทบทวน [รายการนี้](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) ซึ่งรวบรวมคำศัพท์ที่มีประโยชน์ไว้!\n",
+    "\n",
+    "#### ขอขอบคุณ:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) สำหรับการสร้างภาพประกอบที่น่าทึ่งซึ่งทำให้ R ดูน่าสนใจและเข้าถึงได้มากขึ้น ค้นหาภาพประกอบเพิ่มเติมได้ที่ [แกลเลอรีของเธอ](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM)\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) และ [Jen Looper](https://www.twitter.com/jenlooper) สำหรับการสร้างเวอร์ชัน Python ดั้งเดิมของโมดูลนี้ ♥️\n",
+    "\n",
+    "เรียนรู้อย่างมีความสุข,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>ภาพประกอบโดย @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/th/4-Classification/3-Classifiers-2/solution/notebook.ipynb
new file mode 100644
index 000000000..3afd7a4c8
--- /dev/null
+++ b/translations/th/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -0,0 +1,300 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "C = 10\n",
+    "# Create different classifiers.\n",
+    "classifiers = {\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
+    "    'KNN classifier': KNeighborsClassifier(C),\n",
+    "    'SVC': SVC(),\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "    \n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy (train) for Linear SVC: 76.4% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.64      0.66      0.65       242\n",
+      "      indian       0.91      0.86      0.89       236\n",
+      "    japanese       0.72      0.73      0.73       245\n",
+      "      korean       0.83      0.75      0.79       234\n",
+      "        thai       0.75      0.82      0.78       242\n",
+      "\n",
+      "    accuracy                           0.76      1199\n",
+      "   macro avg       0.77      0.76      0.77      1199\n",
+      "weighted avg       0.77      0.76      0.77      1199\n",
+      "\n",
+      "Accuracy (train) for KNN classifier: 70.7% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.65      0.63      0.64       242\n",
+      "      indian       0.84      0.81      0.82       236\n",
+      "    japanese       0.60      0.81      0.69       245\n",
+      "      korean       0.89      0.53      0.67       234\n",
+      "        thai       0.69      0.75      0.72       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.73      0.71      0.71      1199\n",
+      "weighted avg       0.73      0.71      0.71      1199\n",
+      "\n",
+      "Accuracy (train) for SVC: 80.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.71      0.69      0.70       242\n",
+      "      indian       0.92      0.92      0.92       236\n",
+      "    japanese       0.77      0.78      0.77       245\n",
+      "      korean       0.87      0.77      0.82       234\n",
+      "        thai       0.75      0.86      0.80       242\n",
+      "\n",
+      "    accuracy                           0.80      1199\n",
+      "   macro avg       0.80      0.80      0.80      1199\n",
+      "weighted avg       0.80      0.80      0.80      1199\n",
+      "\n",
+      "Accuracy (train) for RFST: 82.8% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.80      0.75      0.77       242\n",
+      "      indian       0.90      0.91      0.90       236\n",
+      "    japanese       0.82      0.78      0.80       245\n",
+      "      korean       0.85      0.82      0.83       234\n",
+      "        thai       0.78      0.89      0.83       242\n",
+      "\n",
+      "    accuracy                           0.83      1199\n",
+      "   macro avg       0.83      0.83      0.83      1199\n",
+      "weighted avg       0.83      0.83      0.83      1199\n",
+      "\n",
+      "Accuracy (train) for ADA: 71.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.60      0.57      0.58       242\n",
+      "      indian       0.87      0.84      0.86       236\n",
+      "    japanese       0.71      0.60      0.65       245\n",
+      "      korean       0.68      0.78      0.72       234\n",
+      "        thai       0.70      0.78      0.74       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.71      0.71      0.71      1199\n",
+      "weighted avg       0.71      0.71      0.71      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "    y_pred = classifier.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
+    "    print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามอย่างเต็มที่เพื่อให้การแปลมีความถูกต้อง โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามนุษย์ที่เป็นมืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "7ea2b714669c823a596d986ba2d5739f",
+   "translation_date": "2025-09-06T14:43:00+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/4-Applied/notebook.ipynb b/translations/th/4-Classification/4-Applied/notebook.ipynb
new file mode 100644
index 000000000..d2b566c12
--- /dev/null
+++ b/translations/th/4-Classification/4-Applied/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2f3e0d9e9ac5c301558fb8bf733ac0cb",
+   "translation_date": "2025-09-06T14:41:38+00:00",
+   "source_file": "4-Classification/4-Applied/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/4-Classification/4-Applied/solution/notebook.ipynb b/translations/th/4-Classification/4-Applied/solution/notebook.ipynb
new file mode 100644
index 000000000..c4a2e0f50
--- /dev/null
+++ b/translations/th/4-Classification/4-Applied/solution/notebook.ipynb
@@ -0,0 +1,290 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "49325d6dd12a3628fc64fa7ccb1a80ff",
+   "translation_date": "2025-09-06T14:42:03+00:00",
+   "source_file": "4-Classification/4-Applied/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: skl2onnx in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.8.0)\n",
+      "Requirement already satisfied: protobuf in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (3.8.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.19.2)\n",
+      "Requirement already satisfied: onnx>=1.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.9.0)\n",
+      "Requirement already satisfied: six in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from skl2onnx) (1.12.0)\n",
+      "Requirement already satisfied: onnxconverter-common<1.9,>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.8.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.19 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (0.24.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.4.1)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->skl2onnx) (45.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnx>=1.2.1->skl2onnx) (3.10.0.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (0.16.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install skl2onnx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 60
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 61
+    }
+   ],
+   "source": [
+    "X = data.iloc[:,2:]\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "  cuisine\n",
+       "0  indian\n",
+       "1  indian\n",
+       "2  indian\n",
+       "3  indian\n",
+       "4  indian"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 62
+    }
+   ],
+   "source": [
+    "y = data[['cuisine']]\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 65
+    }
+   ],
+   "source": [
+    "model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
+    "model.fit(X_train,y_train.values.ravel())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.72      0.70      0.71       236\n      indian       0.91      0.88      0.89       243\n    japanese       0.80      0.75      0.77       240\n      korean       0.80      0.81      0.81       230\n        thai       0.76      0.85      0.80       250\n\n    accuracy                           0.80      1199\n   macro avg       0.80      0.80      0.80      1199\nweighted avg       0.80      0.80      0.80      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}\n",
+    "onx = convert_sklearn(model, initial_types=initial_type, options=options)\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
+    "    f.write(onx.SerializeToString())\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/5-Clustering/1-Visualize/notebook.ipynb b/translations/th/5-Clustering/1-Visualize/notebook.ipynb
new file mode 100644
index 000000000..abd29b9fc
--- /dev/null
+++ b/translations/th/5-Clustering/1-Visualize/notebook.ipynb
@@ -0,0 +1,50 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python383jvsc74a57bd0e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95",
+   "display_name": "Python 3.8.3 64-bit (conda)"
+  },
+  "coopTranslator": {
+   "original_hash": "40e0707e96b3e1899a912776006264f9",
+   "translation_date": "2025-09-06T14:08:08+00:00",
+   "source_file": "5-Clustering/1-Visualize/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามนุษย์ที่เป็นมืออาชีพ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/translations/th/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
new file mode 100644
index 000000000..fa4bb2087
--- /dev/null
+++ b/translations/th/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **การวิเคราะห์เพลงไนจีเรียที่ดึงข้อมูลจาก Spotify**\n",
+    "\n",
+    "การจัดกลุ่ม (Clustering) เป็นรูปแบบหนึ่งของ [การเรียนรู้แบบไม่มีผู้สอน](https://wikipedia.org/wiki/Unsupervised_learning) ซึ่งสมมติว่าชุดข้อมูลไม่มีการติดป้ายกำกับ หรือข้อมูลนำเข้าไม่ได้จับคู่กับผลลัพธ์ที่กำหนดไว้ล่วงหน้า โดยใช้หลากหลายอัลกอริทึมเพื่อจัดเรียงข้อมูลที่ไม่มีป้ายกำกับและสร้างกลุ่มตามรูปแบบที่พบในข้อมูล\n",
+    "\n",
+    "[**แบบทดสอบก่อนการบรรยาย**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)\n",
+    "\n",
+    "### **บทนำ**\n",
+    "\n",
+    "[การจัดกลุ่ม](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) มีประโยชน์มากสำหรับการสำรวจข้อมูล ลองมาดูกันว่ามันสามารถช่วยค้นหาแนวโน้มและรูปแบบในวิธีที่ผู้ฟังชาวไนจีเรียบริโภคเพลงได้หรือไม่\n",
+    "\n",
+    "> ✅ ลองใช้เวลาสักครู่คิดถึงการใช้งานของการจัดกลุ่ม ในชีวิตจริง การจัดกลุ่มเกิดขึ้นเมื่อคุณมีกองผ้าซักและต้องแยกเสื้อผ้าของสมาชิกในครอบครัวออกจากกัน 🧦👕👖🩲 ในด้านวิทยาศาสตร์ข้อมูล การจัดกลุ่มเกิดขึ้นเมื่อพยายามวิเคราะห์ความชอบของผู้ใช้ หรือกำหนดลักษณะของชุดข้อมูลที่ไม่มีป้ายกำกับ การจัดกลุ่มช่วยให้เข้าใจความยุ่งเหยิง เช่น ลิ้นชักถุงเท้า\n",
+    "\n",
+    "ในสภาพแวดล้อมการทำงาน การจัดกลุ่มสามารถใช้เพื่อกำหนดสิ่งต่าง ๆ เช่น การแบ่งส่วนตลาด หรือการระบุว่ากลุ่มอายุใดซื้อสินค้าอะไร ตัวอย่างเช่น อีกการใช้งานหนึ่งคือการตรวจจับความผิดปกติ เช่น การตรวจจับการฉ้อโกงจากชุดข้อมูลธุรกรรมบัตรเครดิต หรือคุณอาจใช้การจัดกลุ่มเพื่อระบุเนื้องอกจากชุดภาพสแกนทางการแพทย์\n",
+    "\n",
+    "✅ ลองคิดสักครู่เกี่ยวกับวิธีที่คุณอาจเคยพบการจัดกลุ่มในชีวิตจริง เช่น ในธนาคาร อีคอมเมิร์ซ หรือการตั้งค่าทางธุรกิจ\n",
+    "\n",
+    "> 🎓 น่าสนใจที่การวิเคราะห์การจัดกลุ่มมีต้นกำเนิดในสาขามานุษยวิทยาและจิตวิทยาในช่วงปี 1930 คุณจินตนาการได้ไหมว่ามันอาจถูกใช้อย่างไร?\n",
+    "\n",
+    "อีกทางหนึ่ง คุณสามารถใช้มันเพื่อจัดกลุ่มผลการค้นหา เช่น ลิงก์การช็อปปิ้ง รูปภาพ หรือรีวิว การจัดกลุ่มมีประโยชน์เมื่อคุณมีชุดข้อมูลขนาดใหญ่ที่ต้องการลดขนาดลงและต้องการวิเคราะห์ในเชิงลึกมากขึ้น ดังนั้นเทคนิคนี้สามารถใช้เพื่อเรียนรู้เกี่ยวกับข้อมูลก่อนที่จะสร้างโมเดลอื่น ๆ\n",
+    "\n",
+    "✅ เมื่อข้อมูลของคุณถูกจัดระเบียบในกลุ่ม คุณสามารถกำหนดรหัสกลุ่มให้กับมัน เทคนิคนี้มีประโยชน์เมื่อคุณต้องการรักษาความเป็นส่วนตัวของชุดข้อมูล คุณสามารถอ้างถึงจุดข้อมูลโดยใช้รหัสกลุ่มแทนที่จะใช้ข้อมูลที่สามารถระบุตัวตนได้ คุณคิดเหตุผลอื่น ๆ ได้ไหมว่าทำไมคุณถึงเลือกใช้รหัสกลุ่มแทนองค์ประกอบอื่น ๆ ของกลุ่มเพื่อระบุข้อมูล?\n",
+    "\n",
+    "### เริ่มต้นกับการจัดกลุ่ม\n",
+    "\n",
+    "> 🎓 วิธีที่เราสร้างกลุ่มมีความเกี่ยวข้องอย่างมากกับวิธีที่เรารวบรวมจุดข้อมูลเข้าด้วยกัน ลองมาทำความเข้าใจคำศัพท์บางคำ:\n",
+    ">\n",
+    "> 🎓 ['Transductive' vs. 'Inductive'](https://wikipedia.org/wiki/Transduction_(machine_learning))\n",
+    ">\n",
+    "> การอนุมานแบบ Transductive มาจากกรณีการฝึกอบรมที่สังเกตได้ซึ่งจับคู่กับกรณีทดสอบเฉพาะ การอนุมานแบบ Inductive มาจากกรณีการฝึกอบรมที่จับคู่กับกฎทั่วไปซึ่งจะถูกนำไปใช้กับกรณีทดสอบในภายหลัง\n",
+    ">\n",
+    "> ตัวอย่าง: ลองจินตนาการว่าคุณมีชุดข้อมูลที่มีการติดป้ายกำกับบางส่วน บางรายการเป็น 'แผ่นเสียง' บางรายการเป็น 'ซีดี' และบางรายการไม่มีป้ายกำกับ งานของคุณคือการให้ป้ายกำกับกับข้อมูลที่ไม่มีป้ายกำกับ หากคุณเลือกวิธี Inductive คุณจะฝึกโมเดลเพื่อค้นหา 'แผ่นเสียง' และ 'ซีดี' และนำป้ายกำกับเหล่านั้นไปใช้กับข้อมูลที่ไม่มีป้ายกำกับ วิธีนี้จะมีปัญหาในการจัดประเภทสิ่งที่จริง ๆ แล้วเป็น 'เทปคาสเซ็ต' ในทางกลับกัน วิธี Transductive จะจัดการกับข้อมูลที่ไม่รู้จักได้อย่างมีประสิทธิภาพมากกว่า โดยทำงานเพื่อจัดกลุ่มรายการที่คล้ายกันเข้าด้วยกันและนำป้ายกำกับไปใช้กับกลุ่ม ในกรณีนี้ กลุ่มอาจสะท้อนถึง 'สิ่งดนตรีทรงกลม' และ 'สิ่งดนตรีทรงสี่เหลี่ยม'\n",
+    ">\n",
+    "> 🎓 ['Non-flat' vs. 'Flat' Geometry](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)\n",
+    ">\n",
+    "> มาจากคำศัพท์ทางคณิตศาสตร์ 'Non-flat' vs. 'Flat' Geometry หมายถึงการวัดระยะทางระหว่างจุดโดยใช้วิธีการทางเรขาคณิตแบบ 'Flat' ([Euclidean](https://wikipedia.org/wiki/Euclidean_geometry)) หรือ 'Non-flat' (Non-Euclidean)\n",
+    ">\n",
+    "> 'Flat' ในบริบทนี้หมายถึงเรขาคณิตแบบยูคลิด (บางส่วนของมันถูกสอนเป็นเรขาคณิต 'Plane') และ 'Non-flat' หมายถึงเรขาคณิตแบบไม่ใช่ยูคลิด เรขาคณิตเกี่ยวข้องอะไรกับการเรียนรู้ของเครื่อง? เนื่องจากทั้งสองสาขามีรากฐานมาจากคณิตศาสตร์ จึงต้องมีวิธีการทั่วไปในการวัดระยะทางระหว่างจุดในกลุ่ม ซึ่งสามารถทำได้ในแบบ 'Flat' หรือ 'Non-flat' ขึ้นอยู่กับลักษณะของข้อมูล [ระยะทางแบบยูคลิด](https://wikipedia.org/wiki/Euclidean_distance) ถูกวัดเป็นความยาวของเส้นตรงระหว่างสองจุด [ระยะทางแบบไม่ใช่ยูคลิด](https://wikipedia.org/wiki/Non-Euclidean_geometry) ถูกวัดตามเส้นโค้ง หากข้อมูลของคุณเมื่อแสดงภาพดูเหมือนจะไม่อยู่บนระนาบ คุณอาจต้องใช้อัลกอริทึมเฉพาะเพื่อจัดการกับมัน\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/flat-nonflat.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>อินโฟกราฟิกโดย Dasani Madipalli</figcaption>\n",
+    "\n",
+    "> 🎓 ['Distances'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)\n",
+    ">\n",
+    "> กลุ่มถูกกำหนดโดยเมทริกซ์ระยะทาง เช่น ระยะทางระหว่างจุด ระยะทางนี้สามารถวัดได้หลายวิธี กลุ่มแบบยูคลิดถูกกำหนดโดยค่าเฉลี่ยของค่าจุด และมี 'Centroid' หรือจุดศูนย์กลาง ระยะทางจึงถูกวัดโดยระยะทางไปยัง Centroid นั้น ระยะทางแบบไม่ใช่ยูคลิดหมายถึง 'Clustroids' ซึ่งเป็นจุดที่ใกล้ที่สุดกับจุดอื่น ๆ Clustroids สามารถกำหนดได้หลายวิธี\n",
+    ">\n",
+    "> 🎓 ['Constrained'](https://wikipedia.org/wiki/Constrained_clustering)\n",
+    ">\n",
+    "> [การจัดกลุ่มแบบมีข้อจำกัด](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) แนะนำการเรียนรู้แบบกึ่งมีผู้สอนเข้าสู่วิธีการแบบไม่มีผู้สอน ความสัมพันธ์ระหว่างจุดถูกกำหนดเป็น 'ไม่สามารถเชื่อมโยง' หรือ 'ต้องเชื่อมโยง' ดังนั้นจึงมีการบังคับกฎบางอย่างในชุดข้อมูล\n",
+    ">\n",
+    "> ตัวอย่าง: หากอัลกอริทึมถูกปล่อยให้ทำงานกับชุดข้อมูลที่ไม่มีป้ายกำกับหรือมีป้ายกำกับบางส่วน กลุ่มที่มันสร้างขึ้นอาจมีคุณภาพต่ำ ในตัวอย่างข้างต้น กลุ่มอาจจัดกลุ่ม 'สิ่งดนตรีทรงกลม' 'สิ่งดนตรีทรงสี่เหลี่ยม' 'สิ่งทรงสามเหลี่ยม' และ 'คุกกี้' หากมีการให้ข้อจำกัดหรือกฎบางอย่าง (\"รายการต้องทำจากพลาสติก\", \"รายการต้องสามารถผลิตเสียงดนตรีได้\") สิ่งนี้สามารถช่วย 'จำกัด' อัลกอริทึมให้เลือกได้ดีขึ้น\n",
+    ">\n",
+    "> 🎓 'Density'\n",
+    ">\n",
+    "> ข้อมูลที่มี 'เสียงรบกวน' ถือว่าเป็น 'หนาแน่น' ระยะทางระหว่างจุดในแต่ละกลุ่มอาจพิสูจน์ได้ว่ามีความหนาแน่นมากหรือน้อยเมื่อวิเคราะห์ และข้อมูลนี้จำเป็นต้องได้รับการวิเคราะห์ด้วยวิธีการจัดกลุ่มที่เหมาะสม [บทความนี้](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) แสดงให้เห็นความแตกต่างระหว่างการใช้ K-Means clustering กับ HDBSCAN เพื่อสำรวจชุดข้อมูลที่มีความหนาแน่นของกลุ่มไม่เท่ากัน\n",
+    "\n",
+    "เพิ่มความเข้าใจของคุณเกี่ยวกับเทคนิคการจัดกลุ่มใน [โมดูลการเรียนรู้](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "### **อัลกอริทึมการจัดกลุ่ม**\n",
+    "\n",
+    "มีอัลกอริทึมการจัดกลุ่มมากกว่า 100 แบบ และการใช้งานขึ้นอยู่กับลักษณะของข้อมูลที่มีอยู่ ลองมาพูดถึงบางส่วนที่สำคัญ:\n",
+    "\n",
+    "-   **การจัดกลุ่มแบบลำดับชั้น** หากวัตถุถูกจัดประเภทตามความใกล้ชิดกับวัตถุใกล้เคียงมากกว่ากับวัตถุที่อยู่ไกลออกไป กลุ่มจะถูกสร้างขึ้นตามระยะทางของสมาชิกไปยังและจากวัตถุอื่น ๆ การจัดกลุ่มแบบลำดับชั้นมีลักษณะโดยการรวมสองกลุ่มซ้ำ ๆ\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/hierarchical.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>อินโฟกราฟิกโดย Dasani Madipalli</figcaption>\n",
+    "\n",
+    "-   **การจัดกลุ่มแบบ Centroid** อัลกอริทึมยอดนิยมนี้ต้องการการเลือก 'k' หรือจำนวนกลุ่มที่จะสร้าง หลังจากนั้นอัลกอริทึมจะกำหนดจุดศูนย์กลางของกลุ่มและรวบรวมข้อมูลรอบจุดนั้น [K-means clustering](https://wikipedia.org/wiki/K-means_clustering) เป็นเวอร์ชันยอดนิยมของการจัดกลุ่มแบบ Centroid ซึ่งแยกชุดข้อมูลออกเป็น K กลุ่มที่กำหนดไว้ล่วงหน้า จุดศูนย์กลางถูกกำหนดโดยค่าเฉลี่ยที่ใกล้ที่สุด จึงเป็นที่มาของชื่อ ระยะทางยกกำลังสองจากกลุ่มจะถูกลดลง\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/centroid.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>อินโฟกราฟิกโดย Dasani Madipalli</figcaption>\n",
+    "\n",
+    "-   **การจัดกลุ่มแบบอิงการกระจาย** อิงตามการสร้างแบบจำลองทางสถิติ การจัดกลุ่มแบบอิงการกระจายมุ่งเน้นไปที่การกำหนดความน่าจะเป็นที่จุดข้อมูลจะอยู่ในกลุ่ม และกำหนดให้ตามนั้น วิธี Gaussian mixture เป็นส่วนหนึ่งของประเภทนี้\n",
+    "\n",
+    "-   **การจัดกลุ่มแบบอิงความหนาแน่น** จุดข้อมูลจะถูกกำหนดให้กับกลุ่มตามความหนาแน่น หรือการรวมกลุ่มรอบ ๆ กัน จุดข้อมูลที่อยู่ไกลจากกลุ่มจะถือว่าเป็นข้อมูลรบกวนหรือเสียงรบกวน DBSCAN, Mean-shift และ OPTICS เป็นส่วนหนึ่งของประเภทนี้\n",
+    "\n",
+    "-   **การจัดกลุ่มแบบอิงกริด** สำหรับชุดข้อมูลหลายมิติ จะมีการสร้างกริดและข้อมูลจะถูกแบ่งออกเป็นเซลล์ของกริดนั้น ซึ่งจะสร้างกลุ่มขึ้นมา\n",
+    "\n",
+    "วิธีที่ดีที่สุดในการเรียนรู้เกี่ยวกับการจัดกลุ่มคือการลองทำด้วยตัวเอง และนั่นคือสิ่งที่คุณจะทำในแบบฝึกหัดนี้\n",
+    "\n",
+    "เราจะต้องใช้แพ็กเกจบางอย่างเพื่อเริ่มต้นโมดูลนี้ คุณสามารถติดตั้งได้โดยใช้: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`\n",
+    "\n",
+    "หรือใช้สคริปต์ด้านล่างเพื่อตรวจสอบว่าคุณมีแพ็กเกจที่จำเป็นสำหรับการทำโมดูลนี้หรือไม่ และติดตั้งให้คุณในกรณีที่บางแพ็กเกจขาดหายไป\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## แบบฝึกหัด - จัดกลุ่มข้อมูลของคุณ\n",
+    "\n",
+    "การจัดกลุ่มข้อมูล (Clustering) เป็นเทคนิคที่ได้รับประโยชน์อย่างมากจากการแสดงผลข้อมูลที่เหมาะสม ดังนั้นเรามาเริ่มต้นด้วยการแสดงผลข้อมูลเพลงของเรากัน การฝึกหัดนี้จะช่วยให้เราตัดสินใจได้ว่าวิธีการจัดกลุ่มข้อมูลแบบใดที่เหมาะสมที่สุดสำหรับลักษณะของข้อมูลนี้\n",
+    "\n",
+    "มาเริ่มต้นกันเลยด้วยการนำเข้าข้อมูล!\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# Import the data into a tibble\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows of the data set\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "บางครั้งเราอาจต้องการข้อมูลเพิ่มเติมเล็กน้อยเกี่ยวกับข้อมูลของเรา เราสามารถดู `data` และ `โครงสร้างของมัน` ได้โดยใช้ฟังก์ชัน [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Glimpse into the data set\r\n",
+    "df %>% \r\n",
+    "  glimpse()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมมาก!💪\n",
+    "\n",
+    "เราสามารถสังเกตได้ว่า `glimpse()` จะแสดงจำนวนแถวทั้งหมด (observations) และจำนวนคอลัมน์ทั้งหมด (variables) จากนั้นจะแสดงข้อมูลบางส่วนของแต่ละตัวแปรในแถวถัดจากชื่อของตัวแปร นอกจากนี้ *ประเภทข้อมูล* ของตัวแปรจะถูกแสดงอยู่ถัดจากชื่อของตัวแปรภายใน `< >`\n",
+    "\n",
+    "`DataExplorer::introduce()` สามารถสรุปข้อมูลเหล่านี้ได้อย่างเป็นระเบียบ:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe basic information for our data\r\n",
+    "df %>% \r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# A visual display of the same\r\n",
+    "df %>% \r\n",
+    "  plot_intro()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เยี่ยมไปเลย! เราเพิ่งทราบว่าข้อมูลของเราไม่มีค่าที่หายไป\n",
+    "\n",
+    "ในขณะที่เรากำลังทำสิ่งนี้ เราสามารถสำรวจสถิติที่แสดงถึงแนวโน้มศูนย์กลางทั่วไป (เช่น [ค่าเฉลี่ย](https://en.wikipedia.org/wiki/Arithmetic_mean) และ [ค่ามัธยฐาน](https://en.wikipedia.org/wiki/Median)) และการวัดการกระจายตัว (เช่น [ส่วนเบี่ยงเบนมาตรฐาน](https://en.wikipedia.org/wiki/Standard_deviation)) โดยใช้ `summarytools::descr()`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe common statistics\r\n",
+    "df %>% \r\n",
+    "  descr(stats = \"common\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "มาดูค่าทั่วไปของข้อมูลกันก่อน โปรดทราบว่าความนิยม (popularity) อาจมีค่าเป็น `0` ซึ่งแสดงถึงเพลงที่ไม่มีการจัดอันดับ เราจะลบข้อมูลเหล่านั้นในภายหลัง\n",
+    "\n",
+    "> 🤔 ถ้าเรากำลังทำงานกับการจัดกลุ่ม (clustering) ซึ่งเป็นวิธีการแบบไม่มีผู้สอนที่ไม่ต้องการข้อมูลที่มีป้ายกำกับ ทำไมเราถึงแสดงข้อมูลนี้พร้อมป้ายกำกับ? ในขั้นตอนการสำรวจข้อมูล ป้ายกำกับเหล่านี้มีประโยชน์ แต่ไม่ได้จำเป็นสำหรับการทำงานของอัลกอริทึมการจัดกลุ่ม\n",
+    "\n",
+    "### 1. สำรวจแนวเพลงยอดนิยม\n",
+    "\n",
+    "มาลองค้นหาแนวเพลงที่ได้รับความนิยมมากที่สุด 🎶 โดยการนับจำนวนครั้งที่ปรากฏกันเถอะ\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Popular genres\r\n",
+    "top_genres <- df %>% \r\n",
+    "  count(artist_top_genre, sort = TRUE) %>% \r\n",
+    "# Encode to categorical and reorder the according to count\r\n",
+    "  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())\r\n",
+    "\r\n",
+    "# Print the top genres\r\n",
+    "top_genres\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "นั่นไปได้ดี! เขาว่าภาพหนึ่งภาพมีค่ามากกว่าพันแถวของข้อมูล (จริงๆ แล้วไม่มีใครพูดแบบนั้นเลย 😅) แต่คุณเข้าใจความหมายใช่ไหม?\n",
+    "\n",
+    "วิธีหนึ่งในการแสดงข้อมูลประเภทหมวดหมู่ (ตัวแปรประเภทตัวอักษรหรือแฟคเตอร์) คือการใช้กราฟแท่ง ลองมาสร้างกราฟแท่งของ 10 อันดับแรกของประเภทแนวเพลงกัน:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Change the default gray theme\r\n",
+    "theme_set(theme_light())\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ตอนนี้มันง่ายขึ้นมากที่จะระบุว่าเรามี `missing` ประเภทเพลง 🧐!\n",
+    "\n",
+    "> การแสดงผลข้อมูลที่ดีจะช่วยให้คุณเห็นสิ่งที่คุณไม่คาดคิด หรือทำให้เกิดคำถามใหม่เกี่ยวกับข้อมูล - Hadley Wickham และ Garrett Grolemund, [R For Data Science](https://r4ds.had.co.nz/introduction.html)\n",
+    "\n",
+    "โปรดทราบว่า เมื่อประเภทเพลงที่อยู่ด้านบนถูกระบุว่า `Missing` นั่นหมายความว่า Spotify ไม่ได้จัดประเภทให้ ดังนั้นเรามาลบมันออกไปกันเถอะ\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  filter(artist_top_genre != \"Missing\") %>% \r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "จากการสำรวจข้อมูลเบื้องต้น เราได้เรียนรู้ว่าแนวเพลงสามอันดับแรกมีอิทธิพลอย่างมากในชุดข้อมูลนี้ ดังนั้นเรามาเน้นที่ `afro dancehall`, `afropop` และ `nigerian pop` และกรองชุดข้อมูลเพิ่มเติมเพื่อเอาข้อมูลที่มีค่า popularity เท่ากับ 0 ออก (ซึ่งหมายความว่าไม่ได้ถูกจัดประเภทด้วยค่าความนิยมในชุดข้อมูล และสามารถพิจารณาได้ว่าเป็นสัญญาณรบกวนสำหรับวัตถุประสงค์ของเรา):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "nigerian_songs <- df %>% \r\n",
+    "  # Concentrate on top 3 genres\r\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \r\n",
+    "  # Remove unclassified observations\r\n",
+    "  filter(popularity != 0)\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "nigerian_songs %>%\r\n",
+    "  count(artist_top_genre) %>%\r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "มาดูกันว่ามีความสัมพันธ์เชิงเส้นที่ชัดเจนระหว่างตัวแปรเชิงปริมาณในชุดข้อมูลของเราหรือไม่ ความสัมพันธ์นี้ถูกวัดในเชิงคณิตศาสตร์โดย [สถิติความสัมพันธ์](https://en.wikipedia.org/wiki/Correlation)\n",
+    "\n",
+    "สถิติความสัมพันธ์เป็นค่าที่อยู่ระหว่าง -1 ถึง 1 ซึ่งบ่งบอกถึงความแข็งแกร่งของความสัมพันธ์ ค่าเหนือ 0 แสดงถึงความสัมพันธ์แบบ *บวก* (ค่าที่สูงของตัวแปรหนึ่งมักจะสอดคล้องกับค่าที่สูงของอีกตัวแปรหนึ่ง) ในขณะที่ค่าต่ำกว่า 0 แสดงถึงความสัมพันธ์แบบ *ลบ* (ค่าที่สูงของตัวแปรหนึ่งมักจะสอดคล้องกับค่าที่ต่ำของอีกตัวแปรหนึ่ง)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Narrow down to numeric variables and fid correlation\r\n",
+    "corr_mat <- nigerian_songs %>% \r\n",
+    "  select(where(is.numeric)) %>% \r\n",
+    "  cor()\r\n",
+    "\r\n",
+    "# Visualize correlation matrix\r\n",
+    "corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  \r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ข้อมูลไม่ได้มีความสัมพันธ์ที่ชัดเจน ยกเว้นระหว่าง `energy` และ `loudness` ซึ่งสมเหตุสมผล เพราะเพลงที่เสียงดังมักจะมีพลังงานสูง `Popularity` มีความสัมพันธ์กับ `release date` ซึ่งก็สมเหตุสมผลเช่นกัน เนื่องจากเพลงที่ออกใหม่มักจะได้รับความนิยมมากกว่า นอกจากนี้ ความยาวและพลังงานดูเหมือนจะมีความสัมพันธ์กันด้วย\n",
+    "\n",
+    "น่าสนใจที่จะดูว่าอัลกอริทึมการจัดกลุ่มจะสามารถวิเคราะห์ข้อมูลนี้ได้อย่างไร!\n",
+    "\n",
+    "> 🎓 โปรดทราบว่าความสัมพันธ์ไม่ได้หมายถึงเหตุและผล! เรามีหลักฐานของความสัมพันธ์ แต่ไม่มีหลักฐานของเหตุและผล เว็บไซต์ [ที่น่าสนุก](https://tylervigen.com/spurious-correlations) มีภาพประกอบที่เน้นประเด็นนี้\n",
+    "\n",
+    "### 2. สำรวจการกระจายตัวของข้อมูล\n",
+    "\n",
+    "ลองถามคำถามที่ลึกซึ้งขึ้นอีกหน่อย แนวเพลงต่าง ๆ มีความแตกต่างกันอย่างมีนัยสำคัญในแง่ของการเต้นได้หรือไม่ โดยพิจารณาจากความนิยม? ลองตรวจสอบการกระจายตัวของข้อมูลในสามแนวเพลงยอดนิยมของเราสำหรับความนิยมและการเต้นได้ตามแกน x และ y ที่กำหนด โดยใช้ [density plots](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Perform 2D kernel density estimation\r\n",
+    "density_estimate_2d <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +\r\n",
+    "  geom_density_2d(bins = 5, size = 1) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  xlim(-20, 80) +\r\n",
+    "  ylim(0, 1.2)\r\n",
+    "\r\n",
+    "# Density plot based on the popularity\r\n",
+    "density_estimate_pop <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  theme(legend.position = \"none\")\r\n",
+    "\r\n",
+    "# Density plot based on the danceability\r\n",
+    "density_estimate_dance <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Patch everything together\r\n",
+    "library(patchwork)\r\n",
+    "density_estimate_2d / (density_estimate_pop + density_estimate_dance)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เราสังเกตเห็นว่ามีวงกลมที่เรียงตัวกันเป็นชั้นๆ ไม่ว่าจะเป็นแนวเพลงใดก็ตาม อาจเป็นไปได้ว่ารสนิยมของชาวไนจีเรียมาบรรจบกันที่ระดับความสามารถในการเต้นสำหรับแนวเพลงนี้?\n",
+    "\n",
+    "โดยทั่วไปแล้ว แนวเพลงทั้งสามมีความสอดคล้องกันในแง่ของความนิยมและความสามารถในการเต้น การกำหนดกลุ่มในข้อมูลที่เรียงตัวกันอย่างหลวมๆ นี้จะเป็นความท้าทาย ลองมาดูกันว่าการใช้ scatter plot จะช่วยสนับสนุนเรื่องนี้ได้หรือไม่\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# A scatter plot of popularity and danceability\r\n",
+    "scatter_plot <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +\r\n",
+    "  geom_point(size = 2, alpha = 0.8) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"futurevisions::mars\")\r\n",
+    "\r\n",
+    "# Add a touch of interactivity\r\n",
+    "ggplotly(scatter_plot)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "แผนภาพกระจายของแกนเดียวกันแสดงรูปแบบการบรรจบกันที่คล้ายกัน\n",
+    "\n",
+    "โดยทั่วไป สำหรับการจัดกลุ่มข้อมูล คุณสามารถใช้แผนภาพกระจายเพื่อแสดงกลุ่มของข้อมูล ดังนั้นการเชี่ยวชาญการสร้างภาพประเภทนี้จึงมีประโยชน์มาก ในบทเรียนถัดไป เราจะนำข้อมูลที่กรองแล้วนี้มาใช้กับการจัดกลุ่มแบบ k-means เพื่อค้นหากลุ่มในข้อมูลที่มีการทับซ้อนกันในรูปแบบที่น่าสนใจ\n",
+    "\n",
+    "## **🚀 ความท้าทาย**\n",
+    "\n",
+    "เพื่อเตรียมตัวสำหรับบทเรียนถัดไป สร้างแผนภูมิที่เกี่ยวกับอัลกอริทึมการจัดกลุ่มต่าง ๆ ที่คุณอาจค้นพบและใช้ในสภาพแวดล้อมการผลิต อัลกอริทึมการจัดกลุ่มเหล่านี้พยายามแก้ไขปัญหาแบบใด?\n",
+    "\n",
+    "## [**แบบทดสอบหลังบทเรียน**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)\n",
+    "\n",
+    "## **ทบทวนและศึกษาด้วยตัวเอง**\n",
+    "\n",
+    "ก่อนที่คุณจะใช้การจัดกลุ่มข้อมูลตามที่เราได้เรียนรู้มา การทำความเข้าใจลักษณะของชุดข้อมูลของคุณเป็นความคิดที่ดี อ่านเพิ่มเติมเกี่ยวกับหัวข้อนี้ [ที่นี่](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)\n",
+    "\n",
+    "เพิ่มพูนความเข้าใจเกี่ยวกับเทคนิคการจัดกลุ่มข้อมูล:\n",
+    "\n",
+    "-   [ฝึกฝนและประเมินโมเดลการจัดกลุ่มด้วย Tidymodels และเครื่องมืออื่น ๆ](https://rpubs.com/eR_ic/clustering)\n",
+    "\n",
+    "-   Bradley Boehmke & Brandon Greenwell, [*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/)*.*\n",
+    "\n",
+    "## **งานที่ได้รับมอบหมาย**\n",
+    "\n",
+    "[ค้นคว้าการสร้างภาพอื่น ๆ สำหรับการจัดกลุ่มข้อมูล](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)\n",
+    "\n",
+    "## ขอขอบคุณ:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) สำหรับการสร้างเวอร์ชัน Python ดั้งเดิมของโมดูลนี้ ♥️\n",
+    "\n",
+    "[`Dasani Madipalli`](https://twitter.com/dasani_decoded) สำหรับการสร้างภาพประกอบที่ยอดเยี่ยมซึ่งช่วยให้แนวคิดเกี่ยวกับการเรียนรู้ของเครื่องเข้าใจง่ายและเข้าถึงได้มากขึ้น\n",
+    "\n",
+    "เรียนรู้อย่างมีความสุข,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "99c36449cad3708a435f6798cfa39972",
+   "translation_date": "2025-09-06T14:16:59+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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diff --git a/translations/th/5-Clustering/1-Visualize/solution/notebook.ipynb b/translations/th/5-Clustering/1-Visualize/solution/notebook.ipynb
new file mode 100644
index 000000000..f648adf97
--- /dev/null
+++ b/translations/th/5-Clustering/1-Visualize/solution/notebook.ipynb
@@ -0,0 +1,817 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: seaborn in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (0.11.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (3.5.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.21.4)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.3.4)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.7.2)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (4.28.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (21.2)\n",
+      "Requirement already satisfied: setuptools-scm>=4 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (6.3.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
+      "Requirement already satisfied: tomli>=1.0.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (1.2.2)\n",
+      "Requirement already satisfied: setuptools in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (59.1.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "รับข้อมูลเกี่ยวกับดาต้าเฟรม\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 530 entries, 0 to 529\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   name              530 non-null    object \n",
+      " 1   album             530 non-null    object \n",
+      " 2   artist            530 non-null    object \n",
+      " 3   artist_top_genre  530 non-null    object \n",
+      " 4   release_date      530 non-null    int64  \n",
+      " 5   length            530 non-null    int64  \n",
+      " 6   popularity        530 non-null    int64  \n",
+      " 7   danceability      530 non-null    float64\n",
+      " 8   acousticness      530 non-null    float64\n",
+      " 9   energy            530 non-null    float64\n",
+      " 10  instrumentalness  530 non-null    float64\n",
+      " 11  liveness          530 non-null    float64\n",
+      " 12  loudness          530 non-null    float64\n",
+      " 13  speechiness       530 non-null    float64\n",
+      " 14  tempo             530 non-null    float64\n",
+      " 15  time_signature    530 non-null    int64  \n",
+      "dtypes: float64(8), int64(4), object(4)\n",
+      "memory usage: 66.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name                0\n",
+       "album               0\n",
+       "artist              0\n",
+       "artist_top_genre    0\n",
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+       "liveness            0\n",
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+       "tempo               0\n",
+       "time_signature      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ดูค่าทั่วไปของข้อมูล โปรดทราบว่าความนิยมสามารถเป็น '0' ได้ - และมีหลายแถวที่มีค่านั้น\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530.000000</td>\n",
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+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
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+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2015.390566</td>\n",
+       "      <td>222298.169811</td>\n",
+       "      <td>17.507547</td>\n",
+       "      <td>0.741619</td>\n",
+       "      <td>0.265412</td>\n",
+       "      <td>0.760623</td>\n",
+       "      <td>0.016305</td>\n",
+       "      <td>0.147308</td>\n",
+       "      <td>-4.953011</td>\n",
+       "      <td>0.130748</td>\n",
+       "      <td>116.487864</td>\n",
+       "      <td>3.986792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.131688</td>\n",
+       "      <td>39696.822259</td>\n",
+       "      <td>18.992212</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.208342</td>\n",
+       "      <td>0.148533</td>\n",
+       "      <td>0.090321</td>\n",
+       "      <td>0.123588</td>\n",
+       "      <td>2.464186</td>\n",
+       "      <td>0.092939</td>\n",
+       "      <td>23.518601</td>\n",
+       "      <td>0.333701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>89488.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>0.111000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028300</td>\n",
+       "      <td>-19.362000</td>\n",
+       "      <td>0.027800</td>\n",
+       "      <td>61.695000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>199305.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681000</td>\n",
+       "      <td>0.089525</td>\n",
+       "      <td>0.669000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.075650</td>\n",
+       "      <td>-6.298750</td>\n",
+       "      <td>0.059100</td>\n",
+       "      <td>102.961250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>218509.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.761000</td>\n",
+       "      <td>0.220500</td>\n",
+       "      <td>0.784500</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.103500</td>\n",
+       "      <td>-4.558500</td>\n",
+       "      <td>0.097950</td>\n",
+       "      <td>112.714500</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>242098.500000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.829500</td>\n",
+       "      <td>0.403000</td>\n",
+       "      <td>0.875750</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.164000</td>\n",
+       "      <td>-3.331000</td>\n",
+       "      <td>0.177000</td>\n",
+       "      <td>125.039250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>511738.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>0.966000</td>\n",
+       "      <td>0.954000</td>\n",
+       "      <td>0.995000</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.811000</td>\n",
+       "      <td>0.582000</td>\n",
+       "      <td>0.514000</td>\n",
+       "      <td>206.007000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       release_date         length  popularity  danceability  acousticness  \\\n",
+       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
+       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
+       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
+       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
+       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
+       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
+       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
+       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
+       "\n",
+       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
+       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
+       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
+       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
+       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
+       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
+       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
+       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
+       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
+       "\n",
+       "            tempo  time_signature  \n",
+       "count  530.000000      530.000000  \n",
+       "mean   116.487864        3.986792  \n",
+       "std     23.518601        0.333701  \n",
+       "min     61.695000        3.000000  \n",
+       "25%    102.961250        4.000000  \n",
+       "50%    112.714500        4.000000  \n",
+       "75%    125.039250        4.000000  \n",
+       "max    206.007000        5.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "โปรดลบประเภทเพลง 'Missing' เนื่องจากไม่ได้ถูกจัดประเภทใน Spotify\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[df['artist_top_genre'] != 'Missing']\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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A+4ATkuwDXA7s0p9+CrAjcAlwE/DyxjVLkiSNpGUKVlW12xIObbuYcwvYbyJFSZIkTUXuvC5JktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIzNW9IFJtgSOH2jaHDgYWAd4JbCwbz+oqk5Z0deRJEmaKlY4WFXVxcBMgCSrAlcBXwFeDnywqj7QokBJkqSpotVQ4LbApVV1eaPnkyRJmnJaBatdgeMG7u+f5LwkxyRZd3EPSDInybwk8xYuXLi4UyRJkqaUCQerJKsBLwS+1DcdBTyUbphwPnDY4h5XVXOralZVzRobG5toGZIkSUPXosdqB+CcqroGoKquqao7qupO4Ghg6wavIUmSNPJaBKvdGBgGTLLRwLEXARc0eA1JkqSRt8KrAgGSrAk8F3jVQPN/JJkJFHDZIsckSZKmrQkFq6q6EVh/kbY9J1SRJEnSFOXO65IkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNTJj2AVIWn6/PeTRwy5B08yDDj5/2CVI04I9VpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIzMm+gRJLgP+BNwB3F5Vs5KsBxwPbAZcBuxSVddP9LUkSZJGWaseq2dV1cyqmtXfPxA4vaq2AE7v70uSJE1rkzUUOBs4tr99LLDTJL2OJEnSyGgRrAo4NcnZSeb0bRtW1fz+9tXAhos+KMmcJPOSzFu4cGGDMiRJkoZrwnOsgKdW1VVJ7g+cluSiwYNVVUlq0QdV1VxgLsCsWbP+5rgkSdJUM+Eeq6q6qv93AfAVYGvgmiQbAfT/Lpjo60iSJI26CQWrJGsmud/4beB5wAXAycBe/Wl7AV+dyOtIkiRNBRMdCtwQ+EqS8ef6QlX9b5KfACck2Qe4HNhlgq8jSZI08iYUrKrq18DfL6b9WmDbiTy3JEnSVOPO65IkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1ssLBKsmmSc5IcmGSnyd5Xd/+ziRXJTm3/9mxXbmSJEmja8YEHns78KaqOifJ/YCzk5zWH/tgVX1g4uVJkiRNHSscrKpqPjC/v/2nJL8ANm5VmCRJ0lTTZI5Vks2AxwI/6pv2T3JekmOSrLuEx8xJMi/JvIULF7YoQ5IkaagmHKyS3Bc4CXh9Vd0AHAU8FJhJ16N12OIeV1Vzq2pWVc0aGxubaBmSJElDN6FgleRedKHq81X1ZYCquqaq7qiqO4Gjga0nXqYkSdLom8iqwACfBH5RVYcPtG80cNqLgAtWvDxJkqSpYyKrAp8C7Amcn+Tcvu0gYLckM4ECLgNeNYHXkCRJmjImsirwu0AWc+iUFS9HkiRp6nLndUmSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWpkItstSJI0aZ7ykacMuwRNM997zfcm/TXssZIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGJi1YJdk+ycVJLkly4GS9jiRJ0qiYlGCVZFXgSGAHYCtgtyRbTcZrSZIkjYrJ6rHaGrikqn5dVX8BvgjMnqTXkiRJGgmpqvZPmvw/YPuqekV/f0/giVW1/8A5c4A5/d0tgYubF6K7swHw+2EXIU0y3+daGfg+v+c9uKrGFndgxj1dybiqmgvMHdbrr+ySzKuqWcOuQ5pMvs+1MvB9PlomayjwKmDTgfub9G2SJEnT1mQFq58AWyR5SJLVgF2BkyfptSRJkkbCpAwFVtXtSfYHvgGsChxTVT+fjNfSCnMYVisD3+daGfg+HyGTMnldkiRpZeTO65IkSY0YrCRJkhoxWKmJJJsnWX3YdUiSNEwGK01YknWBA4C3G64kaepJkmHXMF0YrDQhSTarquuBk4C1gQMMV5rK/IDRyiZJqqqSPCXJPkm27bdK0gowWGmFJVkH+ECSt1fV6cCXgQdiuNIUMB6gkjy6/0DZGKD/gDFcaaXRv+efBXwWeBjwIeC1SR421MKmKLdb0ApLci/gScC+wDlV9YEkzwB2AX4HfKCqbh1mjdLiDHxDfw7wEWA+cBnwc+Dw8g+jViJJtgQOAT5fVScneRzwKuDcqjpquNVNPfZYabmNf5uvqtuAHwJHAtskOaCqzgROAO4PHGzPlUZRH6oeB7wZ2Kmqng18ie5SXLOHWpx0D0kPeDrwUGC7JGtW1TnAccCcfg6tloPBSstl/Jt+f3ttgKr6HnA48KSBcPU1YDXgvkMrVlqCfv7IM4BnARv3zd8FbgCeMKy6pHvCwFD3BsCMqjoaeA8QukvQAVwN/Klv03KYlEvaaPoaCFWvBbYFrk1yalV9sf9dfV2Sg6vqkCTfraqbh1mvNG5g+O9ewG10Pa3rAm9Ncn1V/TTJucDuSdYAbnZIUNNR/3uwI93w31VJbgT2AdYE9kqyO10++I+qum6IpU5JBisttyT7Av8I7AEcChyWZP2qOjLJDGCfJOv5C6lR0n+YvAB4Ed1Q9aF0w9bXAicnOYauB+vQqrppeJVKkyvJI4F3A/sD5wJfAD5VVbsmuQXYDji/qr7Wnx+/ZCw7g5WWS5J7A7fSfTi9DLgP8FLgk0nurKqjkvy4qm4ZZp3SopI8Hng/8Aq6OSV7AmfQrYR6AF0P7FFV9d9JVq2qO4ZWrDS5bgUupFt0dAuwU5Izk+wHfAJYh25qx67A8Yaq5eMcK92tJH/1HqmqW6rqU3RdxtsD+1XVGcDPgH9Oso6hSiPqkcCPq+r7VfU+4H+B1wP3Bg6j673aJ8mjDFWaTga2Flm1Hwq/DtgImDVw2heBO6rqduBY4CzgDEPV8jNYaYmSbFJVd/a3X5Pk8CQHJFmLuyY2PiTJHOAa4NlV9YfhVSzdZeDDZPzv3IXAaklmAVTVicBFwBZV9Xu6VYFfBv44hHKlSdMPg8+m+/JwPN2XjCOBjyTZP8kr6IYFL+nPv62qjq2qa4ZW9BTmPlb6G/0H0lp0Y+/vBc4DjgD+E3gM3UqSvej2OXkc8Hhgj6o6bxj1SkvS71P1JOAPwGl0e67NB64CLqZbUv7i8feuQ4CajpI8gu7v93vp/n6/k24o/Da6+VSbACdW1anDqnE6MVhpiZI8Gfgk3Tf9I6vqW0keCLyVLnjtV1U3JVm7qvyWr5GS5CnAMXTDfC+j2wLkcmAMeDLdcPbR/YaITs7VtJTkUXS/AxdX1Wv7tu2ATwNPq6pLhljetORQoP7KwPBJqur7dBPT/w7YEaCqfge8D7gDODLJKoYqjZokDwf+GTiiquYCOwHrA4+pqiOqaldgb0OVVgK/pJu68cgkWyRZvaq+QXd917HhljY92WOl/7PI5p/PARbSDZmsC/wP3TL0T/THH0A3dO8YvEZOkh2A19KtfnpDVf2mnxt4JvCSqvrlUAuU7gHjQ9v9hPVPAjcD3wQW0A2Dz66qnwyzxunIYKW/keQNdN/wz6DbnXoXYHO6lSIfr6oPDa04aTEGNv/cHLiJbtXTlnTzAH9L9+08dJPT/6GqrhhasdI9YOB3YkZV3d5fbeBIuisLnAl8vapOtce2PYcC9VeSbAVsV1XPANYGrgf+UFU/opunsmeSdYZYovQ3+g+QHejmUR0O/IRudd9xdEvKv0T3ofJWQ5Wmo4FpHFv0IwoA9KFqRlX9hW54fB6wBnCOoWpyGKxWcgPXjBr3F+C3SQ4CHg7sXlW3JZldVT8GnuyWCho1/QfJu4FXVtXudEHqZOBXdCuhvg98m64XdnHve2nKGuid2o7uff9fwH5JHgZ/Fa5uowtX9wfehpuETwqD1UpskTlVs/tVgL8GNgT2pht/v6Xf4+SA/rI1tw6vYuku4/tTJXkC3WKKc+lW/VFV7wa+AxxUVT+lmyP4ULoNQGf4LV3TSR+qZtFdVeAFwJvoFh3ttEi4WrXvudoZOKwPWmrMYLUSGwhV+wP/BizoNwT9MN23+xOTHEi3cdyrq+raYdUqjUtyH4CqujPJU+km5T6Kbn+efxg49bt0k3Wpqq/TbY54Yr+ztDRtJLkf8HLgcVV1SVV9j+76f5sDL+lXydJPZF+lqv7Sr/DWJLAbcCXXf5vZGdh+4BftO8D5dFstXE+3iuriIZUo/Z9+T54PJXk+3SU5/hX4SFWdkeTPwNwkD6XbEPQlwEHjj3XzQ00ngyMOVfWnJEcAD0tyJPCaqvpOklWBPeg2AqU/987hVLzycFXgSmbRyYr9RPSPAwcCvwNu67uVNwMud8hEo6JfMn4U8CPgv4Hn0Q17rAvsVVVXJXkk3UrW9YEf9JvaOkFX08rAnKrnAg+i+yz/z75n6kDgBuCNfa/uulV1/VALXsk4FLgSWWRO1Qb9kMqNwOrADn33cCXZnW5i4xpDLFdanKvogtOX6FY3HUI3t+o1STaqql9U1cer6j1V9S24a8hbmg76obxK8g90O6pfCRyS5AP9/mz/DjwA+Gj/EDdwvofZY7WSWCRUvRHYDfgZ3eqRn9J9UF0E3A5sDexZVecPp1rpry3yDf2zwLf73dPHL13zfLovAv/uprWajpI8BFilqi5NsgHd78Eb6FZvHwhsDHyzqvZJsiWwenn91qGwx2olMRCqnkB34eTXAF8HXk+3z8/z6Cb3/oDuorSGKo2ERTb/HANeDKyS5N39MMf3gFPovhSsM8RSpcn0ZGD9/pI0vwfmAKsB7wKeCjwLeHmSj1bVxYaq4XHy+kqkX0H1ZeDDVfXDfijwT8CbgQdW1VFDLVBajD5UvZBu2O8Sui1BPkG3pPy1SY7oJ+qe7x5rmq6q6vNJ7gv8JMlLq+q8JA8Ezu7nUj2AbnPcbwy3UtljNY0tugliVX2Xrvt4lyQbVNXNwFl02ys8N8m6bpyoUZNkG7rVf9sBX6Hb4PB5wAfo5lu9qd+b6g9DK1KaJAM7qm9Ht63IZ4Gj+xWylwFrJ/kY3WWbvlpVp/l3fLicYzVNLTKnalvgfsBpVXVjkg8C29BtALqgX211r6q6aYglS4uVZBO6rRXWpdtdfXe6lazXAZ8GFpYXktU0lmRrui/Ab+hHG95I93uwU3/KY4Abq+rMIZWoAQaraS7dBZX/ke7SHhsC76+qM5McSjfh9+lVtXCYNUrLIsl76Dax/XCSlwGvBf6xqi4fcmnSpEmyKfBB4LqqmjPQ/kbglcDOVXXBsOrT33KO1TTTj7NfX1W39iuonltVT+13UH8y3eTGqqo3J7kVWBMwWGkqOB94Vd/D+mLgdYYqrQRuB84DZifZvqr+F6CqDu83AF1nmMXpb9ljNU30Y+pjwInAfwKfoxs6uS/dipG9gBcCnwIeBrylqs4YTrXS8kuyFvAiuvfxMf1laqRpZWAV7JPo/qb/lm7Bxt501/87oapOH2KJWgonr08fqaoFdNf8242ue/ja/hv9w4H/qapbgO/RDQvadawppapuqKpj6S6x9HUn6Go66kPV84Bj6KZvnA08BTiZrudq7/64RpRDgdPEwPWf1gDuAxyTZI2q+hTwfeBj/eUOnkQXuhz+01R1B7ijuqafJKvQDe29iq53dj3gQuCcqromyZforpQxf2hFaqkcCpxGkrwEeCuwA91qkZcBHwOOp/vG81zgc1V10bBqlCTdvSRvBdYGng3s0e+2vjfd9jiXeSHl0WaP1fSyPnBWf0mPTyS5FpgLrFFVRwMuxZWkEZRkJt0WOO+iW1S0J/CsPlT9Pd2X5kuq6tdDLFPLwGA1vfwWmNnv+3NVVZ3YL0t/QZIvVtWfhlyfJKk3MFH9acDOwHZJFlTVwUkeAbwjye3ATOCt/SbPGnEOBU4jSdam2zjxcroLK69BNyT4uqq6bHiVSZLGLbKB89OBzwP7A48AHg98v6o+1F+GbF3gd1V19uDjNLoMVlPMkn6x+kt63J5kPbqJjw8EtgAOcPM4SRoN/fX9Hgl8u6ruSPJSYOOqen9/LcCZwPvptlX48BBL1QoyWE0hi3zLmQ0EuLOqTu7bxsPVKv1FOdesqhuHWbMk6S793+5fAVfSbf65Ld3O6s+tqt/0KwOPpZtndWJVfWFoxWqFuI/VFDIQqval269qS+CjSd7SH7+9/6UcT8te+0+SRkhVfRW4mm7F9k7AqXRTOD7cz6t6DN21MX8FbDykMjUBTl6fAhbpqbo3sAuwV1X9NMkJwDeS/LmqPja4DNexeEkaDYN/x6vquiRnAs8D/gJ8hW4E4rN0vVj7AI8Dnttfwul2/55PHQarKaTvqboY+CVwn37I7zdJ9gd2HW51kqQl6Vf/PQN4NPCtqjo6yZ/pLtF0Z1UdmuRj/elbAwcDL6qq24ZUslaQQ4EjLMmW8H+/kC+mW477c+Ba4A1015EC2BRYo78gpyRpRIxfeinJE+mG/54BvCXJK6vqOOBrwEuT7ALcAtwbeDLdnlY/H1LZmgAnr4+oJNsBR9F1B28EHAZcWlWv6Y8fQ7cM90a6uVYvd/WfJI2eJFsDhwBvqarzkuxKF57O73uudgMurKqf9eevVlV/GWLJmgCHAkdQkhl0XcH/CmxFt/z2DGB2khdU1X9X1T/134BWpdsM9PKhFSxJujvrAM+hu6zYecCJwJ30c6iq6mNw1zwsQ9XUZrAaQf3qvkuBf6G74Oyz6LqIb6bbRf2Oqjqlqn40zDolSUtXVaf20znem+R3VXVckhPpvhj/bOA8h5CmAYPV6DqPbruEG4C1q+r3Sb5M9y1nzyS3VtXpQ61QkrRMqurk/vI0/9YP9R0LHDfsutSec6xGxCJbKqwG3NHvynsA3RXO31FVP+mvA7gD8LWqmj/EkiVJyynJC4H30Q0NXj24RY6mB4PVCFgkVO1PN6/qBuCdVXVLkoOAJwLvq6ofJFm1qu4YYsmSpBWUZKyqFg67Dk0Og9UISfLPwEuA3YFzgG8CB1fVpUneDTwM2LuqbhlimZIkaQkMViMiyVrA4XQrAXcGdgQW0G218OqquiTJ+lV17RDLlCRJd8NgNUKSrA48AvhQVT2r31huIfAJumFBd+CVJGmEuSpwhFTVrUluAmYkeTTwYOB04BOGKkmSRp89ViOm77V6Pd2KkQcCO1fVhUMtSpIkLROD1Qjqr2b+ALoLc1417HokSdKyMVhJkiQ1ssqwC5AkSZouDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpkf8PwQ8MlHUQ3OMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corrmat = df.corr()\n",
+    "f, ax = plt.subplots(figsize=(12, 9))\n",
+    "sns.heatmap(corrmat, vmax=.8, square=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "\n",
+    "# Show the joint distribution using kernel density estimation\n",
+    "g = sns.jointplot(\n",
+    "    data=df,\n",
+    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
+    "    kind=\"kde\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1350b1a90>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 468.975x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
+    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
+    "   .add_legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "c61deff2839902ac8cb4ed411eb10fee",
+   "translation_date": "2025-09-06T14:09:46+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/5-Clustering/2-K-Means/notebook.ipynb b/translations/th/5-Clustering/2-K-Means/notebook.ipynb
new file mode 100644
index 000000000..b5b8958ff
--- /dev/null
+++ b/translations/th/5-Clustering/2-K-Means/notebook.ipynb
@@ -0,0 +1,229 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "3e5c8ab363e8d88f566d4365efc7e0bd",
+   "translation_date": "2025-09-06T14:19:51+00:00",
+   "source_file": "5-Clustering/2-K-Means/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "เราจะมุ่งเน้นเพียง 3 ประเภทเท่านั้น บางทีเราอาจสร้าง 3 กลุ่มขึ้นมาได้!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/translations/th/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
new file mode 100644
index 000000000..c08e56927
--- /dev/null
+++ b/translations/th/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
@@ -0,0 +1,639 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "colab": {
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+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "coopTranslator": {
+   "original_hash": "ad65fb4aad0a156b42216e4929f490fc",
+   "translation_date": "2025-09-06T14:29:51+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb",
+   "language_code": "th"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GULATlQXLXyR"
+   },
+   "source": [
+    "## สำรวจการจัดกลุ่ม K-Means ด้วย R และหลักการข้อมูลแบบ Tidy\n",
+    "\n",
+    "### [**แบบทดสอบก่อนเรียน**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)\n",
+    "\n",
+    "ในบทเรียนนี้ คุณจะได้เรียนรู้วิธีสร้างกลุ่มโดยใช้แพ็กเกจ Tidymodels และแพ็กเกจอื่นๆ ในระบบนิเวศของ R (เราจะเรียกพวกมันว่าเพื่อน 🧑‍🤝‍🧑) รวมถึงชุดข้อมูลเพลงไนจีเรียที่คุณนำเข้าไว้ก่อนหน้านี้ เราจะครอบคลุมพื้นฐานของ K-Means สำหรับการจัดกลุ่ม โปรดจำไว้ว่า ตามที่คุณได้เรียนรู้ในบทเรียนก่อนหน้า มีหลายวิธีในการทำงานกับการจัดกลุ่ม และวิธีที่คุณใช้ขึ้นอยู่กับข้อมูลของคุณ เราจะลองใช้ K-Means เนื่องจากเป็นเทคนิคการจัดกลุ่มที่พบได้บ่อยที่สุด มาเริ่มกันเลย!\n",
+    "\n",
+    "คำศัพท์ที่คุณจะได้เรียนรู้:\n",
+    "\n",
+    "-   การให้คะแนน Silhouette\n",
+    "\n",
+    "-   วิธี Elbow\n",
+    "\n",
+    "-   ความเฉื่อย (Inertia)\n",
+    "\n",
+    "-   ความแปรปรวน (Variance)\n",
+    "\n",
+    "### **บทนำ**\n",
+    "\n",
+    "[K-Means Clustering](https://wikipedia.org/wiki/K-means_clustering) เป็นวิธีที่มาจากสาขาการประมวลผลสัญญาณ ใช้ในการแบ่งและจัดกลุ่มข้อมูลออกเป็น `k clusters` โดยอิงจากความคล้ายคลึงกันของคุณลักษณะ\n",
+    "\n",
+    "กลุ่มเหล่านี้สามารถแสดงผลเป็น [แผนภาพ Voronoi](https://wikipedia.org/wiki/Voronoi_diagram) ซึ่งประกอบด้วยจุด (หรือ 'seed') และพื้นที่ที่เกี่ยวข้อง\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/voronoi.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>อินโฟกราฟิกโดย Jen Looper</figcaption>\n",
+    "\n",
+    "ขั้นตอนของ K-Means clustering มีดังนี้:\n",
+    "\n",
+    "1. นักวิทยาศาสตร์ข้อมูลเริ่มต้นโดยกำหนดจำนวนกลุ่มที่ต้องการสร้าง\n",
+    "\n",
+    "2. จากนั้น อัลกอริทึมจะสุ่มเลือก K ข้อมูลจากชุดข้อมูลเพื่อใช้เป็นจุดศูนย์กลางเริ่มต้นสำหรับกลุ่ม (หรือที่เรียกว่า centroids)\n",
+    "\n",
+    "3. ต่อมา ข้อมูลที่เหลือทั้งหมดจะถูกจัดกลุ่มไปยัง centroid ที่ใกล้ที่สุด\n",
+    "\n",
+    "4. จากนั้น คำนวณค่าเฉลี่ยใหม่ของแต่ละกลุ่ม และ centroid จะถูกย้ายไปยังตำแหน่งค่าเฉลี่ยนั้น\n",
+    "\n",
+    "5. เมื่อจุดศูนย์กลางถูกคำนวณใหม่แล้ว ข้อมูลทุกชิ้นจะถูกตรวจสอบอีกครั้งเพื่อดูว่ามันอาจใกล้กับกลุ่มอื่นมากกว่า ข้อมูลทั้งหมดจะถูกจัดกลุ่มใหม่โดยใช้ค่าเฉลี่ยของกลุ่มที่อัปเดต ขั้นตอนการจัดกลุ่มและการอัปเดต centroid จะถูกทำซ้ำจนกว่าการจัดกลุ่มจะหยุดเปลี่ยนแปลง (หรือเมื่อเกิดการลู่เข้า) โดยทั่วไป อัลกอริทึมจะหยุดเมื่อการเคลื่อนที่ของ centroid ในแต่ละรอบใหม่มีน้อยมาก และกลุ่มกลายเป็นคงที่\n",
+    "\n",
+    "<div>\n",
+    "\n",
+    "> โปรดทราบว่าเนื่องจากการสุ่มเลือกข้อมูลเริ่มต้น k ที่ใช้เป็น centroid การดำเนินการอาจให้ผลลัพธ์ที่แตกต่างกันเล็กน้อยในแต่ละครั้ง ด้วยเหตุนี้ อัลกอริทึมส่วนใหญ่จึงใช้การเริ่มต้นแบบสุ่มหลายครั้ง (*random starts*) และเลือกการวนซ้ำที่มีค่า WCSS ต่ำที่สุด ดังนั้นจึงแนะนำอย่างยิ่งให้รัน K-Means ด้วยค่าของ *nstart* หลายค่าเพื่อหลีกเลี่ยง *local optimum* ที่ไม่พึงประสงค์\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "แอนิเมชันสั้นๆ นี้ใช้ [งานศิลปะ](https://github.com/allisonhorst/stats-illustrations) ของ Allison Horst อธิบายกระบวนการจัดกลุ่ม:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/kmeans.gif\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>งานศิลปะโดย @allison_horst</figcaption>\n",
+    "\n",
+    "คำถามพื้นฐานที่เกิดขึ้นในการจัดกลุ่มคือ: คุณจะรู้ได้อย่างไรว่าควรแยกข้อมูลออกเป็นกี่กลุ่ม? ข้อเสียของการใช้ K-Means คือคุณจะต้องกำหนด `k` ซึ่งก็คือจำนวน `centroids` โชคดีที่ `elbow method` ช่วยประมาณค่าที่ดีสำหรับการเริ่มต้น `k` คุณจะได้ลองใช้ในอีกสักครู่\n",
+    "\n",
+    "### \n",
+    "\n",
+    "**ข้อกำหนดเบื้องต้น**\n",
+    "\n",
+    "เราจะเริ่มต้นจากจุดที่เราหยุดไว้ใน [บทเรียนก่อนหน้า](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb) ซึ่งเราได้วิเคราะห์ชุดข้อมูล สร้างภาพจำนวนมาก และกรองชุดข้อมูลไปยังข้อมูลที่น่าสนใจ อย่าลืมตรวจสอบ!\n",
+    "\n",
+    "เราจะต้องใช้แพ็กเกจบางตัวเพื่อดำเนินการในโมดูลนี้ คุณสามารถติดตั้งได้โดยใช้: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`\n",
+    "\n",
+    "หรือใช้สคริปต์ด้านล่างเพื่อตรวจสอบว่าคุณมีแพ็กเกจที่จำเป็นสำหรับโมดูลนี้หรือไม่ และติดตั้งให้ในกรณีที่ขาดหาย\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ah_tBi58LXyi"
+   },
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7e--UCUTLXym"
+   },
+   "source": [
+    "มาลุยกันเลย!\n",
+    "\n",
+    "## 1. เต้นรำกับข้อมูล: คัดเลือก 3 แนวเพลงที่ได้รับความนิยมมากที่สุด\n",
+    "\n",
+    "นี่คือการทบทวนสิ่งที่เราได้ทำในบทเรียนก่อนหน้า มาลองจัดการข้อมูลกันเถอะ!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Ycamx7GGLXyn"
+   },
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data into a tibble\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\", show_col_types = FALSE)\n",
+    "\n",
+    "# Narrow down to top 3 popular genres\n",
+    "nigerian_songs <- df %>% \n",
+    "  # Concentrate on top 3 genres\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \n",
+    "  # Remove unclassified observations\n",
+    "  filter(popularity != 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Visualize popular genres using bar plots\n",
+    "theme_set(theme_light())\n",
+    "nigerian_songs %>%\n",
+    "  count(artist_top_genre) %>%\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\n",
+    "                       fill = artist_top_genre)) +\n",
+    "  geom_col(alpha = 0.8) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\n",
+    "  ggtitle(\"Top genres\") +\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b5h5zmkPLXyp"
+   },
+   "source": [
+    "🤩 นั่นไปได้ดีมาก!\n",
+    "\n",
+    "## 2. การสำรวจข้อมูลเพิ่มเติม\n",
+    "\n",
+    "ข้อมูลนี้สะอาดแค่ไหน? มาลองตรวจสอบค่าผิดปกติด้วยการใช้กล่องแสดงค่ากัน เราจะเน้นไปที่คอลัมน์ตัวเลขที่มีค่าผิดปกติน้อยกว่า (แม้ว่าคุณจะสามารถลบค่าผิดปกติออกได้) กล่องแสดงค่าจะช่วยแสดงช่วงของข้อมูลและช่วยเลือกว่าคอลัมน์ใดควรนำมาใช้ อย่างไรก็ตาม กล่องแสดงค่าไม่ได้แสดงค่าความแปรปรวน ซึ่งเป็นองค์ประกอบสำคัญของข้อมูลที่สามารถจัดกลุ่มได้ดี กรุณาดู [การอภิปรายนี้](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot) เพื่ออ่านเพิ่มเติม\n",
+    "\n",
+    "[กล่องแสดงค่า](https://en.wikipedia.org/wiki/Box_plot) ถูกใช้เพื่อแสดงการกระจายของข้อมูล `ตัวเลข` ในรูปแบบกราฟิก ดังนั้นเรามาเริ่มต้นด้วยการ *เลือก* คอลัมน์ตัวเลขทั้งหมดควบคู่ไปกับแนวเพลงยอดนิยมกัน\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HhNreJKLLXyq"
+   },
+   "source": [
+    "# Select top genre column and all other numeric columns\n",
+    "df_numeric <- nigerian_songs %>% \n",
+    "  select(artist_top_genre, where(is.numeric)) \n",
+    "\n",
+    "# Display the data\n",
+    "df_numeric %>% \n",
+    "  slice_head(n = 5)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uYXrwJRaLXyq"
+   },
+   "source": [
+    "ดูว่า `where` ใน selection helper ทำให้เรื่องนี้ง่ายขึ้นแค่ไหน 💁? ลองสำรวจฟังก์ชันอื่นๆ ได้ [ที่นี่](https://tidyselect.r-lib.org/)  \n",
+    "\n",
+    "เนื่องจากเราจะสร้าง boxplot สำหรับแต่ละคุณสมบัติที่เป็นตัวเลข และเราต้องการหลีกเลี่ยงการใช้ loops ลองปรับรูปแบบข้อมูลของเราให้เป็น *longer* format ซึ่งจะช่วยให้เราใช้ `facets` ได้ - subplots ที่แสดงข้อมูลแต่ละชุดแยกกัน\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "gd5bR3f8LXys"
+   },
+   "source": [
+    "# Pivot data from wide to long\n",
+    "df_numeric_long <- df_numeric %>% \n",
+    "  pivot_longer(!artist_top_genre, names_to = \"feature_names\", values_to = \"values\") \n",
+    "\n",
+    "# Print out data\n",
+    "df_numeric_long %>% \n",
+    "  slice_head(n = 15)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-7tE1swnLXyv"
+   },
+   "source": [
+    "ถึงเวลาสนุกกับ `ggplots` กันแล้ว! แล้วเราจะใช้ `geom` อะไรดี?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "r88bIsyuLXyy"
+   },
+   "source": [
+    "# Make a box plot\n",
+    "df_numeric_long %>% \n",
+    "  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_wrap(~ feature_names, ncol = 4, scales = \"free\") +\n",
+    "  theme(legend.position = \"none\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EYVyKIUELXyz"
+   },
+   "source": [
+    "ง่ายมาก!\n",
+    "\n",
+    "ตอนนี้เราสามารถเห็นได้ว่าข้อมูลนี้ค่อนข้างมีความแปรปรวน: เมื่อดูแต่ละคอลัมน์ในรูปแบบกล่องแสดงค่ากระจาย คุณจะเห็นค่าผิดปกติ คุณอาจจะไล่ดูชุดข้อมูลและลบค่าผิดปกติเหล่านี้ออกไป แต่การทำเช่นนั้นจะทำให้ข้อมูลเหลือน้อยลงมาก\n",
+    "\n",
+    "สำหรับตอนนี้ เรามาเลือกคอลัมน์ที่เราจะใช้สำหรับการฝึกการจัดกลุ่มกันดีกว่า เราจะเลือกคอลัมน์ตัวเลขที่มีช่วงค่าคล้ายกัน เราสามารถเข้ารหัส `artist_top_genre` เป็นตัวเลขได้ แต่ตอนนี้เราจะตัดมันออกไปก่อน\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-wkpINyZLXy0"
+   },
+   "source": [
+    "# Select variables with similar ranges\n",
+    "df_numeric_select <- df_numeric %>% \n",
+    "  select(popularity, danceability, acousticness, loudness, energy) \n",
+    "\n",
+    "# Normalize data\n",
+    "# df_numeric_select <- scale(df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D7dLzgpqLXy1"
+   },
+   "source": [
+    "## 3. การคำนวณ k-means clustering ใน R\n",
+    "\n",
+    "เราสามารถคำนวณ k-means ใน R ได้โดยใช้ฟังก์ชัน `kmeans` ที่มีอยู่แล้ว ดูเพิ่มเติมได้ที่ `help(\"kmeans()\")` ฟังก์ชัน `kmeans()` รับข้อมูลในรูปแบบ data frame ที่มีคอลัมน์เป็นตัวเลขทั้งหมดเป็นอาร์กิวเมนต์หลัก\n",
+    "\n",
+    "ขั้นตอนแรกในการใช้ k-means clustering คือการกำหนดจำนวนคลัสเตอร์ (k) ที่จะสร้างขึ้นในผลลัพธ์สุดท้าย เราทราบว่ามี 3 ประเภทของเพลงที่เราแบ่งออกมาจากชุดข้อมูล ดังนั้นลองใช้ค่า k เป็น 3:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "uC4EQ5w7LXy5"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Kmeans clustering for 3 clusters\n",
+    "kclust <- kmeans(\n",
+    "  df_numeric_select,\n",
+    "  # Specify the number of clusters\n",
+    "  centers = 3,\n",
+    "  # How many random initial configurations\n",
+    "  nstart = 25\n",
+    ")\n",
+    "\n",
+    "# Display clustering object\n",
+    "kclust\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hzfhscWrLXy-"
+   },
+   "source": [
+    "วัตถุ kmeans มีข้อมูลหลายส่วนที่อธิบายได้ดีใน `help(\"kmeans()\")` สำหรับตอนนี้ เรามาเน้นที่บางส่วนกัน เราเห็นว่าข้อมูลถูกจัดกลุ่มเป็น 3 กลุ่ม โดยมีขนาด 65, 110, 111 ผลลัพธ์ยังมีจุดศูนย์กลางของกลุ่ม (ค่าเฉลี่ย) สำหรับ 3 กลุ่มใน 5 ตัวแปร\n",
+    "\n",
+    "เวกเตอร์การจัดกลุ่มคือการกำหนดกลุ่มสำหรับแต่ละการสังเกตการณ์ ลองใช้ฟังก์ชัน `augment` เพื่อเพิ่มการกำหนดกลุ่มลงในชุดข้อมูลเดิม\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "0XwwpFGQLXy_"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "augment(kclust, df_numeric_select) %>% \n",
+    "  relocate(.cluster) %>% \n",
+    "  slice_head(n = 10)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NXIVXXACLXzA"
+   },
+   "source": [
+    "เยี่ยมเลย! ตอนนี้เราได้แบ่งชุดข้อมูลของเราออกเป็น 3 กลุ่มแล้ว ทีนี้คำถามคือ การจัดกลุ่มของเราดีแค่ไหน 🤷? มาดูที่ `Silhouette score` กันเถอะ\n",
+    "\n",
+    "### **Silhouette score**\n",
+    "\n",
+    "[การวิเคราะห์ Silhouette](https://en.wikipedia.org/wiki/Silhouette_(clustering)) สามารถใช้เพื่อศึกษาระยะห่างระหว่างกลุ่มที่ได้จากการจัดกลุ่ม คะแนนนี้มีค่าตั้งแต่ -1 ถึง 1 โดยถ้าคะแนนใกล้ 1 หมายความว่ากลุ่มนั้นมีความหนาแน่นและแยกออกจากกลุ่มอื่นได้ดี ค่าใกล้ 0 หมายถึงกลุ่มที่มีการทับซ้อนกัน โดยตัวอย่างอยู่ใกล้กับเส้นแบ่งเขตของกลุ่มข้างเคียง [แหล่งที่มา](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam)\n",
+    "\n",
+    "วิธีการเฉลี่ย Silhouette จะคำนวณค่าเฉลี่ยของ Silhouette ของตัวอย่างสำหรับค่าต่าง ๆ ของ *k* คะแนน Silhouette เฉลี่ยที่สูงบ่งบอกถึงการจัดกลุ่มที่ดี\n",
+    "\n",
+    "ฟังก์ชัน `silhouette` ในแพ็กเกจ cluster ใช้สำหรับคำนวณค่าเฉลี่ยความกว้างของ Silhouette\n",
+    "\n",
+    "> Silhouette สามารถคำนวณได้ด้วย [ระยะทาง](https://en.wikipedia.org/wiki/Distance \"Distance\") เมตริกใด ๆ เช่น [ระยะทางแบบยุคลิด](https://en.wikipedia.org/wiki/Euclidean_distance \"Euclidean distance\") หรือ [ระยะทางแบบแมนฮัตตัน](https://en.wikipedia.org/wiki/Manhattan_distance \"Manhattan distance\") ซึ่งเราได้พูดถึงใน [บทเรียนก่อนหน้า](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Jn0McL28LXzB"
+   },
+   "source": [
+    "# Load cluster package\n",
+    "library(cluster)\n",
+    "\n",
+    "# Compute average silhouette score\n",
+    "ss <- silhouette(kclust$cluster,\n",
+    "                 # Compute euclidean distance\n",
+    "                 dist = dist(df_numeric_select))\n",
+    "mean(ss[, 3])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QyQRn97nLXzC"
+   },
+   "source": [
+    "คะแนนของเราคือ **.549** ซึ่งอยู่ตรงกลางพอดี นี่บ่งชี้ว่าข้อมูลของเราไม่ได้เหมาะสมกับการจัดกลุ่มประเภทนี้มากนัก ลองมาดูกันว่าเราสามารถยืนยันข้อสันนิษฐานนี้ด้วยการมองเห็นได้หรือไม่ [แพ็กเกจ factoextra](https://rpkgs.datanovia.com/factoextra/index.html) มีฟังก์ชัน (`fviz_cluster()`) สำหรับการแสดงผลการจัดกลุ่มแบบภาพ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "7a6Km1_FLXzD"
+   },
+   "source": [
+    "library(factoextra)\n",
+    "\n",
+    "# Visualize clustering results\n",
+    "fviz_cluster(kclust, df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IBwCWt-0LXzD"
+   },
+   "source": [
+    "การที่กลุ่มข้อมูลมีการทับซ้อนกันแสดงให้เห็นว่าข้อมูลของเราอาจไม่เหมาะสมกับการจัดกลุ่มประเภทนี้นัก แต่เรามาลองทำต่อกันเถอะ\n",
+    "\n",
+    "## 4. การกำหนดจำนวนกลุ่มที่เหมาะสม\n",
+    "\n",
+    "คำถามพื้นฐานที่มักเกิดขึ้นใน K-Means clustering คือ - หากเราไม่มีป้ายกำกับคลาสที่รู้ล่วงหน้า เราจะทราบได้อย่างไรว่าควรแบ่งข้อมูลออกเป็นกี่กลุ่ม?\n",
+    "\n",
+    "วิธีหนึ่งที่เราสามารถลองหาคำตอบได้คือการใช้ตัวอย่างข้อมูลเพื่อ `สร้างโมเดลการจัดกลุ่มหลายชุด` โดยเพิ่มจำนวนกลุ่มทีละขั้น (เช่น จาก 1-10) และประเมินเมตริกการจัดกลุ่ม เช่น **Silhouette score**\n",
+    "\n",
+    "เรามาลองกำหนดจำนวนกลุ่มที่เหมาะสมโดยการคำนวณอัลกอริธึมการจัดกลุ่มสำหรับค่าต่าง ๆ ของ *k* และประเมิน **Within Cluster Sum of Squares** (WCSS) กันดีกว่า โดย WCSS หรือผลรวมของกำลังสองภายในกลุ่มทั้งหมดนั้นใช้วัดความกระชับของการจัดกลุ่ม ซึ่งเราต้องการให้ค่าต่ำที่สุดเท่าที่จะเป็นไปได้ เพราะค่าที่ต่ำกว่าหมายความว่าจุดข้อมูลอยู่ใกล้กันมากขึ้น\n",
+    "\n",
+    "เรามาสำรวจผลกระทบของการเลือกค่า `k` ที่แตกต่างกัน ตั้งแต่ 1 ถึง 10 ต่อการจัดกลุ่มนี้กันเถอะ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "hSeIiylDLXzE"
+   },
+   "source": [
+    "# Create a series of clustering models\n",
+    "kclusts <- tibble(k = 1:10) %>% \n",
+    "  # Perform kmeans clustering for 1,2,3 ... ,10 clusters\n",
+    "  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),\n",
+    "  # Farm out clustering metrics eg WCSS\n",
+    "         glanced = map(model, ~ glance(.x))) %>% \n",
+    "  unnest(cols = glanced)\n",
+    "  \n",
+    "\n",
+    "# View clustering rsulsts\n",
+    "kclusts\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m7rS2U1eLXzE"
+   },
+   "source": [
+    "ตอนนี้เรามีผลรวมของภายในคลัสเตอร์ทั้งหมด (tot.withinss) สำหรับแต่ละอัลกอริทึมการจัดกลุ่มที่มีศูนย์ *k* เราใช้ [วิธีข้อศอก](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) เพื่อหาจำนวนคลัสเตอร์ที่เหมาะสมที่สุด วิธีนี้ประกอบด้วยการวาดกราฟ WCSS เป็นฟังก์ชันของจำนวนคลัสเตอร์ และเลือก [ข้อศอกของกราฟ](https://en.wikipedia.org/wiki/Elbow_of_the_curve \"Elbow of the curve\") เป็นจำนวนคลัสเตอร์ที่ใช้\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "o_DjHGItLXzF"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Use elbow method to determine optimum number of clusters\n",
+    "kclusts %>% \n",
+    "  ggplot(mapping = aes(x = k, y = tot.withinss)) +\n",
+    "  geom_line(size = 1.2, alpha = 0.8, color = \"#FF7F0EFF\") +\n",
+    "  geom_point(size = 2, color = \"#FF7F0EFF\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLYyt5XSLXzG"
+   },
+   "source": [
+    "กราฟแสดงการลดลงอย่างมากของ WCSS (ซึ่งหมายถึง *ความกระชับ* ที่มากขึ้น) เมื่อจำนวนคลัสเตอร์เพิ่มขึ้นจากหนึ่งเป็นสอง และมีการลดลงที่สังเกตได้ชัดเจนอีกครั้งจากสองเป็นสามคลัสเตอร์ หลังจากนั้น การลดลงจะไม่เด่นชัดเท่าเดิม ทำให้เกิด `elbow` 💪ในกราฟประมาณที่สามคลัสเตอร์ นี่เป็นสัญญาณที่ดีที่บ่งบอกว่ามีคลัสเตอร์ของจุดข้อมูลที่แยกกันได้ดีประมาณสองถึงสามคลัสเตอร์\n",
+    "\n",
+    "ตอนนี้เราสามารถดำเนินการและดึงโมเดลการจัดกลุ่มที่ `k = 3` ได้แล้ว:\n",
+    "\n",
+    "> `pull()`: ใช้สำหรับดึงคอลัมน์เดียว\n",
+    ">\n",
+    "> `pluck()`: ใช้สำหรับเข้าถึงโครงสร้างข้อมูล เช่น ลิสต์\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JP_JPKBILXzG"
+   },
+   "source": [
+    "# Extract k = 3 clustering\n",
+    "final_kmeans <- kclusts %>% \n",
+    "  filter(k == 3) %>% \n",
+    "  pull(model) %>% \n",
+    "  pluck(1)\n",
+    "\n",
+    "\n",
+    "final_kmeans\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l_PDTu8tLXzI"
+   },
+   "source": [
+    "เยี่ยมเลย! มาลองดูการจัดกลุ่มที่ได้กันดีกว่า สนใจเพิ่มความโต้ตอบด้วย `plotly` ไหม?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dNcleFe-LXzJ"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "results <-  augment(final_kmeans, df_numeric_select) %>% \n",
+    "  bind_cols(df_numeric %>% select(artist_top_genre)) \n",
+    "\n",
+    "# Plot cluster assignments\n",
+    "clust_plt <- results %>% \n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +\n",
+    "  geom_point(size = 2, alpha = 0.8) +\n",
+    "  paletteer::scale_color_paletteer_d(\"ggthemes::Tableau_10\")\n",
+    "\n",
+    "ggplotly(clust_plt)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6JUM_51VLXzK"
+   },
+   "source": [
+    "บางทีเราอาจคาดหวังว่ากลุ่มแต่ละกลุ่ม (แสดงด้วยสีต่างๆ) จะมีประเภทที่แตกต่างกัน (แสดงด้วยรูปร่างต่างๆ)\n",
+    "\n",
+    "ลองมาดูความแม่นยำของโมเดลกัน\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HdIMUGq7LXzL"
+   },
+   "source": [
+    "# Assign genres to predefined integers\n",
+    "label_count <- results %>% \n",
+    "  group_by(artist_top_genre) %>% \n",
+    "  mutate(id = cur_group_id()) %>% \n",
+    "  ungroup() %>% \n",
+    "  summarise(correct_labels = sum(.cluster == id))\n",
+    "\n",
+    "\n",
+    "# Print results  \n",
+    "cat(\"Result:\", label_count$correct_labels, \"out of\", nrow(results), \"samples were correctly labeled.\")\n",
+    "\n",
+    "cat(\"\\nAccuracy score:\", label_count$correct_labels/nrow(results))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C50wvaAOLXzM"
+   },
+   "source": [
+    "ความแม่นยำของโมเดลนี้ไม่แย่ แต่ก็ไม่ได้ดีมากนัก อาจเป็นเพราะข้อมูลไม่เหมาะสมกับการใช้ K-Means Clustering ข้อมูลนี้มีความไม่สมดุลกันมาก มีความสัมพันธ์ระหว่างคอลัมน์น้อย และมีความแปรปรวนระหว่างค่าของคอลัมน์สูงเกินไปที่จะจัดกลุ่มได้ดี ในความเป็นจริง กลุ่มที่เกิดขึ้นอาจได้รับอิทธิพลหรือถูกบิดเบือนอย่างมากจากสามหมวดหมู่ของประเภทที่เรากำหนดไว้ข้างต้น\n",
+    "\n",
+    "อย่างไรก็ตาม นี่เป็นกระบวนการเรียนรู้ที่น่าสนใจมาก!\n",
+    "\n",
+    "ในเอกสารของ Scikit-learn คุณจะเห็นว่าโมเดลแบบนี้ ที่มีการจัดกลุ่มที่ไม่ชัดเจน มีปัญหาเรื่อง 'ความแปรปรวน':\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/problems.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>อินโฟกราฟิกจาก Scikit-learn</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "## **ความแปรปรวน**\n",
+    "\n",
+    "ความแปรปรวนถูกนิยามว่าเป็น \"ค่าเฉลี่ยของผลต่างกำลังสองจากค่าเฉลี่ย\" [แหล่งข้อมูล](https://www.mathsisfun.com/data/standard-deviation.html) ในบริบทของปัญหาการจัดกลุ่มนี้ หมายถึงข้อมูลที่ค่าต่างๆ ในชุดข้อมูลมีแนวโน้มที่จะเบี่ยงเบนจากค่าเฉลี่ยมากเกินไป\n",
+    "\n",
+    "✅ นี่เป็นช่วงเวลาที่ดีในการคิดถึงวิธีต่างๆ ที่คุณสามารถแก้ไขปัญหานี้ได้ ลองปรับข้อมูลอีกเล็กน้อย? ใช้คอลัมน์ที่แตกต่างกัน? ใช้อัลกอริทึมที่แตกต่างกัน? เคล็ดลับ: ลอง [ปรับขนาดข้อมูลของคุณ](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) เพื่อทำให้ข้อมูลเป็นมาตรฐานและทดสอบคอลัมน์อื่นๆ\n",
+    "\n",
+    "> ลองใช้ '[เครื่องคำนวณความแปรปรวน](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)' เพื่อทำความเข้าใจแนวคิดนี้เพิ่มเติม\n",
+    "\n",
+    "------------------------------------------------------------------------\n",
+    "\n",
+    "## **🚀ความท้าทาย**\n",
+    "\n",
+    "ใช้เวลาสักครู่กับโน้ตบุ๊กนี้ ปรับพารามิเตอร์ต่างๆ คุณสามารถปรับปรุงความแม่นยำของโมเดลได้โดยการทำความสะอาดข้อมูลเพิ่มเติม (เช่น การลบค่าผิดปกติ)? คุณสามารถใช้น้ำหนักเพื่อให้ความสำคัญกับตัวอย่างข้อมูลบางตัวมากขึ้น คุณสามารถทำอะไรอีกเพื่อสร้างกลุ่มที่ดีกว่า?\n",
+    "\n",
+    "เคล็ดลับ: ลองปรับขนาดข้อมูลของคุณ มีโค้ดที่ถูกคอมเมนต์ไว้ในโน้ตบุ๊กที่เพิ่มการปรับขนาดมาตรฐานเพื่อทำให้คอลัมน์ข้อมูลมีลักษณะคล้ายกันมากขึ้นในแง่ของช่วง คุณจะพบว่าแม้คะแนน silhouette จะลดลง แต่ 'จุดหัก' ในกราฟข้อศอกจะเรียบขึ้น นี่เป็นเพราะการปล่อยให้ข้อมูลไม่ได้ปรับขนาดทำให้ข้อมูลที่มีความแปรปรวนน้อยมีน้ำหนักมากขึ้น อ่านเพิ่มเติมเกี่ยวกับปัญหานี้ [ที่นี่](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226)\n",
+    "\n",
+    "## [**แบบทดสอบหลังการบรรยาย**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)\n",
+    "\n",
+    "## **การทบทวนและการศึกษาด้วยตนเอง**\n",
+    "\n",
+    "-   ลองดูตัวจำลอง K-Means [เช่นนี้](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/) คุณสามารถใช้เครื่องมือนี้เพื่อแสดงภาพจุดข้อมูลตัวอย่างและกำหนดจุดศูนย์กลางของมัน คุณสามารถแก้ไขความสุ่มของข้อมูล จำนวนกลุ่ม และจำนวนจุดศูนย์กลาง สิ่งนี้ช่วยให้คุณเข้าใจวิธีการจัดกลุ่มข้อมูลได้หรือไม่?\n",
+    "\n",
+    "-   นอกจากนี้ ลองดู [เอกสารประกอบเกี่ยวกับ K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) จาก Stanford\n",
+    "\n",
+    "ต้องการลองใช้ทักษะการจัดกลุ่มที่คุณเพิ่งเรียนรู้กับชุดข้อมูลที่เหมาะสมกับ K-Means clustering? โปรดดู:\n",
+    "\n",
+    "-   [การฝึกและประเมินโมเดลการจัดกลุ่ม](https://rpubs.com/eR_ic/clustering) โดยใช้ Tidymodels และเพื่อนๆ\n",
+    "\n",
+    "-   [การวิเคราะห์กลุ่มด้วย K-Means](https://uc-r.github.io/kmeans_clustering), UC Business Analytics R Programming Guide\n",
+    "\n",
+    "- [การจัดกลุ่มด้วย K-Means โดยใช้หลักการข้อมูลที่เป็นระเบียบ](https://www.tidymodels.org/learn/statistics/k-means/)\n",
+    "\n",
+    "## **งานที่ได้รับมอบหมาย**\n",
+    "\n",
+    "[ลองใช้วิธีการจัดกลุ่มที่แตกต่างกัน](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)\n",
+    "\n",
+    "## ขอขอบคุณ:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) สำหรับการสร้างเวอร์ชัน Python ดั้งเดิมของโมดูลนี้ ♥️\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) สำหรับการสร้างภาพประกอบที่น่าทึ่งซึ่งทำให้ R น่าสนใจและเข้าถึงได้มากขึ้น ค้นหาภาพประกอบเพิ่มเติมได้ที่ [แกลเลอรีของเธอ](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM)\n",
+    "\n",
+    "เรียนรู้อย่างมีความสุข,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Student Ambassador.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>ภาพประกอบโดย @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/5-Clustering/2-K-Means/solution/notebook.ipynb b/translations/th/5-Clustering/2-K-Means/solution/notebook.ipynb
new file mode 100644
index 000000000..79dfd2f95
--- /dev/null
+++ b/translations/th/5-Clustering/2-K-Means/solution/notebook.ipynb
@@ -0,0 +1,544 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "e867e87e3129c8875423a82945f4ad5e",
+   "translation_date": "2025-09-06T14:21:39+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "เราจะมุ่งเน้นเพียง 3 ประเภทเท่านั้น บางทีเราอาจสร้าง 3 กลุ่มขึ้นมาได้!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "ข้อมูลนี้สะอาดแค่ไหน? ตรวจสอบค่าผิดปกติโดยใช้กล่องแผนภาพ เราจะมุ่งเน้นไปที่คอลัมน์ที่มีค่าผิดปกติน้อยกว่า (แม้ว่าคุณจะสามารถลบค่าผิดปกติออกได้) กล่องแผนภาพสามารถแสดงช่วงของข้อมูลและช่วยเลือกคอลัมน์ที่จะใช้ โปรดทราบว่ากล่องแผนภาพไม่ได้แสดงความแปรปรวน ซึ่งเป็นองค์ประกอบสำคัญของข้อมูลที่สามารถจัดกลุ่มได้ดี (https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "# scaler = StandardScaler()\n",
+    "\n",
+    "X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
+    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "y = le.transform(y)\n",
+    "\n"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 0, 2, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0,\n",
+       "       2, 0, 0, 1, 1, 2, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2,\n",
+       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 0,\n",
+       "       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 2,\n",
+       "       2, 1, 1, 0, 1, 2, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0,\n",
+       "       1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       1, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 1, 1, 2, 1],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "source": [
+    "ตัวเลขเหล่านั้นไม่ได้มีความหมายมากนักสำหรับเรา ดังนั้นมาหา 'คะแนนซิลลูเอต' เพื่อดูความแม่นยำกัน คะแนนของเราอยู่ในระดับกลาง\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "source": [
+    "ใช้โมเดลนั้นเพื่อตัดสินใจโดยใช้วิธี Elbow Method ว่าควรสร้างจำนวนคลัสเตอร์ที่เหมาะสมที่สุดกี่คลัสเตอร์\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
+     ]
+    },
+    {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Looks like 3 is a good number after all. Fit the model again and create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "ความแม่นยำของโมเดลนี้ไม่ได้แย่ แต่ก็ไม่ได้ดีนัก อาจเป็นไปได้ว่าข้อมูลอาจไม่เหมาะสมกับการจัดกลุ่มแบบ K-Means คุณอาจลองใช้วิธีอื่นดู\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 811,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/5-Clustering/2-K-Means/solution/tester.ipynb b/translations/th/5-Clustering/2-K-Means/solution/tester.ipynb
new file mode 100644
index 000000000..d27a38ec2
--- /dev/null
+++ b/translations/th/5-Clustering/2-K-Means/solution/tester.ipynb
@@ -0,0 +1,341 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "6f92868513e59d321245137c1c4c5311",
+   "translation_date": "2025-09-06T14:22:55+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/tester.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 105
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "เราจะมุ่งเน้นเพียง 3 ประเภทเท่านั้น บางทีเราอาจสร้าง 3 กลุ่มขึ้นมาได้!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 106
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 107
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# X = df.loc[:, ('danceability','energy')]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Unknown label type: 'continuous'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-110-3756ba7b0bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# we create an instance of SVM and fit out data. We do not scale our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# data since we want to plot the support vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mls30\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 30% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mls50\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 50% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mls100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 100% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m# actual graph construction (implementations should override this)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    181\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    182\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.semi_supervised import LabelSpreading\n",
+    "from sklearn.semi_supervised import SelfTrainingClassifier\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "X = df[['danceability','acousticness']].values\n",
+    "y = df['energy'].values\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "# step size in the mesh\n",
+    "h = .02\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "y_rand = rng.rand(y.shape[0])\n",
+    "y_30 = np.copy(y)\n",
+    "y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled\n",
+    "y_50 = np.copy(y)\n",
+    "y_50[y_rand < 0.5] = -1\n",
+    "# we create an instance of SVM and fit out data. We do not scale our\n",
+    "# data since we want to plot the support vectors\n",
+    "ls30 = (LabelSpreading().fit(X, y_30), y_30, 'Label Spreading 30% data')\n",
+    "ls50 = (LabelSpreading().fit(X, y_50), y_50, 'Label Spreading 50% data')\n",
+    "ls100 = (LabelSpreading().fit(X, y), y, 'Label Spreading 100% data')\n",
+    "\n",
+    "# the base classifier for self-training is identical to the SVC\n",
+    "base_classifier = SVC(kernel='rbf', gamma=.5, probability=True)\n",
+    "st30 = (SelfTrainingClassifier(base_classifier).fit(X, y_30),\n",
+    "        y_30, 'Self-training 30% data')\n",
+    "st50 = (SelfTrainingClassifier(base_classifier).fit(X, y_50),\n",
+    "        y_50, 'Self-training 50% data')\n",
+    "\n",
+    "rbf_svc = (SVC(kernel='rbf', gamma=.5).fit(X, y), y, 'SVC with rbf kernel')\n",
+    "\n",
+    "# create a mesh to plot in\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                     np.arange(y_min, y_max, h))\n",
+    "\n",
+    "color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}\n",
+    "\n",
+    "classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)\n",
+    "for i, (clf, y_train, title) in enumerate(classifiers):\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    colors = [color_map[y] for y in y_train]\n",
+    "    plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')\n",
+    "\n",
+    "    plt.title(title)\n",
+    "\n",
+    "plt.suptitle(\"Unlabeled points are colored white\", y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามนุษย์ที่เป็นมืออาชีพ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb b/translations/th/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
new file mode 100644
index 000000000..6613ba3c6
--- /dev/null
+++ b/translations/th/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
@@ -0,0 +1,100 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "27de2abc0235ebd22080fc8f1107454d",
+   "translation_date": "2025-09-06T15:22:18+00:00",
+   "source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You should download the book text, clean it, and import it here\n",
+    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
+    "    file_contents = f.read()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "book_pride = TextBlob(file_contents)\n",
+    "positive_sentiment_sentences = []\n",
+    "negative_sentiment_sentences = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sentence in book_pride.sentences:\n",
+    "    if sentence.sentiment.polarity == 1:\n",
+    "        positive_sentiment_sentences.append(sentence)\n",
+    "    if sentence.sentiment.polarity == -1:\n",
+    "        negative_sentiment_sentences.append(sentence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
+    "for sentence in positive_sentiment_sentences:\n",
+    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
+    "for sentence in negative_sentiment_sentences:\n",
+    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/6-NLP/4-Hotel-Reviews-1/notebook.ipynb b/translations/th/6-NLP/4-Hotel-Reviews-1/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/th/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb b/translations/th/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
new file mode 100644
index 000000000..b7c5a3aa1
--- /dev/null
+++ b/translations/th/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
@@ -0,0 +1,174 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2d05e7db439376aa824f4b387f8324ca",
+   "translation_date": "2025-09-06T15:21:57+00:00",
+   "source_file": "6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EDA\n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_difference_review_avg(row):\n",
+    "    return row[\"Average_Score\"] - row[\"Calc_Average_Score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "print(\"Loading data file now, this could take a while depending on file size\")\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n",
+    "end = time.time()\n",
+    "print(\"Loading took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What shape is the data (rows, columns)?\n",
+    "print(\"The shape of the data (rows, cols) is \" + str(df.shape))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# value_counts() creates a Series object that has index and values\n",
+    "#                in this case, the country and the frequency they occur in reviewer nationality\n",
+    "nationality_freq = df[\"Reviewer_Nationality\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What reviewer nationality is the most common in the dataset?\n",
+    "print(\"The highest frequency reviewer nationality is \" + str(nationality_freq.index[0]).strip() + \" with \" + str(nationality_freq[0]) + \" reviews.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the top 10 most common nationalities and their frequencies?\n",
+    "print(\"The top 10 highest frequency reviewer nationalities are:\")\n",
+    "print(nationality_freq[0:10].to_string())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many unique nationalities are there?\n",
+    "print(\"There are \" + str(nationality_freq.index.size) + \" unique nationalities in the dataset\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What was the most frequently reviewed hotel for the top 10 nationalities - print the hotel and number of reviews\n",
+    "for nat in nationality_freq[:10].index:\n",
+    "   # First, extract all the rows that match the criteria into a new dataframe\n",
+    "   nat_df = df[df[\"Reviewer_Nationality\"] == nat]   \n",
+    "   # Now get the hotel freq\n",
+    "   freq = nat_df[\"Hotel_Name\"].value_counts()\n",
+    "   print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many reviews are there per hotel (frequency count of hotel) and do the results match the value in `Total_Number_of_Reviews`?\n",
+    "# First create a new dataframe based on the old one, removing the uneeded columns\n",
+    "hotel_freq_df = df.drop([\"Hotel_Address\", \"Additional_Number_of_Scoring\", \"Review_Date\", \"Average_Score\", \"Reviewer_Nationality\", \"Negative_Review\", \"Review_Total_Negative_Word_Counts\", \"Positive_Review\", \"Review_Total_Positive_Word_Counts\", \"Total_Number_of_Reviews_Reviewer_Has_Given\", \"Reviewer_Score\", \"Tags\", \"days_since_review\", \"lat\", \"lng\"], axis = 1)\n",
+    "# Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found\n",
+    "hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')\n",
+    "# Get rid of all the duplicated rows\n",
+    "hotel_freq_df = hotel_freq_df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "print()\n",
+    "print(hotel_freq_df.to_string())\n",
+    "print(str(hotel_freq_df.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# While there is an `Average_Score` for each hotel according to the dataset, \n",
+    "# you can also calculate an average score (getting the average of all reviewer scores in the dataset for each hotel)\n",
+    "# Add a new column to your dataframe with the column header `Calc_Average_Score` that contains that calculated average. \n",
+    "df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n",
+    "# Add a new column with the difference between the two average scores\n",
+    "df[\"Average_Score_Difference\"] = df.apply(get_difference_review_avg, axis = 1)\n",
+    "# Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)\n",
+    "review_scores_df = df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "# Sort the dataframe to find the lowest and highest average score difference\n",
+    "review_scores_df = review_scores_df.sort_values(by=[\"Average_Score_Difference\"])\n",
+    "print(review_scores_df[[\"Average_Score_Difference\", \"Average_Score\", \"Calc_Average_Score\", \"Hotel_Name\"]])\n",
+    "# Do any hotels have the same (rounded to 1 decimal place) `Average_Score` and `Calc_Average_Score`?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/6-NLP/5-Hotel-Reviews-2/notebook.ipynb b/translations/th/6-NLP/5-Hotel-Reviews-2/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/th/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb b/translations/th/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
new file mode 100644
index 000000000..1c1eaff22
--- /dev/null
+++ b/translations/th/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
@@ -0,0 +1,172 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "033cb89c85500224b3c63fd04f49b4aa",
+   "translation_date": "2025-09-06T15:22:39+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import time\n",
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replace_address(row):\n",
+    "    if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Amsterdam, Netherlands\"\n",
+    "    elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Barcelona, Spain\"\n",
+    "    elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
+    "        return \"London, United Kingdom\"\n",
+    "    elif \"Milan\" in row[\"Hotel_Address\"]:        \n",
+    "        return \"Milan, Italy\"\n",
+    "    elif \"France\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Paris, France\"\n",
+    "    elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Vienna, Austria\" \n",
+    "    else:\n",
+    "        return row.Hotel_Address\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dropping columns we will not use:\n",
+    "df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace all the addresses with a shortened, more useful form\n",
+    "df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop `Additional_Number_of_Scoring`\n",
+    "df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
+    "# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
+    "df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
+    "df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process the Tags into new columns\n",
+    "# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
+    "# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
+    "# Family with young children, Family with older children, With a pet\n",
+    "df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
+    "df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
+    "df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
+    "df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
+    "df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
+    "df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
+    "df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
+    "df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No longer need any of these columns\n",
+    "df.drop([\"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_Filtered.csv\n",
+      "Filtering took 23.74 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Saving new data file with calculated columns\n",
+    "print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
+    "df.to_csv(r'../../data/Hotel_Reviews_Filtered.csv', index = False)\n",
+    "end = time.time()\n",
+    "print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb b/translations/th/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
new file mode 100644
index 000000000..7f2b88014
--- /dev/null
+++ b/translations/th/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
@@ -0,0 +1,137 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "341efc86325ec2a214f682f57a189dfd",
+   "translation_date": "2025-09-06T15:23:00+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV (you can )\n",
+    "import pandas as pd \n",
+    "\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We want to find the most useful tags to keep\n",
+    "# Remove opening and closing brackets\n",
+    "df.Tags = df.Tags.str.strip(\"[']\")\n",
+    "# remove all quotes too\n",
+    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
+    "# Now split the strings into a list\n",
+    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove leading and trailing spaces\n",
+    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
+    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
+    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
+    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
+    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
+    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the 6 columns into one with melt\n",
+    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "The shape of the tags with no filtering: (2514684, 2)\n",
+      "                        index   count\n",
+      "0                Leisure trip  338423\n",
+      "1                      Couple  205305\n",
+      "2               Solo traveler   89779\n",
+      "3               Business trip   68176\n",
+      "4                       Group   51593\n",
+      "5  Family with young children   49318\n",
+      "6  Family with older children   21509\n",
+      "7      Travelers with friends    1610\n",
+      "8                  With a pet    1078\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the value counts\n",
+    "tag_vc = df_tags.value.value_counts()\n",
+    "# print(tag_vc)\n",
+    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
+    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
+    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
+    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
+    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
+    "print(tag_vc[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb b/translations/th/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
new file mode 100644
index 000000000..3ac54a358
--- /dev/null
+++ b/translations/th/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
@@ -0,0 +1,260 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "705bf02633759f689abc37b19749a16d",
+   "translation_date": "2025-09-06T15:23:20+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[nltk_data] Downloading package vader_lexicon to\n[nltk_data]     /Users/jenlooper/nltk_data...\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import pandas as pd\n",
+    "import nltk as nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    "nltk.download('vader_lexicon')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vader_sentiment = SentimentIntensityAnalyzer()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are 3 possibilities of input for a review:\n",
+    "# It could be \"No Negative\", in which case, return 0\n",
+    "# It could be \"No Positive\", in which case, return 0\n",
+    "# It could be a review, in which case calculate the sentiment\n",
+    "def calc_sentiment(review):    \n",
+    "    if review == \"No Negative\" or review == \"No Positive\":\n",
+    "        return 0\n",
+    "    return vader_sentiment.polarity_scores(review)[\"compound\"]    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "df = pd.read_csv(\"../../data/Hotel_Reviews_Filtered.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove stop words - can be slow for a lot of text!\n",
+    "# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches\n",
+    "# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends\n",
+    "start = time.time()\n",
+    "cache = set(stopwords.words(\"english\"))\n",
+    "def remove_stopwords(review):\n",
+    "    text = \" \".join([word for word in review.split() if word not in cache])\n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the stop words from both columns\n",
+    "df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   \n",
+    "df.Positive_Review = df.Positive_Review.apply(remove_stopwords)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Removing stop words took 5.77 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "end = time.time()\n",
+    "print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Calculating sentiment columns for both positive and negative reviews\n",
+      "Calculating sentiment took 201.07 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a negative sentiment and positive sentiment column\n",
+    "print(\"Calculating sentiment columns for both positive and negative reviews\")\n",
+    "start = time.time()\n",
+    "df[\"Negative_Sentiment\"] = df.Negative_Review.apply(calc_sentiment)\n",
+    "df[\"Positive_Sentiment\"] = df.Positive_Review.apply(calc_sentiment)\n",
+    "end = time.time()\n",
+    "print(\"Calculating sentiment took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                          Negative_Review  Negative_Sentiment\n",
+      "186584  So bad experience memories I hotel The first n...             -0.9920\n",
+      "129503  First charged twice room booked booking second...             -0.9896\n",
+      "307286  The staff Had bad experience even booking Janu...             -0.9889\n",
+      "452092  No WLAN room Incredibly rude restaurant staff ...             -0.9884\n",
+      "201293  We usually traveling Paris 2 3 times year busi...             -0.9873\n",
+      "...                                                   ...                 ...\n",
+      "26899   I would say however one night expensive even d...              0.9933\n",
+      "138365  Wifi terribly slow I speed test network upload...              0.9938\n",
+      "79215   I find anything hotel first I walked past hote...              0.9938\n",
+      "278506  The property great location There bakery next ...              0.9945\n",
+      "339189  Guys I like hotel I wish return next year Howe...              0.9948\n",
+      "\n",
+      "[515738 rows x 2 columns]\n",
+      "                                          Positive_Review  Positive_Sentiment\n",
+      "137893  Bathroom Shower We going stay twice hotel 2 ni...             -0.9820\n",
+      "5839    I completely disappointed mad since reception ...             -0.9780\n",
+      "64158   get everything extra internet parking breakfas...             -0.9751\n",
+      "124178  I didnt like anythig Room small Asked upgrade ...             -0.9721\n",
+      "489137  Very rude manager abusive staff reception Dirt...             -0.9703\n",
+      "...                                                   ...                 ...\n",
+      "331570  Everything This recently renovated hotel class...              0.9984\n",
+      "322920  From moment stepped doors Guesthouse Hotel sta...              0.9985\n",
+      "293710  This place surprise expected good actually gre...              0.9985\n",
+      "417442  We celebrated wedding night Langham I commend ...              0.9985\n",
+      "132492  We arrived super cute boutique hotel area expl...              0.9987\n",
+      "\n",
+      "[515738 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=[\"Negative_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Negative_Review\", \"Negative_Sentiment\"]])\n",
+    "df = df.sort_values(by=[\"Positive_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Positive_Review\", \"Positive_Sentiment\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)\n",
+    "df = df.reindex([\"Hotel_Name\", \"Hotel_Address\", \"Total_Number_of_Reviews\", \"Average_Score\", \"Reviewer_Score\", \"Negative_Sentiment\", \"Positive_Sentiment\", \"Reviewer_Nationality\", \"Leisure_trip\", \"Couple\", \"Solo_traveler\", \"Business_trip\", \"Group\", \"Family_with_young_children\", \"Family_with_older_children\", \"With_a_pet\", \"Negative_Review\", \"Positive_Review\"], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_NLP.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
+    "df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/7-TimeSeries/1-Introduction/solution/notebook.ipynb b/translations/th/7-TimeSeries/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..38736668f
--- /dev/null
+++ b/translations/th/7-TimeSeries/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,169 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ในสมุดบันทึกนี้ เราจะแสดงวิธีการ:\n",
+    "- ตั้งค่าข้อมูลอนุกรมเวลาเพื่อใช้งานในโมดูลนี้\n",
+    "- แสดงภาพข้อมูล\n",
+    "\n",
+    "ข้อมูลในตัวอย่างนี้นำมาจากการแข่งขันพยากรณ์ GEFCom2014  \n",
+    "ประกอบด้วยข้อมูลการใช้ไฟฟ้ารายชั่วโมงและค่าความร้อนรายชั่วโมงเป็นเวลา 3 ปี ระหว่างปี 2012 ถึง 2014  \n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli และ Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, กรกฎาคม-กันยายน, 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "from common.utils import load_data\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "โหลดข้อมูลจากไฟล์ CSV ลงใน Pandas DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2698.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2558.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2444.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2402.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2403.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "data_dir = './data'\n",
+    "energy = load_data(data_dir)[['load']]\n",
+    "energy.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "แสดงข้อมูลโหลดทั้งหมดที่มีอยู่ (มกราคม 2012 ถึง ธันวาคม 2014)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 1080x576 with 1 Axes>",
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305.362902 \nL 79.573915 293.40237 \nL 79.669379 184.770013 \nL 79.764844 193.658115 \nL 79.89213 211.544048 \nL 79.923951 210.556481 \nL 79.987594 155.911114 \nL 80.051237 167.981376 \nL 80.305809 316.445597 \nL 80.33763 306.02128 \nL 80.56038 175.113803 \nL 80.687666 194.426222 \nL 80.751309 161.178137 \nL 80.814952 183.562986 \nL 81.069524 323.358566 \nL 81.101345 319.847217 \nL 81.292274 220.761339 \nL 81.387739 239.52511 \nL 81.451381 245.99916 \nL 81.515024 189.378658 \nL 81.578667 204.52135 \nL 81.833239 325.22397 \nL 81.896882 311.836952 \nL 82.087811 223.065662 \nL 82.151454 230.417549 \nL 82.183275 235.794302 \nL 82.215097 234.477546 \nL 82.310561 169.517591 \nL 82.342382 184.550553 \nL 82.565133 312.714789 \nL 82.628776 298.449934 \nL 82.819704 179.832178 \nL 82.851526 183.343527 \nL 82.94699 198.26676 \nL 82.978812 195.19433 \nL 83.074276 135.830587 \nL 83.106098 148.998145 \nL 83.360669 296.58453 \nL 83.392491 286.269943 \nL 83.58342 176.650018 \nL 83.615241 177.857044 \nL 83.742527 195.962437 \nL 83.837991 148.230038 \nL 83.869813 161.617056 \nL 84.092563 314.141275 \nL 84.156206 304.046147 \nL 84.378956 188.061902 \nL 84.506242 207.92297 \nL 84.601707 158.873814 \nL 84.633528 171.382995 \nL 84.856278 323.687755 \nL 84.919921 312.60506 \nL 85.11085 190.805144 \nL 85.142671 192.121899 \nL 85.238136 198.595949 \nL 85.269957 197.937571 \nL 85.365422 150.095442 \nL 85.397243 159.641922 \nL 85.619993 299.437501 \nL 85.683636 287.806158 \nL 85.810922 179.39326 \nL 85.874565 183.233797 \nL 86.033672 208.691077 \nL 86.065494 196.291626 \nL 86.129137 164.140838 \nL 86.160958 174.784614 \nL 86.41553 312.166141 \nL 86.479173 297.133178 \nL 86.63828 206.606214 \nL 86.670102 211.763507 \nL 86.765566 233.599709 \nL 86.797388 232.831601 \nL 86.892852 171.382995 \nL 86.956495 197.388923 \nL 87.179245 301.412635 \nL 87.242888 288.354806 \nL 87.401995 205.728377 \nL 87.465638 212.641345 \nL 87.529281 226.577011 \nL 87.561103 223.394851 \nL 87.624746 169.956509 \nL 87.688389 203.753243 \nL 87.94296 312.49533 \nL 87.974782 303.497498 \nL 88.102068 192.670548 \nL 88.165711 195.742978 \nL 88.324818 223.72404 \nL 88.356639 210.666211 \nL 88.420282 165.457594 \nL 88.452104 179.832178 \nL 88.674854 324.565592 \nL 88.738497 313.373167 \nL 88.929426 202.875406 \nL 88.961247 205.508917 \nL 89.056712 216.372153 \nL 89.088533 214.397019 \nL 89.183998 154.155439 \nL 89.24764 180.490556 \nL 89.438569 296.035882 \nL 89.470391 295.048315 \nL 89.534034 251.485642 \nL 89.597677 175.333262 \nL 89.661319 181.148934 \nL 89.820427 213.080263 \nL 89.852248 211.10513 \nL 89.947713 153.716521 \nL 90.011356 182.46569 \nL 90.202284 308.435333 \nL 90.234106 308.215873 \nL 90.297749 265.640768 \nL 90.361392 191.682981 \nL 90.425035 198.815408 \nL 90.615963 224.711607 \nL 90.711428 166.884079 \nL 90.775071 192.560818 \nL 90.966 312.60506 \nL 90.997821 311.398033 \nL 91.061464 268.384009 \nL 91.125107 195.962437 \nL 91.18875 202.875406 \nL 91.284214 219.005665 \nL 91.379679 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212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 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200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 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191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 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345.414226 \nL 247.654894 330.600723 \nL 247.845823 238.208354 \nL 247.877645 242.048892 \nL 247.941288 246.438078 \nL 247.973109 242.59754 \nL 248.068573 234.916464 \nL 248.100395 239.41538 \nL 248.323145 354.192598 \nL 248.354967 351.120168 \nL 248.482252 275.296977 \nL 248.577717 229.539711 \nL 248.64136 231.075927 \nL 248.673181 230.966197 \nL 248.736824 220.651609 \nL 248.832289 201.010001 \nL 248.86411 206.496484 \nL 249.08686 340.586121 \nL 249.150503 302.94885 \nL 249.309611 191.573251 \nL 249.405075 187.513254 \nL 249.468718 192.01217 \nL 249.500539 189.378658 \nL 249.596004 178.844611 \nL 249.691468 261.6905 \nL 249.850575 324.785051 \nL 249.914218 283.526701 \nL 250.073326 161.507326 \nL 250.16879 158.764085 \nL 250.264255 163.043541 \nL 250.296076 161.946245 \nL 250.327897 161.507326 \nL 250.359719 153.387332 \nL 250.39154 165.567323 \nL 250.614291 321.602891 \nL 250.677934 284.953187 \nL 250.868862 168.420294 \nL 250.996148 160.958678 \nL 251.02797 161.946245 \nL 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258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 315.75283 171.382995 \nL 315.880116 189.488388 \nL 315.911937 182.026771 \nL 315.97558 135.281939 \nL 316.039223 155.252736 \nL 316.134688 222.955932 \nL 316.293795 295.487234 \nL 316.357438 280.125082 \nL 316.516545 184.221364 \nL 316.548367 185.977039 \nL 316.643831 197.279193 \nL 316.675652 191.682981 \nL 316.739295 154.704087 \nL 316.802938 177.198666 \nL 317.05751 323.578025 \nL 317.152974 296.4748 \nL 317.312082 212.531615 \nL 317.343903 215.165127 \nL 317.407546 223.065662 \nL 317.439368 216.262423 \nL 317.503011 165.238134 \nL 317.566653 190.256495 \nL 317.789404 338.610988 \nL 317.853047 324.785051 \nL 318.043975 214.28729 \nL 318.171261 228.332685 \nL 318.203083 224.272688 \nL 318.266726 161.397596 \nL 318.330369 174.674884 \nL 318.553119 330.381263 \nL 318.616762 319.518028 \nL 318.807691 200.790542 \nL 318.839512 203.424054 \nL 318.934976 209.349455 \nL 318.966798 204.411621 \nL 319.030441 154.923547 \nL 319.094084 172.480291 \nL 319.348656 336.745583 \nL 319.380477 327.089374 \nL 319.475941 219.993232 \nL 319.603227 220.212691 \nL 319.635049 220.871069 \nL 319.66687 219.225124 \nL 319.698692 219.334854 \nL 319.825978 144.27977 \nL 319.88962 169.956509 \nL 320.080549 296.145611 \nL 320.112371 295.158045 \nL 320.176014 248.852131 \nL 320.239657 173.906777 \nL 320.303299 176.979207 \nL 320.462407 199.583516 \nL 320.494228 195.413789 \nL 320.557871 129.246808 \nL 320.621514 138.13491 \nL 320.844264 294.719126 \nL 320.907907 283.087783 \nL 321.003372 177.308396 \nL 321.098836 183.233797 \nL 321.226122 204.74081 \nL 321.257943 199.144597 \nL 321.321586 157.227869 \nL 321.385229 179.722449 \nL 321.639801 331.039641 \nL 321.703444 314.360734 \nL 321.862551 207.264592 \nL 321.926194 212.641345 \nL 321.989837 222.297554 \nL 322.021659 217.46945 \nL 322.085302 175.881911 \nL 322.148944 200.900272 \nL 322.403516 336.196935 \nL 322.467159 323.248836 \nL 322.658088 232.392682 \nL 322.689909 235.245653 \nL 322.753552 243.475378 \nL 322.785374 235.026194 \nL 322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 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304.375335 \nL 344.614899 260.593204 \nL 344.774006 175.662451 \nL 344.901292 193.658115 \nL 344.933114 188.83001 \nL 345.028578 123.650595 \nL 345.060399 137.695991 \nL 345.28315 282.868323 \nL 345.346793 271.456439 \nL 345.474078 169.298131 \nL 345.537721 177.418126 \nL 345.665007 199.473786 \nL 345.696829 196.291626 \nL 345.760472 135.940317 \nL 345.824115 150.973279 \nL 346.046865 299.87642 \nL 346.110508 287.03805 \nL 346.205972 176.10137 \nL 346.301437 181.148934 \nL 346.428722 187.952173 \nL 346.460544 184.550553 \nL 346.524187 138.354369 \nL 346.58783 157.557058 \nL 346.842401 309.532629 \nL 346.874223 297.681827 \nL 347.065152 190.146766 \nL 347.192438 207.264592 \nL 347.224259 204.960269 \nL 347.287902 148.339767 \nL 347.351545 162.494893 \nL 347.574295 305.801821 \nL 347.637938 294.170478 \nL 347.797045 186.196498 \nL 347.828867 187.513254 \nL 347.956153 199.583516 \nL 347.987974 199.473786 \nL 348.051617 159.641922 \nL 348.11526 183.014338 \nL 348.369832 324.565592 \nL 348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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360.111952 184.660283 \nL 360.175595 198.37649 \nL 360.207417 194.97487 \nL 360.302881 126.942485 \nL 360.334703 142.633825 \nL 360.557453 272.992654 \nL 360.621096 260.044555 \nL 360.780203 149.546794 \nL 360.812025 150.314901 \nL 360.939311 163.921378 \nL 360.971132 160.848948 \nL 361.034775 119.261409 \nL 361.098418 149.107875 \nL 361.35299 288.683995 \nL 361.416633 247.206186 \nL 361.480275 172.91921 \nL 361.543918 174.016507 \nL 361.57574 174.235966 \nL 361.703026 187.184065 \nL 361.734847 185.757579 \nL 361.830312 137.695991 \nL 361.862133 152.180305 \nL 362.116705 306.899118 \nL 362.148526 299.437501 \nL 362.339455 182.136501 \nL 362.371277 183.014338 \nL 362.466741 195.413789 \nL 362.498562 194.865141 \nL 362.562205 153.497061 \nL 362.625848 173.138669 \nL 362.88042 323.907214 \nL 362.912241 314.799653 \nL 363.10317 206.715943 \nL 363.134992 209.568914 \nL 363.230456 219.993232 \nL 363.294099 194.97487 \nL 363.357742 150.534361 \nL 363.389563 159.861381 \nL 363.612314 292.853722 \nL 363.675957 281.002919 \nL 363.866885 170.505158 \nL 363.96235 181.478123 \nL 364.025993 187.952173 \nL 364.121457 139.451665 \nL 364.153279 150.095442 \nL 364.40785 275.187248 \nL 364.471493 258.61807 \nL 364.630601 179.064071 \nL 364.662422 182.026771 \nL 364.757886 197.827841 \nL 364.789708 195.633248 \nL 364.853351 150.534361 \nL 364.916994 166.774349 \nL 365.171565 292.524533 \nL 365.235208 280.125082 \nL 365.426137 192.560818 \nL 365.48978 199.802975 \nL 365.521602 202.436487 \nL 365.553423 196.511086 \nL 365.617066 144.060311 \nL 365.680709 175.772181 \nL 365.903459 291.427236 \nL 365.967102 276.613733 \nL 366.158031 168.420294 \nL 366.189852 168.091105 \nL 366.285317 178.954341 \nL 366.317138 176.979207 \nL 366.412603 119.480868 \nL 366.476246 149.107875 \nL 366.667174 268.493739 \nL 366.698996 267.835361 \nL 366.762639 222.626743 \nL 366.826282 150.973279 \nL 366.889925 159.971111 \nL 367.01721 180.161367 \nL 367.049032 186.635417 \nL 367.080853 185.318661 \nL 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 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431.328392 247.974293 \nL 431.392035 232.173223 \nL 431.582963 356.167732 \nL 431.646606 344.865578 \nL 431.869357 232.941331 \nL 432.092107 242.70727 \nL 432.15575 225.479714 \nL 432.346679 353.53422 \nL 432.410321 343.439092 \nL 432.633072 225.369985 \nL 432.792179 237.769435 \nL 432.855822 235.904031 \nL 432.919465 223.50458 \nL 433.110394 348.925575 \nL 433.142215 348.706116 \nL 433.205858 313.592626 \nL 433.364965 235.684572 \nL 433.396787 234.038627 \nL 433.428608 235.904031 \nL 433.46043 237.001328 \nL 433.619537 252.582939 \nL 433.651359 251.266183 \nL 433.68318 237.988895 \nL 433.715002 256.752666 \nL 433.90593 355.070436 \nL 433.969573 341.573688 \nL 434.128681 259.825096 \nL 434.192324 264.543471 \nL 434.287788 275.955355 \nL 434.319609 273.431573 \nL 434.446895 246.767267 \nL 434.669646 356.167732 \nL 434.733288 346.840712 \nL 434.892396 267.94509 \nL 434.98786 278.259678 \nL 435.019682 281.990486 \nL 435.051503 280.67373 \nL 435.21061 244.682404 \nL 435.401539 358.581785 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185.099202 \nL 454.52624 325.443429 \nL 454.589883 296.803989 \nL 454.780812 140.110043 \nL 454.908097 122.11438 \nL 454.939919 122.553299 \nL 455.099026 159.422463 \nL 455.321776 309.093711 \nL 455.353598 306.460199 \nL 455.671813 138.903017 \nL 455.735456 135.611128 \nL 455.862741 167.652187 \nL 456.085492 302.400202 \nL 456.117313 302.729391 \nL 456.371885 139.341936 \nL 456.499171 152.509494 \nL 456.626457 201.44892 \nL 456.817385 311.288304 \nL 456.881028 279.795893 \nL 457.040136 174.235966 \nL 457.1356 161.068407 \nL 457.231064 161.397596 \nL 457.294707 170.285698 \nL 457.421993 250.498075 \nL 457.5811 334.112072 \nL 457.644743 306.569929 \nL 457.803851 209.568914 \nL 457.835672 207.70351 \nL 457.867494 210.446752 \nL 457.899315 209.349455 \nL 458.058422 217.140261 \nL 458.153887 233.489979 \nL 458.344816 343.329363 \nL 458.408459 316.116408 \nL 458.567566 219.005665 \nL 458.758495 209.020266 \nL 458.853959 217.798638 \nL 458.885781 212.531615 \nL 459.108531 339.598554 \nL 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228.991063 \nL 545.853845 225.479714 \nL 545.917488 211.434319 \nL 545.949309 227.015929 \nL 546.17206 353.973139 \nL 546.203881 351.668816 \nL 546.39481 248.193753 \nL 546.522096 261.471041 \nL 546.553917 261.141852 \nL 546.681203 229.100793 \nL 546.713025 245.88943 \nL 546.935775 361.325026 \nL 546.967596 358.910974 \nL 547.063061 308.215873 \nL 547.190347 255.545639 \nL 547.222168 257.740233 \nL 547.285811 265.201849 \nL 547.317632 262.458608 \nL 547.444918 213.189993 \nL 547.47674 231.844034 \nL 547.667669 352.985572 \nL 547.69949 352.327194 \nL 547.763133 311.398033 \nL 547.92224 218.676476 \nL 548.017705 215.933234 \nL 548.081348 223.72404 \nL 548.113169 220.761339 \nL 548.208633 186.086768 \nL 548.240455 207.593781 \nL 548.463205 347.169901 \nL 548.526848 306.679658 \nL 548.685955 214.616478 \nL 548.78142 209.129996 \nL 548.813241 210.9954 \nL 548.845063 211.982967 \nL 548.876884 209.678644 \nL 548.972349 183.782446 \nL 549.22692 344.865578 \nL 549.258742 335.867746 \nL 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222.846203 \nL 584.803318 176.320829 \nL 584.866961 205.838106 \nL 585.089712 342.232066 \nL 585.153355 329.503426 \nL 585.344283 212.202426 \nL 585.471569 232.063493 \nL 585.503391 230.746738 \nL 585.567034 173.797047 \nL 585.630677 192.999737 \nL 585.853427 331.259101 \nL 585.91707 316.226138 \nL 586.107999 200.571083 \nL 586.13982 200.022435 \nL 586.171641 202.985135 \nL 586.235284 202.546217 \nL 586.267106 198.15703 \nL 586.330749 137.476532 \nL 586.394392 154.813817 \nL 586.617142 296.365071 \nL 586.680785 283.636431 \nL 586.776249 181.807312 \nL 586.871714 191.463522 \nL 586.999 207.92297 \nL 587.030821 201.887839 \nL 587.094464 141.975448 \nL 587.158107 154.155439 \nL 587.380857 293.182911 \nL 587.4445 276.723463 \nL 587.539965 171.163536 \nL 587.603607 183.892175 \nL 587.762715 213.628912 \nL 587.794536 206.496484 \nL 587.858179 154.045709 \nL 587.921822 167.761916 \nL 588.144572 314.360734 \nL 588.208215 302.400202 \nL 588.399144 188.500821 \nL 588.430966 189.488388 \nL 588.52643 200.351623 \nL 588.558251 198.37649 \nL 588.621894 165.896512 \nL 588.653716 173.797047 \nL 588.940109 325.22397 \nL 589.003752 304.704524 \nL 589.162859 221.090528 \nL 589.194681 223.065662 \nL 589.258324 230.527278 \nL 589.290145 230.198089 \nL 589.353788 186.745146 \nL 589.385609 165.238134 \nL 589.449252 187.732713 \nL 589.703824 296.145611 \nL 589.767467 280.78346 \nL 589.958396 181.478123 \nL 590.022039 191.024603 \nL 590.05386 194.206763 \nL 590.085682 186.196498 \nL 590.149325 127.930052 \nL 590.212968 148.559227 \nL 590.435718 272.992654 \nL 590.499361 258.069421 \nL 590.626647 153.387332 \nL 590.69029 158.325166 \nL 590.817575 178.515422 \nL 590.849397 172.480291 \nL 590.91304 119.15168 \nL 590.976683 132.319238 \nL 591.199433 289.122914 \nL 591.263076 278.259678 \nL 591.454005 168.530024 \nL 591.581291 183.562986 \nL 591.613112 180.929475 \nL 591.676755 142.194907 \nL 591.740398 162.604622 \nL 591.99497 315.56776 \nL 592.058613 281.332108 \nL 592.21772 177.418126 \nL 592.249541 177.527855 \nL 592.376827 189.159199 \nL 592.44047 163.263 \nL 592.472292 172.480291 \nL 592.599577 261.58077 \nL 592.726863 326.211537 \nL 592.758685 325.443429 \nL 592.822328 302.729391 \nL 592.981435 180.710015 \nL 593.045078 201.33919 \nL 593.140542 239.854299 \nL 593.172364 221.419717 \nL 593.204185 212.092696 \nL 593.29965 215.604045 \nL 593.490578 290.32994 \nL 593.554221 281.551567 \nL 593.74515 201.887839 \nL 593.776972 202.765676 \nL 593.872436 212.312156 \nL 593.936079 168.968942 \nL 593.9679 149.437064 \nL 594.031543 163.043541 \nL 594.158829 247.864564 \nL 594.254294 279.466704 \nL 594.286115 277.162381 \nL 594.349758 261.909959 \nL 594.477044 186.964606 \nL 594.540687 196.401356 \nL 594.636151 208.800807 \nL 594.667973 200.461353 \nL 594.731616 147.57166 \nL 594.795259 172.370562 \nL 594.890723 237.220787 \nL 595.04983 304.923984 \nL 595.081652 304.923984 \nL 595.177116 257.959692 \nL 595.336223 201.668379 \nL 595.399866 206.496484 \nL 595.431688 200.461353 \nL 595.495331 155.252736 \nL 595.558974 179.28353 \nL 595.781724 316.555327 \nL 595.845367 303.05858 \nL 596.036296 183.672716 \nL 596.068117 183.892175 \nL 596.163582 190.256495 \nL 596.195403 185.099202 \nL 596.259046 138.903017 \nL 596.322689 158.105707 \nL 596.577261 316.774786 \nL 596.609082 306.350469 \nL 596.736368 196.840275 \nL 596.800011 199.693246 \nL 596.927297 210.556481 \nL 596.959118 205.947836 \nL 597.022761 145.815985 \nL 597.086404 157.557058 \nL 597.309154 306.899118 \nL 597.372797 294.060748 \nL 597.468262 183.014338 \nL 597.563726 194.426222 \nL 597.595548 194.097033 \nL 597.691012 204.411621 \nL 597.722833 201.668379 \nL 597.786476 142.633825 \nL 597.850119 153.716521 \nL 598.072869 307.008847 \nL 598.168334 263.446175 \nL 598.327441 177.198666 \nL 598.359263 177.527855 \nL 598.454727 184.550553 \nL 598.550191 139.890584 \nL 598.613834 158.215436 \nL 598.836585 319.847217 \nL 598.900228 310.081277 \nL 599.091156 187.842443 \nL 599.122978 189.268928 \nL 599.218442 199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 653.155826 278.369407 \nL 653.283112 194.316493 \nL 653.346755 198.815408 \nL 653.442219 210.337022 \nL 653.47404 208.032699 \nL 653.569505 162.824082 \nL 653.601326 175.99164 \nL 653.855898 294.938585 \nL 653.919541 283.416972 \nL 654.078648 200.241894 \nL 654.142291 206.277025 \nL 654.205934 217.908368 \nL 654.237756 213.299723 \nL 654.33322 143.401933 \nL 654.396863 174.894344 \nL 654.587792 277.162381 \nL 654.619613 275.077518 \nL 654.683256 237.988895 \nL 654.810542 157.447329 \nL 654.842363 157.995977 \nL 654.969649 182.794879 \nL 655.001471 181.368393 \nL 655.096935 123.101947 \nL 655.160578 148.998145 \nL 655.351507 262.348878 \nL 655.383328 260.154285 \nL 655.446971 218.895935 \nL 655.606079 143.840852 \nL 655.765186 156.350032 \nL 655.797007 149.875983 \nL 655.86065 118.603031 \nL 655.892472 134.623561 \nL 656.115222 283.746161 \nL 656.178865 270.688332 \nL 656.369794 172.480291 \nL 656.528901 175.113803 \nL 656.592544 145.157608 \nL 656.624366 136.598694 \nL 656.656187 150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 211.434319 \nL 680.554107 315.56776 \nL 680.61775 312.934248 \nL 680.713215 263.885093 \nL 680.808679 219.554313 \nL 680.840501 221.419717 \nL 680.935965 240.402947 \nL 680.999608 233.928898 \nL 681.095072 181.258664 \nL 681.126894 185.867309 \nL 681.286001 288.025617 \nL 681.317823 286.489402 \nL 681.381465 272.224547 \nL 681.47693 164.689486 \nL 681.572394 173.02894 \nL 681.731502 204.960269 \nL 681.795145 191.682981 \nL 681.858787 160.958678 \nL 681.890609 167.981376 \nL 682.081538 281.880756 \nL 682.113359 278.808326 \nL 682.177002 231.734305 \nL 682.240645 161.726785 \nL 682.304288 173.797047 \nL 682.495217 211.873237 \nL 682.527038 207.154862 \nL 682.622503 166.335431 \nL 682.654324 173.577588 \nL 682.845253 297.023449 \nL 682.877074 295.267774 \nL 682.940717 253.680235 \nL 683.099825 166.55489 \nL 683.195289 175.333262 \nL 683.258932 182.685149 \nL 683.290753 181.039204 \nL 683.386218 153.277602 \nL 683.418039 162.275434 \nL 683.608968 278.918056 \nL 683.64079 276.504003 \nL 683.704432 234.806735 \nL 683.768075 167.761916 \nL 683.831718 175.333262 \nL 684.022647 212.312156 \nL 684.08629 201.668379 \nL 684.149933 174.784614 \nL 684.181754 180.490556 \nL 684.372683 305.582362 \nL 684.404505 305.362902 \nL 684.468148 268.16455 \nL 684.627255 183.014338 \nL 684.754541 204.301891 \nL 684.786362 209.568914 \nL 684.850005 206.496484 \nL 684.881827 206.167295 \nL 684.913648 200.571083 \nL 684.94547 207.154862 \nL 685.16822 314.251004 \nL 685.231863 307.447766 \nL 685.359149 226.467281 \nL 685.422792 232.173223 \nL 685.518256 253.460776 \nL 685.581899 246.876997 \nL 685.677363 216.701342 \nL 685.709185 223.61431 \nL 685.931935 323.468295 \nL 685.963756 321.273702 \nL 686.059221 269.042387 \nL 686.186507 199.034868 \nL 686.218328 200.132164 \nL 686.313793 211.763507 \nL 686.345614 206.496484 \nL 686.441078 186.086768 \nL 686.4729 194.426222 \nL 686.663829 312.056411 \nL 686.69565 309.31317 \nL 686.759293 263.665634 \nL 686.9184 172.91921 \nL 686.950222 172.480291 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205.179728 \nL 690.005083 213.189993 \nL 690.132368 229.649441 \nL 690.259654 203.094865 \nL 690.291476 210.446752 \nL 690.514226 315.348301 \nL 690.546047 312.056411 \nL 690.736976 209.129996 \nL 690.832441 232.282953 \nL 690.896084 240.073758 \nL 690.927905 237.549976 \nL 691.023369 214.506749 \nL 691.055191 219.883502 \nL 691.277941 322.480728 \nL 691.309763 319.188839 \nL 691.405227 272.334277 \nL 691.500691 235.684572 \nL 691.532513 238.427813 \nL 691.659799 261.58077 \nL 691.69162 255.874828 \nL 691.818906 217.35972 \nL 692.009835 320.725054 \nL 692.041656 317.872083 \nL 692.105299 273.980221 \nL 692.168942 206.386754 \nL 692.232585 213.628912 \nL 692.264407 212.970534 \nL 692.423514 225.589444 \nL 692.455335 220.43215 \nL 692.5508 196.511086 \nL 692.582621 204.74081 \nL 692.77355 327.199104 \nL 692.805371 325.662888 \nL 692.869014 285.172646 \nL 693.028122 203.643513 \nL 693.187229 223.175392 \nL 693.219051 217.798638 \nL 693.314515 197.718112 \nL 693.346336 206.606214 \nL 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697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 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\nL 711.707322 255.106721 \nL 711.898251 345.962874 \nL 711.961894 333.343964 \nL 712.121001 255.984558 \nL 712.184644 263.775363 \nL 712.248287 274.089951 \nL 712.31193 270.578602 \nL 712.439216 250.607805 \nL 712.661966 354.302328 \nL 712.693787 352.327194 \nL 712.789252 308.764522 \nL 712.916538 252.473209 \nL 712.948359 253.460776 \nL 713.043823 260.702933 \nL 713.202931 237.879165 \nL 713.393859 344.31693 \nL 713.425681 342.341796 \nL 713.489324 302.290472 \nL 713.648431 219.773772 \nL 713.680253 220.102961 \nL 713.807539 240.402947 \nL 713.871181 235.245653 \nL 713.934824 229.97863 \nL 713.966646 220.322421 \nL 714.157575 340.805581 \nL 714.189396 338.391528 \nL 714.253039 301.522364 \nL 714.412146 231.514845 \nL 714.443968 229.649441 \nL 714.475789 234.148357 \nL 714.571254 240.841866 \nL 714.603075 239.63484 \nL 714.730361 218.566746 \nL 714.92129 339.927743 \nL 714.984933 325.772618 \nL 715.112219 234.367816 \nL 715.175862 237.001328 \nL 715.36679 245.121322 \nL 715.462255 236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 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\nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 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188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 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158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 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339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 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204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "dddca9ad9e34435494e0933c218e1579",
+   "translation_date": "2025-09-06T14:01:46+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/7-TimeSeries/1-Introduction/working/notebook.ipynb b/translations/th/7-TimeSeries/1-Introduction/working/notebook.ipynb
new file mode 100644
index 000000000..78a284060
--- /dev/null
+++ b/translations/th/7-TimeSeries/1-Introduction/working/notebook.ipynb
@@ -0,0 +1,64 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# การตั้งค่าข้อมูล\n",
+    "\n",
+    "ในโน้ตบุ๊กนี้ เราจะแสดงวิธีการ:\n",
+    "\n",
+    "ตั้งค่าข้อมูลอนุกรมเวลาเพื่อใช้งานในโมดูลนี้  \n",
+    "แสดงภาพข้อมูล  \n",
+    "\n",
+    "ข้อมูลในตัวอย่างนี้นำมาจากการแข่งขันพยากรณ์ GEFCom2014 โดยข้อมูลประกอบด้วยการใช้ไฟฟ้ารายชั่วโมงและค่าความร้อนรายชั่วโมงเป็นเวลา 3 ปี ระหว่างปี 2012 ถึง 2014  \n",
+    "\n",
+    "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli และ Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, กรกฎาคม-กันยายน, 2016.  \n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5e2bbe594906dce3aaaa736d6dac6683",
+   "translation_date": "2025-09-06T14:02:38+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/working/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/translations/th/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
new file mode 100644
index 000000000..d164cbb88
--- /dev/null
+++ b/translations/th/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -0,0 +1,1095 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# การพยากรณ์ข้อมูลอนุกรมเวลาด้วย ARIMA\n",
+    "\n",
+    "ในโน้ตบุ๊กนี้ เราจะแสดงวิธีการ:\n",
+    "- เตรียมข้อมูลอนุกรมเวลาเพื่อฝึกโมเดลการพยากรณ์อนุกรมเวลาด้วย ARIMA\n",
+    "- สร้างโมเดล ARIMA แบบง่ายเพื่อพยากรณ์ค่าล่วงหน้าในช่วง HORIZON (ตั้งแต่เวลา *t+1* ถึง *t+HORIZON*) ในอนุกรมเวลา\n",
+    "- ประเมินผลโมเดล\n",
+    "\n",
+    "ข้อมูลในตัวอย่างนี้นำมาจากการแข่งขัน GEFCom2014 ด้านการพยากรณ์ ซึ่งประกอบด้วยข้อมูลโหลดไฟฟ้าและอุณหภูมิรายชั่วโมงเป็นเวลา 3 ปี ระหว่างปี 2012 ถึง 2014 โดยมีเป้าหมายเพื่อพยากรณ์ค่าโหลดไฟฟ้าในอนาคต ในตัวอย่างนี้ เราจะแสดงวิธีการพยากรณ์ค่าล่วงหน้าเพียงหนึ่งช่วงเวลา โดยใช้ข้อมูลโหลดในอดีตเท่านั้น\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli และ Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, กรกฎาคม-กันยายน, 2016.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## ติดตั้ง Dependencies\n",
+    "เริ่มต้นด้วยการติดตั้ง dependencies ที่จำเป็น ไลบรารีเหล่านี้พร้อมกับเวอร์ชันที่ระบุเป็นที่ทราบกันดีว่าสามารถใช้งานได้กับโซลูชันนี้:\n",
+    "\n",
+    "* `statsmodels == 0.12.2`\n",
+    "* `matplotlib == 3.4.2`\n",
+    "* `scikit-learn == 0.24.2`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "!pip install statsmodels"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "/bin/sh: pip: command not found\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from pandas.plotting import autocorrelation_plot\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "%matplotlib inline\n",
+    "pd.options.display.float_format = '{:,.2f}'.format\n",
+    "np.set_printoptions(precision=2)\n",
+    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load\n",
+       "2012-01-01 00:00:00 2,698.00\n",
+       "2012-01-01 01:00:00 2,558.00\n",
+       "2012-01-01 02:00:00 2,444.00\n",
+       "2012-01-01 03:00:00 2,402.00\n",
+       "2012-01-01 04:00:00 2,403.00\n",
+       "2012-01-01 05:00:00 2,453.00\n",
+       "2012-01-01 06:00:00 2,560.00\n",
+       "2012-01-01 07:00:00 2,719.00\n",
+       "2012-01-01 08:00:00 2,916.00\n",
+       "2012-01-01 09:00:00 3,105.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "แสดงข้อมูลโหลดทั้งหมดที่มีอยู่ (มกราคม 2012 ถึง ธันวาคม 2014)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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JJOklaEOiTUyiU+9RzfrI+kWEpAcv9fLCwq50j6M2AupLufKZu1YC8K9MO3lvqlt7/V2EJAa999LMTXUR9oSUA1QSSWoJehKjjkl0f5VxnF0RkhrioxoGA9e23OHV3VTDyeJj4KEVJLY4zW5qBnMhED1UEklqCTpFu50l0SnjaEkkJHXE8XP30iXt72AJDGd4uU36d8VRMXQ+CuKQB1ccLun1nttSj81H2kt6TeIcKomEBIR+HHaTVKConfy5VBIJIaXmeNcJDGe0Oone26FeUsz2ug7UtPYF0pbezXRhJeOYiT1VLX22LuSVJSqvo/HrF3bhXx/ZUNJrEudQSSSpJWi3Ti+ZTJXtBNAGISQZxO07f2WnTuFgncRAueyh9fh0PhbRD8Y4xB31nZbHU2FPDyrLtF7GLNzdmD9QfT69TYkeKokktQQfkxhQO3kpT1lNSHqIy/c+4nOWqFkq6G0aHm4XFuj6SzRaewct9/tJVkXKDyqJhMSMIDILEkKSxdbaDgyOeC+LEBQZXXHEOGVcJYTYY5ekaLyNC5X+i+cchFBJJKml5KurDuWtlriG8pmQ8keviBW4ekZEUG7zJDykLHw2do9JP9L98tntYXSJxBi9jBmf/77NZj/6XAj8/gmVRJJa4uqAo/VLE9b7G7uZljpBVLf0YtXBlqi7QYhv/EmduErY9KFfD31xx7HoOkJCx27te5ydJdHF4kMYSCnR0TcUwZWJCiqJhMQMvZA/2NSDL923BvctOxhdh4grPnv3Knz/8c0YGI7edZDEn3k66+G2uo4IexIMXM4iJBqklNjf2GN5zAQbJbHQklj6r/mhlVX44E1LcbzrRMmvTYqhkkjSSwAL3UMjWfuDXPKbFyoB5Fb0tBW1ebuid0Mj7vhsAFkMSfkza3Pd6M8zNtUV7JNSomdwpKT9GVfgbup+kjhWJzGoHhG/8FGkA7N6g/rP2C4mseA8vx3ywJK9TQCA410DEVydGCm5kiiEeKcQYkAI8Zf8758WQmSFEL26f9/XHf9GIcRcIUSfEKJWCHGlob0r89v7hBAvCiHeWOq/iSQTuwBvJ/x81jbHx7pNAiEBnDQh94keae1Dcw+FZpI4xkGOeGDvse7Rn5ftay759fXKnZ9JIhUTZ7T0DNoq47saOjGSKVyQdJNghNlN00HfkP2CkqYkmr0SUddnZgkw5wxngjdSGInCkvgggC2GbceklKfq/j1lOH4IwJsBXAXgYSHEuQCQ/+8jAL6b398P4KGw/wBCNBbvaQq1ff2qX2sP/fQJKXe+MnXN6M/9DiZ9QeNfneDsTsX6w62m+zTriYp9x7tx6QPr8IX7Vpse8/zWBl99I+lhvM2CQTZ8vcMRYXhplROHmnrwzt8txILKcL3MSqokCiG+DaATwHKHx78WwOUArpNS9kop1wJ4GTmlEMgpjfOklKullL0ArgPwTSHEaU7a39/YHfoNJvGl5MlNbeZOBwyxBKsOthRYO6Ne4SOElD8FlkQPIofupmqunL5p9Offza0s2NfWa74A2NKTq2tX3dJnekxn/zC6B4ZN9/NRpINymCLsqO8EADxXUY+B4QyT9plQebQLALBkT2Oo1ymZkiiEOB3AjQB+pdh9lhCiSQhxRAhxb145BIB3ARiRUuqzduwEcG7+53PzvwMApJRVyFkd3+WkT1+6bw2unuHcXZCUF3EbOP/fn9cX/K5NDjSoJBJCwka/MKV3kd9Q1Ya6tv4oulR2GGNPrWS7mbJtdDG1nEybtLHnWBc6++mhUi4EMUXwu0gUFFUtvXjPdYtw28J90XUixpRqEa6UlsSbADwmpTT6RewH8AEAfwPgswA+DOCe/L5TAXQbju8CcJpuf5fF/lGEED8WQlQIISpaWpienpQeO3mrcq/QT9K4oEZIuohiklYw+chff2gkiyse3YiL7lxhe77W5SBivklhIqGguWTqWnzzofX2B5JEkAlAYMTFA0CbDz27uT7insSbsIeIkiiJQogPALgYwL3GfVLKRinlXillVkp5BMCvkXMxBYBeAKcbTjkdQI/D/frrTJNSXiClvODMM8/EvuNG3ZOkjVIIQzfJagZHsrjg5mWF5+tOpyWREBIF33x4neNj6W7qHqNkP9Z5ApOnzMeO+k7HqrbV8GClsFe3mruxkmRhlsBItdnsnYjL4s6E8bl+cN6jplTPqVSWxE8DmAygTgjRCOB/AVwuhFD5ekpdvw4CmCCEeKdu//kA9uR/3pP/HQAghHg7gJPz51ly+8L97v4CUnaUWhiqBLhxItXaW+hiqj+DvvmEkLDRZ8LUJM7uo+4XVakkusAwNqw5lPN2mrGxNorekITiJNml3SyiMLtxdHOOscUmCpIoKZWSOA3AO5BzK/0AgD8DmA/gi0KIzwgh3iZy/B2A2wG8BABSyj4AcwDcKIR4rRDiEwC+DuCZfLszAHxNCHFhPo7xRgBzpJTW1UTBAYwkD6qI5U8UxYsJ0TPOb+IaSirXGNf/tAVMCfNJsvEu+7nrPRZJb0hyCMLqVkqlLOykK2kg7ClDSZREKWV/3q20UUrZiJyb6ICUsgXABwGsB9CX/28lgP/SnX41gNcAaAYwC8BPpZR78u3uAfAT5JTFZuRiEa920ifqiMQLe451eU7N7OVb1isN1B/Kn1mMvyA6olC4CkMS3V9/1ALAUdY7ultXijn7HYsOhH8REjpmSqLqOw7ivWrqHkCjj3rAP35mq+k+znesKZUuP6E0lylESnm97ud7MJaoRnVsO4BvWOyfCWCm2z44XS3ZXteB889+PcaN44CXdura+nHJ1LX4/sfehhu+/r6i/SqZZjdREibnaehXmGllKn/WV5nXUiPpI8ykJWYEZkngkOkYq1gy1W082nki0OsPDGcCbY9EQ9Dxe3bN/fOtuWp2NbdfEuh1AX0CLGJFWSSuSSrrDrfisofW4zuPbbI/mJQ9rX25eMGdDcaEuuboV/C8ye+xkxbutnfN+OFTFfjq/WtsjyPxhPEXRE8U74PvOonBdaUsGM5k8Z3p1nOIYnfTHBLS8TvARUSSNXFy4qtBvJJaJdGJ2G3oyNWEWl/V5sukTsoDLXGMmVE5jOmcXrg/ub7G9vhl+5o8JZkg8YAqItET9fvgx0U+6r7HhcV7GrH2sLWHgPE+6xVDMx1xQ1WbZRuEqEiKwjj5jFNyP1CQWBL24lB6lUQHq3N6V0G6Y5CRvJI4YZz6sxmxyT6qjguwfg8TIs9JQNCQmC6qW3ot90fx/RdkN/UxAaFVPEfGR1bqxbsbTRcljxlcTt3c7U3VbfYHkbKhpWewaFtcv85vXfB3AID3vfV1AOLbz6gJQr5mshK1bdYlcFKsJDo5aOxHTtaJNtiPNxm1T4SwkJCUVT8SDFHEoJHo+Ozdqyz3R+FCyDcwWJxM5ozPWTujbyiD4yF4MV09Yxv2HHMeNkGSzQ+froi6C47RPheW/HKGn7t037KD+NSdKy2PSa2SSIhbtHHcxJDooAEv16SgTBNeJujDmSzWHmLCm3Li2c11uOb5nZFcuz4fZgF4jEkczW5KvKLXKweGvWXTNmsPyA1Fl0xdW/C7FRU17Zg8ZT52H6VimXSSUqKGOqI1o5+0j/u00YFHQWqVRA5gxCtB6m1276GbS/11a4OfrpCIGRzJYM72o67Pu2vxAXznsU3YWtseQq9IFEyZU4nntzbYypq23sHA4+UfWVUdSDs0iud44NVDvs63ynzqvA1fXcDSfU0AgDVcjCobnFm4S9ARs2vnZz90W1cTxG1xUqYovUqigxtcUC+KFh3iE29JIJwfe/vC/R6uQKyYuakOP3zK2lVn99GuQFxjqpqtYwNMz2vJndfWO+S7DyRe2K36f/jmZfjobcvDu76n7KYcK/UcbLKOO1XhZH7ip9yB1/kMny0pFZxylwAHcia9SqKDu2NcwWjqHqCySEqKm0GZpTyD59q5lViWX0VXsam6DV+9fy0eX3ekhL1SQ8lUfpiltI8zaXE3zWZlYHFT7X2FCzz6+UkQlhRjE72DIwW/23mhmM2XZm6qw476Tl99I+lkJGMt3EbrJJa7IIk5qVUS3XKktQ//fOtyTFsdjCsOiTcnhjJFgf1ehJV+TcG4vjCcydpmRHWDWUIdEh71HbkMg3uP+y87YvZ+XTFtI/71zxsKth1p7cNP/7IVgyPMukzCw4vlaFRJLPPZ3duvXYArp28MpK2HVlYV/F5YqzL45Z/hTHGbRw3ZUp1w7dxKfOPBdUF0icSQMC3Hdyw+YH1tltJxxPzK456rLzi5t6lVEt2OX/+Rdzmzq3dEkoPVO/CbF3bhkqlri1Z4g+S3cypdnzNzUx2au9UxSMyMGSEhmvE2VLdhc01hvOHv5lZi4e5GVNR0hHdhEjhuB/OorcN+9JM0SKON1dHFAQetOw6P2Jut6UhVPkT9fe40sUBr7xjfNWv01v0GXbIxV23Q3dQctzGJGnxxywerZ7mtLjf57h0YMT/IAQUrwoYp32tPGm/fgKGP186txPce32x7LVIagrjlmaxEfXu/Z0s1n3s4DAxnMGzjEuWWG+btdXV81OENnuKoA+9F+iioVemzraGRrO+kZpQxJGjs3mvGv8aD1CqJTlAJRr646UL5DgT0Ckwcb//5qS7VrCiMC9CSGCbXv7wntLbvW3YQF96xAnVtxauBbmKeuIAVLO+5bhH+xeDm6xe3K76JfqQURyXl57O2K7cfaOxx3dblD6/HPUus3QGHHFgeSfwoZVZc68bVm0frJCZa+MWfjr4hR54QVBIBLNtrnpjCCCdi6UD1nP3OeYxtOtHpVP0wsy5QRwyPJ9fXWO63EgurD7Zg9cEW0/3rq3K1ilTK/83z91led2A4g6Uu5BdxR+RJOSIeb3xZMjlWeqZAlJvcR+OCtSZHitryMC5sre3A1FcPWx7z2btXum+YEBs4xy4N2+udhaqkVknU+/P+8GnrFPd6+AITN1i9L04SO6jSnJs1SUtiPPne45tNXYQB68WHF7ZZu4m5kV0ketyOH5kEupsS/zi576WytAxnsnjYkFgHABo63Ce6IeGSpCkAvfLc8ak7V+CL964e/d3v/XNS4QFIsZLo5AbT3ZQAuYLVQaB/c25dsC/wTLlJGiDKkfr2fnSdGHZ9ntVzc2fJoWyKO27HDz+18ILA1+Upj2KB03HB7FGf0CVb2ljdhhaTcAeSMJx4MrlsUkqJW+bvdTQOmsmW2RX1lvvTSm1bPw40uXcdN8WhXEitkugEp5o2KT+0yfnqQy348M3LsPJAc6DtO1UQVXLSKDx7B0fQNzhCS2IEaLdcSokL71iBz9+zytX5O+o7sSWfoVT1rAeG1XE/bh61lDLyBCgkR2FJHPtnkui4nCT3PQL074P+847TwvSaQ634yC3LXJ93y/y92FobXSZYMkaYb9OcbUfx6JojOP+GJZ76cWKoOPtzfN7+ePGzmeoYZKc4nUKkVkn0qgByrpUuttflYpL0sUleB20vE3Wlu6lh2/v+sBjn37AELJMYDJuq2xxnA/zVczsLfjdLKmRGQY0xxbMeCiC75qUPrMM5v13gux3iH+0Rrz7Y4uiZuC3W/u7/W4hvPhRk3ToOeCrCKI3kdkGg26HXgp/F7t5Bf9m9NR5dcwSXPxxsEigSP5p61OW5VDidD3X2u/fOIcGRWiXRK1QS04GTx1zZ0BV6P1Qd0W9aWHkcADCSlbR8B8S3pm3E/z6/0/7AhFB5tATvKXGEtsC0wqFnglt308GRLLbVeU+2Y5y40d1UzYV/fDXwNt0uIj6kiBNU4cfB5Fezd3g/mcQaJ6+F23fy3W8+zfGxSi8p3Vb9z6rM38QfTnJiAFQSC1h3uBVbdEWrGZOYXjTZOE4U/q5n3q5j4ffDRku85q+7Rn+mt6l7DjX1YPKU+aN1MZMMF7Dij/aMJjg0+0f9TP1cflOEhebDpk/hFueXqD9flUJQ3dpXzrp+Kgkz9ODUkycAAD4y+Q0O+uFsGwC09TEONmjobuqBq6ZvCrwuFkk2mnVO5n4pwK2w9SKblYJU97N+rsmYRPesypemmL8rZ5Ft9ZikKIhhN+pJIgkf7Rk7XcUtdeKaIC6n/WVHO5n90g1mz9rvM4liWPjUnSssMz7OJVwAACAASURBVDqT4NhW14GB4eAXLexQXXNcfkLiNZba7LSoE3iVI07lQnqVRIsb1NIziGV7m9RBtHxXU0GRBU8ZG+ikHZ/9sKmTOGH82Cc8Lr1fsy09A8N4aOXhohgvbbKuDUK/eNZfMLgfKFtSQP4ZO8446fGd2He8Gy/tOOrtZJ/X52scLH7vp5swBCklegb8xYDVt/ejtq3fsjYsCYZjnSfwzYfW49o5laG0b/Xu+b2mOimfVB4QQGg+8ciEqDsQR66avhEHm3qV+zgApgNpMZnT9pUi86Dab3+M8TpTIi2J5tz0yl48V9GAd551Gj7/j28e3W50Jw46SP7pDTWBtkeSjbb45HTi7jW84ct/WgMA+PoH3urqPOPV0hhesXxfE045aQI+9o4zou5KILgpgfHEuhrc+MresW0eVgk+c9dK1+cQb/TlEwvt8hB37tSbwYw5230uQrmoAZ1JdJrnYOk6MYzXvWai73ZYJ9EHNa3mQbJMJZ8uavMB089srMWqA4Uro05cIPy+L3bn6z9zv0K/nNGy9A2OFHoHaHdMe5b6W/jY2iOO2zd7TE+uq3Hchhu8JCkayWRxuDnAOkvENVaLTyqinht5EV9Jl0L/8VQFrnh0Yyonpov2NPpuY8Tivmlu/SQYRt07Y/Kuuvn2nZT30qC76Rjn37AEK/b7L8lGd1M/WNw8TsTTgSaSNEvd4EgWjzisbWjapgc5p5L9Zu2wBIY5o7GlhnunDbKjk3fdx3+TbkXdK1EPbXO3j5XyuHPxAVx8z2rUtPZF2KN0o70PTr/VoCdHaw61KMMoNIJYBI36nQ+KP7y8u6TXC2seHPWwoH+n/nPmNkeF1okzNO+hTEgvj1Wzp08K3hFR/67oL53GBRsrNh0xTwq2/nAr9h3vtm2DiWtCYmttB5q61bVgjrT2YX+j/cMh8UeTVef+7ekAgG9+qNhty8mEKowpl94FTL9mEfVkINbkb87PZ23Hz2ZuM25WWhLd8PJO+0y3VpPzIFC9a3ctPjj6c0VtLoNri8fkPGmlti04pVqTGY7dTQOcGx1q6sF3H9uM9/5+kfn1Qrx+0nhpR/jZq8Oie2AYn75zBXYf7Yo067VqjOSEPzjGa0qiz3s6ogv6U8WSZrOy6FmecpK5kuhkbqSuAa0+NiwlOGqe3VyHyVPmY2gkuKDLK6dvwpf/tAZTlx/CuRay3ilUEj1Q3aKeNHzmrpX40n1rStwb4hU3g6dKRrmVy17ie9ykiSZqVhxoLnBzekX3s+YZMJp1MuBr6wfLq2dstTzWr9WILjnh8P0AszS6tSQGGd7gxYLjRWaVzWJVgj+njVVtqGnrx33LDtofnMfPq9beN6Tcft+yQ94bJbZMGJ/72rTYRK9ocahVLepcHG+/dgHO+e2Cgm16GTbsIbNMVnGK/hXUy75MJsEfowV3LD4AwH0maCdy+Z6lB61L9dDd1Dt2985vMP9IJsvVtBjgd/7l5D3wfw1n24g5P3hii+k+zV1ndEAKcdl9Q3Wb5X67K1ebDOAamazEZ+9eiVdKUL8zTQwGuMqrvWaHmq2fpfH4IHAy5Biv5ze7aZJj+PWLLjvrO/HLZ7eXLPZLL4b+uGi/h/P1bvTOZZrxSKd/7e9fUrvmzthUy0XNENFurd/XcuneJgBAt3EhyaJdfejV/zy30/U1bbOb6ijXBVDtDrpO9hTA7WDiGh/YTQpae9WrZk459w+L8ck/vuqrDRI2Uvf/homP9l+3lkQPH/acbQ3FG8tTXkaCNs5pq5pWFp7fzqn0tbgzMJxFXdtYUiy3qeY/d8+q0Z9Vuuwvnt2B6pY+XPP8LuX5ZWPhKTHjAwz21d4eM8uLkSB1kigUtnKZ2/3w6Qq8uOOYrat298Cwo3ggO/T3zUvG5VJ/60G6yxHnaN/0qBu7xwc/brQUlLfz51ceL7j+trpOW+umqqtmly9bJVF3EyZPmY+VB/wnpDFy1fSNvs5PrZLoR4j+1yx/tdQGR7I43qWOayTxwIlMciZQ/Qm3ZfuKhUZBTCKn/r4w3j2ruzlrcx3ece0CiyOKMT79F3W16z5+m7uFIilzMQy3Ldzn6jxVO8Q5DR0n0D/kz51rlPzNd1quJsjJkSNLYsArUEl+1fR9156W3eO46tFcPJCUEhuq2iK3pPq9enVLH4Z9uPpJqYpzLdzSOziCP6+qik2GziTiZuFadcz4cQaPGgfoRZhKTtnNcdWWxLGf9xwbW2yxypibbArHgdlb6h2d5eZurDvchsGRTJFLK7Obhsx3pm/C9rqOqLtBIsTtBCAoMedn3rG1tsN3/EI5cyTkzJ/6Z9fj4TlMmVOJR1b5y7JL3BPUe+E6jtniY3frVlyQOdChEPHtLp/gFQkvXa/M16ubX3kcVzy6EbM2O5v0GfHr9V6Q0MxnWwsq7ctWmF2jrW/INl7t9oX7cPvC/VgcQPmN1OLzM9OURDfySb/QpfpWvHjd6Bep1leNhWcwPKuQaS4z7f9i1g584vZXC+4js5uGzNrDrfj1X9VuXST5GAe9AiEoFdtKiNfLdvUP4/KH1+PnPi3hUdDeN4SHVh4ObdIpIbGroRMdHly7koKW3ZS4R5VkwQ+fe+9Zjo6zett/NtPdd6yfZ5lNuurbC2sEW1kWpZTosvleegaSuyClyiLd3OPMA6i+Pbdq7yYzbpCiTeuvO3kZTjzY8xXWirL2jgQZ+5sWjI/G64KA9p4Yn/VDKw+bnqO+1thGO8VO6W5qckq5lp4L6s+yKokBjNU/9aJsU0n0Adc2yp+xFZvip+02cU1QCo7XdobyK7o76zsD6UcpmfLCLtyx6AA22whDI8e7nGcNqzNMkIOgKBEIpUYiCcrtU3v+kyaOD6Q9N+j/BrO5wsX3rHbc3rTV1Tj/xiU4ZpGZL8kLE6pHfukD64q2zdpcV7RtxqbaXBserz047F1hqm/vx6GmXGKkOBhgBgx/S1N3YVxngo3NscHNLZy+NjenUc0jjJusakPbuczbyUw7d9PC7eX5khQlivL4Z/5urrOarl7mH1QSCfFIVANwQaxMeS6wFdGbd810Gx/zMYdxfyL/v7AJaqzzmzyLWGOclASlJAZtkbRDX/OsUEn0726quQcalUT9V5RkN7HCmERz2TBzU7GS2NCRuyeVDV2Or6d3ab5+3h7H5xm58I4VuG2h+4yoZqjeAWMtPav7Y5yYfmWqukxYWsayMHCjRO0+WpxYqSafUM1VTKLjI53DRVTvuFkQdwOVRB/4Xd1YtNve159ERTAiUJUVNSo0AZzEwTisPk+ZUwkgvMEprHaDyKBInBOUruO2meWKxFVu+J6uxqN+uAozW6C+5QATw5Ye3R/iVf7Ylb3Ro1eevLrp7j1WKBdcOZu6sOJc81d1yYPO/uLFK6evWpkai0pCELcum5Xu5JzNN+FFxqTtHSgKa/LxJD9+u/2CuJf7m1olUV9QOyr+a9aOqLtAHBIn4WU6mNueqP2Q5Jlb9Axnsnhs7RFHad/bXFj8SvWKlavrTpgE7SrutL29AS4G6CcgbiaDxvfcTeHsJMcSFWaRTgY/eHKz/UEmBPGorn/ZuwWUeCcI8TRrS13kpSbSNjIF6b0U1qNLpZLYF1Q6c5+4XTV4vqI+NJMyscbrB1gYkxhMX/R4ETEJnreF6o7i9L78ZWMtbnplLx5be8T22P6hTMHvVr0P6rHY/R36iftLO45ixf7gazMlHeO3GrTX5HUvlX4yrf+bnLqBSgC/eq5wMfOdv1uIGotsr8Lk56Th2AIWo6mtcdIZxOKGmxaM8Yduzyfu8D4vKT7xWOeJAJREbwtRGoPDGfuDiClVLb2W+6evcZ8ZPZVKokpqLd3b5LqZqpY+/HlVVQAdsqdnYBjX/HUXrpq+qSTXI+Y4nRSEbbFZsqcR3Tq3JLsJWZIH61LECzq9guYK1j2Q/Eyov3h2B37w5JaouxF7AktcE+FHmC1YsHLeEZXXzeFm88lIkuWMnsLYb2vpIKXEPUsP+r7m5CnzXcUxGlF1043kVJ2velXCyESa5MXLUvDo6mpc//IedcIZl1+d6pmOE8JVK3aPy63MrG/vVy4ylDNGd3y/48O1+fAZM+5aMiajnHp5pFNJVHDTK3s9nXe7IkD8Bh9B52ZoCQ9aegatDySB4MRX3O4TW7i70SDQg5s+tfcN4cfPbB1N6OKkdU1ocyz2h/ZIvQj0qcsPmU7Q6zvoJRBXgs5uGgVOsps6Zfx4MZrd0Kqtkycmd4rhRpFu6R3E1OWHArnu0r3e6wX6yZZoduyrCk+DThelgujd7p/Dzb24ZcE+PLm+RpmF23X9VcU2gVxcop82Cva7fPDzdh3DSROSKy+8kAR3/HQ9EQuCHLyfWFeDHgdWBgrP5ODlWRlrjq077DyJgR1erJSa/E+AXDIlrG9GSuf3ZWeDvxIiZq5+TtxXSTQE9d5FKfP1MsOpu6lV3O2Ykmje1htfe5LD3pWe3/x1F+ZsazDdr5/A2ckG1WKxVxoCXCwKYl6z9nBrAD0xh9Mge07owhYKPAJGiza7a085fxDCkbJ5uLnHtOxNYXIsd30aGMog7W+D378+jLkdlcSQOO/6JWjvY5r6cqHzhH/Xwj8EGNTvxRKgrRKWwnUzaOKk2Gor614nYOkeBpOB8RkFZ0mMB04Xmf71kQ2m+8blZw9G64O+7TgvhM6uqMevnlNn6QSc3yMpgTnbjgbVLczZ7r0to2Vi3eE2fPbuVY7O/da0jZ6va0WcYjaTiv6xKmWRy/HRzJLo5J2/+J7VoWTSHE5wuZy4EIa8TaeSqPig6tuDd/UK1DU0RpPkNKKKWXWiuIQh9oSwF+ZWhXLjpHC5JaxhxNM9CSCZURj0D2WYwTRgAiuBEelzGXvJM377IYHx+YAa320lACv5UC5/vp9FbavySnb3Z97OY46OIzmUr6JrS6KiXeFfzulPb+oecN2ntL0DR00sskbCMDg5nfOkUkmMyxzZ0/eQP2nR7uNo6Cj2TSfO8aosaUkborTIeXl30jCZ80ru1rh7nl6tS6VYWX/Bo2XjPdctxKOr3WdAK3fcxOroOdTUU/B7pCpigTXCf3vHu3KTwC01Hf4bizlJ8b5wOukMmiDuT5IXL0vJxPFj03bP2U0hC5LehcHPZ2237oOh82mzOKuyips9z//38PqQe2NOKpXEUiOlxOqDLdh91DprWW1bn+0xGj/5yzZcMnVtEN1LLVYC1mq86ugfRr/DMiphxtC5Yf3hVnz+npzbUZLH4p31/uIB40ApdPXjHieLA8NZ3LJgX8C9SR5BWfwW7TYkIYlwHqT/7odGsvjSfaux6mCL7Xn/cNapRdskJKpbcmUwnt1cZ9hXfugVmIESpOm/8J1vCv0aSWPR7uOYPGU+ugII/Uga+vcvCGVaNc+MfA056uuXGLtyFXqqLUoO6QnjFlJJLAE/enorvvf4Znz1fmul7lN3rrQ9Rk8ahWVcGBrJYjhrna45Tiuj//fSbozkzQdJlsVBpJkHcq4wxpqjbp9X5IMqiT3FWZKjQx+vdrzzBPY39timTAeASQnOUBoGpShD9f6zXxf6NUpFUAsuD6/KeThUu5hclyNB3M4DjcX3MIin5KZvxvjZrEyXLVGd2dTfHTB6rgTBhMBbTCGNXQN4y+smFW3XXvll+9Q1GN0IT+19StNHFGcGhrOOkhWUUuzpRY5Vts4kKjdBp4r+3mObccCnQPV6G5N4/9OO1+9YNRGKA4fzE22v7onTdC7Jp5w0PpA+xRn9U9xaW+heG48nmnxsZXxMvp2o0d8G4x1x6varkmdm81Enmfq9UORuysfrmw4XpWmcUvIlQiHEO4UQA0KIv+i2XSmEqBVC9AkhXhRCvFG3741CiLn5fbVCiCsN7ZmeWyo+etvyUl+ShIzdeNXn0N00LJRCXvdzc88gntlYW7oOJYzmHndB9SriHJN43GXSAFJIOc5X9CJt77FuX21trG4f/fmnn36Hr7bihL7UgFPKKUmU19hCt4l9NJfdsBSQcqNQMfT/vqmeidnCsmlsobIN875lshLTVleZhupkLRLXlNEnZon2dx7vOoFbF+zzHAsfJFH4kTwIYIv2ixDiXACPAPgugDcD6AfwkOH4ofy+qwA8nD/HybmREuSL3d7LchpxwmmdMaMgnfLCLnSXYGD88TMVuO7F3WPJjQIeZErJdS/uxmoHsVN+eH5rA4Yz1u7DQSFlcQ3NoJm5qc7XAPOHl3bjh09VBNijdFLkbhplTKKuLx/4u9cDcOZKatfnSRMNlsRkiZcCrpoeThkIt5TTpFj1pzxXUQ8A2N9Y6M1x37KD+PKf1qgbilP8RoQU1En0vFCp2iaVSwRaoj6/vLLrGG5dsB93L8mFjBgtx0mbl/jF6m3+n+d2Ytrqamytiz4pWEmVRCHEtwF0AtCb3q4CME9KuVpK2QvgOgDfFEKcJoR4LYDLAVwnpeyVUq4F8DJySqHluVb9iPuruKGquOj6p+9aWfqOEFNUmamc8OyWerz/+iW+r283NrTlFxW0sMkjbc4Cn+NIqSyifYPurMNeJ3JZKfGzmdu8newCu+5ZxTQ/taHW1E0+jXh91kbLTJQTIf2cbEI+Q+I7zixOShMkSVN2ttWNJcbSdz1od3c7EnbbsNCYoEmH6h0YyeRLZhi237fsEPYd7w4s9rxckA4VQ8fyRdHGgyuqQn3vevPjq2ZJVLmbpklRtPpLtQVrKYEn1x0J5fpOJVrJlEQhxOkAbgTwK8OucwGMVrSVUlYhZzl8V/7fiJRSLzF25s+xOzexXPFoPFYz04yd281tC/c7aiMqkdfWl6vROTiSc+tJ2mQtCnoHw89aCACv7m/GcKb4gZRyHvp8RT3Ov8H/YkW5EtT3EitLok6maa7STvpDAw5xwv5GtQuzXWiEhv41m7r8UDCdShiZrLT1mlHdu6FMFq29zutym332QyP+vGmsxIlmAR1nIlDiEq8dJ5q6B3D9vL2R9qGUlsSbADwmpWwwbD8VgDEfbxeA0/L7jJJH22d3bgFCiB8LISqEEBVdnc7KTPgljHfeuPri1vpB7OnsH0JjADFdYa6KaTXKzBgYzgn7Odu91ctLKm29g45ccFRH3PRKaYTxL57dodxuNniGwcqQ3XfLjR6PNcWMTzQu8yCtX04yZNv12bi/XKwBUo5ZP6K4dtLodxHPubG6DdvrOvDSjmOj26zEX317P3pTEL84bXU1vvf4Ziw3eHHovyn9+KZ/TY609vmuVxmmoqb122ycS+I77werJ6U9R8dhTSFSEiVRCPEBABcDuFexuxfA6YZtpwPosdlnd24BUsppUsoLpJQXvO71waeXVrlHhDHnM7q9/CmlK25hsnRvcG52aw61BtaWhgBwucPiquUueDdWt6Ern9Fr77FufPjmZaPxLlZ0hpAFzC9BDwjllFAjan45W63Y26EaA0ZKFPtqpGCimf/v0c4TofYn6Qrj0xtqbI8JZTE4ofdN9b6r7s/SvU247KH1jkMJLrxjBapakhsy4RQth8AxQ9bhQndT9bluxg+zNlQeLm6w+ha0/o0zmRdnpSz7+QqQy458m8NaxHHw4iiVJfHTACYDqBNCNAL4XwCXCyG2AdgD4HztQCHE2wGcDOBg/t8EIcQ7dW2dnz8HNueWlCfWuvcbzkrYJsuQUqLJwqpVisK+aSOoybqAwJ9XVQXSllcyNrUck8zAcAbfnrYR59+4BFJKHGrOrQ2tPVwc00vGMC+N4u69v/mVvZg8ZX5ZKqNBTdKLYhKlRCYO90vXhSGbMchuohKHiYwfVhxoxs9nbceDKw4X7YvDoyolu4+VxsvKD+X8SDQrm9UUxGyfGyug2RxH1UZQ3/eokmiiJUoA23UxwYX7yuepX/7wejyiKyEUFU6fa6mUxGkA3gHgA/l/fwYwH8AXAcwA8DUhxIX5RDU3ApgjpeyRUvYBmAPgRiHEa4UQnwDwdQDP5Ns1PbdEf5clTpSNp9bXWO5/dks9/vlWltgoJSMhm/g/+vbSVWl56+tfU7JrlRr99/V8xZgXezkqLV74rUmh9KBuz3QPC2NpwzgQW6V594Ofd96v9cBI0j6/J9bVYN7OY7hz8QHTY6zmU37rrSqJ6B56dau24qkNNY6OW+bQgyfhaxKWaPpTUVKXgp/1rqdj292sB9eaJLKzkyOP+5D5WtPjTd1NJa6dqx6zygXH7usxeslLoiRKKfullI3aP+TcRAeklC1Syj0AfoKcwteMXDzh1brTrwbwmvy+WQB+mj8HDs4tGapP66v3r7U9z04ob6ymVaTUhO0H7ncSZXa6fhXuda+ZCAA4+w2n+LtYjNGPNU3dAyXPQOiHUnT1+a3G8G9r7N7LbFZia2279UEkEpzIFLNaa3GIewmbxq4BzN5Sp9znNYY5TMrpiTh17X9RF58IjLleGmtXJknOu0WYWBL176heGXx+61hohRtL4lMbvGUMv9Embt/K4qf1z9SSWE4vvQl6bwUnf24c3vUo6iRCSnm9lPI7ut9nSin/Xkr5Winl16WU7bp97VLKb+T3/b2UcqahLdNz44BdjZk/LT/ka5BOw4dVasK2JJbikWkJKVTXKpd3pjBbY+mvn1RXb6/jzrQ11bj84Q1Yf7gwzrZc3qdSIBFO3I2fJkdsTBB++huXd+Mzd63Eb16oRHufu3rDUXklmFl60sQn/7gCQK52op7op83hoclm41un/10fc//IqjG3xSDc2FVNOG12tCazCVr/zMafNGQ31cd/P7zSPBRp85H2ouOjIhIlMU3cMG9Pwe/GVTHAfpAmpSX0OD6fstDNIJkGwQtEE7Pw7Bb7BDlJob1vyPYOHsy71R01JFUoR7x+Np+7eyUes3DJCmsxw5E1zCT5hV9L4pM2IRNRs+ZQC07kF3TcjrXarSm1YrJ4T/JqlLb2DPrOrqmix8RFr769H+sOB58YLkq0+6eqIahh9r1lAxAuflr45B9XWHrG2bub+rh4QtBbBp2ULOlzkTHYQ28cHUUlscT8z/PusuSV86pZXAnfklg6aVjOglc/1uj/zld2HY/FClyS+METm22PMZsAlvEr5pqqlj7LUiotPYOobrX2LvGCW5GVdaEk2lmet9d1FiwcxO19ONBoHTNYzjKylPz4ma2htGtUKlp6cpPrT9+1EldN3xTKNaNC88Q0Lu46WQQKwm1cdRnTRGeKbVZKota/uSZluVLg9R4znN3wdCqJESYOqDxanD1sYMj7hLacsj6VGjPhlwkokYN5FslAmic6JAoXVJp6nBcWThuq96+2vd+xDEvD69sS4vuztbYj8DbdjgP640ds5J2T18LMOvDI6mizOzvB6t5RVkfPeEMM2w+frgBQnrG02t9q/NOc/KlBeA35baN/yFxJ1No2q/FsNf6Uy3doZ/SpajEsIMbgD0+nkhgCZmZhu1VMADj/xiVBd4c4wPj97TvejfVVraEnFYn+sy8PCupZelh5jZIYxKO7ZrTP8b61gXDhHSuKtsX9nbJD33v9pHPqq/5r7U4cr36hF1Q2+m47UFzGXHERNnqSKCs948OSmJXe67kGRd+guXuknaIbty9ta20HXjSxenrG5l2uaesv8MoIdyGE7qaxoNnjivT/Pr/TtoYiCZYv/2kNrnx0U+gZpdISJxg2P5+1ffTnokB/3mJThADm7zpesE1K+0Ha7KtIuvLklKAG7CBih7zwo7wFBih8ZnO2+Z8I6f+icnodyulvSSrjUqQlan+r8b1zIjICcTdVbXPR7Kknj/d87bjNiy5/eH0kSndlw1iW+oCrExXQPeAs6zCVxJjy160NSrckozk/Zt9Voohq7In6mZXjKxO3ASbOvLq/2df5ErJA0fngTUv9dilVjB8f/LDr9vXXP78L3vaGkl+/lNgt+llbErVGAusOcYnR3bScGXXW8GRJVB/jZhEvzAU/u6cYZxnihRNDGVQ2dKFNl6DGSWKnn/xl2+jPVvHtfvnBE1scHUclMcaoYkXK0A0/MsyEUtjK4476TvuDSsSm6jZMnjIfdW3W6atJ+WCWettukB5Nzy6BGZvG6myFUYC7nInDnFf/qP/jk+dYHutWHoaeHdoHD68qfvc3WNUiLreZawKhJdHZwm4QlkSVl4Ob2++0CypltFwWem9buA9L9zbhq/evwdceWIuP3f7q6L4kvspUEkuMm+/gO4/ZZ+4qj88qXoSRxjuuaAXXNx6xmCglACnNs52SYNDuqRDA7qPdrs6ta+tHo0nCgqQQ1CsVhrtpVkq8vPOY+X7DNfW/zrGJu3HyLelj9657aY/FkdHyxLoa1+dIKVHdwrqFURGHRZVSIUZjEu2P3WRY3AhizHPj3qhS9JxaIlV/X7kM2Y+sqsaPnq5AVV5mDI3Ed9HMCVQSY47KVUb/Ie5qiI9VipCoMA46f90a7xqGpVqIcFrTUAjnSToEBGZXuLu/F925Ah+9bbmrc8oBlfwOwxvksTVH8F+6GF0jxkLb+jGkIAGUV2I2w5NSYmN1G6SUvr40CWCeIX6XlJYkWl+8oskLoyxW6V7fmrbRUZulXDBt6xtydNzVM4rLpcQpu2lYCWOS+CpTSUwg+kFr99FudPY7+zCJM9I0KGnCt7492e6mKwxxdlNfPRxRT0i5EtRERe+qGxRNPdZWWuOkJ2jXrpjpiJixqQ7fnrYRC3f7y656tOME2h0UvSbhUNnQlSp3U+0v9aKjBPEN+o1JvN9i3NU/xsV7ihem4uSlfveSA4G219DRj7WHws+cHwZUEmPOlpr2om3GCf3AcIy+rjIggd+xb6yEexI40NSDhg5nVjOihi66peFgU6/9QS6xs0w/urq64Pegn3Xc3p2a1pyr11GfMmHO9qO4fl54ySOINV97YG2qxuNx+sBvl5gpeFF+mtp3CNjLKCeeLK29g7h2bqVrF84jrX1Yd7jV8fHrqorDbzJZie8+tgnrq5y3o/HZP5TmDwAAIABJREFUu1Y5Ch+LI1QSY4JZnIqTiS9rOQVLUKs9S4Jw4wqZJK5sAWrL5+0L90fQE2/E7Zs1m5OoEho5TZ1N4sPdSw8W/J6WBGhx+86IB5I6SHlAi780esI4eY/nV/p3i3ajmzopFfbpu1Y6bs+JTLpx3l7M3FSHRXvceQh85q6VuGq6PyWtvW8Iaw61Wrr1mzGUL2f34Ap10rg4QyUxJtyxOFjzNvFOUPFim48UW4GDIMw6jltrO3Dn4mCVratnbA3cxe7z964KtL1S4zbxS9h0nRhG32BxltKL7swVlZ+38xg2570abp6/r6R9iwtuFY7GrgG098XTVTFJmQSPdp7AZ+9e6SrxkZ2IZIhGSIQwNKVHRfSnD6880OL7+m5kXNDlMpw0p7nNR5HM6LoXdwMAWnuHEp+Mxg1UEmPCggBWgUgwlNvC5coDhfF6VsL48ofX48EVVYEOAAsqG/G7ubsDaw+gi3UYzNhUZ7rv57O2o5ZlUlzx0duWl2zl+JmN7hZhjN/3h29a6qsMTpgWuxkba1Hd0uc4GdVv51Ti0TVHLI+58I8rgugaKQHlNh5bYbYA7Gc4dlcn0ft1SoG2uBVFnKreeuk2cVuceGRVFSZPme/4eCqJJSaIj7C40Kr/Nkn50txTaM1o7R3EwHAGAHC4WR0fFcY7FWahXkLCppxeX+Pf0tY3hF+/sDOazjjEqQfFrM2Fix2q03oUVnMST9JUkirqMTLMqweh140pif7bcoPxuQwn2JLodkGRSmKJGQzg5SqnyUqUpGWFUhXv+t18EPWO+uBLqPQMDKPrRHHc2jRD8gwSL8zkSkOHvYUp6skN8U9Yad+9sOdYF04MZaLuBokB9y47aH9QmdDZH228t0qMt/YE45p9jyEuen9jj+s2djV0AQg35AYodnH+z5nblMc9vaEGk6fMT5T7qdtbRyWxxLQynXZsMJvXllvKbdXcb0tNh+U5fqaL512/BOffsKRo+1oX2cVI6TFzGfykA9e8WxekM04xqaie9IgPJTHINYKegWFcMnUtfvFsLkGEWdPvuW7haJxQKfpF4kO5Lkq99Q2vUW7389f6vVMnhkuzWGM17fr1C7swecp8HM/HJZd6jragsjBRjnZ5TfFVxfPHFbeWeSqJMUFleSHRUGY6Ymoz/O07Hq/kMHHHz7zLLgaMRMdIpniVW/Ws42JJ3FSdS5C0rc56IWtgOFvkOqWyPpaZOCcAHl1THl4pzT0DoSsYAy6UvLDmCgccWA2txh+jbIoicY2RE0OZUctvPCRnOFBJjAlulETjC/lcgoNoo2TjkeJaOOWH9FaYN8YrtTe/4qxu2bVzK0PuSXnhpo6UGSeGMkqlhESHKnO2ajLox2UqSGnxw6cr8j+5nwl++9GNAfaEuGX+rtIk4Asrc3ip+adbluMrU9eEeo3zri/26jEjrGH/i/etDrQ9r5ZEVd1xr+gztu8MIWwnLOhumgKM6cvvW3Yoop4kl/VVrabZGmOwSBUocVb4vDB9rTOrVbk9x7CpqLW23Djhvb9fhH97YkvBNv2EzsmKclxJ6me0SpEaX/W3BBEv75dhh1ZPPVUtY8m3jJM1pVstFzEST0yM3oHgJGt0uY3hKnpc1N/16u31L3/e4O1EBXrr5g+e3GJxZLKhkphA0pTty4z+oRHcs/Sg59Xvlh7z2NCwg6JLiZTqxDUaH3v7GY7a2dXQiUsfWJuoZBLl9ByThDH29F8fGRuYr1BYevqHRrChKg1W/Wg40ORMMY9D7cR/t5hsmX3On7t7FWpa+xxfwywJBUkOr+5vtj8o4UShGLoqlxHwtTe5sA67sSS6UT6BnHwII6FfXGjvdZeIiEpiAklrjJmeh1dWYeryQ3h2i3ltNz1Pra/BQl0tylaLD6W9r7wKLde1nyjadtqkCQDMJ4bGrde/vAe7Grqw51iX536Uesyjihg/VFac/31+J654dCOOdxW/p3GinOSu6i8pVS02K9YcGltgcLPG4yYh3OI9TW66REgk6Nd2SyV5YrBO5Ag3SqJdcisjpXKZjgq35X+oJCaQID7kHz61Bc9sqPHfUEQMZ3I3oWfA2Qv/h5f34KczxlaQ3/XmU02P/dPy8nHflQAeX1fsnvm9j70tt9/hu6QdZpTNaw+1FqzUOZ0s/s9zOx0VdE2S5ZLYozJqa6nQ+wb5rEtGDGaDgyMZ/PfsHTjaab044EQ5j/6vISRYIrEklvyK3pg43rmSaDQI6I0A7X1D2FTt3otFoHgu1Nw94LqdJEAlMYEEocQs29eM617aE0BvouGkvJB4vqIe020yne09VpzlstzKXLhF+/v1lkS9W6r5+DR235q7B/CdxzbhF8/uGN1228L9jq7/wrYGy/0zNtViR30nPnPXSkftqUj5I44l1o8kKVOU5KO0JJb4/q860IK524/iojvUJVaM74rXMAu6nZMkov8aX9p+tHBfSApkGM2G0dcJLpRE4+d/9YytAHL9+tBNS/GtacEku/pCwMl54kIqlcQjLuIXSEzJf/k1bf24eb51jbaws4clmYxOgM/YbO66q5LzWv0kfXzIk+tqzNuAREffUEHK71f3q12/fjd3N77x4Do0+lidY+xudOxqcB7ToT2lGBi3LIl7/9wQ9N/itL31h1vR3JP7prVTMlmJQw7jJs2YZSK7yumZkXShX8B9ccexCHuixknCHQB4wmJO4JWJ472rLsc6c/JnzrYxxTuImHitHEa5kUolkeNG8mlzEYOiIi3qg90kSe/+19Q1ppCZWRXMFuXXHCrOoGhk3eE2fPCmpQXWwX9/siJRhWiJMy59YB2qdVknrdCs2pTLpUP1fZdCobpy+iZc9uD6ou29VjJA16/9jd3oUkzG9BM+QlQMjmQwb+exxGQKtepmWH9CRW3wpUX2NwZfr9iPJ5h2alPP2HxHlVDNC9ttarsmkVQqieVI/9BIwc8Pr6yKTXHkMOh2GItYYVIXhy5IOZzW97F7kxo6iuOKzFLNNxsyy577h8XhJAviI46UDtXKquKZaJ9iHLJrWhHv3plz5mknF217dI2zMjJhoMUgzts5Zh1xWr/sS/etwbemOU9j/8dF++l2TgAA9yw5iJ/P2o5VB+0XNONAFOJQnzgqKEZKPA8dGM6gqqXXdjEgjPt72UPFC2BJh0pimfBN3ct51+KD+OOi/QWDcFpZvKcx6i4EjhvlX0Li6x/4W+U+Y8IIy5ikvETVVvCue3E3PnXnytHdoxN83YTsR6NFse2ZvaUeT2+oweQp8wsWPPzAuWG0DAw7S0Qzakm0eP3aegfRkaCsw3G3VqhKB5W6y6/osgjeuqA4lrm5ZxAnhjJFSbP2u6y1uSIF5RKIPdp413UiGW6BVuNxvKVLIWEYK6xk1XuuW4TP3b0Kty7Yh4MKN/batn40dDhzlTVDCJGacBYqiWWCfuDsHcwJwcER+0nakj2NsU89DwCzt9Rh0e6xSYXTz/M1E8eH06EywDiJ1wte/c8X37MKOxtypS+0+/7MxtqCc7NZicGRTMHkc4WiiLcZEhLTVucSELX2BKMM0IJQWuoMMSpXTd9UdIwqG7FQJFEy8uGbl+GDNy312cPS8det1omZSkmSP4OZuljDXofeI0bcyCFS/iTFi6hcHMFKbUnUeHTNEXzhXnUymWV7i3Mh+A1hKlcmWO0UQjwDB4sWUsrvBdYj4ovhTBbPVeQmKE5Whn/8TC7TU83tl4TZLd/85oVKAMDqaz6Dvz/jFN/tlVPNMyvM3gHVMGl2Rw43O4ste3xtjaPjzNDG7rQ8m3Ljz6urPJ2XnMQ1zjtYr3C/LmfCenZSytH7/sCKw+FchCSa7XUdmDRxPN77N6dbHhdz8VKElbyJu6eCnqT09av3r8WG334u6m7EDjtL4mEAVfl/XQC+AWA8gIb8uV8H4DyNHQmd+nZ/ZvS4820X8ShA8gaGMHC6bupElpvWNBNiNNupV+rbTzjuhxPS4g4SF/w+NyeeD3FGmpniI8YYAxwVXiaLQog43UoSQy57aD2+/Kc1WHGgGb2DI/jV7B3o7Fd4oxRHRMQa7bXPKixxWp3oJBBKWQ1InBjK4M7F+/Hx25bjE7e/6up8lTX5eJc6k7pKbs2vPJ4aTyVLJVFKeYP2D8C7AFwipbxKSnmtlPI7AC4B8O5SdJS458Udyc/41tg1gF88u33092MmH7IZxu9Yy6T5WISJG0qNmYz2IryvnrHNdJ8fmRnGQJIWIR4XvK4Y7z2ey34XRqr0IHHz1019NXlWL7ui9l7Ze6wbk6fMxyGHHgmEeKGquRd/2ViLOduPmroZAsBShathFEx5YVfRtoJ1pnzkxqAiflir9ZcEwkpI9tDKw3hwRRWOdQ2EJrsAYMam4vI6m48EnwU2rriJSfwoAGOe2E0APhZcd0iQbKxO/ot88/y9eElRI8ix2DFoCuf+YTG6TgxjeUqSGVjdJ+OkXu/mGWVm3OSskRI9ficDh5qSrUSk2eJl5SI+b1dOfj/gUXEul9gsEi76Mau5ZxAfvXV5wX7tHX05Jgn9nt1Sb7lf6+84xSw9SXG2YX2/ThOjqeSymwXktSFkfE0SbpTE7QBuFUK8BgDy/70FwI4wOkYIUFwP5/RJlmG0Rahkwdxt8UkqESWft1htff8NS1y1JaAWvF5qJAUVw+CnlhJxj0nFE8eMGxfv55XJSGwrwzpYduw55vwb3n20y3Sf18n54+vS4/VBvGMsw9TY7c7rKA7oh6zuEzmvp6QvPoURk6hqUuWWa91Gwm9siXCjJP4bgE8A6BJCNCEXo/hJAExaEyPK7bUfb5g4Thife2X/7g2vcXS+Sk+IKttWFEgp0dztMCZJd1usLIl7XUwan/CQzCY9T6e88JtwaHzMc23fvfQAvvnQeuw+2oVN1W2YPGU+qluSbf0Miu11udQEX71/bdE+P3OxeC8bkDhhzLitJ5uVWFCZrHJYF925AusOJ9+KVarp1m/nVJbmQnlunr+vpNeLCsfDspSyRkr5cQD/AOBSAP8gpfy4lLImrM4Rd7y04yh+/ddiP/ckY7QGaS5t737LaY7OVyUvGfJr8kgYG6rbijf6sLJ9Zeoax8d6URz6B5OdwCS1OHzU+lI2esbH3PJbmS8D0943hBfy3gh6l/40L278cjYdikj80MrybHXpAXDrgn34/uObw+iSElXdUiBXRmjfcffeOHEijJhEVYuzK6zdd4k3XK/dSinrAGwG0CCEGCeEiPn6b3r4xbM7sLW2vNyhJhgsiW5dCpSWxARlBislxrtyuNld0eqgsol+7YFia4QXYq5zlB1Ovyoz98Uvvu8twXXGBc09A/j9S7sxbLN4pGUUnDBOjJYZqmnrC71/SYclbUhUVNTmFnHsvm0j01ZXY9XB0sX93bYwZ5VSJUSpbUt2xvpSOm6psvurvD0EnHs4DCQ867ZfHCt4Qoi/FULMFUK0ARgBMKz7RxLAzvpOtPcFU6i8VIwfXzjTdytvVHrCSMosiSrWK9xYjD76VhninOJUEDM+IPk4fYZmuvtZp03y1a4batv68IeXdiOTlfj9i3vw9IZavGqTzEpzwR43TuAT/3AGgMIYab7DznH8rgjgg3//+pB7Q8oVTUHJxnzIP9iUW5BduLvYJTbp4TGllIsX3rGiaJtZRnynvVqZoCRBYeDGCvgIgCEAnwPQC+BDAF4G8JMQ+kVC4OsPrsM3H1oXdTdcMdFgSQxC3gwnXOgGQYXC4my8t25uE612pKXXYeyr7mWpc7BK3pMvWxMk/z17B57aUIvKo12jli67yYzmNjVOiNG43LuWHMSjq6sD71+5M9sms6OerhNchybe0OolhlWGISjGq1KY5nHrPRU3QnE39dumyYQlyqzuccWNkvhxAP8updwBQEopdwL4DwD/E0rPSKDM3a65RyXLdcEoPHsHR9DVr540fPX+NTjv+sUF21SyYNjE/78ccSNL/YjH383dHXibJFmsO6yIfbXhojuLV35LgVZMeTiTHXUrs5sfaPHRmawsOPaWBTlXMb7rznGaMXU4k0V1C116iTe05CJNMc90apXYORNzBdeOuFtx9UxfwwU/I27qCWSQczMFgE4hxJkAugG8NfBekcD579k7bY+RUkLKeKWinzC+uC9rTTJ+7T5aPPEQCi0x6e4bcUWlkNcyZosYiIN00UTc/a8exsBwbhajrXibrVJrclFKWZR12W3MU6pQ3E6nE1/KauKGJ0zKpVwT84R+Kw+0oM/EYyLp30BYVly/zarOr+9IlhGlFLixJG4C8JX8z4sBzAYwB0BF0J0i0fDL2Tvw9msXBN7uusOto5ZMtzQrVgCdTDCyWWkae5imCZ2bpBG+PTgU27bUOEuklPDFUuKB70zfZLrv1gX78H6DV0CQaEreal1yCu0dPOe3ahmoLXhkpCzKunzN8zv5Dpugui1+MzY6LaRN0kWT03JPMaROkXQFSH6scxjdT/YdSRZulMTvAliV//mXAFYA2A3gyqA7RaLhpR3qYsdba9t9Caqrpm/Cf8/eiZ4BZ7ElN87bi6V7mwAAzT3FQt9JX741bQP+4XcLldYt+p3Hj7nbj0bdBVIi5uWLqpt5BAC57ILdA/li0gGt6TR3D2Awn6nOaAkE7Cce/UO5czNZWVTT8UUT2UnUGJVst9y1+EDRtn97onQlC0h5kM1KTJ4yH4+sqjLdXyruXnJQuT3p85U4xoMeanKXuT3NuKmT2CmlbM//fEJKeZOU8jdSSnXRK1IWLN/XhMsf3mBZqNYpxzrt4wKGRrJ4fN0R/OjpnIH69EkTi45xInQ0C5aqLEMMZVYsiDJVfXVr8G6ptDZEz/GuE0Xb3D7r37+sjnd1yz/duhw/n7kdgDpRhNOFMCnVNR27HS6CpQ3V4qDTmphm9ePmKBaV0p6FkLhjz7Gu0djYOxWLDgDwSAmTUi3b16TcTiUxeJ7eUKuc78Swq5HjpgTGRCHEDUKII0KIASFEdf73k8LsIPGPn8myVnfmcHNxrRkjU5cfsgz8dTIveHV/oaBUpT/PZtWxhk6vmaa6XU5iUTXKTUA6dXUl4fHAq4d9t2Hm4aAim5X44VNbsKm6MImOpgAuyXsotCo9FBxeQ0pl3PZft3pzqS93Zm0uzmRqkcyxgPuWHVJuH+QCEPHJJVPXjtbkNfv0Dzq0OO091o3Khq6AelZIHJUsN4Sh4yb8liQKN+6mdwC4GMD/B+B85EpffBbAH52cLIT4ixDiuBCiWwhxUAjxw/z2yUIIKYTo1f27TnfeyUKIx/PnNQohfmVo93NCiP1CiH4hxAohxNtc/E1lTyYr8Z7rFnk+36kyBgD3LD04mk1MhZN8OA0dxZYHI3YxiUn34Y8K1X3bfdT5wMcyGMSIl1Vwo4Lnho7+ISzb14xvTdtYsN34ah9TWDidLh5lJfDevzm9aHuasib7RSWiZ26qc3x+3xCVRBI+ThW0r0xdM6pwBt+HUJotGTvqO0Np1+9tUcVFtygWD9OOGyXxXwBcKqVcIqU8IKVcAuAyAP/q8PzbAEyWUp4O4FIANwshPqzb/3op5an5fzfptl8P4J0A3gbgMwB+LYT4EgAIId6EXPKc6wC8EbkkOrNd/E1lz188uIn+fNZ2TJ4y33JlrH9oZDS+xzn2WoSVkqlhpwTqd6sUU+qQalS3ZYaLiRvvKzFipiRq9cv0SClR1dJbpOC5QX81vZww9kIVE+f0/X1y/RF8ZPIbirYnPVV9KVHdqWvnVpa8H4QA5nIqDp900t1Nw+B41wnfz2bxnmL33g0+FijLFTdKotkM35H9QEq5R0qpqeky/+8dDk79PoCbpJQdUsp9AB4F8G/5fd8EsEdK+byUcgA5hfJ8IcR7nPQpDbT1FU/G7NASS6w+VJz5T+Mff78YX7h3tat27SxNTi2AdslJfzZr29g1VTGJjq6SPhbtboy6C6TMMJvgfOm+NcrtxsLpZhmKzdCLkL26lWKjbFEmrnEoGMzqQXIu5xyzWENCSsHaQ+qkWVLKAot2HFw9S5k8Jyn8bOb2UMKG/CbUKkfcKInPA5gnhPiiEOK9eWvei/ntjhBCPCSE6AewH8BxAPpc47VCiAYhxBN5CyGEEG8A8DcA9IFVOwGcm//5XP0+KWUfgCrdfuKDTFZaKna1be5qyuibymYl7l16EK29Y+Z9NzFBRqpbxmImF1RS2fHCyRPdiANC7DGzrjWaFLc2ihujZ8Gqg86Tk1wydcz9yzjPUnoYOG5ZDSdzzgnLBY0QJzyyWp3NdPGepgKLttUXXdPah2km7aiob+/Hot3u8zzSQ6F0xKhEeGxwMyv8NYBlAB4EsBXA/ciVwbjGaQNSyqsBnAbgQuTcRAcBtAL4CHLupB/O75+RP+XU/H/1fo9d+WO0/UafSP3+UYQQPxZCVAghUl/XUTVAm1nxhjP5AtMBrNoMjmSx8kAzAKCitgN/Wn4I1zzvPLGKhqqvn717leJIk8Q1lLlK/lERZ8WFNeIHv9/aRoP7z5EW6wRaZnJKv31gOKPMrimlxPoq87IcdsTB6kAIsWcko15oPjFcWNDeyrvpykc34tYF+9HVb5/VeGttBy68YwV+8pdttsca4eKTmjDEbYeDZ5k2JljtFEJ81rBpZf6fwNgiyycBvOr0glLKDIC1QojvAPiplHIqcrGEANAkhPgZgONCiNMAaDOC0wEM6H7WUk715n/Xo9+vv+40ANMA4OS/eWeqvzpVxq5pilTPAsBNr+zN/+xfW/jyn3IuZiv/99OjE6q+wbG4RtVDUW3z66OfpuymbhhWDJxu4F0lRtwqTsajjae7/fS7+ofxulMmFrSz6Ui7MjuplLkshV7hXI6Q5PL0hlq8/+zXFWzLWnhF9wzmFMotNe22bd++0D7XghmUKyRKLJVEAI+ZbNdeW01ZfLvHa6tiErW2x0kpO4QQx5HLpro0v/18AHvyP+9BLmYx1xkhXptvU9tPFKj8rlWFrfWHHe20zzrqlN7BEUzIT9LMXCnOOu1k0/PdCE032VnTzrDL+C8j9yxVFwMm6cXt/OZHTxU6ehgXdOza21BVaHn87uOb8MJPP47GrjH31l//dScmTRxfdK6EVMYqOoWWRELih8oaqFooFqJ4UWrEYrLRM5BTEo+Y1H0dGM7g5AnjIIQYPRYAatv68LYzXuuk6wDobkqixdLdVEp5jsm/t+f/nSOltFUQhRBnCSG+LYQ4VQgxXgjxRQBXAFguhPhnIcS7hRDjhBBnAJgKYKWUUnMjfRrA/wkh3pBPSPMjAE/m980F8D4hxOVCiEkAfg9gl5Ryv6e7kRK8ZPx8dX/zaFFkv+4P+jpj2+s6cKAxZ9lcqPPXt9LtslI6tmsqj6PMVaJSEjk+ET/M3+UuBseYaMsoaroUWVH1rD5YuNi1/3gPrp1TiU/ftXJ021mnTcLrTyku7yulwyxsJjALISHxQ/VZqsY1KYu9rLQC91JK3L3kAKoU7u63LCi2EjZ2DeA91y3Cn1flPLROmjA21X6+wl09VbqbqmGps9JQqkwVEsBPATQA6ABwF4BfSilfRs4KuQg5F9HdyMUpXqE79w/IJaOpBbAKwJ1SykUAIKVsAXA5gFvy7f4zgG+X4O9JNMr07w40p968e4U+2YwXsrrJWFYCX7wvlyX1ZzO3OzyfbpFhsLG62G1m8xGmhCbRYfzWp7562NX5GSlHJ3oaEhIXvK24hIWEOuupU2hJJCR+fHvahqJtytAWKfGIIuwGAB5bewT3v3oY33tss6NrNnTkkvr9cdF+1LT24TU6z4UHVhx25dbOxSc1vCulwc7dNBDyytynTPbNAjDL4txBAP+e/6favwwAS164wGsylxX7W/CFc9/s20deSn9RgUFcnzhD7yZDSJioJ27u2nhhW+EqfSZbLGvMvKql9OeezhV/QuLHlpqOom2bjxQviJp9vXuPdY9mWR6xClLUoY95rmrpxYTxhXLl8ofXO2oHoLupGbwtpYE571OI00LSxgnTtXMrcfE9qxwLSjP8zqXcrNirjqVscY7fOEVCnLJEUdw4iAWdTkPGOmnirp6V/mIS0z6Z67RxBSYkzph9vvqMp04T+J00fmxqnZXFcy431sGUixUSMVQSU4iykLRCderoKx70O/uHAxBaEgNDGcsjmrrNXVq5Yl86VKnCCQmD3sHi9ONhfOpmEzQJKEtjOCXtYukDNy61P4iQmGLu3zQmE/5/9u47TK6y7B/4957tve8mm81ms8luyibZ9E3vCamUhIRAKCHEEIo0aUpCkRZpgl2UpqIvivhTRFGxoLzWCK8FRTESQARF6R2S5/fHzJk9M3POzDlnTpuZ7+e6cmV35pwzz0555qn3/dwrb1kaOC1J6CRaC5xjhstNjX3rd/8MuggFgZ3EAtNYVWoYuMZoP9rnH3rC8eM88Kd/4Yd/Tp0ZAKKNqY/96PGM1zDLP5Qu4ljqYxlU0KxzLSv02RHyj9XBqzlX/zDh9888uA/ff/Q5y49j+p5WKqu8oBy8IspdZtVCcp3w1b1Pm17j10+8gEM++lO8895gR9JoNYSdqoLfwcZefpM5Df3ATmKBqa8ocTUthNkG7O1f2IuTksLZa5Qy7pQme9Fk+dKNDzyOf79qLXgO69fsvJFhxpfILUZLuYxWtv9Tl84CAPZ89zHs+OJvs378g4qBa4gK1Q///G/D25NrhG8+Yj6Ddem3HsVf/vVqQhTUAwdTl5va2jLDwScKEDuJhUaAR595OfNxaejrtx//xbhi1fzt36+m3KaUwsRhdQZHJ7o6TQLay7/9p4znAybhry2dSUR+MsrVmqkx9T+/fsr24/z9eeO8ZirLPYlsyxHlrudeecvw9uRB9V/vNx/g1uqPd3RLUg8ohRV9bQnH2RlP4uATBYmdxDz39rupM0F2w8ink2m9/JP/fSPlNqsj9maNOTuMl5uy0iXKBc++bNxw01x4zx88qc2IAAAgAElEQVRceywF46BeVnHEnyj/2KkR/hAbgD//7t/Hbztw8GDCHkW77GyvIXIbO4l5zizvTzb0+4Tefi+1E/pv3YicYdJaWNv749WGbVa5RPnnlbey26OiFFBZWpT5QBPcO0SUf4zaKl+xsYIh2wDhHHyiILGTWGDcmJ3T++SP96XcNvOqwcAShtWbxTrv7/95Pes9haxgifLPC6+/g56LvpNw29MvpK5asEMBCUmv07nqO4+l3Maqhij/GO2V/uyDqe0eMwcCThlGlA12EslTZpG9kqtdsyWgH7k/tTFm6/Et30hEueI3+1/Au0npWbIdUHr0ny9nVTVwQIoo/xjNJNpZNZDtTCJTPVCQ2Ekk2+w0xowOVVBoqSlzrTzpZJujiIjCx6gOeifL1tg9Dz+T1fkMMEFUGOxMDnIZOuUydhLJU0YpMpQChjdUJtz2lV+b5x7Kxo0PpOZjZJ1NlH/qK0qyvkY2dYM+NxoR5QejmUS7KSy++htv2jdEXmMnkWxLrh7TRQu96YcGnTQAn3/oiYTbrrjPWkoLIiKjNQrFkWC/zn74WPp0QESUe4z2JNqJOPreQYW9T77oZpGIfMNOImVt5Ae/k/kgHaNROD+XanEmkSi37TMIwJVF9oo4LkUnIr1/v5qahsdOGi3uVaZcxk4i2fbSG+9kdf57Bww6iVypRUQWXfu9v6Tc9vSL2UU3JSJK9tIbqal17PT7XsyyvUQUJHYSKa2e1uqU2954JzU3oh1/+/drKbf5OpPI2QKivPPcy6kj/nb95okXXCgJEeULo/bCC68bd/xmdTem3Papn1hPl0EUNuwkUlpdzVUptxkluX/TRsfxXYMohH5GAHvrXU5bEuUbN1Z1PW4wgEVEhctO02RIbbl3BSEKADuJlNYb77yXcpvRrN8HvvZ/lq/55V89lXKbn/sEH/zr8/49GBH54s13s1vhAABFERc2NhJ5aMnY1qCLUFD+89rblo/lGiXKN+wkkm1GHbqHn3zJ+vmsSonIZVd823qE5P+aLBdjJ5HCbmBk6pJG8s5V33ks6CIQBYadRErLKPyz0UziS29a35y9bFxbVmUiIkr2to08hf96xXj/IjuJFHZuRPElbxgNoG+bO9L/ghC5hJ1Ess1o74+dfX6cRySiMCpiC5yIHDKKsMxxJ8pl7CRSWg/97T8ptxkFrrGDeQqJKIw4k0hhZ7S6h8Lhkaesb7shygXsJJJtdhLJEhGFzVMvGOdUFM4kUh6rKCkKughElEPYSSTbsg81z04mEQXnHy++aXj7K2+mJs4mCpNsxjG+fcY89wpCRHmPnUSy7Rd/T12CSkSU6+77w7NBF4HIM6NaqoMuQsFxIzUPUVDYSSTbvvTL1DyHRERERDToToO80ES5gp1E8t1jz70adBGIQq2qlHuH0uHzQ4WK+2aJyC/sJJLvGAGMKD02BNOrKisOughEgWDNQER+YSeRiChk2BBM79+vvh10EYgCwfEjIvILO4lERGHDhiARGWDVQER+YSeRiIiIKAdwKToR+YWdRCKikGEzkIiMJPcRT5g9IpiCEFHeYyeRiIiIKAckDyCdtawXE4fVBVIWIspv7CQSEYVMJMK5RCIywOWmROQTdhKJiEKGzUAiMpJcN7DPSEReYSeRiIiIKAewU0hEfmEnkYgoZAZGNgVdBCLy2PDGCtvnRJJ6icJ1B0TkEXYSiYhC5sbNk4MuAhF5bOuckbbPYZeQiPzCTiIRUciUlxQFXQQKme+eOR+XrhuPzsbKoItCAUpZbspeIxF5pDjoAhAREVF644bWYtzQWvzz5bdw80//HnRxyAVO+ndcXkpEfuFMIhERUY5gF6GwdTVXJfxuNZDNl04a8KA0RJTP2EkkIiIi8sENm/rjPzuJVDqls97R484c2ejoPCIqXOwkEhEREflAqcGf7fYRW2rKUm6zeg2zDun1G/uN7yCigsdOIhEREZHPxOZU4uiW6pROod1rpJYhq9OJKI8xcA0RERGRD3QTibY6aLedOANTOxtSbrc8kxj7f+7oJvzv3/5r/YGJqGBxJpGIiIjIB0q33tTOJN7iMa2oqyhJmTm0OxN4x4kz7Z0Q8/4lox2dR0S5y7dOooh8SUSeFZFXROSvIrJdd99SEXlMRN4QkR+LyAjdfWUicmvsvOdE5Jyk65qeS0QUhMpS5jkkj3B5YE5TmQ+xxW5KDKedzLmjm209DhHlPj9nEq8G0KWUqgVwKIArRGSaiDQDuAfAbgCNAPYCuEt33qUAegCMALAYwPkishIALJxLROS7hsrSoItARCFUpO+VOdgQmLon0eJ5sQNTzrfYyZza2YCmKtZrRIXEt06iUupRpdTb2q+xf6MArAfwqFLqa0qptxDtFPaLyNjYsScAuFwp9aJS6s8APgdga+y+TOcSERERhUJnU2X853yfFL7s0L6gi0BEWfB1T6KIfEpE3gDwGIBnAXwHQB+A32nHKKVeB7APQJ+INAAYqr8/9rNW85ie61aZL1473q1LERERpRg3tDboIpBPZnQ1Yl1/u+Pzk2cOLc8kmhxfUmStGcgoqESFx9dOolLqVAA1AOYjukz0bQDVAF5OOvTl2HHVut+T70OGcxOIyA4R2Ssie7P5G4iIiNy0ZGxL0EUgH0zprAcAVJdFA8u70fGyuydRb/LwelSWcf80ERnzPbqpUuqAUuohAB0ATgHwGoDkYdRaAK/G7kPS/dp9yHBu8uPerJSarpSant1fQEREROQ/EcH0EQ263+2fr9E6rZbOs/cwOHtZr80ziChsgkyBUYzonsRHAfRrN4pIlXa7UupFRJel9uvO64+dg3TnelpyIqI0uDSL3HTXjlnxn7OZOaKwyC7G6cLewZlny3kSDQ708r3UVluWkO6DiHKPL51EEWkVkc0iUi0iRSJyCICjAfwQwDcATBCRDSJSDuBiAL9XSj0WO/0LAHaJSEMsIM37ANweuy/TuUREOenrp8y2lJvsisMn+FAaCtJAd1PQRSAXJPeZBIK+9uD2o3Iwi4jS8WsmUSG6tPQfAF4EcB2As5RS31JKPQ9gA4ArY/cNANisO/cSRIPRPAngQQDXKqXuBwAL5xIR5aRpIxrxgRVjUFqcvppmQ48oNxRFoh9WfWfxsMnOg9gAqXkP7Rxnp+qw+jhElD+K/XiQWGduYZr7HwBgmLYiljZjW+yfrXPdwMUSRBSkoXXlePK/bwRdDAoJttVz19C6cgCDnUR3Atc4F4l492Yq8vDaROSPIPck5gRWc0Rkl58Nee5RI8oNV6+fmPC7G5/cbOoaATBvdLPp/WVJqxjsDJozrQtR7mMnkQpeR0NF0EUgIqI8V1Ne4vo1RQTK4ZqnUxePRklRxLSjePKC7sHHsXHd2vJiTBhW56hMRBQe7CRmwOWm+a++0v0vbips2czuPbx7ua3jnTYQKTw4G1xY3P7MOg0iWlfhzXdfQ1WpJ9clIn+xk5gBv7qJyK5sloA1soFFlNeOn92F4ohg0ZjWrK5z+uLM0Y+tMOu06m8VAVNaEBUYXwLX5DJWiUQUZmy3EeWWCcPq8LerVjs+P/kj71UdwLqFqLBxJpGIyGV2JxLXThrq+LHYjissXN2SX9xYapxtHWDUGTxyWofrj0O5I1PqJSoMfBdkwC9kIvKaFhrfidndjS6WhIjCYl2/tRyKXiwDXT6+LWEZqoj4MrNYUVLk/YMQEQDg+o39ae9nJzED5qQiO+7eOTvoIlAI+JV4ev+eNRjdWoMvnTTgy+MRUf4x6vwplXq71c5oNp3JjdNTZzCJyBsbDFYM6BV8J7GlpizoIlAemd7FWR2yvwIh2xH6eT3muc6IKPfcvXO2aT3i9hCU1WiryfXUJhsdutLiCC5YOdZOsYgojZV9Qzx/jILvJH7ztLlp7+dEIhEFiXUQUeFJF+XY7cA1RueLGDxO0u/XHGm8VM1oP9ufLjsEpywalbEsrO+IrJnf6/3gcMF3Etvr0ydST142tnVOl4elIaK84GJLh8EiiArP8MZKy8d6kSs1m+WmNx83LeU2v5bgE+WiGzal3xsYlILvJGair9duOWE6Lj20L7jCEBFRQWNbO78YvZ5rJg5FSVHEctcv65lE09uVpeOSdbdUOy4LO5NE4cFOog2su8gLs7ubgi4CuWyEjVmATKxWO1WljApIVIi8WG0gknrhbDqjbD755wdnLwi6COQDN9LnZMJOIlHAvnjSzKCLQC67cfMU1661a814S8fd+/55rj0mEQXH7vJRJykwDp+sS69hFt009rO2PzKbZa1WB9kPHEx9jIW9LY4ftxD1tNUEXQSyqbgonN2xcJaKqICEtXIg5+oqSly71rLxbZaOy2aJF+UOP0aPKTOrn/Fl41o9K4PW8Tpy2nBb5/31ilW4YdPkjMdtnjEcdRUl8QGobKIpWl1G2j+8PuW2bPLIEoVde105Rof0+5utU6IQuHvnbOxcmDnyGxERBa+h0upAkHedem0CcefCblvnlRZHEIkMlstohrC2vBjdLdX43SUrMCwW4M8smmkms7qtp4ZaO2moo8eg9Ni+CK+ff3ApKhxsF/EiYFUydhKJQmB6VyOOmdkZdDEoh3xt5+ygi0AuiXByMKfVlhf7+nha51B732Qb7KW7OXEW44rDJ2D2qNS98kapLawYY2P5I2M/EFnjx6oSf2s2IiIyNTAy84j70TM70dFQgRld1kfnKdwY0TH36F8zv9PUHNR6iS69by47rA937X06/vuxs0a4cl0KDz9mnci5sH4DsJNoA/eCEJEXtK/vZeMy7z+8ev1EbwtDvouwk5i3vHhptfrCrUuXl3gbGdlO98SonZVtig9i+zUfcbkpEcWVOVzqQ0ThtrZ/KB66YDGKuO604Ew2CNSSUazXFMTgQnssiMz8nuaMxxp1Zu8/a777hSJH1vW3Zz6IfBHWcUK2OpOcsbQn4ff6ytKASkKFRj8q1N9Rl3DfRzZMRHsseAAR5Y+mqlKMaqlGR0MlKi0ELwhrY6LQTBhWl/mgmKKI4LDJxg3y6V2NpjlOzVJbHLS52nSYi98dPz5vER7ZvRy3bp2R8djBVbGDBR07pDbtOXx/++fjR7uXqonCaccCe0GtkrGTmCS5fpoyvB41Pm9KJzpyWkfC74dPGRZQSfLfKYuCjfp24tyu+M9eN5CWW0ynQdTTGs6Q7GFxzYZJlo/dd9Vq3JQmd2pNuXGkVLPFZNqAotXq4mSb0U/TKSsuQkNVKUospG6yuhjOyqwkZc9p4CHynpPlwFaWYW9MakvaxXdMkvVTBxvjwxsrMLyxEtNGNARYIipISb0F7ifwzsimqkAff9UE/0K+V5dxwCvUQrT3aqKNmbJ8YyUap9WQ9V7U3Hbj1gT17aHNhGYqZ5euDjY79NcfWmorSiqlum6jsxQmXrKeSoaSZVpuXlYcQU+Wnxl2EpOM0FVW/R3RvQLcNJ3fDh4MugSpkrcmcQmOd4w2f+tn97zm52trtnyNvPW546e7di1WBd5bOcFm0nivPlYm143v9UtTebTVlg3+4mElk7zqxYgbg5ytteUod5BLjsKNkZ2d8+OpYyeRCt5BFxrOt26djod3LwcAlJc4+1jpi1ESiSTMagvYwHfD/j1rLB33odXjPC7JIMYqyX9uLvMttrDMj7LjZuMrm2uZRS+0MpN47ooxg2VwXoSMtgyY5/d1kqmDnQZ33HfGvKCLQB7L9Elxo8XIb5s0vKyszlrWk/kg8kVrbXnW11gytg2NVdEgRz8+d1HW1wMSlzwxRL6/9Ptt3OjEvX/JaNP7Ohv9W+7KYYbw0X+0qywsBz5hTpd3hdHheyV4ZuOC6/qjS9RXpBl8KNOltQj664PfXv7ra09dLs6B5vzCmcSApTz/Lr4gDYyaGho3HTXZ1esNrXMnktwRU4ahu7kKPzt/MSKcbsppZq/e1M56tNSUcUl7ATp3RW/Kbf+zY1bG8+oqgtnD01xdlvkgcpVZvdDXXof9e9ZgdKv5fqNh9eXxtElB7WmPB9ixM5NodBu//rLG5zD/+PG5ZicxDS/b5cfOGuHdxcmWhqpwdNiT2wOtteX40bmLMLyxMpDyUHasRE31e2kVO6PhYfRadDUHG0SJnPFuS2I2VxYcPjm6ZSGoDoJRCgzD45L+zn4nuSMpJxm9M46eOdz3cuQki5/r5JRqdrCTmIZZxXbByrFZXhdMmkyU5y5YOZbLyslTayb6Fxm3voCiENodoU93dDaj/dkM6ojYT5XhNqvFP6g7UAS43UIORnLHvacHu3fR6D0yqYODBEbWJ6VCy/i5jj25x6TZN5wJO4lpaC9A8ps46LxqVAA4hpBzPnpUanhxqyPpfuFEYn4Z354+Mbmb7tg207fHykUzRzYa3p5d4BrnosHOsi9DNhpj22paMixVTu4Mm63u4di6+yZmMcvkhuSXdOnYVsfB//LdDUdNdjVSthUF/UpkSiwaloYd5ba5o5tM77twVXaz0mTNyQu68f2zFzg6N11DrbGqFFM76/GB5b04YkpqKHgtcm6QgYdmdDHPaxgNvq9y43tmWL07e63z1e0nuj/7ld1MogymygjoPXbktA589Kh+bJs3MuW+Kl06iym65aXp2l1Xr5/obgHJE3ZWOCS/3txek15Hg7/1cEF3En9+4RLD26/ZMAkAN/qSO8YPNR/tXzbOvdD4ZO7khaPQa5JUNpuG2NTOetxz6ly8f6nxstKWmugIemut8Uj64GqF9IXoy2LG6IsnDcR/ZnS78NvqU/TSdO6yEECHdAQoNUlNkk074pgB53uzhtVXxNMoDXQbz3J6LRIRHDGlI2V7zQPnLMCD5y+O/75xeuZciwAwdoh/M+f5yK/q384KB7az09u1JjEd17ihtaiMDbBY3eubzSBRQXcSq03CjZut4w/be3kPR9VygpMZaaMzDrJ978iQ2vJ4ehK3XBTPo5j+tT12YAQ+ccwUHDU9u43438pi30h5CRNQ55JLD+3Dnz58SKBlYDRl+/T1fLrUFHYsGduG+T3Nts9b0NuClpoyzBnVjP171mBEU7gCIo1urUmIlstVW94TZBsIyRt85dMz+mxoqzr8qKYLupNoRhtt0ZaIaaPvQVdkycsWWa/mPv1rmGmWJ4wVfC7w4nNiNbR7JCJYO6k960a31UBXx87qxOqJQ9BeZ5z70+130LD6Cpy8oNvlqxaGdB/3ytJiTLYQ4dGrmeFM7zY/A+bkIv2g1JqJ7Vld644TZ2LfVattnWM2q+mFCcOc72m7+bhpOHtZaioYAPjVh5bijDT5ZSk/MAd0lJ32ndGRRjER3FDQnUSz9+bc0dGRu00zoksg4pu/s3is0a3V8Z+dfq/ftjUxcEBQ+wzIPdpyRCu4UjAYbj3vpy32PuDVFYdPxKe2TMPPP7jU88f6+NFT8LWds/HB1eMyH5yjVk0YEthje/Vx15YgppOp3fZRl3PLho2TATmzp2zNpOw61JGI2I6G7me7u6QogpV9zj4nK/qG4EyTCNBtteUYyn2wee8cg3yxhcisnWH0UR6cuBq87cBB69e0o6A7iWaGN1Zi/541mDYicR1/kAMeNeXFqYF22Ef0nZNGo9nLtH7qMNSWG4eVN5q1ZifRGS8+JlM7o8FgTrSxf+y8Q8ZikkkkOd+ioLr4HlrX3472PG/E9ZjsY3WD1dlot6OK9qRJwK4RkbSzlJmCvgHh2Fvpl5StKTY/xgddrNx7WqtxznI2vCk4dt7/m7LcilGIjGqLBo9SFBV0J9HqTJwbmz+9EK7SFIbrN3kzpQ8kfvCNIqL6Ge4+nzjteH1kw0R88piphvc1V5dh/541mDPa3n6hTG1BfqbDJ+jgUrXlqXvnp3ZGl6J6NXCULthWIbD7XZ/6MmR3/shm53sIf3DOQozL4vVzknibWyFyg9X64mNHT/HlcWiQ2VNm1HzR9iTq80lGRPCjDyzE2CHuDmwWdCdRkykAzMHYNK7dtuaXtw8kjKZuySKhpZGg90gWmqrSIlSWGgc7SsvmyzSyuQpD61JnaG48ajK+tnO2/ccnR46a0Wm6VMzpR8+sMWW0fMQLbMzZIwCWjWv15NoZG1Jp9sJ/afsAfqaLDummJWNbCz7YkZPPYVb5EHXvhY3TOkz36fnhazvnBPbYyTJ9Rm47cQYuXjvel6X8heTQ/uz20R7MIsqeUqogO5l29pd//Ogp+MQxUzCqpRoLe1vit3e3VOP+swZTfcWvmEXdVNCdRK1SP3xK5j0agP3nec7oZhwT6xgKEN+7w74dAfZHq6vKijGjK5hQ5vksyO8jbcR/MJ+Zt5aO9WZW7KbNmfeoHT2Ty4rcUllaHHg+sWNcHvTMZdkuN7143fj4z9du7Eexj4FnkllZSuyFm4+bltI5eS82Ql9i8nwsHtOKbfNGFmSnwi4/2518OdKbOTK1HWfnOauvLMXaSdY78vqYKHYVdifR4nF2Rt9/8cHE3IuDe42yawAancu+pr+cztxa7QxWxEbvRzQxmayRGoNldwBwzZGT0p6X7Zej/vrDG6MzvE6Xnic3ZnatHZdwu1erA+7YNhN71k/EhmnW8pGlM21Eg6Pzupudf1GRsSAbY1cdkb8pmATAeoPB43vTpKLJ5rOb7cxNPljRNyRlmeM770U7ifqO64PnLUo5t8oknRkl8qszfYD5utK640R395lnMrWzId6+tKugO4lW2WnAJS8T1Dak6xuVTr5KjB6bM5K5QQT47HHTsC5DQ6C9vgKfP3561vsB8tWK8cZBg7L9GGT64tRvrHf7S7asOFpxWw1i4tTC3hZsnunOzM+XThrAry/yPnpqGIh41xHza/aYnJnfm7rfeGLSfr0dTP8CwLvOx8Zpw7GgtyUhzY5Rzsft80d6UwDK6PPHT0+5bblLeUKPsLjKL9dUlKZ22Mw+Q2bBDdOpMRg0cbrPuaA7iVZH/uJf5g6+zRNmEtkayGlmL983T5ub8dxD+obg40mdP6MZ6mXj2xxVCpqTF+Zno+WE2SMsrdm//PAJKbdl+tw5Cndvcs0LVo7FNRvMZzbN/gQ30uzY1ddei08eMxUzbS5hrigtQmuNcR7GdFYGmE4ilDLmRc3OrG4uTXfK6nf1qYsS98LN7o4GHDOahcxnc0YNBlq70cX0KHWVJfjCtplorU1f35QVF6HYj8ziOaw44n5z//6z5mNZUofw8MntmDCsDkMyvGZWeJUHNkweOGdh7CfjvzXTdjijuurhi5dj4rA6S9tAMuEcPSx8IaR5n1aVFuH1dw6kOTW7N/molirse/71rK5B3uq3kPTaL0V5OhKxbHwbvvHwM4b36Qd7jP76yhL3qrlM31mnLHIWQGFwg7l/r9+9p89DJCJYM2koui68L+X+lpoyPP/q2wm3mUV7zaSvvbbgl4Rl+q4w4/QdEbZo3LnE7tJR7fiv7Jjl+DG/edpcvPb2e47Pz9bdO2fj7w7bGifM6cKl9/4JgPUYD+SNK49IHSgFgJ0LR+E7f3jW+oUsNF3HDkmNomsn9zMN7hc0aluUFkUy5kg1Oq+kKIJ732++NF5z3cbM0foLeybR4nHa1HBJkdGST2svoIg4+tI+OmmJmH4Dap72B8LLaURL0ySp7r+A+TzuZhoiOsN5t2xNXQ6TcF0fnzSzhwpiJjGS4cvnp+elRs90mhicdRXw0AVLMh9kwO5z96ktU7FrzThLA5S71owzvc/Kx4Kzle7pH16PuTZT6rhpelcjNs1wFlyKkdbDr6K0KOEzfdysEfGf6w1y7IUhEraImMYioChtS1umzqTRR7S+IvOqtYLuJGq0xrrZc3zdxn6cs7w3nkA78Vyrj6H7OfZqWQnbXBbbsD0v9uVxgi6lhrafibylreXO9CG0Il/X2PvBLOG0vvLraEhNHdLRkD4QkJOvQvfbRP6kwLDDaN+EU7m6asjNgZyGqtKE3y1mwLB9zOqJQ7F9frfhfTO6nAUdMpM8iJlP3HjPHsVE4b4x+37IJ06Dhmn0HS59ELPvnDE/5VinT6cbL8OYWK6/uaOb8zZGw/qpwwyjnNo1ObaSbWhd9st7jfjSSRSRMhG5RUSeFJFXReT/RGRV7L4uEVEi8pru3+6kc28VkVdE5DkROSfp2ktF5DEReUNEfiwiI5If37xcKeU0PK6lpgxnLO1JuL9O64Fn2u8U+8BEIqmPZ2XvWXlJEX587qKUJO4MP+6OxqSGW7K22rJ4bsKICy34j7q4X6OQKAWcsbTH8D7tc3TSvJHO8lja+FbLdo+E2fmDM4nudUru2jELnznW2vLQ3rbEyKOruH8wzuu2p1cDA0bFttKpC9E4Reile2/s37MGH8kQeZncUwgBNVf2ZVcvrzAJKGP01Dl9OjPVZ5m+WxSAvvY6PLJ7OY50IRp3WN2waTK+evJg3utRLc6if5+1rBc/OHsBetpq3CpaAr9mEosBPA1gIYA6ALsAfFVEunTH1CulqmP/LtfdfimAHgAjACwGcL6IrAQAEWkGcA+A3QAaAewFcJfdwmnT6nYmih7ZvRwLe1vw6S3TLF3bKLqp1Q/hyOaqgk9u7BWjcNp6I5urUFdRgpqyYly8dnzaY3PB4Cbp3GNWiS4d14rrNvbjvEPGOLqus5lEd5vSWs4jN0YWNQPdTVg5wdry0C0D0bG19VOG4Y+XHZISZInc58bKhOQlYfr9QAMuvpfsujXDEu9cUAATU67aPm+kK+9pp4y2A+Ubo68dLdeutfMF/UkRes2cvni05esmP4ZVPzs/dUuDJnnlRb6LRATLxrXaPq8oIpY6iEYvi5UqzpdOolLqdaXUpUqp/Uqpg0qpbwN4AkD6HlbUCQAuV0q9qJT6M4DPAdgau289gEeVUl9TSr2FaIeyX0TGWimX9mbWvgzsLN+MRAR3bJuJeT3p9xDoo5ua3UfBSVehbRnoxGeOnYaSogj+cNkh8Q356fbxhF02SVXDqKasGCKCI6d1+DKQ4hN5q48AACAASURBVNVHdl5PM/bvWRP461NdXozqsuJAk3kXirWx/Z0Le1sM7zcaYMzk/jMHl42dtaw34/HpUivlf5PbXJiWfeeKXWvH429Xrgrs8f/faXNxxhJnHZtc1p2U2sCt1ShnL+/F/j1rXLmWnr7da7REMqX0bCe7wml/I5CWgIi0AegF8Kju5idF5B8icltshhAi0gBgKIDf6Y77HYC+2M99+vuUUq8D2Ke7X/+YO0Rkr4jsTb6vvKQIZy/rxddPmePo79FPGQPApI46fGh1tJ86mCdRX5bo/5uTNomfv3KMq+GjKTtXHjER9ZW5NZpVaAMPRyV9hqwsB71odWIn38lz5vRrONvXZ55HgS2yXUZbCPuB3DR2SA1Gt9bgoQsW44rDjZPSX7ByLNpqyzCq1Xp+q6bqwZlE57M6scFTh2eHVXeLszxhVoShU/nDDyx03IZxi18BbPasn4gvbEtMSN7XXoczLQyMkH2HT06f41kvc7KAwZpFe7/cduIM3f2Fy8uv0ZzpJIpICYA7AdyhlHoMwH8AzEB0Oek0ADWx+wFAG1Z/WXeJl2PHaPfr70u+P04pdbNSarpSynAdzJnLeuKbZe3Slog1xCJEfev0edixIBqUpq+9DivGt+EjR05K+SJpqCqNnwMApy4anRI+Ol2lG4LvpZznx3NoFiXMre/Tn5y7yPCx9qxPbHyeNC//Ew5bqQe3JT0PdjpI2VbiNxyVOeS0mb9esQp3JDWM3KLt53G67/bgwcGfL1hpaSFHTih2eQnb8bMTt8x3NFSitNj4a3h+Twt+9aFlaffZZvt+9LL+C0PHSa/NRm5Pq7MxYWrQjmqpzjqwSa7YPLMTC0xm4POZG50Iu5f4yxUrcf2mwcmL5DoshWiPk/mRtHGsuaOadbclBwuxVMyc8f2zF5jeF6b6RONrJ1FEIgC+COAdAKcDgFLqNaXUXqXUe0qpf8VuXyEiNQBei52qX3RdC+DV2M+vJd2XfL9v7toxC/eflfrilxZHcPPx0xPyyei/gML4pigkXjVk9MmFk22Y6u5m7C79chPdGyr5b9udB3sqg9JWW5YwoOP0fdPXbm0/iJHS4sw5k6xYMzF1n6I2E+i0k7hwzGCDrbEqc0CuXLFtrrsDK8kz336z8vKmO+Z7Zy3A9wy+51KuEcKWnZ3E0iLm383fOn1ufGBOy0s71uEAM7krfO867xmlr3BTWXFRwvfOhw8zzsNolUpoo0js/3QnZPVwoTOsPjUCuybdgHXyykO7DIMTWRh18K2TKNF3wy0A2gBsUEq9a3KoVuqIUupFAM8C0A+/92Nwmeqj+vtEpArAKCQuY/XFQHcT2mrTj1QavR4HY0P4i8dkNypWCJu2c8kn0iQdH/Awt1ie1aeeSP6kWHnOfnHhUvx21/JQ5I7K1ieOmYJ9V61OuG3x2OiG+cOnJC4rshoMqLm6DLecMB29bdU5t0TbTETcTQMSRnbHBMYMqXG84iZorUnfz/2x0PFmmqqN38eTOurjA3N1lSX44kkz8dnjcj9QTz4ZbyOYS667yIUYCVY6C2ct67G07FQbIDK7ZKZHciOKfK46NPn5jT0V+65ajavXG29LsMrplhI/ZxI/DWAcgHVKqTe1G0VkQETGiEhERJoAfAzAT5RS2jLSLwDYJSINsYA07wNwe+y+bwCYICIbRKQcwMUAfh9bxhpeus/AjZsno7+jDp8/YUamQ9PiLJEz2Y54/+aiZY7P9WL9ub4iCONovteSN/G7JRIRRCKCoXXRUcDiSO4GdhGRlBnJUS3V2L9nDSZ1JDac+zvSN6T1lo5rw/fPXohig9lOblmMUlku6025nitXSTTSo89Q2Bi9TzWlRREssriccX5Py2BKLApUJCK4a8cs3Ll9wPQYp1E7w6DNINCL3bRPg6tGBm+zUj+ftawXN262HvXa6JKXH9aX8bHyvY+Yru4/YorxKrOiiGS933dGl7PJCb/yJI4AcDKAyQCe0+VD3AKgG8D9iC4R/SOAtwEcrTv9EkSD0TwJ4EEA1yql7gcApdTzADYAuBLAiwAGAGz2429yy5Kxbfjm6fNMl5Elvy+0TkCRCOZmiKxK3lBplnTGb0/42b9az6/AAWHVWluORRlm5bN5ij5/wnTctHlyQqoBsua9AwczHxRCbs4eV5VFG3R2wtZ7yeijcO6K1NnjNZOMU6ks6rUfsj0XbJnVCRFBT55Fgy4EA91NaKgqNVwFccXhE3Cuw1RJYbDO5HNoR6aBqh/rYhw4oV3WqDM4vr0OUzpTBx71JQkwi4ovgmqiOZ1I8isFxpNKKVFKletyIVYrpe5USn1FKTVSKVWllBqqlDpeKfWc7ty3lVLblFK1Sqk2pdQNSdd+QCk1VilVoZRapJTa78fflI16G6OOyW8obXlqRKJJxPkllh0nH9jyEnsfm+RGppb8PduORmdjZcqI6Wn6UdI8qmztNNPtzlrZOb65ugyHTR6W+cACdsAkq3U2y1DzZaZmZHMVvvy+AVx5RHb7ejTZblMweqWMAuncsMk44FJdZUne7cerKSuOp8PiBHjuOs1gxvDYWRmCroSAWfChjdM6ICJp97RZoVXPRm2fYfUVjlYS7Fk/EX3t0YGvdBE3RIBTFo5Ke618D1xji4sVkFG9Hpo8iRRVXlKEyw+fgLt3Wg9TnTwLNSm2j2KuC6HwV00YkvU1CtHmmZ3xn53UX4f0teHaIyfh7OU9WZVjVEtVyvsgXxrTfhrWkN2Xbj474GCd6OKxrTh2VmfK7aXFEWx3GGG3KoC9gW7Nyn/lfbMSfp8zqtm1nJ5TOhts5TIrxCXo6WgRoLuaKgdvTFiGN/j+T7c8lcgtZkHvpnS6E7l2WH10yWpN2WBbQUtcv3Nht6VrXLpuPKbqZgQ3z+yMtye1anOhwSoDQXRJcMrturo2pd7N4ZEaoxWCubbn0t5iZsracTZHsuqSIldN7WzAHy87BNVl2b90c0Y14bt/fC7zgZSgpCiChsoSvPjGu6YNyXT1gIhg4/TsoxzqE553NlbiqRfeyPqahSD5Nettq0F7XTn++fJb2DqnC4fZyAmV7+aOasLWOV3YsaAbjVXWZgJLiiK44vCJ+NIvn/K4dLmhtdb7pcm71ozD628fsHRsQp4yi9fP187ljFj6qoRGqsmx2S7DI8pGpqjDh9z4UwCZVzpdv3Eyfr7vP+jUDYxUlxXbGmzaOncktmaI/Hz1+on4+sP/SLjNysDbIX1tlssRdqNbqvGXfyUmW8ixPiI7iWF00ryRGDOkBuUlRYab593oIJK3/AjUoV+y9s3T5uLfr76dVIYcHoLzWWdTJf758ltYMb7NtRHbfFBcFMGlh/a5dr21/e34/ENPuHY9ito+39oMwJC6cjzzUjxuHIY3VqY5Ojthr34Eg2U0a7fp/wQvnysK1pi2mpTGfC4ZM6QGj12+Erf9734c2p9+kLOusgSrDNIgZWvwsxT9NJnlfzWi//zlw/fv2ct68dEH/mrYIcyxPiKXm4bR7rXjsWn6cBza355x5CXk38Oh53RUJ9Pz7sfr0qpLDt1QVZqzoendZve5F9NfyC3aazJ5eD0uXDXW9vlBBmQa3pBd5yBMy4tmdQ8uZYtINCKtGSsBunJfai8xIe0Jv2ALwsbp7uYuzobTj1p5SRFOWTQqYYWRn7SgXDXl5pMYblQjWwZStzKEyTdOnYPl483rVVvfBx7Xu1YG8thJpILmdBnVsQPRZcOVJnulDoZgGD0ERXBNkY2K1ckMavIoKGVvl0n+rp0GgQs2TE3fSAsy48gCi6kQzITlHaUFOdM+H9rrYCVQRZg6um4ymknU7yFtZhRj8tjpi0dbXsofZsfNHoFda8bhxAzLUPPdlM6GhCX9v7tkBf5w6Yr477lWlbKTmONy7P2WNz6wohf7rlptGoAinzpoYaAtXZlqED7bDd0tseTYDPzjGv0SyEz1lFmwBs2WAf+jEuoHG4Y3Og9uFLYOVrxjFCvWHSfOzHiOk78g7MvdRQYnCvUz1d0tgxHDP71lqs+lIj+EqZ4/95Ax+MTRmfMPhqsWSVVSFMH2+d1pl5mapgxL88fpO1ybpnfkxOT+YB0rqKsoQU354Pst19KUsZOY47Tolk4qvdoQVZRBMfu8plsuED0vNSG5np31+F7JhcrUqumxsOBfPMk8SbJTbbXluGRdH24/cQbGt4cjf12+yfa9ePICa3vu3OTWl3mQs6DfOn0uzlneC2AwSEu8YxRrdnY2VeLB8xalnKuPbJtj7Zq4IbWpycf1Mu1JbKrmTGI++tbpc4Mugm3aZzBXP4sA0BFbum+2cmSHQT2vb9tedmh2qYO2zunK6vxMlo1LjOjq5KWa0eXnnszM38yMgJLjPrh6LLYMdOL5197OfHCSKcMbcMe2mTioFE687TcelC533HfGPLz61nvYfPMvAUSXgGSjrqIEG6Z2pET3csNFq8ehp815fswtA52481e5E3ly/NDaeNjsKgtBm46c1oGfPf4fS9fWR3RbNMbfxODdDvJRFaogRl/1j5jNpFimjoqXJnXUY1JHPVZPHILOxuj7LXkmEQBGNKW+Fy9eOx73/f7Z2LH2n/9cGKTSZilyueFN9iWncg160ltfHu3H8UNr8adnX0k5NuiyOvXE1avj9cj1m/px3cZJ8ftExDS66rQRjbj9xBmYO7oZJVnst9y/Zw2eeelN3P7z/Sn3nbm0Bzf98HFL1ykpErx7IPVF0P99RnWsVV/aPoCX3ngXA1f90P7JNlmp14Of7qCslBUXoaetJmXZ4/GzrS3PWtjbgsU+N47DRPuI9LXXYVZ3E/buWoYLVo7FpI66rK+tjQg1ubzf4H0LutN2aOb3RGeXzb5M1kxyP7JZmFhJdr9kbCtuPm6aD6VJ9fMLl+DWrdPxzRwczfbLeYeMCboIrtg6pyuwQBJ6o1tr4qsb4h2jDOe0Zdm5zYXGLPciF6Z0KS9Ptpgr0E3KoyGV31y0zJPrWvXI7uXxn5M7JHYGnhaNac2qg6hpM9hjPKWzHmfHVltY8YEVid9Nd2ybib9ftTrh72mriz7Oal0U2ZuPm4YlYzO3tcuKiwaD/3hch1rJ1xv8txe5YmpS2ODKUk4SO9FcXYZTFo1yZeZi0/ThuPbISdjm80buzx0/HT87f7Gvj5lrbt06Ayv6hgTy2O31FVgyti1hn0K+2r12vKPzTtPN5A+tC2YmLmx7CV3n5d8n3jV8nTh/pfGgg/YUFBfl+WtNCUY0VSXUTfr3aksAS4z1M4na8kqzrQ92guJpA9SdAaVvaQhZQJ7iokjKIMAd24z3ZH/zNGuDuAt7W+IrnTStNeX4/aUrcOqiwSBtK/qG4NatMyxd049Bq/NXjsH82Ha1dNiTyCPze5rjy+zOXNqDzzy4L+AShccRU4bhG488k3K7l8vYIhHBxunDPbu+mfKSIgxvrEz44ls/NfPsGpHbBmL74KyqKS/GtUf2J9z24HkBDXjkab8h3T68Y9wKLx+e/iEA4PDY6oIvbJuJ42/9dfz23tYa7FjQjeNmjcB7B1Uo9pKTe6Z01uORp14yvO+keSOxasIQ7Hv+NfzZYFmnn/Qdv46GSnz9lNnoa6/D3b8d3K6idRzszNBHIoJbTpiOiS6sjMqGlRk0v3QlLa2v1Q3W6peS9g9PDJJ3z6lz8JsnXrBctdW6MQjs4XfQqYusbalijZinKkxSM1h12uLUMPVhdPYya8sESgp8pDiXl+/l+4ROPtMaP8Xp1nfpTB5ej5UTEmd4c7nhPqolfPtOByN6Jt6+f88aXHXERPceJ2QdRSA1nUkkIvjQ6nEY3liJkc1VGFbvPIothc/JC9K3Y9rrKzC/pyXhvaqtIFg9MbEeuuzQPtfLF5f0WZk2otF0KaDd9FpLx7Ul5FT222OXr8Tnjp/u2vWybQ5snmE8cP/9sxfgVx8yX547tbMBJy8cFcp6zUu5++1LnogI8MljpqIoyJB8HvjQauOcbfnc/yiEysysAzmr294MFnljaF200Z08k33JuvFZpZXwg34WtNnhErRjZ/mfuiMTLTWF10uagq5+Lna41DnZA+csxL2nz3PlWhROm2cMzqBrA8rJbSAvByutdPy0aOpBf67sKi8pShsJ3ksVBh1tETHck9rbVmMpX+XI5sGlu17tXw3TwHh+9QTIsYtinajRrdV5GdikvrIUzdWpFUCYPoxuC9uXybr+dleuU2ZhZunL22dlPIa811JThsevXJUSevzEuSNx98458d+tjEnduX0Ad+2Y5Uu00K+fMhuf1QU2+tzx03HNhklpzjAWxpxYI2NRdXuziJCsZ9a+DXKQateacdg2z5294KNbqwNfrkfZqS4rxg2b+k3vr6sswS8/uBTXbezHnNg+rZVJe9anDPcuNYHZZ+XnFy6J/3zo5PbYsYMHX36Yh7ObOeTYWYOd/PVTBgckT5o3Etdt7MeX35eYOkt7Br9zxnzbj7VywmD7+CSX6hi/tdvY589OIgEA5oyOJrPOtxlEve+cmVoh5PVsW8B/3EyL+9EqbS6N/rWFiG3JG8kpOCVFEcPOUltteXy2rrw483tg7uhmDHQ3OSyD4I+XHWL5+GkjGhOCf7XUlGGTyTIlfaMkFxwxZRi+edpcrJro7WDgxunGudDCIowdeHJb9DtwzqgmrDfJzacZUleOI6d1YFRLNfZdtTplsHxiR517e3aTNFQl7ovTaEFsSosj8eie+q/142Z3eVKeXGO20uOsZT04cloH5oxKDNCiPYdOB8qcriyxSptZ9qoZ890zF1g+Nn97BJSRUUSv5NGzsLMTQS95Xf6iMalRqfJJkKH35/c044wlPQm3TRqWOhp/2aF9+OCqsbauXV4y+HexoZfbbj5+Ov5nxyyUxwYKvHo9BYJqCzk27br5uGm44ajJCbctDVGQBiMikhKUwQvJEbeDkG4/ax5X/RTz9nsHAdiP0ZC8PLLf45nkaSMacd4hY9DVVGk4uKovznVpZkQLgVGLb5NJgMBMbSCj75tbTpgeb5MEtcxci3Zb7NGkTV2l9aA6BdlJtBpEoZC01pbjkd3L8f4l0YhH6/JwyWmyGV35vW9tQ4aRU80mD0b8ZyY9t3+5YqVrSzPKiotwyqLcCKxE6dVVlGBWd1N8CZVnVbNL101uwBmlUbnFYpjzvGbx+d46pwtfOmkg84ExOxZY2wO0bFwbAOCzx03DtrkjDdOo5H2KE8IhfUOwcVoHdq1xtj81+b2Z7eKcdN9bpy0ejZ+ctzghNZL2cPr3aiHntQYSl9sCwK8+tBTt9RU4Y+ngoPQ5y3vxPzsybzkx+r5ZOq4NJy+Mvk7my8y9XaVVVVqEHQu6cdfJwW+bKchOYk9bTV5uRE9umNvVUFUan1nraatBVZYRUt20MBaV7oKVibNO+d7Ry0ZpcSS+fyvdl9s1SSkH3Hjdd+q+DOeMakJZcZH5rG2GxprR3doyHMoPVbGlnVqgG7ele4fZCXK0M4BE20FqNUg+bcXYITWWjtuxoBvzejLn6gKiS8fOWtaT8bhh9RXoiu27HDe0FhevG284Y8BOYv4rLynCtRv70eLwfVxdnrj6YJLLM4q3bk0f9VOb0DjKZKl7IUpuy7TF2jj6XJBnLO3BrDRbE7SPvtOVK1p6i1KPVmuJRKMu97W7+377wdkL8JX32et4FmQnsTgiebkR/dTFo3HXjlm4/yz7m3HD7qI14zB9RAPW9SfOcM4d3Zwx+Izd5YyFbrHN5XKfOXZaym0lJpXncUnRHkVg2oPVZg38SCxLwZo5shEfPao/ISLl/WfNTxtsIp2+pETUWh2RbZQ9fcMjUwNPvyw6V33nzPm47wx7A6rzRjdn1dkfP9Q4ifhZy3oT9olqTpjtLIIs+4hkl1n6BKeWjG1Le395SRH+cOkK7HY4E5qPzAa8tY+zlTm+e0+fhzOXZh5wMnPHtpm4/LA+1FdmjoYaJj1tNZg9yt6+/tz/FqO4oohgoLsJY4dEv2QzzQjZ2bPmViQ8p3rbanD3KXMMGwnJe43uOXVOQr6rIbqlRv938XLvCpknrt/Uj4cusJ7APDmvXTqXHz4h4XcRMa3UtdnjdI05tvPyg4jgiCkdCXuHxg6pzRhswsz0EYn74bSBhhuT9g/aVVlajEVjWlJuT+5IXbex31ZwgLBqri7LOJp92BTrUYu17QyZ3LCpH+NMOot6zdWluOywCRmPM2IlSjKRXhB74GvKS/I6doJdh002rm8OxLcsZH6uJgyrw9nLreXYNjK8sbJgggaxlsxj92UI72snlPztJ87E1evdS7TsqqRextTOBlxvMgOhD2SwJOQBJrzSkGH0q6y4CB0NlWmPSUeLAKcFFUpXZxeJ4ODB1G7iSfNGoipNoJG8jkpLWUt+e2jvQa/eNlpaCc2R0zpSbstXpyx0f3/w+qkdWD4uc/18zylzHT/GVWH9PqOcc9URfC/5RUtRkkzrHJblwQqOMOGzmce6khop+un1VROG2Fp61V5fgaNnZhf++eNHT7F87Ke3TDW8XV/iyw6N5giy0/DTL120MlKd67RQyvrX2s2/+55T56TcdkVsZF9bcqbNCBpZ1z/U8PU7d8WY+M9p36UcYCUDyYMI8aVIHF1wnYjgy9sHLOVBtbPH0WDsKEVnU+pgltWAgMnRronMZHorDmswXl59RdLKmfkmHRzK3qH97Thp3siUuBXZ+ul5i/HrDy119Zq5hJ3EAuLHRv2fX7gEf7tyVfz3c1dEp/Tn9zRbTqZ+0+bJlpZ1xGcHdA0/bc+b6br1AutUaJ3E5L/7W6fbG4FfPXFIQnJxjRbmXp/MVlsaM7q1Gr/44BK8b75xsI9h9RWoKS9BV1PqjEtFaVHaBv0xA504pK8NJy9glNNCkyntzdY5XViaNAvl5jIxoysV+r7ZOaObM0ZJvnTdeGwZGIG/XbkKbbXmncUVfdF9WgczdOgXmAw+zexKv+fmmg2T8NWTZ6c9hgjQ56tL//meM6rJsG2R/A6eM7oZj+vaR06t629P2XddSJqro6uh9AGySosj2L12vOtB7TqbKtFqY9VdvnE/cRSF1oLeZnz0gb9aPl6r4H5lYxSlXbcXMCLA6Ut6sGVgBCrLrEfMPGzyMNz/x2fjv+vzjmWqiD98WHR2savZ+XLJfDJmSA3+87f/oiwpWfmkjnqMaKrEk/99w9J1zlnei9GtNVgxvg3f/9O/ACRGE7v8sAn40i+fSjkvXQAL7bU0C5Sj7SVdOi51c39dRQk+e1z6wCEAMKXT+3xw5K9MM0xLxramdCAGZxJTj180phVD6yowqqUK133fev1IiTJN0m6dG02BE4Ggt60G/3rl7XhU20kddfj9P14GgHh+VX2exU8ek7qypMik3X7V+vR7FDcxUiRZpEWvzLQ1JyKC/o56/N/TLyXeYfChMAvqZoedVVn56P+dNhePPPUS1hZAqragsZNYIJ64erXt0XRtFK2m3P7b5MOH9cUjATZUWY8AVRL75tc3BHea5BYyavhpf+PQugqsnTQU3/79s6knFpBPHzsNjz7ziuHo2r3vn4eXXn/X1vVuPn46ui68L+V2JzM1+lOG1VfgmZfeTLh/aF0FfrtrWcY9lGb271nj6DwKt+QZ5qOmD8dde59Of1J8T2LiuXt3LUNTVWn8/eu0k1hoKxSy9cktU6P1Uiyp81dPno2xu+8HMLgSYdn4wcEho/3jbbqG+7ffPw9rP/4QAKQMiBE5NWFYHa7ZMAkrJ5oHZ9sy0ImiiHEANv0gqdFKHHKmo6Eyq7gJZB2XmxYIJ414rS3mZJnq8bO70NtmLVeWnrZsy2xUOuH2WLnYQDNXW15iGvK4trzEcE+PVdk+7+cdMrh3wGxpWVN1GSO7UYIbj0ocRR/RXJkxIfsRU4YBSK1XmqvLEupGK1GcuavRWLr64Le7liX8nlwvlZcYd+y0QGP6az9x9Wpcs2ESLl43mBZgwrD8S2lF4bBpxvB4XjwjjWkGwStLi/DY5SuxZ/1ErNANeowdUoPhjd7khCVyEzuJZGrxmOjobba5xdJJaViYjPhr9JGrtFPv3pkaPCV6bXYu7Hp492CKkE8c482SlpMXduPCVWNxqG6P6gErUSqIgJTk6ycvGIV5Pc3xVQiaS3SdiEvWRZehz80QOOKeU+fify9ckvYY7b1apIuQUlYcQWVpEW7anF2KjVymDfAZfV80VTtLZm70NSAi2DRjuGE6JCKvrUla4rhlIJan02Cgs6GqFOUlRdg8szOhPXL/WQvws/PT1zNEYcBOYoGy0n+6cfNk/Oz8xaZr6I+c1oHbT5yRVTn+b/eKhA3Y6fYOAdE8Zdo6dO1vGG+ygdso8InWgJk8nHvVjDRUOt/0fe/p8/Dt92dOvP3BVeOwMyls/ieOmYrFBvnniDLRPtMzuhoBDNYLJ8b2wOmPaastx6QO81mn6rLihByrRuKdRF0lKiL404dX4rDJw+z/AXliVncjjps1AtccOcm1a9bH6iM39nERueEmXa7V4ogk5GHWa6wqLYgI6pTfWPOSqfKSIgxvNF+OuGWgE4vG2Ms1OD9pFqCusgRf3j4r/ruVfGbaHslMEQWrY3n29HsqS4oi+POHV+KeU4xnHwvVhGHRLzP9aGemQBQXrR6X8PvEjjrHy75mjmzEbSfOdHQuERBNibN4TEu8s2hG6+Q5HZQYnEnkSgW94qIILj98QsJewWzdvXMOrtkwic81hUaxxQGLgZHp6yGiXMD1GgUqqHRhH1w1Dj97/GcJt9XpZq+0jt/UNFEptVHl4gwNh4vWjMPo1ur4sllNRSkDGyS7c/ss/DMpcIy2H8jMij7zzfxEfutpq7E00KCtbP6ALhenHWsnDcWvnniBEZR90NlUmdW+aSIvlem+I5ObVEcxii7lAXYS89zHjp6CKoNOkdOciXduH8CWz//KcXky5b7Slm50NFRi2ogGT3xSTgAAE95JREFU/PbJF1OO+cCKMSgtiuDwKYNLuxb0tuDlN95JOK6mvATbTXL0UaK6ipKUCKjLDVJPEOU6ZTH3mZljZ43AphnDGUWTqMB947TUfMNLxrbi1q3ZbcMhCgt2EvPcoSYJ7IOK6ZJpBvPL7xuMUqg15pKLWldRgl1rxyfc9oVtXKrotkhEMKqlCvuefz3oohAZOmtZT9r7P3PstJQVB/EE2Q43W4gIO4gWdTRU4B8vvpn5QAB3bJuJJhvpkpLduX0gvsWAyA9GEdzPWJq+TiLKJdyTWKCanUabM7BhaoflY/VRSy87tC/lfn1eIe79CZ72had/XYjC4qxlvWnvXzlhSEK+PQCoiEXFLHbaSyTLvnHqXHxt52xLxy7sbckqlcXc0c3oZ0AyCsjuteMxurUaYxyk/iIKKw67FZjm6jIURwQXrhqb+WCLrt/Uj68//A9Lx45srgIQHeFfOSH9nrayWO4s7iEMzmGTh6VEbLx03XgMYaeRctRnjp2Kex5+BqNaqoIuSt5rqSlDS417A5JEYTWjqxEPnLMw6GIQuYqdxAKzNympcTYaKu0vDaopL8H+PWssHfvxo6fgnoef4chcyGzVpRYgCkJfey1eeuNdR+cOravAaYtHu1wiIiKi/MJOIjlSHBF0NQ+OxP/momUZo43a1VZbjlMWjcp8IBEVlPvOmB90EYiIiPIaO4nkyMykHEBcUkRERERElB/YSSRHgsqzSERERBSUR3YvDyxCPJGf2EkkW7INH29m15pxENa6REREFGINWaRqIcol7CSSLbGsFI4TUZth0nsiIiIionBgoiiypSjWOawoYVoKIiIiIqJ8xJlEsmXOqCacsWQ0TpjTFXRRiIiIiIjIA+wkki2RiOCcFWOCLgYREREREXmEy02JiIiIiIgojp1EIiIiIiIiivOlkygiZSJyi4g8KSKvisj/icgq3f1LReQxEXlDRH4sIiOSzr1VRF4RkedE5Jyka5ueS0RERERERPb4NZNYDOBpAAsB1AHYBeCrItIlIs0A7gGwG0AjgL0A7tKdeymAHgAjACwGcL6IrAQAC+cSERERERGRDb4ErlFKvY5oZ0/zbRF5AsA0AE0AHlVKfQ0ARORSAP8RkbFKqccAnABgq1LqRQAvisjnAGwFcD+A9RnOJSIiIiIiIhsC2ZMoIm0AegE8CqAPwO+0+2Idyn0A+kSkAcBQ/f2xn/tiP5uea/CYO0Rkr4jsff755939g4iIiIiIiPKE751EESkBcCeAO2KzfdUAXk467GUANbH7kHS/dh8ynJtAKXWzUmq6Ump6S0tLdn8EERERERFRnvK1kygiEQBfBPAOgNNjN78GoDbp0FoAr8buQ9L92n2ZziWiHDSqpSroIhAREREVNF/2JAKAiAiAWwC0AVitlHo3dtejiO471I6rAjAK0b2GL4rIswD6Afwgdkh/7Jy053r4pxCRR352/mLUVZYEXQwiIiKigubnTOKnAYwDsE4p9abu9m8AmCAiG0SkHMDFAH6vCzzzBQC7RKRBRMYCeB+A2y2eS0Q5ZHhjJWrL2UkkIiIiCpJfeRJHADgZwGQAz4nIa7F/W5RSzwPYAOBKAC8CGACwWXf6JYgGo3kSwIMArlVK3Q8AFs4lIiIiIiIiG0QpFXQZfDd9+nS1d+/eoItBREREREQUCBH5rVJqutF9gaTAICIiIiIionBiJ5GIiIiIiIji2EkkIiIiIiKiOHYSiYiIiIiIKI6dRCIiIiIiIopjJ5GIiIiIiIji2EkkIiIiIiKiOHYSiYiIiIiIKI6dRCIiIiIiIopjJ5GIiIiIiIji2EkkIiIiIiKiOHYSiYiIiIiIKI6dRCIiIiIiIooTpVTQZfCdiLwK4C8ePkQdgJdz6Lp+XJ/Xzr/r89rGmgH8x8Pre1H+XH0f5mq5vb6219fP1Wt7ff1crlty8TOaq9f2+vosu//X9vr6Xl57jFKqxvAepVTB/QOw1+Pr35xL1/Xj+rx2/l2f1za9fs7VL7n6PszVcrPsfF4cXt+zuiUXP6O5em2WPf+unctlT1evcLmpN+7Nsev6cX1eO/+uz2sHw4vy5+r7MFfL7fW1vb5+rl7b6+vnct2Si5/RXL2219dn2f2/ttfXD6RuKdTlpnuVUtODLgcR5R/WL0TkBdYtROS2dPVKoc4k3hx0AYgob7F+ISIvsG4hIreZ1isFOZNIRERERERExgp1JpHIERG5XUSuCLocRJRfWLcQkRdYt5BT7CQSARCRn4jI9qDLQUT5hXULEXmBdQt5jZ1EIiIiIiIiimMnkUhHRLaKyENJtykRGR1UmYgo97FuISIvsG4hr+RcJ1FEcq7MRJQbWL8QkdtYrxBRLsqpiktEipRSB4MuBxHlH9YvROQ21itElKtyopMoIkUAoJQ6ICLNIvIxETlbRPqCLhsR5TbWL0TkNtYrRJTrcqKTqJQ6AAAiMhfAgwDaABwK4FoRmRy7Lyf+Fgq91wFUar+IyJAAy0I+YP1CPmHdUkBYr5CPWLeQJ0JZQYmIJP1eJiJfBnAJgI8rpY4CcDqAfQDOBwAu5yCX/A5An4hMFpFyAJcGXB5yGesXCgjrljzGeoUCxLqFPBGqTqJEFSmllP52pdTbAH4KYCKAmthtjwL4LoDhInJk7PxQ/T2Uc5RS6q8APgzgAQCPA3go/SmUK1i/UIBYt+Qp1isUMNYt5BlJqteCKYRIRD+iJiLVAC4C8CqA3yqlvhcbpfsmgMcA3KSUekZEWgCcCmABgDVKqbcCKD7lARF5GMCHlVL/L+iykLtYv1CQWLfkJ9YrFDTWLeS1wEewRGQlgCtFpDP2+3YAfwcwDkA/gI+LyHGxUbpbAMyK/YNS6nkAPwYgAOYFUHzKA7FAAuMAPBJ0WchdrF8oSKxb8hPrFQoa6xbyQ+CdRADFAJYBmCkilQCmA3i/Uurw2Br+HwG4EgCUUt8E8FcAK0VkfOz8XwPYoJR6wP+iU64TkY8A+D6AC5RSTwZdHnId6xcKBOuWvMZ6hQLDuoX8Epblpp8EUAvgCgCvKqX+KSI9AD4PoAPR9fxfUUqdKSJTAXwF0c3gd2n7ALRN48n7AoiosLF+ISK3sV4honwX6EyiLhrYTQC6ACwB8IKIdAP4KoBfKKVGAbgZwOkiMlIp9TCA7Uqp/9FXrCrG37+AiMKK9QsRuY31ChEVikA7iUopJSISi8z0XQBrEF1jPQrAC0qpC2OHliG68XtD7LyfAakhp4mINKxfiMhtrFeIqFCEYrkpEI8M9g1E1/K/BWA9ohXsAgB7AZyqlHo5uBISUa5i/UJEbmO9QkT5LAyBa7RQ0q8B+CKAuQCeQ3SdfwmA65RSW5RSL8fyEaUts4iU6q/rZbmJKPxcrl+q9df1stxEFF5u1iux63WLSG3sZ842ElHgQjOTqBGRuwA8D+ASpdR/dbcXKaUOpDmvE8AeAO8A+IdSapfnhSWinJJl/XITgPcQzYO2Qyn1ntflJaLwc1qv6I47DcC1AI5XSt3tXUmJiKwLzUi4buTsYwBmILq+HyJSBAAZGnA7EV3a8Syiyz6OEpFbY/eF5m8komBkWb9cBOBhAE8D+DCA5QA+mXRdIiow2dQrSfoBvIhoSo0et8tJROREaDpQsc3gEaXU/yKaZPaQ2O1pK1kRqQfQA+B0pdQHlFJfALARwHoRqVVKHfS67EQUblnULwLgIICVSqkzlFJ/APAQgNpY8IpwLcUgIt84rVc0WmcSwOMA7gIwAGCeiJR5UV4iIjtC00kEAKXUwVhi2jcB/MXsOK0CjTXg3kY0/9D9sdsiAOoB/BnRSpuIyEn9UhzrBN6glNorItNE5C8ADgXwdwCH6/dAE1HhsVqvAAl1S/JM42wAtwH4NoDDAIz0rMBERBaFqpMYcziARwDck3yHiDTElpF+BojnGHpTKbVXKfVKbGT/IKKhp18F8JqfBSei0LNTv7wX+//t2CHtAD6hlKoCcAOiibEvEpEaPwpORKFlWq8AhnXLgdjtWhvsaQDDAdwCoBzA0SJyhYhM8rrgRERmwhi4xnAJl4hMBPBxAE0AXgFwvVLqHqON4SLyaQDvKqXO8KXQRJQTXKpfJLbM7EgA1wMYr5R63Y/yE1H4pFt6nqZuiWjbYUTkpwBOVErtE5F7AawCcB+ALbEIqkREvgvdTGKaPT6liIaa3grghwDeJyKlSqkD2uZxEYnElnFMQ3R9P0Rku4ic4n3JiSjssqlfdIpj/7+KaLCJWi/KSkS5IcPeZLO65aBuufqvAFwmIn9AtD55CMB+AFWeFZqIKIPQdRI1IjJWRBaKSGvspj8AuFsp9VsA3wOgAJyuHR77XyFawf4bQIeI/AjAlYgu5SAiAuC4ftFC2r8rIuMQzYn2XaXUs36WnYjCy07dopR6J7bkdCiAPgA3KqUWAvgIgEb/S09ENCiMy02LEF23vwnAbxGtPM9XSt2rO6YawEkANgA4Tin1pLZ0Q0SWI1oRvwDgY0qpD/v+RxBRKGVRvwiAakQDTJwGYAGAa5VSV/n8JxBRCDmtW2K3jwTwL6XUG74XnIjIRBhnEvsAjEY039AKALcDuElEFmgHxNbo/xDAPwGcHbvtYKySfgnApQC62EEkoiRO6xeFaCCsxxHdK9TJDiIR6TiqW2KeVkq9oQWyYf5VIgqDUHQSRaROF+VrFoARSqn/ADiolPoIouv1TxCRbt1pf0U09cUEEblKRH4BYKFS6jdKqQ9zszcRAa7WL8uUUk8opW5WSr3q6x9BRKHjUt3yvwCWAtHB7tj/4VriRUQFKdBOooj0iMj3ANwJ4OsiMgLAnwA8JSKTtQoTwNUA+gHEw0Erpd4BcADRivkEAJ9VSv3I1z+AiELLg/rlB77+AUQUSi7XLZ9TSn3P1z+AiMiCwDqJInISgB8hmlvofEQ3ae9GNHLgvxBdrgEAUEr9HtHN38fFzi2K7T28G8CnlFLDlFK3+/oHEFFosX4hIi+wbiGiQhFY4BoRuQLAk0qpz8V+7wDwGIBeRCvUqdDNDorIOgB7AMyIrd0fBuB1pdRLgfwBRBRarF+IyAusW4ioUBRnPsQznwHwNgCISBmANwDsA1AB4GuIbv4+S0T2xSKAzQDwfS36l1LqmUBKTUS5gPULEXmBdQsRFYTAOolKqX8A0SheSqm3RWQ8ostfn47lDvoYonnI7hORlwCMAbAlqPISUe5g/UJEXmDdQkSFIsiZRAAJUbwWAfhLbFM3lFJ/FJENAKYA6FNK3RFQEYkoR7F+ISIvsG4honwXeCdRRIqUUgcAzARwf+y2UxAdfbtSKbUXwN4Ai0hEOYr1CxF5gXULEeW7wDuJSqkDIlKMaISwVhH5KYAuANuUUs8HWjgiymmsX4jIC6xbiCjfBRbdNKEQIhMB/A7R8NHXK6WuC7hIRJQnWL8QkRdYtxBRPgtLJ7EUwOmI5g16K+jyEFH+YP1CRF5g3UJE+SwUnUQiIiIiIiIKh0jQBSAiIiIiIqLwYCeRiIiIiIiI4thJJCIiIiIiojh2EomIiIiIiCiOnUQiIiIiIiKKYyeRiIgIgIh0ishrIlIUdFmIiIiCxE4iEREVLBHZLyLLAEAp9ZRSqlopdcDHx18kIv/w6/GIiIisYCeRiIiIiIiI4thJJCKigiQiXwTQCeDe2DLT80VEiUhx7P6fiMgVIvLz2P33ikiTiNwpIq+IyG9EpEt3vbEi8gMReUFE/iIim3T3rRaRP4nIqyLyjIicKyJVAL4LoD12/ddEpF1EZorIL0TkJRF5VkQ+ISKlumspETlVRB6PXe9yERkVK+crIvJV7XhtplJEPiQi/4nNnG7x5xkmIqJcxU4iEREVJKXUcQCeArBOKVUN4KsGh20GcByAYQBGAfgFgNsANAL4M4BLACDW4fsBgC8DaI2d9ykRGR+7zi0ATlZK1QCYAOBHSqnXAawC8M/YMtdqpdQ/ARwAcDaAZgCzASwFcGpSuQ4BMA3ALADnA7gZwLEAhseuf7Tu2CGxaw0DcAKAm0VkjK0ni4iICgo7iUREROZuU0rtU0q9jOis3z6l1ANKqfcAfA3AlNhxawHsV0rdppR6Tyn1CICvA9gYu/9dAONFpFYp9aJS6mGzB1RK/VYp9cvYdfYD+CyAhUmHXaOUekUp9SiAPwL4vlLq77pyTkk6frdS6m2l1IMA7gOwCURERCbYSSQiIjL3L93Pbxr8Xh37eQSAgdgS0ZdE5CUAWxCdxQOA/9/OHbJmGUZhHP9fwVnUKbYhBsExP4DBIJgMFoMmZX3rJllZUfwEBqsiYjHsCyz7BZbEIYzXNNhsgsfw3Lt9w1bePaDu/f/gbg/nnHo4F88j4AGwm2Q7yZ2TGiZZTrKVZJLkAHjBcAmcZS6A/Xa1PLILLJ3UX5Ikl0RJ0jyrkep8A7ar6vLUu1BV6wBV9bmqHjJEUT/xJ9p6XP/XwA5ws6ouAc+BnGK2Ky0Oe+Q6sHeKepKkM84lUZI0z74DN0aoswUsJ1lNcq6920luJVlI8jTJYlX9BA6AX1P9ryZZnKp1sX3zI8kKsD7CfJttjrsM0diPI9SUJJ1RLomSpHn2Etho8dDHsxapqkPgPsMPa/aACfAKON8+WQW+tvjoGkMUlaraAd4DX1pMdQl4BjwBDoE3wIdZ52omwH6b6x2w1vpKknSsVI2VtJEkSf+SJPeAt1V17W/PIkn6f3hJlCRJkiR1LomSJEmSpM64qSRJkiSp85IoSZIkSepcEiVJkiRJnUuiJEmSJKlzSZQkSZIkdS6JkiRJkqTOJVGSJEmS1P0G8XdW8bXHiqkAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## สร้างชุดข้อมูลสำหรับการฝึกและการทดสอบ\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-11-01 00:00:00  0.10\n",
+       "2014-11-01 01:00:00  0.07\n",
+       "2014-11-01 02:00:00  0.05\n",
+       "2014-11-01 03:00:00  0.04\n",
+       "2014-11-01 04:00:00  0.06\n",
+       "2014-11-01 05:00:00  0.10\n",
+       "2014-11-01 06:00:00  0.19\n",
+       "2014-11-01 07:00:00  0.31\n",
+       "2014-11-01 08:00:00  0.40\n",
+       "2014-11-01 09:00:00  0.48"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "ข้อมูลดั้งเดิมเทียบกับข้อมูลที่ปรับขนาด:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "มาปรับขนาดข้อมูลทดสอบกันเถอะ\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-12-30 00:00:00  0.33\n",
+       "2014-12-30 01:00:00  0.29\n",
+       "2014-12-30 02:00:00  0.27\n",
+       "2014-12-30 03:00:00  0.27\n",
+       "2014-12-30 04:00:00  0.30"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Forecasting horizon: 3 hours\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "order = (4, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
+    "results = model.fit()\n",
+    "\n",
+    "print(results.summary())\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Thu, 30 Sep 2021   AIC                          -6942.477\n",
+      "Time:                                    14:36:28   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "สร้างจุดข้อมูลทดสอบสำหรับแต่ละขั้นตอนของ HORIZON\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load  load+1  load+2\n",
+       "2014-12-30 00:00:00  0.33    0.29    0.27\n",
+       "2014-12-30 01:00:00  0.29    0.27    0.27\n",
+       "2014-12-30 02:00:00  0.27    0.27    0.30\n",
+       "2014-12-30 03:00:00  0.27    0.30    0.41\n",
+       "2014-12-30 04:00:00  0.30    0.41    0.57"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2014-12-30 00:00:00\n",
+      "1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
+      "2014-12-30 01:00:00\n",
+      "2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
+      "2014-12-30 02:00:00\n",
+      "3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
+      "2014-12-30 03:00:00\n",
+      "4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
+      "2014-12-30 04:00:00\n",
+      "5 : predicted = [0.3  0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
+      "2014-12-30 05:00:00\n",
+      "6 : predicted = [0.4  0.55 0.66] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
+      "2014-12-30 06:00:00\n",
+      "7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
+      "2014-12-30 07:00:00\n",
+      "8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
+      "2014-12-30 08:00:00\n",
+      "9 : predicted = [0.75 0.8  0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
+      "2014-12-30 09:00:00\n",
+      "10 : predicted = [0.77 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
+      "2014-12-30 10:00:00\n",
+      "11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
+      "2014-12-30 11:00:00\n",
+      "12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
+      "2014-12-30 12:00:00\n",
+      "13 : predicted = [0.7  0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
+      "2014-12-30 13:00:00\n",
+      "14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
+      "2014-12-30 14:00:00\n",
+      "15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
+      "2014-12-30 15:00:00\n",
+      "16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
+      "2014-12-30 16:00:00\n",
+      "17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
+      "2014-12-30 17:00:00\n",
+      "18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
+      "2014-12-30 18:00:00\n",
+      "19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
+      "2014-12-30 19:00:00\n",
+      "20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
+      "2014-12-30 20:00:00\n",
+      "21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
+      "2014-12-30 21:00:00\n",
+      "22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
+      "2014-12-30 22:00:00\n",
+      "23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
+      "2014-12-30 23:00:00\n",
+      "24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
+      "2014-12-31 00:00:00\n",
+      "25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
+      "2014-12-31 01:00:00\n",
+      "26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
+      "2014-12-31 02:00:00\n",
+      "27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
+      "2014-12-31 03:00:00\n",
+      "28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
+      "2014-12-31 04:00:00\n",
+      "29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
+      "2014-12-31 05:00:00\n",
+      "30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
+      "2014-12-31 06:00:00\n",
+      "31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
+      "2014-12-31 07:00:00\n",
+      "32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
+      "2014-12-31 08:00:00\n",
+      "33 : predicted = [0.79 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
+      "2014-12-31 09:00:00\n",
+      "34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
+      "2014-12-31 10:00:00\n",
+      "35 : predicted = [0.8  0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
+      "2014-12-31 11:00:00\n",
+      "36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
+      "2014-12-31 12:00:00\n",
+      "37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
+      "2014-12-31 13:00:00\n",
+      "38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
+      "2014-12-31 14:00:00\n",
+      "39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
+      "2014-12-31 15:00:00\n",
+      "40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
+      "2014-12-31 16:00:00\n",
+      "41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
+      "2014-12-31 17:00:00\n",
+      "42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
+      "2014-12-31 18:00:00\n",
+      "43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
+      "2014-12-31 19:00:00\n",
+      "44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
+      "2014-12-31 20:00:00\n",
+      "45 : predicted = [0.7  0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
+      "2014-12-31 21:00:00\n",
+      "46 : predicted = [0.6  0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
+      "CPU times: user 12min 15s, sys: 2min 39s, total: 14min 54s\n",
+      "Wall time: 2min 36s\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": true
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "เปรียบเทียบการคาดการณ์กับภาระงานจริง\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp    h  prediction   actual\n",
+       "0 2014-12-30 00:00:00  t+1    3,008.74 3,023.00\n",
+       "1 2014-12-30 01:00:00  t+1    2,955.53 2,935.00\n",
+       "2 2014-12-30 02:00:00  t+1    2,900.17 2,899.00\n",
+       "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
+       "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "คำนวณ **ค่าเฉลี่ยเปอร์เซ็นต์ความคลาดเคลื่อนสัมบูรณ์ (MAPE)** สำหรับการพยากรณ์ทั้งหมด\n",
+    "\n",
+    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "One step forecast MAPE:  0.5570581332313952 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Multi-step forecast MAPE:  1.1460048657704118 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "แสดงกราฟการคาดการณ์เทียบกับค่าจริงสำหรับสัปดาห์แรกของชุดทดสอบ\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "if(HORIZON == 1):\n",
+    "    ## Plotting single step forecast\n",
+    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
+    "\n",
+    "else:\n",
+    "    ## Plotting multi step forecast\n",
+    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
+    "\n",
+    "    fig = plt.figure(figsize=(15, 8))\n",
+    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        x = plot_df['timestamp'][(t-1):]\n",
+    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
+    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
+    "    \n",
+    "    ax.legend(loc='best')\n",
+    "    \n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "c193140200b9684da27e3890211391b6",
+   "translation_date": "2025-09-06T13:59:09+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/th/7-TimeSeries/2-ARIMA/working/notebook.ipynb b/translations/th/7-TimeSeries/2-ARIMA/working/notebook.ipynb
new file mode 100644
index 000000000..21ea12e6c
--- /dev/null
+++ b/translations/th/7-TimeSeries/2-ARIMA/working/notebook.ipynb
@@ -0,0 +1,61 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "523ec472196307b3c4235337353c9ceb",
+   "translation_date": "2025-09-06T14:00:42+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/working/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# การพยากรณ์ข้อมูลอนุกรมเวลาด้วย ARIMA\n",
+    "\n",
+    "ในโน้ตบุ๊กนี้ เราจะแสดงวิธีการ:\n",
+    "- เตรียมข้อมูลอนุกรมเวลาเพื่อฝึกโมเดลการพยากรณ์อนุกรมเวลาด้วย ARIMA\n",
+    "- สร้างโมเดล ARIMA แบบง่ายเพื่อพยากรณ์ค่าล่วงหน้าในช่วง HORIZON (ตั้งแต่เวลา *t+1* ถึง *t+HORIZON*) ในอนุกรมเวลา\n",
+    "- ประเมินผลโมเดล\n",
+    "\n",
+    "ข้อมูลในตัวอย่างนี้นำมาจากการแข่งขัน GEFCom2014 ด้านการพยากรณ์ \n",
+    "\n",
+    "ประกอบด้วยข้อมูลโหลดไฟฟ้ารายชั่วโมงและค่าอุณหภูมิเป็นเวลา 3 ปี ระหว่างปี 2012 ถึง 2014 โดยมีเป้าหมายเพื่อพยากรณ์ค่าโหลดไฟฟ้าในอนาคต ในตัวอย่างนี้ เราจะแสดงวิธีพยากรณ์ค่าล่วงหน้าเพียงหนึ่งช่วงเวลา โดยใช้ข้อมูลโหลดในอดีตเท่านั้น\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli และ Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, กรกฎาคม-กันยายน, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install statsmodels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/7-TimeSeries/3-SVR/solution/notebook.ipynb b/translations/th/7-TimeSeries/3-SVR/solution/notebook.ipynb
new file mode 100644
index 000000000..2aa997fe3
--- /dev/null
+++ b/translations/th/7-TimeSeries/3-SVR/solution/notebook.ipynb
@@ -0,0 +1,1013 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ในสมุดบันทึกนี้ เราจะแสดงวิธีการ:\n",
+    "\n",
+    "- เตรียมข้อมูลชุดเวลาแบบ 2 มิติสำหรับการฝึกโมเดล SVM regressor  \n",
+    "- ใช้ SVR ด้วย RBF kernel  \n",
+    "- ประเมินโมเดลโดยใช้กราฟและค่า MAPE  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### โหลดข้อมูล\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ตอนนี้คุณจำเป็นต้องเตรียมข้อมูลสำหรับการฝึกโดยการกรองและปรับขนาดข้อมูลของคุณ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ปรับขนาดข้อมูลให้อยู่ในช่วง (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.101611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.065801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.046106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.042525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.059087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-11-01 00:00:00  0.101611\n",
+       "2014-11-01 01:00:00  0.065801\n",
+       "2014-11-01 02:00:00  0.046106\n",
+       "2014-11-01 03:00:00  0.042525\n",
+       "2014-11-01 04:00:00  0.059087"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.329454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.290063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.273948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.268129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.302596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-12-30 00:00:00  0.329454\n",
+       "2014-12-30 01:00:00  0.290063\n",
+       "2014-12-30 02:00:00  0.273948\n",
+       "2014-12-30 03:00:00  0.268129\n",
+       "2014-12-30 04:00:00  0.302596"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "สำหรับ SVR ของเรา เราแปลงข้อมูลอินพุตให้อยู่ในรูปแบบ `[batch, timesteps]` ดังนั้น เราจึงปรับรูปร่าง `train_data` และ `test_data` ที่มีอยู่ให้มีมิติใหม่ซึ่งหมายถึง timesteps สำหรับตัวอย่างของเรา เรากำหนดให้ `timesteps = 5` ดังนั้น อินพุตของโมเดลจะเป็นข้อมูลสำหรับ 4 timesteps แรก และเอาต์พุตจะเป็นข้อมูลสำหรับ timestep ที่ 5\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1412, 5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=np.array([[j for j in train_data[i:i+timesteps]] for i in range(0,len(train_data)-timesteps+1)])[:,:,0]\n",
+    "train_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(44, 5)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=np.array([[j for j in test_data[i:i+timesteps]] for i in range(0,len(test_data)-timesteps+1)])[:,:,0]\n",
+    "test_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 4) (1412, 1)\n",
+      "(44, 4) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train, y_train = train_data_timesteps[:,:timesteps-1],train_data_timesteps[:,[timesteps-1]]\n",
+    "x_test, y_test = test_data_timesteps[:,:timesteps-1],test_data_timesteps[:,[timesteps-1]]\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = SVR(kernel='rbf',gamma=0.5, C=10, epsilon = 0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVR(C=10, cache_size=200, coef0=0.0, degree=3, epsilon=0.05, gamma=0.5,\n",
+       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit model on training data\n",
+    "\n",
+    "model.fit(x_train, y_train[:,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 1) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = model.predict(x_train).reshape(-1,1)\n",
+    "y_test_pred = model.predict(x_test).reshape(-1,1)\n",
+    "\n",
+    "print(y_train_pred.shape, y_test_pred.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
+    "\n",
+    "print(len(y_train_pred), len(y_test_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)\n",
+    "\n",
+    "print(len(y_train), len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)].index[timesteps-1:]\n",
+    "test_timestamps = energy[test_start_dt:].index[timesteps-1:]\n",
+    "\n",
+    "print(len(train_timestamps), len(test_timestamps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "plt.plot(train_timestamps, y_train, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(train_timestamps, y_train_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for training data:  1.7195710200875551 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "plt.plot(test_timestamps, y_test, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(test_timestamps, y_test_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for testing data:  1.2623790187854018 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor shape:  (26300, 5)\n",
+      "X shape:  (26300, 4) \n",
+      "Y shape:  (26300, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = energy.copy().values\n",
+    "\n",
+    "# Scaling\n",
+    "data = scaler.transform(data)\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=np.array([[j for j in data[i:i+timesteps]] for i in range(0,len(data)-timesteps+1)])[:,:,0]\n",
+    "print(\"Tensor shape: \", data_timesteps.shape)\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = data_timesteps[:,:timesteps-1],data_timesteps[:,[timesteps-1]]\n",
+    "print(\"X shape: \", X.shape,\"\\nY shape: \", Y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "Y_pred = model.predict(X).reshape(-1,1)\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = scaler.inverse_transform(Y_pred)\n",
+    "Y = scaler.inverse_transform(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "plt.plot(Y, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(Y_pred, color = 'blue', linewidth=1)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE:  2.0572089029888656 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาต้นทางควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "f8f3967282314d3995245835bdaa8418",
+   "translation_date": "2025-09-06T14:04:42+00:00",
+   "source_file": "7-TimeSeries/3-SVR/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/th/7-TimeSeries/3-SVR/working/notebook.ipynb b/translations/th/7-TimeSeries/3-SVR/working/notebook.ipynb
new file mode 100644
index 000000000..573efb259
--- /dev/null
+++ b/translations/th/7-TimeSeries/3-SVR/working/notebook.ipynb
@@ -0,0 +1,689 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ในสมุดบันทึกนี้ เราจะแสดงวิธีการ:\n",
+    "\n",
+    "- เตรียมข้อมูลชุดเวลาแบบ 2 มิติสำหรับการฝึกโมเดล SVM regressor  \n",
+    "- ใช้ SVR ด้วย RBF kernel  \n",
+    "- ประเมินโมเดลโดยใช้กราฟและค่า MAPE  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### โหลดข้อมูล\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ตอนนี้คุณจำเป็นต้องเตรียมข้อมูลสำหรับการฝึกโดยการกรองและปรับขนาดข้อมูลของคุณ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ปรับขนาดข้อมูลให้อยู่ในช่วง (0, 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "สำหรับ SVR ของเรา เราแปลงข้อมูลอินพุตให้อยู่ในรูปแบบ `[batch, timesteps]` ดังนั้น เราจึงปรับรูปร่าง `train_data` และ `test_data` ที่มีอยู่ให้มีมิติใหม่ซึ่งอ้างอิงถึง timesteps สำหรับตัวอย่างของเรา เรากำหนดให้ `timesteps = 5` ดังนั้น อินพุตของโมเดลจะเป็นข้อมูลสำหรับ 4 timesteps แรก และเอาต์พุตจะเป็นข้อมูลสำหรับ timestep ที่ 5\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [],
+   "source": [
+    "x_train, y_train = None\n",
+    "x_test, y_test = None\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit model on training data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = None\n",
+    "y_test_pred = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = None\n",
+    "test_timestamps = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = None\n",
+    "\n",
+    "# Scaling\n",
+    "data = None\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=None\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = None, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = None\n",
+    "Y = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "e86ce102239a14c44585623b9b924a74",
+   "translation_date": "2025-09-06T14:07:12+00:00",
+   "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/th/8-Reinforcement/1-QLearning/notebook.ipynb b/translations/th/8-Reinforcement/1-QLearning/notebook.ipynb
new file mode 100644
index 000000000..03f175e77
--- /dev/null
+++ b/translations/th/8-Reinforcement/1-QLearning/notebook.ipynb
@@ -0,0 +1,411 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "17e5a668646eabf5aabd0e9bfcf17876",
+   "translation_date": "2025-09-06T15:05:51+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# ปีเตอร์กับหมาป่า: บทเรียนพื้นฐานเกี่ยวกับการเรียนรู้แบบเสริมกำลัง\n",
+    "\n",
+    "ในบทเรียนนี้ เราจะเรียนรู้วิธีการนำการเรียนรู้แบบเสริมกำลังมาใช้กับปัญหาการค้นหาเส้นทาง สถานการณ์นี้ได้รับแรงบันดาลใจจากนิทานดนตรีเรื่อง [ปีเตอร์กับหมาป่า](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) โดยนักประพันธ์ชาวรัสเซีย [เซอร์เกย์ โปรโกฟีเยฟ](https://en.wikipedia.org/wiki/Sergei_Prokofiev) เป็นเรื่องราวเกี่ยวกับปีเตอร์ เด็กชายผู้กล้าหาญที่ออกจากบ้านไปยังลานป่าเพื่อไล่ล่าหมาป่า เราจะฝึกอัลกอริทึมการเรียนรู้ของเครื่องที่จะช่วยปีเตอร์สำรวจพื้นที่โดยรอบและสร้างแผนที่นำทางที่เหมาะสมที่สุด\n",
+    "\n",
+    "ก่อนอื่น มาเริ่มต้นด้วยการนำเข้าไลบรารีที่มีประโยชน์หลายตัว:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## ภาพรวมของการเรียนรู้แบบเสริมกำลัง\n",
+    "\n",
+    "**การเรียนรู้แบบเสริมกำลัง** (Reinforcement Learning หรือ RL) เป็นเทคนิคการเรียนรู้ที่ช่วยให้เราสามารถเรียนรู้พฤติกรรมที่เหมาะสมที่สุดของ **ตัวแทน** (agent) ใน **สภาพแวดล้อม** (environment) โดยการทดลองทำซ้ำหลายๆ ครั้ง ตัวแทนในสภาพแวดล้อมนี้ควรมี **เป้าหมาย** ซึ่งกำหนดโดย **ฟังก์ชันรางวัล** (reward function)\n",
+    "\n",
+    "## สภาพแวดล้อม\n",
+    "\n",
+    "เพื่อความเข้าใจง่าย ลองพิจารณาโลกของปีเตอร์เป็นกระดานสี่เหลี่ยมขนาด `width` x `height` แต่ละช่องในกระดานนี้สามารถเป็นได้ดังนี้:\n",
+    "* **พื้นดิน** ซึ่งปีเตอร์และสิ่งมีชีวิตอื่นๆ สามารถเดินได้\n",
+    "* **น้ำ** ซึ่งแน่นอนว่าไม่สามารถเดินได้\n",
+    "* **ต้นไม้** หรือ **หญ้า** - สถานที่ที่สามารถพักผ่อนได้\n",
+    "* **แอปเปิล** ซึ่งเป็นสิ่งที่ปีเตอร์จะดีใจที่ได้พบเพื่อใช้เป็นอาหาร\n",
+    "* **หมาป่า** ซึ่งเป็นอันตรายและควรหลีกเลี่ยง\n",
+    "\n",
+    "ในการทำงานกับสภาพแวดล้อม เราจะกำหนดคลาสที่เรียกว่า `Board` เพื่อไม่ให้เนื้อหาในสมุดบันทึกนี้ดูรกเกินไป เราได้ย้ายโค้ดทั้งหมดที่เกี่ยวข้องกับการทำงานของกระดานไปยังโมดูล `rlboard` แยกต่างหาก ซึ่งเราจะนำเข้าในตอนนี้ คุณสามารถดูรายละเอียดเพิ่มเติมเกี่ยวกับการทำงานภายในของการนำไปใช้ในโมดูลนี้ได้\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "ตอนนี้มาสร้างกระดานแบบสุ่มและดูว่ามันเป็นอย่างไร:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## การกระทำและนโยบาย\n",
+    "\n",
+    "ในตัวอย่างของเรา เป้าหมายของปีเตอร์คือการหาแอปเปิ้ล ในขณะที่หลีกเลี่ยงหมาป่าและสิ่งกีดขวางอื่น ๆ กำหนดการกระทำเหล่านั้นเป็นพจนานุกรม และจับคู่กับคู่ของการเปลี่ยนแปลงพิกัดที่สอดคล้องกัน\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 2"
+   ]
+  },
+  {
+   "source": [
+    "กลยุทธ์ของตัวแทนของเรา (ปีเตอร์) ถูกกำหนดโดยสิ่งที่เรียกว่า **นโยบาย** ลองพิจารณานโยบายที่ง่ายที่สุดที่เรียกว่า **การเดินแบบสุ่ม**\n",
+    "\n",
+    "## การเดินแบบสุ่ม\n",
+    "\n",
+    "มาแก้ปัญหาของเราด้วยการใช้งานกลยุทธ์การเดินแบบสุ่มกันก่อน\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Let's run a random walk experiment several times and see the average number of steps taken: code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 4"
+   ]
+  },
+  {
+   "source": [
+    "## ฟังก์ชันรางวัล\n",
+    "\n",
+    "เพื่อทำให้กลยุทธ์ของเราฉลาดขึ้น เราจำเป็นต้องเข้าใจว่าการเคลื่อนไหวใด \"ดีกว่า\" การเคลื่อนไหวอื่นๆ\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning\n",
+    "\n",
+    "สร้าง Q-Table หรืออาเรย์หลายมิติ เนื่องจากกระดานของเรามีขนาด `width` x `height` เราสามารถแสดง Q-Table ได้ด้วย numpy array ที่มีรูปร่างเป็น `width` x `height` x `len(actions)`\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 6"
+   ]
+  },
+  {
+   "source": [
+    "ส่ง Q-Table ไปยังฟังก์ชัน `plot` เพื่อแสดงตารางบนกระดาน:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'm' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8b8a9e4cb2b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## สาระสำคัญของ Q-Learning: สมการ Bellman และอัลกอริทึมการเรียนรู้\n",
+    "\n",
+    "เขียน pseudo-code สำหรับอัลกอริทึมการเรียนรู้ของเรา:\n",
+    "\n",
+    "* เริ่มต้น Q-Table Q ด้วยค่าที่เท่ากันสำหรับทุกสถานะและการกระทำ\n",
+    "* กำหนดอัตราการเรียนรู้ $\\alpha\\leftarrow 1$\n",
+    "* ทำการจำลองซ้ำหลายครั้ง\n",
+    "   1. เริ่มต้นที่ตำแหน่งสุ่ม\n",
+    "   1. ทำซ้ำ\n",
+    "        1. เลือกการกระทำ $a$ ที่สถานะ $s$\n",
+    "        2. ดำเนินการโดยย้ายไปยังสถานะใหม่ $s'$\n",
+    "        3. หากพบเงื่อนไขสิ้นสุดเกม หรือรางวัลรวมมีค่าน้อยเกินไป - ออกจากการจำลอง  \n",
+    "        4. คำนวณรางวัล $r$ ที่สถานะใหม่\n",
+    "        5. อัปเดต Q-Function ตามสมการ Bellman: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. อัปเดตรางวัลรวมและลดค่า $\\alpha$\n",
+    "\n",
+    "## การใช้ประโยชน์ vs. การสำรวจ\n",
+    "\n",
+    "วิธีที่ดีที่สุดคือการสร้างสมดุลระหว่างการสำรวจและการใช้ประโยชน์ เมื่อเราเรียนรู้เกี่ยวกับสภาพแวดล้อมมากขึ้น เราจะมีแนวโน้มที่จะเลือกเส้นทางที่เหมาะสมที่สุด แต่ควรเลือกเส้นทางที่ยังไม่ได้สำรวจบ้างเป็นครั้งคราว\n",
+    "\n",
+    "## การใช้งาน Python\n",
+    "\n",
+    "ตอนนี้เราพร้อมที่จะใช้อัลกอริทึมการเรียนรู้ ก่อนหน้านั้น เราต้องมีฟังก์ชันที่จะแปลงตัวเลขใน Q-Table ให้เป็นเวกเตอร์ของความน่าจะเป็นสำหรับการกระทำที่เกี่ยวข้อง:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 7"
+   ]
+  },
+  {
+   "source": [
+    "เราเพิ่มค่า `eps` เล็กน้อยลงในเวกเตอร์ต้นฉบับเพื่อหลีกเลี่ยงการหารด้วย 0 ในกรณีเริ่มต้น เมื่อทุกองค์ประกอบของเวกเตอร์มีค่าเหมือนกัน\n",
+    "\n",
+    "อัลกอริทึมการเรียนรู้ที่เราจะใช้งานจริงจะถูกดำเนินการใน 5000 การทดลอง ซึ่งเรียกว่า **epochs**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "# code block 8"
+   ]
+  },
+  {
+   "source": [
+    "หลังจากดำเนินการอัลกอริทึมนี้ ตาราง Q-Table ควรได้รับการอัปเดตด้วยค่าที่กำหนดความน่าสนใจของการกระทำต่าง ๆ ในแต่ละขั้นตอน แสดงตารางที่นี่:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## การตรวจสอบนโยบาย\n",
+    "\n",
+    "เนื่องจาก Q-Table แสดง \"ความน่าสนใจ\" ของแต่ละการกระทำในแต่ละสถานะ การใช้งานมันเพื่อกำหนดการนำทางที่มีประสิทธิภาพในโลกของเราจึงค่อนข้างง่าย ในกรณีที่ง่ายที่สุด เราสามารถเลือกการกระทำที่สอดคล้องกับค่าที่สูงที่สุดใน Q-Table ได้:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "# code block 9"
+   ]
+  },
+  {
+   "source": [
+    "หากคุณลองรันโค้ดด้านบนหลายครั้ง คุณอาจสังเกตเห็นว่าบางครั้งมันจะ \"ค้าง\" และคุณจำเป็นต้องกดปุ่ม STOP ในโน้ตบุ๊กเพื่อหยุดการทำงาน\n",
+    "\n",
+    "> **งานที่ 1:** แก้ไขฟังก์ชัน `walk` เพื่อจำกัดความยาวสูงสุดของเส้นทางโดยกำหนดจำนวนก้าว (เช่น 100) และสังเกตว่าโค้ดด้านบนจะคืนค่านี้เป็นครั้งคราว\n",
+    "\n",
+    "> **งานที่ 2:** แก้ไขฟังก์ชัน `walk` เพื่อไม่ให้กลับไปยังตำแหน่งที่เคยไปมาก่อนหน้านี้ สิ่งนี้จะช่วยป้องกันไม่ให้ `walk` เกิดการวนซ้ำ อย่างไรก็ตาม ตัวแทนอาจยังคงติดอยู่ในตำแหน่งที่ไม่สามารถหลบหนีได้\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 5.31, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# code block 10"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26dab295a48>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 57
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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107.315281 213.750715 \r\nL 107.34572 214.756364 \r\nL 107.37616 208.492013 \r\nL 107.437039 214.756364 \r\nL 107.467478 214.756364 \r\nL 107.589236 201.284868 \r\nL 107.650115 214.735413 \r\nL 107.710993 208.659621 \r\nL 107.741433 201.452476 \r\nL 107.771872 214.756364 \r\nL 107.802312 214.756364 \r\nL 107.832751 213.478352 \r\nL 107.86319 203.317115 \r\nL 107.924069 214.756364 \r\nL 108.045827 214.756364 \r\nL 108.106706 208.785327 \r\nL 108.137145 214.756364 \r\nL 108.167584 214.46305 \r\nL 108.198024 203.023801 \r\nL 108.258903 214.735413 \r\nL 108.350221 214.756364 \r\nL 108.38066 197.555589 \r\nL 108.471979 204.427518 \r\nL 108.593736 214.756364 \r\nL 108.624176 214.756364 \r\nL 108.776373 206.96259 \r\nL 108.837251 214.756364 \r\nL 108.89813 211.215644 \r\nL 108.92857 212.367949 \r\nL 108.959009 208.952935 \r\nL 108.989448 213.939274 \r\nL 109.019888 214.756364 \r\nL 109.050327 211.278497 \r\nL 109.111206 214.756364 \r\nL 109.141646 214.756364 \r\nL 109.172085 207.968238 \r\nL 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115.016451 213.43645 \r\nL 115.046891 214.46305 \r\nL 115.07733 207.339708 \r\nL 115.168648 209.66527 \r\nL 115.229527 213.268842 \r\nL 115.290406 211.215644 \r\nL 115.320845 205.642677 \r\nL 115.351285 214.756364 \r\nL 115.381724 209.749074 \r\nL 115.533921 214.756364 \r\nL 115.564361 213.687862 \r\nL 115.655679 206.606423 \r\nL 115.746997 214.756364 \r\nL 115.716558 193.700604 \r\nL 115.777436 214.735413 \r\nL 115.838315 193.763457 \r\nL 115.868755 193.910114 \r\nL 115.899194 214.756364 \r\nL 115.990512 212.346998 \r\nL 116.051391 210.754722 \r\nL 116.11227 214.756364 \r\nL 116.142709 211.76037 \r\nL 116.203588 214.756364 \r\nL 116.234027 214.756364 \r\nL 116.264467 213.478352 \r\nL 116.294906 214.756364 \r\nL 116.325346 184.670721 \r\nL 116.386225 214.756364 \r\nL 116.416664 214.044029 \r\nL 116.447103 214.756364 \r\nL 116.477543 196.047117 \r\nL 116.538422 212.786969 \r\nL 116.5993 210.964232 \r\nL 116.690619 214.756364 \r\nL 116.812376 200.404926 \r\nL 116.903694 214.756364 \r\nL 116.934134 196.403284 \r\nL 116.995013 214.756364 \r\nL 117.055891 214.756364 \r\nL 117.086331 213.939274 \r\nL 117.11677 199.650689 \r\nL 117.177649 212.158439 \r\nL 117.208089 214.756364 \r\nL 117.238528 213.939274 \r\nL 117.268967 205.684579 \r\nL 117.329846 214.756364 \r\nL 117.360286 214.484001 \r\nL 117.421164 195.774754 \r\nL 117.451604 210.000486 \r\nL 117.512483 214.756364 \r\nL 117.542922 176.709339 \r\nL 117.63424 184.64977 \r\nL 117.725558 183.874583 \r\nL 117.755998 214.756364 \r\nL 117.786437 214.756364 \r\nL 117.877756 198.959306 \r\nL 117.908195 210.566163 \r\nL 117.938634 212.766018 \r\nL 117.969074 206.522619 \r\nL 118.029953 214.756364 \r\nL 118.060392 214.756364 \r\nL 118.18215 199.399277 \r\nL 118.273468 214.756364 \r\nL 118.303907 205.035098 \r\nL 118.364786 214.756364 \r\nL 118.456104 214.232589 \r\nL 118.486544 214.756364 \r\nL 118.516983 209.476711 \r\nL 118.577862 214.756364 \r\nL 118.608301 213.352646 \r\nL 118.66918 214.735413 \r\nL 118.69962 186.786773 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207.004492 \r\nL 120.465105 200.90775 \r\nL 120.495545 214.756364 \r\nL 120.617302 181.423316 \r\nL 120.678181 214.756364 \r\nL 120.73906 211.13184 \r\nL 120.769499 187.247695 \r\nL 120.830378 208.931984 \r\nL 120.891257 214.756364 \r\nL 120.921696 195.544293 \r\nL 121.013014 204.888441 \r\nL 121.043454 214.735413 \r\nL 121.134772 213.583107 \r\nL 121.165212 209.979535 \r\nL 121.22609 214.756364 \r\nL 121.25653 214.148785 \r\nL 121.286969 210.92233 \r\nL 121.347848 214.756364 \r\nL 121.560924 214.756364 \r\nL 121.621803 198.728845 \r\nL 121.652242 209.686221 \r\nL 121.713121 214.756364 \r\nL 121.74356 207.779679 \r\nL 121.774 203.610429 \r\nL 121.895757 214.756364 \r\nL 121.926197 182.785131 \r\nL 121.987076 214.693511 \r\nL 122.017515 214.756364 \r\nL 122.078394 212.954577 \r\nL 122.108833 214.756364 \r\nL 122.230591 203.694233 \r\nL 122.352348 214.756364 \r\nL 122.382788 214.756364 \r\nL 122.413227 183.979338 \r\nL 122.474106 205.621726 \r\nL 122.504545 214.756364 \r\nL 122.534985 200.090661 \r\nL 122.595864 211.927978 \r\nL 122.687182 211.383252 \r\nL 122.717621 214.756364 \r\nL 122.7785 214.756364 \r\nL 122.80894 210.335702 \r\nL 122.900258 212.493655 \r\nL 122.930697 214.756364 \r\nL 123.022015 183.958387 \r\nL 123.052455 208.533915 \r\nL 123.082894 214.756364 \r\nL 123.113334 201.703888 \r\nL 123.143773 210.482359 \r\nL 123.174212 210.419506 \r\nL 123.265531 200.48873 \r\nL 123.29597 214.756364 \r\nL 123.326409 214.735413 \r\nL 123.356849 187.017234 \r\nL 123.417728 214.756364 \r\nL 123.569925 191.291239 \r\nL 123.691682 214.756364 \r\nL 123.722122 214.735413 \r\nL 123.783001 205.579824 \r\nL 123.81344 189.279942 \r\nL 123.874319 214.735413 \r\nL 123.935198 214.735413 \r\nL 123.996076 214.756364 \r\nL 124.026516 206.669276 \r\nL 124.148273 214.756364 \r\nL 124.178713 214.756364 \r\nL 124.239592 209.581466 \r\nL 124.270031 211.96988 \r\nL 124.300471 214.023078 \r\nL 124.33091 201.201064 \r\nL 124.422228 208.471062 \r\nL 124.513546 214.756364 \r\nL 124.543986 212.326047 \r\nL 124.604865 199.818297 \r\nL 124.635304 207.360659 \r\nL 124.726622 214.756364 \r\nL 124.787501 174.467582 \r\nL 124.81794 214.756364 \r\nL 124.909259 214.735413 \r\nL 124.939698 211.173742 \r\nL 124.970137 186.283949 \r\nL 125.031016 211.907027 \r\nL 125.091895 214.756364 \r\nL 125.152774 214.735413 \r\nL 125.274532 173.252424 \r\nL 125.36585 214.756364 \r\nL 125.396289 214.127834 \r\nL 125.426729 214.735413 \r\nL 125.457168 198.267923 \r\nL 125.518047 214.106882 \r\nL 125.548486 210.775673 \r\nL 125.609365 214.756364 \r\nL 125.639804 211.006134 \r\nL 125.731123 214.735413 \r\nL 125.761562 214.421148 \r\nL 125.822441 214.735413 \r\nL 125.913759 173.399081 \r\nL 125.974638 214.756364 \r\nL 126.035517 206.648325 \r\nL 126.065956 206.585472 \r\nL 126.187714 214.756364 \r\nL 126.248593 214.756364 \r\nL 126.309471 213.562156 \r\nL 126.339911 205.852187 \r\nL 126.37035 214.735413 \r\nL 126.40079 212.996479 \r\nL 126.431229 214.756364 \r\nL 126.461668 214.588756 \r\nL 126.522547 198.41458 \r\nL 126.552987 213.059332 \r\nL 126.705184 214.756364 \r\nL 126.735623 192.192132 \r\nL 126.796502 213.750715 \r\nL 126.91826 214.756364 \r\nL 126.979138 210.021437 \r\nL 127.009578 214.756364 \r\nL 127.040017 214.756364 \r\nL 127.131335 210.461408 \r\nL 127.192214 207.402561 \r\nL 127.253093 214.756364 \r\nL 127.283532 210.377604 \r\nL 127.344411 214.735413 \r\nL 127.374851 214.756364 \r\nL 127.40529 214.46305 \r\nL 127.466169 214.756364 \r\nL 127.527048 199.524983 \r\nL 127.557487 214.735413 \r\nL 127.587927 211.404203 \r\nL 127.618366 177.673086 \r\nL 127.648805 214.756364 \r\nL 127.679245 184.251701 \r\nL 127.801002 214.756364 \r\nL 127.861881 214.756364 \r\nL 127.953199 207.612071 \r\nL 128.014078 214.756364 \r\nL 128.044518 204.532273 \r\nL 128.074957 198.309825 \r\nL 128.105396 214.756364 \r\nL 128.166275 214.756364 \r\nL 128.257593 210.105241 \r\nL 128.348912 214.756364 \r\nL 128.501109 186.430606 \r\nL 128.531548 210.314751 \r\nL 128.592427 206.459766 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214.693511 \r\nL 130.236155 214.735413 \r\nL 130.297034 204.972245 \r\nL 130.388352 214.756364 \r\nL 130.449231 190.620806 \r\nL 130.47967 213.331695 \r\nL 130.540549 214.756364 \r\nL 130.601428 214.358295 \r\nL 130.692746 203.547576 \r\nL 130.753625 197.953658 \r\nL 130.814504 214.756364 \r\nL 130.844943 210.649967 \r\nL 130.905822 211.425154 \r\nL 130.936261 214.756364 \r\nL 130.99714 214.546854 \r\nL 131.058019 203.945645 \r\nL 131.088458 213.939274 \r\nL 131.149337 214.756364 \r\nL 131.179777 207.653973 \r\nL 131.240655 214.756364 \r\nL 131.51461 214.756364 \r\nL 131.54505 182.827033 \r\nL 131.605928 214.106882 \r\nL 131.636368 214.756364 \r\nL 131.758125 205.998844 \r\nL 131.788565 214.756364 \r\nL 131.819004 200.111612 \r\nL 131.849444 213.813568 \r\nL 131.879883 194.643399 \r\nL 131.910322 214.756364 \r\nL 131.940762 212.849822 \r\nL 131.971201 214.756364 \r\nL 132.092959 153.16041 \r\nL 132.184277 214.756364 \r\nL 132.214716 191.752161 \r\nL 132.306035 214.756364 \r\nL 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214.756364 \r\nL 143.446858 214.756364 \r\nL 143.477297 213.520254 \r\nL 143.507737 206.836884 \r\nL 143.599055 210.566163 \r\nL 143.629494 210.524261 \r\nL 143.690373 206.250256 \r\nL 143.751252 201.578182 \r\nL 143.781691 214.756364 \r\nL 143.84257 180.815736 \r\nL 143.87301 214.756364 \r\nL 143.903449 214.756364 \r\nL 143.933888 214.421148 \r\nL 143.994767 214.756364 \r\nL 144.025207 214.735413 \r\nL 144.055646 212.472704 \r\nL 144.086085 214.756364 \r\nL 144.116525 161.729371 \r\nL 144.177404 214.756364 \r\nL 144.207843 212.912675 \r\nL 144.238282 184.733574 \r\nL 144.268722 214.735413 \r\nL 144.299161 210.063339 \r\nL 144.36004 204.406567 \r\nL 144.39048 214.756364 \r\nL 144.420919 191.961671 \r\nL 144.481798 205.642677 \r\nL 144.573116 214.756364 \r\nL 144.542677 202.772389 \r\nL 144.603555 214.735413 \r\nL 144.633995 214.735413 \r\nL 144.755752 201.159162 \r\nL 144.87751 214.756364 \r\nL 144.907949 189.112334 \r\nL 144.968828 214.756364 \r\nL 145.060146 207.046394 \r\nL 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204.846539 \r\nL 152.700438 214.756364 \r\nL 152.791756 194.685301 \r\nL 152.822195 214.756364 \r\nL 152.913514 214.735413 \r\nL 152.943953 214.756364 \r\nL 152.974392 193.470143 \r\nL 153.035271 214.735413 \r\nL 153.09615 213.038381 \r\nL 153.126589 213.771666 \r\nL 153.157029 214.756364 \r\nL 153.187468 214.735413 \r\nL 153.278787 212.17939 \r\nL 153.370105 214.756364 \r\nL 153.491862 210.000486 \r\nL 153.522302 214.106882 \r\nL 153.552741 212.996479 \r\nL 153.583181 180.208157 \r\nL 153.644059 186.807724 \r\nL 153.704938 209.811927 \r\nL 153.765817 197.031814 \r\nL 153.857135 214.148785 \r\nL 153.887575 203.400919 \r\nL 154.039772 214.756364 \r\nL 154.161529 209.309102 \r\nL 154.222408 214.756364 \r\nL 154.283287 195.565244 \r\nL 154.313726 210.901379 \r\nL 154.374605 208.994837 \r\nL 154.465923 214.756364 \r\nL 154.61812 153.495627 \r\nL 154.678999 214.756364 \r\nL 154.739878 214.714462 \r\nL 154.770317 214.735413 \r\nL 154.831196 214.756364 \r\nL 154.861636 198.959306 \r\nL 154.952954 214.756364 \r\nL 155.074712 200.823946 \r\nL 155.16603 214.756364 \r\nL 155.196469 214.735413 \r\nL 155.257348 214.735413 \r\nL 155.379106 197.869854 \r\nL 155.500863 214.756364 \r\nL 155.531303 209.141494 \r\nL 155.622621 211.634664 \r\nL 155.65306 209.204347 \r\nL 155.6835 214.756364 \r\nL 155.713939 211.383252 \r\nL 155.744379 213.813568 \r\nL 155.774818 195.167174 \r\nL 155.805257 214.756364 \r\nL 155.835697 213.729764 \r\nL 155.866136 214.756364 \r\nL 156.018333 198.770747 \r\nL 156.048773 210.398555 \r\nL 156.079212 135.854881 \r\nL 156.109651 214.756364 \r\nL 156.140091 195.984264 \r\nL 156.20097 214.756364 \r\nL 156.231409 210.754722 \r\nL 156.261848 183.706975 \r\nL 156.322727 214.756364 \r\nL 156.353167 214.756364 \r\nL 156.474924 196.466137 \r\nL 156.627121 214.756364 \r\nL 156.657561 214.756364 \r\nL 156.688 192.485446 \r\nL 156.748879 214.735413 \r\nL 156.779318 213.520254 \r\nL 156.840197 214.735413 \r\nL 156.870637 214.735413 \r\nL 156.901076 213.289793 \r\nL 156.961955 201.180113 \r\nL 157.022834 208.806278 \r\nL 157.053273 205.956942 \r\nL 157.175031 214.756364 \r\nL 157.23591 214.756364 \r\nL 157.266349 213.64596 \r\nL 157.296788 214.756364 \r\nL 157.357667 189.175187 \r\nL 157.388107 214.735413 \r\nL 157.418546 214.735413 \r\nL 157.509864 208.659621 \r\nL 157.540304 209.749074 \r\nL 157.662061 214.756364 \r\nL 157.692501 214.25354 \r\nL 157.72294 191.794063 \r\nL 157.783819 208.659621 \r\nL 157.844698 205.475069 \r\nL 157.936016 214.756364 \r\nL 157.966455 178.846342 \r\nL 158.027334 204.092302 \r\nL 158.057774 205.307461 \r\nL 158.088213 214.756364 \r\nL 158.179531 209.560515 \r\nL 158.209971 203.652331 \r\nL 158.24041 214.756364 \r\nL 158.270849 207.255904 \r\nL 158.362168 214.756364 \r\nL 158.392607 211.676566 \r\nL 158.483925 214.756364 \r\nL 158.514365 207.234953 \r\nL 158.575243 214.735413 \r\nL 158.605683 214.756364 \r\nL 158.666562 206.732129 \r\nL 158.72744 211.425154 \r\nL 158.849198 214.756364 \r\nL 158.910077 214.735413 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168.589809 199.88115 \r\nL 168.650687 214.756364 \r\nL 168.772445 205.056049 \r\nL 168.833324 207.255904 \r\nL 168.955081 214.756364 \r\nL 169.076839 204.720832 \r\nL 169.168157 214.756364 \r\nL 169.198597 212.912675 \r\nL 169.289915 214.756364 \r\nL 169.320354 214.735413 \r\nL 169.442112 194.287232 \r\nL 169.56387 214.756364 \r\nL 169.685627 181.884238 \r\nL 169.807385 214.756364 \r\nL 169.837824 200.048758 \r\nL 169.898703 208.072993 \r\nL 169.959582 214.756364 \r\nL 169.990021 207.695875 \r\nL 170.020461 210.189045 \r\nL 170.111779 214.756364 \r\nL 170.142218 187.059136 \r\nL 170.203097 213.059332 \r\nL 170.233537 210.796624 \r\nL 170.263976 213.331695 \r\nL 170.324855 211.404203 \r\nL 170.385734 214.756364 \r\nL 170.446612 214.756364 \r\nL 170.477052 210.733771 \r\nL 170.537931 214.735413 \r\nL 170.629249 214.756364 \r\nL 170.659688 204.406567 \r\nL 170.720567 208.21965 \r\nL 170.842325 214.756364 \r\nL 170.933643 196.298529 \r\nL 170.964082 211.446105 \r\nL 170.994522 214.756364 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178.695692 214.756364 \r\nL 178.726131 205.077 \r\nL 178.78701 214.693511 \r\nL 178.847889 214.756364 \r\nL 178.878328 214.421148 \r\nL 178.908768 214.714462 \r\nL 178.939207 211.844174 \r\nL 178.969647 193.051123 \r\nL 179.030525 214.756364 \r\nL 179.060965 207.255904 \r\nL 179.121844 214.756364 \r\nL 179.213162 211.027085 \r\nL 179.274041 192.548299 \r\nL 179.30448 213.750715 \r\nL 179.334919 214.756364 \r\nL 179.426238 214.358295 \r\nL 179.456677 214.756364 \r\nL 179.487116 214.609707 \r\nL 179.547995 189.887521 \r\nL 179.578435 214.756364 \r\nL 179.639313 214.756364 \r\nL 179.669753 214.504952 \r\nL 179.700192 205.349363 \r\nL 179.730632 214.756364 \r\nL 179.761071 214.756364 \r\nL 179.882829 200.237318 \r\nL 179.913268 214.756364 \r\nL 180.004586 206.564521 \r\nL 180.065465 205.097951 \r\nL 180.095905 195.062419 \r\nL 180.126344 208.994837 \r\nL 180.156783 203.715184 \r\nL 180.278541 214.756364 \r\nL 180.400299 206.145501 \r\nL 180.430738 214.756364 \r\nL 180.461177 208.114895 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200.551583 \r\nL 184.266103 214.756364 \r\nL 184.296543 214.756364 \r\nL 184.387861 203.191409 \r\nL 184.509619 214.756364 \r\nL 184.540058 208.994837 \r\nL 184.600937 214.756364 \r\nL 184.631376 214.756364 \r\nL 184.661816 195.523342 \r\nL 184.722695 214.756364 \r\nL 184.783573 214.756364 \r\nL 184.93577 200.404926 \r\nL 184.96621 214.735413 \r\nL 185.057528 213.541205 \r\nL 185.118407 214.044029 \r\nL 185.240164 192.275936 \r\nL 185.331483 187.373401 \r\nL 185.361922 214.756364 \r\nL 185.392362 203.882792 \r\nL 185.45324 213.981176 \r\nL 185.48368 214.756364 \r\nL 185.544559 180.103402 \r\nL 185.574998 214.756364 \r\nL 185.605437 214.756364 \r\nL 185.757634 190.599855 \r\nL 185.788074 192.171181 \r\nL 185.818513 181.695679 \r\nL 185.909831 214.756364 \r\nL 185.940271 203.149507 \r\nL 185.97071 214.756364 \r\nL 186.031589 202.16481 \r\nL 186.062028 213.22694 \r\nL 186.092468 195.271929 \r\nL 186.153347 207.130198 \r\nL 186.214226 214.756364 \r\nL 186.244665 207.402561 \r\nL 186.305544 213.981176 \r\nL 186.396862 211.236595 \r\nL 186.427301 207.758728 \r\nL 186.457741 214.756364 \r\nL 186.48818 212.661263 \r\nL 186.51862 210.733771 \r\nL 186.609938 214.756364 \r\nL 186.640377 168.035624 \r\nL 186.701256 210.461408 \r\nL 186.762135 208.303454 \r\nL 186.792574 195.942362 \r\nL 186.823014 214.756364 \r\nL 186.853453 198.184119 \r\nL 186.975211 214.756364 \r\nL 187.00565 191.668357 \r\nL 187.066529 209.476711 \r\nL 187.096968 214.756364 \r\nL 187.188287 170.486891 \r\nL 187.310044 214.756364 \r\nL 187.340484 214.756364 \r\nL 187.431802 200.928701 \r\nL 187.462241 214.316393 \r\nL 187.492681 199.294522 \r\nL 187.553559 208.84818 \r\nL 187.614438 210.901379 \r\nL 187.644878 194.978615 \r\nL 187.705756 212.80792 \r\nL 187.736196 210.042388 \r\nL 187.857954 183.853632 \r\nL 187.827514 210.92233 \r\nL 187.888393 186.996283 \r\nL 187.949272 214.756364 \r\nL 187.979711 214.735413 \r\nL 188.010151 192.024524 \r\nL 188.04059 214.756364 \r\nL 188.071029 214.756364 \r\nL 188.162348 206.648325 \r\nL 188.192787 164.432051 \r\nL 188.253666 208.701523 \r\nL 188.284105 207.653973 \r\nL 188.314545 213.897372 \r\nL 188.344984 202.332418 \r\nL 188.405863 210.000486 \r\nL 188.436302 214.756364 \r\nL 188.466742 190.055129 \r\nL 188.52762 214.756364 \r\nL 188.55806 214.044029 \r\nL 188.588499 214.756364 \r\nL 188.710257 204.993196 \r\nL 188.801575 214.756364 \r\nL 188.832015 214.735413 \r\nL 188.862454 214.567805 \r\nL 188.892893 199.105963 \r\nL 188.984212 206.585472 \r\nL 189.136409 214.756364 \r\nL 189.166848 164.369198 \r\nL 189.227727 214.756364 \r\nL 189.319045 200.048758 \r\nL 189.440803 214.756364 \r\nL 189.471242 214.023078 \r\nL 189.501682 204.322763 \r\nL 189.56256 212.724116 \r\nL 189.623439 214.756364 \r\nL 189.653879 194.475791 \r\nL 189.714757 214.756364 \r\nL 189.836515 194.203428 \r\nL 189.866954 205.894089 \r\nL 189.927833 194.894811 \r\nL 189.958273 214.756364 \r\nL 189.988712 193.931065 \r\nL 190.049591 214.274491 \r\nL 190.08003 214.756364 \r\nL 190.11047 202.772389 \r\nL 190.201788 207.59112 \r\nL 190.323546 214.756364 \r\nL 190.353985 214.421148 \r\nL 190.384424 214.756364 \r\nL 190.414864 197.953658 \r\nL 190.475743 209.183396 \r\nL 190.62794 214.756364 \r\nL 190.658379 214.756364 \r\nL 190.688818 189.824668 \r\nL 190.749697 214.714462 \r\nL 190.810576 214.756364 \r\nL 190.932334 201.578182 \r\nL 191.023652 214.756364 \r\nL 191.084531 211.802272 \r\nL 191.175849 214.756364 \r\nL 191.236728 206.836884 \r\nL 191.267167 213.771666 \r\nL 191.328046 214.756364 \r\nL 191.419364 208.680572 \r\nL 191.480243 214.756364 \r\nL 191.541122 214.735413 \r\nL 191.571561 214.756364 \r\nL 191.63244 180.962393 \r\nL 191.662879 212.3889 \r\nL 191.693319 214.756364 \r\nL 191.815077 180.627177 \r\nL 191.936834 214.756364 \r\nL 191.967274 214.756364 \r\nL 191.997713 214.190687 \r\nL 192.028152 179.831039 \r\nL 192.058592 214.756364 \r\nL 192.089031 214.756364 \r\nL 192.14991 210.2938 \r\nL 192.180349 213.331695 \r\nL 192.271668 214.756364 \r\nL 192.362986 189.971325 \r\nL 192.393425 202.269565 \r\nL 192.454304 214.735413 \r\nL 192.484743 207.067345 \r\nL 192.515183 185.655418 \r\nL 192.576062 209.183396 \r\nL 192.636941 209.141494 \r\nL 192.697819 214.756364 \r\nL 192.728259 214.756364 \r\nL 192.758698 210.230947 \r\nL 192.819577 214.756364 \r\nL 192.880456 214.735413 \r\nL 192.941335 206.606423 \r\nL 192.971774 177.798792 \r\nL 193.002213 213.01743 \r\nL 193.032653 208.764376 \r\nL 193.123971 214.735413 \r\nL 193.15441 211.467056 \r\nL 193.18485 211.152791 \r\nL 193.215289 202.79334 \r\nL 193.245729 214.756364 \r\nL 193.276168 214.756364 \r\nL 193.306607 214.756364 \r\nL 193.337047 213.64596 \r\nL 193.397926 214.756364 \r\nL 193.580562 214.756364 \r\nL 193.67188 201.180113 \r\nL 193.793638 214.756364 \r\nL 193.824077 200.132563 \r\nL 193.915396 204.46942 \r\nL 193.976274 214.756364 \r\nL 194.006714 210.901379 \r\nL 194.037153 199.336424 \r\nL 194.098032 214.756364 \r\nL 194.18935 203.338066 \r\nL 194.280669 214.756364 \r\nL 194.311108 208.931984 \r\nL 194.341547 208.177748 \r\nL 194.463305 214.756364 \r\nL 194.493744 194.203428 \r\nL 194.585063 195.586195 \r\nL 194.73726 214.756364 \r\nL 194.767699 212.053684 \r\nL 194.828578 214.756364 \r\nL 194.859017 214.756364 \r\nL 194.919896 191.144582 \r\nL 194.950335 195.020517 \r\nL 194.980775 214.756364 \r\nL 195.072093 202.479075 \r\nL 195.193851 214.756364 \r\nL 195.315608 201.515329 \r\nL 195.376487 201.431525 \r\nL 195.467805 214.756364 \r\nL 195.589563 203.966596 \r\nL 195.650442 196.382333 \r\nL 195.74176 214.756364 \r\nL 195.833078 206.606423 \r\nL 195.863518 213.562156 \r\nL 195.893957 214.756364 \r\nL 195.924397 213.43645 \r\nL 195.954836 211.425154 \r\nL 195.985275 212.640312 \r\nL 196.015715 194.685301 \r\nL 196.076594 214.756364 \r\nL 196.167912 214.756364 \r\nL 196.25923 192.736858 \r\nL 196.289669 206.711178 \r\nL 196.380988 202.79334 \r\nL 196.411427 214.756364 \r\nL 196.502745 214.756364 \r\nL 196.624503 197.932707 \r\nL 196.715821 214.756364 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214.756364 \r\nL 198.785701 212.619361 \r\nL 198.81614 188.798069 \r\nL 198.877019 214.756364 \r\nL 198.937898 214.756364 \r\nL 198.998777 194.769105 \r\nL 199.029216 210.817575 \r\nL 199.059656 214.735413 \r\nL 199.090095 186.891528 \r\nL 199.150974 208.911033 \r\nL 199.272731 214.735413 \r\nL 199.303171 210.629016 \r\nL 199.33361 214.756364 \r\nL 199.36405 212.724116 \r\nL 199.394489 179.810088 \r\nL 199.455368 214.756364 \r\nL 199.485807 200.258269 \r\nL 199.577125 214.756364 \r\nL 199.698883 191.794063 \r\nL 199.729322 201.242966 \r\nL 199.790201 191.207435 \r\nL 199.85108 214.756364 \r\nL 199.911959 174.593288 \r\nL 199.972838 201.180113 \r\nL 200.003277 212.367949 \r\nL 200.033717 159.759977 \r\nL 200.064156 214.756364 \r\nL 200.094595 214.756364 \r\nL 200.125035 214.756364 \r\nL 200.155474 212.409851 \r\nL 200.216353 135.792028 \r\nL 200.277232 204.343714 \r\nL 200.36855 214.756364 \r\nL 200.398989 200.48873 \r\nL 200.459868 214.735413 \r\nL 200.490308 214.756364 \r\nL 200.520747 199.441179 \r\nL 200.612065 203.023801 \r\nL 200.703384 214.67256 \r\nL 200.672944 201.368672 \r\nL 200.733823 208.533915 \r\nL 200.764262 205.077 \r\nL 200.794702 214.756364 \r\nL 200.825141 195.104321 \r\nL 200.855581 160.87038 \r\nL 200.88602 214.756364 \r\nL 200.916459 207.423512 \r\nL 200.977338 214.735413 \r\nL 201.038217 177.777841 \r\nL 201.129535 212.17939 \r\nL 201.159975 210.126192 \r\nL 201.251293 214.232589 \r\nL 201.281732 184.545015 \r\nL 201.40349 213.604058 \r\nL 201.464369 208.911033 \r\nL 201.494808 198.393629 \r\nL 201.525248 214.756364 \r\nL 201.555687 214.756364 \r\nL 201.586126 214.421148 \r\nL 201.738323 146.89606 \r\nL 201.829642 214.756364 \r\nL 201.860081 208.63867 \r\nL 201.89052 206.040746 \r\nL 201.92096 212.053684 \r\nL 201.951399 212.493655 \r\nL 201.981839 214.756364 \r\nL 202.012278 214.735413 \r\nL 202.042717 142.140182 \r\nL 202.103596 159.843781 \r\nL 202.164475 214.756364 \r\nL 202.225354 211.844174 \r\nL 202.255793 157.664876 \r\nL 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272.449067 214.756364 \r\nL 272.509946 210.147143 \r\nL 272.570825 209.707172 \r\nL 272.631704 214.756364 \r\nL 272.753461 199.839248 \r\nL 272.844779 214.756364 \r\nL 272.875219 209.728123 \r\nL 272.936098 202.101957 \r\nL 272.996977 214.756364 \r\nL 273.057855 212.011782 \r\nL 273.149174 214.735413 \r\nL 273.179613 213.22694 \r\nL 273.240492 214.756364 \r\nL 273.301371 214.735413 \r\nL 273.362249 209.853829 \r\nL 273.392689 212.430802 \r\nL 273.423128 214.756364 \r\nL 273.453568 213.185038 \r\nL 273.484007 203.903743 \r\nL 273.514446 214.756364 \r\nL 273.544886 209.078641 \r\nL 273.575325 214.504952 \r\nL 273.605765 204.218008 \r\nL 273.636204 210.838526 \r\nL 273.666643 209.66527 \r\nL 273.697083 212.074635 \r\nL 273.818841 214.756364 \r\nL 273.879719 214.735413 \r\nL 273.910159 214.442099 \r\nL 273.971038 214.756364 \r\nL 274.031916 205.831236 \r\nL 274.092795 214.756364 \r\nL 274.123235 214.46305 \r\nL 274.153674 172.623894 \r\nL 274.184113 214.756364 \r\nL 274.214553 213.834519 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "## แบบฝึกหัด\n",
+    "## โลกของปีเตอร์กับหมาป่าที่สมจริงยิ่งขึ้น\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/translations/th/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
new file mode 100644
index 000000000..e6058844f
--- /dev/null
+++ b/translations/th/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
@@ -0,0 +1,447 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "eadbd20d2a075efb602615ad90b1e97a",
+   "translation_date": "2025-09-06T15:15:11+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# ปีเตอร์กับหมาป่า: สภาพแวดล้อมที่สมจริง\n",
+    "\n",
+    "ในสถานการณ์ของเรา ปีเตอร์สามารถเคลื่อนที่ไปมาได้เกือบจะโดยไม่รู้สึกเหนื่อยหรือหิวเลย ในโลกที่สมจริงมากขึ้น เขาจะต้องนั่งพักและพักผ่อนเป็นครั้งคราว รวมถึงต้องหาอาหารกินด้วย มาเพิ่มความสมจริงให้โลกของเราด้วยการนำกฎเหล่านี้มาใช้:\n",
+    "\n",
+    "1. เมื่อเคลื่อนที่จากที่หนึ่งไปยังอีกที่หนึ่ง ปีเตอร์จะสูญเสีย **พลังงาน** และเพิ่ม **ความเหนื่อยล้า** ขึ้น\n",
+    "2. ปีเตอร์สามารถเพิ่มพลังงานได้โดยการกินแอปเปิ้ล\n",
+    "3. ปีเตอร์สามารถลดความเหนื่อยล้าได้โดยการพักผ่อนใต้ต้นไม้หรือบนพื้นหญ้า (เช่น เดินไปยังตำแหน่งที่มีต้นไม้หรือหญ้า - พื้นที่สีเขียว)\n",
+    "4. ปีเตอร์ต้องค้นหาและกำจัดหมาป่า\n",
+    "5. เพื่อที่จะกำจัดหมาป่า ปีเตอร์จำเป็นต้องมีระดับพลังงานและความเหนื่อยล้าในระดับที่เหมาะสม มิฉะนั้นเขาจะพ่ายแพ้ในการต่อสู้\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "from rlboard import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "## การกำหนดสถานะ\n",
+    "\n",
+    "ในกฎของเกมใหม่ เราจำเป็นต้องติดตามพลังงานและความเหนื่อยล้าในแต่ละสถานะของกระดาน ดังนั้นเราจะสร้างออบเจกต์ `state` ที่จะเก็บข้อมูลทั้งหมดที่จำเป็นเกี่ยวกับสถานะปัจจุบันของปัญหา รวมถึงสถานะของกระดาน ระดับพลังงานและความเหนื่อยล้าในปัจจุบัน และความเป็นไปได้ที่เราจะชนะหมาป่าเมื่ออยู่ในสถานะสุดท้าย:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class state:\n",
+    "    def __init__(self,board,energy=10,fatigue=0,init=True):\n",
+    "        self.board = board\n",
+    "        self.energy = energy\n",
+    "        self.fatigue = fatigue\n",
+    "        self.dead = False\n",
+    "        if init:\n",
+    "            self.board.random_start()\n",
+    "        self.update()\n",
+    "\n",
+    "    def at(self):\n",
+    "        return self.board.at()\n",
+    "\n",
+    "    def update(self):\n",
+    "        if self.at() == Board.Cell.water:\n",
+    "            self.dead = True\n",
+    "            return\n",
+    "        if self.at() == Board.Cell.tree:\n",
+    "            self.fatigue = 0\n",
+    "        if self.at() == Board.Cell.apple:\n",
+    "            self.energy = 10\n",
+    "\n",
+    "    def move(self,a):\n",
+    "        self.board.move(a)\n",
+    "        self.energy -= 1\n",
+    "        self.fatigue += 1\n",
+    "        self.update()\n",
+    "\n",
+    "    def is_winning(self):\n",
+    "        return self.energy > self.fatigue"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(state):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(board,policy):\n",
+    "    n = 0 # number of steps\n",
+    "    s = state(board)\n",
+    "    while True:\n",
+    "        if s.at() == Board.Cell.wolf:\n",
+    "            if s.is_winning():\n",
+    "                return n # success!\n",
+    "            else:\n",
+    "                return -n # failure!\n",
+    "        if s.at() == Board.Cell.water:\n",
+    "            return 0 # died\n",
+    "        a = actions[policy(m)]\n",
+    "        s.move(a)\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 5, won: 1 times, drown: 94 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        elif z==0:\n",
+    "            n+=1\n",
+    "        else:\n",
+    "            s+=1\n",
+    "    print(f\"Killed by wolf = {w}, won: {s} times, drown: {n} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## ฟังก์ชันรางวัล\n",
+    "\n",
+    "### คำอธิบาย\n",
+    "ฟังก์ชันรางวัลเป็นส่วนสำคัญในการกำหนดพฤติกรรมของตัวแทนในระบบการเรียนรู้แบบเสริมกำลัง (Reinforcement Learning - RL) โดยฟังก์ชันนี้จะให้ค่าตอบแทนแก่ตัวแทนตามการกระทำที่เกิดขึ้นในสภาพแวดล้อม\n",
+    "\n",
+    "### ตัวอย่าง\n",
+    "ตัวอย่างฟังก์ชันรางวัลที่เรียบง่าย:\n",
+    "\n",
+    "```python\n",
+    "def reward_function(state, action):\n",
+    "    if state == \"goal\":\n",
+    "        return 1  # รางวัลสำหรับการบรรลุเป้าหมาย\n",
+    "    else:\n",
+    "        return 0  # ไม่มีรางวัลสำหรับสถานะอื่น\n",
+    "```\n",
+    "\n",
+    "### หลักการออกแบบ\n",
+    "- **ความชัดเจน**: ฟังก์ชันรางวัลควรสะท้อนเป้าหมายของระบบอย่างชัดเจน\n",
+    "- **ความสมดุล**: หลีกเลี่ยงการให้รางวัลที่มากเกินไปหรือไม่เพียงพอ\n",
+    "- **ความสอดคล้อง**: รางวัลควรสอดคล้องกับพฤติกรรมที่ต้องการ\n",
+    "\n",
+    "### ข้อควรระวัง\n",
+    "[!WARNING] การออกแบบฟังก์ชันรางวัลที่ไม่เหมาะสมอาจนำไปสู่พฤติกรรมที่ไม่คาดคิดของตัวแทน\n",
+    "\n",
+    "### เคล็ดลับ\n",
+    "[!TIP] ทดสอบฟังก์ชันรางวัลในสถานการณ์จำลองก่อนใช้งานจริงเพื่อให้แน่ใจว่ามันทำงานตามที่คาดหวัง\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reward(s):\n",
+    "    r = s.energy-s.fatigue\n",
+    "    if s.at()==Board.Cell.wolf:\n",
+    "        return 100 if s.is_winning() else -100\n",
+    "    if s.at()==Board.Cell.water:\n",
+    "        return -100\n",
+    "    return r"
+   ]
+  },
+  {
+   "source": [
+    "## อัลกอริทึม Q-Learning\n",
+    "\n",
+    "อัลกอริทึมการเรียนรู้จริงๆ ยังคงเหมือนเดิม เพียงแค่เราใช้ `state` แทนที่จะใช้ตำแหน่งของกระดานเพียงอย่างเดียว\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    s = state(m)\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = s.board.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        while True:\n",
+    "            a = random.choices(list(actions),weights=v)[0]\n",
+    "            dpos = actions[a]\n",
+    "            if s.board.is_valid(s.board.move_pos(s.board.human,dpos)):\n",
+    "                break \n",
+    "        s.move(dpos)\n",
+    "        r = reward(s)\n",
+    "        if abs(r)==100: # end of game\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## ผลลัพธ์\n",
+    "\n",
+    "มาดูกันว่าเราสามารถฝึกปีเตอร์ให้ต่อสู้กับหมาป่าได้สำเร็จหรือไม่!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 1, won: 9 times, drown: 90 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0987e7dd8>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/translations/th/8-Reinforcement/1-QLearning/solution/notebook.ipynb
new file mode 100644
index 000000000..392e8e168
--- /dev/null
+++ b/translations/th/8-Reinforcement/1-QLearning/solution/notebook.ipynb
@@ -0,0 +1,577 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "488431336543f71f14d4aaf0399e3381",
+   "translation_date": "2025-09-06T15:11:50+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# ปีเตอร์กับหมาป่า: บทเรียนพื้นฐานเกี่ยวกับการเรียนรู้แบบเสริมกำลัง\n",
+    "\n",
+    "ในบทเรียนนี้ เราจะเรียนรู้วิธีการนำการเรียนรู้แบบเสริมกำลังมาใช้กับปัญหาการค้นหาเส้นทาง สถานการณ์นี้ได้รับแรงบันดาลใจจากนิทานดนตรีเรื่อง [ปีเตอร์กับหมาป่า](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) โดยนักประพันธ์ชาวรัสเซีย [เซอร์เก โปรโกเฟียฟ](https://en.wikipedia.org/wiki/Sergei_Prokofiev) เป็นเรื่องราวเกี่ยวกับปีเตอร์ เด็กชายผู้กล้าหาญที่ออกจากบ้านไปยังลานป่าเพื่อไล่ล่าหมาป่า เราจะฝึกอัลกอริทึมการเรียนรู้ของเครื่องที่จะช่วยปีเตอร์สำรวจพื้นที่โดยรอบและสร้างแผนที่การนำทางที่เหมาะสมที่สุด\n",
+    "\n",
+    "ก่อนอื่น มาเริ่มต้นด้วยการนำเข้าไลบรารีที่มีประโยชน์หลายตัว:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## ภาพรวมของการเรียนรู้แบบเสริมกำลัง\n",
+    "\n",
+    "**การเรียนรู้แบบเสริมกำลัง** (Reinforcement Learning หรือ RL) เป็นเทคนิคการเรียนรู้ที่ช่วยให้เราสามารถเรียนรู้พฤติกรรมที่เหมาะสมที่สุดของ **ตัวแทน** (agent) ใน **สภาพแวดล้อม** (environment) โดยการทดลองทำซ้ำหลายๆ ครั้ง ตัวแทนในสภาพแวดล้อมนี้ควรมี **เป้าหมาย** ซึ่งกำหนดโดย **ฟังก์ชันรางวัล** (reward function)\n",
+    "\n",
+    "## สภาพแวดล้อม\n",
+    "\n",
+    "เพื่อความเข้าใจง่าย ลองพิจารณาโลกของปีเตอร์เป็นกระดานสี่เหลี่ยมขนาด `width` x `height` แต่ละช่องในกระดานนี้สามารถเป็นได้ดังนี้:\n",
+    "* **พื้นดิน** ซึ่งปีเตอร์และสิ่งมีชีวิตอื่นๆ สามารถเดินได้\n",
+    "* **น้ำ** ซึ่งแน่นอนว่าไม่สามารถเดินได้\n",
+    "* **ต้นไม้** หรือ **หญ้า** - สถานที่ที่คุณสามารถพักผ่อนได้\n",
+    "* **แอปเปิ้ล** ซึ่งเป็นสิ่งที่ปีเตอร์จะดีใจที่ได้พบเพื่อใช้เป็นอาหาร\n",
+    "* **หมาป่า** ซึ่งเป็นอันตรายและควรหลีกเลี่ยง\n",
+    "\n",
+    "ในการทำงานกับสภาพแวดล้อม เราจะกำหนดคลาสที่เรียกว่า `Board` เพื่อไม่ให้เนื้อหาในสมุดบันทึกนี้ดูรกเกินไป เราได้ย้ายโค้ดทั้งหมดที่เกี่ยวข้องกับการทำงานของกระดานไปยังโมดูล `rlboard` แยกต่างหาก ซึ่งเราจะนำเข้าในตอนนี้ คุณสามารถดูรายละเอียดเพิ่มเติมเกี่ยวกับการทำงานภายในของการนำไปใช้ในโมดูลนี้ได้\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rlboard import *"
+   ]
+  },
+  {
+   "source": [
+    "ตอนนี้มาสร้างกระดานแบบสุ่มและดูว่ามันเป็นอย่างไร:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#pa5c3528530)\">\n    <image height=\"327\" id=\"image7eb26753ab\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "source": [
+    "## การกระทำและนโยบาย\n",
+    "\n",
+    "ในตัวอย่างของเรา เป้าหมายของปีเตอร์คือการหาลูกแอปเปิ้ล ในขณะเดียวกันก็ต้องหลีกเลี่ยงหมาป่าและสิ่งกีดขวางอื่นๆ เพื่อทำสิ่งนี้ เขาสามารถเดินไปรอบๆ ได้จนกว่าจะเจอลูกแอปเปิ้ล ดังนั้น ในแต่ละตำแหน่ง เขาสามารถเลือกทำหนึ่งในสี่การกระทำต่อไปนี้: ขึ้น, ลง, ซ้าย และขวา เราจะกำหนดการกระทำเหล่านี้เป็นพจนานุกรม และจับคู่กับการเปลี่ยนแปลงพิกัดที่สอดคล้องกัน ตัวอย่างเช่น การเคลื่อนที่ไปทางขวา (`R`) จะสอดคล้องกับคู่พิกัด `(1,0)`\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "กลยุทธ์ของตัวแทนของเรา (ปีเตอร์) ถูกกำหนดโดยสิ่งที่เรียกว่า **นโยบาย** ลองพิจารณานโยบายที่ง่ายที่สุดที่เรียกว่า **การเดินแบบสุ่ม**\n",
+    "\n",
+    "## การเดินแบบสุ่ม\n",
+    "\n",
+    "มาแก้ปัญหาของเราด้วยการใช้กลยุทธ์การเดินแบบสุ่ม\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(m):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(m,policy,start_position=None):\n",
+    "    n = 0 # number of steps\n",
+    "    # set initial position\n",
+    "    if start_position:\n",
+    "        m.human = start_position \n",
+    "    else:\n",
+    "        m.random_start()\n",
+    "    while True:\n",
+    "        if m.at() == Board.Cell.apple:\n",
+    "            return n # success!\n",
+    "        if m.at() in [Board.Cell.wolf, Board.Cell.water]:\n",
+    "            return -1 # eaten by wolf or drowned\n",
+    "        while True:\n",
+    "            a = actions[policy(m)]\n",
+    "            new_pos = m.move_pos(m.human,a)\n",
+    "            if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:\n",
+    "                m.move(a) # do the actual move\n",
+    "                break\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "มาลองทำการทดลองเดินสุ่มหลายครั้งและดูจำนวนก้าวเฉลี่ยที่ใช้:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 32.87096774193548, eaten by wolf: 7 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        else:\n",
+    "            s += z\n",
+    "            n += 1\n",
+    "    print(f\"Average path length = {s/n}, eaten by wolf: {w} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## ฟังก์ชันรางวัล\n",
+    "\n",
+    "เพื่อทำให้กลยุทธ์ของเราฉลาดขึ้น เราจำเป็นต้องเข้าใจว่าการเคลื่อนไหวใด \"ดีกว่า\" การเคลื่อนไหวอื่นๆ\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "move_reward = -0.1\n",
+    "goal_reward = 10\n",
+    "end_reward = -10\n",
+    "\n",
+    "def reward(m,pos=None):\n",
+    "    pos = pos or m.human\n",
+    "    if not m.is_valid(pos):\n",
+    "        return end_reward\n",
+    "    x = m.at(pos)\n",
+    "    if x==Board.Cell.water or x == Board.Cell.wolf:\n",
+    "        return end_reward\n",
+    "    if x==Board.Cell.apple:\n",
+    "        return goal_reward\n",
+    "    return move_reward"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning\n",
+    "\n",
+    "สร้าง Q-Table หรืออาร์เรย์หลายมิติ เนื่องจากกระดานของเรามีขนาด `width` x `height` เราสามารถแสดง Q-Table ด้วย numpy array ที่มีรูปร่าง `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "source": [
+    "ส่ง Q-Table ไปยังฟังก์ชัน plot เพื่อแสดงตารางบนกระดาน:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p267d475764)\">\n    <image height=\"327\" id=\"image00b8757863\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## สาระสำคัญของ Q-Learning: สมการ Bellman และอัลกอริทึมการเรียนรู้\n",
+    "\n",
+    "เขียน pseudo-code สำหรับอัลกอริทึมการเรียนรู้ของเรา:\n",
+    "\n",
+    "* เริ่มต้น Q-Table Q ด้วยค่าที่เท่ากันสำหรับทุกสถานะและการกระทำ\n",
+    "* กำหนดอัตราการเรียนรู้ $\\alpha\\leftarrow 1$\n",
+    "* ทำการจำลองซ้ำหลายครั้ง\n",
+    "   1. เริ่มต้นที่ตำแหน่งสุ่ม\n",
+    "   1. ทำซ้ำ\n",
+    "        1. เลือกการกระทำ $a$ ที่สถานะ $s$\n",
+    "        2. ดำเนินการโดยย้ายไปยังสถานะใหม่ $s'$\n",
+    "        3. หากพบเงื่อนไขสิ้นสุดเกม หรือรางวัลรวมมีค่าน้อยเกินไป - ออกจากการจำลอง  \n",
+    "        4. คำนวณรางวัล $r$ ที่สถานะใหม่\n",
+    "        5. อัปเดต Q-Function ตามสมการ Bellman: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. อัปเดตรางวัลรวมและลดค่า $\\alpha$\n",
+    "\n",
+    "## ใช้ประโยชน์ vs. สำรวจ\n",
+    "\n",
+    "วิธีที่ดีที่สุดคือการสร้างสมดุลระหว่างการสำรวจและการใช้ประโยชน์ เมื่อเราเรียนรู้เกี่ยวกับสภาพแวดล้อมมากขึ้น เราจะมีแนวโน้มที่จะเลือกเส้นทางที่เหมาะสมที่สุด แต่ควรเลือกเส้นทางที่ยังไม่ได้สำรวจเป็นครั้งคราว\n",
+    "\n",
+    "## การใช้งาน Python\n",
+    "\n",
+    "ตอนนี้เราพร้อมที่จะใช้อัลกอริทึมการเรียนรู้ ก่อนหน้านั้น เราต้องมีฟังก์ชันที่จะแปลงตัวเลขใน Q-Table ให้เป็นเวกเตอร์ของความน่าจะเป็นสำหรับการกระทำที่เกี่ยวข้อง:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "source": [
+    "เราเพิ่มค่า `eps` เล็กน้อยลงในเวกเตอร์ต้นฉบับเพื่อหลีกเลี่ยงการหารด้วย 0 ในกรณีเริ่มต้น เมื่อทุกองค์ประกอบของเวกเตอร์มีค่าเหมือนกัน\n",
+    "\n",
+    "อัลกอริทึมการเรียนรู้ที่เราจะใช้งานจริงสำหรับการทดลอง 5000 ครั้ง ซึ่งเรียกว่า **epochs**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    m.random_start()\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        dpos = actions[a]\n",
+    "        m.move(dpos,check_correctness=False) # we allow player to move outside the board, which terminates episode\n",
+    "        r = reward(m)\n",
+    "        cum_reward += r\n",
+    "        if r==end_reward or cum_reward < -1000:\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "source": [
+    "หลังจากดำเนินการอัลกอริทึมนี้ ตาราง Q-Table ควรได้รับการอัปเดตด้วยค่าที่กำหนดความน่าสนใจของการกระทำต่าง ๆ ในแต่ละขั้นตอน แสดงตารางที่นี่:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p31952a86ca)\">\n    <image height=\"327\" id=\"image3fd90cd4cb\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## การตรวจสอบนโยบาย\n",
+    "\n",
+    "เนื่องจาก Q-Table แสดง \"ความน่าสนใจ\" ของแต่ละการกระทำในแต่ละสถานะ การใช้งานเพื่อกำหนดการนำทางที่มีประสิทธิภาพในโลกของเราจึงค่อนข้างง่าย ในกรณีที่ง่ายที่สุด เราสามารถเลือกการกระทำที่สอดคล้องกับค่าที่สูงที่สุดใน Q-Table ได้:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "def qpolicy_strict(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = list(actions)[np.argmax(v)]\n",
+    "        return a\n",
+    "\n",
+    "walk(m,qpolicy_strict)"
+   ]
+  },
+  {
+   "source": [
+    "หากคุณลองรันโค้ดด้านบนหลายครั้ง คุณอาจสังเกตเห็นว่าบางครั้งมันจะ \"ค้าง\" และคุณจำเป็นต้องกดปุ่ม STOP ในโน้ตบุ๊กเพื่อหยุดการทำงาน\n",
+    "\n",
+    "> **งานที่ 1:** แก้ไขฟังก์ชัน `walk` เพื่อจำกัดความยาวสูงสุดของเส้นทางโดยกำหนดจำนวนก้าว (เช่น 100) และดูว่าโค้ดด้านบนคืนค่านี้เป็นครั้งคราว\n",
+    "\n",
+    "> **งานที่ 2:** แก้ไขฟังก์ชัน `walk` เพื่อไม่ให้กลับไปยังตำแหน่งที่เคยไปมาก่อนหน้านี้ สิ่งนี้จะช่วยป้องกันไม่ให้ `walk` วนลูป อย่างไรก็ตาม ตัวแทนอาจยังคงติดอยู่ในตำแหน่งที่ไม่สามารถหลบหนีได้\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 3.45, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbab852fd68>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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127.527048 214.756364 \nL 127.557487 211.325525 \nL 127.618366 214.756364 \nL 127.648805 204.39335 \nL 127.709684 214.61537 \nL 127.740124 211.20803 \nL 127.801002 214.685867 \nL 127.831442 214.521375 \nL 127.861881 212.030492 \nL 127.892321 214.756364 \nL 127.92276 214.756364 \nL 127.953199 214.098395 \nL 128.014078 214.756364 \nL 128.044518 214.756364 \nL 128.074957 209.563107 \nL 128.166275 210.644057 \nL 128.196715 210.409068 \nL 128.257593 213.933902 \nL 128.318472 212.077489 \nL 128.379351 201.502985 \nL 128.409791 210.57356 \nL 128.531548 214.756364 \nL 128.592427 213.087942 \nL 128.622866 208.411661 \nL 128.653306 214.756364 \nL 128.683745 214.450878 \nL 128.714185 214.756364 \nL 128.744624 209.210624 \nL 128.805503 214.756364 \nL 128.896821 212.594465 \nL 128.92726 213.651915 \nL 128.9577 214.756364 \nL 128.988139 208.85814 \nL 129.049018 214.756364 \nL 129.079457 214.756364 \nL 129.201215 209.093129 \nL 129.231655 214.756364 \nL 129.292533 214.121893 \nL 129.322973 207.659697 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133.949763 214.756364 \nL 133.980202 214.756364 \nL 134.132399 206.226264 \nL 134.254157 214.756364 \nL 134.284596 214.685867 \nL 134.315036 198.00165 \nL 134.345475 214.756364 \nL 134.375914 199.106098 \nL 134.436793 214.756364 \nL 134.528111 214.497876 \nL 134.558551 214.309885 \nL 134.58899 214.756364 \nL 134.649869 214.685867 \nL 134.680308 213.252434 \nL 134.710748 214.756364 \nL 134.741187 214.756364 \nL 134.802066 212.946949 \nL 134.832506 214.756364 \nL 134.954263 193.184376 \nL 135.076021 214.756364 \nL 135.10646 212.594465 \nL 135.167339 214.756364 \nL 135.289097 214.756364 \nL 135.502172 214.756364 \nL 135.532612 206.766738 \nL 135.593491 214.756364 \nL 135.62393 214.662368 \nL 135.65437 214.756364 \nL 135.745688 195.722257 \nL 135.806567 214.756364 \nL 135.867445 203.923372 \nL 135.989203 214.756364 \nL 136.050082 213.111441 \nL 136.080521 214.756364 \nL 136.110961 214.756364 \nL 136.202279 206.837235 \nL 136.293597 214.756364 \nL 136.324036 213.510922 \nL 136.384915 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138.150401 209.375116 \nL 138.21128 214.756364 \nL 138.363477 206.672743 \nL 138.454795 214.756364 \nL 138.485234 205.967776 \nL 138.546113 210.338571 \nL 138.576553 214.732865 \nL 138.637431 211.41952 \nL 138.667871 207.659697 \nL 138.69831 213.604918 \nL 138.789629 213.299432 \nL 138.820068 214.756364 \nL 138.911386 201.737974 \nL 139.002704 214.756364 \nL 139.033144 213.322931 \nL 139.094023 214.756364 \nL 139.124462 210.033085 \nL 139.21578 210.503063 \nL 139.276659 209.7276 \nL 139.337538 214.756364 \nL 139.398417 212.92345 \nL 139.428856 214.756364 \nL 139.459295 214.756364 \nL 139.520174 204.863328 \nL 139.611493 211.114034 \nL 139.641932 211.255028 \nL 139.672371 210.738052 \nL 139.702811 214.756364 \nL 139.794129 213.957401 \nL 139.824568 214.756364 \nL 139.855008 213.581419 \nL 139.885447 207.119222 \nL 139.915887 214.756364 \nL 139.946326 214.004399 \nL 140.037644 211.325525 \nL 140.128962 214.756364 \nL 140.159402 207.260215 \nL 140.220281 214.380381 \nL 140.25072 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151.300225 211.889498 \nL 151.330664 190.435005 \nL 151.361104 214.756364 \nL 151.391543 211.748505 \nL 151.421983 212.429973 \nL 151.452422 204.322853 \nL 151.482861 214.756364 \nL 151.513301 207.80069 \nL 151.665498 214.756364 \nL 151.726377 208.975635 \nL 151.756816 210.15058 \nL 151.787256 214.756364 \nL 151.817695 211.748505 \nL 151.848134 206.155767 \nL 151.909013 214.521375 \nL 151.969892 214.756364 \nL 152.000331 214.145392 \nL 152.06121 214.756364 \nL 152.182968 197.625667 \nL 152.243847 214.756364 \nL 152.304725 213.181937 \nL 152.335165 212.71196 \nL 152.365604 214.756364 \nL 152.426483 205.897279 \nL 152.456923 214.756364 \nL 152.548241 208.458659 \nL 152.57868 213.111441 \nL 152.60912 214.756364 \nL 152.700438 214.19239 \nL 152.730877 214.215889 \nL 152.822195 214.756364 \nL 152.852635 208.364663 \nL 152.913514 214.121893 \nL 152.943953 184.654276 \nL 153.065711 214.756364 \nL 153.09615 210.926043 \nL 153.157029 214.756364 \nL 153.187468 214.756364 \nL 153.248347 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154.952954 214.756364 \nL 155.013833 213.534421 \nL 155.044272 206.461253 \nL 155.105151 214.756364 \nL 155.16603 214.756364 \nL 155.196469 204.487345 \nL 155.257348 214.756364 \nL 155.379106 213.228935 \nL 155.439984 211.959995 \nL 155.561742 214.756364 \nL 155.622621 214.756364 \nL 155.713939 205.215811 \nL 155.805257 214.756364 \nL 155.835697 202.865921 \nL 155.896576 212.641463 \nL 156.018333 214.756364 \nL 156.048773 213.322931 \nL 156.109651 214.756364 \nL 156.140091 196.356727 \nL 156.261848 214.756364 \nL 156.322727 207.777191 \nL 156.353167 213.040944 \nL 156.414045 214.756364 \nL 156.566243 204.017367 \nL 156.657561 214.756364 \nL 156.688 214.239388 \nL 156.748879 214.756364 \nL 156.779318 208.035679 \nL 156.840197 207.80069 \nL 156.870637 214.756364 \nL 156.961955 202.912919 \nL 157.022834 214.756364 \nL 157.083712 214.356882 \nL 157.175031 200.539531 \nL 157.23591 214.756364 \nL 157.296788 207.847688 \nL 157.357667 214.756364 \nL 157.388107 204.463846 \nL 157.418546 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172.790447 211.466518 \nL 172.851326 214.756364 \nL 172.912204 214.756364 \nL 172.942644 208.623151 \nL 173.033962 211.772004 \nL 173.12528 214.756364 \nL 173.15572 212.030492 \nL 173.216598 214.756364 \nL 173.338356 214.709366 \nL 173.368796 202.724928 \nL 173.429674 214.756364 \nL 173.460114 214.756364 \nL 173.581871 205.709288 \nL 173.703629 214.756364 \nL 173.734068 214.756364 \nL 173.764508 214.544874 \nL 173.855826 214.662368 \nL 173.977584 214.756364 \nL 174.129781 210.855547 \nL 174.16022 204.134862 \nL 174.19066 214.638869 \nL 174.221099 214.215889 \nL 174.251538 210.620558 \nL 174.281978 214.756364 \nL 174.312417 214.756364 \nL 174.342857 214.756364 \nL 174.464614 204.745833 \nL 174.495054 207.871187 \nL 174.616811 214.756364 \nL 174.647251 214.709366 \nL 174.67769 214.145392 \nL 174.708129 214.709366 \nL 174.769008 203.429895 \nL 174.799448 214.756364 \nL 174.890766 214.756364 \nL 174.982084 213.416926 \nL 175.012524 214.756364 \nL 175.042963 211.560514 \nL 175.073402 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177.112843 214.756364 \nL 177.143282 214.027898 \nL 177.173721 208.270668 \nL 177.2346 208.811143 \nL 177.295479 214.756364 \nL 177.356358 212.241982 \nL 177.417237 214.756364 \nL 177.447676 214.568372 \nL 177.508555 208.106176 \nL 177.538994 214.756364 \nL 177.569434 212.359476 \nL 177.599873 212.335977 \nL 177.660752 214.756364 \nL 177.691191 200.022555 \nL 177.78251 214.756364 \nL 177.812949 213.087942 \nL 177.843388 214.756364 \nL 177.873828 214.638869 \nL 177.904267 196.427223 \nL 177.965146 214.756364 \nL 177.995585 208.599652 \nL 178.056464 209.892092 \nL 178.178222 214.756364 \nL 178.208661 214.756364 \nL 178.29998 208.482158 \nL 178.421737 214.756364 \nL 178.482616 211.443019 \nL 178.513055 214.756364 \nL 178.573934 212.030492 \nL 178.634813 213.158439 \nL 178.665252 214.756364 \nL 178.756571 205.380303 \nL 178.847889 214.756364 \nL 178.878328 207.683195 \nL 178.939207 214.756364 \nL 178.969647 214.756364 \nL 179.030525 210.879046 \nL 179.060965 214.19239 \nL 179.121844 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214.756364 \nL 191.023652 214.356882 \nL 191.054091 201.385491 \nL 191.084531 214.568372 \nL 191.14541 208.64665 \nL 191.267167 214.732865 \nL 191.328046 211.959995 \nL 191.358485 214.756364 \nL 191.388925 214.427379 \nL 191.449804 214.638869 \nL 191.541122 203.594387 \nL 191.571561 213.675414 \nL 191.63244 202.701429 \nL 191.662879 198.283636 \nL 191.784637 214.732865 \nL 191.815077 208.200171 \nL 191.845516 214.756364 \nL 191.906395 210.291573 \nL 191.936834 214.756364 \nL 191.967274 210.174079 \nL 191.997713 199.294089 \nL 192.058592 214.756364 \nL 192.119471 213.205436 \nL 192.14991 214.756364 \nL 192.210789 214.309885 \nL 192.241228 214.756364 \nL 192.271668 203.805877 \nL 192.332546 210.197577 \nL 192.454304 214.756364 \nL 192.606501 208.928637 \nL 192.728259 214.756364 \nL 192.789138 214.286386 \nL 192.819577 201.1975 \nL 192.850016 214.756364 \nL 192.880456 213.651915 \nL 192.941335 211.231529 \nL 192.971774 212.26548 \nL 193.032653 214.756364 \nL 193.15441 190.622996 \nL 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209.657103 \nL 208.404554 214.756364 \nL 208.434993 210.268074 \nL 208.465432 207.941683 \nL 208.495872 213.416926 \nL 208.556751 214.756364 \nL 208.58719 203.500392 \nL 208.678508 207.354211 \nL 208.708948 206.29676 \nL 208.800266 213.463924 \nL 208.830705 212.429973 \nL 208.861145 203.946871 \nL 208.891584 214.732865 \nL 208.952463 208.317666 \nL 208.982902 213.792909 \nL 209.013342 207.777191 \nL 209.043781 212.899951 \nL 209.135099 214.756364 \nL 209.165539 194.688305 \nL 209.226418 214.756364 \nL 209.287296 212.899951 \nL 209.317736 214.756364 \nL 209.348175 209.586606 \nL 209.409054 213.463924 \nL 209.439493 214.756364 \nL 209.469933 201.573482 \nL 209.530812 213.534421 \nL 209.561251 214.286386 \nL 209.591691 204.064365 \nL 209.652569 209.774597 \nL 209.713448 207.424708 \nL 209.774327 214.756364 \nL 209.804766 206.578747 \nL 209.865645 214.450878 \nL 209.896085 214.756364 \nL 209.987403 208.670149 \nL 210.017842 214.756364 \nL 210.10916 214.215889 \nL 210.1396 208.082677 \nL 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198.84761 \nL 212.118161 212.899951 \nL 212.148601 211.701507 \nL 212.17904 199.411583 \nL 212.20948 214.756364 \nL 212.239919 214.333383 \nL 212.300798 214.756364 \nL 212.331237 199.952058 \nL 212.392116 208.85814 \nL 212.422555 211.983494 \nL 212.452995 182.468878 \nL 212.483434 213.158439 \nL 212.513874 203.194906 \nL 212.544313 214.756364 \nL 212.635631 212.735458 \nL 212.666071 206.437754 \nL 212.72695 210.409068 \nL 212.757389 214.756364 \nL 212.818268 213.322931 \nL 212.848707 211.725006 \nL 212.879147 198.236639 \nL 212.909586 214.756364 \nL 212.940025 199.223592 \nL 213.000904 214.756364 \nL 213.061783 212.171485 \nL 213.092222 207.307213 \nL 213.122662 214.756364 \nL 213.153101 210.009586 \nL 213.183541 214.756364 \nL 213.244419 208.341165 \nL 213.274859 214.568372 \nL 213.335738 214.756364 \nL 213.396616 213.510922 \nL 213.427056 214.756364 \nL 213.457495 212.735458 \nL 213.487935 213.9809 \nL 213.518374 209.234123 \nL 213.579253 214.756364 \nL 213.609692 214.756364 \nL 213.761889 197.155689 \nL 213.883647 214.756364 \nL 213.914086 214.756364 \nL 213.944526 214.497876 \nL 214.005405 214.756364 \nL 214.035844 214.756364 \nL 214.066283 214.61537 \nL 214.127162 214.756364 \nL 214.188041 209.422114 \nL 214.309799 214.756364 \nL 214.370678 209.539608 \nL 214.401117 214.662368 \nL 214.431556 214.756364 \nL 214.522875 194.218327 \nL 214.553314 210.244575 \nL 214.614193 209.539608 \nL 214.675072 214.756364 \nL 214.76639 213.275933 \nL 214.857708 214.756364 \nL 214.827269 212.92345 \nL 214.888147 214.450878 \nL 214.918587 204.087864 \nL 215.009905 210.197577 \nL 215.101223 214.756364 \nL 215.131663 214.732865 \nL 215.192542 214.756364 \nL 215.25342 206.484752 \nL 215.28386 212.05399 \nL 215.314299 195.745755 \nL 215.375178 203.006915 \nL 215.496936 214.756364 \nL 215.527375 206.531749 \nL 215.588254 214.756364 \nL 215.618693 214.756364 \nL 215.649133 206.790237 \nL 215.710011 214.732865 \nL 215.77089 214.756364 \nL 215.831769 213.534421 \nL 215.862208 199.90506 \nL 215.923087 208.717147 \nL 216.014406 214.756364 \nL 216.044845 212.92345 \nL 216.105724 202.842423 \nL 216.136163 214.756364 \nL 216.227481 206.978229 \nL 216.257921 214.756364 \nL 216.349239 214.709366 \nL 216.440557 202.372445 \nL 216.410118 214.756364 \nL 216.470997 211.889498 \nL 216.531875 209.868593 \nL 216.592754 214.215889 \nL 216.684072 205.544796 \nL 216.775391 214.756364 \nL 216.80583 206.790237 \nL 216.866709 214.756364 \nL 216.927588 211.396021 \nL 216.958027 186.628183 \nL 216.988467 214.591871 \nL 217.018906 212.758957 \nL 217.049345 209.398615 \nL 217.110224 213.863406 \nL 217.262421 214.756364 \nL 217.292861 201.549983 \nL 217.353739 213.463924 \nL 217.384179 213.675414 \nL 217.414618 200.398537 \nL 217.445058 214.521375 \nL 217.475497 211.560514 \nL 217.505937 214.756364 \nL 217.597255 212.735458 \nL 217.658134 214.756364 \nL 217.719012 202.489939 \nL 217.779891 212.077489 \nL 217.84077 210.832048 \nL 217.932088 214.756364 \nL 217.962528 211.349023 \nL 218.023406 212.876452 \nL 218.114725 214.756364 \nL 218.145164 214.732865 \nL 218.206043 214.756364 \nL 218.297361 200.610027 \nL 218.388679 214.756364 \nL 218.419119 207.095723 \nL 218.540876 214.756364 \nL 218.571316 214.591871 \nL 218.632195 203.077411 \nL 218.662634 211.678008 \nL 218.693073 214.756364 \nL 218.723513 201.385491 \nL 218.784392 211.043538 \nL 218.87571 214.756364 \nL 218.936589 201.878968 \nL 218.967028 214.756364 \nL 218.997467 214.756364 \nL 219.027907 201.737974 \nL 219.088786 214.756364 \nL 219.149665 214.756364 \nL 219.180104 197.696164 \nL 219.240983 214.756364 \nL 219.271422 213.087942 \nL 219.301862 214.450878 \nL 219.332301 195.581263 \nL 219.39318 210.503063 \nL 219.423619 210.080083 \nL 219.454059 212.030492 \nL 219.514937 202.771926 \nL 219.545377 214.756364 \nL 219.575816 212.547467 \nL 219.636695 214.756364 \nL 219.697574 214.756364 \nL 219.819331 213.487423 \nL 219.91065 214.756364 \nL 219.971529 195.722257 \nL 220.001968 214.732865 \nL 220.032407 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228.068411 214.756364 \nL 228.09885 210.291573 \nL 228.159729 214.756364 \nL 228.190169 214.756364 \nL 228.220608 214.380381 \nL 228.251047 199.505579 \nL 228.281487 214.756364 \nL 228.311926 211.560514 \nL 228.342366 212.852953 \nL 228.372805 190.998978 \nL 228.433684 214.521375 \nL 228.464123 214.61537 \nL 228.494563 214.239388 \nL 228.555441 214.756364 \nL 228.61632 203.335899 \nL 228.677199 192.808393 \nL 228.738078 214.756364 \nL 228.890275 207.330712 \nL 228.920714 213.087942 \nL 228.951154 197.437676 \nL 228.981593 214.756364 \nL 229.012033 208.458659 \nL 229.042472 214.756364 \nL 229.13379 213.322931 \nL 229.225108 214.756364 \nL 229.255548 213.440425 \nL 229.285987 214.756364 \nL 229.346866 203.335899 \nL 229.377305 208.43516 \nL 229.468624 214.756364 \nL 229.529502 191.844938 \nL 229.559942 196.192234 \nL 229.6817 214.756364 \nL 229.712139 213.181937 \nL 229.773018 214.732865 \nL 229.864336 213.228935 \nL 229.894775 214.40388 \nL 229.925215 214.732865 \nL 229.955654 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231.72114 210.221076 \nL 231.782019 214.756364 \nL 231.812458 213.76941 \nL 231.903776 200.892014 \nL 231.934216 212.382975 \nL 231.964655 214.756364 \nL 232.055973 213.910403 \nL 232.086413 214.168891 \nL 232.177731 214.239388 \nL 232.20817 196.450722 \nL 232.23861 214.756364 \nL 232.329928 214.286386 \nL 232.360367 214.732865 \nL 232.451686 200.633526 \nL 232.421246 214.756364 \nL 232.482125 208.43516 \nL 232.543004 214.756364 \nL 232.603883 213.745911 \nL 232.664761 213.275933 \nL 232.72564 197.155689 \nL 232.786519 206.132268 \nL 232.877837 214.756364 \nL 232.847398 198.518625 \nL 232.908277 213.252434 \nL 232.999595 214.756364 \nL 233.030034 211.278527 \nL 233.121353 213.275933 \nL 233.24311 200.563029 \nL 233.27355 210.479564 \nL 233.303989 214.756364 \nL 233.395307 212.570966 \nL 233.425747 208.200171 \nL 233.456186 186.581185 \nL 233.517065 211.20803 \nL 233.547504 214.756364 \nL 233.608383 209.140127 \nL 233.638823 207.871187 \nL 233.669262 188.249607 \nL 233.730141 214.756364 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237.839461 199.881561 \nL 237.8699 214.756364 \nL 237.90034 214.685867 \nL 238.052537 203.382897 \nL 238.143855 214.756364 \nL 238.174294 205.615292 \nL 238.235173 211.137533 \nL 238.296052 214.756364 \nL 238.326491 212.547467 \nL 238.356931 198.84761 \nL 238.38737 214.756364 \nL 238.41781 205.967776 \nL 238.539567 214.756364 \nL 238.661325 198.072146 \nL 238.691764 214.756364 \nL 238.783082 212.241982 \nL 238.813522 200.093051 \nL 238.874401 214.756364 \nL 238.90484 203.547389 \nL 239.057037 214.756364 \nL 239.087476 207.213217 \nL 239.148355 211.090536 \nL 239.270113 214.756364 \nL 239.361431 191.539453 \nL 239.42231 214.756364 \nL 239.483189 205.87378 \nL 239.544068 214.756364 \nL 239.604946 210.009586 \nL 239.635386 214.427379 \nL 239.665825 201.385491 \nL 239.696265 214.756364 \nL 239.726704 211.41952 \nL 239.787583 214.450878 \nL 239.818022 209.445613 \nL 239.878901 214.756364 \nL 240.000659 209.375116 \nL 240.031098 191.492455 \nL 240.091977 214.662368 \nL 240.122416 214.61537 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254.733332 214.756364 \nL 254.763771 214.756364 \nL 254.794211 198.730115 \nL 254.885529 206.813736 \nL 254.946408 214.756364 \nL 254.976847 213.487423 \nL 255.007287 141.345807 \nL 255.068165 214.756364 \nL 255.098605 213.886904 \nL 255.159484 196.239232 \nL 255.189923 212.664962 \nL 255.220362 210.808549 \nL 255.250802 212.735458 \nL 255.281241 212.030492 \nL 255.311681 214.662368 \nL 255.34212 201.925966 \nL 255.402999 214.756364 \nL 255.463878 211.513516 \nL 255.494317 199.599575 \nL 255.555196 214.756364 \nL 255.616075 214.756364 \nL 255.646514 213.275933 \nL 255.676954 213.698913 \nL 255.707393 201.033007 \nL 255.737832 214.732865 \nL 255.768272 211.067037 \nL 255.798711 214.145392 \nL 255.829151 194.194828 \nL 255.85959 214.756364 \nL 255.890029 214.756364 \nL 255.920469 214.756364 \nL 255.950908 191.210468 \nL 256.011787 212.71196 \nL 256.042226 208.811143 \nL 256.103105 212.946949 \nL 256.133545 214.756364 \nL 256.163984 214.638869 \nL 256.224863 167.923061 \nL 256.255302 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "สิ่งที่เราเห็นในที่นี้คือในตอนแรกความยาวเฉลี่ยของเส้นทางเพิ่มขึ้น นี่อาจเป็นเพราะว่าเมื่อเราไม่รู้อะไรเกี่ยวกับสภาพแวดล้อมเลย เรามักจะติดอยู่ในสถานะที่ไม่ดี เช่น น้ำหรือหมาป่า เมื่อเราเรียนรู้มากขึ้นและเริ่มใช้ความรู้นี้ เราสามารถสำรวจสภาพแวดล้อมได้นานขึ้น แต่เรายังไม่รู้แน่ชัดว่าแอปเปิ้ลอยู่ที่ไหน\n",
+    "\n",
+    "เมื่อเราเรียนรู้มากพอ มันจะง่ายขึ้นสำหรับตัวแทนในการบรรลุเป้าหมาย และความยาวของเส้นทางก็เริ่มลดลง อย่างไรก็ตาม เรายังคงเปิดรับการสำรวจ ดังนั้นเรามักจะเบี่ยงเบนออกจากเส้นทางที่ดีที่สุด และลองสำรวจตัวเลือกใหม่ ๆ ซึ่งทำให้เส้นทางยาวกว่าที่ควรจะเป็น\n",
+    "\n",
+    "สิ่งที่เราสังเกตได้อีกอย่างจากกราฟนี้คือในบางจุดความยาวเพิ่มขึ้นอย่างรวดเร็ว สิ่งนี้บ่งชี้ถึงธรรมชาติแบบสุ่มของกระบวนการ และในบางครั้งเราอาจ \"ทำให้ค่าสัมประสิทธิ์ใน Q-Table เสียหาย\" โดยการเขียนทับด้วยค่าที่ใหม่กว่า ซึ่งควรลดผลกระทบนี้ให้น้อยที่สุดโดยการลดอัตราการเรียนรู้ (เช่น ในช่วงท้ายของการฝึก เราปรับค่าของ Q-Table เพียงเล็กน้อย)\n",
+    "\n",
+    "โดยรวมแล้ว สิ่งสำคัญคือต้องจำไว้ว่าความสำเร็จและคุณภาพของกระบวนการเรียนรู้ขึ้นอยู่กับพารามิเตอร์อย่างมาก เช่น อัตราการเรียนรู้ การลดอัตราการเรียนรู้ และตัวคูณลดค่า พารามิเตอร์เหล่านี้มักถูกเรียกว่า **ไฮเปอร์พารามิเตอร์** เพื่อแยกความแตกต่างจาก **พารามิเตอร์** ซึ่งเราปรับแต่งระหว่างการฝึก (เช่น ค่าสัมประสิทธิ์ใน Q-Table) กระบวนการค้นหาค่าที่ดีที่สุดของไฮเปอร์พารามิเตอร์เรียกว่า **การปรับแต่งไฮเปอร์พารามิเตอร์** ซึ่งเป็นหัวข้อที่ควรศึกษาแยกต่างหาก\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## แบบฝึกหัด\n",
+    "#### โลกของปีเตอร์กับหมาป่าที่สมจริงยิ่งขึ้น\n",
+    "\n",
+    "ในสถานการณ์ของเรา ปีเตอร์สามารถเคลื่อนที่ไปมาได้แทบจะไม่รู้สึกเหนื่อยหรือหิวเลย ในโลกที่สมจริงมากขึ้น เขาจะต้องนั่งพักเป็นครั้งคราว และต้องหาอาหารกินด้วย ลองทำให้โลกของเราสมจริงขึ้นโดยการเพิ่มกฎดังต่อไปนี้:\n",
+    "\n",
+    "1. เมื่อปีเตอร์เคลื่อนที่จากที่หนึ่งไปยังอีกที่หนึ่ง เขาจะสูญเสีย **พลังงาน** และเพิ่ม **ความเหนื่อยล้า** ขึ้น\n",
+    "2. ปีเตอร์สามารถเพิ่มพลังงานได้โดยการกินแอปเปิ้ล\n",
+    "3. ปีเตอร์สามารถลดความเหนื่อยล้าได้โดยการพักผ่อนใต้ต้นไม้หรือบนพื้นหญ้า (เช่น เดินไปยังตำแหน่งบนกระดานที่มีต้นไม้หรือหญ้า - พื้นที่สีเขียว)\n",
+    "4. ปีเตอร์ต้องค้นหาและฆ่าหมาป่า\n",
+    "5. เพื่อที่จะฆ่าหมาป่า ปีเตอร์จำเป็นต้องมีระดับพลังงานและความเหนื่อยล้าในระดับที่เหมาะสม มิฉะนั้นเขาจะพ่ายแพ้ในการต่อสู้\n",
+    "\n",
+    "ปรับฟังก์ชันรางวัลตามกฎของเกมนี้ จากนั้นรันอัลกอริทึมการเรียนรู้แบบเสริมกำลังเพื่อเรียนรู้กลยุทธ์ที่ดีที่สุดในการชนะเกม และเปรียบเทียบผลลัพธ์ของการเดินแบบสุ่มกับอัลกอริทึมของคุณในแง่ของจำนวนเกมที่ชนะและแพ้\n",
+    "\n",
+    "> **Note**: คุณอาจต้องปรับไฮเปอร์พารามิเตอร์เพื่อให้ระบบทำงานได้ โดยเฉพาะจำนวนรอบการฝึกฝน เนื่องจากความสำเร็จของเกม (การต่อสู้กับหมาป่า) เป็นเหตุการณ์ที่เกิดขึ้นได้ยาก คุณอาจต้องใช้เวลาฝึกฝนนานขึ้น\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/8-Reinforcement/2-Gym/notebook.ipynb b/translations/th/8-Reinforcement/2-Gym/notebook.ipynb
new file mode 100644
index 000000000..7694d20f4
--- /dev/null
+++ b/translations/th/8-Reinforcement/2-Gym/notebook.ipynb
@@ -0,0 +1,392 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.4 64-bit ('base': conda)"
+  },
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "coopTranslator": {
+   "original_hash": "f22f8f3daed4b6d34648d1254763105b",
+   "translation_date": "2025-09-06T15:18:01+00:00",
+   "source_file": "8-Reinforcement/2-Gym/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## การเล่นสเก็ต CartPole\n",
+    "\n",
+    "> **ปัญหา**: หากปีเตอร์ต้องการหนีจากหมาป่า เขาจำเป็นต้องเคลื่อนที่ได้เร็วกว่า เราจะมาดูกันว่าปีเตอร์สามารถเรียนรู้การเล่นสเก็ตได้อย่างไร โดยเฉพาะการรักษาสมดุล ด้วยการใช้ Q-Learning\n",
+    "\n",
+    "ก่อนอื่น มาติดตั้ง gym และนำเข้าห้องสมุดที่จำเป็น:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## สร้างสภาพแวดล้อมรถเข็นเสา\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 2"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "เพื่อดูว่าโครงสร้างของสิ่งแวดล้อมทำงานอย่างไร ลองรันการจำลองสั้น ๆ เป็นเวลา 100 ขั้นตอน\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "ระหว่างการจำลอง เราจำเป็นต้องได้รับการสังเกตเพื่อที่จะตัดสินใจว่าจะดำเนินการอย่างไร ในความเป็นจริง ฟังก์ชัน `step` จะส่งคืนการสังเกตปัจจุบัน ฟังก์ชันรางวัล และธง `done` ที่ระบุว่าควรดำเนินการจำลองต่อไปหรือไม่:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 4"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "เราสามารถหาค่าต่ำสุดและค่าสูงสุดของตัวเลขเหล่านั้น:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 6"
+   ]
+  },
+  {
+   "source": [
+    "มาสำรวจวิธีการแยกส่วนอื่น ๆ โดยใช้ถัง:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 7"
+   ]
+  },
+  {
+   "source": [
+    "มาทำการจำลองสั้น ๆ และสังเกตค่าของสภาพแวดล้อมแบบแยกส่วนเหล่านั้นกันเถอะ\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -2, -2)\n(0, 1, -2, -5)\n(0, 2, -3, -8)\n(0, 3, -5, -11)\n(0, 3, -7, -14)\n(0, 4, -10, -17)\n(0, 3, -14, -15)\n(0, 3, -17, -12)\n(0, 3, -20, -16)\n(0, 4, -23, -19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 8"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 9"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 22.0, alpha=0.3, epsilon=0.9\n",
+      "5000: 70.1384, alpha=0.3, epsilon=0.9\n",
+      "10000: 121.8586, alpha=0.3, epsilon=0.9\n",
+      "15000: 149.6368, alpha=0.3, epsilon=0.9\n",
+      "20000: 168.2782, alpha=0.3, epsilon=0.9\n",
+      "25000: 196.7356, alpha=0.3, epsilon=0.9\n",
+      "30000: 220.7614, alpha=0.3, epsilon=0.9\n",
+      "35000: 233.2138, alpha=0.3, epsilon=0.9\n",
+      "40000: 248.22, alpha=0.3, epsilon=0.9\n",
+      "45000: 264.636, alpha=0.3, epsilon=0.9\n",
+      "50000: 276.926, alpha=0.3, epsilon=0.9\n",
+      "55000: 277.9438, alpha=0.3, epsilon=0.9\n",
+      "60000: 248.881, alpha=0.3, epsilon=0.9\n",
+      "65000: 272.529, alpha=0.3, epsilon=0.9\n",
+      "70000: 281.7972, alpha=0.3, epsilon=0.9\n",
+      "75000: 284.2844, alpha=0.3, epsilon=0.9\n",
+      "80000: 269.667, alpha=0.3, epsilon=0.9\n",
+      "85000: 273.8652, alpha=0.3, epsilon=0.9\n",
+      "90000: 278.2466, alpha=0.3, epsilon=0.9\n",
+      "95000: 269.1736, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 11"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "จากกราฟนี้ ไม่สามารถบอกอะไรได้เลย เนื่องจากลักษณะของกระบวนการฝึกแบบสุ่มทำให้ระยะเวลาของการฝึกแต่ละครั้งแตกต่างกันอย่างมาก เพื่อให้กราฟนี้มีความหมายมากขึ้น เราสามารถคำนวณ **ค่าเฉลี่ยเคลื่อนที่** จากชุดการทดลอง เช่น 100 ซึ่งสามารถทำได้อย่างสะดวกโดยใช้ `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "#code block 12"
+   ]
+  },
+  {
+   "source": [
+    "## การปรับเปลี่ยนไฮเปอร์พารามิเตอร์และดูผลลัพธ์ที่เกิดขึ้น\n",
+    "\n",
+    "ตอนนี้จะน่าสนใจมากขึ้นถ้าเราได้เห็นว่ารูปแบบที่ผ่านการฝึกฝนทำงานอย่างไร ลองรันการจำลองดู และเราจะใช้กลยุทธ์การเลือกการกระทำแบบเดียวกับที่ใช้ในระหว่างการฝึก: การสุ่มตัวอย่างตามการกระจายความน่าจะเป็นใน Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 13"
+   ]
+  },
+  {
+   "source": [
+    "## การบันทึกผลลัพธ์เป็นภาพ GIF แบบเคลื่อนไหว\n",
+    "\n",
+    "หากคุณต้องการสร้างความประทับใจให้เพื่อน ๆ คุณอาจต้องการส่งภาพ GIF แบบเคลื่อนไหวของเสาเพื่อความสมดุลให้พวกเขา สำหรับการทำเช่นนี้ เราสามารถเรียกใช้ `env.render` เพื่อสร้างเฟรมภาพ และบันทึกเฟรมนั้นเป็นภาพ GIF แบบเคลื่อนไหวโดยใช้ไลบรารี PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามืออาชีพ เราจะไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/8-Reinforcement/2-Gym/solution/notebook.ipynb b/translations/th/8-Reinforcement/2-Gym/solution/notebook.ipynb
new file mode 100644
index 000000000..9db8feddb
--- /dev/null
+++ b/translations/th/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -0,0 +1,524 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "5c0e485e58d63c506f1791c4dbf990ce",
+   "translation_date": "2025-09-06T15:20:54+00:00",
+   "source_file": "8-Reinforcement/2-Gym/solution/notebook.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## การเล่นสเก็ต CartPole\n",
+    "\n",
+    "> **ปัญหา**: หากปีเตอร์ต้องการหนีจากหมาป่า เขาจำเป็นต้องเคลื่อนที่ได้เร็วกว่า เราจะมาดูกันว่าปีเตอร์สามารถเรียนรู้การเล่นสเก็ตได้อย่างไร โดยเฉพาะการรักษาสมดุล ด้วยการใช้ Q-Learning\n",
+    "\n",
+    "ก่อนอื่น มาติดตั้ง gym และนำเข้าไลบรารีที่จำเป็น:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: gym in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.18.3)\n",
+      "Requirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
+      "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
+      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.6.0)\n",
+      "Requirement already satisfied: pyglet<=1.5.15,>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.5.15)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!pip install gym \n",
+    "\n",
+    "import gym\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "source": [
+    "## สร้างสภาพแวดล้อมรถเข็นและเสา\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "เพื่อดูว่าโครงสร้างของสิ่งแวดล้อมทำงานอย่างไร ลองรันการจำลองสั้น ๆ เป็นเวลา 100 ขั้นตอน\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "ระหว่างการจำลอง เราจำเป็นต้องได้รับการสังเกตเพื่อที่จะตัดสินใจว่าจะดำเนินการอย่างไร ในความเป็นจริง ฟังก์ชัน `step` จะส่งคืนการสังเกตการณ์ปัจจุบัน ฟังก์ชันรางวัล และธง `done` ที่ระบุว่ามีเหตุผลที่จะดำเนินการจำลองต่อไปหรือไม่:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[ 0.03044442 -0.19543914 -0.04496216  0.28125618] -> 1.0\n",
+      "[ 0.02653564 -0.38989186 -0.03933704  0.55942606] -> 1.0\n",
+      "[ 0.0187378  -0.19424049 -0.02814852  0.25461393] -> 1.0\n",
+      "[ 0.01485299 -0.38894946 -0.02305624  0.53828712] -> 1.0\n",
+      "[ 0.007074   -0.19351108 -0.0122905   0.23842953] -> 1.0\n",
+      "[ 0.00320378  0.00178427 -0.00752191 -0.05810469] -> 1.0\n",
+      "[ 0.00323946  0.19701326 -0.008684   -0.35315131] -> 1.0\n",
+      "[ 0.00717973  0.00201587 -0.01574703 -0.06321931] -> 1.0\n",
+      "[ 0.00722005  0.19736001 -0.01701141 -0.36082863] -> 1.0\n",
+      "[ 0.01116725  0.39271958 -0.02422798 -0.65882671] -> 1.0\n",
+      "[ 0.01902164  0.19794307 -0.03740452 -0.37387001] -> 1.0\n",
+      "[ 0.0229805   0.39357584 -0.04488192 -0.67810827] -> 1.0\n",
+      "[ 0.03085202  0.58929164 -0.05844408 -0.98457719] -> 1.0\n",
+      "[ 0.04263785  0.78514572 -0.07813563 -1.2950295 ] -> 1.0\n",
+      "[ 0.05834076  0.98116859 -0.10403622 -1.61111521] -> 1.0\n",
+      "[ 0.07796413  0.78741784 -0.13625852 -1.35259196] -> 1.0\n",
+      "[ 0.09371249  0.98396202 -0.16331036 -1.68461179] -> 1.0\n",
+      "[ 0.11339173  0.79106371 -0.1970026  -1.44691436] -> 1.0\n",
+      "[ 0.12921301  0.59883361 -0.22594088 -1.22169133] -> 1.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "เราสามารถหาค่าต่ำสุดและค่าสูงสุดของตัวเลขเหล่านั้น:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(env.observation_space.low)\n",
+    "print(env.observation_space.high)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretize(x):\n",
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+   ]
+  },
+  {
+   "source": [
+    "มาสำรวจวิธีการแยกส่วนอื่น ๆ โดยใช้ถังข้อมูล:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_bins(i,num):\n",
+    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
+    "\n",
+    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
+    "\n",
+    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
+    "nbins = [20,20,10,10] # number of bins for each parameter\n",
+    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
+    "\n",
+    "def discretize_bins(x):\n",
+    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+   ]
+  },
+  {
+   "source": [
+    "มาลองรันการจำลองสั้น ๆ และสังเกตค่าของสภาพแวดล้อมแบบแยกส่วนเหล่านั้นกันเถอะ\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 2, -4, -9)\n(0, 3, -6, -12)\n(0, 2, -8, -9)\n(0, 3, -10, -13)\n(0, 4, -13, -16)\n(0, 4, -16, -19)\n(0, 4, -20, -17)\n(0, 4, -24, -20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   #env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   #print(discretize_bins(obs))\n",
+    "   print(discretize(obs))\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = {}\n",
+    "actions = (0,1)\n",
+    "\n",
+    "def qvalues(state):\n",
+    "    return [Q.get((state,a),0) for a in actions]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hyperparameters\n",
+    "alpha = 0.3\n",
+    "gamma = 0.9\n",
+    "epsilon = 0.90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 108.0, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v\n",
+    "\n",
+    "Qmax = 0\n",
+    "cum_rewards = []\n",
+    "rewards = []\n",
+    "for epoch in range(100000):\n",
+    "    obs = env.reset()\n",
+    "    done = False\n",
+    "    cum_reward=0\n",
+    "    # == do the simulation ==\n",
+    "    while not done:\n",
+    "        s = discretize(obs)\n",
+    "        if random.random()<epsilon:\n",
+    "            # exploitation - chose the action according to Q-Table probabilities\n",
+    "            v = probs(np.array(qvalues(s)))\n",
+    "            a = random.choices(actions,weights=v)[0]\n",
+    "        else:\n",
+    "            # exploration - randomly chose the action\n",
+    "            a = np.random.randint(env.action_space.n)\n",
+    "\n",
+    "        obs, rew, done, info = env.step(a)\n",
+    "        cum_reward+=rew\n",
+    "        ns = discretize(obs)\n",
+    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
+    "    cum_rewards.append(cum_reward)\n",
+    "    rewards.append(cum_reward)\n",
+    "    # == Periodically print results and calculate average reward ==\n",
+    "    if epoch%5000==0:\n",
+    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
+    "        if np.average(cum_rewards) > Qmax:\n",
+    "            Qmax = np.average(cum_rewards)\n",
+    "            Qbest = Q\n",
+    "        cum_rewards=[]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 128.423241 194.265898 \r\nL 128.514551 142.035299 \r\nL 128.56325 208.729756 \r\nL 128.566293 51.234412 \r\nL 128.624123 150.47255 \r\nL 128.730651 46.011353 \r\nL 128.715433 208.327982 \r\nL 128.736739 103.465011 \r\nL 128.751957 172.971885 \r\nL 128.74587 80.162129 \r\nL 128.803699 140.428204 \r\nL 128.858485 33.958137 \r\nL 128.864573 204.712018 \r\nL 128.913271 151.276098 \r\nL 128.919359 192.658803 \r\nL 128.998494 63.287627 \r\nL 129.016756 110.295166 \r\nL 129.077629 60.876984 \r\nL 129.025887 186.230421 \r\nL 129.114153 117.928869 \r\nL 129.117197 166.945277 \r\nL 129.175027 50.832639 \r\nL 129.223725 161.320443 \r\nL 129.321123 63.287627 \r\nL 129.308948 199.890732 \r\nL 129.333297 84.983415 \r\nL 129.357647 198.283636 \r\nL 129.409389 53.243282 \r\nL 129.442869 145.24949 \r\nL 129.518961 20.297827 \r\nL 129.491568 197.078315 \r\nL 129.549398 161.722217 \r\nL 129.640708 202.301375 \r\nL 129.610271 33.556364 \r\nL 129.655926 179.80204 \r\nL 129.716799 19.092506 \r\nL 129.668101 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 91.81357 \r\nL 189.47311 17.083636 \r\nL 189.409193 213.952816 \r\nL 189.479197 119.535965 \r\nL 189.518765 198.283636 \r\nL 189.530939 30.743947 \r\nL 189.549201 146.454812 \r\nL 189.652686 17.083636 \r\nL 189.576594 191.051707 \r\nL 189.658773 186.632195 \r\nL 189.683123 17.083636 \r\nL 189.774433 63.287627 \r\nL 189.874874 212.747494 \r\nL 189.810957 17.083636 \r\nL 189.884005 86.59051 \r\nL 189.899223 17.083636 \r\nL 189.908354 197.078315 \r\nL 189.944878 185.0251 \r\nL 189.947922 195.872993 \r\nL 189.969227 17.083636 \r\nL 190.030101 136.812239 \r\nL 190.121411 17.083636 \r\nL 190.094018 205.515565 \r\nL 190.139673 154.892062 \r\nL 190.252288 17.083636 \r\nL 190.227939 211.140399 \r\nL 190.258376 52.439734 \r\nL 190.279681 191.855255 \r\nL 190.27055 17.083636 \r\nL 190.374035 170.159468 \r\nL 190.377079 174.177206 \r\nL 190.380122 54.046829 \r\nL 190.413603 17.083636 \r\nL 190.422734 191.453481 \r\nL 190.486651 50.029091 \r\nL 190.532306 202.703149 \r\nL 190.501869 17.083636 \r\nL 190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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320.128594 80.965676 \r\nL 320.162075 22.306696 \r\nL 320.204686 185.0251 \r\nL 320.235123 116.321774 \r\nL 320.320345 194.667672 \r\nL 320.286865 17.083636 \r\nL 320.338607 38.779424 \r\nL 320.402524 17.083636 \r\nL 320.387306 184.221552 \r\nL 320.432961 155.69561 \r\nL 320.51514 50.430865 \r\nL 320.442092 205.515565 \r\nL 320.554708 56.055698 \r\nL 320.652105 196.274767 \r\nL 320.566882 17.083636 \r\nL 320.66428 80.162129 \r\nL 320.691673 17.083636 \r\nL 320.703847 188.23929 \r\nL 320.75559 124.759024 \r\nL 320.840812 202.301375 \r\nL 320.776895 17.083636 \r\nL 320.859074 93.018891 \r\nL 320.959515 17.083636 \r\nL 320.941253 188.23929 \r\nL 320.97169 53.243282 \r\nL 321.047782 191.453481 \r\nL 321.063 17.083636 \r\nL 321.081262 67.305366 \r\nL 321.09648 188.641064 \r\nL 321.178659 17.083636 \r\nL 321.184747 71.323104 \r\nL 321.2791 198.68541 \r\nL 321.291275 17.083636 \r\nL 321.330843 183.418004 \r\nL 321.403891 92.617118 \r\nL 321.483026 17.083636 \r\nL 321.501288 176.989623 \r\nL 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "จากกราฟนี้ ไม่สามารถบอกอะไรได้เลย เพราะเนื่องจากลักษณะของกระบวนการฝึกแบบสุ่ม ความยาวของช่วงการฝึกจะแตกต่างกันอย่างมาก เพื่อให้เข้าใจกราฟนี้มากขึ้น เราสามารถคำนวณ **ค่าเฉลี่ยเคลื่อนที่** จากชุดการทดลอง เช่น 100 ซึ่งสามารถทำได้อย่างสะดวกโดยใช้ `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "def running_average(x,window):\n",
+    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
+    "\n",
+    "plt.plot(running_average(rewards,100))"
+   ]
+  },
+  {
+   "source": [
+    "## การปรับเปลี่ยนค่าพารามิเตอร์และดูผลลัพธ์ที่เกิดขึ้น\n",
+    "\n",
+    "ตอนนี้จะน่าสนใจมากขึ้นถ้าเราได้เห็นว่ารูปแบบการทำงานของโมเดลที่ผ่านการฝึกฝนเป็นอย่างไร ลองรันการจำลองดู และเราจะใช้กลยุทธ์การเลือกการกระทำแบบเดียวกับที่ใช้ในระหว่างการฝึกฝน: การสุ่มตัวอย่างตามการกระจายความน่าจะเป็นใน Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset()\n",
+    "done = False\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   env.render()\n",
+    "   v = probs(np.array(qvalues(s)))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## การบันทึกผลลัพธ์เป็นภาพ GIF แบบเคลื่อนไหว\n",
+    "\n",
+    "หากคุณต้องการสร้างความประทับใจให้เพื่อน ๆ คุณอาจต้องการส่งภาพ GIF แบบเคลื่อนไหวของเสาเพื่อความสมดุลให้พวกเขา สำหรับการทำเช่นนี้ เราสามารถเรียกใช้ `env.render` เพื่อสร้างเฟรมภาพ และบันทึกเฟรมเหล่านั้นเป็นภาพ GIF แบบเคลื่อนไหวโดยใช้ไลบรารี PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้องมากที่สุด แต่โปรดทราบว่าการแปลโดยอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่ถูกต้อง เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ ขอแนะนำให้ใช้บริการแปลภาษามนุษย์ที่เป็นมืออาชีพ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความผิดที่เกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/th/PyTorch_Fundamentals.ipynb b/translations/th/PyTorch_Fundamentals.ipynb
new file mode 100644
index 000000000..18fa658fd
--- /dev/null
+++ b/translations/th/PyTorch_Fundamentals.ipynb
@@ -0,0 +1,2828 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4",
+   "authorship_tag": "ABX9TyOgv0AozH1FKQBD+RkgT2bV",
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "coopTranslator": {
+   "original_hash": "0ca21b6ee62904d616f2e36dc1cf0da7",
+   "translation_date": "2025-09-06T13:08:15+00:00",
+   "source_file": "PyTorch_Fundamentals.ipynb",
+   "language_code": "th"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "view-in-github",
+    "colab_type": "text"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EHh5JllMh1rG",
+    "outputId": "f55755ad-c369-414c-85ec-6e9d4f061a02",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'2.2.1+cu121'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(\"I am excited to run this\")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UPlb-duwXAfz",
+    "outputId": "cfd687e4-1238-49f4-ab6b-ee1305b740d2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "I am excited to run this\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(torch.__version__)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "byWVlJ9wXDSk",
+    "outputId": "fd74a5c4-4d4a-41b2-ef3c-562ea3e4811f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2.2.1+cu121\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "Osm80zoEYklS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# scalar\n",
+    "scalar = torch.tensor(7)\n",
+    "scalar"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-o8wvJ-VXZmI",
+    "outputId": "558816f5-1205-4de1-fe1f-2f96e9bd79e6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(7)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mCZ2tXC4Y_Sg",
+    "outputId": "2d86dbdc-56e1-45c6-d3dd-14515f2a457a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ssN00By0ZQgS",
+    "outputId": "490f40d1-5135-4969-a6d3-c8c902cdc473"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# vector\n",
+    "vector = torch.tensor([7, 7])\n",
+    "vector\n",
+    "#vector.ndim\n",
+    "#vector.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Bws__5wlZnmF",
+    "outputId": "944e38f9-5ba1-4ddc-a9c6-cfb6a19bb488"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([7, 7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "vector.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9pjCvnsZZzNG",
+    "outputId": "e030a4da-8f81-4858-fbce-86da2aaafe52"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([2])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Matrix\n",
+    "MATRIX = torch.tensor([[7, 8],[9, 10]])\n",
+    "MATRIX"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a747hI9SaBGW",
+    "outputId": "af835ddb-81ff-4981-badb-441567194d15"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 7,  8],\n",
+       "        [ 9, 10]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XdTfFa7vaRUj",
+    "outputId": "0fbbab9c-8263-4cad-a380-0d2a16ca499e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX[0]\n",
+    "MATRIX[1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TFeD3jSDafm7",
+    "outputId": "69b44ab3-5ba7-451a-c6b2-f019a03d0c96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Tensor\n",
+    "TENSOR = torch.tensor([[[1, 2, 3],[3,6,9], [2,4,5]]])\n",
+    "TENSOR"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ic3cE47tah42",
+    "outputId": "f250e295-91de-43ec-9d80-588a6fe0abde"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 2, 3],\n",
+       "         [3, 6, 9],\n",
+       "         [2, 4, 5]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wvjf5fczbAM1",
+    "outputId": "9c72b5b8-bafe-4ae7-9883-b051e209eada"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mwtXZwiMbN3m",
+    "outputId": "331a5e36-b1b0-4a5f-a9b8-e7049cbaa8f9"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vzdZu_IfbP3J",
+    "outputId": "e24e7e71-e365-412d-ff50-fc094b56d2f3"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [3, 6, 9],\n",
+       "        [2, 4, 5]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "A8OL9eWfcRrJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor = torch.rand(3,4)\n",
+    "random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hAqSDE1EcVS_",
+    "outputId": "946171c3-d054-400c-f893-79110356888c"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.4414, 0.7681, 0.8385, 0.3166],\n",
+       "        [0.0468, 0.5812, 0.0670, 0.9173],\n",
+       "        [0.2959, 0.3276, 0.7411, 0.4643]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "g4fvPE5GcwzP",
+    "outputId": "8737f36b-6864-4059-eaed-6f9156c22306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XsAg99QmdAU6",
+    "outputId": "35467c11-257c-4f16-99aa-eca930bcbc36"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.size()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cii1pNdVdB68",
+    "outputId": "fc8d2de6-9215-43de-99f7-7b0d7f7d20fa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_image_tensor = torch.rand(size=(3, 224, 224)) #color channels, height, width\n",
+    "random_image_tensor.ndim, random_image_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aTKq2j0cdDjb",
+    "outputId": "6be42057-20b9-4faf-d79d-8b65c42cc27e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([3, 224, 224]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor_ofownsize = torch.rand(size=(5,10,10))\n",
+    "random_tensor_ofownsize.ndim, random_tensor_ofownsize.shape\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IyhDdj-Pd6nC",
+    "outputId": "43e5e334-6d4d-4b67-f87d-7d364c6d8c67"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([5, 10, 10]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "UOJW08uOert_"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero = torch.zeros(size=(3, 4))\n",
+    "zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGvXtaXyefie",
+    "outputId": "d40d3e28-8667-4d2f-8b62-f0829c6162ad"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero*random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OyUkUPkDe0uH",
+    "outputId": "26c2e4be-36ba-4c6c-9a90-2704ec135828"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones = torch.ones(size=(3, 4))\n",
+    "ones\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "y_Ac62Aqe82G",
+    "outputId": "291de5d9-b9df-49de-c9d1-d098e3e9f4d8"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TvGOA9odfIEO",
+    "outputId": "45949ef4-6649-4b6c-d6af-2d4bfb8de832"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones*zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "--pTyge-fI-8",
+    "outputId": "c4d9bb7e-829b-43db-e2db-b1a2d64e61f0"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qDcc7Z36fSJF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "one_to_ten = torch.arange(start = 1, end = 11, step = 1)\n",
+    "one_to_ten"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w3CZB4zUfR1s",
+    "outputId": "197fcba1-da0a-4b4a-ed11-3974bd6c01aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ten_zeros = torch.zeros_like(one_to_ten)\n",
+    "ten_zeros"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WZh99BwVfRy8",
+    "outputId": "51ef8bfb-6fa0-4099-ff66-b97d65b2ddea"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "pGGhgsbUgqbW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor = torch.tensor([3.0, 6.0,9.0], dtype = None, device = None, requires_grad = False)\n",
+    "float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JORJl4XkfRsx",
+    "outputId": "71114171-0f49-481f-b6fc-6cb48e2fb895"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3., 6., 9.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6wOPPwGyfRLn",
+    "outputId": "f23776a1-b682-404a-9f67-d5bcb0402666"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor = float_32_tensor.type(torch.float16)\n",
+    "float_16_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tFsHCvmZfOYe",
+    "outputId": "d3aa305a-7591-47f5-97fd-61bff60b44bd"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float16"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 31
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TQiCGTPuwq0q",
+    "outputId": "98750fce-1ca3-4889-e269-8b753efdea96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor = torch.tensor([3, 6, 9], dtype = torch.int32)\n",
+    "int_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5hlrLvGUw5D_",
+    "outputId": "41d890a0-9aee-446c-d906-631ce2ab0995"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9], dtype=torch.int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ihApD9u3xTNW",
+    "outputId": "d295eed0-6996-4e0f-8502-ff4b55cd1373"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(0,100,10)"
+   ],
+   "metadata": {
+    "id": "utKhlb_KxWDQ"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p78D74E9Rj7Y",
+    "outputId": "781a1614-a900-41f5-9e5d-358f0b2390aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.min()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4BcSs5NeRkcj",
+    "outputId": "3f24a8dc-58e9-4a5f-9834-e85856a34f9d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.max()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hinqvXVLRm4q",
+    "outputId": "5c7d8a53-3913-4ac1-bba3-5ba8ff68250a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 38
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mean(x.type(torch.float32))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k7okc0_vRpnB",
+    "outputId": "91e5494f-dc57-417c-ea4d-25dbc547c893"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.type(torch.float32).mean()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "29QcDTjHRq10",
+    "outputId": "62937c6c-78e0-49f2-dde3-1543ee8f7907"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wlpY_G_sbdKF",
+    "outputId": "475d8258-af65-4011-a258-b93d4d8142d4"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(450)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmax()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GT6HJzwhbk4n",
+    "outputId": "2e455c20-c322-4bcf-d07c-1259d3ccefc6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmin()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "egL3oi2Mb19P",
+    "outputId": "f71fb32f-6338-44a3-b377-75bea0a3ab54"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p2U8DZKib3DP",
+    "outputId": "b9f613b9-74e9-45f4-ed01-05babb6a6793"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[9]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24qBFlGYcABe",
+    "outputId": "5813cfcb-7f63-4bd7-ee46-f95ccbfda939"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(1, 10)\n",
+    "x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0GPOxEzkcBHO",
+    "outputId": "aefbd903-4f4c-4d2c-c90f-eccd682fe018"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped = x.reshape(1,9)\n",
+    "x_reshaped, x_reshaped.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "spmRgQjwddgp",
+    "outputId": "85a7c55c-2909-4ea2-fc68-386dddc65742"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), torch.Size([1, 9]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped.view(1,9)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tH2ahWGydqqP",
+    "outputId": "65d92263-4fc4-434a-c06d-c5e08436f7fe"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked = torch.stack([x, x, x, x], dim = 1)\n",
+    "x_stacked"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jgCeJcaud_-1",
+    "outputId": "7f293a37-6ef1-43b6-aee5-9d6d91c94f9e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XhJHIK6cfPse",
+    "outputId": "06c47b89-3a9e-453e-bcc3-00cbcb0b8b49"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=1)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ej2c3Xxzf0tq",
+    "outputId": "94024061-eb37-446d-c4a8-e4d16cb6de81"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 52
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4DJYo1a0f5M0",
+    "outputId": "efca2b47-1b14-44de-9a9a-2c83629d153f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 53
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=-2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J4iEjn2ah2HL",
+    "outputId": "22395593-7c16-4162-beae-dd2bbe7bda35"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 55
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "tensor = torch.tensor([1, 2, 3])\n",
+    "tensor = tensor - 10\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cFfiD7Nth7Z_",
+    "outputId": "1139e1f8-fc1a-46ca-d636-f2bc4fd2eef6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-9, -8, -7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mul(tensor, 10)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dyA7BM_GHhqE",
+    "outputId": "0e3b9671-d9e8-4a32-87bb-59bc05986142"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-90, -80, -70])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.sub(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "owtUsZ1KNegI",
+    "outputId": "189b7b23-0041-4e09-b991-cd209a48506a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-109, -108, -107])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.add(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K5STXlQONsyc",
+    "outputId": "00cbb79a-0a1d-4e21-86ec-5c91c37a2d01"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([91, 92, 93])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.divide(tensor, 2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xqMGnzIUNvp0",
+    "outputId": "c894cf3e-f148-45f8-cfc8-d78740735306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-4.5000, -4.0000, -3.5000])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(tensor, tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ruGzKpV8NyBc",
+    "outputId": "fddb63bf-006f-48b6-ae28-287fbcda8bc5"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8GS3r9yTeGfD",
+    "outputId": "c80b12ac-30b5-4f3d-c38c-9e41ba511b0e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QmuYHqXTemC0",
+    "outputId": "402fe3ba-70b5-4bb2-c83b-254db84ff810"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 622 µs, sys: 0 ns, total: 622 µs\n",
+      "Wall time: 516 µs\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "torch.matmul(tensor,tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dGr1fzdNepd8",
+    "outputId": "97bd6c91-bc25-4b38-cdf5-f22dcdef243e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 424 µs, sys: 998 µs, total: 1.42 ms\n",
+      "Wall time: 1.43 ms\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.rand(3,2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pGYDoK2gevfo",
+    "outputId": "2c8783d5-0453-47c5-c7ed-af10d25d6989"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5999, 0.0073],\n",
+       "        [0.9321, 0.3026],\n",
+       "        [0.3463, 0.3872]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(torch.rand(3,2), torch.rand(2,3))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KGBGQoB8e2DP",
+    "outputId": "4c2ef361-a2d0-41ee-c328-3992cbbc138d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.3528, 0.1893, 0.0714],\n",
+       "        [1.2791, 0.7110, 0.2563],\n",
+       "        [0.8812, 0.4553, 0.1803]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch"
+   ],
+   "metadata": {
+    "id": "ib8DMtkBe_LJ"
+   },
+   "execution_count": 1,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.rand(2,9)"
+   ],
+   "metadata": {
+    "id": "nJo8ZBdrQY1b"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wi6oRv4MQfgf",
+    "outputId": "55c99f55-31f6-4cf5-ba4e-19a47c3a0167"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5894, 0.4391, 0.2018, 0.5417, 0.3844, 0.3592, 0.9209, 0.9269, 0.0681],\n",
+       "        [0.0746, 0.1740, 0.6821, 0.6890, 0.0999, 0.7444, 0.2391, 0.4625, 0.8302]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "y=torch.randn(2,3,5)\n",
+    "y"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zpx8myAUQgoc",
+    "outputId": "07756d70-56bd-437c-c74e-9aecc1a77311"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[ 1.5552, -0.4877,  0.5175, -1.7958, -0.6187],\n",
+       "         [-0.3359, -1.9710,  0.0112, -1.7578, -1.5295],\n",
+       "         [ 0.0932,  1.4079,  0.9108,  0.3328, -0.6978]],\n",
+       "\n",
+       "        [[-0.9406, -1.0809, -0.2595,  0.1282,  1.6605],\n",
+       "         [ 1.1624,  1.0902,  1.7092, -0.2842, -1.3780],\n",
+       "         [-0.1534, -1.2795, -0.5495,  0.9902,  0.1822]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original = torch.rand(size=(224,224,3))\n",
+    "x_original"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s4U-X9bJQnWe",
+    "outputId": "657a7a76-962c-4b41-a76b-902d0482266c"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[0.4549, 0.6809, 0.2118],\n",
+       "         [0.4824, 0.9008, 0.8741],\n",
+       "         [0.1715, 0.1757, 0.1845],\n",
+       "         ...,\n",
+       "         [0.8741, 0.6594, 0.2610],\n",
+       "         [0.0092, 0.1984, 0.1955],\n",
+       "         [0.4236, 0.4182, 0.0251]],\n",
+       "\n",
+       "        [[0.9174, 0.1661, 0.5852],\n",
+       "         [0.1837, 0.2351, 0.3810],\n",
+       "         [0.3726, 0.4808, 0.8732],\n",
+       "         ...,\n",
+       "         [0.6794, 0.0554, 0.9202],\n",
+       "         [0.0864, 0.8750, 0.3558],\n",
+       "         [0.8445, 0.9759, 0.4934]],\n",
+       "\n",
+       "        [[0.1600, 0.2635, 0.7194],\n",
+       "         [0.9488, 0.3405, 0.3647],\n",
+       "         [0.6683, 0.5168, 0.9592],\n",
+       "         ...,\n",
+       "         [0.0521, 0.0140, 0.2445],\n",
+       "         [0.3596, 0.3999, 0.2730],\n",
+       "         [0.5926, 0.9877, 0.7784]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[0.4794, 0.5635, 0.3764],\n",
+       "         [0.9124, 0.6094, 0.5059],\n",
+       "         [0.4528, 0.4447, 0.5021],\n",
+       "         ...,\n",
+       "         [0.0089, 0.4816, 0.8727],\n",
+       "         [0.2173, 0.6296, 0.2347],\n",
+       "         [0.2028, 0.9931, 0.7201]],\n",
+       "\n",
+       "        [[0.3116, 0.6459, 0.4703],\n",
+       "         [0.0148, 0.2345, 0.7149],\n",
+       "         [0.8393, 0.5804, 0.6691],\n",
+       "         ...,\n",
+       "         [0.2105, 0.9460, 0.2696],\n",
+       "         [0.5918, 0.9295, 0.2616],\n",
+       "         [0.2537, 0.7819, 0.4700]],\n",
+       "\n",
+       "        [[0.6654, 0.1200, 0.5841],\n",
+       "         [0.9147, 0.5522, 0.6529],\n",
+       "         [0.1799, 0.5276, 0.5415],\n",
+       "         ...,\n",
+       "         [0.7536, 0.4346, 0.8793],\n",
+       "         [0.3793, 0.1750, 0.7792],\n",
+       "         [0.9266, 0.8325, 0.9974]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted=x_original.permute(2, 0, 1)\n",
+    "print(x_original.shape)\n",
+    "print(x_permuted.shape)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DD19_zvbQzHo",
+    "outputId": "1d64ce1b-eb48-47e3-90b6-7f1340e7f2b2"
+   },
+   "execution_count": 9,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "torch.Size([224, 224, 3])\n",
+      "torch.Size([3, 224, 224])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NnPmMk4ZRF7w",
+    "outputId": "2cd5da7f-4a23-4a76-8c4a-bb982113f2a4"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z0ylNoAARgTo",
+    "outputId": "ddca0298-cddf-4048-9b71-a791655e5bed"
+   },
+   "execution_count": 11,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]=0.989"
+   ],
+   "metadata": {
+    "id": "RXw0xXsDRi4L"
+   },
+   "execution_count": 13,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1sFdV6wzRo3f",
+    "outputId": "1cf87d2c-6d88-453a-d136-0f625a2800f1"
+   },
+   "execution_count": 14,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xTX-hx2SR1wp",
+    "outputId": "0d4908c4-c3bc-44e3-8ec6-1487104cc209"
+   },
+   "execution_count": 15,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x=torch.arange(1,10).reshape(1,3,3)\n",
+    "x, x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mZomOe7gR4Q8",
+    "outputId": "0b3c922f-ec11-46de-b8a5-9f9533d866ad"
+   },
+   "execution_count": 18,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[[1, 2, 3],\n",
+       "          [4, 5, 6],\n",
+       "          [7, 8, 9]]]),\n",
+       " torch.Size([1, 3, 3]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3y7v4SQvSBs1",
+    "outputId": "8c53307d-e628-404d-db66-56c6bdffab7c"
+   },
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [4, 5, 6],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hf9uG4xLSNya",
+    "outputId": "3075bc42-9ffa-426b-8a86-95628ffcd824"
+   },
+   "execution_count": 21,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zA4G2Se4SRB3",
+    "outputId": "324312d2-ed0a-49eb-f81f-e904e53992fe"
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(1)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][2][2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mwy3zmKKSdbk",
+    "outputId": "d35172c3-b099-40a6-ddf1-a453c2adfa44"
+   },
+   "execution_count": 23,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[:,1,1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fE3nCM1KS7XT",
+    "outputId": "01f5d755-9737-4235-9f73-dce89ff6ba16"
+   },
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([5])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,0,:]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "luNDINKNTTxp",
+    "outputId": "091195ef-2f71-4602-e95f-529a69193150"
+   },
+   "execution_count": 25,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,:,2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KG8A4xbfThCL",
+    "outputId": "5866bc41-9241-4619-be7b-e9206b3f80ab"
+   },
+   "execution_count": 26,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import numpy as np"
+   ],
+   "metadata": {
+    "id": "CZ3PX0qlTwHJ"
+   },
+   "execution_count": 27,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array = np.arange(1.0, 8.0)"
+   ],
+   "metadata": {
+    "id": "UOBeTumiT3Lf"
+   },
+   "execution_count": 28,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RzcO32E9UCQl",
+    "outputId": "430def24-c42c-461f-e5e7-398544c695d3"
+   },
+   "execution_count": 29,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 4., 5., 6., 7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.from_numpy(array)\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JJIL0q1DUC6O",
+    "outputId": "8a3b1d7c-4482-4d32-f34f-9212d9d3a177"
+   },
+   "execution_count": 32,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1., 2., 3., 4., 5., 6., 7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array[3]=11.0"
+   ],
+   "metadata": {
+    "id": "j3Ce6q3DUIEK"
+   },
+   "execution_count": 33,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dc_BCVdjUsCc",
+    "outputId": "65537325-8b11-4f36-fc73-e56f30d6a036"
+   },
+   "execution_count": 34,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3., 11.,  5.,  6.,  7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VG1e_eITUta2",
+    "outputId": "a26c5198-23b6-4a6d-d73a-ba20cd9782b8"
+   },
+   "execution_count": 35,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1.,  2.,  3., 11.,  5.,  6.,  7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.ones(7)\n",
+    "tensor, tensor.dtype\n",
+    "numpy_tensor = tensor.numpy()\n",
+    "numpy_tensor, numpy_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Swt8JF8vUuev",
+    "outputId": "c9e5bf6a-6d2c-41d6-8327-366867ffdd2d"
+   },
+   "execution_count": 37,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(array([1., 1., 1., 1., 1., 1., 1.], dtype=float32), dtype('float32'))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "random_tensor_A = torch.rand(3,4)\n",
+    "random_tensor_B = torch.rand(3,4)\n",
+    "print(random_tensor_A)\n",
+    "print(random_tensor_B)\n",
+    "print(random_tensor_A == random_tensor_B)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGcagTteVFTD",
+    "outputId": "49405790-08e7-4210-b7f1-f00b904c7eb9"
+   },
+   "execution_count": 38,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.9870, 0.6636, 0.6873, 0.8863],\n",
+      "        [0.8386, 0.4169, 0.3587, 0.0265],\n",
+      "        [0.2981, 0.6025, 0.5652, 0.5840]])\n",
+      "tensor([[0.9821, 0.3481, 0.0913, 0.4940],\n",
+      "        [0.7495, 0.4387, 0.9582, 0.8659],\n",
+      "        [0.5064, 0.6919, 0.0809, 0.9771]])\n",
+      "tensor([[False, False, False, False],\n",
+      "        [False, False, False, False],\n",
+      "        [False, False, False, False]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "RANDOM_SEED = 42\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_C = torch.rand(3,4)\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_D = torch.rand(3,4)\n",
+    "print(random_tensor_C)\n",
+    "print(random_tensor_D)\n",
+    "print(random_tensor_C == random_tensor_D)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HznyXyEaWjLM",
+    "outputId": "25956434-01b6-4059-9054-c9978884ddc1"
+   },
+   "execution_count": 46,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[True, True, True, True],\n",
+      "        [True, True, True, True],\n",
+      "        [True, True, True, True]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!nvidia-smi"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vltPTh0YXJSt",
+    "outputId": "807af6dc-a9ca-4301-ec32-b688dbde8be8"
+   },
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Thu May 23 02:57:59 2024       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   60C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|  No running processes found                                                           |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "torch.cuda.is_available()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L6mMyPDyYh1j",
+    "outputId": "279c5dd8-c2a8-4fbd-f321-2f5d7c6e90e6"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "oOdiYa7ZYytx",
+    "outputId": "d73b04fc-8963-4826-9722-08d118d5ab91"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.cuda.device_count()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vOdsazLqZFM5",
+    "outputId": "8189cd6a-9017-4663-a652-3e15c517d9c3"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.tensor([1,2,3], device = \"cpu\")\n",
+    "print(tensor, tensor.device)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cdik9Vw3ZMv0",
+    "outputId": "044a68fd-83a1-409d-8e3b-655142ca0270"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([1, 2, 3]) cpu\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu = tensor.to(device)\n",
+    "tensor_on_gpu"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zmp835rrZp-z",
+    "outputId": "37fa3413-18a3-47bf-ae51-5b36ff85a3ef"
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3], device='cuda:0')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu.numpy()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 159
+    },
+    "id": "jhriaa8uZ1yM",
+    "outputId": "bc5a3226-1a12-4fea-8769-a44f21cdc323"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "TypeError",
+     "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-e0c96c7436fd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtensor_on_gpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_cpu = tensor_on_gpu.cpu().numpy()"
+   ],
+   "metadata": {
+    "id": "LHGXK3GgaOzL"
+   },
+   "execution_count": 12,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "id": "j-El4LlCajfq"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/2-Regression/1-Tools/notebook.ipynb b/translations/tr/2-Regression/1-Tools/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/tr/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/tr/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
new file mode 100644
index 000000000..a317a9da4
--- /dev/null
+++ b/translations/tr/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -0,0 +1,448 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_1-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "c18d3bd0bd8ae3878597e89dcd1fa5c1",
+   "translation_date": "2025-09-06T13:45:03+00:00",
+   "source_file": "2-Regression/1-Tools/solution/R/lesson_1-R.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "YJUHCXqK57yz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Regresyona Giriş - Ders 1\n",
+    "\n",
+    "#### Perspektif Kazanma\n",
+    "\n",
+    "✅ Regresyon yöntemlerinin birçok türü vardır ve hangisini seçeceğiniz, aradığınız cevaba bağlıdır. Örneğin, belirli bir yaşta bir kişinin muhtemel boyunu tahmin etmek istiyorsanız, **sayısal bir değer** aradığınız için `doğrusal regresyon` kullanırsınız. Eğer bir mutfak türünün vegan olarak kabul edilip edilmemesi gerektiğini keşfetmek istiyorsanız, **kategori ataması** arıyorsunuz demektir ve bu durumda `lojistik regresyon` kullanırsınız. Lojistik regresyon hakkında daha fazla bilgi edineceksiniz. Verilere sorabileceğiniz bazı soruları düşünün ve bu yöntemlerden hangisinin daha uygun olacağını değerlendirin.\n",
+    "\n",
+    "Bu bölümde, [diyabet hakkında küçük bir veri seti](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) ile çalışacaksınız. Diyabet hastaları için bir tedaviyi test etmek istediğinizi hayal edin. Makine Öğrenimi modelleri, değişkenlerin kombinasyonlarına dayanarak hangi hastaların tedaviye daha iyi yanıt vereceğini belirlemenize yardımcı olabilir. Görselleştirildiğinde, çok basit bir regresyon modeli bile teorik klinik deneylerinizi organize etmenize yardımcı olacak değişkenler hakkında bilgi gösterebilir.\n",
+    "\n",
+    "Öyleyse, bu göreve başlayalım!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/encouRage.jpg\"\n",
+    "   width=\"630\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış sanat eseri</figcaption>\n",
+    "\n",
+    "<!--![@allison_horst tarafından yapılmış sanat eseri](../../../../../../2-Regression/1-Tools/images/encouRage.jpg)<br>@allison_horst tarafından yapılmış sanat eseri-->\n"
+   ],
+   "metadata": {
+    "id": "LWNNzfqd6feZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Araç setimizi yükleme\n",
+    "\n",
+    "Bu görev için aşağıdaki paketlere ihtiyacımız olacak:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/), veri bilimini daha hızlı, kolay ve eğlenceli hale getirmek için tasarlanmış bir [R paketleri koleksiyonudur](https://www.tidyverse.org/packages).\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) çerçevesi, modelleme ve makine öğrenimi için bir [paketler koleksiyonudur](https://www.tidymodels.org/packages).\n",
+    "\n",
+    "Bu paketleri şu şekilde yükleyebilirsiniz:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+    "\n",
+    "Aşağıdaki script, bu modülü tamamlamak için gerekli paketlere sahip olup olmadığınızı kontrol eder ve eksik olanları sizin için yükler.\n"
+   ],
+   "metadata": {
+    "id": "FIo2YhO26wI9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse, tidymodels)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "id": "cIA9fz9v7Dss",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Şimdi, bu harika paketleri yükleyelim ve mevcut R oturumumuzda kullanılabilir hale getirelim. (Bu sadece bir örnek için, `pacman::p_load()` bunu zaten sizin için yaptı)\n"
+   ],
+   "metadata": {
+    "id": "gpO_P_6f9WUG"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# load the core Tidyverse packages\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# load the core Tidymodels packages\r\n",
+    "library(tidymodels)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NLMycgG-9ezO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Diyabet veri seti\n",
+    "\n",
+    "Bu alıştırmada, diyabet veri seti üzerinde tahminler yaparak regresyon becerilerimizi sergileyeceğiz. [Diyabet veri seti](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt), diyabetle ilgili `442 örnek` veri içerir ve 10 tahmin edici özellik değişkeni, `yaş`, `cinsiyet`, `vücut kitle indeksi`, `ortalama kan basıncı` ve `altı kan serumu ölçümü` ile bir sonuç değişkeni `y`: başlangıçtan bir yıl sonra hastalık ilerlemesinin nicel bir ölçüsünü içerir.\n",
+    "\n",
+    "|Gözlem Sayısı|442|\n",
+    "|----------------------|:---|\n",
+    "|Tahmin Edici Sayısı|İlk 10 sütun sayısal tahmin edici|\n",
+    "|Sonuç/Hedef|11. sütun başlangıçtan bir yıl sonra hastalık ilerlemesinin nicel bir ölçüsüdür|\n",
+    "|Tahmin Edici Bilgileri|- yaş (yıl olarak)\n",
+    "||- cinsiyet\n",
+    "||- bmi vücut kitle indeksi\n",
+    "||- bp ortalama kan basıncı\n",
+    "||- s1 tc, toplam serum kolesterol\n",
+    "||- s2 ldl, düşük yoğunluklu lipoproteinler\n",
+    "||- s3 hdl, yüksek yoğunluklu lipoproteinler\n",
+    "||- s4 tch, toplam kolesterol / HDL\n",
+    "||- s5 ltg, muhtemelen serum trigliserit seviyesinin logaritması\n",
+    "||- s6 glu, kan şekeri seviyesi|\n",
+    "\n",
+    "\n",
+    "> 🎓 Unutmayın, bu denetimli öğrenmedir ve adlandırılmış bir 'y' hedefine ihtiyacımız var.\n",
+    "\n",
+    "R ile veri üzerinde işlem yapmadan önce, veriyi R'nin belleğine aktarmanız veya R'nin veriye uzaktan erişebilmesi için bir bağlantı oluşturmanız gerekir.\n",
+    "\n",
+    "> [readr](https://readr.tidyverse.org/) paketi, Tidyverse'in bir parçası olup, dikdörtgen verileri R'ye hızlı ve kullanıcı dostu bir şekilde aktarmanın bir yolunu sunar.\n",
+    "\n",
+    "Şimdi, bu kaynak URL'de sağlanan diyabet veri setini yükleyelim: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+    "\n",
+    "Ayrıca, verilerimiz üzerinde bir kontrol yapacağız ve `glimpse()` kullanarak veri yapısını inceleyeceğiz, ardından `slice()` ile ilk 5 satırı görüntüleyeceğiz.\n",
+    "\n",
+    "Daha ileri gitmeden önce, R kodunda sıkça karşılaşacağınız bir şeyi tanıtalım 🥁🥁: pipe operatörü `%>%`\n",
+    "\n",
+    "Pipe operatörü (`%>%`), bir nesneyi mantıksal bir sırayla bir fonksiyona veya çağrı ifadesine ileterek işlemleri gerçekleştirir. Pipe operatörünü kodunuzda \"ve sonra\" demek gibi düşünebilirsiniz.\n"
+   ],
+   "metadata": {
+    "id": "KM6iXLH996Cl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import the data set\r\n",
+    "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Get a glimpse and dimensions of the data\r\n",
+    "glimpse(diabetes)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Select the first 5 rows of the data\r\n",
+    "diabetes %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Z1geAMhM-bSP"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`glimpse()` bize bu verinin 442 satır ve 11 sütundan oluştuğunu, tüm sütunların veri türünün `double` olduğunu gösteriyor.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "> glimpse() ve slice(), [`dplyr`](https://dplyr.tidyverse.org/) paketindeki fonksiyonlardır. Dplyr, Tidyverse'in bir parçası olup, veri manipülasyonu için bir dil sunar ve en yaygın veri manipülasyonu zorluklarını çözmenize yardımcı olan tutarlı bir dizi fiil sağlar.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Artık elimizde veri olduğuna göre, bu alıştırma için bir özelliği (`bmi`) hedef alarak daraltalım. Bunun için istediğimiz sütunları seçmemiz gerekecek. Peki bunu nasıl yaparız?\n",
+    "\n",
+    "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html), bir veri çerçevesindeki sütunları *seçmemize* (ve isteğe bağlı olarak yeniden adlandırmamıza) olanak tanır.\n"
+   ],
+   "metadata": {
+    "id": "UwjVT1Hz-c3Z"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select predictor feature `bmi` and outcome `y`\r\n",
+    "diabetes_select <- diabetes %>% \r\n",
+    "  select(c(bmi, y))\r\n",
+    "\r\n",
+    "# Print the first 5 rows\r\n",
+    "diabetes_select %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RDY1oAKI-m80"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Eğitim ve Test Verileri\n",
+    "\n",
+    "Denetimli öğrenmede, verileri iki alt kümeye *bölmek* yaygın bir uygulamadır; modelin eğitilmesi için kullanılan (genellikle daha büyük) bir küme ve modelin performansını görmek için kullanılan daha küçük bir \"ayrılmış\" küme.\n",
+    "\n",
+    "Artık veriler hazır olduğuna göre, bu veri kümesindeki sayılar arasında mantıklı bir ayrım yapıp yapamayacağını görmek için bir makineye başvurabiliriz. Verileri nasıl böleceğimize dair bilgileri içeren bir nesne oluşturmak için Tidymodels çerçevesinin bir parçası olan [rsample](https://tidymodels.github.io/rsample/) paketini kullanabiliriz. Daha sonra oluşturulan eğitim ve test kümelerini çıkarmak için iki rsample fonksiyonunu kullanabiliriz:\n"
+   ],
+   "metadata": {
+    "id": "SDk668xK-tc3"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\r\n",
+    "# Split 67% of the data for training and the rest for tesing\r\n",
+    "diabetes_split <- diabetes_select %>% \r\n",
+    "  initial_split(prop = 0.67)\r\n",
+    "\r\n",
+    "# Extract the resulting train and test sets\r\n",
+    "diabetes_train <- training(diabetes_split)\r\n",
+    "diabetes_test <- testing(diabetes_split)\r\n",
+    "\r\n",
+    "# Print the first 3 rows of the training set\r\n",
+    "diabetes_train %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "EqtHx129-1h-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Tidymodels ile bir doğrusal regresyon modeli eğitmek\n",
+    "\n",
+    "Artık modelimizi eğitmeye hazırız!\n",
+    "\n",
+    "Tidymodels'de modelleri `parsnip()` kullanarak üç kavramı belirterek tanımlarsınız:\n",
+    "\n",
+    "-   Model **türü**, doğrusal regresyon, lojistik regresyon, karar ağacı modelleri gibi modelleri birbirinden ayırır.\n",
+    "\n",
+    "-   Model **modu**, regresyon ve sınıflandırma gibi yaygın seçenekleri içerir; bazı model türleri her iki modu desteklerken bazıları yalnızca bir moda sahiptir.\n",
+    "\n",
+    "-   Model **motoru**, modeli eğitmek için kullanılacak hesaplama aracıdır. Genellikle bunlar R paketleridir, örneğin **`\"lm\"`** veya **`\"ranger\"`**\n",
+    "\n",
+    "Bu modelleme bilgisi bir model spesifikasyonunda tutulur, o halde bir tane oluşturalım!\n"
+   ],
+   "metadata": {
+    "id": "sBOS-XhB-6v7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- \r\n",
+    "  # Type\r\n",
+    "  linear_reg() %>% \r\n",
+    "  # Engine\r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  # Mode\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the model specification\r\n",
+    "lm_spec"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "20OwEw20--t3"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bir model *belirlendikten* sonra, model [`fit()`](https://parsnip.tidymodels.org/reference/fit.html) fonksiyonu kullanılarak genellikle bir formül ve bazı verilerle `tahmin edilebilir` veya `eğitilebilir`.\n",
+    "\n",
+    "`y ~ .` ifadesi, `y`'yi tahmin edilen değer/hedef olarak, tüm tahmin ediciler/özellikler tarafından açıklanacak şekilde (yani `.`) uyarlayacağımız anlamına gelir (bu durumda yalnızca bir tahmin edicimiz var: `bmi`).\n"
+   ],
+   "metadata": {
+    "id": "_oDHs89k_CJj"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>%\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Train a linear regression model\r\n",
+    "lm_mod <- lm_spec %>% \r\n",
+    "  fit(y ~ ., data = diabetes_train)\r\n",
+    "\r\n",
+    "# Print the model\r\n",
+    "lm_mod"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YlsHqd-q_GJQ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Model çıktısından, eğitim sırasında öğrenilen katsayıları görebiliriz. Bu katsayılar, gerçek ve tahmin edilen değişken arasındaki toplam hatayı en aza indiren en iyi uyum çizgisinin katsayılarını temsil eder.\n",
+    "<br>\n",
+    "\n",
+    "## 5. Test seti üzerinde tahminler yapma\n",
+    "\n",
+    "Artık bir model eğittiğimize göre, test veri seti için hastalık ilerlemesi y'yi [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html) kullanarak tahmin edebiliriz. Bu, veri grupları arasındaki çizgiyi çizmek için kullanılacaktır.\n"
+   ],
+   "metadata": {
+    "id": "kGZ22RQj_Olu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\r\n",
+    "predictions <- lm_mod %>% \r\n",
+    "  predict(new_data = diabetes_test)\r\n",
+    "\r\n",
+    "# Print out some of the predictions\r\n",
+    "predictions %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "nXHbY7M2_aao"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woohoo! 💃🕺 Bir model eğittik ve tahminler yapmak için kullandık!\n",
+    "\n",
+    "Tahmin yaparken, tidymodels geleneği her zaman standartlaştırılmış sütun adlarına sahip bir tibble/veri çerçevesi üretmektir. Bu, orijinal verilerle tahminleri birleştirerek, grafik oluşturma gibi sonraki işlemler için kullanılabilir bir format oluşturmayı kolaylaştırır.\n",
+    "\n",
+    "`dplyr::bind_cols()` birden fazla veri çerçevesini sütun olarak verimli bir şekilde birleştirir.\n"
+   ],
+   "metadata": {
+    "id": "R_JstwUY_bIs"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Combine the predictions and the original test set\r\n",
+    "results <- diabetes_test %>% \r\n",
+    "  bind_cols(predictions)\r\n",
+    "\r\n",
+    "\r\n",
+    "results %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RybsMJR7_iI8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 6. Modelleme Sonuçlarını Görselleştirme\n",
+    "\n",
+    "Şimdi bunu görsel olarak inceleme zamanı 📈. Test setindeki tüm `y` ve `bmi` değerlerinin bir dağılım grafiğini oluşturacağız, ardından modelin tahminlerini kullanarak, modelin veri gruplamaları arasında en uygun yere bir çizgi çizeceğiz.\n",
+    "\n",
+    "R, grafik oluşturmak için birkaç sisteme sahiptir, ancak `ggplot2` bunların en zarif ve en esnek olanlarından biridir. Bu, grafikleri **bağımsız bileşenleri birleştirerek** oluşturmanıza olanak tanır.\n"
+   ],
+   "metadata": {
+    "id": "XJbYbMZW_n_s"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plot\r\n",
+    "theme_set(theme_light())\r\n",
+    "# Create a scatter plot\r\n",
+    "results %>% \r\n",
+    "  ggplot(aes(x = bmi)) +\r\n",
+    "  # Add a scatter plot\r\n",
+    "  geom_point(aes(y = y), size = 1.6) +\r\n",
+    "  # Add a line plot\r\n",
+    "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R9tYp3VW_sTn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ Burada neler olduğunu biraz düşünün. Bir doğru, birçok küçük veri noktasının içinden geçiyor, ancak tam olarak ne yapıyor? Bu doğruyu kullanarak yeni, görülmemiş bir veri noktasının grafiğin y ekseniyle olan ilişkisini nasıl tahmin edebileceğinizi görebiliyor musunuz? Bu modelin pratik kullanımını kelimelerle ifade etmeye çalışın.\n",
+    "\n",
+    "Tebrikler, ilk doğrusal regresyon modelinizi oluşturdunuz, onunla bir tahmin yaptınız ve bunu bir grafikte gösterdiniz!\n"
+   ],
+   "metadata": {
+    "id": "zrPtHIxx_tNI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/2-Regression/1-Tools/solution/notebook.ipynb b/translations/tr/2-Regression/1-Tools/solution/notebook.ipynb
new file mode 100644
index 000000000..a2f6959ed
--- /dev/null
+++ b/translations/tr/2-Regression/1-Tools/solution/notebook.ipynb
@@ -0,0 +1,677 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diyabet veri seti için Doğrusal Regresyon - Ders 1\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gerekli kütüphaneleri içe aktar\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model, model_selection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Diyabet veri setini yükleyin, `X` verileri ve `y` özelliklerine ayırın\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "print(X.shape)\n",
+    "print(X[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bu alıştırma için hedeflemek üzere sadece bir özellik seçin\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selecting the 3rd feature\n",
+    "X = X[:, 2]\n",
+    "print(X.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 1)\n",
+      "[[ 0.06169621]\n",
+      " [-0.05147406]\n",
+      " [ 0.04445121]\n",
+      " [-0.01159501]\n",
+      " [-0.03638469]\n",
+      " [-0.04069594]\n",
+      " [-0.04716281]\n",
+      " [-0.00189471]\n",
+      " [ 0.06169621]\n",
+      " [ 0.03906215]\n",
+      " [-0.08380842]\n",
+      " [ 0.01750591]\n",
+      " [-0.02884001]\n",
+      " [-0.00189471]\n",
+      " [-0.02560657]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [ 0.01211685]\n",
+      " [-0.0105172 ]\n",
+      " [-0.01806189]\n",
+      " [-0.05686312]\n",
+      " [-0.02237314]\n",
+      " [-0.00405033]\n",
+      " [ 0.06061839]\n",
+      " [ 0.03582872]\n",
+      " [-0.01267283]\n",
+      " [-0.07734155]\n",
+      " [ 0.05954058]\n",
+      " [-0.02129532]\n",
+      " [-0.00620595]\n",
+      " [ 0.04445121]\n",
+      " [-0.06548562]\n",
+      " [ 0.12528712]\n",
+      " [-0.05039625]\n",
+      " [-0.06332999]\n",
+      " [-0.03099563]\n",
+      " [ 0.02289497]\n",
+      " [ 0.01103904]\n",
+      " [ 0.07139652]\n",
+      " [ 0.01427248]\n",
+      " [-0.00836158]\n",
+      " [-0.06764124]\n",
+      " [-0.0105172 ]\n",
+      " [-0.02345095]\n",
+      " [ 0.06816308]\n",
+      " [-0.03530688]\n",
+      " [-0.01159501]\n",
+      " [-0.0730303 ]\n",
+      " [-0.04177375]\n",
+      " [ 0.01427248]\n",
+      " [-0.00728377]\n",
+      " [ 0.0164281 ]\n",
+      " [-0.00943939]\n",
+      " [-0.01590626]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.04931844]\n",
+      " [ 0.04121778]\n",
+      " [-0.06332999]\n",
+      " [-0.06440781]\n",
+      " [-0.02560657]\n",
+      " [-0.00405033]\n",
+      " [ 0.00457217]\n",
+      " [-0.00728377]\n",
+      " [-0.0374625 ]\n",
+      " [-0.02560657]\n",
+      " [-0.02452876]\n",
+      " [-0.01806189]\n",
+      " [-0.01482845]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [-0.06979687]\n",
+      " [ 0.03367309]\n",
+      " [-0.00405033]\n",
+      " [-0.02021751]\n",
+      " [ 0.00241654]\n",
+      " [-0.03099563]\n",
+      " [ 0.02828403]\n",
+      " [-0.03638469]\n",
+      " [-0.05794093]\n",
+      " [-0.0374625 ]\n",
+      " [ 0.01211685]\n",
+      " [-0.02237314]\n",
+      " [-0.03530688]\n",
+      " [ 0.00996123]\n",
+      " [-0.03961813]\n",
+      " [ 0.07139652]\n",
+      " [-0.07518593]\n",
+      " [-0.00620595]\n",
+      " [-0.04069594]\n",
+      " [-0.04824063]\n",
+      " [-0.02560657]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.00457217]\n",
+      " [-0.06440781]\n",
+      " [-0.01698407]\n",
+      " [-0.05794093]\n",
+      " [ 0.00996123]\n",
+      " [ 0.08864151]\n",
+      " [-0.00512814]\n",
+      " [-0.06440781]\n",
+      " [ 0.01750591]\n",
+      " [-0.04500719]\n",
+      " [ 0.02828403]\n",
+      " [ 0.04121778]\n",
+      " [ 0.06492964]\n",
+      " [-0.03207344]\n",
+      " [-0.07626374]\n",
+      " [ 0.04984027]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03207344]\n",
+      " [ 0.00457217]\n",
+      " [ 0.02073935]\n",
+      " [ 0.01427248]\n",
+      " [ 0.11019775]\n",
+      " [ 0.00133873]\n",
+      " [ 0.05846277]\n",
+      " [-0.02129532]\n",
+      " [-0.0105172 ]\n",
+      " [-0.04716281]\n",
+      " [ 0.00457217]\n",
+      " [ 0.01750591]\n",
+      " [ 0.08109682]\n",
+      " [ 0.0347509 ]\n",
+      " [ 0.02397278]\n",
+      " [-0.00836158]\n",
+      " [-0.06117437]\n",
+      " [-0.00189471]\n",
+      " [-0.06225218]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.09618619]\n",
+      " [-0.06979687]\n",
+      " [-0.02129532]\n",
+      " [-0.05362969]\n",
+      " [ 0.0433734 ]\n",
+      " [ 0.05630715]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04984027]\n",
+      " [ 0.11127556]\n",
+      " [ 0.06169621]\n",
+      " [ 0.01427248]\n",
+      " [ 0.04768465]\n",
+      " [ 0.01211685]\n",
+      " [ 0.00564998]\n",
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+      " [ 0.12852056]\n",
+      " [ 0.05954058]\n",
+      " [ 0.09295276]\n",
+      " [ 0.01535029]\n",
+      " [-0.00512814]\n",
+      " [ 0.0703187 ]\n",
+      " [-0.00405033]\n",
+      " [-0.00081689]\n",
+      " [-0.04392938]\n",
+      " [ 0.02073935]\n",
+      " [ 0.06061839]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03315126]\n",
+      " [-0.06548562]\n",
+      " [ 0.0433734 ]\n",
+      " [-0.06225218]\n",
+      " [ 0.06385183]\n",
+      " [ 0.03043966]\n",
+      " [ 0.07247433]\n",
+      " [-0.0191397 ]\n",
+      " [-0.06656343]\n",
+      " [-0.06009656]\n",
+      " [ 0.06924089]\n",
+      " [ 0.05954058]\n",
+      " [-0.02668438]\n",
+      " [-0.02021751]\n",
+      " [-0.046085  ]\n",
+      " [ 0.07139652]\n",
+      " [-0.07949718]\n",
+      " [ 0.00996123]\n",
+      " [-0.03854032]\n",
+      " [ 0.01966154]\n",
+      " [ 0.02720622]\n",
+      " [-0.00836158]\n",
+      " [-0.01590626]\n",
+      " [ 0.00457217]\n",
+      " [-0.04285156]\n",
+      " [ 0.00564998]\n",
+      " [-0.03530688]\n",
+      " [ 0.02397278]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00297252]\n",
+      " [-0.06656343]\n",
+      " [-0.01267283]\n",
+      " [-0.04177375]\n",
+      " [-0.03099563]\n",
+      " [-0.00512814]\n",
+      " [-0.05901875]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00349435]\n",
+      " [ 0.05415152]\n",
+      " [-0.04500719]\n",
+      " [-0.05794093]\n",
+      " [-0.05578531]\n",
+      " [ 0.00133873]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00672779]\n",
+      " [ 0.04660684]\n",
+      " [ 0.02612841]\n",
+      " [ 0.04552903]\n",
+      " [ 0.04013997]\n",
+      " [-0.01806189]\n",
+      " [ 0.01427248]\n",
+      " [ 0.03690653]\n",
+      " [ 0.00349435]\n",
+      " [-0.07087468]\n",
+      " [-0.03315126]\n",
+      " [ 0.09403057]\n",
+      " [ 0.03582872]\n",
+      " [ 0.03151747]\n",
+      " [-0.06548562]\n",
+      " [-0.04177375]\n",
+      " [-0.03961813]\n",
+      " [-0.03854032]\n",
+      " [-0.02560657]\n",
+      " [-0.02345095]\n",
+      " [-0.06656343]\n",
+      " [ 0.03259528]\n",
+      " [-0.046085  ]\n",
+      " [-0.02991782]\n",
+      " [-0.01267283]\n",
+      " [-0.01590626]\n",
+      " [ 0.07139652]\n",
+      " [-0.03099563]\n",
+      " [ 0.00026092]\n",
+      " [ 0.03690653]\n",
+      " [ 0.03906215]\n",
+      " [-0.01482845]\n",
+      " [ 0.00672779]\n",
+      " [-0.06871905]\n",
+      " [-0.00943939]\n",
+      " [ 0.01966154]\n",
+      " [ 0.07462995]\n",
+      " [-0.00836158]\n",
+      " [-0.02345095]\n",
+      " [-0.046085  ]\n",
+      " [ 0.05415152]\n",
+      " [-0.03530688]\n",
+      " [-0.03207344]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04768465]\n",
+      " [ 0.06061839]\n",
+      " [ 0.05630715]\n",
+      " [ 0.09834182]\n",
+      " [ 0.05954058]\n",
+      " [ 0.03367309]\n",
+      " [ 0.05630715]\n",
+      " [-0.06548562]\n",
+      " [ 0.16085492]\n",
+      " [-0.05578531]\n",
+      " [-0.02452876]\n",
+      " [-0.03638469]\n",
+      " [-0.00836158]\n",
+      " [-0.04177375]\n",
+      " [ 0.12744274]\n",
+      " [-0.07734155]\n",
+      " [ 0.02828403]\n",
+      " [-0.02560657]\n",
+      " [-0.06225218]\n",
+      " [-0.00081689]\n",
+      " [ 0.08864151]\n",
+      " [-0.03207344]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00888341]\n",
+      " [ 0.00672779]\n",
+      " [-0.02021751]\n",
+      " [-0.02452876]\n",
+      " [-0.01159501]\n",
+      " [ 0.02612841]\n",
+      " [-0.05901875]\n",
+      " [-0.03638469]\n",
+      " [-0.02452876]\n",
+      " [ 0.01858372]\n",
+      " [-0.0902753 ]\n",
+      " [-0.00512814]\n",
+      " [-0.05255187]\n",
+      " [-0.02237314]\n",
+      " [-0.02021751]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00620595]\n",
+      " [-0.01698407]\n",
+      " [ 0.05522933]\n",
+      " [ 0.07678558]\n",
+      " [ 0.01858372]\n",
+      " [-0.02237314]\n",
+      " [ 0.09295276]\n",
+      " [-0.03099563]\n",
+      " [ 0.03906215]\n",
+      " [-0.06117437]\n",
+      " [-0.00836158]\n",
+      " [-0.0374625 ]\n",
+      " [-0.01375064]\n",
+      " [ 0.07355214]\n",
+      " [-0.02452876]\n",
+      " [ 0.03367309]\n",
+      " [ 0.0347509 ]\n",
+      " [-0.03854032]\n",
+      " [-0.03961813]\n",
+      " [-0.00189471]\n",
+      " [-0.03099563]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06492964]\n",
+      " [ 0.04013997]\n",
+      " [-0.02345095]\n",
+      " [ 0.05307371]\n",
+      " [ 0.04013997]\n",
+      " [-0.02021751]\n",
+      " [ 0.01427248]\n",
+      " [-0.03422907]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00457217]\n",
+      " [ 0.03043966]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.06169621]\n",
+      " [-0.00728377]\n",
+      " [ 0.00564998]\n",
+      " [ 0.05415152]\n",
+      " [-0.00836158]\n",
+      " [ 0.114509  ]\n",
+      " [ 0.06708527]\n",
+      " [-0.05578531]\n",
+      " [ 0.03043966]\n",
+      " [-0.02560657]\n",
+      " [ 0.10480869]\n",
+      " [-0.00620595]\n",
+      " [-0.04716281]\n",
+      " [-0.04824063]\n",
+      " [ 0.08540807]\n",
+      " [-0.01267283]\n",
+      " [-0.03315126]\n",
+      " [-0.00728377]\n",
+      " [-0.01375064]\n",
+      " [ 0.05954058]\n",
+      " [ 0.02181716]\n",
+      " [ 0.01858372]\n",
+      " [-0.01159501]\n",
+      " [-0.00297252]\n",
+      " [ 0.01750591]\n",
+      " [-0.02991782]\n",
+      " [-0.02021751]\n",
+      " [-0.05794093]\n",
+      " [ 0.06061839]\n",
+      " [-0.04069594]\n",
+      " [-0.07195249]\n",
+      " [-0.05578531]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03315126]\n",
+      " [ 0.04984027]\n",
+      " [-0.08488624]\n",
+      " [ 0.00564998]\n",
+      " [ 0.02073935]\n",
+      " [-0.00728377]\n",
+      " [ 0.10480869]\n",
+      " [-0.02452876]\n",
+      " [-0.00620595]\n",
+      " [-0.03854032]\n",
+      " [ 0.13714305]\n",
+      " [ 0.17055523]\n",
+      " [ 0.00241654]\n",
+      " [ 0.03798434]\n",
+      " [-0.05794093]\n",
+      " [-0.00943939]\n",
+      " [-0.02345095]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03422907]\n",
+      " [-0.00297252]\n",
+      " [ 0.06816308]\n",
+      " [ 0.00996123]\n",
+      " [ 0.00241654]\n",
+      " [-0.03854032]\n",
+      " [ 0.02612841]\n",
+      " [-0.08919748]\n",
+      " [ 0.06061839]\n",
+      " [-0.02884001]\n",
+      " [-0.02991782]\n",
+      " [-0.0191397 ]\n",
+      " [-0.04069594]\n",
+      " [ 0.01535029]\n",
+      " [-0.02452876]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06924089]\n",
+      " [-0.06979687]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [ 0.01858372]\n",
+      " [ 0.00133873]\n",
+      " [-0.03099563]\n",
+      " [-0.00405033]\n",
+      " [ 0.01535029]\n",
+      " [ 0.02289497]\n",
+      " [ 0.04552903]\n",
+      " [-0.04500719]\n",
+      " [-0.03315126]\n",
+      " [ 0.097264  ]\n",
+      " [ 0.05415152]\n",
+      " [ 0.12313149]\n",
+      " [-0.08057499]\n",
+      " [ 0.09295276]\n",
+      " [-0.05039625]\n",
+      " [-0.01159501]\n",
+      " [-0.0277622 ]\n",
+      " [ 0.05846277]\n",
+      " [ 0.08540807]\n",
+      " [-0.00081689]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00888341]\n",
+      " [ 0.08001901]\n",
+      " [ 0.07139652]\n",
+      " [-0.02452876]\n",
+      " [-0.0547075 ]\n",
+      " [-0.03638469]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.07786339]\n",
+      " [-0.03961813]\n",
+      " [ 0.01103904]\n",
+      " [-0.04069594]\n",
+      " [-0.03422907]\n",
+      " [ 0.00564998]\n",
+      " [ 0.08864151]\n",
+      " [-0.03315126]\n",
+      " [-0.05686312]\n",
+      " [-0.03099563]\n",
+      " [ 0.05522933]\n",
+      " [-0.06009656]\n",
+      " [ 0.00133873]\n",
+      " [-0.02345095]\n",
+      " [-0.07410811]\n",
+      " [ 0.01966154]\n",
+      " [-0.01590626]\n",
+      " [-0.01590626]\n",
+      " [ 0.03906215]\n",
+      " [-0.0730303 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Reshaping to get a 2D array\n",
+    "X = X.reshape(-1, 1)\n",
+    "print(X.shape)\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hem `X` hem de `y` için eğitim ve test verilerini ayırın\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modeli seçin ve eğitim verileriyle eğitin\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Test verilerini kullanarak bir çizgi tahmin edin\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sonuçları bir grafikte göster\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test,  color='black')\n",
+    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "16ff1a974f6e4348e869e4a7d366b86a",
+   "translation_date": "2025-09-06T13:39:43+00:00",
+   "source_file": "2-Regression/1-Tools/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/tr/2-Regression/2-Data/notebook.ipynb b/translations/tr/2-Regression/2-Data/notebook.ipynb
new file mode 100644
index 000000000..d9eba010d
--- /dev/null
+++ b/translations/tr/2-Regression/2-Data/notebook.ipynb
@@ -0,0 +1,46 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  },
+  "coopTranslator": {
+   "original_hash": "1b2ab303ac6c604a34c6ca7a49077fc7",
+   "translation_date": "2025-09-06T13:46:05+00:00",
+   "source_file": "2-Regression/2-Data/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/tr/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
new file mode 100644
index 000000000..f6d4d5865
--- /dev/null
+++ b/translations/tr/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -0,0 +1,672 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_2-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "f3c335f9940cfd76528b3ef918b9b342",
+   "translation_date": "2025-09-06T13:55:20+00:00",
+   "source_file": "2-Regression/2-Data/solution/R/lesson_2-R.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Bir regresyon modeli oluşturma: verileri hazırlama ve görselleştirme\n",
+    "\n",
+    "## **Balkabağı için Doğrusal Regresyon - Ders 2**\n",
+    "#### Giriş\n",
+    "\n",
+    "Tidymodels ve Tidyverse ile makine öğrenimi modeli oluşturma sürecine başlamak için gerekli araçları kurduğunuza göre, artık verilerinizle ilgili sorular sormaya hazırsınız. Verilerle çalışırken ve ML çözümleri uygularken, veri setinizin potansiyelini doğru bir şekilde açığa çıkarmak için doğru soruyu nasıl soracağınızı anlamak çok önemlidir.\n",
+    "\n",
+    "Bu derste öğreneceksiniz:\n",
+    "\n",
+    "-   Model oluşturma için verilerinizi nasıl hazırlayacağınızı.\n",
+    "\n",
+    "-   Veri görselleştirme için `ggplot2`'yi nasıl kullanacağınızı.\n",
+    "\n",
+    "Cevaplanmasını istediğiniz soru, hangi tür ML algoritmalarını kullanacağınızı belirleyecektir. Ve alacağınız cevabın kalitesi, büyük ölçüde verilerinizin doğasına bağlı olacaktır.\n",
+    "\n",
+    "Bunu pratik bir alıştırma ile görelim.\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/unruly_data.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış sanat eseri</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![@allison_horst tarafından yapılmış sanat eseri](../../../../../../2-Regression/2-Data/images/unruly_data.jpg)<br>@allison_horst tarafından yapılmış sanat eseri-->\n"
+   ],
+   "metadata": {
+    "id": "Pg5aexcOPqAZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Kabak verilerini içe aktarma ve Tidyverse'i çağırma\n",
+    "\n",
+    "Bu dersin verilerini işlemek için aşağıdaki paketlere ihtiyacımız olacak:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/), veri bilimini daha hızlı, kolay ve eğlenceli hale getirmek için tasarlanmış bir [R paketleri koleksiyonudur](https://www.tidyverse.org/packages).\n",
+    "\n",
+    "Paketleri şu şekilde yükleyebilirsiniz:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\"))`\n",
+    "\n",
+    "Aşağıdaki script, bu modülü tamamlamak için gerekli paketlere sahip olup olmadığınızı kontrol eder ve eksik olanları sizin için yükler.\n"
+   ],
+   "metadata": {
+    "id": "dc5WhyVdXAjR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "GqPYUZgfXOBt"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Şimdi, bazı paketleri çalıştıralım ve bu ders için sağlanan [veriyi](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) yükleyelim!\n"
+   ],
+   "metadata": {
+    "id": "kvjDTPDSXRr2"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n =50)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VMri-t2zXqgD"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hızlı bir `glimpse()` işlemi, boşluklar olduğunu ve metin (`chr`) ile sayısal verilerin (`dbl`) karışık olduğunu hemen gösteriyor. `Date` karakter türünde ve ayrıca verilerin `sacks`, `bins` ve diğer değerlerin karışımı olduğu garip bir `Package` adlı sütun da var. Aslında, veri biraz karışık 😤.\n",
+    "\n",
+    "Aslında, tamamen kullanıma hazır bir veri setinin size sunulması ve doğrudan bir ML modeli oluşturmak için uygun olması pek yaygın değildir. Ama endişelenmeyin, bu derste, standart R kütüphanelerini kullanarak ham bir veri setini nasıl hazırlayacağınızı öğreneceksiniz 🧑‍🔧. Ayrıca, veriyi görselleştirmek için çeşitli teknikleri de öğreneceksiniz. 📈📊\n",
+    "<br>\n",
+    "\n",
+    "> Bir hatırlatma: Pipe operatörü (`%>%`), bir nesneyi ileriye doğru bir fonksiyona veya çağrı ifadesine geçirerek işlemleri mantıksal bir sırayla gerçekleştirir. Pipe operatörünü kodunuzda \"ve sonra\" demek gibi düşünebilirsiniz.\n"
+   ],
+   "metadata": {
+    "id": "REWcIv9yX29v"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Eksik verileri kontrol et\n",
+    "\n",
+    "Veri bilimcilerin en sık karşılaştığı sorunlardan biri eksik veya kayıp verilerdir. R, eksik veya bilinmeyen değerleri özel bir işaret değeri olan `NA` (Not Available) ile temsil eder.\n",
+    "\n",
+    "Peki, veri çerçevesinde eksik değerler olduğunu nasıl anlayabiliriz?\n",
+    "<br>\n",
+    "-   En basit yöntem, mantıksal nesneler `TRUE` veya `FALSE` döndüren temel R fonksiyonu `anyNA`yı kullanmaktır.\n"
+   ],
+   "metadata": {
+    "id": "Zxfb3AM5YbUe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  anyNA()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "G--DQutAYltj"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Harika, bazı eksik veriler var gibi görünüyor! Bu başlamak için iyi bir yer.\n",
+    "\n",
+    "-   Bir başka yöntem, hangi bireysel sütun elemanlarının eksik olduğunu `TRUE` mantıksal değeriyle gösteren `is.na()` fonksiyonunu kullanmaktır.\n"
+   ],
+   "metadata": {
+    "id": "mU-7-SB6YokF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "W-DxDOR4YxSW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tamam, iş tamamlandı ancak bu kadar büyük bir veri çerçevesiyle, tüm satırları ve sütunları tek tek incelemek verimsiz ve pratikte imkansız olurdu😴.\n",
+    "\n",
+    "-   Daha sezgisel bir yöntem, her sütundaki eksik değerlerin toplamını hesaplamak olacaktır:\n"
+   ],
+   "metadata": {
+    "id": "xUWxipKYY0o7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  colSums()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZRBWV6P9ZArL"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Çok daha iyi! Eksik veriler var, ancak belki de bu, yapılacak görev için önemli olmayabilir. Bakalım, daha fazla analiz ne ortaya çıkaracak.\n",
+    "\n",
+    "> R, harika paketler ve fonksiyonlar setlerinin yanı sıra çok iyi bir dokümantasyona sahiptir. Örneğin, `help(colSums)` veya `?colSums` kullanarak bu fonksiyon hakkında daha fazla bilgi edinebilirsiniz.\n"
+   ],
+   "metadata": {
+    "id": "9gv-crB6ZD1Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Dplyr: Veri Manipülasyonu için Bir Dilbilgisi\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_wrangling.png\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış bir illüstrasyon</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![@allison_horst tarafından yapılmış bir illüstrasyon](../../../../../../2-Regression/2-Data/images/dplyr_wrangling.png)<br/>@allison_horst tarafından yapılmış bir illüstrasyon-->\n"
+   ],
+   "metadata": {
+    "id": "o4jLY5-VZO2C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`dplyr`](https://dplyr.tidyverse.org/), Tidyverse içinde yer alan bir paket olup, veri manipülasyonu için bir dilbilgisi sunar ve en yaygın veri manipülasyonu sorunlarını çözmenize yardımcı olan tutarlı bir fiil seti sağlar. Bu bölümde, dplyr'ın bazı fiillerini keşfedeceğiz!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "i5o33MQBZWWw"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::select()\n",
+    "\n",
+    "`select()` fonksiyonu, `dplyr` paketinde bulunan ve belirli sütunları seçmenize veya hariç tutmanıza yardımcı olan bir işlevdir.\n",
+    "\n",
+    "Veri çerçevenizi daha kolay çalışılabilir hale getirmek için, `select()` kullanarak ihtiyacınız olan sütunları tutup diğerlerini kaldırabilirsiniz.\n",
+    "\n",
+    "Örneğin, bu alıştırmada analizimiz `Package`, `Low Price`, `High Price` ve `Date` sütunlarını içerecek. Hadi bu sütunları seçelim.\n"
+   ],
+   "metadata": {
+    "id": "x3VGMAGBZiUr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(Package, `Low Price`, `High Price`, Date)\n",
+    "\n",
+    "\n",
+    "# Print data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "F_FgxQnVZnM0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::mutate()\n",
+    "\n",
+    "`mutate()`, `dplyr` paketinde bulunan ve mevcut sütunları korurken yeni sütunlar oluşturmanıza veya mevcut sütunları değiştirmenize yardımcı olan bir fonksiyondur.\n",
+    "\n",
+    "`mutate` fonksiyonunun genel yapısı şu şekildedir:\n",
+    "\n",
+    "`data %>%   mutate(yeni_sutun_adi = icerigi)`\n",
+    "\n",
+    "`mutate` fonksiyonunu `Date` sütunu üzerinde şu işlemleri yaparak deneyelim:\n",
+    "\n",
+    "1. Tarihleri (şu anda karakter türünde) ay formatına dönüştürün (bunlar ABD tarih formatında, yani `MM/DD/YYYY`).\n",
+    "\n",
+    "2. Tarihlerden ay bilgisini çıkararak yeni bir sütuna ekleyin.\n",
+    "\n",
+    "R dilinde, [lubridate](https://lubridate.tidyverse.org/) paketi Tarih-Zaman verileriyle çalışmayı kolaylaştırır. Bu nedenle, yukarıdaki hedeflere ulaşmak için `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` fonksiyonlarını kullanalım. Date sütununu, sonraki işlemlerde artık ihtiyaç duymayacağımız için kaldırabiliriz.\n"
+   ],
+   "metadata": {
+    "id": "2KKo0Ed9Z1VB"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load lubridate\n",
+    "library(lubridate)\n",
+    "\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  # Convert the Date column to a date object\n",
+    "  mutate(Date = mdy(Date)) %>% \n",
+    "  # Extract month from Date\n",
+    "  mutate(Month = month(Date)) %>% \n",
+    "  # Drop Date column\n",
+    "  select(-Date)\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5joszIVSZ6xe"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woohoo! 🤩\n",
+    "\n",
+    "Şimdi, bir yeni sütun olan `Price` oluşturalım. Bu sütun, bir kabağın ortalama fiyatını temsil ediyor. Şimdi, `Low Price` ve `High Price` sütunlarının ortalamasını alarak yeni Price sütununu dolduralım.\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "nIgLjNMCZ-6Y"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new column Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Zo0BsqqtaJw2"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Evet!💪\n",
+    "\n",
+    "\"Ama dur bir dakika!\", tüm veri setini `View(pumpkins)` ile hızlıca gözden geçirdikten sonra diyeceksiniz ki, \"Burada bir gariplik var!\"🤔\n",
+    "\n",
+    "Eğer `Package` sütununa bakarsanız, kabakların birçok farklı şekilde satıldığını göreceksiniz. Bazıları `1 1/9 bushel` ölçüsünde, bazıları `1/2 bushel` ölçüsünde, bazıları kabak başına, bazıları pound başına, ve bazıları da farklı genişliklerde büyük kutular içinde satılıyor.\n",
+    "\n",
+    "Hadi bunu doğrulayalım:\n"
+   ],
+   "metadata": {
+    "id": "p77WZr-9aQAR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Verify the distinct observations in Package column\n",
+    "pumpkins %>% \n",
+    "  distinct(Package)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "XISGfh0IaUy6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Harika!👏\n",
+    "\n",
+    "Balkabaklarını tutarlı bir şekilde tartmak oldukça zor görünüyor, bu yüzden onları filtreleyelim. Bunun için `Package` sütununda *bushel* kelimesini içeren balkabaklarını seçip yeni bir veri çerçevesi olan `new_pumpkins` içine koyalım.\n"
+   ],
+   "metadata": {
+    "id": "7sMjiVujaZxY"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::filter() ve stringr::str_detect()\n",
+    "\n",
+    "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): Verilerin yalnızca **satırlarının** koşullarınızı sağladığı bir alt kümesini oluşturur, bu durumda `Package` sütununda *bushel* kelimesini içeren kabaklar.\n",
+    "\n",
+    "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): Bir dizgede bir desenin varlığını veya yokluğunu algılar.\n",
+    "\n",
+    "[`stringr`](https://github.com/tidyverse/stringr) paketi, yaygın dizge işlemleri için basit fonksiyonlar sağlar.\n"
+   ],
+   "metadata": {
+    "id": "L8Qfcs92ageF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Retain only pumpkins with \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "       filter(str_detect(Package, \"bushel\"))\n",
+    "\n",
+    "# Get the dimensions of the new data\n",
+    "dim(new_pumpkins)\n",
+    "\n",
+    "# View a few rows of the new data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "hy_SGYREampd"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kabakları kile ile içeren yaklaşık 415 satıra kadar daralttığımızı görebilirsiniz.🤩\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "VrDwF031avlR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::case_when()\n",
+    "\n",
+    "**Ama durun! Yapılacak bir şey daha var**\n",
+    "\n",
+    "Her satırda buşel miktarının değiştiğini fark ettiniz mi? Fiyatlandırmayı normalize etmeniz gerekiyor, böylece fiyatlandırmayı 1 1/9 veya 1/2 buşel yerine buşel başına gösterebilirsiniz. Şimdi fiyatları standartlaştırmak için biraz matematik yapma zamanı.\n",
+    "\n",
+    "Fiyat sütununu bazı koşullara bağlı olarak *değiştirmek* için [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) fonksiyonunu kullanacağız. `case_when`, birden fazla `if_else()` ifadesini vektörleştirmenize olanak tanır.\n"
+   ],
+   "metadata": {
+    "id": "mLpw2jH4a0tx"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(Price = case_when(\n",
+    "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+    "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+    "    TRUE ~ Price))\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 30)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "P68kLVQmbM6I"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Şimdi, bushel ölçümüne göre birim fiyat analizini yapabiliriz. Ancak, tüm bu kabak bushel'leri üzerine yapılan çalışma, `verinizin doğasını anlamanın` ne kadar `önemli` olduğunu gösteriyor!\n",
+    "\n",
+    "> ✅ [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308)'e göre, bir bushel'in ağırlığı ürün türüne bağlıdır çünkü bu bir hacim ölçüsüdür. \"Örneğin, bir domates bushel'i 56 pound ağırlığında olmalı... Yapraklar ve yeşillikler daha az ağırlıkla daha fazla alan kaplar, bu yüzden bir ıspanak bushel'i sadece 20 pound'dur.\" Oldukça karmaşık bir konu! Bushel'den pound'a dönüşüm yapmaya uğraşmayalım, bunun yerine bushel üzerinden fiyatlandırma yapalım. Ancak, tüm bu kabak bushel'leri üzerine yapılan çalışma, verinizin doğasını anlamanın ne kadar önemli olduğunu gösteriyor!\n",
+    ">\n",
+    "> ✅ Yarım bushel ile satılan kabakların çok pahalı olduğunu fark ettiniz mi? Nedenini çözebilir misiniz? İpucu: Küçük kabaklar büyük olanlardan çok daha pahalıdır, muhtemelen bir bushel'de daha fazla yer kapladıkları için. Büyük, içi boş bir turta kabağının kapladığı kullanılmayan alan nedeniyle bushel başına daha fazla küçük kabak bulunur.\n"
+   ],
+   "metadata": {
+    "id": "pS2GNPagbSdb"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Son olarak, sırf macera olsun diye 💁‍♀️, Haydi Ay sütununu da ilk sıraya, yani `Package` sütununun `öncesine` taşıyalım.\n",
+    "\n",
+    "Sütunların yerini değiştirmek için `dplyr::relocate()` kullanılır.\n"
+   ],
+   "metadata": {
+    "id": "qql1SowfbdnP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new data frame new_pumpkins\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(Month, .before = Package)\n",
+    "\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "JJ1x6kw8bixF"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tebrikler! 👌 Artık yeni regresyon modelinizi oluşturabileceğiniz temiz ve düzenli bir veri kümesine sahipsiniz!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "y8TJ0Za_bn5Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. ggplot2 ile Veri Görselleştirme\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/data-visualization.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Dasani Madipalli tarafından hazırlanan infografik</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Dasani Madipalli tarafından hazırlanan infografik](../../../../../../2-Regression/2-Data/images/data-visualization.png){width=\"600\"}-->\n",
+    "\n",
+    "Şöyle bir *bilgece* söz vardır:\n",
+    "\n",
+    "> \"Basit bir grafik, veri analistinin zihnine diğer tüm araçlardan daha fazla bilgi taşımıştır.\" --- John Tukey\n",
+    "\n",
+    "Bir veri bilimcinin rolü, üzerinde çalıştığı verinin kalitesini ve doğasını göstermektir. Bunu yapmak için genellikle verinin farklı yönlerini gösteren ilginç görselleştirmeler, grafikler, çizimler ve tablolar oluştururlar. Bu şekilde, görsel olarak ilişkileri ve aksi takdirde ortaya çıkarması zor olan boşlukları gösterebilirler.\n",
+    "\n",
+    "Görselleştirmeler, veriye en uygun makine öğrenimi tekniğini belirlemeye de yardımcı olabilir. Örneğin, bir çizgiye uyar gibi görünen bir dağılım grafiği, verinin doğrusal regresyon çalışması için iyi bir aday olduğunu gösterir.\n",
+    "\n",
+    "R, grafik oluşturmak için birkaç sistem sunar, ancak [`ggplot2`](https://ggplot2.tidyverse.org/index.html) en zarif ve en esnek olanlardan biridir. `ggplot2`, grafikleri **bağımsız bileşenleri birleştirerek** oluşturmanıza olanak tanır.\n",
+    "\n",
+    "Hadi Price ve Month sütunları için basit bir dağılım grafiğiyle başlayalım.\n",
+    "\n",
+    "Bu durumda, [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html) ile başlayacağız, bir veri seti ve estetik eşleme ([`aes()`](https://ggplot2.tidyverse.org/reference/aes.html) ile) sağlayacağız, ardından dağılım grafikleri için katmanlar ekleyeceğiz (örneğin [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)).\n"
+   ],
+   "metadata": {
+    "id": "mYSH6-EtbvNa"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plots\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+    "p + geom_point()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "g2YjnGeOcLo4"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bu faydalı bir grafik mi 🤷? Sizi şaşırtan bir şey var mı?\n",
+    "\n",
+    "Pek faydalı değil, çünkü tek yaptığı verilerinizi belirli bir ayda noktalar halinde göstermek.  \n"
+   ],
+   "metadata": {
+    "id": "Ml7SDCLQcPvE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Nasıl faydalı hale getirebiliriz?**\n",
+    "\n",
+    "Grafiklerin faydalı veriler göstermesini sağlamak için genellikle verileri bir şekilde gruplandırmanız gerekir. Örneğin, bizim durumumuzda, her ay için kabakların ortalama fiyatını bulmak, verilerimizdeki temel desenlere dair daha fazla içgörü sağlayacaktır. Bu bizi bir başka **dplyr** özelliğine yönlendiriyor:\n",
+    "\n",
+    "#### `dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "R'de gruplandırılmış toplama işlemleri kolayca şu şekilde yapılabilir:\n",
+    "\n",
+    "`dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "-   `dplyr::group_by()` analiz birimini tüm veri setinden, ay gibi bireysel gruplara değiştirir.\n",
+    "\n",
+    "-   `dplyr::summarize()` her bir gruplama değişkeni için bir sütun ve belirttiğiniz özet istatistikler için bir sütun içeren yeni bir veri çerçevesi oluşturur.\n",
+    "\n",
+    "Örneğin, **Month** sütunlarına göre kabakları gruplandırmak ve her ay için **ortalama fiyatı** bulmak için `dplyr::group_by() %>% summarize()` kullanabiliriz.\n"
+   ],
+   "metadata": {
+    "id": "jMakvJZIcVkh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "6kVSUa2Bcilf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Kısa ve öz!✨\n",
+    "\n",
+    "Aylar gibi kategorik özellikler, bir çubuk grafik kullanılarak daha iyi temsil edilir 📊. Çubuk grafiklerden sorumlu katmanlar `geom_bar()` ve `geom_col()`dur. Daha fazla bilgi için `?geom_bar` komutuna göz atabilirsiniz.\n",
+    "\n",
+    "Hadi bir tane oluşturalım!\n"
+   ],
+   "metadata": {
+    "id": "Kds48GUBcj3W"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price)) %>% \r\n",
+    "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"Pumpkin Price\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VNbU1S3BcrxO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩Bu, daha kullanışlı bir veri görselleştirme! Görünüşe göre en yüksek kabak fiyatları Eylül ve Ekim aylarında gerçekleşiyor. Bu beklentinizi karşılıyor mu? Neden veya neden değil?\n",
+    "\n",
+    "İkinci dersi tamamladığınız için tebrikler 👏! Verilerinizi model oluşturma için hazırladınız, ardından görselleştirmeler kullanarak daha fazla içgörü ortaya çıkardınız!\n"
+   ],
+   "metadata": {
+    "id": "zDm0VOzzcuzR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/2-Regression/2-Data/solution/notebook.ipynb b/translations/tr/2-Regression/2-Data/solution/notebook.ipynb
new file mode 100644
index 000000000..1e0cf5a57
--- /dev/null
+++ b/translations/tr/2-Regression/2-Data/solution/notebook.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/8/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    City Name Type               Package   Variety Sub Variety  Grade  \\\n",
+       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "\n",
+       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
+       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
+       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "\n",
+       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
+       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "\n",
+       "    Unnamed: 25  \n",
+       "70          NaN  \n",
+       "71          NaN  \n",
+       "72          NaN  \n",
+       "73          NaN  \n",
+       "74          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Month               Package  Low Price  High Price  Price\n",
+      "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
+      "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "...     ...                   ...        ...         ...    ...\n",
+      "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
+      "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
+      "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
+      "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Get an average between low and high price for the base pumpkin price\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "# Convert the date to its month only\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "\n",
+    "# Create a new dataframe with this basic data\n",
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
+    "\n",
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
+    "\n",
+    "print(new_pumpkins)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "price = new_pumpkins.Price\n",
+    "month = new_pumpkins.Month\n",
+    "plt.scatter(price, month)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluğu sağlamak için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "95726f0b8283628d5356a4f8eb8b4b76",
+   "translation_date": "2025-09-06T13:46:31+00:00",
+   "source_file": "2-Regression/2-Data/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/tr/2-Regression/3-Linear/notebook.ipynb b/translations/tr/2-Regression/3-Linear/notebook.ipynb
new file mode 100644
index 000000000..9ae5293d1
--- /dev/null
+++ b/translations/tr/2-Regression/3-Linear/notebook.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kabak Fiyatlandırması\n",
+    "\n",
+    "Gerekli kütüphaneleri ve veri setini yükleyin. Verileri aşağıdaki alt küme verilerini içeren bir dataframe'e dönüştürün:\n",
+    "\n",
+    "- Sadece kile başına fiyatlandırılmış kabakları alın\n",
+    "- Tarihi bir aya dönüştürün\n",
+    "- Fiyatı, yüksek ve düşük fiyatların ortalaması olarak hesaplayın\n",
+    "- Fiyatı, kile miktarına göre fiyatlandırmayı yansıtacak şekilde dönüştürün\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Temel bir saçılım grafiği, elimizde yalnızca Ağustos'tan Aralık'a kadar olan ay verilerinin bulunduğunu hatırlatır. Muhtemelen doğrusal bir şekilde sonuçlar çıkarabilmek için daha fazla veriye ihtiyacımız var.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter('Month','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "b032d371c75279373507f003439a577e",
+   "translation_date": "2025-09-06T13:09:14+00:00",
+   "source_file": "2-Regression/3-Linear/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/tr/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/tr/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
new file mode 100644
index 000000000..8082d76ed
--- /dev/null
+++ b/translations/tr/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -0,0 +1,1086 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_3-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "5015d65d61ba75a223bfc56c273aa174",
+   "translation_date": "2025-09-06T13:24:39+00:00",
+   "source_file": "2-Regression/3-Linear/solution/R/lesson_3-R.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "EgQw8osnsUV-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Kabak Fiyatlandırması için Doğrusal ve Polinom Regresyon - Ders 3\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Dasani Madipalli tarafından hazırlanan bilgi grafiği</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Dasani Madipalli tarafından hazırlanan bilgi grafiği](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n",
+    "\n",
+    "#### Giriş\n",
+    "\n",
+    "Şimdiye kadar, bu derste kullanacağımız kabak fiyatlandırma veri setinden toplanan örnek verilerle regresyonun ne olduğunu keşfettiniz. Ayrıca bunu `ggplot2` kullanarak görselleştirdiniz. 💪\n",
+    "\n",
+    "Artık ML için regresyona daha derinlemesine dalmaya hazırsınız. Bu derste, iki tür regresyon hakkında daha fazla bilgi edineceksiniz: *temel doğrusal regresyon* ve *polinom regresyon*, bu tekniklerin altında yatan matematikle birlikte.\n",
+    "\n",
+    "> Bu müfredat boyunca, matematik bilgisi gereksinimini minimumda tutmayı hedefliyoruz ve diğer alanlardan gelen öğrenciler için erişilebilir hale getirmeye çalışıyoruz. Bu nedenle notlar, 🧮 matematiksel açıklamalar, diyagramlar ve kavramayı kolaylaştıracak diğer öğrenme araçlarına dikkat edin.\n",
+    "\n",
+    "#### Hazırlık\n",
+    "\n",
+    "Hatırlatma olarak, bu veriyi yükleyerek ona sorular sormayı amaçlıyorsunuz.\n",
+    "\n",
+    "-   Kabak almak için en iyi zaman ne zaman?\n",
+    "\n",
+    "-   Mini kabakların bir kasasının fiyatı ne kadar olabilir?\n",
+    "\n",
+    "-   Kabakları yarım sepetlik sepetlerde mi yoksa 1 1/9 sepetlik kutularda mı almalıyım? Bu veriyi daha fazla incelemeye devam edelim.\n",
+    "\n",
+    "Önceki derste, bir `tibble` (veri çerçevesinin modern bir yeniden tasarımı) oluşturdunuz ve orijinal veri setinin bir kısmını fiyatlandırmayı sepet başına standartlaştırarak doldurdunuz. Ancak bunu yaparak, yalnızca yaklaşık 400 veri noktası ve yalnızca sonbahar ayları için veri toplayabildiniz. Belki veriyi daha fazla temizleyerek onun doğası hakkında biraz daha ayrıntı elde edebiliriz? Göreceğiz... 🕵️‍♀️\n",
+    "\n",
+    "Bu görev için aşağıdaki paketlere ihtiyacımız olacak:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/), veri bilimini daha hızlı, kolay ve eğlenceli hale getirmek için tasarlanmış bir [R paketleri koleksiyonudur](https://www.tidyverse.org/packages).\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) çerçevesi, modelleme ve makine öğrenimi için bir [paketler koleksiyonudur](https://www.tidymodels.org/packages).\n",
+    "\n",
+    "-   `janitor`: [janitor paketi](https://github.com/sfirke/janitor), kirli verileri incelemek ve temizlemek için basit araçlar sağlar.\n",
+    "\n",
+    "-   `corrplot`: [corrplot paketi](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html), değişkenler arasındaki gizli desenleri tespit etmeye yardımcı olmak için otomatik değişken sıralamasını destekleyen korelasyon matrisinde görsel bir keşif aracı sağlar.\n",
+    "\n",
+    "Bu paketleri şu şekilde yükleyebilirsiniz:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\n",
+    "\n",
+    "Aşağıdaki script, bu modülü tamamlamak için gereken paketlere sahip olup olmadığınızı kontrol eder ve eksik olanları sizin için yükler.\n"
+   ],
+   "metadata": {
+    "id": "WqQPS1OAsg3H"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "tA4C2WN3skCf",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bu harika paketleri daha sonra yükleyip mevcut R oturumumuzda kullanılabilir hale getireceğiz. (Bu sadece bir örnekleme için, `pacman::p_load()` bunu zaten sizin için yapıyor)\n",
+    "\n",
+    "## 1. Bir doğrusal regresyon çizgisi\n",
+    "\n",
+    "1. Derste öğrendiğiniz gibi, doğrusal regresyon çalışmasının amacı, aşağıdakilere en uygun *çizgiyi* çizebilmektir:\n",
+    "\n",
+    "-   **Değişken ilişkilerini göstermek**. Değişkenler arasındaki ilişkiyi göstermek.\n",
+    "\n",
+    "-   **Tahminlerde bulunmak**. Yeni bir veri noktasının bu çizgiye göre nerede yer alacağını doğru bir şekilde tahmin etmek.\n",
+    "\n",
+    "Bu tür bir çizgiyi çizmek için **En Küçük Kareler Regresyonu** adı verilen bir istatistiksel teknik kullanırız. `En küçük kareler` terimi, regresyon çizgisinin çevresindeki tüm veri noktalarının karelerinin alınması ve ardından toplanması anlamına gelir. İdeal olarak, bu toplam mümkün olduğunca küçük olmalıdır, çünkü daha az hata, yani `en küçük kareler` istiyoruz. Bu nedenle, en uygun çizgi, kare hataların toplamı için en düşük değeri veren çizgidir - bu yüzden adı *en küçük kareler regresyonu*.\n",
+    "\n",
+    "Bunu yapmamızın nedeni, tüm veri noktalarımızdan en az toplam mesafeye sahip bir çizgi modellemek istememizdir. Ayrıca, büyüklüğüyle ilgilendiğimiz için terimleri toplama işleminden önce karesini alırız, yönüyle değil.\n",
+    "\n",
+    "> **🧮 Matematiği Göster**\n",
+    ">\n",
+    "> Bu çizgi, *en uygun çizgi* olarak adlandırılır ve [bir denklemle](https://en.wikipedia.org/wiki/Simple_linear_regression) ifade edilebilir:\n",
+    ">\n",
+    ">     Y = a + bX\n",
+    ">\n",
+    "> `X`, '`açıklayıcı değişken` veya `tahmin edici`'dir. `Y`, '`bağımlı değişken` veya `sonuç`'tur. Çizginin eğimi `b` ve `a` ise y-keseni, yani `X = 0` olduğunda `Y`'nin değerini ifade eder.\n",
+    ">\n",
+    "\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/slope.png \"eğim = $y/x$\")\n",
+    "    Jen Looper tarafından hazırlanan infografik\n",
+    ">\n",
+    "> İlk olarak, eğim `b` hesaplanır.\n",
+    ">\n",
+    "> Başka bir deyişle, ve kabak verilerimizin orijinal sorusuna atıfta bulunarak: \"Bir ay boyunca bir kabak sepetinin fiyatını tahmin edin\", `X` fiyatı ifade ederken, `Y` satış ayını ifade eder.\n",
+    ">\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/calculation.png)\n",
+    "    Jen Looper tarafından hazırlanan infografik\n",
+    "> \n",
+    "> `Y` değerini hesaplayın. Eğer yaklaşık 4 dolar ödüyorsanız, bu Nisan olmalı!\n",
+    ">\n",
+    "> Çizgiyi hesaplayan matematik, çizginin eğimini göstermelidir, bu da aynı zamanda kesişim noktasına, yani `X = 0` olduğunda `Y`'nin konumuna bağlıdır.\n",
+    ">\n",
+    "> Bu değerlerin hesaplama yöntemini [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html) web sitesinde gözlemleyebilirsiniz. Ayrıca, sayıların değerlerinin çizgiyi nasıl etkilediğini görmek için [bu En Küçük Kareler hesaplayıcısını](https://www.mathsisfun.com/data/least-squares-calculator.html) ziyaret edin.\n",
+    "\n",
+    "Korkutucu değil, değil mi? 🤓\n",
+    "\n",
+    "#### Korelasyon\n",
+    "\n",
+    "Anlamanız gereken bir diğer terim, verilen X ve Y değişkenleri arasındaki **Korelasyon Katsayısı**dır. Bir dağılım grafiği kullanarak bu katsayıyı hızlıca görselleştirebilirsiniz. Verilerin düzgün bir çizgi üzerinde sıralandığı bir grafik yüksek korelasyona sahiptir, ancak X ve Y arasında her yere dağılmış veri noktalarına sahip bir grafik düşük korelasyona sahiptir.\n",
+    "\n",
+    "İyi bir doğrusal regresyon modeli, En Küçük Kareler Regresyonu yöntemiyle ve bir regresyon çizgisiyle yüksek (1'e daha yakın, 0'dan uzak) bir Korelasyon Katsayısına sahip olan modeldir.\n"
+   ],
+   "metadata": {
+    "id": "cdX5FRpvsoP5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **2. Verilerle dans: modelleme için kullanılacak bir veri çerçevesi oluşturma**\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/janitor.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış bir sanat eseri</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![@allison_horst tarafından yapılmış bir sanat eseri](../../../../../../2-Regression/3-Linear/images/janitor.jpg){width=\"700\"}-->\n"
+   ],
+   "metadata": {
+    "id": "WdUKXk7Bs8-V"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Gerekli kütüphaneleri ve veri setini yükleyin. Verileri, aşağıdaki alt küme verilerini içeren bir veri çerçevesine dönüştürün:\n",
+    "\n",
+    "-   Sadece kile başına fiyatlandırılan kabakları alın\n",
+    "\n",
+    "-   Tarihi bir aya dönüştürün\n",
+    "\n",
+    "-   Fiyatı, yüksek ve düşük fiyatların ortalaması olarak hesaplayın\n",
+    "\n",
+    "-   Fiyatı, kile miktarına göre fiyatlandırmayı yansıtacak şekilde dönüştürün\n",
+    "\n",
+    "> Bu adımları [önceki derste](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb) ele almıştık.\n"
+   ],
+   "metadata": {
+    "id": "fMCtu2G2s-p8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "library(lubridate)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ryMVZEEPtERn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Saf bir macera ruhuyla, kirli verileri incelemek ve temizlemek için basit işlevler sağlayan [`janitor paketi`](../../../../../../2-Regression/3-Linear/solution/R/github.com/sfirke/janitor)'ni keşfedelim. Örneğin, verilerimiz için sütun adlarına bir göz atalım:\n"
+   ],
+   "metadata": {
+    "id": "xcNxM70EtJjb"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5XtpaIigtPfW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤔 Daha iyisini yapabiliriz. Bu sütun adlarını [snake_case](https://en.wikipedia.org/wiki/Snake_case) kuralına dönüştürerek `janitor::clean_names` kullanarak `friendR` yapalım. Bu fonksiyon hakkında daha fazla bilgi edinmek için: `?clean_names`\n"
+   ],
+   "metadata": {
+    "id": "IbIqrMINtSHe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Clean names to the snake_case convention\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  clean_names(case = \"snake\")\n",
+    "\n",
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "a2uYvclYtWvX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Çok düzenli 🧹! Şimdi, önceki derste olduğu gibi `dplyr` kullanarak verilerle bir dans! 💃\n"
+   ],
+   "metadata": {
+    "id": "HfhnuzDDtaDd"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(variety, city_name, package, low_price, high_price, date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Extract the month from the dates to a new column\n",
+    "pumpkins <- pumpkins %>%\n",
+    "  mutate(date = mdy(date),\n",
+    "         month = month(date)) %>% \n",
+    "  select(-date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a new column for average Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(price = (low_price + high_price)/2)\n",
+    "\n",
+    "\n",
+    "# Retain only pumpkins with the string \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "  filter(str_detect(string = package, pattern = \"bushel\"))\n",
+    "\n",
+    "\n",
+    "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(price = case_when(\n",
+    "    str_detect(package, \"1 1/9\") ~ price/(1.1),\n",
+    "    str_detect(package, \"1/2\") ~ price*2,\n",
+    "    TRUE ~ price))\n",
+    "\n",
+    "# Relocate column positions\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(month, .before = variety)\n",
+    "\n",
+    "\n",
+    "# Display the first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "X0wU3gQvtd9f"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tebrikler! 👌 Artık yeni regresyon modelinizi oluşturabileceğiniz temiz ve düzenli bir veri setine sahipsiniz!\n",
+    "\n",
+    "Bir dağılım grafiği ister misiniz?\n"
+   ],
+   "metadata": {
+    "id": "UpaIwaxqth82"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set theme\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Make a scatter plot of month and price\n",
+    "new_pumpkins %>% \n",
+    "  ggplot(mapping = aes(x = month, y = price)) +\n",
+    "  geom_point(size = 1.6)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "DXgU-j37tl5K"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bir dağılım grafiği, elimizde yalnızca Ağustos'tan Aralık'a kadar olan ay verilerinin bulunduğunu hatırlatıyor. Doğrusal bir şekilde sonuçlara varabilmek için muhtemelen daha fazla veriye ihtiyacımız var.\n",
+    "\n",
+    "Modelleme verilerimize tekrar bir göz atalım:\n"
+   ],
+   "metadata": {
+    "id": "Ve64wVbwtobI"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Display first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HFQX2ng1tuSJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bir kabağın `fiyatını`, karakter türündeki `şehir` veya `paket` sütunlarına dayanarak tahmin etmek isteseydik ne olurdu? Ya da daha basit bir şekilde, örneğin `paket` ve `fiyat` arasında (her iki girdinin de sayısal olması gerektiği) korelasyonu nasıl bulabilirdik? 🤷🤷\n",
+    "\n",
+    "Makine öğrenimi modelleri, metin değerlerinden ziyade sayısal özelliklerle daha iyi çalışır, bu nedenle genellikle kategorik özellikleri sayısal temsillere dönüştürmeniz gerekir.\n",
+    "\n",
+    "Bu, tahmin edicilerimizi bir modelin etkili bir şekilde kullanmasını kolaylaştıracak şekilde yeniden biçimlendirme yolunu bulmamız gerektiği anlamına gelir; bu sürece `özellik mühendisliği` denir.\n"
+   ],
+   "metadata": {
+    "id": "7hsHoxsStyjJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Modeller için verileri tariflerle ön işleme 👩‍🍳👨‍🍳\n",
+    "\n",
+    "Tahmin edici değerleri yeniden biçimlendirerek bir modelin bunları daha etkili kullanmasını sağlama faaliyetlerine `özellik mühendisliği` denir.\n",
+    "\n",
+    "Farklı modellerin farklı ön işleme gereksinimleri vardır. Örneğin, en küçük kareler yöntemi `ay, çeşit ve şehir_adı gibi kategorik değişkenlerin kodlanmasını` gerektirir. Bu, basitçe `kategorik değerler` içeren bir sütunun, orijinal sütunun yerine geçen bir veya daha fazla `sayısal sütuna` dönüştürülmesini içerir.\n",
+    "\n",
+    "Örneğin, verilerinizde aşağıdaki kategorik özellik bulunduğunu varsayalım:\n",
+    "\n",
+    "|  şehir   |\n",
+    "|:--------:|\n",
+    "| Denver   |\n",
+    "| Nairobi  |\n",
+    "|  Tokyo   |\n",
+    "\n",
+    "*Ordinal kodlama* uygulayarak her kategoriye benzersiz bir tam sayı değeri atayabilirsiniz, şöyle:\n",
+    "\n",
+    "| şehir |\n",
+    "|:-----:|\n",
+    "|   0   |\n",
+    "|   1   |\n",
+    "|   2   |\n",
+    "\n",
+    "Ve işte bunu verilerimize uygulayacağız!\n",
+    "\n",
+    "Bu bölümde, verilerinizi modelinizi eğitmeden **önce** ön işleme konusunda size yardımcı olmak için tasarlanmış bir başka harika Tidymodels paketi olan [recipes](https://tidymodels.github.io/recipes/) paketini keşfedeceğiz. Temelde bir tarif, bir veri setine modelleme için hazır hale getirmek amacıyla hangi adımların uygulanması gerektiğini tanımlayan bir nesnedir.\n",
+    "\n",
+    "Şimdi, tahmin edici sütunlardaki tüm gözlemler için benzersiz bir tam sayı atayarak verilerimizi modelleme için hazırlayan bir tarif oluşturalım:\n"
+   ],
+   "metadata": {
+    "id": "AD5kQbcvt3Xl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\n",
+    "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "# Print out the recipe\n",
+    "pumpkins_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "BNaFKXfRt9TU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Harika! 👏 İlk tarifimizi oluşturduk ve bu tarif bir sonucu (fiyat) ve buna karşılık gelen tahmin edicileri belirtiyor, ayrıca tüm tahmin edici sütunların bir dizi tam sayıya kodlanması gerektiğini ifade ediyor 🙌! Hadi bunu hızlıca parçalarına ayıralım:\n",
+    "\n",
+    "-   `recipe()` çağrısı, bir formül ile birlikte, `new_pumpkins` verilerini referans alarak değişkenlerin *rollerini* tarife bildirir. Örneğin, `price` sütunu bir `outcome` rolüne atanmışken, diğer sütunlar bir `predictor` rolüne atanmıştır.\n",
+    "\n",
+    "-   `step_integer(all_predictors(), zero_based = TRUE)` tüm tahmin edicilerin 0'dan başlayan bir numaralandırma ile bir dizi tam sayıya dönüştürülmesi gerektiğini belirtir.\n",
+    "\n",
+    "Eminiz ki şu tür düşünceleriniz olabilir: \"Bu çok havalı!! Ama ya tariflerin tam olarak beklediğim gibi çalıştığını doğrulamam gerekirse? 🤔\"\n",
+    "\n",
+    "Bu harika bir düşünce! Görüyorsunuz, tarifiniz bir kez tanımlandıktan sonra, veriyi gerçekten ön işlemek için gereken parametreleri tahmin edebilir ve ardından işlenmiş veriyi çıkarabilirsiniz. Tidymodels kullanırken genellikle bunu yapmanız gerekmez (birazdan normal yöntemi göreceğiz-\\> `workflows`), ancak tariflerin beklediğiniz gibi çalıştığını doğrulamak için bir tür kontrol yapmak istediğinizde işe yarayabilir.\n",
+    "\n",
+    "Bunun için iki ek fiile ihtiyacınız olacak: `prep()` ve `bake()`. Her zamanki gibi, [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) tarafından hazırlanan küçük R arkadaşlarımız bunu daha iyi anlamanıza yardımcı oluyor!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış sanat eseri</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "KEiO0v7kuC9O"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): bir eğitim setinden gerekli parametreleri tahmin eder ve bu parametreler daha sonra diğer veri setlerine uygulanabilir. Örneğin, belirli bir tahmin edici sütunu için hangi gözlem 0, 1, 2 gibi bir tam sayı ile atanacak.\n",
+    "\n",
+    "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): hazırlanmış bir tarifi alır ve işlemleri herhangi bir veri setine uygular.\n",
+    "\n",
+    "Öyleyse, tariflerimizi hazırlayıp uygulayalım ve gerçekten doğrulayalım ki perde arkasında tahmin edici sütunlar önce kodlanacak, ardından bir model oluşturulacak.\n"
+   ],
+   "metadata": {
+    "id": "Q1xtzebuuTCP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep the recipe\n",
+    "pumpkins_prep <- prep(pumpkins_recipe)\n",
+    "\n",
+    "# Bake the recipe to extract a preprocessed new_pumpkins data\n",
+    "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\n",
+    "\n",
+    "# Print out the baked data set\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FGBbJbP_uUUn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woo-hoo!🥳 İşlenmiş veri `baked_pumpkins` tüm tahmin edicilerinin kodlandığını doğruladı, bu da tarif olarak tanımlanan ön işleme adımlarının beklendiği gibi çalışacağını gösteriyor. Bu, sizin için okumayı zorlaştırabilir ama Tidymodels için çok daha anlaşılır hale getirir! Hangi gözlemin ilgili bir tam sayıya eşlendiğini bulmak için biraz zaman ayırın.\n",
+    "\n",
+    "Ayrıca, `baked_pumpkins` üzerinde hesaplamalar yapabileceğimiz bir veri çerçevesi olduğunu belirtmekte fayda var.\n",
+    "\n",
+    "Örneğin, verilerinizdeki iki nokta arasında iyi bir korelasyon bulmaya çalışabiliriz, böylece potansiyel olarak iyi bir tahmin modeli oluşturabiliriz. Bunu yapmak için `cor()` fonksiyonunu kullanacağız. Fonksiyon hakkında daha fazla bilgi edinmek için `?cor()` yazabilirsiniz.\n"
+   ],
+   "metadata": {
+    "id": "1dvP0LBUueAW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the correlation between the city_name and the price\n",
+    "cor(baked_pumpkins$city_name, baked_pumpkins$price)\n",
+    "\n",
+    "# Find the correlation between the package and the price\n",
+    "cor(baked_pumpkins$package, baked_pumpkins$price)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "3bQzXCjFuiSV"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Görünüşe göre, Şehir ve Fiyat arasında yalnızca zayıf bir ilişki var. Ancak Paket ve Fiyatı arasında biraz daha iyi bir ilişki bulunuyor. Bu mantıklı, değil mi? Genelde, ürün kutusu ne kadar büyükse, fiyat da o kadar yüksek olur.\n",
+    "\n",
+    "Bu sırada, tüm sütunların bir korelasyon matrisini `corrplot` paketi kullanarak görselleştirmeyi de deneyelim.\n"
+   ],
+   "metadata": {
+    "id": "BToPWbgjuoZw"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the corrplot package\n",
+    "library(corrplot)\n",
+    "\n",
+    "# Obtain correlation matrix\n",
+    "corr_mat <- cor(baked_pumpkins %>% \n",
+    "                  # Drop columns that are not really informative\n",
+    "                  select(-c(low_price, high_price)))\n",
+    "\n",
+    "# Make a correlation plot between the variables\n",
+    "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZwAL3ksmutVR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Çok daha iyi.\n",
+    "\n",
+    "Bu verilerle şimdi sorulabilecek iyi bir soru şu olabilir: '`Belirli bir kabak paketi için hangi fiyatı bekleyebilirim?`' Haydi başlayalım!\n",
+    "\n",
+    "> Not: **`pumpkins_prep`** tarifini **`new_data = NULL`** ile **`bake()`** ettiğinizde, işlenmiş (yani kodlanmış) eğitim verilerini elde edersiniz. Örneğin, başka bir veri setiniz (örneğin bir test seti) varsa ve bir tarifin onu nasıl ön işleyeceğini görmek istiyorsanız, **`pumpkins_prep`** tarifini **`new_data = test_set`** ile **`bake()`** etmeniz yeterlidir.\n",
+    "\n",
+    "## 4. Doğrusal regresyon modeli oluşturun\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Dasani Madipalli tarafından hazırlanan bilgi grafiği</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Dasani Madipalli tarafından hazırlanan bilgi grafiği](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "YqXjLuWavNxW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Artık bir tarif oluşturduğumuza ve verilerin uygun şekilde ön işleneceğini doğruladığımıza göre, şimdi şu soruyu yanıtlamak için bir regresyon modeli oluşturalım: `Belirli bir kabak paketi için hangi fiyatı bekleyebilirim?`\n",
+    "\n",
+    "#### Eğitim seti kullanarak bir doğrusal regresyon modeli eğitin\n",
+    "\n",
+    "Muhtemelen zaten fark etmişsinizdir, *price* sütunu `sonuç` değişkeni iken *package* sütunu `tahmin edici` değişkendir.\n",
+    "\n",
+    "Bunu yapmak için, önce verileri %80'i eğitim setine ve %20'si test setine gidecek şekilde böleceğiz, ardından tahmin edici sütunu bir dizi tam sayıya kodlayacak bir tarif tanımlayacağız ve ardından bir model spesifikasyonu oluşturacağız. Tarifimizi hazırlayıp pişirmeyeceğiz çünkü verileri beklendiği gibi ön işleyeceğini zaten biliyoruz.\n"
+   ],
+   "metadata": {
+    "id": "Pq0bSzCevW-h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\n",
+    "# Split the data into training and test sets\n",
+    "pumpkins_split <- new_pumpkins %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "\n",
+    "# Extract training and test data\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a recipe for preprocessing the data\n",
+    "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a linear model specification\n",
+    "lm_spec <- linear_reg() %>% \n",
+    "  set_engine(\"lm\") %>% \n",
+    "  set_mode(\"regression\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "CyoEh_wuvcLv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Harika iş çıkardınız! Artık bir tarifimiz ve bir model spesifikasyonumuz olduğuna göre, bunları bir araya getirip, önce veriyi ön işleme (arka planda hazırlık + pişirme), ardından ön işlenmiş veri üzerinde modeli eğitme ve potansiyel olarak son işlem aktivitelerine olanak tanıyan bir nesneye dönüştürmenin bir yolunu bulmamız gerekiyor. İçiniz rahatladı mı!🤩\n",
+    "\n",
+    "Tidymodels'de, bu kullanışlı nesne [`workflow`](https://workflows.tidymodels.org/) olarak adlandırılır ve modelleme bileşenlerinizi pratik bir şekilde barındırır! Python'da buna *pipelines* derdik.\n",
+    "\n",
+    "O halde her şeyi bir workflow içinde bir araya getirelim!📦\n"
+   ],
+   "metadata": {
+    "id": "G3zF_3DqviFJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Hold modelling components in a workflow\n",
+    "lm_wf <- workflow() %>% \n",
+    "  add_recipe(lm_pumpkins_recipe) %>% \n",
+    "  add_model(lm_spec)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "lm_wf"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "T3olroU3v-WX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Üstelik, bir iş akışı tıpkı bir model gibi uygun hale getirilebilir/eğitilebilir.\n"
+   ],
+   "metadata": {
+    "id": "zd1A5tgOwEPX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Train the model\n",
+    "lm_wf_fit <- lm_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the model coefficients learned \n",
+    "lm_wf_fit"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NhJagFumwFHf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Model çıktısından, eğitim sırasında öğrenilen katsayıları görebiliriz. Bu katsayılar, gerçek ve tahmin edilen değişken arasındaki toplam hatayı en aza indiren en iyi uyum çizgisinin katsayılarını temsil eder.\n",
+    "\n",
+    "#### Test seti kullanarak model performansını değerlendirme\n",
+    "\n",
+    "Modelin nasıl performans gösterdiğini görme zamanı 📏! Bunu nasıl yaparız?\n",
+    "\n",
+    "Artık modeli eğittiğimize göre, test_set için tahminler yapmak için `parsnip::predict()` fonksiyonunu kullanabiliriz. Ardından, bu tahminleri gerçek etiket değerleriyle karşılaştırarak modelin ne kadar iyi (ya da kötü!) çalıştığını değerlendirebiliriz.\n",
+    "\n",
+    "Hadi test seti için tahminler yaparak başlayalım ve ardından sütunları test setine bağlayalım.\n"
+   ],
+   "metadata": {
+    "id": "_4QkGtBTwItF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\n",
+    "predictions <- lm_wf_fit %>% \n",
+    "  predict(new_data = pumpkins_test)\n",
+    "\n",
+    "\n",
+    "# Bind predictions to the test set\n",
+    "lm_results <- pumpkins_test %>% \n",
+    "  select(c(package, price)) %>% \n",
+    "  bind_cols(predictions)\n",
+    "\n",
+    "\n",
+    "# Print the first ten rows of the tibble\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "UFZzTG0gwTs9"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Evet, bir model eğittiniz ve tahminler yapmak için kullandınız!🔮 Peki, bu model ne kadar iyi? Hadi modelin performansını değerlendirelim!\n",
+    "\n",
+    "Tidymodels'de bunu `yardstick::metrics()` kullanarak yapıyoruz! Lineer regresyon için şu metriklere odaklanalım:\n",
+    "\n",
+    "-   `Root Mean Square Error (RMSE)`: [MSE](https://en.wikipedia.org/wiki/Mean_squared_error)'nin karekökü. Bu, etiketle (bu durumda bir kabağın fiyatı) aynı birimde mutlak bir metrik sağlar. Değer ne kadar küçükse, model o kadar iyidir (basit bir anlamda, tahminlerin ortalama olarak ne kadar yanlış olduğunu temsil eder!)\n",
+    "\n",
+    "-   `Coefficient of Determination (genellikle R-squared veya R2 olarak bilinir)`: Daha yüksek bir değerin daha iyi bir uyumu temsil ettiği göreceli bir metrik. Temelde, bu metrik modelin tahmin edilen ve gerçek etiket değerleri arasındaki varyansın ne kadarını açıklayabildiğini gösterir.\n"
+   ],
+   "metadata": {
+    "id": "0A5MjzM7wW9M"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Evaluate performance of linear regression\n",
+    "metrics(data = lm_results,\n",
+    "        truth = price,\n",
+    "        estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "reJ0UIhQwcEH"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Model performansı düşüyor. Paket ve fiyatın bir dağılım grafiğini görselleştirerek daha iyi bir gösterge elde edip, ardından yapılan tahminleri kullanarak en iyi uyum çizgisini üzerine ekleyebilir miyiz, bir bakalım.\n",
+    "\n",
+    "Bu, test setini hazırlayıp işleyerek paket sütununu kodlamamız ve ardından bunu modelimizin yaptığı tahminlerle birleştirmemiz gerektiği anlamına geliyor.\n"
+   ],
+   "metadata": {
+    "id": "fdgjzjkBwfWt"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Encode package column\n",
+    "package_encode <- lm_pumpkins_recipe %>% \n",
+    "  prep() %>% \n",
+    "  bake(new_data = pumpkins_test) %>% \n",
+    "  select(package)\n",
+    "\n",
+    "\n",
+    "# Bind encoded package column to the results\n",
+    "lm_results <- lm_results %>% \n",
+    "  bind_cols(package_encode %>% \n",
+    "              rename(package_integer = package)) %>% \n",
+    "  relocate(package_integer, .after = package)\n",
+    "\n",
+    "\n",
+    "# Print new results data frame\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 5)\n",
+    "\n",
+    "\n",
+    "# Make a scatter plot\n",
+    "lm_results %>% \n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\n",
+    "  geom_point(size = 1.6) +\n",
+    "  # Overlay a line of best fit\n",
+    "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\n",
+    "  xlab(\"package\")\n",
+    "  \n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R0nw719lwkHE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Harika! Gördüğünüz gibi, doğrusal regresyon modeli bir paketin fiyatı ile arasındaki ilişkiyi gerçekten iyi bir şekilde genelleştiremiyor.\n",
+    "\n",
+    "🎃 Tebrikler, birkaç çeşit kabak fiyatını tahmin etmeye yardımcı olabilecek bir model oluşturdunuz. Tatil kabak bahçeniz harika görünecek. Ancak muhtemelen daha iyi bir model oluşturabilirsiniz!\n",
+    "\n",
+    "## 5. Polinom regresyon modeli oluşturun\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Dasani Madipalli tarafından hazırlanan bilgi grafiği</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Dasani Madipalli tarafından hazırlanan bilgi grafiği](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "HOCqJXLTwtWI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bazen verilerimiz doğrusal bir ilişkiye sahip olmayabilir, ancak yine de bir sonucu tahmin etmek isteyebiliriz. Polinom regresyon, daha karmaşık doğrusal olmayan ilişkiler için tahmin yapmamıza yardımcı olabilir.\n",
+    "\n",
+    "Örneğin, kabak veri setimizdeki paket ve fiyat arasındaki ilişkiyi ele alalım. Bazen değişkenler arasında doğrusal bir ilişki olabilir - kabak hacmi büyüdükçe fiyatın artması gibi - ancak bazen bu ilişkiler bir düzlem veya doğru olarak çizilemez.\n",
+    "\n",
+    "> ✅ İşte [polinom regresyon](https://online.stat.psu.edu/stat501/lesson/9/9.8) kullanabilecek verilere dair bazı örnekler\n",
+    ">\n",
+    "> Önceki grafikte Çeşit ve Fiyat arasındaki ilişkiye tekrar bir göz atın. Bu dağılım grafiği mutlaka bir doğru ile analiz edilmesi gereken bir ilişki gibi mi görünüyor? Belki de hayır. Bu durumda, polinom regresyonu deneyebilirsiniz.\n",
+    ">\n",
+    "> ✅ Polinomlar, bir veya daha fazla değişken ve katsayıdan oluşabilen matematiksel ifadelerdir.\n",
+    "\n",
+    "#### Eğitim seti kullanarak bir polinom regresyon modeli eğitin\n",
+    "\n",
+    "Polinom regresyon, doğrusal olmayan verilere daha iyi uyum sağlamak için *eğri bir çizgi* oluşturur.\n",
+    "\n",
+    "Bir polinom modelinin tahmin yapmada daha iyi performans gösterip göstermeyeceğini görelim. Daha önce izlediğimiz prosedüre benzer bir yol izleyerek devam edeceğiz:\n",
+    "\n",
+    "-   Verilerimizi modellemeye hazırlamak için uygulanması gereken ön işleme adımlarını belirten bir tarif oluşturun, örneğin: tahmin edicileri kodlama ve *n* dereceli polinomlar hesaplama\n",
+    "\n",
+    "-   Bir model spesifikasyonu oluşturun\n",
+    "\n",
+    "-   Tarif ve model spesifikasyonunu bir iş akışında birleştirin\n",
+    "\n",
+    "-   İş akışını uydurarak bir model oluşturun\n",
+    "\n",
+    "-   Modelin test verilerinde ne kadar iyi performans gösterdiğini değerlendirin\n",
+    "\n",
+    "Haydi başlayalım!\n"
+   ],
+   "metadata": {
+    "id": "VcEIpRV9wzYr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\r\n",
+    "poly_pumpkins_recipe <-\r\n",
+    "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+    "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+    "  step_poly(all_predictors(), degree = 4)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model specification\r\n",
+    "poly_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Bundle recipe and model spec into a workflow\r\n",
+    "poly_wf <- workflow() %>% \r\n",
+    "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+    "  add_model(poly_spec)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model\r\n",
+    "poly_wf_fit <- poly_wf %>% \r\n",
+    "  fit(data = pumpkins_train)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print learned model coefficients\r\n",
+    "poly_wf_fit\r\n",
+    "\r\n",
+    "  "
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "63n_YyRXw3CC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Model performansını değerlendirin\n",
+    "\n",
+    "👏👏Bir polinom modeli oluşturdunuz, şimdi test seti üzerinde tahminler yapalım!\n"
+   ],
+   "metadata": {
+    "id": "-LHZtztSxDP0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make price predictions on test data\r\n",
+    "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+    "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+    "  relocate(.pred, .after = last_col())\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the results\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YUFpQ_dKxJGx"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woo-hoo, hadi `yardstick::metrics()` kullanarak modelin test_set üzerindeki performansını değerlendirelim.\n"
+   ],
+   "metadata": {
+    "id": "qxdyj86bxNGZ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "metrics(data = poly_results, truth = price, estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "8AW5ltkBxXDm"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Çok daha iyi performans.\n",
+    "\n",
+    "`rmse` yaklaşık 7'den yaklaşık 3'e düştü, bu da gerçek fiyat ile tahmin edilen fiyat arasındaki hatanın azaldığını gösteriyor. Bunu *kabaca* şu şekilde yorumlayabilirsiniz: Ortalama olarak, yanlış tahminler yaklaşık \\$3 kadar yanlıştır. `rsq` yaklaşık 0.4'ten 0.8'e yükseldi.\n",
+    "\n",
+    "Tüm bu metrikler, polinom modelinin doğrusal modelden çok daha iyi performans gösterdiğini gösteriyor. Harika iş!\n",
+    "\n",
+    "Hadi bunu görselleştirebilir miyiz bir bakalım!\n"
+   ],
+   "metadata": {
+    "id": "6gLHNZDwxYaS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Bind encoded package column to the results\r\n",
+    "poly_results <- poly_results %>% \r\n",
+    "  bind_cols(package_encode %>% \r\n",
+    "              rename(package_integer = package)) %>% \r\n",
+    "  relocate(package_integer, .after = package)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print new results data frame\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+    "  xlab(\"package\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "A83U16frxdF1"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Verilerinize daha iyi uyan bir eğri çizgisi görebilirsiniz! 🤩\n",
+    "\n",
+    "Bunu daha da düzgün hale getirmek için `geom_smooth` fonksiyonuna bir polinom formülü geçirerek şu şekilde yapabilirsiniz:\n"
+   ],
+   "metadata": {
+    "id": "4U-7aHOVxlGU"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+    "  xlab(\"package\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5vzNT0Uexm-w"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tıpkı pürüzsüz bir eğri gibi!🤩\n",
+    "\n",
+    "İşte yeni bir tahmin yapmanın yolu:\n"
+   ],
+   "metadata": {
+    "id": "v9u-wwyLxq4G"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a hypothetical data frame\r\n",
+    "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+    "\r\n",
+    "# Make predictions using linear model\r\n",
+    "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Make predictions using polynomial model\r\n",
+    "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Return predictions in a list\r\n",
+    "list(\"linear model prediction\" = lm_pred, \r\n",
+    "     \"polynomial model prediction\" = poly_pred)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "jRPSyfQGxuQv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`polynomial model` tahmini, `price` ve `package` dağılım grafikleri göz önüne alındığında mantıklı görünüyor! Ve eğer bu model önceki modelden daha iyiyse, aynı verilere bakarak, bu daha pahalı kabaklar için bütçe ayırmanız gerekecek!\n",
+    "\n",
+    "🏆 Tebrikler! Bir derste iki regresyon modeli oluşturdunuz. Regresyonun son bölümünde, kategorileri belirlemek için lojistik regresyonu öğreneceksiniz.\n",
+    "\n",
+    "## **🚀Meydan Okuma**\n",
+    "\n",
+    "Bu not defterinde birkaç farklı değişkeni test edin ve korelasyonun model doğruluğuyla nasıl ilişkili olduğunu görün.\n",
+    "\n",
+    "## [**Ders sonrası test**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)\n",
+    "\n",
+    "## **Gözden Geçirme ve Kendi Kendine Çalışma**\n",
+    "\n",
+    "Bu derste Doğrusal Regresyon hakkında bilgi edindik. Regresyonun diğer önemli türleri de vardır. Stepwise, Ridge, Lasso ve Elasticnet teknikleri hakkında okuyun. Daha fazla bilgi edinmek için çalışabileceğiniz iyi bir kurs [Stanford Statistical Learning course](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning).\n",
+    "\n",
+    "Harika Tidymodels çerçevesini nasıl kullanacağınızı öğrenmek istiyorsanız, lütfen aşağıdaki kaynaklara göz atın:\n",
+    "\n",
+    "-   Tidymodels web sitesi: [Tidymodels ile Başlayın](https://www.tidymodels.org/start/)\n",
+    "\n",
+    "-   Max Kuhn ve Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+    "\n",
+    "###### **TEŞEKKÜRLER:**\n",
+    "\n",
+    "[R için daha sıcak ve ilgi çekici hale getiren harika illüstrasyonları oluşturan Allison Horst](https://twitter.com/allison_horst?lang=en). Daha fazla illüstrasyonu onun [galerisinde](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM) bulabilirsiniz.\n"
+   ],
+   "metadata": {
+    "id": "8zOLOWqMxzk5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/2-Regression/3-Linear/solution/notebook.ipynb b/translations/tr/2-Regression/3-Linear/solution/notebook.ipynb
new file mode 100644
index 000000000..fa297ac73
--- /dev/null
+++ b/translations/tr/2-Regression/3-Linear/solution/notebook.ipynb
@@ -0,0 +1,1113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Kabak Fiyatlandırması için Doğrusal ve Polinomial Regresyon - Ders 3\n",
+    "\n",
+    "Gerekli kütüphaneleri ve veri setini yükleyin. Verileri aşağıdaki alt küme içeren bir veri çerçevesine dönüştürün:\n",
+    "\n",
+    "- Sadece kile ile fiyatlandırılan kabakları alın\n",
+    "- Tarihi bir aya dönüştürün\n",
+    "- Fiyatı, yüksek ve düşük fiyatların ortalaması olarak hesaplayın\n",
+    "- Fiyatı kile miktarına göre fiyatlandırmayı yansıtacak şekilde dönüştürün\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bir dağılım grafiği, elimizde yalnızca Ağustos'tan Aralık'a kadar olan ay verilerinin bulunduğunu hatırlatır. Muhtemelen doğrusal bir şekilde sonuçlar çıkarabilmek için daha fazla veriye ihtiyacımız var.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Month', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('Month','Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Görünüşe göre korelasyon oldukça küçük, ancak başka daha önemli bir ilişki var - çünkü yukarıdaki grafikteki fiyat noktaları birkaç belirgin küme oluşturuyor gibi görünüyor. Haydi farklı kabak çeşitlerini gösterecek bir grafik yapalım:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax=None\n",
+    "colors = ['red','blue','green','yellow']\n",
+    "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    ax = new_pumpkins[new_pumpkins['Variety']==var].plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.2669192282197318\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n",
+    "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))\n",
+    "pie_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Doğrusal Regresyon\n",
+    "\n",
+    "Doğrusal regresyon modelini eğitmek için Scikit Learn kullanacağız:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a3232f8dc0>]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(X_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Doğrunun eğimi, doğrusal regresyon katsayılarından belirlenebilir:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-0.01751876]), 21.133734359909326)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.coef_, lin_reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eğitilmiş modeli fiyat tahmini yapmak için kullanabiliriz:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.64893156])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pumpkin price on programmer's day\n",
+    "\n",
+    "lin_reg.predict([[256]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Polinom Regresyon\n",
+    "\n",
+    "Bazen özellikler ile sonuçlar arasındaki ilişki doğası gereği doğrusal olmayabilir. Örneğin, kabak fiyatları kış aylarında (aylar=1,2) yüksek olabilir, yaz aylarında (aylar=5-7) düşebilir ve ardından tekrar yükselebilir. Doğrusal regresyon bu ilişkiyi doğru bir şekilde bulamaz.\n",
+    "\n",
+    "Bu durumda, ek özellikler eklemeyi düşünebiliriz. Basit bir yöntem, giriş özelliklerinden polinomlar kullanmaktır, bu da **polinom regresyon** ile sonuçlanır. Scikit Learn'de, polinom özelliklerini otomatik olarak önceden hesaplamak için boru hatlarını kullanabiliriz:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.73 (17.0%)\n",
+      "Model determination:  0.07639977655280217\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a323368ac0>]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)\n",
+    "\n",
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Çeşitlerin Kodlanması\n",
+    "\n",
+    "İdeal bir dünyada, farklı balkabağı çeşitleri için fiyatları aynı modelle tahmin edebilmek isteriz. Çeşidi hesaba katmak için, önce onu sayısal bir forma dönüştürmemiz, yani **kodlamamız** gerekir. Bunu yapmanın birkaç yolu vardır:\n",
+    "\n",
+    "* Basit sayısal kodlama, farklı çeşitlerin bir tablosunu oluşturur ve ardından çeşit adını bu tablodaki bir indeksle değiştirir. Bu, doğrusal regresyon için en iyi fikir değildir, çünkü doğrusal regresyon indeksin sayısal değerini dikkate alır ve bu sayısal değer muhtemelen fiyatla sayısal olarak ilişki kurmaz.\n",
+    "* Tekil kodlama (one-hot encoding), `Variety` sütununu 4 farklı sütunla değiştirir. Her sütun, ilgili satırın belirli bir çeşide ait olup olmadığını göstermek için 1 veya 0 içerir.\n",
+    "\n",
+    "Aşağıdaki kod, bir çeşidi nasıl tekil kodlayabileceğimizi gösteriyor:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FAIRYTALE</th>\n",
+       "      <th>MINIATURE</th>\n",
+       "      <th>MIXED HEIRLOOM VARIETIES</th>\n",
+       "      <th>PIE TYPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE\n",
+       "70            0          0                         0         1\n",
+       "71            0          0                         0         1\n",
+       "72            0          0                         0         1\n",
+       "73            0          0                         0         1\n",
+       "74            0          0                         0         1\n",
+       "...         ...        ...                       ...       ...\n",
+       "1738          0          1                         0         0\n",
+       "1739          0          1                         0         0\n",
+       "1740          0          1                         0         0\n",
+       "1741          0          1                         0         0\n",
+       "1742          0          1                         0         0\n",
+       "\n",
+       "[415 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(new_pumpkins['Variety'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Çeşit Üzerinde Doğrusal Regresyon\n",
+    "\n",
+    "Şimdi yukarıdakiyle aynı kodu kullanacağız, ancak `DayOfYear` yerine giriş olarak tek-seçenekli kodlanmış çeşidimizi kullanacağız:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety'])\n",
+    "y = new_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 5.24 (19.7%)\n",
+      "Model determination:  0.774085281105197\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_linear_regression(X,y):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "    lin_reg = LinearRegression()\n",
+    "    lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "    pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "    mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "    print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "    score = lin_reg.score(X_train,y_train)\n",
+    "    print('Model determination: ', score)\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aynı şekilde diğer özellikleri kullanmayı deneyebilir ve bunları `Month` veya `DayOfYear` gibi sayısal özelliklerle birleştirebiliriz:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.84 (10.5%)\n",
+      "Model determination:  0.9401096672643048\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['Package']))\n",
+    "y = new_pumpkins['Price']\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Polinom Regresyon\n",
+    "\n",
+    "Polinom regresyon, tekil sıcak kodlanmış kategorik özelliklerle de kullanılabilir. Polinom regresyonu eğitmek için kullanılan kod, yukarıda gördüğümüzle temelde aynı olacaktır.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.23 (8.25%)\n",
+      "Model determination:  0.9652870784724543\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Orijinal belgenin kendi dilindeki hali yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan herhangi bir yanlış anlama veya yanlış yorumlama durumunda sorumluluk kabul edilmez.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d77bd89ae7e79780c68c58bab91f13f8",
+   "translation_date": "2025-09-06T13:12:16+00:00",
+   "source_file": "2-Regression/3-Linear/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/tr/2-Regression/4-Logistic/notebook.ipynb b/translations/tr/2-Regression/4-Logistic/notebook.ipynb
new file mode 100644
index 000000000..df7a180bf
--- /dev/null
+++ b/translations/tr/2-Regression/4-Logistic/notebook.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Balkabağı Çeşitleri ve Renk\n",
+    "\n",
+    "Gerekli kütüphaneleri ve veri setini yükleyin. Verileri, verilerin bir alt kümesini içeren bir veri çerçevesine dönüştürün:\n",
+    "\n",
+    "Renk ve çeşit arasındaki ilişkiye bir göz atalım\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
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@@ -0,0 +1,686 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lojistik Regresyon Modeli Oluşturma - Ders 4\n",
+    "\n",
+    "![Lojistik ve doğrusal regresyon infografiği](../../../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)\n",
+    "\n",
+    "#### **[Ders Öncesi Test](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
+    "\n",
+    "#### Giriş\n",
+    "\n",
+    "Regresyon üzerine olan bu son derste, temel *klasik* ML tekniklerinden biri olan Lojistik Regresyonu inceleyeceğiz. Bu tekniği, ikili kategorileri tahmin etmek için desenleri keşfetmek amacıyla kullanabilirsiniz. Bu şeker çikolata mı, değil mi? Bu hastalık bulaşıcı mı, değil mi? Bu müşteri bu ürünü seçecek mi, seçmeyecek mi?\n",
+    "\n",
+    "Bu derste şunları öğreneceksiniz:\n",
+    "\n",
+    "-   Lojistik regresyon teknikleri\n",
+    "\n",
+    "✅ Bu tür regresyonla çalışmayı daha iyi anlamak için şu [Learn modülüne](https://learn.microsoft.com/training/modules/introduction-classification-models/?WT.mc_id=academic-77952-leestott) göz atabilirsiniz.\n",
+    "\n",
+    "## Ön Koşul\n",
+    "\n",
+    "Balkabağı verileriyle çalıştıktan sonra, üzerinde çalışabileceğimiz bir ikili kategori olduğunu fark edecek kadar aşina olduk: `Renk`.\n",
+    "\n",
+    "Şimdi, bazı değişkenlere dayanarak *belirli bir balkabağının muhtemelen hangi renkte olduğunu* (turuncu 🎃 veya beyaz 👻) tahmin etmek için bir lojistik regresyon modeli oluşturalım.\n",
+    "\n",
+    "> Neden regresyonla ilgili bir ders grubunda ikili sınıflandırmadan bahsediyoruz? Sadece dilsel kolaylık açısından, çünkü lojistik regresyon [aslında bir sınıflandırma yöntemi](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), ancak doğrusal tabanlı bir yöntemdir. Verileri sınıflandırmanın diğer yollarını bir sonraki ders grubunda öğrenin.\n",
+    "\n",
+    "Bu ders için aşağıdaki paketlere ihtiyacımız olacak:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/), veri bilimini daha hızlı, kolay ve eğlenceli hale getirmek için tasarlanmış bir [R paketleri koleksiyonudur](https://www.tidyverse.org/packages).\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) çerçevesi, modelleme ve makine öğrenimi için bir [paketler koleksiyonudur](https://www.tidymodels.org/packages/).\n",
+    "\n",
+    "-   `janitor`: [janitor paketi](https://github.com/sfirke/janitor), kirli verileri incelemek ve temizlemek için basit araçlar sağlar.\n",
+    "\n",
+    "-   `ggbeeswarm`: [ggbeeswarm paketi](https://github.com/eclarke/ggbeeswarm), ggplot2 kullanarak beeswarm tarzı grafikler oluşturmak için yöntemler sunar.\n",
+    "\n",
+    "Bu paketleri şu şekilde yükleyebilirsiniz:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+    "\n",
+    "Alternatif olarak, aşağıdaki script, bu modülü tamamlamak için gerekli paketlere sahip olup olmadığınızı kontrol eder ve eksikse sizin için yükler.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## **Soruyu Tanımlayın**\n",
+    "\n",
+    "Bizim amacımız için bunu bir ikili olarak ifade edeceğiz: 'Beyaz' veya 'Beyaz Değil'. Veri setimizde ayrıca 'çizgili' bir kategori var, ancak çok az örneği olduğu için bunu kullanmayacağız. Zaten veri setinden eksik değerleri çıkardığımızda bu kategori kayboluyor.\n",
+    "\n",
+    "> 🎃 Eğlenceli bilgi, bazen beyaz balkabaklarına 'hayalet' balkabakları diyoruz. Oyması çok kolay değil, bu yüzden turuncu olanlar kadar popüler değiller ama oldukça havalı görünüyorlar! Bu yüzden sorumuzu şu şekilde de yeniden formüle edebiliriz: 'Hayalet' veya 'Hayalet Değil'. 👻\n",
+    "\n",
+    "## **Lojistik Regresyon Hakkında**\n",
+    "\n",
+    "Lojistik regresyon, daha önce öğrendiğiniz doğrusal regresyondan birkaç önemli şekilde farklıdır.\n",
+    "\n",
+    "#### **İkili Sınıflandırma**\n",
+    "\n",
+    "Lojistik regresyon, doğrusal regresyonla aynı özellikleri sunmaz. İlki, `ikili bir kategori` (\"turuncu veya turuncu değil\") hakkında bir tahmin sunarken, ikincisi `sürekli değerler` tahmin edebilir, örneğin bir balkabağının kökeni ve hasat zamanı verildiğinde, *fiyatının ne kadar artacağı*.\n",
+    "\n",
+    "![Dasani Madipalli tarafından hazırlanan infografik](../../../../../../2-Regression/4-Logistic/images/pumpkin-classifier.png)\n",
+    "\n",
+    "### Diğer Sınıflandırmalar\n",
+    "\n",
+    "Lojistik regresyonun başka türleri de vardır, bunlar arasında çok kategorili ve sıralı olanlar bulunur:\n",
+    "\n",
+    "- **Çok kategorili**, birden fazla kategoriye sahip olmayı içerir - \"Turuncu, Beyaz ve Çizgili\".\n",
+    "\n",
+    "- **Sıralı**, mantıksal olarak sıralanmış kategorileri içerir, örneğin sonuçlarımızı sınırlı sayıda boyuta göre sıralamak istersek (mini, küçük, orta, büyük, xl, xxl).\n",
+    "\n",
+    "![Çok kategorili vs sıralı regresyon](../../../../../../2-Regression/4-Logistic/images/multinomial-vs-ordinal.png)\n",
+    "\n",
+    "#### **Değişkenlerin İLİŞKİLİ Olması GEREKMEZ**\n",
+    "\n",
+    "Doğrusal regresyonun daha fazla ilişkili değişkenlerle daha iyi çalıştığını hatırlıyor musunuz? Lojistik regresyon bunun tersidir - değişkenlerin uyumlu olması gerekmez. Bu, zayıf korelasyonlara sahip olan bu veri için işe yarar.\n",
+    "\n",
+    "#### **Çok Temiz Veriye İhtiyacınız Var**\n",
+    "\n",
+    "Lojistik regresyon, daha fazla veri kullanırsanız daha doğru sonuçlar verir; küçük veri setimiz bu görev için ideal değil, bunu aklınızda bulundurun.\n",
+    "\n",
+    "✅ Lojistik regresyona uygun olabilecek veri türlerini düşünün\n",
+    "\n",
+    "## Alıştırma - Veriyi Düzenleyin\n",
+    "\n",
+    "Öncelikle, eksik değerleri çıkararak ve yalnızca bazı sütunları seçerek veriyi biraz temizleyin:\n",
+    "\n",
+    "1. Aşağıdaki kodu ekleyin:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the core tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data and clean column names\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \n",
+    "  clean_names()\n",
+    "\n",
+    "# Select desired columns\n",
+    "pumpkins_select <- pumpkins %>% \n",
+    "  select(c(city_name, package, variety, origin, item_size, color)) \n",
+    "\n",
+    "# Drop rows containing missing values and encode color as factor (category)\n",
+    "pumpkins_select <- pumpkins_select %>% \n",
+    "  drop_na() %>% \n",
+    "  mutate(color = factor(color))\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins_select %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Yeni veri çerçevenize her zaman bir göz atabilirsiniz, aşağıdaki gibi [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) fonksiyonunu kullanarak:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pumpkins_select %>% \n",
+    "  glimpse()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "İkili sınıflandırma problemi yapacağımızı doğrulayalım:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Subset distinct observations in outcome column\n",
+    "pumpkins_select %>% \n",
+    "  distinct(color)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Görselleştirme - kategorik grafik\n",
+    "Şimdiye kadar balkabağı verilerini tekrar yüklediniz ve birkaç değişkeni içeren bir veri setini koruyacak şekilde temizlediniz, bunlar arasında Renk de bulunuyor. Hadi ggplot kütüphanesini kullanarak veri çerçevesini not defterinde görselleştirelim.\n",
+    "\n",
+    "ggplot kütüphanesi, verilerinizi görselleştirmek için bazı güzel yöntemler sunar. Örneğin, her Çeşit ve Renk için verilerin dağılımlarını kategorik bir grafikte karşılaştırabilirsiniz.\n",
+    "\n",
+    "1. Balkabağı verilerimizi kullanarak, her balkabağı kategorisi (turuncu veya beyaz) için bir renk eşlemesi belirterek geombar fonksiyonunu kullanarak böyle bir grafik oluşturun:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Specify colors for each value of the hue variable\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# Create the bar plot\n",
+    "ggplot(pumpkins_select, aes(y = variety, fill = color)) +\n",
+    "  geom_bar(position = \"dodge\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(y = \"Variety\", fill = \"Color\") +\n",
+    "  theme_minimal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Verilere bakarak, Renk verilerinin Çeşitlilik ile nasıl ilişkili olduğunu görebilirsiniz.\n",
+    "\n",
+    "✅ Bu kategorik grafiğe bakarak, hangi ilginç keşifleri hayal edebilirsiniz?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Veri Ön İşleme: Özellik Kodlama\n",
+    "\n",
+    "Kabak veri setimizdeki tüm sütunlar metin değerleri içeriyor. Kategorik verilerle çalışmak insanlar için sezgisel olsa da makineler için aynı durum geçerli değil. Makine öğrenimi algoritmaları sayılarla daha iyi çalışır. Bu nedenle, kodlama veri ön işleme aşamasında çok önemli bir adımdır; çünkü kategorik verileri sayısal verilere dönüştürmemizi sağlar ve bu süreçte hiçbir bilgi kaybı yaşanmaz. İyi bir kodlama, iyi bir model oluşturmanın temelini oluşturur.\n",
+    "\n",
+    "Özellik kodlama için iki ana kodlayıcı türü vardır:\n",
+    "\n",
+    "1. **Ordinal kodlayıcı**: Bu kodlayıcı, sıralı değişkenler için uygundur. Sıralı değişkenler, verilerin mantıksal bir sıralamayı takip ettiği kategorik değişkenlerdir. Örneğin, veri setimizdeki `item_size` sütunu gibi. Bu kodlayıcı, her kategorinin bir sayı ile temsil edildiği bir eşleme oluşturur. Bu sayı, sütundaki kategorinin sırasını ifade eder.\n",
+    "\n",
+    "2. **Kategorik kodlayıcı**: Bu kodlayıcı, nominal değişkenler için uygundur. Nominal değişkenler, verilerin mantıksal bir sıralamayı takip etmediği kategorik değişkenlerdir. Örneğin, veri setimizdeki `item_size` dışındaki tüm özellikler. Bu kodlama yöntemi \"one-hot encoding\" olarak adlandırılır. Her kategori, ikili bir sütunla temsil edilir: kodlanmış değişken, kabak o çeşide aitse 1, değilse 0 değerini alır.\n",
+    "\n",
+    "Tidymodels, veri ön işleme için başka bir kullanışlı paket sunar: [recipes](https://recipes.tidymodels.org/) - veri ön işleme için bir paket. Bir `recipe` tanımlayacağız; bu, tüm tahmin edici sütunların bir dizi tam sayıya kodlanması gerektiğini belirtir. Ardından, gerekli miktarları ve istatistikleri tahmin etmek için `prep` işlemini gerçekleştireceğiz ve son olarak yeni verilere hesaplamaları uygulamak için `bake` işlemini kullanacağız.\n",
+    "\n",
+    "> Normalde, recipes genellikle modelleme için bir ön işleyici olarak kullanılır. Bu durumda, bir veri setine modelleme için hazır hale getirilmesi amacıyla hangi adımların uygulanması gerektiğini tanımlar. Bu durumda, bir tarifi manuel olarak `prep` ve `bake` ile tahmin etmek yerine bir `workflow()` kullanmanız **şiddetle tavsiye edilir**. Bunu birazdan daha ayrıntılı göreceğiz.\n",
+    ">\n",
+    "> Ancak şu an için, recipes + prep + bake kullanarak bir veri setine hangi adımların uygulanması gerektiğini belirliyoruz. Bu adımları uygulayarak ön işlenmiş veriyi çıkarıyoruz.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Preprocess and extract data to allow some data analysis\n",
+    "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>%\n",
+    "  # Define ordering for item_size column\n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  # Convert factors to numbers using the order defined above (Ordinal encoding)\n",
+    "  step_integer(item_size, zero_based = F) %>%\n",
+    "  # Encode all other predictors using one hot encoding\n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE) %>%\n",
+    "  prep(data = pumpkin_select) %>%\n",
+    "  bake(new_data = NULL)\n",
+    "\n",
+    "# Display the first few rows of preprocessed data\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "✅ Öğe Boyutu sütunu için bir sıralı kodlayıcı kullanmanın avantajları nelerdir?\n",
+    "\n",
+    "### Değişkenler arasındaki ilişkileri analiz etme\n",
+    "\n",
+    "Artık verilerimizi ön işleme tabi tuttuğumuza göre, özellikler ve etiket arasındaki ilişkileri analiz ederek modelin, özellikler verildiğinde etiketi ne kadar iyi tahmin edebileceği hakkında bir fikir edinebiliriz. Bu tür bir analizi gerçekleştirmenin en iyi yolu verileri görselleştirmektir. \n",
+    "Bu analiz için tekrar ggplot geom_boxplot_ fonksiyonunu kullanacağız ve Öğe Boyutu, Çeşit ve Renk arasındaki ilişkileri kategorik bir grafikte görselleştireceğiz. Verileri daha iyi görselleştirmek için kodlanmış Öğe Boyutu sütununu ve kodlanmamış Çeşit sütununu kullanacağız.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define the color palette\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins_select_plot<-pumpkins_select\n",
+    "pumpkins_select_plot$item_size <- baked_pumpkins$item_size\n",
+    "\n",
+    "# Create the grouped box plot\n",
+    "ggplot(pumpkins_select_plot, aes(x = `item_size`, y = color, fill = color)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_grid(variety ~ ., scales = \"free_x\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(x = \"Item Size\", y = \"\") +\n",
+    "  theme_minimal() +\n",
+    "  theme(strip.text = element_text(size = 12)) +\n",
+    "  theme(axis.text.x = element_text(size = 10)) +\n",
+    "  theme(axis.title.x = element_text(size = 12)) +\n",
+    "  theme(axis.title.y = element_blank()) +\n",
+    "  theme(legend.position = \"bottom\") +\n",
+    "  guides(fill = guide_legend(title = \"Color\")) +\n",
+    "  theme(panel.spacing = unit(0.5, \"lines\"))+\n",
+    "  theme(strip.text.y = element_text(size = 4, hjust = 0)) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Bir swarm plot kullanın\n",
+    "\n",
+    "Renk, ikili bir kategori olduğu için (Beyaz veya Değil), görselleştirme için '[özel bir yaklaşım](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf)' gerektirir.\n",
+    "\n",
+    "Renk dağılımını item_size ile ilişkili olarak göstermek için bir `swarm plot` deneyin.\n",
+    "\n",
+    "[ggbeeswarm paketi](https://github.com/eclarke/ggbeeswarm)'ni kullanacağız. Bu paket, ggplot2 ile arı kovanı tarzı grafikler oluşturmak için yöntemler sağlar. Arı kovanı grafikleri, normalde üst üste binecek noktaları yan yana düşecek şekilde düzenlemenin bir yoludur.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create beeswarm plots of color and item_size\n",
+    "baked_pumpkins %>% \n",
+    "  mutate(color = factor(color)) %>% \n",
+    "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\n",
+    "  geom_quasirandom() +\n",
+    "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\n",
+    "  theme(legend.position = \"none\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Artık renklerin ikili kategorileri ile daha büyük boyut grubu arasındaki ilişki hakkında bir fikrimiz olduğuna göre, bir kabağın muhtemel rengini belirlemek için lojistik regresyonu inceleyelim.\n",
+    "\n",
+    "## Modelinizi oluşturun\n",
+    "\n",
+    "Sınıflandırma modelinizde kullanmak istediğiniz değişkenleri seçin ve veriyi eğitim ve test setlerine ayırın. [rsample](https://rsample.tidymodels.org/), Tidymodels içinde yer alan bir paket, verilerin verimli bir şekilde bölünmesi ve yeniden örneklenmesi için altyapı sağlar:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Split data into 80% for training and 20% for testing\n",
+    "set.seed(2056)\n",
+    "pumpkins_split <- pumpkins_select %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "# Print out the first 5 rows of the training set\n",
+    "pumpkins_train %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🙌 Artık modeli, eğitim özelliklerini eğitim etiketi (renk) ile eşleştirerek eğitmeye hazırız.\n",
+    "\n",
+    "Verilerimizi modellemeye hazırlamak için yapılması gereken ön işleme adımlarını belirten bir tarif oluşturarak başlayacağız, yani: kategorik değişkenleri bir dizi tam sayıya kodlama. Tıpkı `baked_pumpkins` gibi, bir `pumpkins_recipe` oluşturuyoruz ancak bunu `prep` ve `bake` yapmıyoruz çünkü bu, birkaç adım sonra göreceğiniz bir iş akışına dahil edilecek.\n",
+    "\n",
+    "Tidymodels'de lojistik regresyon modelini belirtmenin oldukça fazla yolu vardır. `?logistic_reg()`'e bakabilirsiniz. Şimdilik, varsayılan `stats::glm()` motoru aracılığıyla bir lojistik regresyon modeli belirteceğiz.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a recipe that specifies preprocessing steps for modelling\n",
+    "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  step_integer(item_size, zero_based = F) %>%  \n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)\n",
+    "\n",
+    "# Create a logistic model specification\n",
+    "log_reg <- logistic_reg() %>% \n",
+    "  set_engine(\"glm\") %>% \n",
+    "  set_mode(\"classification\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Artık bir tarif ve model spesifikasyonuna sahip olduğumuza göre, bunları bir araya getirerek bir nesne oluşturmanın bir yolunu bulmamız gerekiyor. Bu nesne, önce veriyi ön işleme tabi tutacak (arka planda hazırlık + pişirme), modeli ön işlenmiş veri üzerinde eğitecek ve ayrıca olası son işlem aktivitelerine izin verecek.\n",
+    "\n",
+    "Tidymodels'da, bu kullanışlı nesne [`workflow`](https://workflows.tidymodels.org/) olarak adlandırılır ve modelleme bileşenlerinizi pratik bir şekilde bir arada tutar.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Bundle modelling components in a workflow\n",
+    "log_reg_wf <- workflow() %>% \n",
+    "  add_recipe(pumpkins_recipe) %>% \n",
+    "  add_model(log_reg)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "log_reg_wf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bir iş akışı *belirlendikten* sonra, bir model [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html) fonksiyonu kullanılarak `eğitilebilir`. İş akışı, bir tarifi tahmin edecek ve verileri eğitmeden önce ön işleyecektir, bu yüzden bunu manuel olarak prep ve bake kullanarak yapmamıza gerek kalmayacak.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "wf_fit <- log_reg_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the trained workflow\n",
+    "wf_fit\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modelin eğitim sırasında öğrendiği katsayılar çıktı olarak gösterilir.\n",
+    "\n",
+    "Artık modeli eğitim verileriyle eğittik, test verileri üzerinde tahminler yapabiliriz. Bunun için [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html) fonksiyonunu kullanabiliriz. Hadi başlayalım ve modelimizi test seti için etiketleri ve her etiket için olasılıkları tahmin etmek için kullanalım. Olasılık 0.5'ten büyük olduğunda tahmin edilen sınıf `WHITE`, aksi takdirde `ORANGE` olur.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions for color and corresponding probabilities\n",
+    "results <- pumpkins_test %>% select(color) %>% \n",
+    "  bind_cols(wf_fit %>% \n",
+    "              predict(new_data = pumpkins_test)) %>%\n",
+    "  bind_cols(wf_fit %>%\n",
+    "              predict(new_data = pumpkins_test, type = \"prob\"))\n",
+    "\n",
+    "# Compare predictions\n",
+    "results %>% \n",
+    "  slice_head(n = 10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Çok güzel! Bu, lojistik regresyonun nasıl çalıştığına dair daha fazla bilgi sağlıyor.\n",
+    "\n",
+    "### Bir karmaşıklık matrisi ile daha iyi anlama\n",
+    "\n",
+    "Her tahmini, karşılık gelen \"gerçek değer\" ile karşılaştırmak, modelin ne kadar iyi tahmin yaptığını belirlemek için çok verimli bir yöntem değildir. Neyse ki, Tidymodels'in elinde birkaç numara daha var: [`yardstick`](https://yardstick.tidymodels.org/) - performans metriklerini kullanarak modellerin etkinliğini ölçmek için kullanılan bir paket.\n",
+    "\n",
+    "Sınıflandırma problemleriyle ilişkili performans metriklerinden biri [`karmaşıklık matrisi`](https://wikipedia.org/wiki/Confusion_matrix). Bir karmaşıklık matrisi, bir sınıflandırma modelinin ne kadar iyi performans gösterdiğini açıklar. Karmaşıklık matrisi, her sınıftaki kaç örneğin model tarafından doğru bir şekilde sınıflandırıldığını tablo halinde gösterir. Bizim durumumuzda, kaç turuncu balkabağının turuncu olarak sınıflandırıldığını ve kaç beyaz balkabağının beyaz olarak sınıflandırıldığını gösterecek; ayrıca karmaşıklık matrisi, **yanlış** kategorilere sınıflandırılanların sayısını da gösterir.\n",
+    "\n",
+    "Yardstick paketindeki [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) fonksiyonu, gözlemlenen ve tahmin edilen sınıfların bu çapraz tablosunu hesaplar.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Confusion matrix for prediction results\n",
+    "conf_mat(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hadi karışıklık matrisini yorumlayalım. Modelimizden balkabaklarını iki ikili kategori arasında sınıflandırması isteniyor: kategori `beyaz` ve kategori `beyaz değil`.\n",
+    "\n",
+    "-   Eğer modeliniz bir balkabağını beyaz olarak tahmin ederse ve gerçekte 'beyaz' kategorisine aitse, buna `doğru pozitif` denir ve bu, sol üstteki sayı ile gösterilir.\n",
+    "\n",
+    "-   Eğer modeliniz bir balkabağını beyaz değil olarak tahmin ederse ve gerçekte 'beyaz' kategorisine aitse, buna `yanlış negatif` denir ve bu, sol alttaki sayı ile gösterilir.\n",
+    "\n",
+    "-   Eğer modeliniz bir balkabağını beyaz olarak tahmin ederse ve gerçekte 'beyaz değil' kategorisine aitse, buna `yanlış pozitif` denir ve bu, sağ üstteki sayı ile gösterilir.\n",
+    "\n",
+    "-   Eğer modeliniz bir balkabağını beyaz değil olarak tahmin ederse ve gerçekte 'beyaz değil' kategorisine aitse, buna `doğru negatif` denir ve bu, sağ alttaki sayı ile gösterilir.\n",
+    "\n",
+    "| Gerçek |\n",
+    "|:-----:|\n",
+    "\n",
+    "\n",
+    "|               |        |       |\n",
+    "|---------------|--------|-------|\n",
+    "| **Tahmin**    | BEYAZ  | TURUNCU |\n",
+    "| BEYAZ         | DP     | YP      |\n",
+    "| TURUNCU       | YN     | DN      |\n",
+    "\n",
+    "Tahmin edebileceğiniz gibi, daha fazla doğru pozitif ve doğru negatif sayısına sahip olmak ve daha az yanlış pozitif ve yanlış negatif sayısına sahip olmak tercih edilir, bu da modelin daha iyi performans gösterdiğini ifade eder.\n",
+    "\n",
+    "Karışıklık matrisi faydalıdır çünkü bir sınıflandırma modelinin performansını daha iyi değerlendirmemize yardımcı olabilecek diğer metriklerin ortaya çıkmasını sağlar. Şimdi bunlardan bazılarını inceleyelim:\n",
+    "\n",
+    "🎓 Kesinlik: `DP/(DP + YP)` tahmin edilen pozitiflerin gerçekten pozitif olma oranı olarak tanımlanır. Ayrıca [pozitif öngörü değeri](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\") olarak da adlandırılır.\n",
+    "\n",
+    "🎓 Duyarlılık: `DP/(DP + YN)` gerçekte pozitif olan örnekler arasından pozitif sonuçların oranı olarak tanımlanır. Ayrıca `hassasiyet` olarak da bilinir.\n",
+    "\n",
+    "🎓 Özgüllük: `DN/(DN + YP)` gerçekte negatif olan örnekler arasından negatif sonuçların oranı olarak tanımlanır.\n",
+    "\n",
+    "🎓 Doğruluk: `DP + DN/(DP + DN + YP + YN)` Bir örnek için doğru bir şekilde tahmin edilen etiketlerin yüzdesi.\n",
+    "\n",
+    "🎓 F Ölçüsü: Kesinlik ve duyarlılığın ağırlıklı ortalaması, en iyi değer 1 ve en kötü değer 0'dır.\n",
+    "\n",
+    "Haydi bu metrikleri hesaplayalım!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Combine metric functions and calculate them all at once\n",
+    "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\n",
+    "eval_metrics(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bu modelin ROC eğrisini görselleştirin\n",
+    "\n",
+    "Hadi, sözde [`ROC eğrisi`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic)'ni görmek için bir görselleştirme daha yapalım:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make a roc_curve\n",
+    "results %>% \n",
+    "  roc_curve(color, .pred_ORANGE) %>% \n",
+    "  autoplot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ROC eğrileri, bir sınıflandırıcının çıktısını doğru ve yanlış pozitifler açısından değerlendirmek için sıklıkla kullanılır. ROC eğrileri genellikle Y ekseninde `Doğru Pozitif Oranı`/Duyarlılık ve X ekseninde `Yanlış Pozitif Oranı`/1-Özgüllük gösterir. Bu nedenle, eğrinin dikliği ve orta çizgi ile eğri arasındaki boşluk önemlidir: eğrinin hızla yukarı çıkıp çizginin üzerine geçmesini istersiniz. Bizim durumumuzda, başlangıçta yanlış pozitifler vardır ve ardından çizgi düzgün bir şekilde yukarı çıkıp geçer.\n",
+    "\n",
+    "Son olarak, gerçek Eğri Altındaki Alanı hesaplamak için `yardstick::roc_auc()` kullanabiliriz. AUC'yi yorumlamanın bir yolu, modelin rastgele bir pozitif örneği rastgele bir negatif örnekten daha yüksek sıralama olasılığı olarak değerlendirilmesidir.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate area under curve\n",
+    "results %>% \n",
+    "  roc_auc(color, .pred_ORANGE)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sonuç yaklaşık `0.975`. AUC'nin 0 ile 1 arasında değiştiğini göz önünde bulundurursak, yüksek bir skor elde etmek istersiniz çünkü tahminlerinde %100 doğru olan bir modelin AUC değeri 1 olur; bu durumda model *oldukça iyi*.\n",
+    "\n",
+    "Gelecekteki sınıflandırma derslerinde, modelinizin skorlarını nasıl iyileştirebileceğinizi öğreneceksiniz (bu durumda dengesiz veriyle başa çıkmak gibi).\n",
+    "\n",
+    "## 🚀Meydan Okuma\n",
+    "\n",
+    "Lojistik regresyon hakkında keşfedilecek çok şey var! Ancak öğrenmenin en iyi yolu denemektir. Bu tür bir analize uygun bir veri seti bulun ve onunla bir model oluşturun. Ne öğreniyorsunuz? ipucu: İlginç veri setleri için [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets)'ı deneyin.\n",
+    "\n",
+    "## Gözden Geçirme ve Kendi Kendine Çalışma\n",
+    "\n",
+    "Stanford'dan [bu makalenin](https://web.stanford.edu/~jurafsky/slp3/5.pdf) ilk birkaç sayfasını okuyarak lojistik regresyonun bazı pratik kullanım alanlarını öğrenin. Şimdiye kadar incelediğimiz regresyon türlerinden hangisinin hangi görevler için daha uygun olduğunu düşünün. Hangisi daha iyi çalışır?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "langauge": "R",
+   "name": "ir"
+  },
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+   "file_extension": ".r",
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+  },
+  "coopTranslator": {
+   "original_hash": "feaf125f481a89c468fa115bf2aed580",
+   "translation_date": "2025-09-06T13:37:00+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
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new file mode 100644
index 000000000..2c5be3543
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+++ b/translations/tr/2-Regression/4-Logistic/solution/notebook.ipynb
@@ -0,0 +1,1257 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lojistik Regresyon - Ders 4\n",
+    "\n",
+    "Gerekli kütüphaneleri ve veri setini yükleyin. Verileri, verilerin bir alt kümesini içeren bir veri çerçevesine dönüştürün:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date   \n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17  \\\n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition   \n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN  \\\n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Origin</th>\n",
+       "      <th>Item Size</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>DELAWARE</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>VIRGINIA</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>36 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name       Package      Variety    Origin Item Size   Color\n",
+       "2  BALTIMORE  24 inch bins  HOWDEN TYPE  DELAWARE       med  ORANGE\n",
+       "3  BALTIMORE  24 inch bins  HOWDEN TYPE  VIRGINIA       med  ORANGE\n",
+       "4  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "5  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "6  BALTIMORE  36 inch bins  HOWDEN TYPE  MARYLAND       med  ORANGE"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select the columns we want to use\n",
+    "columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Drop rows with missing values\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Haydi verilerimize bir göz atalım!\n",
+    "\n",
+    "Seaborn ile görselleştirerek\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56d0c650>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 609.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# Specify colors for each values of the hue variable\n",
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# Plot a bar plot to visualize how many pumpkins of each variety are orange or white\n",
+    "sns.catplot(\n",
+    "    data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
+    "    palette=palette, \n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Veri Ön İşleme\n",
+    "\n",
+    "Verileri daha iyi görselleştirmek ve modeli eğitmek için özellikleri ve etiketleri kodlayalım.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the different values of the 'Item Size' column\n",
+    "pumpkins['Item Size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "# Encode the 'Item Size' column using ordinal encoding\n",
+    "item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
+    "ordinal_features = ['Item Size']\n",
+    "ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "# Encode all the other features using one-hot encoding\n",
+    "categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
+    "categorical_encoder = OneHotEncoder(sparse_output=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_MICHIGAN</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_MICHIGAN  cat__Origin_NEW JERSEY   \n",
+       "2                   0.0  ...                   0.0                     0.0  \\\n",
+       "3                   0.0  ...                   0.0                     0.0   \n",
+       "4                   0.0  ...                   0.0                     0.0   \n",
+       "5                   0.0  ...                   0.0                     0.0   \n",
+       "6                   0.0  ...                   0.0                     0.0   \n",
+       "\n",
+       "   cat__Origin_NEW YORK  cat__Origin_NORTH CAROLINA  cat__Origin_OHIO   \n",
+       "2                   0.0                         0.0               0.0  \\\n",
+       "3                   0.0                         0.0               0.0   \n",
+       "4                   0.0                         0.0               0.0   \n",
+       "5                   0.0                         0.0               0.0   \n",
+       "6                   0.0                         0.0               0.0   \n",
+       "\n",
+       "   cat__Origin_PENNSYLVANIA  cat__Origin_TENNESSEE  cat__Origin_TEXAS   \n",
+       "2                       0.0                    0.0                0.0  \\\n",
+       "3                       0.0                    0.0                0.0   \n",
+       "4                       0.0                    0.0                0.0   \n",
+       "5                       0.0                    0.0                0.0   \n",
+       "6                       0.0                    0.0                0.0   \n",
+       "\n",
+       "   cat__Origin_VERMONT  cat__Origin_VIRGINIA  \n",
+       "2                  0.0                   0.0  \n",
+       "3                  0.0                   1.0  \n",
+       "4                  0.0                   0.0  \n",
+       "5                  0.0                   0.0  \n",
+       "6                  0.0                   0.0  \n",
+       "\n",
+       "[5 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "ct = ColumnTransformer(transformers=[\n",
+    "     ('ord', ordinal_encoder, ordinal_features),\n",
+    "     ('cat', categorical_encoder, categorical_features)\n",
+    "     ])\n",
+    "# Get the encoded features as a pandas DataFrame\n",
+    "ct.set_output(transform='pandas')\n",
+    "encoded_features = ct.fit_transform(pumpkins)\n",
+    "encoded_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_NEW JERSEY  cat__Origin_NEW YORK   \n",
+       "2                   0.0  ...                     0.0                   0.0  \\\n",
+       "3                   0.0  ...                     0.0                   0.0   \n",
+       "4                   0.0  ...                     0.0                   0.0   \n",
+       "5                   0.0  ...                     0.0                   0.0   \n",
+       "6                   0.0  ...                     0.0                   0.0   \n",
+       "\n",
+       "   cat__Origin_NORTH CAROLINA  cat__Origin_OHIO  cat__Origin_PENNSYLVANIA   \n",
+       "2                         0.0               0.0                       0.0  \\\n",
+       "3                         0.0               0.0                       0.0   \n",
+       "4                         0.0               0.0                       0.0   \n",
+       "5                         0.0               0.0                       0.0   \n",
+       "6                         0.0               0.0                       0.0   \n",
+       "\n",
+       "   cat__Origin_TENNESSEE  cat__Origin_TEXAS  cat__Origin_VERMONT   \n",
+       "2                    0.0                0.0                  0.0  \\\n",
+       "3                    0.0                0.0                  0.0   \n",
+       "4                    0.0                0.0                  0.0   \n",
+       "5                    0.0                0.0                  0.0   \n",
+       "6                    0.0                0.0                  0.0   \n",
+       "\n",
+       "   cat__Origin_VIRGINIA  Color  \n",
+       "2                   0.0      0  \n",
+       "3                   1.0      0  \n",
+       "4                   0.0      0  \n",
+       "5                   0.0      0  \n",
+       "6                   0.0      0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "# Encode the 'Color' column using label encoding\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n",
+    "encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
+    "encoded_pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ORANGE', 'WHITE']"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the mapping between the encoded values and the original values\n",
+    "list(label_encoder.inverse_transform([0, 1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56322210>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1620 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=pumpkins,\n",
+    "    x=\"Item Size\", y=\"Color\", row='Variety',\n",
+    "    kind=\"box\", orient=\"h\",\n",
+    "    sharex=False, margin_titles=True,\n",
+    "    height=1.8, aspect=4, palette=palette,\n",
+    ")\n",
+    "# Defining axis labels \n",
+    "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
+    "g.set_titles(row_template=\"{row_name}\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Color', ylabel='ord__Item Size'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Suppressing warning message claiming that a portion of points cannot be placed into the plot due to the high number of data points\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')\n",
+    "\n",
+    "palette = {\n",
+    "    0: 'orange',\n",
+    "    1: 'wheat'\n",
+    "}\n",
+    "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Dikkat edin**: Uyarıları görmezden gelmek, genellikle iyi bir uygulama değildir ve mümkün olduğunca kaçınılmalıdır. Uyarılar, kodumuzu geliştirmemize ve bir sorunu çözmemize yardımcı olabilecek faydalı mesajlar içerir.  \n",
+    "Bu özel uyarıyı görmezden gelmemizin nedeni, grafiğin okunabilirliğini garanti altına almaktır. Tüm veri noktalarını daha küçük bir işaretçi boyutuyla çizerken, palet rengindeki tutarlılığı korumak, net olmayan bir görselleştirme oluşturur.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modelinizi oluşturun\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# X is the encoded features\n",
+    "X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
+    "# y is the encoded label\n",
+    "y = encoded_pumpkins['Color']\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.94      0.98      0.96       166\n",
+      "           1       0.85      0.67      0.75        33\n",
+      "\n",
+      "    accuracy                           0.92       199\n",
+      "   macro avg       0.89      0.82      0.85       199\n",
+      "weighted avg       0.92      0.92      0.92       199\n",
+      "\n",
+      "Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
+      " 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
+      " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+      " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
+      "F1-score:  0.7457627118644068\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Train a logistic regression model on the pumpkin dataset\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model and print the results\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('F1-score: ', f1_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[162,   4],\n",
+       "       [ 11,  22]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "y_scores = model.predict_proba(X_test)\n",
+    "# calculate ROC curve\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "# Plot the diagonal 50% line\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "# Plot the FPR and TPR achieved by our model\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9749908725812341\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate AUC score\n",
+    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+    "print(auc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "vscode": {
+   "interpreter": {
+    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "ef50cc584e0b79412610cc7da15e1f86",
+   "translation_date": "2025-09-06T13:28:21+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/tr/3-Web-App/1-Web-App/notebook.ipynb b/translations/tr/3-Web-App/1-Web-App/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/tr/3-Web-App/1-Web-App/solution/notebook.ipynb b/translations/tr/3-Web-App/1-Web-App/solution/notebook.ipynb
new file mode 100644
index 000000000..795c3f821
--- /dev/null
+++ b/translations/tr/3-Web-App/1-Web-App/solution/notebook.ipynb
@@ -0,0 +1,267 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5fa2e8f4584c78250ca9729b46562ceb",
+   "translation_date": "2025-09-06T14:32:21+00:00",
+   "source_file": "3-Web-App/1-Web-App/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           datetime                  city state country     shape  \\\n",
+       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
+       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
+       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
+       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
+       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
+       "\n",
+       "   duration (seconds) duration (hours/min)  \\\n",
+       "0              2700.0           45 minutes   \n",
+       "1              7200.0              1-2 hrs   \n",
+       "2                20.0           20 seconds   \n",
+       "3                20.0             1/2 hour   \n",
+       "4               900.0           15 minutes   \n",
+       "\n",
+       "                                            comments date posted   latitude  \\\n",
+       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
+       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
+       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
+       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
+       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
+       "\n",
+       "    longitude  \n",
+       "0  -97.941111  \n",
+       "1  -98.581082  \n",
+       "2   -2.916667  \n",
+       "3  -96.645833  \n",
+       "4 -157.803611  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ufos = pd.read_csv('../data/ufos.csv')\n",
+    "ufos.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "\n",
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()\n",
+    "\n",
+    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufos.dropna(inplace=True)\n",
+    "\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "\n",
+    "X = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       1.00      0.02      0.05       250\n",
+      "           2       0.00      0.00      0.00         8\n",
+      "           3       0.94      1.00      0.97       131\n",
+      "           4       0.95      1.00      0.97      4743\n",
+      "\n",
+      "    accuracy                           0.95      5173\n",
+      "   macro avg       0.78      0.60      0.60      5173\n",
+      "weighted avg       0.95      0.95      0.93      5173\n",
+      "\n",
+      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
+      "Accuracy:  0.9512855209742895\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "model_filename = 'ufo-model.pkl'\n",
+    "pickle.dump(model, open(model_filename,'wb'))\n",
+    "\n",
+    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
+    "print(model.predict([[50,44,-12]]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/1-Introduction/notebook.ipynb b/translations/tr/4-Classification/1-Introduction/notebook.ipynb
new file mode 100644
index 000000000..bec34b0ae
--- /dev/null
+++ b/translations/tr/4-Classification/1-Introduction/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d544ef384b7ba73757d830a72372a7f2",
+   "translation_date": "2025-09-06T14:50:59+00:00",
+   "source_file": "4-Classification/1-Introduction/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/tr/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
new file mode 100644
index 000000000..101918c25
--- /dev/null
+++ b/translations/tr/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -0,0 +1,727 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_10-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "2621e24705e8100893c9bf84e0fc8aef",
+   "translation_date": "2025-09-06T15:01:10+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Lezzetli Asya ve Hint Mutfağı: Bir sınıflandırma modeli oluşturun\n"
+   ],
+   "metadata": {
+    "id": "ItETB4tSFprR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Sınıflandırmaya Giriş: Verilerinizi Temizleyin, Hazırlayın ve Görselleştirin\n",
+    "\n",
+    "Bu dört derste, klasik makine öğreniminin temel odak noktalarından biri olan *sınıflandırmayı* keşfedeceksiniz. Asya ve Hindistan'ın tüm muhteşem mutfakları hakkında bir veri seti kullanarak çeşitli sınıflandırma algoritmalarını inceleyeceğiz. Umarım acıkmışsınızdır!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/pinch.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Bu derslerde pan-Asya mutfaklarını kutlayın! Görsel: Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Bu derslerde pan-Asya mutfaklarını kutlayın! Görsel: Jen Looper](../../../../../../4-Classification/1-Introduction/solution/R/images/pinch.png)-->\n",
+    "\n",
+    "Sınıflandırma, regresyon teknikleriyle birçok ortak noktası olan bir [denetimli öğrenme](https://wikipedia.org/wiki/Supervised_learning) biçimidir. Sınıflandırmada, bir öğenin hangi `kategoriye` ait olduğunu tahmin etmek için bir model eğitirsiniz. Makine öğrenimi, veri setlerini kullanarak değerleri veya şeylerin isimlerini tahmin etmekle ilgiliyse, sınıflandırma genellikle iki gruba ayrılır: *ikili sınıflandırma* ve *çoklu sınıflandırma*.\n",
+    "\n",
+    "Unutmayın:\n",
+    "\n",
+    "-   **Doğrusal regresyon**, değişkenler arasındaki ilişkileri tahmin etmenize ve yeni bir veri noktasının bu çizgiyle ilişkili olarak nerede yer alacağını doğru bir şekilde tahmin etmenize yardımcı oldu. Örneğin, *Eylül ve Aralık aylarında bir kabağın fiyatının ne olacağını* tahmin edebilirsiniz.\n",
+    "\n",
+    "-   **Lojistik regresyon**, \"ikili kategorileri\" keşfetmenize yardımcı oldu: bu fiyat noktasında, *bu kabak turuncu mu yoksa turuncu değil mi*?\n",
+    "\n",
+    "Sınıflandırma, bir veri noktasının etiketini veya sınıfını belirlemenin diğer yollarını belirlemek için çeşitli algoritmalar kullanır. Bu mutfak verileriyle çalışarak, bir grup malzemeyi gözlemleyerek, bu malzemelerin hangi mutfağa ait olduğunu belirleyip belirleyemeyeceğimizi görelim.\n",
+    "\n",
+    "### [**Ders Öncesi Testi**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)\n",
+    "\n",
+    "### **Giriş**\n",
+    "\n",
+    "Sınıflandırma, makine öğrenimi araştırmacısının ve veri bilimcisinin temel faaliyetlerinden biridir. Basit bir ikili değerin sınıflandırılmasından (\"bu e-posta spam mi değil mi?\"), bilgisayarla görme kullanarak karmaşık görüntü sınıflandırma ve segmentasyona kadar, verileri sınıflara ayırmak ve onlara sorular sormak her zaman faydalıdır.\n",
+    "\n",
+    "Bu süreci daha bilimsel bir şekilde ifade etmek gerekirse, sınıflandırma yöntemi, giriş değişkenleri ile çıkış değişkenleri arasındaki ilişkiyi haritalamanıza olanak tanıyan bir tahmin modeli oluşturur.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/binary-multiclass.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Sınıflandırma algoritmalarının ele alması gereken ikili ve çoklu sınıf problemleri. Bilgilendirme görseli: Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Verilerimizi temizleme, görselleştirme ve makine öğrenimi görevlerimiz için hazırlama sürecine başlamadan önce, makine öğreniminin verileri sınıflandırmak için kullanılabileceği çeşitli yollar hakkında biraz bilgi edinelim.\n",
+    "\n",
+    "[İstatistikten](https://wikipedia.org/wiki/Statistical_classification) türetilen klasik makine öğrenimi ile sınıflandırma, `sigara içen`, `kilo` ve `yaş` gibi özellikleri kullanarak *X hastalığını geliştirme olasılığını* belirler. Daha önce gerçekleştirdiğiniz regresyon egzersizlerine benzer bir denetimli öğrenme tekniği olarak, verileriniz etiketlenir ve makine öğrenimi algoritmaları bu etiketleri kullanarak bir veri setinin sınıflarını (veya 'özelliklerini') sınıflandırır ve bunları bir gruba veya sonuca atar.\n",
+    "\n",
+    "✅ Bir mutfaklar hakkında bir veri seti hayal etmek için bir an durun. Çoklu sınıf modeli neyi cevaplayabilir? İkili model neyi cevaplayabilir? Belirli bir mutfağın çemen otu kullanma olasılığını belirlemek isteseydiniz ne olurdu? Ya yıldız anason, enginar, karnabahar ve yaban turpu dolu bir market çantası hediye aldığınızda, tipik bir Hint yemeği yapıp yapamayacağınızı görmek isteseydiniz?\n",
+    "\n",
+    "### **Merhaba 'sınıflandırıcı'**\n",
+    "\n",
+    "Bu mutfak veri setine sormak istediğimiz soru aslında bir **çoklu sınıf sorusu**, çünkü çalışabileceğimiz birkaç olası ulusal mutfak var. Bir grup malzeme verildiğinde, bu birçok sınıftan hangisine veri uyacak?\n",
+    "\n",
+    "Tidymodels, çözmek istediğiniz problemin türüne bağlı olarak verileri sınıflandırmak için kullanabileceğiniz çeşitli algoritmalar sunar. Önümüzdeki iki derste, bu algoritmalardan birkaçını öğreneceksiniz.\n",
+    "\n",
+    "#### **Ön Koşul**\n",
+    "\n",
+    "Bu ders için, verilerimizi temizlemek, hazırlamak ve görselleştirmek için aşağıdaki paketlere ihtiyacımız olacak:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/), veri bilimi işlemlerini daha hızlı, kolay ve eğlenceli hale getirmek için tasarlanmış bir [R paketleri koleksiyonudur](https://www.tidyverse.org/packages).\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) çerçevesi, modelleme ve makine öğrenimi için bir [paketler koleksiyonudur](https://www.tidymodels.org/packages/).\n",
+    "\n",
+    "-   `DataExplorer`: [DataExplorer paketi](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html), EDA sürecini ve rapor oluşturmayı basitleştirmek ve otomatikleştirmek için tasarlanmıştır.\n",
+    "\n",
+    "-   `themis`: [themis paketi](https://themis.tidymodels.org/), Dengesiz Verilerle Başa Çıkmak için Ek Tarif Adımları sağlar.\n",
+    "\n",
+    "Bu paketleri şu şekilde yükleyebilirsiniz:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Alternatif olarak, aşağıdaki script, bu modülü tamamlamak için gereken paketlere sahip olup olmadığınızı kontrol eder ve eksik olanları sizin için yükler.\n"
+   ],
+   "metadata": {
+    "id": "ri5bQxZ-Fz_0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "KIPxa4elGAPI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Daha sonra bu harika paketleri yükleyip mevcut R oturumumuzda kullanılabilir hale getireceğiz. (Bu sadece bir örnekleme için, `pacman::p_load()` bunu zaten sizin için yaptı)\n"
+   ],
+   "metadata": {
+    "id": "YkKAxOJvGD4C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Alıştırma - Verilerinizi temizleyin ve dengeleyin\n",
+    "\n",
+    "Bu projeye başlamadan önceki ilk görev, daha iyi sonuçlar elde etmek için verilerinizi temizlemek ve **dengelemektir**.\n",
+    "\n",
+    "Haydi verilerle tanışalım!🕵️\n"
+   ],
+   "metadata": {
+    "id": "PFkQDlk0GN5O"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Qccw7okxGT0S"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "İlginç! Görünüşe göre, ilk sütun bir tür `id` sütunu. Hadi veri hakkında biraz daha bilgi edinelim.\n"
+   ],
+   "metadata": {
+    "id": "XrWnlgSrGVmR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Basic information about the data\r\n",
+    "df %>%\r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# Visualize basic information above\r\n",
+    "df %>% \r\n",
+    "  plot_intro(ggtheme = theme_light())"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "4UcGmxRxGieA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Çıktıdan hemen görebiliyoruz ki `2448` satır ve `385` sütun ile `0` eksik değerimiz var. Ayrıca 1 ayrık sütunumuz var, *cuisine*.\n",
+    "\n",
+    "## Alıştırma - mutfaklar hakkında bilgi edinme\n",
+    "\n",
+    "Şimdi işler daha ilginç hale gelmeye başlıyor. Haydi, veri dağılımını her bir mutfak için keşfedelim.\n"
+   ],
+   "metadata": {
+    "id": "AaPubl__GmH5"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Count observations per cuisine\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(n)\r\n",
+    "\r\n",
+    "# Plot the distribution\r\n",
+    "theme_set(theme_light())\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"cuisine\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FRsBVy5eGrrv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Mutfak türleri sınırlıdır, ancak veri dağılımı dengesizdir. Bunu düzeltebilirsiniz! Ancak önce biraz daha keşfetmeye devam edin.\n",
+    "\n",
+    "Sonraki adımda, her bir mutfak türünü kendi tibble'ına atayalım ve her mutfak türü için ne kadar veri olduğunu (satırlar, sütunlar) öğrenelim.\n",
+    "\n",
+    "> Bir [tibble](https://tibble.tidyverse.org/) modern bir veri çerçevesidir.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_filter.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış bir eser</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "vVvyDb1kG2in"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create individual tibble for the cuisines\r\n",
+    "thai_df <- df %>% \r\n",
+    "  filter(cuisine == \"thai\")\r\n",
+    "japanese_df <- df %>% \r\n",
+    "  filter(cuisine == \"japanese\")\r\n",
+    "chinese_df <- df %>% \r\n",
+    "  filter(cuisine == \"chinese\")\r\n",
+    "indian_df <- df %>% \r\n",
+    "  filter(cuisine == \"indian\")\r\n",
+    "korean_df <- df %>% \r\n",
+    "  filter(cuisine == \"korean\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Find out how much data is available per cuisine\r\n",
+    "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
+    "    \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
+    "    \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
+    "    \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
+    "    \"korean_df:\", dim(korean_df))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "0TvXUxD3G8Bk"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Alıştırma - dplyr ile mutfaklara göre en popüler malzemeleri keşfetmek**\n",
+    "\n",
+    "Artık verilere daha derinlemesine bakabilir ve her mutfak için tipik malzemelerin neler olduğunu öğrenebilirsiniz. Mutfaklar arasında kafa karışıklığına neden olan tekrar eden verileri temizlemeniz gerekiyor, bu sorunu birlikte inceleyelim.\n",
+    "\n",
+    "R'de bir `create_ingredient()` fonksiyonu oluşturun ve bu fonksiyon bir malzeme veri çerçevesi döndürsün. Bu fonksiyon, işe yaramaz bir sütunu kaldırarak başlayacak ve malzemeleri sayısına göre sıralayacak.\n",
+    "\n",
+    "R'deki bir fonksiyonun temel yapısı şu şekildedir:\n",
+    "\n",
+    "`myFunction <- function(arglist){`\n",
+    "\n",
+    "**`...`**\n",
+    "\n",
+    "**`return`**`(value)`\n",
+    "\n",
+    "`}`\n",
+    "\n",
+    "R fonksiyonlarına dair düzenli bir giriş [burada](https://skirmer.github.io/presentations/functions_with_r.html#1) bulunabilir.\n",
+    "\n",
+    "Hadi başlayalım! Daha önceki derslerimizde öğrendiğimiz [dplyr fiillerini](https://dplyr.tidyverse.org/) kullanacağız. Hatırlatma olarak:\n",
+    "\n",
+    "-   `dplyr::select()`: hangi **sütunları** tutacağınızı veya hariç tutacağınızı seçmenize yardımcı olur.\n",
+    "\n",
+    "-   `dplyr::pivot_longer()`: veriyi \"uzatmanıza\" yardımcı olur, satır sayısını artırır ve sütun sayısını azaltır.\n",
+    "\n",
+    "-   `dplyr::group_by()` ve `dplyr::summarise()`: farklı gruplar için özet istatistikler bulmanıza ve bunları düzenli bir tabloya koymanıza yardımcı olur.\n",
+    "\n",
+    "-   `dplyr::filter()`: yalnızca koşullarınızı karşılayan satırları içeren bir veri alt kümesi oluşturur.\n",
+    "\n",
+    "-   `dplyr::mutate()`: sütunlar oluşturmanıza veya mevcut sütunları değiştirmenize yardımcı olur.\n",
+    "\n",
+    "Allison Horst tarafından hazırlanan, dplyr *(Tidyverse'in bir parçası)* içindeki bazı kullanışlı veri düzenleme fonksiyonlarını tanıtan bu [*sanat*-dolu learnr eğitimine](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) göz atabilirsiniz.\n"
+   ],
+   "metadata": {
+    "id": "K3RF5bSCHC76"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Creates a functions that returns the top ingredients by class\r\n",
+    "\r\n",
+    "create_ingredient <- function(df){\r\n",
+    "  \r\n",
+    "  # Drop the id column which is the first colum\r\n",
+    "  ingredient_df = df %>% select(-1) %>% \r\n",
+    "  # Transpose data to a long format\r\n",
+    "    pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
+    "  # Find the top most ingredients for a particular cuisine\r\n",
+    "    group_by(ingredients) %>% \r\n",
+    "    summarise(n_instances = sum(count)) %>% \r\n",
+    "    filter(n_instances != 0) %>% \r\n",
+    "  # Arrange by descending order\r\n",
+    "    arrange(desc(n_instances)) %>% \r\n",
+    "    mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
+    "  \r\n",
+    "  \r\n",
+    "  return(ingredient_df)\r\n",
+    "} # End of function"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "uB_0JR82HTPa"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Şimdi, mutfağa göre en popüler on malzeme hakkında bir fikir edinmek için fonksiyonu kullanabiliriz. Hadi bunu `thai_df` ile deneyelim.\n"
+   ],
+   "metadata": {
+    "id": "h9794WF8HWmc"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Call create_ingredient and display popular ingredients\r\n",
+    "thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
+    "\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "agQ-1HrcHaEA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Önceki bölümde `geom_col()` kullandık, şimdi de çubuk grafikler oluşturmak için `geom_bar` nasıl kullanılır görelim. Daha fazla bilgi için `?geom_bar` kullanın.\n"
+   ],
+   "metadata": {
+    "id": "kHu9ffGjHdcX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a bar chart for popular thai cuisines\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10) %>% \r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "fb3Bx_3DHj6e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Haydi Japonca veriler için de aynısını yapalım.\n"
+   ],
+   "metadata": {
+    "id": "RHP_xgdkHnvM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
+    "create_ingredient(df = japanese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "019v8F0XHrRU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Peki ya Çin mutfağı?\n"
+   ],
+   "metadata": {
+    "id": "iIGM7vO8Hu3v"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
+    "create_ingredient(df = chinese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lHd9_gd2HyzU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ir8qyQbNH1c7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Indian cuisines and make bar chart\r\n",
+    "create_ingredient(df = indian_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ApukQtKjH5FO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Son olarak, Kore malzemelerini çiz.\n"
+   ],
+   "metadata": {
+    "id": "qv30cwY1H-FM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Korean cuisines and make bar chart\r\n",
+    "create_ingredient(df = korean_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lumgk9cHIBie"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Veri görselleştirmelerinden, artık farklı mutfaklar arasında karışıklık yaratan en yaygın malzemeleri `dplyr::select()` kullanarak çıkarabiliriz.\n",
+    "\n",
+    "Herkes pirinci, sarımsağı ve zencefili sever!\n"
+   ],
+   "metadata": {
+    "id": "iO4veMXuIEta"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "iHJPiG6rIUcK"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Tarifler kullanarak veri ön işleme 👩‍🍳👨‍🍳 - Dengesiz veriyle başa çıkma ⚖️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Çizim: @allison_horst</figcaption>\n",
+    "\n",
+    "Bu dersin mutfaklarla ilgili olduğunu göz önünde bulundurarak, `tarifleri` bağlama oturtmamız gerekiyor.\n",
+    "\n",
+    "Tidymodels, veri ön işleme için başka bir harika paket sunar: `recipes` - veri ön işleme için bir paket.\n"
+   ],
+   "metadata": {
+    "id": "kkFd-JxdIaL6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Haydi mutfaklarımızın dağılımına bir kez daha göz atalım.\n"
+   ],
+   "metadata": {
+    "id": "6l2ubtTPJAhY"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "old_label_count <- df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "old_label_count"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "1e-E9cb7JDVi"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Gördüğünüz gibi, mutfak türlerinin sayısında oldukça eşitsiz bir dağılım var. Kore mutfakları, Tayland mutfaklarının neredeyse 3 katı. Dengesiz veriler genellikle model performansı üzerinde olumsuz etkilere sahiptir. Bir ikili sınıflandırmayı düşünün. Eğer verilerinizin çoğu bir sınıfa aitse, bir ML modeli, sadece o sınıfa ait daha fazla veri olduğu için, o sınıfı daha sık tahmin edecektir. Verilerin dengelenmesi, herhangi bir dengesizliği giderir ve bu eşitsizliği ortadan kaldırmaya yardımcı olur. Birçok model, gözlem sayılarının eşit olduğu durumlarda en iyi performansı gösterir ve bu nedenle dengesiz verilerle çalışırken zorlanır.\n",
+    "\n",
+    "Dengesiz veri setleriyle başa çıkmanın temel olarak iki yolu vardır:\n",
+    "\n",
+    "-   azınlık sınıfına gözlem eklemek: `Over-sampling` örneğin, bir SMOTE algoritması kullanarak\n",
+    "\n",
+    "-   çoğunluk sınıfından gözlem çıkarmak: `Under-sampling`\n",
+    "\n",
+    "Şimdi bir `recipe` kullanarak dengesiz veri setleriyle nasıl başa çıkılacağını gösterelim. Bir recipe, bir veri setine hangi adımların uygulanması gerektiğini tanımlayan bir plan olarak düşünülebilir ve veri analizi için hazır hale getirilmesini sağlar.\n"
+   ],
+   "metadata": {
+    "id": "soAw6826JKx9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HS41brUIJVJy"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ön işleme adımlarımızı inceleyelim.\n",
+    "\n",
+    "-   Bir formül ile `recipe()` çağrısı, `df_select` verilerini referans alarak değişkenlerin *rollerini* tarif eder. Örneğin, `cuisine` sütunu bir `outcome` rolü alırken, diğer sütunlar bir `predictor` rolü almıştır.\n",
+    "\n",
+    "-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html), bu vakaların en yakın komşularını kullanarak azınlık sınıfının sentetik olarak yeni örneklerini oluşturan bir tarif adımının *spesifikasyonunu* oluşturur.\n",
+    "\n",
+    "Şimdi, ön işlenmiş veriyi görmek istersek, tarifimizi [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) ve [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) ile işlememiz gerekir.\n",
+    "\n",
+    "`prep()`: Eğitim setinden gerekli parametreleri tahmin eder ve bu parametreler daha sonra diğer veri setlerine uygulanabilir.\n",
+    "\n",
+    "`bake()`: Hazırlanmış bir tarifi alır ve işlemleri herhangi bir veri setine uygular.\n"
+   ],
+   "metadata": {
+    "id": "Yb-7t7XcJaC8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep and bake the recipe\r\n",
+    "preprocessed_df <- cuisines_recipe %>% \r\n",
+    "  prep() %>% \r\n",
+    "  bake(new_data = NULL) %>% \r\n",
+    "  relocate(cuisine)\r\n",
+    "\r\n",
+    "# Display data\r\n",
+    "preprocessed_df %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Quick summary stats\r\n",
+    "preprocessed_df %>% \r\n",
+    "  introduce()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "9QhSgdpxJl44"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Haydi şimdi mutfaklarımızın dağılımını kontrol edelim ve bunları dengesiz verilerle karşılaştıralım.\n"
+   ],
+   "metadata": {
+    "id": "dmidELh_LdV7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "new_label_count <- preprocessed_df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "list(new_label_count = new_label_count,\r\n",
+    "     old_label_count = old_label_count)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "aSh23klBLwDz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Yum! Veriler temiz, dengeli ve çok lezzetli 😋!\n",
+    "\n",
+    "> Normalde, bir tarif genellikle modelleme için bir ön işlemci olarak kullanılır ve bir veri setine modellemeye hazır hale getirmek için hangi adımların uygulanması gerektiğini tanımlar. Bu durumda, genellikle (önceki derslerimizde gördüğümüz gibi) bir `workflow()` kullanılır, tarifleri manuel olarak tahmin etmek yerine.\n",
+    ">\n",
+    "> Bu nedenle, tidymodels kullanırken genellikle tarifleri **`prep()`** ve **`bake()`** yapmanız gerekmez, ancak tariflerin beklentilerinizi karşılayıp karşılamadığını doğrulamak için bizim durumumuzda olduğu gibi bu işlevler faydalı olabilir.\n",
+    ">\n",
+    "> Bir hazırlanmış tarifi **`new_data = NULL`** ile **`bake()`** ettiğinizde, tarifi tanımlarken sağladığınız veriyi geri alırsınız, ancak ön işleme adımlarından geçmiş olur.\n",
+    "\n",
+    "Şimdi bu verinin bir kopyasını gelecekteki derslerde kullanmak için kaydedelim:\n"
+   ],
+   "metadata": {
+    "id": "HEu80HZ8L7ae"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Save preprocessed data\r\n",
+    "write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "cBmCbIgrMOI6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bu yeni CSV artık kök veri klasöründe bulunabilir.\n",
+    "\n",
+    "**🚀Meydan Okuma**\n",
+    "\n",
+    "Bu müfredat birkaç ilginç veri seti içeriyor. `data` klasörlerini inceleyin ve ikili veya çok sınıflı sınıflandırma için uygun olabilecek veri setleri içerip içermediğini kontrol edin. Bu veri seti hakkında hangi soruları sorardınız?\n",
+    "\n",
+    "## [**Ders sonrası test**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)\n",
+    "\n",
+    "## **Gözden Geçirme ve Kendi Kendine Çalışma**\n",
+    "\n",
+    "-   [package themis](https://github.com/tidymodels/themis)'e göz atın. Dengesiz veriyle başa çıkmak için başka hangi teknikleri kullanabiliriz?\n",
+    "\n",
+    "-   Tidy models [referans web sitesi](https://www.tidymodels.org/start/).\n",
+    "\n",
+    "-   H. Wickham ve G. Grolemund, [*R for Data Science: Görselleştirme, Modelleme, Dönüştürme, Düzenleme ve Veri İçe Aktarma*](https://r4ds.had.co.nz/).\n",
+    "\n",
+    "#### TEŞEKKÜRLER:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) R'yi daha sıcak ve ilgi çekici hale getiren harika çizimler oluşturduğu için. Daha fazla çizimi onun [galerisinde](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM) bulabilirsiniz.\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) ve [Jen Looper](https://www.twitter.com/jenlooper) bu modülün orijinal Python versiyonunu oluşturdukları için ♥️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış sanat eseri</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "WQs5621pMGwf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/1-Introduction/solution/notebook.ipynb b/translations/tr/4-Classification/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..6bb9c545f
--- /dev/null
+++ b/translations/tr/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Lezzetli Asya ve Hint Mutfağı\n",
+    "\n",
+    "## Giriş\n",
+    "\n",
+    "Asya ve Hint mutfağı, zengin tatları ve çeşitli malzemeleriyle dünya çapında popülerdir. Bu mutfaklar, baharatların ustaca kullanımı ve benzersiz pişirme teknikleriyle tanınır.\n",
+    "\n",
+    "## Neden Asya ve Hint Mutfağı?\n",
+    "\n",
+    "Asya ve Hint yemekleri, sadece lezzetli olmakla kalmaz, aynı zamanda genellikle sağlıklı ve besleyicidir. Bu mutfaklar, sebzeler, baklagiller, baharatlar ve otlar gibi doğal malzemelere dayanır. Ayrıca, farklı kültürlerin etkilerini bir araya getirerek her damak tadına hitap eden bir çeşitlilik sunar.\n",
+    "\n",
+    "## Popüler Yemekler\n",
+    "\n",
+    "### 1. Sushi\n",
+    "\n",
+    "Sushi, Japon mutfağının en ünlü yemeklerinden biridir. Genellikle çiğ balık, pirinç ve deniz yosunu ile hazırlanır. Farklı türleri arasında nigiri, maki ve sashimi bulunur.\n",
+    "\n",
+    "### 2. Butter Chicken\n",
+    "\n",
+    "Butter Chicken, Hint mutfağının ikonik yemeklerinden biridir. Tavuk, kremalı bir domates sosunda pişirilir ve genellikle naan ekmeği veya pirinçle servis edilir.\n",
+    "\n",
+    "### 3. Pad Thai\n",
+    "\n",
+    "Pad Thai, Tayland mutfağından gelen bir erişte yemeğidir. Pirinç eriştesi, karides veya tavuk, yumurta, yer fıstığı ve özel bir sosla hazırlanır.\n",
+    "\n",
+    "### 4. Dim Sum\n",
+    "\n",
+    "Dim Sum, Çin mutfağından gelen küçük porsiyonlarda servis edilen bir dizi yemektir. Buharda pişirilmiş veya kızartılmış hamur işleri, köfteler ve tatlılar içerir.\n",
+    "\n",
+    "## Baharatların Önemi\n",
+    "\n",
+    "Baharatlar, Asya ve Hint mutfağının temel taşlarından biridir. Yemeklere derinlik ve karakter kazandırır. Örneğin:\n",
+    "\n",
+    "- **Zerdeçal**: Hint yemeklerinde yaygın olarak kullanılır ve parlak sarı rengiyle bilinir.\n",
+    "- **Zencefil**: Hem tatlı hem de tuzlu yemeklerde kullanılır.\n",
+    "- **Soya Sosu**: Asya mutfağında tuzlu bir tat için sıklıkla tercih edilir.\n",
+    "\n",
+    "## Evde Denemek İçin İpuçları\n",
+    "\n",
+    "- Taze malzemeler kullanın: Sebzeler, otlar ve baharatlar ne kadar taze olursa, yemekleriniz o kadar lezzetli olur.\n",
+    "- Baharatları dikkatli ölçün: Baharatlar güçlüdür, bu yüzden miktarları dengeli kullanmaya özen gösterin.\n",
+    "- Yeni tarifler denemekten korkmayın: Asya ve Hint mutfağı, keşfedilecek sonsuz bir çeşitlilik sunar.\n",
+    "\n",
+    "## Sonuç\n",
+    "\n",
+    "Asya ve Hint mutfağı, hem lezzetli hem de kültürel açıdan zengin bir deneyim sunar. Bu mutfakları keşfetmek, sadece yemek yapmayı değil, aynı zamanda farklı kültürleri anlamayı da içerir. Şimdi mutfağa girin ve bu harika tatları kendiniz deneyimleyin!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Imblearn'i yükleyin, bu SMOTE'u etkinleştirecektir. Bu, sınıflandırma yaparken dengesiz verileri yönetmeye yardımcı olan bir Scikit-learn paketidir. (https://imbalanced-learn.org/stable/)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
+      "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df  = pd.read_csv('../../data/cuisines.csv')"
+   ]
+  },
+  {
+   "source": [
+    "Bu veri kümesi, belirli bir mutfak setinden çeşitli mutfaklardaki her türlü malzemeyi gösteren 385 sütun içermektedir.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0          65  indian       0         0      0           0      0   \n",
+       "1          66  indian       1         0      0           0      0   \n",
+       "2          67  indian       0         0      0           0      0   \n",
+       "3          68  indian       0         0      0           0      0   \n",
+       "4          69  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 385 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>67</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>69</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 385 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2448 entries, 0 to 2447\nColumns: 385 entries, Unnamed: 0 to zucchini\ndtypes: int64(384), object(1)\nmemory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "korean      799\n",
+       "indian      598\n",
+       "chinese     442\n",
+       "japanese    320\n",
+       "thai        289\n",
+       "Name: cuisine, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts()"
+   ]
+  },
+  {
+   "source": [
+    "Mutfakları bir çubuk grafikle göster\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18799710>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "thai df: (289, 385)\njapanese df: (320, 385)\nchinese df: (442, 385)\nindian df: (598, 385)\nkorean df: (799, 385)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "thai_df = df[(df.cuisine == \"thai\")]\n",
+    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
+    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
+    "indian_df = df[(df.cuisine == \"indian\")]\n",
+    "korean_df = df[(df.cuisine == \"korean\")]\n",
+    "\n",
+    "print(f'thai df: {thai_df.shape}')\n",
+    "print(f'japanese df: {japanese_df.shape}')\n",
+    "print(f'chinese df: {chinese_df.shape}')\n",
+    "print(f'indian df: {indian_df.shape}')\n",
+    "print(f'korean df: {korean_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ingredient_df(df):\n",
+    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    # drop ingredients that have a 0 sum\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    # sort df\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
+    "    return ingredient_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d5917db00>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
+    "thai_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d389634a8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+   ],
+   "source": [
+    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
+    "japanese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18817f98>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
+    "chinese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1880a550>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
+    "indian_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1896ce80>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
+    "korean_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\n",
+    "labels_df = df.cuisine #.unique()\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "source": [
+    "SMOTE aşırı örnekleme ile verileri en yüksek sınıfa dengeleyin. Daha fazla bilgi için burayı okuyun: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oversample = SMOTE()\n",
+    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "new label count: korean      799\nchinese     799\njapanese    799\nindian      799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
+    "print(f'old label count: {df.cuisine.value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "source": [
+    "transformed_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "     cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  \\\n",
+       "0     indian       0         0      0           0      0             0   \n",
+       "1     indian       1         0      0           0      0             0   \n",
+       "2     indian       0         0      0           0      0             0   \n",
+       "3     indian       0         0      0           0      0             0   \n",
+       "4     indian       0         0      0           0      0             0   \n",
+       "...      ...     ...       ...    ...         ...    ...           ...   \n",
+       "3990    thai       0         0      0           0      0             0   \n",
+       "3991    thai       0         0      0           0      0             0   \n",
+       "3992    thai       0         0      0           0      0             0   \n",
+       "3993    thai       0         0      0           0      0             0   \n",
+       "3994    thai       0         0      0           0      0             0   \n",
+       "\n",
+       "      apricot  armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
+       "0           0         0          0  ...        0            0           0   \n",
+       "1           0         0          0  ...        0            0           0   \n",
+       "2           0         0          0  ...        0            0           0   \n",
+       "3           0         0          0  ...        0            0           0   \n",
+       "4           0         0          0  ...        0            0           0   \n",
+       "...       ...       ...        ...  ...      ...          ...         ...   \n",
+       "3990        0         0          0  ...        0            0           0   \n",
+       "3991        0         0          0  ...        0            0           0   \n",
+       "3992        0         0          0  ...        0            0           0   \n",
+       "3993        0         0          0  ...        0            0           0   \n",
+       "3994        0         0          0  ...        0            0           0   \n",
+       "\n",
+       "      whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                           0     0     0    0      0       0         0  \n",
+       "1                           0     0     0    0      0       0         0  \n",
+       "2                           0     0     0    0      0       0         0  \n",
+       "3                           0     0     0    0      0       0         0  \n",
+       "4                           0     0     0    0      0       1         0  \n",
+       "...                       ...   ...   ...  ...    ...     ...       ...  \n",
+       "3990                        0     0     0    0      0       0         0  \n",
+       "3991                        0     0     0    0      0       0         0  \n",
+       "3992                        0     0     0    0      0       0         0  \n",
+       "3993                        0     0     0    0      0       0         0  \n",
+       "3994                        0     0     0    0      0       0         0  \n",
+       "\n",
+       "[3995 rows x 381 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": [
+    "# export transformed data to new df for classification\n",
+    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
+    "transformed_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df.info()"
+   ]
+  },
+  {
+   "source": [
+    "Dosyayı gelecekte kullanmak için kaydedin\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "1da12ed6d238756959b8de9cac2a35a2",
+   "translation_date": "2025-09-06T14:53:01+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/2-Classifiers-1/notebook.ipynb b/translations/tr/4-Classification/2-Classifiers-1/notebook.ipynb
new file mode 100644
index 000000000..bc18d6b21
--- /dev/null
+++ b/translations/tr/4-Classification/2-Classifiers-1/notebook.ipynb
@@ -0,0 +1,41 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "68829b06b4dcd512d3327849191f4d7f",
+   "translation_date": "2025-09-06T14:32:49+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "Sınıflandırma Modelleri Oluştur\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Orijinal belgenin kendi dilindeki hali, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/tr/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
new file mode 100644
index 000000000..e658c02a5
--- /dev/null
+++ b/translations/tr/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -0,0 +1,1302 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_11-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "6ea6a5171b1b99b7b5a55f7469c048d2",
+   "translation_date": "2025-09-06T14:40:20+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Lezzetli Asya ve Hint Mutfağı: Bir Sınıflandırma Modeli Oluşturun\n"
+   ],
+   "metadata": {
+    "id": "zs2woWv_HoE8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Mutfak sınıflandırıcıları 1\n",
+    "\n",
+    "Bu derste, *bir grup malzemeye dayanarak belirli bir ulusal mutfağı tahmin etmek* için çeşitli sınıflandırıcıları keşfedeceğiz. Bunu yaparken, algoritmaların sınıflandırma görevlerinde nasıl kullanılabileceği hakkında daha fazla bilgi edineceğiz.\n",
+    "\n",
+    "### [**Ders öncesi test**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
+    "\n",
+    "### **Hazırlık**\n",
+    "\n",
+    "Bu ders, [önceki dersimize](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb) dayanır. Önceki derste:\n",
+    "\n",
+    "-   Asya ve Hindistan'ın tüm muhteşem mutfakları hakkında bir veri seti kullanarak sınıflandırmalara nazik bir giriş yaptık 😋.\n",
+    "\n",
+    "-   Verilerimizi hazırlamak ve temizlemek için bazı [dplyr fiilleri](https://dplyr.tidyverse.org/) keşfettik.\n",
+    "\n",
+    "-   ggplot2 kullanarak güzel görselleştirmeler yaptık.\n",
+    "\n",
+    "-   [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html) kullanarak dengesiz verileri ön işleme ile nasıl ele alacağımızı gösterdik.\n",
+    "\n",
+    "-   Tarifimizi `prep` ve `bake` ederek, beklenildiği gibi çalışacağından emin olmayı gösterdik.\n",
+    "\n",
+    "#### **Ön Koşul**\n",
+    "\n",
+    "Bu ders için, verilerimizi temizlemek, hazırlamak ve görselleştirmek için aşağıdaki paketlere ihtiyacımız olacak:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/), veri bilimini daha hızlı, kolay ve eğlenceli hale getirmek için tasarlanmış bir [R paketleri koleksiyonudur](https://www.tidyverse.org/packages).\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) çerçevesi, modelleme ve makine öğrenimi için bir [paketler koleksiyonudur](https://www.tidymodels.org/packages/).\n",
+    "\n",
+    "-   `themis`: [themis paketi](https://themis.tidymodels.org/), dengesiz verilerle başa çıkmak için ekstra tarif adımları sağlar.\n",
+    "\n",
+    "-   `nnet`: [nnet paketi](https://cran.r-project.org/web/packages/nnet/nnet.pdf), tek bir gizli katmanlı ileri beslemeli sinir ağlarını ve çoklu lojistik regresyon modellerini tahmin etmek için işlevler sağlar.\n",
+    "\n",
+    "Bu paketleri şu şekilde yükleyebilirsiniz:\n"
+   ],
+   "metadata": {
+    "id": "iDFOb3ebHwQC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Alternatif olarak, aşağıdaki betik bu modülü tamamlamak için gerekli paketlere sahip olup olmadığınızı kontrol eder ve eksikse sizin için yükler.\n"
+   ],
+   "metadata": {
+    "id": "4V85BGCjII7F"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, here)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "an5NPyyKIKNR",
+    "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Haydi başlayalım!\n",
+    "\n",
+    "## 1. Veriyi eğitim ve test setlerine ayırın.\n",
+    "\n",
+    "Önceki dersimizden birkaç adımı seçerek başlayacağız.\n",
+    "\n",
+    "### Farklı mutfaklar arasında karışıklığa neden olan en yaygın malzemeleri `dplyr::select()` kullanarak çıkarın.\n",
+    "\n",
+    "Herkes pirinci, sarımsağı ve zencefili sever!\n"
+   ],
+   "metadata": {
+    "id": "0ax9GQLBINVv"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# Load the original cuisines data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
+    "  # Encode cuisine column as categorical\r\n",
+    "  mutate(cuisine = factor(cuisine))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Display distribution of cuisines\r\n",
+    "df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "New names:\n",
+      "* `` -> ...1\n",
+      "\n",
+      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
+      "\n",
+      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+      "\u001b[31mchr\u001b[39m   (1): cuisine\n",
+      "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
+      "\n",
+      "\n",
+      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
+      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 indian  0      0        0     0          0     0            0       0       \n",
+       "2 indian  1      0        0     0          0     0            0       0       \n",
+       "3 indian  0      0        0     0          0     0            0       0       \n",
+       "4 indian  0      0        0     0          0     0            0       0       \n",
+       "5 indian  0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       0    0   \n",
+       "2 0         ⋯ 0       0           0          0                       0    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     1      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   799\n",
+       "2 indian   598\n",
+       "3 chinese  442\n",
+       "4 japanese 320\n",
+       "5 thai     289"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 799 |\n",
+       "| indian   | 598 |\n",
+       "| chinese  | 442 |\n",
+       "| japanese | 320 |\n",
+       "| thai     | 289 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 799\\\\\n",
+       "\t indian   & 598\\\\\n",
+       "\t chinese  & 442\\\\\n",
+       "\t japanese & 320\\\\\n",
+       "\t thai     & 289\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>799</td></tr>\n",
+       "\t<tr><td>indian  </td><td>598</td></tr>\n",
+       "\t<tr><td>chinese </td><td>442</td></tr>\n",
+       "\t<tr><td>japanese</td><td>320</td></tr>\n",
+       "\t<tr><td>thai    </td><td>289</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 735
+    },
+    "id": "jhCrrH22IWVR",
+    "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Harika! Şimdi verileri %70'i eğitim ve %30'u test olacak şekilde bölelim. Ayrıca, verileri bölerken `stratifikasyon` tekniğini uygulayarak `her bir mutfağın oranını` eğitim ve doğrulama veri setlerinde koruyacağız.\n",
+    "\n",
+    "[rsample](https://rsample.tidymodels.org/), Tidymodels içinde verimli veri bölme ve yeniden örnekleme için altyapı sağlar:\n"
+   ],
+   "metadata": {
+    "id": "AYTjVyajIdny"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# Load the core Tidymodels packages into R session\r\n",
+    "library(tidymodels)\r\n",
+    "\r\n",
+    "# Create split specification\r\n",
+    "set.seed(2056)\r\n",
+    "cuisines_split <- initial_split(data = df_select,\r\n",
+    "                                strata = cuisine,\r\n",
+    "                                prop = 0.7)\r\n",
+    "\r\n",
+    "# Extract the data in each split\r\n",
+    "cuisines_train <- training(cuisines_split)\r\n",
+    "cuisines_test <- testing(cuisines_split)\r\n",
+    "\r\n",
+    "# Print the number of cases in each split\r\n",
+    "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
+    "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
+    "\r\n",
+    "# Display the first few rows of the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Display distribution of cuisines in the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training cases: 1712\n",
+      "Test cases: 736"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 chinese 0      0        0     0          0     0            0       0       \n",
+       "2 chinese 0      0        0     0          0     0            0       0       \n",
+       "3 chinese 0      0        0     0          0     0            0       0       \n",
+       "4 chinese 0      0        0     0          0     0            0       0       \n",
+       "5 chinese 0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       1    0   \n",
+       "2 0         ⋯ 0       0           0          0                       1    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     0      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   559\n",
+       "2 indian   418\n",
+       "3 chinese  309\n",
+       "4 japanese 224\n",
+       "5 thai     202"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 559 |\n",
+       "| indian   | 418 |\n",
+       "| chinese  | 309 |\n",
+       "| japanese | 224 |\n",
+       "| thai     | 202 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 559\\\\\n",
+       "\t indian   & 418\\\\\n",
+       "\t chinese  & 309\\\\\n",
+       "\t japanese & 224\\\\\n",
+       "\t thai     & 202\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>559</td></tr>\n",
+       "\t<tr><td>indian  </td><td>418</td></tr>\n",
+       "\t<tr><td>chinese </td><td>309</td></tr>\n",
+       "\t<tr><td>japanese</td><td>224</td></tr>\n",
+       "\t<tr><td>thai    </td><td>202</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 535
+    },
+    "id": "w5FWIkEiIjdN",
+    "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Dengesiz Verilerle Başa Çıkmak\n",
+    "\n",
+    "Orijinal veri setinde ve eğitim setimizde fark etmiş olabileceğiniz gibi, mutfakların sayısında oldukça eşitsiz bir dağılım var. Kore mutfakları, *neredeyse* Tay mutfaklarının 3 katı kadar. Dengesiz veriler genellikle model performansı üzerinde olumsuz etkiler yaratır. Birçok model, gözlem sayısı eşit olduğunda en iyi performansı gösterir ve bu nedenle dengesiz verilerle mücadele etmekte zorlanır.\n",
+    "\n",
+    "Dengesiz veri setleriyle başa çıkmanın iki ana yolu vardır:\n",
+    "\n",
+    "-   azınlık sınıfına gözlem eklemek: `Over-sampling` örneğin, SMOTE algoritması kullanarak azınlık sınıfının yeni örneklerini bu vakaların en yakın komşularını kullanarak sentetik olarak oluşturmak.\n",
+    "\n",
+    "-   çoğunluk sınıfından gözlem çıkarmak: `Under-sampling`\n",
+    "\n",
+    "Önceki dersimizde, dengesiz veri setleriyle nasıl başa çıkılacağını bir `recipe` kullanarak göstermiştik. Recipe, bir veri setine veri analizi için hazır hale getirmek amacıyla hangi adımların uygulanması gerektiğini açıklayan bir taslak olarak düşünülebilir. Bizim durumumuzda, `eğitim setimiz` için mutfaklarımızın sayısında eşit bir dağılım elde etmek istiyoruz. Hadi başlayalım.\n"
+   ],
+   "metadata": {
+    "id": "daBi9qJNIwqW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing training data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "# Print recipe\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Data Recipe\n",
+       "\n",
+       "Inputs:\n",
+       "\n",
+       "      role #variables\n",
+       "   outcome          1\n",
+       " predictor        380\n",
+       "\n",
+       "Operations:\n",
+       "\n",
+       "SMOTE based on cuisine"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 200
+    },
+    "id": "Az6LFBGxI1X0",
+    "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tarifi beklendiği gibi çalışacağından emin olmak için (hazırlık + pişirme kullanarak) kontrol edebilirsiniz - tüm mutfak etiketlerinin `559` gözlemi olduğunu göreceksiniz.\n",
+    "\n",
+    "Bu tarifi modelleme için bir ön işleyici olarak kullanacağımızdan, bir `workflow()` bizim için tüm hazırlık ve pişirme işlemlerini gerçekleştirecek, böylece tarifi manuel olarak tahmin etmemize gerek kalmayacak.\n",
+    "\n",
+    "Şimdi bir model eğitmeye hazırız 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Sınıflandırıcınızı Seçmek\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/parsnip.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış bir illüstrasyon</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "NBL3PqIWJBBB"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Şimdi hangi algoritmayı kullanacağımıza karar vermeliyiz 🤔.\n",
+    "\n",
+    "Tidymodels'de, [`parsnip paketi`](https://parsnip.tidymodels.org/index.html), farklı motorlar (paketler) arasında modellerle çalışmak için tutarlı bir arayüz sağlar. [Model türleri ve motorları](https://www.tidymodels.org/find/parsnip/#models) ile ilgili belgeleri ve bunlara karşılık gelen [model argümanlarını](https://www.tidymodels.org/find/parsnip/#model-args) incelemek için parsnip belgelerine göz atabilirsiniz. Çeşitlilik ilk bakışta oldukça kafa karıştırıcı olabilir. Örneğin, aşağıdaki yöntemlerin tümü sınıflandırma tekniklerini içerir:\n",
+    "\n",
+    "-   C5.0 Kural Tabanlı Sınıflandırma Modelleri\n",
+    "\n",
+    "-   Esnek Ayrımcı Modeller\n",
+    "\n",
+    "-   Doğrusal Ayrımcı Modeller\n",
+    "\n",
+    "-   Düzenlenmiş Ayrımcı Modeller\n",
+    "\n",
+    "-   Lojistik Regresyon Modelleri\n",
+    "\n",
+    "-   Multinom Regresyon Modelleri\n",
+    "\n",
+    "-   Naive Bayes Modelleri\n",
+    "\n",
+    "-   Destek Vektör Makineleri\n",
+    "\n",
+    "-   En Yakın Komşular\n",
+    "\n",
+    "-   Karar Ağaçları\n",
+    "\n",
+    "-   Toplu Yöntemler\n",
+    "\n",
+    "-   Sinir Ağları\n",
+    "\n",
+    "Liste uzayıp gidiyor!\n",
+    "\n",
+    "### **Hangi sınıflandırıcıyı seçmeli?**\n",
+    "\n",
+    "Peki, hangi sınıflandırıcıyı seçmelisiniz? Çoğu zaman, birkaçını denemek ve iyi bir sonuç aramak test etmenin bir yoludur.\n",
+    "\n",
+    "> AutoML, bu karşılaştırmaları bulutta çalıştırarak, verileriniz için en iyi algoritmayı seçmenize olanak tanıyarak bu sorunu kolayca çözer. [Buradan](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott) deneyin.\n",
+    "\n",
+    "Ayrıca sınıflandırıcı seçimi problemimize bağlıdır. Örneğin, sonuç `iki sınıftan daha fazla` kategorize edilebiliyorsa, bizim durumumuzda olduğu gibi, `ikili sınıflandırma` yerine `çok sınıflı sınıflandırma algoritması` kullanmanız gerekir.\n",
+    "\n",
+    "### **Daha iyi bir yaklaşım**\n",
+    "\n",
+    "Ancak rastgele tahmin etmekten daha iyi bir yol, bu indirilebilir [ML Cheat Sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott) üzerindeki fikirleri takip etmektir. Burada, çok sınıflı problemimiz için bazı seçeneklerimiz olduğunu keşfediyoruz:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/cheatsheet.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Microsoft'un Algoritma Cheat Sheet'inin bir bölümü, çok sınıflı sınıflandırma seçeneklerini detaylandırıyor</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "a6DLAZ3vJZ14"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Mantık**\n",
+    "\n",
+    "Elimizdeki kısıtlamalar göz önüne alındığında farklı yaklaşımları değerlendirelim:\n",
+    "\n",
+    "-   **Derin sinir ağları çok ağır**. Temiz ama minimal bir veri setimiz olduğu ve eğitim işlemini yerel olarak notebooklar üzerinden gerçekleştirdiğimiz için, derin sinir ağları bu görev için fazla ağır kalıyor.\n",
+    "\n",
+    "-   **İki sınıflı sınıflandırıcı yok**. İki sınıflı bir sınıflandırıcı kullanmıyoruz, bu nedenle one-vs-all (birine karşı tümü) yöntemi devre dışı kalıyor.\n",
+    "\n",
+    "-   **Karar ağacı veya lojistik regresyon işe yarayabilir**. Karar ağacı veya çok sınıflı lojistik regresyon/multinomial regresyon çok sınıflı veri için işe yarayabilir.\n",
+    "\n",
+    "-   **Çok sınıflı Boosted Karar Ağaçları farklı bir problemi çözüyor**. Çok sınıflı boosted karar ağacı, sıralama oluşturma gibi parametrik olmayan görevler için en uygun olanıdır, bu nedenle bizim için kullanışlı değil.\n",
+    "\n",
+    "Ayrıca, genellikle daha karmaşık makine öğrenimi modellerine (örneğin ensemble yöntemleri) geçmeden önce, en basit modeli oluşturmak ve neler olup bittiğini anlamak iyi bir fikirdir. Bu ders için, `multinomial regression` modeliyle başlayacağız.\n",
+    "\n",
+    "> Lojistik regresyon, sonuç değişkeni kategorik (veya nominal) olduğunda kullanılan bir tekniktir. İkili lojistik regresyonda sonuç değişkeni sayısı iki iken, multinomial lojistik regresyonda sonuç değişkeni sayısı ikiden fazladır. Daha fazla bilgi için [İleri Regresyon Yöntemleri](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html) bağlantısına bakabilirsiniz.\n",
+    "\n",
+    "## 4. Multinomial lojistik regresyon modelini eğitmek ve değerlendirmek.\n",
+    "\n",
+    "Tidymodels'de, `parsnip::multinom_reg()`, multinomial dağılımı kullanarak çok sınıflı veriyi tahmin etmek için doğrusal tahminciler kullanan bir modeli tanımlar. Bu modeli fit etmek için kullanabileceğiniz farklı yollar/motorlar hakkında bilgi almak için `?multinom_reg()`'e bakabilirsiniz.\n",
+    "\n",
+    "Bu örnek için, varsayılan [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) motoru üzerinden bir Multinomial regresyon modeli fit edeceğiz.\n",
+    "\n",
+    "> `penalty` değerini rastgele seçtim. Bu değeri seçmek için daha iyi yöntemler var, örneğin `resampling` ve modeli `tuning` yaparak, ki bunu daha sonra tartışacağız.\n",
+    ">\n",
+    "> Model hiperparametrelerini nasıl ayarlayacağınızı öğrenmek isterseniz [Tidymodels: Başlangıç](https://www.tidymodels.org/start/tuning/) bağlantısına göz atabilirsiniz.\n"
+   ],
+   "metadata": {
+    "id": "gWMsVcbBJemu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# Create a multinomial regression model specification\r\n",
+    "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
+    "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
+    "  set_mode(\"classification\")\r\n",
+    "\r\n",
+    "# Print model specification\r\n",
+    "mr_spec"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 166
+    },
+    "id": "Wq_fcyQiJvfG",
+    "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Harika iş çıkardınız 🥳! Artık bir tarifimiz ve bir model spesifikasyonumuz olduğuna göre, bunları bir araya getirip önce veriyi ön işleyen, ardından ön işlenmiş veri üzerinde modeli eğiten ve potansiyel olarak son işlem aktivitelerine de olanak tanıyan bir nesneye dönüştürmenin bir yolunu bulmamız gerekiyor. Tidymodels'de, bu kullanışlı nesne [`workflow`](https://workflows.tidymodels.org/) olarak adlandırılır ve modelleme bileşenlerinizi pratik bir şekilde bir arada tutar! Python'da buna *pipelines* derdik.\n",
+    "\n",
+    "Şimdi her şeyi bir workflow içinde bir araya getirelim!📦\n"
+   ],
+   "metadata": {
+    "id": "NlSbzDfgJ0zh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# Bundle recipe and model specification\r\n",
+    "mr_wf <- workflow() %>% \r\n",
+    "  add_recipe(cuisines_recipe) %>% \r\n",
+    "  add_model(mr_spec)\r\n",
+    "\r\n",
+    "# Print out workflow\r\n",
+    "mr_wf"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow ════════════════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 333
+    },
+    "id": "Sc1TfPA4Ke3_",
+    "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "İş akışları 👌👌! Bir **`workflow()`** tıpkı bir model gibi uyarlanabilir. Öyleyse, bir model eğitme zamanı!\n"
+   ],
+   "metadata": {
+    "id": "TNQ8i85aKf9L"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# Train a multinomial regression model\n",
+    "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
+    "\n",
+    "mr_fit"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Call:\n",
+       "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
+       "    trace = FALSE)\n",
+       "\n",
+       "Coefficients:\n",
+       "         (Intercept)     almond angelica         anise anise_seed       apple\n",
+       "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
+       "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
+       "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
+       "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
+       "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
+       "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
+       "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
+       "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
+       "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
+       "             avocado       bacon baked_potato balm     banana     barley\n",
+       "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
+       "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
+       "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
+       "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
+       "         bartlett_pear      basil        bay       bean         beech\n",
+       "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
+       "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
+       "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
+       "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
+       "               beef beef_broth   beef_liver         beer        beet\n",
+       "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
+       "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
+       "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
+       "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
+       "         bell_pepper bergamot       berry bitter_orange black_bean\n",
+       "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
+       "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
+       "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
+       "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
+       "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
+       "indian               0             0.38935801   -0.4453495               0\n",
+       "japanese             0            -0.05452887   -0.5440869               0\n",
+       "korean               0            -0.03929970    0.8025454               0\n",
+       "thai                 0            -0.21498372   -0.9854806               0\n",
+       "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
+       "indian          -0.2759246  0.3079977  0.191256164                 0\n",
+       "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
+       "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
+       "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
+       "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
+       "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
+       "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
+       "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
+       "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
+       "\n",
+       "...\n",
+       "and 308 more lines."
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GMbdfVmTKkJI",
+    "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Modelin eğitim sırasında öğrendiği katsayılar çıktı olarak gösterilir.\n",
+    "\n",
+    "### Eğitilmiş Modeli Değerlendirme\n",
+    "\n",
+    "Modelin nasıl bir performans sergilediğini görmek zamanı geldi 📏! Bunu bir test seti üzerinde değerlendirerek yapacağız. Hadi, test seti üzerinde tahminler yaparak başlayalım.\n"
+   ],
+   "metadata": {
+    "id": "tt2BfOxrKmcJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# Make predictions on the test set\n",
+    "results <- cuisines_test %>% select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
+    "\n",
+    "# Print out results\n",
+    "results %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class\n",
+       "1 indian  thai       \n",
+       "2 indian  indian     \n",
+       "3 indian  indian     \n",
+       "4 indian  indian     \n",
+       "5 indian  indian     "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
+       "|---|---|\n",
+       "| indian | thai   |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & .pred\\_class\\\\\n",
+       " <fct> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai  \\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "CqtckvtsKqax",
+    "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Harika iş! Tidymodels'de, model performansını değerlendirmek [yardstick](https://yardstick.tidymodels.org/) kullanılarak yapılabilir - performans metrikleri kullanarak modellerin etkinliğini ölçmek için kullanılan bir paket. Lojistik regresyon dersimizde yaptığımız gibi, bir karmaşıklık matrisi hesaplayarak başlayalım.\n"
+   ],
+   "metadata": {
+    "id": "8w5N6XsBKss7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "# Confusion matrix for categorical data\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "          Truth\n",
+       "Prediction chinese indian japanese korean thai\n",
+       "  chinese       83      1        8     15   10\n",
+       "  indian         4    163        1      2    6\n",
+       "  japanese      21      5       73     25    1\n",
+       "  korean        15      0       11    191    0\n",
+       "  thai          10     11        3      7   70"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 133
+    },
+    "id": "YvODvsLkK0iG",
+    "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Birden fazla sınıfla uğraşırken, bunu bir ısı haritası olarak görselleştirmek genellikle daha sezgiseldir, şöyle:\n"
+   ],
+   "metadata": {
+    "id": "c0HfPL16Lr6U"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
+    "# Visualize confusion matrix\n",
+    "results %>% \n",
+    "  conf_mat(cuisine, .pred_class) %>% \n",
+    "  autoplot(type = \"heatmap\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "plot without title"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {
+      "image/png": {
+       "width": 420,
+       "height": 420
+      }
+     }
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 436
+    },
+    "id": "HsAtwukyLsvt",
+    "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Karmaşıklık matrisi grafiğindeki daha koyu kareler, yüksek vaka sayılarını gösterir ve umarım tahmin edilen ve gerçek etiketin aynı olduğu durumları gösteren koyu karelerden oluşan bir diyagonal çizgi görebilirsiniz.\n",
+    "\n",
+    "Şimdi karmaşıklık matrisi için özet istatistikleri hesaplayalım.\n"
+   ],
+   "metadata": {
+    "id": "oOJC87dkLwPr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# Summary stats for confusion matrix\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
+    "summary()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "   .metric              .estimator .estimate\n",
+       "1  accuracy             multiclass 0.7880435\n",
+       "2  kap                  multiclass 0.7276583\n",
+       "3  sens                 macro      0.7780927\n",
+       "4  spec                 macro      0.9477598\n",
+       "5  ppv                  macro      0.7585583\n",
+       "6  npv                  macro      0.9460080\n",
+       "7  mcc                  multiclass 0.7292724\n",
+       "8  j_index              macro      0.7258524\n",
+       "9  bal_accuracy         macro      0.8629262\n",
+       "10 detection_prevalence macro      0.2000000\n",
+       "11 precision            macro      0.7585583\n",
+       "12 recall               macro      0.7780927\n",
+       "13 f_meas               macro      0.7641862"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 13 × 3\n",
+       "\n",
+       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| accuracy             | multiclass | 0.7880435 |\n",
+       "| kap                  | multiclass | 0.7276583 |\n",
+       "| sens                 | macro      | 0.7780927 |\n",
+       "| spec                 | macro      | 0.9477598 |\n",
+       "| ppv                  | macro      | 0.7585583 |\n",
+       "| npv                  | macro      | 0.9460080 |\n",
+       "| mcc                  | multiclass | 0.7292724 |\n",
+       "| j_index              | macro      | 0.7258524 |\n",
+       "| bal_accuracy         | macro      | 0.8629262 |\n",
+       "| detection_prevalence | macro      | 0.2000000 |\n",
+       "| precision            | macro      | 0.7585583 |\n",
+       "| recall               | macro      | 0.7780927 |\n",
+       "| f_meas               | macro      | 0.7641862 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 13 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " .metric & .estimator & .estimate\\\\\n",
+       " <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t accuracy             & multiclass & 0.7880435\\\\\n",
+       "\t kap                  & multiclass & 0.7276583\\\\\n",
+       "\t sens                 & macro      & 0.7780927\\\\\n",
+       "\t spec                 & macro      & 0.9477598\\\\\n",
+       "\t ppv                  & macro      & 0.7585583\\\\\n",
+       "\t npv                  & macro      & 0.9460080\\\\\n",
+       "\t mcc                  & multiclass & 0.7292724\\\\\n",
+       "\t j\\_index              & macro      & 0.7258524\\\\\n",
+       "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
+       "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
+       "\t precision            & macro      & 0.7585583\\\\\n",
+       "\t recall               & macro      & 0.7780927\\\\\n",
+       "\t f\\_meas               & macro      & 0.7641862\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 13 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
+       "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
+       "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
+       "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
+       "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
+       "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
+       "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
+       "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
+       "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 494
+    },
+    "id": "OYqetUyzL5Wz",
+    "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Eğer doğruluk, duyarlılık, ppv gibi bazı metriklere odaklanırsak, başlangıç için fena değiliz 🥳!\n",
+    "\n",
+    "## 4. Daha Derine İnmek\n",
+    "\n",
+    "Hadi ince bir soru soralım: Tahmin edilen sonuç olarak belirli bir mutfak türüne karar vermek için hangi kriterler kullanılıyor?\n",
+    "\n",
+    "Aslında, lojistik regresyon gibi istatistiksel makine öğrenimi algoritmaları `olasılık` temellidir; yani bir sınıflandırıcı tarafından tahmin edilen şey, olası sonuçlar kümesi üzerinde bir olasılık dağılımıdır. En yüksek olasılığa sahip sınıf, verilen gözlemler için en olası sonuç olarak seçilir.\n",
+    "\n",
+    "Hadi bunu hem kesin sınıf tahminleri hem de olasılıklarla uygulayarak görelim.\n"
+   ],
+   "metadata": {
+    "id": "43t7vz8vMJtW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "# Make hard class prediction and probabilities\n",
+    "results_prob <- cuisines_test %>%\n",
+    "  select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
+    "\n",
+    "# Print out results\n",
+    "results_prob %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
+       "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
+       "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
+       "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
+       "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
+       "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
+       "  .pred_thai  \n",
+       "1 5.388194e-01\n",
+       "2 1.577948e-06\n",
+       "3 6.874989e-03\n",
+       "4 3.863391e-03\n",
+       "5 5.653283e-03"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 7\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
+       "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
+       "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
+       "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
+       "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 7\n",
+       "\\begin{tabular}{lllllll}\n",
+       " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
+       " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
+       "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
+       "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
+       "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
+       "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "xdKNs-ZPMTJL",
+    "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Çok daha iyi!\n",
+    "\n",
+    "✅ Modelin neden ilk gözlemin Tayland mutfağı olduğundan oldukça emin olduğunu açıklayabilir misiniz?\n",
+    "\n",
+    "## **🚀Meydan Okuma**\n",
+    "\n",
+    "Bu derste, temizlenmiş verilerinizi kullanarak bir dizi malzemeye dayanarak ulusal bir mutfağı tahmin edebilen bir makine öğrenimi modeli oluşturdunuz. Tidymodels'in verileri sınıflandırmak için sunduğu [birçok seçeneği](https://www.tidymodels.org/find/parsnip/#models) ve multinomial regresyonu uyarlamak için [diğer yolları](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) incelemek için biraz zaman ayırın.\n",
+    "\n",
+    "#### TEŞEKKÜRLER:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) R'yi daha sıcak ve ilgi çekici hale getiren harika çizimler oluşturduğu için. Daha fazla çizimi onun [galerisinde](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM) bulabilirsiniz.\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) ve [Jen Looper](https://www.twitter.com/jenlooper) bu modülün orijinal Python versiyonunu oluşturdukları için ♥️\n",
+    "\n",
+    "<br>\n",
+    "Biraz espri eklemek isterdim ama yemek kelime oyunlarını anlamıyorum 😅.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Keyifli öğrenmeler,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Öğrenci Elçisi.\n"
+   ],
+   "metadata": {
+    "id": "2tWVHMeLMYdM"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/translations/tr/4-Classification/2-Classifiers-1/solution/notebook.ipynb
new file mode 100644
index 000000000..4eb07caa7
--- /dev/null
+++ b/translations/tr/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Sınıflandırma Modelleri Oluştur\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy is 0.8181818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print (\"Accuracy is {}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n       'vegetable_oil'],\n      dtype='object')\ncuisine: korean\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test an item\n",
+    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[50]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                 0\n",
+       "korean    0.392231\n",
+       "chinese   0.372872\n",
+       "japanese  0.218825\n",
+       "thai      0.013427\n",
+       "indian    0.002645"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>korean</th>\n      <td>0.392231</td>\n    </tr>\n    <tr>\n      <th>chinese</th>\n      <td>0.372872</td>\n    </tr>\n    <tr>\n      <th>japanese</th>\n      <td>0.218825</td>\n    </tr>\n    <tr>\n      <th>thai</th>\n      <td>0.013427</td>\n    </tr>\n    <tr>\n      <th>indian</th>\n      <td>0.002645</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "#rehsape to 2d array and transpose\n",
+    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
+    "# predict with score\n",
+    "proba = model.predict_proba(test)\n",
+    "classes = model.classes_\n",
+    "# create df with classes and scores\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
+    "\n",
+    "# create df to show results\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.75      0.73      0.74       223\n      indian       0.93      0.88      0.90       255\n    japanese       0.78      0.78      0.78       253\n      korean       0.87      0.86      0.86       236\n        thai       0.76      0.84      0.80       232\n\n    accuracy                           0.82      1199\n   macro avg       0.82      0.82      0.82      1199\nweighted avg       0.82      0.82      0.82      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\r\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluğu sağlamak için çaba göstersek de, otomatik çeviriler hata veya yanlışlıklar içerebilir. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan herhangi bir yanlış anlama veya yanlış yorumlama durumunda sorumluluk kabul edilmez.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "9408506dd864f2b6e334c62f80c0cfcc",
+   "translation_date": "2025-09-06T14:33:19+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/3-Classifiers-2/notebook.ipynb b/translations/tr/4-Classification/3-Classifiers-2/notebook.ipynb
new file mode 100644
index 000000000..0e7f65b8b
--- /dev/null
+++ b/translations/tr/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
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+   "translation_date": "2025-09-06T14:42:31+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
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diff --git a/translations/tr/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/translations/tr/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
new file mode 100644
index 000000000..5dc70d53e
--- /dev/null
+++ b/translations/tr/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -0,0 +1,650 @@
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+   "name": "lesson_12-R.ipynb",
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+  },
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+   "name": "R"
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+   "source_file": "4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jsFutf_ygqSx"
+   },
+   "source": [
+    "# Lezzetli Asya ve Hint Mutfağı: Bir sınıflandırma modeli oluşturun\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HD54bEefgtNO"
+   },
+   "source": [
+    "## Mutfak Sınıflandırıcıları 2\n",
+    "\n",
+    "Bu ikinci sınıflandırma dersinde, kategorik verileri sınıflandırmanın `daha fazla yolunu` keşfedeceğiz. Ayrıca bir sınıflandırıcıyı diğerine tercih etmenin sonuçlarını öğreneceğiz.\n",
+    "\n",
+    "### [**Ders Öncesi Testi**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
+    "\n",
+    "### **Ön Koşul**\n",
+    "\n",
+    "Önceki dersleri tamamladığınızı varsayıyoruz, çünkü daha önce öğrendiğimiz bazı kavramları burada devam ettireceğiz.\n",
+    "\n",
+    "Bu ders için aşağıdaki paketlere ihtiyacımız olacak:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/), veri bilimini daha hızlı, kolay ve eğlenceli hale getirmek için tasarlanmış bir [R paketleri koleksiyonudur](https://www.tidyverse.org/packages).\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) çerçevesi, modelleme ve makine öğrenimi için bir [paketler koleksiyonudur](https://www.tidymodels.org/packages/).\n",
+    "\n",
+    "-   `themis`: [themis paketi](https://themis.tidymodels.org/), Dengesiz Verilerle Çalışmak için Ek Tarif Adımları sağlar.\n",
+    "\n",
+    "Bu paketleri şu şekilde yükleyebilirsiniz:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\n",
+    "\n",
+    "Alternatif olarak, aşağıdaki script, bu modülü tamamlamak için gerekli paketlere sahip olup olmadığınızı kontrol eder ve eksik olanları sizin için yükler.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vZ57IuUxgyQt"
+   },
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z22M-pj4g07x"
+   },
+   "source": [
+    "## **1. Bir sınıflandırma haritası**\n",
+    "\n",
+    "[Önceki dersimizde](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1), şu soruyu ele almaya çalıştık: birden fazla model arasında nasıl seçim yaparız? Büyük ölçüde, bu seçim veri özelliklerine ve çözmek istediğimiz problemin türüne (örneğin sınıflandırma veya regresyon) bağlıdır.\n",
+    "\n",
+    "Daha önce, Microsoft'un cheat sheet'ini kullanarak verileri sınıflandırırken sahip olduğunuz çeşitli seçenekleri öğrenmiştik. Python'un Makine Öğrenimi çerçevesi olan Scikit-learn, benzer ancak daha ayrıntılı bir cheat sheet sunar ve bu, tahmincilerinizi (sınıflandırıcılar için başka bir terim) daha da daraltmanıza yardımcı olabilir:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/map.png\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption></figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u1i3xRIVg7vG"
+   },
+   "source": [
+    "> İpucu: [bu haritayı çevrimiçi ziyaret edin](https://scikit-learn.org/stable/tutorial/machine_learning_map/) ve belgeleri okumak için yol boyunca tıklayın.\n",
+    ">\n",
+    "> [Tidymodels referans sitesi](https://www.tidymodels.org/find/parsnip/#models) ayrıca farklı model türleri hakkında mükemmel bir dokümantasyon sunar.\n",
+    "\n",
+    "### **Plan** 🗺️\n",
+    "\n",
+    "Bu harita, verilerinizi net bir şekilde anladığınızda çok faydalıdır, çünkü yolları boyunca 'yürüyerek' bir karara varabilirsiniz:\n",
+    "\n",
+    "-   \\>50 örneğimiz var\n",
+    "\n",
+    "-   Bir kategori tahmin etmek istiyoruz\n",
+    "\n",
+    "-   Etiketlenmiş verilerimiz var\n",
+    "\n",
+    "-   100K'dan az örneğimiz var\n",
+    "\n",
+    "-   ✨ Linear SVC seçebiliriz\n",
+    "\n",
+    "-   Eğer bu işe yaramazsa, çünkü sayısal verilerimiz var\n",
+    "\n",
+    "    -   ✨ KNeighbors Classifier deneyebiliriz\n",
+    "\n",
+    "        -   Eğer bu da işe yaramazsa, ✨ SVC ve ✨ Ensemble Classifiers deneyin\n",
+    "\n",
+    "Bu takip edilmesi çok faydalı bir yol. Şimdi, [tidymodels](https://www.tidymodels.org/) modelleme çerçevesini kullanarak işe koyulalım: iyi istatistiksel uygulamaları teşvik etmek için geliştirilmiş, tutarlı ve esnek bir R paketleri koleksiyonu 😊.\n",
+    "\n",
+    "## 2. Veriyi böl ve dengesiz veri setiyle başa çık.\n",
+    "\n",
+    "Önceki derslerimizden, mutfaklarımız arasında ortak olan bir dizi bileşen olduğunu öğrendik. Ayrıca, mutfakların sayısında oldukça eşitsiz bir dağılım vardı.\n",
+    "\n",
+    "Bunlarla şu şekilde başa çıkacağız:\n",
+    "\n",
+    "-   Farklı mutfaklar arasında kafa karışıklığı yaratan en yaygın bileşenleri `dplyr::select()` kullanarak çıkaracağız.\n",
+    "\n",
+    "-   Veriyi modellemeye hazırlamak için bir `recipe` kullanarak bir `over-sampling` algoritması uygulayacağız.\n",
+    "\n",
+    "Yukarıdakilere önceki derste zaten baktık, bu yüzden bu iş kolay olacak 🥳!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "6tj_rN00hClA"
+   },
+   "source": [
+    "# Load the core Tidyverse and Tidymodels packages\n",
+    "library(tidyverse)\n",
+    "library(tidymodels)\n",
+    "\n",
+    "# Load the original cuisines data\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
+    "\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\n",
+    "df_select <- df %>% \n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\n",
+    "  # Encode cuisine column as categorical\n",
+    "  mutate(cuisine = factor(cuisine))\n",
+    "\n",
+    "\n",
+    "# Create data split specification\n",
+    "set.seed(2056)\n",
+    "cuisines_split <- initial_split(data = df_select,\n",
+    "                                strata = cuisine,\n",
+    "                                prop = 0.7)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "cuisines_train <- training(cuisines_split)\n",
+    "cuisines_test <- testing(cuisines_split)\n",
+    "\n",
+    "# Display distribution of cuisines in the training set\n",
+    "cuisines_train %>% \n",
+    "  count(cuisine) %>% \n",
+    "  arrange(desc(n))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFin5yw3hHb1"
+   },
+   "source": [
+    "### Dengesiz Verilerle Başa Çıkma\n",
+    "\n",
+    "Dengesiz veriler genellikle model performansı üzerinde olumsuz etkilere sahiptir. Birçok model, gözlem sayısının eşit olduğu durumlarda en iyi performansı gösterir ve bu nedenle dengesiz verilerle başa çıkmakta zorlanır.\n",
+    "\n",
+    "Dengesiz veri setleriyle başa çıkmanın temel olarak iki yolu vardır:\n",
+    "\n",
+    "-   azınlık sınıfına gözlem eklemek: `Over-sampling` (Aşırı örnekleme), örneğin, bir SMOTE algoritması kullanarak. Bu algoritma, azınlık sınıfının yeni örneklerini, bu vakaların en yakın komşularını kullanarak sentetik olarak üretir.\n",
+    "\n",
+    "-   çoğunluk sınıfından gözlem çıkarmak: `Under-sampling` (Azaltılmış örnekleme)\n",
+    "\n",
+    "Önceki dersimizde, dengesiz veri setleriyle bir `recipe` (tarif) kullanarak nasıl başa çıkılacağını göstermiştik. Bir tarif, bir veri setine hangi adımların uygulanması gerektiğini tanımlayan bir plan olarak düşünülebilir. Bizim durumumuzda, `training set` (eğitim seti) için mutfak türlerimizin sayısında eşit bir dağılım elde etmek istiyoruz. Hadi başlayalım.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "cRzTnHolhLWd"
+   },
+   "source": [
+    "# Load themis package for dealing with imbalanced data\n",
+    "library(themis)\n",
+    "\n",
+    "# Create a recipe for preprocessing training data\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
+    "  step_smote(cuisine) \n",
+    "\n",
+    "# Print recipe\n",
+    "cuisines_recipe"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KxOQ2ORhhO81"
+   },
+   "source": [
+    "Artık modelleri eğitmeye hazırız 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Multinom regresyon modellerinin ötesinde\n",
+    "\n",
+    "Önceki dersimizde multinom regresyon modellerine baktık. Şimdi sınıflandırma için daha esnek modelleri keşfedelim.\n",
+    "\n",
+    "### Destek Vektör Makineleri\n",
+    "\n",
+    "Sınıflandırma bağlamında, `Destek Vektör Makineleri` sınıfları \"en iyi\" şekilde ayıran bir *hiper düzlem* bulmaya çalışan bir makine öğrenimi tekniğidir. Basit bir örneğe bakalım:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/svm.png\"\n",
+    "   width=\"300\"/>\n",
+    "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C4Wsd0vZhXYu"
+   },
+   "source": [
+    "H1~ sınıfları ayırmaz. H2~ ayırır, ancak yalnızca küçük bir boşlukla. H3~ ise sınıfları maksimum boşlukla ayırır.\n",
+    "\n",
+    "#### Doğrusal Destek Vektör Sınıflandırıcı\n",
+    "\n",
+    "Destek-Vektör kümeleme (SVC), ML teknikleri ailesinden Destek-Vektör makinelerinin bir alt dalıdır. SVC'de, hiper düzlem, eğitim gözlemlerinin `çoğunu` doğru bir şekilde ayıracak şekilde seçilir, ancak bazı gözlemleri `yanlış sınıflandırabilir`. Bazı noktaların yanlış tarafta olmasına izin vererek, SVM aykırı değerlere karşı daha dayanıklı hale gelir ve dolayısıyla yeni verilere daha iyi genelleme yapar. Bu ihlali düzenleyen parametre `maliyet` olarak adlandırılır ve varsayılan değeri 1'dir (bkz. `help(\"svm_poly\")`).\n",
+    "\n",
+    "Bir polinom SVM modelinde `degree = 1` ayarlayarak doğrusal bir SVC oluşturalım.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vJpp6nuChlBz"
+   },
+   "source": [
+    "# Make a linear SVC specification\n",
+    "svc_linear_spec <- svm_poly(degree = 1) %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svc_linear_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svc_linear_spec)\n",
+    "\n",
+    "# Print out workflow\n",
+    "svc_linear_wf"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rDs8cWNkhoqu"
+   },
+   "source": [
+    "Artık ön işleme adımlarını ve model spesifikasyonunu bir *iş akışına* dahil ettiğimize göre, linear SVC'yi eğitip sonuçları değerlendirerek devam edebiliriz. Performans metrikleri için, `accuracy`, `sensitivity`, `Positive Predicted Value` ve `F Measure` değerlendirecek bir metrik seti oluşturalım.\n",
+    "\n",
+    "> `augment()` verilen verilere tahminler için sütun(lar) ekleyecektir.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "81wiqcwuhrnq"
+   },
+   "source": [
+    "# Train a linear SVC model\n",
+    "svc_linear_fit <- svc_linear_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svc_linear_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0UFQvHf-huo3"
+   },
+   "source": [
+    "#### Destek Vektör Makinesi\n",
+    "\n",
+    "Destek vektör makinesi (SVM), sınıflar arasındaki doğrusal olmayan bir sınırı karşılamak için destek vektör sınıflandırıcısının bir uzantısıdır. Temelde, SVM'ler sınıflar arasındaki doğrusal olmayan ilişkileri uyarlamak için özellik uzayını genişletmek amacıyla *çekirdek hilesi*ni kullanır. SVM'ler tarafından kullanılan popüler ve son derece esnek bir çekirdek fonksiyonu *Radyal tabanlı fonksiyon*dur. Şimdi, verilerimiz üzerinde nasıl bir performans göstereceğini görelim.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-KX4S8mzhzmp"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Make an RBF SVM specification\n",
+    "svm_rbf_spec <- svm_rbf() %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svm_rbf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svm_rbf_spec)\n",
+    "\n",
+    "\n",
+    "# Train an RBF model\n",
+    "svm_rbf_fit <- svm_rbf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svm_rbf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QBFSa7WSh4HQ"
+   },
+   "source": [
+    "Çok daha iyi 🤩!\n",
+    "\n",
+    "> ✅ Lütfen bakınız:\n",
+    ">\n",
+    "> -   [*Destek Vektör Makineleri*](https://bradleyboehmke.github.io/HOML/svm.html), R ile Uygulamalı Makine Öğrenimi\n",
+    ">\n",
+    "> -   [*Destek Vektör Makineleri*](https://www.statlearning.com/), R ile İstatistiksel Öğrenime Giriş\n",
+    ">\n",
+    "> daha fazla bilgi için.\n",
+    "\n",
+    "### En Yakın Komşu Sınıflandırıcılar\n",
+    "\n",
+    "*K*-en yakın komşu (KNN), her bir gözlemin diğer gözlemlere olan *benzerliğine* dayanarak tahmin edildiği bir algoritmadır.\n",
+    "\n",
+    "Haydi, verilerimize bir tane uygulayalım.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "k4BxxBcdh9Ka"
+   },
+   "source": [
+    "# Make a KNN specification\n",
+    "knn_spec <- nearest_neighbor() %>% \n",
+    "  set_engine(\"kknn\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "knn_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(knn_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "knn_wf_fit <- knn_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "knn_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HaegQseriAcj"
+   },
+   "source": [
+    "Bu modelin performansı pek iyi görünmüyor. Muhtemelen modelin argümanlarını değiştirmek (bkz. `help(\"nearest_neighbor\")`) model performansını artıracaktır. Mutlaka denemelisiniz.\n",
+    "\n",
+    "> ✅ Lütfen bakınız:\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> *K*-En Yakın Komşu sınıflandırıcıları hakkında daha fazla bilgi edinmek için.\n",
+    "\n",
+    "### Topluluk sınıflandırıcıları\n",
+    "\n",
+    "Topluluk algoritmaları, birden fazla temel tahmin ediciyi birleştirerek optimal bir model oluşturur. Bu, şu yöntemlerle yapılır:\n",
+    "\n",
+    "`bagging`: temel modellerin bir koleksiyonuna *ortalama alma fonksiyonu* uygulamak\n",
+    "\n",
+    "`boosting`: birbiri üzerine inşa edilen bir model dizisi oluşturarak tahmin performansını iyileştirmek.\n",
+    "\n",
+    "Hadi bir Random Forest modelini deneyerek başlayalım. Bu model, büyük bir karar ağacı koleksiyonu oluşturur ve ardından daha iyi bir genel model için bir ortalama alma fonksiyonu uygular.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "49DPoVs6iK1M"
+   },
+   "source": [
+    "# Make a random forest specification\n",
+    "rf_spec <- rand_forest() %>% \n",
+    "  set_engine(\"ranger\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "rf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(rf_spec)\n",
+    "\n",
+    "# Train a random forest model\n",
+    "rf_wf_fit <- rf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "rf_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RGVYwC_aiUWc"
+   },
+   "source": [
+    "Tebrikler 👏!\n",
+    "\n",
+    "Hadi bir de Boosted Tree modeliyle deney yapalım.\n",
+    "\n",
+    "Boosted Tree, bir dizi ardışık karar ağacı oluşturan ve her ağacın önceki ağaçların sonuçlarına bağlı olduğu bir topluluk yöntemini tanımlar. Amaç, hatayı kademeli olarak azaltmaktır. Bu yöntem, yanlış sınıflandırılmış öğelerin ağırlıklarına odaklanır ve bir sonraki sınıflandırıcıyı düzeltmek için uyumu ayarlar.\n",
+    "\n",
+    "Bu modeli oluşturmanın farklı yolları vardır (bkz. `help(\"boost_tree\")`). Bu örnekte, Boosted Tree'leri `xgboost` motoru aracılığıyla oluşturacağız.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Py1YWo-micWs"
+   },
+   "source": [
+    "# Make a boosted tree specification\n",
+    "boost_spec <- boost_tree(trees = 200) %>% \n",
+    "  set_engine(\"xgboost\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "boost_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(boost_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "boost_wf_fit <- boost_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "boost_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zNQnbuejigZM"
+   },
+   "source": [
+    "> ✅ Lütfen bakınız:\n",
+    ">\n",
+    "> -   [Sosyal Bilimciler için Makine Öğrenimi](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
+    ">\n",
+    "> -   [R ile Uygulamalı Makine Öğrenimi](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [R Uygulamalarıyla İstatistiksel Öğrenime Giriş](https://www.statlearning.com/)\n",
+    ">\n",
+    "> -   <https://algotech.netlify.app/blog/xgboost/> - xgboost'a iyi bir alternatif olan AdaBoost modelini inceler.\n",
+    ">\n",
+    "> Ensemble sınıflandırıcılar hakkında daha fazla bilgi edinmek için.\n",
+    "\n",
+    "## 4. Ekstra - birden fazla modeli karşılaştırma\n",
+    "\n",
+    "Bu laboratuvarda oldukça fazla model oluşturduk 🙌. Farklı ön işleme setlerinden ve/veya model tanımlarından birçok iş akışı oluşturmak ve ardından performans metriklerini tek tek hesaplamak yorucu veya zahmetli olabilir.\n",
+    "\n",
+    "Bunu, eğitim seti üzerinde bir iş akışı listesini eğiten ve ardından test setine dayalı performans metriklerini döndüren bir fonksiyon oluşturarak çözebilir miyiz, bir bakalım. Liste içindeki her bir elemana fonksiyon uygulamak için [purrr](https://purrr.tidyverse.org/) paketinden `map()` ve `map_dfr()` fonksiyonlarını kullanacağız.\n",
+    "\n",
+    "> [`map()`](https://purrr.tidyverse.org/reference/map.html) fonksiyonları, birçok for döngüsünü daha özlü ve okunması daha kolay bir kodla değiştirmenize olanak tanır. [`map()`](https://purrr.tidyverse.org/reference/map.html) fonksiyonları hakkında bilgi edinmek için en iyi yer, R for Data Science kitabındaki [iterasyon bölümü](http://r4ds.had.co.nz/iteration.html)dir.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Qzb7LyZnimd2"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "# Define a function that returns performance metrics\n",
+    "compare_models <- function(workflow_list, train_set, test_set){\n",
+    "  \n",
+    "  suppressWarnings(\n",
+    "    # Fit each model to the train_set\n",
+    "    map(workflow_list, fit, data = train_set) %>% \n",
+    "    # Make predictions on the test set\n",
+    "      map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
+    "    # Select desired columns\n",
+    "      select(model, cuisine, .pred_class) %>% \n",
+    "    # Evaluate model performance\n",
+    "      group_by(model) %>% \n",
+    "      eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
+    "      ungroup()\n",
+    "  )\n",
+    "  \n",
+    "} # End of function"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fwa712sNisDA"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "3i4VJOi2iu-a"
+   },
+   "source": [
+    "# Make a list of workflows\n",
+    "workflow_list <- list(\n",
+    "  \"svc\" = svc_linear_wf,\n",
+    "  \"svm\" = svm_rbf_wf,\n",
+    "  \"knn\" = knn_wf,\n",
+    "  \"random_forest\" = rf_wf,\n",
+    "  \"xgboost\" = boost_wf)\n",
+    "\n",
+    "# Call the function\n",
+    "set.seed(2056)\n",
+    "perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
+    "\n",
+    "# Print out performance metrics\n",
+    "perf_metrics %>% \n",
+    "  group_by(.metric) %>% \n",
+    "  arrange(desc(.estimate)) %>% \n",
+    "  slice_head(n=7)\n",
+    "\n",
+    "# Compare accuracy\n",
+    "perf_metrics %>% \n",
+    "  filter(.metric == \"accuracy\") %>% \n",
+    "  arrange(desc(.estimate))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KuWK_lEli4nW"
+   },
+   "source": [
+    "[**workflowset**](https://workflowsets.tidymodels.org/) paketi, kullanıcıların çok sayıda modeli oluşturmasını ve kolayca eğitmesini sağlar, ancak esas olarak `çapraz doğrulama` gibi yeniden örnekleme teknikleriyle çalışmak üzere tasarlanmıştır. Bu, henüz ele almadığımız bir yaklaşımdır.\n",
+    "\n",
+    "## **🚀Meydan Okuma**\n",
+    "\n",
+    "Bu tekniklerin her birinin, örneğin SVM'lerdeki `cost`, KNN'deki `neighbors`, Random Forest'taki `mtry` (Rastgele Seçilen Tahminciler) gibi ayarlayabileceğiniz çok sayıda parametresi vardır.\n",
+    "\n",
+    "Her birinin varsayılan parametrelerini araştırın ve bu parametreleri değiştirmenin modelin kalitesi açısından ne anlama gelebileceğini düşünün.\n",
+    "\n",
+    "Belirli bir model ve parametreleri hakkında daha fazla bilgi edinmek için şu komutu kullanabilirsiniz: `help(\"model\")` örneğin `help(\"rand_forest\")`.\n",
+    "\n",
+    "> Uygulamada, genellikle bu parametrelerin *en iyi değerlerini* tahmin etmek için bir `simüle edilmiş veri seti` üzerinde birçok model eğitir ve bu modellerin ne kadar iyi performans gösterdiğini ölçeriz. Bu sürece **tuning** (ince ayar) denir.\n",
+    "\n",
+    "### [**Ders Sonrası Testi**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
+    "\n",
+    "### **Gözden Geçirme ve Kendi Kendine Çalışma**\n",
+    "\n",
+    "Bu derslerde çok fazla teknik terim var, bu yüzden [bu listeyi](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) gözden geçirmek için bir dakikanızı ayırın. Faydalı terminolojiler içeriyor!\n",
+    "\n",
+    "#### TEŞEKKÜRLER:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) R'ı daha sıcak ve ilgi çekici hale getiren harika illüstrasyonlar oluşturduğu için. Daha fazla illüstrasyonu [galerisinde](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM) bulabilirsiniz.\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) ve [Jen Looper](https://www.twitter.com/jenlooper) bu modülün orijinal Python versiyonunu oluşturdukları için ♥️\n",
+    "\n",
+    "Keyifli Öğrenmeler,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Öğrenci Elçisi.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış bir çalışma</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/tr/4-Classification/3-Classifiers-2/solution/notebook.ipynb
new file mode 100644
index 000000000..810c44b1b
--- /dev/null
+++ b/translations/tr/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Farklı sınıflandırıcıları deneyin\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "C = 10\n",
+    "# Create different classifiers.\n",
+    "classifiers = {\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
+    "    'KNN classifier': KNeighborsClassifier(C),\n",
+    "    'SVC': SVC(),\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "    \n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy (train) for Linear SVC: 76.4% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.64      0.66      0.65       242\n",
+      "      indian       0.91      0.86      0.89       236\n",
+      "    japanese       0.72      0.73      0.73       245\n",
+      "      korean       0.83      0.75      0.79       234\n",
+      "        thai       0.75      0.82      0.78       242\n",
+      "\n",
+      "    accuracy                           0.76      1199\n",
+      "   macro avg       0.77      0.76      0.77      1199\n",
+      "weighted avg       0.77      0.76      0.77      1199\n",
+      "\n",
+      "Accuracy (train) for KNN classifier: 70.7% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.65      0.63      0.64       242\n",
+      "      indian       0.84      0.81      0.82       236\n",
+      "    japanese       0.60      0.81      0.69       245\n",
+      "      korean       0.89      0.53      0.67       234\n",
+      "        thai       0.69      0.75      0.72       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.73      0.71      0.71      1199\n",
+      "weighted avg       0.73      0.71      0.71      1199\n",
+      "\n",
+      "Accuracy (train) for SVC: 80.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.71      0.69      0.70       242\n",
+      "      indian       0.92      0.92      0.92       236\n",
+      "    japanese       0.77      0.78      0.77       245\n",
+      "      korean       0.87      0.77      0.82       234\n",
+      "        thai       0.75      0.86      0.80       242\n",
+      "\n",
+      "    accuracy                           0.80      1199\n",
+      "   macro avg       0.80      0.80      0.80      1199\n",
+      "weighted avg       0.80      0.80      0.80      1199\n",
+      "\n",
+      "Accuracy (train) for RFST: 82.8% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.80      0.75      0.77       242\n",
+      "      indian       0.90      0.91      0.90       236\n",
+      "    japanese       0.82      0.78      0.80       245\n",
+      "      korean       0.85      0.82      0.83       234\n",
+      "        thai       0.78      0.89      0.83       242\n",
+      "\n",
+      "    accuracy                           0.83      1199\n",
+      "   macro avg       0.83      0.83      0.83      1199\n",
+      "weighted avg       0.83      0.83      0.83      1199\n",
+      "\n",
+      "Accuracy (train) for ADA: 71.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.60      0.57      0.58       242\n",
+      "      indian       0.87      0.84      0.86       236\n",
+      "    japanese       0.71      0.60      0.65       245\n",
+      "      korean       0.68      0.78      0.72       234\n",
+      "        thai       0.70      0.78      0.74       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.71      0.71      0.71      1199\n",
+      "weighted avg       0.71      0.71      0.71      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "    y_pred = classifier.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
+    "    print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "7ea2b714669c823a596d986ba2d5739f",
+   "translation_date": "2025-09-06T14:43:04+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/4-Applied/notebook.ipynb b/translations/tr/4-Classification/4-Applied/notebook.ipynb
new file mode 100644
index 000000000..b5d56e471
--- /dev/null
+++ b/translations/tr/4-Classification/4-Applied/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2f3e0d9e9ac5c301558fb8bf733ac0cb",
+   "translation_date": "2025-09-06T14:41:41+00:00",
+   "source_file": "4-Classification/4-Applied/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/4-Classification/4-Applied/solution/notebook.ipynb b/translations/tr/4-Classification/4-Applied/solution/notebook.ipynb
new file mode 100644
index 000000000..8f1c9442d
--- /dev/null
+++ b/translations/tr/4-Classification/4-Applied/solution/notebook.ipynb
@@ -0,0 +1,290 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "49325d6dd12a3628fc64fa7ccb1a80ff",
+   "translation_date": "2025-09-06T14:42:05+00:00",
+   "source_file": "4-Classification/4-Applied/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: skl2onnx in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.8.0)\n",
+      "Requirement already satisfied: protobuf in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (3.8.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.19.2)\n",
+      "Requirement already satisfied: onnx>=1.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.9.0)\n",
+      "Requirement already satisfied: six in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from skl2onnx) (1.12.0)\n",
+      "Requirement already satisfied: onnxconverter-common<1.9,>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.8.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.19 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (0.24.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.4.1)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->skl2onnx) (45.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnx>=1.2.1->skl2onnx) (3.10.0.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (0.16.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install skl2onnx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 60
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 61
+    }
+   ],
+   "source": [
+    "X = data.iloc[:,2:]\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "  cuisine\n",
+       "0  indian\n",
+       "1  indian\n",
+       "2  indian\n",
+       "3  indian\n",
+       "4  indian"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 62
+    }
+   ],
+   "source": [
+    "y = data[['cuisine']]\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 65
+    }
+   ],
+   "source": [
+    "model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
+    "model.fit(X_train,y_train.values.ravel())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.72      0.70      0.71       236\n      indian       0.91      0.88      0.89       243\n    japanese       0.80      0.75      0.77       240\n      korean       0.80      0.81      0.81       230\n        thai       0.76      0.85      0.80       250\n\n    accuracy                           0.80      1199\n   macro avg       0.80      0.80      0.80      1199\nweighted avg       0.80      0.80      0.80      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}\n",
+    "onx = convert_sklearn(model, initial_types=initial_type, options=options)\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
+    "    f.write(onx.SerializeToString())\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluğu sağlamak için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/5-Clustering/1-Visualize/notebook.ipynb b/translations/tr/5-Clustering/1-Visualize/notebook.ipynb
new file mode 100644
index 000000000..5fad876be
--- /dev/null
+++ b/translations/tr/5-Clustering/1-Visualize/notebook.ipynb
@@ -0,0 +1,50 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python383jvsc74a57bd0e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95",
+   "display_name": "Python 3.8.3 64-bit (conda)"
+  },
+  "coopTranslator": {
+   "original_hash": "40e0707e96b3e1899a912776006264f9",
+   "translation_date": "2025-09-06T14:08:11+00:00",
+   "source_file": "5-Clustering/1-Visualize/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/translations/tr/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
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@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Spotify'den Toplanan Nijerya Müziği - Bir Analiz**\n",
+    "\n",
+    "Kümeleme, bir tür [Denetimsiz Öğrenme](https://wikipedia.org/wiki/Unsupervised_learning) yöntemidir ve bir veri setinin etiketlenmemiş olduğunu veya girdilerinin önceden tanımlanmış çıktılarla eşleşmediğini varsayar. Bu yöntem, çeşitli algoritmalar kullanarak etiketlenmemiş verileri analiz eder ve verideki desenlere göre gruplamalar sağlar.\n",
+    "\n",
+    "[**Ders öncesi test**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)\n",
+    "\n",
+    "### **Giriş**\n",
+    "\n",
+    "[Kümeleme](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124), veri keşfi için oldukça faydalıdır. Nijeryalı dinleyicilerin müzik tüketim alışkanlıklarında trendleri ve desenleri keşfetmeye yardımcı olup olamayacağını görelim.\n",
+    "\n",
+    "> ✅ Kümelemenin kullanım alanlarını düşünmek için bir dakika ayırın. Gerçek hayatta, kümeleme çamaşır yığınınızı aile üyelerinizin kıyafetlerine göre ayırmanız gerektiğinde gerçekleşir 🧦👕👖🩲. Veri biliminde ise, kümeleme bir kullanıcının tercihlerini analiz ederken veya etiketlenmemiş bir veri setinin özelliklerini belirlerken gerçekleşir. Kümeleme, bir anlamda, kaosu anlamlandırmaya yardımcı olur, tıpkı bir çorap çekmecesi gibi.\n",
+    "\n",
+    "Profesyonel bir ortamda, kümeleme pazar segmentasyonu belirlemek, örneğin hangi yaş gruplarının hangi ürünleri satın aldığını anlamak için kullanılabilir. Bir diğer kullanım alanı ise anomali tespiti olabilir; örneğin, kredi kartı işlemleri veri setinden dolandırıcılığı tespit etmek. Ya da tıbbi taramalardaki tümörleri belirlemek için kümeleme kullanılabilir.\n",
+    "\n",
+    "✅ Bankacılık, e-ticaret veya iş dünyasında 'doğada' kümelemeyle karşılaştığınız durumları düşünmek için bir dakika ayırın.\n",
+    "\n",
+    "> 🎓 İlginç bir şekilde, kümeleme analizi 1930'larda Antropoloji ve Psikoloji alanlarında ortaya çıkmıştır. Sizce o zamanlar nasıl kullanılmış olabilir?\n",
+    "\n",
+    "Alternatif olarak, arama sonuçlarını gruplamak için kullanılabilir - örneğin alışveriş bağlantıları, görseller veya incelemeler. Kümeleme, büyük bir veri setini küçültmek ve daha ayrıntılı analiz yapmak istediğinizde faydalıdır, bu nedenle diğer modeller oluşturulmadan önce veri hakkında bilgi edinmek için kullanılabilir.\n",
+    "\n",
+    "✅ Verileriniz kümeler halinde organize edildikten sonra, her birine bir küme kimliği atarsınız. Bu teknik, bir veri setinin gizliliğini korumak için faydalı olabilir; bir veri noktasına daha açıklayıcı ve tanımlayıcı veriler yerine küme kimliğiyle atıfta bulunabilirsiniz. Küme kimliğiyle diğer küme öğelerine atıfta bulunmak yerine başka nedenler düşünebilir misiniz?\n",
+    "\n",
+    "### Kümelemeye Başlangıç\n",
+    "\n",
+    "> 🎓 Kümeleri nasıl oluşturduğumuz, veri noktalarını gruplara nasıl topladığımızla yakından ilgilidir. Bazı terimleri açalım:\n",
+    ">\n",
+    "> 🎓 ['Transdüktif' vs. 'indüktif'](https://wikipedia.org/wiki/Transduction_(machine_learning))\n",
+    ">\n",
+    "> Transdüktif çıkarım, belirli test durumlarına eşlenen gözlemlenen eğitim durumlarından türetilir. İndüktif çıkarım ise genel kurallara eşlenen eğitim durumlarından türetilir ve bu kurallar yalnızca test durumlarına uygulanır.\n",
+    ">\n",
+    "> Bir örnek: Elinizde yalnızca kısmen etiketlenmiş bir veri seti olduğunu hayal edin. Bazı şeyler 'plak', bazıları 'cd' ve bazıları boş. Göreviniz, boş olanlara etiket vermektir. İndüktif bir yaklaşım seçerseniz, 'plak' ve 'cd' arayan bir model eğitirsiniz ve bu etiketleri etiketlenmemiş verinize uygularsınız. Bu yaklaşım, aslında 'kaset' olan şeyleri sınıflandırmakta zorlanır. Transdüktif bir yaklaşım ise bu bilinmeyen veriyi daha etkili bir şekilde ele alır, benzer öğeleri gruplandırır ve ardından bir gruba etiket uygular. Bu durumda, kümeler 'yuvarlak müzik şeyleri' ve 'kare müzik şeyleri' gibi görünebilir.\n",
+    ">\n",
+    "> 🎓 ['Düz' vs. 'düz olmayan' geometriler](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)\n",
+    ">\n",
+    "> Matematiksel terminolojiden türetilen düz ve düz olmayan geometriler, noktalar arasındaki mesafelerin 'düz' ([Öklid](https://wikipedia.org/wiki/Euclidean_geometry)) veya 'düz olmayan' (Öklid dışı) geometrik yöntemlerle ölçülmesini ifade eder.\n",
+    ">\n",
+    "> Bu bağlamda 'düz', Öklid geometrisini (bazı bölümleri 'düzlem' geometrisi olarak öğretilir) ifade ederken, 'düz olmayan' Öklid dışı geometriyi ifade eder. Geometri, makine öğrenimiyle nasıl ilişkilidir? Matematiğe dayalı iki alan olarak, kümelerdeki noktalar arasındaki mesafeleri ölçmek için ortak bir yol bulunmalıdır ve bu, verinin doğasına bağlı olarak 'düz' veya 'düz olmayan' şekilde yapılabilir. [Öklid mesafeleri](https://wikipedia.org/wiki/Euclidean_distance), iki nokta arasındaki doğru parçasının uzunluğu olarak ölçülür. [Öklid dışı mesafeler](https://wikipedia.org/wiki/Non-Euclidean_geometry) ise bir eğri boyunca ölçülür. Verileriniz görselleştirildiğinde bir düzlemde bulunmuyorsa, bunu ele almak için özel bir algoritma kullanmanız gerekebilir.\n",
+    "\n",
+    "<p>\n",
+    "   <img src=\"../../images/flat-nonflat.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Dasani Madipalli tarafından hazırlanan infografik</figcaption>\n",
+    "\n",
+    "> 🎓 ['Mesafeler'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)\n",
+    ">\n",
+    "> Kümeler, mesafe matrisleriyle tanımlanır, örneğin noktalar arasındaki mesafeler. Bu mesafe birkaç şekilde ölçülebilir. Öklid kümeleri, nokta değerlerinin ortalamasıyla tanımlanır ve bir 'merkez' veya merkez noktası içerir. Mesafeler, bu merkeze olan uzaklıkla ölçülür. Öklid dışı mesafeler ise 'clustroid' olarak adlandırılan, diğer noktalara en yakın noktayı ifade eder. Clustroid'ler çeşitli şekillerde tanımlanabilir.\n",
+    ">\n",
+    "> 🎓 ['Kısıtlı'](https://wikipedia.org/wiki/Constrained_clustering)\n",
+    ">\n",
+    "> [Kısıtlı Kümeleme](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf), bu denetimsiz yönteme 'yarı denetimli' öğrenme ekler. Noktalar arasındaki ilişkiler 'bağlanamaz' veya 'bağlanmalı' olarak işaretlenir, böylece veri setine bazı kurallar uygulanır.\n",
+    ">\n",
+    "> Bir örnek: Bir algoritma etiketlenmemiş veya yarı etiketlenmiş bir veri setinde serbest bırakılırsa, ürettiği kümeler düşük kaliteli olabilir. Yukarıdaki örnekte, kümeler 'yuvarlak müzik şeyleri', 'kare müzik şeyleri', 'üçgen şeyler' ve 'kurabiyeler' olarak gruplandırılabilir. Eğer algoritmaya bazı kısıtlamalar veya kurallar verilirse (\"öğe plastikten yapılmış olmalı\", \"öğe müzik üretebilmelidir\"), bu algoritmanın daha iyi seçimler yapmasına yardımcı olabilir.\n",
+    ">\n",
+    "> 🎓 'Yoğunluk'\n",
+    ">\n",
+    "> 'Gürültülü' olarak kabul edilen veri 'yoğun' olarak değerlendirilir. Kümelerindeki noktalar arasındaki mesafeler, incelendiğinde daha az veya daha çok yoğun, yani 'kalabalık' olabilir ve bu veri uygun kümeleme yöntemiyle analiz edilmelidir. [Bu makale](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html), düzensiz küme yoğunluğuna sahip gürültülü bir veri setini keşfetmek için K-Means kümeleme ve HDBSCAN algoritmalarını kullanmanın farkını göstermektedir.\n",
+    "\n",
+    "Kümeleme tekniklerini daha iyi anlamak için bu [Öğrenme modülünü](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott) inceleyin.\n",
+    "\n",
+    "### **Kümeleme Algoritmaları**\n",
+    "\n",
+    "100'den fazla kümeleme algoritması vardır ve bunların kullanımı, eldeki verinin doğasına bağlıdır. Önemli olanlardan bazılarını tartışalım:\n",
+    "\n",
+    "-   **Hiyerarşik kümeleme**. Bir nesne, yakınındaki bir nesneye olan yakınlığına göre sınıflandırıldığında, kümeler üyelerinin diğer nesnelere olan mesafelerine göre oluşturulur. Hiyerarşik kümeleme, iki kümeyi tekrar tekrar birleştirerek karakterize edilir.\n",
+    "\n",
+    "<p>\n",
+    "   <img src=\"../../images/hierarchical.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Dasani Madipalli tarafından hazırlanan infografik</figcaption>\n",
+    "\n",
+    "-   **Merkez kümeleme**. Bu popüler algoritma, oluşturulacak küme sayısını ('k') seçmeyi gerektirir, ardından algoritma bir kümenin merkez noktasını belirler ve veriyi bu nokta etrafında toplar. [K-means kümeleme](https://wikipedia.org/wiki/K-means_clustering), önceden tanımlanmış K gruplarına bir veri setini ayıran popüler bir merkez kümeleme versiyonudur. Merkez, en yakın ortalama ile belirlenir, bu nedenle adı buradan gelir. Kümeden olan kare mesafesi minimize edilir.\n",
+    "\n",
+    "<p>\n",
+    "   <img src=\"../../images/centroid.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Dasani Madipalli tarafından hazırlanan infografik</figcaption>\n",
+    "\n",
+    "-   **Dağılım tabanlı kümeleme**. İstatistiksel modellemeye dayalı olan dağılım tabanlı kümeleme, bir veri noktasının bir kümeye ait olma olasılığını belirler ve buna göre atama yapar. Gaussian karışım yöntemleri bu türe aittir.\n",
+    "\n",
+    "-   **Yoğunluk tabanlı kümeleme**. Veri noktaları, yoğunluklarına veya birbirleri etrafındaki gruplarına göre kümelere atanır. Gruptan uzak olan veri noktaları, aykırı değerler veya gürültü olarak kabul edilir. DBSCAN, Mean-shift ve OPTICS bu tür kümelemeye aittir.\n",
+    "\n",
+    "-   **Izgara tabanlı kümeleme**. Çok boyutlu veri setleri için bir ızgara oluşturulur ve veri, ızgaranın hücreleri arasında bölünerek kümeler oluşturulur.\n",
+    "\n",
+    "Kümelemeyi öğrenmenin en iyi yolu, onu kendiniz denemektir, bu yüzden bu alıştırmada bunu yapacaksınız.\n",
+    "\n",
+    "Bu modülü tamamlamak için bazı paketlere ihtiyacımız olacak. Şu şekilde yükleyebilirsiniz: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`\n",
+    "\n",
+    "Alternatif olarak, aşağıdaki script eksik olan paketleri kontrol eder ve bu modülü tamamlamak için gerekenleri sizin için yükler.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Alıştırma - Verilerinizi kümeleyin\n",
+    "\n",
+    "Kümeleme tekniği, doğru görselleştirme ile büyük ölçüde desteklenir, bu yüzden müzik verilerimizi görselleştirerek başlayalım. Bu alıştırma, bu verilerin doğasına en uygun kümeleme yöntemini seçmemize yardımcı olacak.\n",
+    "\n",
+    "Verileri içe aktararak işe koyulalım.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# Import the data into a tibble\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows of the data set\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bazen verilerimiz hakkında biraz daha fazla bilgiye ihtiyaç duyabiliriz. `data` ve `yapısını` [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) fonksiyonunu kullanarak inceleyebiliriz:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Glimpse into the data set\r\n",
+    "df %>% \r\n",
+    "  glimpse()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "İyi iş!💪\n",
+    "\n",
+    "`glimpse()` fonksiyonunun, toplam satır (gözlem) ve sütun (değişken) sayısını verdiğini, ardından değişken adından sonra her bir değişkenin ilk birkaç girişini satır halinde gösterdiğini gözlemleyebiliriz. Ayrıca, değişkenin *veri tipi* her değişken adının hemen ardından `< >` içinde belirtilir.\n",
+    "\n",
+    "`DataExplorer::introduce()` bu bilgiyi düzenli bir şekilde özetleyebilir:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe basic information for our data\r\n",
+    "df %>% \r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# A visual display of the same\r\n",
+    "df %>% \r\n",
+    "  plot_intro()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Harika! Verilerimizde eksik değer olmadığını yeni öğrendik.\n",
+    "\n",
+    "Bu sırada, yaygın merkezi eğilim istatistiklerini (örneğin [ortalama](https://en.wikipedia.org/wiki/Arithmetic_mean) ve [medyan](https://en.wikipedia.org/wiki/Median)) ve dağılım ölçülerini (örneğin [standart sapma](https://en.wikipedia.org/wiki/Standard_deviation)) `summarytools::descr()` kullanarak inceleyebiliriz.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe common statistics\r\n",
+    "df %>% \r\n",
+    "  descr(stats = \"common\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hadi verilerin genel değerlerine bir göz atalım. Popülerliğin `0` olabileceğini unutmayın, bu sıfır sıralamaya sahip şarkıları gösterir. Bunları birazdan kaldıracağız.\n",
+    "\n",
+    "> 🤔 Eğer etiketlenmiş verilere ihtiyaç duymayan, denetimsiz bir yöntem olan kümeleme ile çalışıyorsak, neden bu verileri etiketlerle gösteriyoruz? Veri keşfi aşamasında bu etiketler işe yarar, ancak kümeleme algoritmalarının çalışması için gerekli değildir.\n",
+    "\n",
+    "### 1. Popüler türleri keşfet\n",
+    "\n",
+    "Hadi en popüler türleri 🎶 bulalım ve göründükleri örneklerin sayısını hesaplayalım.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Popular genres\r\n",
+    "top_genres <- df %>% \r\n",
+    "  count(artist_top_genre, sort = TRUE) %>% \r\n",
+    "# Encode to categorical and reorder the according to count\r\n",
+    "  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())\r\n",
+    "\r\n",
+    "# Print the top genres\r\n",
+    "top_genres\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bu iyi gitti! Bir resmin binlerce veri çerçevesi satırına bedel olduğu söylenir (aslında kimse bunu söylemez 😅). Ama ne demek istediğimi anladınız, değil mi?\n",
+    "\n",
+    "Kategorik verileri (karakter veya faktör değişkenleri) görselleştirmenin bir yolu çubuk grafikler kullanmaktır. Haydi, en popüler 10 türün çubuk grafiğini oluşturalım:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Change the default gray theme\r\n",
+    "theme_set(theme_light())\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Şimdi `eksik` türlerin olduğunu fark etmek çok daha kolay 🧐!\n",
+    "\n",
+    "> İyi bir görselleştirme, beklemediğiniz şeyleri size gösterebilir veya veri hakkında yeni sorular ortaya çıkarabilir - Hadley Wickham ve Garrett Grolemund, [R For Data Science](https://r4ds.had.co.nz/introduction.html)\n",
+    "\n",
+    "Not: En üst tür `Eksik` olarak tanımlandığında, bu Spotify'ın onu sınıflandırmadığı anlamına gelir, bu yüzden ondan kurtulalım.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  filter(artist_top_genre != \"Missing\") %>% \r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Veriler üzerinde yapılan küçük bir keşif sonucunda, en üst üç türün bu veri setine hakim olduğunu öğreniyoruz. Hadi `afro dancehall`, `afropop` ve `nigerian pop` türlerine odaklanalım, ayrıca veri setini filtreleyerek popülerlik değeri 0 olan her şeyi kaldıralım (bu, veri setinde popülerlik ile sınıflandırılmamış ve bizim amaçlarımız için gürültü olarak kabul edilebilir anlamına gelir):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "nigerian_songs <- df %>% \r\n",
+    "  # Concentrate on top 3 genres\r\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \r\n",
+    "  # Remove unclassified observations\r\n",
+    "  filter(popularity != 0)\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "nigerian_songs %>%\r\n",
+    "  count(artist_top_genre) %>%\r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Veri setimizdeki sayısal değişkenler arasında belirgin bir doğrusal ilişki olup olmadığını görelim. Bu ilişki, matematiksel olarak [korelasyon istatistiği](https://en.wikipedia.org/wiki/Correlation) ile ölçülür.\n",
+    "\n",
+    "Korelasyon istatistiği, -1 ile 1 arasında bir değer alır ve ilişkinin gücünü gösterir. 0'ın üzerindeki değerler *pozitif* bir korelasyonu ifade eder (bir değişkenin yüksek değerleri genellikle diğer değişkenin yüksek değerleriyle örtüşür), 0'ın altındaki değerler ise *negatif* bir korelasyonu ifade eder (bir değişkenin yüksek değerleri genellikle diğer değişkenin düşük değerleriyle örtüşür).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Narrow down to numeric variables and fid correlation\r\n",
+    "corr_mat <- nigerian_songs %>% \r\n",
+    "  select(where(is.numeric)) %>% \r\n",
+    "  cor()\r\n",
+    "\r\n",
+    "# Visualize correlation matrix\r\n",
+    "corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  \r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Veriler güçlü bir şekilde ilişkili değil, sadece `energy` ve `loudness` arasında bir bağlantı var, ki bu mantıklı, çünkü yüksek sesli müzik genellikle oldukça enerjik olur. `Popularity` ile `release date` arasında bir ilişki var, bu da mantıklı, çünkü daha yeni şarkılar muhtemelen daha popülerdir. Uzunluk ve enerji arasında da bir korelasyon olduğu görülüyor.\n",
+    "\n",
+    "Bu verilerle bir kümeleme algoritmasının neler yapabileceğini görmek ilginç olacak!\n",
+    "\n",
+    "> 🎓 Korelasyonun nedensellik anlamına gelmediğini unutmayın! Korelasyonun kanıtına sahibiz, ancak nedenselliğin kanıtına sahip değiliz. Bu noktayı vurgulayan bazı görsellerin bulunduğu [eğlenceli bir web sitesi](https://tylervigen.com/spurious-correlations) var.\n",
+    "\n",
+    "### 2. Veri dağılımını keşfet\n",
+    "\n",
+    "Daha ince sorular soralım. Türler, popülerliklerine göre dans edilebilirlik algısında önemli ölçüde farklı mı? İlk üç türümüzün popülerlik ve dans edilebilirlik verilerinin dağılımını belirli bir x ve y ekseni boyunca [yoğunluk grafikleri](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves) kullanarak inceleyelim.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Perform 2D kernel density estimation\r\n",
+    "density_estimate_2d <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +\r\n",
+    "  geom_density_2d(bins = 5, size = 1) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  xlim(-20, 80) +\r\n",
+    "  ylim(0, 1.2)\r\n",
+    "\r\n",
+    "# Density plot based on the popularity\r\n",
+    "density_estimate_pop <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  theme(legend.position = \"none\")\r\n",
+    "\r\n",
+    "# Density plot based on the danceability\r\n",
+    "density_estimate_dance <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Patch everything together\r\n",
+    "library(patchwork)\r\n",
+    "density_estimate_2d / (density_estimate_pop + density_estimate_dance)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nijerya'daki zevklerin bu tür için belirli bir dans edilebilirlik seviyesinde birleştiği söylenebilir mi? Türden bağımsız olarak, eşleşen eşmerkezli daireler görüyoruz.\n",
+    "\n",
+    "Genel olarak, üç tür popülerlik ve dans edilebilirlik açısından uyum gösteriyor. Bu gevşek bir şekilde hizalanmış verilerde kümeleri belirlemek zor olacak. Bakalım bir dağılım grafiği bunu destekleyebilir mi?\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# A scatter plot of popularity and danceability\r\n",
+    "scatter_plot <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +\r\n",
+    "  geom_point(size = 2, alpha = 0.8) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"futurevisions::mars\")\r\n",
+    "\r\n",
+    "# Add a touch of interactivity\r\n",
+    "ggplotly(scatter_plot)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Aynı eksenlerin bir dağılım grafiği, benzer bir yakınsama modelini gösterir.\n",
+    "\n",
+    "Genel olarak, kümeleme için dağılım grafikleri kullanarak veri kümelerini gösterebilirsiniz, bu nedenle bu tür görselleştirmeyi öğrenmek oldukça faydalıdır. Bir sonraki derste, bu filtrelenmiş veriyi alıp k-means kümeleme yöntemini kullanarak ilginç şekillerde örtüşen grupları keşfedeceğiz.\n",
+    "\n",
+    "## **🚀 Meydan Okuma**\n",
+    "\n",
+    "Bir sonraki derse hazırlık olarak, üretim ortamında keşfedebileceğiniz ve kullanabileceğiniz çeşitli kümeleme algoritmaları hakkında bir grafik oluşturun. Kümeleme hangi tür problemleri çözmeye çalışıyor?\n",
+    "\n",
+    "## [**Ders Sonrası Test**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)\n",
+    "\n",
+    "## **Gözden Geçirme ve Kendi Kendine Çalışma**\n",
+    "\n",
+    "Kümeleme algoritmalarını uygulamadan önce, öğrendiğimiz gibi, veri kümenizin doğasını anlamak iyi bir fikirdir. Bu konu hakkında daha fazla bilgi edinin [buradan](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html).\n",
+    "\n",
+    "Kümeleme teknikleri hakkındaki bilginizi derinleştirin:\n",
+    "\n",
+    "-   [Tidymodels ve arkadaşları ile Kümeleme Modellerini Eğitme ve Değerlendirme](https://rpubs.com/eR_ic/clustering)\n",
+    "\n",
+    "-   Bradley Boehmke & Brandon Greenwell, [*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/)*.*\n",
+    "\n",
+    "## **Ödev**\n",
+    "\n",
+    "[Kümeleme için diğer görselleştirme yöntemlerini araştırın](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)\n",
+    "\n",
+    "## TEŞEKKÜRLER:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper), bu modülün orijinal Python versiyonunu oluşturduğu için ♥️\n",
+    "\n",
+    "[`Dasani Madipalli`](https://twitter.com/dasani_decoded), makine öğrenimi kavramlarını daha anlaşılır ve kolay hale getiren harika illüstrasyonları oluşturduğu için.\n",
+    "\n",
+    "Mutlu öğrenmeler,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Öğrenci Elçisi.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel bir insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan herhangi bir yanlış anlama veya yanlış yorumlama durumunda sorumluluk kabul edilmez.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "99c36449cad3708a435f6798cfa39972",
+   "translation_date": "2025-09-06T14:18:06+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: seaborn in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (0.11.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (3.5.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.21.4)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.3.4)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.7.2)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (4.28.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (21.2)\n",
+      "Requirement already satisfied: setuptools-scm>=4 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (6.3.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
+      "Requirement already satisfied: tomli>=1.0.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (1.2.2)\n",
+      "Requirement already satisfied: setuptools in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (59.1.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Veri çerçevesi hakkında bilgi alın\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 530 entries, 0 to 529\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   name              530 non-null    object \n",
+      " 1   album             530 non-null    object \n",
+      " 2   artist            530 non-null    object \n",
+      " 3   artist_top_genre  530 non-null    object \n",
+      " 4   release_date      530 non-null    int64  \n",
+      " 5   length            530 non-null    int64  \n",
+      " 6   popularity        530 non-null    int64  \n",
+      " 7   danceability      530 non-null    float64\n",
+      " 8   acousticness      530 non-null    float64\n",
+      " 9   energy            530 non-null    float64\n",
+      " 10  instrumentalness  530 non-null    float64\n",
+      " 11  liveness          530 non-null    float64\n",
+      " 12  loudness          530 non-null    float64\n",
+      " 13  speechiness       530 non-null    float64\n",
+      " 14  tempo             530 non-null    float64\n",
+      " 15  time_signature    530 non-null    int64  \n",
+      "dtypes: float64(8), int64(4), object(4)\n",
+      "memory usage: 66.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name                0\n",
+       "album               0\n",
+       "artist              0\n",
+       "artist_top_genre    0\n",
+       "release_date        0\n",
+       "length              0\n",
+       "popularity          0\n",
+       "danceability        0\n",
+       "acousticness        0\n",
+       "energy              0\n",
+       "instrumentalness    0\n",
+       "liveness            0\n",
+       "loudness            0\n",
+       "speechiness         0\n",
+       "tempo               0\n",
+       "time_signature      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Verilerin genel değerlerine bakın. Popülerliğin '0' olabileceğini unutmayın - ve bu değere sahip birçok satır var.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2015.390566</td>\n",
+       "      <td>222298.169811</td>\n",
+       "      <td>17.507547</td>\n",
+       "      <td>0.741619</td>\n",
+       "      <td>0.265412</td>\n",
+       "      <td>0.760623</td>\n",
+       "      <td>0.016305</td>\n",
+       "      <td>0.147308</td>\n",
+       "      <td>-4.953011</td>\n",
+       "      <td>0.130748</td>\n",
+       "      <td>116.487864</td>\n",
+       "      <td>3.986792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.131688</td>\n",
+       "      <td>39696.822259</td>\n",
+       "      <td>18.992212</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.208342</td>\n",
+       "      <td>0.148533</td>\n",
+       "      <td>0.090321</td>\n",
+       "      <td>0.123588</td>\n",
+       "      <td>2.464186</td>\n",
+       "      <td>0.092939</td>\n",
+       "      <td>23.518601</td>\n",
+       "      <td>0.333701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>89488.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>0.111000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028300</td>\n",
+       "      <td>-19.362000</td>\n",
+       "      <td>0.027800</td>\n",
+       "      <td>61.695000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>199305.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681000</td>\n",
+       "      <td>0.089525</td>\n",
+       "      <td>0.669000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.075650</td>\n",
+       "      <td>-6.298750</td>\n",
+       "      <td>0.059100</td>\n",
+       "      <td>102.961250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>218509.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.761000</td>\n",
+       "      <td>0.220500</td>\n",
+       "      <td>0.784500</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.103500</td>\n",
+       "      <td>-4.558500</td>\n",
+       "      <td>0.097950</td>\n",
+       "      <td>112.714500</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>242098.500000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.829500</td>\n",
+       "      <td>0.403000</td>\n",
+       "      <td>0.875750</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.164000</td>\n",
+       "      <td>-3.331000</td>\n",
+       "      <td>0.177000</td>\n",
+       "      <td>125.039250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>511738.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>0.966000</td>\n",
+       "      <td>0.954000</td>\n",
+       "      <td>0.995000</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.811000</td>\n",
+       "      <td>0.582000</td>\n",
+       "      <td>0.514000</td>\n",
+       "      <td>206.007000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       release_date         length  popularity  danceability  acousticness  \\\n",
+       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
+       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
+       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
+       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
+       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
+       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
+       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
+       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
+       "\n",
+       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
+       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
+       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
+       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
+       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
+       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
+       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
+       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
+       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
+       "\n",
+       "            tempo  time_signature  \n",
+       "count  530.000000      530.000000  \n",
+       "mean   116.487864        3.986792  \n",
+       "std     23.518601        0.333701  \n",
+       "min     61.695000        3.000000  \n",
+       "25%    102.961250        4.000000  \n",
+       "50%    112.714500        4.000000  \n",
+       "75%    125.039250        4.000000  \n",
+       "max    206.007000        5.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Haydi türleri inceleyelim. Oldukça fazla sayıda 'Eksik' olarak listelenmiş, bu da onların veri kümesinde bir türle kategorize edilmediği anlamına geliyor.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Eksik türleri kaldırın, çünkü Spotify'da sınıflandırılmamış.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[df['artist_top_genre'] != 'Missing']\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corrmat = df.corr()\n",
+    "f, ax = plt.subplots(figsize=(12, 9))\n",
+    "sns.heatmap(corrmat, vmax=.8, square=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "\n",
+    "# Show the joint distribution using kernel density estimation\n",
+    "g = sns.jointplot(\n",
+    "    data=df,\n",
+    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
+    "    kind=\"kde\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Genel olarak, üç tür popülerlik ve dans edilebilirlik açısından uyum gösterir. Aynı eksenlerin bir dağılım grafiği benzer bir yakınsama modeli gösterir. Tür başına veri dağılımını kontrol etmek için bir dağılım grafiği deneyin.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1350b1a90>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 468.975x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
+    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
+    "   .add_legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "c61deff2839902ac8cb4ed411eb10fee",
+   "translation_date": "2025-09-06T14:09:59+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/tr/5-Clustering/2-K-Means/notebook.ipynb b/translations/tr/5-Clustering/2-K-Means/notebook.ipynb
new file mode 100644
index 000000000..248dea39a
--- /dev/null
+++ b/translations/tr/5-Clustering/2-K-Means/notebook.ipynb
@@ -0,0 +1,231 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "3e5c8ab363e8d88f566d4365efc7e0bd",
+   "translation_date": "2025-09-06T14:19:57+00:00",
+   "source_file": "5-Clustering/2-K-Means/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Son derste bitirdiğimiz yerden başlayın, veriler içe aktarılmış ve filtrelenmiş durumda.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Yalnızca 3 türe odaklanacağız. Belki 3 küme oluşturabiliriz!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/translations/tr/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
new file mode 100644
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--- /dev/null
+++ b/translations/tr/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
@@ -0,0 +1,639 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "colab": {
+   "name": "lesson_14.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "coopTranslator": {
+   "original_hash": "ad65fb4aad0a156b42216e4929f490fc",
+   "translation_date": "2025-09-06T14:30:55+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GULATlQXLXyR"
+   },
+   "source": [
+    "## R ve Tidy veri prensiplerini kullanarak K-Means kümeleme keşfi.\n",
+    "\n",
+    "### [**Ders öncesi quiz**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)\n",
+    "\n",
+    "Bu derste, Tidymodels paketi ve R ekosistemindeki diğer paketleri (onlara arkadaşlar 🧑‍🤝‍🧑 diyeceğiz) kullanarak kümeler oluşturmayı ve daha önce içe aktardığınız Nijerya müzik veri setini nasıl kullanacağınızı öğreneceksiniz. K-Means kümeleme için temel bilgileri ele alacağız. Unutmayın, önceki derste öğrendiğiniz gibi, kümelerle çalışmanın birçok yolu vardır ve kullandığınız yöntem verinize bağlıdır. En yaygın kümeleme tekniği olduğu için K-Means yöntemini deneyeceğiz. Haydi başlayalım!\n",
+    "\n",
+    "Öğreneceğiniz terimler:\n",
+    "\n",
+    "-   Siluet skoru\n",
+    "\n",
+    "-   Dirsek yöntemi\n",
+    "\n",
+    "-   Atalet\n",
+    "\n",
+    "-   Varyans\n",
+    "\n",
+    "### **Giriş**\n",
+    "\n",
+    "[K-Means Kümeleme](https://wikipedia.org/wiki/K-means_clustering), sinyal işleme alanından türetilmiş bir yöntemdir. Verileri özelliklerindeki benzerliklere göre `k kümeye` ayırmak ve bölmek için kullanılır.\n",
+    "\n",
+    "Kümeler, bir nokta (veya 'tohum') ve ona karşılık gelen bölgeyi içeren [Voronoi diyagramları](https://wikipedia.org/wiki/Voronoi_diagram) olarak görselleştirilebilir.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/voronoi.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Jen Looper tarafından hazırlanan infografik</figcaption>\n",
+    "\n",
+    "K-Means kümeleme şu adımları içerir:\n",
+    "\n",
+    "1. Veri bilimci, oluşturulacak kümelerin istenen sayısını belirleyerek başlar.\n",
+    "\n",
+    "2. Daha sonra algoritma, veri setinden rastgele K gözlem seçerek kümeler için başlangıç merkezleri (yani, merkez noktalar) olarak kullanır.\n",
+    "\n",
+    "3. Ardından, kalan her bir gözlem en yakın merkez noktaya atanır.\n",
+    "\n",
+    "4. Daha sonra, her bir kümenin yeni ortalamaları hesaplanır ve merkez noktası bu ortalamaya taşınır.\n",
+    "\n",
+    "5. Merkezler yeniden hesaplandığına göre, her bir gözlem tekrar kontrol edilir ve farklı bir kümeye daha yakın olup olmadığına bakılır. Tüm nesneler, güncellenmiş küme ortalamalarını kullanarak yeniden atanır. Küme atama ve merkez noktası güncelleme adımları, küme atamaları değişmeyi bırakana kadar (yani, yakınsama sağlandığında) tekrarlanır. Genellikle, algoritma her yeni iterasyonda merkez noktalarının hareketi önemsiz hale geldiğinde ve kümeler statik hale geldiğinde sona erer.\n",
+    "\n",
+    "<div>\n",
+    "\n",
+    "> Başlangıç merkez noktaları olarak kullanılan ilk k gözlemlerin rastgele seçilmesi nedeniyle, prosedürü her uyguladığımızda biraz farklı sonuçlar alabiliriz. Bu nedenle, çoğu algoritma birkaç *rastgele başlangıç* kullanır ve en düşük WCSS'ye sahip iterasyonu seçer. Bu nedenle, K-Means'i her zaman birkaç *nstart* değeriyle çalıştırmanız ve *istenmeyen yerel optimumdan* kaçınmanız şiddetle tavsiye edilir.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Allison Horst'un [çizimleri](https://github.com/allisonhorst/stats-illustrations) kullanılarak hazırlanan bu kısa animasyon, kümeleme sürecini açıklıyor:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/kmeans.gif\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>@allison_horst tarafından hazırlanan çizim</figcaption>\n",
+    "\n",
+    "Kümeleme ile ilgili temel bir soru şudur: Verilerinizi kaç kümeye ayırmanız gerektiğini nasıl bileceksiniz? K-Means kullanmanın bir dezavantajı, `k` yani `merkez noktalarının` sayısını belirlemeniz gerekmesidir. Neyse ki, `dirsek yöntemi` `k` için iyi bir başlangıç değeri tahmin etmenize yardımcı olur. Birazdan bunu deneyeceksiniz.\n",
+    "\n",
+    "### \n",
+    "\n",
+    "**Ön Koşul**\n",
+    "\n",
+    "[Önceki derste](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb) kaldığımız yerden devam edeceğiz. Bu derste veri setini analiz ettik, birçok görselleştirme yaptık ve ilgi çekici gözlemleri filtreledik. Mutlaka göz atın!\n",
+    "\n",
+    "Bu modülü tamamlamak için bazı paketlere ihtiyacımız olacak. Şu şekilde yükleyebilirsiniz: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`\n",
+    "\n",
+    "Alternatif olarak, aşağıdaki script, bu modülü tamamlamak için gerekli paketlere sahip olup olmadığınızı kontrol eder ve eksik olanları sizin için yükler.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ah_tBi58LXyi"
+   },
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7e--UCUTLXym"
+   },
+   "source": [
+    "Haydi başlayalım!\n",
+    "\n",
+    "## 1. Verilerle dans: En popüler 3 müzik türünü belirleyin\n",
+    "\n",
+    "Bu, önceki derste yaptıklarımızın bir özeti. Hadi biraz veri inceleyip analiz edelim!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Ycamx7GGLXyn"
+   },
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data into a tibble\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\", show_col_types = FALSE)\n",
+    "\n",
+    "# Narrow down to top 3 popular genres\n",
+    "nigerian_songs <- df %>% \n",
+    "  # Concentrate on top 3 genres\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \n",
+    "  # Remove unclassified observations\n",
+    "  filter(popularity != 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Visualize popular genres using bar plots\n",
+    "theme_set(theme_light())\n",
+    "nigerian_songs %>%\n",
+    "  count(artist_top_genre) %>%\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\n",
+    "                       fill = artist_top_genre)) +\n",
+    "  geom_col(alpha = 0.8) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\n",
+    "  ggtitle(\"Top genres\") +\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b5h5zmkPLXyp"
+   },
+   "source": [
+    "🤩 Bu harika oldu!\n",
+    "\n",
+    "## 2. Daha fazla veri keşfi.\n",
+    "\n",
+    "Bu veriler ne kadar temiz? Aykırı değerleri kutu grafikleri kullanarak kontrol edelim. Daha az aykırı değere sahip sayısal sütunlara odaklanacağız (ancak aykırı değerleri temizleyebilirsiniz). Kutu grafikleri, verilerin aralığını gösterebilir ve hangi sütunların kullanılacağına karar vermenize yardımcı olabilir. Ancak unutmayın, kutu grafikleri varyansı göstermez, bu da iyi kümeleme yapılabilir veriler için önemli bir unsurdur. Daha fazla bilgi için lütfen [bu tartışmaya](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot) göz atın.\n",
+    "\n",
+    "[Kutu grafikleri](https://en.wikipedia.org/wiki/Box_plot), `sayısal` verilerin dağılımını görsel olarak göstermek için kullanılır, bu yüzden popüler müzik türleriyle birlikte tüm sayısal sütunları *seçerek* başlayalım.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HhNreJKLLXyq"
+   },
+   "source": [
+    "# Select top genre column and all other numeric columns\n",
+    "df_numeric <- nigerian_songs %>% \n",
+    "  select(artist_top_genre, where(is.numeric)) \n",
+    "\n",
+    "# Display the data\n",
+    "df_numeric %>% \n",
+    "  slice_head(n = 5)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uYXrwJRaLXyq"
+   },
+   "source": [
+    "Seçim yardımcısı `where`'in bunu ne kadar kolaylaştırdığını görüyor musunuz 💁? Bu tür diğer fonksiyonları [burada](https://tidyselect.r-lib.org/) keşfedin.\n",
+    "\n",
+    "Her bir sayısal özellik için bir kutu grafiği oluşturacağımız ve döngü kullanmaktan kaçınmak istediğimiz için, verilerimizi *daha uzun* bir formata dönüştürelim. Bu, `facets`'ten - her biri verinin bir alt kümesini gösteren alt grafiklerden - faydalanmamıza olanak tanıyacak.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "gd5bR3f8LXys"
+   },
+   "source": [
+    "# Pivot data from wide to long\n",
+    "df_numeric_long <- df_numeric %>% \n",
+    "  pivot_longer(!artist_top_genre, names_to = \"feature_names\", values_to = \"values\") \n",
+    "\n",
+    "# Print out data\n",
+    "df_numeric_long %>% \n",
+    "  slice_head(n = 15)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-7tE1swnLXyv"
+   },
+   "source": [
+    "Daha uzun! Şimdi biraz `ggplot` zamanı! Peki hangi `geom`'u kullanacağız?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "r88bIsyuLXyy"
+   },
+   "source": [
+    "# Make a box plot\n",
+    "df_numeric_long %>% \n",
+    "  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_wrap(~ feature_names, ncol = 4, scales = \"free\") +\n",
+    "  theme(legend.position = \"none\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EYVyKIUELXyz"
+   },
+   "source": [
+    "Kolay-gg!\n",
+    "\n",
+    "Şimdi bu verilerin biraz gürültülü olduğunu görebiliyoruz: Her sütunu bir kutu grafiği olarak gözlemlediğinizde, aykırı değerleri fark edebilirsiniz. Bu aykırı değerleri veri setinden çıkarabilirsiniz, ancak bu durumda veri oldukça sınırlı hale gelir.\n",
+    "\n",
+    "Şimdilik, kümeleme çalışmamızda kullanacağımız sütunları seçelim. Benzer aralıklara sahip sayısal sütunları seçelim. `artist_top_genre` sütununu sayısal olarak kodlayabiliriz, ancak şimdilik bunu çıkaracağız.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-wkpINyZLXy0"
+   },
+   "source": [
+    "# Select variables with similar ranges\n",
+    "df_numeric_select <- df_numeric %>% \n",
+    "  select(popularity, danceability, acousticness, loudness, energy) \n",
+    "\n",
+    "# Normalize data\n",
+    "# df_numeric_select <- scale(df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D7dLzgpqLXy1"
+   },
+   "source": [
+    "## 3. R'de k-means kümeleme hesaplama\n",
+    "\n",
+    "R'de yerleşik `kmeans` fonksiyonunu kullanarak k-means hesaplayabiliriz, bkz. `help(\"kmeans()\")`. `kmeans()` fonksiyonu, birincil argüman olarak tüm sütunları sayısal olan bir veri çerçevesini kabul eder.\n",
+    "\n",
+    "K-means kümeleme kullanırken ilk adım, nihai çözümde oluşturulacak küme sayısını (k) belirtmektir. Veri setinden çıkardığımız 3 şarkı türü olduğunu biliyoruz, o halde 3 deneyelim:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "uC4EQ5w7LXy5"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Kmeans clustering for 3 clusters\n",
+    "kclust <- kmeans(\n",
+    "  df_numeric_select,\n",
+    "  # Specify the number of clusters\n",
+    "  centers = 3,\n",
+    "  # How many random initial configurations\n",
+    "  nstart = 25\n",
+    ")\n",
+    "\n",
+    "# Display clustering object\n",
+    "kclust\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hzfhscWrLXy-"
+   },
+   "source": [
+    "kmeans nesnesi, `help(\"kmeans()\")` içinde iyi bir şekilde açıklanan birkaç bilgi içerir. Şimdilik, birkaçına odaklanalım. Verilerin 65, 110, 111 boyutlarında 3 kümeye ayrıldığını görüyoruz. Çıktı ayrıca 5 değişken üzerinden 3 grup için küme merkezlerini (ortalama değerlerini) içerir.\n",
+    "\n",
+    "Kümeleme vektörü, her bir gözlem için küme atamasını ifade eder. Küme atamasını orijinal veri setine eklemek için `augment` fonksiyonunu kullanalım.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "0XwwpFGQLXy_"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "augment(kclust, df_numeric_select) %>% \n",
+    "  relocate(.cluster) %>% \n",
+    "  slice_head(n = 10)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NXIVXXACLXzA"
+   },
+   "source": [
+    "Harika, veri setimizi 3 gruba ayırdık. Peki, kümelememiz ne kadar iyi 🤷? Hadi `Silhouette skoru`na bir göz atalım.\n",
+    "\n",
+    "### **Silhouette skoru**\n",
+    "\n",
+    "[Silhouette analizi](https://en.wikipedia.org/wiki/Silhouette_(clustering)), ortaya çıkan kümeler arasındaki ayrım mesafesini incelemek için kullanılabilir. Bu skor -1 ile 1 arasında değişir ve skor 1'e yakınsa, küme yoğun ve diğer kümelerden iyi bir şekilde ayrılmıştır. 0'a yakın bir değer, komşu kümelerin karar sınırına çok yakın örneklerle örtüşen kümeleri temsil eder. [kaynak](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).\n",
+    "\n",
+    "Ortalama silhouette yöntemi, farklı *k* değerleri için gözlemlerin ortalama silhouette skorunu hesaplar. Yüksek bir ortalama silhouette skoru, iyi bir kümelemeyi gösterir.\n",
+    "\n",
+    "`silhouette` fonksiyonu, cluster paketinde ortalama silhouette genişliğini hesaplamak için kullanılır.\n",
+    "\n",
+    "> Silhouette, [mesafe](https://en.wikipedia.org/wiki/Distance \"Distance\") metriği ile hesaplanabilir, örneğin [Öklid mesafesi](https://en.wikipedia.org/wiki/Euclidean_distance \"Euclidean distance\") veya [Manhattan mesafesi](https://en.wikipedia.org/wiki/Manhattan_distance \"Manhattan distance\") gibi. Bu metrikleri [önceki derste](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb) tartışmıştık.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Jn0McL28LXzB"
+   },
+   "source": [
+    "# Load cluster package\n",
+    "library(cluster)\n",
+    "\n",
+    "# Compute average silhouette score\n",
+    "ss <- silhouette(kclust$cluster,\n",
+    "                 # Compute euclidean distance\n",
+    "                 dist = dist(df_numeric_select))\n",
+    "mean(ss[, 3])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QyQRn97nLXzC"
+   },
+   "source": [
+    "Skorumuz **.549**, yani tam ortada. Bu, verilerimizin bu tür bir kümeleme için özellikle uygun olmadığını gösteriyor. Bu tahminimizi görsel olarak doğrulayıp doğrulayamayacağımıza bakalım. [factoextra paketi](https://rpkgs.datanovia.com/factoextra/index.html), kümelemeyi görselleştirmek için (`fviz_cluster()`) işlevlerini sağlar.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "7a6Km1_FLXzD"
+   },
+   "source": [
+    "library(factoextra)\n",
+    "\n",
+    "# Visualize clustering results\n",
+    "fviz_cluster(kclust, df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IBwCWt-0LXzD"
+   },
+   "source": [
+    "Kümeler arasındaki örtüşme, verilerimizin bu tür bir kümeleme için pek uygun olmadığını gösteriyor, ancak devam edelim.\n",
+    "\n",
+    "## 4. Optimum küme sayısını belirleme\n",
+    "\n",
+    "K-Means kümeleme ile ilgili sıkça ortaya çıkan temel bir soru şudur: Bilinen sınıf etiketleri olmadan, verilerinizi kaç kümeye ayırmanız gerektiğini nasıl bilebilirsiniz?\n",
+    "\n",
+    "Bunu öğrenmenin bir yolu, bir veri örneği kullanarak `artan küme sayısıyla bir dizi kümeleme modeli oluşturmak` (örneğin 1'den 10'a kadar) ve **Silhouette skoru** gibi kümeleme metriklerini değerlendirmektir.\n",
+    "\n",
+    "Optimum küme sayısını belirlemek için farklı *k* değerleri için kümeleme algoritmasını çalıştırıp **Küme İçi Kareler Toplamı** (WCSS) değerini değerlendirelim. Toplam küme içi kareler toplamı (WCSS), kümeleme kompaktlığını ölçer ve mümkün olduğunca küçük olmasını isteriz; daha düşük değerler, veri noktalarının birbirine daha yakın olduğunu gösterir.\n",
+    "\n",
+    "Bu kümeleme üzerinde `k` için 1'den 10'a kadar farklı seçimlerin etkisini inceleyelim.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "hSeIiylDLXzE"
+   },
+   "source": [
+    "# Create a series of clustering models\n",
+    "kclusts <- tibble(k = 1:10) %>% \n",
+    "  # Perform kmeans clustering for 1,2,3 ... ,10 clusters\n",
+    "  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),\n",
+    "  # Farm out clustering metrics eg WCSS\n",
+    "         glanced = map(model, ~ glance(.x))) %>% \n",
+    "  unnest(cols = glanced)\n",
+    "  \n",
+    "\n",
+    "# View clustering rsulsts\n",
+    "kclusts\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m7rS2U1eLXzE"
+   },
+   "source": [
+    "Her bir kümeleme algoritması için merkez *k* ile toplam küme içi kareler toplamını (tot.withinss) elde ettikten sonra, optimal küme sayısını bulmak için [dirsek yöntemi](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) kullanılır. Bu yöntem, WCSS'yi küme sayısının bir fonksiyonu olarak çizmek ve kullanılacak küme sayısı olarak [eğrinin dirseğini](https://en.wikipedia.org/wiki/Elbow_of_the_curve \"Eğrinin dirseği\") seçmekten oluşur.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "o_DjHGItLXzF"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Use elbow method to determine optimum number of clusters\n",
+    "kclusts %>% \n",
+    "  ggplot(mapping = aes(x = k, y = tot.withinss)) +\n",
+    "  geom_line(size = 1.2, alpha = 0.8, color = \"#FF7F0EFF\") +\n",
+    "  geom_point(size = 2, color = \"#FF7F0EFF\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLYyt5XSLXzG"
+   },
+   "source": [
+    "Grafik, kümelerin sayısı birden ikiye çıkarken WCSS'de (dolayısıyla daha fazla *sıkılık*) büyük bir azalma ve iki kümeden üç kümeye geçerken daha belirgin bir azalma gösteriyor. Bundan sonra azalma daha az belirgin hale geliyor ve grafikte yaklaşık üç kümede bir `dirsek` 💪 oluşuyor. Bu, veri noktalarının iki ila üç makul şekilde ayrılmış küme oluşturduğuna dair iyi bir göstergedir.\n",
+    "\n",
+    "Şimdi `k = 3` olduğu durumda kümeleme modelini çıkarabiliriz:\n",
+    "\n",
+    "> `pull()`: tek bir sütunu çıkarmak için kullanılır\n",
+    ">\n",
+    "> `pluck()`: listeler gibi veri yapılarında indeksleme yapmak için kullanılır\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JP_JPKBILXzG"
+   },
+   "source": [
+    "# Extract k = 3 clustering\n",
+    "final_kmeans <- kclusts %>% \n",
+    "  filter(k == 3) %>% \n",
+    "  pull(model) %>% \n",
+    "  pluck(1)\n",
+    "\n",
+    "\n",
+    "final_kmeans\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l_PDTu8tLXzI"
+   },
+   "source": [
+    "Harika! Hadi elde edilen kümeleri görselleştirelim. `plotly` kullanarak biraz etkileşim eklemeye ne dersiniz?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dNcleFe-LXzJ"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "results <-  augment(final_kmeans, df_numeric_select) %>% \n",
+    "  bind_cols(df_numeric %>% select(artist_top_genre)) \n",
+    "\n",
+    "# Plot cluster assignments\n",
+    "clust_plt <- results %>% \n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +\n",
+    "  geom_point(size = 2, alpha = 0.8) +\n",
+    "  paletteer::scale_color_paletteer_d(\"ggthemes::Tableau_10\")\n",
+    "\n",
+    "ggplotly(clust_plt)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6JUM_51VLXzK"
+   },
+   "source": [
+    "Belki de her kümenin (farklı renklerle temsil edilen) farklı türlere (farklı şekillerle temsil edilen) sahip olmasını beklerdik.\n",
+    "\n",
+    "Şimdi modelin doğruluğuna bir göz atalım.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HdIMUGq7LXzL"
+   },
+   "source": [
+    "# Assign genres to predefined integers\n",
+    "label_count <- results %>% \n",
+    "  group_by(artist_top_genre) %>% \n",
+    "  mutate(id = cur_group_id()) %>% \n",
+    "  ungroup() %>% \n",
+    "  summarise(correct_labels = sum(.cluster == id))\n",
+    "\n",
+    "\n",
+    "# Print results  \n",
+    "cat(\"Result:\", label_count$correct_labels, \"out of\", nrow(results), \"samples were correctly labeled.\")\n",
+    "\n",
+    "cat(\"\\nAccuracy score:\", label_count$correct_labels/nrow(results))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C50wvaAOLXzM"
+   },
+   "source": [
+    "Bu modelin doğruluğu fena değil, ama harika da değil. Bunun nedeni, verilerin K-Means Kümeleme için uygun olmaması olabilir. Bu veri seti çok dengesiz, çok az ilişkilendirilmiş ve sütun değerleri arasında çok fazla varyans var, bu da iyi bir kümeleme yapılmasını zorlaştırıyor. Aslında, oluşan kümeler muhtemelen yukarıda tanımladığımız üç tür kategorisinden büyük ölçüde etkileniyor veya çarpıtılıyor.\n",
+    "\n",
+    "Yine de, bu oldukça öğretici bir süreçti!\n",
+    "\n",
+    "Scikit-learn dokümantasyonunda, bu tür kümelerin çok iyi ayrılmadığı bir modelin 'varyans' problemi yaşadığını görebilirsiniz:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/problems.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Scikit-learn'den bir bilgi grafiği</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "## **Varyans**\n",
+    "\n",
+    "Varyans, \"ortalamanın karesel farklarının ortalaması\" olarak tanımlanır [kaynak](https://www.mathsisfun.com/data/standard-deviation.html). Bu kümeleme problemi bağlamında, veri setimizdeki sayıların ortalamadan biraz fazla sapma eğiliminde olduğunu ifade eder.\n",
+    "\n",
+    "✅ Bu sorunu düzeltmek için düşünebileceğiniz tüm yolları değerlendirmek için harika bir an. Verileri biraz daha düzenlemek mi? Farklı sütunlar kullanmak mı? Farklı bir algoritma mı denemek? İpucu: Verilerinizi normalize etmek için [verilerinizi ölçeklendirmeyi deneyin](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) ve diğer sütunları test edin.\n",
+    "\n",
+    "> Bu '[varyans hesaplayıcıyı](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)' kullanarak kavramı biraz daha iyi anlayabilirsiniz.\n",
+    "\n",
+    "------------------------------------------------------------------------\n",
+    "\n",
+    "## **🚀Meydan Okuma**\n",
+    "\n",
+    "Bu not defteriyle biraz zaman geçirin ve parametreleri değiştirin. Verileri daha fazla temizleyerek (örneğin, aykırı değerleri kaldırarak) modelin doğruluğunu artırabilir misiniz? Belirli veri örneklerine daha fazla ağırlık vermek için ağırlıklar kullanabilirsiniz. Daha iyi kümeler oluşturmak için başka neler yapabilirsiniz?\n",
+    "\n",
+    "İpucu: Verilerinizi ölçeklendirmeyi deneyin. Not defterinde, veri sütunlarının aralık açısından birbirine daha çok benzemesini sağlamak için standart ölçeklendirme ekleyen yorumlanmış kodlar var. Verilerin ölçeklendirilmemiş haliyle bırakılması, daha az varyansa sahip verilerin daha fazla ağırlık taşımasına izin verdiği için, silüet puanı düşse de dirsek grafiğindeki 'kırılma' yumuşar. Bu sorun hakkında biraz daha fazla bilgi edinmek için [burayı okuyun](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
+    "\n",
+    "## [**Ders Sonrası Testi**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)\n",
+    "\n",
+    "## **Gözden Geçirme ve Kendi Kendine Çalışma**\n",
+    "\n",
+    "-   Bir K-Means Simülatörüne göz atın [örneğin bu](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). Bu aracı kullanarak örnek veri noktalarını görselleştirebilir ve merkezlerini belirleyebilirsiniz. Verinin rastgeleliğini, küme sayılarını ve merkez sayılarını düzenleyebilirsiniz. Bu, verilerin nasıl gruplanabileceği hakkında bir fikir edinmenize yardımcı oluyor mu?\n",
+    "\n",
+    "-   Ayrıca, Stanford'dan [bu K-Means el kitabına](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) göz atın.\n",
+    "\n",
+    "Yeni edindiğiniz kümeleme becerilerinizi K-Means kümeleme için uygun veri setlerinde denemek ister misiniz? Şunlara göz atabilirsiniz:\n",
+    "\n",
+    "-   [Kümeleme Modellerini Eğit ve Değerlendir](https://rpubs.com/eR_ic/clustering) Tidymodels ve arkadaşlarıyla\n",
+    "\n",
+    "-   [K-means Kümeleme Analizi](https://uc-r.github.io/kmeans_clustering), UC İş Analitiği R Programlama Rehberi\n",
+    "\n",
+    "- [Tidy veri ilkeleriyle K-means kümeleme](https://www.tidymodels.org/learn/statistics/k-means/)\n",
+    "\n",
+    "## **Ödev**\n",
+    "\n",
+    "[Farklı kümeleme yöntemlerini deneyin](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)\n",
+    "\n",
+    "## TEŞEKKÜRLER:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) bu modülün orijinal Python versiyonunu oluşturduğu için ♥️\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) R'ı daha sıcak ve çekici hale getiren harika illüstrasyonları için. Daha fazla illüstrasyonu [galerisinde](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM) bulabilirsiniz.\n",
+    "\n",
+    "Keyifli Öğrenmeler,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Gold Microsoft Learn Öğrenci Elçisi.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>@allison_horst tarafından yapılmış bir çalışma</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
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+   "metadata": {},
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+     "output_type": "stream",
+     "name": "stdout",
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+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Son derste bitirdiğimiz yerden başlayın, veriler içe aktarılmış ve filtrelenmiş durumda.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Yalnızca 3 türe odaklanacağız. Belki 3 küme oluşturabiliriz!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Bu veri ne kadar temiz? Aykırı değerleri kutu grafikleri kullanarak kontrol edin. Daha az aykırı değere sahip sütunlara odaklanacağız (ancak aykırı değerleri temizleyebilirsiniz). Kutu grafikleri, verinin aralığını gösterebilir ve hangi sütunların kullanılacağını seçmeye yardımcı olur. Not: Kutu grafikleri, iyi kümeleme yapılabilir verinin önemli bir unsuru olan varyansı göstermez (https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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/91pV9fBNpvm9MdOPEuPFmcqxdMz7zruAXamqQ6vqe6rqN9PdyHNw/9ZTdlGcfdiUTTk+27WTa+M3rap/q6ovV9VFVXV2VZ1aVQ8ac9PYvjPj+Oz2moXzvMy97vxMkkP78b9urbVtzDOoulNVRyZ5av/yxNba+ZOm36HdHDMOTvI9uylnP9adGcdnu9aOPZdk/fvdzI9V1buq6sKq+npVfaKqXlJVP7Xfnno9z9hU1Wuq6tz+uHNuVZ1RVY+pqmtsMeta3bmgT3Ydq7V2TtavM6k7e7ebJyMNpu707l9V7+8/64VV9ZGqen5V3XmP5TruTCChgpXQ9zq11hvRZo+ZSZK01r6UrjfgJLn+LNcLxlmm7bWqfijrNwW9t7UmoYJvWdS22v9waVXVkpyX5K1JfjlJpbtA/tOttS/vZRnsL/PeVvsf1WsNko9prZ23m3IYpgWeBxyX5DuTXCnJtZL8SLpEtY9W1a/ssWyAldY3Av9iuh5Qbp/kzP7i3o9W1V2r6g/SXZg8JMm/59tvzP2ukfGJ+/Yknx4Z1yaxPKYVw92UUxvmY0G22UayFquvbeN36VqMr11VV544JVM1hd9s4ry8Ri/EHUjXg9mvJzly5P+HJLlrktdX1YmblCPGy+3/JnlBP/6MqnpOVd27qm5dVferqlOy3ivin7TWXjumDDFegKo6kOQxI/966ZjJnHcBM1dVJ4xcZ/pauqc8PDnJdfpJnpDkRbso2j5sCmYYn+0se6fXxq+T5LZJjkjXu/j1ktwn3Y1976qqpb/pa6fmGJ/jsrtrFoM9z1tg3Rm9qfUFm051RUOrO09McnSStyR53pTLnvaxZ3B1J7ONz5aq6l5JfrB/+eoNnTWNc8N0HUpcLclV0z3p6gFJTknypn3WWd48Y3O3JNdOd9y5drokl8cn+XhV3XfCfGt1Z6v6l6zXnf1y7WchdadPfviF/uU3kvzdNmcdUt1JunbSm6X7rFdLcpN0x+zXV9UpVXXELst13Jng4K0ngaVw9ZHxr25j+q+le9z71WazOjDRUmyv/QHouUnWel993ITJGaal2FZH/EWSP15QjwEst3lvq09K1xD4tiTP2WUZDNe8t9ePJ3lZuu117YfojZL853S9Cl0lybOqqrXW/nKXywBYea2106rqVumSJR6Sb+/x5vPpekJ+Tmvt6xve28m+/Wsj49oklse0YmhbWFE7aCNZi/F2z+PWXC3JN3e3duzCXn+zifPyuubI+Inpfs/8c5LfT/KeJIen+63zhHQ37jyhqj7YWjt1QzlivMRaa5cleXBVvSLJ7yR5aD+MekOSP90kmSIR40V5VLob55LkZa21d46ZxnkXsEjvSvKw1tqZu5zfPmy29hqfiXZ4bfzyJK9L8k9J3p3kC+ni9p+S/Eq6m9dunuQNVXXb1tqnZrHOS2Za8dnrNQvned9uZnWnqr476z3rv7W1tlXP+oOrO1V1x3S/Vy5N8vBtPsFjJ6Z97BlU3ZlDfLZa/jWTPKN/eVm69ovNXJzktCSvSfK+JBek60Di2CSPSHez8u2TnF5Vx7bWLpjVes/DHGPz3iQvT/L2JJ9Nl1DxfUl+Psnd033H/1BV926tvWrM/LupOyt/zrbgunOHdOcGSXJKa+3CLaYfVN1J1zncaemOtx9Mt22uJQk9PF2i6k8lObWq7tZau2SH5TvuTCChglVxlZHxi7cx/Vqlu+oM1gW2sizb69OT3Loff35r7RVTLp/Vt6ht9ZfS3Txc6U5yb53uJPfXktyoqh7aWvv8HpfB/jK3bbWqfizdE1MW0ujCvjDPfesp6Y7xG7fTM5O8pKqOT3fh4kpJ/ryqTpv0qFKA/ayqDknXc8t9052HbnSddD3ifCJdQ+WonezbRxsBtUksj2nF0LawurbbRrIW452cxyViPDdT+s0mzsvrsJHxqyQ5Pcnx/Q34Sfek02dV1fvSPV3qQJLH97912oZ5EzFeWn2PtQ9KcotNJjk2yUOq6gOttc+MeV+M56yq7pQumSlJzk3XnjuO8y5gHl6e7klWSVfvb5yud9qfTvLiqvqN1tord1Gufdh0zCo+W9nJtfH7bdKT7puq6pnpErcfnK696ClJ7jfVNV2sWcZnGtcshnyet4i68wtZbyvdztMpBlV3+jblv0z3Hf15a+19M1jMtI89g6k7c4rPpOUflOSFSW7Q/+t/ttb+34RZbrtJ/Tmjqp6e5O/TJQDcLMkfJPnNaa7vPM0xNk9prZ005v//luQF/RORnpUu2fK5VXXj1tpFG6bdTd1Z2XqTLL7upHui/JrtHHsGU3d619vk855eVU9L8qokt0yXYPGIdJ0X74TjzgQHFr0CsE2jB7NDtjH92qNhvjGDdYGtLHx7rarHZr1XrzOT/Oq0ymZfWci22lr7RGvtfa2197bW3tRa+/N0j0D8pyTHJzmzqvbFY42Zmrlsq33vRWs/HJ/aWnvPTuaH3tz2ra21CybdQNY3rP9R//LQdD2yAwxOVR2W5LVJHpuu5+snpmtYvXK63q3vnuTN6S66v7yqNja27mTfPvqoWm0Sy2NaMbQtrKAdtpGsxXgn53GJGM/FFH+zifPy2nhR+8SRZIpvaa29Od2NWEl3TN94U74YL7G+F8S3Jbl3ks+ku5B+dLp4XT/dfvrrSR6Y5O1V9f1jihHjOepjcEq6jvouSnL/1tq5m0zuvAuYudbal/vrTO9rrZ3ZWvvb1tr90iXr3Shdb60n7KJo+7ApmGF8NrXTa+Ob3KS29t4lfVkf6v/101V1vWms5zKYZXymdM1isOd5i6g7Wb+p9ZtJXrKddZzw3n6sO7+T5KZJPpXkD2e0jGkfe4ZUd+YRn0memeQn+vFXJvnjSRNvUX8uTJdA9cX+Xw/rb3pfVXOJzaTvtH//2Ume17+8brqnJW20m7qzyvUmWWDdqaqrJLl///Kz6a7dTTSwurPV5/18uid+rT2V4pG7WITjzgQSKlgVo4/22c5jk9Z6strOI2Vg2ha6vfYZtn/av/xgknu21r42YRaGa2n2rX0W+C+lu1h6/XQ3ucGaeW2rj0v3+MdPp8tch91Ymn1r7y+TrF3AuNOkCQH2sZOS3LEff0hr7cTW2gdbaxe31r7SWjs9yZ2TvCHdTbpPqqofGpl/J/v20Z61tUksj2nF0LawYnbRRrIW452cxyViPC/T+s0mzstrdD973hY9O756ZPw2m5QjxkumT4x6cbqk1s8l+dHW2t+01j7fWruktXZ2a+2ZSX4s3YXZ6yZ5/piixHhOquqGSV6T5BpJLkvywNbav0yYxXkXsDCttb9O8nfp7oN5elVdc4dF2IfN0BTiM9Ysro231i7N+g2YyQDa1mcVnzG2umbhPG+DGdad26a7oTZJTtvqxuTt2E91p6pumq6DniR55AzvuZn2sWcQdWeO8dls+Y9P8rD+5ZuSPGBchxA70Vq7IMnf9i8Py/pTl1bKomMzxrNHxqd13FnJepMsRXzuk+TIfvyFe603yf6pO9vVWvt4uqf6JslNquq6OyzCcWeCgxe9ArAdrbWLquoLSa6VZGKv5VV1jaxXwk/Pet1go0Vur1X1X9JlQSfJJ5PcrbV2/l7LZX9atn1ra+38qnpLkrsluW9VXanvyYKBm+O2emL/97VJ7l1V46ZZK/uwqnpgP35ua+31O1wW+9QS7lvP7dfnqCSr3hMQwI5Vd0D/5f7lh1tr427IS2vt0qr6vXRPqjiQ5IQkj+rfPntk0q2epHb9kXFtEstjWjHcWM6k39tr5bQN8zEnu2wjOTvJj6Q73z9yi5sJ1mJ8XmvtmxOmY3qm9ZtNnJfX6H53q33n6LTX3vCeGC+vn8j6b9OntdY+N26i1tp/VNXfpOvh9lZV9UOttXePTCLGc9BflH9tusSWluSXW2unbjGb8y5g0U5N10vtYemOOy/awbz2YbO3l/h8mxlfG3//yPhQ2tanGp9xtnHNwnneeLOIzYNGxl8whfLW7Je686h0vW5/PMmhI20Mo35gZPzHq+rofvwVO7hJeeMx4x0Tpt3q2DOkujOv+HybqjoxyWP6l/+e5PjW2rR6XN8P9WdhsdnEVt/p2Umuk63P/ZL1urPK134WHR/Hnul4f5J79uPXS/e0j+1y3JlAQgWr5P3pepS8SVUd3Gc2j3PTkfEPzH61YKy5b69VdZ90JxsHkpyT5C6ttSE0vLE3y7ZvPa//e2i6hrRzZrgsVss8ttW1R9H9Uj9MclS6HhWT5I1JJFQwatn2rZs+YhtgAK6TZK3Xtkm9XCfJO0fGR/fR79/k/+Nok1hO04rhxnLetY1yPr0EvXANzh7aSN6f9Ue/3zTJv25S/sFJbty/VNfnZ1q/2cR5ef3HyPhBW0w7+v7G31xivLxuNjL+71tM+850CRVJF8fRhAoxnrGqOipdj4c36v/1yNbadm52cN4FLNp5I+M32OG89mGzt5f4XMEcro0PsV19avHZwqTv1nneeFONTVVdKcm3OiFI8s97LXPEfqk7V+7/3ijr7QuT/N7I+A2TbHefvptjz6VJPjKmnCHVnXnF5wqq6r8leUL/8gNJ7tFa+6RtGjwAACAASURBVMpuytrEfqg/C4nNBFt9p+9PcqskR1TV0Zt1/FBV35nk8P6lurMLVfUdSe7Rv/z31tr7dlvWGPuh7uzEXj6v484EBxa9ArADb+7/HpbuQLaZ0cczvWV2qwMTzXV7raq7JHlpukS5L6TrfeNjuy2PQVm2fetopvBSP+aLuVu2bRUmWZrttaqune5msmRnPRMA7BejN1hu1bHIlTaZ7xNZ34du9Zj6H+v/fibJWVutHHMzrRi+eWR803L6Hpu+t3/pnHTO9thGsq0Yp3ts+NqTEMR49YjzkmqtfTLJp/qXx9QmjyHp3Xhk/DMb3hPj5TWNc7NEjGeqqo5I8uokN+//9ZjW2jO2ObvzLmDR9nKdyT5s9qZyHXBO18ZvPjI+lLb1mV+n3cY1C+d54007NvdK96T1JHnRhI7BdmO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V9Yj0vzKynwvWzG6R++6FSS7YJaaXJHlZVT06/UnvSUmeV1WvmvRT1OxpUbEd/TnP2yZ5fZJHDzdrJ/2vB51XVZcmeVP6X7H4xSG+9u/9WdaYO+p704+9iV+nmKeFxraq7p/+hO8rx8xyTpIfrKr3dV339/upg1ssJLZd1/1dVZ2X/lj4bkn+oqp+In1yzI3pn5Z5bpJvGd6fvJ96AJZslj71hpHXO/u6eZdzkP591dZpnc3jvHM/MU2O347zGvu1jQmq6pHpk/eT5Mr01wR3s2rbUb9xyGZoG4l+Y7dlmWc5y3ZR+l9TTfplu0/6p9k+PsnvV9WPdF336h2fWbVtuIntaxXsp20kfd9wz11ubnhXkouq6kXpb/q7Q5JnV9XLuq77vzvmNZ6ML2dpqurkJC9Kf83zeV3XXTpjEau2LbUPYO7W+BxsrOF7khcPbz+X5MkH/P5ypb4PX/OYPWTML7RdXFW/luQV6W8yvH/6ZNZn7lHfblZujFrzmN3K8NTorYSlv09/M/FBtb6fzcMiv5NepfW+xZrGbaIZr4dN4/ld1527y///ZZKXDL8Cc16SE5L8ZlXdp+u66w9Y51hrHrO7jRnTXl9V/z39QxMfnD7B4unpHyQ+q5Vb7zWP2a1U1d3T/zJSkryt67oPzKFY+9n4ZTkl/Xi+9cstP9x13UcPuCyjy7PWY9qReRfI3I3uqCePnWvb1s9Nf+4glXZd9+Gu6y7tuu49Xde9ueu65yX5qiR/kv5pQJcMnRkHs5D4Dj9DvnXB+le6rnv3LJ9nXxa273Zdd/WkC1DDF1Q/O7w9JckPzloHx1lUbHceqD1rJJniFl3X/Xm2n+hy/4y/gZu9LWXM3eHJw783JHnZHMtt3cJiOzxx761JHpP+AuaTk9x1qPceSf5t+iclPCnJ26vqK2atg+Mscr/9sfTHwklyv/Q3iVw9lPXW9MkU70jyWyOfmefPKwMctln61NuMvN7Zp867nIP076u2TmtrTued+4lpcvx2nNfYr22MMRyfXpj+QTzXJ/muruuuHDP7qm1H/cYhmrFt6Dd2X5Z5lrNUXdd9evj+4NKu6y7puu4Puq77jvQPF7h3+if/HdvxsVXbhpvYvpZun20jXdddO+lJkV3XvT39Qw7y/9u773BbqvLw498XEFGkgwo2lKIi/ARBIwH0Ioiggr0glgtGjYVEDRgbckGi0Whi7CJR7A0iFmyxXIrGCKgRjYoIFytVmvTy/v5Ya3vm7rvrOfucfc7e38/zzLP37JlZs2bWlD1rVqG853hZh9m8n3QPZ5xeCzwA+A1wzCyWX2z70uND0kgt8WewjiJiK0pe+gZAAodl5s8HWbabxfQ+fKmnWZeCp61p11Iqw/6p/vTCWjlyWIvqHrXU06yLJwIb1u8fz8zbB1yuK8+zkVjI95aLabuBJZ1uXQ2bHzaIXtfhOv0DzLxv3orSQ8y8WOpp1ueedgml4lkrr+HwWa5mUW33Uk+zLp7NTDn2kfS45HnWNS7rAJ8DHlx/el9mnjiCuDTjs6TvaVaoWPyahbAG6aKk1aL5IN2sDKXWwjqUUgjwXsBbR72OKbRQ6fs6SpdFv6W0IqD5t2jO3ep4SoYVlJq3mr2FStvmei7LzB/1mPfrje8PHXI9mjHW8zYiHkZ5uQjwxX5/8DWUBUnbWoHxU5Sa3BcDD8/Mj2fmJZl5S2b+LjPfCzyC8kCwFaPrgndaLdh5m5k3USrKvAD4MTP3VSitCPwTsBczvcwAXDnseiRpjIa5pjZ7U2u/po46nLlc3xfbNk26fs+ds0lTWH0/jure77HRQUTcl9L69ybAbcAzM/P0Hosstv3odWOezOLYGJTXjdmHsyhl5scoL+XWAt4dEZs2Ji+2fTiJx9ei1efYGNSngWvq917XDPB+siiOjYh4APCaOnp4Zl43i2AW2770+JA0MhPwDLaGeo//BrB1/enwzPx0v+VGZN7fh09imrXLzKsp/7taYe02bBgsonvUBKfZcxvfPzrgMqMwyefZKCxkeYPFtN1LPd06msf8sEF8oPHdc22WMvMC4L/q6La10uewFs12T3Cajavh2ak6zyIigBOBx9afPkvnBktmE5dmfJb0Pc0KFYtcrcRwRR3t2SNERGzCzAHz23mKz+XAd+voEyLiDvOxnmmxgOn7j/Xzm8CBEfHM9qER9vqN3x815HpULcJz99JGfO4xH+uYFguYts35fzfEvFsMuR5Vi+C8HVfG18RbwLTdn5lr7Lu6dXObmT8DPl5Hd42IB3eaT/0t9Hmbmbdn5gmZuQul4sx2lDTfMjNfX+OzXWOR/5vNeiRpTJr/Ofv1SHmvxvf2a+pswknW/M/bGh+kd8xWOO1x+f0s4tIpnFHtm4k2wHNnaz+uHxEb9wmutR8vq5UaW+sY1b1/nMd7p3DGrr5M+ial0m+rBdEv9FnM68YMj41Z8LqxtI+NHlrHx/qU5+SWxbYPJ/H4Wuy6HRsDycxbgfPqaK9rBng/WSzHxisoLQteANy5y7upHRvzP6oxrXW++H9jxqQdH9JUm5BnsNVExAbA14BWz9xHZeZ7+oQ/MvP9PnwS06yH5ruN2ezLRXGPmtQ0i4i7AfvV0XMyc8HeRU34eTZnC/zectFs91JPt07mMz9sQHO9Dvc0iWnWg/e0YlGmWUTsBuxQR7+cmQvZYOW0nWfvAQ6p378KPLtXD1fTek+zQsXS0Dp5t63drnTzgMb3OXWZ2Mdl9fPOwObzuJ5psRDp2+oK51BKy9mdhlZabt747Q1DrkerW2znbtfuGTW0hUjbnzW+r91n3ub0W4dcj1Y3lvO2VlB8Zh29lJLhrNFaiLR9YOP7D/vMe06XdWp4YzlvM/PazDw/M//QetCMiLWBnessF9TKyJK0VDQzDvvdm3pdU2cTzm87tFbbCmejiLh7twAiYktmurdfLS6ZeS0zmWkLvU2dwpkGvZ47B9qP9X6+TR3ttA9Hce8/j9IaUM+4DBDObI6NW4Ff9Zl3QUXE5pSWuu5Xfzo8Mwep5O11Y8ZEXjfmcGwMw+tG/3AW3XWjj8sa3+/T+L7Y9uEkHl+LXbdjYxhzvma0Tfd+Mr/uWD/vR/d3U09pzH9U4/dWw0H+35gxaceHNLUm6BnsLyLiTsCXgIfWn/4lM4/rE/Z8mJf34ZOYZn3MdT+O/R414Wl2CDPlAz4ywPyjNqnn2ags1HvLRbHdE5Ruf7FA+WH9zFv5rklMsz7GcU8baV7ehKdZs+HZhb6nTc15FhFvAV5cR08HnpKZtwwRn6m4p4EVKpaKM+vn+sCuPeZrdj3z3a5zzV2zRpbdws7d0d1e9wAAIABJREFUYktfjc6iSduI2IKZSjN/mI91TJl5T9vMvAj4TR3duna91c02je+/7zqXBjGu8/ZxwGb1+ydri3sarYVI22a69XqYAGj28mV6z82iud8CezNzLi9kd5SSNAoXMvOs0K9r20fUz98Dq9qmndn43jWcWvho+zra6bo8UDj0v763wrl/rwJPfcIZ1b6ZaAM8dw6aprsx05JNrzSd9b0/M28GflBHd4+IdemuFc5NwNlt084Cbu6wvtXU8B/eWmbAjOoFEREbAV9nphWoVw/RgqjXje7hLPnrxhyPjUHX4XWji8V83RhAx/cHi3AfTuLxtdjN6d1SfXHbug/M+prh/WTJ8f9G93A8PqQlaMKewVrz3QE4uRHW+zPzVX3iN3Lz9T58EtNsADs0vs9mX471HjUFadYqfHoLpSLqgpnw82xUFuq95di3e8LSDViY/LABzfU63NEkptkA5rovx5qXN8lp1tbw7GWUXhMW0lScZxHxeqD13/ws4PGZecOA8Zmae1qLFSqWhlMa3w/tNENErMXMn+argO/MR0Qi4p7A7nX0otqKiuZm3tM3M6PfAFxUZ7+o8fuyIbdFq1s05y7wQqBVIP+0eVrHNFmotD25fm4I7NNjvic3vp/ZdS4NYlzn7ThrXU+LhUjbCxvf9+ozb/Mh4MKuc2kQi+J+Wyu+raijtwAfHPU6JGk+ZWYCra5mHxARD+80X/291frHF+pyzXDOY6ZFkKdHxJ27rHJ54/vnO0z/ItDqarbj9b0tnNvrMu2a94nlHaZT4/j0Ovp/dRv+YlT7Zgr0e+5cCVxdvz+vR6Xx5Y3vnY6NUd37W+FsyOrPVM1w7gnsW0e/1Z4PVse/VUf3rfN38mRmWijutE1jUY/9U4GH1J/+KTPfMujyXjcm97ox12NjCF43lth1Y0BPa3w/t23aYtqHK5mw42sJ6HVsDOIZwEb1+xrXDO8ni+9+kpnLB3g3dUxjkb0b01bVMPy/UUzc8SFNowl8Bmv12vxJ4ID608eAl/TYjPk08vfhk5hm/dRCgK3Cjdczi0q847xHTXqaRcROwIPr6Fdy4XtKn9jzbIQW5L3luLd7AtNtIfPDBvGixnfPtVmKiPsCj66jv87MoRuoHWde3hSk2QHM9E45joZnJ/48i4i/B95YR88F9h8yP3Eq7mntkXFYAgOlq5WkFM7avcP0I+v0BFZ0mL6sMf3EDtO3Bx7VJw4bNeKRwLHj3i+TMsx3+g4Yh1V1+VXj3h+TNCzAubs1sEufODye0mJZUjI97jHu/TIJw0Kct8C9gRvqPD8BNuwwz7Mb4Xx53PtlEoaFviYDmzbO0Z+Me/sneViAa/LGwHV1+jXATl3icQBwW53vd8Ba4943S31YoGvyZsAdu0xbG3hPI4xjxr1PHBwcHOqzwlDPiZS8gVvrMmcBd2qbfqf6e+uau12XcA5rrPvdHaZvQynAmJRuj9fpEs5HG+E8tcP0pw1w/b4D8Os6z9XANh3maV7Dl8/nvlkMw7DHBiN87gSObaz7yA7Td6/7L4GVPdY3p3t/nWdTSuZuUvJENmubvjal8FsrnGVdwnlUY54vAGu3Td+c0ohFAlcCm4z7GKjxWpfSSlIr7u+YZTheNybsujGKY8PrxsReN5YD6/WZ5xWNbbugw7Ytqn04icfXUjw2gE36bQ/wsJqWSSmgvmuX+byfLJL7yRDHz4oBznn/b0zp8eHgMEkDE/gMRilU/aFGOCfR9r9swG1a1uu6yZjeh09omu3fvv626Xdp2+Z3zibNRrnd055mHZZ5WyPMJw+xTZ5nszzeKJXxE8ghlhnFM+KiPM8mNd1GuE3Le6UtsBOwbZ8wXtgI44/A+qZZx/kPpMd1E7gb8MPGNr9yNmlW51nwvLxJTLMOy5/U2L6HDLGc59lg/0MOpeShJfBL4G6zjM9E39PWiMd8BOowDwkFu1D+kCZwLfAaSjdBewMfaBxwvwQ26LB8z4OyMf3HlMzLA4GH1vUeABxXLx6tMM4F7jzu/TIpw3yn74BxWFWXXzXu/TFJwwKeu9+rYT+W0g39bpSWiz7buDkm8JJx75NJGRbqvGX1Px6/oPzh2bWu513M/Jm4er7+LEzbsNDXZEpLPa35/2Hc2z/Jw0KkLXBUY55rgTfV8HcGHgO8l5nCGAk8e9z7ZRKGBUrbpwIXA+8AnlSvxXsALwZ+1Fj+K8C6494nDg4O0zcAe1IyEVvDEY1r05lt05b3COfNjeV+SGkJeLf62cx8flOPMNau62zNe1K9Dz4MeBlwSf39NuCAHuHcC7iUmcyxf67buWf93rqnXgrcs0c4j2WmMuPFNQ4Pq3FqZtieQY8X/6PYN0vx2GCEz53ABpT7cWveD1Du1w+vYV9bf78e2LlHOHO69zfCeVFj3vMpz1u7AQcB325M+2SfffypxrzfrsvvVsM7vzHtheM+HhpxPrkRr29RXjLs2GPY3uvGdFw3RnFs4HVjUq8bq4ArgOMprY7tQWkZdU/Kc1HzHL4J2Hex78NJPb6W2rHBTGXP/6X0VnAQ5f3UQ4AnAicwU3Argbf2iIv3k0VwTAx5/KxoxH/ZfO4Dj4+ld3w4OEzSwAQ+gwFvbyx/LuXe3WubduwSzrJGOCf2mL6g78MnNM1WUv63fRB4HuXetTOlV/XXMFMYNCnvpTedTZqNcrunPc06hPeHuswVDPEuql+a4XnWCuPutOWV1nOhtWz7tI4FdhnBM2K/NBvXeTap6TaqbaJ/Qe/llDI+/wW8ktJ7wkMo14Xnsnph7FuBA02zrmm2Cvg98E7gYEqjGDtTevY8DrisEd4ZdG84sWeaNeZb0Ly8SUyztvA2AW6sy5475L7pmWZ4nkHJT2uWJ9y/T1x2pEulEib8nrZGHOYrYId5SKxSyeHqxoHRPvyy28Wo30HZNr3f8GVgi3Hvj0kb5jN9B1z/qrr8qnHvi0kbFsm5ex2L6AXspAwLdd7WPwy391jPJXSoBeqw+NO2zv99Zv6o333c2z7pw3ynLaVFpn/rc84mcDNwxLj3xyQNC5C2T+2TprcD/0GXzBgHBweH+R6AEwd8Nkgge4SzVr2e9Vr+BPr0sERpkecHPcK4EfibAbbrr1i9gYf24Y/AXw0QzgtYvSBe+/A/wOZ9whjJvllqxwYjfu4EtgXO6xHO1cDjBwhn1vf+tnCOofd/t1Pp3+r2nep83cK4jR4vY8Z0XAx8TNRh1XyfG3jdGPtxMapjA68bk3rdWDVguv4WePRS2YeTeHwttWOD1XvP6jXcChwNRJ/4eD9ZQgODV6jw/8YUHh8ODpM0DHivaw6reoS1KK6JDP4f4C9Dl3CWNeY5sc/0XsNI34dPaJqtHHBbVtKj94F+aTbq7Z7mNGsLa//Gcu8Zct/0TDM8z4bdD61heY+w5vSM2C/NxnWeTWq6jWqbGKyg9yDhXw48wTTrmWarBlz2JGDjHnHpmWaN+RY0L28S06wtvL9tzLtGr7F9lu2ZZniewZDvBeuwrEd8JvaetkYc5itgh3lKMLgP8K/1ILyO0k3QWcCr6NFjRL+DktK17H7AWym16M6rJ8EtlJrNZwPvBvYY9z6Y5GG+0nfAda/qd7F2WHxpS2lF7ZB6fn6f0mrEdZQM+IspNR5fC9x13PtgUoeFOm8ptak/ClxIyVi5ipL58npgo3Hvh0kcFiJtge0a83513Ns8LcMCpe2uwPsorTJdQ3npf1X9T/V2etQ2d1icaUvpFvQISg8UF9Twr63rej8DvCR3cHBwmM+BEVWoaIT3WOAUSgs/N9XPUxigtbRGGOtQWiU+g5IxeQPwa0rrxQ8aIpzNgTfW++q1dfhJ/W2zIcLZsa771zUul9e4/S09uoaej32zlI4N5uG5E1i/3p/Pqvfr6yitGf0rcJ8hwpnVvb9DOH8NfAL4Td2uS4BvAAcPua+fVZe7pIbzmxruoqsAP8wxwYD5RV435nffLKVjw+vGUPt7KV037k9p1e1kSk8CF1PeH1xDaYnvJMoLy4H242Lah5N6fC2VYwNYl9KIwdsp19gL6rI3U1qUPJPSuuTWQ8TJ+8kSGRiwQsUo94HHh4ODwzgGJvAZjIWrUDGW9+ETmma7Af9Y1/lzyn+tWyhlg35OyUN6DP0rsPZMs/nY7mlNs7ZwPtGI+1DvpDzPBkszRl9geNbPiIv1PJvUdBvVNtG/oPddgcMoPQWdTWl44HrKdeH3lPfQfwdsaJr1TbNHAm8Avko5x66g3NOupDyXvZ8B8ob6pVmH+RckL28S06wtvO/W+W4Fthpy33ie9UkzRlyhooZ5HybwntY+RI2AJEmSJEmSJEmSJEmSJEmSJEnS1Fhr3BGQJEmSJEmSJEmSJEmSJEmSJElaaFaokCRJkiRJkiRJkiRJkiRJkiRJU8cKFZIkSZIkSZIkSZIkSZIkSZIkaepYoUKSJEmSJEmSJEmSJEmSJEmSJE0dK1RIkiRJkiRJkiRJkiRJkiRJkqSpY4UKSZIkSZIkSZIkSZIkSZIkSZI0daxQIUmSJEmSJEmSJEmSJEmSJEmSpo4VKiRJkiRJkiRJkiRJkiRJkiRJ0tSxQoUkSZIkSZIkSZIkSZIkSZIkSZo6VqiQJEmSJEmSJEmSJEmSJEmSJElTxwoVkiRJkiRJkiRJkiRJkiRJkiRp6lihQpIkSZIkSZIkSZIkSZIkSZIkTR0rVEiSJEmSJEmSJEmSJEmSJEmSpKljhQpJkiRJkiRJkiRJkiRJkiRJkjR1rFAhSZIkSZIkSZIkSZIkSZIkSZKmjhUqJEmSJEmSJEmSJEmSJEmSJEnS1LFChSRJkiRJkiRJkiRJkiRJkiRJmjpWqJAkLZiIWB4RWYetxx2fSdbYzyvmEMayRjjLOkxf0ZreZfmVdfrK2cZBkiRJkiTNnXkynUXEiXWfrBp3XCRJkiRJ0poiYlV9dj9x3HGZVpb9kCRNAytUSJIkSZIkSZIkSZIkSZIkSZKkqWOFCkmSNBaj6EVDkiRJkiSpyd44JEmSJEmSlo6I2LqRl7N83PGRJE2ndcYdAUmStDhl5kog5rD8spFFRpIkSZIkSZIkSZIkSZIkacTsoUKSJEmSJEmSJEmSJEmSJEmSJE0dK1RIkiRJkiRJkiRJkiRJkiRJkqSpY4UKSdLIRMQmEfHPEfGLiLghIi6NiG9GxNMGWHbdiDgwIt4dEWdFxJURcUtEXBER/xMRKyJi8z5hrIqIjIgT6/j9I+KD9febIuKSiPh8RDx8wO3ZOiLeEhHn1HjcEhGXR8QZNT7367HsRhHxmoj4bkRcFhE3R8QfI+JLEfHUiIgey64fEc+IiBMi4scRcXVd92URcVpEHBERdxlkGxph7hsRX6xxuDEiLqj7+h49lllW92dGxLJh1leXX1mXXdn2+6qIyMZPRzfW0xpOrPP+Zx2/MiLW67O+dSLi4jr/V4aNryRJkiRJ0ywi1o6I50XElyPiDzUv5YqIODMiXhkRd+qx7Gp5ABFxj4j414g4v+YRXRERX4+IAwaMy3NrHsiVEfHniDg3It4QERvW6a38gxWNZZbV/IYPN4K6sEOew7Ie6904Io6NiJ9FxHURcVVEnB4RhwwSb0mSJEmSJl0tK5Gtd/61bMRREfGj+hydEbG8bZlZl58YIl7bRsS/1TyEq2t+xAURcWJE7NZn2S0j4iURcVJE/KrmCdwUEb+PiC/U8hs9y1lGxHoR8Xc1j+SyWsbjTxHxy4j4as1b2brH8rPOl5mriHh4RHyulre4MSIujIjjI+L+Ay4/6/1Xj6MLGz99uENezoouyz4kIt5f9/Gf63p/GRHvi4jth94RkqSpts64IyBJmgwR8UDgm8BWjZ/XA/YB9omIDwOn9wjieOB5HX7fFHhYHV4WEU/IzO8OEJ8nAR8H7tz4+a7AE4EDI+KQzPxMj+WPAN4E3KFt0mbAnnVYVof2ZfcBPlPnbbo78Pg6fCUinpGZf+6w+lOBR3b4fXPgEXV4SUQ8NjN/0W0bGvE5GljR9vN9gZcCz46IAzPzjH7hjMkJwJOAjSlp9+ke8z4WuFv9/qF5jpckSZIkSRMjIu4NfBF4cNukTYE96vDiiHhcZp7XJ6w9gFMo+Rgt6wH7AftFxJGZ+bYuy94B+BzwhLZJO9bh2RHx6MG2aji1kMDXgK3bJu0F7BURu2fmy+Zj3ZIkSZIkLUURsR3wDdZ8lm7OM9fyE4PEo1v5jvvW4bkRcVxmvqHDsmsDv6Nzw9RbAQfV4fkR8eROcYyILSnlZXZom7RJHbYH9q/hHdFh+ZHlywwrIl4BvI3Vt39r4AXAsyLi6X2Wn/P+m0Wc16pxfjnQXhln+zr8TUS8NDOPn+v6JEnTwQoVkqQ5i9I64NeZqUzxGeAjwKWUB5VXAodSXnx3sw5wAfB54AfAb4BbgfsA+wKHUR6wPx8RO2bmpT3C2gl4BvBH4O3A2ZSHqMcAr6a8xD8+Ir6dmZd12J6jgGPr6FXAe4HvAFdQCvY/BHgykB2W3QP4KuVB/RLgXcD/An+o++cZwLMphf8/Ajyly744l/LAfHZdNuq+eBLwdMpD/ykRsXNm3thjXzwO2A34JfBW4CfARsDTKA/AGwFfrvv0tz3CGaX9gHUp2wjwPso+brqyfn6N8vB9T8ox1KtCxaH183LKvpMkSZIkSX1ExGbAmcC9gJuADwKnAauAu1Ce4/8e2Bb4akQ8JDOv7hLclpTKFLdT8mDOBG6mNEzxBkq+ypsj4quZ+bMOy/87M5UpfkZ5Of5TYENKnsiLKflOnZxFyRN6AnBc/e0xlHyVpgtZ052BL1Hyno6jFIL4M7ALcDQlX+KlEfGlzPx6l/VLkiRJkjRtTgLuQSkX8UXKe/7tgItgZOUneoqIIyllIaCUh3gf8CtKWY/7Ay8DdgeOiojLM/Od7UHUz2/XuJ4LXAZsANyPUq5id+DRwHvo3FDou5ipTPFx4D/rNt5GySvZjTUbj2jFf5T5MkOpDZX+ax29GngLsLKOPwp4FfAJyv7oGkz9nO3+24lyLLTyW14PfKFtnvbyQe8CXlK/nw6cSClvdD2lUsrLgQcBH4iIizPT8iOSpL4ic42yoJIkDSUi/oWZWvSvzcw3t02/A/BlyoNey30zc1Vjnm2AC7LLjSkidgK+R3lgPC4zj+owzypKpQOAc4BHZeY1bfMcQnmABXhlZv5b2/RdKJUY1gLOA/bJzN91idO9mpUQ6naeR6mt/zXgKZl5fYflXkDpkQNgv8z8r7bp22Xmrzqts07fl/IwuRbwN5n5Hx3mae7HHwKPbK/pHxHPAT5aRz+XmU9vm76MUpEEYO/MXNk2fQWlUAGZuUYXnBGxktLTxmmZuaxHHI/JzBVrbOjMfMcCR1EKY9ynU3pExF0pFS/uAPx7Zr68W3iSJEmSJE2biFgOfLiOtufJfAJ4FqWww96ZuUaFg5pfcgawPvCmzHxd2/SVzPS2eRGwR2b+vm2ePSkvuQN4Z2b+fYd1nFOn/zclT+aGtnmeSunBomWNPIVe29phu05k5kX+1TXeP2ubZ1tKYYD1gC9mZscCEJIkSZIkTYNmOQHKO/wDMvMbHeYbVfmJVZRyIB/JzOVt03YAfkwpJ3AMJZ8g2+ZZi1JZ49mUxhPunZlXNqYHsE1mnt9jm4+hNBSRwP2b5TkiYj3gmhqHt2fmGj1QNObdNDP/1PbbnPNlZiMi1qU0OrEVJU9k98z8eds8OwLfpTR0AR3Kfsx1/9XpWzPTAMahmXlij7AeTekVBbqXl1kPOJVSKeQiYNvMvLVbmJIkQeeuliRJGlh9yHp+Hf0J8M/t82TmLXWeW7qFk5m/7laZok4/Fzihjj5xgKgd1l6ZovokMy0T7tVh+pGU+2MCz+xWmaLGqb1Hh2dSMgNuBJ7bKTOgLvdBSi8cAMs7TO9amaJO/yYzPTAMsi9e2KnbxMz8GKWFAIAnRcTdBwhrHD5ESY+16NzaA5TMjzs05pckSZIkSX3UF9bPqKMv6/TSHiAzf0RpRRA65GW0Oby9MkUN40zgf+popzyZFzLTquEL2itT1DBOovRuOh+O6tRrRi0QcEod3XOe1i1JkiRJ0lJ0YqfKFNVIyk/08Q+UcgJn06EyRQ3/duBwSu8PdwGe2jY9e1UGqI4FLqfkWxzUNm1TZsoqnN4rkA6VKbZm9Pkyg3oCpTIFwBvbK1PU9f4U+KdegYxg/w3r1fXz5E6VKWqcbqT0TAKlMs7ec1ynJGkKWKFCkjRXuwKb1O8f6VYpolZM6PYgvYaI2CQitomIB0XEjrXm+1V18g61NYNuzs3Mn3SJRwI/qqP3a1vnWsABdXRlfSgdRuvB77TM7NXlIcw8SO/eL9CI2CIitmvth7ovWuE/uM/i52bmOT2mtyofrAMs6xeXcagtSX6zji7vMtuh9fOcbmkvSZIkSZLW8DhgbeB6Zhpd6KaVl7FVRNy7yzxXUVoA7KaVR3G/DtP2rZ8/6lSxoeGjPabNVlIa4eimFe9NI2LjeVi/JEmSJElL0Sd6TJuX8hNtDqyfJ/dpwPMqSu+TfdcREWtFxFYRcf9G+YwHAq3GONvLaFwB3Fy/Pyci1hki/qPOlxlGKx8mKT14dPPhOs9AZrH/BhYRGzJTruWkXvPWCiKX19FhjytJ0hQa5gYuSVInOzW+n9Vn3h9QHgg7ioidgFdQKjX06i1hLUoljku7TP9Fn3i0av1v0Pb7fYHWS/Ez+oTRyW718zERMegDZcftjIg9gL+jPMRu2mP5zfuEP0iatOwEfLrP/ONyAvBoYNuI2Csz/5I+EbEbsGMdtXcKSZIkSZIG18rLuDNwa0T0mrfp7sBvOvz+q9ryYzcd82QiYj1g2zraq2EIKK1OjtrlmXlFj+nNFiQ3YKbRD0mSJEmSplmvxg5HVn6ik4i4D7BFHX1zRLx5tuuIkiFyCPB84K+AO/VYfrUyGpl5U0R8BngOpfeLh0bEZ4GVwPdqZY5uRp0vM4xWWZ8LM/PybjNl5mURsYpSnqajuey/Ie3CTAPin4qITw243MDHlSRpelmhQpI0V83C/t0qOLRc0m1CRDwfeD+D35t6PYB17CqyofVif+2235sPbn8cMB5Nd53FMmtsR0SsAI6e7fJthkmTXhU3xu0USusBm1N6o2hWeDmsft5I79YkJUmSJEnS6maTlwHlRX8ng+bJtPee3ez1oV+rlf2mz8ag8YY185MkSZIkSZpWV/aYNpLyEyMOH9ryNGojD/9JafhzEJ3i+DJK3saBwH2AI+twe0T8EPgscHxmXt223KjzZYbRKh/Sr0wJlHIlHStUjGj/DWqc+0uSNOGsUCFJGqWBu/lriogHMFOZ4lLgX4BvA6uAazPzljrfYcB/tBaba2TnQeuF+leBV80mgIjYh5nKFBcAbwPOpLQucF1m3lrnOxY4aoAgZ5Umi01m3hwRH6P0YPK0iDg8M6+rD+cH19k+36d1B0mSJEmStLpWXsblwN5DLHfhPMRFkiRJkiQtIZl5W4/Jcy4/0UezwYNjgc8NuNx1beOvY6YywGnAe4AfAhcDN7R64oyI04G96FBWJTOvAQ6KiIcBTweWATvXOO5WhyMi4omZ+d8dtmGc+TJzLVMy5/03hGaavwj43oDL9ar4I0kSYIUKSdLcNR887gac12Peu3X5fTnlnnQb8MjM/EWX+ea7B4VmN4ZbzmL5K4CtgHUz86ezjMML6ueVwMMzs1uri4Pui277vNP0Pw0Y5ricQKlQcRfgacCJwBOZacXyQ+OJliRJkiRJS9YV9XMD4Od9CkLMp2YDCVv0mbffdEmSJEmSNH6jKD/RL/yWW2azjogI4G/q6BnAo1oVADroW0YjM38A/KCGvQGlYsVy4MmU3hVOjohtMvOGtm0YR75Mq6xPvzIlXecZ9f4bQDPNr5+n40qSNKXau9WWJGlY5za+P7TPvN2mP6h+/m+PyhRQau3PpwuZeYH/iFks/6P6uVtErDvLOLT2xXd6VKaAwffFMGmyqB82M/P/gFZrDYfWz8Pq50XAtxY8UpIkSZIkLW2tvIw7Mv/5Ll1l5o3Ar+vorn1m7xfPieitU5IkSZKkJW4U5Sd6uQC4un7fY5ZhbArcvX7/XLfKABFxF+D+wwScmddm5pcy8ynAO+vPWwJ7NmYbZ75Mq6zPfSNis24zRcQWwNZdJo9q/w2al/PjxryzTXNJkjqyQoUkaa7OYabm+nNqDfQ1RMQ9gP26hNHqMWn9biuJiC2Bg2YbyUHUh7tT6+gjI2KXIYP4Yv3ciJkC/8MaZF/sAvzVgOHt1Gc7WhUSbgNWDhjmqNxYP+84xDIn1M+9ImJvYJ86fmJmWmBCkiRJkqThfImZF9EvH2dEmGkoYZeIeFCP+Z7bJ5wbG9+HyXOQJEmSJEmjM4ryE13V3hy+Ukf3i4gHziKYdRrfu5bRoPTCsE6P6f00G4fcvPF9nPky36yfQe+8luV1nk5Gtf8GysupjZJ+v44+q1b2kCRpJKxQIUmak8y8CfhwHd0ZOLJ9nohYB/gg0K3VgV/Vz+0i4q87LH9n4JPAneYc4f7eBtxOeSD8dETcs9uMHaZ9BPhtK5yI6NnLRUTsGRGPbPu5tS/2jIhtOyyzBfCxXuF2cHxErPHwGhHPAh5bR0/JzD8OGe5ctda3zRDLfAa4lpI+n6T8l0lmjkFJkiRJkjSgzPwl8Lk6+syIeGWv+SPivhFx8DxF53hmChF8MCLWyAeKiKcAT+oTTjN/Y5g8B0mSJEmSNDqjKD/Rz5spjUeuBZzUp3zH2hFxSNs8lwFX1e8HR8Qahfkj4qHAG3uEe78B4t1sfPTC1pcx58ucwkweylERsUYPEhGxA/C6HmHMef9VVwA31+/98nKOq58bUtJ8424zRsQdI+KlEbFenzAlSbJChSRpJI4Ffle/vyUiPhkR+0fEQyLimcD3gAOAs7ss36ogsBZwakS8NiIeEREPi4gXU7qQ5UZvAAAHxklEQVTtWwZ8d/42ocjMHwNH19HtgXMj4riI2Ccido6IZRHx8og4nbaKDbVyydOBm4C7AN+OiI9HxFMjYteIeGhEHBQRx0TET4AzgJ3aovDR+rk+cFpEHB4Rf12HI4D/BXYA/nvATTqb0jXk2RGxvMbjURHx3kb8rwWOGDC8Ufpe/TwoIl4UETtGxLZ1uGunBTLzOuDTdbTVdeS3M/Oi+Y6sJEmSJEkT6sXABfX72yPitIh4fkQ8PCJ2iYh9I+IfIuK/gPOBp8xHJDLzHEqDHAC7A2dFxPNqXsbeEfEuSkMLP2gu1iGoHzHTsuEbI+LREbF9I89hIRrskCRJkiRpqo2o/ES/dZzLTFmHHYCfRsRba3mVXSJi94g4OCLeSanc8XFg48bytwOfqKP/Dzizzr9bLSPyduB0Sj7DeV2icW9gZUT8rJYteWLdtodGxJMj4jPAS+u8Pwb+p235seTLZObNwOF1dBPg+xHx6rre3SPiNcyU6Ti/Sxij2H9k5q3AWXX0sBrGAxt5OZs25v0K8O919BHAzyPi6EaZnj1qftIJlAoj72ZuvYtIkqaENwtJ0pxl5tURsT+lS8C7AwfXoelE4DQ69CSQmWdFxNHAMZSH13/qsJq3Az8F9hhdzDvLzOMi4vZGfF5H51r3p3VY9vsRsQz4LHAv4JA6dHNN2/InRcSHKV1ebgW8s23+24BXUB5odx9gc06tw9F07sXhGuCgzFw1QFij9jbgqZQuG9/fNu0jlK4jOzkBeEFj/EMjj5kkSZIkSVMiM/8UEXtQ8jL2oryM7tVq5DU9ps3V4ZT8kMcDD6LkJzVdCDyLmRf5N7ZNJzOvrQUlXgU8BPhG2yx7AytHFmNJkiRJktTRXMtPDLiOd0TEdcA7gI2AI+vQyc2smZfwOko5lJ0pjVV+sm36nyiVGI6lNMrZzQ516OYXwJMzc7XGIcaZL5OZJ0fEkcBbKWVj3tw2y/WUSjFHAtt2CWZU++/NwJeAzTqEcQywojH+ihruUZQySivo7jpKORtJknqyhwpJ0khk5s8oL7rfCvyK0srA5cB3gGdl5qF9lj8WeBzlJfeVlAfZ3wH/CeyXmQvag0JmvonysPsOSkWOa4BbKV0Wnga8HnhOl2W/D2wH/C2lMsMfmHkw/y1lG18HPCAzP9ph+cNq2GdQeo+4CbiI0qPEX2fmv7cv02dbVgD717hcUuOyCngv8KDMXKNiyEKovYHsDnwK+A1lOwdZ7gfMtF5wFeUYkSRJkiRJs5SZF2fmIygVGT5BaRnxeuAWSl7I9yiNXTyy5lvMVzxuBg6iNDRxJnB1jcfPgTcBuwJXNBa5uktQr6Y0xnAG5QW7L84lSZIkSRqDuZafGHAdHwTuR2lo8ruUsiq3UgrTnwecXNd/j8w8v23ZqykVAo4Czq3x+jMlL+JtwIMz8/Qeqz8DWEapEPAdSiMQ11LyVC6p2/e3wM6ZeWGX+I8tXyYz3wbsSSl3cSkz5VM+BOyWmaf2WX6u+68VzqnAPsAXKMfILT3mzVrGaHtKGaWzmcn/uRb4P8p+fB6wZWbe0G/9kiRFW6VHSZKkRS0iNgQuBu4EvC8zXzLmKEmSJEmSpAUSEXtSCisA7JuZ3xpnfCRJkiRJkiRJ0tJmDxWSJGmpOZhSmQLgP8YZEUmSJEmStOAOrp+3AOeMMyKSJEmSJEmSJGnps0KFJElaMiJiHeCVdfTszLTghCRJkiRJEyIiNo+IjXtMfwzwojr6xcy8amFiJkmSJEmSJEmSJtU6446AJElSLxGxKbApsBnwD8D2ddKbxhYpSZIkSZI0H3YEvhARnwO+CfwauB24D3AQ8GxgbeAG4LXjiqQkSZIkSZIkSZockZnjjoMkSVJXEbECOLrt5y9n5oFjiI4kSZIkSZonEbEM+E6f2a4BnpaZ35j/GEmSJEmSJC0+EXFX4K6zWPTmzDxv1PGRJGmps4cKSZK0VNwKXAR8CnjzmOMiSZIkSZJG72xgObA/8GBgC2BjSiWK84GvAe/OzMvGFUFJkiRJkqRF4CWs2TDlIC4Cth5tVCRJWvrsoUKSJEmSJEmSJEmSJEmSJGkJiIgVzLJCRWZuPdrYSJK09FmhQpIkSZIkSZIkSZIkSZIkSZIkTZ21xh0BSZIkSZIkSZIkSZIkSZIkSZKkhWaFCkmSJEmSJEmSJEmSJEmSJEmSNHWsUCFJkiRJkiRJkiRJkiRJkiRJkqaOFSokSZIkSZIkSZIkSZIkSZIkSdLUsUKFJEmSJEmSJEmSJEmSJEmSJEmaOlaokCRJkiRJkiRJkiRJkiRJkiRJU8cKFZIkSZIkSZIkSZIkSZIkSZIkaepYoUKSJEmSJEmSJEmSJEmSJEmSJE0dK1RIkiRJkiRJkiRJkiRJkiRJkqSpY4UKSZIkSZIkSZIkSZIkSZIkSZI0daxQIUmSJEmSJEmSJEmSJEmSJEmSpo4VKiRJkiRJkiRJkiRJkiRJkiRJ0tSxQoUkSZIkSZIkSZIkSZIkSZIkSZo6VqiQJEmSJEmSJEmSJEmSJEmSJElTxwoVkiRJkiRJkiRJkiRJkiRJkiRp6lihQpIkSZIkSZIkSZIkSZIkSZIkTR0rVEiSJEmSJEmSJEmSJEmSJEmSpKljhQpJkiRJkiRJkiRJkiRJkiRJkjR1/j+aeOcrmpqnWAAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "source": [
+    "Benzer aralıklara sahip birkaç sütun seçin. Türlerimizi düzenli tutmak için artist_top_genre sütununu eklediğinizden emin olun.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "# scaler = StandardScaler()\n",
+    "\n",
+    "X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
+    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "y = le.transform(y)\n",
+    "\n"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 0, 2, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0,\n",
+       "       2, 0, 0, 1, 1, 2, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2,\n",
+       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 0,\n",
+       "       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 2,\n",
+       "       2, 1, 1, 0, 1, 2, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0,\n",
+       "       1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       1, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 1, 1, 2, 1],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "source": [
+    "Bu sayılar bizim için pek bir şey ifade etmiyor, bu yüzden doğruluğu görmek için bir 'silüet skoru' alalım. Skorumuz ortada.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "source": [
+    "Bu modeli kullanarak, Dirsek Yöntemi ile oluşturulacak en iyi küme sayısını belirleyin\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Looks like 3 is a good number after all. Fit the model again and create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Bu modelin doğruluğu fena değil, ancak harika da değil. Verilerin K-Means Kümeleme için uygun olmayabileceği düşünülebilir. Farklı bir yöntem deneyebilirsiniz.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 811,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/5-Clustering/2-K-Means/solution/tester.ipynb b/translations/tr/5-Clustering/2-K-Means/solution/tester.ipynb
new file mode 100644
index 000000000..94266a981
--- /dev/null
+++ b/translations/tr/5-Clustering/2-K-Means/solution/tester.ipynb
@@ -0,0 +1,343 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "6f92868513e59d321245137c1c4c5311",
+   "translation_date": "2025-09-06T14:23:00+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/tester.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Son derste bitirdiğimiz yerden başlayın, veriler içe aktarılmış ve filtrelenmiş durumda.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 105
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Yalnızca 3 türe odaklanacağız. Belki 3 küme oluşturabiliriz!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 106
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 107
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# X = df.loc[:, ('danceability','energy')]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Unknown label type: 'continuous'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-110-3756ba7b0bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# we create an instance of SVM and fit out data. We do not scale our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# data since we want to plot the support vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mls30\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 30% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mls50\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 50% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mls100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 100% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m# actual graph construction (implementations should override this)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    181\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    182\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.semi_supervised import LabelSpreading\n",
+    "from sklearn.semi_supervised import SelfTrainingClassifier\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "X = df[['danceability','acousticness']].values\n",
+    "y = df['energy'].values\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "# step size in the mesh\n",
+    "h = .02\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "y_rand = rng.rand(y.shape[0])\n",
+    "y_30 = np.copy(y)\n",
+    "y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled\n",
+    "y_50 = np.copy(y)\n",
+    "y_50[y_rand < 0.5] = -1\n",
+    "# we create an instance of SVM and fit out data. We do not scale our\n",
+    "# data since we want to plot the support vectors\n",
+    "ls30 = (LabelSpreading().fit(X, y_30), y_30, 'Label Spreading 30% data')\n",
+    "ls50 = (LabelSpreading().fit(X, y_50), y_50, 'Label Spreading 50% data')\n",
+    "ls100 = (LabelSpreading().fit(X, y), y, 'Label Spreading 100% data')\n",
+    "\n",
+    "# the base classifier for self-training is identical to the SVC\n",
+    "base_classifier = SVC(kernel='rbf', gamma=.5, probability=True)\n",
+    "st30 = (SelfTrainingClassifier(base_classifier).fit(X, y_30),\n",
+    "        y_30, 'Self-training 30% data')\n",
+    "st50 = (SelfTrainingClassifier(base_classifier).fit(X, y_50),\n",
+    "        y_50, 'Self-training 50% data')\n",
+    "\n",
+    "rbf_svc = (SVC(kernel='rbf', gamma=.5).fit(X, y), y, 'SVC with rbf kernel')\n",
+    "\n",
+    "# create a mesh to plot in\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                     np.arange(y_min, y_max, h))\n",
+    "\n",
+    "color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}\n",
+    "\n",
+    "classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)\n",
+    "for i, (clf, y_train, title) in enumerate(classifiers):\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    colors = [color_map[y] for y in y_train]\n",
+    "    plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')\n",
+    "\n",
+    "    plt.title(title)\n",
+    "\n",
+    "plt.suptitle(\"Unlabeled points are colored white\", y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb b/translations/tr/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
new file mode 100644
index 000000000..368002742
--- /dev/null
+++ b/translations/tr/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
@@ -0,0 +1,100 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "27de2abc0235ebd22080fc8f1107454d",
+   "translation_date": "2025-09-06T15:22:21+00:00",
+   "source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You should download the book text, clean it, and import it here\n",
+    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
+    "    file_contents = f.read()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "book_pride = TextBlob(file_contents)\n",
+    "positive_sentiment_sentences = []\n",
+    "negative_sentiment_sentences = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sentence in book_pride.sentences:\n",
+    "    if sentence.sentiment.polarity == 1:\n",
+    "        positive_sentiment_sentences.append(sentence)\n",
+    "    if sentence.sentiment.polarity == -1:\n",
+    "        negative_sentiment_sentences.append(sentence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
+    "for sentence in positive_sentiment_sentences:\n",
+    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
+    "for sentence in negative_sentiment_sentences:\n",
+    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dilindeki hali, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/6-NLP/4-Hotel-Reviews-1/notebook.ipynb b/translations/tr/6-NLP/4-Hotel-Reviews-1/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/tr/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb b/translations/tr/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
new file mode 100644
index 000000000..3ca92b264
--- /dev/null
+++ b/translations/tr/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
@@ -0,0 +1,174 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2d05e7db439376aa824f4b387f8324ca",
+   "translation_date": "2025-09-06T15:21:59+00:00",
+   "source_file": "6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EDA\n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_difference_review_avg(row):\n",
+    "    return row[\"Average_Score\"] - row[\"Calc_Average_Score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "print(\"Loading data file now, this could take a while depending on file size\")\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n",
+    "end = time.time()\n",
+    "print(\"Loading took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What shape is the data (rows, columns)?\n",
+    "print(\"The shape of the data (rows, cols) is \" + str(df.shape))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# value_counts() creates a Series object that has index and values\n",
+    "#                in this case, the country and the frequency they occur in reviewer nationality\n",
+    "nationality_freq = df[\"Reviewer_Nationality\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What reviewer nationality is the most common in the dataset?\n",
+    "print(\"The highest frequency reviewer nationality is \" + str(nationality_freq.index[0]).strip() + \" with \" + str(nationality_freq[0]) + \" reviews.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the top 10 most common nationalities and their frequencies?\n",
+    "print(\"The top 10 highest frequency reviewer nationalities are:\")\n",
+    "print(nationality_freq[0:10].to_string())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many unique nationalities are there?\n",
+    "print(\"There are \" + str(nationality_freq.index.size) + \" unique nationalities in the dataset\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What was the most frequently reviewed hotel for the top 10 nationalities - print the hotel and number of reviews\n",
+    "for nat in nationality_freq[:10].index:\n",
+    "   # First, extract all the rows that match the criteria into a new dataframe\n",
+    "   nat_df = df[df[\"Reviewer_Nationality\"] == nat]   \n",
+    "   # Now get the hotel freq\n",
+    "   freq = nat_df[\"Hotel_Name\"].value_counts()\n",
+    "   print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many reviews are there per hotel (frequency count of hotel) and do the results match the value in `Total_Number_of_Reviews`?\n",
+    "# First create a new dataframe based on the old one, removing the uneeded columns\n",
+    "hotel_freq_df = df.drop([\"Hotel_Address\", \"Additional_Number_of_Scoring\", \"Review_Date\", \"Average_Score\", \"Reviewer_Nationality\", \"Negative_Review\", \"Review_Total_Negative_Word_Counts\", \"Positive_Review\", \"Review_Total_Positive_Word_Counts\", \"Total_Number_of_Reviews_Reviewer_Has_Given\", \"Reviewer_Score\", \"Tags\", \"days_since_review\", \"lat\", \"lng\"], axis = 1)\n",
+    "# Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found\n",
+    "hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')\n",
+    "# Get rid of all the duplicated rows\n",
+    "hotel_freq_df = hotel_freq_df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "print()\n",
+    "print(hotel_freq_df.to_string())\n",
+    "print(str(hotel_freq_df.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# While there is an `Average_Score` for each hotel according to the dataset, \n",
+    "# you can also calculate an average score (getting the average of all reviewer scores in the dataset for each hotel)\n",
+    "# Add a new column to your dataframe with the column header `Calc_Average_Score` that contains that calculated average. \n",
+    "df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n",
+    "# Add a new column with the difference between the two average scores\n",
+    "df[\"Average_Score_Difference\"] = df.apply(get_difference_review_avg, axis = 1)\n",
+    "# Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)\n",
+    "review_scores_df = df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "# Sort the dataframe to find the lowest and highest average score difference\n",
+    "review_scores_df = review_scores_df.sort_values(by=[\"Average_Score_Difference\"])\n",
+    "print(review_scores_df[[\"Average_Score_Difference\", \"Average_Score\", \"Calc_Average_Score\", \"Hotel_Name\"]])\n",
+    "# Do any hotels have the same (rounded to 1 decimal place) `Average_Score` and `Calc_Average_Score`?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/6-NLP/5-Hotel-Reviews-2/notebook.ipynb b/translations/tr/6-NLP/5-Hotel-Reviews-2/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/tr/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb b/translations/tr/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
new file mode 100644
index 000000000..56ba72294
--- /dev/null
+++ b/translations/tr/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
@@ -0,0 +1,172 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "033cb89c85500224b3c63fd04f49b4aa",
+   "translation_date": "2025-09-06T15:22:42+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import time\n",
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replace_address(row):\n",
+    "    if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Amsterdam, Netherlands\"\n",
+    "    elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Barcelona, Spain\"\n",
+    "    elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
+    "        return \"London, United Kingdom\"\n",
+    "    elif \"Milan\" in row[\"Hotel_Address\"]:        \n",
+    "        return \"Milan, Italy\"\n",
+    "    elif \"France\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Paris, France\"\n",
+    "    elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Vienna, Austria\" \n",
+    "    else:\n",
+    "        return row.Hotel_Address\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dropping columns we will not use:\n",
+    "df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace all the addresses with a shortened, more useful form\n",
+    "df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop `Additional_Number_of_Scoring`\n",
+    "df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
+    "# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
+    "df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
+    "df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process the Tags into new columns\n",
+    "# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
+    "# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
+    "# Family with young children, Family with older children, With a pet\n",
+    "df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
+    "df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
+    "df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
+    "df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
+    "df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
+    "df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
+    "df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
+    "df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No longer need any of these columns\n",
+    "df.drop([\"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_Filtered.csv\n",
+      "Filtering took 23.74 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Saving new data file with calculated columns\n",
+    "print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
+    "df.to_csv(r'../../data/Hotel_Reviews_Filtered.csv', index = False)\n",
+    "end = time.time()\n",
+    "print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb b/translations/tr/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
new file mode 100644
index 000000000..63beaabc7
--- /dev/null
+++ b/translations/tr/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
@@ -0,0 +1,137 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "341efc86325ec2a214f682f57a189dfd",
+   "translation_date": "2025-09-06T15:23:02+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV (you can )\n",
+    "import pandas as pd \n",
+    "\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We want to find the most useful tags to keep\n",
+    "# Remove opening and closing brackets\n",
+    "df.Tags = df.Tags.str.strip(\"[']\")\n",
+    "# remove all quotes too\n",
+    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
+    "# Now split the strings into a list\n",
+    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove leading and trailing spaces\n",
+    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
+    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
+    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
+    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
+    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
+    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the 6 columns into one with melt\n",
+    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "The shape of the tags with no filtering: (2514684, 2)\n",
+      "                        index   count\n",
+      "0                Leisure trip  338423\n",
+      "1                      Couple  205305\n",
+      "2               Solo traveler   89779\n",
+      "3               Business trip   68176\n",
+      "4                       Group   51593\n",
+      "5  Family with young children   49318\n",
+      "6  Family with older children   21509\n",
+      "7      Travelers with friends    1610\n",
+      "8                  With a pet    1078\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the value counts\n",
+    "tag_vc = df_tags.value.value_counts()\n",
+    "# print(tag_vc)\n",
+    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
+    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
+    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
+    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
+    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
+    "print(tag_vc[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb b/translations/tr/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
new file mode 100644
index 000000000..8fa44997f
--- /dev/null
+++ b/translations/tr/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
@@ -0,0 +1,260 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "705bf02633759f689abc37b19749a16d",
+   "translation_date": "2025-09-06T15:23:22+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[nltk_data] Downloading package vader_lexicon to\n[nltk_data]     /Users/jenlooper/nltk_data...\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import pandas as pd\n",
+    "import nltk as nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    "nltk.download('vader_lexicon')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vader_sentiment = SentimentIntensityAnalyzer()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are 3 possibilities of input for a review:\n",
+    "# It could be \"No Negative\", in which case, return 0\n",
+    "# It could be \"No Positive\", in which case, return 0\n",
+    "# It could be a review, in which case calculate the sentiment\n",
+    "def calc_sentiment(review):    \n",
+    "    if review == \"No Negative\" or review == \"No Positive\":\n",
+    "        return 0\n",
+    "    return vader_sentiment.polarity_scores(review)[\"compound\"]    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "df = pd.read_csv(\"../../data/Hotel_Reviews_Filtered.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove stop words - can be slow for a lot of text!\n",
+    "# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches\n",
+    "# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends\n",
+    "start = time.time()\n",
+    "cache = set(stopwords.words(\"english\"))\n",
+    "def remove_stopwords(review):\n",
+    "    text = \" \".join([word for word in review.split() if word not in cache])\n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the stop words from both columns\n",
+    "df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   \n",
+    "df.Positive_Review = df.Positive_Review.apply(remove_stopwords)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Removing stop words took 5.77 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "end = time.time()\n",
+    "print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Calculating sentiment columns for both positive and negative reviews\n",
+      "Calculating sentiment took 201.07 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a negative sentiment and positive sentiment column\n",
+    "print(\"Calculating sentiment columns for both positive and negative reviews\")\n",
+    "start = time.time()\n",
+    "df[\"Negative_Sentiment\"] = df.Negative_Review.apply(calc_sentiment)\n",
+    "df[\"Positive_Sentiment\"] = df.Positive_Review.apply(calc_sentiment)\n",
+    "end = time.time()\n",
+    "print(\"Calculating sentiment took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                          Negative_Review  Negative_Sentiment\n",
+      "186584  So bad experience memories I hotel The first n...             -0.9920\n",
+      "129503  First charged twice room booked booking second...             -0.9896\n",
+      "307286  The staff Had bad experience even booking Janu...             -0.9889\n",
+      "452092  No WLAN room Incredibly rude restaurant staff ...             -0.9884\n",
+      "201293  We usually traveling Paris 2 3 times year busi...             -0.9873\n",
+      "...                                                   ...                 ...\n",
+      "26899   I would say however one night expensive even d...              0.9933\n",
+      "138365  Wifi terribly slow I speed test network upload...              0.9938\n",
+      "79215   I find anything hotel first I walked past hote...              0.9938\n",
+      "278506  The property great location There bakery next ...              0.9945\n",
+      "339189  Guys I like hotel I wish return next year Howe...              0.9948\n",
+      "\n",
+      "[515738 rows x 2 columns]\n",
+      "                                          Positive_Review  Positive_Sentiment\n",
+      "137893  Bathroom Shower We going stay twice hotel 2 ni...             -0.9820\n",
+      "5839    I completely disappointed mad since reception ...             -0.9780\n",
+      "64158   get everything extra internet parking breakfas...             -0.9751\n",
+      "124178  I didnt like anythig Room small Asked upgrade ...             -0.9721\n",
+      "489137  Very rude manager abusive staff reception Dirt...             -0.9703\n",
+      "...                                                   ...                 ...\n",
+      "331570  Everything This recently renovated hotel class...              0.9984\n",
+      "322920  From moment stepped doors Guesthouse Hotel sta...              0.9985\n",
+      "293710  This place surprise expected good actually gre...              0.9985\n",
+      "417442  We celebrated wedding night Langham I commend ...              0.9985\n",
+      "132492  We arrived super cute boutique hotel area expl...              0.9987\n",
+      "\n",
+      "[515738 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=[\"Negative_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Negative_Review\", \"Negative_Sentiment\"]])\n",
+    "df = df.sort_values(by=[\"Positive_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Positive_Review\", \"Positive_Sentiment\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)\n",
+    "df = df.reindex([\"Hotel_Name\", \"Hotel_Address\", \"Total_Number_of_Reviews\", \"Average_Score\", \"Reviewer_Score\", \"Negative_Sentiment\", \"Positive_Sentiment\", \"Reviewer_Nationality\", \"Leisure_trip\", \"Couple\", \"Solo_traveler\", \"Business_trip\", \"Group\", \"Family_with_young_children\", \"Family_with_older_children\", \"With_a_pet\", \"Negative_Review\", \"Positive_Review\"], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_NLP.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
+    "df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/7-TimeSeries/1-Introduction/solution/notebook.ipynb b/translations/tr/7-TimeSeries/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..06a44991b
--- /dev/null
+++ b/translations/tr/7-TimeSeries/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,168 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bu defterde, aşağıdaki konuları nasıl yapacağımızı gösteriyoruz:\n",
+    "- bu modül için zaman serisi verilerini ayarlama\n",
+    "- verileri görselleştirme\n",
+    "\n",
+    "Bu örnekteki veriler, GEFCom2014 tahmin yarışmasından alınmıştır. 2012 ile 2014 yılları arasında 3 yıl boyunca saatlik elektrik yükü ve sıcaklık değerlerini içermektedir.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli ve Rob J. Hyndman, \"Olasılıksal enerji tahmini: Global Energy Forecasting Competition 2014 ve sonrası\", International Journal of Forecasting, cilt 32, sayı 3, s. 896-913, Temmuz-Eylül, 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "from common.utils import load_data\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CSV'den verileri bir Pandas veri çerçevesine yükle\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2698.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2558.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2444.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2402.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2403.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "data_dir = './data'\n",
+    "energy = load_data(data_dir)[['load']]\n",
+    "energy.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tüm mevcut yük verilerini grafiğe dök (Ocak 2012 - Aralık 2014)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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231.185656 \nL 91.475143 185.318661 \nL 91.506964 197.827841 \nL 91.761536 330.381263 \nL 91.825179 313.153708 \nL 91.984286 214.726208 \nL 92.047929 223.065662 \nL 92.143394 231.953764 \nL 92.238858 182.026771 \nL 92.302501 208.252159 \nL 92.525251 302.290472 \nL 92.557073 297.901286 \nL 92.652537 247.645104 \nL 92.779823 197.169464 \nL 92.811645 201.229461 \nL 92.875287 210.446752 \nL 92.907109 207.81324 \nL 93.002573 138.13491 \nL 93.066216 169.84678 \nL 93.257145 279.905623 \nL 93.288967 277.820759 \nL 93.352609 237.440247 \nL 93.416252 165.896512 \nL 93.479895 170.175969 \nL 93.670824 198.925138 \nL 93.702646 189.488388 \nL 93.766288 144.3895 \nL 93.79811 156.679221 \nL 94.052682 299.65696 \nL 94.084503 288.245076 \nL 94.179968 188.500821 \nL 94.275432 193.109466 \nL 94.434539 214.506749 \nL 94.466361 204.850539 \nL 94.530004 162.714352 \nL 94.561825 175.881911 \nL 94.816397 318.969379 \nL 94.848218 308.545062 \nL 95.039147 197.169464 \nL 95.134611 205.947836 \nL 95.198254 212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 197.217872 308.106144 \nL 197.440622 167.981376 \nL 197.59973 160.410029 \nL 197.727015 185.318661 \nL 197.917944 322.480728 \nL 197.981587 297.352638 \nL 198.236159 139.122477 \nL 198.331623 119.919787 \nL 198.363445 120.578165 \nL 198.490731 168.420294 \nL 198.713481 322.261269 \nL 198.745302 321.383432 \nL 199.063517 158.325166 \nL 199.12716 147.791119 \nL 199.158981 152.290035 \nL 199.286267 213.519182 \nL 199.477196 317.542894 \nL 199.509017 319.847217 \nL 199.604482 229.539711 \nL 199.763589 153.82625 \nL 199.827232 152.180305 \nL 199.890875 142.194907 \nL 199.922697 149.437064 \nL 200.049982 220.212691 \nL 200.20909 311.398033 \nL 200.272733 292.305074 \nL 200.495483 131.441401 \nL 200.527304 126.064648 \nL 200.590947 133.965183 \nL 200.622769 133.087346 \nL 200.65459 133.306805 \nL 200.781876 176.2111 \nL 200.972805 319.627757 \nL 201.036448 302.94885 \nL 201.227377 151.851116 \nL 201.259198 152.399765 \nL 201.29102 152.180305 \nL 201.322841 152.619224 \nL 201.354662 152.948413 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 218.983754 162.055974 \nL 219.015576 162.385163 \nL 219.11104 187.622984 \nL 219.301969 310.300737 \nL 219.365612 281.880756 \nL 219.556541 157.995977 \nL 219.683826 161.726785 \nL 219.620183 157.666788 \nL 219.715648 160.410029 \nL 219.747469 161.178137 \nL 219.874755 183.562986 \nL 220.065684 319.737487 \nL 220.097505 319.079109 \nL 220.161148 297.462367 \nL 220.352077 172.80948 \nL 220.383899 175.442992 \nL 220.63847 211.763507 \nL 220.829399 325.22397 \nL 220.861221 324.016944 \nL 220.924864 310.191007 \nL 221.115792 208.471618 \nL 221.147614 210.117563 \nL 221.179435 211.214859 \nL 221.211257 210.88567 \nL 221.2749 200.461353 \nL 221.306721 201.44892 \nL 221.434007 256.533206 \nL 221.593114 330.271534 \nL 221.656757 303.168309 \nL 221.911329 131.55113 \nL 222.006793 125.40627 \nL 222.038615 127.052215 \nL 222.165901 168.420294 \nL 222.356829 309.861818 \nL 222.420472 285.392105 \nL 222.675044 125.845188 \nL 222.738687 129.246808 \nL 222.865973 155.252736 \nL 222.897794 153.058143 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227.702836 315.238571 \nL 227.766479 291.427236 \nL 228.02105 135.391668 \nL 228.116515 126.503566 \nL 228.148336 128.039781 \nL 228.275622 168.200835 \nL 228.466551 311.727222 \nL 228.530194 285.831024 \nL 228.784765 105.764662 \nL 228.848408 100.278179 \nL 228.912051 102.143583 \nL 229.039337 142.085177 \nL 229.230266 295.377504 \nL 229.262087 291.536966 \nL 229.548481 84.696568 \nL 229.643945 97.864127 \nL 229.803052 143.182474 \nL 229.993981 278.369407 \nL 230.057624 251.156453 \nL 230.280374 103.789528 \nL 230.344017 117.396005 \nL 230.598589 209.239725 \nL 230.757696 299.108312 \nL 230.789518 298.559664 \nL 230.853161 280.564001 \nL 231.075911 157.666788 \nL 231.26684 168.310565 \nL 231.298661 167.761916 \nL 231.553233 301.632094 \nL 231.585054 296.4748 \nL 231.648697 262.239148 \nL 231.839626 164.908945 \nL 231.935091 146.254904 \nL 231.966912 138.573828 \nL 232.030555 148.120308 \nL 232.062376 146.035445 \nL 232.285127 300.863987 \nL 232.316948 295.048315 \nL 232.635163 98.961423 \nL 232.698806 100.168449 \nL 232.730627 101.704665 \nL 232.857913 151.302468 \nL 233.048842 295.487234 \nL 233.080663 291.207777 \nL 233.367056 101.046287 \nL 233.430699 107.191147 \nL 233.557985 130.014915 \nL 233.589807 127.710593 \nL 233.812557 294.280207 \nL 233.844378 289.561832 \nL 234.194415 111.690063 \nL 234.258057 119.480868 \nL 234.385343 155.801384 \nL 234.576272 292.414803 \nL 234.608094 288.245076 \nL 234.894487 125.625729 \nL 234.926308 127.491133 \nL 235.085416 148.559227 \nL 235.117237 147.68139 \nL 235.339987 312.166141 \nL 235.371809 306.569929 \nL 235.721845 120.687895 \nL 235.753666 121.346273 \nL 235.880952 144.608959 \nL 236.135524 304.046147 \nL 236.167345 296.4748 \nL 236.549203 193.877574 \nL 236.676489 220.322421 \nL 236.899239 337.513691 \nL 236.962882 327.528292 \nL 237.185632 225.589444 \nL 237.217454 226.79647 \nL 237.249275 224.272688 \nL 237.408383 203.314324 \nL 237.631133 332.466127 \nL 237.662954 326.211537 \nL 237.949347 160.958678 \nL 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345.414226 \nL 247.654894 330.600723 \nL 247.845823 238.208354 \nL 247.877645 242.048892 \nL 247.941288 246.438078 \nL 247.973109 242.59754 \nL 248.068573 234.916464 \nL 248.100395 239.41538 \nL 248.323145 354.192598 \nL 248.354967 351.120168 \nL 248.482252 275.296977 \nL 248.577717 229.539711 \nL 248.64136 231.075927 \nL 248.673181 230.966197 \nL 248.736824 220.651609 \nL 248.832289 201.010001 \nL 248.86411 206.496484 \nL 249.08686 340.586121 \nL 249.150503 302.94885 \nL 249.309611 191.573251 \nL 249.405075 187.513254 \nL 249.468718 192.01217 \nL 249.500539 189.378658 \nL 249.596004 178.844611 \nL 249.691468 261.6905 \nL 249.850575 324.785051 \nL 249.914218 283.526701 \nL 250.073326 161.507326 \nL 250.16879 158.764085 \nL 250.264255 163.043541 \nL 250.296076 161.946245 \nL 250.327897 161.507326 \nL 250.359719 153.387332 \nL 250.39154 165.567323 \nL 250.614291 321.602891 \nL 250.677934 284.953187 \nL 250.868862 168.420294 \nL 250.996148 160.958678 \nL 251.02797 161.946245 \nL 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216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 315.75283 171.382995 \nL 315.880116 189.488388 \nL 315.911937 182.026771 \nL 315.97558 135.281939 \nL 316.039223 155.252736 \nL 316.134688 222.955932 \nL 316.293795 295.487234 \nL 316.357438 280.125082 \nL 316.516545 184.221364 \nL 316.548367 185.977039 \nL 316.643831 197.279193 \nL 316.675652 191.682981 \nL 316.739295 154.704087 \nL 316.802938 177.198666 \nL 317.05751 323.578025 \nL 317.152974 296.4748 \nL 317.312082 212.531615 \nL 317.343903 215.165127 \nL 317.407546 223.065662 \nL 317.439368 216.262423 \nL 317.503011 165.238134 \nL 317.566653 190.256495 \nL 317.789404 338.610988 \nL 317.853047 324.785051 \nL 318.043975 214.28729 \nL 318.171261 228.332685 \nL 318.203083 224.272688 \nL 318.266726 161.397596 \nL 318.330369 174.674884 \nL 318.553119 330.381263 \nL 318.616762 319.518028 \nL 318.807691 200.790542 \nL 318.839512 203.424054 \nL 318.934976 209.349455 \nL 318.966798 204.411621 \nL 319.030441 154.923547 \nL 319.094084 172.480291 \nL 319.348656 336.745583 \nL 319.380477 327.089374 \nL 319.475941 219.993232 \nL 319.603227 220.212691 \nL 319.635049 220.871069 \nL 319.66687 219.225124 \nL 319.698692 219.334854 \nL 319.825978 144.27977 \nL 319.88962 169.956509 \nL 320.080549 296.145611 \nL 320.112371 295.158045 \nL 320.176014 248.852131 \nL 320.239657 173.906777 \nL 320.303299 176.979207 \nL 320.462407 199.583516 \nL 320.494228 195.413789 \nL 320.557871 129.246808 \nL 320.621514 138.13491 \nL 320.844264 294.719126 \nL 320.907907 283.087783 \nL 321.003372 177.308396 \nL 321.098836 183.233797 \nL 321.226122 204.74081 \nL 321.257943 199.144597 \nL 321.321586 157.227869 \nL 321.385229 179.722449 \nL 321.639801 331.039641 \nL 321.703444 314.360734 \nL 321.862551 207.264592 \nL 321.926194 212.641345 \nL 321.989837 222.297554 \nL 322.021659 217.46945 \nL 322.085302 175.881911 \nL 322.148944 200.900272 \nL 322.403516 336.196935 \nL 322.467159 323.248836 \nL 322.658088 232.392682 \nL 322.689909 235.245653 \nL 322.753552 243.475378 \nL 322.785374 235.026194 \nL 322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 340.923609 150.973279 \nL 340.95543 151.083009 \nL 341.082716 162.933811 \nL 341.114538 159.312733 \nL 341.178181 98.741964 \nL 341.241824 109.38574 \nL 341.464574 253.460776 \nL 341.528217 240.293218 \nL 341.623681 133.087346 \nL 341.719146 138.464099 \nL 341.846431 158.434896 \nL 341.878253 156.350032 \nL 341.941896 100.05872 \nL 342.005539 111.141415 \nL 342.26011 265.201849 \nL 342.291932 254.448343 \nL 342.451039 145.377067 \nL 342.482861 145.596526 \nL 342.641968 174.674884 \nL 342.705611 132.209508 \nL 342.769254 155.362465 \nL 343.023826 302.619661 \nL 343.087469 286.489402 \nL 343.246576 199.802975 \nL 343.310219 208.910536 \nL 343.373862 219.225124 \nL 343.405683 214.616478 \nL 343.469326 153.716521 \nL 343.532969 171.053806 \nL 343.787541 298.011016 \nL 343.851184 287.476969 \nL 344.010291 198.705679 \nL 344.073934 203.862972 \nL 344.137577 216.372153 \nL 344.169398 214.17756 \nL 344.233041 148.668956 \nL 344.296684 173.138669 \nL 344.519435 307.228306 \nL 344.551256 304.375335 \nL 344.614899 260.593204 \nL 344.774006 175.662451 \nL 344.901292 193.658115 \nL 344.933114 188.83001 \nL 345.028578 123.650595 \nL 345.060399 137.695991 \nL 345.28315 282.868323 \nL 345.346793 271.456439 \nL 345.474078 169.298131 \nL 345.537721 177.418126 \nL 345.665007 199.473786 \nL 345.696829 196.291626 \nL 345.760472 135.940317 \nL 345.824115 150.973279 \nL 346.046865 299.87642 \nL 346.110508 287.03805 \nL 346.205972 176.10137 \nL 346.301437 181.148934 \nL 346.428722 187.952173 \nL 346.460544 184.550553 \nL 346.524187 138.354369 \nL 346.58783 157.557058 \nL 346.842401 309.532629 \nL 346.874223 297.681827 \nL 347.065152 190.146766 \nL 347.192438 207.264592 \nL 347.224259 204.960269 \nL 347.287902 148.339767 \nL 347.351545 162.494893 \nL 347.574295 305.801821 \nL 347.637938 294.170478 \nL 347.797045 186.196498 \nL 347.828867 187.513254 \nL 347.956153 199.583516 \nL 347.987974 199.473786 \nL 348.051617 159.641922 \nL 348.11526 183.014338 \nL 348.369832 324.565592 \nL 348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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159.203003 \nL 389.3877 188.72028 \nL 389.578629 316.884516 \nL 389.610451 316.555327 \nL 389.674094 271.12725 \nL 389.833201 188.61055 \nL 389.865022 189.488388 \nL 389.992308 201.55865 \nL 390.087773 157.337599 \nL 390.151416 185.099202 \nL 390.342344 310.959115 \nL 390.374166 310.520196 \nL 390.437809 269.152117 \nL 390.596916 186.745146 \nL 390.660559 196.181897 \nL 390.756024 215.494316 \nL 390.787845 210.666211 \nL 390.851488 177.747315 \nL 390.883309 188.939739 \nL 391.10606 317.981813 \nL 391.201524 297.462367 \nL 391.32881 229.320252 \nL 391.392453 237.111058 \nL 391.519739 263.007256 \nL 391.55156 256.094288 \nL 391.615203 206.606214 \nL 391.678846 227.015929 \nL 391.901596 326.211537 \nL 391.933418 325.22397 \nL 392.028882 286.92832 \nL 392.156168 237.440247 \nL 392.187989 242.158622 \nL 392.283454 261.141852 \nL 392.315275 256.423477 \nL 392.41074 198.486219 \nL 392.442561 210.446752 \nL 392.63349 331.47856 \nL 392.665311 330.161804 \nL 392.728954 288.793725 \nL 392.888062 207.045132 \nL 392.983526 220.43215 \nL 393.047169 227.345118 \nL 393.07899 222.078095 \nL 393.174455 181.807312 \nL 393.206276 196.511086 \nL 393.429027 329.283967 \nL 393.49267 289.232643 \nL 393.651777 198.37649 \nL 393.683598 194.097033 \nL 393.747241 199.254327 \nL 393.810884 210.117563 \nL 393.842706 202.875406 \nL 393.93817 178.625152 \nL 393.969991 193.548385 \nL 394.192742 330.929912 \nL 394.256385 290.11048 \nL 394.415492 207.154862 \nL 394.542778 223.394851 \nL 394.574599 229.8689 \nL 394.606421 226.9062 \nL 394.701885 188.391091 \nL 394.733707 197.059734 \nL 394.924635 317.542894 \nL 394.956457 316.006679 \nL 395.0201 274.748329 \nL 395.147386 195.633248 \nL 395.179207 195.19433 \nL 395.338314 210.446752 \nL 395.370136 207.045132 \nL 395.4656 164.908945 \nL 395.497422 172.041373 \nL 395.688351 294.170478 \nL 395.720172 293.621829 \nL 395.783815 253.460776 \nL 395.879279 177.966774 \nL 395.911101 178.734882 \nL 395.974744 187.622984 \nL 396.10203 221.309987 \nL 396.133851 219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 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214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 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319.737487 \nL 413.540193 227.564578 \nL 413.603836 244.024026 \nL 413.667478 254.119154 \nL 413.6993 249.400779 \nL 413.794764 219.883502 \nL 413.826586 221.748906 \nL 414.017515 342.561255 \nL 414.049336 341.463959 \nL 414.112979 295.926152 \nL 414.272086 212.860804 \nL 414.463015 243.255918 \nL 414.494837 237.659706 \nL 414.558479 213.409452 \nL 414.590301 214.06783 \nL 414.78123 341.244499 \nL 414.813051 340.91531 \nL 414.876694 297.572097 \nL 415.035801 207.264592 \nL 415.099444 211.982967 \nL 415.22673 231.075927 \nL 415.290373 222.846203 \nL 415.322195 208.471618 \nL 415.354016 210.007833 \nL 415.544945 343.658552 \nL 415.576766 343.329363 \nL 415.640409 301.741824 \nL 415.767695 215.823505 \nL 415.799517 216.701342 \nL 415.958624 234.367816 \nL 415.990445 231.734305 \nL 416.08591 204.850539 \nL 416.117731 213.738641 \nL 416.30866 345.194767 \nL 416.340482 343.768281 \nL 416.404124 299.327771 \nL 416.53141 221.858636 \nL 416.563232 221.968365 \nL 416.595053 222.517014 \nL 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331.58829 \nL 498.089826 330.381263 \nL 498.153469 308.435333 \nL 498.344397 190.914873 \nL 498.376219 191.024603 \nL 498.439862 185.099202 \nL 498.503505 173.797047 \nL 498.535326 177.418126 \nL 498.662612 228.661874 \nL 498.853541 322.151539 \nL 498.917184 307.886684 \nL 499.171756 188.171632 \nL 499.203577 190.037036 \nL 499.26722 177.637585 \nL 499.330863 181.039204 \nL 499.362684 183.233797 \nL 499.585435 312.824519 \nL 499.617256 303.716958 \nL 499.935471 127.381404 \nL 499.967292 128.039781 \nL 499.999114 127.600863 \nL 500.1264 151.192738 \nL 500.34915 310.849385 \nL 500.412793 287.696428 \nL 500.635543 147.791119 \nL 500.79465 143.072744 \nL 500.921936 184.331094 \nL 501.112865 318.530461 \nL 501.176508 292.743992 \nL 501.399258 149.875983 \nL 501.526544 138.573828 \nL 501.558365 140.439232 \nL 501.685651 179.832178 \nL 501.87658 315.896949 \nL 501.940223 291.317507 \nL 502.162973 136.269506 \nL 502.194795 131.66086 \nL 502.226616 135.830587 \nL 502.449366 185.977039 \nL 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168.639753 \nL 528.288396 168.639753 \nL 528.352039 166.66462 \nL 528.383861 176.2111 \nL 528.606611 328.625589 \nL 528.670254 294.389937 \nL 528.829361 206.057565 \nL 528.861183 203.972702 \nL 528.924826 205.508917 \nL 528.988468 210.666211 \nL 529.083933 210.007833 \nL 529.115754 200.790542 \nL 529.147576 207.374321 \nL 529.370326 349.913142 \nL 529.433969 315.56776 \nL 529.593076 224.382418 \nL 529.688541 222.187825 \nL 529.784005 230.966197 \nL 529.847648 229.97863 \nL 529.879469 222.517014 \nL 529.911291 228.442415 \nL 530.134041 351.449357 \nL 530.165863 348.376927 \nL 530.261327 296.58453 \nL 530.388613 240.841866 \nL 530.420434 241.061325 \nL 530.515899 245.560241 \nL 530.643185 222.078095 \nL 530.675006 231.734305 \nL 530.897756 350.46179 \nL 530.929578 348.486656 \nL 531.025042 307.776955 \nL 531.18415 239.195921 \nL 531.279614 254.228884 \nL 531.311435 247.315915 \nL 531.4069 224.272688 \nL 531.438721 234.477546 \nL 531.62965 357.484488 \nL 531.661472 357.045569 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 569.910873 275.626166 \nL 569.974516 190.037036 \nL 570.038159 197.827841 \nL 570.197266 223.61431 \nL 570.229087 227.125659 \nL 570.260909 219.444583 \nL 570.324552 164.689486 \nL 570.388195 190.037036 \nL 570.610945 322.04181 \nL 570.674588 280.234812 \nL 570.738231 193.109466 \nL 570.801874 195.633248 \nL 570.833695 195.523519 \nL 570.992803 226.68674 \nL 571.024624 222.407284 \nL 571.088267 172.480291 \nL 571.15191 197.498652 \nL 571.342839 325.333699 \nL 571.37466 325.00451 \nL 571.438303 282.539134 \nL 571.59741 195.962437 \nL 571.629232 195.852708 \nL 571.756518 203.094865 \nL 571.820161 186.086768 \nL 571.883804 197.388923 \nL 571.979268 275.845625 \nL 572.138375 340.476392 \nL 572.202018 305.143443 \nL 572.361126 204.192161 \nL 572.392947 204.850539 \nL 572.552054 230.966197 \nL 572.615697 199.254327 \nL 572.647519 214.06783 \nL 572.90209 352.436924 \nL 572.965733 333.782883 \nL 573.124841 247.535375 \nL 573.156662 250.498075 \nL 573.283948 267.835361 \nL 573.31577 258.947259 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199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 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150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 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693.537265 325.443429 \nL 693.569087 321.493162 \nL 693.63273 277.71103 \nL 693.760015 206.386754 \nL 693.791837 206.606214 \nL 693.919123 221.858636 \nL 693.950944 226.357551 \nL 693.982766 225.369985 \nL 694.110052 198.26676 \nL 694.30098 312.934248 \nL 694.332802 310.849385 \nL 694.396445 266.628335 \nL 694.460088 201.229461 \nL 694.523731 207.92297 \nL 694.714659 233.819168 \nL 694.778302 226.9062 \nL 694.873767 204.960269 \nL 695.096517 328.625589 \nL 695.16016 286.92832 \nL 695.255624 214.616478 \nL 695.287446 217.798638 \nL 695.351089 223.72404 \nL 695.478375 242.487811 \nL 695.542017 237.549976 \nL 695.60566 226.79647 \nL 695.637482 228.661874 \nL 695.828411 340.476392 \nL 695.892054 335.648287 \nL 696.051161 243.804566 \nL 696.146625 257.081855 \nL 696.24209 271.23698 \nL 696.273911 268.384009 \nL 696.401197 244.024026 \nL 696.592126 346.511523 \nL 696.655769 344.975307 \nL 696.751233 293.182911 \nL 696.878519 221.968365 \nL 696.910341 223.285121 \nL 697.005805 239.964029 \nL 697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 \nL 707.729639 249.839697 \nL 707.856925 227.893767 \nL 708.047853 338.062339 \nL 708.079675 334.66072 \nL 708.143318 293.621829 \nL 708.302425 218.566746 \nL 708.461532 233.489979 \nL 708.493354 229.649441 \nL 708.588818 213.738641 \nL 708.62064 214.616478 \nL 708.84339 336.635854 \nL 708.907033 300.425068 \nL 709.034319 224.272688 \nL 709.06614 225.589444 \nL 709.225248 245.99916 \nL 709.28889 238.866732 \nL 709.384355 216.701342 \nL 709.607105 343.878011 \nL 709.670748 303.497498 \nL 709.829855 209.568914 \nL 709.92532 215.933234 \nL 709.988963 221.419717 \nL 710.020784 216.701342 \nL 710.116249 201.33919 \nL 710.14807 205.728377 \nL 710.338999 329.722886 \nL 710.37082 328.515859 \nL 710.434463 290.000751 \nL 710.593571 208.910536 \nL 710.657213 216.481883 \nL 710.752678 232.502412 \nL 710.848142 231.514845 \nL 710.911785 218.127827 \nL 711.102714 340.91531 \nL 711.166357 331.259101 \nL 711.357286 232.941331 \nL 711.389107 233.819168 \nL 711.611857 256.752666 \nL 711.6755 241.171055 \nL 711.707322 255.106721 \nL 711.898251 345.962874 \nL 711.961894 333.343964 \nL 712.121001 255.984558 \nL 712.184644 263.775363 \nL 712.248287 274.089951 \nL 712.31193 270.578602 \nL 712.439216 250.607805 \nL 712.661966 354.302328 \nL 712.693787 352.327194 \nL 712.789252 308.764522 \nL 712.916538 252.473209 \nL 712.948359 253.460776 \nL 713.043823 260.702933 \nL 713.202931 237.879165 \nL 713.393859 344.31693 \nL 713.425681 342.341796 \nL 713.489324 302.290472 \nL 713.648431 219.773772 \nL 713.680253 220.102961 \nL 713.807539 240.402947 \nL 713.871181 235.245653 \nL 713.934824 229.97863 \nL 713.966646 220.322421 \nL 714.157575 340.805581 \nL 714.189396 338.391528 \nL 714.253039 301.522364 \nL 714.412146 231.514845 \nL 714.443968 229.649441 \nL 714.475789 234.148357 \nL 714.571254 240.841866 \nL 714.603075 239.63484 \nL 714.730361 218.566746 \nL 714.92129 339.927743 \nL 714.984933 325.772618 \nL 715.112219 234.367816 \nL 715.175862 237.001328 \nL 715.36679 245.121322 \nL 715.462255 236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 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158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 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339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 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\nL 834.569999 204.850539 \nL 834.633642 221.090528 \nL 834.856392 341.13477 \nL 834.888214 337.73315 \nL 835.0155 250.607805 \nL 835.079143 228.332685 \nL 835.142785 239.41538 \nL 835.23825 262.348878 \nL 835.270071 256.423477 \nL 835.333714 219.554313 \nL 835.397357 235.684572 \nL 835.588286 339.598554 \nL 835.651929 335.977476 \nL 835.747393 285.392105 \nL 835.811036 245.88943 \nL 835.874679 254.777532 \nL 835.970144 270.578602 \nL 836.001965 269.920224 \nL 836.097429 215.494316 \nL 836.161072 235.026194 \nL 836.383823 340.366662 \nL 836.447466 308.654792 \nL 836.606573 211.324589 \nL 836.76568 228.113226 \nL 836.797502 223.61431 \nL 836.861145 186.415957 \nL 836.892966 193.767844 \nL 837.147538 345.523956 \nL 837.179359 334.880179 \nL 837.40211 216.262423 \nL 837.497574 219.225124 \nL 837.529395 221.858636 \nL 837.561217 219.554313 \nL 837.62486 186.854876 \nL 837.688503 213.189993 \nL 837.879431 339.379095 \nL 837.911253 339.269365 \nL 837.974896 301.193176 \nL 838.134003 204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 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222.626743 \nL 852.103626 191.682981 \nL 852.135448 200.461353 \nL 852.39002 332.356397 \nL 852.421841 330.052075 \nL 852.485484 283.85589 \nL 852.61277 217.908368 \nL 852.644591 218.127827 \nL 852.676413 219.444583 \nL 852.771877 228.881334 \nL 852.83552 202.217028 \nL 852.867342 172.480291 \nL 852.930985 188.939739 \nL 853.153735 324.016944 \nL 853.217378 310.191007 \nL 853.408307 208.032699 \nL 853.535592 220.761339 \nL 853.567414 217.140261 \nL 853.631057 177.308396 \nL 853.6947 192.451088 \nL 853.91745 332.356397 \nL 853.981093 320.725054 \nL 854.108379 216.481883 \nL 854.203843 220.980798 \nL 854.299308 232.831601 \nL 854.331129 231.624575 \nL 854.394772 181.258664 \nL 854.458415 197.169464 \nL 854.681165 338.940176 \nL 854.744808 325.882348 \nL 854.872094 210.9954 \nL 854.935737 211.214859 \nL 854.967558 210.775941 \nL 854.99938 215.274856 \nL 855.063023 214.06783 \nL 855.126666 188.281362 \nL 855.158487 172.041373 \nL 855.22213 193.109466 \nL 855.44488 330.052075 \nL 855.508523 320.834784 \nL 855.699452 202.875406 \nL 855.731273 204.850539 \nL 855.826738 214.506749 \nL 855.858559 211.653778 \nL 855.922202 171.931643 \nL 855.985845 192.341359 \nL 856.240417 316.555327 \nL 856.30406 296.365071 \nL 856.431346 226.028363 \nL 856.494989 233.489979 \nL 856.590453 250.498075 \nL 856.622275 243.914296 \nL 856.685917 189.268928 \nL 856.74956 213.738641 \nL 857.004132 334.002342 \nL 857.067775 323.468295 \nL 857.258704 241.500244 \nL 857.290525 247.645104 \nL 857.354168 254.996991 \nL 857.38599 247.535375 \nL 857.449633 187.952173 \nL 857.513276 210.117563 \nL 857.736026 333.782883 \nL 857.799669 320.615324 \nL 857.990598 211.434319 \nL 858.117883 227.015929 \nL 858.149705 226.028363 \nL 858.213348 175.772181 \nL 858.276991 193.877574 \nL 858.499741 328.625589 \nL 858.563384 316.116408 \nL 858.754313 219.554313 \nL 858.786134 224.492147 \nL 858.881599 231.734305 \nL 858.91342 228.661874 \nL 858.977063 177.308396 \nL 859.040706 198.486219 \nL 859.263456 338.610988 \nL 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JJOklaEOiTUyiU+9RzfrI+kWEpAcv9fLCwq50j6M2AupLufKZu1YC8K9MO3lvqlt7/V2EJAa999LMTXUR9oSUA1QSSWoJehKjjkl0f5VxnF0RkhrioxoGA9e23OHV3VTDyeJj4KEVJLY4zW5qBnMhED1UEklqCTpFu50l0SnjaEkkJHXE8XP30iXt72AJDGd4uU36d8VRMXQ+CuKQB1ccLun1nttSj81H2kt6TeIcKomEBIR+HHaTVKConfy5VBIJIaXmeNcJDGe0Oone26FeUsz2ug7UtPYF0pbezXRhJeOYiT1VLX22LuSVJSqvo/HrF3bhXx/ZUNJrEudQSSSpJWi3Ti+ZTJXtBNAGISQZxO07f2WnTuFgncRAueyh9fh0PhbRD8Y4xB31nZbHU2FPDyrLtF7GLNzdmD9QfT69TYkeKokktQQfkxhQO3kpT1lNSHqIy/c+4nOWqFkq6G0aHm4XFuj6SzRaewct9/tJVkXKDyqJhMSMIDILEkKSxdbaDgyOeC+LEBQZXXHEOGVcJYTYY5ekaLyNC5X+i+cchFBJJKml5KurDuWtlriG8pmQ8keviBW4ekZEUG7zJDykLHw2do9JP9L98tntYXSJxBi9jBmf/77NZj/6XAj8/gmVRJJa4uqAo/VLE9b7G7uZljpBVLf0YtXBlqi7QYhv/EmduErY9KFfD31xx7HoOkJCx27te5ydJdHF4kMYSCnR0TcUwZWJCiqJhMQMvZA/2NSDL923BvctOxhdh4grPnv3Knz/8c0YGI7edZDEn3k66+G2uo4IexIMXM4iJBqklNjf2GN5zAQbJbHQklj6r/mhlVX44E1LcbzrRMmvTYqhkkjSSwAL3UMjWfuDXPKbFyoB5Fb0tBW1ebuid0Mj7vhsAFkMSfkza3Pd6M8zNtUV7JNSomdwpKT9GVfgbup+kjhWJzGoHhG/8FGkA7N6g/rP2C4mseA8vx3ywJK9TQCA410DEVydGCm5kiiEeKcQYkAI8Zf8758WQmSFEL26f9/XHf9GIcRcIUSfEKJWCHGlob0r89v7hBAvCiHeWOq/iSQTuwBvJ/x81jbHx7pNAiEBnDQh94keae1Dcw+FZpI4xkGOeGDvse7Rn5ftay759fXKnZ9JIhUTZ7T0DNoq47saOjGSKVyQdJNghNlN00HfkP2CkqYkmr0SUddnZgkw5wxngjdSGInCkvgggC2GbceklKfq/j1lOH4IwJsBXAXgYSHEuQCQ/+8jAL6b398P4KGw/wBCNBbvaQq1ff2qX2sP/fQJKXe+MnXN6M/9DiZ9QeNfneDsTsX6w62m+zTriYp9x7tx6QPr8IX7Vpse8/zWBl99I+lhvM2CQTZ8vcMRYXhplROHmnrwzt8txILKcL3MSqokCiG+DaATwHKHx78WwOUArpNS9kop1wJ4GTmlEMgpjfOklKullL0ArgPwTSHEaU7a39/YHfoNJvGl5MlNbeZOBwyxBKsOthRYO6Ne4SOElD8FlkQPIofupmqunL5p9Offza0s2NfWa74A2NKTq2tX3dJnekxn/zC6B4ZN9/NRpINymCLsqO8EADxXUY+B4QyT9plQebQLALBkT2Oo1ymZkiiEOB3AjQB+pdh9lhCiSQhxRAhxb145BIB3ARiRUuqzduwEcG7+53PzvwMApJRVyFkd3+WkT1+6bw2unuHcXZCUF3EbOP/fn9cX/K5NDjSoJBJCwka/MKV3kd9Q1Ya6tv4oulR2GGNPrWS7mbJtdDG1nEybtLHnWBc6++mhUi4EMUXwu0gUFFUtvXjPdYtw28J90XUixpRqEa6UlsSbADwmpTT6RewH8AEAfwPgswA+DOCe/L5TAXQbju8CcJpuf5fF/lGEED8WQlQIISpaWpienpQeO3mrcq/QT9K4oEZIuohiklYw+chff2gkiyse3YiL7lxhe77W5SBivklhIqGguWTqWnzzofX2B5JEkAlAYMTFA0CbDz27uT7insSbsIeIkiiJQogPALgYwL3GfVLKRinlXillVkp5BMCvkXMxBYBeAKcbTjkdQI/D/frrTJNSXiClvODMM8/EvuNG3ZOkjVIIQzfJagZHsrjg5mWF5+tOpyWREBIF33x4neNj6W7qHqNkP9Z5ApOnzMeO+k7HqrbV8GClsFe3mruxkmRhlsBItdnsnYjL4s6E8bl+cN6jplTPqVSWxE8DmAygTgjRCOB/AVwuhFD5ekpdvw4CmCCEeKdu//kA9uR/3pP/HQAghHg7gJPz51ly+8L97v4CUnaUWhiqBLhxItXaW+hiqj+DvvmEkLDRZ8LUJM7uo+4XVakkusAwNqw5lPN2mrGxNorekITiJNml3SyiMLtxdHOOscUmCpIoKZWSOA3AO5BzK/0AgD8DmA/gi0KIzwgh3iZy/B2A2wG8BABSyj4AcwDcKIR4rRDiEwC+DuCZfLszAHxNCHFhPo7xRgBzpJTW1UTBAYwkD6qI5U8UxYsJ0TPOb+IaSirXGNf/tAVMCfNJsvEu+7nrPRZJb0hyCMLqVkqlLOykK2kg7ClDSZREKWV/3q20UUrZiJyb6ICUsgXABwGsB9CX/28lgP/SnX41gNcAaAYwC8BPpZR78u3uAfAT5JTFZuRiEa920ifqiMQLe451eU7N7OVb1isN1B/Kn1mMvyA6olC4CkMS3V9/1ALAUdY7ultXijn7HYsOhH8REjpmSqLqOw7ivWrqHkCjj3rAP35mq+k+znesKZUuP6E0lylESnm97ud7MJaoRnVsO4BvWOyfCWCm2z44XS3ZXteB889+PcaN44CXdura+nHJ1LX4/sfehhu+/r6i/SqZZjdREibnaehXmGllKn/WV5nXUiPpI8ykJWYEZkngkOkYq1gy1W082nki0OsPDGcCbY9EQ9Dxe3bN/fOtuWp2NbdfEuh1AX0CLGJFWSSuSSrrDrfisofW4zuPbbI/mJQ9rX25eMGdDcaEuuboV/C8ye+xkxbutnfN+OFTFfjq/WtsjyPxhPEXRE8U74PvOonBdaUsGM5k8Z3p1nOIYnfTHBLS8TvARUSSNXFy4qtBvJJaJdGJ2G3oyNWEWl/V5sukTsoDLXGMmVE5jOmcXrg/ub7G9vhl+5o8JZkg8YAqItET9fvgx0U+6r7HhcV7GrH2sLWHgPE+6xVDMx1xQ1WbZRuEqEiKwjj5jFNyP1CQWBL24lB6lUQHq3N6V0G6Y5CRvJI4YZz6sxmxyT6qjguwfg8TIs9JQNCQmC6qW3ot90fx/RdkN/UxAaFVPEfGR1bqxbsbTRcljxlcTt3c7U3VbfYHkbKhpWewaFtcv85vXfB3AID3vfV1AOLbz6gJQr5mshK1bdYlcFKsJDo5aOxHTtaJNtiPNxm1T4SwkJCUVT8SDFHEoJHo+Ozdqyz3R+FCyDcwWJxM5ozPWTujbyiD4yF4MV09Yxv2HHMeNkGSzQ+froi6C47RPheW/HKGn7t037KD+NSdKy2PSa2SSIhbtHHcxJDooAEv16SgTBNeJujDmSzWHmLCm3Li2c11uOb5nZFcuz4fZgF4jEkczW5KvKLXKweGvWXTNmsPyA1Fl0xdW/C7FRU17Zg8ZT52H6VimXSSUqKGOqI1o5+0j/u00YFHQWqVRA5gxCtB6m1276GbS/11a4OfrpCIGRzJYM72o67Pu2vxAXznsU3YWtseQq9IFEyZU4nntzbYypq23sHA4+UfWVUdSDs0iud44NVDvs63ynzqvA1fXcDSfU0AgDVcjCobnFm4S9ARs2vnZz90W1cTxG1xUqYovUqigxtcUC+KFh3iE29JIJwfe/vC/R6uQKyYuakOP3zK2lVn99GuQFxjqpqtYwNMz2vJndfWO+S7DyRe2K36f/jmZfjobcvDu76n7KYcK/UcbLKOO1XhZH7ip9yB1/kMny0pFZxylwAHcia9SqKDu2NcwWjqHqCySEqKm0GZpTyD59q5lViWX0VXsam6DV+9fy0eX3ekhL1SQ8lUfpiltI8zaXE3zWZlYHFT7X2FCzz6+UkQlhRjE72DIwW/23mhmM2XZm6qw476Tl99I+lkJGMt3EbrJJa7IIk5qVUS3XKktQ//fOtyTFsdjCsOiTcnhjJFgf1ehJV+TcG4vjCcydpmRHWDWUIdEh71HbkMg3uP+y87YvZ+XTFtI/71zxsKth1p7cNP/7IVgyPMukzCw4vlaFRJLPPZ3duvXYArp28MpK2HVlYV/F5YqzL45Z/hTHGbRw3ZUp1w7dxKfOPBdUF0icSQMC3Hdyw+YH1tltJxxPzK456rLzi5t6lVEt2OX/+Rdzmzq3dEkoPVO/CbF3bhkqlri1Z4g+S3cypdnzNzUx2au9UxSMyMGSEhmvE2VLdhc01hvOHv5lZi4e5GVNR0hHdhEjhuB/OorcN+9JM0SKON1dHFAQetOw6P2Jut6UhVPkT9fe40sUBr7xjfNWv01v0GXbIxV23Q3dQctzGJGnxxywerZ7mtLjf57h0YMT/IAQUrwoYp32tPGm/fgKGP186txPce32x7LVIagrjlmaxEfXu/Z0s1n3s4DAxnMGzjEuWWG+btdXV81OENnuKoA+9F+iioVemzraGRrO+kZpQxJGjs3mvGv8aD1CqJTlAJRr646UL5DgT0Ckwcb//5qS7VrCiMC9CSGCbXv7wntLbvW3YQF96xAnVtxauBbmKeuIAVLO+5bhH+xeDm6xe3K76JfqQURyXl57O2K7cfaOxx3dblD6/HPUus3QGHHFgeSfwoZVZc68bVm0frJCZa+MWfjr4hR54QVBIBLNtrnpjCCCdi6UD1nP3OeYxtOtHpVP0wsy5QRwyPJ9fXWO63EgurD7Zg9cEW0/3rq3K1ilTK/83z91led2A4g6Uu5BdxR+RJOSIeb3xZMjlWeqZAlJvcR+OCtSZHitryMC5sre3A1FcPWx7z2btXum+YEBs4xy4N2+udhaqkVknU+/P+8GnrFPd6+AITN1i9L04SO6jSnJs1SUtiPPne45tNXYQB68WHF7ZZu4m5kV0ketyOH5kEupsS/zi576WytAxnsnjYkFgHABo63Ce6IeGSpCkAvfLc8ak7V+CL964e/d3v/XNS4QFIsZLo5AbT3ZQAuYLVQaB/c25dsC/wTLlJGiDKkfr2fnSdGHZ9ntVzc2fJoWyKO27HDz+18ILA1+Upj2KB03HB7FGf0CVb2ljdhhaTcAeSMJx4MrlsUkqJW+bvdTQOmsmW2RX1lvvTSm1bPw40uXcdN8WhXEitkugEp5o2KT+0yfnqQy348M3LsPJAc6DtO1UQVXLSKDx7B0fQNzhCS2IEaLdcSokL71iBz9+zytX5O+o7sSWfoVT1rAeG1XE/bh61lDLyBCgkR2FJHPtnkui4nCT3PQL074P+847TwvSaQ634yC3LXJ93y/y92FobXSZYMkaYb9OcbUfx6JojOP+GJZ76cWKoOPtzfN7+ePGzmeoYZKc4nUKkVkn0qgByrpUuttflYpL0sUleB20vE3Wlu6lh2/v+sBjn37AELJMYDJuq2xxnA/zVczsLfjdLKmRGQY0xxbMeCiC75qUPrMM5v13gux3iH+0Rrz7Y4uiZuC3W/u7/W4hvPhRk3ToOeCrCKI3kdkGg26HXgp/F7t5Bf9m9NR5dcwSXPxxsEigSP5p61OW5VDidD3X2u/fOIcGRWiXRK1QS04GTx1zZ0BV6P1Qd0W9aWHkcADCSlbR8B8S3pm3E/z6/0/7AhFB5tATvKXGEtsC0wqFnglt308GRLLbVeU+2Y5y40d1UzYV/fDXwNt0uIj6kiBNU4cfB5Fezd3g/mcQaJ6+F23fy3W8+zfGxSi8p3Vb9z6rM38QfTnJiAFQSC1h3uBVbdEWrGZOYXjTZOE4U/q5n3q5j4ffDRku85q+7Rn+mt6l7DjX1YPKU+aN1MZMMF7Dij/aMJjg0+0f9TP1cflOEhebDpk/hFueXqD9flUJQ3dpXzrp+Kgkz9ODUkycAAD4y+Q0O+uFsGwC09TEONmjobuqBq6ZvCrwuFkk2mnVO5n4pwK2w9SKblYJU97N+rsmYRPesypemmL8rZ5Ft9ZikKIhhN+pJIgkf7Rk7XcUtdeKaIC6n/WVHO5n90g1mz9rvM4liWPjUnSssMz7OJVwAACAASURBVDqT4NhW14GB4eAXLexQXXNcfkLiNZba7LSoE3iVI07lQnqVRIsb1NIziGV7m9RBtHxXU0GRBU8ZG+ikHZ/9sKmTOGH82Cc8Lr1fsy09A8N4aOXhohgvbbKuDUK/eNZfMLgfKFtSQP4ZO8446fGd2He8Gy/tOOrtZJ/X52scLH7vp5swBCklegb8xYDVt/ejtq3fsjYsCYZjnSfwzYfW49o5laG0b/Xu+b2mOimfVB4QQGg+8ciEqDsQR66avhEHm3qV+zgApgNpMZnT9pUi86Dab3+M8TpTIi2J5tz0yl48V9GAd551Gj7/j28e3W50Jw46SP7pDTWBtkeSjbb45HTi7jW84ct/WgMA+PoH3urqPOPV0hhesXxfE045aQI+9o4zou5KILgpgfHEuhrc+MresW0eVgk+c9dK1+cQb/TlEwvt8hB37tSbwYw5230uQrmoAZ1JdJrnYOk6MYzXvWai73ZYJ9EHNa3mQbJMJZ8uavMB089srMWqA4Uro05cIPy+L3bn6z9zv0K/nNGy9A2OFHoHaHdMe5b6W/jY2iOO2zd7TE+uq3Hchhu8JCkayWRxuDnAOkvENVaLTyqinht5EV9Jl0L/8VQFrnh0Yyonpov2NPpuY8Tivmlu/SQYRt07Y/Kuuvn2nZT30qC76Rjn37AEK/b7L8lGd1M/WNw8TsTTgSaSNEvd4EgWjzisbWjapgc5p5L9Zu2wBIY5o7GlhnunDbKjk3fdx3+TbkXdK1EPbXO3j5XyuHPxAVx8z2rUtPZF2KN0o70PTr/VoCdHaw61KMMoNIJYBI36nQ+KP7y8u6TXC2seHPWwoH+n/nPmNkeF1okzNO+hTEgvj1Wzp08K3hFR/67oL53GBRsrNh0xTwq2/nAr9h3vtm2DiWtCYmttB5q61bVgjrT2YX+j/cMh8UeTVef+7ekAgG9+qNhty8mEKowpl94FTL9mEfVkINbkb87PZ23Hz2ZuM25WWhLd8PJO+0y3VpPzIFC9a3ctPjj6c0VtLoNri8fkPGmlti04pVqTGY7dTQOcGx1q6sF3H9uM9/5+kfn1Qrx+0nhpR/jZq8Oie2AYn75zBXYf7Yo067VqjOSEPzjGa0qiz3s6ogv6U8WSZrOy6FmecpK5kuhkbqSuAa0+NiwlOGqe3VyHyVPmY2gkuKDLK6dvwpf/tAZTlx/CuRay3ilUEj1Q3aKeNHzmrpX40n1rStwb4hU3g6dKRrmVy17ie9ykiSZqVhxoLnBzekX3s+YZMJp1MuBr6wfLq2dstTzWr9WILjnh8P0AszS6tSQGGd7gxYLjRWaVzWJVgj+njVVtqGnrx33LDtofnMfPq9beN6Tcft+yQ94bJbZMGJ/72rTYRK9ocahVLepcHG+/dgHO+e2Cgm16GTbsIbNMVnGK/hXUy75MJsEfowV3LD4AwH0maCdy+Z6lB61L9dDd1Dt2985vMP9IJsvVtBjgd/7l5D3wfw1n24g5P3hii+k+zV1ndEAKcdl9Q3Wb5X67K1ebDOAamazEZ+9eiVdKUL8zTQwGuMqrvWaHmq2fpfH4IHAy5Biv5ze7aZJj+PWLLjvrO/HLZ7eXLPZLL4b+uGi/h/P1bvTOZZrxSKd/7e9fUrvmzthUy0XNENFurd/XcuneJgBAt3EhyaJdfejV/zy30/U1bbOb6ijXBVDtDrpO9hTA7WDiGh/YTQpae9WrZk459w+L8ck/vuqrDRI2Uvf/homP9l+3lkQPH/acbQ3FG8tTXkaCNs5pq5pWFp7fzqn0tbgzMJxFXdtYUiy3qeY/d8+q0Z9Vuuwvnt2B6pY+XPP8LuX5ZWPhKTHjAwz21d4eM8uLkSB1kigUtnKZ2/3w6Qq8uOOYrat298Cwo3ggO/T3zUvG5VJ/60G6yxHnaN/0qBu7xwc/brQUlLfz51ceL7j+trpOW+umqqtmly9bJVF3EyZPmY+VB/wnpDFy1fSNvs5PrZLoR4j+1yx/tdQGR7I43qWOayTxwIlMciZQ/Qm3ZfuKhUZBTCKn/r4w3j2ruzlrcx3ece0CiyOKMT79F3W16z5+m7uFIilzMQy3Ldzn6jxVO8Q5DR0n0D/kz51rlPzNd1quJsjJkSNLYsArUEl+1fR9156W3eO46tFcPJCUEhuq2iK3pPq9enVLH4Z9uPpJqYpzLdzSOziCP6+qik2GziTiZuFadcz4cQaPGgfoRZhKTtnNcdWWxLGf9xwbW2yxypibbArHgdlb6h2d5eZurDvchsGRTJFLK7Obhsx3pm/C9rqOqLtBIsTtBCAoMedn3rG1tsN3/EI5cyTkzJ/6Z9fj4TlMmVOJR1b5y7JL3BPUe+E6jtniY3frVlyQOdChEPHtLp/gFQkvXa/M16ubX3kcVzy6EbM2O5v0GfHr9V6Q0MxnWwsq7ctWmF2jrW/INl7t9oX7cPvC/VgcQPmN1OLzM9OURDfySb/QpfpWvHjd6Bep1leNhWcwPKuQaS4z7f9i1g584vZXC+4js5uGzNrDrfj1X9VuXST5GAe9AiEoFdtKiNfLdvUP4/KH1+PnPi3hUdDeN4SHVh4ObdIpIbGroRMdHly7koKW3ZS4R5VkwQ+fe+9Zjo6zett/NtPdd6yfZ5lNuurbC2sEW1kWpZTosvleegaSuyClyiLd3OPMA6i+Pbdq7yYzbpCiTeuvO3kZTjzY8xXWirL2jgQZ+5sWjI/G64KA9p4Yn/VDKw+bnqO+1thGO8VO6W5qckq5lp4L6s+yKokBjNU/9aJsU0n0Adc2yp+xFZvip+02cU1QCo7XdobyK7o76zsD6UcpmfLCLtyx6AA22whDI8e7nGcNqzNMkIOgKBEIpUYiCcrtU3v+kyaOD6Q9N+j/BrO5wsX3rHbc3rTV1Tj/xiU4ZpGZL8kLE6pHfukD64q2zdpcV7RtxqbaXBserz047F1hqm/vx6GmXGKkOBhgBgx/S1N3YVxngo3NscHNLZy+NjenUc0jjJusakPbuczbyUw7d9PC7eX5khQlivL4Z/5urrOarl7mH1QSCfFIVANwQaxMeS6wFdGbd810Gx/zMYdxfyL/v7AJaqzzmzyLWGOclASlJAZtkbRDX/OsUEn0726quQcalUT9V5RkN7HCmERz2TBzU7GS2NCRuyeVDV2Or6d3ab5+3h7H5xm58I4VuG2h+4yoZqjeAWMtPav7Y5yYfmWqukxYWsayMHCjRO0+WpxYqSafUM1VTKLjI53DRVTvuFkQdwOVRB/4Xd1YtNve159ERTAiUJUVNSo0AZzEwTisPk+ZUwkgvMEprHaDyKBInBOUruO2meWKxFVu+J6uxqN+uAozW6C+5QATw5Ye3R/iVf7Ylb3Ro1eevLrp7j1WKBdcOZu6sOJc81d1yYPO/uLFK6evWpkai0pCELcum5Xu5JzNN+FFxqTtHSgKa/LxJD9+u/2CuJf7m1olUV9QOyr+a9aOqLtAHBIn4WU6mNueqP2Q5Jlb9Axnsnhs7RFHad/bXFj8SvWKlavrTpgE7SrutL29AS4G6CcgbiaDxvfcTeHsJMcSFWaRTgY/eHKz/UEmBPGorn/ZuwWUeCcI8TRrS13kpSbSNjIF6b0U1qNLpZLYF1Q6c5+4XTV4vqI+NJMyscbrB1gYkxhMX/R4ETEJnreF6o7i9L78ZWMtbnplLx5be8T22P6hTMHvVr0P6rHY/R36iftLO45ixf7gazMlHeO3GrTX5HUvlX4yrf+bnLqBSgC/eq5wMfOdv1uIGotsr8Lk56Th2AIWo6mtcdIZxOKGmxaM8Yduzyfu8D4vKT7xWOeJAJREbwtRGoPDGfuDiClVLb2W+6evcZ8ZPZVKokpqLd3b5LqZqpY+/HlVVQAdsqdnYBjX/HUXrpq+qSTXI+Y4nRSEbbFZsqcR3Tq3JLsJWZIH61LECzq9guYK1j2Q/Eyov3h2B37w5JaouxF7AktcE+FHmC1YsHLeEZXXzeFm88lIkuWMnsLYb2vpIKXEPUsP+r7m5CnzXcUxGlF1043kVJ2velXCyESa5MXLUvDo6mpc//IedcIZl1+d6pmOE8JVK3aPy63MrG/vVy4ylDNGd3y/48O1+fAZM+5aMiajnHp5pFNJVHDTK3s9nXe7IkD8Bh9B52ZoCQ9aegatDySB4MRX3O4TW7i70SDQg5s+tfcN4cfPbB1N6OKkdU1ocyz2h/ZIvQj0qcsPmU7Q6zvoJRBXgs5uGgVOsps6Zfx4MZrd0Kqtkycmd4rhRpFu6R3E1OWHArnu0r3e6wX6yZZoduyrCk+DThelgujd7p/Dzb24ZcE+PLm+RpmF23X9VcU2gVxcop82Cva7fPDzdh3DSROSKy+8kAR3/HQ9EQuCHLyfWFeDHgdWBgrP5ODlWRlrjq077DyJgR1erJSa/E+AXDIlrG9GSuf3ZWeDvxIiZq5+TtxXSTQE9d5FKfP1MsOpu6lV3O2Ykmje1htfe5LD3pWe3/x1F+ZsazDdr5/A2ckG1WKxVxoCXCwKYl6z9nBrAD0xh9Mge07owhYKPAJGiza7a085fxDCkbJ5uLnHtOxNYXIsd30aGMog7W+D378+jLkdlcSQOO/6JWjvY5r6cqHzhH/Xwj8EGNTvxRKgrRKWwnUzaOKk2Gor614nYOkeBpOB8RkFZ0mMB04Xmf71kQ2m+8blZw9G64O+7TgvhM6uqMevnlNn6QSc3yMpgTnbjgbVLczZ7r0to2Vi3eE2fPbuVY7O/da0jZ6va0WcYjaTiv6xKmWRy/HRzJLo5J2/+J7VoWTSHE5wuZy4EIa8TaeSqPig6tuDd/UK1DU0RpPkNKKKWXWiuIQh9oSwF+ZWhXLjpHC5JaxhxNM9CSCZURj0D2WYwTRgAiuBEelzGXvJM377IYHx+YAa320lACv5UC5/vp9FbavySnb3Z97OY46OIzmUr6JrS6KiXeFfzulPb+oecN2ntL0DR00sskbCMDg5nfOkUkmMyxzZ0/eQP2nR7uNo6Cj2TSfO8aosaUkborTIeXl30jCZ80ru1rh7nl6tS6VYWX/Bo2XjPdctxKOr3WdAK3fcxOroOdTUU/B7pCpigTXCf3vHu3KTwC01Hf4bizlJ8b5wOukMmiDuT5IXL0vJxPFj03bP2U0hC5LehcHPZ2237oOh82mzOKuyips9z//38PqQe2NOKpXEUiOlxOqDLdh91DprWW1bn+0xGj/5yzZcMnVtEN1LLVYC1mq86ugfRr/DMiphxtC5Yf3hVnz+npzbUZLH4p31/uIB40ApdPXjHieLA8NZ3LJgX8C9SR5BWfwW7TYkIYlwHqT/7odGsvjSfaux6mCL7Xn/cNapRdskJKpbcmUwnt1cZ9hXfugVmIESpOm/8J1vCv0aSWPR7uOYPGU+ugII/Uga+vcvCGVaNc+MfA056uuXGLtyFXqqLUoO6QnjFlJJLAE/enorvvf4Znz1fmul7lN3rrQ9Rk8ahWVcGBrJYjhrna45Tiuj//fSbozkzQdJlsVBpJkHcq4wxpqjbp9X5IMqiT3FWZKjQx+vdrzzBPY39timTAeASQnOUBoGpShD9f6zXxf6NUpFUAsuD6/KeThUu5hclyNB3M4DjcX3MIin5KZvxvjZrEyXLVGd2dTfHTB6rgTBhMBbTCGNXQN4y+smFW3XXvll+9Q1GN0IT+19StNHFGcGhrOOkhWUUuzpRY5Vts4kKjdBp4r+3mObccCnQPV6G5N4/9OO1+9YNRGKA4fzE22v7onTdC7Jp5w0PpA+xRn9U9xaW+heG48nmnxsZXxMvp2o0d8G4x1x6varkmdm81Enmfq9UORuysfrmw4XpWmcUvIlQiHEO4UQA0KIv+i2XSmEqBVC9AkhXhRCvFG3741CiLn5fbVCiCsN7ZmeWyo+etvyUl+ShIzdeNXn0N00LJRCXvdzc88gntlYW7oOJYzmHndB9SriHJN43GXSAFJIOc5X9CJt77FuX21trG4f/fmnn36Hr7bihL7UgFPKKUmU19hCt4l9NJfdsBSQcqNQMfT/vqmeidnCsmlsobIN875lshLTVleZhupkLRLXlNEnZon2dx7vOoFbF+zzHAsfJFH4kTwIYIv2ixDiXACPAPgugDcD6AfwkOH4ofy+qwA8nD/HybmREuSL3d7LchpxwmmdMaMgnfLCLnSXYGD88TMVuO7F3WPJjQIeZErJdS/uxmoHsVN+eH5rA4Yz1u7DQSFlcQ3NoJm5qc7XAPOHl3bjh09VBNijdFLkbhplTKKuLx/4u9cDcOZKatfnSRMNlsRkiZcCrpoeThkIt5TTpFj1pzxXUQ8A2N9Y6M1x37KD+PKf1qgbilP8RoQU1En0vFCp2iaVSwRaoj6/vLLrGG5dsB93L8mFjBgtx0mbl/jF6m3+n+d2Ytrqamytiz4pWEmVRCHEtwF0AtCb3q4CME9KuVpK2QvgOgDfFEKcJoR4LYDLAVwnpeyVUq4F8DJySqHluVb9iPuruKGquOj6p+9aWfqOEFNUmamc8OyWerz/+iW+r283NrTlFxW0sMkjbc4Cn+NIqSyifYPurMNeJ3JZKfGzmdu8newCu+5ZxTQ/taHW1E0+jXh91kbLTJQTIf2cbEI+Q+I7zixOShMkSVN2ttWNJcbSdz1od3c7EnbbsNCYoEmH6h0YyeRLZhi237fsEPYd7w4s9rxckA4VQ8fyRdHGgyuqQn3vevPjq2ZJVLmbpklRtPpLtQVrKYEn1x0J5fpOJVrJlEQhxOkAbgTwK8OucwGMVrSVUlYhZzl8V/7fiJRSLzF25s+xOzexXPFoPFYz04yd281tC/c7aiMqkdfWl6vROTiSc+tJ2mQtCnoHw89aCACv7m/GcKb4gZRyHvp8RT3Ov8H/YkW5EtT3EitLok6maa7STvpDAw5xwv5GtQuzXWiEhv41m7r8UDCdShiZrLT1mlHdu6FMFq29zutym332QyP+vGmsxIlmAR1nIlDiEq8dJ5q6B3D9vL2R9qGUlsSbADwmpWwwbD8VgDEfbxeA0/L7jJJH22d3bgFCiB8LISqEEBVdnc7KTPgljHfeuPri1vpB7OnsH0JjADFdYa6KaTXKzBgYzgn7Odu91ctLKm29g45ccFRH3PRKaYTxL57dodxuNniGwcqQ3XfLjR6PNcWMTzQu8yCtX04yZNv12bi/XKwBUo5ZP6K4dtLodxHPubG6DdvrOvDSjmOj26zEX317P3pTEL84bXU1vvf4Ziw3eHHovyn9+KZ/TY609vmuVxmmoqb122ycS+I77werJ6U9R8dhTSFSEiVRCPEBABcDuFexuxfA6YZtpwPosdlnd24BUsppUsoLpJQXvO71waeXVrlHhDHnM7q9/CmlK25hsnRvcG52aw61BtaWhgBwucPiquUueDdWt6Ern9Fr77FufPjmZaPxLlZ0hpAFzC9BDwjllFAjan45W63Y26EaA0ZKFPtqpGCimf/v0c4TofYn6Qrj0xtqbI8JZTE4ofdN9b6r7s/SvU247KH1jkMJLrxjBapakhsy4RQth8AxQ9bhQndT9bluxg+zNlQeLm6w+ha0/o0zmRdnpSz7+QqQy458m8NaxHHw4iiVJfHTACYDqBNCNAL4XwCXCyG2AdgD4HztQCHE2wGcDOBg/t8EIcQ7dW2dnz8HNueWlCfWuvcbzkrYJsuQUqLJwqpVisK+aSOoybqAwJ9XVQXSllcyNrUck8zAcAbfnrYR59+4BFJKHGrOrQ2tPVwc00vGMC+N4u69v/mVvZg8ZX5ZKqNBTdKLYhKlRCYO90vXhSGbMchuohKHiYwfVhxoxs9nbceDKw4X7YvDoyolu4+VxsvKD+X8SDQrm9UUxGyfGyug2RxH1UZQ3/eokmiiJUoA23UxwYX7yuepX/7wejyiKyEUFU6fa6mUxGkA3gHgA/l/fwYwH8AXAcwA8DUhxIX5RDU3ApgjpeyRUvYBmAPgRiHEa4UQnwDwdQDP5Ns1PbdEf5clTpSNp9bXWO5/dks9/vlWltgoJSMhm/g/+vbSVWl56+tfU7JrlRr99/V8xZgXezkqLV74rUmh9KBuz3QPC2NpwzgQW6V594Ofd96v9cBI0j6/J9bVYN7OY7hz8QHTY6zmU37rrSqJ6B56dau24qkNNY6OW+bQgyfhaxKWaPpTUVKXgp/1rqdj292sB9eaJLKzkyOP+5D5WtPjTd1NJa6dqx6zygXH7usxeslLoiRKKfullI3aP+TcRAeklC1Syj0AfoKcwteMXDzh1brTrwbwmvy+WQB+mj8HDs4tGapP66v3r7U9z04ob6ymVaTUhO0H7ncSZXa6fhXuda+ZCAA4+w2n+LtYjNGPNU3dAyXPQOiHUnT1+a3G8G9r7N7LbFZia2279UEkEpzIFLNaa3GIewmbxq4BzN5Sp9znNYY5TMrpiTh17X9RF58IjLleGmtXJknOu0WYWBL176heGXx+61hohRtL4lMbvGUMv9Embt/K4qf1z9SSWE4vvQl6bwUnf24c3vUo6iRCSnm9lPI7ut9nSin/Xkr5Winl16WU7bp97VLKb+T3/b2UcqahLdNz44BdjZk/LT/ka5BOw4dVasK2JJbikWkJKVTXKpd3pjBbY+mvn1RXb6/jzrQ11bj84Q1Yf7gwzrZc3qdSIBFO3I2fJkdsTBB++huXd+Mzd63Eb16oRHufu3rDUXklmFl60sQn/7gCQK52op7op83hoclm41un/10fc//IqjG3xSDc2FVNOG12tCazCVr/zMafNGQ31cd/P7zSPBRp85H2ouOjIhIlMU3cMG9Pwe/GVTHAfpAmpSX0OD6fstDNIJkGwQtEE7Pw7Bb7BDlJob1vyPYOHsy71R01JFUoR7x+Np+7eyUes3DJCmsxw5E1zCT5hV9L4pM2IRNRs+ZQC07kF3TcjrXarSm1YrJ4T/JqlLb2DPrOrqmix8RFr769H+sOB58YLkq0+6eqIahh9r1lAxAuflr45B9XWHrG2bub+rh4QtBbBp2ULOlzkTHYQ28cHUUlscT8z/PusuSV86pZXAnfklg6aVjOglc/1uj/zld2HY/FClyS+METm22PMZsAlvEr5pqqlj7LUiotPYOobrX2LvGCW5GVdaEk2lmet9d1FiwcxO19ONBoHTNYzjKylPz4ma2htGtUKlp6cpPrT9+1EldN3xTKNaNC88Q0Lu46WQQKwm1cdRnTRGeKbVZKota/uSZluVLg9R4znN3wdCqJESYOqDxanD1sYMj7hLacsj6VGjPhlwkokYN5FslAmic6JAoXVJp6nBcWThuq96+2vd+xDEvD69sS4vuztbYj8DbdjgP640ds5J2T18LMOvDI6mizOzvB6t5RVkfPeEMM2w+frgBQnrG02t9q/NOc/KlBeA35baN/yFxJ1No2q/FsNf6Uy3doZ/SpajEsIMbgD0+nkhgCZmZhu1VMADj/xiVBd4c4wPj97TvejfVVraEnFYn+sy8PCupZelh5jZIYxKO7ZrTP8b61gXDhHSuKtsX9nbJD33v9pHPqq/5r7U4cr36hF1Q2+m47UFzGXHERNnqSKCs948OSmJXe67kGRd+guXuknaIbty9ta20HXjSxenrG5l2uaesv8MoIdyGE7qaxoNnjivT/Pr/TtoYiCZYv/2kNrnx0U+gZpdISJxg2P5+1ffTnokB/3mJThADm7zpesE1K+0Ha7KtIuvLklKAG7CBih7zwo7wFBih8ZnO2+Z8I6f+icnodyulvSSrjUqQlan+r8b1zIjICcTdVbXPR7Kknj/d87bjNiy5/eH0kSndlw1iW+oCrExXQPeAs6zCVxJjy160NSrckozk/Zt9Voohq7In6mZXjKxO3ASbOvLq/2df5ErJA0fngTUv9dilVjB8f/LDr9vXXP78L3vaGkl+/lNgt+llbErVGAusOcYnR3bScGXXW8GRJVB/jZhEvzAU/u6cYZxnihRNDGVQ2dKFNl6DGSWKnn/xl2+jPVvHtfvnBE1scHUclMcaoYkXK0A0/MsyEUtjK4476TvuDSsSm6jZMnjIfdW3W6atJ+WCWettukB5Nzy6BGZvG6myFUYC7nInDnFf/qP/jk+dYHutWHoaeHdoHD68qfvc3WNUiLreZawKhJdHZwm4QlkSVl4Ob2++0CypltFwWem9buA9L9zbhq/evwdceWIuP3f7q6L4kvspUEkuMm+/gO4/ZZ+4qj88qXoSRxjuuaAXXNx6xmCglACnNs52SYNDuqRDA7qPdrs6ta+tHo0nCgqQQ1CsVhrtpVkq8vPOY+X7DNfW/zrGJu3HyLelj9657aY/FkdHyxLoa1+dIKVHdwrqFURGHRZVSIUZjEu2P3WRY3AhizHPj3qhS9JxaIlV/X7kM2Y+sqsaPnq5AVV5mDI3Ed9HMCVQSY47KVUb/Ie5qiI9VipCoMA46f90a7xqGpVqIcFrTUAjnSToEBGZXuLu/F925Ah+9bbmrc8oBlfwOwxvksTVH8F+6GF0jxkLb+jGkIAGUV2I2w5NSYmN1G6SUvr40CWCeIX6XlJYkWl+8oskLoyxW6V7fmrbRUZulXDBt6xtydNzVM4rLpcQpu2lYCWOS+CpTSUwg+kFr99FudPY7+zCJM9I0KGnCt7492e6mKwxxdlNfPRxRT0i5EtRERe+qGxRNPdZWWuOkJ2jXrpjpiJixqQ7fnrYRC3f7y656tOME2h0UvSbhUNnQlSp3U+0v9aKjBPEN+o1JvN9i3NU/xsV7ihem4uSlfveSA4G219DRj7WHws+cHwZUEmPOlpr2om3GCf3AcIy+rjIggd+xb6yEexI40NSDhg5nVjOihi66peFgU6/9QS6xs0w/urq64Pegn3Xc3p2a1pyr11GfMmHO9qO4fl54ySOINV97YG2qxuNx+sBvl5gpeFF+mtp3CNjLKCeeLK29g7h2bqVrF84jrX1Yd7jV8fHrqorDbzJZie8+tgnrq5y3o/HZP5TmDwAAIABJREFUu1Y5Ch+LI1QSY4JZnIqTiS9rOQVLUKs9S4Jw4wqZJK5sAWrL5+0L90fQE2/E7Zs1m5OoEho5TZ1N4sPdSw8W/J6WBGhx+86IB5I6SHlAi780esI4eY/nV/p3i3ajmzopFfbpu1Y6bs+JTLpx3l7M3FSHRXvceQh85q6VuGq6PyWtvW8Iaw61Wrr1mzGUL2f34Ap10rg4QyUxJtyxOFjzNvFOUPFim48UW4GDIMw6jltrO3Dn4mCVratnbA3cxe7z964KtL1S4zbxS9h0nRhG32BxltKL7swVlZ+38xg2570abp6/r6R9iwtuFY7GrgG098XTVTFJmQSPdp7AZ+9e6SrxkZ2IZIhGSIQwNKVHRfSnD6880OL7+m5kXNDlMpw0p7nNR5HM6LoXdwMAWnuHEp+Mxg1UEmPCggBWgUgwlNvC5coDhfF6VsL48ofX48EVVYEOAAsqG/G7ubsDaw+gi3UYzNhUZ7rv57O2o5ZlUlzx0duWl2zl+JmN7hZhjN/3h29a6qsMTpgWuxkba1Hd0uc4GdVv51Ti0TVHLI+58I8rgugaKQHlNh5bYbYA7Gc4dlcn0ft1SoG2uBVFnKreeuk2cVuceGRVFSZPme/4eCqJJSaIj7C40Kr/Nkn50txTaM1o7R3EwHAGAHC4WR0fFcY7FWahXkLCppxeX+Pf0tY3hF+/sDOazjjEqQfFrM2Fix2q03oUVnMST9JUkirqMTLMqweh140pif7bcoPxuQwn2JLodkGRSmKJGQzg5SqnyUqUpGWFUhXv+t18EPWO+uBLqPQMDKPrRHHc2jRD8gwSL8zkSkOHvYUp6skN8U9Yad+9sOdYF04MZaLuBokB9y47aH9QmdDZH228t0qMt/YE45p9jyEuen9jj+s2djV0AQg35AYodnH+z5nblMc9vaEGk6fMT5T7qdtbRyWxxLQynXZsMJvXllvKbdXcb0tNh+U5fqaL512/BOffsKRo+1oX2cVI6TFzGfykA9e8WxekM04xqaie9IgPJTHINYKegWFcMnUtfvFsLkGEWdPvuW7haJxQKfpF4kO5Lkq99Q2vUW7389f6vVMnhkuzWGM17fr1C7swecp8HM/HJZd6jragsjBRjnZ5TfFVxfPHFbeWeSqJMUFleSHRUGY6Ymoz/O07Hq/kMHHHz7zLLgaMRMdIpniVW/Ws42JJ3FSdS5C0rc56IWtgOFvkOqWyPpaZOCcAHl1THl4pzT0DoSsYAy6UvLDmCgccWA2txh+jbIoicY2RE0OZUctvPCRnOFBJjAlulETjC/lcgoNoo2TjkeJaOOWH9FaYN8YrtTe/4qxu2bVzK0PuSXnhpo6UGSeGMkqlhESHKnO2ajLox2UqSGnxw6cr8j+5nwl++9GNAfaEuGX+rtIk4Asrc3ip+adbluMrU9eEeo3zri/26jEjrGH/i/etDrQ9r5ZEVd1xr+gztu8MIWwnLOhumgKM6cvvW3Yoop4kl/VVrabZGmOwSBUocVb4vDB9rTOrVbk9x7CpqLW23Djhvb9fhH97YkvBNv2EzsmKclxJ6me0SpEaX/W3BBEv75dhh1ZPPVUtY8m3jJM1pVstFzEST0yM3oHgJGt0uY3hKnpc1N/16u31L3/e4O1EBXrr5g+e3GJxZLKhkphA0pTty4z+oRHcs/Sg59Xvlh7z2NCwg6JLiZTqxDUaH3v7GY7a2dXQiUsfWJuoZBLl9ByThDH29F8fGRuYr1BYevqHRrChKg1W/Wg40ORMMY9D7cR/t5hsmX3On7t7FWpa+xxfwywJBUkOr+5vtj8o4UShGLoqlxHwtTe5sA67sSS6UT6BnHwII6FfXGjvdZeIiEpiAklrjJmeh1dWYeryQ3h2i3ltNz1Pra/BQl0tylaLD6W9r7wKLde1nyjadtqkCQDMJ4bGrde/vAe7Grqw51iX536Uesyjihg/VFac/31+J654dCOOdxW/p3GinOSu6i8pVS02K9YcGltgcLPG4yYh3OI9TW66REgk6Nd2SyV5YrBO5Ag3SqJdcisjpXKZjgq35X+oJCaQID7kHz61Bc9sqPHfUEQMZ3I3oWfA2Qv/h5f34KczxlaQ3/XmU02P/dPy8nHflQAeX1fsnvm9j70tt9/hu6QdZpTNaw+1FqzUOZ0s/s9zOx0VdE2S5ZLYozJqa6nQ+wb5rEtGDGaDgyMZ/PfsHTjaab044EQ5j/6vISRYIrEklvyK3pg43rmSaDQI6I0A7X1D2FTt3otFoHgu1Nw94LqdJEAlMYEEocQs29eM617aE0BvouGkvJB4vqIe020yne09VpzlstzKXLhF+/v1lkS9W6r5+DR235q7B/CdxzbhF8/uGN1228L9jq7/wrYGy/0zNtViR30nPnPXSkftqUj5I44l1o8kKVOU5KO0JJb4/q860IK524/iojvUJVaM74rXMAu6nZMkov8aX9p+tHBfSApkGM2G0dcJLpRE4+d/9YytAHL9+tBNS/GtacEku/pCwMl54kIqlcQjLuIXSEzJf/k1bf24eb51jbaws4clmYxOgM/YbO66q5LzWv0kfXzIk+tqzNuAREffUEHK71f3q12/fjd3N77x4Do0+lidY+xudOxqcB7ToT2lGBi3LIl7/9wQ9N/itL31h1vR3JP7prVTMlmJQw7jJs2YZSK7yumZkXShX8B9ccexCHuixknCHQB4wmJO4JWJ472rLsc6c/JnzrYxxTuImHitHEa5kUolkeNG8mlzEYOiIi3qg90kSe/+19Q1ppCZWRXMFuXXHCrOoGhk3eE2fPCmpQXWwX9/siJRhWiJMy59YB2qdVknrdCs2pTLpUP1fZdCobpy+iZc9uD6ou29VjJA16/9jd3oUkzG9BM+QlQMjmQwb+exxGQKtepmWH9CRW3wpUX2NwZfr9iPJ5h2alPP2HxHlVDNC9ttarsmkVQqieVI/9BIwc8Pr6yKTXHkMOh2GItYYVIXhy5IOZzW97F7kxo6iuOKzFLNNxsyy577h8XhJAviI46UDtXKquKZaJ9iHLJrWhHv3plz5mknF217dI2zMjJhoMUgzts5Zh1xWr/sS/etwbemOU9j/8dF++l2TgAA9yw5iJ/P2o5VB+0XNONAFOJQnzgqKEZKPA8dGM6gqqXXdjEgjPt72UPFC2BJh0pimfBN3ct51+KD+OOi/QWDcFpZvKcx6i4EjhvlX0Li6x/4W+U+Y8IIy5ikvETVVvCue3E3PnXnytHdoxN83YTsR6NFse2ZvaUeT2+oweQp8wsWPPzAuWG0DAw7S0Qzakm0eP3aegfRkaCsw3G3VqhKB5W6y6/osgjeuqA4lrm5ZxAnhjJFSbP2u6y1uSIF5RKIPdp413UiGW6BVuNxvKVLIWEYK6xk1XuuW4TP3b0Kty7Yh4MKN/batn40dDhzlTVDCJGacBYqiWWCfuDsHcwJwcER+0nakj2NsU89DwCzt9Rh0e6xSYXTz/M1E8eH06EywDiJ1wte/c8X37MKOxtypS+0+/7MxtqCc7NZicGRTMHkc4WiiLcZEhLTVucSELX2BKMM0IJQWuoMMSpXTd9UdIwqG7FQJFEy8uGbl+GDNy312cPS8det1omZSkmSP4OZuljDXofeI0bcyCFS/iTFi6hcHMFKbUnUeHTNEXzhXnUymWV7i3Mh+A1hKlcmWO0UQjwDB4sWUsrvBdYj4ovhTBbPVeQmKE5Whn/8TC7TU83tl4TZLd/85oVKAMDqaz6Dvz/jFN/tlVPNMyvM3gHVMGl2Rw43O4ste3xtjaPjzNDG7rQ8m3Ljz6urPJ2XnMQ1zjtYr3C/LmfCenZSytH7/sCKw+FchCSa7XUdmDRxPN77N6dbHhdz8VKElbyJu6eCnqT09av3r8WG334u6m7EDjtL4mEAVfl/XQC+AWA8gIb8uV8H4DyNHQmd+nZ/ZvS4820X8ShA8gaGMHC6bupElpvWNBNiNNupV+rbTzjuhxPS4g4SF/w+NyeeD3FGmpniI8YYAxwVXiaLQog43UoSQy57aD2+/Kc1WHGgGb2DI/jV7B3o7Fd4oxRHRMQa7bXPKixxWp3oJBBKWQ1InBjK4M7F+/Hx25bjE7e/6up8lTX5eJc6k7pKbs2vPJ4aTyVLJVFKeYP2D8C7AFwipbxKSnmtlPI7AC4B8O5SdJS458Udyc/41tg1gF88u33092MmH7IZxu9Yy6T5WISJG0qNmYz2IryvnrHNdJ8fmRnGQJIWIR4XvK4Y7z2ey34XRqr0IHHz1019NXlWL7ui9l7Ze6wbk6fMxyGHHgmEeKGquRd/2ViLOduPmroZAsBShathFEx5YVfRtoJ1pnzkxqAiflir9ZcEwkpI9tDKw3hwRRWOdQ2EJrsAYMam4vI6m48EnwU2rriJSfwoAGOe2E0APhZcd0iQbKxO/ot88/y9eElRI8ix2DFoCuf+YTG6TgxjeUqSGVjdJ+OkXu/mGWVm3OSskRI9ficDh5qSrUSk2eJl5SI+b1dOfj/gUXEul9gsEi76Mau5ZxAfvXV5wX7tHX05Jgn9nt1Sb7lf6+84xSw9SXG2YX2/ThOjqeSymwXktSFkfE0SbpTE7QBuFUK8BgDy/70FwI4wOkYIUFwP5/RJlmG0Rahkwdxt8UkqESWft1htff8NS1y1JaAWvF5qJAUVw+CnlhJxj0nFE8eMGxfv55XJSGwrwzpYduw55vwb3n20y3Sf18n54+vS4/VBvGMsw9TY7c7rKA7oh6zuEzmvp6QvPoURk6hqUuWWa91Gwm9siXCjJP4bgE8A6BJCNCEXo/hJAExaEyPK7bUfb5g4Thife2X/7g2vcXS+Sk+IKttWFEgp0dztMCZJd1usLIl7XUwan/CQzCY9T6e88JtwaHzMc23fvfQAvvnQeuw+2oVN1W2YPGU+qluSbf0Miu11udQEX71/bdE+P3OxeC8bkDhhzLitJ5uVWFCZrHJYF925AusOJ9+KVarp1m/nVJbmQnlunr+vpNeLCsfDspSyRkr5cQD/AOBSAP8gpfy4lLImrM4Rd7y04yh+/ddiP/ckY7QGaS5t737LaY7OVyUvGfJr8kgYG6rbijf6sLJ9Zeoax8d6URz6B5OdwCS1OHzU+lI2esbH3PJbmS8D0943hBfy3gh6l/40L278cjYdikj80MrybHXpAXDrgn34/uObw+iSElXdUiBXRmjfcffeOHEijJhEVYuzK6zdd4k3XK/dSinrAGwG0CCEGCeEiPn6b3r4xbM7sLW2vNyhJhgsiW5dCpSWxARlBislxrtyuNld0eqgsol+7YFia4QXYq5zlB1Ovyoz98Uvvu8twXXGBc09A/j9S7sxbLN4pGUUnDBOjJYZqmnrC71/SYclbUhUVNTmFnHsvm0j01ZXY9XB0sX93bYwZ5VSJUSpbUt2xvpSOm6psvurvD0EnHs4DCQ867ZfHCt4Qoi/FULMFUK0ARgBMKz7RxLAzvpOtPcFU6i8VIwfXzjTdytvVHrCSMosiSrWK9xYjD76VhninOJUEDM+IPk4fYZmuvtZp03y1a4batv68IeXdiOTlfj9i3vw9IZavGqTzEpzwR43TuAT/3AGgMIYab7DznH8rgjgg3//+pB7Q8oVTUHJxnzIP9iUW5BduLvYJTbp4TGllIsX3rGiaJtZRnynvVqZoCRBYeDGCvgIgCEAnwPQC+BDAF4G8JMQ+kVC4OsPrsM3H1oXdTdcMdFgSQxC3gwnXOgGQYXC4my8t25uE612pKXXYeyr7mWpc7BK3pMvWxMk/z17B57aUIvKo12jli67yYzmNjVOiNG43LuWHMSjq6sD71+5M9sms6OerhNchybe0OolhlWGISjGq1KY5nHrPRU3QnE39dumyYQlyqzuccWNkvhxAP8updwBQEopdwL4DwD/E0rPSKDM3a65RyXLdcEoPHsHR9DVr540fPX+NTjv+sUF21SyYNjE/78ccSNL/YjH383dHXibJFmsO6yIfbXhojuLV35LgVZMeTiTHXUrs5sfaPHRmawsOPaWBTlXMb7rznGaMXU4k0V1C116iTe05CJNMc90apXYORNzBdeOuFtx9UxfwwU/I27qCWSQczMFgE4hxJkAugG8NfBekcD579k7bY+RUkLKeKWinzC+uC9rTTJ+7T5aPPEQCi0x6e4bcUWlkNcyZosYiIN00UTc/a8exsBwbhajrXibrVJrclFKWZR12W3MU6pQ3E6nE1/KauKGJ0zKpVwT84R+Kw+0oM/EYyLp30BYVly/zarOr+9IlhGlFLixJG4C8JX8z4sBzAYwB0BF0J0i0fDL2Tvw9msXBN7uusOto5ZMtzQrVgCdTDCyWWkae5imCZ2bpBG+PTgU27bUOEuklPDFUuKB70zfZLrv1gX78H6DV0CQaEreal1yCu0dPOe3ahmoLXhkpCzKunzN8zv5Dpugui1+MzY6LaRN0kWT03JPMaROkXQFSH6scxjdT/YdSRZulMTvAliV//mXAFYA2A3gyqA7RaLhpR3qYsdba9t9Caqrpm/Cf8/eiZ4BZ7ElN87bi6V7mwAAzT3FQt9JX741bQP+4XcLldYt+p3Hj7nbj0bdBVIi5uWLqpt5BAC57ILdA/li0gGt6TR3D2Awn6nOaAkE7Cce/UO5czNZWVTT8UUT2UnUGJVst9y1+EDRtn97onQlC0h5kM1KTJ4yH4+sqjLdXyruXnJQuT3p85U4xoMeanKXuT3NuKmT2CmlbM//fEJKeZOU8jdSSnXRK1IWLN/XhMsf3mBZqNYpxzrt4wKGRrJ4fN0R/OjpnIH69EkTi45xInQ0C5aqLEMMZVYsiDJVfXVr8G6ptDZEz/GuE0Xb3D7r37+sjnd1yz/duhw/n7kdgDpRhNOFMCnVNR27HS6CpQ3V4qDTmphm9ePmKBaV0p6FkLhjz7Gu0djYOxWLDgDwSAmTUi3b16TcTiUxeJ7eUKuc78Swq5HjpgTGRCHEDUKII0KIASFEdf73k8LsIPGPn8myVnfmcHNxrRkjU5cfsgz8dTIveHV/oaBUpT/PZtWxhk6vmaa6XU5iUTXKTUA6dXUl4fHAq4d9t2Hm4aAim5X44VNbsKm6MImOpgAuyXsotCo9FBxeQ0pl3PZft3pzqS93Zm0uzmRqkcyxgPuWHVJuH+QCEPHJJVPXjtbkNfv0Dzq0OO091o3Khq6AelZIHJUsN4Sh4yb8liQKN+6mdwC4GMD/B+B85EpffBbAH52cLIT4ixDiuBCiWwhxUAjxw/z2yUIIKYTo1f27TnfeyUKIx/PnNQohfmVo93NCiP1CiH4hxAohxNtc/E1lTyYr8Z7rFnk+36kyBgD3LD04mk1MhZN8OA0dxZYHI3YxiUn34Y8K1X3bfdT5wMcyGMSIl1Vwo4Lnho7+ISzb14xvTdtYsN34ah9TWDidLh5lJfDevzm9aHuasib7RSWiZ26qc3x+3xCVRBI+ThW0r0xdM6pwBt+HUJotGTvqO0Np1+9tUcVFtygWD9OOGyXxXwBcKqVcIqU8IKVcAuAyAP/q8PzbAEyWUp4O4FIANwshPqzb/3op5an5fzfptl8P4J0A3gbgMwB+LYT4EgAIId6EXPKc6wC8EbkkOrNd/E1lz188uIn+fNZ2TJ4y33JlrH9oZDS+xzn2WoSVkqlhpwTqd6sUU+qQalS3ZYaLiRvvKzFipiRq9cv0SClR1dJbpOC5QX81vZww9kIVE+f0/X1y/RF8ZPIbirYnPVV9KVHdqWvnVpa8H4QA5nIqDp900t1Nw+B41wnfz2bxnmL33g0+FijLFTdKotkM35H9QEq5R0qpqeky/+8dDk79PoCbpJQdUsp9AB4F8G/5fd8EsEdK+byUcgA5hfJ8IcR7nPQpDbT1FU/G7NASS6w+VJz5T+Mff78YX7h3tat27SxNTi2AdslJfzZr29g1VTGJjq6SPhbtboy6C6TMMJvgfOm+NcrtxsLpZhmKzdCLkL26lWKjbFEmrnEoGMzqQXIu5xyzWENCSsHaQ+qkWVLKAot2HFw9S5k8Jyn8bOb2UMKG/CbUKkfcKInPA5gnhPiiEOK9eWvei/ntjhBCPCSE6AewH8BxAPpc47VCiAYhxBN5CyGEEG8A8DcA9IFVOwGcm//5XP0+KWUfgCrdfuKDTFZaKna1be5qyuibymYl7l16EK29Y+Z9NzFBRqpbxmImF1RS2fHCyRPdiANC7DGzrjWaFLc2ihujZ8Gqg86Tk1wydcz9yzjPUnoYOG5ZDSdzzgnLBY0QJzyyWp3NdPGepgKLttUXXdPah2km7aiob+/Hot3u8zzSQ6F0xKhEeGxwMyv8NYBlAB4EsBXA/ciVwbjGaQNSyqsBnAbgQuTcRAcBtAL4CHLupB/O75+RP+XU/H/1fo9d+WO0/UafSP3+UYQQPxZCVAghUl/XUTVAm1nxhjP5AtMBrNoMjmSx8kAzAKCitgN/Wn4I1zzvPLGKhqqvn717leJIk8Q1lLlK/lERZ8WFNeIHv9/aRoP7z5EW6wRaZnJKv31gOKPMrimlxPoq87IcdsTB6kAIsWcko15oPjFcWNDeyrvpykc34tYF+9HVb5/VeGttBy68YwV+8pdttsca4eKTmjDEbYeDZ5k2JljtFEJ81rBpZf6fwNgiyycBvOr0glLKDIC1QojvAPiplHIqcrGEANAkhPgZgONCiNMAaDOC0wEM6H7WUk715n/Xo9+vv+40ANMA4OS/eWeqvzpVxq5pilTPAsBNr+zN/+xfW/jyn3IuZiv/99OjE6q+wbG4RtVDUW3z66OfpuymbhhWDJxu4F0lRtwqTsajjae7/fS7+ofxulMmFrSz6Ui7MjuplLkshV7hXI6Q5PL0hlq8/+zXFWzLWnhF9wzmFMotNe22bd++0D7XghmUKyRKLJVEAI+ZbNdeW01ZfLvHa6tiErW2x0kpO4QQx5HLpro0v/18AHvyP+9BLmYx1xkhXptvU9tPFKj8rlWFrfWHHe20zzrqlN7BEUzIT9LMXCnOOu1k0/PdCE032VnTzrDL+C8j9yxVFwMm6cXt/OZHTxU6ehgXdOza21BVaHn87uOb8MJPP47GrjH31l//dScmTRxfdK6EVMYqOoWWRELih8oaqFooFqJ4UWrEYrLRM5BTEo+Y1H0dGM7g5AnjIIQYPRYAatv68LYzXuuk6wDobkqixdLdVEp5jsm/t+f/nSOltFUQhRBnCSG+LYQ4VQgxXgjxRQBXAFguhPhnIcS7hRDjhBBnAJgKYKWUUnMjfRrA/wkh3pBPSPMjAE/m980F8D4hxOVCiEkAfg9gl5Ryv6e7kRK8ZPx8dX/zaFFkv+4P+jpj2+s6cKAxZ9lcqPPXt9LtslI6tmsqj6PMVaJSEjk+ET/M3+UuBseYaMsoaroUWVH1rD5YuNi1/3gPrp1TiU/ftXJ021mnTcLrTyku7yulwyxsJjALISHxQ/VZqsY1KYu9rLQC91JK3L3kAKoU7u63LCi2EjZ2DeA91y3Cn1flPLROmjA21X6+wl09VbqbqmGps9JQqkwVEsBPATQA6ABwF4BfSilfRs4KuQg5F9HdyMUpXqE79w/IJaOpBbAKwJ1SykUAIKVsAXA5gFvy7f4zgG+X4O9JNMr07w40p968e4U+2YwXsrrJWFYCX7wvlyX1ZzO3OzyfbpFhsLG62G1m8xGmhCbRYfzWp7562NX5GSlHJ3oaEhIXvK24hIWEOuupU2hJJCR+fHvahqJtytAWKfGIIuwGAB5bewT3v3oY33tss6NrNnTkkvr9cdF+1LT24TU6z4UHVhx25dbOxSc1vCulwc7dNBDyytynTPbNAjDL4txBAP+e/6favwwAS164wGsylxX7W/CFc9/s20deSn9RgUFcnzhD7yZDSJioJ27u2nhhW+EqfSZbLGvMvKql9OeezhV/QuLHlpqOom2bjxQviJp9vXuPdY9mWR6xClLUoY95rmrpxYTxhXLl8ofXO2oHoLupGbwtpYE571OI00LSxgnTtXMrcfE9qxwLSjP8zqXcrNirjqVscY7fOEVCnLJEUdw4iAWdTkPGOmnirp6V/mIS0z6Z67RxBSYkzph9vvqMp04T+J00fmxqnZXFcy431sGUixUSMVQSU4iykLRCderoKx70O/uHAxBaEgNDGcsjmrrNXVq5Yl86VKnCCQmD3sHi9ONhfOpmEzQJKEtjOCXtYukDNy61P4iQmGLu3zQmE/5/9u47TK6y7B/4957tve8mm81ms8luyibZ9E3vCamUhIRAKCHEEIo0aUpCkRZpgl2UpqIvivhTRFGxoLzWCK8FRTESQARF6R2S5/fHzJk9M3POzDlnTpuZ7+e6cmV35pwzz0555qn3/dwrb1kaOC1J6CRaC5xjhstNjX3rd/8MuggFgZ3EAtNYVWoYuMZoP9rnH3rC8eM88Kd/4Yd/Tp0ZAKKNqY/96PGM1zDLP5Qu4ljqYxlU0KxzLSv02RHyj9XBqzlX/zDh9888uA/ff/Q5y49j+p5WKqu8oBy8IspdZtVCcp3w1b1Pm17j10+8gEM++lO8895gR9JoNYSdqoLfwcZefpM5Df3ATmKBqa8ocTUthNkG7O1f2IuTksLZa5Qy7pQme9Fk+dKNDzyOf79qLXgO69fsvJFhxpfILUZLuYxWtv9Tl84CAPZ89zHs+OJvs378g4qBa4gK1Q///G/D25NrhG8+Yj6Ddem3HsVf/vVqQhTUAwdTl5va2jLDwScKEDuJhUaAR595OfNxaejrtx//xbhi1fzt36+m3KaUwsRhdQZHJ7o6TQLay7/9p4znAybhry2dSUR+MsrVmqkx9T+/fsr24/z9eeO8ZirLPYlsyxHlrudeecvw9uRB9V/vNx/g1uqPd3RLUg8ohRV9bQnH2RlP4uATBYmdxDz39rupM0F2w8ink2m9/JP/fSPlNqsj9maNOTuMl5uy0iXKBc++bNxw01x4zx88qc2IAAAgAElEQVRceywF46BeVnHEnyj/2KkR/hAbgD//7t/Hbztw8GDCHkW77GyvIXIbO4l5zizvTzb0+4Tefi+1E/pv3YicYdJaWNv749WGbVa5RPnnlbey26OiFFBZWpT5QBPcO0SUf4zaKl+xsYIh2wDhHHyiILGTWGDcmJ3T++SP96XcNvOqwcAShtWbxTrv7/95Pes9haxgifLPC6+/g56LvpNw29MvpK5asEMBCUmv07nqO4+l3Maqhij/GO2V/uyDqe0eMwcCThlGlA12EslTZpG9kqtdsyWgH7k/tTFm6/Et30hEueI3+1/Au0npWbIdUHr0ny9nVTVwQIoo/xjNJNpZNZDtTCJTPVCQ2Ekk2+w0xowOVVBoqSlzrTzpZJujiIjCx6gOeifL1tg9Dz+T1fkMMEFUGOxMDnIZOuUydhLJU0YpMpQChjdUJtz2lV+b5x7Kxo0PpOZjZJ1NlH/qK0qyvkY2dYM+NxoR5QejmUS7KSy++htv2jdEXmMnkWxLrh7TRQu96YcGnTQAn3/oiYTbrrjPWkoLIiKjNQrFkWC/zn74WPp0QESUe4z2JNqJOPreQYW9T77oZpGIfMNOImVt5Ae/k/kgHaNROD+XanEmkSi37TMIwJVF9oo4LkUnIr1/v5qahsdOGi3uVaZcxk4i2fbSG+9kdf57Bww6iVypRUQWXfu9v6Tc9vSL2UU3JSJK9tIbqal17PT7XsyyvUQUJHYSKa2e1uqU2954JzU3oh1/+/drKbf5OpPI2QKivPPcy6kj/nb95okXXCgJEeULo/bCC68bd/xmdTem3Papn1hPl0EUNuwkUlpdzVUptxkluX/TRsfxXYMohH5GAHvrXU5bEuUbN1Z1PW4wgEVEhctO02RIbbl3BSEKADuJlNYb77yXcpvRrN8HvvZ/lq/55V89lXKbn/sEH/zr8/49GBH54s13s1vhAABFERc2NhJ5aMnY1qCLUFD+89rblo/lGiXKN+wkkm1GHbqHn3zJ+vmsSonIZVd823qE5P+aLBdjJ5HCbmBk6pJG8s5V33ks6CIQBYadRErLKPyz0UziS29a35y9bFxbVmUiIkr2to08hf96xXj/IjuJFHZuRPElbxgNoG+bO9L/ghC5hJ1Ess1o74+dfX6cRySiMCpiC5yIHDKKsMxxJ8pl7CRSWg/97T8ptxkFrrGDeQqJKIw4k0hhZ7S6h8Lhkaesb7shygXsJJJtdhLJEhGFzVMvGOdUFM4kUh6rKCkKughElEPYSSTbsg81z04mEQXnHy++aXj7K2+mJs4mCpNsxjG+fcY89wpCRHmPnUSy7Rd/T12CSkSU6+77w7NBF4HIM6NaqoMuQsFxIzUPUVDYSSTbvvTL1DyHRERERDToToO80ES5gp1E8t1jz70adBGIQq2qlHuH0uHzQ4WK+2aJyC/sJJLvGAGMKD02BNOrKisOughEgWDNQER+YSeRiChk2BBM79+vvh10EYgCwfEjIvILO4lERGHDhiARGWDVQER+YSeRiIiIKAdwKToR+YWdRCKikGEzkIiMJPcRT5g9IpiCEFHeYyeRiIiIKAckDyCdtawXE4fVBVIWIspv7CQSEYVMJMK5RCIywOWmROQTdhKJiEKGzUAiMpJcN7DPSEReYSeRiIiIKAewU0hEfmEnkYgoZAZGNgVdBCLy2PDGCtvnRJJ6icJ1B0TkEXYSiYhC5sbNk4MuAhF5bOuckbbPYZeQiPzCTiIRUciUlxQFXQQKme+eOR+XrhuPzsbKoItCAUpZbspeIxF5pDjoAhAREVF644bWYtzQWvzz5bdw80//HnRxyAVO+ndcXkpEfuFMIhERUY5gF6GwdTVXJfxuNZDNl04a8KA0RJTP2EkkIiIi8sENm/rjPzuJVDqls97R484c2ejoPCIqXOwkEhEREflAqcGf7fYRW2rKUm6zeg2zDun1G/uN7yCigsdOIhEREZHPxOZU4uiW6pROod1rpJYhq9OJKI8xcA0RERGRD3QTibY6aLedOANTOxtSbrc8kxj7f+7oJvzv3/5r/YGJqGBxJpGIiIjIB0q33tTOJN7iMa2oqyhJmTm0OxN4x4kz7Z0Q8/4lox2dR0S5y7dOooh8SUSeFZFXROSvIrJdd99SEXlMRN4QkR+LyAjdfWUicmvsvOdE5Jyk65qeS0QUhMpS5jkkj3B5YE5TmQ+xxW5KDKedzLmjm209DhHlPj9nEq8G0KWUqgVwKIArRGSaiDQDuAfAbgCNAPYCuEt33qUAegCMALAYwPkishIALJxLROS7hsrSoItARCFUpO+VOdgQmLon0eJ5sQNTzrfYyZza2YCmKtZrRIXEt06iUupRpdTb2q+xf6MArAfwqFLqa0qptxDtFPaLyNjYsScAuFwp9aJS6s8APgdga+y+TOcSERERhUJnU2X853yfFL7s0L6gi0BEWfB1T6KIfEpE3gDwGIBnAXwHQB+A32nHKKVeB7APQJ+INAAYqr8/9rNW85ie61aZL1473q1LERERpRg3tDboIpBPZnQ1Yl1/u+Pzk2cOLc8kmhxfUmStGcgoqESFx9dOolLqVAA1AOYjukz0bQDVAF5OOvTl2HHVut+T70OGcxOIyA4R2Ssie7P5G4iIiNy0ZGxL0EUgH0zprAcAVJdFA8u70fGyuydRb/LwelSWcf80ERnzPbqpUuqAUuohAB0ATgHwGoDkYdRaAK/G7kPS/dp9yHBu8uPerJSarpSant1fQEREROQ/EcH0EQ263+2fr9E6rZbOs/cwOHtZr80ziChsgkyBUYzonsRHAfRrN4pIlXa7UupFRJel9uvO64+dg3TnelpyIqI0uDSL3HTXjlnxn7OZOaKwyC7G6cLewZlny3kSDQ708r3UVluWkO6DiHKPL51EEWkVkc0iUi0iRSJyCICjAfwQwDcATBCRDSJSDuBiAL9XSj0WO/0LAHaJSEMsIM37ANweuy/TuUREOenrp8y2lJvsisMn+FAaCtJAd1PQRSAXJPeZBIK+9uD2o3Iwi4jS8WsmUSG6tPQfAF4EcB2As5RS31JKPQ9gA4ArY/cNANisO/cSRIPRPAngQQDXKqXuBwAL5xIR5aRpIxrxgRVjUFqcvppmQ48oNxRFoh9WfWfxsMnOg9gAqXkP7Rxnp+qw+jhElD+K/XiQWGduYZr7HwBgmLYiljZjW+yfrXPdwMUSRBSkoXXlePK/bwRdDAoJttVz19C6cgCDnUR3Atc4F4l492Yq8vDaROSPIPck5gRWc0Rkl58Nee5RI8oNV6+fmPC7G5/cbOoaATBvdLPp/WVJqxjsDJozrQtR7mMnkQpeR0NF0EUgIqI8V1Ne4vo1RQTK4ZqnUxePRklRxLSjePKC7sHHsXHd2vJiTBhW56hMRBQe7CRmwOWm+a++0v0vbips2czuPbx7ua3jnTYQKTw4G1xY3P7MOg0iWlfhzXdfQ1WpJ9clIn+xk5gBv7qJyK5sloA1soFFlNeOn92F4ohg0ZjWrK5z+uLM0Y+tMOu06m8VAVNaEBUYXwLX5DJWiUQUZmy3EeWWCcPq8LerVjs+P/kj71UdwLqFqLBxJpGIyGV2JxLXThrq+LHYjissXN2SX9xYapxtHWDUGTxyWofrj0O5I1PqJSoMfBdkwC9kIvKaFhrfidndjS6WhIjCYl2/tRyKXiwDXT6+LWEZqoj4MrNYUVLk/YMQEQDg+o39ae9nJzED5qQiO+7eOTvoIlAI+JV4ev+eNRjdWoMvnTTgy+MRUf4x6vwplXq71c5oNp3JjdNTZzCJyBsbDFYM6BV8J7GlpizoIlAemd7FWR2yvwIh2xH6eT3muc6IKPfcvXO2aT3i9hCU1WiryfXUJhsdutLiCC5YOdZOsYgojZV9Qzx/jILvJH7ztLlp7+dEIhEFiXUQUeFJF+XY7cA1RueLGDxO0u/XHGm8VM1oP9ufLjsEpywalbEsrO+IrJnf6/3gcMF3Etvr0ydST142tnVOl4elIaK84GJLh8EiiArP8MZKy8d6kSs1m+WmNx83LeU2v5bgE+WiGzal3xsYlILvJGair9duOWE6Lj20L7jCEBFRQWNbO78YvZ5rJg5FSVHEctcv65lE09uVpeOSdbdUOy4LO5NE4cFOog2su8gLs7ubgi4CuWyEjVmATKxWO1WljApIVIi8WG0gknrhbDqjbD755wdnLwi6COQDN9LnZMJOIlHAvnjSzKCLQC67cfMU1661a814S8fd+/55rj0mEQXH7vJRJykwDp+sS69hFt009rO2PzKbZa1WB9kPHEx9jIW9LY4ftxD1tNUEXQSyqbgonN2xcJaKqICEtXIg5+oqSly71rLxbZaOy2aJF+UOP0aPKTOrn/Fl41o9K4PW8Tpy2nBb5/31ilW4YdPkjMdtnjEcdRUl8QGobKIpWl1G2j+8PuW2bPLIEoVde105Rof0+5utU6IQuHvnbOxcmDnyGxERBa+h0upAkHedem0CcefCblvnlRZHEIkMlstohrC2vBjdLdX43SUrMCwW4M8smmkms7qtp4ZaO2moo8eg9Ni+CK+ff3ApKhxsF/EiYFUydhKJQmB6VyOOmdkZdDEoh3xt5+ygi0AuiXByMKfVlhf7+nha51B732Qb7KW7OXEW44rDJ2D2qNS98kapLawYY2P5I2M/EFnjx6oSf2s2IiIyNTAy84j70TM70dFQgRld1kfnKdwY0TH36F8zv9PUHNR6iS69by47rA937X06/vuxs0a4cl0KDz9mnci5sH4DsJNoA/eCEJEXtK/vZeMy7z+8ev1EbwtDvouwk5i3vHhptfrCrUuXl3gbGdlO98SonZVtig9i+zUfcbkpEcWVOVzqQ0ThtrZ/KB66YDGKuO604Ew2CNSSUazXFMTgQnssiMz8nuaMxxp1Zu8/a777hSJH1vW3Zz6IfBHWcUK2OpOcsbQn4ff6ytKASkKFRj8q1N9Rl3DfRzZMRHsseAAR5Y+mqlKMaqlGR0MlKi0ELwhrY6LQTBhWl/mgmKKI4LDJxg3y6V2NpjlOzVJbHLS52nSYi98dPz5vER7ZvRy3bp2R8djBVbGDBR07pDbtOXx/++fjR7uXqonCaccCe0GtkrGTmCS5fpoyvB41Pm9KJzpyWkfC74dPGRZQSfLfKYuCjfp24tyu+M9eN5CWW0ynQdTTGs6Q7GFxzYZJlo/dd9Vq3JQmd2pNuXGkVLPFZNqAotXq4mSb0U/TKSsuQkNVKUospG6yuhjOyqwkZc9p4CHynpPlwFaWYW9MakvaxXdMkvVTBxvjwxsrMLyxEtNGNARYIipISb0F7ifwzsimqkAff9UE/0K+V5dxwCvUQrT3aqKNmbJ8YyUap9WQ9V7U3Hbj1gT17aHNhGYqZ5euDjY79NcfWmorSiqlum6jsxQmXrKeSoaSZVpuXlYcQU+Wnxl2EpOM0FVW/R3RvQLcNJ3fDh4MugSpkrcmcQmOd4w2f+tn97zm52trtnyNvPW546e7di1WBd5bOcFm0nivPlYm143v9UtTebTVlg3+4mElk7zqxYgbg5ytteUod5BLjsKNkZ2d8+OpYyeRCt5BFxrOt26djod3LwcAlJc4+1jpi1ESiSTMagvYwHfD/j1rLB33odXjPC7JIMYqyX9uLvMttrDMj7LjZuMrm2uZRS+0MpN47ooxg2VwXoSMtgyY5/d1kqmDnQZ33HfGvKCLQB7L9Elxo8XIb5s0vKyszlrWk/kg8kVrbXnW11gytg2NVdEgRz8+d1HW1wMSlzwxRL6/9Ptt3OjEvX/JaNP7Ohv9W+7KYYbw0X+0qywsBz5hTpd3hdHheyV4ZuOC6/qjS9RXpBl8KNOltQj664PfXv7ra09dLs6B5vzCmcSApTz/Lr4gDYyaGho3HTXZ1esNrXMnktwRU4ahu7kKPzt/MSKcbsppZq/e1M56tNSUcUl7ATp3RW/Kbf+zY1bG8+oqgtnD01xdlvkgcpVZvdDXXof9e9ZgdKv5fqNh9eXxtElB7WmPB9ixM5NodBu//rLG5zD/+PG5ZicxDS/b5cfOGuHdxcmWhqpwdNiT2wOtteX40bmLMLyxMpDyUHasRE31e2kVO6PhYfRadDUHG0SJnPFuS2I2VxYcPjm6ZSGoDoJRCgzD45L+zn4nuSMpJxm9M46eOdz3cuQki5/r5JRqdrCTmIZZxXbByrFZXhdMmkyU5y5YOZbLyslTayb6Fxm3voCiENodoU93dDaj/dkM6ojYT5XhNqvFP6g7UAS43UIORnLHvacHu3fR6D0yqYODBEbWJ6VCy/i5jj25x6TZN5wJO4lpaC9A8ps46LxqVAA4hpBzPnpUanhxqyPpfuFEYn4Z354+Mbmb7tg207fHykUzRzYa3p5d4BrnosHOsi9DNhpj22paMixVTu4Mm63u4di6+yZmMcvkhuSXdOnYVsfB//LdDUdNdjVSthUF/UpkSiwaloYd5ba5o5tM77twVXaz0mTNyQu68f2zFzg6N11DrbGqFFM76/GB5b04YkpqKHgtcm6QgYdmdDHPaxgNvq9y43tmWL07e63z1e0nuj/7ld1MogymygjoPXbktA589Kh+bJs3MuW+Kl06iym65aXp2l1Xr5/obgHJE3ZWOCS/3txek15Hg7/1cEF3En9+4RLD26/ZMAkAN/qSO8YPNR/tXzbOvdD4ZO7khaPQa5JUNpuG2NTOetxz6ly8f6nxstKWmugIemut8Uj64GqF9IXoy2LG6IsnDcR/ZnS78NvqU/TSdO6yEECHdAQoNUlNkk074pgB53uzhtVXxNMoDXQbz3J6LRIRHDGlI2V7zQPnLMCD5y+O/75xeuZciwAwdoh/M+f5yK/q384KB7az09u1JjEd17ihtaiMDbBY3eubzSBRQXcSq03CjZut4w/be3kPR9VygpMZaaMzDrJ978iQ2vJ4ehK3XBTPo5j+tT12YAQ+ccwUHDU9u43438pi30h5CRNQ55JLD+3Dnz58SKBlYDRl+/T1fLrUFHYsGduG+T3Nts9b0NuClpoyzBnVjP171mBEU7gCIo1urUmIlstVW94TZBsIyRt85dMz+mxoqzr8qKYLupNoRhtt0ZaIaaPvQVdkycsWWa/mPv1rmGmWJ4wVfC7w4nNiNbR7JCJYO6k960a31UBXx87qxOqJQ9BeZ5z70+130LD6Cpy8oNvlqxaGdB/3ytJiTLYQ4dGrmeFM7zY/A+bkIv2g1JqJ7Vld644TZ2LfVattnWM2q+mFCcOc72m7+bhpOHtZaioYAPjVh5bijDT5ZSk/MAd0lJ32ndGRRjER3FDQnUSz9+bc0dGRu00zoksg4pu/s3is0a3V8Z+dfq/ftjUxcEBQ+wzIPdpyRCu4UjAYbj3vpy32PuDVFYdPxKe2TMPPP7jU88f6+NFT8LWds/HB1eMyH5yjVk0YEthje/Vx15YgppOp3fZRl3PLho2TATmzp2zNpOw61JGI2I6G7me7u6QogpV9zj4nK/qG4EyTCNBtteUYyn2wee8cg3yxhcisnWH0UR6cuBq87cBB69e0o6A7iWaGN1Zi/541mDYicR1/kAMeNeXFqYF22Ef0nZNGo9nLtH7qMNSWG4eVN5q1ZifRGS8+JlM7o8FgTrSxf+y8Q8ZikkkkOd+ioLr4HlrX3472PG/E9ZjsY3WD1dlot6OK9qRJwK4RkbSzlJmCvgHh2Fvpl5StKTY/xgddrNx7WqtxznI2vCk4dt7/m7LcilGIjGqLBo9SFBV0J9HqTJwbmz+9EK7SFIbrN3kzpQ8kfvCNIqL6Ge4+nzjteH1kw0R88piphvc1V5dh/541mDPa3n6hTG1BfqbDJ+jgUrXlqXvnp3ZGl6J6NXCULthWIbD7XZ/6MmR3/shm53sIf3DOQozL4vVzknibWyFyg9X64mNHT/HlcWiQ2VNm1HzR9iTq80lGRPCjDyzE2CHuDmwWdCdRkykAzMHYNK7dtuaXtw8kjKZuySKhpZGg90gWmqrSIlSWGgc7SsvmyzSyuQpD61JnaG48ajK+tnO2/ccnR46a0Wm6VMzpR8+sMWW0fMQLbMzZIwCWjWv15NoZG1Jp9sJ/afsAfqaLDummJWNbCz7YkZPPYVb5EHXvhY3TOkz36fnhazvnBPbYyTJ9Rm47cQYuXjvel6X8heTQ/uz20R7MIsqeUqogO5l29pd//Ogp+MQxUzCqpRoLe1vit3e3VOP+swZTfcWvmEXdVNCdRK1SP3xK5j0agP3nec7oZhwT6xgKEN+7w74dAfZHq6vKijGjK5hQ5vksyO8jbcR/MJ+Zt5aO9WZW7KbNmfeoHT2Ty4rcUllaHHg+sWNcHvTMZdkuN7143fj4z9du7Eexj4FnkllZSuyFm4+bltI5eS82Ql9i8nwsHtOKbfNGFmSnwi4/2518OdKbOTK1HWfnOauvLMXaSdY78vqYKHYVdifR4nF2Rt9/8cHE3IuDe42yawAancu+pr+cztxa7QxWxEbvRzQxmayRGoNldwBwzZGT0p6X7Zej/vrDG6MzvE6Xnic3ZnatHZdwu1erA+7YNhN71k/EhmnW8pGlM21Eg6Pzupudf1GRsSAbY1cdkb8pmATAeoPB43vTpKLJ5rOb7cxNPljRNyRlmeM770U7ifqO64PnLUo5t8oknRkl8qszfYD5utK640R395lnMrWzId6+tKugO4lW2WnAJS8T1Dak6xuVTr5KjB6bM5K5QQT47HHTsC5DQ6C9vgKfP3561vsB8tWK8cZBg7L9GGT64tRvrHf7S7asOFpxWw1i4tTC3hZsnunOzM+XThrAry/yPnpqGIh41xHza/aYnJnfm7rfeGLSfr0dTP8CwLvOx8Zpw7GgtyUhzY5Rzsft80d6UwDK6PPHT0+5bblLeUKPsLjKL9dUlKZ22Mw+Q2bBDdOpMRg0cbrPuaA7iVZH/uJf5g6+zRNmEtkayGlmL983T5ub8dxD+obg40mdP6MZ6mXj2xxVCpqTF+Zno+WE2SMsrdm//PAJKbdl+tw5Cndvcs0LVo7FNRvMZzbN/gQ30uzY1ddei08eMxUzbS5hrigtQmuNcR7GdFYGmE4ilDLmRc3OrG4uTXfK6nf1qYsS98LN7o4GHDOahcxnc0YNBlq70cX0KHWVJfjCtplorU1f35QVF6HYj8ziOaw44n5z//6z5mNZUofw8MntmDCsDkMyvGZWeJUHNkweOGdh7CfjvzXTdjijuurhi5dj4rA6S9tAMuEcPSx8IaR5n1aVFuH1dw6kOTW7N/molirse/71rK5B3uq3kPTaL0V5OhKxbHwbvvHwM4b36Qd7jP76yhL3qrlM31mnLHIWQGFwg7l/r9+9p89DJCJYM2koui68L+X+lpoyPP/q2wm3mUV7zaSvvbbgl4Rl+q4w4/QdEbZo3LnE7tJR7fiv7Jjl+DG/edpcvPb2e47Pz9bdO2fj7w7bGifM6cKl9/4JgPUYD+SNK49IHSgFgJ0LR+E7f3jW+oUsNF3HDkmNomsn9zMN7hc0aluUFkUy5kg1Oq+kKIJ732++NF5z3cbM0foLeybR4nHa1HBJkdGST2svoIg4+tI+OmmJmH4Dap72B8LLaURL0ySp7r+A+TzuZhoiOsN5t2xNXQ6TcF0fnzSzhwpiJjGS4cvnp+elRs90mhicdRXw0AVLMh9kwO5z96ktU7FrzThLA5S71owzvc/Kx4Kzle7pH16PuTZT6rhpelcjNs1wFlyKkdbDr6K0KOEzfdysEfGf6w1y7IUhEraImMYioChtS1umzqTRR7S+IvOqtYLuJGq0xrrZc3zdxn6cs7w3nkA78Vyrj6H7OfZqWQnbXBbbsD0v9uVxgi6lhrafibylreXO9CG0Il/X2PvBLOG0vvLraEhNHdLRkD4QkJOvQvfbRP6kwLDDaN+EU7m6asjNgZyGqtKE3y1mwLB9zOqJQ7F9frfhfTO6nAUdMpM8iJlP3HjPHsVE4b4x+37IJ06Dhmn0HS59ELPvnDE/5VinT6cbL8OYWK6/uaOb8zZGw/qpwwyjnNo1ObaSbWhd9st7jfjSSRSRMhG5RUSeFJFXReT/RGRV7L4uEVEi8pru3+6kc28VkVdE5DkROSfp2ktF5DEReUNEfiwiI5If37xcKeU0PK6lpgxnLO1JuL9O64Fn2u8U+8BEIqmPZ2XvWXlJEX587qKUJO4MP+6OxqSGW7K22rJ4bsKICy34j7q4X6OQKAWcsbTH8D7tc3TSvJHO8lja+FbLdo+E2fmDM4nudUru2jELnznW2vLQ3rbEyKOruH8wzuu2p1cDA0bFttKpC9E4Reile2/s37MGH8kQeZncUwgBNVf2ZVcvrzAJKGP01Dl9OjPVZ5m+WxSAvvY6PLJ7OY50IRp3WN2waTK+evJg3utRLc6if5+1rBc/OHsBetpq3CpaAr9mEosBPA1gIYA6ALsAfFVEunTH1CulqmP/LtfdfimAHgAjACwGcL6IrAQAEWkGcA+A3QAaAewFcJfdwmnT6nYmih7ZvRwLe1vw6S3TLF3bKLqp1Q/hyOaqgk9u7BWjcNp6I5urUFdRgpqyYly8dnzaY3PB4Cbp3GNWiS4d14rrNvbjvEPGOLqus5lEd5vSWs4jN0YWNQPdTVg5wdry0C0D0bG19VOG4Y+XHZISZInc58bKhOQlYfr9QAMuvpfsujXDEu9cUAATU67aPm+kK+9pp4y2A+Ubo68dLdeutfMF/UkRes2cvni05esmP4ZVPzs/dUuDJnnlRb6LRATLxrXaPq8oIpY6iEYvi5UqzpdOolLqdaXUpUqp/Uqpg0qpbwN4AkD6HlbUCQAuV0q9qJT6M4DPAdgau289gEeVUl9TSr2FaIeyX0TGWimX9mbWvgzsLN+MRAR3bJuJeT3p9xDoo5ua3UfBSVehbRnoxGeOnYaSogj+cNkh8Q356fbxhF02SVXDqKasGCKCI6d1+DKQ4hN5q48AACAASURBVNVHdl5PM/bvWRP461NdXozqsuJAk3kXirWx/Z0Le1sM7zcaYMzk/jMHl42dtaw34/HpUivlf5PbXJiWfeeKXWvH429Xrgrs8f/faXNxxhJnHZtc1p2U2sCt1ShnL+/F/j1rXLmWnr7da7REMqX0bCe7wml/I5CWgIi0AegF8Kju5idF5B8icltshhAi0gBgKIDf6Y77HYC+2M99+vuUUq8D2Ke7X/+YO0Rkr4jsTb6vvKQIZy/rxddPmePo79FPGQPApI46fGh1tJ86mCdRX5bo/5uTNomfv3KMq+GjKTtXHjER9ZW5NZpVaAMPRyV9hqwsB71odWIn38lz5vRrONvXZ55HgS2yXUZbCPuB3DR2SA1Gt9bgoQsW44rDjZPSX7ByLNpqyzCq1Xp+q6bqwZlE57M6scFTh2eHVXeLszxhVoShU/nDDyx03IZxi18BbPasn4gvbEtMSN7XXoczLQyMkH2HT06f41kvc7KAwZpFe7/cduIM3f2Fy8uv0ZzpJIpICYA7AdyhlHoMwH8AzEB0Oek0ADWx+wFAG1Z/WXeJl2PHaPfr70u+P04pdbNSarpSynAdzJnLeuKbZe3Slog1xCJEfev0edixIBqUpq+9DivGt+EjR05K+SJpqCqNnwMApy4anRI+Ol2lG4LvpZznx3NoFiXMre/Tn5y7yPCx9qxPbHyeNC//Ew5bqQe3JT0PdjpI2VbiNxyVOeS0mb9esQp3JDWM3KLt53G67/bgwcGfL1hpaSFHTih2eQnb8bMTt8x3NFSitNj4a3h+Twt+9aFlaffZZvt+9LL+C0PHSa/NRm5Pq7MxYWrQjmqpzjqwSa7YPLMTC0xm4POZG50Iu5f4yxUrcf2mwcmL5DoshWiPk/mRtHGsuaOadbclBwuxVMyc8f2zF5jeF6b6RONrJ1FEIgC+COAdAKcDgFLqNaXUXqXUe0qpf8VuXyEiNQBei52qX3RdC+DV2M+vJd2XfL9v7toxC/eflfrilxZHcPPx0xPyyei/gML4pigkXjVk9MmFk22Y6u5m7C79chPdGyr5b9udB3sqg9JWW5YwoOP0fdPXbm0/iJHS4sw5k6xYMzF1n6I2E+i0k7hwzGCDrbEqc0CuXLFtrrsDK8kz336z8vKmO+Z7Zy3A9wy+51KuEcKWnZ3E0iLm383fOn1ufGBOy0s71uEAM7krfO867xmlr3BTWXFRwvfOhw8zzsNolUpoo0js/3QnZPVwoTOsPjUCuybdgHXyykO7DIMTWRh18K2TKNF3wy0A2gBsUEq9a3KoVuqIUupFAM8C0A+/92Nwmeqj+vtEpArAKCQuY/XFQHcT2mrTj1QavR4HY0P4i8dkNypWCJu2c8kn0iQdH/Awt1ie1aeeSP6kWHnOfnHhUvx21/JQ5I7K1ieOmYJ9V61OuG3x2OiG+cOnJC4rshoMqLm6DLecMB29bdU5t0TbTETcTQMSRnbHBMYMqXG84iZorUnfz/2x0PFmmqqN38eTOurjA3N1lSX44kkz8dnjcj9QTz4ZbyOYS667yIUYCVY6C2ct67G07FQbIDK7ZKZHciOKfK46NPn5jT0V+65ajavXG29LsMrplhI/ZxI/DWAcgHVKqTe1G0VkQETGiEhERJoAfAzAT5RS2jLSLwDYJSINsYA07wNwe+y+bwCYICIbRKQcwMUAfh9bxhpeus/AjZsno7+jDp8/YUamQ9PiLJEz2Y54/+aiZY7P9WL9ub4iCONovteSN/G7JRIRRCKCoXXRUcDiSO4GdhGRlBnJUS3V2L9nDSZ1JDac+zvSN6T1lo5rw/fPXohig9lOblmMUlku6025nitXSTTSo89Q2Bi9TzWlRREssriccX5Py2BKLApUJCK4a8cs3Ll9wPQYp1E7w6DNINCL3bRPg6tGBm+zUj+ftawXN262HvXa6JKXH9aX8bHyvY+Yru4/YorxKrOiiGS933dGl7PJCb/yJI4AcDKAyQCe0+VD3AKgG8D9iC4R/SOAtwEcrTv9EkSD0TwJ4EEA1yql7gcApdTzADYAuBLAiwAGAGz2429yy5Kxbfjm6fNMl5Elvy+0TkCRCOZmiKxK3lBplnTGb0/42b9az6/AAWHVWluORRlm5bN5ij5/wnTctHlyQqoBsua9AwczHxRCbs4eV5VFG3R2wtZ7yeijcO6K1NnjNZOMU6ks6rUfsj0XbJnVCRFBT55Fgy4EA91NaKgqNVwFccXhE3Cuw1RJYbDO5HNoR6aBqh/rYhw4oV3WqDM4vr0OUzpTBx71JQkwi4ovgmqiOZ1I8isFxpNKKVFKletyIVYrpe5USn1FKTVSKVWllBqqlDpeKfWc7ty3lVLblFK1Sqk2pdQNSdd+QCk1VilVoZRapJTa78fflI16G6OOyW8obXlqRKJJxPkllh0nH9jyEnsfm+RGppb8PduORmdjZcqI6Wn6UdI8qmztNNPtzlrZOb65ugyHTR6W+cACdsAkq3U2y1DzZaZmZHMVvvy+AVx5RHb7ejTZblMweqWMAuncsMk44FJdZUne7cerKSuOp8PiBHjuOs1gxvDYWRmCroSAWfChjdM6ICJp97RZoVXPRm2fYfUVjlYS7Fk/EX3t0YGvdBE3RIBTFo5Ke618D1xji4sVkFG9Hpo8iRRVXlKEyw+fgLt3Wg9TnTwLNSm2j2KuC6HwV00YkvU1CtHmmZ3xn53UX4f0teHaIyfh7OU9WZVjVEtVyvsgXxrTfhrWkN2Xbj474GCd6OKxrTh2VmfK7aXFEWx3GGG3KoC9gW7Nyn/lfbMSfp8zqtm1nJ5TOhts5TIrxCXo6WgRoLuaKgdvTFiGN/j+T7c8lcgtZkHvpnS6E7l2WH10yWpN2WBbQUtcv3Nht6VrXLpuPKbqZgQ3z+yMtye1anOhwSoDQXRJcMrturo2pd7N4ZEaoxWCubbn0t5iZsracTZHsuqSIldN7WzAHy87BNVl2b90c0Y14bt/fC7zgZSgpCiChsoSvPjGu6YNyXT1gIhg4/TsoxzqE553NlbiqRfeyPqahSD5Nettq0F7XTn++fJb2DqnC4fZyAmV7+aOasLWOV3YsaAbjVXWZgJLiiK44vCJ+NIvn/K4dLmhtdb7pcm71ozD628fsHRsQp4yi9fP187ljFj6qoRGqsmx2S7DI8pGpqjDh9z4UwCZVzpdv3Eyfr7vP+jUDYxUlxXbGmzaOncktmaI/Hz1+on4+sP/SLjNysDbIX1tlssRdqNbqvGXfyUmW8ixPiI7iWF00ryRGDOkBuUlRYab593oIJK3/AjUoV+y9s3T5uLfr76dVIYcHoLzWWdTJf758ltYMb7NtRHbfFBcFMGlh/a5dr21/e34/ENPuHY9ito+39oMwJC6cjzzUjxuHIY3VqY5Ojthr34Eg2U0a7fp/wQvnysK1pi2mpTGfC4ZM6QGj12+Erf9734c2p9+kLOusgSrDNIgZWvwsxT9NJnlfzWi//zlw/fv2ct68dEH/mrYIcyxPiKXm4bR7rXjsWn6cBza355x5CXk38Oh53RUJ9Pz7sfr0qpLDt1QVZqzoendZve5F9NfyC3aazJ5eD0uXDXW9vlBBmQa3pBd5yBMy4tmdQ8uZYtINCKtGSsBunJfai8xIe0Jv2ALwsbp7uYuzobTj1p5SRFOWTQqYYWRn7SgXDXl5pMYblQjWwZStzKEyTdOnYPl483rVVvfBx7Xu1YG8thJpILmdBnVsQPRZcOVJnulDoZgGD0ERXBNkY2K1ckMavIoKGVvl0n+rp0GgQs2TE3fSAsy48gCi6kQzITlHaUFOdM+H9rrYCVQRZg6um4ymknU7yFtZhRj8tjpi0dbXsofZsfNHoFda8bhxAzLUPPdlM6GhCX9v7tkBf5w6Yr477lWlbKTmONy7P2WNz6wohf7rlptGoAinzpoYaAtXZlqED7bDd0tseTYDPzjGv0SyEz1lFmwBs2WAf+jEuoHG4Y3Og9uFLYOVrxjFCvWHSfOzHiOk78g7MvdRQYnCvUz1d0tgxHDP71lqs+lIj+EqZ4/95Ax+MTRmfMPhqsWSVVSFMH2+d1pl5mapgxL88fpO1ybpnfkxOT+YB0rqKsoQU354Pst19KUsZOY47Tolk4qvdoQVZRBMfu8plsuED0vNSG5np31+F7JhcrUqumxsOBfPMk8SbJTbbXluGRdH24/cQbGt4cjf12+yfa9ePICa3vu3OTWl3mQs6DfOn0uzlneC2AwSEu8YxRrdnY2VeLB8xalnKuPbJtj7Zq4IbWpycf1Mu1JbKrmTGI++tbpc4Mugm3aZzBXP4sA0BFbum+2cmSHQT2vb9tedmh2qYO2zunK6vxMlo1LjOjq5KWa0eXnnszM38yMgJLjPrh6LLYMdOL5197OfHCSKcMbcMe2mTioFE687TcelC533HfGPLz61nvYfPMvAUSXgGSjrqIEG6Z2pET3csNFq8ehp815fswtA52481e5E3ly/NDaeNjsKgtBm46c1oGfPf4fS9fWR3RbNMbfxODdDvJRFaogRl/1j5jNpFimjoqXJnXUY1JHPVZPHILOxuj7LXkmEQBGNKW+Fy9eOx73/f7Z2LH2n/9cGKTSZilyueFN9iWncg160ltfHu3H8UNr8adnX0k5NuiyOvXE1avj9cj1m/px3cZJ8ftExDS66rQRjbj9xBmYO7oZJVnst9y/Zw2eeelN3P7z/Sn3nbm0Bzf98HFL1ykpErx7IPVF0P99RnWsVV/aPoCX3ngXA1f90P7JNlmp14Of7qCslBUXoaetJmXZ4/GzrS3PWtjbgsU+N47DRPuI9LXXYVZ3E/buWoYLVo7FpI66rK+tjQg1ubzf4H0LutN2aOb3RGeXzb5M1kxyP7JZmFhJdr9kbCtuPm6aD6VJ9fMLl+DWrdPxzRwczfbLeYeMCboIrtg6pyuwQBJ6o1tr4qsb4h2jDOe0Zdm5zYXGLPciF6Z0KS9Ptpgr0E3KoyGV31y0zJPrWvXI7uXxn5M7JHYGnhaNac2qg6hpM9hjPKWzHmfHVltY8YEVid9Nd2ybib9ftTrh72mriz7Oal0U2ZuPm4YlYzO3tcuKiwaD/3hch1rJ1xv8txe5YmpS2ODKUk4SO9FcXYZTFo1yZeZi0/ThuPbISdjm80buzx0/HT87f7Gvj5lrbt06Ayv6hgTy2O31FVgyti1hn0K+2r12vKPzTtPN5A+tC2YmLmx7CV3n5d8n3jV8nTh/pfGgg/YUFBfl+WtNCUY0VSXUTfr3aksAS4z1M4na8kqzrQ92guJpA9SdAaVvaQhZQJ7iokjKIMAd24z3ZH/zNGuDuAt7W+IrnTStNeX4/aUrcOqiwSBtK/qG4NatMyxd049Bq/NXjsH82Ha1dNiTyCPze5rjy+zOXNqDzzy4L+AShccRU4bhG488k3K7l8vYIhHBxunDPbu+mfKSIgxvrEz44ls/NfPsGpHbBmL74KyqKS/GtUf2J9z24HkBDXjkab8h3T68Y9wKLx+e/iEA4PDY6oIvbJuJ42/9dfz23tYa7FjQjeNmjcB7B1Uo9pKTe6Z01uORp14yvO+keSOxasIQ7Hv+NfzZYFmnn/Qdv46GSnz9lNnoa6/D3b8d3K6idRzszNBHIoJbTpiOiS6sjMqGlRk0v3QlLa2v1Q3W6peS9g9PDJJ3z6lz8JsnXrBctdW6MQjs4XfQqYusbalijZinKkxSM1h12uLUMPVhdPYya8sESgp8pDiXl+/l+4ROPtMaP8Xp1nfpTB5ej5UTEmd4c7nhPqolfPtOByN6Jt6+f88aXHXERPceJ2QdRSA1nUkkIvjQ6nEY3liJkc1VGFbvPIothc/JC9K3Y9rrKzC/pyXhvaqtIFg9MbEeuuzQPtfLF5f0WZk2otF0KaDd9FpLx7Ul5FT222OXr8Tnjp/u2vWybQ5snmE8cP/9sxfgVx8yX547tbMBJy8cFcp6zUu5++1LnogI8MljpqIoyJB8HvjQauOcbfnc/yiEysysAzmr294MFnljaF200Z08k33JuvFZpZXwg34WtNnhErRjZ/mfuiMTLTWF10uagq5+Lna41DnZA+csxL2nz3PlWhROm2cMzqBrA8rJbSAvByutdPy0aOpBf67sKi8pShsJ3ksVBh1tETHck9rbVmMpX+XI5sGlu17tXw3TwHh+9QTIsYtinajRrdV5GdikvrIUzdWpFUCYPoxuC9uXybr+dleuU2ZhZunL22dlPIa811JThsevXJUSevzEuSNx98458d+tjEnduX0Ad+2Y5Uu00K+fMhuf1QU2+tzx03HNhklpzjAWxpxYI2NRdXuziJCsZ9a+DXKQateacdg2z5294KNbqwNfrkfZqS4rxg2b+k3vr6sswS8/uBTXbezHnNg+rZVJe9anDPcuNYHZZ+XnFy6J/3zo5PbYsYMHX36Yh7ObOeTYWYOd/PVTBgckT5o3Etdt7MeX35eYOkt7Br9zxnzbj7VywmD7+CSX6hi/tdvY589OIgEA5oyOJrPOtxlEve+cmVoh5PVsW8B/3EyL+9EqbS6N/rWFiG3JG8kpOCVFEcPOUltteXy2rrw483tg7uhmDHQ3OSyD4I+XHWL5+GkjGhOCf7XUlGGTyTIlfaMkFxwxZRi+edpcrJro7WDgxunGudDCIowdeHJb9DtwzqgmrDfJzacZUleOI6d1YFRLNfZdtTplsHxiR517e3aTNFQl7ovTaEFsSosj8eie+q/142Z3eVKeXGO20uOsZT04cloH5oxKDNCiPYdOB8qcriyxSptZ9qoZ890zF1g+Nn97BJSRUUSv5NGzsLMTQS95Xf6iMalRqfJJkKH35/c044wlPQm3TRqWOhp/2aF9+OCqsbauXV4y+HexoZfbbj5+Ov5nxyyUxwYKvHo9BYJqCzk27br5uGm44ajJCbctDVGQBiMikhKUwQvJEbeDkG4/ax5X/RTz9nsHAdiP0ZC8PLLf45nkaSMacd4hY9DVVGk4uKovznVpZkQLgVGLb5NJgMBMbSCj75tbTpgeb5MEtcxci3Zb7NGkTV2l9aA6BdlJtBpEoZC01pbjkd3L8f4l0YhH6/JwyWmyGV35vW9tQ4aRU80mD0b8ZyY9t3+5YqVrSzPKiotwyqLcCKxE6dVVlGBWd1N8CZVnVbNL101uwBmlUbnFYpjzvGbx+d46pwtfOmkg84ExOxZY2wO0bFwbAOCzx03DtrkjDdOo5H2KE8IhfUOwcVoHdq1xtj81+b2Z7eKcdN9bpy0ejZ+ctzghNZL2cPr3aiHntQYSl9sCwK8+tBTt9RU4Y+ngoPQ5y3vxPzsybzkx+r5ZOq4NJy+Mvk7my8y9XaVVVVqEHQu6cdfJwW+bKchOYk9bTV5uRE9umNvVUFUan1nraatBVZYRUt20MBaV7oKVibNO+d7Ry0ZpcSS+fyvdl9s1SSkH3Hjdd+q+DOeMakJZcZH5rG2GxprR3doyHMoPVbGlnVqgG7ele4fZCXK0M4BE20FqNUg+bcXYITWWjtuxoBvzejLn6gKiS8fOWtaT8bhh9RXoiu27HDe0FhevG284Y8BOYv4rLynCtRv70eLwfVxdnrj6YJLLM4q3bk0f9VOb0DjKZKl7IUpuy7TF2jj6XJBnLO3BrDRbE7SPvtOVK1p6i1KPVmuJRKMu97W7+377wdkL8JX32et4FmQnsTgiebkR/dTFo3HXjlm4/yz7m3HD7qI14zB9RAPW9SfOcM4d3Zwx+Izd5YyFbrHN5XKfOXZaym0lJpXncUnRHkVg2oPVZg38SCxLwZo5shEfPao/ISLl/WfNTxtsIp2+pETUWh2RbZQ9fcMjUwNPvyw6V33nzPm47wx7A6rzRjdn1dkfP9Q4ifhZy3oT9olqTpjtLIIs+4hkl1n6BKeWjG1Le395SRH+cOkK7HY4E5qPzAa8tY+zlTm+e0+fhzOXZh5wMnPHtpm4/LA+1FdmjoYaJj1tNZg9yt6+/tz/FqO4oohgoLsJY4dEv2QzzQjZ2bPmViQ8p3rbanD3KXMMGwnJe43uOXVOQr6rIbqlRv938XLvCpknrt/Uj4cusJ7APDmvXTqXHz4h4XcRMa3UtdnjdI05tvPyg4jgiCkdCXuHxg6pzRhswsz0EYn74bSBhhuT9g/aVVlajEVjWlJuT+5IXbex31ZwgLBqri7LOJp92BTrUYu17QyZ3LCpH+NMOot6zdWluOywCRmPM2IlSjKRXhB74GvKS/I6doJdh002rm8OxLcsZH6uJgyrw9nLreXYNjK8sbJgggaxlsxj92UI72snlPztJ87E1evdS7TsqqRextTOBlxvMgOhD2SwJOQBJrzSkGH0q6y4CB0NlWmPSUeLAKcFFUpXZxeJ4ODB1G7iSfNGoipNoJG8jkpLWUt+e2jvQa/eNlpaCc2R0zpSbstXpyx0f3/w+qkdWD4uc/18zylzHT/GVWH9PqOcc9URfC/5RUtRkkzrHJblwQqOMOGzmce6khop+un1VROG2Fp61V5fgaNnZhf++eNHT7F87Ke3TDW8XV/iyw6N5giy0/DTL120MlKd67RQyvrX2s2/+55T56TcdkVsZF9bcqbNCBpZ1z/U8PU7d8WY+M9p36UcYCUDyYMI8aVIHF1wnYjgy9sHLOVBtbPH0WDsKEVnU+pgltWAgMnRronMZHorDmswXl59RdLKmfkmHRzK3qH97Thp3siUuBXZ+ul5i/HrDy119Zq5hJ3EAuLHRv2fX7gEf7tyVfz3c1dEp/Tn9zRbTqZ+0+bJlpZ1xGcHdA0/bc+b6br1AutUaJ3E5L/7W6fbG4FfPXFIQnJxjRbmXp/MVlsaM7q1Gr/44BK8b75xsI9h9RWoKS9BV1PqjEtFaVHaBv0xA504pK8NJy9glNNCkyntzdY5XViaNAvl5jIxoysV+r7ZOaObM0ZJvnTdeGwZGIG/XbkKbbXmncUVfdF9WgczdOgXmAw+zexKv+fmmg2T8NWTZ6c9hgjQ56tL//meM6rJsG2R/A6eM7oZj+vaR06t629P2XddSJqro6uh9AGySosj2L12vOtB7TqbKtFqY9VdvnE/cRSF1oLeZnz0gb9aPl6r4H5lYxSlXbcXMCLA6Ut6sGVgBCrLrEfMPGzyMNz/x2fjv+vzjmWqiD98WHR2savZ+XLJfDJmSA3+87f/oiwpWfmkjnqMaKrEk/99w9J1zlnei9GtNVgxvg3f/9O/ACRGE7v8sAn40i+fSjkvXQAL7bU0C5Sj7SVdOi51c39dRQk+e1z6wCEAMKXT+3xw5K9MM0xLxramdCAGZxJTj180phVD6yowqqUK133fev1IiTJN0m6dG02BE4Ggt60G/3rl7XhU20kddfj9P14GgHh+VX2exU8ek7qypMik3X7V+vR7FDcxUiRZpEWvzLQ1JyKC/o56/N/TLyXeYfChMAvqZoedVVn56P+dNhePPPUS1hZAqragsZNYIJ64erXt0XRtFK2m3P7b5MOH9cUjATZUWY8AVRL75tc3BHea5BYyavhpf+PQugqsnTQU3/79s6knFpBPHzsNjz7ziuHo2r3vn4eXXn/X1vVuPn46ui68L+V2JzM1+lOG1VfgmZfeTLh/aF0FfrtrWcY9lGb271nj6DwKt+QZ5qOmD8dde59Of1J8T2LiuXt3LUNTVWn8/eu0k1hoKxSy9cktU6P1Uiyp81dPno2xu+8HMLgSYdn4wcEho/3jbbqG+7ffPw9rP/4QAKQMiBE5NWFYHa7ZMAkrJ5oHZ9sy0ImiiHEANv0gqdFKHHKmo6Eyq7gJZB2XmxYIJ414rS3mZJnq8bO70NtmLVeWnrZsy2xUOuH2WLnYQDNXW15iGvK4trzEcE+PVdk+7+cdMrh3wGxpWVN1GSO7UYIbj0ocRR/RXJkxIfsRU4YBSK1XmqvLEupGK1GcuavRWLr64Le7liX8nlwvlZcYd+y0QGP6az9x9Wpcs2ESLl43mBZgwrD8S2lF4bBpxvB4XjwjjWkGwStLi/DY5SuxZ/1ErNANeowdUoPhjd7khCVyEzuJZGrxmOjobba5xdJJaViYjPhr9JGrtFPv3pkaPCV6bXYu7Hp492CKkE8c482SlpMXduPCVWNxqG6P6gErUSqIgJTk6ycvGIV5Pc3xVQiaS3SdiEvWRZehz80QOOKeU+fify9ckvYY7b1apIuQUlYcQWVpEW7anF2KjVymDfAZfV80VTtLZm70NSAi2DRjuGE6JCKvrUla4rhlIJan02Cgs6GqFOUlRdg8szOhPXL/WQvws/PT1zNEYcBOYoGy0n+6cfNk/Oz8xaZr6I+c1oHbT5yRVTn+b/eKhA3Y6fYOAdE8Zdo6dO1vGG+ygdso8InWgJk8nHvVjDRUOt/0fe/p8/Dt92dOvP3BVeOwMyls/ieOmYrFBvnniDLRPtMzuhoBDNYLJ8b2wOmPaastx6QO81mn6rLihByrRuKdRF0lKiL404dX4rDJw+z/AXliVncjjps1AtccOcm1a9bH6iM39nERueEmXa7V4ogk5GHWa6wqLYgI6pTfWPOSqfKSIgxvNF+OuGWgE4vG2Ms1OD9pFqCusgRf3j4r/ruVfGbaHslMEQWrY3n29HsqS4oi+POHV+KeU4xnHwvVhGHRLzP9aGemQBQXrR6X8PvEjjrHy75mjmzEbSfOdHQuERBNibN4TEu8s2hG6+Q5HZQYnEnkSgW94qIILj98QsJewWzdvXMOrtkwic81hUaxxQGLgZHp6yGiXMD1GgUqqHRhH1w1Dj97/GcJt9XpZq+0jt/UNFEptVHl4gwNh4vWjMPo1ur4sllNRSkDGyS7c/ss/DMpcIy2H8jMij7zzfxEfutpq7E00KCtbP6ALhenHWsnDcWvnniBEZR90NlUmdW+aSIvlem+I5ObVEcxii7lAXYS89zHjp6CKoNOkdOciXduH8CWz//KcXky5b7Slm50NFRi2ogGT3xSTgAAE95JREFU/PbJF1OO+cCKMSgtiuDwKYNLuxb0tuDlN95JOK6mvATbTXL0UaK6ipKUCKjLDVJPEOU6ZTH3mZljZ43AphnDGUWTqMB947TUfMNLxrbi1q3ZbcMhCgt2EvPcoSYJ7IOK6ZJpBvPL7xuMUqg15pKLWldRgl1rxyfc9oVtXKrotkhEMKqlCvuefz3oohAZOmtZT9r7P3PstJQVB/EE2Q43W4gIO4gWdTRU4B8vvpn5QAB3bJuJJhvpkpLduX0gvsWAyA9GEdzPWJq+TiLKJdyTWKCanUabM7BhaoflY/VRSy87tC/lfn1eIe79CZ72had/XYjC4qxlvWnvXzlhSEK+PQCoiEXFLHbaSyTLvnHqXHxt52xLxy7sbckqlcXc0c3oZ0AyCsjuteMxurUaYxyk/iIKKw67FZjm6jIURwQXrhqb+WCLrt/Uj68//A9Lx45srgIQHeFfOSH9nrayWO4s7iEMzmGTh6VEbLx03XgMYaeRctRnjp2Kex5+BqNaqoIuSt5rqSlDS417A5JEYTWjqxEPnLMw6GIQuYqdxAKzNympcTYaKu0vDaopL8H+PWssHfvxo6fgnoef4chcyGzVpRYgCkJfey1eeuNdR+cOravAaYtHu1wiIiKi/MJOIjlSHBF0NQ+OxP/momUZo43a1VZbjlMWjcp8IBEVlPvOmB90EYiIiPIaO4nkyMykHEBcUkRERERElB/YSSRHgsqzSERERBSUR3YvDyxCPJGf2EkkW7INH29m15pxENa6REREFGINWaRqIcol7CSSLbGsFI4TUZth0nsiIiIionBgoiiypSjWOawoYVoKIiIiIqJ8xJlEsmXOqCacsWQ0TpjTFXRRiIiIiIjIA+wkki2RiOCcFWOCLgYREREREXmEy02JiIiIiIgojp1EIiIiIiIiivOlkygiZSJyi4g8KSKvisj/icgq3f1LReQxEXlDRH4sIiOSzr1VRF4RkedE5Jyka5ueS0RERERERPb4NZNYDOBpAAsB1AHYBeCrItIlIs0A7gGwG0AjgL0A7tKdeymAHgAjACwGcL6IrAQAC+cSERERERGRDb4ErlFKvY5oZ0/zbRF5AsA0AE0AHlVKfQ0ARORSAP8RkbFKqccAnABgq1LqRQAvisjnAGwFcD+A9RnOJSIiIiIiIhsC2ZMoIm0AegE8CqAPwO+0+2Idyn0A+kSkAcBQ/f2xn/tiP5uea/CYO0Rkr4jsff755939g4iIiIiIiPKE751EESkBcCeAO2KzfdUAXk467GUANbH7kHS/dh8ynJtAKXWzUmq6Ump6S0tLdn8EERERERFRnvK1kygiEQBfBPAOgNNjN78GoDbp0FoAr8buQ9L92n2ZziWiHDSqpSroIhAREREVNF/2JAKAiAiAWwC0AVitlHo3dtejiO471I6rAjAK0b2GL4rIswD6Afwgdkh/7Jy053r4pxCRR352/mLUVZYEXQwiIiKigubnTOKnAYwDsE4p9abu9m8AmCAiG0SkHMDFAH6vCzzzBQC7RKRBRMYCeB+A2y2eS0Q5ZHhjJWrL2UkkIiIiCpJfeRJHADgZwGQAz4nIa7F/W5RSzwPYAOBKAC8CGACwWXf6JYgGo3kSwIMArlVK3Q8AFs4lIiIiIiIiG0QpFXQZfDd9+nS1d+/eoItBREREREQUCBH5rVJqutF9gaTAICIiIiIionBiJ5GIiIiIiIji2EkkIiIiIiKiOHYSiYiIiIiIKI6dRCIiIiIiIopjJ5GIiIiIiIji2EkkIiIiIiKiOHYSiYiIiIiIKI6dRCIiIiIiIopjJ5GIiIiIiIji2EkkIiIiIiKiOHYSiYiIiIiIKI6dRCIiIiIiIooTpVTQZfCdiLwK4C8ePkQdgJdz6Lp+XJ/Xzr/r89rGmgH8x8Pre1H+XH0f5mq5vb6219fP1Wt7ff1crlty8TOaq9f2+vosu//X9vr6Xl57jFKqxvAepVTB/QOw1+Pr35xL1/Xj+rx2/l2f1za9fs7VL7n6PszVcrPsfF4cXt+zuiUXP6O5em2WPf+unctlT1evcLmpN+7Nsev6cX1eO/+uz2sHw4vy5+r7MFfL7fW1vb5+rl7b6+vnct2Si5/RXL2219dn2f2/ttfXD6RuKdTlpnuVUtODLgcR5R/WL0TkBdYtROS2dPVKoc4k3hx0AYgob7F+ISIvsG4hIreZ1isFOZNIRERERERExgp1JpHIERG5XUSuCLocRJRfWLcQkRdYt5BT7CQSARCRn4jI9qDLQUT5hXULEXmBdQt5jZ1EIiIiIiIiimMnkUhHRLaKyENJtykRGR1UmYgo97FuISIvsG4hr+RcJ1FEcq7MRJQbWL8QkdtYrxBRLsqpiktEipRSB4MuBxHlH9YvROQ21itElKtyopMoIkUAoJQ6ICLNIvIxETlbRPqCLhsR5TbWL0TkNtYrRJTrcqKTqJQ6AAAiMhfAgwDaABwK4FoRmRy7Lyf+Fgq91wFUar+IyJAAy0I+YP1CPmHdUkBYr5CPWLeQJ0JZQYmIJP1eJiJfBnAJgI8rpY4CcDqAfQDOBwAu5yCX/A5An4hMFpFyAJcGXB5yGesXCgjrljzGeoUCxLqFPBGqTqJEFSmllP52pdTbAH4KYCKAmthtjwL4LoDhInJk7PxQ/T2Uc5RS6q8APgzgAQCPA3go/SmUK1i/UIBYt+Qp1isUMNYt5BlJqteCKYRIRD+iJiLVAC4C8CqA3yqlvhcbpfsmgMcA3KSUekZEWgCcCmABgDVKqbcCKD7lARF5GMCHlVL/L+iykLtYv1CQWLfkJ9YrFDTWLeS1wEewRGQlgCtFpDP2+3YAfwcwDkA/gI+LyHGxUbpbAMyK/YNS6nkAPwYgAOYFUHzKA7FAAuMAPBJ0WchdrF8oSKxb8hPrFQoa6xbyQ+CdRADFAJYBmCkilQCmA3i/Uurw2Br+HwG4EgCUUt8E8FcAK0VkfOz8XwPYoJR6wP+iU64TkY8A+D6AC5RSTwZdHnId6xcKBOuWvMZ6hQLDuoX8Epblpp8EUAvgCgCvKqX+KSI9AD4PoAPR9fxfUUqdKSJTAXwF0c3gd2n7ALRN48n7AoiosLF+ISK3sV4honwX6EyiLhrYTQC6ACwB8IKIdAP4KoBfKKVGAbgZwOkiMlIp9TCA7Uqp/9FXrCrG37+AiMKK9QsRuY31ChEVikA7iUopJSISi8z0XQBrEF1jPQrAC0qpC2OHliG68XtD7LyfAakhp4mINKxfiMhtrFeIqFCEYrkpEI8M9g1E1/K/BWA9ohXsAgB7AZyqlHo5uBISUa5i/UJEbmO9QkT5LAyBa7RQ0q8B+CKAuQCeQ3SdfwmA65RSW5RSL8fyEaUts4iU6q/rZbmJKPxcrl+q9df1stxEFF5u1iux63WLSG3sZ842ElHgQjOTqBGRuwA8D+ASpdR/dbcXKaUOpDmvE8AeAO8A+IdSapfnhSWinJJl/XITgPcQzYO2Qyn1ntflJaLwc1qv6I47DcC1AI5XSt3tXUmJiKwLzUi4buTsYwBmILq+HyJSBAAZGnA7EV3a8Syiyz6OEpFbY/eF5m8komBkWb9cBOBhAE8D+DCA5QA+mXRdIiow2dQrSfoBvIhoSo0et8tJROREaDpQsc3gEaXU/yKaZPaQ2O1pK1kRqQfQA+B0pdQHlFJfALARwHoRqVVKHfS67EQUblnULwLgIICVSqkzlFJ/APAQgNpY8IpwLcUgIt84rVc0WmcSwOMA7gIwAGCeiJR5UV4iIjtC00kEAKXUwVhi2jcB/MXsOK0CjTXg3kY0/9D9sdsiAOoB/BnRSpuIyEn9UhzrBN6glNorItNE5C8ADgXwdwCH6/dAE1HhsVqvAAl1S/JM42wAtwH4NoDDAIz0rMBERBaFqpMYcziARwDck3yHiDTElpF+BojnGHpTKbVXKfVKbGT/IKKhp18F8JqfBSei0LNTv7wX+//t2CHtAD6hlKoCcAOiibEvEpEaPwpORKFlWq8AhnXLgdjtWhvsaQDDAdwCoBzA0SJyhYhM8rrgRERmwhi4xnAJl4hMBPBxAE0AXgFwvVLqHqON4SLyaQDvKqXO8KXQRJQTXKpfJLbM7EgA1wMYr5R63Y/yE1H4pFt6nqZuiWjbYUTkpwBOVErtE5F7AawCcB+ALbEIqkREvgvdTGKaPT6liIaa3grghwDeJyKlSqkD2uZxEYnElnFMQ3R9P0Rku4ic4n3JiSjssqlfdIpj/7+KaLCJWi/KSkS5IcPeZLO65aBuufqvAFwmIn9AtD55CMB+AFWeFZqIKIPQdRI1IjJWRBaKSGvspj8AuFsp9VsA3wOgAJyuHR77XyFawf4bQIeI/AjAlYgu5SAiAuC4ftFC2r8rIuMQzYn2XaXUs36WnYjCy07dopR6J7bkdCiAPgA3KqUWAvgIgEb/S09ENCiMy02LEF23vwnAbxGtPM9XSt2rO6YawEkANgA4Tin1pLZ0Q0SWI1oRvwDgY0qpD/v+RxBRKGVRvwiAakQDTJwGYAGAa5VSV/n8JxBRCDmtW2K3jwTwL6XUG74XnIjIRBhnEvsAjEY039AKALcDuElEFmgHxNbo/xDAPwGcHbvtYKySfgnApQC62EEkoiRO6xeFaCCsxxHdK9TJDiIR6TiqW2KeVkq9oQWyYf5VIgqDUHQSRaROF+VrFoARSqn/ADiolPoIouv1TxCRbt1pf0U09cUEEblKRH4BYKFS6jdKqQ9zszcRAa7WL8uUUk8opW5WSr3q6x9BRKHjUt3yvwCWAtHB7tj/4VriRUQFKdBOooj0iMj3ANwJ4OsiMgLAnwA8JSKTtQoTwNUA+gHEw0Erpd4BcADRivkEAJ9VSv3I1z+AiELLg/rlB77+AUQUSi7XLZ9TSn3P1z+AiMiCwDqJInISgB8hmlvofEQ3ae9GNHLgvxBdrgEAUEr9HtHN38fFzi2K7T28G8CnlFLDlFK3+/oHEFFosX4hIi+wbiGiQhFY4BoRuQLAk0qpz8V+7wDwGIBeRCvUqdDNDorIOgB7AMyIrd0fBuB1pdRLgfwBRBRarF+IyAusW4ioUBRnPsQznwHwNgCISBmANwDsA1AB4GuIbv4+S0T2xSKAzQDwfS36l1L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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "dddca9ad9e34435494e0933c218e1579",
+   "translation_date": "2025-09-06T14:01:55+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/tr/7-TimeSeries/1-Introduction/working/notebook.ipynb b/translations/tr/7-TimeSeries/1-Introduction/working/notebook.ipynb
new file mode 100644
index 000000000..aeab6371c
--- /dev/null
+++ b/translations/tr/7-TimeSeries/1-Introduction/working/notebook.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Veri Kurulumu\n",
+    "\n",
+    "Bu not defterinde, aşağıdaki işlemleri nasıl yapacağımızı göstereceğiz:\n",
+    "\n",
+    "bu modül için zaman serisi verilerini hazırlamak  \n",
+    "verileri görselleştirmek  \n",
+    "Bu örnekte kullanılan veriler, GEFCom2014 tahmin yarışmasından alınmıştır. Veriler, 2012 ile 2014 yılları arasında 3 yıllık saatlik elektrik yükü ve sıcaklık değerlerinden oluşmaktadır.\n",
+    "\n",
+    "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli ve Rob J. Hyndman, \"Olasılıklı enerji tahmini: Küresel Enerji Tahmin Yarışması 2014 ve sonrası\", International Journal of Forecasting, cilt.32, sayı.3, s. 896-913, Temmuz-Eylül, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluğu sağlamak için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5e2bbe594906dce3aaaa736d6dac6683",
+   "translation_date": "2025-09-06T14:02:43+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/working/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/tr/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/translations/tr/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
new file mode 100644
index 000000000..5445e429a
--- /dev/null
+++ b/translations/tr/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -0,0 +1,1143 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# ARIMA ile Zaman Serisi Tahmini\n",
+    "\n",
+    "Bu not defterinde, aşağıdaki adımları nasıl gerçekleştireceğimizi göstereceğiz:\n",
+    "- ARIMA zaman serisi tahmin modeli için zaman serisi verilerini eğitime hazırlama\n",
+    "- Zaman serisinde bir ARIMA modelini kullanarak bir sonraki HORIZON adımlarını (zaman *t+1*'den *t+HORIZON*'a kadar) tahmin etme\n",
+    "- Modeli değerlendirme\n",
+    "\n",
+    "Bu örnekteki veriler, GEFCom2014 tahmin yarışmasından alınmıştır. 2012 ile 2014 yılları arasındaki 3 yıllık saatlik elektrik yükü ve sıcaklık değerlerinden oluşmaktadır. Görev, elektrik yükünün gelecekteki değerlerini tahmin etmektir. Bu örnekte, yalnızca geçmiş yük verilerini kullanarak bir zaman adımı ilerisini tahmin etmeyi göstereceğiz.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli ve Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, cilt 32, sayı 3, s. 896-913, Temmuz-Eylül, 2016.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Bağımlılıkları Yükleme\n",
+    "Çözüm için gerekli olan bazı bağımlılıkları yükleyerek başlayın. Bu kütüphaneler ve ilgili sürümleri çözümle uyumlu olarak çalışmaktadır:\n",
+    "\n",
+    "* `statsmodels == 0.12.2`\n",
+    "* `matplotlib == 3.4.2`\n",
+    "* `scikit-learn == 0.24.2`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "!pip install statsmodels"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "/bin/sh: pip: command not found\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from pandas.plotting import autocorrelation_plot\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "%matplotlib inline\n",
+    "pd.options.display.float_format = '{:,.2f}'.format\n",
+    "np.set_printoptions(precision=2)\n",
+    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load\n",
+       "2012-01-01 00:00:00 2,698.00\n",
+       "2012-01-01 01:00:00 2,558.00\n",
+       "2012-01-01 02:00:00 2,444.00\n",
+       "2012-01-01 03:00:00 2,402.00\n",
+       "2012-01-01 04:00:00 2,403.00\n",
+       "2012-01-01 05:00:00 2,453.00\n",
+       "2012-01-01 06:00:00 2,560.00\n",
+       "2012-01-01 07:00:00 2,719.00\n",
+       "2012-01-01 08:00:00 2,916.00\n",
+       "2012-01-01 09:00:00 3,105.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tüm mevcut yük verilerini çiz (Ocak 2012 - Aralık 2014)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Eğitim ve test veri setlerini oluşturun\n",
+    "\n",
+    "### Eğitim ve test veri setlerini ayırma\n",
+    "\n",
+    "Makine öğrenimi modellerini eğitmek ve değerlendirmek için veri setinizi genellikle iki ana bölüme ayırmanız gerekir: eğitim veri seti ve test veri seti. Eğitim veri seti, modelin öğrenmesi için kullanılırken, test veri seti modelin performansını değerlendirmek için kullanılır.\n",
+    "\n",
+    "### Veri setini ayırma yöntemleri\n",
+    "\n",
+    "Veri setinizi ayırmanın birkaç yaygın yöntemi vardır:\n",
+    "\n",
+    "1. **Rastgele ayırma**: Veri setinizi rastgele bir şekilde eğitim ve test veri setlerine bölersiniz. Örneğin, veri setinizin %80'ini eğitim için, %20'sini test için ayırabilirsiniz.\n",
+    "2. **Zaman temelli ayırma**: Eğer verileriniz zaman serisi içeriyorsa, daha eski verileri eğitim için, daha yeni verileri test için kullanabilirsiniz.\n",
+    "3. **Katmanlı ayırma**: Veri setinizdeki sınıf dağılımını koruyarak ayırma işlemi yapılır. Bu, özellikle dengesiz veri setlerinde faydalıdır.\n",
+    "\n",
+    "### Örnek kod\n",
+    "\n",
+    "Aşağıda, veri setinizi rastgele ayırmak için bir örnek kod verilmiştir:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Veri setini yükleyin\n",
+    "data = @@INLINE_CODE_1@@\n",
+    "\n",
+    "# Özellikler ve hedef değişkeni ayırın\n",
+    "X = data['features']\n",
+    "y = data['target']\n",
+    "\n",
+    "# Eğitim ve test veri setlerini ayırın\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(\"Eğitim veri seti boyutu:\", len(X_train))\n",
+    "print(\"Test veri seti boyutu:\", len(X_test))\n",
+    "```\n",
+    "\n",
+    "### Veri setini ayırırken dikkat edilmesi gerekenler\n",
+    "\n",
+    "[!IMPORTANT] Veri setinizi ayırırken aşağıdaki noktalara dikkat edin:\n",
+    "- Eğitim ve test veri setlerinin birbirinden bağımsız olması gerekir. Test veri seti, modelin daha önce görmediği verilerden oluşmalıdır.\n",
+    "- Veri setinizin boyutuna bağlı olarak, test veri seti için %20-%30 arasında bir oran genellikle yeterlidir.\n",
+    "- Eğer veri setiniz dengesizse, katmanlı ayırma kullanmayı düşünün.\n",
+    "\n",
+    "### Veri setini ayırmanın önemi\n",
+    "\n",
+    "Veri setini doğru bir şekilde ayırmak, modelinizin gerçek dünyadaki performansını değerlendirmek için kritik öneme sahiptir. Eğer test veri seti modelin daha önce gördüğü verilerden oluşuyorsa, modelin performansı olduğundan daha iyi görünebilir. Bu nedenle, veri setinizi ayırırken özenli olun.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-11-01 00:00:00  0.10\n",
+       "2014-11-01 01:00:00  0.07\n",
+       "2014-11-01 02:00:00  0.05\n",
+       "2014-11-01 03:00:00  0.04\n",
+       "2014-11-01 04:00:00  0.06\n",
+       "2014-11-01 05:00:00  0.10\n",
+       "2014-11-01 06:00:00  0.19\n",
+       "2014-11-01 07:00:00  0.31\n",
+       "2014-11-01 08:00:00  0.40\n",
+       "2014-11-01 09:00:00  0.48"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Orijinal ve ölçeklendirilmiş veri:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hadi test verilerini de ölçeklendirelim\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-12-30 00:00:00  0.33\n",
+       "2014-12-30 01:00:00  0.29\n",
+       "2014-12-30 02:00:00  0.27\n",
+       "2014-12-30 03:00:00  0.27\n",
+       "2014-12-30 04:00:00  0.30"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Forecasting horizon: 3 hours\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "order = (4, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
+    "results = model.fit()\n",
+    "\n",
+    "print(results.summary())\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Thu, 30 Sep 2021   AIC                          -6942.477\n",
+      "Time:                                    14:36:28   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Modeli değerlendirin\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Her bir HORIZON adımı için bir test veri noktası oluşturun.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load  load+1  load+2\n",
+       "2014-12-30 00:00:00  0.33    0.29    0.27\n",
+       "2014-12-30 01:00:00  0.29    0.27    0.27\n",
+       "2014-12-30 02:00:00  0.27    0.27    0.30\n",
+       "2014-12-30 03:00:00  0.27    0.30    0.41\n",
+       "2014-12-30 04:00:00  0.30    0.41    0.57"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Test verileri üzerinde tahminler yapın\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2014-12-30 00:00:00\n",
+      "1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
+      "2014-12-30 01:00:00\n",
+      "2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
+      "2014-12-30 02:00:00\n",
+      "3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
+      "2014-12-30 03:00:00\n",
+      "4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
+      "2014-12-30 04:00:00\n",
+      "5 : predicted = [0.3  0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
+      "2014-12-30 05:00:00\n",
+      "6 : predicted = [0.4  0.55 0.66] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
+      "2014-12-30 06:00:00\n",
+      "7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
+      "2014-12-30 07:00:00\n",
+      "8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
+      "2014-12-30 08:00:00\n",
+      "9 : predicted = [0.75 0.8  0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
+      "2014-12-30 09:00:00\n",
+      "10 : predicted = [0.77 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
+      "2014-12-30 10:00:00\n",
+      "11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
+      "2014-12-30 11:00:00\n",
+      "12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
+      "2014-12-30 12:00:00\n",
+      "13 : predicted = [0.7  0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
+      "2014-12-30 13:00:00\n",
+      "14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
+      "2014-12-30 14:00:00\n",
+      "15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
+      "2014-12-30 15:00:00\n",
+      "16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
+      "2014-12-30 16:00:00\n",
+      "17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
+      "2014-12-30 17:00:00\n",
+      "18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
+      "2014-12-30 18:00:00\n",
+      "19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
+      "2014-12-30 19:00:00\n",
+      "20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
+      "2014-12-30 20:00:00\n",
+      "21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
+      "2014-12-30 21:00:00\n",
+      "22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
+      "2014-12-30 22:00:00\n",
+      "23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
+      "2014-12-30 23:00:00\n",
+      "24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
+      "2014-12-31 00:00:00\n",
+      "25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
+      "2014-12-31 01:00:00\n",
+      "26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
+      "2014-12-31 02:00:00\n",
+      "27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
+      "2014-12-31 03:00:00\n",
+      "28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
+      "2014-12-31 04:00:00\n",
+      "29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
+      "2014-12-31 05:00:00\n",
+      "30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
+      "2014-12-31 06:00:00\n",
+      "31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
+      "2014-12-31 07:00:00\n",
+      "32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
+      "2014-12-31 08:00:00\n",
+      "33 : predicted = [0.79 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
+      "2014-12-31 09:00:00\n",
+      "34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
+      "2014-12-31 10:00:00\n",
+      "35 : predicted = [0.8  0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
+      "2014-12-31 11:00:00\n",
+      "36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
+      "2014-12-31 12:00:00\n",
+      "37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
+      "2014-12-31 13:00:00\n",
+      "38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
+      "2014-12-31 14:00:00\n",
+      "39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
+      "2014-12-31 15:00:00\n",
+      "40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
+      "2014-12-31 16:00:00\n",
+      "41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
+      "2014-12-31 17:00:00\n",
+      "42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
+      "2014-12-31 18:00:00\n",
+      "43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
+      "2014-12-31 19:00:00\n",
+      "44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
+      "2014-12-31 20:00:00\n",
+      "45 : predicted = [0.7  0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
+      "2014-12-31 21:00:00\n",
+      "46 : predicted = [0.6  0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
+      "CPU times: user 12min 15s, sys: 2min 39s, total: 14min 54s\n",
+      "Wall time: 2min 36s\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": true
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tahminleri gerçek yükle karşılaştır\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp    h  prediction   actual\n",
+       "0 2014-12-30 00:00:00  t+1    3,008.74 3,023.00\n",
+       "1 2014-12-30 01:00:00  t+1    2,955.53 2,935.00\n",
+       "2 2014-12-30 02:00:00  t+1    2,900.17 2,899.00\n",
+       "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
+       "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tüm tahminler için **ortalama mutlak yüzde hatasını (MAPE)** hesaplayın\n",
+    "\n",
+    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "One step forecast MAPE:  0.5570581332313952 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Multi-step forecast MAPE:  1.1460048657704118 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Test setinin ilk haftası için tahminleri ve gerçek değerleri karşılaştırın.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "if(HORIZON == 1):\n",
+    "    ## Plotting single step forecast\n",
+    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
+    "\n",
+    "else:\n",
+    "    ## Plotting multi step forecast\n",
+    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
+    "\n",
+    "    fig = plt.figure(figsize=(15, 8))\n",
+    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        x = plot_df['timestamp'][(t-1):]\n",
+    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
+    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
+    "    \n",
+    "    ax.legend(loc='best')\n",
+    "    \n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "c193140200b9684da27e3890211391b6",
+   "translation_date": "2025-09-06T13:59:36+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/tr/7-TimeSeries/2-ARIMA/working/notebook.ipynb b/translations/tr/7-TimeSeries/2-ARIMA/working/notebook.ipynb
new file mode 100644
index 000000000..044eae09b
--- /dev/null
+++ b/translations/tr/7-TimeSeries/2-ARIMA/working/notebook.ipynb
@@ -0,0 +1,59 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "523ec472196307b3c4235337353c9ceb",
+   "translation_date": "2025-09-06T14:00:49+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/working/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# ARIMA ile Zaman Serisi Tahmini\n",
+    "\n",
+    "Bu not defterinde, aşağıdaki adımları nasıl gerçekleştireceğimizi göstereceğiz:\n",
+    "- ARIMA zaman serisi tahmin modeli için zaman serisi verilerini eğitime hazırlama\n",
+    "- Zaman serisinde bir sonraki HORIZON adımlarını (zaman *t+1*'den *t+HORIZON*'a kadar) tahmin etmek için basit bir ARIMA modeli uygulama\n",
+    "- Modeli değerlendirme\n",
+    "\n",
+    "Bu örnekteki veriler, GEFCom2014 tahmin yarışmasından alınmıştır. 2012 ile 2014 yılları arasında 3 yıllık saatlik elektrik yükü ve sıcaklık değerlerinden oluşmaktadır. Görev, elektrik yükünün gelecekteki değerlerini tahmin etmektir. Bu örnekte, yalnızca geçmiş yük verilerini kullanarak bir zaman adımı ileriye tahmin yapmayı göstereceğiz.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli ve Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, cilt 32, sayı 3, s. 896-913, Temmuz-Eylül, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install statsmodels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/7-TimeSeries/3-SVR/solution/notebook.ipynb b/translations/tr/7-TimeSeries/3-SVR/solution/notebook.ipynb
new file mode 100644
index 000000000..84b2a0097
--- /dev/null
+++ b/translations/tr/7-TimeSeries/3-SVR/solution/notebook.ipynb
@@ -0,0 +1,1023 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bu not defterinde şunları nasıl yapacağımızı gösteriyoruz:\n",
+    "\n",
+    "- 2D zaman serisi verilerini bir SVM regresör modeli için eğitime hazırlamak  \n",
+    "- RBF çekirdeği kullanarak SVR'yi uygulamak  \n",
+    "- Modeli grafikler ve MAPE kullanarak değerlendirmek  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modüllerin İçe Aktarılması\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": [
+    "## Veri hazırlama\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Verileri yükle\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/+UxirEmMiIiIiIiIaMBkErdizz4AwP7SSv4Mo3fkhHFN4ELNbIER60MjIiK6otiu3DQfAjukLIlBYkRERERExEUGs5/fVuzZBwDolYHeWQaTSAszqkkM7ZNoBubx+EdERHRFZyZRGsnBPFByygwmY5AYERERERFxkcGsnYtMlgItsjhMopab6hYYYQu14TgyuREREdsHGdew68wLY94JlZwW4fXaQAwSIyIiIiIiLjqYjNcwOm0CqGoDNxnMHh3GSm4aNtaUpXK2FxEREWGi6FyTmAPZrPo9Dwz+Qt9XIgaJERERERERFxlMxisyWQoUuIVKRgGTSSS5adhCzVzQjaLTbEREREfQdNXJuKZHQWKgeoLMbgIRg8SIiIiIiIiLDKbENPbsU6CAj8OsauOaXncmMfasjIiI6AqSmxaS6ZQsc6A3p34PbYMRg8SIiIiIiIhLG2ZgMg256TgvsDHc3YwlBXybDGaVxvRTXp/EGpMYa0IjIiI6wgwMWfXlNSYxUEZqBomyfa6LQWJERERERMRFhtGUmcRX//mn8fSfeMeOb2c7oIw8p0awkBJCAH2mcY0ZJLKbYEdERESUGHcJEknxwJWbmsFkAKsYg8SIiIiIiIiLDNM2rvnHWx8DAJxeDZQ1XQBQRp5To5kXEqkQyBIBoKvcNDKJERER3VDIDqoQCvDYNYlGkBggUY1BYkRERERExEWG0ZTlpocXBwCAu4+v7vi2umLcxd1USiSJQMaWm1bbCB0TYccjZ9bxvjtPXOjdiIi4IDBVCcFzOfVIZAeJZm/F9jExSIyIiLgosTHM8fr33B2dHSMuS5iM1zTkpk88NA8AuOfEyo5vqyuqFhjhc0JRMom9tGQSA1nBGpMY3U23hX//+zfhP/zBJ1jnbdo4szbE/3nfvXwHyoiIFnQKEjWTWBrXdJGbRiYxIiLiUsUffORBvP499+APP/Lghd6ViIipw1xMDPOdX1wfnFdM4qnV8Eb10wbVJHKC5nEhkSYCWZLo/4egiO6m5w3E/N7x2LkLvCdu/NK77sIvvvMuvPuO4xd6VyIuMZhy02ATrKLBJI47yE3zGCRGRERcoiCjiaNLmxd4TyIipo9xl+zztrantrGb2R5aXw3HRbCVfFEGicQkhi7SxtHd9Lzh6VfvAQDcemT5Au+JGzNlH82HTq9d4D2JuNRgMonBCa5mkNilT2JAYBmDxIiIiIsSVyz0AQAnmUYa/3LvKfzMP9yxE7sUETE1jKfsbkps2W5ug1F0WGzlUgWJQgikiYjuphcAh8p616NLG6xx77r9GG564MxO7NIErtk3AwB4lLmPERFtqBnXhCacdE0iyU0D10G5ESRGJjEiIuJSBfU1O7nCCxL/ze98HL/74Qd2NSMSEdGG4bT7JJaB0MYuvm/GtSAxbD/zAkiEYhGzRLCYRC77GGFH5UrLO46v+uNb8Io3fnQndmkC5H776NkYJEacX2yvJpHbJ9GsSYxMYkRExCUKqj86xQwS9872AABHpvCwH44LfOdvfwyffPjsjm8r4vLC1JnEi0BuambkQx1OldxU/d5LE1ZNIkkQo5nJ9tCllnTaoOvi9BqvJvevbzmCH33brTuxSxGXCMwcE78mkWlcY8pNi/bAMgaJERERFyVoYXaKKTe9eq+SDT1ydv2871MTD5xaw0fuO40ffstnd3xbEZcXRnmBmZ56hE9jcU1tHnZzkJh3YRKlcjcFACHqgaYP40Ji0FNBYjSu2R5yzSTu3muLrn8ZeH0Q/ttffQZ/9vGHd2KXIi4RdOqT2GyBEeBUCqAhN41BYkRExCUKWlhwe5TpIPHMzgeJxFDkzIVFREQbRrlEP03QT5OpyE3zi0BumndkEpNSSpgmItjwJi+kDtJHsQXGtjDuKDedJoi570oa7+bkSsSFRTe5aZNJDJWbju2/OxCDxIiIiIsSNLFypV7755ThzWPLO++KSrsWuvCMiAjFKC/QSxP0s+kEibRI5hrX3PLQGdx/cnUndmkC5n0Wuign4xoASIUIDgLGRYGZyCSeF+iaxI6BFJfd64JRx+cNgat4mSbe+skjuP41/4i1rfagIeL8w0xujUKvLwoSs5nyQwKvL1NiGoPEiIiISxUk0eA2sqas9TQeiLR4j0xixPnGOJfopQkGWTIVmV4lN+Xdby//zY/iK3/5AzuxSxMYdwkSC1NuKoLv1aIABmUbnmhcsz3QHN6VSZwGu01JkmD3yRKUgDjBrJ2fJt70wfsBAA/G9h4XBEUhq7lkp41rzPdFuWlERMSlCloQchO7uQ4Sd35hQYv3qEaLON8YFUXZ3y+ZSpBC981uls2ZTGKoDL2Qptw0nJWqMYnMSWh5PXBBd5mALt8tZgKCsDSF40nnmMva759TRmlcF+4uWN4Y4cW/9H7cfpTXb5LM3OJ1eWGQG0FicNKbahKzASAShnGNMX9HJjEiIuJSRVcJJy2o14c7zyRSZjy6H0acb1AT+CwN7+23HVDd3cVSkxgaOJtMYiJE8L1q1iSOGUH6Zx5ZwrN/+l34x88+FjzmUkeumcTwa8sM5pc3dj64oeuJGyTuK8sbpsEkfuKBM3jg1Bp+6Z13scZRCcbSFI5jxCQKKXXCaRg6l1OAl2RA2g83rqnJTSOTGBERcYmia+Cl5aZTaAq+FeWmETuEXKJiEqeQhKBAlBMkTrsWNy+k7p8aKgvMC2gmMWHVJEoMyhYYHPOsO4+dAwC8764TwWMuddDh48hNzfl/GkEiXf9cafd8X10j0yhvWJzJAAArm7xt7SvZzjPM9h4R5wd5ITHoceWm5ftECqSDjnLTyCRGRERcouhKnnStSXz49DpOnOOZ3VDWeRrGChGXFwopkQiglwoWk9UVtA2O3HRzyi0NVEaeVydYyKpPYpKEB7Z5IZElAmkiWHXR/VJWNg2zoYsFeYeaRDMwn2afUO55owREVyktB73y2uIGiXtKueluNte5lJFL6J6rwXOJZhJTIO117JMYg8SIiIhLFLkxmXJYRVrscoPEL//F9+ELf/a9rDFRbhqxUygKiUQIZMl0ahLHuiaxCA6kplH3a2KcV7KtLnJT5W7KCBJTgSzhyX17aTezm1seOoMfePOnLkmnZM3SMRIQJlPc1RWVAwpKucY12rl1CgkTes6sbHZjVmOQeGFQmExi6FxCNYlJquSmwe6mZpAY5aYRERGXKMxnNSe7S4vd9SnITYcxSIzYIeRlTWIvFSy544mVTfz3v/oMu5VFzTk0cMHL3cZ2Ydb2dDGuSYQIViio45+UxkHhxz8rt8VlpL7nj2/B33/m6CW5kKfAnMMImsePG7h1ASUXR7lkBeqjnP/duoISD1wmkcYtb8QWGBcCeU26zmUSMyDNuvVJjO6mERERlyrMjD8rSJyqcU3pbhpjxIjzDCU3FcjShCV3/JV334O33HIEf/PpR1nbMxcvoffb+mi6i85ag/suxjVJOJM4LiRSAWUcxDj+w46MFBmgnL4E68bGBT+Q6nI9Ela3xuwSADNJwjl34w6mPF2hmUSmSoaY3OGU5eERCrmspOvhQWJ5rkSqAsUi8NxFd9OIiIjLAabEaysPf7httwUGpyaL6lAikxhxvlFjEsfh19dsybStMtmGvJCYK004goPEKTOJuUTFJAbu47g8jgCQCF5NYpokpdw3/PjTseMyS/tmp9dKYdroIsnsGiQurQ/xzP/5TrzhvfeG7yAaz5sOypVp1CR2dTnuWm8ZcX5QGHN58DmkYC/JVKAoQ4PEWJMYERFxGSDvyCSODJfGLsHbY8vh5jWUcY7uphHnG7lUzJdyNw2//hfIAbED20BBYugi2ZSbTsO8qSja5abL66NaPXJRCxKZNYl6YceXSbL77c0rJpEbJN70wBl87P7TrDHTBgVSo1wGz8lmkMgJ2lbLc//HH3uIsYd1QxGecmX6clP+uG77eMtDZ/Hhe0512mZEhVyWQWKShLPUuiYx6c4kRrlpRETEpQoz4895aJuLkC6S0+MMh1PKHl+KZhMRFxZSlnJHpnHK4kAFiVwmcVQUmOursaGLUZNJnErdWGH2G5vc3tY4x7N/+l34j3/wCf0aLdAA6pMYvq00LftUMu5vkvR1bcrO7bf3ijd+FN/+po+xxkwb5pwcyiYOx93mf8oBnFnjHUcz6cCSm+bTl5t2HccNEl/+mx/BK3/34522GVGBTMh6WcJgEsv5W6TKvCY0SJRRbhoREXEZoMYkMh7aJuvSxViDM2ZYymDHRXiGPCIiBDktLFKeu2lZfofVrXAHxKKQkBKV3DQ4SKwWIZvDnQ8Si0JiJnPXJL77juMAgI8/cKY2JiF300QEM555USDt4C471AYovONBrqgnVnhteC4G1ILEQFlmTW7Kmf/L93Kn465M4qhjANYFXful0jGJctMLA5K8Z11qEpMMEAlTbiqM3/2IQWJERMRFifw8MIld2A1OM3FzwTONTHLE5YO8kJXclHEd02J1lSE3pcTKLLMm0UyoTKNnYi6rthS2Y3LOcG+kxuF1JjFcGm4u7DgJIC03Zc499H24DPDFgC5zcle5KYf1rW9ve88bbk3i8saIVf8OoCZ75qhXKklsfEZdCFT15Yz6ZrMFBldummTqX5SbRkREXKroGiSOc9nZhh7gGdfUe3nFLG3E+UMhlSsnX+7It8mnReQ8U25qJlSm0Q6jqLGrk8fE7K16x9Fz5WvQTKJyNw3fVpYod9kuxjWhxjqEada2TRtmYB56bZlzK68mvdvxG+fdEn6jjnLTZ//Uu/CKN36UNWb7Dqzdjs006o0vZdBcrtoZdWASkzSIFVTjxuWYXuyTGBERcemic5BYFJoRCV3cmQ/BrkziNJiUiMsHZvaZU5NIi0dO0EaLz1mmcY15j06NSSzNZGxzgnm/3350GYBqxE5tMxIhghkYqknsMVtgbJHclMlo0fu57NLFADNxFzonj2puo4xr2RjHCW7GRbfk4naC+88eWWa9v+bAykhK0v3dVW56KSYuponaXB46l+gWGAnT3TRXQWXaC2IfY5AYERFxUaJWx8IyEqhcGkOzduaCZJPx8DUXL5xxERFtqNxNBYs10ExWB/MNbgsM8x6dBpM4zpUEt5/ZJbi0P4szGe54TDGJm6NctwVJme6mqVC9zbrITbmMFjGPmx0X5Lu53qyQleFQqFOsycRyk4QEXl9GfgsYc3vTOP7178YPnLsGe2tMp+SIOopyLs/SpGbI5B9UHnOSjoYGl7IMEpM0yk0jIiIuXXRtgTEupHZpDF1cmw9fTia/HlxeegxAxIVD5W6asFow0OJxyGAfiWmo3E0DA6mO92hXVLIte5BI3+Pzr9uLOx9bAaCUARSgCBHuEEnMUteaRLbclCSBHeeRLk7O04JypVXL0S41iV1aIAG8OXmcF5gvnYFZSckpGtd07eWoJbEdry1OfXPEJFTCCehzVAm1msTELTddPQl85i8qW98oN42IiLgc0LUFxjgv2A23uy4sanK7GCRGnEeQu2nGacCMbkzWqCOTaN6j01gkk2wrS4W3JvHGQwt45Ow6pJTYHFXyc+Vu2r4d+l5pkiARvJpQOpacOkbz/V2ZxLUpMLldURQSg4yYxLDjUqtJZCpJCBx1h0ou8pl0up6mYQrTmUkkuWnHek1OfXPEJMiELOOYkJlMok9uetObgLe9CvjYb5TjcvX+tBfdTSMiInY/pJT45Xfdhdse5dVfdK1JHJkP+1Am0XgfK/tcdFuQRFxe+KV33oX/9Q93sMbQwqKfMhowo1uQmGsmkSfTHne8R7uikJXjq+2Y0P48/sAc1oc5zqwNFZOYVTWJIe6m9DlZ2SeR4yRpupvyauK2x/bsZknguJAY9NytS2ygoHm2l7Lq70adVSEFFohJDE4udpO2djWC6VoWQdfzKO/Wqmk3X1sXA2rGNcFyU6pJJHdTxzkYrqqfD5cmSJpJTIE8BokRERG7HFvjAr/2z/fiW3+L5+SWFxK9tDQSYC54t7PY5RjXmPVN0bgmwoVff9+9+J0PP4CzZVuGEHR2NyUmq4NEj9sC40IwiVkZONu+n2JfgccdmAMA3H9qDQAwU34v5W7afixpIZ0IwWYSzbmKxUCO+bJFM9jYzQt51d+SZyZGc/f8IOvMJLJaZ+RGmULgOPP8cgLZrj11u7Z3GneU7hKi3HR7qLXA4BrXUH2hy4RmVfWGxcZSNS5Jo9w0IiLi4gBlc7lSl9wwOwh9sEmpMqWzehxvQQLwM7SDkqXoygBEXD745MNng9+rpZVJUsraAmV6mskKX4jS4pNaYITWZNVqEvNpyO3MFhh2JjFLEly3fxYAcO8JlWWnACURYf3liNWjmsRQsxugPlexJL8dmEQzSFnb2r3zz7iQhuM0zxRprp+yyw0IPFVIgfkB73lD25rvp9ga58H3aNdejrU2HZznVEenWEIMErcHUwERPCc0+yRKx7jVE+rn+plqHLmbRuOaiIiI3Q4OM2eiHuzxMrvcBYn5EGUxiYXUZgdRbhrhQtmmj3WNkCNePyOZXtjCcmsbclMyFwllIacuN9UZeTu7N84LpInA1XtVkPggMYk1d9OQ7aifaaLcTTk1oSZ7FSwtQzUHcWoSzWO+mxfyuWFcE2rc0TVINFuPcE3IZjWTGDZOGz4NMhQyPPir9dfl9GQsugV7JnvFSbgSYr399kBOyT1OfXmtJtFnXENM4tlqnHZEjS0wIiIidjnIGp9bh1HILrWF3WqrarUlHbPP8UEa4QL1X+Ms7Artbkr95cKu5S41icSUaeY+cOzU5aaykm3ZFrvjQiJLBfbN9pAI4NGlDQDAbF8thYQIcyqlRXWWdm+BAfDUE10cKEcdWbOukFLirmMr7HG5rOSmoeqOKnGRMts9GMeEcU0WhcQc8/qn5w23ltEMFDgMcN7xfusiwR1uU6IaUaEy3GLUl9dqEj1y0xVLkKjrGCOTGBERscvRlUkc55XcNHThRFnq2R6vtqSrIUBRGBK9+CCNcCBLSkky4xqp3E1LBobpCtmFSeQy93lRsaTTMa5R2/P1ScwSgSQR2D/Xx9EySKQAJU0QJB2lxNZML1VyX47c1NivLg6zXZnErvMsB2/71KP4mtd/EO+780TwGCoB0H0SA5lEOk+zvbRTuweAb0JGCpRQKWdVN8l8Thn7uMpwDu1qlJMXUisSQgNu89qKz7btoZCqvrmfJgwmkeSmJStoczctcmBrGchmgPEGMNooaxKzKDeNiIi4OEAPakGryUAUUi2S+1kSXCPVlUk0Fy6cxZZiEkluGpnECDuIDeRcI0VBdSwlkxi4uK6YxPA6RgqCelmCRITfN4WsJOHTWEjKWp9Ei9y0kEjLgPzAfB9HlzYBGMY1Iqy+kBbSM70UCZNJNN/LCRJJqpgXMrgvpnnMN6bQAuOessbzjsfOBY+hw8F1NzUl0Lxgu2M7I6nqy4Xg9Ndt1PKGJiXNWlJGf8saI7jjzq3dTHIiJqGYRPUc6FSTKFK73DQvjdAWr1I/N5YaxjXR3TQiImKXY2OoJkWu3JQkGgOHtMyGZk1iqHkHPRB7qWAuLKqANNYkKjx0ei0GzA2kKclNGdl/IyAC+HJTNSbs+td9AYVbymnDuCgq46YpuZsmQjgXW3kudUC+f76PY+fKIJGMawIDPpqzZnspMmaQaC7kWUGiGdx0kAROg0mkhADXEAaopMyh12RuSKA5Ri9dzV2o3rXPuP6pLydfbtqNpRt3lZsajt/BclNTNh2ZxG0hl2afREZNokiUdCJJq0Lp2geXQeLClernxpnKuCZxBJYNxCAxIiLigqKz3JQe2hlv0QpUi5lw4xqSDWW8ILEo0EsT9NMktsCAWmh9xS++H9/zx7dc6F3ZVdA1iSybfBXU0NhQmVLNOIXJ2tD9FrqQzAsgSxPWPbodkJmPq06Q5gwAODjf16/PGkxiSK5qUzOJieqtyGQSKwluN5lq6Bw07ZpEMp/hsJa0tq1aYPDqXQcZQ6KHZiDFYxK51z8t+Od0kBh63roxguO8wDwz2FPbK3SQGCxbj0HieUNRGteEyt3VoFwxiIA74CM5qQ4Sz1bGNbtVbiqEeLIQYlMI8SfGa98phHhICLEmhPgbIcQB428HhBBvK//2kBDiOxuf5xwbERGx+9E1SCxkhyCx0e+N69I43+f15MoLtbAe9JLInqFa6H7g7pMXeE92F2hdwDauScBmEs33BQeJsuoL2HfYtC+tD3VLCb2P5eJnkCadrPU5kHofS0bQstjKiwJZydoeMIJECm5SgaD6QgqCOjGJRcFOUgEqAOCyssMpy001k8gxktFMIq+2lg7dIEuZBkD8+nIpJWRZNzbIws1FKuMargt3N3OjcSHZASlQdwoPViRE45rzBkpApJyEkyxrCwEVLNpqEieYxLOGcc3ulZv+HwCfoP8IIZ4B4I0A/i2AKwGsA/iNxvuH5d/+DYDfLMeEjI2IiNjl2CR3U+Y4so3uMx7alKXrp7zaEtOAgPNAzIsCqRCY6aVRbopocOACJSE4x6co5aYU9IRK7rrUEpktH1xJmVf90S34qtd9oPY3WvwMejvPJNLXT4RabNn6HZpM4g1XzOvXqz6JYTWJlNiimkSW3LHDghxQAccCs7552sY1g/J7kRw3BKZLKcBht4tym0lwjSbQrU+iyaQPsjSY8c+1uU53d1NWv0NDNsptr8I9/l1deiPqMBMQSWKft6yg2kLA3c7CGiQWRguMXRYkCiG+HcASgPcaL/8bAH8vpfyglHIVwGsB/GshxKIQYh7AywG8Vkq5KqX8MIC/gwoKvWOn9JUiIiK2ic5MYsnS8WpEqod9j2E3XRneZMwgUSJNBWZ6CUs2dKniYsk4P3JmHc/6yXdOMGM7BboueVJmCVEGROZntKEoDTiA8PovYjaUTbs9KLr/lDpWn3r4bG0fk0QlZXY6QVAYTGLqYBLHuUSvNK751uc9DouDDE8+vIDr9qu+iWqR1r6tTSNIzJKwwNLcB24rEXrvwkwZbAQGDtMOEula5DCJEz04GTWJNI8H13GhMoXJEhG8n3Qt6SQJU6Y919EVFeC2sijQT3nmUoAyvarM3EKTTZFJPB8w1ySpsM9bVtSCREcLDC03Pax+arlpCqTZ7pKbCiH2APhpAP+18adnAPgM/UdKeR8Uc/iU8t9YSnm38f7PlGPaxkZERFwE6F6TWLDlpnohWRrehGZbuzOJpdwuS2NNIrrb/08b77z9GFY2x/jTjz80le3pHnjMvm1p2c4BCA8Sx3nBllsXsrGQsWzr+U9QlR6fePCMfk21nEgw6PHumy6gfRRCKEbQsjmTSdw718P7f+hFePurv0y3EUmEvybo6NIGfuYf7tCN6Wf7KdJEsJis3GR7mLV0s8w2EV3qGLcDWuBuMqSt3ZlEaCadK9vNEp66gw43ya2HocFlwygtVALatW5ylEtkaaIMUEJbiRSKydItSKLcdKowExCpIwFnBclGAbfcdLylfs7uU/LSddO4prfr+iT+DIDflVIeaby+AGC58doygMXyb00vZfpb29gahBCvEkLcLIS4+eTJWA8TEbFbQLUyUvIcTvPSpIKX2VU/UyHQc/RSs4Gyz+yaRCnLBUkS5aaoLyZWGP2/po3FkrGZ1j7SYpLtbppUTGIom5UXkm0SYt43rno/WgifWh3W9jERYLH9XUG7lJQGELZA1qxJBICDCwNd0wnAaXhD+P4//SR+98MP4JaHFFs6kyWtY5ow++2FBunUS5DbuoeOuRDTqUnUjDjTEAaoSgBCg5RCKpa6lyRsuW+WqtYxoduqFvJgJSVz/dzgmcmY1wW3vCFLBHqJCK7t1I7fTHa71idxFyf8djvMBIRLJq/x6CeBT79Z/W7WJDqZxHIuTgfA3IG6cc1ukpsKIZ4D4KsA/Irlz6sA9jRe2wNgpeVvbWNrkFK+SUr5fCnl8w8dOsTa/4iIiJ2DmeFmMymCJ2WrpB2qnUUXd9NRLoPrBvJcWVvPZOmOG3dcDKi3X9i9C4uFQQ8AsLLZnmndLqSUVU0iq08iBURMJtEIUriyuSSBt94PAM6uG0FibjpC8q7/kytbrKRRkNzUYBJtEELAdxg//cgSAMUoAiowzhzbciEvCjZrRozjPLOVAp23xUE2FbkpbY8TkFIwkyQCvSQJbktEKo1emiAvwufkUV6glyi2jbMtALovL7cGfq7PdDfdRiuLLOUlQGkfB0y5b00SGxOgnWEmIFzzlsZvvxj4m+9Vv5NsFGh3N017wOz+hrtpBuS7JEgE8CIA1wN4WAhxDMB/B/ByIcQnAdwO4Nn0RiHEEwEMANxd/suEEE82PuvZ5Ri0jI2IiABw65Fldg/CacJcvPD6a6nG2F0yu2mS8Pq9NVxRgxfXmkmMxjXAxWObTnHENJhEc20b2v8OICYRup1CsEpJSnZPuprc1NVeorwnzq5XgbViEnmOkICqCX3B/34P3vTB+4PHmMY1iSOQVfJXd5CYeoxrzDn0odPrAJThTWhvRYIpGw1vyq7eN8s8bxS4Lwwy9vzz7juO48TKJmtMVVvLM2ACqAcng90rqLecOp+h8spxrgIpl0uvdR9rxjX8580ssyax3ieRF3BniUCWhNdp0j4OMp7c1Kyli8Y13ZFTkqSct6REe8JDvaldbqqZxL4RJBaqv+Iuk5u+CcCNAJ5T/vstAP8I4GsA/CmArxdCfFlpVPPTAN4qpVyRUq4BeCuAnxZCzAshvgTANwL44/JznWOn9L0iInY1PnLvKXz9r38Yf/iRBy/0rjhRt+RnMAeFWiSzHtqmbChNgmUyVQsMPgOTaLlpZBKHeW78vnsXFpTJn0aQaNaXcZhEYlKIGQuVm6oghdc2Qy8IKQBzsHSAaoVBKEpmg9sn8ZGzKgh7310ngsdUNYndmURfTeKSEfw+tryJfpboPpWsIDGXnYO9rrWkCzO8/q6jvMB3/9HN+I43fSx4DNDNgIkOXaqbiYd/N2VcUwaJDBOmLE06yk2ZfUK1uylTbmo6EDMTRwk32JbdakLpmpzrpcE1mhGTMK8tSmC1KhNG6w0mMQNkgYkmr9Yg0eiTuFvkplLKdSnlMfoHJRPdlFKelFLeDuB7oQK+E1D1hN9vDP9+ALPl394M4PvKMQgYGxFxWePossoEf/ZIs3R398BsKB1qyADUmxuzZXOl4UEenG2tZ/I5meQsERj00hgkolsj9wsBWoRPQ25qBhihi0ipAyKeu2nTpIIbbHgb1Zfn88xavSaR2tSw3E07CB8K4972M4nuZY+PFTyxslX7P80FSSlRDVVr5GZNIpPJ4rbOGBsyVY7clD7/vpNrwWOA6hrk1AjSGCFUz89R4NhxObdSTWloUDTKJXo6IA1k0k25KUOBQuPm2MY1JpMYft9Iw/E19BzQM5DtLmskLnazKmS3g45j1mZCZtYcbq1UBjRA9bNZl1iTm5Y1iVTLmITJTTPWtzlPkFL+ZOP/fwbgzxzvPQPgmzyf5RwbEXG5o89svnwhUGMSGb2dilJumorwoM2UzYX2RAPMB6KaMsObIqvM7kwW5abAxSM3pUXauakwifwg0ZR60cIipCarq0lFjUlMBGzryIpJHNXGJUyJXldUctMqcJvYx7xAOnAve0juZUNTerm8ob5nZizsTFMcF8ZFwQ4S6dhyW2cQI7EwyPDo2Y2gMQBvHrZtj8OsmnNyLxXhiYtybiVn2mAmMScmkZFcbLbA2HG5abeaxLyQGGQqAcr9bp2ZxH66q1Uhux3NBJz5Wg0rx6rfN89VjCCg5KNAKTk15rcak7hPuZsuXlm6m2a7Sm4aERGxTbzkl9+PN7z3HtaYgQ4Sdy+LZWZNQ+tK1LjSuKZLTaJHNufaFlBlhDmZZHI33c3nYFq4WIxryNAidMG6HZhsdqjctGabHipRQrX4mGG2YKj38rIHpLSwXd0a6/NMkth+lvIYkeB3VqjXTdoDlXEhtTzRBp9T6Ylzikl8w3c8FwB0Y3sK0kOYm6KQKCS0uyyXkSJzEe58N9NLWfdb10U/bY/lUm3MySwGrJQO9xKSmwYyiSSBZtY/mvsY3F6CTGGyFIngyE07StBlZQAU7BJL10jGlaCr981Mob3NpQzz2vKqQlaPV79vrSjWUBhyU2BSPmoGiXMHgPEGsLW6++SmERER28d9J9fwunfzPJn66e5nEmtyU0ZNIrEULLc57dLols25tgXwGRiqgYrGNQrmcdvN1yQFhxzXyq7owiSatukJQ27aZBK5Doi++8Z8bWljqF/jmn0A3YKUWp9ER00i7Y8LwlOTSHLTlzztMD7ymq/EW77vhQDqTGIbKtamlEiGzj+yed5485YKEsOv5a4JHNoe574xry0uA5YIU24aLq/M2HJT6H109eC0bqt8n7oHwh2uzeuCcy9IqRKnvSy8BYZuQZIx62Rzg0ncxXP5bkfeWJMAsF9fG2er37eWVZBotsAA2uWmALB+qjSuKVtgtNyrMUiMiLiEQQ/gXb0g79hgvZCVBXoo42MyMC7ZnG/cLJdJlNG4hnBiZRMPn1nX/+csWqcNYrc5srmuyDssCE0DJop5Qtblk/VHoayB+kkMvC0IMNmVs2tqcULmItyaRJLlCbTLNwm1PomeNh2+mkSfu+mp1S3M9lLMDzJcs28WT7tqjxrDYHKbjeND2R7trsxuOF+xRJxgY9tBIkduaiQ8siS8TrwoA36S+PKDS74kU7UpCP9+5rhBL/weoOtitpey2kvkJMFN+HX6WWmcwr225vpZDBK3AdPdN9WqBMvxrAWJVJNYzmUULDYdTpvGNYQkU+6mQCubeEFqEiMiIqYDWohPK0j8i088jFEu8covfkLwmJrclNOovpQNpY5Fq2sMQAvJsDouoFpczzGbIpvmCuNC6nqYyxFf8ysfrLVH2M0LC7pvQuXI24HZ3oC7QOP2SWwyUlxX4KTs5WUblxdSS7+pV+K4kJjpibKXaXiShN4rwmPEoD6JbUyiq5YRAM6uDXFgvj/xuj7+AcENMURUBhDKJGqZcMcgcbafBgekQPd7k7bHSQDVEx6MOnFZ1ggSkxgqAS0qc5e1rbCa4+b9Fvq8MQ1vegzmsuqLyZNpFyQ3TcOZRJMl7THcZc3EaaxJ7A5bfbn1+jKDxImaRBeT2JCbEqhPIlCxjQ5cnquViIiLDF37HNKDc1oL8h/+61vx439zG2uMaZLAWlyUWVOX/E1KiZseOFM7diH93qzb6rC4JifJRAjN3HD64F1qMANEYJfXJBo1dTuNKiOfBjPiUlaLTy03DZgjuvbb033iPIvkcS5xaGEAoGqDQeYigx5PbtolqVUZ11T9xprz5rgovH0Sfe6mZ9aHOLgwGSQG29ajYoh6aYJEMCSSHeXuZnBZyPDruXNNoqQgkZfsAwxXWkYAlgjopBtHXpkmvKBtwvCMWaZA7Q3y0F6O5fvmBxkruULHRCUl+SxpLxVsKfNcnxfIRtRRk/KLULlpsyaxDOW8clOTSUzddYwNxCAxIuIigPlMCn1AAdVEvptNU4Z5UWXWOYuL8mGfJPaFxVs/+She8caP4u8+c7QaEyCbs26rfPhq44iA/aTPzsqaRIBnQnApwSa13c1BIi2wCrnz+2k6V7LdRmt1LAy5Y0d3zSxJnIvkcVHgikUVJFJCIJdlC5hUSe1Ck12dgsRyn6hPIjAZFOV5e59EwJ6UO7M2xP65ySDRa1vfgD6OaVkTF7qQ72hc02Qug81dOkrBKyaRV0cHVNdy6ONNBfwJX25aVHLTLn1COUxibiRz0kQEM8eaSexnPHdTSRLcRJtvtY4pr8E0SVhMoimB5ngJRNRhrkm8Caf1M0BvDoAANpeA8RaQqfk2TG7aYBID5aYxSIyIuAhgZgXPMXq3abnpLjZNGReFlnGG9sgCKulY5nj4nlxVRhO3Gj0iqyJxtSgJXbSSAU0/DWcSzYJ0cjO8XJnEI2fXJ17bzdlnc5G80/tpSgJHuQy6JnMz+8xissogMWMa1zTuG1ejemISSW6aF9DmUmp7gUFih2RKrSbRcUzGhb9Nhc9d8MzaEActclOWcY0RbPcSvrlIL02Qdqgb48pUt1uTGHodm2MUk8io96Nri8kkFgaTyO6vSwoULgOvmUTeOC5LV1C9ZcJxblU/tbtsJ1Ok3TuX73ZUCT8j4WQ7BxtngbkrgJm9KmAcrgL9BfU3LTdtupsSk9iUm6ZRbhoRcSnBnLibsj3/ODV57+oF+Vhiruw/yGk5QPb6LmnZ/jmVKTvb6NsGVIwI56GdGotdTpCYJQK9rCxIv0wfpncdW514bTcvLMzzu9OGQ6ZLIBAWSBXGwo7jbkqMuzZg6tAnTt03k+8Z5xILgxQzvQRn1yhILJAK5ezI2V43uWkVyCYO2RYle1zQPScth/LM2hD7LUFiF3fZrKO7MtXgdXE3BcKD9K6tX7ZjwpQItyrEBhXsVUE6p50FNyCqmYtwFCgNozS2my03SCwqx1dusJcmiuHu0rtzXEiWwimiQlFjm9VrzprEuf0q2Ns4AwzXgP68+pvT3XQIQKi/92aBbEa9LqLcNCLikoL5cOEsWomZG+5iuekor5pLh9ZRABWT6JKWUeBJ9VFAwySB4W5Krog6SMwnj+ev//M9+JG3fhY3PXCmsS2BNGEaVRQS956YDKwuVrz/rhPYM1P3SdvNxjXmdchZpH3onpN49Z9/irWt5kKeI2VOk0paGbJurRZ2JbMX2DTdZHtctVXKSCrB/rl+JTdtJFdCGUI65hzjoJDF1jgvvO6mOrhsjNsc5Vgf5lbjGmImQ+5tYgiqmji+3LGX2o2DfOO4clPzGuzS81BtK3SuUz/TsiYr2LimDPaoJpHDCqZJR7kpM7gvmvdNcFCqfs70UharXkhox1d2exuhEhDhx7Eo97G8thjP7ogKNeMaX8JpuAr0F5VsdL0ZJJLctHEO8qFiEckBjCSnNblpZBIjIi56mJMGZ9FK2dXd3G5gmBcsFoVA7SXc0jL13c8YQaL50Ga5m5Y1iT4m8ZfedTfefNMjeMUbP4pRXtQWhBxJGgD8n/fdi6963Qdw57FzQe/f7fjEg2fwwhsP1l7bzUyiGTxxkjL/9ndvwt9++iiLMabrVN8DgaZIQCXRA3hyR65ssQhYJI9yZQqzZ6aHlU1qgYE6Ax/KJJbHnJNIqPVJdAR7eSG9xjUUXDbHkXzW7m6a6M9uA51rVZPIkJuaTGLGCy7NeatLTSLreWMGiYw2QUDlbhocgJXzP1duqssUGEG6aS7CYTvrwWXCShImQgX3HBOhvJAQpXENt96VEhehx9FkEgFej+OICrbSAev1Nd4Esj4wd9BgEkluShOXRW6aGnNWb1b9zPrKzAYA8sgkRkRc9DAXnZzMIj0ApmHl3xXjXLL7fwHVgs8lLaOF/pJFbsp2Ny1Zkl7JGrQFsx+7/3SDSSRJVNj2bn7oLADgseXNoPfvdqxu5Ti4MMA3Puca/dpuZhJHtfuNv5/rHe5RjnOlmf1nuZvmhgSaw6QYcrvEYS5CLWkWZjKslq0FxkVRNhInJpEnN+UskGmfEmHKTRtBYhlYuODK5J9eVUGizbjGV8fYxNiYfzJO0CDrC3lO65IsSXQgFd6Cofr8jSHPXdP2GW37CJTBfeJwdrSN00wizcmh26tqGcNlu+qnlpsGy0arcRknuDT7izLmn6p3ML/eNWEykLkRyAK7O+m3m2E6R2e+dcJ4qOSicweAtdMls9hgEm1y09RQ8KydUj8PPinKTSMiLiWYiwlOZpcegrs4RsTIYBJDH2xSStUTyuMIRpnU5Q0jSGwsrkMf2uOy1iN1MBQAcP3BOXz5Uw4BAD57ZHnC/hwIZxI1pnTeVLuOndvY5ijHbC/F67/tOXjg574WAILd97aLI2fXWQtdoG6gxHEGJlXP+lb4mHEzSGTUu3LdTatgI2EZd1TGNYqBdzOJCRYGGVY3x+W4UsbGZRLLY85dIAP1ptTN/Sykv/dixUDWXycm0dYCg9On0gzSs1QEy+vH25CpJgnYgZT5Pk6gbj6nuNcWnTcOS0c9AYFw07OikEgFOslNk7JMoZBhMtym4RMnKZCUtbxc4xo2S1qTMifBx5FKMLhy34g6bHJT6z2Qb1UupeeOAJDAoGQSE4dxjdlLEQC2ShO/w59nBIlRbhoRcdFju3LT0EJ7wj989ihe+HPv7Wy0wmrcnBe6frBLCwCXIxjJnUzmNUQ2Z99eoc0mzO3XtpdLXLHQx+HFAR44tTZhmw7wai6niSf+6Nvx3X90y458tpQSG2WQKISAoNqXKTGJX/r/vg///vdvYo0Z1YxrwveTkgjrw7Am3UDd3RQIW8gXFolSWJ/EKkjpMxtn03ZcvQSJ2V+YybBSMolK2ofKuCbwnFNwyDPtUD+FcDelluXi2wUiGZsB95k1D5PYwd00TRLlysxlewTJTRkKiDIhADBqEo3j3kXyq7bFl2Ry2hJVrBmvfdJ25KZpLVEYsC2DAeb0SdT9RbOE5SeQF+r67yX8foe6TpNR76qMlMLUNZcDRnmBL/7Z9+Lttz4WPKbZJgVwzCXU8mLO6HdITCK5mzZbYDSDxF75/gNPNOSmMUiMOE/43GPn2E5/m6Mcf3nzIzvKUlwOMB9mXYxrciZT9BN/ezseW95kOamaYGWfc1kZ13RwWyRHe5vdPVBvO9F0mwvvk1hnbWyT+Lgo0EsSXH9wHg+dXkOdSQyvW7pQeM/nju/I545yibyozjHAy+JvB3TNf7w0EwpFTW7KWKRRcLLOYC4nmMQQ4xqLu2lYn8RC72cvTRjGNVUA7DIXGRUSaSqwaDCJKrisDJ9CjyUFh6xG4nqxVe1rc00uZRUI2uCqCaIg0dsCIyhIr2oSOYwgfQ8ax+sJiA5y045JyQ5y00omzGtUT+1MMmYArNtElHLToJYzteRi/TUTR86u41t+8yO47+Rq7T26TyJD3pomAoNewjr+khxf04QVNAN8lnqcy1K2zu9xfKni3MYIx85t4kffdmvwGNO4yZXcAqCCxLQP7H189Vq/wSQ2jWtkUQWQAPDd7wW+5fdUgBj7JF482BrneN2772ZLoqaJ1a0xXvarH8IP/vmnWeN+9b334H+85bN45+07swC9XNCVSTQzl5xMHy1YOQGp+bDlyMRGeYF57W7Kz366AjdaSJkBct24RgTLcPOytsobJOZq0fKEg3N48PR6g+0E6/vpz9zFQWUoaF4jgwMALPON7aDr8RsVUgc23ZhETva/aVwzuc9SStxzfEVfx6ZtfRXYtG+rVpOY8RwQ6Rp2MfB5IdEjuWmDSaQgJXTu0nJT1gJZ/fS5mxZSQsAdJQpHfefZtSESAeyd7U2M0Qu7AJbIbIvDUTJQcKkcKHkyySxNtic3ZbZg4I4zW5ekjnpX17bMcgPOs8NkwIJcaWvMvTvh93P/dCdufugs/vAjD+p9BCrHUZYpjwAGqQoSg3tONgLg0DHVPobLTfOiQJoKNkt9OYCzbjKdqr1lKfmWqkk8+KTqNc0kknFN4xwUeRVAAsDhpwPPfLn63SVRbSAGibsAb7nlCN7w3nvwa/98z4XeFScoS/S+u06wxlG/LMrERnRDvSaRwVIYDwoOu0e21iub4bK5LvUoUkqMC4nZrnJTQ1rWzP6PLdnwurTJPhk/cGoNf/vpR+uf1TDJsWX6RnmBXprg6n2zOLmypR+a22ESpyHJ3GmWf6N8YM72TCZxOnLTrguX0bjA4kBdk5z7jQK2NYbcVDe4170LJ7f3mr++FV/9Kx/E++86CaBpXKPeEyR3bBqgMHvLAbC6O0op9eJ7caaH9WGOvJB63KAX3l8UqOaSLjWJ9T6Jjf1EGJPYvCVOrw2xf65vNb2pFnbt+1gzrmEsyLdjXJMI/kK+a01ipz6J5lzumJOt40oJNIdJp3FJwmMgtVN1Sy+7zx5ZAqCMywDjfAumu2n53QYMdQFAzHEpG2W6m7Idv6XUBlgAMAxUJVzKoGPJSSw2E9fm59QwHiq56cEnVq81g0Sr3DSFFVFuevGAFk9HlzYu8J64QVkpbpNjrvV2hB21gIfF0vEzu0BVH0WMQNi2DIle4D7S/s0ybbTp+aeMNOwTq7k/mtko30LBpW0yfuXvfByv/vNP17KB9ND2mYToRXIZXJwr2wDU3E2ZQSInQOmKrmzb2tYYr3jjR/G5x/xtOnSQ2K8eN/1sSkFix4XLKC+wUPZ15NxvFENwVCET7qaWff5Mufi8pXS9pWtzppc6kyS+bbFrEovKFdTm7kjXUK90NwXUPVcU9TYFoc8P+nxODa/ZAsPG+EspIWXFFtrgCriXN0ZWFhGAcW9zmMQEvUQwJIEoxwmWAUrekASGskvDjkzidlpgJAnPTCwvAuq4bNsr6rWMIcekVqbgeN5sjXM8elat4Y6VrtTmd8tYNfBVTSIQfg6krJKSUoYdEzMBwXX8To1ra7fW208TXZ6lNn8Fa6CujWvMmsQ2uWlel5uaiH0SLx7sL+scTu9itq3rBEATyG62u78Y0LW5tzmOFSSWC1bqdxYCc0EeGtzQInWml0AIfk2iz0zGfPhXbouGtMmxIKHj+9Dpdf3aOJe62TNgfxiMiqoFAFC5qmaJ6W4a9v1oHcvJSHYFN/FDuP3oOdz0wBm85q8/630fBUwmk5gxFrrbQVfHvVEhsbAdJpGRXKHjMOcxrqHPve2ocqcjOetcP+3UgkHXJAY3966Ma2wLycp9M9FJktWtsWYbuItd+vzQmjG1j+pnLXAwxtKvYe6m9W3mhdTPsiY4QUpNXZAygj1Dbpom4XV7xLZpB9DA42/O5TttXGOawnCCxKKUMuvrP3A6IVVInyHBrbE9joX8I2fWUUjghivmcW5zjHFeaDMnoIO7qXHfcJIriQB6mf+7PXJmXc9rzfrmYMfvvBlsxzVel16RVuOa5jkocsUKZgP1/2/7U+CpX6vko4AhN20yiXnduMaEq21G821B3yJiR0HPrFOruzhI7OhcpYPEOIFsC+YChGVc05FJpPoxDpNonuO2h9r6cIybHjijHyy9NEEvSYLbIowNAw7XIm1sZRLbs6aPP6AazpL5AFAtkpNEQDgkOeO80DVZQBUkmpM/9z6aBpPI6btpgoKazx1b8b5vYzRZk8gxDdoOzIULx1hhNC70eWTVJJbneaNDn8QZTwsMum5ue3QZUkodJM72U5a7aW7WJDJq28bGYlctJJt/p/vYYBI3x6p1TGK0wGAGiUB4sCH1YssuN6Xf/O6mdsMb+h6+MUG1bQaTmyUccxH1U89bjDYRJtsT7G5qSJ5t8mcXuhjX0NtonmTLTXU5VnhwyZabUlIydS/kHzilkorPfdw+AMDSxqjWl5PnbqqCtj4zSKTgspe4v9vmKMeX/cL78MNvUck9s70Hm0lMqz6VUW7ajUxpOq4DloTTeEv9pCDx6f838B1vBgaL6v/CwSQ2axJNUP/EKDfd/aDMzanVrQu8J250zRJxFwcXCnccPber3bm6us3lNQYy/GFPrM85Rk3iiBEkfs8f34JXvPGjeLSUWPcyZa4Qeg60I5gn+2buD9VWNqUdtqzp4w7MAQDuN4JEc5GcWmzai0L1bTSZxKXSGTYzHqTcmsSuLF/XbYQutIBq3mq7tzctNYlZIiZaluwEzGuA49Sbd2QSKWBY20afRNtcS4uPU6tDHD+3hY2Rup7n+hnP3bRjbRuZhKixtoRM9bkLmkkcaWkfJ5AF6p8fuvBqYxJN91MXXPtpMkKuMSGMp25BkjKZxIbcMZhJbAaJgeO6JheLQrJcemkMUPUgDM0d0TXpcqR1IS9bZ3gblzfHGEyi635b3VLzy/VXqDqxpfWhvv4BHpOYk3FN2TomNJFHzq2VbHHyPZT8/JtPH9VjaP84yTvqk9iPTKJGF/dys07ZqQrJy9ggHdg/xFWTKPPqb01EuenFA7q3Tu/iILGrNIwkHbuZSXzw1Bq+9g0fws//050XelecqLubdjOu4QQcxBKtdgwSfQuLopD40D2nAACfLGusegmzSbThCOZ6aJsLItNtEaj6XdkmdXroHTu3WW3PrMmyPOxHmkmp5HYmk8ixyQcqaRynHq4rzOuCZbgSOCdsGKwXgdMPbTswr6dzDOl0IZW7aSK6SX45fRLpup3xLK7HhcR1+xXDfdujy3W5qV4kt2/LrInjOMxSsAHYWeAq+El0kmRlc6zZHq5xk/n54W061PuEwSSa2zNrFl2gP02YYBnfvwlXuw3fPmZJgizh9DusZKrdmESe3NScv7nlDTNMk6J6ABw+R4aYiVm3V/gVKDY06/Zs26Nn7cEFVT50dn2kW1kAYNUkEtupZdpMgylXWygAuPfEau3/Zu/UdBsJiFiT2G2dbJqQuZnEUmWYTbbgUYNdTOLYLTcl45ooN939oAtiCiU6ndE1S0RzVOjD6UKAFlwUuOxGdK1JNAMlTqBOUhxOTWJ9YeGeeNaNrCgZcfTSpHRkC3xAGTVQTibR2B/6Hs3WGfZ+h+o105G3WVvSfJCanztfBonk7NtPE69tuvX7le+bitzU2AZHXhy6mLC5m3LqqrYDUwJFQXsISCY2yFLWOaB7jNUCo7xuKYh2yU2fec1eAIoJMANvl9mKDVXWGmybfM0kWhaSuv9fImpJEinVdrjGTUWHeUsaiy06JuaUEFKT6Ko3KzxMoiuwtKGqSeTJD+t1Y2EBqRrXrW6say17UfgZcesYoybRlbizgRJ3XHfTouwlyGEgzePvYnvoc65YUGzPmbWhbmUBqGdVcJ/EMrmi5aaBiapCtgfAZpA4zouJesvwIL2omSJFuWnHmkRT3eSoiQ5mEttaYJig16PcdPcjVCbRxPvvOoHv/qObz/Pe2FFnpDg1ceqi3ZzCYrcraCLmMA2Ayui/4o0fZdUIdoV5/DnbGweye03QxMVpgRFak7huBCI3lU3OZ3opepwGwBYm0ea4uH9OZctOl/W+hZQQQrEJQkxa3QPVNWsGiSaToBYyzTFVNpbkdo8uKSZyz2yPJW0CuvWJ6wpzAdK15YlPamerScwuQE3iOUaQSDKxmV7CYhLp3uxyr81ktLiePC6jvMC+uR56qcDSxqiqSeyl+loOeY5U9S8JqyaxKBrGNQ4WJUsqufXR8vpfGGQVk86ogSRwm7K7antol4NqEpuJ/LJPqn9M+/EnGfL8ICvl9bygOU2Z5iId5abmIeckF8dFoVu5cK4tQM3JLLmpbEiZmYGzYJy3muGZqwa+qAeJS+vDGgPPYhKles5ouWnAfFIFe1XvTtt3O7lSKdZOrw1rQYpoSUB87rFz+NTDKrE7wVLvYrXYtNCFTa3VKaeOa1nXJM7YP8QnN3Ua14TJTR2jI6aJrlLO//AHn4CUamFiLsB2AuYEMMolBoFXDhmRrDNqdKYNmkg5i0gA+Mm/ux03P3QWtz66jBdcfyB43CNn1rGyOcbnXbMneEy9TyLnoW1k5DuM2wmTHGKrskTgaGkVfnjPAFkqGPKrKvtJC9Dm/DzMC+yf72NrXOCxcjtmvzdbbSFQLXibTGKvlFElFgaAgttemmBRL5JVveXe2R7bpp3O8XSYxOq7sJoA1xbyEv3Mvoimz5zrN4xrpsAkmvMWl0lMmUyilFIfS86CieYfahEytGxvXCjn3L2zPf09ekYj6zYG5uzaULkHGwsSVp9EWS2OE6EW8lJKvdA2a+0oSULX/+JMhpTRuBxoXls8RspcJNtqEj1EojPhlBdSJxOb4Mh96dztne0pJjdwUVnvt8dryq4ax5dBIqMGdZCpRu6shIc0Wrkw53Jf71rXOBqjth0ecNdbJwVsyyY3tbDNAHCwdKtfLo1rTJk2h0kXArq/aMgcZDKyvqbs5hx/cmWr7q7ZkoB42a9+CADw4M9/nZb7RnfTCnljveWaM0w0W8AAlmBTB4kOuakOEi1MoqsFRuyTePGgq+yKHsZLDEOGrjAX7yyXwPK9nFqnaYNu7DWGRAyomBFOTzQA+LJfeB++9g0fYo0xs02cOrWuTCKNY0lbQ5nE8nh98RMP6teuXJxBLwmvkaKJNUtMKdtk4NZPE1y1dwbHy/pC022OKzclyaiNBTMXyfOaSVSL5D0zBpMYGiSW53gaNYnmoqELAwb4FzG2mkSOrGw7MIMgjglTUahFE4dJHOaFZkG6NCAn1sB2jYzzAlmSYM9sD8vrI2wM85p812c4sTHM8dyfeTe+/U0fq1opJLw+icokRP1uWySbSZv5vrr+KTGzONNjtelovo87JwhDEsh3N61/FmFcSOc4jtx3eWOEXiow20tZ5k16IZ/wnIFJkpkx2Z5cSq/82b29Qj8XQwNSs3ctR+5ITp5CS3AZUk4hQB1NOP1Fqb+ubRzdt8Skrw/zmuETT15MSarw+s7cEmzYrknTefn02nDCXTPkOs4LWTGJ5T52dcC/lBCqgHhseQM//JbP4vTqVl3K7GKA2+SmJB21tsBwGdfEFhgXDboulnSQuLHzrTNqNQpdgsRdzCR2Pf70MJyK3LQ8/nN9Xo3UuJCY74dLVsxxagyDSRyHBaTUQ+6FN1ZB4uE9g1JuGiij0jWJbmnNKFfsy9V7Z/DYsgrYTLc5l7spHeuz6yP9AFUMJPS4SblpySQmCXppgpleguWNEYRQTIpmNgIXCV3lpjc/eAZ/dfMjrDFbtVrSrkGie5yWm2b1msTpMInVNlhyU8NdMPQeMI9BKCMOVMeRMvKuxEWWVEzi+nCMuX4l5/AZTrzrjmMAgDuPrUwyiYyWFKbcFGgyN1WQmJSSa7rn9sxk7JrEXEotYwutdTLlpLZAtgoi3Z/hWqT53E190j7CfSdX8XP/9Dn81gfuw97ZHoRQgVuo/LPQ5y1hm4tkHYLEmkspa07g1yRKMwAWwild3xrntb/VVCHMwDlNKrkpx7jGx0DS5/SzBIMswcYwn7hvWH0SjVrSMLmp+uk1QAGwMSp0GcYpg0n0OX6rz69eP7q0gXGu3E17u9yc8AN3n8T3/+ktU9mWuX7xXf/vv+sk/uLmR/D9f/rJWgLIyW63Gde4WmB45aYUJEYmcdeja23ONJnEcY1JDN9fulE4fcOmja5yX3Jxm8Z3o2M+20/Zx3+uvE5Y/a7K88ZZINRrEj3GNSWz9Lwn7EeaCOyf62Gml5ZyU660zP3QHuUFemmCK/fM4Pi5Lf2eem2hW26aF1LXqY4NJlGNq2/MlEwBwFOuVP2LFgcqQGQziR3lpt/yWx/FD73F39x+clvbZxJ9iZKNUY5BltT6zE0tSDS+DydIpMUdi0k0g8QODchpseWSQGdpgn06SMxr8t00mexdCAD/fOdxvPrPPz2xX9ruPrSOyzCusfUSpG3T31SQWNYkzmRe+Zt1e4XUSbjwFhiG3NTieEnrJ5+7qbMmMa/uf9cY32P8u//wZrzxA/erzzICvlBVzoTclBMQiapvHud897MEQnBZ8crdlOOcC1TtJWzXyIOn1vCF//u9+K7f/4RuTVRz3GXUadI4zTYzmURiIG2SZHrPXD/F2nBcu2+yJDy4r4LE8OdG5cDqbuUCAJvDHNftV22eTq9t1Y1rPAqP04ay5oFTayVLDW9Pxt2A7/q9m/D2W49NNZkP+O8bUiktGYloxVKrv09cJ+PSaZ1bk1iMo9z0UoB5QXCknPNTlZtW+8UJUigLvJv7JG6XSewqCeTIjGkfZ3spqzh6nFdMYpeaRJ7cNIxZoprEg/N9PPnwAq7coya+LE0Yxgpm9rP+WrU/qrn94cUZnFjZhJSy5jaXlAvrZubavNbp3jLdDVMrk1gu/MqH+vOesB8ANNvTVpP4X//i0/jTjz+k/0/Hr0v7BS7M65fLUlfj3Pu5OcxrUlOAl/nfDrrWJJKUjcMkmvczZ8FEbyXZlm1eGBUFemVN4pGz6/iHzz6mWQZAsWO2a+tX33tv7f/HymRJlqjFbujCum5cU+63rd6vvLcWZjK9EFqc4dfkmkEi17im1jvPGCo12+n+jMSxn14mMUBuah4rSlZ0Ma4hto3lblomBNQ1wmCOhZIkcxNHJJvu0gLDlTz6i5sfwfLGCB+8+yRe9qsfwpm14WSQyJGbJqa0L2CMPv5uB0rze8z1My031fcNpy9m0Wwdw5CbCr/j6+Y4x8GFPgZZglOrw3oLDE/yjpQBgGISc1kyibtcbkpkCtdzoguadfounCjLX8ZFUVvL0PmeuE7yMkBvk5s253Ovuykxif4yjBgk7gKYDxBO1o4MMpaZctPbHl3GncfOscbUDFA6yE13a5YJ6B4kkqymK5O4zjGFKR8SM7002BAAUOdttgxUurBEXeoY28ZRD7n5QYYf/dqn44e+5qkAVE/N8DoWY2HheGiPS7np/rkeRrnEWlP+42ANzOuhYhILY5E8mbWmwJ0W7l90gzIyol6Lzsm/xFs/9Sh+7G236f9P8745L3JTTzC7MarXzwFTbIFRHr9EcPskqjEDBpPYdR6na6mf2tmeopCQUh2zvbM9nC0TF3cdX9HvsV2TAPDs6/bW/v/ImXUAlWwuOGiTZpBY7qcxD1VST/VzwXA2W5zJVJ0gx3ClJncMZ18AFajaA1n1029co342E0e5lNp8Z3JMOyM1yBI87sBsbT96aRIcNJjsY5owgm3jvGVcuWOiWjBw5wSqdw19TplMlstxmuaXL37iAWyNC3zmkaXGNRnGikup7iXaFu1z6z4GGNdUbZkEZvupkpvKbn0Sc1lvZREiX2+2slCfMzluo1QhXLEwwKnVrYkA2LWLJ85VrqjLGyPDOXd3y00XGn2LdxK1mkTPfXOidJhd3RrXjWss85Z6gYLEnv0DdQsMW01im7tpDBJ3PWo9oRgTMhkEcC/+//vXPoyXvp5nnFJjEhlMFo3bzUyi+X18Vv5NkKymq4xhjdGTTtvk91IW21xIqWVprJrEnM8kmhObj33RNvD9DF/+lEN4ydOvBFDKrxiZbqDMvqX2hzbJTffPlc2N14aNhQXs44qKfT23MdbvqS9IJgNS9R3Ue77kSVfU/h7KpBwzXFjpO3QBZ5x5rnbifK8PLUGiCF+wbge0uNoz22Mx/sSkKCaRd02q7fKD7cyxsBsZCYi9c1VNyg9+1ZP1766Aj+bdP3/VFwMAHjy9pt+feAJ1KSU2RzlOrZJM2+yTiIn9bDaq3zNbLWYomcmpyRoXki1brPdJtBjXGIsxF1wGO2b9WxMhNYmnV4d4/hPqDtjqeDCltAmPhR+3zFsuVNd/uAOu2k91HfczjgkZ9P65HKcLKbF3todf/JZnA1CunFvjAjMli5UIpmw08ZiEWMeV+2gY17jMy0huWhnX0PdTSYGQNYaUqgZeN6oPCRKNRI2uAbaM2xjlmMlSVd+8PmoEwO5n1MpWtc5c2hghpz6Ju1xuOj9Qz56pBImBNYk6SNwc16XMLgaYgj8XK+hyN5V59bcmkkT9LcpNdz+atrmhIHvd3V2TyA82pg3zOc1ZuJKspiuTyGlcrmsSeylrMh7nVZDI7XcFdK9RCzGumRvUJ7xexmjuber4HQu7Ua7ML/aVRfpL6yPkebVocvXJGucFDixUNuYASmmNe7GljWvKh/riTA//+rnX4odf+jQAVQBgu3fMoN9clAPhNvJNcJq5d2cSw8bZWvRMuwXGwiBj9Wolm/yZXoKtwPvbnEe6GNeQnf9Eo3ojAXH13qom5Qe/6in6d5fhxOrWGE+8Yh7Pedw+JAK4/eg5CKHmEdeC/LZHl3HDj7wdT3vtO/D8//UevQ/EGPh6EFIY9eKnHtJ/o3mS4+5YyEq2uB25ad24ptxHT01iNSfUX6fG4Ta4FAmEvJA4uz7EdfsVk/htz38cAKCXqJY/IUGDOd8Jj7lLE6bcMUvCmcu8qBxwOc8A6kPL6cFZ1SS6GXE6/gfLefnhM+uQsnISbQuA/+rmR1TfQpsChcEkJgmc9bX03ZNEBYlN4xrdqingFFBSpkqABshNC8t3s9UkjnLM9FNtgtUMgF0JCEqYCqGepeO8kjInYhfLTWfU8386TGJYTSL1qlwb5vr+8rHUmu1zsYLOmkQPkwgoNjEyibsf5r3VRe7FCTZMcORX5sXPCVI0k7hLs0xA9+9Gaw3OgtwEh0kca7lpuAMoUK/t4TIpAK9GzXwg+a7jtWGOfpbUaqoAWjQFLggNiYy7uXHJJJZ9q86WiwTTEa+534B62B0oGRu6R/LcaJ1hYW2axjUA8Lpvew6+70U36v101QSZ9wY1s9dMYsfkCqctS9c2KeapajOumahJZBhNbAdmkMi5/qlRN4tJNL4Pt0+iEKgkmZbrEVA1u084OGf9DBeTuLY1xvwgw0wvxfVXzAOozJRcC/Lbjy7X/r85yrE5rgJ9G3NmOosCwMufdx2e87h9+LInV4w6l0nkumSawYbVuIbYTs9nON1NjeRSE/Sy63peWh+ikKoG+4Gf+1r8v9/y+QDU+TT324dxIwAITbB0ZRJVf0WViGYFiUXlyskxIaPrXzjqLUnGOtfPMNdP8UDJiC+WAYCPXb33xCp+6C2fxX/7y8/UHECF5Rpxwcb2TD5vqkTiXD/D+qhuXONq1QQoI5MP3H1y4vvS54UknUym3Kdc2RwVmO1VQWKNpfbUdq6Uz8LH7Z/D8oaqCc2MxNE0asy7YKFMRk+DTAmtSVwx1t7HVzbRSxVr72S3teuWq77Q1QJj7GYfARVAxiBx96MmN+0QgHWVOz54ao2xrbCLf3Lc7pebmjdkF3OXrkE6py0IbWu2nwY3YAZKl7pU2VTzrq2SyeoYNLQxifP9yYmLY+RgFtvbFoT0niQR2u777PpQM0SAW1o2LgocXFAF4ucsTKIt29o0rrHBVRNkBi/08KDP7yrh4bDbdcOh8HGhTGKzpx/AM3HYDug6XJzJ2FJaYhJD59euihBTymgz4KjqXQWuPzhv/QyXu+naVq7lVk8tHXf3lveDa0HeZH2PLW9iY1gFibb7xlxoAqo36N/85y/BH/+nL9LvYdVkFVI3Eg+ubbMsks9nn0TXva3ZR8d3I1fIgwuDGovJMU4h2WKX2k46XxlT3poKwQ8Spbo+VHuV8HNdJe7sQZtpHHRwoa/XLlRv5jOuOXJW1eHeeWzFYBKN4x/w9epGafbzbfZEnO2nWN/KG0yuO3D7/j+9Bd/1ezdptkuWx5ESCSHnrWZc4wg2pJS6RnzvbK+UjRoBsCfYW9kcY5AlOLw4wNk1qklM9HGZRo15F1ywmkTPs3tzVGBPyYI/trSJPTM9fW83PweAITd1hGy+FhiuwBIA0izKTS8G5B2DFLq5uzogHjcKkdtgsg2cmriLwbjGDEy4WVOAxwiaYNUklvs400sxCnz4Ag0jgQ7XVhf5YZqIVndTkgmZ6KVJcACctywIgepBu69kBZfWR7WaxMSxSBgXEoulbb9mEot6LaMtsKTv4IJrcWceq5XNsTYqAbrfN2QOFIKuwU2ou+nGqJgIPDi93rYDCoAXBhmPFS/UYovDJJoSQFZNojQSF7Z6V2OBetUeuwW6sMhUgfJeKxdJJHdcHBD7Yl+QNxM1R5c3sDUudKBvcwDVNYkeni5lyR0NJjH4+KuftZpEY3NmiwwXfO6mLiaxLdg7vVoGifP1HmfCEZDaUJu3POYiTYyLQhvucGsSK+MaRuJIqrYIfUct46NLG/iW3/wIbnu0YqsLiVrizi43rQKwKxYGuP2oMt4z5aau4/jQaRUkrg+r+q/EaGURcvw3x3nZEsRtClNjEnuqJrFprkPva+LIWeUcSjJEOo6c1kl2trP+nlEuS3VRgn1zJDc1AmAhIKXdm+Hc5giLMz3sm1PBpZpbqsTRNJJ+XdDVu6MLajWJjnmLAvUrFlUi+ujypq7bdpXOdJebFlFueimg6yKN2gV0ZRK7LghD2xQAVRZ4NzOJeeBitwk6Jp3lpoyFPG1rhtkCgzKw3IwwBWu8IFH9nOv52wasbI70QtUES6LUyH4Ckw9tkjHtm62YxFrW2mLAAUA3Cd4z29N1GHXZVtJqXGODqybIPC+rW+OGbLHbg5clNy1kKf/1B/dNmEGJr25v0yY3nZI8SctNZ3psJjFNlLtp6CKZvs+AUVsL1NkeW52grndNVK/JX3j55+Nv//OX1N7jOp5rw7FulXR4cUa/V2/Lcj3S/PJn361YwKNLmzU22FbvRB/jIekUk8ioOd5On0Svu6kvSCz/OGEuKKV2KJ4cU99+E6fX1MKf1AkEl5LBhrYklQvE7AElk8hgZXULDEbCQ8qqv59tkXzXsXO4+aGz+IZf/3BtW3QMhSNIKQwm9+B8dRz3GItr126SWdPK5lgnuNOEJzfdMPqSuhby5jmaH2Q6KDX7JAKw3gNUN39ipTIuS4Tw1rI3IY3r2yU3JYXJTC/FntkehuMC68NxTe5uGwcA5zbH2DOTYe9sH8vrQ/UcL+W+06ox7wLaKyrl2EnkATWJ9By6opwPji5taKMvOv4TQToFf21y0+a4YuxmH4FSbhqZxF2PrnJTuiA3OwZgnObqo4AMiXVc+d5xIXetHKFrLdF2HSg5MtW8lBt1YQTTDkFilxYYdBxn+6l33MqmnUnkLGJsLnXNh5SUKIv/EyzOZKpxbYO1sY1TrqgCe2ayyrjGXKSJyQVh07jGBjeTWN2HK5ujetKoM5PIq0nMkpI1Y6gSgpnEYY7ZXv24pIyAYTsY5QUSUSYuOtQk9hh1dKYDMVembbLbTuOacpH8ihc8Ds9+3L7ae9pqEgHgUJm5puvNJZujuf5xZcPt4+c2a3Wl9vumYmhc4NQkFtJogREc2EDvg8/d1Gdc43Q3zaXzu7nYRwIxiQcaTCLPXdOQJHsSLFvjHG/71BHcXbZHMWWaacpkEjvKTX01ibTbhazmzLrc1F3vR+/5yW/4PP36woCCFPdxJCZxXEgsUY/KhGdcs7aVa0bKWbdqPCNm+yk2RnlZ20nH390GaW8ZJFCbCRrnYx+bCHkmEqEwWxrXAMCZtVHNlA2wB84rm2MszvZwxUIfJ1e3sDkqsDhoZ3IvNKqez92S+V22BbgTvPQcovl4eWNkYRIbg7TctIVJbNYktspNe0AemcRdj65yU7oIuUwiZe04ssWa3LRDCwxg95rXdF2UbzdIZBnJlJlsTq8roJQb0cOe6YoKqOMRGtzT++b6foneSpmRbKLHsE23Obm53OYAYP9cf4JJdNVtUEH+wYVBJf8p6u6mzWDWZlzThKsmqyk3rfUk7Wpcw5gTSCalrhFOTWJgkGjrk+hw1jzfGOYFsjRhMYLUS01l1hNIGeiAWB6CmV74dQzUmRSrcU35wZknAeFzN12YCBJLNsVVy1vuuzK8SbC8McLGKNc1grb7xgzQXMjScHfTWguMwHug3idxck5omuvY4JKA+moSXewj4fTaEEJA10ZX2+LVxNUkmY7r8e8+fRT/5S8+g+/6vZsgpayN47ibFgVK2Sg34VEmMx3PG3P7955YnfxuDumuGWxdt78yb9JyU4+ZDzGJAHCqnM9rZQqOQ/LXtxzBj//NrdgY5tgYjaskiWMhr9QH5G6dYZRLrG6Oa0wufd8mdJBYMomUpBIlmxhS4tNsZUH7ZEIHiT0zSNzS13DiuSbPbYywZybDjYcW9NqTghtOvfG0QXPONNRsdeMa+/bo2XzlYlU6sIcYWZcqoWsLjGLcIjeNTOJFgZpsi2Uc0S1IJNnMFqe2MCBDYh13EQSJXWsSaQLmTj60EOEGpEnCC6TUOJVB7qe82pJQK+fmPgKKSfEGiVuVTMUEWcIHbav2QPQziYBaoJ1dV3bfTeMaGyuYJQmu3TeLR5c2IKXEcFzoRbptIU/3R89jXJM4AnzzvJzbNOtm+AkIaovTJjf9uX/6HN55+zEAFBQnGGRJJwdcoN3ddKYhN1XypOBNdcY4l+glyjmO2+/Q7MEZlMmXBpPIvLfNerPmUNOkyQXbInmcF9gcFZoBoSCRzpWLAdPbSwUWZ1QvtaFRk2hbJNMzzCflDGUSqSZ3wHQ3tfVJtPZy9HxGCEvUBL3sSnqcXt3C/rn+RJDvkrvb0ExSucZ89P7TAIDHljdx66PL2Cpr6Wg/g5nEMihTzw1mwiNxM4nm9v/l3lMAyp6A5XdzBukOd1nNwDiOSV5IPHJmXZs2kYmQCsAm94nw7juO47/91WfwJx97GL//kQdKJpFqcu3jlAOu+v3afar+96Ez6/r9PndTWpMdW6akZJVEyDwmX2fXhnjJL78fv/bee2q9Sm1MOlCtVXppoueDx5Y3a1JmwMUkKsbrxsML+jUtN21x3H3RL74Pv/3B+51/30loVdQUHjh1g0f79mj+veFQZUJG17Eor8tJd1OSm7qMa3w1idHd9KJHVyaRsktsJjHpvi2A2wJj+6zITqOz3FSzbbwMGk3gXRwQOb211LhCW/l37XkYukigia2NSVzdrNgNE700CTZFMg0IXIxgIaVeEO6b62Npfajq44gRabEyv2bfLB5b3sDmqMC4kHqfbU5utN+uuiX1NzuTUmcSK7kptycmoOTIgF9uuro1xhs/cD++549vUfteFJUkmdVLs/2+yQsVYM/16uc7m5I8iViKmV6KzVEe1pMuIAHh2hYAzGS881Y3t5hc2IXUuyomsf4aXVfEyFENzP7SyMkVENE10EuUTPtk2btTB4mWRTL91lqTyAy2ge31SazLTcu/+0p0LIY3gL9PYuJINhHOrA0npKZqPxhy04Zxjctc5JaHzuJpV6mA6N4TqzUmWTGJ4fOrMm7izQnk8NlP7XW5NGccWhzgdz70gP5uuk1EQL2fCS0BdbCrR5c2MMolnvv4fQCqPrR1JnFy3MfuP43ZXoqnXbWID99zqqxJrI6jbZxZt/q4A4rtXFof6dd8TCIlCqm8QbmiQm/PVYZx/6lV3HdyDb/87rt10qZWp+9KACUCjy/38YFTazV3Zdc+qtYZGZ5UCxLb+1SO8gIPnl7H/37756x/32l0KZ3pvi0jue7YHvXrPTjf1+qCPUbS3MqKt8lNXS0wZEufxLSH6G56EcCcgzlZu0puyrv4adJiNWU3LlpOn75xwE1zPvFXNz+Cj9x3ijWmq3FNFyZRSqkfLmwZT5mhpf+HgAxX+gwmxRzH2U8d3PRTp5GJlFLVNthqEh0LC9+2zNqS5uU8ySQOsWrUaDndTXPFrF27fxajXOL+U0oWZdYNNDO7pgOlCy4mxTwvq1vj2nHkJASAisn0uZt+9shSfd9Nto1TtxcQJFY1MJM1iRyX5K4gmdogS1DIMEbQlCVW7oLt+yp1cMM3rjEXyU7jGp/cVFiCy8Y1eWC+j5/95mfhd77r+XoMYAmI8ooV3zPT0xK42YZxR11uKmt/syHU3ZSu/0HWsQWGKTdl72N9HwCas933dqvcdHU44WxqjguTMk/222sOk1Li6NIGnvv4/Xq7JpPM75NYBoncFhhJaVxjZRLVay952mEcO7eJta2xUndMfDdbAFYd/5/+xmfgaVct1pxDbd+NXEM//7p9ACq5aVpzwJ0cd3ZtiIMLfVy7bxbLGyOsDcfauIaukea1TGofoKrnBeqBlG0cUD0DyPHclNcqJtF+Dszef2dKltR1/dM+AurZd9WeGc0UT8h9Hfs46CXYO9vD4ZKFrDGJjhtg3Wj1xXmWnS/Q8Z7G+jOkJpFUPrO9VJtZmcoqW8KvcjftIDd1sY9AZBIvFnTt09dVbspd/AP1RSCrT18h9eQ6jZv0h97yWXznb3+cNaaru2yXmsS8qNobsDK05UODJEuhi9Ci6OZuOs6r88Z1d5ztZc7vRqycVW6aivAWGMYCOHFIZJT0Sf2+b66PpbUR1odjY9FkHzculHHNdaVkiEwgzMWWrf8U/c0FZ01iGZjtn+thxZCbDjJiUsIfrLR935jbH1X28ZRJzguJNC3Z5g6JI5+b54ZRA2OCY+O/HRADT8cyJFFiyn27MImDXoq8kCyTEK9xjSH/dMEmQaTPMRfX3/lFj9c1XU4m3XCAXJzJtJnGTOY2rtHOip7vGerKSfdWlhAjxWMSXXK7kNMhLIFDbjmOJrTc1LGBU2tbmsU14TMJacJsr+Da3tn1EUa5xI2H5iGEajcBQPfJ9MkWm6D7hm14VtaB9xwGa5SAoHYsjy1v1lgzV52mWbcIAP/uhdfjHT/45fr/rvlkeUMFTk8spX0nSxMhavdAn93EmXXF/lLD+Y1hjrlB3VykeZ8qSTCx9n095+1tOFf6ngHkeK6OY3stqRkkPlL2gzRZUludPlAGyYnAdQfUeahUAu5rcmuc68TNk69UbKIZALuSHatGwvJUefynCToGnCT5drcF+BKn6vVBL8HZMrB/4Y0H9d8Tr9zUFSSm9fcRitwvN01jC4yLAl2NU2hBPZUWGMYikMckmi514du798Qq3n3H8eD3bwf1G5v33dSYnZffarlpuVAMDaaIEeTKhsZFlYEO3c+acY2DkaJm8a4+iVLyFpJJIvSDOW98P/UxxCT2sbI1xvLGqGISLYuEoqhYg2vKIPGuY6u1fbYFe6bUzQU3k6ju3ysWBkpuSsF2nye3U6Ag0T3m+DnFDNGujsrFDbcnGhmu9D11spQ1bfZJzBhOi9sB1UiR6YqvVQfBlJuy+pR1lEnW5HaWYI/mW18CwlYT1MZuu1iDUSHRS5VphmISyyBR12RNLiQrKef2mURzv12tFGyoahK34cBqCRJpf1zfrc2l1Ck3dTC5Nph1vS6ZKjG+V+2dwd7Zng4STZk8y920gwRdloYrPcc42j7JMY8tb2qTFqCq07SaiXnl1vbjT/LNa/fNIkuEVW5qO21n14bYP9fH3jlVk7s2HGOuGUjZmMRyF4UQuHKPSgzsLSWFvpYnJEHUTGIha4Y3LtXFktH775Ez63o7Ptmu+R2I8dxfXp8+dntrXOj61icdUkEiySR9hnrrhov76tYYtzx0Br/6nnus790JTFNuGlSTOK6eib/ybc/Bf/vqp+ALbzig/26Xm5af1SY3tQWXbcY1UW66+2FeEJwATDOJzIufJgBu0KDd5ph1e7MdmMSvet0H8N1/dHPw+7eDrm6SRYfJxwzueD0IZblo4jv+kQEBNyitmMTwgBTw1ySulA8Mm7spMSUh1xe9pSY3nbh1qof2wQX1EDx+bktn1m2LOzo/vTTBNfuU+xgxiQtGcOnK0HrWMciSxNr2gc7LwYW+YhJzCjb49xsthH1STpIlURY6L+ut1IKclyTJksTL9piW6ybajA7OFyomsQwSA65luq+T0t2UPqd9nPo5k/HOW2EwiVYDGuOadMHGblduh/ZxzqbgpXETAOyZre7TiT6JNZYu5PoPczc1j38vC3fkNOuUbXNCxTS6P8Pm7tjGJFbs4+TfRnmBpfWRnn/s22r/futb41r9nblfBGJ8Dy/OYN9sT0st5wfu5JYLtT6JHVpgDBxzAp1LYhKPLm/UWDNbAoLGtQX3tu9GQeK+uR6u3T+LB0+t6ff7GODTZWC/b1YlF1c2x5gbNPokWhQoZk06JRWJSfS1StFMYinNNGtQs9TdGmp5vWLmKEhMEr9zN1Dd+yQbJTm0a04g8zZSZLzghgNYHGS4YrGvv5srSWK2+lofjvHy3/wofuU9d3d2hedCt8CYwvbysr4fcK+vNw256Zc/5RB+4CVPrv1dseLNIDFQbmrWJEqp5Ke+FhhJZBIvCpj6c1adoNGontODsItGe5RLXbjNYdty2Y1JnCbqfSr5DqAsJtE45l36HXLcFul9JDcNDfaklOp8D3hBIh3HfpY4F4PU0NZWk9hj1MrS5yvjmvr29f7I6oH+zGv36tdp0WRbbJkmIYszPeyZyXDXsXqQaMvI6/96FqBtNYlXLAywujnW11WX+0ZLoD3XBzn8nStNcsg5sJcmbCl5kqjgxRVcuuSmaRIms9suqAchR25ascLYJpMYGNzIRp9EB5PIdjfVC0L7GFeQMsqrdg+mLHxCkmYJEn03QAiTNc6Lmrw2S8KZLH3eEvucYNaaumCTrlfHsSVIt3w3kpMd9MhNQ4xr1oxm7q6FPDG+hxcH2DvXx6Ol/NCct8L7VKIys+rQAsM1J9D5v3afYrAeW9qsJUlc12RRuFuQAO4gZXljhDQRWBhkeOIV87inbLuRJu5gDzCYxDJJsm45/pNy0zrbTCZi+2bLAMxTA0kKjlXNJFb710s9ctOyx54QlZSzliRpYRLJ4bSN7aR5k5JtX/esq3Hza79Krwl9LUhME7X1Ya7nMWovtdPokswHgKX1oU4yhGJcyCpJ6HgmmkyiDbaSAyUjFe4MlxDq72ZNYpvZDQCksSbxokBuXlgdTWE6Ga6wAtLKAp1jODEutleTOI1C565MIh0Glmy3o7SYJGnEJoRsk6zkk4RnQEC7OMeUm1L209ZHkKDlpgN7TSIQdg+YkjTXosnsP/eMa/bo9803ZXNmkKgXqOo4X7t/rpJtmU5uE7KOisVwIXUwKbUgcVj1SeQGG0AVUPiYZmISpVS9r8aFRC8te3AylQxZkqCX2U0qgHqRvok0Scr63J29v6kutWISA+Smtmsr4LgU+rzxmMS8KKo+iZbFVohxjRCTSqNCfw8mk1gUeluLhgvxTNMV2JSblj/bmUT7cZRS4tvf9FH8q9d/sMYk9rcrN7Ua17g/ozKhqca1MYn0mbbFPyVkfMY1IezehhGkuAKp06WU8uBCH/tmezhXJuRoXFbecyEoDLnpuJBBSWh9/BPhnBNobpvtp9g/18PJ1c1aksRlyqPKJjxBuiNIWVofYe9sD0II3HiocuVMk8SQm9bHbY5yrA1zHJjvYd9cdd7mjJp0wGYKU3fApWfIvrn2mkSqUyPDsWbvVJ9xzYH5Phb6Gc6Vz1bT3XciSGw8p4hJpL12mbnRWoXmUWHUedM413RXZxJzLW09VpY97DQqUoRXlvWcn343nv1T72KNyQuJfqaurbaaRJpPm1CqkMaLRYtsFFBsolmTqNnHFuOaKDedLv7gXx7A5x47xxpTSKl7QrECMGPhwqlL7CaTlLquh2vU0kVuSphGsXHNOIi1SC7KMeH7OKwxibxzRjU6AI/Z4BrX0Pmd5xrXFCjrJt2M1KqHSaSHasg9QBPtbC91LraKopIx9dJES5yaTKL5rK9aWVDNxqz+m68FRsU+tQSJltO2ZVhiU+AGGEFihySE7/o4vbqlFxFn14e6bjVjmIQAdQm06x5YL+elZp/EKqsevLlO0HLTcu4KcYKu1bvq+y18HJ03jiuwySRONOk2mDUXbBKlNibRxRqM86r+6/Ou2aNfJ/OV1LKQlIFJEtd1+dDpdXzs/jO4/+SalqRniWD1hTXdS30OrG1sJ4DaOaBz76sJFQ53x9OrniCRxSSOtXGKKwAY6YV8qgMTwJjvGExiLiVSUfVdDXnGmXJfFwNMz8wsETi4MMCplWEtINISUItM0nP5l/eNnUkkuafZl67GJDZ2k+oCF2d6mmEDlBkNfT/AwiTK+jVCTCIlyHznu3I3zavvS3JTT/Juqfx+CzOZZr3M5NaEBL2Afg9Q1SJWdc/0XRpMojZbsbNfvpZGptP2+tYYB8rA+/jydIJEXZM4pT6JaZI43X0Bo04/czCJwsJuF2O/AQ2g/m4yiW1mN0ApN41B4lTxk39/B172qx9ijRnnUk8oPpnYxLgyawFUFHYI6ALkSVsL9OjiD9xHKZXD3yzTyMFEW1Pw8wFzAuY5uamfXcxuumzLrEkMCaRMu2uOAQGN4zKJxNr4Hmo+uSnnHiAZ4yBLjEVrfZxEXZ1B5hFVCwz1uk1aRgvyL35i5Tq2YASXzcVWCEvhYhKrmkS1CD9b1gpuR27qGiOlxOm1IW64Qi2YlJtqVZMYuoikbaWJQC/x1CQ6mcT6/u4UcjKuIblpQDLNdADs5G7KrEmstwCYXNjpYK8lAGvuYsWI2h/ztGhtrkdGudTzzIufehhfdMMB/KcvvQHXl9eMbSGpfRU8++hjEk224dGylq5qyh7IfhmJGttCvpKbuj+Ddt/mbuptbyOE1QDl9FrF7tnGmPvtw8Yw18Ypvro9QB3nq/dOJrdCa0IBw7gmZdTylrtDQYqNfTQVIFcs9HFqdavW3sPnHNrGJLrkpnvKIPHgfCX5NcsUXG0islTg2tK87KuefiW+6bnXqtcdEnSzHg2oXGXN+US9b3L/KVE4zAtVPiSr8+xzpd0sGeaFQaaTiyaTOLGPsn4t6/WEEeCbx6C5fzS3NZF4EhCrRguMtWGO/fPqfByfEpM4TeOavHRG9yVOW+WmwiY3baktBBSTaNYkhspNc7/ctIW/jOCAUxdogmR6PhcrG8Z5gYVBhjPjYXB/MymNFgxMd1OqEQndRzoc22ESN0Y59rNH8RDSANU3juzufYsIgrlw5C3+i/IaCc/smouGfpqyZKNAdd5C2VxibbKyhkIahgQEksQsWuSm2rgmYHtboxwzvUQ9SMu3Nx9SUtYXrZTNp8nZ9kDUC8Lyby95+mH89D/cAaCSbdlqBui/ze9rwiWJoppAyngvlWYEJEfpYozkukc3RwW2xgUef2AO955YxbnNka5B49zbQOWcq6Rl9rnP2QKDwaJsB+QSyKotrDEi4RJo+i6UuAuv//Ib15jMpgvCIndsXstNaNamySQWhb4Xk0Tgz1/1xbXrurpvqmuFtu0zhUk9TcHNuiUyXMlaWOomzH2wMYIhNYm2urGQIDER9ud/xSRO1iS6jr8Na0Z/18o4qP6evFDtJ5JE4DmP26tfrzGJjNZJpuFTUHlD4/jbmFWSbWeJwBULA9z26DIOzPctclNbkOjedpoI2PLJ5zZG2FsyVzS/Aio4qpQk7qTMU65cxMd/9CU4vDiYMNexJWXMa+SnvuGZOLgwwJc++YpyXPW+JrZGhepTOy6wPhzX2NXMk4QbFQUWehkWZjLcd1LVWybCF2zXWfFnlbX6L/+Ca2uvN49/syaxidRx/QMVMwsAG8OxLjU5uTqdmsRptsAggyWfmRvVKvZdAbfVuCZEbtpgEtvMbgCE9EmMQeJ5BMf0wUQlJeTJvUaFxD6SqTL7ywF8uWmWEo0e9qCpDDhKRqoDk9i1vcc4L7R8sQ3U20lKvpSWMMoLpG1yADTlpkxpnxDoZ+ViN+AcmEwCR25qtrLg7CdlnzO9QJs0GyDGwNYCQ8tNA67ljVFek/HYFsmFlDVh2f5ysUB1kbYHYnNB/oSD8/jFb/l8fOqRJaNv1eQCSAYtku21JRRs0TEh2RCXgacaVMB9fdDxv3qvcm6lvoz9XoqMcW8DZeJC+OetDYe7KSdo2w4qw6dwdk+zYgnP3ZQ+mljL0AW5ae9vM+AIMlwRPibRPs7tblpvN9BMfOiWM2YApt/r3EUvk7hmSNKOlIYrypXZLdtqwjxONtliSCDrSxz5WzA45KZrW7UEUHNMc1su1I1Tyv2yBDe0j899fJVaJYdaTtsZqoHnyE2bcl/bNF5nEgc4tTrEjYcmmURbLV3mYRKFcARf40L7PZjnYP9c372txn1z5Z6Z2t/pMvD1SQRUK5Kf/eZn6f+7jGuKQmKYF7hm7wyOLm9idWusn/eAqtV3nTe6VxcG2QSTa/9u9X153IE5PPjzXzexjxNM4qglSPTc22YLjLVhru/naTB7QLWe6Lq9UV5468FNmGt51/aGeV47R02klpIDJTdt2YcJuWn5e5Sb7h5wFlgmSErIX6RJwyQhfPIncI1resxAtpIt8hgpExsdg8R1xjgy7kgTwQ7cCKHHsm5cwznXqPUEDHMApYwotNw0hO2mcbPMmkRiRHwOrCubY8z3U+sE2WcY12wM8xo7ZZMbkWkP4SVPvxJA1ePJ9kCkjzD37luf/7jaw95W/xLEUjgepCT3JAkutaaY0X0SQ5My7dcjybdp4bOyOaoYwSQJTjap/UZrcsvVJ5Fj2rEdFNrwiSEb1ZIsM5gNuN/KcYMsPNmh9zGASfTpFGxMVluQ6HY39S+KbIxIUE1i6jbg2LAwiQmpEhjslxqnAtvmMaHf/O6mkyxRW79J+kwbKX6mdMm0scC2mugmTpzbxPL6qGYAJxznzWSyrtwzg9965RfgXf/ly3XSImUY19Dzps9iEqvv5XIvpn0UQuDQ4gCrW2OcXhtW7YU8TKK/B6ddbmoek32N+kJXkN41uWJLilrHNbZHc/WBUpK8Psy1cRCNc90DozIZbpZvCOFuC1WVoNj30XVMKrmpPeBwMceACgxneymEUAEjPSu6rpe5oK/SNUjkOJwW5Vrep65Rcn7fHGQxbJR5gNxUOOSmnnFpr1VuGoPE8wiOVMuE0uST1XG4bNQ0heFkrQmd5Kaenj2ubXWRm1ItRIjRhA2cWsa8zMb5io2t4zocS/r8mR63/5SS22SeQOoTD57Bc376XdpaWj/s0kQHYBzDm9kuBhxCeFtZrGyOrCwigKAAmCzlzebSAMq+e/X3FiVDTHjpM6/CTT/2EnxRWWdYOblNBoncnlzBNYmWw09BGi0Cie3jMonm3OG6R4mxuWpPxSSOc1VHkTIb3OelLLHvqRvbdLbAsC90zzdokVi1GGrfnsmIcGoS6bsMmE7VJmtgSySEJiAmFtaGcZVrjPk+wjhwsVtn4NXPzkyiwTZQHR/NyewWGOaxZDOJ5XutTKIncHYwWadWh9rwZGKMgxEkHD+3iS/82ffiP//ZJwGg1m4AsNSu5nUm66XPvBpPuXJR/9/X8LwJet700/BnAH1/ITzSeiNooz60dxw9p4McV52mybbb4NqeGSTWmERD4to8/M26vSZspki0rZAERPN80/xOrTJWt8a6TyUArxHcKC/QTxMdZNN+p6l9fi2k/1p2XVvDNrmp594e5QUGvQRzvRTrw1x/FichuR3QdkJdeptYMnpRtoFUaT6Z/HBc6PWtDYnNBOsCyk1jkHge0dU9SUuiGDUD2iY/45lbdGG/ACWl7aVJJyZxpoNxDWVauspN1zlBojTlvjwmlxC8kC8/f76fsdxNtdzUI5u7/dFlLK2PcNujy7X9y5iyOZq7uQxwc0Fu29bq1rjWe81E24L8de++G8/9mXfjnuMr2BzlNXcwW/atWZMIqEbTE9uzyk3t35HGTQaJ5X60MomTx5IWQBRIkbnPDNO4ZhxwPdJ9cWjPAEIA50q5qWISmUoGSW62Hie3UQ7TlZcwNbmprAxQ1PZC5KZVkMjpS1rVJPKcqk2WxBbshQQ3wio3Vdt3MTAuNneUF96AyMfAd3U3peuynyU4uzbS+8153jSPU9MAopKE8wLgyvmRz2SdWRtaTWto/wD3fPdXNz8CAPjwvacAYLJPoiUAaNtH30L+6a99B/6y3GZV3hDOJEojuLIl4NTnVm0iPu/qveqz80InDrVM2MOS2uByNzWb0s8ZScVemnidVOl7WLflYnJleyBrHVf+n2TB61u5Ntyi/XDKTcuEjvlMTQ0msXm/tToeO5nEMkh0tG1IHEE6UCkTZvtZTW7Kabe0HZjti7qs0clILgRUy9tPE6e3wigvnPWIgCPhUeTt7qbNFhgywLgmyaLcdJroLjeteuAFLwjzOksXbCZjvI3NJNJDO3BhRxNSlz6JPXJt7dDaA6hnptugF8ncFgCmdDfwu9EidW6QsiYsWgD4Ainqf3fPiZXatkzjjpAG5k0mkRsk+vodrmyOa1lPEz4zEykl3vDeewAA955YVTWJE0zi5KLJL9GbPJYmi+SCPUgMYSnsvaSoBxgd79WtRk1i6PE3jrfrHiU78oVBhoV+hpXNkZYsKWkfh0kvdL2f6/rfGBal1Kh+YKZlXKPqJquglCc3FUb9XcC4BpPIYm0CmMQ2BszW3BtoZxKbcfO4VQ5lYxLpvnHvo49JpCDxqj0zOnOvDLd4QaIQVRDYPJZVIOv+DFsPyBDjGluQDqh2MwcspjXm57l6hR5ttAiYa5Fkjht9+ppQTKL9WN5zXM2pv/COu9Rnl4kLrVwJkVsbyZXUchzVPlZB242H5vW90pSb2syUvO6+NrMP1Fn6iTmopSax9b5pbG6ct0tifdvbUwZ6q1tjFEW1f8q4xhEkluxxk0mkHI+t/hFwP99c11ZlXOOWm7rm8lHp3j8/SLE+HOvP2ukEIcG8BruUPJ1jyE1D1pLDsV/OT/4YNYTITZs1icQQ+sZFuel0welnZsLsgRfOJJJskYxrwsaZNwzLJIcWkkm4A2uzJpEVJJY3EacmsT4Z8Fi6LnJT81yFSwLLY9LL2EF6W5B4qgwS7z2hXM7Mnkh6HKMp+IAbJJbJDp8BjS+L5rMIf/jMuv792LlNR01ifYxEm9lH+T5jXMhtZKu/0CyFJyzNErstPMmdST5LvbIquWnYvT2qyU39TOJsL8XiTKaNa7JyQchqwVNek76F/OY4t0qUXAYJ5xtkJlFJmcODPZ+VvA1Nd1OO4oK2o6RG9b9LtCcu7H0SC+84twEKn0kM60GYeJjEMYQADi0OdOZeM4nj8GDb/K6paMpN1c+24wjU54Q22S5ty3ZvL22MsM9iWmNuy3UPnDi3iaddtYgXXL8fi4MMT7myWUtdf38b2+ZjpEh9cuUeFdBS4qLHUaDQ8TWYRFtARMcxSxM8sawPr5hEhwS0gJZQcr5b4TkmriDdvP9tcLGdRQuT6JOpAtCtOtZIblregsq4xs1K9VKhxwLAFYt95/zazpLSd6m/Tusp37PbzyQK/bypmMTpyE27llh1GWPWwLtrEluYRNu1HNIncaIFRrnfrXJTfxAc3U3PI7r0AQQqSUTGqEnUTCJTytm5JrFQ2eWMIck0M+tC8Kj+LjWJ9e8WvvikWgJfxs41jm7o0O3ReZsbpDixwmMtzf5f9sbNqpaHgkSzCTSnKbiZSe1nSbhxjSGbBuxykryQziyazyL8I/ed1r8fO7eJjVGBA/PV5CeE3d3UxxrYA+7ABXnjMIawFG3uplpuWrLggx4/2CC45abqs+cHGRZnepPGNR3Ybd8Dcex4IHJq/baDolDXlb7+A74fnUvzWg7pL1e5m4YvrIFKEkvbdLVXaat3ci0+XfWFrkXrMJeY6fEZEfWZzmEtNYk55vsZ9s72dGIwFQK9jMMkosY2NVvVhBoAAfXv1ibbpXFWJit313f65nIAOLGyhSv3zOB3v+v5KGS1QHf1GG260jbhq0m847FzAKqELq1JUs9c3kRlXuRupUCqCcKBsmcesWiuxEXewiQmid04yGQuAeBHXvY0bWCTeIJtwH3fCGF30x4XfibRJV2n/+8pA+X14Vg/7wG/cQ3JTa8yHFgPzg/cst0WpcxOuJsSwbB/ro+z60O9DuQkJLeDcaEMHjdHRSe5KU/xVZm5ucaZfWhtcLfAaAsSm+6m5bpN+GpnekAeg8SpYTs1idSTi8sakLtpsANcjUnksAYqu8yxJK+yVgn6HkmaDfRA5DCJ5ndjObeW2c0+Y0ECqAl3tpdidWvMqAkt5ab98L6FgArA+p7G8UDVk+ueE6va2AjwW2Jb99GQ2w0YrTNMl17Azr6oCdv/gJp0KZX4hXfciWdeuwdn10Y4tryJrYbc1CZ3kRJejZ5tkRayILc594WNs0vSyACmV7aYWd2sy02Dpcx5+71NTOJcP8We2QznNsZKXpgkyFK1f0XLYkdvr6gk6K7gy/VAnFaQmMvquwFhC5OKSQBrkazdTZkMsKpjQbnNyYV8qJR5kn1pY0Tsc8k4bzFW8MpN/de/6zrZGI0x209rLo1845q6UVWT8a9ku+0BsM2UxxekuOSmvjo1l3EK4XjJJDZbOfkYKT/bljiVJFSeQaZnKrlSzeWc50ZNbtoMUhqBLJnxaLmp77u1BOm2YNt0DgaA7/mKG2tjzP0mNBvL22CTt7aZ67TJW8lYZ2VrXKunbzOuyZIEV++brLW3BbKtiSPH+iJEbuq6RoZj9QzYO9vDkbMblXHNFJnEuX6GzdEQWx08LkJ7kNO2EqHWr6uOkqetFrmpNUiUAcY1SWJvgeFlEmMLjKmia00iZeT7WXgmn27I2W30SeS4S+mG2xy202SkGPWWQGVcw7mpa0wKMwBLU+Gt2XBtj2vKQ9fIXD9jHY9mZtf2QKSaxJXNMU6ubNVkUhyjELNugRr8hoBcekmqZruWfQ9SV+AwyiXOro/wNZ93Fa7dN4vHljfLPonV9NWsSTSz2i7YAu4gl1LLJB7WTNzPJAJKPk4Plx0xrtmqgsQrFgY4ubqltp9W5i6h/V7JcKWXJc52LsNSatSE7zo+nzBVAgCQh7SO0eZFvEVy092UMyfT8UgTmyOh+tlWk+gy4ODeb2MP+wXY5Y70VdsCWddhVExiqhkloJSbehqJN1EUdblpc3shvUw1S2TOCUXYXGJjEn3skovtAYC//MQjOH5ua6JHH23LNk4lbdxLuix1M4k0x58gZ+xSbqpdgUMMn4xgugo26u9pMnvEXFLCz90EvkVK65A7mjWJTVCblKbctM0BlPazi9zX/HxzHKDWBImojMvo/VkinME9SRep760JG3OvE8dOCbp9Xh62yE1dfUIBNQ/2U6GZRFoDTc24ppBVwrVDYLrFVNe0GW4pR1r/dWI1rmltgdGUmwYyiS2IQaIDeSHx6/98D06VEr4QmItiVzG6a1tkLhLcE6183wwza21KQDlZ/HFRqIw8Yx91JrsMgDvVJDJcSmumPEwmkZqCc2Wquk8lw9wFUA/HUR5uyUwLIB8Dc3Z9iCcemgeg2ES6RthNwWtMYjjjGWJcQ3VsNrgswmmx3csSXLd/FkfOrJdBYloba361UGavub2Qxa4oWRvzHg9xTnQ1mDYD59leitVykTDb50rJzZrENiYxw5V7ZnB8eRPkOOiTCdu3J1vNRUaOrKmPET+fCOndOTHGWEhxkit0b9FCiqPucLVtAKCppi7MBn2mDS6546goJtir2jiLJDCMSXTPP+vDMWb7mXZ4BEomMQuv098aFzX3xaQR7En9up8lbwaXVZDOl5uS26F1jCdR8vbbHgMAvOiph4PHbacmkeb49WGOtbKZOwXpAFi17EJAM+M22aiZgGg6aDvNXdrq/SwybWAyKJ0YZwkuq+emcxiSxMF2ttyj6n3113UyNxWYL83EACNI9NSKk8TYdO3W+2j5bpV7d8ucYElAmPtk+26uuZzcTffP9bC8MdJmhNOUm3bxxSBwe2drx2/HWrKtJrG5lgFQyk1bwjWn3LSlJrEFMUh04AN3n8Avvetu/MI77gweYwYmHBelqm4mPGuqW2B0rEkcZLz6O8oucySZzdo2zs1Gc21Xt1Fuv8M04dW/0Dh9/AMnPPp8PWkxzneWuGU8gDpHT796DwBVl1hlRLstdhNBNYlM45rEzaT4FjIui3CabLNE4AkH53F0eRNL66NGn0Q+I2h7IJJJSIgkrT7Ovy3af+t5K6o6nbl+irVh07iGz9y7Mv/rwzEGpWz5yj0zWNkaY3ljpMxdUl5wo2sZPRJ0V2N2l0X7+YZ+aLcY13zq4bP4b3/5GSxvjGoJBlbrGFnNrWpb3YxrutQkCksCoi1I9LqbtrA2atxkAOZlEj1swzBXcnrTyv/AfJ/l+D0cF7WaqWZQFDInAJOGNyGqBFcLAJq3XdsxP9/E0aUN/KvPuxLPe8L+4HHbcTc1686XSjdHk0nkGDf5yhuaQRsxpfSdKrlp/bODmERXkN4SXLoZeL95k43t9DHw2nHUxe4lAnODVDOJdC/Z5gRAnX9Vk1iVoTzz2j3VPm6DSXQpZVzH0lfvOhqr47Jvrg8pq0TltOSmRYcg0by3uCaIbUxim7upNeEUIjedaIERIDcNYBJjTaID959cA1Bp5kNgLkA2R7kOINpAdTMpwo1axo1gIzj7LynbnWJjGN4mghZ7vF6OVXawn/HkpvRWl4zNPsa8sTsEiR3kplpaxnZ8VdfVMC+CrhOafHxM4riQuGbvDBZnMtx7YhXPum4vADTGBciGyrdUNYk845rUW5Podk50LRAo4OlnCQ4tVnbyVxjW8k0mpZJ/8uQ/lQFNyLjqtaazog2J40FqMonmtcBVCZjqAl+fRJozrtpLToYqW10lEsLlfb1e4k1ukeFVEy73w/ONyfvGvp/f9qaPYTgu8LJnXoV5bcfP6+fYDBI5jtO1nmguKbPnM2yyuTZXTrdJSN1cZHKcJUkSynY6GZECvUTgwFzVU/CKxQGrBcbWuKjVTDWbUleMoP9zmrVcOkhvCTYmvCYMJY11jINZklLiyNkNfOmTDrHGhTCJhbTXHJvPS2pBkhqGT0EKFCO56Gql0Cw3+N6vuBGDLMW3PO+6ciys41oZwcTeXsjsk2gdJyxOqjogcg6buLaAStrugkvKSfN2lgjMDzIsle6+lNiyqgtQzS+U0Pnka796wvHb2QPSEcy62M4qUWX/bi4mF1BrnMVehv3z9YBkGi0wKJCeZQaJ5q5xyA0p1XPDV141ygv9jLHB2s6lGHdogUGyKJ/c1N7Dtfaxre+4TPFIablv03q7YEoOWa0bioqBCQ02JphExsIOUAsZzk1KGVEO22kyiewehOX34Yypm/Iw5aZJwrJbV/so2Qt5Yhy5mS2yJPf1l6PF3ZMOL+CeEyu1489iEo2HJIdJ1EYmiZuR8i1kXO6mdC57aYInHJzXr7/i+Y/Tv4uJ7D+97t5fa21VAGsgLAuZQrbL2FKLRAmoL4BMMx6SMnPkvjTOlcgxk1dmvRO1wKD9CUHtvsmllRFxZU0zz3V8PqGt/D0SaKBK8pxZG1ZZc8FLrlTupu1tiT732Dncd5Ja1Rh9Ei2MVEjiwrVoBdqDlEnmUvoXyJZzFxKAWQ0ZSpBS5br9s/q1xUGmr60QbDXarTSdYkMk4bZxYaoEe2ADuIN013x3dn2E9WGOa41jETLOx1qa+2ELOIa1ILFqQVIxieE1iYlH8dJ0N53ppfi+F91oOLdOjisKCSndLJYa55L7ticuJtk2Sm63SAIt903bttTnu1g61e/w9JoqcaL53yUT1sFlebMemO/Xewdb9rGNSXRdW20mWC4mF6CaxAT75uoByTSYRPoauudz8Pq6eh9Lbmr4iziZxFI14YJVlRDkbtqoSaSAMcpNdwZHzm4AaM86mjAvCk5RbmUl7zaAaKLZAoPLJLKDxFzJGjiZ3VpNItPdlMbymntX34fb26bqE8ebENgmFZoBzlj7ScGVr5cgyY2edGgB955Yq8lYWC6NxgNhkCXB7l5UtO1rNxDi9jex2DIyrU+/ehHf8YWPw9/+5y/B3rlebaw5LGRh7Qr2zL9Z99PysG86K9qQJfY64NyQic0ZD/ksSVi9Oysmy80kmkH6YYOVNYN7zvZSAV2Eb5tPXPUXSeIecz5B31eUAZ/rPqV59NTaVk1atS0m0XMcf/Rtt+In/+52tY8G2+FafAL+hLCtjqWrcU0hpbffp+36Dwpky320JRNGhUomXLd/Tr8myjrxvJDW+6aJrVG9JlExMNXfQ/aR/t5UCdD++MZw678qd9P6uGPLmwDcCWqXC3Q7k+iWTm+NCxyYV4t4ChJrNbnMFhghfRJtsCUgdNKSWZMLtEtw7a1jqs90bs9yvxUtgawrwVvV+6n5n1zKKZnnMuWhtYpNqQGU5Q0OdUGrBL2pZij841xMLqDkpr00qT1vgOkwiRPKLWbCFeCr0hIhvMmtUat79GQJAGQRIDd11ST6FjPRuKYziAnkBBsmvcyuiSuz3eFMonpf115q/dK4JtRgRz3EhdchzbWtqt8eJ3BTP7vKTblMIgWyrPOWV0wip44L6MIkomxSXG7bMonTQ+rJVy7g1OqWftjUGZHwxa5aJIcbHJnXMWCv08w9xjWuTCvdV/0swSBL8XP/+vPx7Mftq70nEZNBG+CX6NnMU0INaMxtqHHtCSVn3ZJxTGpyISYDbwYprgeUGZAcMOW6RpuI4Gs5r5hEwH4tjxy921z1p+cbdN8A/roZOt+nV4f6HAnmfVPVe7fPCadXhzoRabIdtoy8ZsU927bJTdus/F3tBkgy5YLV8EnfN55xDjk5UBlwXNUIjHoZMcDt98DWuL74SoS9Trkt79ucS0KZ3Ob3amUSXcZB5Xed6dlPgteV1isTru+Xia1xjisWyiBxY6j3j1gqjuGZ2QKDLRu1jGuTSAKlkmSCgZe1+9+1vUm2rW6iYx9neQa3rKXaG9yXTGIzSHQwiaRcczPVlsRFACNo3ceWQD2zBNt6P/MCvSzB1XtnG69PL0icbRgktcG8R7iEg35uO8YpdU1bAqKZgRj7s4SAmny7tMBoQQwSHaCTxAlSzIuekyWhDKBqL8ELNqoWGGHjKEMxYIzLS7lHliSlJNY95tzmCL/23nswyouaIxbXuIZu7u5yU87xLypJLEduajCJXHMRvnFNUTOumXiwGfVHTzq8AAC46/iKei3lGRCYkhSVFAhPQNSdVCfHUfNfG5yZVs0ktjmCGQs7ep1ZI6IlUy2MCFDP9smAmsQsddu00zGZsQaJgfd2+Taf1MV04KO+XLRv2riGcb6zxH9tOY1rGMHXdqC+r/rdVU8tpcR6WZ9dk5smhgkTg4En5tSnSji3OcLRpQ3dz5QOkS0jT2ZK3CCF5oi2Zu6TJhWBSRJbcsXHQDqkbEAlQ2yyzhx2e9ioSWwuroOZxETUAu4guWliqQkNDNJtkky1/+52A4CrlrT9vNmO/3Bc4IoFlTQy5aa81knVdpwupS3Mnk0p0yaRBCYlwkA1H7aNmzyO1d9csJrJyLDnhs9MxmyNNWsEia4yBUC5fttgl9Kqn77AEnAziT5W3EVwDMt64/1zjZrEKchN6VnG7Tlsuvl2cjfN3P1dW91NbdLdELlpkmInWmBE4xoHaGHGYqQ6Mom0kOol4dIyep+2yWc4UAKVJCovJNp8U2hbyt3Uv4//55/vxRs/eD+u3T+Lq8qapyxJMPA0F7XuZ4cgcbvGNVy56biQfJOKjvIHYhvcD98q2/qkQ4sAgLuOnVOvlcE9EFYDZj4QfLbpE+OkLBMJlP23B0RcJrGqSWxbJPOYDd9i17cgpN3g1iT6HBDnyvNjNhOnIDHYXZaYrF7qDGrM5tLmeZjppdoAIbjFjaz37rSxgsMyi9zENINEzSSm9prvYV7oBeWp1a2a3CxlGHfQtdNrYWSllDhXuqgurY9q7K6ttioocSE8wYaLNUgmF+S0fyEMvDmuSsq4x/nqqcdGP80/+o9fiIMlq9VWS2pia5xj/3xV9zRpXNM+JwCTbC4d1jYJrk3dAfiYRPWzeThaZcKOROG4KDDoeQwxPPfpVi1IJAWKea5DahKrudOX8PMFX7YSgDaJJP3N6VLa+tyovzYOYhLtvXK9zw3HMTHLQkxDE5NJdCXggCqRYtvepLmResEZ7HmYxPbjYf8brW/NxJPrO51v0GU7N2AGicY54ribUoLN2xbKkTglWI9lkLtpU24aaxJ3FHQxcbIIo1qQyGGyFJPQS/0sXXMMAHYLBpokOAyYzlilKrPuG0P79ejZjZrWnm9cww8SuxrXUPaHLTctpCEt4wX3zf5QQfuYtNc1ZInAtftnkSYCD51W5ks1K/MQRqRRk8Xpi0mBjWtbvobPrsDBNK5xQTknVv/XSosQJrEmSVM/fQtCWyY/pCYxTSaz/7R9WhCazcTTRKDfqSYxwagorFLysUP++fgDc+wWGLpO1sFs0GfZ6i98Y84nyLgGUPOXbWGyOayO7+nViknkups2g2bXmNWtsb7Oji5v1JrA27L/QQkPC9vQZlvvcjeVaA9Im+PaZGyAewEKkMJA7dCXP+UQnnHNXgAwrskwuanPuEbf2y33qWgs0mTA8W+OAVBT0tjgbhNRLuRbg/vJYKMtkALs99zWuMCe2Qy9VGB5o+rT50v4NVFrgeGonTdVE759NM9brpUkbcze5Lbob+5xduMmc19c+2lzAPVJW33HBFDB7MKgWtDP9hO9/1Ja5OR5Nc4GK7vdEuzZnLvp/35G1p1IG+dSy8YJL3naYZaTfFdMMolhAV/N34KzBi3Xab00QSHtx2QrpAXGxAkIcDedaIFxfpjEGCQ6QAszTtBgSlM5VPooN5isUPmhMXFmHqq/icqBL7zWgD47K+uWfItI+rhHzq7XtPZs4xqS+7LcRqvfOdsiCWTbdzNBMrEZXRMavrBORHX8OZmt+sPXlY1UPZMOLw7waFnzRNeWbZxrHwFox93gmsTyIVm5ZFqMazwLGVcATMfWN7EK0b1Gqm5uUWXDXbAzkO1MYprY64DNOp09pgQ04fXuLIx7WzoeUC4HvusPzunzFsqmkwTa5ZIJUNZ0cns+6dv5hHm9pQ65qelEfW5zVAt4uO6mqVAmOb45+dxmpag4tryp2Nby2rYZ1+iaxJZrsrn2p+/qvN8c560t4SGEsNT7te+jL0gZlZK0JnyOnE00W2BMskvtQYMaZ/9ubQknd7DR1vKHx4C5xrW5m3oZ//LY7Znp4azF3ZT73HBJi5vups59NIa1Bdvqbxb5rT7+7n22KWXGIUGpJQArWhh43Sdx4rxVSQGz5Rpdy5njHNC4zPEF7Y7H/mBPM7nNa7nwzwlpknh6oFbz2+//+xfgl7712dg722OZO3YF7VOXvtSEUOM+2p6ZKLc9u9vkplb1VlGEyU1tLTBiTeLOgBbvXNlo9Xv4DTAuCvS0lTzvItaBG1PuSBNQyH5Wi/SSbfMsmE6sKGe2+0+u1ditadQkdjWuoUw+zyRE/axqO0OZRMWk0SQROmnRProWWk2J0tV7VaN0AA12I1w2lCZKbsfpm5cKg32xMonumhRXplUnKVoy0OZDm37z14hU+62/g17s8rKtQUyiw7jDrNPZ05CbctjtkD59riD9cQfmWE6GQPncamESXdKaabbAqIxr7HMX1SPum+thZXNcY9LpnIW6m9I15ZuTz5VMDQA8dHodK5tj7SxpN64JZETYTKJ98RmS8MiSev08/catZSS4rsuEcU1ujfLa4qsp7w4J9ujvdVOq8vh7++a51R2uIMXXS1CNczNEalz99VYm0XMtU/uQxZkMy4a7qVaFBF3/6qcQ/gDYzwiW77Mk/LzBpbX+LuS+sbmU0vn2j7Mx8N7nhiMxRtvLEoEFi9zU5QSt12We56ntu4UxiZPHsjVId1wj5jPgxU87jG953nVlK7WdDxJput9WTWKntaRwjh051DUEa6sgGdoCgyk3TaPctDPo5HJq28y6QA6VTj2ifO57k2OqhXOvRQJqgi6+PodJNDJWiu10jzmxonr8nFkf1h52XYNEznE0byw2k1hmf9g96bRsNzRIL8razm5Momuh1ayjMJ3EzEbpYRlh9ZMs0EPZHsoS+wxQvEwiZTEbEyTdi206fpu7qY8RtAU3YUwiLOPCGArAlV1XYxdNuamgazLw2tJBojtrSmwv4auefiUAtSDRJi1MJtEnI3T1SeTIn7cD06in5zAOIibx8OIAK5uj2uIySYS13q91W5452QwSb3t0GQB0PZgti6wX355t2+pd2xgYl7tmSMIjadROcu4b26Ec5dLKiLQlE/7+M0fxlB/7J6xujTHM63LTRNTr/UJrEtWxNMdVr7sgbDVqhrrDhlb5YUsyzc4k+s29zM8393NU9m5bnOlV7qYJj0mXsrre3FLaQHdTY1x1P7q3Td+tZjjUchzVOH/phgv2fqZhNYku5jJNhK6dAyq/CVfg1uyT2IQQlnVCS02oa07IDdm+Da42HYC6t5tqkh4j+bwd5MZ6V4jw9Za5bzxX/qpPIjDpFZIXsvQBYQaJwXJTM0gMaIERmcTuqJhE/+Lg5MoWfvfDD0BK2WASeUFKL01YjeprdYIMmWTFJDJqEmvSVn/rjJNlkLi0PqoFiZwekEA1uXGb2xM42Z+qlq4DI8tsgUHSYnaQmDfqv1oWFmaPrUOLg9Y6KdtnJQmvwFzr8ctt2e4dnySqzd3Ua1yT2Hub+SZIW7Y7hEm0LWTaTAuAarFoy3hrJnG2yuwlTLmplPV723ZNEttLeOO/fR7u+l8vBdBuuNJEXjKJNrdXgm2BALiZhvONELnpZhkkXrlnBoUEVko5KJ3PJmvm21ZlkuOek28/ek7/fqsOEhWTaKstpP+2OSc2D2UbI0WvN+dyKf01ucDkorBKyrSzFPaaRLssuW3e+u9/9RkM8wIPn15XfRKD3E2du6j+3pASBiWcLDVx7Uxi/fOb41zHUuh7p/56KJPoSsINshR7ZjMtN00Fj0lvJlcAm+Klxd3U8gwIZfbMfTD3ue2Y2IJm8zOt27Pcb4Vsr1sVLYx/jUks53FXgrdKnrrvb665TuK4tgpjbrOO08+AyWNpC4raXPLPF8xz2U8TbDFbxQFME0RZqdKAyTWQ9lfI/PfAxO0W4m46UZMYIDeN7qbdQTdgm2voD7z5k/jY/WfwFU85VAuCWHLTvNBF4uGsDTFHPJmqmVkBwjLkpnGI6TbXt1zoZ9ZUFnJpfajH0XfjLAhpgmcFex2ZRNNwhX0cU5WhCsm00rZ6aaLlBsM8sJC6zOTZWCxgsv7ouv0Vk7g4yHSGOeR8N5uJ+ybzWx46g0ICL7j+gGY7Xb21ikK1UmnPrDsm1rZi7wazR6+7x0w+2Kp2A+5x9oWMP7BU49RP27mjY2Ia1wBgGdfoeuOe2+yjuZBUiYey9oXdAqNkEh3fiz7LKjfVNbJBm+qMQlbn2XV/rw/VPXiobPZsGnfQT1/N9xveew9eeOPBWhsU1c7IPub177kbADDfT3HPiVUAwBXltjsb11gWnzpIdAysFtb119sYEQATPdhMqaFvjHqv5Tpx9Phrq4mjBdzptS1Vk2j0FnS5m7Z1Spx0N6Xj72ekms98s07cOqZzTWK5XxY1SUgA1jyWVHM1yBIsDnqV3DSpmPSQBb3JuLodWFuCDcszQOf7PNs2VSG0qA0KLi3rkhAm0aYuCGmDZLu/zWB23qhJJCbR2Sql5bloUxe0JRJc11ar4Y3BQCbGmSJ5f3Mfe4zn2nbQVLOF1hd27pNYoBEk1sdqcsfD+NsY4CB30y4tMCKT2B0UHLYFKfefXAOgstHmQoKTJRkVSm7aZEOa2BjmeOi02t7IYPdYvdQaTGLIgnBsPLR8BblSSqxsjjDXT1FI4GxppU291DhmPvSQnkZNIk2cpJN3saS1MXn1AOolSbC77LgoGnKEUNZG7aPNNIL+DlQLixsOLei/mU3BuX0S0xYW5eW/+VF86299FMBk3aRrgmyr0ZkIEg3W3IV0oo6o/MwgZqN6jcMk1hcyftMCtb0QJrFXbkP9jVcnS/e2T27qXshwnAyBSjrmWsQodYVdWkNjdlpylBdVD0JXneDGkOSmin2nIFE7jiaTToaEe0+s4nXvvhuvfvOnagspV3JlbWuMc5tjfN+LbsQLbjigXz9Uyk3pGVCrrw0JUiwSpdDatkm5aUCbiKTD/eaRJY9LGX4ToTJ5MgDyuZsSWgNg0XQ3rV73jXHViTtbkDiOR6vc1BFsk9rEuY+OcWTx3y9rEmneMGt5Q2tyAXV83XOC3zjFto+hxx+oqxnajj+Nc9033JrEkERhkniuk4bcdCary01dwaW7xcrkejLU3XTymmy5tx3GeCOHCoijUNoOzPryQZYGkw70PVSZVHgLDHreuGoSzXWjC5OGW1ABX6vcNLUzid6axBgkdoZmElsuqnObakFxbmNUey+vJlEZ17iyOABw5Ow6XvRL78NX/OL7ta4ZoCbY4fpuujF5xjVlTaJZb2ZbAA1zFFLZ6QOq5xhQMVIcJ0MtN+0cJIZviybOvnblDAikjGJzJS0LPP5lCwIKEkPlD83+dhNsVCNrfcPB+drfq8VWuHFNkoiyjss+xux7eW6z6vfWcxzHtsy6a/FDCZvzXZNoY2XDGmfbFzLtWeRye5aHPT1kyYWNako5Mu3CeLAB9ns0L9wW9BwHXNqe2V7F5Uprc3JzjTnfyGv3jX2xu6HlpipQIzUEXW8qSLTfA++8/RgA4Lr9c7WFlCsgfWxZGXs99cpF/NK3Plu/rmsSLXIvifbAhlowmMFlXihG0LVwdbE9EnxGJLRNB+1XE67WLK7ESvO1R5eUk7PP3TREEkv72ZSSq3GeMbYFeWBSzD2ujYGsvz723NuAuwaS2NhBltTcled6pDBwX/8mTAWKy1yntZeg5buZLWmc4ywJj7YkCW2PG6TTZ1oTOe5d1J+ZOxjnpnFNs59t85lf9a92XCeWesuuiYTWekuXCsjx7M5Sf9nS+YIpNx0wfDFo7p7rp2y5qc/dVAetLcdysiYx998AgGIaC6MXeWyBsXOgDDjQTjVvlvT18saotpgLb5IuUUg1EZuUfRMv/qX34/i50hRmbVhvS9EiCWxuD+C1wCA51vwgreSmlocGGTI84WAZJK6UTXmJSWQEifReTrBnPthZxjV5XSYZ1juyyrZyegnSg3yQut228kLiZ/7hDtxbStHoNbo+VE8o/4PtWkNuCvCMQupMovu83XpkufY77aM780nXbFtmvf56m9U3QA/E6v960ep5bNtY2TDWhrZR317bQt6dpa1kYtfsncV3vfAJ+IP/8AIAYJkp0cf66o3zwh0A+JgeGyi4bZMJ2853lbQI2lRnFMZ903OoGagmkeSmx8+pQI6y+tbMbglKhPUyoWty1bbsDPBjyyqYuXrvDK5YGOCDP/Ri/MK3fL5hUqHe1wzAQoI2oG7Ukku/kYnregxlRGzGNW0N59XnTy5AzT6J9X1UP33NxAHodj9Nd9NasEeJ9VYm0V6n3OYuO9GTrjXYo/1yJPycNYn2ca01iQ7jLFoEE5NIMK9/nrupcJ5rZYrUPrdaExC+3rWWOSgkSLSpt0z2ybc9e71rQHLFU4N6o6EA0mNcz9OWWn1bsNFmQOOTQAcxkM1A1iU3ZSibtgPzXHLME+lYz/VS1lpSlte3i0nX16S3nddkfTNk0c4kJmmdStcX5fZaYMSaRAtMijiUnl4umUQqlg92KTUW98K4sHqN82oGISdXtmrjtlOTGDKOgr89Mz1vo3QyfHhCyWKdNJhEWxG1C+bDj8UkGuwey7ZYVu6mapsBD8Ty42kch8mttcCwTEC3PbqM3/3wA3j3Hcfxwf/xYkipEgmmlM0VgJl1VL/w8s/H06/eU3s9JAAwH64+Bvj02pb+/cjZdV3c7rT6Lr9qu7V7M9jwPwwB9XAzAwBdxxIQuDVdStXnecZYvl/IwpoWi7YaGDomSSLwU9/4TP23fsaoSdRyU/d17LNA99WM2UCN6p0yYU/NTDVmZ6NE89i6zGRoLqU2FMdKto+YFJ/cdLWc85bWR7h6b7UAc22LmERiih9/cA6PL5NqgP0cqGvL/z2rxEVVE6SMdDxjPKxB633jXCR7xrTI5mxW/j4m0XzGPnJ2HQAm5Ka1exu0jy0BcAeW1OZK29anz7WwpnsidS3+Heetzd3UdSzHxkLedFcmVitUBVSxNu5zLVuuZatKo/zZdvzNfQBCgz1LsN3SX5T207y9Q9hmGmczdwHUnDE/mFySu56nVdLV5W5qN+XxSh0dLLWvTAGw1/cD7me3qUhrrnXPJ3SrQAFWr24dJA4ynQgMHUeu8Ob2m5/rdYq1MMAqSGzh9JrGNSE1iQEtMGKQaIG5uPIt0LYMrfJSGSTO9VKsbI3D5YcGS0LXRdsa7cTKpmF/LJzSJhtMrbX5fx8o+Nsz2zPqlixMYim9fcY1KjC5+/iK2kdiO0P3UYYd/4lx5efPMLM/tJCsTHnCmcRUcN1li5rc1LafNz90FgDw8Jl1bI5yfczNQKKtTyIAvOIFj9O/6x54QRnhalHkq0k0j9PJlS1sjgvM9JJ2JtGT+bSN08FGCytiD/baF4Tcxtl2ual/Qa72Uf20yXBd7ConAVS5m7p7d5qM9MT+dWASzRYYzuDeJjd1sMbnE02XwixJsD4eT7yPvi8FiUeXNjDIKmm9CojsO7o2rIJEcwGm6rgmxxwvg8Qr9w6sn2c7BzIgAWG6SdJD3WUG49sWbS+EFee2iWhzL/a1wLBLVKsdePi0ChJNJmyylUV7sEfjzM2F9KkUwiY3pcQd17hG/fTVmgG24DKQSXTV4AlR69NKjd3TJMzzwJSb0rsne34Gss0WdUdIfXktuRLAJNpUApUk0LefTQdc9ZOblAQmmeP3/Ncvx6NLm7UxgC3gqP/dtq3mWrKtJpH23xZc+sZljhITU/FmwlSkzWLnokRTYt7PkmDygObuuX4abHYDVPdg5QHhkJv6kneW4D4oSGwa14TITSOT2A3mwt1nLLK2VZ0QYhJn+ypIDJUfmgY0dEE3J5FmIFFnEpNWcxETZiBlbr/5nj+76WFASvzznSfwFU85BEA9gH3GNZXcdB7X7pvFkVICpG4aFQS3ZbLMfXTtnwt0/GZ64cwebc+8scOypuqnYtvCjYOoBQb9s7mb3l7a4gPAxx84gy9+4gG9Lfrpc0izgVMDlte+m7ve0rw3Tq0OsTHMMdtLnVbfbfKfVlmNJdggNBdpoQvCJsMd5iQ5+dAupGxtG+DucVm5mzbBsQqn40YOj065KfP422A61brG0fb7lqSAi0U5nzCVBYC7TpCO76GFAYRQtdUUMNK+utYWK5pJHNZkoa5tbY0LbaJgg+24hLiN0vUqG/dAF4lY0LXcyHaHOrCq99Zf98nQfQZH5oLvaBl8k/kQUGbkLcFGO+PfnBNQjnOPSYRNblp+nmOgaz7IG6oQ5zjLcfQ7ctrvU1MVY9YkzpdyU8Uktj9PzWCTbvkJ1qzlWq4CYHNM/W82CEvg3CbbpXGTwb1i0luNa6xscwhLPbk9oDrfTzq8iCcdXtR/bw84XNeJXe4YMidMXMvSH2xXbVns65JmYlgnrafQJxfglzzRuNleuNkNAO2m7UqIVSy1r3TGorqTeQCT6DCu2WYLjFiTaIG5uPJdIGuGccfyxgjDsdTGE+G29dVCxjWJU43Mj3/d0wEoGafO0KRqQraZ3dhAN8lMz80kfurhs3jt39yG1/7t7XjfXSd1sLc4k+mb3RYUacZxJsOzrt2rX1f1fuFMVi1IZLWyUD/7qXthvbo1xk/+3e34D79/E+4/uaq3lyUJi0kx5Z0c4yAlJU70ftqYxPVhjscfmEM/S/Av954yJBNlkOgttncHG0BgTaKRSVULJvv1RffG4iDD8XOb2BjlmKXss2Uf29zYKLhsPqCGuXsRSWjeA8EmFY2HdlBtlSUjL9HOvrjuAR+TqBj4rjWJk+eMJKI2cOSmZkbUNc4nN9UByg5SiU2XQlcyx0ycHZhTwSHN47SvrmNCz4C1YY7NUVGx/bZsMLo1Ew8J2mxsLjkpu2ALLIGwa3lCbqo/s50lciWBrO6mqZ1FMceZIPMhoJuUnP5u7dPXwmQ5649akzL119uCm0Sft24BwORCvnqW7Z+rkiPEJIYqlcxgzic39bPN9D5eAqJiIKvXmve/fZy9ttP3rKHPtN3frTWJiX17gPvZ7Qw4ApIJ1trCANlo85pUzuXOYdbjT9uz7WMlN93ZcgO6/8k7grtOnh9kqnyMsZ5PE3cQHMIkppbEBWThD/aASeOaoBYYUW7aCebC3SdbXG0EieOi0BNrcE2i4VAloX5vTv4nygb1Nx5ewMIgw8mVLV07QO0lQiViekFE7qaWpy85xREeOrOOQZZgkKVe+Q/JTRdnerjhUOWu2U8TZwBs3ceOtYUVk5I6j/+H7zmFP/jIgwCAL3vySTzx0ALIzMPnvufaVsW2hR1/qlsFKACYHDfKCyzOZOhnc3jkzPrExGLt7dRSR+GyqLahMB6uJAtp9j+i/QSAawzWeLZkqP1W3y0upY7v5nM3bS7kq0Wrc0i5vaZstHy9RQ4CTNaNhQSkQFNKKL2LO+UsGHZtNVtguJhE5zXCkICa59I1buS5JiuTrvZtdYUpfwPgXCSYLPyhxQFOrw1r/cp8GWjzGXDs3KYO0F2BZVszcZvDbIj808Yu5UWL1M5xPYbKW7lsp7vFTfUMnNhHB4sCVPPC4kymE5SH91RMYhcpOe2nrd7SN0w4FuTmd5jYjmZWHeOcfRIdwXZLcOOsFTfuE5NBp0RJcE2iXpADKOyBjTKucX+Gr0+iN0ixJO7092oJ7m3Hv6u7b1ByxSX3dTxzXCZfIUz1BPvYmqQq32c5byFSZqeUvBkklv8PbR3WFVpyLIhJDAz2yv0mQ7FhXniN8/T2SjWJTxIOtK2BLOs0KQPlpmbGu/z9YmmBIYT4EyHEY0KIc0KIu4UQ/0/5+vVCCCmEWDX+vdYYNxBC/F457pgQ4r82PvclQog7hRDrQoj3CSGesN19JbevhUHmZQRNJnF1c6zlpkA4k2j2gHNN/hSozmQp9s6qZrd5mSUmJ6VQ2VbFJKr9bNoxA9AL/t965RcAAN59x3Fd6+GqowAqJnFxJsPVe2fK96v6Rz3RBexnYewjpyaxWiQnzszPyZVK60/BN/XoctVW2WBKPVjGQYXUgZetiB1QE1IvTXD13hkcXdqoyXiAFibR1d5AL7bCH/bUJ9H8fBNVkDiDh8+omiBaWKRi8ruFNClOLXKcUV7UzBBsaBbph9SxAJMMQMhC0sn2tDGJFuMaWy1pfUy4RGaiB6qDNWszDgq5R2sKCIdroi9Drk2KdnCR0DQJSB2Mf27IHcnh1OxXlljYBsLq5lgf7xPnNiuXUmFxqEM4k1hf7LZfx7ZaotbsvyVoCwmIgMn7u821ksbQe03oJJAvmWCbf8oDTO1DZnpJrYVAk0msXDL9aDIwQTVxNtliixywTZbvrEnUzFL1min/dkGPczC5aSJw0AgSzRYwvD6JJpNYf49EC9tsWSeESDltc3JbsE2faZu32pnEyXtUfZ53mPWZXyXF/bWrzbmrSubat0UqoNoY6Q+AyfHbxlIHyX2dbPpkCwzAvv48n2jKTTn19kBlXhbuMK7KOVwKiBDjGqvctAiRmzaMa85TTeI05aY/B+B6KeUeAN8A4H8JIZ5n/H2flHKh/Pczxus/CeDJAJ4A4MUA/ocQ4qUAIIS4AsBbAbwWwAEANwP4i+3uKC1+5wd+PTJlkef6KVa3xhiNJfqpqtEJZZbMwt7EMkECpvuYwL65nmItjb5SqkA5bHsk79ILSQeTuH+uh2v3Va57p1ardhaA/aG9OcohhPpscu8z6+jU9tv305TEFjKM/TL3qZ+5azRPrGwhEcBVe2ZwopTxFoVfImPdR+PByjEOonYbNNa2IB/n6jq6dt8sji5vGteIMW5CouSXnlTfjVFbkghvcElM0VV7Z3UD8lmPI2SbPEZt0yJbLAoviwioSbdej6V+ti52HQtJ/uI6IPtMxjXGPTBuWcikjJpE2h2qSbTNXT7jAlffPBvGxjXiWvxXD+jJ8Vr+vINBopZp033jkYAC6rxSTZvJJLpMaAD1DKCE2ImVLX39C2EPttsWoDaTikLK1sjGVkvkkxarfZwMUug3bnIlhO10za8+xs03J9Nz+ooFFdjMNmwSJ4K98mdIMFs//tXnudBUJKh9bp/vfLL8NgbSPNeu2q/athzri9wI7vbOTi4aVV10+3PDPE4uk5w2xpmOsTlMBhx/23USsiC3tYkYF+2+CV0SCbSfXCbRaVzTElwKCyOVF/5emoBdKt8WXDoTQI56Y18rtfMJ89ruUpNIbrMcV9TUYBJtTC7gl5vaWpx1M66R1esu7CYmUUp5u5SSvGRl+e/GgKHfBeBnpJRnpZSfA/DbAP59+bd/DeB2KeVfSSk3oQLKZwshnradfaULYn6QeWviyLjmqj0zWN0cKwaoZM1CmSVzcrf1ugLqTVP3zvawtDGqSUtc9S++7Q16ae3/Jh49u4Fr98/iwEJ/4m+uQBZAzbikYhKF/n5qewEPmwbbGXos60yiI0g8t4WDCwNcvW+mziQmTOMaQxKbOXqi2TA2Ah5bETugvm+WCly9dxYnV7Z0c2+SGFub5LY8EFM9aYV/t5qZj+V4EuNO7AtQyTNsi6ZwJrERJI5la5DYzOyGLCzo77ZaRt+q3CbtK4ow18T6NgKYRE69a0NuarsHKCFiA+f6N/fbNa66jjxMYmByqwuajYtdrCyxq0IIXdNW67dnSXgAamG4ujXGNftUQmxcyFqSxCVt9bE9rl65oVJmc5N5ILtnNeDwjppcJBcy3IG1U+2q7ViW1zcxiY87MFf7e9II9qqFvHc3JwL8kMSRvf5L/fSayVjYnra53NcTkBtImf/PUmENjnyS97WtMX7nQ/djlBe1FhguUx51nTh3sZI7Mq9JWwDcJvelv9nYtlYm0SFl5t5v5n66/QRc5EF7cNkkD8YtjKD6PPs1GVbvOjnO/DuB45GwHdQNBjk1iWogrWe2AoJEalVmJtdtcmu1P+FO7erDA4LEpnFNUE1iirbZfqrGNUKI3xBCrAO4E8BjAN5u/PkhIcQRIcTvlwwhhBD7AVwN4DPG+z4D4Bnl788w/yalXANwn/F3c9uvEkLcLIS4+eTJk979pIeWkpu6LyqSm165Z0YxiXmBfirQT8PdLivNdmKdINX+qP/XmcRKI93s2ePdnsHSmds3sbI5wr7ZvjZxAIDv+YonAvCzDRujXC+SriqDxMPloou1AJXdgkRdk5ilzoX1iZVNHF4c4MrFGRw/t1nrQchxXDSZlF5LC4xxXuhrxZSb2YrYAfV9e2mCq/epY3jPCWWwQ85znbLPibBr3UvcdWwFr3/P3WWNHPR2Mk/GT+2nwIG5KhtVWyQ7ZGV+JtH2EPUbcAC2RWvYgtAlQfEvCOvbAACJ9oevrd6sOm/uBUJoAqhqgeFWCVBhvQ0s4xrj+ncrINxZU267DQ7+4bNHcf1r/hFn1+vqB3/gpt7z1KuUqyD1MwTqztMmNkcFCln1PASAmb7BpNuYxNy/ALVJAtscIQHHNSn9tbW0PWtypWWDk0xi+z66ald9LJhfyUA1/Or/L7j+QGMfJ+W3QJi7qb29QUuw51BOcBnIcKfq6jWJ9vnOdc+1BZg+yfuPvPVW/K9//Bw+ct9pe3KxMa5NceGTQIfUiZvnzWSRXLCxbW2ScNrPZk0uEJBcsT0XC/+5c/WUNevtrOMsZUghjL+VFZf+OcEmdwfcieHKAHE6TGKalKqcwOeNdsrPOEGi+kn1j8Dk+tr3TCQ0W/CoDw+QmzaNa3RNYltvLj+bONUgUUr5/QAWAXwZlEx0C8ApAC+AkpM+r/z7n5ZDFsqfy8bHLJfvob+bf2v+3dz2m6SUz5dSPv/QoUPe/dRMYj/DMC+cUk6Sm161dwYrmyO9uOdk/yuW0G2cYhp37J3tYWm9ziRy3E21cY2HSSRJwqzh8PcjL1POqr5gb2OU6889ON/H/3jpU/GH/+ELy30MZ7Lo+85oK3/ed2uTmx5eHODwngGOn9usZbo45jqFMfn55GgA8Nq/vR3P+J/vxMfvP43huNBZNFsROwAMc8WckdPcQ2UPMJICefskeqQkmWeS/Ppf/zBe/557cHptWDMg8Mq9xup6328xO7Cx2yH7aAsuKYj3oQsjqPfTGlzyFjJBTIrlHjifNYlmkgSwS2R8clNfvbFrW7U+iYzFJydpxMUvvvMuAMBtZSsZ7W7qkIWbElDq8frgqTX998RxDqhHoumoqY2bbJIhtC9AtUyvcS23XVvCcg7aJGKAvbYwBLZm4iFtMwBbErQquZjYP0/igs7JN3/Btfier3gi/stXP2VibNOBGAhZJLvqm31jbOoO9dM739mkfQazbQO9bGvT0YVJNFUxrnGu+/Qfb30MgEosm/e7y7ijkG1B26TDNacm0Yw5Oj9vAoLESZaaXvcOczCJhfd8u5IrbUxpcx+BdgMa2t4EA1xI+FSqrvu0Ogf2Pok7WW4AGHWbpQQ6vHRJ/aT1TIjcNDfmibbj0WZcY5WbtrqbNo1rAphEoLUuceruplLKHMCHhRCvBPB9Uso3QNUSAsBxIcT/D8BjQohFAKvl63sAbBq/r5S/r5b/N2H+vRO2dE2iOjyjXKKfTd4hTSZxOKYgkcEkFhQAumt7TF333tk+zlFNYlplyIPlpjktJN0WxOOiqmX5+mdfgy+6ocrQuiZ/ANgaVcY9Qgh8/4uepP/WRco543Fp9I3zyU3XtsZYPLSAfbO9Wj9LM/sZEt+bNuVZKrAxcn+vzzyyBAB4x+3HsD7MsTCoHECtC8m8QD9TrDEAbQqzZ7ZqL+G20fY/EG3HvyikngQfObNekzv1PLVjlBTZZzDOxKT4MqZtcjvbIqYtQ9uUDYU+tJOk0VuO6thaFjK0X9X22pkUm3FNey2p6i/aJvdRn6t+9jP3OVNmJrzFiA2mKUe73HRye2SQsBNBIjU/vvu4egzQ93K2pTDqhG+4QuUlX/TUKpHoSsLRvHRwYTJIdDKJpUmWC7b5NST7b2OXCumvx6Jx9vumbSFZPyZhDMUk2wNU10DPclxcGXmgao+0Z6ank5jN7Vklga3BrL3nalsA5npu+1s+2OfJEHMjW3Dvl3I61hcNhcdHXvOVtfvE16u1V0pRT61saWf3JKmOsK1PYhsmE3fqp++s2ZIrQRJci0zYXP+497EZpJfbagsuE1utfrskVr3PFXC4gkS73DGol6PlHm2rraXPN+FixSu56c4yieYzyJcknxhXvq+Sm072s27CVNfoect1zphzSZjctGFcQ3LTtuCyhUm8kC0wMthrEnXCT0p5VgjxGIBnA3h3+fqzAdxe/n47VM0iAEAIMV9+Jv29EzaH6uDSonyUF7UaFcLqcIx+lmD/XA+FVO6evTRBLwmvSayK2xPrg15tnx6iCfbN9TDMC6xsjfSNZquHILzpg/fhSYcX8JVPuxKAenAJUS0kbRbE5oTwa9/x3NrffIGUYhIdsjlGC4Ym2xleNKx+9jN3jeCoDK73zPYgZdW2Qy12y88JeJAVxuTXS/1M4onSUfXWI8tY2xrr5IOrkHqUK7aRmMNHztSZRFuwF5KhcrXqIFkeADxydqP28PHVJA5ziX6WYJ9hdmC6m7prL3iLJol2lq4pGwpf7LqaInvGWLKEikn0bkozKeY5CKlJBKjnnX+yb7qbWltg+JhERp2gbgpssAbcFgCuQGo7WNsa40x5PX/yoSUA1TyS+QI3UmUkAjf96EtqTcWzJPHWxC3OZPqenK0x6ZP7184k2gKAEJaa3lu/ltuuyYn7JkC2SPvZZNJDTDuAyfnV2yrFp2QoX7MlcPU+2iSBbYn1iQCAXvcnjiZ6y3mSJNW2bAk/f5uUakFevaaVZUFMon8/qcbWHOd6vi0MMmyOhji1OsR1+6t6dGcyOeQ6mTiW7cGevS1R+/G3nrdCeh1R6TOb1z/QLjdNrX4CRWvQoN7XGNfy/VyqnPbEqYVJbAku3e6mduMa3QJjSu6mZFwTrLgrj9ssYw0aIrduMykCqJ2Omq+EotXLP3CNa2hS2F6QOBW5qRDisBDi24UQC0KIVAjxNQC+A8B7hRBfJIR4qhAiEUIcBPAGAO+XUi6Xw/8IwI8LIfaXhjTfDeAPyr+9DcAzhRAvF0LMAPgJAJ+VUt65nf3dLLMGe0qjENcFMhwXmMkSLJTtIc6sD9HPBHqeFgxN6AAwEU5dtylJ3V+yS2+/9VgrkyilxM++/U78xz+4GXcdU1n1cSHRSxIdYNqaWfuyRr5Aioxr7OPC3QyrIJHngFgZd9gXdfTZWSL0uSVTmCzhyU3NDJmvT+LGMNfOsLcdXcbZ9aG2aXfJTUel3JSCwocbQaItKRBiCqOymJPnm1qXACogpcWlMGoSbYsEVYOb1Bow+9xNQzLrttYBofVONokSuyar/OkNEnUmv7mP7Ysfc9+A9jpNX1/SJjST7pFp53m7u2nY9V/NSa7FZ6uZEuOhHYp33n4Mw3GBa/bO4KP3nwZQZYPTJLEmO5pmMof3zOjAElDXkDVINBgwuqdnetVC2VamYEpbbbAZ18gAJt3NbocwiZbFbgArOCHTDmQSJ8sp3MY1+jllk5t6ZKp6HzsmjmzBhp+lmzzfrh5xtW1ZEhdtTJZeJ9Tmrfbg3vXsbquBzFLhXMzT6ydXtmo1gG7jmpB+mk25KfTnumBblIcYB9l8AYKYxMZ9Q8e/m3FN+z4Ck+etjSm1KSfC54T6a21OyS7ywPV80y0wdkBJYqLZloVbumH2SQwdkwrhfJZW0u528zI9NMSABpg0rjlPctNp1SRKAN8H4AiAswB+CcAPSin/DsATAbwDSiJ6G1Sd4ncYY/8nlBnNQwA+AOAXpZTvAAAp5UkALwfwv8vP/SIA377dnd0YkoylYhJtIIaRFghSqpOfJcLbJPSmB87omhf9IElNJtH+EM2SBF/9eVfp132Ny4EqAAKA3/+XBwBUAaCLDjffY4Nr8gfqNYlNcBagXY1rKiYlxbiQ1kWa6omY6L6PprmFr/7FtY80zvW9Hl1SAd7LnnmVNrowmURXn8R+JmpBYpaIRgBmz9j5sqau+jYzSHy07Muoe8t5ghQlrxbYP28Y12i5qa8+wZcRttSxBDqH2nuiBSwIawvJ9qy1bZEWwvbY2PS2Y0LHPyTjOulu6mASPUFbc/9cqD9869vX22pjEkX4QzsUtx89h9leil81FBDUziJNPDXYzIU8UJ+Xib313aNAu7uprS5dSTnbmMTJRZrPyZbQXLSGWvnbHC9DmUROgkuz71bFRfu9Y5Vkevdykl0KCS5tCb8QCaLNpKKNybL1sgsJpFzP7rZgwyp/K0HP5lOrW7V9cEvQA+dy87zp/fOMsbDUIcZBdrYttAVG9f/QBIRNKZMXhXd7NF/Y6gQBfz/N5mkLYfxtz+BWualDheJ6vvkM8c4ntAlfyzqtCfoeWm46CmES1U8h3M/SEOOaiWMZakAzYVxDF2Ubk+gXlE4lSJRSnpRSfoWUcp+Uco+U8llSyt8u//ZmKeUNUsp5KeXVUsp/J6U8ZozdklL+x3LclVLK1zU++z1SyqdJKWellC+SUj7YZR9Pr27h+tf8I/75zuPYHJHctGQSXUHiWCJLqoADUNnQXupmEt9+62N4xRs/ihf/8vtx9/EVfZP4mrnTw7CfJjgw38c3PucaACrjDdhr1ADg+Lkt/fvnHjsHALq/om9BmEv3osk3bnPkYxLdjNTE9jWTyJWblscpc2epyPCHzu3SuiE35QSyRobMFaQDSr4JAN/6/Ov0a2aQaJexqVq/mV6qv8ve2Z4OQmxutkHtJRwPe5LcAkqulxsPSZ8shGoSqTUHAF2X4pObeuVXlsA5xDm0WUdEv7YtSFyyoZCsNd+BcvL6aqvT7DEyrnRqvXJTz8Pe1dybsDXO8TefehSbo7x2vTkXny2LZJuT7XYxygvM9BKtFACAuYHBJDoDN/8izSp3NFyn+zpINB2nOwSktmAvhEm3LNJC5KbNRWvo6ZhYyAey/cAk4+x3N3UrXuj67jtWXG7jmrZgthGABQQpwiobpe/gT4rZmKygFgxMtlMfS8e87DPPct2ntSDROE4VI15/f8hc3gy49aZ9LKllDjIDBBeEJbgPIMWRCkxc/0CATNvyXGw73y7Tv5B+mrbERUiQPlE3LP3JDi03DUwAaa+DnZab6mQ+Jcl5a0ktNw3pE2qcD2eSpOV5D1iku8FBYkobKX9eXEzirscdZRD1Wx+4HxujMLnpKC/QywQWBtVB7mXCK8/48L2nAKiJ6B23HdPBRpYIp67blHYBwKHSKOHwYtVewrYgOV42i7/x0DyOlb/nRYHUCEhdzdydCzvPuM1RXnNENaH7xoSYYugg0b3YtcE0rgEcrEGuAns6tyaTaLOfd+6jySQ6gnRA9ZwEgM+7eq9+bbEMEl21pKNyHwFoltrsRWgz0ggJwNLELotdMYLE9WFeOpkRk+gOUihIBICbfvQl+LXveK7eX58Da5tsheuIB0wutkIZkS6LLVvtcAjbY5dE+RdoXZIrut7Y1iexJUvuajgPAD/2ttvwg3/xafzxRx+y1602r8kWuZ2rtcR2MCpbAy0YiTuqk3XXJPobTDcXhOY4QN0jZF4za9TkdnM3nTzfISy1vSaLzyRSJMWXqQYYYjjcTc26fNt2gMlgAzCfi265aTPYBjowWeWvbfV+zUsrlMmyBW2+a6Tax+r/QQ6gzuPfFmy4y1lojjm1OtTrpH6WVPXNFiaxLQITom4mJgNqEm1zshkgOMdZ7tPwpKTtGvEOsybG2hxHXefNLAuxwWbKEzqX2IIbr7tpC0vdvLd1GctOG9cYLLmtBMaFZpAYZFxjrAnbelsGSXc1k8iQm5rvlwUAEZApj0FiEIhRyguJrVEOIerupjYMy0UyLYwBldX0NVe/9cgyvuRJB/Gsa/fiX+49ZTiXGnLTxtCmrIYWJLRdIezBFwWJz37cPlUzUEiMChWA0D1rW6MVAUxiW5/EiXGe2rYmdJCo3U3DFpKTi2RL9rl0FyT2l857jV21HJRHzqzjx952K37krbdinBcTi2QXk3jk7AZ6qcDhxYFerLYxicMy+QBUkuEf/KrK3t1mpGH223TB5loGAOdKuenhxQE2hjlMtzVfTSIZ1wCK1f76Z1+j/+ZzYPUukm0PqIBAomttVfMcyIBxtPtNeWtr9tlyfbW5m+o+cQH3gGllnyV28yxTSmyDjxX/5MNnAai6P/NcumrN2vqUcWpEQjHKJfqNObmSm6pzbWsw3YlJNJJ3V5eqDkqauOaENlMSW69KiRADGlviQrbW5DYXTl0ZEZUkad+WuQ2Cj3GrEhA2JrE9CWHOWyH3NuCuN/N9P5dEz7d/atyk3DSsJq5RA6mDe/cY1/OtzeDF1TrGfC6fXNnCemn4N9tL3c/SwGQat5ZUlwCYwX0Aa2OtvwuQaTcNh7QpUkBSxuYu6y8TmXTFBtrnctFIkpQ7GnSfNo9Ju9x0cv4BPEyilpue3/m/iYl1WiCTSMea0wLDDEhd7exCjGsmFG3EJLa2wKDMDAWJAb0VgVYm0StGFUL8MSqVhhNSyn/Xvie7G0eXFOOTFxKb4wIzWeoNNuj1fjopN+2nbsvo+06u4ttf8HicWdvCpx5ZqkmWXLpu2j5R9M0bzjbxAGriBoBnXbsXb/3kozi1uqWbOfuklb66GV8gtTF01yR2MeDg1iQ2a7Jc2X9TbkpBWK23k2Xc7/3LA/jTjz8MAPjer3jihNzONfccObuOa/bNIkkE5voZ1oc55gdmLenkmHF5XQHAa172NEgJvPSZVS1qknjqv3x9Ei11REBVk3jV3hmsD8c16ZjvvI3GhV/qZcsioyUAs7FLMsCAxlnH0p4RNs9BCANpk1fuNJMYdN8U1ULFtrCTUip2qSVItyWA8kLiyBk1R372yLK+J2tMYnPxGcBQnG/jmnGukkDzhqJhzmhwD0wyh61uki7jGm06luCqvSpIPL1azSe2OaFtQWiTZIbVJKqfTXav/fqfHAMEsA3JJNsWskAG3EyibV+r+Wfy88z+wdbtJXWXQJ66oPp/CJPoMxPz1iRaardD3DUnFRDV6y64jn+bbDFNEmftPKBULidXtnB8ZVMlydOq1YAt4RcWpNiuSc8YC5MewtoI4WbovPvYSLiGBLKAegZvjieTaSH1frZ2Cm3j7O6m/n1UpIPtvPGCdPV/exJ0Wi0wzEQlz7hG/eSUPFmZREfC25vMb5JFoXJTG5PYFlgC265JvBfKNOY+qCb13wQghTKgSQB8I4Cl9r3Y/aAg8ez6sAx2Eh0kbjnlpnKCSVR9Et3a561xgbl+irlBhrWtvCaZceq6czWx0sVDP0lW6WKktsaqSet1++cAAMeWN3XW3FuTWEinRMNfk1i0GteE3KT08GfLTQsKEu1yO5LHZImYMK7JUveNDdQniaX1UXCT1iNnN3BtaSlOwWHlbmqXZxSyWvh871fciO97Ub1TjM3wJqgm0SEbOreh2NRDCwOsD/Paw8dncDQyGM8mhJjMRoZYmbuYxJCFnSWxHiAt6+6kx63/sl1fbQs0ug5C7ptCVp/TSyfVDPQR7b00J18/fm4Tw7zAM6/dg2Fe4Egpoyajrub3Aqp5rEutU1fQ/W1KEE3m3twvggrc/GyDVZZvLIC+8mmHAQBPunKhfM3OgLdJW23SstBFK723GhcmLauzZvR6+/YmnVT9Y2wOrIC/dszPJJIrqvv6UmNluV31elAPSOZ3U7XU9deKlutfbcseXPoWkYBHbtqyj+Z79di2IFE45v/ymUgtMx45s244CU8m0mjbbWZiwhkAu8fY1iX6Gd0mN20cD4mwJEk9kdB+/AF7Yqwt2PMZDrWN6xqk29jmkAREqCmVXlfscE2ieW1znjdNosIVA9THqJ/mWr2TcQ0F3HQsg2sLyziEzGuKKTCJUsqfot+FEO8E8HVSyg8Zr30pgNe278Xux2PLSpr52NKmlk1qStzDJPZSoRchgHpgZWmCteGkhrkoJPJCBZbz/RRrW+OaTbZL1z0qHTkJ3/aCx+HOx87he79CBQ9W9gWVjIpqF5XkVGXafSYVuech5c4GSwzzotW4JsgUZoJJ5GV/XMY19N80SXTPSUoO+BzZABUAE86uD43JL/HKTU+ubOELbzgAoDJ16XuCe7PdiQv2Zrft7qYu45qVzTHm+ykWZjJsjPJaZtNldkD7atZ+1bdlz2LS/rtgazYckv1syr1M900fnH0SvduavE6k9B9717jcuI6s++dZJDeRGwsAW5DYJm1V+2ifEx46rVx6v+zJh3Dbo+dw30nlzpwk7sVnJa1xMIk7EiQWE8wSJY2cwWzR5m5qZ1LGhgrkhTcewk0/9hIcXlSMoquOa1xIzAXZn9evrfbsfyP7jMAFoeP6b4tKJw2f2hf/Lrmp77r0JRerudJRk2gkuLKUt5CfNOVpn39cC2TuQj6sJrEpSa4+z4VWJtEpC3cZN6njf+2+GXzmEeXCbfbJBSYTMhJdpMzleWt5bjS3F1rLOBEkhiQFGglezSSGyLuZcnen4ZBsLx3oUpOYWhK84XJT+zOgOZaT/NwO6i707tZoE+PK9/lMEJswiQMnk1gEXJNNszo9J4ca1xhMYluPROC81iR+MYCPNV77OIAXMj5j14Ikd8O8wLHlTeUsWZ4sr3FNWjGOgLqoeomw0uhmDcv8QC3IyUl1kCXOIGVc1tkQFgYZfvFbn43986o/nYshIhkVLeTXhmOMTJbIOc79UHTtIxX2msfChM3+3wVa6HSVm7pkws0A7AkH5vBA2YokSxKr+QNhs6xTBYDljVEtS2lKmZo4szbEgfI8/V83HgQA7QZqC/ZIwuOScQL24DK0INqWuVvdGmFhJsNcX7HbhXH+6XzbzsHWeHJBrrflyGICbaYwtuAyrN6vubAGAhaESXNBUr4esCCZkPYFLBCABpMY2CcxJFFiLmatcl8qbWi5Rmz3KNU3v/CJ6hq+/+RquX+JN3EE+Baf7uRKV9iCRNG4lm0ugX4JqH0x0zROoQARcCfu2gNSCyMSwCTSR3KDy4n9NLLh7eOMYTJs8Q9MSkd9NXFJIqzyN6Dep9KGrHGfBsa/E60DwpJUlgV5SJBouU7GLc3VbeMqqbl/DOCrk3IfR1ftPABcvVcxiQ+fWa+15TL3C1DBlwwJUizXFhAWANcdp9vHNdud0LhW5r4xd4UmJZsMMO2zv05Z/bSaybS0k2qetjDG38YA+2XTTtNFh1eCz+vgfCJU8dUEvc9ngugaY9bpuxxpQ/okTtQkBstNi+pnEJN4/lpgfArAzwohZgGg/Pm/AXya8Rm7FuRoCgAPnVnDoBdSkygnFiRZQi0wbJm3MohJE22mcLKsYZkfZEaQUh9HdTYuuBZ2tPghN82Vzf+PvT+NtmTbzsLALyL23qfLzJuZt3vvvr650tN7T3pCEtJTh1os0ckGWbRFQRmQjI1MW1AYYfCQGaYQrqrhYVcVyOAqFwUlMwYYsA1VBgRlG1OgKiMjGdGo19Nr7715sztnNxFRP1bMWE2s5ptxmnsyc88xcpyT++y1Y+1o1ppzft/85g5t24+1jSmRil2XPl4KNSht2DmVzNjxgXl9EuvKziHG4Qes8/Cuu8djmxCfgjv97LNtO4pTvPEoQBITwfbjzQ6n2xbP3zBB4h/8JR/Cf/ndX4P3vXAyHnNCGy3U2QCpAMz8zPdbSquprhY1jlcNTjc7T5I7W5PYpmsSY86P3eyTU4x+tx7lTTss0u+J8yHHi9a/ZFZG65Db1zRIit/La7iPUo6uogWGF9zHEIrRGc/PMbYmvDbU7n70Hc+hrgy1TD6r5HymaWwXjyTuuj6zBg3zbKfzLInJ5GoSo4IrGSSxFDQAmNQ7MegXEKtJzI+bIon+56XHhYEsQ9Eb3ju5T8zPvABN7Pxb0bfoHIOkQN/n1SDHeU6+G9c2oOt96jqVuKumNFVXOCx/PF1yK4X2lGixdeL8iz/z9qEet+/hqZuHawkbpIdBCoMAp5IrQImmGj8fWuEadhlr6un9v8sk5c2Y6Zog40r31pzSjZif0HYFddMSkhgMFh+UZYnNNbueGR801T87NHkP04Lqzcdb/A8//tqkdzYQCbYdcCFlk77DdAuMQLima8vQNnChSOJvBvDVAN6squrTMDWKXwPgiRetAUwQ8MLgzP/s66c4WtbjDVKim7p292RlWmBEMiQ7B8mS3l2ffWAy9EfLJpoNBoyKZEm1MnYPj9TWIUh8tN55TkpKpKLLbFLJbHDBIVwEG3bOLB88j+SGJjRJS88I6HajSqz5+3uePx7/Zh5s+zmhne1avDxshvdOt5MFIbaIiIjF8wOSuGhqfPQdz3nHDI9l62zyhc2T4xGOTKpwW2pgjlcNHm9bnG5bHErbgETAbebaJ5HjPJKYz0jG6T+ljW3qIAMEujEDSYwhZ0wgG0USWXVT6rmxnxOrLRwRgxxKmlgTXn+0RlNXuH20xN2TAye5Uhedz7QgxuXQTVP0w7QseVege6Xp/EA8wE99t6JIThQR4Rw78177mgluyoiUtrbNjnOfG279AabnfxSuSQUpiYB749B9s8cb3tcRwbaMUyOyw99DBBIoITDx85FLCsvxwmstn5ey1FpSosWm7n/Zl1+6dTius8dukBigZvIJlLqpMgCOJdjH3zOHiyHADNo59bnKaysQ3/O7rtCCJ3HdDHtFt5eyqHgs4VcSPAMwSXikahKvqgWGm6hcRNbI5LjBl5Rp5/bf7/hTfw+/7vv/Pj41lKzVlS0dmwAVY0Im788Dzh5wnhYYFN10lf0zHST2ff9Tfd9/FYAPAvg2AB/s+/6r5javv272eNPiPc+fjP8/XDajo54qWt1E6HavvnwjWg8EWHqGK3bzmftrHK8a1HW6Jm4XCUZdS6rvDQHh8apBVQEP1ztvA0qJVOToLnUikCoJp9h6LKYFhvk5pwWGywcPx9l2I+bvIugjr6UKxAGj3HqyWuDm4QL3Hm/H5s5CLYitIaKcevfkYPpHxB1y2XjzfdvmLf4lx/Vo1aDvgXuPNyNtqFSTmMs+TxBBMts9pW2Rvc0iQWLJLQzVTTUCEKHgDVP/CKRqEvOOLrOZtp111FOqie5npo6XSnjcOV6hriu8cGM19l1tqiqZOCqrJl6OcI2slW4SyBwvUdtDOGmp5AoQ3/BTawLbJzFMXDCKhECYKGGSJPraNmC6BvXkGCB2/s1PrcDRblh/Ug59mBRg+t8BEeGUjhH7GN7rBTekkmckaKNqEjvddUvtb0XhmjpRPjO8drioxz3OFa5LKecy6LY2AK4T598cL39O5om7TJ+10rHk79HrraTSAoLuFfbSyHdjUXHX+r7UbmOYUyIBFN5bueTzRdp4z2X865i1nTnvVSWCN/H999F6h3/2aVN68bf+yWfMsarK0uQTfnIO4JuUZtEtMES4RtsC4+Lopua4ff8zAP4BgJ+rqqquKmYW199Ot63nVBw5NYmpIMWoO/pf/z13jw09pkA3FRGTzzxYj7/nqJx5RbxET65B6a+qKtxYLfAwQBLryE0sMvklJJFtmiqmWRTks2TDYXnrQhsTJCG8BiG17+VbNngrLSJGubXGneMV7j3ejIGFIJCxMRIkCt00tBySqK1JnEMtExNZ/uPhfL/2cDNpG5BEIDM1iSlkKTfNON2UVzcVx4ChtprjzXe2QiSFcQjDcUwW331fzvo+X2/sot8pi9XJAoZuKoj4izftc3PjcBF10IBykX4si39e2zk1if/N7/46/Nj3fuv4t5SUPEP3igvX+Eknf0x8TShSWxO0UcaxA6Y1YGrhJvmduJen4i6FOSYpaekWGHKs9PqTu25+UqDry8kmIBakMEG6H5CacWUkN03t043j6r0LyZzM+c/VJK4W9bgmTJHE2BzLCQ9tbWGU3UEmJcMliKV3x79bflwUSez1iSPz/9JaHqtJZOqUI89oYS1J0StT95alm14RkljpgkRXYyC1/gAYe4MCwA/99OvD+83/YzT5rvCsAY5/oaabinDNzo6jWmBcEN20qqpXqqr6y1VVvQZgB2Dr/Hvi7WzT4vbRamxXcLjiahLFmb9zbE70oqmxXNTRwNKlm0ofr88+WNu+eREHQY6/LMDTMZ61GxDeOFzg4ZlRU5XPii3+LLKREk65iH5v8p6jlZJuOlAirGMdCNcEc3SFJlwkMUU3PVw2uHuywmuPNl6GLBWkj0jicTxIjBWx29rOPN00PI3GASJQs5Tj2lRjsuL1R5sxQLc1idNrYBTP4seK3VsUbaieUlao7xbQjRhlu9g8KWdrvE/cOXIOIZBCElPJFb4m0d3IY/ckiyTG6aabMdnxwg0bJD5/Y1UWrkkFwIm+necxl5q/WtQesiFBQyzBVaKAxmvi/KSTa0nFaaLdhpmTfW3O/S+/q4Vrgs9jxzFzLN0nub0jldzKCkAESQHmGQXm1yQCPt3UOOT5cVU17ZXLIYmhuEt5vZOPTK0LWeGayD0iLTBWTT2W6sgeIp8XC9qoQCpIdpTGpcTEgPw1CK81QCYlw/t/FAXLDosm78r3ceq5ydPko3vAzGRy2+Vp0zG6u8y5rqZjBWm77BYYsd6FDOjgJmoWdTWpYxfbOAv1Jx26qfxMtypTXG+2BUYoXHNBLTA0KOCfArAB8E0AHgL4EgB/FcC/rviMa2l93+PxtsXRyjZF/oK33RxpS3l1U/OeH/x9X49/8G9/EwBgWVfRwNKtN5M6wc8+tEhikrYVEchxLS2SYOmAJweLgW7q98BL0UZzsvVAJNNEqjSmnMKf+OzDcTOQOR0o6aaSkUuhljJHeUBfchCRXLExYJIIh8sGL908wGfurz2KVKq2U2jKyd6RkSJ2udey9OLYxtaXFUBTtSVG8bYe62Q/93A90k3lnKSSHqnFLqRxAny2eyKbTny3kO7FKHmauegFIOQr98E4Fkl0j8dQMt335cwNCmL3JCOkEcs+A75KrziEq0WNmweLtBPT+99heiy+uTFru7bDKtG7M1mTVQjc6hSSmEnopFDSYkAapfMzKJ35OQeBnEsJ1NYkJoXZCntOU9fRIKXk8IZJAWHWlCwMAPpC7Zc5lvmpFfOJ9crtiCAxhbblDldV1YSmCkhNej4AiJcbDPf/osaLN6Z001htJ4CiwNdElMd5PTnHSALCnhNlINWxdGv7f0lKUv0VI0mSvJBJPHnd9oWEXwQ8oER5In6C8a0yYxIJiFzPz+UgJHMZNrIHZG1RIoluDWYOSVw7gpeffWDq9MW3j9Hk2d6d3jxHJLFENw1bYPRkTeLF0U2/CsC/1vf9PwLQ933/wwB+C4Dfq/iMa2nb1vQvPFo24/L1pe+5O1JJ88I15j23j1d4aVC/XDR1gsMvTkU1oodt14+oYoq2lVMbNeOmympmnIMkHgjd1H5WLNNRUjpL91rK19LlnN1/+qkH+Mb/4O/i//x3f8LMYXiPugXGkP2RRSnMUm2DOT7vICJHyya5GAPA2c7QTV+6dYDPPDjzKFKxwMbMJy9KcpHCNT041Cx2/sdWKcPi1vWWNpRrXdJlNqmmmt6PTCY5ViPS9/lF1XymP0+a/hM6yR2RtU44JCyS6F6DNqiTDW0MbIhnwK2dyrEEcg5vChV/vNmN98fLwzonNRupnqvM/X/hdNOMU2JVWP1zWQrcUkhKqb9fsk680AMVmAYArHDKtCax7OzGHevssAjaw9HYgEhQVNhzUpl8N+GZGgfY563UV04sTLB0/Tx1WbomUUk/jI1jklsybhpsFERJEvvGpjXO6LKp8YGXbgAwyt9iKUpmMeExI3ERr+UlkpKJQKp0wUMEkj7/Vfz8M0ImsYCjWDowGQPiu+kp0Kl619wzumjita7ntf/wb/1zfODf/q+xbbsxme+CAKx4orx/0aT7KwqSePvYonHye0wVmBKuCe/l0XEqOUGSvdbWJF4cktjC0EwB4F5VVS8CeATgHYrPuJYm7S+OVgv8e7/yo/iOL30nvuy9d2yfREULDACDumks8zZFEgGbeUg9aJs2XfsFOHWCwSFbh8Zw8zCBJKbQhgKyMS1Qthmb7LjIeXntocnA/LUf/nlvDgeLGlWlaIExbHapHjwhtc/9jh99x3OFmsQWR8sGL908xBuPtzjbWuc+rIcTY8R8Ug5TSUksVv9FOSQxx3XIsEv/RsBKmY8BdwGpDi0a7M1sbqxRdxxp/MPramfXmX/pWK3SIYxt9sU+iYpaXndji230o9poISMfu0fcdePzXr4JwG8blKIo5Y53GcI1MTExMctm8F/fdV0+cEs4yWPSLyZck0jcFdVNI2uQribRvkbdk/V0jPt5ueO5p6Sj0P743sHsOdHkViGYCpMCXVdGBGVciBLxQbp9jVm3qpgjTyScUuqmVAAWCTZK92Tfx1gvw/3fVPi2j70CwF+n5iCCcjz/u5mfWropxQqJ7lPEeZTjhUnJ/LCJmjAw1EQTyY5YYj4buAXnEeDrlCclH4U5xlgygPUtYpZqbXNe+wv/4GcAAP/jz9wb15q6StdNxswNis36E/dB14Mf+M47R+Nrd4byohhzyyZq08cO+7taddPSTRkI11xQC4w8zujb/wfALwXwlwH8PwH8AIBTAD+k+IxraadD8enRssGH3nYL3/cdHwMA1JW5SAzd1LVVEkm0zZffdusQ73/xBD/x2UcjqpjcRNsOy+wDan6GWRsXSTxZLfDp+2dY1PXo+MdEKkq00VQge56axPtnJvfwyTdPvc+u6wrLpk4G6aFZJFHQF/9Ycv7d7/Y7vuGDeOX2kck0JTJ2fd+bthDLZizQH/nntU9jcJHUsSYrg65OawaGvxWClJhjwSz+cSSxx8Gyxs1DuxxMahIj16DNbBwxtFP+W6INzXOahjkN55N2dicOSdnZitUfMQ7hIrJJhWJK0zGKmkSHJpVDEks1iSkETOb/4VduTf6eqn/JHc/cjxebSd51aSVom0nWIYnpwDnNnnATd+6fi30SZQ0KnN0ySo3heH5wyTjkbhKO9dlCYR4KScwgIu7fY3OMIc5uX9CYRZFEJkgM9kXN+qNFgGPPAEOvryoEcxzTYtlxMQocq67Z9j1q5/PlvjlY1HjX3WP82d/8ZfjoK8/ZcUECgmV3TFE6GVeeo3otd5I51biu96gLzrU9njknY0Ba8MmjfRmJIB2IC9dkWSHOPTkqciN/PoBEW5YCCp96tttMAi7VBeC89pFXnsMn3zzD3/vxz43Jc1EpBfiEq5yzlBAlYAGkd905xo984j4AiyTGkluukE7K6nCe5xGuuWIk8TcC+LvD778LwA8C+BEAv17xGdfSLJLonw6BqBm6qWuLukbXTx8Yl25aVRV+9Ze9CwBw77HR/kk9aLu2rG4KxOleMu6lWwf4+XtnXtuCGLJk4fBSsOe/XhSpyDi79x4bisobw3kYi42ragi4Oc9F1L5S6FdMIOH3fcvn49d/xbsBRGD+wTZth77HWJMImIDWPY/uvHPHcy0aEHXsxjZ1yLWomZjUJLpB4qhumkGycs51Tt209N2mQWLZaQrpXkxAauY5o45FAtJgHHP+Af9cssg9s5ka52aYY52uCc07FolaOqduT4Rrvvhdt+08I4FUCSW9DCRxm1kr0xn5Mm0xKtyR+X6xGjX5P+MQTvskJocAsPdrKKbBIFL+HPnkylTJMz/HVCa/lGBMIQ4lumnoXLtJlJzVVVhvPE+5lT0nE7QHZUc+pEla1Kw8z5jiYg5JbxIlB2FpxDd+6OWx5EbmGOt3SNXtqRFBTObIJiXdY8g45v4H7J7PKrdGhWsKyY4Unb9UJ9jE1gQycRSj4DKofVSZP/VcN+ng6zwmh/vMg7WXGEq1QIqZJ1yTEVgTJPG9L9j2ea6eQ7oLAJHwCIPEklLppE9ie7U1iX3f3+v7/vXh99O+77+37/s/0Pf9J9nPuK72eGMi76Pl9GQ1EXoAYDaOHN0UmDp2YXuDb/6ClwAAP/rz98djmc/2P2/bpSlUQK5O0D4gH33Hc3i43uFffPbhuOg3EUeyVLcUIjbhuDk1iRIcAua8uo7zskkH6aGJbHGqUWuu+bU7x3ChE2rpwaLGK4P67c+8/tij9gGYUDRKAUCsiJ0JUqJtInpOXTO22AkFzqObTtRNp45F1+sCgPG/uSCxmt7/8nrOQoSbzloH81Q1BZ+Myw+MbfYhBTq0ZcJBi5mrbhoTxCjRP4ECkug8Nz/0Pd+MP//bviIYN50PkL52sfXnvJZK3AGRDO1gpcCtruN0u92Y9IvTTYG400Spm4ZIYnLEcLwkup0fF6J0fHJlWrfHjAGme0dJXdM8oxEmQ8HhDdcuU8OVnaKZZ/AMMEImMbopg65WVWRtJRz5MLhnAikgjsqWkMTUHlCqnw/3t76wHoxzrMJ7kksuuu8Nf0+Oi/gzc+jd475dOl40cMhT0IF4oqQtrCVhICvz1CeOygmnEbWM+JM54GB7wUwSAHgwMNMerXfoOrsvp1ogxcxtL7LICOxIXe6H327ZNePxIsyt1kE2UzbxL9RIYmfHMS0wLgpJrKpqWVXVv1tV1U9WVXVWVdVPDP+P6/s/QXY2IonTE2oc8rQIzWoxPYXLBPri0k0B4AMv3sCv+pJ34H//az4GIJ4NA/K8biCPZEm9zBe+4zkAZjORz6ojjmQpq5tTSDNzKdUDTc/lvVNb7H627Typfg0lQbI/qfM/StYn5phSl5X743DZ4B0D9/zn3jgdERW7QCYC58zxYkqe7mdGxyUCMCaLHBXgGO6vY0eZ7kgUd1OOtYP2po41WVcJCmicgsvXJMp5YTPrk6bglGMnDrluHDDd7C9a3dSto0jTTdOfEUMgzfF9ufUXbhz4cvexTHdhQ2wSzv95LKcEHaP7AkTglghu2q5DVcWvXaqd0RwkkauJs58/jiMDt5i4CJMo2XnHIhzrzPkvCgdF7v+icEfw7LDCNUbx2DkOgyQmUBsOSdQ55ECGJl+iO8YCgD5fbybfLVyDrBJ3Yn+boM3mJ5PwiLI7MiNTiXJ3/tFjRRN+c+jd5b1N5hnz03JIrnxuVBQmm3CdJo5YVHwCHpCoZQyoSLd3ungmCQA8WBvQ4eHZziCg456YRxL/3b/2o/h1f/rv4/FmNwIOZlx6nvIMvM9BEsWaJs7Uy4nWyPHkvQDAt8AIhGvYFhgXWJP4JwB8OYDvAvDTAN4D4A8DuAXgdys+59rZ6cZc6KNIq4IU1cVm0aZPnCyaIZLl0k0BsxD97371F49/T/XpK2aMQnh6MGmSDpiA1H6ndJ9EG6Dls91qamXG2b33yCKJ98+2Hm972dRjgXzJRKWtGemmIZKYV2BNqcvKQrBa1Lh1uMStwwXun+3wyu1D77sl24kkFuVYETuFJEYpSlxNXBxJNCiR6yhYJDGuFFust4xmyM3PkpDAVFiBQwSBGfSfCZJS3kRlXJh9ZsaF36+sbho//zEzWVN7nFSNYJ5+NV1HgHky+Yxq30WXpKTqxIFMwqP03Rw0190itplx8ebefZkiGRmnQbIm9yShChyiX+bz8uPcYLauK0pdWcbNUdeMrlt9nrYVJgVY4RqX7iXB2KXVhEbo3RyS5SeprHAKE1zqktCp5Eroz4QWa5Mic8jP0WflaPok+s8Nl3A1x/CPxyYF7H7jv547Xix5V0peJMdlg3v7PjEO8Y8JbhVqEhPMiTySyLPENPZwQBKNUKOLCKYTCQDwn/73PwUA+Hv/4jWfblpnhGsG3zAFMEWRxGLdqjxvwwt0C4xAuKbvObppnQ8DNUHidwD4WN/3rw3//6dVVf3/APwwnvAgUSDjGCoYywYAeaqFIIWh4EqJnmEXrBiSkp5/CurfdR0OBgrtalHj+aERvEUS030S8z1xYj3YynL3QNwBfeOxRRLvn269AERFNx0ebHm4Q8faIom8Y+f+X8a9884x/udP3sfbnzOoYlr+2dBI0kjKdFG1CqCpb5lGEufUQ5h5TpMQUp/b1NVAiUopxaYC7njGGshnkmNOZI8ylXMRLKzyESWXsAoCFeY8AuLc+eNYJNE9L7SacGKTevPxFvfPtnjX3WOvbqSpq4ngFkNJSyWO+oJDnkoclVTcGKU51oSqXmYKhOtCvil1blyp3YZ72UrPjDuu9e6tnpKtB6aBAxPchPWPxspriZmnEe7gkyTTfYoR7ki17mFqEvXCNcO8ehEl4YJtMycfkSodLlx/wjmk5xhXZS4il5E9oETbbSTpndoXc3TTGJJIBERugn1ctxgEXkmdHgOpkJKZnWGkvKErB7JAPEg3yTTinGivW/SccAGwNrjPCRqmvtvRqhlLeS7SRrqpIILD4VPCZaE9XO+8BE8OSZQgcdXU+H/97l/kXY/UnlhKCFjkPnRmtMI1bfmGBIAmTwbVCNekjkbM4nrbNhM8pJDETUAddU2USFNBSpLDn6A7GvpDev5p9St/Uxz7mzk1ienC2gKSWAikJmMSlBXAPtTAgCQ6VEY13bSuxu+XpH8WgvTYJur+/b0vHAOwAh6pcSVBjJSSJ1Di/8dpqkz2P5rwiNRE3DiwFITYM1CiF8cy5FSQkjgnxe8WLKxMuw1gGqgzyoJArJk1Ny4UjmgLiYvlWN8c36S+4T/4O/jaP/GD5rO6YGNLIYmFBFAqcZTt3ZmkQ5Wc/4tzEkrIRqrnJ0sBjTaKziDp4ZjxmcmJhESSaT14ZK/1EBgS2Qho0yCOFyI3XQ/KIYknGMv3STK5VVhL3DkyDhoQZyWwSK57i7AIZKwNklaVmVVzjtUOd5n7GMhTCd2/x8bFgg0mAIspt2aTixHfiaFOx8QCNfRuGcYG6dHawraMcMdoyW3huo1iVu456cqJoymVeXidedaCxzSXyDleLUY9kIs08ScfngUt3zKUZPe1MUh0EMhUuYcEiQfLGp/38k188CXL2GvqaX/XEvor4wDHL+lZumkoXEPWJBboppog8S8C+GtVVX1LVVVfUFXVtwL4L4bXn2jLChAk0BfbIysSWI50U3/cJkNRBazzFkNSGF59SVnq+RsmY3C4sOpLWtqojEsHDanvZmoZUw+o0Bvvn+7G99S1yCTHH9DQZHNN1RaONaGJOaabgss5MRfo2z5mWoPeGNRAYwXiAIY+lfnrluyTmE0KRBwLgpKWzMg7tORXh0Xuaz74gjcuWd+WOGQc7Rw27UJNXPjdTOBGLqwiJCCCYEp0tSfGAFNEkKljkXl2scAh89wAcRGGtuvx+tC8etd2HpoZpe0SlLTZimyJeySbfa6ntOnzmAScaaZGvCalmMyR5zuS9NO0/KHW1ghtlOptFtk7WNSgC+5jeT1/PP++ZJMrKaQ6GzgnGRB5dCNMCpRq78RioiTs+Q8TTlTibpLMYejFetQsdTxauCaRdMqtXV7SYpx7OeAORZFK42KlIjZwKz/bHgJP1ISGzzdD5Ze/971/vBKVH0ggwF0BSRwDWf/5pu6tCLsgN0U5/5Ne0W16bT1ZNXi8aaN/m2tn23b0sx+uW1/dNCMCd89hsz2SfuIEkihsnYMmQjet60TpWP78T/r5juqmSuEatibx7V+cn0/5E0b7/QC+B8B/DOAVAJ8A8P8A8L2Kz7iWlqtVS9ZxCQIQQxIl+x9kyYt00whlCChvGjmapHtDfuKe6UP45e+7AyAeAMu5KNEYJoFUQe5e/pZqyn73ZIVP3DvFm6dBTeJCjySmgm2LJOocklDK/1s+8jL+k//ll+HjH3h+HAP4GTvzvQq1NglEECDQthl1LLHrJvOUe/YHvusrUcHn2MfUvSwlOH4vh7VOgAJJjCCQLI9f7l+Nuqm3IXZ8TWIo3MGMC9cTuWdTTkmujuKff+bB+PtnHqynxfaTZJOxEithipjlkytAugYyHxCl60PEcmql0/em12Rg2jdPrJSRTyOJXZZqByCREMgpEto5iRlEKjnEjIvQvbjgJoHalNaSYM8pSeS742KIbBlxnt4nxRYAQVKARRLDgE+jdulft3JwXyWeG4Ze7Lc7Ia9b5FyWrl0uSQLkdAimVEcz9/IcPZSUKMGI3f+q2sKQFULsG3IM89P/vJS5weXCYTwx6FIs6coIN2kTR7E6faDMbgLKPqhrx6sFfub1x/nJKO3R2qCIh8t6VDeVueX20jcmQSIcJDHdhm29M0HuwTJSqlZHzkef1xcBIueSVTed2wLjPV+Z/XM2SKyq6huDl/7O8K+C9Tm+BsDfLs/k+ppFBeM1ifGiednwp58nTk0Y3PB0U10AkOrvF9bnfOPnv4Sf+OxP4mtefXGYe1xZDShnu5PUEyXaIPOWINETrqkrrBQ1iW1vzsWkz8xgWyKTH1MADRGAqqrwzR9+2flecvyI86k8HwwC5kry+01yyxtNqd/Y3ZMpPz2sEZExAO8gABxtKC7mw6ubdsGmzTi7YT0KLcChdMiBaWKGba4eu24PHZr2z9879c5TTBSGSUCkaKNA+dmOq4bmxtTZIPET907x1X/8b+NP/KtfNPaUzZltMZRANsb11a136um2FDFhsBiTBHASRxFHPssSSDq7OmRDjk3d/wGSbuZYQBsC5F5D5Ywh3FkBmqYa+5G5xtJNx5rEAvI4zjFAcxn0xfaptK9RQXrkeQM9zhsyzCM7bIISAXm0B5gm4MTkOUqd06lyLj/H+DgiUT4JiLj7OKRc08Glk0gwc8yPcxNO4nyX1kkZp6XJxxV3y89AmLigkruJRFquDvj4EpBEQRGfPznAJ+6dYuPUm4c1yq697ognPly3XgIqVxaxcWoSQ4vtb4a1lf8OU7qptgWGo25aEKVhrPQJfybx+rgmDb+//9wzeQtt16aRxJhCEWAXsdiDM2asg+zDNnMcIB3slTapmEMin+M+oH/gl3wIv/3rP4AbB4txXApJLGWoUgFATs1wkXAKd12P54f6vvun2zHbVVW6mkQjXBN3mMz/5bvl5jjlkY/npOB8aqX1s+IuhCMpohEyjsl2x6nTaQEOwCDjU3px580lNsfJfezMI2Xx4JKn/8g8+ZrESAuAsh+JJkBXmfMfO16pR1ZKWRCw9RAA8PNvngU1ibFaJ/OzKFzTh+tWOblixvmvuVnc+Jg8kvgTn30IAPgv/sdPUEHiroQkjnQjZ47D4RkKaIxiX1oTJvcWysE2EDq7evobQFLLQiR9zK5kh01qLruujPbLPCdIYiFIqRN7cIk+GqMEMqD0WF840k3LVNpU43IqaAufGwLtqaoUSqcPNkrBVEwABUBRcCX0E6xKaeGerFLjMmMi6ySDwFv2ln2NqUkMkXQbSOWPF1KZ5TOoIDHi35USfuGx2MSRmm4aOY9mbPqePDlYqILEXdvhB37oZ/Grv+xdSaBF9oDnjpb4xL1TPDjb2T0xAcIAGMs2gCnddNFUOEso7G92Rk07tg7FFd750oFxzWNbYITCNV3L1SQWLBsk9n3/vnMf4QmwEWGKBYkJ9Cu34aeQxLG3YkE4JRaAZbPPSSTRd0CXTT0GYwAmUtPuZ2gFV86LJB4tazR1ZfokOtmnRVPjEbmQWLpp/DyW6DFAnA5VRs3i57+o2hejVg4/WeRYJPkZhzDXAmNOvZn8LTXHZE1cYbOZCt4QTlqQmGEConGewfHYmsRQXIStSdQgibnsp1BdAOBnX3/sZcBjTgVDSYshkPZa54RrpkEpdV9F6M9zrVRzHENE2KQYMH2+c8mVmLopg4iMddEBksje/2pq2QzUwDueE4DFmDihRe+vEpKYW7cy8wxrEmm0MwyAGURq+LO6/isatHG1bTsPETc/S98uRgsv0a1TNG2tMJtmjuF5lNdzFq4nPZFcGa+1khVi6aYyhguAY6wj5r5M7cH59dWfm8yXqe8PEXGgxECxc3ItV75hkEReuOb/+9Nv4A/95R/Be+6e4GtefSH6HtkDbh8bMZY3T7eOL5neS4WmumwqPNxMhWtSycz1rsv683Oebdlnxz2AbYER0k27HbA4SL+fNI1wzVNrgiTGNrkU+uXWzU3GJBQJ2ZrEeGax7MTEgqJSrc1c4ZqpI1muW0r1mxFn62jZ4HTbeovfqqmw3XFIokhCl9TYcuckFtzQbQrC8084yX0fNmUvZ4RjyQS21iCF5OadhHRNYlrdMV5bCDBByjRwZpxdwK8jKh1Lxvkb4uXWJIbPXIkyVFXVRKRCzKXg/fhnHnrPTYw2yiQgYggkU5M4z4nJt8DQxo8j3TTSygiIZ5KZ9S5Vy5KjScacQVbdcRK4gb//tTVxde3vN3b9yR8v3HNKcvzjPCP3V/k+Se/BXBN4W6dM9UkMvpsJNvJj4kkBErWJJIWZpIB/3fx5ZMdF9jfGv5j0yi0hwHU8SJzF7gD3DPjqvlzjePcY4++lcUFQpAmA3XHye064CYgn5kv9RaNtWcCh1LHzn9839GvkycEC27aftGpK2eOtCX7ePN0m3yN78p1jUzZz3wkSU4wvwA0uVxHhmnRN4mbXpfebFGpfopuG142mmwZ9ErvdhSCJ+yARLk1pejPn6riA+KI8NsCOCNdUVTrYqIKFR6zrkF19UmqeRbpjbOHJfK9xXIQSGIq7xMflkazDZW2DxOE7LZualsnvhs/JBW1AnhKbC5xT2b7U4tN2HSeIEWQx3c+Mjos6hPrG8XaeMxqlE4HzlDbNBcBzJOGbIEvIBKRmLnrHWubp1rYxGUI7zh6v9IzKmDiSaI7/jttH+BeffTg4pdU4JtkCo0BRSqr75pIrMYSigBClqPxip4NTwAaLlhbLt7hhGRBAZD3JqJvGpfX9v6UsrMvV1FaF9UcUtS+y/hTRhuD7lajF3vEi91deuCZOEet6UpVTiSRWwXrOInsyJzEKSYwyJ8rXLeXIU4nCyLpQQnLdY4gxz3eUElvydYfkqR1nfpZbN8QSp+R1c10M4rqFa4KmvMEcj08UyripfkSZqQRM27IwVFovcU3oJAgDIhRdbLt0y43jQRyPRRMlKfpwnQ4SJdh7bkAS7z22QWIqkHXH3TlejkEihyS2OFjEA7Hks0bcx4Cz36hrEp0+iRdQk7gPEmFVSGMIX+gMio100wySGA7btnlKTkogASjXcblzEmOc/9jCD+iFaxhHclFX0YyMSEAfLhucbVsvQ6ZpgSGbVipoY4QjYnSoEgJpM8/Tcfr6I2KOkax1z2StI8EGI9yxqKfiQaUeWfG61eFvhSBxSpPk+k+Z94pjx23aYS1LR6AGcryw4TmFJAbPTqlNCpBG3M6GIOqj77iFf/GZh94GFK0JJbLdMeEmNpDS9kmMofauifPQg3v+LVMjfsxYn8RSn0ogvS7nnNAU2g9wiIgveKNXGwX4FgwxtJNFYMYArBAwjOMSdMd8kDKVkmfGhU5hCXkMx42JfCL7Lx+rrkmsp/sNe936yL3FJAUmDBtauEbvX0QRqUICoq4SCDzz3ISJ0/yQKOLPrOW2Bk+SJP7r6eNN9/ySuq+Mm/TdLq6vmByL/W5anyQ2DjB7TmqOJysTwLDlRFJe4fbVDk3O0e0jEyR+5sEZTla25RswTfYBwEbGHa+scI3spU1auCbH1gt1CwCWbhrsN2wLDOl32A5B9AUJ1+yDRORr1WKCDEAeSQlpLmKlzSZPN02Pi9EKAEOjvYzawvM4kqnNftE4QaKz+SybmqYjSDbbOgfB33tmjungMhVIhdQT73vlzmMki2mzpslh0UbdTNZa6K1hFhOYRyWUv8XnmBHlyczRIBv+az0IlHR85qxD6L5eGucqGfK1hQ6SmMmYTscFDkIpSEwgboIkvvf5EzzetKZIf7hFsz04S9n/VAJIicAzCEWuJvHRWqd8J+co3Sdx6uzaBveZ71ZNxwH5ICWF9gN6uh1z/8fqJlkELDbHsnCNHwQzfVqBTDJBuf4w42J9Ei+tJjGSKOTWZH3LKzPHON209O1iqEjXl4M9IF6+UfYTnDkOP5lgbw5NNQxSWCQXiCDw+Sk6TAEZwwWyYQK75Fu442JIIkO3trTpnqvTrKclGPJ6zkK6LyCU2Pj7pc3WqRJJzAaJnSCChm7a9YbWCnBI4u0jiySOtYwZJDHX47Kp4olFNuE37jc0kjgEiZ0EiXu66YWZ1CTGFrxFAUmM002nC4/8Pw/Zm58xZ2uuummx3UO4YTP9DjOOZIn+k6qJa4aaxLNt59ckLjQtMARJtP93jaGtpPp4yfxjFtto3O+VPFaubqmAtgExmmRyiBkXBEQyR/czY7Zo9DWJstH0weYLlGviYr0jmcw64COCMo+chc61prbQzewaR54b594nJUqyjIk9N5JZff6G2RBfe7QZqS+5mtDc94sFexSVfAZCUUeQbddON1q6KVfzra1JTAnXSMud3BgfEeQ8+bqahwhOjkeO8wWYhn2NRUScAIx9biYquCXaYh3P5DN0R8BPHLFoJxCyC7hgUa45QgAA3VBJREFUQ7smx9AXVhU1rDVz55EcF1lLaMZLJHmaP/++k8yUG8gcfYqkjMsOiyr1aoM2gFQ3DfqZ0gFwgGbZ/Tc/bhFpxVZUlw2+Gx9sY4LIAhzdN0Y3Taubmr2KTQYySKKwzoRuao5j1fxlTpNxQ8L1uaOlKXly1vZUuQeQX4NitdSadkbjWs6qm45Ioqib7rg+iQXbB4kw6qaLOt7QOgbzAzaDFAuKxox1Gy7+ZV53+IDKOG3QAOQl2mXuKbqpNrPL1vul6jsN3bTG6ab1guJFzbfAaIdsduo8slLasdpCmX/qe8n3iH2v5LGiFDH/b6k5Avr6iyZGtxvRF11wX2oMHquHwLBpF4P0WPaNUGRz58VQi91xruDNnNpCJmst40Ikq4gkpoLEre0JBQCffbAes7OpfpNAuSlySvFYS11nkls5uukjoZsqg8RU0C1rk4/kltVNk0m/zPMdYwmwTlp4Lqn7P0A2zO9ldHtKNzU/S3fyRLiGDsCm6zITpMTYXm0BvQyf7VxQ788xtibkx9j1Trcmp4S6tG0iaLpjbM8vBntp/6LILogGG9kpRurEh3HEHhAmXBkhHyCgZHZ8UsBF6dzXk8cLzqVlN5WRxFiilmkBI8Pm1q3SFPTYvZwJog6XUpPIBonlmsSQbgpgpJumBB4Bqxdy43BhfNCuHxlbOSQxJ94Uq6VmGBcTf5JVNxVqaTu089jTTS/OhO4YsxzVBYjThGNZXfk/k2maFrLnN6nUzX8eQZLc4p+iPgDIB6X1tAchYB7Qph7oprvWE4TQ1CSazKZzrCSSUpijFkmMbDQyjsnQansS5ahsOYuNKwV7Ms8UkpgW8xneFyCJ2hpBYAjAyAa07egQlpMdQGzTptokTqiSjCKhzCcM0ss1iVMkFzCbZlNXuHNiN8SjpdRfTNcDJgCIbWxSs11KOMXWkvPQTcV5ONtxToSsE6mERy5JwiCJsV6hyT6hMZYAys+2GRtJQBTFPszPWcIpsUCWfm7M/0tKi+O4xJ5Tuk9iSKK73sfHDQJy0hbHcfxKcwTsmsqdR/MzXJOZID18BChVziqOtjH+hTaZmWodYIKU/BzD+1FeL84x8twwqGDYA7WY7Igmc/iaRJtIADfH2j8eiyTG/MJizXdwT3bks50Ss2IC4Jh4WWrcwSLeKs61n37tEf7JJ+8DsEFiFknsrEqpmCCJKZVqwNQkLpt6bMvhIqBNnfZBc2yxWC01W6fszZOlmzbDd+72NYkXbtu2SwrKpOrocsI1qUJvZrOpovSTQmATySIzgiQh99x8BhGkZJHEXFAUd3YtktjgdBMI1ywqbEgkcdfZGsxYCwA2AJtTfwdMM+RtV6gJjWUxyUDWnRfA1ySGxxOqdakFQEi5Lp2TeI0U35R6EoCx321YzO19zAal5v894djJ8cJgg0FSwvuLUzed3luAKKvVuHFgg0RRjGuqary2YgwlLbbecYEUpghkX6Co1tMaWddEuObhmqtZ2ZF002hNIoGkTGuV08FbnH7o/y1lE5SIuP/DgEjGlZ3PKSPBzKE8DnAc0ILDKpYUrikwJ6KJ2gLdtA7myD6jMhUNlXZURHWmyQUbEbSBSqbF9w3GAQ393RwlELAJxFiJiQ5J5O4tEwDPCS6nCVc2uJ+qAufnGO7BmjkCTjJzpPKXFa61yZUYIg5wwbZ7KA3dN3SVc8hZqp+42CffPMXXfd/fwa/7/r8PAFgPQm25/UDWv+NVM7amuDEEiXVk/Rfbtqbf4fFqga43iUlf3TQ+x9waG++BSjC+wr2DDhIjdNN9TeLFWE6hKFdHB6TbZgCxxZ+jrWiLXcPN0P09r1Ian6P5zMKmPdloONpWDOoXWuzRssF613m1Dqumnji7KXM319iiyjhpMVVOK2zES+vLOGoRj2ykuesdQ6oZBb4YclkK9uRvYSatVJMYnSMUQZsSEQkD4I74XsDUSWDOIzD0jgwccorKNgNJjPWpBExm1QSJNlsoSGIsAcRQ0mLJldL9D8SDy1wjZSCeEHDt8VCr8pisWWFbYPg1oeXvlmomnq9HmR5rLtrT9wwiYufkHk8r9mHnWx7nvt+svYVJQqijOmc3tQeX2m6M9GIlJVbeMypXKpJUYQ02F7Tp9nsgxubhkmIxMY1Sn9w5wk3A9Ltp7i0vuCQS1zLPsL8onVx0nzeiJjEUKtIEwO7xGDV5IFWGUd633bmpgu3YusWo0ip83lKQ+A9/6g0Apo0FAJwNSOL9rLqpLTk4HILEULgmlpTctR2Wgw8KAI/Wu/G+XzTpmsQ8kqi/Zu489S0wpE/iXrjmwm3XdVhkFPFiNYl24YoEiQkOP5VFqGOS2ByS6D7YbOP4KYd8+Fv2ePGNxp1LdFydrklc1vVYk+jSNJdNja6PUwRinyPjYtQHxkmLtekYA6mUSEvC2WXovuG48wnXcItPDEnJ1iRGiuaF/pU6Zqq5N+P8AD4qRSGQwTkZ6aba4JI4j8CQyQ+RRGJcmChhaxJjyZX1tsPBosHNQydIHJDElIohUE5AJJHEUp/EyPGyTkyE/uma1CQ+ItXvxNlYLdIJP8BH29j+rkAcSdHc/8yzDUzrj5jkSqwO2wQp5WP1vZ1bP76eHTZ93joySTIJboiauNS+Ubq/ImsCVzcZrgmM82/nJEaJVASomRnH0oT9a+3OPXm82LpQYDOkNQ/SdGtgmvC2vkV2ihEE0vzkVDnD858/1vz+ovLe3vvJBqXy/nEfJfaA6Pkn1q2xTl8RyIZJKoBMbkV8rtQcJUjcJKicP/nZRwCA24MIzYgknqVrEjcjM6oer8Ux2QJjMdBNzTF24/tzNYmluvRZQFG434wtMAoBX1WZQFFaYOz7JF6cmf6F6YxwriYx9gCkkUSuaFtbyxg7HockxsUmgFLdUpz6UFf5xS4G2/d9PzoJR0NNYtdbh7uUber7Hv/4594c5zDSTSMZ69FJy3y7mBNTotLah9p/nVXfiy3IuYUkKdvNblBRJDGHpGTUTVMLZOSc9MT9H6/lKm9sYb3BmMQpIon+Oel6tibR1hv0fW8cSRKl2Hnnv6P6JMaRxBYHSx9JPHaFaybPtvmZZSVENjZG3CVVM1MSKZL3xex0EOZhW+DIPFOooKVA289j1snx3lIkgVK1tQDnJE9pc/kxMm4apJTHAC5tjgxkI8kVim5aR1goxDqZ2oNLSUnAr0mkEkBBoqrvy8JBsRYYbJAecyQ5P8EfA3COfFSFOzMu1l/U/L+8JkRFkYoJD1+siq3lnQTOhEhIbC+leg4H+8b43fLDpjXwwz3G7AETVeCScNNkb5PzWL6X+8i9xZxLjbrpWJOYWN9/8nMPAdhzytQk7py6dDlsSDeNIYlCN5Uk66ONpZumyqSAfKIkhSSy7bxGl3dEEolNoF4GSOI+SLwQ27VpJDHZyDfz4KSCxJ7ZNCKLeCmzGMuGsYIkc5DEWF1JSelM5plC6byaRA9JND9TdYnf/9/+BH7Ff/Tf4Yd+6nXPUckL16TnGA+AC6hZJLCR78YIYsT6JHI1ie44vt4vhiRedE1irJcj46TFa7n04xhaBzDNLvYob4Yybhc4CGxNlrtJlSjJcqx4TaKhm55E6KZx5dDyZp+iAwLlhFNsLWGc+BTdVDLHrLrxdjfMs6DU69//ZVGelHBNDgGzzqd9TYX2BE4aUycbQ5e0NG06kA1pcwQibsbFEdkS4pykm+bW12AN4pHE4fNdJLHE9Iom7jgkq+t9miqFJNZh3ao/95TFEk5tAUm0tVzhHpBv3xOKItH1roF/wSKJ8eemfCzA30vN+c+PG3tTj/sGH4CZ45n3j+sPhQAH578vCTdN90TG6ipExGXupXHxoCj1jFokMREkvvYYgA0OrbppuU/ioqnx3udPAPjsGvOeeJDo0k0B68PkkcS0nxFr8aRBt8drwLbAAIBm6dQk7pHEC7Ntp1c3zdU8pW5GKrM7A6KOblBdeUGOBaQsJU270QOFVgpNhYOhJtF1nEckMZFt+os/9HMAgAdBA9RQ6cx8N4x/S1ks0033SQy/W6vvk8ic/1ggxWxsseRFrkeoOy4Z3BdqEv1aD95pDTPCpQg4bG/QsujLJLgsOxbmeHbTbknnZxznXO82s/a4c4ypO5og0RboA8DRytZfpJDEueq+xfqj2LpVUD8E4pldwDoPXQ+qLlmU7VYZzyn8fmwAHJtnLnmRe7bLiMgUgWTuyZhQCIO+ADZRxQayMQd0juCTfEY5SRVP1Oac66qq/Oe0gDy6cwTcWi6FAMpkTeZQgxC5Kc0yTAhoqMzxvTs9JtUCZldI+oVKyfIbswdoaaPmc4PzTxwrpgqsKd0YnxuyBcmU8eJ/XnKegT9jGVh5YUL3WCCf7dAHZZHEWKmUSdzH3y8AQCoJKLRSGyQONeqbNrkfjArXdYWv/uDz4/uByPnwxnWDuqkNqtw+iW3XT1BSIE/5jWlwsOUsXmKebYEBmKDQRRKZwLI0l3N/wlNgu4K6aaqRr/w9NgaI00a5DKH/WmlcLBvWZebnjkshiaUgcSIHTyIiufYSY9HwxvLBLd007kj+888YSsLYX3F8sONOXfG7JTLdQBptSCHHRSQxGuyVF+TY/cU1AI4EiQySGK1JHM5J4pipXo4MHQqYokvsZu/STbnaQn+zZxxCGSeBsxyTFa7xauK6HseUst30dVE3dW0UrqlsrZlVXuQSEOk+iZngq9JviClpfTHpAwmYgDHF9hAbBXYy71vUfhseSt0083wXkcSIs1XKeISBFBNsAD4C0/c9xVwJg9lY7WvMQjoto6QKxFkoOUoaIEmSSJDY5RFgwE9wlZBHd44yL4Cnjcp7xbgg3fxs+x41bMDItCCJoj0U2jZdy5maxGnfZ6LFViRJUoqAqwgCOScBoauBd8cRtNEguKSVQ4P9zSJf5eAyVoOqYWpoVEoBu3ewSG5dYxJI5Z7t5bB3pcoJts7+um07bz94tG7x3PH0nrXCNTW++xtfxeGiwb/yxe8AkPbLzRxMCwxBHQF4rDSZR3idcgreZu3xvxtDgQYk6Tr8hxWuAQYkcd8C48Itp25qULPp66NjmKGbThdVhtcdC24KDbAj6qYdsR5H6aYsJW2yGOSLqIF4RtiVgD45MA/og7PdePxctsn9rAdnWy9THBOuYUVhYs6gzD9mMSQXGLJMhb6R4TgO7fHnBXCLTywAs4hgzkmYcvJLSYhUvSWTIQemznVpY5uvZAhvnGmkXBzmZQmZhExsnMyTqkeJIYnbbkQR5diTIv3g/AMluvU0QN+SaPPkuSmtWwUkce30RxQqac5knjkRppACRAXAqSAxQ1+ce/7N3+f14HTrsNmgIQyINMEGEDxvpCOvppsmWzDkhVMAn15cQh7FwvpCTd+8aZBSTgrHx5XmOA1IAa4mLob4UwJrysTwFO20r2fnGKxBDEtGPnc6Tr8n9sR1m9JN5fX8HENWQs6PDOfpghW23CM/xn3vGCQSqKU7ziaus8OiCaBcwmNVAABcn2+960ZEEQDuJ8RrXMHGw2WD7/6mV8fALwamuMdaLqxwDWDPX64sIuf3NhHApyWZSl7StVfQTeulQRCBfU3iRZqhm6ZlbKONfGciicwiEgtuSsheeDwmIKqrGGpp/5adY8RhopqCBwuCnNtlU40Fxm882oyO3irTcPWTb56Ovz84C+imEWekx7yFroQ2pILEUgCQUgAF8pt9TE1VhdJpkcTIM1Bqi5ASBGBU3IDIdysKRwzz0jqE4YaI8rFknrtgo2cd0LAmlEPgp69vu35E2l+8cQAAOFymldwYBb5cn9BSfe2UlVBogZHZfAG/VmXdlttgjFSjApIYr93mM/JiDJI4F+3R0hZlnBo1GOmOvkPIIjAeck8lZRJIYoHJAOhbMJixtVM3SaL9w1vcmsRicmu45cIWGKXDyfzdU8IGN/EERDkonZZF5BO8OeGmYuIo4pMUz0k1rbdkkcQwuUgjidrzHwR7DANI5gj4ZRFAWbgmZJhZ/zPP7nCPxSKC1p+E91NLU5Vjp75aSZTQCxK3Lc62dg9I1SVauun0vOSQRCNc49ckunRT+S6h5cSbYixElqnkPd+suikANIO6adcB6LkxBdsHiRC6aWKzjzg/gM1GxBzDEUmcOE3kIh7LxuScrRhqI59XQKRiWUWgQElLoG1lZzdO2TJ/s0Hip+6fjb/n6KY/+7ofJLpIbYwSy9YapIU7Sn0S/ddLAUAsi8xs9imUgt3YtDWJIUXP/YxkM3FZkANRnjlzNNnI7LDx2oz0N9JpDZ1k1pF0UfFxE1WOM8fj1E1jSKI79sWbJkiU5EpYawaQolTjdYskErK91KZOZEnxMrf5AgYplVPDKJxaJJFHxeW8zkFScuhZtLa2zz8z7thJv0OGaTTjngy/G7NvuONcNH2OcBNg7pvU/uvOJSqKQSCJcl+wlNgwACjRMb0xXrBBJLeGP2uR41Td2JwkdNfPpFtnkHRgSLh6czQ/S+ekqoK2LCSSPq1l5JhbgJ65kgqk6OTKME9Jtpae03DfsMmt8hy1iKB8h5ApU0w41b4qqozNBVFNXSXX9s2uG4M2QRIFNEgpnLp9EmPHM++Z7je71ggvHkfopmEdqWs5NkOMhVh61tyxNkiUm4tFErcWTdwHiRdjObppqIYnZiX2p2NSiyorJKARSDBzmGZImAc7JpPfE4tdrP6oVFcCxJViXSTrxtDvreuBG4fL8XUg7iT+7BuPx98tkjjMMfLdqCAlMk7mmDqXc9VNY/0tx80+t/gnqU3pMWaekSCRQRIjzWTtuFTgbH5qnR9bD2FfYzLJoXIrq2QYq9ugaqtqv9YJ4ChRiwBNp5HEyBrk0r2+6gOmSP9gIUiieU8UScwdK4KAcZTMerohdmXU0v380Na7bkwWpShJru3aDlVVcnjh1yQSfRKTfW8ziYjQiTS/iyNfclxjzjVxL1dukMgFDbFWFmYO5WMBNjHWEiwB+dzwcpcEvvJIVv54i8avHWYC2ZBK2BHHCccAw35DqqKGCV4tIjgmgAhEaprMzCeqUs41U3MfE9dhAzA3ccGgL1U1Q900siea/aZ0LPhzJBJw7vGsUFR5/5XP9ZOL/fh6box7DA0i6I9j1624z5sbt2yqJJK4abvRJ1zvOmx2He4erwAAD9dxuuk248/EEjLusZaBUrgkG1NJKnktqVVRTZFEVhgviiRqahKForqnm16MbbsumX1OOWhZ4ZpzZD5j1LKSkxwPGszPEm001UuwqIAY+W5znF2LJPr93uT3sUdT5MF+87FZKG4eLPBwvfUcldh36/q+6GvF1d/MJpq6Bkl100IGOl+3RCQFlI5F7HhcTWKcXux+5mSOCeeHbVLsnxMis97456RlC8SDebKOpEtbLJ0L18JNkapJjNB4xrHD9/5ff8vn4y/8to/jC9/5HIDE+dfcW2pKpj4pVkISN7sON4dkEYUkdn1SgEws7Plp7/+ykxxFUpJrgn2PmEVS8jat+Vb0SRwFmGQepEMYqJuWg0vz09JNFZTYGCLLICJhkEIEpou6HlVvNZRYwJ5DJuFkx/jrFrsm+zRVUgVdmQAC0u2McuclRdMr1SQ2k7pJ81NNd+xYpWpMAme2JneuCJxWuCZM3jFrq4zzNQjK40KatpaCrg2AY6U6XR9n24ktmzrZAmPb9rg5Boktdl2HOycmSMwhiaZH4vSYVVVFNT/MsQzd1EUSbx1ZpXAgEySm9oB62t6GiQGA4FyOLTCIh6BeGBRxRBL3QeKFmIGa4xcgJb+de7hzSGI5SAkXuvKDHc2GER5JCm0rDEsqsJbu4VhtmzjMi7oKgkSr0gjEH1BZXJ6/sTJ0U+eBjQb3PZfFjKu/zXAiC87PXAQ4WbfHZk2VAUDoWANA2+Zpeqk6WTqLPHGaOGfX9i7kHWvAz+xyjqQ9JxZJJILLAJUtoSgyJrlBDWMXTY2vHNBEM79pIoFVNzXjnOMUrrUcL0Y31ar0ivV9j/WuHZ2Eza7DG482Xh1yaNtdlxWtkWPGAuB5dFMks8jxxBF3n4RrV9tx2WcjZqJzCEOnlW2lEF67Us+2cY4RFgqtrhlm5YmEztJFEjNBvX88Oy85DktbnCJSXJDiUgJ7k00rzDGoSQw+jx0nx6aQxIg/UyqniNUksiidi4DNSUBQzJUom4dnrnTBc0MHpU5SUuaePV7AwmJqGacqvdyzHZaK8GtJFVU3zX23VVMnRQnbrsfNwSdcbzu0XY87xyZpmAwSu3y/7lTfc2mBUVXVeP9JgjJXO7/LPDsxmirvlzh7h6YFhiCJEiQyYwq2DxJhbpAUkhJriAk4mZxMxiIepOicGAYRjAUb498KdIS+n2Y/S+OayHdjsnZNna5ta2pLNwWAGwf+Axr7bqJ2dedkhYfrnZfxjTkjbE3ihFZDqLgB00Vk13bFtgGAX8tIXe8EcjwPSeQCgFDKeaTgpoLE2BwJtCHmXPfgKUqj01rYnOw481Ov0uigjwokcVHXaiQxpBqJ5Tao2MYmv+a+Xoym6vYyzc0xpsrMJFdi6+uu6w3tfHASNm2H7/krP4Kv/Pf/Nv6HH38t+nm7Li1AJhaWD7hMhtI8p3THfD2K+/mAju4V1vJS92SEbqp93sZ7hDgW4DugrLppLHlKBenh+ScQvkVTj8EluybImi1rHnOcuACKvnG2RW3K47TJRZlnOMfS3pFKkpQc8pDNQweyseCGSZJUYVCq2RPtayWhQMChF49zHF4vzDE8l3yQGGcAaRBgnl0Abxy7bsX2qa6Q4Fo2dVS5WvZIl266bXvcOc4jicaXz/nKccBh2/YTNuGtwzKS2GXWlLkJbwD4hs9/CR9+5Zb5j1bdtN1Y9PECkMR9n0SUswExeLr0cMfQF7YmMVqQnh0j7/WP5f4tZu7NLw8WG5TOqbdsIgG3m8m/OQSGgF0ccnVL612L1aLGzcMlXn+0Rt9bHnmKEks5aDORRK2THG9dMv2eyeNpUbrIudwSNVmxutxSLcVcaffowtqVJcnDupmWXIxjdFNOgKYenUj5ioyT7CIbwHBvET2y4vLb6e8YC7aZwCGWgBjXuoIjGUMSGdQyyhLY+U7CZtfhn33qAQDgT/+/f9xDTccxbbpsYDxm7dPtNEiipnVDVIF4RJyzU0RTVx4FixZhcq7B6LQqE0dMC6RwHNuTMZyjGK2uGWTkmSBg4ST9eJaAn7xgEkdxVggfEMkwTSAVS0AwfTGj5QYFJon7XndsKUiJUWIZH8h9v/tazpraF0Hh9ht/bgDXcmMagOmeG60ydni9mXH2uYE3V23iQlPfPC2Vyj87y0W8JlFeE59wvWvRdj1ODhos6ipZk7iLBHuupUo3Nju7d8itcOsoQBITycxikBgkypl18o9/+xfZ/4wBH4kkdjvdmILtkURIkJSqSTTBXgijl3rOuNQfdwyTjYlmCHOLcQJZks9LjktkOoCycMr0u+lRUnfOi6bG4dIeVOimYSNw1za7DgeLGjcPFvj0/TUA2ycutWAxmb4Ly3R3hZqszHcr1ZKaz/fnqHUI3d9z2bcY3bFEU419tx48shHW0hU3qOHvvtgE7xBqVRrNvYzxWADZX9FBNgCDRjHy51FFtszYKJIon8dQQCNrSQlN17ILFuOxpn8TloAVrunG8/vf/4vXook7qUfJWUxdFiggnpXv2Inl1rzceSyWHNTTRCGLJE4l+Qtjwvtf1NYLx3Pl61k0JJyjWAlNl73PfQbYY7r0bpYlIN/NFaYq95bD+F4xLimMcW4yxn09Oa72653o1iVB4MaxBPz3ihWvW8BUsoFseY5AUAJArK3ToLR8H8f8C015Q1jLy4j3ucdj9l/A+KFdp7v/baLWT2Zq6zTpALjCxE8u1Q2vmhrraJBoPkeShGfbzux3TY0bh4sM3TS/n6a6FWzbDquFP+6W0E0zycysemvUL+fuZc80IjQh3XRfk3gxlruxRkcmuD9KmZxQxVA+g3Hk3WMxFLFcbRuTyY/J5OdmmQqkSot/DJFyM/nuuZnQTROO5MGixs3DBT77YD2MEwRymvmngo16Wm9ZqplJCdeUMte5WlIWARbTZD/dcSWVUjNumigR2nAxSJmJJHr3JLjNvnFQorbnnVZzPOvIcJS0yqOxuXMvjdu29lwy6qZhbz+xXE1crgcndW8p15Kmjjv/zLFifWgFEXCFa8Qx2LQdPvdoPRmTU6l2jxm//xlnK+Ikp85/NEjnnS33erPKoe53Yx3C0ElmUQNXFENDt07WfOeSVJH9jT2m0LtHWiXzjDb+fcnUMsZaYDD7fXhvsXVj4fF4KmEVDzYy48K11R2rqdWn610l4HbuSQqljlBpi/tNIsFeOp78OUTbSgmgFJJYTrD7QTqz54TJZBoRnBkAR+mmhUDd0E3TSKL4c0a4xrTKuXm4wMMk3TSPJKb20m2kNIgRrmGQxC5I5jPrpGejcA2BCu5bYFyO5WqlUo5MaZOKoWZUjUI1df7N68xiMB2XVUWN1B8xjky8lyODZNWRmsR4Jn+kmybaSwDGcVw1virq8YF9sKfIBtHMvYo4gxlhI3fuWiQlpHUAOgRYXWwfOR5TkxhLlJSQxDoyhgnSU60b2EyyLzZRHhNK1/MiIfb+0tYkAva8UDWJkeSKGZtObqX6VALIOjLRmlAik5yim+aet1wt9XpnNsZRuKbtcP90i/c8fwwA+Pl7Z5MxDN00pLwzzlYsSJGxqXMiL8cSflpxEVY51GWh0GITgSM53iLFhJ+t22O/1zjHwCcsIXyx2lU2MSP07hENJ+Yo19u2zigHlzFEitvvq+G98H7S66QSgQyfU3btMvek/9ouU5PrfmZYb1m6AlO6Y/l7AeY951X8HgPZwrFSFPTSPC0F1JxMJkkLTBFg5rolzz8RkAIuu4C7t6JlSIV9eLWIC9fYJKEVrhGRtxsHS9zPqJuWlKqjNfAR3Ylbh2VdjFyiJBY7MOj2xLoWQGXrk3LWLIF2r2564cbQtqY9wPKOU1PHeqQQ2ZhaX5Aeb8pOjJtLU41ljDpmg5pSVnYBIvWxd90GEGsKHg8SD5bNiDYAwMnKqqLO6ZMYW+iKNYljoOG/XgpuQhRLxgCkuEhwvWkk0ZkoU5MYX+zM8dJtQez7/DmWs5FADIEsL6xulpwXrgmzpqy4hR+QunPPjhvua9kYmUbdqbrofBbT/PSeATLYC8cxa0LM+WfppjEF9JBuerZt8WjT4vNfvgkA+OS9qcrpri23wAgDsO24/jDqmkGQ2KcVPXPPNhM4zKEoudRKGpEK1hIW7VmO93HvoCHlOdZVJNju9Uk4tpbLaAO4lNjyHKX0xBPlKfY7xPheMVPeQJ7/wJFn9in3eHJqysczFNBxnSTWf0D2/AiSWBCzAuzayiLpMTElav0PkytMkF7Hg3SekmmPxYyb1M6TyY5wD2D2HLvfmwvAUtCTojzFc+L3NzZj8/vpsqmjPXCnSKKhmy6bAUlM9UksJPRjAmuAef7CUoWwJjGeqE0nuFKsHG2MiG7HI4L1wiCJooi6DxIvxnKZwjGrOFkgzc9cFiF0fihxlyoVJJYXA7Uq6uwgJYLSUb2F6mJt2/f/xi/Fb/z4e/Al777jvZ4Urhk46mLHK4skRumfyvNvjp3PTkn9Zqxulblu7uLDOGlx2W6+t1O0UTpB9woFVzjlVv+eZOcYCgkwC6sXuBHnwxzP/NSrm1pna0QpCC952fjXnEESTVPe+AaVRBLPkQCajis7F009RftLz5vMI083Nc/z6482AIAPvc0EiZ+IBInbtsNykT+XyZpEok9iVLim5CBEzj9DuQ6plcy95QaXemc3HJc/lgRSu67T000DZ7fvkU2UxIRrxtrJknPdGHo3U9sfHk/6K5ZUqoH49e77cvI/PP96BDgI7gvHswnN3vtZWoMWEX+mtHaFaytb72r3APN/tm3AVM9B0ZdXef7r8LuRz/bY99lZ/wEuSPeQdOK6hclrZv13x01rErPDJig1o5y7bKpon0R5TQCA9a4d0b6bB+maxG2BTRLzXQE/WfttH3sFgFO6lPFBc4mSdFmQMkrsWz7Ym9Qk7ummF2JZ2lbEaQLsQ5p6cGKZN27R0quWpYRMgHJtIRDQTYnFLuT+yzxL3y2m0jjSLYYH7aVbh/jef+WjOFwGfRIjUP9m1w3qpvYBOhkEb+L0N6BEJIktIjSSOAlKS3TfWJDOLcbh8ToikJpfkzgNZo2yXfpYdfS7lbNocbojt7A2jU0MsEFieLySYp/Ywnl2NEiKT9PrKcQzlvAA8ihkPHFUnmeOplpag2J1ypxIzvRvQjeVjfpzD02Q+M67x1jUFV4bgkbXtoXEhRxzF73/y8+3O27M5Kf2jWiyo5wAkrGu80/f//UMummwJrCO5BhItb0KSQ+ZMuH6Hx0TWV/HwLTklA/nkkUeZQzgCNcQ5z+t5kwG6SECTOxTwHwEchqkFJ6bZurP7Lr88x1eN74mzvx0A7fS+QCme/ecmkQ6kTPShHXnf1KTSCZYwv1U7s2s5kETv7fYNcGipP4cknNM+K6lmsRNrCZxaIvhC9eYpIRBEjN9EgsJv5RSuDz3f/I7PoZ/+Ie+eTwPOeEafaJwhnBN1/L9DmtRN933SbxQyzlqYeZHTLjWqU0ghiQyi1Zd+04Tw5GX9T1EX4ACIhVx5Ln6o1j9HUd3bLtAAGWsics7uyFKB5hsk6ibip0Mv8dpBRz9JFZ7pFU3ZbJvKXGLeQqgZYcklpFnGqUvA/oVMIiEKHvL9T1RjxJJCjCqtICfbdXSTT1qGensyjiWagTEaXpFJLHWI4k5xeOcw5WiqZbXrela0hbkvnP1xuutjyS+9tAI1dw6XOBgEXcsGHXT0JFk6HbR71bI5OfqxClxkdBBI+nW2mAvKXdfOJarbqpB0pNS/pmxiwB9Z8eZsabmaU7dsL+W5MdE1ZyJ4HIS7A2v0wm/1r9ufAAwBBvjeSyMi7AZ2H1xpI0Or2sThT2ROAUwqdtjzr+lm8ocyUTOzAA4bCfCqCubcfHnNFsTGiS3WAq6/Flb72rUbKdJydy9lVrLNyPd1AQ5p1uTNFw0puVZtk9iwS+J+a6uv7Ba1Hjx5oE3BkgEib12D+aS1551GiRxMSCJF9cncR8kIu9Qpm6QEgXI3Iz+zc9uGjFkLxtszKWIjRubfc06kmmL9bpisq2xIGVXcNLsYjz923rbjX0SxU4cuulkjoQARAwRYTOmMbn7nEOeogTOpWTOCS4ZCfRUTWLWsY45yRQlWc6lfa0H6SQ7AX7bc9SyMChlg0tLQ+9phxXwaXrMuZfPjQlF5eaaq1POUdJm35OR4L7v8851MzpMOSfBPNuCHN46XGKVcCxKVCM5ZvT+V7IgRvS4tG8okVwZGzpoTC2dm2Bk27KEtZPWkS/fk3Vl1m8Nkh46abuunKRq6mmSiqWPLoc6zTl1w9IHlVGXjbEtuBYY/nrXKymZE7pjCYEMgxsWSUwkT5l9w6Kd3DUI97euJ2sSg8Sw5vyradp1OE6SK7r7RPwaRrzMPR6z54TggRbttK1LuOAy9LnarjxuESlBAmxN4uGiwbKp8GhADpu6wslBWt3U9EnMB84TX75wLmP+FmCp8slStQjAxPZJ9A+0K/PWx8mugHazDxIv2nIB3yKy+ANlZ9I4Ff5r7KIVpYgRDnkMSSwhgkCcblqqW5pT71dHzmWpJi6rbtpO6abHB45wzZya0Ahq0xMJAfNd/DFmHvljybzcOfK0Ufsa+93MuKmTzKibhs4dlUXu/QWyvEGZn7PVTZ3eZiUqmjdPB4Hh2g1Yx7UlnlExl8p2HiSxuLHNXhPi9yT7bGuypjGavJhcx6NBiOre4834/4NFM9JRXdu26X63YtOaRPPdilSqOqGKmvh+ucQRk8wJnUFauENLPwwShWy/PWBA6bpOVe8XliowdWqxJChLHzWtqHS9HEPk0qwlbHJLl1yR6WhpwhO0Ddx1C+nkLJK4qKe9Wkv121MqLbg5TgK3fGmDmAlk7f8Zuu+0bo9MroyBFLyfpesWqptqkcSwdUbeB50KMDFzDBOnmrUk1k6t5DvFatIlSFwtahwsmjFIXDaVSRIO5RqhGb8k064skuwo9nx29uzYOJ0uQJmVMDFNTeLiENitsa9JvGDrOgbJUi6QESSRrVGIIlJK2iiz2J1HuCbWWHoO3ZF90JLqposazx1ZJPF4KTWJ0zHMHOOCN/kFUqYeVYTMLZARJ1mFCAbHO49wDRMEh82std9tTk2i1GRpnWQt3VSuF60k6dzLsllpGnVvWwdJVCpyAvzGFkNfcrOc2zcsHlwWnpuISq+YfL/DZY2qwliDslrUWSRxVcgM1NW0JrFUxwhME2Ml+qJ1/u1rMQn16LHqadDA3MtucGkDt8KYibqp/aySLYfAQVPvF5YqjEgig0hFgkRWuIZ9P+DWDQ9rSSFJCMxf72J0d0DfzohGiYK9iq1JTKl+a8owtCid+wwwNYlVFVm3CmPka09rCwvjgvPI+GnAFEksreNiY8CnUEWVP1m6qbxOJo6C/orMOQkFt0rjYnsbYIPEZVPjYFGP639Tm/8DcbXRbaFXbpgkBOyciz2fJ+y5/JoSUoRljJ5uuuNrCxeHwO50HyRetOX6/aRqEktOcizzZpym/FymdFPuAZ2MG35SEsnd9HhawRVVCwZnnrsxk5avSYwtJOtdh9WiGXunAZbOF6Ob9uA27XCcoA0pqypDvVIH6UIH8egImobz/vFKS08USRzu0VzrgFRNUL4BuX2fWA+F8xM4rcy66m44LbkYh5t9Kfkzjhs3+250DFVUtk6BJEYoMqWNLSqcIn8jEk6TetfsDBPBZeFetj0jp8+2fM6yqXG4aHD/1Gx6BwvjJKyjNYnlgG8RJO/arqPpxV4AIEFH4gvG11Zkx7hjw9pCVnF3ikhxwY0WyQLMWrtrlUhikMnX1CTGgsRSDepSWmAUnhfXmrpCVZlnmxUOqocx7r3FjgMcSqC8XtoDErTR4rigvpxdg0K6NUNTnYrCkHMM0W1ijIzz1hLm/CfnqBvHBESAEzi0/nUr19ba/cY/Xnpc6JdohYNcum/pWMA04GPOpatI7tpmZ9f/g0WNx5t2+H/l1PVH9oAuX3IQiuuYMflrkPJBiwBHJLnOJFwnpqlJXBwazvpuUP/e003PbyLTm75B4lmLUk1Wqo5IC9nTD2hAr2QRQQCT4JLeoJQZknCBBKZ9EkMrqps2Naqqwu/9xZ+Hr331BW9ciAiyKN2kJ2bP1aSoabvJ8885dqEDqg3AzO8dqqqAeEZqx4qKr7EghaEthlSX4PWcuQh3RyKJE0pUIfkjNjZY71xUozhsDGIMkshRjYyyoHJjSyAbQGFNqKbXjXm246qopSbp5me0ubFzbo5XDd48NX2xlk0GSew6LBeMSqObpCq3IAGmzJCSKmdVmaAhpm7KtEUYa2s7zrED/GSCFtmYjCOQm2VTYdv1dGsDeU9U3TSLiPgOsjuuuFYO11vulxXJ9RLnVUNTbapqsieWRoWlIiySGD5v9PUO9g45p0wAEFMFzp3O6RxlfGkPhjdHhiUjc5zQ3UuCPMEerH1uxrq9Li9kGI6bIInKcXJOc6iZjJtbE6oV5Zn0+CaenRhzC3DpphUOlo2DJFaWjbOL7BuFPokx5LJlfdAIUGQ+M36TjT6C0geaWNdyzgUALA/Nz80j8/MCgsTzf8ITbmxtj5ZumqJkMjUKWtl6wNxDUYckS5P0HWQ5nqZoe2B3DgW5+Tk2zTTgpouGE0jiwdJ85nd/06uTcdq+bWZcnFbABZe665YSHCovxpiMo5DcyPG2hJO8DDYo+QxGuEZDPwSm11uDbKyaGtvBGWRpHeGG2BYCm3Dc1kFSOLqpzS6eB0ksbmzRe2T4G0EBdQ9nkivZKU5obPIZVC115Nl2z83RqhmFa1YZJHHbduO9mrJYTWJJOAiYridUM+sqdFpRHDMea0TSFUFKZFx53wiSMuIQkgmPnaccWh6TVjct90mM7Ysl5HhZG7rpGCQWkgj2mOF3Y8+//T+TlAyTOTzabH7aZBp3vVO9C5m66LbVXbeUngB9T7p7ALH+T4IUct8G/PIGQKHCPZw/tpdpVVU+40XOI4GIA25wyQf3YQJIjW4r0FV3KWeOt2jiSGJIN328GWoS6xr94HPG+iuWxMuM6KL/mhWQ44M9wK2LTh/L/XzzGVzCw7Nup0MSAWD9cJjEHkk8t5U2AZvBmdYX5tGXOEWMojtGgg1GtSzmkGiRRAOHl+fozg0QKqcuiwmUM/JhzZhr612bzAzHKXpcpnUyruMyfVqqRbyXmiLTpwzAwkJ2+b2IZEVoEyUkMVW0zd5bE0l+YmGVgnYznqS/Bchl23HjlhHaqKYGctf1RWVfsVx/0dTGFkUECVTqvIq7muudo5K7SOnR0tZVrDJI4q5QjwJgIuXPIonh880ED6EKNFMTCviUqBGlViIpdEA6cZJBzREYHDxP3ZR43mrjSPa9//3yKriR9Yd9dgZK7FobJI6qqMO86fPvK0CzNaFTJLEwbkKTxDBOFxTRbIa69mvZCfrueamc4+FIxzrmA5XPh32ve8wy48X8tME2X2vmAghsPe+4VgYtT5ha2ZCSzN5barpppa8bLtUkLpoaB8sGj9bt+P6VU9cfWrFPYjVNSpaStSnhGhvgF4LLCQiTnF7cNMI1yyPzczMEiUy2r2D7ILHg4Lm0snBcqUdNLEhhsjgxRUIG6lejBnNpi0lHMj9Hq9Dl1gTJAxq/FatgMXZNhGtiVkdoDFyfyviDXQxuJlQjmX/+HgGmjnV5g0qMU4pUAINkdKmOKyrlTPbI6nX3VmrTZvbf5dATTebHqJuG341VH/P7xJU3w3Ccq7hYCmzqukLf+/cku7GFNaEAR4HWKu7OSXikBAHceS/qGscrGyQuB7W7uLop0wIjEKBpSYGjFAJWeL67yPmnxLOGN2uUc92glK7/Cu4Ttk8cYAL2jYukk+fRzM/8nxFvit2TbGJmMVBi1UFibQJgpkXHOM8qLPnQB21skBKeE2Zvc8dNkKxikBgEAEwz92COsu2XE4XDMZx7mXGsw4CDqqWOoZYoP29S7+cmO5j7H/DromnhGqnDG1VRh/kT/uQ0kZCfX/iM8my2hDJ/ZtyirpMCNIBhAhws6lHddNFUWC7MByYThVlWQj0BfMZESeLExNYfoLwHhH0qZcysFhga4RoAOL1nfkrQeA7bB4klZysoGLbjylzrMEikgpQAsufpJ/EHlMnk+7LFvNz9dByXDXPHsbVVsYBPWmCkxk2Ea4ggfW69ZRiUMnSvOW0DgDgFl0JJI05523VEn74pTdggiYxogX1NEwBPnSbSad3JJqpTN/UaZxPHcmsL2Qy5GWePx6qbxlTSSghASCME5lOg+wJrIjWuLVxvGklcTZHEON20LwaJIb2JVjetQ+Ea/zukxvhrOR+4tcH9T1GgnaDU1jKSQYoEpaTzCVgkUXP/N0GiiqFch88owCFZwCBc03bjunDA1iQ2tY8kMmtJ4yOJc8TcaHXT4Hlj9rbY8WjKe+DMU/0twzkOr9MUUC9IJJIkVQV3y+9RftbC2uExkCoezUf8u557ZgAfSezI8x+ygDStM6woEvecjijpDJQ6VnJTKsuKrf9usO4FiXXtJWhDM4nC9PHcXrLjsQpreWz9AcprUMxPY5/TyQS1dNPT1/3/n8P2QWKBKpOqSSwhNym6qRayZ6XMp1SL8oKQ6klXprbGxhF0x0jA3RY2m5Qj2XZGdS7lFNaBU2fmOLffW3kxriufEstsNnHUhs/0hSq42qwpwNHtbG8/HwHOixaYn+ECOTezyyyry0WNjUPH4WT8LbIn81XRTdte6VjbDY510GL3JJvFvJh2OoyUfDzhUULawjFicj0WdYXjld0gV0ONSjyLnHcQAN+xM8fm1E1DKiFD06tSaznhbIVOPI2kBMFG+d7y58buN+azB+XQwh7qH89f85jzGFu3WCdZAnVBnqWGvWQGSXRqEpnzP2GTKGrihmFsUixWt8fcI2ESjqEEAkOwEQnSS3TrcI6MTcTEuGGoK31yF4CniqpB0kPknvX93bpoFkl0a+ABJ5lDjJNjaO+taU1idtjk/mfWBSMQNV3L3f3tYNHg0cbSTcXni9Uk7rp8ojCmVVFqw5MUrimg6bHgkmUqedbtuAUZsMI1p28M/z8/krgXrhFHLXGDxHr7yf+LGZIokpWfT0gbZR2LKoFA5kal6KZ09jOYZ5lGMh23LdSWxGhsAFNLCsT6VNIonZJuF15vq2TIOMn2NYqSWQ/ZT/e6QYGSukEiQbeLO2kFJDGCZPXgnaY56qZzkMQY3ZRCBEcRJsdJpmiqNrhk64HCRsru76Va3gn9in1GldctrIFk+lumnm133k1jaxKXTYW6rrJI4qKEJNbnqEn0kkDE8x2yOxR0u4lwEzFHV5l5VP9UIsBsjRQwqJu2PYWqlo6XuwbR/rpkTaI8b6eDk7lqONqWIM6aeuMwMckkTuVjJ20KCseati7he8kCDpJIJhNMLe+0TCQb3AdzlMWcUQp3j8EqVU90AToy2HNowhrmSl3Z92vopi4qK8G9tsc0Kxblons0bXQM0hGMK/slMR90Tk2im7xwEzvLphrnsQ0VaGCC6Fz5RpgkdI9VLDlL+KClFhjn7pOoqUlcDEHhnm56cVZEElNBYoGCFUcSORpJ791U5meZSjhFbQBdzYAcT5tpYselONpABklMZXEK4xZ1HT3/JUv1e9Orm5qfeSdZ3us78hRqEM1aF8YkkcSCQuCMPonx88jRps17A6eJOCerRWUzrQRFEnBpo3bzZTZ726vJ0u2ooLSeBpdFtCdG5SxsbDF1WYbqkhKumdvfsqT+GR5LTO61ZV2PdFPJEJuaRD9I7PvetMAoOqD1pLazVBNqxgVICoEeh+wONuHhPtsa4ZRFXXk1uaX5AdO1XHUvN34PQkrddIIklo9XZ/YNRrgGwIhEqNRNu14VpIc9OOckTu2+rR1XZpK44yTIppMJtd8aignSk/V+bJAiARi5Jsdq4ph9w1WG16hpu883O0dguE+c869JZoY0VaZUQUsbTSngUsr80Wc0P79YTaK7N7q6Ey6SGBWuKZQcLIJ7BCi34UkL1wwKs4Xg0lUFZpM5ns2qSXxj+P8+SDy3sbziCR+50I8llFoH+D5xUUSqtNlEgobSuLktGJJ1SzODFPdvyTGJBzvnJIdrD+Mkx1FSjtYRng8zj8yYxPlnaVthc+/S4iPF9iFtq9xraaqKuuu6rAMaTyTokaxe4SS7SCJzrQFn8x2da25cTLhG0wJj2/ZFFH2cYwLJdf8Wmr2P7WuaRII2cRFeb6a5dIyiKuYGASJcIw6+oZu2k/f3fZp+LmaETMJ+n0RNYhJJSY9JCTmUbhMRKur7XhWALRcR1UQlkmgVENmgtHe+V3mMvGdEKYh5yjMaozuyVHmR0NcJ19hEDvNsu4gUIPR6LikzDVKUSRliDDBFRUqtdMZxjX//M3TmaQLCvF4+J/4xmAQ0ME3KuJ9VGhdSW7ng0j7f51E3VSUXg+BeJ1yjDdJ1idqQTcWVPNUTUTbAZ4odLBzhsqa2e2iiDVKpvjmpUprYB9LCNeanpsf3/D6JrLqpEyRWNdAslQeb2jMfJO5IXnEUScw5yZEFixVACSkrALGw1vGiYW39kaGkcdlnbXATU8lsO/NQp445kcOWcQUHO1RjM5+haK4bOOTa4F6GU+qOQUCqraMAFEFRIGXOZDJjdMfS5hZPJCjkt2cgia66qaGDl8fId9t6GWF+nNsCgHKsHZoq27tNJLbjSGJqYxveF9yTTCsd8/n2NQoBDsYxCEyKyg/YdXlRW7rpqrFBYogkWvptWd00PI+zWmAQmfwJksgGAM6zoxGFCe9/mXf2WME10CAiy6G9BItaAlOFawZJjDFQ2FouSRqIhD7fAqP26KaUcE0drskKoa4wkFIGidLMvWRh7za2D2Toz2iu20jlpHs5zv9uXe80uGcTrs7e3RN+kztPlzZNI4mOwJFZf8r3ZKjnMPZpJfyZ8LvRdbITdLs8Lt4rupwAmvQU7+zYCZK4iNck9n0/tMDI9Ems0khialgKKCr1qowlt1j/zrOuBeoZSOLiiHOcCvbMB4mljFistxxQ5snHmoQyN0hT24cZ0C2sbRA0AJxwil9/xKGWQIASEfz/tEpmLtg2P2NBOpDng4foIxuku58v4yjZ+ugCmR4TKqvZY2UPNR5PiwADZlMJa3uYRsrmvT6SkuX+j9k3+xpV2xbJkAMkkrjwaxIZx842N3aDS84hBwIkkcoID0Fp248OfarXp1i2v2ghi+nXrTKtLDA5FodA+uMoummCJWA+p0NVmftI6KbyfumH6a6TtvlyOeGhWX/GccF6ziB8TSLhx65BRl1zZpDIOv8hskT0hBWbU7c3qYkb72NlCwy6T6L5u6gjlp61cdyAJGpYAuG91fUcjRNwAikJUgrHClEK1vmc1LaR1y7s1cqMC9cSNgCe3pN8mxr3OOw5cRPszLol5l7vEmjgzTNAEpnYMl2TWL6/bLBtXmPZPPY8cj5oyN6Se7oEppj3Bv6dU+d86PTJXdRun8R4sJcrOYiVgVkRrIQIYmQvdedcKpUKhWvUSGKvCBKlBvH0DYsqntOe+SCRpTvG5G/zfPx6clMxjvwEkRof7HL22a9/KTsX8fojvSMv40rfLUqbKwQpSSS3hKQEWS0zR4UgQEAbYprdxoQt1NetJ6kuVYDSQV9HAXB0uzlIovxJm0Wzyq0yhtugAOMkS3axpKzpmvREs9lZLmgAMDjJ/txLcwRMcM4iibEWGCV1x7hwDU9jmyruktnnCW2OGBNDEh2ET+im8l0OFoai5DoJY1+tonCNGWud5Dw9yY6LI4m581lVfsJPQ9uS98sh2TpZQWCZ+QFxBJgW0hvo3ZZarA9SKETKQe3FWCdZqGoPzrYANEjiPOEadX/doC6dTYrZ2m1nPJlcBKZBIpMo9M4/MS4mZgXom7mzAVh4LjXnJAyImJ2jqW1tISuuA/hrSVtAvtxjAZg83wydPAwsyyKI8I7BJrdC9pZNnqbHxHpnm2MLSgecOC2QFk26BcbIPlGqm5ZooznxSgDJFmLhXsqIuUVtTk1i315IPSKwDxKLNRG5ICUvyjANLBknLaR10FmcOq6kx2Typw5h+VjhOGqO1XRBKGXyY2ibjAPyNVkTdVnoi7bl92Kj+iqk7crr5azplLPObIjTeid2XEj3LaIvEbrjroC4xVAixmmqhvPsUobMH8rf7WChVzcFBIFx6r+0SCJJ/QF82pAEtCXHdUS3HSdtV6glitVDMLStKE2YQCCnIhX+PPJjpn9zr9+NA1NXETZEd+lGY8uM4r3sr0GMui9ggkvXKWHQ4yS7Q3ENbLBXnKKhfw7oI4tuj8kced4K+5p/PBNIyffSIInW2SX67cWSiwQCCQA3Dkwtz+uPTZB4oKpJ1AnXxBJ+jCo5oEuuAPF9ii03AGJIop6mXRoXo40CRMI1TFyQa3kVrl3E+QeGhOvoyA+v0S1gJADj7n/AnDMZtyOftzG5GF43Jnkdnn8S3Q73YMYv9ND+Tsbpnm3AgjBVVeH4wNbjLerKqev3Nw6pNc/5MzEkke05PAGKCtcgXOs0CT/PNDWJbl/EPZJ4MVYqAE5RojoCSYzXJObn09RBQ1g2i5MILnPD5rZgSDdK5xw0TyWt68p1RFWEIlCoJa0rK/4gpqoRmYGuRs9//nATxJOlyEyPp6+jAC6vJjGGUuvuLbvRm8/LDgPg0+0MIkJmdocaEU39kfRXdIU7mOsmTup669QkEm0bgBBJ5JJbk36HSqoXQCa3gjlaByEzRjLWEbrptrW1Os/fWAGwaJCgQ+utFa/ZjHRTEhV3MvKMuqm5R2JB+sXXJNpaca6uR2xMXHRdMYkwOZaD2vAIfO2JuzCPW1iXzvR7GwOb3l+3zLj88SRIfOPRBoCGbmqUW0vJSNdcGiFA7vfBescyh2K12xSTJFjLWQGUMOHBnP/JmkD6MvJn755UJCDcgJs5J/E2EdyaMNYIEgnQcVxdeX152XsLsEkVFkl0a/BYsacwwaipZQz3DSB/b8V0Ksz/7bgbB433fkkSTurSiTUv6ksWnvFkCwwCqABcSji/TnqmqUlcHlnU8QLaXwD7ILH4sC2Ch9Mdl0dSpjc+U9tWV3HIvohk1QnBm5yTFqk/4lRKp+OY4MZuUEFtG+HITCgChQUvRYmdQ6WlEeAAkTVzzw6L1C2RtYWTBZl1EupJsMHWJLbhdStk7IAQXVUo4I4Lq3mdrUnseoyOq4ZuunUQEa0qKkt9AzDWV5xtWzqwqSPnf44qMIUIJino3Dhb21M+J6K2G6Obuk3uXxiCRHlbmFUHrIPAouKuSiClblpXHr11dBAKdbkhSwMo319uUkBT7+r24KRVMidBii654iLwnLhLiIiUe4VG62RJBOxkcDBfe7TBoq5U380TrrkkdkfY7kFLSbbIfdm38McFrVKIWl41khgGsiTjItw7WHR7ci/PSLhGlqKkuTWJnWK/cZMJdE308J5ta6+3fFZ2XDNVPGYEaNz3s+tWGIAx+2IKSXRp78crH0lcJemmwibJCNfU0zKk2SVnhfMZe0aBGXRTTU1iVQHHd83ve7rpxRibkQ9lc7suv9iFzjhAqjvWYbDB3Vih4A2ImsQ43bRXy3aP4wrfLdUUmWnkOy0aJmuyAgouvfkGgRQT3IQBEcAEl3rURo43RRKLw0zyIqAtzkcSyw5CGDhrldU0NSKWAmpqufgakToI9ogxQnnp9OIiTV3hbNeOSGKJAmefG/taad2KCtcoEjlzE0Bax8KlbLm26/ox6Hn+5MAfM7y+cTLJWzLgXgZ0UyZJIp/rI/Dl4CbJ7igcy1WP1gQptudnRyF04bEAHrUBDJq+6+apm4Y1ifl2UnG6e2kc4COJbD2ifK6pUzb/Z2sSQ+GaMnI/vDd4Bth10qVJUkhusJZoWiloaxKnwQYX2EyFU/jzD/h1msyd7Auu6J43CUw05Q1+TWJHjQtLPlh1X7d8SUNRBSwSqCt5sv9nEh4x3wIQv9B85xMXSaydmsQASRSF8lyiMBRgAlAUwUoK1yiBCk0S2jNNTSIAHL9gfu7pphdjpWxHiTOdsngLBi4bE23Knh2VQbJyQWKCEsiiPbvJOG7xmQQbxT59056HvLqjfc0gKbogBeCoiyEdxyK5xLjgWOzG5tOEuWzrtAVGV6zriSVKTE1iZn4RJJH5bmGdmoxmkUTABA+mb2RxCIAh29rqHHKhm3q91Egn4XBR42zLt8CI1fKWarJSSDqTpJL3qsYFTivvWExpPDLvEUm86QeJqwiSuCXon0CcbsdS2bZeDSThJNfhMzq8TqJ7rnANRYF2ajU16odyLPOTd2KWC4MksvexezxNqw75k79v5BtZi51ITaI2SJQWGJrEUV1NkHR6vVM+NxO0jQhIveNJkoRFpAKUtCT/DzgB8DCMFU6RqbjUdWZpbYJxTHmDzDNsQaJHIGeqm/YclXkx47mRv7sqscyY8fwHCT+GBQdMkeNSWZZ7LDGXvXLiIIlNbVtgTNRNBUnM7AFu38hxXGGesSStOy6JJCaSJKSbYE1TkwgAx8+bn3sk8WKMpptG+MjZ/i/nqEn0nSZ2YdUL3sQdQn2PJvkMdmMLM8Ilx66qpotIub+l+TlFssg5KtG9qvID2V6x2YfI8TxqE78hhrShothHpHVJCUmUOYbnkd1oRqepK2cjxVaCMA1Oskrd1EEEKWrfwkFthq/IHu9w2Xh0U1bd1K/lLSS3xqDNvsYmqQBMEhDsmhA2Sdeq+4q564Krbgc4KK4btHVyLvPHWzpoG2CcZQ5JjPeJy7duSKibFul2GObWqxyLlUM3ZWicwPR6sz3pAFMbunbuY0YUZoIkEsG9tKnpouefCxI3bUfXI8rn+vWWxJpcTWmLPN1UNy7cp3oyuRgiN+3oWJeP56GkogjJlBwEa/kcNgm7JwIucsMGl/qG84AVbpJj0khi4yOJbA9OOQ7gMCcUgkOsCFN4T9K+zCRR6H9ezFJIons+TxzhmuNVMyKFYZ/EMVGYuycj+418z9QaFCv3AOz9ybbP0yQg/APteLopYOmmeyTxYoylm8ZurJJwRzxI5IKNfsamEat/yQ2LI4m8kqG2/iLaAoNYXKeBs/tgx8fGhVP4GpFwjuUFMkTN/M/LHS+kBHK00crbDM1nlceFyQu31UByTDNdJKlaxmpab1maY1inpqFouKqXuk3b0OY0tYVujUhPOv9iJkjsRrpMiSKZquU1c0/c/xEaG3P+x2zwJEmSHxcmZeSwDN0xFiS61y8MNGMS6Fsii+zOx6VtcUhiPUmSAIWaxGqaJJHXc+auQSxFDEjQTVm6o4M2sM/N0bLB2a7D2SAgdLAsOzKLAAUenzmCTeIHKdyzesNxMDVI4sGixnqn64HqJuDYNTlFry9FfOE+xV632P1v5l4+/3Po1u6x2DUh9C/Y7ybfwfWdKHVTx79gewnKPEcBrJ5D+8242gsuKSQxaDq/a81aXrxuHt3UvpazEDxg65tj/S1L40JkW8xl6h07ScK7J6tJok9M7sncfhrex2Zc+RmP0VR3he8X1lLLTypG/LH/CvjeF4FP/yjQdzok8ei2+Xn4HD8mY1cWJFZV9eeqqvpkVVX3q6r6Z1VV/Vbnb99UVdWPVVX1uKqqH6yq6j3O3w6qqvqzw7hPVVX1e4LPTY5lrLQJp2oSSw93qIYHkCjdmMUZxow3YnZYUklPKz/cQ4M2+JnFMtowpRZs23ImP5b9YYuNp+qO2UNFA9lSQkCOp0Vy4+PIja2al6FyxwF8kA4EQiFdmYIbUglVAXCwQTHj3FoFlbppbWhz2tpCAB5NlUUSD5a1qUlsTcN4VjgoRvdS1eQS60+Kbjq3llTPnDAWJi/+3G/5Cvz13/m1AOBIoLvrSDloc/8u76drEusKfuseQnAlwe4oHc293poWDC4NixYJCa6bpin44dIknB6tTZDIIHXJZu4U4jwVbmKenaMheNUEiUerBU63rTpIn6Ao9LNt/k9TkkOHXIm2WVl+7jyGyUXmvKSDhvwcJwEwuZbHKO/MreyKwEl/aYaVI8lFAINwTflYgFlLXJVSTb2xUCq3hCq8mWM1ScjQa4L4oMrklte7E/nrnUQSHa0EN9FTVUZ8yuzZM9RN62npEtOGJ6w3BhwkMXHhQ1VmRszNHnAJtBtgezbUJCpCtXs/Y36+52v4MbmpXMincPbvA3hv3/e3AHwbgH+vqqovrarqBQB/CcAfBnAXwA8B+AFn3B8F8CqA9wD4BgC/v6qqbwUAYmzRShnhmIMm47J8/Ehgo1EODeknTAF82ABe5pEek3II83OM1ZsxCOScVgpyvLT8cKEmS+nspmu5ygvkHMGh8D7pe66ZdYxGwuxRYVBqgnRSOMUTLigH9xOUFNyG6I6T0RTdNEQSSWdXREk0SGJTm/6dWgQScGh6O0OBY6mcGuGIFJJeDFBiCSAmuRU8N7TgVopu2vqCDl/z6gv4grffAjB1mMzv5jPKwjWBAETLq5uqm4nX89RN3XPJ9LsVW47otr0nS8/2xLFWIImi1Pvm6dCaZMnXJI59KklE0Ih92P+PAQcxV6Gr3T5aFt8rdrRscLppnWCbWLdia3IxIDI/tQhkWCfFXrdwX5wjgOLOV4ckGh9BvZaQa3l4vB5knWblsnLMa1zg5iCJiufGXUt2Hd+CR94PmHVrSV7vMdgm2RbhPckr7g7jggRvviZxurfJZ8j1dPskii2b2hMuAzjxsiZI9rnHLt3LSVVUEknUJPNHqujuVF+T+Pm/1Pz8wDfyYzJ2ZUFi3/c/2vf9Wv47/PsAgF8F4Ef7vv+Lfd+fwQSFH6uq6kPDe38TgO/t+/6Nvu//CYDvB/Cbh7+VxhatBInH6rFkXAmeDm9GBiUKG8Kep7atNC7e71CxiLsoETHHlJT/nD6JtiYxcawgQw6QNXHRwJlERIJjufPIjZtTk+gGpRq0IdoCg6B6AfrgvpkEzizaOS2216B7p5sWXc83zm6GrKQWEVw2NTZtp0IgAYPAnG07rHedSuwj3qcsn9xqg3uSpQxNadr5OcbUFt3XUxaj/wB5GqhcZ7cmhUH23L+7AhAc3aueIOml403YHSNKkT+WFySOjlZxih4N19II82PChF/X88mOSZA4oyaRrYmrax9JZIJ0Memz9s47x8X3ih2taoMkahJHFdTBxpTapxtnBWhYmnxQJ8UG6Y3vzzB061jiSKOA6yJgGrqpTJOtSXRVObUJ153z3dj1369J1LXAsMktEkmsa48SC+jpplqa/GTvzvnJiT6Jbiue4wiNPUwaAQ67oFACELjl1L0cO14pUTKtSeR8eTN4EJ3ZnuprEr/8O4E/9Gngxov8mIxdaU1iVVX/x6qqHgP4MQCfBPBfA/gIgB+W9/R9/wjAjwP4SFVVdwC83f378PtHht+TY9k50YhUGCQWKBACa4fCBUywIe81P83rTLFxmP0HkG1nUQeZH4BDBGMohaYmMQw2GMpQ+ICWio2jgSxz/qOBc3lzSwXppfUg7D/V9dwGtWhcJT3zGkfl9K8bU5MlwhFz+ltO1E2pOdrrrakJkoDrwdkOAIdsAIMAQdurziMwUBDb3tZ6sM71wgjXbEkxjdgaVFy3ks8od6xpkkSX/WepZakgse36ZEbY7Qdo58gFDcswI9+V1ZXlc2PIZV5JT8/uMHOcqpQyiEiMbkohMM7a1ZLPKICRxilBouZenjRzZ/r0KZIkrr04qOO+8w6v9ne0bNB2Pc62ZRVPscahZNJ068CxpinJwT7FCg4lzz/xfEeFs1R0UxKhG6n8QuVkz7/56VJ+2RKMSQ08mZRxrzevblp76yT7jAL2vG+7PtvqQcwFK+ZS0NmSpzAxz6x3Yd9UMTcxIPfRb/rK99hxzUxEsPZ9QvfYpXs51isdyAjeVP7nq/okLt0gUYkkVtWFidYAgOLI57e+7/+Nqqq+G8BXAvh6AGsANwB8NnjrmwBuDn+T/4d/Q2GsZ1VVfSeA7wSAd7/73ePrpYxrCg4vUSBcp0kyFEaWv7AYT/jg5KZRT5urA5xwzdxeanNVUX2VunKfoKoKe0Aq1B2DAKycocVkXEtct5gAjft56eMF1w0Kiowy0wf4GxTAIymhcAQTXIaBM4PkAsOmHWQjmY30cBEiG1z2bTH0e2MoVN64xvTJ0kpbHy5rPFhvDd1UgyRGkKwkkl4bOqz6GU0kSTTol4xxX09ZSNMWY5DEWEuK8tol2V2duqlb2+OOLyEp7hxZZ8sNgjXBnkc3VdzLrsCUpim4iySuFmXaNOBk11v/+eaQxGmShJnrO24f4R/iDTx/46D4XrGjQXb/4XpLzQ8wDqha7CN43myiitynnHVShUg5SHpVEbWTle+UU1TCyXfjSilCaiWT3AKmyWtVUjIM0snAzWUkHBSUlcUWtV4V1bZcGsYR/Y2BeE0isyab95v/s+h2KlGYe0ZjGhDAdP3/qT/+yybjJjWCBMVe/B9XsZu5l6P9FQt7cKjKrPHTvCCxb3V9Ei/YrlzdtO/7tu/7/w7AOwH8dgAPAdwK3nYLwIPhbwj+Ln9DYWx43D/d9/2X9X3/ZS++aGHYovztcD0nN0iBApGiZJbuD/m7y6s3r5cX8VAkBMgv/sn6u/wUkzL5pTXrXEji5AHN05RC6gmg+27hudQGRHNrsjQUmTk0BpcSBRjUganJWtaVV5PIiPnEezkSc6yryUbDUGlFBe2NxxsAPN1UNlJtbeGyqbDterrWRkzUTTctGSRGEh5MfUns3iqyBIKaaDNOkQAKGBDMuLDWA8gHb6H4jHl/7/0tZTaQskgK61yHfULl9ZTVVUpxujBHJwhmHTTAIom7tqeDdACewJSmtupoZY735ulWRe0GIuqmhWOG518TBH/8/aZv2Es3FUHiEAA/HER52Ho/G9iY19hgb267Aa3gUB2ef0WS0GVGsUi6zE2OpelBu1XW+4VrEJPwBnwWlmVglc1ti6NTN7WsBPa7SdJP/J4t0d8YmNZNAnyi3KrEii9TnqN5P+hxsR7AAAfCtAFwY4GD9PFCEAZg7+Up44Wp+zbJFb2fhoVbk6ikm16wXSmSGDn2BwD8KEzdIQCgqqoTeb3v+zeqqvokgI8B+G+Gt3xsGIPcWHYSpRurqqqBEhjexCUYferYqVA65Y1VBQ45My5Wf8fMMYa2zaGkAWYjOFzqAimgTC2I9UnUOLtaml4YELHOXdhwm+4J5TigbNYasKiZGIukxJDEcnCPgG7Kop3zUFIJEu9JkEjSTWUj1dYWmnoPnUgO4PRJ3J2Dbtr7f4tZHSK5IJ7RuTTVYJyulnr6+i6TKZdz5t3HJN10EQjXaGoStcIdMeEm83rhWI6TXMpW++MkAO6KTAvXwj5xdG2tg9xrEjKAW5PYj3PIzjG4l8W5ZhIzv+YXvgvvunuMr/rA89QcARsAPxyo6yxLowvufzopGQQprCqqJ+6iQBI7ZeDmIj5uTV3uvowFslRN4oikd+NcqXr7CHWXuZXryu4zGgVKt5WFRt10tajHeupN2+HGIeeGL2qTlAQG4RpK8Kb27hEz73JyF/DXciYJKh8b+iVZhC5Rk1jW/KinSOL4/XJI4vTzx7Vc2V+RqfsOE3DADCSxa9/SIPFKkMSqql6qqurXVlV1o6qqpqqqbwHw6wD8LQB/GcBHq6r69qqqDgH8OwD+p77vf2wY/p8B+J6qqu4MgjS/DcD/ZfhbaWzRGMcwJn9rbrL056ZaKTBOEwDHSeZurJhDDuRrElMOIUst06qipgRQqNq2AEksLUDpusn8HOvg/MtnaM8/7yQH55Gsv/CRxOLbR1s0U0SEU1fzN5ueyJxOhWu4hs+uc22RhvK4oxFJlBopkm7a1J4iJI0kLkzrjG3bFVU1XRPhms2OGxevSSwLfoSKbKr1Z2Zyqw2uG9XfNYokpu/LRYRuym7AoXCNQRK5jPw22oIhr6QXCjcBZWdrtbBz1DitXi2jwiEJ1xK6tnblBoncs2b3AJ26aVi7ulMkZqqqwld/8AUa6QcskvhovaPmBxiWhovQMeNibRuAcnA5qfcjmB3ufNyaRCZJMkEgKWpfGGxwaNuY7HDOJXOtp6J/iqRkcN1oddNOhwgCQw/OodaV3QPM8ewevGNbYNSVt9YBXEIGcBFB8jxO6L7mdS2YYsbmfS6TqAj7JJa/XwwYYVghUV0MYg9whYMYdt9oy1C45q3D866KbtrDUEt/DsAbAP4kgN/V9/1f7fv+swC+HcAfG/72FQB+rTP2j8CI0fw0gL8L4Pv6vv8bAECMLRqz2S8i0HZpUZ4r7hIqRLES6Cm6Y+5+jFFiu15Tt+QGN0S9ZWRBmK1uWniwY4Es5ewG2c9xHOPERBxC5njaIF3GuSpuzLGAISBSBumAjyTSPcqaSJ0m891mbtrHQx3RG490dFOhDakyfTC0qG3b0bWFYgeLBuuhTyIzbhF5Til1zQlKXX62qypWy6hoi6Ndt6qpAAGQD95ifRJLIgLhWJeCRSGJdY2+j9TbFPaAOXRrq6jd0cE2YIWbvFpG8tmWNcQkxMpzBBwk8bECSQyTCSQiGAaJLVHLfh6zNYkSJJbHuMG27UlXToACjtjH8Lp2D1YjgkoqZ4hAMutPrE8ic8mklqtVBmCWhYXxuGxS0hVuktdK5vqFGgT+YNFg7SCJ7N7hBnzblmVA+CyBqirfk/I1XESQuW6pvphaMEX+X6r3ngI3ZYXraOkGM66eGZRW/nmU14omdNPtYwD9W1qTeCXh6RDMfV3m738TQLRtxdA2418b/qnGMsYoPsVq4rrCohyD0RknLdWQtMgHn1CbMIwrB7IhAqZ1CM248sIaSgKb34l+e5EsTslJOy9KqkUgqyqsPyKvWz2tJWX7JG6cugZ37jkzipw+IsIiKbvJ8fLjQkoIK4Eea+/BfLdJTSJJNxXakDxzvHCNQWU1Gz1g6KbrLR9cxtpStGMdBbdBAVxNIpBKXLBOqx0jc8hZ7NkGhkx5YnBUuIZYxwEfSen7nneSRzGNDk3djMfOZ59Ddkf5PAIOIrjTJS5cuumIis943tjaKkHuNff/eP6VNaHhPdl2nJjMXAuRRPb8h0kSVrnSojbccxMigkyNeGycNgAbx7V6h5xFOwG/lo5Gsibqpvx3k+dZlkt2TdgqzyNgkpebnWEJsArXgEm6jkgiyV5ZDInMcY4zEUFOcC7uuzJIYhjw7Ur+dT0FDmQ7YNqyRNsZlY7nH84m4nKtMxpb3sMCBwCM47g4AjaPhkFPP5J4bY1GEsMbcgaSSLVgiMg4u5+XHhcqSSqQRDfbzRxrLpVz+LteJdN3doFyTWK03xsUNaEu3ZTIEsaofWCOFzg/dMP5GVlrIEI3bfmaRDmeBKelmoimnrbNYDftztl8Ac7ZOljUqCpLN2UpcMvBSdBQW+Xz17sW621HIymA6du2aTs8XO9m9ZYDNEiiLpEDTGsZu56hv5mf+j6J02cbyNckjgJYs5BEB6UjEfHYMQUVKQmDhTXRjB+5dAJSex6ZcTZ4bjsONTCfbQN1TX3toZOE4ZFEX/K+63kmwwRJZIvAZpgVrlHQTeuYkmT5WHU1LS9hxY28Zu4kJbOuwsBNQXkPe+4V7n85BiD+D3fNlk09MgVMsFceM1E37fjgZspcKR/PpX+WWqK5JgmV9a6j69LleB5NmGyBoZ3jlCbMCs5hPA7A+UAx4EDG5u+taU2i+BpMUNp1/t5RWitjLTA4JHHazotmvS8PgfX94YOe8prE62zWwU6/Jw5tlzMd7ucDXEZs2muGu7Em2X8iAy3PxLSZe+FYUbSNUW6digBRNYnVtCaxVD+WqgllkcSdc/7ZmqzQIQS42pLwPHL8/ym1jxMFqMfaqq7r0fVlRUjAbNpjj6Zh1SuhB6EiIS1AEMusE+OqqsLxsrHCNRp1U0cRkq1dOlo2ON22WCuRxOeOVwCAT98/owJZqwDnb2wlml6Mgk5t9kHCA8z9H6xbHbGuyrjQQQDM90slIWw/wGlNYsmZd4M9tkef+7l+LVf+y4XsDjYjHwZ77vFztnL6JGqCPRfxbDsFkug0uabbzYwMm6FOipTyP09N4hwT4Zr7Z0MLDMKRd6+3RgTLHyev5ceYZ9+ex67jGCjAENwIJbDlArDQn2FqokNfhqXEAqI4bREwtrYQCOs0y8eqHZRIk3BtHHYNm4AD7L60aTts2x5LsnXGsqmw3dnnhkquOGgn295GSg5cdVP2Ppb3AxZ8YBIJUc0PJZJoOxXwiQs5VrF0JrJPsa0zQl+evU+wPAYev25+X51wYy7B9kEiIyVfT8UVSjdW7TgjYoyTFtJUZTSjkhkVrskMk6ziZBxxLPPeeQiYhyQSLRjqyANaQg5iAjTMQj7tteS/nhsXtttw55GysHWJqrdT6JAzaIMTuLGKkOHxtiOSWAgSm2kAzG1STjaSrG0TO1otLJKoppuWM4OuHa4anG512WAAuH20BAB87uEGd0+Wxfe7fVbFGAR+GqT0WSErsWktY7m/WUq4ptwCYJoAks9J1iQGEvnucctBokWyNEiiRW74OqkYu4NCKJyaS51ysYzrVMiGi1CwaCdg+yQC5YRROEdLd+RqC8O+aCyVcK7Jd3vjkVlL3IA4ZW4vNU1w3zjPALvfy/G2zvPGng93LWeSHWaMj/iwTAYzBuNPnm7qI4kadVOv3pVMuI4BkWIPmPQ7JG/Hg+FeGksOSIG1ZWNVUXdkC4yli3aqzr/fzkUVJAZ+SW6oTeZPuweU7q0Jklhgl8XmaI5NruVBWQRzvKaetsWhg8TFIfDoc+Z3EbJ5C2wfJA4XLp+1mELbpRsrRLIEkSoGYAFqQNcoVIFDMvykBAGUjky84TaXJQ9FgHK1R+4cJ3TTwiYVE6CZo9xqs0WlcSG12PxkMsJzkMQoRYZxtoYG8O44uiZRejTtzLhSkBhSQljhAhfJ0lJAj1cN3jxV0k2HrLXGsQOA42WD080O652Obnr72AaGd05Wxfc3wZoADM4182wH9ySzP9VVTLhGlzgaVTkL42IZYSCvumiFa6Y1icUg0ZHX3ynu/zAIZjL502eb7BPn9Unkn20XYW1JpAEIUBtFsHGwqMfkiLZPoh+k6AIpgEdS5pqIYL0+iGAdEgmn2tnbNErJcylpHt1Rg9LVbr0fi+San27LB/N6ziE3Pz26KblMLpuglyCZgAbMd+oUe2KsTyIrAucGiWxS5mCkmxrxMhZJXC1qX7iGZEC0nam/1iRWwiQ0MypE6Zh7JKxRFtsVkkfRtnQksufO0RyrnCiJCde0hG/urlsaKjMAExg+fm34/ZgcdPH2zAeJTPFpHQQAQDm7NeXjD59VQhKHm9UGKeZ1JiMfKukxx5sI3jBoW8A9l+NRAUAQlLZdX6R7xaD+UuF8jBJL0UaDAJilIMaofcw4l+pi5sifx0kgq8x+MuIb7vG0NYlhANCRG6m3QSloW4AVrwF0dVK7tlcp2wFGuON022Kza+mAFABuH9nA8O4xESRGkHvGua6r8J5kBSCmiQut2qIGgQ8TcPI5pWfbFWBi1TytAI0WSRwC0zG4KdfETQWA2CbdjkopkcQUk4BtMyCJLCK+bHxxC/b+r6oKL9ww9y/fk9SvSdTUKYc1qKxTPsdODszz/NqjNQCr5JozN1Gro5tOkysM4r9orOCKJgBw66to4SDxS0IkMfP9QqYSU9svZhIXEtzwwZ6Zo25P9Or7x+CSmGNtA1lWlA1wg0SDJB6QGdBVYwRvALP+MMI1S2e9KwVeroXsAja5CLjCNfJ6JmhL9UksXPNwPQA41fUYw49nhfivCQuiVPIRAj50Kx43SNzTTd8645SNEkhi4eaQ9wE8jWTM2LX+plEO9ux7AYVwSiTbXbqFR4fQddJIBzQMHHZdj2XJ2a2BLoT6C05h2H/KzLGMJMrmMK0RKTu7k3YPxLjG2UTtHOdtbKwim7aXFyDy235wWaJYToRryI3UrUHVontHM4LE5aIa60MA26eOOdbpRt8Cw0US7zJIYmRj65jkSh3ek2xz6VBxV9/f1TrJ5XGxPok5hkFVVVgFrVzYtixjkNJ2472pcZIFKWKC9JBuzdYtLRykVJN9HoPEXUcnZAD/2dbSOF+8eQCAR+0ntGQiiy/jvORif7lI4sGiMfdY22O1qLnklnO9NUiiRzeVfYMOUpxgW4MkOkm4OUgiK4zkXjfdHIcSAEWSxBVOUdUWVtV43jVq2ovG1M53Ha+SDNh96fG6BVBm5IitFjXW2prE2iacWrIHsxnn+xeqIL33n4E56qamfjJ9rHBtlXkCheAymKM5djl4jiGJnOjitN6YXrdWJ8DZPfP7Hkl864xZFMIbhMluhXRTXto6RBIVwV6AGgCk4I2ybm9UpHKeUVoUI6QgEjWJ4XeT45m55NGGzkNXufMITBW6ynRTfZ/KcZxHI2SFa+aJu7iS2HIdSohgOE+6JjGSgNCKVGjRPQ9JJOqIAIMSbHYdzrZm02Yd3iOHbqoTrpkXJGqRxDD72YNENuqwTpYQYAro3WxNYuzZBgg6vyPiYObI3ScukigOtg5JHBC3lhOu0QpnATbY82oSGSetNsHzetepaKMujU2DiABWhOkdt7mamZhK7GzhmksMEgGLJh6yVNpYCYCydOAq6I5uULojaOtmjNQkWsSZeW7c5GlPIoKARelskrA8xl2DNLXsdR3Z7xU1wO1A5eTppua+ejCIIrF7x2phkcQt2QLDVUo2CQHqUF55CZuQmZTqEAnelLppribdfKafJHQ/gxKu8XwuItiLsNk6Zg+u7HnUJCUBAMcv2N/3QeJbZ0zx6dTZNT9L0rfANEgsO03DvILsQxEBq6ZZa3ceyXF1RNxCibZJvSWDgDU1gprEMrc+JlxTrEmMUGIp5dbEdWOQxNg9QiGQc2m7o4Pgzz1ny8bPPpvPImoSnX4/Y5BY2NzC4nJaktwJbrSKYC6Vk0USRaTiwZmRu2c3bVE33ShrEm8e2J5HVE1iJNvKbGwu1QtQJHLCtYQQpQpFKuSWLs8xjiSWmkW7arsA6IDPFU4Rh4u53rFauiK1tQ4TcJwj6QrQaMWUVosa622nQjZ8REpBh4LtI/jBl25Q769rX5VTQ3f0HDtSFfU8duPQPKeHZLLJTXhoeq66SRmmzklsLt1xgkAq7kmvVYo24ddx6w8wqHArFafdZ1RTy+7eW/KTO56L0unppg+GZ4dFEg8WrnANX5M4zlFxj4QlHxrhGg0wkkIS20JAG6tJpJDESMKVSXgYRNB/jVFYNucR3jHpdevGS/b31T5IfMuMWZTDAIDJbk1USsmgIczYaZDEWJ++IpI4cWS4MUCs3pJDsrTOrtukeBxX6pMYIBsAp+4YCnDYIP0Sz7/yfABx4RpKytwJ9liKnrxnrEkchGtKdNMwucJKkrsUXEZG27V33DGIRlXxtA7ZtEXwhlUqPVo16HrTS02DJFZVhVeeOwQAvH34mbNY9pMvtg8QQbom0f6fCe7lVE97ZBHHSiKJ6e+3bKrRYTLv7yj6m0s3lfHMtZNxtnavKye3al+ki0FkAQx1LmaOrWJtBcy9vN61KqfVVU3UoA0AxnYz73+Rr5mZqEIq1x9AgsvLdV9OVvogse8H+qEmSKmsA6qpZQxLB2hRksbfO5hgwwrqOcE9yXhpnQCY75NoEBiNY+2WmGiQ3KaaBjbM8VyUTnP+Zb15qExKejWJZJJEWreIercGbXYTCRRqHPhOmqAtJgpTAm7CmkQNkqjv1e2XzgB8yYfb7sedQ9FOXrS/75HEt86Y4tOplH/55p+rUhpm7OhWCpGstTuP3Dw9wRsCNZiTMXLn6QZ8W0rdtE4WKac2qphjbeguhfklvltpH519/oNxfMNbPdoJSON4IwggWWhaOGI4nu2TqLtubLY7RjdlHVcJvlhkG7BO4P1TXesMkcW/93iraoEBAH/79309/ubv+UV4+3Nlml54T8rvDEoX3pNUTWIdUzfNjwlFKmSqFN20m75uhBlyTkLtCdewWfIokqigbWmRRNcZYdsEVVWFZV1jMzynAFejBgxow66DRu5+6SoeK2sS/61vehUAjyQC/vPNqFsD5l721Wzz98dF2M0BSWTaXwCOKm3XqYKUurLrnKYGe+kkXLuep3IaCpy9jzXtVdzgkkZJHcaRVoG1LezzrtkSk56iOtpxFknU9Bx2Ubqu1yRyzP30cEAS2b3DpZvuum5sBZQzu951qjresJZU14LE/J8BD8KWOGIl9DJWk8goXIe+q/yuLd2Q41FI4jBOWzrjIYlvYZC4KL/l6TZmMw0V+JiFK6VSygZgtpEsiseScX6fPvOzdDu6jZRlHEstCx1ClkrlFs33ffn8pxqn1hnkIC5cw6mNApEAjLhHoue/FFxWU8EbtkZkjmMhmcW2s83EmUyyew3m1CRqKMkuBVerbvqOO/rFVOTtpXG2BkkEgNNtSweW9pgNPvjSTeq9qewnR5EJkiRs9n/OOMexoIVrqiltCCg7octF5fVJZGmLdW2C2V3bjyIQGrqpHJNrgVGr1x8xaQGgCTYAU4e73pnEJx8kOsI1ZNAg9i9/8TvwL3/xO+j3A34PPDaYXTZTxsVlCtcAwMmBIInKHpAOTVIbSGkcSYP2uG0iqGn6VEK23mySJOnGvaR4LGefoinQg+LuWFtIJjIB85xp+4uGSUnmEXADHJbKD9gk5KyaxNaiUlq66U6BJIbnhE0kA5icS0rgMUQFC/dlSD93j8scz91yWCQx1nJDM24UrmEf1JM93fRaGNNvLEabAwoZi0CllKUf2sbZ8MYVna0aAWowzKMU8AVoA9VLMAjAYs2w0/P05bcBot9eM4X6S3S7MJAFuHq/ScaUrImoK/888PTiaUsQDpGdioRwfRLtxmbpGcxmX0/UTZnrNgp9KALZ86ibvu2WQRK/5N23qfcDLpJoMrus4I0rksM2RJ5jIUoHWAZEzsJ7i3VkQnSbFVxx6wvnqiuLlYLgpSPABPAqjYB1QNc7Xqho6SRXZH5lwa2QyaCtyepUKpmApZtqhF3kWIBe3XSOuXtASyKJrkgLYNYgZt06j0mQyK4HI7VPiYDFkmJswm+OcI3bX5GlLcYa1bMIsGVF8WjncqDS2kCjPEY+Wq1u2viJa0CfcNUEwHNrEl26qV64ph8UQ3kkcU6bGnk/wPm8oS8ptmvz93PI0nCPW6KphsfjRcj815hnJ2RNyGdRtkcSr4e1Xbl2aVLbQ2R3QySxV7RSAOwNRdNNg+w/G5SGwjUM2jNyz4OHlM9+6gKAGJJoNsX8cYCpcE2pJjFVb8nRCqbnv7S2hgikhtoxaVpL0U1tbZWGI+8Wim9aLriPZWjZzWa90wUbYh9+5RZ++9d/AL/pK99LvR+wSIG2JtGtVdLUJM6xsJaX2aCi6ppEVVyIbrNN4EORCnktO8faqvSOxxsZBrmaxDqgH/JO2tGAts0RrrHITZnuKNluQ/OthtpOaorm+w3nAeDvf6GkLeqaDkhXbp9EBSVwrrkOHnvdXLVFwNz/l/3M3TzQ1SSGSpIAj4CFlDQWXZ0jXBMGAFxNrh8AlISl7LHgJY7Ye9KooLfq5CLgq5vSdZOOkid7PMsu6FTqprNrEj26qa4FRqusm1zU9YjusfXNoe/EBNzC7IgxxUqIYNQnrPL+q2WK+XtHuZZ9KrDGIKyuumlJkX9iL36+M/HLS0KXbB8kdh1V77fZ+TcVkIeNJyqlLJI4UiYCZ0sRuNV1NdYfaSXoVeqafejIc+Nc+W2AC9LDrNGukP2Jcc97Yo4TKX+SNldXRrRgdAhJBDJEG9jNfm6fRDfbx55/wBdJ2JK1XNKkHtCJG7nqppoWAID5fn/gWz9EvVdMGmXfP9uiqriWIIBfq3TZDmtYy8ts+OEYQ3fkjhU+N9S4aromlDfRqYPArK+Lxhcu0NAPTeuSdlzTGWVaSYi46oKMgwCY895UfE2iOZ5p8SHfkfUrDoZeagcLDZLoIimKTPdMc+uJNMI17vXedj2OlXXAWhMk8Yikm7q0cE2w4bJQNPT6OWgPIOdfV4MaokS7lqSbOmuCSkypNgGwZv1356hTN3UbnpvXqPr+gJXDq5v6NYnsfuMFiW1PnX/pQS1KsZo+ifORRAmKhteLTL1EH/IikjgVuymKuQV1k2aeXOnG5HjEeVk0NuGtFq5Zcm2FLtv2QSJ5g3goEbGQhyqlMpqt9wvRNqYmDhiydah4Jb0J2qAZZ8e4c8hZqGwHzEMSS7QoW5/gXzcmaDZzs8cBOOEgM87UCsylm9K9BKt5dSxjv7fW1iSWFLqARE1iQbjGQxLJYBtAXN30Eh3XA0e4ZtXUdN3YrSPb71DTAmOOuQg8oFi33OemByqy31hInab7KzoIkcyhNCZZW5IZK0ibO4ZGEldD6xKFuqlcX3HSmKDUVbhuBsSUDxKN47RpWzR1RTmEZp7muxnnnxuzbOox8WPmSg2bba5TSCOJTe3XoLbd6ABflr148wAAcLaNKCtFzGVp6IRrqklSkkISnSScBslyNRa0AYB73ShV1NqnoNOU2CEJpGGSuDoEKpVYd46KfUpKDDa7TkWlleTiyFxRIInrYc3aksJNXuA8M5Gg8UnMsTAeE9ArvDPHTCKJhVMpf3fHsjWJIeOFFo8LgCJVEu7X/nng5/8R//5LsH2QSELN2prEpEop8cC476cz8g7UvwCftQ4bZ/PjpnPUohSy6VPqphO0IV/LEssYMc6ufKS2vUd4/nV0U9fZVbQpGLPP/hxyZiWxrSPDqKS5SqWamsTRGVHSeEKa6mWWH43CNadbFSLotq+4iiDRpchQWcyAAdH2PXmtpzRVLbuAdZJjCaAtgXAL0iamcYAOnf6WAEcvluu73vFIYkwFmvUPFkOLj/W2o5u5yzzvnW5Uwd6yqcaAW6tuOsdCWvKKoHMuG//+Z4U7zmNf9I7nAAD/7NMPqPeP9d5K4Rp/vRteY4KbpnJasuj6JLpMJU2jdHm+GeEmGefuU+w6vmhqTyVWrW6qOf+NPnEN2DXh8WZHzxEw+82qqfGZ+2sAfHnDQSPKxZzgH+Ao7g7tdOYkEvhepiGSWKZ/AvF2FqVjxmoSS+wyM84vA5P5zkYSFSCARihwtA/9MvPvLbS9cA2xSE6y+MTCZR0En4/MIll20ULxWO7xOgfdm6NkyDY8d8ex9ZaAT0G0C7Ku356MzbYgGT5y0iexMMVQJERDNzXHk2P5r6csjiTmjwVIhtb8rqHWuAp8+ppE835dTaLtvwYoMusz6i3n2ihcc7ajREzEXrhxMP5++TWJ/qZIbYjNtCeplu4L8DWJvkgFmwCa9kks9UAFBGkLgkTyHjla1jhzgkRGmXYVIIk7QjjFlZ8HeEQWEJSoM9RRsiZO5imOpKZPogQbQpe/TJMAGNAhWe4ezKprnse+8J0mSHRrX3PmJ+DMa7xwjfnd1jKWjzeXbhrqAnDr/8CMEnXfjhMOcv0ETQJiOcxxjrqpvk9iBO0hjif7xummHY5fHALA+Bi3j5f4zIMhSFQgiQBwtjXHY4RrfCSRE4kCpiwglv5s3m/+T9eyBwkg5piNUzMpxqmNmp9z6vtDEW66v2LICrzk9fWi7ZlHEpk+QZPaHmLhslQj838ZzkDvZly4aJXGmZ9uTRBzLxq6l/2/blwIozPjnLoGTU1isFGXMplh5hPgahJlrLq2KnL+AUI4KCIuom1SrAnAvMyiIrPlKsyOfRJLSGIdq/UoHsrLrGsywnNNNvuH6x2ecyikJXPn9J7n+WbicyxWg6elafO0oSC50oHioLsiFWwCIkTSATfjmr6/Fk2NR4NzBgzng60lXZmaxLUKSTT3yHrHBzcW2TD/1yCJ0uJjvWtVKLXUJKqofUHi7jITMoCv0sgiWcsBWRLbdf2l001vHi7xv/32L8TH3nWber/Mx1WOZoI9l5WjE65x6aa887loqpFCy7eOwfh+wLCAGLqju25pauIWQ+9OTQLUltzogr1FZL9n5ikMFFmH2OcNAO4cr/BPB4SaVjdd+K2amHUhVDPXiBu5e/ActLnt+PMf65OYRRKbCJJIKH6HmhNmnn0xURgteSIo142b8NYK11wTe+aDROZCh4W1zMI17XfIISKTZu5kABBSm/qeo3/W1bR1A+vIhzc/tyDUOG1bb67l8x9BEkuc9YhwjQYR0TYFn55/7ro1dUg35TL5EqT3TtaUo5vaTUOcLlo4QhyEndBN+eumUqmL0BYvE91w6XxzaaO/QNFyY44t6tBJ7nCwzC/fTbD5alTqwueNzsgr14QoktiVN1MRthArtcRx7WjZ4N7jrUrd1CKJZu1inGTLCrFCDpqaxG3b4WzbKYPEButtRwdfgAlItcjeeewgUGmcI1xjsv+XT4T6Nb/w3fR7XT0BFd2xmq6TLHLj0U3J07Fq6rHdj3HGGXVTn6Znau+5cTuHTaISbup0iKCc6q7rVUhuU9foe/04SRxp6aYA8NyxTUbeOODccElmSS0jo7or101QWZ7uW2EtiYRCOwqxkG6qEkGM+HclEGDKLlOUZblIYtfjqOTfxeitDN3UCWbVwjXXxJ55uilzoUNxBWbhSqmUagRQACcgJWmqEnD0LNQffDe2JtGnaJjX2Fo6l+vuzj05JpI1KjlBdXD+ZZ50vZ8SJQ3Pvz0n5XHud2P7vS2d+0QjXCObxrbtRroGU6fmSmJv2w5VxSzItYMk8omEpvLFDsxnXT6SCOhpo//Sh1/Gc0dL3DrkEcg5tpyJJLqBFFN/DQzodkCB5lgCrgCHnUPxWJGMMFCmm7o0wJacI+DUJM4Rrmm78WdpXJhZN04yOceFoJ0t3YIBMNTZTdtRDBkxobYCukB2rh0smrFHJdPvE7BJKkm+mT5x18vZirE0WKXqOX0Sl41P5eR7ENpnx/g/5TFhGcy25a7bsqlG7QEN3VT2YG1tJ2BFWsxr5WO5iVOWuQVYJPGxIImK5+aOEyTeOVlRY1YLv5/vERMkOm1ZdOff37upGtmAbsomJVPqpqU+idsJRbXc8zzGMKNEyAI2IUDSW6/Yl7kM2yOJxIWO0baA/MV2s4qArt8eEEES2eByzOTr679U45yHRjZu5tZ369QsYqCH+ku9bWJIorQFKVlUyl+5+GgEb7wianIhXy7EIenVjgUw1CQqEUh5/6btsSRUQN3rxp5HQNBt8/uVqJueA0n8U7/xSxHsHZdii6AGj+mTNUkA9Wxm3Re8UfVJHJNiXOPgEEk3Y8tI4oR+S9Roih0tG5wp6aYSEEp23dBA807aJOGnQOmOVw0++eYWN3Y6JHHV1FgP9ZY3D7ntfdnU6HqMTvllq5uuFvWIvmjUTQFBsCpaXfMqLR5sKJFExTrpOteaRuluK4WWRBJDlGjX9WOQlJ1j4yKJCkpsXc8KtgGzXs1SRe10qqjy/D8aWllonP87xyYwXDYVTlZcEkjWIEESj4hxrgYBW1so49x163DJn3+3BQbdJzSyB5T6JAr6K8dgawTl871jlVpn1PGSJyp2CIRrnrQgcY8kEhc6vImZhatxFiyAV2kcszHOOIqiF3Ct2fqX8Lt1fc/VH7nCNcEcsvN0aEMs/C5Zrb4PncIykuh/N7ZucipJztJNw6xREUmc1CTqags3Xt0Gn21162bYcfL+bdtRjrWMcSmxtHDKBIEsDptti6Yeg2ctklhVlaoWZa4t6sprAcBs+NJGYRzTlyXCgXjiSCtmxQR68vcYjQfI35erph5RPRlDS9CvrLrpasG1PFnUFarKQRKJ4C3GCmGdtOODBU63LdbbTiWmdLA0NYlnWx6BlOBGnPLLRhJXC3vtOiLZ4c7RUrc44ZSrNI+loQhu/D6VfHDp9orToESroW4VMDW2zJoXokQsvdtdt1hGgsxRmtQD/Ppj5qgLEl2fSxOku6rYACeAJXZ7CBJvH6/oUoqxJlGCRA3ddEwA8UiuK4qkOo9a4ZpgD+j7vhhgiv8TsrCYGkEzRx2S6IpLjeMooRynJlfhA10nu16r7FtgLGQcKukB+Zs4pGewTnIY3LQdn/0HfCSLuRWbwCEEGaSYAMD8rhGucTNUrHCNpe7a10p9wMIgHRiQROKsuI6rRQTzY6bnn7/eXW/fz1LSVoFjxxwLcBbXtrMCNKSTsHWEayjRAucZUKnUVb5Eu7x2mSZKpZetUjrXlk09rcEjauLaYBOdo27K1pa47AIZX+zvGqHxMAyDEElkxRUA41ydbg2V84CEzaqqGkVhAAwN6wtBYqS+nEYSlw0eb3Zmjgrn82DRYNf1eLzhg8SVQ5PUCN7MtYNFbeudSCRrWftO4a7jhFOu0mQ+Pt2RRARbfbA39hLsepVy7oETpG9IYaQQJTJBOrffjN9Ncf9L3aqmvZO7B2sToIBJPGuC9ANHFdvMmU/mvHzL7DchgpYzqZ1/4/HG/F9JN2XblgAB40sJVLhMMT4B7TJX7BxyY+QYYmzpmDmGO64rip4tmgTaWfJdI+1VnjThmuvpEV2hURB1M1UABTgkUavS6NIDgMFBo7L/Mjc44xiH0KrvyTw5h9B+JzmmlpLGNnMPBSCAcvbHDVAALjsl5tWIkNctPP/sOZF7aKRXsnTTwLFj5gj450UcBbYpr9A7ti0nP9+4TtM4x+KwaE3oZVM0Xrpleh5qNvqrtFDNjRGccJEGgL+3JuqmPdkD1UMSOVGkEEkHrHpuqSYxpN+ywc3hssHZtqNRFDFXlXO9LY8N0S9NAHC0avB406qFawTdeOPxhu6v6Pb3uxJ1UydIYQV2bPmGFdO47BYYWosJ11CIoFtbSCaFzbga27ZXMUkAvyZx03L3V7ifsn0q3ZpEjbrpwcJQoKV2VaVu2unPP2D8i67nz+NBQP9k6Ldin/fyTQDA64829BhR3v7Um2f08cZaalE8Js+/K9TCCMIAcZ+XBQ7C9jbu56XGuO+V4zH7jTtH+X1O7TxDU3UTQHI+r4J5dJF2vVbZt8CYbIBxZHyHBMgHN5MsslaAxkFSNLx6VzhFG7TJOOYWDushAM6RjCleloRTFvX0wS45hbHzAZCo7Ay1uRTdtIxAYjxO3/MZ4TFI3OkU2RZOcMm2sgB8J2FNOq7LgOoC6Otd7b11uQvr24bM7jFZH3LVtqx9oRa2JnEidjNHzIp0Ltx2OrRwTYCkyzyBfPJi6QRsAE9bBGwtz/3TrSpIPFhawRVNTaJLXafppisrXKPpk3gyKCU+ONvRSKKfcLp8J8YNttngfjkyJ8y53HbdpbfA0JovXGNeYwU/vECKVaAcEBgNagbY89/3PZ0oCe/lLdmncuEEpJp7a6z3U4jCWN9JV8vu1iRqBLAOFjWqygaJmgTjqy/foN8rJhTVT903QSJTk+i27tEgub6fVhaEAVwk1/xfQzeNtphTgDAAXzoGBH0SiX1xEfjJcuxSnsSjkvd7JPGJNMZxiilQAgUksfFvYra2bUJTJZWl5mZxwgwJq25aO2gDK9Ji5lnPqEmcPtilxrD2fJj/d2okS8aZn1pVWhlXrGV0kgIaRFaEazZtp2rA7ArXbMZWFooWAG2HM1JxMUrj0QbpCvTlPCab6YdfuXXpx5pjc/okhjWJHemQzxWzajx2AXe9QyQd4GoSlwGyyvTIEpNanjdPt3pRmF2H3RBMsXRTTyWQpZuuDG304XqnmqMrp88iG7ImCJX2spFEqZsEeCTRb1RvkmlX0QJDY15POkUt9aKufXEjhbiLtt0GYIVrdsN5pOrLg3uZvW7LwElmGcJy755ueFEYeYtL9+XOvwQ3vapNh1DQx5pExXP64lDewPbgBKZIIlOTaAW3WpUCrpso1NBGzft1QkVhXTrDOgqRbYBEEoNkh5lveZzbcswfR7B5xN9VJnOuiz3z6qaslHx4UwEcHG7rUVAc4/7drWNR9ajxKKDzsjiz20RQAdg0cGb6JAIYWzDI2Ny5HDeMoLaN60Fov5OtLcyPSdckFsY5Wcy64gNZryZR4SRIMLRxkMQlsbnJhnS6aWkkMZTfZufoF81fvtMKAJ99sAYAfOhtNy/9WHMsbB6/KyRJgCkiqEk4hWJWWropmwBaOMm0cO3L1yQGLTA6Xt1U0OLXH22USKIJbiTAKdUKRtVNyXv5aGW25jceb1UIxfHKDRJ1SKJ8r8v2YVZNE6hr8vvbru3H637t1E0d4RomkSzWNLbeuyX3e0B6CepEcgATOOy6Hmdbs54wNa9h0ntHqgl79ZaKAMz2IBzopsS4qjLiUl1vnXlNgl0Cbo0Tf7BoHCSRX0uqqsLf+998I24d8a2Tbg9tMz6pCBJlTutdp1LA9ZXJdaUsI5uELW8I96nRv85pTtTeewGeXSNzc4/HCt50PcZEh1m7ssMCdV8+UX6d7Hql4t4Co+imiUxH7mLXYeaNRLJCmupcdVOAbGQaIomkQ+j2UtNQCd3+Owz3HACaiJJVKZNZVZUJgINAlqvJuji6KYsct72ujmJuTy7ZNM627UhxYjLJ4rSeDq0DVEhi11MUErHVwqLNGvrVeewP/tIP4Svf/zw+/v7nL/9gMyxsHs8mt/w6Xr4m191EWQq0L1zDJUnqYL0DuABzOVD0eicJxAYNd4e+ZD/1uUeq/pZC02NbZ0wTfrxKrwSyjIqqaycH9rnk1U0FtWm9/1+WhS0YWEQK8FWZnxbhmmWQcNUoUHprKx1cmuv7cGjdoCk3kHnS4mVuTaIiAD4IexCy52TYu1vFXrrwrpvOiT9c1jZIVNDCAeCV20ce8l8+VoPVosanB7rpIUU3tTWJc/sksiyNULiG3bsXDrvMjBd/JjdmiiQyCZapn8wL0LhzA8weV0qUuPuUzHVPN33CjJWxjXGmGSRxWqOmQxLZRWuCJJLj3HYDgGlnQQWXXgBmXmOl5OVcyMLAqptOlKyI67YdF6zhNVrd0f9u9HVz1GXNuMKxIsX2zBxjwjXMhiiO43rXOXTT8rgRSdy2ONtyinieuikZNMi4sY5Fkf08j33RO2/jL3znxz0k5jpZSDfdEkp1YU0iq1LqNgAGeOp6KFzT1BWRJDE/w8wukEeKQgl0DUohQeKjTTv+zpgoLkpdYskpDNcEjZPm1saywR7g003Z4FJYCRI0aHuFak3UNbuup5QFAUfKv+2cfeN6uS/zhWtqj5I2W7iGXCbl+kp/vxWBVIdJb7ZP5bJ2kJSeRztljg/OZI7ctZa9W6P47X43TZsOwDybc5DEuXb7aDleg0Piui2aGk1dYb3rsN3xisAekqigjQJOiQ+5JqeRxPRYt5TFHcf6kmFNYmktiSUzmaDU7e+qUc69Tna9Vtm3wPiM/BRJZG7iSf+jwkNTVRXqyqebapS9tDWJYQuMOY2zWWqljAvVnlhFqlDdlLlusohYuikxx2qKQGoREXVw2ek2trFP4s7JWiuQxPW2HbPBTHAvTquR5efQDZcSosmsLxrbF1PTW+5ptkVTjwkPYJCgLywM7nkElPSfYE3gaNqBSqMq++xu2oS63djKxX43NkPrBoa6INEI10j7BnWfREUg61LJdEiinm4q1L4HZ8bZvew2MG598xwkUZ6D64ckDok7JQXURfw1bVJEuIdRA3ZtFQRgmoSf7Itsn0o3ucX2aTVzkvYSwz1Jotuyd+sUv81nd0NwqaOb2vraKwkSB8ppU1f0/W/m2NJaAvL5Lk2YeUbDNilsUsCIu/gqpe7npcYAvk+4I9bXKCJI+pKAn8xkSj7COuUnDUUE9kEi9QDUtZX/N2PK/f0k2Jv0eyMXLZfKqeHVyzPT9fraQjuuOMwTvBmRRLIHYUjlLAmnxJSs2KxRGKRrOfJs3eT0/HNBae0sPhra0GphF0lbpE8EiQON52xAEhnRGsAqqQmSSPVoqv0Fkp3j0hmnoSg9zbZqfErOlsjkh/1F2SClriqvLU5Prgluwkm/bk3pprnnWxwkaaXAKNuJzQ0ShSYpx2TVTV3qNI8kOoigQlr/ZEZNoji39xVBw3nMrZNikbOmtkmB8f64Zi0w3PZVmhpsL5BSBCny/SVpwa6TEnBpUDqZ09ZJ8LJ9El1WCF2TuJQ56tpLiOicRt00RIB1dNN5iP9cu31k1qujZUMrfksgy2oJAGE/a6XgjZOUpPaNBJKo7ZPYUXuiMLB0wd7op7m9eYlEqOsDac7jdbLrtcq+BcY4TmEWQaPKGdYkchRQvyaRC/bgHacHWVtYV56yYK8ILjvnWIACSXS47vJazsJ+YwDLI7dUF82m7ao7suje5Pxr1R07qGhDc/skjpLY225AEskg0RWuoZFEm1nUCAmIkI6I8jxpamCXYYvar0nctV2xdYxlM+hQCldcCuBrEl25bzZoiwkJcOqmln4I6Jxrl5KppZuKswWUnWsXoRjnSAvXWIfzWNUCw3VauWd7bAp+ejVIoqwdjzc79D1XyyV7wLazglvXLSvvthfSiFS47aQ0zInxPG4HBVBy3KQmkbjeouS5Vvap9BK1irVc9qk3T6VuknsGJDGvEdNzy1k6hQIoAK+m+SqQxJefM/18NQHpwaLB2XYGkuhdN+5Y3jhyTZ4w9YhnJ0UbLaqNNtYncY9XVK8f/hwiieXWGXaf0rRpuk62DxKJGzlUUmKdcrduT+4tGklUPmhjbZtTE8e1GwiyMbPqjwQ1m4nSEecRCNAGgkfuK3Sdd446zjpPNzU/XeEaSuzAo5vKsYrDRprKetdi0/a0Q2jppnokcdu6c+Q2DXfcnm4qdFN7T3Z9GYF3kQ3gfHRT5t5y5b5ZhzD2bDMMg1hNIuuAumuAFklcuzWJRbopJnNkHdCXbh6Mv7/jzjE9R0/dlFRFtUiivt/bHJtDd1y6SCLJ7rhqc4ONEclineRWl8gBLNr2aK0Td5Hz/1CJHK8W9ZggYdAXwK+31KmbDvfkKArD1yR2vbLlkpNM0yRyAOCOs35ohWvm2Cu3D72fjB0sazxat+h7PrgMfSC2/jcs1bm8mkS/3EDGlZaEsJWLjCv6oI0fA5jfUayndvu7auqNr5Ptg0QqSDQ/3aJtgAhuqspzEADOka+dwK3tdbU9fuNm4lgTuil3I9e1pVaywZ68Z0RkSQroXCUrz2lV0k/Ca11aI0dnd6L4mh/nCdcogu2ocA29ATc4G5BEttbj0BGu0SKJu1bXt8pFidia3Kfdlo11JNkWANO6aF7IYSpcwzmEWyWyF9u0uZpE8zcRX9q0XFNwsbcPGXlNkHi0bEYkHWCCxAFJdDPy5DP6yu2j8fd33+WDRPecsQ7h4YgkXg3ddBUEABq6o3HkBUm8XgtDtE8ik7hrXMYRX1t4uLB14gCPJI5BugJJBKQmtxtq/rggfdlU2LSGSdKRiWvAouAPznSiMKG6qSpRNcxRw1y5e3y1SOI7h3VBo8p8sKjV4jqun6YJ7he17THNJnhdJB3gWF8LZz0YxxFIYl2bMjDrF3LXPOw6YH4nkMRAuOZJpJteTym/KzRO3dTPIrA1YF42Rql25iKJLELkzZGkTYTCNW3H0lsrzyF055AfV4/1nSxKF+uJw2V/pnRfJuBwqbT0HBNIIt0CQ7mx2QyV05OLdS6WppCdlTEHLJJonOSWypoKbXTXOZRYVQCsrxF5Ws2VCZd7unTtlkH2syVpQ1MkkU1cVF7NEquu7M7R/T33fK8CJFGT8ACAv/o7vgZ/7u//NL78fXfpMUcrQ9vajH0SCzWJQQCsQTvd973zzlHmnWmj6aYTJPGy6aaDUI5CTXUUrmn7sZ7o2gnXBH0Sq4p8bhwnmd3vARvcj70ELxlJFHVfKxzEXDfHSVbQFq1wjV7dVARoAB1zZdf243VjzUUSr4JKePvYHI99tgFzLsdnm0YSfT+NvW4GdDD3R69hrrhJScKfSQvQlOcogm6A9Qtphf0+PB43Tkpn9nTTJ9CY2pmwBQOLnIXBHsA5yXWAQKpUAnt7vFmNsxXHC6mcmp5Eu64fkZG5SGJpXLj5yrxL5lFpycBNnAFL91UG90MWU45fMq9PoiIAAyySqBGuERqboZt2OCQ27VUQ7AG6e0S+2z5ItI2zAVuHV06u+NlWmiXg3P9SS8pcgWXjP2/MhtgEz42ZL+8kbEd0tafvZQB48eYBfvcv/jxVy5PjVYPHQ5IEmNEnsZ8nf36i6KUGAM8Pjit7rMMrrkmcI5wyqtl217cFhotStArUWPbgXqmu6dZ2yvEZk/P/cK2jF4tKJtu6CgjQVQ3ddBmgzQp1UwlIAZKFFSRqVUiiEySyQjLnsVdfvgEA+NaPvp0es1rUlrbLIokBKl5C6MZxtS9cw/pAoUopkPdnQpaMHI9S3HUCWQtwlBFIAJOWGxo9DU25wXWyZx5JZBynOnC2WJqkG+yNAQBJyZzD6wYsrZLpIwj4DbABXd2SRduG10gH1Izhm4u6AihiTE2EK+OsoWT6VFoukI2pm1JtA6J00+IwW5PY6vvvHHhIog5t0DQO9rJoiiDdlbvX1Og8zeYqIIq65rKw4bsZ8r7vdaJUY5BoXqNpQzPqH4EUkliuSRyDREXCY64drRY43bY43UqfRF2QyNTMuPbXf+fX4pNvnqrn+Z/8pi/Dv/NXfhQffOkG9f7RIb+imkQJSt98vKGP597Lkiy5bjWJgNOqRpEQWAYOOU/JNOdNahK1dFONcI28bzMo0gIk48VFVxWiMG4LjEVd0Uq2c9RN/X6+/HkEgDvHPF39IuxDb7uFH/qeb8YLNw7Kbx7sYFGPiKymJhGQ+lotkqhLijV1NQm+5LNStkiwy9jjbQNWTtmXHI7Rh0FpCeCwpTNsK5HrZs98kMg0hV0EAQBLr3SDvVYRAITKUlyQOMzNRRLJTH7YAkONtik2DZdLPo4jz39YpMw0Mg1rQlnH1VJp/Tmkxwzz8oSDiofykETNHEeUbqdXAJXaEo1wTV1XOFo2uDc6dgTVyOmH1isyu97CqqwReVpN6jb63rYAWBbvSX+jd18rjXNb8ADcvezSTWkGhKytanVTSUDYwHm5uNz75GgMbrisfIjkajPJX/D2W/iCt99Sz/MXvPsO/tp3fw39fqltk5rEy0YSRYH19Uc8uuEzUIRuer2QRABeb14eSbQiHJqkmFAOx5pE8nRMkFxFonC968bEK3P+Fw5NuCP3RDkWAJxtu7HUgbG69tVNWX8LGNg8Srqppqb5okwTIALzaxIBvZiPJxZIIscu+giAKk2JIYmsmNLSYfjJs1ouHZOgVKeK6iaA2BKM62bPfJDI3MhhM3fW4QqDPWaMvMeFw2cJ15D85xjdlFnI/cbZuu9mjmMRES1tS35neOQTtVEllZZFEmPnX4MkqmsSFy5Kp8t+HixqnG0NbUhTx3W8avD6oyFIJDKSY91Y2490RQ2VdjPU9uxjxGCzIfvEuQqgmnurquzzonlu3BYY7Lplqfz2NUunzQWJAZKorEmcY+KsyjNQoqraPpV2D7iO6nbLpkJVAQ/WV1OTKPTZN4aEE0U3dRAptkzhrTDpC1jXFT0/+2zrEn4Hi1BwiAumJuqydO1qY+imgr4QSO7CWcvbjqeEu/eg5n4UjQVNnf6ISrU6ui8AfOQVk8T52Dufo8dctR0smtGf0SKJUn/N0k0npVLnUTfN3F9237AbB9O3UI5nWYFcO51UyROPJD65rKhnPkhkgqkYbQjgaoJCARSW/iCgWauA7MM5sgGRc9/zNDG3l6BCFGbhbIjbHZcRjvVJZOi0LiKoQUTc68ZSayb1R4oF0ryf760ITNVNNeU5RrjGBGCaRt03Dhf43MM1AF32f9t24/fkqLRu9vnJXFgv2hZOwG2FI9h7shtZECxLwKW703OsHXVTsh6lDhJwAJwgOP0B4hBvdh3YliDnNeld+NoYJBaEawIGhNYBvSqTHngjQqpYE+aYnLc3FHRTd02QXn1XoSapNUHbDhY1ndwKVaC1CqBvKmtJpU/oa4/MWk4jicsaj9Y7mqIHWLaDiOuwc6yqaqwB1iDbkuBV9UUePl5L9wWMkMyPfe+3juvedTT3eeZFikJRJO5YbqmCKbkpj0n1SaSQxNb1Cctqo+PxQrqpks0mqqjFHutOf9fdE0o3vX6r7BVb2zK0RT8AYAprgTiSSAVuDpLYkXUsoXANn8Wx30vqlrS9beb0JGq7Hpu2RUNkXOeqmy6bCNpJOsnuYuDOO2XT889mtTCOm9NLcCPZTxWSKHRTXR3XneMVPvXmGQAuI+kFsprMriNSoXGanmYb6zudZuJ0n0TPaSofy6WbatrbLJ26ybblRATCWl4AlHqiODtSW1t6/0WYBDevPdygqni6qYskXlfhgsNlY5UkL/k8SpAyshIodVO7lpwNjqumofhV2eHSKOB2Cpr8wkH8NUmxsbZTSSUUiuTP3zNrOat4uWp8uiklEjJ8t0dr/b11+8i0edDUyM7pk+jWt7GKnK4dLhvcVLSkuGpz7wv2mZExUn/NrlsuwMH3yq2jNYl6ddNy6ZKMlXHj3lHquR0cj1dFdZDEa7z+52wfJBJI4qRROllLF2ZV3M/KmUuTpPuNDe/pe9242qs/8j+rNE7ezyKrgEvdNVLmzKYRg/p3BFLa1H5zb5l3yVzOulZcxxccKh7Kp5t2vCNfVRVWA7VJW+t0sKixHqT8dUHiEj8/BIk3Dni66bad195js9u3wBBbOkgiqy4YIhQA3yex781aorlupgbYQRKVSRIx5vtJdvxs21khn0sWMhmDxEdrHC0bur2Nln71VphH77vk4EtouhIkMkiRTQp0OBvUZY+uYZAoSKKGNroc7xNd/ddcwaHjVYPDZY3XH22wbCqckDV/BwMDRSiIJeEswD6Tgkhp0F9p96Clm86pwQasLsDTtt+4tHi6Lc5Y72quG4uAeQAHmShxyxQAuxfkrkOc/skiiTW24t8RrBXAqRsOQYcSAjkyIJ5c4ZpnPkhkgo2wRwodOFSW+6xVAHUzFqxj5x6HDi6rqbgOcx8vPF63BGDlcR6SuOP69MXUTRmJfSP2IYiseY2ici78puAA1xMTCJBcxQblFtuzm9SyqbDddWpK5uHSIIlbZQNyV8ntpZuHxfe7dNOxtk1BpR2dpidwYb1oc88li5y5NYka4SD3GdVk5Je1UY3re6m/KA6ZJOAAUDVn4hCvdy22ZHP785pk4V97uKHENKLqptf0XnYRhstGEleLGqumxj1SAAiw81vvTAse97XrZCOSqAg23GRO13H7KODWJOpowlVV4fkTI35y53hFt244WDTY7Lrx/DNBuiSOHw3iOpqk5O2hUb1mjxIV6DHhrWDltF1Hl/c8Sfa8I67DtzsZ6KYDAszeyyHAwapp+7oYw2dlfMMYu4xV9HdbYIw1iSwrJ+wNXvh+nnLxE5qAeOaDRCbYmNaWmBulKHhTV2PQ1ikCKROkWAroHHVTjQT9RMmQrWV02j3IZ5XMhe03bYeVRv7cocWaBaFMEZj2OyweDsumGtEJlm4XOrua2k4Zpw4SF/UoCKBBKI5XDR6udzSSK3bbCRJfvFlWWIvRTdkEBCD9xp6+TXuOuVLy8hyUHC43uaJBEucq7spGK+1tGLGDkJIJANuuH8RUckHigCxtO6e5+mXTTaWWazPWJ+Zs6VCNAF45+q0wOZ9VdTVN6o8PmrEmjkEuXbXLs63QTa+f+zIiiYqEQCgwpVU31dJNAUs51ahzGrppOyK5zPmX/f7xWleTCNikpOZ7HQx74qhuqkhcS6uga9hZ5Vz2grNX00ji2INTahJJJDHoscucSxdwAGzgpkUS6d68DsNs3DtYEGDiT5aBCjPPAUl8Am+u67fKXqGNwQZJG7IZEvM6E1xa6J13tlZzpOQDdU1WScmjlmnq9mo/Q86PsxviZtdhpUASXWcL4BSptiHdlEJELJK4I53rSf0RmTUaWwAoKTKAka439S86uumdkxXuPd7QSK7Y3RNbd/E84VwsXbqp4ru5weWWvEeednOl5AU50xTbt4oEkNc6QzHOFZjqSAchKkDQdkXqukWWeGT1vCbo4ZunWxwvy5pvrgIxIGv55c3vPCbnc9XUV9IU/GS1GBEpquSgqbGoK6x3tk/ldUYSNQIoYTKHHWeuFfCmUt0UsMGhps+f0E3PFOdf9hdBEjVB4nPH+prEg0WD9bZT+iTOeveEoj05c1tm8EjiECRudUFimJhnk5Jdj0lpVu6Ydt9wg0sSSXTorbxSuB+Ujm1uSj6o06rpSW2BcU23rKuxMdhgM/IjlbBDVXEUxF2QeWDpdlYlcJ66KRP8huM0gezSqT9SIYnO8Vi6Yyh3L8dlsjhzxHWWCxtcskiiRQQxjtOcj51z/tmA72jV4HSrRxLvHq+wbXu89mit2rRdJJFpbrxyrpv2/jfjBG1+ppcpANa52rbdWE/Bqpvuun5E/XX0K904D+1khWskueUiiW054+oK14w1iZd8n7j0OgpJDNYttk7zrTA5n1elGHri1DTzLRjqAUm8/uqmGrl7t+enpnRAVGltTSJ/Pp6/MQ9JdOmmh0TAIUGJticjYIVrNM+1CWRbXS11WJN4TZ/RufbCDXuNRTSqZILuC91UgySOYo0dmVyc+Nfm9dyeE0USyWdHeg4DViSNrUk8r3DNdS03yNn1W2Wv0DTBBgBPNpd1tlxkCeCd5I2jrjlX3VQVXPbWIWQyycthwwDsedHUO23bjhZOcYMGwHLWi6qoTTUuAlokS9Aadpz4w25bEH0ri2HetAT6kLUmlVTFxDHYtr1Kle3lW+U6RNfGLNqus+09VIhUN6DNz/QyBcA6Vxp1QRfZ06qbAj4CqWpvM4zT1iiL7bryuuDTTQWRutwN2HWwmJpEV4EY4NvivBUmiNJVoXOumAb7fJta6hbrbYuDxdUgnlqTNVnaYDDmOqDahN/hshn3YU0y7cNvN/39Hg8IH2NTJLF8vCMHfdfOUe5J2YupOTpBOqCj14/1/dfwvjqPuUgie/4ndFMFKu6pOVNskiAAI0qz4vvGjJrEkW5K1iRO5sghibvOJE73QeITZqxKoM30KSmgDvf5PEGKSgDFgewZfralqTo8a+I+XjbzUFJZpDYtL5ziNht2fzJ00zDzQ82xqb3gsq7KgfNcuq8bJGqEgwCzSVu6KTcG8LPHNw/5Vqlf//kv4ju+9J34nd/0KvV+27ahp7KDYhaRGijJ1xAxuGo78OiVXA2elXbXOU2eJP+4bnGUQMBkZ2m6e5BFBjCgkKXNt0ZTV77a4iUnE547Wo7fiRHtqKrKiEs5SOJ1dRLe/+INAMC77x5fyfE8JFHhuEpNIoPkvhXmzpENuF1RKi2SdbzUn0cA+IYPvQQAIwrPmDRll8CS+X4nQzLgzaEnpibh9+Xvu4uPves2/hcff49qjqYm1Pyfed5kbd0MiVrNXvokmOgHfOxdt+kxc1tgNIHPq2VTyTjzevpCuHsUYPytvueBCtlDx4SrouewP8dC7OAATNdZ3TpnvIf4FJptpJlfFdzABuB69AFSE2flgAHOSV4t/CbwWrEJgFNtNeMwjtOKVIxzVIwT5+psY7KtjGMn12ejRPcWdT0uWOKDMmudofva4FLT780KB5H1pwuLNlhxI5JuOmStN60ObbvjBYk8krhsanzfd3yMfr/XpmNEEonjOLVcrLjR024r5xmwNXjcxqatCbWbthXJoQSfHARy1/U41tBNnYzwtu2pdcE45FfXJ7GuK9w5XuJzDznhGpnTru1UPWjfCnv/CycAgBOSjnZeE+r6SoEIiipzXXFUx7fCDoY5apBEj96tRLJuHZm2RE1dUSUAYu9/4QR/7Fd+FL/o1RfpMeIHvalQUxXE/Y3HeiTxY++6jb/yb341/X7AtneyGgTlMYeOKNLTWJN4uGzwn3/XV+JDb79Jj5HAWfpb0kiiq+jfc+qmY6LQ8bmA/J6zCIM2kv4JmDVZgl+2dGPh7KXmuFzS1WNFtR1uKJLy18WevBlfoNHZgCBI4QOwCqdbvxiXUdtyaxJZHnMUyVK1zuhVlEAJpNxeaswDOgaJQxNsHZLYez/LQaLbgkSH5Ar15zzCQWxAChiqi6aOApAG2Fust51OtMCpLbx1yYvWYmjTMbZgUDQ33rV7uqmYOGTrXUvLdrt1jHNqdLT9FcfsbqsXrgnppowKnFDLNjsOWb0Iu3uywufIFhiAXSc15/GtsBdvGbThfUOweNn2toG6fqgIGlZDUqDC9VQ2Bcy81tsWZ9vWo9TmbOEwJ7RtUm6P4i6681FVFX7DV/AIHWD37tcfmYCPQRLlObk3g246x4QS2/U9KoIBBFhRJGHlXFe0/zz25e+7q3q/3E8P1zrBoUVTYb2zgVRJNRTwk5IAV5oV60EL8PodgiDypRtxSmxRPM6pNzZ+2vVct3L2TAeJbE2iXFgXyWKldsMehBTdzkWyyMxWTKKXnSMwqGuqKJn2IdU8oLKxnG5MAMBspG4gJXN1556yWAsMKgB2kCyWRz63J1qsJpF1JA2S2GG9a1Wb790b8+imc0wSHhon2RVyMEji07dpa81HEnXJrW1rg3TOabLPtk7MSujFfAuAaJBI0E0BoZZZJPEq7hOharMBgKkv71S1nW+Ffd2rL+KP/cqP4lf+gndcyfFeHoLSW0c8k0GQxArXU9kUsHTHs22Huye8Yw0Ikqhr+XP7SN8mYq7dOjL3/GfunwHgKNfHId30soPEkW6qr+08G9rpMMyhp90OAtSYfd7cFhi7tqfQ7Zjv5L4esxFJVAoMyntG4ZqWDPaCoJRVvV849ZabVpfMvy72bAeJM5wtQGpLOCpVWLfHBg4bh6aqlZ8HzMOjFa7RBLIuaqAZJxvL6bY1ffqITcOtbQMcmjCFJArdlJ/jygvclP0OB193lnCNsibxYFnjdKMTSQB8AQ4N3XSOLZt6qEnUCNdIvcEeSRSzap7duG6Vnp0RSdzpakJdJFe1JoT1F8r1R2zbcjR0QQ2uim4KWCGHD7zIIW7LAUkXsa3rqpxY13pk6TwmIlgsIgvYGmyA6634Vtjh0uzdp9uWdgjt/tZh13WqPn23Z7SJmGvPDQH9Zx6s0dQV9bwdLk2bjhFJvORn1LALeJZSOG6za59ItOeiTe6n+8oenH4AxrXYmqqbln3lEElkgzbABw8EKNL0HAYsM5AVj9u23Si49aTZkzfjCzQLGZM1iYIkkpnuxhFO0QhHTPokEpuGDWxsUKqhm3Zdr3JkRgquixIxSOLKFkSzffpEAGLXhtTR0oNdO0XU5jVtC4Zd15ECHOanVrhmRIiUdWOACbjXuyFInOk0XT6SaJxkTabPQxL3wjUA7Kbt1iSyglvuM0qplDobsC64txuilqYdqtQxdNPDoSfaVQaJP/6ZhwCAj7//eer9gqR3iiTVs2CiuHhEIrKA9MBrcbZpVTTVqzTXuWZbeyydhCvbNkDMre28bJMg8dP3z+jzX1UVjpcN3nhkkMTLfkYPFg22bY/HG51DLkjivuWSsWVToa70SOJq4feY1ug57CY1iennoKqqqH/NIYnOHEmgaIIkttx+6iZ3jZ/25N1bT96ML9BYRGrap4/Pko83sVY4xaG2aoI2G1yaFhDMHIEBSVRQosY6QccBYh7QsQn2VrJ9bLZ12uC+NE+jwOoHlgyTZI6zOxWuIemmDrVVQ/cFzLk83bTnylBdCZLYdiqZdlEEW+/2m7bYytlsxiCxlNwKEHFgXm0hwNLkneCSZVsI3T1AEhnnQnqiSYuJqwgS/w+/9hfga199AR986Qb1/mVTGSRdWW/8tNsrt48AAN/ykZfpMYfSgmHHK4detUmt5JunW5VjDZgEEFvKIiZIoptkuSy7dWiRRM35Pz5Y4P7QJ/GykRRxwu+f7VTo6sGyxtmufWLrxi7aTA/OZrxubA2w3xqNS7C7dGv3Z2nPcfuQjz4hJYRo/XI2wTgJZEmgYtXUqCs4bXGu57qVs2eabtqSNYmugwbwYjJNXakgdLGFU5PIqp01tcn82CCx48Y5mXyNI+mjbfx3G+mmA02SpZ8YKXk5l3Ldyg+2VTfV0X0B3aYdIiJsLalbb9krg8SjZYOzoQXA/CDxCpDEgY9fVVwioa4r3Dxc4N7jDdqux6p58hbWizZbF92Oz1sJhY+1V9Gqm4qxTYoBS1OlhGvG58a+tmt7imEg7QYkoXYVtORf/OGX8Ys/zAc2kvDTMEmeBXvfCyf4b3//N+Cdd47oMQeLZhQXua7CNeIE7rpe1doDwNgEXiVcM6B7QsO9TBMk8c3TLd5xm79uLqX48msShyBRgeQClpWwT0paO1jWVsmWDG4MkmgDN2YdFz9ubGdB6ke4/Q41ZRGx4LJck+jPkRVrrKoKR5LMV2pHXBd78mZ8gWb7jRWCxIDKuVMI17TOjc+qba2aCptBbEKjdubWMmp6OQKmT6Kql5rUUbQ6B2g5KImdjkgi/90kABY1w1Lz7EVTY9f5Cqxsn0RgEK5RCge5fSo1Drlx5P3PKtnh0qiwPlzrsqYA8Od/61fgV3zsFdy8ZMl7cZKltpCVu3/uaInPPlgDuBoq1XU3D0nccYpsbiJHI9w0V7jGVXIzgk/l6xbStM1xSSQxEK5ZXkOBI6Ffaankz4K96+4xvR4AFkm8f7obUa3rZkcre9+yaJtLJe/I/Ubs7UOw9tpA57xMc0WGNAGYK/J0FcI1AHD/bKtKnI6shH0N/GgHi3qsSdQgieudpXIywjVuCxgZ576eMjfY0yjsLxzwgFc39WsS5Seznx6tGjza7LBt+cTRdbJnHEkkIeOJummnkNp11C7JxX+sURjUztiNdNXU2DrywxzaaX76dFMikHLl9dXBjcP/JxfkReM82B35YI9UNr6vDRCcf7I+ygrX6K639BLctDq6L2CdkDeVWVMA+KoPvoCv+uALqjFzTIL7tbK28PaxDRKZbOTTbu4aJKhsGUmsxjGdItPqCtDII61B4HedUdxl7skwQwuYIPNwyVDXa7z+qBudkuvo3JleubogfW9xEyTxbNuNqNZ1s+dPDsbf2V6Oq7BuSZHw+9oPvoB/9UvfiVeeO9RNdIYdLhusFoZOqOlT6SGJVyBcAxgkkVUgBsy1Oht0EvZJSWMmCbcef+fG2GT+tuuoFhhWhMYXheGQRCvUyIwBLHgA8EDRtOWGnUPJjlYN7j3WIbLXyZ7pIJGtSZxSOTkkcVFX4w3f9TzVaOkEYAaRooZh6TygLG3FpUmqeqK5vZ0UjWuBoZZu22K741QMAR9JZGWLbTPxDlLypJHy3+w6uv7UIrJSSM3VP8rx3DYFmppEAGp106s0Uxcq8s/8HJ87WuJnXz8FcDXy7tfdFsMaZOT1Wxwtm2LyyCYgbHsVDSLoijZp2AW7tsfZtqMk8msnSSW267h14Xi1wKPNDo82pm7mqhrBa0zYHcLc3QvXzLfjVYN7p1v0Pa5tkPg2J1hjE3cu3ZRNrojVdYU/+R0f003yHCYMD835lyCxqi4fSXdrEu84/YBLdris8ZkHW+w6TnH9WTB3PeWRxGos0+n7MgADTJFEvibRaRWnRBIFCWR7DoctN2Qcsy8eLd0g8cm7t568GV+gsTWJgMn2aamcLhze9b0iaHACMBUCaVVRu05Hrdx18wIpgyQaZ5JFPI9WRspcw/+XVgpyTHfupTn6ATBxLA8l5c9/XVmBIk1tyXKh7yUI+H2qrmuGajEUsmtpPLePVnu6qWNVVY1rkEa4I1SXZW4tV8mtU7AE3BrIs21LORbybHl0U1I9+sbhAo/WOzxa79DU1bXcgFeL2tRoXvM+iU+CvXDjYNyjRLDlupm09gBAK4DKfft4Y9pCaVC6q7a3Dd/vfWQLGMAq2WrKDebaHHVZGfdgFNe5vuf/Ks3VK9DUJG7azgm+NEgir24KhDWJvC/vMvzYnsMj4OCAAAD3/Y6WzdgCZq9u+oQZi0gBgWqTIkjsnOwIG2ysnACsI9VNAVEAtZkOrt2ACKcoAylPFEOXIT9aNng8cLR5JNG2wBg560VFKkubm1eTqKsJDYWK2GBvNaCkVlaZr9sTu44OMjCIi0iQqEESj5c4HcQY9kGiMWkBcLrhUDrAJiA0KqWLyHNDtc4Y1q2zAYFnnN2wkTIwqJsSa/KNgwUenO3waN3iZFVGVt8KWw4iZHvhmvPbCzctMnTrmiKJtw5d9IVU7h4UEO+f6pQk3woTwZr3v8AHiSJOdKToiTnX3JYDmmDvcFnj/tnQy3G/3wDw72XWv1g2RidBfGWuT6L57JA6WvKDouqmpM8btrIo+aG25YaPQDLr+eGywZuPTc3wk5iAeKafBtu7jRFJmIEkVk5hrSLYcwMwViQHsCIJfW9qGanWGVLr5LZgUNUf9SqUFDAPjWyIbGbFbYGxJbNUI9rZdbPUTQXdYxxW+WxXqIimFzc1Nrse650Jiljn4s6JdZqua4bqZLXA6WanDxIdJ3CvbmrMRxL5TVvbg9Mt0tf1d7X1QAB3H480bY9uyvXXunGwwHrX4c3TLW5cQ6opYNkde+Ga89uLNyxKd13ppm6iQotkvalsXP5WmNBpNedfAsvLVtIG/DVH2ydR1q19kGhM2mOtmpr2XeXcnW6ML3OuPomFPWfptLLQrK8muLTBHkuDXgTMQHmtZMerBm88fnLvreu5s16RabMPbu9CTt20Hp2sXhk0AAbd01EyqwE1MP/XNS63yCOFNsi4sU2EbkH+zIMzAKDVNZeutHIr6qYFwSGvlYV5jROuGWoShyBdIzgkGTRW3RSwwb0IcLDOxV0nSLyOoh2AqWt4tNZRiwEr7Q48mQvrZdjBYugTt+HppoJSq9RNx1reHota0d5mZYWUAA4RCetRAFHFKx9PamY+ff8Mx9c0SFwMNYn7PonntxdvWlGY6xokAqa9x09+7pEKNV4tLJJ1XXtAAsDv+uZXUVXAL/+iV+gx0hPz5sHlX7NbR3r0CzDnXJagg2u6l161SVCvSXaIH/JIgkRNr+6RysntVZ66qUKYbTmIifV9b9hspO+6qCtbk6hgfR2tmpEVdZ0TQCl78mZ8gSbQMQOJuzWJfFsEn2etqVEDgNNti7braYhaxF12mlpLr+G2zFuBtg31fhrf59bhEp+4Z0RJbpDZRfNgi3ANiyQ6iODoJBPHcnoXaoI9V9mrJWtCzfEqP0gkF5K7xy6SeD0di5MDI/+srkk83geJoa2GIPF0EK5hbEwcadRNG0v/0dC0ZU6vD3L8zD0pn+urm3bUxn3TCRKvo2gNYIP0Pd30/PbCDbve3VaIkly1/Y5v+CAAqNp0+O0GrudaDpjz/kd+xUdU1NF33T0GAHzLR952WdMazT3nGmqfu+deV1bOVdsYJCrOo+zVjwcxMRWSKPWFdPuwegzaxiCR6a87PF+mVRNX2iDzDJFEZp5z0e3rYtdzZ70i2yqyAa66Ztf11ELijtEEDVKT+HBtHjSNuMumtWITqmbure1Nxkxz5QRSmro9AHj+ZIWzrZkkm110W2CMQaKidYnGSXaFg0y/Nx5JdO8RWpVW2kRsW1QVjwrenFEzcNV2vFrg8VovLe4iBfsWGMZWA1J9tm3poEjuLRXd1FEFPuhqepxshm8M9ReMsxv2FwVAU7zlHHzm/tpTlbxOZoSDdO2F9hY3V63ybbeu5/UGgG//0nfii999W1W35zcuv55r+Vx73wsn+Nu/9+vw3uf58zHXLqSX4x5JBGDppr1TClAy8Z1OFUjipE8iyd5yaaMaJFGer7NdqyrnWgz1ljJHmUPJ3BYwT2JN4jMdJGpqEleNL1xzzIxZmALZdui3pwkaAOCRBInkwNVAidXIAcuxNrseBwsN2mCzPxqUFPBr6WgksanxYGvOhy02zh/T7T/VKRARX7lVFySOvTQ1yPEQ3EsrC1aAw6VjXFfH4mTVYNN2eLTZ4fkbB+UBgz135KCk1/S7XbUdLE0vr9Nth7snCrW5Xa+6/21NYk+3CQLsdRK5b0bdMewvCgxIIrHmydrxYL3DiaIn2lWaEUnQKxfvbWp1XeFv/p6vw92T1ZWIoJzHPvDiDdX7V009qmteZyRxrr1feT7m2smqGbUBNPvGnZM9cyU0SUKLT8OY+KqPhyCRSfCGdNOWLDdYNJUj1Kjfp9bbjt5rZJ6ToJShm7pI4hOIUj95M75AU9UkLowjD4hSafnzJWuwGYIUbU2iIInsjbVcDCIJisaiSzfYU9ZoAhiQS12QeNdZkNlidnG2AIsklh7uEUl020uovpsJgNl6y5VTN9lp1U1nNFJ27bpmqKRW7N7j7Wy66XWuP7pKu3W4wMP1zvRJJJ1kQRIFqGNuZVfum21sDBjRjqNlo0IS66H/oyR+ANDZ3RsH9vOvK91UEkeaViJ7S9sHX7rh1WI/LXawsMIpT6IjeV2sqqrRp9AE2y5KvQ8SjQmSKGVWjC0DuinVJ7Hxg0R2/V+4QoEK31V8pfWupevfAVPyJPuUBM6MT3P0hNNNn7wZX6BpahIPmhqbQX3StMDgFFGBQTjlHEEi61xLL0GrDsUfy5XJVwWXSnEXwF+QWbrp0qWbStaohCQGtYUAuYgsLQK5U6rSjgq4qhpUqUls1YvIb/6q9wKwMuPXzcSRf+PxRvXd3MDwbc9dz+921XbrcIk3T7cmSCQdSWFAsBRtAGM9YOvSVOkeqA3efKyrrVo09ahYDIhwDaNuau+Rk4PrmSRZDtnufZ/EveXsYFnjwXrfp+8iTFgFmv3mSRCBu2p730AP/g1f8R56zARJVJQ32HYWHbX+L5zyHg3g4/p3266jQYDGUVMVMIBJKNx06mQ1dcrXxa5n+vWKTFOTuFrUo0JRR2Y6LN2xVaFtchNrJZlFgdUW8RIOoTSOV1LSvOBSEUgBwPOOAAGLJC6cAEz6JZbELVwkcRTlUQhwnG1a+loDppZ04yjgalRRH57tsN526izyH/22j+D3fcvnX9sWAFLr8XjT6tRNHSTxun63q7ZbR0vcP91h13V0ALZcVFhvO6yHGmCqwX1jN21t6wYfSSTXLSdDC5iWNUziznXsrjOSaHqg8kH63p49cwOT69wn8UkwCQ41wba731xXEbirti9853P4x3/0X/KCnJIdLPwgkQn2mqBP4q7lfK5lU43aFpqab7kvzrYGSWQ1Dxa17a/IstkA4O23bf30y9e4ljplz/RqpKlJXDoBwLbrOBUlpyau7XlHSzJhb4wNONkgMejJpWz3IKwCVQuMIUuuqkk81jt3riM5PqCF8+K3wDBjmbhNHPCzbTugxmSQuPCFa3QIZD+bbnqdgygX4dFkaFn1zmfJnjta4v7pFqcbjbppPaLUAIfuSfJlp6SbAibB9cYMJFGCqLbr0ffcmuy2RPjgFdU8aU3q0sVpehLpRnu7fHMDk6exJvEqTRx5TcJ1jyTGTRMgAucTrtk5KB0TfC1qZ99QAD4ukni25dtJuX2wbZBYPp60gAGeTCrzkzfjCzQNRO0GAJtdR/XSCYVTWEamqCGJs6VtgaGhNrktMGzdnn6chm76Pkf1TYWSji0wuCLlqLopWZPY1BXOdi3arpvVJ7Ht+XOyGqjMZ1s93fS6m6cap/hurHjPs2S3jhbYDK1SaCRxSEBIxpW5v2xzY13iCPCD+0N63apGCjnb3ia0j7//edX7r8rsWs63Bdnbs2fuc7kPEs9nv+jzXgAAfPn77tJj9jWJF2Ny7h6NLTA0wjW2wT2z/i8jwjUckmiFa0QskLGF04Zto0AS33H7yS6Xub4QxBVYq8gGuAEAK+fvFsh2ikDqZBT7MI6FLpDqnYCIQUhtsNcrIPtFGCQqkMTbx6ux4TBri8aKwlh10/z3cwVoNHRTwCgznm6GRYSlzTX1qEjbdjz6crxq8HjTqpz/J8VOZgaJAPBrf+G78OrLNy96Sk+suXWa7H2yCpBEJuHkZnY1/UWBIEhUPDfbnV9bwlKA/qNf/wvwN37kU2MvtutmRysr3ATskcS9xc1dG/f3yPnsD//yD+N7ftmHVXup+16XobA3ncm6PdJNGeGasCaRpJsa+ufA3Op5VqBc6/WuNToQc5DEHeeDAsALClX362jPdJCoRRI3ThaBChKXFsnS0BaFoidNqXV9Ejvne3FjAHjiCkxNoifKowwSAeCv/86vHSW/GTtYuMJBHaqqfN1C4SCAo5sCRoDjbED3WERk5Sjgdj3f8uTm4RIPznazhGuuu7m1Htog8Y9/+xdd9HSeaHOL3tnWMcKAWO861BUXfLmKo5r+ogA81VV28100leMg6Gr3fvkXvYJf/kWvUO99K+w46B35tD3fe7sYc/eYpy1ReNU2V/jnr/ybX407x6u9mvY5TPZ4UTed0wJj13FtKdze2Rqf1y0DO9t2Kl0Ml/HS1BXl9zZ1ha943118w4deoo5z3WwfJELfJ3Gz6yjeulBS17vOUFRJB2HV1FjUlTr7vGqqgSKm7wno1u2xwjWLusLptlW19xA7XDaqzfBgUWO9s0E6s4h4wjVdj7riaYwHC9OT7mzb0YiI0EYBnXDNzaG1weNNi5duPl2P5NueO0RVAX2/r/U4r7nOy8tktvtgUeN0I1Tmhr7/F42ppdOskYDv4LK1sst6SiVnkcTrbpLwG9fyfQCwt4i9/TkraMH0F93bxdvH3nX7rZ7CE2+huinFSpMaeIcpxpYFiSq2hj3nCtesdx1eZEs36mpMYpr+ivwe9QPf9ZX0e6+bPdOr0ShcQ1xsD0lk6aZLP0hkkZSqqnC8amYhidu2V0HvVVWNgjeaNhGAoZadbuYhiVo7WDZY7wwldtf2lLSyK1zTKcV1Dpc11ltdYbPLkdcI10gm6/VHm6dO/nzZ1JA+6S/cePr6m12luXUzrErajQOTgNDQpgFDAdq1tpaXpZvKsyJNraljeRlhqUl8Oramo5kiZHt7tkxaGDV19dTc+3t79mxEEtdDCwxG3TTok7jtuBZIizqCJCpYcOtdh7VCB2I1A6h4GuzZ+JYJkxuMUwE1dTPdkF3X1CRudh1NURU7OViMjgXdJ3EIZMfvRR5uOaAGnVKk4nDV4FSpADrXDt3ehS3XR2ds7zGI+WjEUA6XDR5vdqo6QVfcSCNcIzTCzz1cP9WNlL/kPXfe6ik80fbeF2zdHRsk3jxc4vGmxaM1T5sGhg3Y7blKJ47M/cvSYQFflGpHilI9KTYVIXt6n++9zbd33jHP9v7+2NuTbFJucP/MrHdz1E13bcf1V3R0KkT0huo64PiSGv/OlDxZJPFZYUY9G98yYbvO1OkwiI8giYImMgGf2yeRpaiKHa+a8QFgKUoirqOhjXrjlHV7Bm1r1SjdHHMpAhtSItlDEpUKrEfLBm+e6qT8PXVTReAsSOK27Z9KJ+HzXjbtCT70tltv8UyebHPlyFlUVu6t1x7pEhCGbqpfS6TBvaYtS1jrAXAZ6CfBRMjn3ogkPl1Mgb1djL0y9FLbK2vu7Um2m8P6L/R6hs0me8uobtr2tLqpME82ijKFUbhmq1OUX7lB4o7zQZ8Ge7oKoJS263q61kaonNKUmqpJdKR2NXRTwHey2OBS0DYpGmZl5CWTr2kTAQx0022rboExx0IkkVOktfWWnaJPpTleg5+/d+odu3w8S0nueg3d1Gnk+xQ6kX/ht30cn3u4ufREwrNkLCXt1lDH+NkHa1UCYjEouWnXhOeH4FWzgbq1HjtFCcCTYG4LjIoUDtrbs2fvunuMX/jeO/i3vunVt3oqe9vbbDtcmvZhkmDnVEqDPoldhxvLcmhi+iT6gmcqv3zXqXpTrxbN6N9t2w7LxbOxlj/TQWLbcRkLwN5Y0v9FgyQKArlSBABz+suJkp6ohjJFvIARvPH7K+qCxB2J7J3HDh0kcUdet6qqDI+8NYGltibxnhJJPHDppkrhGrETBQLzpNjzNw7w/BMuA31d7C/9G181Ji8Yk3vrcw/XNEUVMEHe2mEXsPfy80NTasm4MubWJI59EtkiyGtuso7fe7TFwaLe9//cW9SWTY2/+K9/1Vs9jb3t7VxWVRVOVs1YKsX4aXVdoaqs+AzLwhItDcDtr8szzNZb0wJDJ0y4r0l8pmzbahqlm/c9HPrgcRkLC2tr6aaiimc+hxsnfPAxuGEFbxZ+f0WWWnawbIxy4q4dM+aXZUKVO9t2JotDB8DmwTbcc/78HyybkSKmQhIHcZ2u5/skukHiy7f2wdTe0vYl776javkg95YWSRThJitcw93Ld4cgca0IEl2Vut1Tpm4qa/KD9e6pZAnsbW9725trUgcP8IySRV05JQccw89tnbRV1LJXVYWDRY3Hm3Yo8eE1J9b7msTLsaqqDqqq+jNVVf10VVUPqqr6R1VV/ZLhb++tqqqvquqh8+8PB2P/bFVV96uq+lRVVb8n+Oxvqqrqx6qqelxV1Q9WVfWe0nz+ySfv4/7Z1rSlUGQRACdIpIRrfFhbQzd9/sQGC+zNaBs3D/Uvmlo6p+E8C7gdLU2biNNNi8NLDhIFSVzv2kF+mOeRS584zfk/Wjbj+ThSnMeutwsWm4C47ahWvk2B9uxtbyUTUaSu11GZDxYN1rvWCDApEHihm6qDxJFu+nSpm7rJs6ex3nhve9vb3lxzS6XYNU+EIQHQ5USLukbb9ej7Xl3LfrRqRjExFgQwbdhM8Lu9AvbcdbGr4rYtAPwsgK8D8DMAfimA/7yqqi903nO77/tYd/U/CuBVAO8B8DYAP1hV1f/c9/3fqKrqBQB/CcBvBfDXAHwvgB8A8PHcZHZdj7NNq+Iji1LmI0WQONJNdx02uxYrRXb81UHsA+Cz+EczGzcv6gpbR/BGQzf91LbD6aalA6m5JpTPs21HFzYDFkncKK61OZ49d2ywLdf7bCc9grhj3XEazr/83D5I3NvFmYtSa5HEs22nVi6+OyS3pF8oY66U+dgn8SmpX1029dga52lWLt7b3va2N8BXtqZ75XqJQq4FhgSS29a0Rasr3nd97miJzzw4A8Dvi6G66dPCdinZlexafd8/6vv+j/Z9/1N933d93/+XAH4SwJcSw38TgO/t+/6Nvu//CYDvB/Cbh7/9KgA/2vf9X+z7/gwmoPxYVVUfKn3o4zFI1CGJjxR000Vdoa6GPonKFhif9/JNAMAthZS8DRJ1tXSCtkmfRDYoPVzWON22ON1efpBo6aYtNmQLDMAqUq13vIoV4KOHbOsAWTTOtsZBZs+jW6ekqRvb295K5ooiseuBvPds26pVgYVuyt77QKIFxlOUpRVHaU833dve9va0m6x3R8tG56e5ojBkCwzAsE+2na5G8LmjJT5zfw1A5ydvWlNOtNntaxIv1aqqehnA5wH4Uefln66q6ueqqvpPB4QQVVXdAfB2AD/svO+HAXxk+P0j7t/6vn8E4Medv7vH/M6qqn6oqqofAoDTbYv1tqUDN3mfiMIw46qqwtGyweNNq1Y3/fy3mSDxO77sXfSYsSZRiSSuBrrpiCSSTuHR0CfxdNuOx74ss3TTDuttN/ZjK44bEBEt3fS5I9e51t0jZ5uhZ4/Cuf7AiycAgJdu7msS93Zx9tzRcsyuapIkB4saZ7sWbadTBb51uMB3fd378ed+y1fQY7zakpFu+vRkafc98Pa2t709KyZB4k0FwGEYX6JUyjHFpP5w2/bqlhQekqgoOet7g3RulaDPk2xXLqVYVdUSwP8dwP+17/sfq6rqBoBfCOAfAXgewH88/P1bAAjn8k3nI94EcHP4/QaAzwaHcP8+Wt/3fxrAnwaAg7e/2p9uW12j9BBJJG+Qm4dL3DvdoOuBVcMHUi/fOsQP/r6vx7vvHpffPNiIJD7SNW4+EJGKsSaRRRIbPF7vcLblz+NcO3SQxMfbHV66ySFuJwcLPNrsVKgx4AeJbAAs98jpVuimvKP7A9/1lfhnn3rwzGSn9nY11tQVXr55gJ9/80xVN3y4bIY1oadrlAGTGPuDv+QLVHP061GEbvr0PAfvfeEE//gTb+6DxL3tbW9Pvc0KEh0k0ZQ4MHTTAUlsO+w6Hf3zuaOlZdwphGsAUz62r0m8JKuqqgbwfwOwAfA7AKDv+4cAfmh4y6erqvodAD5ZVdVNAA+H128BOHN+fzD8/nD4v2vu35N2utE10pQbQoRr2HG3jhb43EOD7GkzD+974UT1frcnF8DX0h0uTON4Szfljne0bPBoULG6dHVTpwXGY0UN5MlqgUfrHdoeuO0EfiW75SKJ5CIigbK0SWGDbQB44cYBXvjgHkXc28WbJDneo0g4HS6bAUnU1STOsWVjle127dOHJL73eXPe931C97a3vT3tdutIgkTe31q5iUIy4JM9QpA9TYmCCwLQSKIXJO5rEi/cKlN49WcAvAzg2/u+3ybeOuBZqPu+fwPAJwF8zPn7x2Bpqj/q/q2qqhMAH4BPY43aqdQkKkVJHq5NUMSigjcPl3jt4dr7jMsycQbfVLbAOBgomVq6qYseXr5wjVWKPdvw9NbjlaH7aqjFQEg31QWJj9d6JHFve7ssEznyD750o/BOaweLYU1QqpvOMdMUeahH6Z6uFhgA8OpQX86KOOxtb3vb25Nqb3/uCIBVqmZsuagskki2wBC2ybYdkD3FPuX6d666fM7EfxSNkT2SePH2fwLwBQC+ue/7sRt0VVVfAeAegH8O4A6A/xDA3+n7Xiim/xmA7xlqCV8G8NsA/K+Gv/1lAN9XVdW3A/ivAPw7AP6nvu9/rDQZoZvqkUQTgLEBx63DBT7xxqlqzFw7cpDEpq7ozMrhIHe/2XWqcR4l89KFaxwkccv3ZRS66bKpVXQvd+F44Qa3iMj5F7RZI96xt71dlsn9qAkSDd20RdteQZDYWHXTEUl8iuimv/Sjb8NL3/nxUYxsb3vb296eVnvnHRMk3nucwoGm5jaqZ1G6EUlsDZK4nAkCvHiDY3BJOZEgifs+iRdoQ+/C7wLwxQA+5fRD/A0A3g/gb8BQRH8EwBrAr3OG/xEYMZqfBvB3AXxf3/d/A///9u48SLKrOtD4d2rprVq9S4LWOpKQQGhAAiSBxmYZCTBbMCAQCCFAWGaRwQ4CDAQDAsxiBk/gGcIsBssaEGI1iw3YCkMYEZ4AwshmMfLIgEBiES20dXepu7q7ljN/vKVSRS+Z1ZX5KjO/X0RFV2W+l3kqO+vdPO+cdy+QmXcAFwLvAO4BzgWe205MRSWx/XbTKsHbVVUSO7gm8a5dRSVxZZffVKtblonoaJKK8dFycpfZjt74W1r+uLq9TmLrmpOdLLmxZsUou/fOsne6syUwWg8i7Sfb921JbmdhV6nb/uL5D+f8Bx5VT6DSjpXjI+yZmWPPzGzXrzceHx2pJ6yZn910cP52xkZHeORJm+uZXyVpUFXjzM6p9pPEaq1uqJbA6Gx206L62P6YsaFl2bEj25wssCpU7Jud7XiinH7Wk0piZt4KHOx/8BMH2Xcv8OLya3/3fxU45JIXCxWzm7afOFTJUzW7abvtUEesGqvX/up2JXF8dKRYc2wuO5/JsKqsdrCWV2uFreuVxLHid9u5Z5q9M3Ntt5tWlcSIWHS7absWtvs6UYWWg/NO2cJ5p2zpaJ9VY6PsK0/ItHtN7mJV6wjC/OymwzIAS9IgOaasJE7u3d+y5/u3YmyEyT0zZCazc+22m87Pbtpp++fxm+bn++h08sq63XRscE5kHszQXiRRtJvOdry8weSeztYgbJ0ApRdT5q5ZMcrOPTMdr4m2d7pacL6DJLHlDEy3J66JCNa1rG3TdrvpijH2TM8xGjMd/W6drE9ZqRLlHeXEQd2uwEjdUp0s2jE13fYxcrHGRkaYnUsyc76SaBVekvrO+tXjXHzOcTztIVvb3mfl2Ah3z87VSyG1c/yvK4mzyUyHSeLpWxfOd9lejFC0m+6Z7v7J0+ViaJPEYjKT9iuJVeXwznISmnVtztzUOg1wL3qYN02sYOeezhKiVeNFqX/3vtmOWjLv027ag4Ro3aoxbt9ZTHLb9uymK6sZR2c7qpKOjY7wR088jd/qoAJTvQbbrSSqz1UD4I6p6fuc6OqG6tg6XV5bAu23eEuSlpc/eeZDOtp+vLwmcf5yg0Mf/6uW1Om5IrnsdAmMTrVOXNOLtcGXi6FMEkciOm6vrBKAbTv2sGbFaAcT1/S2krhl7UpuuWt3R8lete3knumOYmyttvVi5r51q8fZViWJK9p7vjUt23V6TejvP+6Ujrav3iPV+jud/B9Iy0n1Xt4xNd32NRuLdZ9rSwZwdlNJ0oFV6yTOX25w6ON/NbvpzGyyb6azJTAArr7sbNZ0UNyoVzjYM0Pm8HSKDWWSGFFMLrJvtv32ymrSgV37Zrn/+vYWcgfYumF+204WF12sqrrXSdWsaifrtAIZETzqpM2sWz3GqUe3P3PiYq1bNc5P7tgFdNBuunJ+u3aXO1ms6szS9nqdSqsh6k/VcWD77umeTFwDMD2TAzm7qSTpwMbLdRI7udxgfnbT4uRip5dFPO60ozravh4Tpzq75KzfDWWSOBLBjg6rPavGR1m7cox79850VKo+vmUB65O2dD+R2nJEkcx20i/dWjXo9Fq8T7zkkR1tfzjWrR6rZw5tf3bTlkpilyu51eymO+t1KofjIKLBUx0Tpqa7P7vp/MzFs/UENl6TKEnDoaokznRwuUHrjPfTs3NdL8JMlN1yd5erFXT7Wv3lYjh+ywVGI7ijvLawk8RhczmbZyfX6LROO79+TXev7YH5SmInJ+Kr12DHVGftpr3W2rrbbj/45pYZWLudJI6NjrBidGT+msQhOYho8LRW4Fd1+e+m6grYvW+Wmbk5RsI1RiVpWFTrJE53cLlB9Rlwaro4udjt7pPqkqq77i06xbo9o/9yMZSfYkdHop4ApZMP8pvLltMNHSSJvS5JV2fgT+tg4eYqxp1T08v6OrrW5LzdayBbK7m9SIBXjY/Ui8g6cY361REtJ2S6fQyrBtvd+2aL9a6ctEaShkZVSdw3M1f/fCjVuDG1b7ZY3L7LS1JUJzOrAtOwJIlD2W46OhL8akeRJHbSErhpoqjSdToz0juecQabJ7o7+UPlv511DL/asYfX/k77S0dWZfO9HS6B0WutrbAnH9le6+7mlgWsjzqi/WtJF2vV+Ci/ntxbfy/1o/Wr5//Wut1WM39GeIbp2azXv5IkDb6qklgniaOH/uxUJWl7ZmaZmZ3reiVxYsV9K4nD8vluKJPEsdGo34wbJ9pP+KqEo9Mk8ZJzT+ho+8Nx7MY1vOMZ/7mjfVqrh92e3OVwHNdSFWy33TRi/gPn2f9p05LHtFBrXMs54ZYOppeVxOq64arddNy/G0kaGuOjI8xl0ToK7VUSV61orSRmR+skLsbISLBmxWi9DJ5J4gAbbTlT3UmF74LTj+Znd+/mqQ9tf5HQftBaKejFWo6L9YTT7wfASVsmOtrvokccy0/v3NWTZTpaWxCWc+uudDCtkwB0P0mcH+yHaZFiSdJ8UrirnJiwk3bTPdNFu2kvlk2aWDnWUklcvp+Vl9LQJ4lbOlgD7PGnH83jTz+6GyE16j4zgC7jN/7qFaP8w6se3XEl993PemiXIvpNrbFZSVS/uu/Jjl61m3a2dq0kqf9VSeHkniJJbGudxNERxkaCqelZZuayXhKjm9auHOPnd+8G2u9m63dDmSSORTBbft96zdqw2jTRuxlAD9epHUzI04SNa4rXcsXoiDM0qm+1tmn3qpK4e98se6fnrCRK0hBZUSZ4VSWx3c+hq8ZHmdo3x/TMXNfbTaGY9XumnIF1WMap5Z0RdMnoaO8+APWDDS1LcyznJTD6wcYy4V7uybbUrl7ObrpnZtZKoiQNkerSnMk9xczw7UxcA2WSWHag9OKz60RL192wVBKHcjRuba9U8Qc6Ub7hvY7u8GwqJ0Iy2dagmOjyYFi3m+6bsZIoSUOmGgN2TLV/TWKx3wiTe6bZNzvH2h58rm+d12JYxqmh/CQ7NhI882HHcPE5xzUdyrIxUb75rYAdnqrddN/sXMORSIfnggcV118/+tQju/o8K0ZHGB2Jot3USqIkDZWqm2T7VDEpTNtJ4vgod+8q9lnTg4kJ167qj/k7ltLQltTec9GZTYewrGT5by9mAB1kVZI4tW/2EFtKy9v7L3kYc5ldbzeNCNaMjxbtptNzbJoYjsFXkjR/XfqO3WW7aQdJYjXbaLc7XgCO2bAaKD4nD0tBxYxAANxRLgD/8BM2NhxJf6sqsse3rOko9aNetkyvXjHKVF1JHI42HklSa7tpdU1i+xPX/HL7FNCbSuIpR60Fio671sndBtlwpMI6pOrNf/r91zUcSX87+8SNXPCgo/jIi89pOhSpb0ysHGPXvhn2TM8NzRlaSdJ8kri9wyRx9YpR7uxhJbH6nDxMM9dbSRQAn3rJI5ncMzNUb/5u2Lx2JX/5wrObDkPqK+tWjbFzzwx7Z+accVqShsia8SIVqSuJHbSb1o/Rg4lrqiTx1Y8/tevPtVyYJAookpvNa1c2HYakIbRu9Tg7pqaLdlMriZI0NOpKYofXJLYu3zaxsvsnF9esGOOWdz2l68+znDgaS5IatX71ODunptk7PecyPJI0RKokcefUNKMjwWibHW3VRIHg0nbdYpIoSWrU+tXj3LN7H/tm51g1JFOLS5Lm20b3zc61fT0iwKaJ+SSxF5XEYeRoLElq1PrV43WrkZVESRoeoyNRt5h2Mqu2lcTuM0mUJDVq/er5a0u8JlGShku1VmInSeKmta1JoicXu8HRWJLUqNYk0dlNJWm4rCmP+x21m7ZUEsc72E/t81WVJDXKSqIkDa9Vi6gkVu2mR69zZv5usYlXktSoo1oG+fuvX9VgJJKkXts8sYKf3LGro5OEx25czSv/6ylc9IjjuhjZcDNJlCQ16qQta+vvT9gy0WAkkqReu9/61cA93K+Dk4QjI8Grn3Ba94KS7aaSpGZtbJnK/P7rrCRK0jDZWiaHx25c3XAkamWSKElaNkbaXEhZkjQYqiUs1q4cP8SW6iXbTSVJjfvcFeexZ3q26TAkSQ3pZOIadZ9JoiSpcQ87fmPTIUiSGnDpo07gh7dPctl5JzYdilqYJEqSJElqxKaJFbzvkoc1HYYWsK4rSZIkSaqZJEqSJEmSaiaJkiRJkqSaSaIkSZIkqWaSKEmSJEmqmSRKkiRJkmomiZIkSZKkmkmiJEmSJKlmkihJkiRJqpkkSpIkSZJqJomSJEmSpJpJoiRJkiSpZpIoSZIkSaqZJEqSJEmSaiaJkiRJkqSaSaIkSZIkqWaSKEmSJEmqmSRKkiRJkmqRmU3H0HMRMQn8xyJ2XQ/sWOb7GWOz+/VDjIvdzxib3a8fYlzsfv0Q42L32wLc2aPnWux+/fA6Lna/fohxsfsZY7P79UOMi92vH2Jc7H7GeF+nZeYR+70nM4fuC7hhkft9aLnvZ4z+bsP8u/VDjP5u/RnjYfxuy3686YfX0d/NGJfbfv0Qo7+bMbaxzwHHKNtNO/PFPtjPGJvdrx9iXOx+xtjsfv0Q42L364cYD2e/Xj6Xr39zz9Xr/Yyx2f36IcbF7tcPMS52P2Ns07C2m96QmY9oOg5J0mBzvJEkLVcHG6OGtZL4oaYDkCQNBccbSdJydcAxaigriZIkSZKk/RvWSmJXRMSmiPh8ROyKiFsj4nnl7Y+LiH+LiO0RcVe5zTFNxztoIuIVEXFDROyNiP+z4L7zI+KmiNgdEV+LiBMaCnNgHej1j4hLIuLelq/dEZER8fAGwx0oEbEyIq4qjzuTEfHdiHjSfra7snztL2giTqlbDjT+lve9MiJ+GhE7y2PUbzUZ6yA6yPH/xPKY0zoGvKnBUAfSwcaAiHhkRHwlIu6OiDsi4jMRcf+mYx4khxqDI+LyiPhx+f6/LiK2Nhlvu0wSl9b7gH3A0cAlwAci4sHAvwNPzMwNwFbgR8AHmgpygN0GvB34q9YbI2IL8DngTcAm4AbgUz2PbvDt9/XPzGszc231BVwB/AT41wZiHFRjwM+Bx1BMgf1G4NMRcWK1QUScDDwb+FUTAUpdtt/xNyLOBd4FPIvib+Mq4PMRMdpYpINpv8f/FhtaxoG39TCuYXGwMWAjRUvhicAJwCRwdSNRDq4Dvv4R8VjgncDTKT6D/hT4RDNhdsZ20yUSERPAPcAZmfnD8rZrgF9m5utbtlsJvAV4emae3kSsgy4i3g4cm5kvKn9+CfCizDyv/HmCYt2yszLzpsYCHVALX//93P814PrMfGtPAxsyEfF94K2Z+dny5+uA9wLvBy7PzK82GZ+0VA42/gLfAV6dmee0bHsvsDUzPWGyxPYz/p5I8aF4PDNnGgxt6CwcA1pufxjw9TzQ2nhaEtXrDzwKWJ2Zv1/evpXi2HRKZt7cYIiHZCVx6ZwKzFQDVOl7wIMBIuL4iNgOTAGvAd7d8wiH14Mp/i8AyMxdwM3l7eqhss330cBHm45lkEXE0RTHpBvLn58N7M3Mv2s0MKk7Djb+/j0wGhHnltXDFwPfBbb1PMrhdmtE/CIiri67e9RFC8eABR59gNu1RPbz+kfr3eW/Z/Q0qEUYazqAAbIW2Lngth3AEQCZ+TNgQ0RsAn4PsILVO2uBOxbcVv/fqKdeAPxTZv606UAGVUSMA9cCH8nMmyLiCIpWl8c3G5nUNQcbfyeBzwL/l+LD2XbgSWkbVa/cCZxNkZhvpmgLvhZ4YoMxDbSFY8CC+x4CXEnR+qgu2M8YfB3wyYj4IMXlZlcCCaxpMMy2WElcOvcC6xbcto5igKpl5t3AR4C/iQiT9N5o6/9GPfECive/uiAiRoBrKK7NekV581uAazLzlobCkrrtYMf43wUuo6gqrgCeD3ypXyaO6HeZeW9m3pCZM5l5O8Vx6QnlySstsQOMAdV9p1BU1v8wM/+pgfAG3v5e//LSjjdTnKy6pfyaBH7RSJAdMElcOj8ExiLiAS23PZT9l/THgKP4zUFN3XEjxf8FUF+TcjK2W/RURPwXiomb/rrpWAZRRATFpBxHAxdm5nR51/nAH0TEtojYBhxHcUH96xoKVVpqBxt/zwS+lJk/zMy5zLyOYvKm83ofpigqKODnzyV3kDGgutTjq8DbMvOahkIcaAd7/TPzfZn5gMw8miJZHAN+0Eyk7fOPdImU17l9DvjjiJgoPxA/HbgmIp4ZEadFxEhEHAm8B/hOWVXUEomIsYhYBYxSXIOyqqzWfh44IyIuLO+/Evi+k9YsrYO8/pUXAp/NTCu43fEB4EHA0zJzquX28ymufTiz/LoNeClF25fU9w42/gLfBp4SESdF4fEU1wot+w9o/eRAx//yWtDq889mismzrs/MHc1GPJD2OwZEseTaPwJ/npkfbCq4IXCg139VRJxRHn+Op5hp9n9n5j1NBdouk8SldQWwGvg1xfS2L8/MG4FjgOsoysv/BswBz2gqyAH2RoqJgV5P0VI0BbwxM+8ALgTeQTED3rnAc5sKcoDt9/WH4iAJXIStpl1RniV+KUUSuC3m1yO7JDPvysxt1RcwC9yTmfc2GbO0xA40/n4U+CRwPcV1i+8FXupJwiV3oOP/Scx//vkBsBe4uKEYB9bBxgDgcor/h7e03O7xfwkd4vVfBXycoi3+n4FvUizJtuy5BIYkSZIkqWYlUZIkSZJUM0mUJEmSJNVMEiVJkiRJNZNESZIkSVLNJFGSJEmSVDNJlCRJkiTVTBIlSZIkSTWTREmSJElSzSRRkiRJklQzSZQkSZIk1UwSJUmSJEk1k0RJkiRJUs0kUZIkSZJUM0mUJEmSJNVMEiVJkiRJNZNESZIkSVLNJFGSJEmSVDNJlCRJkiTVTBIlSZIkSTWTREmSJElSzSRRkiRJklQzSZQkSZIk1QYqSYyIWyLi1xEx0XLb5RFxfYNhSZIGTDneTEXEZERsj4hvRMTLImKgxlVJ0nAaxMFsFPjDpoOQJA28p2XmEcAJwLuA1wFXNRuSJEmHbxCTxD8FXhMRGxbeERHnRcS3I2JH+e955e3PiYgbFmz7qoj4296ELEnqV5m5IzP/FngO8MKIOCMiVkbE/4yIn0XE7RHxwYhYXe0TEU+PiO9GxM6IuDkifqe530CSpPsaxCTxBuB64DWtN0bEJuDLwHuBzcB7gC9HxGbgi8BpEfGAll2eB3y8FwFLkvpfZv4z8Avgtykqi6cCZwKnAMcAVwJExDnAR4E/AjYAjwZu6XW8kiQdyCAmiVAMxK+MiCNbbnsK8KPMvCYzZzLzE8BNFO1Cu4G/AS4GKJPFBwJWEiVJnbgN2AS8BHhVZt6dmZPAO4Hnltv8LvBXmfmVzJzLzF9m5k0NxStJ0m8YyCQxM38AfAl4fcvNW4FbF2x6K8XZXSiqhheX3z8P+EKZPEqS1K5jgDFgDfAv5aQ224HrgOrE5XHAzc2EJ0nSoQ1kklh6M/B7zCeBt1FMLtDqeOCX5fdfAY6MiDMpkkVbTSVJbYuIsynGnC8AU8CDM3ND+bU+M9eWm/4cOLmhMCVJOqSBTRIz88fAp4A/KG/6O+DUiHheRIxFxHOA0ykqjmTmNPAZiolvNlEkjZIkHVRErIuIpwKfBD6Wmd8DPgz8WUQcVW5zTEQ8sdzlKuCyiDg/IkbK+x7YTPSSJP2mgU0SS38MTABk5l3AU4FXA3cBrwWempl3tmz/ceAC4DOZOdPjWCVJ/eWLETFJURn87xQTol1W3vc64MfAtyJiJ/BV4DSoJ7i5DPgzYAfwdX6z00WSpMZEZjYdgyRJkiRpmRj0SqIkSZIkqQMmiZIkSZKkmkmiJEmSJKlmkihJkiRJqpkkSpIkSZJqfZ0kRsTKiLgqIm6NiMmI+G5EPKnl/vMj4qaI2B0RX4uIE1ruuygivlHed/1BnuMFEZERcXmXfx1JkiRJalxfJ4nAGMX6VI8B1gNvBD4dESdGxBbgc8CbgE3ADcCnWva9G/hfwLsO9OARsRF4A3BjN4KXJEmSpOVm4NZJjIjvA28FNgMvyszzytsngDuBszLzppbtLween5mP3c9jfRD4PnAR8LHM/Mvu/waSJEmS1Jx+ryTeR0QcDZxKUfl7MPC96r7M3AXcXN7ezmOdAzwC+ODSRypJkiRJy9PAJIkRMQ5cC3ykrBSuBXYs2GwHcEQbjzUKvB94RWbOLXWskiRJkrRcDUSSGBEjwDXAPuAV5c33AusWbLoOmGzjIa8Avp+Z31qyICVJkiSpD4w1HcDhiogArgKOBp6cmdPlXTcCL2zZbgI4mfYmoTkfeExEPLn8eRNwVkScmZmvOMh+kiRJktTX+j5JBD4APAi4IDOnWm7/PPCnEXEh8GXgSorq4E1Qt5SOU7wGIxGxCpgtk8wXAataHutzwF9TJKOSJEmSNLD6ut20XPfwpcCZwLaIuLf8uiQz7wAuBN4B3AOcCzy3ZfdLgSmKJPO3y+8/DJCZ2zNzW/VF0ca6MzMXXuMoSZIkSQNl4JbAkCRJkiQtXl9XEiVJkiRJS8skUZIkSZJUM0mUJEmSJNVMEiVJkiRJNZNESZIkSVLNJFGSJEmSVDNJlCQJiIjjy7V2R5uORZKkJpkkSpKGVkTcEhEXAGTmzzJzbWbO9vD5HxsRv+jV80mS1A6TREmSJElSzSRRkjSUIuIa4Hjgi2Wb6WsjIiNirLz/+oh4e0R8o7z/ixGxOSKujYidEfHtiDix5fEeGBFfiYi7I+I/IuKilvueHBH/HhGTEfHLiHhNREwAfw9sLR//3ojYGhHnRMQ3I2J7RPwqIv48Ila0PFZGxBUR8aPy8d4WESeXce6MiE9X21eVyoh4Q0TcWVZOL+nRSyxJ6lMmiZKkoZSZlwI/A56WmWuBT+9ns+cClwLHACcD3wSuBjYB/w94M0CZ8H0F+DhwVLnf+yPi9PJxrgJemplHAGcA/5iZu4AnAbeVba5rM/M2YBZ4FbAFeBRwPnDFgrieCDwceCTwWuBDwPOB48rHv7hl2/uVj3UM8ELgQxFxWkcvliRpqJgkSpJ0YFdn5s2ZuYOi6ndzZn41M2eAzwBnlds9FbglM6/OzJnM/A7wWeDZ5f3TwOkRsS4z78nMfz3QE2bmv2Tmt8rHuQX4C+AxCzZ7d2buzMwbgR8A/5CZP2mJ86wF278pM/dm5teBLwMXIUnSAZgkSpJ0YLe3fD+1n5/Xlt+fAJxbtohuj4jtwCUUVTyAC4EnA7dGxNcj4lEHesKIODUivhQR2yJiJ/BOikrgYuICuKesWlZuBbYe6PklSTJJlCQNs1yix/k58PXM3NDytTYzXw6Qmd/OzKdTtKJ+gfnW1v09/weAm4AHZOY64A1AHEZsG8t22MrxwG2H8XiSpAFnkihJGma3AyctweN8CTg1Ii6NiPHy6+yIeFBErIiISyJifWZOAzuBuZbn3xwR61se64hym3sj4oHAy5cgvreWcfw2RWvsZ5bgMSVJA8okUZI0zP4EeGPZHvqsxT5IZk4CT6CYsOY2YBvwP4CV5SaXAreU7aMvo2hFJTNvAj4B/KRsU90KvAZ4HjAJfBj41GLjKm0D7injuhZ4Wfm8kiTtV2QuVaeNJElaTiLiscDHMvPYhkORJPURK4mSJEmSpJpJoiRJkiSpZrupJEmSJKlmJVGSJEmSVDNJlCRJkiTVTBIlSZIkSTWTREmSJElSzSRRkiRJklQzSZQkSZIk1f4/WCe+dKm1/scAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Şimdi, verilerinizi filtreleme ve ölçeklendirme işlemleri yaparak eğitim için hazırlamanız gerekiyor.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Verileri (0, 1) aralığında olacak şekilde ölçeklendir.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.101611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.065801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.046106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.042525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.059087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-11-01 00:00:00  0.101611\n",
+       "2014-11-01 01:00:00  0.065801\n",
+       "2014-11-01 02:00:00  0.046106\n",
+       "2014-11-01 03:00:00  0.042525\n",
+       "2014-11-01 04:00:00  0.059087"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.329454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.290063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.273948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.268129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.302596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-12-30 00:00:00  0.329454\n",
+       "2014-12-30 01:00:00  0.290063\n",
+       "2014-12-30 02:00:00  0.273948\n",
+       "2014-12-30 03:00:00  0.268129\n",
+       "2014-12-30 04:00:00  0.302596"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": [
+    "### Zaman adımlarıyla veri oluşturma\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "SVR için, giriş verilerini `[batch, timesteps]` biçiminde dönüştürüyoruz. Bu nedenle, mevcut `train_data` ve `test_data` verilerini, zaman adımlarını ifade eden yeni bir boyut olacak şekilde yeniden şekillendiriyoruz. Örneğimizde, `timesteps = 5` olarak alıyoruz. Bu durumda, modelin girdileri ilk 4 zaman adımına ait veriler olacak ve çıktı 5. zaman adımına ait veriler olacaktır.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1412, 5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=np.array([[j for j in train_data[i:i+timesteps]] for i in range(0,len(train_data)-timesteps+1)])[:,:,0]\n",
+    "train_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(44, 5)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=np.array([[j for j in test_data[i:i+timesteps]] for i in range(0,len(test_data)-timesteps+1)])[:,:,0]\n",
+    "test_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 4) (1412, 1)\n",
+      "(44, 4) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train, y_train = train_data_timesteps[:,:timesteps-1],train_data_timesteps[:,[timesteps-1]]\n",
+    "x_test, y_test = test_data_timesteps[:,:timesteps-1],test_data_timesteps[:,[timesteps-1]]\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = SVR(kernel='rbf',gamma=0.5, C=10, epsilon = 0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVR(C=10, cache_size=200, coef0=0.0, degree=3, epsilon=0.05, gamma=0.5,\n",
+       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit model on training data\n",
+    "\n",
+    "model.fit(x_train, y_train[:,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Model tahmini yap\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 1) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = model.predict(x_train).reshape(-1,1)\n",
+    "y_test_pred = model.predict(x_test).reshape(-1,1)\n",
+    "\n",
+    "print(y_train_pred.shape, y_test_pred.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
+    "\n",
+    "print(len(y_train_pred), len(y_test_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)\n",
+    "\n",
+    "print(len(y_train), len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)].index[timesteps-1:]\n",
+    "test_timestamps = energy[test_start_dt:].index[timesteps-1:]\n",
+    "\n",
+    "print(len(train_timestamps), len(test_timestamps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "plt.plot(train_timestamps, y_train, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(train_timestamps, y_train_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for training data:  1.7195710200875551 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "plt.plot(test_timestamps, y_test, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(test_timestamps, y_test_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for testing data:  1.2623790187854018 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Tam veri seti tahmini\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor shape:  (26300, 5)\n",
+      "X shape:  (26300, 4) \n",
+      "Y shape:  (26300, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = energy.copy().values\n",
+    "\n",
+    "# Scaling\n",
+    "data = scaler.transform(data)\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=np.array([[j for j in data[i:i+timesteps]] for i in range(0,len(data)-timesteps+1)])[:,:,0]\n",
+    "print(\"Tensor shape: \", data_timesteps.shape)\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = data_timesteps[:,:timesteps-1],data_timesteps[:,[timesteps-1]]\n",
+    "print(\"X shape: \", X.shape,\"\\nY shape: \", Y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "Y_pred = model.predict(X).reshape(-1,1)\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = scaler.inverse_transform(Y_pred)\n",
+    "Y = scaler.inverse_transform(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "plt.plot(Y, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(Y_pred, color = 'blue', linewidth=1)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE:  2.0572089029888656 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "f8f3967282314d3995245835bdaa8418",
+   "translation_date": "2025-09-06T14:05:02+00:00",
+   "source_file": "7-TimeSeries/3-SVR/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/tr/7-TimeSeries/3-SVR/working/notebook.ipynb b/translations/tr/7-TimeSeries/3-SVR/working/notebook.ipynb
new file mode 100644
index 000000000..e8a425cb7
--- /dev/null
+++ b/translations/tr/7-TimeSeries/3-SVR/working/notebook.ipynb
@@ -0,0 +1,699 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bu not defterinde, aşağıdaki konuları nasıl yapacağımızı gösteriyoruz:\n",
+    "\n",
+    "- 2D zaman serisi verilerini bir SVM regresör modeli için eğitime hazırlamak  \n",
+    "- RBF çekirdeği kullanarak SVR'yi uygulamak  \n",
+    "- Modeli grafikler ve MAPE ile değerlendirmek  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Modülleri içe aktarma\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": [
+    "## Verileri Hazırlama\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Verileri yükle\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Artık, verilerinizi filtreleme ve ölçeklendirme işlemleri yaparak eğitim için hazırlamanız gerekiyor.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Verileri (0, 1) aralığında ölçeklendirin.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": [
+    "### Zaman adımlarıyla veri oluşturma\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "SVR için, girdi verilerini `[batch, timesteps]` biçiminde dönüştürüyoruz. Bu nedenle, mevcut `train_data` ve `test_data` verilerini, zaman adımlarını ifade eden yeni bir boyut olacak şekilde yeniden şekillendiriyoruz. Örneğimizde, `timesteps = 5` olarak alıyoruz. Yani, modelin girdileri ilk 4 zaman adımına ait veriler olacak ve çıktı 5. zaman adımına ait veri olacaktır.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [],
+   "source": [
+    "x_train, y_train = None\n",
+    "x_test, y_test = None\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit model on training data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Model tahmini yap\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = None\n",
+    "y_test_pred = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = None\n",
+    "test_timestamps = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Tam veri seti tahmini\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = None\n",
+    "\n",
+    "# Scaling\n",
+    "data = None\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=None\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = None, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = None\n",
+    "Y = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluğu sağlamak için çaba göstersek de, otomatik çeviriler hata veya yanlışlıklar içerebilir. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan herhangi bir yanlış anlama veya yanlış yorumlama durumunda sorumluluk kabul edilmez.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "e86ce102239a14c44585623b9b924a74",
+   "translation_date": "2025-09-06T14:07:32+00:00",
+   "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/tr/8-Reinforcement/1-QLearning/notebook.ipynb b/translations/tr/8-Reinforcement/1-QLearning/notebook.ipynb
new file mode 100644
index 000000000..135b01619
--- /dev/null
+++ b/translations/tr/8-Reinforcement/1-QLearning/notebook.ipynb
@@ -0,0 +1,411 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "17e5a668646eabf5aabd0e9bfcf17876",
+   "translation_date": "2025-09-06T15:06:30+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
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+ "cells": [
+  {
+   "source": [
+    "# Peter ve Kurt: Pekiştirmeli Öğrenme Giriş\n",
+    "\n",
+    "Bu eğitimde, bir yol bulma problemine Pekiştirmeli Öğrenme uygulamayı öğreneceğiz. Ayar, Rus besteci [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev) tarafından yazılan [Peter ve Kurt](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) adlı müzikal masaldan esinlenmiştir. Bu, genç öncü Peter'in cesurca evinden çıkıp kurtu kovalamak için orman açıklığına gittiği bir hikayedir. Peter'in çevresini keşfetmesine ve en uygun navigasyon haritasını oluşturmasına yardımcı olacak makine öğrenme algoritmalarını eğiteceğiz.\n",
+    "\n",
+    "Öncelikle, bir dizi faydalı kütüphaneyi içe aktaralım:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Pekiştirmeli Öğrenmeye Genel Bakış\n",
+    "\n",
+    "**Pekiştirmeli Öğrenme** (RL), bir **ajanın** belirli bir **ortamda** en iyi davranışını öğrenmesini sağlayan ve birçok deney yaparak gerçekleştirilen bir öğrenme tekniğidir. Bu ortamda bir ajanın, bir **ödül fonksiyonu** ile tanımlanan bir **hedefi** olmalıdır.\n",
+    "\n",
+    "## Ortam\n",
+    "\n",
+    "Basitlik açısından, Peter'ın dünyasını `genişlik` x `yükseklik` boyutlarında bir kare tahta olarak düşünelim. Bu tahtadaki her bir hücre şu şekilde olabilir:\n",
+    "* Peter ve diğer canlıların yürüyebileceği **zemin**\n",
+    "* Üzerinde yürüyemeyeceğiniz **su**\n",
+    "* Dinlenebileceğiniz bir yer olan **ağaç** veya **çimen**\n",
+    "* Peter'ın kendini beslemek için bulmaktan memnun olacağı bir şey olan **elma**\n",
+    "* Tehlikeli olan ve kaçınılması gereken bir **kurt**\n",
+    "\n",
+    "Ortamla çalışmak için `Board` adında bir sınıf tanımlayacağız. Bu defteri çok fazla karmaşık hale getirmemek adına, tahtayla çalışmak için gereken tüm kodu ayrı bir `rlboard` modülüne taşıdık ve şimdi bu modülü içe aktaracağız. Uygulamanın iç detaylarını öğrenmek için bu modülün içine bakabilirsiniz.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Şimdi rastgele bir tahta oluşturalım ve nasıl göründüğüne bakalım:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Eylemler ve Politika\n",
+    "\n",
+    "Örneğimizde, Peter'ın amacı bir elma bulmak, aynı zamanda kurt ve diğer engellerden kaçınmaktır. Bu eylemleri bir sözlük olarak tanımlayın ve bunları ilgili koordinat değişiklikleriyle eşleştirin.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 2"
+   ]
+  },
+  {
+   "source": [
+    "Ajanımızın (Peter) stratejisi, **politika** olarak adlandırılan bir şeyle tanımlanır. Şimdi, **rastgele yürüyüş** adı verilen en basit politikayı ele alalım.\n",
+    "\n",
+    "## Rastgele yürüyüş\n",
+    "\n",
+    "Öncelikle, rastgele yürüyüş stratejisini uygulayarak problemimizi çözelim.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Let's run a random walk experiment several times and see the average number of steps taken: code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 4"
+   ]
+  },
+  {
+   "source": [
+    "## Ödül Fonksiyonu\n",
+    "\n",
+    "Politikamızı daha akıllı hale getirmek için, hangi hamlelerin diğerlerinden \"daha iyi\" olduğunu anlamamız gerekiyor.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Öğrenme\n",
+    "\n",
+    "Bir Q-Tablosu veya çok boyutlu bir dizi oluşturun. Tahtamızın boyutları `genişlik` x `yükseklik` olduğundan, Q-Tablosunu `genişlik` x `yükseklik` x `len(actions)` şeklinde bir numpy dizisi ile temsil edebiliriz:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Q-Tablosunu tahtada görselleştirmek için `plot` fonksiyonuna aktarın:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'm' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8b8a9e4cb2b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Öğrenmenin Özeti: Bellman Denklemi ve Öğrenme Algoritması\n",
+    "\n",
+    "Öğrenme algoritmamız için bir sözde kod yazın:\n",
+    "\n",
+    "* Tüm durumlar ve eylemler için Q-Tablosu Q'yu eşit sayılarla başlatın\n",
+    "* Öğrenme oranını $\\alpha\\leftarrow 1$ olarak ayarlayın\n",
+    "* Simülasyonu birçok kez tekrarlayın\n",
+    "   1. Rastgele bir pozisyonda başlayın\n",
+    "   1. Tekrarla\n",
+    "        1. Durum $s$'de bir eylem $a$ seçin\n",
+    "        2. Yeni bir duruma $s'$ geçerek eylemi gerçekleştirin\n",
+    "        3. Eğer oyun sonu koşuluyla karşılaşırsak veya toplam ödül çok küçükse - simülasyondan çıkın  \n",
+    "        4. Yeni durumda ödül $r$'yi hesaplayın\n",
+    "        5. Bellman denklemine göre Q-Fonksiyonunu güncelleyin: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$ yapın\n",
+    "        7. Toplam ödülü güncelleyin ve $\\alpha$'yı azaltın.\n",
+    "\n",
+    "## Keşfet vs. Sömür\n",
+    "\n",
+    "En iyi yaklaşım, keşfetme ve sömürme arasında bir denge kurmaktır. Çevremiz hakkında daha fazla bilgi edindikçe, optimal yolu izleme olasılığımız artar, ancak ara sıra keşfedilmemiş bir yolu seçmek faydalı olabilir.\n",
+    "\n",
+    "## Python Uygulaması\n",
+    "\n",
+    "Artık öğrenme algoritmasını uygulamaya hazırız. Bundan önce, Q-Tablosundaki rastgele sayıları ilgili eylemler için bir olasılık vektörüne dönüştürecek bir fonksiyona da ihtiyacımız var:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Orijinal vektöre, tüm bileşenlerin aynı olduğu başlangıç durumunda sıfıra bölünmeyi önlemek için küçük bir miktar `eps` ekliyoruz.\n",
+    "\n",
+    "Gerçek öğrenme algoritmasını, **epoklar** olarak da adlandırılan 5000 deney için çalıştıracağız:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "# code block 8"
+   ]
+  },
+  {
+   "source": [
+    "Bu algoritmayı çalıştırdıktan sonra, Q-Tablosu her adımda farklı eylemlerin çekiciliğini tanımlayan değerlerle güncellenmelidir. Tabloyu burada görselleştirin:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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uG4447jwgsvpKSkxDqnW7duHH/88dx3333ous5///tfSktLa+yTTnm6FUWhqqqKt956i+HDh/OnP/2J8ePHs2rVKlavXg0kd9/cdNNNPP/883z99ddWrGQikfhB2Ya0pvMYMGAAABMnTuS+++6r8+ltQUEBV155JU8//TStWrXi1ltv5dxzz+Xcc8+tcd5bb73FgQMHrL9DoRAjRoyocU7fvn0ZNWpUndr7c3G5XJx33nnMnTuXiy++ONPmNFpef/11LrzwwrTPGiKRiOUhTqUsC1aUI0vg79Ydhz8LTVaqQ3BI3ksyCnIy6FsIJNMEU+DtfCQOQ+AyBXFhEjMgbpjETUHcSP5tyipSPA5OF5IpkA2BYoJiGrTr0SOtr+37SJLEyJEjmT17NldddVXa2095mL1eLyNHjqyxtpjqf/z48TidThwOB3l5eTz44INkZ2fz6quv8uGHH5JIJDj++OO56qqrOP300633W1VVJk+ezPnnn2+1t3v3bp599lmGDRtGXl7ejw6Q0p7raMCAAeTn5zNixAgee+yxOnXSZGVlcdVVV7Fv3z5ycnIOWbDnggsuqPG3aZqceuqpNdZqPvnkE3r37l3jvIkTJzJo0CAgOdqsL4eTw+GgZ8+eLF682BbHBkgqkPjgnRgul47icaMXtEKRQZGV5JS6+qYpSY+zIQSYIEyBappgCIRpIkwQhgkCSJhIQiCZAmGYSG43mmGSME1kU0IxBIppohgmTncVdTn/kSSJgQMHMmXKlLSLYyoDDyR/ZAYMGPADcVy/fj2DBw9myZIlKIrCqaeeaiXHnThxIs888wyQXGa78sorOfHEE600ZpFIhIsuuoivvvrqB33369fPCvM5VFbxOkkEd+SRR3LDDTcwZcoUxo4dW+dbDVu0aPGLzpdlma5du9Z4rGvXrlx22WU1Hps4cSLTp08HYMSIEVxxxRWHZ6jNb4LUl6lmyQITSVaQdTeqIiPJMrIsIcmADJKsJO8FYCaFTzYFsmkiGwLVBGGalnBiCoQwEYZANkmeV/08xTBRTIEiQK9svCFsqazekUiEq666ivfee6/G8dzcXAoLCzEMg9tuu41Zs2ZZWcBTSW9TBAIBioqK8Pv9uFwugsEgkyZNQtd1TjjhBLZu3cr+/fut82+88UZefvllXnvtNSvP4/epE3GUJIlevXpRVlbGww8/zLhx4xp8oGoqgv5gJk2alCFrbBoyqeBiSM5CHA4H8UgYveNRxEqK0dt1QJElkOVkJLEEhpz0RgukpAgKkEyQTDMplAIU08Q0Qa0WR0zTOkcWIBkmshBJYTQFctxk37btHHHkkRm9Hr+WVEJgWZZ57LHHaN++fY3jLVu2ZPr06Rx77LEA1mhQVVW++OILFixYYJ27YMECFi1axB/+8Adr/fDvf/87kCwtO3r06BriKITg4Ycftval13uy20GDBnHmmWdy0003NdpwAxub75OKvYtGo1ZWam9WNtmnno0ZjqD5ckB3I7ncoLsxdQ+m003C6cFwuhG6G5xu6xzF7UFyuZFdHmSXG9ntRnW7Ud1eNLcHR+re48Hh8eLweHF6vGi6g3iwitP+3x8zfUl+FSlPcyKRoKCg4AeDkbVr1zJy5EhKS0txOp2Yponf76eoqIjx48ezceNG69xoNMr999/Pxo0bcTqdBAIBNE1j//79XHXVVXz66ac12h4/fjwej6dGPsfva1Sd51c/7rjjyM7O5rrrrmP69Ol4vd667tLGpk5JxeelQkmysrIor6igxdG9WPvmC7jLS/C2bAMigUDGlARIkrU2aCIQqUgVMzmelIVIxj6aAkkITAGSKUgIqqffAgUT2QBVQDgU4v3Zsyjo1h1f8+YZuhKHh8/no7y83EpAe+2115JIJLj33nut2MV169bRu3dvaz90ajthbbvy1q9fz6BBg6xSFal92QefqygK1157LbfccgtZWVlWuQu32/2Ddcc6F0dJkujatSuXX345//znPxk1alQyhfzWrfTs2bOuu7exSTuplGVAjZRlaovWFFx8GTv+dS+dho7A074TIhYBJAQCJL6bWovvgrcdHh8mIIVCSLEYpgmJ5AnJPkyBZIIiktPriuIiPnrheVp36861Tz5V65SwMVBZWUl+fj7l5eXk5OSQSCQYP348iUSC6dOnW9f4l2xPrqioOOQxSZIYMWIEf/nLX/B6vZSVlSHLMrquEwqFcDqdmSnN2r9/f6LRKNOmTUMIgcfj4dlnn+XOO+8kNze3vsywsTlsUjtjUk6ZVMoyIQTNTjsb3aWz5YG/0ub/jSD/+AFIEsSKi7CGjpKEq2UbhASRfXvZ//FHyZ02+fnkH90HVI1oOEQsHKpejxSokoxAoqK0lA+fexZ/m7Zccf9MHA5Ho43XdbvdVrXGYDBoXc/x48fz0EMPEQgEDruP8ePH88ADDwDJ923y5Mm4XC6rrG7qB+r7wgj1XJr1lFNO4ZZbbiEej/P2228zfPhwysrKyMnJabRvsE3TI5WyLJUjMJXSPysri4qKCvIHnIXhy0batZW10/+C5vLQY+IMkDUkRcYo3sfXk8dhSDJmxOTN9V/T4pjenH7KaaycNZ1QoBJvmw50vfiPOHOyMRNx1n3wPl+8+zaOrBx+P2kK3hatyGnenIqKika7XTYej1ve6tSaYqpmTIobbriBwYMHc8YZZ9S6nfDHeOCBBxg6dChOp5OpU6dimiYzZ85k3Lhx6LpuVT5MZQf6PvUqjjNnzuS2226jS5cuPPvsszz88MNMmDCBV155pT7NsLE5LDwej7UvN1VUy+/3EwqFyM7OJhgM0rzncVS0L+SoQRcQLi9h9azpRDatR295BN1umkyne2dbCVfl+yZRLEu0v+TPtDxvKF6vl1g8zoY3X+Xzl59D0nS6XTiEy556kZy8PIzq+MpUv421NOt3YVDfhUYBXHzxxdaosV27dvTu3duqN/1L6Ny5M7qu07lzZ6u/119/nTFjxlh/p0aOtTmM6/Wqjh8/nuOPP56xY8dy/vnnM3ToUF5++WV71GjTqEil2oKkxzXlWEil/c/JyeHdVe+yL7CPWCSGT/cS7tWVaLcOICQWv/8SbQvaMOi4QeS3aEmLXq3Yufwrdu7YROeuR1NSUoLH46HzBf9Ht8FDrC12qSw2uq5TXFxMfn4+xcXF+Hy+GttjGwspr7+qqoTDYWu3TCrjESR/fKLRKIZh/OL233jjDU466aQaI9HUls/UCDU1Gq2t1ES9/+TcfffdbN++nf/85z+MGTOG/Pz8+jbBxuawSC3gp2Jjg8EgbrebaDTCB5sWMu/rt1mxdjn7SvbTzJWHT/XjVbyYMRMSsP6bdfyu31l89OlHNO8v8fbieXQtUnj/tYfJvuousnOb10jKmlrfrKiosNbLsrOziUQi+P3+ek8VmC5SlQBT9VwSiQSxWKyGgykcDv/q7Z9PPvkkkUjE2uUGyZ04lZWVZGdnEwgEkCQJXdetsgwZccikOPfcc4lGo1Z1MRubxkYq8DtVR0bTNPZV7OOy569g465vKK+oQMRM5LiEO8tDnicXn+bHLbtwaW56tj8WXXGwpPgVtLk6j42bQ/GGlbz70jS+/bwfPfpfjNPltcoFpL6wqRHkwanSUvcH01hmYimPsdvtprS01MrMffBuFU3TGDx48K8aOUKyXMJZByXlUFWV/Px8QqFQjVAel8uVWYdMCqfTech90DY2DZ2DU/ObwmRL6WYu+vfFBKtC+HU/Ba4CvJKbRNRgX9Fetm/bxnn9zqdtfjsOHNiPv52DBcveIawlmDV8Fh1adcadiNDt6D4sffPvfPP5q5x1+QNkN29v1TpJ1T5JORFSu0BSX+6DaSwbLgKBgFUJMDc31xo5Hiz248aNq/GcHj164PF4WLly5Q9etyzL9OzZEyEEX3zxBQD79++vse03kUhQVFREdnY2FRUV1sgxo6E8Nja/FdxuN0VFRZiYrK1cyw3/vYGqcBVHNj8SRSiYMQO37Mbj93Bki27s2beH4rJiPH6NfWzm66rd5HZuw9fffkvUTJYIiDTvzB9Gz+Lzd2eweulslv53HF16nkGXfn8moTpRFIWSkhLy8/MJBoPk5eVRVFT0k5llGjIul4tQKISqqlYoD8Cf/vQnli9fXsMB06pVKy6//HIGDx5M+/btmTFjBkIIDMOwxFTTNK6++mqqqqp44YUXmDNnDps2bbLakCSJa665xsoAnoo4AGoNibLF0cbmF5JKzb/hwDeMem0kG/dvRkuobEpsRMTAjJn8/Y/38vHXK2jbrC3bdm3niOMMNuxYibO5FyWcS7Go4I9nDaV/j/5UVVWRm5tLNBrluLPGsP2LZwkfWMnq9z+jc88h6J5kfeXc3FyCwSCqqlJaWlpjWtrQcxfURiKRsEJqUssUhmEwfPhwbr75ZkscHQ4HL7zwAr169cLhcBAOh5k4caK1PpkqxGWaJqZpkpeXx9ixYznvvPM4++yzrcBwRVG45pprrJKwqfZTNce/jy2ODZA9e/Zw4MABKisr2bFjB61bt260sWy/RXRdZ93ODQz57xByHDkM7HwabslNOBjGrTrxO7088/4TbNi3lp7HH0Fui1z2BMvIKWiGKjtol5dPJBbj9jP/ghkx8eR6KC8vT8b8STqXTf6GYDCIpioISbdEoLy8nNzc3O+2LFbvLKnLkeP+/fvZu3cvwWCQHTt20LJly1o9u7+GeDyO1+u16kinktamRnMul4t77rmHYcOGWeUUUjGJqqpaSSgOzvQdi8WsutfHHHMMn332GQsXLuTmm2+2vN6pmjGptc1Uydbvpy6zxbEBMnDgQFq2bElRURFz5sxh/fr1dZ72zebnEzVC3PnZaLq2y6e1vzUdczriUjw8/cFzJHQHLdq1JF8yObJZc7p1PJrcbs2JGyal4RIUU6V7fm+aOQvIcmeTSCSsmjRCCDRNQ0gOHHqyRk0ikUCu/sI6nU5rlBSPx1FVlWg0iqZpdfbjOWTIEJxOJxs3buS0007j7bffplu3bmlp2+PxUFVVZcWKpuryVFZWcu6559K3b19rv3U0GrWOpQplpWIfU2KdKqIVi8XIzs4mFotxxBFHcNlll2EYBgsXLrSOpYQ5JYoZ3yFj89M8/fTT3HjjjXTr1o0PP/yQPn36MHPmTCZOnJhp02yqMYXJjcddR1Wigl1iLSWRHciyjz+dfyZeORtN9rIu+zOOVY6ljbcTslBIGIK1mz7lrHZ/4PR2Z1OgH1EjADklbikPeOr+4ISwqeOpY4B1bl3wxhtvcP7553Peeecxbdo0rrjiCp5++mmmTp2aFjFOjYBTweypH4qsrCweeughcnJyqKqqsmrEpApxlZWVkZ2dTSgUQtd1wuGwNa1OeaFTO5aqqqpQVZXLLruMSy65BL/fT1lZGX6/31rnTCXP/b7H2hbHBsbAgQOZMGECl19+OUcddRRTp05l2LBhmTarUTJ69Og6yQLlUr0c7Tqt+o9zqQyWk5WVzVcHFrEsNI9AuAycEgk5wJrAMkLRIFd1nMjxJw2ipbc1OZ48q3ZyJBLB6/Um69D4/UQiEbKysggGg9Z6WspTnTo3FVeZSu5aV/Tr149XXnmFESNGMGnSJJ5//nkGDhyYtmm81+u1YjfLysrQNM1aT/X5fJSVlVk7gSRJQtM0iouLrYJcKYdOqvRqqs2qqiry8vIIBoNkZWWRSCSsNdtAIEBeXh6hUAi/P7mWm0gkag/liUajNTw6DY1du3ZRUVHRoG0sKSlBCJE2GydNmmTVvLn++utRFOWw2w4Gg/h829D1kp8+OUOYZizt73MwGKyR5PRwqKysJBgMsnXr1oMeBHBSURWmHcfTVuuHUAWpBGWC5JdWjitIcYnyUCXlySfVsPHg+x/LLJM6p7i42LLpYPbv3095eXnaruOdd95pFby65JJLUBSFzZs3H1abFRUV7Nq1y/q7tqw7qWtQXl7+g2Op1/xj1+ngIl3fp7S09CdtjEajqIFAgIULF/7kyT+X//3vf/Tt25fs7Oy0tJe6kOm0Md1s2bKFxYvdhEIQEVM1AAAgAElEQVQHfvrkDNG8eRF//evSBh1f+u67VQwe3HDfZ0WJkNfvY95c+GamTTkk7r1uBkYG1pr2v6Gwc+dOXuIlYt/8sr3S9UmrQCsYM2aMSCfDhg0T3377bdra27hxo3j88cfT1l5d8Prrr4sWLZZXJ+BrmLe+fSeL0tLSTF+qQ2Kaprjkkuszfp1+7OZ0log+U/oIGvB/LT5qIebOnZvW92bPnj3i4YcfTlt7jz32mMjamJXxa/Vj/w24cYCw40NsbGx+lEAgwCeffJJpM+odWxxtbGxsasEWRxsbG5tasMXRxsbGphZscbSxsbGpBVscbWxsbGrBFkcbGxubWrDF0cbGxqYWbHG0sbGxqQVbHG1sbGxqwRZHGxsbm1qwxdHGxsamFmxxtLGxsakFWxxtbGxsaqFRi6OorlZ27733snXrVit1vM2hEQel2LexsTk0jVocN2/eTOvWrenYsSNjx47ljDPOyLRJDZpEIsELL7zAgw8+yIYNG2yRtLH5ERqtOAohWLhwIX/7299o1aoVZ599Nrt27WL27NnMnj2bcDicaRMbHKFQiNLSUtq1a8fAgQOJRCKZNsnGpsHSqAtsybKMaZrIsoyiKECyQDfAjTfeaBXt/j4ul4tHH320Tuv9NkT8fj8nn3wyM2fOxDRNrrnmGuu6NQS+Vw7FxiajpF0cp02bxvDhw5k3b16dFqKXJImBAwcyYMAAHnzwQebNm0ebNm244oorADjllFNIJBK1PjcSidCnT59DlrTs0qULTz75ZI3HHA4HDocjvS+inhFC0KZNGyZMmMDq1au55ZZb0HU902ZZ3HbbzEybYPM9TNNk7NixP/g+NAXSLo4tWrRg79696W62Vjp16sSuXbu4//77+ec//0nbtm2tYx07djzk84QQrFy58pDHN23axPnnn1/jseHDhzf6Eqnl5eVMnz6dvLw8srOzKSwsbDDiKIRoUKNYm+/Yu3cvLVq0yLQZ9U6jnlZLkoQkSdxyyy2/6nmHorCwkMWLFx+mdQ2PnJwcbrzxRoqLi5kyZUqmzbGxadA0anG0+eUcddRRmTbBxqZR0Gi91TY2NjZ1iS2ONjY2NrVgi6ONjY1NLdjiaGNjY1MLtjja2NjY1IItjjY2Nja1YIujjY2NTS3Y4mhjY2NTC7Y42tjY2NSCLY42NjY2tWCLo42NjU0t2OJoY2NjUwtpFcfNmzfz4osvUlZWxpw5c1i9enU6m7exsalHvvrqK+bMmUNZWRkvvvgimzdvzrRJ9Upas/IsWrSIJUuWYBgGn332GWvXruWYY45JZxc2Njb1xJw5c4hEIhiGwTvvvEMwGKRTp06ZNqveSNvIcdu2baxZs4YHH3yQli1bcs8996DrOsuXL09XFzY2NvXEsmXL8Hg83HPPPbRq1YoHH3yQNWvWsG3btkybVm+kTRzbtWvHqaeeyieffMLy5cvZsmULeXl5nHDCCenqwsbGpp7o378/2dnZbNmyhWXLlvHJJ59w6qmn0q5du0ybVm+oFRUVvPnmm2lpTFEUDMPg7bffBqB169bMmzfvsNosLi5my5YtabOxLli7di3Nm+/A5SrKtCmHxOPZx4IFC3C5XJk25ZAYRiUdOjTc91lVQ3j2eejwZodMm3JI/Fv9rA2uTUvxuLZt27Jz50527twJJL/fb7311mG3u3XrVlotaUXuutzDbquucFQ4UFVVJS8vL9O2HJJ4PI7L5WrQNno8HsaO9VFY2HBtfPVVBzk5OXg8nkybckhyclSeey591/Cll16iR48edO/ePS3tVVU5mTy5H+GXp6alvbpAz/kGz7WBBv190XWdv2X/jYK8gsNu66677uKOO+7A6XTy8MMPM3jwYFq1anXY7T6lPoXq8Xg46aSTDruxumLTpk2UlpY2aBuLioooKCigf//+mTblkCxatIh+/fqRk5OTaVNqRQjBnDlz0vo+v/vuu3Tp0iVtbZaWlhKJfM6+fQ33swjQvn1x2l6zaZqcc845vPfee2lpD2DNmjUcc8wxdO7c+bDamTlzJqNGjaJNmzY8+uijTJs2jRtuuIHXXnvtsCuf/ve//7VryNjY2Pw4Bw4cyLQJtTJmzBhOPfVUhgwZwpo1axg+fDgPPfRQ2kpC20HgNjY2jRJJkhgxYgSbN2+mpKSE0047jWbNmqWtfXvkaGNj02gZNmwYxx13HBUVFdxxxx1pbdseOdrY2NjUgi2ONjY2NrVgi6ONjY1NLfxmxVEIQTQazbQZNjY2jZSMieO+ffsoLi6uk7bXr1/PggULuPHGG1m+fDmBQKBO+rGxsfntkhFxPHDgAI8//ji7du2qk/ZvueUWFi1axI033siYMWNYu3ZtnfRjY2Pz26XexTGRSHDHHXdw8cUX07Nnz7S3/+qrr3LWWWcxduxY5s6dy9NPP82TTz5JLBZLe182Nja/XepVHAOBAJdeeimTJ0+uszyPZ599NkuWLKG0tJROnToxaNAghgwZgqZpddKfzaEpLy/HMIxMm2Fj86uoN3Hcs2cPf//735kyZQotWrSos348Hg/HHHMMkyZNoqioCJ/Ph9frTUuWkl+CEILFixfXa58Nic2bN3P33XdTXl6eaVNsbH4V9bJDpqKigkceeYTf//73h73Z/Odw5513EgwGWbBgAU888QRvvfUWffr0qbfR4/z581m8eDG5ubnMmzePoUOH0q9fv3rpO9Oklk10Xee6665r0NlhbGx+jDofORqGwciRIxk2bBh9+vSp6+4sPB4PgwcP5rTTTmPo0KFcffXV9TLFE0KwdOlSCgsLGT16NA6Hg9WrVyOEqPO+M41pmlxzzTVcdNFFXHnllRQWFmbaJBubX02djhyLioq49dZbmTFjRp1OpX+Ko48+mltuuYVbb72VO++8k+zs7Drra9myZfh8PoYOHcq5557Lrl272LNnDy1btqR9+/bk5eVRUHD4eewaGhUVFUyZMoVx48bRvXv3el/GsLFJN3Umjnv37uXxxx9n3LhxGRVGSGbvOOqooygsLOStt97i8ssvr7O+Tj75ZObNm8fUqVOZO3cuN998M1u3buX1118HoGXLlrRp0waA9u3bc+aZZ9aZLfWBEIJnn32WvXv3cu6559KjR49Mm2RjkxbqRBwjkQjTpk3jiiuuaFBfltNOO43Zs2ezf//+Oh29/eEPf2DNmjXcfffdDBo0iBNPPNFaa12zZg2rVq0CkqUvn3vuOet5AwcO5Morr6wzu+qC6dOn06xZM0466SROOeWUTJtjY5M20i6O4XCYyy+/nJkzZ9K6det0N39YFBYWMm7cOEaNGsVzzz1XZyUD+vTpQ+/evRk0aJA1SkzRo0cP6wejqqqKffv2Wcfef/99evXqBUDv3r2ZOXMmkBz5er3eOrH115JIJHj44YcpLCzkwgsvRFGUTJv0A5xOJ9FoFCGEPc0/DFwuF+FwuMHWH5JlGUVRiMfjaXW6plUc9+7dy7Rp05gxYwZHHHFEOptOGwUFBTz33HNcf/313HXXXXToUDfFkiRJ+oEwfh+v11vDe9+pUyeuvfZaAL744gsuvPBCALKysrjppptqPK8+nVvfJ5FI8Nxzz5GVlcXFF1/cYIXnjjvu4IwzzqB///5kZWVl2pxGiSRJPPXUU4waNYqnn3460+bUSpcuXejfvz//+c9/+POf/5y2dtMmjsXFxTz++OMMGzbsJ0Uh03g8Hu666y6eeeYZLr/88gZTqFySJEto+vTpw6JFi4BkMPV9991nnSfLMu+++y6QLFY0bty4ehOo119/nbVr19KqVasGvwTQUEW7MZH6TDbkaIvU+5xuG9MijoZhMGHCBMaPH9+g1hh/jA4dOqCqKhs3bqRjx44N+ouUnZ3NlClTrL8rKipYsWIFkFzfPeecc4Dkh+TVV1+1pj/pfE1CCObNm8eOHTvo378/gwYNSlvbNjYNkcMWx/LyckaPHs2MGTMaXYjK+PHjueGGG+jYsWOjisnLysri7LPPBpKxhSlHiBCCQYMGEQ6Hueuuu/j973//q/swDIPy8nLy8vIQQrBs2TI+++wzqwymjc1vncMSxz179vDII49w9913NzphhORC87///W+uu+46Lr/88gZdWvVQyLJcYxfKxx9/fNhtLl26lK1bt/LFF19w4oknIoRg5cqVTJ8+/bDbtrFpLPzqHTKBQICHHnqIwYMH07Vr13TaVK9IksS0adNYtGgRS5YsybQ5DYKpU6dSVlbGSSedxJAhQ1i5ciVTpzbcQvY2NnXBzxZHIQSJRAJIeitHjBjBiBEjMuo1TRc+n4/CwkK++uorTNPMtDkZ5emnn2bIkCEMGTKEhQsX8sknn7Bz584mf11smh4/Sxx37tzJypUrGTZsGF9++SWjR49m5syZDcbLmw4GDx5MKBRi/vz5TVoILrvsMl599VW2bNnC5MmTefjhhxk7dqyd8s2myfGz1hwnTZpELBbj+uuvp1evXsydO5eWLVvWtW31iqIo3Hrrrdx///2cc845yPJvtrzOj6KqKueeey4vv/wyBQUF+Hw+WrZs2aC9+TY2dcFPiuOSJUvo3Lkzw4cPZ+LEibz44ossXryYU089tU4TOGSKm2++OdMmZJzRo0cTDodZtWoVJ510UqbNsbHJCD85POrTpw87duxg+/bt3H777ezfv5++ffvi8/nqwz6bDOFyuWxhtGnS/KQ4ejweOnfuzF133cX777/P/Pnz6dChQ4PcS2tjY2OTLn7WwtpNN93EG2+8gRCCBQsWcOKJJ9a1XTY2NjYZ5Wd7HTRNa/B7aW1sbGzSRdN0ydrY2Nj8BLY42tjY2NSCLY42v0nWrVvHyJEj2bBhAzfddBNPPvlkpk1qlDzwwAPcfffdfPzxx4wcObJGcubfOrY42vzmEEKwfft2dF2ndevWnHfeeSxatMja/mrz84jH47zzzjv8/ve/p1u3bpSXl3PgwIEGndsxndjiaPObIxAI8NJLLzF+/Hj69OnDaaedxvHHH8+8efMybVqj4oknnuDKK6+kb9++9O7dmylTpjB16tQGub22WbNm5Ofnp7VNWxxtfnP4/X5uv/121q9fzyOPPAIkEwZffPHFGbascTFq1CjKyspo2bIld999NytXruQf//hHg4xxvuCCC7jgggvS2qY0dOhQccwxx6S10XQSDAYpKiqiffv2mTblkOzduxen00lubm6mTTkk33zzDR07dmzQCSS++uorjj322EybcUji8TgffbSFsrKGm6JP10vp1SvaoHMfbN26lebNm9dZgbt0sHr1atS9e5sxd+7ITNtySPz+bfz1r8u49NJLM23KIXn33Xdp1qxZg07f9o9//INhw4Y16EJT1157J5MmNdzPotNZTrdxf2TNDS9l2pRDUvBZAdeV3stZZ52VaVMOyXPPPccpp5ySlgHPlVdeydSpU/nwww+RJIkBAwbwl7/8haeeeuqw2v3rX/+KKoRCNNpwRzyxWCm6rtfZqEwIwUcffXRYNZfdbjder7dBjxw1TSM7O5ucnJxMm1IryUX+hv1ZBDA0g2huNNNmHJKYN4Y74m7Qn0Vd1/H7/Wmx8bXXXuOoo47inHPOQZIkZs6cybp16w576q+qavrrVjcmPvjgAxYsWIDP52P+/PlceOGF9tZIG5tGhCRJXHPNNVZ5kGuuuSZt6fWarDgKIVi+fDnNmzfn6quvZubMmXz++ef079//V19c0zQpLS3F7XYTj8eBZGF5IQROp5NAIICqqoTDYbKzsyktLaVowwbef3QWlcVFSbuA/n+6lKX/eR4hwDQFqstNm6OPZv2KFZgCBBK5LVtw6V/+Qm6bNqiahqqq1npiJBLB4/HUKPVqY/NbRJZlxo8fz5NPPokkSWnd4txkxXHVqlXEYjFuuOEGzjzzTN555x2mTJnChg0bOPLII39Vm4ZhkEgkrJssy1ZsXeoYJMtMxKJRVvznad65dwqJWCw5rUz+z44vVhKLxTFNgWEKTGDdh4uIxeMkTBCahsufxVeLP+SGWQ9zzOkDEUIgSZLVZyKRQFVVWxxtbH4lTTaUp0+fPjgcDiZNmsTu3bu54447aNeu3a8WRoBwOIzf70eSJHRdR1VVTNPENE2CwSC6riNJEn6/nw+f/BcL/nYPqhFHVyRcqoxLU3CpMg5h4DroMV2RcUgmuqrgVCWcCNwunRbt2/LcxLvZ+MknyLJMNBolEomgqirBYLDJBOva2NQFTVYcAc4//3zee+89gsEgffr04fTTTz+s9txuN5WVlUiSRCQSsUZvqqri9XqJRCJIksSnr7/MkhnTcGsKLvW7m67K6GpSDJP/ltEVCV2V0BUFpyqjKzKqJIhVlkEsSssO7Xl+4j18PG8+LpcLj8dDIpHA5/NlpNRDPB5nypQp9d6vjU26adLi2LNnT1q3bs3//vc/rrrqKo466qjDaq+yspLc3FxM08TtdqNpGvF4nHg8Tnl5OW63GyMeZ8f7b6PGw7hVGZcq4VIk3KqMW6n+W60eNVbfkuIo4ZIlnIqMQ5YgEuHA5k1ICQO3P4vdGzZQWVFBeXk5mqZRWlqKYRhpulI/n1AoxPvvv1/v/dqknwceeIDPPvss02ZkjCYtjinSFWDu9/spKipClmVCoRCJRAJN09A0jZycHIKhEGvnPMOuRQtwycmRoUdT8DiU5ChSk3GrCi5NwaNVC6OmoKsqTk3BqVWPLFUZhyKjGHG++ehDcvPzWPW/99m9Zg3Z2dnE43Hy8/MzspPhd7/7He+9916992uTfoqLiwmHw5k2I2M0aXFcvnw5ffv2Rdf1tLQXiUTw+XwIIXA4HMiyjGEYGIaRFMvyUvZ8uhynKiXXF7Wk0LlUKXmvVd9UGaeiJKfUCtW35KjRqUg4ZQmnApoiI4sEaz/4Hy6Hg+VvziVQWYmiKBlZc3zvvfc466yzUNUm6+ez+Q3RpMXxgw8+4OSTT8btdqelvVgshq7rCCFQFAVZljFNEyEEsViM6IH97Fv2QXItsVoE3aqCS1Wrp89J8UuuOUrfrT8q1WuPSnLE6DhIKDVZIVpZgUt38Mnbb7Nv+3YURSEej9erOAohmDt3LpdcckmD3Htr88u54ooreP7555vs6LFJi2O68Xq9VFRUWA6ZeDxuiaRb0/hgzJWW4LkVObnmqMm4tep/K9XOGU35ziGjKThUJSmKqTVHVUKVZRyyglMGVZHZsPwj+gwcyKxrryEWi+H1euvVIZPaqXC467Y2DYdu3brx7bffZmTtuiHQZMWxuLgYwzAoKChIW5uBQMBKm+T1enE6nRiGQTweZ+sny5BDVeiqjFtT0BWl2hmTHBm6VBm3JlWLpGR5r52KjC5LOBUJhwJOmaQoKhKaQlIkFQkpEWfFy3PIyc9j+5o1FBUV1Vv+wlAoxPbt2+nSpYs9pbb5zdBkxXHt2rXE43F69eqVtjZ1XaeyshLACuWRZRlFUdj27KM4FZFcW9Qd1tqiFcuYCuNJOV0UyZpiO6pF0qHKqKqEQwZNkVBlCU2RcGgquq5jRKKY0TjvPv4v/H5/vU1vDxw4wMqVKzn77LPrpT8bm/qgyYpjXRCLxXC5XAghUFXVWnOUhIlZXoSqJNcR3V53UhwVuTrG8eCQHdm6JcN2qu9T02pZRpNlNAkcsoQiJ7c5KYAiS2xYtgQDk1AkXC9JSYUQTJgwgUmTJtV5Xzb1j6Io9rTaJj2ktusdvG1vy/OPYBbvw60ouN06nqysam919Zpj9VQ7FffoVMGpyjhVGYeaFEFNkZM3WUKpXnNUZQmHIiMj8Pg9uFwuVFXmq/nvsuQ/L9Tba964cSOdO3eut/5s6o/58+fzu9/9LtNmZIQmuUAUi8VYuXIl/fr1S2u7DoeDYDCIoijEYjEguTE+vHs3qhHHocq4fD7cWdkIYaIIEyIhiEUxAcOUMGWBbJgoEoAJQkYoJiYC05QxFYEmTEwZEqZkjRiduk5CSsZXhqsq0Z3OenHIzJ8/P+0ZmG0aDg6Hw/osNzWapDhGo1HmzZvHokWL0tpuKBQiPz+fQCCAruskEgni8TgupwNVlnC6PfgKWqG53Wj+LBRJYOzfhaQ6weEgFqggWnwAkJEkgUly9GkIGUMIDBkSQqDIEoqskNWqgGg8QZbHCw4dj5BwePxs27iRLV+vJlxVhcfvT+tr/D4zZ87klVdeqdM+bGwyQZMUx7oiKyuLAwcO4Ha7CQaDyVHj6k8pWfEB3vwCsjp1weF0JtcMFQlVEiAdgeR0g6ZhJFoSb9OZaCRE+Ya1kEggJIFDFpgJGUM20QzI7dIDw+EgahgYkkpCkogbgqhh4srKQWgqS198gf8bM7ZOxXHmzJmMGjUKn89XZ33Y/HKaWiam1EaLdMUrp2iS4rhq1Sp69uyZ9nZTC9cHpw+LFhdBPIb/6N44XS5r7VCVQZMlVF82EmAiiJsmsgmymY3SrCVxQxA3BTFhEk9AzDBICIgZJjEDqrZvxd28BXFZQTZAMgWqCW26dSdQFUbU4UJ6aWkp5eXlDBgwwA76bmCkO/i/e/furF+//rAyVtUlmzZtYsWKFcyePTut7TZJcRw7diyff/552ts9+NcrFe+XMOL4uhyJMzc/6VmWFVQZVCXpVNFkCRmJhBBgCjBNTFOgGd/9LUxB9aIksinAMJFMKOjWnYRpopgCxQDFTAqsYph1Xkhr1apVSJJE796967Qfm8wiyzJ33XUXU6ZM4Zlnnsm0ObWS+jFI94i5SYpjXeFyudi/fz8+n49oNIqiKOi6juzUkV0eFDn5YZNlkGUpeVNkJAlkMznyk02BYgpMw0AxqzOBm2a1OAoQAtMUYAik6pGmZJrIhkAxTWTTRDEllDoUx6qqKl566SVuu+22OuvDxibTNDlxLCkpSXvx7xQVFRU0a9aMcDiMx+PBNE2KonGMmIHs0JEdKpIsgwSSIoEEpiwlp9Wi+hfQBMkEuXqUqAiBKQyEIVUfF9UjSJBNE0mAZBgoAuImyIZBLCGQVQ3qaO1pz549BINBOnXqVCft29g0BJqcOI4dO5ZHH320TsJcsrKy2Lt3L1lZWVRVVeFwOGh52lmUf7QAWdFQXG4kWQJJQshgSkmBBAkBCNNEEhKSaSIJgSwEskFSIM3kyFAyq88zSZ5TLZIJkRRS1YAdKz/Hl5uLt44qDQ4ZMoQvvviiTtq2sWkoNDlxrEtCoRDZ2dkIIaydMjhd+AqPoXzDGlqdfSFmKJAUR0nClJI1Y5AkEAIhBEJIgKgWyKQASiZJYRQiOcI0Qa6egseFQBImsilhmoJYIMC2r7/izFHXkdWsWYaviM1vgebNm1NYWMjy5cubVHVOe4dMGklV/RNCoGmatfWq2QVDKN25md3vvY6W3xJ0N0J3Y+puhO5COHVM3YVweRCu5DFcXiS3F8XtQ3W5Ud1eVJcX2eVBdXvQvF5Urxenx4vD48Xp8aC5dJbPeYGCDp3oPnBQnaQsu+2225gxY0ba27VpuGRnZ9O6dWvWrVuXaVPqlSY1ciwuLsbv9+N0Ouukfb/fT3FxMbquEwqFkGUZTdNQc/I4/om5fDnyD+x951VanncJsqKCMBDxRHJqLQSSoiI7nEhCkIhEkFUHhmGSCFXicHkxhAnxBJJhYIqkA0cgoQhBRUkJb/7jQbJateH6p5/F7fWkPcSmqKiI8vJyunbt2uRi6WyaHk1KHF977TVOOOEE2rRpUyftV1ZWkpeXR1VVFW63G9M0iUajSJJEOG5w9PR/s/U/j1H29ecoLjd6sxb4u/ZASEmnTHjXdoKbNmCaCfZ+8RUff/QBR/XpR7NuPZBklXg8hqv5EWR37IKBQBgm3yxbQkVJKbF4go7H9efiv/wV3e2ivLycvLy8tKYQe/nllzn11FNp1apV2tq0sWmoNClxrGs8Ho9V4CoSiQDfxTt6vV5iDgftr7uT4pXLccTDVBzYT+krzxIrLyP76F5k9TwBqaqKaDhMsEMhyz/7mO6nn0+rzl0p2rgBr8uF5M3h24XvsmPNV3ibtcTdsZAWPY7liO7dKejS1coCnpWVlVan0/bt29m5cyfnn39+2tq0sWnINBlxNAyDRCKBw+Gosz4ikQh+v59QKGQluk0kEgghiEaj6C6d8qpyvL17UxWownO0xO4t39IsK4+wAMmfjd73ZAqyc8irLOPovZtxZWWhHdGWTp27UVVVhd/vx9GiJT3+eAVxwySv1REgyyQSCStBgK7rVFRUkJeXlxaBrKioYPfu3SQSCdq2bXvY7dk0LgzDYNCgQdxzzz306tWLLl264K/jPfsNgSYjjhs2bODLL7/kX//6V531IVeLlCRJVu2Y5NqcYMWWFewP7mfqK1PZXbwHv+wlV8/DgYOS4mKioTiRqjCjLh7F6ScOosK9ixVb32PDJ28zfvK/6XZ0fxRFwTRNvPnNcDqdyUzf1TkjU/0DVg2bw10X3L9/P19++SXPP/88K1as4M033zzcS2TTCNm8eTMDBw5EkiTWrVuHLMssW7Ys02bVOU1GHFPUpSNB13XC4TCqqhKPx5EkiW+KvuWtr9/i0Q8epbi0GGICn+qjY0EnHJIDt+ymc/NOqKaGZMC6Tev4374XaJXbmqHnjmbTh6+x8JX7KWgxk7yCdsRiMUsYU6nRJElCkiRrVJzaxni4r3Xp0qU8+eST9O3bl6ysLGbNmsUjjzySjktl00gQQvDSSy/x6KOP8uabbzJmzBjmzp1rVZr8LdMkQnlEdQxhXXtYg8Egfr8fIQS6rrM/tI/hLw7nkQ8fwSFrtM9rT7eW3XDIDjbu/pbi0mKaZzcnx5vLEc1a4dbdBHK3Ea4U3H3RTP7vghEUHnkM5fvW8dbsMaz59G3LE+5wOIjH47jdbksUw+EwoVAIRVEIBAKHlQm8uLiYDz74gH/961+8//77TJw4kQ4dOvDBBx+k63LZNBKOPvpo1q5dy80330yrVq3YtGlTk0hu3CRGjk13rs4AACAASURBVIZhMHLkSP5/e/cdH0W59///NTPbk00PBAIYihRBBcGDIFWaHCxgw+P3xH6QJqIixcJRFNSDHkGkNwEREBDErkhXkK7Um5oQDIEkm7qb7GZ35vdHyq0/CTfIbmYg19PHPkhjrreT8Mk1M1dZu3ZtSNupWLIs3MGhjIP0nNkLq2ajljMBC2Zs2AmTHTSKbYTil/npl59QJIWa8XEsW7+Q1n0akLfDz7m8IhrWbYwkSXTuN4IOPe7hs5kD+OGjIcgyNGjaGo9HxuFw4HK5SocLmUyEh4ejKAqFhYXExcVd1v3GmJgYOnXqxPz58/nyyy/Zvn07p06dYvjw4UE8Y4LRSZJEy5YtGT16NDExMaxZswaHw1Etpo5Wi54jlD4ssdlsIW2jpKQEs8XM1tNbuffje8kqzEYNBIiyRVLi82NVLFxXpznxznia1GtKxzbtSPXsZ83ZRdzYrQk7DqfhcQa4v/f9mE2lC0c4I6Kocc3NdOrzNFEOLztWPMH38/4fZsVEIBDAYrFUFMGSkhJ8Pl/pOpJFRZc1CFyWZRo2bMjRo0dZvHgxS5YsoU2bNiFf7UcwnqSkJA4fPoyiKMyePZvly5frHalKVIueoyzLvPjiiyFvR1VVjriO8ca68ah+jYaxDbGqFgqL3TgsDhzmMNLOpZHuPom1Rn1S8g9Sp0UMZzNkAlipV6M219e8kZGdXwKoWBMSSSHxul6ERcZQkF9ATI3aaNL//r+V33Msz/D79y/HzTffzKJFi1i9ejWDBw8W6zZWc08++aTeEapUtSmO99xzT8jbKcLDgC8fJ8YWQ5/retO0RlNOpKewdMsSGjWOxBrm5+ivZ1AS/ShRDenQoR2SyURkWE1qOeoR8Gn0adYXm1z6wMVsNlNYWEiNGjWwOmvR+JaHcWVnExkZSV5eXsWeNWazGbPZXNGLdLvdhIWFBe0ea9++fYNyHEEIhVA9S6gWxbGqZHpSebBbKyRZIsO7jxPaUfxJGo8kdSDOmoiMjTpJh4mz1SbGmoBJMrP7zHZinNFYJTMPtnyC2o66WK1WNE3D7/cTHx+P2+3GYrHgys7G4XBQUFCA3W7H6/VWPAAKBAK43W6gdFOknJwcoqKigjpDRhCMKBRrCIAojkHVKOpGHvW+jc1pYXvOF9SwXYNfLuZ44S+c8aWSW5yJ4vBjt5tx+U5Tw1yH3g3v4WT2MYa2fQmft3T4T0FBAZIkYbVaK9afLH/Icu7cOWJiYsjNzf3DjByLxYLD4cBkMlVMYxSE6iAmJiYkw4pEcQwiRVGIjY3FbDbT3f7P0g/KKrXDGpHjP4umapiwABJFJR4cSgQNI5oj1VcwKWYka+n9w/IB3LIsY7FYKp5EAxXTAiMiIlAUhZiYmIqvLb/XWP61glAd1KhRg/79+wf9uFLv3vdobrdxFxJQFB+RkYVYLDF6R6mU359PVJQp6LufBdO5c+eIjY019EOV06fTMZmM+7MIAUrkdMw1jPvEXvWohPvDDT29z+VyER4eHtKpvJcrPT0dU2FhXTZvNu76fJGRx6lTZz0HDhj3SVmDBp/x0Uc1ueWWW/SOUqnx48czZMgQoqKi9I5SqWHDhvH+++/rHaNSOTk5zJgxgzFjxugdpVI//fQTWVlZ3HXXXXpHqdSsWbPo1q2bocdKDh8+vPyy+kpYm8/4Ga+ENQ6NmjFUO8iFgsgYHEbPWG0GgQuCIFwKURwFQRDOQxRHQRCE8xDFURAE4TxEcRQE4YJSUlIYNWqU3jGqnCiOgiBckM/nIyMjQ+8YVU4UR0EQhPMQxVEQhCte+Wr/wSSK41UkKyvrsrZGEIQr1ZIlS1i6dGlQjykWnrgKZGRksGXLFtatW0enTp1o2LAhN998s96xBKHKFBcXB33Gjeg5XgX27t3LvHnzGDNmDEeOHGHKlCl6R7pi/Oc//wnZeoDBUFBQwJw5c/SOUS2J4niFy8vL4+OPP6ZNmzb079+foUOHcvPNN/PZZ5/pHc2wNE2jpKSEUaNG0bZtW73jnJeqqpSUlPCvf/2Lzp076x2nWhLF8QqWmprKgQMH+Omnn3C5XHz++eds376dXbt2cccdd+gdz5BUVWXNmjW0b9+e7t2706lTJ8MtgODxeHj33Xdp37497733Htdee63ekaolcc/xCpSZmcmKFStITU3F7/fz2Wef8cEHHzBv3jxSU1Pp2rWrrus2njt3jpUrV9KzZ0/DLUu1bNky0tLS2LFjh95Rzsvv9zNjxgwaNGhg2IzVhSiOV5gxY8aQm5tLx44dGTx4MPXq1QNg+vTp/PDDDwwfPlzX7VNHjx5NXl4eHTt2NNw2rnPnzkXTNEaMGKF3lEq9/PLLdOnShdtvv13vKNWeuKw2OLfbzZEjR2jZsiUtW7akR48evPzyyzz00EMVhbFct27dqrwgaZpGfn4+K1eupGXLlvTq1YtXXnnlvPn0oGkahw8fpmXLlvj9fh5++OGKfb6NIhAI8Mknn9CyZUv69OkTkv1QhEsneo4G5XK52L17N8uWLcPtdrNnz56KzxnlHtmJEyc4ceIEkyZNon379hUZjZIPYPfu3UybNs2Q2aC0eK9Zs4aTJ08aNmN1JYqjAU2ePJmcnBw0TWP06NGGu29XXFzM22+/XTEEZsWKFdhsNp1T/dn69evZuHEjU6dONWzBWbBgAXl5eYZd2EHTNFasWGHovcuzsrI4deoUd999d1CPK4qjAZQXmS+//JJJkyYxaNAgOnbsyE033aRrps2bN5OZmck999xT8fEJEyawdetWhg8fToMGDWjQoIFuGSujaRq7du1i8+bNDBs2zHCFu/z7/eGHHwIwZMgQHdNcmKZpLF++/A9XLkZTXhxbtWoV1OMa6+ZLNeN2u0lJSeH++++nVatW/PzzzxW/pfUujE2aNGHFihUcOnSIxMRE1q5dS6tWrWjatCmLFy+mW7duhiyMmZmZ9OjRgxkzZjBy5EhiYoy3a+Xhw4dp1aoVhYWFJCcnYzKJPsrlCNVVgfiu6EDTNFatWkVKSgo7duzg7bffNtSl88aNG/n73//O888/z5YtW7jzzjsZOXIke/fu1TvaBR05coSZM2eydOlS4uLi9I5zXlu2bGHZsmXs2bPHsJf6V5pQzXASPccq9t133zFkyBBOnz7Nddddx5IlSwxVGAEKCwtxOp34fD5ycnJwOp2MHDlS71h/snv3bjZs2ABAWloaixYtYtCgQYYqjOXjKgE2bNjA5s2befPNN0VhvAKInmMVKCkpwePx0KtXL3r27MlTTz1F8+bNDXs51adPH4YMGYLX6yU+Pp4FCxYwfvx4vWNV0DSNLl260Lx5cxwOB8OHD6dhw4ZMnjyZOnXq6B0PgDNnztCnTx8efvhhhgwZQlpaGr169WL06NGEh4frHU+4CMb813mVeeutt/jhhx/YuHEjJpNJ19krF0OSJI4dO8aGDRvIzMwkPT3dUIX82LFj1KhRg9dee423334bq9XKyy+/bJjCqGkahw4domfPnvTt25cNGzaQlJTEwIEDiYqK0juecJHEZXUVeOWVV9iwYQNWq9XwhbGc2WymR48ePPTQQ5jNZkNdBq5du5aePXty+vRp7rrrLp555hnWrl2rd6w/WLduHZ07d2bnzp28//77dOjQgV27dukd66qzfPlyFi1axMGDByuGwAWLcboDgnCRBg4cSJ06dXjuueeIjIzkxRdfrLivZwSSJDFgwABuvfVWZs2axaRJk9i0aRM7d+7UO9pVZ8qUKdx55504HA5Wr15Nr169iI6ODsqxRc9RuCLt2LGDxMRESkpK2LNnDxaLRe9If1C7dm22bt3Kli1b6Nu3L1999ZXeka46kyZNYtiwYdx99900b96chQsXMnr06KCthi96jsIVR5IkateuTf/+/SveNxqTyUSdOnV44403AGNmvNI988wzdO7cmVdeeYVRo0bx2GOPMWXKlKDNnRfFUbhiXQkF50rIWJnvvvuODRs2cObMGV566SX69etHmzZt9I5VQZIknnzySbZt28aWLVvo0qUL8fHxQTu+KI6CIPyJpmmsX7+eWrVq4XQ60TSNvXv30rp1a0MV/IcffhiPx0NaWhpNmjQJ6rHFPUdBEP7kxx9/JCwsjEcffZRmzZrx/PPPs2/fPlJSUvSO9icOhyPohRFEcRQE4Tw6dOiA2+3mrbfeYv78+YwePZrrr7+e+vXr6x2tyojiKAjCed1zzz00aNCAcePG0bZtWzp27Kh3pCol7jkKgnBeN998M23atCElJaVa9RjLiZ6jIAiVkiSpWhZGEMVREAThvERxFARBOA+TJAWwWII3WTvYzOYCFMVr6IyKUoTb7Q7qpPdgKykpIS8vT+8YFxQIBAx9DvPy8igpKTF0Ro/HQ1FRkaEzer1eCgsLDZ0xEAgg9erVX8vObqF3lkqZzW6aN8/immuu0TtKpTIyMvjlFyvFxcGZ8B4K0dFHaNeuvuH2kv69ffv2cf311+sdo1IlJSWkpKRw7bXX6h2lUi6XC5/PR0JCgt5RKpWSksLB+IOUhJXoHaVScfvioGPHZzTQDPuKjDyqzZo1SzOyVatWaQkJP+l+ri70atPmdc3lcul9qiqlqqo2dOjQoB93+vTpWlFRUVCOlZ2drY0fPz4oxwqVLVu2aKtXr9Y7xgXNmDFDizwaqWHg/zoN66SJe47CVe2TTz7B6/XqHUO4AolxjtWMqqoEAgEURUFV1Yp5suUrmaiqWrFhkSzLBAIBvIWF5J3NAEr7oZIs4YiKotDlqjiuyWIFScJXXFzxMYvNRo169VA1raI9RVEqjl/edijn6hppHrBwZRHFsZrx+XxkZ2cTFxeHy+UiLCyMQCBAeHg4qqri9Xrxer0EAgEiIyP5Zf1a9q5cwbaVy0vXydM0ZIuFRu07sH/dD6gaBFQVZ80EkBXOpqURUCUkk4maDepz77BhtLnjTiKio/B6vcTExFBUVASAzWYL2vJSV4tVq1Zx9913i/NiAOI7UI1omobX6yUqKori4mIiIiKA0rUH3W43Xq8XSZKw2Ww4nU6Obd/GF2PHsOfTT7BKGnZFwm6SsQT8nNy8AZtJxm6SsZkUirPOUZiRjk1RsChgtZrxeTysmjKF7+fNQ9M0bDYbubm5BAIB/H4/BQUFOp8R43njjTeCtlircHlEz7EakSQJs9lMQUEBkZGRFBQUYLfbCQQC2O12VFXF7/fj8/lwnU5j6eDHKczMxG4u/x0qoWmgopX+qWoEgEBAww/4VRW/qiGrEpKkYbOYiYiJZs/a74mIjqLTQw8RGRWFz+dD0zSsVquOZ8N4iouLsdvtescQyoieYzWiaRqapmGxWNDK7gNqmoYkSaiqWtFjkWWZL18ZgZqTjaOsd2g3KThMMg6TRJhJwWaSsZnLPydjV8CuyKUfVxRkfwn5p1PxFxYQGRPDp5Pf53+2bycQCFTkKCkx7lAOPQwbNoyZM2deMZuwXe1Ez7GaKX8YU1JSgtlsriiOfr+/4u1T677Fc/gANpNU9ttTKnuVFlgVSu81ahIBDfyyhqJKKKpW+pI05ICGLMG5Y0dwRESS1Pw69q1fT9O2bTGXFefyBzNCqfLzLx4iGYPoOVYj5ZfVhYWFWK1W3G53xRNpm82GxWJB1TQOrVmBUlKMXVGwmRTsZgW7ufTeos2k4Cj7s7THKGEzSdgUGatJxqqUviyKhEWRMKGS9usezGhsXrESpcSHyWTCZDKJze1/R/yiMB5RHKsRVVUpLi6mRo0auN1uYmNjCQQCmM1mcnNzKSoq4siyhZzdtrm0+FkUHObyy2kZh1nGYZGwmv63INoqCiTYFQmrScYsy6XFUpEwKwqSv4T9677n5m5dGNOvL97iYkpKSsjOztb7lBjGsmXLuOmmm2jcuLHeUYQy4rK6GpEkCUVR8Hg8mM1mioqKkCSp4oGMNzeH7P17saCimCRkqexV9vdVQEVGDaioQEDV8GvgVyWUgIZf0pDQkEwS3oAKKGiaCpqMFgiQ89tvOJ0R7Nu0iVbduhluO1U9+Xz/26MWjEH0HKuZ8gKpaVrFWLrye445x/6H09+tKbt8Ln0IUzpUR64YtmNXJOzm0s9by76u9DJaxqxIWBUJiyxhURTM5W/LYFJkDm7agKL5+WnlCvx+v3ggIxiaKI7ViKZpBAIBHA4HJSUl2Gy2iqfWRQUFHPl4LjaThN0kYTcp2JTSt22/v8dYPrZRkbGZJKyyVHafUSq7jJawKBoWmdJ7joqESZYwyzJmSeLs0aNExESz9fM1FYPBq7u0tDS2b99Ov3799I4i/I4ojtVI+QOZrKwsHA4HeXl5yLJMSUkJzjAHBTs2YVVKH7yEO8NxlA3VcZjlsrel3xVIBXvZQxibScFqUrAqChZTaa/RosiYZan0ZZIJC3dgsVooyMzk2xkzCRQXERMTo/cpMQSv10t+fj5xcXF6RwmJs2fPkp+fr3eMSyZucFQj5TNk4uLiyM7OJjo6msLCQsxmM6c3fY1JBrMkY7WYsTod4C9G/t3QEk2TCGgaAU3DpEJJQEYqu8+oaRJoEioSKBDQQFUl/HLpcB9fcTE2mx2fzweKwt4NG2jWqQsNQ7ClpmAMPp+P2bNnk5WVhc1mIyEhgUceeUTvWBdN9ByrEUmSsFgs5ObmYrfbKSgoQJZl/H4/KYvnVFwih8XE4Ah3lvYiFRm7IuNQfn/fUS6dIvi7ITvmspdJllGk0ktpU1nP0STLyBJExEQjyzKSqrJj5adoxeKy+mrm9XqZNm0a/fr1o23btrz11lt6R7okojiW8fl85OTk8I9//INz587h8Xj0jhR0mqbh8/lwOp14vd6KKYOyLKN4i7DIEjazgiMiEkdkNNFNmxPd5Drs4XbsdjN2qwW72VRxCf2/D1zKiqNcOoyn/D6jIoPJbEZRZMxWC/bwcGJq1USWAE2lqNCt9ynRnaqqDBo0iPfee0/vKJctPz+fnJycilfPnj1JTU0lOTkZs9nMzJkzeeGFF/SOedHEZXWZ1atX8+9//5tPP/2U++67jxtuuIEPPvhA71hBVz7oW5blihkZ3vRTyH4fpjAHzsR6hMXVqOj1KRJYCnMw122IBhSdTackPw/F60UqLCxbGxT8mkxACqBIYDYpKOGRmFGIr1uPYo8XKSyMkgDY42oQFhvP8QMH+WL6dFq0b1/tZ4RkZWURGxurd4xLcvLkSY4ePfqHjy1evJgzZ85UvG+322nYsCErVqwgPz+f5ORkDhw4wKxZs6o67l9yVRfHw4cP8/HHH1/U1x48eJCkpCQ++eQTlixZwuzZs9m/fz8tWhh3C4lLJcsyFouFzMxM4uPjycnJwel0curThfhdWdS4oTX2qKjSHqBSdkksgdSgCZLVjoqGyXEtfhUs7gJMWZlYAipev4pV1fAGVLx+jYBswhRXkxIkMtNOEVkrEZ+qogRACgQwO8LQFDN7v/umdIFIwdCys7OZPHnyHz4my/KfllUbP348derUqXjf6/Xy9ttvs3jxYiRJYsCAAVfUL8KrujjGxsbSpUuXi/76nJwcOnXqhNvtxuPxEB8fH7pwOlBVFZ/PR2xsLB6Ph6iyFXKK8/OIatEKe0LtistisyyV/SljIhIoXXlHVlUkVUWz2ZGj4ylRVWwBDZ+mURIoe6kaPr+KokJk7ToojrDSudZq6ZzrElWjZsMwTqek6n1KdDdixAhdL6nPN23xwQcfJCsrq+L9mJgYBg0a9IevqVevHo0aNbrgsa1WK2PHjiUlJQWn03nF9Y6v6uIYHx/PbbfddlFfW1RUxIgRI7jjjjvo378/d955JzVr1gxxwqolyzJWq5Xc3FxiYmLIy8vD4SgdYiMl1EYJC0cpe4CiyGD6XaEESlebUEFTVdSAhqaqoIEWUNFUDSkAkqoiqRqoGpKqEWG349dK35YDEkoggKKqmFTEgq7Anj17eO2116qkraKiIjIyMv7wsZkzZ/LNN9/84WMrV678w7AiWZZxOp1/ud2kpKS//Hf1dFUXx0vRp08f+vTpw7///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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Politikayı Kontrol Etme\n",
+    "\n",
+    "Q-Tablosu, her durumdaki her bir eylemin \"çekiciliğini\" listelediği için, dünyamızda verimli bir gezinmeyi tanımlamak için onu kullanmak oldukça kolaydır. En basit durumda, sadece en yüksek Q-Tablosu değerine karşılık gelen eylemi seçebiliriz:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "# code block 9"
+   ]
+  },
+  {
+   "source": [
+    "Eğer yukarıdaki kodu birkaç kez denerseniz, bazen sadece \"takıldığını\" fark edebilirsiniz ve bunu durdurmak için not defterinde DURDUR düğmesine basmanız gerekir.\n",
+    "\n",
+    "> **Görev 1:** `walk` fonksiyonunu, yolun maksimum uzunluğunu belirli bir adım sayısıyla (örneğin, 100) sınırlayacak şekilde değiştirin ve yukarıdaki kodun bu değeri zaman zaman döndürdüğünü gözlemleyin.\n",
+    "\n",
+    "> **Görev 2:** `walk` fonksiyonunu, daha önce bulunduğu yerlere geri dönmemesini sağlayacak şekilde değiştirin. Bu, `walk` fonksiyonunun döngüye girmesini engelleyecektir, ancak ajan yine de kaçamayacağı bir konumda \"sıkışabilir\".\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 5.31, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# code block 10"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26dab295a48>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 57
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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109.232964 214.756364 \r\nL 109.263403 214.085931 \r\nL 109.293843 201.703888 \r\nL 109.385161 202.919046 \r\nL 109.44604 214.756364 \r\nL 109.476479 192.192132 \r\nL 109.537358 214.735413 \r\nL 109.598237 214.756364 \r\nL 109.719994 198.100315 \r\nL 109.811312 214.756364 \r\nL 109.841752 210.230947 \r\nL 109.872191 214.735413 \r\nL 109.93307 208.63867 \r\nL 110.054828 214.756364 \r\nL 110.085267 200.509681 \r\nL 110.146146 208.052042 \r\nL 110.207025 214.756364 \r\nL 110.267904 213.268842 \r\nL 110.298343 193.470143 \r\nL 110.328782 214.756364 \r\nL 110.359222 209.811927 \r\nL 110.420101 214.756364 \r\nL 110.480979 214.044029 \r\nL 110.541858 214.756364 \r\nL 110.572298 212.032733 \r\nL 110.633176 214.756364 \r\nL 110.663616 214.756364 \r\nL 110.694055 199.441179 \r\nL 110.785374 202.981899 \r\nL 110.907131 214.756364 \r\nL 110.937571 211.110889 \r\nL 110.998449 214.756364 \r\nL 111.089768 209.916682 \r\nL 111.181086 214.756364 \r\nL 111.211525 203.882792 \r\nL 111.272404 207.842532 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115.016451 213.43645 \r\nL 115.046891 214.46305 \r\nL 115.07733 207.339708 \r\nL 115.168648 209.66527 \r\nL 115.229527 213.268842 \r\nL 115.290406 211.215644 \r\nL 115.320845 205.642677 \r\nL 115.351285 214.756364 \r\nL 115.381724 209.749074 \r\nL 115.533921 214.756364 \r\nL 115.564361 213.687862 \r\nL 115.655679 206.606423 \r\nL 115.746997 214.756364 \r\nL 115.716558 193.700604 \r\nL 115.777436 214.735413 \r\nL 115.838315 193.763457 \r\nL 115.868755 193.910114 \r\nL 115.899194 214.756364 \r\nL 115.990512 212.346998 \r\nL 116.051391 210.754722 \r\nL 116.11227 214.756364 \r\nL 116.142709 211.76037 \r\nL 116.203588 214.756364 \r\nL 116.234027 214.756364 \r\nL 116.264467 213.478352 \r\nL 116.294906 214.756364 \r\nL 116.325346 184.670721 \r\nL 116.386225 214.756364 \r\nL 116.416664 214.044029 \r\nL 116.447103 214.756364 \r\nL 116.477543 196.047117 \r\nL 116.538422 212.786969 \r\nL 116.5993 210.964232 \r\nL 116.690619 214.756364 \r\nL 116.812376 200.404926 \r\nL 116.903694 214.756364 \r\nL 116.934134 196.403284 \r\nL 116.995013 214.756364 \r\nL 117.055891 214.756364 \r\nL 117.086331 213.939274 \r\nL 117.11677 199.650689 \r\nL 117.177649 212.158439 \r\nL 117.208089 214.756364 \r\nL 117.238528 213.939274 \r\nL 117.268967 205.684579 \r\nL 117.329846 214.756364 \r\nL 117.360286 214.484001 \r\nL 117.421164 195.774754 \r\nL 117.451604 210.000486 \r\nL 117.512483 214.756364 \r\nL 117.542922 176.709339 \r\nL 117.63424 184.64977 \r\nL 117.725558 183.874583 \r\nL 117.755998 214.756364 \r\nL 117.786437 214.756364 \r\nL 117.877756 198.959306 \r\nL 117.908195 210.566163 \r\nL 117.938634 212.766018 \r\nL 117.969074 206.522619 \r\nL 118.029953 214.756364 \r\nL 118.060392 214.756364 \r\nL 118.18215 199.399277 \r\nL 118.273468 214.756364 \r\nL 118.303907 205.035098 \r\nL 118.364786 214.756364 \r\nL 118.456104 214.232589 \r\nL 118.486544 214.756364 \r\nL 118.516983 209.476711 \r\nL 118.577862 214.756364 \r\nL 118.608301 213.352646 \r\nL 118.66918 214.735413 \r\nL 118.69962 186.786773 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200.090661 \r\nL 122.595864 211.927978 \r\nL 122.687182 211.383252 \r\nL 122.717621 214.756364 \r\nL 122.7785 214.756364 \r\nL 122.80894 210.335702 \r\nL 122.900258 212.493655 \r\nL 122.930697 214.756364 \r\nL 123.022015 183.958387 \r\nL 123.052455 208.533915 \r\nL 123.082894 214.756364 \r\nL 123.113334 201.703888 \r\nL 123.143773 210.482359 \r\nL 123.174212 210.419506 \r\nL 123.265531 200.48873 \r\nL 123.29597 214.756364 \r\nL 123.326409 214.735413 \r\nL 123.356849 187.017234 \r\nL 123.417728 214.756364 \r\nL 123.569925 191.291239 \r\nL 123.691682 214.756364 \r\nL 123.722122 214.735413 \r\nL 123.783001 205.579824 \r\nL 123.81344 189.279942 \r\nL 123.874319 214.735413 \r\nL 123.935198 214.735413 \r\nL 123.996076 214.756364 \r\nL 124.026516 206.669276 \r\nL 124.148273 214.756364 \r\nL 124.178713 214.756364 \r\nL 124.239592 209.581466 \r\nL 124.270031 211.96988 \r\nL 124.300471 214.023078 \r\nL 124.33091 201.201064 \r\nL 124.422228 208.471062 \r\nL 124.513546 214.756364 \r\nL 124.543986 212.326047 \r\nL 124.604865 199.818297 \r\nL 124.635304 207.360659 \r\nL 124.726622 214.756364 \r\nL 124.787501 174.467582 \r\nL 124.81794 214.756364 \r\nL 124.909259 214.735413 \r\nL 124.939698 211.173742 \r\nL 124.970137 186.283949 \r\nL 125.031016 211.907027 \r\nL 125.091895 214.756364 \r\nL 125.152774 214.735413 \r\nL 125.274532 173.252424 \r\nL 125.36585 214.756364 \r\nL 125.396289 214.127834 \r\nL 125.426729 214.735413 \r\nL 125.457168 198.267923 \r\nL 125.518047 214.106882 \r\nL 125.548486 210.775673 \r\nL 125.609365 214.756364 \r\nL 125.639804 211.006134 \r\nL 125.731123 214.735413 \r\nL 125.761562 214.421148 \r\nL 125.822441 214.735413 \r\nL 125.913759 173.399081 \r\nL 125.974638 214.756364 \r\nL 126.035517 206.648325 \r\nL 126.065956 206.585472 \r\nL 126.187714 214.756364 \r\nL 126.248593 214.756364 \r\nL 126.309471 213.562156 \r\nL 126.339911 205.852187 \r\nL 126.37035 214.735413 \r\nL 126.40079 212.996479 \r\nL 126.431229 214.756364 \r\nL 126.461668 214.588756 \r\nL 126.522547 198.41458 \r\nL 126.552987 213.059332 \r\nL 126.705184 214.756364 \r\nL 126.735623 192.192132 \r\nL 126.796502 213.750715 \r\nL 126.91826 214.756364 \r\nL 126.979138 210.021437 \r\nL 127.009578 214.756364 \r\nL 127.040017 214.756364 \r\nL 127.131335 210.461408 \r\nL 127.192214 207.402561 \r\nL 127.253093 214.756364 \r\nL 127.283532 210.377604 \r\nL 127.344411 214.735413 \r\nL 127.374851 214.756364 \r\nL 127.40529 214.46305 \r\nL 127.466169 214.756364 \r\nL 127.527048 199.524983 \r\nL 127.557487 214.735413 \r\nL 127.587927 211.404203 \r\nL 127.618366 177.673086 \r\nL 127.648805 214.756364 \r\nL 127.679245 184.251701 \r\nL 127.801002 214.756364 \r\nL 127.861881 214.756364 \r\nL 127.953199 207.612071 \r\nL 128.014078 214.756364 \r\nL 128.044518 204.532273 \r\nL 128.074957 198.309825 \r\nL 128.105396 214.756364 \r\nL 128.166275 214.756364 \r\nL 128.257593 210.105241 \r\nL 128.348912 214.756364 \r\nL 128.501109 186.430606 \r\nL 128.531548 210.314751 \r\nL 128.592427 206.459766 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214.693511 \r\nL 130.236155 214.735413 \r\nL 130.297034 204.972245 \r\nL 130.388352 214.756364 \r\nL 130.449231 190.620806 \r\nL 130.47967 213.331695 \r\nL 130.540549 214.756364 \r\nL 130.601428 214.358295 \r\nL 130.692746 203.547576 \r\nL 130.753625 197.953658 \r\nL 130.814504 214.756364 \r\nL 130.844943 210.649967 \r\nL 130.905822 211.425154 \r\nL 130.936261 214.756364 \r\nL 130.99714 214.546854 \r\nL 131.058019 203.945645 \r\nL 131.088458 213.939274 \r\nL 131.149337 214.756364 \r\nL 131.179777 207.653973 \r\nL 131.240655 214.756364 \r\nL 131.51461 214.756364 \r\nL 131.54505 182.827033 \r\nL 131.605928 214.106882 \r\nL 131.636368 214.756364 \r\nL 131.758125 205.998844 \r\nL 131.788565 214.756364 \r\nL 131.819004 200.111612 \r\nL 131.849444 213.813568 \r\nL 131.879883 194.643399 \r\nL 131.910322 214.756364 \r\nL 131.940762 212.849822 \r\nL 131.971201 214.756364 \r\nL 132.092959 153.16041 \r\nL 132.184277 214.756364 \r\nL 132.214716 191.752161 \r\nL 132.306035 214.756364 \r\nL 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154.952954 214.756364 \r\nL 155.074712 200.823946 \r\nL 155.16603 214.756364 \r\nL 155.196469 214.735413 \r\nL 155.257348 214.735413 \r\nL 155.379106 197.869854 \r\nL 155.500863 214.756364 \r\nL 155.531303 209.141494 \r\nL 155.622621 211.634664 \r\nL 155.65306 209.204347 \r\nL 155.6835 214.756364 \r\nL 155.713939 211.383252 \r\nL 155.744379 213.813568 \r\nL 155.774818 195.167174 \r\nL 155.805257 214.756364 \r\nL 155.835697 213.729764 \r\nL 155.866136 214.756364 \r\nL 156.018333 198.770747 \r\nL 156.048773 210.398555 \r\nL 156.079212 135.854881 \r\nL 156.109651 214.756364 \r\nL 156.140091 195.984264 \r\nL 156.20097 214.756364 \r\nL 156.231409 210.754722 \r\nL 156.261848 183.706975 \r\nL 156.322727 214.756364 \r\nL 156.353167 214.756364 \r\nL 156.474924 196.466137 \r\nL 156.627121 214.756364 \r\nL 156.657561 214.756364 \r\nL 156.688 192.485446 \r\nL 156.748879 214.735413 \r\nL 156.779318 213.520254 \r\nL 156.840197 214.735413 \r\nL 156.870637 214.735413 \r\nL 156.901076 213.289793 \r\nL 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162.83676 214.756364 \r\nL 162.897639 198.016511 \r\nL 162.988958 214.756364 \r\nL 163.019397 209.392906 \r\nL 163.049836 209.351004 \r\nL 163.080276 211.508958 \r\nL 163.141155 204.951294 \r\nL 163.171594 214.735413 \r\nL 163.232473 214.756364 \r\nL 163.262912 211.299448 \r\nL 163.323791 210.649967 \r\nL 163.35423 214.756364 \r\nL 163.415109 201.641035 \r\nL 163.445549 214.735413 \r\nL 163.506427 214.756364 \r\nL 163.536867 213.729764 \r\nL 163.597746 214.756364 \r\nL 163.628185 214.756364 \r\nL 163.719503 191.815014 \r\nL 163.780382 214.756364 \r\nL 163.841261 214.023078 \r\nL 163.8717 202.688585 \r\nL 163.932579 208.743425 \r\nL 163.963019 214.756364 \r\nL 164.023897 212.912675 \r\nL 164.054337 202.939997 \r\nL 164.084776 214.756364 \r\nL 164.145655 205.097951 \r\nL 164.206534 214.756364 \r\nL 164.236973 202.541928 \r\nL 164.267413 189.971325 \r\nL 164.297852 214.756364 \r\nL 164.328291 205.852187 \r\nL 164.38917 214.756364 \r\nL 164.480489 212.870773 \r\nL 164.510928 212.59841 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168.589809 199.88115 \r\nL 168.650687 214.756364 \r\nL 168.772445 205.056049 \r\nL 168.833324 207.255904 \r\nL 168.955081 214.756364 \r\nL 169.076839 204.720832 \r\nL 169.168157 214.756364 \r\nL 169.198597 212.912675 \r\nL 169.289915 214.756364 \r\nL 169.320354 214.735413 \r\nL 169.442112 194.287232 \r\nL 169.56387 214.756364 \r\nL 169.685627 181.884238 \r\nL 169.807385 214.756364 \r\nL 169.837824 200.048758 \r\nL 169.898703 208.072993 \r\nL 169.959582 214.756364 \r\nL 169.990021 207.695875 \r\nL 170.020461 210.189045 \r\nL 170.111779 214.756364 \r\nL 170.142218 187.059136 \r\nL 170.203097 213.059332 \r\nL 170.233537 210.796624 \r\nL 170.263976 213.331695 \r\nL 170.324855 211.404203 \r\nL 170.385734 214.756364 \r\nL 170.446612 214.756364 \r\nL 170.477052 210.733771 \r\nL 170.537931 214.735413 \r\nL 170.629249 214.756364 \r\nL 170.659688 204.406567 \r\nL 170.720567 208.21965 \r\nL 170.842325 214.756364 \r\nL 170.933643 196.298529 \r\nL 170.964082 211.446105 \r\nL 170.994522 214.756364 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178.695692 214.756364 \r\nL 178.726131 205.077 \r\nL 178.78701 214.693511 \r\nL 178.847889 214.756364 \r\nL 178.878328 214.421148 \r\nL 178.908768 214.714462 \r\nL 178.939207 211.844174 \r\nL 178.969647 193.051123 \r\nL 179.030525 214.756364 \r\nL 179.060965 207.255904 \r\nL 179.121844 214.756364 \r\nL 179.213162 211.027085 \r\nL 179.274041 192.548299 \r\nL 179.30448 213.750715 \r\nL 179.334919 214.756364 \r\nL 179.426238 214.358295 \r\nL 179.456677 214.756364 \r\nL 179.487116 214.609707 \r\nL 179.547995 189.887521 \r\nL 179.578435 214.756364 \r\nL 179.639313 214.756364 \r\nL 179.669753 214.504952 \r\nL 179.700192 205.349363 \r\nL 179.730632 214.756364 \r\nL 179.761071 214.756364 \r\nL 179.882829 200.237318 \r\nL 179.913268 214.756364 \r\nL 180.004586 206.564521 \r\nL 180.065465 205.097951 \r\nL 180.095905 195.062419 \r\nL 180.126344 208.994837 \r\nL 180.156783 203.715184 \r\nL 180.278541 214.756364 \r\nL 180.400299 206.145501 \r\nL 180.430738 214.756364 \r\nL 180.461177 208.114895 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200.551583 \r\nL 184.266103 214.756364 \r\nL 184.296543 214.756364 \r\nL 184.387861 203.191409 \r\nL 184.509619 214.756364 \r\nL 184.540058 208.994837 \r\nL 184.600937 214.756364 \r\nL 184.631376 214.756364 \r\nL 184.661816 195.523342 \r\nL 184.722695 214.756364 \r\nL 184.783573 214.756364 \r\nL 184.93577 200.404926 \r\nL 184.96621 214.735413 \r\nL 185.057528 213.541205 \r\nL 185.118407 214.044029 \r\nL 185.240164 192.275936 \r\nL 185.331483 187.373401 \r\nL 185.361922 214.756364 \r\nL 185.392362 203.882792 \r\nL 185.45324 213.981176 \r\nL 185.48368 214.756364 \r\nL 185.544559 180.103402 \r\nL 185.574998 214.756364 \r\nL 185.605437 214.756364 \r\nL 185.757634 190.599855 \r\nL 185.788074 192.171181 \r\nL 185.818513 181.695679 \r\nL 185.909831 214.756364 \r\nL 185.940271 203.149507 \r\nL 185.97071 214.756364 \r\nL 186.031589 202.16481 \r\nL 186.062028 213.22694 \r\nL 186.092468 195.271929 \r\nL 186.153347 207.130198 \r\nL 186.214226 214.756364 \r\nL 186.244665 207.402561 \r\nL 186.305544 213.981176 \r\nL 186.396862 211.236595 \r\nL 186.427301 207.758728 \r\nL 186.457741 214.756364 \r\nL 186.48818 212.661263 \r\nL 186.51862 210.733771 \r\nL 186.609938 214.756364 \r\nL 186.640377 168.035624 \r\nL 186.701256 210.461408 \r\nL 186.762135 208.303454 \r\nL 186.792574 195.942362 \r\nL 186.823014 214.756364 \r\nL 186.853453 198.184119 \r\nL 186.975211 214.756364 \r\nL 187.00565 191.668357 \r\nL 187.066529 209.476711 \r\nL 187.096968 214.756364 \r\nL 187.188287 170.486891 \r\nL 187.310044 214.756364 \r\nL 187.340484 214.756364 \r\nL 187.431802 200.928701 \r\nL 187.462241 214.316393 \r\nL 187.492681 199.294522 \r\nL 187.553559 208.84818 \r\nL 187.614438 210.901379 \r\nL 187.644878 194.978615 \r\nL 187.705756 212.80792 \r\nL 187.736196 210.042388 \r\nL 187.857954 183.853632 \r\nL 187.827514 210.92233 \r\nL 187.888393 186.996283 \r\nL 187.949272 214.756364 \r\nL 187.979711 214.735413 \r\nL 188.010151 192.024524 \r\nL 188.04059 214.756364 \r\nL 188.071029 214.756364 \r\nL 188.162348 206.648325 \r\nL 188.192787 164.432051 \r\nL 188.253666 208.701523 \r\nL 188.284105 207.653973 \r\nL 188.314545 213.897372 \r\nL 188.344984 202.332418 \r\nL 188.405863 210.000486 \r\nL 188.436302 214.756364 \r\nL 188.466742 190.055129 \r\nL 188.52762 214.756364 \r\nL 188.55806 214.044029 \r\nL 188.588499 214.756364 \r\nL 188.710257 204.993196 \r\nL 188.801575 214.756364 \r\nL 188.832015 214.735413 \r\nL 188.862454 214.567805 \r\nL 188.892893 199.105963 \r\nL 188.984212 206.585472 \r\nL 189.136409 214.756364 \r\nL 189.166848 164.369198 \r\nL 189.227727 214.756364 \r\nL 189.319045 200.048758 \r\nL 189.440803 214.756364 \r\nL 189.471242 214.023078 \r\nL 189.501682 204.322763 \r\nL 189.56256 212.724116 \r\nL 189.623439 214.756364 \r\nL 189.653879 194.475791 \r\nL 189.714757 214.756364 \r\nL 189.836515 194.203428 \r\nL 189.866954 205.894089 \r\nL 189.927833 194.894811 \r\nL 189.958273 214.756364 \r\nL 189.988712 193.931065 \r\nL 190.049591 214.274491 \r\nL 190.08003 214.756364 \r\nL 190.11047 202.772389 \r\nL 190.201788 207.59112 \r\nL 190.323546 214.756364 \r\nL 190.353985 214.421148 \r\nL 190.384424 214.756364 \r\nL 190.414864 197.953658 \r\nL 190.475743 209.183396 \r\nL 190.62794 214.756364 \r\nL 190.658379 214.756364 \r\nL 190.688818 189.824668 \r\nL 190.749697 214.714462 \r\nL 190.810576 214.756364 \r\nL 190.932334 201.578182 \r\nL 191.023652 214.756364 \r\nL 191.084531 211.802272 \r\nL 191.175849 214.756364 \r\nL 191.236728 206.836884 \r\nL 191.267167 213.771666 \r\nL 191.328046 214.756364 \r\nL 191.419364 208.680572 \r\nL 191.480243 214.756364 \r\nL 191.541122 214.735413 \r\nL 191.571561 214.756364 \r\nL 191.63244 180.962393 \r\nL 191.662879 212.3889 \r\nL 191.693319 214.756364 \r\nL 191.815077 180.627177 \r\nL 191.936834 214.756364 \r\nL 191.967274 214.756364 \r\nL 191.997713 214.190687 \r\nL 192.028152 179.831039 \r\nL 192.058592 214.756364 \r\nL 192.089031 214.756364 \r\nL 192.14991 210.2938 \r\nL 192.180349 213.331695 \r\nL 192.271668 214.756364 \r\nL 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194.311108 208.931984 \r\nL 194.341547 208.177748 \r\nL 194.463305 214.756364 \r\nL 194.493744 194.203428 \r\nL 194.585063 195.586195 \r\nL 194.73726 214.756364 \r\nL 194.767699 212.053684 \r\nL 194.828578 214.756364 \r\nL 194.859017 214.756364 \r\nL 194.919896 191.144582 \r\nL 194.950335 195.020517 \r\nL 194.980775 214.756364 \r\nL 195.072093 202.479075 \r\nL 195.193851 214.756364 \r\nL 195.315608 201.515329 \r\nL 195.376487 201.431525 \r\nL 195.467805 214.756364 \r\nL 195.589563 203.966596 \r\nL 195.650442 196.382333 \r\nL 195.74176 214.756364 \r\nL 195.833078 206.606423 \r\nL 195.863518 213.562156 \r\nL 195.893957 214.756364 \r\nL 195.924397 213.43645 \r\nL 195.954836 211.425154 \r\nL 195.985275 212.640312 \r\nL 196.015715 194.685301 \r\nL 196.076594 214.756364 \r\nL 196.167912 214.756364 \r\nL 196.25923 192.736858 \r\nL 196.289669 206.711178 \r\nL 196.380988 202.79334 \r\nL 196.411427 214.756364 \r\nL 196.502745 214.756364 \r\nL 196.624503 197.932707 \r\nL 196.715821 214.756364 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200.520747 199.441179 \r\nL 200.612065 203.023801 \r\nL 200.703384 214.67256 \r\nL 200.672944 201.368672 \r\nL 200.733823 208.533915 \r\nL 200.764262 205.077 \r\nL 200.794702 214.756364 \r\nL 200.825141 195.104321 \r\nL 200.855581 160.87038 \r\nL 200.88602 214.756364 \r\nL 200.916459 207.423512 \r\nL 200.977338 214.735413 \r\nL 201.038217 177.777841 \r\nL 201.129535 212.17939 \r\nL 201.159975 210.126192 \r\nL 201.251293 214.232589 \r\nL 201.281732 184.545015 \r\nL 201.40349 213.604058 \r\nL 201.464369 208.911033 \r\nL 201.494808 198.393629 \r\nL 201.525248 214.756364 \r\nL 201.555687 214.756364 \r\nL 201.586126 214.421148 \r\nL 201.738323 146.89606 \r\nL 201.829642 214.756364 \r\nL 201.860081 208.63867 \r\nL 201.89052 206.040746 \r\nL 201.92096 212.053684 \r\nL 201.951399 212.493655 \r\nL 201.981839 214.756364 \r\nL 202.012278 214.735413 \r\nL 202.042717 142.140182 \r\nL 202.103596 159.843781 \r\nL 202.164475 214.756364 \r\nL 202.225354 211.844174 \r\nL 202.255793 157.664876 \r\nL 202.286233 214.756364 \r\nL 202.316672 213.85547 \r\nL 202.377551 205.642677 \r\nL 202.40799 214.756364 \r\nL 202.43843 214.484001 \r\nL 202.468869 188.588559 \r\nL 202.499309 214.756364 \r\nL 202.529748 214.484001 \r\nL 202.651506 198.330776 \r\nL 202.681945 214.756364 \r\nL 202.742824 212.849822 \r\nL 202.773263 182.994641 \r\nL 202.834142 190.055129 \r\nL 202.92546 214.756364 \r\nL 202.9559 214.735413 \r\nL 202.986339 170.067871 \r\nL 203.047218 214.756364 \r\nL 203.077657 208.869131 \r\nL 203.168976 211.089938 \r\nL 203.229854 214.337344 \r\nL 203.260294 211.739419 \r\nL 203.321173 211.948929 \r\nL 203.351612 193.449192 \r\nL 203.382051 214.756364 \r\nL 203.47337 202.311467 \r\nL 203.503809 203.547576 \r\nL 203.534248 214.735413 \r\nL 203.564688 165.66816 \r\nL 203.656006 192.715907 \r\nL 203.686445 193.009221 \r\nL 203.716885 214.400197 \r\nL 203.747324 206.627374 \r\nL 203.777764 170.549744 \r\nL 203.808203 214.756364 \r\nL 203.838643 214.756364 \r\nL 203.869082 208.198699 \r\nL 203.929961 209.288151 \r\nL 203.99084 214.756364 \r\nL 204.021279 210.964232 \r\nL 204.082158 176.353172 \r\nL 204.112597 214.756364 \r\nL 204.173476 213.918323 \r\nL 204.203915 214.756364 \r\nL 204.234355 210.482359 \r\nL 204.295234 214.567805 \r\nL 204.356112 211.96988 \r\nL 204.386552 214.756364 \r\nL 204.447431 186.975332 \r\nL 204.47787 211.446105 \r\nL 204.508309 212.870773 \r\nL 204.538749 209.2672 \r\nL 204.569188 207.989189 \r\nL 204.599628 190.159884 \r\nL 204.660507 214.756364 \r\nL 204.690946 214.756364 \r\nL 204.721385 211.907027 \r\nL 204.782264 214.379246 \r\nL 204.812704 192.213083 \r\nL 204.843143 214.756364 \r\nL 204.904022 205.91504 \r\nL 204.934461 205.244608 \r\nL 204.99534 214.756364 \r\nL 205.056219 211.488007 \r\nL 205.086658 213.771666 \r\nL 205.117098 213.352646 \r\nL 205.147537 171.052568 \r\nL 205.208416 207.549218 \r\nL 205.238855 213.394548 \r\nL 205.299734 204.86749 \r\nL 205.360613 214.442099 \r\nL 205.391052 157.74868 \r\nL 205.421492 214.756364 \r\nL 205.451931 214.756364 \r\nL 205.51281 202.688585 \r\nL 205.543249 213.373597 \r\nL 205.604128 211.865125 \r\nL 205.634568 214.756364 \r\nL 205.665007 199.734493 \r\nL 205.725886 214.756364 \r\nL 205.756325 213.310744 \r\nL 205.786765 193.428241 \r\nL 205.847643 214.756364 \r\nL 205.878083 212.849822 \r\nL 205.908522 212.975528 \r\nL 205.969401 188.777118 \r\nL 206.03028 199.650689 \r\nL 206.091159 214.756364 \r\nL 206.152037 205.077 \r\nL 206.212916 214.756364 \r\nL 206.243356 182.76418 \r\nL 206.304235 214.756364 \r\nL 206.425992 191.354092 \r\nL 206.456432 214.756364 \r\nL 206.51731 204.427518 \r\nL 206.54775 198.142217 \r\nL 206.578189 214.756364 \r\nL 206.669507 197.031814 \r\nL 206.760826 214.756364 \r\nL 206.791265 196.256627 \r\nL 206.852144 210.943281 \r\nL 206.882583 214.756364 \r\nL 206.913023 188.588559 \r\nL 207.004341 205.28651 \r\nL 207.03478 214.756364 \r\nL 207.06522 213.373597 \r\nL 207.095659 198.435531 \r\nL 207.156538 205.768383 \r\nL 207.278296 214.756364 \r\nL 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "## Alıştırma\n",
+    "## Daha gerçekçi bir Peter ve Kurt dünyası\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan herhangi bir yanlış anlama veya yanlış yorumlama durumunda sorumluluk kabul edilmez.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/translations/tr/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
new file mode 100644
index 000000000..fa287a7b8
--- /dev/null
+++ b/translations/tr/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
@@ -0,0 +1,462 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "eadbd20d2a075efb602615ad90b1e97a",
+   "translation_date": "2025-09-06T15:15:31+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter ve Kurt: Gerçekçi Bir Çevre\n",
+    "\n",
+    "Bizim senaryomuzda, Peter neredeyse hiç yorulmadan ya da acıkmadan dolaşabiliyordu. Daha gerçekçi bir dünyada, zaman zaman oturup dinlenmesi ve kendini beslemesi gerekir. Dünyamızı daha gerçekçi hale getirelim ve şu kuralları uygulayalım:\n",
+    "\n",
+    "1. Bir yerden başka bir yere hareket ettiğinde, Peter **enerji** kaybeder ve biraz **yorgunluk** kazanır.\n",
+    "2. Peter, elma yiyerek daha fazla enerji kazanabilir.\n",
+    "3. Peter, bir ağacın altında ya da çimenlerin üzerinde dinlenerek yorgunluğunu atabilir (yani, bir ağaç ya da çimen bulunan bir tahtanın konumuna yürüyerek - yeşil alan).\n",
+    "4. Peter, kurdu bulmalı ve öldürmelidir.\n",
+    "5. Kurdu öldürmek için, Peter'ın belirli bir enerji ve yorgunluk seviyesine sahip olması gerekir, aksi takdirde savaşı kaybeder.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "from rlboard import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "## Durum Tanımlama\n",
+    "\n",
+    "Yeni oyun kurallarımızda, her bir tahta durumunda enerji ve yorgunluğu takip etmemiz gerekiyor. Bu nedenle, mevcut problem durumuyla ilgili tüm gerekli bilgileri taşıyacak bir `state` nesnesi oluşturacağız. Bu bilgiler arasında tahtanın durumu, mevcut enerji ve yorgunluk seviyeleri ve terminal durumdayken kurdu yenip yenemeyeceğimiz yer alacak:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class state:\n",
+    "    def __init__(self,board,energy=10,fatigue=0,init=True):\n",
+    "        self.board = board\n",
+    "        self.energy = energy\n",
+    "        self.fatigue = fatigue\n",
+    "        self.dead = False\n",
+    "        if init:\n",
+    "            self.board.random_start()\n",
+    "        self.update()\n",
+    "\n",
+    "    def at(self):\n",
+    "        return self.board.at()\n",
+    "\n",
+    "    def update(self):\n",
+    "        if self.at() == Board.Cell.water:\n",
+    "            self.dead = True\n",
+    "            return\n",
+    "        if self.at() == Board.Cell.tree:\n",
+    "            self.fatigue = 0\n",
+    "        if self.at() == Board.Cell.apple:\n",
+    "            self.energy = 10\n",
+    "\n",
+    "    def move(self,a):\n",
+    "        self.board.move(a)\n",
+    "        self.energy -= 1\n",
+    "        self.fatigue += 1\n",
+    "        self.update()\n",
+    "\n",
+    "    def is_winning(self):\n",
+    "        return self.energy > self.fatigue"
+   ]
+  },
+  {
+   "source": [
+    "Hadi rastgele yürüyüş kullanarak problemi çözmeyi deneyelim ve başarılı olup olmadığımızı görelim:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(state):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(board,policy):\n",
+    "    n = 0 # number of steps\n",
+    "    s = state(board)\n",
+    "    while True:\n",
+    "        if s.at() == Board.Cell.wolf:\n",
+    "            if s.is_winning():\n",
+    "                return n # success!\n",
+    "            else:\n",
+    "                return -n # failure!\n",
+    "        if s.at() == Board.Cell.water:\n",
+    "            return 0 # died\n",
+    "        a = actions[policy(m)]\n",
+    "        s.move(a)\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 5, won: 1 times, drown: 94 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        elif z==0:\n",
+    "            n+=1\n",
+    "        else:\n",
+    "            s+=1\n",
+    "    print(f\"Killed by wolf = {w}, won: {s} times, drown: {n} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Ödül Fonksiyonu\n",
+    "\n",
+    "### Amaç\n",
+    "Bu bölümde, bir ödül fonksiyonunun nasıl oluşturulacağını ve bu fonksiyonun bir modelin performansını optimize etmek için nasıl kullanılacağını öğreneceksiniz.\n",
+    "\n",
+    "### Ödül Fonksiyonu Nedir?\n",
+    "Ödül fonksiyonu, bir modelin belirli bir görevi ne kadar iyi yerine getirdiğini ölçmek için kullanılan bir metrik sağlar. Modelin çıktısını değerlendirir ve bir \"ödül\" değeri döndürür. Bu değer, modelin öğrenme sürecinde yönlendirilmesine yardımcı olur.\n",
+    "\n",
+    "### Ödül Fonksiyonu Nasıl Çalışır?\n",
+    "1. **Girdi:** Modelin tahminleri ve gerçek değerler.\n",
+    "2. **İşlem:** Tahminler ile gerçek değerler arasındaki farkı hesaplar.\n",
+    "3. **Çıktı:** Modelin performansını temsil eden bir ödül değeri.\n",
+    "\n",
+    "### Örnek\n",
+    "Aşağıda basit bir ödül fonksiyonu örneği verilmiştir:\n",
+    "\n",
+    "```python\n",
+    "def reward_function(predictions, actuals):\n",
+    "    # Tahminler ile gerçek değerler arasındaki farkı hesapla\n",
+    "    error = abs(predictions - actuals)\n",
+    "    # Hata ne kadar küçükse ödül o kadar büyük\n",
+    "    reward = 1 / (1 + error)\n",
+    "    return reward\n",
+    "```\n",
+    "\n",
+    "### İyi Bir Ödül Fonksiyonu Tasarlama\n",
+    "İyi bir ödül fonksiyonu tasarlarken aşağıdaki noktaları göz önünde bulundurun:\n",
+    "- **Hedefe Uygunluk:** Ödül fonksiyonu, modelin optimize etmeye çalıştığı hedefle uyumlu olmalıdır.\n",
+    "- **Hassasiyet:** Küçük performans değişikliklerini ayırt edebilmelidir.\n",
+    "- **Denge:** Modeli aşırı uyumdan kaçınmaya teşvik etmelidir.\n",
+    "\n",
+    "### Yaygın Hatalar\n",
+    "- **Aşırı Karmaşıklık:** Ödül fonksiyonunu gereksiz yere karmaşık hale getirmek, modelin öğrenme sürecini zorlaştırabilir.\n",
+    "- **Yanlılık:** Ödül fonksiyonunun belirli bir tür tahmini veya davranışı ödüllendirmesi, modelin dengesiz sonuçlar üretmesine neden olabilir.\n",
+    "\n",
+    "### Sonuç\n",
+    "Ödül fonksiyonu, modelin performansını optimize etmek için kritik bir bileşendir. İyi tasarlanmış bir ödül fonksiyonu, modelin doğru ve etkili bir şekilde öğrenmesine yardımcı olur.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reward(s):\n",
+    "    r = s.energy-s.fatigue\n",
+    "    if s.at()==Board.Cell.wolf:\n",
+    "        return 100 if s.is_winning() else -100\n",
+    "    if s.at()==Board.Cell.water:\n",
+    "        return -100\n",
+    "    return r"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Öğrenme algoritması\n",
+    "\n",
+    "Gerçek öğrenme algoritması neredeyse hiç değişmez, sadece tahta pozisyonu yerine `state` kullanırız.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    s = state(m)\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = s.board.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        while True:\n",
+    "            a = random.choices(list(actions),weights=v)[0]\n",
+    "            dpos = actions[a]\n",
+    "            if s.board.is_valid(s.board.move_pos(s.board.human,dpos)):\n",
+    "                break \n",
+    "        s.move(dpos)\n",
+    "        r = reward(s)\n",
+    "        if abs(r)==100: # end of game\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Sonuçlar\n",
+    "\n",
+    "Peter'i kurda karşı savaşması için eğitmede başarılı olup olmadığımızı görelim!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 1, won: 9 times, drown: 90 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [
+    "Artık çok daha az boğulma vakası görüyoruz, ancak Peter hala her zaman kurdu öldüremiyor. Hiperparametrelerle oynayarak bu sonucu iyileştirip iyileştiremeyeceğinizi denemeye çalışın.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0987e7dd8>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluğu sağlamak için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/translations/tr/8-Reinforcement/1-QLearning/solution/notebook.ipynb
new file mode 100644
index 000000000..9100c9729
--- /dev/null
+++ b/translations/tr/8-Reinforcement/1-QLearning/solution/notebook.ipynb
@@ -0,0 +1,577 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
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+   "source_file": "8-Reinforcement/1-QLearning/solution/notebook.ipynb",
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+  {
+   "source": [
+    "# Peter ve Kurt: Pekiştirmeli Öğrenme Giriş\n",
+    "\n",
+    "Bu eğitimde, bir yol bulma problemine Pekiştirmeli Öğrenme uygulamayı öğreneceğiz. Ayar, Rus besteci [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev) tarafından yazılan [Peter ve Kurt](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) adlı müzikal masaldan esinlenmiştir. Bu, genç öncü Peter'in cesurca evinden çıkıp kurtu kovalamak için orman açıklığına gittiği bir hikayedir. Peter'in çevresini keşfetmesine ve en uygun navigasyon haritasını oluşturmasına yardımcı olacak makine öğrenimi algoritmalarını eğiteceğiz.\n",
+    "\n",
+    "Öncelikle, bir dizi kullanışlı kütüphane ithal edelim:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Pekiştirmeli Öğrenmeye Genel Bakış\n",
+    "\n",
+    "**Pekiştirmeli Öğrenme** (RL), bir **ajanın** belirli bir **ortamda** birçok deney yaparak en uygun davranışı öğrenmesini sağlayan bir öğrenme tekniğidir. Bu ortamda bir ajanın, bir **ödül fonksiyonu** ile tanımlanan bir **hedefi** olmalıdır.\n",
+    "\n",
+    "## Ortam\n",
+    "\n",
+    "Basitlik açısından, Peter'ın dünyasını `genişlik` x `yükseklik` boyutlarında bir kare tahta olarak düşünelim. Bu tahtadaki her bir hücre şu şekilde olabilir:\n",
+    "* Peter ve diğer canlıların yürüyebileceği bir **zemin**\n",
+    "* Üzerinde yürüyemeyeceğiniz açık bir şekilde belli olan **su**\n",
+    "* Dinlenebileceğiniz bir yer olan **ağaç** veya **çimen**\n",
+    "* Peter'ın kendini beslemek için bulmaktan memnun olacağı bir **elma**\n",
+    "* Tehlikeli olan ve kaçınılması gereken bir **kurt**\n",
+    "\n",
+    "Ortamla çalışmak için `Board` adında bir sınıf tanımlayacağız. Bu defteri çok fazla karmaşıklaştırmamak adına, tahtayla çalışmak için gereken tüm kodu ayrı bir `rlboard` modülüne taşıdık ve şimdi bu modülü içe aktaracağız. Uygulamanın iç detaylarını görmek için bu modülün içine bakabilirsiniz.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rlboard import *"
+   ]
+  },
+  {
+   "source": [
+    "Şimdi rastgele bir tahta oluşturalım ve nasıl göründüğüne bakalım:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "source": [
+    "## Eylemler ve Politika\n",
+    "\n",
+    "Örneğimizde, Peter'ın amacı bir elma bulmak, aynı zamanda kurttan ve diğer engellerden kaçınmaktır. Bunu yapmak için, elmayı bulana kadar etrafta dolaşabilir. Bu nedenle, herhangi bir konumda yukarı, aşağı, sola ve sağa olmak üzere dört eylemden birini seçebilir. Bu eylemleri bir sözlük olarak tanımlayacağız ve bunları ilgili koordinat değişiklik çiftlerine eşleyeceğiz. Örneğin, sağa hareket etmek (`R`) bir çift `(1,0)` ile eşleşir.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "Ajanımızın (Peter) stratejisi, **politika** olarak adlandırılan bir kavramla tanımlanır. En basit politika olan **rastgele yürüyüş**ü ele alalım.\n",
+    "\n",
+    "## Rastgele yürüyüş\n",
+    "\n",
+    "Öncelikle, rastgele yürüyüş stratejisini uygulayarak problemimizi çözelim.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(m):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(m,policy,start_position=None):\n",
+    "    n = 0 # number of steps\n",
+    "    # set initial position\n",
+    "    if start_position:\n",
+    "        m.human = start_position \n",
+    "    else:\n",
+    "        m.random_start()\n",
+    "    while True:\n",
+    "        if m.at() == Board.Cell.apple:\n",
+    "            return n # success!\n",
+    "        if m.at() in [Board.Cell.wolf, Board.Cell.water]:\n",
+    "            return -1 # eaten by wolf or drowned\n",
+    "        while True:\n",
+    "            a = actions[policy(m)]\n",
+    "            new_pos = m.move_pos(m.human,a)\n",
+    "            if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:\n",
+    "                m.move(a) # do the actual move\n",
+    "                break\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "Rastgele yürüyüş deneyini birkaç kez gerçekleştirelim ve alınan ortalama adım sayısını görelim:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 32.87096774193548, eaten by wolf: 7 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        else:\n",
+    "            s += z\n",
+    "            n += 1\n",
+    "    print(f\"Average path length = {s/n}, eaten by wolf: {w} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Ödül Fonksiyonu\n",
+    "\n",
+    "Politikamızı daha akıllı hale getirmek için, hangi hamlelerin diğerlerinden \"daha iyi\" olduğunu anlamamız gerekiyor.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "move_reward = -0.1\n",
+    "goal_reward = 10\n",
+    "end_reward = -10\n",
+    "\n",
+    "def reward(m,pos=None):\n",
+    "    pos = pos or m.human\n",
+    "    if not m.is_valid(pos):\n",
+    "        return end_reward\n",
+    "    x = m.at(pos)\n",
+    "    if x==Board.Cell.water or x == Board.Cell.wolf:\n",
+    "        return end_reward\n",
+    "    if x==Board.Cell.apple:\n",
+    "        return goal_reward\n",
+    "    return move_reward"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Öğrenme\n",
+    "\n",
+    "Bir Q-Tablosu veya çok boyutlu bir dizi oluşturun. Tahtamızın boyutları `genişlik` x `yükseklik` olduğundan, Q-Tablosunu `genişlik` x `yükseklik` x `len(actions)` şeklinde bir numpy dizisi ile temsil edebiliriz:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "source": [
+    "Q-Tablosunu tahtada görselleştirmek için tabloyu plot fonksiyonuna geçirin:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p267d475764)\">\n    <image height=\"327\" id=\"image00b8757863\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Öğrenmenin Özeti: Bellman Denklemi ve Öğrenme Algoritması\n",
+    "\n",
+    "Öğrenme algoritmamız için bir sözde kod yazın:\n",
+    "\n",
+    "* Tüm durumlar ve eylemler için Q-Tablosu Q'yu eşit sayılarla başlatın\n",
+    "* Öğrenme oranını $\\alpha\\leftarrow 1$ olarak ayarlayın\n",
+    "* Simülasyonu birçok kez tekrarlayın\n",
+    "   1. Rastgele bir pozisyonda başlayın\n",
+    "   1. Tekrarla\n",
+    "        1. Durum $s$'de bir eylem $a$ seçin\n",
+    "        2. Yeni bir duruma $s'$ geçerek eylemi gerçekleştirin\n",
+    "        3. Eğer oyun sonu koşuluyla karşılaşırsak veya toplam ödül çok küçükse - simülasyondan çıkın  \n",
+    "        4. Yeni durumda ödül $r$'yi hesaplayın\n",
+    "        5. Bellman denklemine göre Q-Fonksiyonunu güncelleyin: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Toplam ödülü güncelleyin ve $\\alpha$'yı azaltın.\n",
+    "\n",
+    "## Keşfet vs. Sömür\n",
+    "\n",
+    "En iyi yaklaşım, keşfetme ve sömürme arasında bir denge kurmaktır. Çevremiz hakkında daha fazla bilgi edindikçe, optimal yolu izleme olasılığımız artar, ancak ara sıra keşfedilmemiş bir yolu seçmek faydalı olabilir.\n",
+    "\n",
+    "## Python Uygulaması\n",
+    "\n",
+    "Artık öğrenme algoritmasını uygulamaya hazırız. Bundan önce, Q-Tablosundaki rastgele sayıları ilgili eylemler için olasılık vektörüne dönüştürecek bir fonksiyona da ihtiyacımız var:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "source": [
+    "Orijinal vektöre, tüm vektör bileşenlerinin aynı olduğu başlangıç durumunda sıfıra bölünmeyi önlemek için küçük bir miktar `eps` ekliyoruz.\n",
+    "\n",
+    "Gerçek öğrenme algoritmasını, **epoklar** olarak da adlandırılan 5000 deney için çalıştıracağız:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    m.random_start()\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        dpos = actions[a]\n",
+    "        m.move(dpos,check_correctness=False) # we allow player to move outside the board, which terminates episode\n",
+    "        r = reward(m)\n",
+    "        cum_reward += r\n",
+    "        if r==end_reward or cum_reward < -1000:\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "source": [
+    "Bu algoritmayı çalıştırdıktan sonra, Q-Tablosu her adımda farklı eylemlerin çekiciliğini tanımlayan değerlerle güncellenmelidir. Tabloyu burada görselleştirin:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p31952a86ca)\">\n    <image height=\"327\" id=\"image3fd90cd4cb\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Politikanın Kontrolü\n",
+    "\n",
+    "Q-Tablosu, her durumdaki her bir eylemin \"çekiciliğini\" listelediği için, dünyamızda verimli bir gezinmeyi tanımlamak için onu kullanmak oldukça kolaydır. En basit durumda, sadece en yüksek Q-Tablosu değerine karşılık gelen eylemi seçebiliriz:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "def qpolicy_strict(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = list(actions)[np.argmax(v)]\n",
+    "        return a\n",
+    "\n",
+    "walk(m,qpolicy_strict)"
+   ]
+  },
+  {
+   "source": [
+    "Eğer yukarıdaki kodu birkaç kez denerseniz, bazen sadece \"takıldığını\" fark edebilirsiniz ve bunu durdurmak için not defterinde DURDUR düğmesine basmanız gerekir.\n",
+    "\n",
+    "> **Görev 1:** `walk` fonksiyonunu, yolun maksimum uzunluğunu belirli bir adım sayısıyla (örneğin, 100) sınırlayacak şekilde değiştirin ve yukarıdaki kodun bu değeri zaman zaman döndürdüğünü gözlemleyin.\n",
+    "\n",
+    "> **Görev 2:** `walk` fonksiyonunu, daha önce bulunduğu yerlere geri dönmemesini sağlayacak şekilde değiştirin. Bu, `walk` fonksiyonunun döngüye girmesini önleyecektir, ancak ajan yine de kaçamayacağı bir konumda \"sıkışabilir\".\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 3.45, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbab852fd68>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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133.949763 214.756364 \nL 133.980202 214.756364 \nL 134.132399 206.226264 \nL 134.254157 214.756364 \nL 134.284596 214.685867 \nL 134.315036 198.00165 \nL 134.345475 214.756364 \nL 134.375914 199.106098 \nL 134.436793 214.756364 \nL 134.528111 214.497876 \nL 134.558551 214.309885 \nL 134.58899 214.756364 \nL 134.649869 214.685867 \nL 134.680308 213.252434 \nL 134.710748 214.756364 \nL 134.741187 214.756364 \nL 134.802066 212.946949 \nL 134.832506 214.756364 \nL 134.954263 193.184376 \nL 135.076021 214.756364 \nL 135.10646 212.594465 \nL 135.167339 214.756364 \nL 135.289097 214.756364 \nL 135.502172 214.756364 \nL 135.532612 206.766738 \nL 135.593491 214.756364 \nL 135.62393 214.662368 \nL 135.65437 214.756364 \nL 135.745688 195.722257 \nL 135.806567 214.756364 \nL 135.867445 203.923372 \nL 135.989203 214.756364 \nL 136.050082 213.111441 \nL 136.080521 214.756364 \nL 136.110961 214.756364 \nL 136.202279 206.837235 \nL 136.293597 214.756364 \nL 136.324036 213.510922 \nL 136.384915 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138.150401 209.375116 \nL 138.21128 214.756364 \nL 138.363477 206.672743 \nL 138.454795 214.756364 \nL 138.485234 205.967776 \nL 138.546113 210.338571 \nL 138.576553 214.732865 \nL 138.637431 211.41952 \nL 138.667871 207.659697 \nL 138.69831 213.604918 \nL 138.789629 213.299432 \nL 138.820068 214.756364 \nL 138.911386 201.737974 \nL 139.002704 214.756364 \nL 139.033144 213.322931 \nL 139.094023 214.756364 \nL 139.124462 210.033085 \nL 139.21578 210.503063 \nL 139.276659 209.7276 \nL 139.337538 214.756364 \nL 139.398417 212.92345 \nL 139.428856 214.756364 \nL 139.459295 214.756364 \nL 139.520174 204.863328 \nL 139.611493 211.114034 \nL 139.641932 211.255028 \nL 139.672371 210.738052 \nL 139.702811 214.756364 \nL 139.794129 213.957401 \nL 139.824568 214.756364 \nL 139.855008 213.581419 \nL 139.885447 207.119222 \nL 139.915887 214.756364 \nL 139.946326 214.004399 \nL 140.037644 211.325525 \nL 140.128962 214.756364 \nL 140.159402 207.260215 \nL 140.220281 214.380381 \nL 140.25072 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147.221344 214.591871 \nL 147.312663 210.57356 \nL 147.373541 211.231529 \nL 147.403981 214.756364 \nL 147.495299 214.19239 \nL 147.525738 214.732865 \nL 147.556178 208.317666 \nL 147.617057 214.262887 \nL 147.677936 213.816408 \nL 147.799693 214.756364 \nL 147.95189 201.502985 \nL 148.043208 214.756364 \nL 148.073648 211.513516 \nL 148.104087 210.78505 \nL 148.134527 197.79016 \nL 148.164966 214.756364 \nL 148.195405 214.568372 \nL 148.225845 214.756364 \nL 148.347602 211.8425 \nL 148.438921 214.756364 \nL 148.46936 209.093129 \nL 148.530239 212.617964 \nL 148.651997 214.756364 \nL 148.682436 201.549983 \nL 148.743315 214.756364 \nL 148.773754 213.698913 \nL 148.804194 214.756364 \nL 148.834633 202.724928 \nL 148.895512 212.335977 \nL 149.017269 214.756364 \nL 149.047709 214.662368 \nL 149.078148 213.698913 \nL 149.108588 207.213217 \nL 149.169466 214.027898 \nL 149.199906 211.137533 \nL 149.291224 208.787644 \nL 149.321664 214.756364 \nL 149.352103 214.756364 \nL 149.382542 188.484596 \nL 149.443421 206.860734 \nL 149.534739 214.756364 \nL 149.565179 207.401209 \nL 149.626058 214.756364 \nL 149.656497 212.547467 \nL 149.717376 214.756364 \nL 149.778255 214.756364 \nL 149.869573 212.312478 \nL 149.900012 214.262887 \nL 149.930452 207.518703 \nL 149.99133 214.685867 \nL 150.02177 208.740646 \nL 150.052209 206.226264 \nL 150.082649 209.7276 \nL 150.234846 214.756364 \nL 150.265285 201.573482 \nL 150.326164 213.9809 \nL 150.356603 213.792909 \nL 150.387043 214.756364 \nL 150.417482 209.539608 \nL 150.478361 213.651915 \nL 150.5088 213.487423 \nL 150.53924 214.756364 \nL 150.569679 208.811143 \nL 150.630558 214.051397 \nL 150.660997 214.756364 \nL 150.691437 207.659697 \nL 150.752316 214.756364 \nL 150.843634 212.852953 \nL 150.904513 213.628417 \nL 150.934952 214.756364 \nL 151.02627 208.388162 \nL 151.05671 209.116628 \nL 151.117589 214.756364 \nL 151.148028 213.463924 \nL 151.178467 204.275855 \nL 151.239346 214.732865 \nL 151.269786 208.01218 \nL 151.300225 211.889498 \nL 151.330664 190.435005 \nL 151.361104 214.756364 \nL 151.391543 211.748505 \nL 151.421983 212.429973 \nL 151.452422 204.322853 \nL 151.482861 214.756364 \nL 151.513301 207.80069 \nL 151.665498 214.756364 \nL 151.726377 208.975635 \nL 151.756816 210.15058 \nL 151.787256 214.756364 \nL 151.817695 211.748505 \nL 151.848134 206.155767 \nL 151.909013 214.521375 \nL 151.969892 214.756364 \nL 152.000331 214.145392 \nL 152.06121 214.756364 \nL 152.182968 197.625667 \nL 152.243847 214.756364 \nL 152.304725 213.181937 \nL 152.335165 212.71196 \nL 152.365604 214.756364 \nL 152.426483 205.897279 \nL 152.456923 214.756364 \nL 152.548241 208.458659 \nL 152.57868 213.111441 \nL 152.60912 214.756364 \nL 152.700438 214.19239 \nL 152.730877 214.215889 \nL 152.822195 214.756364 \nL 152.852635 208.364663 \nL 152.913514 214.121893 \nL 152.943953 184.654276 \nL 153.065711 214.756364 \nL 153.09615 210.926043 \nL 153.157029 214.756364 \nL 153.187468 214.756364 \nL 153.248347 206.320259 \nL 153.309226 207.706694 \nL 153.370105 214.756364 \nL 153.430984 213.158439 \nL 153.461423 213.816408 \nL 153.491862 207.518703 \nL 153.522302 214.756364 \nL 153.552741 212.617964 \nL 153.644059 214.756364 \nL 153.674499 214.356882 \nL 153.704938 214.756364 \nL 153.735378 213.839907 \nL 153.765817 207.542202 \nL 153.796256 214.756364 \nL 153.826696 214.239388 \nL 153.887575 214.756364 \nL 153.918014 204.839829 \nL 153.978893 213.792909 \nL 154.039772 214.756364 \nL 154.070211 213.322931 \nL 154.100651 188.790082 \nL 154.161529 214.215889 \nL 154.191969 214.756364 \nL 154.222408 200.11655 \nL 154.283287 202.630932 \nL 154.405045 214.756364 \nL 154.435484 214.756364 \nL 154.465923 214.591871 \nL 154.496363 208.975635 \nL 154.526802 214.756364 \nL 154.557242 214.756364 \nL 154.587681 214.756364 \nL 154.678999 213.698913 \nL 154.739878 214.756364 \nL 154.770317 214.145392 \nL 154.800757 213.13494 \nL 154.831196 214.756364 \nL 154.861636 214.756364 \nL 154.892075 205.02782 \nL 154.952954 214.756364 \nL 155.013833 213.534421 \nL 155.044272 206.461253 \nL 155.105151 214.756364 \nL 155.16603 214.756364 \nL 155.196469 204.487345 \nL 155.257348 214.756364 \nL 155.379106 213.228935 \nL 155.439984 211.959995 \nL 155.561742 214.756364 \nL 155.622621 214.756364 \nL 155.713939 205.215811 \nL 155.805257 214.756364 \nL 155.835697 202.865921 \nL 155.896576 212.641463 \nL 156.018333 214.756364 \nL 156.048773 213.322931 \nL 156.109651 214.756364 \nL 156.140091 196.356727 \nL 156.261848 214.756364 \nL 156.322727 207.777191 \nL 156.353167 213.040944 \nL 156.414045 214.756364 \nL 156.566243 204.017367 \nL 156.657561 214.756364 \nL 156.688 214.239388 \nL 156.748879 214.756364 \nL 156.779318 208.035679 \nL 156.840197 207.80069 \nL 156.870637 214.756364 \nL 156.961955 202.912919 \nL 157.022834 214.756364 \nL 157.083712 214.356882 \nL 157.175031 200.539531 \nL 157.23591 214.756364 \nL 157.296788 207.847688 \nL 157.357667 214.756364 \nL 157.388107 204.463846 \nL 157.418546 211.278527 \nL 157.448985 210.103582 \nL 157.479425 213.816408 \nL 157.570743 213.534421 \nL 157.631622 214.756364 \nL 157.692501 207.988681 \nL 157.753379 214.756364 \nL 157.814258 214.521375 \nL 157.844698 214.756364 \nL 157.996895 205.403802 \nL 158.027334 205.521297 \nL 158.149092 214.732865 \nL 158.179531 214.756364 \nL 158.209971 204.510844 \nL 158.270849 211.983494 \nL 158.392607 214.756364 \nL 158.453486 214.756364 \nL 158.483925 212.970447 \nL 158.544804 213.416926 \nL 158.605683 214.756364 \nL 158.666562 210.503063 \nL 158.697001 214.756364 \nL 158.72744 213.816408 \nL 158.788319 214.756364 \nL 158.818759 214.756364 \nL 158.940516 210.832048 \nL 158.970956 211.372522 \nL 159.092713 192.620402 \nL 159.031835 213.76941 \nL 159.123153 200.32804 \nL 159.24491 214.756364 \nL 159.366668 212.194984 \nL 159.397107 212.805955 \nL 159.427547 212.335977 \nL 159.457986 214.756364 \nL 159.518865 212.852953 \nL 159.549304 212.429973 \nL 159.640623 214.756364 \nL 159.671062 214.709366 \nL 159.701502 213.675414 \nL 159.731941 214.756364 \nL 159.76238 214.685867 \nL 159.79282 202.25495 \nL 159.853699 210.855547 \nL 159.975456 214.756364 \nL 160.066774 211.865999 \nL 160.158093 214.756364 \nL 160.27985 211.325525 \nL 160.340729 207.565701 \nL 160.371168 214.756364 \nL 160.492926 200.657025 \nL 160.584244 214.168891 \nL 160.614684 209.28112 \nL 160.736441 214.732865 \nL 160.675563 208.670149 \nL 160.766881 214.568372 \nL 160.858199 214.756364 \nL 160.888638 197.155689 \nL 161.010396 214.756364 \nL 161.071275 214.756364 \nL 161.101714 208.905138 \nL 161.162593 214.756364 \nL 161.284351 211.678008 \nL 161.375669 211.231529 \nL 161.406108 214.756364 \nL 161.527866 209.892092 \nL 161.588745 208.411661 \nL 161.649624 214.756364 \nL 161.740942 213.416926 \nL 161.771381 214.756364 \nL 161.801821 213.440425 \nL 161.83226 207.142721 \nL 161.893139 214.756364 \nL 162.045336 214.756364 \nL 162.167094 208.153173 \nL 162.319291 214.662368 \nL 162.410609 214.756364 \nL 162.441048 205.192312 \nL 162.593245 214.756364 \nL 162.745442 205.591794 \nL 162.775882 214.756364 \nL 162.8672 212.899951 \nL 162.958518 214.756364 \nL 162.928079 212.171485 \nL 162.988958 213.675414 \nL 163.049836 214.756364 \nL 163.080276 213.55792 \nL 163.110715 210.949542 \nL 163.171594 214.756364 \nL 163.202033 214.662368 \nL 163.232473 213.040944 \nL 163.262912 197.696164 \nL 163.323791 214.756364 \nL 163.35423 208.693648 \nL 163.415109 213.910403 \nL 163.445549 214.756364 \nL 163.567306 207.119222 \nL 163.719503 214.756364 \nL 163.749943 214.638869 \nL 163.780382 208.059178 \nL 163.841261 214.756364 \nL 163.8717 212.852953 \nL 163.90214 214.756364 \nL 163.932579 199.670071 \nL 163.993458 209.234123 \nL 164.023897 213.76941 \nL 164.115216 212.476971 \nL 164.145655 214.61537 \nL 164.176094 211.63101 \nL 164.206534 212.735458 \nL 164.236973 212.641463 \nL 164.328291 214.756364 \nL 164.358731 213.510922 \nL 164.38917 214.40388 \nL 164.480489 209.915591 \nL 164.510928 211.725006 \nL 164.541367 214.40388 \nL 164.571807 207.424708 \nL 164.602246 212.735458 \nL 164.663125 209.915591 \nL 164.693564 212.71196 \nL 164.724004 212.970447 \nL 164.784883 211.255028 \nL 164.845761 214.756364 \nL 164.876201 214.756364 \nL 165.028398 193.983338 \nL 165.119716 214.756364 \nL 165.150155 213.393428 \nL 165.180595 211.889498 \nL 165.211034 213.487423 \nL 165.241474 214.756364 \nL 165.271913 207.683195 \nL 165.363231 212.147986 \nL 165.393671 214.756364 \nL 165.42411 200.915513 \nL 165.484989 214.756364 \nL 165.515428 214.756364 \nL 165.576307 210.291573 \nL 165.637186 212.382975 \nL 165.758944 214.756364 \nL 165.819822 212.241982 \nL 165.850262 214.756364 \nL 165.880701 214.756364 \nL 165.911141 213.581419 \nL 165.972019 214.662368 \nL 166.032898 214.756364 \nL 166.063338 214.309885 \nL 166.124217 214.756364 \nL 166.154656 214.61537 \nL 166.185095 214.756364 \nL 166.215535 212.006993 \nL 166.276414 213.487423 \nL 166.306853 214.756364 \nL 166.337292 211.912997 \nL 166.367732 214.239388 \nL 166.428611 214.709366 \nL 166.45905 206.273262 \nL 166.580808 214.756364 \nL 166.611247 212.288979 \nL 166.672126 213.55792 \nL 166.733005 214.756364 \nL 166.885202 209.445613 \nL 167.037399 214.756364 \nL 167.159156 212.500469 \nL 167.220035 214.756364 \nL 167.250475 204.957323 \nL 167.311353 214.756364 \nL 167.433111 212.547467 \nL 167.46355 214.40388 \nL 167.49399 207.80069 \nL 167.585308 207.965182 \nL 167.615747 214.756364 \nL 167.646187 195.22878 \nL 167.707066 214.756364 \nL 167.767945 214.756364 \nL 167.798384 211.865999 \nL 167.859263 214.098395 \nL 167.920142 214.756364 \nL 167.950581 213.299432 \nL 168.01146 214.756364 \nL 168.041899 214.732865 \nL 168.072339 214.333383 \nL 168.163657 193.818846 \nL 168.285414 214.756364 \nL 168.315854 208.787644 \nL 168.376733 211.184531 \nL 168.407172 214.756364 \nL 168.437611 214.450878 \nL 168.468051 203.429895 \nL 168.52893 214.756364 \nL 168.589809 212.100988 \nL 168.620248 212.147986 \nL 168.650687 214.756364 \nL 168.681127 211.513516 \nL 168.742006 213.463924 \nL 168.772445 210.526562 \nL 168.802884 184.325291 \nL 168.863763 214.685867 \nL 168.924642 214.756364 \nL 168.955081 214.027898 \nL 168.985521 209.351617 \nL 169.0464 214.756364 \nL 169.076839 214.756364 \nL 169.107278 207.612699 \nL 169.168157 214.591871 \nL 169.198597 213.628417 \nL 169.229036 214.756364 \nL 169.259475 214.756364 \nL 169.320354 214.027898 \nL 169.442112 200.398537 \nL 169.381233 214.756364 \nL 169.472551 206.132268 \nL 169.56387 214.756364 \nL 169.685627 200.586528 \nL 169.807385 214.756364 \nL 169.868264 214.756364 \nL 169.898703 214.568372 \nL 169.929142 207.824189 \nL 169.959582 214.756364 \nL 169.990021 214.756364 \nL 170.0509 211.396021 \nL 170.081339 214.756364 \nL 170.233537 195.886749 \nL 170.263976 214.756364 \nL 170.355294 213.228935 \nL 170.416173 212.26548 \nL 170.446612 213.76941 \nL 170.477052 213.510922 \nL 170.56837 200.563029 \nL 170.629249 203.782378 \nL 170.751006 214.756364 \nL 170.842325 206.907732 \nL 170.872764 207.824189 \nL 170.994522 214.756364 \nL 171.024961 207.753692 \nL 171.08584 214.662368 \nL 171.116279 214.756364 \nL 171.268476 212.71196 \nL 171.298916 214.638869 \nL 171.329355 210.738052 \nL 171.359795 209.351617 \nL 171.481552 214.709366 \nL 171.511992 214.756364 \nL 171.542431 214.286386 \nL 171.57287 206.884233 \nL 171.60331 214.756364 \nL 171.664189 209.116628 \nL 171.755507 214.756364 \nL 171.785946 214.286386 \nL 171.877265 214.756364 \nL 171.907704 212.124487 \nL 171.938143 214.756364 \nL 171.968583 206.437754 \nL 172.059901 211.396021 \nL 172.09034 213.651915 \nL 172.12078 213.534421 \nL 172.151219 205.756286 \nL 172.181659 214.756364 \nL 172.212098 214.756364 \nL 172.272977 214.756364 \nL 172.303416 213.252434 \nL 172.364295 214.333383 \nL 172.394734 214.756364 \nL 172.486053 208.43516 \nL 172.577371 206.343758 \nL 172.60781 214.756364 \nL 172.63825 191.680446 \nL 172.699129 214.756364 \nL 172.729568 214.333383 \nL 172.760007 214.756364 \nL 172.790447 211.466518 \nL 172.851326 214.756364 \nL 172.912204 214.756364 \nL 172.942644 208.623151 \nL 173.033962 211.772004 \nL 173.12528 214.756364 \nL 173.15572 212.030492 \nL 173.216598 214.756364 \nL 173.338356 214.709366 \nL 173.368796 202.724928 \nL 173.429674 214.756364 \nL 173.460114 214.756364 \nL 173.581871 205.709288 \nL 173.703629 214.756364 \nL 173.734068 214.756364 \nL 173.764508 214.544874 \nL 173.855826 214.662368 \nL 173.977584 214.756364 \nL 174.129781 210.855547 \nL 174.16022 204.134862 \nL 174.19066 214.638869 \nL 174.221099 214.215889 \nL 174.251538 210.620558 \nL 174.281978 214.756364 \nL 174.312417 214.756364 \nL 174.342857 214.756364 \nL 174.464614 204.745833 \nL 174.495054 207.871187 \nL 174.616811 214.756364 \nL 174.647251 214.709366 \nL 174.67769 214.145392 \nL 174.708129 214.709366 \nL 174.769008 203.429895 \nL 174.799448 214.756364 \nL 174.890766 214.756364 \nL 174.982084 213.416926 \nL 175.012524 214.756364 \nL 175.042963 211.560514 \nL 175.073402 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177.112843 214.756364 \nL 177.143282 214.027898 \nL 177.173721 208.270668 \nL 177.2346 208.811143 \nL 177.295479 214.756364 \nL 177.356358 212.241982 \nL 177.417237 214.756364 \nL 177.447676 214.568372 \nL 177.508555 208.106176 \nL 177.538994 214.756364 \nL 177.569434 212.359476 \nL 177.599873 212.335977 \nL 177.660752 214.756364 \nL 177.691191 200.022555 \nL 177.78251 214.756364 \nL 177.812949 213.087942 \nL 177.843388 214.756364 \nL 177.873828 214.638869 \nL 177.904267 196.427223 \nL 177.965146 214.756364 \nL 177.995585 208.599652 \nL 178.056464 209.892092 \nL 178.178222 214.756364 \nL 178.208661 214.756364 \nL 178.29998 208.482158 \nL 178.421737 214.756364 \nL 178.482616 211.443019 \nL 178.513055 214.756364 \nL 178.573934 212.030492 \nL 178.634813 213.158439 \nL 178.665252 214.756364 \nL 178.756571 205.380303 \nL 178.847889 214.756364 \nL 178.878328 207.683195 \nL 178.939207 214.756364 \nL 178.969647 214.756364 \nL 179.030525 210.879046 \nL 179.060965 214.19239 \nL 179.121844 212.993946 \nL 179.182722 210.78505 \nL 179.213162 214.756364 \nL 179.243601 210.432566 \nL 179.274041 202.395943 \nL 179.30448 214.756364 \nL 179.334919 214.756364 \nL 179.365359 214.756364 \nL 179.395798 208.129674 \nL 179.456677 214.215889 \nL 179.487116 214.756364 \nL 179.517556 211.114034 \nL 179.578435 214.756364 \nL 179.608874 214.756364 \nL 179.669753 196.544718 \nL 179.730632 212.05399 \nL 179.791511 214.732865 \nL 179.82195 214.168891 \nL 179.852389 200.821517 \nL 179.913268 214.756364 \nL 180.035026 208.576154 \nL 180.126344 214.756364 \nL 180.156783 214.732865 \nL 180.187223 214.427379 \nL 180.217662 214.756364 \nL 180.278541 203.735381 \nL 180.30898 213.158439 \nL 180.369859 214.756364 \nL 180.400299 214.239388 \nL 180.430738 204.769332 \nL 180.491617 213.933902 \nL 180.552496 214.756364 \nL 180.674253 205.920778 \nL 180.735132 214.756364 \nL 180.765572 211.748505 \nL 180.796011 204.275855 \nL 180.82645 214.756364 \nL 180.85689 211.278527 \nL 180.887329 213.910403 \nL 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204.299354 \nL 187.066529 210.879046 \nL 187.218726 214.756364 \nL 187.340484 211.748505 \nL 187.370923 214.380381 \nL 187.431802 201.808471 \nL 187.462241 214.756364 \nL 187.492681 211.137533 \nL 187.52312 214.756364 \nL 187.553559 209.06963 \nL 187.614438 214.709366 \nL 187.644878 195.369773 \nL 187.736196 196.403725 \nL 187.827514 214.756364 \nL 187.857954 213.675414 \nL 187.888393 214.756364 \nL 188.010151 209.892092 \nL 188.04059 214.544874 \nL 188.101469 187.732631 \nL 188.131908 214.756364 \nL 188.162348 213.017445 \nL 188.192787 202.88942 \nL 188.223226 214.427379 \nL 188.253666 213.158439 \nL 188.344984 214.756364 \nL 188.405863 200.069553 \nL 188.466742 212.359476 \nL 188.52762 214.756364 \nL 188.588499 187.591638 \nL 188.679818 214.756364 \nL 188.710257 214.380381 \nL 188.771136 211.63101 \nL 188.801575 214.286386 \nL 188.832015 214.756364 \nL 188.862454 214.732865 \nL 188.923333 214.756364 \nL 188.984212 202.771926 \nL 189.07553 214.756364 \nL 189.105969 211.278527 \nL 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214.756364 \nL 191.023652 214.356882 \nL 191.054091 201.385491 \nL 191.084531 214.568372 \nL 191.14541 208.64665 \nL 191.267167 214.732865 \nL 191.328046 211.959995 \nL 191.358485 214.756364 \nL 191.388925 214.427379 \nL 191.449804 214.638869 \nL 191.541122 203.594387 \nL 191.571561 213.675414 \nL 191.63244 202.701429 \nL 191.662879 198.283636 \nL 191.784637 214.732865 \nL 191.815077 208.200171 \nL 191.845516 214.756364 \nL 191.906395 210.291573 \nL 191.936834 214.756364 \nL 191.967274 210.174079 \nL 191.997713 199.294089 \nL 192.058592 214.756364 \nL 192.119471 213.205436 \nL 192.14991 214.756364 \nL 192.210789 214.309885 \nL 192.241228 214.756364 \nL 192.271668 203.805877 \nL 192.332546 210.197577 \nL 192.454304 214.756364 \nL 192.606501 208.928637 \nL 192.728259 214.756364 \nL 192.789138 214.286386 \nL 192.819577 201.1975 \nL 192.850016 214.756364 \nL 192.880456 213.651915 \nL 192.941335 211.231529 \nL 192.971774 212.26548 \nL 193.032653 214.756364 \nL 193.15441 190.622996 \nL 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209.774597 \nL 195.467805 208.881639 \nL 195.498245 214.756364 \nL 195.528684 205.944277 \nL 195.589563 214.638869 \nL 195.680881 206.508251 \nL 195.711321 214.756364 \nL 195.802639 211.490017 \nL 195.833078 214.756364 \nL 195.863518 188.837079 \nL 195.924397 214.544874 \nL 196.046154 181.411428 \nL 196.167912 214.756364 \nL 196.289669 200.281043 \nL 196.380988 214.756364 \nL 196.350548 179.696008 \nL 196.411427 213.181937 \nL 196.441866 211.114034 \nL 196.472306 214.756364 \nL 196.502745 212.641463 \nL 196.533185 212.547467 \nL 196.563624 213.76941 \nL 196.594063 211.983494 \nL 196.624503 210.385569 \nL 196.685382 214.756364 \nL 196.746261 213.393428 \nL 196.807139 214.756364 \nL 196.837579 214.568372 \nL 196.898458 214.756364 \nL 196.928897 209.304619 \nL 197.020215 214.756364 \nL 197.050655 212.171485 \nL 197.141973 214.756364 \nL 197.172412 183.526329 \nL 197.29417 214.756364 \nL 197.415928 213.675414 \nL 197.476806 214.756364 \nL 197.507246 214.333383 \nL 197.568125 205.051319 \nL 197.598564 206.555248 \nL 197.629003 214.756364 \nL 197.689882 207.260215 \nL 197.720322 196.803206 \nL 197.750761 214.756364 \nL 197.7812 214.756364 \nL 197.81164 211.302026 \nL 197.872519 214.756364 \nL 197.902958 214.756364 \nL 197.933397 175.842189 \nL 197.994276 213.487423 \nL 198.055155 212.382975 \nL 198.116034 214.756364 \nL 198.176913 187.168658 \nL 198.237792 204.910326 \nL 198.268231 214.756364 \nL 198.32911 206.061771 \nL 198.359549 203.594387 \nL 198.389989 186.111207 \nL 198.450867 214.756364 \nL 198.542186 202.654431 \nL 198.572625 213.55792 \nL 198.603064 213.463924 \nL 198.633504 214.756364 \nL 198.663943 207.542202 \nL 198.724822 214.568372 \nL 198.81614 214.756364 \nL 198.877019 206.320259 \nL 198.907458 214.521375 \nL 198.998777 213.886904 \nL 199.029216 200.516032 \nL 199.090095 201.291495 \nL 199.181413 214.756364 \nL 199.211853 213.087942 \nL 199.242292 214.756364 \nL 199.272731 213.863406 \nL 199.303171 189.753537 \nL 199.394489 205.004321 \nL 199.485807 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205.02782 \nL 204.325673 202.536937 \nL 204.447431 214.756364 \nL 204.47787 214.756364 \nL 204.508309 211.114034 \nL 204.569188 212.664962 \nL 204.660507 214.756364 \nL 204.751825 206.484752 \nL 204.843143 214.756364 \nL 204.873582 213.322931 \nL 204.904022 211.20803 \nL 204.934461 214.756364 \nL 204.964901 183.338337 \nL 205.025779 214.756364 \nL 205.056219 214.521375 \nL 205.117098 214.756364 \nL 205.147537 201.455988 \nL 205.208416 214.756364 \nL 205.360613 214.756364 \nL 205.391052 199.0591 \nL 205.451931 214.756364 \nL 205.482371 213.722412 \nL 205.543249 214.709366 \nL 205.573689 214.756364 \nL 205.665007 207.448206 \nL 205.695446 189.988525 \nL 205.756325 212.617964 \nL 205.786765 214.756364 \nL 205.817204 203.852875 \nL 205.878083 211.443019 \nL 205.938962 214.756364 \nL 205.99984 214.662368 \nL 206.03028 214.756364 \nL 206.152037 191.492455 \nL 206.273795 214.756364 \nL 206.304235 197.625667 \nL 206.365113 214.685867 \nL 206.395553 214.544874 \nL 206.425992 208.411661 \nL 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209.657103 \nL 208.404554 214.756364 \nL 208.434993 210.268074 \nL 208.465432 207.941683 \nL 208.495872 213.416926 \nL 208.556751 214.756364 \nL 208.58719 203.500392 \nL 208.678508 207.354211 \nL 208.708948 206.29676 \nL 208.800266 213.463924 \nL 208.830705 212.429973 \nL 208.861145 203.946871 \nL 208.891584 214.732865 \nL 208.952463 208.317666 \nL 208.982902 213.792909 \nL 209.013342 207.777191 \nL 209.043781 212.899951 \nL 209.135099 214.756364 \nL 209.165539 194.688305 \nL 209.226418 214.756364 \nL 209.287296 212.899951 \nL 209.317736 214.756364 \nL 209.348175 209.586606 \nL 209.409054 213.463924 \nL 209.439493 214.756364 \nL 209.469933 201.573482 \nL 209.530812 213.534421 \nL 209.561251 214.286386 \nL 209.591691 204.064365 \nL 209.652569 209.774597 \nL 209.713448 207.424708 \nL 209.774327 214.756364 \nL 209.804766 206.578747 \nL 209.865645 214.450878 \nL 209.896085 214.756364 \nL 209.987403 208.670149 \nL 210.017842 214.756364 \nL 210.10916 214.215889 \nL 210.1396 208.082677 \nL 210.200479 212.312478 \nL 210.261357 214.756364 \nL 210.291797 213.628417 \nL 210.322236 197.625667 \nL 210.352676 214.756364 \nL 210.383115 214.756364 \nL 210.413555 208.22367 \nL 210.474433 214.544874 \nL 210.504873 214.756364 \nL 210.535312 214.121893 \nL 210.596191 208.85814 \nL 210.62663 210.244575 \nL 210.717949 214.756364 \nL 210.748388 193.066881 \nL 210.809267 214.756364 \nL 210.931024 206.108769 \nL 211.052782 214.756364 \nL 211.113661 214.756364 \nL 211.204979 209.328118 \nL 211.296297 214.756364 \nL 211.326737 209.022633 \nL 211.418055 210.244575 \nL 211.448494 211.701507 \nL 211.509373 213.275933 \nL 211.539813 189.965027 \nL 211.570252 214.756364 \nL 211.66157 201.573482 \nL 211.69201 214.732865 \nL 211.722449 191.915435 \nL 211.752888 211.396021 \nL 211.813767 195.722257 \nL 211.844207 214.756364 \nL 211.874646 214.40388 \nL 211.905086 206.014774 \nL 211.935525 214.756364 \nL 211.965964 214.027898 \nL 211.996404 213.252434 \nL 212.026843 214.756364 \nL 212.057283 198.84761 \nL 212.118161 212.899951 \nL 212.148601 211.701507 \nL 212.17904 199.411583 \nL 212.20948 214.756364 \nL 212.239919 214.333383 \nL 212.300798 214.756364 \nL 212.331237 199.952058 \nL 212.392116 208.85814 \nL 212.422555 211.983494 \nL 212.452995 182.468878 \nL 212.483434 213.158439 \nL 212.513874 203.194906 \nL 212.544313 214.756364 \nL 212.635631 212.735458 \nL 212.666071 206.437754 \nL 212.72695 210.409068 \nL 212.757389 214.756364 \nL 212.818268 213.322931 \nL 212.848707 211.725006 \nL 212.879147 198.236639 \nL 212.909586 214.756364 \nL 212.940025 199.223592 \nL 213.000904 214.756364 \nL 213.061783 212.171485 \nL 213.092222 207.307213 \nL 213.122662 214.756364 \nL 213.153101 210.009586 \nL 213.183541 214.756364 \nL 213.244419 208.341165 \nL 213.274859 214.568372 \nL 213.335738 214.756364 \nL 213.396616 213.510922 \nL 213.427056 214.756364 \nL 213.457495 212.735458 \nL 213.487935 213.9809 \nL 213.518374 209.234123 \nL 213.579253 214.756364 \nL 213.609692 214.756364 \nL 213.761889 197.155689 \nL 213.883647 214.756364 \nL 213.914086 214.756364 \nL 213.944526 214.497876 \nL 214.005405 214.756364 \nL 214.035844 214.756364 \nL 214.066283 214.61537 \nL 214.127162 214.756364 \nL 214.188041 209.422114 \nL 214.309799 214.756364 \nL 214.370678 209.539608 \nL 214.401117 214.662368 \nL 214.431556 214.756364 \nL 214.522875 194.218327 \nL 214.553314 210.244575 \nL 214.614193 209.539608 \nL 214.675072 214.756364 \nL 214.76639 213.275933 \nL 214.857708 214.756364 \nL 214.827269 212.92345 \nL 214.888147 214.450878 \nL 214.918587 204.087864 \nL 215.009905 210.197577 \nL 215.101223 214.756364 \nL 215.131663 214.732865 \nL 215.192542 214.756364 \nL 215.25342 206.484752 \nL 215.28386 212.05399 \nL 215.314299 195.745755 \nL 215.375178 203.006915 \nL 215.496936 214.756364 \nL 215.527375 206.531749 \nL 215.588254 214.756364 \nL 215.618693 214.756364 \nL 215.649133 206.790237 \nL 215.710011 214.732865 \nL 215.77089 214.756364 \nL 215.831769 213.534421 \nL 215.862208 199.90506 \nL 215.923087 208.717147 \nL 216.014406 214.756364 \nL 216.044845 212.92345 \nL 216.105724 202.842423 \nL 216.136163 214.756364 \nL 216.227481 206.978229 \nL 216.257921 214.756364 \nL 216.349239 214.709366 \nL 216.440557 202.372445 \nL 216.410118 214.756364 \nL 216.470997 211.889498 \nL 216.531875 209.868593 \nL 216.592754 214.215889 \nL 216.684072 205.544796 \nL 216.775391 214.756364 \nL 216.80583 206.790237 \nL 216.866709 214.756364 \nL 216.927588 211.396021 \nL 216.958027 186.628183 \nL 216.988467 214.591871 \nL 217.018906 212.758957 \nL 217.049345 209.398615 \nL 217.110224 213.863406 \nL 217.262421 214.756364 \nL 217.292861 201.549983 \nL 217.353739 213.463924 \nL 217.384179 213.675414 \nL 217.414618 200.398537 \nL 217.445058 214.521375 \nL 217.475497 211.560514 \nL 217.505937 214.756364 \nL 217.597255 212.735458 \nL 217.658134 214.756364 \nL 217.719012 202.489939 \nL 217.779891 212.077489 \nL 217.84077 210.832048 \nL 217.932088 214.756364 \nL 217.962528 211.349023 \nL 218.023406 212.876452 \nL 218.114725 214.756364 \nL 218.145164 214.732865 \nL 218.206043 214.756364 \nL 218.297361 200.610027 \nL 218.388679 214.756364 \nL 218.419119 207.095723 \nL 218.540876 214.756364 \nL 218.571316 214.591871 \nL 218.632195 203.077411 \nL 218.662634 211.678008 \nL 218.693073 214.756364 \nL 218.723513 201.385491 \nL 218.784392 211.043538 \nL 218.87571 214.756364 \nL 218.936589 201.878968 \nL 218.967028 214.756364 \nL 218.997467 214.756364 \nL 219.027907 201.737974 \nL 219.088786 214.756364 \nL 219.149665 214.756364 \nL 219.180104 197.696164 \nL 219.240983 214.756364 \nL 219.271422 213.087942 \nL 219.301862 214.450878 \nL 219.332301 195.581263 \nL 219.39318 210.503063 \nL 219.423619 210.080083 \nL 219.454059 212.030492 \nL 219.514937 202.771926 \nL 219.545377 214.756364 \nL 219.575816 212.547467 \nL 219.636695 214.756364 \nL 219.697574 214.756364 \nL 219.819331 213.487423 \nL 219.91065 214.756364 \nL 219.971529 195.722257 \nL 220.001968 214.732865 \nL 220.032407 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222.102287 214.756364 \nL 222.132726 213.228935 \nL 222.193605 214.756364 \nL 222.224045 197.437676 \nL 222.284923 211.889498 \nL 222.345802 210.78505 \nL 222.406681 214.756364 \nL 222.437121 214.497876 \nL 222.46756 212.406474 \nL 222.497999 214.756364 \nL 222.528439 213.722412 \nL 222.650196 214.756364 \nL 222.680636 214.004399 \nL 222.802393 203.147908 \nL 222.832833 206.08527 \nL 222.863272 214.685867 \nL 222.924151 208.64665 \nL 222.95459 206.437754 \nL 223.076348 214.756364 \nL 223.106787 214.732865 \nL 223.137227 213.205436 \nL 223.167666 194.241826 \nL 223.228545 211.278527 \nL 223.289424 210.597059 \nL 223.350303 214.756364 \nL 223.380742 189.236561 \nL 223.47206 204.722334 \nL 223.532939 211.114034 \nL 223.563379 189.941528 \nL 223.593818 214.732865 \nL 223.624257 211.63101 \nL 223.715576 214.756364 \nL 223.746015 203.735381 \nL 223.806894 211.584012 \nL 223.837333 214.756364 \nL 223.867773 206.08527 \nL 223.928652 209.798096 \nL 223.98953 214.756364 \nL 224.01997 197.672665 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228.068411 214.756364 \nL 228.09885 210.291573 \nL 228.159729 214.756364 \nL 228.190169 214.756364 \nL 228.220608 214.380381 \nL 228.251047 199.505579 \nL 228.281487 214.756364 \nL 228.311926 211.560514 \nL 228.342366 212.852953 \nL 228.372805 190.998978 \nL 228.433684 214.521375 \nL 228.464123 214.61537 \nL 228.494563 214.239388 \nL 228.555441 214.756364 \nL 228.61632 203.335899 \nL 228.677199 192.808393 \nL 228.738078 214.756364 \nL 228.890275 207.330712 \nL 228.920714 213.087942 \nL 228.951154 197.437676 \nL 228.981593 214.756364 \nL 229.012033 208.458659 \nL 229.042472 214.756364 \nL 229.13379 213.322931 \nL 229.225108 214.756364 \nL 229.255548 213.440425 \nL 229.285987 214.756364 \nL 229.346866 203.335899 \nL 229.377305 208.43516 \nL 229.468624 214.756364 \nL 229.529502 191.844938 \nL 229.559942 196.192234 \nL 229.6817 214.756364 \nL 229.712139 213.181937 \nL 229.773018 214.732865 \nL 229.864336 213.228935 \nL 229.894775 214.40388 \nL 229.925215 214.732865 \nL 229.955654 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231.72114 210.221076 \nL 231.782019 214.756364 \nL 231.812458 213.76941 \nL 231.903776 200.892014 \nL 231.934216 212.382975 \nL 231.964655 214.756364 \nL 232.055973 213.910403 \nL 232.086413 214.168891 \nL 232.177731 214.239388 \nL 232.20817 196.450722 \nL 232.23861 214.756364 \nL 232.329928 214.286386 \nL 232.360367 214.732865 \nL 232.451686 200.633526 \nL 232.421246 214.756364 \nL 232.482125 208.43516 \nL 232.543004 214.756364 \nL 232.603883 213.745911 \nL 232.664761 213.275933 \nL 232.72564 197.155689 \nL 232.786519 206.132268 \nL 232.877837 214.756364 \nL 232.847398 198.518625 \nL 232.908277 213.252434 \nL 232.999595 214.756364 \nL 233.030034 211.278527 \nL 233.121353 213.275933 \nL 233.24311 200.563029 \nL 233.27355 210.479564 \nL 233.303989 214.756364 \nL 233.395307 212.570966 \nL 233.425747 208.200171 \nL 233.456186 186.581185 \nL 233.517065 211.20803 \nL 233.547504 214.756364 \nL 233.608383 209.140127 \nL 233.638823 207.871187 \nL 233.669262 188.249607 \nL 233.730141 214.756364 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237.839461 199.881561 \nL 237.8699 214.756364 \nL 237.90034 214.685867 \nL 238.052537 203.382897 \nL 238.143855 214.756364 \nL 238.174294 205.615292 \nL 238.235173 211.137533 \nL 238.296052 214.756364 \nL 238.326491 212.547467 \nL 238.356931 198.84761 \nL 238.38737 214.756364 \nL 238.41781 205.967776 \nL 238.539567 214.756364 \nL 238.661325 198.072146 \nL 238.691764 214.756364 \nL 238.783082 212.241982 \nL 238.813522 200.093051 \nL 238.874401 214.756364 \nL 238.90484 203.547389 \nL 239.057037 214.756364 \nL 239.087476 207.213217 \nL 239.148355 211.090536 \nL 239.270113 214.756364 \nL 239.361431 191.539453 \nL 239.42231 214.756364 \nL 239.483189 205.87378 \nL 239.544068 214.756364 \nL 239.604946 210.009586 \nL 239.635386 214.427379 \nL 239.665825 201.385491 \nL 239.696265 214.756364 \nL 239.726704 211.41952 \nL 239.787583 214.450878 \nL 239.818022 209.445613 \nL 239.878901 214.756364 \nL 240.000659 209.375116 \nL 240.031098 191.492455 \nL 240.091977 214.662368 \nL 240.122416 214.61537 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254.733332 214.756364 \nL 254.763771 214.756364 \nL 254.794211 198.730115 \nL 254.885529 206.813736 \nL 254.946408 214.756364 \nL 254.976847 213.487423 \nL 255.007287 141.345807 \nL 255.068165 214.756364 \nL 255.098605 213.886904 \nL 255.159484 196.239232 \nL 255.189923 212.664962 \nL 255.220362 210.808549 \nL 255.250802 212.735458 \nL 255.281241 212.030492 \nL 255.311681 214.662368 \nL 255.34212 201.925966 \nL 255.402999 214.756364 \nL 255.463878 211.513516 \nL 255.494317 199.599575 \nL 255.555196 214.756364 \nL 255.616075 214.756364 \nL 255.646514 213.275933 \nL 255.676954 213.698913 \nL 255.707393 201.033007 \nL 255.737832 214.732865 \nL 255.768272 211.067037 \nL 255.798711 214.145392 \nL 255.829151 194.194828 \nL 255.85959 214.756364 \nL 255.890029 214.756364 \nL 255.920469 214.756364 \nL 255.950908 191.210468 \nL 256.011787 212.71196 \nL 256.042226 208.811143 \nL 256.103105 212.946949 \nL 256.133545 214.756364 \nL 256.163984 214.638869 \nL 256.224863 167.923061 \nL 256.255302 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288.947226 213.064443 \nL 288.977665 214.756364 \nL 289.038544 214.215889 \nL 289.099423 204.111363 \nL 289.129863 208.811143 \nL 289.190741 214.756364 \nL 289.25162 212.876452 \nL 289.28206 214.756364 \nL 289.434257 196.521219 \nL 289.556014 214.756364 \nL 289.586454 212.218483 \nL 289.677772 212.735458 \nL 289.708211 189.04857 \nL 289.76909 212.852953 \nL 289.829969 214.756364 \nL 289.860408 205.051319 \nL 289.951727 211.231529 \nL 289.982166 210.526562 \nL 290.012605 211.043538 \nL 290.043045 212.735458 \nL 290.073484 208.059178 \nL 290.103924 214.732865 \nL 290.134363 212.030492 \nL 290.195242 214.286386 \nL 290.225681 207.213217 \nL 290.28656 214.756364 \nL 290.408318 199.552577 \nL 290.469196 198.283636 \nL 290.530075 214.756364 \nL 290.621394 214.685867 \nL 290.651833 203.523891 \nL 290.712712 214.756364 \nL 290.743151 214.756364 \nL 290.864909 204.557842 \nL 290.925788 214.756364 \nL 291.017106 214.427379 \nL 291.047545 214.356882 \nL 291.077985 190.834486 \nL 291.108424 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297.561578 209.351617 \nL 297.592018 214.756364 \nL 297.622457 201.174001 \nL 297.652897 213.322931 \nL 297.805094 203.899873 \nL 297.896412 214.756364 \nL 297.926851 203.194906 \nL 298.01817 209.962589 \nL 298.139927 214.756364 \nL 298.170367 205.192312 \nL 298.231245 214.756364 \nL 298.261685 214.756364 \nL 298.292124 205.779785 \nL 298.353003 206.696242 \nL 298.383442 214.121893 \nL 298.474761 213.181937 \nL 298.535639 214.756364 \nL 298.566079 211.325525 \nL 298.626958 214.544874 \nL 298.687837 214.756364 \nL 298.748715 213.205436 \nL 298.809594 214.756364 \nL 298.840034 213.886904 \nL 298.870473 203.194906 \nL 298.931352 208.176672 \nL 299.02267 214.756364 \nL 299.053109 208.035679 \nL 299.113988 214.756364 \nL 299.144428 214.662368 \nL 299.235746 207.424708 \nL 299.327064 214.756364 \nL 299.357503 211.772004 \nL 299.387943 211.41952 \nL 299.418382 206.531749 \nL 299.448822 207.612699 \nL 299.479261 214.756364 \nL 299.509701 202.019961 \nL 299.54014 212.641463 \nL 299.601019 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301.366504 207.777191 \nL 301.396944 214.756364 \nL 301.427383 211.090536 \nL 301.457823 202.583935 \nL 301.488262 212.218483 \nL 301.518701 203.923372 \nL 301.640459 214.756364 \nL 301.670898 214.098395 \nL 301.701338 214.756364 \nL 301.731777 196.779707 \nL 301.823095 204.534343 \nL 301.883974 214.756364 \nL 301.914414 211.161032 \nL 301.944853 199.247091 \nL 302.005732 214.732865 \nL 302.036171 209.892092 \nL 302.066611 214.756364 \nL 302.12749 211.137533 \nL 302.157929 210.926043 \nL 302.188368 197.061694 \nL 302.249247 211.748505 \nL 302.279687 208.975635 \nL 302.310126 214.756364 \nL 302.340565 213.910403 \nL 302.401444 214.756364 \nL 302.431884 202.25495 \nL 302.492762 207.918184 \nL 302.553641 214.756364 \nL 302.584081 208.811143 \nL 302.61452 196.004243 \nL 302.644959 214.756364 \nL 302.705838 202.513438 \nL 302.736278 202.536937 \nL 302.888475 214.756364 \nL 303.010232 203.453394 \nL 303.13199 214.756364 \nL 303.192869 206.061771 \nL 303.223308 211.278527 \nL 303.314626 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "Burada gördüğümüz şey, başlangıçta ortalama yol uzunluğunun arttığıdır. Bu muhtemelen çevre hakkında hiçbir şey bilmediğimizde - kötü durumlara, suya veya kurda takılma olasılığımızın yüksek olmasından kaynaklanıyor. Daha fazla bilgi edindikçe ve bu bilgiyi kullanmaya başladıkça, çevreyi daha uzun süre keşfedebiliriz, ancak elmaların nerede olduğunu hâlâ iyi bilmiyoruz.\n",
+    "\n",
+    "Yeterince öğrendiğimizde, ajan için hedefe ulaşmak daha kolay hale gelir ve yol uzunluğu azalmaya başlar. Ancak, hâlâ keşfe açık olduğumuz için, genellikle en iyi yoldan sapar ve yeni seçenekleri keşfederiz, bu da yolu optimalden daha uzun hale getirir.\n",
+    "\n",
+    "Bu grafikte ayrıca gözlemlediğimiz bir diğer şey, bir noktada uzunluğun aniden arttığıdır. Bu, sürecin stokastik doğasını gösterir ve Q-Tablo katsayılarını yeni değerlerle üzerine yazarak \"bozabileceğimiz\" anlamına gelir. Bu durum, öğrenme oranını azaltarak (örneğin, eğitimin sonuna doğru Q-Tablo değerlerini yalnızca küçük bir miktarla ayarlayarak) ideal olarak en aza indirgenmelidir.\n",
+    "\n",
+    "Genel olarak, öğrenme sürecinin başarısı ve kalitesinin, öğrenme oranı, öğrenme oranı azalması ve indirim faktörü gibi parametrelere önemli ölçüde bağlı olduğunu unutmamak önemlidir. Bunlar genellikle **hiperparametreler** olarak adlandırılır, çünkü eğitim sırasında optimize ettiğimiz **parametrelerden** (örneğin, Q-Tablo katsayıları) ayrılırlar. En iyi hiperparametre değerlerini bulma sürecine **hiperparametre optimizasyonu** denir ve bu ayrı bir konu başlığını hak eder.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## Alıştırma\n",
+    "#### Daha Gerçekçi Bir Peter ve Kurt Dünyası\n",
+    "\n",
+    "Bizim senaryomuzda, Peter neredeyse hiç yorulmadan veya acıkmadan etrafta dolaşabiliyordu. Daha gerçekçi bir dünyada, zaman zaman oturup dinlenmesi ve kendini beslemesi gerekiyor. Dünyamızı daha gerçekçi hale getirmek için aşağıdaki kuralları uygulayalım:\n",
+    "\n",
+    "1. Bir yerden başka bir yere hareket ettiğinde, Peter **enerji** kaybeder ve biraz **yorgunluk** kazanır.\n",
+    "2. Peter, elma yiyerek daha fazla enerji kazanabilir.\n",
+    "3. Peter, ağacın altında veya çimenlerin üzerinde dinlenerek yorgunluğunu giderebilir (yani, tahtada bir ağaç veya çimen bulunan bir konuma yürümek - yeşil alan).\n",
+    "4. Peter, kurdu bulmalı ve öldürmelidir.\n",
+    "5. Kurdu öldürmek için Peter'ın belirli seviyelerde enerjiye ve yorgunluğa sahip olması gerekir, aksi takdirde savaşı kaybeder.\n",
+    "\n",
+    "Yukarıdaki ödül fonksiyonunu oyunun kurallarına göre değiştirin, oyunu kazanmak için en iyi stratejiyi öğrenmek üzere pekiştirmeli öğrenme algoritmasını çalıştırın ve rastgele yürüyüş sonuçlarını algoritmanızla karşılaştırın; kazanılan ve kaybedilen oyun sayısı açısından değerlendirin.\n",
+    "\n",
+    "> **Not**: Çalışması için hiperparametreleri ayarlamanız gerekebilir, özellikle epoch sayısını. Çünkü oyunun başarısı (kurdu yenmek) nadir bir olaydır, bu nedenle çok daha uzun bir eğitim süresi bekleyebilirsiniz.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/8-Reinforcement/2-Gym/notebook.ipynb b/translations/tr/8-Reinforcement/2-Gym/notebook.ipynb
new file mode 100644
index 000000000..c661ae1ea
--- /dev/null
+++ b/translations/tr/8-Reinforcement/2-Gym/notebook.ipynb
@@ -0,0 +1,394 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.4 64-bit ('base': conda)"
+  },
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "coopTranslator": {
+   "original_hash": "f22f8f3daed4b6d34648d1254763105b",
+   "translation_date": "2025-09-06T15:18:22+00:00",
+   "source_file": "8-Reinforcement/2-Gym/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Kartopu Kayma\n",
+    "\n",
+    "> **Problem**: Peter, kurttan kaçmak istiyorsa ondan daha hızlı hareket edebilmelidir. Peter'ın özellikle dengeyi koruyarak kaymayı nasıl öğrenebileceğini Q-Öğrenme kullanarak göreceğiz.\n",
+    "\n",
+    "Öncelikle, gym'i yükleyelim ve gerekli kütüphaneleri içe aktaralım:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Bir cartpole ortamı oluştur\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 2"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Çevrenin nasıl çalıştığını görmek için, 100 adımlık kısa bir simülasyon çalıştıralım.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Simülasyon sırasında, nasıl hareket edeceğimize karar vermek için gözlemler almamız gerekir. Aslında, `step` fonksiyonu bize mevcut gözlemleri, ödül fonksiyonunu ve simülasyona devam etmenin mantıklı olup olmadığını gösteren `done` bayrağını geri döndürür:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 4"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Bu sayıların minimum ve maksimum değerlerini alabiliriz:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Haydi, diğer ayrıklaştırma yöntemini de kutular kullanarak keşfedelim:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Haydi şimdi kısa bir simülasyon çalıştıralım ve bu ayrık ortam değerlerini gözlemleyelim.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -2, -2)\n(0, 1, -2, -5)\n(0, 2, -3, -8)\n(0, 3, -5, -11)\n(0, 3, -7, -14)\n(0, 4, -10, -17)\n(0, 3, -14, -15)\n(0, 3, -17, -12)\n(0, 3, -20, -16)\n(0, 4, -23, -19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 8"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Tablo Yapısı\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 9"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 22.0, alpha=0.3, epsilon=0.9\n",
+      "5000: 70.1384, alpha=0.3, epsilon=0.9\n",
+      "10000: 121.8586, alpha=0.3, epsilon=0.9\n",
+      "15000: 149.6368, alpha=0.3, epsilon=0.9\n",
+      "20000: 168.2782, alpha=0.3, epsilon=0.9\n",
+      "25000: 196.7356, alpha=0.3, epsilon=0.9\n",
+      "30000: 220.7614, alpha=0.3, epsilon=0.9\n",
+      "35000: 233.2138, alpha=0.3, epsilon=0.9\n",
+      "40000: 248.22, alpha=0.3, epsilon=0.9\n",
+      "45000: 264.636, alpha=0.3, epsilon=0.9\n",
+      "50000: 276.926, alpha=0.3, epsilon=0.9\n",
+      "55000: 277.9438, alpha=0.3, epsilon=0.9\n",
+      "60000: 248.881, alpha=0.3, epsilon=0.9\n",
+      "65000: 272.529, alpha=0.3, epsilon=0.9\n",
+      "70000: 281.7972, alpha=0.3, epsilon=0.9\n",
+      "75000: 284.2844, alpha=0.3, epsilon=0.9\n",
+      "80000: 269.667, alpha=0.3, epsilon=0.9\n",
+      "85000: 273.8652, alpha=0.3, epsilon=0.9\n",
+      "90000: 278.2466, alpha=0.3, epsilon=0.9\n",
+      "95000: 269.1736, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 11"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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203.506696 \r\nL 112.459209 153.284967 \r\nL 112.489645 199.488958 \r\nL 112.556606 78.555033 \r\nL 112.562693 130.383858 \r\nL 112.599217 78.153259 \r\nL 112.571824 191.453481 \r\nL 112.666178 102.25969 \r\nL 112.772706 209.533304 \r\nL 112.769663 17.083636 \r\nL 112.778794 159.713348 \r\nL 112.812274 79.358581 \r\nL 112.803143 211.140399 \r\nL 112.894453 127.169667 \r\nL 112.976632 211.943947 \r\nL 112.912715 82.974545 \r\nL 113.004025 154.088514 \r\nL 113.070986 90.206475 \r\nL 113.067942 212.345721 \r\nL 113.110553 168.552373 \r\nL 113.171427 204.310244 \r\nL 113.159252 93.822439 \r\nL 113.210994 126.36612 \r\nL 113.226213 46.8149 \r\nL 113.256649 203.506696 \r\nL 113.311435 139.222882 \r\nL 113.408833 207.926208 \r\nL 113.390571 44.404257 \r\nL 113.421007 168.552373 \r\nL 113.436226 94.224213 \r\nL 113.497099 192.257029 \r\nL 113.530579 136.812239 \r\nL 113.603627 205.917339 \r\nL 113.588409 32.351042 \r\nL 113.634064 158.106253 \r\nL 113.637108 72.528426 \r\nL 113.734505 190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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125.656548 212.345721 \r\nL 125.738727 132.392727 \r\nL 125.811775 195.872993 \r\nL 125.805688 47.216674 \r\nL 125.817862 130.383858 \r\nL 125.820906 33.958137 \r\nL 125.924391 194.667672 \r\nL 125.991351 17.083636 \r\nL 125.970046 202.301375 \r\nL 126.046137 122.750155 \r\nL 126.137447 186.230421 \r\nL 126.122229 70.519557 \r\nL 126.155709 132.794501 \r\nL 126.16484 85.786962 \r\nL 126.177015 195.069446 \r\nL 126.259194 131.187406 \r\nL 126.271369 203.104922 \r\nL 126.353548 85.786962 \r\nL 126.37181 178.998492 \r\nL 126.453989 86.992284 \r\nL 126.472251 209.533304 \r\nL 126.55443 81.769224 \r\nL 126.584866 133.196275 \r\nL 126.633565 199.087184 \r\nL 126.603128 63.689401 \r\nL 126.694438 104.268559 \r\nL 126.706613 209.13153 \r\nL 126.7127 70.117783 \r\nL 126.807054 158.9098 \r\nL 126.886189 78.153259 \r\nL 126.840534 201.497827 \r\nL 126.916626 132.392727 \r\nL 127.001849 201.497827 \r\nL 126.931844 82.974545 \r\nL 127.023154 138.017561 \r\nL 127.074897 102.661463 \r\nL 127.111421 205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 128.423241 194.265898 \r\nL 128.514551 142.035299 \r\nL 128.56325 208.729756 \r\nL 128.566293 51.234412 \r\nL 128.624123 150.47255 \r\nL 128.730651 46.011353 \r\nL 128.715433 208.327982 \r\nL 128.736739 103.465011 \r\nL 128.751957 172.971885 \r\nL 128.74587 80.162129 \r\nL 128.803699 140.428204 \r\nL 128.858485 33.958137 \r\nL 128.864573 204.712018 \r\nL 128.913271 151.276098 \r\nL 128.919359 192.658803 \r\nL 128.998494 63.287627 \r\nL 129.016756 110.295166 \r\nL 129.077629 60.876984 \r\nL 129.025887 186.230421 \r\nL 129.114153 117.928869 \r\nL 129.117197 166.945277 \r\nL 129.175027 50.832639 \r\nL 129.223725 161.320443 \r\nL 129.321123 63.287627 \r\nL 129.308948 199.890732 \r\nL 129.333297 84.983415 \r\nL 129.357647 198.283636 \r\nL 129.409389 53.243282 \r\nL 129.442869 145.24949 \r\nL 129.518961 20.297827 \r\nL 129.491568 197.078315 \r\nL 129.549398 161.722217 \r\nL 129.640708 202.301375 \r\nL 129.610271 33.556364 \r\nL 129.655926 179.80204 \r\nL 129.716799 19.092506 \r\nL 129.668101 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 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139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 182.212683 \r\nL 140.649651 195.069446 \r\nL 140.613127 74.135521 \r\nL 140.652694 174.57898 \r\nL 140.704437 51.636186 \r\nL 140.674 201.096053 \r\nL 140.76531 133.598049 \r\nL 140.832271 195.872993 \r\nL 140.847489 76.14439 \r\nL 140.874882 188.23929 \r\nL 140.887057 55.252151 \r\nL 140.963148 203.90847 \r\nL 140.987498 135.205144 \r\nL 141.087939 206.720887 \r\nL 141.002716 22.70847 \r\nL 141.09707 189.042838 \r\nL 141.197511 62.885854 \r\nL 141.194467 194.265898 \r\nL 141.206642 74.537295 \r\nL 141.218816 183.819778 \r\nL 141.319257 136.812239 \r\nL 141.352738 17.083636 \r\nL 141.419698 199.087184 \r\nL 141.498834 17.083636 \r\nL 141.489703 211.140399 \r\nL 141.532314 80.965676 \r\nL 141.608406 199.890732 \r\nL 141.611449 17.887184 \r\nL 141.641886 78.956807 \r\nL 141.675366 210.336851 \r\nL 141.672323 17.083636 \r\nL 141.751458 115.92 \r\nL 141.787982 17.083636 \r\nL 141.839724 205.917339 \r\nL 141.864074 99.849047 \r\nL 141.964515 189.846386 \r\nL 141.897554 48.020222 \r\nL 141.973646 169.757694 \r\nL 141.98582 37.574102 \r\nL 142.064956 184.221552 \r\nL 142.089305 38.779424 \r\nL 142.159309 194.265898 \r\nL 142.119742 31.949268 \r\nL 142.201921 134.80337 \r\nL 142.241488 46.413126 \r\nL 142.232357 203.506696 \r\nL 142.265838 93.420665 \r\nL 142.320624 199.087184 \r\nL 142.332798 48.823769 \r\nL 142.37541 111.098714 \r\nL 142.478894 199.890732 \r\nL 142.396715 48.020222 \r\nL 142.484982 120.741286 \r\nL 142.488025 21.503149 \r\nL 142.503244 201.497827 \r\nL 142.59151 74.939069 \r\nL 142.597597 203.506696 \r\nL 142.701082 53.645055 \r\nL 142.704126 146.454812 \r\nL 142.743693 44.002483 \r\nL 142.801523 186.230421 \r\nL 142.813698 87.394058 \r\nL 142.868484 199.890732 \r\nL 142.850222 52.841508 \r\nL 142.926313 193.46235 \r\nL 142.944575 65.698271 \r\nL 142.965881 193.864124 \r\nL 143.035885 142.437073 \r\nL 143.066322 194.265898 \r\nL 143.072409 66.501818 \r\nL 143.142414 93.822439 \r\nL 143.215462 202.301375 \r\nL 143.233724 80.965676 \r\nL 143.255029 187.435743 \r\nL 143.35547 208.729756 \r\nL 143.370689 33.15459 \r\nL 143.373732 205.515565 \r\nL 143.431562 31.145721 \r\nL 143.483304 125.964346 \r\nL 143.586789 60.876984 \r\nL 143.577658 195.069446 \r\nL 143.59592 73.733747 \r\nL 143.662881 195.47122 \r\nL 143.605051 23.913792 \r\nL 143.708536 147.660133 \r\nL 143.77854 32.752816 \r\nL 143.732885 207.926208 \r\nL 143.818108 68.108914 \r\nL 143.878981 204.310244 \r\nL 143.875937 39.181197 \r\nL 143.930723 164.13286 \r\nL 143.985509 67.70714 \r\nL 143.967247 208.327982 \r\nL 144.043339 105.875654 \r\nL 144.107256 209.13153 \r\nL 144.076819 17.083636 \r\nL 144.152911 91.81357 \r\nL 144.253352 194.265898 \r\nL 144.241177 17.083636 \r\nL 144.256396 85.786962 \r\nL 144.259439 35.565233 \r\nL 144.280745 196.274767 \r\nL 144.35988 162.123991 \r\nL 144.365968 207.122661 \r\nL 144.381186 17.083636 \r\nL 144.463365 105.875654 \r\nL 144.515107 28.735078 \r\nL 144.512064 182.212683 \r\nL 144.572937 118.732417 \r\nL 144.615548 187.837517 \r\nL 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191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 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332.202821 102.661463 \r\nL 332.236301 199.488958 \r\nL 332.306305 31.547494 \r\nL 332.358048 17.083636 \r\nL 332.415877 204.712018 \r\nL 332.522406 17.083636 \r\nL 332.491969 209.533304 \r\nL 332.531537 43.60071 \r\nL 332.635021 209.533304 \r\nL 332.543711 17.083636 \r\nL 332.644152 87.795831 \r\nL 332.711113 44.002483 \r\nL 332.662414 199.890732 \r\nL 332.735462 117.928869 \r\nL 332.738506 196.274767 \r\nL 332.750681 17.083636 \r\nL 332.841991 81.769224 \r\nL 332.863296 192.257029 \r\nL 332.930257 17.083636 \r\nL 332.954606 193.864124 \r\nL 333.039829 80.965676 \r\nL 333.042873 17.083636 \r\nL 333.061135 206.720887 \r\nL 333.149401 39.582971 \r\nL 333.161576 180.605588 \r\nL 333.240711 17.083636 \r\nL 333.258973 87.795831 \r\nL 333.262017 17.083636 \r\nL 333.344196 193.46235 \r\nL 333.365501 76.546164 \r\nL 333.462899 206.319113 \r\nL 333.371589 17.083636 \r\nL 333.475073 121.14306 \r\nL 333.578558 47.618448 \r\nL 333.560296 195.872993 \r\nL 333.587689 55.653925 \r\nL 333.685086 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334.93299 84.581641 \r\nL 334.990819 192.658803 \r\nL 334.996907 23.913792 \r\nL 335.042562 86.188736 \r\nL 335.127784 198.68541 \r\nL 335.082129 17.083636 \r\nL 335.155177 144.044169 \r\nL 335.228225 17.083636 \r\nL 335.203876 202.301375 \r\nL 335.261706 65.698271 \r\nL 335.362147 196.274767 \r\nL 335.307361 17.083636 \r\nL 335.371278 166.141729 \r\nL 335.453457 17.083636 \r\nL 335.380409 208.729756 \r\nL 335.483893 78.153259 \r\nL 335.587378 195.069446 \r\nL 335.520417 40.386519 \r\nL 335.593465 81.36745 \r\nL 335.617815 192.658803 \r\nL 335.690863 17.083636 \r\nL 335.794347 198.283636 \r\nL 335.803478 147.258359 \r\nL 335.84609 195.069446 \r\nL 335.818697 33.15459 \r\nL 335.891745 115.116452 \r\nL 335.967836 17.083636 \r\nL 335.995229 209.13153 \r\nL 335.998273 163.731086 \r\nL 336.077408 32.752816 \r\nL 336.007404 205.515565 \r\nL 336.123063 69.314235 \r\nL 336.199155 191.051707 \r\nL 336.156544 25.520887 \r\nL 336.235679 101.456142 \r\nL 336.269159 33.15459 \r\nL 336.314814 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Bu grafikten herhangi bir şey söylemek mümkün değildir, çünkü stokastik eğitim sürecinin doğası gereği eğitim oturumlarının uzunluğu büyük ölçüde değişir. Bu grafiği daha anlamlı hale getirmek için, diyelim ki 100 deney serisi üzerinde **hareketli ortalama** hesaplayabiliriz. Bu, `np.convolve` kullanılarak kolayca yapılabilir:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "#code block 12"
+   ]
+  },
+  {
+   "source": [
+    "## Hiperparametreleri Değiştirmek ve Sonucu Görmek\n",
+    "\n",
+    "Artık eğitilmiş modelin nasıl davrandığını gerçekten görmek ilginç olurdu. Simülasyonu çalıştıralım ve eğitim sırasında kullandığımız aynı eylem seçme stratejisini takip edeceğiz: Q-Tablosundaki olasılık dağılımına göre örnekleme yaparak:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 13"
+   ]
+  },
+  {
+   "source": [
+    "## Sonucu animasyonlu bir GIF olarak kaydetme\n",
+    "\n",
+    "Arkadaşlarınızı etkilemek istiyorsanız, denge çubuğunun animasyonlu GIF resmini onlara göndermek isteyebilirsiniz. Bunu yapmak için, bir görüntü karesi oluşturmak üzere `env.render` çağrısını yapabilir ve ardından bunları PIL kütüphanesini kullanarak animasyonlu bir GIF olarak kaydedebilirsiniz:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlık içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalardan sorumlu değiliz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/8-Reinforcement/2-Gym/solution/notebook.ipynb b/translations/tr/8-Reinforcement/2-Gym/solution/notebook.ipynb
new file mode 100644
index 000000000..5b23ba030
--- /dev/null
+++ b/translations/tr/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -0,0 +1,526 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "5c0e485e58d63c506f1791c4dbf990ce",
+   "translation_date": "2025-09-06T15:21:21+00:00",
+   "source_file": "8-Reinforcement/2-Gym/solution/notebook.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## CartPole Kayma\n",
+    "\n",
+    "> **Problem**: Peter, kurttan kaçmak istiyorsa ondan daha hızlı hareket edebilmelidir. Peter'ın özellikle dengeyi koruyarak kaymayı nasıl öğrenebileceğini Q-Learning kullanarak göreceğiz.\n",
+    "\n",
+    "Öncelikle, gym'i yükleyelim ve gerekli kütüphaneleri içe aktaralım:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: gym in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.18.3)\n",
+      "Requirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
+      "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
+      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.6.0)\n",
+      "Requirement already satisfied: pyglet<=1.5.15,>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.5.15)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!pip install gym \n",
+    "\n",
+    "import gym\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "source": [
+    "## Bir cartpole ortamı oluştur\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Çevrenin nasıl çalıştığını görmek için, 100 adımlık kısa bir simülasyon çalıştıralım.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Simülasyon sırasında, nasıl hareket edeceğimize karar vermek için gözlemler almamız gerekir. Aslında, `step` fonksiyonu bize mevcut gözlemleri, ödül fonksiyonunu ve simülasyona devam etmenin mantıklı olup olmadığını gösteren `done` bayrağını geri döndürür:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[ 0.03044442 -0.19543914 -0.04496216  0.28125618] -> 1.0\n",
+      "[ 0.02653564 -0.38989186 -0.03933704  0.55942606] -> 1.0\n",
+      "[ 0.0187378  -0.19424049 -0.02814852  0.25461393] -> 1.0\n",
+      "[ 0.01485299 -0.38894946 -0.02305624  0.53828712] -> 1.0\n",
+      "[ 0.007074   -0.19351108 -0.0122905   0.23842953] -> 1.0\n",
+      "[ 0.00320378  0.00178427 -0.00752191 -0.05810469] -> 1.0\n",
+      "[ 0.00323946  0.19701326 -0.008684   -0.35315131] -> 1.0\n",
+      "[ 0.00717973  0.00201587 -0.01574703 -0.06321931] -> 1.0\n",
+      "[ 0.00722005  0.19736001 -0.01701141 -0.36082863] -> 1.0\n",
+      "[ 0.01116725  0.39271958 -0.02422798 -0.65882671] -> 1.0\n",
+      "[ 0.01902164  0.19794307 -0.03740452 -0.37387001] -> 1.0\n",
+      "[ 0.0229805   0.39357584 -0.04488192 -0.67810827] -> 1.0\n",
+      "[ 0.03085202  0.58929164 -0.05844408 -0.98457719] -> 1.0\n",
+      "[ 0.04263785  0.78514572 -0.07813563 -1.2950295 ] -> 1.0\n",
+      "[ 0.05834076  0.98116859 -0.10403622 -1.61111521] -> 1.0\n",
+      "[ 0.07796413  0.78741784 -0.13625852 -1.35259196] -> 1.0\n",
+      "[ 0.09371249  0.98396202 -0.16331036 -1.68461179] -> 1.0\n",
+      "[ 0.11339173  0.79106371 -0.1970026  -1.44691436] -> 1.0\n",
+      "[ 0.12921301  0.59883361 -0.22594088 -1.22169133] -> 1.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Bu sayıların minimum ve maksimum değerlerini alabiliriz:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(env.observation_space.low)\n",
+    "print(env.observation_space.high)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretize(x):\n",
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+   ]
+  },
+  {
+   "source": [
+    "Haydi, diğer ayrıklaştırma yöntemlerini de kutular kullanarak keşfedelim:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_bins(i,num):\n",
+    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
+    "\n",
+    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
+    "\n",
+    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
+    "nbins = [20,20,10,10] # number of bins for each parameter\n",
+    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
+    "\n",
+    "def discretize_bins(x):\n",
+    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+   ]
+  },
+  {
+   "source": [
+    "Haydi şimdi kısa bir simülasyon çalıştıralım ve bu ayrık ortam değerlerini gözlemleyelim.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 2, -4, -9)\n(0, 3, -6, -12)\n(0, 2, -8, -9)\n(0, 3, -10, -13)\n(0, 4, -13, -16)\n(0, 4, -16, -19)\n(0, 4, -20, -17)\n(0, 4, -24, -20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   #env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   #print(discretize_bins(obs))\n",
+    "   print(discretize(obs))\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Tablo Yapısı\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = {}\n",
+    "actions = (0,1)\n",
+    "\n",
+    "def qvalues(state):\n",
+    "    return [Q.get((state,a),0) for a in actions]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hyperparameters\n",
+    "alpha = 0.3\n",
+    "gamma = 0.9\n",
+    "epsilon = 0.90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 108.0, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v\n",
+    "\n",
+    "Qmax = 0\n",
+    "cum_rewards = []\n",
+    "rewards = []\n",
+    "for epoch in range(100000):\n",
+    "    obs = env.reset()\n",
+    "    done = False\n",
+    "    cum_reward=0\n",
+    "    # == do the simulation ==\n",
+    "    while not done:\n",
+    "        s = discretize(obs)\n",
+    "        if random.random()<epsilon:\n",
+    "            # exploitation - chose the action according to Q-Table probabilities\n",
+    "            v = probs(np.array(qvalues(s)))\n",
+    "            a = random.choices(actions,weights=v)[0]\n",
+    "        else:\n",
+    "            # exploration - randomly chose the action\n",
+    "            a = np.random.randint(env.action_space.n)\n",
+    "\n",
+    "        obs, rew, done, info = env.step(a)\n",
+    "        cum_reward+=rew\n",
+    "        ns = discretize(obs)\n",
+    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
+    "    cum_rewards.append(cum_reward)\n",
+    "    rewards.append(cum_reward)\n",
+    "    # == Periodically print results and calculate average reward ==\n",
+    "    if epoch%5000==0:\n",
+    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
+    "        if np.average(cum_rewards) > Qmax:\n",
+    "            Qmax = np.average(cum_rewards)\n",
+    "            Qbest = Q\n",
+    "        cum_rewards=[]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Bu grafikten herhangi bir şey söylemek mümkün değildir, çünkü stokastik eğitim sürecinin doğası gereği eğitim oturumlarının uzunluğu büyük ölçüde değişir. Bu grafiği daha anlamlı hale getirmek için, diyelim ki 100 deney serisi üzerinde **hareketli ortalama** hesaplayabiliriz. Bu, `np.convolve` kullanılarak kolayca yapılabilir:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "def running_average(x,window):\n",
+    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
+    "\n",
+    "plt.plot(running_average(rewards,100))"
+   ]
+  },
+  {
+   "source": [
+    "## Hiperparametreleri Değiştirmek ve Sonucu Görmek\n",
+    "\n",
+    "Şimdi, eğitilmiş modelin nasıl davrandığını gerçekten görmek ilginç olurdu. Simülasyonu çalıştıralım ve eğitim sırasında kullandığımız aynı eylem seçme stratejisini takip edeceğiz: Q-Tablosundaki olasılık dağılımına göre örnekleme yaparak:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset()\n",
+    "done = False\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   env.render()\n",
+    "   v = probs(np.array(qvalues(s)))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Sonucu animasyonlu bir GIF olarak kaydetme\n",
+    "\n",
+    "Arkadaşlarınızı etkilemek istiyorsanız, denge çubuğunun animasyonlu bir GIF resmini onlara göndermek isteyebilirsiniz. Bunu yapmak için, `env.render` çağrısını kullanarak bir görüntü karesi üretebilir ve ardından bu kareleri PIL kütüphanesi kullanarak animasyonlu bir GIF olarak kaydedebilirsiniz:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, AI çeviri hizmeti [Co-op Translator](https://github.com/Azure/co-op-translator) kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/tr/PyTorch_Fundamentals.ipynb b/translations/tr/PyTorch_Fundamentals.ipynb
new file mode 100644
index 000000000..45a04806d
--- /dev/null
+++ b/translations/tr/PyTorch_Fundamentals.ipynb
@@ -0,0 +1,2830 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4",
+   "authorship_tag": "ABX9TyOgv0AozH1FKQBD+RkgT2bV",
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "coopTranslator": {
+   "original_hash": "0ca21b6ee62904d616f2e36dc1cf0da7",
+   "translation_date": "2025-09-06T13:08:20+00:00",
+   "source_file": "PyTorch_Fundamentals.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "view-in-github",
+    "colab_type": "text"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EHh5JllMh1rG",
+    "outputId": "f55755ad-c369-414c-85ec-6e9d4f061a02",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'2.2.1+cu121'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(\"I am excited to run this\")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UPlb-duwXAfz",
+    "outputId": "cfd687e4-1238-49f4-ab6b-ee1305b740d2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "I am excited to run this\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(torch.__version__)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "byWVlJ9wXDSk",
+    "outputId": "fd74a5c4-4d4a-41b2-ef3c-562ea3e4811f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2.2.1+cu121\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "Osm80zoEYklS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# scalar\n",
+    "scalar = torch.tensor(7)\n",
+    "scalar"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-o8wvJ-VXZmI",
+    "outputId": "558816f5-1205-4de1-fe1f-2f96e9bd79e6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(7)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mCZ2tXC4Y_Sg",
+    "outputId": "2d86dbdc-56e1-45c6-d3dd-14515f2a457a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ssN00By0ZQgS",
+    "outputId": "490f40d1-5135-4969-a6d3-c8c902cdc473"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# vector\n",
+    "vector = torch.tensor([7, 7])\n",
+    "vector\n",
+    "#vector.ndim\n",
+    "#vector.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Bws__5wlZnmF",
+    "outputId": "944e38f9-5ba1-4ddc-a9c6-cfb6a19bb488"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([7, 7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "vector.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9pjCvnsZZzNG",
+    "outputId": "e030a4da-8f81-4858-fbce-86da2aaafe52"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([2])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Matrix\n",
+    "MATRIX = torch.tensor([[7, 8],[9, 10]])\n",
+    "MATRIX"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a747hI9SaBGW",
+    "outputId": "af835ddb-81ff-4981-badb-441567194d15"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 7,  8],\n",
+       "        [ 9, 10]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XdTfFa7vaRUj",
+    "outputId": "0fbbab9c-8263-4cad-a380-0d2a16ca499e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX[0]\n",
+    "MATRIX[1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TFeD3jSDafm7",
+    "outputId": "69b44ab3-5ba7-451a-c6b2-f019a03d0c96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Tensor\n",
+    "TENSOR = torch.tensor([[[1, 2, 3],[3,6,9], [2,4,5]]])\n",
+    "TENSOR"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ic3cE47tah42",
+    "outputId": "f250e295-91de-43ec-9d80-588a6fe0abde"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 2, 3],\n",
+       "         [3, 6, 9],\n",
+       "         [2, 4, 5]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wvjf5fczbAM1",
+    "outputId": "9c72b5b8-bafe-4ae7-9883-b051e209eada"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mwtXZwiMbN3m",
+    "outputId": "331a5e36-b1b0-4a5f-a9b8-e7049cbaa8f9"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vzdZu_IfbP3J",
+    "outputId": "e24e7e71-e365-412d-ff50-fc094b56d2f3"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [3, 6, 9],\n",
+       "        [2, 4, 5]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "A8OL9eWfcRrJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor = torch.rand(3,4)\n",
+    "random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hAqSDE1EcVS_",
+    "outputId": "946171c3-d054-400c-f893-79110356888c"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.4414, 0.7681, 0.8385, 0.3166],\n",
+       "        [0.0468, 0.5812, 0.0670, 0.9173],\n",
+       "        [0.2959, 0.3276, 0.7411, 0.4643]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "g4fvPE5GcwzP",
+    "outputId": "8737f36b-6864-4059-eaed-6f9156c22306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XsAg99QmdAU6",
+    "outputId": "35467c11-257c-4f16-99aa-eca930bcbc36"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.size()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cii1pNdVdB68",
+    "outputId": "fc8d2de6-9215-43de-99f7-7b0d7f7d20fa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_image_tensor = torch.rand(size=(3, 224, 224)) #color channels, height, width\n",
+    "random_image_tensor.ndim, random_image_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aTKq2j0cdDjb",
+    "outputId": "6be42057-20b9-4faf-d79d-8b65c42cc27e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([3, 224, 224]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor_ofownsize = torch.rand(size=(5,10,10))\n",
+    "random_tensor_ofownsize.ndim, random_tensor_ofownsize.shape\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IyhDdj-Pd6nC",
+    "outputId": "43e5e334-6d4d-4b67-f87d-7d364c6d8c67"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([5, 10, 10]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "UOJW08uOert_"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero = torch.zeros(size=(3, 4))\n",
+    "zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGvXtaXyefie",
+    "outputId": "d40d3e28-8667-4d2f-8b62-f0829c6162ad"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero*random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OyUkUPkDe0uH",
+    "outputId": "26c2e4be-36ba-4c6c-9a90-2704ec135828"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones = torch.ones(size=(3, 4))\n",
+    "ones\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "y_Ac62Aqe82G",
+    "outputId": "291de5d9-b9df-49de-c9d1-d098e3e9f4d8"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TvGOA9odfIEO",
+    "outputId": "45949ef4-6649-4b6c-d6af-2d4bfb8de832"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones*zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "--pTyge-fI-8",
+    "outputId": "c4d9bb7e-829b-43db-e2db-b1a2d64e61f0"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qDcc7Z36fSJF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "one_to_ten = torch.arange(start = 1, end = 11, step = 1)\n",
+    "one_to_ten"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w3CZB4zUfR1s",
+    "outputId": "197fcba1-da0a-4b4a-ed11-3974bd6c01aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ten_zeros = torch.zeros_like(one_to_ten)\n",
+    "ten_zeros"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WZh99BwVfRy8",
+    "outputId": "51ef8bfb-6fa0-4099-ff66-b97d65b2ddea"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tensor Veri Türleri\n"
+   ],
+   "metadata": {
+    "id": "pGGhgsbUgqbW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor = torch.tensor([3.0, 6.0,9.0], dtype = None, device = None, requires_grad = False)\n",
+    "float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JORJl4XkfRsx",
+    "outputId": "71114171-0f49-481f-b6fc-6cb48e2fb895"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3., 6., 9.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6wOPPwGyfRLn",
+    "outputId": "f23776a1-b682-404a-9f67-d5bcb0402666"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor = float_32_tensor.type(torch.float16)\n",
+    "float_16_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tFsHCvmZfOYe",
+    "outputId": "d3aa305a-7591-47f5-97fd-61bff60b44bd"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float16"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 31
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TQiCGTPuwq0q",
+    "outputId": "98750fce-1ca3-4889-e269-8b753efdea96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor = torch.tensor([3, 6, 9], dtype = torch.int32)\n",
+    "int_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5hlrLvGUw5D_",
+    "outputId": "41d890a0-9aee-446c-d906-631ce2ab0995"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9], dtype=torch.int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ihApD9u3xTNW",
+    "outputId": "d295eed0-6996-4e0f-8502-ff4b55cd1373"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(0,100,10)"
+   ],
+   "metadata": {
+    "id": "utKhlb_KxWDQ"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p78D74E9Rj7Y",
+    "outputId": "781a1614-a900-41f5-9e5d-358f0b2390aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.min()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4BcSs5NeRkcj",
+    "outputId": "3f24a8dc-58e9-4a5f-9834-e85856a34f9d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.max()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hinqvXVLRm4q",
+    "outputId": "5c7d8a53-3913-4ac1-bba3-5ba8ff68250a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 38
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mean(x.type(torch.float32))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k7okc0_vRpnB",
+    "outputId": "91e5494f-dc57-417c-ea4d-25dbc547c893"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.type(torch.float32).mean()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "29QcDTjHRq10",
+    "outputId": "62937c6c-78e0-49f2-dde3-1543ee8f7907"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wlpY_G_sbdKF",
+    "outputId": "475d8258-af65-4011-a258-b93d4d8142d4"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(450)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmax()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GT6HJzwhbk4n",
+    "outputId": "2e455c20-c322-4bcf-d07c-1259d3ccefc6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmin()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "egL3oi2Mb19P",
+    "outputId": "f71fb32f-6338-44a3-b377-75bea0a3ab54"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p2U8DZKib3DP",
+    "outputId": "b9f613b9-74e9-45f4-ed01-05babb6a6793"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[9]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24qBFlGYcABe",
+    "outputId": "5813cfcb-7f63-4bd7-ee46-f95ccbfda939"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(1, 10)\n",
+    "x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0GPOxEzkcBHO",
+    "outputId": "aefbd903-4f4c-4d2c-c90f-eccd682fe018"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped = x.reshape(1,9)\n",
+    "x_reshaped, x_reshaped.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "spmRgQjwddgp",
+    "outputId": "85a7c55c-2909-4ea2-fc68-386dddc65742"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), torch.Size([1, 9]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped.view(1,9)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tH2ahWGydqqP",
+    "outputId": "65d92263-4fc4-434a-c06d-c5e08436f7fe"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked = torch.stack([x, x, x, x], dim = 1)\n",
+    "x_stacked"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jgCeJcaud_-1",
+    "outputId": "7f293a37-6ef1-43b6-aee5-9d6d91c94f9e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XhJHIK6cfPse",
+    "outputId": "06c47b89-3a9e-453e-bcc3-00cbcb0b8b49"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=1)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ej2c3Xxzf0tq",
+    "outputId": "94024061-eb37-446d-c4a8-e4d16cb6de81"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 52
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4DJYo1a0f5M0",
+    "outputId": "efca2b47-1b14-44de-9a9a-2c83629d153f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 53
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=-2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J4iEjn2ah2HL",
+    "outputId": "22395593-7c16-4162-beae-dd2bbe7bda35"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 55
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "tensor = torch.tensor([1, 2, 3])\n",
+    "tensor = tensor - 10\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cFfiD7Nth7Z_",
+    "outputId": "1139e1f8-fc1a-46ca-d636-f2bc4fd2eef6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-9, -8, -7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mul(tensor, 10)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dyA7BM_GHhqE",
+    "outputId": "0e3b9671-d9e8-4a32-87bb-59bc05986142"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-90, -80, -70])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.sub(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "owtUsZ1KNegI",
+    "outputId": "189b7b23-0041-4e09-b991-cd209a48506a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-109, -108, -107])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.add(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K5STXlQONsyc",
+    "outputId": "00cbb79a-0a1d-4e21-86ec-5c91c37a2d01"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([91, 92, 93])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.divide(tensor, 2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xqMGnzIUNvp0",
+    "outputId": "c894cf3e-f148-45f8-cfc8-d78740735306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-4.5000, -4.0000, -3.5000])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(tensor, tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ruGzKpV8NyBc",
+    "outputId": "fddb63bf-006f-48b6-ae28-287fbcda8bc5"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8GS3r9yTeGfD",
+    "outputId": "c80b12ac-30b5-4f3d-c38c-9e41ba511b0e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QmuYHqXTemC0",
+    "outputId": "402fe3ba-70b5-4bb2-c83b-254db84ff810"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 622 µs, sys: 0 ns, total: 622 µs\n",
+      "Wall time: 516 µs\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "torch.matmul(tensor,tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dGr1fzdNepd8",
+    "outputId": "97bd6c91-bc25-4b38-cdf5-f22dcdef243e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 424 µs, sys: 998 µs, total: 1.42 ms\n",
+      "Wall time: 1.43 ms\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.rand(3,2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pGYDoK2gevfo",
+    "outputId": "2c8783d5-0453-47c5-c7ed-af10d25d6989"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5999, 0.0073],\n",
+       "        [0.9321, 0.3026],\n",
+       "        [0.3463, 0.3872]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(torch.rand(3,2), torch.rand(2,3))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KGBGQoB8e2DP",
+    "outputId": "4c2ef361-a2d0-41ee-c328-3992cbbc138d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.3528, 0.1893, 0.0714],\n",
+       "        [1.2791, 0.7110, 0.2563],\n",
+       "        [0.8812, 0.4553, 0.1803]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch"
+   ],
+   "metadata": {
+    "id": "ib8DMtkBe_LJ"
+   },
+   "execution_count": 1,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.rand(2,9)"
+   ],
+   "metadata": {
+    "id": "nJo8ZBdrQY1b"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wi6oRv4MQfgf",
+    "outputId": "55c99f55-31f6-4cf5-ba4e-19a47c3a0167"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5894, 0.4391, 0.2018, 0.5417, 0.3844, 0.3592, 0.9209, 0.9269, 0.0681],\n",
+       "        [0.0746, 0.1740, 0.6821, 0.6890, 0.0999, 0.7444, 0.2391, 0.4625, 0.8302]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "y=torch.randn(2,3,5)\n",
+    "y"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zpx8myAUQgoc",
+    "outputId": "07756d70-56bd-437c-c74e-9aecc1a77311"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[ 1.5552, -0.4877,  0.5175, -1.7958, -0.6187],\n",
+       "         [-0.3359, -1.9710,  0.0112, -1.7578, -1.5295],\n",
+       "         [ 0.0932,  1.4079,  0.9108,  0.3328, -0.6978]],\n",
+       "\n",
+       "        [[-0.9406, -1.0809, -0.2595,  0.1282,  1.6605],\n",
+       "         [ 1.1624,  1.0902,  1.7092, -0.2842, -1.3780],\n",
+       "         [-0.1534, -1.2795, -0.5495,  0.9902,  0.1822]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original = torch.rand(size=(224,224,3))\n",
+    "x_original"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s4U-X9bJQnWe",
+    "outputId": "657a7a76-962c-4b41-a76b-902d0482266c"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[0.4549, 0.6809, 0.2118],\n",
+       "         [0.4824, 0.9008, 0.8741],\n",
+       "         [0.1715, 0.1757, 0.1845],\n",
+       "         ...,\n",
+       "         [0.8741, 0.6594, 0.2610],\n",
+       "         [0.0092, 0.1984, 0.1955],\n",
+       "         [0.4236, 0.4182, 0.0251]],\n",
+       "\n",
+       "        [[0.9174, 0.1661, 0.5852],\n",
+       "         [0.1837, 0.2351, 0.3810],\n",
+       "         [0.3726, 0.4808, 0.8732],\n",
+       "         ...,\n",
+       "         [0.6794, 0.0554, 0.9202],\n",
+       "         [0.0864, 0.8750, 0.3558],\n",
+       "         [0.8445, 0.9759, 0.4934]],\n",
+       "\n",
+       "        [[0.1600, 0.2635, 0.7194],\n",
+       "         [0.9488, 0.3405, 0.3647],\n",
+       "         [0.6683, 0.5168, 0.9592],\n",
+       "         ...,\n",
+       "         [0.0521, 0.0140, 0.2445],\n",
+       "         [0.3596, 0.3999, 0.2730],\n",
+       "         [0.5926, 0.9877, 0.7784]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[0.4794, 0.5635, 0.3764],\n",
+       "         [0.9124, 0.6094, 0.5059],\n",
+       "         [0.4528, 0.4447, 0.5021],\n",
+       "         ...,\n",
+       "         [0.0089, 0.4816, 0.8727],\n",
+       "         [0.2173, 0.6296, 0.2347],\n",
+       "         [0.2028, 0.9931, 0.7201]],\n",
+       "\n",
+       "        [[0.3116, 0.6459, 0.4703],\n",
+       "         [0.0148, 0.2345, 0.7149],\n",
+       "         [0.8393, 0.5804, 0.6691],\n",
+       "         ...,\n",
+       "         [0.2105, 0.9460, 0.2696],\n",
+       "         [0.5918, 0.9295, 0.2616],\n",
+       "         [0.2537, 0.7819, 0.4700]],\n",
+       "\n",
+       "        [[0.6654, 0.1200, 0.5841],\n",
+       "         [0.9147, 0.5522, 0.6529],\n",
+       "         [0.1799, 0.5276, 0.5415],\n",
+       "         ...,\n",
+       "         [0.7536, 0.4346, 0.8793],\n",
+       "         [0.3793, 0.1750, 0.7792],\n",
+       "         [0.9266, 0.8325, 0.9974]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted=x_original.permute(2, 0, 1)\n",
+    "print(x_original.shape)\n",
+    "print(x_permuted.shape)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DD19_zvbQzHo",
+    "outputId": "1d64ce1b-eb48-47e3-90b6-7f1340e7f2b2"
+   },
+   "execution_count": 9,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "torch.Size([224, 224, 3])\n",
+      "torch.Size([3, 224, 224])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NnPmMk4ZRF7w",
+    "outputId": "2cd5da7f-4a23-4a76-8c4a-bb982113f2a4"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z0ylNoAARgTo",
+    "outputId": "ddca0298-cddf-4048-9b71-a791655e5bed"
+   },
+   "execution_count": 11,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]=0.989"
+   ],
+   "metadata": {
+    "id": "RXw0xXsDRi4L"
+   },
+   "execution_count": 13,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1sFdV6wzRo3f",
+    "outputId": "1cf87d2c-6d88-453a-d136-0f625a2800f1"
+   },
+   "execution_count": 14,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xTX-hx2SR1wp",
+    "outputId": "0d4908c4-c3bc-44e3-8ec6-1487104cc209"
+   },
+   "execution_count": 15,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x=torch.arange(1,10).reshape(1,3,3)\n",
+    "x, x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mZomOe7gR4Q8",
+    "outputId": "0b3c922f-ec11-46de-b8a5-9f9533d866ad"
+   },
+   "execution_count": 18,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[[1, 2, 3],\n",
+       "          [4, 5, 6],\n",
+       "          [7, 8, 9]]]),\n",
+       " torch.Size([1, 3, 3]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3y7v4SQvSBs1",
+    "outputId": "8c53307d-e628-404d-db66-56c6bdffab7c"
+   },
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [4, 5, 6],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hf9uG4xLSNya",
+    "outputId": "3075bc42-9ffa-426b-8a86-95628ffcd824"
+   },
+   "execution_count": 21,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zA4G2Se4SRB3",
+    "outputId": "324312d2-ed0a-49eb-f81f-e904e53992fe"
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(1)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][2][2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mwy3zmKKSdbk",
+    "outputId": "d35172c3-b099-40a6-ddf1-a453c2adfa44"
+   },
+   "execution_count": 23,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[:,1,1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fE3nCM1KS7XT",
+    "outputId": "01f5d755-9737-4235-9f73-dce89ff6ba16"
+   },
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([5])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,0,:]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "luNDINKNTTxp",
+    "outputId": "091195ef-2f71-4602-e95f-529a69193150"
+   },
+   "execution_count": 25,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,:,2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KG8A4xbfThCL",
+    "outputId": "5866bc41-9241-4619-be7b-e9206b3f80ab"
+   },
+   "execution_count": 26,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import numpy as np"
+   ],
+   "metadata": {
+    "id": "CZ3PX0qlTwHJ"
+   },
+   "execution_count": 27,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array = np.arange(1.0, 8.0)"
+   ],
+   "metadata": {
+    "id": "UOBeTumiT3Lf"
+   },
+   "execution_count": 28,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RzcO32E9UCQl",
+    "outputId": "430def24-c42c-461f-e5e7-398544c695d3"
+   },
+   "execution_count": 29,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 4., 5., 6., 7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.from_numpy(array)\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JJIL0q1DUC6O",
+    "outputId": "8a3b1d7c-4482-4d32-f34f-9212d9d3a177"
+   },
+   "execution_count": 32,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1., 2., 3., 4., 5., 6., 7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array[3]=11.0"
+   ],
+   "metadata": {
+    "id": "j3Ce6q3DUIEK"
+   },
+   "execution_count": 33,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dc_BCVdjUsCc",
+    "outputId": "65537325-8b11-4f36-fc73-e56f30d6a036"
+   },
+   "execution_count": 34,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3., 11.,  5.,  6.,  7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VG1e_eITUta2",
+    "outputId": "a26c5198-23b6-4a6d-d73a-ba20cd9782b8"
+   },
+   "execution_count": 35,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1.,  2.,  3., 11.,  5.,  6.,  7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.ones(7)\n",
+    "tensor, tensor.dtype\n",
+    "numpy_tensor = tensor.numpy()\n",
+    "numpy_tensor, numpy_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Swt8JF8vUuev",
+    "outputId": "c9e5bf6a-6d2c-41d6-8327-366867ffdd2d"
+   },
+   "execution_count": 37,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(array([1., 1., 1., 1., 1., 1., 1.], dtype=float32), dtype('float32'))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "random_tensor_A = torch.rand(3,4)\n",
+    "random_tensor_B = torch.rand(3,4)\n",
+    "print(random_tensor_A)\n",
+    "print(random_tensor_B)\n",
+    "print(random_tensor_A == random_tensor_B)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGcagTteVFTD",
+    "outputId": "49405790-08e7-4210-b7f1-f00b904c7eb9"
+   },
+   "execution_count": 38,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.9870, 0.6636, 0.6873, 0.8863],\n",
+      "        [0.8386, 0.4169, 0.3587, 0.0265],\n",
+      "        [0.2981, 0.6025, 0.5652, 0.5840]])\n",
+      "tensor([[0.9821, 0.3481, 0.0913, 0.4940],\n",
+      "        [0.7495, 0.4387, 0.9582, 0.8659],\n",
+      "        [0.5064, 0.6919, 0.0809, 0.9771]])\n",
+      "tensor([[False, False, False, False],\n",
+      "        [False, False, False, False],\n",
+      "        [False, False, False, False]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "RANDOM_SEED = 42\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_C = torch.rand(3,4)\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_D = torch.rand(3,4)\n",
+    "print(random_tensor_C)\n",
+    "print(random_tensor_D)\n",
+    "print(random_tensor_C == random_tensor_D)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HznyXyEaWjLM",
+    "outputId": "25956434-01b6-4059-9054-c9978884ddc1"
+   },
+   "execution_count": 46,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[True, True, True, True],\n",
+      "        [True, True, True, True],\n",
+      "        [True, True, True, True]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!nvidia-smi"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vltPTh0YXJSt",
+    "outputId": "807af6dc-a9ca-4301-ec32-b688dbde8be8"
+   },
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Thu May 23 02:57:59 2024       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   60C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|  No running processes found                                                           |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "torch.cuda.is_available()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L6mMyPDyYh1j",
+    "outputId": "279c5dd8-c2a8-4fbd-f321-2f5d7c6e90e6"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "oOdiYa7ZYytx",
+    "outputId": "d73b04fc-8963-4826-9722-08d118d5ab91"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.cuda.device_count()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vOdsazLqZFM5",
+    "outputId": "8189cd6a-9017-4663-a652-3e15c517d9c3"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.tensor([1,2,3], device = \"cpu\")\n",
+    "print(tensor, tensor.device)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cdik9Vw3ZMv0",
+    "outputId": "044a68fd-83a1-409d-8e3b-655142ca0270"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([1, 2, 3]) cpu\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu = tensor.to(device)\n",
+    "tensor_on_gpu"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zmp835rrZp-z",
+    "outputId": "37fa3413-18a3-47bf-ae51-5b36ff85a3ef"
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3], device='cuda:0')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu.numpy()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 159
+    },
+    "id": "jhriaa8uZ1yM",
+    "outputId": "bc5a3226-1a12-4fea-8769-a44f21cdc323"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "TypeError",
+     "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-e0c96c7436fd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtensor_on_gpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_cpu = tensor_on_gpu.cpu().numpy()"
+   ],
+   "metadata": {
+    "id": "LHGXK3GgaOzL"
+   },
+   "execution_count": 12,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "id": "j-El4LlCajfq"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlamalar veya yanlış yorumlamalar için sorumluluk kabul etmiyoruz.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/1-Tools/notebook.ipynb b/translations/vi/2-Regression/1-Tools/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/vi/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb b/translations/vi/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
new file mode 100644
index 000000000..b12e3c64e
--- /dev/null
+++ b/translations/vi/2-Regression/1-Tools/solution/R/lesson_1-R.ipynb
@@ -0,0 +1,448 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_1-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "c18d3bd0bd8ae3878597e89dcd1fa5c1",
+   "translation_date": "2025-09-06T13:45:46+00:00",
+   "source_file": "2-Regression/1-Tools/solution/R/lesson_1-R.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "YJUHCXqK57yz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Giới thiệu về Hồi quy - Bài học 1\n",
+    "\n",
+    "#### Đặt vấn đề vào bối cảnh\n",
+    "\n",
+    "✅ Có nhiều phương pháp hồi quy khác nhau, và việc bạn chọn phương pháp nào phụ thuộc vào câu trả lời mà bạn đang tìm kiếm. Nếu bạn muốn dự đoán chiều cao có thể xảy ra của một người ở một độ tuổi nhất định, bạn sẽ sử dụng `hồi quy tuyến tính`, vì bạn đang tìm kiếm một **giá trị số**. Nếu bạn muốn khám phá liệu một loại ẩm thực có nên được coi là thuần chay hay không, bạn đang tìm kiếm một **phân loại danh mục**, vì vậy bạn sẽ sử dụng `hồi quy logistic`. Bạn sẽ học thêm về hồi quy logistic sau này. Hãy suy nghĩ một chút về một số câu hỏi bạn có thể đặt ra với dữ liệu, và phương pháp nào trong số này sẽ phù hợp hơn.\n",
+    "\n",
+    "Trong phần này, bạn sẽ làm việc với [một tập dữ liệu nhỏ về bệnh tiểu đường](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html). Hãy tưởng tượng rằng bạn muốn thử nghiệm một phương pháp điều trị cho bệnh nhân tiểu đường. Các mô hình Machine Learning có thể giúp bạn xác định bệnh nhân nào sẽ phản ứng tốt hơn với phương pháp điều trị, dựa trên sự kết hợp của các biến số. Ngay cả một mô hình hồi quy rất cơ bản, khi được trực quan hóa, cũng có thể cho thấy thông tin về các biến số giúp bạn tổ chức các thử nghiệm lâm sàng lý thuyết của mình.\n",
+    "\n",
+    "Vậy thì, hãy bắt đầu nhiệm vụ này nhé!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/encouRage.jpg\"\n",
+    "   width=\"630\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n",
+    "\n",
+    "<!--![Tác phẩm nghệ thuật của \\@allison_horst](../../../../../../2-Regression/1-Tools/images/encouRage.jpg)<br>Tác phẩm nghệ thuật của @allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "LWNNzfqd6feZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Tải bộ công cụ của chúng ta\n",
+    "\n",
+    "Để thực hiện nhiệm vụ này, chúng ta sẽ cần các gói sau:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) là một [bộ sưu tập các gói R](https://www.tidyverse.org/packages) được thiết kế để làm cho khoa học dữ liệu trở nên nhanh hơn, dễ dàng hơn và thú vị hơn!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) là một [bộ sưu tập các gói](https://www.tidymodels.org/packages/) dành cho mô hình hóa và học máy.\n",
+    "\n",
+    "Bạn có thể cài đặt chúng bằng lệnh sau:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\"))`\n",
+    "\n",
+    "Đoạn mã dưới đây sẽ kiểm tra xem bạn đã có các gói cần thiết để hoàn thành mô-đun này chưa và sẽ cài đặt chúng cho bạn nếu thiếu.\n"
+   ],
+   "metadata": {
+    "id": "FIo2YhO26wI9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse, tidymodels)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "id": "cIA9fz9v7Dss",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2df7073b-86b2-4b32-cb86-0da605a0dc11"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bây giờ, hãy tải các gói tuyệt vời này và làm cho chúng khả dụng trong phiên làm việc R hiện tại của chúng ta. (Đây chỉ là minh họa, `pacman::p_load()` đã làm điều đó cho bạn)\n"
+   ],
+   "metadata": {
+    "id": "gpO_P_6f9WUG"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# load the core Tidyverse packages\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# load the core Tidymodels packages\r\n",
+    "library(tidymodels)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NLMycgG-9ezO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Bộ dữ liệu tiểu đường\n",
+    "\n",
+    "Trong bài tập này, chúng ta sẽ áp dụng kỹ năng hồi quy bằng cách dự đoán trên bộ dữ liệu tiểu đường. [Bộ dữ liệu tiểu đường](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt) bao gồm `442 mẫu` dữ liệu liên quan đến bệnh tiểu đường, với 10 biến đặc trưng dự đoán: `tuổi`, `giới tính`, `chỉ số khối cơ thể`, `huyết áp trung bình`, và `sáu phép đo huyết thanh máu`, cùng với một biến kết quả `y`: một thước đo định lượng về mức độ tiến triển của bệnh sau một năm kể từ thời điểm ban đầu.\n",
+    "\n",
+    "|Số lượng quan sát|442|\n",
+    "|------------------|:---|\n",
+    "|Số lượng biến dự đoán|10 cột đầu tiên là các biến dự đoán dạng số|\n",
+    "|Kết quả/Mục tiêu|Cột thứ 11 là thước đo định lượng về mức độ tiến triển của bệnh sau một năm kể từ thời điểm ban đầu|\n",
+    "|Thông tin về biến dự đoán|- tuổi tính theo năm\n",
+    "||- giới tính\n",
+    "||- bmi chỉ số khối cơ thể\n",
+    "||- bp huyết áp trung bình\n",
+    "||- s1 tc, tổng cholesterol trong huyết thanh\n",
+    "||- s2 ldl, lipoprotein mật độ thấp\n",
+    "||- s3 hdl, lipoprotein mật độ cao\n",
+    "||- s4 tch, tổng cholesterol / HDL\n",
+    "||- s5 ltg, có thể là logarit của mức triglycerides trong huyết thanh\n",
+    "||- s6 glu, mức đường trong máu|\n",
+    "\n",
+    "> 🎓 Hãy nhớ rằng đây là học có giám sát, và chúng ta cần một mục tiêu 'y' được đặt tên.\n",
+    "\n",
+    "Trước khi bạn có thể thao tác dữ liệu với R, bạn cần nhập dữ liệu vào bộ nhớ của R hoặc tạo một kết nối để R có thể truy cập dữ liệu từ xa.\n",
+    "\n",
+    "> Gói [readr](https://readr.tidyverse.org/), một phần của Tidyverse, cung cấp cách nhanh chóng và thân thiện để đọc dữ liệu dạng hình chữ nhật vào R.\n",
+    "\n",
+    "Bây giờ, hãy tải bộ dữ liệu tiểu đường từ URL nguồn này: <https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html>\n",
+    "\n",
+    "Ngoài ra, chúng ta sẽ kiểm tra dữ liệu bằng cách sử dụng `glimpse()` và hiển thị 5 hàng đầu tiên bằng `slice()`.\n",
+    "\n",
+    "Trước khi tiếp tục, hãy giới thiệu một điều mà bạn sẽ thường xuyên gặp trong mã R 🥁🥁: toán tử pipe `%>%`\n",
+    "\n",
+    "Toán tử pipe (`%>%`) thực hiện các thao tác theo trình tự logic bằng cách chuyển một đối tượng vào một hàm hoặc biểu thức gọi. Bạn có thể nghĩ toán tử pipe như đang nói \"và sau đó\" trong mã của bạn.\n"
+   ],
+   "metadata": {
+    "id": "KM6iXLH996Cl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import the data set\r\n",
+    "diabetes <- read_table2(file = \"https://www4.stat.ncsu.edu/~boos/var.select/diabetes.rwrite1.txt\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Get a glimpse and dimensions of the data\r\n",
+    "glimpse(diabetes)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Select the first 5 rows of the data\r\n",
+    "diabetes %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Z1geAMhM-bSP"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`glimpse()` cho chúng ta thấy rằng dữ liệu này có 442 hàng và 11 cột, với tất cả các cột đều thuộc kiểu dữ liệu `double`.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "> glimpse() và slice() là các hàm trong [`dplyr`](https://dplyr.tidyverse.org/). Dplyr, một phần của Tidyverse, là một ngữ pháp thao tác dữ liệu cung cấp một tập hợp các động từ nhất quán giúp bạn giải quyết các thách thức phổ biến trong việc thao tác dữ liệu.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Bây giờ chúng ta đã có dữ liệu, hãy thu hẹp lại một đặc điểm (`bmi`) để làm mục tiêu cho bài tập này. Điều này sẽ yêu cầu chúng ta chọn các cột mong muốn. Vậy làm thế nào để thực hiện điều này?\n",
+    "\n",
+    "[`dplyr::select()`](https://dplyr.tidyverse.org/reference/select.html) cho phép chúng ta *chọn* (và tùy chọn đổi tên) các cột trong một khung dữ liệu.\n"
+   ],
+   "metadata": {
+    "id": "UwjVT1Hz-c3Z"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select predictor feature `bmi` and outcome `y`\r\n",
+    "diabetes_select <- diabetes %>% \r\n",
+    "  select(c(bmi, y))\r\n",
+    "\r\n",
+    "# Print the first 5 rows\r\n",
+    "diabetes_select %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RDY1oAKI-m80"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Dữ liệu huấn luyện và kiểm tra\n",
+    "\n",
+    "Trong học máy có giám sát, việc *chia* dữ liệu thành hai tập hợp là một thực hành phổ biến; một tập (thường lớn hơn) để huấn luyện mô hình, và một tập nhỏ hơn \"giữ lại\" để kiểm tra xem mô hình hoạt động như thế nào.\n",
+    "\n",
+    "Bây giờ chúng ta đã có dữ liệu sẵn sàng, chúng ta có thể xem liệu máy có thể giúp xác định một cách chia hợp lý giữa các số trong tập dữ liệu này hay không. Chúng ta có thể sử dụng gói [rsample](https://tidymodels.github.io/rsample/), một phần của khung làm việc Tidymodels, để tạo một đối tượng chứa thông tin về *cách* chia dữ liệu, và sau đó sử dụng hai hàm rsample khác để trích xuất các tập huấn luyện và kiểm tra đã được tạo:\n"
+   ],
+   "metadata": {
+    "id": "SDk668xK-tc3"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\r\n",
+    "# Split 67% of the data for training and the rest for tesing\r\n",
+    "diabetes_split <- diabetes_select %>% \r\n",
+    "  initial_split(prop = 0.67)\r\n",
+    "\r\n",
+    "# Extract the resulting train and test sets\r\n",
+    "diabetes_train <- training(diabetes_split)\r\n",
+    "diabetes_test <- testing(diabetes_split)\r\n",
+    "\r\n",
+    "# Print the first 3 rows of the training set\r\n",
+    "diabetes_train %>% \r\n",
+    "  slice(1:10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "EqtHx129-1h-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Huấn luyện mô hình hồi quy tuyến tính với Tidymodels\n",
+    "\n",
+    "Bây giờ chúng ta đã sẵn sàng để huấn luyện mô hình!\n",
+    "\n",
+    "Trong Tidymodels, bạn định nghĩa mô hình bằng cách sử dụng `parsnip()` và chỉ định ba khái niệm:\n",
+    "\n",
+    "-   **Loại mô hình** phân biệt các mô hình như hồi quy tuyến tính, hồi quy logistic, mô hình cây quyết định, và nhiều loại khác.\n",
+    "\n",
+    "-   **Chế độ mô hình** bao gồm các tùy chọn phổ biến như hồi quy và phân loại; một số loại mô hình hỗ trợ cả hai chế độ này, trong khi một số chỉ có một chế độ duy nhất.\n",
+    "\n",
+    "-   **Công cụ mô hình** là công cụ tính toán sẽ được sử dụng để khớp mô hình. Thường thì đây là các gói R, chẳng hạn như **`\"lm\"`** hoặc **`\"ranger\"`**\n",
+    "\n",
+    "Thông tin về mô hình này được lưu trong một đặc tả mô hình, vì vậy hãy cùng xây dựng một đặc tả!\n"
+   ],
+   "metadata": {
+    "id": "sBOS-XhB-6v7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- \r\n",
+    "  # Type\r\n",
+    "  linear_reg() %>% \r\n",
+    "  # Engine\r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  # Mode\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the model specification\r\n",
+    "lm_spec"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "20OwEw20--t3"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Sau khi một mô hình đã được *xác định*, mô hình có thể được `ước lượng` hoặc `huấn luyện` bằng cách sử dụng hàm [`fit()`](https://parsnip.tidymodels.org/reference/fit.html), thường sử dụng một công thức và một số dữ liệu.\n",
+    "\n",
+    "`y ~ .` có nghĩa là chúng ta sẽ khớp `y` làm giá trị dự đoán/mục tiêu, được giải thích bởi tất cả các biến dự đoán/đặc trưng, tức là `.` (trong trường hợp này, chúng ta chỉ có một biến dự đoán: `bmi`).\n"
+   ],
+   "metadata": {
+    "id": "_oDHs89k_CJj"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Build a linear model specification\r\n",
+    "lm_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>%\r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Train a linear regression model\r\n",
+    "lm_mod <- lm_spec %>% \r\n",
+    "  fit(y ~ ., data = diabetes_train)\r\n",
+    "\r\n",
+    "# Print the model\r\n",
+    "lm_mod"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YlsHqd-q_GJQ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Từ kết quả đầu ra của mô hình, chúng ta có thể thấy các hệ số được học trong quá trình huấn luyện. Chúng đại diện cho các hệ số của đường hồi quy tốt nhất, giúp giảm thiểu tổng lỗi giữa biến thực tế và biến dự đoán.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "## 5. Dự đoán trên tập kiểm tra\n",
+    "\n",
+    "Bây giờ chúng ta đã huấn luyện xong một mô hình, chúng ta có thể sử dụng nó để dự đoán sự tiến triển của bệnh y cho tập dữ liệu kiểm tra bằng [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Điều này sẽ được sử dụng để vẽ đường phân cách giữa các nhóm dữ liệu.\n"
+   ],
+   "metadata": {
+    "id": "kGZ22RQj_Olu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\r\n",
+    "predictions <- lm_mod %>% \r\n",
+    "  predict(new_data = diabetes_test)\r\n",
+    "\r\n",
+    "# Print out some of the predictions\r\n",
+    "predictions %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "nXHbY7M2_aao"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woohoo! 💃🕺 Chúng ta vừa huấn luyện một mô hình và sử dụng nó để tạo dự đoán!\n",
+    "\n",
+    "Khi tạo dự đoán, quy ước của tidymodels luôn là tạo ra một tibble/data frame kết quả với các tên cột được chuẩn hóa. Điều này giúp dễ dàng kết hợp dữ liệu gốc và các dự đoán trong một định dạng có thể sử dụng cho các thao tác tiếp theo như vẽ biểu đồ.\n",
+    "\n",
+    "`dplyr::bind_cols()` kết hợp các data frame theo cột một cách hiệu quả.\n"
+   ],
+   "metadata": {
+    "id": "R_JstwUY_bIs"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Combine the predictions and the original test set\r\n",
+    "results <- diabetes_test %>% \r\n",
+    "  bind_cols(predictions)\r\n",
+    "\r\n",
+    "\r\n",
+    "results %>% \r\n",
+    "  slice(1:5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "RybsMJR7_iI8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 6. Hiển thị kết quả mô hình\n",
+    "\n",
+    "Bây giờ, đã đến lúc xem kết quả một cách trực quan 📈. Chúng ta sẽ tạo một biểu đồ phân tán cho tất cả các giá trị `y` và `bmi` của tập kiểm tra, sau đó sử dụng các dự đoán để vẽ một đường ở vị trí phù hợp nhất, giữa các nhóm dữ liệu của mô hình.\n",
+    "\n",
+    "R có nhiều hệ thống để tạo biểu đồ, nhưng `ggplot2` là một trong những hệ thống thanh lịch và linh hoạt nhất. Điều này cho phép bạn tạo biểu đồ bằng cách **kết hợp các thành phần độc lập**.\n"
+   ],
+   "metadata": {
+    "id": "XJbYbMZW_n_s"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plot\r\n",
+    "theme_set(theme_light())\r\n",
+    "# Create a scatter plot\r\n",
+    "results %>% \r\n",
+    "  ggplot(aes(x = bmi)) +\r\n",
+    "  # Add a scatter plot\r\n",
+    "  geom_point(aes(y = y), size = 1.6) +\r\n",
+    "  # Add a line plot\r\n",
+    "  geom_line(aes(y = .pred), color = \"blue\", size = 1.5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R9tYp3VW_sTn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "✅ Hãy suy nghĩ một chút về điều đang diễn ra ở đây. Một đường thẳng đang chạy qua nhiều điểm dữ liệu nhỏ, nhưng nó thực sự đang làm gì? Bạn có thấy cách mà bạn có thể sử dụng đường này để dự đoán vị trí của một điểm dữ liệu mới, chưa được thấy trước đó, trong mối quan hệ với trục y của biểu đồ không? Hãy thử diễn đạt bằng lời về ứng dụng thực tế của mô hình này.\n",
+    "\n",
+    "Chúc mừng bạn, bạn đã xây dựng mô hình hồi quy tuyến tính đầu tiên, tạo ra một dự đoán từ nó, và hiển thị nó trên biểu đồ!\n"
+   ],
+   "metadata": {
+    "id": "zrPtHIxx_tNI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/1-Tools/solution/notebook.ipynb b/translations/vi/2-Regression/1-Tools/solution/notebook.ipynb
new file mode 100644
index 000000000..cc1e127ab
--- /dev/null
+++ b/translations/vi/2-Regression/1-Tools/solution/notebook.ipynb
@@ -0,0 +1,675 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nhập các thư viện cần thiết\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn import datasets, linear_model, model_selection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tải tập dữ liệu bệnh tiểu đường, chia thành dữ liệu `X` và các đặc trưng `y`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 10)\n",
+      "[ 0.03807591  0.05068012  0.06169621  0.02187239 -0.0442235  -0.03482076\n",
+      " -0.04340085 -0.00259226  0.01990749 -0.01764613]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X, y = datasets.load_diabetes(return_X_y=True)\n",
+    "print(X.shape)\n",
+    "print(X[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chọn chỉ một tính năng để nhắm mục tiêu cho bài tập này\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Selecting the 3rd feature\n",
+    "X = X[:, 2]\n",
+    "print(X.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(442, 1)\n",
+      "[[ 0.06169621]\n",
+      " [-0.05147406]\n",
+      " [ 0.04445121]\n",
+      " [-0.01159501]\n",
+      " [-0.03638469]\n",
+      " [-0.04069594]\n",
+      " [-0.04716281]\n",
+      " [-0.00189471]\n",
+      " [ 0.06169621]\n",
+      " [ 0.03906215]\n",
+      " [-0.08380842]\n",
+      " [ 0.01750591]\n",
+      " [-0.02884001]\n",
+      " [-0.00189471]\n",
+      " [-0.02560657]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [ 0.01211685]\n",
+      " [-0.0105172 ]\n",
+      " [-0.01806189]\n",
+      " [-0.05686312]\n",
+      " [-0.02237314]\n",
+      " [-0.00405033]\n",
+      " [ 0.06061839]\n",
+      " [ 0.03582872]\n",
+      " [-0.01267283]\n",
+      " [-0.07734155]\n",
+      " [ 0.05954058]\n",
+      " [-0.02129532]\n",
+      " [-0.00620595]\n",
+      " [ 0.04445121]\n",
+      " [-0.06548562]\n",
+      " [ 0.12528712]\n",
+      " [-0.05039625]\n",
+      " [-0.06332999]\n",
+      " [-0.03099563]\n",
+      " [ 0.02289497]\n",
+      " [ 0.01103904]\n",
+      " [ 0.07139652]\n",
+      " [ 0.01427248]\n",
+      " [-0.00836158]\n",
+      " [-0.06764124]\n",
+      " [-0.0105172 ]\n",
+      " [-0.02345095]\n",
+      " [ 0.06816308]\n",
+      " [-0.03530688]\n",
+      " [-0.01159501]\n",
+      " [-0.0730303 ]\n",
+      " [-0.04177375]\n",
+      " [ 0.01427248]\n",
+      " [-0.00728377]\n",
+      " [ 0.0164281 ]\n",
+      " [-0.00943939]\n",
+      " [-0.01590626]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.04931844]\n",
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+      " [ 0.01211685]\n",
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+      " [ 0.00996123]\n",
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+      " [ 0.07139652]\n",
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+      " [-0.04824063]\n",
+      " [-0.02560657]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.00457217]\n",
+      " [-0.06440781]\n",
+      " [-0.01698407]\n",
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+      " [ 0.00996123]\n",
+      " [ 0.08864151]\n",
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+      " [-0.06440781]\n",
+      " [ 0.01750591]\n",
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+      " [ 0.02828403]\n",
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+      " [-0.03207344]\n",
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+      " [ 0.04552903]\n",
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+      " [-0.03207344]\n",
+      " [ 0.00457217]\n",
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+      " [ 0.11019775]\n",
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+      " [-0.02129532]\n",
+      " [-0.0105172 ]\n",
+      " [-0.04716281]\n",
+      " [ 0.00457217]\n",
+      " [ 0.01750591]\n",
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+      " [ 0.0347509 ]\n",
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+      " [-0.00836158]\n",
+      " [-0.06117437]\n",
+      " [-0.00189471]\n",
+      " [-0.06225218]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.09618619]\n",
+      " [-0.06979687]\n",
+      " [-0.02129532]\n",
+      " [-0.05362969]\n",
+      " [ 0.0433734 ]\n",
+      " [ 0.05630715]\n",
+      " [-0.0816528 ]\n",
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+      " [-0.0191397 ]\n",
+      " [-0.06656343]\n",
+      " [-0.06009656]\n",
+      " [ 0.06924089]\n",
+      " [ 0.05954058]\n",
+      " [-0.02668438]\n",
+      " [-0.02021751]\n",
+      " [-0.046085  ]\n",
+      " [ 0.07139652]\n",
+      " [-0.07949718]\n",
+      " [ 0.00996123]\n",
+      " [-0.03854032]\n",
+      " [ 0.01966154]\n",
+      " [ 0.02720622]\n",
+      " [-0.00836158]\n",
+      " [-0.01590626]\n",
+      " [ 0.00457217]\n",
+      " [-0.04285156]\n",
+      " [ 0.00564998]\n",
+      " [-0.03530688]\n",
+      " [ 0.02397278]\n",
+      " [-0.01806189]\n",
+      " [ 0.04229559]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00297252]\n",
+      " [-0.06656343]\n",
+      " [-0.01267283]\n",
+      " [-0.04177375]\n",
+      " [-0.03099563]\n",
+      " [-0.00512814]\n",
+      " [-0.05901875]\n",
+      " [ 0.0250506 ]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00349435]\n",
+      " [ 0.05415152]\n",
+      " [-0.04500719]\n",
+      " [-0.05794093]\n",
+      " [-0.05578531]\n",
+      " [ 0.00133873]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00672779]\n",
+      " [ 0.04660684]\n",
+      " [ 0.02612841]\n",
+      " [ 0.04552903]\n",
+      " [ 0.04013997]\n",
+      " [-0.01806189]\n",
+      " [ 0.01427248]\n",
+      " [ 0.03690653]\n",
+      " [ 0.00349435]\n",
+      " [-0.07087468]\n",
+      " [-0.03315126]\n",
+      " [ 0.09403057]\n",
+      " [ 0.03582872]\n",
+      " [ 0.03151747]\n",
+      " [-0.06548562]\n",
+      " [-0.04177375]\n",
+      " [-0.03961813]\n",
+      " [-0.03854032]\n",
+      " [-0.02560657]\n",
+      " [-0.02345095]\n",
+      " [-0.06656343]\n",
+      " [ 0.03259528]\n",
+      " [-0.046085  ]\n",
+      " [-0.02991782]\n",
+      " [-0.01267283]\n",
+      " [-0.01590626]\n",
+      " [ 0.07139652]\n",
+      " [-0.03099563]\n",
+      " [ 0.00026092]\n",
+      " [ 0.03690653]\n",
+      " [ 0.03906215]\n",
+      " [-0.01482845]\n",
+      " [ 0.00672779]\n",
+      " [-0.06871905]\n",
+      " [-0.00943939]\n",
+      " [ 0.01966154]\n",
+      " [ 0.07462995]\n",
+      " [-0.00836158]\n",
+      " [-0.02345095]\n",
+      " [-0.046085  ]\n",
+      " [ 0.05415152]\n",
+      " [-0.03530688]\n",
+      " [-0.03207344]\n",
+      " [-0.0816528 ]\n",
+      " [ 0.04768465]\n",
+      " [ 0.06061839]\n",
+      " [ 0.05630715]\n",
+      " [ 0.09834182]\n",
+      " [ 0.05954058]\n",
+      " [ 0.03367309]\n",
+      " [ 0.05630715]\n",
+      " [-0.06548562]\n",
+      " [ 0.16085492]\n",
+      " [-0.05578531]\n",
+      " [-0.02452876]\n",
+      " [-0.03638469]\n",
+      " [-0.00836158]\n",
+      " [-0.04177375]\n",
+      " [ 0.12744274]\n",
+      " [-0.07734155]\n",
+      " [ 0.02828403]\n",
+      " [-0.02560657]\n",
+      " [-0.06225218]\n",
+      " [-0.00081689]\n",
+      " [ 0.08864151]\n",
+      " [-0.03207344]\n",
+      " [ 0.03043966]\n",
+      " [ 0.00888341]\n",
+      " [ 0.00672779]\n",
+      " [-0.02021751]\n",
+      " [-0.02452876]\n",
+      " [-0.01159501]\n",
+      " [ 0.02612841]\n",
+      " [-0.05901875]\n",
+      " [-0.03638469]\n",
+      " [-0.02452876]\n",
+      " [ 0.01858372]\n",
+      " [-0.0902753 ]\n",
+      " [-0.00512814]\n",
+      " [-0.05255187]\n",
+      " [-0.02237314]\n",
+      " [-0.02021751]\n",
+      " [-0.0547075 ]\n",
+      " [-0.00620595]\n",
+      " [-0.01698407]\n",
+      " [ 0.05522933]\n",
+      " [ 0.07678558]\n",
+      " [ 0.01858372]\n",
+      " [-0.02237314]\n",
+      " [ 0.09295276]\n",
+      " [-0.03099563]\n",
+      " [ 0.03906215]\n",
+      " [-0.06117437]\n",
+      " [-0.00836158]\n",
+      " [-0.0374625 ]\n",
+      " [-0.01375064]\n",
+      " [ 0.07355214]\n",
+      " [-0.02452876]\n",
+      " [ 0.03367309]\n",
+      " [ 0.0347509 ]\n",
+      " [-0.03854032]\n",
+      " [-0.03961813]\n",
+      " [-0.00189471]\n",
+      " [-0.03099563]\n",
+      " [-0.046085  ]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06492964]\n",
+      " [ 0.04013997]\n",
+      " [-0.02345095]\n",
+      " [ 0.05307371]\n",
+      " [ 0.04013997]\n",
+      " [-0.02021751]\n",
+      " [ 0.01427248]\n",
+      " [-0.03422907]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00457217]\n",
+      " [ 0.03043966]\n",
+      " [ 0.0519959 ]\n",
+      " [ 0.06169621]\n",
+      " [-0.00728377]\n",
+      " [ 0.00564998]\n",
+      " [ 0.05415152]\n",
+      " [-0.00836158]\n",
+      " [ 0.114509  ]\n",
+      " [ 0.06708527]\n",
+      " [-0.05578531]\n",
+      " [ 0.03043966]\n",
+      " [-0.02560657]\n",
+      " [ 0.10480869]\n",
+      " [-0.00620595]\n",
+      " [-0.04716281]\n",
+      " [-0.04824063]\n",
+      " [ 0.08540807]\n",
+      " [-0.01267283]\n",
+      " [-0.03315126]\n",
+      " [-0.00728377]\n",
+      " [-0.01375064]\n",
+      " [ 0.05954058]\n",
+      " [ 0.02181716]\n",
+      " [ 0.01858372]\n",
+      " [-0.01159501]\n",
+      " [-0.00297252]\n",
+      " [ 0.01750591]\n",
+      " [-0.02991782]\n",
+      " [-0.02021751]\n",
+      " [-0.05794093]\n",
+      " [ 0.06061839]\n",
+      " [-0.04069594]\n",
+      " [-0.07195249]\n",
+      " [-0.05578531]\n",
+      " [ 0.04552903]\n",
+      " [-0.00943939]\n",
+      " [-0.03315126]\n",
+      " [ 0.04984027]\n",
+      " [-0.08488624]\n",
+      " [ 0.00564998]\n",
+      " [ 0.02073935]\n",
+      " [-0.00728377]\n",
+      " [ 0.10480869]\n",
+      " [-0.02452876]\n",
+      " [-0.00620595]\n",
+      " [-0.03854032]\n",
+      " [ 0.13714305]\n",
+      " [ 0.17055523]\n",
+      " [ 0.00241654]\n",
+      " [ 0.03798434]\n",
+      " [-0.05794093]\n",
+      " [-0.00943939]\n",
+      " [-0.02345095]\n",
+      " [-0.0105172 ]\n",
+      " [-0.03422907]\n",
+      " [-0.00297252]\n",
+      " [ 0.06816308]\n",
+      " [ 0.00996123]\n",
+      " [ 0.00241654]\n",
+      " [-0.03854032]\n",
+      " [ 0.02612841]\n",
+      " [-0.08919748]\n",
+      " [ 0.06061839]\n",
+      " [-0.02884001]\n",
+      " [-0.02991782]\n",
+      " [-0.0191397 ]\n",
+      " [-0.04069594]\n",
+      " [ 0.01535029]\n",
+      " [-0.02452876]\n",
+      " [ 0.00133873]\n",
+      " [ 0.06924089]\n",
+      " [-0.06979687]\n",
+      " [-0.02991782]\n",
+      " [-0.046085  ]\n",
+      " [ 0.01858372]\n",
+      " [ 0.00133873]\n",
+      " [-0.03099563]\n",
+      " [-0.00405033]\n",
+      " [ 0.01535029]\n",
+      " [ 0.02289497]\n",
+      " [ 0.04552903]\n",
+      " [-0.04500719]\n",
+      " [-0.03315126]\n",
+      " [ 0.097264  ]\n",
+      " [ 0.05415152]\n",
+      " [ 0.12313149]\n",
+      " [-0.08057499]\n",
+      " [ 0.09295276]\n",
+      " [-0.05039625]\n",
+      " [-0.01159501]\n",
+      " [-0.0277622 ]\n",
+      " [ 0.05846277]\n",
+      " [ 0.08540807]\n",
+      " [-0.00081689]\n",
+      " [ 0.00672779]\n",
+      " [ 0.00888341]\n",
+      " [ 0.08001901]\n",
+      " [ 0.07139652]\n",
+      " [-0.02452876]\n",
+      " [-0.0547075 ]\n",
+      " [-0.03638469]\n",
+      " [ 0.0164281 ]\n",
+      " [ 0.07786339]\n",
+      " [-0.03961813]\n",
+      " [ 0.01103904]\n",
+      " [-0.04069594]\n",
+      " [-0.03422907]\n",
+      " [ 0.00564998]\n",
+      " [ 0.08864151]\n",
+      " [-0.03315126]\n",
+      " [-0.05686312]\n",
+      " [-0.03099563]\n",
+      " [ 0.05522933]\n",
+      " [-0.06009656]\n",
+      " [ 0.00133873]\n",
+      " [-0.02345095]\n",
+      " [-0.07410811]\n",
+      " [ 0.01966154]\n",
+      " [-0.01590626]\n",
+      " [-0.01590626]\n",
+      " [ 0.03906215]\n",
+      " [-0.0730303 ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Reshaping to get a 2D array\n",
+    "X = X.reshape(-1, 1)\n",
+    "print(X.shape)\n",
+    "print(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chia dữ liệu huấn luyện và kiểm tra cho cả `X` và `y`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.33)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chọn mô hình và huấn luyện nó với dữ liệu đào tạo\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = linear_model.LinearRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sử dụng dữ liệu kiểm tra để dự đoán một dòng\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hiển thị kết quả trong một biểu đồ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test, y_test,  color='black')\n",
+    "plt.plot(X_test, y_pred, color='blue', linewidth=3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "16ff1a974f6e4348e869e4a7d366b86a",
+   "translation_date": "2025-09-06T13:39:56+00:00",
+   "source_file": "2-Regression/1-Tools/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/2-Data/notebook.ipynb b/translations/vi/2-Regression/2-Data/notebook.ipynb
new file mode 100644
index 000000000..e1c99098e
--- /dev/null
+++ b/translations/vi/2-Regression/2-Data/notebook.ipynb
@@ -0,0 +1,46 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3",
+   "language": "python"
+  },
+  "coopTranslator": {
+   "original_hash": "1b2ab303ac6c604a34c6ca7a49077fc7",
+   "translation_date": "2025-09-06T13:46:08+00:00",
+   "source_file": "2-Regression/2-Data/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/2-Data/solution/R/lesson_2-R.ipynb b/translations/vi/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
new file mode 100644
index 000000000..560e03fb6
--- /dev/null
+++ b/translations/vi/2-Regression/2-Data/solution/R/lesson_2-R.ipynb
@@ -0,0 +1,673 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_2-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "f3c335f9940cfd76528b3ef918b9b342",
+   "translation_date": "2025-09-06T13:56:29+00:00",
+   "source_file": "2-Regression/2-Data/solution/R/lesson_2-R.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Xây dựng mô hình hồi quy: chuẩn bị và trực quan hóa dữ liệu\n",
+    "\n",
+    "## **Hồi quy tuyến tính cho bí ngô - Bài học 2**\n",
+    "#### Giới thiệu\n",
+    "\n",
+    "Bây giờ bạn đã có các công cụ cần thiết để bắt đầu xây dựng mô hình học máy với Tidymodels và Tidyverse, bạn đã sẵn sàng để bắt đầu đặt câu hỏi về dữ liệu của mình. Khi làm việc với dữ liệu và áp dụng các giải pháp ML, điều rất quan trọng là phải hiểu cách đặt câu hỏi đúng để khai thác tối đa tiềm năng của tập dữ liệu.\n",
+    "\n",
+    "Trong bài học này, bạn sẽ học:\n",
+    "\n",
+    "-   Cách chuẩn bị dữ liệu của bạn để xây dựng mô hình.\n",
+    "\n",
+    "-   Cách sử dụng `ggplot2` để trực quan hóa dữ liệu.\n",
+    "\n",
+    "Câu hỏi bạn cần trả lời sẽ quyết định loại thuật toán ML mà bạn sẽ sử dụng. Và chất lượng của câu trả lời bạn nhận được sẽ phụ thuộc rất nhiều vào bản chất của dữ liệu.\n",
+    "\n",
+    "Hãy cùng xem điều này qua một bài tập thực hành.\n",
+    "\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/unruly_data.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Tác phẩm nghệ thuật của \\@allison_horst](../../../../../../2-Regression/2-Data/images/unruly_data.jpg)<br>Tác phẩm nghệ thuật của \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "Pg5aexcOPqAZ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Nhập dữ liệu về bí ngô và triệu hồi Tidyverse\n",
+    "\n",
+    "Chúng ta sẽ cần các gói sau để phân tích và xử lý bài học này:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) là một [bộ sưu tập các gói R](https://www.tidyverse.org/packages) được thiết kế để làm cho khoa học dữ liệu trở nên nhanh hơn, dễ dàng hơn và thú vị hơn!\n",
+    "\n",
+    "Bạn có thể cài đặt chúng bằng cách:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\"))`\n",
+    "\n",
+    "Đoạn mã dưới đây kiểm tra xem bạn đã có các gói cần thiết để hoàn thành module này chưa và sẽ cài đặt chúng cho bạn nếu thiếu.\n"
+   ],
+   "metadata": {
+    "id": "dc5WhyVdXAjR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "pacman::p_load(tidyverse)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "GqPYUZgfXOBt"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bây giờ, hãy khởi động một số gói và tải [dữ liệu](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/data/US-pumpkins.csv) được cung cấp cho bài học này!\n"
+   ],
+   "metadata": {
+    "id": "kvjDTPDSXRr2"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n =50)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VMri-t2zXqgD"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Một `glimpse()` nhanh chóng cho thấy rằng có các giá trị trống và sự kết hợp giữa chuỗi ký tự (`chr`) và dữ liệu số (`dbl`). Cột `Date` thuộc kiểu ký tự và còn có một cột kỳ lạ tên là `Package`, nơi dữ liệu là sự pha trộn giữa `sacks`, `bins` và các giá trị khác. Thực tế, dữ liệu này khá lộn xộn 😤.\n",
+    "\n",
+    "Thực tế, không thường xuyên bạn nhận được một tập dữ liệu hoàn toàn sẵn sàng để sử dụng nhằm tạo ra một mô hình ML ngay lập tức. Nhưng đừng lo, trong bài học này, bạn sẽ học cách chuẩn bị một tập dữ liệu thô bằng cách sử dụng các thư viện R tiêu chuẩn 🧑‍🔧. Bạn cũng sẽ học các kỹ thuật khác nhau để trực quan hóa dữ liệu. 📈📊\n",
+    "<br>\n",
+    "\n",
+    "> Một lời nhắc lại: Toán tử pipe (`%>%`) thực hiện các thao tác theo trình tự logic bằng cách chuyển một đối tượng vào một hàm hoặc biểu thức gọi. Bạn có thể nghĩ toán tử pipe như đang nói \"và sau đó\" trong mã của bạn.\n"
+   ],
+   "metadata": {
+    "id": "REWcIv9yX29v"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Kiểm tra dữ liệu bị thiếu\n",
+    "\n",
+    "Một trong những vấn đề phổ biến nhất mà các nhà khoa học dữ liệu phải đối mặt là dữ liệu không đầy đủ hoặc bị thiếu. R biểu thị các giá trị bị thiếu hoặc không xác định bằng một giá trị đặc biệt: `NA` (Not Available).\n",
+    "\n",
+    "Vậy làm thế nào để chúng ta biết rằng khung dữ liệu chứa các giá trị bị thiếu?\n",
+    "<br>\n",
+    "-   Một cách đơn giản là sử dụng hàm cơ bản của R `anyNA`, hàm này trả về các đối tượng logic `TRUE` hoặc `FALSE`.\n"
+   ],
+   "metadata": {
+    "id": "Zxfb3AM5YbUe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  anyNA()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "G--DQutAYltj"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tuyệt vời, có vẻ như đang thiếu một số dữ liệu! Đây là một điểm tốt để bắt đầu.\n",
+    "\n",
+    "-   Một cách khác là sử dụng hàm `is.na()` để xác định các phần tử bị thiếu trong từng cột với giá trị logic `TRUE`.\n"
+   ],
+   "metadata": {
+    "id": "mU-7-SB6YokF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "W-DxDOR4YxSW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Được rồi, đã hoàn thành công việc nhưng với một khung dữ liệu lớn như thế này, việc xem xét từng hàng và cột riêng lẻ sẽ không hiệu quả và gần như không thể😴.\n",
+    "\n",
+    "-   Một cách trực quan hơn là tính tổng số giá trị bị thiếu cho mỗi cột:\n"
+   ],
+   "metadata": {
+    "id": "xUWxipKYY0o7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "pumpkins %>% \n",
+    "  is.na() %>% \n",
+    "  colSums()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZRBWV6P9ZArL"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tốt hơn nhiều! Có dữ liệu bị thiếu, nhưng có lẽ điều đó sẽ không ảnh hưởng đến nhiệm vụ hiện tại. Hãy xem phân tích tiếp theo sẽ mang lại điều gì.\n",
+    "\n",
+    "> Bên cạnh các bộ gói và hàm tuyệt vời, R còn có tài liệu hướng dẫn rất tốt. Ví dụ, sử dụng `help(colSums)` hoặc `?colSums` để tìm hiểu thêm về hàm này.\n"
+   ],
+   "metadata": {
+    "id": "9gv-crB6ZD1Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Dplyr: Ngữ pháp của việc xử lý dữ liệu\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_wrangling.png\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Tác phẩm nghệ thuật của \\@allison_horst](../../../../../../2-Regression/2-Data/images/dplyr_wrangling.png)<br/>Tác phẩm nghệ thuật của \\@allison_horst-->\n"
+   ],
+   "metadata": {
+    "id": "o4jLY5-VZO2C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`dplyr`](https://dplyr.tidyverse.org/), một gói trong Tidyverse, là một ngữ pháp xử lý dữ liệu cung cấp một tập hợp các động từ nhất quán giúp bạn giải quyết các thách thức xử lý dữ liệu phổ biến nhất. Trong phần này, chúng ta sẽ khám phá một số động từ của dplyr!  \n",
+    "<br>  \n"
+   ],
+   "metadata": {
+    "id": "i5o33MQBZWWw"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::select()\n",
+    "\n",
+    "`select()` là một hàm trong gói `dplyr` giúp bạn chọn các cột để giữ lại hoặc loại bỏ.\n",
+    "\n",
+    "Để làm cho khung dữ liệu của bạn dễ làm việc hơn, hãy loại bỏ một số cột bằng cách sử dụng `select()`, chỉ giữ lại các cột bạn cần.\n",
+    "\n",
+    "Ví dụ, trong bài tập này, phân tích của chúng ta sẽ liên quan đến các cột `Package`, `Low Price`, `High Price` và `Date`. Hãy chọn các cột này.\n"
+   ],
+   "metadata": {
+    "id": "x3VGMAGBZiUr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(Package, `Low Price`, `High Price`, Date)\n",
+    "\n",
+    "\n",
+    "# Print data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "F_FgxQnVZnM0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::mutate()\n",
+    "\n",
+    "`mutate()` là một hàm trong gói `dplyr`, giúp bạn tạo hoặc chỉnh sửa các cột, đồng thời giữ nguyên các cột hiện có.\n",
+    "\n",
+    "Cấu trúc chung của `mutate` là:\n",
+    "\n",
+    "`data %>%   mutate(new_column_name = what_it_contains)`\n",
+    "\n",
+    "Hãy thử sử dụng `mutate` với cột `Date` bằng cách thực hiện các thao tác sau:\n",
+    "\n",
+    "1. Chuyển đổi các ngày (hiện tại thuộc kiểu ký tự) sang định dạng tháng (đây là ngày tháng kiểu Mỹ, nên định dạng là `MM/DD/YYYY`).\n",
+    "\n",
+    "2. Trích xuất tháng từ các ngày và lưu vào một cột mới.\n",
+    "\n",
+    "Trong R, gói [lubridate](https://lubridate.tidyverse.org/) giúp làm việc với dữ liệu ngày giờ dễ dàng hơn. Vì vậy, hãy sử dụng `dplyr::mutate()`, `lubridate::mdy()`, `lubridate::month()` để đạt được các mục tiêu trên. Chúng ta có thể loại bỏ cột `Date` vì sẽ không cần sử dụng nó nữa trong các thao tác tiếp theo.\n"
+   ],
+   "metadata": {
+    "id": "2KKo0Ed9Z1VB"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load lubridate\n",
+    "library(lubridate)\n",
+    "\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  # Convert the Date column to a date object\n",
+    "  mutate(Date = mdy(Date)) %>% \n",
+    "  # Extract month from Date\n",
+    "  mutate(Month = month(Date)) %>% \n",
+    "  # Drop Date column\n",
+    "  select(-Date)\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5joszIVSZ6xe"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woohoo! 🤩\n",
+    "\n",
+    "Tiếp theo, hãy tạo một cột mới `Price`, đại diện cho giá trung bình của một quả bí ngô. Bây giờ, hãy lấy trung bình của các cột `Low Price` và `High Price` để điền vào cột Price mới.  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "nIgLjNMCZ-6Y"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new column Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(Price = (`Low Price` + `High Price`)/2)\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Zo0BsqqtaJw2"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Yeees!💪\n",
+    "\n",
+    "\"Nhưng khoan đã!\", bạn sẽ nói sau khi lướt qua toàn bộ tập dữ liệu với `View(pumpkins)`, \"Có điều gì đó kỳ lạ ở đây!\"🤔\n",
+    "\n",
+    "Nếu bạn nhìn vào cột `Package`, bí ngô được bán theo nhiều cách khác nhau. Một số được bán theo đơn vị `1 1/9 bushel`, một số theo đơn vị `1/2 bushel`, một số theo từng quả bí ngô, một số theo cân nặng, và một số được đóng trong các hộp lớn với kích thước khác nhau.\n",
+    "\n",
+    "Hãy kiểm tra điều này:\n"
+   ],
+   "metadata": {
+    "id": "p77WZr-9aQAR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Verify the distinct observations in Package column\n",
+    "pumpkins %>% \n",
+    "  distinct(Package)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "XISGfh0IaUy6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tuyệt vời!👏\n",
+    "\n",
+    "Bí ngô dường như rất khó để cân một cách nhất quán, vì vậy hãy lọc chúng bằng cách chỉ chọn những quả bí ngô có chuỗi *bushel* trong cột `Package` và đặt chúng vào một khung dữ liệu mới `new_pumpkins`.\n"
+   ],
+   "metadata": {
+    "id": "7sMjiVujaZxY"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::filter() và stringr::str_detect()\n",
+    "\n",
+    "[`dplyr::filter()`](https://dplyr.tidyverse.org/reference/filter.html): tạo một tập con của dữ liệu chỉ chứa **các hàng** thỏa mãn điều kiện của bạn, trong trường hợp này là các hàng có từ *bushel* trong cột `Package`.\n",
+    "\n",
+    "[stringr::str_detect()](https://stringr.tidyverse.org/reference/str_detect.html): phát hiện sự có mặt hoặc vắng mặt của một mẫu trong chuỗi.\n",
+    "\n",
+    "Gói [`stringr`](https://github.com/tidyverse/stringr) cung cấp các hàm đơn giản cho các thao tác chuỗi thông dụng.\n"
+   ],
+   "metadata": {
+    "id": "L8Qfcs92ageF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Retain only pumpkins with \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "       filter(str_detect(Package, \"bushel\"))\n",
+    "\n",
+    "# Get the dimensions of the new data\n",
+    "dim(new_pumpkins)\n",
+    "\n",
+    "# View a few rows of the new data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "hy_SGYREampd"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bạn có thể thấy rằng chúng tôi đã thu hẹp xuống còn khoảng 415 dòng dữ liệu chứa bí ngô theo giạ.🤩\n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "VrDwF031avlR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### dplyr::case_when()\n",
+    "\n",
+    "**Nhưng khoan đã! Còn một việc nữa cần làm**\n",
+    "\n",
+    "Bạn có nhận thấy rằng số lượng giạ thay đổi theo từng hàng không? Bạn cần chuẩn hóa giá để hiển thị giá theo mỗi giạ, không phải theo 1 1/9 hay 1/2 giạ. Đã đến lúc thực hiện một số phép toán để chuẩn hóa.\n",
+    "\n",
+    "Chúng ta sẽ sử dụng hàm [`case_when()`](https://dplyr.tidyverse.org/reference/case_when.html) để *biến đổi* cột Price dựa trên một số điều kiện. `case_when` cho phép bạn vector hóa nhiều câu lệnh `if_else()`.\n"
+   ],
+   "metadata": {
+    "id": "mLpw2jH4a0tx"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(Price = case_when(\n",
+    "    str_detect(Package, \"1 1/9\") ~ Price/(1 + 1/9),\n",
+    "    str_detect(Package, \"1/2\") ~ Price/(1/2),\n",
+    "    TRUE ~ Price))\n",
+    "\n",
+    "# View the first few rows of the data\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 30)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "P68kLVQmbM6I"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bây giờ, chúng ta có thể phân tích giá theo đơn vị dựa trên đo lường bằng giạ. Tuy nhiên, tất cả việc nghiên cứu về giạ bí ngô này cho thấy rằng việc `hiểu rõ bản chất của dữ liệu` của bạn là điều `rất quan trọng`!\n",
+    "\n",
+    "> ✅ Theo [The Spruce Eats](https://www.thespruceeats.com/how-much-is-a-bushel-1389308), trọng lượng của một giạ phụ thuộc vào loại nông sản, vì đây là một đơn vị đo thể tích. \"Ví dụ, một giạ cà chua được cho là nặng 56 pound... Lá và rau xanh chiếm nhiều không gian hơn nhưng lại nhẹ hơn, vì vậy một giạ rau chân vịt chỉ nặng 20 pound.\" Thật là phức tạp! Chúng ta không cần phải bận tâm đến việc chuyển đổi từ giạ sang pound, thay vào đó hãy định giá theo giạ. Tuy nhiên, tất cả việc nghiên cứu về giạ bí ngô này cho thấy rằng việc hiểu rõ bản chất của dữ liệu của bạn là điều rất quan trọng!\n",
+    ">\n",
+    "> ✅ Bạn có để ý rằng bí ngô được bán theo nửa giạ có giá rất đắt không? Bạn có thể tìm ra lý do tại sao không? Gợi ý: những quả bí ngô nhỏ đắt hơn rất nhiều so với những quả lớn, có lẽ vì có nhiều quả nhỏ hơn trong một giạ, do không gian bị chiếm bởi một quả bí ngô lớn rỗng ruột.\n"
+   ],
+   "metadata": {
+    "id": "pS2GNPagbSdb"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bây giờ cuối cùng, chỉ để thêm phần thú vị 💁‍♀️, hãy di chuyển cột Month lên vị trí đầu tiên, tức là `trước` cột `Package`.\n",
+    "\n",
+    "`dplyr::relocate()` được sử dụng để thay đổi vị trí các cột.\n"
+   ],
+   "metadata": {
+    "id": "qql1SowfbdnP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create a new data frame new_pumpkins\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(Month, .before = Package)\n",
+    "\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 7)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "JJ1x6kw8bixF"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Làm tốt lắm!👌 Giờ bạn đã có một tập dữ liệu sạch sẽ, gọn gàng để xây dựng mô hình hồi quy mới của mình!  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "y8TJ0Za_bn5Y"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 4. Trực quan hóa dữ liệu với ggplot2\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/data-visualization.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Đồ họa thông tin bởi Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Đồ họa thông tin bởi Dasani Madipalli](../../../../../../2-Regression/2-Data/images/data-visualization.png){width=\"600\"}-->\n",
+    "\n",
+    "Có một câu nói *thông thái* như sau:\n",
+    "\n",
+    "> \"Biểu đồ đơn giản đã mang lại nhiều thông tin hơn cho nhà phân tích dữ liệu so với bất kỳ công cụ nào khác.\" --- John Tukey\n",
+    "\n",
+    "Một phần vai trò của nhà khoa học dữ liệu là thể hiện chất lượng và bản chất của dữ liệu mà họ đang làm việc. Để làm điều này, họ thường tạo ra các hình ảnh trực quan thú vị, hoặc các biểu đồ, đồ thị, và sơ đồ, thể hiện các khía cạnh khác nhau của dữ liệu. Bằng cách này, họ có thể trực quan hóa các mối quan hệ và khoảng trống mà nếu không sẽ khó phát hiện.\n",
+    "\n",
+    "Các hình ảnh trực quan cũng có thể giúp xác định kỹ thuật học máy phù hợp nhất với dữ liệu. Ví dụ, một biểu đồ phân tán có xu hướng theo một đường thẳng cho thấy dữ liệu là ứng viên tốt cho bài toán hồi quy tuyến tính.\n",
+    "\n",
+    "R cung cấp một số hệ thống để tạo biểu đồ, nhưng [`ggplot2`](https://ggplot2.tidyverse.org/index.html) là một trong những hệ thống thanh lịch và linh hoạt nhất. `ggplot2` cho phép bạn tạo biểu đồ bằng cách **kết hợp các thành phần độc lập**.\n",
+    "\n",
+    "Hãy bắt đầu với một biểu đồ phân tán đơn giản cho các cột Price và Month.\n",
+    "\n",
+    "Trong trường hợp này, chúng ta sẽ bắt đầu với [`ggplot()`](https://ggplot2.tidyverse.org/reference/ggplot.html), cung cấp một tập dữ liệu và ánh xạ thẩm mỹ (với [`aes()`](https://ggplot2.tidyverse.org/reference/aes.html)) sau đó thêm các lớp (như [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html)) cho biểu đồ phân tán.\n"
+   ],
+   "metadata": {
+    "id": "mYSH6-EtbvNa"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set a theme for the plots\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Create a scatter plot\n",
+    "p <- ggplot(data = new_pumpkins, aes(x = Price, y = Month))\n",
+    "p + geom_point()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "g2YjnGeOcLo4"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Đây có phải là một biểu đồ hữu ích không 🤷? Có điều gì khiến bạn ngạc nhiên về nó không?\n",
+    "\n",
+    "Nó không thực sự hữu ích vì tất cả những gì nó làm chỉ là hiển thị dữ liệu của bạn dưới dạng một loạt các điểm trong một tháng nhất định.  \n",
+    "<br>\n"
+   ],
+   "metadata": {
+    "id": "Ml7SDCLQcPvE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Làm thế nào để chúng ta làm cho nó hữu ích?**\n",
+    "\n",
+    "Để biểu đồ hiển thị dữ liệu hữu ích, bạn thường cần nhóm dữ liệu theo một cách nào đó. Ví dụ, trong trường hợp của chúng ta, tìm giá trung bình của bí ngô theo từng tháng sẽ cung cấp thêm thông tin chi tiết về các mẫu ẩn trong dữ liệu. Điều này dẫn chúng ta đến một công cụ khác của **dplyr**:\n",
+    "\n",
+    "#### `dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "Việc tính toán tổng hợp theo nhóm trong R có thể được thực hiện dễ dàng bằng cách sử dụng\n",
+    "\n",
+    "`dplyr::group_by() %>% summarize()`\n",
+    "\n",
+    "-   `dplyr::group_by()` thay đổi đơn vị phân tích từ toàn bộ tập dữ liệu sang các nhóm riêng lẻ, chẳng hạn như theo từng tháng.\n",
+    "\n",
+    "-   `dplyr::summarize()` tạo một khung dữ liệu mới với một cột cho mỗi biến nhóm và một cột cho mỗi thống kê tóm tắt mà bạn đã chỉ định.\n",
+    "\n",
+    "Ví dụ, chúng ta có thể sử dụng `dplyr::group_by() %>% summarize()` để nhóm các bí ngô thành các nhóm dựa trên cột **Month** và sau đó tìm **giá trung bình** cho từng tháng.\n"
+   ],
+   "metadata": {
+    "id": "jMakvJZIcVkh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "6kVSUa2Bcilf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Ngắn gọn!✨\n",
+    "\n",
+    "Các đặc điểm phân loại như tháng được biểu diễn tốt hơn bằng biểu đồ cột 📊. Các lớp chịu trách nhiệm cho biểu đồ cột là `geom_bar()` và `geom_col()`. Tham khảo `?geom_bar` để tìm hiểu thêm.\n",
+    "\n",
+    "Hãy cùng tạo một cái nhé!\n"
+   ],
+   "metadata": {
+    "id": "Kds48GUBcj3W"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the average price of pumpkins per month then plot a bar chart\r\n",
+    "new_pumpkins %>%\r\n",
+    "  group_by(Month) %>% \r\n",
+    "  summarise(mean_price = mean(Price)) %>% \r\n",
+    "  ggplot(aes(x = Month, y = mean_price)) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"Pumpkin Price\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "VNbU1S3BcrxO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Đây là một biểu đồ trực quan hóa dữ liệu hữu ích hơn! Dường như nó chỉ ra rằng giá cao nhất của bí ngô xảy ra vào tháng 9 và tháng 10. Điều này có đúng với mong đợi của bạn không? Tại sao hoặc tại sao không?\n",
+    "\n",
+    "Chúc mừng bạn đã hoàn thành bài học thứ hai 👏! Bạn đã chuẩn bị dữ liệu của mình để xây dựng mô hình, sau đó khám phá thêm nhiều thông tin chi tiết bằng cách sử dụng biểu đồ trực quan hóa!\n"
+   ],
+   "metadata": {
+    "id": "zDm0VOzzcuzR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/2-Data/solution/notebook.ipynb b/translations/vi/2-Regression/2-Data/solution/notebook.ipynb
new file mode 100644
index 000000000..0cde53f4d
--- /dev/null
+++ b/translations/vi/2-Regression/2-Data/solution/notebook.ipynb
@@ -0,0 +1,437 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/1/16</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>10/8/16</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    City Name Type               Package   Variety Sub Variety  Grade  \\\n",
+       "70  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "71  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "72  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "73  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "74  BALTIMORE  NaN  1 1/9 bushel cartons  PIE TYPE         NaN    NaN   \n",
+       "\n",
+       "       Date  Low Price  High Price  Mostly Low  ...  Unit of Sale Quality  \\\n",
+       "70  9/24/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "71  9/24/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "72  10/1/16       18.0        18.0        18.0  ...           NaN     NaN   \n",
+       "73  10/1/16       17.0        17.0        17.0  ...           NaN     NaN   \n",
+       "74  10/8/16       15.0        15.0        15.0  ...           NaN     NaN   \n",
+       "\n",
+       "   Condition Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  \\\n",
+       "70       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "71       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "72       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "73       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "74       NaN        NaN     NaN   NaN      N         NaN          NaN   \n",
+       "\n",
+       "    Unnamed: 25  \n",
+       "70          NaN  \n",
+       "71          NaN  \n",
+       "72          NaN  \n",
+       "73          NaN  \n",
+       "74          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "City Name            0\n",
+       "Type               406\n",
+       "Package              0\n",
+       "Variety              0\n",
+       "Sub Variety        167\n",
+       "Grade              415\n",
+       "Date                 0\n",
+       "Low Price            0\n",
+       "High Price           0\n",
+       "Mostly Low          24\n",
+       "Mostly High         24\n",
+       "Origin               0\n",
+       "Origin District    396\n",
+       "Item Size          114\n",
+       "Color              145\n",
+       "Environment        415\n",
+       "Unit of Sale       404\n",
+       "Quality            415\n",
+       "Condition          415\n",
+       "Appearance         415\n",
+       "Storage            415\n",
+       "Crop               415\n",
+       "Repack               0\n",
+       "Trans Mode         415\n",
+       "Unnamed: 24        415\n",
+       "Unnamed: 25        391\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "      Month               Package  Low Price  High Price  Price\n",
+      "70        9  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "71        9  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "72       10  1 1/9 bushel cartons      18.00        18.0  16.20\n",
+      "73       10  1 1/9 bushel cartons      17.00        17.0  15.30\n",
+      "74       10  1 1/9 bushel cartons      15.00        15.0  13.50\n",
+      "...     ...                   ...        ...         ...    ...\n",
+      "1738      9    1/2 bushel cartons      15.00        15.0  30.00\n",
+      "1739      9    1/2 bushel cartons      13.75        15.0  28.75\n",
+      "1740      9    1/2 bushel cartons      10.75        15.0  25.75\n",
+      "1741      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "1742      9    1/2 bushel cartons      12.00        12.0  24.00\n",
+      "\n",
+      "[415 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# A set of new columns for a new dataframe. Filter out nonmatching columns\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Get an average between low and high price for the base pumpkin price\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "# Convert the date to its month only\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "\n",
+    "# Create a new dataframe with this basic data\n",
+    "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
+    "\n",
+    "# Convert the price if the Package contains fractional bushel values\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n",
+    "\n",
+    "print(new_pumpkins)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0hYh4ombWf4qIt2eo0czMMsjyjv5VwO6I+FlEvAz8I3DJ+JRlZmbjJUvQPwq8UdKZkk4F1gHz68x3oaRvSvoHSf8ybWWSNkoqSSoNDw9nKMvMzKp1fOomIh6T9CHgPuBFYB8wWjPbN4BXRsRPJa0DdgCLU9a3DdgG5ZuDd1qXmZmdKNOHsRHx8Yh4XURcBLwAfLdm+o8j4qfJ4y8C0yWdlWWbZmbWnqxX3cxJvi+gfH7+b2qm/wtJSh5fkGzvh1m2aWZm7en41E3i7yWdCRwDro6Io5LeBxARHwN+D/gPkl4GRoBLI8KnZczMuihT0EfEG+u0fazq8UeBj2bZhpmZZeO/jDUzyzkHvZlZzjnozcxyzkFvZpZzDnozs5xz0JuZ5ZyD3sws5xz0ZmY556A3M8s5B72ZWc456M3Mcs5Bb2aWcw56M7Occ9CbmeWcg97MLOcy/T96SR8ArgIE3BYRt9ZMF/ARyjcO/xnwroj4RpZtdssNO/azffczjEYgoHK3lILErBm/xo9/8avb4y6eM4td16zi8tse4qtPHmlp/QKe2rIeYMxyKxfN5q6rLgTgt67/Ij8f/dW9Wk4piO9sXgfA8s27eP4nLx2fNve0Gey+fvUJtVcM9vexac0SNiwbbHv/CxKXLZ/PTRuWtrTs6lse4PHDLx5/XukfgB17h9i68wCHjo4wr6qmTrbX6BhVlm9lvQuvvXfMug9uWV+3HwsSK37jDA7+cIRDR0c4dUaBn700StRst+I1H/zSCWPl9JkFHrlxbcPtVjQaF82WbaTRmGom7fhN9LJZNBsDjepqdAza1Wg7E9036vSGT5LOAz4NXAC8BHwJeF9EPFE1zzrgP1EO+uXARyJiebN1F4vFKJVKHdU1Hm7YsZ9PPfx0W8ucUtAJL55WCHj9otl1fzisXDSbPQdfqLvOUwriFadOPyHkW6mjb3qBmy9Z2nQApe3/FSsWNA3f2pCvWDxnFle/aTHX3b2fkWO/Cr6+6QVeu+AVdfug0fZaOUaL58yqW0v1eusFZlaV9deGfMXpMwt12ysOblmf+qZhZcp4qV62kdqQr2gl7HfsHap7/FoZU1mWzaLZWG5U19+Vnk49Bu2GfaPtAOPSN5L2RESx3rQsp25eBeyOiJ9FxMvAP1K+b2y1i4FPRtnDQL+kszNssyu2736m7WXaDXkovwNNe9F+9ckjqev8+WjUDflmdYwcG2XrzgNN60rb/1b6pV6wVtq37jxwwmCu1JTWB422l6WWTo5vOyrrTwvzRiFf0WhcZNFoTDWTdvxaGVNZls2i2VhuVNd4HoNG2+lG32QJ+keBN0o6U9KplN+1z6+ZZxCo7ulnk7YxJG2UVJJUGh4ezlBWdqM5vq3toaMjTedJ2/+s/dLKtlvdXpZaJvr45nX8pB2/Vo5rlmWzaDaWu1VXo+10o4aOgz4iHgM+BNxH+bTNPqD5W5X09W2LiGJEFAcGBjpdzbgoSD3d/kSa19/XdJ60/c/aL61su9XtZalloo9vXsdP2vFr5bhmWTaLZmO5W3U12k43ash01U1EfDwiXhcRFwEvAN+tmWWIE9/ln5O0TWqXLa/9xaS5Uwrtv7hF+XxfPSsXzU5d5ykFMfe0GW3X0Te9wKY1S5rWlbb/rfTL4jmzUts3rVlC3/TCmJrS+qDR9rLU0snxbUdl/afPLNSdntZerdG4yKLRmGom7fi1MqayLJtFs7HcqK7xPAaNttONvskU9JLmJN8XUD4//zc1s9wD/HuVrQB+FBHPZdlmN9y0YSlXrFhw/Kd+9UugII15oS6eM4vvbF7X1gCoXHVz11UXjlmu8mHPdzavG/MCrHxotvv61WPCfu5pM/jO5nUn1F4x2N/X8oc7tftfkFr6IBZg1zWrxgRs5aqbDcsGufmSpQz296Gqmu666sK2t9fsGF2xYgG7rlnVdL1pH14e3LK+bj8WJFYumn18H2bNKBzfdu36H7lx7ZixUrnqptF2gYbjotmyjTQaU82kHb9WxlSWZbNoNpYb1dXoGLSr0Xa60TcdX3UDIOmfgDOBY8A1EXG/pPcBRMTHkssrPwqspXx55bsjounlNL2+6sbMbKppdNVNpuvoI+KNddo+VvU4gKuzbMPMzLLxX8aameWcg97MLOcc9GZmOeegNzPLOQe9mVnOOejNzHLOQW9mlnMOejOznHPQm5nlnIPezCznHPRmZjnnoDczyzkHvZlZzjnozcxyzkFvZpZzDnozs5zLeivBP5L0LUmPStou6ZSa6e+SNCxpX/L13mzlmplZuzoOekmDwPuBYkScBxSAS+vM+pmIOD/5ur3T7ZmZWWeynrqZBvRJmgacChzKXpKZmY2njoM+IoaAvwSeBp4DfhQR99WZ9d9IekTSZyXNT1ufpI2SSpJKw8PDnZZlZmY1spy6OQO4GDgXmAfMknRFzWz/G1gYEa8BdgF3pq0vIrZFRDEiigMDA52WZWZmNbKcunkL8FREDEfEMeBu4PXVM0TEDyPiF8nT24HXZdiemZl1IEvQPw2skHSqJAFvBh6rnkHS2VVP31E73czMJt60TheMiN2SPgt8A3gZ2Atsk/TnQCki7gHeL+kdyfQjwLuyl2xmZu1QRPS6hjGKxWKUSqVel2FmNmVI2hMRxXrT/JexZmY556A3M8s5B72ZWc456M3Mcs5Bb2aWcw56M7Occ9CbmeWcg97MLOcc9GZmOeegNzPLOQe9mVnOOejNzHLOQW9mlnMOejOznHPQm5nlnIPezCznOr7DFICkPwLeCwSwH3h3RPy8avpM4JOU7xX7Q+CdEXEwyzbzaMfeIbbuPMChoyPM6+9j05olbFg22HSaWSc8pk4+HQe9pEHg/cCrI2JE0t8ClwKfqJrtPcALEfGbki4FPgS8M0O9ubNj7xDX3b2fkWOjAAwdHeG6u/cfn542zS9M60Sj8eYxlV9ZT91MA/okTQNOBQ7VTL8YuDN5/FngzcmNxC2xdeeB4y+6ipFjo2zdeaDhNLNOeEydnDoO+ogYAv4SeBp4DvhRRNxXM9sg8Ewy/8vAj4Az661P0kZJJUml4eHhTsuacg4dHUltbzTNrBMeUyenjoNe0hmU37GfC8wDZkm6otP1RcS2iChGRHFgYKDT1Uw58/r7UtsbTTPrhMfUySnLqZu3AE9FxHBEHAPuBl5fM88QMB8gOb3zCsofylpi05ol9E0vnNDWN73ApjVLGk4z64TH1Mkpy1U3TwMrJJ0KjABvBko189wDXAk8BPwe8OWIiAzbzJ3KB2CNroLwFRI2XloZb5Y/ypK7km6kfBXNy8BeypdaXg+UIuIeSacAfw0sA44Al0bE95qtt1gsRqlU+zPDzMzSSNoTEcW60ybjG2wHvZlZexoFvf8y1sws5xz0ZmY556A3M8s5B72ZWc5Nyg9jJQ0D328y21nAP3ehnHZMxppgctblmlo3GeuajDXB5KyrWzW9MiLq/rXppAz6VkgqpX3C3CuTsSaYnHW5ptZNxromY00wOeuaDDX51I2ZWc456M3Mcm4qB/22XhdQx2SsCSZnXa6pdZOxrslYE0zOunpe05Q9R29mZq2Zyu/ozcysBQ56M7OcmxJBL+kOSYclPVrVNlvSLkmPJ9/PmAQ1/ZmkIUn7kq91Xa5pvqSvSPq2pG9J+kDS3rO+alBTr/vqFElfk/TNpK4bk/ZzJe2W9ISkz0iaMQlq+oSkp6r66vxu1VRVW0HSXklfSJ73rJ+a1NXTvpJ0UNL+ZNulpK2nWQVTJOgp33B8bU3btcD9EbEYuD953uuaAD4cEecnX1/sck0vA/85Il4NrACulvRqettXaTVBb/vqF8DvRMRvA+cDayWtoHwD+w9HxG8CL1C+wX2vawLYVNVX+7pYU8UHgMeqnveyn6rV1gW976s3JduuXDvf66yaGkEfEQ9S/n/21apvPH4nsGES1NRTEfFcRHwjefwTyi+AQXrYVw1q6qko+2nydHryFcDvUL6RPXS/r9Jq6ilJ5wDrgduT56KH/ZRW1yTW06yCKRL0KeZGxHPJ4x8Ac3tZTJX/KOmR5NRO139Fq5C0kPINX3YzSfqqpibocV8lv/bvAw4Du4AngaPJjewBnqXLP5Rqa4qISl9tTvrqw5JmdrMm4Fbgj4FfJs/PpMf9lFJXRS/7KoD7JO2RtDFp6/nrbyoH/XHJ7Ql7/s4H+J/AIsq/dj8H/LdeFCHp14G/B/4wIn5cPa1XfVWnpp73VUSMRsT5wDnABcBvdbuGWrU1SToPuI5ybf8KmA38SbfqkfR24HBE7OnWNlvRoK6e9VXiDRHxWuBtlE9TXlQ9sVevv6kc9M9LOhsg+X64x/UQEc8nL9RfArdRDo+ukjSdcqDeFRF3J8097at6NU2GvqqIiKPAV4ALgX6Vb2QP5bAd6nFNa5PTXxERvwD+iu721UrgHZIOAp+mfMrmI/S+n8bUJelTPe4rImIo+X4Y+Fyy/Z5n1VQO+sqNx0m+f76HtQDHD2LF7wKPps07QdsX8HHgsYi4pWpSz/oqraZJ0FcDkvqTx33AasqfH3yF8o3soft9Va+m71SFhCif3+1aX0XEdRFxTkQsBC4FvhwRl9PDfmpQ1xW97CtJsySdVnkMvDXZfu+zKiIm/RewnfKv98conw98D+XzhPcDjwP/B5g9CWr6a2A/8Ajlg3t2l2t6A+VfCx8B9iVf63rZVw1q6nVfvYbyDe0fofxi/NOk/TeArwFPAH8HzJwENX056atHgU8Bv97NvqqqbxXwhV73U5O6etZXSZ98M/n6FnB90t7TrIoI/wsEM7O8m8qnbszMrAUOejOznHPQm5nlnIPezCznHPRmZjnnoDczyzkHvZlZzv1/N8s9l//aWz4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "price = new_pumpkins.Price\n",
+    "month = new_pumpkins.Month\n",
+    "plt.scatter(price, month)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel(\"Pumpkin Price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "95726f0b8283628d5356a4f8eb8b4b76",
+   "translation_date": "2025-09-06T13:46:35+00:00",
+   "source_file": "2-Regression/2-Data/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/3-Linear/notebook.ipynb b/translations/vi/2-Regression/3-Linear/notebook.ipynb
new file mode 100644
index 000000000..2a2e5f4f0
--- /dev/null
+++ b/translations/vi/2-Regression/3-Linear/notebook.ipynb
@@ -0,0 +1,128 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Giá Bí Ngô\n",
+    "\n",
+    "Tải các thư viện và tập dữ liệu cần thiết. Chuyển đổi dữ liệu thành một dataframe chứa một phần dữ liệu:\n",
+    "\n",
+    "- Chỉ lấy những quả bí ngô được định giá theo giạ\n",
+    "- Chuyển đổi ngày thành tháng\n",
+    "- Tính giá trung bình giữa giá cao và giá thấp\n",
+    "- Chuyển đổi giá để phản ánh mức giá theo số lượng giạ\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Một biểu đồ phân tán cơ bản nhắc nhở chúng ta rằng chúng ta chỉ có dữ liệu tháng từ tháng Tám đến tháng Mười Hai. Chúng ta có lẽ cần thêm dữ liệu để có thể đưa ra kết luận theo cách tuyến tính.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.scatter('Month','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3-final"
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "b032d371c75279373507f003439a577e",
+   "translation_date": "2025-09-06T13:09:20+00:00",
+   "source_file": "2-Regression/3-Linear/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb b/translations/vi/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
new file mode 100644
index 000000000..8d3640228
--- /dev/null
+++ b/translations/vi/2-Regression/3-Linear/solution/R/lesson_3-R.ipynb
@@ -0,0 +1,1086 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_3-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "5015d65d61ba75a223bfc56c273aa174",
+   "translation_date": "2025-09-06T13:26:17+00:00",
+   "source_file": "2-Regression/3-Linear/solution/R/lesson_3-R.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "EgQw8osnsUV-"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Hồi quy tuyến tính và hồi quy đa thức để định giá bí ngô - Bài học 3\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Đồ họa thông tin bởi Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Đồ họa thông tin bởi Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n",
+    "\n",
+    "#### Giới thiệu\n",
+    "\n",
+    "Cho đến nay, bạn đã tìm hiểu về hồi quy với dữ liệu mẫu được thu thập từ tập dữ liệu định giá bí ngô mà chúng ta sẽ sử dụng xuyên suốt bài học này. Bạn cũng đã trực quan hóa nó bằng cách sử dụng `ggplot2`.💪\n",
+    "\n",
+    "Bây giờ bạn đã sẵn sàng đi sâu hơn vào hồi quy trong học máy. Trong bài học này, bạn sẽ tìm hiểu thêm về hai loại hồi quy: *hồi quy tuyến tính cơ bản* và *hồi quy đa thức*, cùng với một số toán học cơ bản liên quan đến các kỹ thuật này.\n",
+    "\n",
+    "> Xuyên suốt chương trình học này, chúng tôi giả định rằng bạn có kiến thức toán học tối thiểu và cố gắng làm cho nó dễ tiếp cận hơn đối với học viên đến từ các lĩnh vực khác, vì vậy hãy chú ý đến các ghi chú, 🧮 các điểm nhấn, sơ đồ và các công cụ học tập khác để hỗ trợ việc hiểu bài.\n",
+    "\n",
+    "#### Chuẩn bị\n",
+    "\n",
+    "Như đã nhắc lại, bạn đang tải dữ liệu này để đặt câu hỏi về nó.\n",
+    "\n",
+    "-   Khi nào là thời điểm tốt nhất để mua bí ngô?\n",
+    "\n",
+    "-   Giá của một thùng bí ngô nhỏ sẽ là bao nhiêu?\n",
+    "\n",
+    "-   Tôi nên mua chúng trong giỏ nửa bushel hay trong hộp 1 1/9 bushel? Hãy tiếp tục khám phá dữ liệu này.\n",
+    "\n",
+    "Trong bài học trước, bạn đã tạo một `tibble` (một cách tái hiện hiện đại của khung dữ liệu) và điền vào đó một phần của tập dữ liệu gốc, chuẩn hóa giá theo bushel. Tuy nhiên, bằng cách làm như vậy, bạn chỉ có thể thu thập khoảng 400 điểm dữ liệu và chỉ trong các tháng mùa thu. Có lẽ chúng ta có thể tìm hiểu thêm chi tiết về bản chất của dữ liệu bằng cách làm sạch nó hơn nữa? Chúng ta sẽ xem... 🕵️‍♀️\n",
+    "\n",
+    "Để thực hiện nhiệm vụ này, chúng ta sẽ cần các gói sau:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) là một [bộ sưu tập các gói R](https://www.tidyverse.org/packages) được thiết kế để làm cho khoa học dữ liệu nhanh hơn, dễ dàng hơn và thú vị hơn!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) là một [khung làm việc](https://www.tidymodels.org/packages/) bao gồm các gói dành cho mô hình hóa và học máy.\n",
+    "\n",
+    "-   `janitor`: [gói janitor](https://github.com/sfirke/janitor) cung cấp các công cụ đơn giản để kiểm tra và làm sạch dữ liệu bẩn.\n",
+    "\n",
+    "-   `corrplot`: [gói corrplot](https://cran.r-project.org/web/packages/corrplot/vignettes/corrplot-intro.html) cung cấp một công cụ trực quan để khám phá ma trận tương quan, hỗ trợ sắp xếp lại biến tự động nhằm giúp phát hiện các mẫu ẩn giữa các biến.\n",
+    "\n",
+    "Bạn có thể cài đặt chúng bằng lệnh:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"corrplot\"))`\n",
+    "\n",
+    "Đoạn mã dưới đây kiểm tra xem bạn đã có các gói cần thiết để hoàn thành module này chưa và cài đặt chúng cho bạn nếu chúng bị thiếu.\n"
+   ],
+   "metadata": {
+    "id": "WqQPS1OAsg3H"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, corrplot)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "tA4C2WN3skCf",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "c06cd805-5534-4edc-f72b-d0d1dab96ac0"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Chúng ta sẽ tải các gói tuyệt vời này và làm cho chúng khả dụng trong phiên làm việc R hiện tại của chúng ta. (Đây chỉ là minh họa, `pacman::p_load()` đã làm điều này cho bạn)\n",
+    "\n",
+    "## 1. Đường hồi quy tuyến tính\n",
+    "\n",
+    "Như bạn đã học trong Bài học 1, mục tiêu của bài tập hồi quy tuyến tính là vẽ được một *đường* *phù hợp nhất* để:\n",
+    "\n",
+    "-   **Hiển thị mối quan hệ giữa các biến**. Hiển thị mối quan hệ giữa các biến.\n",
+    "\n",
+    "-   **Dự đoán**. Dự đoán chính xác vị trí mà một điểm dữ liệu mới sẽ nằm trong mối quan hệ với đường đó.\n",
+    "\n",
+    "Để vẽ loại đường này, chúng ta sử dụng một kỹ thuật thống kê gọi là **Hồi quy Bình phương Tối thiểu**. Thuật ngữ `bình phương tối thiểu` có nghĩa là tất cả các điểm dữ liệu xung quanh đường hồi quy được bình phương và sau đó cộng lại. Lý tưởng nhất, tổng cuối cùng này càng nhỏ càng tốt, vì chúng ta muốn số lỗi thấp, hay `bình phương tối thiểu`. Do đó, đường phù hợp nhất là đường cho chúng ta giá trị thấp nhất của tổng các lỗi bình phương - vì vậy mới có tên gọi *hồi quy bình phương tối thiểu*.\n",
+    "\n",
+    "Chúng ta làm điều này vì muốn mô hình hóa một đường có khoảng cách tích lũy nhỏ nhất từ tất cả các điểm dữ liệu của chúng ta. Chúng ta cũng bình phương các giá trị trước khi cộng chúng lại vì chúng ta quan tâm đến độ lớn của chúng hơn là hướng của chúng.\n",
+    "\n",
+    "> **🧮 Hiển thị toán học**\n",
+    ">\n",
+    "> Đường này, gọi là *đường phù hợp nhất*, có thể được biểu diễn bằng [một phương trình](https://en.wikipedia.org/wiki/Simple_linear_regression):\n",
+    ">\n",
+    ">     Y = a + bX\n",
+    ">\n",
+    "> `X` là '`biến giải thích` hoặc `biến dự đoán`'. `Y` là '`biến phụ thuộc` hoặc `kết quả`'. Độ dốc của đường là `b` và `a` là giao điểm trục y, tức là giá trị của `Y` khi `X = 0`.\n",
+    ">\n",
+    "\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/slope.png \"slope = $y/x$\")\n",
+    "    Đồ họa thông tin bởi Jen Looper\n",
+    ">\n",
+    "> Đầu tiên, tính độ dốc `b`.\n",
+    ">\n",
+    "> Nói cách khác, và liên hệ với câu hỏi ban đầu về dữ liệu bí ngô của chúng ta: \"dự đoán giá của một giạ bí ngô theo tháng\", `X` sẽ là giá và `Y` sẽ là tháng bán.\n",
+    ">\n",
+    "> ![](../../../../../../2-Regression/3-Linear/solution/images/calculation.png)\n",
+    "    Đồ họa thông tin bởi Jen Looper\n",
+    "> \n",
+    "> Tính giá trị của Y. Nếu bạn đang trả khoảng \\$4, chắc hẳn là tháng Tư!\n",
+    ">\n",
+    "> Phép toán tính toán đường này phải thể hiện độ dốc của đường, điều này cũng phụ thuộc vào giao điểm, hoặc vị trí của `Y` khi `X = 0`.\n",
+    ">\n",
+    "> Bạn có thể quan sát phương pháp tính toán các giá trị này trên trang web [Math is Fun](https://www.mathsisfun.com/data/least-squares-regression.html). Cũng hãy ghé thăm [máy tính hồi quy bình phương tối thiểu này](https://www.mathsisfun.com/data/least-squares-calculator.html) để xem cách các giá trị số ảnh hưởng đến đường.\n",
+    "\n",
+    "Không quá đáng sợ, đúng không? 🤓\n",
+    "\n",
+    "#### Tương quan\n",
+    "\n",
+    "Một thuật ngữ nữa cần hiểu là **Hệ số Tương quan** giữa các biến X và Y cho trước. Sử dụng biểu đồ phân tán, bạn có thể nhanh chóng hình dung hệ số này. Một biểu đồ với các điểm dữ liệu phân tán theo một đường gọn gàng có tương quan cao, nhưng một biểu đồ với các điểm dữ liệu phân tán khắp nơi giữa X và Y có tương quan thấp.\n",
+    "\n",
+    "Một mô hình hồi quy tuyến tính tốt sẽ là mô hình có Hệ số Tương quan cao (gần 1 hơn 0) sử dụng phương pháp Hồi quy Bình phương Tối thiểu với một đường hồi quy.\n"
+   ],
+   "metadata": {
+    "id": "cdX5FRpvsoP5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **2. Một điệu nhảy với dữ liệu: tạo một khung dữ liệu sẽ được sử dụng để mô hình hóa**\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/janitor.jpg\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Tác phẩm nghệ thuật của \\@allison_horst](../../../../../../2-Regression/3-Linear/images/janitor.jpg){width=\"700\"}-->\n"
+   ],
+   "metadata": {
+    "id": "WdUKXk7Bs8-V"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tải các thư viện cần thiết và tập dữ liệu. Chuyển dữ liệu thành một khung dữ liệu chứa một phần của tập dữ liệu:\n",
+    "\n",
+    "-   Chỉ lấy bí ngô được định giá theo đơn vị bushel\n",
+    "\n",
+    "-   Chuyển đổi ngày thành tháng\n",
+    "\n",
+    "-   Tính giá trung bình dựa trên giá cao và giá thấp\n",
+    "\n",
+    "-   Chuyển đổi giá để phản ánh mức giá theo số lượng bushel\n",
+    "\n",
+    "> Chúng ta đã đề cập đến các bước này trong [bài học trước](https://github.com/microsoft/ML-For-Beginners/blob/main/2-Regression/2-Data/solution/lesson_2-R.ipynb).\n"
+   ],
+   "metadata": {
+    "id": "fMCtu2G2s-p8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core Tidyverse packages\n",
+    "library(tidyverse)\n",
+    "library(lubridate)\n",
+    "\n",
+    "# Import the pumpkins data\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\")\n",
+    "\n",
+    "\n",
+    "# Get a glimpse and dimensions of the data\n",
+    "glimpse(pumpkins)\n",
+    "\n",
+    "\n",
+    "# Print the first 50 rows of the data set\n",
+    "pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ryMVZEEPtERn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Với tinh thần phiêu lưu thuần túy, hãy cùng khám phá [`gói janitor`](../../../../../../2-Regression/3-Linear/solution/R/github.com/sfirke/janitor) cung cấp các hàm đơn giản để kiểm tra và làm sạch dữ liệu bẩn. Ví dụ, hãy xem qua tên các cột trong dữ liệu của chúng ta:\n"
+   ],
+   "metadata": {
+    "id": "xcNxM70EtJjb"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5XtpaIigtPfW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤔 Chúng ta có thể làm tốt hơn. Hãy làm cho các tên cột này `friendR` bằng cách chuyển chúng sang quy ước [snake_case](https://en.wikipedia.org/wiki/Snake_case) bằng cách sử dụng `janitor::clean_names`. Để tìm hiểu thêm về hàm này: `?clean_names`\n"
+   ],
+   "metadata": {
+    "id": "IbIqrMINtSHe"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Clean names to the snake_case convention\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  clean_names(case = \"snake\")\n",
+    "\n",
+    "# Return column names\n",
+    "pumpkins %>% \n",
+    "  names()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "a2uYvclYtWvX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Rất gọn gàng 🧹! Bây giờ, hãy nhảy múa với dữ liệu bằng cách sử dụng `dplyr` như trong bài học trước! 💃\n"
+   ],
+   "metadata": {
+    "id": "HfhnuzDDtaDd"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Select desired columns\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  select(variety, city_name, package, low_price, high_price, date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Extract the month from the dates to a new column\n",
+    "pumpkins <- pumpkins %>%\n",
+    "  mutate(date = mdy(date),\n",
+    "         month = month(date)) %>% \n",
+    "  select(-date)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a new column for average Price\n",
+    "pumpkins <- pumpkins %>% \n",
+    "  mutate(price = (low_price + high_price)/2)\n",
+    "\n",
+    "\n",
+    "# Retain only pumpkins with the string \"bushel\"\n",
+    "new_pumpkins <- pumpkins %>% \n",
+    "  filter(str_detect(string = package, pattern = \"bushel\"))\n",
+    "\n",
+    "\n",
+    "# Normalize the pricing so that you show the pricing per bushel, not per 1 1/9 or 1/2 bushel\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  mutate(price = case_when(\n",
+    "    str_detect(package, \"1 1/9\") ~ price/(1.1),\n",
+    "    str_detect(package, \"1/2\") ~ price*2,\n",
+    "    TRUE ~ price))\n",
+    "\n",
+    "# Relocate column positions\n",
+    "new_pumpkins <- new_pumpkins %>% \n",
+    "  relocate(month, .before = variety)\n",
+    "\n",
+    "\n",
+    "# Display the first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "X0wU3gQvtd9f"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Làm tốt lắm!👌 Giờ bạn đã có một tập dữ liệu sạch sẽ, gọn gàng để xây dựng mô hình hồi quy mới của mình!\n",
+    "\n",
+    "Bạn có muốn xem biểu đồ phân tán không?\n"
+   ],
+   "metadata": {
+    "id": "UpaIwaxqth82"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Set theme\n",
+    "theme_set(theme_light())\n",
+    "\n",
+    "# Make a scatter plot of month and price\n",
+    "new_pumpkins %>% \n",
+    "  ggplot(mapping = aes(x = month, y = price)) +\n",
+    "  geom_point(size = 1.6)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "DXgU-j37tl5K"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Một biểu đồ phân tán nhắc nhở chúng ta rằng chúng ta chỉ có dữ liệu tháng từ tháng Tám đến tháng Mười Hai. Chúng ta có lẽ cần thêm dữ liệu để có thể đưa ra kết luận theo cách tuyến tính.\n",
+    "\n",
+    "Hãy cùng xem lại dữ liệu mô hình của chúng ta:\n"
+   ],
+   "metadata": {
+    "id": "Ve64wVbwtobI"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Display first 5 rows\n",
+    "new_pumpkins %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HFQX2ng1tuSJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nếu chúng ta muốn dự đoán `price` của một quả bí ngô dựa trên các cột `city` hoặc `package`, vốn thuộc loại ký tự, thì sao? Hoặc đơn giản hơn, làm thế nào để tìm mối tương quan (yêu cầu cả hai đầu vào phải là số) giữa, ví dụ, `package` và `price`? 🤷🤷\n",
+    "\n",
+    "Các mô hình học máy hoạt động tốt nhất với các đặc trưng dạng số thay vì giá trị văn bản, vì vậy bạn thường cần chuyển đổi các đặc trưng phân loại thành các biểu diễn dạng số.\n",
+    "\n",
+    "Điều này có nghĩa là chúng ta phải tìm cách định dạng lại các biến dự đoán của mình để làm cho chúng dễ sử dụng hơn đối với mô hình, một quá trình được gọi là `feature engineering`.\n"
+   ],
+   "metadata": {
+    "id": "7hsHoxsStyjJ"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 3. Tiền xử lý dữ liệu để mô hình hóa với recipes 👩‍🍳👨‍🍳\n",
+    "\n",
+    "Các hoạt động định dạng lại giá trị dự đoán để giúp mô hình sử dụng hiệu quả hơn được gọi là `kỹ thuật đặc trưng`.\n",
+    "\n",
+    "Các mô hình khác nhau có yêu cầu tiền xử lý khác nhau. Ví dụ, phương pháp bình phương tối thiểu yêu cầu `mã hóa các biến phân loại` như tháng, loại và tên thành phố. Điều này đơn giản chỉ là `chuyển đổi` một cột với các `giá trị phân loại` thành một hoặc nhiều `cột số` thay thế cho cột ban đầu.\n",
+    "\n",
+    "Ví dụ, giả sử dữ liệu của bạn bao gồm đặc trưng phân loại sau:\n",
+    "\n",
+    "|  thành phố   |\n",
+    "|:------------:|\n",
+    "|   Denver     |\n",
+    "|   Nairobi    |\n",
+    "|    Tokyo     |\n",
+    "\n",
+    "Bạn có thể áp dụng *mã hóa thứ tự* để thay thế một giá trị số nguyên duy nhất cho mỗi danh mục, như sau:\n",
+    "\n",
+    "| thành phố |\n",
+    "|:---------:|\n",
+    "|     0     |\n",
+    "|     1     |\n",
+    "|     2     |\n",
+    "\n",
+    "Và đó chính là điều chúng ta sẽ làm với dữ liệu của mình!\n",
+    "\n",
+    "Trong phần này, chúng ta sẽ khám phá một gói tuyệt vời khác của Tidymodels: [recipes](https://tidymodels.github.io/recipes/) - được thiết kế để giúp bạn tiền xử lý dữ liệu **trước khi** huấn luyện mô hình. Cốt lõi của nó, một recipe là một đối tượng định nghĩa các bước cần áp dụng cho một tập dữ liệu để chuẩn bị cho việc mô hình hóa.\n",
+    "\n",
+    "Bây giờ, hãy tạo một recipe để chuẩn bị dữ liệu của chúng ta cho việc mô hình hóa bằng cách thay thế một số nguyên duy nhất cho tất cả các quan sát trong các cột dự đoán:\n"
+   ],
+   "metadata": {
+    "id": "AD5kQbcvt3Xl"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\n",
+    "pumpkins_recipe <- recipe(price ~ ., data = new_pumpkins) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "# Print out the recipe\n",
+    "pumpkins_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "BNaFKXfRt9TU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tuyệt vời! 👏 Chúng ta vừa tạo ra công thức đầu tiên để xác định một kết quả (giá cả) và các yếu tố dự đoán tương ứng, đồng thời tất cả các cột dự đoán đều được mã hóa thành một tập hợp các số nguyên 🙌! Hãy cùng phân tích nhanh:\n",
+    "\n",
+    "-   Lệnh gọi `recipe()` với một công thức cho phép công thức xác định *vai trò* của các biến bằng cách sử dụng dữ liệu `new_pumpkins` làm tham chiếu. Ví dụ, cột `price` được gán vai trò là `outcome`, trong khi các cột còn lại được gán vai trò là `predictor`.\n",
+    "\n",
+    "-   `step_integer(all_predictors(), zero_based = TRUE)` chỉ định rằng tất cả các yếu tố dự đoán sẽ được chuyển đổi thành một tập hợp các số nguyên, bắt đầu đánh số từ 0.\n",
+    "\n",
+    "Chúng tôi chắc chắn rằng bạn có thể đang nghĩ: \"Thật tuyệt vời!! Nhưng nếu tôi cần xác nhận rằng các công thức đang thực hiện đúng như mong đợi thì sao? 🤔\"\n",
+    "\n",
+    "Đó là một ý tưởng tuyệt vời! Bạn thấy đấy, một khi công thức của bạn được định nghĩa, bạn có thể ước tính các tham số cần thiết để thực sự tiền xử lý dữ liệu, sau đó trích xuất dữ liệu đã được xử lý. Bạn thường không cần làm điều này khi sử dụng Tidymodels (chúng ta sẽ thấy cách thông thường ngay sau đây-\\> `workflows`), nhưng nó có thể rất hữu ích khi bạn muốn kiểm tra nhanh để xác nhận rằng các công thức đang hoạt động đúng như mong đợi.\n",
+    "\n",
+    "Để làm điều đó, bạn sẽ cần hai động từ nữa: `prep()` và `bake()`. Và như mọi khi, những người bạn nhỏ R của chúng ta từ [`Allison Horst`](https://github.com/allisonhorst/stats-illustrations) sẽ giúp bạn hiểu rõ hơn về điều này!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "KEiO0v7kuC9O"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "[`prep()`](https://recipes.tidymodels.org/reference/prep.html): ước tính các tham số cần thiết từ tập dữ liệu huấn luyện, sau đó có thể áp dụng cho các tập dữ liệu khác. Ví dụ, đối với một cột dự đoán cụ thể, quan sát nào sẽ được gán giá trị nguyên 0, 1, 2, v.v.\n",
+    "\n",
+    "[`bake()`](https://recipes.tidymodels.org/reference/bake.html): sử dụng công thức đã được chuẩn bị và áp dụng các thao tác cho bất kỳ tập dữ liệu nào.\n",
+    "\n",
+    "Vậy nên, hãy chuẩn bị và áp dụng công thức của chúng ta để thực sự xác nhận rằng, trong quá trình xử lý, các cột dự đoán sẽ được mã hóa trước khi mô hình được huấn luyện.\n"
+   ],
+   "metadata": {
+    "id": "Q1xtzebuuTCP"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep the recipe\n",
+    "pumpkins_prep <- prep(pumpkins_recipe)\n",
+    "\n",
+    "# Bake the recipe to extract a preprocessed new_pumpkins data\n",
+    "baked_pumpkins <- bake(pumpkins_prep, new_data = NULL)\n",
+    "\n",
+    "# Print out the baked data set\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FGBbJbP_uUUn"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woo-hoo!🥳 Dữ liệu đã xử lý `baked_pumpkins` đã được mã hóa tất cả các biến dự đoán, xác nhận rằng các bước tiền xử lý được định nghĩa trong công thức của chúng ta hoạt động đúng như mong đợi. Điều này khiến việc đọc dữ liệu trở nên khó khăn hơn đối với bạn nhưng lại dễ hiểu hơn nhiều đối với Tidymodels! Hãy dành chút thời gian để tìm hiểu xem quan sát nào đã được ánh xạ thành số nguyên tương ứng.\n",
+    "\n",
+    "Cũng cần lưu ý rằng `baked_pumpkins` là một khung dữ liệu mà chúng ta có thể thực hiện các phép tính trên đó.\n",
+    "\n",
+    "Ví dụ, hãy thử tìm mối tương quan tốt giữa hai điểm dữ liệu của bạn để có thể xây dựng một mô hình dự đoán tốt. Chúng ta sẽ sử dụng hàm `cor()` để làm điều này. Gõ `?cor()` để tìm hiểu thêm về hàm này.\n"
+   ],
+   "metadata": {
+    "id": "1dvP0LBUueAW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Find the correlation between the city_name and the price\n",
+    "cor(baked_pumpkins$city_name, baked_pumpkins$price)\n",
+    "\n",
+    "# Find the correlation between the package and the price\n",
+    "cor(baked_pumpkins$package, baked_pumpkins$price)\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "3bQzXCjFuiSV"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hóa ra, mối tương quan giữa Thành phố và Giá chỉ ở mức yếu. Tuy nhiên, có một mối tương quan tốt hơn giữa Gói hàng và Giá của nó. Điều này hợp lý, đúng không? Thông thường, hộp sản phẩm càng lớn thì giá càng cao.\n",
+    "\n",
+    "Nhân tiện, hãy thử hình dung ma trận tương quan của tất cả các cột bằng cách sử dụng gói `corrplot`.\n"
+   ],
+   "metadata": {
+    "id": "BToPWbgjuoZw"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the corrplot package\n",
+    "library(corrplot)\n",
+    "\n",
+    "# Obtain correlation matrix\n",
+    "corr_mat <- cor(baked_pumpkins %>% \n",
+    "                  # Drop columns that are not really informative\n",
+    "                  select(-c(low_price, high_price)))\n",
+    "\n",
+    "# Make a correlation plot between the variables\n",
+    "corrplot(corr_mat, method = \"shade\", shade.col = NA, tl.col = \"black\", tl.srt = 45, addCoef.col = \"black\", cl.pos = \"n\", order = \"original\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ZwAL3ksmutVR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Tuyệt vời hơn nhiều.\n",
+    "\n",
+    "Một câu hỏi hay để đặt ra từ dữ liệu này sẽ là: '`Giá mà tôi có thể mong đợi cho một gói bí ngô nhất định là bao nhiêu?`' Hãy bắt đầu ngay thôi!\n",
+    "\n",
+    "> Lưu ý: Khi bạn **`bake()`** công thức đã chuẩn bị **`pumpkins_prep`** với **`new_data = NULL`**, bạn sẽ trích xuất dữ liệu huấn luyện đã được xử lý (tức là đã được mã hóa). Nếu bạn có một tập dữ liệu khác, ví dụ như tập kiểm tra, và muốn xem cách công thức xử lý trước nó, bạn chỉ cần bake **`pumpkins_prep`** với **`new_data = test_set`**\n",
+    "\n",
+    "## 4. Xây dựng mô hình hồi quy tuyến tính\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Đồ họa thông tin bởi Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Đồ họa thông tin bởi Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "YqXjLuWavNxW"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bây giờ chúng ta đã xây dựng một công thức và thực sự xác nhận rằng dữ liệu sẽ được tiền xử lý một cách phù hợp, hãy cùng xây dựng một mô hình hồi quy để trả lời câu hỏi: `Giá của một gói bí ngô cụ thể sẽ là bao nhiêu?`\n",
+    "\n",
+    "#### Huấn luyện mô hình hồi quy tuyến tính bằng tập huấn luyện\n",
+    "\n",
+    "Như bạn có thể đã nhận ra, cột *price* là biến `kết quả` trong khi cột *package* là biến `dự đoán`.\n",
+    "\n",
+    "Để thực hiện điều này, trước tiên chúng ta sẽ chia dữ liệu sao cho 80% vào tập huấn luyện và 20% vào tập kiểm tra, sau đó định nghĩa một công thức để mã hóa cột dự đoán thành một tập hợp các số nguyên, rồi xây dựng một đặc tả mô hình. Chúng ta sẽ không chuẩn bị và nướng công thức của mình vì đã biết rằng nó sẽ tiền xử lý dữ liệu như mong đợi.\n"
+   ],
+   "metadata": {
+    "id": "Pq0bSzCevW-h"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "set.seed(2056)\n",
+    "# Split the data into training and test sets\n",
+    "pumpkins_split <- new_pumpkins %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "\n",
+    "# Extract training and test data\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a recipe for preprocessing the data\n",
+    "lm_pumpkins_recipe <- recipe(price ~ package, data = pumpkins_train) %>% \n",
+    "  step_integer(all_predictors(), zero_based = TRUE)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Create a linear model specification\n",
+    "lm_spec <- linear_reg() %>% \n",
+    "  set_engine(\"lm\") %>% \n",
+    "  set_mode(\"regression\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "CyoEh_wuvcLv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Làm tốt lắm! Bây giờ chúng ta đã có một công thức và một mô tả mô hình, chúng ta cần tìm cách kết hợp chúng lại thành một đối tượng để trước tiên xử lý dữ liệu (prep+bake phía sau hậu trường), huấn luyện mô hình trên dữ liệu đã được xử lý và cũng cho phép các hoạt động xử lý hậu kỳ tiềm năng. Thật tuyệt vời để bạn yên tâm, đúng không!🤩\n",
+    "\n",
+    "Trong Tidymodels, đối tượng tiện lợi này được gọi là [`workflow`](https://workflows.tidymodels.org/) và nó tiện lợi lưu giữ các thành phần mô hình của bạn! Đây là thứ mà chúng ta gọi là *pipelines* trong *Python*.\n",
+    "\n",
+    "Vậy hãy gói gọn mọi thứ vào một workflow nhé!📦\n"
+   ],
+   "metadata": {
+    "id": "G3zF_3DqviFJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Hold modelling components in a workflow\n",
+    "lm_wf <- workflow() %>% \n",
+    "  add_recipe(lm_pumpkins_recipe) %>% \n",
+    "  add_model(lm_spec)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "lm_wf"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "T3olroU3v-WX"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "👌 Thêm vào đó, một quy trình làm việc có thể được điều chỉnh/huấn luyện theo cách tương tự như một mô hình.\n"
+   ],
+   "metadata": {
+    "id": "zd1A5tgOwEPX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Train the model\n",
+    "lm_wf_fit <- lm_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the model coefficients learned \n",
+    "lm_wf_fit"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "NhJagFumwFHf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Từ đầu ra của mô hình, chúng ta có thể thấy các hệ số được học trong quá trình huấn luyện. Chúng đại diện cho các hệ số của đường hồi quy tốt nhất, giúp giảm thiểu tổng lỗi giữa biến thực tế và biến dự đoán.\n",
+    "\n",
+    "#### Đánh giá hiệu suất mô hình bằng tập kiểm tra\n",
+    "\n",
+    "Đã đến lúc xem mô hình hoạt động như thế nào 📏! Chúng ta làm điều này như thế nào?\n",
+    "\n",
+    "Bây giờ chúng ta đã huấn luyện xong mô hình, có thể sử dụng nó để đưa ra dự đoán cho `test_set` bằng cách dùng `parsnip::predict()`. Sau đó, chúng ta có thể so sánh các dự đoán này với các giá trị nhãn thực tế để đánh giá mức độ hiệu quả (hoặc không hiệu quả!) của mô hình.\n",
+    "\n",
+    "Hãy bắt đầu bằng cách tạo dự đoán cho tập kiểm tra, sau đó gắn các cột vào tập kiểm tra.\n"
+   ],
+   "metadata": {
+    "id": "_4QkGtBTwItF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make predictions for the test set\n",
+    "predictions <- lm_wf_fit %>% \n",
+    "  predict(new_data = pumpkins_test)\n",
+    "\n",
+    "\n",
+    "# Bind predictions to the test set\n",
+    "lm_results <- pumpkins_test %>% \n",
+    "  select(c(package, price)) %>% \n",
+    "  bind_cols(predictions)\n",
+    "\n",
+    "\n",
+    "# Print the first ten rows of the tibble\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "UFZzTG0gwTs9"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vâng, bạn vừa huấn luyện một mô hình và sử dụng nó để đưa ra dự đoán! 🔮 Nó có tốt không? Hãy đánh giá hiệu suất của mô hình nhé!\n",
+    "\n",
+    "Trong Tidymodels, chúng ta thực hiện việc này bằng cách sử dụng `yardstick::metrics()`! Đối với hồi quy tuyến tính, hãy tập trung vào các chỉ số sau:\n",
+    "\n",
+    "-   `Root Mean Square Error (RMSE)`: Căn bậc hai của [MSE](https://en.wikipedia.org/wiki/Mean_squared_error). Đây là một chỉ số tuyệt đối có cùng đơn vị với nhãn (trong trường hợp này là giá của một quả bí ngô). Giá trị càng nhỏ, mô hình càng tốt (hiểu một cách đơn giản, nó đại diện cho mức giá trung bình mà dự đoán bị sai lệch!).\n",
+    "\n",
+    "-   `Coefficient of Determination (thường được gọi là R-squared hoặc R2)`: Một chỉ số tương đối, trong đó giá trị càng cao, mô hình càng phù hợp. Về cơ bản, chỉ số này thể hiện mức độ mà mô hình có thể giải thích được sự biến thiên giữa giá trị dự đoán và giá trị thực tế của nhãn.\n"
+   ],
+   "metadata": {
+    "id": "0A5MjzM7wW9M"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Evaluate performance of linear regression\n",
+    "metrics(data = lm_results,\n",
+    "        truth = price,\n",
+    "        estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "reJ0UIhQwcEH"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hiệu suất của mô hình giảm đi. Hãy xem liệu chúng ta có thể có được một chỉ báo tốt hơn bằng cách trực quan hóa biểu đồ phân tán của gói hàng và giá, sau đó sử dụng các dự đoán đã thực hiện để vẽ một đường phù hợp nhất.\n",
+    "\n",
+    "Điều này có nghĩa là chúng ta sẽ phải chuẩn bị và xử lý tập kiểm tra để mã hóa cột gói hàng, sau đó kết hợp nó với các dự đoán được tạo ra bởi mô hình của chúng ta.\n"
+   ],
+   "metadata": {
+    "id": "fdgjzjkBwfWt"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Encode package column\n",
+    "package_encode <- lm_pumpkins_recipe %>% \n",
+    "  prep() %>% \n",
+    "  bake(new_data = pumpkins_test) %>% \n",
+    "  select(package)\n",
+    "\n",
+    "\n",
+    "# Bind encoded package column to the results\n",
+    "lm_results <- lm_results %>% \n",
+    "  bind_cols(package_encode %>% \n",
+    "              rename(package_integer = package)) %>% \n",
+    "  relocate(package_integer, .after = package)\n",
+    "\n",
+    "\n",
+    "# Print new results data frame\n",
+    "lm_results %>% \n",
+    "  slice_head(n = 5)\n",
+    "\n",
+    "\n",
+    "# Make a scatter plot\n",
+    "lm_results %>% \n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\n",
+    "  geom_point(size = 1.6) +\n",
+    "  # Overlay a line of best fit\n",
+    "  geom_line(aes(y = .pred), color = \"orange\", size = 1.2) +\n",
+    "  xlab(\"package\")\n",
+    "  \n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "R0nw719lwkHE"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Thật tuyệt! Như bạn có thể thấy, mô hình hồi quy tuyến tính không thực sự khái quát tốt mối quan hệ giữa một gói hàng và giá tương ứng của nó.\n",
+    "\n",
+    "🎃 Chúc mừng, bạn vừa tạo ra một mô hình có thể giúp dự đoán giá của một vài loại bí ngô. Vườn bí ngô cho kỳ nghỉ của bạn sẽ thật đẹp. Nhưng có lẽ bạn có thể tạo ra một mô hình tốt hơn!\n",
+    "\n",
+    "## 5. Xây dựng mô hình hồi quy đa thức\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/linear-polynomial.png\"\n",
+    "   width=\"800\"/>\n",
+    "   <figcaption>Đồ họa thông tin bởi Dasani Madipalli</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Đồ họa thông tin bởi Dasani Madipalli](../../../../../../2-Regression/3-Linear/images/linear-polynomial.png){width=\"800\"}-->\n"
+   ],
+   "metadata": {
+    "id": "HOCqJXLTwtWI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Đôi khi dữ liệu của chúng ta không có mối quan hệ tuyến tính, nhưng chúng ta vẫn muốn dự đoán kết quả. Hồi quy đa thức có thể giúp chúng ta đưa ra dự đoán cho các mối quan hệ phi tuyến phức tạp hơn.\n",
+    "\n",
+    "Hãy xem xét mối quan hệ giữa kích thước gói và giá cả trong bộ dữ liệu bí ngô của chúng ta. Mặc dù đôi khi có mối quan hệ tuyến tính giữa các biến - bí ngô có thể tích lớn hơn thì giá cao hơn - nhưng đôi khi những mối quan hệ này không thể được biểu diễn dưới dạng mặt phẳng hoặc đường thẳng.\n",
+    "\n",
+    "> ✅ Đây là [một số ví dụ khác](https://online.stat.psu.edu/stat501/lesson/9/9.8) về dữ liệu có thể sử dụng hồi quy đa thức  \n",
+    ">  \n",
+    "> Hãy xem lại mối quan hệ giữa Loại bí ngô và Giá cả trong biểu đồ trước đó. Biểu đồ phân tán này có nhất thiết phải được phân tích bằng một đường thẳng không? Có lẽ là không. Trong trường hợp này, bạn có thể thử hồi quy đa thức.  \n",
+    ">  \n",
+    "> ✅ Đa thức là các biểu thức toán học có thể bao gồm một hoặc nhiều biến và hệ số  \n",
+    "\n",
+    "#### Huấn luyện mô hình hồi quy đa thức bằng tập dữ liệu huấn luyện\n",
+    "\n",
+    "Hồi quy đa thức tạo ra một *đường cong* để phù hợp hơn với dữ liệu phi tuyến.\n",
+    "\n",
+    "Hãy xem liệu mô hình đa thức có hoạt động tốt hơn trong việc đưa ra dự đoán hay không. Chúng ta sẽ thực hiện quy trình tương tự như trước:\n",
+    "\n",
+    "-   Tạo một công thức chỉ định các bước tiền xử lý cần thực hiện trên dữ liệu để chuẩn bị cho việc mô hình hóa, ví dụ: mã hóa các biến dự đoán và tính toán đa thức với bậc *n*\n",
+    "\n",
+    "-   Xây dựng một mô hình cụ thể\n",
+    "\n",
+    "-   Kết hợp công thức và mô hình cụ thể vào một quy trình làm việc\n",
+    "\n",
+    "-   Tạo mô hình bằng cách khớp quy trình làm việc\n",
+    "\n",
+    "-   Đánh giá mức độ hiệu quả của mô hình trên dữ liệu kiểm tra\n",
+    "\n",
+    "Hãy bắt đầu ngay thôi!\n"
+   ],
+   "metadata": {
+    "id": "VcEIpRV9wzYr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Specify a recipe\r\n",
+    "poly_pumpkins_recipe <-\r\n",
+    "  recipe(price ~ package, data = pumpkins_train) %>%\r\n",
+    "  step_integer(all_predictors(), zero_based = TRUE) %>% \r\n",
+    "  step_poly(all_predictors(), degree = 4)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model specification\r\n",
+    "poly_spec <- linear_reg() %>% \r\n",
+    "  set_engine(\"lm\") %>% \r\n",
+    "  set_mode(\"regression\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Bundle recipe and model spec into a workflow\r\n",
+    "poly_wf <- workflow() %>% \r\n",
+    "  add_recipe(poly_pumpkins_recipe) %>% \r\n",
+    "  add_model(poly_spec)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Create a model\r\n",
+    "poly_wf_fit <- poly_wf %>% \r\n",
+    "  fit(data = pumpkins_train)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print learned model coefficients\r\n",
+    "poly_wf_fit\r\n",
+    "\r\n",
+    "  "
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "63n_YyRXw3CC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "#### Đánh giá hiệu suất mô hình\n",
+    "\n",
+    "👏👏Bạn đã xây dựng một mô hình đa thức, hãy thực hiện dự đoán trên tập kiểm tra!\n"
+   ],
+   "metadata": {
+    "id": "-LHZtztSxDP0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make price predictions on test data\r\n",
+    "poly_results <- poly_wf_fit %>% predict(new_data = pumpkins_test) %>% \r\n",
+    "  bind_cols(pumpkins_test %>% select(c(package, price))) %>% \r\n",
+    "  relocate(.pred, .after = last_col())\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print the results\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "YUFpQ_dKxJGx"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Woo-hoo, hãy đánh giá cách mô hình hoạt động trên test_set bằng cách sử dụng `yardstick::metrics()`.\n"
+   ],
+   "metadata": {
+    "id": "qxdyj86bxNGZ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "metrics(data = poly_results, truth = price, estimate = .pred)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "8AW5ltkBxXDm"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "🤩🤩 Hiệu suất tốt hơn nhiều.\n",
+    "\n",
+    "`rmse` đã giảm từ khoảng 7 xuống khoảng 3, cho thấy lỗi giữa giá thực tế và giá dự đoán đã giảm. Bạn có thể *hiểu một cách đơn giản* rằng trung bình, các dự đoán sai lệch khoảng \\$3. `rsq` đã tăng từ khoảng 0.4 lên 0.8.\n",
+    "\n",
+    "Tất cả các chỉ số này đều cho thấy rằng mô hình đa thức hoạt động tốt hơn nhiều so với mô hình tuyến tính. Làm tốt lắm!\n",
+    "\n",
+    "Hãy xem liệu chúng ta có thể trực quan hóa điều này không!\n"
+   ],
+   "metadata": {
+    "id": "6gLHNZDwxYaS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Bind encoded package column to the results\r\n",
+    "poly_results <- poly_results %>% \r\n",
+    "  bind_cols(package_encode %>% \r\n",
+    "              rename(package_integer = package)) %>% \r\n",
+    "  relocate(package_integer, .after = package)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Print new results data frame\r\n",
+    "poly_results %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_line(aes(y = .pred), color = \"midnightblue\", size = 1.2) +\r\n",
+    "  xlab(\"package\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "A83U16frxdF1"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bạn có thể thấy một đường cong phù hợp với dữ liệu của mình tốt hơn! 🤩\n",
+    "\n",
+    "Bạn có thể làm cho đường này mượt mà hơn bằng cách truyền một công thức đa thức vào `geom_smooth` như sau:\n"
+   ],
+   "metadata": {
+    "id": "4U-7aHOVxlGU"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a scatter plot\r\n",
+    "poly_results %>% \r\n",
+    "  ggplot(mapping = aes(x = package_integer, y = price)) +\r\n",
+    "  geom_point(size = 1.6) +\r\n",
+    "  # Overlay a line of best fit\r\n",
+    "  geom_smooth(method = lm, formula = y ~ poly(x, degree = 4), color = \"midnightblue\", size = 1.2, se = FALSE) +\r\n",
+    "  xlab(\"package\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "5vzNT0Uexm-w"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Giống như một đường cong mượt mà!🤩\n",
+    "\n",
+    "Đây là cách bạn tạo một dự đoán mới:\n"
+   ],
+   "metadata": {
+    "id": "v9u-wwyLxq4G"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a hypothetical data frame\r\n",
+    "hypo_tibble <- tibble(package = \"bushel baskets\")\r\n",
+    "\r\n",
+    "# Make predictions using linear model\r\n",
+    "lm_pred <- lm_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Make predictions using polynomial model\r\n",
+    "poly_pred <- poly_wf_fit %>% predict(new_data = hypo_tibble)\r\n",
+    "\r\n",
+    "# Return predictions in a list\r\n",
+    "list(\"linear model prediction\" = lm_pred, \r\n",
+    "     \"polynomial model prediction\" = poly_pred)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "jRPSyfQGxuQv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Dự đoán của `polynomial model` thực sự hợp lý, dựa trên biểu đồ phân tán của `price` và `package`! Và nếu đây là một mô hình tốt hơn so với mô hình trước đó, khi nhìn vào cùng một dữ liệu, bạn cần dự trù ngân sách cho những quả bí ngô đắt tiền hơn này!\n",
+    "\n",
+    "🏆 Làm tốt lắm! Bạn đã tạo hai mô hình hồi quy trong một bài học. Trong phần cuối cùng về hồi quy, bạn sẽ học về hồi quy logistic để xác định các danh mục.\n",
+    "\n",
+    "## **🚀Thử thách**\n",
+    "\n",
+    "Kiểm tra một số biến khác nhau trong notebook này để xem cách mối tương quan ảnh hưởng đến độ chính xác của mô hình.\n",
+    "\n",
+    "## [**Câu hỏi sau bài giảng**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/14/)\n",
+    "\n",
+    "## **Ôn tập & Tự học**\n",
+    "\n",
+    "Trong bài học này, chúng ta đã học về Hồi quy tuyến tính. Có những loại hồi quy quan trọng khác. Hãy tìm hiểu về các kỹ thuật Stepwise, Ridge, Lasso và Elasticnet. Một khóa học hay để học thêm là [Khóa học Stanford Statistical Learning](https://online.stanford.edu/courses/sohs-ystatslearning-statistical-learning).\n",
+    "\n",
+    "Nếu bạn muốn tìm hiểu thêm về cách sử dụng framework Tidymodels tuyệt vời, hãy tham khảo các tài nguyên sau:\n",
+    "\n",
+    "-   Trang web Tidymodels: [Bắt đầu với Tidymodels](https://www.tidymodels.org/start/)\n",
+    "\n",
+    "-   Max Kuhn và Julia Silge, [*Tidy Modeling with R*](https://www.tmwr.org/)*.*\n",
+    "\n",
+    "###### **CẢM ƠN ĐẾN:**\n",
+    "\n",
+    "[Allison Horst](https://twitter.com/allison_horst?lang=en) vì đã tạo ra những hình minh họa tuyệt vời giúp R trở nên thân thiện và hấp dẫn hơn. Tìm thêm các hình minh họa tại [bộ sưu tập của cô ấy](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n"
+   ],
+   "metadata": {
+    "id": "8zOLOWqMxzk5"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
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+++ b/translations/vi/2-Regression/3-Linear/solution/notebook.ipynb
@@ -0,0 +1,1111 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hồi quy tuyến tính và hồi quy đa thức để định giá bí ngô - Bài học 3\n",
+    "\n",
+    "Tải các thư viện cần thiết và tập dữ liệu. Chuyển đổi dữ liệu thành một dataframe chứa một phần dữ liệu:\n",
+    "\n",
+    "- Chỉ lấy bí ngô được định giá theo đơn vị bushel\n",
+    "- Chuyển đổi ngày thành tháng\n",
+    "- Tính giá trung bình dựa trên giá cao và giá thấp\n",
+    "- Chuyển đổi giá để phản ánh mức giá theo số lượng bushel\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from datetime import datetime\n",
+    "\n",
+    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>DayOfYear</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>9</td>\n",
+       "      <td>267</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>16.363636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>10</td>\n",
+       "      <td>274</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>15.454545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>10</td>\n",
+       "      <td>281</td>\n",
+       "      <td>PIE TYPE</td>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>1 1/9 bushel cartons</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>13.636364</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
+       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
+       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
+       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
+       "\n",
+       "    High Price      Price  \n",
+       "70        15.0  13.636364  \n",
+       "71        18.0  16.363636  \n",
+       "72        18.0  16.363636  \n",
+       "73        17.0  15.454545  \n",
+       "74        15.0  13.636364  "
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "\n",
+    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
+    "\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
+    "\n",
+    "new_pumpkins = pd.DataFrame(\n",
+    "    {'Month': month, \n",
+    "     'DayOfYear' : day_of_year, \n",
+    "     'Variety': pumpkins['Variety'], \n",
+    "     'City': pumpkins['City Name'], \n",
+    "     'Package': pumpkins['Package'], \n",
+    "     'Low Price': pumpkins['Low Price'],\n",
+    "     'High Price': pumpkins['High Price'], \n",
+    "     'Price': price})\n",
+    "\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
+    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
+    "\n",
+    "new_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Biểu đồ phân tán nhắc nhở chúng ta rằng chúng ta chỉ có dữ liệu tháng từ tháng Tám đến tháng Mười Hai. Chúng ta có lẽ cần thêm dữ liệu để có thể đưa ra kết luận theo cách tuyến tính.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Month', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('Month','Price')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.14878293554077535\n",
+      "-0.16673322492745407\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
+    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Có vẻ như mối tương quan khá nhỏ, nhưng có một mối quan hệ quan trọng hơn - vì các điểm giá trong biểu đồ trên dường như có một số cụm riêng biệt. Hãy tạo một biểu đồ để hiển thị các loại bí ngô khác nhau:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax=None\n",
+    "colors = ['red','blue','green','yellow']\n",
+    "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n",
+    "    ax = new_pumpkins[new_pumpkins['Variety']==var].plot.scatter('DayOfYear','Price',ax=ax,c=colors[i],label=var)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Variety'>"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.2669192282197318\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='DayOfYear', ylabel='Price'>"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n",
+    "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))\n",
+    "pie_pumpkins.plot.scatter('DayOfYear','Price')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hồi quy tuyến tính\n",
+    "\n",
+    "Chúng ta sẽ sử dụng Scikit Learn để huấn luyện mô hình hồi quy tuyến tính:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.77 (17.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
+    "y = pie_pumpkins['Price']\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "lin_reg = LinearRegression()\n",
+    "lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a3232f8dc0>]"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(X_test,pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Độ dốc của đường thẳng có thể được xác định từ các hệ số hồi quy tuyến tính:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([-0.01751876]), 21.133734359909326)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lin_reg.coef_, lin_reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([16.64893156])"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pumpkin price on programmer's day\n",
+    "\n",
+    "lin_reg.predict([[256]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hồi quy đa thức\n",
+    "\n",
+    "Đôi khi mối quan hệ giữa các đặc điểm và kết quả vốn dĩ không tuyến tính. Ví dụ, giá bí ngô có thể cao vào mùa đông (tháng 1, 2), sau đó giảm vào mùa hè (tháng 5-7), rồi lại tăng lên. Hồi quy tuyến tính không thể tìm ra mối quan hệ này một cách chính xác.\n",
+    "\n",
+    "Trong trường hợp này, chúng ta có thể cân nhắc thêm các đặc điểm bổ sung. Một cách đơn giản là sử dụng các đa thức từ các đặc điểm đầu vào, điều này sẽ dẫn đến **hồi quy đa thức**. Trong Scikit Learn, chúng ta có thể tự động tính trước các đặc điểm đa thức bằng cách sử dụng pipelines:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.73 (17.0%)\n",
+      "Model determination:  0.07639977655280217\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a323368ac0>]"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAD4CAYAAAATpHZ6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAbw0lEQVR4nO3de3Cc1Znn8e+jm93ClmRb8kWyjYBgDb6ATQQhFwIhFzu7meBQNVOVyu5Sm9RQSWWnJlMTZ3BIZWq2dpcMnprZzM5WTbEDFVLDsJOZOM4USTAEkkBYMJExjOwYY8AXkGRLsi35otb92T+6JbfurXa3ut+j36eqS2+ffvvto0f2T6/Oe/q0uTsiIhKGonx3QEREskehLiISEIW6iEhAFOoiIgFRqIuIBKRkLl+surra6+vr5/IlRUQib//+/Z3uXpPOvnMa6vX19TQ1Nc3lS4qIRJ6ZnUh3Xw2/iIgERKEuIhIQhbqISEAU6iIiAVGoi4gEZMbZL2a2Bvg+sBIYBh529++a2S7gd4F+4G3gP7t7Vw77KnNgz4EWdu09QmtXnNqqGDu2NrB9S12+uyUiaUrnTH0Q+BN3vwG4Dfiqma0HngE2uvuNwJvAztx1U+bCngMt7NzdTEtXHAdauuLs3N3MngMt+e6aiKRpxlB39zZ3fzW5fQE4DNS5+9PuPpjc7WVgde66KXNh194jxAeGxrTFB4bYtfdInnokIrM1qzF1M6sHtgD7xj30ReBnUzznPjNrMrOmjo6OjDopc6O1Kz6rdhEpPGmHupktAn4IfM3dz6e0P0BiiObxyZ7n7g+7e6O7N9bUpPUuV8mT2qrYrNpFpPCkFepmVkoi0B93990p7fcCnwG+4PoIpcjbsbWBWGnxmLZYaTE7tjbkqUciMlvpzH4x4BHgsLv/VUr7NuBPgTvcvSd3XZS5MjLLRbNfRKLLZjrBNrOPAC8AzSSmNAJ8E/gbYAFwJtn2srt/ebpjNTY2uhb0EhGZHTPb7+6N6ew745m6u/8asEke+ulsOyYiIrmld5SKiAREoS4iEhCFuohIQBTqIiIBUaiLiAREoS4iEhCFuohIQBTqIiIBUaiLiAREoS4iEhCFuohIQBTqIiIBUaiLiAREoS4iEhCFuohIQBTqIiIBUaiLiAREoS4iEhCFuohIQGb8jFKRXNtzoIVde4/Q2hWntirGjq0NbN9SV/DHlolU7/xTqEte7TnQws7dzcQHhgBo6Yqzc3czwBWHQS6PLROp3oVBwy+SV7v2HhkNgRHxgSF27T1S0MeWiVTvwqBQl7xq7YrPqr1Qji0Tqd6FQaEueVVbFZtVe6EcWyZSvQuDQl3yasfWBmKlxWPaYqXF7NjaUNDHlolU78KgC6WSVyMX0HIxYyKXx5aJVO/CYO4+Zy/W2NjoTU1Nc/Z6IiIhMLP97t6Yzr4znqmb2Rrg+8BKYBh42N2/a2ZLgX8C6oHjwO+7+7lMO50PmlMrIqFJZ0x9EPgTd78BuA34qpmtB+4HnnX364Fnk/cjY2RObUtXHOfynNo9B1ry3TURkYzNGOru3uburya3LwCHgTrgbuCx5G6PAdtz1Mec0JxaEQnRrGa/mFk9sAXYB6xw9zZIBD+wfIrn3GdmTWbW1NHRcYXdzR7NqRWREKUd6ma2CPgh8DV3P5/u89z9YXdvdPfGmpqaTPqYE5pTKyIhSivUzayURKA/7u67k82nzWxV8vFVQHtuupgbmlMrIiGaMdTNzIBHgMPu/lcpD/0rcG9y+17gx9nvXu5s31LHg/dsoq4qhgF1VTEevGeTZr+ISKTNOE/dzD4CvAA0k5jSCPBNEuPqPwDWAieB33P3s9MdS/PURURmL6vz1N3914BN8fDHZ9MxERHJLa39IiISEIW6iEhAFOoiIgFRqIuIBEShLiISEIW6iEhAFOoiIgFRqIuIBEShLiISEH1GqUiG9MlZUogU6iIZGPnkrJEPWhn55CxAwS55peEXkQzok7OkUCnURTKgT86SQqVQF8mAPjlLCpVCXSQD+uQsKVS6UCqSgZGLoZr9IoVGoS6Soe1b6hTiUnA0/CIiEhCFuohIQBTqIiIBUaiLiAREoS4iEhCFuohIQAp+SmNUV8KLar9FJNoKOtSjuhJeVPstItFX0MMvUV0JL6r9FpHomzHUzexRM2s3s4MpbZvN7GUze83Mmszs1lx0Lqor4UW13yISfemcqX8P2Dau7SHgz919M/Dt5P2si+pKeFHtt4hE34yh7u7PA2fHNwMVye1KoDXL/QKiuxJeVPstItGX6YXSrwF7zewvSfxi+FDWepQiqivhRbXfIhJ95u4z72RWDzzp7huT9/8G+JW7/9DMfh+4z90/McVz7wPuA1i7du37T5w4ka2+i4jMC2a2390b09k309kv9wK7k9v/DEx5odTdH3b3RndvrKmpyejFuuMD9A8OZ/RcEZH5JNNQbwXuSG7fBRzNTncm97+ePcqt/+PnfGtPM/tPnCWdvy5EROajGcfUzewJ4E6g2szeA/4M+APgu2ZWAvSSHF7JlbtuWE77hT7+Zf97/MPLJ1m7tDzxAQWba7m2ZlEuX1pEJFLSGlPPlsbGRm9qasr4+Rf7Btl78BR7Xmvhxbc6GXa4aU0Vn9tcy+/eVMuyRQuy2FsRkcIwmzH1SIV6qtPne/nX11r50YEWftt2nuIi4451NWzfUscnb1hBrKx45oOIiETAvAj1VEdOXWDPay38+EALrd29XFVWzLaNq/jcljo+eN0yioss668pIjJX5l2ojxgedvYdO8ueAy38tLmNC32DrKhYwN2b69i+uY4bVi3GTAEvItEyb0M9Ve/AEM+90c6PDrTwyyPtDAw5DSsWs31LHXdvrtVb9kUkMhTq45y71M+TzW3sOdDC/hPnMIPbrlnG57bUsW3TSioWls55n0RE0qVQn8aJM5f4cfIC67HOS5SVFHFXw3I+vWklH/ud5Qp4ESk4CvU0uDuvv9c9Ov7efqGP0mLjw++rZtuGlXxi/QqqNUVSRAqAQn2WhoedA+92sffQKZ46eIqTZ3soMrilfinbNq5k64aVGoMXkbxRqF8Bd+dw2wWeOnSKvQdPceT0BQBuWl3JpzasZNvGlVynd7GKyBxSqGfROx0X2XvoNE8dOsXr73YBcP3yRaNn8BtqKzRNUkRySqGeI23dcZ4+dJqnDp5i37EzDDusXhJjW/IM/ua1SyjSG51EJMsU6nPgzMU+nj3czlOHTvHro530Dw1Ts3gBn1q/gm0bV3LbtcsoLS7oz/UWkYhQqM+xC70D/OJIB3sPnuIXR9rp6R+iYmEJn7hhBZ9Yv4IPX1dNZbmmSopIZhTqedQ7MMQLRzt56uApfn74NN3xAYossZrk7dfX8NHrq9m8pooSncWLSJoU6gVicGiY197t4vmjnbxwtIPX3+1i2GHxghI+9L5lyZCvYe2y8nx3VUQKmEK9QHX3DPDi24mAf/7NTlq64gBcvaycj15fw+3XV/PB65axWO9qFZEUCvUIcHeOdV7ihaOdPP9mBy+9c4ae/iGKi4yb11YlQn5dDZvqKrV0sMg8p1CPoP7BYV49eY4XjnbwwtFOmlu6cYfKWCkfeV81t19fze3raqjTO1tF5h2FegDOXOzjxbfP8MKbiZA/db4XgOtqrkqMxa+r5rZrl1FeNuPHzIpIxCnUA+PuvNV+kV8lA37fsTP0DgxTWmw0Xr2U29dV89Hra1i/qkJvfhIJkEI9cL0DQ+w/cY7nkxdcD7edB2DpVWWjQzUfuGYZa5bGtISBSAAU6mnac6CFXXuP0NoVp7Yqxo6tDWzfUpfvbs1a+4VeXnyrkxfe7OT5o510XuwDYPniBdxSv5TG+iXcUr+U31m5WPPjRSJIoZ6GPQda2Lm7mfjA0GhbrLSYB+/ZFMlgHzE87LzZfoGm4+doOn6W3xw/Nzp18qqyYm6+egmNVyeCfvOaKq5aoDF5kUKnUE/Dh7/z3GjYpaqrivHi/XfloUe509oVp+nE5ZB/49R53KG4yNhQW0Hj1Uu5pX4J769fwvLFC/PdXREZZzahPm9P01onCfTp2qOstirGZ6tifPamWgDO9w5w4GRXMuTP8o+vnODRF48BUL+snMb6pdy0poqNtRXcsKqChaXF+ey+iMzCvA312qrYpGfq8+ETjioWlnLHuhruWFcDJObIH2rtpun4OX5z/CzPvdHOv+x/D4Aig/ctX8TG2krW11awsS7xNZuf5RrKtQ2RQjBvh19CHVPPBnentbuXgy3dHGrp5lDreQ62dnP6fN/oPlcvK2djbSUb6irYUFvJxtoKlmXwma76OYjMLKvDL2b2KPAZoN3dN6a0/yHwX4BB4Cfu/o0M+5sXI4GhM8SJzIy6qhh1VTG2blg52t5xoY9DrcmQb+mmuaWbnzS3jT6+qnIhG2qTIV9XyYbaClZVLpx2WuWuvUfGBDpAfGCIXXuP6GchkoF0hl++B/wt8P2RBjP7GHA3cKO795nZ8tx0L7e2b6lTcMxCzeIF3NmwnDsbLv+4u3sGONTWzW+TQX+w9TzPvdHOcPIPwKVXlaUEfeLr1UvLR98kNZ+ubYjMhRlD3d2fN7P6cc1fAb7j7n3Jfdpz0DeZpXyMTVeWl/Kh66r50HXVo209/YMcbruQOKtvSQzdPPLrdxgYSiT9ogUlrK+tYENtBVXlpZzrGZhw3Gxd29B4vcw3mV4oXQfcbmb/HegFvu7uv5lsRzO7D7gPYO3atRm+nMxk/Nh0S1ecnbubAeY8xMrLSnj/1Ut4/9VLRtv6Boc4evoih1q7OZgM+ideOUnvwPCkx7imupyDLd3UV1/Fogzn0hdSTUTmSloXSpNn6k+OjKmb2UHgOeCPgFuAfwKu9RkOVkgXSkMTxXn3Q8POBx98lvYLfdPut3zxAq6pvmrM7dqaq1iztJwFJVNPt4xiTSQcQ8NOW3eck2d7ePdsDx9rWM7yiszeBzIX89TfA3YnQ/wVMxsGqoGODI8nVyiKY9PFRUbHNIH+d//hZt7pvMSxjksc67zEM789zZlL/aOPFxmsXlI+JuhHtmsrY5GsiUTL+d4BTp5JhPbJlNu7Z3to6YqPDjkC/J//1Mgn1+f+zX2Zhvoe4C7gl2a2DigDOrPVKZm9qM67n6rfdVUxtm1cNaG9u2eAY2cucazzIsc6LvFO5yWOn7lE0/GzXOq/PIumrKSI4iJjcHjiH48rKxbi7lrsTGY0MDRMW1fv5bA+dzm0T57toWvc9aCq8lLWLi1nQ10ln960irVLy0dvqyrn5t3a6UxpfAK4E6g2s/eAPwMeBR5NDsP0A/fONPQiubVja8Ok8713bG3IY69mNtt+V5aXsrm8is1rqsa0uzsdF/oSZ/bJ2/97q5NDrecZ/w+z7Xwv67+9l5WVC1lZsZBVlQtZUZn4urJiYaK9ciHVVy3QUsaBcncu9Q/R1dNPV88AXT0DnO3p571zY8+6W7t6GUo5MSgtNlYvKWfN0nJuXF05GthrkrdsvikvU/P2zUchiupMj1z2e8+BFh566g1au3upXlTGv9+0ijVLyznV3Uvb+V5OdSdup8/3TjirLykyVqSE/MgvgJUp4b988ULKSrTyZT71Dgxxrqefc5cG6IpfDulzPf10xwc4d6mfrvjAaICf6xmgO94/ZmgkVfWiskRILykfE9prl5WzsmJhXj5eUgt6iczS8LDTeamP0919tHXHOZUS+G3J0G/tjk+YrWMGVbFSqsrLqIiVUhUrpTJ5qyq/vH25rWz0Ma2pM1b/4HAieKcJ4smCu29w8hlUAAtLi6iKlVFVnqj5kvKR7TKqYon7lcn2JeWl1FbFCnLlUi3oNU/pTD1zRUXG8sWJM+9Nqysn3cfdOR8fpO18fEzgd17sozs+kDgr7Onn+JlLo/enO2cqKylKBHzKL4GK5HZbdy8vvX2G7vgAS8pL2b6ljtuvr6asuJiykqLErTjxdUHylto+3br5uaq3uzM07Ay5c7F3cFwQpwb05cA+d+ly3Xr6h6Y8dmmxjQnixPBHMpxHwjo27v48/cWpM/VARHUNlaj2Ox3Dw86FvkG6ewZGQ74r3j+6PdLelfJ4d3yAzot90559pqPIGA34BaXFia8lRcQHhjh1vnfMLxszWF0VY/HCUoZHgjkZzkPDzvDoNqOPj7QNpmynEyVFxmjwTnamXJn8Ov7suryseF5f2NaZ+jwU1TVUotrvdBQV2eiwy2xMNb++ZtEC/vcXbqZ/cJj+oSH6BobpHxqmb3A40TaYuJ+63TcwNGafnx8+PSF83aH9Qh8NKxdTZEZxUcrNjKLUr0WMaSsuTn4tsjHPXbSgZNJhjsULSnTxOccU6oGI6pzsqPY7l6b63jsv9nHrNUuv6NjX3P+TSdv7B4f5+3tvuaJjS2HQZftATDUfPQrz1GfTPh/ksiaqd/gU6oHYsbWB2LiLQlGZpx7FfudSLmuieodPwy+BiOr68FHtdy7lsiaqd/g0+0VEpMDNZvaLhl9ERAKiUBcRCYhCXUQkIAp1EZGAKNRFRAKiUBcRCYhCXUQkIAp1EZGA6B2lMkYhrG0uIplTqMuo8Wubt3TF2bm7GUDBLhIRGn6RUdOtbS4i0aBQl1Fa21wk+hTqMkprbYtEn0JdRmmtbZHo04VSGaW1tkWiT6EuY2zfUqcQF4kwhbrknebGT5TLmkT12FE11zVRqEteaW78RLmsSVSPHVX5qMmMF0rN7FEzazezg5M89nUzczOrzknvJHiaGz9RLmsS1WNHVT5qks7sl+8B28Y3mtka4JPAySz3SeYRzY2fKJc1ieqxoyofNZkx1N39eeDsJA/9NfANYO4+uVqCo7nxE+WyJlE9dlTloyYZzVM3s88CLe7+ehr73mdmTWbW1NHRkcnLScA0N36iXNYkqseOqnzUZNYXSs2sHHgA+FQ6+7v7w8DDAI2NjTqrlzE0N36iXNYkqseOqnzUxNxnzlkzqweedPeNZrYJeBboST68GmgFbnX3U9Mdp7Gx0Zuamq6sxyIi84yZ7Xf3xnT2nfWZurs3A8tTXuw40OjunbM9lkiuad60zDfpTGl8AngJaDCz98zsS7nvlsiVG5kj3NIVx7k8R3jPgZZ8d00kZ2Y8U3f3z8/weH3WeiOSRdPNEdbZuoRKqzRKsDRvWuYjLRMgwaqtitEySYDP53nTuRbVaxhR7fdkdKYuwdK86bkV1WsYUe33VBTqEqztW+p48J5N1FXFMKCuKsaD92yK7BlYoYvq2i9R7fdUNPwiQdP68HMnqtcwotrvqehMXUSyIqprv0S131NRqItIVkT1GkZU+z0VDb+ISFZEde2XqPZ7Kmmt/ZItWvtFRGT2ZrP2i4ZfREQColAXEQmIQl1EJCAKdRGRgCjURUQColAXEQmIQl1EJCAKdRGRgCjURUQColAXEQmIQl1EJCAKdRGRgCjURUQColAXEQmIQl1EJCAKdRGRgCjURUQCMmOom9mjZtZuZgdT2naZ2Rtm9m9m9iMzq8ppL0VEJC3pnKl/D9g2ru0ZYKO73wi8CezMcr9ERCQDM4a6uz8PnB3X9rS7DybvvgyszkHfRERklrIxpv5F4GdTPWhm95lZk5k1dXR0ZOHlRERkKlcU6mb2ADAIPD7VPu7+sLs3untjTU3NlbyciIjMoCTTJ5rZvcBngI+7u2evSyIikqmMQt3MtgF/Ctzh7j3Z7ZKIiGQqnSmNTwAvAQ1m9p6ZfQn4W2Ax8IyZvWZmf5fjfoqISBpmPFN3989P0vxIDvoiIiJXSO8oFREJiEJdRCQgCnURkYAo1EVEAqJQFxEJiEJdRCQgCnURkYAo1EVEAqJQFxEJiEJdRCQgCnURkYBkvPSuiOTOt/Y088S+dxlyp9iMz39gDf9t+6asHHvPgRZ27T1Ca1ec2qoYO7Y2sH1LXVaOLfmnUBcpMN/a08w/vHxy9P6Q++j9Kw32PQda2Lm7mfjAEAAtXXF27m4GULAHQsMvIgXmiX3vzqp9NnbtPTIa6CPiA0Ps2nvkio8thUGhLlJghqb4ILGp2mejtSs+q3aJHoW6SIEpNptV+2zUVsVm1S7Ro1AXKTCf/8CaWbXPxo6tDcRKi8e0xUqL2bG14YqPLYVBF0pFCszIxdBczH4ZuRiq2S/hMs/COF26Ghsbvampac5eT0QkBGa2390b09lXwy8iIgFRqIuIBEShLiISEIW6iEhAFOoiIgGZ09kvZtYBnACqgc45e+HCpTqoBqAajFAdpq7B1e5ek84B5jTUR1/UrCnd6TkhUx1UA1ANRqgO2amBhl9ERAKiUBcRCUi+Qv3hPL1uoVEdVANQDUaoDlmoQV7G1EVEJDc0/CIiEhCFuohIQLIe6ma2xsx+YWaHzeyQmf3RuMe/bmZuZtUpbTvN7C0zO2JmW7Pdp3yYrg5m9ofJ7/WQmT2U0h5UHaaqgZltNrOXzew1M2sys1tTnhNUDQDMbKGZvWJmryfr8OfJ9qVm9oyZHU1+XZLynKDqME0NdpnZG2b2b2b2IzOrSnlOUDWAqeuQ8viV56O7Z/UGrAJuTm4vBt4E1ifvrwH2knwDUrJtPfA6sAC4BngbKM52v+b6NlUdgI8BPwcWJB9bHmodpqnB08Cnk+3/DvhlqDVIfl8GLEpulwL7gNuAh4D7k+33A38Rah2mqcGngJJk+1+EXIPp6pC8n5V8zPqZuru3ufurye0LwGFgZAX+vwa+AaRenb0b+L/u3ufux4C3gFuJuGnq8BXgO+7el3ysPfmU4OowTQ0cqEjuVgm0JreDqwGAJ1xM3i1N3pzE9/tYsv0xYHtyO7g6TFUDd3/a3QeT7S8Dq5PbwdUApv23AFnKx5yOqZtZPbAF2GdmnwVa3P31cbvVAakfk/4el38JBCG1DsA64HYz22dmvzKzW5K7BV2HcTX4GrDLzN4F/hLYmdwt2BqYWbGZvQa0A8+4+z5ghbu3QeIXILA8uXuQdZiiBqm+CPwsuR1kDWDyOmQzH3MW6ma2CPghif/Ag8ADwLcn23WStmDmWabWwd3Pk/gIwSUk/vTcAfzAzIyA6zBJDb4C/LG7rwH+GHhkZNdJnh5EDdx9yN03kzgTvdXMNk6ze5B1mK4GZvYAiZx4fKRpskPkvJNzYJI63EgW8zEnoW5mpST+Ez/u7ruB60iMB71uZsdJfDOvmtlKEr95Uj9RdzWX/xyPtEnqAInvd3fyz7BXgGESi/gEWYcpanAvMLL9z1z+czLIGqRy9y7gl8A24LSZrQJIfh0Zigu6DuNqgJndC3wG+IInB5IJvAYwpg53k818zNGFgO8D/3OafY5z+ULABsZeCHiHcC6ITKgD8GXgvya315H408pCrMM0NTgM3Jnc/jiwP/B/CzVAVXI7BrxAIsR2MfZC6UOh1mGaGmwDfgvUjNs/uBpMV4dx+1xRPpZMk/eZ+jDwH4Hm5LgRwDfd/aeT7ezuh8zsByR+sIPAV919KAf9mmuT1gF4FHjUzA4C/cC9nvjphViHqWrwB8B3zawE6AXug6D/LawCHjOzYhJ/Hf/A3Z80s5dIDL99CTgJ/B4EW4epavAWicB6JjEKycvu/uVAawBT1GGqnTOpg5YJEBEJiN5RKiISEIW6iEhAFOoiIgFRqIuIBEShLiISEIW6iEhAFOoiIgH5/+EaqS+WjFbpAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)\n",
+    "\n",
+    "plt.scatter(X_test,y_test)\n",
+    "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Các phương pháp mã hóa\n",
+    "\n",
+    "Trong thế giới lý tưởng, chúng ta muốn có thể dự đoán giá cho các loại bí ngô khác nhau bằng cùng một mô hình. Để tính đến loại bí ngô, trước tiên chúng ta cần chuyển đổi nó sang dạng số, hay còn gọi là **mã hóa**. Có một số cách để thực hiện điều này:\n",
+    "\n",
+    "* Mã hóa số đơn giản sẽ tạo một bảng các loại khác nhau, sau đó thay thế tên loại bằng một chỉ số trong bảng đó. Đây không phải là ý tưởng tốt nhất cho hồi quy tuyến tính, vì hồi quy tuyến tính sẽ tính đến giá trị số của chỉ số, và giá trị số này có khả năng không tương quan một cách tuyến tính với giá cả.\n",
+    "* Mã hóa one-hot, sẽ thay thế cột `Variety` bằng 4 cột khác nhau, mỗi cột đại diện cho một loại, chứa giá trị 1 nếu hàng tương ứng thuộc loại đó, và 0 nếu không.\n",
+    "\n",
+    "Đoạn mã dưới đây cho thấy cách chúng ta có thể mã hóa one-hot cho một loại:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FAIRYTALE</th>\n",
+       "      <th>MINIATURE</th>\n",
+       "      <th>MIXED HEIRLOOM VARIETIES</th>\n",
+       "      <th>PIE TYPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1738</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1739</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1740</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1741</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1742</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>415 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      FAIRYTALE  MINIATURE  MIXED HEIRLOOM VARIETIES  PIE TYPE\n",
+       "70            0          0                         0         1\n",
+       "71            0          0                         0         1\n",
+       "72            0          0                         0         1\n",
+       "73            0          0                         0         1\n",
+       "74            0          0                         0         1\n",
+       "...         ...        ...                       ...       ...\n",
+       "1738          0          1                         0         0\n",
+       "1739          0          1                         0         0\n",
+       "1740          0          1                         0         0\n",
+       "1741          0          1                         0         0\n",
+       "1742          0          1                         0         0\n",
+       "\n",
+       "[415 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.get_dummies(new_pumpkins['Variety'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hồi quy tuyến tính trên các loại\n",
+    "\n",
+    "Bây giờ chúng ta sẽ sử dụng cùng một đoạn mã như trên, nhưng thay vì `DayOfYear`, chúng ta sẽ sử dụng loại đã được mã hóa one-hot làm đầu vào:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety'])\n",
+    "y = new_pumpkins['Price']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 5.24 (19.7%)\n",
+      "Model determination:  0.774085281105197\n"
+     ]
+    }
+   ],
+   "source": [
+    "def run_linear_regression(X,y):\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "    lin_reg = LinearRegression()\n",
+    "    lin_reg.fit(X_train,y_train)\n",
+    "\n",
+    "    pred = lin_reg.predict(X_test)\n",
+    "\n",
+    "    mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "    print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "    score = lin_reg.score(X_train,y_train)\n",
+    "    print('Model determination: ', score)\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chúng ta cũng có thể thử sử dụng các đặc điểm khác theo cách tương tự, và kết hợp chúng với các đặc điểm số, chẳng hạn như `Month` hoặc `DayOfYear`:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.84 (10.5%)\n",
+      "Model determination:  0.9401096672643048\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = pd.get_dummies(new_pumpkins['Variety']) \\\n",
+    "        .join(new_pumpkins['Month']) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['City'])) \\\n",
+    "        .join(pd.get_dummies(new_pumpkins['Package']))\n",
+    "y = new_pumpkins['Price']\n",
+    "\n",
+    "run_linear_regression(X,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Hồi quy đa thức\n",
+    "\n",
+    "Hồi quy đa thức cũng có thể được sử dụng với các đặc trưng phân loại đã được mã hóa one-hot. Mã nguồn để huấn luyện hồi quy đa thức về cơ bản sẽ giống như chúng ta đã thấy ở trên.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean error: 2.23 (8.25%)\n",
+      "Model determination:  0.9652870784724543\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn.pipeline import make_pipeline\n",
+    "\n",
+    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "\n",
+    "pipeline.fit(X_train,y_train)\n",
+    "\n",
+    "pred = pipeline.predict(X_test)\n",
+    "\n",
+    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
+    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
+    "\n",
+    "score = pipeline.score(X_train,y_train)\n",
+    "print('Model determination: ', score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.5"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d77bd89ae7e79780c68c58bab91f13f8",
+   "translation_date": "2025-09-06T13:12:40+00:00",
+   "source_file": "2-Regression/3-Linear/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/4-Logistic/notebook.ipynb b/translations/vi/2-Regression/4-Logistic/notebook.ipynb
new file mode 100644
index 000000000..4bf58d7c5
--- /dev/null
+++ b/translations/vi/2-Regression/4-Logistic/notebook.ipynb
@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Các Loại Bí Ngô và Màu Sắc\n",
+    "\n",
+    "Tải các thư viện cần thiết và tập dữ liệu. Chuyển dữ liệu thành một dataframe chứa một phần dữ liệu:\n",
+    "\n",
+    "Hãy xem xét mối quan hệ giữa màu sắc và loại bí ngô.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.1"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
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+  "coopTranslator": {
+   "original_hash": "dee08c2b49057b0de8b6752c4dbca368",
+   "translation_date": "2025-09-06T13:26:53+00:00",
+   "source_file": "2-Regression/4-Logistic/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb b/translations/vi/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
new file mode 100644
index 000000000..7cef80b66
--- /dev/null
+++ b/translations/vi/2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb
@@ -0,0 +1,686 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Xây dựng mô hình hồi quy logistic - Bài học 4\n",
+    "\n",
+    "![Đồ họa thông tin hồi quy logistic vs. hồi quy tuyến tính](../../../../../../2-Regression/4-Logistic/images/linear-vs-logistic.png)\n",
+    "\n",
+    "#### **[Câu hỏi trước bài học](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/15/)**\n",
+    "\n",
+    "#### Giới thiệu\n",
+    "\n",
+    "Trong bài học cuối cùng về Hồi quy, một trong những kỹ thuật *cổ điển* cơ bản của ML, chúng ta sẽ tìm hiểu về Hồi quy Logistic. Bạn sẽ sử dụng kỹ thuật này để khám phá các mẫu nhằm dự đoán các danh mục nhị phân. Đây có phải là kẹo sô-cô-la hay không? Bệnh này có lây hay không? Khách hàng này có chọn sản phẩm này hay không?\n",
+    "\n",
+    "Trong bài học này, bạn sẽ học:\n",
+    "\n",
+    "-   Các kỹ thuật cho hồi quy logistic\n",
+    "\n",
+    "✅ Nâng cao hiểu biết của bạn về cách làm việc với loại hồi quy này trong [module học này](https://learn.microsoft.com/training/modules/introduction-classification-models/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "## Điều kiện tiên quyết\n",
+    "\n",
+    "Sau khi làm việc với dữ liệu bí ngô, chúng ta đã đủ quen thuộc để nhận ra rằng có một danh mục nhị phân mà chúng ta có thể làm việc: `Color`.\n",
+    "\n",
+    "Hãy xây dựng một mô hình hồi quy logistic để dự đoán, dựa trên một số biến, *màu sắc của một quả bí ngô có khả năng là gì* (cam 🎃 hoặc trắng 👻).\n",
+    "\n",
+    "> Tại sao chúng ta lại nói về phân loại nhị phân trong một bài học nhóm về hồi quy? Chỉ vì sự tiện lợi về ngôn ngữ, vì hồi quy logistic thực chất là [một phương pháp phân loại](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression), mặc dù dựa trên tuyến tính. Tìm hiểu về các cách khác để phân loại dữ liệu trong nhóm bài học tiếp theo.\n",
+    "\n",
+    "Để thực hiện bài học này, chúng ta sẽ cần các gói sau:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) là một [bộ sưu tập các gói R](https://www.tidyverse.org/packages) được thiết kế để làm cho khoa học dữ liệu nhanh hơn, dễ dàng hơn và thú vị hơn!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) là một [bộ khung các gói](https://www.tidymodels.org/packages/) dành cho mô hình hóa và học máy.\n",
+    "\n",
+    "-   `janitor`: [Gói janitor](https://github.com/sfirke/janitor) cung cấp các công cụ đơn giản để kiểm tra và làm sạch dữ liệu bẩn.\n",
+    "\n",
+    "-   `ggbeeswarm`: [Gói ggbeeswarm](https://github.com/eclarke/ggbeeswarm) cung cấp các phương pháp để tạo biểu đồ kiểu beeswarm sử dụng ggplot2.\n",
+    "\n",
+    "Bạn có thể cài đặt chúng bằng lệnh:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"janitor\", \"ggbeeswarm\"))`\n",
+    "\n",
+    "Ngoài ra, đoạn mã dưới đây sẽ kiểm tra xem bạn đã có các gói cần thiết để hoàn thành module này chưa và cài đặt chúng nếu chúng bị thiếu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, janitor, ggbeeswarm)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## **Xác định câu hỏi**\n",
+    "\n",
+    "Đối với mục đích của chúng ta, chúng ta sẽ biểu diễn điều này dưới dạng nhị phân: 'Trắng' hoặc 'Không Trắng'. Trong tập dữ liệu của chúng ta cũng có một danh mục 'sọc', nhưng số lượng trường hợp rất ít, vì vậy chúng ta sẽ không sử dụng nó. Dù sao thì danh mục này cũng biến mất khi chúng ta loại bỏ các giá trị null khỏi tập dữ liệu.\n",
+    "\n",
+    "> 🎃 Một sự thật thú vị, đôi khi chúng ta gọi bí ngô trắng là bí ngô 'ma'. Chúng không dễ khắc lắm, vì vậy chúng không phổ biến như bí ngô màu cam, nhưng trông chúng rất ngầu! Vì vậy, chúng ta cũng có thể diễn đạt lại câu hỏi của mình thành: 'Ma' hoặc 'Không Ma'. 👻\n",
+    "\n",
+    "## **Về hồi quy logistic**\n",
+    "\n",
+    "Hồi quy logistic khác với hồi quy tuyến tính, mà bạn đã học trước đây, ở một vài điểm quan trọng.\n",
+    "\n",
+    "#### **Phân loại nhị phân**\n",
+    "\n",
+    "Hồi quy logistic không cung cấp các tính năng giống như hồi quy tuyến tính. Hồi quy logistic đưa ra dự đoán về một `danh mục nhị phân` (\"màu cam hoặc không màu cam\"), trong khi hồi quy tuyến tính có khả năng dự đoán `giá trị liên tục`, ví dụ như dựa trên nguồn gốc của một quả bí ngô và thời gian thu hoạch, *giá của nó sẽ tăng bao nhiêu*.\n",
+    "\n",
+    "![Đồ họa thông tin của Dasani Madipalli](../../../../../../2-Regression/4-Logistic/images/pumpkin-classifier.png)\n",
+    "\n",
+    "### Các loại phân loại khác\n",
+    "\n",
+    "Có các loại hồi quy logistic khác, bao gồm hồi quy đa thức và hồi quy thứ bậc:\n",
+    "\n",
+    "- **Đa thức**, liên quan đến việc có nhiều hơn một danh mục - \"Màu cam, Trắng và Sọc\".\n",
+    "\n",
+    "- **Thứ bậc**, liên quan đến các danh mục có thứ tự, hữu ích nếu chúng ta muốn sắp xếp các kết quả theo logic, như các quả bí ngô được sắp xếp theo một số kích thước hữu hạn (mini, nhỏ, vừa, lớn, rất lớn, cực lớn).\n",
+    "\n",
+    "![Hồi quy đa thức vs hồi quy thứ bậc](../../../../../../2-Regression/4-Logistic/images/multinomial-vs-ordinal.png)\n",
+    "\n",
+    "#### **Các biến KHÔNG CẦN phải tương quan**\n",
+    "\n",
+    "Bạn còn nhớ hồi quy tuyến tính hoạt động tốt hơn với các biến có tương quan cao không? Hồi quy logistic thì ngược lại - các biến không cần phải tương quan. Điều này phù hợp với tập dữ liệu của chúng ta, vốn có các mối tương quan khá yếu.\n",
+    "\n",
+    "#### **Bạn cần nhiều dữ liệu sạch**\n",
+    "\n",
+    "Hồi quy logistic sẽ cho kết quả chính xác hơn nếu bạn sử dụng nhiều dữ liệu hơn; tập dữ liệu nhỏ của chúng ta không phải là tối ưu cho nhiệm vụ này, vì vậy hãy ghi nhớ điều đó.\n",
+    "\n",
+    "✅ Hãy suy nghĩ về các loại dữ liệu phù hợp với hồi quy logistic\n",
+    "\n",
+    "## Bài tập - làm sạch dữ liệu\n",
+    "\n",
+    "Đầu tiên, làm sạch dữ liệu một chút, loại bỏ các giá trị null và chỉ chọn một số cột:\n",
+    "\n",
+    "1. Thêm đoạn mã sau:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load the core tidyverse packages\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data and clean column names\n",
+    "pumpkins <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/2-Regression/data/US-pumpkins.csv\") %>% \n",
+    "  clean_names()\n",
+    "\n",
+    "# Select desired columns\n",
+    "pumpkins_select <- pumpkins %>% \n",
+    "  select(c(city_name, package, variety, origin, item_size, color)) \n",
+    "\n",
+    "# Drop rows containing missing values and encode color as factor (category)\n",
+    "pumpkins_select <- pumpkins_select %>% \n",
+    "  drop_na() %>% \n",
+    "  mutate(color = factor(color))\n",
+    "\n",
+    "# View the first few rows\n",
+    "pumpkins_select %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bạn luôn có thể xem nhanh dataframe mới của mình bằng cách sử dụng hàm [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html) như dưới đây:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pumpkins_select %>% \n",
+    "  glimpse()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hãy xác nhận rằng chúng ta thực sự sẽ thực hiện một bài toán phân loại nhị phân:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Subset distinct observations in outcome column\n",
+    "pumpkins_select %>% \n",
+    "  distinct(color)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trực quan hóa - biểu đồ phân loại\n",
+    "Đến thời điểm này, bạn đã tải lại dữ liệu về bí ngô và làm sạch nó để giữ lại một tập dữ liệu chứa một vài biến, bao gồm Màu sắc. Hãy trực quan hóa dataframe trong notebook bằng thư viện ggplot.\n",
+    "\n",
+    "Thư viện ggplot cung cấp một số cách thú vị để trực quan hóa dữ liệu của bạn. Ví dụ, bạn có thể so sánh phân bố dữ liệu cho từng Loại và Màu sắc trong một biểu đồ phân loại.\n",
+    "\n",
+    "1. Tạo biểu đồ như vậy bằng cách sử dụng hàm geombar, sử dụng dữ liệu bí ngô của chúng ta, và chỉ định ánh xạ màu cho từng loại bí ngô (màu cam hoặc màu trắng):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Specify colors for each value of the hue variable\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# Create the bar plot\n",
+    "ggplot(pumpkins_select, aes(y = variety, fill = color)) +\n",
+    "  geom_bar(position = \"dodge\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(y = \"Variety\", fill = \"Color\") +\n",
+    "  theme_minimal()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bằng cách quan sát dữ liệu, bạn có thể thấy cách dữ liệu Màu sắc liên quan đến Loại.\n",
+    "\n",
+    "✅ Dựa trên biểu đồ phân loại này, bạn có thể hình dung ra những khám phá thú vị nào?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Xử lý dữ liệu: mã hóa đặc trưng\n",
+    "\n",
+    "Bộ dữ liệu pumpkins của chúng ta chứa các giá trị dạng chuỗi cho tất cả các cột. Làm việc với dữ liệu phân loại rất trực quan đối với con người nhưng không phải đối với máy móc. Các thuật toán học máy hoạt động tốt với dữ liệu dạng số. Đó là lý do tại sao mã hóa là một bước rất quan trọng trong giai đoạn xử lý dữ liệu, vì nó cho phép chúng ta chuyển đổi dữ liệu phân loại thành dữ liệu dạng số mà không làm mất thông tin. Mã hóa tốt sẽ giúp xây dựng một mô hình tốt.\n",
+    "\n",
+    "Đối với mã hóa đặc trưng, có hai loại mã hóa chính:\n",
+    "\n",
+    "1. Bộ mã hóa thứ tự (Ordinal encoder): phù hợp với các biến thứ tự, là các biến phân loại mà dữ liệu của chúng tuân theo một thứ tự logic, như cột `item_size` trong bộ dữ liệu của chúng ta. Nó tạo ra một ánh xạ sao cho mỗi danh mục được biểu diễn bằng một con số, con số này là thứ tự của danh mục trong cột.\n",
+    "\n",
+    "2. Bộ mã hóa phân loại (Categorical encoder): phù hợp với các biến danh nghĩa, là các biến phân loại mà dữ liệu của chúng không tuân theo một thứ tự logic, như tất cả các đặc trưng khác ngoài `item_size` trong bộ dữ liệu của chúng ta. Đây là một dạng mã hóa one-hot, nghĩa là mỗi danh mục được biểu diễn bằng một cột nhị phân: biến được mã hóa sẽ bằng 1 nếu quả bí thuộc về loại đó và bằng 0 nếu không.\n",
+    "\n",
+    "Tidymodels cung cấp một gói rất tiện lợi khác: [recipes](https://recipes.tidymodels.org/) - một gói dùng để xử lý dữ liệu. Chúng ta sẽ định nghĩa một `recipe` để chỉ định rằng tất cả các cột dự đoán nên được mã hóa thành một tập hợp các số nguyên, `prep` để ước tính các số lượng và thống kê cần thiết cho bất kỳ thao tác nào, và cuối cùng `bake` để áp dụng các tính toán cho dữ liệu mới.\n",
+    "\n",
+    "> Thông thường, recipes thường được sử dụng như một bộ tiền xử lý cho việc mô hình hóa, nơi nó định nghĩa các bước cần được áp dụng cho một tập dữ liệu để chuẩn bị cho việc mô hình hóa. Trong trường hợp đó, **rất khuyến khích** bạn sử dụng `workflow()` thay vì tự ước tính một recipe bằng prep và bake. Chúng ta sẽ thấy tất cả điều này ngay sau đây.\n",
+    ">\n",
+    "> Tuy nhiên, hiện tại chúng ta đang sử dụng recipes + prep + bake để chỉ định các bước cần được áp dụng cho một tập dữ liệu nhằm chuẩn bị cho việc phân tích dữ liệu và sau đó trích xuất dữ liệu đã được xử lý với các bước đã áp dụng.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Preprocess and extract data to allow some data analysis\n",
+    "baked_pumpkins <- recipe(color ~ ., data = pumpkins_select) %>%\n",
+    "  # Define ordering for item_size column\n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  # Convert factors to numbers using the order defined above (Ordinal encoding)\n",
+    "  step_integer(item_size, zero_based = F) %>%\n",
+    "  # Encode all other predictors using one hot encoding\n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE) %>%\n",
+    "  prep(data = pumpkin_select) %>%\n",
+    "  bake(new_data = NULL)\n",
+    "\n",
+    "# Display the first few rows of preprocessed data\n",
+    "baked_pumpkins %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "✅ Những lợi ích của việc sử dụng bộ mã hóa thứ tự (ordinal encoder) cho cột Item Size là gì?\n",
+    "\n",
+    "### Phân tích mối quan hệ giữa các biến\n",
+    "\n",
+    "Bây giờ, sau khi đã tiền xử lý dữ liệu, chúng ta có thể phân tích mối quan hệ giữa các đặc trưng và nhãn để hiểu rõ hơn về khả năng dự đoán nhãn của mô hình dựa trên các đặc trưng. Cách tốt nhất để thực hiện loại phân tích này là vẽ biểu đồ dữ liệu.  \n",
+    "Chúng ta sẽ tiếp tục sử dụng hàm ggplot geom_boxplot_ để trực quan hóa mối quan hệ giữa Item Size, Variety và Color trong một biểu đồ phân loại. Để biểu diễn dữ liệu tốt hơn, chúng ta sẽ sử dụng cột Item Size đã được mã hóa và cột Variety chưa được mã hóa.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Define the color palette\n",
+    "palette <- c(ORANGE = \"orange\", WHITE = \"wheat\")\n",
+    "\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins_select_plot<-pumpkins_select\n",
+    "pumpkins_select_plot$item_size <- baked_pumpkins$item_size\n",
+    "\n",
+    "# Create the grouped box plot\n",
+    "ggplot(pumpkins_select_plot, aes(x = `item_size`, y = color, fill = color)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_grid(variety ~ ., scales = \"free_x\") +\n",
+    "  scale_fill_manual(values = palette) +\n",
+    "  labs(x = \"Item Size\", y = \"\") +\n",
+    "  theme_minimal() +\n",
+    "  theme(strip.text = element_text(size = 12)) +\n",
+    "  theme(axis.text.x = element_text(size = 10)) +\n",
+    "  theme(axis.title.x = element_text(size = 12)) +\n",
+    "  theme(axis.title.y = element_blank()) +\n",
+    "  theme(legend.position = \"bottom\") +\n",
+    "  guides(fill = guide_legend(title = \"Color\")) +\n",
+    "  theme(panel.spacing = unit(0.5, \"lines\"))+\n",
+    "  theme(strip.text.y = element_text(size = 4, hjust = 0)) \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Sử dụng biểu đồ swarm\n",
+    "\n",
+    "Vì Color là một danh mục nhị phân (Trắng hoặc Không), nó cần 'một [phương pháp chuyên biệt](https://github.com/rstudio/cheatsheets/blob/main/data-visualization.pdf) để trực quan hóa'.\n",
+    "\n",
+    "Hãy thử sử dụng `biểu đồ swarm` để hiển thị sự phân bố của màu sắc liên quan đến kích thước vật phẩm.\n",
+    "\n",
+    "Chúng ta sẽ sử dụng [gói ggbeeswarm](https://github.com/eclarke/ggbeeswarm), cung cấp các phương pháp để tạo biểu đồ kiểu beeswarm bằng ggplot2. Biểu đồ beeswarm là một cách để vẽ các điểm mà thông thường sẽ chồng lên nhau, sao cho chúng nằm cạnh nhau thay vì chồng lấp.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create beeswarm plots of color and item_size\n",
+    "baked_pumpkins %>% \n",
+    "  mutate(color = factor(color)) %>% \n",
+    "  ggplot(mapping = aes(x = color, y = item_size, color = color)) +\n",
+    "  geom_quasirandom() +\n",
+    "  scale_color_brewer(palette = \"Dark2\", direction = -1) +\n",
+    "  theme(legend.position = \"none\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bây giờ chúng ta đã hiểu mối quan hệ giữa các danh mục nhị phân của màu sắc và nhóm lớn hơn của kích thước, hãy cùng khám phá hồi quy logistic để xác định màu sắc có khả năng của một quả bí ngô.\n",
+    "\n",
+    "## Xây dựng mô hình của bạn\n",
+    "\n",
+    "Chọn các biến bạn muốn sử dụng trong mô hình phân loại và chia dữ liệu thành tập huấn luyện và tập kiểm tra. [rsample](https://rsample.tidymodels.org/), một gói trong Tidymodels, cung cấp cơ sở hạ tầng để chia dữ liệu và lấy mẫu lại một cách hiệu quả:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Split data into 80% for training and 20% for testing\n",
+    "set.seed(2056)\n",
+    "pumpkins_split <- pumpkins_select %>% \n",
+    "  initial_split(prop = 0.8)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "pumpkins_train <- training(pumpkins_split)\n",
+    "pumpkins_test <- testing(pumpkins_split)\n",
+    "\n",
+    "# Print out the first 5 rows of the training set\n",
+    "pumpkins_train %>% \n",
+    "  slice_head(n = 5)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🙌 Chúng ta đã sẵn sàng huấn luyện một mô hình bằng cách khớp các đặc trưng huấn luyện với nhãn huấn luyện (màu sắc).\n",
+    "\n",
+    "Chúng ta sẽ bắt đầu bằng việc tạo một công thức (recipe) để chỉ định các bước tiền xử lý cần thực hiện trên dữ liệu nhằm chuẩn bị cho việc mô hình hóa, ví dụ: mã hóa các biến phân loại thành một tập hợp các số nguyên. Tương tự như `baked_pumpkins`, chúng ta tạo một `pumpkins_recipe` nhưng không `prep` và `bake` vì nó sẽ được tích hợp vào một quy trình làm việc (workflow), điều này sẽ được giải thích trong vài bước tiếp theo.\n",
+    "\n",
+    "Có khá nhiều cách để chỉ định một mô hình hồi quy logistic trong Tidymodels. Xem `?logistic_reg()`. Hiện tại, chúng ta sẽ chỉ định một mô hình hồi quy logistic thông qua engine mặc định `stats::glm()`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a recipe that specifies preprocessing steps for modelling\n",
+    "pumpkins_recipe <- recipe(color ~ ., data = pumpkins_train) %>% \n",
+    "  step_mutate(item_size = ordered(item_size, levels = c('sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo'))) %>%\n",
+    "  step_integer(item_size, zero_based = F) %>%  \n",
+    "  step_dummy(all_nominal(), -all_outcomes(), one_hot = TRUE)\n",
+    "\n",
+    "# Create a logistic model specification\n",
+    "log_reg <- logistic_reg() %>% \n",
+    "  set_engine(\"glm\") %>% \n",
+    "  set_mode(\"classification\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bây giờ chúng ta đã có một công thức và một mô tả mô hình, chúng ta cần tìm cách kết hợp chúng lại thành một đối tượng có thể thực hiện các nhiệm vụ sau: tiền xử lý dữ liệu (chuẩn bị + xử lý ngầm), huấn luyện mô hình trên dữ liệu đã được tiền xử lý, và cũng hỗ trợ các hoạt động hậu xử lý tiềm năng.\n",
+    "\n",
+    "Trong Tidymodels, đối tượng tiện lợi này được gọi là một [`workflow`](https://workflows.tidymodels.org/) và nó giúp bạn lưu trữ các thành phần mô hình một cách thuận tiện.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Bundle modelling components in a workflow\n",
+    "log_reg_wf <- workflow() %>% \n",
+    "  add_recipe(pumpkins_recipe) %>% \n",
+    "  add_model(log_reg)\n",
+    "\n",
+    "# Print out the workflow\n",
+    "log_reg_wf\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sau khi một quy trình làm việc được *xác định*, một mô hình có thể được `huấn luyện` bằng cách sử dụng hàm [`fit()`](https://tidymodels.github.io/parsnip/reference/fit.html). Quy trình làm việc sẽ ước tính một công thức và tiền xử lý dữ liệu trước khi huấn luyện, vì vậy chúng ta sẽ không cần phải thực hiện thủ công bằng cách sử dụng prep và bake.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Train the model\n",
+    "wf_fit <- log_reg_wf %>% \n",
+    "  fit(data = pumpkins_train)\n",
+    "\n",
+    "# Print the trained workflow\n",
+    "wf_fit\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mô hình hiển thị các hệ số đã học được trong quá trình huấn luyện.\n",
+    "\n",
+    "Bây giờ chúng ta đã huấn luyện mô hình bằng dữ liệu huấn luyện, chúng ta có thể thực hiện dự đoán trên dữ liệu kiểm tra bằng [parsnip::predict()](https://parsnip.tidymodels.org/reference/predict.model_fit.html). Hãy bắt đầu bằng cách sử dụng mô hình để dự đoán nhãn cho tập kiểm tra và xác suất cho từng nhãn. Khi xác suất lớn hơn 0.5, lớp dự đoán là `WHITE`, ngược lại là `ORANGE`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make predictions for color and corresponding probabilities\n",
+    "results <- pumpkins_test %>% select(color) %>% \n",
+    "  bind_cols(wf_fit %>% \n",
+    "              predict(new_data = pumpkins_test)) %>%\n",
+    "  bind_cols(wf_fit %>%\n",
+    "              predict(new_data = pumpkins_test, type = \"prob\"))\n",
+    "\n",
+    "# Compare predictions\n",
+    "results %>% \n",
+    "  slice_head(n = 10)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rất tuyệt! Điều này cung cấp thêm một số hiểu biết về cách hồi quy logistic hoạt động.\n",
+    "\n",
+    "### Hiểu rõ hơn thông qua ma trận nhầm lẫn\n",
+    "\n",
+    "So sánh từng dự đoán với giá trị thực tế \"ground truth\" tương ứng không phải là cách hiệu quả nhất để xác định mức độ chính xác của mô hình. May mắn thay, Tidymodels có một vài thủ thuật khác: [`yardstick`](https://yardstick.tidymodels.org/) - một gói dùng để đo lường hiệu quả của mô hình thông qua các chỉ số hiệu suất.\n",
+    "\n",
+    "Một chỉ số hiệu suất liên quan đến các vấn đề phân loại là [`ma trận nhầm lẫn`](https://wikipedia.org/wiki/Confusion_matrix). Ma trận nhầm lẫn mô tả mức độ hiệu quả của một mô hình phân loại. Ma trận nhầm lẫn liệt kê số lượng ví dụ trong mỗi lớp được mô hình phân loại chính xác. Trong trường hợp của chúng ta, nó sẽ cho bạn biết có bao nhiêu quả bí ngô màu cam được phân loại là màu cam và bao nhiêu quả bí ngô màu trắng được phân loại là màu trắng; ma trận nhầm lẫn cũng cho thấy có bao nhiêu quả bị phân loại vào các danh mục **sai**.\n",
+    "\n",
+    "Hàm [**`conf_mat()`**](https://tidymodels.github.io/yardstick/reference/conf_mat.html) từ yardstick tính toán sự đối chiếu giữa các lớp quan sát và dự đoán.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Confusion matrix for prediction results\n",
+    "conf_mat(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hãy cùng phân tích ma trận nhầm lẫn. Mô hình của chúng ta được yêu cầu phân loại bí ngô thành hai danh mục nhị phân, danh mục `trắng` và danh mục `không trắng`.\n",
+    "\n",
+    "-   Nếu mô hình của bạn dự đoán một quả bí ngô là trắng và thực tế nó thuộc danh mục 'trắng', chúng ta gọi đó là `đúng dương`, được biểu thị bằng số ở góc trên bên trái.\n",
+    "\n",
+    "-   Nếu mô hình của bạn dự đoán một quả bí ngô là không trắng và thực tế nó thuộc danh mục 'trắng', chúng ta gọi đó là `sai âm`, được biểu thị bằng số ở góc dưới bên trái.\n",
+    "\n",
+    "-   Nếu mô hình của bạn dự đoán một quả bí ngô là trắng và thực tế nó thuộc danh mục 'không trắng', chúng ta gọi đó là `sai dương`, được biểu thị bằng số ở góc trên bên phải.\n",
+    "\n",
+    "-   Nếu mô hình của bạn dự đoán một quả bí ngô là không trắng và thực tế nó thuộc danh mục 'không trắng', chúng ta gọi đó là `đúng âm`, được biểu thị bằng số ở góc dưới bên phải.\n",
+    "\n",
+    "| Thực tế |\n",
+    "|:-----:|\n",
+    "\n",
+    "\n",
+    "|               |        |       |\n",
+    "|---------------|--------|-------|\n",
+    "| **Dự đoán**   | TRẮNG | CAM   |\n",
+    "| TRẮNG         | TP     | FP    |\n",
+    "| CAM           | FN     | TN    |\n",
+    "\n",
+    "Như bạn có thể đoán, sẽ tốt hơn nếu có số lượng lớn các giá trị đúng dương và đúng âm, đồng thời giảm số lượng sai dương và sai âm, điều này cho thấy mô hình hoạt động tốt hơn.\n",
+    "\n",
+    "Ma trận nhầm lẫn rất hữu ích vì nó dẫn đến các chỉ số khác giúp chúng ta đánh giá hiệu quả của một mô hình phân loại tốt hơn. Hãy cùng tìm hiểu một số chỉ số này:\n",
+    "\n",
+    "🎓 Độ chính xác: `TP/(TP + FP)` được định nghĩa là tỷ lệ các dự đoán dương thực sự là dương. Còn được gọi là [giá trị dự đoán dương](https://en.wikipedia.org/wiki/Positive_predictive_value \"Positive predictive value\").\n",
+    "\n",
+    "🎓 Độ nhạy: `TP/(TP + FN)` được định nghĩa là tỷ lệ kết quả dương trên tổng số mẫu thực sự là dương. Còn được gọi là `độ nhạy cảm`.\n",
+    "\n",
+    "🎓 Độ đặc hiệu: `TN/(TN + FP)` được định nghĩa là tỷ lệ kết quả âm trên tổng số mẫu thực sự là âm.\n",
+    "\n",
+    "🎓 Độ chính xác tổng thể: `TP + TN/(TP + TN + FP + FN)` Tỷ lệ nhãn được dự đoán chính xác trên tổng số mẫu.\n",
+    "\n",
+    "🎓 F Measure: Trung bình trọng số của độ chính xác và độ nhạy, với giá trị tốt nhất là 1 và kém nhất là 0.\n",
+    "\n",
+    "Hãy cùng tính các chỉ số này!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Combine metric functions and calculate them all at once\n",
+    "eval_metrics <- metric_set(ppv, recall, spec, f_meas, accuracy)\n",
+    "eval_metrics(data = results, truth = color, estimate = .pred_class)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hiển thị đường cong ROC của mô hình này\n",
+    "\n",
+    "Hãy thực hiện một hình ảnh hóa nữa để xem cái gọi là [`đường cong ROC`](https://en.wikipedia.org/wiki/Receiver_operating_characteristic):\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Make a roc_curve\n",
+    "results %>% \n",
+    "  roc_curve(color, .pred_ORANGE) %>% \n",
+    "  autoplot()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Đường cong ROC thường được sử dụng để đánh giá đầu ra của một bộ phân loại dựa trên tỷ lệ dương tính thật so với dương tính giả. Đường cong ROC thường hiển thị `True Positive Rate`/Độ nhạy trên trục Y, và `False Positive Rate`/1-Đặc hiệu trên trục X. Do đó, độ dốc của đường cong và khoảng cách giữa đường trung điểm và đường cong rất quan trọng: bạn muốn một đường cong nhanh chóng đi lên và vượt qua đường trung điểm. Trong trường hợp của chúng ta, ban đầu có một số dương tính giả, sau đó đường cong đi lên và vượt qua đúng cách.\n",
+    "\n",
+    "Cuối cùng, hãy sử dụng `yardstick::roc_auc()` để tính toán Diện Tích Dưới Đường Cong thực tế. Một cách để diễn giải AUC là xác suất mà mô hình xếp hạng một ví dụ dương tính ngẫu nhiên cao hơn một ví dụ âm tính ngẫu nhiên.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "r"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate area under curve\n",
+    "results %>% \n",
+    "  roc_auc(color, .pred_ORANGE)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Kết quả là khoảng `0.975`. Vì AUC dao động từ 0 đến 1, bạn muốn có một điểm số cao, bởi vì một mô hình dự đoán chính xác 100% sẽ có AUC bằng 1; trong trường hợp này, mô hình *khá tốt*.\n",
+    "\n",
+    "Trong các bài học tương lai về phân loại, bạn sẽ học cách cải thiện điểm số của mô hình (chẳng hạn như xử lý dữ liệu không cân bằng trong trường hợp này).\n",
+    "\n",
+    "## 🚀Thử thách\n",
+    "\n",
+    "Có rất nhiều điều để khám phá về hồi quy logistic! Nhưng cách tốt nhất để học là thử nghiệm. Tìm một tập dữ liệu phù hợp với loại phân tích này và xây dựng một mô hình với nó. Bạn học được gì? mẹo: thử [Kaggle](https://www.kaggle.com/search?q=logistic+regression+datasets) để tìm các tập dữ liệu thú vị.\n",
+    "\n",
+    "## Ôn tập & Tự học\n",
+    "\n",
+    "Đọc vài trang đầu của [bài báo này từ Stanford](https://web.stanford.edu/~jurafsky/slp3/5.pdf) về một số ứng dụng thực tiễn của hồi quy logistic. Hãy suy nghĩ về các nhiệm vụ phù hợp hơn với từng loại hồi quy mà chúng ta đã học cho đến thời điểm này. Loại nào sẽ hoạt động tốt nhất?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "langauge": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "feaf125f481a89c468fa115bf2aed580",
+   "translation_date": "2025-09-06T13:38:14+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/R/lesson_4-R.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/vi/2-Regression/4-Logistic/solution/notebook.ipynb b/translations/vi/2-Regression/4-Logistic/solution/notebook.ipynb
new file mode 100644
index 000000000..35e8203df
--- /dev/null
+++ b/translations/vi/2-Regression/4-Logistic/solution/notebook.ipynb
@@ -0,0 +1,1256 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hồi quy Logistic - Bài học 4\n",
+    "\n",
+    "Tải các thư viện cần thiết và tập dữ liệu. Chuyển đổi dữ liệu thành một dataframe chứa một tập con của dữ liệu:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Type</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Sub Variety</th>\n",
+       "      <th>Grade</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Low Price</th>\n",
+       "      <th>High Price</th>\n",
+       "      <th>Mostly Low</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Unit of Sale</th>\n",
+       "      <th>Quality</th>\n",
+       "      <th>Condition</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Storage</th>\n",
+       "      <th>Crop</th>\n",
+       "      <th>Repack</th>\n",
+       "      <th>Trans Mode</th>\n",
+       "      <th>Unnamed: 24</th>\n",
+       "      <th>Unnamed: 25</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4/29/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5/6/17</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>280.0</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>E</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9/24/16</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>11/5/16</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>N</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date   \n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17  \\\n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition   \n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN  \\\n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "full_pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
+    "\n",
+    "full_pumpkins.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>Package</th>\n",
+       "      <th>Variety</th>\n",
+       "      <th>Origin</th>\n",
+       "      <th>Item Size</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>DELAWARE</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>VIRGINIA</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>24 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>lge</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BALTIMORE</td>\n",
+       "      <td>36 inch bins</td>\n",
+       "      <td>HOWDEN TYPE</td>\n",
+       "      <td>MARYLAND</td>\n",
+       "      <td>med</td>\n",
+       "      <td>ORANGE</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City Name       Package      Variety    Origin Item Size   Color\n",
+       "2  BALTIMORE  24 inch bins  HOWDEN TYPE  DELAWARE       med  ORANGE\n",
+       "3  BALTIMORE  24 inch bins  HOWDEN TYPE  VIRGINIA       med  ORANGE\n",
+       "4  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "5  BALTIMORE  24 inch bins  HOWDEN TYPE  MARYLAND       lge  ORANGE\n",
+       "6  BALTIMORE  36 inch bins  HOWDEN TYPE  MARYLAND       med  ORANGE"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select the columns we want to use\n",
+    "columns_to_select = ['City Name','Package','Variety', 'Origin','Item Size', 'Color']\n",
+    "pumpkins = full_pumpkins.loc[:, columns_to_select]\n",
+    "\n",
+    "# Drop rows with missing values\n",
+    "pumpkins.dropna(inplace=True)\n",
+    "\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hãy cùng xem dữ liệu của chúng ta!\n",
+    "\n",
+    "Bằng cách trực quan hóa nó với Seaborn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56d0c650>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 609.375x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "# Specify colors for each values of the hue variable\n",
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# Plot a bar plot to visualize how many pumpkins of each variety are orange or white\n",
+    "sns.catplot(\n",
+    "    data=pumpkins, y=\"Variety\", hue=\"Color\", kind=\"count\",\n",
+    "    palette=palette, \n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tiền xử lý dữ liệu\n",
+    "\n",
+    "Hãy mã hóa các đặc trưng và nhãn để dễ dàng hơn trong việc vẽ biểu đồ dữ liệu và huấn luyện mô hình.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['med', 'lge', 'sml', 'xlge', 'med-lge', 'jbo', 'exjbo'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the different values of the 'Item Size' column\n",
+    "pumpkins['Item Size'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OrdinalEncoder\n",
+    "# Encode the 'Item Size' column using ordinal encoding\n",
+    "item_size_categories = [['sml', 'med', 'med-lge', 'lge', 'xlge', 'jbo', 'exjbo']]\n",
+    "ordinal_features = ['Item Size']\n",
+    "ordinal_encoder = OrdinalEncoder(categories=item_size_categories)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "# Encode all the other features using one-hot encoding\n",
+    "categorical_features = ['City Name', 'Package', 'Variety', 'Origin']\n",
+    "categorical_encoder = OneHotEncoder(sparse_output=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_MICHIGAN</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 48 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_MICHIGAN  cat__Origin_NEW JERSEY   \n",
+       "2                   0.0  ...                   0.0                     0.0  \\\n",
+       "3                   0.0  ...                   0.0                     0.0   \n",
+       "4                   0.0  ...                   0.0                     0.0   \n",
+       "5                   0.0  ...                   0.0                     0.0   \n",
+       "6                   0.0  ...                   0.0                     0.0   \n",
+       "\n",
+       "   cat__Origin_NEW YORK  cat__Origin_NORTH CAROLINA  cat__Origin_OHIO   \n",
+       "2                   0.0                         0.0               0.0  \\\n",
+       "3                   0.0                         0.0               0.0   \n",
+       "4                   0.0                         0.0               0.0   \n",
+       "5                   0.0                         0.0               0.0   \n",
+       "6                   0.0                         0.0               0.0   \n",
+       "\n",
+       "   cat__Origin_PENNSYLVANIA  cat__Origin_TENNESSEE  cat__Origin_TEXAS   \n",
+       "2                       0.0                    0.0                0.0  \\\n",
+       "3                       0.0                    0.0                0.0   \n",
+       "4                       0.0                    0.0                0.0   \n",
+       "5                       0.0                    0.0                0.0   \n",
+       "6                       0.0                    0.0                0.0   \n",
+       "\n",
+       "   cat__Origin_VERMONT  cat__Origin_VIRGINIA  \n",
+       "2                  0.0                   0.0  \n",
+       "3                  0.0                   1.0  \n",
+       "4                  0.0                   0.0  \n",
+       "5                  0.0                   0.0  \n",
+       "6                  0.0                   0.0  \n",
+       "\n",
+       "[5 rows x 48 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.compose import ColumnTransformer\n",
+    "ct = ColumnTransformer(transformers=[\n",
+    "     ('ord', ordinal_encoder, ordinal_features),\n",
+    "     ('cat', categorical_encoder, categorical_features)\n",
+    "     ])\n",
+    "# Get the encoded features as a pandas DataFrame\n",
+    "ct.set_output(transform='pandas')\n",
+    "encoded_features = ct.fit_transform(pumpkins)\n",
+    "encoded_features.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ord__Item Size</th>\n",
+       "      <th>cat__City Name_ATLANTA</th>\n",
+       "      <th>cat__City Name_BALTIMORE</th>\n",
+       "      <th>cat__City Name_BOSTON</th>\n",
+       "      <th>cat__City Name_CHICAGO</th>\n",
+       "      <th>cat__City Name_COLUMBIA</th>\n",
+       "      <th>cat__City Name_DALLAS</th>\n",
+       "      <th>cat__City Name_DETROIT</th>\n",
+       "      <th>cat__City Name_LOS ANGELES</th>\n",
+       "      <th>cat__City Name_MIAMI</th>\n",
+       "      <th>...</th>\n",
+       "      <th>cat__Origin_NEW JERSEY</th>\n",
+       "      <th>cat__Origin_NEW YORK</th>\n",
+       "      <th>cat__Origin_NORTH CAROLINA</th>\n",
+       "      <th>cat__Origin_OHIO</th>\n",
+       "      <th>cat__Origin_PENNSYLVANIA</th>\n",
+       "      <th>cat__Origin_TENNESSEE</th>\n",
+       "      <th>cat__Origin_TEXAS</th>\n",
+       "      <th>cat__Origin_VERMONT</th>\n",
+       "      <th>cat__Origin_VIRGINIA</th>\n",
+       "      <th>Color</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 49 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ord__Item Size  cat__City Name_ATLANTA  cat__City Name_BALTIMORE   \n",
+       "2             1.0                     0.0                       1.0  \\\n",
+       "3             1.0                     0.0                       1.0   \n",
+       "4             3.0                     0.0                       1.0   \n",
+       "5             3.0                     0.0                       1.0   \n",
+       "6             1.0                     0.0                       1.0   \n",
+       "\n",
+       "   cat__City Name_BOSTON  cat__City Name_CHICAGO  cat__City Name_COLUMBIA   \n",
+       "2                    0.0                     0.0                      0.0  \\\n",
+       "3                    0.0                     0.0                      0.0   \n",
+       "4                    0.0                     0.0                      0.0   \n",
+       "5                    0.0                     0.0                      0.0   \n",
+       "6                    0.0                     0.0                      0.0   \n",
+       "\n",
+       "   cat__City Name_DALLAS  cat__City Name_DETROIT  cat__City Name_LOS ANGELES   \n",
+       "2                    0.0                     0.0                         0.0  \\\n",
+       "3                    0.0                     0.0                         0.0   \n",
+       "4                    0.0                     0.0                         0.0   \n",
+       "5                    0.0                     0.0                         0.0   \n",
+       "6                    0.0                     0.0                         0.0   \n",
+       "\n",
+       "   cat__City Name_MIAMI  ...  cat__Origin_NEW JERSEY  cat__Origin_NEW YORK   \n",
+       "2                   0.0  ...                     0.0                   0.0  \\\n",
+       "3                   0.0  ...                     0.0                   0.0   \n",
+       "4                   0.0  ...                     0.0                   0.0   \n",
+       "5                   0.0  ...                     0.0                   0.0   \n",
+       "6                   0.0  ...                     0.0                   0.0   \n",
+       "\n",
+       "   cat__Origin_NORTH CAROLINA  cat__Origin_OHIO  cat__Origin_PENNSYLVANIA   \n",
+       "2                         0.0               0.0                       0.0  \\\n",
+       "3                         0.0               0.0                       0.0   \n",
+       "4                         0.0               0.0                       0.0   \n",
+       "5                         0.0               0.0                       0.0   \n",
+       "6                         0.0               0.0                       0.0   \n",
+       "\n",
+       "   cat__Origin_TENNESSEE  cat__Origin_TEXAS  cat__Origin_VERMONT   \n",
+       "2                    0.0                0.0                  0.0  \\\n",
+       "3                    0.0                0.0                  0.0   \n",
+       "4                    0.0                0.0                  0.0   \n",
+       "5                    0.0                0.0                  0.0   \n",
+       "6                    0.0                0.0                  0.0   \n",
+       "\n",
+       "   cat__Origin_VIRGINIA  Color  \n",
+       "2                   0.0      0  \n",
+       "3                   1.0      0  \n",
+       "4                   0.0      0  \n",
+       "5                   0.0      0  \n",
+       "6                   0.0      0  \n",
+       "\n",
+       "[5 rows x 49 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "# Encode the 'Color' column using label encoding\n",
+    "label_encoder = LabelEncoder()\n",
+    "encoded_label = label_encoder.fit_transform(pumpkins['Color'])\n",
+    "encoded_pumpkins = encoded_features.assign(Color=encoded_label)\n",
+    "encoded_pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ORANGE', 'WHITE']"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's look at the mapping between the encoded values and the original values\n",
+    "list(label_encoder.inverse_transform([0, 1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7f8c56322210>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x1620 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "palette = {\n",
+    "    'ORANGE': 'orange',\n",
+    "    'WHITE': 'wheat',\n",
+    "}\n",
+    "# We need the encoded Item Size column to use it as the x-axis values in the plot\n",
+    "pumpkins['Item Size'] = encoded_pumpkins['ord__Item Size']\n",
+    "\n",
+    "g = sns.catplot(\n",
+    "    data=pumpkins,\n",
+    "    x=\"Item Size\", y=\"Color\", row='Variety',\n",
+    "    kind=\"box\", orient=\"h\",\n",
+    "    sharex=False, margin_titles=True,\n",
+    "    height=1.8, aspect=4, palette=palette,\n",
+    ")\n",
+    "# Defining axis labels \n",
+    "g.set(xlabel=\"Item Size\", ylabel=\"\").set(xlim=(0,6))\n",
+    "g.set_titles(row_template=\"{row_name}\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Color', ylabel='ord__Item Size'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Suppressing warning message claiming that a portion of points cannot be placed into the plot due to the high number of data points\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')\n",
+    "\n",
+    "palette = {\n",
+    "    0: 'orange',\n",
+    "    1: 'wheat'\n",
+    "}\n",
+    "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Lưu ý**: Bỏ qua cảnh báo KHÔNG phải là một phương pháp tốt và nên tránh bất cứ khi nào có thể. Các cảnh báo thường chứa những thông điệp hữu ích giúp chúng ta cải thiện mã và giải quyết vấn đề.  \n",
+    "\n",
+    "Lý do chúng ta bỏ qua cảnh báo cụ thể này là để đảm bảo tính dễ đọc của biểu đồ. Việc vẽ tất cả các điểm dữ liệu với kích thước dấu nhỏ hơn, đồng thời giữ sự nhất quán với màu sắc của bảng màu, sẽ tạo ra một hình ảnh không rõ ràng.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# X is the encoded features\n",
+    "X = encoded_pumpkins[encoded_pumpkins.columns.difference(['Color'])]\n",
+    "# y is the encoded label\n",
+    "y = encoded_pumpkins['Color']\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.94      0.98      0.96       166\n",
+      "           1       0.85      0.67      0.75        33\n",
+      "\n",
+      "    accuracy                           0.92       199\n",
+      "   macro avg       0.89      0.82      0.85       199\n",
+      "weighted avg       0.92      0.92      0.92       199\n",
+      "\n",
+      "Predicted labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0\n",
+      " 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+      " 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0\n",
+      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0\n",
+      " 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1\n",
+      " 0 0 0 1 0 0 0 0 0 0 0 0 1 1]\n",
+      "F1-score:  0.7457627118644068\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "# Train a logistic regression model on the pumpkin dataset\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "# Evaluate the model and print the results\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('F1-score: ', f1_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[162,   4],\n",
+       "       [ 11,  22]])"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "confusion_matrix(y_test, predictions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_curve, roc_auc_score\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "y_scores = model.predict_proba(X_test)\n",
+    "# calculate ROC curve\n",
+    "fpr, tpr, thresholds = roc_curve(y_test, y_scores[:,1])\n",
+    "\n",
+    "# plot ROC curve\n",
+    "fig = plt.figure(figsize=(6, 6))\n",
+    "# Plot the diagonal 50% line\n",
+    "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "# Plot the FPR and TPR achieved by our model\n",
+    "plt.plot(fpr, tpr)\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9749908725812341\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calculate AUC score\n",
+    "auc = roc_auc_score(y_test,y_scores[:,1])\n",
+    "print(auc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "vscode": {
+   "interpreter": {
+    "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "ef50cc584e0b79412610cc7da15e1f86",
+   "translation_date": "2025-09-06T13:28:32+00:00",
+   "source_file": "2-Regression/4-Logistic/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/3-Web-App/1-Web-App/notebook.ipynb b/translations/vi/3-Web-App/1-Web-App/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/vi/3-Web-App/1-Web-App/solution/notebook.ipynb b/translations/vi/3-Web-App/1-Web-App/solution/notebook.ipynb
new file mode 100644
index 000000000..11f6df572
--- /dev/null
+++ b/translations/vi/3-Web-App/1-Web-App/solution/notebook.ipynb
@@ -0,0 +1,267 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5fa2e8f4584c78250ca9729b46562ceb",
+   "translation_date": "2025-09-06T14:32:24+00:00",
+   "source_file": "3-Web-App/1-Web-App/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "           datetime                  city state country     shape  \\\n",
+       "0  10/10/1949 20:30            san marcos    tx      us  cylinder   \n",
+       "1  10/10/1949 21:00          lackland afb    tx     NaN     light   \n",
+       "2  10/10/1955 17:00  chester (uk/england)   NaN      gb    circle   \n",
+       "3  10/10/1956 21:00                  edna    tx      us    circle   \n",
+       "4  10/10/1960 20:00               kaneohe    hi      us     light   \n",
+       "\n",
+       "   duration (seconds) duration (hours/min)  \\\n",
+       "0              2700.0           45 minutes   \n",
+       "1              7200.0              1-2 hrs   \n",
+       "2                20.0           20 seconds   \n",
+       "3                20.0             1/2 hour   \n",
+       "4               900.0           15 minutes   \n",
+       "\n",
+       "                                            comments date posted   latitude  \\\n",
+       "0  This event took place in early fall around 194...   4/27/2004  29.883056   \n",
+       "1  1949 Lackland AFB&#44 TX.  Lights racing acros...  12/16/2005  29.384210   \n",
+       "2  Green/Orange circular disc over Chester&#44 En...   1/21/2008  53.200000   \n",
+       "3  My older brother and twin sister were leaving ...   1/17/2004  28.978333   \n",
+       "4  AS a Marine 1st Lt. flying an FJ4B fighter/att...   1/22/2004  21.418056   \n",
+       "\n",
+       "    longitude  \n",
+       "0  -97.941111  \n",
+       "1  -98.581082  \n",
+       "2   -2.916667  \n",
+       "3  -96.645833  \n",
+       "4 -157.803611  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ufos = pd.read_csv('../data/ufos.csv')\n",
+    "ufos.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ],
+   "source": [
+    "\n",
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()\n",
+    "\n",
+    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 25863 entries, 2 to 80330\nData columns (total 4 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Seconds    25863 non-null  float64\n 1   Country    25863 non-null  object \n 2   Latitude   25863 non-null  float64\n 3   Longitude  25863 non-null  float64\ndtypes: float64(3), object(1)\nmemory usage: 1010.3+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ufos.dropna(inplace=True)\n",
+    "\n",
+    "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
+    "\n",
+    "ufos.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "    Seconds  Country   Latitude   Longitude\n",
+       "2      20.0        3  53.200000   -2.916667\n",
+       "3      20.0        4  28.978333  -96.645833\n",
+       "14     30.0        4  35.823889  -80.253611\n",
+       "23     60.0        4  45.582778 -122.352222\n",
+       "24      3.0        3  51.783333   -0.783333"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
+    "\n",
+    "ufos.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "Selected_features = ['Seconds','Latitude','Longitude']\n",
+    "\n",
+    "X = ufos[Selected_features]\n",
+    "y = ufos['Country']\n",
+    "\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
+      "  FutureWarning)\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
+      "  \"this warning.\", FutureWarning)\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       1.00      1.00      1.00        41\n",
+      "           1       1.00      0.02      0.05       250\n",
+      "           2       0.00      0.00      0.00         8\n",
+      "           3       0.94      1.00      0.97       131\n",
+      "           4       0.95      1.00      0.97      4743\n",
+      "\n",
+      "    accuracy                           0.95      5173\n",
+      "   macro avg       0.78      0.60      0.60      5173\n",
+      "weighted avg       0.95      0.95      0.93      5173\n",
+      "\n",
+      "Predicted labels:  [4 4 4 ... 3 4 4]\n",
+      "Accuracy:  0.9512855209742895\n",
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
+      "  'precision', 'predicted', average, warn_for)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score, classification_report \n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "predictions = model.predict(X_test)\n",
+    "\n",
+    "print(classification_report(y_test, predictions))\n",
+    "print('Predicted labels: ', predictions)\n",
+    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "model_filename = 'ufo-model.pkl'\n",
+    "pickle.dump(model, open(model_filename,'wb'))\n",
+    "\n",
+    "model = pickle.load(open('ufo-model.pkl','rb'))\n",
+    "print(model.predict([[50,44,-12]]))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/1-Introduction/notebook.ipynb b/translations/vi/4-Classification/1-Introduction/notebook.ipynb
new file mode 100644
index 000000000..0e10f1ada
--- /dev/null
+++ b/translations/vi/4-Classification/1-Introduction/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "d544ef384b7ba73757d830a72372a7f2",
+   "translation_date": "2025-09-06T14:51:02+00:00",
+   "source_file": "4-Classification/1-Introduction/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb b/translations/vi/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
new file mode 100644
index 000000000..46b61ef2c
--- /dev/null
+++ b/translations/vi/4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
@@ -0,0 +1,721 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_10-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": []
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "2621e24705e8100893c9bf84e0fc8aef",
+   "translation_date": "2025-09-06T15:02:12+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ItETB4tSFprR"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Giới thiệu về phân loại: Làm sạch, chuẩn bị và trực quan hóa dữ liệu của bạn\n",
+    "\n",
+    "Trong bốn bài học này, bạn sẽ khám phá một trọng tâm cơ bản của học máy cổ điển - *phân loại*. Chúng ta sẽ cùng nhau sử dụng các thuật toán phân loại khác nhau với một tập dữ liệu về các món ăn tuyệt vời của châu Á và Ấn Độ. Hy vọng bạn đang đói!\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/pinch.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Hãy cùng khám phá các món ăn châu Á trong những bài học này! Hình ảnh bởi Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "<!--![Hãy cùng khám phá các món ăn châu Á trong những bài học này! Hình ảnh bởi Jen Looper](../../../../../../4-Classification/1-Introduction/solution/R/images/pinch.png)-->\n",
+    "\n",
+    "Phân loại là một dạng [học có giám sát](https://wikipedia.org/wiki/Supervised_learning) có nhiều điểm tương đồng với các kỹ thuật hồi quy. Trong phân loại, bạn huấn luyện một mô hình để dự đoán một `danh mục` mà một mục thuộc về. Nếu học máy là về việc dự đoán giá trị hoặc tên của các đối tượng bằng cách sử dụng tập dữ liệu, thì phân loại thường chia thành hai nhóm: *phân loại nhị phân* và *phân loại đa lớp*.\n",
+    "\n",
+    "Hãy nhớ:\n",
+    "\n",
+    "-   **Hồi quy tuyến tính** giúp bạn dự đoán mối quan hệ giữa các biến và đưa ra dự đoán chính xác về vị trí mà một điểm dữ liệu mới sẽ nằm trong mối quan hệ với đường đó. Ví dụ, bạn có thể dự đoán giá trị số như *giá của một quả bí ngô vào tháng 9 so với tháng 12*.\n",
+    "\n",
+    "-   **Hồi quy logistic** giúp bạn khám phá \"danh mục nhị phân\": ở mức giá này, *quả bí ngô này có màu cam hay không màu cam*?\n",
+    "\n",
+    "Phân loại sử dụng các thuật toán khác nhau để xác định các cách khác nhau nhằm xác định nhãn hoặc lớp của một điểm dữ liệu. Hãy làm việc với dữ liệu về món ăn này để xem liệu, bằng cách quan sát một nhóm nguyên liệu, chúng ta có thể xác định nguồn gốc của món ăn đó hay không.\n",
+    "\n",
+    "### [**Câu hỏi trước bài giảng**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/19/)\n",
+    "\n",
+    "### **Giới thiệu**\n",
+    "\n",
+    "Phân loại là một trong những hoạt động cơ bản của nhà nghiên cứu học máy và nhà khoa học dữ liệu. Từ việc phân loại cơ bản một giá trị nhị phân (\"email này có phải là spam hay không?\"), đến phân loại hình ảnh phức tạp và phân đoạn bằng cách sử dụng thị giác máy tính, việc có thể phân loại dữ liệu thành các lớp và đặt câu hỏi về nó luôn hữu ích.\n",
+    "\n",
+    "Nói theo cách khoa học hơn, phương pháp phân loại của bạn tạo ra một mô hình dự đoán cho phép bạn ánh xạ mối quan hệ giữa các biến đầu vào và biến đầu ra.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/binary-multiclass.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Vấn đề nhị phân và đa lớp mà các thuật toán phân loại cần xử lý. Đồ họa thông tin bởi Jen Looper</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "Trước khi bắt đầu quá trình làm sạch dữ liệu, trực quan hóa nó và chuẩn bị cho các nhiệm vụ học máy, hãy tìm hiểu một chút về các cách khác nhau mà học máy có thể được sử dụng để phân loại dữ liệu.\n",
+    "\n",
+    "Xuất phát từ [thống kê](https://wikipedia.org/wiki/Statistical_classification), phân loại sử dụng học máy cổ điển dựa vào các đặc điểm như `người hút thuốc`, `cân nặng`, và `tuổi` để xác định *khả năng phát triển bệnh X*. Là một kỹ thuật học có giám sát tương tự như các bài tập hồi quy bạn đã thực hiện trước đó, dữ liệu của bạn được gắn nhãn và các thuật toán học máy sử dụng các nhãn đó để phân loại và dự đoán các lớp (hoặc 'đặc điểm') của một tập dữ liệu và gán chúng vào một nhóm hoặc kết quả.\n",
+    "\n",
+    "✅ Hãy dành một chút thời gian để tưởng tượng một tập dữ liệu về các món ăn. Một mô hình phân loại đa lớp có thể trả lời điều gì? Một mô hình phân loại nhị phân có thể trả lời điều gì? Nếu bạn muốn xác định liệu một món ăn cụ thể có khả năng sử dụng cỏ cà ri hay không thì sao? Nếu bạn muốn xem liệu, với một túi quà gồm hồi, atisô, súp lơ, và cải ngựa, bạn có thể tạo ra một món ăn Ấn Độ điển hình hay không?\n",
+    "\n",
+    "### **Xin chào 'bộ phân loại'**\n",
+    "\n",
+    "Câu hỏi mà chúng ta muốn đặt ra với tập dữ liệu về món ăn này thực sự là một câu hỏi **đa lớp**, vì chúng ta có nhiều món ăn quốc gia tiềm năng để làm việc. Với một nhóm nguyên liệu, dữ liệu sẽ phù hợp với lớp nào trong số nhiều lớp này?\n",
+    "\n",
+    "Tidymodels cung cấp một số thuật toán khác nhau để sử dụng nhằm phân loại dữ liệu, tùy thuộc vào loại vấn đề bạn muốn giải quyết. Trong hai bài học tiếp theo, bạn sẽ tìm hiểu về một số thuật toán này.\n",
+    "\n",
+    "#### **Yêu cầu trước**\n",
+    "\n",
+    "Để bài học này, chúng ta sẽ cần các gói sau để làm sạch, chuẩn bị và trực quan hóa dữ liệu:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) là một [bộ sưu tập các gói R](https://www.tidyverse.org/packages) được thiết kế để làm cho khoa học dữ liệu nhanh hơn, dễ dàng hơn và thú vị hơn!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) là một [khung làm việc](https://www.tidymodels.org/packages/) gồm các gói dành cho mô hình hóa và học máy.\n",
+    "\n",
+    "-   `DataExplorer`: [Gói DataExplorer](https://cran.r-project.org/web/packages/DataExplorer/vignettes/dataexplorer-intro.html) được thiết kế để đơn giản hóa và tự động hóa quá trình EDA và tạo báo cáo.\n",
+    "\n",
+    "-   `themis`: [Gói themis](https://themis.tidymodels.org/) cung cấp các bước bổ sung trong công thức để xử lý dữ liệu không cân bằng.\n",
+    "\n",
+    "Bạn có thể cài đặt chúng bằng lệnh:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Ngoài ra, đoạn mã dưới đây sẽ kiểm tra xem bạn đã có các gói cần thiết để hoàn thành module này chưa và cài đặt chúng nếu chúng bị thiếu.\n"
+   ],
+   "metadata": {
+    "id": "ri5bQxZ-Fz_0"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "KIPxa4elGAPI"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Chúng ta sẽ tải các gói tuyệt vời này sau và làm cho chúng khả dụng trong phiên làm việc R hiện tại của chúng ta. (Đây chỉ là để minh họa, `pacman::p_load()` đã làm điều đó cho bạn)\n"
+   ],
+   "metadata": {
+    "id": "YkKAxOJvGD4C"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Bài tập - làm sạch và cân bằng dữ liệu của bạn\n",
+    "\n",
+    "Nhiệm vụ đầu tiên trước khi bắt đầu dự án này là làm sạch và **cân bằng** dữ liệu của bạn để đạt được kết quả tốt hơn.\n",
+    "\n",
+    "Hãy cùng khám phá dữ liệu nào! 🕵️\n"
+   ],
+   "metadata": {
+    "id": "PFkQDlk0GN5O"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Import data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "Qccw7okxGT0S"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Thú vị! Nhìn qua thì cột đầu tiên là một loại cột `id`. Hãy tìm hiểu thêm một chút thông tin về dữ liệu.\n"
+   ],
+   "metadata": {
+    "id": "XrWnlgSrGVmR"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Basic information about the data\r\n",
+    "df %>%\r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# Visualize basic information above\r\n",
+    "df %>% \r\n",
+    "  plot_intro(ggtheme = theme_light())"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "4UcGmxRxGieA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Từ kết quả, chúng ta có thể thấy ngay rằng chúng ta có `2448` hàng và `385` cột và `0` giá trị bị thiếu. Chúng ta cũng có 1 cột rời rạc, *cuisine*.\n",
+    "\n",
+    "## Bài tập - tìm hiểu về các loại ẩm thực\n",
+    "\n",
+    "Bây giờ công việc bắt đầu trở nên thú vị hơn. Hãy khám phá sự phân bố dữ liệu theo từng loại ẩm thực.\n"
+   ],
+   "metadata": {
+    "id": "AaPubl__GmH5"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Count observations per cuisine\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(n)\r\n",
+    "\r\n",
+    "# Plot the distribution\r\n",
+    "theme_set(theme_light())\r\n",
+    "df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  ggplot(mapping = aes(x = n, y = reorder(cuisine, -n))) +\r\n",
+    "  geom_col(fill = \"midnightblue\", alpha = 0.7) +\r\n",
+    "  ylab(\"cuisine\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "FRsBVy5eGrrv"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Có một số lượng hữu hạn các nền ẩm thực, nhưng phân bố dữ liệu lại không đồng đều. Bạn có thể khắc phục điều đó! Trước khi làm vậy, hãy khám phá thêm một chút.\n",
+    "\n",
+    "Tiếp theo, hãy gán mỗi nền ẩm thực vào một tibble riêng và tìm hiểu xem có bao nhiêu dữ liệu (số hàng, số cột) cho từng nền ẩm thực.\n",
+    "\n",
+    "> Một [tibble](https://tibble.tidyverse.org/) là một dạng khung dữ liệu hiện đại.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/dplyr_filter.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "vVvyDb1kG2in"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Create individual tibble for the cuisines\r\n",
+    "thai_df <- df %>% \r\n",
+    "  filter(cuisine == \"thai\")\r\n",
+    "japanese_df <- df %>% \r\n",
+    "  filter(cuisine == \"japanese\")\r\n",
+    "chinese_df <- df %>% \r\n",
+    "  filter(cuisine == \"chinese\")\r\n",
+    "indian_df <- df %>% \r\n",
+    "  filter(cuisine == \"indian\")\r\n",
+    "korean_df <- df %>% \r\n",
+    "  filter(cuisine == \"korean\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Find out how much data is available per cuisine\r\n",
+    "cat(\" thai df:\", dim(thai_df), \"\\n\",\r\n",
+    "    \"japanese df:\", dim(japanese_df), \"\\n\",\r\n",
+    "    \"chinese_df:\", dim(chinese_df), \"\\n\",\r\n",
+    "    \"indian_df:\", dim(indian_df), \"\\n\",\r\n",
+    "    \"korean_df:\", dim(korean_df))"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "0TvXUxD3G8Bk"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Bài tập - Khám phá các nguyên liệu hàng đầu theo từng loại ẩm thực bằng dplyr**\n",
+    "\n",
+    "Bây giờ bạn có thể đi sâu vào dữ liệu và tìm hiểu những nguyên liệu đặc trưng cho từng loại ẩm thực. Bạn nên loại bỏ dữ liệu lặp lại gây nhầm lẫn giữa các loại ẩm thực, vì vậy hãy cùng tìm hiểu vấn đề này.\n",
+    "\n",
+    "Hãy tạo một hàm `create_ingredient()` trong R để trả về một dataframe nguyên liệu. Hàm này sẽ bắt đầu bằng cách loại bỏ một cột không hữu ích và sắp xếp các nguyên liệu theo số lượng của chúng.\n",
+    "\n",
+    "Cấu trúc cơ bản của một hàm trong R là:\n",
+    "\n",
+    "`myFunction <- function(arglist){`\n",
+    "\n",
+    "**`...`**\n",
+    "\n",
+    "**`return`**`(value)`\n",
+    "\n",
+    "`}`\n",
+    "\n",
+    "Một giới thiệu ngắn gọn về hàm trong R có thể được tìm thấy [tại đây](https://skirmer.github.io/presentations/functions_with_r.html#1).\n",
+    "\n",
+    "Hãy bắt đầu ngay thôi! Chúng ta sẽ sử dụng các [động từ dplyr](https://dplyr.tidyverse.org/) mà chúng ta đã học trong các bài học trước. Tóm tắt lại:\n",
+    "\n",
+    "-   `dplyr::select()`: giúp bạn chọn những **cột** cần giữ lại hoặc loại bỏ.\n",
+    "\n",
+    "-   `dplyr::pivot_longer()`: giúp bạn \"kéo dài\" dữ liệu, tăng số lượng hàng và giảm số lượng cột.\n",
+    "\n",
+    "-   `dplyr::group_by()` và `dplyr::summarise()`: giúp bạn tìm các thống kê tóm tắt cho các nhóm khác nhau và đưa chúng vào một bảng gọn gàng.\n",
+    "\n",
+    "-   `dplyr::filter()`: tạo một tập hợp con của dữ liệu chỉ chứa các hàng thỏa mãn điều kiện của bạn.\n",
+    "\n",
+    "-   `dplyr::mutate()`: giúp bạn tạo hoặc chỉnh sửa các cột.\n",
+    "\n",
+    "Hãy xem hướng dẫn [*nghệ thuật*](https://allisonhorst.shinyapps.io/dplyr-learnr/#section-welcome) của Allison Horst, giới thiệu một số hàm xử lý dữ liệu hữu ích trong dplyr *(một phần của Tidyverse)*.\n"
+   ],
+   "metadata": {
+    "id": "K3RF5bSCHC76"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Creates a functions that returns the top ingredients by class\r\n",
+    "\r\n",
+    "create_ingredient <- function(df){\r\n",
+    "  \r\n",
+    "  # Drop the id column which is the first colum\r\n",
+    "  ingredient_df = df %>% select(-1) %>% \r\n",
+    "  # Transpose data to a long format\r\n",
+    "    pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
+    "  # Find the top most ingredients for a particular cuisine\r\n",
+    "    group_by(ingredients) %>% \r\n",
+    "    summarise(n_instances = sum(count)) %>% \r\n",
+    "    filter(n_instances != 0) %>% \r\n",
+    "  # Arrange by descending order\r\n",
+    "    arrange(desc(n_instances)) %>% \r\n",
+    "    mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
+    "  \r\n",
+    "  \r\n",
+    "  return(ingredient_df)\r\n",
+    "} # End of function"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "uB_0JR82HTPa"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bây giờ chúng ta có thể sử dụng hàm để có cái nhìn tổng quan về mười nguyên liệu phổ biến nhất theo từng loại ẩm thực. Hãy thử nghiệm với `thai_df`.\n"
+   ],
+   "metadata": {
+    "id": "h9794WF8HWmc"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Call create_ingredient and display popular ingredients\r\n",
+    "thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
+    "\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "agQ-1HrcHaEA"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Trong phần trước, chúng ta đã sử dụng `geom_col()`, hãy xem cách bạn có thể sử dụng `geom_bar` để tạo biểu đồ cột. Sử dụng `?geom_bar` để đọc thêm.\n"
+   ],
+   "metadata": {
+    "id": "kHu9ffGjHdcX"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Make a bar chart for popular thai cuisines\r\n",
+    "thai_ingredient_df %>% \r\n",
+    "  slice_head(n = 10) %>% \r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "fb3Bx_3DHj6e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "RHP_xgdkHnvM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
+    "create_ingredient(df = japanese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "019v8F0XHrRU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Còn về các món ăn Trung Quốc thì sao?\n"
+   ],
+   "metadata": {
+    "id": "iIGM7vO8Hu3v"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
+    "create_ingredient(df = chinese_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lHd9_gd2HyzU"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "ir8qyQbNH1c7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Indian cuisines and make bar chart\r\n",
+    "create_ingredient(df = indian_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "ApukQtKjH5FO"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qv30cwY1H-FM"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Get popular ingredients for Korean cuisines and make bar chart\r\n",
+    "create_ingredient(df = korean_df) %>% \r\n",
+    "  slice_head(n = 10) %>%\r\n",
+    "  ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
+    "  geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
+    "  xlab(\"\") + ylab(\"\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "lumgk9cHIBie"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Từ các biểu đồ trực quan, giờ đây chúng ta có thể loại bỏ những nguyên liệu phổ biến nhất gây nhầm lẫn giữa các nền ẩm thực khác nhau, sử dụng `dplyr::select()`.\n",
+    "\n",
+    "Ai cũng yêu thích gạo, tỏi và gừng!\n"
+   ],
+   "metadata": {
+    "id": "iO4veMXuIEta"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "iHJPiG6rIUcK"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Xử lý dữ liệu bằng recipes 👩‍🍳👨‍🍳 - Đối phó với dữ liệu không cân bằng ⚖️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/recipes.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n",
+    "\n",
+    "Vì bài học này liên quan đến ẩm thực, chúng ta cần đặt `recipes` vào ngữ cảnh.\n",
+    "\n",
+    "Tidymodels cung cấp thêm một gói tiện ích khác: `recipes` - một gói dùng để tiền xử lý dữ liệu.\n"
+   ],
+   "metadata": {
+    "id": "kkFd-JxdIaL6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hãy cùng xem lại sự phân bố của các món ăn của chúng ta.\n"
+   ],
+   "metadata": {
+    "id": "6l2ubtTPJAhY"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "old_label_count <- df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "old_label_count"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "1e-E9cb7JDVi"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Như bạn có thể thấy, số lượng các món ăn không được phân bố đồng đều. Các món ăn Hàn Quốc gần gấp 3 lần các món ăn Thái. Dữ liệu không cân bằng thường có ảnh hưởng tiêu cực đến hiệu suất của mô hình. Hãy nghĩ về một bài toán phân loại nhị phân. Nếu phần lớn dữ liệu của bạn thuộc về một lớp, mô hình học máy sẽ dự đoán lớp đó thường xuyên hơn, chỉ vì có nhiều dữ liệu hơn cho lớp đó. Việc cân bằng dữ liệu sẽ điều chỉnh bất kỳ sự lệch lạc nào và giúp loại bỏ sự mất cân bằng này. Nhiều mô hình hoạt động tốt nhất khi số lượng quan sát là bằng nhau và do đó thường gặp khó khăn với dữ liệu không cân bằng.\n",
+    "\n",
+    "Có hai cách chính để xử lý các tập dữ liệu không cân bằng:\n",
+    "\n",
+    "-   thêm các quan sát vào lớp thiểu số: `Over-sampling`, ví dụ sử dụng thuật toán SMOTE\n",
+    "\n",
+    "-   loại bỏ các quan sát từ lớp đa số: `Under-sampling`\n",
+    "\n",
+    "Bây giờ chúng ta sẽ minh họa cách xử lý các tập dữ liệu không cân bằng bằng cách sử dụng một `recipe`. Recipe có thể được xem như một bản thiết kế mô tả các bước cần thực hiện trên một tập dữ liệu để chuẩn bị cho việc phân tích dữ liệu.\n"
+   ],
+   "metadata": {
+    "id": "soAw6826JKx9"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "HS41brUIJVJy"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hãy cùng phân tích các bước tiền xử lý.\n",
+    "\n",
+    "-   Lệnh gọi `recipe()` với một công thức cho phép recipe xác định *vai trò* của các biến bằng cách sử dụng dữ liệu `df_select` làm tham chiếu. Ví dụ, cột `cuisine` được gán vai trò `outcome`, trong khi các cột còn lại được gán vai trò `predictor`.\n",
+    "\n",
+    "-   [`step_smote(cuisine)`](https://themis.tidymodels.org/reference/step_smote.html) tạo ra một *đặc tả* cho một bước trong recipe, bước này sẽ tạo ra các ví dụ mới cho lớp thiểu số một cách tổng hợp bằng cách sử dụng các điểm lân cận gần nhất của các trường hợp này.\n",
+    "\n",
+    "Bây giờ, nếu chúng ta muốn xem dữ liệu đã được tiền xử lý, chúng ta cần [**`prep()`**](https://recipes.tidymodels.org/reference/prep.html) và [**`bake()`**](https://recipes.tidymodels.org/reference/bake.html) recipe của mình.\n",
+    "\n",
+    "`prep()`: ước tính các tham số cần thiết từ tập huấn luyện để sau đó có thể áp dụng cho các tập dữ liệu khác.\n",
+    "\n",
+    "`bake()`: sử dụng một recipe đã được chuẩn bị và áp dụng các thao tác lên bất kỳ tập dữ liệu nào.\n"
+   ],
+   "metadata": {
+    "id": "Yb-7t7XcJaC8"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Prep and bake the recipe\r\n",
+    "preprocessed_df <- cuisines_recipe %>% \r\n",
+    "  prep() %>% \r\n",
+    "  bake(new_data = NULL) %>% \r\n",
+    "  relocate(cuisine)\r\n",
+    "\r\n",
+    "# Display data\r\n",
+    "preprocessed_df %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Quick summary stats\r\n",
+    "preprocessed_df %>% \r\n",
+    "  introduce()"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "9QhSgdpxJl44"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bây giờ hãy kiểm tra phân bố của các món ăn và so sánh chúng với dữ liệu mất cân bằng.\n"
+   ],
+   "metadata": {
+    "id": "dmidELh_LdV7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Distribution of cuisines\r\n",
+    "new_label_count <- preprocessed_df %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))\r\n",
+    "\r\n",
+    "list(new_label_count = new_label_count,\r\n",
+    "     old_label_count = old_label_count)"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "aSh23klBLwDz"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Yum! Dữ liệu thật sạch sẽ, cân bằng và rất ngon miệng 😋!\n",
+    "\n",
+    "> Thông thường, một công thức thường được sử dụng như một bộ tiền xử lý cho việc mô hình hóa, nơi nó xác định các bước cần áp dụng cho một tập dữ liệu để chuẩn bị cho việc mô hình hóa. Trong trường hợp đó, một `workflow()` thường được sử dụng (như chúng ta đã thấy trong các bài học trước) thay vì tự tay ước tính một công thức.\n",
+    ">\n",
+    "> Vì vậy, bạn thường không cần phải **`prep()`** và **`bake()`** công thức khi sử dụng tidymodels, nhưng chúng là những hàm hữu ích để kiểm tra rằng công thức đang hoạt động đúng như mong đợi, giống như trong trường hợp của chúng ta.\n",
+    ">\n",
+    "> Khi bạn **`bake()`** một công thức đã được chuẩn bị với **`new_data = NULL`**, bạn sẽ nhận lại dữ liệu mà bạn đã cung cấp khi định nghĩa công thức, nhưng đã trải qua các bước tiền xử lý.\n",
+    "\n",
+    "Bây giờ hãy lưu một bản sao của dữ liệu này để sử dụng trong các bài học sau:\n"
+   ],
+   "metadata": {
+    "id": "HEu80HZ8L7ae"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Save preprocessed data\r\n",
+    "write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
+   ],
+   "outputs": [],
+   "metadata": {
+    "id": "cBmCbIgrMOI6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tệp CSV mới này hiện có thể được tìm thấy trong thư mục dữ liệu gốc.\n",
+    "\n",
+    "**🚀Thử thách**\n",
+    "\n",
+    "Chương trình học này chứa một số bộ dữ liệu thú vị. Hãy khám phá các thư mục `data` và xem liệu có bộ dữ liệu nào phù hợp cho phân loại nhị phân hoặc đa lớp không? Bạn sẽ đặt câu hỏi gì với bộ dữ liệu này?\n",
+    "\n",
+    "## [**Câu hỏi sau bài giảng**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/20/)\n",
+    "\n",
+    "## **Ôn tập & Tự học**\n",
+    "\n",
+    "-   Xem qua [gói themis](https://github.com/tidymodels/themis). Có những kỹ thuật nào khác mà chúng ta có thể sử dụng để xử lý dữ liệu mất cân bằng?\n",
+    "\n",
+    "-   Trang web tham khảo [Tidy models](https://www.tidymodels.org/start/).\n",
+    "\n",
+    "-   H. Wickham và G. Grolemund, [*R for Data Science: Visualize, Model, Transform, Tidy, and Import Data*](https://r4ds.had.co.nz/).\n",
+    "\n",
+    "#### CẢM ƠN ĐẾN:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) vì đã tạo ra những hình minh họa tuyệt vời giúp R trở nên thân thiện và hấp dẫn hơn. Tìm thêm hình minh họa tại [bộ sưu tập của cô ấy](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) và [Jen Looper](https://www.twitter.com/jenlooper) vì đã tạo ra phiên bản Python gốc của module này ♥️\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "WQs5621pMGwf"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/1-Introduction/solution/notebook.ipynb b/translations/vi/4-Classification/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..0b0f6b621
--- /dev/null
+++ b/translations/vi/4-Classification/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,701 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Các Món Ăn Ngon Châu Á và Ấn Độ\n",
+    "\n",
+    "## Giới thiệu  \n",
+    "Ẩm thực Châu Á và Ấn Độ nổi tiếng với hương vị phong phú, đa dạng và cách chế biến độc đáo. Từ các món ăn cay nồng của Ấn Độ đến các món hấp dẫn từ Nhật Bản, Trung Quốc và Thái Lan, mỗi món ăn đều mang một câu chuyện riêng.\n",
+    "\n",
+    "## Các món ăn phổ biến  \n",
+    "\n",
+    "### 1. Sushi  \n",
+    "Sushi là một món ăn truyền thống của Nhật Bản, thường được làm từ cơm trộn giấm kết hợp với cá sống, rau củ hoặc các nguyên liệu khác.  \n",
+    "- **Nguyên liệu chính**: Cá sống, cơm, rong biển.  \n",
+    "- **Mẹo nhỏ**: Để có hương vị ngon nhất, hãy sử dụng cá tươi và cơm vừa nấu.  \n",
+    "\n",
+    "### 2. Cà ri Ấn Độ  \n",
+    "Cà ri Ấn Độ là một món ăn đậm đà, thường được nấu với các loại gia vị như nghệ, thì là, và bột cà ri.  \n",
+    "- **Nguyên liệu chính**: Thịt gà, thịt cừu hoặc rau củ, nước cốt dừa, gia vị.  \n",
+    "- **Mẹo nhỏ**: Hãy để cà ri nấu chậm để gia vị thấm đều vào nguyên liệu.  \n",
+    "\n",
+    "### 3. Phở Việt Nam  \n",
+    "Phở là một món ăn truyền thống của Việt Nam, nổi tiếng với nước dùng thơm ngon và sợi phở mềm mại.  \n",
+    "- **Nguyên liệu chính**: Bánh phở, thịt bò hoặc gà, hành lá, rau thơm.  \n",
+    "- **Mẹo nhỏ**: Nước dùng ngon là yếu tố quan trọng nhất, hãy ninh xương trong nhiều giờ để đạt được hương vị đậm đà.  \n",
+    "\n",
+    "### 4. Pad Thai  \n",
+    "Pad Thai là một món ăn nổi tiếng của Thái Lan, được làm từ mì xào với tôm, đậu phụ, trứng và nước sốt đặc biệt.  \n",
+    "- **Nguyên liệu chính**: Mì gạo, tôm, đậu phụ, trứng, nước sốt me.  \n",
+    "- **Mẹo nhỏ**: Thêm một chút nước cốt chanh và đậu phộng rang để tăng hương vị.  \n",
+    "\n",
+    "## Kết luận  \n",
+    "Ẩm thực Châu Á và Ấn Độ không chỉ là những món ăn, mà còn là một phần của văn hóa và truyền thống. Hãy thử khám phá và thưởng thức những món ăn này để cảm nhận sự đa dạng và phong phú của ẩm thực thế giới!  \n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Cài đặt Imblearn để kích hoạt SMOTE. Đây là một gói Scikit-learn giúp xử lý dữ liệu mất cân bằng khi thực hiện phân loại. (https://imbalanced-learn.org/stable/)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: imblearn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.0)\n",
+      "Requirement already satisfied: imbalanced-learn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imblearn) (0.8.0)\n",
+      "Requirement already satisfied: numpy>=1.13.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=0.19.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (1.4.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.24 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.24.2)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from imbalanced-learn->imblearn) (0.16.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.24->imbalanced-learn->imblearn) (2.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install imblearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib as mpl\n",
+    "import numpy as np\n",
+    "from imblearn.over_sampling import SMOTE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df  = pd.read_csv('../../data/cuisines.csv')"
+   ]
+  },
+  {
+   "source": [
+    "Tập dữ liệu này bao gồm 385 cột biểu thị tất cả các loại nguyên liệu trong các nền ẩm thực khác nhau từ một tập hợp các nền ẩm thực được cho.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0          65  indian       0         0      0           0      0   \n",
+       "1          66  indian       1         0      0           0      0   \n",
+       "2          67  indian       0         0      0           0      0   \n",
+       "3          68  indian       0         0      0           0      0   \n",
+       "4          69  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 385 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>65</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>66</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>67</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>68</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>69</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 385 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 2448 entries, 0 to 2447\nColumns: 385 entries, Unnamed: 0 to zucchini\ndtypes: int64(384), object(1)\nmemory usage: 7.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "korean      799\n",
+       "indian      598\n",
+       "chinese     442\n",
+       "japanese    320\n",
+       "thai        289\n",
+       "Name: cuisine, dtype: int64"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts()"
+   ]
+  },
+  {
+   "source": [
+    "Hiển thị các món ăn trong biểu đồ thanh\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18799710>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df.cuisine.value_counts().plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "thai df: (289, 385)\njapanese df: (320, 385)\nchinese df: (442, 385)\nindian df: (598, 385)\nkorean df: (799, 385)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "thai_df = df[(df.cuisine == \"thai\")]\n",
+    "japanese_df = df[(df.cuisine == \"japanese\")]\n",
+    "chinese_df = df[(df.cuisine == \"chinese\")]\n",
+    "indian_df = df[(df.cuisine == \"indian\")]\n",
+    "korean_df = df[(df.cuisine == \"korean\")]\n",
+    "\n",
+    "print(f'thai df: {thai_df.shape}')\n",
+    "print(f'japanese df: {japanese_df.shape}')\n",
+    "print(f'chinese df: {chinese_df.shape}')\n",
+    "print(f'indian df: {indian_df.shape}')\n",
+    "print(f'korean df: {korean_df.shape}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_ingredient_df(df):\n",
+    "    # transpose df, drop cuisine and unnamed rows, sum the row to get total for ingredient and add value header to new df\n",
+    "    ingredient_df = df.T.drop(['cuisine','Unnamed: 0']).sum(axis=1).to_frame('value')\n",
+    "    # drop ingredients that have a 0 sum\n",
+    "    ingredient_df = ingredient_df[(ingredient_df.T != 0).any()]\n",
+    "    # sort df\n",
+    "    ingredient_df = ingredient_df.sort_values(by='value', ascending=False, inplace=False)\n",
+    "    return ingredient_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d5917db00>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "thai_ingredient_df = create_ingredient_df(thai_df)\r\n",
+    "thai_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d389634a8>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+   ],
+   "source": [
+    "japanese_ingredient_df = create_ingredient_df(japanese_df)\r\n",
+    "japanese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d18817f98>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "chinese_ingredient_df = create_ingredient_df(chinese_df)\r\n",
+    "chinese_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1880a550>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "indian_ingredient_df = create_ingredient_df(indian_df)\r\n",
+    "indian_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d1896ce80>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "korean_ingredient_df = create_ingredient_df(korean_df)\r\n",
+    "korean_ingredient_df.head(10).plot.barh()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ],
+   "source": [
+    "feature_df= df.drop(['cuisine','Unnamed: 0','rice','garlic','ginger'], axis=1)\n",
+    "labels_df = df.cuisine #.unique()\n",
+    "feature_df.head()\n"
+   ]
+  },
+  {
+   "source": [
+    "Cân bằng dữ liệu với SMOTE oversampling đến lớp cao nhất. Đọc thêm tại đây: https://imbalanced-learn.org/dev/references/generated/imblearn.over_sampling.SMOTE.html\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "oversample = SMOTE()\n",
+    "transformed_feature_df, transformed_label_df = oversample.fit_resample(feature_df, labels_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "new label count: korean      799\nchinese     799\njapanese    799\nindian      799\nthai        799\nName: cuisine, dtype: int64\nold label count: korean      799\nindian      598\nchinese     442\njapanese    320\nthai        289\nName: cuisine, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'new label count: {transformed_label_df.value_counts()}')\r\n",
+    "print(f'old label count: {df.cuisine.value_counts()}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "source": [
+    "transformed_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "     cuisine  almond  angelica  anise  anise_seed  apple  apple_brandy  \\\n",
+       "0     indian       0         0      0           0      0             0   \n",
+       "1     indian       1         0      0           0      0             0   \n",
+       "2     indian       0         0      0           0      0             0   \n",
+       "3     indian       0         0      0           0      0             0   \n",
+       "4     indian       0         0      0           0      0             0   \n",
+       "...      ...     ...       ...    ...         ...    ...           ...   \n",
+       "3990    thai       0         0      0           0      0             0   \n",
+       "3991    thai       0         0      0           0      0             0   \n",
+       "3992    thai       0         0      0           0      0             0   \n",
+       "3993    thai       0         0      0           0      0             0   \n",
+       "3994    thai       0         0      0           0      0             0   \n",
+       "\n",
+       "      apricot  armagnac  artemisia  ...  whiskey  white_bread  white_wine  \\\n",
+       "0           0         0          0  ...        0            0           0   \n",
+       "1           0         0          0  ...        0            0           0   \n",
+       "2           0         0          0  ...        0            0           0   \n",
+       "3           0         0          0  ...        0            0           0   \n",
+       "4           0         0          0  ...        0            0           0   \n",
+       "...       ...       ...        ...  ...      ...          ...         ...   \n",
+       "3990        0         0          0  ...        0            0           0   \n",
+       "3991        0         0          0  ...        0            0           0   \n",
+       "3992        0         0          0  ...        0            0           0   \n",
+       "3993        0         0          0  ...        0            0           0   \n",
+       "3994        0         0          0  ...        0            0           0   \n",
+       "\n",
+       "      whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                           0     0     0    0      0       0         0  \n",
+       "1                           0     0     0    0      0       0         0  \n",
+       "2                           0     0     0    0      0       0         0  \n",
+       "3                           0     0     0    0      0       0         0  \n",
+       "4                           0     0     0    0      0       1         0  \n",
+       "...                       ...   ...   ...  ...    ...     ...       ...  \n",
+       "3990                        0     0     0    0      0       0         0  \n",
+       "3991                        0     0     0    0      0       0         0  \n",
+       "3992                        0     0     0    0      0       0         0  \n",
+       "3993                        0     0     0    0      0       0         0  \n",
+       "3994                        0     0     0    0      0       0         0  \n",
+       "\n",
+       "[3995 rows x 381 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3990</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3991</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3992</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3993</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3994</th>\n      <td>thai</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>3995 rows × 381 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ],
+   "source": [
+    "# export transformed data to new df for classification\n",
+    "transformed_df = pd.concat([transformed_label_df,transformed_feature_df],axis=1, join='outer')\n",
+    "transformed_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 3995 entries, 0 to 3994\nColumns: 381 entries, cuisine to zucchini\ndtypes: int64(380), object(1)\nmemory usage: 11.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "transformed_df.info()"
+   ]
+  },
+  {
+   "source": [
+    "Lưu tệp để sử dụng sau này\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transformed_df.to_csv(\"../../data/cleaned_cuisines.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "1da12ed6d238756959b8de9cac2a35a2",
+   "translation_date": "2025-09-06T14:53:18+00:00",
+   "source_file": "4-Classification/1-Introduction/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/2-Classifiers-1/notebook.ipynb b/translations/vi/4-Classification/2-Classifiers-1/notebook.ipynb
new file mode 100644
index 000000000..facf06172
--- /dev/null
+++ b/translations/vi/4-Classification/2-Classifiers-1/notebook.ipynb
@@ -0,0 +1,41 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "68829b06b4dcd512d3327849191f4d7f",
+   "translation_date": "2025-09-06T14:32:53+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Xây dựng các mô hình phân loại\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb b/translations/vi/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
new file mode 100644
index 000000000..40b4c2304
--- /dev/null
+++ b/translations/vi/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb
@@ -0,0 +1,1298 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "metadata": {
+  "colab": {
+   "name": "lesson_11-R.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "name": "ir",
+   "display_name": "R"
+  },
+  "language_info": {
+   "name": "R"
+  },
+  "coopTranslator": {
+   "original_hash": "6ea6a5171b1b99b7b5a55f7469c048d2",
+   "translation_date": "2025-09-06T14:41:18+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "zs2woWv_HoE8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Bộ phân loại ẩm thực 1\n",
+    "\n",
+    "Trong bài học này, chúng ta sẽ khám phá nhiều bộ phân loại để *dự đoán một nền ẩm thực quốc gia dựa trên nhóm nguyên liệu.* Trong quá trình này, chúng ta sẽ tìm hiểu thêm về cách các thuật toán có thể được sử dụng cho các nhiệm vụ phân loại.\n",
+    "\n",
+    "### [**Câu hỏi trước bài học**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
+    "\n",
+    "### **Chuẩn bị**\n",
+    "\n",
+    "Bài học này dựa trên [bài học trước](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb) nơi chúng ta:\n",
+    "\n",
+    "-   Đã giới thiệu nhẹ nhàng về phân loại bằng cách sử dụng một tập dữ liệu về tất cả các nền ẩm thực tuyệt vời của châu Á và Ấn Độ 😋.\n",
+    "\n",
+    "-   Đã khám phá một số [động từ dplyr](https://dplyr.tidyverse.org/) để chuẩn bị và làm sạch dữ liệu.\n",
+    "\n",
+    "-   Đã tạo các hình ảnh trực quan đẹp mắt bằng ggplot2.\n",
+    "\n",
+    "-   Đã trình bày cách xử lý dữ liệu không cân bằng bằng cách tiền xử lý nó bằng [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html).\n",
+    "\n",
+    "-   Đã trình bày cách `prep` và `bake` công thức của chúng ta để xác nhận rằng nó hoạt động như mong đợi.\n",
+    "\n",
+    "#### **Điều kiện tiên quyết**\n",
+    "\n",
+    "Đối với bài học này, chúng ta sẽ cần các gói sau để làm sạch, chuẩn bị và trực quan hóa dữ liệu:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) là một [bộ sưu tập các gói R](https://www.tidyverse.org/packages) được thiết kế để làm cho khoa học dữ liệu nhanh hơn, dễ dàng hơn và thú vị hơn!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) là một [khung làm việc gồm các gói](https://www.tidymodels.org/packages/) dành cho mô hình hóa và học máy.\n",
+    "\n",
+    "-   `themis`: [gói themis](https://themis.tidymodels.org/) cung cấp các bước bổ sung trong công thức để xử lý dữ liệu không cân bằng.\n",
+    "\n",
+    "-   `nnet`: [gói nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) cung cấp các hàm để ước tính mạng nơ-ron truyền thẳng với một lớp ẩn, và các mô hình hồi quy logistic đa thức.\n",
+    "\n",
+    "Bạn có thể cài đặt chúng như sau:\n"
+   ],
+   "metadata": {
+    "id": "iDFOb3ebHwQC"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
+    "\n",
+    "Ngoài ra, đoạn mã dưới đây sẽ kiểm tra xem bạn đã có các gói cần thiết để hoàn thành mô-đun này chưa và sẽ cài đặt chúng nếu chúng bị thiếu.\n"
+   ],
+   "metadata": {
+    "id": "4V85BGCjII7F"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, here)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Loading required package: pacman\n",
+      "\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "an5NPyyKIKNR",
+    "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 1. Chia dữ liệu thành tập huấn luyện và tập kiểm tra.\n",
+    "\n",
+    "Chúng ta sẽ bắt đầu bằng cách chọn một vài bước từ bài học trước.\n",
+    "\n",
+    "### Loại bỏ các nguyên liệu phổ biến nhất gây nhầm lẫn giữa các nền ẩm thực khác nhau, sử dụng `dplyr::select()`.\n",
+    "\n",
+    "Ai cũng yêu thích cơm, tỏi và gừng!\n"
+   ],
+   "metadata": {
+    "id": "0ax9GQLBINVv"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "source": [
+    "# Load the original cuisines data\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
+    "\r\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
+    "df_select <- df %>% \r\n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
+    "  # Encode cuisine column as categorical\r\n",
+    "  mutate(cuisine = factor(cuisine))\r\n",
+    "\r\n",
+    "# Display new data set\r\n",
+    "df_select %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "# Display distribution of cuisines\r\n",
+    "df_select %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "New names:\n",
+      "* `` -> ...1\n",
+      "\n",
+      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
+      "\n",
+      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
+      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
+      "\u001b[31mchr\u001b[39m   (1): cuisine\n",
+      "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
+      "\n",
+      "\n",
+      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
+      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
+      "\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 indian  0      0        0     0          0     0            0       0       \n",
+       "2 indian  1      0        0     0          0     0            0       0       \n",
+       "3 indian  0      0        0     0          0     0            0       0       \n",
+       "4 indian  0      0        0     0          0     0            0       0       \n",
+       "5 indian  0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       0    0   \n",
+       "2 0         ⋯ 0       0           0          0                       0    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     1      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   799\n",
+       "2 indian   598\n",
+       "3 chinese  442\n",
+       "4 japanese 320\n",
+       "5 thai     289"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 799 |\n",
+       "| indian   | 598 |\n",
+       "| chinese  | 442 |\n",
+       "| japanese | 320 |\n",
+       "| thai     | 289 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 799\\\\\n",
+       "\t indian   & 598\\\\\n",
+       "\t chinese  & 442\\\\\n",
+       "\t japanese & 320\\\\\n",
+       "\t thai     & 289\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>799</td></tr>\n",
+       "\t<tr><td>indian  </td><td>598</td></tr>\n",
+       "\t<tr><td>chinese </td><td>442</td></tr>\n",
+       "\t<tr><td>japanese</td><td>320</td></tr>\n",
+       "\t<tr><td>thai    </td><td>289</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 735
+    },
+    "id": "jhCrrH22IWVR",
+    "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tuyệt vời! Bây giờ, chúng ta sẽ chia dữ liệu sao cho 70% dữ liệu dành cho huấn luyện và 30% dành cho kiểm tra. Chúng ta cũng sẽ áp dụng kỹ thuật `phân tầng` khi chia dữ liệu để `duy trì tỷ lệ của mỗi loại ẩm thực` trong các tập dữ liệu huấn luyện và kiểm tra.\n",
+    "\n",
+    "[rsample](https://rsample.tidymodels.org/), một gói trong Tidymodels, cung cấp cơ sở hạ tầng cho việc chia dữ liệu và lấy mẫu lại một cách hiệu quả:\n"
+   ],
+   "metadata": {
+    "id": "AYTjVyajIdny"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "source": [
+    "# Load the core Tidymodels packages into R session\r\n",
+    "library(tidymodels)\r\n",
+    "\r\n",
+    "# Create split specification\r\n",
+    "set.seed(2056)\r\n",
+    "cuisines_split <- initial_split(data = df_select,\r\n",
+    "                                strata = cuisine,\r\n",
+    "                                prop = 0.7)\r\n",
+    "\r\n",
+    "# Extract the data in each split\r\n",
+    "cuisines_train <- training(cuisines_split)\r\n",
+    "cuisines_test <- testing(cuisines_split)\r\n",
+    "\r\n",
+    "# Print the number of cases in each split\r\n",
+    "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
+    "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
+    "\r\n",
+    "# Display the first few rows of the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  slice_head(n = 5)\r\n",
+    "\r\n",
+    "\r\n",
+    "# Display distribution of cuisines in the training set\r\n",
+    "cuisines_train %>% \r\n",
+    "  count(cuisine) %>% \r\n",
+    "  arrange(desc(n))"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training cases: 1712\n",
+      "Test cases: 736"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
+       "1 chinese 0      0        0     0          0     0            0       0       \n",
+       "2 chinese 0      0        0     0          0     0            0       0       \n",
+       "3 chinese 0      0        0     0          0     0            0       0       \n",
+       "4 chinese 0      0        0     0          0     0            0       0       \n",
+       "5 chinese 0      0        0     0          0     0            0       0       \n",
+       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
+       "1 0         ⋯ 0       0           0          0                       1    0   \n",
+       "2 0         ⋯ 0       0           0          0                       1    0   \n",
+       "3 0         ⋯ 0       0           0          0                       0    0   \n",
+       "4 0         ⋯ 0       0           0          0                       0    0   \n",
+       "5 0         ⋯ 0       0           0          0                       0    0   \n",
+       "  yam yeast yogurt zucchini\n",
+       "1 0   0     0      0       \n",
+       "2 0   0     0      0       \n",
+       "3 0   0     0      0       \n",
+       "4 0   0     0      0       \n",
+       "5 0   0     0      0       "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 381\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 381\n",
+       "\\begin{tabular}{lllllllllllllllllllll}\n",
+       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
+       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 381</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine  n  \n",
+       "1 korean   559\n",
+       "2 indian   418\n",
+       "3 chinese  309\n",
+       "4 japanese 224\n",
+       "5 thai     202"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
+       "|---|---|\n",
+       "| korean   | 559 |\n",
+       "| indian   | 418 |\n",
+       "| chinese  | 309 |\n",
+       "| japanese | 224 |\n",
+       "| thai     | 202 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & n\\\\\n",
+       " <fct> & <int>\\\\\n",
+       "\\hline\n",
+       "\t korean   & 559\\\\\n",
+       "\t indian   & 418\\\\\n",
+       "\t chinese  & 309\\\\\n",
+       "\t japanese & 224\\\\\n",
+       "\t thai     & 202\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>korean  </td><td>559</td></tr>\n",
+       "\t<tr><td>indian  </td><td>418</td></tr>\n",
+       "\t<tr><td>chinese </td><td>309</td></tr>\n",
+       "\t<tr><td>japanese</td><td>224</td></tr>\n",
+       "\t<tr><td>thai    </td><td>202</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 535
+    },
+    "id": "w5FWIkEiIjdN",
+    "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 2. Xử lý dữ liệu không cân bằng\n",
+    "\n",
+    "Như bạn có thể đã nhận thấy trong tập dữ liệu gốc cũng như trong tập huấn luyện của chúng ta, có sự phân bố không đồng đều về số lượng các loại ẩm thực. Các món ăn Hàn Quốc *gần như* gấp 3 lần các món ăn Thái. Dữ liệu không cân bằng thường ảnh hưởng tiêu cực đến hiệu suất của mô hình. Nhiều mô hình hoạt động tốt nhất khi số lượng quan sát là bằng nhau và do đó thường gặp khó khăn với dữ liệu không cân bằng.\n",
+    "\n",
+    "Có hai cách chính để xử lý các tập dữ liệu không cân bằng:\n",
+    "\n",
+    "-   thêm các quan sát vào lớp thiểu số: `Over-sampling`, ví dụ sử dụng thuật toán SMOTE, thuật toán này tạo ra các ví dụ mới cho lớp thiểu số một cách tổng hợp bằng cách sử dụng các hàng xóm gần nhất của các trường hợp này.\n",
+    "\n",
+    "-   loại bỏ các quan sát từ lớp đa số: `Under-sampling`\n",
+    "\n",
+    "Trong bài học trước, chúng ta đã minh họa cách xử lý các tập dữ liệu không cân bằng bằng cách sử dụng một `recipe`. Recipe có thể được coi như một bản hướng dẫn mô tả các bước cần áp dụng cho một tập dữ liệu để chuẩn bị cho việc phân tích dữ liệu. Trong trường hợp của chúng ta, chúng ta muốn có sự phân bố đồng đều về số lượng các loại ẩm thực trong `training set`. Hãy bắt đầu ngay thôi.\n"
+   ],
+   "metadata": {
+    "id": "daBi9qJNIwqW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "source": [
+    "# Load themis package for dealing with imbalanced data\r\n",
+    "library(themis)\r\n",
+    "\r\n",
+    "# Create a recipe for preprocessing training data\r\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
+    "  step_smote(cuisine)\r\n",
+    "\r\n",
+    "# Print recipe\r\n",
+    "cuisines_recipe"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Data Recipe\n",
+       "\n",
+       "Inputs:\n",
+       "\n",
+       "      role #variables\n",
+       "   outcome          1\n",
+       " predictor        380\n",
+       "\n",
+       "Operations:\n",
+       "\n",
+       "SMOTE based on cuisine"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 200
+    },
+    "id": "Az6LFBGxI1X0",
+    "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bạn có thể xác nhận (bằng cách chuẩn bị và thực hiện) rằng công thức này sẽ hoạt động như mong đợi - tất cả các nhãn ẩm thực đều có `559` quan sát.\n",
+    "\n",
+    "Vì chúng ta sẽ sử dụng công thức này như một bộ tiền xử lý cho việc mô hình hóa, một `workflow()` sẽ thực hiện toàn bộ việc chuẩn bị và thực hiện cho chúng ta, vì vậy chúng ta sẽ không phải ước tính công thức một cách thủ công.\n",
+    "\n",
+    "Bây giờ chúng ta đã sẵn sàng để huấn luyện một mô hình 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Lựa chọn bộ phân loại của bạn\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/parsnip.jpg\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "NBL3PqIWJBBB"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bây giờ chúng ta cần quyết định thuật toán nào sẽ được sử dụng cho công việc này 🤔.\n",
+    "\n",
+    "Trong Tidymodels, [`gói parsnip`](https://parsnip.tidymodels.org/index.html) cung cấp giao diện nhất quán để làm việc với các mô hình trên các engine (gói) khác nhau. Vui lòng xem tài liệu của parsnip để khám phá [các loại mô hình & engine](https://www.tidymodels.org/find/parsnip/#models) và [các tham số mô hình tương ứng](https://www.tidymodels.org/find/parsnip/#model-args). Ban đầu, sự đa dạng này có thể khiến bạn choáng ngợp. Ví dụ, các phương pháp sau đây đều bao gồm các kỹ thuật phân loại:\n",
+    "\n",
+    "-   Mô hình phân loại dựa trên quy tắc C5.0\n",
+    "\n",
+    "-   Mô hình phân biệt linh hoạt\n",
+    "\n",
+    "-   Mô hình phân biệt tuyến tính\n",
+    "\n",
+    "-   Mô hình phân biệt có điều chỉnh\n",
+    "\n",
+    "-   Mô hình hồi quy logistic\n",
+    "\n",
+    "-   Mô hình hồi quy đa thức\n",
+    "\n",
+    "-   Mô hình Naive Bayes\n",
+    "\n",
+    "-   Máy vector hỗ trợ\n",
+    "\n",
+    "-   Láng giềng gần nhất\n",
+    "\n",
+    "-   Cây quyết định\n",
+    "\n",
+    "-   Phương pháp tập hợp\n",
+    "\n",
+    "-   Mạng nơ-ron\n",
+    "\n",
+    "Danh sách này còn tiếp tục!\n",
+    "\n",
+    "### **Nên chọn bộ phân loại nào?**\n",
+    "\n",
+    "Vậy, bạn nên chọn bộ phân loại nào? Thông thường, thử nghiệm qua nhiều bộ phân loại và tìm kiếm kết quả tốt là một cách để kiểm tra.\n",
+    "\n",
+    "> AutoML giải quyết vấn đề này một cách gọn gàng bằng cách chạy các so sánh này trên đám mây, cho phép bạn chọn thuật toán tốt nhất cho dữ liệu của mình. Hãy thử tại đây [here](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "Ngoài ra, việc lựa chọn bộ phân loại còn phụ thuộc vào vấn đề của chúng ta. Ví dụ, khi kết quả có thể được phân loại thành `nhiều hơn hai lớp`, như trong trường hợp của chúng ta, bạn phải sử dụng một `thuật toán phân loại đa lớp` thay vì `phân loại nhị phân.`\n",
+    "\n",
+    "### **Một cách tiếp cận tốt hơn**\n",
+    "\n",
+    "Tuy nhiên, một cách tốt hơn thay vì đoán mò là làm theo các ý tưởng trong [bảng tham khảo nhanh ML](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott) có thể tải xuống này. Tại đây, chúng ta phát hiện rằng, đối với vấn đề phân loại đa lớp của mình, chúng ta có một số lựa chọn:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/cheatsheet.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Một phần của Bảng tham khảo thuật toán của Microsoft, chi tiết các tùy chọn phân loại đa lớp</figcaption>\n"
+   ],
+   "metadata": {
+    "id": "a6DLAZ3vJZ14"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### **Lý do**\n",
+    "\n",
+    "Hãy xem xét các cách tiếp cận khác nhau dựa trên các giới hạn mà chúng ta có:\n",
+    "\n",
+    "-   **Mạng nơ-ron sâu quá nặng**. Với tập dữ liệu sạch nhưng tối thiểu của chúng ta, và việc huấn luyện được thực hiện cục bộ qua notebook, mạng nơ-ron sâu là quá nặng nề cho nhiệm vụ này.\n",
+    "\n",
+    "-   **Không sử dụng bộ phân loại hai lớp**. Chúng ta không sử dụng bộ phân loại hai lớp, vì vậy loại bỏ phương pháp one-vs-all.\n",
+    "\n",
+    "-   **Cây quyết định hoặc hồi quy logistic có thể phù hợp**. Cây quyết định có thể hoạt động, hoặc hồi quy đa thức/hồi quy logistic đa lớp cho dữ liệu đa lớp.\n",
+    "\n",
+    "-   **Cây quyết định tăng cường đa lớp giải quyết vấn đề khác**. Cây quyết định tăng cường đa lớp phù hợp nhất cho các nhiệm vụ phi tham số, ví dụ như các nhiệm vụ thiết kế xếp hạng, vì vậy nó không hữu ích cho chúng ta.\n",
+    "\n",
+    "Ngoài ra, thông thường trước khi bắt đầu với các mô hình học máy phức tạp hơn như phương pháp ensemble, nên xây dựng mô hình đơn giản nhất có thể để hiểu rõ vấn đề. Vì vậy, trong bài học này, chúng ta sẽ bắt đầu với mô hình `hồi quy đa thức`.\n",
+    "\n",
+    "> Hồi quy logistic là một kỹ thuật được sử dụng khi biến kết quả là biến phân loại (hoặc danh nghĩa). Đối với hồi quy logistic nhị phân, số lượng biến kết quả là hai, trong khi số lượng biến kết quả đối với hồi quy logistic đa thức là nhiều hơn hai. Xem [Phương pháp hồi quy nâng cao](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html) để tìm hiểu thêm.\n",
+    "\n",
+    "## 4. Huấn luyện và đánh giá mô hình hồi quy logistic đa thức.\n",
+    "\n",
+    "Trong Tidymodels, `parsnip::multinom_reg()` định nghĩa một mô hình sử dụng các dự đoán tuyến tính để dự đoán dữ liệu đa lớp bằng cách sử dụng phân phối đa thức. Xem `?multinom_reg()` để biết các cách/engine khác nhau mà bạn có thể sử dụng để huấn luyện mô hình này.\n",
+    "\n",
+    "Trong ví dụ này, chúng ta sẽ huấn luyện mô hình hồi quy đa thức thông qua engine mặc định [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf).\n",
+    "\n",
+    "> Tôi đã chọn giá trị cho `penalty` một cách ngẫu nhiên. Có những cách tốt hơn để chọn giá trị này, đó là sử dụng `resampling` và `tuning` mô hình, điều mà chúng ta sẽ thảo luận sau.\n",
+    ">\n",
+    "> Xem [Tidymodels: Bắt đầu](https://www.tidymodels.org/start/tuning/) nếu bạn muốn tìm hiểu thêm về cách tinh chỉnh siêu tham số của mô hình.\n"
+   ],
+   "metadata": {
+    "id": "gWMsVcbBJemu"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "source": [
+    "# Create a multinomial regression model specification\r\n",
+    "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
+    "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
+    "  set_mode(\"classification\")\r\n",
+    "\r\n",
+    "# Print model specification\r\n",
+    "mr_spec"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 166
+    },
+    "id": "Wq_fcyQiJvfG",
+    "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Làm tốt lắm 🥳! Bây giờ chúng ta đã có một công thức và một mô tả mô hình, chúng ta cần tìm cách kết hợp chúng lại thành một đối tượng để trước tiên xử lý dữ liệu, sau đó khớp mô hình trên dữ liệu đã được xử lý, và cũng cho phép các hoạt động xử lý sau tiềm năng. Trong Tidymodels, đối tượng tiện lợi này được gọi là [`workflow`](https://workflows.tidymodels.org/) và nó tiện lợi lưu giữ các thành phần mô hình của bạn! Đây là điều chúng ta gọi là *pipelines* trong *Python*.\n",
+    "\n",
+    "Vậy hãy kết hợp mọi thứ lại thành một workflow!📦\n"
+   ],
+   "metadata": {
+    "id": "NlSbzDfgJ0zh"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "source": [
+    "# Bundle recipe and model specification\r\n",
+    "mr_wf <- workflow() %>% \r\n",
+    "  add_recipe(cuisines_recipe) %>% \r\n",
+    "  add_model(mr_spec)\r\n",
+    "\r\n",
+    "# Print out workflow\r\n",
+    "mr_wf"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow ════════════════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Multinomial Regression Model Specification (classification)\n",
+       "\n",
+       "Main Arguments:\n",
+       "  penalty = 1\n",
+       "\n",
+       "Engine-Specific Arguments:\n",
+       "  MaxNWts = 2086\n",
+       "\n",
+       "Computational engine: nnet \n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 333
+    },
+    "id": "Sc1TfPA4Ke3_",
+    "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Quy trình làm việc 👌👌! Một **`workflow()`** có thể được điều chỉnh tương tự như cách một mô hình có thể. Vậy, đến lúc huấn luyện một mô hình rồi!\n"
+   ],
+   "metadata": {
+    "id": "TNQ8i85aKf9L"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "source": [
+    "# Train a multinomial regression model\n",
+    "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
+    "\n",
+    "mr_fit"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
+       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
+       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
+       "\n",
+       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
+       "1 Recipe Step\n",
+       "\n",
+       "• step_smote()\n",
+       "\n",
+       "── Model ───────────────────────────────────────────────────────────────────────\n",
+       "Call:\n",
+       "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
+       "    trace = FALSE)\n",
+       "\n",
+       "Coefficients:\n",
+       "         (Intercept)     almond angelica         anise anise_seed       apple\n",
+       "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
+       "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
+       "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
+       "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
+       "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
+       "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
+       "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
+       "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
+       "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
+       "             avocado       bacon baked_potato balm     banana     barley\n",
+       "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
+       "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
+       "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
+       "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
+       "         bartlett_pear      basil        bay       bean         beech\n",
+       "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
+       "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
+       "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
+       "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
+       "               beef beef_broth   beef_liver         beer        beet\n",
+       "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
+       "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
+       "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
+       "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
+       "         bell_pepper bergamot       berry bitter_orange black_bean\n",
+       "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
+       "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
+       "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
+       "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
+       "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
+       "indian               0             0.38935801   -0.4453495               0\n",
+       "japanese             0            -0.05452887   -0.5440869               0\n",
+       "korean               0            -0.03929970    0.8025454               0\n",
+       "thai                 0            -0.21498372   -0.9854806               0\n",
+       "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
+       "indian          -0.2759246  0.3079977  0.191256164                 0\n",
+       "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
+       "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
+       "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
+       "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
+       "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
+       "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
+       "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
+       "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
+       "\n",
+       "...\n",
+       "and 308 more lines."
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GMbdfVmTKkJI",
+    "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Các hệ số mà mô hình đã học được trong quá trình huấn luyện được hiển thị ở đầu ra.\n",
+    "\n",
+    "### Đánh giá Mô hình Đã Huấn Luyện\n",
+    "\n",
+    "Đã đến lúc xem mô hình hoạt động như thế nào 📏 bằng cách đánh giá nó trên tập kiểm tra! Hãy bắt đầu bằng việc tạo dự đoán trên tập kiểm tra.\n"
+   ],
+   "metadata": {
+    "id": "tt2BfOxrKmcJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "source": [
+    "# Make predictions on the test set\n",
+    "results <- cuisines_test %>% select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
+    "\n",
+    "# Print out results\n",
+    "results %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class\n",
+       "1 indian  thai       \n",
+       "2 indian  indian     \n",
+       "3 indian  indian     \n",
+       "4 indian  indian     \n",
+       "5 indian  indian     "
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 2\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
+       "|---|---|\n",
+       "| indian | thai   |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "| indian | indian |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 2\n",
+       "\\begin{tabular}{ll}\n",
+       " cuisine & .pred\\_class\\\\\n",
+       " <fct> & <fct>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai  \\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\t indian & indian\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 2</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "CqtckvtsKqax",
+    "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Làm tốt lắm! Trong Tidymodels, việc đánh giá hiệu suất mô hình có thể được thực hiện bằng cách sử dụng [yardstick](https://yardstick.tidymodels.org/) - một gói dùng để đo lường hiệu quả của các mô hình bằng các chỉ số hiệu suất. Như chúng ta đã làm trong bài học hồi quy logistic, hãy bắt đầu bằng cách tính ma trận nhầm lẫn.\n"
+   ],
+   "metadata": {
+    "id": "8w5N6XsBKss7"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "source": [
+    "# Confusion matrix for categorical data\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "          Truth\n",
+       "Prediction chinese indian japanese korean thai\n",
+       "  chinese       83      1        8     15   10\n",
+       "  indian         4    163        1      2    6\n",
+       "  japanese      21      5       73     25    1\n",
+       "  korean        15      0       11    191    0\n",
+       "  thai          10     11        3      7   70"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 133
+    },
+    "id": "YvODvsLkK0iG",
+    "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Khi xử lý nhiều lớp, thường trực quan hơn khi hình dung điều này dưới dạng bản đồ nhiệt, như thế này:\n"
+   ],
+   "metadata": {
+    "id": "c0HfPL16Lr6U"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "source": [
+    "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
+    "# Visualize confusion matrix\n",
+    "results %>% \n",
+    "  conf_mat(cuisine, .pred_class) %>% \n",
+    "  autoplot(type = \"heatmap\")"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "plot without title"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {
+      "image/png": {
+       "width": 420,
+       "height": 420
+      }
+     }
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 436
+    },
+    "id": "HsAtwukyLsvt",
+    "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Các ô vuông tối màu hơn trong biểu đồ ma trận nhầm lẫn biểu thị số lượng trường hợp cao, và hy vọng bạn có thể thấy một đường chéo các ô vuông tối màu cho biết các trường hợp mà nhãn dự đoán và nhãn thực tế trùng khớp.\n",
+    "\n",
+    "Bây giờ, chúng ta hãy tính toán các thống kê tóm tắt cho ma trận nhầm lẫn.\n"
+   ],
+   "metadata": {
+    "id": "oOJC87dkLwPr"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "source": [
+    "# Summary stats for confusion matrix\n",
+    "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
+    "summary()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "   .metric              .estimator .estimate\n",
+       "1  accuracy             multiclass 0.7880435\n",
+       "2  kap                  multiclass 0.7276583\n",
+       "3  sens                 macro      0.7780927\n",
+       "4  spec                 macro      0.9477598\n",
+       "5  ppv                  macro      0.7585583\n",
+       "6  npv                  macro      0.9460080\n",
+       "7  mcc                  multiclass 0.7292724\n",
+       "8  j_index              macro      0.7258524\n",
+       "9  bal_accuracy         macro      0.8629262\n",
+       "10 detection_prevalence macro      0.2000000\n",
+       "11 precision            macro      0.7585583\n",
+       "12 recall               macro      0.7780927\n",
+       "13 f_meas               macro      0.7641862"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 13 × 3\n",
+       "\n",
+       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
+       "|---|---|---|\n",
+       "| accuracy             | multiclass | 0.7880435 |\n",
+       "| kap                  | multiclass | 0.7276583 |\n",
+       "| sens                 | macro      | 0.7780927 |\n",
+       "| spec                 | macro      | 0.9477598 |\n",
+       "| ppv                  | macro      | 0.7585583 |\n",
+       "| npv                  | macro      | 0.9460080 |\n",
+       "| mcc                  | multiclass | 0.7292724 |\n",
+       "| j_index              | macro      | 0.7258524 |\n",
+       "| bal_accuracy         | macro      | 0.8629262 |\n",
+       "| detection_prevalence | macro      | 0.2000000 |\n",
+       "| precision            | macro      | 0.7585583 |\n",
+       "| recall               | macro      | 0.7780927 |\n",
+       "| f_meas               | macro      | 0.7641862 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 13 × 3\n",
+       "\\begin{tabular}{lll}\n",
+       " .metric & .estimator & .estimate\\\\\n",
+       " <chr> & <chr> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t accuracy             & multiclass & 0.7880435\\\\\n",
+       "\t kap                  & multiclass & 0.7276583\\\\\n",
+       "\t sens                 & macro      & 0.7780927\\\\\n",
+       "\t spec                 & macro      & 0.9477598\\\\\n",
+       "\t ppv                  & macro      & 0.7585583\\\\\n",
+       "\t npv                  & macro      & 0.9460080\\\\\n",
+       "\t mcc                  & multiclass & 0.7292724\\\\\n",
+       "\t j\\_index              & macro      & 0.7258524\\\\\n",
+       "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
+       "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
+       "\t precision            & macro      & 0.7585583\\\\\n",
+       "\t recall               & macro      & 0.7780927\\\\\n",
+       "\t f\\_meas               & macro      & 0.7641862\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 13 × 3</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
+       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
+       "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
+       "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
+       "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
+       "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
+       "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
+       "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
+       "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
+       "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
+       "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
+       "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 494
+    },
+    "id": "OYqetUyzL5Wz",
+    "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Nếu chúng ta thu hẹp lại một số chỉ số như độ chính xác, độ nhạy, ppv, thì khởi đầu của chúng ta không tệ lắm 🥳!\n",
+    "\n",
+    "## 4. Đi sâu hơn\n",
+    "\n",
+    "Hãy đặt một câu hỏi tinh tế: Tiêu chí nào được sử dụng để quyết định một loại ẩm thực cụ thể là kết quả dự đoán?\n",
+    "\n",
+    "Thực ra, các thuật toán học máy thống kê, như hồi quy logistic, dựa trên `xác suất`; vì vậy, điều mà một bộ phân loại thực sự dự đoán là một phân phối xác suất trên tập hợp các kết quả có thể xảy ra. Lớp có xác suất cao nhất sau đó được chọn làm kết quả có khả năng xảy ra nhất cho các quan sát đã cho.\n",
+    "\n",
+    "Hãy xem điều này hoạt động như thế nào bằng cách thực hiện cả dự đoán lớp cứng và xác suất.\n"
+   ],
+   "metadata": {
+    "id": "43t7vz8vMJtW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "source": [
+    "# Make hard class prediction and probabilities\n",
+    "results_prob <- cuisines_test %>%\n",
+    "  select(cuisine) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
+    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
+    "\n",
+    "# Print out results\n",
+    "results_prob %>% \n",
+    "  slice_head(n = 5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": [
+       "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
+       "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
+       "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
+       "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
+       "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
+       "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
+       "  .pred_thai  \n",
+       "1 5.388194e-01\n",
+       "2 1.577948e-06\n",
+       "3 6.874989e-03\n",
+       "4 3.863391e-03\n",
+       "5 5.653283e-03"
+      ],
+      "text/markdown": [
+       "\n",
+       "A tibble: 5 × 7\n",
+       "\n",
+       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
+       "|---|---|---|---|---|---|---|\n",
+       "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
+       "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
+       "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
+       "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
+       "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
+       "\n"
+      ],
+      "text/latex": [
+       "A tibble: 5 × 7\n",
+       "\\begin{tabular}{lllllll}\n",
+       " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
+       " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
+       "\\hline\n",
+       "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
+       "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
+       "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
+       "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
+       "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
+       "\\end{tabular}\n"
+      ],
+      "text/html": [
+       "<table class=\"dataframe\">\n",
+       "<caption>A tibble: 5 × 7</caption>\n",
+       "<thead>\n",
+       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
+       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
+       "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
+       "</tbody>\n",
+       "</table>\n"
+      ]
+     },
+     "metadata": {}
+    }
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 248
+    },
+    "id": "xdKNs-ZPMTJL",
+    "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tốt hơn nhiều!\n",
+    "\n",
+    "✅ Bạn có thể giải thích tại sao mô hình lại khá chắc chắn rằng quan sát đầu tiên là món ăn Thái không?\n",
+    "\n",
+    "## **🚀Thử thách**\n",
+    "\n",
+    "Trong bài học này, bạn đã sử dụng dữ liệu đã được làm sạch để xây dựng một mô hình học máy có thể dự đoán ẩm thực quốc gia dựa trên một loạt các nguyên liệu. Hãy dành thời gian để đọc qua [nhiều tùy chọn](https://www.tidymodels.org/find/parsnip/#models) mà Tidymodels cung cấp để phân loại dữ liệu và [các cách khác](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) để áp dụng hồi quy đa thức.\n",
+    "\n",
+    "#### CẢM ƠN ĐẾN:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) vì đã tạo ra những hình minh họa tuyệt vời giúp R trở nên thân thiện và hấp dẫn hơn. Tìm thêm các hình minh họa tại [bộ sưu tập](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM) của cô ấy.\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) và [Jen Looper](https://www.twitter.com/jenlooper) vì đã tạo ra phiên bản Python gốc của module này ♥️\n",
+    "\n",
+    "<br>\n",
+    "Đã định thêm vài câu đùa nhưng tôi không hiểu các trò chơi chữ về đồ ăn 😅.\n",
+    "\n",
+    "<br>\n",
+    "\n",
+    "Học vui nhé,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Đại sứ Sinh viên Vàng của Microsoft Learn.\n"
+   ],
+   "metadata": {
+    "id": "2tWVHMeLMYdM"
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/2-Classifiers-1/solution/notebook.ipynb b/translations/vi/4-Classification/2-Classifiers-1/solution/notebook.ipynb
new file mode 100644
index 000000000..1b286f7e0
--- /dev/null
+++ b/translations/vi/4-Classification/2-Classifiers-1/solution/notebook.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Xây dựng Mô hình Phân loại\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "from sklearn.svm import SVC\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy is 0.8181818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n",
+    "model = lr.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "accuracy = model.score(X_test, y_test)\n",
+    "print (\"Accuracy is {}\".format(accuracy))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n       'vegetable_oil'],\n      dtype='object')\ncuisine: korean\n"
+     ]
+    }
+   ],
+   "source": [
+    "# test an item\n",
+    "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
+    "print(f'cuisine: {y_test.iloc[50]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                 0\n",
+       "korean    0.392231\n",
+       "chinese   0.372872\n",
+       "japanese  0.218825\n",
+       "thai      0.013427\n",
+       "indian    0.002645"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>korean</th>\n      <td>0.392231</td>\n    </tr>\n    <tr>\n      <th>chinese</th>\n      <td>0.372872</td>\n    </tr>\n    <tr>\n      <th>japanese</th>\n      <td>0.218825</td>\n    </tr>\n    <tr>\n      <th>thai</th>\n      <td>0.013427</td>\n    </tr>\n    <tr>\n      <th>indian</th>\n      <td>0.002645</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "#rehsape to 2d array and transpose\n",
+    "test= X_test.iloc[50].values.reshape(-1, 1).T\n",
+    "# predict with score\n",
+    "proba = model.predict_proba(test)\n",
+    "classes = model.classes_\n",
+    "# create df with classes and scores\n",
+    "resultdf = pd.DataFrame(data=proba, columns=classes)\n",
+    "\n",
+    "# create df to show results\n",
+    "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
+    "topPrediction.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.75      0.73      0.74       223\n      indian       0.93      0.88      0.90       255\n    japanese       0.78      0.78      0.78       253\n      korean       0.87      0.86      0.86       236\n        thai       0.76      0.84      0.80       232\n\n    accuracy                           0.82      1199\n   macro avg       0.82      0.82      0.82      1199\nweighted avg       0.82      0.82      0.82      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\r\n",
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "9408506dd864f2b6e334c62f80c0cfcc",
+   "translation_date": "2025-09-06T14:33:23+00:00",
+   "source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/3-Classifiers-2/notebook.ipynb b/translations/vi/4-Classification/3-Classifiers-2/notebook.ipynb
new file mode 100644
index 000000000..ca5928421
--- /dev/null
+++ b/translations/vi/4-Classification/3-Classifiers-2/notebook.ipynb
@@ -0,0 +1,163 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
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+   "mimetype": "text/x-python",
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+   "language_code": "vi"
+  }
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+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb b/translations/vi/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
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--- /dev/null
+++ b/translations/vi/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb
@@ -0,0 +1,648 @@
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+   "name": "R"
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+   "source_file": "4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jsFutf_ygqSx"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HD54bEefgtNO"
+   },
+   "source": [
+    "## Bộ phân loại ẩm thực 2\n",
+    "\n",
+    "Trong bài học phân loại thứ hai này, chúng ta sẽ khám phá `nhiều cách hơn` để phân loại dữ liệu dạng danh mục. Chúng ta cũng sẽ tìm hiểu về hậu quả của việc chọn một bộ phân loại thay vì bộ phân loại khác.\n",
+    "\n",
+    "### [**Câu hỏi trước bài giảng**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
+    "\n",
+    "### **Điều kiện tiên quyết**\n",
+    "\n",
+    "Chúng tôi giả định rằng bạn đã hoàn thành các bài học trước vì chúng ta sẽ tiếp tục sử dụng một số khái niệm đã học trước đó.\n",
+    "\n",
+    "Đối với bài học này, chúng ta sẽ cần các gói sau:\n",
+    "\n",
+    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) là một [bộ sưu tập các gói R](https://www.tidyverse.org/packages) được thiết kế để làm cho khoa học dữ liệu nhanh hơn, dễ dàng hơn và thú vị hơn!\n",
+    "\n",
+    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) là một [khung làm việc](https://www.tidymodels.org/packages/) bao gồm các gói dành cho mô hình hóa và học máy.\n",
+    "\n",
+    "-   `themis`: [gói themis](https://themis.tidymodels.org/) cung cấp các bước bổ sung trong công thức để xử lý dữ liệu không cân bằng.\n",
+    "\n",
+    "Bạn có thể cài đặt chúng bằng lệnh:\n",
+    "\n",
+    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\n",
+    "\n",
+    "Ngoài ra, đoạn mã dưới đây sẽ kiểm tra xem bạn đã có các gói cần thiết để hoàn thành module này chưa và cài đặt chúng cho bạn nếu chúng bị thiếu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vZ57IuUxgyQt"
+   },
+   "source": [
+    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "z22M-pj4g07x"
+   },
+   "source": [
+    "## **1. Một bản đồ phân loại**\n",
+    "\n",
+    "Trong [bài học trước](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1), chúng ta đã cố gắng giải quyết câu hỏi: làm thế nào để chọn giữa nhiều mô hình? Phần lớn điều này phụ thuộc vào đặc điểm của dữ liệu và loại vấn đề mà chúng ta muốn giải quyết (ví dụ như phân loại hay hồi quy?)\n",
+    "\n",
+    "Trước đây, chúng ta đã tìm hiểu về các tùy chọn khác nhau khi phân loại dữ liệu bằng bảng gian lận của Microsoft. Framework Machine Learning của Python, Scikit-learn, cung cấp một bảng gian lận tương tự nhưng chi tiết hơn, giúp bạn thu hẹp các bộ ước lượng (một thuật ngữ khác cho các bộ phân loại):\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/map.png\"\n",
+    "   width=\"700\"/>\n",
+    "   <figcaption></figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u1i3xRIVg7vG"
+   },
+   "source": [
+    "> Mẹo: [truy cập bản đồ trực tuyến này](https://scikit-learn.org/stable/tutorial/machine_learning_map/) và nhấp theo đường dẫn để đọc tài liệu.\n",
+    ">\n",
+    "> Trang [tham khảo Tidymodels](https://www.tidymodels.org/find/parsnip/#models) cũng cung cấp tài liệu tuyệt vời về các loại mô hình khác nhau.\n",
+    "\n",
+    "### **Kế hoạch** 🗺️\n",
+    "\n",
+    "Bản đồ này rất hữu ích khi bạn đã hiểu rõ dữ liệu của mình, vì bạn có thể 'đi bộ' theo các đường dẫn để đưa ra quyết định:\n",
+    "\n",
+    "-   Chúng ta có \\>50 mẫu\n",
+    "\n",
+    "-   Chúng ta muốn dự đoán một danh mục\n",
+    "\n",
+    "-   Chúng ta có dữ liệu được gắn nhãn\n",
+    "\n",
+    "-   Chúng ta có ít hơn 100K mẫu\n",
+    "\n",
+    "-   ✨ Chúng ta có thể chọn Linear SVC\n",
+    "\n",
+    "-   Nếu cách đó không hiệu quả, vì chúng ta có dữ liệu dạng số\n",
+    "\n",
+    "    -   Chúng ta có thể thử ✨ KNeighbors Classifier\n",
+    "\n",
+    "        -   Nếu cách đó không hiệu quả, thử ✨ SVC và ✨ Ensemble Classifiers\n",
+    "\n",
+    "Đây là một lộ trình rất hữu ích để làm theo. Bây giờ, hãy bắt đầu ngay với [tidymodels](https://www.tidymodels.org/): một bộ sưu tập các gói R nhất quán và linh hoạt được phát triển để khuyến khích thực hành thống kê tốt 😊.\n",
+    "\n",
+    "## 2. Chia dữ liệu và xử lý tập dữ liệu không cân bằng.\n",
+    "\n",
+    "Từ các bài học trước, chúng ta đã học rằng có một tập hợp các thành phần chung giữa các món ăn của chúng ta. Ngoài ra, có sự phân bố không đồng đều về số lượng món ăn.\n",
+    "\n",
+    "Chúng ta sẽ xử lý những điều này bằng cách:\n",
+    "\n",
+    "-   Loại bỏ các thành phần phổ biến nhất gây nhầm lẫn giữa các món ăn khác nhau, sử dụng `dplyr::select()`.\n",
+    "\n",
+    "-   Sử dụng một `recipe` để tiền xử lý dữ liệu, chuẩn bị cho việc mô hình hóa bằng cách áp dụng thuật toán `over-sampling`.\n",
+    "\n",
+    "Chúng ta đã xem qua những điều trên trong bài học trước nên việc này sẽ rất dễ dàng 🥳!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "6tj_rN00hClA"
+   },
+   "source": [
+    "# Load the core Tidyverse and Tidymodels packages\n",
+    "library(tidyverse)\n",
+    "library(tidymodels)\n",
+    "\n",
+    "# Load the original cuisines data\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
+    "\n",
+    "# Drop id column, rice, garlic and ginger from our original data set\n",
+    "df_select <- df %>% \n",
+    "  select(-c(1, rice, garlic, ginger)) %>%\n",
+    "  # Encode cuisine column as categorical\n",
+    "  mutate(cuisine = factor(cuisine))\n",
+    "\n",
+    "\n",
+    "# Create data split specification\n",
+    "set.seed(2056)\n",
+    "cuisines_split <- initial_split(data = df_select,\n",
+    "                                strata = cuisine,\n",
+    "                                prop = 0.7)\n",
+    "\n",
+    "# Extract the data in each split\n",
+    "cuisines_train <- training(cuisines_split)\n",
+    "cuisines_test <- testing(cuisines_split)\n",
+    "\n",
+    "# Display distribution of cuisines in the training set\n",
+    "cuisines_train %>% \n",
+    "  count(cuisine) %>% \n",
+    "  arrange(desc(n))"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zFin5yw3hHb1"
+   },
+   "source": [
+    "### Xử lý dữ liệu không cân bằng\n",
+    "\n",
+    "Dữ liệu không cân bằng thường ảnh hưởng tiêu cực đến hiệu suất của mô hình. Nhiều mô hình hoạt động tốt nhất khi số lượng quan sát là bằng nhau và do đó thường gặp khó khăn với dữ liệu không cân bằng.\n",
+    "\n",
+    "Có hai cách chính để xử lý các tập dữ liệu không cân bằng:\n",
+    "\n",
+    "-   thêm các quan sát vào lớp thiểu số: `Over-sampling`, ví dụ sử dụng thuật toán SMOTE, thuật toán này tạo ra các ví dụ mới cho lớp thiểu số một cách tổng hợp bằng cách sử dụng các điểm lân cận gần nhất của các trường hợp này.\n",
+    "\n",
+    "-   loại bỏ các quan sát từ lớp đa số: `Under-sampling`\n",
+    "\n",
+    "Trong bài học trước, chúng ta đã minh họa cách xử lý các tập dữ liệu không cân bằng bằng cách sử dụng một `recipe`. Recipe có thể được xem như một bản thiết kế mô tả các bước cần áp dụng cho một tập dữ liệu để chuẩn bị cho việc phân tích dữ liệu. Trong trường hợp của chúng ta, mục tiêu là có một phân phối đồng đều về số lượng các loại món ăn trong `training set`. Hãy bắt đầu ngay thôi.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "cRzTnHolhLWd"
+   },
+   "source": [
+    "# Load themis package for dealing with imbalanced data\n",
+    "library(themis)\n",
+    "\n",
+    "# Create a recipe for preprocessing training data\n",
+    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
+    "  step_smote(cuisine) \n",
+    "\n",
+    "# Print recipe\n",
+    "cuisines_recipe"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KxOQ2ORhhO81"
+   },
+   "source": [
+    "Bây giờ chúng ta sẵn sàng để huấn luyện các mô hình 👩‍💻👨‍💻!\n",
+    "\n",
+    "## 3. Vượt ra ngoài các mô hình hồi quy đa thức\n",
+    "\n",
+    "Trong bài học trước, chúng ta đã tìm hiểu về các mô hình hồi quy đa thức. Hãy khám phá một số mô hình linh hoạt hơn cho bài toán phân loại.\n",
+    "\n",
+    "### Máy Vector Hỗ Trợ (Support Vector Machines)\n",
+    "\n",
+    "Trong bối cảnh phân loại, `Máy Vector Hỗ Trợ` là một kỹ thuật học máy cố gắng tìm một *siêu phẳng* (*hyperplane*) để \"tách biệt tốt nhất\" các lớp. Hãy xem một ví dụ đơn giản:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/svm.png\"\n",
+    "   width=\"300\"/>\n",
+    "   <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C4Wsd0vZhXYu"
+   },
+   "source": [
+    "H1~ không tách biệt các lớp. H2~ có tách biệt, nhưng chỉ với một khoảng cách nhỏ. H3~ tách biệt chúng với khoảng cách tối đa.\n",
+    "\n",
+    "#### Bộ phân loại tuyến tính SVM\n",
+    "\n",
+    "Hỗ trợ phân cụm vector (SVC) là một nhánh của họ các kỹ thuật máy học (ML) thuộc máy vector hỗ trợ (SVM). Trong SVC, siêu phẳng được chọn để phân tách `phần lớn` các quan sát huấn luyện một cách chính xác, nhưng `có thể phân loại sai` một vài quan sát. Bằng cách cho phép một số điểm nằm ở phía sai, SVM trở nên mạnh mẽ hơn trước các giá trị ngoại lai, do đó cải thiện khả năng tổng quát hóa với dữ liệu mới. Tham số điều chỉnh sự vi phạm này được gọi là `cost`, với giá trị mặc định là 1 (xem `help(\"svm_poly\")`).\n",
+    "\n",
+    "Hãy tạo một SVC tuyến tính bằng cách đặt `degree = 1` trong một mô hình SVM đa thức.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "vJpp6nuChlBz"
+   },
+   "source": [
+    "# Make a linear SVC specification\n",
+    "svc_linear_spec <- svm_poly(degree = 1) %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svc_linear_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svc_linear_spec)\n",
+    "\n",
+    "# Print out workflow\n",
+    "svc_linear_wf"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rDs8cWNkhoqu"
+   },
+   "source": [
+    "Bây giờ khi chúng ta đã lưu lại các bước tiền xử lý và đặc tả mô hình vào một *workflow*, chúng ta có thể tiến hành huấn luyện SVC tuyến tính và đánh giá kết quả trong quá trình đó. Để đo lường hiệu suất, hãy tạo một tập hợp các chỉ số để đánh giá: `accuracy`, `sensitivity`, `Positive Predicted Value` và `F Measure`.\n",
+    "\n",
+    "> `augment()` sẽ thêm cột (hoặc các cột) cho dự đoán vào dữ liệu được cung cấp.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "81wiqcwuhrnq"
+   },
+   "source": [
+    "# Train a linear SVC model\n",
+    "svc_linear_fit <- svc_linear_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svc_linear_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0UFQvHf-huo3"
+   },
+   "source": [
+    "#### Máy Vector Hỗ Trợ\n",
+    "\n",
+    "Máy vector hỗ trợ (SVM) là một sự mở rộng của bộ phân loại vector hỗ trợ nhằm đáp ứng ranh giới phi tuyến giữa các lớp. Về cơ bản, SVM sử dụng *kernel trick* để mở rộng không gian đặc trưng, thích nghi với các mối quan hệ phi tuyến giữa các lớp. Một hàm kernel phổ biến và cực kỳ linh hoạt được SVM sử dụng là *Hàm cơ sở xuyên tâm.* Hãy cùng xem cách nó hoạt động trên dữ liệu của chúng ta.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-KX4S8mzhzmp"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Make an RBF SVM specification\n",
+    "svm_rbf_spec <- svm_rbf() %>% \n",
+    "  set_engine(\"kernlab\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle specification and recipe into a worklow\n",
+    "svm_rbf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(svm_rbf_spec)\n",
+    "\n",
+    "\n",
+    "# Train an RBF model\n",
+    "svm_rbf_fit <- svm_rbf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "svm_rbf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QBFSa7WSh4HQ"
+   },
+   "source": [
+    "Tuyệt vời hơn 🤩!\n",
+    "\n",
+    "> ✅ Vui lòng xem:\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://bradleyboehmke.github.io/HOML/svm.html), Hands-on Machine Learning with R\n",
+    ">\n",
+    "> -   [*Support Vector Machines*](https://www.statlearning.com/), An Introduction to Statistical Learning with Applications in R\n",
+    ">\n",
+    "> để đọc thêm.\n",
+    "\n",
+    "### Bộ phân loại Hàng xóm Gần nhất\n",
+    "\n",
+    "*K*-nearest neighbor (KNN) là một thuật toán trong đó mỗi quan sát được dự đoán dựa trên *sự tương đồng* của nó với các quan sát khác.\n",
+    "\n",
+    "Hãy thử áp dụng nó vào dữ liệu của chúng ta.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "k4BxxBcdh9Ka"
+   },
+   "source": [
+    "# Make a KNN specification\n",
+    "knn_spec <- nearest_neighbor() %>% \n",
+    "  set_engine(\"kknn\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "knn_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(knn_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "knn_wf_fit <- knn_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "knn_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HaegQseriAcj"
+   },
+   "source": [
+    "Có vẻ như mô hình này không hoạt động tốt lắm. Có lẽ việc thay đổi các tham số của mô hình (xem `help(\"nearest_neighbor\")`) sẽ cải thiện hiệu suất của mô hình. Hãy chắc chắn thử nghiệm điều này.\n",
+    "\n",
+    "> ✅ Vui lòng tham khảo:\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> để tìm hiểu thêm về bộ phân loại *K*-Nearest Neighbors.\n",
+    "\n",
+    "### Bộ phân loại tập hợp (Ensemble classifiers)\n",
+    "\n",
+    "Các thuật toán tập hợp hoạt động bằng cách kết hợp nhiều bộ ước lượng cơ sở để tạo ra một mô hình tối ưu thông qua:\n",
+    "\n",
+    "`bagging`: áp dụng một *hàm trung bình* cho một tập hợp các mô hình cơ sở\n",
+    "\n",
+    "`boosting`: xây dựng một chuỗi các mô hình, trong đó mỗi mô hình kế tiếp cải thiện hiệu suất dự đoán dựa trên mô hình trước.\n",
+    "\n",
+    "Hãy bắt đầu bằng cách thử nghiệm mô hình Random Forest, mô hình này xây dựng một tập hợp lớn các cây quyết định, sau đó áp dụng một hàm trung bình để tạo ra một mô hình tổng thể tốt hơn.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "49DPoVs6iK1M"
+   },
+   "source": [
+    "# Make a random forest specification\n",
+    "rf_spec <- rand_forest() %>% \n",
+    "  set_engine(\"ranger\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "rf_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(rf_spec)\n",
+    "\n",
+    "# Train a random forest model\n",
+    "rf_wf_fit <- rf_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "rf_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "RGVYwC_aiUWc"
+   },
+   "source": [
+    "Làm tốt lắm 👏!\n",
+    "\n",
+    "Hãy cùng thử nghiệm với mô hình Boosted Tree.\n",
+    "\n",
+    "Boosted Tree định nghĩa một phương pháp tập hợp, tạo ra một chuỗi các cây quyết định tuần tự, trong đó mỗi cây phụ thuộc vào kết quả của các cây trước đó nhằm giảm dần lỗi. Phương pháp này tập trung vào trọng số của các mục bị phân loại sai và điều chỉnh việc khớp cho bộ phân loại tiếp theo để sửa lỗi.\n",
+    "\n",
+    "Có nhiều cách khác nhau để khớp mô hình này (xem `help(\"boost_tree\")`). Trong ví dụ này, chúng ta sẽ khớp Boosted trees thông qua công cụ `xgboost`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Py1YWo-micWs"
+   },
+   "source": [
+    "# Make a boosted tree specification\n",
+    "boost_spec <- boost_tree(trees = 200) %>% \n",
+    "  set_engine(\"xgboost\") %>% \n",
+    "  set_mode(\"classification\")\n",
+    "\n",
+    "# Bundle recipe and model specification into a workflow\n",
+    "boost_wf <- workflow() %>% \n",
+    "  add_recipe(cuisines_recipe) %>% \n",
+    "  add_model(boost_spec)\n",
+    "\n",
+    "# Train a boosted tree model\n",
+    "boost_wf_fit <- boost_wf %>% \n",
+    "  fit(data = cuisines_train)\n",
+    "\n",
+    "\n",
+    "# Make predictions and Evaluate model performance\n",
+    "boost_wf_fit %>% \n",
+    "  augment(new_data = cuisines_test) %>% \n",
+    "  eval_metrics(truth = cuisine, estimate = .pred_class)"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "zNQnbuejigZM"
+   },
+   "source": [
+    "> ✅ Vui lòng xem:\n",
+    ">\n",
+    "> -   [Machine Learning for Social Scientists](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
+    ">\n",
+    "> -   [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
+    ">\n",
+    "> -   [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
+    ">\n",
+    "> -   <https://algotech.netlify.app/blog/xgboost/> - Khám phá mô hình AdaBoost, một lựa chọn thay thế tốt cho xgboost.\n",
+    ">\n",
+    "> để tìm hiểu thêm về các bộ phân loại Ensemble.\n",
+    "\n",
+    "## 4. Bổ sung - so sánh nhiều mô hình\n",
+    "\n",
+    "Chúng ta đã xây dựng khá nhiều mô hình trong bài thực hành này 🙌. Việc tạo ra nhiều quy trình làm việc từ các bộ tiền xử lý và/hoặc các đặc tả mô hình khác nhau, sau đó tính toán từng chỉ số hiệu suất một cách riêng lẻ có thể trở nên tẻ nhạt hoặc khó khăn.\n",
+    "\n",
+    "Hãy xem liệu chúng ta có thể giải quyết vấn đề này bằng cách tạo một hàm để huấn luyện danh sách các quy trình làm việc trên tập huấn luyện, sau đó trả về các chỉ số hiệu suất dựa trên tập kiểm tra. Chúng ta sẽ sử dụng `map()` và `map_dfr()` từ gói [purrr](https://purrr.tidyverse.org/) để áp dụng các hàm cho từng phần tử trong danh sách.\n",
+    "\n",
+    "> Các hàm [`map()`](https://purrr.tidyverse.org/reference/map.html) cho phép bạn thay thế nhiều vòng lặp for bằng mã ngắn gọn hơn và dễ đọc hơn. Nơi tốt nhất để tìm hiểu về các hàm [`map()`](https://purrr.tidyverse.org/reference/map.html) là chương [iteration](http://r4ds.had.co.nz/iteration.html) trong sách R for Data Science.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Qzb7LyZnimd2"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "\n",
+    "# Create a metric set\n",
+    "eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
+    "\n",
+    "# Define a function that returns performance metrics\n",
+    "compare_models <- function(workflow_list, train_set, test_set){\n",
+    "  \n",
+    "  suppressWarnings(\n",
+    "    # Fit each model to the train_set\n",
+    "    map(workflow_list, fit, data = train_set) %>% \n",
+    "    # Make predictions on the test set\n",
+    "      map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
+    "    # Select desired columns\n",
+    "      select(model, cuisine, .pred_class) %>% \n",
+    "    # Evaluate model performance\n",
+    "      group_by(model) %>% \n",
+    "      eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
+    "      ungroup()\n",
+    "  )\n",
+    "  \n",
+    "} # End of function"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fwa712sNisDA"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "3i4VJOi2iu-a"
+   },
+   "source": [
+    "# Make a list of workflows\n",
+    "workflow_list <- list(\n",
+    "  \"svc\" = svc_linear_wf,\n",
+    "  \"svm\" = svm_rbf_wf,\n",
+    "  \"knn\" = knn_wf,\n",
+    "  \"random_forest\" = rf_wf,\n",
+    "  \"xgboost\" = boost_wf)\n",
+    "\n",
+    "# Call the function\n",
+    "set.seed(2056)\n",
+    "perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
+    "\n",
+    "# Print out performance metrics\n",
+    "perf_metrics %>% \n",
+    "  group_by(.metric) %>% \n",
+    "  arrange(desc(.estimate)) %>% \n",
+    "  slice_head(n=7)\n",
+    "\n",
+    "# Compare accuracy\n",
+    "perf_metrics %>% \n",
+    "  filter(.metric == \"accuracy\") %>% \n",
+    "  arrange(desc(.estimate))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KuWK_lEli4nW"
+   },
+   "source": [
+    "[**workflowset**](https://workflowsets.tidymodels.org/) cho phép người dùng tạo và dễ dàng huấn luyện một số lượng lớn mô hình, nhưng chủ yếu được thiết kế để hoạt động với các kỹ thuật lấy mẫu lại như `cross-validation`, một phương pháp mà chúng ta chưa đề cập đến.\n",
+    "\n",
+    "## **🚀Thử thách**\n",
+    "\n",
+    "Mỗi kỹ thuật này có một số lượng lớn các tham số mà bạn có thể điều chỉnh, ví dụ như `cost` trong SVMs, `neighbors` trong KNN, `mtry` (Các Dự đoán Được Chọn Ngẫu Nhiên) trong Random Forest.\n",
+    "\n",
+    "Hãy nghiên cứu các tham số mặc định của từng kỹ thuật và suy nghĩ về việc điều chỉnh các tham số này sẽ ảnh hưởng như thế nào đến chất lượng của mô hình.\n",
+    "\n",
+    "Để tìm hiểu thêm về một mô hình cụ thể và các tham số của nó, sử dụng: `help(\"model\")` ví dụ `help(\"rand_forest\")`\n",
+    "\n",
+    "> Trong thực tế, chúng ta thường *ước lượng* các *giá trị tốt nhất* cho các tham số này bằng cách huấn luyện nhiều mô hình trên một `bộ dữ liệu mô phỏng` và đo lường hiệu suất của tất cả các mô hình này. Quá trình này được gọi là **tuning**.\n",
+    "\n",
+    "### [**Câu hỏi sau bài giảng**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
+    "\n",
+    "### **Ôn tập & Tự học**\n",
+    "\n",
+    "Có rất nhiều thuật ngữ chuyên ngành trong các bài học này, vì vậy hãy dành một chút thời gian để xem lại [danh sách này](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) các thuật ngữ hữu ích!\n",
+    "\n",
+    "#### CẢM ƠN ĐẾN:\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) vì đã tạo ra những hình minh họa tuyệt vời giúp R trở nên thân thiện và hấp dẫn hơn. Tìm thêm các hình minh họa tại [bộ sưu tập của cô ấy](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "[Cassie Breviu](https://www.twitter.com/cassieview) và [Jen Looper](https://www.twitter.com/jenlooper) vì đã tạo ra phiên bản Python gốc của module này ♥️\n",
+    "\n",
+    "Chúc học tập vui vẻ,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Đại sứ Sinh viên Microsoft Learn Vàng.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"569\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/3-Classifiers-2/solution/notebook.ipynb b/translations/vi/4-Classification/3-Classifiers-2/solution/notebook.ipynb
new file mode 100644
index 000000000..9ea5d7931
--- /dev/null
+++ b/translations/vi/4-Classification/3-Classifiers-2/solution/notebook.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
+    "cuisines_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0    indian\n",
+       "1    indian\n",
+       "2    indian\n",
+       "3    indian\n",
+       "4    indian\n",
+       "Name: cuisine, dtype: object"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 2
+    }
+   ],
+   "source": [
+    "cuisines_label_df = cuisines_df['cuisine']\n",
+    "cuisines_label_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ],
+   "source": [
+    "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
+    "cuisines_feature_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Thử các bộ phân loại khác\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
+    "from sklearn.model_selection import train_test_split, cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "C = 10\n",
+    "# Create different classifiers.\n",
+    "classifiers = {\n",
+    "    'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n",
+    "    'KNN classifier': KNeighborsClassifier(C),\n",
+    "    'SVC': SVC(),\n",
+    "    'RFST': RandomForestClassifier(n_estimators=100),\n",
+    "    'ADA': AdaBoostClassifier(n_estimators=100)\n",
+    "    \n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Accuracy (train) for Linear SVC: 76.4% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.64      0.66      0.65       242\n",
+      "      indian       0.91      0.86      0.89       236\n",
+      "    japanese       0.72      0.73      0.73       245\n",
+      "      korean       0.83      0.75      0.79       234\n",
+      "        thai       0.75      0.82      0.78       242\n",
+      "\n",
+      "    accuracy                           0.76      1199\n",
+      "   macro avg       0.77      0.76      0.77      1199\n",
+      "weighted avg       0.77      0.76      0.77      1199\n",
+      "\n",
+      "Accuracy (train) for KNN classifier: 70.7% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.65      0.63      0.64       242\n",
+      "      indian       0.84      0.81      0.82       236\n",
+      "    japanese       0.60      0.81      0.69       245\n",
+      "      korean       0.89      0.53      0.67       234\n",
+      "        thai       0.69      0.75      0.72       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.73      0.71      0.71      1199\n",
+      "weighted avg       0.73      0.71      0.71      1199\n",
+      "\n",
+      "Accuracy (train) for SVC: 80.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.71      0.69      0.70       242\n",
+      "      indian       0.92      0.92      0.92       236\n",
+      "    japanese       0.77      0.78      0.77       245\n",
+      "      korean       0.87      0.77      0.82       234\n",
+      "        thai       0.75      0.86      0.80       242\n",
+      "\n",
+      "    accuracy                           0.80      1199\n",
+      "   macro avg       0.80      0.80      0.80      1199\n",
+      "weighted avg       0.80      0.80      0.80      1199\n",
+      "\n",
+      "Accuracy (train) for RFST: 82.8% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.80      0.75      0.77       242\n",
+      "      indian       0.90      0.91      0.90       236\n",
+      "    japanese       0.82      0.78      0.80       245\n",
+      "      korean       0.85      0.82      0.83       234\n",
+      "        thai       0.78      0.89      0.83       242\n",
+      "\n",
+      "    accuracy                           0.83      1199\n",
+      "   macro avg       0.83      0.83      0.83      1199\n",
+      "weighted avg       0.83      0.83      0.83      1199\n",
+      "\n",
+      "Accuracy (train) for ADA: 71.1% \n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "     chinese       0.60      0.57      0.58       242\n",
+      "      indian       0.87      0.84      0.86       236\n",
+      "    japanese       0.71      0.60      0.65       245\n",
+      "      korean       0.68      0.78      0.72       234\n",
+      "        thai       0.70      0.78      0.74       242\n",
+      "\n",
+      "    accuracy                           0.71      1199\n",
+      "   macro avg       0.71      0.71      0.71      1199\n",
+      "weighted avg       0.71      0.71      0.71      1199\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_classifiers = len(classifiers)\n",
+    "\n",
+    "for index, (name, classifier) in enumerate(classifiers.items()):\n",
+    "    classifier.fit(X_train, np.ravel(y_train))\n",
+    "\n",
+    "    y_pred = classifier.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n",
+    "    print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "7ea2b714669c823a596d986ba2d5739f",
+   "translation_date": "2025-09-06T14:43:09+00:00",
+   "source_file": "4-Classification/3-Classifiers-2/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/4-Applied/notebook.ipynb b/translations/vi/4-Classification/4-Applied/notebook.ipynb
new file mode 100644
index 000000000..6aa02291f
--- /dev/null
+++ b/translations/vi/4-Classification/4-Applied/notebook.ipynb
@@ -0,0 +1,39 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2f3e0d9e9ac5c301558fb8bf733ac0cb",
+   "translation_date": "2025-09-06T14:41:44+00:00",
+   "source_file": "4-Classification/4-Applied/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/4-Classification/4-Applied/solution/notebook.ipynb b/translations/vi/4-Classification/4-Applied/solution/notebook.ipynb
new file mode 100644
index 000000000..59d001fc8
--- /dev/null
+++ b/translations/vi/4-Classification/4-Applied/solution/notebook.ipynb
@@ -0,0 +1,290 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "49325d6dd12a3628fc64fa7ccb1a80ff",
+   "translation_date": "2025-09-06T14:42:08+00:00",
+   "source_file": "4-Classification/4-Applied/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: skl2onnx in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.8.0)\n",
+      "Requirement already satisfied: protobuf in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (3.8.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.19.2)\n",
+      "Requirement already satisfied: onnx>=1.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.9.0)\n",
+      "Requirement already satisfied: six in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from skl2onnx) (1.12.0)\n",
+      "Requirement already satisfied: onnxconverter-common<1.9,>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.8.1)\n",
+      "Requirement already satisfied: scikit-learn>=0.19 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (0.24.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.4.1)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->skl2onnx) (45.1.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.6.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnx>=1.2.1->skl2onnx) (3.10.0.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (0.16.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install skl2onnx"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   Unnamed: 0 cuisine  almond  angelica  anise  anise_seed  apple  \\\n",
+       "0           0  indian       0         0      0           0      0   \n",
+       "1           1  indian       1         0      0           0      0   \n",
+       "2           2  indian       0         0      0           0      0   \n",
+       "3           3  indian       0         0      0           0      0   \n",
+       "4           4  indian       0         0      0           0      0   \n",
+       "\n",
+       "   apple_brandy  apricot  armagnac  ...  whiskey  white_bread  white_wine  \\\n",
+       "0             0        0         0  ...        0            0           0   \n",
+       "1             0        0         0  ...        0            0           0   \n",
+       "2             0        0         0  ...        0            0           0   \n",
+       "3             0        0         0  ...        0            0           0   \n",
+       "4             0        0         0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 382 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>cuisine</th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>indian</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>indian</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 60
+    }
+   ],
+   "source": [
+    "data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "   almond  angelica  anise  anise_seed  apple  apple_brandy  apricot  \\\n",
+       "0       0         0      0           0      0             0        0   \n",
+       "1       1         0      0           0      0             0        0   \n",
+       "2       0         0      0           0      0             0        0   \n",
+       "3       0         0      0           0      0             0        0   \n",
+       "4       0         0      0           0      0             0        0   \n",
+       "\n",
+       "   armagnac  artemisia  artichoke  ...  whiskey  white_bread  white_wine  \\\n",
+       "0         0          0          0  ...        0            0           0   \n",
+       "1         0          0          0  ...        0            0           0   \n",
+       "2         0          0          0  ...        0            0           0   \n",
+       "3         0          0          0  ...        0            0           0   \n",
+       "4         0          0          0  ...        0            0           0   \n",
+       "\n",
+       "   whole_grain_wheat_flour  wine  wood  yam  yeast  yogurt  zucchini  \n",
+       "0                        0     0     0    0      0       0         0  \n",
+       "1                        0     0     0    0      0       0         0  \n",
+       "2                        0     0     0    0      0       0         0  \n",
+       "3                        0     0     0    0      0       0         0  \n",
+       "4                        0     0     0    0      0       1         0  \n",
+       "\n",
+       "[5 rows x 380 columns]"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>almond</th>\n      <th>angelica</th>\n      <th>anise</th>\n      <th>anise_seed</th>\n      <th>apple</th>\n      <th>apple_brandy</th>\n      <th>apricot</th>\n      <th>armagnac</th>\n      <th>artemisia</th>\n      <th>artichoke</th>\n      <th>...</th>\n      <th>whiskey</th>\n      <th>white_bread</th>\n      <th>white_wine</th>\n      <th>whole_grain_wheat_flour</th>\n      <th>wine</th>\n      <th>wood</th>\n      <th>yam</th>\n      <th>yeast</th>\n      <th>yogurt</th>\n      <th>zucchini</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 61
+    }
+   ],
+   "source": [
+    "X = data.iloc[:,2:]\n",
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "  cuisine\n",
+       "0  indian\n",
+       "1  indian\n",
+       "2  indian\n",
+       "3  indian\n",
+       "4  indian"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>cuisine</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>indian</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>indian</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 62
+    }
+   ],
+   "source": [
+    "y = data[['cuisine']]\n",
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "SVC(C=10, kernel='linear', probability=True, random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 65
+    }
+   ],
+   "source": [
+    "model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
+    "model.fit(X_train,y_train.values.ravel())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "              precision    recall  f1-score   support\n\n     chinese       0.72      0.70      0.71       236\n      indian       0.91      0.88      0.89       243\n    japanese       0.80      0.75      0.77       240\n      korean       0.80      0.81      0.81       230\n        thai       0.76      0.85      0.80       250\n\n    accuracy                           0.80      1199\n   macro avg       0.80      0.80      0.80      1199\nweighted avg       0.80      0.80      0.80      1199\n\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skl2onnx import convert_sklearn\n",
+    "from skl2onnx.common.data_types import FloatTensorType\n",
+    "\n",
+    "initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
+    "options = {id(model): {'nocl': True, 'zipmap': False}}\n",
+    "onx = convert_sklearn(model, initial_types=initial_type, options=options)\n",
+    "with open(\"./model.onnx\", \"wb\") as f:\n",
+    "    f.write(onx.SerializeToString())\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/5-Clustering/1-Visualize/notebook.ipynb b/translations/vi/5-Clustering/1-Visualize/notebook.ipynb
new file mode 100644
index 000000000..3012140c8
--- /dev/null
+++ b/translations/vi/5-Clustering/1-Visualize/notebook.ipynb
@@ -0,0 +1,50 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python383jvsc74a57bd0e134e05457d34029b6460cd73bbf1ed73f339b5b6d98c95be70b69eba114fe95",
+   "display_name": "Python 3.8.3 64-bit (conda)"
+  },
+  "coopTranslator": {
+   "original_hash": "40e0707e96b3e1899a912776006264f9",
+   "translation_date": "2025-09-06T14:08:14+00:00",
+   "source_file": "5-Clustering/1-Visualize/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb b/translations/vi/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
new file mode 100644
index 000000000..f76379f5d
--- /dev/null
+++ b/translations/vi/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb
@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## **Phân tích âm nhạc Nigeria được thu thập từ Spotify**\n",
+    "\n",
+    "Clustering (phân cụm) là một loại [Học không giám sát](https://wikipedia.org/wiki/Unsupervised_learning) giả định rằng một tập dữ liệu không được gắn nhãn hoặc các đầu vào của nó không được ghép với các đầu ra được xác định trước. Nó sử dụng các thuật toán khác nhau để sắp xếp dữ liệu không gắn nhãn và cung cấp các nhóm dựa trên các mẫu mà nó nhận ra trong dữ liệu.\n",
+    "\n",
+    "[**Câu hỏi kiểm tra trước bài giảng**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/27/)\n",
+    "\n",
+    "### **Giới thiệu**\n",
+    "\n",
+    "[Phân cụm](https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-30164-8_124) rất hữu ích trong việc khám phá dữ liệu. Hãy xem liệu nó có thể giúp khám phá các xu hướng và mẫu trong cách khán giả Nigeria tiêu thụ âm nhạc hay không.\n",
+    "\n",
+    "> ✅ Dành một phút để suy nghĩ về các ứng dụng của phân cụm. Trong đời sống thực, phân cụm xảy ra bất cứ khi nào bạn có một đống quần áo cần phân loại theo từng thành viên trong gia đình 🧦👕👖🩲. Trong khoa học dữ liệu, phân cụm xảy ra khi cố gắng phân tích sở thích của người dùng hoặc xác định các đặc điểm của bất kỳ tập dữ liệu không gắn nhãn nào. Phân cụm, theo một cách nào đó, giúp làm sáng tỏ sự hỗn loạn, giống như ngăn kéo đựng tất.\n",
+    "\n",
+    "Trong môi trường chuyên nghiệp, phân cụm có thể được sử dụng để xác định các phân khúc thị trường, ví dụ như xác định nhóm tuổi nào mua những mặt hàng nào. Một ứng dụng khác có thể là phát hiện bất thường, chẳng hạn để phát hiện gian lận từ một tập dữ liệu giao dịch thẻ tín dụng. Hoặc bạn có thể sử dụng phân cụm để xác định khối u trong một loạt các bản quét y tế.\n",
+    "\n",
+    "✅ Dành một phút để suy nghĩ về cách bạn có thể đã gặp phân cụm 'trong thực tế', trong ngân hàng, thương mại điện tử hoặc môi trường kinh doanh.\n",
+    "\n",
+    "> 🎓 Thật thú vị, phân tích cụm bắt nguồn từ các lĩnh vực Nhân học và Tâm lý học vào những năm 1930. Bạn có thể tưởng tượng nó đã được sử dụng như thế nào không?\n",
+    "\n",
+    "Ngoài ra, bạn có thể sử dụng nó để nhóm các kết quả tìm kiếm - ví dụ như theo liên kết mua sắm, hình ảnh hoặc đánh giá. Phân cụm rất hữu ích khi bạn có một tập dữ liệu lớn mà bạn muốn giảm bớt và thực hiện phân tích chi tiết hơn, vì vậy kỹ thuật này có thể được sử dụng để tìm hiểu về dữ liệu trước khi xây dựng các mô hình khác.\n",
+    "\n",
+    "✅ Khi dữ liệu của bạn được tổ chức thành các cụm, bạn gán cho nó một Id cụm, và kỹ thuật này có thể hữu ích khi bảo vệ quyền riêng tư của tập dữ liệu; bạn có thể thay thế việc tham chiếu đến một điểm dữ liệu bằng Id cụm của nó, thay vì bằng dữ liệu nhận dạng tiết lộ nhiều hơn. Bạn có thể nghĩ đến những lý do khác tại sao bạn lại tham chiếu đến Id cụm thay vì các yếu tố khác của cụm để xác định nó không?\n",
+    "\n",
+    "### Bắt đầu với phân cụm\n",
+    "\n",
+    "> 🎓 Cách chúng ta tạo cụm phụ thuộc rất nhiều vào cách chúng ta nhóm các điểm dữ liệu thành các nhóm. Hãy cùng tìm hiểu một số thuật ngữ:\n",
+    ">\n",
+    "> 🎓 ['Transductive' vs. 'inductive'](https://wikipedia.org/wiki/Transduction_(machine_learning))\n",
+    ">\n",
+    "> Suy diễn truyền dẫn (transductive inference) được rút ra từ các trường hợp huấn luyện quan sát được ánh xạ đến các trường hợp kiểm tra cụ thể. Suy diễn quy nạp (inductive inference) được rút ra từ các trường hợp huấn luyện ánh xạ đến các quy tắc chung, sau đó mới được áp dụng cho các trường hợp kiểm tra.\n",
+    ">\n",
+    "> Một ví dụ: Hãy tưởng tượng bạn có một tập dữ liệu chỉ được gắn nhãn một phần. Một số là 'đĩa nhạc', một số là 'cd', và một số để trống. Nhiệm vụ của bạn là cung cấp nhãn cho các mục trống. Nếu bạn chọn cách tiếp cận quy nạp, bạn sẽ huấn luyện một mô hình tìm kiếm 'đĩa nhạc' và 'cd', và áp dụng các nhãn đó cho dữ liệu chưa được gắn nhãn. Cách tiếp cận này sẽ gặp khó khăn trong việc phân loại những thứ thực sự là 'băng cassette'. Một cách tiếp cận truyền dẫn, mặt khác, xử lý dữ liệu chưa biết này hiệu quả hơn vì nó hoạt động để nhóm các mục tương tự lại với nhau và sau đó áp dụng một nhãn cho một nhóm. Trong trường hợp này, các cụm có thể phản ánh 'những thứ âm nhạc hình tròn' và 'những thứ âm nhạc hình vuông'.\n",
+    ">\n",
+    "> 🎓 ['Non-flat' vs. 'flat' geometry](https://datascience.stackexchange.com/questions/52260/terminology-flat-geometry-in-the-context-of-clustering)\n",
+    ">\n",
+    "> Được lấy từ thuật ngữ toán học, hình học không phẳng (non-flat) và phẳng (flat) đề cập đến cách đo khoảng cách giữa các điểm bằng các phương pháp hình học 'phẳng' ([Euclidean](https://wikipedia.org/wiki/Euclidean_geometry)) hoặc 'không phẳng' (phi Euclid).\n",
+    ">\n",
+    "> 'Phẳng' trong ngữ cảnh này đề cập đến hình học Euclid (một phần được dạy như hình học 'mặt phẳng'), và không phẳng đề cập đến hình học phi Euclid. Hình học có liên quan gì đến học máy? Vâng, vì cả hai lĩnh vực đều bắt nguồn từ toán học, nên phải có một cách chung để đo khoảng cách giữa các điểm trong các cụm, và điều đó có thể được thực hiện theo cách 'phẳng' hoặc 'không phẳng', tùy thuộc vào bản chất của dữ liệu. [Khoảng cách Euclid](https://wikipedia.org/wiki/Euclidean_distance) được đo bằng độ dài của một đoạn thẳng giữa hai điểm. [Khoảng cách phi Euclid](https://wikipedia.org/wiki/Non-Euclidean_geometry) được đo dọc theo một đường cong. Nếu dữ liệu của bạn, khi được hình dung, dường như không tồn tại trên một mặt phẳng, bạn có thể cần sử dụng một thuật toán chuyên biệt để xử lý nó.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/flat-nonflat.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infographic bởi Dasani Madipalli</figcaption>\n",
+    "\n",
+    "> 🎓 ['Distances'](https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)\n",
+    ">\n",
+    "> Các cụm được xác định bởi ma trận khoảng cách của chúng, ví dụ: khoảng cách giữa các điểm. Khoảng cách này có thể được đo bằng một vài cách. Các cụm Euclid được xác định bởi giá trị trung bình của các điểm và chứa một 'centroid' hoặc điểm trung tâm. Khoảng cách được đo bằng khoảng cách đến centroid đó. Khoảng cách phi Euclid đề cập đến 'clustroids', điểm gần nhất với các điểm khác. Clustroids lần lượt có thể được định nghĩa theo nhiều cách khác nhau.\n",
+    ">\n",
+    "> 🎓 ['Constrained'](https://wikipedia.org/wiki/Constrained_clustering)\n",
+    ">\n",
+    "> [Phân cụm có ràng buộc](https://web.cs.ucdavis.edu/~davidson/Publications/ICDMTutorial.pdf) giới thiệu học 'bán giám sát' vào phương pháp không giám sát này. Các mối quan hệ giữa các điểm được đánh dấu là 'không thể liên kết' hoặc 'phải liên kết' để một số quy tắc được áp dụng cho tập dữ liệu.\n",
+    ">\n",
+    "> Một ví dụ: Nếu một thuật toán được tự do áp dụng trên một loạt dữ liệu không gắn nhãn hoặc gắn nhãn một phần, các cụm mà nó tạo ra có thể có chất lượng kém. Trong ví dụ trên, các cụm có thể nhóm 'những thứ âm nhạc hình tròn', 'những thứ âm nhạc hình vuông', 'những thứ hình tam giác' và 'bánh quy'. Nếu được cung cấp một số ràng buộc hoặc quy tắc để tuân theo (\"mục phải được làm bằng nhựa\", \"mục cần có khả năng tạo ra âm nhạc\"), điều này có thể giúp 'ràng buộc' thuật toán để đưa ra các lựa chọn tốt hơn.\n",
+    ">\n",
+    "> 🎓 'Density'\n",
+    ">\n",
+    "> Dữ liệu 'nhiễu' được coi là 'dày đặc'. Khoảng cách giữa các điểm trong mỗi cụm của nó có thể, khi được kiểm tra, dày đặc hơn hoặc ít dày đặc hơn, hoặc 'đông đúc', và do đó dữ liệu này cần được phân tích bằng phương pháp phân cụm phù hợp. [Bài viết này](https://www.kdnuggets.com/2020/02/understanding-density-based-clustering.html) minh họa sự khác biệt giữa việc sử dụng phân cụm K-Means và các thuật toán HDBSCAN để khám phá một tập dữ liệu nhiễu với mật độ cụm không đồng đều.\n",
+    "\n",
+    "Nâng cao hiểu biết của bạn về các kỹ thuật phân cụm trong [module học này](https://docs.microsoft.com/learn/modules/train-evaluate-cluster-models?WT.mc_id=academic-77952-leestott)\n",
+    "\n",
+    "### **Thuật toán phân cụm**\n",
+    "\n",
+    "Có hơn 100 thuật toán phân cụm, và việc sử dụng chúng phụ thuộc vào bản chất của dữ liệu. Hãy thảo luận một số thuật toán chính:\n",
+    "\n",
+    "-   **Phân cụm phân cấp**. Nếu một đối tượng được phân loại dựa trên sự gần gũi của nó với một đối tượng gần đó, thay vì với một đối tượng xa hơn, các cụm được hình thành dựa trên khoảng cách của các thành viên với các đối tượng khác. Phân cụm phân cấp được đặc trưng bởi việc liên tục kết hợp hai cụm.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/hierarchical.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infographic bởi Dasani Madipalli</figcaption>\n",
+    "\n",
+    "-   **Phân cụm centroid**. Thuật toán phổ biến này yêu cầu chọn 'k', hoặc số lượng cụm cần tạo, sau đó thuật toán xác định điểm trung tâm của một cụm và thu thập dữ liệu xung quanh điểm đó. [Phân cụm K-means](https://wikipedia.org/wiki/K-means_clustering) là một phiên bản phổ biến của phân cụm centroid, chia một tập dữ liệu thành K nhóm được xác định trước. Trung tâm được xác định bởi giá trị trung bình gần nhất, do đó có tên gọi này. Khoảng cách bình phương từ cụm được giảm thiểu.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/centroid.png\"\n",
+    "   width=\"600\"/>\n",
+    "   <figcaption>Infographic bởi Dasani Madipalli</figcaption>\n",
+    "\n",
+    "-   **Phân cụm dựa trên phân phối**. Dựa trên mô hình thống kê, phân cụm dựa trên phân phối tập trung vào việc xác định xác suất rằng một điểm dữ liệu thuộc về một cụm và gán nó tương ứng. Các phương pháp hỗn hợp Gaussian thuộc loại này.\n",
+    "\n",
+    "-   **Phân cụm dựa trên mật độ**. Các điểm dữ liệu được gán vào các cụm dựa trên mật độ của chúng, hoặc sự nhóm lại xung quanh nhau. Các điểm dữ liệu xa nhóm được coi là nhiễu hoặc ngoại lệ. DBSCAN, Mean-shift và OPTICS thuộc loại phân cụm này.\n",
+    "\n",
+    "-   **Phân cụm dựa trên lưới**. Đối với các tập dữ liệu đa chiều, một lưới được tạo ra và dữ liệu được chia giữa các ô của lưới, từ đó tạo ra các cụm.\n",
+    "\n",
+    "Cách tốt nhất để học về phân cụm là tự mình thử nghiệm, và đó là điều bạn sẽ làm trong bài tập này.\n",
+    "\n",
+    "Chúng ta sẽ cần một số gói để hoàn thành module này. Bạn có thể cài đặt chúng bằng lệnh: `install.packages(c('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork'))`\n",
+    "\n",
+    "Ngoài ra, đoạn mã dưới đây sẽ kiểm tra xem bạn đã có các gói cần thiết để hoàn thành module này chưa và cài đặt chúng nếu thiếu.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\r\n",
+    "\r\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'DataExplorer', 'summarytools', 'plotly', 'paletteer', 'corrplot', 'patchwork')\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Bài tập - phân cụm dữ liệu của bạn\n",
+    "\n",
+    "Phân cụm là một kỹ thuật được hỗ trợ rất nhiều bởi việc trực quan hóa đúng cách, vì vậy hãy bắt đầu bằng cách trực quan hóa dữ liệu âm nhạc của chúng ta. Bài tập này sẽ giúp chúng ta quyết định phương pháp phân cụm nào sẽ được sử dụng hiệu quả nhất cho bản chất của dữ liệu này.\n",
+    "\n",
+    "Hãy bắt đầu ngay bằng cách nhập dữ liệu.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\r\n",
+    "library(tidyverse)\r\n",
+    "\r\n",
+    "# Import the data into a tibble\r\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\")\r\n",
+    "\r\n",
+    "# View the first 5 rows of the data set\r\n",
+    "df %>% \r\n",
+    "  slice_head(n = 5)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Đôi khi, chúng ta có thể muốn biết thêm một chút thông tin về dữ liệu của mình. Chúng ta có thể xem `dữ liệu` và `cấu trúc của nó` bằng cách sử dụng hàm [*glimpse()*](https://pillar.r-lib.org/reference/glimpse.html):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Glimpse into the data set\r\n",
+    "df %>% \r\n",
+    "  glimpse()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Làm tốt lắm!💪\n",
+    "\n",
+    "Chúng ta có thể thấy rằng `glimpse()` sẽ cung cấp cho bạn tổng số hàng (quan sát) và cột (biến), sau đó là một vài giá trị đầu tiên của mỗi biến được hiển thị theo hàng sau tên biến. Ngoài ra, *kiểu dữ liệu* của biến được hiển thị ngay sau tên biến trong dấu `< >`.\n",
+    "\n",
+    "`DataExplorer::introduce()` có thể tóm tắt thông tin này một cách gọn gàng:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe basic information for our data\r\n",
+    "df %>% \r\n",
+    "  introduce()\r\n",
+    "\r\n",
+    "# A visual display of the same\r\n",
+    "df %>% \r\n",
+    "  plot_intro()\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tuyệt vời! Chúng ta vừa biết rằng dữ liệu của mình không có giá trị bị thiếu.\n",
+    "\n",
+    "Nhân tiện, chúng ta có thể khám phá các thống kê xu hướng trung tâm phổ biến (ví dụ như [mean](https://en.wikipedia.org/wiki/Arithmetic_mean) và [median](https://en.wikipedia.org/wiki/Median)) và các thước đo độ phân tán (ví dụ như [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation)) bằng cách sử dụng `summarytools::descr()`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Describe common statistics\r\n",
+    "df %>% \r\n",
+    "  descr(stats = \"common\")\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hãy xem các giá trị tổng quát của dữ liệu. Lưu ý rằng độ phổ biến có thể là `0`, điều này cho thấy các bài hát không có xếp hạng. Chúng ta sẽ loại bỏ chúng ngay sau đây.\n",
+    "\n",
+    "> 🤔 Nếu chúng ta đang làm việc với phân cụm, một phương pháp không giám sát không yêu cầu dữ liệu được gắn nhãn, tại sao lại hiển thị dữ liệu này với nhãn? Trong giai đoạn khám phá dữ liệu, chúng rất hữu ích, nhưng chúng không cần thiết để các thuật toán phân cụm hoạt động.\n",
+    "\n",
+    "### 1. Khám phá các thể loại phổ biến\n",
+    "\n",
+    "Hãy cùng tìm hiểu các thể loại phổ biến nhất 🎶 bằng cách đếm số lần chúng xuất hiện.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Popular genres\r\n",
+    "top_genres <- df %>% \r\n",
+    "  count(artist_top_genre, sort = TRUE) %>% \r\n",
+    "# Encode to categorical and reorder the according to count\r\n",
+    "  mutate(artist_top_genre = factor(artist_top_genre) %>% fct_inorder())\r\n",
+    "\r\n",
+    "# Print the top genres\r\n",
+    "top_genres\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Điều đó thật tốt! Người ta thường nói một bức tranh đáng giá ngàn dòng của một khung dữ liệu (thực ra chẳng ai nói vậy cả 😅). Nhưng bạn hiểu ý rồi đúng không?\n",
+    "\n",
+    "Một cách để trực quan hóa dữ liệu phân loại (biến ký tự hoặc biến nhân tố) là sử dụng biểu đồ cột. Hãy tạo một biểu đồ cột cho 10 thể loại hàng đầu:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Change the default gray theme\r\n",
+    "theme_set(theme_light())\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bây giờ dễ dàng hơn nhiều để nhận ra rằng chúng ta có các thể loại `thiếu` 🧐!\n",
+    "\n",
+    "> Một hình ảnh trực quan tốt sẽ cho bạn thấy những điều mà bạn không ngờ tới, hoặc gợi lên những câu hỏi mới về dữ liệu - Hadley Wickham và Garrett Grolemund, [R For Data Science](https://r4ds.had.co.nz/introduction.html)\n",
+    "\n",
+    "Lưu ý, khi thể loại hàng đầu được mô tả là `Thiếu`, điều đó có nghĩa là Spotify đã không phân loại nó, vì vậy hãy loại bỏ nó đi.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Visualize popular genres\r\n",
+    "top_genres %>%\r\n",
+    "  filter(artist_top_genre != \"Missing\") %>% \r\n",
+    "  slice(1:10) %>% \r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"rcartocolor::Vivid\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5),\r\n",
+    "        # Rotates the X markers (so we can read them)\r\n",
+    "    axis.text.x = element_text(angle = 90))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Từ việc khám phá dữ liệu ban đầu, chúng ta nhận thấy rằng ba thể loại hàng đầu chiếm ưu thế trong tập dữ liệu này. Hãy tập trung vào `afro dancehall`, `afropop`, và `nigerian pop`, đồng thời lọc tập dữ liệu để loại bỏ bất kỳ mục nào có giá trị phổ biến bằng 0 (nghĩa là không được phân loại với mức độ phổ biến trong tập dữ liệu và có thể được coi là nhiễu đối với mục đích của chúng ta):\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "nigerian_songs <- df %>% \r\n",
+    "  # Concentrate on top 3 genres\r\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \r\n",
+    "  # Remove unclassified observations\r\n",
+    "  filter(popularity != 0)\r\n",
+    "\r\n",
+    "\r\n",
+    "\r\n",
+    "# Visualize popular genres\r\n",
+    "nigerian_songs %>%\r\n",
+    "  count(artist_top_genre) %>%\r\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\r\n",
+    "                       fill = artist_top_genre)) +\r\n",
+    "  geom_col(alpha = 0.8) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\r\n",
+    "  ggtitle(\"Top genres\") +\r\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hãy xem liệu có bất kỳ mối quan hệ tuyến tính rõ ràng nào giữa các biến số trong tập dữ liệu của chúng ta hay không. Mối quan hệ này được định lượng một cách toán học bằng [thống kê tương quan](https://en.wikipedia.org/wiki/Correlation).\n",
+    "\n",
+    "Thống kê tương quan là một giá trị nằm trong khoảng từ -1 đến 1, biểu thị mức độ mạnh của mối quan hệ. Các giá trị lớn hơn 0 cho thấy mối tương quan *dương* (giá trị cao của một biến thường đi kèm với giá trị cao của biến kia), trong khi các giá trị nhỏ hơn 0 cho thấy mối tương quan *âm* (giá trị cao của một biến thường đi kèm với giá trị thấp của biến kia).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Narrow down to numeric variables and fid correlation\r\n",
+    "corr_mat <- nigerian_songs %>% \r\n",
+    "  select(where(is.numeric)) %>% \r\n",
+    "  cor()\r\n",
+    "\r\n",
+    "# Visualize correlation matrix\r\n",
+    "corrplot(corr_mat, order = 'AOE', col = c('white', 'black'), bg = 'gold2')  \r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Dữ liệu không có sự tương quan mạnh ngoại trừ giữa `energy` và `loudness`, điều này khá hợp lý vì nhạc lớn thường rất sôi động. `Popularity` có sự liên hệ với `release date`, điều này cũng hợp lý vì các bài hát mới hơn có thể phổ biến hơn. Độ dài và năng lượng dường như cũng có sự tương quan.\n",
+    "\n",
+    "Sẽ rất thú vị khi xem một thuật toán phân cụm có thể làm gì với dữ liệu này!\n",
+    "\n",
+    "> 🎓 Lưu ý rằng sự tương quan không đồng nghĩa với nguyên nhân! Chúng ta có bằng chứng về sự tương quan nhưng không có bằng chứng về nguyên nhân. Một [trang web thú vị](https://tylervigen.com/spurious-correlations) có một số hình ảnh minh họa nhấn mạnh điểm này.\n",
+    "\n",
+    "### 2. Khám phá phân bố dữ liệu\n",
+    "\n",
+    "Hãy đặt ra một số câu hỏi tinh tế hơn. Liệu các thể loại có khác biệt đáng kể trong cách chúng được cảm nhận về khả năng nhảy múa, dựa trên mức độ phổ biến của chúng? Hãy kiểm tra phân bố dữ liệu của ba thể loại hàng đầu về mức độ phổ biến và khả năng nhảy múa dọc theo trục x và y bằng cách sử dụng [biểu đồ mật độ](https://www.khanacademy.org/math/ap-statistics/density-curves-normal-distribution-ap/density-curves/v/density-curves).\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# Perform 2D kernel density estimation\r\n",
+    "density_estimate_2d <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre)) +\r\n",
+    "  geom_density_2d(bins = 5, size = 1) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  xlim(-20, 80) +\r\n",
+    "  ylim(0, 1.2)\r\n",
+    "\r\n",
+    "# Density plot based on the popularity\r\n",
+    "density_estimate_pop <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  theme(legend.position = \"none\")\r\n",
+    "\r\n",
+    "# Density plot based on the danceability\r\n",
+    "density_estimate_dance <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = danceability, fill = artist_top_genre, color = artist_top_genre)) +\r\n",
+    "  geom_density(size = 1, alpha = 0.5) +\r\n",
+    "  paletteer::scale_fill_paletteer_d(\"RSkittleBrewer::wildberry\") +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"RSkittleBrewer::wildberry\")\r\n",
+    "\r\n",
+    "\r\n",
+    "# Patch everything together\r\n",
+    "library(patchwork)\r\n",
+    "density_estimate_2d / (density_estimate_pop + density_estimate_dance)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Chúng ta thấy rằng có các vòng tròn đồng tâm xếp thẳng hàng, bất kể thể loại. Liệu có thể rằng sở thích của người Nigeria hội tụ ở một mức độ nhảy múa nhất định cho thể loại này?\n",
+    "\n",
+    "Nhìn chung, ba thể loại này tương đồng về mức độ phổ biến và khả năng nhảy múa. Việc xác định các cụm trong dữ liệu không hoàn toàn đồng nhất này sẽ là một thách thức. Hãy xem liệu biểu đồ phân tán có thể hỗ trợ điều này không.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [
+    "# A scatter plot of popularity and danceability\r\n",
+    "scatter_plot <- nigerian_songs %>% \r\n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = artist_top_genre, shape = artist_top_genre)) +\r\n",
+    "  geom_point(size = 2, alpha = 0.8) +\r\n",
+    "  paletteer::scale_color_paletteer_d(\"futurevisions::mars\")\r\n",
+    "\r\n",
+    "# Add a touch of interactivity\r\n",
+    "ggplotly(scatter_plot)\r\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Một biểu đồ phân tán của cùng các trục cho thấy một mô hình hội tụ tương tự.\n",
+    "\n",
+    "Nhìn chung, đối với việc phân cụm, bạn có thể sử dụng biểu đồ phân tán để hiển thị các cụm dữ liệu, vì vậy việc thành thạo loại hình trực quan hóa này rất hữu ích. Trong bài học tiếp theo, chúng ta sẽ sử dụng dữ liệu đã được lọc này và áp dụng phương pháp phân cụm k-means để khám phá các nhóm trong dữ liệu này, những nhóm có xu hướng chồng lấn theo những cách thú vị.\n",
+    "\n",
+    "## **🚀 Thử thách**\n",
+    "\n",
+    "Để chuẩn bị cho bài học tiếp theo, hãy tạo một biểu đồ về các thuật toán phân cụm khác nhau mà bạn có thể khám phá và sử dụng trong môi trường sản xuất. Những loại vấn đề nào mà phân cụm đang cố gắng giải quyết?\n",
+    "\n",
+    "## [**Câu hỏi kiểm tra sau bài học**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/28/)\n",
+    "\n",
+    "## **Ôn tập & Tự học**\n",
+    "\n",
+    "Trước khi bạn áp dụng các thuật toán phân cụm, như chúng ta đã học, việc hiểu rõ bản chất của tập dữ liệu là một ý tưởng tốt. Đọc thêm về chủ đề này [tại đây](https://www.kdnuggets.com/2019/10/right-clustering-algorithm.html)\n",
+    "\n",
+    "Nâng cao hiểu biết của bạn về các kỹ thuật phân cụm:\n",
+    "\n",
+    "-   [Huấn luyện và Đánh giá Mô hình Phân cụm bằng Tidymodels và các công cụ liên quan](https://rpubs.com/eR_ic/clustering)\n",
+    "\n",
+    "-   Bradley Boehmke & Brandon Greenwell, [*Hands-On Machine Learning with R*](https://bradleyboehmke.github.io/HOML/)*.*\n",
+    "\n",
+    "## **Bài tập**\n",
+    "\n",
+    "[Nghiên cứu các cách trực quan hóa khác cho phân cụm](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/assignment.md)\n",
+    "\n",
+    "## CẢM ƠN ĐẾN:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) vì đã tạo phiên bản Python gốc của mô-đun này ♥️\n",
+    "\n",
+    "[`Dasani Madipalli`](https://twitter.com/dasani_decoded) vì đã tạo ra những hình minh họa tuyệt vời giúp các khái niệm học máy trở nên dễ hiểu và dễ tiếp cận hơn.\n",
+    "\n",
+    "Chúc bạn học vui,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Đại sứ Sinh viên Microsoft Learn Vàng.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "coopTranslator": {
+   "original_hash": "99c36449cad3708a435f6798cfa39972",
+   "translation_date": "2025-09-06T14:19:17+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/vi/5-Clustering/1-Visualize/solution/notebook.ipynb b/translations/vi/5-Clustering/1-Visualize/solution/notebook.ipynb
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index 000000000..eeb1ef963
--- /dev/null
+++ b/translations/vi/5-Clustering/1-Visualize/solution/notebook.ipynb
@@ -0,0 +1,817 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: seaborn in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (0.11.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (3.5.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.21.4)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.3.4)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from seaborn) (1.7.2)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (4.28.1)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.4.7)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (1.3.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (8.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (21.2)\n",
+      "Requirement already satisfied: setuptools-scm>=4 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (6.3.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2017.3 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from pandas>=0.23->seaborn) (2021.3)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
+      "Requirement already satisfied: tomli>=1.0.0 in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (1.2.2)\n",
+      "Requirement already satisfied: setuptools in /Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages (from setuptools-scm>=4->matplotlib>=2.2->seaborn) (59.1.1)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>album</th>\n",
+       "      <th>artist</th>\n",
+       "      <th>artist_top_genre</th>\n",
+       "      <th>release_date</th>\n",
+       "      <th>length</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>danceability</th>\n",
+       "      <th>acousticness</th>\n",
+       "      <th>energy</th>\n",
+       "      <th>instrumentalness</th>\n",
+       "      <th>liveness</th>\n",
+       "      <th>loudness</th>\n",
+       "      <th>speechiness</th>\n",
+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Sparky</td>\n",
+       "      <td>Mandy &amp; The Jungle</td>\n",
+       "      <td>Cruel Santino</td>\n",
+       "      <td>alternative r&amp;b</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>144000</td>\n",
+       "      <td>48</td>\n",
+       "      <td>0.666</td>\n",
+       "      <td>0.8510</td>\n",
+       "      <td>0.420</td>\n",
+       "      <td>0.534000</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-6.699</td>\n",
+       "      <td>0.0829</td>\n",
+       "      <td>133.015</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>shuga rush</td>\n",
+       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2020</td>\n",
+       "      <td>89488</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.0822</td>\n",
+       "      <td>0.683</td>\n",
+       "      <td>0.000169</td>\n",
+       "      <td>0.1010</td>\n",
+       "      <td>-5.640</td>\n",
+       "      <td>0.3600</td>\n",
+       "      <td>129.993</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>LITT!</td>\n",
+       "      <td>AYLØ</td>\n",
+       "      <td>indie r&amp;b</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>207758</td>\n",
+       "      <td>40</td>\n",
+       "      <td>0.836</td>\n",
+       "      <td>0.2720</td>\n",
+       "      <td>0.564</td>\n",
+       "      <td>0.000537</td>\n",
+       "      <td>0.1100</td>\n",
+       "      <td>-7.127</td>\n",
+       "      <td>0.0424</td>\n",
+       "      <td>130.005</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Confident / Feeling Cool</td>\n",
+       "      <td>Enjoy Your Life</td>\n",
+       "      <td>Lady Donli</td>\n",
+       "      <td>nigerian pop</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>175135</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.7980</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.000187</td>\n",
+       "      <td>0.0964</td>\n",
+       "      <td>-4.961</td>\n",
+       "      <td>0.1130</td>\n",
+       "      <td>111.087</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>wanted you</td>\n",
+       "      <td>rare.</td>\n",
+       "      <td>Odunsi (The Engine)</td>\n",
+       "      <td>afropop</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>152049</td>\n",
+       "      <td>25</td>\n",
+       "      <td>0.702</td>\n",
+       "      <td>0.1160</td>\n",
+       "      <td>0.833</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.3480</td>\n",
+       "      <td>-6.044</td>\n",
+       "      <td>0.0447</td>\n",
+       "      <td>105.115</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lấy thông tin về dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 530 entries, 0 to 529\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   name              530 non-null    object \n",
+      " 1   album             530 non-null    object \n",
+      " 2   artist            530 non-null    object \n",
+      " 3   artist_top_genre  530 non-null    object \n",
+      " 4   release_date      530 non-null    int64  \n",
+      " 5   length            530 non-null    int64  \n",
+      " 6   popularity        530 non-null    int64  \n",
+      " 7   danceability      530 non-null    float64\n",
+      " 8   acousticness      530 non-null    float64\n",
+      " 9   energy            530 non-null    float64\n",
+      " 10  instrumentalness  530 non-null    float64\n",
+      " 11  liveness          530 non-null    float64\n",
+      " 12  loudness          530 non-null    float64\n",
+      " 13  speechiness       530 non-null    float64\n",
+      " 14  tempo             530 non-null    float64\n",
+      " 15  time_signature    530 non-null    int64  \n",
+      "dtypes: float64(8), int64(4), object(4)\n",
+      "memory usage: 66.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name                0\n",
+       "album               0\n",
+       "artist              0\n",
+       "artist_top_genre    0\n",
+       "release_date        0\n",
+       "length              0\n",
+       "popularity          0\n",
+       "danceability        0\n",
+       "acousticness        0\n",
+       "energy              0\n",
+       "instrumentalness    0\n",
+       "liveness            0\n",
+       "loudness            0\n",
+       "speechiness         0\n",
+       "tempo               0\n",
+       "time_signature      0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hãy xem các giá trị tổng quát của dữ liệu. Lưu ý rằng mức độ phổ biến có thể là '0' - và có nhiều hàng có giá trị đó.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "      <th>tempo</th>\n",
+       "      <th>time_signature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "      <td>530.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2015.390566</td>\n",
+       "      <td>222298.169811</td>\n",
+       "      <td>17.507547</td>\n",
+       "      <td>0.741619</td>\n",
+       "      <td>0.265412</td>\n",
+       "      <td>0.760623</td>\n",
+       "      <td>0.016305</td>\n",
+       "      <td>0.147308</td>\n",
+       "      <td>-4.953011</td>\n",
+       "      <td>0.130748</td>\n",
+       "      <td>116.487864</td>\n",
+       "      <td>3.986792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3.131688</td>\n",
+       "      <td>39696.822259</td>\n",
+       "      <td>18.992212</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.208342</td>\n",
+       "      <td>0.148533</td>\n",
+       "      <td>0.090321</td>\n",
+       "      <td>0.123588</td>\n",
+       "      <td>2.464186</td>\n",
+       "      <td>0.092939</td>\n",
+       "      <td>23.518601</td>\n",
+       "      <td>0.333701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1998.000000</td>\n",
+       "      <td>89488.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.255000</td>\n",
+       "      <td>0.000665</td>\n",
+       "      <td>0.111000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.028300</td>\n",
+       "      <td>-19.362000</td>\n",
+       "      <td>0.027800</td>\n",
+       "      <td>61.695000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2014.000000</td>\n",
+       "      <td>199305.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.681000</td>\n",
+       "      <td>0.089525</td>\n",
+       "      <td>0.669000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.075650</td>\n",
+       "      <td>-6.298750</td>\n",
+       "      <td>0.059100</td>\n",
+       "      <td>102.961250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2016.000000</td>\n",
+       "      <td>218509.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>0.761000</td>\n",
+       "      <td>0.220500</td>\n",
+       "      <td>0.784500</td>\n",
+       "      <td>0.000004</td>\n",
+       "      <td>0.103500</td>\n",
+       "      <td>-4.558500</td>\n",
+       "      <td>0.097950</td>\n",
+       "      <td>112.714500</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>242098.500000</td>\n",
+       "      <td>31.000000</td>\n",
+       "      <td>0.829500</td>\n",
+       "      <td>0.403000</td>\n",
+       "      <td>0.875750</td>\n",
+       "      <td>0.000234</td>\n",
+       "      <td>0.164000</td>\n",
+       "      <td>-3.331000</td>\n",
+       "      <td>0.177000</td>\n",
+       "      <td>125.039250</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>511738.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "      <td>0.966000</td>\n",
+       "      <td>0.954000</td>\n",
+       "      <td>0.995000</td>\n",
+       "      <td>0.910000</td>\n",
+       "      <td>0.811000</td>\n",
+       "      <td>0.582000</td>\n",
+       "      <td>0.514000</td>\n",
+       "      <td>206.007000</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       release_date         length  popularity  danceability  acousticness  \\\n",
+       "count    530.000000     530.000000  530.000000    530.000000    530.000000   \n",
+       "mean    2015.390566  222298.169811   17.507547      0.741619      0.265412   \n",
+       "std        3.131688   39696.822259   18.992212      0.117522      0.208342   \n",
+       "min     1998.000000   89488.000000    0.000000      0.255000      0.000665   \n",
+       "25%     2014.000000  199305.000000    0.000000      0.681000      0.089525   \n",
+       "50%     2016.000000  218509.000000   13.000000      0.761000      0.220500   \n",
+       "75%     2017.000000  242098.500000   31.000000      0.829500      0.403000   \n",
+       "max     2020.000000  511738.000000   73.000000      0.966000      0.954000   \n",
+       "\n",
+       "           energy  instrumentalness    liveness    loudness  speechiness  \\\n",
+       "count  530.000000        530.000000  530.000000  530.000000   530.000000   \n",
+       "mean     0.760623          0.016305    0.147308   -4.953011     0.130748   \n",
+       "std      0.148533          0.090321    0.123588    2.464186     0.092939   \n",
+       "min      0.111000          0.000000    0.028300  -19.362000     0.027800   \n",
+       "25%      0.669000          0.000000    0.075650   -6.298750     0.059100   \n",
+       "50%      0.784500          0.000004    0.103500   -4.558500     0.097950   \n",
+       "75%      0.875750          0.000234    0.164000   -3.331000     0.177000   \n",
+       "max      0.995000          0.910000    0.811000    0.582000     0.514000   \n",
+       "\n",
+       "            tempo  time_signature  \n",
+       "count  530.000000      530.000000  \n",
+       "mean   116.487864        3.986792  \n",
+       "std     23.518601        0.333701  \n",
+       "min     61.695000        3.000000  \n",
+       "25%    102.961250        4.000000  \n",
+       "50%    112.714500        4.000000  \n",
+       "75%    125.039250        4.000000  \n",
+       "max    206.007000        5.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top[:5].index,y=top[:5].values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Xóa các thể loại 'Missing', vì chúng không được phân loại trên Spotify\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[df['artist_top_genre'] != 'Missing']\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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A+4ATkuwDXA7s0p9+CrAjcAlwE/DyxjVLkiSNpGUKVlW12xIObbuYcwvYbyJFSZIkTUXuvC5JktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIzNW9IFJtgSOH2jaHDgYWAd4JbCwbz+oqk5Z0deRJEmaKlY4WFXVxcBMgCSrAlcBXwFeDnywqj7QokBJkqSpotVQ4LbApVV1eaPnkyRJmnJaBatdgeMG7u+f5LwkxyRZd3EPSDInybwk8xYuXLi4UyRJkqaUCQerJKsBLwS+1DcdBTyUbphwPnDY4h5XVXOralZVzRobG5toGZIkSUPXosdqB+CcqroGoKquqao7qupO4Ghg6wavIUmSNPJaBKvdGBgGTLLRwLEXARc0eA1JkqSRt8KrAgGSrAk8F3jVQPN/JJkJFHDZIsckSZKmrQkFq6q6EVh/kbY9J1SRJEnSFOXO65IkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNTJj2AVIWn6/PeTRwy5B08yDDj5/2CVI04I9VpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIzMm+gRJLgP+BNwB3F5Vs5KsBxwPbAZcBuxSVddP9LUkSZJGWaseq2dV1cyqmtXfPxA4vaq2AE7v70uSJE1rkzUUOBs4tr99LLDTJL2OJEnSyGgRrAo4NcnZSeb0bRtW1fz+9tXAhos+KMmcJPOSzFu4cGGDMiRJkoZrwnOsgKdW1VVJ7g+cluSiwYNVVUlq0QdV1VxgLsCsWbP+5rgkSdJUM+Eeq6q6qv93AfAVYGvgmiQbAfT/Lpjo60iSJI26CQWrJGsmud/4beB5wAXAycBe/Wl7AV+dyOtIkiRNBRMdCtwQ+EqS8ef6QlX9b5KfACck2Qe4HNhlgq8jSZI08iYUrKrq18DfL6b9WmDbiTy3JEnSVOPO65IkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1ssLBKsmmSc5IcmGSnyd5Xd/+ziRXJTm3/9mxXbmSJEmja8YEHns78KaqOifJ/YCzk5zWH/tgVX1g4uVJkiRNHSscrKpqPjC/v/2nJL8ANm5VmCRJ0lTTZI5Vks2AxwI/6pv2T3JekmOSrLuEx8xJMi/JvIULF7YoQ5IkaagmHKyS3Bc4CXh9Vd0AHAU8FJhJ16N12OIeV1Vzq2pWVc0aGxubaBmSJElDN6FgleRedKHq81X1ZYCquqaq7qiqO4Gjga0nXqYkSdLom8iqwACfBH5RVYcPtG80cNqLgAtWvDxJkqSpYyKrAp8C7Amcn+Tcvu0gYLckM4ECLgNeNYHXkCRJmjImsirwu0AWc+iUFS9HkiRp6nLndUmSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWpkItstSJI0aZ7ykacMuwRNM997zfcm/TXssZIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpEYOVJElSIwYrSZKkRgxWkiRJjRisJEmSGjFYSZIkNWKwkiRJasRgJUmS1IjBSpIkqRGDlSRJUiMGK0mSpEYMVpIkSY0YrCRJkhoxWEmSJDVisJIkSWrEYCVJktSIwUqSJKkRg5UkSVIjBitJkqRGJi1YJdk+ycVJLkly4GS9jiRJ0qiYlGCVZFXgSGAHYCtgtyRbTcZrSZIkjYrJ6rHaGrikqn5dVX8BvgjMnqTXkiRJGgmpqvZPmvw/YPuqekV/f0/giVW1/8A5c4A5/d0tgYubF6K7swHw+2EXIU0y3+daGfg+v+c9uKrGFndgxj1dybiqmgvMHdbrr+ySzKuqWcOuQ5pMvs+1MvB9PlomayjwKmDTgfub9G2SJEnT1mQFq58AWyR5SJLVgF2BkyfptSRJkkbCpAwFVtXtSfYHvgGsChxTVT+fjNfSCnMYVisD3+daGfg+HyGTMnldkiRpZeTO65IkSY0YrCRJkhoxWKmJJJsnWX3YdUiSNEwGK01YknWBA4C3G64kaepJkmHXMF0YrDQhSTarquuBk4C1gQMMV5rK/IDRyiZJqqqSPCXJPkm27bdK0gowWGmFJVkH+ECSt1fV6cCXgQdiuNIUMB6gkjy6/0DZGKD/gDFcaaXRv+efBXwWeBjwIeC1SR421MKmKLdb0ApLci/gScC+wDlV9YEkzwB2AX4HfKCqbh1mjdLiDHxDfw7wEWA+cBnwc+Dw8g+jViJJtgQOAT5fVScneRzwKuDcqjpquNVNPfZYabmNf5uvqtuAHwJHAtskOaCqzgROAO4PHGzPlUZRH6oeB7wZ2Kmqng18ie5SXLOHWpx0D0kPeDrwUGC7JGtW1TnAccCcfg6tloPBSstl/Jt+f3ttgKr6HnA48KSBcPU1YDXgvkMrVlqCfv7IM4BnARv3zd8FbgCeMKy6pHvCwFD3BsCMqjoaeA8QukvQAVwN/Klv03KYlEvaaPoaCFWvBbYFrk1yalV9sf9dfV2Sg6vqkCTfraqbh1mvNG5g+O9ewG10Pa3rAm9Ncn1V/TTJucDuSdYAbnZIUNNR/3uwI93w31VJbgT2AdYE9kqyO10++I+qum6IpU5JBisttyT7Av8I7AEcChyWZP2qOjLJDGCfJOv5C6lR0n+YvAB4Ed1Q9aF0w9bXAicnOYauB+vQqrppeJVKkyvJI4F3A/sD5wJfAD5VVbsmuQXYDji/qr7Wnx+/ZCw7g5WWS5J7A7fSfTi9DLgP8FLgk0nurKqjkvy4qm4ZZp3SopI8Hng/8Aq6OSV7AmfQrYR6AF0P7FFV9d9JVq2qO4ZWrDS5bgUupFt0dAuwU5Izk+wHfAJYh25qx67A8Yaq5eMcK92tJH/1HqmqW6rqU3RdxtsD+1XVGcDPgH9Oso6hSiPqkcCPq+r7VfU+4H+B1wP3Bg6j673aJ8mjDFWaTga2Flm1Hwq/DtgImDVw2heBO6rqduBY4CzgDEPV8jNYaYmSbFJVd/a3X5Pk8CQHJFmLuyY2PiTJHOAa4NlV9YfhVSzdZeDDZPzv3IXAaklmAVTVicBFwBZV9Xu6VYFfBv44hHKlSdMPg8+m+/JwPN2XjCOBjyTZP8kr6IYFL+nPv62qjq2qa4ZW9BTmPlb6G/0H0lp0Y+/vBc4DjgD+E3gM3UqSvej2OXkc8Hhgj6o6bxj1SkvS71P1JOAPwGl0e67NB64CLqZbUv7i8feuQ4CajpI8gu7v93vp/n6/k24o/Da6+VSbACdW1anDqnE6MVhpiZI8Gfgk3Tf9I6vqW0keCLyVLnjtV1U3JVm7qvyWr5GS5CnAMXTDfC+j2wLkcmAMeDLdcPbR/YaITs7VtJTkUXS/AxdX1Wv7tu2ATwNPq6pLhljetORQoP7KwPBJqur7dBPT/w7YEaCqfge8D7gDODLJKoYqjZokDwf+GTiiquYCOwHrA4+pqiOqaldgb0OVVgK/pJu68cgkWyRZvaq+QXd917HhljY92WOl/7PI5p/PARbSDZmsC/wP3TL0T/THH0A3dO8YvEZOkh2A19KtfnpDVf2mnxt4JvCSqvrlUAuU7gHjQ9v9hPVPAjcD3wQW0A2Dz66qnwyzxunIYKW/keQNdN/wz6DbnXoXYHO6lSIfr6oPDa04aTEGNv/cHLiJbtXTlnTzAH9L9+08dJPT/6GqrhhasdI9YOB3YkZV3d5fbeBIuisLnAl8vapOtce2PYcC9VeSbAVsV1XPANYGrgf+UFU/opunsmeSdYZYovQ3+g+QHejmUR0O/IRudd9xdEvKv0T3ofJWQ5Wmo4FpHFv0IwoA9KFqRlX9hW54fB6wBnCOoWpyGKxWcgPXjBr3F+C3SQ4CHg7sXlW3JZldVT8GnuyWCho1/QfJu4FXVtXudEHqZOBXdCuhvg98m64XdnHve2nKGuid2o7uff9fwH5JHgZ/Fa5uowtX9wfehpuETwqD1UpskTlVs/tVgL8GNgT2pht/v6Xf4+SA/rI1tw6vYuku4/tTJXkC3WKKc+lW/VFV7wa+AxxUVT+lmyP4ULoNQGf4LV3TSR+qZtFdVeAFwJvoFh3ttEi4WrXvudoZOKwPWmrMYLUSGwhV+wP/BizoNwT9MN23+xOTHEi3cdyrq+raYdUqjUtyH4CqujPJU+km5T6Kbn+efxg49bt0k3Wpqq/TbY54Yr+ztDRtJLkf8HLgcVV1SVV9j+76f5sDL+lXydJPZF+lqv7Sr/DWJLAbcCXXf5vZGdh+4BftO8D5dFstXE+3iuriIZUo/Z9+T54PJXk+3SU5/hX4SFWdkeTPwNwkD6XbEPQlwEHjj3XzQ00ngyMOVfWnJEcAD0tyJPCaqvpOklWBPeg2AqU/987hVLzycFXgSmbRyYr9RPSPAwcCvwNu67uVNwMud8hEo6JfMn4U8CPgv4Hn0Q17rAvsVVVXJXkk3UrW9YEf9JvaOkFX08rAnKrnAg+i+yz/z75n6kDgBuCNfa/uulV1/VALXsk4FLgSWWRO1Qb9kMqNwOrADn33cCXZnW5i4xpDLFdanKvogtOX6FY3HUI3t+o1STaqql9U1cer6j1V9S24a8hbmg76obxK8g90O6pfCRyS5AP9/mz/DjwA+Gj/EDdwvofZY7WSWCRUvRHYDfgZ3eqRn9J9UF0E3A5sDexZVecPp1rpry3yDf2zwLf73dPHL13zfLovAv/uprWajpI8BFilqi5NsgHd78Eb6FZvHwhsDHyzqvZJsiWwenn91qGwx2olMRCqnkB34eTXAF8HXk+3z8/z6Cb3/oDuorSGKo2ERTb/HANeDKyS5N39MMf3gFPovhSsM8RSpcn0ZGD9/pI0vwfmAKsB7wKeCjwLeHmSj1bVxYaq4XHy+kqkX0H1ZeDDVfXDfijwT8CbgQdW1VFDLVBajD5UvZBu2O8Sui1BPkG3pPy1SY7oJ+qe7x5rmq6q6vNJ7gv8JMlLq+q8JA8Ezu7nUj2AbnPcbwy3UtljNY0tugliVX2Xrvt4lyQbVNXNwFl02ys8N8m6bpyoUZNkG7rVf9sBX6Hb4PB5wAfo5lu9qd+b6g9DK1KaJAM7qm9Ht63IZ4Gj+xWylwFrJ/kY3WWbvlpVp/l3fLicYzVNLTKnalvgfsBpVXVjkg8C29BtALqgX211r6q6aYglS4uVZBO6rRXWpdtdfXe6lazXAZ8GFpYXktU0lmRrui/Ab+hHG95I93uwU3/KY4Abq+rMIZWoAQaraS7dBZX/ke7SHhsC76+qM5McSjfh9+lVtXCYNUrLIsl76Dax/XCSlwGvBf6xqi4fcmnSpEmyKfBB4LqqmjPQ/kbglcDOVXXBsOrT33KO1TTTj7NfX1W39iuonltVT+13UH8y3eTGqqo3J7kVWBMwWGkqOB94Vd/D+mLgdYYqrQRuB84DZifZvqr+F6CqDu83AF1nmMXpb9ljNU30Y+pjwInAfwKfoxs6uS/dipG9gBcCnwIeBrylqs4YTrXS8kuyFvAiuvfxMf1laqRpZWAV7JPo/qb/lm7Bxt501/87oapOH2KJWgonr08fqaoFdNf8242ue/ja/hv9w4H/qapbgO/RDQvadawppapuqKpj6S6x9HUn6Go66kPV84Bj6KZvnA08BTiZrudq7/64RpRDgdPEwPWf1gDuAxyTZI2q+hTwfeBj/eUOnkQXuhz+01R1B7ijuqafJKvQDe29iq53dj3gQuCcqromyZforpQxf2hFaqkcCpxGkrwEeCuwA91qkZcBHwOOp/vG81zgc1V10bBqlCTdvSRvBdYGng3s0e+2vjfd9jiXeSHl0WaP1fSyPnBWf0mPTyS5FpgLrFFVRwMuxZWkEZRkJt0WOO+iW1S0J/CsPlT9Pd2X5kuq6tdDLFPLwGA1vfwWmNnv+3NVVZ3YL0t/QZIvVtWfhlyfJKk3MFH9acDOwHZJFlTVwUkeAbwjye3ATOCt/SbPGnEOBU4jSdam2zjxcroLK69BNyT4uqq6bHiVSZLGLbKB89OBzwP7A48AHg98v6o+1F+GbF3gd1V19uDjNLoMVlPMkn6x+kt63J5kPbqJjw8EtgAOcPM4SRoN/fX9Hgl8u6ruSPJSYOOqen9/LcCZwPvptlX48BBL1QoyWE0hi3zLmQ0EuLOqTu7bxsPVKv1FOdesqhuHWbMk6S793+5fAVfSbf65Ld3O6s+tqt/0KwOPpZtndWJVfWFoxWqFuI/VFDIQqval269qS+CjSd7SH7+9/6UcT8te+0+SRkhVfRW4mm7F9k7AqXRTOD7cz6t6DN21MX8FbDykMjUBTl6fAhbpqbo3sAuwV1X9NMkJwDeS/LmqPja4DNexeEkaDYN/x6vquiRnAs8D/gJ8hW4E4rN0vVj7AI8Dnttfwul2/55PHQarKaTvqboY+CVwn37I7zdJ9gd2HW51kqQl6Vf/PQN4NPCtqjo6yZ/pLtF0Z1UdmuRj/elbAwcDL6qq24ZUslaQQ4EjLMmW8H+/kC+mW477c+Ba4A1015EC2BRYo78gpyRpRIxfeinJE+mG/54BvCXJK6vqOOBrwEuT7ALcAtwbeDLdnlY/H1LZmgAnr4+oJNsBR9F1B28EHAZcWlWv6Y8fQ7cM90a6uVYvd/WfJI2eJFsDhwBvqarzkuxKF57O73uudgMurKqf9eevVlV/GWLJmgCHAkdQkhl0XcH/CmxFt/z2DGB2khdU1X9X1T/134BWpdsM9PKhFSxJujvrAM+hu6zYecCJwJ30c6iq6mNw1zwsQ9XUZrAaQf3qvkuBf6G74Oyz6LqIb6bbRf2Oqjqlqn40zDolSUtXVaf20znem+R3VXVckhPpvhj/bOA8h5CmAYPV6DqPbruEG4C1q+r3Sb5M9y1nzyS3VtXpQ61QkrRMqurk/vI0/9YP9R0LHDfsutSec6xGxCJbKqwG3NHvynsA3RXO31FVP+mvA7gD8LWqmj/EkiVJyynJC4H30Q0NXj24RY6mB4PVCFgkVO1PN6/qBuCdVXVLkoOAJwLvq6ofJFm1qu4YYsmSpBWUZKyqFg67Dk0Og9UISfLPwEuA3YFzgG8CB1fVpUneDTwM2LuqbhlimZIkaQkMViMiyVrA4XQrAXcGdgQW0G218OqquiTJ+lV17RDLlCRJd8NgNUKSrA48AvhQVT2r31huIfAJumFBd+CVJGmEuSpwhFTVrUluAmYkeTTwYOB04BOGKkmSRp89ViOm77V6Pd2KkQcCO1fVhUMtSpIkLROD1Qjqr2b+ALoLc1417HokSdKyMVhJkiQ1ssqwC5AkSZouDFaSJEmNGKwkSZIaMVhJkiQ1YrCSJElqxGAlSZLUiMFKkiSpkf8PwQ8MlHUQ3OMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x648 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "corrmat = df.corr()\n",
+    "f, ax = plt.subplots(figsize=(12, 9))\n",
+    "sns.heatmap(corrmat, vmax=.8, square=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iUGsR1FoUGl1o2fU+tK5DUGtCx8j0i9XhpbpLnCobbJTVdlLZYMMfCP0Geq2SrOSQQOWkmMlNtWA2w7r6zSyrXEdJWzkAeTFZjE8dyfjUEXIkCJnDQhakAaTd1cm/N7/PutotZFhS+fWkW0mMjMfl8bO7qoNdFW3sKm9nT3UHPn9IgNITIxmaHRKgodkxRJl0A2K7JEmIHicBa0voZWshYG0l6LKFxMdlI+gOrUs+z1FcqbuVeAycGdRaFFoDCl0ECm0ECp0h/F7Zva17v26/dW2onKDW/ui6pQJBkZomO2W1VspqOymrs1Jeb8XrC/0fNSoFWclmslPNxMYo6FRUU+raRpW1GoBMSyrjUkcwPHEwOVEZcrgrmQMiC9IAYPPY+aJkEd+ULiEgipybcS7p6qGUVHWys6KNijorogQKAbJTzAzOimFIduhlNmpPiI2SJCG6bPg7m7vEpuvV9d5vbUXyunocI6g0KCPMKPQmlIZIlAYTCoMJpcGEUh+JQqtHUOtCrReNLtRy0YRaMChVCIIi1ArqWiIowgIQai0FkcTuZTDUihKDSMEAUsC3r7Xl94aXkt+D6PeFlj43oseF6HEieruXToIeJ6LHBWJvp4AeKJQotPougdonVGFh2/8VFrKe5QXNgZ0ITgWCokRds53yOitldVb21nZSXmcNO1WolAIp8Qb0Ji92ZR2tVCAYbBh1OoYlFDI8YRDDEgcRZ4g+5b8LmWOLLEgnCEmS2NNcwVc717G2pByf3UBkMAWvw4DLHbqRNWolhRlRDM6KYXBWNAUZURh06uNjjxgkaG8nYGsjYG3Bb23eJzrWZgLWVqSAr8cxgtaA2hyHyhyHyhzftYwLb1MYTKdsBSNJElLAFxIs736C1f3yunq++trmdYe6HQ+EoAh1K2r0IbHqWu57b0Ch0XWJlx6FVhfa1lVW2P8Y9ckjbqIo0dju7NGSKqvtDDvXAOgjgohaG0FNJ4LegckE+alxDE7KJD8mi+zoDHSqE/PAJXNyIgvSMUSSJJxuP21WD21WD9XNneysqaOisZO2Dh8Bt5buLiiVUiAzOeT51t0nn51iRqU8ui4NSZKQfO5Q15nTSsDRTtDWRsDW2vUKrQcdnb0qT4XBhMoUh9oSFxab/YVHqYs4KttOd0LfvacPofqhgLlD42k+d3hd9LoQfZ7wtoMKGwBCl0CFBEyhCa0L3evdIvYD8eu9zYCg0YZapsf4+2jpdFNWa6Wy3kpNs4OaJju1zfaec9VUXgStG4XWQ0SERKxFR3KMiYz4WLLj48mMjSc2wiJ39/0IkAWJru4pKfSUFwyK4TkzP1x6fAEcbj82p5dOhwery4PN6cXqCLna2hwBAj/s9VEEUOjcWCwK8pJjGZOVQ0F6LGkJkaiUitAcGzGIFPQjBQKhZdDf1Q3lD73CFZerRwUmda13j9sEnVZElw0p6O/1GQWVBpUpBqUpFpUpBlVk19IUGxYchWZgxqNkehJurXldXb+9p8dvL4X/A/uWkq/7/+HpKhMSONHrOkRxIyRS+7XKBK0ehUoTcghR7XspfvBeUGtQqLTh9X371AgKJYJSCQpV17qKIAKtNj+1rW5qW11UNlmpbu6gpcONwyEhin20+pR+VJoAWp2EwaAkQq/AoFMTodNg1GswGbSY9AZMBi1GvQa9Roteo+5aatCp1aE5a11z2eRpDycnp6QgBQIBGhsbD1jG6vDywgdbcHr8BEWJYFBCEiWCkoQohl5BUULsen+4CIjoFD50gh+d4MOocBOpcGNUeDEpvZiVXqJUoaU6/OQpgtQ10VOSIBgAMcgRDdgLyq6nYy1KnRFBF4nSYAyN1eiMoTEcfSQKgxlVZBSCNuKk6d6ROXFIkgRBf2hczedG9HlDLTG/p2tczbOvZeb3dnk/uhG9XiS/e99DUdCHFPAjBvwQ8IMUPIZWCiAoQBnynHQKejoDeqyiDoeowhnU4BA1OEUNrqAGp6TBJWrwSWpEjrDVJIihCCDCfvde13r39q6gISgEAZPOiFJQ7BetZN+91L3avWV4fhxzz8g9qAmJiYmoVKojs/805ZQUpNraWmbNmjXQZsjIyMgcMYsWLSI1NXWgzTipOCUF6VBaSEdKY2Mj11xzDf/9739JTEw8Ltc4Xsi2Dwyy7QPDqWw7yC2kvjglvw2VSnXcnywSExNP2acX2faBQbZ9YDiVbZfpiey2IiMjIyNzUiALkoyMjIzMSYEsSDIyMjIyJwWyIP0Ak8nEnXfeiclkGmhTDhvZ9oFBtn1gOJVtl+mbU9LLTkZGRkbm9ENuIcnIyMjInBTIgiQjIyMjc1JwSgpSIBCgtraWQK/AcTIyMjKnDz+2uu6UFKTGxkZmzZp13KI1yMjIyJwM/NjquuMuSA6HgwsuuIDa2tpe+xYuXMicOXO46KKLuOOOO7BarcfbHBkZGRmZk5TjKkhbt27lqquuorKystc+h8PBY489xquvvsr8+fMpKCjghRdeOJ7myMjIyMicxBzXWHYffPABv/vd77j//vt77fP7/Tz22GMkJCQAUFBQwOeff96rnM1mw2az9dj2Y2m+ysjI/HiQ67rjLEhPPPFEv/uioqI488wzAfB4PLz66qtcd911vcq99dZbvPjii8fNRhkZGZmTAbmuOwmifdvtdu644w4KCwu55JJLeu2/4YYbem3vDjsvI/Njw+/3U1tbi8fjGWhTZA4BnU5HamoqarX6oGXlum6ABam5uZlbbrmFCRMm8Nvf/rbPMiaTSQ4NIiPTRW1tLZGRkWRmZsoZgE9yJEmira2N2tpasrKyDlperusG0O07GAzy85//nHPPPZeHHnpIvrlkZA4Bj8dDTEyMfL+cAgiCQExMjNyaPQxOeAvp1ltv5e6776axsZFdu3YRDAb59ttvARg6dOgBx51kZGSQxegUQv6tDo8TIkiLFy8Or//rX/8CYNiwYezevftEXF5GRkZG5hTglIzUICMjc+zYtm0bjz76KADbt2/n7rvvPuTyx6KcjEw3siDJyPzI2bt3L01NTUCo5+L5558/5PLHopyMTDcD7vYtIyNzfBBFkT/96U9s3boVp9OJJEn88Y9/5MMPP6Szs5OamhqGDx/OqlWrsNvt/OY3v+Hiiy/mD3/4A1988QUbNmzgySefRBRFAH72s59RVFTE888/Hy7/5z//uc9rNzQ09Cr3/vvv8/bbb6NQKIiNjeWRRx4hKyuLBx98EEEQKCsro729ncmTJ/Pwww8f0FU6GAzyl7/8hcWLFxMZGUlRURFlZWW8/fbb2O12nnjiCUpKSvD7/UycOJH7778flUrFsGHDuO2221i5ciXNzc1cf/313HjjjXz88cd89NFHuN1ujEYjb7/9Nh9++CHvvvsuoihisVh45JFHyMnJOS6/lUwX0ilITU2NlJ+fL9XU1Ay0KTIyJ5Rdu3YdctlNmzZJd911lxQMBiVJkqRXXnlF+tnPfiY98MAD0g033BAuN2/ePOm2226TJEmS1qxZI51//vmSJEnS9ddfL33xxReSJElScXGx9Nhjj/UqfyD2L7dq1SrpzDPPlNra2sL7zj33XEkURemBBx6QLr74YsnhcEher1e65pprpLfffvuA53733Xela665RvJ4PJLX65Vuvvlm6dprr5UkSZIefPBB6T//+Y8kSZIUCASke++9V3r11VclSZKk/Pz88Lm3b98uDR06VPJ4PNK8efOksWPHSna7XZIkSVq7dq109dVXSy6XS5IkSVq+fLl07rnnHvQz98Xh/GY/5MdW18ktJBmZ05SRI0diNpt57733qKmpYe3atURERGCxWBg9evRBjz/33HN5/PHHWbx4MZMmTeL//u//jtiW5cuXc9555xEdHQ3A3LlzeeKJJ8JBly+55BIiIiIAmDNnDosWLeLaa6/t93xLly5lzpw5aLVaAK644grefvttAJYsWcL27dv56KOPAHq5Xc+aNQuAIUOG4PP5cLlcQCh8mdFoDJ+jqqqKK6+8Mnyc1Wqls7MTi8VyxN+DzIGRBUlG5jRlyZIlPPHEE9x0003MmjWL7Oxs5s+fD4DBYDjo8VdeeSUzZsxg5cqVLF++nBdffDF8/OEiSVKf27rz/CiVyh7bFYoDD2+rVD2rrv3Li6LIc889F+5es9lsPdyvu0Wse1u3bft/J6IoMmfOHO67777w++bmZsxm80E+qczRIDs1yMicpqxcuZIZM2Zw9dVXM2zYMBYuXEgwGOxVTqlU9pkA7sorr6S4uJi5c+fyhz/8AZvNhtVq7bf8gc47ZcoUvvrqK9rb2wGYN28eFouFjIwMAL7++mt8Ph9er5dPPvmEGTNmHPDc06dPZ/78+fh8PgKBAJ988kl435QpU3jzzTeRJAmfz8ftt9/OO++8c1B792fy5Ml8+eWXNDc3A/Duu+9yww03HNY5ZA4fWZBkZE5TrrzyStavX8+FF17IFVdcQVpaGrW1tWEnhW5GjhxJeXk5v/jFL3psv/fee3n++ee5+OKLuf7667nzzjtJTU3tt/wP2b/c5MmTufHGG7nhhhs4//zz+fTTT3nllVfCLRudTsfVV1/NhRdeyJgxY7j00ksPeO65c+dSVFTExRdfzJVXXolarUav1wPw0EMP4XK5uPDCC7nwwgvJz8/npz/96WF9d1OnTuXWW2/l5ptv5sILL+SLL77gxRdflCe6HmcEqa+29ElObW0ts2bNYtGiRaSmpg60OTIyJ4zi4mIGDRo00GYcUx588EHy8vK45ZZbDvmYFStW0NbWxpw5cwD44x//iFarDXexnUwczW/2Y6vr5DEkGRmZI6K8vJxf/epXfe7Lysri2WefParzX3311Tidzj73/fOf/+T111/n9ddfJxgMUlhYyGOPPXZU15MZeGRBkpGROSKys7P57LPPjvo8Tz75ZJ/b//e//x3wuDfeeOOory1zciGPIcnIyMjInBTIgiQjIyMjc1IgC5KMjIyMzEmBLEgyMjIyMicFsiDJyMjIyJwUyIIkIyNzTHn++eeZNWuW7AUnc9jIbt8yMjLHlM8++4zXXnuNrKysgTZF5hRDFiQZmVOUxRuqWbCu+ric+6xx6cwck37AMoFAgMcee4zS0lJaW1vJysoiOTmZpqYmfvGLX/C3v/2Nm266iSFDhtDa2spHH33E66+/zvz581EqlUyePJn77ruPhoYGbr/9dtLS0qiqqiI5OZmnn34ai8XC999/z7PPPosoiqSlpfH4448TGxvLzJkzmTlzJhs2bADgT3/6E4MHDz4u34XMiUPuspORkTkiNm/ejFqt5v3332fBggV4vV4mT55MfHw8r776KoMGDaKjo4PbbruNzz77jFWrVrF48WI+/vhjPvnkE6qqqnjvvfcAKCkp4YYbbuDLL78kJyeHF198kba2Nh599FH+8Y9/8PnnnzNq1Cgef/zx8PUtFguffvopd999Nw888MBAfQ0yxxC5hSQjc4oyc8zBWzHHk7Fjx2KxWPjvf/9LeXk5lZWV4dxC+zN8+HAA1qxZw/nnn49OpwPg0ksv5dNPP2X69OlkZmYyfvx4AC6++GLuvfdeJk+eTFFRUTiG2xVXXMGrr74aPu/ll18OwMyZM3nwwQdpb28P51uSOTWRW0gyMjJHxKJFi7j33nvR6XTMnTuXsWPH9pn3qFuAfhhlHAinp9g/v5EkSSiVyl7l98+f9MNjRFHskVNJ5tREFiQZGZkjYvXq1Zx77rlceumlxMbGsn79+j7zLXUzYcIEvvzySzweD4FAgHnz5jFhwgQAKioqKC4uBkK5kqZNm8bw4cPZunVrOKvs+++/H25FAXz55ZcALFiwgJycHDl53mmA3GUnIyNzRFx22WXce++9fPPNN2g0GkaMGBEWj76YMWMGxcXFXHrppQQCAaZOncq1115LY2MjZrOZ559/nurqagoKCvjjH/+IwWDg8ccf584778Tv95OcnMwTTzwRPt+mTZv46KOP0Ov1/QZolTm1kAVJRkbmiCgoKODzzz/vtX3/lBR79uzpse+OO+7gjjvu6HWMXq/npZde6rW925uuL37961//KHIE/ZiQu+xkZGRkZE4K5BaSjIzMgJKamsrixYsP65jDLS9zaiC3kGRkZGRkTgpkQZKRkZGROSmQBUlGRkZG5qRAFiQZGRkZmZMCWZBkZGSOKU1NTdx6663H5FzPPfccixYtOibnkjn5kb3sZGRkjikJCQn861//Oibnuueee47JeWRODWRBkpGROSLWrl3LK6+8gk6no6ysjIKCAv7617/S3NzM9ddfz+LFi2lsbOTee+/FarWSn5/P+vXrWbZsGU6nk8cff5zS0lKCwSC33norF1xwQTgSeGdnJzNmzKC5uZlx48Yxd+5cnnnmGVavXo3VaiUqKooXXniBuLg4pkyZwtlnn83GjRtRKpU8++yzpKWl9bC1v3QVFRUVPProo3R2dmIwGHjooYcoKiriwQcfRBAESkpKcDgc3H777Vx88cUD8C3/uJAFSUbmFMW+bQn2rcdnPk7k8JlEFp1x0HKbN2/m66+/Jj4+nssvv5wVK1aQn58f3v/EE09w7rnncs0117BgwQK++OILAF566SWGDBnCU089hcPh4MorrwxHBW9qauKrr75CpVLx4IMPAlBVVUV5eTnvvfceCoWC+++/n88//5ybb76ZlpYWJk6cyCOPPMKTTz7Jf//73/Bx+9OdrmLx4sU88MADfP7559x3333cdtttzJ49my1btnDPPffw7bffhu147733aGtrY+7cuUyePJm4uLij/WqPiL6C1p6OyGNIMjIyR0xeXh6JiYkoFApycnKwWq099q9cuZI5c+YAcNZZZ2EymQBYtWoV7733HnPmzOGaa67B5XJRWloKwODBg3tE8gbIyMjggQce4MMPP+TJJ59ky5YtPVJdTJ06NWzPD23oZv90FU1NTTQ2NlJdXc3s2bMBGDFiBGazmfLycgDmzp2LWq0mMTGRUaNGsXHjxqP6ro6OH4cgyS0kGZlTlMiiMw6pFXM80Wq14XVBEHo9ySuVyj6f7kVR5Omnn2bIkCEAtLa2Yjab+fzzz8PpKvZnx44d/PrXv+bGG2/k7LPPRqFQ9Dhvtx192dDND9NVBIPBXmUlSQpHLN8/nYUoir1E8oQS8A/ctU8gcgtJRkbmuDFp0qRwANalS5dis9mAUCqKd999F4Dm5mYuuugiGhoa+j3P+vXrGTduHFdddRW5ubmsXLnygKku+uKH6SpSUlJIS0vju+++A2DLli20traSl5cHwNdff40kSdTV1bFt2zZGjx59eB/+GCIGfxyCJLeQZGRkjhu//e1veeCBB/jggw8oLCwMd9ndeeedPPbYY1xwwQUEg0Huu+8+0tPTw04HP+S8887jzjvv5MILL0StVlNQUHDAVBd90Ve6iqeffprHHnuMF154AbVazQsvvIBGowHA4/Fw6aWX4vP5ePzxx4mKijqKb+LocDl6Z+I9LZFOQWpqaqT8/HyppqZmoE2RkTmh7Nq1a6BNOCzeeustqbS0VJIkSdqxY4d0ySWXDIgdM2bMOKz64oEHHpDmzZt3TK59NL9Zd123esnSY2LLyc5xbyF1e9C8/PLLvXKXFBcX8/DDD+NwOBgzZgy///3vB7afVua0R5IkvAEvLr8HT8CDN+jHH/QTlLrHEwRUCiUqhRKNSoNepSNCrUer0iIIwkCbf8qRkZHB//3f/6FQKNBqtfzhD38YaJNOSaxW50CbcEI4rrX/1q1befjhh6msrOxz/3333ccf//hHRowYwW9/+1s++OADrr766uNpksxpjCRJWD02Gh2tNDtbaXW10+7qpNXdQYe7k06PDbvXSUAMHPa51QoVJl0k0XoLcYZo4o2xxEfEkhwZT4opEbPOdBw+0anP9OnTmT59+kCbcdjpKk62DLStVvtAm3BCOK6C9MEHH/C73/2O+++/v9e+uro6PB4PI0aMAEIuls8//3wvQbLZbOGB0G4aGxuPm80yJz+iKNLkbKXGWk+drZE6eyN11kbq7U24A54eZY2aCGIMUUTrzWRYUjFpI4nURBCh0aNTadEoNWiUahSCAoUgIAFBUSQg+vEGfbj9Xlx+Fzavg063jXZ3J+Ud1ayt3UxQEsPXMetMZEelkR2VQU50OnkxWSdUpCRJQpQkRDG0HnrtcxYWAEEIeaEpFAKKrqXMycOB6rqmDlmQjponnnii333Nzc09JpnFxcXR1NTUq9xbb73Fiy++eFzskzn56fTYqLHWU2Otp9paT1VnLTXWenz7eR1F6c2kmpKYnjmBpMh4kiLjiYuIIdYQjValOS52iaJIq7uDelsTtbYGqjprKe+oZkvjrrArcVJkPIPi8hgcl8fg+DxiDdFHdC2vP0hjm5Omdhcqb4DWTjeBoEgwKBEQQ0tRlA57pooggEohoFIqUKkUqLteGpUSjVopC9YJ5kB1XWOnp8/tpxsDNmAj9TFXoK8++htuuIFLLrmkx7bGxkauueaa42abzIlFlETa3Z002JuptzVRbw9V8tWddVi9+54MIzURZFhSOStnGmnmZNLNySSbEjCo9SfcZoVCQXxEDPERMYxIGhze7g34qOiopqStnF0te1lTs4nF5SsBSDTGMTShkCHxeQyJy8eiN4ePkySJDruX6kYb1U126pod1LU4qGt20GrdVxnde2kqnQ4vKoWAUhkSD6VGQKEUUO7X+uluDe1PqBXVNddG7HoFRQJBCY8viMPl79GiUqsV6DQq9BolOq0KjVqJzPHjQHVdu0Ps56jTiwETpISEBFpbW8PvW1paiI+P71XOZDKFXUVlTi0CwQBOvwunz4Xd58TudWD12On02OhwW2l1d9DiaKXJ2dqjxaNVaUmNTGRk8lDSzSlkWFJIMydj1kae9I4FWpWGwrhcCuNyuahwNqIkUt1Zz87mPWxv3sPK6vUsLFsOgEUdQ0QwgYDVQmu9DodVTUgKIEKnIiXeyNDcWFLijCTFRJAQYyDoaCQnxXxcvgdJkvAHRHz+IF5/EK8viNPtx+b0AaBWKYjQqYjQq9FrVSf9b3GqcaC6rsP943gYGDBBSklJQavVsnHjRkaPHs2nn37KtGnTBsocmX5w+z20uTpod3di9dixee04fC6cfhcuvxtPwIs34MXj9+IJeHEHPKGl34P/AM4DkZoIovUWEoxxDE8cTGJXV1tyZALRestpU9lZHT7amtS46tJQ1pvR1+fT6axHEdlGm6mdzsgSiAxAAUQpDKQaU8mPyyQ/PpF0SzKJxnhUin2VUXFx03H7bgRBQKMOddcZu7ZJkoQvIOL2BHB5/FidPjodPpQKgUiDmsgIDTqN7Bl7vHEGdDTZOkkwWQbalOPKCf8n3Xrrrdx9990MGzaMv/71rzz88MM4nU4GDx7M9ddff6LNkemi022lvKOGamsdtbYGGuzNNDpasHsdvcoKCOjVIXdonUqLTqVFq9Ji0kWiU2nRq3To1DoMah0GtZ4ItYFIbQSRWiMmrRGLzoRaqT7un0mSJFx+Nx0eKzaPHbvPidPnxhPw4Au7e4uAhEJQoFKo0Cg16FVaIjSGkK16MzH6qIOORYmiRGO7k/I6K+V1VirqbZTXWWm37etui4/Sk5VsZtqIcWQlm8hKNhNr0VFra6CkrYyS1grKOqr4unwBX5WFOs+UgoJEY3yXV18MI9T5OLzOLtd0FUqF8riKtyAIaNVKtGollkgtoijh8vixu/aJk06jxGzUEmlQh215/vnn+eyzz7j22mu56aabjsqGF154AYC77rrrqD/PgaitrQ1HKT9UZs6cyX/+8x/WrVvHunXrjqN3nsCCbdu4dsrp/dB+QgRp/x94/zwphYWFfPTRRyfCBJn9kCSJBnsTO5r3UNyyl5LWclpc7eH90XoLSZHxjE8ZQbwxllhDNNF6MxadCZM2EoNGj0LoHXVKCvrxNlXha64i0N5AwF6D6HYgBkJdPoJShag1YI0wozLForYkoo5NQR2dhKA4si4JURJpcbZRZ2uk3t5Mo6OZFmcbLc52WlzteAPeAx4vIIBw8GjKJq2RRGOoFZcYkYA2GIXfbqSpOUhFfUiA3N5Qi1CpEEiNN1KUF0tOioWcFDNZKWaM+r5FODMqlcyoVGbnhtyjfQEftbZGam0N1NoaqLc10eRspaS1jLz0VJqc+7q6BUClUIUFSqVQoVaqUCvUqJUhwTqWKBQCRoMGo0FDMCh2CZOXpnYXbVaBqEgdpggNn332Ga+99hpZWVnH9Po/Zlbu2SsLkszpQSAYYEfzHjbUbWNL406anW1AyEOtICaHc/NnkB2VQYYlhQiN4ZDPG3TZcBavxlmyHk/1TqQu8UGhQmW0oNBHIqhDk0pFr4tARyMBpxXJuy8UiqDSoEnMRpeSjy5tELr0wSj1xl7XcvicVHbUUNlZR3VnHTW2emqtDXiDvnAZg1pPQkQsyZEJFCUOIkYfRZTejKmrdWbQGDCodGhUGlQKZVhYQwP9wZCrd8CDw+ui02OnqrWZypYmaq0t1De3Utq0BUm1n8eTT48hMo68kWkMSchnTGYumUnmo3IA0Kg0ZEenkx2d3mvfzl27SDUlERCDLK9cy/LqdSEXb6Twcn8EBBSCIuTu3eXaLnDwFtWMrElMz5pwwDKSJPLXp/5ASUkJra1tJKem8+Cjf+b1l5+lsbGRX/ziF/ztb3/jpptuYsiQIbS2tvLRRx/x+uuvM3/+fJRKJZMnT+a+++7rEcgU4LXXXuODDz4gKioKk8lEUVERAO+88w6fffYZbrcbQRB49tlnycnJYebMmVx00UWsWLECt9vNU089xdChQykuLubRRx/F4/FgNpv561//SmJiIq+++ipff/01wWCQKVOmcN999wGhcEG/+tWvKC0txWQy8Y9//IOoqKh+r3uiMCq9NDRCVWctGZbUgx9wiiIL0mmMKInsbC5hedU61tdtxelzoVVpGRZfwEWFsylKHERCROxBu3yCotTlZizS3ZAINFfg2vgF7j1rQQygjk4icsSZ6NIHoU3IQmWJP2CrJ+hxEmhvwNdag7epEm9dKdYNX2FdOx9JUOBJzqI5KZ0mk4maoIvKzlpaukQUQvN+0s1JzMqeTJo5mRRTEsmmBCI1EYfdheUPiDS2OalttlPT5KCm2U5NU2jd5w8CWgQhlcToAoqSIkmO1hIR5SKg6aDRXc/e9kpKXSspbVjJgjYDQ+LyGZpQQFFCIUmRCce0S00hCGhVGrSAXq1Dreh9C4thgRJD65JEUNoXiFSBAoVCgVJQ9NnSPVQ2b96MWq3mgw8+QBRFrr/+esp3b+L/7nuIDevW8Ps/PUN2Tg4dHR3cdtttjB8/nqVLl7J48WI+/vhjVCoVd911F++9914Pr9nt27czb948PvnkEwRB4IorrqCoqAiHw8HChQt5++230el0PPfcc/zvf//jkUceAUL5jj766CPefvttXnnlFV544QXuvfde7r33XmbMmMH//vc/3nrrLSZOnMiOHTv46KOPEASB++67j/nz5zN69Gja29u56aabKCoq4u677+arr75izpw5B7zuiSBbZ2W3LZt5O7/h/yb/9IRd90QjC9JpSKurncXlK1lSsYZWVzt6tY4xyUVMTBtNUeIgxKBAU5uL6ioXGzsqaLN56LR76XR4sTt9ONx+XJ4AHl8Ary9IUNz31B2jsHORYSMjNNV4JDVrvbms9+fR5opF16xCu8mFXrsHg66MCJ2aCL2aSIOGSIMao0GDKWLfy2xMRR2dgjU1m+qcAio7qqlqKaPG3oRdtEPnToQOidggpOksTE8eQ172WLJjMg866VSURGweO+1uK50eG802Ky02G+1OJ1anC7vLg8Ptx+H243QHkYIKpKAKAipMWiNJ5mhmZSWSlxRHRpKJ9IRIdNr+b5dWZzu7WkrZ0byHnU17WFe3BYBYQ3RYnIYmFGI5hpNlp2dNOGgrphtREvEGQq2/bgcUCQmloCBCY8CoiUB3mOGRxo4di8Vi4b///S/l5eVUVVUR9HtJjTeiVAgEgiI1TaExyGFdLZw1a9Zw/vnnh1NMXHrppXz66ac9BGndunVMnz6diIgIAM455xxEUcRoNPK3v/2NL7/8ksrKSpYvX86gQYPCx+2fE+m7776jvb2dlpYWZsyYARCedP/UU0+xbds25s6dC4RaRcnJyYwePZr4+Phwayw3N5eOjo6DXvdEkK1ppdhbyKqSPZyXX0Zh3IlrnZ1IZEE6TZAkieKWvXxVupgNdduQJImhCYWclXY2OncKNY0uPtti5x/Ni3vMa4HQuIDFqMVi1GKK0BBj1mPQqdBrVWg1StQqJWqlRGL9chJrFiIpFDQkn0ljwmR0aBjf5Srs8QXx+AK4vQFcngBN7S7sbh8unwsPDgSNG4XOhaB1I+icoZfGQ3cdKIhKNKKZCDLJVsYRrzST57WR5CjDXLMH5e5SxJWLqIwZhDN+GPaofDokL62eFjp8LXT627EHOnGLdrw4QTjA3A1JAK0AWglVVM9uLg9QAVR4YUN9BAm2WFLqE0kzJ5MVlUZ2VDpGbUSPY2IjopkWMZ5pmeMBaHK0sLWxmG1Nxayv28qSitUApJqSGBSXS0FsDvmx2YfUQj0WKAQFerUOvVoH+tDEXlfAHXbJt3kdqJWqrkgWRpSKg7ecFi1axPPPP8/111/P3Llz6ejoQJKkcDSIlDgjWkNo3KzVFiBRFUQUe/8mgUBPb0xBEHqUU6lU+Hw+GhoauO6667j22muZNm0asbGxFBcXh8vtnxMJQK3uOWbn9Xppbm4mGAxyww03hJ0tbDYbSqWSjo6OHrE0u3MrHey6J4IsQqk5dO40Xtv4Ln8+64ET4hh0opEF6RRHlEQ21G3jk+JvKGuvQqfUk6Ecga8xlS2bAqwLdAAd6DRK0hIiGZYbS3KckeTYCBKiDcRFGbAYtQeclR+wt9P8yd/x1BQTUTiRmLNuIivCTKfXRqfbRqfHhtVjw+q1d63bET023B4rQbcVAl72T7mmVeowq6OIUMSjk8yo/WYErwm/S4fLHcTh9tPo8VPhDbDUG4kojUCpGEx6ZCWxhgZUUiVtnVU0uVT49rNb8msQfBGoRTNGIRWjMhKT1kS0wUy8yUJSlJnkKDMpcWZMhn2J5URJxB8M4Pa7cfhd2DwOOj1WWl3tNDlaaXS0sKN5D8uq1oaPSTTGkR+bzaDYXIbE55NgjOshLAnGOGbnxjE7dxqiKFLRWcP2pt0Ut5Syono9C7rmIkVqIsiMSiPdnEKaOYkUUyJJkUfW9Xg4KBQKjJoIjJoIREnE6QuFR2pzddDhtoa9IQ/kFLF69WrOPfdcLr30Upqamli/fj0TJ07c7xoCCTEh4Q4ERGqa7YwYNYY3Xv8XV1xxBSqVinnz5jFhQs9W3sSJE7nnnnu466670Gg0LFiwgOnTp7N9+3YyMjK48cYb8fl8vPzyy0RH9x/9IjIyksTERFauXMnkyZP57LPPWLduHeeddx7PP/88l19+OVqtll/84hdccskljBs3rs/zHO51jwcmyUp6QiR48qm2fsb/tn3GDSN/ckJtOBHIgnSKIkoiq6s38v62L2l0NaEKGPHXDcHdnIxdoSY3NYJzJ0WTl2YhN81CUkzEYYWCCYpBGh0tVFZtYe+aebQJIs4Rw7EpPbQvfrJPd3AIjWtYtCYsehOZljRGJg0lWm8hPiKGuK7IBsaDVLa+gI/KzlrK2qsoa6+ivKOaensTjZJIIxChtpCssTDeFyC+vZmEznbi/EHM8VkYcoZhyB2FOikbp9+D1WvH7nXi9Lvw+DtoCDZTUx+K7B1y9VaiVWkxqPVEaiOw6Ewkxcb3WRF3O1Xsba+itK2CrQ27WFYZEql9XXODKEochEm7zylDoVCQE51BTnQGFw86G1EUqbbWU9JWTnlHNZUdNXxXtgz/fpOD9SodsYaoUBw+QxTReksosGswEm/AF3bIOBaipRAURGqNRGqNeANeOj1dDxleOxatCbPO1GeL6bLLLuPee+/lm2++QaPRMGLEiH5zFKUlGGloc5E7eCzjJxZz6aWXEggEmDp1Ktdee22PsoMGDeKGG27gJz/5CSaTieTkZAAmT57Mu+++y3nnnYdGo6GoqCic9rw/uvMd/eUvfyEqKoq//OUvxMfHs3v3bi6//HKCwSBTp07lkksuoa6urs9zHMl1jwfTiuJ4Z0E5s0fP4MuSRRTEZjMhbdQJt+N4IkgH83c9CamtrWXWrFksWrSoV0qL0x1RFPlqxzo+2f0FdqkN0R1BoD6HTEMho/ITGJEfR0FGNNrD8PIKikGqOmspaatgb3slVR211NmbekTFjlDpiDfGEW0IVYxRejMWXcgV3KIzYdZFYtZGojnM2HEun5vKzhoqOmqo6FrW2RoRuwKXmnUmcqLSyezqKsuOTidGHxXu1mmwN1NZt4Pq6q3UtdfQ4nPQqVJgUykIHmFlLQgC0ToLCcZYkk2JpJuTybCkkGVJQ6fe19aTJIl6exM7mvawozn0cvpcCAhkRqWGxakgNgfNQbpXRFGk2dlKvb2JenvIdb3V1R6KWO4OzaOSkLgz5xpSstNCdnanylCqUCtUqJVqNEo1GqWmx2TaI8EX9NPh7sThc6EUlEQbLEfdahNFiaZ2Fw63H4tRQ6xFf9pMgD4QxcXFRzzm1F3XvTk3n/TbXuTnL27hytl5FCu/pKKzht+d8UvyY7OPscUDh9xCOkWwOrzMW72JRXXf4Nc3I3kMpElTmV0wifGXJhIVqTv4SbqQJIk6WyObGnawvWk3e1rL8HTN1+mOWj3EEI9x2wqS9FEMveQBzNHJR2W/w+ekwd5MXdf8mhprA9XWOtpcHeEyFp2JrKh0xqYUdUXNzghHbfAEvFR21LKudgsVHTVUd4Ym8O4fDcISYSIhLokCEYwOB4b2ZiJcdiKCIgatEVNiNsakPAzJ+Wjj0pAUCgLBAJ6u/Eh2376I3i2uNprsLayu2RgO9SMgkGZOJj8mi0FxeQyJzyfFlEiKKZGz86YjiiLlHdVsbdzFtqbdfLFnIZ/t/g61QkVe1zGFcTnkRWdh0PSMv6dQKEiMjCcxMp6+nnkDYpBOj5X68joSjLEExCBBMYhfDBAQAzh8rrCIA6GWn1KDTq1Dr9KhUaoPq/LXKNUkGOMwB7y0uTpocbZh9zqIi4g5qLj2h0IhkBhjoNXqptPuQ5IgLurHIUrHglh9kKE5MSzbVM9ffvkzHl78V55c/k8eOeMesqLSBtq8Y4IsSCc5e2s7mbdsJ+valqGIq0ap1TA+6kxunnQeUcZDDyoqSRKlbRWsqtnI+rqtYRfqFFMi0zLHUxibS2FsDjGGKHxNFdS//QgqcxzJVz2O0nBwzzBPV8XV6mqnxdlGs7ON5q44dU2OVhy+fQnGVAoVKaZEBsXmkmZOJjMqlSxLWjjYaCAYoNpax8b6bextq6KsvZJae2N48qpZG0lmVCrnJJxBmjmZ1C6X7x8GWZUkiUBHA+7KHXhqivFU7yKweyMuwK3SoEnMQpuUQ1RSDolJuaiTMnu5qkuSRIfbSkVnDWVdXXWrajaysHwFEIroPSyhkOGJgxkSn09uTCa5MZlcOuQ83H4PxS2lbG/aw66WEj4u/hppV1ckcGM8GVGpZJhDcfqSIxOIj4jpt4WpUiiJNUTTomzCqInos0xQDOIL+vAG/XgDPrwBL06/O3y8Qa0nQmNAr9IdsgjoVFqSIxOw+5y0uzqotTZ0tY5NRyQkgiAQaw6JUIfNiyDwo2kpHS2i18WM0Wm88MEWGpsDPDz9Lh77/hkeX/Isj0y/m+zojIE28aiRu+xOQiRJYntZKx8sLGFH+1Y0GXsQVH4mp0zi5rGX9PLwOhBNjha+r1jN8sq1tLjaUSlUFCUOYnTSMEYmD+mVEiHg6KDu3/eDoCDlxj+jigzt9/g9NDpaaXK20ORo3a9LKSRCTp+rx3mUgoLYrjGjBGMcica4rmR2ScRHxITHaERRpN7eFB4vKmuvpLKzNtzyMWmN5ERnkhOd3tVll0GU7siDiwZsrXjqSvDW7sFTvxdfY3l4Mq+g1qFJyESbmI02KQdtUjbqmJReIiWKIpWdtexsLmFH8x52tZTiDXhRCgryYrIoShxMUUIhOdEZPcaiXH43e9sqKW2roLyjmqrO2vAE5W4sOhMxhihi9FHhrtHwS2fGUW+lsLAQhUKxX86j0C0s0DvKdyAYwBXw4PK7cfndSJKESqEMhXHSGFEpD/2ZNCAGw7+1TqUlPiLmiD29JEkKt5RizTqiTIfewj+VkCSJ3bt3H5Muu6IbH4b0UVz/2DfMHp/Bz+YW0exo5fffP4Pd5+T/Jt3WI/L8qYgsSCcZu6va+c+XxeyoqcGQuwvR2EJOVCY/H3fNIc/QFkWRDfXb+HbvErY37UEQBIoSBjElfSxjU4f3m65BEoPU/Pd3VLdW4ppxOXWiJ5QEz95Ih9vao6xBrSfOEE1MRDSx+tDge6whmtiIKOIMMUTrLSh+MBDuCXiptYZyB1V21nZFXagNR1rQqrRkR6WTE51BbnQmudEZxEXEHNenZ0kM4m+tw9tYjrexDG9DOb6mCiR/qAtTUGtDApWShy6lAG1qASpjVI9zBIIB9rSVh7rqGoup6KhBQkKv0jEoLpfB8XkUxuaSFZXWqwL3+D3U2ZuotzXR7GylxdlOg72FVkcHNp8dr9jTRf/y1HMoTM5HZ4wAlCAqQFKAqETqWlcoQKlQoFKGch2pVUq0agUajQJf0Ivd58TldyMQSmBo0ZsPuRtOkiQcPhetrnYkJGIN0Uc8tiRJoTElu8tPcmwEEf2EVjpVkSSJtrY27Hb7EYdQ2l+Qhl71f0QWzeCp/6xn295W3vrd2aiUCtpdnfx5+T+osdbz09FXcmbO1GP8SU4ccpfdSUJLh5s3vtjJ8i21RKY2EzlyJwoFXDP8CmbnTjukGfWegJfF5Sv5qmQxzc42YgxRXD70QmZkTSTGENXnMaFupb3sailhR9laqlVWAsmRsOdrtCotaaYkihIGhWK4GeNJNMYSb4ztt9tIkiTsXgel7RXU25qoszdSa2ukztpAs7Mt/DSvV+nIsKQwM3syWVFp5ERnkBKZ2EvEDgdPwEurqz3sumzzOnD6XLj9HnxBH4GuaAUKQYFGoUan1mLURGDSGomKiyUuoyD01K9Q4m+r7xKoMrz1e7Gu/wrrmvkAqKOT0WUMQZ9VhD5zGCp9JEPi8xkSn8/VRRdj8zrY0bSbHU2h1tOmhh0AKBVKMswpZEalkRaZjE6KwufQ0dEGdS0KalsMNLSKuL2R+z6UIojBGMBkEdFFBNlQY0VHExatDoRQNIb9wwUJKFCgROgSK1Gkx8RmpSIU0VutEgjgoy5QiyRJ6FRaItSGQ/7+g6KI3eegNliNVqXBqIk4oqgP3XmgGmolok3aQ5r/dCqh0+mO2UOz2BVua8aYNFZsrWfT7mbGDUkk2mDh8Zm/5tnVr/Hqhv+xt62Sm0dfecRjfQOJLEgDTFCU+Hx5Ge98sxsJH7mTq6jzlzAoNpdfjLuBeGPsQc/hCXj5tnQp8/cswO51UBCbw7XD5zI2ZXif7sv19ibW125lU8N2SlrLCUoiKkFJitvDFH0cKYXnEqNORC+Y8AdCidzwA53Q5lBgUznwCy24JSsO0UqHt51mZyuNjmaaHK24usYtIDRelBQZT050BtOzJpBmTibDnEK8MfaIw9aEPPNqqbbWUd2VxrzB3kSnx9arbPeEUI1SjUro6iaUJHyiH08/KTLiDNGkmpPJtKSSM3g0uVMvJVkdgbepAk/1LjzVu3DsWol98wIQFOhSCzDkjyNi0ATU5nhMWiOT0scwKX0MAFWtLazcu4Pi5nIa2uqpaF2PpNwXf08SFSiJQJ8QSXKamRiDhURzDKmWGNJj4kg0R2PWmfr0nJMkCavHRrW1nsrOGva2VbGntYwOT6hFm25OYWzyCNK1hbQ0CWwva2VraT1ubxCzUcPUMbFIcWUsq1iJUqHkJ4PP4/yCWYfkpSeKIvP3LOD97e9i1pm4c/wNDE0oPLQfcT8a25zc8/clZCaZ+NMdU1DKmWr7RPSEBGlUQTymCA2LN9YwbkgiEJpu8cCUO/hg5+d8vOsbKjpquGfizSSbEgfS5MNG7rIbQOpbHPz93U3sqepg6BA1ttjVtHnauXzoBVxcePZBn1YDYpCFZcuZt+trrB4bwxMHc+ngcymMy+1VttnZxoqqdaysWk+NLTTr26KKI8KfjNgZxcVt3xMlOviz9SJcUtekUUFE0DlR6B0IekdoqXMi6FwIin0eXZIooAgY0EkmIlUW4gyxpEclUpiYzrD0NIx6bS97DhV/0E9FRw2lXS7p5e3VNDiaw/sj1HpSTUkkmRJINMYRHxFDjCGKKJ0Zky7yoAP43oAPm9dOm6uTVlcbjY5W6m2NVFnrqLc1dqWnCIlUYVxoEuzQ+ALiDFF46/fiKtuEq2QDvuZKAJRJ+XTEj2aXkEtpg4fyuk7abfsijsda9GQmR5IYr8Rg8aDUu/EKNto87bQ622lzd4ZdvPdHQMCsiyTGEEVcREzXmFwCqaYk0szJPdJjdHtRbm7Yyfq6LexuLQNgWEIB5+bNZFj8YLaWtLJwfTVrdzSgUCiYPiEKZ/RWtjbtIMOcwu3jrjvkQfLy9iqeW/NvGuzNnJ8/i6uGXXTY7v+LN1TzzLubue3iYVw49fRxYz5awl12lw8jf/oFxJx5IwCvfLyNb9dW8Z/HzukVRX5j/XZeXPsm/qCfa4fPPeQelpMBWZAGiO831vDPj7aiUiqYdZaapS1fYFDr+eWkWxgUl3fQ4zfV7+CtLR/SYG9mSHw+Vw67iILYnvGtAsEA6+q28vWepexpD03kU7qjcTfHI3YkIPn0REVqmWkqZ6p7EWszzqc5Lgar1ESrr5FWT3M4KKeAQIw+hhhdLFGaaIxKC1rJjDIQgd+lpdPuo7XTTVO7i5YOF/v1EoXzAOWkmMlJs5CfFoUlsm+Rsnkd7GktC71ayijrqA7Ph4rWW8iNziQ7Op1MSyoZltTjmszPF/RT2SWGe1rLKW4pDadUj4+IYXBsATHKNALWaNoqGzA0bGawVEKi0opHUlGsHERL8mQSM7PJSTWTlWTCaDh4Rd3t4h2KgmGlw22jw9NJu6uT1i4X7GZXG0Gx67cRBFJNSRTE5jA0Pp9hCYVE7jcxt9XZztLKNSwsW0Gbu4NUUxI/GXIeE9JG0drh4YNFJSxYV41Rr+asszSsbl+AzWvn0iHnc8mgsw8phYUn4OWdrR/z3d5lJEcmcMe46w9rfowkSfzu1dXsqe7glQfP7Pf/8WOju677z7XjyB45kbgL7gCgpLqDXz+3jDsvG8HZE3o/OLS7O3l53dtsadxFUcIgbh93Xb/d9icTsiCdYAJBkdc/28EXKysYnB3NsImdzC/9ioKYbH49+baw63N/tLk6+Pem91lft5WkyHiuH/ETRiUN7VEpO3xOPtq6kMUVy/BILkSvnmBLChGeLIamplGQEUVumgWLRWRP207WrnqXMp0KhxD6KxjUenKiM8iKSg+7JaeYEg7Zo8ofEGlqd4YiZzfZqWywUVFvpa7FEY4WHh9toCDDQkqKgMrUSXuwgZLWcursjUCoqy87Kp382GwKYrPJi84i2mA5/C/8GNE9AL+qZA+baoupdlbgVjchqAJIEqh8FuJUaQyKzWOiyUBCw3rce1ZDMEjE4ElETbkMTdyxmysSFIM0OVuptTZ0ddVVsqetHLffg4BAYVwOE9NGMyl9TDhqRFAMsrpmI5/s+oYaWwPZUencOPJyCuNyqGqw8cIHW9hT3cGZE5OQkrezqmYDhbE53D3x5l7emP2xrbGYl9a/Tburk3PyzuDKYReF4ucdAjVNdu786/ecNzGTn80tOuLv5nSiu65755ZppOcUkHDpvUDo/3j7U4uwROp48hdT+jxWkiQWlC3n7S3zUCqUXDnsIs7KmXrMc2QdS2RBOoG4PH6e+s8GNu1p5qKpWUgp21lYvoIpGeO4fey1B6zwJUliUflK3t4yj6AU5CdDzueC/Fk93HbbnFZeWfEpW9s3ICkCBK2xJElDmJ47krGDk8hIjKTJ0cKqmo2sq91CeUc1AKZAkKFJQxiWMZqCuBySIxOOSxO/3WFnZWkxW+tLqbJWYZWaQNU1lhJQEyHFk2XKZHT6IKYVDiFSN3CuwMGgSEW9jV0VbRRXtlNc2U5bV1BavVZJXloUBRkWTHEunKpGSjpKKWkrJygGEQSBTEsqeaYUUtrbiN6zmRi3B3PRGURNvxpV5PF5UhVFkbKOKjY37GBtzWZqbA2oFCompo3i/PxZ4fxKoiiyono97277jDZ3BzOzJ3Pt8EvQK/W8/XUx877fy5hBCUybAW9seQ+VQsWd429kVPLQQ7LD5Xfz7rbP+G7vMqL0Zm4Y+RMmpI46pJbsix9uYdH6Gl5/+CyiT1NX8MOhu6773+3nkBwfTdLVvwvve3/BHt75ZjevP3QW8dH95zBrtDfz6ob/saN5D2nmZG4aeTlDEwpOhPmHjSxIJwib08fv/rWa8jort186lGJxMatrNnLxoLO5aticA96sVo+Nl9a/w6b67QyNL+BnY68hwRi3b7/TyXPfz2OHbT2SIoDGmcaMtDOYM3YkcVF63H4PK6rW833FKva2VwKQF5PFmITBJH7/EZkJeSRd8dAx/bwev4fKzjoqOqop66iivL2aOltjeGwkOTKB/Jhskgyp4Iiivk6guKKd6iZ7qMWhFMhNtTAoK4aCjCgKM6KIMR/aRGBvwIfVa+/ysHPjFwOIktgVakcV8ijTGDBrI9GrQ2NMHXYPJVUd7KnuYE9VByXVHXh8oS6xuCg9gzKiGZwVzaCsGDKSTH0OvHsCXkpayylu2cvu1r3sbasMu7SrUZDg8REfEElPHUpG4WQSjPHERURj0kYel27Hqs5aFpWvZGnFGtwBDyMSB3PlsDlhYfIEvHy080u+2LMIi87EHeOupyhxEF+vquCf87YxqSiJ6y5O57k1r1PVWcvcwedw+ZALD9kTr6S1nNc2vktlZy1D4vO5YcRlZEYd+H6tb3Xw8ycXccWZBVxzzuE7SJxudNd1795zCQk6iZSbnwrva2xzcuufFnLTBUOYO6P3uPH+SJLEurot/GfzR7S42pmQNorrh19KbMSJDRJ7MGRBOgE43H4eemklNU127r9+NCs7v2Bt7WauHX4JFxXOPuCxxS2lPLvqdRw+J9cMv4Rz8s4It14CgSAvL/mWZY0LQO3B6EvnimEXMnv4EARBoN7exFcli1lWuRZPwEuaOZnpmROYlD6aWEM01nVf0LbgDZJv/DO6lPwj+mzdKcSrrfVUd9ZRZa2jqqOWRkdLWHzMOlMouGhUOnkxWeRGZ/Y7udfh8rGrop2d5aGWSWlNJ4FgyLEgxqwjJ8VCVrKJtEQjmkgXLtppcjVRb2+iydFKq7MtHJ3gUFBIKiSfnoBLj+iOQPBEkmRMYmhKJoMzYxmcFUNc1KFHxNifoBikztYYCqDaWUt1awU1bdVYhWCPcmqFiii9uWsirCW8HmOwEKMPOTFE6c1H5ZX4XdkyPt+9ALvPyRmZE7lm+MXhnFJl7VW8uPZN6myNXFR4FlcOm8MXyyt4ff5Orjt3EBefkcnrm97n+4pVDEso5J4JN2PSRR7kqiFEUWRh+Qre3z4fh8/FjKyJXD7sQqL1ln6Peexfq6lssPH6w7N/9B533XXd+/dfTYyvlbTbX+ix/5fPLEGpEPjbPdMP6Xy+gI/5exbwSfG3CMDFg87mooKzDtsJ5XghC9JxxusP8ugrqyip7uChm8ay1vYdSyvXcP2In3BBwawDHvtN6RLe2vwh8RGx/GrSrT2eLteVlvP8qv/g0zWh8Udz3fCfcPawkUDoyXjezq9ZW7sZpULJlPSxzM6dRk50RvhJXBKD1PzzTlSmGJKv/+MhfRarx0ZVZx011nqqrfXUWOupsTXgDezzIkuIiCXDkkpmVCqZllBA1Cj9kUdW8AeClNVZ2VJRw7b6Emqd1bgUrQgG2z5PP0lAGTCilUwYFJHoBCMaQY9S1CIGlXi94HIHsDq8uHxeUAYRlH4UGh9GcxCd0Y+kduAQO8NOHBqlumsMK4fC2BwK43L6nXt1OEiSRNv2JZQueYs2gviHTsYRHUeH20q7uzO83D8tO4REK9EYR4o5iQxzCjnRGeTFZB1WunmXz83Hxd/wZcki9CodN468jKkZ4xAEAV/Ax5tbPmJh2XIKYnP45cRbeH1eGau21fPMr6aTlWxmcflKXt/4HiZtJL+a9NPDclpw+Jx8vPNrvt67BJWg5IKCM7mo8Kw+x5eWba7l6Xc28uc7JjM05+DTHk5nuuu6Dx+5BXPrbjJ++e8e+z9cVMJ/vio+aLfdD2lxtvH21o9ZU7OJOEM014/8CeNSRgx4CCdZkI4jkiTxt/9uYunmWu6/bgxVwjrm7/6Oy4acz2VDL+j3OFEUeXPLh3xTuoRRycO4e/xN4WCcgaDI0199yib7YgQEzkg+k59NvQClUkmzo5X/bf+MVdUb0Kt1nJN7Bufmz+gzS6mrdCONH/yJ+Ln3Yhw0sdf+dlcne9srKWuvCkfj3n+eT6TWGHZ4SDMnk9710h3iAPbB6PTYwlG0dzWX0OhoAUJCkWVJJ06bhEGMQXKbcNu02BwBbE4fTo8ff0AkKEooBQGtRoleqyLSoCHarCPOoicpNoKUOCOp8UY0+0VFD4pBGuzNVHTUUNZeSWl7JeUd1aFxIQTSLSkMictjcHw+g+PzjkqgAvYOWj5/HnfFNoxFZxB77s9QdD2lSpKE2++h3d1Jq6udZmcbTY4W6u1N1FobaHK2AoSjio9MGsK4lJFkRaUdUoVSa2vglXXvsKetnAlpo/jZmGvCwraiaj2vbPgvepWWn4+6mb/9q5ysZBN//PlkACo6avj7yldpdbVz7fC5nJc/87AqsUZHC//b9ilrajZh0hq5dPB5nJUztcdYqMvj5+pHvubi6TnceMGQQz736Uh3XTfvj3cSUbaSrAff67G/odXJbX9eyK1zhnLRtMPPIruzuYQ3Nn1AtbWOYQkF3DjyctLMRxdI+WiQBek4Mn9ZGf/6bAfXnltIfHYbL61/m9k507hl9JX93sQBMciLa95gVc1Gzs+fxXXD54b77Fttdh747J/YNZWYxCQenv0zMmMS8AV8fFL8LfN3f4cgCJyfP4sLC888YIXZ+OGTeOtKSb/rFVAoQ0nomvaws7mE3a1ltLtDUbgVKIg3xJNmSiE7Oo3c2HQyo1IOmkL8cPEEvOxu2cu2xmK2Ne2m2hrKTWNQ6xkcl8eguDwGxeWSGZV21KkVDgdfwMfe9ip2tZSyq7mEPW3l+IN+BAQyLCkh2+LzKIjNOez05JIk0rHsAzpXfIg2JZ+EnzyAymg56HEuv5vy9iqKW/ayvWk3JW0ViJJIcmQCM7MnMTN78kHFct+k1vnEGqL5v8m3hSNGV3fW8fTKV2hzdTDGOIvvFwo8fddUCjND4w1On4t/rPsPG+q2MjZlOLePu+6wxXlvWyX/3fYJO5tLSDTGcXXRxYxPHRm+L+5/YTmSJPH03dMO67ynG9113cd/uQ/9jq/JeuA9BFVP56efP7mQpFgjv/vpoaWz/yFBMciCsuW8v+Nz3H4P5+RO57KhFxxW6/tYIQvScaKqwcYvn1nKqIJ4rro4gUcX/41Bcbn8dtqd/bpdBsQgz61+vc/xpd31tfx+0QsE1DbGRE3jvtlXoBAU7G4p46X1/6HB3syU9LFcO3zuQd2jg04r5c/fSvOIKawzWNjesgtHsCtWnV9LwBaF6LAgOixIrkiQ9tmrUgrERRlIiTOSlWyiID2KIdkxhzS/Zn+6UzVsayruSoFRTkAMoFaoKIjNoShxEEPjC8iOSj+qcELHGn/Qz972SnY2l7CzuYSStopwYr0EYxx50ZnkRGeQHZ1Ohjm1V5qJvnDuXkPzZ8+hjIwm6apHUEcd3ux6u9fButotLK1cw+7WMrRKDWflTuPiwtkHHespaS3nmVWvYfc5uGPc9eHoEg6vk2dW/4vtTXugOZvxMTP49dVjwsdJksSXJYv479ZPiNZbuGfiLYedl0eSJLY07uSdLR9TY2tgcFweN468nMyoVP716Xa+WVPFh386/7ASS55udNd1nz77CNpNn5Dxy3+jjOg5NeSf87by/YYa3v3jeaiUR36v2LwO3t8+n4VlKzBqI7h62BxmZE06ofefLEjHgaAocd/zy2jucPH0PZP406q/EpCC/GX2b3tMWNwfURJ5ce1brKhaxw0jfsL5+40vbaou4anl/0RC5OpBV3PxqAmIoshHu75k3q6viTVE87Mx11CUePCIwhUdNXy69B22OytxqBRIogLRGoPel0x6RBaZ0UnEWgyYIjTotSqUSgFRlPD4gjhcPtptHpraXdQ2O6husiOKEoIA+elRTByaxPRRqcRaelfCoiRSY63vipBdwq7mknCIoQxLKkUJhRQlDmJQbO5RD7D6gn46PTZsHjvugAdf0B/2stMo1ejVulBQUZ3pkOfI9EcgGKC8o5rdrXspaQ1Fk2h3d4b3xxqiSTMnkRSZQHJXPMD4iBhiDdE9uqk8dSU0vv8EglJD0rWPoYlJOSJ7Kjtq+WLPQpZXr0On0nLZkPM5N2/GAeeedHps/H3lq+xuLePyoRdy6eBzEQSBgBjkjU3vh9Ktdybw+vUPEKnv+duWtlXw3OrXaXV1cMXQC5kzaPZhO18ExSCLy1fx3o75OHxOzs6dTqx7BP/6eDevP3wW8VEn/kn9ZKG7rpv/0p9Rr/4vqT9/AU1Mzy61lVvrefI/6/nr3VMpyDh6r7mKjhre2PQ+u1vLyI5K56ZRl/eadH+8kAXpOPDtmipe/HALv75mNMWB71lSuZrHZ/76gD/qf7d+wme7v+PKYRcxd/C54e2b63bz5LJ/IPnV3D3uZ0wpLMDudfDs6tfZ3rSb6ZkTuHnUFQesWEVRZE3tJt7f8i0N7loUIuQ4g4i6s5hRMJrR+UmH7FK9P15/kJLqDraVtrJhdxN7azoRhFCsrQumZhAZ42ZPWzm7W/ZS3Lo3nKIiISKWoQmFDE0IheE50u6/7lTn3V5sddYGGh0t4WgKh4JBrSc+IoakyATSzElkWEK5mWIMUUc8wNvptlLeUUNVZ23Y8aPB3oRvvxTlAgIWvYlYQzRxhmhiI2KIFgWUaz4nNigw5KrH0cUceV9+ra2Bt7fMY3PDTrKj0rlrwk2kHCCumT/o55X1/2VZ1VpmZk/m1tFXoVQokSSJV5Z/xqL6b0mOSOH3Z97Va/K2y+fm1Q3/ZVXNRobGF3DnhBsP6EXXHw6vk/d2zGfB3uVEqI207cjnz9ddwuCsmMM+1+lCd133xevPolz6Gsk3PYUuuaeLd1O7i58+sYA7Li3i3ElHFlX8h0iSxMrqDbyz9WPa3Z1MyxjPNcMvIeogE/ePFlmQjjE+f5Db/ryQWIue6y6P44mlzzOncDbXDL+k32NWVK3n+TX/5sycqdw6+qpwRbintZzfLXyGgEfLXaN/zvRhuTQ5WvjT0hdpcbXz09FXMTN7Ur/nFSWRlVUbeHfb57S6WxE9BizOLO6xLSN+9AUknHX9MfnMoiTS5GhlQ9UeVpTupNJajajtRFDsS0ZXGJfLoK5YcHERR1bBePweilv3sqNpD8Ute6noqA7HmovURJBqTibJGNflJm3BpDViUOvRqjQoBAWiJOIP+nH5PTh8Tjrc1i6ngdau9A/7opGbtEZyozPJj82mMDaH3OjMo2q5iZJIh9tKk6OVJkcLLa5Q7LruNOWtro4eKeOVUmiuVmZMKHNuYWwOmZa0w+o+kSSJNbWbeG3je/gCPm4dczXTMscfsPz7O+bz8a5vGJcygnsm3oxaqcbl8XPNX99Al7edaIOJ30y7k1RzUq9jv69YzRub3kejVPOL8TcwKnnY4X9RdLW6Vr5Js7uZ4dGjuW/G9SeNW/KJpruu+/LtV1AseIHEqx7FkD28RxlJkrj6ka+ZPDyZOy8bcUyv7/F7+HT3t8zfvRCVQsllQy7g3PwZx20cV472fYxZtL6aNquHu68czr83vUSiMe6AHnV1tkZeWf8OhbE53DzqirAY1dub+OP3LxLwargo5WqmD8ul1tbA498/S0AM8ugZv6Qwrv8WV0lrOa9vei+Um8cVibptDDdMmcnEyHpaP/0ec+G4I/p8oiTS6GihoqOa8vZqyjuqqeioCXe/aZRq8tPSUXvzKNkN1uYIskfkcf3MoYed70aSJKo6a9ncsJMtjbvCkRBUChV5MZlcWHhWV96kzKNyLe/GE/BS3VnX5WUXyg67f+qInKgMBsXlMiguj4LY7MMa9FUIilDiPUMUg+N7xyoUJZFOt41mZys1NTso3TCf5mArOwJulletA0K5i0YkDmZS+mhGJA09aKUgCAIT00ZTEJvD86v/zYtr36TaWs/VRXP67FYTBIErh83BpI3kzc0f8pcVL3Hv5J9j0GlI0eZidKbQplvGw4ue5v4pt/f4HIIgMDN7EgWx2Ty7+nWeXP5PLsifxdXDLznsyisvJotfjb6Hez/8F1vZyMOLmrh/yu0n3STOE4lCE+oBEX+QCBNC331GkomapkPvGThUdGodVw6bwxlZk3hz0we8vXUeSyvX8NPRVx2w/jlSZEE6hkiSxBcrK8hNNdNEMQ32Zh6ceke/eUmCYpAX176JRqnml5N+Gr5xXT43Ty79J15fkBTHbK6ZNZJGRwuPf/8sAI/P/HWvJ9RuvAEf/9v2KV+Xfo+WCHxlRYyIH8Gvfj4Ks1FLy5ffoNAa0KYcPICrJEm0uToo6Yq0XdZeRWVHDe5AKISOWqEi3ZLCpPQx5EZnkB2VQZo5KTxe4Z4V4P0Fe/hkyV62lbXymxvGkptqOeA1nT4X25t2s6lhB1sadoZdzTMtqZyfP4uihEIKYnN6RLc+VuhUWvJjs3sMztu9DkraKkLdji17+aJkEZ/t/g4BgTRzMoWxORTE5pAXk0mCMe6IRVEhKIg2WIg2WCiMy2VyZBqNHz6JIX8sqgvuZ3drGVsbi9lUv50V1esx60zMzpnKufkzDurhFq238PAZ9/DGpveZv/s7Oj1Wbh97Xb/jSuflz0Sv0vHy+nd4cvk/eHDqL8hMMrOzIsBfrrifPy17kT8ufZ67J9zEhLRRPY5NMSXyxJn38/aWeXxRsojStgp+NfnWw+7CUylUBGoKuWTMeBY1f8ZvFjzJb6bdGY4y8WNDodEhApK370nfCdEGtpW2HLfrJxrjeGDqHayv28obmz/g0cV/ZWbWJK4efkk4VuKxQBakY0hZrZXqRjs/u3QQH+96jSHx+YxM6j/+15cliyhrr+KXE38avmElSeLlDe/Q5GzBWzqWO2+ZhNPv5ImlLxAUg/z+AGJUZ2vkbytfpdbWQLZ2BDtXxnLBxDx+evGw8Ix3d9UOdOlDeqXl7qbZ0cq2pt3saN7DnpYy2rrcv9UKFZmWVKZmjiM7KoPsqHRSzUkHfPrVa1XceMEQJgxN4qm3N/DAiyt44PoxjBu8bywjKAYpa69iW1MxWxt2UdJegSRJRKj1FCUOZmTSEIYnDj7ufdf9Eak1Mjp5GKO7up+8AR+lbRXsbt3L7pYyllWt5buyZUCoBZMTHcp4m2lJI8OSSkJE7BF5KRnyRhM963raF75JVFIuUybPZUrGOAJikK2Nu1hQtpwPd37JFyWL+Mng8zkv/8COCyqFkp+OvooovYUPdnyOKEncOf6Gfh0QZmRPQqVQ8eK6N3lq+T/JTTiLpZvdRKrM/GHmvTy14iWeWfUat465qleGUo1SzS2jr2RQXB4vrX+bB7/7M/dN+Tl5MYc+vuELhCYoF1gKmFl0P39a+iK/X/IMD0+/+7DOc7rQLUiiz9Pn/oRoA202D/6AiFp1fLziBEFgXOoIihIK+WjX13y5ZyEb67dz29hrGJsy/OAnOARkQTqGrNhah1IhEDTXYK2x8+uht/X7xNzpsfHRzq8YlTyMifs9Za6oWs+amk0omwYxKnUQOSlmnlj2PO2uDn4341f9itG2xmL+tvJV1EoVV+Vcz7/fbWbmmDRuu2RY2IaAvYNARyOm0WeHjwt1i9WxqmYD6+u2UmcLRduO0pkZFJ9HYWwOeTFZZJhTeniFHQ6FmdH8/ZfTePz1tfzpzTXceFkqisgOdjWXUNyyF3cgFKE6OyqdSwadw4jEIeTFZJ6UUYm1Kg1DEwrCwSmDYpBaW0NXvqYqytqr+LT4O8SusS21Uk1aV86iVFMSKaZEUk2JxB+CUJnHXYC3vpSOpe+iTx+ELm0QKoUyLJBVnbX8b9tnvL11HquqN3DPxJtJjIzv93yCIPCTIeehEATe2z4fo8bAzaOu6Lf81MxxSEi8uPZNnEYJyKShzUlWspmHp9/N31f9i1c3/A9f0M95+TN7HT8pfTSppkT+suIlHvv+GX458ZZDrrhc7tB4WoReTaoplj/MupfHvn+GPy19gT+eef8BHTRORwR1d5dd3y0ks1GLJIHT7T/uqTt0ah3XDr+EaRnj+Mfat3h6xctMyxzPzSOvOKRpDgdCFqRjyMbdzQzOjub7qoXkRmf2mSivm492fok/6OeGET8JC4bD6+TNLR+SpE+hvCqd83+axae7v2V70x5+Pva6fud5rKnZxHOrXyfFlMT9k2/n4Rc3kxIXwR0/Gd5DEL11ewDQpRbi8XtYUrmGRWUrqLLWoRAUDInP48zsKQxPGkxKZOIxCSNi8zrY21ZJSVs5pmFlaBPK+V95qLJJioxnSsbYUAUfX9CvS/yR4Al4aXa00ubuwOoJBVr1Bf1ISCgFZTjtdpTeTKwhihhD9BEN1CoVSjK6cjN1txR8QT811vpwmKUaaz3bGotZWrkmfJxaoSLVnESWJY3s6HRyozPJsKT2EGFBEIg77+d4G8po/vRZUm/9Owrdvu65DEsqv5n2C9bUbOKVDf/lwQVP9hrb6YtLBp2Dw+vki5JFpEQmcnZe/3HQpmWOx+V38+9N76POdFHXPI6sZDNalYb7Jv+MZ9e8zpubP0QhKDgn74xex6dbUvjTmQ/w5PJ/8reVr3Ln+BuZkjH2oN9rpyMUjsoUEeqajTFE8cgZ9/DbBU/y9IqXefKsB49ZVJBTAUGpAoUqnMb8h+g0of+NxxcATkwuqXRLCk+ceT/zdn3NJ8XfUNpawa8n30a65cimLIAsSMcMp9tPVaONM2caWGFv4s7xN/Zbtt3dyeLyVczInkzSfk+0H+36CofPSZ50Dk06D7GJAf628EsmpY1mRlbv8D4Amxt28Nzq18mJzuS30+5kU3E7Da1OfnvjOLTqnhWst6EMr1LJ5+17+Grtyzj9brKi0vjp6CuZkDb6qPuCXX43lR01lLWHInyXtVWGw9woBAUZ5hTOyJrImrVeJHs0j//yHMzGo795XD43e9sr9wt1VEuLs+2wzqFUKEmNTCSrSxwKYrNJMycfUUBTjVIdCib7g4yrTp+LOlsjtbZGam0NVHfWsb5uK4srVgGhMayihEGMSx3BmJQiDGo9Cq2B+Dn3UP/WQ7QtfJO4C37R63oT0kaRHZ3Bn5e9yBNLn+eh6XcxOL7/YLmCIHDt8LnU25t4c8uH5ERnkBuT2W/5c/LOoMXRyed8y5KaZUwZcRUAKqWKX078KX9f9S/+vel9jJqIPsXGpIvk0TPu4cnl/+SFtW+gU2kYc5CWUoc91DW1fwqK+IgYfjXpVh7//lne2foJPx1z1QHPcbqh0OqQ+umy02lDVbnbG+hz//FCpVRxxbALKUos5JlVr/HQwr9w+7jrmZQ++sjOd4zt+9FSXm9FksCqKkev0jEhdWS/ZReWLScoBrmo8KzwtnZ3Jwv2LuOMzIls+x6GZMfy9taP0Km0Pbzv9qfGWs/fV71GujmF306/E4Naz7LNdUSbdIwf0rNLQ5IkVjTt5LOMGOy7vmJMchEXDzr7sGfXd+P2e0KpJbrEp6Kjmgb7vtTisYZosqPTmZUzhfyYLLKjM9CpQuJzdpqVXz+3jH98tJXf3DD2sFpioiTSaG+mtCsh3Z7WMmqtDWF37SRjPHnRmczMmkRiZByxhmgsOhMRGgMapQYFAkFJxB3w4PA66fBYaXG202Bvoqqzlo3121lSsRoIjQkNTxzEqKRhDE8afNSCHaEx9HKakCSJFlc7e9sq2NFcwqb67ayr24JWqeHMnKnMKTwLS0o+lolz6Fz1Ccah09Bn9nanjo+I4fGZv+bRxX/jLyte5snZvyFxvxQlP0ShUHDnhBu579sneGHtGzw9+6EDulZfM+Iivtq4nW3ScopbxoSzGqsUSn458RaeWPoCL637D4nGuD7FTafW8eDUO/j9kmd5bvW/eeLM+w/4JN3a6UGjVvbyzBwSn885eWfwTekSZudOO6qn8VMNhcbQb5edXhOqyj3eYJ/7jzeD4vJ4avZv+fvKV3lu9es4fA5m5x5aBPL9kQXpGFHf4gQkKhyljE4p6vfmFkWRReUrGZE0uEeF8VXJ9wSkIBcVzOabD9eSPzjI6qbd3Djysj7Dv/gCPv6+8l/oVFoemHoHBnWo73ZXRRtjBiX0CLdi9zp4ad3bbBA6yFTo+M2sew74RPxDJEmi3t7E7pa97GkrZ29bZY/cRjGGKLKj0pmWMZ7s6HSyo9IPONk1O8XMdecW8sYXu1i5rZ4pw/uuVDwBL/W2RmqsDVRZQ7mV9ncx16t05MdmMTFtNPkxWeREZxySK7aK0FiQRWfqcz5Nk7OVPS1lbG/ezdaGXays3oBCUDA8cRBT0scxNqXomHUXCYJAfEQM8RExTEofgyiJlLZVsKBsOV+Xfs/i8pX8dPRVTJ5yGY7i1bR+/Sqpt/0doQ/PzUitkd9Mu5MHvn2CF9e8yeOzfn3AFp5RE8Ed467nD0ue49Pd33L50Av7LasQFOSI0ykLfs7za97g7+c8Gp6MrVGquXfybTz43Z95ZtW/ePqch8P/x/3RqXU8MOV27vvuTzy35t88ddZv+h2XbOl0EWfR9/mwctmQ81lSsZpPir/hnom39Gvz6YZCq0Psx8tO26PLbmCI0pt55Ix7+Pvq10Jz34J+Lig487DOIQvSMaLd6kaIsOLwOxl1AM+6nS0ldLit3DTy8vC2QDDA9+UrGZsyHK1kQhQlKoObiNKbe3kwdfPBzi+oszfy8PS7w7HrPL4AVoePlLh9T/K1tgaeWvZP2twdnN9i54LhM4k5BDFy+JxsadjJpoadbG/ajbXL/TpSE0FeTBaT0keHYrYdRHz6Y860HJZuruPV+ZuITvBi83fS7Gyl0dFKk6OZenszba6OcHm1QkW6eZ+LeW50JqmmpGMeZ0sQBBKNcSQa45ieNQFREilvr2Zd3RZWVK3nhbVvoFfrmJU9hXPyziD+CCf59odCUFDQ5Up+6eDzeHn927yw9g06hs/lzNm30Pj+E1jXf4Vlwpw+j4+PiOGGkZfxz3X/YVX1xoOO1wxLKGRS2mjm717A7NzpBwwQm5MUw65Nw2gvXMOHO77g+pE/Ce+L1Bq5Z+ItPLL4r7y3bT43j+7bWcKiN/OzMdfwlxUv8XXpEi4s7LvCau5wE99PHiqjNoIzsiayoGw5Lr+7T/E7HRE0+v5bSF1ddgMpSAAalYZ7J/+M59f8m/9smUdcRAzjD9Bb9ENkQTpGONx+dNGdABQl9J/pcn3dVtRKdQ938K1Nxdh9TmZmTcLtDSDoHDR4q7hq2Jw+5zDV25v4cs8iZmZN6hG/LhgMtVi63T6rO+v4/ZJnUSDw0PCr0H/4d7QHCEfjD/pZX7eNpZVr2Na4i6AkEqk1UpRQyJD4AgbH5ZIUmXBYXWxBMUirq51GRwtNjpZQpAJnK82OVjrSW/EEPTy29Ktw+UhNBInGOIbE5ZMUGR/ySjMnkWSMHxCvO4WgIDcmk9yYTK4cdhF7Wsv4bu8yvipZzNel33N+/ix+Mvjc4zLAnhQZz6Nn/JLn17zBO1s/Jn/mvZhzRtK5ch6Rw2ei1PcdOHVa5ng+2/0dX5YsOiQHgiuGXcTq2k18XfI9VxX1LXQAeekW/EvMTIgfzdd7l3Bu/oweUTfyY7M5K2cq35Ut47z8Gf16/I1JKaIoYRDzd3/HuXln9NlKam53kTOsb49SgPGpI/i69Ht2NpccM5fjkx2FSoMY8PW5T6ftmvs3QF12+6NSKLlz/I20Odt5cc2bpM5OOmSvSFmQjhG+gIjC2ElSZOIBIyzvbNrD4LjcHhM719duQa/WUZQwiKZ2D8qYhlCuo34cGebt/AqVQsWVP6g89FoVKqVAp91Lq6udPy59HrVCxWMzfkVkfSVNgMqS0Ot8br+Hb/cu5auSxXR6bMQYoji/YBbjU0eSE51xSAP7/qCfOlsj1dZ6am0N1NoaabA10ehsISjuu0nUSjXxhhjijbEUxOawu9TD3jIf910xjRHpGUftNno8UQiKrjQYeVwz/BI+2P4F83d/x9bGXTx6xj3H1EuwG6VCyR3jrmdXSylf7FnIXTOupe61e+lc/SkxM6/r184ZWRN5Z+sntLk6iDFEHfAaSZHxjEwaypLK1Vwx7MJ+f++C9FCkhDRGsVnaxNelS7h+xKU9ylw6+DwWl6/im71LuXHkZf1e89z8GTy1/J9sayruFWLI4w3ltjpQUNWc6EwEBKo6a380goRSjeTp28suPIY0wC2kbjRKNfdO+Tn/9/XveX3jezxyxj2H9CArC9IxQgBEnZWsqP6765w+F7W2xnCIfwiNWWxtKqYoYRAqpQqjXo0yqok4TUqfk0E73FZWVW/g7LwzenWvKBQC6QkmSmvbeWblF3gDPp44834SI+Ox2jcBoDLte6KVJIklFav537ZPsXrtDE8cxB3511OUMOiAXWGiKFJtraekrZy97ZWUt1dTZ2sIx5VTCgoSI+NJNiUwJqWIpK4o14nGOCx6U48Kz1rg5fanFvHxV82Mv7PgwF/ySUSsIZo7xoe8iZ5e8TJvb/2YO8YdXmxASZJweUKZbO0uH15/EFGUUCoUGHQqTBFaok1atCoNg+PyKO+oRpuQiXHIFGwbvsEy/qJeqQi6KYwNTTmo6Kg5qCABTE4bw6b67VR01PTyDuwmLkpPfLSB8kofI3OHsrJ6PdcNn9ujoonSmxmZNIQ1NZt6TGn4IcMSClEr1exo2tNLkFo6Q91S/XXZQWgMMFIbQbvbetDPdrogqNRI+wXo3Z/wGNJJ0ELqJkpv5qqiOby28T021m9nTErRQY+RBekYoVKLEHQfsGla0VGDhNTDoaDN3UGbq4M5XbmPRIUbhcGBWew7MOXyqrUEJZHZuX0nLivKi+WrvQtQtlfyq0k/DQ/aBx0dIChQGkIiZvM6+MfaN9ncsJOC2Bzun3r7AWfAt7ra2VS/nS2NxT1SR0RqjeREpTM6eRjplmQyzKkkRsYf8pwes1HLbZcU8bf/buSdr4u54fzBh3TcycKIpCHkx2ZT1lbZbxlJkmjpdFNWa6WiPhTNo67FQVO786BdLAqFQFJMBL6MWrRaJTVNduInX4pj5wqsG74ienrfrs/mrla6w+c8pM8xKD4kYHvbKvsVJIDhubGs2t7ALVOHsaF+Gw32JpJ/8J8fnjiY9XVbaXG2EW/sOwW5RqkmOTKBentTr32tXYIUd5C0EwpBgSiePBXw8UZQqqAfQdJ1tZBc3r73DxSzsqcwb9fXLChbLgvSiUTQecADUbr+n0ZrbQ0APVIEV3bUAJAdFYrRVdpeCYC7ve/B5bU1m8mJyiA5snfXG8CooWa+cZSRpstjYtq+uQBBtw2F3oigUNJgb+aPS5+n023l5lFXMDt3Wp/dNE6fi+VV61hetY7StgoA4iJimJA2isFxoSgOcRExRz2B9oxRqewoa+WjxaUkxhg4e0LmUZ3vRLKudgu7Wko5P29fpAKXx09JdQfFlR3sqWqntKYTmzPU9y8IkBgTSqE+LDeWWLMeS6QWU4QGrUaJQhAIBEVcngCdDi8tHS52teyhXNFER3ked6xcTFJMBLeaC2H911gmzUWh7j2XqzvVx6Hme4rRR6FSqGhxtR+w3Ij8OBasq0bpDbXMam2NvQSp+6GsydnaryBBKPWHJ+Dttb3NGhKkGPOBU6o4fC6Mx6Gb9GRFUKqRgn13ySkUAnqt6oTPQzoYSoWS6ZkT+Gz3dzh8zoPGXTyugvT555/z0ksv4ff7ufHGG7nmmmt67N+5cyePPvoofr+fpKQknn76aUymY5sa+0Sh1YWe1BTB/m+iFmcbaqWaKN2+bpb6rrk73Tdxd+rumkp6xaVy+Jzsba/i0iHn9XuNnfb1CAqRll0ZeM4JhCfMiR4nSl0EzY5WHvv+7+G4eH25f3e4rXy2+zsWla/EG/CSYU7hqmFzGJ864rCdGkRRxOl34faHEuV1p1hQCAq0Kg0GtZ4IjYGfXVJEa6ebFz/cSpvVwxVnFYTj752MBMUgnxR/y4c7vyA9Mo143whe/HALuyvbqW6yI0kh8UlLiGTc4ERy0yzkpJrJTDKFn2YPhZLWcr5ftpwUfSL/d8PNbC/tYPW2Bv5Xmc5dkcW8+/JbjDj/UoZk9/T229NaDoSiORwKgiCgVqh6pMDoi+F5cQgCVNSEhMTudfQqo1WGxkd9/TzNd+PyuYjpI4J3m7X3pNgf0uhoJiAGSD5AqKTTDUGpQgr0/50adCrcnpNLkACGxhfwafG3lLdXHzSJ6HETpKamJp555hk+/vhjNBoNV155JePHjyc3d184nSeeeIK7776b6dOn8+STT/L666/zq1/96niZdFyJMIY83Jy9788wnR4bFp2pR4Xe5upAr9KF58802luIVJto9gjsKm9jeP6+uUpl7VVISAzqJySRP+hncfkqBkcPYeN6Fe8t2MONFwwBQPJ58Ku1PLviJXxBP7+f8X+9JhUGxSCf71nIvJ1fERADTE4fy3n5M8g+QBcOhFKv11obqOysoc7WSIO9mRZnG+3uTmxeR3i+Un8IgoBFayI2M5r0SBUfbqtgZfk2fn7ONIZlnTwxyyRJorXTw5q9u5lf+SlWsRmhI4Xd6/PZLe4kQqeiICOayUXJFGREU5ARddgpN7oJBAPM37OAD3d8QWxEDL+ddidxERbS4iycNymL1s4RNPx7C+mdG3nwHwmMyIvjpguHkJ1iRpREFpavIMOccsDJsfvjC/hwBzwHfYI1G7XkplrYXtYEsfTp+ej0h1pnhgO0zgLBAA2OZob24ZHabvNg1KvRqPvv9t3RXAJwwjKZngyExpD6FxyDToXrJBSkzK6Holpbw8AJ0qpVq5gwYQIWiwWAs88+m2+++YY777wzXEYURZzOUB+32+3GbO49QGuz2bDZbD22NTY2Hi+zjxhTZOjmaW7t2y0TwOl3Y1T37Be3ee09vPI6PJ3EG6OxapQs31rXQ5CqO+sByLKk9Xn+7U27cficXDR+OtEdfj5ZspdxQxIZnBWDFPTzpT5IjbWe3067q5cYtbk6+Puqf1HaVsHYlOFcN3xuv267kiRR0VHD5oYd7GjeQ2lbRfhpWKVQkWCMJT4ilpzoDMw6E5HaCAxqPRqlGqVCiYBAUAriDfhw+d3YvA463Faana0EDE2o0620sIfH1yxBvzKGooQhXDJiKjlxJ25Wvs3po67ZQU2znaoGG5UNNsob2/FG70SZUAUBDVG2iQyLHUb+iGgKM6NIi4/sMSH5SAiIQVZVb+DDnV/S5GhhQtoobht9NUZtT6GItRhQTz2fiO/+zS9mRvGfNZ386pklXDAlm/i8Jmqs9fzyMCaNVnbWApDWT/De/RlVEM9H68rQxNKnw0StNdQ1nWTsv/VS0vWf6SunTofdS9QBWkcAK6s3kGSM77fr+lTkYHXdgbrsAAw6NU73yTWGBIQftp195HL6IcdNkJqbm4mL21eZxsfHs23bth5lHnzwQW666Sb+9Kc/odfr+eCDD3qd56233uLFF188XmYeMzTqrrk/jf03kfxBX695RW6/B4Nq383n8LqIMliYOCyJFVvq+OlFQ8Pdbk3OFiI0hl6VUzebG3aiVWkZllBA4RzYUdbGU/9Zz99/OZ0G0csKlZezc6czIqmn40CdrZE/LHkOl9/NLyfe0sMLcH9aXe0sKlvJ8qq1NDvbEBDItKQyK3sKeTFZZEWlkWiMO+r5Qjavg12NZXyzbRO720tY176UdYuXovHFkBcxgklpY8hIMBMXpcccoT0sEZAkCY8viM3pw+rw0m7z0NbppqXTTXOHm4Y2J42tThz73dhajZL4NAeqwRsJ4mBM/DhuGXspMcZj171s9dj4vmI135Yupc3dQYYlld9Ou5MRSUP6PcY4ZBptC99irK6SKb+5kv98XcyXW9ej8WygIKqgxxjiwVhXtxWFoGBw3MHzZA3Pj2Pezk5g39Pv/uxo3kN8REyvVOf7s6ZmE2qFiqKE3k/MnXYvUQeIWF3ZUUtxSynXFF1yTAIAnywcrK4TlKp+vewg1MVZ23zsk/QdLYIgdD2Eigcte9wEqa/M6Pv/eTweDw899BBvvfUWRUVFvPHGGzzwwAO8+uqrPY654YYbuOSSnum/Gxsbe41HDTTdn620phNJkvq8UURJ6uVOHRCDPSYGeoJetEoNsydksmRjLd9vquXciZkAdLptROv6v8n3tJaRH5OFWqlGrYSHbhrHfS8s4/evrSElyYUaemWvbXd18sclzxOURP4w694+xxxanG18sOMLVlStQ5QkihILuXTweYxKHnpEURoOhklrZELGcCZkDEcUJdaVVvJN8SpKA9vY6V/E9uJVBBbmEGxNQaFQYIrQEKFTo9cqUauUKBQCCkFAlCQCARFfIIjHF8TtCeBw+wkEe98YKqVAXJSBhGgDU0ekkBxnJDkuguRYAwtrv+Wr0qWkmBK5feztRxz/74cExSBbG4v5vmIVG+q2EpREhsTnc8voKxmVPPSg87+UhkgM2SNw7lpJ9MzrGDsBVgQ3I3oi2f59Gt+aqjin679zIDwBL9+Xr2RU8rBDmktVkB6FKqqFSCG2V3mX3822pt3Myprc7/Fuv4elVWsYnzqyT6eLdpuHQZn9Z4edt+sr9CodZ+ZMOaitpxKHVNf1Ua92E2fRs6Wkud/6Z6Bw+z1ISBgPMazXcSEhIYENGzaE3zc3NxMfv68JX1JSglarpago5Ap4xRVX8Nxzz/U6j8lkOiUcHZRdQmNzeahuspOR2NtmhSDg/0FlKAj0+JOJkoRSUDA4K5rcVDOfLNnL7PEZKBUCTr+r31htoihSa2vkvPwZ4W0ZSSYevH4cf3hrOc0pXiYGND0ChIqSyPNr/o3T7+Lxmb/uJUaiJPL57oV8sPMLBODs3OmcVzDrmIfLORAKhcCEgiwmFGQhSVezoW477237ghrtDmIKWhmingmeSJxuPx5fEH8gSFCUQuIvCBh0KixqLVqNEoNOTYROhdGgwRyhwWzUEmXSEmPWYzH2bml5Az7+uvJltjYWc07eGVw7fG6/2X8Ph0Z7M4srVrG0Yg0dHiuRWiPn5s1gZs5kUk0H7zLbn4jCCVjLNvLWqjf4qnY9OVEZ3DX2Nl7x7uEfH22lucPFdecOOmAF9Vnxd9h9Ti7umnpwMFrcLQjGTlT2Eb32rahahz/oZ2rmuH6P/3bvUtx+D+cXzOq1T5Ik2m2efrvsdrfsZW3tZi4bcv5hpZA/FTikuu4Av2OsRY/bG8Tp9mM0HPuMykdKd1ddhHoABWnSpEm88MILtLe3o9fr+e677/jDH/4Q3p+RkUFjYyPl5eVkZ2ezaNEihg3re+7NqYBW2dXFoAyyeU9zn4KkVmqwe3vOC1EqVAT2m0uhFBSIkoggCFw2K58/v7WepZtqmDkmHX8w0G/q7nZ3JwExQOIP+u1HFcZz4XkRfFMPGc0ebE5fOMfM9+Wr2NVSyu1jryMzque4lMfv4ZnVr7O5YQfjUkZw46jLiDX0/9R6IhAEgbGpRYxJGcbyqnW8vWUeqzwfcMOInzA7d9oxfSoUJZFnV7/Gtsbd/HzstczM7v+J/1DPt6l+B1+Xfs/2pt0IgsDIpKHcnDWR0UnDjij5oSRJlJgjeT0tmtba9ZyZM5UbR16GRqnm0ZvH89LH2/hwUSkA15/X9/yu/2fvvMOjKtM+fJ9pmZmUmfTeeyAJCaH3KoioKApi1/WzrH11i72srrruuq517b03RJDeWyChJJCekN57Mpk+5/tjkkBIIaEISO7r8pLMnHPmzWTmPO/7vM/z+xU1lvBTzhomB40Z9MpvRe56JEhpLPbAZhO7A7nNZmNl7kbCXO0WHn3RYdLzc846knxH9Nnv1NZhxmyx9VnybbFZeS/9K9zVriw8Rin/wkHE3oLfN56djcR1zfpzKiDVdlrQePRRUXk8Z3SF9MADD3DDDTdgNptZvHgxCQkJ3Hbbbdx7773Ex8fzj3/8g/vvvx9RFHF3d+f5558/U8M543QJPHp7yNlzuIbLp/WuhFPLVZSZK3s8ppQ5oLcc9ThRSOUYrfbCiPEjfQkP0PD56px+FbG7aO4UP+1L3cHkUIdSlOCr0/PXN7bx1B8m4KZ14LusVUS5h/WSKDJaTDy/9XXyGo7wh9FLmRN+em/2p4ogCEwNGcconzje2PMJ7+/7ipKWCv6QvPS0ia2uyd9CemUmtyQvOaVgZLPZ2FGaxg9Zv1LRVo27ypUlIxcyI3RityjuUBFFkey6fL49vJLDtXl4yBTcYVQzM2VZ9zFSqYQ/LrZL6ny7IR8vV3Wv9F2zoZWXd7yD1sGFm5OvZjBUt9ex5cguopwS2d8ho6HF0H0j3FmWRlV7LQ9OvK3fz8uP2atpN+lY0o+yeF2TfTbtoe2t0vBT9hpKWyp4ePId3VYmFxKiKA4Uj7rfs/pmPaF+/af2f2sqWu3Nz/7OJ66YPaN9SAsXLmThwp4fvHfffbf739OmTWPatKF7ZpyLOHcWGkSEqNm5tZ7mNmMvK2EXhSPtx62QnOTqHtUnKrmKDrM9QEkkAjcvGMFj/9vJ8q2FyCTSfvtEuoKaStZ7ZnmkuYwQuSMeylYamw089N+tLF6koaGjiZuTru5183h/31fk1Bdy/4Q/nLTR1m+Bi9KZv0y5ky8zlrM8Zy06Uwf3jL/5pJxfj8VsNfND1q/Ee0dz0Ul4unSRW1/Ie+lfUdJcTrDGn3vH38L4wOSTHp/VZmVPxQF+yd1AfsMRNEoXbk66mtHl5bSnrsBm1CNxOHojFwSBO69MpK5Jzzs/ZRIT4kaI71Gljr9v/i9txnaenvngoHX4PjnwPTKpnFlBM9nPYbtNhKsKi9XC15krCNb4MzZgVJ/n1uoaWJW3sdOmpO9WgupG+3fB261neqe4qZzvD69kYlDKhaNd1wfCQCsk7dEV0rlERVs1SplDn5Pl4zm92v0XMF2b+wF+CmwibD9Y0ecxeosB4zGKvS5KZ9pNHd1pO2cHxx7NholRnowb4cM36/OQIEdv7tsx0mqz7031dbOraa/HR+GCYOrgxbsmIJdL+WT7JhQSh146YhnV2Ww+sotFsfPO6WDUhUSQcG3iIq5LvIJdZem8uecTbIOo5hmI3PoiWoxtzIuccVIrQ6vNyucHf+SJDf+i3aTj3vG38OJFjzA5eMxJBaNmQys/ZP3K3b88zis736PV0MatyUt5Y8GzzI+agVPwSLBZMVbm9zpXKhF4cFkyjko5r39zAFEUqdU18OTGf1HVXsvDk+84YZ9ZF3srDpJWcZAr4+bj72pXYOhSoFhTsIUaXT3LEi/vtxjj84M/IgjCgIrilXX2z76fx9FKUrPVzOupH+Hk4MStyX3bWlwQdHVb94PWWYlUInRLL50rVLbW4O/sM6jv0nBAOk04KdTIJDJEuYFQPxc2ppX1OsZNpQWg4Rh5FjeVBhGRZoNdJFKrdKHJ0FMw8rbL47GJUFFlpK0fbbKuogrLcdpeFquFDrO+uwTX38nKv++bioO2BX2DC9+sy+9REfnd4ZV4qt1YPIAaxLnIpTFzWBp/KdtL9vB15opTulZFq733o799kIEwWky8sO1NluesZVbYJP497wkmB485KSv0goZi/rv7Q+5c8QhfZf6Mn4s3f558B69e/DQXRU7rNoF08LfblRsq8vq8jsbJgesvjiW3tInv9uzkb+teoFnfwqNT7z5ho2IXbcZ23k37gmCNP5dEz0bRqSBiNttoM7bz3eGVJHjH9rBVOZacugJ2laVzWczcAcVeS6vb8NAoUSuPFo98e3glpS0V3DnmujOiqH7+MHCDuVQi4K5VnZMrJD+XwfWLDWvZnSYkggR3lZb6jkZmjI7ngxWHKatpI9D7aNNrl3dMXUdjt/5X15ezXteEh9oNd5UrOlMHBouxO0/u7aZm6ZwovsjIQilvxSbaet3guo49Xhusq2FVpbKv4Cyt9Tj5u2GVtxOkDePLtblU1LZz39IkmoyN5NQXcm3CIuSnoZrst2ZR7DzqdI38mL2aUNdAxgcmn9R1zDb7ezbUijqrzcrLO/5HRk02t6dcy6yTKEsWRZF9VYf4KXsNufWFqGRK5oRPYV7EtF6acV1IlY7IXH0w1RT3e93JSd58lJ7Pt0dWE6Dx5aFJ/9fv9foa09t7P6PNpOORqXfbU8ed3ltSqcDXmSvQW4zceIxh3/Hnf37wR1yVmhMWIxSUNxPmr+3+uaixlJ9z1jE9dEKv1fwFh8iAKySwp+3qms6dgGQwG2joaBq0H9LwCuk04uXkTl17PdOTA5BIBDbsLe3xvHen0GRNe93Rcxztj3VVonQHLV1Dj3MXTY/ATaXFKlqpbm7ieJw7JV9ajcc1xnV+fgWVfWZpbqxCZ+rAJtqYkxzJjQvi2Hqggife2UV6eTbAoFR5z0UEQeDW5CVEuoXw1t5PqT3uPRwsLg72SUSXS+5g+T5rFQers7ht9LKTCkYZ1dn8dd0/eHHbmzR2NHFT0lW8fek/uCV5yQmDh8IrGFNtSZ/PHag6zF/WPY/FvRBbfRBPT3940MEIYFXeRvZWHOTahMu7qzHbOlN1HTSyrmgbcyOm9hANPpb9VYfJbShi8YgFAxYjtOpMlNe2Ex1sn6TZRBvvpn2Bi4NTL9+lC5OBq+wAPDSqcypl16XVOVhFjeGAdBrxcvSkWlePq4uSsXHebNhbhtlydD/DVaVBLpVT1XZsQHJHQKC63f6H69Ieqz4maAHIpBIun2APFB+vT+N4XDvTgY365h6PKzvL0Y0yGUhkmBoqutN6CqmcxTMjefi60eSWNPLN9n3IJLLzWo5FJpVx38Q/gAhv7fmkzwbtExHQebMubu69D9gf1W21/Ji9hinBY4fcsNmob+al7W/z9y3/RWfq4K6xN/Dqgme4OGrmoNW6Fe7+mJtrekjLlDSX84+tr/P81teRCAKXB1yL8UgcdY39y1sdz+HaPD49+ANj/BNZEHW0b6iqQQeIbK5Zg6NczdUjLun3Gj9mr8ZT7caMsIkDvlZGgf0zPzLcPinbXrKXwqYSrk+88oQaexcCNrMRQT5wObenq4qGFj0229A/92eCyjZ7+nvYMfYs4OvsSZuxHZ2pg4vGh7D7UDWph6u6S7YlggQfR48eKyS5VI6noxuVnaWRvp36cZWtNXBcpXdKWBif5kBqQQFZR1KICz3aoOogU6BxcKa2vb7HORKJBBcHJ5qN7cjd/TDXleEks6eiDJ3FFVOTAnBSKXh+wyFkZhk6gwWnkxQFPRfwcnTnusQreDf9C7YWpzItdPyQzg/U+KOQKjhQmUO4UywmsxWzxYbFasMmit0TValEQCaVIJdJ+LFgDQIC1yYsOuH1jyWt4iBvpH6M2WZhWcLlLIiaeVLpUpnWG2xWLG2N1EisfHd4FbtK01HLlVyfeCXzIqeRX9rKl2ynua235UNfVLXV8q8d7+Dr7MUfx93YY1O6oLwZtVcjeY0F3JK8pF85q6LGEnLrC7kp6aoTFnSkZ9fiqJITHeSK1Wblm0MrCHUNZFJw31JWFxqi2YjkBBMUd40Si1WkrcOExunsl8ZXtNYgCMKgRX6HA9JpxLdzZVHVVktSdDAeWhXrUkt79BD5OHt1B58u/F18Ke/cSHdUqNEqXbq9k47Fy9EduUSG3NXAG98d5NUHpyOTHl3k+rn4dG/IH4u3owfV7bU4eIegLz6Et1yNXCrvsZpKjvEipdyX9JoqXvx4L0/dNh6p9PxdQM8Kn8SmIzv5IvMnJgQmIyCjoUVPXZOe+hY9jS0GmtqMNLcZadHZHVvbOszo9Gb0BjOySBc2tu/j128HsYku2FAm7cHa7MUtT25F46TAzUWJl5saf08ngnxciArU4uvh2H1TF0WRn7LX8GXmcsJdg7l3wi3dk5GTQabxoFoh5ce0z9jTUIBCpuCy2LlcGjOne3Vh7Zw1D8bWo8XQyvNbX0cQBP4y5a7uPruusR/Ir0ERmoe7szezw6f0e50NRTtQSOVMD5nQ7zEAVquNPVnVjI7xQiqVkFq+n1pdAw+NWnxSBSG/R2wmA4Ji4IDUZdnR0GI4RwJSNd6OHoOeZA0HpNNIV6qrsq2GCPcQZo0J5Jv1edQ2deDV6X7p6+zNvqpDWG3WbhHSABcfMmtysNlsSCQSAjV+3YrJxyKVSAlw8cWmspGzqY2ftxZxxYyjDbhBGj+2FO/uVfQQqPFjb8VBFH7TaD+0FWtbI35OXpS39GzSjQvwZ1/DHg4UVfD5mpx+u/vPZdo6TBRX2tW5HZvjKbCt4Na3PqClxLeXDJiDQoqrswMaRwdcnZUEejvjpJSjUsootxk4oNvMLVeE4q52Qy6TIJVKkAgCgmCvwO3SyitoKeCXSgszw8fhEhpAc5uRhhYDJVVtpB6q7g4EWmcHkqI8mZYcQIF5D99nrWJy0BjuGHv9KUkSFTWW8m3RRtKD3HFoOsJlsXO5JGpWDxV5gJoGe4WmxwDW4GD33Xpuy2s06Zt5Yvr9vWa3RRUt1AsFKCQtXJOwpN+Vj81mY3f5flL8E1ErBn7N9NxaWnUmpo6yT962FKfiqtSQ4nd+7meeCUSzAcmJAlKnwkVjq4Ew/7PfHFvcXEaQZvAq/cMB6TTi7ejRYz9o9pggvl6Xx4a9ZVwzNxoAf2dvrDYrtbqG7hmxv4svFpuFWl09Ps5eBGn8WVe4tTtAHUuwawD7KjMZHTuZr9blMjMlsLsBN8w1iDUFW6hsq+mhiRbuFsLGIztpcbcHTENFLuFuwaSW7+/xGl2utaNHy/l+Yz4T4n2JDOy/RPdsI4oiFXXtZBTUc7iogdySJmoajzYZq1Uy5DGuSD3LWRI5HW83NZ5aFe5aJW4uPUuLj6e0WcOBNZtx8+tgaujAN8UjB1ORVkn4w+xpvTbtzRYb5bVt5JU2kVnQwN6sGraW7EYRdohIx3juGnsjMunJNcqWNJfzdeYK0iozUMuUzGrUsXDsUvwS+i7ZP1TUgJNKjo97//sx7UYdz219jfLWav48+Y4+5YR+3VWE3L+QEE0gY/1H9XutI81ltBnbSRlEddyvO4vROjswOtYbk9XMweosZoVOOm3KG78HbCZDn+7Ax3LsCuls027SUd1e10sJZiCGA9JpRCaV4a52pbpzH8fH3ZGECA82pZWxdE4UgiB0b+5VtFZ3B6SuTfTy1qrOgOSHyWqmRlffK40T5hrE5iO7uHJuAPtfq+PrdbncfoX9hhndefPIrSvsEZDivOyWArliB5FyJYaSw8THjWLjkZ3kNRzp9qSJ9ghHJVPi6tuES7Yv7y0/xAt/nHxOyQaJokhuSRNb9peTeri6u8TVzUVJbIgb8yaEEOanIdjXGTcXJSty5Xx28EcWTPdDOwRl8gAXXxykCo40lzGNgfegcuoKCHML7rOCTC6TEOqnIdRPw0XjQ8irO8KTm1YhN/iQsceXx0t38bcbx3brCw6GRn0zX2YsZ2txKiq5kiUjFzI3IIW61+/EsZ/NbKPZyu5D1YyJ8+43Zdesb+G5ra9T0VrNgxNv69P6oqnVwOaiVCQhepYkLBzws5FbXwhAnGfUgL9PWU0badk1XDM3GplUQnZdIWarmQSf3uZ9FzKiyYCgHvgz3GXb0dI+uH3CM0lho73qsy/Nwv4YDkinGW8njx6FBTNGB/Dq1wfIL2smKsi1R0DqKq8O6DRFK2upIsU/sds8r6S5vFdA6vrjdkjqmTM2iNW7S7hyZiQeWhW+zt5olC4crs3rUXbs5+yNl6M76VWHiQ+KRX8kg6RZ1yGXyNhRurc7IMmlciYGpbCtJJVFs8bw0U+FZBTUkxg5uA3JM4nRbGVdagkrthVRWa9DIZOQFO3FVbOiSIz0wNfdsc+bY1cpcnVb3ZACkkQiwcvRvVf5/fGYLCYKmkpYEDXzhNc0WUy8ufdjtEoXXrz0QfbENPHm9wd55M3tvHzf1BNam9tsNn7N38RXh1ZgtVlZGDOby2MvwknhiM2gow4QbX2rVKxPLUGnN3PRuL5vDmUtlbyw9Q1aje38ZcqdJPr0na79Yk0OeBfh6+hDcj9NsF2UtlTi7OB0Qs2+7zbmo5BLuXhiKAAlndWNg1WQuFCwmY1ITpD6lMukyKQCeuPZd449UJWFXCIbkqvv8Hr4NOPp6N7dUwR2gVSpRGBnhn2/pqtooaLtaPGBWq7CXe1KWWchQ6CLL4IgUNrSu+w4RBuATCIjr76Iq2dFYRNFlm+1z0QFQSDeO4aMmuwe8jmCIDAuIMn+eHAc5sZK5O0tjAtIYmtxKh2mo30LC6NnYbFZaVBmoHFS8Mv2otP7Bg0RURTZsLeU255bx/9+zMTZUcF9S5L49Ol5PHbLOOZPCMHPw6nfmbrebJ8pnswejVLmgMk6cIl0Vl0BVpuVEV4DrwLArjhQ2VbDnWOvx0XpzOyxQTx681hKqtv4bkNv2Z9jqW6v44mN/+LjA98R5xnBK/Of4LrEK7oLFrrK2/t6H3R6M1+tzyMu1I0RYb2tQ3aX7ePR9S9htll4auaD/Qaj/LIm1mWnIVG1c8WIi064cq7T1Z+wuqq4qpVN6WVcPDGkO/Vcq2tAIZXjOoD314WIaDIgnCBlB6BykJ31gGSz2dhTvp8RXlFDEsIdDkinGS9HD5oNrd0KCU5qBfERHuw+dLRIwc/Zu1elXaCLb3eRgUKmwM/Ju88+GLlUTphrEHn1RXi5qZmU4Me6PaUYTPYPYLLvCFqN7RQ0FPc4b2rIOKw2K+lK+02kIz+NS6Jno7cYWJW/8ejYXHy4KGIaG4q2kzhKQlp2DW0dg+9bOZ3o9Gae/SCV/3y1Hy83Nf+4axIv3zuV2WODBtz/6UIURTYf2YmTwpGgfpo2B6LVpDuhh0taxUEUUvkJ01LFTeWsyF3PjNCJPeR6Rsd4M26ED+v2lPbbM7W7bB9/WfM85a1V3DPuZv465Y94H3ejFztL+IU+7Ek+/OUwre1G/nDZyJ4mmWYD7+z9nH/vfJdAjR8vzPlbv+kVo9nKK1/uR+lfjouDM5P6cRU+lmZ964BBRRRF3v0pEyeVnKtnH33/Wg1taBycz6lU8bmAbRBFDXBuBKQD1VnUdTSesPfseIYD0mnGs9MzqKHjqJrCmDhvKup0VHdWOfk6e1PVWfjQRYDGj4q2GmydKZdgrT8lTb318ABiPMMpbCrFZDExf0IIOr2Z3Zn2gJfkOxKpRMru8v09zgnWBhDlHsa6in1IPAPR5ewmzC2Isf6jWJ6zjvpj9PWuib8UX2cvsmzrsMraST3Uu5T8TNNhMPPImzvYl1PLbZeP5KW7pzAy3GPQ59tEG58e+J4D1VlcGTd/yH5DTfoWatrr+nTQ7cJoMbGjLI0Uv4R+farAPlt8J+1znBRqrk+8otfzsSFuNLYaet1EbKKNrzNX8O+d7xLg4sPLFz3GlJCxfbsRG+yipBJlz4KF7QcrWLO7hEXTI3oUqByszuJPa/7OhqIdXBozl6dnPNhvak0URf73QwblTTXYnGqZHT55UO9n+wCGkgCb0svIKKjn+vmxOB/j39NhMaD+nZnvnSqiKCKajINaIUkkdrfks4VNtPH94ZW4qbSMGaDopS+GA9Jpxr3zS32sgOqozj2YjAJ7Ks/byYM2Y3sP5e4AF1/MVnN3ui/ENZC6jkba+xBTjfWMxGKzkN9YzIgwdzw0SrYfPJoSTPSOZVdpei/V60tj5lCjqycrOAxDWTaW1nq7JIso8vaez7qPV8qV/HnKnUgkoIpLY2t2zml6dwbPf77aT0l1K4/dMo5Lp4T3cnMdiIKGYh5f/09+ydvAvIjpzD/GRXewrM7fDMC4fqwUADYUbbc3QUcObFHxS94GChqLuSnp6j4bSHUGMxIBFPKj1XYWq4XXd3/E91mrmBE6kadn/mlAgzNLu30CJHXUdj9WVNHCq1/tJzrYlWvn2Vdlte31/GvHOzy35TVkgpSnZj7AdYmLBgwwK3ccYd2eUkaMbUciCMwZoO/oWI7VYzye+mY97/x0iNgQNy4aH9LjOZPFhIP03DGYOyewmADxhHtIYO83G0yv2Zlie8le8huLWRp/6ZDV7YcD0mmmS9G7SX9UBy3Q2xknlZzcEvtNw7PzxnLshrl/pxpuRZs9lRfcWdhQ2kfaLsYjHEEQOFybZ7f4HunL/rw6TGa7JNDUkHE06Js4VJPb47wU/wTCXINYYajEJED7oW14OXlwY9JiMmqy+ebQL93H+jl78/j0+5DJIUe+gu3FveWKzhQ5xY3syqxi2UUxpMQOTsbIYDawtTiVJzf+i0fWv0htRyN3j7uJm5OvHnJj5ZGmMn7JXc/EoJR+Nd9aDW18f3gVI7yiiPHobcbYhb00+2dS/BP7THOJokhadg2Rga7dTc4Gi5EXt7/J9tK9LI2/lDvGXHfCFYml2b7ilmk6pacadDz93m6c1Ar+duMYDNYOPjnwPff/+jT7qw5x9ciF/HPeY8R6Rg543V2Zlbz7UyYpI9ypFrMZGzBqQLXuY3+v/gKS1Sby7y/2YbXauP+apF6TDZPVhPwkHHR/z3SnZAchJWW1ikjOUrqzur2O9/d9RaRbCFNDxg35/OGAdJrp8kVqMR4NSIIgEBGgpaiiGQBPtX1juf6YtN6xKg9Ad6qopI+A5KhQE6YN4lCNfeWSFO2FyWztDnhj/BNxVjiytmBrj/MkgoQbkxbTYGhhY3AQbQc3Ioois8ImMzN0Ij9k/cqqvKP7SSGugdwQeRs2vSP/TX2ff25/u5fG3plgZ2YVMqmEhVMGttSu1TWwvnAbL2x7k1uX/5nXUz+iUd/CdYlX8OrFTzE1ZNyQ9yFKmyt4fuvrODs4cXPSVX0eYxNtvLX3UzosBm5JXtLva3SY9byy8z0cFWpuT1nW53F7s2s4UtnKnHH2HjCdqYPnNv+XjJoc7hhzPVfEzR/U72CqK0OQOyDTeFDb1MFjb+/EZLby15uS2Fy+ibtXPs7KvA1MDhrDqxc/zeIRF5+w0GNfTi0vfZpOVJArCWN16Mx6LjlGz24gjFYToij2qcX3+epsMgvruX1RAn4evZUwjFYTiuEVUg9spk7TTsWJU3YWq62HgstvRYdZz793vIMEgfsm/uGkFDYGNQ1ZtGgRy5Yt45JLLkGlOvGS8UJGLVchFSS0HmOyBxDk68zqXSXYbGJ3rv7YfSZnhSOOclV3U62rUoOTwpHS49QUuoj3iWFFzjo6zHriQu0rrqziBuIj7DIds8InszxnLdXtdT0qnWI9I5kTPoX1hdsI1zfhUXoYVfBI/pCyjHZzBx/t/5YmfQvXxF+GRCJh2ohI3vlmAuGjG8moPsD9q55iQmAyF0VMI7pzpXa6aWjR46FVonI4+vG02KyUt1SR33CE7PoCcuoKuve9PB3dmR02mXEBo4jxjDipL4JNtLH5yC4+3P8tarmSx6bf2z25OBZRFPki4yfSKzO5KemqfhWurTYr/9n5nr06bvr9fV6rpd3IW99nEOjtxMyUIJoNrTy35TXKW6t4YMIfhmSfYawqQOEVQlVDB4+/vZN2g4F5lwi8lP6SvTnVP5GlIxd2txSciLTsGp7/aA9B3s48eEM8j216jnjvmD4bZfui1WBXndc49FSL2La/gm835HPR+GBmjw3q81y92dA9QRvGTvcK6QRFDVabSHuHaUh9bacDg8XIP7a+QVlLJX+ecidejr2rOQfDoALS448/ztdff82rr77K3Llzueaaa4iMHHipf6EiCAKOCjXtx9iSA/h7OmEyW2lqM+DqrEFA6KElJwgC3k6e3cKrgiAQpPHrs/QbYJRPHD9lr+FQTS5jA0bh7+lIfunR682PnMHK3A38mLWaO8de3+Pc60ddSXZdPl/YRHz2LicxeCQyiZQHJvyBD/Z9zfKcteQ1HOGusdfj7eTJ6Bhfsg858O+HL2N1wSbWF25nR2kaHmo3UvwTSPCOIco9rJdUzckgiiJqZyuN1gp+ydlIZVsVR5rLKG2uwNxp365RuhDrEcGlMXMY4RVFQGeZ/Mlgs9k4UH2Ybw+tpLCphJFe0fxx3I19pqVsoo3PD/7Iitz1zI2YyvzIvvembDYbb+z5hAPVWfxfyrXdjcnHYjRb+cfHe2lpN/LITZNpMjTy9y3/pVHfzF8m38Uo38HLNtlMeoyVhYixc3jy9a1YXEpwHlHMryWtxHvHsDT+UiLdQwd9va37y3nly30E+7rw9G0T+Pzw1+jM+iFZQHTthR6775V9pJFXvtpHbIgbty/qX72h1diOywVtxNcbm9mIACcUV23vMGET+U117FqN7by8/W3yGoq4f8Kt/Zo0DoZBBaTk5GSSk5NpbW1lxYoV3HnnnXh5eXH99dczf/78k37x3ytquYoOc09Pki4tu7omPe4aFS5K517OsD5OnhQ2HfVQCtL6s/nIrj4N+aI8wlHLVaRXZjI2YBRh/lpyS44WUriqNMwJn8KvBZtZEDWzx8xYKXPgr1P+yCOrnuG/xhIeL8skJDAeqUTKH0ZfQ5R7GB/s+5oHf7VbIExMTmRXZhXZ+R1cn3QlV41YwO7y/aSW72dj0Y7uAgB3lSsBGh+8HT1xU2vRODjjqFCjkCq6Nzdtog2T1YzBYkRn6qDN1E6zvpUGfTP1ugZqdQ0YrSakUfDJwVQcFWpCtYFcFDmdMNcgItyC8XbyPKWVmdVmpaiplL0VB9lRspe6jkY81G7cNfYGpoaM63OF1W7S8WbqJ6RVZnBRxDRuTr66zzFYrBZe3/MxO0vTuCb+sj6tKDoMZp7/aA9ZRxp46NrRSBxbeXTDm1hsFp6Yfv+gVyHd1yvcj2iz8O/cZszhuYiKdnycw3gg4dZB9Ud1IXb2tH2w4jBxoe48dss4dpTvZHPxLq6Imz9gxeHxFDeXA/Z2BoDS6lae/WA3nloVj948Frms783uDpOeDrMej0HsU11IiF0B6QQrpC4vpC5NuzNNZWs1/+j077pv/K1MCBx9Stcb9M5ha2sry5cv57vvvsPZ2Zn58+ezfPlyNm3axEsvvXRKg/i9oZIpMZh7akl1SXo0ttofd1W60HycAZyXkwe7y/d3C68Ga/wxWIzU6hp6NRjKJFKSfEeQVpmBzWYj1M+FbQcq0OnNOHZaR1w54mK2lKTywb6veWLG/T1utF5OHjw26Q6e2fwqT+38H/dPtc/KBUFgWuh44r1j+CLjJ37OWYdcuglNrD+fbTMyIf4KlHIl00MnMD10AiarmYKGYgoaiylpLqeitZqCxhJ0x60Q+0NAwNnBETeVFm8nT+K9Y/B28mT99kZKim389dZZRAf3X102EFablWZDK3W6Bqrb6yhvraKosZSCxmIMFiMSQUKCdwzXJi5irP+oPgsHRFEkrTKD99K/pNXQxs1JVzMvcnqfwajdqONfO9/hcG0e1yYs4rLYub2OqarX8fxHeyitaeP+pcnIPWp4cuPHaByceXLG/T0knwaDzSayeevPbPJzo05diI+jF9cnXUeKX8KQgrbFauPtHzJYs7uESQl+PLAsmW2lO/lg/9ck+8UP6HfUF5k1uXg7eaJVaaiq1/H4/3Yhk0p4+v8mDDh7L2u1p6jPZ0+uM4HY2eB9oqKGslr7VkGA55lfYe4sTeN/aZ8jl8h4csYDQ55I9cWgAtKf/vQntm7dyvTp03nqqadISkoC4JprrmHixKE1Pl0IOMgUGI/r8O/qQm/pdNrUKF16OZL6OHlhE23UdzTi7eTZ7c5Z3FTWZ8f7uIAkdpSmcag2lxBf+4yyuKq1uxvf2cGJ6xOv4O29n7EqbyOXRM/ucX6IfxwPu8TyVvNhnt/6GvMiprM04VLUchVuai13j7+Jy+MuYmXuRrbYUml2LubWH/cwPjiBkV7RRLmH4u3kSZxXZK+0lMliotXYjs7cgdFiwipaERAQBAG5RI5S7oCTXI2jQt2ten4s430MPPzaNp58Zxd/vXEMo6K8MFnNtBrbaDPqaDO202psp91k/3ebUUebyf7/FmMbLYZWWoxtPZpNZRIZQRo/poWMJ9YzgnjvGJwHSA0VNpbwRcZPZNbkEKjx4y+T7+xXzqagoZhXdr5Lk6GVu8fd1KvCyK44Uca7yzORSgQeu3UMucZd/LxzHdHuYTw0+fY+95kGorShhhdWvUe9tgWlzYGbR13F3MjJfb6fA9HcZuTFT/dyqLCBxTMjWTw7lI8PfMX6ou0k+Y7ggQl/GJLIaauhjYyabOZHTKemsYNH396B2WLjH3dNGlDYFSC/4QgwLBt0PDazESknLmoor2lDIoCf55kzNDRYjHy8/zs2FG0nyj2M+ybc0u10faoMKiBFRkby6KOP4ubWc6Yqk8n48ssvT8tAfk8opAr0lp4rJKfOxr/2jq6A5NzL86gr6FS11eHt5Emgxg+pIKGoqbTPDe5k35Go5Sq2FO9mWexSAAormnvIw8wInci+ykN8fvBHQrSBjPSO7nGNiMlLuet/97IxOoY1BVvYUZbGZTFzmR02GbVCRYCLL7ePuZbrE6/g8S9/oqQjn93CfjYf2QXYV4MBLj74Onvj6eiOm0qLRumMk8IRtVyFUqawBx1BigDYELHZbJhtFpoMLVS116I3G+kw69GZOtCZO7oDTOD4ZnIranhu1wZk+y1YMff7njvKVTg5OOGicMRD7Uq4axBalQY3lRYvR3e8nDzwdvQ44c3aarOyv+oQq/I2cag2FyeFIzclXcXciGl99lRYrBZ+zF7N91m/4qbS8szMPxHhHtLjmNLqVv73YyYZBfWMCHPnhstD+CL7M/IaipgbPpUbkxYPyZTPYDbw3u7lbC3fikRmY3qTnmuveg6N++Bl/rvIKWnkhY/30tZh5v5rEhHcKnlwzdM061u5LGYuS+MvHXKAW12wGavNyki3JP725nb0Bgt/v2Miwb4nDrgHqrLwd/bpbp8Yxs7Rsu+Bi8ryy5sJ8HbuNyV6quyrPMT76V9S39HE5bEXcfXIhUPuNRqIQQWktLQ07rjjjh6PXX311XzzzTeEhw9eOO9CQS6V0WrsefNUyCTIpAI6vf1xrVJDi8E+g+9KrXQJqVa11TDKNw6FVE6Q1p/CxuI+X0chUzApKIXNxbu5cdRiXJ0dyC9r7nGMIAjcNfYGHtvwT17a/haPTbu3x9Ja7uqDe8Is5h3cyOxlf+GbI9v57OAPfHd4JRODUpgclEKMZyRqhYqnrrqSP726ldYKI/dcF4ZZ0ciRpjLKW6s4XJdHY2nzSVmG937/5LgonHBxcCI6wJO6OiuV1WaUUjVjo4MYGxWEm9oFJwdHnBWOOCkch3zTPBaL1UJOfSF7Kw6ysyydFkMrrioN1yYsYk7ElB7mdMeSVZvHe+lfUd5axeTgsdyavKSHMkFVvY6v1+eyKa0MlVLO7YviUfpW8sLulxEEgfsm3MKkoDGDHqfVZmVj4S4+2f8jRrEDrc6d2xtyCR17+ZCDkSiK/LytiI9+OYyrG1w818Z3le/QUNhEhFsIf5r4fyeVgqnTNbAiZz0JnvG8+lE+ZovI3++YSHiA9oTnthraOFyb22slPwzYTPaU3UB7SFabSG5xI1OSBr/XN1ia9S18uP9bdpWlE+Diy9MzHyTGs//+u5NlwIB07733cuTIEcrKyli4cGH34xaLZdinZADkEnl3RVgXgiCgcpDT0SkPo1W6YLFZaDfputNGWqULarmqh+trpFso20r29OmNBHBRxDTWFW5jbeE24kI9OVTY0CPIAagVKh6bdi9Pbvo3z25+lXvG38zYYxQIXKdcRXvmZjT7NvPYogcpbCxhdf5mdpSmsbFoB2q5ihFeUcR6RnDj1T6893UJr31SyAPXjObW0ZO6r2OxWe2pMkMb7SYdBosRg8WI2WrGeoxqhEwiRS6RI5fKUMocUMmVqOUqHDtTeH3J8GQdaeCjX7LYsLqR9B21zJ+gZs5YLRrl0NoQRFGkQd9EaXMlR5pKya0vJLu+EKPFiFwiI8lvJNNCxpPkO7LfmV9FazVfZi5nT/kBPNVu/GXKXYzu9Pyx2UQyC+tZueMIuw9VIZdKWDglnGnjtXyV/T2ZaTmM8IrirrE3DDrNIYoi+6oO8WH699R21GBr1zBGOYcl7auQanzRTl48pPegpd3Iy99sI7P+MK6jmtBJa1lTIjLCK4o/pFxDsu/IkyoasdisvJ76MaIIWTu8kYrwjz9OIthncKnIjUd2YhVtTAkeO+TX/r1zdA+p/3Lu0upWdAYLsSEnt+faFzbRxsaiHXx+8EdMVjNLRi7kspi5Q5biGiwDXvXPf/4zFRUVPP744zz++OPdj0ul0uGy7wGQS2WYrb3TS04qOR16e0A6KjHU3B2QBEEgwMW3RyovxjOctYVbKW4u6zOvHqT1J9kvnpV5G7gs4jZ2ZFRSWtPW6ybgptby7KyHeGnbW7y843/MjZjKsoTLUctVyJzd0Iy/jObt32JImU94YCx/HHcjt45eSkZ1NvuqDnG4Jpe9FQftFwsHqVXFy7t34ZflybioULyd3bpTdY4KFT5OnihlDsilcuQSGVKJtNdNThRFRFHEIlqx2uz/6c162oztWGwWLDZr9/+lzlZuXOJNXqmCnZkVfJ26nW/2bCfQx4m4EFfCAjQ4OUqx2uxVfCarCb3ZQIdZT5tRR5OhhcaOpu4qvi4CXHyZFjKOBO9YErxjUA6waVzeWsVPWWvYVroHhVTBkpELuSR6NgqpnMLyZnZkVLJlXzm1TXqc1XIWz4xkznh/tlVu5amta5FJZPxh9FJmh08ZdK9UVm0enx9cTn5jEaJBjUPDGO6ZMhG/fW9hMenxuuaxE5q2gb1p8XBtHpty9pNeeQjRUYfCEVxdfLk48GKmBI/F5xQs1G2ijXfTviC7Lh9KRuEsOPPsHyfiN8jNdYPFyMrcDcR7xwy6V+pCQrQYEWQKhAEyAXuz7CoviZGD13wciPKWKt5J+5yc+kJGeEVxW8qyM15sMmBACggIICAggDVr1gwr7w4BB6kCYx8ByVEtp11vvxm6q+xFCI36JkJcjy6xg7T+7CpN617ljPSy7/lk1OT0u9G7dOSl/GXt81RJ9yERHNmyr7xP+3Gt0oWnZj7IlxnLWZW3kdSy/SyKm8essMloJy6iLWMT9avfxf/WfyJIpChlDowNGNW9mmrWt1DUVEpxczllLVUcLi+j2lDMz7m5MIiPhyAICJ0Hiognn95Tg0PnVlgNUNMImxr7PrQr/adROuPt7EWCTxy+zl4EuPgSog04obW2TbRxqCaXVfmb2FeZiUIqZ0HkTCZ4T6G8ysTb3x1mf14tDS0GJAKMivLi+vmxjIv3YW/lPp7Z/iIN+iYmBaVww6jFuKoGZ6mQU1fIt4d+IbM2B8xKTOVxzAqfxHVTHWlb+R8sumZ8lj6Gg3dIn+dbbVbyG4rJqMkiozqHgsZibKIN0SpBYfHiougZzI0dc0pB6NjXejf9SzYd2Ym1MgJvInn2ngm4awa/el2evZYWYxtLRi488cEXIKLJeMKm2D1Z1UQGaof0vveFyWrmx6zV/JSzBpVMyV1jb2BayPjfJAYMGJCuueYavvzyS5KTk3sMputmuW/fvjM+wPMRpcyhV9k3gIujorvKritdU3ucAVyYaxDrC7dR3V6Hr7MXWpWGMNcg9lYc5PLYi/p8vRDXAOZGTGVtwVaiR8xlXWopS+dE9xDr7EIhlXNj0mImB4/hkwPf89H+b/nu8CqmBo8leeIlqFd/RMuelWjHX9rrXK1KQ7IqnuRjLKmPVLbw7vIMMksq0Whg/Cg34iI0CFILRqsJk9XUudKx2m+Ix/j2SAQBiSBBKkiRSqTIuv+TIZPIkEokR/8tSJFKJJ3H2/8vdJ5f26gnt7iZ3JImCsta0RsAmwS1QkmAj5ZAb2f8XZzwclbjrlWidXLAxVGBSt7/x7+ypY5NhbvZXpZKg6EBB4maUEkK1IXw6wEj3+p3A/ZVb2KkJymx3qTEeqNxUrC/6hBPbPqEkuZywlyDuG/CLYPKt4uiyKHaXH7I+tWuU2h1wFwRTbA8njuujMarYitNXy1H6qjB97qnUfr37DFqNrSyv/IQ+6oOkVmTQ4dZb3cpdvTHoTmK5kpn5o4cxa2LE05oBjhYWg1t/Hf3h2TUZGOpDCNUksJTd0/ood59IkqbK/gpZw2Tg8acltLh3yM2i2nAptj6Zj15pU0su+jUXHYP1+bxTtrnVLXVMjV4HDeMuvK0NLwPlgE/la+++ioAv/zyy0CHDXMcaoUaY+eN+Nh9CFdnB0qq7KXeWqULSplDt3ZdFzGd7orZdfndRQ7jA5P5IuMnatrrevngdHFtwuUcrM6iXraDZmMKq3Ye4fJp/d8Ew92CeXrmg+TUFbAqfxNrC7exymbBOcKHiMPLGaUWiA5IIFDjN2AFWKifhufumExGQT3fb8xnzfo6NmwyMDHBl9ljIkiI9EB6hnW1wt1gQuevarXaOFLVSkFZM0UVLZRUt7Izo6pPTyeJAA4KGXKZxC7ZL2vH4liNzaUSwcku62RtdcVal4C+0QejQkGQj5xJie5EBGiJDnYlyMcFqURAFEUya3L4JvUX8hqK8Hb04L4JtzAhcPQJ03M20UZaRQbLs9eQ31iM1KbCVBaDxhjBH6d4EWvJpv3nj2k26HAaORX3OTcjVbsgiiIVbdXsLT9IWsVB8juLX9xUWsYHJjPCM4acTIFfNlfg5uzAk9ckkRx96iuiLtIqMng37QtaDO2YikaS4JbEIzeNRekw+GBnMBt4ddf73dWMw/SNzWwYcIW0fm8pogjTk0+uoEFn6uDTgz+wsWgH3o4ePDbt3h6+Xb8VA35yDhw4MODJ/v7Dud6+cO508WwztvdI0XhoVTS1GjBbbMhlEgJcfHtJA/m7+OCq1HCgOouZYfaCgcnBY/gyczkbi3ZyTcJlfb6mUq7koUm38/iGl9EmHOCLDTImxvvh5Tawr0yMZwQxnhHoTB2kV2ayr3QfGeUH2J+9CrJXIREkeDt54OvkhZejBx6ObriptLiqNGgcnHF2sFe5JUZ6khjpSUlVK6t3F7MpvZyt+yvQOjkwMcGXiQl+jAhzP+Oij1KphIgALRHHVXW1d5iobdLT0KKnuc1IW4eZxo4WaozlNFjKaRDLMdIMgLPgRrBqIrGakQTEeuOhVeGpVaF1duhzH2xfZSbfZ/1KfsMR3FWu3DZ6GTPCJp6wHNZkNbO1eDcrctdT1VaLzOqEvDyMOL2SOYFmfExrsOyooFWQ4Bg9Fu3EK1H4hFLUVEpqwUb2lB+gslMdPtwtmCUjFzLaL55gbQCHihp446sDVNTpmDM2iFsuHYmTauiuuX1R3VbLpwd/YG/FQZwl7nRkjmNSVAwPLhuNXDb4v6/NZuP11I8pb6vm0an3/KYz8fMN0WxEoup7r9BmE1mXWsKoSM8T9nn1xd6Kg7yX9iUtxjYujZnLVSMWDOjvdSYZMCB9+umn/T4nCAJz5/buRB8GtCp7QUFzZ/lwF77ujthEuzVAoLczoa6BbC/d20MaSBAEkv3i2VG6F5PVjEIqt2vG+SWwrnAbi2Iv6nfjPUjrz8OT7+CFrW9CxE6e/ULOv25f0Gfq7ngcFWqmhoxjasg4Wg9sIHfN27SkzKbBy4/y1iqq22rJrivo1V/VxbGVcioXJfGzHejogMZmMxsqD7K2RIJCqiDQU0u4rzuR/h64OTmilDngIHOwV9vJlJ0/K06pjPt4DBYjjaZ66sRaysUqSiwVFOlKutOlCqmcWM8IRvnMZrRf/KD2VSw2K7vL0lmes46S5nI81W7cNnoZ00PH91hRilYzNkMHVoMOm7EDm0FHS3s9G6sPsbG5kDbRjIdBYF6DhfEdJSiFIpCBUKdAHhSLJnku6piJHDE1s6ZsH6lp71PX0YhEkDDCK5L5kTMY45/YLdjb0m7kv18fYP3eUrzd1Dx7+wRGRZ2eVVFNex3Ls9ey6chOZFI5kbLxZOxyYe7YUO5anDgkDx5RFHl/31fsqTjATUlXnZXZ+PmEaDYh0Wj7fO5gfh21TXpuWjBiSNdsMbTywb5v2FWWTrA2gL9M6b/x+7fipAPSMP3joT7qdxTaqbYAdFe+lVS3EujtTLRHOOsKt1HaXNGtygAwITCZDUXbSavIYGKQXRtqUew89la8yC95G1g8YkG/rz3SO5onZtzH85vfpFq2loe/aOTFa65FqRj87Ng5cSaBBem479nIlJuexyHevtEsiiJ6s4FGfTPNhhZajG20GtppM+nQmTroMNt1yPRmA+0mHXoM2JwMODrYy79FREqB0lrYVDvwGORSOapjgpRS3hmspAoUUjlyqRyZRNodyLuq9UxWMwazAZ25g1ZjO82G1l4yRt6OHoS6BjE3YirRHuGEuQYNujG1w6xnY9FOVuVtpL6jEV+llpu9kkgRVYhZ+6nfswmrrgWrvg2bob27XBegXi5lm1ZFurMKi0QgWmdkTKMZb7MaR3dfPEZOROkZiIN3CHKvIIpaKllXmsauLS/T0NGETCIjwTuGxSMWMMY/sYfZn9VqY01qCZ+uykZvtHDljAiWzo0+5b0iURTJqS/g1/zNpJbvRypImR0+BUNZKKu3VXPJpFBuuzx+SAaKNpuN9/d9xbrCbVwWM5eLo2ae0hgvBESzsV/ZoLWpJTir5YyP79u7qy8OVB3mjdSP0Zn1LI2/lEtj5p7WBteTZcBP63PPPcejjz7aqym2i7fffvuMDOp856jiQs+7brCvMzKphNySJiYn+nerJhyozuoRkEZ6ReOhdmNd4dbugBThHsL4gGR+yl7DlOCx/e4lAUR5hPHvBY/x9Jq3qTKmctu3+dw+4SomhSYOqlJGEAQ8F9xJ+XsPUfPjvwm45SUkSkcEQUCtUNkVHDRD01wTRRGzzUKHyUBeeR0ZRdVkldRRXNOIFQsSmRUvdzleHg64aWU4O0uwCRYMZgMGixGj1UiHqYNGSxMmqxmzzYLFZum2ahYEAZkgRSGVo5QrcZSr8HfxYaRXNO5qVzwd3fBx8sLf2XvA0u7+qGyqYNXB5WytzcIgWgk1WLm4sY2Yjlok5NEmkSJzckXq5IrMxR2FTyhSlTMSpSNFNgPLGwrIMdYgiAKyBk/UjaH4xMQTOyesh7V4aXMF20v3smPfR9TpGpBKpIzyieOa+MsY7RffpyX4gbxa3lt+iJLqNhIiPLh9UTxBg+z96Y9mQyvbS/awqWgnZa1VOMpVLIyezcVRM1m1uYqftuWxcEoYt102tJ4lo8XEG6kfs7t8H5fFzGVZwuWnNM4LBZvF2GdTbEu7kd2Hqrh4Uuig1BksVgufZ/zEyrwNBGr8eHz6fedUmf2AAWnChAkAXHRR39Vdw/SNo0KNm0pLyXH7Q3KZlJgQVzLy7dL8biqtvYKu/ECPCjqJRML8yBl8evB78uqLuiuPbkxazMGaLF5P/ZgnZzww4IzGTaXlP5f9hXe3rGFd6Rr+u/d/fJnpxYLYaUwMSkF7At00qcoZ78sfoPLTx6ld8Treix9GOAmfoS4EQUAhlaNQyRkb6czYSPvvZDBZyC1uIrOwnszCeg7tbsZitTfR+rq7ER3iSlyQK5GhWkL9NINKP54urGYje/b9zNriXWSJHQhAQruJGTJXIr2jUYwMRu4RgNzVB6mTtkePSE2jjuUZqWwv20q7pAbRIkesi2CkJplZk6MYE+fdvXpp0rewvWQvW4p3U9pSgUSQEO8dw1WdK6G+ghBAYXkzn6zKZl9uLV5uav56wxgmJpy8FYfO1GFXQC9NI7MmB5toI9IthDvGXMfEoBSUMgdWbi/i6/V5zB0XPORgVK9r5F873qGoqZQbRl05rMgwBPor+96UXobFKjJ37IlTbS2GVv69812y6wqYFzGd6xIXoThLe0X9MWBAmjnTvpRetGgRTU1NHDhwAJlMRmJiIi4upzYD+70T7hZMQadQ5LGMjvHm45VZ1DZ14OWq7q6gq2qr7a6qA5gTPpmfc9by2cEfeHrmnxAEAXe1K/+XsoxXd33AR/u+4dbRSwe8IQiCwP9Nn8eM8jH8Y/mP1Kjy+Wj/t3y8/zsi3EOI944hxiOCcLegPkVGlYExuM++kYZ1H9K8/Xtcp5z+KiilQkZilCeJUfYVn8lsJb+smZziRnJKGjmYV8fmdLuVgVQiEOjtTJi/hlA/DSG+zgT5uODaR7HBqdDUVMnqnZ+xpbmQRpkEJ6vIfLUfc6Jm4BuRguS43iWj2UpReSv5pU1klzaQUXuIDk02EnUbWFWEMIGLR0xlbIw/aqU9NWi1WUmrOMj6oh3srzqEKIpEuoVwS/ISJgQmDyi0WlzVyldrc9mRUYmTSs6tl47g4omhJxWsW43tpFVkkFq+n4yabKw2K56O7lwaM4epweN6rIT35dTyzk+ZjBvhw11XDk1NfF/lId7Y8zEWm4WHJ99Oin/ikMd6IWMv++5Z1CCKImtTS4gOdj2hTmBpcwX/2PYGbcZ27h1/C5ODBy9Z9VsyqATz5s2b+ctf/kJkZCQ2m43S0lJeeeUVxow5N3+pc4FYzwj2VhykoaOph9nbpAQ/Pl6ZxZZ95Vw1K4ppIeP5OvNn1hRs6VH2qpQrWRJ/Ke+kfc7mI7uYEWZXVZ8UNIYjTWX8nLMOR4WapfGXnvDGEBngzv9uv4UfNxfw3c50TE6VVNhaKGhYg8ivwFEvIx8nL7ydPPBQ26vptHHjcajMp2nrVyg8A3GMGX8G3q2jKORSRoS5dwvEiqJIfbOBgvIm8suaKaxoYX9uLRvTyrrPcVTJCfB0wtfTET93R7zd1Xi6qvHQqHDTKHEYxI3aJtrIrDzM6vTvONBRg1UQCJepuDpsElOSLkMQZDS2Gsgu76C6oY6qeh3lte2UVLdSWa/DZrMhdavGIbAQ0bcdrdSNeeFXc1nCFOTHyKy0GtpYX7SdtQVbadQ3o1W6cFnMXKaHjMfPZeA9gLzSJr7dkMfuQ9WoHGQsmRPF5dMihlw9V9tez96Kg+ypOEhOfQGiKOKpdmN+5AwmBCYT4RbS6zNV16Tn5c/TCPJx4U/Xjh50Kb/RYuKLjJ/4NX8TQRp/Hpx027C1xMlg7r1CyiluoqymnXuuHjXgqXn1Rfxj6+s4yBx4ZuZDhLn17dR7LjCogPTqq6/y2WefdcsFHT58mMcff5wffvjhjA7ufKZLYSGzJofpoRO6H/f1cGREmDtrU0u4YkYkrioNE4NS2FC0gyvi5vdwypwZNpFtJXv4aP+3xHhGdK+gliVcjs6k58fs1bSZdNySvOSEG5JymYSrZ0dx0fhglm8tZNXOYjqMejz8jASF2VA6d9BibCCvIRV9H0296nBvHPd+gPuRDbhpvNEqXdCqNLgqNbirtbir3XBXu6IYgmr1YBAEAU9XFZ6uKibEH7ULb24zUlLdSml1G2W1bVTWtXO4qIEt+8o5XgBCrZShcXTASS3HUSVH5SBDqZCikEsxCW3UkE+N5RAdgh6V1Ua8XgXWcbSYAviuxMT7KzbQ1mHqcV2JAN7ujgR6OxEea6TIlkq9sRZ/F18Wj1jK+ICkHtqDNe11rMhZz6biXZitZhK8Y7kleQnJfvED/u0sVhu7D1Xx89YisosbcVTJWTInisumhg+6+VQURY40lZFWeZC95Qe7U8mBLr5cETufsQGjCNEG9DuxEUWR177Zj9li4283julhLT8QOXUFvLX3U6raapkXOZ3rEq847Z+PCwex1wpp3Z4SVA5Spozqfw8op66Q57a+hlbpwuPT7ztpa/HfikF9sgRB6KFdN2LEiNOi6vx7Jkjrj5tKy96Kgz0CEsDCyWG88MledmZUMmWUP4vi5rG9dC8/Zq3mxqSjQpkSQcI9427iL2uf5+Xtb/PMrIdwVKiRCBJuS7kGZwdHfspeQ1lLJfeMuwkvpxNrWGmcHLjh4jiunhXFjoxKNqaVcWBbPTbRGXdNMImRHkSFOOHpBTKlkRZjG036Fhrb6qjJ201rXSmF5g5azHbx1ONxU2nxcfLEz8WHABcfgjR+BGkDTrsltdbZAa2zvffpWMwWK3XNemobO6hvNtDUZqCpzUhLu5H2DjM6vZm61jY6HEoxOZVic6wHEcL1JuJaRIraJlAiC0XpIMNJJSHAy5mRYQ5onR1w1yjxdFXj7abGy1VNaWsZnxz4jj11Bfg4eXJv0i1MDBzdIxDV6Rr49vBKthTvRipImRoyjkuiZp2wKKSmsYN1e0pYl1pKY6sBbzc1t102ktljg7rTfgNhsVrIqssnrTKje6UuCALR7mHcMOpKUvwT+/TY6ovdh6rYn1fH/10ePyhtug6zni8yfmJdwTY8HN14fPp9xHufmoLAMD3N+QxGC9sPVjA50b/fCUJ5SxUvbn8TN5WGp2Y8OGjZqrPJgAGpubkZgJEjR/L++++zdOlSJBIJP/zwA+PHn9nUzfmORJAwPiCJtYXbaDO299ijGR/vS6C3E5+vzmFCvC8BLr7MCJ3I6vxNzAid0KPqxcPRjQcm/oHntr7Oi9ve5JGpd6OUK5EIEpYlXE6Qxp9307/gT2v+zlUjLubiyJmDUuJVOsiYNSaIWWOCaG4zsjermvScWtKyatmUZt+zcVTJCffXEOoXRKjvCKaOGoNi3UtI2xrxu/7vWNRONOlbaOhooqGjibqORmra66hqq2VXWXqPcmtPtRsR7qFEuYcS7RFOiGvgGSkzlcuk+Hk44efR88ZpsBg5UHWYHaWZ7KvMxGyz4OPkSYrBkxEF2QREjMNz2V1IHAZuJAb75vB7+z5n05GdaByc+cPoa5gZNqnH76M3G/gxezW/5G5AAOZHzuDSmDkD+vy0683szKhkc3o5mYX1CAIkR3vxx8WJjI71PmGfT4dZz8HqLPZWZLCvMpMOsx65VE6idyxLRi4k2XfkkJtPRVHkq3V5+Hs6cfHEkBMeu7t8Hx/t+5ZmQyvzIqdzTfylJ1XVOExvjlX63plZhd5oZdaYvtNv7UYd/9j6OnKJjEen3nNeBCMAQRxgqRMTE4MgCH2uhgRBIDs7+4wOrj/Ky8uZNWsWGzZsICDg9Ht/nC5Kmyt4aM3fuS7xCi6NmdPjuT2Hq3n2g1RuWTiCRdMjaDW288CvT+OhduW5WX/uFVR2laXz6q4PCHcN4i9T/9hjxVGna+D9fV+zrzITT0d3roybz5TgsUMyfevCZhMpq20jp7iJgvJmCsqbKa1uw2S2AhAkredul3XopM6kBt6Am5cXvu5qvN3sezddBQaiKNJiaKW0pZLi5nIKGospaCimvsOuguogcyDWI5wRXtGM8Ioi1DXwtDbDgn2/5kB1FnsrDrK/6hAmqxmN0oUJgclMDkjGecsP6PP24Dp1CdrJV51wL04URTYf2cUnB77DYDGyIHoWV8TN7+WXtK/yEO+mfUGDvompweNYmnBpd2/a8TS3GdmTVc2uzCoO5NVisYr4eTgyMyWQGSmBeLkOHCCb9C2kV2ayt+IgmTU5WGwWnBWOjPZLYExAIgnesafUdV9S1crdL2/ijkXxLJjcv85cTXsd76d/xYHqLEK1gdyWsqyXUeEwQ6frXvfRFVHEX/dnnEZMAeCxt3dQ09jBO3+b3ad6yD+3v83+6sM8O/Oh8+rvMOBUOicn55QuvmLFCt566y3MZjM33XQT1157bY/ni4qKePLJJ2lpacHT05N///vfaDTnRyQfDEFaf2I8wllbsIUFUTN73HDHxHkzJs6bz1bnMHaED/6eTtyeci0v7/gfnx38gZuSr+5xrQmBo5EKUl7d/QGPrHuBhybd3t275Onozl+n3MWBqiy+ylzO23s/48uM5UwPncC0kPFD6hmSSASCfVwI9nHhIuylpFabSE2DjtKaNipq29lX6sGYis9JLv6EVw/MRicenQErZBI8XdX4ejji46bG290RH/eRJISOw2e0mg5bOzl1hWTV5XG4No/PM34E7M6zMZ4RxHpGEOkeSphrEKohzqxbDW0UNJaQU19AZnUORU2liIi4KjVMD53A+IBk4jwjEQSoXf4qurw9uM+9Bc2Y/huNu2jsaOatvZ9ysDqLGI9w/m/MtQS49HxfzVYznxz4njUFWwhw8eXvEx/uJRZqtdrIL29mf04t6Tm15JU1IYrg5aZm4ZRwJif6ERmoHTA41ukaSC0/QGr5fvLqixAR8XJ0Z17ENMYEJBLlHnbagvuhIruaxZi4vgsuLFYLv+Rt4NvDK5EKEm5KuoqLIqad9snFMCDI7HtITW0GMgrqWTonus/PyeYju0irzODGUYvPq2AEg9xDMplMbNmyBZ1OB4DVaqW0tJQHHnig33Nqamp45ZVX+OGHH1AoFCxdupRx48YREWFXwRRFkTvvvJNHH32UqVOn8vLLL/POO+/w8MMPn4Zf69xhYcwc/rn9bbaV7OmxlyQIAn9cnMjd/9zEy5+l8eLdUxgbMIr5kTNYlb+JQI0fs8In97jW2IBRPDXjAf614x0eXf8S1yRcxsVRM7vVCkb5xpHoE0tmTQ6r8zezInc9y3PWEujiy2j/BEb5xBHlHjZkcy2pRMDP0+mY/YNIOo6EIP/mBV6K3AVzH6LWIKemQUd1Ywc1jR3UNHSQfaQBnaGnUaGrswO+Ho74ekQzwWM02gjokFVTZSglr6GQ/VWH7O8PAl5OHvi7+OCldkercsFJoUYukSMCJqsJnamDJn0LNbp6yluqaNDbBVGlgoQItxCuGrmAJN+RhLoG9hA4bdz0GbrD23Gbce2ggtH+qkO8vvsjTFYztyQvYW7E1F6CqW3Gdv65/W1y6gtZEDWLZQmXIZfKMVusFFa0kFXUSGZhPVlHGugwWBAEiAzUsuyiGMbG+RDq5zJgEDJYjOwqTWdL8W6y6vIBCNL4c9XISxjrn0igxu+M2AM0tRoQBPB07W1pUNRYwlt7PqWkpYKxAaO4JWlJt4TRMKcfobOoIS2rBlGECfG9J5rtJh2fZfxItEf4eamAMag70wMPPEBZWRl1dXXExcVx8OBBxo4d2NVx586djB8/Hq1WC9iba1evXs3dd98N2Cv11Go1U6dOBeCOO+6gtbW113VaW1t7PV5dXd3ruHOV0X7xhLsG882hX5gYlNKjyshdo+L+pUn8/cM9vPV9BvcuGcX1o66ksq2Gd9O/xMnBkXEBST2uF+keyotz/8b/9n7OJwe+Z0dpGrcmL+2eCQmCQIJPLAk+sTTrW9hVto89FQf4OWcdP2WvQS6VE+kWQpRHGGGuQYS5BuHp6D7km5k6NBGfq/9G9bcvIFv9IqOWPYEstndKp73DRFWDjur6DqobdVTV66is17E/t5YNe8uOOdIZd814wrxkOLrrENStGKVNVLXUkV2b36+GnqNCjbejB7GeEYS4BhDhFkK4W0i/aar2nF007/wR56Q5aCYsGvB3FEWRH7NX81XmzwRr/Hmgn5LldpOOZza/SkVrNctiluFiDubDFTnklTZRWN7S3ejr7+nIlFH+jIryJCHCExfHE6fSDGYDq/I3sTJ3A20mHb5OXiyNv5SJgaNPi5fRiVArZYiifX+rq6rParPyU/Yavj28Eo2DMw9PvoMxw31Fp8yJ7nVdVXaph6vxdFUR0kfv0c8562g36rh12pLz0sNuUAEpOzubtWvX8tRTT3HzzTcjiiJPP/30gOfU1tbi6Xm0isfLy4uMjIzun0tLS/Hw8OAvf/kLWVlZREVF9XCl7eLjjz/m9ddfH+zvc84hESRcm3g5z2x+lRU567hyxMU9nh830pelc6L5al0uvh6OXD07ij9NvI2/b3mN/+x8j/sm3Mr4wOQe52iULjw8+Q52lO7l4wPf88j6F5kYOJrFIxf0SCNpVRrmR81gftQMdKYOsuryOVybR05dASty1nXbijvKVQRpA+wVcRp/grR+BGsDUMoGdiJVhSbge83jVH/9PBUfP4rvNY+h8Oy5yeqkVhCpVvSQx+lCb7RQWddOZb2Oyrp2Kjr/O3xAis7gDDgDQUgkAl5uDnh6yvB0dcDLVY2PqzMB7q74urvgrJYP6stnbq6l7pc3cfCLxGPurQOeY7PZeCf9CzYW7WBy0BjuGHMdCpkCURRp6zBT0xlcy2vb2Nj4Ha1CNZb8ZN7f3Qg04qCQEhGg5ZLJocSEuBEX4oary9BSkEWNJbyy631q2utI8h3J5bFzifGI+E1vNNHB9r2vtOwaZowOpNXQxqu73yezJpeJQSn8YfRSnBRDV5gepjcnutcJcgVGs5X9eXXMGRvU63OgM3WwpmAL4wOTe0iRnU8MKiB5eXkhk8kICQkhLy+P+fPno9frBzynv0KILiwWC3v27OGzzz4jPj6e//znP7zwwgu88MILPc658cYbWbSo50y2urq6137UucxI7xjGBybzQ9avTAhM7tUAec3caKobdXz6azZOajkXTwzlkal3849tb/DKzve4Kekq5kfN6HGOIAhMDh5Lsl88P+esZWXuRnaV7SPFP4EFUTOJ9Yzs8X47KtSM8U/snsmarGZKmysobi6jqKmM0uYKthandq9EBAT8XLwJdw0mwj2EGI9wgrT+vVJVysBYfK9/luovn6Xyk8fwXvxnVMEjB/W+qBxkhAdoCT/OKkIURVp1JirrdFTUtVPVoOsOXEVHWtAZepoaKuRS3DVK3DVKXJ2VaJwUuDg64NLZd6RWyVHJJKi2/BuJzYZl8v9R0WhAEIzYbCI2m4jZasNktmIwWekwmFlV9iMFusOEy1KwFCfybEYaDS0G6ps70But3a8t8ypFHlKJn2ECo5JGE+LrQpi/Bn8v5yGpXx9PZVsNT2/+D2q5iqdmPECcV9SJTzoDxIS4EeDlxLcb8ggPk/LPHW/RaGjhjjHXM7OzWXuY08OJ7nWC3IG80iZMZmufvlbbS/aiNxu47LgCqvOJQQUktVrNihUriImJ4ZtvviEsLKy7JLw/vL29SUtL6/65trYWL6+jb6KnpyfBwcHEx9vdRy+55BLuvffeXtdxcXH5XcgU3ZJ0NZnV2byR+jHPzHqox6avRCJw35IkOvQW3vo+A0EQmD8hhMem3curu97nw/3fUNZSyc3JV/eqnFPLVSyNv4yLo2axKm8Dawu2sbfiYHcp+ZTgMWj7KPlUSOVEuIf02PQURZH6jkZKmsspairjSFMpGTXZbC1JBcDZwYkE7xhG+9ldY7uqyxy8Q/C7+R9Uf/UcVV88i+fFt+OcePL5a0EQ0Dg5oHFyIDa0Z3WaKIq0683UNHRQ29RBXbOe+mY9DS0GGlsNFJY309Ju7LV3Nc0hmysc8/msfRJ7384c8PVlgbnIfY9gLo8kt9aLGucGXJ0dCPByYlSUJ16uqs6qQiUv7H0BP+dInpxx/WlduXxy4HukgpRnZv6p2134bCCVCNx66Uie+WwDj6xdidJBytMzHjzvNsvPB050r5PIHcgpse+TRgf3zjhsK9lDoMbvrFtInAqDCkhPPPEE33zzDQ8//DDfffcd1113HQ8++OCA50ycOJHXXnuNxsZGVCoVa9eu5dlnn+1+PikpicbGRnJycoiJiWHjxo2MGDE0P4/zCa1Kw20py/jPrvf55tAvvYz2ZFIJf70xhec/2sub3x1Eb7BwxYwIHpp0O18d+pmfstdQ1FTK/RNu7XPvwMXBiaXxl7Eodj47S9NYX7iNTw9+z2cZP5DgHcP4gGTGBozqU7OuC0EQ8HR0x9PRvVtrrCtIZdXmk1mTw8HqLHaUpiGXyhnjl8CMsInEe8cg13jhd+Pz1P7wMnW/vIGptgS3WTf0EBw9HQiCgLNagbNaQUSgtt/jLFYbbR0mOgwWdLWVSFd8hclzJLNTljDDBlZRBFG0q4RLJUilAgq5lIK2LL4rPMKUwInccukS1Mr+04E5dYU0G1q5Ofnq0xqMrDYrB6oOc3HkjLMajLqICXfCNeEgHWYr872Hy7nPFoLMgZziRnw9HNE49Uyn17TXkddQdN6rpw8qIIWEhPDnP/+Z1tZW/vOf/wzqwt7e3jzwwAPccMMNmM1mFi9eTEJCArfddhv33nsv8fHxvPHGGzz22GPo9Xp8fHx46aWXTuV3OeeZGJRCRnU2P2avJtI9lBT/hB7Py2VSHrlpLK98uY8PfzlMc7uRmxbEsSzhciLdQ3kz9WMeXvs8N41azMywSX3eBB1kCmaETWRG2EQqWqvZWpzKztI0/pf2Oe+mf8kIryjG+CcyNmDUgE2aXXQFqWmh7kwLHY9NtJHfcITtJXvZUZrGzrJ0/Jy9uThqJtNDJ+Cz9DEa1n1Ey55fMNYcwevyB5E5nfh1TjcyqQRXZyVaJ5Hq9V9jkEqIuOoeZC79q1k0G1p57dcVRLqHcuf4ZSds3O1yaw1zPb3aYHqzAZto66GBeDb5eP93mIR2ws3z+PqXCnydvJiZcu7qof1eEeQK8suaeqmTAByszgLoVQR1vjGogFRUVMQ999xDa2sr3333HTfddBOvv/464eHhA563cOFCFi5c2OOxd999t/vfiYmJfPfddycx7POXW5KXUNxczmu7P+S5OX/u1csil0n407WjcXFU8OPmAuqb9dy/NIkx/on8c95jvJH6Mf9L+5w9FQe4bfQyPBz7brgEux36NQmXsTT+Uo40lbG7fB+p5fv5YN/XfLjvGyLdQxkXkMSEoOR+GzePRyJIiPYIJ9ojnBtGXUlq+X5W5m7kvfQv+f7wKi6Lncvs2Tfg4BtO/a//o+L9h/C6/P5B7yudbnTZO9EXHcB97i0DBiOAbzJXYLAYuWvsDYNSkZB27qdZbdYTHDk0lHIlAgI688D7tL8F5S1VbCnezWUxc1kcdzHPvr+b/3y1H4lEwvTkc7cp/feIzizQ2GrsNvo8lqza/G7ZrvOZQUn2/v3vf+eRRx7B3d0db29vrrvuOp544okzPbbfJQqZgocm345CpuCFrW/QYuhd6i6VCNy+KJ6bFsSx7UAFj729k5Z2Ix5quy7YLclLyKrN50+rn2VN/hZsndVy/SEIAmFuQSxLuJxXL36af89/gqtGXoLJauLTg9/zxxWP8dTGf7OxaEef+nT9IZfKmRw8lufn/IUnpt+Pn4s3H+3/lvtWPkmqowzvG55DolBR9dlTNG7+AtFqOfFFTyNWfRsNa9/HwTccl9HzBjy2Sd/CpiM7mRU2Cf8TqG53Edwp8ZTXh83IqSCTSHFVaajTNZz44DPM9tI9SAQJC6Nn4yCX8tgt4xgZ5sErX6SzM6PybA/vwkGmoKre3gfal55gdl1BZ9P3+VfqfSyDCkjNzc1MmjSp++drr72W9vb2Mzao3zseajf+MvlOmg2tvLjtLYwWU69jBEHgypmR/PWGMRSWN/Pwf7dRUdeORJAwL3I6/5r3OBHuIby/7yue3vQKla2D780KcPFl8YiLeemiR/nvgme4auQlNBtaeXvvZ9z+81/5YN/X1LbXD/p6giAw0juaJ2c8wBPT78NNreWdtM/5276PKbnoGtTx02ne8T0VH/0NU23poK97qjSs+whrRxseC+464V7WluLdWEUbl0TNGvT1g7UBeDt6sK5w22kXG/ZUu3XLLJ1NipsrCHDx7dbAUypkPH7rOCKDXPnnZ+lkHTn7QfNCQJApqOwMSP6ePcvsWwytNBlaCD+Pixm6GLQFqNFo7I6+dXV12GwDz8qHGZgI9xDuHX8LhY0lvLrr/X7TPpMS/XjurknoDGYe/u9Wso/Yb1JeTh48Nu1e7hxzPaUtlTy89nlW5Kw/4WrpeHycPFk84mJemf8kz8x8iNF+Cawr3Ma9q57kjdSPhxSYwF7i/vdZD/PnyXcgl8r5795PeVnRTNmsqzC11lP+wcM0bf8O0Woe0nWHii5nN+2Zm9FOvAIH75ATHp9emUm4a/CQmk0FQeDSmLnkNxwhtXz/yQ+2D1yUzrQZdaf1mieDVJBgsfVc2aocZDxx63i8XFU8/9EeGlrOfmrx944gtSuhAPi49wxIpS32lWqgxq/XeecbgwpIy5Yt49Zbb6WhoYF//etfLFmyhGuuueZMj+13z9iAUdycfDVplRl8uO+bfmfZMcFuvHzvVJzUCh57ewd7s+yrIUEQmBE2kX/Pe4JEnzg+Pfg9z25+lSZ9y5DHIggCMZ7h3Dv+Zt5Y8HfmR85gZ1k69//6NJ8d/AFDHx5JA10rxT+Rl+Y+wr3jb8FsM/NGyRbeiA4mKyKW+i1fUv7un9AXD1x+fbKYm2uoW/UWCp/wQbnc2mw2ihpLiPWMGPJrzQybSLA2gA/3fUO76fQFEJlE1isQnA1iPSOobKshr76ox+Mujgoeu2UcBpOV1789OGxHc4YRZDKa242olbJezsAVndmRoWhWnqsMKiAtXryY++67j4ULF2KxWHj22WdZtmzZmR7bBcG8yOlcGjOXtYVb+TV/U7/H+Xo48s97phDk48xzH+5hx8Gj+XtXlYaHJ93OHWOuo6ChmL+u/QcFDcUnPSY3tZYbkxbz2sXPMDloDD/nrOOBX58hrSLjxCcfg0QiYXLwGF6Z9yT3jLsZBAmfWGt4OTaE9Qoz+V8+Tc33/8TcePr2ImxGPTXfvgiiiPeiBxAGodvXZGjBbLPgexJOplKJlDvHXE+rsY330r86mSH3icFiOCWV7tPFrLDJuKm0/HPH/yhqLOnxXKC3M9fPjyUtu4a07JqzNMILA0EipbXdhMaxt3pKi6ENAQGtw/nfrzmogNTe3s6+fft4+OGHue6669i8eTMdHR0nPnGYQbEs4TLG+CfyyYHvOVST2+9xGicHnrtzElFBrrz0WRq7D1V1PycIAjPDJvHc7D8jl8p4evN/yKw5NbV2N7WWu8bdwLOzHkKtUPHS9rd4ffdHQ14JSCQSpoSM5Z/zHuOvU+4iwC2QXx3hH2FevNuSy/pPHqZm5dtYWupOabw2k57qr5/DVFeG1+UPIHcb3IyxyyHXUdFbQHQwhLkFsXjEAnaWprGzNP2krnE89bpG3FVnv+xbJVfy+PT7kAlSHln/Eu+lf9kjjbtgUihebmp+2FxwFkf5+0eQymnRGXFx6j1JaTG04uzg2MMY8nxlUL/B3/72t25lBhcXuypxX7pzw5wcEkHC3eNuwsfJk9d2f0ibsf+CEbVSzlO3jSciQMNLn6Z17yl1EaT15++zHsbL0Z2Xtr9NcVP5KY8v2iOcF+f8jSvjLmZ76V4eXv1cd9/DUJAIEpL94nl8+v38e94TXBQ5nSKtCx/6uvBI837e+Pphdv/4AvqK/CFf29xUTeXHj2Eoz8Xr8vtRhw++H6Pri2w5hfLty2MvItw1mA/3fX3KqTuDxUhFWw1B2nNjT8DfxYd/znuU2WGT2VC4nXtWPcFL294irSIDQRCZPSaIQ4UNtLQPvkJzmKEhSGW09LdCMrahcRia8eK5yqD6kIqLi3nttdcAcHZ25pFHHuHSSy89owO70FDJldw/4Vb+tu4FPj3wA3eNu6HfY9VKOU/cOp6HX9vG8x/t4ZUHpuGhPTq716o0PDrtHv627gX+s+s9Xrro0R4q4yeDTCpjSfxCUvwTeD31I57b8hpzwqdwXeIVQ/YtAnu++6bkq7k2cRHplZlsK9xOak0OO0wluGz+JyNtDozxTyRx5FwcPQP7LWe16lpoSVtFy+6fEWQKfJY8MqRgBODcKQ460ERgIAxGC01tRub4XcLbh9/glQ3fEOcwGZ3BjN5gwWCyYrJYsVhtiCIIgr1xV6mQ4aSSo3FywNNVRaC3M/6eTuTUFWATbcR4RJ7UeM4ETgpH/pByDVfEzWdNwRY2HtlJWmUGrkoNI7SjQC5wpLKFUVFnXoH8QkSQymltMRLZhzpJq6ENjfL8T9fBIAOSxWKhvb0dJyd7/btOpxvexDwDhLgGsiB6Fity1nNJ9KweVubHo3Fy4PFbxvGnV7fw8ufpPHfnpB5inm4qLXeOuYHnt77GuoKtLIgefDnzQIS7BfPinL/x1aEVrMzdwP6qw9w6eimj/eJP6npyqZzxgcmMD0ymw6xnb/FeduVuJq29ip116cg3pBFphpFKDxJcg/Fy8gRRxKprxlh9BEN5DtisOMZOwH32zchchi6146RwxEHmQO1xfT8ms5XGVoNdJ6/FQEPrUc28xlYDTa0GGluN6I1Hiw/kYX5kWNPZc8AZiahA7SBDqZAil0uRSSVIBLCJdmkjg9FCu96M2XK0MlIuk6CNzUWqkqM0eSJ2yhudK7iptVyTcBlXjbyE/VWH2FC4nR1VW1Emwk9FzQQGXH3OKEz8rpBIadWZ+rQsaTG2ndf6dccyqIB0+eWXc9VVVzFv3jwEQWDdunVcccUVZ3psFySXx1zEmvwtrMrbyB1jrx/w2EBvZ25flMB/vtrPyu1FXDq1p3LGKN84oj3CWV+4/bQFJLA3994w6krGBYzinb2f8+K2N0nxS+CGUVeekkePWq5iWuRUpkVOxWQxcfBIKnsLdpLRWk6W2MA3jQ14VFuI7DARZRKJVnvjOm4hzgkzUHgMXjXAbLHS2GqkscVAY5s92ChsTuzOK6A4bVd3EGrr6N0fppBLcXNxwM1FSaifhtExSrTO9p+1zg60ieG8mfEGd93qybyoqYOyRe8wWKht6qC0uo2csjo2dqzDUu/Jw//dSYivC/MmhDBrTCBKxdCMFc8kMom0Wz1+Y0YOr2/+kVxpJvetOszS+Et7GEcOc+oYkWOxir007MBe1KC9kFJ2t99+OxEREezatQuZTMZDDz3EtGnTzvTYLkicHBwZF5hEavl+bktZdkIr6JkpgWw7UMFnq7OZPMoft+M8dyYEJvPR/m9p6Gg67TPXaI9wXpz7CCvzNvJ91ioe+PVpZodPYVHcvEHp5A2EQqZgTOQUxkROQRRFqtpq2F91mIyqLPbVF7DLakIqdBBFLaNqD5EkFQnWBmA0Walt6qC2SU9dpxp4V4Bp6lzZtHX07oFSREmRKVtQd5jwdlMTG+qGm4sSdxcl7hqV3d5Cq8JRKRswyIiiF1/nu5LTkM984cTfEUEQcFTJCVVpCPXTYHQpREw388ilV1Fb5sDaPaW8/UMGX63N5erZUcybEIJcdm7d6EtLrVjLRvD8spv5NvtHPjnwPVl1Bdw/4dZTThUPY0cn2gOR5riiBpPFhN5i6G5cPt8Z9JRrypQppKSkdKfqmpubu91ghzm9JHjHsrU4laq22hP2FgiCwP8tiuePL23k89U53HP1qB7PdzXLVbfXnZFUikwq47LYuUwLGce3h1eyvnAbG4t2MCtsMpfGzBlQa2+wCIKAn4sPfi4+LIiehdlqZl9ZLjuLD5LdkEt23XK+zFwOZgcsTZ5Ym7yxtbqBKEUiEXB1dsBdo8TXw5G4MHfcXZS4uijtAafTQ+nDzDqKm8t45cZTm2gJgkCoWxDlLUMvZbfarKzI3UC4WzDJAdEIgQLzJ4ZyuKiBz1fn8M5PmazccYT/WxTfpx/O2aCtw8Sa3SWMifUm1MOHhyffwaq8jXx84Dv+s+t9Hpr0f8MrpdNAh80eiFyOK2po7dz3vKCKGj7++GP+9a9/YTbbZ5Zdee3s7OwzOrgLlS6BxLqOhkE1u/l5ODF/YigrtxexaHo4AV5HP5xyiX2Gaj7DOnJd9hqXxszhh6zVrCvcytrCrUwMHM2C6FmnJGsiiiJV9ToO5NdxuKiB3JImaho7ACdgNBqtiMa3BZtzDS1e5Vi8ylFIFIz0imNKSAqj/UagHKDwQhRFKttqTnlV14WLgxMFJ6Fvt61kDzXtdVw/6fYeq7ARYe48d+dE0rJreHf5IZ58ZxeTE/34w2UjcdecXKn66UAURV7/9gB6o4Vr58UA9oDclR7++MB3bCrayazwyWdtjL8X2q327/HxK6QWY5v98QupqOHTTz/lyy+//F37FZ1LdKU5hhJErp4VxbrUEr5ck8vD16d0P95uss+gnBTq0zvIfvB28uTOsddz1YgFrMzbyMaiHWwv3Uu0RzjzI2cwNmDUoJS0bTaRnJJGdmRUsudwNdUN9r43NxclsSFuXDwxhHB/LSF+Lj3y6marmUO1ueytyGBP+X72VR9ALpWT6BNHil88CT6xPZTNbaKNX/M2UdJczi3JS07Le2Cz2U6Yaj0ek9XMN4d+Idw1uNvV91gEQWBMnA+jojz5YVMBX6/PIz2nhmvmxnDJ5LDfPI1ns4m8+1MmOzOquPmSOEL9eppAXhw1kx2laazIXT8ckE4DXQFJ69RzYtUlzqy5kFJ2np6ew8HoN6Sj03ZgKOXUWmcHFk4J47uN+SyeFdl9gyjvlBX5rWXpPRzduDFpMVeNXMCmop2szt/Mf3a9h6tKw9zwqcwOn9znrK6uSc/a1BI2ppVS26RHLpOQGOnJ5dMiSIryxNfDccA9HLlUTpLvSJJ8R/KH5KVk1xeQWr6fvRUHSas4CICrUoOPsycyiZTKtloaOppI9IllVtikfq87FOo7GnFT9nbpHYhf8zZR39HInWMHdp6Vy6QsmRPN1KQA3vkpkw9WHGb1rmJuWBDHhJG+SE7BNn2wtLQb+e/XB9iTVc3l08JZNL235JIgCEwOHsNH+7+lSd+Cax+uxcMMHp3FfqvWOh+3QjJ0rpAupJTdpEmT+OKLL5g1axYODkdno8N7SGeGms5O+MF6FHVxxfQIft1ZzAc/H+aZ2ycgCAL5DUfwdvTAycHxxBc4A6jlKhZEz2J+5Az2Vx9mdf4mvj60gu+zfmVSUAoXR80k1DWQrCMN/LSlkNRDVYhAYqQn186LZfxIH9TKk9sYl0gkjPCKYoRXFDcnXU1ZSyWHanM50lRGna4Bg8VEtHsYKQkJTAoac1rKq202G0eayxgfkDzoc5r1LfyQ9SvJfvHEe8cM6hxfD0eeuHUc6Tm1fLDiEC98vJcwPw1XzIhgUqIfMunpXzGZLTbW7y3ls1+z6TBYuH1RPAsmhfb7vjl19nfpLQZcGQ5Ip0KbRYqTSo5c1nPl3dy9QrqAUnbvvPMOJpOJZ555pvux4T2kM8eR5jIcpAq8HQc2lDseJ7WCay6K5t2fDrEjo5KJCb7k1BUw2i/hxCefYSQSCaP94hntF09FazW/5m9iS3EqW4p342DyorU4ALXJj0XTI5g/MRRvt9ObYhQEgSCt/4C9XaeDgsZidKYORngNvqn184yfMNnM3Dhq8ZBeSxAEUmK9SYr2Yuv+cr5el8vLn6fz/s+HmDUmiKlJ/oT4upxyoG1o0bMpvZxVO49Q16QnNsSNP16V2KdR3LEUNBYjk8iGPLEapjdtZila594l3436ZhzlqnNC9/B0MKiAlJExNFHNYU6N7LoCIt1DT0qbasHEUDallfHW9xk4uXXQZtIx0jv6DIzy5PFz9ibeYTqZZe6UmrMQfEtxiNqHt3MlIbFuuGt7f/HOF7aX7kUmkZHkOziH3Oy6fLYU7+by2IvwPckeLqlEYMboQKYlBZCWU8PqXcX8sLmA7zbl4uZrxC/QjMrZhEJpw1Elx1Xlgr+zDzGeEb1eUxRFmtuMFFa0kFPSyMG8OnJKmgAYGe7OXVcmMjrG64RBrrGjmS3Fuxnrnzhc+n0aaDcLuDr3TuE36Jtx+x01Ig8qIJlMJrZs2YJOZ9foslqtlJaW8sADD5zRwV2ItBhaKWkuZ2n8yUkzSaUSHlw2mgf/s4U3Vm8EF86ZgGSziew+VMXX6/MoqmjBx13NXbMWMSXZj7TK/SzPWcebez7h20O/cFnsXGaETkR+Ht3MOkx6thTvZlzAKBwHUURisVl5L/0rPNRuXBE3/5RfXyIRSIn1QuHaiCoqgwNVh9GLJgpFEJukiBY5AiDITSCxq0MorC64mMNQd4TS0SalvlmP3mjX9JMIEB6g5bp5MUwZ5d+nU2lftJt0vLT9LWyiyJKT/BwP05N2o0BoHyukpo7m01Ydei4wqID0wAMPUFZWRl1dHXFxcRw8eJCxY8ee6bFdkHQpdCd4x570NQK9nXlw2Whe3rEXpdUZpXB29o+6sFhtbDtQwXcb8ymtbsPXw5H7lyYxPTkAaedex+TgsUwKGsP+qsP8kPUr76V/xY9Za1gUN4+ZoRORDcJG4myzKn8TerOBhdGzB3X8L7nrKWup5M+T70ApO7VVoc1mY3vpXn7MWk1FWzVOCkemho4lyXcE4a4htLcKFFe1UVbbZm8cbq+lyVaBzqGMeuUBcDiIRhPAqPAERniOIMxfS7i/Zsj7d7n1hbye+jENHU08NOn2k171DdOTNpPQZ8quQd90xtPQvyWD+pZnZ2ezdu1annrqKW6++WZEUeTpp58+02O7IDlYnY2zwpEw16BTus6EeF+0eSaaa5346xvb+fP1KT36k34LWnUm1qWW8MuOI9Q36wnyceZP145myij/Hrp7XQiCQLLfSJJ8R5BZk8O3h37hvfQv+TlnLVePXMjkoDHnrMR+Y0czP+esZYx/4qB0xarbavn28ErGBowipY8y76GQXZfP++lfU9pSQbA2gHvG3cz4wKQeq0t3Rwj27buwoKa9jg1FO9hYtIMDxpWUNe9kkiYFpS6JcEXwCd9zq83Kodpcfs3fzL7KTDwd3Xl8+r3Eep474rDnOwYLvVRYOkx6mg2tv3kF7ZlkUAHJy8sLmUxGSEgIeXl5zJ8/H71+2Lb4dCOKIpk1OYzwjj7lG68oinRYW5kUM5K0jXru+/cWls6J4rKp4b0cJ08nVpvIocJ6NuwtZcfBSkwWG/HhHtx1ZQKjY7wHVZYsCAIJPrHEe8dwoPowX2X8zOupH/FzzjqWJVxGku/Ic0pwVBRF3k3/Aqto44ZRV57weJto439pnyOTSE+p98lgMfLZwR9YW7AVD7Ub90+4lfGByUNWRvB28mRZwuVcNWIBeysOsqU4lZW5G/g5Zx1quYpwtyACXPzwdHTDUa5GEAT0ZgMN+mbKWirIqS9EbzbgrHBkyciFXBw186QU4IcZGHdNz/e0vNXuh/Z7sC7vYlABSa1Ws2LFCmJiYvjmm28ICwvr9kca5vRR015Ho76ZkV6nvucjImIVbQR4arjxoRm89X0Gn6zKZuWOI1wyOYzZY4L6TAGcDGaLlcNFDaQermZXZhUNLQbUShmzxgRx8aRQQnxPriRVEASSfEeS6BPH7rJ9fJn5My9se5MRXlFcn3jFOaNwvKZgC+mVmdwwajHeg5itri/czuHaPG5Pufak8//lrVX8a8c7VLbWsCBqFkvjLz3lSiu5VM7EoBQmBqXQbtRxoDqLrLp8ihpL2HhkJ0ZLT78jmUSGn7M3k4LGkOgTS7LvyPNqz+984/iAVNopTxX4O7Au72JQAemJJ57g22+/5eGHH+b777/n+uuvHy5oOAPkdcrNxHiEn+DIEyMRJDg7OFHf0Yi7RsVjt4wjo6COr9fl8fHKLD79NZuECA+SoryIC3MjzE8zqJWTKIrUNesprmqloKyZ7OJGsosbMZqsKGQSkqK9uHVhAGNH+uBwmlZiEkHCxKAUxvqPYn3Rdr49vJK/rnuByUFjWJpwGV6OQ7ecOF1k1uTw8f5vSfYdycVRM054fE17HZ8d/IEE71hmnmQj7v6qQ7yy8z0cpAoen34vIwfZuzQUnBwcmRw8hsnBYwD7311n7qDDbABRxEGmwFnhdM6mUH+PHC8TVdJcjoPMAc+z+Pk/3QwYkK6/vmfX+A033IAoikRHR/Prr79yzTXXnPEBXkiUNJcjl8jwd/E5LdeL84xkX2UmJosJhUxBQoQnCRGelFS3sjm9nNTDVXz4y2HAXlHl4arGQ6NE4+SAykGGRBCwiSJGk5W2DhNNbQZqm/QYTfYqLEGAYB8X5owJIinGi4RwD5QOZ674QCaVMS9yOlNDxrE8ey0r8zawu3w/8yKmsShuHs4Og6sCO13k1BXw0va38XP25t7xt5wwVWaz2Xg99WMEQeCOsdedVNpxU9FO3k77jBBNAH+Zchduau1Jjn5oCIKAk8Kxu9l1mN8e9+P2kHLqC4lyD/ldidcOePe47rrrAFi3bh3t7e1ceeWVSKVSli9fjovL76Mz+FyiRlePl6PHkHXQ+mN+5HRSy/fzZebP3DDqyu4bYLCPCzcuiOPGBXE0tOjJK22iqKKVqnodDa16ymvbMZos2GwigkRAqZDipFIQ4OVMcrQ3fp6OBPu4EOrnctIqCqeCWq7imoTLmBsxla8PrWBl/kY2FO1gYcxsLo6aiVp+5gVH95Qf4LXdH+Km1vLY9PtQK078mt9lrSK3vpB7xt18Us2iq/M388G+r0n0ieVPE/9vQMHYYX5fKOVCj8leh0lPaXMFi0dcfBZHdfoZMCBddNFFALz//vt89dVX3cvz6dOns2TJ6RGiHOYobcZ2tKrTF+jjvKK4KGIaK/M20GHWc/2oK3rNcN01KibEq5gQf/5tjLqrXblr7A0sjJ7NV5k/882hX1iVt4kFUTOZFzl9UL1AQ8VsNfP1oRX8nLOOCLcQ/jzlTrSDkG3JqM7m+8OrmBoyjikhQ2+ZWFewjQ/2fU2KfyIPTLh1eK/mAkPr2PNWndtQiIhIjGdvHcHzmUHlV5qamjAajahU9lmgTqejpaXljA7sQsRitQxqpj0Ubk6+GkeFmh+yfmVXWTpTg8cxyncEYa5BaFUufS73bTYbbaZ2WgxtNBlaaNK30GxopVnfQrOxjTZjO+0mHQazEaPVhMVmVyWXSqQopArUciUuDk64KrV4OLrh6+RFgMYXfxefM9K1H6jx4+HJd1DYWMK3h1fy9aEV/JSzlpmhE5kbMfW0pEBFUWR/1WE+OfAdlW01zA6fwk2jFqMYRCFBra6BV3e9j7+LD38YPfQ0987SdN5L/5Jkv3genPCH86Ina5jTi1bd829+sCoLuURGpHvoWRrRmWFQn+xLLrmEq6++mjlz5iCKIqtXr+bqq68+02O74FDIFBjMxhMfOAQkgoSl8ZcyITCZ5Tnr2FK8m7WFWwF7lZSzgyMOUvtN1Wy1oLcY0JsNiIi9rqWSKdEonXFxcEar1KByVuIgVXTbSVhFGyaLCZ1ZT6uxjbKWKpr0Ld3XkgoSgjT+RHmEEecVyQivaFxO475PuFswf51yF8VNZfySu4G1hVv5NX8T0R7hTAkeQ4p/4pCr2vRmA6nl+/k1fxNHmsrwdfLikan3MMo3blDn60wdvLD1DWyijYdPogE2qzaP11M/ItojbDgYXcBonY7+3UVRJK0yg3jvmFNuqD7XGNSn+7777mPEiBHs3r0bgL/+9a/DFuZnAHeVK5m1OWfk2sHaAO4dfzMmi4nCphJKmiuo72ik3ajDaDUB9rJfpcwBJ4UaFwdnNEpntEoXXJUatEqXk9qzMFnN1LTXUdZSRXFzGYWNxWwu3s2agi0ICIS7BZPin8BY/1GDMiMcDCGugdw9/iauG3UFm4/sYmtxKu+lf8V76V8RqPEjxiOcUNcg/Jy9cVNpUMtVSCVSzDYLrYY2anX1lLZUkl2Xz+HafCw2C37O3twx5jqmBo8bdFAwWky8tP0tqtpreXTq3UNWLShvreKf29/G28mDP0+5c1CrsWF+n2jVRzMLZS2V1OoauDz2orM4ojPDoKdbs2fPZvbswUmiDHNyhLgGsrUklYaOpjNiNw72VVisZ+Rv1kWvkMoJ1PgRqPFjYtBowK7hVtRYQkZNNvsqD/FV5s98lfkzgS6+TA4ey5SQsadFIVqrdOHy2Iu4LGYuFa3VpFVmcKgml+2le1lXuO2E5we4+DIvYhpjA5KI9ggbUlWcwWLkn9vfIqeukHsn3Dzk0uxWYzsvbn0TmUTG36bePVzddoHjfExA2tvp63UuqPifbobX/+cQo3zi+ARILd/PxVEzz/ZwzhgyiZQojzCiPMJYPGIBjfpm9pQfYEdpGl9mLuerzJ9J8IlldvhkRvslDMphdiAEQSBA40uAxpfLYy/CJtqo1zVS1V5Lk74FvdmAVbQil8hxdnDCQ+1KgIvvSe/ntRnbeXHbW+Q3HuGusTcwKWjMkM43W828vP1tGvXNPDnjgbPaZzXMuYGz6uitOq0yg0i3kN+l6eFwQDqHCND4Eu4azJqCLcyLmH7BNB26qbTMi5zOvMjp1LTXsaV4N5uKdvGvHe/gqtIwJ3wKs8OnDKqabTBIBAleTh54OQ3Nb2owlDZX8M/OYPLAhD8wPnDwRn1g3x/4X9rn5NQXcv+EW4nyCDvtYxzm/EOttqdrG/XNFDaWnLQbwLnOhXHHO4+4LHYuVW21g0op/R7xdvLk6pELeeOSv/PnyXcSpPHnm0O/cNeKR3kj9WOKm8rO9hD7RBRF1hdu55H1L2KwmnhyxgNDDkYA3x1eydbiVK4eeQkTg1LOwEiHOR9xUtlTdukVmQCMOUVB3nOV4RXSOca4gCTivWP47OAPxHlF/q6EE4eCRCIhxT+BFP8EKttq+DVvE5uLd7OleDcjvKKYHzmDFL+Ec2IVWdlWwwfpX5NRk028dwz3jLsJ7UmkU9YX2mWRpodM4Mq431fD4zCnhrPKvkJKqzyIt6MHAS6/H/26Yzn73+ZheiAIAn8ceyMquZJ/bH2DWl3D2R7SWcfP2ZtbRy/l7YXPc13iFdS01/Pyjv9xz6on+Cl7DS2G1rMyrmZ9Cx/u+4Y/rX6W/IYj3Jq8lEen3XNSwWh32T7eTf+CJN8R/N+Ya88pNfNhzj5OjgoMZgOZNbmk+Cf+bj8fwyukcxA3tZa/Tb2bZza9wuMb/smfJ99J+DmibH02cVSouTRmDguiZrK34iBrCrbwRcZPfH1oBSl+CUwLGcconxFnvFfnSFMZawq2sK04FatoY0boRJbELzzpPa69FQd5ddf7RLqF8sDE2065iGOY3x9KBzkZNTlYbBZG+8Wf7eGcMYYD0jlKqGsgT8/8Ey9se5PHNvyTJSMXcknUrOHGSOyKEOMDkxkfmEx5axUbC3ewtSSV1PL9OMpVjPZLYLR/PPFeMTg5nHq5tCiKlLZUkF6Zya7SdEpaKpBL5UwLGc+lMXPwOQVX1J2laby2+0PCXIN4ZOrdv7tGx2FOD4JExr7KdFRy5e9OLuhYhu9u5zBBWn9enPs33kn7gi8yfmLTkZ0sjlvAxKDRp02A9XwnwMWXG5IWsyxxERnVWewsS2df5SG2lqQiIBCk8SPCPZRQ1wACXHzxcvJAq9T0uwoxWIzUdzRS3VZHaUsFRY2l5NYX0mJsAyDKPYxbkpcwOXjMKfcG/Zq3iY/2f0uMZzh/mXLXbyIKO8x5iiBhf9VhEr3jftcr6OGAdI7j7ODEnyb9H/sqD/H5wR94LfVDvsj8iRmhE5gQOJoAF98zmk82Wc006ptp7GimxdhKi6ENnakDvcWAyWrGZrMBdmsIlUyJs4MjWqULHmo3/Jy9T8sKZTDIJFKS/eJJ9ovHarNS2FhCRk0OOXUF7C5LZ0PR9h7HO8pVKOVKZBIZomjDbLPQYdJ3q1Z04ePkSYJPLCO8oknyHXFaej8sNisf7/+WNQVbSPFP5P7xtwyrMAwzIBW6WpoMLST7jTzbQzmjnNGAtGLFCt566y3MZjM33XQT1157bZ/Hbd68mWeeeYaNGzeeyeGc1yT7jWSUbxz7KjNZU7CF7w//yneHV+Hp6M4Irygi3UIJ1vrj6+yFk8JxUEHKJtpoN+po1LfQqG+ivqOROl0jdboG6nQN1OoaulcGxyOXyFDIFN3irGarGaPF1EsDT6t0IcwtmGj3MEZ4RRHuFnzGV3fSYxpvwZ5ya9A3UdlaQ62uniZ9C21GHQaLEYvNgiAIyCUy1HIVLkpn3FRafJw8T6k5tj/qOxp5ddcH5NYXckn0bK5LWHROVAoOc26TVV8IwCjfEWd5JGeWMxaQampqeOWVV/jhhx9QKBQsXbqUcePGERHRM/9ZX1/Piy++eKaG8btCIkhI8U8kxT+RJn0LeysOcKA6m/SKDDYf2dV9nIPMAa3SBSe5GgeZAqlEioCAxWbBZDWjNxtoN+loM+mwibYeryGVSPFQu+Hl6MZo/wQ81G54qF1xU2nRKl1wcXDCSeHY516WTbShM3XQpG+hVtdAVVstJS3lFDaUsK/S3j+hkitJ8h3JuIBRJPmO/E32TARB6Pw9Tl2O6GQRRZFtJXv4cN/XWEUb90+4dbjPaJhBk99QTJDG/7Q1h5+rnLGAtHPnTsaPH49WqwXs3kqrV6/m7rvv7nHcY489xt13382//vWvPq/T2tpKa2vPst7q6uozMubzCVeVhrkR05gbMQ1RFKnV1VPWUkl1ez0NHU00G1rQmTowHbNykUmkOCrUeKjd7AKqSic0Di64qjS4qexWEVoHl5OesXfZpjs7OBGk9e/xXKuxnazaPPZXHSa9MoOdpWk4yBwY5z+K6aHjifOK+l05Xx5LdVstH+7/hv1Vh4lyD+Pu8Tfh4+R5toc1zDnGQPe6Iy1lzAmbcTaG9ZtyxgJSbW0tnp5Hv3ReXl5kZGT0OOaTTz4hLi6OxMT+u44//vhjXn/99TM1zN8FgiDg7eSJ9zl8k3NxcOqujLPZbGTV5bO9dC+7ytLZWpKKl6M7s8ImMyN0wkn18ZyLtBnb+TF7DavzNyOTSLkp6aoLShJqmKEx0L3OaDER4/H7ra7r4owFJFHs7adz7L5GXl4ea9eu5aOPPhpwxXPjjTeyaNGiHo9VV1f3ux81zLmPRCJhpHc0I72juSXpavZUHGB94Xa+zFzON4dWMDYgibkRU4nzjDwvGwBbDK2sytvE6vzNGCxGpoWM55qEy36XYpjDnD5OdK+L8Qg/G8P6TTljAcnb25u0tLTun2tra/HyOtqvsXr1aurq6rjyyisxm83U1taybNkyvvjiix7XcXFxwcXl9503vZBRyBRMDh7L5OCxVLZWs65wO5uLd7GrLB0/Z29mhk1iasi4cz53LooihY0lrCvcxvaSPVhsVsYFJrE47uJe6cthhumLge51KrnqjFnSnEucsYA0ceJEXnvtNRobG1GpVKxdu5Znn322+/l7772Xe++9F4Dy8nJuuOGGXsFomAsLPxcfbkxazDXxl7KrbB/ri7bz2cEf+DLjJ0b5jmBK8FiS/eLPqebRmvY6dpXtY1vJHspaKnGQOTA9dAILombidxqs04cZBuzfjfMxWzBUzugK6YEHHuCGG27AbDazePFiEhISuO2227j33nuJj//9yl8Mc2ooZAqmhY5nWuh4ylur2HxkN9tKUkmvzEQhlZPoE8dovwRG+cYN2ZL8VDFZTOQ2FJFZk8O+ykOUtlQA9obZ20YvY1JQymkvFR9mGP9TUAM5nxDEvjZ7znHKy8uZNWsWGzZsICAg4GwPZ5jfAJvNRk59ATvL0kmryKBR3wyAv4sPsR4RRLiHEuYaRICLz2mTVzJbzVS21VDSXEFRUymFDcUUNpVisVmQChKiPcJJ8U9kbMCoYRO9Yc4IXfe6J95/jmsnLz7bwznjDCs1DHNeIJFIiPOKIs4riluTl1LaUsHB6mwO1+ayqyyd9Z1KDFJBgreTJz5Onnio3dCqNLg4OOKoUOMgdUAhlSMRBETo0ZelM3XQamynydBCQ0cjte0N1HY0dBfnyKVywrSBXBw1g7hOC3iVXHkW35FhLiR+60zA2WI4IA1z3iEIAsHa/2/vTmOjKhs2jl8DbdlKKYUuPKC8AbENYYuC7CU8ULpRdkNZLAqCgCyWSNjEQMSASFKIJGyi4UNRSkGwBAHZZGmDghrW+kLCIn0ZSi1SaEs7Mz3vBx4m1hateRznHvr/JU2Yc8+cueZOOdec6cw9rdQ6uJWGRMWowqqQ/X6+rv76s278+n/Ku29X/oMC/e8vV/WgvLjG+61jq6Mm9RurWYOmatvsf9Sn9Ut6pkkLPdvk0QoYrB8Ib2lav3a8Q5NCgs+rY6ujfwVF6F9BEer9bOUxp8up++XFKnaUqMxZLofLoYr/nPX41amrgLoBauBfT438G6phQIOn9sO58G215SMDFBKean51/dS0QZNa8x8aT6fashI8TwcBwHC14S3fEoUEADAEhQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMAKFBAAwAoUEADAChQQAMIJHCykrK0sJCQmKiYlRenp6lfGDBw9q6NChGjJkiKZPn6579+55Mg4AwGAeK6Tbt28rLS1NW7du1e7du7VtEpZtFQAADjxJREFU2zZduXLFPf7gwQMtWbJEGzdu1JdffqnIyEh99NFHnooDADCcn6d2nJ2drR49eig4OFiSFBsbq3379mnGjBmSJIfDoSVLlig8PFySFBkZqaysrCr7KSoqUlFRUaVtdrvdU7EBwCs41nmwkPLz8xUaGuq+HBYWprNnz7ovN23aVAMHDpQkPXz4UBs3btQrr7xSZT9btmzR2rVrPRUTAIzAsc6DhWRZVpVtNputyrb79+9r+vTpioqK0vDhw6uMT5gwocp2u92ucePG/X1hAcDLONZ5sJDCw8N1+vRp9+X8/HyFhYVVuk5+fr4mTZqkHj16aOHChdXuJygoSEFBQZ6KCQBG4FjnwTc19OrVSzk5OSosLFRpaakOHDig6Oho97jL5dLUqVMVHx+vRYsWVXv2BACoPTx6hpSamqqUlBQ5HA6NGjVKnTp10uTJkzVr1izZ7XZdvHhRLpdL+/fvlyR16NBB77//vqciAQAM5rFCkqSkpCQlJSVV2rZp0yZJUseOHZWbm+vJuwcA+BBWagAAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAgAYgUICABiBQgIAGMGjhZSVlaWEhATFxMQoPT29yvilS5c0cuRIxcbGatGiRXI6nZ6MAwAwmMcK6fbt20pLS9PWrVu1e/dubdu2TVeuXKl0nblz52rx4sXav3+/LMtSRkaGp+IAAAznsULKzs5Wjx49FBwcrIYNGyo2Nlb79u1zj+fl5enhw4fq0qWLJGnEiBGVxh8rKirSzZs3K/3Y7XZPxQYAr+BYJ/l5asf5+fkKDQ11Xw4LC9PZs2efOB4aGqrbt29X2c+WLVu0du1aT8UEACNwrPNgIVmWVWWbzWar8fhjEyZM0PDhwytts9vtGjdu3N+QEgDMwLHOg4UUHh6u06dPuy/n5+crLCys0nhBQYH78p07dyqNPxYUFKSgoCBPxQQAI3Cs8+DfkHr16qWcnBwVFhaqtLRUBw4cUHR0tHu8ZcuWqlevns6cOSNJ2rVrV6VxAEDt4rFCCg8PV2pqqlJSUjRs2DANHjxYnTp10uTJk3Xu3DlJ0qpVq7R8+XLFx8ertLRUKSkpnooDADCcx16yk6SkpCQlJSVV2rZp0yb3v6OiopSZmenJCAAAH8FKDQAAI1BIAAAjUEgAACN49G9InuJyuSSp1n2KGcDTIyIiQn5+PnkI9hifnI07d+5IUq36wBiAp8uhQ4fUqlUrb8cwis2qbskEwz18+FDnz59XaGio6tat+7fu+/Eno9PT0xUREfG37tvTyO4dZPcOX84u1ewMyel0ym6315qzKZ98hPXr11fXrl09eh8RERE+++yF7N5Bdu/w5ex/xs/P76l9bNXhTQ0AACNQSAAAI1BIAAAjUEi/ExQUpBkzZvjkqrtk9w6ye4cvZ0f1fPJddgCApw9nSAAAI1BIAAAjUEj/cebMGY0cOVJDhw7VhAkTlJeXJ0kqKirSlClTFB8fr3HjxrlXiTBNVlaWEhISFBMTo/T0dG/H+VNr165VYmKiEhMTtXLlSklSdna2kpKSNGjQIKWlpXk54Z/74IMPNH/+fEnSpUuXNHLkSMXGxmrRokVyOp1eTle9w4cPa8SIEYqLi9OyZcsk+c6879692/0788EHH0jynXlHDVmwLMuy+vfvb126dMmyLMvavn27NXXqVMuyLGvp0qXWhg0bLMuyrC+++MKaPXu2tyI+kd1ut/r372/dvXvXKi4utpKSkqzLly97O9YTnTx50ho9erRVVlZmlZeXWykpKVZWVpbVr18/68aNG5bD4bAmTpxoHT161NtRnyg7O9vq3r27NW/ePMuyLCsxMdH64YcfLMuyrAULFljp6eleTFe9GzduWH369LFu3bpllZeXW2PGjLGOHj3qE/NeUlJidevWzfrll18sh8NhjRo1yjp58qRPzDtqjjMkSeXl5Zo9e7aioqIkSZGRkbp165Yk6ejRo+4vGRw8eLCOHTsmh8PhtazVyc7OVo8ePRQcHKyGDRsqNjZW+/bt83asJwoNDdX8+fMVEBAgf39/tW3bVteuXVPr1q31zDPPyM/PT0lJScY+hl9//VVpaWmaOnWqJCkvL08PHz5Uly5dJEkjRowwMvvXX3+thIQERUREyN/fX2lpaWrQoIFPzLvL5VJFRYVKS0vldDrldDrl5+fnE/OOmqOQJAUEBGjo0KGSpIqKCq1du1YDBw6UJOXn5ys0NFTSo2U8AgMDVVhY6LWs1fltRkkKCwvT7du3vZjoj7Vr1859ELl27Zr27t0rm83mM4/h3XffVWpqqvvtxr+f/9DQUCOzX79+XS6XS5MmTdKQIUO0detWn/ndCQwM1OzZsxUfH6/o6Gi1bNlS/v7+PjHvqLlaV0hfffWVoqOjK/28+uqrkh6dKb399ttyOp164403nriPOnXMmjarmnfu22w2LyT5ay5fvqyJEydq3rx5evbZZ6uMm/gYtm/frhYtWqhnz57ubb4y/y6XSzk5Ofrwww+VkZGhc+fO6ebNm1WuZ2L23Nxc7dixQ0eOHNGJEydUp04dnTx5ssr1TMyOmvPJxVX/G/Hx8YqPj6+yvbi4WNOmTVNwcLDWrVsnf39/SY+eMRYUFCgiIkJOp1MPHjxQcHDwP5z6j4WHh+v06dPuy/n5+QoLC/Nioj935swZzZo1SwsXLlRiYqK+/fZbFRQUuMdNfQx79+7VnTt3NHToUN27d08lJSWy2WyVst+5c8fI7M2bN1fPnj0VEhIiSRowYID27dtXacV8U+f9xIkT6tmzp5o1aybp0ctzmzdv9ol5R82Z9VTfi+bOnavWrVtrzZo1CggIcG/v16+fdu3aJenRwahr167usjJFr169lJOTo8LCQpWWlurAgQOKjo72dqwnunXrlt58802tWrVKiYmJkqTOnTvr6tWr7peV9uzZY+Rj+PTTT7Vnzx7t3r1bs2bN0r///W8tX75c9erV05kzZyRJu3btMjJ7//79deLECRUVFcnlcun48eOKi4vziXmPiopSdna2SkpKZFmWDh8+rJdeeskn5h01V+vOkKpz8eJFHTp0SM8995yGDRsm6dGZ0aZNmzR79mzNnz9fiYmJaty4sVatWuXdsNUIDw9XamqqUlJS5HA4NGrUKHXq1MnbsZ5o8+bNKisr04oVK9zbkpOTtWLFCs2cOVNlZWXq16+f4uLivJjyr1m1apXeeecdFRcXq3379kpJSfF2pCo6d+6s119/XWPHjpXD4VDv3r01ZswYtWnTxvh579Onjy5evKgRI0bI399fHTt21JQpUxQTE2P8vKPmWDoIAGAEXrIDABiBQgIAGIFCAgAYgUICABiBQgIAGIFCAp5g/vz52rx581+6zaFDh9yraB89elRr1qzxRDTgqcTnkIC/0YABAzRgwABJ0rlz53Tv3j0vJwJ8B4UEn3Pq1CmtXLlS4eHh+vnnn1W/fn2tWLFCYWFhWrp0qXJzc2Wz2dS3b1/NmTNHfn5+at++vSZMmKBTp06ppKREc+bM0aBBg7Rz507t379fGzZskKQqlx/LzMzUtm3b5HA4dO/ePU2ePFljx47Vzp07lZmZqdLSUgUGBmr48OHav3+/pk+frs8//1wul0uNGzfW2bNnFRcXp9GjR0uS1q1bp7t372rhwoX/+PwBpqKQ4JMuXryoBQsWqGvXrvrss880d+5ctWvXTsHBwcrKypLD4dC0adP0ySefaMqUKXK5XGrSpIl27typ3NxcjR8/Xl27dq3RfRUXF2v79u3auHGjmjZtqh9//FGvvfaaxo4dK0m6cuWKDh8+rMDAQO3cuVPSo1URkpOTdffuXaWmpurgwYNav369Ro8erYqKCm3fvl0ff/yxx+YH8EX8DQk+KSoqyl0oI0eO1KVLl7Rnzx6NHz9eNptNAQEBSk5O1rFjx9y3GT9+vPu2zz//vL777rsa3VejRo20fv16ffPNN1q9erXWr1+vkpIS93hkZKQCAwP/cB/9+/dXQUGBcnNzdfz4cbVq1Upt2rT5qw8beKpRSPBJv12hWnr0FRC/XwWroqKi0lda//Y2FRUVqlu3rmw2W6XbVffli3a7XcOGDVNeXp5efPFFvfXWW5XGGzZsWKO8ycnJyszM1I4dO5ScnPyntwFqGwoJPik3N1e5ubmSpG3btumFF15QfHy80tPTZVmWysvLlZGRoV69erlv83jV9gsXLujq1avq1q2bQkJCdPnyZZWVlcnpdOrIkSNV7uv8+fMKCQnR9OnT1bdvX/d1XC7XH2asW7dupUJ8+eWXdfDgQV24cEExMTH/7RQATx3+hgSf1Lx5c61evVp5eXkKCQnRypUr1ahRIy1btkxJSUlyOBzq27ev+2vGJen7779XRkaGKioqlJaWpiZNmqh3797q1q2b4uPjFRoaqu7du+unn36qdF+9e/dWZmam4uLi1KBBA3Xq1EkhISG6fv36H2bs2bOnZs6cKX9/fy1evFjNmjVThw4d1LZtW+O+wgQwAat9w+ecOnVK7733nvbs2VPj20RGRionJ8f95XTeUFhYqFGjRik9PV0tWrTwWg7AVLxkB/wDMjIylJCQoJSUFMoIeALOkAAARuAMCQBgBAoJAGAECgkAYAQKCQBgBAoJAGAECgkAYIT/B5VRJ992K6JTAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x432 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"ticks\")\n",
+    "\n",
+    "# Show the joint distribution using kernel density estimation\n",
+    "g = sns.jointplot(\n",
+    "    data=df,\n",
+    "    x=\"popularity\", y=\"danceability\", hue=\"artist_top_genre\",\n",
+    "    kind=\"kde\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jenniferlooper/Library/Python/3.8/lib/python/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1350b1a90>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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l13sWXPrBEHR0rA4Fl35Au5jWgubZpEhKGtCqN+cQ8oBWvfHjtSKfGkL0pqBNfIvPD4dWaZSCj7tjyMsW/TY91yurTYpvF5wqNnmcrSEwzqzJvx73aepC0U5Ahy4fx0v5szAmZxpeyp+FQ5ePA7BvyEus30HM9+R7LX1gK79bARb3Apv+sxecKsaUBbvx5D+2IuvQesGf7bnHx2JQ674IYIz+mWpl2PXrQZQr74CB6TA/DSESYj+f7wlau4JnmACMyZnm8LZBgGuGTu3ZPZvvatoVyTD2ljcbHtkOLb5ZhYvKRSY1U8VqqxhZpcbDi9Ze6yWeQtja8iYm1YN08mpYrgS1/v7PPT4Wzz0+FisP7MCea3U7kzAAIFdBp2MQLAuGiq2hIUQH0AJ9AvhBEOQaJgTuJR2U363AsuNfAnDtkJ1QYu2e7YoMPHvKm+lrpmo4aqaK2VZns0qN39Paa/EFUPOLGFYVDCaoxuKxtj7bvj/2gFGYXgwxASxUKgY5E5bxPldMvhI4aIE+0fP54VDzYULzISQA0Oi0+OLHDW4ZsrPF2iJ+IYv7jYcma9S1kAfITO4Xe/jMnh5c5YFsi5qprKYWlQey7c4WtDYEaw9nMhT5sm3NL1Y0xQ/CPNdFxshsvo9Ozn3RY+24OTG+I1vDvt7EE6c+iHv4fBAE6gLhp6kLkTNmmUWlGL3bqiq7UstdtdYuPbk9ghSmwUvI4n7zE9YddRVYFnYvvbCHPUsvNMobnI/VKG/Y9TuIdWIW8p6OzHdyX6yYXogxXOOjZgI03Bc91o4bO3T5OJYd/9LkO1p2/Eu7vyNfChy0QJ/o+fxwqL34hs+MszRDHgsBOBbhizG8aD7kNCipN344HGjXNj1cJywtq0WQPAirRnzgdBu5cA09yxk5bv/WCk/+Y6tJ2+XhDaFRllu8hjy8IQDhw5hiVr/he09Ht3PSJhebzAnKm58HE2B6IabRaW22N/G+gYY5QT1WK8OA+wba/Fxf/LgBGp3pUKp+9MOe78hagCi/W2Exv+7paIE+0aMgKJA+S1N/Mqu93BaKlmdMTkpiDC9ynWwP1u7EC8+MR58WgwS/jjuudPu06Ibq4nPIvXIENwOABlog/M8YnC+pq9VpvG1Vl4TxJvsoAgAjD0JkgmVJMz6u+pyObudkvqNIQKDlfCBfe40viILlwahVy8DKVAjQhGDAfQPxfEKyzbZzFpDnOW4NX3Uj41444Pnzat5WVYdIR9IgmJ+fj2XLlkGtVmPSpEkYP970BHfmzBnMmTMHarUaTZo0wfvvv4/w8HApm4T6VnaVqB8Yxvs88wQH/a7xQS0uAIpq0a6CxerZuONK93bRQbQ9vBOZRoFNxVZgfWB9nFK1AmCU2fpWXUCvPJANjfKGSXaoPVz1OZ0JtsY7iryUf9zh5KtatgaBQYF4oetktwQZa0lmxryl8DUt0Cd6kgXB0tJSLF68GHl5eQgMDERaWhq6d++ONm3aGB6zcOFCzJgxA/369cO7776LVatWISMjQ6omAQAmdX4ay45/aTI8JA+QYVLnp3mfx5WNqa1oiuqKpvjmQ/ckl/Bxx5UuV7JLIKPF0JCfDEEQuPdd1u/Q1+6gZ85Vm8uKFWzHxg3j/PuzN/nKns9QTxHGWUi+noL/ws+ceeCwskGLpFV0xEQL9AkgYWLMkSNHEB8fj4iICISGhiIpKQk7d+40eYxOp0NVVd0/zurqagQHB0vVHIM+LbphWrd0kwSIad3Sbf5jcCZL0x5i1fV0Vf1I42SRBZEsfqoXZPGYyADTE7CY3xnf5xQzm1HM+pbm23tZ2+5LrAuiyV2ehowxTa6SMTJM7sJ/4cfFOMksWqS/VV/KOiXeR7KeYFlZGWJiYgy3Y2NjUVhYaPKYzMxMTJ48GW+//TZCQkKwYcMGi9dRKpVQKk2ru5SUlDjVNkeuANOT25vMCQLSbMEkZg9O6itd8+G6mwoZ8mLDASjx2J17PcJK3b0ehxTfmSs2lxVr+Gxd4VZoWdMkFS3LnRgjVu9TqqE/sf5WaT9B4k6SBUGW4/KWMcoFr6mpwaxZs7BmzRrExcVh9erVeP3117Fy5UqT56xZswZZWVlSNVMw8wQHqbZg8qa5Cq6TlzqAwa6G9QxBUBegwHdsd7u3rRJjeEzspBkxLirsaZOnXxCJ9bdKyxWIO0kWBBs1aoSTJ08abpeVlSE2NtZw+/z58wgKCkJcXBwAYMyYMViyZInF60ycOBEjRowwOVZSUmKRZOMKxgkOUpKqByf2vAvfBrYAY0h2+UeHvviHne0Uo5qHJ6bB29MmqS6IxPw7EONv1RN/J+I/JAuCPXv2xNKlS1FRUYGQkBDs3r0b8+fPN9zfokULlJSU4OLFi2jVqhX27duHjh07WrxOeHi45Bmjnk6Mk5YUZaKsnbyiwxqi1azlDr0mIN7wmCemwdvbJrEviDyxXJgn/k7Ef0jaE8zIyEB6ejrUajVGjx6NuLg4TJ06FTNmzEDHjh3xzjvv4P/+7//AsiwaNmyIt99+W6rmCMYXcFyVwWa8KL9B83Jom/4MDVu3H5T5SUtom6SYd5Hq5OWOAtqu4u42eeL8m7u/E+LfGJZr8s7DXb16FYmJidi3bx+aNWsm2uty7bkXKAvEC13rhl6t3SfmP9aCU8XI2vst0OQcmL8WVnOV1dLvASi0TWNypnGmtDMAcsY4XoBZigsDa/sA6rd9Io4T++9AyF6XhHgyqhhjxFZtRFdcQa86tBtM80IwMssdxY3duFth11W9VPMuUsxf0vCYdMT8OzCvomRcEYgCIfEWFASNODIMJ3YGW3VUEQJsBECg7qRlq73GvbQwRRjkATKTRdruDizmvcjOTTrgx2tFuHG3AvUCwxAYoECVuspmD1OqYerbRQedrmjjaexZrG+Ltb0uP/z6R3y54yz1ColXoCBoxNZVsisy2KzVljSmD17rCrdabdOhy8ex4tiXUP21Ju2OugoyMKgfGIY7KtuBRWpcCRq7fztouP+2qgqBskBMj+cvESZVood+v0OWY79Dbw+EQhfr28K3pyX1Com38IsgKLSnYOsq2RVDdMGyENTouE4uDBiwFu231qavT+UYAqCeFiwCWdapOUBzjvbCuIZyzQkZbrY30UNoe/n2O/TmIGjPYn1boiNDcJ0nEBrqxFIQJB7M54PgocvHsezYWpPsymXH1gLg7ilYu0qWMoPN+MRs7aK8niIU/xlpugUSX5uWHv0PZ0bNDSs7BzgSzJzphQkdRrb1OHuGsO1pL99+h1KSOtFEzIXpXFWUzPH1FgnxBD4fBFef3GQIgHoaVoPVJzdZnPhsXSVLkQTClZHKpYqjADJgPTElQqPDTbPNePXHbbVBaDBzJt2eb1se88c58jpcz7OnvVz7Hf5ULwi7YsJxS6K981yRaCJmYoxxFSVrPUKxa+sSIjafD4J31ErzjbwNx803AhXzKtlWz0p/v5BAANh/kkq+yyC3Pgt1wL0Pr9CxSL7LWLSPYQKgY02Do5Bg5sz3JWRbHiHDzfZkktrT3kiz/Q5/qheEvNhww/cpxSJzrkQTTf1iLDu7H8t+FWe7LrEzb/VVlMwDOCBNnVhCxObzQVCnCkZAEEeyCWO5EWg9K3sN1rOy16C17EFbPSuhvT89GSNDraYWY3KmIUwRBoaBzeSWAT3HQ1ewCrsignBTHoAIjQ5JN2sx4IlnLd6fZbmzUW0FM2d6FVxDucbZoUJP+LaGqY2HF0MeCwEUlj0Wrvbq5/30v++umHCTCwpA/CUy5kOHsqg/oWhZBPavbGExAq9Uw/quqq1LiNh8PgiGVHRATeyPvOvu9CczlZW5Da7jfNmD637fxTvsJiQpJIAJAMvqEKYIQ422xhCcjfeF4zsp1u/QF4MAdD2QDY2y/K8g/Szqd+iLdfmzBAVgW8HM2V6FWMPL1l7HvHdSe7ktFC3PgJEJWybyc/1grHsgGjfuBoi6d5415okm8ubnLf5uxQi8UtWmdVVtXULE5PNB8Nk+g5C1VwPWRgWWG3cr6pJgOO6r1Vn2JPmyB2/Ecm/TqD9h2jpxGld9eSl/FueGqHp8J0Vrm9YKOXELCWauLHflSMKI+fCitqIpACCoxQVAwT+8KLS3LuYSGfNEE8bKchlX7q7w+cl12Hvxe+hYHQKYAAxo1RvPPT7WZe9PiNR8PgjWnSiH4MsdLf8aEvsOrJUhsbLKu5xDpzqV5Wa/fNmDDR9oxztMyJcUEm12YhZywrP3pGjt/fW9T3uCmSt253Y0YYQrM1Fb0RTVFU3xzYf8AV5Ib13sJTLmQ4oBmhCrf6uu8PnJdSZrN3WsznCbAiHxFT4fBAHTYZpDl5tYHcJbufkXi6FTVhuAkIoOFq/JlT2oP25rmNCemp9CsijtPSkKeX/9jvGeUNDYWmUSW2vQrK1jE5KxyHdhwQCSfSdC/1ZdYe/F760epyBIfIVfBEFjfEN42j5NTIZOWVUwcO0hPDtgkMXrmGcPAgAjD0Jkwnjcb2OY0J5hxLFxw5D1wxdgrcxKOXJStPX+QhJ7XFnx39paM1tr0LjWsQnNWLS6TZQLi3i7e3cF84xhruNUQJt4O78LgoD1ITzzoVO+f9Tm2YPmtSVtDRMKHUY8e/EGdDqAMZpmZNm6eU3zoVN78L2/rULirt6PztEenTMZi55SxJvvd5K6tmkAx9IZ/XGACmgT3+CXQZCPeYZb3bDgcs4rcWuJJ2La98ceMArTXiDDAIw6xKkeCV9vjm89nTv2o3OmR+doxqK7e2G2uKK26YBWvU3mBI2PA44PUxPiSSgI8vCEXbh18mquhFXo5I6Xo7JVSs7aUKB+XSUXsTMWjXs5rcIb4rVeg7H8dJjdPTpnhm5dlfTjSE/VFbVN9fN+1rJDHR2mJsSTUBDk4Qm7cFvLEAzQOF6OylYpOSHVXMw5krFoLUBx9XJi/rsRS4a/iPodLOdn+V7f3RcxfJwZTnRVbdPnHh9rNQnGmcQjQjwFBUEeriyjZk3ifQOx59p2kwXerFaGAfcNtLsNenyl5ADToUAhZd0cmSvjC1AtROrluOsiRmjvzpkyaXzZya7izDA1IZ6CgqAZ85qaXCXFWAAv5c8SHMic6ZE8n5AMHKibG9TJ69aODbhvYN1xB1krJWe8HlI/FDgmZ5rVainOLBXgC1CvitTLsfciRoysV3t6d86USePLTnYVKpVGfAEFQSNCa2oC9gUyZ3skzyck43lwBz1HTtxcpeSsrYeUaqkAX4ASq5djT21TsYZO7UkWcaZMmq3sZFehUmnE23HX9/JT1qqEBDDcX5PxsgE+jvRIXsqfhTE50/BS/iwcunzc6uNWnMhG+V/7EOpP3NYer/dsn0Fgi+Ogqw0Gy6Luf4vj8Gwfy/m2sXHDECgLNDkmxlIBa3OIDUOjEJkwHow8yOS4I70ce9pua1mIUPYki6Qnt0eQ0XZX9pZJq9+hL+7/2wq0mpWL+/+2wqs3+yXEXagnaMTayYZldWAAzmFBIfODUvVIHO1hClkPaTyv1aB5HOo1P48qjRINQ6PQuF4MPjm2Bkt/WO1wPcnOTTpwpt93btJBtF6OPcscxJr/tSdZxNPKpBHijygIGrEVrBzdNsiehdf2BDZnTtx8w1jm81o3i6MRVNII05/qhF/Z70WpJ/njtSLe42KtwRS6zEHIhYqQoWd7k0U8qUwaIf7IL4dDrQ038g2fOTMs2KdFN7zQdTyiQ6PAoG4+jatOKGBfYOMbUnQG37wWXz1Je4iZeSsGW7+v0KHnJ7o0x/SnOiEmMgQMgJjIELzWS41WRxbg4sLRuLL0BdwusuwBA/b9nRBCxOF3PUEhw418V/tSL7wOU4Rxbp0UprDc2Nfe0l7GPRm+zXn55rWCBdSTFMKZDXmlYOu3t6eHbty7q1vz+AU0Aiu7uGKBPiHkHr8LgrZOZuYnIVfspmAcnDgX8IF7D0QhhbD199ULDMNddQ20bF0Pj29zXr55rbs26kkKJWVtTkeXOvAFIEd7rq6o7GIve6rUUIFs4uv8LgjaczJzRcURy81buVfl3VFxb6xr7cRt/rq3rTxfz/hCgG9e61f2Bm89SaHsrc3JF9hMk3jKoW36s0lJODF+M0d7rq6q7CKUPesYqUA28Qd+FwTtOZm5ouKIkM1brbVPjNc1pr8Q4FsE/QT460nawzyAF5wqxpS1uw3BTPFXRmqYIgw12hpodHUnY+PApi1vYnKiro4qQoBZSTjj38zRXqKjPVdPqOxizJ51jFQgm/gDvwuC9pzMXJG8IeS1HBkmdKSNxoGWL3uUr56ko4x7HbKoP1ETW4RaTd2wK9ccqT6w1ZzuZ3Ki5ltr50zP3tFdJTyhsosxe9Yxilkg29V7UBIilN8FQXtOZq5I3rD2HgF/lWzjm+dzpO3WuHs+zrjXwVU5hcuNuxWoNjshs6pgMBwl4RqGRjnds3ckacVTKrvo2bOOUawC2Z5eyJz4N0mDYH5+PpYtWwa1Wo1JkyZh/Ph7V79nz55FZmam4XZFRQUaNGiAbdu2SdkkAJYnM2vJL67YWNXae3Clxtuz43u9wDDIGJkhEQYA5AEyBMuCUaWuQmBAIFQ6NViwCGAC8MQD8Q6dkMSajzPuXVjrzZlrGBqFGrMTtab4QShaFpkEUf1vlvXDas7XkXpZhiv2nRTKnnWMYhXI9oTdWAixRrIgWFpaisWLFyMvLw+BgYFIS0tD9+7d0aZNGwBA+/btsXVrXUmq6upqPPXUU5g7d65UzbHK2SUTzhLyHvrgxtWzs7bj+21VFeQBMtSTh6FKbboMQv+Z2b+ScHSsDgWXfkC7mNZ2fTbzxAlb83F8jHsd1npzxvSBTdu0CbL2fgs0OQcmsAasKhhsRTPUb3LTUOFG/7mtfYcME4AxOdP8YpjOnqLXYhXI9rQ1oYQYkywIHjlyBPHx8YiIiAAAJCUlYefOnZg+fbrFY1esWIGuXbvi8ccfl6o5Vtm7ZEIKfO9hmT1qydqO7xqdFsHBQfjPyA9Mjot1ZW6eOGFv7Utjxr0OTfGDULT6BUzAvUxZBgzqBYZarGs8hOMIbFlk6H0yQTWQx/6JyY8/Y9I7zvphNWfvGLi3xtH8Akis5QGfn1wnSiKRWOwpei1GgWxPWxNKiDHJgmBZWRliYmIMt2NjY1FYWGjxOKVSiQ0bNiA/P5/zdZRKJZRKpcmxkpIS0drp6VepQrI8G4ZG2fU5xPrM5gkSfPNxthj3OqyvlrS0rnAr5wbBQnrHDMeaR/3FgHnWqaPLAz4/uU6UMnPezBXTCoQ4SrIgyLKW690YjhXf+fn5GDBgABo25E4ZX7NmDbKyskRvn56nX6XaCkzyABlq1LVW9/yrF2hZacbez2wt2cU8cYJvPk4Ifa/jpfxZKL9r+olYsIa1jsY9Nr6Abqt3PCZnmtXnirU8gK/MnL8EQVdMKxDiKMmCYKNGjXDy5EnD7bKyMsTGxlo8bu/evXjhhResvs7EiRMxYsQIk2MlJSUmSTbO8PSrVL4sz/p/VYHhWkKgx3EtYtdn5pszNU+c0FY0hVwegPptLlrMx9lDSI9U32PjC+i2erx8z70q0vIAa+Xk7C0z5+2oHBzxVJIFwZ49e2Lp0qWoqKhASEgIdu/ejfnz55s8hmVZnDlzBo899pjV1wkPD0d4eLhUzfT4q9Thke2w5s4RqAPu9aIVOhYTm/fElsr/2awEU8URIO35zHzzh/pNdU3mzQYMkWwOydyNuxWYHj/ZakC3lgij7/HyXQysPn2Td3mA0PnCAJHKzAH+s9bOXz4n8QyS9gQzMjKQnp4OtVqN0aNHIy4uDlOnTsWMGTPQsWNHVFRUQKFQICgoyPYLSsiTr1If+ukwRupuY1fDergpD0CERoekG3fwUPlh3Ii1fSK1NsQp9DPb6k1JsbM4V3Di0jA0ymZA5+vx8j1Xm1xsdXkAZzmxvd/iy0uWPeABrXqLUmbOX9ba+cvnJJ6DYbkm7zzc1atXkZiYiH379qFZs2bubo6BFFewFxeOBnc9UQYfxLXj7TFZW29oj7r5OY5lBeoQVP/UT7KiyuY7XhiXTQP4P5vQ3TJssdbbm7Jgt0kvURb1J+dcqL59YmSHWvsdokOjDD1yX+Avn5N4Dr+rGCMVqa5g+WpP8vWYokUKwlzvwWplUF1uCxbSFVXmKmhgfIHRuUkHw9IHrjWQ+vbeUVchUBaI6fGT7f4urPVyzecFuSrcGC85EaPMnKdnMYvFXz4n8RwUBJ1gfGLmS7d3JhDx1Z683w2L+aEOgepyW2grmhoe44qiysZBke+CwxXVScyzYp1ZHymUp2cxi8VfPifxHH65s7wYDl0+jmXH1hp2GreW7WdP/U4u9Tv0RXTKi5CHRwNgIA+PRnTKiy4tw9WnRTd8mroQOWOWofqnfiYBUM+RosqO4gt0ruhJpCe3R5BCZrjNqoI5HyfmiXtsHP/O977CXz4n8RzUE3TQ6pObLBZpc3EkC9CctdqT7kgiEKuosi182Zd8gc4VPQnzcmIhFR1MaqYC4p+4PT2L2Rah8+Xe/jmJ9xEUBEeMGIFx48Zh6NChCAkR92TnDmKUw7qjVgoqayLlejB3FCZOT26PJTk/QaO9l6wjlzF2F1XmY2szV75A56p1n+bzha5I6/fkLGY+9l6seevnJN5JUBCcPXs2cnJysGTJEgwaNAhjx45F27ZtpW6bJMTaLVunCkaAjSLPQF2CilTclURgnk/saH6xtcBhq1pL5yYdOJcddG7SwW09CTpxW0e7SBBPJigIdu7cGZ07d4ZSqUR+fj6mTZuG2NhYPPPMM0hOTpa6jU4zPtlCHQJN/baAk4kdIRUdUBP7o0lWIMsCxpXh5AEySecyrPWIGqi1uLL0BUn2rftyx1lodaZRT6tj7f7++HoHtjZz/fFaEef9+uPeHJDEKtrtSSjjk3gywXOCSqUSW7duRW5uLurXr4/k5GRs3boVBw4cwKJFi6Rso1MsdmFQVEPRsu5kaZzgYW9ix7N9BiFrrwasfgsfjRyMTGMyRCrFCkzjk2RY2wZApNmJhGXxUFUtNMo7KN++HABEDYTlldWQRf1Ztyzgr62LNMUPopwjWYYPX+8gOrIf77yjr55UxRql8DSU8Uk8maCsjX/84x9ITExEYWEh5s6di82bN+OZZ55BVlYWCgoKJG6ic7hOtoxMB3nz8ybH7E3seKJLc0wfMAThV5JRe2IwZFDAPAdGy2oNuxmIQX+SvF5ZDRaAOvSa5YMYBufC6irwsJpaVB7IFu39AaBB83IoWhYhIKgGDAMEBNVA0bIIDZpbrmXkwxfIzLMvAdPNXK2dPPXHC04VY8qC3XjyH1sxZcFuFJwqtqtt7sI3DOzNKOOTeDJBPcG2bdti1qxZiIoyPfnI5XKsW7dOkoaJxdrJ1nhtlyO7ZQOmyRFP5+zkfIyzSySMCd2/76b8XjTWKG+I9v4AoGh+HrUa02QfRqaDwuyiwha+3oGtzVz5kl+8uTdlaxjYW1HGJ/FkgoLgyZMn8eKLL5oce/rpp7Fhwwa0bt1akoaJxdrJNkATAgYQbd6FUYeAVVierBi1eNm0QvfvizAKUvJw7i2qHFWlUdp13BpbWZx8NUn5TqpT1u4WZQskW/jm7hyd13PV8hN38OZ5WuLbeIPgjBkz8Pvvv6O4uBipqamG4xqNBgEB3rHO3lom4cCHuuK5CeINx9RebmtRP5LVBkB1WbwsWiH79yl0LJJu3AFwr7KMmMSa33G2d2DtpOqK3hRfbxOAwz1R862pAMdHKQghwvAGwddeew1//PEHZs+ejdmzZxuOy2Qyr1kiYSuTUCxRbGtU/A6LhJEoVryesq39+6IUoRhUfged7qggD4+WJDtUzHV49vQOhK7Dc0VvytbcnaM9UVvDwIQQ8fEGwWbNmqFZs2bYtWsX567w3sDanJyYc3WAPkCpUHv6XpZkkEKG9KfEu4rnPEmKsH+fPdwxv2PPYmtX9KYc6W0K7YlKsTUVIcQ63iA4duxYrFu3Dp07dzYJgizLgmEY/Pjjj5I30FlibmrKx1VX8Z5wknT1/I49i61d8TvY6m16+rwebVpLyD28QXDJkiUAgG3btrmkMVKwVrZMinJmnhCgfJG96wKl/h1s9TY9eV6PNq0lxBRvEPz55595n3zfffeJ2RZJRFtJ5JCynBkRl6ctthbS2/TUeT0qYUaIKd4guHbtWqv3MQyDQYMGid4gsbmqoDKRjif+hny9TU8eEfDVajuEOMrhIOgthCRy0ByJZ6PF1uLxtF41Ie7GGwQXLlyIWbNmWSyU11u+fLkkjRIbXyIHzZF4B1u/IQVIYTyxV02IO/EGwR49egAAkpKSXNIYd6A5Eu9GFzH2oV41IaZ4g2D//v0B1G2qW1lZiZ9//hlyuRydOnVCeHi4SxooNZojEZere2V0EWM/KmFGyD2CFssVFBRg8ODBWLVqFZYtW4YhQ4bgxIkTUrfNJWztSECE0/fKyu9WgMW9Xtmhy8c5Hy/Gbg90EUN8SWFhIebMmQMA+OWXXzBjxgzBjxfjcf5IUBBcsmQJvvrqK3z11Vf4+uuvsWLFCrzzzjtSt80laJsX8fD1ysyZbwulr7FpbyCkixjiS3799VeUlpYCADp27IiPP/5Y8OPFeJw/ErSLBMMwJrVCH3nkEbBS7BjrBlLNkfhjsoY9vTK++pv2LC+gRA/iDXQ6Hd5++22cPn0aVVVVYFkWCxYswMaNG3Hz5k0UFxejU6dOOHLkCG7fvo033ngDw4cPx/z587Ft2zacPHkS7777LnS6uiIfL7zwAuLi4vDxxx8bHm+tY3Lt2jWLx+Xk5GDt2rUICAhAdHQ0Zs+ejZYtWyIzMxMMw+C3335DRUUFevXqhbfeegsKhcLqZ9NqtVi0aBH279+P+vXrIy4uDr/99hvWrl2L27dvY+HChTh//jzUajV69OiB1157DXK5HB07dsTzzz+Pw4cPo6ysDOnp6Zg0aRLy8vKQm5uL6upq1KtXD2vXrsXGjRuxbt066HQ6REREYPbs2aLtYMQbBG/evAkA6NChA1atWoW0tDQEBAQgLy8P8fHxojTAE4g9R+ILyRqOBHF70u/F2u2BEj2INzh9+jTKysqQk5ODgIAArFy5Ep999hkiIiJQU1OD7du3AwDy8vKwa9cuvPPOOzh27Jjh+UuXLsXkyZORkpKCc+fOIScnB0lJSZgxY4bh8dY0adLE5HFHjx7F559/jpycHERFRSEvLw8vv/yyoQ3nzp3DV199BYVCgSlTpiAnJwcTJkyw+vobN27EmTNnsG3bNjAMg2nTphnue/vtt/HII4/g3XffhVarRWZmJlavXo2pU6dCpVIhMjIS69evR1FREcaOHYuxY8cCqOu57t+/H/Xq1cPx48exZcsWZGdnIyQkBN9//z3+9re/4dtvv3XqN9HjDYLx8fFgGMbQ63v//fcN9zEMg9dff12URvgab0/WsCeIGwfLeoFhkDEyaNl7PTxrvTIxd3ugRA/i6R577DE0aNAA69evR3FxMY4dO4awsDBERESgS5cuNp+fnJyMefPmYf/+/ejZsydeeeUVh9ty6NAhDBkyxLBJ+siRI7Fw4UJcvXoVQF0iZFhYGABg2LBh2LdvH28Q/O677zBs2DAEBQUBAMaMGWNYY15QUIBffvkFubm5AICaGtP9TxMTEwHUjS6qVCrcvXsXANCuXTvUq1fP8BqXL19GWlqa4Xm3bt3CzZs3ERER4fD3oMcbBM+dO+f0G/gjb0/WEBrEzYPlbVUV5AEy1JOHoUpdxdsro73ziD8pKCjAwoULMXnyZCQmJqJVq1b45ptvAAChoaE2n5+WloaEhAQcPnwYhw4dQlZWluH59uKaymJZFhqNBkDdVnnGx23tHSuXm4YR48frdDosWbLEMHSpVCpNNmPQB079MX3bjL8TnU6HYcOGYebMmYbbZWVlaNCggY1PKoygxBiVSoU9e/Zgy5Yt2LJlCzZt2oTFixeL0gBXECML0R7enqwhNIhzBUuNTotgRRByxizDp6kLrfbQnujSHNOf6oSYyBAwAGIiQzD9qU4eW26MEGccPnwYCQkJGDduHDp27Ii9e/dCq9VaPE4mkxmCkbG0tDScPXsWI0eOxPz586FUKnHr1i2rj+d73d69e+Pbb79FRUXdv+dNmzYhIiICLVq0AADs2LEDKpUKtbW12Lx5MxISEnhfu1+/fvjmm2+gUqmg0WiwefNmw329e/fGF198AZZloVKpMG3aNHz11Vc222usV69e2L59O8rKygAA69atw8SJE+16DT6CEmMyMjJQXFyM69ev4+GHH8bp06fRrZt3DD/x7QIu1QnX25M1hM7tOdvj9eQam4SIKS0tDa+++ipSU1Mhk8nw+OOPY/fu3WjWrJnJ4x577DF89NFHePnll5Genm44/uqrr+Ltt9/GRx99hICAAEyfPh3NmjWDTqczPP6TTz6x+v7Gr/vJJ59g0qRJmDhxInQ6HaKiorBixQpDDy44OBjjxo2DUqlEUlISRo0axfvZRo4cid9//x3Dhw9HaGgomjVrhpCQummNWbNmYeHChUhNTYVarUbPnj3x3HPP2fXd9enTB1OnTsWUKVPAMAzq1auHrKws0fa4ZVgBaZ79+/fH7t27MXfuXEyePBksy+Jf//qX22qLXr16FYmJidi3b5/FH5G5KQt2c849xUSG4D9vSVcA3JuzQ82HOYG6IP5C1/Emn+Gl/FlWd+j4NHWhS9pKCBFPZmYm2rZti2effVbwc77//nvcuHEDw4bVXeQvWLAAQUFBhuFLTyeoJxgbGwu5XI4HHngA58+fR3JyMqqrbWfx5efnY9myZVCr1Zg0aRLGjx9vcv/Fixfxz3/+E7du3UJMTAz+/e9/izbOqydWFqK9vDlZQ2jGpbf3eAnxFRcvXkRGRgbnfS1btsRHH33k1OuPGzcOVVVVnPd9+umnWLVqFVatWgWtVouHHnoIc+fOder9XElQEAwNDUV+fj4eeughbNiwAa1atTIsn7CmtLQUixcvRl5eHgIDA5GWlobu3bujTZs2AOomQKdNm4ZZs2ahb9+++OCDD7By5UrRrx7EzEL0Np+fXIe9F7+HjtUhgAnAgFa98dzjYwU9V0gQp+UJhHiGVq1aYetWy6IU9nr33Xc5j3/99de8z1u9erXT7+0ugoLgnDlzsGHDBsycORO5ubmYMGGCzRTdI0eOID4+3pDCmpSUhJ07d2L69OkAgDNnziA0NBR9+/YFALz44otQKpUWr6NUKi2Ol5SUCGk2gL+yEPd+CzQ5ByawBqwqGLj2ENIHDBH8Gt7o85PrsPu3g4bbOlZnuC00EArhzT1eQggRFAQfeOABvPbaa1AqlYK71WVlZYiJiTHcjo2NRWFhoeH2lStXEB0djddffx3//e9/8eCDD2L27NkWr7NmzRpkZWUJek8usuhrCGxZBA1blxnFBNVA3rIIsujHAPhuUsbei99bPS5mECSEEG8maInExYsXkZKSgpSUFJSWliI5ORm//fYb73O48m2Ms3k0Gg2OHz+OCRMmID8/H82bN+fsik+cOBH79u0z+S87O1tIswHUDdXpA6DhvVkNZz1LX6JjdXYdJ4QQfyQoCC5YsABvvvkmGjZsiEaNGmHChAk2K5I3atQI5eXlhttlZWWIjY013I6JiUGLFi3QsWNHAMDQoUNNeop64eHhaNasmcl/jRs3FvThAO9fuO6oAIb7p7V2nBBC/JGgM+LNmzfRq1cvw+3x48fjzp07vM/p2bMnjh49ioqKClRXV2P37t2G+T+gbt1KRUWFoSrN/v378cgjjzjyGXh5+8J1Rw1o1duu44QQ4o8Edwtqa2sNw5nXr183VDO3plGjRsjIyEB6ejqGDx+OoUOHIi4uDlOnTsUvv/yC4OBgfPLJJ3jrrbeQkpKCY8eOITMz07lPw8Fft0p67vGxGNS6r6HnF8AEYFDrvg7NB94uOogrS1/AxYWjcWXpC7hddND2kwghbvfxxx8jMTHRq7M3pSZosXxubi62bNmCK1euYNiwYdi+fTuee+45jBs3zhVttGDPYnnAuxeuu9vtooMo374crKbWcIyRByE65UXU79DX4vH0XbsHfe/eoeBUMb7ccRblldWIjgxBenJ7SasmJSYm4vPPP0fLli0lew9vJygIAsCJEydQUFAAnU6H3r17mwyPupq9QZA47srSF6BRllscl4dH4/6/rTA5JrTSDBEXfe/ewbyEI1BXNF6MmrkajQZz587FhQsXUF5ejpYtW6Jp06bIy8vD/fffjw8//BCTJ0/GI488gvLycuTm5mLVqlX45ptvIJPJ0KtXL8ycORPXrl3DtGnT0Lx5c1y+fBlNmzbF+++/j4iICBw4cAAfffQRdDodmjdvjnnz5iE6Ohr9+/dH//79cfLkSQB12yc9/PDDTn0eVxI0HHrnzh38+OOPmDlzJiZMmICCggLDlhfEt2mUNwQft2dneWe5uii6J3Pl904cx7eRtLN++uknKBQK5OTkYM+ePaitrUWvXr0QGxuLlStXon379qisrMTzzz+PrVu34siRI9i/fz/y8vKwefNmXL58GevXrwcAnD9/HhMnTsT27dvRunVrZGVl4caNG5gzZw4++eQT5Ofno3Pnzpg3b57h/SMiIrBlyxbMmDHD67bYExQE33jjDUOFmPDwcDAMw7mmj/geeXhDwcddlYmrv6K+XlkNFveKovtrIPTXDGhvI2UJx65du2LcuHHIzs7GwoULcenSJc6OSqdOnQAAP/zwA1JSUhAcHAy5XI5Ro0bh6NGjAOrWhXfv3h0AMHz4cPzwww8oLCxEXFycYeRtzJgx+OGHHwyv+/TTTwOoqzNdWlpq2KHCGwgKgpcuXTJE9/r16+PNN9/EhQsXJG0Y8QyRCePByINMjjHyIEQmjLd4rKsycaW8ovZG/poB7W2slWoUo4Tjvn378OqrryI4OBgjR45E165dOddqBwcHAwBnYqN+qyXj/QFZloVMJrN4vPH+g+bP0el0JnsSejpBQVCj0ZgsiaiqquL8gonvqd+hL6JTXoQ8PBoAA3l4tNWkmLFxwyBjTP/4ZYxM9ExcdxVF91TuzICmzGHh0pPbI0hh+u9DrI2kjx49iuTkZIwaNQrR0dE4ceIE536FevHx8di+fTtqamqg0WiwadMmxMfHAwB+//13nD1bd0G5adMm9O3bF506dcLp06cNu8/n5OQYeosAsH37dgDAnj170Lp1a9E3QpCSoLJpw4cPx1NPPYXBgweDYRjs2bMHI0eOlLptxEPU79CXM+hxYRgArNltkflzUXQu7ipkbp45rFGWo3z7cgAQ/PfiT/TJL1Jkhz711FN49dVXsXPnTgQGBuLRRx81BCwuCQkJOHv2LEaNGgWNRoM+ffpgwoQJKCkpQYMGDfDxxx/jypUraNeuHRYsWIDQ0FDMmzcP06dPh1qtRtOmTbFw4b3t0n788Ufk5uYiJCTEahFuTyU4O3Tfvn04evQo5HI5evTogX79+kndNqsoO9QzuWp/QSmz7Ihw9mQOE+9w9epVpKenY//+/YKf079/f3z55Zdeey4W1BME6nb3ffzxxw3DoDdv3jTsEEGk5S1rwFyVoGHritpbvi9vZ0/mMCGeSlAQXLNmDT788EOo1WoAdZOiDMMYxo2JdMzXgJXfrcCKE3UFxD3txN4wNIqzJyhFgsYTXZpz9vq86fvydvLwhlZ6gtwZxcTzNWvWzK5eIAC7H+9pBCXGrF27FuvWrcPZs2dx9uxZnDt3jgKgi3jTGjBPKFHnTd+Xt7Mnc5gQTyWoJxgTEyNJcWtimzetAfOEnea96fvydvU79MXZ329AfnoLGuAObqEeNA8PR0tKiiFeRFAQ7NWrF77++mskJiYiKOjelR/NCUrPlUOMYnD3TvPe9n15s4JTxcg6rECt+l6meNBhGaY3LaYEJeI1BA2Hrly5EvPmzUO/fv0QHx+P+Ph49OjRQ+q2EXjGEKM3oe/LdahoAfEFgnqCXJvdEtfwhCFGb2Lv90WZpI6jogWer7S0FG+99RY+++wzp19ryZIl6NChAxITE0VomecQtE5QpVLhu+++Q1VVFQBAq9XiypUryMjIkLyBXGidIBED7b7gnCkLdnMWLYiJDMF/3hrkhhYRYj9BPcGMjAwUFxfj+vXrePjhh3H69Gl060YnCeLd+DJJKQjalp7cnrNogRhlwHzV7aKDqDyQDY3yBuThDRGZMF6U6jrHjh3DihUrEBwcjN9++w3t2rXDBx98gLKyMsPi95KSErz66qu4desWHnzwQZw4cQIHDx5EVVUV5s2bhwsXLkCr1WLq1KkYOnSoYYeJmzdvIiEhAWVlZejWrRtGjhyJxYsX4+jRo7h16xYiIyOxdOlSxMTEoHfv3khKSsKpU6cgk8nw0UcfoXlz0/lha1sv/f7775gzZw5u3ryJ0NBQzJo1C3FxccjMzATDMDh//jzu3LmDadOmYfjw4U5/Z3qC5gTPnj2LvLw8JCYm4s0338T69etx+/Zt0RpBiDtQJqlznujSHNOf6oSYyBAwqOsBUtUe6/Rl5urWVrKGMnNi1Vv96aefMGfOHOzYsQN//vknvv/+e5P7Fy5ciOTkZOTn52Pw4MEoLS0FACxbtgyPPPII8vLykJ2djeXLl6O4uG5HltLSUmzevBmvvPKK4XUuX76MixcvYv369di1axfuv/9+5OfnAwCuX7+OHj16YMuWLejatSuys7M528q19dLMmTPxzDPPID8/H2+88Qb+/ve/Q6VSGdqxfv16rFmzBosWLcL169dF+c4AgT3B2NhYyOVyPPDAAzh//jySk5NRXU3j/v7KV+bR3JVJ6ivfH2C9aAGxVHkg21BnVY/V1KLyQLYovcG2bduicePGAIDWrVvj1q1bJvcfPnwY77zzDgBg4MCBCA8PBwAcOXIENTU12LRpEwDg7t27hl2CHn74YZMdIgCgRYsWeP3117Fx40b8/vvv+Pnnn3H//fcb7u/Tp4+hPfrenjnjrZcyMzNRUlKCK1euYNCgumH0Rx99FA0aNMDFixcBACNHjoRCoUDjxo3RuXNnnDp1CoMHD3bwmzIlKAiGhoYiPz8fDz30EDZs2IBWrVoZ9hck/sWXKrKMjRvGOScoZSapL31/xD5Sl5kzXr7GMIzFTj8ymYxz9x+dTof333/fsBa8vLwcDRo0QH5+vmHrJWNFRUX4xz/+gUmTJiEpKQkBAQEmr6tvB1cb9My3XtJqtRaPZVnWsBOG8dZMOp3OIjA7Q9Bw6Jw5c3Du3Dn07t0bMpkMzzzzDJ599lnRGkG8hy9VZOnTohte6Doe0aFRYFBX6FvqpBhf+v6IfezZoFoKPXv2NAxbfvfdd1AqlQDqtlVat24dAKCsrAxPPvkkrl27ZvV1Tpw4gW7dumHs2LFo06YNDh8+zLttExfzrZfuu+8+NG/eHLt37wYA/PzzzygvL0fbtm0BADt27ADLsvjjjz9QWFiILl262PfhefCG02eeeQaM0V446enpYFkW7dq1w44dOzB27FjRGuINXDWMVXCqWJLtVsTga/Norl7c72vfHxEuMmG8ydZTgGvLzL355pt4/fXXsWHDBjz00EOG4dDp06dj7ty5GDp0KLRaLWbOnIn777/f6lDmkCFDMH36dKSmpkKhUKBdu3a82zZx4dp66f3338fcuXOxdOlSKBQKLF26FIGBdWt+a2pqMGrUKKhUKsybNw+RkZFOfBOmeJdI7Nq1C0BdtL5z5w5GjRoFmUyGrVu3Ijw8HPPnzxetIfZwxxIJV6XTe/o2Qa7aLslX0ffn36TKDhXiyy+/RM+ePdGmTRucOXMGs2fPRl5enkve25i9Wy9lZmYaslKlwNsTTEpKAgCsWrUK69evR0BA3ejpE088gTFjxkjSIE/lqnR6viocnhAE3TGP5kvo+/Nv9mxQLbYWLVrglVdeQUBAAIKCgtzWifE0gmYXKysrUVtbi5CQup27q6qqLDKPfJ2rhrE8vQoHVbBxji9/f548jE+Afv36uXUzdD17t16Seqd6QUFw6NChePrppzFw4ECwLIudO3caUlz9havS6aMjQzircERHhoj6Ps5wd5Fsb+eL35/5MP71ympkbTwNABQIiUcTlB3697//HX//+9+hVCpx+/ZtZGZm4rnnnpO6bR7FVYWZ05PbI0ghMznmL1U4Dl0+jpfyZ2FMzjS8lD8Lhy4fd3eTiEBUTJt4K8GLLQYMGIABAwZI2RaP5qphLP1Vs78NK9H6Oe8m5jC+LxUTIJ5PvBWHfsBVw1j+WIWD6nh6N7GG8eliiLiaoOFQQqTmTOIRDaO6n1jD+FRMgLgaBUHiEawlGNlKPNL3HMrvVoDFvZ4DBULXEquYNhUTEN/HH3+MxMRErF692unXWrp0KZYuXSpCq/hdvXoV/fv3t+s5/fv3x9WrV5GXl4fMzEzBz5N0ODQ/Px/Lli2DWq3GpEmTMH68aWWErKwsbNq0yVC54Omnn7Z4DPEPjq6fo2FUzyHGML67ipq7ijvmO7du3YrPP/8cLVu2lPR9vJVkQbC0tBSLFy9GXl4eAgMDkZaWhu7du6NNmzaGxxQVFeHf//43HnvsMamaQbyEo4lH1HPwLb5cTEDK+U6NRoO5c+fiwoULKC8vR8uWLZGVlYW3334bpaWlePnll/Hhhx9i8uTJeOSRR1BeXo7c3FysWrUK33zzDWQyGXr16oWZM2eaFKsGgM8//xwbNmxAZGQkwsPDERcXBwD46quvsHXrVlRXV4NhGHz00Udo3bo1+vfvjyeffBLff/89qqur8d5776FDhw44e/Ys5syZg5qaGjRo0AAffPABGjdujJUrV2LHjh3QarXo3bs3Zs6cCaCuVFpGRgYuXLiA8PBwfPLJJ4iMjLT6vo6SbDj0yJEjiI+PR0REBEJDQ5GUlISdO3eaPKaoqAifffYZUlNTMW/ePNTW1lq8jlKpxNWrV03+KykpkarZxI36tOiGT1MXImfMMnyaulDQicHRYVTimdxR1NxVpJzv/Omnn6BQKJCTk4M9e/agtrYW3333HebNm4fY2FisXLkS7du3R2VlJZ5//nls3boVR44cwf79+w2b516+fBnr1683ed1ffvkFmzZtwubNm7F69WrDuffOnTvYu3cv1q5di23btmHAgAH4+uuvDc+LiIhAbm4u0tLSsGLFCgDAq6++ipdeegn5+fkYMmQI1qxZg4MHD6KoqAi5ubnYsmULSktL8c033wAAKioqMHnyZGzbtg3R0dH49ttvbb6vIyTrCZaVlSEmJsZwOzY2FoWFhYbbVVVVaN++PV5//XXcd999yMzMxKeffoqMjAyT11mzZg2ysrKkaibxcr7cc/BXvlhMAJB21KJr166IiIhAdnY2Ll68iEuXLuHu3bucj+3UqRMA4IcffkBKSophu6RRo0Zhy5YtJlNSx48fR79+/RAWFgYAGDx4MHQ6HerVq4cPP/wQ27dvx6VLl3Do0CG0b38vCcp4T8Hdu3ejoqIC169fR0JCAgBg3LhxAID33nsPhYWFhrqgNTU1aNq0Kbp06YLY2FhDr7NNmzaorKy0+b6OkCwIctXlNt6RIiwsDJ999pnh9pQpU/Dmm29aBMGJEydixIgRJsdKSkrcMndIZaE8jy+XISO+Rcr5zn379uHjjz9Geno6Ro4cicrKSqt7+emDnk6ns7hPo9GY3GYYxuRxcrkcKpUK165dwzPPPIMJEyagb9++iI6Oxtmz9wojGO8pCAAKhcLkdWtra1FWVgatVouJEydi8uTJAOpG/mQyGSorK032DNTvTWjrfR0h2XBoo0aNUF5ebrhdVlaG2NhYw+0///wTubm5htssy3JulBgeHo5mzZqZ/KffPdmV9GWhrldWg8W9slAFp4pd3hZiypFhVEJcTcqqU0ePHkVycjJGjRqF6OhonDhxwuYef/Hx8di+fTtqamqg0WiwadMmxMfHmzymR48eKCgowO3bt1FbW4s9e/YAqBsmbdGiBSZNmoROnTrh4MGDvO9Xv359NG7cGIcPHwZQl6yzZMkSxMfHY+vWraiqqoJGo8HLL79s2L2Ii73vK4RkPcGePXti6dKlqKioQEhICHbv3m1StTw4OBjvv/8+unfvjmbNmiE7OxsDBw6UqjlO85TdHaiaBiHeScpRi6eeegqvvvoqdu7cicDAQDz66KM29/hLSEjA2bNnMWrUKGg0GvTp0wcTJkwweUz79u0xceJEjB49GuHh4WjatCkAoFevXli3bh2GDBmCwMBAxMXF4cKFC7zvp98vcNGiRYiMjMSiRYsQGxuLc+fO4emnn4ZWq0WfPn0wYsQI/PHHH5yv4cj72sK7n6Cz8vPzsWLFCqjVaowePRpTp07F1KlTMWPGDHTs2BG7du3C0qVLoVar0blzZ/zrX/8ybKLIxx37CT75j63g+qIYAN986Jr5J1ftaUgIIf5C0nWCqampSE1NNTlmPA+YlJRk2LPQ03nC7g6+vibOnRuOAtTLJsQfUe1QG/QnxjttKxCsCoH6SltoK+qGBFy9u4Mvr4m7XXQQ5duXg9XULZPRKMtRvn05ALgkEFLNSkL8E5VN42FckgsAmMBqBLY6A1nUnw6XhXKGL6+JqzyQbQiAeqymFpUHsl3y/lSzkhD/RD1BHlwnRgRo0TTuKj5Nneby9vjymjiN8oZdx42JMYzpy71sQoh1FAR5eNqJ0ZVr4lw9PyYPbwiNspzzOB+xhjF9vWYlIYQbBUEennhidEU1DXfMj0UmjDeZEwQARh6EyAT+oghiJQv5ci+bEGIdzQnykHJxqydzx/xY/Q59EZ3yIuTh0QAYyMOjEZ3yos2kGLF6675cs5IQYh31BHn4a0kudw0D1+/Q1+5MUDF7675as5IQYh0FQRv88cToicPA1tAwJiHEGTQcSix40zAwDWMSQpxBPUFiwduGgf2xt04IEYdfBEEqh2U/CiyEEH/g80GQymERQgixxufnBKkcFiGEEGt8Pgh6WtUXQgghnsPng6AvF50mhBDiHJ8Pgt6U7k8IIcS1fD4xxtvS/QkhhLiOzwdBgNL9CSGEcPOLIEiINbSGlBD/RkGQ+C1aQ0oI8fnEGEKsoTWkhBAKgsRv0RpSQggNhxK3cfd8nDdtGUUIkQb1BIlb6Ofjyu9WgMW9+bhDl4+7rA20hpQQQkGQuIUnzMfRXoSEEBoOJW7hKfNxtIaUEP9GPUHiFlTTlRDiCSgIEreg+ThCiCeg4VDiFlTTlRDiCSgIEreh+ThCiLtJOhyan5+PIUOGYODAgcjOzrb6uIKCAvTv31/KphBCCCEWJOsJlpaWYvHixcjLy0NgYCDS0tLQvXt3tGnTxuRx5eXleO+996RqBiGEEGKVZD3BI0eOID4+HhEREQgNDUVSUhJ27txp8bi33noL06dPl6oZhBBCiFWS9QTLysoQExNjuB0bG4vCwkKTx3z55Zd4+OGH0alTJ6uvo1QqoVQqTY6VlJSI21hCCCF+SbIgyLKsxTGGYQz///z589i9eze++OIL3qC2Zs0aZGVlSdJGQggh/k2yINioUSOcPHnScLusrAyxsbGG2zt37sT169cxatQoqNVqlJWVYdy4cfj6669NXmfixIkYMWKEybGSkhKMHz9eqqYTQgjxEwzL1WUTQWlpKcaOHYvc3FyEhIQgLS0N8+fPR1xcnMVjr169ivT0dOzfv1/Qa1+9ehWJiYnYt28fmjVrJnbTCSGE+AnJEmMaNWqEjIwMpKenY/jw4Rg6dCji4uIwdepU/PLLL1K9LSGEECKYZD1BKVFPkBBCiBiodighhBC/RUGQEEKI36IgSAghxG9RECSEEOK3KAgSQgjxWxQECSGE+C0KgoQQQvwWBUFCCCF+i4IgIYQQv0VBkBBCiN+iIEgIIcRvURAkhBDitygIEkII8VsUBAkhhPgtCoKEEEL8FgVBQgghfouCICGEEL9FQZAQQojfoiBICCHEb1EQJIQQ4rcoCBJCCPFbFAQJIYT4LQqChBBC/BYFQUIIIX6LgiAhhBC/RUGQEEKI36IgSAghxG9RECSEEOK3KAgSQgjxWxQECSGE+C1Jg2B+fj6GDBmCgQMHIjs72+L+PXv2IDU1FSkpKcjMzIRKpZKyOYQQQogJyYJgaWkpFi9ejK+//hpbt25FTk4Ofv31V8P9d+/exbx587B69Wps374dtbW12Lx5s1TNIYQQQixIFgSPHDmC+Ph4REREIDQ0FElJSdi5c6fh/tDQUOzfvx/R0dG4e/cubty4gfDwcKmaQwghhFiQS/XCZWVliImJMdyOjY1FYWGhyWMUCgW+++47vPbaa4iNjUXv3r0tXkepVEKpVJocKykpkabRhBBC/IpkQZBlWYtjDMNYHOvXrx+OHTuGf//735g7dy4+/PBDk/vXrFmDrKwsqZpJCCHEj0kWBBs1aoSTJ08abpeVlSE2NtZw++bNmygqKjL0/lJTU5GRkWHxOhMnTsSIESNMjpWUlGD8+PEStZwQQoi/kGxOsGfPnjh69CgqKipQXV2N3bt3o2/fvob7WZbFzJkz8eeffwIAduzYgc6dO1u8Tnh4OJo1a2byX+PGjaVqNiGEED8iaU8wIyMD6enpUKvVGD16NOLi4jB16lTMmDEDHTt2xPz58/HCCy+AYRi0adMG//rXv6RqDiGEEGKBYbkm7zzc1atXkZiYiH379qFZs2bubg4hhBAvRRVjCCGE+C0KgoQQQvwWBUFCCCF+i4IgIYQQv0VBkBBCiN+iIEgIIcRvURAkhBDitygIEkII8VsUBAkhhPgtCoKEEEL8lmS1Qwkxd7voICoPZEOjvAF5eENEJoxH/Q59bT+REEIkQkGQuMTtooMo374crKYWAKBRlqN8+3IAoEBICHEbGg4lLlF5INsQAPVYTS0qD2S7qUWEEEJBkLiIRnnDruOEEOIKFASJS8jDG9p1nBBCXIGCIHGJyITxYORBJscYeRAiE8a7qUWEEEKJMcRF9MkvlB1KCPEkFASJy9Tv0JeCHiHEo9BwKCGEEL9FQZAQQojfoiBICCHEb1EQJIQQ4rcoCBJCCPFbFAQJIYT4LQqChBBC/JZXrhPUarUAgJKSEje3hBDirxo3bgy53CtPocSIV/6C169fBwCMH08ltwgh7rFv3z40a9bM3c0gTmJYlmXd3Qh71dTUoKioCDExMZDJZFYfV1JSgvHjxyM7OxuNGzd2YQupPY7ytDZRe2zztDa5qj3UE/QNXvkLBgcH4/HHHxf8+MaNG3vUFRu1xzZPaxO1xzZPa5OntYd4JkqMIYQQ4rcoCBJCCPFbFAQJIYT4LZ8OguHh4Zg+fTrCw8Pd3RQA1B4hPK1N1B7bPK1NntYe4tm8MjuUEEIIEYNP9wQJIYQQPhQECSGE+C2fDYL5+fkYMmQIBg4ciOzsbLe1486dOxg6dCiuXr0KADhy5AhSU1MxaNAgLF682KVtycrKQkpKClJSUrBo0SK3twcAlixZgiFDhiAlJQWrV6/2iDYBwHvvvYfMzEwAwNmzZzFq1CgkJSVh1qxZ0Gg0Lm1Leno6UlJSMGzYMAwbNgynT59269/3/v37MXLkSAwePBgLFiwA4L7fbOPGjYbvZdiwYejSpQvmzZvnEX9DxEuwPqikpIRNSEhgKysr2aqqKjY1NZW9cOGCy9vx888/s0OHDmUfeeQRtri4mK2urmb79evHXrlyhVWr1eyUKVPYgoICl7Tl8OHD7JgxY9ja2lpWpVKx6enpbH5+vtvaw7Ise+zYMTYtLY1Vq9VsdXU1m5CQwJ49e9atbWJZlj1y5AjbvXt39vXXX2dZlmVTUlLYn376iWVZln3jjTfY7Oxsl7VFp9OxvXr1YtVqteGYO/++r1y5wvbu3Zu9du0aq1Kp2LFjx7IFBQVu/81YlmXPnz/PDhw4kP3zzz89oj3EO/hkT/DIkSOIj49HREQEQkNDkZSUhJ07d7q8HRs2bMA///lPxMbGAgAKCwvRokULNG/eHHK5HKmpqS5rV0xMDDIzMxEYGAiFQoHWrVvj0qVLbmsPAHTr1g1ffvkl5HI5bty4Aa1WC6VS6dY23bx5E4sXL8aLL74IAPjjjz9QU1ODRx99FAAwcuRIl7bn4sWLYBgGU6dOxZNPPomvvvrKrX/fe/bswZAhQ9C4cWMoFAosXrwYISEhbv3N9ObOnYuMjAwUFxd7RHuId/DJIFhWVoaYmBjD7djYWJSWlrq8HQsXLjQp7+bOdrVt29ZwIr906RK+/fZbMAzj9u9JoVDg448/RkpKCnr06OH2327OnDnIyMgwpNebtycmJsal7VEqlejRowc++eQTfPHFF1i/fj3+/PNPt31Hly9fhlarxbPPPosnn3wSX3/9tdt/M6DuwrempgbJycke0R7iPXwyCLIcqz4YhnFDS0x5QrsuXLiAKVOm4PXXX8f999/v9vYAwIwZM3D06FFcu3YNly5dclubNm7ciCZNmqBHjx6GY+7+zR577DEsWrQIoaGhiIqKwujRo/Hxxx+7rU1arRZHjx7F+++/jw0bNuCXX34xzHe7oz1669evx+TJkwG4/zcj3sUrC2jb0qhRI5w8edJwu6yszDAk6U6NGjVCeXm54bar23Xq1CnMmDEDb775JlJSUnD8+HG3tue3336DSqVC+/btERISgkGDBmHnzp0mO4O4sk3ffvstrl+/jmHDhuHWrVu4e/cuGIYx+Y6uX7/u0u/o5MmTUKvVhsDMsizuu+8+t/1u0dHR6NGjB6KiogAAiYmJbv3NAEClUuHEiRN49913Abj/3xnxLj7ZE+zZsyeOHj2KiooKVFdXY/fu3ejbt6+7m4VOnTrh999/Nwwpbdu2zWXtunbtGl5++WV88MEHSElJcXt7AODq1at46623oFKpoFKpsG/fPqSlpbmtTatXr8a2bduwdetWzJgxA/3798c777yDoKAgnDp1CgCwZcsWl35Ht2/fxqJFi1BbW4s7d+5g8+bNeP/99932952QkIDvv/8eSqUSWq0Whw4dwuDBg936d/S///0PDzzwAEJDQwG4/++aeBef7QlmZGQgPT0darUao0ePRlxcnLubhaCgILz77rv429/+htraWvTr1w+DBw92yXuvWrUKtbW1hqtlAEhLS3NbewCgX79+OH36NIYPHw6ZTIZBgwYhJSUFUVFRbmsTlw8++ABvvfUWqqqq8PDDDyM9Pd1l752QkGD4jnQ6HcaNG4cuXbq47e+7U6dOeO655zBu3Dio1Wr06tULY8eORatWrdz2mxUXF5vsG+jOf2fE+1DZNEIIIX7LJ4dDCSGEECEoCBJCCPFbFAQJIYT4LQqChBBC/BYFQUIIIX6LgiDxW5mZmVi1apVdz9m3b59h54SCggIsWbJEiqYRQlzEJ9cJEiKVxMREJCYmAgB++eUX3Lp1y80tIoQ4g4Ig8TjHjh3DokWL0KhRIxQXFyM4OBjvvvsuYmNj8a9//Qvnzp0DwzDo06cPXnnlFcjlcjz88MOYOHEijh07hrt37+KVV17BoEGDkJeXh127dmHFihUAYHFbLzc3Fzk5OVCr1bh16xamTp2KcePGIS8vD7m5uaiurka9evUwYsQI7Nq1Cy+99BLWr18PrVaL+vXro7CwEIMHD8aYMWMAAMuWLUNlZSXefPNNl39/hBDhKAgSj/Tf//4Xb7zxBh5//HGsW7cOM2fORNu2bREREYH8/Hyo1WpMmzYN//nPf/D8889Dq9WiQYMGyMvLw7lz5zBhwgSTHTz4VFVVYePGjVi5ciUiIyPx888/Y/LkyRg3bhwA4Ndff8X+/ftRr1495OXlAairnJKWlobKykpkZGRg7969WL58OcaMGQOdToeNGzfi888/l+z7IYSIg+YEiUd66KGHDEFs1KhROHv2LLZt24YJEyaAYRgEBgYiLS0NBw8eNDxnwoQJhuc++OCDOHHihKD3CgsLw/Lly/Hdd9/ho48+wvLly3H37l3D/e3atUO9evV4XyMhIQHl5eU4d+4cDh06hGbNmqFVq1b2fmxCiItRECQeyXhXAqBu9wTzCn86nQ4ajYbzOTqdDjKZDAzDmDxPrVZbvFdJSQmGDx+OP/74A126dMH//d//mdyvL8xsq71paWnIzc3Fpk2bkJaWZvM5hBD3oyBIPNK5c+dw7tw5AEBOTg46d+6M5ORkZGdng2VZqFQqbNiwAT179jQ8Z8uWLQCAM2fO4Pfff0fXrl0RFRWFCxcuoLa2FhqNBgcOHLB4r6KiIkRFReGll15Cnz59DI/RarW8bZTJZCZB+KmnnsLevXtx5swZDBw40NmvgBDiAjQnSDxSdHQ0PvroI/zxxx+IiorCokWLEBYWhgULFiA1NRVqtRp9+vTBiy++aHjOjz/+iA0bNkCn02Hx4sVo0KABevXqha5duyI5ORkxMTHo3r07/ve//5m8V69evZCbm4vBgwcjJCQEcXFxiIqKwuXLl3nb2KNHD/ztb3+DQqHA7Nmz0bBhQ3To0AGtW7eGQqGQ5HshhIiLdpEgHufYsWOYP38+tm3bJvg57dq1w9GjRw2bvbpDRUUFRo8ejezsbDRp0sRt7SCECEfDoYSIYMOGDRgyZAjS09MpABLiRagnSAghxG9RT5AQQojfoiBICCHEb1EQJIQQ4rcoCBJCCPFbFAQJIYT4LQqChBBC/Nb/AzTNtGFHfP2fAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 468.975x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.FacetGrid(df, hue=\"artist_top_genre\", size=5) \\\n",
+    "   .map(plt.scatter, \"popularity\", \"danceability\") \\\n",
+    "   .add_legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.0 64-bit ('3.7')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "c61deff2839902ac8cb4ed411eb10fee",
+   "translation_date": "2025-09-06T14:10:11+00:00",
+   "source_file": "5-Clustering/1-Visualize/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/5-Clustering/2-K-Means/notebook.ipynb b/translations/vi/5-Clustering/2-K-Means/notebook.ipynb
new file mode 100644
index 000000000..a6d4f7bec
--- /dev/null
+++ b/translations/vi/5-Clustering/2-K-Means/notebook.ipynb
@@ -0,0 +1,231 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "3e5c8ab363e8d88f566d4365efc7e0bd",
+   "translation_date": "2025-09-06T14:20:03+00:00",
+   "source_file": "5-Clustering/2-K-Means/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Bắt đầu từ nơi chúng ta đã kết thúc trong bài học trước, với dữ liệu đã được nhập và lọc.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Chúng ta sẽ chỉ tập trung vào 3 thể loại. Có lẽ chúng ta có thể tạo được 3 cụm!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb b/translations/vi/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
new file mode 100644
index 000000000..56a79dba6
--- /dev/null
+++ b/translations/vi/5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb
@@ -0,0 +1,639 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "anaconda-cloud": "",
+  "kernelspec": {
+   "display_name": "R",
+   "language": "R",
+   "name": "ir"
+  },
+  "language_info": {
+   "codemirror_mode": "r",
+   "file_extension": ".r",
+   "mimetype": "text/x-r-source",
+   "name": "R",
+   "pygments_lexer": "r",
+   "version": "3.4.1"
+  },
+  "colab": {
+   "name": "lesson_14.ipynb",
+   "provenance": [],
+   "collapsed_sections": [],
+   "toc_visible": true
+  },
+  "coopTranslator": {
+   "original_hash": "ad65fb4aad0a156b42216e4929f490fc",
+   "translation_date": "2025-09-06T14:31:58+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/R/lesson_15-R.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GULATlQXLXyR"
+   },
+   "source": [
+    "## Khám phá phân cụm K-Means bằng R và nguyên tắc dữ liệu Tidy.\n",
+    "\n",
+    "### [**Câu hỏi trước bài giảng**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/29/)\n",
+    "\n",
+    "Trong bài học này, bạn sẽ học cách tạo các cụm bằng gói Tidymodels và các gói khác trong hệ sinh thái R (chúng ta sẽ gọi chúng là bạn bè 🧑‍🤝‍🧑), cùng với bộ dữ liệu âm nhạc Nigeria mà bạn đã nhập trước đó. Chúng ta sẽ tìm hiểu những điều cơ bản về K-Means để phân cụm. Hãy nhớ rằng, như bạn đã học trong bài trước, có nhiều cách để làm việc với các cụm và phương pháp bạn sử dụng phụ thuộc vào dữ liệu của bạn. Chúng ta sẽ thử K-Means vì đây là kỹ thuật phân cụm phổ biến nhất. Bắt đầu nào!\n",
+    "\n",
+    "Các thuật ngữ bạn sẽ học:\n",
+    "\n",
+    "-   Điểm số Silhouette\n",
+    "\n",
+    "-   Phương pháp Elbow\n",
+    "\n",
+    "-   Quán tính (Inertia)\n",
+    "\n",
+    "-   Phương sai (Variance)\n",
+    "\n",
+    "### **Giới thiệu**\n",
+    "\n",
+    "[Phân cụm K-Means](https://wikipedia.org/wiki/K-means_clustering) là một phương pháp xuất phát từ lĩnh vực xử lý tín hiệu. Nó được sử dụng để chia và phân nhóm dữ liệu thành `k cụm` dựa trên sự tương đồng trong các đặc điểm của chúng.\n",
+    "\n",
+    "Các cụm có thể được hình dung dưới dạng [biểu đồ Voronoi](https://wikipedia.org/wiki/Voronoi_diagram), bao gồm một điểm (hoặc 'hạt giống') và vùng tương ứng của nó.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/voronoi.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Đồ họa thông tin bởi Jen Looper</figcaption>\n",
+    "\n",
+    "Phân cụm K-Means có các bước sau:\n",
+    "\n",
+    "1.  Nhà khoa học dữ liệu bắt đầu bằng cách xác định số lượng cụm mong muốn sẽ được tạo.\n",
+    "\n",
+    "2.  Tiếp theo, thuật toán chọn ngẫu nhiên K quan sát từ bộ dữ liệu để làm trung tâm ban đầu cho các cụm (tức là các centroid).\n",
+    "\n",
+    "3.  Sau đó, mỗi quan sát còn lại được gán cho centroid gần nhất của nó.\n",
+    "\n",
+    "4.  Tiếp theo, trung bình mới của mỗi cụm được tính toán và centroid được di chuyển đến vị trí trung bình.\n",
+    "\n",
+    "5.  Bây giờ các trung tâm đã được tính toán lại, mỗi quan sát được kiểm tra lại để xem liệu nó có thể gần hơn với một cụm khác hay không. Tất cả các đối tượng được gán lại bằng cách sử dụng các trung bình cụm đã cập nhật. Các bước gán cụm và cập nhật centroid được lặp lại cho đến khi việc gán cụm không còn thay đổi (tức là khi đạt được sự hội tụ). Thông thường, thuật toán kết thúc khi mỗi lần lặp mới dẫn đến sự di chuyển không đáng kể của các centroid và các cụm trở nên ổn định.\n",
+    "\n",
+    "<div>\n",
+    "\n",
+    "> Lưu ý rằng do sự ngẫu nhiên của các quan sát k ban đầu được sử dụng làm centroid khởi đầu, chúng ta có thể nhận được kết quả hơi khác nhau mỗi lần áp dụng quy trình. Vì lý do này, hầu hết các thuật toán sử dụng nhiều *khởi đầu ngẫu nhiên* và chọn lần lặp có WCSS thấp nhất. Do đó, rất khuyến khích luôn chạy K-Means với nhiều giá trị *nstart* để tránh một *điểm cực tiểu cục bộ không mong muốn.*\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "Hình ảnh động ngắn này sử dụng [tác phẩm nghệ thuật](https://github.com/allisonhorst/stats-illustrations) của Allison Horst giải thích quá trình phân cụm:\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/kmeans.gif\"\n",
+    "   width=\"550\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật bởi @allison_horst</figcaption>\n",
+    "\n",
+    "Một câu hỏi cơ bản nảy sinh trong phân cụm là: làm thế nào để bạn biết nên chia dữ liệu của mình thành bao nhiêu cụm? Một nhược điểm của việc sử dụng K-Means là bạn sẽ cần xác định `k`, tức là số lượng `centroid`. May mắn thay, `phương pháp elbow` giúp ước tính một giá trị khởi đầu tốt cho `k`. Bạn sẽ thử nó ngay bây giờ.\n",
+    "\n",
+    "### \n",
+    "\n",
+    "**Điều kiện tiên quyết**\n",
+    "\n",
+    "Chúng ta sẽ tiếp tục từ nơi đã dừng lại trong [bài học trước](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb), nơi chúng ta đã phân tích bộ dữ liệu, tạo nhiều hình ảnh trực quan và lọc bộ dữ liệu để lấy các quan sát quan trọng. Hãy chắc chắn kiểm tra nó!\n",
+    "\n",
+    "Chúng ta sẽ cần một số gói để hoàn thành module này. Bạn có thể cài đặt chúng bằng: `install.packages(c('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork'))`\n",
+    "\n",
+    "Ngoài ra, đoạn mã dưới đây sẽ kiểm tra xem bạn đã có các gói cần thiết để hoàn thành module này chưa và cài đặt chúng nếu thiếu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "ah_tBi58LXyi"
+   },
+   "source": [
+    "suppressWarnings(if(!require(\"pacman\")) install.packages(\"pacman\"))\n",
+    "\n",
+    "pacman::p_load('tidyverse', 'tidymodels', 'cluster', 'summarytools', 'plotly', 'paletteer', 'factoextra', 'patchwork')\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7e--UCUTLXym"
+   },
+   "source": [
+    "Hãy bắt đầu ngay thôi nào!\n",
+    "\n",
+    "## 1. Một điệu nhảy với dữ liệu: Thu hẹp xuống 3 thể loại nhạc phổ biến nhất\n",
+    "\n",
+    "Đây là phần ôn lại những gì chúng ta đã làm trong bài học trước. Hãy cùng phân tích và xử lý dữ liệu nào!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Ycamx7GGLXyn"
+   },
+   "source": [
+    "# Load the core tidyverse and make it available in your current R session\n",
+    "library(tidyverse)\n",
+    "\n",
+    "# Import the data into a tibble\n",
+    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/5-Clustering/data/nigerian-songs.csv\", show_col_types = FALSE)\n",
+    "\n",
+    "# Narrow down to top 3 popular genres\n",
+    "nigerian_songs <- df %>% \n",
+    "  # Concentrate on top 3 genres\n",
+    "  filter(artist_top_genre %in% c(\"afro dancehall\", \"afropop\",\"nigerian pop\")) %>% \n",
+    "  # Remove unclassified observations\n",
+    "  filter(popularity != 0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Visualize popular genres using bar plots\n",
+    "theme_set(theme_light())\n",
+    "nigerian_songs %>%\n",
+    "  count(artist_top_genre) %>%\n",
+    "  ggplot(mapping = aes(x = artist_top_genre, y = n,\n",
+    "                       fill = artist_top_genre)) +\n",
+    "  geom_col(alpha = 0.8) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"ggsci::category10_d3\") +\n",
+    "  ggtitle(\"Top genres\") +\n",
+    "  theme(plot.title = element_text(hjust = 0.5))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "b5h5zmkPLXyp"
+   },
+   "source": [
+    "🤩 Điều đó thật tuyệt!\n",
+    "\n",
+    "## 2. Khám phá dữ liệu thêm.\n",
+    "\n",
+    "Dữ liệu này sạch đến mức nào? Hãy kiểm tra các giá trị ngoại lai bằng cách sử dụng biểu đồ hộp. Chúng ta sẽ tập trung vào các cột số với ít giá trị ngoại lai hơn (mặc dù bạn có thể loại bỏ các giá trị ngoại lai). Biểu đồ hộp có thể hiển thị phạm vi của dữ liệu và sẽ giúp chọn những cột nào để sử dụng. Lưu ý rằng, biểu đồ hộp không hiển thị độ biến thiên, một yếu tố quan trọng của dữ liệu có thể phân cụm tốt. Vui lòng xem [thảo luận này](https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot) để tìm hiểu thêm.\n",
+    "\n",
+    "[Biểu đồ hộp](https://en.wikipedia.org/wiki/Box_plot) được sử dụng để mô tả phân phối dữ liệu `số` một cách trực quan, vì vậy hãy bắt đầu bằng cách *chọn* tất cả các cột số cùng với các thể loại nhạc phổ biến.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HhNreJKLLXyq"
+   },
+   "source": [
+    "# Select top genre column and all other numeric columns\n",
+    "df_numeric <- nigerian_songs %>% \n",
+    "  select(artist_top_genre, where(is.numeric)) \n",
+    "\n",
+    "# Display the data\n",
+    "df_numeric %>% \n",
+    "  slice_head(n = 5)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uYXrwJRaLXyq"
+   },
+   "source": [
+    "Hãy xem cách công cụ chọn `where` giúp việc này trở nên dễ dàng 💁? Khám phá các hàm khác tương tự [tại đây](https://tidyselect.r-lib.org/).\n",
+    "\n",
+    "Vì chúng ta sẽ tạo biểu đồ hộp cho từng đặc điểm số và muốn tránh sử dụng vòng lặp, hãy định dạng lại dữ liệu của chúng ta thành dạng *dài hơn* để có thể tận dụng `facets` - các biểu đồ con, mỗi biểu đồ hiển thị một tập hợp con của dữ liệu.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "gd5bR3f8LXys"
+   },
+   "source": [
+    "# Pivot data from wide to long\n",
+    "df_numeric_long <- df_numeric %>% \n",
+    "  pivot_longer(!artist_top_genre, names_to = \"feature_names\", values_to = \"values\") \n",
+    "\n",
+    "# Print out data\n",
+    "df_numeric_long %>% \n",
+    "  slice_head(n = 15)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-7tE1swnLXyv"
+   },
+   "source": [
+    "Lâu hơn nhiều! Bây giờ là lúc cho một số `ggplots`! Vậy chúng ta sẽ sử dụng `geom` nào?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "r88bIsyuLXyy"
+   },
+   "source": [
+    "# Make a box plot\n",
+    "df_numeric_long %>% \n",
+    "  ggplot(mapping = aes(x = feature_names, y = values, fill = feature_names)) +\n",
+    "  geom_boxplot() +\n",
+    "  facet_wrap(~ feature_names, ncol = 4, scales = \"free\") +\n",
+    "  theme(legend.position = \"none\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EYVyKIUELXyz"
+   },
+   "source": [
+    "Dễ dàng!\n",
+    "\n",
+    "Bây giờ chúng ta có thể thấy dữ liệu này hơi nhiễu: bằng cách quan sát từng cột dưới dạng biểu đồ hộp, bạn có thể thấy các giá trị ngoại lai. Bạn có thể duyệt qua tập dữ liệu và loại bỏ các giá trị ngoại lai này, nhưng điều đó sẽ làm cho dữ liệu trở nên khá tối giản.\n",
+    "\n",
+    "Hiện tại, hãy chọn những cột mà chúng ta sẽ sử dụng cho bài tập phân cụm. Hãy chọn các cột số với phạm vi tương tự. Chúng ta có thể mã hóa `artist_top_genre` dưới dạng số nhưng tạm thời sẽ bỏ qua nó.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "-wkpINyZLXy0"
+   },
+   "source": [
+    "# Select variables with similar ranges\n",
+    "df_numeric_select <- df_numeric %>% \n",
+    "  select(popularity, danceability, acousticness, loudness, energy) \n",
+    "\n",
+    "# Normalize data\n",
+    "# df_numeric_select <- scale(df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "D7dLzgpqLXy1"
+   },
+   "source": [
+    "## 3. Tính toán phân cụm k-means trong R\n",
+    "\n",
+    "Chúng ta có thể tính toán k-means trong R bằng hàm tích hợp sẵn `kmeans`, xem `help(\"kmeans()\")`. Hàm `kmeans()` chấp nhận một khung dữ liệu (data frame) với tất cả các cột là số làm đối số chính.\n",
+    "\n",
+    "Bước đầu tiên khi sử dụng phân cụm k-means là xác định số cụm (k) sẽ được tạo ra trong giải pháp cuối cùng. Chúng ta biết có 3 thể loại bài hát được tách ra từ tập dữ liệu, vì vậy hãy thử với 3:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "uC4EQ5w7LXy5"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Kmeans clustering for 3 clusters\n",
+    "kclust <- kmeans(\n",
+    "  df_numeric_select,\n",
+    "  # Specify the number of clusters\n",
+    "  centers = 3,\n",
+    "  # How many random initial configurations\n",
+    "  nstart = 25\n",
+    ")\n",
+    "\n",
+    "# Display clustering object\n",
+    "kclust\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hzfhscWrLXy-"
+   },
+   "source": [
+    "Đối tượng kmeans chứa nhiều thông tin được giải thích rõ trong `help(\"kmeans()\")`. Hiện tại, chúng ta hãy tập trung vào một vài điểm. Chúng ta thấy rằng dữ liệu đã được phân thành 3 cụm với kích thước lần lượt là 65, 110, 111. Kết quả cũng bao gồm các trung tâm cụm (giá trị trung bình) cho 3 nhóm trên 5 biến số.\n",
+    "\n",
+    "Vector phân cụm là sự phân bổ cụm cho từng quan sát. Hãy sử dụng hàm `augment` để thêm sự phân bổ cụm vào tập dữ liệu gốc.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "0XwwpFGQLXy_"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "augment(kclust, df_numeric_select) %>% \n",
+    "  relocate(.cluster) %>% \n",
+    "  slice_head(n = 10)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NXIVXXACLXzA"
+   },
+   "source": [
+    "Hoàn hảo, chúng ta vừa phân chia tập dữ liệu thành 3 nhóm. Vậy, việc phân cụm của chúng ta tốt đến mức nào 🤷? Hãy cùng xem xét `Silhouette score`.\n",
+    "\n",
+    "### **Silhouette score**\n",
+    "\n",
+    "[Phân tích Silhouette](https://en.wikipedia.org/wiki/Silhouette_(clustering)) có thể được sử dụng để nghiên cứu khoảng cách phân tách giữa các cụm kết quả. Điểm số này dao động từ -1 đến 1, và nếu điểm số gần 1, cụm đó dày đặc và được phân tách tốt khỏi các cụm khác. Giá trị gần 0 biểu thị các cụm chồng lấn với các mẫu rất gần ranh giới quyết định của các cụm lân cận. [nguồn](https://dzone.com/articles/kmeans-silhouette-score-explained-with-python-exam).\n",
+    "\n",
+    "Phương pháp silhouette trung bình tính toán silhouette trung bình của các quan sát cho các giá trị khác nhau của *k*. Điểm silhouette trung bình cao cho thấy việc phân cụm tốt.\n",
+    "\n",
+    "Hàm `silhouette` trong gói cluster được sử dụng để tính toán độ rộng silhouette trung bình.\n",
+    "\n",
+    "> Silhouette có thể được tính toán với bất kỳ [khoảng cách](https://en.wikipedia.org/wiki/Distance \"Distance\") nào, chẳng hạn như [khoảng cách Euclid](https://en.wikipedia.org/wiki/Euclidean_distance \"Euclidean distance\") hoặc [khoảng cách Manhattan](https://en.wikipedia.org/wiki/Manhattan_distance \"Manhattan distance\") mà chúng ta đã thảo luận trong [bài học trước](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/1-Visualize/solution/R/lesson_14-R.ipynb).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Jn0McL28LXzB"
+   },
+   "source": [
+    "# Load cluster package\n",
+    "library(cluster)\n",
+    "\n",
+    "# Compute average silhouette score\n",
+    "ss <- silhouette(kclust$cluster,\n",
+    "                 # Compute euclidean distance\n",
+    "                 dist = dist(df_numeric_select))\n",
+    "mean(ss[, 3])\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QyQRn97nLXzC"
+   },
+   "source": [
+    "Điểm số của chúng ta là **0.549**, nằm ở mức trung bình. Điều này cho thấy dữ liệu của chúng ta không thực sự phù hợp với loại phân cụm này. Hãy xem liệu chúng ta có thể xác nhận nhận định này một cách trực quan hay không. [Gói factoextra](https://rpkgs.datanovia.com/factoextra/index.html) cung cấp các hàm (`fviz_cluster()`) để trực quan hóa phân cụm.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "7a6Km1_FLXzD"
+   },
+   "source": [
+    "library(factoextra)\n",
+    "\n",
+    "# Visualize clustering results\n",
+    "fviz_cluster(kclust, df_numeric_select)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IBwCWt-0LXzD"
+   },
+   "source": [
+    "Sự chồng chéo giữa các cụm cho thấy rằng dữ liệu của chúng ta không thực sự phù hợp với loại phân cụm này, nhưng hãy tiếp tục.\n",
+    "\n",
+    "## 4. Xác định số cụm tối ưu\n",
+    "\n",
+    "Một câu hỏi cơ bản thường xuất hiện trong phân cụm K-Means là - khi không có nhãn lớp đã biết, làm thế nào để bạn biết nên chia dữ liệu thành bao nhiêu cụm?\n",
+    "\n",
+    "Một cách chúng ta có thể thử tìm ra là sử dụng một mẫu dữ liệu để `tạo một loạt các mô hình phân cụm` với số cụm tăng dần (ví dụ từ 1-10), và đánh giá các chỉ số phân cụm như **điểm Silhouette.**\n",
+    "\n",
+    "Hãy xác định số cụm tối ưu bằng cách tính toán thuật toán phân cụm với các giá trị khác nhau của *k* và đánh giá **Tổng bình phương khoảng cách trong cụm** (WCSS). Tổng bình phương khoảng cách trong cụm (WCSS) đo lường mức độ chặt chẽ của phân cụm và chúng ta muốn giá trị này càng nhỏ càng tốt, với các giá trị thấp hơn có nghĩa là các điểm dữ liệu gần nhau hơn.\n",
+    "\n",
+    "Hãy khám phá ảnh hưởng của các lựa chọn khác nhau về `k`, từ 1 đến 10, đối với phân cụm này.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "hSeIiylDLXzE"
+   },
+   "source": [
+    "# Create a series of clustering models\n",
+    "kclusts <- tibble(k = 1:10) %>% \n",
+    "  # Perform kmeans clustering for 1,2,3 ... ,10 clusters\n",
+    "  mutate(model = map(k, ~ kmeans(df_numeric_select, centers = .x, nstart = 25)),\n",
+    "  # Farm out clustering metrics eg WCSS\n",
+    "         glanced = map(model, ~ glance(.x))) %>% \n",
+    "  unnest(cols = glanced)\n",
+    "  \n",
+    "\n",
+    "# View clustering rsulsts\n",
+    "kclusts\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m7rS2U1eLXzE"
+   },
+   "source": [
+    "Bây giờ chúng ta đã có tổng bình phương trong cụm (tot.withinss) cho mỗi thuật toán phân cụm với tâm *k*, chúng ta sử dụng [phương pháp khuỷu tay](https://en.wikipedia.org/wiki/Elbow_method_(clustering)) để tìm số lượng cụm tối ưu. Phương pháp này bao gồm việc vẽ biểu đồ WCSS như một hàm của số lượng cụm, và chọn [điểm khuỷu tay của đường cong](https://en.wikipedia.org/wiki/Elbow_of_the_curve \"Elbow of the curve\") làm số lượng cụm cần sử dụng.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "o_DjHGItLXzF"
+   },
+   "source": [
+    "set.seed(2056)\n",
+    "# Use elbow method to determine optimum number of clusters\n",
+    "kclusts %>% \n",
+    "  ggplot(mapping = aes(x = k, y = tot.withinss)) +\n",
+    "  geom_line(size = 1.2, alpha = 0.8, color = \"#FF7F0EFF\") +\n",
+    "  geom_point(size = 2, color = \"#FF7F0EFF\")\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pLYyt5XSLXzG"
+   },
+   "source": [
+    "Biểu đồ cho thấy sự giảm đáng kể trong WCSS (tức là *độ chặt chẽ* lớn hơn) khi số lượng cụm tăng từ một lên hai, và một sự giảm đáng chú ý khác từ hai lên ba cụm. Sau đó, mức giảm ít rõ rệt hơn, dẫn đến một điểm `khuỷu tay` 💪 trên biểu đồ ở khoảng ba cụm. Đây là một dấu hiệu tốt cho thấy có hai đến ba cụm dữ liệu được phân tách khá rõ ràng.\n",
+    "\n",
+    "Bây giờ chúng ta có thể tiếp tục và trích xuất mô hình phân cụm với `k = 3`:\n",
+    "\n",
+    "> `pull()`: được sử dụng để trích xuất một cột duy nhất  \n",
+    ">\n",
+    "> `pluck()`: được sử dụng để truy cập các cấu trúc dữ liệu như danh sách  \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JP_JPKBILXzG"
+   },
+   "source": [
+    "# Extract k = 3 clustering\n",
+    "final_kmeans <- kclusts %>% \n",
+    "  filter(k == 3) %>% \n",
+    "  pull(model) %>% \n",
+    "  pluck(1)\n",
+    "\n",
+    "\n",
+    "final_kmeans\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l_PDTu8tLXzI"
+   },
+   "source": [
+    "Tuyệt vời! Hãy cùng xem qua các cụm đã thu được. Bạn có muốn thêm tính tương tác bằng cách sử dụng `plotly` không?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "dNcleFe-LXzJ"
+   },
+   "source": [
+    "# Add predicted cluster assignment to data set\n",
+    "results <-  augment(final_kmeans, df_numeric_select) %>% \n",
+    "  bind_cols(df_numeric %>% select(artist_top_genre)) \n",
+    "\n",
+    "# Plot cluster assignments\n",
+    "clust_plt <- results %>% \n",
+    "  ggplot(mapping = aes(x = popularity, y = danceability, color = .cluster, shape = artist_top_genre)) +\n",
+    "  geom_point(size = 2, alpha = 0.8) +\n",
+    "  paletteer::scale_color_paletteer_d(\"ggthemes::Tableau_10\")\n",
+    "\n",
+    "ggplotly(clust_plt)\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6JUM_51VLXzK"
+   },
+   "source": [
+    "Có lẽ chúng ta đã kỳ vọng rằng mỗi cụm (được biểu thị bằng các màu sắc khác nhau) sẽ có các thể loại riêng biệt (được biểu thị bằng các hình dạng khác nhau).\n",
+    "\n",
+    "Hãy cùng xem xét độ chính xác của mô hình.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "HdIMUGq7LXzL"
+   },
+   "source": [
+    "# Assign genres to predefined integers\n",
+    "label_count <- results %>% \n",
+    "  group_by(artist_top_genre) %>% \n",
+    "  mutate(id = cur_group_id()) %>% \n",
+    "  ungroup() %>% \n",
+    "  summarise(correct_labels = sum(.cluster == id))\n",
+    "\n",
+    "\n",
+    "# Print results  \n",
+    "cat(\"Result:\", label_count$correct_labels, \"out of\", nrow(results), \"samples were correctly labeled.\")\n",
+    "\n",
+    "cat(\"\\nAccuracy score:\", label_count$correct_labels/nrow(results))\n"
+   ],
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "C50wvaAOLXzM"
+   },
+   "source": [
+    "Độ chính xác của mô hình này không tệ, nhưng cũng không quá tốt. Có thể là do dữ liệu không phù hợp để áp dụng K-Means Clustering. Dữ liệu này quá mất cân đối, ít tương quan và có quá nhiều sự biến đổi giữa các giá trị cột, khiến việc phân cụm trở nên khó khăn. Thực tế, các cụm được hình thành có lẽ bị ảnh hưởng hoặc lệch nhiều bởi ba danh mục thể loại mà chúng ta đã định nghĩa ở trên.\n",
+    "\n",
+    "Dù vậy, đây vẫn là một quá trình học hỏi rất thú vị!\n",
+    "\n",
+    "Trong tài liệu của Scikit-learn, bạn có thể thấy rằng một mô hình như thế này, với các cụm không được phân định rõ ràng, gặp phải vấn đề về 'phương sai':\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/problems.png\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Infographic từ Scikit-learn</figcaption>\n",
+    "\n",
+    "\n",
+    "\n",
+    "## **Phương sai**\n",
+    "\n",
+    "Phương sai được định nghĩa là \"trung bình của bình phương các độ lệch so với giá trị trung bình\" [nguồn](https://www.mathsisfun.com/data/standard-deviation.html). Trong bối cảnh của bài toán phân cụm này, nó ám chỉ việc các giá trị trong tập dữ liệu của chúng ta có xu hướng lệch quá nhiều so với giá trị trung bình.\n",
+    "\n",
+    "✅ Đây là thời điểm tuyệt vời để suy nghĩ về tất cả các cách bạn có thể khắc phục vấn đề này. Điều chỉnh dữ liệu thêm một chút? Sử dụng các cột khác? Dùng một thuật toán khác? Gợi ý: Hãy thử [chuẩn hóa dữ liệu của bạn](https://www.mygreatlearning.com/blog/learning-data-science-with-k-means-clustering/) để đưa nó về cùng một thang đo và kiểm tra các cột khác.\n",
+    "\n",
+    "> Hãy thử '[máy tính phương sai](https://www.calculatorsoup.com/calculators/statistics/variance-calculator.php)' để hiểu rõ hơn về khái niệm này.\n",
+    "\n",
+    "------------------------------------------------------------------------\n",
+    "\n",
+    "## **🚀Thử thách**\n",
+    "\n",
+    "Dành thời gian với notebook này, điều chỉnh các tham số. Bạn có thể cải thiện độ chính xác của mô hình bằng cách làm sạch dữ liệu hơn (ví dụ như loại bỏ các giá trị ngoại lai)? Bạn có thể sử dụng trọng số để tăng trọng số cho các mẫu dữ liệu nhất định. Còn cách nào khác để tạo ra các cụm tốt hơn?\n",
+    "\n",
+    "Gợi ý: Hãy thử chuẩn hóa dữ liệu của bạn. Có đoạn mã đã được chú thích trong notebook để thêm chuẩn hóa tiêu chuẩn, giúp các cột dữ liệu giống nhau hơn về mặt phạm vi. Bạn sẽ thấy rằng mặc dù điểm silhouette giảm xuống, nhưng 'gấp khúc' trong đồ thị khuỷu tay trở nên mượt mà hơn. Điều này là do việc để dữ liệu không được chuẩn hóa cho phép dữ liệu có ít phương sai hơn mang nhiều trọng số hơn. Đọc thêm về vấn đề này [tại đây](https://stats.stackexchange.com/questions/21222/are-mean-normalization-and-feature-scaling-needed-for-k-means-clustering/21226#21226).\n",
+    "\n",
+    "## [**Câu hỏi sau bài giảng**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/30/)\n",
+    "\n",
+    "## **Ôn tập & Tự học**\n",
+    "\n",
+    "-   Xem qua một trình mô phỏng K-Means [như thế này](https://user.ceng.metu.edu.tr/~akifakkus/courses/ceng574/k-means/). Bạn có thể sử dụng công cụ này để trực quan hóa các điểm dữ liệu mẫu và xác định các tâm cụm. Bạn có thể chỉnh sửa độ ngẫu nhiên của dữ liệu, số lượng cụm và số lượng tâm cụm. Điều này có giúp bạn hình dung cách dữ liệu có thể được nhóm lại không?\n",
+    "\n",
+    "-   Ngoài ra, hãy xem [tài liệu về K-Means](https://stanford.edu/~cpiech/cs221/handouts/kmeans.html) từ Stanford.\n",
+    "\n",
+    "Muốn thử áp dụng kỹ năng phân cụm mới học vào các tập dữ liệu phù hợp với K-Means clustering? Hãy xem:\n",
+    "\n",
+    "-   [Huấn luyện và Đánh giá Mô hình Phân cụm](https://rpubs.com/eR_ic/clustering) sử dụng Tidymodels và các công cụ liên quan\n",
+    "\n",
+    "-   [Phân tích Cụm K-means](https://uc-r.github.io/kmeans_clustering), Hướng dẫn Lập trình R của UC Business Analytics\n",
+    "\n",
+    "- [Phân cụm K-means với nguyên tắc dữ liệu gọn gàng](https://www.tidymodels.org/learn/statistics/k-means/)\n",
+    "\n",
+    "## **Bài tập**\n",
+    "\n",
+    "[Thử các phương pháp phân cụm khác nhau](https://github.com/microsoft/ML-For-Beginners/blob/main/5-Clustering/2-K-Means/assignment.md)\n",
+    "\n",
+    "## CẢM ƠN ĐẾN:\n",
+    "\n",
+    "[Jen Looper](https://www.twitter.com/jenlooper) vì đã tạo phiên bản Python gốc của module này ♥️\n",
+    "\n",
+    "[`Allison Horst`](https://twitter.com/allison_horst/) vì đã tạo ra những hình minh họa tuyệt vời giúp R trở nên thân thiện và hấp dẫn hơn. Tìm thêm các hình minh họa tại [bộ sưu tập của cô ấy](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
+    "\n",
+    "Chúc bạn học vui,\n",
+    "\n",
+    "[Eric](https://twitter.com/ericntay), Đại sứ Sinh viên Microsoft Learn Vàng.\n",
+    "\n",
+    "<p >\n",
+    "   <img src=\"../../images/r_learners_sm.jpeg\"\n",
+    "   width=\"500\"/>\n",
+    "   <figcaption>Tác phẩm nghệ thuật của @allison_horst</figcaption>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
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@@ -0,0 +1,546 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
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+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
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+  "kernelspec": {
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+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "e867e87e3129c8875423a82945f4ad5e",
+   "translation_date": "2025-09-06T14:22:10+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Bắt đầu từ nơi chúng ta đã kết thúc trong bài học trước, với dữ liệu đã được nhập và lọc.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Chúng ta sẽ chỉ tập trung vào 3 thể loại. Có lẽ chúng ta có thể xây dựng được 3 cụm!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Dữ liệu này sạch đến mức nào? Kiểm tra các giá trị ngoại lai bằng cách sử dụng biểu đồ hộp. Chúng ta sẽ tập trung vào các cột có ít giá trị ngoại lai hơn (mặc dù bạn có thể loại bỏ các giá trị ngoại lai). Biểu đồ hộp có thể hiển thị phạm vi của dữ liệu và sẽ giúp chọn cột nào để sử dụng. Lưu ý, biểu đồ hộp không hiển thị phương sai, một yếu tố quan trọng của dữ liệu có thể phân cụm tốt (https://stats.stackexchange.com/questions/91536/deduce-variance-from-boxplot)\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbc18790a20>"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,20), dpi=200)\n",
+    "\n",
+    "plt.subplot(4,3,1)\n",
+    "sns.boxplot(x = 'popularity', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,2)\n",
+    "sns.boxplot(x = 'acousticness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,3)\n",
+    "sns.boxplot(x = 'energy', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,4)\n",
+    "sns.boxplot(x = 'instrumentalness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,5)\n",
+    "sns.boxplot(x = 'liveness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,6)\n",
+    "sns.boxplot(x = 'loudness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,7)\n",
+    "sns.boxplot(x = 'speechiness', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,8)\n",
+    "sns.boxplot(x = 'tempo', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,9)\n",
+    "sns.boxplot(x = 'time_signature', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,10)\n",
+    "sns.boxplot(x = 'danceability', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,11)\n",
+    "sns.boxplot(x = 'length', data = df)\n",
+    "\n",
+    "plt.subplot(4,3,12)\n",
+    "sns.boxplot(x = 'release_date', data = df)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "# scaler = StandardScaler()\n",
+    "\n",
+    "X = df.loc[:, ('artist_top_genre','popularity','danceability','acousticness','loudness','energy')]\n",
+    "\n",
+    "y = df['artist_top_genre']\n",
+    "\n",
+    "X['artist_top_genre'] = le.fit_transform(X['artist_top_genre'])\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "y = le.transform(y)\n",
+    "\n"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([2, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 0, 2, 1, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 0, 2, 0, 0, 2, 2, 1, 1, 0, 2, 2, 2, 2, 1, 1, 0, 2, 0, 2, 0,\n",
+       "       2, 0, 0, 1, 1, 2, 1, 0, 0, 2, 2, 2, 2, 1, 1, 0, 1, 2, 2, 1, 2, 2,\n",
+       "       1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 2, 1, 2, 0,\n",
+       "       1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 0, 1, 1, 1, 1, 0, 1, 2, 1, 2,\n",
+       "       1, 2, 2, 2, 0, 2, 1, 1, 1, 2, 1, 0, 1, 2, 2, 1, 1, 1, 0, 1, 2, 2,\n",
+       "       2, 1, 1, 0, 1, 2, 1, 1, 1, 1, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 1, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 2, 0, 2, 2, 0, 2, 2, 1, 1, 0,\n",
+       "       1, 1, 0, 0, 1, 0, 2, 0, 1, 0, 2, 0, 0, 2, 2, 2, 1, 1, 1, 1, 1, 0,\n",
+       "       2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 0, 1, 1, 1, 0, 2, 2, 2,\n",
+       "       1, 1, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 2, 0, 0, 2, 1, 1, 1, 2, 2, 2,\n",
+       "       1, 2, 1, 2, 1, 1, 1, 0, 2, 2, 2, 1, 2, 1, 0, 1, 2, 1, 1, 1, 2, 1],\n",
+       "      dtype=int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "nclusters = 3 \n",
+    "seed = 0\n",
+    "\n",
+    "km = KMeans(n_clusters=nclusters, random_state=seed)\n",
+    "km.fit(X)\n",
+    "\n",
+    "# Predict the cluster for each data point\n",
+    "\n",
+    "y_cluster_kmeans = km.predict(X)\n",
+    "y_cluster_kmeans"
+   ]
+  },
+  {
+   "source": [
+    "Những con số đó không có ý nghĩa nhiều đối với chúng ta, vì vậy hãy lấy 'điểm silhouette' để xem độ chính xác. Điểm của chúng ta nằm ở mức trung bình.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0.5466747351275563"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ],
+   "source": [
+    "from sklearn import metrics\n",
+    "score = metrics.silhouette_score(X, y_cluster_kmeans)\n",
+    "score"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "wcss = []\n",
+    "\n",
+    "for i in range(1, 11):\n",
+    "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 42)\n",
+    "    kmeans.fit(X)\n",
+    "    wcss.append(kmeans.inertia_)"
+   ]
+  },
+  {
+   "source": [
+    "Sử dụng mô hình đó để quyết định, bằng phương pháp Elbow, số lượng cụm tốt nhất để xây dựng\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n  FutureWarning\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x360 with 1 Axes>",
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NMANcuzrqqOKBBp9GlSSp7Rjg2tXmm8PBBxcBLrPqaiRJ0gAywLWzWg2eeAIeeKDqSiRJ0gAywLWzmTOho8NhVEmS2sx6A1xE7BMRE+v2PxkRcyLi3IgY1/jytFG22gqmT3c6EUmS2kxvPXD/DiwHiIgDgK8DFwJLgfMaW5oGRFcXPPJI8ZIkSW2htwA3LDOXlNsfA87LzB9m5l8COza2NA2IWbOKd4dRJUlqG70GuIgYXm4fBNxUd2x4D+er2UyaBPvtZ4CTJKmN9BbgLgZujYg5wJvA7QARsSPFMKpaQa0G990HTz5ZdSWSJGkArDfAZeb/C3wB+D6wf+ZvJhTrAD7T2NI0YGq14t1eOEmS2kJvT6GOBu7NzCsy8/WI2CUizgR2z8z7BqdEbbQddoA99zTASZLUJnobQr0WmAq/GTb9ObADcFpE/F1jS9OAqtXgZz8r1keVJEktrbcANzYzHyu3TwIuzszPAEcARzW0Mg2srq5iSa3Zs6uuRJIkbaTeAlz9IpozgBsAMnM5sLJRRakB3vte2GUXJ/WVJKkN9BbgHoyIfyjve9sRuB4gIrZseGUaWBFFL9wtt8CLL1ZdjSRJ2gi9BbhPA7+muA/u0Mx8o2zfDfiHBtalRqjV4J134Ec/qroSSZK0EXoLcGOAH2XmGZn5i7r2pRQPOKiV7L03TJniMKokSS2utwD3z8D4HtrHAd8a+HLUUBFFL9z118Orr1ZdjSRJ2kC9BbgdM/O2NRsz83bgtxtTkhqqVoPly+Hqq6uuRJIkbaDeAty71nNsxEAWokHyoQ/BxIlO6itJUgvrLcDNj4iPrtkYEUcAjzemJDVURwfMmgXXXANvvll1NZIkaQMM7+X454CrI+J/AfeWbZ3AfjiRb+vq6oJ/+7fiXriZM6uuRpIk9VNvPXBHAr8H3AFMKV+3Ar+dmY82uDY1yvTpMHasw6iSJLWo3nrgtgPOAd4LPEgR5BYBo4G3GluaGmbECDjmGJgzp3igYeTIqiuSJEn9sN4euMw8KzM/BGwDfAlYAvw+8FBE/HIQ6lOjdHXByy8XKzNIkqSW0tsQardNgc2BLcrXs8BdjSpKg+CQQ2DMGCf1lSSpBa03wEXEeRFxB/ADigcXfgYcn5mdmfn7g1GgGmSTTeDII2H27GJ5LUmS1DJ664F7NzAKeB54BlgIvNzoojRIajVYtAjuuKPqSiRJUj/0dg/c4cA+rFq4/gvAPRFxfUT8daOLU4N99KMwapRPo0qS1GJ6vQcuCw8B1wA/pngS9T3AGQ2uTY02ZgwcdlgR4DKrrkaSJPVRb/fAfTYiLomIpyjmfzsK+G+gRrGgvVpdrQZPPw1z51ZdiSRJ6qPe5oGbCvx/wJmZ+Vzjy9GgO/poGD686IXbZ5+qq5EkSX3Q2z1wn8/MH25MeIuIYRFxf0RcVe5vHxF3RcT8iPhBRIws20eV+/PL41PrvuNLZfuvIuKwuvbDy7b5EfHFDa1xSBs3Dg48sJhOxGFUSZJaQl/ngdsYZwCP1O2fDXwzM3cEXgJOLttPBl4q279ZnkdE7AacALwPOBz41zIUDgP+BTgC2A04sTxX/dXVBY89Bg8/XHUlkiSpDxoa4CJiO4r1VL9b7gcwA7isPOUCYFa5PbPcpzx+UHn+TOCSzFyWmU8A84F9y9f8zHw8M5cDl5Tnqr9mzoQIJ/WVJKlFNLoH7hzgfwMry/3xwMuZuaLcXwhMKrcnAU8DlMeXluf/pn2Nz6yrXf01cSLsv7/TiUiS1CIaFuAi4ihgUWbe26if0Y9aTomIuRExd/HixVWX05xqNXjwQZg/v+pKJElSLxrZA/dh4JiIeJJieHMG8C1gy4jofvp1O4oVHijfJwOUx7cAXqxvX+Mz62pfS2aeVy7/1TlhwoSNv7J2dOyxxbu9cJIkNb2GBbjM/FJmbpeZUykeQrgpMz8O3AwcV552EjCn3L6y3Kc8flNmZtl+QvmU6vbATsDdwD3ATuVTrSPLn3Flo66n7U2ZAp2dBjhJklrAYDyFuqY/BT4fEfMp7nE7v2w/Hxhftn8e+CJAZj4MXAr8ErgWOC0z3ynvkzsduI7iKddLy3O1oWo1uOsuWLiw6kokSdJ6RA6xub86OztzrqsO9OzRR2GXXeDcc+Ezn6m6GkmShryIuDczO9dsr6IHTs1q553hfe9zOhFJkpqcAU6r6+qC22+HRYuqrkSSJK2DAU6rq9Vg5Uq40udBJElqVgY4re63fxt22MFhVEmSmpgBTquLKIZRb7wRXn656mokSVIPDHBaW60Gb78NV11VdSWSJKkHBjitbd99YdIkJ/WVJKlJGeC0to6OYmmta6+F11+vuhpJkrQGA5x61tUFb75ZhDhJktRUDHDq2f77w1ZbOYwqSVITMsCpZ8OHw8yZxYMMy5ZVXY0kSapjgNO6dXXBK68UU4pIkqSmYYDTus2YAZtv7qS+kiQ1GQOc1m3UKDj6aJgzB1asqLoaSZJUMsBp/Wo1ePFFuO22qiuRJEklA5zW7/DDYdNNfRpVkqQmYoDT+o0eDUccAVdcAStXVl2NJEnCAKe+qNXg2WfhrruqrkSSJGGAU18cdRSMGOEwqiRJTcIAp95tsQUcfHAxnUhm1dVIkjTkGeDUN11d8MQT8ItfVF2JJElDngFOfXPMMdDR4aS+kiQ1AQOc+mbCBJg+3fvgJElqAsOrLkAt5NRTi4cZ5s+HMWNg662LXjlJkjSo/Ourvlm5EnbaCc48s3ifNg3mzXNuOEmSKmCAU98sWgTHHgsLFhT7CxbAzJlFuyRJGlQGOPXNsmWrwlu3BQuKdkmSNKgMcOqbUaNgypTV26ZMcV44SZIqYIBT32y9NcyZsyrETZkC3/senHyyT6ZKkjTIDHDqm44O2GMPuPNOePLJ4n3XXeHVV4tJfs86C95+u+oqJUkaEgxw6ruODpg4seh9mzgRtt0Wbr8dTjsN/vEfYcaMYtF7SZLUUAY4bZxRo+Db34aLLoL77oO99oKbb666KkmS2poBTgPjd38X7rkHxo0rFr7/u79zjjhJkhrEAKeBs9tucPfdcPzx8Gd/VswT99JLVVclSVLbMcBpYL3rXXDxxfDP/wzXXQcf+EAxtCpJkgaMAU4DLwJOPx1uuw1WrIAPfQjOO8854yRJGiAGODXOtGlF79v06fCHfwif+hS88UbVVUmS1PIMcGqsrbaCa66Br3wF/s//KULdo49WXZUkSS3NAKfGGzYM/uqv4Mc/LuaJ6+yEH/6w6qokSWpZBjgNnsMOK4ZUd9sNjjsOPv95V2+QJGkDGOA0uN797uLhhtNPh29+Ew48EJ55puqqJElqKQY4Db6RI4tpRi6+GB54APbeG266qeqqJElqGQY4VeeEE4rVG8aPh0MOgb/9W1dvkCSpDwxwqtZ731us3vCxj8Gf/zkccwwsWVJ1VZIkNTUDnKo3ZgxcdBF8+9tw/fXF6g1z51ZdlSRJTcsAp+YQAaedBrffDu+8Ax/+MPz7v7t6gyRJPTDAqbl88INw//0wYwaceiqcdBK8/nrVVUmS1FQaFuAiYnJE3BwRv4yIhyPijLJ9XETcEBGPle9jy/aIiHMjYn5EPBgRe9d910nl+Y9FxEl17R+IiHnlZ86NiGjU9WgQjR8PV18Nf/3X8F//Vaze8KtfVV2VJElNo5E9cCuAL2TmbsA04LSI2A34InBjZu4E3FjuAxwB7FS+TgG+A0XgA74CfBDYF/hKd+grz/l03ecOb+D1aDB1dMCXvwzXXgvPPQf77AOXXVZ1VZIkNYWGBbjMfC4z7yu3XwUeASYBM4ELytMuAGaV2zOBC7NwJ7BlRPwWcBhwQ2YuycyXgBuAw8tjm2fmnZmZwIV136V2ceihxZDq+94Hxx8PZ57p6g2SpCFvUO6Bi4ipwF7AXcA2mflceeh5YJtyexLwdN3HFpZt62tf2EN7Tz//lIiYGxFzFy9evFHXogpMngy33gqf/Syccw78zu/AwoW9fkySpHbV8AAXEWOAHwKfy8xX6o+VPWcNf8wwM8/LzM7M7JwwYUKjf5waYeRI+Na34JJL4MEHi9UbfvKTqquSJKkSDQ1wETGCIrxdlJmXl80vlMOflO+LyvZngMl1H9+ubFtf+3Y9tKudfexjxeoNEyYUw6tf+5qrN0iShpxGPoUawPnAI5n5T3WHrgS6nyQ9CZhT1/7J8mnUacDScqj1OuDQiBhbPrxwKHBdeeyViJhW/qxP1n2X2tmuu8Jdd8GJJ8Jf/iUcfbSrN0iShpRG9sB9GPgEMCMiHihfHwW+DhwSEY8BB5f7ANcAjwPzgf8A/hggM5cAXwXuKV9/U7ZRnvPd8jP/A/y4gdejZjJmTDHFyL/+azGUuvfert4gSRoyIofYTPednZ051z/07eWee+C44+D554uHHE49tVjZQZKkFhcR92Zm55rtrsSg1rfPPnDffXDQQfDHfwyf+ISrN0iS2poBTu1h/Hi46ir46lfh//7fYkkuV2+QJLUpA5zaR0cH/MVfwPXXwwsvQGcnXHpp1VVJkjTgDHBqPwcfXKzesMcexbQjZ5wBy5dXXZUkSQPGAKf2tN12cMstRXg791xXb5AktRUDnNrXyJHFU6mXXgrz5sFee8ENN1RdlSRJG80Ap/Z3/PHFHHHbbAOHHVY86ODqDZKkFmaA09Cwyy7F6g0f/zh8+ctw5JHw4otVVyVJ0gYxwGno2GwzuPBC+M534KabitUb7r676qokSeo3A5yGlohipYY77ii299+/WI5riK1IIklqbQY4DU2dncXqDYccAqedBr/3e/Daa1VXJUlSnxjgNHSNGwc/+hF87WtwySXF6g2PP16sqbpgQfHuww6SpCZkgNPQ1tEBf/7nxeoNkyfDk0/CtGkwdWrxPm+eIU6S1HSGV12A1BQOOgh22gkOOKDofYPifeZMuPHGItANG1ZpiZIkdTPASd0yV4W3bgsWFCs4vP/9sO++sN9+xWvaNNhqq2rqlCQNeQY4qduoUTBlyuohbsoUGD8ePvUp+PnP4eyz4Z13imM77bQq0O23H+y+u710kqRBETnEpk/o7OzMuXPnVl2GmtHKlcU9bzNnFiFuyhSYMwf22KO4Vw7g9deLVR1+/vNVr8WLi2ObbWYvnSRpQEXEvZnZuVa7AU6qs3IlLFoEy5YVPXJbb70qvPUkE554YvVA94tf2EsnSRoQBriSAU4NZy+dJGmArCvAeQ+cNNA22wymTy9e0HMvnffSSZI2gj1wUhXqe+nuvLN4X7SoOGYvnSSpZA+c1Ew2ppdu2rSil264//eVpKHKHjipWdlLJ0lDnj1wUqtpRC9df5+ylSQ1JXvgpFbWn166D30Inn56/fPcSZKaitOIlAxwamvrm5fu8svhzDPXXmnijjtg0qTqapYkrZNDqNJQEAE77FC8Pv7xoq27l27s2J7Xep0/v1jrdfvtV7122GHV9pQpMHLk4F+LJGmdDHBSu+u+l+7553te63XMGDjuOHj8cbj/fpg9G95+e9U5EUUP3ZrBrvu17bYOwUrSIDPASUPF1lsX97z1dA/cv/3bqvPeeQeefbYYin3iiSLYdW//5CfFsfpbL0aOhKlT1w523a9x44oQKEkaMN4DJw0lA/EU6rJlRQCsD3b1ryVLVj9/8817DnY77FAEv9GjB+zyJKndeA+cpCKsTZy4cd8xahTsvHPx6snSpT0Hu1/9Cq67Dt58c/Xzt9lm3fffTZ7shMWS1AP/ZZQ0sLbYAvbcs3itKRNeeGH1YNfdk/fzn8Oll66a1w6KNWEnT+453G2/fRH+6odnnedO0hBhgJM0eCKKHsCJE4u56db09tuwcOHa99498QRcfXUR/uptuumq++8OPBD23x9OOGHVPX6zZxf3+A0bNiiXJ0mDxQAnqXmMGLGqd23GjLWPv/EGPPlkz/ff7bzzqvAGxfusWXDOOXDqqcXDFP15bb65vXeSmpYBTlLrGD0adtuteK1pwYKe57mbOhWOOaZ4uGLJEnjqqWJy4yVL4LXX1v2zOjpgyy37H/zGjvW+PUkN578yktrDqFE9z3M3cSKcd17Pn1m+HF56aVW4W9/r17+GRx8ttl9+ef21bL75hgW/TTbp/Tq9z08SBlFgrMMAAApnSURBVDhJ7WJd89xtvfW6PzNyZPEgxDbb9O9nvfNOEeLWF/jqg+HTT6/arn9IY02jR68e6NYMeTvvXDzUcfzxq67x8suLkDpsWBHoNtmkeG/VufcMqFKfGOAktYeOjuKBhTvvbPwf/2HDYPz44tUfmfDqq33r8VuyZFWP34svFr2Fl1++KrxB8V6rwTe/WbzX6w5z9a+e2vpzvD/fsSHDyCtXwrx5PU82bYiTVuNEvpLU7DKL+fOeew523HHt4w89BLfdBm+9tfZr2bKe23s7vrGGDet/APyDP4ATT1x7GHz2bLjnnqLHdOTI4ju6t9fcX9exESOaIwTaw6h+ciJfSWpVEcXw6mab9Xyf3/jx8Ed/NHA/L7Po8duYANiX40uXrn78U5/q+UGUpUvhlFM2/rpGjOh/8Ovvues7tuWWxZPUtdqqHsYrrijmNxw2rOi17A6arToEDu0fUpvk+gxwktQqNuQ+vw0RUfxhGjVqYL+3N88/33NA3WGHYn7AZcuKYNn9qt9f37H+nLtsWdHbuXTp+j+3bNnqawL3xeWXw5lnrj4EfuyxPQ+BjxixKtDVb6/53te2RhzrqW2zzYr7P+tD6uzZxe+wo6M4b/jwIrC2YkhtomF+h1AlqZU0yX/9N0QT/XHskxUr+hcSp07teQqcBx8slpl7++3iO99+e/XtNd/7e6y38wcyB6wZUqH4PfYUUrsDXXfv48ZsD8R39GV7zz3hqKPWvr4779z4ZQrXwSFUSWoHA7GebbMazAdRBkL3H/fRo/t2/rp6GCdMgLPOakyNfbFy5cAFxV126XkYfMcd4e//vngKe8WK4jVQ28uXb9hn69/76pZber6+Zcs2+tfQXwY4SVLzaOeAOlhD4P3V0TFwQ+brC6l/8icb//2NkFmE2L4Ev2HDer6+wb7dAAOcJEmDo9V6GDdEs4bU9Ykogln3XIrrs3Jl01yfAU6SpMHSzj2M0P4htYmur+UDXEQcDnwLGAZ8NzO/XnFJkiQNXUMhpDbB9bV0JI6IYcC/AEcAuwEnRkQPj/hIkiS1j5YOcMC+wPzMfDwzlwOXADMrrkmSJKmhWj3ATQKerttfWLZJkiS1rVYPcH0SEadExNyImLt48eKqy5EkSdoorR7gngEm1+1vV7atJjPPy8zOzOycMGHCoBUnSZLUCK0e4O4BdoqI7SNiJHACcGXFNUmSJDVUS08jkpkrIuJ04DqKaUS+l5kPV1yWJElSQ7V0gAPIzGuAa6quQ5IkabBEZlZdw6CKiMXAgl5P1PpsBfy66iK0Ufwdtj5/h63N31/rG6zf4ZTMXOsG/iEX4LTxImJuZnZWXYc2nL/D1ufvsLX5+2t9Vf8OW/0hBkmSpCHHACdJktRiDHDaEOdVXYA2mr/D1ufvsLX5+2t9lf4OvQdOkiSpxdgDJ0mS1GIMcOqziJgcETdHxC8j4uGIOKPqmtR/ETEsIu6PiKuqrkX9FxFbRsRlEfHfEfFIROxXdU3qn4g4s/w39KGIuDgiNqm6Jq1fRHwvIhZFxEN1beMi4oaIeKx8HzuYNRng1B8rgC9k5m7ANOC0iNit4prUf2cAj1RdhDbYt4BrM3NX4P34u2wpETEJ+CzQmZm7U6widEK1VakPvg8cvkbbF4EbM3Mn4MZyf9AY4NRnmflcZt5Xbr9K8YdjUrVVqT8iYjvgSOC7Vdei/ouILYADgPMBMnN5Zr5cbVXaAMOBTSNiODAaeLbietSLzLwNWLJG80zggnL7AmDWYNZkgNMGiYipwF7AXdVWon46B/jfwMqqC9EG2R5YDPxnOQz+3YjYrOqi1HeZ+QzwD8BTwHPA0sy8vtqqtIG2ycznyu3ngW0G84cb4NRvETEG+CHwucx8pep61DcRcRSwKDPvrboWbbDhwN7AdzJzL+B1BnnYRhunvE9qJkUY3xbYLCJ+r9qqtLGymNJjUKf1MMCpXyJiBEV4uygzL6+6HvXLh4FjIuJJ4BJgRkT8V7UlqZ8WAgszs7vn+zKKQKfWcTDwRGYuzsy3gcuBD1VckzbMCxHxWwDl+6LB/OEGOPVZRATFvTePZOY/VV2P+iczv5SZ22XmVIqbpm/KTP/Lv4Vk5vPA0xGxS9l0EPDLCktS/z0FTIuI0eW/qQfhgyit6krgpHL7JGDOYP5wA5z648PAJyh6bh4oXx+tuihpiPkMcFFEPAjsCfxtxfWoH8re08uA+4B5FH+HXZWhyUXExcDPgV0iYmFEnAx8HTgkIh6j6Fn9+qDW5EoMkiRJrcUeOEmSpBZjgJMkSWoxBjhJkqQWY4CTJElqMQY4SZKkFmOAk9Q0IiIj4h/r9s+KiL8aoO/+fkQcNxDf1cvPOT4iHomImxtZV0RMjYjf7X+FktqBAU5SM1kG1CJiq6oLqVcuOt5XJwOfzswDG1VPaSrQrwDXz+uQ1MQMcJKayQqKSU3PXPPAmj1VEfFa+f47EXFrRMyJiMcj4usR8fGIuDsi5kXEe+q+5uCImBsRj5ZrwxIRwyLiGxFxT0Q8GBF/WPe9t0fElfSw2kFEnFh+/0MRcXbZ9mVgf+D8iPhGD5/50/Izv4iItSb9jIgnu8NrRHRGxC3l9vS6ybPvj4h3UUwa+pGy7cy+XkdEbBYRV5c1PBQRH+vLL0ZSc/G/xiQ1m38BHoyIv+/HZ94PvBdYAjwOfDcz942IMyhWLvhced5UYF/gPcDNEbEj8ElgaWbuExGjgDsi4vry/L2B3TPzifofFhHbAmcDHwBeAq6PiFmZ+TcRMQM4KzPnrvGZIygWMf9gZr4REeP6cX1nAadl5h0RMQZ4i2IR+7MyszuIntKX64iILuDZzDyy/NwW/ahDUpOwB05SU8nMV4ALgc/242P3ZOZzmbkM+B+gO7jMowht3S7NzJWZ+RhF0NsVOBT4ZEQ8ANwFjAd2Ks+/e83wVtoHuKVckHwFcBFwQC81Hgz8Z2a+UV7nkn5c3x3AP0XEZ4Ety5+5pr5exzyK5X/OjoiPZObSftQhqUkY4CQ1o3Mo7iXbrK5tBeW/WRHRAYysO7asbntl3f5KVh9pWHPtwAQC+Exm7lm+ts/M7gD4+kZdRf/95hqBTX5TZObXgT8ANqXoWdu1h8/26Toy81GKHrl5wNfKYV9JLcYAJ6nplL1Tl1KEuG5PUgxZAhwDjNiArz4+IjrK++J2AH4FXAf8UUSMAIiInSNis/V9CXA3MD0itoqIYcCJwK29fOYG4PcjYnT5c3oaQn2SVdfY1d0YEe/JzHmZeTZwD0XP4avAu+o+26frKId/38jM/wK+QRHmJLUY74GT1Kz+ETi9bv8/gDkR8QvgWjasd+wpivC1OXBqZr4VEd+lGGa9LyICWAzMWt+XZOZzEfFF4GaKnq+rM3NOL5+5NiL2BOZGxHLgGuDP1jjtrykegPgqcEtd++ci4kCKHsWHgR+X2++U/3t8H/hWH69jD+AbEbESeBv4o/XVLak5ReaaIwqSJElqZg6hSpIktRgDnCRJUosxwEmSJLUYA5wkSVKLMcBJkiS1GAOcJElSizHASZIktRgDnCRJUov5/wEEXUm8vjXJ1AAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.lineplot(range(1, 11), wcss,marker='o',color='red')\n",
+    "plt.title('Elbow')\n",
+    "plt.xlabel('Number of clusters')\n",
+    "plt.ylabel('WCSS')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Looks like 3 is a good number after all. Fit the model again and create a scatterplot of your clusters. They do group in bunches, but they are pretty close together."
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "kmeans = KMeans(n_clusters = 3)\n",
+    "kmeans.fit(X)\n",
+    "labels = kmeans.predict(X)\n",
+    "plt.scatter(df['popularity'],df['danceability'],c = labels)\n",
+    "plt.xlabel('popularity')\n",
+    "plt.ylabel('danceability')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "source": [
+    "Độ chính xác của mô hình này không tệ, nhưng cũng không tốt. Có thể dữ liệu không phù hợp với Phân cụm K-Means. Bạn có thể thử một phương pháp khác.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 811,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Result: 109 out of 286 samples were correctly labeled.\nAccuracy score: 0.38\n"
+     ]
+    }
+   ],
+   "source": [
+    "labels = kmeans.labels_\n",
+    "\n",
+    "correct_labels = sum(y == labels)\n",
+    "\n",
+    "print(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y.size))\n",
+    "\n",
+    "print('Accuracy score: {0:0.2f}'. format(correct_labels/float(y.size)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/5-Clustering/2-K-Means/solution/tester.ipynb b/translations/vi/5-Clustering/2-K-Means/solution/tester.ipynb
new file mode 100644
index 000000000..fb76cf147
--- /dev/null
+++ b/translations/vi/5-Clustering/2-K-Means/solution/tester.ipynb
@@ -0,0 +1,343 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "6f92868513e59d321245137c1c4c5311",
+   "translation_date": "2025-09-06T14:23:06+00:00",
+   "source_file": "5-Clustering/2-K-Means/solution/tester.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.1.2)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (3.1.0)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.4.1)\n",
+      "Requirement already satisfied: pytz>=2017.2 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2019.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7.3 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pandas>=0.23->seaborn) (2.8.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (2.4.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.7.3->pandas>=0.23->seaborn) (1.12.0)\n",
+      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "source": [
+    "Bắt đầu từ nơi chúng ta đã kết thúc trong bài học trước, với dữ liệu đã được nhập và lọc.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "0                    Sparky            Mandy & The Jungle   \n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "2                     LITT!                         LITT!   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "0        Cruel Santino  alternative r&b          2019  144000          48   \n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "2                 AYLØ        indie r&b          2018  207758          40   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "0         0.666        0.8510   0.420          0.534000    0.1100    -6.699   \n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "2         0.836        0.2720   0.564          0.000537    0.1100    -7.127   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "0       0.0829  133.015               5  \n",
+       "1       0.3600  129.993               3  \n",
+       "2       0.0424  130.005               4  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 105
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv(\"../../data/nigerian-songs.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "source": [
+    "Chúng ta sẽ chỉ tập trung vào 3 thể loại. Có lẽ chúng ta có thể xây dựng được 3 cụm!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Top genres')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 106
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 720x504 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"474.468454pt\" version=\"1.1\" viewBox=\"0 0 598.4875 474.468454\" width=\"598.4875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 474.468454 \nL 598.4875 474.468454 \nL 598.4875 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 402.838125 \nL 591.2875 402.838125 \nL 591.2875 22.318125 \nL 33.2875 22.318125 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"patch_3\">\n    <path clip-path=\"url(#p0704da7660)\" d=\"M 51.8875 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "df = df[(df['artist_top_genre'] == 'afro dancehall') | (df['artist_top_genre'] == 'afropop') | (df['artist_top_genre'] == 'nigerian pop')]\n",
+    "df = df[(df['popularity'] > 0)]\n",
+    "top = df['artist_top_genre'].value_counts()\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=top.index,y=top.values)\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.title('Top genres',color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       name                         album  \\\n",
+       "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
+       "3  Confident / Feeling Cool               Enjoy Your Life   \n",
+       "4                wanted you                         rare.   \n",
+       "5                    Kasala                      Pioneers   \n",
+       "6                   Pull Up             Everything Pretty   \n",
+       "\n",
+       "                artist artist_top_genre  release_date  length  popularity  \\\n",
+       "1  Odunsi (The Engine)          afropop          2020   89488          30   \n",
+       "3           Lady Donli     nigerian pop          2019  175135          14   \n",
+       "4  Odunsi (The Engine)          afropop          2018  152049          25   \n",
+       "5          DRB Lasgidi     nigerian pop          2020  184800          26   \n",
+       "6          prettyboydo     nigerian pop          2018  202648          29   \n",
+       "\n",
+       "   danceability  acousticness  energy  instrumentalness  liveness  loudness  \\\n",
+       "1         0.710        0.0822   0.683          0.000169    0.1010    -5.640   \n",
+       "3         0.894        0.7980   0.611          0.000187    0.0964    -4.961   \n",
+       "4         0.702        0.1160   0.833          0.910000    0.3480    -6.044   \n",
+       "5         0.803        0.1270   0.525          0.000007    0.1290   -10.034   \n",
+       "6         0.818        0.4520   0.587          0.004490    0.5900    -9.840   \n",
+       "\n",
+       "   speechiness    tempo  time_signature  \n",
+       "1       0.3600  129.993               3  \n",
+       "3       0.1130  111.087               4  \n",
+       "4       0.0447  105.115               4  \n",
+       "5       0.1970  100.103               4  \n",
+       "6       0.1990   95.842               4  "
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 107
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "\n",
+    "# X = df.loc[:, ('danceability','energy')]\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "ValueError",
+     "evalue": "Unknown label type: 'continuous'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-110-3756ba7b0bc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# we create an instance of SVM and fit out data. We do not scale our\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# data since we want to plot the support vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mls30\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 30% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mls50\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 50% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mls100\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mLabelSpreading\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Label Spreading 100% data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/semi_supervised/_label_propagation.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;31m# actual graph construction (implementations should override this)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    181\u001b[0m     if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',\n\u001b[1;32m    182\u001b[0m                       'multilabel-indicator', 'multilabel-sequences']:\n\u001b[0;32m--> 183\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.semi_supervised import LabelSpreading\n",
+    "from sklearn.semi_supervised import SelfTrainingClassifier\n",
+    "from sklearn import datasets\n",
+    "\n",
+    "X = df[['danceability','acousticness']].values\n",
+    "y = df['energy'].values\n",
+    "\n",
+    "# X = scaler.fit_transform(X)\n",
+    "\n",
+    "# step size in the mesh\n",
+    "h = .02\n",
+    "\n",
+    "rng = np.random.RandomState(0)\n",
+    "y_rand = rng.rand(y.shape[0])\n",
+    "y_30 = np.copy(y)\n",
+    "y_30[y_rand < 0.3] = -1  # set random samples to be unlabeled\n",
+    "y_50 = np.copy(y)\n",
+    "y_50[y_rand < 0.5] = -1\n",
+    "# we create an instance of SVM and fit out data. We do not scale our\n",
+    "# data since we want to plot the support vectors\n",
+    "ls30 = (LabelSpreading().fit(X, y_30), y_30, 'Label Spreading 30% data')\n",
+    "ls50 = (LabelSpreading().fit(X, y_50), y_50, 'Label Spreading 50% data')\n",
+    "ls100 = (LabelSpreading().fit(X, y), y, 'Label Spreading 100% data')\n",
+    "\n",
+    "# the base classifier for self-training is identical to the SVC\n",
+    "base_classifier = SVC(kernel='rbf', gamma=.5, probability=True)\n",
+    "st30 = (SelfTrainingClassifier(base_classifier).fit(X, y_30),\n",
+    "        y_30, 'Self-training 30% data')\n",
+    "st50 = (SelfTrainingClassifier(base_classifier).fit(X, y_50),\n",
+    "        y_50, 'Self-training 50% data')\n",
+    "\n",
+    "rbf_svc = (SVC(kernel='rbf', gamma=.5).fit(X, y), y, 'SVC with rbf kernel')\n",
+    "\n",
+    "# create a mesh to plot in\n",
+    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
+    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
+    "                     np.arange(y_min, y_max, h))\n",
+    "\n",
+    "color_map = {-1: (1, 1, 1), 0: (0, 0, .9), 1: (1, 0, 0), 2: (.8, .6, 0)}\n",
+    "\n",
+    "classifiers = (ls30, st30, ls50, st50, ls100, rbf_svc)\n",
+    "for i, (clf, y_train, title) in enumerate(classifiers):\n",
+    "    # Plot the decision boundary. For that, we will assign a color to each\n",
+    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
+    "    plt.subplot(3, 2, i + 1)\n",
+    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
+    "\n",
+    "    # Put the result into a color plot\n",
+    "    Z = Z.reshape(xx.shape)\n",
+    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)\n",
+    "    plt.axis('off')\n",
+    "\n",
+    "    # Plot also the training points\n",
+    "    colors = [color_map[y] for y in y_train]\n",
+    "    plt.scatter(X[:, 0], X[:, 1], c=colors, edgecolors='black')\n",
+    "\n",
+    "    plt.title(title)\n",
+    "\n",
+    "plt.suptitle(\"Unlabeled points are colored white\", y=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb b/translations/vi/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
new file mode 100644
index 000000000..1c0510d01
--- /dev/null
+++ b/translations/vi/6-NLP/3-Translation-Sentiment/solution/notebook.ipynb
@@ -0,0 +1,100 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "27de2abc0235ebd22080fc8f1107454d",
+   "translation_date": "2025-09-06T15:22:23+00:00",
+   "source_file": "6-NLP/3-Translation-Sentiment/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from textblob import TextBlob\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You should download the book text, clean it, and import it here\n",
+    "with open(\"pride.txt\", encoding=\"utf8\") as f:\n",
+    "    file_contents = f.read()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "book_pride = TextBlob(file_contents)\n",
+    "positive_sentiment_sentences = []\n",
+    "negative_sentiment_sentences = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for sentence in book_pride.sentences:\n",
+    "    if sentence.sentiment.polarity == 1:\n",
+    "        positive_sentiment_sentences.append(sentence)\n",
+    "    if sentence.sentiment.polarity == -1:\n",
+    "        negative_sentiment_sentences.append(sentence)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(positive_sentiment_sentences)) + \" most positive sentences:\")\n",
+    "for sentence in positive_sentiment_sentences:\n",
+    "    print(\"+ \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"The \" + str(len(negative_sentiment_sentences)) + \" most negative sentences:\")\n",
+    "for sentence in negative_sentiment_sentences:\n",
+    "    print(\"- \" + str(sentence.replace(\"\\n\", \"\").replace(\"      \", \" \")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/6-NLP/4-Hotel-Reviews-1/notebook.ipynb b/translations/vi/6-NLP/4-Hotel-Reviews-1/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb b/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
new file mode 100644
index 000000000..ecb4369e5
--- /dev/null
+++ b/translations/vi/6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb
@@ -0,0 +1,174 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 4,
+  "coopTranslator": {
+   "original_hash": "2d05e7db439376aa824f4b387f8324ca",
+   "translation_date": "2025-09-06T15:22:02+00:00",
+   "source_file": "6-NLP/4-Hotel-Reviews-1/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EDA\n",
+    "import pandas as pd\n",
+    "import time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_difference_review_avg(row):\n",
+    "    return row[\"Average_Score\"] - row[\"Calc_Average_Score\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "print(\"Loading data file now, this could take a while depending on file size\")\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n",
+    "end = time.time()\n",
+    "print(\"Loading took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What shape is the data (rows, columns)?\n",
+    "print(\"The shape of the data (rows, cols) is \" + str(df.shape))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# value_counts() creates a Series object that has index and values\n",
+    "#                in this case, the country and the frequency they occur in reviewer nationality\n",
+    "nationality_freq = df[\"Reviewer_Nationality\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What reviewer nationality is the most common in the dataset?\n",
+    "print(\"The highest frequency reviewer nationality is \" + str(nationality_freq.index[0]).strip() + \" with \" + str(nationality_freq[0]) + \" reviews.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is the top 10 most common nationalities and their frequencies?\n",
+    "print(\"The top 10 highest frequency reviewer nationalities are:\")\n",
+    "print(nationality_freq[0:10].to_string())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many unique nationalities are there?\n",
+    "print(\"There are \" + str(nationality_freq.index.size) + \" unique nationalities in the dataset\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What was the most frequently reviewed hotel for the top 10 nationalities - print the hotel and number of reviews\n",
+    "for nat in nationality_freq[:10].index:\n",
+    "   # First, extract all the rows that match the criteria into a new dataframe\n",
+    "   nat_df = df[df[\"Reviewer_Nationality\"] == nat]   \n",
+    "   # Now get the hotel freq\n",
+    "   freq = nat_df[\"Hotel_Name\"].value_counts()\n",
+    "   print(\"The most reviewed hotel for \" + str(nat).strip() + \" was \" + str(freq.index[0]) + \" with \" + str(freq[0]) + \" reviews.\") \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# How many reviews are there per hotel (frequency count of hotel) and do the results match the value in `Total_Number_of_Reviews`?\n",
+    "# First create a new dataframe based on the old one, removing the uneeded columns\n",
+    "hotel_freq_df = df.drop([\"Hotel_Address\", \"Additional_Number_of_Scoring\", \"Review_Date\", \"Average_Score\", \"Reviewer_Nationality\", \"Negative_Review\", \"Review_Total_Negative_Word_Counts\", \"Positive_Review\", \"Review_Total_Positive_Word_Counts\", \"Total_Number_of_Reviews_Reviewer_Has_Given\", \"Reviewer_Score\", \"Tags\", \"days_since_review\", \"lat\", \"lng\"], axis = 1)\n",
+    "# Group the rows by Hotel_Name, count them and put the result in a new column Total_Reviews_Found\n",
+    "hotel_freq_df['Total_Reviews_Found'] = hotel_freq_df.groupby('Hotel_Name').transform('count')\n",
+    "# Get rid of all the duplicated rows\n",
+    "hotel_freq_df = hotel_freq_df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "print()\n",
+    "print(hotel_freq_df.to_string())\n",
+    "print(str(hotel_freq_df.shape))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# While there is an `Average_Score` for each hotel according to the dataset, \n",
+    "# you can also calculate an average score (getting the average of all reviewer scores in the dataset for each hotel)\n",
+    "# Add a new column to your dataframe with the column header `Calc_Average_Score` that contains that calculated average. \n",
+    "df['Calc_Average_Score'] = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n",
+    "# Add a new column with the difference between the two average scores\n",
+    "df[\"Average_Score_Difference\"] = df.apply(get_difference_review_avg, axis = 1)\n",
+    "# Create a df without all the duplicates of Hotel_Name (so only 1 row per hotel)\n",
+    "review_scores_df = df.drop_duplicates(subset = [\"Hotel_Name\"])\n",
+    "# Sort the dataframe to find the lowest and highest average score difference\n",
+    "review_scores_df = review_scores_df.sort_values(by=[\"Average_Score_Difference\"])\n",
+    "print(review_scores_df[[\"Average_Score_Difference\", \"Average_Score\", \"Calc_Average_Score\", \"Hotel_Name\"]])\n",
+    "# Do any hotels have the same (rounded to 1 decimal place) `Average_Score` and `Calc_Average_Score`?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/6-NLP/5-Hotel-Reviews-2/notebook.ipynb b/translations/vi/6-NLP/5-Hotel-Reviews-2/notebook.ipynb
new file mode 100644
index 000000000..e69de29bb
diff --git a/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
new file mode 100644
index 000000000..a1b469d46
--- /dev/null
+++ b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb
@@ -0,0 +1,172 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "033cb89c85500224b3c63fd04f49b4aa",
+   "translation_date": "2025-09-06T15:22:44+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/1-notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import time\n",
+    "import ast"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replace_address(row):\n",
+    "    if \"Netherlands\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Amsterdam, Netherlands\"\n",
+    "    elif \"Barcelona\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Barcelona, Spain\"\n",
+    "    elif \"United Kingdom\" in row[\"Hotel_Address\"]:\n",
+    "        return \"London, United Kingdom\"\n",
+    "    elif \"Milan\" in row[\"Hotel_Address\"]:        \n",
+    "        return \"Milan, Italy\"\n",
+    "    elif \"France\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Paris, France\"\n",
+    "    elif \"Vienna\" in row[\"Hotel_Address\"]:\n",
+    "        return \"Vienna, Austria\" \n",
+    "    else:\n",
+    "        return row.Hotel_Address\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "start = time.time()\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dropping columns we will not use:\n",
+    "df.drop([\"lat\", \"lng\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace all the addresses with a shortened, more useful form\n",
+    "df[\"Hotel_Address\"] = df.apply(replace_address, axis = 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop `Additional_Number_of_Scoring`\n",
+    "df.drop([\"Additional_Number_of_Scoring\"], axis = 1, inplace=True)\n",
+    "# Replace `Total_Number_of_Reviews` and `Average_Score` with our own calculated values\n",
+    "df.Total_Number_of_Reviews = df.groupby('Hotel_Name').transform('count')\n",
+    "df.Average_Score = round(df.groupby('Hotel_Name').Reviewer_Score.transform('mean'), 1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Process the Tags into new columns\n",
+    "# The file Hotel_Reviews_Tags.py, identifies the most important tags\n",
+    "# Leisure trip, Couple, Solo traveler, Business trip, Group combined with Travelers with friends, \n",
+    "# Family with young children, Family with older children, With a pet\n",
+    "df[\"Leisure_trip\"] = df.Tags.apply(lambda tag: 1 if \"Leisure trip\" in tag else 0)\n",
+    "df[\"Couple\"] = df.Tags.apply(lambda tag: 1 if \"Couple\" in tag else 0)\n",
+    "df[\"Solo_traveler\"] = df.Tags.apply(lambda tag: 1 if \"Solo traveler\" in tag else 0)\n",
+    "df[\"Business_trip\"] = df.Tags.apply(lambda tag: 1 if \"Business trip\" in tag else 0)\n",
+    "df[\"Group\"] = df.Tags.apply(lambda tag: 1 if \"Group\" in tag or \"Travelers with friends\" in tag else 0)\n",
+    "df[\"Family_with_young_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with young children\" in tag else 0)\n",
+    "df[\"Family_with_older_children\"] = df.Tags.apply(lambda tag: 1 if \"Family with older children\" in tag else 0)\n",
+    "df[\"With_a_pet\"] = df.Tags.apply(lambda tag: 1 if \"With a pet\" in tag else 0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# No longer need any of these columns\n",
+    "df.drop([\"Review_Date\", \"Review_Total_Negative_Word_Counts\", \"Review_Total_Positive_Word_Counts\", \"days_since_review\", \"Total_Number_of_Reviews_Reviewer_Has_Given\"], axis = 1, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_Filtered.csv\n",
+      "Filtering took 23.74 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Saving new data file with calculated columns\n",
+    "print(\"Saving results to Hotel_Reviews_Filtered.csv\")\n",
+    "df.to_csv(r'../../data/Hotel_Reviews_Filtered.csv', index = False)\n",
+    "end = time.time()\n",
+    "print(\"Filtering took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
new file mode 100644
index 000000000..ef047753d
--- /dev/null
+++ b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb
@@ -0,0 +1,137 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "341efc86325ec2a214f682f57a189dfd",
+   "translation_date": "2025-09-06T15:23:04+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/2-notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV (you can )\n",
+    "import pandas as pd \n",
+    "\n",
+    "df = pd.read_csv('../../data/Hotel_Reviews_Filtered.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We want to find the most useful tags to keep\n",
+    "# Remove opening and closing brackets\n",
+    "df.Tags = df.Tags.str.strip(\"[']\")\n",
+    "# remove all quotes too\n",
+    "df.Tags = df.Tags.str.replace(\" ', '\", \",\", regex = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# removing this to take advantage of the 'already a phrase' fact of the dataset \n",
+    "# Now split the strings into a list\n",
+    "tag_list_df = df.Tags.str.split(',', expand = True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove leading and trailing spaces\n",
+    "df[\"Tag_1\"] = tag_list_df[0].str.strip()\n",
+    "df[\"Tag_2\"] = tag_list_df[1].str.strip()\n",
+    "df[\"Tag_3\"] = tag_list_df[2].str.strip()\n",
+    "df[\"Tag_4\"] = tag_list_df[3].str.strip()\n",
+    "df[\"Tag_5\"] = tag_list_df[4].str.strip()\n",
+    "df[\"Tag_6\"] = tag_list_df[5].str.strip()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Merge the 6 columns into one with melt\n",
+    "df_tags = df.melt(value_vars=[\"Tag_1\", \"Tag_2\", \"Tag_3\", \"Tag_4\", \"Tag_5\", \"Tag_6\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "The shape of the tags with no filtering: (2514684, 2)\n",
+      "                        index   count\n",
+      "0                Leisure trip  338423\n",
+      "1                      Couple  205305\n",
+      "2               Solo traveler   89779\n",
+      "3               Business trip   68176\n",
+      "4                       Group   51593\n",
+      "5  Family with young children   49318\n",
+      "6  Family with older children   21509\n",
+      "7      Travelers with friends    1610\n",
+      "8                  With a pet    1078\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get the value counts\n",
+    "tag_vc = df_tags.value.value_counts()\n",
+    "# print(tag_vc)\n",
+    "print(\"The shape of the tags with no filtering:\", str(df_tags.shape))\n",
+    "# Drop rooms, suites, and length of stay, mobile device and anything with less count than a 1000\n",
+    "df_tags = df_tags[~df_tags.value.str.contains(\"Standard|room|Stayed|device|Beds|Suite|Studio|King|Superior|Double\", na=False, case=False)]\n",
+    "tag_vc = df_tags.value.value_counts().reset_index(name=\"count\").query(\"count > 1000\")\n",
+    "# Print the top 10 (there should only be 9 and we'll use these in the filtering section)\n",
+    "print(tag_vc[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
new file mode 100644
index 000000000..5cca6770d
--- /dev/null
+++ b/translations/vi/6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb
@@ -0,0 +1,260 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "705bf02633759f689abc37b19749a16d",
+   "translation_date": "2025-09-06T15:23:25+00:00",
+   "source_file": "6-NLP/5-Hotel-Reviews-2/solution/3-notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "[nltk_data] Downloading package vader_lexicon to\n[nltk_data]     /Users/jenlooper/nltk_data...\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import pandas as pd\n",
+    "import nltk as nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
+    "nltk.download('vader_lexicon')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vader_sentiment = SentimentIntensityAnalyzer()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# There are 3 possibilities of input for a review:\n",
+    "# It could be \"No Negative\", in which case, return 0\n",
+    "# It could be \"No Positive\", in which case, return 0\n",
+    "# It could be a review, in which case calculate the sentiment\n",
+    "def calc_sentiment(review):    \n",
+    "    if review == \"No Negative\" or review == \"No Positive\":\n",
+    "        return 0\n",
+    "    return vader_sentiment.polarity_scores(review)[\"compound\"]    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the hotel reviews from CSV\n",
+    "df = pd.read_csv(\"../../data/Hotel_Reviews_Filtered.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove stop words - can be slow for a lot of text!\n",
+    "# Ryan Han (ryanxjhan on Kaggle) has a great post measuring performance of different stop words removal approaches\n",
+    "# https://www.kaggle.com/ryanxjhan/fast-stop-words-removal # using the approach that Ryan recommends\n",
+    "start = time.time()\n",
+    "cache = set(stopwords.words(\"english\"))\n",
+    "def remove_stopwords(review):\n",
+    "    text = \" \".join([word for word in review.split() if word not in cache])\n",
+    "    return text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remove the stop words from both columns\n",
+    "df.Negative_Review = df.Negative_Review.apply(remove_stopwords)   \n",
+    "df.Positive_Review = df.Positive_Review.apply(remove_stopwords)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Removing stop words took 5.77 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "end = time.time()\n",
+    "print(\"Removing stop words took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Calculating sentiment columns for both positive and negative reviews\n",
+      "Calculating sentiment took 201.07 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add a negative sentiment and positive sentiment column\n",
+    "print(\"Calculating sentiment columns for both positive and negative reviews\")\n",
+    "start = time.time()\n",
+    "df[\"Negative_Sentiment\"] = df.Negative_Review.apply(calc_sentiment)\n",
+    "df[\"Positive_Sentiment\"] = df.Positive_Review.apply(calc_sentiment)\n",
+    "end = time.time()\n",
+    "print(\"Calculating sentiment took \" + str(round(end - start, 2)) + \" seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                          Negative_Review  Negative_Sentiment\n",
+      "186584  So bad experience memories I hotel The first n...             -0.9920\n",
+      "129503  First charged twice room booked booking second...             -0.9896\n",
+      "307286  The staff Had bad experience even booking Janu...             -0.9889\n",
+      "452092  No WLAN room Incredibly rude restaurant staff ...             -0.9884\n",
+      "201293  We usually traveling Paris 2 3 times year busi...             -0.9873\n",
+      "...                                                   ...                 ...\n",
+      "26899   I would say however one night expensive even d...              0.9933\n",
+      "138365  Wifi terribly slow I speed test network upload...              0.9938\n",
+      "79215   I find anything hotel first I walked past hote...              0.9938\n",
+      "278506  The property great location There bakery next ...              0.9945\n",
+      "339189  Guys I like hotel I wish return next year Howe...              0.9948\n",
+      "\n",
+      "[515738 rows x 2 columns]\n",
+      "                                          Positive_Review  Positive_Sentiment\n",
+      "137893  Bathroom Shower We going stay twice hotel 2 ni...             -0.9820\n",
+      "5839    I completely disappointed mad since reception ...             -0.9780\n",
+      "64158   get everything extra internet parking breakfas...             -0.9751\n",
+      "124178  I didnt like anythig Room small Asked upgrade ...             -0.9721\n",
+      "489137  Very rude manager abusive staff reception Dirt...             -0.9703\n",
+      "...                                                   ...                 ...\n",
+      "331570  Everything This recently renovated hotel class...              0.9984\n",
+      "322920  From moment stepped doors Guesthouse Hotel sta...              0.9985\n",
+      "293710  This place surprise expected good actually gre...              0.9985\n",
+      "417442  We celebrated wedding night Langham I commend ...              0.9985\n",
+      "132492  We arrived super cute boutique hotel area expl...              0.9987\n",
+      "\n",
+      "[515738 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "df = df.sort_values(by=[\"Negative_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Negative_Review\", \"Negative_Sentiment\"]])\n",
+    "df = df.sort_values(by=[\"Positive_Sentiment\"], ascending=True)\n",
+    "print(df[[\"Positive_Review\", \"Positive_Sentiment\"]])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reorder the columns (This is cosmetic, but to make it easier to explore the data later)\n",
+    "df = df.reindex([\"Hotel_Name\", \"Hotel_Address\", \"Total_Number_of_Reviews\", \"Average_Score\", \"Reviewer_Score\", \"Negative_Sentiment\", \"Positive_Sentiment\", \"Reviewer_Nationality\", \"Leisure_trip\", \"Couple\", \"Solo_traveler\", \"Business_trip\", \"Group\", \"Family_with_young_children\", \"Family_with_older_children\", \"With_a_pet\", \"Negative_Review\", \"Positive_Review\"], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Saving results to Hotel_Reviews_NLP.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Saving results to Hotel_Reviews_NLP.csv\")\n",
+    "df.to_csv(r\"../../data/Hotel_Reviews_NLP.csv\", index = False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/7-TimeSeries/1-Introduction/solution/notebook.ipynb b/translations/vi/7-TimeSeries/1-Introduction/solution/notebook.ipynb
new file mode 100644
index 000000000..b46440636
--- /dev/null
+++ b/translations/vi/7-TimeSeries/1-Introduction/solution/notebook.ipynb
@@ -0,0 +1,164 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dữ liệu này bao gồm 3 năm giá trị tải điện và nhiệt độ theo giờ từ năm 2012 đến năm 2014.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli và Rob J. Hyndman, \"Dự báo năng lượng xác suất: Cuộc thi Dự báo Năng lượng Toàn cầu 2014 và hơn thế nữa\", Tạp chí Quốc tế về Dự báo, tập 32, số 3, trang 896-913, tháng 7-tháng 9, năm 2016.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "from common.utils import load_data\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tải dữ liệu từ csv vào một Pandas dataframe\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ],
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>load</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2012-01-01 00:00:00</th>\n      <td>2698.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 01:00:00</th>\n      <td>2558.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 02:00:00</th>\n      <td>2444.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 03:00:00</th>\n      <td>2402.0</td>\n    </tr>\n    <tr>\n      <th>2012-01-01 04:00:00</th>\n      <td>2403.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "data_dir = './data'\n",
+    "energy = load_data(data_dir)[['load']]\n",
+    "energy.head()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vẽ tất cả dữ liệu tải có sẵn (tháng 1 năm 2012 đến tháng 12 năm 2014)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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83.742527 195.962437 \nL 83.837991 148.230038 \nL 83.869813 161.617056 \nL 84.092563 314.141275 \nL 84.156206 304.046147 \nL 84.378956 188.061902 \nL 84.506242 207.92297 \nL 84.601707 158.873814 \nL 84.633528 171.382995 \nL 84.856278 323.687755 \nL 84.919921 312.60506 \nL 85.11085 190.805144 \nL 85.142671 192.121899 \nL 85.238136 198.595949 \nL 85.269957 197.937571 \nL 85.365422 150.095442 \nL 85.397243 159.641922 \nL 85.619993 299.437501 \nL 85.683636 287.806158 \nL 85.810922 179.39326 \nL 85.874565 183.233797 \nL 86.033672 208.691077 \nL 86.065494 196.291626 \nL 86.129137 164.140838 \nL 86.160958 174.784614 \nL 86.41553 312.166141 \nL 86.479173 297.133178 \nL 86.63828 206.606214 \nL 86.670102 211.763507 \nL 86.765566 233.599709 \nL 86.797388 232.831601 \nL 86.892852 171.382995 \nL 86.956495 197.388923 \nL 87.179245 301.412635 \nL 87.242888 288.354806 \nL 87.401995 205.728377 \nL 87.465638 212.641345 \nL 87.529281 226.577011 \nL 87.561103 223.394851 \nL 87.624746 169.956509 \nL 87.688389 203.753243 \nL 87.94296 312.49533 \nL 87.974782 303.497498 \nL 88.102068 192.670548 \nL 88.165711 195.742978 \nL 88.324818 223.72404 \nL 88.356639 210.666211 \nL 88.420282 165.457594 \nL 88.452104 179.832178 \nL 88.674854 324.565592 \nL 88.738497 313.373167 \nL 88.929426 202.875406 \nL 88.961247 205.508917 \nL 89.056712 216.372153 \nL 89.088533 214.397019 \nL 89.183998 154.155439 \nL 89.24764 180.490556 \nL 89.438569 296.035882 \nL 89.470391 295.048315 \nL 89.534034 251.485642 \nL 89.597677 175.333262 \nL 89.661319 181.148934 \nL 89.820427 213.080263 \nL 89.852248 211.10513 \nL 89.947713 153.716521 \nL 90.011356 182.46569 \nL 90.202284 308.435333 \nL 90.234106 308.215873 \nL 90.297749 265.640768 \nL 90.361392 191.682981 \nL 90.425035 198.815408 \nL 90.615963 224.711607 \nL 90.711428 166.884079 \nL 90.775071 192.560818 \nL 90.966 312.60506 \nL 90.997821 311.398033 \nL 91.061464 268.384009 \nL 91.125107 195.962437 \nL 91.18875 202.875406 \nL 91.284214 219.005665 \nL 91.379679 231.185656 \nL 91.475143 185.318661 \nL 91.506964 197.827841 \nL 91.761536 330.381263 \nL 91.825179 313.153708 \nL 91.984286 214.726208 \nL 92.047929 223.065662 \nL 92.143394 231.953764 \nL 92.238858 182.026771 \nL 92.302501 208.252159 \nL 92.525251 302.290472 \nL 92.557073 297.901286 \nL 92.652537 247.645104 \nL 92.779823 197.169464 \nL 92.811645 201.229461 \nL 92.875287 210.446752 \nL 92.907109 207.81324 \nL 93.002573 138.13491 \nL 93.066216 169.84678 \nL 93.257145 279.905623 \nL 93.288967 277.820759 \nL 93.352609 237.440247 \nL 93.416252 165.896512 \nL 93.479895 170.175969 \nL 93.670824 198.925138 \nL 93.702646 189.488388 \nL 93.766288 144.3895 \nL 93.79811 156.679221 \nL 94.052682 299.65696 \nL 94.084503 288.245076 \nL 94.179968 188.500821 \nL 94.275432 193.109466 \nL 94.434539 214.506749 \nL 94.466361 204.850539 \nL 94.530004 162.714352 \nL 94.561825 175.881911 \nL 94.816397 318.969379 \nL 94.848218 308.545062 \nL 95.039147 197.169464 \nL 95.134611 205.947836 \nL 95.198254 212.970534 \nL 95.293719 165.238134 \nL 95.32554 177.308396 \nL 95.548291 323.578025 \nL 95.611933 311.288304 \nL 95.739219 205.947836 \nL 95.802862 208.581348 \nL 95.96197 221.090528 \nL 96.057434 169.078672 \nL 96.089255 180.819745 \nL 96.312006 327.638022 \nL 96.375649 318.420731 \nL 96.566577 201.997568 \nL 96.598399 206.386754 \nL 96.725685 225.040796 \nL 96.757506 216.591612 \nL 96.821149 183.892175 \nL 96.852971 196.511086 \nL 97.107542 326.650455 \nL 97.171185 309.31317 \nL 97.330293 220.651609 \nL 97.362114 223.72404 \nL 97.457578 247.974293 \nL 97.4894 247.425645 \nL 97.584864 193.658115 \nL 97.648507 221.419717 \nL 97.871258 323.687755 \nL 97.903079 320.176406 \nL 97.998543 272.553736 \nL 98.094008 231.185656 \nL 98.125829 231.405116 \nL 98.221294 247.754834 \nL 98.253115 246.328349 \nL 98.348579 178.076504 \nL 98.412222 210.007833 \nL 98.603151 314.470464 \nL 98.634973 312.49533 \nL 98.698616 281.222378 \nL 98.857723 193.328926 \nL 98.921366 202.546217 \nL 99.01683 215.933234 \nL 99.112295 153.82625 \nL 99.175938 190.146766 \nL 99.366866 308.874251 \nL 99.398688 306.350469 \nL 99.462331 265.860227 \nL 99.557795 193.438655 \nL 99.589617 196.181897 \nL 99.65326 203.972702 \nL 99.748724 214.506749 \nL 99.780545 213.409452 \nL 99.87601 158.544625 \nL 99.939653 195.413789 \nL 100.162403 323.578025 \nL 100.226046 283.96562 \nL 100.385153 202.875406 \nL 100.544261 229.320252 \nL 100.576082 220.871069 \nL 100.639725 175.552722 \nL 100.671546 185.757579 \nL 100.894297 332.575857 \nL 100.95794 321.82235 \nL 101.148868 197.937571 \nL 101.18069 198.15703 \nL 101.307976 217.579179 \nL 101.339797 212.970534 \nL 101.40344 170.505158 \nL 101.435262 181.148934 \nL 101.658012 326.979644 \nL 101.721655 316.555327 \nL 101.944405 195.523519 \nL 102.071691 204.301891 \nL 102.167155 173.138669 \nL 102.198977 187.403524 \nL 102.453549 332.246668 \nL 102.48537 327.308833 \nL 102.70812 224.162958 \nL 102.835406 234.806735 \nL 102.93087 184.550553 \nL 102.994513 217.908368 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171.821913 \nL 106.653982 187.293795 \nL 106.685803 182.794879 \nL 106.749446 146.693823 \nL 106.781268 159.751651 \nL 107.004018 306.899118 \nL 107.067661 296.255341 \nL 107.25859 192.670548 \nL 107.417697 214.616478 \nL 107.449519 208.142429 \nL 107.513161 174.894344 \nL 107.544983 185.208931 \nL 107.799555 318.420731 \nL 107.863198 305.472632 \nL 108.054126 207.374321 \nL 108.085948 214.945667 \nL 108.181412 246.108889 \nL 108.213234 240.073758 \nL 108.276877 196.181897 \nL 108.34052 222.187825 \nL 108.56327 330.929912 \nL 108.595091 327.857481 \nL 108.690556 282.100216 \nL 108.817842 233.489979 \nL 108.849663 236.45268 \nL 108.945127 248.742401 \nL 109.040592 189.159199 \nL 109.104235 220.102961 \nL 109.295164 319.518028 \nL 109.358806 304.375335 \nL 109.549735 190.146766 \nL 109.6452 205.947836 \nL 109.708843 214.835938 \nL 109.740664 205.618647 \nL 109.804307 152.728954 \nL 109.86795 179.39326 \nL 110.058879 298.011016 \nL 110.0907 296.145611 \nL 110.154343 252.034291 \nL 110.217986 185.867309 \nL 110.281629 191.79271 \nL 110.408915 211.10513 \nL 110.472558 219.334854 \nL 110.504379 213.080263 \nL 110.568022 160.629489 \nL 110.631665 184.879742 \nL 110.822594 306.679658 \nL 110.854415 306.350469 \nL 110.918058 264.543471 \nL 110.981701 199.583516 \nL 111.045344 206.935403 \nL 111.17263 224.601877 \nL 111.236273 235.135924 \nL 111.268094 228.881334 \nL 111.331737 174.894344 \nL 111.39538 204.082432 \nL 111.61813 328.625589 \nL 111.681773 287.367239 \nL 111.777238 218.895935 \nL 111.809059 219.334854 \nL 111.872702 220.54188 \nL 111.999988 231.295386 \nL 112.095452 184.550553 \nL 112.159095 214.06783 \nL 112.350024 337.074772 \nL 112.381846 336.855313 \nL 112.445489 296.4748 \nL 112.604596 215.494316 \nL 112.763703 234.806735 \nL 112.795525 229.97863 \nL 112.859168 192.999737 \nL 112.922811 216.481883 \nL 113.145561 326.211537 \nL 113.177382 320.615324 \nL 113.368311 227.125659 \nL 113.463775 244.901863 \nL 113.527418 252.912128 \nL 113.55924 245.88943 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117.02778 297.572097 \nL 117.186887 209.129996 \nL 117.282351 220.212691 \nL 117.345994 231.405116 \nL 117.377816 229.97863 \nL 117.47328 192.670548 \nL 117.505102 203.643513 \nL 117.727852 329.283967 \nL 117.759673 321.712621 \nL 117.982424 197.498652 \nL 118.109709 212.970534 \nL 118.173352 209.678644 \nL 118.236995 199.254327 \nL 118.491567 340.366662 \nL 118.55521 326.650455 \nL 118.714317 237.769435 \nL 118.77796 249.839697 \nL 118.873425 274.638599 \nL 118.937067 268.05482 \nL 119.00071 241.829433 \nL 119.032532 247.754834 \nL 119.255282 350.352061 \nL 119.287104 347.27963 \nL 119.382568 302.290472 \nL 119.478032 259.386177 \nL 119.541675 265.750497 \nL 119.63714 281.990486 \nL 119.668961 274.967788 \nL 119.764426 231.075927 \nL 119.796247 240.622406 \nL 119.987176 360.447189 \nL 120.018997 359.459622 \nL 120.08264 317.762353 \nL 120.241748 227.784037 \nL 120.273569 227.015929 \nL 120.30539 228.881334 \nL 120.337212 229.649441 \nL 120.432676 241.829433 \nL 120.464498 237.879165 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254.119154 \nL 124.092145 255.765099 \nL 124.219431 278.259678 \nL 124.251252 272.992654 \nL 124.346717 247.315915 \nL 124.378538 256.533206 \nL 124.601288 363.30016 \nL 124.63311 360.337459 \nL 124.728574 316.335868 \nL 124.887681 243.585107 \nL 124.983146 258.947259 \nL 125.014967 251.814831 \nL 125.110432 225.699174 \nL 125.142253 239.086191 \nL 125.333182 350.57152 \nL 125.365003 349.145034 \nL 125.428646 307.667225 \nL 125.587754 215.055397 \nL 125.746861 231.075927 \nL 125.778683 226.467281 \nL 125.874147 188.391091 \nL 125.905968 196.730545 \nL 126.096897 316.116408 \nL 126.128719 314.580193 \nL 126.192362 272.773195 \nL 126.256004 202.655946 \nL 126.319647 206.386754 \nL 126.446933 220.980798 \nL 126.510576 233.05106 \nL 126.574219 225.260255 \nL 126.637862 195.962437 \nL 126.669684 202.875406 \nL 126.892434 330.271534 \nL 126.956077 292.414803 \nL 127.115184 205.508917 \nL 127.178827 212.970534 \nL 127.274291 224.053229 \nL 127.306113 219.773772 \nL 127.401577 194.865141 \nL 127.433399 209.568914 \nL 127.624328 337.184502 \nL 127.656149 336.196935 \nL 127.719792 296.803989 \nL 127.878899 205.069999 \nL 127.910721 205.289458 \nL 128.038007 221.858636 \nL 128.069828 218.127827 \nL 128.165292 194.97487 \nL 128.197114 205.179728 \nL 128.388043 329.613156 \nL 128.419864 327.967211 \nL 128.483507 287.915887 \nL 128.642614 216.042964 \nL 128.833543 247.974293 \nL 128.897186 241.390514 \nL 128.929008 223.285121 \nL 128.992651 245.011593 \nL 129.183579 337.513691 \nL 129.247222 321.82235 \nL 129.40633 234.148357 \nL 129.469972 247.535375 \nL 129.565437 273.651032 \nL 129.62908 265.311579 \nL 129.692723 243.475378 \nL 129.756366 260.922392 \nL 129.947294 343.329363 \nL 130.010937 330.052075 \nL 130.170045 253.789965 \nL 130.265509 264.87266 \nL 130.297331 268.05482 \nL 130.329152 264.76293 \nL 130.456438 223.50458 \nL 130.488259 237.111058 \nL 130.679188 345.962874 \nL 130.71101 343.878011 \nL 130.774653 300.754257 \nL 130.93376 219.883502 \nL 131.029224 229.97863 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225.479714 \nL 134.147728 258.50834 \nL 134.211371 253.680235 \nL 134.243192 250.717535 \nL 134.306835 236.672139 \nL 134.338657 256.972125 \nL 134.529585 346.730982 \nL 134.62505 311.507763 \nL 134.752336 252.692668 \nL 134.784157 256.752666 \nL 134.879622 273.321843 \nL 134.911443 272.114817 \nL 135.038729 241.609973 \nL 135.07055 245.121322 \nL 135.293301 357.045569 \nL 135.325122 353.53422 \nL 135.420586 300.863987 \nL 135.547872 252.912128 \nL 135.579694 257.081855 \nL 135.675158 285.940754 \nL 135.738801 277.930489 \nL 135.802444 249.400779 \nL 135.834266 249.839697 \nL 136.025194 352.985572 \nL 136.057016 352.107735 \nL 136.120659 307.776955 \nL 136.279766 216.042964 \nL 136.343409 219.225124 \nL 136.438873 234.587276 \nL 136.470695 233.27052 \nL 136.566159 208.910536 \nL 136.597981 214.835938 \nL 136.788909 346.840712 \nL 136.820731 345.414226 \nL 136.884374 303.716958 \nL 136.979838 226.357551 \nL 137.043481 228.442415 \nL 137.202589 245.121322 \nL 137.23441 240.293218 \nL 137.298053 231.844034 \nL 137.329874 218.018098 \nL 137.361696 220.980798 \nL 137.552625 348.157467 \nL 137.584446 346.730982 \nL 137.648089 305.911551 \nL 137.743553 227.125659 \nL 137.807196 230.746738 \nL 137.934482 244.682404 \nL 137.966304 247.974293 \nL 137.998125 246.438078 \nL 138.09359 220.980798 \nL 138.125411 223.175392 \nL 138.31634 350.352061 \nL 138.348161 349.145034 \nL 138.411804 305.472632 \nL 138.507269 231.624575 \nL 138.53909 231.953764 \nL 138.730019 245.88943 \nL 138.793662 239.854299 \nL 138.857305 220.871069 \nL 138.889126 221.529447 \nL 139.080055 347.38936 \nL 139.111876 346.292063 \nL 139.175519 305.143443 \nL 139.334627 233.928898 \nL 139.589198 261.141852 \nL 139.652841 244.353215 \nL 139.684663 265.201849 \nL 139.875592 359.788811 \nL 139.939235 341.354229 \nL 140.098342 263.994823 \nL 140.161985 275.077518 \nL 140.257449 289.452103 \nL 140.289271 286.489402 \nL 140.416557 266.189416 \nL 140.639307 371.420154 \nL 140.671128 368.237994 \nL 140.766593 324.016944 \nL 140.893879 275.845625 \nL 140.9257 278.698596 \nL 140.989343 287.14778 \nL 141.021164 285.721294 \nL 141.180272 253.460776 \nL 141.3712 365.165564 \nL 141.403022 364.507186 \nL 141.466665 328.735319 \nL 141.657594 220.871069 \nL 141.721237 218.457016 \nL 141.753058 218.786205 \nL 141.880344 223.065662 \nL 141.943987 213.080263 \nL 142.166737 353.64395 \nL 142.23038 319.408298 \nL 142.421309 225.369985 \nL 142.45313 225.808903 \nL 142.484952 224.162958 \nL 142.516773 226.577011 \nL 142.644059 235.794302 \nL 142.675881 230.307819 \nL 142.707702 227.564578 \nL 142.930452 357.484488 \nL 142.994095 323.248836 \nL 143.153203 243.585107 \nL 143.185024 242.268351 \nL 143.344131 254.777532 \nL 143.375953 252.034291 \nL 143.471417 233.489979 \nL 143.662346 354.521787 \nL 143.694167 353.973139 \nL 143.75781 317.213705 \nL 143.916918 239.964029 \nL 143.948739 239.195921 \nL 144.076025 250.717535 \nL 144.171489 249.729968 \nL 144.235132 233.16079 \nL 144.457883 352.217465 \nL 144.489704 343.439092 \nL 144.712454 236.891598 \nL 144.744276 238.427813 \nL 144.776097 238.537543 \nL 144.935205 255.765099 \nL 144.967026 251.595372 \nL 144.998848 245.121322 \nL 145.221598 362.861241 \nL 145.253419 359.020703 \nL 145.348884 306.679658 \nL 145.444348 259.825096 \nL 145.507991 265.860227 \nL 145.571634 271.785628 \nL 145.603455 270.029954 \nL 145.762563 252.14402 \nL 145.985313 365.055834 \nL 146.017134 363.0807 \nL 146.112599 320.725054 \nL 146.271706 257.411044 \nL 146.335349 265.421308 \nL 146.367171 263.665634 \nL 146.462635 239.744569 \nL 146.526278 249.949427 \nL 146.717207 351.339627 \nL 146.749028 349.693683 \nL 146.812671 310.739655 \nL 146.971778 211.544048 \nL 147.0036 210.446752 \nL 147.130886 226.247822 \nL 147.162707 221.309987 \nL 147.194529 215.823505 \nL 147.258172 222.626743 \nL 147.321814 258.61807 \nL 147.512743 351.998005 \nL 147.576386 316.774786 \nL 147.735494 229.759171 \nL 147.767315 230.198089 \nL 147.926422 245.88943 \nL 147.958244 241.500244 \nL 148.021887 234.148357 \nL 148.053708 228.113226 \nL 148.276458 351.888276 \nL 148.340101 313.153708 \nL 148.499209 235.684572 \nL 148.53103 235.574842 \nL 148.658316 251.705102 \nL 148.690137 250.827264 \nL 148.817423 231.953764 \nL 149.008352 350.022872 \nL 149.040174 348.486656 \nL 149.103817 310.191007 \nL 149.262924 240.073758 \nL 149.422031 248.852131 \nL 149.453853 247.096456 \nL 149.549317 227.454848 \nL 149.581139 232.063493 \nL 149.803889 354.412058 \nL 149.867532 319.847217 \nL 150.026639 223.61431 \nL 150.05846 225.150525 \nL 150.217568 246.328349 \nL 150.249389 245.340782 \nL 150.281211 244.682404 \nL 150.344854 231.514845 \nL 150.567604 345.194767 \nL 150.599425 339.379095 \nL 150.790354 249.949427 \nL 150.885819 269.591035 \nL 150.949462 280.67373 \nL 150.981283 276.174814 \nL 151.108569 250.278616 \nL 151.331319 352.436924 \nL 151.394962 343.219633 \nL 151.585891 264.543471 \nL 151.649534 274.41914 \nL 151.713177 279.905623 \nL 151.744998 272.444006 \nL 151.872284 238.866732 \nL 152.063213 348.596386 \nL 152.095034 346.401793 \nL 152.158677 306.24074 \nL 152.317785 231.514845 \nL 152.413249 238.537543 \nL 152.476892 247.864564 \nL 152.540535 243.475378 \nL 152.635999 225.040796 \nL 152.826928 352.985572 \nL 152.858749 352.327194 \nL 152.922392 313.263437 \nL 153.0815 213.189993 \nL 153.113321 207.593781 \nL 153.176964 213.189993 \nL 153.240607 220.54188 \nL 153.272428 217.24999 \nL 153.367893 208.252159 \nL 153.399714 214.06783 \nL 153.622465 345.194767 \nL 153.686108 307.996414 \nL 153.845215 222.297554 \nL 153.877036 222.626743 \nL 154.004322 237.220787 \nL 154.036144 233.16079 \nL 154.131608 216.591612 \nL 154.16343 218.018098 \nL 154.38618 347.060171 \nL 154.449823 309.752089 \nL 154.60893 218.566746 \nL 154.640751 218.566746 \nL 154.768037 235.135924 \nL 154.799859 234.806735 \nL 154.927145 218.786205 \nL 155.149895 351.559087 \nL 155.181716 342.780714 \nL 155.372645 215.823505 \nL 155.404467 219.005665 \nL 155.531753 237.879165 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255.545639 \nL 158.968471 357.045569 \nL 159.032114 319.627757 \nL 159.191221 222.517014 \nL 159.223042 221.968365 \nL 159.350328 234.367816 \nL 159.38215 230.856467 \nL 159.477614 222.297554 \nL 159.509436 224.162958 \nL 159.732186 352.546654 \nL 159.764007 343.329363 \nL 159.986758 218.786205 \nL 160.018579 223.285121 \nL 160.114044 235.026194 \nL 160.209508 231.953764 \nL 160.273151 224.931066 \nL 160.495901 352.107735 \nL 160.527723 342.780714 \nL 160.750473 231.295386 \nL 160.973223 254.996991 \nL 161.005045 255.32618 \nL 161.036866 245.121322 \nL 161.068687 259.166718 \nL 161.259616 358.142866 \nL 161.323259 345.853145 \nL 161.482367 261.361311 \nL 161.546009 268.493739 \nL 161.609652 277.381841 \nL 161.673295 271.34671 \nL 161.800581 256.204017 \nL 162.023331 368.567183 \nL 162.118796 349.803412 \nL 162.277903 268.16455 \nL 162.309725 270.029954 \nL 162.373368 276.833192 \nL 162.405189 275.955355 \nL 162.564296 242.487811 \nL 162.755225 361.325026 \nL 162.818868 350.352061 \nL 163.041618 218.566746 \nL 163.07344 221.200258 \nL 163.105261 221.529447 \nL 163.168904 229.429982 \nL 163.200726 227.674307 \nL 163.328011 222.736473 \nL 163.51894 353.095302 \nL 163.550762 352.766113 \nL 163.614405 316.665057 \nL 163.773512 213.409452 \nL 163.805333 211.982967 \nL 163.837155 213.628912 \nL 163.900798 219.554313 \nL 163.932619 224.053229 \nL 163.996262 220.651609 \nL 164.028084 219.225124 \nL 164.091727 219.444583 \nL 164.314477 351.668816 \nL 164.346298 342.341796 \nL 164.60087 209.568914 \nL 164.632692 210.117563 \nL 164.696334 215.933234 \nL 164.759977 212.421885 \nL 164.82362 211.982967 \nL 164.855442 208.471618 \nL 165.078192 347.608819 \nL 165.110014 339.818014 \nL 165.332764 222.955932 \nL 165.364585 225.479714 \nL 165.396407 226.138092 \nL 165.555514 237.001328 \nL 165.587336 237.111058 \nL 165.619157 224.821336 \nL 165.650978 243.146189 \nL 165.841907 350.681249 \nL 165.90555 321.163973 \nL 166.064658 229.8689 \nL 166.096479 228.881334 \nL 166.1283 230.746738 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223.394851 \nL 171.187913 342.561255 \nL 171.251556 315.019112 \nL 171.442485 205.399188 \nL 171.7607 233.928898 \nL 171.951629 347.060171 \nL 171.98345 344.536389 \nL 172.047093 321.493162 \nL 172.2062 225.918633 \nL 172.238022 226.79647 \nL 172.301665 232.721871 \nL 172.333486 230.198089 \nL 172.397129 224.162958 \nL 172.428951 224.931066 \nL 172.556236 274.748329 \nL 172.715344 356.93584 \nL 172.747165 356.71638 \nL 172.778987 356.167732 \nL 173.001737 255.874828 \nL 173.129023 256.972125 \nL 173.256309 251.046724 \nL 173.479059 360.447189 \nL 173.574523 342.232066 \nL 173.765452 244.901863 \nL 173.797274 247.425645 \nL 173.860916 243.036459 \nL 173.956381 225.479714 \nL 174.020024 225.808903 \nL 174.210953 350.790979 \nL 174.274596 341.13477 \nL 174.497346 186.635417 \nL 174.560989 188.83001 \nL 174.624632 200.022435 \nL 174.656453 196.620815 \nL 174.688275 191.024603 \nL 174.720096 191.90244 \nL 174.81556 242.487811 \nL 175.006489 340.695851 \nL 175.070132 309.31317 \nL 175.229239 200.022435 \nL 175.324704 191.244062 \nL 175.388347 194.755411 \nL 175.420168 194.426222 \nL 175.515633 201.55865 \nL 175.547454 196.620815 \nL 175.770204 339.488825 \nL 175.833847 311.288304 \nL 176.024776 192.999737 \nL 176.088419 189.488388 \nL 176.120241 191.573251 \nL 176.342991 227.454848 \nL 176.53392 347.938008 \nL 176.597562 321.93208 \nL 176.75667 218.676476 \nL 176.852134 214.726208 \nL 176.883956 217.140261 \nL 177.106706 247.425645 \nL 177.297635 356.496921 \nL 177.361278 345.962874 \nL 177.552206 240.622406 \nL 177.647671 250.388346 \nL 177.679492 250.388346 \nL 177.743135 238.318084 \nL 177.806778 242.70727 \nL 177.870421 264.433741 \nL 178.06135 360.776378 \nL 178.124993 354.302328 \nL 178.538672 237.220787 \nL 178.602315 240.951595 \nL 178.793244 352.875843 \nL 178.856887 342.232066 \nL 179.079637 205.179728 \nL 179.111458 207.81324 \nL 179.175101 215.823505 \nL 179.206923 219.115394 \nL 179.238744 215.274856 \nL 179.270566 209.898103 \nL 179.334209 217.24999 \nL 179.36603 220.761339 \nL 179.58878 344.09747 \nL 179.620602 336.526124 \nL 179.843352 213.299723 \nL 179.875173 214.835938 \nL 179.906995 214.616478 \nL 179.970638 223.833769 \nL 180.034281 219.005665 \nL 180.161567 237.549976 \nL 180.352495 348.706116 \nL 180.416138 317.213705 \nL 180.575246 219.773772 \nL 180.607067 218.127827 \nL 180.67071 219.005665 \nL 180.861639 230.637008 \nL 180.89346 225.808903 \nL 181.116211 349.364494 \nL 181.179853 317.872083 \nL 181.338961 213.519182 \nL 181.402604 217.140261 \nL 181.434425 216.042964 \nL 181.625354 230.198089 \nL 181.657175 223.50458 \nL 181.688997 236.34295 \nL 181.879926 349.364494 \nL 181.943569 320.286135 \nL 182.134497 208.142429 \nL 182.229962 210.556481 \nL 182.452712 247.535375 \nL 182.643641 353.863409 \nL 182.675462 352.985572 \nL 182.739105 325.882348 \nL 182.898213 241.500244 \nL 182.930034 244.353215 \nL 182.993677 252.473209 \nL 183.05732 248.193753 \nL 183.089141 245.231052 \nL 183.120963 246.876997 \nL 183.216427 253.789965 \nL 183.439178 359.349892 \nL 183.470999 358.033136 \nL 183.852857 233.928898 \nL 183.884678 231.405116 \nL 183.916499 234.148357 \nL 183.948321 231.844034 \nL 184.011964 280.234812 \nL 184.171071 348.047738 \nL 184.234714 317.872083 \nL 184.425643 195.304059 \nL 184.489286 192.231629 \nL 184.58475 193.658115 \nL 184.616572 194.426222 \nL 184.743858 219.444583 \nL 184.934786 339.708284 \nL 184.998429 310.849385 \nL 185.189358 195.0846 \nL 185.253001 189.598117 \nL 185.316644 193.987304 \nL 185.380287 195.304059 \nL 185.475751 204.63108 \nL 185.698502 335.209368 \nL 185.730323 329.174237 \nL 185.953073 187.513254 \nL 186.016716 193.548385 \nL 186.207645 208.032699 \nL 186.239466 208.691077 \nL 186.462217 342.451525 \nL 186.52586 314.031545 \nL 186.716788 199.802975 \nL 186.74861 199.912705 \nL 186.780431 197.937571 \nL 186.812253 198.595949 \nL 187.035003 223.285121 \nL 187.225932 344.2072 \nL 187.289575 319.408298 \nL 187.480504 198.486219 \nL 187.671432 207.70351 \nL 187.83054 264.324012 \nL 187.989647 348.925575 \nL 188.05329 344.31693 \nL 188.244219 237.111058 \nL 188.307862 245.7797 \nL 188.371505 252.692668 \nL 188.403326 250.059157 \nL 188.435148 245.7797 \nL 188.466969 247.754834 \nL 188.594255 283.526701 \nL 188.753362 357.923407 \nL 188.785184 357.155299 \nL 188.817005 357.484488 \nL 189.007934 255.43591 \nL 189.13522 262.458608 \nL 189.294327 245.7797 \nL 189.35797 287.915887 \nL 189.517077 350.790979 \nL 189.58072 319.956946 \nL 189.771649 197.608382 \nL 190.026221 216.042964 \nL 190.058042 215.823505 \nL 190.121685 268.932657 \nL 190.280793 345.194767 \nL 190.344435 316.445597 \nL 190.535364 190.037036 \nL 190.599007 184.879742 \nL 190.66265 187.293795 \nL 190.694472 187.403524 \nL 190.726293 186.525687 \nL 190.853579 199.364057 \nL 191.044508 320.834784 \nL 191.108151 293.841289 \nL 191.362722 85.245216 \nL 191.458187 75.040359 \nL 191.490008 75.808466 \nL 191.617294 106.642499 \nL 191.808223 263.116986 \nL 191.840044 260.044555 \nL 191.903687 199.473786 \nL 192.094616 70.431713 \nL 192.126438 68.895498 \nL 192.19008 69.224687 \nL 192.285545 80.746301 \nL 192.381009 111.031685 \nL 192.571938 275.955355 \nL 192.635581 257.630503 \nL 192.890153 73.284684 \nL 192.985617 89.744132 \nL 193.176546 191.463522 \nL 193.367475 301.302905 \nL 193.399296 297.791556 \nL 193.622046 170.285698 \nL 193.685689 175.223533 \nL 193.90844 229.759171 \nL 194.099368 337.294232 \nL 194.13119 336.745583 \nL 194.163011 336.306665 \nL 194.57669 192.560818 \nL 194.703976 246.986726 \nL 194.863084 324.455862 \nL 194.926726 298.011016 \nL 195.117655 169.73705 \nL 195.21312 181.368393 \nL 195.43587 220.871069 \nL 195.626799 337.184502 \nL 195.690442 309.20344 \nL 195.88137 189.378658 \nL 195.913192 189.598117 \nL 195.945013 187.952173 \nL 195.976835 191.353792 \nL 196.199585 229.320252 \nL 196.390514 338.391528 \nL 196.454157 313.482897 \nL 196.645086 195.962437 \nL 196.708729 193.219196 \nL 196.9633 222.736473 \nL 197.154229 336.526124 \nL 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183.124068 \nL 205.236881 168.749483 \nL 205.268703 171.821913 \nL 205.395989 238.976462 \nL 205.555096 330.381263 \nL 205.618739 309.20344 \nL 205.841489 163.811649 \nL 205.873311 160.410029 \nL 205.968775 161.178137 \nL 206.000596 159.422463 \nL 206.127882 202.655946 \nL 206.318811 334.880179 \nL 206.382454 314.251004 \nL 206.605204 166.66462 \nL 206.73249 155.143006 \nL 206.764312 155.472195 \nL 206.891597 191.024603 \nL 207.082526 326.869915 \nL 207.146169 308.106144 \nL 207.368919 143.621392 \nL 207.496205 128.259241 \nL 207.528027 128.58843 \nL 207.655313 173.248399 \nL 207.846241 316.445597 \nL 207.909884 291.975885 \nL 208.164456 105.545202 \nL 208.25992 101.046287 \nL 208.291742 102.582502 \nL 208.387206 125.40627 \nL 208.482671 231.624575 \nL 208.609957 299.76669 \nL 208.641778 297.462367 \nL 208.737242 200.900272 \nL 208.959993 76.796033 \nL 208.991814 77.015492 \nL 209.055457 83.270083 \nL 209.214564 173.797047 \nL 209.405493 293.29264 \nL 209.437315 289.561832 \nL 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213.987784 319.627757 \nL 214.274177 177.747315 \nL 214.33782 181.148934 \nL 214.465106 204.850539 \nL 214.496928 204.082432 \nL 214.560571 248.193753 \nL 214.719678 337.074772 \nL 214.751499 336.526124 \nL 214.783321 332.466127 \nL 215.006071 208.691077 \nL 215.101535 208.910536 \nL 215.165178 199.034868 \nL 215.197 201.229461 \nL 215.547036 339.269365 \nL 215.578857 325.992077 \nL 215.865251 180.271097 \nL 215.928894 161.726785 \nL 215.992536 169.84678 \nL 216.024358 169.73705 \nL 216.119822 257.081855 \nL 216.247108 305.143443 \nL 216.27893 297.133178 \nL 216.660787 79.539274 \nL 216.72443 91.499807 \nL 217.010823 277.930489 \nL 217.074466 254.119154 \nL 217.297217 85.793865 \nL 217.329038 90.40251 \nL 217.647253 257.191584 \nL 217.774539 315.238571 \nL 217.80636 309.971548 \nL 218.188218 149.766253 \nL 218.25186 158.873814 \nL 218.410968 266.628335 \nL 218.538254 315.458031 \nL 218.570075 309.4229 \nL 218.856468 159.751651 \nL 218.88829 162.165704 \nL 218.920111 163.811649 \nL 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216.591612 \nL 254.814724 217.46945 \nL 254.910188 212.312156 \nL 254.94201 199.144597 \nL 254.973831 212.312156 \nL 255.196582 354.082869 \nL 255.260225 316.555327 \nL 255.419332 221.200258 \nL 255.514796 205.838106 \nL 255.578439 207.81324 \nL 255.673904 204.850539 \nL 255.705725 192.780277 \nL 255.737547 205.728377 \nL 255.960297 346.730982 \nL 256.02394 310.849385 \nL 256.214869 196.511086 \nL 256.373976 180.490556 \nL 256.437619 184.879742 \nL 256.46944 172.260832 \nL 256.501262 185.318661 \nL 256.724012 339.927743 \nL 256.787655 304.704524 \nL 256.978584 188.061902 \nL 257.042227 175.881911 \nL 257.10587 178.076504 \nL 257.264977 196.840275 \nL 257.487727 333.563423 \nL 257.519549 332.685586 \nL 257.710477 228.113226 \nL 257.837763 243.475378 \nL 257.869585 245.7797 \nL 257.901406 243.585107 \nL 257.996871 230.527278 \nL 258.028692 241.719703 \nL 258.251442 359.130433 \nL 258.283264 357.594218 \nL 258.378728 313.592626 \nL 258.537835 257.959692 \nL 258.601478 263.116986 \nL 258.6333 261.58077 \nL 258.760586 219.334854 \nL 258.792407 237.659706 \nL 258.983336 356.058003 \nL 259.015157 355.180165 \nL 259.0788 314.141275 \nL 259.237908 219.225124 \nL 259.365194 213.299723 \nL 259.397015 213.628912 \nL 259.428836 212.751074 \nL 259.524301 189.817577 \nL 259.556122 208.252159 \nL 259.747051 346.401793 \nL 259.778873 346.072604 \nL 259.842516 307.886684 \nL 260.001623 202.875406 \nL 260.097087 198.925138 \nL 260.128909 200.571083 \nL 260.16073 201.119731 \nL 260.256195 178.515422 \nL 260.288016 179.612719 \nL 260.542588 336.416394 \nL 260.574409 329.174237 \nL 260.797159 204.192161 \nL 260.988088 218.237557 \nL 261.051731 199.144597 \nL 261.083553 215.055397 \nL 261.306303 349.035305 \nL 261.369946 309.31317 \nL 261.529053 223.833769 \nL 261.592696 226.028363 \nL 261.624518 225.369985 \nL 261.688161 231.405116 \nL 261.751803 227.893767 \nL 261.815446 202.655946 \nL 261.847268 216.372153 \nL 262.038197 347.38936 \nL 262.070018 347.27963 \nL 262.133661 309.093711 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226.9062 \nL 265.634022 196.291626 \nL 265.665844 218.018098 \nL 265.888594 352.546654 \nL 265.952237 310.629926 \nL 266.111344 206.606214 \nL 266.143166 203.424054 \nL 266.206809 203.753243 \nL 266.270452 211.324589 \nL 266.302273 207.484051 \nL 266.397737 186.415957 \nL 266.652309 345.962874 \nL 266.684131 337.95261 \nL 266.906881 220.651609 \nL 267.034167 229.8689 \nL 267.065988 228.771604 \nL 267.129631 210.227292 \nL 267.161453 196.950004 \nL 267.193274 215.384586 \nL 267.416024 349.803412 \nL 267.479667 312.275871 \nL 267.638775 214.726208 \nL 267.670596 213.299723 \nL 267.702417 215.384586 \nL 267.734239 214.506749 \nL 267.797882 220.322421 \nL 267.829703 216.811072 \nL 267.893346 200.680812 \nL 267.925168 206.277025 \nL 268.179739 353.863409 \nL 268.243382 338.062339 \nL 268.40249 234.258087 \nL 268.466133 240.402947 \nL 268.561597 255.545639 \nL 268.593418 250.607805 \nL 268.657061 227.125659 \nL 268.688883 230.637008 \nL 268.943455 363.519619 \nL 269.007098 351.668816 \nL 269.229848 235.026194 \nL 269.293491 248.193753 \nL 269.325312 251.156453 \nL 269.357134 243.914296 \nL 269.420777 219.554313 \nL 269.452598 220.322421 \nL 269.70717 358.691514 \nL 269.738991 350.022872 \nL 269.961741 217.140261 \nL 270.089027 228.661874 \nL 270.120849 228.003496 \nL 270.216313 193.219196 \nL 270.248135 214.506749 \nL 270.439063 352.107735 \nL 270.470885 350.790979 \nL 270.534528 312.934248 \nL 270.693635 220.322421 \nL 270.725457 217.24999 \nL 270.7891 218.895935 \nL 270.852743 225.369985 \nL 270.884564 223.943499 \nL 270.980028 196.840275 \nL 271.01185 218.347287 \nL 271.202779 350.681249 \nL 271.2346 349.145034 \nL 271.298243 312.49533 \nL 271.45735 212.202426 \nL 271.489172 210.556481 \nL 271.616458 220.54188 \nL 271.648279 217.579179 \nL 271.711922 188.83001 \nL 271.775565 210.88567 \nL 271.966494 347.718549 \nL 271.998315 347.49909 \nL 272.061958 311.946682 \nL 272.221066 207.154862 \nL 272.252887 204.960269 \nL 272.284708 208.032699 \nL 272.31653 209.129996 \nL 272.380173 219.334854 \nL 272.411994 214.835938 \nL 272.475637 190.914873 \nL 272.507459 194.316493 \nL 272.730209 348.267197 \nL 272.793852 338.720717 \nL 273.016602 212.970534 \nL 273.048424 215.604045 \nL 273.080245 214.17756 \nL 273.175709 225.369985 \nL 273.207531 225.260255 \nL 273.271174 205.179728 \nL 273.302995 221.200258 \nL 273.525746 346.182334 \nL 273.557567 344.646119 \nL 273.653031 288.354806 \nL 273.780317 237.659706 \nL 273.812139 240.512677 \nL 273.907603 245.560241 \nL 274.003068 219.444583 \nL 274.034889 224.272688 \nL 274.257639 352.875843 \nL 274.353104 344.536389 \nL 274.575854 256.533206 \nL 274.639497 258.727799 \nL 274.671318 258.069421 \nL 274.766783 217.24999 \nL 274.830426 241.171055 \nL 275.021354 357.265029 \nL 275.053176 356.167732 \nL 275.116819 324.675321 \nL 275.275926 217.140261 \nL 275.307748 217.688909 \nL 275.435034 236.34295 \nL 275.466855 233.819168 \nL 275.530498 194.645681 \nL 275.594141 216.481883 \nL 275.78507 350.900709 \nL 275.816891 349.035305 \nL 275.880534 306.679658 \nL 276.039641 217.46945 \nL 276.071463 217.579179 \nL 276.198749 229.649441 \nL 276.23057 226.357551 \nL 276.294213 189.159199 \nL 276.357856 212.202426 \nL 276.548785 347.27963 \nL 276.580606 346.950441 \nL 276.644249 308.215873 \nL 276.803357 213.628912 \nL 276.835178 211.653778 \nL 276.866999 212.970534 \nL 276.962464 219.225124 \nL 277.057928 182.026771 \nL 277.08975 190.695414 \nL 277.3125 345.085037 \nL 277.376143 334.002342 \nL 277.53525 220.651609 \nL 277.598893 221.748906 \nL 277.726179 237.769435 \nL 277.758 235.794302 \nL 277.853465 193.109466 \nL 277.885286 211.10513 \nL 278.108037 344.536389 \nL 278.17168 305.582362 \nL 278.330787 208.581348 \nL 278.426251 224.272688 \nL 278.489894 237.440247 \nL 278.521716 234.697005 \nL 278.585359 199.364057 \nL 278.649001 215.713775 \nL 278.871752 337.513691 \nL 278.903573 332.136938 \nL 279.094502 239.744569 \nL 279.158145 254.887262 \nL 279.253609 269.920224 \nL 279.285431 263.226715 \nL 279.349074 219.005665 \nL 279.412717 237.659706 \nL 279.635467 351.998005 \nL 279.667288 350.790979 \nL 279.762753 298.120745 \nL 279.890039 230.417549 \nL 279.92186 231.514845 \nL 280.017324 241.500244 \nL 280.049146 235.355383 \nL 280.112789 206.825673 \nL 280.14461 211.982967 \nL 280.367361 346.621252 \nL 280.431004 335.648287 \nL 280.653754 206.606214 \nL 280.78104 215.274856 \nL 280.812861 212.751074 \nL 280.876504 178.405693 \nL 280.908326 187.184065 \nL 281.162897 345.743415 \nL 281.194719 337.73315 \nL 281.417469 209.349455 \nL 281.544755 229.539711 \nL 281.576576 225.589444 \nL 281.640219 181.039204 \nL 281.703862 205.728377 \nL 281.894791 334.77045 \nL 281.926612 334.112072 \nL 281.990255 293.40237 \nL 282.08572 216.591612 \nL 282.117541 219.334854 \nL 282.244827 230.637008 \nL 282.30847 239.195921 \nL 282.340291 236.781869 \nL 282.403934 191.79271 \nL 282.467577 214.945667 \nL 282.658506 342.780714 \nL 282.690328 342.341796 \nL 282.753971 301.741824 \nL 282.849435 219.773772 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348.376927 \nL 286.572546 308.874251 \nL 286.668011 227.345118 \nL 286.699832 227.454848 \nL 286.731654 227.125659 \nL 286.763475 227.784037 \nL 286.890761 241.939162 \nL 286.922582 238.976462 \nL 286.986225 190.585684 \nL 287.049868 218.566746 \nL 287.240797 344.646119 \nL 287.272619 344.536389 \nL 287.336262 302.94885 \nL 287.399904 221.200258 \nL 287.463547 223.285121 \nL 287.622655 238.757002 \nL 287.654476 241.280784 \nL 287.686298 238.427813 \nL 287.749941 191.90244 \nL 287.813583 218.566746 \nL 288.004512 344.646119 \nL 288.036334 343.000174 \nL 288.099977 303.936417 \nL 288.227263 226.247822 \nL 288.259084 226.357551 \nL 288.418191 241.061325 \nL 288.450013 237.440247 \nL 288.513656 192.451088 \nL 288.577299 216.042964 \nL 288.768227 342.232066 \nL 288.800049 341.683418 \nL 288.863692 304.923984 \nL 289.022799 218.018098 \nL 289.086442 226.79647 \nL 289.181906 243.036459 \nL 289.213728 242.158622 \nL 289.277371 208.691077 \nL 289.341014 235.355383 \nL 289.563764 353.095302 \nL 289.595586 348.815845 \nL 289.722871 265.092119 \nL 289.786514 246.547808 \nL 289.850157 255.874828 \nL 289.945622 268.274279 \nL 289.977443 262.678067 \nL 290.041086 222.626743 \nL 290.104729 250.168886 \nL 290.327479 357.594218 \nL 290.359301 355.728814 \nL 290.454765 307.008847 \nL 290.613872 243.914296 \nL 290.709337 250.498075 \nL 290.804801 196.730545 \nL 290.868444 231.185656 \nL 291.059373 348.047738 \nL 291.091194 346.950441 \nL 291.154837 308.435333 \nL 291.313945 196.511086 \nL 291.345766 193.219196 \nL 291.377588 195.742978 \nL 291.568516 224.711607 \nL 291.85491 381.076364 \nL 291.918552 350.681249 \nL 292.109481 236.672139 \nL 292.173124 235.904031 \nL 292.236767 239.086191 \nL 292.332232 202.107298 \nL 292.364053 213.958101 \nL 292.586803 351.339627 \nL 292.650446 343.000174 \nL 292.841375 220.871069 \nL 292.873196 221.419717 \nL 293.000482 232.502412 \nL 293.032304 226.79647 \nL 293.095947 212.860804 \nL 293.127768 221.639176 \nL 293.191411 257.849962 \nL 293.350518 349.803412 \nL 293.38234 349.474223 \nL 293.445983 310.520196 \nL 293.60509 221.200258 \nL 293.700555 230.08836 \nL 293.764197 238.976462 \nL 293.796019 232.392682 \nL 293.859662 189.598117 \nL 293.923305 216.701342 \nL 294.114234 346.401793 \nL 294.146055 346.182334 \nL 294.209698 308.983981 \nL 294.368805 225.808903 \nL 294.400627 226.467281 \nL 294.527913 238.757002 \nL 294.623377 202.655946 \nL 294.655199 213.628912 \nL 294.90977 348.157467 \nL 294.973413 331.36883 \nL 295.13252 241.500244 \nL 295.196163 248.193753 \nL 295.291628 262.568337 \nL 295.323449 252.582939 \nL 295.387092 214.726208 \nL 295.450735 239.52511 \nL 295.673485 353.973139 \nL 295.705307 346.730982 \nL 296.118986 189.049469 \nL 296.182629 212.641345 \nL 296.405379 336.416394 \nL 296.437201 334.002342 \nL 296.500843 284.843457 \nL 296.659951 201.55865 \nL 296.691772 201.778109 \nL 296.787237 209.678644 \nL 296.819058 207.593781 \nL 296.882701 157.447329 \nL 296.946344 174.674884 \nL 297.169094 316.335868 \nL 297.232737 301.302905 \nL 297.328202 205.399188 \nL 297.423666 212.092696 \nL 297.550952 224.382418 \nL 297.582773 222.955932 \nL 297.678238 161.507326 \nL 297.710059 172.370562 \nL 297.932809 309.4229 \nL 297.996452 298.230475 \nL 298.187381 188.391091 \nL 298.219203 189.049469 \nL 298.251024 192.670548 \nL 298.314667 191.463522 \nL 298.346488 188.391091 \nL 298.410131 141.646259 \nL 298.473774 163.811649 \nL 298.696525 314.251004 \nL 298.728346 313.263437 \nL 298.791989 269.261846 \nL 298.951096 182.136501 \nL 298.982918 182.136501 \nL 299.078382 192.780277 \nL 299.110204 189.159199 \nL 299.173847 153.82625 \nL 299.237489 172.260832 \nL 299.46024 320.615324 \nL 299.523883 308.983981 \nL 299.651169 210.775941 \nL 299.714811 212.860804 \nL 299.842097 233.489979 \nL 299.873919 231.295386 \nL 299.937562 184.331094 \nL 300.001205 204.63108 \nL 300.255776 336.087205 \nL 300.319419 316.445597 \nL 300.446705 246.218619 \nL 300.510348 251.705102 \nL 300.605813 270.249413 \nL 300.637634 265.531038 \nL 300.701277 205.728377 \nL 300.76492 222.846203 \nL 301.019492 343.878011 \nL 301.051313 340.147203 \nL 301.146777 293.29264 \nL 301.242242 248.193753 \nL 301.305885 252.25375 \nL 301.369528 264.653201 \nL 301.401349 262.897526 \nL 301.464992 203.094865 \nL 301.528635 226.9062 \nL 301.751385 345.962874 \nL 301.815028 335.867746 \nL 302.005957 220.651609 \nL 302.037778 225.040796 \nL 302.133243 233.819168 \nL 302.165064 232.941331 \nL 302.228707 177.747315 \nL 302.29235 200.022435 \nL 302.546922 352.546654 \nL 302.578743 342.122336 \nL 302.801494 215.384586 \nL 302.865137 218.237557 \nL 302.896958 221.858636 \nL 302.928779 218.566746 \nL 302.992422 169.62732 \nL 303.056065 186.415957 \nL 303.278816 322.370999 \nL 303.342459 307.996414 \nL 303.437923 201.33919 \nL 303.533387 209.898103 \nL 303.660673 225.808903 \nL 303.692495 220.54188 \nL 303.756138 161.836515 \nL 303.81978 175.99164 \nL 304.042531 312.60506 \nL 304.106174 299.108312 \nL 304.201638 195.304059 \nL 304.297102 202.217028 \nL 304.424388 220.980798 \nL 304.45621 215.384586 \nL 304.519853 162.824082 \nL 304.583496 174.016507 \nL 304.806246 313.592626 \nL 304.869889 299.327771 \nL 304.997175 196.511086 \nL 305.060818 200.351623 \nL 305.188103 226.247822 \nL 305.219925 221.309987 \nL 305.283568 175.772181 \nL 305.347211 191.134333 \nL 305.601783 321.273702 \nL 305.665425 300.754257 \nL 305.792711 229.539711 \nL 305.856354 238.098624 \nL 305.951819 257.849962 \nL 305.98364 250.827264 \nL 306.047283 190.366225 \nL 306.110926 211.214859 \nL 306.365498 319.737487 \nL 306.429141 304.046147 \nL 306.588248 234.477546 \nL 306.651891 243.475378 \nL 306.715534 252.912128 \nL 306.747355 243.585107 \nL 306.810998 176.979207 \nL 306.874641 195.304059 \nL 307.097391 315.67749 \nL 307.161034 300.863987 \nL 307.28832 194.865141 \nL 307.351963 203.753243 \nL 307.479249 227.564578 \nL 307.51107 223.285121 \nL 307.606535 161.068407 \nL 307.638356 173.02894 \nL 307.861107 311.398033 \nL 307.92475 298.449934 \nL 308.052035 196.950004 \nL 308.115678 205.069999 \nL 308.242964 227.235389 \nL 308.274786 223.50458 \nL 308.338429 167.432727 \nL 308.402071 183.124068 \nL 308.656643 327.857481 \nL 308.688465 317.213705 \nL 308.847572 201.33919 \nL 308.911215 205.508917 \nL 309.038501 222.517014 \nL 309.102144 174.565155 \nL 309.165787 192.121899 \nL 309.420358 337.84288 \nL 309.484001 316.335868 \nL 309.643109 217.579179 \nL 309.706752 244.572674 \nL 309.802216 290.439669 \nL 309.834037 274.858059 \nL 309.89768 259.715366 \nL 309.961323 267.506172 \nL 310.152252 342.890444 \nL 310.215895 333.673153 \nL 310.406824 251.375913 \nL 310.470467 260.812663 \nL 310.53411 269.042387 \nL 310.565931 263.885093 \nL 310.629574 210.117563 \nL 310.693217 228.991063 \nL 310.947789 353.863409 \nL 311.011432 337.623421 \nL 311.170539 232.721871 \nL 311.234182 235.904031 \nL 311.297825 243.475378 \nL 311.329646 237.330517 \nL 311.393289 183.453257 \nL 311.456932 199.473786 \nL 311.711504 318.420731 \nL 311.775147 304.704524 \nL 312.157004 142.853285 \nL 312.188826 150.973279 \nL 312.443398 306.679658 \nL 312.50704 295.596963 \nL 312.666148 186.635417 \nL 312.697969 188.171632 \nL 312.825255 203.314324 \nL 312.857077 199.254327 \nL 312.92072 136.708424 \nL 312.984362 148.778686 \nL 313.207113 302.290472 \nL 313.270756 287.915887 \nL 313.398042 177.857044 \nL 313.461684 179.612719 \nL 313.493506 180.710015 \nL 313.58897 192.999737 \nL 313.620792 189.159199 \nL 313.684435 136.818154 \nL 313.748078 151.302468 \nL 313.970828 305.253173 \nL 314.034471 292.853722 \nL 314.129935 180.819745 \nL 314.2254 183.233797 \nL 314.352685 195.0846 \nL 314.384507 189.159199 \nL 314.44815 131.66086 \nL 314.511793 148.010579 \nL 314.734543 296.58453 \nL 314.798186 284.733727 \nL 314.89365 176.2111 \nL 314.989115 182.136501 \nL 315.116401 198.705679 \nL 315.148222 191.90244 \nL 315.211865 135.501398 \nL 315.275508 148.778686 \nL 315.498258 298.779123 \nL 315.561901 283.96562 \nL 315.721008 170.505158 \nL 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322.849017 160.08084 \nL 322.91266 177.966774 \nL 323.13541 325.443429 \nL 323.199053 312.824519 \nL 323.389982 181.258664 \nL 323.421803 182.246231 \nL 323.517267 192.670548 \nL 323.549089 185.757579 \nL 323.612732 140.987881 \nL 323.676375 160.410029 \nL 323.930947 331.36883 \nL 323.962768 318.74992 \nL 324.153697 195.633248 \nL 324.185518 196.950004 \nL 324.280983 202.875406 \nL 324.312804 195.633248 \nL 324.376447 130.673293 \nL 324.44009 142.963014 \nL 324.66284 299.108312 \nL 324.726483 284.843457 \nL 324.853769 172.590021 \nL 324.917412 181.587853 \nL 325.044698 199.693246 \nL 325.076519 193.328926 \nL 325.140162 127.600863 \nL 325.203805 137.257072 \nL 325.426555 297.352638 \nL 325.490198 284.843457 \nL 325.585663 175.772181 \nL 325.681127 189.927306 \nL 325.808413 209.129996 \nL 325.840234 204.301891 \nL 325.935699 133.416535 \nL 325.96752 143.182474 \nL 326.094806 259.715366 \nL 326.190271 296.58453 \nL 326.222092 296.145611 \nL 326.285735 252.912128 \nL 326.349378 175.004073 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195.523519 \nL 330.486168 138.683558 \nL 330.549811 149.875983 \nL 330.677097 273.431573 \nL 330.772562 313.592626 \nL 330.804383 312.824519 \nL 330.868026 268.274279 \nL 330.96349 186.854876 \nL 330.995312 187.403524 \nL 331.154419 198.815408 \nL 331.186241 191.79271 \nL 331.249884 139.341936 \nL 331.313526 150.534361 \nL 331.377169 191.353792 \nL 331.536277 313.153708 \nL 331.568098 307.447766 \nL 331.631741 269.481306 \nL 331.790848 175.333262 \nL 331.82267 175.113803 \nL 331.949956 198.925138 \nL 332.013599 161.617056 \nL 332.077242 184.331094 \nL 332.331813 328.2964 \nL 332.395456 312.166141 \nL 332.586385 206.057565 \nL 332.650028 211.434319 \nL 332.713671 203.972702 \nL 332.777314 149.875983 \nL 332.840957 160.958678 \nL 332.968243 249.949427 \nL 333.095529 293.951018 \nL 333.12735 292.305074 \nL 333.222814 239.195921 \nL 333.318279 195.523519 \nL 333.3501 197.498652 \nL 333.445565 209.129996 \nL 333.477386 206.277025 \nL 333.541029 145.706256 \nL 333.604672 158.544625 \nL 333.731958 260.702933 \nL 333.859244 306.569929 \nL 333.891065 301.302905 \nL 334.081994 188.061902 \nL 334.145637 200.571083 \nL 334.20928 211.544048 \nL 334.241101 207.374321 \nL 334.304744 166.115971 \nL 334.336566 181.587853 \nL 334.622959 310.739655 \nL 334.686602 297.681827 \nL 334.845709 205.289458 \nL 334.909352 213.299723 \nL 334.972995 235.684572 \nL 335.004816 235.245653 \nL 335.068459 195.413789 \nL 335.132102 199.583516 \nL 335.195745 222.846203 \nL 335.354853 305.143443 \nL 335.386674 302.94885 \nL 335.450317 267.067253 \nL 335.609424 172.91921 \nL 335.73671 184.989472 \nL 335.768532 181.368393 \nL 335.832175 127.271674 \nL 335.895817 141.975448 \nL 336.150389 298.888853 \nL 336.214032 270.907791 \nL 336.404961 155.581925 \nL 336.468604 161.068407 \nL 336.500425 170.395428 \nL 336.532247 169.73705 \nL 336.59589 117.944653 \nL 336.659533 145.157608 \nL 336.882283 305.692091 \nL 336.945926 294.719126 \nL 337.136855 181.478123 \nL 337.200498 189.049469 \nL 337.26414 196.401356 \nL 337.295962 195.19433 \nL 337.359605 135.501398 \nL 337.423248 154.704087 \nL 337.677819 302.83912 \nL 337.741462 286.599132 \nL 337.932391 186.525687 \nL 337.964213 189.488388 \nL 338.027856 197.498652 \nL 338.059677 192.560818 \nL 338.12332 139.232206 \nL 338.186963 161.397596 \nL 338.441535 304.265606 \nL 338.505178 290.549399 \nL 338.727928 185.099202 \nL 338.759749 190.256495 \nL 338.791571 193.987304 \nL 338.823392 190.585684 \nL 338.887035 125.954918 \nL 338.950678 146.254904 \nL 339.20525 287.14778 \nL 339.268893 261.032122 \nL 339.428 169.62732 \nL 339.459822 174.345695 \nL 339.555286 192.341359 \nL 339.587107 191.573251 \nL 339.65075 146.035445 \nL 339.682572 162.714352 \nL 339.968965 316.555327 \nL 340.000786 313.373167 \nL 340.128072 253.460776 \nL 340.223537 209.349455 \nL 340.28718 216.701342 \nL 340.319001 216.042964 \nL 340.350823 209.568914 \nL 340.414466 136.598694 \nL 340.478108 154.045709 \nL 340.700859 288.245076 \nL 340.764502 271.34671 \nL 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348.433475 308.654792 \nL 348.592582 208.032699 \nL 348.656225 214.945667 \nL 348.719868 227.784037 \nL 348.751689 226.357551 \nL 348.815332 178.954341 \nL 348.878975 203.424054 \nL 349.133547 338.172069 \nL 349.19719 328.076941 \nL 349.41994 220.761339 \nL 349.483583 226.68674 \nL 349.515405 222.297554 \nL 349.579047 178.734882 \nL 349.64269 205.728377 \nL 349.865441 338.062339 \nL 349.929084 324.565592 \nL 350.120012 203.204594 \nL 350.151834 205.947836 \nL 350.247298 220.322421 \nL 350.27912 218.457016 \nL 350.374584 157.557058 \nL 350.406406 171.053806 \nL 350.660977 314.141275 \nL 350.692799 301.741824 \nL 350.883728 184.221364 \nL 351.011013 194.865141 \nL 351.042835 194.316493 \nL 351.106478 139.012747 \nL 351.170121 153.497061 \nL 351.392871 300.863987 \nL 351.456514 290.11048 \nL 351.679264 163.37273 \nL 351.742907 168.420294 \nL 351.80655 174.455425 \nL 351.870193 126.832755 \nL 351.933836 148.230038 \nL 352.156586 296.365071 \nL 352.220229 283.96562 \nL 352.315694 175.881911 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292.853722 \nL 363.675957 281.002919 \nL 363.866885 170.505158 \nL 363.96235 181.478123 \nL 364.025993 187.952173 \nL 364.121457 139.451665 \nL 364.153279 150.095442 \nL 364.40785 275.187248 \nL 364.471493 258.61807 \nL 364.630601 179.064071 \nL 364.662422 182.026771 \nL 364.757886 197.827841 \nL 364.789708 195.633248 \nL 364.853351 150.534361 \nL 364.916994 166.774349 \nL 365.171565 292.524533 \nL 365.235208 280.125082 \nL 365.426137 192.560818 \nL 365.48978 199.802975 \nL 365.521602 202.436487 \nL 365.553423 196.511086 \nL 365.617066 144.060311 \nL 365.680709 175.772181 \nL 365.903459 291.427236 \nL 365.967102 276.613733 \nL 366.158031 168.420294 \nL 366.189852 168.091105 \nL 366.285317 178.954341 \nL 366.317138 176.979207 \nL 366.412603 119.480868 \nL 366.476246 149.107875 \nL 366.667174 268.493739 \nL 366.698996 267.835361 \nL 366.762639 222.626743 \nL 366.826282 150.973279 \nL 366.889925 159.971111 \nL 367.01721 180.161367 \nL 367.049032 186.635417 \nL 367.080853 185.318661 \nL 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371.058537 188.171632 \nL 371.249465 288.354806 \nL 371.281287 287.14778 \nL 371.34493 244.353215 \nL 371.504037 160.3003 \nL 371.535859 159.312733 \nL 371.663144 176.650018 \nL 371.694966 168.420294 \nL 371.758609 137.366802 \nL 371.79043 155.911114 \nL 372.01318 308.654792 \nL 372.045002 306.350469 \nL 372.108645 263.007256 \nL 372.204109 193.109466 \nL 372.235931 193.328926 \nL 372.267752 192.670548 \nL 372.395038 202.655946 \nL 372.42686 199.034868 \nL 372.522324 151.851116 \nL 372.554145 164.250567 \nL 372.776896 307.338036 \nL 372.840539 293.951018 \nL 372.936003 192.341359 \nL 373.031467 196.730545 \nL 373.190575 214.616478 \nL 373.286039 157.00841 \nL 373.349682 187.074335 \nL 373.540611 308.654792 \nL 373.572432 307.996414 \nL 373.636075 262.568337 \nL 373.699718 189.268928 \nL 373.763361 193.438655 \nL 373.890647 211.873237 \nL 373.95429 218.676476 \nL 373.986111 209.898103 \nL 374.049754 163.37273 \nL 374.113397 188.391091 \nL 374.304326 307.118577 \nL 374.336147 306.460199 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150.314901 \nL 377.900152 164.250567 \nL 378.122902 314.470464 \nL 378.154723 307.228306 \nL 378.218366 268.274279 \nL 378.377474 179.502989 \nL 378.504759 188.83001 \nL 378.536581 193.767844 \nL 378.568402 183.233797 \nL 378.632045 141.31707 \nL 378.663867 153.387332 \nL 378.886617 296.035882 \nL 378.95026 281.112649 \nL 379.141189 171.821913 \nL 379.300296 188.61055 \nL 379.363939 149.107875 \nL 379.39576 133.965183 \nL 379.427582 144.718689 \nL 379.650332 293.182911 \nL 379.713975 282.539134 \nL 379.841261 187.074335 \nL 379.904904 189.378658 \nL 380.064011 217.798638 \nL 380.095833 211.763507 \nL 380.159476 171.931643 \nL 380.191297 182.57542 \nL 380.445869 313.921815 \nL 380.509512 298.011016 \nL 380.668619 205.289458 \nL 380.732262 210.117563 \nL 380.827726 221.200258 \nL 380.923191 181.807312 \nL 380.955012 197.059734 \nL 381.209584 325.882348 \nL 381.241405 322.700188 \nL 381.33687 276.394274 \nL 381.464156 204.082432 \nL 381.495977 205.399188 \nL 381.591442 218.127827 \nL 381.623263 210.88567 \nL 381.686906 170.724617 \nL 381.718727 186.854876 \nL 381.941478 316.884516 \nL 381.973299 315.019112 \nL 382.036942 274.41914 \nL 382.164228 197.169464 \nL 382.196049 197.937571 \nL 382.227871 198.486219 \nL 382.355157 209.239725 \nL 382.450621 159.093274 \nL 382.514264 192.231629 \nL 382.705193 314.689923 \nL 382.737014 314.360734 \nL 382.800657 275.077518 \nL 382.959765 203.533783 \nL 383.118872 214.945667 \nL 383.214336 163.043541 \nL 383.277979 195.0846 \nL 383.500729 316.226138 \nL 383.564372 274.30941 \nL 383.72348 182.685149 \nL 383.755301 179.502989 \nL 383.818944 182.35596 \nL 383.882587 187.184065 \nL 383.978051 146.584093 \nL 384.009873 162.933811 \nL 384.232623 312.49533 \nL 384.296266 301.741824 \nL 384.519016 198.595949 \nL 384.646302 214.945667 \nL 384.741767 166.774349 \nL 384.80541 195.413789 \nL 385.02816 316.774786 \nL 385.091803 278.588867 \nL 385.25091 195.19433 \nL 385.410017 212.970534 \nL 385.441839 205.618647 \nL 385.505482 178.515422 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219.334854 \nL 396.229316 190.256495 \nL 396.261137 199.473786 \nL 396.483887 312.275871 \nL 396.515709 308.106144 \nL 396.706638 212.970534 \nL 396.802102 232.502412 \nL 396.865745 239.964029 \nL 396.897566 238.208354 \nL 396.993031 204.52135 \nL 397.024852 212.860804 \nL 397.215781 315.019112 \nL 397.279424 311.617493 \nL 397.374888 259.495907 \nL 397.470353 213.080263 \nL 397.533996 215.823505 \nL 397.62946 232.173223 \nL 397.661281 228.222956 \nL 397.756746 182.57542 \nL 397.788567 190.146766 \nL 397.947675 298.888853 \nL 398.011318 296.145611 \nL 398.074961 252.473209 \nL 398.138603 173.906777 \nL 398.202246 180.051637 \nL 398.297711 197.059734 \nL 398.393175 214.506749 \nL 398.424997 210.227292 \nL 398.520461 171.821913 \nL 398.552282 183.782446 \nL 398.775033 309.31317 \nL 398.838676 271.23698 \nL 399.029604 164.031108 \nL 399.15689 180.490556 \nL 399.220533 172.480291 \nL 399.284176 162.165704 \nL 399.506926 307.447766 \nL 399.570569 296.365071 \nL 399.79332 192.121899 \nL 399.920605 208.361888 \nL 399.952427 206.386754 \nL 400.047891 171.163536 \nL 400.079713 179.1738 \nL 400.270642 302.290472 \nL 400.302463 301.302905 \nL 400.366106 256.204017 \nL 400.429749 181.697582 \nL 400.493392 188.83001 \nL 400.588856 206.277025 \nL 400.684321 222.846203 \nL 400.747964 212.531615 \nL 400.811607 182.46569 \nL 400.843428 189.159199 \nL 401.066178 308.654792 \nL 401.129821 269.042387 \nL 401.225286 191.353792 \nL 401.257107 192.341359 \nL 401.448036 222.517014 \nL 401.479857 222.407284 \nL 401.575322 194.426222 \nL 401.607143 199.144597 \nL 401.829893 312.714789 \nL 401.861715 306.679658 \nL 402.052644 210.9954 \nL 402.148108 233.928898 \nL 402.211751 240.841866 \nL 402.243572 237.330517 \nL 402.339037 207.045132 \nL 402.370858 213.189993 \nL 402.593609 325.662888 \nL 402.62543 322.700188 \nL 402.720894 275.735896 \nL 402.816359 230.966197 \nL 402.84818 234.148357 \nL 402.975466 254.448343 \nL 403.007288 249.839697 \nL 403.102752 207.374321 \nL 403.134573 214.726208 \nL 403.325502 326.650455 \nL 403.389145 311.617493 \nL 403.548253 204.411621 \nL 403.580074 204.960269 \nL 403.739181 222.078095 \nL 403.771003 218.018098 \nL 403.866467 185.099202 \nL 403.898289 194.206763 \nL 404.089217 324.016944 \nL 404.121039 322.151539 \nL 404.184682 278.259678 \nL 404.248325 201.997568 \nL 404.311968 208.142429 \nL 404.343789 208.691077 \nL 404.502896 232.502412 \nL 404.534718 229.320252 \nL 404.630182 193.548385 \nL 404.662004 199.254327 \nL 404.852933 321.163973 \nL 404.884754 317.213705 \nL 404.948397 272.553736 \nL 405.01204 203.424054 \nL 405.075683 208.252159 \nL 405.139326 212.970534 \nL 405.266612 233.928898 \nL 405.393898 190.914873 \nL 405.425719 199.144597 \nL 405.616648 330.490993 \nL 405.648469 329.064508 \nL 405.712112 287.14778 \nL 405.839398 206.167295 \nL 405.871219 205.289458 \nL 405.998505 227.674307 \nL 406.062148 225.918633 \nL 406.157613 194.097033 \nL 406.189434 197.718112 \nL 406.380363 326.869915 \nL 406.412184 324.455862 \nL 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228.991063 \nL 545.853845 225.479714 \nL 545.917488 211.434319 \nL 545.949309 227.015929 \nL 546.17206 353.973139 \nL 546.203881 351.668816 \nL 546.39481 248.193753 \nL 546.522096 261.471041 \nL 546.553917 261.141852 \nL 546.681203 229.100793 \nL 546.713025 245.88943 \nL 546.935775 361.325026 \nL 546.967596 358.910974 \nL 547.063061 308.215873 \nL 547.190347 255.545639 \nL 547.222168 257.740233 \nL 547.285811 265.201849 \nL 547.317632 262.458608 \nL 547.444918 213.189993 \nL 547.47674 231.844034 \nL 547.667669 352.985572 \nL 547.69949 352.327194 \nL 547.763133 311.398033 \nL 547.92224 218.676476 \nL 548.017705 215.933234 \nL 548.081348 223.72404 \nL 548.113169 220.761339 \nL 548.208633 186.086768 \nL 548.240455 207.593781 \nL 548.463205 347.169901 \nL 548.526848 306.679658 \nL 548.685955 214.616478 \nL 548.78142 209.129996 \nL 548.813241 210.9954 \nL 548.845063 211.982967 \nL 548.876884 209.678644 \nL 548.972349 183.782446 \nL 549.22692 344.865578 \nL 549.258742 335.867746 \nL 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218.127827 \nL 560.173504 218.237557 \nL 560.30079 226.68674 \nL 560.364433 208.471618 \nL 560.396255 185.977039 \nL 560.459898 212.202426 \nL 560.650826 348.486656 \nL 560.682648 347.828278 \nL 560.746291 308.654792 \nL 560.905398 216.262423 \nL 560.93722 214.17756 \nL 561.000862 216.701342 \nL 561.096327 224.272688 \nL 561.15997 190.585684 \nL 561.223613 213.299723 \nL 561.414542 346.730982 \nL 561.446363 346.072604 \nL 561.510006 307.996414 \nL 561.669113 215.494316 \nL 561.732756 221.968365 \nL 561.796399 228.003496 \nL 561.860042 235.574842 \nL 561.891864 228.771604 \nL 561.923685 208.252159 \nL 561.987328 231.185656 \nL 562.210078 354.412058 \nL 562.2419 349.693683 \nL 562.432828 250.607805 \nL 562.528293 266.079686 \nL 562.591936 271.785628 \nL 562.6874 225.918633 \nL 562.751043 250.607805 \nL 562.973793 361.325026 \nL 563.005615 359.240162 \nL 563.101079 311.068844 \nL 563.228365 259.166718 \nL 563.260187 259.386177 \nL 563.323829 269.042387 \nL 563.355651 268.16455 \nL 563.451115 208.581348 \nL 563.514758 237.988895 \nL 563.705687 353.095302 \nL 563.737509 351.339627 \nL 563.801151 305.362902 \nL 563.960259 217.46945 \nL 564.119366 234.367816 \nL 564.151188 231.514845 \nL 564.21483 184.001905 \nL 564.278473 214.726208 \nL 564.501224 346.730982 \nL 564.564867 305.472632 \nL 564.723974 215.384586 \nL 564.755795 213.299723 \nL 564.819438 215.274856 \nL 564.883081 222.187825 \nL 564.914903 216.042964 \nL 564.978546 179.39326 \nL 565.010367 189.049469 \nL 565.264939 342.670985 \nL 565.29676 332.027208 \nL 565.392225 210.88567 \nL 565.487689 215.933234 \nL 565.646796 231.075927 \nL 565.678618 223.50458 \nL 565.742261 179.502989 \nL 565.805904 210.007833 \nL 566.028654 342.561255 \nL 566.092297 297.791556 \nL 566.187761 214.945667 \nL 566.219583 216.042964 \nL 566.251404 215.274856 \nL 566.283226 215.713775 \nL 566.410512 226.68674 \nL 566.474155 196.181897 \nL 566.505976 174.894344 \nL 566.569619 201.997568 \nL 566.792369 335.867746 \nL 566.856012 293.841289 \nL 566.951476 212.970534 \nL 566.983298 214.726208 \nL 567.015119 212.531615 \nL 567.046941 214.17756 \nL 567.174227 236.45268 \nL 567.206048 231.844034 \nL 567.269691 192.341359 \nL 567.333334 214.616478 \nL 567.556084 334.441261 \nL 567.587906 330.490993 \nL 567.778835 227.893767 \nL 567.874299 245.011593 \nL 567.90612 247.974293 \nL 567.937942 247.315915 \nL 568.033406 206.167295 \nL 568.065228 216.811072 \nL 568.3198 346.511523 \nL 568.351621 346.182334 \nL 568.447085 292.743992 \nL 568.54255 246.328349 \nL 568.606193 250.278616 \nL 568.669836 255.984558 \nL 568.701657 254.667802 \nL 568.797121 193.767844 \nL 568.860764 225.260255 \nL 569.051693 339.488825 \nL 569.083515 337.184502 \nL 569.147158 292.414803 \nL 569.242622 205.179728 \nL 569.274443 205.728377 \nL 569.306265 204.082432 \nL 569.338086 205.728377 \nL 569.465372 221.419717 \nL 569.497194 214.506749 \nL 569.560837 166.006242 \nL 569.62448 196.840275 \nL 569.815408 322.151539 \nL 569.84723 320.615324 \nL 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588.52643 200.351623 \nL 588.558251 198.37649 \nL 588.621894 165.896512 \nL 588.653716 173.797047 \nL 588.940109 325.22397 \nL 589.003752 304.704524 \nL 589.162859 221.090528 \nL 589.194681 223.065662 \nL 589.258324 230.527278 \nL 589.290145 230.198089 \nL 589.353788 186.745146 \nL 589.385609 165.238134 \nL 589.449252 187.732713 \nL 589.703824 296.145611 \nL 589.767467 280.78346 \nL 589.958396 181.478123 \nL 590.022039 191.024603 \nL 590.05386 194.206763 \nL 590.085682 186.196498 \nL 590.149325 127.930052 \nL 590.212968 148.559227 \nL 590.435718 272.992654 \nL 590.499361 258.069421 \nL 590.626647 153.387332 \nL 590.69029 158.325166 \nL 590.817575 178.515422 \nL 590.849397 172.480291 \nL 590.91304 119.15168 \nL 590.976683 132.319238 \nL 591.199433 289.122914 \nL 591.263076 278.259678 \nL 591.454005 168.530024 \nL 591.581291 183.562986 \nL 591.613112 180.929475 \nL 591.676755 142.194907 \nL 591.740398 162.604622 \nL 591.99497 315.56776 \nL 592.058613 281.332108 \nL 592.21772 177.418126 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\nL 595.495331 155.252736 \nL 595.558974 179.28353 \nL 595.781724 316.555327 \nL 595.845367 303.05858 \nL 596.036296 183.672716 \nL 596.068117 183.892175 \nL 596.163582 190.256495 \nL 596.195403 185.099202 \nL 596.259046 138.903017 \nL 596.322689 158.105707 \nL 596.577261 316.774786 \nL 596.609082 306.350469 \nL 596.736368 196.840275 \nL 596.800011 199.693246 \nL 596.927297 210.556481 \nL 596.959118 205.947836 \nL 597.022761 145.815985 \nL 597.086404 157.557058 \nL 597.309154 306.899118 \nL 597.372797 294.060748 \nL 597.468262 183.014338 \nL 597.563726 194.426222 \nL 597.595548 194.097033 \nL 597.691012 204.411621 \nL 597.722833 201.668379 \nL 597.786476 142.633825 \nL 597.850119 153.716521 \nL 598.072869 307.008847 \nL 598.168334 263.446175 \nL 598.327441 177.198666 \nL 598.359263 177.527855 \nL 598.454727 184.550553 \nL 598.550191 139.890584 \nL 598.613834 158.215436 \nL 598.836585 319.847217 \nL 598.900228 310.081277 \nL 599.091156 187.842443 \nL 599.122978 189.268928 \nL 599.218442 199.583516 \nL 599.250264 194.755411 \nL 599.313907 153.497061 \nL 599.37755 173.467858 \nL 599.473014 239.63484 \nL 599.6003 308.874251 \nL 599.632121 308.654792 \nL 599.695764 288.025617 \nL 599.82305 200.132164 \nL 599.886693 203.533783 \nL 599.982157 212.641345 \nL 600.0458 165.018675 \nL 600.077622 143.731122 \nL 600.141265 161.068407 \nL 600.236729 223.285121 \nL 600.395836 288.464536 \nL 600.427658 285.831024 \nL 600.841337 116.518168 \nL 600.90498 135.172209 \nL 601.12773 283.197512 \nL 601.191373 268.274279 \nL 601.382302 143.731122 \nL 601.414123 144.938148 \nL 601.509588 156.569492 \nL 601.541409 154.813817 \nL 601.605052 111.580333 \nL 601.668695 132.099779 \nL 601.891445 295.816422 \nL 601.955088 280.78346 \nL 602.146017 160.519759 \nL 602.20966 174.016507 \nL 602.273303 166.335431 \nL 602.305124 163.811649 \nL 602.368767 111.470604 \nL 602.43241 122.772758 \nL 602.527875 213.519182 \nL 602.65516 275.296977 \nL 602.686982 273.321843 \nL 602.750625 226.9062 \nL 602.814268 153.167872 \nL 602.877911 159.312733 \nL 603.037018 171.712184 \nL 603.06884 165.457594 \nL 603.132482 102.582502 \nL 603.196125 114.323575 \nL 603.29159 204.411621 \nL 603.418876 268.16455 \nL 603.450697 266.079686 \nL 603.51434 218.018098 \nL 603.577983 143.840852 \nL 603.641626 148.559227 \nL 603.705269 153.93598 \nL 603.800733 161.178137 \nL 603.864376 116.957086 \nL 603.928019 93.804129 \nL 603.959841 104.557636 \nL 604.055305 190.914873 \nL 604.182591 259.715366 \nL 604.214412 257.959692 \nL 604.278055 216.811072 \nL 604.37352 139.561395 \nL 604.405341 139.671125 \nL 604.564448 158.764085 \nL 604.59627 153.058143 \nL 604.659913 104.886824 \nL 604.723556 119.371139 \nL 604.81902 182.026771 \nL 604.946306 251.266183 \nL 604.978127 250.936994 \nL 605.009949 247.974293 \nL 605.169056 145.925715 \nL 605.296342 165.018675 \nL 605.328164 164.470027 \nL 605.391806 119.261409 \nL 605.423628 107.739795 \nL 605.455449 121.894921 \nL 605.741843 267.94509 \nL 605.837307 242.487811 \nL 606.028236 148.888416 \nL 606.060057 154.484628 \nL 606.091879 159.971111 \nL 606.1237 159.093274 \nL 606.187343 103.24088 \nL 606.250986 127.710593 \nL 606.473736 278.698596 \nL 606.537379 260.264015 \nL 606.632844 150.314901 \nL 606.728308 152.948413 \nL 606.855594 162.714352 \nL 606.887415 154.813817 \nL 606.951058 90.073321 \nL 607.014701 101.814394 \nL 607.110166 186.745146 \nL 607.237451 253.131587 \nL 607.269273 251.924561 \nL 607.332916 200.571083 \nL 607.396559 122.11438 \nL 607.460202 123.431136 \nL 607.492023 123.211677 \nL 607.619309 131.441401 \nL 607.682952 90.841429 \nL 607.714773 72.297117 \nL 607.778416 87.659269 \nL 608.001167 255.545639 \nL 608.096631 210.775941 \nL 608.160274 142.194907 \nL 608.223917 142.853285 \nL 608.383024 164.031108 \nL 608.414846 159.751651 \nL 608.478489 101.814394 \nL 608.542132 114.433304 \nL 608.637596 199.693246 \nL 608.796703 272.334277 \nL 608.860346 229.97863 \nL 608.923989 152.728954 \nL 608.987632 155.143006 \nL 609.051275 162.494893 \nL 609.146739 179.941908 \nL 609.178561 174.565155 \nL 609.242204 119.04195 \nL 609.305847 129.905186 \nL 609.401311 218.018098 \nL 609.560418 294.938585 \nL 609.624061 253.680235 \nL 609.719526 174.565155 \nL 609.783169 175.113803 \nL 609.910455 193.219196 \nL 609.942276 187.184065 \nL 610.005919 143.840852 \nL 610.069562 162.494893 \nL 610.196848 266.957523 \nL 610.324134 313.702356 \nL 610.355955 309.642359 \nL 610.769634 157.447329 \nL 610.801456 165.677053 \nL 610.960563 276.613733 \nL 611.087849 317.323435 \nL 611.11967 314.470464 \nL 611.215135 262.678067 \nL 611.374242 187.184065 \nL 611.437885 195.742978 \nL 611.469706 190.037036 \nL 611.533349 147.242471 \nL 611.596992 166.993809 \nL 611.851564 312.60506 \nL 611.915207 278.698596 \nL 612.074314 170.724617 \nL 612.106136 173.687318 \nL 612.2016 187.293795 \nL 612.233422 182.685149 \nL 612.297064 149.985712 \nL 612.360707 168.530024 \nL 612.456172 240.512677 \nL 612.615279 312.166141 \nL 612.678922 282.758594 \nL 612.838029 182.685149 \nL 612.869851 187.952173 \nL 612.965315 205.399188 \nL 612.997137 201.997568 \nL 613.06078 165.896512 \nL 613.092601 181.478123 \nL 613.347173 285.611565 \nL 613.410816 283.087783 \nL 613.569923 197.169464 \nL 613.665387 210.007833 \nL 613.72903 224.601877 \nL 613.760852 219.334854 \nL 613.824495 176.650018 \nL 613.888138 182.794879 \nL 613.951781 206.715943 \nL 614.110888 282.978053 \nL 614.142709 282.648864 \nL 614.206352 256.752666 \nL 614.397281 142.853285 \nL 614.492746 152.838683 \nL 614.524567 152.728954 \nL 614.58821 106.31331 \nL 614.651853 122.333839 \nL 614.906425 274.967788 \nL 614.970068 243.146189 \nL 615.097353 155.362465 \nL 615.129175 156.020843 \nL 615.256461 172.91921 \nL 615.288282 170.944076 \nL 615.351925 113.774926 \nL 615.415568 133.526264 \nL 615.542854 241.171055 \nL 615.67014 280.67373 \nL 615.701961 277.272111 \nL 615.797426 222.407284 \nL 615.89289 175.004073 \nL 615.924711 178.954341 \nL 616.020176 204.301891 \nL 616.051997 200.571083 \nL 616.11564 150.095442 \nL 616.179283 168.310565 \nL 616.433855 306.789388 \nL 616.497498 294.938585 \nL 616.688427 192.999737 \nL 616.783891 195.852708 \nL 616.879355 137.476532 \nL 616.942998 164.470027 \nL 617.038463 249.400779 \nL 617.165749 307.886684 \nL 617.19757 305.692091 \nL 617.293034 232.502412 \nL 617.42032 166.44516 \nL 617.452142 167.322998 \nL 617.515785 177.418126 \nL 617.547606 176.2111 \nL 617.579428 168.968942 \nL 617.643071 101.924124 \nL 617.706714 114.104115 \nL 617.929464 258.61807 \nL 618.024928 220.761339 \nL 618.184036 129.246808 \nL 618.343143 151.960846 \nL 618.406786 102.801961 \nL 618.438607 116.298709 \nL 618.725 263.226715 \nL 618.756822 260.593204 \nL 619.170501 113.116549 \nL 619.234144 128.36897 \nL 619.456894 260.044555 \nL 619.520537 245.340782 \nL 619.743287 107.300877 \nL 619.775109 108.617633 \nL 619.80693 108.068984 \nL 619.870573 111.141415 \nL 619.934216 62.970097 \nL 619.997859 88.866295 \nL 620.220609 236.23322 \nL 620.284252 226.357551 \nL 620.475181 109.934389 \nL 620.538824 117.944653 \nL 620.634288 129.466267 \nL 620.697931 80.417112 \nL 620.761574 98.193316 \nL 620.984324 235.465113 \nL 621.079789 218.566746 \nL 621.238896 135.720857 \nL 621.270718 140.000314 \nL 621.366182 168.749483 \nL 621.398004 166.774349 \nL 621.461646 111.909522 \nL 621.525289 134.294372 \nL 621.779861 270.249413 \nL 621.875325 242.048892 \nL 622.002611 183.014338 \nL 622.034433 186.525687 \nL 622.129897 202.436487 \nL 622.161719 193.767844 \nL 622.225362 131.990049 \nL 622.289005 159.312733 \nL 622.511755 300.754257 \nL 622.575398 290.988318 \nL 622.798148 169.956509 \nL 622.893612 182.57542 \nL 622.925434 177.966774 \nL 622.989077 128.039781 \nL 623.05272 146.693823 \nL 623.27547 277.820759 \nL 623.339113 260.922392 \nL 623.561863 142.524096 \nL 623.657328 146.913282 \nL 623.689149 141.975448 \nL 623.752792 81.733867 \nL 623.816435 93.913859 \nL 624.039185 239.41538 \nL 624.102828 226.577011 \nL 624.198292 122.443569 \nL 624.293757 131.880319 \nL 624.421043 155.362465 \nL 624.452864 152.619224 \nL 624.548329 93.036022 \nL 624.58015 103.570069 \nL 624.8029 250.498075 \nL 624.866543 237.988895 \nL 624.962008 133.197075 \nL 625.057472 140.658692 \nL 625.184758 164.470027 \nL 625.216579 161.068407 \nL 625.312044 102.692231 \nL 625.343865 111.909522 \nL 625.566615 244.024026 \nL 625.630258 231.953764 \nL 625.725723 129.905186 \nL 625.821187 131.55113 \nL 625.853009 133.635994 \nL 625.980295 161.617056 \nL 626.043937 122.333839 \nL 626.10758 143.731122 \nL 626.362152 289.891021 \nL 626.425795 276.394274 \nL 626.616724 169.84678 \nL 626.680367 178.844611 \nL 626.712188 185.208931 \nL 626.74401 184.440824 \nL 626.807653 155.581925 \nL 626.839474 166.66462 \nL 627.125867 323.248836 \nL 627.18951 311.178574 \nL 627.41226 214.616478 \nL 627.444082 216.811072 \nL 627.475903 218.566746 \nL 627.507725 212.092696 \nL 627.571368 152.180305 \nL 627.635011 175.662451 \nL 627.857761 305.692091 \nL 627.921404 290.768858 \nL 628.016868 180.600286 \nL 628.112333 188.61055 \nL 628.239619 202.436487 \nL 628.27144 200.900272 \nL 628.335083 144.49923 \nL 628.398726 160.410029 \nL 628.621476 310.629926 \nL 628.685119 298.120745 \nL 628.876048 179.832178 \nL 628.907869 179.941908 \nL 628.971512 181.039204 \nL 629.003334 186.306228 \nL 629.035155 181.587853 \nL 629.098798 142.853285 \nL 629.162441 161.178137 \nL 629.385191 306.899118 \nL 629.448834 294.060748 \nL 629.544299 189.159199 \nL 629.639763 196.072167 \nL 629.735227 208.691077 \nL 629.79887 204.850539 \nL 629.894335 145.596526 \nL 629.926156 158.983544 \nL 630.148906 298.559664 \nL 630.212549 284.733727 \nL 630.4353 165.347864 \nL 630.530764 175.772181 \nL 630.562585 174.894344 \nL 630.626228 135.391668 \nL 630.689871 149.217605 \nL 630.944443 296.913719 \nL 630.976265 285.721294 \nL 631.10355 178.515422 \nL 631.199015 183.233797 \nL 631.294479 202.326757 \nL 631.326301 202.217028 \nL 631.389944 154.374898 \nL 631.453587 169.407861 \nL 631.580872 261.141852 \nL 631.708158 301.302905 \nL 631.73998 296.4748 \nL 632.153659 150.205172 \nL 632.18548 156.89868 \nL 632.471873 306.789388 \nL 632.535516 295.267774 \nL 632.758267 191.463522 \nL 632.82191 194.206763 \nL 632.853731 194.97487 \nL 632.917374 155.143006 \nL 632.981017 172.260832 \nL 633.203767 295.487234 \nL 633.26741 283.307242 \nL 633.458339 160.08084 \nL 633.521982 171.821913 \nL 633.585625 176.430559 \nL 633.617446 175.552722 \nL 633.681089 121.456002 \nL 633.744732 137.147343 \nL 633.967482 259.386177 \nL 634.031125 243.914296 \nL 634.222054 134.074912 \nL 634.34934 144.3895 \nL 634.381161 139.780854 \nL 634.444804 88.866295 \nL 634.508447 104.667365 \nL 634.731197 239.744569 \nL 634.79484 225.479714 \nL 634.953948 117.176546 \nL 634.985769 117.286275 \nL 635.144876 140.110043 \nL 635.240341 84.916027 \nL 635.272162 97.315478 \nL 635.494913 237.659706 \nL 635.558556 222.297554 \nL 635.685841 124.199244 \nL 635.749484 131.331671 \nL 635.87677 155.252736 \nL 635.908592 153.716521 \nL 636.004056 97.315478 \nL 636.067699 122.11438 \nL 636.258628 242.378081 \nL 636.290449 240.293218 \nL 636.354092 198.37649 \nL 636.417735 126.284107 \nL 636.481378 131.66086 \nL 636.640485 161.178137 \nL 636.672307 160.519759 \nL 636.73595 113.445737 \nL 636.799593 127.052215 \nL 636.926879 224.492147 \nL 637.054164 263.775363 \nL 637.085986 260.483474 \nL 637.276915 160.08084 \nL 637.372379 177.857044 \nL 637.436022 184.111635 \nL 637.499665 144.170041 \nL 637.563308 167.103538 \nL 637.81788 288.903454 \nL 637.881522 271.456439 \nL 638.072451 175.442992 \nL 638.104273 176.979207 \nL 638.167916 185.318661 \nL 638.199737 178.844611 \nL 638.26338 118.93222 \nL 638.327023 139.451665 \nL 638.549773 276.504003 \nL 638.613416 266.408875 \nL 638.804345 153.82625 \nL 638.836166 154.374898 \nL 638.931631 169.298131 \nL 638.963452 168.749483 \nL 639.058917 114.762493 \nL 639.090738 129.027348 \nL 639.313488 269.152117 \nL 639.377131 251.046724 \nL 639.472596 142.194907 \nL 639.56806 146.803552 \nL 639.695346 167.761916 \nL 639.727167 164.579756 \nL 639.822632 104.886824 \nL 639.886275 132.319238 \nL 640.077204 260.812663 \nL 640.109025 259.166718 \nL 640.172668 216.042964 \nL 640.236311 142.414366 \nL 640.299954 146.145174 \nL 640.459061 175.442992 \nL 640.490883 173.358129 \nL 640.586347 108.617633 \nL 640.64999 135.281939 \nL 640.840919 258.288881 \nL 640.87274 256.642936 \nL 640.936383 211.10513 \nL 641.000026 139.561395 \nL 641.063669 149.766253 \nL 641.222776 180.819745 \nL 641.254598 174.126236 \nL 641.350062 118.712761 \nL 641.413705 143.731122 \nL 641.636455 275.516436 \nL 641.700098 237.220787 \nL 641.763741 163.811649 \nL 641.827384 166.44516 \nL 641.859206 167.871646 \nL 642.018313 190.695414 \nL 642.113777 150.314901 \nL 642.145599 163.043541 \nL 642.400171 292.195344 \nL 642.463813 276.723463 \nL 642.591099 197.388923 \nL 642.654742 206.277025 \nL 642.750207 221.968365 \nL 642.782028 215.604045 \nL 642.845671 170.614887 \nL 642.909314 187.513254 \nL 643.163886 317.433164 \nL 643.227529 305.692091 \nL 643.450279 203.972702 \nL 643.4821 205.508917 \nL 643.513922 208.032699 \nL 643.545743 203.972702 \nL 643.609386 162.933811 \nL 643.641208 174.235966 \nL 643.895779 311.946682 \nL 643.927601 309.093711 \nL 643.991244 265.092119 \nL 644.150351 173.906777 \nL 644.182173 174.345695 \nL 644.277637 186.086768 \nL 644.309458 185.757579 \nL 644.404923 132.099779 \nL 644.468566 161.946245 \nL 644.659495 282.758594 \nL 644.691316 281.002919 \nL 644.754959 231.514845 \nL 644.818602 158.654355 \nL 644.882245 170.724617 \nL 645.041352 196.291626 \nL 645.073174 193.109466 \nL 645.168638 132.209508 \nL 645.232281 161.287867 \nL 645.42321 291.317507 \nL 645.455031 290.22021 \nL 645.550496 211.214859 \nL 645.709603 144.828419 \nL 645.741424 147.46193 \nL 645.836889 161.397596 \nL 645.86871 152.728954 \nL 645.900532 122.333839 \nL 645.964175 140.439232 \nL 646.155103 280.344541 \nL 646.250568 265.640768 \nL 646.346032 160.519759 \nL 646.441497 168.968942 \nL 646.568783 190.585684 \nL 646.600604 188.72028 \nL 646.696068 127.820322 \nL 646.759711 152.180305 \nL 646.95064 267.396442 \nL 646.982462 265.311579 \nL 647.046104 220.871069 \nL 647.109747 150.424631 \nL 647.17339 158.215436 \nL 647.364319 187.513254 \nL 647.396141 176.540289 \nL 647.459784 139.561395 \nL 647.491605 151.521927 \nL 647.587069 213.738641 \nL 647.714355 275.406707 \nL 647.746177 274.30941 \nL 647.80982 252.582939 \nL 647.937106 178.186233 \nL 648.000748 187.622984 \nL 648.096213 213.409452 \nL 648.128034 210.337022 \nL 648.223499 157.776518 \nL 648.287142 185.318661 \nL 648.509892 289.671562 \nL 648.573535 277.930489 \nL 648.732642 202.875406 \nL 648.796285 208.142429 \nL 648.828107 214.506749 \nL 648.891749 207.045132 \nL 648.955392 148.668956 \nL 649.019035 166.006242 \nL 649.241786 288.464536 \nL 649.273607 286.379672 \nL 649.33725 243.804566 \nL 649.432714 170.285698 \nL 649.464536 170.395428 \nL 649.496357 170.505158 \nL 649.591822 177.198666 \nL 649.623643 181.148934 \nL 649.655465 179.502989 \nL 649.750929 124.199244 \nL 649.814572 151.741387 \nL 650.005501 263.665634 \nL 650.037322 261.471041 \nL 650.100965 216.481883 \nL 650.164608 148.010579 \nL 650.228251 157.447329 \nL 650.41918 182.136501 \nL 650.514644 115.201412 \nL 650.578287 140.110043 \nL 650.769216 257.081855 \nL 650.801037 253.570506 \nL 650.86468 208.252159 \nL 650.928323 140.110043 \nL 650.991966 153.716521 \nL 651.151073 190.366225 \nL 651.182895 188.72028 \nL 651.278359 125.954918 \nL 651.342002 146.364634 \nL 651.532931 265.860227 \nL 651.564753 263.665634 \nL 651.628395 226.9062 \nL 651.787503 141.755988 \nL 651.819324 140.219773 \nL 651.94661 152.180305 \nL 652.010253 125.954918 \nL 652.073896 142.633825 \nL 652.296646 290.000751 \nL 652.360289 279.466704 \nL 652.583039 172.91921 \nL 652.614861 176.320829 \nL 652.710325 182.136501 \nL 652.742147 177.088937 \nL 652.80579 148.010579 \nL 652.837611 162.604622 \nL 653.092183 294.938585 \nL 653.155826 278.369407 \nL 653.283112 194.316493 \nL 653.346755 198.815408 \nL 653.442219 210.337022 \nL 653.47404 208.032699 \nL 653.569505 162.824082 \nL 653.601326 175.99164 \nL 653.855898 294.938585 \nL 653.919541 283.416972 \nL 654.078648 200.241894 \nL 654.142291 206.277025 \nL 654.205934 217.908368 \nL 654.237756 213.299723 \nL 654.33322 143.401933 \nL 654.396863 174.894344 \nL 654.587792 277.162381 \nL 654.619613 275.077518 \nL 654.683256 237.988895 \nL 654.810542 157.447329 \nL 654.842363 157.995977 \nL 654.969649 182.794879 \nL 655.001471 181.368393 \nL 655.096935 123.101947 \nL 655.160578 148.998145 \nL 655.351507 262.348878 \nL 655.383328 260.154285 \nL 655.446971 218.895935 \nL 655.606079 143.840852 \nL 655.765186 156.350032 \nL 655.797007 149.875983 \nL 655.86065 118.603031 \nL 655.892472 134.623561 \nL 656.115222 283.746161 \nL 656.178865 270.688332 \nL 656.369794 172.480291 \nL 656.528901 175.113803 \nL 656.592544 145.157608 \nL 656.624366 136.598694 \nL 656.656187 150.75382 \nL 656.878937 298.449934 \nL 656.94258 285.501835 \nL 657.069866 190.366225 \nL 657.133509 196.181897 \nL 657.260795 214.17756 \nL 657.292616 210.9954 \nL 657.388081 158.764085 \nL 657.451724 184.989472 \nL 657.674474 308.545062 \nL 657.738117 270.468872 \nL 657.897224 176.2111 \nL 657.929046 175.223533 \nL 658.056331 192.890007 \nL 658.119974 166.993809 \nL 658.183617 182.46569 \nL 658.438189 316.994246 \nL 658.501832 298.779123 \nL 658.629118 226.028363 \nL 658.692761 231.075927 \nL 658.788225 250.717535 \nL 658.820047 249.18132 \nL 658.915511 194.645681 \nL 658.979154 219.225124 \nL 659.170083 321.82235 \nL 659.201904 321.712621 \nL 659.265547 309.861818 \nL 659.424654 228.991063 \nL 659.488297 232.941331 \nL 659.55194 245.99916 \nL 659.583762 245.231052 \nL 659.679226 184.660283 \nL 659.742869 216.591612 \nL 659.933798 316.555327 \nL 659.965619 314.251004 \nL 660.029262 269.700765 \nL 660.18837 193.767844 \nL 660.347477 203.204594 \nL 660.442941 144.608959 \nL 660.506584 174.565155 \nL 660.697513 285.501835 \nL 660.729335 283.96562 \nL 660.792977 241.061325 \nL 660.920263 177.308396 \nL 660.952085 177.747315 \nL 661.111192 185.64785 \nL 661.206657 131.77059 \nL 661.270299 161.287867 \nL 661.461228 275.955355 \nL 661.49305 273.870492 \nL 661.556693 229.759171 \nL 661.652157 160.629489 \nL 661.7158 163.921378 \nL 661.874907 185.867309 \nL 661.970372 130.234375 \nL 662.034015 155.143006 \nL 662.224943 270.029954 \nL 662.256765 268.05482 \nL 662.320408 222.846203 \nL 662.415872 160.958678 \nL 662.447694 164.579756 \nL 662.479515 165.457594 \nL 662.606801 182.46569 \nL 662.638622 181.697582 \nL 662.702265 152.509494 \nL 662.734087 136.159776 \nL 662.79773 159.422463 \nL 662.988659 266.738064 \nL 663.02048 265.860227 \nL 663.084123 227.125659 \nL 663.179587 160.848948 \nL 663.211409 162.933811 \nL 663.275052 169.84678 \nL 663.402338 191.573251 \nL 663.497802 140.658692 \nL 663.561445 163.153271 \nL 663.784195 263.885093 \nL 663.816017 259.056988 \nL 664.006945 179.28353 \nL 664.070588 194.206763 \nL 664.134231 211.324589 \nL 664.197874 201.778109 \nL 664.261517 169.517591 \nL 664.293339 181.917042 \nL 664.54791 305.253173 \nL 664.611553 294.170478 \nL 664.802482 205.289458 \nL 664.834304 209.788374 \nL 664.897946 224.162958 \nL 664.929768 223.065662 \nL 665.025232 163.48246 \nL 665.088875 192.121899 \nL 665.279804 290.11048 \nL 665.343447 270.359143 \nL 665.470733 168.859213 \nL 665.534376 168.968942 \nL 665.62984 181.039204 \nL 665.661662 186.306228 \nL 665.693483 184.989472 \nL 665.757126 149.546794 \nL 665.788948 126.503566 \nL 665.85259 152.180305 \nL 666.043519 263.116986 \nL 666.075341 259.715366 \nL 666.138984 212.202426 \nL 666.202627 151.960846 \nL 666.266269 161.068407 \nL 666.329912 174.565155 \nL 666.425377 191.79271 \nL 666.457198 190.146766 \nL 666.552663 137.476532 \nL 666.616306 166.55489 \nL 666.807234 283.746161 \nL 666.839056 281.880756 \nL 666.902699 235.355383 \nL 666.998163 163.921378 \nL 667.029985 164.031108 \nL 667.093628 167.542457 \nL 667.220913 183.124068 \nL 667.316378 133.745723 \nL 667.380021 155.143006 \nL 667.539128 260.922392 \nL 667.602771 258.179151 \nL 667.666414 210.007833 \nL 667.730057 148.559227 \nL 667.7937 160.08084 \nL 667.984629 199.802975 \nL 668.01645 191.353792 \nL 668.080093 142.963014 \nL 668.143736 161.617056 \nL 668.334665 267.176983 \nL 668.366486 265.640768 \nL 668.430129 219.444583 \nL 668.493772 156.569492 \nL 668.557415 167.322998 \nL 668.748344 210.117563 \nL 668.780165 201.778109 \nL 668.843808 160.519759 \nL 668.907451 179.722449 \nL 669.09838 294.280207 \nL 669.130201 292.524533 \nL 669.193844 273.212114 \nL 669.32113 205.618647 \nL 669.384773 217.688909 \nL 669.480237 240.951595 \nL 669.512059 240.622406 \nL 669.607523 189.817577 \nL 669.671166 211.434319 \nL 669.893917 306.13101 \nL 669.957559 295.816422 \nL 670.148488 216.262423 \nL 670.212131 227.125659 \nL 670.275774 235.904031 \nL 670.307596 230.198089 \nL 670.40306 175.333262 \nL 670.434881 186.415957 \nL 670.62581 297.791556 \nL 670.657632 297.462367 \nL 670.721275 255.765099 \nL 670.880382 174.784614 \nL 671.039489 203.753243 \nL 671.071311 200.022435 \nL 671.166775 160.629489 \nL 671.198597 174.235966 \nL 671.389525 304.594795 \nL 671.421347 303.826687 \nL 671.48499 265.531038 \nL 671.548633 191.90244 \nL 671.612276 192.341359 \nL 671.675919 198.705679 \nL 671.803204 217.030531 \nL 671.835026 214.726208 \nL 671.93049 177.966774 \nL 671.962312 190.695414 \nL 672.153241 308.545062 \nL 672.185062 307.667225 \nL 672.248705 268.274279 \nL 672.407812 180.929475 \nL 672.439634 178.076504 \nL 672.471455 181.587853 \nL 672.535098 185.977039 \nL 672.56692 185.53812 \nL 672.662384 156.350032 \nL 672.694205 160.958678 \nL 672.916956 296.255341 \nL 672.980599 282.978053 \nL 673.203349 151.192738 \nL 673.23517 154.704087 \nL 673.330635 174.235966 \nL 673.362456 169.956509 \nL 673.457921 139.671125 \nL 673.489742 151.083009 \nL 673.712492 272.005088 \nL 673.776135 233.05106 \nL 673.8716 163.48246 \nL 673.903421 164.140838 \nL 673.967064 174.674884 \nL 674.09435 200.461353 \nL 674.126171 199.034868 \nL 674.221636 172.590021 \nL 674.253457 182.685149 \nL 674.476208 306.02128 \nL 674.508029 302.071013 \nL 674.603493 247.535375 \nL 674.698958 210.666211 \nL 674.730779 213.958101 \nL 674.858065 240.512677 \nL 674.889887 236.562409 \nL 674.985351 208.252159 \nL 675.017172 215.933234 \nL 675.239923 313.592626 \nL 675.271744 310.849385 \nL 675.367209 260.044555 \nL 675.494494 210.007833 \nL 675.526316 212.641345 \nL 675.62178 228.661874 \nL 675.653602 221.858636 \nL 675.749066 171.053806 \nL 675.780888 179.502989 \nL 675.971816 281.551567 \nL 676.003638 278.808326 \nL 676.067281 235.574842 \nL 676.162745 157.337599 \nL 676.194567 158.873814 \nL 676.385495 201.010001 \nL 676.449138 188.171632 \nL 676.512781 158.105707 \nL 676.544603 164.689486 \nL 676.735532 273.980221 \nL 676.767353 272.882925 \nL 676.830996 229.210522 \nL 676.894639 158.544625 \nL 676.958282 170.834347 \nL 677.149211 212.751074 \nL 677.212854 201.010001 \nL 677.276496 165.567323 \nL 677.308318 173.138669 \nL 677.499247 290.000751 \nL 677.531068 288.135347 \nL 677.594711 245.560241 \nL 677.658354 174.016507 \nL 677.721997 186.964606 \nL 677.849283 196.291626 \nL 677.881104 200.680812 \nL 677.912926 200.241894 \nL 678.040212 164.908945 \nL 678.072033 177.966774 \nL 678.262962 303.168309 \nL 678.294783 302.729391 \nL 678.358426 266.189416 \nL 678.517534 180.490556 \nL 678.612998 195.742978 \nL 678.676641 206.715943 \nL 678.740284 199.254327 \nL 678.803927 175.552722 \nL 678.835748 184.879742 \nL 679.058499 308.106144 \nL 679.122141 268.932657 \nL 679.281249 191.024603 \nL 679.472178 218.895935 \nL 679.503999 215.384586 \nL 679.567642 192.670548 \nL 679.599463 197.279193 \nL 679.822214 306.789388 \nL 679.854035 303.607228 \nL 680.044964 197.608382 \nL 680.140428 211.214859 \nL 680.235893 226.68674 \nL 680.267714 223.065662 \nL 680.331357 204.192161 \nL 680.363179 211.434319 \nL 680.554107 315.56776 \nL 680.61775 312.934248 \nL 680.713215 263.885093 \nL 680.808679 219.554313 \nL 680.840501 221.419717 \nL 680.935965 240.402947 \nL 680.999608 233.928898 \nL 681.095072 181.258664 \nL 681.126894 185.867309 \nL 681.286001 288.025617 \nL 681.317823 286.489402 \nL 681.381465 272.224547 \nL 681.47693 164.689486 \nL 681.572394 173.02894 \nL 681.731502 204.960269 \nL 681.795145 191.682981 \nL 681.858787 160.958678 \nL 681.890609 167.981376 \nL 682.081538 281.880756 \nL 682.113359 278.808326 \nL 682.177002 231.734305 \nL 682.240645 161.726785 \nL 682.304288 173.797047 \nL 682.495217 211.873237 \nL 682.527038 207.154862 \nL 682.622503 166.335431 \nL 682.654324 173.577588 \nL 682.845253 297.023449 \nL 682.877074 295.267774 \nL 682.940717 253.680235 \nL 683.099825 166.55489 \nL 683.195289 175.333262 \nL 683.258932 182.685149 \nL 683.290753 181.039204 \nL 683.386218 153.277602 \nL 683.418039 162.275434 \nL 683.608968 278.918056 \nL 683.64079 276.504003 \nL 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205.179728 \nL 690.005083 213.189993 \nL 690.132368 229.649441 \nL 690.259654 203.094865 \nL 690.291476 210.446752 \nL 690.514226 315.348301 \nL 690.546047 312.056411 \nL 690.736976 209.129996 \nL 690.832441 232.282953 \nL 690.896084 240.073758 \nL 690.927905 237.549976 \nL 691.023369 214.506749 \nL 691.055191 219.883502 \nL 691.277941 322.480728 \nL 691.309763 319.188839 \nL 691.405227 272.334277 \nL 691.500691 235.684572 \nL 691.532513 238.427813 \nL 691.659799 261.58077 \nL 691.69162 255.874828 \nL 691.818906 217.35972 \nL 692.009835 320.725054 \nL 692.041656 317.872083 \nL 692.105299 273.980221 \nL 692.168942 206.386754 \nL 692.232585 213.628912 \nL 692.264407 212.970534 \nL 692.423514 225.589444 \nL 692.455335 220.43215 \nL 692.5508 196.511086 \nL 692.582621 204.74081 \nL 692.77355 327.199104 \nL 692.805371 325.662888 \nL 692.869014 285.172646 \nL 693.028122 203.643513 \nL 693.187229 223.175392 \nL 693.219051 217.798638 \nL 693.314515 197.718112 \nL 693.346336 206.606214 \nL 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697.069448 234.806735 \nL 697.133091 219.225124 \nL 697.164912 219.993232 \nL 697.387662 337.074772 \nL 697.451305 293.731559 \nL 697.610413 219.334854 \nL 697.642234 220.212691 \nL 697.76952 234.148357 \nL 697.801342 233.709438 \nL 697.928627 211.982967 \nL 698.119556 343.439092 \nL 698.151378 341.573688 \nL 698.215021 304.046147 \nL 698.374128 205.838106 \nL 698.405949 205.618647 \nL 698.533235 211.982967 \nL 698.660521 200.571083 \nL 698.755985 276.065085 \nL 698.883271 328.625589 \nL 698.915093 325.992077 \nL 698.978736 286.269943 \nL 699.137843 206.277025 \nL 699.169665 207.484051 \nL 699.29695 226.138092 \nL 699.328772 224.162958 \nL 699.456058 191.79271 \nL 699.646987 309.20344 \nL 699.678808 305.692091 \nL 699.742451 267.396442 \nL 699.806094 200.132164 \nL 699.869737 201.44892 \nL 699.901558 202.655946 \nL 700.092487 234.148357 \nL 700.124308 227.125659 \nL 700.219773 197.718112 \nL 700.251594 221.639176 \nL 700.410702 316.335868 \nL 700.474345 305.582362 \nL 700.633452 213.299723 \nL 700.665273 214.397019 \nL 700.792559 240.183488 \nL 700.888024 235.026194 \nL 700.983488 220.212691 \nL 701.206238 327.747752 \nL 701.23806 324.126673 \nL 701.397167 235.574842 \nL 701.524453 258.179151 \nL 701.588096 265.201849 \nL 701.619917 262.787797 \nL 701.747203 235.794302 \nL 701.779025 254.448343 \nL 701.969953 335.758017 \nL 702.033596 319.079109 \nL 702.160882 246.438078 \nL 702.256347 262.897526 \nL 702.351811 290.878588 \nL 702.415454 287.806158 \nL 702.510918 250.168886 \nL 702.54274 270.907791 \nL 702.701847 339.379095 \nL 702.733669 337.184502 \nL 702.797312 301.961283 \nL 702.956419 215.604045 \nL 703.020062 209.788374 \nL 703.051883 211.653778 \nL 703.179169 229.649441 \nL 703.210991 226.028363 \nL 703.274634 212.751074 \nL 703.497384 336.745583 \nL 703.592848 266.628335 \nL 703.720134 223.285121 \nL 703.84742 231.624575 \nL 703.879241 235.574842 \nL 703.911063 234.916464 \nL 704.006527 216.152694 \nL 704.038349 221.968365 \nL 704.229278 340.256932 \nL 704.261099 337.403961 \nL 704.324742 302.94885 \nL 704.483849 218.895935 \nL 704.515671 217.24999 \nL 704.547492 219.005665 \nL 704.579314 218.457016 \nL 704.642957 228.332685 \nL 704.674778 222.846203 \nL 704.770242 208.252159 \nL 704.802064 212.421885 \nL 704.992993 329.283967 \nL 705.024814 326.650455 \nL 705.088457 288.464536 \nL 705.247564 212.312156 \nL 705.438493 241.171055 \nL 705.502136 233.380249 \nL 705.565779 215.384586 \nL 705.756708 332.356397 \nL 705.788529 331.039641 \nL 705.883994 259.056988 \nL 706.01128 224.601877 \nL 706.23403 254.119154 \nL 706.265851 252.25375 \nL 706.329494 233.380249 \nL 706.361316 254.448343 \nL 706.552244 342.561255 \nL 706.615887 328.18667 \nL 706.806816 230.966197 \nL 706.870459 237.111058 \nL 706.934102 243.804566 \nL 706.965924 242.378081 \nL 707.061388 237.549976 \nL 707.093209 239.63484 \nL 707.31596 343.329363 \nL 707.347781 339.818014 \nL 707.443245 293.731559 \nL 707.53871 240.293218 \nL 707.602353 243.365648 \nL 707.697817 257.740233 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236.562409 \nL 715.494076 222.626743 \nL 715.525898 245.560241 \nL 715.685005 343.439092 \nL 715.716826 342.122336 \nL 715.780469 302.83912 \nL 715.939577 234.038627 \nL 715.971398 233.489979 \nL 716.162327 245.450511 \nL 716.194148 244.792133 \nL 716.257791 224.711607 \nL 716.480542 348.047738 \nL 716.512363 340.256932 \nL 716.703292 227.235389 \nL 716.766935 232.502412 \nL 716.862399 240.293218 \nL 716.926042 236.123491 \nL 716.957864 236.562409 \nL 717.021507 235.355383 \nL 717.244257 356.93584 \nL 717.276078 354.631517 \nL 717.371543 291.646696 \nL 717.467007 243.146189 \nL 717.498829 246.657537 \nL 717.626114 269.481306 \nL 717.657936 268.603468 \nL 717.785222 248.632671 \nL 718.007972 367.030968 \nL 718.071615 361.325026 \nL 718.230722 276.504003 \nL 718.326187 289.452103 \nL 718.358008 292.085614 \nL 718.38983 290.32994 \nL 718.548937 247.974293 \nL 718.580758 268.822928 \nL 718.771687 360.447189 \nL 718.83533 325.00451 \nL 718.994437 222.297554 \nL 719.089902 217.688909 \nL 719.121723 219.993232 \nL 719.249009 224.272688 \nL 719.280831 222.517014 \nL 719.312652 214.616478 \nL 719.535402 356.606651 \nL 719.567224 346.292063 \nL 719.789974 233.380249 \nL 719.91726 243.914296 \nL 719.980903 239.086191 \nL 720.044546 236.23322 \nL 720.076367 226.357551 \nL 720.108189 250.827264 \nL 720.267296 353.973139 \nL 720.299117 353.863409 \nL 720.36276 316.884516 \nL 720.521868 231.624575 \nL 720.553689 230.966197 \nL 720.680975 239.744569 \nL 720.840082 224.382418 \nL 721.031011 349.693683 \nL 721.094654 339.269365 \nL 721.317404 219.334854 \nL 721.349226 220.212691 \nL 721.381047 217.908368 \nL 721.412869 222.297554 \nL 721.540155 227.015929 \nL 721.603798 215.933234 \nL 721.826548 346.950441 \nL 721.858369 338.391528 \nL 722.049298 210.775941 \nL 722.112941 216.701342 \nL 722.30387 231.295386 \nL 722.367513 228.881334 \nL 722.590263 352.107735 \nL 722.653906 341.463959 \nL 722.813013 240.732136 \nL 722.876656 247.974293 \nL 722.972121 262.787797 \nL 723.003942 259.605637 \nL 723.099406 260.483474 \nL 723.131228 253.351046 \nL 723.3858 362.641782 \nL 723.449443 343.768281 \nL 723.60855 270.029954 \nL 723.640371 272.005088 \nL 723.704014 277.6013 \nL 723.735836 276.284544 \nL 723.894943 245.231052 \nL 724.117693 360.556918 \nL 724.149515 351.229898 \nL 724.372265 223.394851 \nL 724.404086 225.369985 \nL 724.499551 234.587276 \nL 724.531372 231.405116 \nL 724.563194 225.808903 \nL 724.658658 227.564578 \nL 724.849587 356.606651 \nL 724.881408 356.167732 \nL 724.945051 321.163973 \nL 725.104159 230.527278 \nL 725.167802 232.612142 \nL 725.263266 238.098624 \nL 725.390552 236.891598 \nL 725.422373 228.113226 \nL 725.613302 359.788811 \nL 725.676945 349.474223 \nL 725.931517 227.893767 \nL 725.963338 226.467281 \nL 725.99516 227.235389 \nL 726.154267 235.904031 \nL 726.186089 226.9062 \nL 726.408839 357.155299 \nL 726.44066 347.060171 \nL 726.66341 218.127827 \nL 726.695232 220.871069 \nL 726.727053 221.200258 \nL 726.790696 230.198089 \nL 726.854339 224.821336 \nL 726.949804 221.639176 \nL 727.140732 350.790979 \nL 727.204375 342.122336 \nL 727.427126 219.115394 \nL 727.458947 222.955932 \nL 727.681697 237.988895 \nL 727.713519 234.806735 \nL 727.936269 350.790979 \nL 727.968091 348.596386 \nL 728.063555 290.000751 \nL 728.159019 245.450511 \nL 728.190841 249.291049 \nL 728.318127 264.87266 \nL 728.349948 259.715366 \nL 728.477234 245.560241 \nL 728.699984 354.741247 \nL 728.763627 346.511523 \nL 728.922735 260.593204 \nL 729.018199 270.798061 \nL 729.05002 274.638599 \nL 729.081842 274.089951 \nL 729.240949 253.680235 \nL 729.463699 355.289895 \nL 729.527342 345.633685 \nL 729.718271 244.243485 \nL 729.813736 259.495907 \nL 729.845557 259.166718 \nL 730.004664 231.514845 \nL 730.195593 353.75368 \nL 730.227415 350.900709 \nL 730.291058 314.909382 \nL 730.450165 216.811072 \nL 730.481986 215.274856 \nL 730.513808 218.127827 \nL 730.545629 218.457016 \nL 730.609272 224.492147 \nL 730.641094 221.200258 \nL 730.672915 215.823505 \nL 730.736558 219.993232 \nL 730.800201 254.448343 \nL 730.959308 344.865578 \nL 730.99113 343.878011 \nL 731.054773 306.460199 \nL 731.21388 216.152694 \nL 731.245701 214.726208 \nL 731.277523 217.688909 \nL 731.309344 218.566746 \nL 731.372987 229.210522 \nL 731.43663 223.72404 \nL 731.500273 224.931066 \nL 731.532095 217.35972 \nL 731.723023 345.523956 \nL 731.786666 333.563423 \nL 732.009417 227.345118 \nL 732.136703 240.512677 \nL 732.200345 239.854299 \nL 732.232167 240.732136 \nL 732.263988 239.52511 \nL 732.29581 228.003496 \nL 732.327631 245.121322 \nL 732.51856 351.888276 \nL 732.582203 319.847217 \nL 732.74131 220.871069 \nL 732.773132 219.554313 \nL 732.804953 222.736473 \nL 733.091346 254.009424 \nL 733.282275 350.900709 \nL 733.345918 337.73315 \nL 733.505026 252.363479 \nL 733.568668 260.483474 \nL 733.664133 273.212114 \nL 733.695954 272.224547 \nL 733.82324 259.276448 \nL 733.918705 317.433164 \nL 734.04599 355.838543 \nL 734.077812 354.302328 \nL 734.109633 351.668816 \nL 734.300562 260.264015 \nL 734.364205 264.324012 \nL 734.396027 265.750497 \nL 734.427848 263.885093 \nL 734.586955 236.123491 \nL 734.777884 347.608819 \nL 734.841527 337.623421 \nL 735.12792 197.279193 \nL 735.318849 204.850539 \nL 735.350671 200.351623 \nL 735.573421 331.807749 \nL 735.605242 324.236403 \nL 735.891635 184.001905 \nL 736.146207 220.871069 \nL 736.337136 326.869915 \nL 736.400779 295.048315 \nL 736.559886 204.960269 \nL 736.655351 201.887839 \nL 736.846279 213.189993 \nL 736.878101 211.214859 \nL 737.100851 334.66072 \nL 737.132673 327.089374 \nL 737.355423 206.277025 \nL 737.387244 207.154862 \nL 737.609995 220.761339 \nL 737.641816 217.140261 \nL 737.864566 333.892612 \nL 737.896388 327.308833 \nL 738.119138 208.471618 \nL 738.150959 212.531615 \nL 738.182781 212.641345 \nL 738.437353 235.684572 \nL 738.628281 345.194767 \nL 738.691924 336.526124 \nL 738.914675 236.781869 \nL 738.946496 237.879165 \nL 738.978318 237.659706 \nL 739.073782 225.260255 \nL 739.169246 228.003496 \nL 739.201068 232.392682 \nL 739.423818 345.633685 \nL 739.45564 344.2072 \nL 739.837497 207.264592 \nL 739.90114 207.70351 \nL 739.932961 202.765676 \nL 740.028426 289.671562 \nL 740.155712 329.283967 \nL 740.187533 321.383432 \nL 740.473926 159.861381 \nL 740.505748 161.178137 \nL 740.664855 181.039204 \nL 740.696677 180.490556 \nL 740.919427 322.700188 \nL 740.951248 315.67749 \nL 741.237642 177.198666 \nL 741.492213 223.943499 \nL 741.683142 340.147203 \nL 741.746785 312.166141 \nL 741.905892 206.057565 \nL 742.001357 200.022435 \nL 742.096821 210.007833 \nL 742.128643 209.568914 \nL 742.160464 210.117563 \nL 742.255928 231.624575 \nL 742.415036 336.635854 \nL 742.446857 336.526124 \nL 742.5105 304.704524 \nL 742.669608 205.289458 \nL 742.701429 200.571083 \nL 742.765072 201.997568 \nL 742.828715 210.007833 \nL 742.892358 205.069999 \nL 742.956001 207.264592 \nL 742.987822 210.007833 \nL 743.178751 334.880179 \nL 743.242394 325.992077 \nL 743.433323 201.010001 \nL 743.496966 205.728377 \nL 743.751537 229.320252 \nL 743.974288 343.000174 \nL 744.037931 331.47856 \nL 744.197038 229.429982 \nL 744.292502 239.63484 \nL 744.356145 246.547808 \nL 744.387967 244.353215 \nL 744.45161 237.440247 \nL 744.515252 240.073758 \nL 744.578895 274.967788 \nL 744.738003 347.938008 \nL 744.769824 347.38936 \nL 744.801646 344.09747 \nL 744.992574 247.096456 \nL 745.056217 254.667802 \nL 745.088039 256.752666 \nL 745.11986 253.899695 \nL 745.278968 232.831601 \nL 745.469896 343.439092 \nL 745.565361 313.373167 \nL 745.75629 196.401356 \nL 745.788111 197.718112 \nL 745.947218 189.598117 \nL 746.074504 213.738641 \nL 746.265433 329.174237 \nL 746.329076 299.437501 \nL 746.520005 171.602454 \nL 746.679112 147.791119 \nL 746.742755 154.594358 \nL 746.838219 179.39326 \nL 747.029148 310.520196 \nL 747.092791 284.294809 \nL 747.28372 152.070576 \nL 747.315541 151.412198 \nL 747.347363 147.57166 \nL 747.379184 149.546794 \nL 747.601935 189.159199 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326.321266 \nL 751.675082 301.193176 \nL 751.866011 169.84678 \nL 751.929654 164.579756 \nL 751.961475 167.322998 \nL 752.184226 202.765676 \nL 752.375154 321.054243 \nL 752.438797 292.305074 \nL 752.661548 138.354369 \nL 752.693369 130.892752 \nL 752.757012 135.391668 \nL 752.820655 136.050046 \nL 752.884298 148.998145 \nL 752.947941 182.904608 \nL 753.13887 306.24074 \nL 753.202512 276.394274 \nL 753.393441 159.203003 \nL 753.552549 172.151102 \nL 753.711656 205.838106 \nL 753.902585 331.149371 \nL 753.966228 307.996414 \nL 754.157156 180.929475 \nL 754.316264 177.747315 \nL 754.379907 186.745146 \nL 754.507193 246.328349 \nL 754.6663 331.47856 \nL 754.729943 327.089374 \nL 754.952693 195.304059 \nL 755.016336 195.633248 \nL 755.1118 183.562986 \nL 755.143622 185.867309 \nL 755.270908 243.036459 \nL 755.461837 331.807749 \nL 755.493658 332.356397 \nL 755.907337 169.188402 \nL 756.034623 227.235389 \nL 756.19373 316.665057 \nL 756.257373 292.195344 \nL 756.511945 111.031685 \nL 756.639231 99.729531 \nL 756.671052 103.899258 \nL 756.798338 182.46569 \nL 756.957445 287.257509 \nL 757.021088 266.738064 \nL 757.27566 75.918196 \nL 757.371124 70.212254 \nL 757.402946 71.090091 \nL 757.530232 107.300877 \nL 757.721161 263.446175 \nL 757.784803 242.59754 \nL 758.039375 45.632811 \nL 758.103018 61.104693 \nL 758.484876 283.307242 \nL 758.548519 263.116986 \nL 758.771269 95.450074 \nL 758.834912 89.195484 \nL 758.866733 91.499807 \nL 759.057662 159.971111 \nL 759.248591 299.437501 \nL 759.312234 292.634263 \nL 759.503163 184.001905 \nL 759.566806 192.231629 \nL 759.66227 207.045132 \nL 759.694091 203.862972 \nL 759.725913 204.74081 \nL 759.853199 261.58077 \nL 760.044128 338.062339 \nL 760.10777 318.420731 \nL 760.266878 237.769435 \nL 760.298699 242.487811 \nL 760.362342 251.595372 \nL 760.394164 249.291049 \nL 760.489628 233.16079 \nL 760.52145 239.086191 \nL 760.553271 239.854299 \nL 760.616914 265.860227 \nL 760.776021 340.256932 \nL 760.807843 339.927743 \nL 760.839664 337.84288 \nL 760.96695 244.462944 \nL 761.126057 213.519182 \nL 761.253343 184.001905 \nL 761.316986 193.109466 \nL 761.380629 239.854299 \nL 761.539736 317.433164 \nL 761.603379 293.29264 \nL 761.857951 116.737627 \nL 761.921594 113.994386 \nL 761.953415 114.762493 \nL 762.04888 130.014915 \nL 762.080701 134.84302 \nL 762.303452 289.561832 \nL 762.367094 265.421308 \nL 762.621666 76.576574 \nL 762.685309 71.30955 \nL 762.717131 73.943062 \nL 762.780774 80.417112 \nL 762.908059 173.906777 \nL 763.067167 280.454271 \nL 763.13081 259.056988 \nL 763.35356 102.033853 \nL 763.480846 93.804129 \nL 763.512667 94.901426 \nL 763.639953 149.766253 \nL 763.830882 296.58453 \nL 763.894525 275.845625 \nL 764.117275 146.364634 \nL 764.149097 142.524096 \nL 764.244561 143.840852 \nL 764.276382 144.170041 \nL 764.371847 169.62732 \nL 764.43549 224.382418 \nL 764.594597 320.286135 \nL 764.65824 299.218042 \nL 764.88099 167.542457 \nL 764.912812 163.811649 \nL 765.008276 164.470027 \nL 765.071919 173.358129 \nL 765.231026 272.773195 \nL 765.358312 331.259101 \nL 765.390134 330.490993 \nL 765.421955 327.089374 \nL 765.74017 185.208931 \nL 765.803813 170.395428 \nL 765.835634 172.80948 \nL 765.96292 225.918633 \nL 766.153849 318.420731 \nL 766.18567 317.872083 \nL 766.312956 213.409452 \nL 766.503885 165.567323 \nL 766.599349 153.058143 \nL 766.631171 157.557058 \nL 766.662992 160.848948 \nL 766.885743 294.828856 \nL 766.917564 288.793725 \nL 767.2676 78.222519 \nL 767.331243 85.135487 \nL 767.458529 134.404101 \nL 767.649458 275.516436 \nL 767.713101 247.754834 \nL 767.935851 92.706833 \nL 767.967672 84.586838 \nL 768.031315 93.255481 \nL 768.254066 192.999737 \nL 768.413173 284.404538 \nL 768.476816 258.947259 \nL 768.667745 139.341936 \nL 768.731388 136.050046 \nL 768.79503 154.045709 \nL 768.858673 143.511663 \nL 768.890495 144.49923 \nL 768.985959 172.80948 \nL 769.176888 303.387769 \nL 769.240531 279.466704 \nL 769.463281 151.192738 \nL 769.526924 144.828419 \nL 769.558746 145.596526 \nL 769.717853 172.80948 \nL 769.781496 227.784037 \nL 769.940603 318.74992 \nL 770.004246 297.462367 \nL 770.226996 168.530024 \nL 770.290639 158.544625 \nL 770.354282 161.287867 \nL 770.417925 173.467858 \nL 770.545211 237.330517 \nL 770.704318 330.820182 \nL 770.73614 329.942345 \nL 770.799783 306.789388 \nL 770.990712 205.179728 \nL 771.086176 210.775941 \nL 771.117997 208.910536 \nL 771.149819 204.74081 \nL 771.18164 206.715943 \nL 771.308926 249.291049 \nL 771.499855 328.2964 \nL 771.563498 316.335868 \nL 771.786248 212.860804 \nL 771.81807 213.299723 \nL 771.913534 191.79271 \nL 771.977177 201.668379 \nL 772.008998 203.314324 \nL 772.231749 328.076941 \nL 772.295392 300.095879 \nL 772.518142 151.521927 \nL 772.677249 135.830587 \nL 772.804535 177.857044 \nL 772.963642 311.507763 \nL 772.995464 311.398033 \nL 773.059107 285.611565 \nL 773.313679 102.472772 \nL 773.440964 91.938725 \nL 773.536429 107.849525 \nL 773.759179 267.067253 \nL 773.822822 248.96186 \nL 774.077394 56.166858 \nL 774.172858 60.665774 \nL 774.300144 100.497638 \nL 774.522894 279.137515 \nL 774.554716 274.638599 \nL 774.841109 141.31707 \nL 774.87293 143.840852 \nL 775.000216 154.045709 \nL 775.159324 267.396442 \nL 775.286609 321.82235 \nL 775.318431 317.323435 \nL 775.700288 155.801384 \nL 775.73211 156.569492 \nL 775.891217 228.113226 \nL 776.050325 321.602891 \nL 776.082146 321.493162 \nL 776.145789 301.632094 \nL 776.368539 195.304059 \nL 776.400361 197.827841 \nL 776.432182 194.97487 \nL 776.495825 184.879742 \nL 776.527647 186.306228 \nL 776.654932 238.976462 \nL 776.81404 323.907214 \nL 776.845861 323.139106 \nL 776.877683 320.725054 \nL 776.973147 251.814831 \nL 777.100433 196.950004 \nL 777.132254 201.33919 \nL 777.164076 201.55865 \nL 777.25954 181.807312 \nL 777.355005 189.268928 \nL 777.545933 309.752089 \nL 777.609576 301.193176 \nL 777.89597 136.159776 \nL 777.927791 137.91545 \nL 777.959612 138.573828 \nL 777.991434 136.598694 \nL 778.023255 138.793288 \nL 778.182363 239.52511 \nL 778.34147 315.787219 \nL 778.405113 290.878588 \nL 778.596042 178.076504 \nL 778.691506 171.821913 \nL 778.755149 175.552722 \nL 778.786971 175.772181 \nL 778.914256 207.045132 \nL 779.105185 326.650455 \nL 779.168828 299.76669 \nL 779.391578 180.819745 \nL 779.4234 174.565155 \nL 779.487043 176.2111 \nL 779.614329 188.500821 \nL 779.64615 187.293795 \nL 779.8689 324.785051 \nL 779.900722 316.994246 \nL 780.187115 163.37273 \nL 780.218936 168.749483 \nL 780.378044 182.246231 \nL 780.409865 180.161367 \nL 780.632616 321.054243 \nL 780.696258 295.267774 \nL 780.95083 149.875983 \nL 781.014473 153.93598 \nL 781.205402 192.999737 \nL 781.396331 313.043978 \nL 781.459974 303.168309 \nL 781.682724 192.780277 \nL 781.778188 199.693246 \nL 781.841831 194.865141 \nL 781.969117 221.748906 \nL 782.160046 330.710452 \nL 782.191867 330.600723 \nL 782.25551 317.103975 \nL 782.478261 212.751074 \nL 782.510082 213.299723 \nL 782.573725 203.753243 \nL 782.701011 190.475955 \nL 782.89194 316.335868 \nL 782.955582 308.215873 \nL 783.273797 132.648427 \nL 783.305619 133.087346 \nL 783.369262 130.892752 \nL 783.464726 143.621392 \nL 783.687476 298.669393 \nL 783.719298 292.743992 \nL 784.037512 109.934389 \nL 784.132977 116.079249 \nL 784.260263 164.250567 \nL 784.451191 295.267774 \nL 784.514834 269.700765 \nL 784.737585 140.658692 \nL 784.769406 137.037613 \nL 784.833049 141.31707 \nL 784.896692 141.865718 \nL 785.023978 182.35596 \nL 785.214907 315.56776 \nL 785.278549 291.866155 \nL 785.5013 171.931643 \nL 785.533121 167.761916 \nL 785.564943 174.784614 \nL 785.787693 213.848371 \nL 785.978622 329.503426 \nL 786.042265 303.497498 \nL 786.265015 171.931643 \nL 786.328658 166.006242 \nL 786.392301 168.639753 \nL 786.424122 168.420294 \nL 786.551408 204.960269 \nL 786.742337 328.40613 \nL 786.774158 327.747752 \nL 786.837801 304.155876 \nL 787.02873 199.364057 \nL 787.092373 202.985135 \nL 787.124194 199.583516 \nL 787.187837 187.732713 \nL 787.219659 188.500821 \nL 787.346945 244.792133 \nL 787.537873 334.331531 \nL 787.601516 320.286135 \nL 787.856088 197.059734 \nL 787.951553 175.004073 \nL 788.047017 177.418126 \nL 788.269767 316.006679 \nL 788.301589 309.861818 \nL 788.587982 125.845188 \nL 788.683446 127.710593 \nL 788.715268 125.845188 \nL 788.747089 130.124645 \nL 788.842554 168.749483 \nL 789.033482 305.253173 \nL 789.097125 277.49157 \nL 789.319876 144.49923 \nL 789.351697 138.244639 \nL 789.41534 141.646259 \nL 789.510804 152.509494 \nL 789.606269 182.026771 \nL 789.797198 294.609396 \nL 789.86084 265.421308 \nL 790.051769 151.960846 \nL 790.338162 183.453257 \nL 790.529091 315.787219 \nL 790.592734 310.520196 \nL 790.879127 176.759748 \nL 790.910949 179.612719 \nL 791.070056 193.767844 \nL 791.101878 190.037036 \nL 791.324628 331.039641 \nL 791.356449 324.126673 \nL 791.5792 189.927306 \nL 791.642842 193.658115 \nL 791.833771 213.958101 \nL 791.865593 211.324589 \nL 792.088343 338.062339 \nL 792.151986 327.747752 \nL 792.342915 225.589444 \nL 792.438379 236.45268 \nL 792.502022 230.307819 \nL 792.597486 224.492147 \nL 792.629308 224.711607 \nL 792.852058 340.91531 \nL 792.88388 340.037473 \nL 792.947523 324.565592 \nL 793.10663 221.968365 \nL 793.170273 227.125659 \nL 793.202094 231.185656 \nL 793.233916 229.8689 \nL 793.393023 214.616478 \nL 793.583952 337.074772 \nL 793.615773 336.745583 \nL 793.679416 303.05858 \nL 793.870345 183.892175 \nL 794.124917 196.291626 \nL 794.156738 194.426222 \nL 794.379489 336.855313 \nL 794.41131 328.735319 \nL 794.697703 187.952173 \nL 794.729525 188.83001 \nL 794.824989 181.478123 \nL 794.85681 182.35596 \nL 794.952275 213.519182 \nL 795.143204 327.199104 \nL 795.206847 299.437501 \nL 795.429597 168.859213 \nL 795.588704 156.679221 \nL 795.71599 196.730545 \nL 795.906919 315.67749 \nL 795.970562 286.599132 \nL 796.161491 168.749483 \nL 796.225133 167.652187 \nL 796.384241 180.710015 \nL 796.416062 179.941908 \nL 796.447884 184.001905 \nL 796.670634 319.627757 \nL 796.702455 313.043978 \nL 796.925206 174.235966 \nL 796.988849 177.857044 \nL 797.275242 252.582939 \nL 797.434349 329.064508 \nL 797.497992 316.335868 \nL 797.688921 214.835938 \nL 797.784385 224.601877 \nL 797.816207 225.150525 \nL 797.975314 215.055397 \nL 798.198064 336.855313 \nL 798.229886 336.416394 \nL 798.293529 323.029377 \nL 798.516279 216.481883 \nL 798.579922 210.227292 \nL 798.739029 181.807312 \nL 798.929958 321.163973 \nL 798.96178 320.505595 \nL 799.025422 289.781291 \nL 799.248173 136.269506 \nL 799.40728 118.93222 \nL 799.502744 134.294372 \nL 799.725495 309.752089 \nL 799.757316 304.375335 \nL 800.139174 108.507903 \nL 800.170995 110.263578 \nL 800.298281 164.579756 \nL 800.48921 296.365071 \nL 800.552853 262.897526 \nL 800.775603 108.068984 \nL 800.902889 92.597103 \nL 800.93471 93.804129 \nL 801.061996 144.3895 \nL 801.252925 281.112649 \nL 801.284746 276.723463 \nL 801.507497 147.352201 \nL 801.539318 148.888416 \nL 801.57114 148.998145 \nL 801.79389 177.857044 \nL 802.01664 323.358566 \nL 802.048462 317.542894 \nL 802.334855 186.086768 \nL 802.589427 225.479714 \nL 802.780355 336.855313 \nL 802.843998 325.00451 \nL 803.034927 226.357551 \nL 803.162213 232.282953 \nL 803.289499 213.189993 \nL 803.32132 213.958101 \nL 803.54407 331.807749 \nL 803.607713 324.455862 \nL 803.703178 257.301314 \nL 803.894107 182.794879 \nL 804.053214 162.714352 \nL 804.085035 173.687318 \nL 804.307786 304.046147 \nL 804.339607 303.05858 \nL 804.435072 261.58077 \nL 804.626 146.474363 \nL 804.657822 146.584093 \nL 804.753286 125.625729 \nL 804.816929 127.710593 \nL 804.880572 177.088937 \nL 805.071501 302.180742 \nL 805.135144 265.531038 \nL 805.357894 106.42304 \nL 805.389715 99.071153 \nL 805.421537 104.228447 \nL 805.453358 112.019252 \nL 805.517001 103.02142 \nL 805.580644 102.801961 \nL 805.612466 114.872223 \nL 805.835216 284.733727 \nL 805.898859 254.777532 \nL 806.089788 149.437064 \nL 806.248895 139.890584 \nL 806.280717 140.878151 \nL 806.376181 158.654355 \nL 806.598931 319.956946 \nL 806.662574 283.416972 \nL 806.853503 166.115971 \nL 807.044432 142.414366 \nL 807.139896 152.948413 \nL 807.362646 314.360734 \nL 807.426289 275.626166 \nL 807.64904 122.004651 \nL 807.744504 104.118717 \nL 807.776325 105.545202 \nL 807.935433 154.594358 \nL 808.126361 284.294809 \nL 808.190004 271.017521 \nL 808.380933 127.930052 \nL 808.540041 139.890584 \nL 808.730969 237.111058 \nL 808.890077 318.640191 \nL 808.921898 318.091542 \nL 808.985541 303.936417 \nL 809.17647 222.187825 \nL 809.208291 223.065662 \nL 809.240113 224.711607 \nL 809.271934 221.090528 \nL 809.39922 197.059734 \nL 809.431042 206.825673 \nL 809.62197 337.184502 \nL 809.653792 335.977476 \nL 809.717435 295.706693 \nL 809.876542 197.827841 \nL 809.972006 190.037036 \nL 810.003828 192.341359 \nL 810.035649 193.987304 \nL 810.099292 193.767844 \nL 810.131114 192.341359 \nL 810.162935 182.246231 \nL 810.194757 196.181897 \nL 810.385686 336.635854 \nL 810.417507 336.087205 \nL 810.48115 299.108312 \nL 810.640257 213.519182 \nL 810.735722 209.349455 \nL 810.799365 213.958101 \nL 810.831186 213.409452 \nL 810.92665 193.438655 \nL 810.958472 205.947836 \nL 811.149401 335.428828 \nL 811.181222 334.989909 \nL 811.244865 295.596963 \nL 811.403972 204.082432 \nL 811.658544 194.316493 \nL 811.690366 181.917042 \nL 811.722187 196.401356 \nL 811.913116 331.698019 \nL 811.944937 331.149371 \nL 812.00858 289.891021 \nL 812.167688 187.293795 \nL 812.199509 185.64785 \nL 812.263152 187.842443 \nL 812.294973 189.707847 \nL 812.358616 186.964606 \nL 812.454081 176.540289 \nL 812.517724 220.871069 \nL 812.676831 332.575857 \nL 812.708652 332.136938 \nL 812.772295 296.69426 \nL 812.931403 212.860804 \nL 812.963224 213.299723 \nL 813.154153 228.881334 \nL 813.185974 226.577011 \nL 813.217796 215.494316 \nL 813.249617 224.492147 \nL 813.472368 349.803412 \nL 813.504189 345.085037 \nL 813.599654 289.671562 \nL 813.726939 241.609973 \nL 813.758761 243.255918 \nL 813.822404 249.510508 \nL 813.854225 248.742401 \nL 813.981511 222.736473 \nL 814.013333 235.794302 \nL 814.267904 353.314761 \nL 814.363369 307.447766 \nL 814.458833 257.740233 \nL 814.522476 259.056988 \nL 814.586119 267.396442 \nL 814.61794 266.957523 \nL 814.745226 222.955932 \nL 814.777048 237.001328 \nL 814.967977 352.875843 \nL 814.999798 350.790979 \nL 815.063441 308.545062 \nL 815.222548 226.138092 \nL 815.381656 231.295386 \nL 815.47712 209.788374 \nL 815.508941 196.620815 \nL 815.540763 214.17756 \nL 815.763513 346.292063 \nL 815.827156 310.849385 \nL 815.986263 214.397019 \nL 816.018085 213.409452 \nL 816.145371 225.040796 \nL 816.177192 222.078095 \nL 816.240835 210.556481 \nL 816.272657 197.608382 \nL 816.304478 214.616478 \nL 816.527228 345.633685 \nL 816.590871 305.362902 \nL 816.749979 222.187825 \nL 816.813621 224.382418 \nL 816.845443 224.162958 \nL 816.909086 228.661874 \nL 816.940907 224.601877 \nL 817.036372 196.181897 \nL 817.068193 212.531615 \nL 817.259122 343.000174 \nL 817.290943 341.573688 \nL 817.354586 302.290472 \nL 817.513694 212.970534 \nL 817.545515 212.751074 \nL 817.609158 214.726208 \nL 817.704623 224.492147 \nL 817.736444 221.639176 \nL 817.800087 200.132164 \nL 817.831908 213.189993 \nL 818.022837 341.793147 \nL 818.054659 340.91531 \nL 818.118302 302.290472 \nL 818.213766 222.955932 \nL 818.277409 226.79647 \nL 818.436516 246.876997 \nL 818.500159 239.52511 \nL 818.563802 216.481883 \nL 818.595624 227.784037 \nL 818.818374 346.072604 \nL 818.850195 341.02504 \nL 819.041124 236.891598 \nL 819.16841 258.39861 \nL 819.200231 257.520773 \nL 819.327517 226.357551 \nL 819.359339 240.183488 \nL 819.582089 351.449357 \nL 819.61391 349.145034 \nL 819.709375 300.863987 \nL 819.868482 236.23322 \nL 819.932125 238.647273 \nL 819.963947 236.891598 \nL 820.091232 202.875406 \nL 820.345804 337.184502 \nL 820.377626 327.638022 \nL 820.600376 202.655946 \nL 820.727662 220.54188 \nL 820.759483 218.895935 \nL 820.854948 193.109466 \nL 820.886769 214.726208 \nL 821.077698 342.561255 \nL 821.109519 342.122336 \nL 821.173162 304.814254 \nL 821.33227 222.846203 \nL 821.364091 220.980798 \nL 821.427734 221.858636 \nL 821.491377 226.79647 \nL 821.523198 224.162958 \nL 821.618663 192.999737 \nL 821.650484 212.751074 \nL 821.841413 346.511523 \nL 821.873234 344.975307 \nL 821.936877 303.607228 \nL 822.064163 223.943499 \nL 822.095985 225.699174 \nL 822.127806 217.140261 \nL 822.191449 220.761339 \nL 822.255092 228.552145 \nL 822.286914 226.138092 \nL 822.382378 196.181897 \nL 822.414199 214.06783 \nL 822.63695 344.09747 \nL 822.700593 305.911551 \nL 822.8597 220.43215 \nL 823.018807 227.784037 \nL 823.050629 226.68674 \nL 823.146093 193.877574 \nL 823.177915 212.202426 \nL 823.368843 336.745583 \nL 823.400665 336.635854 \nL 823.464308 299.437501 \nL 823.623415 215.494316 \nL 823.718879 212.531615 \nL 823.782522 218.127827 \nL 823.846165 215.604045 \nL 823.909808 202.765676 \nL 823.94163 217.140261 \nL 824.16438 342.451525 \nL 824.196201 339.159636 \nL 824.323487 256.972125 \nL 824.418952 237.220787 \nL 824.450773 237.659706 \nL 824.514416 239.195921 \nL 824.641702 213.628912 \nL 824.673523 206.825673 \nL 824.705345 222.297554 \nL 824.928095 343.439092 \nL 824.959917 342.451525 \nL 825.055381 300.315338 \nL 825.214488 233.380249 \nL 825.341774 213.519182 \nL 825.405417 186.745146 \nL 825.437239 188.171632 \nL 825.69181 334.441261 \nL 825.723632 324.785051 \nL 825.946382 194.865141 \nL 826.010025 192.560818 \nL 826.041846 194.535952 \nL 826.073668 196.840275 \nL 826.105489 196.511086 \nL 826.169132 176.759748 \nL 826.200954 179.1738 \nL 826.423704 336.745583 \nL 826.487347 326.869915 \nL 826.710097 203.862972 \nL 826.837383 216.811072 \nL 826.869205 214.06783 \nL 826.964669 184.660283 \nL 826.99649 207.593781 \nL 827.187419 336.416394 \nL 827.219241 335.867746 \nL 827.282884 299.65696 \nL 827.441991 205.289458 \nL 827.473812 204.960269 \nL 827.601098 213.958101 \nL 827.63292 211.982967 \nL 827.696563 188.72028 \nL 827.728384 189.817577 \nL 827.951134 335.648287 \nL 828.014777 325.992077 \nL 828.173885 214.726208 \nL 828.237528 217.579179 \nL 828.364813 225.699174 \nL 828.396635 223.833769 \nL 828.492099 193.328926 \nL 828.523921 210.88567 \nL 828.714849 338.610988 \nL 828.746671 337.184502 \nL 828.810314 300.863987 \nL 828.9376 216.152694 \nL 828.969421 218.127827 \nL 829.128529 236.45268 \nL 829.16035 232.173223 \nL 829.255814 208.910536 \nL 829.287636 223.285121 \nL 829.510386 341.13477 \nL 829.542208 338.501258 \nL 829.637672 280.564001 \nL 829.764958 226.577011 \nL 829.796779 228.771604 \nL 829.892244 235.904031 \nL 829.987708 211.763507 \nL 830.01953 220.761339 \nL 830.274101 348.486656 \nL 830.337744 338.940176 \nL 830.496852 252.363479 \nL 830.592316 262.568337 \nL 830.624137 266.079686 \nL 830.655959 263.446175 \nL 830.751423 217.46945 \nL 830.815066 231.624575 \nL 831.005995 343.219633 \nL 831.037816 341.463959 \nL 831.101459 299.65696 \nL 831.260567 218.566746 \nL 831.292388 218.895935 \nL 831.419674 224.711607 \nL 831.451496 223.50458 \nL 831.54696 189.817577 \nL 831.578781 212.531615 \nL 831.76971 339.159636 \nL 831.801532 337.623421 \nL 831.865175 299.327771 \nL 832.024282 203.753243 \nL 832.087925 208.581348 \nL 832.119746 208.142429 \nL 832.183389 217.46945 \nL 832.215211 214.17756 \nL 832.278854 188.83001 \nL 832.342497 209.020266 \nL 832.565247 336.965043 \nL 832.62889 296.035882 \nL 832.787997 204.63108 \nL 832.819819 204.082432 \nL 832.85164 205.399188 \nL 832.947104 216.701342 \nL 833.010747 209.349455 \nL 833.042569 190.695414 \nL 833.106212 210.88567 \nL 833.328962 337.074772 \nL 833.392605 298.230475 \nL 833.551712 221.968365 \nL 833.583534 221.639176 \nL 833.71082 232.392682 \nL 833.742641 227.345118 \nL 833.838105 194.865141 \nL 833.869927 211.214859 \nL 834.060856 337.513691 \nL 834.092677 337.074772 \nL 834.15632 300.973716 \nL 834.251784 218.786205 \nL 834.315427 219.883502 \nL 834.37907 224.053229 \nL 834.474535 234.806735 \nL 834.506356 234.148357 \nL 834.569999 204.850539 \nL 834.633642 221.090528 \nL 834.856392 341.13477 \nL 834.888214 337.73315 \nL 835.0155 250.607805 \nL 835.079143 228.332685 \nL 835.142785 239.41538 \nL 835.23825 262.348878 \nL 835.270071 256.423477 \nL 835.333714 219.554313 \nL 835.397357 235.684572 \nL 835.588286 339.598554 \nL 835.651929 335.977476 \nL 835.747393 285.392105 \nL 835.811036 245.88943 \nL 835.874679 254.777532 \nL 835.970144 270.578602 \nL 836.001965 269.920224 \nL 836.097429 215.494316 \nL 836.161072 235.026194 \nL 836.383823 340.366662 \nL 836.447466 308.654792 \nL 836.606573 211.324589 \nL 836.76568 228.113226 \nL 836.797502 223.61431 \nL 836.861145 186.415957 \nL 836.892966 193.767844 \nL 837.147538 345.523956 \nL 837.179359 334.880179 \nL 837.40211 216.262423 \nL 837.497574 219.225124 \nL 837.529395 221.858636 \nL 837.561217 219.554313 \nL 837.62486 186.854876 \nL 837.688503 213.189993 \nL 837.879431 339.379095 \nL 837.911253 339.269365 \nL 837.974896 301.193176 \nL 838.134003 204.082432 \nL 838.229468 202.436487 \nL 838.293111 208.142429 \nL 838.324932 206.825673 \nL 838.388575 175.004073 \nL 838.452218 200.241894 \nL 838.643147 331.47856 \nL 838.674968 331.039641 \nL 838.738611 293.182911 \nL 838.897718 188.391091 \nL 838.92954 186.745146 \nL 838.961361 188.500821 \nL 838.993183 187.622984 \nL 839.056826 192.999737 \nL 839.088647 191.463522 \nL 839.15229 169.62732 \nL 839.184112 180.271097 \nL 839.438683 330.820182 \nL 839.470505 323.907214 \nL 839.693255 202.875406 \nL 839.852362 225.918633 \nL 839.884184 220.212691 \nL 839.916005 203.862972 \nL 839.979648 226.247822 \nL 840.202398 345.414226 \nL 840.23422 341.902877 \nL 840.425149 237.549976 \nL 840.552435 250.827264 \nL 840.584256 253.021857 \nL 840.616077 249.07159 \nL 840.67972 219.444583 \nL 840.711542 229.210522 \nL 840.966114 351.120168 \nL 841.029757 341.793147 \nL 841.220685 250.498075 \nL 841.284328 256.423477 \nL 841.31615 259.056988 \nL 841.347971 253.131587 \nL 841.443436 197.059734 \nL 841.507079 226.68674 \nL 841.698007 337.95261 \nL 841.729829 336.965043 \nL 841.793472 293.5121 \nL 841.888936 210.775941 \nL 841.920758 211.434319 \nL 841.952579 210.007833 \nL 841.9844 213.409452 \nL 842.111686 232.721871 \nL 842.143508 223.72404 \nL 842.207151 181.148934 \nL 842.270794 207.92297 \nL 842.461722 335.428828 \nL 842.493544 335.319098 \nL 842.557187 301.741824 \nL 842.716294 210.666211 \nL 842.748116 209.788374 \nL 842.84358 216.920801 \nL 842.875402 216.262423 \nL 842.970866 178.405693 \nL 843.002687 190.366225 \nL 843.225438 340.476392 \nL 843.289081 329.174237 \nL 843.480009 202.765676 \nL 843.511831 205.508917 \nL 843.607295 213.189993 \nL 843.639117 210.337022 \nL 843.734581 176.430559 \nL 843.766403 189.707847 \nL 844.020974 337.623421 \nL 844.052796 326.869915 \nL 844.275546 193.767844 \nL 844.402832 206.825673 \nL 844.498296 178.625152 \nL 844.530118 190.585684 \nL 844.752868 334.55099 \nL 844.816511 324.675321 \nL 845.039261 201.887839 \nL 845.166547 220.43215 \nL 845.198368 217.24999 \nL 845.262011 196.730545 \nL 845.293833 206.057565 \nL 845.389297 271.23698 \nL 845.548405 343.109903 \nL 845.612048 326.869915 \nL 845.739333 250.388346 \nL 845.802976 251.485642 \nL 845.898441 269.920224 \nL 845.962084 260.483474 \nL 846.025727 225.260255 \nL 846.08937 249.620238 \nL 846.31212 348.376927 \nL 846.343941 347.169901 \nL 846.439406 300.754257 \nL 846.566691 244.462944 \nL 846.598513 245.011593 \nL 846.662156 257.411044 \nL 846.693977 254.667802 \nL 846.789442 200.461353 \nL 846.853085 229.320252 \nL 847.044013 342.561255 \nL 847.075835 340.91531 \nL 847.139478 298.998582 \nL 847.298585 211.324589 \nL 847.457693 233.489979 \nL 847.489514 225.260255 \nL 847.553157 183.562986 \nL 847.6168 210.9954 \nL 847.807729 337.294232 \nL 847.83955 337.074772 \nL 847.903193 295.816422 \nL 848.030479 208.361888 \nL 848.0623 211.544048 \nL 848.125943 217.24999 \nL 848.221408 225.918633 \nL 848.253229 222.626743 \nL 848.316872 181.258664 \nL 848.380515 210.337022 \nL 848.571444 338.172069 \nL 848.603265 337.294232 \nL 848.666908 297.791556 \nL 848.826016 207.045132 \nL 848.985123 219.225124 \nL 849.080587 184.770013 \nL 849.112409 194.645681 \nL 849.335159 337.513691 \nL 849.398802 325.443429 \nL 849.557909 210.337022 \nL 849.621552 219.993232 \nL 849.748838 230.527278 \nL 849.812481 207.92297 \nL 849.844302 188.83001 \nL 849.907945 206.825673 \nL 850.098874 331.36883 \nL 850.130696 330.600723 \nL 850.194339 289.561832 \nL 850.289803 209.788374 \nL 850.321624 212.092696 \nL 850.480732 237.549976 \nL 850.512553 236.891598 \nL 850.608018 217.908368 \nL 850.639839 228.003496 \nL 850.703482 253.460776 \nL 850.894411 336.855313 \nL 850.958054 321.712621 \nL 851.148982 218.127827 \nL 851.212625 224.382418 \nL 851.244447 226.357551 \nL 851.276268 224.931066 \nL 851.371733 199.144597 \nL 851.403554 213.738641 \nL 851.626304 333.673153 \nL 851.721769 315.128842 \nL 851.912698 206.606214 \nL 851.944519 211.873237 \nL 852.008162 224.821336 \nL 852.039984 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "energy['2014-07-01':'2014-07-07'].plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "dddca9ad9e34435494e0933c218e1579",
+   "translation_date": "2025-09-06T14:02:03+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/7-TimeSeries/1-Introduction/working/notebook.ipynb b/translations/vi/7-TimeSeries/1-Introduction/working/notebook.ipynb
new file mode 100644
index 000000000..b82509f95
--- /dev/null
+++ b/translations/vi/7-TimeSeries/1-Introduction/working/notebook.ipynb
@@ -0,0 +1,63 @@
+{
+ "cells": [
+  {
+   "source": [
+    "# Thiết lập dữ liệu\n",
+    "\n",
+    "Trong notebook này, chúng ta sẽ minh họa cách:\n",
+    "\n",
+    "thiết lập dữ liệu chuỗi thời gian cho mô-đun này  \n",
+    "trực quan hóa dữ liệu  \n",
+    "Dữ liệu trong ví dụ này được lấy từ cuộc thi dự báo GEFCom2014. Nó bao gồm 3 năm dữ liệu tải điện và giá trị nhiệt độ theo giờ từ năm 2012 đến năm 2014.\n",
+    "\n",
+    "1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli và Rob J. Hyndman, \"Dự báo năng lượng xác suất: Cuộc thi Dự báo Năng lượng Toàn cầu 2014 và hơn thế nữa\", Tạp chí Quốc tế về Dự báo, tập 32, số 3, trang 896-913, tháng 7-tháng 9, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
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+  },
+  "kernelspec": {
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+   "display_name": "Python 3.7.0 64-bit ('3.7')"
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+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "coopTranslator": {
+   "original_hash": "5e2bbe594906dce3aaaa736d6dac6683",
+   "translation_date": "2025-09-06T14:02:49+00:00",
+   "source_file": "7-TimeSeries/1-Introduction/working/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/7-TimeSeries/2-ARIMA/solution/notebook.ipynb b/translations/vi/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
new file mode 100644
index 000000000..9488f6d55
--- /dev/null
+++ b/translations/vi/7-TimeSeries/2-ARIMA/solution/notebook.ipynb
@@ -0,0 +1,1137 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# Dự báo chuỗi thời gian với ARIMA\n",
+    "\n",
+    "Trong notebook này, chúng ta sẽ thực hiện:\n",
+    "- chuẩn bị dữ liệu chuỗi thời gian để huấn luyện mô hình dự báo chuỗi thời gian ARIMA\n",
+    "- triển khai một mô hình ARIMA đơn giản để dự báo các bước tiếp theo trong HORIZON (từ thời điểm *t+1* đến *t+HORIZON*) trong chuỗi thời gian\n",
+    "- đánh giá mô hình\n",
+    "\n",
+    "Dữ liệu trong ví dụ này được lấy từ cuộc thi dự báo GEFCom2014. Nó bao gồm 3 năm dữ liệu tải điện và nhiệt độ theo giờ từ năm 2012 đến 2014. Nhiệm vụ là dự báo các giá trị tải điện trong tương lai. Trong ví dụ này, chúng ta sẽ minh họa cách dự báo một bước thời gian tiếp theo, chỉ sử dụng dữ liệu tải điện lịch sử.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli và Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, tháng 7-tháng 9, 2016.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Cài đặt các thư viện cần thiết\n",
+    "Bắt đầu bằng cách cài đặt một số thư viện cần thiết. Các thư viện này cùng với phiên bản tương ứng đã được kiểm chứng hoạt động tốt cho giải pháp:\n",
+    "\n",
+    "* `statsmodels == 0.12.2`\n",
+    "* `matplotlib == 3.4.2`\n",
+    "* `scikit-learn == 0.24.2`\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "source": [
+    "!pip install statsmodels"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "/bin/sh: pip: command not found\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from pandas.plotting import autocorrelation_plot\n",
+    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape\n",
+    "from IPython.display import Image\n",
+    "\n",
+    "%matplotlib inline\n",
+    "pd.options.display.float_format = '{:,.2f}'.format\n",
+    "np.set_printoptions(precision=2)\n",
+    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "source": [
+    "energy = load_data('./data')[['load']]\n",
+    "energy.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2,698.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2,558.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2,444.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2,402.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2,403.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 05:00:00</th>\n",
+       "      <td>2,453.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 06:00:00</th>\n",
+       "      <td>2,560.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 07:00:00</th>\n",
+       "      <td>2,719.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 08:00:00</th>\n",
+       "      <td>2,916.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 09:00:00</th>\n",
+       "      <td>3,105.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        load\n",
+       "2012-01-01 00:00:00 2,698.00\n",
+       "2012-01-01 01:00:00 2,558.00\n",
+       "2012-01-01 02:00:00 2,444.00\n",
+       "2012-01-01 03:00:00 2,402.00\n",
+       "2012-01-01 04:00:00 2,403.00\n",
+       "2012-01-01 05:00:00 2,453.00\n",
+       "2012-01-01 06:00:00 2,560.00\n",
+       "2012-01-01 07:00:00 2,719.00\n",
+       "2012-01-01 08:00:00 2,916.00\n",
+       "2012-01-01 09:00:00 3,105.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vẽ tất cả dữ liệu tải có sẵn (tháng 1 năm 2012 đến tháng 12 năm 2014)\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Tạo tập dữ liệu huấn luyện và kiểm tra\n",
+    "\n",
+    "### Giới thiệu\n",
+    "\n",
+    "Khi xây dựng một mô hình học máy, việc chia dữ liệu thành các tập huấn luyện và kiểm tra là một bước quan trọng. Tập huấn luyện được sử dụng để dạy mô hình, trong khi tập kiểm tra được sử dụng để đánh giá hiệu suất của mô hình trên dữ liệu chưa từng thấy.\n",
+    "\n",
+    "### Tại sao cần chia dữ liệu?\n",
+    "\n",
+    "Việc chia dữ liệu giúp đảm bảo rằng mô hình không chỉ hoạt động tốt trên dữ liệu mà nó đã được huấn luyện, mà còn có khả năng tổng quát hóa để dự đoán chính xác trên dữ liệu mới. Điều này giúp tránh hiện tượng **overfitting** (quá khớp), khi mô hình học quá chi tiết từ dữ liệu huấn luyện và không thể áp dụng tốt cho dữ liệu khác.\n",
+    "\n",
+    "### Cách chia dữ liệu\n",
+    "\n",
+    "Dưới đây là các bước cơ bản để chia dữ liệu:\n",
+    "\n",
+    "1. **Thu thập dữ liệu**: Đảm bảo rằng bạn có một tập dữ liệu đủ lớn và đại diện cho vấn đề bạn đang giải quyết.\n",
+    "2. **Xáo trộn dữ liệu**: Trộn ngẫu nhiên dữ liệu để đảm bảo rằng không có sự thiên vị nào trong cách dữ liệu được sắp xếp.\n",
+    "3. **Chia thành hai tập**:\n",
+    "   - **Tập huấn luyện**: Thường chiếm khoảng 70-80% tổng dữ liệu.\n",
+    "   - **Tập kiểm tra**: Thường chiếm khoảng 20-30% tổng dữ liệu.\n",
+    "\n",
+    "### Ví dụ\n",
+    "\n",
+    "Dưới đây là một ví dụ minh họa cách chia dữ liệu bằng Python:\n",
+    "\n",
+    "```python\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "# Giả sử X là dữ liệu đầu vào và y là nhãn\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "print(\"Kích thước tập huấn luyện:\", len(X_train))\n",
+    "print(\"Kích thước tập kiểm tra:\", len(X_test))\n",
+    "```\n",
+    "\n",
+    "### Lưu ý\n",
+    "\n",
+    "[!NOTE] Đảm bảo rằng bạn sử dụng cùng một giá trị `random_state` để kết quả có thể tái lập.\n",
+    "\n",
+    "[!WARNING] Không sử dụng dữ liệu kiểm tra trong quá trình huấn luyện mô hình. Điều này có thể dẫn đến kết quả không chính xác khi đánh giá mô hình.\n",
+    "\n",
+    "[!TIP] Nếu bạn có một tập dữ liệu rất lớn, bạn có thể cân nhắc sử dụng một phần nhỏ của dữ liệu để kiểm tra, thay vì 20-30%.\n",
+    "\n",
+    "[!IMPORTANT] Đối với các bài toán cụ thể như phân loại hoặc dự đoán chuỗi thời gian, cách chia dữ liệu có thể cần được điều chỉnh để phù hợp với đặc điểm của dữ liệu.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'    "
+   ],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(10)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 05:00:00</th>\n",
+       "      <td>0.10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 06:00:00</th>\n",
+       "      <td>0.19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 07:00:00</th>\n",
+       "      <td>0.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 08:00:00</th>\n",
+       "      <td>0.40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 09:00:00</th>\n",
+       "      <td>0.48</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-11-01 00:00:00  0.10\n",
+       "2014-11-01 01:00:00  0.07\n",
+       "2014-11-01 02:00:00  0.05\n",
+       "2014-11-01 03:00:00  0.04\n",
+       "2014-11-01 04:00:00  0.06\n",
+       "2014-11-01 05:00:00  0.10\n",
+       "2014-11-01 06:00:00  0.19\n",
+       "2014-11-01 07:00:00  0.31\n",
+       "2014-11-01 08:00:00  0.40\n",
+       "2014-11-01 09:00:00  0.48"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Dữ liệu gốc so với dữ liệu đã được chuẩn hóa:\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "source": [
+    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
+    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Hãy cùng chuẩn hóa dữ liệu kiểm tra\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load\n",
+       "2014-12-30 00:00:00  0.33\n",
+       "2014-12-30 01:00:00  0.29\n",
+       "2014-12-30 02:00:00  0.27\n",
+       "2014-12-30 03:00:00  0.27\n",
+       "2014-12-30 04:00:00  0.30"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "source": [
+    "# Specify the number of steps to forecast ahead\n",
+    "HORIZON = 3\n",
+    "print('Forecasting horizon:', HORIZON, 'hours')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Forecasting horizon: 3 hours\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "source": [
+    "order = (4, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
+    "results = model.fit()\n",
+    "\n",
+    "print(results.summary())\n"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "                                     SARIMAX Results                                      \n",
+      "==========================================================================================\n",
+      "Dep. Variable:                               load   No. Observations:                 1416\n",
+      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.239\n",
+      "Date:                            Thu, 30 Sep 2021   AIC                          -6942.477\n",
+      "Time:                                    14:36:28   BIC                          -6911.050\n",
+      "Sample:                                11-01-2014   HQIC                         -6930.725\n",
+      "                                     - 12-29-2014                                         \n",
+      "Covariance Type:                              opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.8403      0.016     52.226      0.000       0.809       0.872\n",
+      "ar.L2         -0.5220      0.034    -15.388      0.000      -0.588      -0.456\n",
+      "ar.L3          0.1536      0.044      3.470      0.001       0.067       0.240\n",
+      "ar.L4         -0.0778      0.036     -2.158      0.031      -0.148      -0.007\n",
+      "ar.S.L24      -0.2327      0.024     -9.718      0.000      -0.280      -0.186\n",
+      "sigma2         0.0004   8.32e-06     47.358      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):              1464.60\n",
+      "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
+      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.02\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tạo một điểm dữ liệu kiểm tra cho mỗi bước HORIZON.\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "source": [
+    "test_shifted = test.copy()\n",
+    "\n",
+    "for t in range(1, HORIZON):\n",
+    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
+    "    \n",
+    "test_shifted = test_shifted.dropna(how='any')\n",
+    "test_shifted.head(5)"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "      <th>load+1</th>\n",
+       "      <th>load+2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.33</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.29</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>0.57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     load  load+1  load+2\n",
+       "2014-12-30 00:00:00  0.33    0.29    0.27\n",
+       "2014-12-30 01:00:00  0.29    0.27    0.27\n",
+       "2014-12-30 02:00:00  0.27    0.27    0.30\n",
+       "2014-12-30 03:00:00  0.27    0.30    0.41\n",
+       "2014-12-30 04:00:00  0.30    0.41    0.57"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "source": [
+    "%%time\n",
+    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
+    "\n",
+    "train_ts = train['load']\n",
+    "test_ts = test_shifted\n",
+    "\n",
+    "history = [x for x in train_ts]\n",
+    "history = history[(-training_window):]\n",
+    "\n",
+    "predictions = list()\n",
+    "\n",
+    "# let's user simpler model for demonstration\n",
+    "order = (2, 1, 0)\n",
+    "seasonal_order = (1, 1, 0, 24)\n",
+    "\n",
+    "for t in range(test_ts.shape[0]):\n",
+    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
+    "    model_fit = model.fit()\n",
+    "    yhat = model_fit.forecast(steps = HORIZON)\n",
+    "    predictions.append(yhat)\n",
+    "    obs = list(test_ts.iloc[t])\n",
+    "    # move the training window\n",
+    "    history.append(obs[0])\n",
+    "    history.pop(0)\n",
+    "    print(test_ts.index[t])\n",
+    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2014-12-30 00:00:00\n",
+      "1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
+      "2014-12-30 01:00:00\n",
+      "2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
+      "2014-12-30 02:00:00\n",
+      "3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
+      "2014-12-30 03:00:00\n",
+      "4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
+      "2014-12-30 04:00:00\n",
+      "5 : predicted = [0.3  0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
+      "2014-12-30 05:00:00\n",
+      "6 : predicted = [0.4  0.55 0.66] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
+      "2014-12-30 06:00:00\n",
+      "7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
+      "2014-12-30 07:00:00\n",
+      "8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
+      "2014-12-30 08:00:00\n",
+      "9 : predicted = [0.75 0.8  0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
+      "2014-12-30 09:00:00\n",
+      "10 : predicted = [0.77 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
+      "2014-12-30 10:00:00\n",
+      "11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
+      "2014-12-30 11:00:00\n",
+      "12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
+      "2014-12-30 12:00:00\n",
+      "13 : predicted = [0.7  0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
+      "2014-12-30 13:00:00\n",
+      "14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
+      "2014-12-30 14:00:00\n",
+      "15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
+      "2014-12-30 15:00:00\n",
+      "16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
+      "2014-12-30 16:00:00\n",
+      "17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
+      "2014-12-30 17:00:00\n",
+      "18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
+      "2014-12-30 18:00:00\n",
+      "19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
+      "2014-12-30 19:00:00\n",
+      "20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
+      "2014-12-30 20:00:00\n",
+      "21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
+      "2014-12-30 21:00:00\n",
+      "22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
+      "2014-12-30 22:00:00\n",
+      "23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
+      "2014-12-30 23:00:00\n",
+      "24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
+      "2014-12-31 00:00:00\n",
+      "25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
+      "2014-12-31 01:00:00\n",
+      "26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
+      "2014-12-31 02:00:00\n",
+      "27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
+      "2014-12-31 03:00:00\n",
+      "28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
+      "2014-12-31 04:00:00\n",
+      "29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
+      "2014-12-31 05:00:00\n",
+      "30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
+      "2014-12-31 06:00:00\n",
+      "31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
+      "2014-12-31 07:00:00\n",
+      "32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
+      "2014-12-31 08:00:00\n",
+      "33 : predicted = [0.79 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
+      "2014-12-31 09:00:00\n",
+      "34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
+      "2014-12-31 10:00:00\n",
+      "35 : predicted = [0.8  0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
+      "2014-12-31 11:00:00\n",
+      "36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
+      "2014-12-31 12:00:00\n",
+      "37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
+      "2014-12-31 13:00:00\n",
+      "38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
+      "2014-12-31 14:00:00\n",
+      "39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
+      "2014-12-31 15:00:00\n",
+      "40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
+      "2014-12-31 16:00:00\n",
+      "41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
+      "2014-12-31 17:00:00\n",
+      "42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
+      "2014-12-31 18:00:00\n",
+      "43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
+      "2014-12-31 19:00:00\n",
+      "44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
+      "2014-12-31 20:00:00\n",
+      "45 : predicted = [0.7  0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
+      "2014-12-31 21:00:00\n",
+      "46 : predicted = [0.6  0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
+      "CPU times: user 12min 15s, sys: 2min 39s, total: 14min 54s\n",
+      "Wall time: 2min 36s\n"
+     ]
+    }
+   ],
+   "metadata": {
+    "scrolled": true
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "So sánh dự đoán với tải thực tế\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "source": [
+    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
+    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
+    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
+    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
+    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
+    "eval_df.head()"
+   ],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>h</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2014-12-30 00:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>3,008.74</td>\n",
+       "      <td>3,023.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2014-12-30 01:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,955.53</td>\n",
+       "      <td>2,935.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2014-12-30 02:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,900.17</td>\n",
+       "      <td>2,899.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2014-12-30 03:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,917.69</td>\n",
+       "      <td>2,886.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2014-12-30 04:00:00</td>\n",
+       "      <td>t+1</td>\n",
+       "      <td>2,946.99</td>\n",
+       "      <td>2,963.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            timestamp    h  prediction   actual\n",
+       "0 2014-12-30 00:00:00  t+1    3,008.74 3,023.00\n",
+       "1 2014-12-30 01:00:00  t+1    2,955.53 2,935.00\n",
+       "2 2014-12-30 02:00:00  t+1    2,900.17 2,899.00\n",
+       "3 2014-12-30 03:00:00  t+1    2,917.69 2,886.00\n",
+       "4 2014-12-30 04:00:00  t+1    2,946.99 2,963.00"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Tính **lỗi phần trăm tuyệt đối trung bình (MAPE)** trên tất cả các dự đoán\n",
+    "\n",
+    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "source": [
+    "if(HORIZON > 1):\n",
+    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
+    "    print(eval_df.groupby('h')['APE'].mean())"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "h\n",
+      "t+1   0.01\n",
+      "t+2   0.01\n",
+      "t+3   0.02\n",
+      "Name: APE, dtype: float64\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "source": [
+    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "One step forecast MAPE:  0.5570581332313952 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "source": [
+    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Multi-step forecast MAPE:  1.1460048657704118 %\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Vẽ biểu đồ dự đoán so với thực tế cho tuần đầu tiên của tập kiểm tra\n"
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "source": [
+    "if(HORIZON == 1):\n",
+    "    ## Plotting single step forecast\n",
+    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
+    "\n",
+    "else:\n",
+    "    ## Plotting multi step forecast\n",
+    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
+    "\n",
+    "    fig = plt.figure(figsize=(15, 8))\n",
+    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
+    "    ax = fig.add_subplot(111)\n",
+    "    for t in range(1, HORIZON+1):\n",
+    "        x = plot_df['timestamp'][(t-1):]\n",
+    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
+    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
+    "    \n",
+    "    ax.legend(loc='best')\n",
+    "    \n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "No handles with labels found to put in legend.\n"
+     ]
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "source": [],
+   "outputs": [],
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, chúng tôi khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "c193140200b9684da27e3890211391b6",
+   "translation_date": "2025-09-06T14:00:04+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/translations/vi/7-TimeSeries/2-ARIMA/working/notebook.ipynb b/translations/vi/7-TimeSeries/2-ARIMA/working/notebook.ipynb
new file mode 100644
index 000000000..17d58e045
--- /dev/null
+++ b/translations/vi/7-TimeSeries/2-ARIMA/working/notebook.ipynb
@@ -0,0 +1,59 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": 3
+  },
+  "orig_nbformat": 2,
+  "coopTranslator": {
+   "original_hash": "523ec472196307b3c4235337353c9ceb",
+   "translation_date": "2025-09-06T14:00:55+00:00",
+   "source_file": "7-TimeSeries/2-ARIMA/working/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Dự báo chuỗi thời gian với ARIMA\n",
+    "\n",
+    "Trong notebook này, chúng ta sẽ thực hiện:\n",
+    "- chuẩn bị dữ liệu chuỗi thời gian để huấn luyện mô hình dự báo chuỗi thời gian ARIMA\n",
+    "- triển khai một mô hình ARIMA đơn giản để dự báo các bước tiếp theo trong HORIZON (từ thời điểm *t+1* đến *t+HORIZON*) trong chuỗi thời gian\n",
+    "- đánh giá mô hình\n",
+    "\n",
+    "Dữ liệu trong ví dụ này được lấy từ cuộc thi dự báo GEFCom2014. Nó bao gồm 3 năm dữ liệu tải điện và nhiệt độ theo giờ từ năm 2012 đến 2014. Nhiệm vụ là dự báo các giá trị tải điện trong tương lai. Trong ví dụ này, chúng ta sẽ minh họa cách dự báo một bước thời gian tiếp theo, chỉ sử dụng dữ liệu tải điện lịch sử.\n",
+    "\n",
+    "Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli và Rob J. Hyndman, \"Dự báo năng lượng xác suất: Cuộc thi Dự báo Năng lượng Toàn cầu 2014 và xa hơn\", Tạp chí Quốc tế về Dự báo, tập 32, số 3, trang 896-913, tháng 7-tháng 9, 2016.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install statsmodels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/7-TimeSeries/3-SVR/solution/notebook.ipynb b/translations/vi/7-TimeSeries/3-SVR/solution/notebook.ipynb
new file mode 100644
index 000000000..a3206fd3d
--- /dev/null
+++ b/translations/vi/7-TimeSeries/3-SVR/solution/notebook.ipynb
@@ -0,0 +1,1019 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trong sổ tay này, chúng ta sẽ trình bày cách:\n",
+    "\n",
+    "- chuẩn bị dữ liệu chuỗi thời gian 2D để huấn luyện mô hình hồi quy SVM  \n",
+    "- triển khai SVR sử dụng kernel RBF  \n",
+    "- đánh giá mô hình bằng biểu đồ và MAPE  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nhập các mô-đun\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Tải dữ liệu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bây giờ, bạn cần chuẩn bị dữ liệu để huấn luyện bằng cách thực hiện lọc và chuẩn hóa dữ liệu của mình.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training data shape:  (1416, 1)\n",
+      "Test data shape:  (48, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chuyển đổi dữ liệu để nằm trong khoảng (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 00:00:00</th>\n",
+       "      <td>0.101611</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 01:00:00</th>\n",
+       "      <td>0.065801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 02:00:00</th>\n",
+       "      <td>0.046106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 03:00:00</th>\n",
+       "      <td>0.042525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-01 04:00:00</th>\n",
+       "      <td>0.059087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-11-01 00:00:00  0.101611\n",
+       "2014-11-01 01:00:00  0.065801\n",
+       "2014-11-01 02:00:00  0.046106\n",
+       "2014-11-01 03:00:00  0.042525\n",
+       "2014-11-01 04:00:00  0.059087"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 00:00:00</th>\n",
+       "      <td>0.329454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 01:00:00</th>\n",
+       "      <td>0.290063</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 02:00:00</th>\n",
+       "      <td>0.273948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 03:00:00</th>\n",
+       "      <td>0.268129</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-30 04:00:00</th>\n",
+       "      <td>0.302596</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         load\n",
+       "2014-12-30 00:00:00  0.329454\n",
+       "2014-12-30 01:00:00  0.290063\n",
+       "2014-12-30 02:00:00  0.273948\n",
+       "2014-12-30 03:00:00  0.268129\n",
+       "2014-12-30 04:00:00  0.302596"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "Đối với SVR của chúng tôi, chúng tôi chuyển đổi dữ liệu đầu vào thành dạng `[batch, timesteps]`. Vì vậy, chúng tôi định hình lại `train_data` và `test_data` hiện có sao cho có một chiều mới đại diện cho các bước thời gian. Trong ví dụ của chúng tôi, chúng tôi chọn `timesteps = 5`. Vì vậy, đầu vào cho mô hình là dữ liệu của 4 bước thời gian đầu tiên, và đầu ra sẽ là dữ liệu của bước thời gian thứ 5.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1412, 5)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=np.array([[j for j in train_data[i:i+timesteps]] for i in range(0,len(train_data)-timesteps+1)])[:,:,0]\n",
+    "train_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(44, 5)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=np.array([[j for j in test_data[i:i+timesteps]] for i in range(0,len(test_data)-timesteps+1)])[:,:,0]\n",
+    "test_data_timesteps.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 4) (1412, 1)\n",
+      "(44, 4) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train, y_train = train_data_timesteps[:,:timesteps-1],train_data_timesteps[:,[timesteps-1]]\n",
+    "x_test, y_test = test_data_timesteps[:,:timesteps-1],test_data_timesteps[:,[timesteps-1]]\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = SVR(kernel='rbf',gamma=0.5, C=10, epsilon = 0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVR(C=10, cache_size=200, coef0=0.0, degree=3, epsilon=0.05, gamma=0.5,\n",
+       "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit model on training data\n",
+    "\n",
+    "model.fit(x_train, y_train[:,0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Thực hiện dự đoán mô hình\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1412, 1) (44, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = model.predict(x_train).reshape(-1,1)\n",
+    "y_test_pred = model.predict(x_test).reshape(-1,1)\n",
+    "\n",
+    "print(y_train_pred.shape, y_test_pred.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)\n",
+    "\n",
+    "print(len(y_train_pred), len(y_test_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)\n",
+    "\n",
+    "print(len(y_train), len(y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1412 44\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)].index[timesteps-1:]\n",
+    "test_timestamps = energy[test_start_dt:].index[timesteps-1:]\n",
+    "\n",
+    "print(len(train_timestamps), len(test_timestamps))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LAH+xoSNkGIZhGIZhGIZhtp4oAgAcbOwEut0tPhiGYRiGYZ6MDJNZzTAMwzAMwzAMw1zsRBEEACEUi9UMwzAMw2wKLFafA7Zt47nPfS6uu+46/MiP/Ag6nc45t/WTP/mTuOmmmwAAb3zjG/Hwww+v+dqvfOUruPPOOzf8N6688krMcqYcwzAMwzAMwzCjEEUQQsERGWQ72uqjYRiGYRjmSQiL1edAqVTCfffdhwcffBCe5+F973vfsuellOfU7gc/+EFce+21az5/rmI1wzAMwzAMwzDMyJgYkLITo91It/hgGIZhGIZ5MsJi9Yi87GUvw4EDB/CVr3wFL3vZy/C6170O1157LdI0xa/92q/h+uuvx7Of/Wy8//3vBwAopfALv/ALeMYznoFXvepVmJ6e7rX1Pd/zPbjnnnsAAJ/73Ofw/Oc/H895znPwfd/3fTh8+DDe97734V3vehee+9zn4vbbb8fMzAx++Id/GNdffz2uv/563HHHHQCAubk5vPrVr8aznvUsvPGNb4SOEWcYhmEYhmEYhhmBOAYAVNwInSaL1QzDMAzD0LOhAovMcqSUuPnmm3HDDTcAAL75zW/iwQcfxFVXXYUPfOADGBsbwze+8Q1EUYTv/u7vxqtf/Wp861vfwmOPPYaHH34YZ86cwbXXXouf+qmfWtbuzMwMfuZnfga33XYbrrrqKszPz2NychJvfvObUa1W8au/+qsAgH//7/89fvmXfxkvfelLcfToUbzmNa/BI488gt/5nd/BS1/6Uvyv//W/8E//9E/40Ic+dN4/G4ZhGIZhGIZhnmSEIQDjrG6xIYZhGIZhGHq2vVh9ww0AZRzzjh3A5z63/mu63S6e+9znAtDO6p/+6Z/GnXfeiRe96EW46qqrAAC33HIL7r///l4e9dLSEvbv34/bbrsNb3jDG2DbNi655BK88pWvPKv9u+66Cy9/+ct7bU1OTq56HF/4wheWZVw3Gg20Wi3cdttt+Pu//3sAwPd///djYmJiQ58BwzAMwzAMwzDMWeTOaidGJ7SAJAFcd4sPimEYhmGYJxPbXqweJCxvBnlm9UoqlUrv30opvOc978FrXvOaZa/553/+Z7LjyLIMd911F4IgIGuTYRiGYRiGYRjmLLJMi9MAyiWFtvSBbpfFaoZhGIZhSOHM6k3iNa95Dd773vciMQO6xx9/HO12Gy9/+cvx8Y9/HGma4tSpU/jyl7981u+++MUvxm233YZDhw4BAObn5wEAtVoNzWaz97pXv/rVeM973tP7ORfQX/7yl+OjH/0oAODmm2/GwsLCprxHhmEYhmEYhmEuEoyrGsJCpZyhIz0tVjMMwzAMwxDCYvUm8cY3vhHXXnstnv/85+O6667Dm970Jkgp8UM/9EN42tOehmuvvRY//uM/ju/6ru8663d37tyJD3zgA/i3//bf4jnPeQ5+9Ed/FADwAz/wA/iHf/iHXoHFP/mTP8E999yDZz/72bj22mvxvve9DwDwlre8Bbfddhue9axn4e///u9x+eWXn9f3zjAMwzAMwzDMk4woglKAEgLlEtBOWKxmGIZhmFtv3eojePIhlLpwC2O88IUvVPfcc8+yxx555BFcc801W3RETy74s2QYhmEYhmEYZiimpyF/6y14yT/9Fn7kBQdxWecxvP5PXwrwfIJhGIa5iLn6auDAga0+igsHIcS9SqkXjtIGO6sZhmEYhmEYhmGY9YkiJJkN11EoV9DPrCbgiSeAN72JpCmGYRiGOW8oBZw4of/P0MFiNcMwDMMwDMMwAzl5Evi939vqo2C2jDjWYrWrUKlapJnVi/c+gf13nNFFHBmGYRhmm9DpAGHYqz/MEMFiNcMwDMMwDMMwA5mZAW6/fauPgtkykqTvrK5aaBOK1fFHb8LpEylw990k7TEMwzDM+WBuTv8/DLf2OJ5sbEux+kLO2d4u8GfIMAzDMAzDbAQpgTNntvoomC1DSi1W2wqVmnFWxzFJ03Fq41RnDFhaImmPYRiGYc4HuVjN9YZp2XZidRAEmJubY7F1BJRSmJubQxAEW30oDMMwDMMwzDaBxeqLnDQtZFYLtBMfiCKSpqPMwWJcQZjYJO0xDMMwzPlg7owEwM5qapytPoCNsm/fPhw/fhwzMzNbfSjbmiAIsG/fvq0+DIZhGIZhGGabIKWOAskywNp2lpeLDyl1hmapRNRgHgPiFpzVFGJ1liFO9bT09LyHK0dvkWEYhmE2n6NHMfdHtwB4I4vVxGw7sdp1XVx11VVbfRgMwzAMwzAMc1GRJECa6i2vO3du9dEwg7j5ZuBrXwPe9jaiBnvOavQzqyliQLpdxJkRq88IFqsZhmGY7cHtt2OuW4ZAhjDkVXxK+NNkGIZhGIZhGGYg8r4HAQBnjtJEPzCbSxQBjz9O2KCUkJkFx1GojNl0mdXtNuLURtUNcer06M0xDMMwzHlh927MRRXsLjURtuRWH82TCharGYZhGIZhGIYZiLzlSwCAM5/75hYfCTMMaQrs30/YYF5g0QHKdRdt6ZOJ1VHm4IrqHE5Pc2Y1wzAMs01wHMyHFVxaWUR4dHqrj+ZJBYvVDMMwDMMwDMMMRGYWSnaM6YPtrT4UZgjSMzM4sD8DWV36XKx2gUrdpiuw2G4jTh1cUZ3H6dltl1LJMAzDXKxIibmogn2VBYRHuAI1JSxWMwzDMAzDMAwzEKksXFpZwJmT6VYfCjME6T/8IzpdC6cfmqNpsCBWl+oubQxI5mAqaKHVFqO3xzAMwzDnAykxF1a1s3q2tdVH86SCxWqGYRiGYRiGYQaSZDYurSzizPTFKyjeeSfw0ENbfRTDkSoLjkix/6tEbq+8wKIL2CUPmRI0YnWngyh1UHdDhF0qGzjDMAzDbDJSopkE2Bk00W1nW300TypYrGYYhmEYhmEYZiASLnYGLSx2faDb3erD2RJuvx34xje2+iiGI1UWrqrP4uDDIU2DBWc1fF8/RhED0mohzmzUvRAh1+5kGIZhtgtSQioLNS9E2GGxmhIWqxmGYRhmizh6FLj55q0+CoZhmCHIMkgJTAUtLMUlYHFxq49oS5AS6HS2+iiGQCnIzMLOoInFU0QLC7lY7QHwPAih6GJAUgc1NyTRvhmGYRjmvJCmkJmFqhOxWE0Mi9UMwzAMs0Xcc8/FLVa320BIZPhjGGaTiWNIZWPKb6ORBBetszpJtslbzzKkysKk30FjmtpZLQDP049RqMudDuLM0c7qxNYrAgxDxPw8cOONW30UzLC0WsD09FYfBcMMSZIgVRbKTswxVsSwWM0wDMMwW8TMzDYRPTaJP/9znkAyzLYhjpFkNnbkzuoLvPNSCvj85+nb3TbOaimRKgsTfhuNxQzICBxfRWd1HgMSx/rDHoUwNJnVXUSpu00+YGa7cPTo9hlrSAn84R9u9VFsLbfcAvzZn231UTDMkKS64HTgJCxWE8NiNcMwDMNsERe7WB2GwNzcVh8FwzBDEYaQmc5l7EjvghcUowh49auB97+ftt0kueDfusaI1eNeF42YyAkvJaSy4bgCsG0IAWSp6k3WR2m3l1mdOhf3jfEiJ4qA7/5u2jaTZPs4dcMQeOc7R1//2c5ICSwtbfVRMMyQSAkBoGSzWE0Ni9UMwzAMs0Vc7GJ1kgALC1t9FAzDDEUcQyoLrmWEyQu880oS4OlPV/jMZ2jb3V7OaoEJv4Nm4uvcpVFJU+OsFgCAwEkRpc7oudVJgjh1UPeMs5oiB5vZlnz0o8Cdd46+/lFESj3e2g4kCTA7C5w5Q9vu4uI26bfAYjWzzTCxVYGdIIzEFh/MkwsWqxmGYRhmi5iZoc9s3k5Rn1LqLEmGYbYBUQSZ2XCEiZO40MXqL38VU41DaJyhPc5tk1ktJdLMwoTfQSMu0YjVvRgQPYUM3BRdSSAuS2liQIyzmsXqi5YbbwSe9jSdW0yFlNvHWZ2P4e6/n7bdD3wA+Nu/pW1zs0hTFquZbUQuVjsJwtiiidxiALBYzTAMwzBbxmY4q3/914GPfIS2zc2CndUMs42IIsjMgmNlEACy9oWt2MqbPoUxt4POEVqVans5q01mdRIQi9X6x8DLEKbu6EUWkwRx5qBcsyEzm8Xqi5hOW+HyyxWpWJ0kQKNBUwt0s8nF6gceoG03joGHH6Ztc7NgsZrZVvSc1VIv3nLleDJYrGYYhmGYrWB2FnPHOuRidbMJ/M7vbA+HdZKws5phtg1RhCSz4YgUVTdEayHZ6iNal2TPZXCFyRIgnDxut8zqmhuhTRUDkovVvg0AKPmpFqspYkAyG17VqOAsVl+cnDoFeeAQxjunyJ3VwPaIAkkW27hib4QHHqDNvpWSxer/8B+4TgqzCeQFFu1E3w+3xdar7QGL1QzDMAyzFbzzncDsLOJFAgGhQD4pO3yYtNlNgZ3VDLONiGNIZcO1Uox5XSzNE4bKbgJJUOvnaz/xBFm7281ZbeexLRQH3XNWm8zq3FlNlFnt1Xz9M4vVFycPPIA0ExifP4jWHJ0NOjHrattBrJY3/QMuV0dx5gH6HSHbRazerMzqI0eAb32Lvl3mIqeYWc1iNSksVjMMwzDMFqAWlyCEonG7FUgaXeyYSNFskja7KXBmNcNsI/IYkGqgxeqFCzuXMQlTOJY+RtWg6xC3k7NaZlqsJrvX5AUWfSNW+0pvex41X0FKRJkWq4VQ2yOvgaGnVEKmLNS9EM27HyFrVsa6H9gOudXywccwFbSwdILQWg493jp1anvEa2yWs1pK4JvfpG+XuchZJlY7HANCCIvVDMMwDLMFNJIAdTekdZAtLEDe9yAmpx/ZFmI1O6sZZhsRRZDKglMvY8wLsbRIu02dGhmlcK0UJTtBtECnLku5TYxTxlndE+ybBOJXkiwrsFjyFamz2h8L9M/srL44kRIKQM0N0Tq+SNZs8k+3YMpvYWY/XZsAoDahC0x2XYqqEyFKnb4lnAApgac+FXjsMbImN43NFKvZWc2QY/qtUt3V90MWq8lgsZphGIZhtoDZZBw7ghaUzOhmPDMzSDIbk24TzTMXuPVPKSQP70dzKePC2QyzHcgzq+tl7awmFBP+/M/po4uSKINrpah7XTSm6SaP2zEGJLATRA0Ct3LurA70FDIIQFhg0e7HgBA6q9/yFuDb3yZrblORshd/enFixNmqG6G1RPdByEPHcEllEdP3HCVrEwB+8RfpxU/plvrxRVSdolKQ334Qe5yZbbGbLU31ehW15pdl20OsZ7YZubN6zOcYEGJYrGYYhmGYLaBbmkTZMe6xxUWaRpWCVBYm/A4aj52iaXOz6HQgz8xiwmuhsXRhOzQZhoGJAbHh1ksY87poNAVZ03fdBdx/P1lzAIAkSuFYKWpuhMYsnVN3O8WApErAtpT+DOYIXJomWsTxdIHFwM90DMgoCqtSOgYkdeCNlfRDEd33deQI8NWvkjUHQL/dP/gD2jYB4E//FPjEJ+jb3TYY0afqhmg16cYFUtnYW17CzCxdnwUArRZw4ABpk8vii8i2yLVaSE7OYqJzAs35C7swLtCvvULtrhZC/8cwpEgJAcAfCxBKFqspYbGaYRiGYbYAmSg4VqrzOakmJHGMJLMx4XXQfOICD2fsdpFkNnaXGpg/vh2UH4a5yIljLVSOV7WzumVTNo0TJ8iaAwDIWPWd1RRCbd5uO0QSbQP7q5RIMwt2taQ/g3lJ0qbOrDbO6jwGZBSxOk3NQqsNu+zDs1LEHYJjNcQxcM89ZM0B0FrE7/1eX1Sjot0GTp6kbXNbkSQQAKpORBpllmQ2pvw2+cK4lMDx46RNQsYZHCuFIzIkTSJrcZJAZsbIcITWWv3KV5I2B0B3CZZFLFa328DcLBwZkl+3zEWOuf85YxWkyuIYEEJYrGYYhmGYLSBJANdKYYsMskm0Ch/HkJmNyaCN5gzdYEkp4Kd+CvjlXyZrsidW7yo1sXCSxWqGueAJQ51ZPVbRzseuBaoMnyShF6t7MSBuSCPUAjoK5dGDcFvbIGw/jwGpllB3QzSXCL6rFWJ1L7N6FPXH/K4QAsL34NsJojadmpQkwL33kjXXa7PdBh54gL7dM2do29xW5JnVXohWm84Cmwu11LU8pASOHSNu02Ttj/sdLE0TxeEkid5153XQPEZrV370UfromjRJMTmpaMXqL3wBaLdRmT1MXdecuchRiblfVSpQABcIJoTFaoZhGIbZAmSi4FoZSk6C7iLdhCTJbEz6HRphwhDHehs16cS800GS2dgZNLF4ml0IDHPBY3ZuOCUHpQDoSo9sUkYuViuFJMrgiAx1L0Rjkag/XFrSgr1IoeQF7q7OxepaWX8GDZo2dWZ1HgOiEKbOaGpVXkROCMDzENgSYYfo+1IK8fEzWJhJSKNbcm3+zjvp2gT0RzF9gW+K2lTyzGonohOrs8yMi9potmmlj80Qq/N+a8zrYnGOqI8pGhlOERRaLRBFdEl2Oemtd2AymSaNb8r7qKoToUX4ERw7Bvzu79K1x2w/0kTvhkClAgFwgWBCWKxmGIZhmC0gSQBHpAjsBGGDaGATx5DKwqTfRqMJssKNcQzs2AHSAT46HcjMxpjXRWeOxWqGueBJU8jMhuPZKJWgs4qJxGryGBCTrayd1V0aoRYAmk0kmY2yEyNZoBV9yMkzq8u+/gzaBLEtvQKLfbG6K73RxOpc+RUAfF8Xg6RyVj/6KOITM7hCHcHMDE2TgL5/79sHfP3rdG3m7V7UYnUvszpCs2PTjGGiqDcuanZtUtfjpjqrvS6W5oiuAzM2nPA6pLvuAP0ZzM2RNgk538Ck38bSfsKLoV4HYIp3nqbru6engc9+lqw5ZhsiY73AhEpFP8AxIGSwWM0wDMMw5xulejEgJTtBd4lQrM4dRJFHVgUsmV1COV5AEhPmPZoYkLoborPILgSGueCRElJZcH0LQQAd/0DkICJ3VhsXuOsJ1P0QjRZRZEmjYRbZQnRmLvC95Lmzuuyj5kVodJzRPwOzeycXq0sljB4DssJZ7VM6q1stxKmDcb9D6qxOEuDyyxSpAA7oj/GijgHJndVuhFZCtHMjinQtD7+DZhyQ2oCThDhjXCmTWZ1h3OtgcYFozGXGhhN+B03CeBVAn7Ozs4QNKoXULC4snSYsVFc8t56gu8jiGHjoIbJELGYbktcg6sWAsLOaDBarGYZhGOZ8k6Y911/JidFtEBX/ygss5pMyosC/5G9uhDt9AlgkzGk1MSB1j8VqhtkW9JzVFkplgW5K56xOWhG6VAIl0BNVHVegXlVoxD5IgkobDSSZjZobojt3gWftSwmZ2bA9G/WyRCMJRp9Er+Kspo8BSRCFRCKdbSM2O3jI4rYAJLfeicmTD6A5QyimgZ3VkBICQM0N0UoCmms2DCEzGyVH9wno0n1nUupCgAlV/VYTs+OIVBexXaQUq01ud4euMC6g3zupszpNkSqBqaCFpTOEDlXT91XdCK3DdOp6kmhfyOHDZE0y2wmlIBMFWyigXIZrpUg6dAWdL3ZYrGYYhmGY800+IbEyBLakE6vNjKnudbUwQSVWP3YQniVBWp3IOKvHvC46DS7NzjAXPCZaw/FtLVZLj8ZBND2N5ImjGO+cpCt8ZcQZ1xOojwk04hJNjlGjAQUteHTmaYVKcpIEqRJwPAv1Sqo/g1G2J2cZkGWIMweer92ZQZ5dTuCsVrDondVhqJ3VXhedM3T3r+Sz/4K620X3xDxZm0BfrCZK8Np+mAKL2lnt0+wOC0O9wGKZBRVKsTrJcOmlim6BIYoglW0KLHaxuETkgs5jQPyO3nU3yvW6AnJndRwjzSxM+p1NEqtDtGbp2s1vgQ89RNYks53IMshMwLEyoFRCxYnRblzg9Sy2ESxWMwzDMMz5Ji9SZZzVYYsul1ABqLkRmpRi9diO/kSPim4XUlna8dZksXo78MEPAkeObPVRbC0/8RNbfQTDc//9oxlezyJfZMud1VSZ1SdOIM5sXBrMY/oMnZMwyWy4ro4qbcQBjUhlwq9LTozOPJ1Td1NIUx0D4tmoV1J9Txji+/rCF9bQsszJ1E4DVKp9sTpMXXJnNVnkp1kUrbm0sS1S6Z1RlKIfoD+Kchl0GevbjUJUQzMJyMRqmemiqEIoOrG61YJ87CB2tI9SDbX6RWzzGJAGkVTTiwFp032u0OtXShE7q5MEUuk4u6VZQodqLlY7EVpLdDfGJAGuvhp48EGyJpnthJRIMwuOnQG+j4oTod26WFcb6WGxmmEYhmHON3n2a55ZTSXWmsF4pazQkR5ZNmNSn4JrpXBEhuQMkZOsFwPSRafJLoTtwF130ReT2m7cfvtWH8Hw/Nf/CnziE4QNpqnutwIbQcXWIiWFWG3bSDIbO4IWlo4sjt4esDyzug6904RQrC47MTrzF3gRJTOJtl0L9ZrSgv0QKvDbfzfGmQ/+I3D69FntAUAn9VAu64dKZTF6DIiUSDMB21aA78O3JaIukbPafOdlJyaNbUnqhQVcKhu0UkgefAz7qosXb261Oce8soModWhE1YJbGQBUh0isfuIJSAlMdk+gsUh0vpodIY4rdAxIiyiywzirx72udqwTCfb5Wg25s1oJLVYvKbrrKx8fuzFaTToxMf7Gt7EvO4LZ02y6uChJU0hlwzH3r7ITo9NmsZoKFqsZhmEY5nzTiwFJUXISdFtEYq0ZjItdO6GUoBOrQ12dvupGaO8nqiZkJqF6YMeVabYDUcRFzjsdspjmTUdK4I/+iLZBmdn9GBCqAovdLpQSGPc6WDpIZNGLYz2B9CzUxu3RIzByGg0I6H6rvUCbtf+qV5E2Z2JAtLO6VlVasB908jabkAeOoPnlbwB/93fLnzPKVFv6qFT0Q0EAhHJ0Z3WS2fActSnOasDcZ2YJxWq/CkdkEABdwb5GA8ncEi7BCUyfvvDvifPzwMGDtG2qWDtpxVhdu6AJY0AcK6O9bj0P0hQCbBwhqucRRXpBsOphzOtiseXQtGuc1YGt+wSqvCUpdZFVUmd1HgMStLEUDbcbZNh2AR0D0iYUE5Pb78J4No/OwdODX8w8+cjj0YxYXXHZWU0Ji9UMwzAMM4D3vIdsbK8xk3PXSvXEnMpFlm+nnpzU/yfa6tkXqwmz/ooiQoe2Oj2zOcQxi9WSOLp9M5FSz/MzKt3LOKsd30ap5uisYgohwfQFY14XjaOLo7cHFJzV0JnVyXCu4oEYZ3XNjdBapHXSHThA2lw/BsS1+rndgz6Dxx6DzMxrH3xQK5I5RqyOUheepx8KypZetBglDkNKRJkDz83oCyx2uxBC6UVhygKL5p7oWCniI6doGjXn7KTfRmuWNg99M5zaX/kK8Cd/QttmlugdXBgb0w9QDLyiqFfQuuaGaM4TRUtEEaT5vhoHiEKr8xiQkouKl6AT2zRRM4V4EQBkY0PZTbB7IsbsNG1x3FSZzOq4RBdZEsawhNIxIC26MWdewLVDZTphthdmp6xj68XWihOjzXMaMlisZhiGYZgBfPKTwDe/Sdhg7lC0FEpOjC6VyyOOtdOrVtM/EymLcaR6zmoysbrorA7FRVxRavsQRdvHVbxZbDexenyc8HjTVAsevo2g6pA6qwGg7oVYOkFQBBEoiNUWahOOzmml6A/Nh1lzQzQJc08BrctRfJw9pESqhM6szgX7QRewcWA2kwDHW+PAHXf0nzOLocLS8dKAdlVSZFaH0kXgqX6BxZjonlBcFF2iy79Noqwvfs7QLeAmma2LQRLmawPADTeQNgdA9y/79xO3GaWwrQyoVvXuMIoLouCsrrshmgu0NULG/S6ds9pcf65voVy1dJwbRWSHiQFxRKrHiFQxIJ/+J+yRxzH3KFWFSRSKQbb1ohmVsB5KOCLT41hCMbF3zdJt3GC2E/l8zgYQBHr3BovVZLBYzTAMwzADiCLgW98ibNCIPm7J0ZnVHSKhNndW1+vwbImoRTM51y6yTDtS5onUlKKIkHhkExJm8+AYkO0lVqcpMDVFWKytEANilzxkSpA7q5fm6SKRZGbB8SzYZV8fK4VAUywAt0S7wCYl/Q6eXgzImIXmMJnVxiXWKO/Biz/9m1C3f7Vvzc/7aKs/fQxKgkSs7kgP5SAFhEDgZYhSt38/GwXznZfsmO4+i76zuuZGaM4R3RONqDrmddGmWhQ2zMzQrwen6SaI1XGmndXVKhwrRdIm+GzzaI2xMmoe4SKT6fvqbheNWaJzoNdv2SjXHS1WU4yN8naNs1q1iGJAvn6vPl8pi82aGJDA1n0RqVidmy46dBJYnDraWd1lgfKiJI8BMTFWFTfS5wIbcEhgsZphGIZhBhCGxGJ1HgNSdvX25C5tARnU69gRtDA7RzN4zl1kVTdCc4HInZaL1RWzjZxUpWE2gzhmZ7WUQIvI/Hs+GBsDlpaIGssLw5YcwPO0Q486BqRNNDXJndW+pe2/wOgrLUr1hNuaG6JJfB6Qn1tSarHad+BWdL7ucM5qC3MTV+NEewKHj9nAww/r5/KDs/pF34KyhS5BZrUWq/Vn63uZLtpIEX9gvvOyE5OKSTJK+85qKrE6d1b7HXQWaDvaMKRfaJQSOHSI5mvqtZlkcKwUKJdRcyO0GwTCcu6sHqvoxQWqRSYz3qp7IRpNonMrj+vwLFRqxllN8cXFse4L6hWdqUskLsugCtcybm0q8mMVxJElRWd1OGJ0Ua9RiThzMO51WKy+WCkWWLQsVPwU7cSjWWxlWKxmGIZhmHVRCipO8OADhJl8+Up82TOTaItmFd5sS0W9jp1BCzMLBMV5lEISF2JAFmmLQZZ3lPWEjMXqCx52VmtNjtpZff/9wKc+RdsmlILqdFEvJXRidZoiUwKW5wC+rx+jdlZTFRTLFwQDS1cBBEY/eQviRs0N0aIS1g1JQixWp2nPXY4g0LEKgz6DJIFUNo7M6yip++Yu09nVQO/gVNFZXba0s3oU4acnVut7YOApXbSR0lntJOiGRGKSUkgSwLEy2gzkMITMndXExTuThHCHhSFN9dd++DBhm1Hac1ZX3RCtBsG4y2RLO+MV1NwQjUWisVxPrO7SidVRBKlMDEjurCaKAQEAMTGuz1miKBQZaAc8qYd0s8TqKDXO6hAt6dN8rlHU2w3BtVcuUpIEMrNgmzXccpChLX12dhDBYjXDMAzDrMdDD8GePgl5mrDcuZQ911+1nKFN6J4BANRq2Bk0MbPkjd6mlEhSqy9WU2yhVaonbrBYvX242J3V+WlLLVYfOADcfTdtm3j0UWBmBmN3fx6NaaIVhlyQtG3A97VAQZRZLYRC3etiqU0kVvccijZQKsEWGWRrxM8hF0+F0IJP1yHd6kvurC7EgAy9uGAm3odnqxirpVqsfuwx/VyrhSSz4Hp9Ucb1LSSZPZqzWkp0Uw/lUu6sVogyGme16nShlOjXRqAgd+1bqY6VoCq0aZzVY14XnUVasVpKerFaSr1zgzIKRCZKZ1ZXKnonV4Pg+gpDXWBxooa616Xrv3sxICEaHXvAi4ck77fyugDEYjXGtVjdmKNZYJFeWS8uAHR9YRzrrH2hd/TFS0T52qHU49iyQishEqu7XcSpjYobQyaKox8uRvLC02boUillek5zMQ+WCWGxmmEYhmHW42tfAwC4cWuk+fgypNTuGReoVoFWEpDmqaJex85SC7NL7uhtGueIZ0ntSGkSDMZ7H6RAMB6wWL1NuNid1Xl0L7VYnSRk9a76LOiCX3W3i6U7Hxq9PaX6161tA55ZCCNyVisltLO6Q9BnAb0iXa6vXcV1L0RzaURHpRFPlRCoVhSaiU92QeQf72bFgCAIYAmFrDOEszqzcWS2jFd8j8C35q8ATp7USmerhY70UCn37wHCsXt/65zJndUmrSXwjbOaQKxOuzEcK9WFjCMiQdHcE3uZ1VSxEgWxmiT+osBmLLKlKXDVVcDx43RtFjOrq05Ecz3kAnAeA9KiW7QATAxI16OJlcizpX0bokwUX2TaBWDE6ogst1sKF46VDte3DIspMulYmb4nzBG5wI1rv7KjpMfcFI5tEzHjWRJQ2SbcyJkLHimRZhbyW2GlrHQMCIvVJLBYzWwJr33tVh8BwzBPRrJs82LCAlvSjUONs9pxhRGraVweWZTo7MBqFTuCFmaawehOj3xi7ig9eaQQq/NJnRCw6lW9PZ1QpZmZIWuKKRDHF7dYnZ+21KKPlJswx01TCKF0DvQMwaRJ5a4xoQvs+T4EABUSZ1ZHXn9VYBQKu1cQBNr9OOr2fxODIoRArS50wUKiLy5fB6COAUmzvrO6NkysQpJAKguHp8t43vMtHEt268cPHgRaLbQTH+VKQezL7WQjOqu1WG1iQHzjrB71Zp5liLsSnpVqZ3Vs05xbRWe1S7AIkhOGkMrSzuoWvVi9GTEg1Sph36UUpFT9zGovpLke8qz98Qqqbog2VVxDLla7XSwlJZoF9xWLbAA2QawO0SSKQkkivbhQshN0Ty2StNmLAXH1tbA0SzCoV0ofq5X1d/NROaszB65lrtftUn2ZocOYj/JbYbkMjgEhhMVqZkt4/HGa8SLDMEyRW24Bfv/3iRsVemJTcmK6SVmep+oJVOuWFqsJJiRpmOhBs+9jZ7WLmW519G36+cS86ukYkDZBlevednoAlYr+N6Gz+tWvJmuKKRBFF/f4e1uJ1eYaG/O6NJnVvQUm87PnwbcTRJ3RRbWsG8EywvpSXKIRZ4pidamkc2VHFetMRIbrZKjVhc49JcpTzbvETYsBCQL9GQwSqYyz88Ssj6kpwA0cxKmt3dW5s7pamD7mQZ2jiNVhqMVqI4L7Pmic1cap7LkK5bKg25odRfo8yMVqqv7AnPcVJybdaJRl+j/yfutr30B15hDCLtGETkrtqLUVEAR0zurcHDBWQdmJ0e4SjGGA5c7qOKAZw5ixoePb/cKwmxADQrXAIkMJx8r0rrsTRMURkkTHgIzX9D1hgeBY0xQy1TnzVr2qI6wondW27P3MXGTkNYjyGJCq0NGOF/NgmRAWq5ktIYq4SCrDbBc+/GHg535ueywwdTrA9DRxo2ZSU7IThB3KSZnO/qzUbRpntVJI4ky7kjwPO8djzITV0QfPUYQ4s+H6NqoVk/U36iBfSmPSFJsiVs/OcnTgZsDOav3/7SJWKyW0kEJR/KuwGwIA4Pu6aF179D4xaUVwrRSlAHSCYppq4csVQBCg5kZojPq9GYemYynUxiw0qeKb0P94SdOQ0lSLPj1ndYSlxoBzwQjcWSYwOQk85coMB5s7emJ1W/oo1wrTx3yGPoqwbETw8phuKwhA46yOY8SpA8/NUKpYOv+XSKxOMhtOVbvVyWIlzLlEWnQZ/XUEcmf1gUOoxvPonqYUKS0tVnt6cbxJkQVtRHC37KLspejENBEziCIoJRDYiS4ySqGsm37L9cTmOas9unM2L1pYcWK0zxDdGHNnNaVYnUeLONDWV4BMrI7NLgulBFSHY0AuOvLMapNgVqkozqwmhMVqZktgsZph6FFqE4p0QYu/n/gEcNtt9G1TkyTA/Dxtm7LRgS0y7ayeo3HRIU1N8S8L1TGbpjJ5PiGzM0AI7ByXmOnWRp/o5M5qXx9rM/FHn5TlrkdbF1ISQpGqNFHE0YHkfP7ziNrJRT3+TvXudFr3K3S/Rb4IUHRWNwmG+0bxUiiI1XaCbmdEQU0pxB1d+EpMTujHiPL7lzmr3S4ag4RaAP/4j+vc6/I+1slQrjtoUxXpQl87Iz23smxZgcWKE6E7KF4iSVB2tLA1MQE841kOHl/cvcxZXa4VimDato5xGsVZ3ROrdQ46mbM6jhFnDjxHoVwxzmqKCy2PxqqVtPDXJhKWe87qCO3EJTu38vkW+SKbslF1I4RNogKTZhHftgH4vt7J1SHou3JndeDoPFmqLfpGABaeUakoxjC5S9Prx4BkbWpnNV1utwylzoF2I7TniYqC5sLyeBX1YaKLhm7TuF8pHev57g0rhWdLJM2LeDX/YiXfEdKLARF6bEBRfJoZXqwWQthCiG8JIT5rfr5KCPF1IcQBIcTHhRCeedw3Px8wz19ZaOO/m8cfE0K8hvzdMNsGFqsZhh4pgZe/XLtKqdvds4deoNkMkqRXV4yMaClEYEstzFCJ1YUYkMqYQ+OszosIGSPSzgmJmZBQrPYExsaF3qY/qiPF5KK6juo5q1WLTqzejGJSFzVLS8BNNyFuS7rdBdsQKbWAty2c1b0t6l0stZ0BLx6CnnDYF6sDO0E4agxIFCFJLXhOBpTLunUKQdEsCLq+zteueyEabXugqPjo/TEevm+NSWYuetmAVS3rreQXslhtBGTh2IDrInAShPGAqV+SoObqz39yEnj68yp4fGk3cOYMsLSEduKhMrZcrLaEQpqM0C/kYvWED0BrdGHqjj5RSBLEqa2d1VUb3dQlc6kmmQ237KBWStGMicTPorNaemQnQ35ukTurlUDNDdGlEqvNuMAxYnXNDWnEauN8dH0L5TLQkS6Zwx6AvikAZGJ1bmRAqYSSEyNsEkyY41gvKo2N6d0ARAssMtY50FUnQmuR6DyIY70jZKKm3fUU99sk6ecK+7qfIam3EIaIU51ZXXZidBYu4tX8i5VeDIgeG1Uq0DEgFLs3mA05q38JwCOFn98B4F1KqasBLAD4afP4TwNYMI+/y7wOQohrAbwewLMA3ADg/wohiMoyM9sNFqsZhh4p9bX1F39B3261uj12NElJ76yOFrvw7QSBk6A7T6Qo5QV/fAtuLYDM7NFFjzxP1daiQS8Le9QvLneR+RbGxkCTKdsTfRRQrcK3JaIlOkeKlNtjcWXbYBYnMgiEs9vjg73vPuDAAdo2pQQmxlI0KYqMrmiXXKyWsl9gsUsgVufCZz5z8Dy926Q94mdhMj9dR/Ucb6pLk1ktM0tnvzoO6qUYjchff/CZZZD/8kU0bvyn1fu4PL6pcKyUmdUenT6pyTItUFkWYNsIbIkwGuCojGM4VobAz7RY/SwXj0VX6JO00zEOaLf/ettGyYkRxSM4NY1YXRo3YnVJIEqd0Sf7SdJzVlvlAJkSpDEgrmfpQpuJTyNUmk6g4kvt/iVaFduU+KIsQ5pZqLgRwhals9qG46i+szocvMA0kDwHOnBQqRJml+fOyclJAEQL7vnYMNA582UnRqdJUGwzP1bfR62S6XOW4KYjoxSOSFFxY7QblGK1BXu8buqk0LQpMwuui96uIJJFgG4XcWbDs6T+rhbZTXvRYQos2svEap/FaiKGEquFEPsAfD+AD5qfBYBXArjJvOSvAPwb8+8fND/DPP995vU/COBGpVSklDoE4ACAFxG8B2aboZSeA/DuCIahRUrg6quBW2+lbTdJtFi9HbJqyWNAlELYiPvO6gWiD2GFewYASQxIkk/0AIgSUd5hQawen7S0WE10rLmzmnqQz2I1Md0ulAJ8SyKa2x4f7C23AF/5Cm2b8p77MDF/EM3Dc7Ttbqaz2g2xFBJMnFY6q11X94nRiM7HYl8QBLpoY4NASCoWWARQL0ndd60nUnW7SDoJGqEP3Hzzmm06jurnnhI6qycmNsdZDVsL9oGdIBwkKpvvuV7VYvU11wCPZM/oPd2p7UZlRWZ1YCcbv80sLOh7k1J9Z/WUvhf6PqGzOrN7AhUAshgQqWy4gY36mKDLLjfH5u2e0GI9obO6ViN2VhunatWJ0KUSq82Cu2MDsCxUA4lmHIzed+X59b6NchlkW/SzKNERZhMTqDgxOvN0hWEdVwClkh4bjSoCK9XvCxwH9ZrSn+uoC21KQUYpXCvV0TUNAlEd6IvV9You3EiRWx7H2rXvAAgCVNyIJl4kX2wNbP1dLbET70Lna18DPvpRwgbzhXFX31tLZUETY8UAGN5Z/ccAfh1AflVPAVhUSuXfwnEAl5p/XwrgGACY55fM63uPr/I7zEWElPq+yc5qhqFFSmB8nF702G7OatIYkDBEFAG+nehiYsRidZ6nCmCoL+7b315nwpmmepujbVxIVMV5CpnV5TGXJqc1SUy+thGrbdpBfpJwDAgp3S6kslB1Q4SL2yMMXEpgcZG4zX++BRN+G80TtPvpk2TzCizW3FCLaQRFUQH0tGo4ju4T4xGFhNz96mZAEOg2F+nF6mopHex2MoJGM/GBu+5atU2p+i5wAaI8WfTv39RitRBquVg9aHEhX+SoKYyP63t/x64jfdPPA69/Pdrf9wM9nR5Az7G9oUWLY8eA3/5t4L3v1Sd+lqGjSijX9LkUlC1dYJEiszp14HmKvFhdYsRP7awOaNo1bYhdO/XPhM5q8vgiKZEqgaoboduiKzydKqvnUKyWM5rdYblbueSgXLXQSWmc1Wk3hmulwMQE6l4XjXkCcaogrPfE6lGd1UoBSunauJZFVxw2TbVIZ2XaAd0g2nGU50uPV3W8SEiwMyjPwTYLV1WHTqyOUwfeZJVmYYHZdI4eBe65h7DBvMCiOU39QOjFVharSRg4shBCvBbAtFLq3vNwPBBC/KwQ4h4hxD0zMzPn408y55l8fMBiNcPQIiVQr5PtSu6x3ZzVi4tARhWr22wiTF3jrI4RNojcv/l2V1NERwg11MThAx8AvvnNNZ7sxYBsjljtlWyIMrULHHpC5sZ6F/UoRboMSm0vZ/V737sNri0zIRvzutti0QrQfcHSEm2bsj6Jkp0gTgkmz8V25SacA2aiZFtKxx+MGlOQX5vCqNW2bZy6I2af5vn9xvEWUG3PNgKV42sBtOyn6Ep3/T7GHEsjLukbycovpZBZjVJJu/6IclqTToJxr00joOTkN8Jlzur1p35ZLCEA/M2fNnqT76uvBp6oPgf43u9FR/p5mYFe2yUnHhwvUuDRP/4cfvOOH8Cxe84Ac3qXQgflngjuB8aZRuKsduC56N8PKWNAAlsLfzGtWI0dO/T/idTlJNFJFeTO6swUWOwSnbN5ZrXZHVarKjKxOlMClufoLfoUbSKPwMiAeh11N0RjkeBzyOt5+FY/BmTUqKUk0V20uUR7YvWo94Qo0t9X7qxuEYnVSaIXLaol1LwIzWhAvz0MvTGnADwPVTdCu0nwfZkCi+5EVbvrKSJbmE1FSuLaTkmCtOCstjxHj7lYrCZhmGXw7wbwOiHEYQA3Qsd/vBvAuBAiH63vA3DC/PsEgMsAwDw/BmCu+Pgqv9NDKfUBpdQLlVIv3Llz54bfEHPhw2L15nDHHeuIWMxFgZTapEt9bcn9h1BdOrEtRKok0boJmUjVaiFKnb6zukH04eYCTaDdM66VIm4NFsLXdWCagmL5RA++D0soZB2iGJCAPrLEdRQgBMYrCU3hRvT1me0iVn/848CJs0ZDFxjdLqLUQd0LEY7qpD1P5AtXlKS1cTiWOcEIFlZyNiMGJAtj2FZhMk7grM6UgJXPHIRAyZVaAB5ldXBFDEjJTtClEKuLBRYBlPxMZ9Wud4M07rtGbITN06eXP58vCLraWV1zIzQXaCak8ubPY3zhMFr7T5G0B6B/jprMat+WA8XqNJJwrBQvfnH/sec+V2fAA1rfWuasHlIE77G4iI98+RKc7tbxP+95HfDQQwCALoK+WF22aZzV5j7reYo2BiTfbVRy4FR8pMoavd18lRXoF+yjigE5ehITzSNoThN2MsYFXXUjdKkMErmr2KgL1SrQkjRiNQDAceBVPR2xQiFWh/paQa2mndUUiwH5OM7XmdUVJ0ZnVOd6miJTArZlFgHGLJrM6tytLDKdWU1VIzuPAQlcVCt0Cxa6CB6AINBOcIrLK18QGy/T5Yszm0qaEovV+a5WI1bDcbQBiYUuEgaOLJRS/10ptU8pdSV0gcQvKaX+A4AvA/h35mU/AeDT5t+fMT/DPP8lpZQyj79eCOELIa4C8DQAd5O9E2bbwGL15nDXXcCXvrTVR/HkJE3pq6hvBlKiN8gnI4qQHDiC6tGHEXZpi4ptBlJq4x9ZbnUU9Z3VToxuh8hBVBRSSiXt8lgaPDE3Na7WfFIqSzsUgZ5LMWqN7k7Tx6onT5ZQo299z9t09Tk1WZOYjyokk/P83rJdxOokAaant/ooBtDtIs4c1NwQsRyhkNp5RMpNcFanQosTANQZui9tM8TqnusvV5dHFavzLd92/z5QciW66YhV702usGcE4JITo9skEICLC4IAyiWlxeoBMSBJZqORBDjeGkd2coVYXdwRUi6jRuislvfch3Gvg9YM4YlQFKuHFJV7543bL6L4nOf0xepOB6vHgAy7iHX0KGbCKn7+2lvx+NJunLlDV0HtZKVeu7ZnI80sGmd1asPzAAQBHJEhaRPsjsrPLU/QObazDCoz9td6XT9GFQPysb/DZDaLxv7Tg188LGbhpuqG2lU/ahFEYLmgCC1WN5OAVKwWvqeFpFEzq7MMMlF68bJS0bUBGgT3RrPrLh8blp1Yd92jfL4mvsjJxepJlyYaKo/rcIXeZdImGhsUnPBarA5G/77yz8BBv3gnkVidZDa8CVN7hSoSh9k0pARIwxtWZFb3OjBCQ8PFzChVUX4DwK8IIQ5AZ1J/yDz+IQBT5vFfAfCbAKCUegjAJwA8DOBzAP6zUoq/xYuQfMzBBRZpSZJt4M47D2yGqHzrrcDv/i59u9QksYKTxQAIRWUzGK26EV0EhuEd7yBtDoC+DqamCHOrw9A4q6WekFPpCPngxhRYHDY/b11n9YoCi3BdBE6CcFSBXUrEqdObPNXcEI2F0bdkyqwvrE+NScyFldG3paI/L90umdVxvA3E6sJ1kGUgzNnZPDbDWS1DCUfofM72E3TCz2ZkVssohS0yYGxMZyu3RndW6y3fBbHaGyJaYxDFhSvfR2BLhBRide7SDHQnUw4ydNPBMSAy01vkf/HO1+Obd54dA9LLpjSLjE2inFaZWah5oT5GKooxILat7wfJ+qKyjDO9ION5vceuvRZ45BFtgv7MZ/TPPSxLi+DSHk5QO3oUM90adu5QeN7UMRx8QPf5nczvi+C5UE7grI4zB54nAN9H2Ylpdkfl96/ApnNs59EHlgJqNbhWimSJxrIsWyEqToxYjlgMdVmjxlntRLoPoHAg9epu6B/LVUu3PYpYXXSsOw7g+1BKjC6A50KtDSAIUPdCNJpUYrWJL3IclD2JTmyPdi3kbZqFxtK4rxfuSGJAbDhlDxUnRrtDJFYXCsNuirPa97W4TuastuGOs1i9XSB3VufjAq8vViuOASFjQ3ctpdRXlFKvNf8+qJR6kVLqaqXUjyilIvN4aH6+2jx/sPD7v6+UeqpS6hlKqVXKbDMXA9vNWf2e99C7szYDKVmsBoBXvIK+zdWiKy9E5L98Ee7D99GGVhunWdWNEDVoP4Q/+zP6c1ZKYNcuWmd1lDkIAoWSHdMJSkmCRPVjQKpDViYf6KzOt9MDfSfdqF9bz1mtxeoxr4ulhREH5KbN3IAwOaG0s5pQrKZ2VifJ5ri1t5Oz2rfMh7sNOsRNEaujFI6VYtJvY/4I3WqIlHrRgnINID9WjI2h4kbozI/4na1cDAMQeNnohYRWZFbrHSw0Ls1lzupguBiQxMSAnOqM4dj+1TOrXSNW19yQTKxOMlsXa6NCqWWiT+6AjpL1M8b1ebPcWX355boo1V/+JfC2twFPeUrhF0wcTDjsosXRo5gJa9j5wy/Hnj3Aqc4YAKAj/V7KFNk26jjWrn3jrC47Mbotgs84vxZMzQkAUF2a68u1M6Bc1uLfELuthmraCeBYKUj3xBix2rV0xATJPSFNkSrRK7DoVxxE6YhitTknFYTedpcvwlCI1Xm+tu/rGJA2QUSWlEiU3c/aD1Ldb41yvLmYZsRqstojuWBfclHxJdox3aKFAADH6bvrSZzVxv2aF1jsECzerHRWE9cMYujZHGe1yUMH+s7q7SJ0XeAQLrEyzHBsN7H65pvphYQkAb7+dfo2WawGDh+m33nTbG6P81V+5atwRAY1T2UrRm9AXnVDcme1lMDdxGFQSaLFajJndRwjlC78sqMzq7t02xxlpqvT6/y8EK3mYNFjOGd1vwCab8nR41uKok8QYNzvYGmRqE2jh0xOgkys3qwYkFtuAf7wD2nbBC5wZ3WS4Ja/b+F3b3waotSBZ28fsXpTYkCMs3rSb2NumjazWhDpPTlpbETHsTEtqs6PeBNLU0SpC9/tK+okzuq8L/AA+L7OrCYUFHOxulSCFn0GOauVhUZaweluHceOrFg9yCelruiL1URrFjKz6MXqHKGFusBN9eLCWp9Bluk4sWJ8DLTWDQD33gu88IVn/9rAdoscPYq29FB57tOw9/WvwOnrfwB4+cshy7W+Pk7prE4deIEWKUtUzsf83DJF8EpOjM7SiNdXbyEk67nAqVya0tERKKRBbrlTNc/Fp1jJ74k++kfhe1oIH0WoXFkYliq2xRSYdGxosdoNacRqE7fUW2Tz0sHxRYPIP9c8wimvkDqqsmrqmTiehWpNaAc0xXlQdFbn7ZI4q82YM48BoRCr852HkzWUHcLcbmbTSFM91SDfKWsWmOC6EEJBJeyspoDFaua8k0/GtoP4B+h7OfWx3n038Na30rYpJXD8OG2b24041k466oiZbSNWw4FjZXBVQne8ubPaiRBRFL0qkCTAN75B2iTk/kPY2T6MxQWiaVkYamd1xUbJienEpFxY9u1eZfJWd/BEZ11ndZ4pW3RWO5vkrB5CrP7iF4GvfW3tNqUqiNU7LMxHZRKFWUod+UkdAxJF9E5dYPOc1V/+MvC+943WRvvPPoxv/+EteOShDHGmC7QB2BZi9WbFgNhWhqmgjfkZWrG6WqWNAullD9dq2kW2NHp0T1e6KPv9dkp+pjOrR4wBiVMHnhERAjuhqY+QZ/iXTAzIhjKrSzjVGcPR0ysE2HyR0YXOrPZCNFs0C5gys7SgqIgWRI1NXxX8tD0n/FrflxHrHVP4tsiVVwKHDunF4JUEbqrjSwYJaq2WXkkWFsTuXdh7icAp61I88Oz/ADiF+BMqZ1oxBsTztADcJnTtG7G65oZoLo5+ffWc1b6v2yQ6txLL74vKVOSxJcK0S3FPWJFZ3fvHiEItUDidPU9HrHRGX1zoRWsEAca8LhodGmf1svgiPx28yDaIXr0B811RFcrO3eWeQKVmoS09mp2daaoXVhwHbsmBVBZdDIi5z1SGHHMPJL8OJuqoOBFdFAqzaeTdCVkUiIkvsgtGIc9KkUQcCUMBi9XMeWe7Oas7HXrx88476fMpkwQ4eXJbRIluGvmNh6DI9zK2jVg9sROOldJuRTPVvqtuhLBNu0pcLtOL1cnBo6g1jiOcJ/oAokg7q6uudvxFRLfN4mTXdVFxYrTCAdUxswzJ4ePo7l9jVaq4nR4w274TXfxoFIqFyoxYvdgY/Dncdx/wrW+t8WQu1puCJJM7bcyHFZKJjpTAxAS9s1rKzcnE3yyx+m1vAx588Nx//+8/FuFVb/8+PLawC0eWxrWr1pJa+tomYjW1szqN+s5qsqgh6GOt14nF6jx7uFbTi2GjitWpFk3K/irOaooYEJMrrHewjHaoAHqij+3rDrFUArpDxIDIzIbMLOwst3GsObF8v3Cv3wLguqh5pt8myKZc5lKlYKWjFEPEtpj376yi4TzjGcB1163+a4GXDhcDcuwYZGbB8h1ACOzZo8etL3kJMDdXeB2ps9rcZz1P38MpImaKC7hBgBpFdnnv/q1jJXaWmphe9Ab/3hBI4cKxUl0cOSYaxxWc1UIoMketVHZ/3SKPgyEQq3vXgREqO02CuhvKuJV9X2dWdwjy5osRM9DxRa0kIOlje9e16+pio+GIn0HeX3g2qnVLO6ApJh/5e7ULmfAUYrWy4bh6zF11Td896qQ5jqEAiGoFVS9GK7Qv7on4NiBN9QYLMrG6WIMI6EUwUus8FyssVjPnHRarN0esllKP70lzmLYZueBz0TqrgyockWmxeppIqevFgESI2rT5Kjt2EGZLG5IY+lhbRF9YnlldNTEghGK1zGzt+hNCi0mxt/4g99AhJLMNdO59ePXM0VUmeoEtEYWEMSClEsa9Lpaagz+HJOmLu+997wp9c0UMyNRuB3NRhcxZvRlidZpuTtFGy6Lvt48f18XQRpk3/s5bUizGJTwwfynmwwriVGe/KmBbiNVS6nOQPgfaiNULdA4qKYFajV6sto2zuuaGQ2Xir9+gREd6KBXE6sBXo2dW92JAcrGaLrMaAIRZvbM8R0cKDIgBSTIbk9UY1181h+PtceB0oZCmETxcV8dqVCtK56kSfHHkmdXmfRbPUv19OWt/BisKsRW57jrg+utX/7XSsDEgR49iLqxgalL/uHevHg9XKn0TLQAtUgJQMUVmtQPPLzirKdax83O25PRd0EujX1+9gn2+jz2lBs4sBQQHq/sCNzcynKHKrZHLndVUYnVmLYsyyx8/Z3p9k2nTnAftJtH35SjAtlH3IzSiEeM68naVBbesB0fVQKKdjNhuPjYsFN+ueQQRRnlmtWehOu7ovpDIWS2A5WL1qBO7NO0vNAqBajlDS/okWdgA9A7JcoY2RWQJs6lIqe89ZONu0xf2xOq8uD3FDjGGxWrm/BNFelBKLShuFu027bEqBTzxBH2R2CTR2zQv5iiQXKy+aJ3ViYJjGbH60BmaRgsFFsMOrVtgnRpP54bJ26w6EcIW0QWWO6vrent6Nya6bZqtg5ZnxOog0a6U9SZlSkEqS28JXc0y2tui3i/yMUyBRaWAD3xg/WNd6axeag3ePhlHGRpL+kt+17v0FvKz2jTVsyd2ezqzmkL0SYDx8c1xVm+GWD0+Tpixblhc1EXQznXeGIZAJW3gZXsO4Fh7AmNeF80kgF9xtDuvc+GL1UkCTE3RuuFlnMERKSaDNuYWCbYQ5+1KhWpFka4B6GPNgHodVTca/dzNndVBwVkdqNEzq3tRDehnVocECwFFdx4wXKSAWZydqiV4yuVSO7FXiNXFHSG1KpFYnWWQqYAjjPs1Ihhw5Ks0hY8y8JV2QK/nrFb2cuHY8IY3AL/1W6v/WuBnw8WAmOKKuy7R38muXcBjjwFvfjPwe79XeJ3rwiNcaPUC46x2EnQJI2byAosk2eU9Z3UGeB52lxo43SiTrLblWfsVJ0LrNNGN0YxhHLPAMnKBSaAvgBcKlalBC0xDtAmgfx2YQpsj54HnfYENQAjUqxkacYlE/OxFxAGollItqo6cWW31i2+7Lk3MjBHBXRcoj3t6bEqcWQ3f1+cASca41eu7qxWlheVRb7r55NB1USkrmnxtZlNJU2DPHlqxeqWzukRR3J4BwGI1swVEkXYQUYt/d9wB/P7v07YJ0DurpezHhVEiJXDJJYTbWrYh+Y3nonVWRykcYdwzc0QWvV4MSIioe4FvbQtDJJmNihsj6hA51HJn9ZguzkTmrM5db8b1V/WlHuQO2KKeZLYWUA4fPvv5XoFF8/OQMSBpCrz73eu8IJ/wl3S+9pgfYqk7IENRKSRf+ioan70V8vffgUMHMxw7trzN4sTBr7qIU4fkQpOzi5hoHkVznrYjSNPNiQHZDPJYiXMt9tO4dz/q8Ry+c+9RPO2pGS6rzuOJxk54VQ++JRE1L/zVZin17g2yKJA07S0ITvltzDcIxeqHHkdt+gl0m0SLbEr1CyxWq3rnxhAFXNc/SIlu6i4Xq4cVKddjRQzIMAtsA1GqL/IVxOqBkQKmj52sJ9hzmY5OkCcKC7+56GMW2Wp1QeMm7HR6kQqBnSBaJBR8ijEgg5zwuaDjnH2uCIFVRWwACNxs6BiQmbCKnZfpQbDjADt3As99LvD61xdely+0jrpAHseIC2K1dlbTLIT0FnB9nya7vFdgEYAQ2F3v4ky3TjKgzXeEVN0I7WmiCnDmWrCFgm9LxE0Cga7nrDY/UzqrRd9ZXXFitFsj9odpavLd9Y/1aoZGEowuVK4cG5YzPTYcVaxW1rIYkJobotkecTzbE+kERKWsHxu1L8wyQCm9g8vS161nS8St0RcBpLJ0DAi0WN1KRnRWK7VMrK5WoRcWWKy+oJGS2NDS29W6MaMQMxwsVjPnnc0Sq2+9FThxgrZNgF6sjqL+riZKkoR+G/F2Y3paj20uWmd1nPWd1QtEH0KhwOLI2ccFlKnfJOiaBMLQRJaEdGJ1GJrMag8llyCfNSdvw8x0qoHUg9z1JmVRBJnZ2r1y5MiqbUrVF1J6BRYHfG9JMmDQliSI84m52freGuRIiSIkSx00Ih9HHmxAphaO3nt29mvxWHsHMyLy7/4B9eZxdI7QZmtslrM6h3KnQV5k8lznjY2vPYQxr4t/9R934/U/7uOK6jz2L+2CXzPbGxsXvlidJFqsJiuyGEWQZtv7pN/GfJMmTxYAksUW6lYL3aNE52yaQmZCF9Qql3UMSHtwZ3vmDPDOd67d5urO6tELLJ6VWT2qs7rophQr+phBzurMxmQ9xd4rfewtL+HkocK9dKWzui7QTPzRB16djhZSrFRfXxQ1F1Zx5A4lVhcEuGEp+QOysAH93MwMZsI6dl5R7j28Zw/wnOeseK3ratF+1GFMXryz56yOyVz7PbE6z6weVfgoZlYD2D0eabGaYECbhNrIUHFitGeI6nkUCiwGdoLuEsE9Ic8VzscFtq0XjKLRxGqlsCyzmqTQZpLovOZcrK4p7aweRaFShcU0I9T3oiU2MO787GdXdMl50caCs7pOIVb3BHvRd2GNKlavXGQzMTsjx1ilqf4Mcmc1hbCcLyyY+0y1Chq3NrOppClxQet8kXdlZjXFvYZhsZo5/0SR7iSoxb977qG/PyhFL1bHMfR2V2LyzMuL+R45Pa1zqLaLs/oDH6AVv5Io6xdYXCI64GJmdUJ3401TwBEpFKVCZ5zVNTdCeI5i9dwc8Pa3Fx6I435mdVnoCTnFCZafUAWxeuCEJIqMs9oFjh49+/kVQgpsG74lEcbrf29SDhare/mc0AV/2kMUKksyG80kwP6l3bh24iSO3lrIAVkpVrsuBNH29+TUrM5+Jc5a2pTMaqWAOILnZuQ7eMbGzn3euHSmi7rXxeUv3IWf+29lXHlNCY+lV8OreNox0rzwV++SxTZ2lNp0zmqzWOSUXEwGHcy1fZpAbCMk1NwQ3RbRIlteXNAGEAQ6BmQIsXp6WmcIr9VmR3oolfp9NmVmdZ4rXHJiHbc0ymdrjkcVpzkmUkAl6xyr6eue94wOrnlegMsqCzh2VC17vpeBDPSLihGI1XlmdWAnCJdoXKqZEssWhAdmVueCzgY3DfTOg/UWLWZngSzDjLUbO/f0/8CHPqQj7JbhOPBtOXrmZ5IgSp3erqCyE6MTEkx9i9FYeQxIa3SXarGOw56JCKc7BGJ1lvWMDBUnQmuBqO/OnapWqqN7KHaFrJJZPXLdjXwRP89h9zxUnGj0aOXesep2yzVbj4sIxE+g7+ToZStvoI/99V9f4Wfo5Wv3x1s1L0SzM+LuoLy/8CxdMR0YvS8sCsAA4Ps0BYKNoJiPOStVsytmlElzPgY2B1upWeys3gbIKEWtJOnE6nzRxu/v4tJGIaL2L3JYrGbOO2G4fcTqONbzpW3hrI4y1GvqondW79tHP05oNDZHrH7nO5fHYY6KTFS/iE6DSKgzomPFjRBJuirXyR13wzl+CE5riU5TzGNAnAjROU5yH3wQ+NSnCg/kmdUVB6WKpYViihNMSr3N0cxMq6V0cAyIcXZ2Uk+r6isxEwe3WGBxSGf1unERxWJSACpBOlhYN+dNQ9Vx4JU/g++79FEcO5Iuq7DbkV5vfgPX1YUbh8jCHoTMLNpCZXm7m+CsVvd9G+LMaVTnj51zZMdq5HFT59pvNWZi1N1QV6oUAs/46Zfi/oXL4JcsncV3oYvVSQJ58Ah2PPF1LC4QLYhFkc5o9W2MjyksxSUaa45x1da8EN15yvgms+3biNWtznBFUddcuOo5q/ufZylQJDEgcWrD9S1ACAQ+dKTEKAOvlTm1QE8AjbvrF1iUysJ/ff1pPP8VNVxencex025/24OUaMQB6jX9c23cpsmsbre16GNcquEiwWA2TZEqAdvqf1++j/UXF7JszQKL6xF42cDs8uzkaZxoj2uxemf/8Re+cJUdVoTO6rb0UakJwHVRdmJ0KXaIFe+JQYC61x1d+OuJ1fqz3zWRYDqsjT7eMIYDx0pRcQkKC+YkCdLMgh2YBUyK3TZGWLbdvkPRt0csVNbLljZtmMzq9qhidV5s1TirRUCQrbxKdE9vJ9swfaxSyB5+FAcPquU1QlYWWHQcvRuA4JzVcW4AymXYIoNsjtgXrlIQs+pEaDYoYlvsnvu1XCUYz68Qq6s1cdFmVn/4w8A73rHVRzEc6W1fRfWbt9LFruU7F87KrGZnNQUsVjPnlyxDtP8oqkFCKgBPTwPBiAukq5GvvJM6q7/xbfj7H4RIYrpt31mG5FsPonbf7Re1WD0zo8Vqamd1u00vVs/O6kKblGMamSg4wsSANInEOiN6eJZ2aVFdZPJTn4VjZSg1z9Cds8b5WHWjc87XfuIJLM9WDkNEmQO/4sAvWXqiTyRWA+hv9cyL6AyIAUkzM8BerSKfcXstz02TCAfkbCeJfktrzoVWOKsrpUxHkQzhrG4kAfYfK+GVz53H0caErqYFaNEnCVCvm9e7ro5WaGxw//kqyMzSheWIyTOrKTcDJPd8G66VohIvkIvVrnvux9qYlxjzujrYD8B11+n7rFeyaRfDNotuF0lmY0fQwtJJomDCKOrlc1YnXC1SUnxpnY7ut5yIxlEL9IufOeg5P1tDCBNJArSb6ervS+qCg+V+ggNKJZAUWEwyI1bnbabeaDfyVUQfOI4WKzvrXBSm33LLLuB5uGyqg2ONsf4qlZRYiku9fqs24dCI1csyqyXNeZBlWkwsdP9DidXK3rhYHQx2Vj92bwu/cMfrMZNNLROrVyV3Vo863IhjtBIftTHtrC7ZCTqhPXonnosTgRarK06MdmiN1m4xsxqAV3GRZPbo440w1IvYVoqqG+rrmwJTDNGpBjq6h2JXSJoiXZFZHYx6HuTfVcFZrQttjh41tCxf2/d1Jv4o39cqi2zlkkJ7GGe1UsDf/i1Ove0vEUUCh/YXXt/Lojc/e57eDdAlEKuV3YsBqbkRmvMjTpZMH6IKMSAlJ0HYHvH8WlEEL6g6o4/nV4jVXtXTtVcuQrH6+HFdJPfBB7f6SAYj2xFqboTuLG1+/zJntZ0M3NXKDAeL1cz55ZvfRHTHPagee5RU/JudBa64YnuI1dFHboKXdhEsnqY73m5Xb2tJ5um2EW9DOh1tBKQeJ9g2vVh99936/2THqtTyzGpSsdqGnYt/RMqyLNfhWqmZNJA02XNWV93onK+tJ57Qua29a944q4O6BxH4vcdGRko9H8ljQIYpohNFcK1UD7DD8OzvIt+W6ha30CYD41vkKZ2T255b40Nb6awuK73ddRhndRzg8GHg+d87jvmo3BerkwRLcQljY+b1uVjddNdqcWiksnVhOWKk1PMoyvtM4pS0WO1G5GK1I86xD0gSLDUt1H2T2QUdrzQxAfgVLfh1Wtuj2OqOoIXFo0R2eLOzwfEsWJWSdtFRfGntNpLMRt0L0V0kuiHkW/QdaGeaG6EZugN3xiRf+wbaB04B/+N/nL0gZmJAyoUYEC1WD+gLhjjWYiRQqSy0AD7gQssy4Jd/ee02AawqVq+7/T93pJd0P3TZpRmOtSaA+fleu8V+qzLh6V0mo2YKmPO1FwNC4VJNU50pXHBWO76NNLMGxIBs3FldGiIOJjo1r53V8dhgsZrQWd1MAlTHbMC2UfYSdJN1YlCGpXhPtCxdaHTU3QD5ok1+C/S1U1eNupc8DHsLuBUnRnvUQqs5uQu6XtHnbJumlkfR/dobw4wYA1LMAofj0BTL7hXs64uqAIjE6n6/Zbm2NooM6GNn7z2CxpfuwRONnbi6fgYHv1iwVhezpYF+gcXuiOOtvL8wMSB1r4vm4ujX1jLyorsbKLaaJMA//MPZD+b3bwAQnqvv4aNM7pLkrDz0kRcsCtx9N2ERwE1GSuDSS/X86UInzSwdu0bprF6xIyRwEnQpIqcYFquZ88z0NKLUQc3uIInp7Gl5ccHtIFbHmXaMlNChE+mMMFRzQ5rto9sUKXVsGrWz2qZLv+hx993AJZcQnrP5oFGktGKS+TB7Y2eikzYpj/W3PFN9BkZMqrohoujc+pcnngCe8Qzg5Ml+mx3poVR3+5ORUU8wU0RnWQxIINFKgoECsBD6fd03uw9q/mwxSarlRQsDOxnsrP7YTQCA1nv/Zo0XmEJKgT7WSikb7PSJY8SZjUbk49QpYO+1E7CFQnxmoXesjYJDEa6LyYBGrE5S69yF2nXI9Q3KKJDYCuBZKapOhNYinVtZfvFWOF/5AoQ8h3N1YUFHHYxbvQtfCO2u9sraWd1uXOCLosZNuCNoYfEkUUGxPLPas4FKRT9GJFZLZWHC76C5QBff1Cv+lRdFlYMLPyWPPIFW4qPVVJj77NeWP9np6L6w1t/94HgWpFpH/ByGPAc6MI638nA7WJIE+NKX1nhSSiSZBdcu3AeNSLVuYbU4XtbXXXa5wNHWZF+4NztCxib0sVqVkhaSRr0v5iKd0GI1lUtVKksX2cyxbX0PWavvVsqIWucYA7LOQEnON7RY3SkP76weNbIjSdBKfC1WAygF0LuCRr2Hm/u35Zk6DiU1ersrMqvh+6i40ej1R0yti15mNZX4lY8L6hVtOqAwSKxccM/HMCOK1R3poeynvTZLdoJuQhGBYS9zVo8cA7LaIlt+QgwQq//yT5r45KHn46C6Eq+69FEcOlD4PlYeq+MYsXrEnWx5H+OKXiHf5tLohRABQBQE4I2K1fffD/zWb61yrCuiGop/75zIF5jyxb38HCCa1Pzf/ws8/DBJU5tOXrTwgq+bZeJgyMXqfKcNUNjVys5qClisZs4vExOIUgdVN0TSouvR4njzxGohiJ3VIoBnS5REROoolcqsFFI5s7YhUmoNgfL7ynd1Um79B4BTp4CnPY3QWZ1v88ud1aNWOs9Z+WFSOauDar8wz3rbsjdC7qx2onP+XA8dzPDyF7T7USBRhNmoih17XRrnDKBPpvyEsvRtuFrFYGd1oYN7w5feiIXDKyrH5dmMK1xJ4YBJWTKthZjWI8dWf4E5JuEWnNVDZla3Y09HUuyawPN2HMW9D5rPMHdWj/edPpN+B/Pt0QP9pdIORQHa61ZK/XU1GnRtJu3YOKtjtM/QWWiSbz0IJ4tXzzYfxOIiGkkJ9Ynl58111wF+HgMy6hbqzcYsXO0IWliaJupkTQyIXRSrR67ShV4ExI6gRZevbe4HtrMiS3OQWB3phaibDj4fN/zadyDuFK7xRkOLPhOFa9Rx9A6RESf8UerAL5sYkNxZPeBGLuU6b8cIVBWvcPy2PnfXvYXlLjtTBfuSKz2c7Iyd5ayuj5nzv1TS4u+o50G+jdjKdJ2BFsEkuhcDUjinBgk0vQKLGzsPXRcDFy2SKMPp7hjmG25/kXKdBkeOfwC0WC191Cb6ovLIETOmXQC9z7McZHqHwSjXQb5g4a4Q6UZ12YdhbyGkSrmDJz9n6+UNi4lrkoufhTGMjoMZ4X6Ti9VBX6zWW/SJ8poLYrUlFNLO6GK1KuaAmKi4QedWcvwMFqMSnpj6Trzy0kdx+Ezp7GPNF0LyAouRRxJdkzura25EJlb3PgLP21ix1UcewV2//VkcO5otf2urjI/XXbgbhnyh1TXvmWqOgH4z1OarzULKbSJWJwlSJVB1I3Spaq+YXUzLM6vjgXMvZjhYrGbOL1IiyhxU3QjJzNLg1w9JkuhOkjr+odMBxsaIxeryBHxLouQkdIKicU7U3BDdpe1xZzt+fEU28KhkGeRiExWrS3oehKHOQ6cmivS5RSpWmyI620KsNkXwSk6M7gyRSFeMATmXLZ5RhPj4NK7efzOO3X1Ku8SSBHNhFVN73J6AMfKXZia6ouCeCQIMLlRmvgsFgcPNKZw5uGLWaSYkRWe1b0uE8fqfhbzkcgDQ0R4r9/BlGZCm2i1iJk2eLxClzlBitetk+mObmMDL9+zH7Y/s6B1rz70LAK6LKb+FuRaBWG1EH99Ozjm7fDXSVEc4Uzqrk5aOdqk4EdrTdDkguTjhJF0knQ0OyBcX0YgDjO30lj38i78IvOw7Y92/XOjbG82MadzvYHGWaEISRf0t6pUKbCuDbBCI1aa43pTfwhLVsKiXJap/rNWFzlYeMJOMwwztxMNCXEEjdHHjHxZu0s0mutJFeaogguRCygYn/MtuI6YIXrVqYkCqtu4LBxyrlOt0xVKinfgoF8VqU2BvUAxI/lpAL7LJzD47BmS8Lygu+71zpRDbEtgSYYco/1ctz6zunRADCyxu7E85rtD5ygPE6kxZWGxaZxdUXEke/zBq5meSoBkHqIzr77NUAjqjxnUA2lmtRO88KfmmjsOIwtcyZ3UQILAlotaI/VceAxI4tHFTJrPaHqvqAq4UwzjjUMwX2fq7w0YTq9vSQyUXq4VAyZUDdwIMdazKhuMtX1yImqNl7WuBdXl8EQCoeJ3zIAwhGx0syCoOxZfimp1zOi837+xyoTb/XC0LNT9BMx5QJ2WI4+25lUsl7awedXyU6vo4oiBWb2gH5nveg7sO7EBJdZbfT1dkVg/rWC/y7W8DP/dzhbnryogZ34dtZaMtWBQIw+0Tf71txGqz66zmhuieww7kMAR+/udXPLhaZrXDmdVUXOCzDeZJR5oiSl3U3AjJPN2MP471nIHa/dpua3GCNAbECuDZRqSbI9qebAajdS+kceScBz7xCeAznyFs8O67Ic/Mo3zrzaTfV7OpXfvURBFQrxMORPJt375D63zMoydKRqCgigGJMhMDIhHOEPUF5jqoutE5TXI7n/48KlkLl1UXcOz2Q70LX8K4nahcE6ts9RSOrSe/AzKrlRLoqDLC1MPpIyuOY6VzJJ/oJQNiQKQ+jlYS6ByUtY41j4RwBwgeQE+snqzE2LsXwNgYXn7pE7jt8GX696TEUlLqbafvxYB0hl8ZOnkS+LM/W/Fglulzy0q1UL9AV3FWSnqxOm7F8CyJihuhdYZQrK5N9HdZHJ3d2C8vLmr36M7lCwfPfCawa4+1bZzVADDmdekE4DRFqkRvy3PdDdGcJejAOx1IZWEqaGOxSTQsl1Ifq7lUh3ZWxwptGWDh0uvwA1fcj298frH33KGjto4BmSpUWBwkfq5CGALPec7yY20lPip1E9VQsbRLdUA/O5Sz2i8cl9n+v+65mw8e8oXJyUk4Vgo5u9hrtxGXMDZpJqWuCwEgi0ccd+WiR9nVjqw2wSJblmkx0V7FWb1uDMjGndWOJ7Sov55YbaL/JieGaDt31I7qTDP3Ia+8Iq5jlJzaLNOTDYHezqiyn+p2R3RWF7Pb4ft6wXXULGiz89Kt+qg4MVoduj5GZjaceln3sy0CF2FetLFYd8MZMbs81d9NJTDXVFGsHtUJny3PgS458fAO4NUwu0wCt3BcZhwXh+v0CWahY1HWMD1rYc8VPlwrRXLsdP9Yi659ALVyikYSjHYtFDOri2L1iG7tZbn55hwYZLoAoLe9pSkeXLgE/+qSh3D8YGEyuEoeulJiQ+fA3/0dcPvtwH33mQeSBF3pouSZ7yYItPGAKCaNndWbQNFZfQ67QWZngfe+F3jggcKDUiJMXXil/rggsCW6o+7eYACwWM2cb6TsxYDEbboeODl6Cu7RAwBo1epOR4sTlMX1olDBtxO9HfXEPFGjkSnQ1KXLYNpkTp8mLlpoQoZ9FZKuRDcfOopq8xSoz61crCa7secxIGUXZTdBJxoxRzQnHymNjelJeZvIWR1KHQPixOhO0xVASzIbJSdGLDcupi3un8G438FVtVk89qgCjh8/q3iKJRTSLoUrC8vMM0O5PMyJXSoDAhlOH1txAUmptyT6yyd6gyb8MtR/s514hbDufpsAIIqjhWGO1YgEO+oR9u0DYFm4Yl+KE+1xYHERaLe1szoXRC1Lx4CE5aHdTrffDnz2syse7Hb1xDwvVEYoVqepLjK4GTEgVSdCe5ZQWE8UHCtFxYnRmd9gJ9NqoZEEqE+tkh/uujpHdbs4q70uFjseTVxH7iJzBVCpoOaGaMwR3MSM1XHc62Cp7dCsuq/IKPWrrs6BHrDYmET62jtduhLfc8l+3PdETZ/4SuGVH/1ptKSP8s5K/xfOIffzyBFg/369u0r/0UQ7H2sms7rq6GMdMQakLf1+Ti3QiwFZV6xeEQOCiQnsDFqYPW7+UKeDtvRQHjfPuy6qbohWe3QHsMxsOGVP99lEkQpnOasHOeFz8WmDUbauM0QMSKwgkGHnjiEatG14luwtpG6EhQXgb/4G+j4ipV5szx3QZaEXQkYshAgsjxUuBWrwzqgh2j1brJaji9W5kaHiayGtS9R3G9HHrpUx7nWw2Box/xjon39+McpsxDiYJNG7LIL+NRV4me5jRo5tWZmvPeKxpila0kc1KNxXHGewc90cy0JcwcICMHHVOHaVmph+dL7XrsysvmsfWqxujipWm7/renqXQS1I0IxHvL5W9lvmc42GafKBB6AUYAmFK6tzOHZnYWdQvvMw6LtfLaGQRsNfXwsn2njetRFOmzUAxDFaiY9ayXyGvq+/qwbNjr7t5qyuVLaBWF10Vp/DkHtxUdeb+vM/LzwoJeajCiZ3mT7QshA4EuGouzcYACxWM+cbKRGmjnZWR3QXcPyxm+AdfIy8bG4uVpM6q8MUXp7Vu0DUq5tiUjU3QpfCkXMeIBerJycBAL4tEXfpin9F7/0LBNNHgRad6xEAolNzGFOLpM7qxBT/qtWAZhzQCDQFsdq3E8RNmgOWsXZWl+yEeIeBDUdkfffTBliaiTHmdfGCZ4W4Z+YKyBtv0gPRcj+PrmQnoy8I5Q6qFYWv8ufWJIoghEK57uK6yZM4fWjF55ZnM1YKYrUtEQ3KrDZ9cUv6Z4vV5iJVq2xLHUasnqonuPRS89jkpG5lehpYXEQ39RDsndDPCYHJaoz5qDx0x3DvvehPGnJMpELPtU9YcFZKLVaTO6ttqTOrF+huNDJRcIRxVi9s8JrtdLR7dEUMCADA0Ts32uEF7hgxM6Yxr4uluATMzIzeZjH3s1xG3euiOU9wEzP9dO9YKQYc5ljz7fSiZHYsDCpaaNyvJ2Z8XL43QSvxkE7PQXW6ONEex7HOFMpjBcXD9FsqGb5PPPRQB4Gf4WtfTXvH2k58VOt6SuKWXR0pMcgFnqwfA7IspxboxYB018u/XemsnpjQos9JvRtE2/QFxKTptxw9nm22RhSr83Or7JkYECqxWpzlrLaEQhqvMUbKsl4cyUZwPGtwDEgC7C0vDS6uCGix2pZ60XmD9/H3vAd4xzuAY/cv4O4zV+hzNHdAVwRJIUQAy9TqckAQAyIl4tTuLzb7PnxLImyPOJ7NBeCKFtLaVAuNUiJTFqxaBeN+F4tdf3RhppdZbe4ved2NATvlokiPB9Zqsy19VMr986jkZ0TOanu5C9xORnNW532hv0KsHlQY0yx2LcZlZBlgX7Ibe0oNnNnf6B9rUVgHUCvJ0cXqFUULa1Wl5x6j7MDMxeq833LdoQqFAwDm5xGmLkp2gn3VBRx7uOAsyD+DgrN6Q074dhsLtz+Ea458DmdOZ702m0mAasmcR76vjQdEzurtJlZvH2e13oHbPYcdgouLwA/+IPDVr5oHzFxzKS7sFAVQ8lKEqUMsdFycsFjNrEu7Taz/Soko1U6UJKJzqiaZLqhF3atvhlgdhVpQLTlEFd+BXjGpqhvSTHJWQB2vAmyCWG0mDp4lEc3RnbR5trJICM+tgwcRHZtG/d4vEWdW23A9LagtxOVNEKvl6MV+DDJK++5XquiaMIQQSidWmJ83wtKcxJjXhf3D/wav2LsfX/pagOmwhl27zQs8TzvBCcRqqax+BXFAFyoTav3t5LmzetzDd+09gtPT9nLl1Gx37YnVeQyIHCxWC2Q6BmQNZ/UyG5kpTLNuhmIcI84cTI1J7awGgIkJfQ499ET/PRdsf1qsrmxIrD5LOO529aKNlen3vkR33aaxxHg1oc2szgssOhFaLbqOViYKrsmv37AI3m6vGgMCoJ/7G13gYrW5Vhwr0/EEVGK1MrmflQpqboTGIsH91jir616oxWqK2Z6peF8s/gVg/baV6l16x08KjF1SwdPHprH/niUsnWghyRzsX9qFciEFBI6jt5sPaz44dQqH3vNZvO7Sb+LOvz+lHzNF8Cpj+mBF4Ot9TEM6q1cdn+Rb/1c4q0tOsv6ugLy/yz+4ceNQnBG9c0gVxE94HupeF43WiNOp/P5d2WA+63r0CiwWHsuzoNfK8s+ycyuwOGQMyNVjM9h7yRACgWXBs1PE6cZ2GnS7wE1/p2AhxU1/ehofOfAi9KvKmYiZIVz767JKEbxSCSTiZzMJUKv2828DJ0E0an55Hg9W1rtiWiHR7o3cYe46GK8kWIzLNNntRQE4r7sxQKjcvx9429v0v9/7XuD971/eZkd6KJcKYrWXju6E7y1erhhvjXLt5pFI/vIYkGGc1UlmYzE2cX0TE9hdauD0if6C4LLPFUCtkmlhedTPQPWzems1aAF8lLlHvshmFWJAhigUDgBotbBoRMPLKgs4fqgwlsw/g0Ku8IYKvB84gIW4jGvGTuL0Y0YETxK0Eh/VgqGlQpXfjm0WA7LUQtVqk4vr7343bXv5LmTPknptbYMLbAt3PIyd930eOzCDU6fQvx8I0RsWAGb3xqj3BAYAi9XMAP7mb4C//mvCBvMYECdCEhM6q1MHni3JOwVysVopxJGCZ0mU7BjdUR0TOWZ0VLITdEdZ1V+F+Xng1a8mbRIAcOoUsVidJFAwzuo5OjVJC18p7VaeVgtR6qDuhvQxIJ6NiQlo0Y8iX7ogVo9cPKaAzhXOyBdtlNKDcQVs7P1nGZbmU4x5XeDZz8Z/fPlRfPixl2CmW8POfUboyZ3Vo4rrSYIodeE6hXPKdXVkw3oFP8wosFyx8F3XNXG6UwcOHuw/LyU60kW5WtxCmyBM1p+cyki/72/P7cM7vvyi5Z9bkiDNlg/C4Lp6e/J6k2jjrH7V8xfwgheYx6amMBW0sHDr/frnFfa9yZouZjlMx6CUzo4bG1txaXY6+joQeiEkatCNnOWtd6D28N10RWyVQtJJdAwIZeErAFKqfmb1Rp3V7baeME+tkh/eE6sv8OHjyo6VQqw2DkXb1QUW614XzUWCvstM7B1Li4sks708s7ogpABYf4xkrlkAOHlSYPzyOp42No1DD3Uwc0Qf41y30itfAKDnThs44ZcSuPFGHHvLB3Fofgz/7in34lv3mXPIbNPPxephawPkGsuq3UWSoJ14y7b+w3F0tES8zrHmnYnV70N3TSSY7laBgwd1VHGx+qDrou6GaLRHXLzJBcWKbwQvgnHcSoci0BPU1vy+egUWN5hZnRdYXGecJBOFG/Y91BMVB+E5CnHmbGhc/+m3P4zXuTfjGZ378MnP+jjSnIJX6vdVQdkaPQZklQVc27ORKmvkGJBmEqBeM5+958G3BtxnhyFfuPIdVAJdQJVk8C2lHme5LsZrKZbiYPS+q9jHAkMX2kyWOph/5DTwvvfh7i828b9/N8NsXqrB9AWV0ioxIBv4vtIUuOWWFceq7LPytUcadpv4omppedZ+1Y3Wjxoy4/+Tzbrun8fGsKfc6NfLlhJxVigGCcAtOTq6Z8hzodEAbr11xYPFaCwYZ3UyorNaSqTFIq+9QuFDLHI1m9rhusvXtWeOF8YpqxTBKznx8Ie6fz8WojKeOX4apw8YNdostNbKBWe1S2c82DbO6iiCvPd+VO+4hdxZ/fa3E5sm41jn4luZ7r82eMCL9z6BCSzgFfYduPUTZwp9yPLzM/AVuumIu20YACxWMwOYnSXuKNMUUaZvvAnhamHPWd2rpEwDuVhtimfkzupOk0gAzR2XToIusYhw443AoUOkTQLYBGe1acyzJaJ5OtWnf24RitW2jTB1UffC0fMIc+K4t82vFwNCceLGsRaAN8FZ3cusphKrVw46NuLuaDaxFAUYq2aAbeNFP/YMHGzuwEMLl2DXU0yFTd/X2wZHjdpZrfiXbaPqhmivM8jNwhiWUPhvvxjjVa8CTnfryy/O3EGUi9VCIHDNVrR1RIQkVpjwO/jSmWvxvkdeDnWqkK1h3F71oPC9O47OvlxvQG6Er3//6llcc4157MUvxqTfxtwZqau9rxCrg5JANOS2ufkv3YedncOYrESYL0b/t9vaoWilOvuV6HwFgDSSqHkhumeIKvZFEeLUgmec1e1Rc29zsgxSCjhCO6s7SxvsaM11I6qVs58zMSCdmCCfdDPJBy6XXAIAUDMbLDK5Gkb8czwLKJd1/EODYMCR99O5+EUx21uRWb2R6B7fTjA/D9Qun8DOoInpg03M/M3n4Nv6PAqKaxi2Pdwi+YMPAl/+Mr7/k/8Jnzz0fDxv6hiWlpRedUoSxJkNr+IOf6yFp/OP633vAz79afNkvsskWL4g6FjZ+t3LSrEawK5dwHS3Dhw4YOIECq93HNS9EM3O6GJ1HtWgxWqCvmC1zGrHgbeeEz7LjAC3sT/lemL9zOosQyIFPDvtF6EagOdkiNP13dor+euP+/iJp38Nzxw/ja9PX4X9jV2o1vqfpRWYeJcRxeo4teEVF5vzGK8RndWNOEC9bn7OF4VHnYPlWb2eQLUm0JaDC60ORX4hOQ7G6xkWozJJ8em04NTtLbhH618P8jP/jIXpBPjWt3DgjtP4lSv/Hh/4H4f1k/kui8LtrBQovWixge/r3e8G3vzmwgMmY3x5QesRM6tzZ3Ww3FldcQaI1Wax6/DiGHbsAFCrYU95Cadn+vFy7cTHslt63s8OeS3ccYd2ra/8u0UBuFbJaKJFlH12DMgwxepaLSxGZYzvLeGKySYOz9V6O5dWusB739ewBq/9+5FkNi6rLuD0KXPtJwmacYBqHjETBDoGhGgKum2c1VHUK24fEhmacqQEHn6YsMEkgVQWbGG+w41csGmKhVMhxr0OXrz7EO7+WtozYXrO8r4k8BXHgBDBYjWzLvPzxB2lEWurbkSbA53pAbinIrp+IcvQueNbGF88THesUaQHuZYczo00LKazHfqGvgE+/vGzDJAjkyR6IYT03JISAoBvScQtwuxXZZkMZELXfhwjUwJlJ0a0RLQMnRdo8m2IYIgt38OSf0n1uj6/mjQXmEwyEwNCkMmYkx+r6+o17o24OxYXtSNj3GS8Xv9C/MyLH8TbH/wB7LrCWAl9X1d4HlWs7m1RX8U9s46DIOlKOCLDK77PxSXfMYXZsLo8tHmlWK0PeaCDKIkyjHtdPDy3G7NhFU/cV9iZYNxetRUFfypOvP6A3JzjdqmQe3zJJZjcV8HjS7vxO/e+FrX6it9xXe10GKITb33qC6jLBew59S2cOVX4PsygWceASLLzFQBkZqPqROiu537fCO12bzGs4sZod4jE6tylaWWoOBE6jQ0uiOVf7LK8B0PPWX2Bi9V537dvn15gOrUwepvFbd+VCupuF40mwXeWX5tGTVEhjbN62RZ1W+e4y/V2tJnonomgi1oNsHbvxM5SEzP3n8bMmQzfMXkCZU8uSwTqb30f8DksLgLQO34OtXbgsr0SdiYhT04vixPIjzV/DwPeIgD9VaepdmD1JrXGoVh0U8LW19q6zebild0fR+3aa2O6WwMOHND3iLHC611XF9qkEKuVBbcWaHcmUQyILpZYGGfaeqfYmudBz1m9sT9luTYyJdYW//I6DY5aHim1Dp67QWd1luH0goenjU3jmonT+I7JE5ju1lAbL/RVeRb5KBOFfOu/t/yeCGDkSIVGUkLNrI3D87RBYNTdkoWs3nLNRjshEqvNmBuui7G60jEgQ4jV/+7f9bqDdY8VQCGzen25QjY6WIj0/aqZBHjTM27Fxz/l6VMnL7ZajAHxMy0kbUCs/su/1LfEniFqlRiQkh0PFNbXxeRrV1f0W1U3QquzzmdgBLhMWVqsrtexu9TEmfl+Mey29FGtFs6lYQplF3j44VW+t17GuD62sVqGxWjEugtmV1CvC87d9XIIySp3Vu9wMP6USR2rdeJEr90odeEFyzOrh5omZBlw9CgAoOpGej0/y3rO6mqlH91TcWO02jTy2rZxVpvzr+JECKlqcRmk1GvdZMSxdu5b5yBWnzyJxa6Pcb+DHUELC3PaJDkXVjBVWn4i9RbE2Fk9MixWM+syN0e8KJSmiFIHNTckbTeBq4UvK6YTJ44eRefwNMYf+zrikM4BHWXaWV12YrpiiIV8zjRbZ8JwDrTbWOZIoGB6Wm/f3xxndYqI6vtC31ltZSndx2q+r8BO6ArAxbGJARH9bdQEqwG9wln1us5oXaJZYEkiZQosEi7ayP6xKmBjzuqlJSzGZYxN9l0XP/bx1yKtj2PnLjP5yDOrR71u8y3qxVxC112/iI5SkKHULn/Pg3XJHiSZjcaRggi3ilitV/fXyU1TCjLOMO53kGYWfvxpd+GWLxSGBkmi3V6lFWK1O9hZDaAvDhimXvQU3HrqafiD+16D+viKIUi+yDCMWI0qqm6I3eUGTn+zkLOdu53yPHSqhRClkCqhndVU/XangyTTBbWqbohWl2ih0UwcnDyzeoPFfpJGV0cfrdbx587qAdEyW04+AdmxQxcunCc4D4rbvstl1LxQF9Yb9XMoiNUlJ6bJWTf52nmBxd73tt5Ci3FWj5cijI8D2L0bu4Imprs1zIRVPG/qGMrlFe/VcfSOrkF9+OIi0kxgT6mB//v2Bvxrn4pLKws4+aVH++8/F09MP5Al639nycFjAIDoyGn882/ehslsBjPTqvf+V+bUwnHgiBQyWeNYlep/lwVBddcVJUx3a1BnpnGsNbGsiFIvs7qzQSvySvIFpmqgF9lGEbxyejEghcccRy9arPUZnGOBxd4fWWsHj+mX3Q18TJ6TIR6Qg72MkychVAbs2IEXvesN+H+feg8m/A6qE4U/mi+IjiimtaR/1gJu/tywHDiwouvIndXjfaeub0tEo0bCFIrgWSV/4xFp67QLALBt1OsYOm9/ZgZYWGvtcEXBvn4ExPpyRRIrzEcVtH7pt1B9xj5UqgLPqh/Ho/e2gUZDmwMm+mORIMCGhKQ0TuFnXezcoXSN1fxYVwrrDlFm9YrCsFqsXt9ZLTMbFS/uO6tLSzi9VNLXZJrqdiuFNjYw3gKARx5ZQ6wu5EBPjme6Xs4o11eaLs/adxz9uSb24Httq6XF6ikX2L0bjpUiOWkiwKREM/FRm3B67epdQUMc0+IiVKp3XWJsTOcMLiyY72t5znzZiUnKBQF6qrgtxGpz/lXdCCGVAavfNB56iLDBUZzVR45gMS5jwuvoceWiAqTEfFTBVGX5ieS6Wj9gsXp0WKxm1oXcWd0rsBghkYLmIlYKsdRh+YEtaRxUho70UPdCxHONwS8ehijq5WsPfZMcst2cc8lgWo/N0CNOnQIuu4xYrDbnkmdJxITFO6URq13EdMdrvi/flmQZ0HmBJsezAd/Xg7T2iG0rhTTJtHBVq2HC72Bxicb9KeNCZjWhWC2EAup1WEIha5+Ds3qqP0v3Agsf+YjAy19uHsgzq6liQIoTEttGxY21+LUaSYIkteDYmd6ivns3fut5N+PnP/l9/UmjlOiudFZ7SkdrrNXXGpFkPAhhWQo/cMX9eOKQWPZ8IymhviJDsTyEsxrAWWL15GVV3GtdDyUsjF1aW/47rqu/v2HE6khnfO8uNXHmUGFmkDsUqcXqfDDuROeUWZ6mqxSKCUPEqQO34mmn+nqF3zZCvstCZIMFylUOdL7hYDLorMh7MAiBip9qd96FXDgmvydOTuKS8hL2nyRYcTV5qo5n6azikkQjGjH/1rQLAKhWMeZ1sThLMC5axfVXGZSLXhCrx8YA7N2LnVMZZsIaZro1PPcN16BUW6E25u60Qc7qhQU9oXtKHW/+jTHghS/ElbU5HP7M/XqQUSxaaNoc5CiV934bABD+6QfxV5+s4Nef/mlMHzJvsNvVfWy1cFyOo2NA5BrHqpTOpAaWi9X/+oWYzqbwwPyleNlnfg31qcJn4OjaE43uiGJ1fs1WAxN7YI0++Mqys2NAbL3jQkZrx3Usi48Zlvy7W6tPyMVqb/jxg+cqXWBxyH4mfPQwAjsBrroKVz+7jN/85Rh7d0hUdxVC1vP70SgKkBGoqsHyeyKADfWJP/zDK0SYJEEjCVAfM5+RiT8YlNc8zPEmea5wEOhYNwoFrCBW2yVPO+uHaFfKdTJo88xqb0UMyCBndawLRD96tIyrnyaAffuwr7KAUw/M4vN31dCWHsq7qr3Xl/xMF1gc8vuav+lLmGocxCXp0X4NanPN9s7pPFZilIUmI1b3CvYBgK134647LDLX185aqMVqx8HuHSnOdEwMhpRoS29ZJE5v5WiYic3Jk3jkznl02iv6pBXRGhPjakNFslclj9ty+guHgTtEsTqlTIHFMsZ3ecCuXbi8Oo+jD7d6x9pMguVi9bDRmXNzZqeOAnbv1uPfQ9NAHKOZ+Kjmp5bZebbROM5mE/ipnzr78TDcJjEgRgDWYjXtAY+PEzurk8RkVqd6UXwj88RTp7AQlTH+1CktVi8JoNXCXFjBZHX5Oe96gsVqIlisZtZlfp5eUFTQgmKibJrVfSmRpH1xIlogWtI0A7yqGyKepROro9SBb+nMajKxeqU4Tdbw5jAzA1x66eY4q32bVqxOhAfH0pEVSUhXCBAAfDtB1CL6EPICi74F+D4m/A4WZkevIi8zC66dAaUSJvw2FhoE7k+lIBPVFxSp1lak1BOxWk0XJtrICv/SEpbikh7kFnjJS4Ddu80Pvk9z3ebbUldUfPftZO0s0Xyyn+f4eR5+9EWH8PD8HvQqCUmJMHXhV/pKQ29r/VqD/DiGVFqguvLSBJdV5jE9XZjQJAmaiY96uTDgyotBrtfVriVWTwL3PVHHD/+IjfGpFeeSmTz13Pzr0G5mqLoRdpcaOHN8lYrvgUsrVpuiLFU3QngOGevz88Cv/Mry1JZcHHTLDipegna8sYJPa5L3BabAYnsjYnWng7moiqlKuOZ2/XKQoSOJinRtEirPUZicxBuf+VW8567rR280n5jnxaTqQtcGGDWgsuCsHid0gadquVhdduKzxYYi5nycKMfaWS0Edl27wzira7jmJRN46lNX/M6QOa3TxyKc7Ixj925zPNddhysmmjgyW9H59W5BHc1jhgbE4shQf26nF3wcaU3hVZc+gpnj5kBaLS1QjS0XlteNAcldxfby62XnVVVM15+Gx596A37yX53AT/1yIQfE1bUnmqE7mricLy6UHJQCE1FAsAhylrPaddePAckXZM7VWT0gBmQjWdgbjQFpHJhG3QuBK67QD7z+9djzgktRqxemuhuMPlgV49BcJlabmJ1BY8S/+zvgC1/Qp8qBA8Btt61styBWmxiQaNTxbH5ueRZ61VGJnNUK0N/9kEVRoRRks4PmyTWKoEupd5+W+s7qwEkQJQOc1eZWdM+DAa6+GsC+fbiksoSTjzXxho++Fs04QGV3X6x2fUsLSUOeWzOfugM7gxb2Nh7DqRPm2smvlQ3maxeZnQUef7zwwGrxRfki23qFYc29qSdWA6jv8LTbvdHoO7arZ4vVKh6ioPUf/R+0p9twuyvqdaxwwgcVW5sjRnRWyxX9lu+pwQUxu11dKD2r6R2Su3bhqtocDj2e6AsuTdGVHkrVYmb1kPOP2VksRGVMjGfA7t3YW17CqUeXgDjWC1f5qZXfZ6ONzZNuuQW4+eazH9+WzmrC6D1A14zoFUulwCwy2ULp738jdW1aLSzGJUw8fSfqbhdLLRuYm9Nj5h3Lr/teH8Ni9ciwWM2sy2Y4qwFo4S8jEqtNzqJnG+FrgUioNYPrihMjphIUo6h3rCV7A1WIh2h3GdTleImJY6Ba3TxnNeXNXWZ6IcS1UiSEDnsAepJ/Di7NVckLLBpn9YTXwcL86JMcHamgtFjtdbDQJBCr80G+SGkXbQoxIL69QbF6fh5LcYCxvavk9Ob4Pk1sSb5FPVgxIRHrTEjyLad24fk9e2AJhfTE6V67AJYLP4NEBCNQ7am18eLrM+wqNTG9UPh9KdGIS/1q5+ZYK4OKyKwVAzKltb3f/E3gf//vFb/j6jinpDv4mmi10Beri5nVeQxI2dNbRztEkR0mZqfqRuieQ5tzc9p8d+ONhQeTRN8PPIFq3UIrCWjuifkuC5Gi7oVoraELrEq7rfP3amvf+Hti9QU8CJfdREfmjI/jxXsO4+DiJNqjFjQ2EzLbCMD1OtBICMTq/NqsVOgiS1ZzVg8SgHNndSXp5TLveNFTMBtWMd2tYddeG1/+8orfMTnQg+7lv/OPz8NH9r8Iuy4x/ZHr4sprSrh39gocbk6hVCocl23rCX93gFht3MH3ZM/Hd+06iEm/jfkZ89k1m7qPnfD7v+A4Wqhd67TNhRdr+XnieUAoHTwx+SK88icvxwtfVJg6WRZqXoRGHKxbxHYg+Y4Q30ZQtrWLcNSBTJqa6I0VmdXrZZcrpfsOarG6FwMyvJDX20Y9pKC4dHgBY263V1QVAPbuRV9IMsc5TMTMuuTO6hW7jYapQ3PoEPDEE3rRcudO4PbbC0+ayK3aWDECI0E0aiRMfm55AiiV9O4l4hiQXKwemLf/6KNITs6i+e6/0LkSq7TZkR4qtWJmtUQ4QKzOz+e77/N6YvXe8hIevuU45roVHGtPorKnv5NL5KHsw9zDlpZ0/1dqapHy3pO9310WtWTE6o24aj/6Ue2w7x1Gu62d1ePLF+8cK4Ucwlm9oxb3xGoxVtfftRGr24l/lrPatdLeot96zM1m2Bk0z47Wy+cIfr9fB0ATA1KYavTi7Nb7vlotPLywFwtqTC+27tyJq2qzugZ5fq4KQFj976vkxOgOc33NzWlH7bjoO7Yf6wKLi2glPmq7zC60cyxA/Y//qIcRxa7ORGJvG2d1qnScXdimGxeq4UscDE8cI1X9AuTdjTjBWy19Hjx9F2xLIZMpMDuL+aiMyV3Lv3N2VtPBYjWzLpvhrAa0WB2nDlkBuDxXOHASmrxHoDfAqzgRhlh4Ho7cWe0YkY6i4jvQ/xzNQPxCd1ZLqQuVUBfvVDDOaqp2lUKSAI4wYnWD6HMtOqupbuzFyuS+j8mgPbpYbUQv18mAINBu7bY32qQc6F2zjie0+Eux5Rnoj/RqNVMMcgMnwtycjgHZV1v7NZ439HXbaAAf+cgaT5otmZUVearugMJX2lldGM3u3q0LoD02r3/O37+zfKKT/81VMd/F3rEOPvJxHf8w1/T7rzcFFuuVFWK1G6O9npi0jrMaAK66Sm/vW4b5DJLOgA43y9DqCFScCHvKDZyZKQxlcpGu4sO3JMIukVhttg7W3BDd9dypazA3B7z61Vgu9hW2xns1n24B1zir3aqPca+DxdYGFpg6HcxFFUzV1+6XgpLQW6gvYGd10kl0YdwgAEol7Co10JoZ8bNNUz3JMVvUx8cUFqPhioqtS8FZPeZ1sbhA0BeulVk9YDdEktmYqCa9a9P9nu9GMjaFmb3Pxs6dq/zOoH7LEHUyfOnkM7FrX188vub5Jdx06Pn4n/e8brmIMsxiGPpi9fQlz8XYDS/Wk8cw7m0Hbyc+KpMrxGqRrX3aKmXGkme/l127gDvuwNnOcgD1ss4aHrVoXy76BCWhhZlRz6ssOztuqid8rbMouhnOarNNfEOZ1a5CnG5ArD7W0M7q9cRq46Ycya1sXKq10vIYr2Hq0EgJtJoZ9t94L37gukN46H7ZH07lu4JK5kPyPH0PGxCBMZCi+7Vc1tcAxVh2hVhd80K0FgeMZxsNSGWhlfhofvyf8aY3rRj6GbG6XOu7lX3b5BWvQ37pfeNbjharn/lM7C0v4WvTTwEAPNHahXJ9+SK+UkNGUT74IGbCKnYGRqx+zJhWpERXuihVCi5wO0G4gdiWb39bRyL+1V+ZB5aWdFzHZGHcNGhHiDkWmdn42VcewItfbB6r6wrWaqnRy1nvLQIAPbE67gz+DObDCnYELQSqu3zanpsoAvPZmvHeMG7tnCgCvud7gHvu6b8XvSOk/zkGXrZ+nB0AtFr4z199A74xfYVebN21S4vVJwq7wMTy+0zJTtAdJn7NiNUTO2xg925cXp3HkccjYG5O77LYa6qFWxbKboJO4m5onnTffcB3f7eu55STd//bwllt3MpVJyL1ymUZNn4vGkTurLYyvVjR2MB9u9XSUTJX7dALM5kCjh/HXFjF1CX+spf2xOoLOS5vm8BiNbMmSm2Cs9pctJ5N7ay2TWY1YcE6s2JcdmLECZGobJzVfk0XautQidX53Wx8HAqA6tCK1ZY1uj5ZREq9G3FTnNW2HD3jL8cU1HKtFK5IETeJRg25WG0RFM/JMYNV17cAz9Mu6MURP4d8Am0DsCxM1CQWosroi0x5MUjXQlC29JZnihFOfkLVatpZvZHtXblYffnY2q/xfd3HDMhSBYCTJ4FPfnLt41zVWb3eVs88S9Ra7qzeW1rCqf39TL68rWK7y55bSeG7gOPA3r1DZ0+e7LuHGvEqYrUTob1edfp1xOrJyd48ajmuC2+YuJ1uV7uEjLP69FxBAckXWCrGWU3VFZqdC1U3Oqfb1tx0imfsXsRCUYjMF1o9XbBvw0VB1yIvsFgrYdzvYrG1gYiCdhvzYQVT42t/B8LbQNblFpF0TTHSIADKZS3Uzo0uVhe3PE/W5ej5nCjE3lQqOld6VAc40F+08ftCir5mh3BWV2XPWQ3LQlap49RiqefYW0ZetHA9vSPUBbW/OXsZdu/rX6vXvGwH7njdO/HQwiWojC3vs4ZxVuf9xMySh/rush5UZBlUqw20WrqPnSzkrueiz1qndi7U2md//i94AfAv/7KGWF1JtcN+lPMgFyp9G0HFphGrc+EvWMVZvVkFFikzq70NxIA0m2gsKYyV42WroK95DfCKVyw/zpITIxxld9RqucJG+Fo3Zgf6FGk9cgwHPvUgnr7wdbwsuAf/8umw/6RpC4DOrHaS0cWqYq5wqYSqG6E9T5tZDV8vjA7cFWLGG80kwG1f9/DhDyt88YvLj7Wd+H2xOo8ZStY/IWWiYIsUDz8q9DW6axcu+e2fwjemr4QtUhxpTCyvF7yRjPEHHsB0t45dlwe4pLyEk8f6NULaRQE4LwQ4jLO63Qb++q/x4D8fwXuvfQ8+9D4zXlpaQisJUNlRyFkftMgG9O75L7lmod9Pj4+j5oZoHpkHUl1noroiEsez5VDxhotqDONeF+NuBwszhc5+ZRSK56HuhmguDX8Pe8cvn8Z1e2bxX39J9dpcllkN9BfwBojVSWbjzqP79P2rXMZTdrdxcGFCuwUAmIoEGpNZPVRsy+wsFuIyJnZ7wNVX44r6Ao4elMCZM7rA4r7+vKHsy43FpEUR7LCFS/ZkOHWq/3AYArXaNhGrTR9TdSO6+bdudnmMFQV5ZrUj9GLFBsVqABC1as95ox56GDNhFVP7SsteymI1HSxWM2vSbAKV0edhy8md1SWHVKzuOattSZeXlMeAuLRidZQ68OrBORVhWK9dAMD4uBZ7WnR3NyHMdkzC82AzxOosSmCLDL4l9fdF4dTtxWCYzOpRCxYa0m4MSygzsCU6t4q5hHlm9ahidZ5Z7eiB50Q9xUJUHv26LbisSlVbV2UnEOmUTHsFFjeUB64UsLCAZhKgetnE2q/LM6uH+M6kXEd/NxOHSnkVZ/U6IoJ2VhcmAXv24JLKEk4djvR7WDnZBYaOAemJCE/RTiR14An9c6eDRhKgNrZ8kO8N2sGwhli9Y8fqgk/+WtdKEXcHi9Uts6V1R9DCbNPvX+8FZ3Vgy6EWFoYiSZBmxll9DouMczd9GZc/8jm0ZwvXThwjTm14gaDNEi1EoYyXYyyGGxDS2m2dvzexzmRz0ALIBYCMdZ8N1wUqFS0kLYzugNZRS/q+PT6msBCXR7uRKQWZ6t1mKJdpCriaY80nZACGE4DN+fh9z57Bb/xG/+FqVQt+1mrDlbxg31r9FgAsLiLJbChY2LW78PevuAKXVhZxYGnn8m3vpthsO1x/lpq7uWcWHb34NTWFmhuidXS+L1ZPrRB9rAxyrQKLeR+7irP6BS/Q6x75zpAi9bLU2eWjitXKgltyUKpYWuwYdQG3J1avdFavEwNyrgUWhxWr/Y0UWATiYSf7p05hKS6hPukuc08+97nA85+//DgpiuA1kwDV0vLPtTxEDIiMM7QePIwDjV24uj6Nn3vKv+C9f7Con8zHQOVyr03fkgPzmgeSL7K5elG04kSDHdBDtisA/d0HwXC7QsyYupkE+OKJa/B7L/4n/Pmfxcuel8qCGyx3Vq/7GSiFRAI7ghYmJoAJM4Tb+6xJdFMPz548gUxZvY81b1cINfjckhJ4+GEdA/LSp2tn9RnRe65VjNawbZSGKAYJALjxRiS3fw1SAlc0H8Qlrcdx/7d1ZEc78VDduVq/tX5m9bI4DgC4+mpM+h3M33cUSJK1ndWDxlsAFsMA434H434Hi48X7L/5faYgVk/6bczPD/4IAADHj+PWTy/id/y3wV88o38vL7JZdFb7Spta1htzhCGkstCJ3d561RXXVnCkNQU8+qhu01q+cKdjW4boC+bnMR9WMLnXByoVXPHsMRxpTOjM6jRAdXd/JaQcqA3FpKlbPg/MzWHv8W8sE6ujSJs6tksMiMxsVNxI74KgmH9Df4TkzmpjPLHrFT2fa26gL8y3e1UqwNVX64XPRoxvzFyJ571khVi9wRgrZm1YrH4S8fa307Y3Pw/s2bM5mdVWraJdZFTO6lSLJ4FNGwPSd1YTXSphiDiz4Y8FelJKJFarMNJb2sbG9LYW4mq85GL1Q4+hdORRJOtNcDfaZpzBsTJ4dopIEhQnAvpibZ5ZTSRWR20J39L/ka1CF90zuVjdGPH8KjqrYba9xyUSsTqvTl+qOehKd/Q2s6wv+lSrG1u4WlrSxYIsG1bgrf26PLM6HBxbkiTraA15ZvWKGBBHpOtnVmcW3ILbBHv26MnTMQk0m8gyQNhiuRVhGGe1srQjHwCuvhrjfgdLDxzVP8/PoxkHqO8826G4bsGfNcTqeh34/OfX+J283UGZ1e02WlLnOjr1MtJM6O8Q6J9bFd9s916/qaEpVDsfanJTZH4ec08sYspvA8VdLz23YaHwFZWz2jiAx+sZFuMNRFXklc13rNN3uK6+31zIzupYwbFSff4bZ3V3cXSnatFZ7fg20swavZhUvmMiCLTTiyK6Jne8ef3s14o7XGZ1rQbs29d/eN8+4Ed/dI3fGRQrAQBRhCSzETgJdu0qPF6pwPFt7AhaqEwt71+GyqyOU3hWgul5B7UagKkp7AxamHmi0SuwWNlZUKjyPnYdsVoqe1Vn9QtfCFx99er5mbVKtmFn9WOP6ZzS/pvpO6srNQtt6ZHFgCxzVg/6vrLs3GJA8pWMtbbgmfG06w0/JvE86LjAYSb7J0+ikQQY2x2s/7q8CN4o6wBJcrazesic9WR2Ca0mcCi+FFf91PfiusmTOH5K55qmSy1YltJ2SqBfYHHU+UdRyCyX9e6NRYK+e4WzuuzEg3eFmILOzUufia8uXIufueoLOPJo4Z63MsrMZOLLbJ3PNcsgUwu7Sk1cfXX/dZUKUPMjvHj3QVgi69WAzNsfKgbk4EEgijBj78bO5+3DvsoCjs3qHRx5nFvPrdyLARni+3riCTy+uBvPeEEVCAK8oLYfj307RLLQ0qLyroINvGdkWKe9PPorKIz9nvEMTAVtzO+fA+bndQTleEFQc114lhwsVicJFjo+xr0uJrwOFk+u8315HiaDNuYXhzxnjx/HfFTGVNDGU50jOLBfrVoYdqjManOeA+jtDAqe8wwdH/Ltb+v4hqBwr85z5ocphtjpYDasYsclejx7+fc+FUdaU/jogevRzKrLYqzKvl4kHFakDO9/HIGdYG/3IE4f7ndMYajHy9vCWW3Gx4Gt58xUB70pYnWSIFMC1lhNf//DFkxP0/6u9XIZeOYzMeZ1cbpTR1e6mHra8pVszqymg8XqJwlSAn/2Z7Rt5mI15Xy0t901H4xRiNVmIqSd1QldwTozeQjsBJkCTYcTxz1ntXZo0uxvSeNUT8zrdS2CE4nVSgHIMj1pINS/5R1fR+nY40gadOFWMkrhiAy+nWg3DkWsRM9ZnertcpRitS3NRITSWd3PrB73OlhojHh+5S4XI5BOTIDGWR3HPadPqeagkxI4q/PvylZAubwxZ3VuA7EHjIosCyU/0+L6gP5gXWd1kujto6s5q9dq1rj+lgkp9Tr2jnVwcrEEHD+OMHVQ8leIEEM6q3ui1tVXY1fQxPSD07oDWFxEIwlQ37Nc9BlYVG21SBJDL2JgJXmBxSFiQFpJoF1CU1MAgGx2vvd3pbLg1gJ9P6Aa6McxUmWh5MRIUmtj94OvfQ3zUQVTQRtQhe/PfPaeL8w5KxEtEtwTexEQNsbqZoFpI2J1VMHU7nWuhfw7vVDF6ixDklraIWtZ/RiQxRH77+LuFaC/EDPK2CBv0876i2HnkIl+FnmRTa8vpJSdGJ318jnzXRbB8vvGX/818NKXrvE7JlZi0Bb1JLPxhuc8giuvXPHc296GK59dR2WisKhl2zqypDugqFqUoeLGmJmztbN6xw6d4X9gCQhDdKS/3FltWUb4WmOxcbXdK4YrrwRuuWX146iWMzQ3KFa/+93AO95ReCBfZAsceFVPT3KpYkBKy92Eg2NA7H5hzmHZlBiQjTurxy6prP+6XFDc4I6bU6cKebK5o7ay/P5dchJ0BjmrFxpoJT4W7Km+uJFIqMUltKSP/5+9946XZDurQ1fl6nj65Mkzd8LNSVlXCIEEKBAkFDEPMGCEn8A8kgFjwGCDwAEeGQuEJWEZkBA85CeCJYEkJF3JQlfh5jgzd/LMOXNip8pV74+9d1V1n67au6pr0J37zvf7zW9mOuzeXb1rh/Wtb612zU+Af0WBofiEqTiteWdIE9K1GjFa7VbA9hsDq2uKxwd+KKjaDRoIGjPoGFZiYB9FyfdMXQNFiuAHOeOGjq2l+oDoVadi32ENL371LOoNaTTRpJB9PdO9zwwqh7YaLWHpIAGRh54GrK3F42DEDFL1uGaQke3gvseauGTP4eCdc8DyMubNAdbP9vG69387vrR2aISpy02yAZOZ1bUa5pZVrFl1nN9sIoIMqZPafInutwYDbDk1zBpDIit2JbWxZWufkmZWD4XB6v6ZNTRUsi4fMy/i1Je7iWa1mnxf1VBI4ooDVgPA/tlBIvly++1oaA76j5wl2tLmmCmqCGksIoakV+0WFveTdWruZbfjkc19+I6PvxUXBp0RTkZsQC24HgzkFhqqS4gn96/EjztXNtFuBNcRs1qGKtGxVAXpAglYrfGPXOJBL6jUbonNWSwGA9iBhpoekqz1TTdhRrfw4Qu34Z7jaxjNhgGyrhJS5i6zeurYBaufJdHtApub1ba5sQEsL1cMUnoRYT62WiSrXaHBom7IZKNQsQyIKtOJpqK+ujS7XVNcWG4FYHUUwXPC+LoSY7lqrkHwufugXjwDbXutWmZ1KKOmevAEXKiF23RDAirLAcmkVwhWx8xqHuNTMJxhAEPxCDjlVmQumC5RpyyXqS8BO0BTsLozJxOWZhUyICFxp2/PKqSEeto2498qBGo1cm2HgpsEBlar/PuxZkYErOYACblgNWNWp8tSRTSrIwWaknpekrD3kIbLwxngscd2tknbjTs0KRhAxZjVe/ZgacbG1dWInM43N9F1a2jtTblU8SRLAAR+BEUKM7QDMoIdnhzOwXw4JKWybRmYm8O82cfG09vx9/RDBWqTgtVV+QIwtrJE+1YESDp3Dut2A/NGH2rgJnOp58EN1RhEaGk2ehsVTLSULa/qMrS6Br9I8q7fJ2Yxe3Kc0DQNshQhdJ+hjJEYAKbjk4HV21NeW2ompWh0TGsaVBFDUE6b3hizuhJ/ZFZKrSVgNRcAzgCr9ZxiE5G5gM3Nv/LNn2W5pSTabRw+ru8wwaurLgYOR6fWDdFUHVxdl2IZkOVaDytPdXGuP4s1t416I3X/SxJUlew/Jh4gGatYnvxddvSdhmxo4mZtILfivfcSXC6WUmXrt6kmh95pF/CYWb3TGyEvKXqtDBZHkqICQQwWxZnV224N7f05Bsm0n2Xk197zHpK0AQB0u+j7Blqzo9I1RAYkvx1vgzBnt4IWZo4RceEZuYfuWbLOtpup7ypJMI2IbyzHi7QJXr2Opuagvz0lgBKGQBQRMEaWE4k0noQRnQvOrTcwv5esMbo/JN4t7HeWpKSEQVGgSgGpnsraJ1Mz2RfuPY/v/M7Rp37iJ2W84K13odEY+70VRWyuvXoVALBqt4jB7N69JCnx5AUKVptozozra+fvI52zV/DWT30Xtmp70ZmVgKUlLJh9rD/dxdPdOZzqLo0awzKppbwhwPYnxuhnzx1o4MPnb8cPf/bbAE0dLQ1RFOiyABg6HGLLraOjD4lh8yp9QxSNJiwAQNOIDMi22Nn2yYdd3DhDANrj7VWc+sJmSr4oGUuSJiA9Rp/7x7f/ffI19+3DDXNdPLm9hE2ngdZMql9Ms5rHhLcsIIqw5s1gYYm8VmrUMT/j42v3PkGA6VTUaxEh4Ajes4N1G03NwZ7aNi5fCuPPtH/td9G+8sT1waymCQZZovdoxWC1WcERMY6Ut1FddTHoC56/+31qrEt/15kZzByawV+v34OXve3Wna/nrYm7IRy7YPWzJLa3ydxQJbA8GBAmZZUgpW1FMBQfcdqzilmYGixqs7T0f1Ads5oBlQAqA6udMMWs5piGCAUr95Wj5FDereYa+H/+QahSCH31QrVJi4jKq0zjyD7eJmVWEzaOWpn2qx/JUKVqNasJWE2Z1YFazU3GQDpTBXRqLjctUMfaZBWZDZOY71XArCYHVwlq04QfyZWA1T5jVtdqdC4Q2yS8+09N9FwjAWxzolYDLAHzK89LXvLgg8CpU6N9HXj6DsMfEc3q8RL1fbd2cGnQAT76UWJMNE4sEzJYTIEIkoSlQzX86ckX4sInTxEZEM8cBQE0jcumdD2JzJ1lwGoRZrVvkEMiA6hOUy05Boo0zYTlVIU7rOsiiCQoUkh0OousXZcuEbayOUBH7mJ7Kxrpq2bIgGmirdnoblXQ15QMSAx8FWVW781BKEXlWr5SwdZuqrVfGVgd63MmYPWcMcTmxnRmbSQJFVKw2q3mUMb6WoZZXSuwLxEEUvKAyiNHsGMubGhOfl+jCJ4boanZ6HalmFm9v7GJi6dsfOfHvw9fd/zsDnMmVcsxPcphVueGphH9W8F1/OGHgRfc5eA1XzNIJJEY+7WmFr9nsyKTWR3A93I0qyMZak6uamIoCiQQ34iJkZ7rBEM3JHGDxUuX0HVrmDmS4zlB+2kqfuHt/LlzwOOP0/90CTu6uTAqXSNimu5vE8DDlQzoLQPodLBodnH1gUvouiZazdHXGwam3yOmq+5qNZK0mtbENQgQRSnskxIkuHMX3W88eamJPQc1oNPBwdoaLnz5KuD7FI8eTTApCkjClZMIOTa/hVe/evSpt76VyBg1JuyLhPwBKJ2+75kkoXbLLTjaWsPTHzudkA7aatymqXhcEpJ/4Qo2nAY2jT1EX3t5GfNGH2tnB/BCBYoUjJIO6L4od45NJ7tSMXe4if+9chTn+3OQxjNQbB3nkQMGA2y5tUSzeo12JN5XpZILTLO6K7aGPPGkhJs7V4AXvhDH2ldx8okA8H2S3O+kvjDTGBdhVi+l7hVJwg2HAnzLh/8V3vHoy9CaS01sTLOaRxqjg3rNbZOEBY0PvKuHn/vae9Fsjc5pMbNaEKzur9loqA5hVq/SvmxtwQlUzGgWnKowjWsZdH6K54OKkGV/4EAdbMHUw0ogGADJXNpuo63b6PUE3zcYUPmnZB6aecFN+IezR/GiV0yo6NkFqyuLXbD6WRJMrpP9XUXYPQ9tw6kUpHRcCYbsJwYiFYHVXqhAn20QJl0V5kTATrC6Qn1to21AVSKiwzYtkJI+mJsmWro9PWuChqfVoTJWcYVJCy9UUFNceBWC1UyjVGk3iI5o5czqcDoGXSqcYQBD9iFLlJlSUV/9UCblu6YpbCL00ENEP3NisAMm23dWZQIXl6gryVwwbSaeMhQ1NSRgvSLOnnr/38/jVHdxRHcuK8yaBNvXuJnBNLP6T/+UMLPi8DxyyEmzfaiWaK6eaiiPMqsBHHjzPTjVW8Sfn34ufuJzb0K9ObasixospkCE5311HQ9v7sP/+B8Aej0iA3KgPdJX3pzg+RSsLmLlTTUUuWD1cEjKbzsaMDeH5VoXK+foWsLuA1OFaYr9VkLBAGC5ILPacYCrV4lp4VyEjjbA1lm6UNNEq15TAF1HW7fQ3a6+ygJAIWZ13zPQ2pNTTi+aVPhKRQwA02vZIEY6wyJGOpOC6UsbdEKkLLL1jenM2mIWOCulr6IaYFyyhALAuaaFZcFqKUAWRsn6MomxzeKrvxq4447UAyLMaqYhr5N7mzGrDzS2cOGqgb5n4Lfe8pkdb9M0kOToJCCBAbUlwOookoTnmY2/+yIWH/4Env/Ie/HgfU7MUIyZkaZZTeUhA6vHgC9iiJnxHmawqBU8HspyvhY2nT8LgdW6oEFVrwf0+9gOmmjva+a/lsmAFGRWj4DV29vEYHFpVBqrrroY5mnCex68vou+bwIaTQZSVu3aw1fQ9Wpoz4y+vxKwmjGrY83qAmzCnDaDKGVYZxgk0Tbk3DuehwjAU5fq2LtXAo4excHmJs59aQ0IArihCl0Z/b1VXUYQZVRDAEmiR5v8nZpN4G1vG3uQaZdz5GB+/38dxunuAqCpBIh77nNxtH0VT39hHbBton2rq6NtckhI3sVVrNtNbCoLBKymY+DKWRtzxgDvestHR7dNVLIkVwaEgYXa6GfPH5vFl9YO4lx/FpK+E6zWFZ9PGhoOseXU0dEtdHQLWxv0N/Z9BOGYvApbE7sC2a4owslzOk7MrAK33YZj7as4dckEggAbTgNzM6mxpGnxZ2aG75M0xxgof/NthEX/xbXDaC3uTDBZHCY8O5tcdVpYWEgevu0bD+Oe//Z9OHbz6Het1SAOVvs+Bts+GpqDeXOATcZId13YgYa2bsGtUC7zmsX4d62IWR387UegnHsatavnqmNWs3W61SLVjH3BtaBPqmLS8k8zM+TP/v0TXn8dGJFfL7ELVj9LgoHUW1vVtWn9yV+i9dkPV3ogdRwQZnW9TlgIvQradhy4gQptrkk2CrzNkmgw7dNrwayuq0lt7bSAPQP+KKO0pTnodity4tVqUOWwWrA6imIZkFx364LhuyEp0a9SD50eIGMZkIruBccKyX3ADqVV9DWtWcfAWgHzxr//e+DjH89ukwDg9P+1GiQA4aA6GRAGgEeDKTc3rE0lSjbMA0NIYsV3AlwadhKjnJyoNWRhZjWbMs6eBT6Txk0GAwIizKZKPXnMaiYXMAZWK3sWcefRAX7uvtfhMyvHUG+OUwkFZUBSQNJrvm8fPvD1f4i/+9Ic/t+n78S54cIoCM7T146iBKye5EiWFbRd1+bLgPQ9E82OmoDVF+kERUEszVRgNhRiylPR3B1EMhQpJAkm0Xn7yhUgitCNWmgfmcOsMcDm6c24r/G113XCrO5O39UYGKqpNHFVwMeg3wcASOMUv3Q805nV4/cKM1icdr+RNrEFAE0jBlabU4DLDEhSUzIgJcDqzU3gN35jvK8TNKvzfDKmYlbz9VSz9Iq//uuBV75ytM0Gr6+OAz9U0DTIfd9qAdizB/v3A0/3Foh828GDO95GZEAyAFCW8FQK7ktEgJRUrH/pLOb0AY7XL+Hkg4P4fU6okWtkGJCkCKFVkcFibdQI8JoYLDItbJdjsFgQrBbSrL58GQDQVTqY6XDuHQYoipjgpWJlBcnczJjVY7rCuhzAzdsnOw78UEbPr0GSaT+Xl7Fo9rHyyBr+ywOvxNe+YDDyFsOUyBo2zQY8XdlQr6Op2WyaLx8UrI4lc3SdJNo4BpPwPDRUF92Bij17ABw8iEPNDZx7tB8zlRv66HdVVZpg4knMZCRYJAn4yZ8ce5ABlXka42GIL5yexQPrBxLG8sICjh708R/+8VX4rS+/LOlg3KYH2+cwqzd7sAIdl4cz6HQALC1h3hzg8a09WDT7+O7XrI6+QVXJvcVJCAJI5iIaczfMwA01BJE8arRK29V5RtlAwqxeNjBnDLC2Qa9zEMAONNS11O+l6yQZwvEbAECIEJaGmXYEHDiAlu6gPyS/87rTwPxs6guLAH80ETI+eX3bd6j461f/Lh7e2IfWcirBpChkzLqcyY6ez9xIG5clRr2+8/yk6AqpQBVZD7pdYgSsumhqDnpDOf5MJ1DR1mw4vetAtNrzyFmWRVXM6ocfhyqFMHur14RZ3dJs9AaCa0G/j55rotlMvufMDPDCF2Ycc3aZ1ZXFLlj9LAkGVlepW233fbQ1G26/OsEkAlZ7QKNBSrfXKtBsZszqdg0Nw0fProhJx8A/plFakayEE6gwmlp1pZ4M8FAiWkpuodutRqfVV00iraH41THs6eFck4NKJ/FYD71NGaBVrGxsE8wA+6qY1QysZk5zFYHVac1Lwh7iT/G+j2yAjCZC0szqtm6huzElSMXkVTQp1lAcTGt+lgaoZBl7mn2sDNtCG0bPDnF5OINmh88GqdUAy9cLaVafO0cOu/F5s9cjBovzoywPVQrh5QHAoRKbXabjNa8Kocohtt0a6jNj30GAWT3CwASATgfLr34O+p6BX3vwG3DvD/zpqJoH09fOAqhYX+WwFFjNPTxZFga+juasBszPk/LJy/RzWMmzoRAWfFVgNRuzlFkdWYJtXrxI/tY0SIsLhJl0kQISbO2qyZRZXaAkMS/SppmGQUp31wXn2l6PHDqaOWC1qLb4VyriihA6jmo1AtROa1zIJLfYvcL0OQXNpCZG2hiWGSyW8LI4exaJpARt1w9lKHqip9pQXQzyAGDfJ8xGs8D3iVl/Oa8pKgERM6vzgXU/lNHQyQe32+R9B37q/8Dnt05g/2EVeO1rdzbNZECymNUTEoLcoDIgUS5SmcTGBjBvDnC4uY4zTyfyISFkMl0zhn13eo31oa+hnvKYjIGvPM1qtjYXCTYOsubudGJOMHRDEtKs/sTfDLFuN7AdzcTbv8wQZFZHEfD5zyf/DkNgaYn46mF7myRL96TmSF4SAIjXkKtWMy5Sw223YdHs4W/O3YGG6uLNrx3dVximJMystm3g7W+fkJ9npAtTSQwWhzka0CLBNPEZWE2BWq5ZneehpZO1c+9exGD1+dNeUgkwBlYrmpytMw8kxKIi0jUUqMzdGmxtwQ8kPDg4hoXF5Hsdu1HBo5t78cT2ctwW+1uEWc0SOqev1Amz+sABzB+fxRPbe7F88yzwpjeNviEmB+QnBKNI2gHUzs1LUOUAL1k+PaqDnWpXiFnt1tE50sGeehcrm/RCT/q9dB26LHhetCxYvoZai5AOAEAKfESejw27gbnOaJKN6wvAxsfYQJBvOIwbWutwAnW0YkzTiBSjzxmzA7pfE5W0U1VxD4OtLQw8YrYuSxEiP6ncswMNLd2GW9G585pG6rtKAMJ+RZrVrVmockATYZU0mcylzSYBq4eCa9JwSJjVqWn/4EHg5S/PeP0uWF1Z7ILVz5LY3iYC9FUyq+1AQ0uz4VUtA0KZ1cu1LlbWK9Bsdl1iUlVTsW/WImZjVciL0AkmxloqNFjU6wRQlOhjUwVjlKoRBRNtdKsAPMCY1RXLgDAwSQ6ShbmKZp2AAEmMWV2htEalBotRlFQYzMwQHbaqwGoGCBgGdZHnT/GelyMfxICUFLN6RrfQ25zyOrB2qYZiRx9ia2161mMaoFpuW7hizQjdX74b4NJwBs25PCcxErWmIsSsToPVlgW8+MXAAw/QJ/t9sslfHHN8zyvPZgzMCUDK679/Ae/+mv+OG2dWUJ/ZWeopAdlmeEyndRxI+tZvxS980xfwJ694F/Y/d3lHm7lzQlntV1GwmjKwak2iWX1Dew2nL9fI4ZslmAwFRkMlv1VVzOqQMKsL6dezDLJKgPWOMcTmJStu0w1UaLQaoqXZ6PYr2JaxiggqKTBSupsXYZgczHaIfKbiOmBWx2siAOg6Aat5rL9UTEz8p6UaAMqs7mN9a4qkOwOSlIiwE1UPVkHWJ0AA0BG2JJsvGLBODeBy2crsvs1gQE8MWabgZz6QMklPNTOoGeQwj/HmuvAjBU3Th64nuf/O8QVctmZx6CUHsYMGByoDkmOwOKJ1LhpMU1bEMDoMsd5VMWcMoCsBvIGHiCH9bLNpmkSypQJd4R0yIIwJn8usVqCoxcHq3GqbEprViibnM2ppvO+vmvjc6g3YDhpx/j+vnyIGi54H/Mt/Sf69vU38e26+GXj4oShhVu9N+TjQJEDAAau9UMHqsEUYtQBw111YXJbw9xdvxlftOYkRnQGQc50TioHVa2vAv/t3wC//MnDmTOqJ9P0ny2jUQgw8fsI9Nxizmq3xqiqWaPM8GLIPVY1iZvWJmRU89rQJeB7x8tBHB5GqSUQGJGtwMYmuIgkWVYWpevnM6l4Pfqjggc1DWFpKHn7pV0X4+Df/Oq5aLQJIyckcayoebF/NTQSwJO+piwYBqzUN2i/8DLS6hqXnThDYpvesF/BlQHYwq+eAG24AbvjqA6OVALRdXfbFmNUOAauXa12sbJvk+/k+rEBDTUv9LrpOAGBP4LdwXfL+GggLxDDQUGwMN2ysOw3MdVL9ot8rysuKZsiAoNOB8R1vwoFlD61OanyyOSvvugKAZcENFPFkSBH5h60tQlxZpJN0GJBrS5nVpuJfH2AnG3+1GkzFq0y6xG/NEma1ypfsEW+U/i7NJlq6je5QcF9i24l2PY3Xvhb4kR/JeP0uWF1Z7ILVz5LY3gYOHaoQrA5D2IGKpubAy9t8FYy0ZvVyLZWhnSYYO62uYt+cjYuDTjVg9XhZS5VM3bpeObOayYC0dQvdXjW3NmNWCwFIosHYaVJIFuaKwveIZjWaTcJyGlYoraHL1cmAuC5h12shUK9XKgMSl6hrGkyVupJzNNF9L0L3S0/hj3/u8ZgEGgdLhLBDQK2GtmZje3P6Q7TPQJF6XRxMywumU0sBqj0dGytWiw9WWxb8QMJle47ISnCi1pAJs5rTLjNYdF1A0yLceCLE6dP0SQpW1xZShwe6cc7TrM5iVjcOL+BFR1Zx08wK6gs7DySKFCJwM8ZuEIxctzjqdXzTu9+EQ7/7b4DXv370OZ7hD+3ruPYkN0TBatdFFEmQdA2o13Fsfhunt2bJHM1YVjUVjZZMtAMrWg+YDEgh1mO3Sw46hgTMz2NWH2Jr1Y3bjGUXKLO6ErCascANhTCr9aHY/WVZ8AKJSFLkaY0/05nVbN5OgdUi5mcs1teJNMWkdv0oxVamBouiZlJZfSWyC1FSmu7mgx2TYnMzyTOwdoMxFnjcdlaUAavjA3/OazL0VPPaJMzqnP2h48ALFTRr/gibVpKIfuThw5PfpupyvmZ1ODkhmBv0fnCHYmXfG3YD820PkGUsqetYPe/AC1PAn2GgWYUJHpMBSYPVqko0xrMk2JgUSgmDRVXOMd0tAVaLHvb9vo0zvQVYoQHTzH1pYrDISQj5PuIql3MPbOLQ4DF8R+tD+I1f8/EPF47Dk7RRuRyWbOaZ4IUELI/BalnGwre8BE/3FnHXW24Gjh4deYtRk4WZ1ZYFvOpVBLT+9V9PPcGqzmiSrdGU0PeN6XRlx5nViiLErA4dD7IUoVUPCLN6Zga3HujhkbUl4MknqZfH6HsUVco3WOTIDE0MNm/lnW9puw+sLI8Y60nLS7i5cwUXhx0Y9dT3lSRiBMepBoiZ1QysprGwgBFQPA5ZJvdsKGevC75PiC9jQO3yMvDz/17BgTvnduaf2bzFy7sPh2Q/tziD2ZkQG3adDDaWDBsDqwmzWuC3cBxYvo5aQyYT99wc0W8/N8SG08D83KgxrKH4+XsOJgMyae/y8pfj6M1GzGVibapyAC/Iua70+6/ZTSzMCO75ioDVvR5JfM3pJDMVRUQS0bZhBxpMxZuuAuKfKth3bbcJsNythuXoyzpUmey57dUqNPKQ3Jv0PNuzNbFrbNvktxLwNgKQjINdsHrq2AWrnyWxtUUc1SuTAXEc2L6GmuoBQUUH0igaZVbXu1jZ5DMZuUFNqrS6hr1zDi4NxdiU3GCTL939VgJ+0n5Jhh5rVkfOlH2NS+CYDIiN7qACeRUQBpIqB0SLr18RxZ5pbMshpKom8SgiYLUUAo0GmppTjckkA0AbenVgteMQsNqIEsPCyljgCtSaRjbNTAKBB6pudNE9vYa/+FMH73vv2GYs1gKk/2eAWm9KmRkKgqsagFoNs8YQm1tTtjkGUC13HFwRmQvW1+GFCi55i2g0+X2ImdUCBotBADz9Px/AwY0HceDB/4UL5+mGqN9HBECeSe2cmVFZXnl2Xon629+Om954O+qdsTmVlSdnaYmGIQFKJrGSJIlQyia4yKtSPrDulmFWaxphG/LA6jSLSJKwZzkiFTW93ogMSKOtoO8ZlTGrI5BLUlNd2D3Bg8v2NrbdGjozAObm0DEsbF714+/hpQ0WNas6sDpUYkmgjmFha1NgM97roeeaaNU4h6zrwGBxxBg2ZlaLXdtuF7j/fuyUZEmNLdburDHAZneK9ZZpSysAJAk1E0Lzy3jsYFaPs5lFWGQlwWoRzWoAO1h/maGqaGguhnnl9FQGpFkLdkg/HDiQA1arOaZ9cUVIcbBalwO4lsD90O1i3W5gfq8OzM3hePsqTn5xG5tOA3M1Ok8xZnV3yvtrErM6BlU5Ek5FxgBAxpeUb7BYVAZEFKz2+jYe39qDWktgfAlqVqfB6rN/8GEc9E7jno2/wd7+SfzOwy/HX33Hn+1oNzcJAMTEmo7pjICUi887BAC443uet0M2q4hmtWUBe71z+DHpN3HhTGoO9/0RI8BmExh404PVxGCR/j9OhnH0mh1SpdhsRASsliQot9+COWOAJ//481i1W6i3RtsgBotSrmb1DikzXigKFCniy7aEMk5vzI6CyIuLaGs2Lg1m0GyP9lU3JDiBJgRW2448UgkwP0/A5R0hSbQiJAewz2BWaxrwnd9J5Ap2KHsphFDAXWpY24YBudMmBJvt7Xh+qempPsXMaoHfgjGr63TMz81h3hhg/ZKDdbvJlEFIMPmeQc5cwFlnjh7FKFgtE4+b3CQAEIPVi7OC8zHdL+eywFk4DtGsbkrAzAxamo3+pW7MrDYU//oAqz2PJEvabfI7bVcjH0sqpgPiu3J5q5I243Gi62jVA/Q8Q2y/RcHq1ozgPMPWr12DxaljF6x+lsT2NgGrK2NWU72kmuJWx34NQ9i+ClMlxnLLtR5Wtng0CIFIMavNukw07iqSAZGkCGg2yeJbhckBmxA1LdEVnjYDyQBFFQSs1gtoMHHCd4KEWd2ryN0gZlZXqFkdH/gjWvY+xOZGBQs8A0AbRnXgjOfBCVUYOgDTrJQFni77NmsSbF9ArmKzj65bw7pVxwffN/Ybx4kQupnUNAKoTZsMYUwfTabM6uH0c5fvEwYrxTc67RBbbo2/CdnchB/KuGx1cmV6WRgmva48thfdnzz+7s/icG0VB/qP48IpJ6FcS9JomTpjVmeBSTnMagBAo4FvfnMN99wz9jgFJwIvD6wux0rK7GtsVHbtZEAAxIcSqd2CLEUItvvJfFjTINcMcrCqqCoGQKIn2xPcgHa72HLrmJmVgYUFzBoDbLHKBFplwQx320VKEjl9TTOr25qVLfWTjn4fPc9Eq86Z59jv9AxmVsemhUAMVnP1VGn0eqQg5XOfG3siCODSvQYAQFVhyP502w02F9Ima3VJqHJjPDY3J4DVoQKFmY7FJl0CYHVB9qsqcUBwdu+IuvYxZrWbAz66LvxIRrMWjgIQAI4dA06cmPw2zZCzQZ8wTPYRRUJVoSu+2P5ge5uUuC+pwMICbmit4cxjRHN5rkHnKcMgusKD/Ka4MYlZzZKXPGZ1CbA6l1nM5mVRKRjaJutTZvR68JwIH798M47dJLAvYaAXB6z2POrlsbWFx0/ruLlzBQDw+zf/Jv6fV/4B9h8ce78Is5rOy7N1O2FWA1hcJGN2fBwDgGYqJLkiAFYPrw5Qu3Iae3tP4uKp1J6SdUpJMas9oyIZkBSzWvG4YLXnhFDlAN/6ymHyfb/6q/Hipafxoj96G/7g0ZehMea7Iav0ns2RAfFKyICoeckVIF5HAIwwq7G0BEkiSa9GZ2dfI/rerGD7m2YjHMFVM5nV4CTZ0p+XMce+4AXAK16xs1EhiUfGWNY0oN0mbOS1bSID4uuop2VbqA60m5e8ZOE4BOxu0HtpdpYwq+0GAXBbqXtM06ArPuxhzp6Dcw1+8ieBl7xk9DFVk/LHFgAMhzjdW8CeRXGw2lB8seSl4xDNagpWd3QLWxf6MQZz3TCr2SCamYGpeBh2qwFofTdMZEDWpnWFpcHuIUVBqxGi55licyFjVrcLgtW7zOqpYxesfpbE9jZhklQNVptVMqt9n4B0WgAYBpEB6db47+MF0/1kOtBShNCuAFhmk2+rhRndwvZ6BZMvO4DqelKavD2lGDTTKlZJu23DQdfWK5kgfZcYt+myX5kGVQwkyGGsMTt1xIA9MamaNSoAP4GEodekYLVTlQyIBkOPYuOvwWY1lQB+mDDpDINqHXKyut5mH13PxNDX0Q428Mgj6SfHDgGahhm9AvYnSy4wzWrDwtb29MzqgW+gYZLvKxmUYcwDfWg5+aVeUwisllSxbDk7kJzensf+xhYONDZx4ckh0OvhdHcBgaSOsqh4DEVaoq5p2RvXF7wAO8FqhZjE5jGr/RRYJhSMWZtlTFNWs1qwLDWuRqHVKWi1cKCxiYunUjIgphJXxVQlAwIAaDZJubMoWM2Y1Qsq0OlgqdbH5Q2TzM+eh223hpk5yqzWrcQNfsq+xgxgwyBA7UDgtxgOKVjNee11IgMSAwGaRvWaxa5tvw/s2wfce+/YE8zHgs0Buj69n0MQwA5UmJSdZtak0szqcRmQga+jwZh/DEzLA5WDgFR8KMWZ1bl6qkWZ1YpCNKs9Pfug7jjwQwWNerSDWf2Hfwjceefkt6kacg0WcxOCWaFp0CRxZvWG08DcXgOYnyeGZeccUvbeGGNWTwtWBwHsQINRS4372GCRkxQtWvQoy/n3wrViVm9uwo9kPLqxF8ePC4zbAsxqxwG8k2fx6OZe3PocA/j2b0/W7Jtu2tEuMVjMb9QLFXTq7ghYvbwMvOMdk98i6fSeEWFWf/4h1BSPVCel/QTY9aOLfKcVYMutT7f3ZjIgDKxmBos8c0FKgPntt/eS7c+JE3jjXSfxxhu+jEc29+2oDovn21yDxXIVIUEeE54mbhQ5HAWR6UZx3hygOT92owiU/vtOgJriYnZmdP08cSK/IiRXv53JVmbMscePA294w85GRcwQA8eHLEWk7ZkZLNV6uHrOSrxDjFSfVBW6EsARkCCE6yYyIACwuIh5c4B1m1xfaf++5LUCsi2Rl6FZTeOWW7BjrVB1OXs9YO0OLfz6g9+A73uDYOk6NQge9gX2R7ZNzi0tGZibw4xuYevcGLM6vI7A6lYL88YAGxvVNBu4QSK9J7rn5jaazIetFsTBaschBoszgmuYohASTZYE424Ixy5Y/SyJawNWU3H/qpjVQUBAOi1KwOpeBWA1Y5GZGqDrWDT7uHqlgj4HAVn8m03C/BQpo+ZFGqxmIELBCTgMgZ//+dQZLs2sliS0myG6nlkJm9B3w8RcsCpmdcpgEUClRoiqAgJW6xXISqTbbZoERLOrGQNOoMZg9ZwxwKaAuWAUAT/90/l9DSIZskE2q4quIMwrnWRv6w6x7ZL78Odu/HP8u58ZLR/1QwWqnoDVbd3G9mBK9iczQNMlKgMywGZvyjYpONOs0e+r61ClkG+ER4G9K926EFgNRSEVF7zr+o9fBACs2i10jhHA+sK5EGG3j1f+7Y/gtbecHH0Dc3znsJVVueAY5MmAlGHSxcAXJoNJ15hZ7dkBdMVPDiWtFo6113DqySApCa5pCXO9IhkQ9lk11YXVF1hjogj/8eMvwvn+LDqLGqCqOHHIwVPbi4QK67rYcuvozDMGtI3usAIfh3hNVAHTJCCCiDmN4xCwuiEIVj9TZUCYJv64DIgjNsf0+8A3fzPwoQ+NYca+T/YEbNxR2Rp3GrCastNqBrnmZkMhlRslmNWDQep29H0Cii6kmdUcBnQUke+Xp1c+Hmx+CaRsYLkos1qSUDcCDPycpDuVAZlpBXxTvVSoeaZ9TLO6KFit69BFmXTb23ADFcZ8E1hYwHKtiyuXIvJbNehvXhWzOgggIQX2AXwGNAOri7BU43ZzwCRq+FmUtS/R92YGBYBlKcTx42JtmqoYWA0Avfsex+Nbe3DzC9vA134t8Ku/Cvyn/wR84zeOvkFViaxE3nbedeGHCmYbo2C1LAPf8A0Z79EKgNXn11BXyRjSAyuZQsYYp4uzPtbs5nRyiROZ1S4sT0wGZARUlSQ876tM/NRdH8HJ7iLqs2PGqGzPxWNWF5EBUdX8fRFt1w8V7O8MRsFqSQK+53uwsN9Ec7E++h6B0n/PA5brXczOjN4rv/VbwI03Tn4PVwaEwyqeGILMantIwEIGVi/XSIINQQDL11BPy4BIEgxTghuqQkQRK9BQa9E+HztGmdVNQFYwcpOwvubsOTyHnFeLXANN4yQBADx5Rse82ceJmwTHVxGw2nHQ9wySVN6zBx1jiO3z26Oa1eCfN77ikdKsXqr1sLJeQYUgEsKcqXiw+xWB1WwfpyjFqkwsixgsCngbAUgqUJ+ppI7rKHbB6mdJbG9HOLwwwGYVgCowWoJSJbM6oMxqXScLXn/c8aFEMPMegwDA+xubuHSpGgkIAIkBXLcC8JPpOlGwuqZ4sApOwO9+N/BLv5TS04xN8Mh/2zMSum6tEtM+slBQsLpfJVhNDBYjoLISfVL2jVijdXO7gumNMfTqGjQlhOdjev0pz6Oa1aBg9VAoC+37BDzJfQFSJlaCJh9e1yJgtarhq5aewuqZIdbWkr4OfB11llPSNGLgOa1UQfpwUauR+6uvTFfu5vuknM6k8xWdY1avcOYvOnb8QBYDqwVNM7wV8qNeXbgF7RfchJpKNluP3u/iBYtn8Ivf+qXRNzCzlxwtUT8qaXyVx6wuo1EqSeR+CDN+M9amWhKs5hyehpZEDuXsYjBm9fkwke6hQK0qB/AHFTCr2cGr2aTztliJ58fPH8cDW4cJexpA49A8Br6B6Nz5+P5qzJJKm5Zmo2tXAFazecskIHhNdWGJyJPaNnqewQerReVavlKRTuACpIpJ8TDklKiz6PeJxufrXw/80R/RB0NaCSQhYVcy0N6bYm9AD/w1nVxz4YqQsdjYIJqn8ZkrCNB1a2jPJeuBCLMaAEHPREOSoMmkOiPP/AuAOLMaRPvVDXIqgxwHfqTgyB4H73mPeHc1PafsO06ylZABEb0fmB7PzAywsIA99W1cWZWJDEgzDVY7GEwhKQxgpNw5jrRsy6Tfi/oN6EYJzeq8uZtd26oNFimgeKjTEwOrVULCsXmgKh0e3U8/QAC1m4iuNFotjAhOp9rN9ZxgfY1kdOrexCYmRgGwetgLiM8QgH3qVVy6EMbzlgTE9/VMi0qkTcus3qFZzWdWMxmQHXPBkSM41NxAEClozI/JQ/L2XPRMUXRscZMLdB2948Am9u8fe+6ee7Bw88JOnxOBfbfvhlgye+h0xOcZVZO4MiASYz8LN0rki1zO+mUNI9TYfqvdxp5aF1cu+IlmtTG6X9BNagrKW8NcF2EkQTFpn48cwbzRx7n+HBr1aLTykJl651S3svNqEbBa1WWiWZ3ze1k9H3PGIPEY4gWt5BI6glPN6uaMTMBq3cLWhQFgWWRfQJNPlfhwXctIMauXaxX5kSH5TWsqR6+8UKNJYkeqFai+pPvj5qy498Yzep98HcUuWP0sie0zmzjy3l/E5vmKNH1cF7ZPweqqmNUxWE3A2qbmYOjyXVjDEDh5MucFYzrQ++rbuHixQmC5XicGcN3pM4W+7UORQtJXXafl5MWu73vfC7zylcDly6xRf8QEr9VCpcxqVQoJe6wKzW4gxRClG5wKzQU1jcmADLDVraicnupA6wbdLE67aXBdUpprImZWbwjoa3seJ/+QyhYDEAOrPQ++7ePSsIPWHsIQefmex/HpTyfPr9lNLM7T30rTKPuzAmY109dWVXQaHrac+nRyDUwGpJaA1Uu1HlZXOe+jwBYwwYBmUtDryzNP8alG9Eq0jPbxJUDTIHsOPvkxHy/dc2rnh/EM0Ma0bYVDkPFWSO8RZArLLJ+cllnNOUNbw4gwfZgMCK1+2V4j+n5WoMOsSYmG/1Y1WvMAKLPaE5PV6HbhhQoe6x9AZ5Ze36NHsWT2cPX+i6OJVqZZbVewyU9LApkmAdcFD04910SryZmPnukyIGz8pYxh66qLoSvIrN7y0Vo/g2/cdz/u+3wUtxmx8/MOsHqKvvo+rEBHzUz0tQEIrTOf/3xSab2xQQD2mI07ZqoWzwN5ZlJhSPY8RcBq0BL1ML9EPa88O6vR3AoW6lWimQoWFgo0mwf6RFGS9C4SikJAH4e/jgdbPVJS32oRGRBaYbjhNDDfoX0yTTQ0F4NpJYEsixAC0iCLJEHVcn6vMh4GQJIU5TG2qwar6f73x1/+Zdx+u1ibpuJxwWoGMDyyuReH2ls7ZT8mtKvKYX4Om/b1+N4+DhwQ6CtQjFk9CInPEICD9TWcf5joCgehBDlVkSUbGtkrTglWB2kZEEmiYHX+mY6dKSaB1abqY299C/WFDLZyzvxSilktBfkyIFRS7AM/9GliBjkWi4sT9owiMiBehEPNDRw9UgCsFpABGfl8kWAJJg6YZlkYZVbXu1i5HMZrV90Y7ZNhAK6Izjrb77MKOE3DgtnHl9cPYnlmbMNC55e8Jsswq+P1gKMxrslBsjbzgnouDAcCv69tE5LNjAYsLxMZkCs2YNs4P5jFgcYWkZKwriOwut7FyraR/3rBiDWrFcE9Ny+iCBEjYMoykY4FxGVAPAPNOXE5s12wuprYBaufJeGu99AxLFgr3WoaHGFWVw1Wh4AsQ9I1spHmLGinTxNjhMxg76c60HPGAJuC0lK5wb53rUZkQHpT3i5BgIGroqm5ZPPFGG8iDL1U9PvArbcCV67QBzxvhHHZaCukrKUCZrXnRlBlarBoVVeC40fEYFECEFkVMavHZUCmlZUAEoaiIScu39Pq38bMagKmzRkDbGzyx5bncczbx9lrIk7EdJNv+TrmDjYBWcbX6P8bn/qYF7933W5ifjYBq2d0C11rSvYnSy7Qg+vsTIhNpz7dmPV9DDx9BKyuqQIMWM+DBGp4IwhWG4oPx8rfOPlOiLrqYHVDxcyCBtx0E/bUt/Hu99fw0j0nd4rnyaTawMsCk5hmddESdQHN6hFgTzByy1Jpm3rRvlINRS5YbYEwq9mhpN0muufrVFNYkgieaBiErbxVwSY3LQOiuLCGYixKP1Tw2OaepKL16FHc1FnBE1/oAq6b6EzSBG7fqQCs9jwiCaSrdJ3xMLQEwCfbJjIgE4y+RoKBtM/UTTjba7DzkqqirlEghaejCaD36fvR/NKncOCTf0J05gFqrkj0OOOItdunSI4z3c9URQgAIbD6Z34m2QdsbxOd7X4fZP4YM1WDJEFVpXzAI0wd4goEAVIEStSLTDK89YvKgKhGMbNfWVeJPFZekq3EvKXJgRBY3e0CM7pFAGSq0bpmNwmz+iCtNNR1wqy2cxILE+Kznx0z2WQbhvoo+KcqFPjK0+0uZbCYz6wOIykx/BRsEwAinw9W//N7nkJDpFCTGSzymNWXSJb7/sEJHH/d7XxWJZOV4DGrQxk//8ZH8cIXCvQVKAxW11UXWFjAgcYWLjw1TJJhaqpjdI6JvR/KRBBQM1L6f0lCzQiJ3j6HVbxDBgQADhHm+pHW+g7Nai5YzXwqihrDyiHfYJF5P0yIhQXsHHMiMiAuAavf8wfi11/LSzABCL2AJMEKJgR1OYCb5T1Cw7Kwk1m9KqfWrtFrGJ+VeFWo7CyVAoHn3/J1+PjFm/Ca7xzLQIrIgDBQucA1UDQZQdZcyNr1AE0OxeWxVJX4UYmA1ZRZ3ZhRgaUldAzqj9Xv4+neAo601oiueP+ZDVaHLtULb7WwXOthtWvy3iIUvhclMiAiUna8YOuQHI2a3BeRAZkTBOJFDUx3gxu7YPWzISjF1lA8OFIFGtDAqMFiWJFeEjNY1IsxiFx31DjoHe8AHnhg7AUAWaB0XQhIEu1vFElEBsSwsDUt+Om6ZKIzklLPWsls4d69KWZ1XPJMDhcS02yoQgbEi6BKAQGQqtInjTWrKQjer6BEnxn2aVIiA9Kvppzepwe3hZYzvc4fkGhWm0Svec4cYGObvwnKZVaH4U5WHDsM8EpoIxkNzcH8sgrccgvuWTyJez9mx8+v2U0szEdxm23dxrY1JaBGNasZE2ZuNsK63ZgerPYNUj4IEB1RWUBH1PPQ1sn3FZUBISXa+Yd5z43Q1BysrisEl77jDvz8c/8GP3z7x3HH/o2dtuSSJAQAl2H9KXmHMmqwWBSciN3pM0AfN1BLGyyKyIDUVHcHs3prg34e+yqmibZmo7ddzXrAPqupORj0BTbO/T78SMYTa/OJru4NN+DmzhW89xOHcK7XIZtmWabszABukAP6FeyrpFGwWnFhOQK/L9OsbnNeyzbhz1Rmte/DDVUYRsL600xFjO0FoH/VQlNzSGUG87+gB/O6lnq/ppE5pgIZkHqKWS1JkdDhyfcpOPnUUwhXr2Km7pL/hyTppSnBSCk1t+R5CrA6t0S9DOuPVxnEkt5F2JS03UzGNjObvYYyIIN+hIbqkLmr1YK8MIcokgiz+jjVhpAkNGoh+qLGTyCX6Z/9MwJYswj6FgGwxoBWVZNy15kgkouBygBlkeUzq6NIKja2KAAeeBxd4ahA0oIyq7lGgE89DQB4wj+OffsF7u/YuDLnNa5LkohagQRLERkQi7CbsX8/9jc2cfFpL5EZ0lIdo3JuU62LQYAgkhJmNUDA6jydeTAZkHAnS9UwgF/5FRx56cGd0hpszFTJrOZVnAFJ1WpGu8vL2KmXL8isVuUJ7PKc4MmAeG5EGOuFwWoxcsAOzep1FRgMyNrVHL0+uiGRtZYHVrvJWZjFwqufD1mR8brvnRt9raZxNcY9Jyx8DWLZxLwx65JzsHC7RZL5jkPOLR3iadKZV7Dl1ICLF9F1TcyYLgzFhzt4ZiOegRsQCZZ6nWhW95uV4EYMg6ipnhhBhNsg8XZSWKWJYUCRQr5UYBQBto2+Z6C1IAhWKwo06Rm8T76OYhesfjbEo49CAggrbwry6x//cYp0lDJYlKQI0VQOQjSYwaKeTBLss/LC80bB6s99jrCtR14AxDrQVYHVoeunZEAG04OfnkdKSAz6IzHGm4gJA43hkJw7xsHq2KwOSA4mVcmAUFBZyDxIqFHKgoi1sCsAqxmzmmpWzxoDbA2q035VDQXLsy5WrVYlYLUV6ITsxDSrpwWr2aYwrfFWgFk9Xxtibg7A856HhubiBuMyPv95JGA1IzmoKtqaNb1UwdjhYn4uwrrT4FDHR+PDH8ZoBUVADLnSYLWh+HCGfLC6pniQ5agAWO1y9UR9L0JTdbC6JhGw+gUvwAtfouK7v82B/NM/hUl1wLmgD2O8lWD9qVKO6z0DwYvKgOj55fTTMBR565hlS8nhCQBaLXSMIba6ErwwZfpkmoRZvV0NIwMAUK+jqTnoDQS2TzTZ5fpKwqxuNPC655yD5Wt41+MvRcek87QkQTINwvGfdo5Jmy3RdcayBforClZfJ8xqPe3hVUALut+L0NRsyFKE0PHjNgeejoY+ClZXway2Aq2UDIjnUe+KX/s1YGihcfEpslfyfWw6Dcyao3sAWVMQXitmdQ7rL5ZMKqinCiC3r37J5F2UxawuO8cqCklaCNy2wyGtCtF1sl7ffDMUOcSl4Qzmbk5c3Br1CANPFwarP/hB8pXOnEkes3o++axxZrUaZSct6BgYMWUUCaqFnXkvsLFV0LyTy1hPVb+JtmmqHmyfA1ZTZvWT20sTJSB2hIgmPEsi6gXuA02DBCC0OYMrCMi6qHrA3r1YrvWwcilI5hd9FKxeMPtYW59u3gpYNSMNXUc+SBkQmTNNCSbPMfPzeO2bdJw4Mfa4CLOaeTSIBjPEzAOrPQ8RkEgpjcX3fR/wPd+T0dc8drlXEPwEJ8FEP06Vg2L3F6sI4WlWs3FFweo99S6ubOrAYEASuDOj3yMGgEXB6lTiYnYW+J3fIf4LI8HTxAe5rhNZ+3khojHug7RbgFldBKzuexSsBjBzaAb3rhzHvVeOkc+cmYEuB3D6z2ywOpb30XUsz3lYsdrVYBBeBKViZrUfyVAZkcYwyH5+kzNWfR/veezFODeYQ71d0GDxmbpPvo5iF6x+NgSt+5MkCJW4TooPfxj4ru9KmfY5Dixfh6l45GYbVFCC4nkEANeTSSKKJO6hbFz+4MqVxKMmfgEQl1Ibig/Hnh6sjsvVmAHctOCn6xKw2kzA9boqWE5OY+W8iz3+eezF5Qma1XTDYdLymyoWCj9KDBYrZ1ZXCFYzlpWGRAZkWJ32q2YqWOq4WKkCrPY8wnSpS4lmdY8/tjyPbJom7qnogxFSm05VJRljm6NZHSqYr9tkc/ic5wCKgh878kH81q+RD1yzm1hYTDRa27qNrjOlHhndMLADZq0hww40YQ3Fv/1b4M1vBj7xidHvMvAMNOtJOb1Q4opq07frvrAMSENz0O/nbPAjcghqajaGQ4mwbxoNomf0trch6/SrMRAhi/VXQltaRLO6sJYoOC7qTF+7BENRkwO4bv53HNryqAxIq0VNOjUC0tXo3Mc0q/N+K9Fg37PRIHIdA4E2KesPGDW2P3BzE//ips/gwY396NRS83QBoDI3xsDquurCckTBagOtGc5racn7M3YTzmRAzFGDJpH9BgD0B0BTJeuSGVkkSRgEhAE1CazOA6h4EQSw/FHNakUK4Q/5/WTMai+UocoBmvYa+n3gIx+O8Ndn78BcbSyjxg7bWfNsSbA6ngsy9p+BQ706CuqpAshlgZMkfaGu5gNftM3CAHgBGRBiVpbS2z90CAtmH6e6izj2goRN2GgAA98QBqvvv5+siTFY7XlkntS8HeANkYPJABXZbygVHNMyISBkYj5lzDsVhcgU5F3XoqxaWYapBgSszjkvMTbcE+frYmA1M67kyIAAENe+BQBVJVWzvLFlWQQ4bEjA/Dxhv64gmV+01HjXdSyafVxdnwICoDIgafwuBuGzLgJjwedgfv/sn6E4WF2WWS0FXI3xWKZrQhjGCCl4tK9Z1yCK4PlSMfATqTk2o90YqC1y36pE1oorA2LTSjZVBZpNLDf6WOnVgc1NkghpjV0fjTMOaISWQ04sqYsoy8AP/MDkvmpymMtSjWVACibEAOQzqz0Ukxdh/jM5LPA4bJsk1lvkGtzwvDnIiPDWT/5zHGxsAu02uf+vA7BaodUCnZmIGLhWgEEEbgBVCinhopp9fBCOMqvbusX3tbFtvOeJl2Bvsyc+vAQSLLshFrtg9bMgvL5DDgIApKgcSPvbvw3ccQfR0wNAndblaqUaXJcwq9kBkm3YCjKrV1ZS/aTtxu3pOpnYq5BBZgtfvU5KzKcFP2Owmk6KrDy7CFj94S9juX8Kez70Tly5Qt/neURLk633tRoB6barYFZH1GBRzDxIKJgEhFTh2EpLoTAZkKFZSO9xYvg+vIhsghfnAly1q2FWx4cKplnd548ttuBNZFczxo40ClabPLCWMasbFgGr63Xg1lvx4qXTeOTLTlwN0GjJcZtt3ULXNaa7tuNagGwTKLiqf/azwFveAjzySOrBICB9ZYAzkwHhJa7oZ7bqgZjmpaKgqToYWDnLJzX/ahkuJGmCrmFGqLqczVYuq6fKNKvzZEBKgOC5faVtlmdW5ycCLEdOmD5Aolnt1rDp1DFbT4x7WpqNbq9CsLpeR1Oz0RcxP6Mmd8AoWI29e3GouYEH1w9gtpGaT3SdHN6qAqs1LdZP5JW9A0gMFjuc3fgznVkdBFSzOtU/0URAFKE/kNGi0kAH9BVcPB8CQYChr6ORZijqemlmddoI0fJHmdVCWvugYHUvwmObe3HTzAqaUQ/9XoQ/+3MZf3zyRZirje0BBNjKhaUaAKhKlMv6i/dSVTLeYlmkgteeIwNSmlmt8BmKwBizGgBe+ELsmXXxfd+8AklO3t9sRBj4uvCB37aBW25JgdXDIc705rGv3d8BYBGWZnZSlH2nQsE0gHNkQAq3K2JcSQFF2RAfW6YekuR4nlSDG6KlEfk/UWa1wmNWpyULRUNVadKdMyaHQwIcNmRgbi6RamDzyyRm9caUYHUoQ03fKzygNt6nF/wsIWa1XIxZLaJZzT6vjHxRrr42vQYFgGVFV/Ln2ICcqcowq3nrV1zJRqtBZpZNYoh+/jw5y8yOIfaC+3l7SBizQskbTSPJII4MSFHNapG+el5BLWxNI5XugsxqCYBUIySzoy87gL95ze+ioTm4YXYTaDaJDMiwIs+oaxQxs1pVIdVMso+oQorUR6JZLSJlJ9Ag0dpPwOqWZqPX5ZwTbRteqODj3/ke8c/aBasri12w+lkQg20fDY0ezksCSFeuAHfeOQpWs9DloBq9pLRWLyBs8jEOVucyqw0DhuzDsfnX4eLFfJw8zayeNYbYHE7HJv2Pv1nDpWEHzRrdbLBDaYH5/MqpAfbUuthb38blp2nnfR/bbg0zraT0va3Z/EyhQDBttUqZ1YxdIYcETKxibDHwU5MARcFs3SG6X7wyNKF2ySZYrWkIQrkiGRDKrDZNzJlDbAxNblUED6wm8gepNigjxx7wNavnGg6RAQGAO++EJAFzShfr22RzFjNmJAmaoXDds7nBmLcMrKYgXeiIjQXLAp7//DGwOtasRtymCLOaSRztmfe5HkoAiAyI5uZrVjsO/FBGU/fQboufSXLZylOUvauygMFiQc3qWAYkQ7PaLykDois+YapmrWVM41f3ElBNVdFeMrFNwepOjd6jVAZESLKDF2mwWnXQtwQOhQGpHpltOKPalnv34kBjE0/35tHZmyrRr4pZndYIVlXijSACVjODRR5YTQ+5uQf9r2QwfwwjNaaZFjTv2g6H6LkGmi0Z6HSwv7aJi4916fyio26k7k06Xl1fEd57Pfgg8P3fDywu0q4wAzQ29+g6+b0EweremoMvXD2M5y+eRUOxMVjp44v3y/jcytEkaZPqb/zGCREF5YDKXPATJUEEEWZ1yUqTzHaZaVwJZrUu+0LghGWPgdX1On7h/30u3vYHzx15XasZoeeawglc2wZuugk4e5Y+MBzik5dvxMuOXpzU3VyDRQCFExbxwTwL+AqCUU8NwTa58irpKhLBMI1ICKyeNUhlwr59Yn1VpRB5XpA8pu7EoIaQ3DONZcHyddRbBKyeNwdY76rJ/JJmVjOwerNgQiIdrNLEHJ0Po0jKZUCX2heIMKujbCPEiSFoiClJUWEAGEA+uzyUCxtaS6pCJMIyZNf8UC7OrJakxCcj5/wxTg6QFqhGx9mzhLXfyQCrOWcEqx8QxvYOevqEEJQBUYsmRYWY1RLRGL9GMiAAkmtA5QG/8/g/4raXLwGGQWRABtcBWM3W+FqN3Fv9KQlzUUTAaomME9vNrt4S76hPtPZTzOqG6mLY5TOrvVCBVi82dz+jSR3XUeyC1c+CGHSDuGQ1CorfFOyM1emkQOAUiqvJAbxhBYCi5xGwukaHnWGQAy9H2N73CRvFdYlMydWrKVA9COgBi4CUMAzoSsAvmQPwn//zqBnNju4yw4p6neih2tOB1R/+BwOPbu5NwGrDIIy3AjpMK+ccLNe6hI17iaKWnocNp465WTqJmyZhv25MubixklgpqNZMiwJZcbuCEjO///s5ksZjut21hkxcyStgKMagahUO6rSvlq8TBowkYa7tY8NpcBlUbJM28Rr4PracOjq11L2kKDAVP1/nizJd3vriR/DSl9LHjh8HANzVOo0HLsyTzXp688f+Pc21ZQkLVrapaZjRLWxvio0xyyLJtZMnUw9SMClmMVNmNe+QFzg+VCnEJ/7gSbFztKIQg0UOs9qPZDRNf6cBT04IaVYXVSPiyYCEISmLK2ioRXRq8zWrC8uASBK0PONGIK5MqOmjY0VdmkMQyYRZzdjKhkFka6oGqzUHfUcAHKE37UtvXh9l1+/dC1P1sVTroXM0ZSSk69VrVssyahoxB+Ru9B0HG04DnQXOdxM0wvynih398P3RKi5AfP7udtH3DTRnNWBxEfsbW7j41JCAM56BhpEal5IETctJ2kyIX/gF4DWvAV72Mqq5zwzQ0mC16gqB1Z4H9K/08cW1w3je4lk0NQcrj22gphKQZa4z1gYHRChVSg6+wWIMVj9DNKsz251Ca19XBDWrLXnUHBbAkSM7PBAx0wqx7dUKgdWzShdDZthtWfjU5RP4mptXdnaX6d9mAPYASsmA5IJ/ZUBwKgOSe9hPV5EIBmFWq7n3rOeEmDMGqJkh8ZwQ6KsqhwjymNWlZUAECDjDIVkXmyowNwdZihD5QcKsNkbB6sVaD1c3pzCNDwKi4W+m1pTriVnNNKs5yQUA1YKf7ExR4hpIWe2yKpOihtbU1DvXHDeKYLkKakpKdm1+nsx3fVolOmeOvkcUrB6ECWObFwJgdWG5DtougPzE1bXSrPZ92I4EQ/WTPlMdlB/9xTl8z6/eHleLP9OZ1YGXMrc0TbQ0B/2N6fexfkgS0mZDheVr07O1Gf6Q8rWpqy7fMJ2ezxkDXih2Nasri12w+lkQg26QYlYXBxRXV4HlhQDtVpSAwKldty5XVILCmNUMrNZ1YnTSy9+MM2b1Bz8I/PAPE+OFGFSnoJfGFmkmAyJQLsJA8LzPHdGstsVkJVZXJz++3ZVwureQaOoaBmFQieow+T6uXAywp96FJBFGaBSRjm46DczO0L7VamhrNrpbU4LLzLRQDgm7vgLZcgCJvrQuF0qE/OVfElZ9dpvJBlAyqR76tJpZdHOtmQSs7hhDbK1PeS8wGZAW2fi0ZyR0PVMYrM5iVm86dcymwWrKyOGB1X6o4PlHN7C4SB/buxeo13Fn4xQ+9dAsOro1ulkvINnhuiTJsCMY87amxm3Om32sr3ObBEDuW3aAjLvh+wRMaiXziyEgX+PZxMW60RbfiDY0J59ZTbPwTdMXO+jS0DRkgwhMo7UMWC2F8HMMFtnrioSs5TB9aIlwYdAHSA5PWQcdZhhljM1v1JFn002B1YxZPZyCQcaCfc9ajYDVtsZfD+h7PvSz/ziK/dC68kPNDXROLCSPC0pjcWOMbVgzQpK84xweI8vGmd48Dh4RY1Y/Uzbhr3kNsLGRemCSZrWuE6NRi7OQ9XpErmtOB+bncaCxiQun3UQGxBi9hppJK00EF8izZ4Fv/VZgYYH22SeJhB3M6gF//fZ9oL86xP3rB3D3/AU0VAf3fgZ47uEN3Dp7GbP7Ro31eCCCF8jFNT9Bwc8c48ZYBqRMeXbVzOq8dpm0QQm2tohRGaIIQ0sizGrORN5uhth2a8Ljyl7rw/z934TZXYVlAdFgiHP9WRzZv3Odjpnwk64Bm9NKyIDE+u2T5sVpZEDyLgFjvxYYW4omI8gzGgVJ3MwZQ+xdDMRwe6ZZHeZUBZXoK1QVhuzzt7KWRUDpFgGLUK9DiiKEG1vUYDE1n+g69XiYDqwe+jrqabCajukoyyE5bYReJNiYyUq2jsvKCbapSgGCvCWxBGtfiFkdlbsGmfstRiySi5/5dC0ipphZ+/lYRiaVxJyfx5LZw6rVImvX3FimTRSsHtfvzwuezBCmXGfymNVMY1y0XdHKM8fBlltPPFZY3H03pG/4eiILxZjVPKP4r2SERP5IlUMCttdqhNCzOT2xi90rjbZCPBymBat9H0EkQ2HoJ/V0Gfb5RA4AiSeYSMTVRqV6uhup2AWrnwUx6EdoqGRSKKNZfebzqzhy6bNon30oAattO7ZqKwIo/uzPjkl0pMN1SWluilltKh63vMXzyJ8rV4APfQi48UaMgOpuoEJTEwDYkH2hs34QcMDqlM6iqsvZIFIqogh45SuT9v/n/ySeaqdPA9s9Gae7iyMGcEQGRPBgdPEiVoZNLNfIl59Tu+Sw63kEqJylr6MAzYiud5lgoDIzQhQxixBtN1SgtmqFxhYzkwLIdY7NQIFkE6wnYwtAJaCPHypQTZW4HNe6WFmZUjfL8xJtQQBSvSak78X2kl/8Iin3vffe0X5uuvXRsm9WPiqgWT1iridJwLFjuGv+PP7qzO2Yrw2RINmI3elFmITr68Dv/u6EJ1IJCwCArmPeGGB9Q+zaWhZQWz2LN36jhTe/mZ4NJzCrRRhJvhMUY/0pChqqi4GdczByXfihgmYtKARWq4ylmceeKXHIUaQQftZtVpZJl7fJL9tXcORFAGIcNi7HAMRjdNNpYHaRfjBlVvcsberywdAPybhXVTRrRB+dCyRlHXYNA/jX/xqHnreIzkJq3FE5nKmrN8Y+1zAAh8MkBICTV5o4MbPKZ48wZvUzZBP+1FNjSWLfhxsqo9XFug5d8fnltP0++p6JxrwJzM8TZvX5MJYZGh93qi4ToFYgeWfbxERV/uy9RP5pA4RZzWShaD9rqisGVts++p/+MixfR/O2w2hqDv72S3vxDdaHcNvsJcwdbo2+QYBZrcpBcYNFlaNZPY2WKIdZXVS+SGTeKpwQVFXCAOaBE0EAy1dR13wuaGvWZdg+P8EEAIgi2E+egwkbN2un8chDIbZXHSyYg52UbXCSC2WMEAFAkqDKdBxMmmuDoLgeOjNY5MiARJFUmE3J28P4XohZY4C9y4IgkSQl1UZZa01aslA0CjCrrUBDfYa2vbhICABPrpNKPmMUVObKq4zF6urY12IyILVRZrWep6/refCjctVh7DMnRhkpMwHwsxRYrSiQpQihm51sL1shByDHI0RJSFsFIt5v5Uis7CAHzM9jT72LK9YMLgw62HdsbI4RNFgkYHV1MiCEYFbSyDeHWR4T1wowq1VZQLPacUZl6yaFqFH8VzKYfFaKrdzUHPS3pid2EXlPYLYTYcOpV8Sslkc0q+uqi+FAjFktNFZZxMzqcl3djSR2wepnQQz6UcKsDqPCstVnPvhlHGlcxcz5hxKg2U2Yabmbj7G47z5gbS3jSdel8gd0QyHC/ESyv7t0iTCRbtrfH2FW9z0DTSMprzMEyzF5zOq4JFbThLVEgwAxO/SDHwTe8x4C3n/uc8B2X8HTvXk0G8kkWVNcDG3B2/DqVWzYDczdeQCQZRw1L+P0kz7g+0QGhFWTqyotfZ+STUhZt6pOdAO9qgwWGVDJwGpLbGx5HgGogwD49m8nf0b6mmZZGYaYPqnAh/qRDLWmUbC6h5XVKcHqMWZ1nKkVZFafOgU897kkCRLf675PQLrGGFitCjCrI5mA8em49VbcPX8Bd81dwJ7lMSYQY8+4fHCm2yXa8DuCGqDpNSVuc94cYGNT7NoOrw5Qf8/v4d9K/wmWFRHXe9+HHWgw6wkArgskrnwnIIwA0dODqvJlQByHGCzWg0IyIFzN6qgkszrPSGgKQ60okvK1X4vKgIBcAzcPWGXriDnW9sICGqqLi4MOZm+ljliShFY9IJULU84FgRcSI2NFQbMRoe9PAVYDwI034ugdTSykiNUMUJ1Kxz8Md/ymkibGdLr36X146Z6T/A15zKwu382qIgjIHLO+TuZEAAmzupa6R9mhjwdWd7uIACgzTQpWb+LiZZlWbuijGq1I6fkLgIpPPgncpJwE/sf/wNylh8lewffJeE6D1YonZLzsDV2sWi1y4P+Wb0HT8NDQHHzToYfwb+/+MF72jc3RN/DAalbuXNRgUZRZXaacnmuwWKir+e0ytnaJNnXZ5zOrmYSRyQce4nElIgPS7cIeBDAVDy9ZPoXPfLiHq5d9LJo9Ypo8FrkyIGU1qwGoKpFRmQjWlmVWXwPNahE2pe8BR1rr+PbXixMeYi3wHOAPQDnNal43mAxIm7a9sEDIFU91qQzIKLNaV8SrJft94EUvGpNNDAJCDkiD1cwfIWvuiiUwymlWR1maHey+1QuMWSZbkyfbUnJsqVKORwg7V1UphcI8YOTi+y1di/L3W0y2MD1+5uawp76NC4MO/FCBPjvmIK6qQh40lgUiA1IArM6TGYrZ5QUTYgC4iRBVicQJHWx/xJu6bZswqxt8sNq1nsHMaibXkQKrG5qDwdaUOnGMBa3KUJsmqYapAKwOIjlZhgyDXykLlJu7dw0WK4tdsPpZEP0+Yma1JnmFS3PPnFdwpLVOpCO2yXtDyyFgX6tVGFBMmyGOhOuSzU2TDjsRTV0kc8TFTzyJQ8113Hjmo+h2o7jNnmeiNQZWO+6UMiBRBM+nZS2SJAxW+z6wtUX+/cADwFvfCtxzDzlIdwcKVqyZBKzWddRVF5YreBuur6PnmWgfbAOLizjavopP/k0fv/ShuwhQOUe/s6YRzer+lLd3SgdOU0K4fgXmBrRdL1SgtuuFEiGeR8b6+94HLC2BAJQjfVVGmNVVyYBEkUTAHsPAUq2H1bVi17XbBX7gB1IPMDmcJl30ajVIUoRoKMasvnRqiLvuItLSMbuayYA0UquiqpL7K+8SpGVO0nHXXZClCO/6mvfiv7zt9OhzmkY2+ZYYWL21lTDi05/rjoPVRh/rm2LX1rpEdFlx5QqOtDaIqRTdxcbAnKgMiEO11oowqzUOs5oZLDai6pjV7EBSouxdlQMEOTIghY2vAD7oE8rQtBKHJ53KgHDKUuvjgE+ng44+xNO9eczecSB+uN0M0ROQ2cmNKELgU9YpA6unYVbTePvbga//+tQDuk4qg6Yx02GfKUnJ4UpAlxEA7r+8B89bOMsvdYyZ1RW4s08Zly+Tr3XhAvDOd9IHg2CnZrWmiV3bXo+sHa0WMD+PffVtXLyq0/3LGJMQKCSL9Mhnt3Fb+BAAYH77dCwDYgUa6o0UWK2KgdW+G+Lp3gKWblsCbrwRx9/yXPz8c/8GuhLgps4KOreOOcNxxsFIKW+BiMHPayADkikpMK3BYh6zuoRmtSYHcHn7TpaoHk+0ZbQJQAystm2SqFU8fNWeU/jsp3ysrQRYMPuTwWpWDZBxDQCUAqs1Jco02oyBxoJAksZjVqfNZAu0CyC/9N+NMKsP8a++Xzwjl+vjAEzHrBYAqy1fR22GnlUWFwm54sltAmI3UtedMqtFQZTf/32CJ545k3qQafjXR5nVpuplSxYwQLEoj4ZWhwVexvmD7Y2KgNWKQjWrBTTGC44tVQ6yweqUDm+h4Gjt+6E8HbM6F6zWRskBCwt47sI5/Nmp5+OG9trO8cw0/O38/lgWCsqA5AB/7CyjFrwGvKQoJVcVWhNEwWrKrJ5t5ryQkW6eycxqRkBjYHWtRkzIeaaFvGAV05qUVAhVIANCpIjGmNU5xEX2PgCF5+5nyj75eo9dsPpZEIMB0KAGi7ocwO0VkD6wLJy8YOKG1hpmdAvdVXKgdwY+TMUD2m3SpmBWjwtWpzVlRZnVFHy/eCHCj97+MbzywKOJzrXnEbC6RicSwyAbOx7DBRywmpVqqRSs1jQC6HBWHyZVEQTAww8Dt99OpFTPn0e8kWgu0CyyaZJDqcO/DVdWgLWne+h6JtoHZoC9e3FDax2/954a/uqhw9h2a5iZTa5rW7PRHU6hRwckZiiaRHU55enBXyApw2nXocmhsAwIA6t/+7eBn//5sT0b6+uYDEhkTykDwjQGKbu+oTr8cqGxeOc7gXe9K7XGMgBcT8DquupiuJnf1xis/uRJdDZO43WvAz71KfokA6ubqYOVopD7a5gvAxKEMmR9bKxQ/V9JAswTB0ef0zQyJwgkGZhUyw52Nfvx2MJPmdXrWwKnGMvCcMslup8ADrtPxWA1k2lgbYokrgrLgFBmdd/Oub8chxgsNqJiBotMbihTl7CkwaKUU+4ahsXLs2m7khRxDH8K9hWAZshCMiA7qtuPHUNnUcXTynHMLieHn1YL6Lq1qcFqP5ShSIRdY9QV2IHGlxnigNW6PnbZKTDBWxOFPhOpcS+iIRkEsDwVTd3lH86fQQaL586RW/eRR0hybDhEzKzWzQnMap72I9vANBrA3BxqqkeGTrdLNKs7YzegYMkzAJx8yMKJGaJXMmcOsXE1SAwWGbOaGS/zwGrXhe9FON1bwNINBJC84bteiv/r3m8DvvZrgW/+5p1JB87B3PdBqgdKgNWZjFqUZFarKhQpROhxgK+i2xwGgk9iaZadY1WVMFUFmNUTq0ImhaYROQEOOxEA4DiwA7KfvrlzBY8/pWBtDdlgdY4pKDEsRzlmtRLBjyavX4VNygA+m5I1DJQz78yVASk+ZhVVygTrAUwFVgtpVgcazHYCVt/QWsPJMyqsQEe9kwIENY2AiYLz98oK8PKXE739OGLN6tRYpqziwM1nQBdOBjHzyiywms47klIMrFalID9/yyRmCv5eqhTCd7LHAJEsEW8SgBizugRYresgmtV5YHUwNmfNz+NrDp/FB888B7d0JhgJUZ31XIA1imDZEjFuFAGrY+Av4/n4zM5vaiR4iSt6bQutCQU0qzedOjqtnEHISDf2M5hZPcG0sKk56Hcr8M2KErDaVDxYm1NiEOOa1apKzt88HMb3iWZ8wcTVM2WffL3HLlh9vUcUYWBJaGoO0GoRc8FuAYDu0iU8vLEPt81eQlu3sb1CJgI7DVYrfmXM6hH5AwamceYeb41oflwazuC1Rx7AcxbOJ5qeE8FqD44AWzkIcvrKFj4lYUEDfC1Rtt53u4SFcHi/j/kZH6dPA8cWiYh0c5mW5qoqAas9lctY/rM/A/78Y3MEpNk3C+zbh6OtqzhzycDl7TrCSIJsJMAf0WmdUgaEmRZqEvSaQkrFptWApu2GkQyl3SATueAi7PsE/PR9Yk4lSanLNs5WMAzC8OhPuUqkDxe6LpRcGY/3vQ9485uBhx6iDzCaENugMcM2jnOyd3kNuuzh8nAGnS9+DCfmN/DUU/TJGKxO3acsGcRhVgOpkuN0/NzPAW98I6n/TAc96IjMCUw3/S//Evid30k9wW4UtlHUNKKvuC0wZvt9AkTJpI3DwWlyiHJdsplg11Ulr3G8/Lkg1mktwKxuag6XWe2FCu440sNrXyvWLMAxLWQAcAlGTq4MSFmNUo72aynJEggYLLruzsMTfePNb7gNmJ3DkSPJw+0ZCT3PmA6sDgIEkRQfBiXTiPuSG0XLiFnJN0+flPOZUQREUkGwmo5ZzVT45a6yTBKNQUXVNlPEuXPAbbeR5DCAWBJokgyIiFl0zOTVdWBuDpAkKKGH4OoGBr6O+uxYybKgmRQADDZdtDQyDuf0PtbPDyfKF5mKzycQnTmDMJJwureIpb2p8SVJRCPrW75l53s48gdBAFJlUrUMiFeCWc3mrSzQhwLLhdiUrN0sQC2eY4s1yeQquAwqtgeuCdzfuk7MYbsCr3UcUkmg+JClCHNaDyfPKLlgdZYMSGwmVoZZzWRAJoyDWPe1ILM6d+2iDRc2LRRgVvteWEweDOS65hk3xtJpZZjVPNY+3cvJJt3/LCzgxcun8b9Xj5JkWCc1b9FkIze5QsOygJtv3glWD3wDjfoYWJ3HKk5LKhQJ5ruR0y6AcmMr7/jBfseCCRY1D6iMz1UlK9ly9lulZEAMSUAGZIxZLUmY/fHvxa2dy7j1zgn3nQizmiVpdV/sd2OJq6wjXcyY5Tc13ldZirKTojEIXoxZrUoCnh7MYLGV88KYWf3MMLOeGONs5VqNSGtUAVazNb5ex4LZx/rKlDrYQTAq9cXA6rzzHO0Le71wiDLsd4Mbu2D19R6um+go1mpkU9MTL1sL1rdgByoamou2ZqG7Rt5rD0KYih/LgIgyq103H6y2AupWDcQlY1xm9aWrUKQAFwcddHQr+SAAv/qOJnqegVY9YVcYsg/H5w/tXGZ17Fodxe3qcgC3LwZWX/mP74HZvwr5p38Kc+//rzh9OsKJDhHzbu6nVEtJQq0GWL4uJi+yTn4DaXEBeNGLcLS9hhvaazjU2iSZcbYBVlXyWw5LIEXpSJXgaDWVHEKmLcEBkotUrxcCqz0PWH16gJriAE8+idnZCJubqb5GKaNAKrEy7E2ZjU6zdhgAXgBIYprSX/3VwJe+RB9kvzX7vWo1cijdzF+E3YeewIxu4fKwjY5h4dh978fJk1Hcz013rJysAFg98fB08CBxCx0HrthBR5BZfegQ8F//K/CZz6SeYBvuFAu6pgiajdIvJO3fB9RqOKxewNmnHETbFBlvUVMxRSGMBA6A4NllDBYdDBwOszpUcGS/h2/6JrFmWdsAqjX/ogyiTCwtishhv4RmNYB8pk8Zg0WDU5bq+xj62iQMBj/zM8QfIDabBdBoSuhPq1lNmSMxG0NQFmpHUoYXdMwWKfl88kkigRGH5yFIl2QCfEkFAHAcuKGSSPPkhSQlSYWv8E78/HngOc9JkoFXroCA1WGGZjWHWe3ZAQzFJ/OBqgKLi1iqdbHyhfOkMmx+jK1cAKy2ex4hAQCYMwfYuGzH908sX6RpqCkuLF5uZXsbTc2B7WtYWuJ+9Ghfc2VASmpWZzFKwxCeL0NTCrbLYyiyhFgJg8VMQC2WLyrWZAzOCDGrJ89dk9ps6xa2twT2GzRRKy2TgXCktoL7Hq5jsdYn6/h407qc+XuVApWTLme3y0DwMsAfh1ldmP0qBFaD3AsFFjHCLM8GqwPHLyY5BlCwWkCzetxocnERd85dxAPrB8i5azY1b+k6NQQV9AgZErD63Ln0lwnI2XMcrOaAyn4ZzWpZJgmmHCkz9jrhYAB4zv4wcsslQlSZp1ldznsEQH7VXVEJDFByQEY1BIDEYHG8ku3mm/ETvzyLl/3cyyY2ymVW+z4hX2mCZzSaZMxMCJYBlVPtZv5eZfayMUjJTzBxmdWsWnwaAsO1jnGPGl0nMiA8HWhejMmALJh9rK1MeaYPAgRhSrNaVdFQXQwczrpUpoKHMat3ZUCmjl2w+noPy6K6YREBq2W/ELP65CNOXJY6o1vobvjEWZyatTAZEC/rwDAWucxqxuI0EuajIfPL27zLRKJk4JuYee3XAABkz0UYAr/yznlsOg206kkG3FB8OD5/ZeGB1SMlRcz4igPQsfns8/cBN8lPAYMB5gfn8PTpCHv1NdRVB80Dnfj1tboES6Cc3PdSwOz8PLBnD+afcwhffP0v44h5hSQWUsBfW7fRtSoAq6MxsLoKGZBxsFpQY90bODj//3wes6tPAP/3/41lbTPRrWaZXcayUhQCfg6mz+wCiJnVIuM1Hf0+0GwSQ8QYrGZtMsDLMMjCznFO9gYu2rqNFXsGnVaA5qkHMNgm7xn0QsKsbqfuU2awWBaszgrmJC+QwOp2gVtvJTIg588njztWSACh1JgVzkCzL1SrAQcP4nBzA+eecuBsWaRNBlYzRhKPWe1SzeoC7NeG5vLB6kjeqQXOizzTwrIardSZPPPAPy2zOlOzusTBAYCmc2RAWCl9Xew6yIZG2OpTgtUEAKbzCbt3OfN2DA4XkJgxBKo3oogYDgPAH/8x8Ed/lHqSeQ2kS4LZpjkvMWjbk/XrM4KrdflPFOfOkfn11CmCy125Ajh2RJjV9dR3YWA1JxEwHEREYoiN70OHcKCxhYuPEu3XxsLYqb0IWN2nYLVpYt4YYGPVj2Wh4nZoxZXNK0v1vJilLQxWC8iAlGFWa3qOZjXbSxUxqKJ9FWJplij7VuUQnjMZrCbM6uKsR00OiK9HXjCDxXHgZ1JoGtmTdwVey+ahvXsBRcER4zK+cH4JC/UhcODAjpfHTPjrgVkt5TOr4zm2SoPFKEr6W5BZnadZ7bthsSou2ldTYB+zA0yZm4NqqujoFs7351CbSw06tocrwKw+fBhYXR39vIFvjCZe2D2bWw1RVgYkfy4AUGxsyTJlVmdfg3hvWBCgUvP0tZlnT4mKEAD5VXfTMKuzNt/MYHHCnPV//J8tHDg+wd+CJe/yzgiMsW2Ig9W5wB8Dq0sZkOecQ6kfUqF2RUFKz8OWWxs9t41HLAPyDNesTmvRszPScEqIkZLQVF2mYPUAa1enBO1ZZUOKiEhkQDjrRxmz1V2wurLgjiRJkkxJkj4vSdIDkiQ9IknSf6CP/5EkSU9LknQ//XM3fVySJOm3JUk6KUnSg5IkPTfV1ndLkvQU/fPd1+xbPcvjne8E/uRP6H8cB33PQKMBwDDIAsFh/6bj/gdl3DV3Adi/n8iAbEekLNVTYGp+DCi6gkwvzyMAcBgCf/EXY0+Oyx+IgGkAvK6Fjm6hWQ+hfsPLAQAtqYfLlyJs9VSc7c2hVU82K4biwwmUhNaaEUFA+hpFwJveBGxvj/aVLHxJplDEnMnbIkj9Ixv7cOhWAprNGQNsbcuYibawaPZHwWojxNDXIF1fVwABAABJREFUuQddr2dj0zbJZow5J995J2aNIQ43NzBnDkjJMpAYLE4LVjPTQk2CXlfhhtXJgAAA6nVafiNzfysA8C0P5/qzmNVJhmGPtJKA1QxYZxtAtgAV1JfeEWzMqiopzxYYr+lYXSVAwp13AvfdR75mLCXD7gNNQ0u30R9wGMCWj7ZmIwhldG4jxlkteYhuF3jjvzmGj1y4bXTToygEXM/7yUpqKOqCjPhuF7jlFgImpRNDfUdDU7MngNViJc8AiB7rwYPYV9/CpQsRBlse0e5PMatFZEA8xqAS0c0DqOO9C8vjy4Co9YLChAJs5bKb8cxy17KGWlymT4mSTBDgy81j7FKmj1ApPZCM7WkYwFRnX2GHQcOALEUIrPy1NvQCogEseiFYspXDonnqKeBHfoT8+/LlsaoFthkfY1bXFA9WP//wWAisfoYwq69eJay/KALuvhv4yEeA7/3jr4M7zqxmGvacazsYgIDVbD44fBjH26v48c+9GZ++chz1xcboGwoY4dn9AKbqATffjDljgPW1KHGfbdB2FYVoVguA1bocQFVCLC5yPzpuW87RgY4NFgtWWciqTOQPJknCxCBCScZb1ppAE2KKXjAhmKcnSkuEyxgs6iKHUiYD0hA4vGoaZnQb212B17J9imnGWsVPbS9j4Uhz4tyjGXKmDIjry9CLMqBpqCoymcXlmdU5iVYAgRsUZkBzTdXY+iUXMx6OkwBZ2u12UFhahKwJXv4+DiCeHWkWsCQB+/bhpXtO4qMXbkFtITVvseo4gQpUgIDV9Trdv7Jbg2pWN9LToQCzupSXBTNDzJkLAJRIhAQI8si/blhKvkjJm7eYGV3FxrClkgBIJZ3zmNW+BrNWoL+MKJK31jL5K12wzzz9+imY1VpegqUMCC4wbwGImdU8sFqXn+HMajam2W2ikfNdvwqwOpShaHLCrF6fEvilkn5pZnVddTF08yelyBvzRBKJXbC6shAZSQ6AV0RRdBeAuwG8WpKkF9PnfjKKorvpn/vpY68BcIL++ZcA3gEAkiTNAfgFAC8C8EIAvyBJUqpYdzdE4uRJIiUbszQti+iGNSXANMkCUUCn96mnVdzUWQFuuIEAnH1iojfwDdT0MNF5FMzqMWb1vfcS2cRPfCL15DhYrSgwFQ6YBmLOM6Nb6MyEBIgyTbSVAR79EkENz/bn0WrQ/kkSDD2CE2i5JX5Awqy+7z5y2P/P/3n0i4wsUIyVxQGr/ZNnAACP+cex71V3AC9/OVQ5RFuz0NZs/J+3fhq1ZjIpEhkQgb4OXawM29DTJa/HjwMAjrTWSdk7ozhoGlqag65T1MFjLFgJji5Dq2vVM6tVFQda2zg/mBNipXlOiPP9OczeRGhky+FlUvId91UZZVaLmFTR+PjHgfe+F3jiCYwymdJsFZawKIDXr6wAy8vkDHn0KPDoo4Brh0mZOQCoKpqqg96AA6o6IdpUBqdzG2FLHW+t4ORJ4NJVDRcHs5idSd2nDADOw9PKgNW6Tg46AnNCrwe87GVEr1pJnY17loqW5oxoVhdmVhsGcPAgFDlCYDm43G1guTkcua5EBiR/ExIfSArIgKhyiCDPRZ7KgKi1gsgy1/G9HCOHHPQynp/CYBFAtfraAHSTw6xm5aMNwf6KSnbkBTVlSZc5NlQHw22O4a4TFAP/NI2w6ByONJaXzFOXLxO95rR+vxOo0NMlwapK5sO8ShPfhxuo0A2x3+yZwqy2rETp4O67iT7+xkAn3yUtaUI17Hnz1nBIwWo2Hxw6hB+/8+/x3q99D37p+R/CDbeNaTgUMFi0hyEM2QdOnMCMbmGrqyRgdTPlZaF4sDiHp9AhJepN0y8kA5KpAx1F8ANqsFiEAQ1ibJaptc+SbCV0anlsyggEKC/cbhagFkXl5i02tgTAaivQxKpCNI3IufUFvh/blBgGsLyMI611AMDCTfOTuyvCrC44BmiXrwGzOv+wX9ggmbabOV5pZ0c8UAQjVw4HJfYaQCKpwPHhibwxGRAA2L8frzzwKC4NZ0fBal0nhqAFwOoaLCwuRAm7OggwmARW50lghCGR6yvJrM5kK5fWlg7hB9lkmZgJX5C1n8suZ2dLo/i8JSHDGJZJVcglDBZNWcBgcYIMSF7Q5J2bt4+h+5SaIdhnVhGTw6wmSYAC/WTtisiAFJkLYjNIERmQBjozOdeJMaun2L5e82AyeePSGvb0YHUQjcmAbEzZpu/DCTTENk2U2DZw8gdO4BYkngCjYLUAIW83soP7q0ck6G4aGv2Td9VfB+C99H2fA9CRJGkvgFcB+LsoijaiKNoE8HcAXj1d9///F7//+8CP/Rg5oAKgwLKOZktKZEAKaFYPuj4pJT1yBBozMrFtbDk1zDbdBKQtCFa/733AH/wB8B/+Q+rJca3e2GAxf0L33RAdY0gmdEkClpawaPZx36cIcHe2P4dWMxmSsUkZ5/DIwOq/+Auiqfu3fzv6RUipiBT32VA8rt6lf5qIuj26sQf79oGUZQKYN/uY0S3825d8cuQMoOoywoxDw0i7QxfnB7NoN1Kvo+WdN7TWML+QupWZZrU9ZgRVNNIGiw2tOs3qlF7x4bkezvbmhLRfPQ8415/D7K17AUnCsnseK5eCpK+hDNVQ4rbrqpst8zIWDz4IfOxjwC/9Ekm0xDFusKh6sB3+puX3fg947DHKrJ4PgCjC618PvOMdwFpXH2XuUcM+Xhbac0K0adl3567DAIBj8tM4dQpQIx/fdvQ+tPakTg4i2f2yMiCKL8ysXtY2cI/yeezbF5F5KwzRd3ViCssOrozpwzMRAhAxMVfTBPbsAQAsK2v4zJVjOLGUKo9g7I68A1kQUDZhAQZVzFTOB6u9UIFWrxCsZqBPUY1Wjut9GESQpWIMMgCJDnLG4cmPSrDAQVh/uYxdJgMiClZrGjmMWBUyq3VdyO28cBkxZdE5vGojjySCADLH3HNPysDV97HhNDDXSGXV2HzYzweri4yvGKz+CjOrLQuYlzZQ1z085+Aarl4FejaZW2MdaECYWR2D1exQcvgwVBW4ob2Otxz7IvTFmdE3iDKrowi2FRJm9YkTJMnmBckPmZIvqql8sDpwfGhygFatAFjNAWqDkJTGFwYqReatqrVEw4SgUCgomDSx7JseugvPsZIETQlJYjTvUMqY1U2BuYvKgGz3xMBqCSB7ikYDR1rEH2Xha2+f+HLVUDLlKqaRASGa1dntFtZDF9jDeG5ExmyVzGoKKBatDMr7/nFfSwAeInJmDptb0m3Pz+PFy6fR1OxRzWoqAyIKogzXh6j/8s/iTvkhPPAAfTAIiATlDrA6P8FUquKKx1Yuw6yWCLsylwlfcmzlVoSkKlYLRV7yLoroebVYkwA5g+YmnZkMiKDsGmmU6qwLMavFweprKgMySRaqbLsFZEB6noF2O+c1VIK0iqJmFmEI/N3fVdfeDnNLTSP7Y6vEgJzUrpFiVm9N3+a2W8NMIyHNNVQHQy+/3djbqMhNJhM5Pi8nIbYbYiE0s0uSpEiSdD+AVRDA+R/pU79MpT5+Q5IkhoztB5BSJ8UF+ljW4+Of9S8lSfqCJElfuHr1arFv8ywPzwM++lHgrW9Fwii1bbJhaEpUBiSAOxA/PA57IRqaAxw5AoACD7aNLbeOTsMHdJ0cnARBPwZWf/azwHd/N/n/xgZ5zrc8kplKy4AIGId4boSObmGGnRGXlvCchfP40Ic1zLY8nO3Nj4DVse4rh63MZEA+9Sng1a8mc1D8Fpb9ZocWlt3kaVYPyJd5eqU+BlYPSP+/67tG3yBg9AIAnuWNMsgBsjE7ehSv2Pc43v7LqUWRykqIMqv7fSKDsvNDE3ZJY0ZFzzOqlQFRVRye7+NMf54PVl+6BD+Use40Mbe/Buzdi2VzGysnu3FfvVDZAVaLYuuXLhHW8wMPpORgwpCytyjrVNcJ61EAUH3kEVIFsfqlC1j6xPuBH/9xvOHu0/C9CD/9qW9EbQysbmm2ILPahmFEMI/tB3QdB5RLOHfShRx4eP/X/zfIy6l68Lxy57jR8jIgIgaL3c0ArT/6HeBd78LB5hbRrfZ99DwTLSP1mzMAXICk6fTcWPcVy8sAgBtbV/C352/H8X2piYrJgOQxq8scSlWVMJXzwGrmOG0UYPrQtgHkalaX24xnH/g9Nypens3azTJAC0N4ZWVADP7haejrqImYlAGArqOl2+h1p9gsxprViQxIU3XQ3+YkL72IADSi11bQx8H3E2a175P5+8/+jD7peVi3d4LVNcXLrzTxPGKweB0yq2sf+O9Y1Ldx55f/O9ptwPaSdSAOUbDakkaZ1bUacOxY8oJxuSBVJZIwDuc6DIewPRVmXQHZHABa6GA4CIkWuknBJEUhMkMcsNqzA6hSiMUZF/OTCbQ7I09TlrIeVaU4O0/IGLZouzyjsjAkAG2JeStzXSyrgy1J0LWISKXl7eVook1cBsTCtgizmu2fDAO49Vbsr29hrm7BuO34xJerusxnVpeUAcliVvtBORmQ3NJ/kH1RYRBBVcnYyfqtKFGjaNJC0eRcg0XfR/FrwM5JnH3nYCgRGbT0BmF2Fpoc4p+f+BzmF1Lvl4lkhJsn/8DC82CfW0UtGuJ5/ufxxS/Q+zEIYAVj8hACCabSOvN5OtAlpcxUhVSrZYHVMWu/jMFiLlgtl6qQy7y2NJleeI4FIKn8xI0VaOLkACBea3OPdQysNgT3Zew8k7XvjpOi4t0caTfr92LX9hqB1X3PJGTDrChR0cuLwQD4qZ+qrr1JOtBNzeabFvKCkQY1GajXsWD2cXWrOAPmscdSt3gQYNutodNKzM/rqouhp2XOA0BqnSl4AMvzcdgN8RCafaIoCqIouhvAAQAvlCTpdgD/FsDNAF4AYA7Av6miQ1EUvTOKoudHUfT8RWEhvv9/xBNPAHfcASwsAFtb9MHhkJRitRUqA8Jn/8YRRRgwM6HlZaBWg4wIweo6ttwacajVdXJwEgT9XJeAn6pK9mOvehUB2GlX0RgD6UyFz1T1PGL+2OnQ1y0t4QWLZ/CPj7Rw1w1dwqxupd4gCAD7PjDY9uE6IWo1cgliDeRYs5r+n7pnc5nVboiGaiOKJHIePXAAkCTM1WzMvO3biXhvOgTNmXyLlAu1W2NP/Kt/BeWnfxJz99yUPKYohCUfimXz1teB+++f9KGJtlqtrcEOtMplQPbPWbg46PDB6rU1Mk4BInmyfz/21rdx+ZyX6usoWF1TPGFm9eXLBFx+7LHUvcWAXEkif3SdaAcKgNW+T67rygNXsGx2geEQ9U/8DX7ih2w8sr4HNT21AVZVMWa1G6GtWyMVBgcaW3jgCy4WdIpcLSwkb2Asl6rBasaC5kgVAEDv9GosXXJQOh+D1X3PQNNIJdViGRD+tR1ue2QsmCaRvmm3cePMCv7+4i04fnAUoItlQLLuA+bMXoT1F2sdcsDqEmXE10Rag8MEL82kY0DCpMMTM1ichlmdc3iKACimoMyRppFk0PYUBjU0+aCwS2QYQszqwkCKoGa17xNCbkCr9V/3OuCv/5peMsqsnm+m5lRFIUnnPBkQdtgTLE/mMuD/icIeBKidewJ/8op345B3Er/wb2wgikh1VRoUYtVRnPl7aE0Afb7ma7LfwEp+eZUmtg07UGG2ddJ2u42jrTU8srGPSK4xhjBjVnOYPr7tQ5UDfOK/fEEc+2KashlAbazTWzRoiXrVzGoemzICSs1budcgkoszq0G2tdzkDTNYbAr8YMwou89/rT90CRHEMIDnPx/Kj/5feOe7sydfwqzO0JZmch1lZEB0kHbHr0EUwQtkaEVZ+2ztyrmkhQ2SabvXQgYkBv6ymNVl2OUU+LPzPDLAwGp39Do8//nAiRP4vV+zRraGAJV/yDPWY7G1hTCSIEsRnrdwFl/6LN1j0e8opQWoeTIgZTXheQBwWbBaBYIcYpPnodTYymWXTyEDkmlIH2uBl5i7eTJWBb0sAMSEltx9jOfBDtTYgokbPEmgKRMhecmFwsxqdvbKI7QAgOcRybZ6TqcZUa7CbZbnAWtr1bVHZPKkEWZ1Q3XRt6f0zWLnKGqw2NGH2B4UZ8C84Q0EP2Ntbrs1zDSTyqy6yfcOK8WsBqBp0TOC1HG9R6HZMoqiLQCfAPDqKIouU6kPB8B7QHSoAeAigIOptx2gj2U9vhuCsb1NPPRG9nqDAfqeicacQcBqARPAOIZDDF0N9QZhjaLTIWZlj25hy6mj0yabX1P1hMFqzyMs1VmqRv6KVxDmMu3qDvkDEVmFGKyeo8N1eRl3zl2AKgW4a+Ys7EAfBat5Tt80/KGL4anLwMWLwP/+3zh0CARMox9KmNX0/xSo5IPVAebNAWQ5IqW59Trw9rdj/oXH0J6Z8D0FwWrPIs+3xyqQ0WyOsr4AMkAK6GhubRGwdgeeR0tiVUNJWF8VgdURQA75Ji1H5YHVnkfkakDH1sICDjQ2ceGiFD8/SQbE4kjMsLh0CbjrLvJzpcHqKEJywxkG0VjnaAfSt2JjPcLqORtLNVrm/cgjODB4Ak91l4i8eAqcaGk2+haHSeeEVLudPrC4iAONTXzpfhlL6nr8WBwMmLgWMiCyoAzIuhdLl+y3T+PSJQBBQJjV5iizmjAR+B9v9XzUGLMaAJaXcWNnBUPfwPEbUn2SJBhaCCdQszPmzJizKLNaDuHngdVU+7TMgQRAvolOKRmQ7HHg+yh+gGbtZpVPUtCnjGa1oiv50khFx6ymoa3Zo1r0RSMIiERCSrO6ztOABmVWFznsMukaDqDKZEDW1sgtbxjAi15E/Bfg+1i3G5hvpe4vplmdt46zQ5koWP1MYFZHEcK1dchShK/acwqaHOLHg18FAtqndI061X7lJdmGtjzKrAYI6PPd3w389E/vfAMDq7PKiFm4LmGS1en1XVjAjZ0V3L9+EDUz9V7mt8ABpxjrr90pcN/mlenTe1YpCVZngn+MWV3CtJAkW/8JZUCm0drXonztVyAhQYjIQzGj7CEfJLIH1LhT18n1uO02vPHbsuccVc82WPRCeQpmtQQ/msAii3XLS7DrOXuYuDKoYHUYgFxfhMpN8JBiVheUQollQHLIJwNLJlWy6eug68BP/ATwmtfseL1qKIQFzgOrbUK+AYDjM1fx1BMJs5r1L2lUhSrlECSY78Y1SFwBKAVW58m2xJVRVcuARCWZ1Zy5u4zBIpfcRceHpBe7v7jM6iCA7RdgVstkXsqUVGASTqXWmXDyegDE+6JCvxdLLAR8k2RJikblysaDyYAIkKREw3WnA6vf8Y4xKIBhBWpypm1qDgZOBTIgKc3qtm6jOyzW5uoq8PjjxJSctUnA6mS8G6ZEyHh5YHWZCh4AWk610W6IB/fukyRpUZKkDv13DcA3AHic6lBDkiQJwLcCeJi+5UMA/rlE4sUAtqMougzgIwBeKUnSLDVWfCV9bDcEo9sF2nUf2N6GaVL54H4fXc9Ea8FIDBZFwWrLIvp5LbpYdTo43NrAuSeGRAZkJqLMak8Y9AvDCGcf7WPBuwScPInFxQT8GwxANlMjMiAc8C8M4fnAUq2HpWXah8OHYao+7l44j7vlBwEArZlUG4pCXLE5h+igb2HbNdFUbOC//3cc1Fdw7hx9MmZWJJq6hsI3Z/KdEAtmH8vzfjKnLSzg+39Qwx13THiDquYzPFi7FBgc+Z55IaqjCZIEGQ4T6czkQ1M6VHRs2d0KXB5SzGroOhqag/4WZ8y6LkzFhyyFMVg9ZwywsSknfY3kBGih7DRRZvXmJvDSl5I/sQwIM3dgh3dNI+WYHO1A9hU3zvaw0jWxvEcCvuqrgCiC+b73oK66qDVSG3yqWd3jgdVuhLZmo8NsaZeWsL+xhceeNrGsbxHwlpl00WtADEnyGi2rWS1osNiX4yRDZ3AR26tOilmd6lgMVvOv7bAXEDCJUTL27MGNM6vo6APM7R2laeg64IQqnzVS1OlbosY8WUFlIwqbf+Ul2sKQtKmVkOuQw+qZ1XkgHTOlKbNXFWD6jLyOF7pOmNXTyIDQyo34XE7lsYYiYHWRDa6gNBZjVl++HMu244UvBL74RfLkutPEXCs197PEVRbbC4jnO0UXG1/PCGb1+fNUB6QGvO1tBKi7cCE5zKaz2KoKXQngcJawiWC1JAEveQlwww0738CSdwJgtRuoyRo1P4+bZlbwJydfiNsOjGrtE4NFvoaiKhfQQwfyS8nDcDQhUySuqWY1B6AqCqrmlX2zipASzGpNA2GqcsBqSYp2Sslk9NOQfSEfB7vvE2ksQZqioiuTZUCiCF6gTG+wON7utFIwOQzFUiACBT9DL98ErwygCCDTINkLZZK8LHJtZRmGGpCkOw+sHmdW50QMPgqA1XFXpAgHW5t4/HFkg9UcZrWXBrVEgxks+tlSDaSDxX4vRZXyZVvcchIzuRWNVLO6DJEhk10+DbNaIHEz8jrBNnWedIXvwwnVmHPCDUmCpmFyMgwonxTNk4UCynnF8IB1FiJ7WSoD4gpUnYqG55HbWvR8PB7veheReo1jgmZ1Q3XQnxasZiC4TsDqlmajV5Ct/elPA8ePA08+mbS55VDlABpxsiAPrGZJ0cJg9a4MSBUhMlvuBfAJSZIeBHAfiGb1XwP4E0mSHgLwEIAFAG+nr/9bAKcBnATwhwB+EACiKNoA8Eu0jfsA/CJ9bDcEY3sbmHnks8C/+3fYu+ASs7J+H5avoTZfB0wTuuLDFWA8AgAcB0NfJ3rXANDp4FBzA2dP+eRm7kSJZrUjtrDWNQ9nTodYWHsC+NVfRevx+2JGGymvLSgDQpkoX33wDH711+jr9u4F3vY2fPybfh03d4h4d+toilEqKgNiueh7BpZaFhBFODh4PGFWMwaMnmQKDVmgPNuLsGD2sW9pdNJ7+csTtvlI0PLZ0OXIgNg+WpqF9qzgwUzTSFmuAJDAkgmXLo1/aIppZxhYMntYvVrBgpne5Oo6Djc3cPZM/nWNHHLIa5o+uY7z82S/H/hkL+B5sH0NRiOR1qirLiwBM0QW//pfEx2vNLOabH7CuE1SjslfrHwf2DizjRWrjaW79wHf8i1kQ2LbONjYHC0DZjIgeWYUASl/m6sNk6TN0hKamoO27hD29sLC6CEo3oRlNxu5JLNflJVEzBA5rwuJxqAiR8CePWhrFrZX7Vizummmxryui2m8AbD6AWpqill9++042NjAD9z6KUhjOjm6IREAIbfctyCoKhMTsiDKMSeieqqSUvxAIklRJugDAJJc8B6k/fXDyUZCpbREaV/zylLLyoDECbysucvzCNOrALO6pdvo9qaYu4IAQSQll8gwiDxWngY0CPu1ELOasuh41Ua+T/6c+ccV7L30ReD978fznxfhC18AlQGpY34mdX/xQATWKCB8XblyLf8UwTYXR48Cz3kO0R0DYS960EfBCyYDwrm2A1sZNVjkBZsPLc6+i06YkkH3PwsLuKmzgk9evglveFFq8ZVlsufytdyDbjy2ioLVWeOAmqKq8jXSrFbLAZWZABXTrK5SBmSKJJtuSPxD6bjJeF6wpLAAP8AeBIXA6szDeVlQmYaqSZPlRaYBqyWODAhLCBa8DxQpypVqKLV+cSujqCZ8wUSAqdMKsZz9/MBWiHxRgXkriiT+/O04IwmWf3HL5/Cud2EyWM2TwGDVYaWY1VEmoBgEBEgvuodRVSDIkMMhiRuJzLEFddZVKX/eKitXoUn5+61S5AAeWO15ZJ4tCFYbCqeKiZ7ZhJnVANQ84I/NMWUNFnOY1YW9YiQJmorcRAiA5H7OS16qKnQ1gONla6sXDbamlGVXd7vA3/996gG2d2DXSNMICU3QNyszKAiuaArRwTZ99FyjEEHi3nuB7/3eFFjNmNXt1O8tQO5znSnB6l0ZkKmCu8uLoujBKIqeE0XRnVEU3R5F0S/Sx18RRdEd9LHvjKKoTx+Poij6V1EUHaPPfyHV1rujKDpO/7zn2n2tZ2d0rzpob58HHAd7lau4fBnYWPEwawwJo5LJgAwFJzTHwSDtTN7p4HBzHWfPyYRZ3ZFizWre4RkAwiBCS+rjqt3G4iyVrfjYB9HdCok+9lAalQGhLLJcZjUDjQ15dL9w991otSXMGQMAQPOWlMIMz+mbRuS4qKsulu4k1LSD9lMTZECkuE0RVpbvhjjY2MSbXj3IfyELWp5tDzgGi06ARbOP1pw4QAMQMJIX29vk5Zcvjz2RLler1bBc62J1rbhD/I5Ib36oiVB3i8NQtH1ocoBmLYiZ1QAwq/awuUm+5xWrTVjMtO2a4mJo5fe31wN+71eHqIUDzD76Gez3zyZgte/D8jWYWrIpNxWPGPZxtMA9D1i/YKPrmmjdfYxkKu6+GwBwqLmBeju14DGDRTtnEXQceKGCA50BPvCBBKwGgAP1DSzVujtZf6wkM++gVwbwoKyJzM0di+1tABHQbgPz8+R33ghiZnWrNsr81OQArgizuh+OMqvvvhvKD/xL/MoPXtihCS+rMgE+c1gjhQ8OkkSMlLIOOcBUeqoSMJnxxcZcifJsRUGuRmlhtheQz9adopw+TrRVyKxuazZ6/enAattPOddTGZAhZ5r3PBDwr6gMiACzGgCe/OPPY+/gKeATn8Dt3c/i4YdBDRabmJ9J3V8M9BEBqwXHl8bAua8ks5rpmtRq5G/qNNjSbMjj7D2WcOZc24nM6rxgVSECzGoAyf7nxAncOLOCA40N3HNXiuIkSagZIaxAyz3oxuZfBefuTNZfFFF2VAl2HpvnJh2myxpfMeCLV/pfdN6iIPjEYRtFoxVaBUJIBqRIQogmcEUYdQSs9oXB6kyAKgaVy7HAtCzAfhoZEB6zugzjLU8OB0gqK6tkVjOgtkQyyDDALVMnMiDFkmwAxJnVN94ISBK+ufEJ/N1HI/h9m+wb2NxL28yVwGAmriV9N7IAYN+LSlWHqVoOs5oxoNWCeyOWGM4z7CspMZPHrCZJgBJgpiizuoRUYK50BTNYrIlfByKpkPF7MX3jsprVnGqAotU2qsaXSfNtn3gN5HVakmAYEklWiWQuBYLd8uvr5d4vScA//EPqgXFmtapS36wpyW2+T6qbdRmQJMh1k5yrRHVpQXCNr/96IgPiOEjA6lbq99Y0buKulNwUUnJ5u8zqqaIC5Gk3/qli++kNzFCzsn24hAsXgCfPGrhpZoVoMzIZEB7Dh4Vtw/J1mE06w8zN4VBzA+c2W8RgcU5OmNUiOr2Pn0JH6QMAFv7Z1wGHD6Nhr2OwYaO3FWDVaqGhe8mGgjGr89qmIN0OcweqyzxnDtBQbcgzo+W+shQhcHOuw/Y2pMBHXfOxeMsCYBg4FDyNc6fpLD5ugqGqhJXFkWz2vQgdY4if/qF+/gtZKAop+eWVk1N5kfa8YKZSVaFIIQJHTLP6xInJzGqflUKaJpZqPayuT+nuCyDyg7iPzDyCpwXuWQSsbjGwenYWkCQcMK7ix380xJ88eAc2nAbmluhYZg6/dv64vXgR+KGfqmNf93Hgve/FzAf+EFtbdDPoebg07GD/DEWkJAmGHpFDAyfD7XsRrl6NoEgRpFtuJg8eOQIAONjcQG0m9TsyZrUAWK0ZclI2t2cPuQbNTSzfsUxcJNLBNs15eo9lSmgZsMwDZ9hOaH4emJlBW7exvRnGmtVNM/WbUwNTj2dIAsAahKgxg0UWz30u8P3fv7N8gadfHzt9FwNoFFVCkKerXFJDMde0MAjIhqpom6DajBmHslgGpHJmdTmjMt7hybP8Ykw6ZrAoCFb/zd8ADzww9mAQwAp01Jl0DVsXLX6lTaH7i60zAprVAPDk1VnsrRMJCe1/fQi6HmHYD4lm9Uzqt9a0/AM0APh+IQaVZihfebCaASlsLpibAwC0NGcnbkelvLgGi45SDKwuoFkNIAGrb7oJbd3GY2/595C9UQS9ZoSEWc3RUCzDKM08mI+biBaJPIPFaTSr80AElhCsUgaEMatLJNk0XYIb5lTxAMWqQmhSWAistiLCrBaRFwGypZZisLqcZJLKZBUy2i0sUxCbA2e/pLAvAGtXCuE7ORVXJUFVAPkGySVY67H3Rs7YGjhqMWa1pgnJJfp9m7CW221g3z5okg8dDrauekTWsd1OXswDasuyimWZaFbn+W5IYbWa1SxhUfTIw0uyUd3uUt4jOZrVZGwV7CvAB6snMegF2uRqVlOwWthgEWTayvy9pmBWa3kmrozQUnAuiCWR8u7ZAdAc15mfEIYpkURoRWD1tMzqRoNs++KtH/0tdTam2feJIi6xi0UUYaevDEvssKQhS4wVAKu7XeDQIeDpp4GbbiJtxjK3LAQSd54TQlf8EgaLAsbLu8GNXbD6OoruuS20KVj9Mv1z+OhHIjx5oY4bZ1YSZrXCl6qIg1KMJJOuFvPzONxax9n+HDFYnFdIubPqCsmAuGtdNDUHshRiYVEGDh2CJBFw8t3/LcRvPPR1aKQBKmawKMis3hGdDjq6hbY+hiArCmFQWTmbwl4PUSShUQuwtEcGDh/G3vo2rpylszgzXtPlkTa5PoBOQWdyEeMrEBbVUq2H1qKgwBdje1n8CXJ7G7j1Vg6z2jCwVOthZXNKd18Q1micTRY0rmRg9du/5yRZr1QV6HRwsLGJP3mfhEdWiQxMXN6qaVS+hg/6HGhu4fY5gtTPhJvYXvfjJy8MZnFgNkk8mEYEm6dLCcDvWXhkfQ/2ztpAp0MepGD1oeYG6rOpQyVlVguB1emkTbsN/MAP4GVvWsKJH/lG4g6ZDmYklSOt4bsU8BA95NJ2dYWvpRmtrRMAY34eaLUwo1uEbM2Y1fVRME1I4w0ErK6Pg9VZwauyYEyfgpt8Sc05lNJ2AVRuWliqTeSXu/qBVNpgMROsZpvMa1CWag2jwszXtm6j2xf7fp/9LPCRcTeNIMDQ11FjzGq2LlocuY5SmtV8QJVdmqe6S9jzspvIHLO1hRv3D/HkWR3rTgNzM6NrbS6TkDYaAdeVDEjQt8gcw+YCmqxqaTYMYyezWlc44F8YYuhphKEoej+oKkm0CYDVI5JLqgq02+SwyoTHaRCwWucyq8vJgOSYdJXVPc0zWJxGs1oO4eeU0wMoPcdmMRSjSCruNQCyhPI0qwM3gCJaaaFp0BVfyHTYHoaFZEAyD+dlGdA0MllkLClclPkpSYk/xCSCQBjCCyTCBC9oWqjKAYIs804GUBnFE7gAspnVUblraxjgy4A46k6DxbwQlAGxul6y36J72P2tLp68UCOPp30BWAVPHrM6LCipELebLa3h+eX2MIomZzOrWeKqRIJFkaLsy1o2IcaSopn7rZKya5w9clydW1SzmreP8X3YgVqIWR2zlfPWmYLEk9jbh2OwWLTaRgSs7veBpupwz1+6KcMJtMrB6jLMap/itTfcAJw5Qx8MAnKma6S04/OqrSbEffcB3/EdOz8sJswBQK1G9nsFwOpejxyVf+M3yDbZsSPCrJ5JvUjTyJ7AvobM6l2weqrYBauvo9hesWJm9UtmHsF9n/PxyMUObuwkYLUu+3ALgNWSFCUb3IUFLNd6uDycgRVoMNs6AVO1AJbHB+i8oQdd8VE3AiwuAvFsEAS4uhLgi2uHUTdSEzcty7XzDOsYSFebsFB+7/dC3b+M7/vnY/1ipdR5ACidret6QNQUDh0i4Gbfj58fWaCYIze3PDsqZnrEzJTymNVRBM+L8FN3fQSveI34YUSTA3hDMc3qW27JYVZrEmCaRAZkq0AaPCNigJSC1brMN+vzLB+qFOJNX7eZPHjwIA40NrFv3sFja4swFD+57jGzmm9a+OoDD+MXnvfXwNGjhA0wTIPVHRyYS0q0VY0yajklPV7fxqrVxuGDqe91kEjVvP7I/fimV43eB03NQc/O2bBkVRjcdRf+7a8v4dixCe/hMQZATLrKlJLreRpvNNyVTZKJjpnVFtEN9n30PAPNWuoayETmJ8jQVU7HcBChpnhiYDWPNVJWT1WgXQDVMqvDkMzXpZnVEzZMUQQvkMsZauUZ00wjAyIAVtdUr7jB4lDsug2HwIMPjj0YBMQbwkjA6rrqYshLMhZl/SkKDF61EZJL88TWMva+8CCYe+9NjQt44oyJLbeO2ZnUGIrLswVkQESZ1WZ5ZvVv/uaYQU/JsPv+aJVFGqw2x8aeppEkdh5Y7XnEdNosUPodV5pw9l3jzGoA+PmfB97yFuAVrxjtqsABJ15Hi8qASBkHc2rg+swyWMzxXCgrA8KuwaR2p0gIxprVOb9ZnGgTBavlAK6AobM9DGGqJZjV4xchiqbTrNblyczHsoxtSUrWrgz2a9xukXEgpFldojJIiFldrEmAgtV5rP0ooszqAjIgPCNjGlbXS/ZbrDpQX8W9Zw/hUGtrpwzItWBWx4mryU/HvhslmNWZ+3kGAJfR2hcA7EvLVeQaLJa4b5lkZEb5wsh5TTTYGSFvaPk+nKIyIHkAcLyXF+8mAL4uPjX5KwNWZ7LAafQGMklWc66tUZMrZVZ79HYuw6xm4O/x48DJk/RB6kPUqo9Ka4j6ZgHEF/vTnx67XEzahYHVmgZFCoVwDRbDIZmiXv960ucLVw24oQKjlvo9mcdV7xoYLO4yqyuJXbD6OopuV0Jbs4E9eyBLEb71RVfwt6duxHPmz4/KgIiC1bZNMuvsoDc/D1mK0NIc9DyTMK4lCbW6RFg+nInSG7jQ5AANwyeSwpRRKgUB1ld89D0TjdaoARxhVufs3hhoPA7SAcD+/cC///f4pXcsjD5OD/y5zGqPGMvVDQpW0/LheKYclwFRFH6mGIDvRlClYkw6Uk6e8xrXhR/KODzbRaMtbrCoyQE8AbPN7W3gzjuBU6fGnmA6VKaSyIBsVwBWexTQpwaLIjIgE/U5b7sNL9/3BH73jf+Af1w5jL217eS6s8WHUxHg9hxosk/ugf37yYNeCqzuz+LAfOrH4YGUrL9DF/NGH0eOpj7fMIA77sDRQz5uetly8jiVw7G8nN/WceCGCvRagZOOplFWUg6zOv1biIagweLw6oAczGdmgHabaJh5YWpjM/qbS7qWb6xHw7IhzqzmyYCwg0OJUk9euwBKm39lgUml2gQrd+UYX5UBfbIYKVMaLALIHAeWBXJ4LsCsbmoO+kOxH9myJoPVBMRMJIxqigfLyW+zDLOaC6iCzId11cGKNYO9LzgA3H47AOCm6HE8cb6GIJShmKnrw0CEvARTURkQBlaX2IR/8YvAlSuF37YjrG2XjAUGmNC/W7oNQx/7rrEMSM794/sJWC0agjIgkTMBrG61gK/7up3XXGCd8d2wpGZ1Bls5ngvLa1ZPBDzixFXBNvM0Wmm7En1doWC/VwZYXcprAKTSwOWAE8NhgbmL7uFEfBxsG9VoVseJhSkNFrPkRUrooatqlO0PwQgVRQGqPO121m4kV8usZuzyEoCiaUSEWZ0j1dD3DCKzWCDJJiIDQgyt6X5r714AwMHgDD568Vbcsmdz9PN41zUMy1VcKQqRAclo1/NLGCEitS/KYu2HClS5bEVIxvNhCC+aQgYkR7NaHfdpEGxXk4NMSRzHjggRqIxUoACzegQ05ISmRpNlhoCp9NAzSRe0n9dKs7o/kAhYzWNW15RKNatdl9zKZZjV3S7Ztpw4QXSgAcTSjq1mMWmNdFy+TPbeDz2UenC8wl3X0dYt9LYK6D9HIaRP/gOwtoaDB4HzV02Ce6XnCro3tHP83jwP5cDqXWZ1JbELVl9HsT1QCbP6zjsBAL/4VR/BQ2/6RexbcMmNx8BqDvs3DvZCtsGl0govXHoaEqKYpVRryMTsh9OwZ/nQpAANMyRgNWVW65KLyxdDaLKPxsyosZzG06l1HAJ6TGJWZwVjQeeB9lQ7sFXzSQUuBavVyMOlS8CXn2pi260lcmwMROCWZxdkVjMG8CAfRPDSpTAiwQ5kAmD11hbwnOeQkp6RdSU2AiGa1YtmD6u9WkYr4uG7YVK+zGRA8hILyGAA3347buqs4FvU/wUJEdFuZQsQlVcZcsAkr2uRdptNYJkCyIzK5ftEBmQhNe55ICVrd+hhud7F4VvGpDl+8AeB//gfRw+VqgpJArnnssK2s5M2WRGbE+X005egSQX1imMtzfyXWUPKgNb1RNcwCIDhEFetFqm+SEcW22u8XQviJc9CMiAlDk/XQuePtpvHVi7VJnI0q9khp+iBjPYjT7O68JzFgjF9Mmg5wyGKyYCwhBhn7mZhWWQuHDkXBAGsQEONOdfTeTs3GRaG8AJ6bUXBL7Z2cZjV3noXc8YALd1GY84Ajh4FANwUPIoHT9Oy7PSgVtV8thdQmFmtGgoZUyWY1efOFarkzAyrN8aspsBJS3NgaGNjnYHVeeCf52HgG6jXCtwPggaL7tAnh34R9isvyYaU5FgpGZAMwGMKZrUqhZNlFaZhvOVUBoVBBLmETm1uOf0UCcFYGirnUDq0pILMaj9XxotFrFktClZnrbXstyphAgjky4CUZWyrSrbfQmlwPW+8ApRZXb0MSGnNap7Boudh4OujMou8EASTrJ5P9nGGEcsVHVQv49OXj+OWg2PePKyCJweoLZXEpnvZrOkw9oYomHAnyZVspm6p5J0IYB+WM1jMS7L5kVwqGcRLtjp2BEMuCFbHc1fOa5h5XoF7jC8DUlJiRg7h5RhiltnLCsmADGUxzepatTIgnkfA6jLM6m6XHOdGwGoq7dhspK6hYOUGi8uXgde8Bvj4x1MPUla7oitxm23NRndLcA4NQ2B1FXjf+4Df+i0c3Bfg/BrFMMb2xzy8pDSzehesriR2werrJYIAXUtD23CA224jjz38MPm72SR/UxkQx4GQqH0wsIl+XnqDa1l40dLT6OgWqZkABat9LTEzyghv6EGTA3Sao8zqljLE0+cUvGDxDOpjxnKqFCLIA6vzmNVZwYDlPGkJuuL/t+/6JBHdp8D8vN7HBz4A/OHHj2Fl2Mae5Shpk6d3CcqsLsikq6kurGE+sF64DKkgs7rTAV7ykrHS7PSG3TSJ5rCtCxsmZIXvg1wjRUlKXTkl1J4d7NRWXlggf2wbR1rr2NvqJ5tVynwc5rH2AXg9G7ockMoEClbroUPuIaZZvZgCq0V0/qIIvu1jyezh8F2d0efSWl4s2AEn77rmyeFkBQOo8jSrfRQvn1RV6ErGpjkVw0FKWzgGq0Og2yWaYUtjB2u2ucnbkEURbFsiJc9VyICUPUSLMqtLSGuoObqEAMoxq9mhLNNQqwQ4kcdIoYmusgaLeYcnywYBKAswq0WSK3H7FlHVYMsrgBSzOoz7WFM9DN2c+9H3i5cRM0CVU/rvr21h1hhiT4euye020G7jmHEB/+/n9+ANN3x59Pqww36W/i+QgBaCa5ek0bmwBFh9/jx3OyEUVj/YKQl0/Dhamg19YWb0xYpCx0GOLn5aBkQ0qD4nTxbJHgTiJni8JBtYeXZxsDovGeaHCpQpEldZxo1lja8yy95Bhp0ql6gIYVrQFcuAxHuDHHDCsgrMXXSddQV8HGxHKmawmAVUTsGABug6k2OwWI5ZnW+qRtjKxQFFJc+8k1VWViwD4kflEsOGKeUzq30fA89AwygAiKgqJAChm/+eYS9IKtmaTaDRwIHmJtxQwy037PQLEpIBKTEX5DKrAzk5UxRpNk+zuiwAzOYtHmBfhlnNkwEp6RGSJ8VYllmtKxwJo4LJcUBQBqQUWJ0jNzWtZnUes9pS0NRsvgxIXancYLEsWJ2WAUmD1T3PGJGvj0knghvvy5eBH/5h4L3vJfgya3ekwoX6z/S6gqaNT59JSJarqzjUfRinVxo7DeVZJYCVA1YXNUun8Uzwdnk2xC5Yfb1Ev4+uZ6I9qxDdMEVJqEmNBvlbUWDoEdncCtwYVj/YWU5fr+OepdO4YbEfb9gJWK3zmdUUrP7of7mf7JkpWN2W+7iwquPr9z+OhT1jbC85X6agFEjH2Gl5bF1qdhQzpylYvaht4gtfAC5tmrhitbG8lDDpRNh5sSt1Uc1qO19Hc6QURiQKGiy228ArXwl87GOpJxiz2iAA60zDR9c1p1swoyhhVqtqwnrkSNfEMiDjhzGqoXekScFqFgxA4JTQej07YVZT1khH7uJ97wMC28NVu4mFTuoaisiAdLvwfeBfv+BTuP3FzdzPH2kz7xKUBKt5lQvEmKYgO02kxA8pfc4Us1qJPPjbAwCA1Bq7NiKZ+DCEE6gwVUGAXVHyD2TMVKxqZnXZEnUOmASgWhkQpstYBqymDKJMpk+oQClTlqqqMBQv8/A0tOTCBosibGUWlgW8+c3AX/5l6sEggOXrqJlJ8rKmuLDykmFMRqnIEKCSQLk+DgD89W3MGkPsXUyNv4MHYao+Xnf4fvzQbZ/ASOkCj+2FlIRDgUQrAG6VyXgEAdEnrAysVr1R3dQf/VG0vu0bYcw1Rl8sSTD0iGi/5jAUh75ejFktKItUCKxmJfp5BotlZUCygBSmWV0GqOQk2cqDCDk6tWyvVXSOlWUKAis7y/+nAasVhSurUIhZLUnQNL5pI6IItivDVAsASllr7ZRgdXwwz2JWlwarqzfBU5ks2aSgmtWVGyyWvAbEYDGHWe37hFldK5Fk45wRrEFI5ljTJImhPXtwsLGJlmZh34Gx+4RXwVNWrkImYHQms7oM6QLI96CZcmwFAYepW4JZnTkfTjG2citNQI79huKVkwHh+EOw1wo3m8eEpyzcayED4kdycbDayEmE0OhbipDBolrTyLxaIbN6bg4YDIq/lzGrl5dToDKVARlnVov6ZgEErL71VuD7vx/4wAeSdoMxsLpVgFntbA7JnovGwf5jeOTSLNq6NTruBMh9notdg8WvYOyC1ddL9HpEh3KGGCkyKRAAMWAHkAycE/BZ0AAw6FKwOs2s/r7vw4Hn78H7/yFxp9fqGpl0OWC1O/ShKz7ml+kk0GwCsoy21IeCAP/h+X+Fl3916oZl2XIBZrVqlgGr80ueo0hKJp5WC5BlLCqb+MJ9ES5t1rFmN7G4PGawmAfQRRFlDRcz1OJqVjOWRxF2uWBpMkD0lbUPfgDHDjg4dy79BHPiJZ/baMnoe+Z0KEO8AaSMKCYDwjNYdDIO5ocPAwBeuuckblu+mjzO5BQ4OArRrKZg9eIiMZLU1vGzPxPiH77QRBRJkI1RhiIPRECvhyCS8c3Pu4x6Q2DjFI8VAWZ1vYQ+aZ5mdRljGioDwmVWD1MssnodUBS01SE2zvUJCybOFCXtSkD+os507sb1aHP6mgd8TmN2AyCXWV1K+/QaaVYrqjR540wNtcqW/pOy1Ml9jQDIajmGoi4HmQksy5aKaVazhBinKiZu3yKed3/1VylCI2NW1xKwuq66AszqguAfreBx8vTrkciA7N2Xur7UwPUvvuGd6BgWcODASLt5LFUgNccWWLsAFAarV1bIYSm9jEQR8Hd/V6gZAGN6qiw0Da397YmKCDFDMWvyomB1ozH56YnBOeyzIGC1oAwIvbZZUjikq1Fxg0UGeGQYuJbS70+3mwGklAIqOcCXH0jk+5cxQ1QjuJOSFmXlmwChRLZlS4USbbpJdbDzFlu6Jpp6ARmELGZ1QEB8XSvJrDaUfIPFkmA1l01ZojJKlQMEWfNhGYIIbRfgaLeXWGtlXUWYV9HneYRZXUQGhFUccfbe1iBETUnNsXv2YF9jC9994+cgzezcw/H0mv2opFRDVpKNmkSrUvEqi1hjPS+5UGZs5RnDRlHpscU1tC7NrPYzq1tLM6uZhFFWVQj7MQsMBjFmdZl1JsyWW2IgeMHEVa5kCQ3CrObLgEiGTvCKCpnVnQ45oxWN7maA1pWnIG2kBK+pDMg4s1pX/Fy2cjquXiVH8L17CYGOtTtusNjWbXS7Yn3trTloaQ5w7BgA4ODgcfyvRw/huQvndxjD8vASz5emkwEpUYG4G0nsgtXXS/R65G82G9xzT/Lcq18d/5NoG6lCgpDDXoC6MnbQO3EC+LEfG2Vlsed5zGqmK8xOibIMtNto6zbmDcp6nUmV5rINYyjArC4C0rFy3zxpCTbps4OjLAOzs1gw+3jiSQlrfYMsUDVtpM3c8mw6sWpqAWdyxtDjgNUjE7Zgu6IyIFi7CnzsY1j61F9gZSX1+FgJjlwzCAA3JVjtRynTEl0XlgHJY1b/4G2fxItuTq1gDJzJS4QA8PopsFqSgEOH8O6veS/+8GfP4od+6zi+4cBjoxsJulDlgQjxolRAt5w0WrEMiERY05kGMmyTX1TrjzJyeJI4I9rCkgTMzmJGs3DygQGWaj2M7mwgpqHoebB9DaYueIBgwGeWgecUQAqAa2KwmFVOHwVTMKuzGERTaIkm+toTnptCXzseXxlzgmVLhOlVWAZEnFk9N7ezzNHytYRZzZKMeaAyS/YVZFbrss81VfMtD4ebG7j9ttQ1SiWtUaslpsFAAiLkgdVsji3KrC7IGDl3jhSxpNc8xwF+4icKNQOAACmTzFZbrcnyvSIMxbLMai5YzXSFBZnVOqcs1Wea1aLSD0AKSMmW7plGszqr3bKSCjxmdRmdWgCUsTxZFglAqTZFZECGtkwSbYL3mG4IsLJ8n6yJWjG5IfbekWDEiLLM6iwWWQymFW9X0wA/ypEBicpVRuXJSpT2XFAUyFKE0KvWYDGutMhlVhto1EtUhHDApBFmNQDccgs0OcTvfNX7d0qxsQqerOE6BatYyQGr4+qwgvetrOQwq8tqVvMMFqdgAOdXspXQwabtankyIA6Ka1bLMnQ1JIm2SWcPIBnLBfaImZUbQAqsLsmszvq9fB8RUp4Eos3qSn6CCUDfVtESkAGJ13hhQ7L88DwCxZRiVj90Bu0n7gN+5mcAROTYygwW00c6ti8SqO4GyDBRFLJ3i8FotodmiQJdR1uzhMHq7rpHWNT79wOtFvbgCv7LC/8Cv/KCD8aV1CN9zcJLogiuJ+2UDxEIzZDhRbvM6mljF6y+XqJPwV42G9xxB/CqVwH/4l/EEhYAcY11Q0VoUhv2QzQ0h2/Kwp7naVZb/k5Acf9+tDQb8wpNlaXBalWFIkXw88BqplldkFEqYrAoSdHoItHpYLHWg2mEmDVt8jzb1ItoVlON0kIbZ0WBqXr5l7aM2QtzkhfJatJJdOHxe7G2Fo087qU/l21MpwGrY0CfbmBiGZD8t3lZB/NDh5IN6pvfnDwemwtywOqBC132EymdI0cgSxFesedRXFgz8dab792h/arzkgClweqc15TRTZMkvoFMJBMzpYJgtSYHcDnGTzEDlv1mc3No6zaeOiVhudZNtPZZaBq5BJyDuROqMA3BAwTPbLXsYZejKRsGEZQy5l+x2cvOp8qWugI55ZNlD2Ssr1KGMc0U+trQNBg5ycahXVAGJGZWi/XFtsmSd+utwBNP0AepwWIMYmoa8RrwcvrA5s8iY0tEGgvEUOrFS6fxcz+akj66/fbk3+HYPR3r12e36TkhdMUvNG9xq0wmxLlzJCeeXkZcF9jcLNQMgJSJ6xho8oIXAN/7vTtfb5hSPlPVdeGWqGLSRcDqYUjAakG94prqwhpkHPRpFVdhpk8e8zGKiHRPaWZ1MFlWoWxCkGOw6AdSORkQALoWZYKq7LMLh4DOeDx3Cf5mqqHwWVm+DzsokMAFsvVEyyYWaKh6Ruk7k5sqwfwUkQEpx37N0ayexgiQYzRalv0KgG+wWEQGhPnFcJjVMemAnQOf//yksveGG0ZfzNNrpnNMaYPFSZ4L6WrNghEDkHmGmCUSIWpeVWcYIoyk4lVnedc2vr/KyYBkaviDyYAUBKvBEoL5Ouvs84W7qmVUCALl5y5eNQD7rKIDgVd5FkXoO6oQsxq6TvZaFTKrm81y2HfvyhBtnWzeGmGPkA58H26gQq+l1s0CUqS+n1yudjvhZiZSpGOa1b2JzeyI7maAtmaTyt7DhyFLEX7g1k9BadVHz5+8SnR2/lZROCGWm2DZDeHYBauvk3DWeuQgycBqWQbe8AbgRS8aed3MDLDl1IUAxWF/MitpR5gmYYzwmNWWT4CvdHvf/u1oN0PMmxnMaukaMKvp4sMzk4oiaRT8XF7GgtnH4YUhlhs9zBmDZBFhoFce4MEY0EUyuzE7kWOwGMlQjGJuzCIyIIHjQ5HIZ6tyiGCY+o19H056AaK/a2RPkd1lbG22pmkakQHhrMGZ+pymCfzIjwA/+qMgTpk0GDgjAFbHzGoglhUxL57CI7/1MZyYWd3BrDYUD/aQb95ZRLccAKI8ZjU78BfciGlqxGUilHE71+UAHo9ZbY2VPM/PY0a38OTWMpZrvYkyIACEWGRFZEB0OacUrWzpoKLkGm3G5l9lmdUT5gPPLw/OZB74KzBYnDgOpjQq03MSDJYjk7LkoprVPt+ojIUkkekkDVYP05rVikJkQDw1t8zVL+pOH8sXCXgjyGPJO01LAOs77hh9A8/4CoDrRMWYI6qA2eyEOHOGXNv0FsXzgK2tQs0AoGD1uGY1SP7+Oc/Z+XpdR75RGd0XSHqx/YaQZrUVEWNYQRkQM2+dSSdCCiYaMwFgpg9ZBkyLK0IqZlbnmar5U8iAaFQGZILBIIDS8xaAfINFp1iiTdIE18QSYLUxqXqFHcqLgok0rokMSB5ANVUiJJ9ZXVaqIdMbgCbGCxnusuDtjZjBYkFmta743DNCbArKznWSBPzgDwJvfztwyy072hSRASlzXTMlI2OTzXJ7GADZGshl/DwUhZCwssgc01bdTRpbUQSvLLOarV9ZMiCuVAqs1vV8g8HQ9YnsXxEZkDz9Xza2yjDWpSBbBqQEA3zk9VnrQRCg7xpoGh5/LLA9Q4Wa1bperoCIAMCkLK4jdQnJYFLiQVWhSWJg9fo6sLBA/t1u72RWK/qYZnVPrOPdDZ8A6/U6cPRo8sTy8ugL2Zk2ay6kFUd6keolGjFYvSsDMlXsgtXXSXRXbczo1s7S+bFYnnWxYrWFboxBP0Jd9cSZ1UVlQABgcRHtl9yOeYPWm4yB1QqPRea6RPvULFbqmuv0DUwGFO+5B4tmH4e1i9hX38KeWjdZJBSFgKo8sLroYS82PMoHq4HUwUWwXRGwenBuHQ11FKBO67S6gQqjQT9X12EqHpz+FJNuXBo/LgOS/7ZMzWqAbJizNs1B/hTnDogpaMysptpWOHkSh4yVuI/pdk3Fhz24RszqLOCrpNM1j4lQSpOvCLM6LdcwP4+2buGp7hKRARlnVouA1Z5XmFmtyhlMJ6C84Q8HmCht/pVjWui50XTM6knjoKzjPZACqyc8Nw1DkTG+Ju3LwxBDV0Vd88TbVhRSkhqohdgN42C15esJJirLqKk+MR7OA6uLsvYZmJiXwAUZCxP1ir//+0kS+9u+bXK7HGa1JhWTASnDrD51imDpaRkQ1yVsmkK4dxRRZrVAwp0G0azWsvdHvj9aUSUSbK3lLIuEWS2oWU33BYGbcW1ZYrxERUgmADxNlYUsZ+t+lly7eNVR8RxbVgYkj1ld0mAx7lhGDB0iH1RYaidvcAUBAatF10TarjnJ2HtKsFpSlcnVUWVNNsESrXmmaiVMQekZIZPQwqQayt5fGZrwXlFZKBYCYLUVaOM5O26bIsxqy8LO6hVJIlKR4/deXMHDMRcsc12zCEjsusrl9jCsXzuC+XmUYVbnVTFNAVarcsYcy/byZe5bBtRlVAbZTjmwOp5jM+YuooVdzLhR0WQEvCqLEusMkQHhaGsXHQi85CXVeW7UBPZPFYPVrltMPSwd3c0ALcqs7qh9QjKYBOgX8M3qdoF2tAW8971oXXgsAauDgOA/uhq32dJs9AZi905vOyQyK7XaKHshLXML8PGSNLO6YGimsmuwWEHsgtXXSXTXXFLOwAGrzbqcbyCUih3lXVlBn48sjgwIAxTHZsHWHUcwf+sSOUSnJ3yagQ7CHMYb+x5FdRnlIBugAil/3HEoPXECt9wc4a3H/wH7lBUs17sjMiA6D6BjbKeCzGrC8Mh5TQkjCtGFov/0VVKGRGPWsJJybHbt2QKk62jrNrbXp5h0Y0ZYAlZzjSvBwJkC+pwM9OOA1bF0DQOrZ2dJ1tVxgM98hjxG2dYAYsabY1XIrJYkQJbJeMzSd2MbsYKO1DwDmfLMar4G8HDcTGpuDnfOXcTfXbiF3FsTwGoJQOhwSp59DYYpeI9pGgHpshJXZXVaOUCd55XUU81hzxA2bUmwWpfJgT8DRCgFUMUsqskHSAClGYoTWX8A4HmwAh21ImZiEDDWSwVrdhKzul5PXsd1+WbmMEWAFCatEuSzwGOgblKlyateNbFqIbc8G/QAU1AGRJFC+E4xsPrkSUIAH5cBASCsRcjeNPR11AzxCgYRg0UA1wastiGuWZ1nhEj76UdTAMAZRoiFk+0j7WZrYZcGq3NAHz+UyZ6gjAyIPrlEPfRDwvYrOW/l6goDsFylkAxIprZ0OjyPGiwWSDqrKmoKlZ9LzzPsUF4SyMgE7KdlVnP2MFqJ9ZvHrC6rWZ3LrE5L4BXsLwCupEIM6gi2madVDIAkBB1KOuCdE2mbZH7JWJuZDEiJKotcZnVUnlkdRdLkfTdLhFStWT0NWJ01H05rsKj4mTgokQEpBioDRG8/TwbEsSOSvC3Sbl5yoWT1afJ7ZRssAigvA5I1EFyXJhkF2tLLGSx+8YvEKHw8PMohEiw0HInudkSwKACzSpeA1ZOuUQEZkG4XaK2eBj7zGbT/9PfR2/LjjkaRlFzLrIqgvL7qFgGr9+1Lnhj/LVlfs/ZbUyRxY2b1Llg9VeyC1ddJbG8EhFmdPi1PCl0nBM1xLboJMbQkwqrlsZIMgwCKHEata4dE/3esvRe9WMJrf/wEOUSnQ5IgKxJhYuSAdACKHUg45aMAENjezsO+JGHueTfgzUe/hAONLeyrb0+QAcnpB9OWLgJWxxsQPrO61AGax6y+sIkm03QCsFzrJiaL7NqzzzUMtDUb3c0ptJeCgGjr6QkATxjrfLB6osFiVjBwJsxxo6bt6spYuzffPNLOiHEZBdJsq/rfC0D2glZyIxZrFWeVzZUpn1RV6EoAjyNVEJc8pzSr33jDl3D3/AUcWrB2btapezRPD9wONFEiZTIXZAFqU2xwAWT+Xn5AS9SLAimaRsDKCSyX2I26YmZ1aUOtPBmQaTSraTJkYgJr3OhQMIyaDGdS2f+kiCLgz/8c7VNfxtYW8IlPINasTjPXuCX6ZcA/SUqYhFlrIgDfj7IrTSYFrzwbqWSzaIcVBQ3VxcAq9htvbpJzw7gMCHtOOByHjIUCbEKuBjB7vKAOtC5na36yKGSwyECvrHmLlqWqRQ9Peaw/qtNbSrOarbc5jNLKDRa98pUmWSXq0/gCxHJeWWK1UUSY1UoBsJpJ7fCY1X5BZrUsw1TJXDqyJsSH8nKa1ZlswmnAahW5MiClEix5RqOpdksngzLug9LJIAGWZhRJ5eatvDNCEGDo6ajrvtg9weatrC0cIweUNBec+PXLki5ou6xfk9otVXUWX4NsGRBJiorPMUwirOpEo5pvEOy4UnGDRfDNYePkbUGwOpMkQtevsgmmTBmQsntZjlQgq8QWWhfpJuf/Y+/Pg23Z7rtO8Jtz5h7OcMc3T9KTLdmSLEtGtrEtY4XBZUDF4HZB0w4bUy6qu6MKKNu0HQW0C6q6mKEg6KKhKLo6gDDNFBDuCgzYBhuwMMaWZcuy9aSn4Q333fkMe++cM/uPtVbu3JlrZe71W3n87kPnF/Hi3XvuuXny5s5cudZ3fX+fb73SS0T8kR8BfviH+18nO6vrGucrNMzqI5zg4UPhki921zsazurzkxLL4gEAYOGlOBcaQ1eD4GvEfTX7szMwYV1MEr/ne5gZ7Vu+Zfcb93VWE8TqfYI2L2u8LsXqt0idnVtsgBgbYTiAfnUy/mBsNthvxzwIWNjPaviYzWK3c7y3vQ34+q9X/KU9RDoAei8KdyTpG0CRlnBsiTONB4Z85zt+Gv/nd/2r7fXmrOK0GDgP4aSb2llNET+bncLhidbqzgZzNwPe8Q4AwE3vQSNWV3kJ26p3nNWHfozTE8KkUFRRYJWHWITbtiHfLoeDK6tqu4jcdyVtWbAcu/n7qspziejT5r0+/ngvYHGQJcoOyv5PEatVi5GqQlnbcDRxFZ43vNCjOqsZBmTMWe3ssoWvXoVlAf/8N/9FfO1XbJTHHdyJ5xz1vcVqgT8YWpQS21IBtTBBFj3EeCBZ7BYlXax2PO6sljBaSSgYYD8MiImzWjYh5W7aWah3vrbv7u9MOT0F/sW/AP7qX8Uf+oM1/vP/HFJnNVx3OBCU6PrzfL6xMDC5bZjVGi7o0YDFrIavI1a7LuZeivVm/zEpTdkrNQzlzmotbnWSaLe+NxsMUzqrRZjxyIarlrN6SPzl50nCF405qylIhfZxp3RWi7FbJvrUNXdWE7pXwJ3VVd/113SvEAMWBzdGxRgSaJzzvjkOOhu4vCKf/b2dY5cl0srdPxOiWyrxzwQDMhQSLY6re8+6I7k24lkgiKrKtUfTxaR3SADjOBhZYPxYCeFnyKWY52xDMNjzfdt08KiFWjIGRDiru+YTgWwhzmGGxE/Sudo2XLtmWUyytUdVsbkIcW6omm+RMSAja7A0t/tC5B7l+ZZ0jBXVbN5qbrAoBWBTDIhqc4FimGt//8AG097dG4eHWHgp1vfi8e9t1cc+xv77gR8AfuZntl8XzmpvgIgmrbMznKUBDg7ZxtiRdYqTewXO1zYzvcmc1UPmI17nr55i6bIJoW3V23WVwLK1NIh98kGa467AMCBi4v6BDwB//I8DN27sfmMjrKtRMGQ81j7dUZc1Wpdi9VukTs9t5qweE5Y9DzejM7xxd/yjjVOLiUlji6cwROTkiM8HBp26VmJABmsPkQ6A3ot9jzCpIimYs7p7rhzCHzgFouuLXWa1XSAtRpzVNQUDMiwiXKSzerVmO5miReaGdRd37vA/29hsoBefke/jwI9xdqq/kPnX/5rPMTmnaxltOWD7ONabF4VmmBSAwZdElll9sfrLvxz48IfZrzsBpoL1OCkGBNgrjAPAVoDfs1xvwN1ADZCxbfgO45kPbQRsMnfXWX31KnDzJtzHr8P6P/2e/l8Qos9QWyp3Vu+NARFYBdVz0Ag0+x2uqRGmbBOGSHTPSJnVOZGDDeYolQqgzYKMIE7wz0slfAGgiT5Dbal5zgRKTWf1XqIPgHyTw12dNL//vR+5j6MjtnGXlS68sPXv2ePdRdkIadreVeda18gLG64OX9rzRp3VWQaas1pDrP7c54Dnn0gQru/3mNWAplidprsc8X3K8wZRDc28QPNdGzjFuFidsnnF3mL1EL6ocWXtf5oAtq4/xTNbtsOPdWoIt2QasCg7V8GTtYnMat9CVvY378j4JmDUoci4wlys3vtE+f06pChQAhYBhF7FxOr2sYtC/5lql2qcNcAU7BWwSBConKGsGB42arsUVMMQE57YxSRc+/kwBkR/jVCNi9Wlv//7dqyDR3TzaeLsYFlwbLZB1ROr+caVNgoGGHdWU7sBnBpFrd5g2fnZ+9aQa108XwYBiyoEQhOwqPmQ7eWs1uH3A/thQHTvrWYjBPIuXKqzegQViCzb/3wPD3Hkb3ByeyTcqVOf/SyLCfubfxPbjmlsndWz2W52yGg9eICzLMLy8QVwdITjYIOHr8dcK0h3P0sxjx9h4gPA+asnTGsQVbJ7phGtxXGH8mxkx13ZjK899kIbe3e3NQjd2nPtcVnDRWkauaw3oc7WDktg3cNZ/Vh0hjduH+DFoe+rKqSFg0M/GR+EubN6MFSuZFgAz9Xc3d9TpNPGgNglykI9SJZZKQ+ounJla/v6nb9zu2hxHCbQDYnVwlmt04Y0tCATRWxN3sdZvV6BoWCuXwdcFzftu7j9Wg7Aw+nGY7wn8XODAIcaYvXpKfv/YgF85CPAK68A86LAeR5isdi+hAInH3bp8pe69kSs/ZJQbPLkOeAFnfvAsoBv/3YmWF+50jtm4BQ7rkDpQQGaWD3WYaA5wW0ctVM6q9Fpo5aNSWWJOHcRea32UccBfuiH2PWViQHinh3aiS8KJOUMYbS/M82zSxQqJnpVIa88eJ4m2kaEw6al9CXaYEB0J7iexxywMmd1QRdS/FDBTeP3wILYPupaJTYyHcUEA8InpFIMSJ4zd2J0MWL15tYpIru1IHjpJRwcXMNqw/4dVlvJcxzGt1UJSZx5GWg+X7Zro1SJM/znNe+Zfe+FsfZstFBLGs7qmZthnew/Jr386QovfPbHEf3ZH0WS/EWAXcHmEmqJ1VmGB+kch4vT/f+OGJMVn1m8rtgCWtNZ7TvFKMoqSSzG/twXA2JVKFS5G032g/47UTnfaFrJCZivkS4LIwyIilNb2bRQNQw4q/Naf7Oidb6D7y8+du3tUgU0nNXu/u9EXqFfMgxIR6xOdDqXujXgrC4qAlYD487qgjKHaQwtij/nopWlu3MzlJdjsjEsNsfzSr5op6wRPA++vR4WqwV2a9971mabqI2o3H0/8Q0x7U0AMAG4zPh7sT2vMMndEMcZYlZTNlhcbDnr3ZveAAPCxm7JPWnwfAkcjCqDpnFW62JA/JGAxRT6eJEhJzwP49M19DAnfIW8dJT3rPjZWrWHtrE3tuTwEEfBKzi5V+CpPX/8yQmLLnn3u4GPfpRdb1F5DnjIMJv52Gz6ESfKWq+xKQ4QHYdAbuPI3+DV2ynOa4eJzc7x9ns9D55d7eesfn3VEavZ38nSDl7E8/ZCromKY2B2mI2L1WOd6E2WwwXkDVzWXnXprH6L1OnKYc7qfcTq2Rlu3xsZWDnnLvAlg3O3goA5q1cDg06aXgj7lY4BqQfRGkVawpEtSiwL+K//a+C7v5u1jLTOk2FAxpzVRAzIELNaXBtNxzpbPI5gQNYWc1b7PnDlCo6DDU5usZfG2cZlvKeWWH3gJTjdMwTrL/0lRhf5n/4nxiJdrdA4qxfRVqz27XIYr5JlNH7gHi+JPAfjrMsmTFev9p8Nx2Fc5SHHOtEJb4E5OKVFbB20PYehCpTOEVrgD2vxGwiNEKJiF9dgDwhsrgvPGher09Ld31ntjgcp0ZmXame1CQZE6awu6YFiXmDLw26oIULAdoI35Kw2wYDkkpbfPKc5//ZsxYsfxIjclm3jU5/C0RFwcs6vefva78GoLWp9N+EoC1uHdSiq2RRV/50s5wGLOhgQN8Um2f8zvvtqipuzM4ROgWS9HRsyjvHVYlZnGf7dnefw/ucf7P93PG+QI5lsKtaarDN22zaCPTpN0szSw4DYFQrVe0YsdCnj1qAATA/pGnKUkuaGgq+tDFUjtv5D7fprNmyIHSFj3MtN4elttO3DrBbOah1mNYDIL/sYEFNntUqgoTqgcUHOan5vqXQkE07tsLPahkd5vvhGSBarkQIAtOecvlMMG1p0N4ctazQQEyAIigAcV527UdQOLbhyD6cuWaweuGdJGJChjJC6Rq7b1ds6WSVaoa65WJ3rY0BCZzBg8SKc1QBIc07Pw+g9q/1OGEKWAHrYksUCR0HCDGB7ip6f/CTwZV8GfMd3AL/n9+yK1dmnPgv/r/0VzOo11joYbH4QKwwaZ/XJ3Qznsct0hI6zem9m9Z2YYW45jtStCxQFsElsRG1UjOcx0+CeYnWSWizEcyzrbczcZ4CxuhSrp6lLsfotUmcbhz3M+2JAxsTqokBauQj3cRSGIWZuhs16YMKSJOQFCYDpndUjoYVFWqqZn297G8M/tEU112UYkHLMWa15DfZgiVI5mmwiOvxt69jeitXLJWNS32cnc5YwRvUuBiTB2fl+/77zc+CP/lHGywoC9ntwZ3WDAdkzuJKUUL+PWC1ERd3gxoFNgCbcVNNZ7VgVynTCTRtg+PlqnNV6hwTUAVVNZZk2T3avQI48Z63qwZ4nLRylqnZ6EyFlgIXdYEAIE1yVuG4S/qVMpDYJWBSu9aIvKldFxTyzFLHasjhmRuKmEwxNXTFlzwljfJIydM1iwb7w8Y/j8KDGyVmrO6B1zNDJ1R1HwvFFQMwMnqsI19Pp4BH31UAoak7BgHh6AYsPXk9w7G9YgGZLeMky1tyj46y++0aJmZthfqDxjI2wXxuxWtdF5tVIy4EAz7pGllv7bwbsMW4x4YvA6lXNN/jGFTlgcYxZrTvGWtb2nu1uWlHDgXn5gXyz1YhZvUcrcazrrPY82FaNKtuDWa3trK4RdzEgkiBZrVLNN0zF6gEhifQciM6ogU1sADSxWnVcA/Fz6/yT3ztlWrB8Gc3uS98u98CA6GG3GqFWEeoNgBYS7dQoZfeBwTx2PGCRiAEZ6gYwvbeU3G4CYx3gmxaKrpiyZOYQVx+3NIoBEULiVNk+FZ9zUjB5LpTYliovaXPZkVwbFMX+JizLwuGywkkWbVuWR+rsDDg+Br76q5mcseOsvn8O384xL06wkcQHKStNmbM9CIDjYxz5Gzy8V+HBysdxsJEHLI50dwPA+VnNnNXPPgsAWLobnJ8Dceaw+XhLrGb5IHt8FnmOJLcRenvcYwMmIQBAWSKr3Eux+k2sS7H6LVKnG39vZ/XN6Ay37488VUXBnNUj2jcAwGcD0cPzgYlQmiIrXfghbUAfZVZrsopZeIr6W8pUwaweOE9HhGbIuFbAbnv2vtWwRAf+fSYYkJEdyNXGZhgQzwMWCyZGPyyBusZZzMI6ewGL5/tNBjYb4P3vB/7H/5GF765WAMqSOatn/HP1PIYBKS8YA6I6dG73mdVDNZT2zqvKCrlrf+RcXbtUi9VlSWsdHOpc4MnslMm472PQNUHCNQinz5BYresgGttcoDoUXR7iKrsP6ppjQAg8VTHBkzmrCyJaBC2xWhawSFyQCReVbEFiJPoA8P0aqSycJ8+xKf1Ro0SvNMTqyMmBF19kqeGrFY7qhzhZ8b/fEatZ8PAQs5omKAIYZB2y9lHCpugIBkQ7YNFNsU73f3ge3M5xJVyzx6L178tzlnmjI1b/y48G+KYnflWv42iEAZxsKvb5a65KggDDnSa6i/6xcYuKFLAsuE6FQhb+VddsI9AACSQ1CJiI1SqHouiIIWJAVJ0m2iicdgnn40ArMfmdOIbGKggBi0GFpIsB4ZkQJhgQi5/TTpkELPoDKDPqhuCQAxowFBQVm9giI4QYYMruA/ncKEsqdYegqoYctaKE015HrBZCrUKsJrmKMSAommxcXaSzWpU7QcWANOOh6hrQAxaVAcHC1EbAHzi+IieFFxOrJ3RWG6DnXFdtvmlMIkTGuBQJBGzHrj3XtUdHNU7SGU5eXe31/avV1nMRBB1nNc8Xm9UbLbG6TlL2/AqxOohxclLjM3cP8LaDu7vzJh1n9YZjRG7eBAAs7TXOTqpt7pGYM/k+R9bscc3imL0X5+74nGusE/3SWf2m16VY/RYpJhzuIVb7PkuNjUceTh1nteviWrjCvbOBny3CAi4KA0JgVg8GLAoMyL4vSotNsKz2OfUOWrDWVF1m9UUGLI7saq7azurFgonRJ4wJdpaHOAjS7UAfBFh6Cc5W+/371mvWffO938s2TIWzekes5knUaeGqNwGoSbxjok9db53VGgKNa5VqliiAPCn1BHB+XMeqB510AKblphkwq73A3gsDouXQcoeDXgAARcGwJhruT+Z0GnAo6qJ7WseVYkB4+BeJL+2qA1dNnNV24LHrpgq+okzCwNPEJSJ4kdfkcwWAwKuZmNQVFQ2c1dJ/f6fi0wyRm7OBi2Ogjjav4+HK200lBwDH4TiLAbGaiJjB0LnyTVFv3+4CfsyxTdEsl4TNjhxz7qVazOoHd0tcCfjqqDUmZRlbp+iI1V94zcWLh3e0xWpAHVSWxLU+sxrsFNIuUqFdfL4V+JpBZUPO6prArLYsuA7k4h/fvKS685QM4KpCDZA5tdJzNXh3AS3XX3fcKkAOsd3LWV1qYkA4o1OJfwAY0k8ndJhX6Fd9DEjJONbRjHAPAOqx61HDgDQuVcWfU8Vq2x5kwpOd1UJYVrigs7jUD8HjrOIxDEhc6t0PgwHBBs5qp82B7hyTfF1HAxZpm3eOo3bq1qUBrsKtBzvkSGO34ADL5gZFgbT09n9vtWoMZ5Zk9rRitUGot+fWyu6NPAfNJLIHFkonEPLo2Mar62P8J99xba/vX62A+Zz9uitW5ykzJsyqcy2xOjnPGSaPi9XXwhVu33fx0u0lXjy8zdIcRe2pQQBcrPYT4LHHAAAHOMf5Sck2d/18u44Sm7dD5j5RWcY2Xmd7XN9GrB7KNnK0pprtYwO4FKsN61KsfitUXeM02dNZ3UxARo6p46wWYvVqwG7RTBqnDb4iteA4DhP+hjons4q9gHRGnz1FBO32bGu4PZuKAdnHWb2OmeAA12XOai/GGReVz7IIB2HrRvJ9FrQ5FC7Yqs1m+7JcLDoYkPl2whZ4FWuhVonVdc1Tuff7uU2Jz2sgAK1Z6OwrKg4tynkVSaFGzAycK9uFH8HhUJ3VA+FEFKeP5w+3+ImWZy0HrFiQxXs4q3U3F1QTJpPF7hCbsiZyoB0HnlhEt5+HukZeEtEigHqcFc8AUfRpxOquQ1FgUIhidYNVkDmrCx/RXH8TwEIL0aMoJlZnbODiLYlH1QO88cDvO249b9g5Q2UA74EBKWrCe8YumaNWVnXNQnd0HESOg7lmwOKD+zWOAw5JLHbF6uvX9ZjVaczdhJQsh3iAo0lxVvs1w9YMfGZai37hhB/sCKF1xbgDog8prI4fVDnfoKDcxGFVwldzrtNiQKZxVg8xq2kbuJnCUSuOW9UWbF/vnKNQggERTlqqWK0yCFDd9cC2e2cA1UDtjFJ2XxqYA5wRZrURBkQhfDFnteZ9K/ivI87qjeY8bmxzgeQqBuA6kAcPiw7BqZ3V4rgm6BqFU5c6N3JVbGU+56Q8X7AseE7Fulu7RiyREUMQq8eQW02GA0WsVnSKAqBfV4ULvJkXERnjyg1n8Q7f8zM7uubgFx88idVqv89i2FnN3nOz4lyLWX12UrFu6zAEjo+x8FLE6wqffPAYXrx5LndW7yFWn8Uull4KHB0x3Ki7wdntmL0v24ZK32fvw33Eap0uobGuqLJETpxvjT0Hl7VfXYrVb4UqCpxlIQ6iYnzAFA/z2HOhE8oixOr1wCxbTEYJ7Djx92WVF5Z+Cw5f6JUDzOoyK/VT38eQJZxBpe+sVjgxeFVpznh0FGf1GAYkdrBwO87qMzYZOs0iHEStA/hMsEnS/V6uwlkNAMtlJ2Bxtn2B267NXI9jorLuvSWu10A7vfZu6RhLFJyHruPa58d1rAqFqt1XCJdTMqu5A5iSTu/4DqqBFj9kmfYiZy/GWZ6zzSsdsXosYJGImHGsCkWqcuQ48Jw9ula6xVs9ewJw+xnQdWvz8wXQ/7zqmp4iD9GibcsdigbOat+rkSkwIGVtwws1V/zckTEo+gCIz3ImSs9mwJUrAIDD6iFu3fOYiL1cbr9ZbFjI7gGALk7ssSmaV87+3HZ+zMFN0apCVjF25d6fGQED8vAEUmd1ngMvvAB89rN7HwppUjE3oaazOnDUTtUkrvU5muDOatn9Kkp30b/HuGUW/iV35xWVDcejhaIqu9lMOLUjrf9UDIgfWn0MSBvfRAyGZYLiCLNaA6nAXI8jrdRFwQRAzZshDOo+BoS7/bTGlnY5Diyr7gdFXxQGxMhZPYBFMglYHGBWU8XPMTGl2bgjrBEGxeo81wu0xsVhQBxXwS5v5rHahwQchzHhZSYRwzFWKqyDuKYVx3UUoio/V0e30wZgGSFuJd8QEu8tzfBWAKOh1klqsfe35jrJtmp5R6PJe2ZgQ6zpEiRhQCrkqvmhLgbkqotffPAEkmS/7x8SqxnyrcA8fzicRdap89MKB17CJjxHRwCAdx98Ab9w/ynceKLzOeo4q2OPYUDCEDg4YB3ct9bcWd26f/j7MCv2ZFaX7n5ZDuJcVSK4wWbraIf3Ze1Vl2L1W6GyDKdZhMPFHjf7PrvlQOsltMfP52L1/c0ezmqqi0zxIJNa38VCb8RZ7ei6X8cGHYqI4A4k3ovDJgRhfU+xep3yFF/OrA6cgjmn8xxnWYjDqPWG832ETo443e+zaDurl8uOs7ql+YxeV2rL75joUxTISnfvNiwAWyf8wEZIznlguo4/x6oH+WYASBMmKUOSH5PqztvH/ZlVDrxQ71nw7Gp0YV4DmozxAYeiEQZE4aoV9yuVp+rxNv2OiEB2PQKjzmrSQg8th70KA0JkVgd+LcfM5Dlb7Oqu+F0ekDvk2gcQnxcMAxJFLJ0GwFFxDy+9sWQi68HBzjFdu1SL1eLa6n5mLk+RH3jPFLoBi7bN295teQcLBbXkMp6gFrP61Nk6qzsYkKtX2amdn+93rCytWWDhXpOY7Tn7A/cB2VkdQI6tEVWWyHQ2RvcJWKxt0kbjkLOazKxub4ZIwhDF90x5roz/S3NWe77d3wxrukwIWQP8ZAfnXPzdZQeaqIaBzRVxXPG9OsWc1f7uNeDCt0VKq4OatU5FIgHbc1F2hxEE4OY8FX9uyKyWDt2GGJChMMQsrfUxIAIxM8R/5feG5e9/3DFnNQDyxpXSWU0NiR7rkLuAUNAir9lGPmGMUXY08k0ACmoJYLeN9P3VbLISDjqyRqBiQLyBTdGaf49uea4CjYXW5gLRWa3cvCxLlLUNx9/vfI+u2vjkyeNIMw2x+tZLwA/8AIL//R9txeqybJBvMyvB5sGerdJgzuqll7AJz8EBYNv46psv49nlfVjHR7vfzOdao8ZJAOeptz3ucomr4Qr3XokZNqsnVu+JAeGbw1pi9UDAokkQvQUMhyRf1mhditVvhdJxKYqHeWyA4Jy7fZ3VV8M17q0HTqARq6cVFEkOPeFSHcOATO2sFmFtOsKXENaH1iJpqd+aOrZTyGuVugwDwsVqywKsuuTM6ggHs9aJBQFCN0ey58uy7axeLDoBi/PWfTImfF4QYkZsLmiJ1U3A4jAP3dVlVgtxYsClCYA0YaqBEWa13iEBjF9bvlums8jZx1ndYBy0MCDjDkXKYtex6uF2X6KwLA0tbCZLNHFG2YpGHbd5qRZPeW7AfgVr05cygClIJGAr+myGJ4yb8xKRwzEg8zngeTiyTvDTrz6Fdx3f2lpV+DFdVcgmwJyaNcGNsScGRGsjaCisjv+svHLgexr3l+Ng7mXYZPu/l/IM8Gz2M6yqaHTNLGMf6dd+LfDTP73fsdIECHQxINxZnSby+50qVrP7dThwVttZPTSHaYQUAlZB3AcyZzVVrLbZeFeUkuBGw+ArZcCiwRjrh3Z/3BKdccSxcJ+Axbq2tFENjFW8BxpLc6wNQyDpClRlSdsMFKW6BuI9Q3Gm7ZO7QdhsdqxKbXYzEasHmNXkELwRl2IaV2zjTnONEDjFsPhF2BxunNWyRSg1KByKeRFgxq8Xa0XZO1yMMZQxdkCwJwf2QWzeya8B9boCrJNN6azWyVroniw/Rq/qGklus3wIzXvWtSr5c2CEAVFvLpDnsq4LzxpGSwDYe2Pw8NhBXrlI9wkXBLA6LbD4lz8CPHyI4PXPbuc8mw2b6zklZm6Gzd39OSBn59YWA2LbwOEhPvT4S/jamy83TuummjFr5KBVhTj3MPP4vOvgAE/MTnHrFY4gCnbFaoYB2eOacXrAvobMQb3E5P01xi6/rL3qUqx+K1TKElitcI+nTjzMYwMaXzztxfMRGJB4rv6esmRtxLqC4hgGhPKiEOFnKvdrzRys2oLimIjAXR5ak5umHVH9dxq+9kVgQBKPYUC4WA0AKKsWeqZ1gCBgGJBsv2GjaBk9Gmc1D2zZ2e3cQ6xmDuC9fuz+xxVitaabcJAlipYTXnMS5liKwD4AdUGciI0w3sjp9O5IYJ248TTd5WOBP3la6TmIRCveIAZEE93THFdxH4jrSuSpel7dR2sUBe0Z2B60Oc5OmQhUaC3KVM5q4oF9H4wBLAlYBKAvpnjefs7qVbkNWLQs4MoVHPoxPnt+HV92o5927toDGBDxPiCMW5ZVD+KLCkKr/mB7dlkiKzXHAoEByTQ+i9a/KUDauH3ynH3mH/oQ8D3fA/wv/8v4obKsZtgSHWf1yH0QxyAFLAahJcfWiBJBVfu2049hQKiufbTuAxWzmooEUuFFjJnVFxCwGPKA4I5Qa8LvH51z5Tl7rrVD8Epkis0VAGRndRiCYUA6gj0A8iajMjDcpI16hCtM2my1bbgOe/dLA9MNmNVsviH5M822/50aEauzpGIbd5r31j5itfjevQ8r0GAK9BwAuqCo2mSrbH0zCz8P11YgIw26zhpRWeHUpaKGlKKqQfcKMIBdE85qzfBWdrK8Q0yBg0kKD6Gr3zXt2SVyGS6xqlgXKfE9o8KAkLEt/FyV73DNsfboKvu+JN/v+1evnGBhM+Ra4BRIhVFjs0FWsXObuRnW9+K9jgcAZ2fAgRdv51zHx3jX8S385V//wwNi9chzyUOwrDBgc+7lEo/PTnHrVs3F6tZn4jhwHKCsJZvi3crz/TuZxCbrmLOa8v7aB295WaN1KVa/FUowPfYRfjyPpaWOidUiQXyfdZ7j4NCPcZKG6hA8A4eiOB9ZkTEglmTCLIqLCI4Nbcf20LkyJ52mWO2OByzmKXeBazrI2E7h8LetMw9zL2uY1QBgVSWqrMCDdI6j+S6zOnRyxJnGS/vOHeCHfgiLT3+MidViN7mtuu2xCUCaNLou45ulw2K1H06MmBFCnebCwbUqJQakKms4lFCWvZjVeofcOe6YWE1hKKra5gAk65ItyrSc1aW63Ve0kBLbiFXsPBMhxXUlDiLBEjXFgKic1ZQ2TwBeYEsxICxBXXPDplVBACWzmv1gAgZkwFErKl5XmLnZtiXk+BhHPpvMv+uZ1e4374uYmToYloKbwkh7diP66J3n3ONitWpe0KqqAux6+3mGVoaYr5My/gr61m8F/vJfBj760fEfnyZA4OTTOqszizGrNT+0IABzVg9gQLSc1WMBi4auP5VDsahsOA4tXM9za+VxAVwABsTAWT1z+8zqsmQCiqeY343VRTirxQbuPs5qXQzIzGIYkM6mKIV/3ZRKoLhAZzUJ4wVs+b+K8RCAPqpBPLdKZzWRWS1a6gcwIDRndY40H5hTivtB11mtyjOZYuNK6azWPuQwY5yv58icdcU1KEp6+LQq0Jp8v4rj+pY8ILgs9YKB26WacwKNYzv0NQ0dQgCWOOHJhh5w9J7KCS/Wc0R+/Zizet9n4fAqu57H0X7YjtXrZwzxCS5Wr/nPW6/ZutcumNngfjKq+4o6X4E5q8Wc6+rV7R92xWqhQYx1+QvHgnBOHhzgidkJXr/tsIDF9j1itbo8xg6ss9EmznUg04WaubDFwVw6q03qUqx+K1SWsQnDPouyfdNSxctiH2e1xVhYgxxNaoAKZ+qqeD4N30vTWe3YAwJwnnPH27QucDMMyICz2gADkg1NRKsKq9xnYrXjNGL10t1gdVrilfUxnrqy2X6/7yNy8713dgEAf+7PAbduYflvf7QJWBTn1z5XAGrh0wADMpjGLLjK2hiQYWc1FQPiDLg0GQ6H0IommNVDvEcDDtekzuo9dqDTpNZzPop7YKCd3qSNWMouN2xR93yLORQ7rr+iJjwDogaY1QUla6B1rnJmdaU/brfK9yHFKpQJIWwWAByHBeuNBSxuaoaBEGL1lSs48jdwrRIvPtvpZxzDgAin6sQ5DgID4mqGTA65vZrOKB3MjGVh7hdYF/64ywXA2WmNQ3f7PonsBEnM7meBAXFd4Ou+br+gxSwDfEI2wFDQZpJYZAyIlLEuir9r/EDTWT2EAalpY7fvKwQPA+EPGBCWDdqzHVexwdKE2NLGQ8d3+mJSUSAufUQ+Uazeg1kNQJ8r7KhFSoDNNxzdTi60MCCta1DnBEG9XSoGsEHA4j4YEMpmq+Oox8OqrGkuTcHUVYjVJuaAQQxIUrOxUNdZbReDYrU2dg0YDcQEYMZrVvHrifeWEhNn4qxWdYSAaMBqH1cmgtc1e24NnNXSd4LAV2k0LzXleUy3kD0MRYGkIIjgLssJkQl/JqHeQ11nxszqicRqbxHgWniOq1G8jzcAq5O8JVbnSDf85202bN3LndV/5u8/j7/wF/Y6BZytHBz48VZY/oZv2P5hO88FGHcri0pT1qUrbrKDAzw+O8Xr94K+sxpg9xWA0WA2nfetONcLCli8dFab16VY/RaoOuE7T/ssykTb4JBICWxfFhptqQCbzEqrqpBThC/BjlPgD/KC70LrTG6E61GFARFBQrrv9X1wFbqLPdvmAYuK4CsYYkCGNi14UJcfWOz6RhFg2zhw1ji9z7EDbSHX91kAY+7u5aRDngEnJwCApZ82AYvi/Joa2wQwCO90rQpFMiGzeowlCpa0THZWK+7ZogDNjcFxHbUMik4ValvHVV2IKsnYhIKwwTI0uUkEp3bf4wrnzIjo43j619W1K5QKZrUJX1rqfBTOakraOz9fcRzZuVK1CS+wpZP8IqdzGQGBVegfN9lUiNyMhAHx7XFndRLXWwwIAFy9igM/wVfd+By848XuNwtBUfHuIl9b3hGiDGThm61azGoMu72oYtI8LLHOA/U7sVUPXk9YuGIYNl06yRlbcAhnNcByLR8+HP/ZaWYxJJCus3rgPkhSi3R/BaGCsS5KFwOyj2uf+NwqhXUe+GRgqpUigcxZomoXOHVDsOns6orVhYfIH7+X5Sc7sigVGBDtd2I1uMmWJjWpGyCaWYgLf2fRX2SVEb5JGYB2Uc7qZtyidBjwwD6Z+9XQTanMsiA6dcfmRlnG3JNan5voMhlYK+ZxoT2XbeYvkpMtS7DNZkoInmJj3CgkWpgOBgIWycxqFQO5sOiiqmojwGCMBbizWoUBqVwEEeG43NAiXdvnOcvMIjqrlWI1MSdlDANCMl7sgZZgP3zPGzcM8Ve/7m8jsPNRnRYAVrGLhZcAV68idAqk8VaszisH/tzD+669gv/q634er7++3ymcrR0svXQrLL/jHcCv//VsvvzCC7vfvCeKtIpTtlYUx1wusfRTrDY24sLHLOx81p43jKAUlefsuPuMXWN6CTdzXGJA3ry6FKvfApVvcjZh2NNZ7duFup1BlAiT2ZfV6rpY+glWJ0NcYcKLne+UqsRqkqNUTBhV10CwRHWZsnuIqtpIgbHgKzAMCEX8bAZflbDMmU7NfWVZwMEBDv0Yn//kBsfBZvdFatuwPQdVbY224NQ10PR4A1i4CVbnbFOiN1l1R9JyuXOCem8NOavzytETfYYWI7yo4Z3KoBewxRMJA8KPK722ZcndeQRRdSQYNVmXTPTRdD2yyc0ABiSu9RLExT0wIPoAgOVMyKw2bFH3PO5Q7LSoZ6VmAF67VJuNJu1tYIsnJQaE4PYTxZjV/cXTZl0zTMdFYEDqGnFisYDFKGJfe8c74NoV/s1H/jQD73eO6VoD44voXNCd4Aq318i4pY0BUTlf+blS2K/zoMC6CNTvxFY9fCNl75TZDAhDhE6B+IRtxOf57mvIVmjq7cpywKcGLKbyP04ymwl/mveXFzpyZ5oowf7c16HmjgTDGvBvVc8W6poszgADbkITDIhnMUFR4aakaqrSzbuyRFx6u23HOjXiziI5oN3xHIdkU+m9E3mFc6cXsBjHYO9tKrNaOB+708OqYrk7FFbDGAaEKig66vGQ7NIU/37ZPWBwrqwjRJ1FlKU1Gwt1xi3bRuAyPJGqM6Z53+o4qwNneqQCxMb4QNgqpUOQz+Ok+D2DzQXHVYufDQaEItirMCAXFbAo8FUUZzXvYpLypXn43V6d3Z1jqjjQjUloYgwI2Vk91tmri8QJQ/zOF34egZ0p5y/tWqUe5m4GXLnCnNUip4OL1e6zT+JquMb/8YWP4v79/U7hbOPsMqsB4Du+g3VQHx7ufnODIh0e79KzlL2/Ws5qAEBZYl34mEWd6zeEl2mXDjKw0UsUn/EUzuoxh/llDdalWP0WqHSVs13zfd4YTVrquLMa6LCDR4575G9wcn9iDIjrwrFqtUhHaZkSwt9A0ndRO9DVp+A4wzt6xDbawfZsEMVP24bnKCYgomQ7j8fHOPASfOxnMjy/vLd1GIoKAjYpGtnaTVMgKLYpw0svwfmDAus1WKBjBwPiOwWyeOjeIrj2xYJfFYBGZVYP8dAhnPD6YrVrqzsMyqKmLZ5GnD5kt65oxVJciHhdIdJtp2+caepvoWBABp3wYpFG2gRQjFuGLepSAVi0qAdEIcXz4MscKUJQJYrVUpGuro0S7wEWgJZWfQZwvKnZfaUjUAKNWJ0NceOqCnHuIvJaDjXuFrEs9O/1xlk9wqymbrKpxq0sQ1nb+wXHtA87FLDYdANoitVRxTAgezirNw/TLQ88ihC5GTYnW2d1+x586ing1VeHj5dm9v7zIlEjmxZMrNbHgFge7zRRrUiEQ003YFF1WQ14qsxFJ3coToIB6aJ7iprsphwKFDNl+NfdTfeyZG3HoSkGRIFqiCuS+3UMA5IkIInV0dzuOauZWK1/rKZsmwk0ZcckYeL83MtZrX/YxiQyJVd4QEwzcgCPYUBEl4nmwQO/HmTtN+9bXWa14j1D7hCEouMMaCGRtA/JO1sVgZgGnYdeYKsFezE3IqBQmu4o1YagkbN6AANCDFhU8vaLAklJY1a7ViU9JjkIEcPoGvZ50RCM0o279oH59+1VXNkPrHxcrK5rlBU3+h0f73aTrdm63Hr2GQDAtex13Lu733v0PHYZs7o95xLugm7tKdIm5znbIBU7F9wUcujHeGNziFl3zTOy9myKJFYPM6t1lx07x750VhvVpVj9Fqh0XbDW9z0DFvcSq3VbUER7n2oBTd2FFjvbKmd1SWjBEW7tAVh+WVskZ7Vt1ajyIWc1QaxWTUB45VlN4hU3bXM6IXjHxzj0Y3zsF208v7zPerLb5ftskTfyttycFZgXp+w3N29i4aU4Py1x78TFlWDdE6sDe0CsprY8C/erame7KFhrj46zekxEAB0DMuisLi0ys1q5u0/pBGidr21VyvDKzYoH1Wm6Hlk4rPpbtDEge7BfAdAcVJYiRd4UreFLuIRNizrdWe3ZZb+dXCyg9+2wkZ1r1Wn9b4QkTXxTq4KZw9yfnU2xzUbf6QWgWTilQ9k0ec6clWHrM23/nK6zemx8MWBWqxZkALYbzb6mWD30niFuWnged+nu4axOVzlzLUcREIZYuCnWJ+y+yTpDxfPPj3Ors9wiOauHgsoasZpwfwEYdVbvvTHaBLWpndVUl6Yf2sMBi0TUkMr1Z+J4U4aCGmJAmhu9N8b65s5qxfsrWZf6GyFi3B7YwE0SsA1cXWf1wkVSejtjLFX4bsqy4MpMEibBjXsELFIcb45roVShGnLQWv/F2C0bBsQG0wW485qARV18USDPhhAVr/WxW0POahMEhnI9Y8gCV5o5RJ4H4fNSOqBhgJVAy9ikCrGlOqsDxbUtS1S1DccnmA5Uc06gwWKFgeb4PYBUaPKtTDZCpnRWT8ysFmJuaKVIxjIWswwWeLbLfL67QS/+8sEBcHCAq94Z7t/Zb4P2LPYYs3ofg4AwoY04q5NzPi8Ux+QO7cdnp3jp9AaiWefvj821eBVJwe6HfQYG24bnMJSttMvk0ln9ptelWP0WqHRT7s9m9Dw4dj2OFM5zvSCVMZcqdfE0FHCBVju5prN6zE1ZUNKjx9p6iG1+g+3ZaO3ETy1WZxlzg7Unoleu4KnFQ/zoJ59hzuorVzoHlSzyJLW+vcLMSVlC8LPPMldjXOLzdyI8t7wvdVY3bUrdom6EeN6w6JNlqGoLTqAxy22C9RR/XtdkxrhrK8RP8IkY0emjRAo0zhG9QwIAHEfu1OW1Wdf67Nd9Fuappbcwb0QfxZ9T0+mHRHCBGbKJzGpfIiaVJWub1J3ct85XOnEuCuS1Jre9fdjAQd4NgxQtnh7RoQjAjxzm9OncDPEGNGb1PhiQPGdiVdfJ8Sf+BPDhDwPf+q29Yw6iGgyc1YPvmYzz4DVtHo6rDn2iuv6azqw9xOrkLNuGV4YhFl7aYMXyrIafnDVuzGefBb7wheHjpbmtz6wW94Fsr7Wuyc7qfcTqurZg+7qbbMNOH4pA4weWHAMisjyozGqPB8N2xWoDfv24m5I+HlpWvXvfliXbEDToXmGLUvlnlia1vvvV5Rk0+zirNa9vtOAYkLazOrGYk5bMVwFcp+477CnhkqL2CFgkYUBcsPt1yhC8xlkt+TMDdI/YbFViQHKLBSwSnNXJAGs/jqF9P9iuzTYBFM5qEwyIepONGD495IQX72/Co6AMg6zZs0F1Vg9dA5OARc9XvBNkGUN7H1TRzcePm1Ac20MYEKqoDIVBpHVc16JtXO3FrN73uFzMDaxsNHulmeCEITCb8bkv/zMx3vs+R4QU2/DFoSoKnKUhlv6emKjm3z8iVot5oRCruZnh6x77NP7dnecxuz7vHXewi00cV+Cx9nznDG0woSzZ/M0lrJN4t1FeKoTwy9qrLsXqt0AxDEi+326W47CXYF0PPxi6zmrPg2tXcv4U0OKG7Xe4plwXjj3sKKVjQIaZ1Y49MVaCOHEeSo4GiO5yjAy+wHawby/2j4/xX3zpT+E73/FR/Lobn+s7q8e43bzW9xPGy5rNtoJ3UeKzt2d9sZoLn8oXppGzehgDAkB78Tjo1OXIEtfW5McNYSXQYihSW9FkE0bhpqS400R7siL4Kd4Q2MKiK2RgctOESelgQOxhJBAAMgtcusipa3o6PQAv4ONBFwNSdFy/WgflrvWuWC2EdaqzWmBAOu30SekipPK1AfgzlwXWdVTFZvGs24/HWcVDoo+4xj2O4o0bwLd/O3p/0Liy1JtsJEFROLYHxq3eJuMeZfsDoahEZvUYu75dyTnHmQlntZdifcr+XvaZL8D7638F+MEfBF57DYsFsNkMHy8rbOYmpDCrMzlTNi74JovueDC2iSucpfuO33sFLNIwICxgUS5WFwQWuigVBqQRqwkiQuP4kzqrJ2BWd8bYjSzQad8aYVancaXXFQQ0nQBj3UYUznq4cPsYkIQHjBqI1Q1+rnNtAdDEtH0wIATH21AIHhlXweeceReDAhh3sg0iZlIesKh7D4TDzurNBtrzOGl4KbZfomJAGrSG0llNC+90Ve5XEwyIyigkNix01568GK5Ccs+K3xM72ZTOah2UQreEs1Y25zJgVqvWM0aboqrrWtfGzmrl1IhvYO891loWEASMP30+krAo3NNBsBWrxVS6LVYfHbFf72E2QJriLA8ZUnqf+0yMWSP5acmqYKKyuBksC7h2Db/3S/4troRrzB476B0XwOick4nV+4+JY2I1APpmkCt5J16WVl2K1W+B0nJWW9Z+D7PujumeGBDSwtxSs3pNAhaV469wEOmKdGMiAmc+Op7eY6VsdeXFdnb1J3hjzuoqzRlLsoMBcewa//1X/WO8eHinL1ZzR1IvqK1Tm5OMTXKjCLh6FQBglQVeurVgYnX7Z3KRciyobHKHorguOmLHmFO3KNiChMDXdqxKHvQCoCgNFk8Daedkp48QlhXols0GJGZ1MOAeAoA41WzTF8/sQJeFBZAXpVInvFiQkJnVEg50WZoxq10XniVxrRcFW+wTndVSsbookBQeIn9cwFRVMHfZIrqHAQENA+I4DAMyRC+SYUCGSvDrB9o8SW3EQ90Q/DzF92nVRbBfhUN1H7FaLEo4s3rhpVidsXPJ7q+YM/DhQ+Cv/BWETt7O55VWLriyU4nVeU7vXtjDWS1+/r7HG+0O0wnIbpUX2HI+aaEZut2pZh6jwoBQAsUCe0BYJ4aqAYDn9ZnVprkAI9zLxlmt+U70nT0wIARntTvz2efVxoCkFlvgUwMWAbgirKx90iYOzdGARdo967g8vFOJaiDMt2wW3F7IXHQm862ReyvLeNisrrM6AMuGGHJW62SE8HMFMIzAoKAaZB1ngFnuxpizurJJc6PGAS1zVtcGc0PFGFuV9FwAYJhZzb6BJlZ7qq4Qo4BFOWPcZFNU2cEjjCcUpJ1AAin0ySov9TawASAIEDoFkrNhsTo7T9l8KgyBKIJt1ajFeNQ2qXGx2qmL8SlcmuI8C7Gc7/l+HONA80pWBeuWbZsxr15F5Ob4Nx/503j2yxa947oDCMrmuHGttVZUdkMARiHRAOB9yfPIf9NvMXqvfrHXpVj9FqjGkbHvC0NwhQd2cRqxcd+Hh+9oDguKBPbpkEMRQF7arAVH5wUkzrVSt9AWVLF6SETgrVi2o/dSczxbHXyFCTAgivugYaF3xOqdkjirA6dAthnBgJxkmHspE6u5s/rdN27jRz7+DJ5b3ttNDt53I4QaVDYm+mjjOgZEhKKgLaKHghABFNRJ/tC1NUg7b1r8FJ0WjbNas0U/dHOkA2L1amNj4aX7L8oa0Uc9FtQAmVk9tMihi9W2tI06LjxEEemQagyIEFQNMCC9cxXIEgNndbRwEHd4qoBw/tEDFqUipSi+gNr7Go88s4JRSkJj2QP4IjFxnlispo6xymN2ijGr2xiQBKsz9m/JNzkTWwDg/n1ED18bFqvrGqhrtn7UFqtzufiX56wjQHcBDciduu3SdVE1HSHq7jAyBiSUBLjyY5aVDdsjOqt9+TwmLyx4unM4Xo3o0z3XhotPOtXtPdO+EQQGhNq90jir0XfVQn/xDKDVbaT+FiYwE9AdkmsQp7a5s9rjXN2pxGr+d2pZe1RZoqxtkgYwhN8zwlW4tRSHI+YapFZyx2Eu1dyW3lsNEonKrFYFLMbQvx8G3gmkTlleF8KsviAzh+M7qGQGpKabkTY3UmFATHjNAHsnZJJAa2Nntcq1zg0NNLFaPu9uQlEJg4HjO2zjShHkS/q8RjIMipywiRuGbB67Gnborh5kWHhJ46wGsJ03Zhmbi3heI1ZfiWI8fDjys9OUrRdn+2+4s/ehfMwSxbIcit3OxWvXAABvP7wL6/q13b8wYpRqjjsxBgQAXax+5gnk73yP0Xv1i70uxeq3QDUp4vu+MMT3DdgxirTUC+QQ/N8BDAhpwiBYvTJndV1vg+V0djUFUmEgYLGoHP1xZ49ALfF9OmW5AyIC9RpggG/Gq9nR7DCrd0q0CYlyXeZ62wwv6DanBRMrWxiQD177DH7pzg08u3iwK1YPtHYBMA4qm9ShKO6BgXuLTW70xepBZzW1fVII9hflrFZgQCjto8JZnWSOcnKzTh3M3VQbAzKU8gyAhgFRbVoY8lSlAk1R6Ll+uzXErDYJWFRgQOLS0096bx935vUFDwCbxNa/r4Cto3bMWV34+19jzxtGNVA7QkSnycBxSd0AQ+5fMW4RApKVx+xUs3iIIuasdlOszpjonMUFW7j9ht8AAAjvvTYcIiTuC8/Tey/yBbR006LZrKBhkQYT6nUxCGMtxMR8DIDz4If4pNQFmSpgUbRRU5zVoaTLBOBjrAEGxPfh2BXKuCNW62xWdcuyeEiTI0XwkZ3VdoFsYJONGrCoFKtNmdWuxCShu1nTriEkDr/OFmHXomGsK0RVegieGgFB5grbDNeWl3Lhpwmb1XVWh9ZwwKJgmOvcswPvBGqXKDA2FhBNF2JjWMEYLyqHFjir2hhuNtkMnNUysdpgcwVovRM6F6JKc3owqhi7ZM7qPEdaEZjVngfXkgvAJs5qy3XYsynZXCB/XmNidVax89W5tmG4FwZk9TBnGE7urAawI1YD2BGrr4Ur3L8/8rOFyL2vOcC2mQteFVrIK17zeWH7uLwbm51cR6wWeJkxsTqu+/rGQA12ohsGmHreJQHEtC7F6rdApSm0hWXLqgefjiTmE2fdxdOQs5oyYXBdJiyr2K9iF1pnQWozN3ZZSbhxQCtIiMisHhOrCcIXAKVYzcRP/UAOpSuJV7op+87qw0Pg5s3+uYniYnWyHmFWnxbsZdlyVn/1/OO4EqxwEKQsibh1zNHgShNntUr0obgGBLZmADHDFtGEe8suURZDzGqCa4Cf79BknNo+6dsDzGpK+6htI/Aq1paquMCr2NVzVgtsy8DmgvjZWiVa0WQYEBOnD7hDsdvqWZYMrWHIrJZhQIraJnNqncDtL56Ea8anByxKnY8A4oTg9AKa+3XcWe3qOavHAhapocMj4yGpG+AiMCCOYpEnqWRdssVDO2BxBeD8HFluw1/4wLvfDQCI7n5h2Fnd5i7qlNi0kHVvCAyI7gIaaHAoVTbCrNbCgJSDrH2qQNO4yCTOau225FapGMAmIoLj2ahk95do0adiQIKAsaDb5gtTZjWG3VkNV1iXWT3SEcJCQfWFyqbtuocBMROrPZ87ljuudbLoNbT2MBARWEejOgiQLKp6Cse2qeCh4p5WFef364vrzFmtVlIaxIzmBosKDdXMY4kYEGm2zwR4FdVGiGXVsByaEx6AfNyqGSqGUm7gSJGRJpsAABBEttRh33SDUF40wtCicFZrBQ6LEgKw5JgmAYvKz0vMi4jO6qEu3Dyr9Z3gYcgwIKthc0DTxSYRq/OkZNephQG56p3i3r2Rny3LuBqpMRQp0LrH2sdt/7prv+ddvXuJ1TrO6iFzn6mz+lKsNq5LsfotUFmiiQFxXbaAHBgg0rjSm5SKttQhVAMRA+LapdxRKlqTdV8UljUcWigcbyRn9a+xWF2WTPwkBHKMvSjSTdl/SVgW8H3fB7zjHcBHPiI918jJkcQjzuqzYsus5onE7z56Ff+Xd/0rYLncvUaC7TXmrKaIPkOBmFSxep/2bN2d+GbTRv7HRQE4ZGf1xTCrfadQfmaNA1ZTTAr8enDxtE4cJlbr7O47TNiQ7u6bBizKbi0RpHQRzuoZLUCncTl0WZp8zGlCkTTL8vmz02VWlx6d/QqoxerUoQUscvbr4IQxz1HWNuxAM7xTtmEB0J8vMR4OsLAB0N8zisk4la+tPGankg2fx+yI1TVw/z7yyoF/7QB429sA20Z471UkQwn14r7YJ3S6XY7DnEm5pHsjz9kmC5FZPbR5R8kIYffWcAcPVaDZOSdRuqHb3cP6NnOqdh4yk+ArJV6FmpEiKgj6rcRlqddZIalGrJbM5Vg4sL7wFzoF0lz9rJtgQCwAddpyVmcO22Q2YVYLZ3Fr7C6Sgs1fqM5q1aaggVvZdSFv/YchW9mFUlS2LM3g7Vb5PuT89qJgLlW/1ja02IGnDt0F22DRdu0PvBNMMCCNUCsbCwxMFyqnrhGnVnUNxLkSndVKDEgBIwxIENnMJNK5EPG60nfWixJzLpkJuCjYfUcwHajWM829Rfy8LKuedlNUdDMq5pykLAfhrB7BgGTrnBkc+dobAFCxf8N6VTNEZ0usvmY/HHVWV0nGuOhTi9WyDbF3vIP9v+2wFiWuq0ovEMeNa61N3MFzLUt2v16K1W9aXQJU3gKlnfTsSQSETiVxrZdMLtpvhgRFIp/TUbmVDbiEjtsKouj+G/m5OlMHLFInoyMBMtQ2pO3ERt4ylKxLBE7Vv68ODoDv/V75QTlXOF4PvyjiVckckDPuoL5yBe7mVfyJr/onwNEzvWMyd4PiXqTyVD0Prp2pHwMqs3rEqcu6ASg8dMWmDVptqSRmda58vkyY1Z4dqzEgicU+f12GomhLzXPILK6r1MP8SAMDgpbjr5CE1Aqkgu4kpJk0S/6s4cnSZiduKHErcyGFJKTx2nFmCZHPsPVf2sUjmNW+wexMfE4dbscmtfEYBQPCxcTBCSNFULRK5QaTyTvRVT2z/LjUexbA8HuGEJC8d8BiXG2d1WnKxOr7NnD/PrIqgnf9iC2uDg8R3UkRn+UAFP9GqrPashD42La+t+8jHrAZzggLfsFET2rIMJxlVrCFnpY5YAADQs0I4ccGoBarp3ZWG2BAms9HIfqYYECazIWai3ymG4JojbGSDy5JLZKzmm2uDIjVmU0KWITvY+49wGZdY86/1DCrSZMCfsq+haLcFWvZcXPA1XxeAf4sqOcwAMihakPOajJbWQRMTu2sVm2EFAWy0oXvTR8Mm2S2nlGqfcyhTQDCWNBsqKtQDURmtRK3ZPJ5jWJAaBv5ju9ITVimGBAn9FgHS1espjDLRfF1nZRGSmXYc7e2DOuX53TXftMhpuKha+6Ji2M2zGZJNZxxzYDFwC6QrofnW9k6Z/dDEABzPrpX7J5ZbWws3BTwDhqx+ggPcfKwBjCQF3RSYOklWnMu34ccOdaqBmPVHmNu3gT+2B/rY0iBcWOAOK7YxNVlVitMgwAuxeo3sS6d1W+B0har+S5hE6KoOKbWjvmYq5jK5xxqpxduL8KLfdBZXVUoKc5qgQEZaPsGML2zurLhuYRrIMMJtCrdlPoMxT2d1cmG78gLwbHNwu6+gPYIFGOhhfuf5va4EwcsjjmrG8cXwVk90DJWlsS21BHeI5lXzNmvUh4dgJjIFh4M/KlrrFJPDwPCTlX5HFQFfYPJtUs5/YDji6hCitRZLXiqBkKK71V9IUU408gnKxGTeBikibAuxPS26w9oiR4UsVqGQWmX7njgecMBi1Se6lBrMj/uhWFAqM7qfQIWk3obpCMCFjdCrHbgX+c5BlGEyM0QrwbeeWITQ9dZDcD3amWYVFa68EKaM8sfYPinuuYAMdcYeM+Qw9ou1FndH2dNMCBDDkUT1BJsG75bIitb52sasIgRZzUFA2LbCJwSaSHnYAP6i/KmfB8HXoKz1fZzWWcee2+bOKu9vrO6cc8RRS/l2sPArex4tlxUhllYW/Pvl9yz7AcbMuFl41blUoZC9WYQryS19J3VAx08jaiq6QAHoB4LeJCx9ruLH1P5Dqd2MAGDWAmSmYWX7TlSJ3ye07E1AJQGt3hT053Vnsd5+5I/E+eve1zbhueU/Q5BAHlp0zdFLwgDMpQ7QXVWh26OZDN8PiywutxiQCwLdVUDVYX1Gtvu1DAEfB9hnYwK4GcnFROrNT6z/Z3VEh3iySe3Qnu7xFx+TKzWRBg1eLRLZvUjWZdi9VugmmAWjYWOY1Uo0+EBQnfxNMYVLmtbf/EkgspUzs/KJrmKWYufgqVJDecZEv74ccX36R4XwMC52iQMSNOir5qIUnhk7n4BizthWsBuO087XJEfc5RZTWynHxJ9mnR5nc9rLGCxwYDonWrDwh50VtOZ1arJOLl9cg8MCGWSG4ZAUnrycNiiwLrwMQ/1FjtDG1cmwZWOVcsfLY7u8ShOJ7TaXbscaJPwLwCeUyOT8KUB0MUJWZt+46w2w4BEbrbbwVHX2KQuE1MI7aOeXSKTsYpF6YrVYiwYcL8C0F+Y75uNQHVWqzAgxMwJ8ffHKkm5AzQImFjtpljFNnB+zjAgVxbsG2czxmUcWpBRndUY2BDjXGkSEkc4YBP5M58lFWvL3fe+tdh4P4QvqmuLtnhSCVSGY4HrD7WoE4UUFdKOuoHdKt+tmetL3Et8jA0i+rJonFmt/04M/Fraoi8qyWwmKOp+bp6HpZfgbM2ROHWNW6slHp+dGjGrGVt4l1ndBPURW/SVcxjxHBBFZSkGxCDUXBxXFoJHfh/w8r0aWSl3VqelSxkKR53VaaYQkojHLCqbvAkwtMlGRiI17lfJnwlMFNWpy8+te0yTgEXVNTDBqwDYfr5d7NqmppkD+DGVBoGiYB5ewjjjOpB2ROQm7nLRIaZwwlMxIK4ld4EDzFmtHbDoshB61TxDVLYpthgQywJmM9bBfrrGamMzDIgIp44ilkN1Pqymnp1UOPD1nNV7idW6GCtuDNjJm5BUnFhaAYvNvE8xPyZ1M/K6FKvN61KsfguU9iSXA+jzeARqrzPB9Ty4imADAM2kUTuMQoh0Q0FlFLFahHEo+Zy2/rgjJs0qDAh1MroPBoSydlDxHnmlccU2QXRmuRwDkoy8LJuWb6GuPfvs9g+7ipt4qQ/cW2Vt6zuAR5zwZV7pT5yF+DuIASFMbvgGkzJotORMOorwpUo7N2RWe0PO6ozIrB5Kp89zrPIQi0gvuO9C2n0FtqVEn31rgC8CWqn3HQ50RQmkaR/Xk7Soi+A3U2e1TKyW8RD2Ld/H3M2wXrWurXA++oTPS0xwVc8toN+aKsYX1WvWQFRWPrMAqpxPnC/AWU3JnAAw2MUlKsms7ftmNmMYkI0DxDFzNC+4NXA2Q+RkiAfE6jLOGAN3SrGa0mkjaqTTRNtwYFmsI0QW1AaYOX1U8wLTVtdAPucywoAMsV+pCCtennDYCzWFL0i1OyF2jjkgVmcWKQixCcEbQDVoC4oA4Dg4CFKcpQH77MsSr68P8eTijCyoAhwDUkvEaipeRCBxZPNuA/erMrBPCIpEsdrzFfMNMU+gPl8q4SfPWWdKYOBWVm2EiA3GKZjVdY28tMnXdZQDTWVW26U8G8DEWa3axBUZRNRgWMVx87ymmVlEKTYwGwwIUawO7KJLctv9OYR5p3SMbTaY6GK1NChabOJTPq9mI0R+rzdueJ3PzPcb3NhQZZuCOatFu8V8zv7eww3WMceAiLlTGDKxeiS08ey0xoEfX4hYrXWPibXnXhgQPUMmAOm5lnkF26Bz4VKsNq9LsfotUGlmkVzQQ2mpzTG10ukvxu2lFOnEi50gVjfM6iG3MolZPdD2TZ2MjjneqG1InjcYtNmI1ReCAeFtz0KY/uqvBp56iv26LVwD423vfMJA2lwY+LyyjBD8NOZ4a8I7afdWWSrE6oqHbBI2QobSzk2Y1f7A5lXjgNV1kYmgF5kdI8uwLnwsZnrPQoMBmTJIybLgOrX8PhDccqKQ0jCrOwIw1YkiynP7k/xG+KQeV7bYLQokpWscsDhzM6w3rfs9z7EpfNpxBecuUz8/dZaza6HzThwJWARA5Myrx8OypG+wNAeQHJSKARmcF7SqYet6HrBcInJzxKkNJAkb3+d8dyOK2IbowDsmXfE2V5JYzZ2qEtGnri2yoDa0iEwTTQwI+CbbwBzGsurJHYoAjJzVcsfbBMFXUzOr0XKqCjWFGvrVqmZxLvnMksxmphNdsdqv1WgsAElu0zAgAA6iDGd5xN63aYrXN0d44kqifZx2NQ77qZzVnqcOnDV4DpSb2GJeROQKKzEgJuInBjZCOLM6CCcUgHklua2/RhDdEKqwOkfPbDB6riamC5s5vfPSkpoOAJhhJRQdIVQMiOq4Rc7NLNSxy/fZZ9aZd29i/txSjut523d859qWKd0koRKr88qBZ7oRomSMEz4vm2FJ8rL/7weIGBDPY8LymFgdl+zYwjGyWDRiNXNWt9ZmQqweCrQGcH5W40CTWa0KBG1XIyprzLv3woDo4rGGWPsmIdG4FKunqMuAxbdApZmFA82ARd8pBsXqBmq/78MnFtADzGo6+1XtrKYm8TYtfgr+UElxVvO2b6UDWPBfJw5YLCqblEujXOjx2mJAFlrHDJ0ccTw8IUgSIFy0nNWWBfzgDwIvvwy8/e29Y7p2PMisrmoLtkdwKFpq0Yc0WbCsXRGh+1lT28aaTRvJnzUCOI2b5lrqABnSdQW24p/CSbjJHNY+qOusjmz1wrwosMoDzCNNsVrVlgsDDAjYbVPk/D5o30MNt1z7kAAAbxGw+6ttSRFCigFLlIkzu9eWcYSJIgKw/XzbixzeTm/urE6xXre+xgPwZhFh4SDcGIqwG4AFzupObscCFgGQXOBDuKlcLExtfW43gOFNUV2xWswLkgJjZ5MKUc33gSiCbdWoywpYr5mjdcbfFVGEyMkRx+pjZZtCvyuIlx/YvecAwPb3RK7u0HhI4RUPjVvkuQY/VwC/dszqwqBFfcRZbSJWz8MS6yLYjrOGYj0w7qxe6obVQaCx1CFVSeaQedAHUY7zPGDjd1HgQTqTZlrpVOOwbz1fDaqE+Gx5qnm3gQCsZFaL8FKiWN04yycOWPQDi3UCSDbZjDEgSsQM4d7iSIU6L3bj2gR+kYA03DlXyXWtaovG77cseG69ddi3/52GAYuW4lzzyoE/NQYkJ6xlZMeVBiwSMSCWhdAvt2NX6xhpXJHHLNcFisTePddm3UU0SKiCosUmvkdQGsW9VUvuLYiARU1ndbMpPvxteVLAt6uts3o2Yy73+yusc577I35uEDBzwHpYrD47g3bA4t4YkLk+OSBLh+eqaQrMdXQzcT0kYyFJK2iV5zFN5LLodemsfgtUmukHLI4xfdIUzJm078uCh0nlU/Oahxbm3FFLwoAokunFueaUtrERrMRFBSySd3bHeHSiNVnXWT3iegNaoQlt5IfrAu94R//6jDmrjUQf9edFfQGx9mx1eCedhz6waUOd5A91A5g4fYT4J2MAlyXi3MPMy7WP3WBAZM7qPMe6CDCb6Z1qs7kwJQYEgOsBpcKZZRL+ZUcBS2Zvq3WGQhIgF1Li1EBEAORidVkiLnyzgEXfx9xLsY5bn0ue4zwPtZ31ALgbQ92SCTA+Iyl0eAAJBIA2bg28Z4q8pnE/BbKjUL8TSQHJdqneaBRV10hyZ7sZ4DjAfM42YO7fZ/8XuxuzGRen1IdLVzljMhJSxVQYkDLO4Ng0tEiziJS1PAPI0hq+rrNaYECmDvwZYFZT+b+AWly/KAyIUcAigEVUYSWEWmCaMXbAWZ0KDA7JWa3AgFQVksJF4NLeYctZibOMO6v5+6bZNCLWoLOauhGkYt8aPAfKLAsRkGyg+0mDEKcI6VJiQIjOas9jJgkZYqWutxuMms5qX/ZOMHGpAmoxyaTLBK3PSxWIacBArjLVJtu0zuo843MCg3mc2GBoF3tuCRkhvKKgRlz4vfl8Y5SaylnddCAbXtcpmdUY6LIA0a3LMSBjwmcWV/CdFgZEOKvvr7DKw93cnzDkGSHD/8azc4sxqzXGgia0cMBWvNNxt08NbV62ir1v9VjYAC6d1Y9oXTqr3wKVZhaCSM9ZPcb0yTJNp49wkQ24igGQhFrHqpWs3rxyEBFeFI6n4NHxcyW9gBwHnp2jUIV0XYRYzYXKiLJj7CrCiXg1rkpdZrUTj74s0xS7AYtDJRzrYw5F4kaI6rh5DiZ4OHoW0MH2bLG5oDuyCme1wgFNDRodvAaGgofvKCYMAtcQ6p8vc1YrFuZZhqq24AR6F1fJpkTLpUppI3awdU20y4ShCGyfmY6zGoCZ68+3kK93RYQkAW/P1hf9APDJcNY716QMEEZ07mnDrE54C6VlAXmOu/ES1w8UiuBQCTTWgLM6Xld6oaAN71Lx5waBu0PMahZYR+WsVyizsj/xM8CADHZcicpzFl4XWttF0sEB+/+dO+z/4RYDYlmQY5Z4pZuSYTUoGJDQkmJAms+fIlYPhUnBwFk9hgEhupVtfh+078yqqGCbiD6hK12YNwu9ifnapNDlVi1n5Y5Y3ThBL8hZnWQ2whkBAyI2cGUDQp4jKV22MUhofz+YlThbhUCW4fxugqWH/eZsA+UFNtJuwKLotJrYWV0VFfkzU7aoNwYRorM6kM836tJQrFa5FEXAIpFZHfAw215/ZVWxe8vTfHbFuzatsTOSNggMovt1gAMNgMxZ99waRSp32AMgi9Xint2ZWYm5oamzunOujemC3M7nMTFzU6K9GopTm+6sBhCFNe6e+r2xizmr9Tfu+Kn2n4OpNkIUXRbUsHTPrZWbl6SARY4BUW2Ki2oQl+Jzm89ZAPS9c6xzH4tl63yCAKFzNhxoDSZWH2o6qy3PlQeNtirONLn4rgvf3iBLh5/LLLcQeHomTwDDGws27fn67b99cDp7WXvUpbP6LVCpYN1pOauLQWd1lkHP6dNwNEeY1dSgMpmj1GC31PFsedI3sOVb6b5/R5jVzWKPKlYrnDNkl8dYi1+iv4BuMCDJwMSw7aLbx/nWOKvViBkApHBBb0BMIjGrsYezurL1JzeOA2cgaJTaYQDHYS6XixCr7QKZ7LhZxnANmg5oALBDn7mKZaqP+CA1H9whd4NJirrjqpmXRq4/Idq1nNVVRk9PF+UHkDreqA4XAEAQ9FmHPGCRslnRlGVhHhRYt52PfBNkPiccz7bZ/Vo6ylljvK5Y0IvWBu5AwCJ1sduMW4qOECpWQYjrMmG5LFGDEC7Hu1fGktmFWL2zgbFcAuB7EcBWJOMDRz0wu8/WORkDohL/NuclibMPgIk+qjApXAAGhAdaU93Kss67JOFhWgYBcD1xXTjTHKJY7XmwrRpld24ggq8C+hJmMa9wnofNRlueVmat9Bhue04p/F/wTgAZ/gFgG4OFR+5iOZiXDbP61qsFnpidGIvVbtBxVtf11j1HvF89xRyGtdLT7i0/ULjguUGEKiiq5humqAY/tJENBSyGtOcrdHKksWSsFRuMuvvY3Anf21wwmcfy40rNNyZsafD5fD3grJ7ynjWdGyoENWa6MAtYDOyidx/EiUV/JwKIggpJ6fWd1QnoGBAP/ffiBBgQW+GEZ58XfSNEJVaTjDIjHVyistxi+o449myGx6IzvPr5EqsiYM5qUYJZPdItHcfA3Eu1zW0WMIwByR3W0ajFrC6RjTmrc5u5y6cUq4nP13IJHB6S/upl8boUq98CleaaAYvC5TPkrM41F08CA6JqzaUunlwXjj3iKCW82G3PYTt6Ct4jWay21KKqieMNULRnU13gAOB50tYuUWwBrekkcxy1o1YUD1jb2+3DRZ+he0v8bK3iTsIh9itJrB5xVhe1rY/sEMgS2UfFN21I70nhcpF1A0wwGZcG1glnNWXN63LOusJFBkBfrPYVbErwgEUis9p1Id8QE+MW1fUnxOpW+0LTqWDUom4z5mWbJSqSuA3aR22rRpV2AxY9M2c1GFN2024hFedNcb5a1lacUEyc4xgEZ3WlRosYbuCqWNgNZ53o2Ja+v6i8XnfPgMUsQ1q6u/fEwQEiJ8df+cQ3snu7hQEBMOqs9qnOakX3RryujMRqtoBSnO/UzmoTN6GYH3b42pvEZv9+KrM6cPrBsEXB5y/EcxUOxS5SQOQ4+PQlzHJe7zirNxsY/fuBlltXgpZIC4eGARE5DrJxS2wCEfMBDhYVzjLmrH7tlRpPzE9hFjYABJGzG5Jc14gLDxEBCwag2XDPin6YsQlixvMtufhr0skGqNntOchzDWAkYJGKAXEHgmHznLYR4jhyfr+p+1UlJhl2namua2M+Mhm3ZII9MYcJgNxZXdd8TmCw0eZ5jFvc6Zhlzmq6WB2GQCwTq2MCgrI5Vck8TqyTDZzV0hBXk/U3+AauAkdKGrsEBmQgKBwA8rxmXcPi+i4W+PrHP42f/PgR1nmARdR1Vo+L1QxlpiH+AqOGOUBw8XUxtwWyoZDJumbOap01k+MoO9FNmdWXZV6XYvWjXnWNNHe0mdWiDUtVWWbpOaublufpMSCuVaEs5AtotqtJc5TunFe7TJjVtiIEDwb8W47B6LmH+LmSg4TE7n6iEKsz6L0k+DGVgX2ixAJq30nuCFuaLPrwDRbVuZLFasESHbi3tIVK4ayWGRRNFk9i0qwIbgRggAEp5MfNc8RUsVqIT7IDiwmv5oPreWr2K9tgIorVKi6+cP1RFyQSsZoF3dBdjwDgCbG2jQERSdwGwTzNokw8p2VJb0tuVROAxs83W2VGra6NOKHqNNlQnNXl9MxqsTGsGLfI96wQlmV86bJkDhiqC3wPsTqvHLhB6z5bLvHCwV18bnUV//Rb/9IOBgTAoFidxSXZWR1FPLCus4DerGv2+ZOZ1TlSxSKy2RjWEasFvkj2AqtrOqeVO6t7YnXqMLGW6lCc82DYTtiqyWJ/O4fpjLFFwTAgBmL1YoEdZ3Uzxk6cCwCgGRMDr9IWv1SMdQDGYbYHy7q5Br/8KRcvHt42dlbPFvYup5YH40Y+0fXIg8oG3cqE97cXOkpntYmo2mBAuudqkI+xc74SZ3VSeDRntegIkTmriwJp5erfW42RofP1plOWjgGRul9Ng2EV8/nGyEAZD1VidVnSMm1axwWwew/UNdLSQegV5HurcVZ3BMA4tfU28DsVRWBjQWfsMnFWN/P5TsAi1dgGoNXdK99gITOrVTx0ENEtohNiRKxm+k7r2PM5vv6xl/CTn7rJQupnrX9PGEo3KvrHrPUNAqp8DFHtLmwN46RyTStKrEEcjXmyOFfJIjzLDXI3LmuSuhSrH/WqKqSloxegwtNSBzEgucVaJHQxICpHLTXkgot0UgFYBMtRxod9xGoCs1oZBgmGFCA73lTuNCoDGdguShU7kElikZzVzpCwDOgvoMSC9KIcigNitU91Vg9iQAj3Ft8MUuFwSmr75EALrWmADGMAW31RKc+ZWBsRbloxYZCccBaX+jv7GBZ9jDAgglktCfwhbViIaocW8mtrFFDFy+u2Z6PlnKEe17Lguezf2/Qm8qA2yzOLxJjPKqzzrehx73aJ69E5WUxpnNWKWW5zjTXSw4c2w4zGLXvEWW2wKToYtkrNBRgLWOSfoRW03jUHB/j//Ib/N/7sV/8DFpopfnYjVqvHuzSu6AGLkc1ak7sYkJWBs9rzWEiRIqG+2RTSEatdKINhUZbMBUTkQMvMDOvEMXNWRx4TETr8+rjwEPkjmBhVCdxU13jBRXAjZ/WCO6v5+W5iy0isB1pjbHeMaXebaRbD1ihyHASzmihWLxd146z+F//+AB9+4lfMxeqlzTpixDXggbvkewBQitUmbmUlKtAg2B3ANmBSimqgi9V+aCOTbVoUBZtvhbSNq0FnNWUjRGCGumtFEVxpunHVPa64zgZitXQjxGRzwXXhWXJn9SQYkE73SlJ4CF368yVE0K671og1DyFWS961YqylYEB8SfdK46ymY0Ckc6MG60g7bLN5qcKA6OoF4nkdE6uFuNoSq69HK5xtXDxI51jMd8XqwB4PbUxTMM1IUy8AoBarOTIw9DXGcI4ZGjJjNt0mOtNDrm2UqRwD4huM3ZdlXpdX/lGvPEeqyw3bx1mdazqrPe/C+JyuPcDqpb4oBvhDRs7qAawEmZ83xBKlip/8uIGKRwce3KlzD4hzHbgGALbtg/tOcpu294ld+0NoDRgwq0eCr0gICBE0KnNp1jV98TS0C20iVlsWfA9s8dS9wEQH9M7fkfTTr08LLDQDPgDenj6EASFOQlzPQllb6oBF3bA6UVZLhOIzyDgG47oZCCl+YDFm85TOaoAFTbZFqrKkuz5bNY/qHWf1nVslboTn5DZ19u9XtNMDiDc1u8YaG7iuXaKoLAZe7lQTqEUQgD27hIwKBRgE1g1tihqE2O7rrK5ra/fZ5cxqALsCGceAuCjUY3dSkQMWG8G8M8bEm5otoCnOat9H5GRKsfo09nDoxwRntXzcqkuzgEXf7odBbjJDZ/XMZwvz9qq36bKiuynVDkUzDMhiaTFXsdgMO/VwNVhPI1ZL3K/a83heduCxHAeFWJ2aOKsPLZzlIYpVgs+8FuHFwzsTOKudXXyTMC9QndUAfK9mCKuuAGyAAWk2U6UYEIPW/0COAWnETwNmdVpJxGoiHg3AVvxSOKuTwtPHiziOPGy2Ca7UP01xrqqxQPxcSnkepPPDPKPz0JUdjaYYEP4Z19muWB2XvtHz1QQsdljIcWYjcgwwIJElZVafbxwsvYTmrJblAhjiOhoUZffzMjGLQb3JBhDHA99H4ORIsvFwwR19h8+13nP8Kv7dned7YnXo5khGQxsJ5rYhHQagbbYKE94+zmqd7KgGL9J/jkyZ1ZdlXpdi9aNeec7aB3UmDGLnaYRZ7euIFILPqRogqMKX6zJntWxhbuJwGHJWmzCrB8RPcnu2OK5SrKZjQJSuCQBJZjFxRvPl49oKXIUoEcwS7d8JMNjWI4QgqutPcVzqC4gF6ymc1dRJE0cKSK8rb5+kitUsCFEyfhhO8n0fcheZyeLJ85ioJfnQVmcV5gQ3xlBQWcP/JTqzpMc1QfeIEq3iLbHa3FntyJ3Vjmnre717XEOnk6j5rMa68BsR/O6dGtejFVlM8QJ7EAOi7ay2LLgDY0GZV3As/bb/LWtf/sdFZdM6eIY2RQ2Z1aPOahlvvJ04016t8M83stN2xuhOpZuSuXwo6p8vF6s3G01mebs48zPObOnGxcnGx5G/oWFAFPcWmX/L54fdz2yTmInVzjxkTlWZs5oYADjkpiy6WBnNWiytHWb13VMfN6Jzo3HLjxyl+zUtXQQ+zXSgzHHgjm0SrxjAwc0IZ1mEj/9Cjfc9c48NVcbO6r5YbeSuR0v06VwDk4BF5RrBlFmtCFhsnJQUBjKAaGYhKSQO+8IgfNnzmKFFJlTlOVIKC1uFARHBlVRBUWwOd99fvJOL7KxWzA+NnNUDYrWRs9r3GTaz7f4UXRsGz1fjrG67a+saceaaOatnFmNWdy7Eam1h4aVksbrX0VgU7N6iGkSazvF+NkJuYDyRhg7zYkYZzXkc7+BKR8TqPMdu5zyfa33F1Vfwq6eP7YaUhyHvChsTwLm7eEoMCKV7Q+gFqowrftyscvTet1wvkYrV1FDzy5qsLq/8o15ikquzJhMYkIEQvCy39VrqxWCucL+SXWRcqFU5q8nt9EPtJ1Rn9QhWosGAUB1vivZs8uSGc8hUDvs0s5k7TedCjLmgASDPUQOwfR2H4gW49m0WdFhUElQF6K6cJrxT5aymCJUCrzJx0ChsG55TIS/74URGzmqw20bmdjJyVvOJUC3ZNl+twCa4EwYsmridGmb11BgQoOesNg5CBOCFfQxIk8RtclwhJLSc1aSw3U7NlzbWeQCs1wCAO3eAG9EZ3VkdKlr0ecUJ2MJM4/5qeJdDiBmSqKwObiQLNCPvGfE9+uc68j4AgCxjglt7ofPEE9tftwUy/uvQSpWtqVla67t8RCm4+E3AHmXcsixEfiXlcwLA6YY7qzXOt+ngUdxbLtXpw+eHfWe1yzYDic+tFfJJalesLn1EF+CsrgFYZJsmsDxydpjVd058XA/PjcatYGbvhguKEqYTwt7KoDstzxkijCjaLx5f4jwPceuVAk8fnLEvGgYszg5ctskonoOiYMzqkChSorUhOqGzekisNmJWh470ncCeWSIDGYAbutObA4ShRSZWFwWq2oITaM4NVBgQge6hTjUakap/XJNOLhVnnox1BIad1QaIGfg+M+C0EZ9lyTpZTZzVvo/Q7WxaiOc2MNtg2eHX8zqPXbKzWroZZOiAHsKAGDGrxblKxWrC2kNsLuUjwnJh953Vto33XXsFANuobSoI4Nt7oEVEzhklYHFKsVo4oGUGLFFcN9PKzVFkeQCG75nLmqQuxepHvcTu9tQYEDGY7fvwed6gUEt2+ghmtWxhLgI5iAgMcYxemQYsDjqrDViiKmd1TThXoHFQqZzVaWbpJzILYXloE1/XWTnEVQbooioP5lG50xiHioBA2IOHri1Ucoei0lldE4NGLWs7GZeIqgDoDEUfDCuhWjxRhCQR3Cjh7a/PK8zdVPu4tuco26jz0qYHLPo2c3ioMCBEzh2A7flwa2nj+jXBgIRyZ7UpBsQTbN2pndVHHhM9NhsAwN17Fq6HK7KYwpzVAxiQRN9l3jhnZIJiXtPuLdvmGBD5JptRNoItSbwHfk0wIAB23zVXr/b/nB8TjoPIyRCfK8KBk1rf5SNK4ayOY9AxIADCoJaysAHgNA70MSCBM/juoo5bUudjVWGTe5h5Gd09FCjE6sK7ELGa7CbltTiwd53V5yFzVhuMhY4K2SFQGBFB9BnIcTASKgHYV45QA7h/u8C14Jx90dRZfeD2MSCGYhpDjg0ELFLEtCFnNXXODXVHhEkYJNDClqjmW1Rnta0Qq/Oc5lhWiUllaRaCpxCAy7wyGgtUQdlGjHHVmsZ0bsgF+533LR9bTDoX4LKgzSRtIc1EUDp17EbLWd15164S18hZnXc3g/KctqYXxTfyZRiQoraNnNVSDEhVoSht/fFAZGMUw++oHrPasoDDQ7z3KhOru85qywJQD3/OTc6ZZie20+0EaBfvitjb2MaP6TvlsFhdlsh0O5kuMSCPdF2K1Y96UTAgvPVf6ayua6S5rRewOOL2ativRGeWVKQzcZTuE7Co+wIaCVhs0qMpbOWBgEVyKIngkKkwILnNUng1d0qHwr8A6C+gxDGnDirDsPORnPA7tFssNlh0J022zXE4Ev5t05aqeUxeKudIVfBJPhUDogisK+IctlWT2+kDp0C67l/bxllNWDyxE5s2YDHwa6QyBjIfXxzP4PXawYCcbHwc+DQniigvdJhYKxYOPIk7csxCxVxxf/EVb11MxKw+8rDKQ/bBA7hz32HOR6KYYvuuEjEDAElKcFZfBGLGsti4JdsIgUFLIp+MS+cFJrkAlkIAb1Wd8nuuvdBpC0z37u3+hSBgbcln8l7PJAHbaCWK1RaAKu04qxNL+/NvVxTWLEyq66qta5wIsVrjfB13YCPEMPyrx5QtS2wKHzNP9RLeoxRidVJ6dP1zLFTNYIxZHrtYFUEzxt458VmAq4mzWLERQjKdiBpxVgMg37M4PgYA3Ltb46rPndWGYvX8yOtjQC7QWU12v6quq5hzUzEgArfVec+Y5GMAUG5a5EnJxoKpndXUe0uEb8vE6tqA16zYGE2Tmq1jiHMjpbN6CgzI1AGLvs/MQkkXA+IhpCCGRFkWQr9kXSHiOhQFNoVv9NyGc4eha7oYkI3NnNWE9zfrXOh8Xty1b4wBUTirycxqVcAi1QneYB0H7sm6ZmbEbqDz4SEO/QRf99hL8KLW18VLSYIva1eac4Ojplg9iKSljDGKzI3ucdNK01nNjVKDGJBLsfpNq0ux+lEvEcyizawecFZXFbLKge9qtPeIiYIM1wED14AIllPgD8jt9BcRsCjaqIcwICbOapkI3rQkap4r0OyWSwf1ukaaOyRntfLzEqXr9GgCxRSfc0UXVVkrtVxMIr+ARjZCipoQsMgFKmk6vWHroIotbcR7hBoDEq8rukNROH0kGyzrNQ0DohwLarYAdm1am2MQWkys7ll9WIu67dAxIJZtM4ceF1I+c/cALxzcNXNAd53VNRPXQs+MxeZ1xOo0rowWj6KuPebiXrJoMCBfuBPgmcUDupg05FAEd1ZrYiD2QjWYbLIp0DUkV62KJQoYYUDYIm/ElbPO5U5osVDqfia+j8jNlc5qxnCnjzEzN8Nm1WE2x7aZszqENEwKRYHTLMLRLNMaZ5rNlYlZ+02bfo7tAlWI1b6BWN0WasVxubM6NGVWdx/ZCbo35lcC5qwWnRtnPgtw5QGfpFJlLgh3McVZLf6NCma1+LmkOjrC3M3whTshrjqn7GuGYnV06DOxWiigRcEcmgail69wKBqxRAed1USDCKAMdDYSPwHA86Ts8s2aB8NSnoVGrJb8WwVegzDf8p0SeXc9IzAgVDyacL92DB1pahY+reqQI2Md+bkOBiwaOKubjIjW2M1CbA3EagCBV+8Ky3mOszzE4YL+TghntrTb6DzmzmrCu9ZVBCzGhcGGmKprmmpsE4f1FJkmvFvWczVd654HywLqIWGZC+G+W+/ON46OAAA/9ZE/K88LkXTxtSvLLX2DgJhzDojVNaA3xggckMI4CQAoCuas1tHNfJ9hQC6Z1Y9kjV55y7JCy7J+xrKsX7As6xOWZf13/OvPW5b17yzL+rRlWX/Xsiyffz3gv/80//PnWsf6Qf71X7Us6zdd2L/qP6YiBiwqF6VA8yDrJqUOuV+LgtjyzBnIZQm5o5S6Ez8WsEgJY9gnYNGIWa3CgBDbkPikUcWjS0uXtWbqnC8PAtwLA6LjrLYq5IrrWuUlbOKkcchZfSFitQE7Temk462DJrv70hZaw8WTylkdnxf0xZPvK9PpVyuQMCDKz0s4qGxam2MjVktEcBPHOtC6b4VYff8Ibzu4a+asjtxeEGJSeohM3JRoidX8uHFiGaNFAODa4x7utsTql2/P8cLBPbqY4nlsYqwSqwUSRQfVoFqMoBWoRRy3lCnyBpuiPUctr7pgGyxUvnYx4qxON6W8i+e3/3b2/2/6pt2vBwEiN0N8Kp/ExInFxhhiwOLcS/tidWLTmdVgt2UscZEhz3GaRTicaz5nAyizpkWd8ozZrKsuK1v3bVliXQSYBQat5LYNvpKezlWrEqvFeRuMh+7Rgm0Oi86Ns8gowBWAekNMtKkHhHctF8Br2QTJBAEBAIsFrkVrfOr+FVyt7rKvGYrV3iJg4l+WsfMrywmc1XxjvLvhbpA5ocy1MTGIoIXrUAUsmkzkJMc1Yu03YrVkfUG9txxn6/5sr+moXYeiLAueW/WcqklqGW2Oq9YIDWppSmY1d5eTmdWOA8/hxh5xDZqARQNmNYDQr5izusWaP8tCHMzo7wQ79JnpoocBcchitedLskfynG22hsRrwA1jMtyUCV7EC+wRZ7Xmul6cyNDtUxTMjNi9tFysBgC87W3bX+/jrBZubQKzWsWBBkB3VjsjzOqSdQlo3V7iuPFAN+Ols/pNq31mTimAb6rr+r0AvgLAt1iW9dUA/hSAv1DX9dsBPATw+/j3/z4AD/nX/wL/PliW9S4AvwvAlwH4FgD/T8uyLj/5sSoK9nLTCVAZw4DwwUyX58MERUXoE7Udz7LgOjWbLHR39vjk5sIwIBRmtaXe0SMHqAgRXNGeTW5Dct3BpO+k9PTX+lycKPdxVuuGdyrvLTqfc8j5SN4tHXNWEyc3jmuhlLkpuahKfU823LRu+6iBmAawwLpMctzNumZ8Zaqz2smlzmpygviAs5pthhHF6gBsci/5vAAY7cKHQc1Yf0KsfmAuVvszh4nrHbE6NGASAsCNoxy3NweNky6JzdpyRdkHC+ZS5GJ1nDoM02DqrFYxq1POrNbGgEwcsMhPVTpu1TXyyqZxWpvg5f7n3WROEDFeyrwBXsm6ZO637pjwjd8I/Lf/LfBt37b79SBgbMaV6rMyQHb4PmZOhs16d4xpBHDiyjSMLLmzOstwQhGrB0KiTTcaPbfe7YrhLd9GzmoAsPj5dF21VP3T8+DJOOsTYECwWLD/c7H6/ibE1WBl7KyWuV+NHNB8EZ3Hks/G1FltWbh2mONXTx7D1foeu5/EdaGWZaG2+eeyWm1d5QZ0FRWqgaE16B0GAKbPnFDMN4q82uXI6pZiI8TcWa1eI9S1pX9vcVE564p0RWHGrAbgOXVvLpskYPMNasCiwrVvgohrxOrummaC8G3X4XMDcQ1EwKKps9rvO6ur2tYP2GyXIsz4PPHMMCDtfz/QbIqSh+5mDdr5uhgLqMxqxbjVYEt0jXj8WbTqSi0uczNi77FtD8DPP9/7ujXkrC5LJoB7mni/AQ40ANqGmBDAh8TqPKc7qyU6zKVY/ebX6Chcs1rx33r8vxrANwH4+/zr/xuA38Z//Z/y34P/+Ycty7L413+4ruu0ruvPAvg0gF83xT/iP+riD7Pl6+08MWaY4s+Fs1pz12lQUDRACjiOYmHetONpH3LQlURu7dkjYJEkIoxgQMgTZ44BUU1E09JFEBGRJSpkBxiz2LEqTWb10EaIgUNRlhwNMNGn5Nz2ycVqGy5hctMgSySLp5L6HKC1uz+lKwlskp9JMBgNBoTIrA6dAkkswYBsLMwpbgyV6CM2gmzaJH8IA7Lzcwn12NUMb2wOm4DF2+dzPBadGfFUD48dnOXhVkwTrZOGjpznHk/xudXV5rirDXeomk7sZjPmdDjZ4PQUOPDZtTARqy1A7azOuBiu46z27ZGARSIGRMXCFl0Wdq3/nhHzAsn7IM9qMr9fynrsVLwqEch4h5YFPPNM/+cGwWDAItu4ICI7fJ9hQDpi9SZ16GgRcGe1AgNSVA68kJiNoELBmGw0dsNxBQbExFkNADa/J8U14JviZLHaZuzNrNw1M+RZTXeWi2qL1XmOqgLLGTBJxlU5qwVSgRqCp8geKRODfAhe166U+NzqKq6Ga+C552jdCt0SYvXZ2STM6gZl1hWrDQVFAOqOK6r7VSFW5znRzNI6rmtVKNLd8zVyVnPsWpL1r1+VFezeItyzvithjJclssqlc4XB2OVFJyQ6STmewCRgUYJteSQDFgG4bkew58+XqVgdhth1Vpvy8Pnftax6951Y11ilHh0DErr99Qx/f5HHmKa7We6sJq+9xEaIYozRFqttG7BtZqxWictFgUzmKm6fQ3sOLb6xrlFXivPJMqYZhYSclCFmtRgfCGiRfECDaJBDBJNnJunqzQr7Uqx+k2uvO8+yLMeyrI8BuAPgnwP4DICTuq7Fm+hVAE/yXz8J4BUA4H9+CuBq++uSv9P+Wf+FZVk/a1nWz969e1f7H/QfXZm0SQxhQAgtEq5dKfEPzeKJyBWWOkpNdqEHXEkmAYvS3VdeRUlszW1Y2HJnNdmN4Hms3Vd2H/BFpLbuIybMAwYs1vKt0S40wqxmzmrai0Ip+oiJs0PgFQ/dWwIxQ7hnHQcoZYxSk00bqJ0jRpNxqDEgm1VFF5KEs1qGAdnYWLgGzGqFg4q6KA0iG2npSe+tnZ9LqCdvFHhtfQRsNijyGnZdstvUwPV35ZqN+8mi76yeQqw+v9a4KV+/H+DJ+YmxsxqLBW5GZ3jjtoXPfAZ42wEP4aMqX0PsVwBxRmBW+7Y6YNGAealkVjdhq4R7Vjg0p3SO7BmwGG9qvY2mIEDo5kjWkglHXSPOHda9QUQNzdxMoIqb2mSOGbM6spgzTeKstqx6uk02mIe1NeJfywE9iVjdCYZthBQKq5mXJxG+GLPcsHvD95kLOC9QPzxhLtLZjJRfsD1ZPnZIAhYB0N2vihyHKfIBrj2/BAAWAPolX0I+zk45/L7kzuq48BHNDO4B32JO3R7KjBjsDgCuC9uqUWade76uUdQGIXhKsRpwLTNndejmiDe798F6Y5kzq7P+Z6NEN+1zqm7NjAzt99cEbGXXtXZRZjDHgDRmjl9LZ7WJu9zFrmAv5nEGnQuA3FnNfuAEm3ft8bAocJ6FWEYF6dr2cHb8XDeFb+Ssdu1Kim0xwYAozQwmIvhI9ooQanvTjW/+ZuDpp4Hv/u7dr3MB3LPL3kZY+5hpqRlYCGzzMRTzwyLO2RyZErBY2Gp3OR93LB2Wk9BLusiSukZeWiyw0mRucFlGtddIUdd1Wdf1VwB4CswN/aUXdUJ1Xf+1uq4/UNf1B65fv35RP+atU5Q2P97um0kmIOKYUqbRUI0gMEyEL8bqVe0+El8UfPJay9R16guocRVL/qxmO/6uRRM/mbNa8meGIZPKFr8sY24X3QWEQJYMrGmTjeYCymbc4LzjnhJV5DVZ9Bna2Sa1YQH7OasJn1ezaSOZ2Jgwq/1ghPdIbSUPHekCMt7UdKePYFZL7tnVxp4WAyLCbhwiBiSymbO6ex9M0KL+5FNgYvX5OV55KcHTIljQAC1ydM3FaRb1ndUGjjcAeO6pAp87v9oIX688mLHzNXUhzOd4YnaKW3ddJlYv77CvGzCrAQyI1a42vqZZjMi4wgYt6sqOEB7MQxKreTCNbPOS7AJvOq6Gv61pVd/32vo+IidHvJK/v+PCR+QTupiAbcBiR6yOU8eMWT23mbNawismtdMPhERPkTeQVe5WrC5LbAoP89BMrK67GBAxxpgIlRKxOkkto1A1UZFfISk9vP5LD3BzdmbMaxbdG1XWb/sGYOaslmzgNnMtA0Hp6q97O64Ea+ainUistoRYfX7ewk0ZMKsvKGDRG+DUGovVnXdCmlnanTs75XmInLzXdbaJDfBFnofQyZHm/evXoJsozmqv7odvlyVjK0+Bg2m9xNLcNsrIUOVOGHWvcPEvLzuCGjWAvVW+V+129E2EAekGBKfrggl0hhuCvcDZLMMqD7CY087XC52eu15siM3m9PBOJQaktuH6xDWSilktnL+US+v7gx2CyHOm73SF5cND4I/8EeCDH5QeM3RyJOfqY5KugzBOKpjVpDHGsnhnmCQvqHW+4ufvXcJZ3Z0fC3OjQ+hmvKzJSuvOq+v6BMBPAPgaAEeWZYk74SkAr/FfvwbgaQDgf34I4H7765K/c1mqIjJ9AqeQTkAAMFep7i7ZHu5X6qTRdWu1o5Qo/MFx4FhV3zXBj1vVFmv51DwmcxVLzqdpHSQIX6oWJH6uRU1MjxaOHBnbiS+gbV9f+HOsahADQllAKRmtYF9yiKKP7/UZdwAuTqw2CVj0FMKXeA7Izmo5BsSYWR05bMLQebtvNtz1ZtCWKhOr14lthgFRdW5QndVBp21SlIkwwevJpx28tjkCzs7wiz+X491XXjMWUnphNxM5q599qmRiNT/uK/fneHr+cBJn9eOzU7x6L8Tf+Ts1PnD8GfZ16mp3yFld10hyW5uD3Cx0JcdsOkIo45ai7b2ZOFPGLeFykfzzTTAgrq14d7WqEav3vbZhyBZPMmd1UTABlMpalzmr6xqb3GVho8T7VsWsTlc5W/RP1RECs042oOVU3RGrDQKqRHXF6jxnCCviYh9gTsILcVYDePJ4jc+eX8OP/4sK3/j4p8x41QDfbO13BlVpzgQGimtfmA4kzmo21zITlK49GeDqYx7wW38r8KXT+JCYuGwje8Cc1TWgP9dslaqLyzRgUY1qIAa78+MC6J1rktlGuAohLHed1Y1YbeSs7v9REtcM3USYxzXz7vb7S4S6m2xaSMTqi3JW56VNx7ZY1naTreMuN2EgA8A8YGN1Lyjb0HQQiEwTjp47vV+wbgvD3QXbqlGlu2K1SbeNG3l9ZzV/f5FPlZvxZGI1oOnQbZXj2WzOrTBLaeWGtc4VAGoV51Uwq3XCfHnXhiojRFwYLRwtsOVLD4nVhLWi76O/GdYu/s7RNnnKhPVGKzCcG12WUY3ezZZlXbcs64j/OgLwzQA+CSZai1Sc7wTwj/mv/wn/Pfif/3hd1zX/+u+yLCuwLOt5AC8C+JmJ/h3/8RalFUe1Q9Q6JslZbatFyqblmfBidxz5zrZAKlBdxa5docwlA4xw7xJcZEpXsWgdJLZnKxf8wlVLvAYMA6IQqwH9xRMP1CoHxOo0rrQn5UonIcza6ZmDTO2s9ikvoH0CFonM6lLJbjfgpimY1UauJDCxOq8dqVhtxFBUOatj1wwD0l2V8utKXZQ6occmoioMiImz+oUAr66O8Tf/1Qv4uf9Q431XXwHmc/LxAPTDbiZgiQLA4sjdWTy9ci/C04uHZoscAPA8PL5c4Qc/+hG868UCv+mJX2TX1DSgSoHviQsPoaf3PAw5q002cH1fHd5ZVHwRrVsDzuo8J4o+ouNqJGCR5KyWtLyLkzW6b4WzOm6N0VmGB+kcx7OU7J6JFg7iwu+NM6cPKxz5sf64NYoBMQzHLbvOanOx2hLM6hZeBICR81fprJ5ArP7Ql9zGv7r1In7s30b48JO/Momz2rfLHqOTuigHsBUUlWK1mbP62jXg6hMh8Ft+y2TOsdkM2BQ+fvcffyd++dN8k9Tgs2ryMdrPQlUhF2OhibNaJlYbYNekm0xVhbRwmIuQ2h3leYjcrCHsiNoktnknm4RZ3SBmKBgQjzPxe85qM1xFE2gsE6uNnNX9eXdRmofY9sRq4ag1cFbPwgrrItjpkEsMUUsAEM4dttnKd3FP7uY49GKzMVE4djet8ZCftxXQcFvezOuvZ/h6lrwh1u7ubTvhTcPSVd1RZcmwGh6tQy5wcqQrxaRLrG11Li/fCFOK1VSMldCiVGI18f2lDK4URVmDNUSCztdNDCKXNVnt8wQ+DuAnLMv6OIB/D+Cf13X9IwD+bwD+G8uyPg3GpP4b/Pv/BoCr/Ov/DYAfAIC6rj8B4P8L4JcB/FMA/9e6rg0BeV8EdVHO6krfWe3aJXIFJ4i1EdNbnssBRykVA+JaFYpsQKwmsaUV7vJGRKA5dYcCFsmTG/FSk90HVB6ZuK5Dzuq41ndW+4oNC5g7FKXtQjzd+EIwIMQ2P8dVXANTZ7WKyVeAPbNUR0rkSp3VcQw6+1UsniQL83XiYO4RHESKRHIjHjygnoiKXX0TZ/XbI/yz196F3/ePfyt+5J8H+MprX5jI9VdsFw6moTSigoCJoGt2fV95MMfT8wfmwg+AJ66m8O0CP/T7b7EvhCFdUPE81m2TyKzFOUNLaJ4yY1YrOkIMNnCV41YjpFCd1QVyyfy+yGvawnxfDIhw/2kwqyM3Q7yWvL8FA9fUWZ20/q15jvvJAlcPRlT3gfIil22MdsbD0wclc6gRNoYBTL4RArScqi2xel0E5s5q22LTw65YbSJUShamccr58oYYkA+9+wF+5PPvwc9+aon3XZ1gjFUgO+JVqc3D3zmmglmdGAiKoh5/HHjiCfJfl9ZsbmGdB/jYywf4+KdCY/e3FAPSON6IBxXs066QYjLnBuScXiEoegbPl+sidArEye55GTmrHYfdr4XTQ/AlcW2OAekyqwvPKL/T8yXO6twxC1gMnP6Gc10zHro1gVjduWeL2tZzvXZqHlXMHNAyHSSGjnUACJberrNavLdMPjDfZ+/x9qaz+OyI2RBe5LLPq4MBsQD6OGhZ8JyqnxNC1QpEDYS4ZpVLc1aLNdJaLSxLMSBDNSZWUzedHUedmwWhFeh3bzQmtF8LDIh4z1ADdy9rkhr9JOu6/jiA90m+/jIYv7r79QTA/0FxrP8BwP+gf5pfvFVlhEGYT5izQvFCpMDyLQuug+1LvTMIFCU96MRxeJiUzPlo4Cp2VGI1dbe04TVbTLBviyYmSIEGLyL5MzG5McCAnKdR/3zznPZyH3HYA3y3lNBOr3RWC2Y1RfRRvdRMgjNEG1ZRoncVBAaE0PbMNm3kTt1JAhbzeOfreVoZJRyHc4cxWrvO6tjSxik0JUSqpP+MrRIXCy/Rd+yqWMXc5bKYWKxu7gsDIeXxt8/x0ukSH7zxMl569Rk887UPgOjp8b84VL6Pq+EKD849PAEARYE78RLXD2RQe42KIjwxP8Hrtyw8B+DW2QyPTcF/BfAbv+IO3h/9RXh3fgf7gmFLqsddj70zE25dzVO2fRfVAL6IuoEbBJCPW8070YRZ3R+7GyQQEQOSqzbGeW1iSw/bEAQInaLHZxUnS/msmmrE6tb4lOcMDRbQBTXLZ7zinlh9UuNgYmd1M98ycFaftZnVImDRUKx2XTDsx5RitVsjT2XOajqnVtS73lHg5+49g7/wn/wzOHY9kVjdR3bE60qbh9+U67IW7aT/R3EMJtQZiNXPPAP88A+T/7q0Zgsbd5MlPntvgZ/4uUM8t3zd6B7wQxt5ZQNF612V50YByUpntWFGCPuMy57zk4nVBv4s7qyOd6dx2KQO5pSOMwCwLIYzEwzkljB5IRiQypRZbSHPO2J1Zk+PAanr7XqOuDnejFs9DIgNN6CPW7Owwvq8K1ZP4KxeeEhKAJs1AOD0pGJitck8zvMQOonEWe2SxWoVBqQGjN4HrltvUZTdjWKqs3pQrHbgU/YBhBlR5awuCmSVDz/UxIA4hRy7BvB7zSflBQ11+VOd1Y2ZYyBkEgABA1L2u3pNkKGXNVnRt/cu69ek8rTSDzngA4TSWc2ZRrrJrqPuVyKqQemsFiIdkVnt2qUcAyKc4brnallwHRak2AsCFI43qlgtSyLmxyW7PGwbvluxdrzO+dZpps90EueqcpfzouyWDjKrDRbmfiBpHwWMmdWuNYyYaQKGdA476Kw2CFiM5EGIjWOdeOBg7rJFjspZTVk82TYOwgxnWdCbiKwSjgHRdXqI0KtU4qyuDdiUCrGaNGZ3KpzZuBat8P/6+r+N7/qmL7D1kikGxHFwNdzg/iZk95jgJxq4fAAAyyVuRme4fZv9tiy5SDuBWO09cZ0J35/4BPvC0ZHBwTiTLpZMyLmYoH3KQ6iG0iY7s3wfSEtvWma1bTOXi+R90CCBCGK1lrN63zEhCBA5GWKZWC2c1dSOAN/H3Nt1Vtepmdtr5+92xq3z0woHXjJpwGJe0F37AAuHTSQYENMuizCometvSrHa7/OKG2e1IQbEWi7wyW//v+N33fhx9oUJMCAyjFW8qekbuCJ7ROKsPl/ZOPAT4+tgmoXbrdnSwX+4+wxevPoAP/GxIzy/vG90bb3A7s9hGsOBoVjdDVg06egEtqFyPWe1a5YPwZ3VSgwIdR7XFqvbx92AfM82THwZBsRE+/QtFHWrI6SqkJYOQrcgi8rSgMWiMOOWYwQDYsDwn8/qXWY1z6AyEcABIFj6OxgQtslKMIfsHDToOauLTQbHqsjvWif0WBB9ZzMIgNE46Lry7g0A5hgQScAiw4AQjilc0BvFWCKY1TooSnFMlVhNdVaLObdCrI7XFek9Ln0ftIuCAfF9Jqx3M75M3zOXNUlditWPeKVxxXa3dR463tqVKZAdKApSsuuQ+9WEoej4jtJZTU7MFS5oBQcaADEMEvK2byEokgMW1RgQk0COwK/ZRLQrqMUFfLsktSY7doWimhYD4vpyVAVgthHih7Y8iEFwvSgvIIFtGULMUO4t35Yzq7kbw3GJDg8Vs1qEqhEneFbIReOuszq1ETlEDAiAg1mJsyxCd8W/Tl22KCM4q0NZQJVwwVPbfRViUpyYMRRF/dPv+Dt479VX8ed+y79kXzB1/VkWrswSPEjmrFVwUxg565s6OMDN6By379o4uVdg6Sbs/jcR/kTduMH+/7GPsf+b9KuLgMFEMiHPMlSUwFkxxnUXI3XNN9lMxi05a58F+Wofkh1XETjbYEB07wXbZmNhZfU3cFulzVUNAuYmjSV/ludMWKWKHo7DnNW515zz6qTAwiO6E0XJ2v8BnK+ApZeQ3rU1MLCBS2dWLxbAeRbuCB6loeMPAK4f5biXLCAUtSYEysRVKzace5xac2c1Dg9xFMTbe3cKZ7VEWI7XlXmOg6Qj4nTlMPejyX17ATU/dPEzd5/DR972Cbz0+gLPL++xm45Y0jkMF/5MMCAXwqyWdXIVBZLCMMzY89gGXrr7zG9Sh35vAQhCSxoUvd5YzLFNwYAI52NHqE0KD2FIdwA364Q2r1lkTVAd0KGjFpUN9oBUAYtlbcPx6NLLLKoZs7rlrAYAi/wgsFoeu1jlQUushnnAYhQhcnLEq+01WJ/ydy11fij+Xut90HQzGuKmemHZU2FAZBjK0qURVnyfvWM2amFZm999UcxqEbB4ARgQ2TxWVJnksK1a73yFua/oo2CM3jOXNUlditWPeKVJrd/mZ1ncmaVISyW+3JowKckAwficNOHD9fhuubLl2QADohCAARicq5qvTWdWD2NAqIJa4FXS+yBelbQJrutyZrV66EgSaLflNa79iRfmQxiQrHTpPHR7Yh46WHJ0j5kG8PBO+j2gYlZnuWUkVjeTxo6ovIkNAn8AHC5KnHbF6rpGVYG1ahOc1aErcSIUhVm774BYrYU8UNT7v5S1YzaWZVMhBcDVeYL76QJIU9y6BTw+OzU+TywWzFn9wMO/+YkMX3PzZeaimyKs6+ZN9n/xzjERqwUGQxb2kufMDTcVqqGumTPLpi2ig2CIWU1vffd8OV4kF8gS3XHLsraOJBnDn1ecao4Jvs8WuRL0gXAlBVTRw7Iwi3bdafdul7genpuJfpJFNACcn4EmhLsubKvud4TAnFm9PLCwKoJGVJ4iFBYAblwtcSdeNsfNUsL8tVNel68NIM4ctiFqOnY93UErTcWs7jirN+uazqwWbd8SWtPZysaB9+iJ1bOjAD9560V8y42fR+AWeG5536gziGFA+q7H1MStLJzVRWccMTHJAIDvw7Gr3eeWIzBIjFpRYh6TdpjVQqymOqtDS9oht16DZYRMhQERaxmDDbFmLBDnKhAYBpsAzbpWxpY2cVaLkMmOWF3XlpGwPJ8D67azWpy34dh99TEP99P5lll9Zk0iVs+9FOvV9jqenfBOIwOx2rLqHR2iQRoaOaslOFID8xGAQQxIWrm0SyDeBypmNaWbib+3htza4vu0ynGGMSAEYxsg1vUKfQtAllQkhFHD2pduil46q9/MuhSrH/FK4woBQfhgzEsF04fYmskERYmYBrPFE2vzlLuVawC2R3gJj6Aa6toycFYrUA01HSshdXjw41a1RbsGAIJAMvgC2Kwq2uKJo1DKWu2ka8RqirN6KGCRyqwewoCYhHdO7doXGyGSIMDShFkdudJd6DTjoip1kqsQZ1iLNj306WBe4iwLd8XqogDAd8oJAYuhI2F+ilZP6jpfIVYnqTVJizoOD9n/33iD/X8Ksfogx/2ELUhee7XGU/OHZosRAPA8PHYY4431Aj/14zm+4fFPTYIAAbB1Vot6/HH6sUSASiIZt8SzQQ3BUwUhEkVlP+BuN8l7hpyNAMAXYXXdLovCJrvsPbdWbjSK0nb/hSEiN0ccSwRpfu4WqYeWVVesvnunxvXo/GIwIOfcWU1poZUFwMEMMQMAy0N7x1ldZQUTAAwFj+vXatxNlo3gEW9qI54sAHiB0xOrk9whh7/t1JNP7l5DE8wQwFqJHf6Ztboa4xhmzGqnL1ICwNnGZYKS6XWYuGZLB6f5HB+6+St47vDE3FkdOv15nKlQKTptpBgQejcjfB+uVe128EzmrM77zurMNXNWBxw51V0jJDaZhc0wIG5PAAZgNMZ4XWc1vwcCg+BKN3D67y9TIwPU3G4ARtdgPu9gQAo+dhuOAQfXA2YSEc7qc9ucWR0EOA5iPDx3m/Xi3Ts1roUr+rvW83qYnc2qMtqwARSd46Z4kTFnNTFgcQwDAkDvnMV7S4bIg0GHFOdA99AavJKEJlZ7gS1f1/PK0pqhGDWfM9YR0u/gSQrPbKPxsozrUqx+xKtxVmsOEvs4q3WP6fr2gPuV6MyC2NnvT5aMeFEXhWoQgqLUBU5nVksdHoAxM8v3Lel90LzcKWK12IFWOOmSBNoLScaikzOry9IAA6LgNRuL1XaJIpX8+w2c1V4omTQD24kzkXPXtDl2ni/TYBrG+Cp7O/ymbamHy6rvrBZKM6VvjjM/e5M7nshtIlbbVo0y270PGAZkghb142P2f/Fvn0CsvnJU4UE6B9ZrvPZqjSfnJ5Mc9+a1Erc3B/g3P23j19/8zCTHZAe+uft7U7HaKZCncrHasurpuMImWCi0Ntkk70STDRbPlzu285y+2TyEBxOl7f4TCzIJ+qC5JgZuUiZWb+ccd98ocT1cGQd42laFMumI1SsLS5/GrA6cAmncuYe4a9+1K3L3wvLQxnm+3RC8/8DC1WBtLlZft5izmgse8aY22xAF4AlXbdtZndrGx2UH93Y/l3e+0+x4to3AKZEWu1z4OKbzf1mwXl+kBIDTtcu4so+Ys3q5BH7fB34Brl3hh977j5izegoMSEf4iwsPYWDmrJYxSo0wIILjn1Xbe0Dwmk32hR2Hj4m7eMdGrJ7aWR3bmHt0DIiS/2u0ccW7D9sYEENnteVJcEsCEUcN7wR7JGXXwHRTcDa3sc5bGJAJrisA2IsZE4H52P35uzMc+Ruzd6Jl4eo8YXNOPo+9c9fCDZONYYlJJk4sY7G64aF3hMq6tujHVTGrRcCiTghic6KefF4giqLvCFyHzMgBIItLhg0lzGGkHGheScy1As3j2r4akQYIk6e+biY1eRYF4tLDfHYpVr+ZdSlWP+KVpmDtDLoPnYJDBoAsVjueAgPS8DnpLc9SYd1kJ9514Vj1xbhflc5qGy7FWS3YeTKx2rANKQhqaXiKCUOxcSIoeFENT3JKZjWVh+7ZyvBOMrNaoFAmvrcaTq2CWe0SnT5u6PavbVUhKXgwDbXFzfex9BKcn+9+Oc64MEVlVh9aOMs7zmrxa8rE2fel7bNioUMOFVMEX00V/oW3vW339xMIwNevVribLIDVCq++bjNn9QQu6Js3anxudRXppmQM2Kmc1WEIPP88+/WVK1u3OaUEBkQ2IRcLnwnF6rxyaFgoDDCrTR3bsgU0zLpXGOtRvtEoapNpbmCJYCaJQDeFk2wW1WzB33VWm9y3vs8C0DqbYquNhSWF0SkWpV3WvgiAc+hi9eLI3RGrb9+1cTM6M8eAPGaz8YULHkkCOv6CV89ZXdfMWT2FWA1sx5TlchLOfuBVvbm3Ueiw6yJyMoWz2sHBI+is/i//S+CP/rZfBAD8rrf/LJu/mWBAIge5REhiQiXx/W1Z8JySdXW2uwSFOYCKq+DdhzsGgQlwFbAsREGFuPS3Y21VYZN7mHn0ZyGYOex+7Yzf69jGnHjPss3WPgcagNEz64qxoOUqTksXYWAgJMk6BCc4bhMMK3NWGzyv8zmYs7rDrDYeC8W7b7PB//q/ss//a26+bNx5d+WgYGI177a5e89iyC1DsbpOs2bThoWBGjqrJRiQMivh2JW5WC0xIKWlZ4YBGWBW7/zsfcr3Wc7ZgFhN6fCH6zLHttJZDaYVEI4LYNhZTcGACGxo5z2zKXzMQoOx+7KM61KsfsTLxFmtbJMgisBN262MqSsW5hSxWuzsq5zVRLHat4t+ix+AqqjIu9uuCzmuoiy314BwUGkqOWAc8BD4kLKdNuua3Jbq+I6SXQ6wNaVua0+DmFExq6ktz67LdsYl91Zm6qwe4qETxWpph0ETiEq7ByzPZeEj7eNOEEwD38eBH/fE6k3mGAkUjbO6tXjIzhLm/KQ4q1UBi0WBhBpyAmwnjZ3jJimmcVa/+OLu7w8OzI4HRtW4Ey+Zs/q2O5mz+rHHLfzYa1+KDz79OvvCVGI1AHzf9wHf+73sPxMOtmj7ljirq6xgzwiVWa3IMKBy7hqmpcqxTUUCifbsHgbEImNAlJkTrdqkmu6/IGDCr0ysnsBZvZxXbEOsLVaHK2OxOnIzxOvd++t8ZdMwIIrNMFMUDAAsjxyc58FWrL7n4ObszFj0vP6Eh7ttZ7XAX5g43gTGqiVWx4WHyMvpG63t+r2/l22I/YE/YH4siFBrrydWRw7xncg3W6XO6o3HWvXJL7GLqfkc8K51NhZNnNUzrxeyabzZbFnwHHkInlHwMjgaqbY7uArXmLgVBRWS9lopz3GWh1hGBfndGIQWksKTO6uJAYsXhgEJJAGLppsA4t8ncVOGvjqHYfRcfUkI3AQu6NnC3mFWV7l5uCA78Ixxi89z/IN/APyZb/z/wXfK6cXqBw6uRwYYENvG0k+xyvzmem42MMeA+P0Q16YzyBADUhdyZzU1YDF0ciSxYtyjoEuGEHlgDGi6s3oAA5JaJAyI9JltVZqC6WYkDEhnfswDvWfRpbP6zaxLsfoRr+ahI7QzKDEgVA7TAH/JZPEURLYclm/yYucCsIz32PC1CZM75qxWhOBVxNZB31czq00xIIECA7KhO32aTYtBZ7XeC8j2HOaAHgpYJG5aAFA6q0lcQsGsnjhgsUGWSFv/DRZPsnT6KSb5vo+ll+J8tXte68xjjhwTZ3UWoQ2ZXp/k7JiUibPnMeGry6wWAU1U94zrInDyXvBVnNjThH9F0fYaXrkCPPOM2fEA3Hzcxu34AFiv8enXIrxwcG8SYXlxPYJt1fiGm7/KvjClWO26wDveAVy7ZnYcjgGRhb0kq4KNWVMxq8uSByHSW9TFcXaqSSYnOqtl7dloYUBMNnBVYnVd63NVgwCRkyHO+udT5+butMODegc1dPc+d3sZO6v7i8jzjUNmVkuD9Qzd9QAQHvhISm9XrI7OzZ3VT3q4Ey/x+TfYKrzBX5iI1WHHWT2FS7VdL7wA/MAP9MMWiRX4VQ+rsEksdh0o70QRNioRq89izyys7CJLYKwAdu8bnGNvwwLY3gcm7len6ovV3BzghQYOYLeWOIDNuaehXyEuWs7qosDDdIbjhXwuvk81zurOvHOdOCxgkfC5+YHEVTyBWB3MbDZudTcBpnBWy8wcBp+Xylld15YZs/rA2WFWNyF1Ezirr4Yr3Lnv4LXXajwb3m6+blJXjiqWkyIwIPcd3IjOjMaDK1HMBHD+mTUBzgbvGd/vIyDY+8ugM8h14VgVyrQ/P0xLF35AWNONYUB4iKfWOfO5cW9jnFcWl4wBTXFWCwxI3T82WawW3z8QsOgTAjeb4MZOl8Wm8CcjG14WrS7F6ke8yGJ1ZMuFL4AuAqt2sxpeM53PKXVWm7Q3CT6pxK1ssjBXspUbcYIwufE8uAMBiyaTG9WmhQkGRCp+tipJLXbPahxb6SQ0RMwMiUlmzOpq8s2FZhNAxqw2SVF3Od+rs1uclNxZTa0gYBiQdevfWpZY5z7mPt315i0C9oy1FqZ3bxVMTDJwVqswIGTjiHBWd8XqzGHCxBQt6t/1XcDb3w78oT9k5irmdfUxD/eTBbBe4/N3IzyzeDCNsLxc4rHoDF+/+Hn2+0dxZicwIJINzHhV0tpIVeOL2Lyk3gJDIjh1UxQimKazgK5rhgGxaBuCQ10xAFireuEj8jSQQ0GAyM2R5P3vz+OCvb8NnNWLYw+rPATWawDAZ14N8bzpxo0QFTddsdrAWS3rCOGblx51IwSAFQbMjSfE6vsuHotOzZ3VTwX493efw2/4W98NQPD7J3ZWc1ZxZPLuusAKJHkxcWLTRQ/H4c9Cv6PvLPaZs/pRF6sNXNUA4M89tp7piAimQqXnSZzVPHiZmhECAK7DQ2d3HMAuwsjsHR6FNRNrW85qUxa2Gzgo2xxoXuvEYc5qiljto4e0m2KT8fiKjZN2190U4Wee1+885BshEZWHjpYLvHXcupiAWb2wsS62zOokrqcJm3UcXIkS/ItXvxTv/4qycUIbO6uP611n9YnHuphMxOoZ52Dz+2ATW8YdPGGI3a4FTNAZxIXl3pyzKFDWxDFG0dHZPjYAvXvM8xBchLPasuC7FROAJRlXCaELG8AoBoQRCfSP2+CLOhtMl87qN78uxepHvJqHTpcv7audquRJg2o3iwu1HtVZrWCmGfG9RPuJxEXXtDxTmNUu1Mxqk4BFp0ZRWVIHMAA6s1qwy7vO6tgyCvwBoBarKcF9Kge0aZjUiFhNmi/tgwExcYGrnNXUxZPsuRVCraEjZeklON+0/q15jlUeYDEzc2wD2Fk83Xq9xmOzM9rE2WFs7iR3emzKpDC4BqJNvyNWGwdXtuv97we+//sZv2OCspdzVLWF9CSGbxWwrXoaYfngAP/8N/9FPGZN48a5kPI8hgGRDFvxumLolkmd1Qbu16HgxpqOAZG5h5qNO4eWCzAasEhpo/R9uHaFouyP+fG6Ml6Y2gdcPOMMo8/dDvHc4r7ZfRsEjI2/3n2HnccuFkRmtW+XUme1KQYEvs82MLsYEEPR8+qTIT51egNvrA9QlxVi4Sg2cbzN3F230xTvrgus5hlrB4AJ5x9lvmVZCEMwVnFn8DpNAhaw+CiK1VeubH9twKsGAHfm74q/AN+08I20NN+t+7hELiTZvsEGi+Df7jiAze/ZMGAInB6v2CRg0/eZgNp1VqcuC1gk3Fte6LDr2prP51nNBC8Dofb4ioWH6WzasUDReRgbHtcP+yiULAPJ8dmu+aHLnNV87Gbc32nmm1eXGf7J59+Lr//KDTtv2zY+7pUrwIN0thWrTz268UQcc57uOKs3sXnAYhhxHI7MWW3ArPbtsu+CNukaFx1c3U5RXnWW62d6eB58R66XACyw0Cdkp/HT7QvAvJLUJgUsjmFAmudMFwMSqp3V0czcLHRZ9LoUqx/xogYsDgmKeVIysZaKAZlSqMUws5qcxCtaviUuujyrjfic0oW5WEDSN2D7fDN+XMuqjTAgPQYTgI0I/KEscvYVq3VeQGPizNQOxYrt9lIxIJ5KrBbPBkVYV11XIXxRxWpXwqwWriQDJh98H0u/I1ZnGdsEiAxcxWIS25qN3bpV4/HZKW2Ca1kI/bLnmhALSGNm9UU6q6eu+Ry2VeNXP+PixSv32NemEJaffpohRUQ9omI1c1b3N1KSTUVzPorv787yDdFYQ/gik3ettD27KIzQIspuI1F5jrj0tMVqAKgr7G4wgYvVhqF9WC6ZWLtaIc8Bu67g2LXZfTubIXJybM77YjXdWa3AgBjcAwC2m35dDIjhxpXj2bgWrfHM4gHO7iS4fRqylm+TgMXQYSF44n3AhSQT1+NFlizUOk648494HaKQi5Ttcaausc495n41eRYuqtoBwa+9ZnQoK+CbKz1mtRkH2nMlGBC+7mi6/QjVoJG6YrUps7rjrK4zItKxXYp55zp1yTg3GW5qClzFlevOjqO2CUI0ua6yd7ipkQFAENq9cSBNzK9BdBwysboVYjtV2OyV4xo/9vqX4oNPvMJ/WGTc0Xd81WafGR+/754GZsxqCLF6u2nBQs3NBPsowm7XAlrjtpGzOu/rECZInCZ4Wf7+K9KSdcnpvBN8f5RZrdstvT10Lce8gn1uFxGwSM56CyREgjxHVdtwiJlRlzVNXYrVj3iluU166IYe5iytzI4pw4AYuMiaNk+Js5rcMjUA9m+c1RRmtQhhkAn2JtdAFsbBjwuAjgGJbKWz+sIwIJmt35Y2wEM3QcyMYkAMmNW5RKyuiooJw1M6q3lbKvllqXJWGzL5GmZ17Gx5ZHmuz0vrVhjCsSoUm5az+g2bidXEVUno1z3XROPKobbmKpiycTYRs/oiaj7HlWCNf/PJY7zr+A32tSmE5eee273nH1EMiG8XyLriHzgGxICd14gGogSqgfoYqMZDwaymjFvgGBCJWF1UNh3L6FnM9agSq7mgoMVntKztxlRnI8A49AgAlksAQH2+wqc/Dbz96gP2dZNnIYpwHGzw8MzZ4TOuE5exX6nM6qxz3fgGLnVzAQBzrlsV8ph9ZrcfuLgZnU3y3P7Y7/4b+MprX8Ctz6V45eECT88fmgWKHXo7jNZGSJqKWT1xNSJV68XQbGBSxWohpLSfBf4us3xvEkTU5OV5wFd9Ffv1299udixJt1WDaqAGLIIFIfbWHmKOYCD+NQJNywGdFAZzDV5hiB1nNfm9tXuy7P/tuVFdc7GathHih33RJ0ktY1zF8TXnQpzVPUyeaXgn5CasBpFocG85B6w7TiCsXn8Qso3GCeabVx7zYaHGl9S/wr5gursCIDgImGEqjvH93w+sEoddAxOxepnjQdLCgCTmzOowstgY295gSQ07g4SzWoLyAmAgVudIFc5qEhbGdRmzWjI3BjgGhKIZAfC9PhKoOVeKsY2fLwA1szoDSVz3Q7tv7jPp8L+syepSrH7Ei8qsHgLQZ0lNa+kYwoAYuIobB0NHpGsSdE0CFmUYEENmtVKsrhy4xDmj53ecGKIMMSDN5LbLrDYUq+vaUovVucN2SzUDHgAonNU2XHvidnpDZrVnl8gl91ZRgPG1KZ/XyPPleIYYEAmz2oj15zhYBhnO0nB7n4qfYbh4OvATnJ1uz+3WHcdQrK56rokmYJG6gHScvphU1+z+f1Sd1YsFbkTn+NFPPI0vP2y5Z0zL83bH6S//cvNjTl2+v91c6IS9rFZgLc+6963vM+dM3HkfCO4pdX472Gliw6UGLAaSTdGiwLoIMAsUYvPYqYoN3AFnNdBi8u99shKBClysNhD+AACLBeNL39/gk58E3nnlDvu6ybPgOLg2j1mYVGvVV9c1w+0QmdUyV5axszoIsPQTrM7ZMd54GODGBM5qAHj3c+d4bHaGN17JmVi9eGg0Fh4c2SxwV1zTR91ZHXGxWjBfAWxSh21gEgWaMLL6zmrx60cRASLqO78T+B2/A/iO7zA7ju8zE4BErDYRgD3hgO44qy3K89qq5azEeR7uOoArF0FoJlbP5tbOxs3DeyWOg800zurOtV0XAeYhbS7rzbxeO33TJWwwFhxdc/Gw46w2dqzLxPqiYBgQg+M2RiGZs9rk8xJIHS5W/8rrB3jn0a1pMCBPhvjA9c/DfvnT7AtTzA35MdYPM/z5Pw/EKf/8DdBAV5b5LgZkIrE67mJAEmIAoCgVs9oEA6IKjOdF4mz7PmNWKzAgWVrTmNUQGBC5szrJiFkOwiSSy+ecaWYxjUsbAyJxVk+BWros47oUqx/xSjP9sDoAg0yfZuCZCgPShD4RF08KkS5PKzrjjLvoZHqqCbO6Cc1QOICp3G7PuxhntRe5fdYfgE3K3Z8X4azOHf0X/ICTsDBpp3ccWFaNKlMwqylpzI1Y3T+nIqcjZoac1ZZV09tShXOk10Jr3pa6jAqc58H22DnnpZksoIMAh16Ms5OtGHHrnkfHgAAI/LovVpsuIEUAWlusrirEhY/ILR5Nt9tshpvRGX785WfxTdd/sfnaJPWN38j+/1VfBRwcTHPMKctxsAhyrDK/9w47PweWRK5w6BRI4n6AjglbWowfvcm4GA9dA2e1hNH6MJ3heC4fz0ePORawSJ3sS3BAAOt+NuJIAsByiUM/xt/9qSfw/d8PfPjpT7GvGy7Orx7muJcsdoRKq65ox+ahR2m3O0wgZkzWTmHIumJWQLIu4Vv5dPz62QyPz05x65Ucr5wu8fT8gdFC7/Cai9Ms2t4HE/F/L6qCuctEqtY9EOcO3RwAIJpZiDvO6jrh4r0B+/XCy/OA3/SbgJs3zY7jSzAgE7iVpc7qojAKNQeAo0XJ7tmWA7iqLdie2Qb2/MBh4Xr83nr4oMZxsDYTUmTz+SzDuvDJeqI/64SigjurDYVaJ/KZq3hHrHbNHgFZwKLoPDSYH4eRxFmdmTuru2L1J28d4p3Hb0wiVr/4ngj/6bO/ALz8MvuC6XMLAFEEy6rxqc96+LZvA/7I1/4Y+7pB6OqVg2IHB3OeeFh4yTTO6i4GxNhZLXEsU0IQRQlntUJYZpv5mvOjEWY104wMmNUqZzXF2AY0ZsQiUYjVRJOnHzl9Z3Wes3fPpbP6Ta1LsfoRLyZWE1x6wv0qdVbzlg7dYw6IabnBAlp1XKPgCMdhg29u9Vx0jbOaGCYl5XPWtZE44QaOVKxu3OVEZ3Uzi+u8LTexbcSslgWyAACqCmnBAu20zlnFaOV8TvLcToSqdTm1ZYmsJDKrxYtSggEpCjBe2MTOagD0CW6HTyp+BmNWm7nTlrMS59nWQVQlGZv4G2JADvwEp6fbL92675k5qwOJWM0XkBYViC7EpLZYXRSIi0fX9Sec1d/85C9jYa3ZPTWVY+A3/2bgu78b+K7vmuZ4F1CLqMQqD3rj4fnawoEXk5zVoZMj3ijcrx4dMePLNsSEUEkNWAzt/nsmZy6l47lipTJS+zqrKWK1ZdWoO6uyeFMbCX8AgMUCh36Mn/jkY/hTfwr49denEauvHRa4n853hMq6JIrVglmd2btzmKJAUdvwqPcWANg2Fnzs/pWf2+BLr9xlXzcMwgMARBETq1+vcetsYTRuA8Dyit9zqT5I5zgibq5cdPkzF2npbe+BskSce4h8erhcNLN6GJD4NKPP4d5q1Xa/imeBb7ibuJX9oM9WJgfQt+pwUeIkjfpBiIaCBxOr/a1Yfb+azlndEatNske8uc9En9b9miQw7zgT90GryyI1vAeUzOrSM8vbjeweViJN+Xre5PPyfQBcsM9z/MobR/jSo2nE6vd/0yH+wLt/fPuFxx4zPibCEId+jH//K0u8973Ad73wU+zrHMVFqSuH5Q6z+tbZnL1nTJjVc4lYnRoi/fg7vCcCG2JAQqdAmsrveZJuIhB5qoDFFPCdC3BW50QMiMifUTC21wltc7gJWGzdA8374NJZ/abWpVj9iFeaW7SAxSFmtUhKpYZJyVzFJgxFxXGNGGeWBd+tpGB/E2e15fNdeBVbmWp+VTCry6Kmi5+AupU6M2ibGnJWc7xEGELPWaraCOH3lmsTxT/HYUGbkslCDdCcLp4H15Yzq/PcDANiAajS/jUAQJ8wBQFsq0YVtwS6qZzVi3pHSIjPcvMFtO8z9uvp9ho+OPNwJViTXWRhiH7Aoul1bWNAOgvoR5WnCt/Hb3zbZ/CH3/uj7PcTBOg0FQTABz/4SDsQlrMSqyLojYfnKxtLnxCC53mI3KzvrG7cr3TEjO8UfdYh37wjd6UqmNUP0xmuLGhi9T4BiwD074sgYB0sq92NhdOVjQNDFxWWSxz4MX7p1lU88wyasCpjZ/VxxZzV/HhlycIbAei7li0LgVcxd14nb8AEOSZqOWdj9yd+LsWXHb1GO0dZzWZ4LDrFG7dqVFXN1uMGx7WjgDmbWgLVeRbiYPFojrHBwkNWOtt7Ks9xmkU4mNEwOwAQzmzEbW43gNMHJXsOvhjEasuCJd5TLQGYPIfj5Xl9cSKJCc7ETh0tS5xks50NFgDmYvWhyzZbeZfBw3sljv2N2XPr+7CtGmWyK1aLP6OUHXYc0GDGK+MgwDBk57pJ2ZxL3AO+wXUV3Zdlve1k5agho4DFmSNlVoemzmrLguVY7PpuNnj9ZIYnZifTjN3Xru2e2xRidRThy49fx9/+t8/hHW8r2b1r20bvWiZWb53Vt84XxmJ1OHN6awTj4EbBgu7mTkzArE4UYjUJA+J58vPklaU1eZPFDyx5wGJdI8ldmiGRs8B7JjRem9RhvH1dDEjksvlxa9zKk5JGIrisSetSrH7EK81sGgaEu197wU9gAw8JVaBCNQg+J3VhrhDWTVOOfa/u84fQEquJLwp2EEXIJFWsDmwprsOIgQyondUpD/whOqsByMVqKkNO5aw23AQQbVi9HViTyQJ3VuddDhmAojS4t/jEpuemNG1LDUN2vvFuiFBausat1AeLakesXj3MGfvXpC8zCHAjOsPtB60xr66Ypkp1VodgbrcOBoQchgkAto3AZdzrZpHDWYeRXw7/3TerLAvv+3UePnjjc+z3j2IQ4gXWotMJIOp8bTEMCOE9GzpF31nduF+JJ+q6jCHYHWMM3zOMyafAgCzpzOqhgMUyyWndFkGA0MmRnO++Z85WNg59ggu+XcslDv0Ev3z/Bp5+omT3QzvUkVjXrta438KArB7mWLgxe78RzlcqVue5WScbr+USTKz+pRpftvwC++IUzuo5c7l95mXuSosi+vwFAIKAvf/4HCaLS/aOfUQXj8HC28WAZBnuJQtcWyrSq/aoaG4zIaU1bj28V7IN3C8GsRpALcTqCQXgBuvXuq6bxMbcVKw+rHYxIBOJ1d4yRFnbW7FaOKtNNtlEh9imNWcx5aH7Puu+bDurJwgXhOviKIxxmvDuqLI0RrbAsuA7Fdtgagdilh6imYFrX/KuTXOLlkHVqVlQIS48rO5sENkpmxtPMZdzHOCd79z+/vHHzY955Qr+s7f9LH7y00/iHU8ydAkWCyOTxJUrYGI1fye8vlriCWOxuu+EZ8G4hs5qyTyuzCs4VkUXq7sdna0ioUv4MSVyEYAJmNUyxKno7g1q/XuBr5V782Ne68Rhoda6GJCZ29tg2mxgzEO/LPO6FKsf8Upzm/ZyE6iCtC+axDGR+ei6bFc7lTmrDQRFhfjZMM6oYrWi/cTEWT0oVhu0Z3si+Kp7rlnNXMXUyZhYfPeY1a5RwCI7OZWzmiCCDgQhGt1bfAd26jYszy7l//zMwAnPW7HSeFdYz7PabHEuHIpx0XcAG6bTHy1Lls7OJ43r04LtaBvB/li6+Z0TtlA6PwdbPALkCXnjrO60egIwWuj0xCTOOjRJkb/wev757a+Pjt6003gzajGrmbO6hwFxsPRozurQyfthNwIzRNVTRUeIZJOtMHBse6EEN1UUeJjNcLwgMqtlbu1WxauS9q7xfURujvhs97xOzx0mVhu2vh9GGaraws3ghH1tgi6Dq1ex46w+eSPBkR+Tjx34dS+kS4zdpqih5YGFVR7goz/n4SsPP8PeWVPwj4+O8MziAX7hM3M8u3hgxCYF0Ntw//wXLDy7vP9oBtiCi9XtgEW+ueCFBu+ZmdPbbH1wr8KV8ItHrMavkVi93vDwcYP76/AAOOkwq402xkWJedUOs3oCsdqZWKzubDABLVexoehzZZ4xoXKzQZEUjLVv6lj3c8YCb4nVaenSMm142UE/iD7NbHN3OYDHj2K8tj7CP/vRGt/0+K+wL05lPPiqr9r++sYN8+PduIGvufkyvuzK63j7jTP2NcN3wuKKz0wHnNt9lgZs/mYiVs+dXvdKnDnMpWwQsOg7ZU9YThNibhg/Zujm/SwLXiQ2fMOsVjirM5CZ1Z5vMR2ma0Rrd2HrljChdYPNea2Fs5ogVneDYTer6lKsfgTqUqx+xCvNbVKqqRDpdiYgvOLEoj18IrSw23ohmNWGzupumJTp5MbzIEVrmDCrh8VqE2e105s0A2w+Tj5XAPB9OHa12+IHYJ26bDAnitXdSVhT/AWkzZBTBYqVJUpDZ/VgGrOBs1rFrDZx7QcSgWoTW8ytTJ3gWhY8p94NvJkopOrKcc0WDlytW58WWHiGYjUPAbz9kC2UPv1p4O1H95o/o1QYAkkn6VugYEwmIYFf74rVIkX+UcWAALti9dd93Zt3Hm9CLRe13Fm94WK17uKcC6q9lsyiwKbwMY+I94F4f3cXD1Vl9K5VBSyeZRGWc9q5zmbApvDVYvWaONkPQ6mz+nTtmjurARwe1Hhq/hD2nTfYFwwRIABweMVhjkouJt1+JcNjs1PyuMXGF6/3eSWla4xwevG5HP/gs18JpBkem51NhwQ6OsLST/HJ7/xT+J+//m+bu7X54jyJWZv+Z19x8fzy/iO7eAyWPvvMWhgQAEaishX0AwYf3K9wJdh80YjVlt0Rq00xXlA4q2Pi+qhVR0fYdVZPFdIVRWzuLcTqh2BitSEGJHDynf3bOuXZIwbOagC7YvVEQu3xImcGifUar9128dT8ofEx50HOWODiWeWObXKoOSBdK07iLgfw4uMrvHR2A//kn/r4yNM/z4431Tjwvvcxkfqd75xm8zIMYR8s8Au/449jdo938BjwqgHAmvP7PY6ZAUeYcw2eLyZUtvSCuubOajMMSODkPbMUw2oQ7wPugk4yuSZAdVb7dsEyviSVZgbM6sCSmxmKAnHh03jzfA2udFanHlsza56vE/koK3v3fXDprH4k6lKsfpSrqpCWDm1Q41wjGYB+E1v6TCNgu4CO+26vxCRUTIh/2e7fn8RZLWNWl/bFiNWGGBCVsG7ErA4CRE6O+Hz3GpylPg4ojFZg1FmdVw7cgHK/SnZKi4Ixqw02QqTBEYbOateSO6sZtoX4eQkXeOcFPIXTx/d4m6OwgIq0c0NndXAQsIkIX5ivTkvmgjZRUpZL3IjOcfskAOoaL70EvHjABSWi8CFL+p5isRsEYGJ1axMgLvxHN2ARAN72Nvb5HB0BH/jAm302v6a1WEDurI5dI2d1K0+PFRerZxFxM0gschQYEKpO64QeE006WAkT7ud8DuZKU4jVm3MDZ7WTI17tvhPONi793dWqwxs+nlk8AD7xCf6FQ6PjAYC9mLHry2+IN17J8Vh0RhbCm/Gl66wuzPMGvu03nuHjD57Et73rl9kXpkCAAE23hnf+AId+Yn5cy8JBkOEsZ+LfZz9v4/nlvUd28bi84uEsC5t7IDnPWfaKyf0qyR55cB9fVBgQy7ZQVtbOuxaA0byo6Whst31PIFYfHlk4aQXAlXk5iQO4eej5PO7l1wM8Fp1O7qxOznO29poQA5Jm0wi1xwesEwibDT73mofnJti4WgQFVp0QV8uqzc5Vsk5indKGAYsA3vF0jF85eQw/9wkfH7j+ebZZMWX2yH/33wF/8A9OczwAuHEDjl0DL7/Mfm/abSM2ZzYbnJ+ULG/Eto2ugeV3srPKkoWlewX9uI7DNJPC2aICwbEaJmK1UyDNB8Rq3VBIwaxWHDMRx6SgzEKrj2AEgDxHVjk0sVoVQM6LmfFoc04AO+uDOIZ5oPdlGdelWP0ol0g69olMH5mwDBYaQJqMiUCxbuiTaVibGHw759rsxBNf7L5XM4GuhwExc7+yg0yLAXHHnNXUSZPvY+Zm2Kxa17aqcJ6FWPpEty53a/eCAIHmumg7EsRu8UbW9m6IAbkgZ7XsRZkXllHAYuDkvedrkxiEYW4PveusFg54U+OEECK4WL0+K80xIL6Pm4cJ7myYY/ull4AXZ6+zP6M6qyVidZmVdG4cryAAa9NvhX9N4Vi/0JrPgT/2x4A/8kce2Vb6iyrB6e2Os2cblxywGMrCbsqSidUzuljNnNWdr4v3jGk+RAcDAoA8GXcCl4U9qcTqdU2b7AcBFl6K89Pdd8LpxjPHgAA4eGKBp+cPgZ/9WfaFJ54wOh6AZnyq12w8fOO1krmWjZzVfWb1FGOMc3yA//1b/jK++/mf2Dl34+qihSYQwQ+jlDlVkwQvf46L1Y8ob//JZ128tjlqxOq7b5S4Hp5PL1Y/tL6oxOpFUOyM3U0XnombMnTYGqHLrPb0W8jbdXRsMQwIH2fTuDZayzQVRaxTcp3g1VeBT78+Z2LlBMzqNspqfVaya2CAAQGYQ1uU6XpO1NXDEnfjJROrX3WZWG24czcPCqzzvrPa6FwlaEN2DSZwVj+X4e+//H6894m7bBNk6rHQJGNAVjdvsv9/5jPs/4bOasxmiNwM8UmK1z+X4fHo1NwF3l3bFwXi0tB4wkOSuzpEmoLWMc/PM3RyJJn87yapxTZHde5d32fM6kKBFsls/WPyimYW4m53GEDXCoDtul5ixkRdY525tDWz5D176ax+NOpSrH6Ui4egBT5hUSLwBypnNcXpoRDTjJEC3P3aMbsZDZBAK4W2F7B4Mc7q3IRZLViinQHdOGDR99lLfd26D7IMWeXAjxzajrHrwrUqKQ+dLHyIVl+ZWH0RzGqT0ELBrJa82E0xIDJndRP4Y+IgcrlY3XJW12BcPaOazWABqM4ZO259XjEMiOHE8cYN4E58AJyd4VO/WuHF6FV2rxIXZUys3h0L0qQ2XjiEEceLtBw5celNQRS42Lp61XzB8BasxZJxensYkMRlAYu6963D2kSTwt1xzjTOaiq7XDCru5lsYjykitVexz0ElnbuWpVRq2v3mO1qmH8Esfo42ODhg91r2IjVhk6Xd38gxO984eeA01P2hSnE6ihim8Mn7P564w3gZmQgVquc1RPkDWC5xFOLE4QnvGvlosRqUxcdgINZydzKmw1eu+3g6cXDSbAtF1HB8WyHWX33To3r4erixOopWvXfAvXU8RqvrY+aa5CnFZ37ykvKrE4ccwzIFZttrvBFTRMWP4GzeuZm2JwV+Ft/C/j9X/NxNoU3eRZ8v2cWun+vxrFvgJjxfczdTCCFAQBJPg0G5EufjfHJk8eB9Zo7q+8ZjwWLqGQdVy2hEoCxs7oGLsRZ/eILFT565wV8+OmX2Bem6oq5qLp+nf3/dW46MX0nRBGuBBs8uFfh1udSPDE/Nb8G3Y38PGfOasMuSd/juROt+ZExBsQpkBYKZ3Vq63O2XZcxqxXHTDKOZKVgQGYuQ891F+F5ztbgxO5uT7auBxqjyDws9fUNmVgtOm0undVval2K1Y9y8ZAH0lxUhLVJxGqys1oIf91jtlNdKcWF9a4I3jCrjTAgMmf1BQQsGooIjVjdGX0bDIhBwOLMzbBZt66tOHeysu7BtUsUiUScEMcmtN+Eio0QUyeh7/RZXI0rh/ICEtiaAtvAwu3p0rEt4jnoCFRTLJ48Hz1mNQBzZ+18jkM/xuk99rmvzmvmyDF0ugRX5khLF//V9wf4yZ8E3n5416jVMYjsnrOa8QPNFg6zmYW49Hec1VNMcC/rYipaOIyv3BOrPYYB0X3ZWhbCoEbc3rAAWs5q4omKFHmJs9pkU7T5i60DnzyscRRsLk6sFs5q3eMHAa4Eazw82X3mzxIPB565s/r5D1zFb3vuF7ZfmEisfmF5Dz/1i0cAgNt3YOasFp0bMma1affGwcHu7ycQlQGwd2r73zuFs6WmeKMAAGnCSURBVHpRMvFvvcbd+w5zKj+iYjWiiC2kV+wZu3u7wvXI8HzFPKUtVp/aX1QBi09djfHq+ri5BlMIwLJ5d9PJZrKJfRSysDa+YZHG1SSOWkQR5m6K1XmNz30OeOfxBLz9KGJOzfXWfPLq6zbbEDJwVi+8BOer7didZjYT6AzH7fd+SYxfuP8UsNngs7dCPLe4b7zRNg9LrPNdsdoyxbZ0xc+6RlK47D4wDfJ9eobjYI3fMPt37AuPaJdJU089tft703fNfI4rwRoPHgCvfi7Hk/OHk2Qj7GQxCeOJ4Vw+8OteTkiaWUYBiwwDIh9LSJxt32e4TKVY7ZDHWsv3enkLAOhaAdBoUVIMSJ5jnQdMrNYt34drl8jj7Wc1BRbqsszrUqx+lEuI1QbO6jTp/9EmsWn8IbED3w1tLEvEhW+GAbH7qIYkpw+QAEuhlbmVL4xZXTn00ODARlHbveMWeW2GAeFidbyRiNUGrglnwFltAbR7yy5YiFK7OAaEPMfnzLDuvZVuSkTUF5BlwRVOZclGiGvkrO4LVMxZbRCwiBa/veWsBmD+Ap7N2KTxLjveeg1zZjUALJfIKgcf/Q8e/rPfGjO3tsFkNJw7fbE6NndWRzOLiZ+tTYCUEjB6Wb8mZYVcjO4yqxOfFrAIIArKPg9dOKsNxGrfKXsbV2I8JJs8JM6RhycWc9FdtFhNdFY/6IjVpxufOatNRbrHH9/9/UQYkD/5wX+IP/z3PoA8B9644xgxZZmzus/an8RZffPm7mfytreZHa9dbXf1FGL1QY2znDmrH5y5zFH8qAo0UYSn5id47R7LXLh7u2TOakP3q23VO0HZD06dL6qAxSevJTvO6iSuWeelybwocnrdl5vUMXfSiXuTW4vjxHxjHAAQhlh4KdZnJV59FXgquLv784jneujHODvbfum1Nxw8OT8xWiMsvRSrjd0YOh7EIQuDNBTsn37GxhdWx8Bmg8/cnuP5A3Mk0DyqWO4Cvw+qfIL5sefBQssYU5ZsPe9VxmK19cTj+Eff/D/jWc8Mj/drVi++uPtvfuEFs+NFEVt3PLTwmU/XeGF5bxKxGsCuWF34iKjdcbx8r+51R2UZ2HhAxFCGDu/mq/vnlqScL61zbMGsLhz5McVG04QZV6aGMWkWFf856yKghZv7Pq6FK9w72T73U2A4L8u8LsXqR7mKAmll4KxWYEDIzmqRGq1gVpMHdYWj1DQ92g8stqMpCS00ZVbXWUesLgrGVvZpj5QXudKAxSKvmfhJHSh5SJUIpgfYuZMEZVGuC9dWiNVU1zbHgPQY6/wZILvIhADc2TFerS1aAMP2sPLPqwA8g4BFqbM6NXdWL2YV4z22mNXGTD5g63C4V6GqgPWqZsKyqVh9cIDjYIPv+PAt/Mk/dJt9zWBCHs4dhutoXdw0hXFbajSzmKu2JVbXACz3i4sF/ZYpiVgLAHlpsbRzwss29CXOau7KIQuKjiNPZ69rlLUN26NvXta1tStWPwRzaBqI1bZVo8zkThYy88/3mbP6dPffukq9acaYMATe9z7262vX+k5jSi2XeHx2hndffwOf+hTwxn0PN02d1SpmteE/H74PvPOd29+/612GB2zV8fH21xOI1QcHaJzVZVGzsK5H1VntOHj64BSvnB8BWYa7d4Br4cpMUOKdZ233ayPaf5GI1U9dz3ac1XFs7qyeL22sO502m9Qxxq5hPmfOXC5Wv3YvwBOz00mY1XM3w3pV4403gMfsO83XTY55FMQ4Od8KVa++4eKpuYGz2raxDFKcZ34zdt1aLfH47NRYrLbmMxwHG7z6hQppZrEQV1MMyKzcwYNNMTdscIEJf2aF+cyboOvu8cfxoSde2v7+URero2hXBH3mGbPjzWa4Eq7x4NTBZz5r420HdycRq22r3oqoRTFJl2Tg12xDrI0BycGc1ZT7y7KURjyAOau1A31tG64DFJWzNTK1qkH4UMYv32d6Q2fOnawKZhgz6G5Wi9U+Way+Hq5w92R7Tpdi9aNRl2L1o1zi5UZx6fE24p4zC8Amc2nMat8fxoBQF+airaV9rnXNBl0TsVqGAalrxg0zCDdwrApl2nlJmGJAFGJ1ntVmPFHhrI63X9qc5mbOEc+Da1VScaIR8Sd0Vhu59h2Hsbg6L7XVCkzwoLr2vQFntUV37fsO3y1uTew2qWvclvrE1Yy5ki7AWX01XOP+feBP/2ngT//4+80DFgFgucTveO7n8W3v/2wT3mjkrF64zP3auhGS1HyxO5tbu1iJqa7rZV1MycTqooCFmj2zhGcsDOq+s5oHNJHCY4Bt5kRXrOb3l+XQMwwA7GJATi0zBrTr9lFTrYpj0BYlgll93rmGdcVMWlOwen//7wf+6B8Fvu/7jN1uABrB+z2HX8DHP84clTM3p4vVoSVlVqelyzInTOvFF7e/FlzRKeo979n+egK8yOGRhbMsYsGVNZ9/PsICzdPHK7zC3Z9371kMW2IoVkcdsfrhuYcjE67wW6yeupH1MCCRqVi9sHbxDwDWKTGcq12LBVyrQn7GJt4vvbHEi4d3JnFWz90U6zVQVTWclM+NTMRa28bRLMNpujUzvHrXNxOrASzDAucZD8Ssa9xaHbAuE1MUymyGD1z/PP7Y3/0yfMPTn2u+ZlLzqGabFnw+n8S1PkqhWyLjSaxrRUeMP4FYHUW73SuP8FjY1Fd/Nfv/V36l+bt2NmPM6jMXL7/i4YWDaZzVO2a8ifJnfL+fO5GmFnwDJI4V+HK0BjgGhDAuWgF/1rtd43WN1KTL3Zcfd31Wsk1ByjEFYzuVzDmLAnnlwAsJ44zv43p0jrun23HvEgPyaNSlWP0ol2gpp6zJxGJX8jDHmU0TK4WorHBWk/Up12Uviaz1AhPH9AiQfF6Ns7rDe4xLH5FPPG53t7x1vkXlwA1oEzE3kDOrGwyIibPa3XVWnz0scWAiTnCuk8xZnW0K1t5EDVjsYmvKkm2EGLj2G4diSwBexzYWXmLkrC4qCbalNOCh2zZ8hydHt8LaNqmDuWcmVj95I8frm8MLYVaLdryf+AnAQcWY1aZC0nKJP/SeH8MT/r3GnWQyIfcXPtu4at1gSWrOUIwWDku6bjGrAVxObB7VCgLmAo5b46wYc4OA9E6IIkgxIADozxd3Vu+8E4HtuGCAhQLQSzsnLxoAwHWZgLKRX7vGWU0Qq68Eazw4a51XVbFx3LKmEeksi/E0205gk+LC7HuWn8XP/1y9/byoGBAhVnfmMMBE3Rsf+hDwwQ8C3/M904j13eMul31eKaGuXbfw915+P37hlxwsXD6GP8ICzdM3Evzgz/x2/D/+pIV//UuHeM/V14wxIN2g7KriDvMvFrH6sQKvtjEgqWXurD5wpM5qY3EiinAlXOP+CZvLvXT7gInVpvMtx8E8KHB3M2fdhmLeYWgOODoocZJFjTHg1bsBw4AYoFCWYc66+bhY/UZ8gMfnZ/SweFHzOf7we/8Z/vHHnsE3P/nL7Gumzup5jZU4V7Rc+yafl8v41I0BZ0pnNbCLsXrUAxYB4Hf/buDbvx34zu80P5bj4MoixYNkhrUwHpm+DzwPoVNskZl5zjAghmJ1EKDvrM5AN8wBfR56q+LcpW20SAJBAexmZBA1E9m5rs/5WpGIFgmdHEl3ftz+OZTjCmf12Xb9eh6z+e1lwOKbW5di9aNcfGdvFhJeboJB1HVW1zV3VlMxIGqxOqA6qzksf+dcJ9iF9kO7x6Mzbu3h7Sd52neXX4izOgdcewJndbplx509LMl8VgAMA6JgVifrknGACfdWKLtfBTeMigGxLPhuyYTKVnuTKQbE9W2ps7oo+OdFnIQEXtXbhZ8CA/LkzQKvrY8bsbbIKjO8jCjOrL536uL2beDHftdfx9c99ulJnNUAgLOzrVhtMCGXsYqbgCaDBclsYbMgJb7IqXPObDddlF7WxZTvY+GmWJ9vx+864fcEcYNFFbAIwCjE1ndKpbOavOAXY37rOTB2jrgua01XidXU4wtn9Wo7Ru98VlOKq1OV6wKzGd5z/Ar+2l8HPvT859nXie7iILJZwKJsI2SKDbEgAL77u4EPfMD8WO2yLHbcP/NntmO5QX3rN6zwbS/8B/z5f/gcrkcr9sVHFQMC4EPvfog//zV/D3/nH4a4Md8w4c8YA1LsZI/UVd382RdD3bhe43Z8sBuwaMisXhy5OyIlAGxyz1ystm08c3iGL5wfA3HMxerbk3xWi1mFXz25iaeup2xOH4bGAvDhssZJOmvE6jcehnhsdmrmrJ6VW/RcniMpJkAXAcBshhvROX769/9v+PDNX2q+ZlLzWY117jfj7BThnRfqrAaAp5/e/voR3rhrKgyBD3/YfG3A68pBiS+srmDu8LbhCZzVodMyTBWFGd6UV+DXvQ3nNLcZdo46dgm0RhcDUtdICgehW+iPCao8Ln4dyPetyLiKd4+7OqvoJgnPYwaJWHL9xDUhitU3onPcOd/OLR6sA1w1weRd1iR1KVY/ylUUyEoX///23jxOsusu737OrbvW1vsy+6qRZrTLkmxJ3mQJy6uIwME2tvEbDMbBBpPYway2QgIkL2sSIASIX3jBsYHYscEYG294xbKFF0kjaSTNPtMzvXdX176d/HHuraW7ZyTdc1pd3f18P5/+VNXtrtu3q8+995znPOf5uX7M/NvVAugbDZTqDpJOjItZVDF2uVu7XkdTCiTi5mgmEkoEr3c4SnUvkFDO6hWCom6FX9e9tFjdTMTOrE4ELpqdlYhDtPOSEgkETg3Fqt0SOpbm68g65fidBsdRzurqys+wXJKxndVe4lLOar1OrmuH1Zg7xeqiZSYGpPP/JaVegUWoYhwrCv5U9WNAdmxrqhiQsOfcKi5oILN6yC/gSyf34MgR4MrUObXsXbdDGi1xnJszEgPSEiI7xeqqpT3YDTJ2V4HFQl4qtwA7Nr2J6yLtVJDPta9dpcWqWjYZc2Du+2vjrPYStZVidXR/1BCrbavRVagtdqZ0hG0j5VzGWR2J1c/2842KKOXb7yvNl9W+TESArBWZDLYnF7B9tI6fu+ULrW1xuJyzekNcYwxNKFiZFF69+2F84ZERjHhhJbgeFqvHd7t43f5v47+880m8/54H1EYDzurIpVkqAZ4VnsO9fC4YxEoFKm8/zLRTzmq9PoybcpSRIRooNZso1hy1kk1TAN49VMCZ/CBQKOD0XBp70nNGRMVUCji2OIadWXPnQX8/sFBNtj7beiOsvWJIrJbFksrwNnHOhtfSQ4njsKtFdY3R7G+mM0C+o8BiK2JGM7O6y9wVOavjGm+W833fB9x5p6q7cMMNZva5gRjsb+KrFw9gfzosMqorVnteuy6AlO04CU/vOuB6YkUcabUm4hdYBNou6OUCT8vcFaOIZ1gQdIVYrTvR5DhqVVC+29xWWGqq1c1x7l+OmlAsllf538StmwWEzuolTOfbGetzRbXCj87q9YVidS8TLfeMk3sZOrNWc6oW41a4XS1buuM4Y194hYDnhPEH0b6i+AcNsdrxLHWTWOGsduPv93IxIDJ+gcVVZzWlRKESLkHRydX1ZZeglpurI6NTlMR1kRDy8s7qGJnVqzqrw8kVzXHeCgG4UBJKrI4bA7JagQspUW8KOImYBRax+ix8oepot4Ht29GVWV0pNfWdIwDgujg8OInFioc3v6HejtnQFavHx9XjxYvA0pJ6rjPQ83010O0ssFjVH+wujwHJ5aAmguis7k08Dxmn3GpSAJCbralYpJiiT5AUK8TqZrWuBucak4yu1UClZnUXJtIVKsO6E521AYw5q0urX/OKJcQTq5NJ9b8qt49rcaamN9H6XJDNQgjgkU+cwFjzQmtbHLzAQmWVPHQAG0OsNkUqhX2ZWcwXPYw4C2pbL7sJ+/oAAHcdPI3rBs6qbTrHG66QK+TVeXvsGHDlYCjSGMgE3xCkUrCtBmqLavJ6ctFXKwQ1zgPhLut3Nxoo1FwkvdWLxT4b9oyWcDo/CJkvtFfcmRCrR5J4aG4n9ljnwg36ERD9AwKLVR8oFjE3B6SdUATTEGjSyWaraOHCZAX9bsmMWD04qPpXuVCs933tSbFUqju7vFQWysigc41dXjcqMt6YmlvKZoE3vAF4xzt6+1q4RmwfraPatPHeI59SG3TPg0QCgddAuW6rcUxULD1u3ZEQz5Mr7uHVmohfYBG4dAxIrabMeG6M69fTOavjTrKEUaSdq4KAMAYkTr8wPNaUU0WhYnf3j6HqZonwZ541QmAkXcJ0Od3qa88VfSVWb6X+Vg9CsbqXCS+WcQPo/UQN5cqym3iYw5QMYlx4ohiQVQTF6HfGxXOa3ctd63U1m6cbA7LclVSr6S3tuVQMSKOBmkZmdevC2jlTGrk83JqWyyPwJUqNdlTB0kJDDfjjOnJs+7LOaj+Oszp015dr3XnNrRuljrPahZoI6Wi4+ZKtZnXjOqtdgbq0VkyE1JsJ6ESJrnBWh20g6dS0OuR9QzYWq0HbWW0gAgMAIAQO7Sjgc6/+XbzqjkX1v0sk9G/s6bTqfJbLwGOPqW2jo/H353kQQqJZ6nBWVzTdDQC8tKMmFzrFarfEjk2vEjqrO8Xq2akGBr34hcr8QKgYkI5rgXbxL8tS8UWNlddDAFqxUFm3hKVC+37SEqs1CiyqJZmXEqut2GK1EB1xBwAmztTV8vReFqtD55+VW1ARRkLEjwG5nLN6K02IJZOwhMTVI9MYsRfUtl5uA9HKoMXFlltVS1BKpTDkFTA7r86xRx8FjvSdV9/bQmL1iJ/HzFQTUgJ/8I0b8ZYrvqF3r11ecDY08xgRq7dVcXppCB/7RAK3jx5XG02I1eMZPDi9F3eLz6sN0cS+Bv0DQsWAlEr43OeAu/afDH9ZfAEwk5atzOoLZw1ety2ruxisic80Y7WEdaDDta/prPbtcAwuZaswrucaclZvcXbs93D0X/57XJcJ26qBduB74Sq5YlHpL1JoO2pdV3Qb8QBUagKupWGUiVzQy2NAIt0kzml2qczqUDPx48a8hvEqK8TqvEZ9I8tCyq2pegON7mt1OV+Pp0GEjGQrmC5lWteC2WISg4wBWXcoVvcyOgPTaOlFZdm/OOqMxczB9hK1VaNFYh9niOdBzT52dBrNZFav5qzWiAGJnNXVlVEojaaFhKspVi871kLdQ9Ktr/6eZ0gyCJ3V4WebW5TIajqrbdFEo7byMywVY4rVQsB3myuX04duBC1ndZRd3iFWF0qW+czqcNmYbcfvjK5wVjcaKNZdpLy6llgdZTbLknI+X5xzMeznzdyAoyXuk5Pq0cSARIj2ICza75498ffneeh3S1hYbH+GJgYkrSzs6Nyis7q3CXOQF3Lt++LsdBNDfj5+ZnXkrF5WtFA3usezmysK82iL1YkE+twyFotOSwQvlQ04q53LOKvLMcVqz1PCRFO2/u6jjwpcPXCht6MPIhf1xIR6TKfjr7RJ2WoSv+PeJWsbKAbEFKFodk3/WQz7S0ZyeteU0FmNxcV2jJVOJyaZxEiwhJkF5SR77DHgcCp0bG+E4momCLOKp6eVWD/oF3Gwb1pbUASwUqyOY+ZZxu7tdZxcGsav/eEgfvmGv1Xt1URm9Y4+HMhO4YgXCuCdhfZi0j+UUDEghQI+8xngnt2hQUBbrPZaYvW25KK56J6xsfZzA/tM99so1L3WxFKpLBAk9HPLvYQSqNFsqhiQJsVqY3RmdgNGroO+D2U8KBZRL9WQ0KkXFeL5Qt3DO8XqqmZmteMo801lmbDcaChndcyV8wBWjRZpFViMg+siSNS6VvMBQH4J8Z3VAJJ+s0vXiGgVboz5fxvpq2K63BarcxVPjem2Un+rB+nh3h6RVY3sHdtGkKitFKujZSJx7u/hEuJKVay+NFlnYO5B3dSjGIHWkqn4N3Y3SFw6szqus/pSmdWNhlr2rVEwAUD3hbdeRyESKjUIkqHzLxzwLuWkWj6pm1m9SgxIPg8VMRKjzfpeKNQu+wzKDVvPWR1lhnUEYudLCb0YkNUyq6OMM02jT9cES6OBQk1/wgLpNPq9Iuan1X4eP5/B4YGLZm7A0cDh1Cn1aMrx1ukY6utrCwBx8DyVRTbf/nvLtYR+FMqyLOzcQlPv3CJri6smaWZy7Q7y7HQTQ14hfgxIIFQUTGfOvG60BtThlBuOWVetEMgGVbXKIrzOatdFiJzV5dWPKbZYLQSQTMJN1FFdUILf0ccTuHpgorfPr2jy7vz57tcxsAJPubs67om1SlMtId5Kg6dQiHjHFV/Ai8ef7Om8agDte9XCQlus1nH+OQ6GU2VMF1NAtYpHH2niSOaMagO9PHFjklQKI/4SpmYTOHYMuHE0nAzScT6G1yRZaYvVSzUfaV+zvwVgxw7g8+evwh1XTGE8mVP/fwMZ7vtu6MOPX/XV9q62b9feZ3LAQ6HuopEv4ZvfBG7JHlPf0BGrM8BSVWVWP/6EhYPZaXPX7U6x2oSzelD9/VEx76WSrcYHmqaDroLpjYZyvZrKrN7qrIVYHYiWs3pmVmDEX9K+z7qeUCu8l9UhStka7ctxVMRMYdl1Klw5H1esFujQnTr2WW448WqnhfsN7GprgVFEoQD1GcQUq1NBsyu6p7XfKF4k5n0hnYa6FoR9LimlutZupf5WD0KxuoepFOrw4uT/AuEFYnWxulh34/X1w2zpFctSI2e1xo3d9a2u5fRGRMrIUbtKZnXssU4UA7LcWa3rLl+lAFzk8kj5eksSW87q5VEFcT/c0FldrzZW5EXlC0DajicCe55c1VndlBYsV6OITpBAuUOsB4B8xdGLAfFWcVZHEyxO/P+X50F1bCJxInRWx1oJ0Uk6jRuHzuLBJ9Ug+vGJLK7qNyRWR86exx9v/S4jdIrVOq5qQInVQR7Tix3F2qoGxOqooxVNBC029VYtkLUlCJRYvdD+n8/OQlX7jhsDkrRWOKtLJQPO6miVxbL7l+7S1L6g1hUJZESsdi4tVpeqMcVqQDlK/TwmT6uJxqNPOjiyUZzVZ892v47DaoVhowinrTR4CgLAsnDr6CnsTC/0/vU1igGZnzdWx2FkoK4cX4UCTp1sYm96Vgk0hopY9jyhs3pq3sGJE8D+9JTarnn/TnYKKY0G5iopDGWWLx999th9KYwnF/GuFzyoNhjKFb7+pQN43+1faW8w4KwWKXVsX/xWGi+8QyJRyqtvaPTlMgO2igHJ5/HZrydx947H1sbIYOBakBpwVQxIOLF0YSFQTnBdV+0ysTpf95AKNPvyRLFzZ/v56KjWpHBEELRjQCanLYwF+mK1qjuxrA5RRfWZtMTqRB2V4rKxpk5spuvCTdRRK62MFpFSxB+Dh87qFTEgBagJIR2xuu6uFKvzUqs/Kzy3bRBoNiEQHjdXy64rFKt7GDVDFPNkdl0EidVjQJpSIOHFO5E9D93VswHUq03YQm+5jOcLJSwvd1brFFgM7JWZ1VEMSFxn9WViQADEv6B53XEC0T4LNU87P69VBC7skeeWgIxTid9xtCzYNlBvWCtuFEt5S8NZjRWij4mImXSqW6wHgEI5oTX76ngW6qYjZqCE9eWTNrmaj4xfu/wbn450Gi/a9iS+clwNbB6/2IfD/RfM3IAjZ8+x0I0zNKS/TwC4/vq20HP4sN6+wirPM0tea4Ll4lJKOZ50PoOO8/bECWBuXuhNBJG1JRKrO53Vc8CQFz8GJEhZajLMsLPa91deD5u1BiydFTwA+pK1bmd1JYHARIHFSzmrK2qVV6xrbTKJA9lpnHhC3V/PTTrYmZrv7fMrcv0tLKhHTbFaAl2rgkolqOJfW2nwZFnAlVe2X+/evX7H8kyI/uedReA0Y0uGhyRmyilU5wuwZBMJS26dCBAASKWUWL3g4sRxiX3eRGt7bFwXKbuKfDH839RqaEoByzFwbqXT+NZ9v45DeEK9NlUETwjgla9sv9ap5RERCr7//xd34a1vrKjYCs/Tun9lRnzkax5qc0s4NeHiQHba3CTTvn3t58sdtjGw+1JoSKstVudSRsTq1j28VALqdcxXkuhP67v2CbonUl72MiOTdi3jQbGIyZkExoKcGWf1MtNcvuIoU5dGgUUvUVvprA7rnMW6ftk23Eu4tQHEN0hEBRaXO6uLIn5mNVbGm7b2m5dKN9M4XgBAtYryUq1tGN0qk8I9yhayZmw8tJYz2DYCp45SLRRru6rHOrEvwK4n2tnSYSexXJLagycvsLCwwlmtl1UsHFsN9JYVWCzpFOyLnNU1KNEruoBFjjddZ3XHoDTKrNZ2Vve76qKeV26J4xdSeMuBWa2OY8IOCwxWuidT8kVLw1mNVZ2E6hfGb1uptEC+7nU7q6uOVgzIpTKrSw0H2zRiW9wggWpTdk3azFVSGExrOn1SKdwxdhz/9ZGXAc0mTkxnsPfmWTOiz3JnjymxemwM+PVfBy5cUGtqdbBtjKSKahl1XXU+zub6cN+OOQNidROyXMH73gfYp0fw8uQTve/826oEAYa8PGbyfuv6PTtv4Sa/ALj9sXbpp8LJwM7Mal23MtSpuTwWqVSUesIyQrG60OGsriSMFFg8W7mEWF1NxHdWp1I4kJ3GU09ehZsLQMqtqVtuLzurly/L1xGro+tzp7M6KmK8lZzVAHDDDe1iu7fdtq6H8rTYNjA8DMzMqNcGhMqRoSamSxk89VgNV+wKR/5bpbgiAFgWRrNlPH5mHCefrGP/tkl1TdGJAXEcFWFUVNGGtXIDtqmInZERtWLnrIqWMCZWA8Ddd6taHkNDZiatwgKmj53L4PlHQle15kRIZjyFpZqP7zxs4+YD8+q6bapftH078IEPKFFdt28IhP+bapdYvX14QT8GJGmhUreBQgGyVoeAGpcSQ7zznSp+8CUvMbK7dFYgN+sDhQImZzJKrNYssOglE93O6mYThZqLlKOx8s5x4CfqK3KglQ5hx7t+hTGv1dJKt7YQUqsYpKqf1i32FopCK7M6lZSrxoAUC1J9tgbE6vkLZQx6BY7negA6q3uYQl6qmac4J50QK7KKAWjPkq0mKLYGTxo39lZWVCTSRVVtdfK9LlG0UEsEFwKOLVfkFcu6ZhRKZ2Z1FK1Rr6NYd7SXjY1uS+Bisa8lVh+7mMWV/ZNaQqXtCDSaVnfbArBUCJ3VMdpXqw0sc1ZLQKttpbNhpe+obUlpLgZktYgZDWd1a8lYx3nQaFqwXc1LtWVhZKiJXDXA4sUS0GzCtppmBlBjY92zzqbEakD9f3btMlJMayRTVsuowzZ7dqkPu9Lz2suIvUQdlVIT09PAt08PIeuU2LnpVSwLw9kqZkqpVjuYnU9oFVi0U566FnRMNLac1dr3xGVidcXSE5YB9KUbK5zVRgosVlZ/f7Fqa8WAHMxO4fgJpVMe3tHhVO1VMplugdpEDEhH29qSMSAAcNNN6vPYtq3bZd2rdLo/Bwe1dzc8amG6nMFjjwFHdunHNGxERocamC6nMXmhoYQkXWe5ZSHtVVGouUCjgcmLUi39NyEAL3c8mxSrLQt4y1uAV73KzP76+tDnlvCvrv82RMGMWJ0eS2GxGuDYCQdXb5tXG01et7dvV1EQJhyP0f8mFKsncmkzMSBJW/XnCwVMTwMjBmIlSAfXXQfce6+xYrujI1BjhGIRk1MCY8mcdl9exZF2GJtqNeRrHtJBI37bdRzlrF4lBgRAvDYWRousEKt1ndWOo2JASt1/a75k6WVWp6CKoq4SA6LrrLaERKNUxdyFihKrTV67SSwoVvcwxXxTa+YpSIaFnzrdutGJHfOG2RKrOwbQ5YrQHjylM0IJipH4GcWA6PRtXBcCWKVgn55j23FWitXVilTLRTQG/BCiVTU6OtZCzdPOKx7c5mGukgSWVCX1Ib+olpJritV1mVixBCdfSsR2VvuBWOmsNhEDkrXUDGxH2yrUPFW4MmZn4VKZ1doxIElbLRnrOFYAZjq46TRetv1x/OzPCbx0x1Nqm4mbsON058eZFKsNMtJXxXQprT5bKTFXTqqOiKazOmlXUSo0MTMDPDndrzKre1lM2+IMDzQwU0m3YpFmF22tAosi8NV9plOsNuCs9nyxIsO/WIKK1NCJAVkuVlcNiNV2BYXK6tf8aj0Bx2poxYA8dcrB0aNoix697KwGgJGR9vPOa+OzxfeREE3Ui8v7Wxp9jY1KNgvcfz/w3vdujCW5+/e3n197rfbu/H4flYaNR49ZOLxtQW3cSjEgUO7yi8U+iHq4wsLA359y62rsUa3iwgUYESkBqH5Qp4jWy4LHyAjec+1n8SO7vtgqMqg7EWIN9CFhNfHY2TQODIbX7V6dxE8m4VgNVJdU33CqkMSov2TGVdtUYvXZ003sTs/17mdAMDomMFXKdMSALGlfY5QBqaMfV62iUHeRSuoZ8bxEHZXysrGmjgvaceBaq4jVOgI4ALgu/MTK+mmFUkI5oGP25VIpqBXjy8XqKF5EQ6xOO2UUFmqYm6xh0Cv29rV7i0CxuodpVUuNedIFKQulZVm9rQtPzH22ihZ2itXlcACt0cHL9gG5sHI0AKDRQLWZgONpNNFLFC1sSgHLi98JWc1ZXS5DLc/WEb6EUKbq6LONYkA0ndUik1YdsYUivvUt4NbxM+obumJ10+qeCAGwVErEdlZ7HlY4CWVNPwYknbW6Y0DqdeTrHtIa8SqOn1B//3JndSNmJeaQ1pKx8HOtFBtqEsSE0yedxn37vos/+vMkXr/3AbXNVMe5M1O6V8Xq/lrLWR21K+HYesJHWDykWJCYmwOkFMg4FKt7mf4BgflKsuWims3ZWgUW4ftq9UfHfaYlVus4q5PWyhgQA1nYfVm5TKy2tcXqpF1FsbrK+xsNABIiYcX7LJJJ7M3M4vSFUKwen1Xbe/386utrPz9yJP5+PA8ZpxItigIAlCvQXsm2YRkc3Dhu4k5n9fXX6+8vFE0eO+7hyGgYL7JRPgtDjIwKfO78VXjhwUm1wYCIkPIbyqFXqeDipMC4KbE6keietOplwSOdxiuveArp+gIwNdXapkU2i32ZGXz2iT042B+21169bjsORpIFTOWTQLWKZkOqTHjN/5mfCou7Fwo4c1ZgV2qeYnUPMzputcXqWdvI6g0n5XavvKtWka/5SKf0xGo/UUO5uFoMSMwC3I4Dd7WijSac1XYN5arVXjUOVTcq7ZRj97udwF5RPw2IdDON/mwQIONUsDRbxexkHQMUq3sCitU9TKEANfOk46yudzur66WaVjHE1ZzVpbLQzqzO9lvI1fyu+IOmFBC2geJny3KgAWh1Rh0bK1y1pbK+u9xOSJUDHR1vrYai7gwsAGQy2JOew+kzAn/918BdO8NCeDpitWupY13urC6Gzuo4BRZXiQGpVZpwrYZeZnU20R0DEi3DSsafBLBdCzW5MrO63LC1xGo35XQVWJydkRj288bE6uePnsTb75vGLQNPqX3GFeiW07ksu6fF6jRQLmN+oqQ6IboDB99H4NRRLEgMDTZhiaZyVnNA0rNYqUB16EOxulBKqEnhuG7dVe4z2tEa6Lgedjqry5Z+ZnW22TUxXKtbcCyNAsm2rZaPNroHIwDa13LXjTcplEzCTTRQqQg88ABwZCgUqXpV9Ii4917lpv2lX9KbDPM8ZJwylpbam8oG+hrkOWDXLuWqP3wYGB/X318qBcdq4KHjKhon2raV8Ps8fOKeP8DvvvbzaoMBsT6dVBmyKJVw4aIw56wGuqNAelnwEKItrJ86pR5121Ymg6v6J/HtqR3YH1xQ23q4XzSWKWGylEV9eh62aKh7lm4MSNppO6snEnRW9zijOxxMlTP4L5+6Al88ts2IWC0Cv7tIcuSs1tlt2OeqlLrHsLIWOqvjjBfDuI7lOdj1cl1LM4JlIXAbKNXtrvHyUtlWWkHcFY3uKjGvUM5qrai8dBppp4z8bAXfeySBqwcmeM72ABSrexhdZ7WftlUMSIfjq1WgKW5mdZSp2+kqjmJAdMTqPgtLtfYAulIKRUqdzsIqjjfUakqo0DhWxxVKrF4m2Osuz245tjscwIW6q9/HTadxIDuNj39rB86cAe4afVht1ymw6Fiodx5ryFLJVs7qGCLoam2rVIK2OJPqd5DvaFuo1VS2tE4UTGBfMrNaR0fJDiTUeRB2bGZmoPJ0DcWAWELif/z4g0o/SSbNLac+eFA9Dg/3rJA0OAjMVVRW8dkTNeVy0T1Wy0IyLXCxmMVofxW7M/PMrO51wv+NLIZFymRTr2if76uVK4X2tSBXCgu46sSABNaKybtSWSBp6wmVfVl0OatbAnPczn1Yx6HaSKgYq050XTnhiO59r3wI3/sesCO5QWJAtm0D3vUuJVjqEInV+fZ1ulwJzQEUq3sb2wZ++ZeBn/kZY7m6ezOz+JV/8R04uXCFQX+//n43EqkUXr7zMYiLF1qvDexSOauLRVyYssyK1Z3F/3pZrAZWitW6EwGWhSvHF7EjuQB/4aLa1qN9QwAY6ytjspTF5LEFFf9g4P/lpZ22s/qCQ7G6xxna5mK2nMKHHzyI//HaTyqjkG47iP7fXc5qT+/SFRZYXC5Wl/P1+BPZrqtWyOWX7bPYVP0NjUicwGuqVf4d4+XZQqC3onG1mmSICjdq9L2z2Zaz+sv/nMKLxp/ccpPCvQjF6h5GN3snSCfUBaLD8bW02ERGY5+tYgFdmdXQzlDMDtrIVYPWsZ46m8C+zIx2niwAozEowCViQCr67vKWYzv6bBsNbVEVQEusfv9nX4j/9OsSohp+HhoDftu1VAzGMrE6rzFb6qcSK5yEJiZCEkkPTSm6XPsSelW51yqzemDIUvnia+SsBtBe5mly8OT7wG/8BvALv2Bun4ZJBG6rKOiJJxvYk541MnAIMg7O5AcxnCzizQe/oTpgPTwo2/IEAdJOBYW5CnI5wBHhORz3fPA8DPt5zMy1u1PTSz5GfL1CXa0lxB332mJFPwu7rw9YrHWI1ZHArNEhd12g2uh2zgAAqlWV5x13QBJes9549UN46CFAFMM81V4Xfkzheci4oVgdTiqUq9bWzKze6qRS+PBdf4IfOPI4MD2tti0v4rfZ2b5dPV4IxWoTMSApqJV3xSIuRs5qU4Liy18OXHON6g/v3Wtmn2vF8LB6vBgKywYEmqt2F3Cwb0q5LoCeFmrHBquYLGXx2c8C1wyeN9K2skMOlmo+GktF/OMjQ7h+6FxPfwZbHbs/jXozgUpV4FU7HzaTix+NBTrE6oa04AQa9++wwOJyF7Qyd8UUlm0bgV2LFhy2KBca8Cy9/kbgN9Uq/07dqJ5Qx2pYrC6WLZVIoOWsrmBuqo75nIWx5BLP2R6Avd0eRs0QacSApEPxr0NQnJsXGPAK8WNAlhcLAFCuWNqCYrrfxlJHEbzjZxwcyF7QG5B5HgSK5mNAXIFa+RLOap39RoUbO+IqJADL0RQq02ncMHQWb77yQdxy3Y1qm+dpuX0Gs3XMzKRXOqvLjnJWxxGrowKLXRmt+nno8H21NKrDWQ1Ab8LicpnVGve1/mEbC9Vk21k9J1TxN5Ni9WS4lN70DTibNbs/03ieqvJcrOALX3Fw37YnjXwGyX4HZ6cGMDy4gP9w89+otm+oOjlZA5JJjAU5XJxo4pPfA9545CG1Pa6TzPcx4p/HzHwCoZSC6UISo2NLejEgSUstIe6KAUkgSOiJ1Zk+S8WAVKvqfiibqr1qTLC4bkc0Vse1v1Gqqmtv3AFJ5BxdWMDu3eqxa/tmx7aRdmtYqrgq/9u2jUzgkg3I4KAqjD052Rb/OjORtwKdRSsBI4JqOmth9pwHlEqYnLYwumsJSB3Q3i8AdXw/9VNqQrDX+wTLJz50CsOGXH9VFf9l5i/bG3p4En9ssI6Hz47gbz8yji/f+d+BpP7fPzhmY66Swke/vg0vOXBeGRkofPUu/f2oNWew3V1Aq1CEKbE6LOitxreuXgSj48Cz6suH3ygVmvHHy66LpJ1Hsdh9jpZLUvU3dJzVPlBqdDirpWyv6Iv7OYTvk5XQEBFSKFlIuRoFFjMZpJ1JfPXRIdy4e05t2yrmiB6mx++eW5tCydKKAXHTrnJBd4i18wtCZbXqxoB0OasNuF9TPpodec1PnfVwsG9aW6QEAFluX9Gb1brq8OuIypGrtuNOUa5aapbQRBZ2R4FF9Q29itRIJnHL2Bn88R1/2r4Ba3aYDu0q4cnc6MrM6ooT21ntpewVy95NONbheSr6Jfx/yaq+WG17iZXO6jAGROejbbnAI2f1rFDOat02ALQFnnPn1ONWW9rk+xgJljBzoYavPujhjvHjZpzV/b5yVsvp1u8hPUwyicMDF3D08QT+/M+BH73ya2q7hlg97OcxvdA+R6cKSYwEes5qN2mvnLwz4Kx2kk47wqkQOpVTKa3JS9eFKry8zFldytXU8cYdkAwMqEdVvXTridUAMkG9KyJNOasZA7Ll2LFDCZ4TE+qakEptvUH0jh3dfSETmdVZSxllikVMzyUwGiyZ7xv1ulANADfc0P06inbTwD60H9cOTrQ39HCfc2ykiY+evBEvv+IU+tyykXNrcNzDbDmFrx0bxr1XhrWCKFb3Lp6HoVQZh7KT6n6rOYkPoP3+SC+I+nM6YzrHgW/XUCl3O6vLxWb8voHjIGlXW5p61z41tY3Al8pZHYrVzWodAqEOE/fa6DiqVkqpuyBkoWypRALNGJDvnhnEoeFQrO7h69ZWYQPcQbcuhZKlVWBR+CtjMOYXBAY1xGo3ubICqwmhdnlkx/FzLg5kNcXqsLptrdJsLXVuzRKaEKs7JgFKBtzltoPuHGgDLnAASoRY7qrV7Igd2lvFEwtj3c5qKVGuJ+Al6rHabCsGpHMSoKKfBd6atCip/1dxqaFXLRhhpMTyApP1OkoNB0FSI59y2ZKxmammcmOYGJRGS2ijz3erdZr7+rAtWMRXv+li13BZtVMDwnIwGCixunI+3LDFPteNRhDgmoEJ/MO3BjA8LJGthfmvcTukoVg9k2tf86YLSYxoZs0L11GTbF0xIPqFGzudPvXFAhKiqS36uJ5Q/YJlYnUxV9cTqzMZde0vFID5eeUuTibNTN5tEDLJRlcdA4rVWxTH6S7UuNUiQADV5nfvbr820C/aNtbERKEfKJXC6MXq1hQnstm2c/2668wI7IcPt58fOtTTfaOxbRYeX9iGFw8dVRsMtK2hHT7mKilM51yMuWG9hR7+DAgw2l/Fob6OcbJuvYEggEBHjZTOotNxsW14Vr1r0TjQUZNMQ6wulrr/3kpZ31ntB0LVTwv/9sXpKvrckra7PGlXUcgtE6tLCTW+1yyweHRyCHsyoVjNc3bdoVjdwxTKCS1n9YqsJABziwkVAxLXWZ1MrBIDYqA6/bJjfeqsp8RqnQ6DEPDsZpcAWio0tQv2OX5ihbO6VLHUfnUyqx2BqVLGvFgNtCMajh9Xj4ODWrvbsa2Jc4WBbrE6vBEJ14l1g/eynso97ZwEKBtoW56nZmCL6vNcmGui340/YQN0TAR19hYaDdSaCTiuRucmjKpoltTnOjMDDHkGinwAwNhY9/9lq7my+vownszhb782iNuuNNcJGdiZxPdmd2LECkVPOqt7myDAtYPn8aGv7MJttzTUddbVWJbpuhgJ8pjOB+1J0bqtP4EbHU+ns7oa5vzpTN6l0xBCorGYR2mmoMRkTXHGccWqmdXFXB3JhIZYLUTbRR0V/tpCrmqgQ6wO77Un5gewMz1PsXor0inUbkWxGgDuvLP93ICovHePxKn8EFAsGsnv39C84x3A3XcDb36zmf11ttebbzazzzVibH8KAk280H9QbTDQBjJjSeRqPqZyAUYTYf+QwldPs2O0hiMD5gq4wvO6Chc2SlW1uls3BiSxSgxIUcaPAXEcBKuI1crgp5dZnU6HdQHC8fLsxZqKt9QplO26SNmV1uLAiEJk6NAQqzNOBScXB7EnWIP6TiQWFKt7mEJZ86RbRayezyUw4BbjZ1YnE2ppcqegWE3oZyh6HhKiiXpJieATMx62Jxe0b+x9QRWLHYUbWxdzzbziFTEgtYT2Bd1xgRf/7XsxcV4t7WlU9CNLWmzbph6Phq4BTbHa8l1YQqJe7HAWR59HzBuQCHxIoKttlStCX5zxPKSdcisBZXG+iX4v/jkAYNVzKxJqdAo3wveRdcrI5dTL7zyRxNWDE2Zulq4LDA21X2+1G3B/P7YlF/HZR8Zx7Q5zYvXbfiqFF28/rmKLDO2TrCFBgIN9UyhVE7jturCnq+MsFgLDmQpmyun29SDK49O5brmuynvuyPkr1pSbRGu/mQxG/DxmJhsozpaMiNUqBmQVZ3W+qdeHAdpRICdOqMetJlanml0DvS+d3Y8Xjz9JsXorsmdP+/mOHet3HOvJLbcA//JfKvfvvn3auxvbYWOymEUtV4IdFdvdqmJ1X5/6bPv6zOzPsoA3vEEJ1bffbmafa8TAvn785JEvqZhMwEj/WKSSAATmSx76q6Hwxf5hT/Off+xJvGz74+qFieuA7yPjtIskF3INFZWp6az2E7UVYnW5JONHdlzCWV024KweGLJULaaweuPsVANDfl5bsE91TAJEFMq2nsnTspDOCEhY2NM8qbZttbFyD8Lebg9TqNgqeyfuSeetEgOylMAhP76z2u0L1KC0I9houhDgKn9SO6oh685jaQnwS4CfqCkTqOaNfSBVwXwliV2Rs7oE7fyllli9LAYkSGg6q12BYt3D3KzEdgDFglQXXZNideRO0xSr4fvYl5nB6QkHrVI0kQMw7mzp8kIUAEomikn5PtJOBYW8xBBU7GmfW9YTUIIAQNN48U74Pga8AuYWExCLQK0KDJuKAQFUFEhUnGmr3YD7+jCeXMTEYhrXbZsCJmFk4OD5Ah/64b8DLoRuDA5GeptUCo7VxEv3n8Xzr7KAz0M7BmMkW8HMCVVwtmIFcK3wWqApVgNoi9X1Oop1F4Hb0Fuamk5je/IcJi4I9M+VkbQT2n9/wrNVLNLyyuxLDb0YEKAtVp8MBw5bTazOoOWsnp0FXKuGjFthgcWtyB13qA7M0FDPi39ryt13qy8DKEERmJ4Ghj1DRdVImzvv7HbD9yhiZBi/98Lfa28w0Y+zLMC2IWt1WJWSuf2SNcMdH0SrYp+JMZJlIe3VsFT1MF6pIJ9rKl3HgLO6XO32m5ZKiG/Gc10lVpe791kshoYxJ34fsW/IxkIlCRRUFM7sVF05q03EgKxwVtsqykljfJ/ps+BaNYzXz6m2sNXGyj0IndU9zHzRUy7ouCf0sqxeAJjLOVrOajvtq0FpOEMGAJNLKYwnc9pRDRmnglwOeOIJtIPtNS8SA6ka5ivJdgxIdDE3HANiIrf74M4KXjB6olVDqlCAdrZyi0isjtAVq10XezKzOH2xLUzX8hXYohm/vQaBymjtclYbyEP3faTsqnJWS4mFBaliQExkrJsWqz0PA14R80sJfP7zwF1XGi6G2FnhfavdgPv7MR7k0OeVsCtpOIusc0l2lA1OepNwdcGnf+CP0G+FSxgyGa1dDvfXMV3OAOUyZiYbKq/asvRyP51lmdVhJn7Sa1z+fU9HOo1tyUVcmEpg+kIdGaeifX0R3srIEoBitQnSaagCcJUKvvJliZeMP6G+QbF66+F5wA/8APCSl2yp3PY1JQgwlszh6MkkRrzwfkCxeusxPNz92lAb8Dtr2CQSXBHT63SuPjW0wiAT1FqrowpLTX1ntePAT9RQrnSbFlTMaS3evsO4klK5e5+5vIWsU9Jqt16fr0yOoW40NyNVLSadGBDHQSo0oXVSqoaJBBpxjOl+G7vS82plO8D7QQ/Aq2YPM1NMYtjPa8WAOFYDtUIV0aVrPu9gYEgjrzcSuDrcrxfzaYwFOQPO6jJySwKPHW3i8JCZJVP96Trmq8l2DEgJYWZ1fKHOCSJndVuwL9VsZG09AfT9/+osGo8WMT+v8qWnZhPq/2+ic7NcQNMVqz0PQ14BcwvtG0J+voa0U4l/AwoCSKjJleh2aSRixrKQ9uvIV12gWMTCglAxIDqDPd8HUFqRWQ1A71htGwN+GfNFHw/8UxMv23UaaMCcsHz33eo45+aAm24ys8+NQjqN7alFXNN/HqIQOqjWQqw+eNDMPsnaMDQECAExPwcsLqptms7i4cGmigGpVDB1roIRf0n/nF2eWR06q7XF6kxGidWzLj7xd+N4/YFPAukrzR5rSKlgIAYkEhGi6+sWE6szGWCpqpzVRx9u4rqhc6pPoFv4iRACJJPYmz6Fbx4fwmhUBG+rTeQTdY/q70fLLWSoDQwOClQqHf1NXrd7myNH1AoWxwFe8Qoju+wskpxfksac1ZVV86VjitWRs7rSPX5dKlrI6K5Ejs6lKAZkRmJQ11ntuioGZNlnIJtSicwaQnhm3xD2pEND0+goV0P0AHRW9zDlegK+XdeKAQkSNZTy7cHtfN7Ru0gsu+gAwMVCRt9ZbVnIpurIVX089r0qDvebWVI/kG1gvpIy66wOnG5ntZQo1cKCWjodEc9Dv1vEwqLax/HzvioyaUKsHhnpdvl1zh7HwfMw6BUwl2vf2E6fktidntOKARnyC5ifb28qVw0UWASQjnI/CwUsLAr0u3ozxatlVsuamYKYg6kK5qtJnDzexP5UR1VqE6RSypn1Yz+29WaLLQv7d9Xwkbv+GJgynB/YKaAZyNEka4htK7eulMCZM2qbpljd3ycxX0li4kwd//4/JjAamBGrHavRquOAeh2luovAl5d/3zPY7/ZsHo/PjeLBJzK4Z+dR/WtBdM1f7qwuSH1n9fJJtauuir+vDUhmwFYD3UIBjx9t4Kr+ixTTCDFFEGBvZhYPnBrFiL2g+skUJ7YmneMiQ/3jwdGwPwCwXW0EbBv4kR8B3vjG9qouTTLJplodVS6jkJfK1GXAWV2pLnNWR2a8OGNw10WQqK2IAckVbGSdst64doVY3VTOap1+TBQDUuz4DKQEZJhhreGsPvDC7fjlm/5OvaD5qCegWN2rNBqAhBI/4zo1fR+BXe0Sq+fyjiogEfemGUU1dDir8zVXXXw1l6VmswK5qo8Hv9XEdX2nW79PByVWt2NAphddjAQabnUATmB3i9WNBsoNB77b1BarB7wiFnLqtDx+ITAnVtu2mi2O0HWneR6G/AJmc+3P8YknBQ71TWo5q7cnFzAx076JzxQC1V4121Y6FVYjzuexmBPoc0vanQUAKvojdPzVKg24VkP7/zWQqWO+klLiv3tRbaQ4YQTR34ed6QXgYvi5anRouhgfbz83tU+ydoyMqMcoWkJTrBaB+p9/858TSLp1vO2qr+kPdqNcwlIoTjcaZpzVALaN1PHh47fglXseU7cs3WONrqXLKv4UCzL+stSITKYtWN94IzA2Fn9fG5DMsKfE6lwOTz4FXJGd4v2AEFMkk3jtnofwmRNXqLFBMkn361blllvUvfDmm7uLmWowtM1ri9VbzSBCAKgiyUtV5axeWBRmCizatZWZ1UWpzHg6zurqMmd1KYGMo+msTqVUbMmCMnedO29hPFjUOx9cFym7gkKns7rRULXNNeN2vCMH8NLtYdyaoesA0YNidY9SK9bgWA110YnbcfJ95awutKul1moCbqIRXwROJiGERCMfitXNZltU18nnBJDtEzhf7MfkJLDPm1AbdcXqfuV4ixywp2dSyv2r46xOOt0FFut1lOqOKnylQxCg3ytifjESq1PmxGpAVeV+0YuA175W+38VOatnc+2b4hMnbCVWa2Ssb08uYmKuLfadnB/AvsyMtlidygg8Mr8DzVweC/NNlVmtI1B1tvcoYqYYRcxoitXDCcxVkqiWGvCqYSeX4oQZovgbGQqAppwuV18NvPWtwAc+YGZ/ZG2JxOrT4aRoNqu3P9/HgFfEd486uOfWeVzRZ0BQdF3VwS+G9+9aTd1ndJ3VALaPNXC+MIC7+x9UGzTF+suJ1cm4Tp9O3vQmtSLkrW/V288GJDOWRK7mQy7mUClJtdqO9wNCzBAEODJ4Eb9w46dxZGCCguJW5s47gd/+beDHf9zYuGtwdxojuwNg927g1a82sk+ysUinpJpwLpXwDw+P4yXbn9CPAbHqKNe6x/HlCrRjQEpVuz0+ApArOsi6+s7qfreEhVklJj/4eAo3Dp/V68fYqpBisdNZHWkyuv3NVKodP3fkiN6+iBEoVvcosxdrGNLJqwYA34dvd8eAaC+RCALsSs3j3JS6GJbmSvDtmtqfphvhRdcv4v0P3otX3jzdvugYEKsXqm1n9em5NPakZzVjQFY6q0sNA2J1Oq0u6Dn1OT510bBYbdvAm98MvOY1+vtKpTDkFzC31OGsPuXiUL+Gs9pxsD2dw0Qu3SpWeGqxX4nVmp/BG140ga9cPIj/83dumFld0h+URO29Mw9dN18bwP79wIPTe1SuWRS3wwGUGQ4d6n5tSqwWArj9dhZX3ChEYnWErnsilcKu9By+9GAS+4YNuaiiIjqdzuqGi6TfvPz7ngHj2wSSdgW3jR5XG3SXu14qBqRoSKxOp4F77tmSy6iDkTTKdQdPHrewYzjsc1CsJsQMiQSwcyc+8LxP4vmjp7ZcJj5ZW66+1sK177gD+MVfBK67br0Ph6wDmaxa2VsvVPCPx7bhZduP6ceA2DWUa91jzVJZxF/JJgSSgUSx4Xb145ZKthFndb9XxMK8xEMPAVdvX4BtNfX6yL4fOqvbMmajUFZ51SZWt77vfcp81FmPiKwbFKt7lJnJBkZ0xeogaDurpUS1CiRkKKjGPZldF/v7ZnB8bgCo1zF5poLxIGdkEHnb82r4/Ts+jB+99RE1s+d52g7ggQF0O6vnMtiT0RSrlzurGw2U62EMiA7pNAa8IuZz6thmljy9AptrSTKpnNUFrzUL++QZDwez0/FvwkJge38R5wv9rYmAqWJaLaHTFIB37QJ+/oZP4wsPpLCQs/Sd1cBKZ3XJjLP6ppuAf5rcj339C0q0t+3ebAMbkcOH289HRjgw3ars2tV+7jj6kwyDg9idnsM3Hs1ib5+hIl2uC9+uoVIOxeqowGKg76z2+gP842t+C14izNk3EAslpVjprC5CP7N6q5PN4vt2PoZ3fvgOvOQ6FoAjxDhXdhSYpaBIDHLffWb8QWTjkslaWKr5+NLXHbxw7zkl1GpnVtdRWeasLpUtNQaNue9kSqBY7xarc2XXmLN6fk7iYx8DXntNGL+nI1YHwYrM6sJ8FSm7YkaszmZpPuohKFb3KNMXG0qo1Mw1Gk/n8aePvwCNcg1PHJO4IhNmtcYVl4XAgeEcTiwNA6USJs/XMZ5cNON46uvDffu+i32N43rH2EH/oNUlVk8sprE9uah14bWTbrezul5XzmpPX6zud4tYKDgoFSXcREOZd005q01i2xjM1jFXVq51KTtmdTVcdDsGS5go9gOlEppNAFKqz0BTrEY6jVtHTuIbR9M4M5NUOdiaYrVlCTSaYqWzWvP/ZY0M4e4dj2OvFxYZpTBhjs5s6bvuYjblVmV5kT7d68vwMHan51BvCGwPQkHRQA60Z3VkVkdxUybMxX19uGW0IwJF9x4T9VNWOKuFGWf1ViabxQ/s+zaemu7Du15zSm3jPYEQc3TeD268cf2OgxCy6ciM+Fiq+fjwZwbwxmseURt1tJ1EAl6ihnLdVlGsIaWKgJ+ox953kFwpVi+VQ2e1Th8uqcbcU/MOPv5x4L6rHmttj00QIOVUVhGr2d/cjFCs7lFmppsYCZa0HZX3v+SLeCo3gmMPVXD0oQauHphQA1ONwen+0TyO50aAYhHf+67EvsysmZmsyN11UVNQ72Bg3MN8NQkUCgCApoSa1dT4XAdHbUwU+7rE6nLDge9pOt5cF/2pGhbKPr78hRpu3xvmdusKKWtEss9BoeYBhQJmZ4GRrH5e1Pbhqvpsy2VcvCCxLVhU3zAgVvt2HRm3ihtHz6tKxJpikuPILod9qSxUJI6u8DM0hHdd/UW8dujr6jUjQMwhBPDud6vc9pe8ZL2PhqwXiURboLj+ev39hc7qXX052BV1rzHlrI4W8KBeR0NaSLgG7gedjpEox10Hz4NtNdAoLROrSxSrtclk8PzRU3j4h/4jgjprGBBinEOHgG3blFA9NLTeR0MI2URkxpKYqyTxzWN9uGPbCbVRR6wWAr4HlBtOKzITAMoVC0EivrPa8l00l62Qy5VcZN2Stljd7xbxZw/fhFe+QiKo5dR2nbGt5yHl1FCsJFqC/fSFutLNWOR+0/G0YrUQYpcQ4otCiEeFEEeFEO8Ot98vhDgvhPhu+PWqjvf8vBDiKSHEMSHEPR3bXxFue0oI8XNr8ydtDqanoO+sBmCnPBzITGN+stoWqzVF4APbSziRG0FjqYj/8eEM/tWVXzPmrAYAzM6qRwMDMn84jUrDBnI55HJAxg4LQ2pcePcf9jBXSWFyLhS8Gw0s1Xykk5rOaiGQGXSQq/n4zN818Ir9T6rtPZrTKdLhjSafxxNPAIfGQmFZ47MdH65jotAPlEo4eayq8qo9T98BG7qoP/jDn8PvvPCjapumCJxN1rvy0EtlYSSzGkNDuGX0NG7tO6ZeMzPLLEeOqHWZukVGycbmJ34CuPdeVXhWl6Eh7E7PYV9qujUxasJZ7dt1FQNSr7cHJSZW2nSK1bp51QDgunCtBqqFWtfmUoVitTaeB3ge0qJgtG9ECAnxPOD++4F3vGO9j4QQsslIj6XwxYkr8YIdZ2HVwwl9TW3HD4TSNjrE6lI1ET+zOjwmAbSd1VJiqeIibVf0+nCWhf5UDZ84fT3e+vqymVpMQiCZBAp1Vy1rBnDuHLAztUCxehPyTEbrdQDvkVIeAfACAO8UQkTlMX9HSnlD+PUpAAi/9wYAVwN4BYA/EEIkhBAJAL8P4JUAjgB4Y8d+yDJmpqXKrNYd5Pk+Bv0C5qdqeOQRiWsGz2uLn3u2VXFscQx/9+kEbr86h2G/YEZQXe5oMLHPTEY9Li3h0aMdMSg6F0nHwVuueAAffvxGNaPXaGCmnMZwuvz0730arEwKDWnhi19O4MWjj6uNutnKa0X4GcpCUYnVI+Hyd42bsJdxUW0mIEtlHP1uDVf2T5oZmIfHuj+4AK8ciuqan+vukRLO5gdazup80ULaqeiLSQMD3ULq/v16+yOErCSZBF796vY9Qod0GlcOz+GDL/r/gJmZ9v51EAK+L5R7pliErK2RWJ3N6u/P8+Am6qiWuosMF8sWM6tNELXRaNUZxWpCCCGk58ns7MPJpRHcMXysLdRqCqpeYKm+YeSClhKlagJ+Qk+sBtAWq+t1NKVQq/k0zT39fRLXDJzH4R25tqFDsx+TylhqdXcoVp+fAHak5mmO2IQ8beuTUl6QUn47fL4E4DEAOy7zlu8H8BEpZUVKeRLAUwBuDb+eklKekFJWAXwk/FmyCrdcsaCEZd1BSRBgwC1ifqaBU6eFiuzQFIG9rIebhs7gp39tDD/5mjOt36NNNgscONB+3ZkvG5dMBiN+Hk+dcfE3/6eBV+98SEWA6MSrCIFbdkzg0fltSqis19FoWrBdA07NdBq5qo87b84hKIfirwkxYS1IpZBxy8hPl5RYPTSntuvcKHwf25OLmDjbwAPfFHj+6EkzbSsSpmdm1ASD52mLPnvGKjiTH2zdKCfmA2xPLug7qxOJ7kKA+/bp7Y8QsrYIATEyjD2ZOWXvAIzE93iRWF0oYGFeot8tmYmF6rz/LcuZjoXnwbEaqBbrXZuL5YQq+MPBgx7RqjOK1YQQQsiGIbNDjeFvzzyihFrb1jZJWIGnIjuinLhGA+W6jcBtxO8jui4k0O4TRo8G+m83HSnj/ud9EpieNuOshhKri53O6gsJOqs3Kc9KXRNC7AVwI4AHwk3vEkI8JIT4oBAiWku6A8DZjredC7ddajtZhVdeP4ED2RkzYrVXxNxME7IhYQmpfyInk3jv9Z/FrYcWcXgsFChNXRxuuaX9/O679feXyeAnDn8Zv/XV5+Mz/wDcs+uokYHezqESzhX6gUoFzVoDQkgzjrdMBu++5gv4lTc/qS7oltW7A9NUCtuTC3jqSanE6r5JtV2nLQQBjgxcwKOPCXzvkQSuHzpnJrM5GuzPhxMABtzqu7fVlFgdzmyfm09hZ3peO2ceAHDbbe3ne/bo748QsrYMD6vHyOliIsYqaDurp6YFRoOcmesLAIyNqccrr9TfVxgDUistE6urCcaAmCD6X0WDvF7tExBCCCGkRbovgdHkEg5mp9SGwUH9aEvfV5EdoVCLahWlhmYB7tBZLSuhSB0J4QZWxu2/ysV9+74LnD+vokt0TYMAkpkECvW2s/rcRZvO6k3KMxarhRBpAB8F8DNSyhyA/w7gAIAbAFwA8FsmDkgI8XYhxINCiAenp6dN7HJjYmjmKRKrz01YSPn11jYtBgZwZOAC/urdX2tfKE3lKt92myp49aY3mcnS9Dy89orH0WgAP/zKBSTtmhHxc3S4iclSFigWMTcrMeQVzDje0mm8+9ovIJ0LiyumUvo3tbUilcJPHP4KfuWDO/Doo8AeJzxmHSd4Xx+ODFzAA99x4SYa8BJ1M20rlerej4E2sHt7HafzQ+0lSAsp7DDhrAaAG25QItKLXsRZYkI2Anv3dr82MCHmpxIoN2wgn8fUbAKjwZK5grvvfS/w9rcDz3++/r5cF65VR7XcXbehWLUpVpvg2mu7X1OsJoQQQnqeRAL46js+1B7Kmyhq7fvKBR0JytUqSnUHQVJDL/A8ZTooKLG6UaoiYTXN9N+i2ksnT6pHA32YVJ/dlVl9ftqls3qT8ozEaiGEAyVUf0hK+TEAkFJOSikbUsomgD+GivkAgPMAdnW8fWe47VLbu5BS/pGU8mYp5c0jIyPP9u/ZPJhy0AQBBr0Cjp10MdYXOr50T+QoW3pmpn2hNCVW+z7wkz8JvPjFZvYHwO5L4Y9e/Bd4z2vCgnUGLpJWJgUpBVAoYHJKYCzImXFWRwLHhQvq0USe6lqRSuGl25/Ajmwe738/YBfCLGidY+7rw5GBCfz2J/bj3tsNZb8CSvDvLFRowlm9RyhndZi/dW4hrZzVJtqB4wD/9t8Cb36z/r4IIWvPwYPt52NjRq7dgwMSs+U0UCgoZ7W/ZOb6AqhJxec9z0yh0dUyq6Vsi9XMrNbj8OHu/xPFakIIIWRDcMXhjn7b8vpccYg0lw6xutxw4Cc1+nOui5RTQSGn+nFLczVknLIZsTrS8556Sj0aMIy5GQ+1ZqIlVk8vOBg2UeuN9BxP26qFEALA/wTwmJTytzu2b+v4sfsAPBI+/xsAbxBCeEKIfQCuAPBNAN8CcIUQYp8QwoUqwvg3Zv6MTYhBsXrAK+Kx0wHG+wy5oKML7eyseWf1WhCJBpNhTIWJgV4yiaRdRWGmhIsTTYwlDS3PjuIqTp9Wjz0uVgPA773hq3jD6yWwtKS2a4rV1w+ew2/e+xW87wfDm5qpttU5+WXAtb9zn6OiYPJ5AMBkPqnEJFPOR0LIxqEzW/6aa4ysiNm1vaGKuBaLmJpLqPuMKbHaJK6rMqs7xepaDdWGDcfTL86z5QkCNbEAqLiZqJ9ACCGEkN7miivaz02I1ZHpsEOsrjZsOL7G+NN1kXXKWFpYA7E6ijKLdILOIt9xibSBUIeSTUNRt6TneCajnjsAvAXAw0KI74bbfgHAG4UQNwCQAE4B+AkAkFIeFUL8FYBHAdQBvFNK2QAAIcS7AHwGQALAB6WUR439JZuNqFqqoRiQExeTGMucb23TIsrmnJ1tC5O9fHGIYikisdpEBnIqhZ2peZw/JTB5UWI8yJkRwXeEMe65nHrcAGI1CoVWoUl4np6Lrr8fvl3Hjx75BlC+QW0z5SLrdFZ3FjCMiTOYQaVhIz9TRhqqbmPCkuYyZQkhGwfPU9eVxx5T8T0G2LVT4mxhAChcwNS0hRv9JSDoN7Jvo4TLR7tiQKLsbrqqzfC2twE/9EPqvssJUUIIIWRjcNVV7eeGYkAAdGVWAzaEp9Hfcl1k3RJyixIAMD/TUEW9TfThIt0owkQtpg6xem4O6PNC4b6X9SgSi6cVq6WUXwWwmkXoU5d5z68C+NVVtn/qcu8jHRh1VhfQlBbGU8oBqn0i9/WpwVIu1xZVe9lZHUU+XLyoHk2In2kV+XDu7AAmJ6FiQJL60RLYsUM58qS6WfS0WN1ZtNCEq7pznwsL5otJdbrRjhzR3186jX93/afwjr++C3/87wBfhEUpuESbkK3J29+uJu8MRZjt3JPAucIAkH8SUzMCo8klM5OtpgljQGqVtlhdnK8gYF61OYTQqwdBCCGEkOeeTrOUiT5cEEAAaJYqsBAVRbT1hGXPQ9YpI7eoVsJdvAhsSy6a6cO5LrB/P3DihHq9e7f+PjvE6ocfBq4bCwtYUqzedHBtZq9isMBikKjBs+sYc2bVNt0Bj2W1ZwbPnWv9np4lOlaTYnUyiZ2pBZw9JzA5LdTybBP7dd32chnASLbymtGZXR6J1bptK5tVg/J8vhWvYaxtRQXQkkkznYVMBm++4gE8NdOHRx9pYmdyVh17L58LhJC1I5k0JlQDQHrYR77m4fx54PhEoAos9uI9wbaVs7oKtcQEwMXzDbXiiGI1IYQQQrYqQgD/+l8DL3uZionTxffhJ2qo5NQKtuJiDSmnotffcl1k3TIWc8qfevEiMJ402Ie7/fb2cxNidShKy2IJDz0EXDsYpgf0Yh+ZaEGxuleJYkB0BdBkEkIAA0EZ440Jta1zhi8ukVBZr6vHXp7J2rmz+7WhGJAjAxN45HiAY6cDHMhOmxMpd3XUIe1lJ1U6rW5ipVJ7IkDXWW1Zah9Smp1cAJRY/Z73AB/4gJn9BQEgBF40+gTe+54mXr7z0dY2QgjRJpWCJSTe8IcvhSeqyuXSi85qIeC6EtWmHS5HVWK1MVcOIYQQQshG5YYbgNe/3kwND9+Hl6ijkq8BAGampSouqO2sLrUWzF+csjAeGOzD3XorMD6uxHoT4/ogUJ/BUlU5q7On1HaK1ZsOitW9iqkIhPFxAMCAk8d4JSzaZ0Ks7iwmlUj0dsGfTvEXMHORTKVw68gpfOOJQRy/mMT+zIw5UfWGG9Tj/v3A9deb2edaIEQ7h+rkSfVoQlyP2lIkVpt0Kh86BPT3m9mXZQGpFO7a8Ti+/o0EfnDfdxgBQggxRyqFsSAHS9bxiTf+JQK71rMdcccRqDYSrYI/FyakcuUws5oQQgghxAyhs7q8ZFCsDp3VuaXQWT2dMNuH8zzg/vuBn/opM/tLpZCyK/jmYxl85zvA1UEYMdKjfWQSnx4sK0/QaKjiREKYyZdOpXDn+OMYE1NqfyZO5KuvBv7+79Xzq67qbffU0JD6u6OquYac1f1eCXN5B4fGFpSZ1pRQefPNwHXXbYxB/vAwcP58O4fKRMb24CBw9my7QFcvC8CZDF687Qn8zk+fxMBSEUiZiwAghGxx0mnsSp3Gke0n27FIveisBuAGCeWszueB/n5cvCDprCaEEEIIMUkQKLG60ACg0jhHdMXqdBpZt4QLOVUz6+KsjW3bFs2unDe58ri/H++57mP406++CC+/p4HUfAGwNXO7SU9CZ3Uv0umq1j2xhQB27MDvv/DD8O26ytM0cbHYv7/93ESxurWk0wEMqCKGuoSCwa3bz+GOXWfUNpMO4I1ysY0+1/NhVpQJZ/UVV3S/7mWxOp1G0q7hJ+/4nnrdy8dKCNlYDAzgZ2/8LH50z+eBxUW1rVfF6mQCtWaiVb/gwqSFcYrVhBBCCCHm8H34dg3lvIpinZmBclbrCMvptCqwGGVWz7mqD9erekR/P16x6yg++OI/xa++L8wuSacZxbkJoVjdi5gqrhjRmdlsIgIEUNEf992nhMU77jCzz7Xk5S9XIvW/+TfAwID+/sL/zc/e9Hn8yJEH1batKFR2TgIAwLXX6u/z6qvbz5NJc7Eda0G0SmFyUj32qJBECNmAWBYO7G0g45RVfYgeLuDqplxUmwlEgYcXpyzlrO7VgQ4hhBBCyEbD9+FZdVQKhsXqjhiQxYKNrFPuXcNBKqWc1OWy+gAARoBsUhgD0oukUsAP/ZC5Qd6ePe3ny4sN6vCKV6ivjcDzn6++TBGKkof9k4AVXhy3olh97bXAX/6ler5790rxOg7btrWf33OPuhn1KsvF6q3YBggha8fQEDA7q56nUj3rGnFTDqpTdkusPnHOxc7rFoDklet7YIQQQgghm4XIWV1sAgBm5hO4ws/rmRnSafS5JeQKCUBKyKZU3c1eFauFUGa2mRng3Dm1jWL1pqSHVaAtTDoN3HWXuf3dfDNQKinx+5ZbzO13K+O66qtaBebn1batKFSOjKhiCR/9KPCDP2hmn0IAP/MzwPHjwPd9n5l9rhWDg+pxYkI9bsU2QAhZO4aG2s97eOWGk3JRbdp44MEEGimgWpEYDZZ4TSSEEEIIMUUQwE/U22L1QgLD/ZpiteMgm2pgseJh/kJZFfQGelesBtRKeYrVmx6K1VsB2wbuvHO9j2JzIQQwNqYKAUave/mCvpZcc436Msnhw+qr1+l0gQM9LSYRQjYgnWJ1D3fE3YyHUjOBX/urg/jH/wTc/9pT6hu8JhJCCCGEmMH34SVqqBQbgJSYXnAwMr6kXQwxO2gjVw3w8z8PvPO276iNvRzlFsWEUqze1DCzmpC4dAqVQdCzy7PJGrJcrKaLkBBikg3irHYzHgo1F6enA/yv/wX8yPOOqm/wmkgIIYQQYgbHge9KlKsWUK3i/FwS48mcdk2TzKCDM/lBfOd7Am88+K1wY8bAAa8RFKu3BBSrCYlLp1DJAfnWZGQEsDouo2wHhBCTjI+3n3cWn+0x3KyPL104hNu2n8GrXw0MiTn1jR4W2AkhhBBCNhp+0kKp4aK5kEOlJlRsh6az2u1P4nR+EHffsgiRW1Qb+/oMHO0aMTCgHuuq0CT7m5sTxoAQEpdOESHKLiZbi0RCzeSGRcU4q0sIMcq+fcBb36omR/ftW++juSRONsDnJ67Cl1/6hwCeBxSL6hucwCOEEEIIMYaXsvGjn/kRnPytPPZlwyLcms5qZDLIumW87Mgk8HhBmbF6eVy7f3/361271uc4yJpCZzUhcel0Vr/iFet3HGR9ufJK9Tg+Dhw8uL7HQgjZXAgB3H57TwvVAPC8Fwb4+Mv/O25NPQpICRQK6ht0uhBCCCGEGOP7bpjGb77gf+O3/qQP1/SfVxs1ndVIp3EwO4XbBx9Xr7PZ3o443bu3+zXH4JsSOqsJicv4OHDzzWqJTA8vzyZrzA/+oCoGeeut3ZEghBCyRdi938buq88CuSawsNB2VlOsJoQQQggxxtWHajgy9TX82rHX4ZrsGbXRgLP6q/f+BsTkAfW6lyNAACWk33038LnPqcdeFtZJbChWExIXIYAf//H1Pgqy3gwMAHfcsd5HQQgh68vIiIpEmpwESiW1TXfwRAghhBBC2mSzEAL45dc9ihcuPQHYtvrSYWRE6b3Hj6vXvS5WA8B99wEHDgDXXbfeR0LWCNoACSGEEEKIHiMj6vHMGRUFEgRcbUIIIYQQYpJMBgDw9hsfxLZkzowxoDPeFAD6+/X3udbYNnDTTfpCPelZOIoghBBCCCF6RGL16dPqkcUVCSGEEELMEorVmJxUj7p51QAwOgokEu3XG8FZTTY9FKsJIYQQQogekVh96pR6ZF41IYQQQohZIrF6ako9mnBWJxLA2Fj7NcVq0gNQrCaEEEIIIXqMjqrHmRn1SGc1IYQQQohZsln1WKmoRxPOaqDbZHDokJl9EqIBxWpCCCGEEKLH2Fh3NfZIvCaEEEIIIWYYHOx+baqY9e23q37cm97U7bImZJ1gGjkhhBBCCNEjmQQOHgSefFK9vvba9T0eQgghhJDNRhAA27cDExPq9d69ZvZ7++3AzTcDrmtmf4RoQmc1IYQQQgjR5+qr28+vumr9joMQQgghZLNy8GD7+eHD5vZLoZr0EBSrCSGEEEKIPnfcAQwNAXfeyQEPIYQQQshasH17+/mePet3HISsIYwBIYQQQggh+mSzwK/92nofBSGEEELI5uXWW4GvfQ248UbAov+UbE4oVhNCCCGEEEIIIYQQ0uukUsAv/dJ6HwUhawqnYQghhBBCCCGEEEIIIYSsOxSrCSGEEEIIIYQQQgghhKw7FKsJIYQQQgghhBBCCCGErDsUqwkhhBBCCCGEEEIIIYSsOxSrCSGEEEIIIYQQQgghhKw7FKsJIYQQQgghhBBCCCGErDsUqwkhhBBCCCGEEEIIIYSsOxSrCSGEEEIIIYQQQgghhKw7FKsJIYQQQgghhBBCCCGErDsUqwkhhBBCCCGEEEIIIYSsOxSrCSGEEEIIIYQQQgghhKw7FKsJIYQQQgghhBBCCCGErDsUqwkhhBBCCCGEEEIIIYSsO0JKud7HcEmEENMATq/3cTyHDAOYWe+DIJsSti2yVrBtkbWCbYsQngdk7WDbImsF2xZZK9i2CFk7TJ5fe6SUIzo76GmxeqshhHhQSnnzeh8H2XywbZG1gm2LrBVsW4TwPCBrB9sWWSvYtshawbZFyNrRa+cXY0AIIYQQQgghhBBCCCGErDsUqwkhhBBCCCGEEEIIIYSsOxSre4s/Wu8DIJsWti2yVrBtkbWCbYsQngdk7WDbImsF2xZZK9i2CFk7eur8YmY1IYQQQgghhBBCCCGEkHWHzmpCCCGEEEIIIYQQQggh6w7F6ssghNglhPiiEOJRIcRRIcS7w+2DQojPCiGeDB8Hwu1XCSH+SQhREUK8d5X9JYQQ3xFCfPIyv/PTQoiF5T8jhHiXEOIpIYQUQgxf5v37hBAPhD/7l0IIN9z+YiHEt4UQdSHE6+J+JsQMm6xt7Q7/lu8IIR4SQrwq7udC9NmgbWvVnxNCvFQIsSiE+G749f44nwkxwwZtWx8SQhwTQjwihPigEMIJt78pvF49LIT4uhDi+rifC9la9Nh5sGr7XuX9l7p/s2/YQ2yytsW+YQ+xQdsW+4YbgA3attg3JBsCk+eXEOJU2La/K4R48DK/8xXh+fGUEOLnOrY/53okxerLUwfwHinlEQAvAPBOIcQRAD8H4PNSyisAfD58DQBzAH4awG9eYn/vBvDY0/zO3wDwllW2fw3A3QBOP837/zOA35FSHgQwD+Bt4fYzAP4fAP/rad5Pnhs2U9v6JQB/JaW8EcAbAPzB0+yHrC0bsW1d7ue+IqW8Ifz6lafZD1lbNmLb+hCAqwBcCyAA8GPh9pMAXiKlvBbAf0CPZbSRnqaXzoNLte/lsG+4MdhMbYt9w95iI7Yt9g03BhuxbbFvSDYKps+vO8Pr5s2rfVMIkQDw+wBeCeAIgDeGvw9YBz2SYvVlkFJekFJ+O3y+BHXh3AHg+wH8WfhjfwbgX4Q/MyWl/BaA2vJ9CSF2Ang1gD95mt/5eQBLq2z/jpTy1OXeK4QQAF4G4H+vcmynpJQPAWhebh/kuWEztS0AEkA2fN4HYOJy+yJry0ZrW8/m58j6skHb1qdkCIBvAtgZbv+6lHI+/LFvRNsJeTp67DxYtX0v+x3sG24QNlPbAvuGPcVGa1vhz7FvuAHYoG2LfUOyITB5fj1DbgXwlJTyhJSyCuAj4e9aFz2SYvUzRAixF8CNAB4AMCalvBB+6yKAsWewi98F8LNY2wHBEIAFKWU9fH0OqjGTHmYTtK37AbxZCHEOwKcA/NQaHgd5FmyQtvV03CaE+J4Q4u+FEFev43GQDjZa2wqXeL4FwKdX+fbbAPz9c3EcZHPRK+fB07Rv9g03IJugbd0P9g17kg3Stp4O9g17kI3Wttg3JBsJA+eXBPAPQoh/FkK8/RI/swPA2Y7Xz7bPaLTPSbH6GSCESAP4KICfkVLmOr8XzsjJp3n/awBMSSn/ee2OkmxENknbeiOAP5VS7gTwKgB/LoTgtWWd2SRt69sA9kgprwfw3wB8fB2PhYRs0Lb1BwC+LKX8yrJjuRNqQPK+5/BYyCagx86DVds32ZhskrbFvmEPsknaFvuGPcgGbVvsG5INge75FfJCKeVNUBEf7xRCvNj8kZqFnYanIZxx+yiAD0kpPxZunhRCbAu/vw3A1NPs5g4A9wohTkFZ6V8mhPgLIcTzRbs4xL0xj+8z4fv/BMAsgH4hhB1+eyeA83H2S9aeTdS23gbgrwBASvlPAHwAlwzdJ2vPBmtbl0RKmZNS5sPnnwLgXK6gA1l7NmLbEkJ8AMAIgH+77Gevg1pq+v1Sytk4v49sTXrpPFitfbNvuHHZRG2LfcMeY4O1rUvCvmHvsRHbFvuGZKNg6PyClPJ8+DgF4P8AuFWoAo7R+fUOqHv4ro63PW2fcS37nPbT/8jWRQghAPxPAI9JKX+741t/A+CtAP5T+PiJy+1HSvnzAH4+3OdLAbxXSvnm8Ns36ByjlPKeZcf8RQCvg7rIP+2xkfVhk7WtMwDuAvCnQojDUAOSaZ3fTeKzEdvWpRBCjAOYlFJKIcStUBOs7DiuExuxbQkhfgzAPQDuklI2O7bvBvAxAG+RUj6h8zvJ1qKXzoNLtW/2DTcmm6xtsW/YQ2zEtnWZ97Nv2ENsxLbFviHZKJg6v4QQKQCWlHIpfP5yAL8ipTyLjvMrFJmvEELsgxKZ3wDghy+37zXtc0op+XWJLwAvhLLUPwTgu+HXq6CyWD4P4EkAnwMwGP78OFQuSw7AQvg8u2yfLwXwycv8zq9AdeZK4fvvCbf/dPi6DlWk5E8u8f79UIUCngLw1wC8cPst4fsLUDf0o+v9+W7lr03Wto5AVYf9Xvh3vHy9P9+t/LVB29aqPwfgXQCOhm3rGwBuX+/Pdyt/bdC2VQdwvON43x9u/xOoCtXR9gfX+/Pl18b46rHzYNX2vcr72TfcAF+brG2xb9hDXxu0bbFvuAG+NmjbYt+QXxviy9T5BXWv/l74dRTAL17md74KwBPhOfKLHdufcz1ShG8khBBCCCGEEEIIIYQQQtYNZlYTQgghhBBCCCGEEEIIWXcoVhNCCCGEEEIIIYQQQghZdyhWE0IIIYQQQgghhBBCCFl3KFYTQgghhBBCCCGEEEIIWXcoVhNCCCGEEEIIIYQQQghZdyhWE0IIIYSQLY8QYkgI8d3w66IQ4nz4PC+E+IM1/L0vFULcvlb7J4QQQgghZCNhr/cBEEIIIYQQst5IKWcB3AAAQoj7AeSllL/5HPzqlwLIA/j6c/C7CCGEEEII6WnorCaEEEIIIeQShM7nT4bP7xdC/JkQ4itCiNNCiB8QQvy/QoiHhRCfFkI44c89TwjxJSHEPwshPiOE2BZu/2khxKNCiIeEEB8RQuwF8A4A/yZ0cb9ICPFaIcQDQojvCCE+J4QYe5a/+1TH9m8KIQ6uywdHCCGEEEJIDChWE0IIIYQQ8sw5AOBlAO4F8BcAviilvBZACcCrQ9H4vwF4nZTyeQA+COBXw/f+HIAbpZTXAXiHlPIUgD8E8DtSyhuklF8B8FUAL5BS3gjgIwB+9pn+7o6fWwy3/x6A3zX89xNCCCGEELJmMAaEEEIIIYSQZ87fSylrQoiHASQAfDrc/jCAvQCuBHANgM8KIRD+zIXwZx4C8CEhxMcBfPwS+98J4C9DN7YL4OSz+N0RH+54/J1n/RcSQgghhBCyTtBZTQghhBBCyDOnAgBSyiaAmpRShtubUEYQAeBo6JS+QUp5rZTy5eHPvBrA7wO4CcC3hBCrGUf+G4DfC53RPwHAfxa/O0Je4jkhhBBCCCE9DcVqQgghhBBCzHEMwIgQ4jYAEEI4QoirhRAWgF1Syi8CeB+APgBpAEsAMh3v7wNwPnz+1pjH8PqOx3+KuQ9CCCGEEEKecxgDQgghhBBCiCGklFUhxOsA/FchRB9Uf/t3ATwB4C/CbQLAf5VSLggh/hbA/xZCfD+AnwJwP4C/FkLMA/gCgH0xDmNACPEQlBP7jbp/EyGEEEIIIc8Vor16kBBCCCGEELKREUKcAnCzlHJmvY+FEEIIIYSQZwtjQAghhBBCCCGEEEIIIYSsO3RWE0IIIYQQQgghhBBCCFl36KwmhBBCCCGEEEIIIYQQsu5QrCaEEEIIIYQQQgghhBCy7lCsJoQQQgghhBBCCCGEELLuUKwmhBBCCCGEEEIIIYQQsu5QrCaEEEIIIYQQQgghhBCy7lCsJoQQQgghhBBCCCGEELLu/F9cCxj9aYoWTAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1800x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "plt.plot(train_timestamps, y_train, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(train_timestamps, y_train_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for training data:  1.7195710200875551 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x216 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "plt.plot(test_timestamps, y_test, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(test_timestamps, y_test_pred, color = 'blue', linewidth=0.8)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE for testing data:  1.2623790187854018 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Dự đoán toàn bộ tập dữ liệu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor shape:  (26300, 5)\n",
+      "X shape:  (26300, 4) \n",
+      "Y shape:  (26300, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = energy.copy().values\n",
+    "\n",
+    "# Scaling\n",
+    "data = scaler.transform(data)\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=np.array([[j for j in data[i:i+timesteps]] for i in range(0,len(data)-timesteps+1)])[:,:,0]\n",
+    "print(\"Tensor shape: \", data_timesteps.shape)\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = data_timesteps[:,:timesteps-1],data_timesteps[:,[timesteps-1]]\n",
+    "print(\"X shape: \", X.shape,\"\\nY shape: \", Y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "Y_pred = model.predict(X).reshape(-1,1)\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = scaler.inverse_transform(Y_pred)\n",
+    "Y = scaler.inverse_transform(Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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EEEISbN4MvPwyMHVq/tqoqgIeegi45578tUEIIYQQQgghhBQDVHARQkggqOAihBCSYMgQYPp04NVX89fG/PmJ/83N+WuDEEIIKWUWLwaefTbhEa0SCtUIIYSQwsP5lxBCAkEFFyGEkASbN+e/DV3PfxuEEEJIKfPUU8C8eZnk8KqggI0QQggpPGF7cHH+J4QUOVRwEUIISaBpYfeAEEIIIbKsXRt2Dwgh+aa5mQZihJQ6VDARQkggqOAihBCSwKjgamzMTxtcvBNCCCHRhHM0IdGiuRm47TbgwQfD7gkhpFDQg4sQQjxDBRchhJAERgXXHXcA69eH1xdCCCGEOLP//mH3gBCST1asSPxfty7cfhBC8kvYIQoJIaTIoYKLEEKINVOnqq+TC3ZCCCEkMR/GYsHrUAnnaEKiBcOHE1IecP4lhJBAUMFFCCEkQYVpSggqeCOEEEKINU8/DdxyC9DSEnZPMlDARki0oIKLEFIIOP8TQoocKrgIIYQkML9E86WaEEIIyQ+LFyf+v/VWIs+OH+jBRUhpw7U4IeUH52JCCPEMFVyEEEISFELBxQU7IYQQkmHyZOD998PuBSGEEEIIIYQUJVRwEUJI2EyeDMyZE3YvCmMlSgUXIYQQko3fNQA9uAgpbejBRUj5EcZczPmfEFLktAu7A4QQUtY0NwO9eyc+v/IKUFkZXl8YopAQQggpHqjgIqS04VqckPKgtTXzmXMxIYR4hh5chBASJm1t1p/DgC/RhBBCSOGhMIsQYgXX5oSUPvX1wP33h9sHrkMIIUUOFVyEEBImup75HI+H1w+AHlyEEEJIGERFsBSVfhBCCCHlwtSp2d/t5uIFC4DHHgNWrMh7lwghpNiggosQQsLEqOAyfg6DQii4KDwjhBBCsvE7N3JOJSQabNoEvPwyMHt22D0hhBQbsu/c//kPsHw58Oqr6vvA9QQhpMihgosQQsLEqNR6912gpSW8vhBCCCEk/0RVkBTVfhESdb74Apg+HXj++bB7Qggpdtzm4uZm9W2GbWhLCCEBoYKLEELCxBiWcNo04JtvwuuLGYYoJIQQQtRjFiT5FSwpUkhpfXpjSf12VHAR4pfNm8PuASGkVAhjLub8TwgpcqjgIoSQsGhpAfr2zd5WWxtOX4DCLGy5eCaEEFLuqLKU9jinPj7lHNww+veW+zY0d+EcTYhfKivD7gEhpFjxOvdyriaEkByo4CKEkLAYNQpYujR7W6l7TXFBTgghpNwxem8DBZv7X551Ml6feyK0Pr1z9lVonJ8J8U2pr98JIYXD7X2Z4QQJISQHKrgIISQsmppytxXyBbmqCnjhhVwlW4oKThGEEEKIclQpuBQajVDBRQghhBQBNBglhJAcKL0khJAoUUgFV//+wKxZwBNPFK5NLsgJIYSUO2br6wh4f1RqtAgnhBBCQieIB9fGjUAsprQ7hBBSDFDBRQghUaKQQq716533R0DgRgghhJQcZg+uCKDRg0sOGuoQQghRiXlecZtn7PZXVwN33QU8/LCafhFCSBFBBRchhISF1eK0kIITvwmxN2wAXnsNWLnS23Hz5gGffeavTUIIIaRUMM/1IYQo/NOYK/HdygOU1VcW6Drw2GPAO++E3RMSNWgURggpFHYeXAsWJP5XVRWuL4QQEhGo4CKEkChRDMKlvn2BiROBp5/2dtyzz+anP4QQQkgxE4KCq9eck/Ha3BN9H1+WLFsGrFgB/PBD2D0h5UifPkBLS9i9IISoxqsHl52Ci4p2QkgZQwUXIYREiSgpuCpspoiamsR/vmQTQggh3gljrndpUwgtWmuQKELhIQmTSZOA777L3rZyJdDQEE5/CCHRgnM4IaSMaRd2BwghpGyxWoQ6JY3NN7KLYjvFFyGEEEK8E4IHFwB8vPgojHp3v0RVoPLGE0JQ4UUKj1GZtW4d8Mgjic+9e4fTH0KIevzO7UHkCFSOEUKKHEopCSGEeIMCHUIIIcQ/qnJwKaBq8zahta2EhobCebAYr1PKm50QoHDPcDye+bxmTWHaJIQUFjdlE9/FCSEkByq4CCEkSoRpPWVu+/33gfnzw+kHrcgIIYSUCyF5cBU9QgB33pn4K/S5oICRhE1lZdg9IISoQNX8ZayHqQQIIWUGFVyEEELs+e9/c7flU4gkBPDMM8Czz+avDUIIIaQUUD0fF7PCjAouQoLT3JwIdzh9etg9IaR88TufGY/761+BtjbXQ2J6BVrjVJYTQoofKrgIISQsrBavxSBcMvZx2jS1dcfjwKJFCc+xYjgXhBBCSDHgMqcW5ZRr7HQsVti2qeAipcjQodBuvgmj/jUk7J4QQuywm3/ME/nata5V/d/w67D3h48p6BQhhIQLFVyEEFKuqJBmvfJK8DrsKLSwihBCCCkCnpx6Fir6vOr5OCedjIAGDBgQoFchoOuZz1wzkDJkSf122NjSWV2FjY0AgOk1u6mrkxDiDRUeXED2HGnDpA17YFVjV3/tEUJIhKCCixBCokQxmFDnO0RhColFOSGksCya04q+z9UDs2Yxvj8hfgk4j07esAcEKtTPx3PnAuvWqa0znxh/f6HXTxV8jSbhs/eHj+P/hl8XdjcIIUFIzl+//uZWPDHlbP/zmfndWaKeCq0IZA+EECIBV+aEEBIWYSuzzKbcYfcHyF6YR6E/hJAsnrxiKq6/Y2sse+y9/HpwElJORGm+KybFdaEVXFG6TiRaaBoW129f2FskuY6vbd1CTX31SeMVQkhhSQ4c36w4FP0WHe0+18iGKJRAA+c1QkhpQAUXIYSERdiCkrDbtyJMa2xCiDutCeH3Xv2ewIKfakLuDCElgsf5Li3a8nycRPmZMz3VGSqF9vo2tkcPLmJE17HPh4/h25UHh90T/9x/f9qDkytwQgqIYf6K6RXAf/+b8Ki2Q6GCS8mxhBASAbgyJ4SQKFEMi8tChSgshnNBSBnTHG8XdhcIKVqmVe+GNU1b+zo2rajyMk+6lE3vnTLFV59CgR5cJAp8/jkwdiwAoKGtY37byuM9WL2xEv8af0He6ieE2GB4ruMiKaLt0ydQPZbfLWCIQkJIqUAFFyGEhMyMml1Q39op8aUYhCdUcBFCCCGB6Pnp/bjsuxt9Hav5FEjZ2HwDAITQUh981R0KRq+tQntwFdN5Ivnlm2/SH+N68YpXhqw8CI9P/XXY3SCk/DDMX2kFlx8vYR8KLoYoJISUCsW7AiOEkGInueg8rP+DuOuni0PuTESg8IgQQkipk5zfWnx6QaYtrj16cEkpxo4/3lefQoEeXCRi6I5qZAUYQ5PZhSlTQGNbR1RV5a16QogRq7nFyWhDVYjCXr3yOYwUjro6oKEh7F4QQkKGCi5CCIkAzfH2YXfBmi0sElfTg4sQQggJjJ6y1Pabg8ujZMrJUlukat1yS091hoYQwIwZ2d8L0SYhDqQ9IRVy8ZCbce/4C50Lvf460NKirM17J1yE7t2VVUcIcUKVB7IXDy4hgClTit+DKx4H/vlP4M47w+4JISRkqOAihJAIUKElF7ZhCk+EwNyNO+W9mf9MOwOt8UrbPlh+JoQQQkqF5PxW6FlOSh9WLHPv1KnAm29mvhciRCEpb1pagCeeAIYNsy2i50HBNWDpEXh/4TG5O5IP9I9V+0C74Xpg+HDlbRNCCouUkrytDfj4Y6uD5RuKxQA4hy4uCpqbM5+LZf1CCMkLVHARQkhYGBZhHyw8Bl3feja09lMc+PHD2NjS2bFM0MXjP376LeZulDAL5SKVEEJICePX2yOvFtfFMvfOm5f9nR5cJN/88AOwdKm1YDlJvkIULt+0Hfb78GHne7CpKVAbvLsJCQk/c4uDol2K1lYA/nN6Roakog5AwpuLEFK2+Av8TgghRCnN8Q5ojncIV3iStARt1V2mBgV9lFpMU5BESOQoektPQiJE3vP1mJBSjBXL3GsWZBXag6tYzhNRh43wVAhgScP2eW9+Yb1LlAXek4QUPYGeYrObttOYUCrKoLa2zGd6chNS1tCDixBCwiIP3lGB2k9+jxstyvPUnwq75TtDFBJSNOQj1wgh5cS06t2x1ZvP+5/vqqqAgQPlygrhqE4ruinXLJwruh9ASoVvVhyCfT58DEB5zIvV1cDq1WH3gpASx2OOTctjnObFpDKo6HNwJT3RAHAdQEiZQwUXIYSQLOK6y9SgYPFYUezhEAiJADU1wLp1YfeCEBKETW2dPB+T5QX99ddK+pFWfxWLgMgYlggojOV2sZwbUjC++w6YvGGPsLsBAHh31B5BoxTmYuHlcdppwK67Km6HEJKNCgWXE8n5zE8zkYIeXISQJFRwEUJIuWLrwZX/qUHThLugyLxI3biRwiVCDJx4bCt22qkEXk4JKTds5t98U1Lhgc0KLubgIvnGYrI94wzg/okXhNAZ5PTn6peOwTffKG5jxYqcTTSsIUQxYcwtpTKfyUR/EYLKL0LKACq4CCEkLKIWojC1GRpWbOqK3w//o6fjvDUtIZE3tjN7NnDXXcBHHwVum5CSIB7H6uVt7uUUsvPOwNvzjytom4QQEytX5m6rrZU6tOhDERmhsIqEwMK6HRDXNQgBHH987n7nQKBy1NQYHKc8rrkL+Rqxfn3h2iKkZInFgAUL8lO304CQ8uBKrQuKVeFl7LfduuDFF4G//S07nCEhpOSggosQQkgWQgDfrToQ7y881lEJd+e4S7CxpbO/NqwEAG1twDvvWB8weHDi/4gRvtojpOQwv8StXZv3JteuBVr19nlvh5Cyw4tg6ZFHcrfdfTewaJHzcU1Nct6epSDkKkQbxXqeSCD2++hRvDHveOg6MHZsftrYbruEPDYSONznO+5YwH4QUqq8/z4wb56aurzMS2kFV5Ejo+CaNQtobgaWLi1Ilwgh4UAFFyGElCNjxyaS01sgoElZef93+pkYs3Y/X80LgdxF+OTJwIwZme/GRWplZebzZ5/5apOQksZOOUwIKTksBVKff+4s3PrnPx3ndqprCJGjavPWtvv6Lz4SL78U/GlatSpwFWqwGFMYFpkQhSQ15Vqf3gCcvUD7LTw6XS4wpWik4fabSvE3E0LSSCu4NE2r1DRtiqZpXyW/v6Vp2hJN06Ym/3omt2uapr2gadpCTdOma5p2pKGOP2iatiD59wflv4YQQoqdQi283n7bvgseqtFlQg1atmFxnDmov/FctGuX+fztt77aJKSksUgETwgpTSxzac2fD0yd6rvORyafiz9//7viFQAVImRhsZ4bEhwhgMZGAIm1r52SZ9CKQ3HrXzT/90qyjbSGy6keapoIKV0snu+5G7s7HyMEZtbskvXdqWyimRKa19zWAZzDiZnx44Hvvgu7F0QRXjy4bgMwx7TtH0KInsm/qclt5wDYL/l3I4BXAUDTtG4AHgRwLIBjADyoaVrXAH0nhJDiJuwcXDZtC6E5L3YNfYwLf47AAsgVyPNFnRBCSDkQcK63nS1HjfJ3HICvlx+GV2efUjwCoDDWDAxRWL689RYwaFD6q+vl37jRXztDhybqnzULeO457/fZlCn+2rXhvc+7KK2PEOKM0yPfvsL07mxR+ND+D2Jpw3bulZVKiEIjbgquOWZxNil7+vYFPvnE/5xNIoWUZFLTtN0AnAvgdYniFwB4RyQYB2BbTdN2BnAWgKFCiBohRC2AoQDO9tlv4sLLLwOnHtMI3HcfY80SQixZv7mLZZiD8wbfiocnnZf44vJi7duDS2jA3/+eSKxrx3PPZSxZmUyekJLhqquAjz4KuxeERAiPQmzbUINOcyokLbWLVXFTrP0mxcG4cemPTiHE0rg8i7YY17tz5wLLl9uXtVLyjh/vr10brvq7c6Kt004DPv5YaZOEEBsqNbn34ZZ4O/dCpfJu7cXwJJXTu6EB+PHHRP5vQgCgpSXsHhAFyJre/w/APwGYR8HHkmEIn9M0rWNy264AVhjKrExus9tO8kD//sDICVsC69cntF2EkOKggAKaplgHy+3z6rpjccMO9gcq8eDSEguJNWty9n29/BBMWLcnUFsLfPllTpuW3wkhRcN77wFvvBF2LwgJEc5hwZH14Fq6NDHg1NcHb5PXjaQYMcJ5v9+wwcacs4Dne64gd2hba/rjiBHAgAGFaJSQMsNijstRcNm8Hw9deSAGrziYIQrtePHFhFfuF1/ktTukiOD6riRwlUxqmnYegHVCiEmmXfcAOADA0QC6AbhLRYc0TbtR07SJmqZNXL9+vYoqiV8LMkJIftE0NLZZK5kKQYdKibEhT2EUszy/TPWdN/gvuHL4dYkvTU3JA0rEyowQVQiR/e4bwsJ87eat4XepxvcIQgx49eCyE0i5KH1sPb+KEfNvtTqHDQ3AE08AP/1EKTwJxIpNXdNRD4asPBD//VeNmordnv0CTpZCIirDd98BsU3NBegNIeVJ2kPUYj6vkFRG/WXs73DON391aSgVorCE1gWy4+WyZYn/M2bkry+kKHhx5qnFnX+WZCFjen88gPM1TVsK4EMAp2ma9p4QYk0yDGELgDeRyKsFAKsA7G44frfkNrvtWQgh+gghjhJCHLXDDg4eBMSZdesyn/mwEhJJ2mIaurz5YvbGAj6vlVoq55b3YxfW7eD7WMBgYdrcDNx9N9CvX1ZlMZG0YE1ZwJobosKLkNA5a9DtOPZYNXWtWcM0fITIknpU3l9wjKPBSM5xMs9Ysb43WPX7zjsznzdtUtLGmDX7oq61U/GeJ+KL5Zu6pT+PrdoXd42/xPkAmfvjgw+Ae+5xDo0UgftMCGDgwMT4ccYZwPrmrcPuEiFlSY6CK8j4YM7BFYGxxhfGfnuVD/j1tCUlwwszTy2u/LPEEVcFlxDiHiHEbkKIvQBcAWC4EOL3ybxa0DRNA3AhgJnJQwYCuFpLcByAOiHEGgDfAjhT07SumqZ1BXBmchvJA9pGg1WZz4f1sMPotUtIPpGK4Z/P9pNDQ5temb3BheUNXbHfR48GbDv526dOTST1HDkya39MT05PdgtVLkJIuRORZ8CvB5dZ0F6jyBiekHIg5cH1+xHXYXXjNpkdbgquUrLUTg4iM2p2weL67d3HRBUadCFw0pf/wKOTzw1eFyGjRiXCcU+fntmWvE9jemUij86SJZ6qlPHCssPOM1QIYN48f3XW1tJJghCvNMXaW273rOAqhxCFXnJwEWIiZfBN4+nSwF/ylATva5o2A8AMANsDSEk7BwFYDGAhgNcA/BkAhBA1AB4BMCH593ByG8kju7//hG/DhBkzgCFD1PaHEJLBcg0WwsIsreCSpDWeKe9XSZc+zphrwOjB5aZ04yKEkEjg24uT76CEeMdscQ3TPOyq4JJvo1g4rP+DOO5zi0j55nWCwt9VXGeIBEFF6jZX2toyn5MKrudnno5ubz8LTJhgf1wRuD3/+c8Jo1k7hABOP71w/SEk6izftB22fOMly30V5hxcZrzkrC4FD66mJuCrrzLfKR8gHkk/U8V4/5McPCm4hBAjhRDnJT+fJoQ4VAhxiBDi90KITcntQghxixBin+T+iYbj3xBC7Jv8e1PtTyFWrGzshljc/+J3wgTgoYfU9YcQUiCEcJ2oU0KxtHAsT/m2LNtOVVthPQ3F3SxQuQghpLA0Niqtbvly4MknlVZJSPHgRQhlJCm8MXpiCQAT1++ZyA9kV08pzpkG4X5zvH3ubzQqDQA1gq9SPI/EkW22AX79a+Bhr157Cu6VpljHwHWoIMhPaXZJ1xWLAcOH+6+fkJLFKgeX2bwiYIjCVY3bYnPc2lvMb50F5aOPgLlz/bfPOb3sqSx2D0aSRRAPLlIGTJgA/PvfYfeCkNIkb2sqXU9Ijvv0cW4/VdxjOBNNZIREfn+CpeeX4YTEBUMUEhIWq1cDL5mNR01hRM0MGpSINirLvHmJ1COEEA+kFFyGF3IhNCyo2zFrvx1FH4rISEUFbh97GYDkmsK8LjCHsOC6gfjkm2+AISsPDrsbAIC1Gzvl5VZuaAA2xzqor9gBXWcKHEK8kBOi0IyXwUEI7Pb+U5hWvXuwTqUYOhT45z+BDRvU1CeDOYQrPbiIR9IeXCF4RC9dyqWpaqjgItY0NeVsmjYN+OyzooiGQEhRkLcQhWvXJmbMyZNd2k95cHlr2m8Oj+emn479Pnw4q027N9uYW9jEWMxXH7LYsCERh9Vs5U2IQhQ7PtmjcIX82mvAX/7i7ZhzzwUefhhYvFhZNwgpH2Sf3+ScaQ5RmDZUUeHBVSxv25qG52cmYptJradUe3AVy3kikeXDhUdhyzdeyN7o8qK9819+iw8+UN+X3XcHrh99teW+fN3qt9wC7LSTe7lVq4ApU4BHHslPPwgpFpTm4FIdxrd//0RM1y++CFaPD/b78GH8bwZjnRLvpKfcENZ0PXoAX35Z8GZLGiq4iDXDhmV/13X07AlcckkovSGkJAlbNpLyotKFw1TgFubQg/fXyDU/w8L6nbKPMyqqDG3pTmETAeDvfwcWLJBu25IXXwQ+/RQYMCBYPYTYMHUq0KVLniov9AAiYd3y3HPAPvsUoC+ElCsWShohXEING47za6ASScwhjt3CPiocM2nrVyb88IP/YyXutx/X7e0ehrBj7v71M9YkPljMy35z49bV2e/L13Jj4sRMjjOnNnbbDTjySOCBB/LTD0KKBc8KLify9WAX8v0k2dbC+p0wbNUBvo8nJCwaGsLuQWlBBRexxuxVsWhROP0gpIQReuFyXlm2L6z/u/XHr4Ds6+WHpj+7hUVMN+t0Pj780Fc/0qxdm/i/cGGwegixYf36AjaWx7FD14HPf5QwsyaE+MNrDi4t2yAkPae6hPUtqSgMbj8mHwouCsPKixA8EcwsWr9V7sbRYwrfkYBoGrBpk/1+PlqEuJMOp2aHF8/lUlBwGWSWUvKJvfbKX18IIaFDBRexxmwVyXi2hBSGQiq4Uh5c0DwFAc5Obm+R98KGuMiEHUxbmNoIqBpjnaD16Z2p26qcijCFhOSRoMLk++7LWDd7RdfVTd3z5gEXPfpzy32NjcF/Z2MjLdhImeFnrt+8GXj9dQCmEIVGgxGXEIVSAqBikTQb3lUENPdwSwp/l1BcH4koQSY3D/fHwhcGJeLwWbS574eP+e+DIsTUaf6PNZwGp5DNfJwIMWEefxYtQoV5DlfpwVWMD6Fh3tc04f4bevTI/l6Mv5mUBqkUGQXLZVAeUMFFrHEL+0EICYzfMCKuGIU8Ds9uxklKA554AqLOpyRdcnz400Ej058vHPInXD3imuzFuxBoirWXr1tVZuqSMmknpcRjjwHff+/v2OOPB847T21/8sUppwC/+EXYvSAk4nz1FTBnDoBcQ5P0d1cFlwTFsuY3KrisvMLNCi+FObjiOl+hS52aGmDqul0K0tZ+Hz2KaXe+6/3AQj2rH32kpBqn7hbLsENIPpC6/59+OjdEYZCKS+GhM6wDnNY3G1s6Y1H99rk7SuEckOJk0KDEfybhUgpX5yVKtmWnjwqo4CIk7+TtsXJLgp7clhIIpRVttbVS1WsiIyQSAtJCo207bE5/3tC8FT5Z/HOgogIPTTwPVU1bAUJgyzdecqyjTa/IhGLiuEQijnkq9YPtbe5y/48bB4wa5b29p57yd5xfNC2RhyMNPcZJOeBn/jIkyTHZhmSEXql6N2wAHnkEmDIla7vmVTgWVVpbgeXL018L7VEVd8pdSkqCv/4VOOKt23wf/9ubunkq39zsELEgZERLq+djUjZoTj8naxwbWcCFByERQ9botTIZovCiITdjbdPWwXJP5suDq5Dv55Lj5Z+/vzLhDUvZATERWm7aVIoMenAphatzQggJCdmcV55x82wytZFSGEnn4PKZ4Na8gEi1++/Jv8EXy3o69lUI4I25v8TWbz6PO8f91lO7lrz1lv9jCZFEhZyq0Pqeu+8GRowwbSxkHs7vvitcW4REBZn5zFAmx4MrNS+nBozhw4GVK4FevdLbn59xGiZv2NN732pro6d4fuklYPHi9FdL4WA+BFkpDy4quEqe+AY5oy87Ph20haKehI/XiBPz5gHt2nls5OOPPR5ASOkg+w6eMmb5fOkR+LFqb/d5rtQ9uCRpaOtkvaOMzgGJFq2610mSyMDVeRmgJAxa1F5sCSkB8ramMj6vVs9ucltqbAjcDcnxwRxWQUADhgxJfBawPiHJbfXNHXDd6D+gOd4B49ftlbXPFz/+6P9YQmRZswZAQtHVr5+/Kvx6cCnlnXcK19bs2YVri5AixWhoImCI3JAaF8we2ULg+ZmnyVVuHFtmzUpovV97zW9X88O8eVlfLdcQybXJ8k1dccIX/1AzZibriDFEYcnTfvHcgrZnGWbTVz1KqgnEunWJ/6efDowZI3fMZ4t7YsaM/PWJkCijS8rrjO/SMStDCy9eWfmS74XkweXkyW3boygMmCQaFPBe+PproOM//XuIE3u4OieEkCihUADjxIcLj8LLs04BAOjJBbKsMlzLWkxq8h5cJgWXLkz1WHmeJetujVemN9WnrLC4KCURYuFCoKkpe5vW74P051mzfFQIQIyVVMZaPA9F+YgUZacJKQBZHlzGzYYcXCmBVceOOcf6MngbPTrxf/Jk78cWGhvB3vh1PfBD1b5Km4qJCo5VJU5lWOE8vbh+F+gedB07bCJHDB8OVFcb6nHo7hXDbsCf/+yjc4QUObrQsMQqP5QFRgVXXA84D5kVXMU4pxllEg5GArb7ivE3k7yw6zGFybkJAEuXFqypsoMKrjJAybhtrmTTJl/VHHooUFWloD+ElAB5W1MZXzRtGrntx8vxn+ln5rcfRtracl6PjS/MutCAWCz3uJS3maGPbXpS2eW34/mKOU7Kmv32A+65x36/59ssmXxWnzzVd5+8EIsB++9fkKac8XiiYjFgwoQ89YWQAnHL6Muwfr18+dwQhakvNnl8hPA31VnNyxHE0tgmlXdMZX4Frhciz4Rek/DEFdOica1WrXLcHb1sWw44uVdNm+arSothyh1DaFJCSoFPFv8c+3/8SO4OyxCFGaVUTFTm7M/ByUur2CM0DRyYcReFN6NbUj5cey0wZ457udVV7XItVUnRQQVXiRI4ibSL9djQJyf50nHNnAnMLWzEB0Iii6+cEUOGAE88ATQ325cxLlht6mtfkVGCpcIi+MkJJj3S3HprbohC88GSgrS0FVaqAq8LdCq4SJ6oq8v+niWE9nCbtbQA01bvACDb0/G997JlOyoFt5s3A/PnK6rMaLJtxvAyaonH57FfP+CYYzwdQkj4mO7zV2aenJv7zkxybV7dvGWWYEs3enDZPT9+5zm3nJ4RwdI6m3N7WfL008C9Hx2eyEEXNqkceAVESWoCc50C0ObPsy8Q96cI95WndNkyX20RElU2tnSWLlthWPfHgnpw5Wt+L9Tc+/XX0kUZorB8eestYMAAycLvv5/PrpACQAVXGTBq9X7BhVYmYfqZj52MF1/0VxXnEUIS+HoWPv004df8/ff2ZdwUXEJkKbi8xv3PqdLth2zeDCBXGC8MU5CABrRvb9uY8YVdGPf16wfcdhtQXy/T9QSlEJKBFJxhw9zLqMrl/MILQM+nfgcgOy7/VVcBDz8sX09otLTY73NxU5m/bltpA7rTTwfuustDvwiJMGKe3GJ9+3eexUuzTs0cB0PYIjsPLi+GIMbBqkgUXAAsjVfu/ukivDrnZOVtqMqXRPKI0zwkgRIDksbG4HVY4LSeGLn6Z/jsM8XteVSa+VJcgctxQtzIycHlZrTp5aEq0gewtmULAM5Gt+kxTMsY9U5Yt2fR/mbiDenLPM/BkCNPBHZMIVlQwVUG/PqrP+Pyy7O3tbQAXbt6qMTCs4LzASHBELq18kmKtjb7fRIeXB0qMs90ykNE+gVWMt51mqSAzOzBZUQXGtDZwoLNQliX7qcQwMiRQGsrMG6cez/s4GBGXGhqAn71K/dyZhmyUcgj4vIC5qROOHGc6RlLt1GI+1ZRG62thi8ukq/9n74O996b+Lxxo3O9w4cDa9YE6hopR4QAxo+P3s0zapTzfptn54WZp+HS725KfLF7ZuNx+Tm+COdEy+Tyuo6npp2NYasOVNiQsP5MokcUwm/5uUcktUNCAOfdlJszpM/ck3DJJR7bzJMizozb6Vi1Cnj88YJ0hZDoYzHfaOYcXF7q8LKvSKhr7YRubz8HwFsOrs+X9sQxn99bEueAuBOF5UAWxWQ8VmRQwVWm1Ne7C47yhV+rLkKIAacHyWOIwrTQSzKufY6QzG1xKJEHQwDWk33aWtqiOYnfacfqxm0wdOWB0Pr09nQcIV7IClE49kfp4yocVme2i/R8vKQ55dxw4PTLumXZxXTsCHz7bfKLxCKgoSHxv2tXJuIleWDRIqBvX+Chh8LuSRauj7DNs7NiU7fMl9QAoWnY0Lxl1nZfXkcVFfhu5QGRlwFJhXxW+CPyEQaOBEfTgGGrD0h8iYAAydKQzQ3JF2VdB74e2cV7/VasXeu4u1DP/7JlwL/+ld82Zs/2aORLSJ6xfeSND56u44Jv/4SLhvw5vSkmKqXfwaVQ9aAXcMHQGm/nq+3qli3dC5GSwe62+P57IC5CUIkMHlz4NssEKrjKhfo61yK/+x1QW5tQfp12d/4SWkT9JZmQQhHoWVCo4Ep7cEn2J0vZ5EHIY9XlIz/9V7JOTdq8JrBgSQicMPAfOHPQ7envhDjhpHAy4mgo2SCfuDLL8ws2Hlwy6AEFfDU1vg4b/kOnHIPw5cv9daFAhuWknKiqCrsHlvid23bqbAjRa/B63uGdZ/HdyqSw36/5qqbhjEF/w8L6Hf0dXyCEQF4VWuY6uWpQz+bNwIYNweupjZDgsmgUoZWV7mXygcwzqjiX2pQp4Rn5EmJFpWY9P2c9HkJg6MqDsvbHrDy4QswzHdMrMHXDbgVrD8j2aHMOUZhN2vuNMoCywM7Y5MQTgRk1hb1nAWSl1nhq6lm4+mreh6qggqtEyfGUMGe+t+DDD4GpUxOGrSOmb2eqsEgW6IQUEZZrKtmFlouC64kpZ2NR/fYOIQotPLgk8fvCbuXBNaV6j0ydkh5ci+p3THheBViU1rcawiFycUtckJ0CzbdS9otXMGWwXRuWpEKYxgIquCIX04EQBUTAs8PqQXZ9tG0GhpjR+tRU74bmpIdHPC6vlLHoW5sekgBcEgGJXCQq2xMa1w6KueYaYIcdwu5FBiV5McK8R7y07WLF4/Vn2K1hBgwwpRBeIWH5smqVt8YJKTLaVVivSXSj17XFejxulYMrRN6ZfxyO+Oz+0Prk5T1LLxbjAxKM5PyhjxsvVdyX17UPjDKx6TW74913uaZUBRVcZUJgC7KIKbgWLOAYQIofqZA6rpWI3Bx5uo57J1yEXrNtEqsLkR2iUCT+amStXrNycDmUmzABeO45YPp0ABI5uKwqc2qgqcmtp7ZkCS44mJQ9jzwCbLONezmVkUCccFRwpV5ynRpTpZjKh4JLYj0R9Dw2Nwc7npQ4xaq4tXl2Plh4bOaLIUQhYBDi6Lq/d4Gk4LsopskCenC9vzB/kS7KFb9evlEmX8+NgOZet5fGXeZlVb/j1luBq64yNCtjbGDuW8RkEoQEpdLm/dis4DKXiukShh1O6x3Fc2ZjrKNzAV0HhgwBqqsDtWOHbRjmn37K2detY1PqoLz0hUSECRMAAGL9eqnioXpdR8H4rgSggqtMcVobWu6L2GLyZz8Dvvgi7F4QEgwlIQr79AFuuSXbSzO5mG3TK20XtlkhCqHhrfm/xK7vP51TrrGtQ26/sz47WJy8/jowdy7wzjuJLjvYj+tCA0aOzN2R8uDSXKYrtxhypiReWX3h4rbsGTcuK1qALUEUXF7uMsvbORm7SZ8+y3vjMuT5OUhVv2Rl+7y209QEdO7sXo6UMVEY8608uPzkyJKoF0DCgyvAzy5Ka+c8Xue4iLZHG0EknnNfwjLJd27XnxdRRb7UZVmxIvNZtQwiJeiM6Pkh5UeFTYjCrFvUwkglFrF5yDWX0WuvAZ9+Ctx7r7I2jesmy6GlrQ14442cfbtvWZOqQFlfSATRUmk4LOYRC0WrrmIdTkKFCi6ShYoxvrY2eB0ybJJPZ0JI6TJ5cvZ/IP0g24YUMnlwPT7lHPxx1B8si65q3Db3cMPU4Ulo7+bBZYXLoKQLDc2xds4vwC0twP33A/37+2qDlCarVgE77ZT4LCs/sZKH6LrB4KK1xf5YD0lsK9auzt04f36inpbWxHeJ+3ZzvIP0b9M++lC2e75pagL2PmOf/FTe0AAgE52REFsiaiVpKQzfuBF46ilg0iS5gco0LqRf6r38ZmMdTsKBlhbg8cfTXtqhU0APLpI/ImZTGQiv4Y7mzgV2uOwURY2ru1cLbtU+c2b+6h44MPE/K1YiIeHRzk7BZZx3LZ5nSw8uM46Wd2rns3iqv3b1GmUVisiRH7h4sDU2J2QjqXnm+u+ukMnkQoqVpMWo8T5Ztiwpr07NBUa4xit6qOAiclg87FOmAO+9l1u0Wzdg8OAC9ImQIsfyxVfhxNqmV1rXN2hQloLr0yVH2taxyS3cgCTLN3VFtUMIRDfrcLvTcu/4C9H5jZftJSJNTcD//pew2Bw6NF1ZKQlQiD/mzwfWrfN2jNV9uGgRcOGFic+tk2dhzZrMPr+3mfbj2Nw206HC8nTz/vhj7jaF45Fmk2ZPGV99lW6HkGLE8nEbOBBYvDjhra1pifyTMpWkQhSmxgu/3gopBZfVaNa7d0JS8PLL/uoOQHOsHeZv3NG5UAFzcpEIYLawDHi9VUwlXhVDU6YAG+pyIydY1j1nrnMBm2e+uhr4xS88dStSHHD1MaipcS4zYYLEWkAyZBUh+abSRsEV100hCk1DmqUha5B5L+CY6cWQTwUxvQLN8UxUCEtj2eRvSuUjzTqnAPrO/gWmvD87f50k4ZJ6dzbMxXvtBVx6qXVxLhOLHyq4iG/698+Oo23Eac0Yi+WmDCKkHPFjEbm2aWs8P+M0i8psksKbt69bBwwebMrB5a0fxn7LJlrf84Mn8eTUc2z32y6KXYRyM2p2TXywC1HYr19COGiCIQpJpeG90FUQkoxfKBpyXYfbGyLufbb0SOyyi3UV3tJhZAr/bvgNOOUUmU76p60N2BzLb+jAvJN03aKCixQrlmsC44JZ5ua2y9Gn61jd1FWyI1YeXBblOqoxgPHDE1PPwf4fP5K90UvuEb8Irh0Kga9TmzJiKmJk56/X5p6A/7uw0bmQzUmcPTsRltkLbtfDtzGPQ2QHO+at2MJqWZ9m6lSmMSDFhV2EEzcPro2tWxQuObAE8QKHd/v98D9ir35PpL/bpU2ob+2E2bU7A7AWF+iDaJkfJVpb7YPueMZmUq2pgXt6i0LDNaUSInZViSpcp5fRoxP/582Tq9DlgauvT0QqkeGMM4rbcowQVVg+Vi7P2r8mXIDbf7zc+ThjiEJzfc3NALJzcDm1aDWW5GP+dYt5bCX4i+sGNZXdImW2tVVW1eZtDJVzQVGOtGsnX1a88SYAQO/7Zs6+9pJ6odQ9PHJkItF6r172Zc13+6hRsFykG7e0xf0v6f7wB2Cnd5/xfXwkSD7HVHCRUuOJKWcnQvH6ID13+nWfdPLg2n57f3UqoL61U+5GC8v1Si2PbqN9+uSv7jLH17IsgoO/198h+xPmbtwZ/Zf83HN/amsTUU+9kq/fIVWBRWVO/TniCOCxx5JfnJTcXPuTiNCuwl8OrriMkWkBPbjieoWSemSZtGGPrO+WBru6jpO+vBOteuJlLSU5yBKbFGOO0RLm229zPaySaai9k17DWmAhO3K7F+bOBfr2td7X2AisXeuxf0Q5VHCVK198nvj/8cdKqttmG+Dqq+XLT5wYvE2uS0mxYzmJutzYb8w7Qbp+SwVXcrVstBZzUi5Z6uA0Yw4uOQ8uN/zk4Gr3ei9MTi1u7d6mrRLyMGwRQbYHlysLFgAA9Lm5RiHarBm527RcT+bUXXbqqYmIXv/7n4f2U5U60OGR+20X3W7MmJGdW6+YiaCMk0QNTcOqxm3DTSZtMe/YzUT3TrgIU6t393Vzp+sMGKKwKBJvWyq4dPv9AdtoWUZJRqSI4ODv7vlU2PVnz57A+edbdcT53P17/Nn4ZsUhSvvSowcwcf2eruU2Nwe4rqNG+T+WEJXMnQu8+aalRbhdiELduC63GEzSCqUoMGqUu6Jo662VvnKbhy3LqoXAwrodco4x9rUo1jdlTF0dsMMO7uVkyBJ9e/TgisWAhx8Grr8+8f3ZZ4F33snsv+46YOedA3SO8iglRGhUJKGQeuFNBbOuqoLWvNlXVQsXZj4X4vmsrQVWr85/O4TkjSAPisSLvGVs7rSCK7OYjgeImb2pTU2IIjcFl92pSnti2ZwPEdfx+dLDcdXwa/H+gmOsy3hMAk6Kn/p6oHH6IgDAb89vcc97nA7TlX2fjRgBfPnINMtDzFZcL8w8HdtIOg5aCr3MnhQWFcyYWt7xf//7X+CKKxKf+Z5AnNjt/afw1rxohROwtD5OzYHQpOb9HJsWP/O7VYhCK8GVuT8tLcB//pNwU80zUroMIVDpIwSaW50pThp4JweaPKHEgytoDi4F907BPZ9cGl++3F9Vz089BcNXHyB/QDJahBNLlwJtuo1navJErFsHbHFirqea9HldtEiyICF55rnnErFBLRLV2ym6zTm4zMREhbvRZqG8GD/4IGM8a1fvIYeg4rXemFUTRAuQwXzenHJwpY+xcvKigivSWNkq++XyZBAku1vU6ZFo3z6R+SLF3/8O3Hln5rsxB7cvuJ5UAhVcZYLt85IKWfJmMuzS++8Db73loYLwuO02YNddw+4FIf5R8VjN27gT/jjyasvKWvXK3IVtspzRgysu7F1Z3EIU/n2cTZZOAK/OPgk9P70PG1s625ZJodtZfcmeJBupQLPoiIuG/BnvLTwOfecdb90EQxOUFTfeCOy2G3D6jfsAAD79sqO0sYT5Xrn4YuDm739vXdbi1k2m8rLdn8LqdtaOPsq9fz+Ndy3jCYVzfyGipLzxBvDVV8HqIOVDbesWobZfYbLadvLq1oU/BVf6u98HwikHF4AmY+6+sWMT3q5GCUCesBQIWvx48zlWyfj1PfKT56tc2VgrVeyHH4CqKosdHrVD8+YBDQ2Z7/feC9x3n6cqXInCPNTcnG2EWhA++sh2l5dz0miTZsy2jlq5eyhVySOP2OcTJyQveIgRag5RaM7VZWnAEmTQ8Xts8jg7T7Q0yTG6avPW/toxV2fuhlVUGSFMucNTZU3HkciiwvBDCC3rgVI1N7e0AIMGJT576acKAxpiDRVc5U7q6TZOtj6DnEZhEU9IMeEnB9cNB4zJ+v7pkiPx5vzjE2/8QOLlLjXTWpHy4AoQFkV2ITho+aGYVr07ur79P/c63XJwuXVXYgBKt2Eqq8c5eJUTr72WLdRy49NPgUcm/RpA7m3mtJjNd97nufMtlnCb/Xlg54sNG4BHHw1Wh+x5iuta1vUQcQqeiQ3JGyXIPKikGx7al7UwzjHeDiq4cfLgqqjAlm+8hNFr9kt8j4XsQWphua48ap0X63jiCa22Jv3Zadw/4YREHsvcCrxd7AMOAG6/PfF5jz2AJ54AHn/cUxWu3Dvu/PTy3C1XtXIha/IkPv44sN9+aqs2M379Xrjppsx3rUpN+E7Pz6/HMeid11vw3ntqPQQIsWLFpq7Y84PHLe9Ru2c/K0ShrluvWfL9siFDdTUAw5rGrs3kA61EuC8E5tV1z9ok48GV2Zyr9CLRYtSoxNisbB33xRfZ363yO3qch+vrgXPPTXz2GPEwF96ISqCCq0xJPbpvTTsCAwYAj4w5JczuEFKW+JnHOlRkL4zTdaRcUJ57Ltu01UYYU2mT0Danj1YbFYeBAfzl4JIhy2rLa9uEAHjoIeDxKecAyF34ui26nQTYxlt7/PiEIk3quOT/A4/uguqWLu6Vh8gXXwD335+nyk2/sd0fr8asWYbdAz7PU8OkVDBbQxeaHOtjh+7IhijUTYYcqkIUOs2Ta5qcQwVbsnAhfndpDD/95L17iabkrl3elZhUcDnSqxfwyis+Dhw92nG35bPiQwqW8qpesSK73uXLbcJ8e+T56afixRcTnzt1CsGTCon8JflmUf2O6NNHXX3PfbanJ0MkO4569AIMH26zU4i0gW+HDsHbIsSJmTW7YPmm7Tytz83zrqVHchQUXMmkxq5rqjwbF1kay0rM0YENgUheOOUUU86soJgnAwUKLqvqvv/edxVEAVRwlTnXfnkxLr4YeGDU6flvzGKCveWWhGCPkHJExZqzvi0Z/u/4ZPg9c9wWm3hFsotLK+FYPtbKcVGButZOuTuM+Ud8oGsZAYVd7iLKp0gai5vBuP71ogxdtw72Cihk34bXXQf89rfGNv09ZHaekJqWrUCz2p9v1q3zJ9wbNw7WOdLMSc5MiNFjHPcTUqGJhPdzGJOAEDnPuZ2XFAC0xNt5U3DZfJftWxqnNv0OHLNmAc88gw/7t8MnTy32V4cVFuudLGMeFYsXt3wnJIs//SnxrucVMeibwG1XHHaw72P33BN4e/4vA/cByL5FamrsywGFmYvTRMzj28gdfQ7Ad9/5ONB0Aict2wHffmtd9K73D0Nty5Y+GiHEO2nlj8V6wy2TSKKQ8GeU45RsyLyO9jufJX+T5paDK+3B5a8ZM2fuNivru2WrQlh6axnXW74MgUj+WDA//TEWU+AZheT1bpfJ+ej5Vp8/371MkhNPdPfYtoTrSSXwaSY5SMW194OFJuuVV4C337Y/pK4OWLIkeNOEFA0uz5pZAPb0tLMSHzpZKIes6hMC8zbuhNVN2/rujtDUTx1vz/8Ftn3refsO+FwNG8M72An/qeAqD2bOBB5/zGUumzPHcbcXD64zzwTO/9ZesuckQ3a624XQfIUCmzrV8yGBMf7G++8HjjxS7jjj+bjhBuDkk10qt9pNi0xiR8qKWBPAO+8AP/4YSjfM+Sos7+jkm31TrIPUPW2r4FLoDW2LpqGxrQPiuktZwzirL8yjgssupJPiNoh6VISYFULLuVxLlyLb07fA8iS39pQquNwa++ILNDYmzkkhWb4cOOggubJ250NsbrbcfvUtuUZFDQvXWhrXPD3wAEcjJEJU4qT8sX0/NYlqLRVcpvqEbirz8cfWwvlJkzJJa53o1w+46SbnuU42/UFyPeMlPLMtQqCdaQ2liwpLuYdbRghGcokYQ4amPwoRcF405pGV0JTZrnXNBuQWCSKN/cxSTstCBZcSqOAickg8cK6Dzxhv1tSaBmy/PbD33p4OI6RosJpEXSMNmL63r3ARdFtUeMDHD+PblXKWrX766Ie1m7ex6YCQa9OmQJaCy+ZQV2EcKQleegn4130u19piRZqV18lDDi4veK7nGxfrdovnoU+fRO6SYkAIAM3N2d89QrkzcSMtZFm1qvCNC+GekB0w5MCSew5EPJ41jgVW9CYFAk65LdIhCgF0efNFPDDxfOc6x40zV6EGC8GWZUgnlVAgkR9cBvAfxui44ALv1ZxyCnDIIf675QdPt0hTU976kUN1Nf76V6BHj8I1mcLFlsgV8fEnltvf/TjX2O/Vz7rnPQcZIW6kZ1AvIQqN45eFB5d0Ta++mrvNKo6aVd9Gjkz8f+89+/pTCq5U/2bPBqZMyS1nNC5SgNT5kMnRraQ3JB+IhYvUVZZjTWqxrrV7ITd4fwGwvK+8KLgoecofVHCVKEqSNxpYvKpj8EpMA8a++7ofYmmk7svnk5DoYbXm8mpFdMtBIxMfbM0cg40FUglb8y3gEcJ3EgFLDy5ziEJaboXOgAH5D82jIsTBru8/hYcfznwP0ufUbbj77sD06R4P9uGOtW4d0snuC4JdXCAZGhqAT6wFWISoIr1WNr+4FohKs3DGai4yhBEzW3NboYsK4N//Nnz34cFlZWHuUK45njx/tbUAgIX1OzrXb0iu41cBJ3uU8nmFHlwFwdaDa948AMDadRUYONC0zyqfhulytbU57w8b7dP+hWtM111DJkaWVN7hAjJmDNCtW8GbJSVC2qDGMkShjQeXOQeXhCpGekzzOndNm2a/Lx7HFd9djxdmnpbZ1qtXbrlUiEII5/okMXuCCaG5yiisQhTaedCR8NHH/ACtbmPii4XXlCvJe25R/Q4YujRj6WD3nDwy7lfWObRM64s9922fvb+tjR5cEYEKrjLFzjW477zj8ers3FhA+1z2c0/1jxoFfPihc5lFSYW852f56689HkBINPGj4DIvwnLe581S/DyEJSpo6C9dB955B2JtlXtZq8PNHlzJ87Fth8wiifKp8Jk9O/9tSCm4XCyymuMdspyRgwhP9Vhi9btyZe4+p/Adq5u2wRdz97fcZ5dnzpVNNtncA4wfQXKoNE+dgyUTq50LuZx8p65feGEw/RspcpL3TjrvwoIFPt9Gg2H2LrKcWysNeSQlwrZtaO6SCKWS/I3xoLklzOfKiPEhGzMGGJoIKxPT5dv0a2AiFU5diCyB4IjFe/pqyxEuIArLihWeihsvz7Bhmcd83pzEjuWz6vHWW9nHXHFFgP5ZYLwtXadUlR5cycbspsqaxo55G/ZUhCBzCk2VDzmgW50//pjW4RPimfS9bBmi0PoYc4SRnDWDhELHFq9zV/v29vt0HR8tPhqLG3ZwriN5EpZt2g4tL/YJ9iBb5DHVrdZQQtjIMgzHUcEVWQQA9O6d+PyWQ14bFz5beiTO/PSm7I0WE8yT40/H7bcnHBY1zT6E7/Iqk/PHoEFZX0N4pSBJqOAqU+wE1B8sPBa95lglu3AgudoThknqjTeA3/3Od/ek2iOkFPG6yEq/RKbCCOmA1qd3pkBAbyvLHFzmbXm2OBE/jMXXyw/1dWy2gkszJMI1lKF8qizwq4wyH6fqdhe19l6JTn2dsL4HLvzgMjWdSOHTQ9INv+fqw0XHYO8Xbs9b2198AXz0UaDqSQmQnm8XL07cFAVGamxJh+nVpOaq9ZuTOWVGjw7WuRTG/AU2feu53cqs8EWfLZVMtof8G8xUVmRO2qp6Bfl26MFVEJTNs4Z6fvWrTBqNAw5KrA0nztsa116bfUw+54aC5uByqXu7e27El18WoOE8kI9xwzVEfMoSy+BVS4gsTh5cduR4cMnk4DIVOX/wn20saj3OXb/4hf0+2bqS48yVw6/H0QPuCaYFELmjgF3UmayfP2YMMHdudhEGjIssQmhpj119gcJwhQ737KRJwFVXJT7L5qjsfOm5GDEi813UbvTdNRIMKrhKkIJrjFMKJ58eFp4pskU4IV5w9eAyfTdbL7XFXYZ1j1IDS2uoQiIElm3aDn8Z66Ixt/ldQjOFKNT1HKtuz/KpZcuATz8FWls9HkjsKIRXvpQHl8XN4DTlaHGXHHg+UZKAOWQaYx3DfUQY6oG4kHWHjB0bVjekEJBTcG3XKTuEi6/HQIhEKIb33zfkALMfCM1tHLfjYuf6t946bYij1HLaxYMrL1baVHA54veVzfZ+81hhFC6P0zOYkxJE5dxfAnOg0lf+tEebz/MyJykUN+QQJESW9H1nGQJYIkShdA6u7K1fLj/cukNWAsNNm3I2/fn73+Hnn93r6sElg9HodEbNbsoHaJm0CkIAeO65rIg49OCKFsZ58JXZJ+PMQbcBUKuIFKvXSE0w6dvHpWxzW7vs1Dp9+/roVPHP2VGACq4SpF07YMjKg7O2mQcEpY/PwoWJ/xT2EuKJQCEKNS37mUuHETIdb148evbgKkyIwp232Gi7L8jCM6evKQWXIcyD5/X1448DQ4YwxplKUmGH8mihIaPg0o48IifNo+OatslHPPAk+VjGph/vCCySd3v/Kdx6q5q6GhuBnXbydozbOBWBU0RCRggNrfFKnP7V38JoPDd/hNU9u/vuqeLQ4+43rXm+9B229IMPEl5gybHZ8ujk5Gnu9xHbL3eu25DzzNdjOG2a9bhs8Ruz5nq7tYQQCTNdc4Imu7JGpkxxP4ZIIaXc8ZhMM+oKLuO+q0Zci8em/Dr/HSoBfI0bVvGgjXW6eXD5aZOQJOnZxzIHlzX+PLgkcmfb9ANz5sCc3HDYqgMweYNLeF9TXV8uOwzNsdzcpuaQi0EHaPP50K1CNpra+O13N2FzrH3WuoUKrmhhvDZTq/fAT+v2TmzXCz8K+31XPK3P5Xj00QI1RrKggosEpq4xnOTcslxwATBypPfjhgzJ1dnNnAkce6ySbhHiKQ63JX/9a444bGNrZ+djPC4mpXJwRWFCtumDcdEqbMr5zlGyfr2/40gO2uRJiQ8TJ+atjYrNcsooYw7bt9+2j9738stAc9zBotEFJwWMXwtjHVrJRs9Zt05tfVEYtkhIGAxCNjR3wfDVB0TihrDsQSr8sKQH17Tq3bCmaWvnOl07kps0yCkJu2fhkC6hdLJDCOCVV+z3mTAKwGx/w6hRwBNPAH36eOsLkCMMJGqwfRzNmk03pcV3w9R0KAANDYmQR4DzMLOofkdMchMk54n11WpFQr69pAw8+ijwpz9Z73Maj2xxG0CjoA0lJUuFgweXHTk5uKxm9Ndf97d+sbvfTbnuLZVqLnWd/+0t+GjxUTnFchRcgUMUmhVc1rlCje9bQ1YejJWNXbOLhB2thkgRxnXyOy3MqNkN/fur7QuRgwquMqFNr8zyqPe1MLRh24tPkytoYwYfj3vMqesxXsHAgcANN3jPCXbWWcDHH2dvGzUKGD/eWz2E2OHLg8tojW1c3CWfi08Xm3JfBMzBZdUf2Sq8tORkWR3kNVlUVGa3kVyprG/OCABlrOIt4cuwepqb81a19uMPno+55hpgkSnkd+r+v/VWoK51C9/9cQxb5LPOV2efgi22ACq3lu9XlUt04WeeAe6dcKHPHoWHqzV2+PoMEhbJi69DS8+pYViGmnGyvJbNwfXb727GlcOuT3/XRUWwmz3pbWUpWEidx2S/F9TtmO4rvvnGvl3DdkuBlBPJY42eWU4YhXO25+/HHxP/p0+Xbt+9UhIIu3vH/A64Zo1jNfrn4Ssghw4FjkrKemtrgY0bkzuWLPFvYCWDh+d+x8N3zl8/fDJlSo6sXQl2MhAx6Bu54wLGTdR1YMaMQFWQIsQpB5fdPWlelpjnPSE0YNUqYMECwzaLiqzereLx7JzdNvhRcAFAdXNuzsscfZbi+TMu671m2tzQ1klpP0ieUPjiJgClIQqt4PIwHKjgKhPmbtzZMTdkmPTpA2y5ZX7bWLgQ+PBD78eZPbgoECP5JqibvKt3ldcQhYF6I09Mr3Qv5ITdAtZwPgQ0y9WG7ncq5ICgHp5TJehmK0kHundPxsK34dFHgc+XHqGiWwWFCi7ixquzT8YpX94BIBovok7WqfeMv0haCTe2au/sDUOGeOtIWxuOGXA3rh5xTTrvhuXzklK+QYMQwM8+eiT5HcDnn9srjLbbLlOFt56lJWSVMkI3U86SvIQh8hgyj8ghnYMr5pwHM2rD/HnnAT//efLL6tV4cZakkahPFiwANq2ozWsbkcHNQMpl0hdjf3Ter8h74PPPgcMOU1IVKSL85eAyzC8WObjSjB6dVc7M8zNOw5CBpudDctFTKWNMYlFXbUuuoV1O1wKGpTd7itp6cLmkW/jz9x6t4Eko+DJEs1FMyY7nQd4VPd/efDFVAlflpUaID8a8up1w1lnB61m2DDjgAA8H1OZv4S4Tjp8Qv/jx4EqTnLA3xTpmVRbTTcN6UAVXgXJwtdkpuKTdxdxDFCY26DllvQo259R2R49+j3Ehkg/yeE5l79r5812NwpVg9Rxdn3S8UJpovlzh80nsSN4bi+p3xML6RHK3giu4hNxMeuHDCeXy4oYdpKtu1dunLbOFAPDZZ5l8uTLU1GDC+h4YtuqAtBe07uLBZbl2SbuqmNh1V0MVHtcTLS14ceapmFGzi21/jBhDOilRcJnboIJLinwZWdY3FF9oqcWLkx8KMEf97GdA38+6uhcsBT74INjxBVozeIpaQ0oPD4uNrJB+FgquxlhHzN+4I1BdnSlmMVff/uPl+Me17mH1jeGNU1Rtzt2Wg8WzYxWm1DIvdgDMv1SHRQ4uieeaObiihd07sN2qOT2nJhk9Wt1wTg+u4oOr8lJj7FjXIiNGAO8tUJ9IqinW0dlIVHJgmDoVmDdPvt0HH+sgX9gjUQhZQ8oL9xCFGZpi7bGsYbus/XttVW06oAAhCq3qlBTepbBVcEFSAGbnwRWLG4rYeHB5WNgKAQxffQCWNmxPAXpA2tospoUInNNf/AL4dQHyvFvdd3375r/dYmbiREjnGHMbgUSbteX/0qWuTgGk2LEYZ6LwIprTrQ0b8MWPO2X2+1iT6kgqn1atkj/IoLRJ9cnOMjqxT0NMZObwxfU74IWZp0qGKPQoNKiqwl/HXoGBy3o6l1u0CHjllayQTkIA+OknoFevHAs2rU9vLK7f3r19Krh8YQyTL4PsUmCbs47Dhg1O9URYcBkw1F2UCeWXzZ4d6HBpr2/TdXvuuYTsghAn0sYWHvJvm+dHs4JrwNIjsP/HjwBHeI+y0BbLfUp3ee8ZTFi3Z9a2dT4VXMfvtMiioIkgCy8hLDy4rA1xrE6vMWf5o0eHH8qWZPAiQZo7F9hnn+xtJ58MLFmSMFjVzjrT/mCZEIWN/i0S4i32L5M0ZM0fXJWXGvPnO+8XAjfeCNwx7rLC9CfJTz8Bv3n9AtdyCxdKvIObBqOH/5uH+IZJDZv2/WiXgoSoRXqtJwSuHnEtBq04NPE1KfjaeYu6nHL+Gkge7vc11aOiIuaQhyCIx5he15D5LCwsu+DtlHzwAXDrD8lQBiUsnCgERiVCVm65CNDYaL9PmVWYlaVhksjcWhG5HimOPhp49lnJwnYncfXqxP/Jky139+gBvPSS976RIsJqHojAK1HOXPfGG9n7fTyO94y/GId88oA3RUyyoZZ4+/QmJ0WUDi3Le3zoqoNw29grpBRcnuf3Dg5Gbcb2nnsOAHJDFL7xRiK5z6RJmbLJsWK9Rc4QVyIzWJcWXu6LtNGDxbUIIym9NPm+dyI2f6vE8rq6nc/k+bDyLDHu99QmgDvuAJ56yrlpIHA0NhJlqqtd75/U7SniFjm4bEMUmhRcdkJx470vEbIfAOpbOlqWa4xZb1eB0hCFdoa0c+Zkf9+wAXGRbUT7yORf47Lvbkp/36KdKScJiSRWt7ad0aOu53p2GVnW0A039znSvkCqzU8/A+DT8LGm2r1MVmOlO2cXkvDf5oha3BZ3M2cWph8mPv8c+Gr23q7l9tsPuOWW/PfHle+/T/xfvsJyt6Yl3o8JCYKfEIVpa9RPPkl4EZnqyjk+4GQpgITbZ9Y2iTY8ths40baxvaoq4N57gR9+gNg1k1tIAJYhCuMe8hWlZOMAKNjKB1zcAYiWZVfUbvOWFrlyZm+XxkagoQEJsz6L/UaqPb6TkCIjCh5clnk4TDQ0ZO/3OSzM2bgLsPvu8gckhU7VLV3S873l2iR50oTQrOdwu5MaxINL8kKJ1jbcP+H8rNVKVlvmJLvw6S0etQEyYng5PVJFLSqMgvelLyoD5p51g+spT1gpHrL2O+yTuc/btQPq6tzLkSJi3DjgppsS75wuyd47VCQk5LVN8gqkXA8um3vUwuvaDTulmq/3cb9jTeAQhdnttsbboe6r0dlWihZGMV8tz06Cx6EyOkybBqxq3NZyn5frJATQ0eFRa4x1Qu+h7rJpUVOLpUuB9r88Wr7xJJ7f5XkjKoEKrlLDbYW1cmVh+qGYX/0qei8wASMhEGKJlxCFWZ9TCi435ZOfHFymRbtUFTYhAeywDH8En2FCv/02IaF+5x3reN8+c3Bt3myy4KFgSwktLZn7vs9Xu+Qt96GXhWYh1pjrNm8NrcL6HrK1MFbBpk3yZYWAs1gnupif/VNOAQ4+GHLx+CO23iD5J5LX3OR1Vf/jLP91eRnULE6GkyfM2/N/gR/W7pO7w846O8uDy2PfPFh8PzrlXDTGMsItXWgYtXo/vDjz1OyCQebynXf2f2xZ4G/+CHi7+qqnkJx2Gko6vGW+jXRWNm7rKeqqDNL3SoDxornZ96Ekgog33sTYtXsnPJhHjnQsm1rXt7TlPvd2t55bDi5D5Vnl7Duc2ZeTs1vicCONjS5NWawZzOVjrQEVXKbzsaKxG7Z96/ns9xwLQ4LaluzoT5H29C0zevYE/vT9/1nu83KdhFAjprn0uxt95xDlfRUOpbuyKlck3fOLjWHDDO+0Fr/xwAMZUogUH5aOTx4mQ6MyzDFPRtZB3haTljm4zH3s1cs5plsAdKHJDVvG39U5E1c76xzBJgeX5FS49dYJI700JSycKCRbbgk8OOl8AMBNzx+EGTPy0w71kUnK0D1J0xL5u1YYnLKdhhWGEipxouDBhVzhTI4HkWnQ+vmAf/lvzMsPNCqg7LzDDTsHrTgUvx78V8d6sjbrxvqt52WZvuUwdWrGq8xKuAbgXxMuxF/HXpG9w4snjbn9gw6SP7bciMcL8t4pJWRdsCDv/fDCiBHgoiQA/zf8ehx+uGljnmUgKvK58ZKXFgvqdsTxA+9C+9dfxfTqXaWO8bK+NM+7trePlisPcGvczoB0ty617h1raUGXLsB7z613L2vA3Lf2e8mdM7vKbM+H8SQXqeyTBEOVgqsp1hFr1wavhxQOSudIXqmpkS87Zozzfqf5ae5cYOhQ+bacGDkS0K6/Tk1lhHhENkSh1qc3plbvkdm+bDnQ2Jh1/JQNFiGJPC70hq46CHWtnWz3/2aPacDy5cCXXwZqxw6/1i9an94YV9UjqxtCwFrBFZfra078Zb6pKsH8smcROargLFyYUGhaUfTvSh4Cie9/w4moqyvO+9ztMjkJq4zDxJo1wLp1avpEIoLFQ+wlVG2+cJvvzFbH3ir36SWVHC88hxIEbBVXxs26jeGJVN9MvNJ/x0TIKFgr5nRdQ/uK5PHG85FUcEmtN4p+AiggmzZJe/IsXJgdGsvWe99jiMJ0LcOHS/WjoOQ5RKHWddu81m/FnrvHsWFDYdqqlZDDe8Ht0U6PDxb3YCyWiI5OyosWvV36c9Vmm5eGJKk1p1UIQLv1qPmetA1RaLgnf/u/45064dhHq75c87OxuYWGDQMALO/3g209a5ucz4cKbKNdZCV5lvjNivpD8kvJL79K/gcWBiq4Sg03jwIhoG1uKkxfAGy3XeK/jBz4pJOCtaVqTJjlEAGG4w5RiZ8cXLZ1zV8A3HVX1vGrm7qits40Jni8iZ+b8StcP+rq7CoMQqDtOyXDAJhyhQAOyXA9IIQPoVPy86zaXbKFW6IiWaEpRKFfq0x6cOUFpxCFa9YUTohgcUsXjPBF7Qnmr+oSdhdySD++eVQwGwWm++wDHHts3poiYWDlwSVp6KCKWQs6YEPzVlnbEhFB89QPL0okQ9nLe5+W2ORnnrT5LcauCKF5NGm3/x23/HBlTlbxZZsyuUp14aLg8nHqy/W9YPFiYK+9XAq1a2e7q1ev7Mu+337AuHWZfBieohk43NpXjfgj3nwT0TRIKsE15PKVlXjjDWD02v0K33jAa/zilBMwerTNTpcUD598AnTv7t6GppXvmFGKvDr7ZOmyqTHNyphGdryrlAhR+PUUa6+oGTW7ZQ2Wdvehea5vV2ExP2/cmN2sRWXXjromZ5vqe1/Kg0sCFd6ZJP94WRf84x/Af5/MU84DEmlKb2VFnBEC2BSi1K4IcFwf19fLlSPEJ+45uKz3d+z7CuItsZzju/28h6kC76vL/kt+bluFgJbwFLOQMKjII6Tr/hVQFZqAqDeNd1YeXH4XthwECs7++wNHHRVuHwohnMhn/gptX4tcOUVE+vy7XAi7F9ZYm8NxqVCrS5ekN23ejIJZpJPwKGiIwrY2HHLuntb78jXAeM3OneSzpUcmNvlRu0t4cAmHcpa4Ca6SCeWt+pul4DLiZS73mcOz1JgyBVi2LHHqvv3WppDDef3Tn4DVq+3rn13THeutol9Z1Dl3rr3+YfCKQ/Daa859CY0SVHABwF13AXeOuzTv7XgeKl0OuGvMefiXXRTYKVPw0KTfeGwwl+OOA26+OXA1JCKs2NRNumzq9rPy4HI7JvXFNgeX7MNgDFFoM6ebt6fX0sY2kmOX07vKEdstl+vTwoVy5cwIYd++h0gVJEJ4yRHtwpdfAl8Obq+sPlI8lObKqpyJ4gJeNXn6jam0JI7vG4MG5aVtUp748eByWsI2xTokwv2Y2bzZuVEXurQ3ZUU21PFj1d448rP7ch8cp4W4B+qaO8oJ1rJNwtP/RENmsWQnSIvLKLhoclk4HASYDQ3A+vXZuZRS3Hqrcy7GfCc9J4VF22Vnx/1240b70060PygVxsqUr6UcllZlhZUHVyEteOfOtdwskEfzfg+amMbNuQthlR5cxiFeFxWZDTNnAq+/DrS02Nfp9jtSObj2zfUgEQIZBZcxLnpSse1LiVemawPjz545U339x3/2d1x+uVzZCy4ATj/dfr8QiKYyKYp9KjIMdqfuRGAiX7wYaEoF0mkqXEQdkh+Mo7+sgapl/m07vZVpTrI1HE1VMH26Yx+s8muambRhD/z0U+a75e9KKbgc3vOv3T83tKFlmwHyI9q27zEHl990CCTB7NmKKnKJO1vyy62S/4GFgSsrUjJYjgmrVkkfv/32idBYEVj/kjImiJCtRW+XEBaZSSbDi8eBb8d5j4l98V5Tsr4bF4Lz6pIxOSxe1FU8SiNW7Ov9nCQHg9rWLbCycdvMZkOuj0O6ZsYGy3NmZsYMb30g/nExJGhpAfbYI3f7yy8Dzz+fpz4VmHzPQ+XqdUAIgPAVXDaC7RwjDJUvux7qWlW7BXbsnC05tjw/bgOVVw+uF18EJkxwzpckOXiJrbfJPVRoGcMbg8vP4pptAQCXfnejRMXZ55GCsfzhRf5vtOOyJIovd1RwBUIIYJvcx9z5ABWoupe++05NPSQ0jJEC2nS5nHpWc4atN5XJg8vVUO+zz5z3Syh+rhl5LY47zrma1DOQ7nWQNZXHcIJphMPs6zlEob8ukAQHHww0N7uXc2PA4M6O+9+fd5Q6ZVoU4Y2oBK6syo1ye3DmzfNUXNez3zei+D5EShvHBaHL89tr9smOllY/fl6Fs+88NEj3Et2wWlJWVOC11wyPnBD2yXA90L4iLrdItjg3d467FGcNuj27iK4DQmC3LhkrIV1mKnSVnhCv2N7ONt4N5US+vc3KIXqHiuXOxImJ/1wLlBgWN4eXsEGBsVNwCZMHl8o1uwettoZcQZqld7jPNo2bdaHllnPSbLj9jtQ5swlHXN/WKfHlrLMSMfbmzcM+zyRihq1p2ta5bmP9JFLoOjBr+VaW+4QAB/FyII/XeMZS63srEOWwECtxjLNBS9w+56Cx7KTVztEH7FsAtu1g8y4qMb/v1LkOF1yRUSDIGmekyxmfr7SCy34+tFTk6R7c16T6ZreD83Sh8XvKR47MRIe/+MbtHcv+edTluOcef+2Q8oEKrnJDxvojorgNnC0twV1khXBeHxfnmSNRxWqh53ifC+GYCPWbFQc736PvvC3dNycs+1hRgRtvBJ58MrNJxatmlw6t3gWPNicxvdg259AwntM2+YSktY0dvPWL5B2n50dFTjgAGDUq/7IyVX21w6/BZBSQfYlS4Vlx9NHyZdescT6vzz4LXHll4C6RPFFQDy4bjF7GiQ3heHBpeu6NbOXp7FqlTYHsn5j5zY1tHbBu81b2CZUA98ErFaLQouljd1uFDhVJoXJlJfD44xD/fda5PhcoRyswNpPvqlXAIX851fNxocKbpzgQAof99ZTM9yjeSyQUjOvM7ls4x8tMvb//X/+LsHixuR7nY9w74j6WVG3eBgO/yeQkcj3E7JJjPCCl4NIs9iWRXlPJGN/YvJvbRmDJ1zqKKOfUU4FnnpEvn3M5izQkCaeR/CEtNdQ0rVLTtCmapn2V/N5D07SfNE1bqGnaR5qmdUhu75j8vjC5fy9DHfckt8/TNO0s5b+GRBYVD7Hb/DR0aMJF1q3hRYvsIxcKwYgRJFzcFoROgu+YXmm92EtO/hX5VNFqubHFVeTg6tKuWUrBta7WkEjUTsFlOLfGz+lzPmlSIpHT6NE5x7a25tY3fNZOrv0iDhTporQU4PueR4T7vbrLLsCrr9rv79sX6NdPYZ+IfywegJdmnYoRI0Loi4G0l3EKlWOkFw8ui/vdcszwubjP+okGpd7/Db8OO737H6C9Q2Jwt8HLkIPTTIfKODqkcnAlF/uBFeEcTO2RPTeFyEVEaVLpo+AaazELQbpNkrmWFuAPVyiIy0WKFuO7ZKdKZwNJ41xj1tfYKbKy5ifFc43r3Dd/vv0+KQ8ui21WHlwua5NB/xyJe475Dli92lSZsG/d49qpFEMNT58OPPVU2L2wZ8aMzJDtJbxhzmPw2aeJ/9OmKelXqHA9qQQvovzbAMwxfH8KwHNCiH0B1AK4Lrn9OgC1ye3PJctB07SDAFwB4GAAZwN4RdM0uWC1hPjFNFBoGrDvvsAJJ1jPfUIAWL++MH0jZY/VgsotRKHTEsw2/vfGjQD8K5yMSrWffkok/7ZDjJ+Q/KAmROEN316CqdW7uffRuMy19eCyJn3O330XA5b0hP7eBzllRk72nruMOCM+/Eh5nRs3Ap9/rrzaglLQ9e3y5QVsrLCc/83NeFuN0yq0Frm3L7vlw6RJnpxDSb6xeMj+M/1M/POf4bUPJNcE+QpR6MWDS+iobu6Stc0qRKFfoZDRCcsYovCLZT0TGw85xFe9iU7ZK7gAoENl0oNL0zB34074ernHsM0UQKhn4EDLzap0UpG9ZJHtWJEicT5/+1ug79wT7AtszihbdT05b9tYxa5eDbzzUSevvSQRZ8IEoHdv78fJ5uACPEQikB0i/BjDBBl/kgYiTka3Vko7yzWDS9+feW9nPDn1HFhZINl6uHlcR5XiSPzcc8Ddd4fdC3uM+spAU+HcZG6MMWOweXMi8nRUKNaoacWOlIJL07TdAJwL4PXkdw3AaQD6J4u8DeDC5OcLkt+R3H96svwFAD4UQrQIIZYAWAjgGAW/gZQJKt8Dli5NRCexbGPQIKk6aAxI8kGQHFw6NOsiHyQUNpVBFE5JF6ZRo2z2//BD4r8hprwKD67Ztbvg3QVu2W6BdhXuvy0tPDSdpLSHmBC4eOifMLs2Nz56xYZ1ch0ucu65p4ApsMaOVV5ldTVw0UXKqy04+Z5e0o+A7QMdXZ56Cth7b/dy46r2xvvvB2jIh8DAbl1w1FHAggUB+kLUYuvlW+B+mNvP2RBeiMKYyF4kW4YodBupJEIUDlzWM+dZO+rGI6X66dimxfMrdJHxZNc0XDfqapz/7S3+24KNRTrxRr11aC+VXoOEAMCnnwLDVx9gX8Bwz/35z8BOOyH3nrOIWEFKh3vvBW6+Wa6s8RZ4bMqvncsaCuuN2bm0CupBlLTEcr1/rQRlKczPhGyIQqtGXdbaaVmChWe3qhxc0qEgiTI6dvR3nO2l1XXccw+w115+e6Qeu+f6hReASev3KHBvygdZD67/AfgngNQItB2AjUKIlCRzJYBdk593BbACAJL765Ll09stjiGq4MI/G8kJLlWsuhrYHGdeHVIYrAQj7h5c9vf0MTssxe+GX5+7o6YGcx/+GIsbdvDTzWTHdOM/W95Z8Iv0Z1WjUatL4l4gswDWdaBqo/WqKcugy7A9dc5bGhNTWsfK3MTPFVMmOVdo4txzgQsucOl0BHnySSjzenHDbuFntz3o9FYss6MQ+bf6EmvW5rX+fBKPA0uWyJUNJIAyeBXQCo8UAiGyPbiUKk+8KGwtykoLq2SqN9dlqmfSvGzvMU9tpjy4bEb8tLBMCPv8HRL1lzvG09CvH3DTTdbljFfhgguAMWOsCuV3dhYi/22QCKA4H8LEiUBtbW69Hw/bLki1aZau28JzPST/aI2bpMsaFSOLG7Z3LmscDU2x4+ymFWnFl5d56Ztv5A5JKrgs17/pEIUOXbLy+rZq02VtMq8umQ6gnUkW4JSXnCEKSxbb+zYeR3V1Qbvim9tuA/rMPSl3B9eXSnBd2Wuadh6AdUIIC+meejRNu1HTtImapk1cz1BxxEAhnvk99wRu/eF3Dn3ITID932nEf/6T/z6R8sJWwaXrCe2DA9+tOgBNMQvljq7jwAcvwxXDbgjQMfvE7QCg9emdXVZRiEIAaJFQcKU61vflZnS/9bfWRZIL2BWrKlDf2jm9PXXOU7+to0UcdStvNKchadAg4Kuv3LtNCkcxKSnyLovzE3+ljHhj3vHY4lJna1wrKEMtEmylSQXKCygZonDuum6F6Y8Elvk0fA6pObKn2lp/FTlUbqccTM3lIhZH3GK9VV8np0BLf6VgDJMmAX36uJcbOBD47DOLHXYDZ31doH6liKzMKLIdK1+0OsNY1NCQ3Jh9fz76diJsupfLJ9pyDed+ee/JnvtH8k/FimSMs2R4fyfO2T2Tn83V9sLwuTUuF84wq0qnBrwodIwxgp2w8eCaORPQLjgfgPN7lVtO8UxB576vauya+NC5c84+q9DJ5jpf/7SrXD9KjGKaXvz09frrgTrTEqHo38GK6aJFGBnTteMBnK9p2lIAHyIRmvB5ANtqmpaSOu4GIBWgeBWA3QEguX8bANXG7RbHpBFC9BFCHCWEOGqHHQJ4GxBrwnhwZs1SVpUQQGOLhLDbR71e+WzwlvjHP5R3hZQ5tgvCRYuweWkVYg4Wx3E/1sgS1LZkrAxFnXUomSyWLIFbvjBP7be6WzmmhFnrvxpnW2ZR/Y6AENijZzeMW5eJcWZeIMf13PNoqeBiSINAKJ2OWlqyvmpaIoFtMVKIaVo0yFuoFjNO51IAaGoybUy+HdW2bInNMe/e3EX/clUu2N0Yy1dYby8Q/5pwYdb3WSu3UVe5h4HFyovZ0ttJ0psqp664abtVaPDp0z3VKYNR6XXwX07FhPU9cspssy0fYmUEncxUmmNzcC57vh2dKyDPwZAsU9TUJD6YFgp+bmvxQb+cbWtqO+Onn7zXRfJLOq9USsHpwK5bbkx/9vLW+9KsU7O+20a0kH3PdPFcziKZSsD1Pq6wliksXZr57DSsqvLgsu2PkweXoaEbHt7duoxbv0gg3ngDeOUV+/1Z944Hp5bUterbF5gyxV/fSGnjKg0VQtwjhNhNCLEXgCsADBdC/B+AEQBSZvJ/APBF8vPA5Hck9w8XQojk9is0TeuoaVoPAPsBGK/sl5DoMmCAkmr22gvo0QPocuOVSuojJGysFn+2Cq54HNu+9RzeNYQAlD42IAOX9cwsnocNdz8guQhVkYMLAJa6hH3Ianpjjef6U4K7zm+8DABYvinXar6dlmvxlq/zTeQYOdKg1zKbcSFaiWajRrm8zLW2AvPmWe/7cNEx2HLL/Pfhlcc3Kq1PCHkDXGKD0wMQy1XuFBRD35R6nXrJwWXRrtV859uDyyVEIQBguMRaw4rUWsWiyufHH5f22pqzcmtf1WsXZsceZg4ub/zvf8A2WycFmlVVif8SyqdFixJ/fhRVDFFIAODsP+7iqXxaeJ4z4drfS6efnmNvlajrB+ucs+NNkrBYDFi40EMniXLS85+E4sX4Hr9th8225aZOTeSUTtHQ1slUjwQSE67UnOzi5exWuWUbQXJwNTZ664cMqWsnm6qEnti+sTvFf/4zcItFitOffrI4ZvJkf4YDpmM4zRNAPgeXFXcBuEPTtIVI5Njqm9zeF8B2ye13ALgbAIQQswB8DGA2gMEAbhFC8BU9BAr+7CuSpK1fnz+BpZhmbSlaTCGtSGlgu8iKxdCq5yZYNZJXhUvyObYNB2CkQ8LzQVWIQhlSw0ylm1LNZhE+eXLm+yNTzs0p061T7gKcgq1g2Obakhx3Tz0VeOcd+/0eQ7BHhkLk4CoXDdeYMcABDvnkZZF9abIqN27A6uAdMHDvvSiIYq4cEUD4Ci4DQsuPV7ZruxbDg6WCy209YDPOxHVrwdfle0/IfG9tda7bDgfh3YC5B2Ftk0KvOOLIF19VWq4Z6xuS9/Wbbyb+SwywBx2U+PNFg0TkAVL0qBZS262SZi7ewnaaGD48IcA1ernE48DRA+6xLG++9V97DdhvP89dJQr5aV2uZ68dxnnx1oNH2JY74gjgpjG/T3+XXYHL3tMff7s19LjI9Y62QssOy2/fuE0o5dVrMlUZfonZEHRja67HpGWVk5yz4HSqtF8L2P6E1AsgXSTzTuqamq+t2eFu0ybg+++B445L/Nc2ZzxjX5h1Gk44wVt7OW1qWvEruMrk3TzfeHpzEkKMFEKcl/y8WAhxjBBiXyHEpUKIluT25uT3fZP7FxuOf0wIsY8QYn8hxDdqfwoBEEnVdSweTp9qaoD9/mDv7ZJFr1757QwhFlgJYGyVVBJCN7uE6deNuspTvyxJW25JPM9CKA1RKEWyf5U+lGpxUYF6g/wjZhGiEFuUtkR52bLoTB9e7hynx4KWXfZwDe2NIArH9g3evUqdmDTJ2kKcyNPn2z0tt0+v3k2Je5wQwJo1LgUsOLH7gvw9nB7qlVVw+cWyrngcndsZ8l/a5kmTM2KxKyZlpOOBY/5+otL6osCgQe6PgcztdOHvOtuuSwFkctxITM6trYm/pmbvSl9RU+teKAw4EatFwfm0nOst6v36K2Hb3MknA4cdlvn+r38BU6v3kGpfIu0TyTM1LV2kyxrnsutHX4ULL5Q7zhwK3+7dWvaWvvyFE7Dy5S/kDPtS463PEMOWiRQtyv53+pkSnXHPR/b3w76z3uEYgzy5zy6Eg7m4VCnixrBhmdvLrOB65hngxORyKRYD8P776X0t8fYYa+3kmkNVFdAvGfFVCGBzLGP8zfd8AgTz4CLFSAiL6fb/vg+PP17wZrFkCbBwlXvuHoCuySQ6pBbLmzcDzz5r2CEh0bQT3LwxT9IsRgIpIVdynFEVolAK2TYtQiHoqMjK9xHTcxfbYptt7ZosCZYsyXwu1AJR9vSNGwfceKNNHRLvN8VGQXJwiUSqiXlr/IXpIiZqkwJUg9n21KkJxbHqcbBY7+socVOvIyy3x0SlkhM8YgSwi7doWACA7lvUYcKkzKvZVh2aA/cljZffZVHWMp+GmxezXQ4u8zpip50AXc+eE/y64NqZEyfp3tnem+dXu8723NyMZaU3hp57bmLeTbHffsAXX9iXLyRbnnqM52OE0CIp+XrmTffw207eCyQb9R5c9vWJWNxxSG02DN3zpstbpHB+D5+9t0rmA5K4GMa5rDnewXqctHjvbF8hZ0hjvgedetQwZip+XL6rVL2AvAdXOidZyvPLw3N22WXAr3/t3OaCuh0d60hFg/l+5rbS7abXD8UayqOIMC655s9PfP7Zz3Jv+xyDVIvUAjJMmQJcmcxWs24d8KfvM6lrIjjNkxCggouULCoGuYa2TtiwIXg9hFjhZCU9dizw978bdkhYlZstwpTiJYFtckFZyBCfmRCFLovZmTNzNulCQ8vgTGiJmIXFsaW3nUtTXGip4e23E2FbvGJ8vnbcEXhj3i/VdSqf+Fz0e0IIvPQScMBdF7iXJdjYuoWtkhUA8PXXAABt+rT0piOOAM45B6hQPA5SAJZnFJzgmlWbfdX1yeKjcMzpW6W/b9UuHAWXdIhCn6cqp66ePRMKLuP2gAouO+Xbjp0bbA+1Mm4hiZxAOSnRli8PpS9+iOqQ+c/ndnYt06myzbUMSVLARbemO7+TZXVl0ya5cuD8HgX26JL0upfJeSXzTpwygDJwyd6Ts75L1ePSn3snXIhT3r/BvZ6UosqrgUpaDmDYZOi3VW2ffAJ88w3gJExrc5l3Uy2ceOexjuWy8KjgkopOU0RoWuLdOfW5qcm5vGoWLChMO1deCUyv2T3xJaVdK2Y4ASiBCi5Scsyb5318sJvk/z7uUnTvbihHTy+SZ1KCn5z3NBlLsnzenxYLW8eyQoTiweWHuNDw78nnGb5bTI2SFu2lQCwGNNjIAJub1elfVLxQOF1243vN+vXAmqZtA7dXCETfNzIWk/lqA/bXuOxocxcg6qLCWclq8xI9Zw7QZ+5JPjtmDd9/8oyKE/zJJ4n/U6cG60pYc4xdwnhFN5/l46LYg8vu3Dn9hJFr9k98cIwvWfykTu38+cCECdZl6upcwqUNHqy6WznoQsOKFYnPQXQXWtAKQqRU15l+2dJK6e83X58FxrPtNFZowtmDK4sK+WsoFi5KfFD4m4g30u+uEnOQZVQTifjoZsMn24i8rj3IMLV6dw+l5T24zHyz/JD05/Tvd6lM9O6T+G8hb4vt6OzubmssKxPCQ/IhLfVxNp92kx5Pdf6Ix4t1ms8Q+kksDajgIiXHAQcA48erq09BOgZCpLFVcPldaKvCSw6ulAdXARVcqTWBpXJKgo0tmWS4Vp5wVovyUl2HPPMMsLVN1KWrrwa23bag3XFE1xMKuV0P385yX1FSAMt4oRetvE89EycGryN5MmW8VmVzNNhRquNOFNi2Q6OSEyw2Jz24JPM/5LByZbIihRc7qAeXVYhCN6GQbIhCAIjHs/I1TZpiM5e7/Y7UwG9TTmqN8Pnn7mWKkBdfTEQGqEwazJ9xBnCMTcS/c89NONYVBJvJaML6HthDLnWRIxwyS5xBgwConxtT45H220ty9rktn7JvafvSOR5cqSgTP/3k3kGSF1Ih8byGKMxsNL18WIxvxrlz40Zgft1OlvV7MQRcvin3PciSVHIknzm4vq/aN1NErkW0rd9oW6Xbujnr9KXWRg79A+C6Dih3mprCt+PJR5SfSL7X8h4sOFRwkZJk82Zvg1ypW26QaGI155329h+wcqU/Dy6/yh0pku27KdE6VrZlPLgKKVaQ7J8dxvOdDk9hxEJb4nZJ4nGgutpXd5Tz6KPAzTfb75ddfy1apKY/gLpxt6UFWL3WIm9aka4pCzIfCRHNF4EwMFqx+L1pNBvDBAuC5rIp1vs6Mji4pWzRrjU0pVIWScmDykvda/RB0mWtum0laHMNcWTB2rXAqIUWeUJ0PWv+PmrAvzzXneiUc4hCqXWShFdnFionRh+0tQE1FssWM3/9aya3669+5W5LsWxZ5nOOIL7A702B5ytOeCWB5WUMQVLrdjtl7XcomxuiMLmB1rWh4cmDy+rimo773+tb5hQxzk433aDjqWlnW9afNc4qXvx59eDSfnMeBg8GZtdmPK50SbmD3pBIyLSpuV3OPlcFl3H/wIGmLto8XEL+GqaLm36vn4hQfpg/Pyt9b14wjzM33eQvT6wVqXP0wAOZ4AWEhAkVXOVGGUlGZN5lzjkHmDbNvRwhhWTCBECrSWpGUpmKJRZpec3BlcRNqCEE0n0taIjCJH7yFZgXyGfsNie3jE8Prr339tydvPDii0Dv3onPCxda5jzOIWzrLhmEsB/ry2i688zYdftg1aqwexENBv3YlfdKGbHpmVdt97WviCsZOH5a18O5gE/L6SD8qV+wUJm+QhRalL/tNuDW/qdYNKDniLlSjnBudVo2bbNWkTKCmTVLqo00IUt17rsP2E7SeD/FsGFy5YIq5FVB/RSxJfnOoVLp+tBDwOqmbWz3a8L5nUzWg8vMg5POT3yooIguLNK5nKUip1hcJ5Ny8m8P5t5HxvfO+okFzh2UysHlwwP7nHNci1iypGF7NDUBB9x9Yc4+N1mBcf8RD18oF6XJaw4u5K5vDjgAOOoo4KqrpKrwzf77A8d6SC/mRkuLxcZ167K+5uP9/rnngBEj7PcXYg4v+nUCX0aVwNmzhBACaGzJtYwwFypk2LCoM3hwIjd8Y6xj2F0hZYjdPBabMQfa228lvqSslfxakqki2b6bcMi4SAwjRGH3LeqdC0qsfuJWluo+f0q9S3fCYL/9gDvvdC+3yy72VuEqFpEq1nGlqOASev5t43/zzS3o1SvPjRQJ5/7zYM/y7Bw8hCgMSuq+1jSm6fBD68p1zgUUxDbdodOmYBV4CQmcB6wMOixDFPq43SvX2UhWdD1HWNinj/f63fJB9J13gvdK3fDq8aUYo6cVkPAcv+MONXUHDakaGYpU8hXWGBBVLOdYhdc29d7y738DNS1dHEoK+fHPgwcXCZ+0QkXiAlsWkVhDGFf5Wtx+/siq36U/O3WWTLQke9PJ/P7U7xDCUWE2o2ZX+2nSpR3jMz+1apcs4wzbNhWFKJw8GXjvvUBVSNHWpkbptHw50KmTxY6UhWse8HOKBw4E+i85Um1Hfv5zoMUiR2PYFKswooihgquEGDgQ6HLtpY5lJizsmt8wZkXIww8D3d56VqosxyhSCMToMRnrwNWrE//DzsGVxE303qa3w73/7ZZY7BbyxVw2RKHVyk/TsrzfLD3hfIQojAo//3mO8VaO4s3ut5hfSArxm1vi7dKOi24UbZ4tF6Zs8JYsmgSjWJ5lILuvlh4uxJE2PTecaRYKboaOlbHEB7/a95BvyHk1O+RsMyqfGhqAFSuAqjorSYoB0+9YtgzQl6+0LWeev/1E6VKyDtoyN6SUIyFrms23y8iRCWvqUoKKAOJGIYdNDYD4ZrD9ftn71a7TvOFDIx1eX1GIQitk71Uv5m5e5z4Vz4tsmy1xewN8x35YhFNPl1eYg0sky771FjBlitQhSqmtVRMy0DYCt09r2+Zm93WYn/vof/8DXph5uq8+2bLrrtAmTFBbp0Luugs47TQAmwIawBFXqOkoIVZavDOaOeauU7HAJpFlKeFk1W/G0pWXkALgtCbIcdn3m+xWFWmLcveiT/TZPn/9sCH1EuB6DiqtczXdeOCY9HcrIwCr310sypXJkzOfHdLPeKZPH2DUKGDqVH/H2724/fqbv+Dgg+XrKTU5gICGZ6afFXY3yorAL/rpHFz5lbAtWpStdC61e78QFELBFdiTLw8ht7xwVv8bcrYZQxRecw2wxx7APv+42FO9e+0FfLT4aNv95rNmOce6XJ/UGiDQZfR6cMj5cgoh2M/XWNPUBLz1/b55bV9AK9rBsr6tc9hdiBQNETkf2uYm4MexsqXtdxkHOeODXKT3aymgefDgMnsdb9uh0bMHl3M5eaQN2GVDFEr8ju/XJsduiXNld0t3ahezP8ii3qxHxu44jyEKdVEBCIFrrwXuvlvqkFBYvBgYM0bt8PC3vwFvv229r3PnRL4uR71RnYvnYAEtDwoRRcMvgwYlQzguXGhfqJisLSMMFVwlBNdCGaZNyxbqFjuy3gykNNA0kZvkNmwPrpSCS1bgtnJlKMI5t3PwxGc/s9y+ZbuMpjtm9ZJgseiwXYbU1jr24ccfgfXrHYt4ZvZsIObwjpCia1d1bd50E3DKKcARR6irEwBiohKLF8uVdVoLFus6UTDuXMFRpazO94i3776J8SPdHtd9nmnV7S2JNQ1qFFxuik63NpIKkyiNYcauVFXJdWz+mq0kK095cGXPvZN/8j4Wpg23gzyN225rvb2hwXp7AS6UrtvLkcK4TyzblEnuaeLrr4Fr+7qHjQwy1hUiPy0Jl1WrgJHLC5f0Vvt8gPN+4/0qG6LQmFyIk3s4CIv3bwfM75wCWvZxNsYPxuHTMo+XD5QruFLpBhyKDFpxqFSTTm1NXbOT/Rym6znnWGa9Xr+pQr4wkj81SgsuC2bPTuRAOylYOtUsvvwy4U3Vq5e9o0TfvsAxx9jXIV5+xbkRGeGECiI4Zi5r6Iabbk58TttXO/Uz4vdgscAVXwkRwec6NP72N+CPfwy7F+ro3Llw8wOJBhXmJLdSFmF5HNLTCi5JCq1hllTA3fvuQTnb/jf9VLwy++T0dythiFW9tiEYXay5f/lL4PbbHYt45uCD7S2wgpDPtZaKqh37VywudiaYc6N4OPJI4IorgJ73nONemESC1gJ4cFXYjG4//ggsWSLRRiyGTz8FTvlKIlligUhZOAOAWL5C6phVNR68LYTIEWT1699B/vgkSjy4Tj7ZevsXX1hvL4BQ4uWX7fVu5uZDk5FE1BpvY2tnviSXOH/+M3Dmh8Ff/GXvEq3VOfxL9u0m6cG1dq1k6ySfpK+WhGdujvJFmBRcqTzaJozrfCfDzKz3AUnv5SjipD+68rPfYqydM6QQaF+RfR3i1RvT++zel1as65guI9c/Q060iJ7Ggw8G5s9XW+f55yf+jxsH7O4SGV/TgKoqix1uIcTKWGnTqrdHn9dMMqWo3mAlBBVcJQSflzwSgcH5/fcTExApHYSN/F0ILSMgSy2wQwyBs/82a9O5XqQtzTStoI9Nqi0/C/xxVXtjek1mZScbojDI78uHwrqpyf+xERjilCOGfhd2F3wRVliyckaYhltZpkwBPvoImLY8uGvkhg3e13Fc93mnICEKba7LL38JXHaZRBu6jvFjo2XVtLAuk5dLtNplizfh8VyqmIaUCPna2Xj52SW9K8AEKhOG/r77EgIoO0ezIFTPXZflYGJJHs9DuYYoJHIovbwSxlGa5pxn2OjF69Q3baFBYs0QhZEgbWAqMZ6Z1+s5Ci4bzY3xOMcUVB7eBzx7cLn9PF3H8uXAovrcnJw5uFS2Y6cGiGXLbfc/8YR9HzpUZK+FxPffu7fpMURhXFQUrVGkEencbj6n6urqXPlFlN5Z8x0mPggc0gsHFVzElU6VDJeEmhr7fQVyrbrmGuCWWwrSFAkZAS0TIiECCq55dd2xxV47AvCwKCq0xsQmSb0fckJOCOC1abn++bY/USZuex7W0eW4eBJLl9qf7hX2L1NRphSVjVFH14Hp04F2F/1G+hirmPR3j/eWkyhFfT0wd27m+9y5wEsv+aqKuNDqkOwcgBoPLs1lgHdp45y7D4c+anTgfqjkvYXHpT//WLWP1DFeT6WUAY2bFXvy1TaQ0MWuDZvtIp5/wViFw6lJdeuxx4Du3fMTweLdwTvi2GPV1yt1j+g6gqg/OaeWOJqmdv2bEqA7Nemyv6FBw5VXuhfWjNEuSkDAXvQIw8zhI0RhXFRkv6/X11set7JxW0Md9oN7jneuw80kreCSqCvFsccCI9fs76leK7p2bIJ4403b/XPm2OywSg+QPOfHnbU1vljW0/owXT7MJADEI+z9pgJVU+DZZ8P7OkAInHQS8Mar0fTwLjgyizkSCCq4iCtR0syHhsOAc+ElFZg2LWHVUOCmSZFjd211oaWtUNKCk4jcCLrseKDrBR070h5ceWgzFgNemfKL3DbdOmP3HWr0lZdfDjz0UPB6gLByeAS/VmLCJNsXyGKFc27hEcIm9IYDTjHpvXLSScCJJyY+//OfwG9/C/zlL+7HlaNSOyiOObgg1HhwOeyTyfUweOL2iLdGT9i5ZLHwdM9lCf+c3IpsDFTO3n0mHnnESw8VhSj0eLCTDO3bbxO584JSDDKR4aNcvCP9MnFiIGNCzqkljmoF16xZ7k1KiIz79cuUloIeXJEg9f6tHX0U9JjzPGyes2J6pZRS5Z7xF2PRIus6/OK1Htd5QwhPaRUdcyJDg15rk0QSQLzRRvlhIUtIff9pUnv7BnVvspOYXhnKRDpqZEQmb0lWrMjNQOH6C4TAmDHAmppO+epWmqIYNTm25x0quEoIPi/5x+ol6YuBFejZE7j55jy1WVxzH1GAcZGaVoRE4UbQdXmlhK4rsxjyghKliek5t1NG3fzBybjqqtztw8eYFt1tmXBOqXFaVsF1112JsENWfPxxfvJuGcnXvFJVBczZuLNc4XXrbHcJAeC119R0ipQ1Uvd6i3PeDekyJhYvznx+5hkp+RrxSXNBPLiEbV2bN8u1sX5zl8D9UM2atQEEaAMGuBY2n5XBKw7BAw+Y0phEMA+JU5vDhiEtyPTL6acD771nvz8Ky0MAOP23wUO1WrJxY6DD1zRti2Xrt1DTFxI9hDfFuyuSlUkrTmX7RgVXJDDm0Gz57GvHsjkRP4BsBddOO9keu+++AGIxR8NM4z02e0F7VDdvaVvWa4hCV4SAkJlcZEI5ChdFSOMm6+0WsgSp+S6VL1TSuzpmyL1dWwt89pnUYYE55dTsa3HZZcC0aXLHLlsmH46YuZ3Dh0N64aCCq4Tgg5NHJGbT1jxFcozKiytRj5MHV4oQIxPmMm6c/AtdSKE28iHcim+0X0FaCZ1yhDwWAm+xbr1U208/DTz1lFRRPPYYcMopcmX79QO6dTNsmD5d6ji/49EbbwBDhmS+X3QRcPKXd0odq82d7dynJUustxfpgn7d5q3C7kL5sXSpo4dEmm+/dS2yse+nrmXmzMmOfMx5vnA8MeUc5wJ5DlE4e7ZcG8aQgFFBxmvBSNbP9KH4TXHBBbapTHJIG27nw5bX5ro5rdOCLIV0PWG/MXw4sDwZcddK1yNWrPDfiE9cz6/ED5e+mxQ8k2c+elLgOkhECWEC9SZzsS9845ir0jZwH/2wq98GiCqEyMrjo//wo3Nxtxxc22zj3N6IEY7vKsY7++DzemBq9R62ZaVzZKeUP25juBDK0hIIaC6/Mzc1QCwGfPJpBT5f2tOxrCXJa5AOVehWHJnrNn48cMklUocp55NPgM8/lyu7117ATTdlbws8FPrwlHZ91y6gPCjKObhST/OgMdbv+Cd0X8CXQUVQwVVC5Gst1BJ3cAEuF4TA1KnAKkPMZDNO51/XgTp7z2y3pkmZYVwIxmP2luAFp7FRvhvxeEEVDPkMURh/uVfACnKlX2L1GunDHcM+GPatXQuMGiVfZ22t4fuw4dL98cN11wF/+lPme1OTmnrfXXAc7vjxUjWVRYT9Pno07C6UHx98ILeGkvAkqJ8wz7XMQQclnokUURjey4XL9pnoXCBfIQqN9RZprhVt5Ajfx26usLc8B+B63ueOtvfkNaIkRKFHnC5nkH6sWwfceGP2tq5dgRkzTG1syFOM9CB8951rESE0T9b4QYjpVBiULELIGahIICsg1eBB+O9CczI62xX/y4RCX1vTAQDQ2AjsuaeadogEQmQ8sAHE23V0LJ7rwVXhaX7/7b8PxcT19hc4n+/RbvdvY5OP0Jp2ReDNAPXpp4H27YHLrtkC36/dL6cuWfS4XOkg53m9nL2qNF7kucb3eCekDX5kLYmS3HcfsN07zzqW0bYsnPd0pGf51asBAOf+yVpJvU2HzXwZVAQVXCUEjX3yyxFHAM/OOMPXsb17A9tu61xG04ANG6TS95ASYXmttRWH8ZKndCORuA8qKjzl4AoDaQs2B8ynOr5idbAKLc5FGGGU/KDyvsuHN+LM2l3Re87J6ismZYcqAZns2KdCyRuJeaHI6NLOxZNIwdxVYSUkXbYs89kQtraY0KZM8lTeeH9ucf2VruW7tLe/Nt1G9M+t1IK0giuIqMOuDTsPLgflSZBntNImpZVZqBVJb+UpU9TVpWKgi1Q4BKIUXS+4DMSTp4APB4edf30Err4auOOOjPcmKQzGy6VXOIc0tnyXM1xQNw+iT3/ojs3xDl66FxzJ8bTLyT9HY6MaJZcQmuOcbJ7DZs50rku2P8MWymmHheEYL4wfD+y4o+fDlOF33LP9qV6SriGhD6MjhBxa3Nk7LpLruCKFCi5CCoBRruHE3nsn4u8aoQCrNHnnHeC3b/zacp8uKtILwUi9k1dWyk/A8XhBc3ClnpN8PC/xoIsOi7fXDc1d8OCDcoe7LWCLxRmgWPpJyhNlCi7JQcj4XHOeLxxOQhYNUOTBZVGHcQAsUgWXZ2TPZbJc50r787LLFnJhEELJwWW4tOvWAUuXZr7nQ8FVKkgrIYXwHB4zp4qWPMWRJ6Gz38NXYfBgNXXF9QosXO8SVg4ePQWqnL1P7caId98F+vRxr37evEARYMuOmhrnSGzGEMPpKCo2WBp9GhVcAZcTazdvnbd3p7yE8bVBF5rj3PyLnRbb7rOqSxZP59/HxTKGGldFPpT1OT+taq1kweIi6DqBlAZUcJUQ9OAqfhoagNGjs7cV+VxTFPzrX8DXznlkPTN0aDKZvA3jx9vvEyJz3edt2A6xWETug4oK6YWl20tBvlARotCsxIvpclKmzZuT+VXMWGgpx6/vgYcf9tO7bJYtKx4hWKEVXIV8eSPFj6c11LBh9vUIuRtdhYLLy3GqBIClTIWmK8rBZVFHe4OVa6QsV+TxOqKmTuVXX0mWd9hnTALvhJIQhR4PNnpwnXkm0KOHXFXTpwPPP2+/307pnhPpQebKRGIRmc3Gls5oHP6Te0FaxxAHFm7YFg32qXI9MXLN/tjvgd+5lvMkSHXJURB0OjjgAOCZZ4LVUU5st51DbmORPZr6MpgwXNCg7yF3jrsUffsGqsKStjZg7Oq91FSWzullP0fHRCVa4vbecMfumJ1L2WGJLXVOv5i8GwBgi0o5za8w506TJB9TUzyeCVvqFelpXiLcutL2iOtd69eLkORCBRchinASjgUZrzjW5Z/HHweefFJtnWeeCbzxhv1+x7xKhmnwuHf+jP/9z+WAQlFRIb1Yn7xsu8K6WyfPj4oQhWbiknU++ihw8MEWOwKugAtx6QtxdxWpTJeUAZ6fsYEDbXd9uqgnUO2eE6fQHlznnFM+jkNOOJ1qTYPSi5FVlXEALNLB0Kt1bOr3u6ZjMlr12BATcpYca+q2yGpbKTaVGvN8pMIHbtqU3Ocw/T/5JHD77Qq6JVNonntuQOVtunDEZ/fjuj7HSjQWvDUavBCVaJq6eyq+aGngOurrnffrOtC9e+BmSoYNG+z3ZeXgcjHWtHzPNQz6KhQg+fAS6tcP+MtoRfmLhYB2nPM4/quv/4a9P3zcdv/fx12aNcy3OjjcyswG933SE3PnAttvIRcLXEBzDqtbQBnMv/8NdO4sV7atDZg/372c9FgVEW+JLu19aviKGK5R1EEFVwkRkTGJKCYKeo1ywClcQT7qdLquuilWdXU1sL7eOdFtQdDkxVtusYZVk8+FgWyIQlsL0gJbHzt5DnrloYeAt99WU5fxNBR0XFuxooCNkWLkbz9ehn//W6KgxI37tx8vA9bahP9QzMEHA7fc4l4u1e18zHPFhpPhhQZ3RYsXsqy/jQNgkXqkeH3PSM3Lk3+S06w6XZuvlh2aKuRYxwNfHZPVtiqqqoALXj3bcp/V5dwqmWLV0Zgpue+TT4A5c3LDmcumApMyJpJdGDhJfpPMnAksqd9erj4FfPL9ztKe9HbwXYqoRINQZhim93ldUU32xOOJMazsSVr5bFFvv0Yzvum6TdVu84yKkLmVG6qUG944KZDC4mRDOmUtZt9BITSp87F2LSBaPVh1paxSJNC0hGxG5rK4KZ+D8N13wP77q69X14Gn78tjx13YZYuNvo6jLJwAVHCVFHyo84jEDFZfDwwfnr1t2jTpw4M0TRSwerWaeoRIePIAzsJEfZV9g+YF8TPPAN2vP09F9wIj65WlLV9WWGuUVIgEBc+Lud+L6ncIVqHDG1I+PCq22ML7MXbXtXdvKAmlCBR+jkrfC6tWFbZhUnT8ULWvu5eJEbeBxmNCL+FT4bFiBTBypHu5VPX04CpMDq5UDVmXtcgVXFfu+5PvU1O5bo1UOadr03vOSZJ1KMDih44bBwyc0cOicHaIwlzlk3szl10GHHQQcMIJ/rpn18TPfpbotxszZhi+SMTDOvRQ4L6JF1rumz9fSkfmicuePkrag88OWkeTqKI3e9c26Lq/aaTsZQqjRgEA2k+dYL3fdIJ8OVsb6hC6gpDH348GFsvnqHJDF5ra+0BRZWPGZD5rLfYePLrQHCxKsxFxuYfENTycxb7f/z4j72lszC0Sjyfm1m22SXjMqZIz+aGutbMX/R3q6oC7Hts6fx1yoVW3D2dZqsgqbok7VHCVEFRw5RGJAWf4cOD00zPfa2qAnj3z0/Rf/0pLLNUsX574v88+Uu/3trS2Avffn/jstDDuPXAX2326KRBQZKIZCRFKAncZUs9JPoQYp331d7nG7XB4C+3QITfa2fz5wLXXZr5rVvlcCoiquSW0dRsnRxJB0rdlWxtETGKQD+AVRg+uDE7jUIXmU2pow4TF22W+JOs9uOuqolRwCaF5znGZEu5pzcHdik/dZV6qI85tqsjBZVmv/T477+Tnnwcm2MhRreo0K6BFo4fwShYsWGAQGjrMg4cdZviSirHok/33TyjsCCllVK7L/bxXnXIKcNZZHtrQs/+XLY2Nnoq7vX9bzguGjcremRW6XAldRF6OXunwfMm+58c3NkiX9ZNSYfDgjAFJly650U4++igzt155JbDrrp6b8IzddT20/4M49VRjOeffG/b9sbTBn4e41zDakSPsE18iUMFFiATazt4DVxsFSao9uF58MTenQXMzMGuW/3bKFtMJXrwY+PZb/9WpCMNW1bQ1jvv8Hv+dyBe6Lr1Y1DR14TsKjfG6KQn3l7wp7O6HJdm5dfH558BbbwGHHpo4IBbTsOWWCvpRbARc6KXvVY/eNIQUlFhMblwdMsRys4z+Vt+YCDWi1zonuy8HHD24lCnzExUd/9AZGQfSpKRM95nMPGz6LToGfedKuhglSZ3rCs0tzlMiNKTTiP+bPadLtZkSKAYxdnn5812xcKF8eaMHl5HbbwfGj7c/zjzFme8/8eZb8p1QhYKHwKMMuSAUNCcsKXlU3k1xUZEbn9SFMWPSzkgA3JfLqf2RMZiMKkKgU7uMpYGvEIVGDy5Vd4ri0MlR9OAyUllhf+Jdva2S6K/JWysLAA2N1u+KGuQMn8yPsNU8mDW9qnZ1BoDRo213eVnTyFKq+pgt26nPAeZmFFGipzIUKPUpIWikHh3Gjwe+/lqu7OTJwOP2eTdtJw/z9X76aeCQQ+TaJAYM2oXUOf3mG//VGV8e9GX2uX+6drSXAMyv28l/B/KJrktbo4U1HKleIPzhDwoqcVFwmUmVmzkzcxab5Ay584JxrLELtzF3bqLf9fUK5yIhsO++wLsLjgtUjZ3wkZC8IfEQpIpMm67l3TNWvJ540dffVJRQr4hxGoZVWX8ahVrpNUGRhygEgNfmnujrOBXnNabLvbKquIK3vvAzPPecfHnj8ys7z59yioQxlaRESkp5IzEm6TqwsUUyu32wpggpajQIqefulFOA0Wv2cyyjC82Xh7aX5yw1LhXp1FNQjNfVSSF44YUCD0+2SB+g2INL9XutritWTORBy3HgtvZhjXVRIdVmXFRId21zrAO2vuLXlvuEpLjc/Dy6PmuvvSZVryfG2Cu4/IwXTvz0k9qc3ypQ5Vnr5EHol6L3LisiqOAiJA/87nfAH/+Y+e40Ufz3v8C//mW/XyywfsE1OyV4ia1bbmyzjUMeAouL49fCbcSIhHdduuqJk2zLtjkkzI5qGEBPi9jOnQtqMZsvK6KlS533X7HPeOkQhVG2dJK2MlxjHUT8wAOBoUOBdetkG5Q4GbqORYuATW2dJCvNZe1aoN1xR/k+nhAjQ2d2x7RpwLtzj3Yu6OFNsudxnRCXyS8TwBPxd71PAeBsdOHEl18mBHWlTj5mrPRQl5oHoCHeVh5SRpH88bttuVGmsOOa4ebvf58u51xN4UMUxn0ouEaNyk0+n+PBJdkXpya9nIeXXgK6PveA/AGElCmyU/yoUUB1SxfHMjr85V7xIrBOCds/+8xzMyVJi12OH5MnsZOS4osv3C+AihxcqimGfIS/2nWu7T4hIPW86EKT/q1OMplMo854VnDlw4NLgpNPBsav3ytwPccd5+yhHgaq7uzGWAdFNcnDHFzqoIKrhKDFXHTwkmTatS6DxiQWA6Yno7TwestTX++QB6FD7iQWj/kTPp12GnDvvZnvdoqqeBy4ej/7zN9e81wUDF2Xtj/R9HDiYKhQqnlZ/HfrKOFalQpN1WZ9TnKEWgrWN17rcCqf1T+HFbvRkuvMMy0KxA1xW1evsijgoVOS1DEiG1HImf85Cz17AlcPvcq5oAcPrqDY1fPgg8CwYYnPA5YeAcC/8cQ332SHQypmXOcIBeOOsQqzgksXGtpdfkngNooBkZwuju+eh9g4NqgIUei9zczrdJDbJ3ctoC5/yCV/38u1zAp/+m9Cyg4NQtkYE5f0Ts3pgw+PjP/7v0RKg3Lnyann2O7L8uDyEwHC8J6kxINLsbFoMYQodPJ2EZAL89ylfYt011RcJ8/v8l6M1iR/iMy9Mno00BTraL2ziIWLKm9DKaNDL5itmSygaksdVHCVEmvt3XlJ4dC03Jw6dqxenZtU2oxxspo+HTj88Ew72eU8dLIMGTMGeP11ix0WCq6OaFHSpt2CqV07YHXTNp6PCx0hsgQ5TuhxeWWYCna/7WI88EA+hFpyv+L554Fp0+yqSNQhPukv12IJPMtDhxq+pBZ2hjiLUr8xcA6uol6rkxJnzhzghhvyV//DDydCFxuJS47fZkopB6DrHKF4ALZScJUbsr9YxZlPne9CzqMy11Qm2qDsfOXFgyvFZ8Ps15yEkPDwa9QYjwMDBiQ+jx4N3H+/fVnjmHHddb6aK3piMaDv0D1cyxnXCHo82ESiJkShYgWX4hCFWmf/UTZykOiYAKRC7XSsdBGwZdWpXsGlNByorYAhm6C/4+d/+QXmzQtURXgIEd0wgGPHRtV0vSShgquU+GZw2D0gFnz6KTJJxk3suivwySfOx9stjvxGKxo7VnqeLCk++cRGmGixmLrhgO+VtOm0sF3VuK3tPr9CyLzjIXbjy9NPLHhS70ceUVNPVgJbmbcAIXD77Q65XZPnTYyyj41tqs6SF15wD5noVocftE3ulkdWfP01cPPNSJuLtsYrMWKEh3Y7Bg8RQAUXCYWqKtciixbZGF3Y4eNmNh/ywszTPNcBAO3b+zoskjh7q6rPwZUmOQ/EVFuGRpgLXvwV3nvPXch32T17K2szdQWrm4LlkvJiRNZnzomu5fbbzz2cT26IQuvzdsstwE03uZcjGVQ924Sopq7Vfaxavz53W1sbcPHFic/jxwOPPmp/vFHYvmCBxw6WCHPnAte/fIRzISGy5qtY3GZsdZgQup5wUKaYgrFZebhdaJEMnQggE/HE4bzpQpOSRzwx5Rwl5//q/X70dRGUXrepUwNXIfMKMXnhNhg7tjgNbSPdZwltp/AZqpbkElEpKvFDZLXWZc5vfwt89JH/4+0mZ7uX786dgZUr7es7/nib8GHlisVk0rmdvNWPE49MOde+WbfFWxSJx6XHmeUNXfPcGWuUvwjIvARI5uCyu66ygrTbbgNefdW9O16ZMQOYvXFn2/1a0vOqRcKx0dj3Pn2A3r2R/oEbW7fEaf7k64QUFdpJJ7oXUtWWw3Rh3vfq7JPy25kiwGnuzYdRRmpM3P+SQwAAMZ/hqIqVXr2AdhXOL/efDNsOI39ojw8XHeNa3/g5Lvlsktfw2OevlO9kQJ6dcUb6s9NywC0hu+xaYN68xPyaKafmvi1l2UqhDa5I6aPqeTnu83tcy+y4Y7A2xA9j05/dIseUKrLXy1jszbm/sC7koGDZ2JDJ76UrELWqFnzrOiDG/qisPqUkf6dT2E4hqeBq0dv5uuZmtmovF9NT1oNr9epkQJOITrjFahgaWaUtIO/OF9F7otgor7esEifsAamL5ARQzjz/lPdzZDfU2V3v5uaEZXi509bmPwyaKgWTLiowYwZw8MFW+5wUXBEdmj14cFU3d0F1S+HjWt0x7jK1FbrcQ1JLkeTCRnbZIp0Py2cdZg47DLhz3KUObSYq69QJmFa9u3S9lZrK+AyEEBmamzPjhGaSA/i1Zi2ldy7Xn6IiB5fhcyoc0PzliTA+rsnMS4wffgC2bOduHXHjnVtJ1XfsLUc77heq84t4RGr+Xr3aen9jg682J67f03efPLNhg2uRsN9HCck3iWBYxXOji48z4WLK9fk0joMX7zXZskz1BoGphvec03eZY11ZLGa93dymFr33eSEAsWx52N2wJrlg7b6FfeQQHXIKrpheIf2Mzq/bybmAxCSao+CqWmdZbtddk9FNXNh7b+nbDACwaRNQ3xo8XKTU+BDRl4LIjm0yHlw0wlFG9EZd4ht6cEWf2+/uJOUFYcRuwHMbxNeujfBAXwA6dADuu0+ioMUk/YRD8lmvHHYYMHt27tzmpOCK6pM8Ye5WWNko55k1pXoPDFzWM78dyhNCKHYTTym4ZBcvra22u9KhSRsbA3Yqwc72jlueMY83FevWWu8oEEJoZT0GkvLAfI8bE8h/800i52MK38Yb1dWJ/x6MHEqVn37yVl6sWJk1ncTKTMEFyClWFyyzSXruEV2Rgsvv3CHV9tix1ttNc7+scK6+LVg4Rk9s3Fi4thTCEIVENUrvqDwJjP/xD+C114AhKzNh88p1XWw8xZ8tPRK3355b5qa/dsDkDRmDAWNEl3ffNdQhqXlQk4NLLbqIsMQwucbsUGF/fmU9uLwYC8/faK/g8qPIvv9+YOh79qHK161zf+SXLDF4fUuMDyeeCJz+9R0eemmNVqSR8qJucMA1SOGggosoozVefi/tfvAsHLELUVi30fE4pzCF5cLjjwNY7mKlpGoWFwK/2Mnedc6ss5habZ/oNqpWHMf88xSMXLN/2N0oKG63h9S1cglRmFPnkKG2+9Ivpi6xjoQAli0D7v2Ls0X42rXu/fH7KlSxeZPtvkLd4+X6Ik9KhBUr0h+HTO9uWcT8fDqNWb49uMZPSHywE8x75J57gEmTlFTlGaexx/gC+vzzwK23Zu9fvRo47jj3vhvbEL16Z6Vkay1HBVcB3+sFFCd3LyA5z7LEMS3N7qWUnn/ZRKARY/mm7cLuAikhhIjuu5qR//wHuPFG4IphmSTUKtbF69cDDf4cTkNj4cLs788/n1umpSX75Jw56HZcey1w6KHA1VcbDIgk4zxG0YMr4VUe0Xs3HfHEvn9rN28tpeDae+v10nOfU84vWYzP1aOPAt+sONSxrMx63MuzOm+efNnAbUZRAyZEpJ093E5rVv53EojojbqkaGnVSygLeR6p8PjU2Y11Wv9PbMtVVdlGQCk/vvoq62uszXRCVU0mLostB6ecHFQstIh/PAuB3e6h5L0hnU8vZn8veQlR+OGHwBMvyYV9UoXxVFTSWomQYNTVpT8OnWnjbrm5Sbq6wNbE9fZhY7zw5JOJ3ExBeeopYMwYb8fI5uC6/Xbg5Zez96cUJ2ecAUeMbejQMHduZl9DIb1tIsLvht/gXkgRQuTH0VA6l0eAac+P4LlTZ+eDlBt5SGgPaVhCiDfGzZAP6T5zZrC2VDyfO+4InHde8HoKSd++7mWExQD+1lsW51zypV6JB5diZVSkZejp92V7vl5+mNQkf8wOS30ZdbU3eY/Jni6vz5Wn61Dgi+baXAStiCJ9X3NRVFCo4Coh6PpYHFR6NN61FYq3mUKZGC7/5ZcDF1zgtWelycgFu2Z9z/F8k5gRGxqAqVNdCrlYjngJTakqBxgJQPq+cL4/pEbdZF3SObgcFuTpNVIBV3Ke1mUGV4V0F8MKUQiGKCRFjsxLpGkscPTgcsr9qEt4mCt8oFRUdffdSU9tD7gOnQ4FUn328m7/zfJD8NBD8uVJMHShKZG9+A9R6H7/2N1jud6Yap43lcuFleslQ0ly8iUlTHO8PRpjasKqAkBTs7tILqVTef+9aMh7ytGQNq1XWbNGqryKuUh16LVEiMKIjs+yJ0xCwdWmV/rKfW057xoLrLPOreVlytM0uZ8axjSqbawtfKOKiKwsXGIRFlmvyiKECi5CCkyFx8HXrjTfHR0wTCSn/i9b06fpcduydjzwAHDEES6FXFYqaQWXMUGKXVWc5CKD24JDCA3fDXO5XimLNJu6zBbnUknqXe7bRPgU527lhZdeMvYihA4QUkIY5hU7AwovITmcvINHjkyE37MiH8KQoGsYu/GtXTvgiSccjnP7LRIKCre+G6u4Y9xlGD3auTyR45rfu4eFqtBEqKnixGb3NZ4d5ttKxXO3fDkwblzgatLs/vuT5QpG2pyakGCcO/hWnPzlncrq69DOXdqdzist8R7phNYS7Ph0PUX2qipSuUSdysgF5AAGDHAumAzlqmIMX7Gpq9LxdG1dZ9S1RtSTXDZntaSCSxZhEImb18k5fXn1Vcs6vCq4VK6rBw92PyVCAJgxw7UubcBn7g1GcH4XemTVtgkjeBf5L0MUqoMKrhIisg81yaJyU517IQN2k3xMr0BTk7FckF6VGA45AnLOk8WJ+799s83Y7d4lNmwwyB9158CCrVVJi5gvv3QolSDuITEqUY8QMNwXzg/W8sZuOOOcdlL12iku//pXoLshvY6UB1cBkRWg2/YtLA8uUXwv4IRkoTgMiNM6wcnLOB/ri6DP5scfW2+Px4Hx4+2PC/JTjH1WFK2ReODt991Dod9y8qy8RM9xegaeObZ/plw8WqF7XnoJOOEEubJ8jyBEjuZ4B6X1dWznLrAfMiSh5Hry+YDKibVy3kdueE25EDoSXlczZzv/qLQSYc89nSuaPRuAGoPVXnMkjQokOeKhC/DQpN8orVMZLiH900iEiIyJCl9eMa7zoI2i1MuadsIE4Iz+N7mWk63znHPcT0k8DmDiRIk2uRBQjqzVFRdhSii2qYmQoqdi6WLH/Tn5eGwm+Uu/uxG77+6vD5oGrF3r79iiwCn5q3mSsZhMzAJ9uwXGDjsYcnTouuOioOXtDxMfNmyw71u6S5TKFwsxXW4abWkB1m7exna/xG0BwFsOrjDIGq9sQhT+6lfA3I3dUQio4CJFjeEGtlU0e0grKRyW/U7HLajfMac/YWOOElNfn+me07tkkPk1Vf/GjcA22wAPPmjTBk3OQmOLDjHoLe6eXp4xe/8b6Nwu2Z7LxOv2+JjXkMUq69Dq6yI1VhASdTqM/961zLRpwGOPBW9L1ZNZbI+4zNy/fIWkguvQQ90aM/4jsshapzS5555NhCj0oeAyrZOb4+3xt39kDFmFHvyirl8PjF61j3tfhPmDf+JxSD20mkxzmzYF7o9qhPAW0aKgyIQo5HuDMqjgIqTAVA7+2nG/eQy0GxPjohI1Ne7lrKiqAna2yVefL267DTj//MK2acVxv+6avcHixJknGaf1QDoGuYsHV8uqpAZDwuTNuSYSJaQWJELgjjuAgz95SLJOezRR+NhLqu/GYcOAmPCYjJCQMsduHsrN26O+7c+XusXo9U5Q4Vgqn+ngwQmj1IaGzL5AIeqcFmG12bkJHn44kZ9z8+YA7RHlxEe5C4vdyBGUfOW8dk8h9fzJ5uAq1rXghvVh94CQoqKQeW9UeWgUnQeXBVdd5a289NoiOcYrSzlQLpqydEh/l3KxmGtVbXqlktO2oG5H/O8lg4LLpk5t5YrgjeURiVOWwbigtmLYsEB9KTvyEVaA2FICUxNJEVmtNcmiQvM2yOXzBXf48IQRRiHyFbzwglR0Pk80NgJ/+pPFjkp74fnEaaawEhYrlfcXHpv13UkQlz7cZfJq1du59i0Fc3CFi4CWvrCTpzmHH5S6VkJg1SoP7TvUWfHj2HSdLk0qex/y/TJcLi9khBQCm+dpyqKts5yW/T52UscpNNkOWlU7w9A8fLh7+ZUrgcMPBx6adJ5r2X7vG+Zzw9wu+n2YU/aII3KFZBz5wkMIQB/4VfCKZs3K/u4Q3spoZR0kxLB5P9eChJBCMGWK92O0NvcwcVEibjGevvce8PbbCY8aqTpWJK1a3QTWKQ8uVTKccnmfkghR2LVjo3OkniRecnA5IZHZAgCg/aQw2aW5LQXXPxaDpAeXAHr1ciyjt4WY6NSGSD8iuu4qpxdCi/iPKB6o4Cohis1VvFypcBngckMU5o/TTwe22grYbrs8NpJHZs2ymYMllEhpPEwmVh7ZsgquRfU7JD4kTd7aV9ib0jAHV/EgK4BSpmyqrXEvpJgZNbtJzS85ZVIPTEgLNgGN8yIpC6qqPBS2eR7tHtPLLjN8CcFk+/rrgR9+yN1uVHDlOF1ZhJDp0QOYPh1Y37y1a5v/figxn1doeraCa/ESy/LLl5vap2IiVJSsodZlP1RSs5gQyq79xx8D/5l+ppK6Ck0Yd3+HijyEpSSkQKjOe7NiBfAbmzRL5udz2TLgyCMTnxsbgZkznetePimhDdICempedpnHtUtAYjYKj2uuAV57Ta6O+FvvJj5IvtcoiGbnqb2iR2GuIr8hCnOaMtVh997/6ZIjcO21gZvLbV8Aq2q3UFKPDJoGiKp1jmXeGb1X4P4oR4jovvO7RHkiaqEUlZACU+GyiM2NjpP/IbGuLnfb3LnR9qitqlLUPyGw3zbOK+zUhDljhuXhCVw68++U1XhSQOikGKFwLFy8vEbILp69vJs4WoBL9k6lB5cXrJpsnu2cdzCfRHaxS4gEk2Z3Tj/HWmuL1DGqPbg++cTwReED9e67UnnC0bcv8M47uduNdiw5fV+4MKe8THiWVr0SG2oq0gOZEMgSuNjN2xMm2OfjIoVF6CI0z6fp04Sll4Asxvn9rruAl2adqqJbBaeutTO+X7JrQdvssVV1QdsjJMqMGgV8JenIatQpPPSQe3qphjUJ4zWniDS1tYl8YR99lMiPacUnnwDjDE4v990HvPhi8HcXXbeuY8v29muojz+Wqzu+KRmP2K2TqRCFuqK5KMoCGZWkPbjs0SD3gquLCiXvwWaZjN07+tiqffHWW1D68h2PA59+Cuz2zysD16XrQEubu+hfg3CVNGxsbB+4P6qJtA5Y4vmNcveLDSq4SgiGKCwOKt1CFJpc/vMhQJfhwAOBzz93LlNVJZXn0xMPPADsv79zmdWrge7dHeYLT9oE97JOMr2RI+XqueeIbxIfkgquOHMQlQSyIQq93JJOdabuxVMu6mpbJmp0PvGosLtASFFy1O8PwPz5yS8O1tLG8cVprLn5wFGZ8KuTgfvv99ghhR5cmzYBX3zhGPkNf/xj4n9rKzBiRPa+yok/pT+3tZki1rQ0++rT/Lru2OHkg7BiTWJ+FqjIkv45rccefhhS5Uj+UaHgmrR+T+myqet9+M/boTHWyb5gyprM4SF1856wo3tnC0u1kJhZuytO7PV/BW2Tb8CkmDmsv1oLCad1gNlbzOiB7JZ6B8gsA6bX7G7rgfX11wmF1RVXJIxZ7PuS+fzYY8Bf/wqMHu3eBye6dQPuvDN3+95bbbA9Zto0ubrTywEXgXXKi5whCr0hYqkcXC7nTVLhJ3v+nTyAZUMUpkkv2oOz7bY2qTh80ukG96RzMt6kUbwdBbTil4VH8cQWIVRwlRCq3dtJfnA1gG7OFsyEOdZZheQz0r17RgClimHD3NcGqYTugwYl/j/yiCldguKTpunZpt9z5mSu44QJyY0ui632FclVsUT4xCAWwEQR0uEnCnutUou3UT92cCxnFaKr0FDIS0hwZCK27LFH4n9bW84SIotuHRvTY9vLLwOPPprxoip0Di4gEaLokENytz/6KLDzzsCbbya+v/cecNpp2WXazZ6e/vzAA4kQhKpoasr8zqNPzCgswh9ViQwqQhQubtg+67sKz3rtw36O++fXdU97T0g9aoaHtrIiuIU/ZSuElAZeHH5OTTqKrl0LtEg4ihvtXB54wL19J7sYq3FOIr2SI3V1wMSJudvP2G1OsIphWI+5DJZzlm8JQOE7YmOjmnoizpwFidjTrvmzJG7w6TW74sQP5LRD3bewcTOEhQeX2zwZ9AY2scFeL5s33Nc7EVwsCBFdWfhOO7kWYQ4udVDBRUgIrFolLycKy4NLts6VK73VqcLLPnXuHnss8f+BB0y5uNw6vi47trCbxYeWjKGgVSdWGRbRjwBdd6xFTwlcJCzgmYMrXLwsMvITotAe2cWb+HqQfIMligBDFJLiJ3UPy9zKV18N7LWX/X4duWNbx47AvfdmQuwNHizRmQAsWpT9vcYireDo0QlhWwqr8IJOAn2VyvWJUzKCFlklB99Rw2Pxhq0xv85dmOCGa7QFA9KXO+nBtWD1lq5FpR61n35yL1Mm0KCGECSSb8HFg8tmxNp5ZyRCrCGRk8ouQssB52SsSazGqblz092wLeOEivnTqk3XeiUajqdCDrqUFUlhhzJjQxnNYwkgYnHMmAHc8oN9SD4NkBImrWrcVrpdJ7mL+Qq6Ki2LfAGowX0+FRGMmCkE8MHCY8LuhjVdu7rKb4r7rokW7dyLEEJUIoRzSJ6c8h7qVY2MMsqrwirVz7q6hADLLRyhFa6LZbdODRyYyFwvSUVrwiS+esZq4Lztrdt3aTO9IJJQcIWVP4IkeHbGGai8N4an/+teNh8hCp2EqNJ3xpAhED3OlW9UAbqencQ5Cos1KrhIsePlHnYLs2McW4z1PvFE5vM55zisJwI+UIsWAfvu615OZrxs50EBQcqHu774JYBfBq7HnC9XlQJl1izgkDvOcy0n9aitWAHguET5SMy44cF1MyFA85padNp9dxcFlzs33gjssgtwruE1YtYsYNIkU10WlR14oHsZmX1e+e4751dxobm8f7e1AXCOjpGu3zUHV7K8qnGpTHJwaULHqFESBSUWiV68rh3zohuemLo6YMb63aXrLUY+WHgMBq84OOxueEcILN+0Xdi9sKbIlZ7FBt0ESggu7UsD8xgoPUFnxehTg8x66scfvdX54ouJMIS33AIccIC/frkuiN0mEmPMJ2EjtjDUkRIcnHfvYZg3z0ZHpVDBRQ+u8Hnm2XZy1nwFvlayQizXF7k8cPHFQM9P70t/r27uUvA+EFJqZDy4gsfF14OGwJDMwTVvnvX2VHhhFXjxsFGFtMcuV+RFj/l5cxSASa7TNU0+b61XwW+533EqQkgSUuz8u3d3AGr0IZ1M6QTvvRf4wx+yt8mMU3fcYR9C2JenlQ1nnAGcdZb9fqFAyJx+53M7wUJxDq5yUXDpcTz5pES5iy50LaN7OPdO88fE9XulP999N3Di5393q0y63SgyYOkR6DvvBMcyKp4l1USwS54QFhE2iD8oRS0hyt16r1hwW+ysb94aY8bIl0+Xq1rnXsgjytZThor+9rfEAsEuv5fMYjmwB1cq6YgDd91lbC/zbNXWZrd/yj7J7LxC5Fj8ZnUptXiSysHFoblYkBKqePXgcnjmpYVeIbguxeNAXGTu7+GrfWqwFUGBFykF/vCHROJ1GVwVXB6FLRs3mjZoGl55Bfilg4PMpk32xit2/TMPVzLjpYqcQ4TYsWzT9jjGEO1GlSW+UvmF4cEJnHuiuto98W6E4RswIUBtfWIN7jTO/FC1r1SaILM9i1Wdr77qXk9TE7B0qf3+trZE3k2VWCvOXMZwXUf7Cot4yAbSObJdF1uJ9Qk9uLyh6RJJZyXxcu5lc5875bhNUw5KijL4iaqhRKJwUIpKSAi4zX0nnSRf1hMSip28YFqYBZW9Wx2ftU0IdKx0WL0bY1nbJKV8+hmD4MCwva0te9HfqTWTmLR7Z/skpWnBoowHl86hORJIPHyyC2jZd5PmZufkutIeXFx8AmCIQlL8/PQT8Oab7uWWLpXx4PI2t4wYnl2hdtKJuP9+Z89tq3xZXpFScIWQTFp2XOXwWxpMmJD4v3Spcz4PWUO0Sk2Xvodk5q7Rs7eXq0yCd/86AXMGL1VWHyGk8KTeSdzGmY8+cq/LPAZ9+aXPTjkQjydCF99/v9p6R45M9N+os3cdp4VA147OLrZCUsGV2q0qBVe5KLgqoLvOfdK5qD2FKKTcxQt3fnhU2F3IIdJyD8UhNYkzzMFFSIHxOv5+ufwwZW2vmFgFQD52sDLhsGlgF8LZmitFPG7t8CQTorBjRQwt8faWux8afBweutO9/XR7hqt28slAO8PIqUsudtOLJwkF17y67vKdI/lD1+FmB+LVI8KNLl2AePxXtvudvASN7Pv+Q6gOSZ8dKVasgJcxj5AosmkT8PjUXzuWsQsBZMQYolBmfm+3aSOArlnbamrcj7NDWkEkUc5bXkP5so71SAjIoGl8US0xEs/WJYHr6TX7JDT8J3A1aU5+8JT056BRPK7+4OyAvQkXhgUlBIjrcgqudLhgh/ecfBqIpTIFXHyxVWoG62Pq6hI5zL2kN9h118RxTvWm0d0NEGTf+VMKKRVrgRO7L8hOrVDCVAj339nY1lGqLi8zouy7fKSVKKT44Q2mBKqrSwhaqpcejzwC3DnuUtdyP/0EVG3e2rXcstXWCh9fbNggX9ZkeTRxIjBtmvMhsVi2IsmI6/jvUqDP+MOzvrsJBozPlhDICu1g9OBxqsVLDi4SEYTAVu2dk8ZIrUU8hCh0e4fZ2NoZM2e617Nu89bl8j7kiPbSi2F3gZDIIABPL1DtNO+DiFP1Kt/dnATaZqFSwYyfZeI+kaKgQ4X8tZS9r9+cfzz691dYIUmjLBQYIUVMymPIbQi58cbkB4fILvmUK/Xqlfgfj+fOz2LWbMtj/vxn4MADvel66g2BVVyV4LruWmZd81ZS7WY8uIKfRAGUjQeXttk9SeXmeAepurwYPcheJ07N4WMXRjTS18YmWlRWkWQ5EhxXSaumaZ00TRuvado0TdNmaZr27+T2tzRNW6Jp2tTkX8/kdk3TtBc0TVuoadp0TdOONNT1B03TFiT//mDTJCEljRdrnqFD5coddxzQf8nPrXcaFkWVFXID54ABif+Oi9uqKrnOAb4G7NQC1nxodTWwetp61/acWlzTILdATVHhkMw+vYASwvHaesnBRSKCEK4LZJmwBgdecwyGDJFozxg604ZHJp+HQw+VqIvQopsQE17DsMiuGYyk5uw//hE5wnzZpcD337uX8ZRfQZGyX3Tt5lwgqeDi2FP8pK7h+PEhND7bWsDrhKzQr1Sh1yQpVs7bY7qyuvSkB5e0PsRhUvaj4Pr2W7lyjiKE777L2fTAA8CkSYnP553nvV+AhEePy3s8AFw7Uk58qdLAUAitbBRcFUMGK1OselkjKjWQoJKCkFCRedNtAXCaEOJwAD0BnK1p2nHJff8QQvRM/k1NbjsHwH7JvxsBvAoAmqZ1A/AggGMBHAPgQU3TsuOekEAEDU9BCkdB575hw9If7TyizIz7MdnBRkUJp30szNLWT6ZDzzgD+MX5OzgeO2t+ezS0dZZuyDXes8M+6RxMqXJ0tSweJDyvZB7lucu3lGtPQsFF5Bmx+mdo1RmJmZAUxkTaUiEKAzw+b74JvP66fPnRozOfZRyhvCiRAgubUtK47i7hg8PKc0ryxrHHhtDorFkQHpPZrZOI4lDK8A2YFCsqhesrqxKRWqTlDA7v5xXw/u4uk34AcA51bNX3Rx4B5s1LfB482N+rtKsSXNddx5H0O4V0iEK5vjkhgLIJUdi+Iq5MTOItB5dCDy4quPKK3XUVyhLe5QEhXOX0NNJRh6uCSyRISbnbJ/+crtAFAN5JHjcOwLaapu0M4CwAQ4UQNUKIWgBDARR3wG9CfFBwy965c9MfZZ2Htli3BACgDRxoX8hLqD0fk/30pEGbeU23aJH7sYdcsr90O7MWdMC6zc4eXU6TknSIQoOnFykSVq509XhQavVF5adSPlx0DD5ZbOPZSki5kpyDZKaidpX+PbiA3DWHU5v/z95Zh8lRpH/8WzOz7prsJht3d3dPiId4QgTiOZwASZCgh7veoXf84LAD7rA73B0O9wQICZIQ192Z/v3RPT3dPS3VMrrv53nyZLu7uqpmprvkVbtW2XZCFbs2fn75ZbFKqw20pOCiaT71sbNWj8m6fudO7+tMY7YesPCuJIgkxctcvi+8nYc//vBGwcUE+xMn7zbmppuMr8VKTsKTQ9OqDK8xeW6mPQMFMwTUHw8uxrxbP9l5r7iisbQHPvvMTY+IWJLU626OzlGIQu/gklAzxvyMsY8A/AZRSfW2dOliKQzhNYyxcMa/RgB+Uty+VTpndJ4g6hfdu8d3/FJoiHitsXP3SyEAzcyoDVaxR44AS5bwdk6n2t27AESsZrUKLq8NpDtNaYkdRjG1pR/KbMHOG/LJbmgoIgnYtctyK0O/a3LjJIcQQaQrSgtBs3XI3o++BwD4X3nRVv333AOcfnrk2I6Cy64Dq2kOLkAlEArWuhQOSYsAq7Xb79u9E2oRiaUuxB9OOiQwLuMrbhijMJcEUU/w2nI/GPRIweXALzKZ00xbfiehkGUZv0UeHbmtoPi9vr+1AVd5S+qJBxcA/PSTdRkegiH+hzHI8Q5++SXwwQcclZGSIjEkcRSczvO74Js9lYnuRr2B680XBCEoCEI3AI0B9GGMdQJwNoB2AHoDKAVwphcdYowtZ4y9xxh77/ffLfLsECqsktcRyYGdeS+8QHIDO+Vk3Hef+DevB1d2QBTSHP/KIhw0yvdpsIrduhW4+271uWAd/4d+7TN15NKtW9XX4+rk8r0o5DPPwQXxR+XNwUWkDn6/pZDJ01GXPLg8x0fzIkFEcfQo8OOPxtevvl60hqnbbE/ScNll6vl/61bg8sv5+2QHszlVm7Mi+MST9ip3yI5fRSEUKSfqF3/7pi9atfKwQloLEES9wev9oS0vGDMF1zdfO2rbS+6+G8jjifLOoQAym5fbF2/j8uBqXfQbR2fEr/Wbb4A1/xzFVd6M+pSDS+jjXUxgOx5cR0MZnrVLxBajoU34zH7u0njx6fe52HbQPDMT7Ru8w5adhSAIuwG8CGCcIAjbpTCERwDcBTGvFgD8DKBGcVtj6ZzReW0btwuC0EsQhF4VFeZ5dgg19FqkITZj8Bvxdtjn8kk+IY9yHbXIKJ+qchWrCIM4dGh00U2XmCwcdu827UubNupjIyWd3UX1rl0chQ4csCwSEhjXwpoUXClIZqbl7+bl73r2+fU7SXwsoAUjQUQIbwyzsoD//tesoFhy2L9PNylkeJvMhx8CZ55pfN02Ci2YpQeXYl4OvfCSq2Y/3FJi2SYAoK4O334rKjyI+sOuI5x5NnlhjPIxEEQ9wcsQhYBof3riibyNmyi4/m2SpsDoHs6P0rZ8h+E15TLh7rthbGirZPt2yyJmy4/irENAKGS5p5vd8j2pMot8OiHBKxFOlEd6OvPOTxZ5Tm2QKLlLOA8eEWe8euESCXn/eYKlgosxVsEYK5b+zgEwGsCXUl4tMMYYgKkAPpVueQLAcUykH4A9giBsB/AsgDGMsRLGWAmAMdI5wiOcuJIT8WfiNSN0lUB6CB4taMILztDWKJ2yLsFQZFHw8MMGzlqKkzffJODDD8W/f9Zp4utvTYaaPXvkP0dxGDp5Ff6glCdcf4a4SDELQxcSfLIgzUz4JS+0aPJKHXw+SyGTlyEK/3wVKbgIgogdt30xFG+8ab3p50nWvHat+ri6WmXrol+vy+mPZWXi1Vet69LmrAi6HKf/2M85NtfWYtMm4KOdTVy1R6QWsVjV0UqRIOoH7/7WzNP6bBl8mhho/uuHrvLfFraoMlx7awBtC3/hKielv7SGQzhgtp8TBIYnnrE2agyHslt0eUfTcp7ro+qJgmvGlQM8q8vtus8pNRM6J6Td+oKRrE2oS+0wnoLgYQK6eg7Pm18F4EXG2McA3oWYg+vfAO5jjH0C4BMA5QAukso/BeB7AN8C+AuA1QAgCMIfAC6U6ngXwAXSOYKoV2zZUZCwMLG8wvgqQa2l0h1vFSvoNTe2x6ZNZu2aNKao/PnnrftmFi7QczJFoZZZbGZuDy7yJEk9QiHrHFz0uyY1tFYkCDVXXMMRq5jjxXnsMfUxhwG1J+/j11LUJFMPLm2IQpfLhrDQyzIBfZ1J3lIibfHaUnz3wUzyPiaIesLe2hxP67NlCGqiOLn4wwny3716mSXt3GujQZEPfq02vObIe5VDq2c2pn67twJTFhXjQF22aR3hvE73Pmfcf0A0EvIqXKPAKWcg1Px2qDDRXSAIIgEErAoIgvAxgO4650cYlBcArDG4dieAO232keCEQranI9Y/Ks+aJ7zY5RUuCbv3WBfSrKDNFtSmhkc2rZLY4UMAPA4HY4T0hZklKg2ByRl9zb5eLz19iDgRDMY1RCHhPSQkJAg1PFMujweXkzXnypX27zHCbOx9YVs7ILRXUdbd/Btuq90F88wLBoMkh6qHeL2+Kzlukqf1EQRB6MK5B//uO5MJ/4kngAULAJivC66/PhI60ZP8uHv3AoWSEoPLg8v42o7DBVxN8noFfbG9GF24SloT0nikEwQRTarv98ke1ztI4koQKU7AUk2tCFHIOfhzbdY1q1htbixlfo+QmcDHroIrnlOAtBquzNlnWCSksBQ3szoz8wIjkpRQCILFNEkKLoIgUgkeQxeBo9DWrfbb/uAD+/c4RqFpcjv/BoOceTsFAc8956opIgUhwQRBEClHKGS5Bz/lFOtq2MIFuPde6e/33zMs94Qipdf8Vu8YluMWVPOEfQGAw4ft1WsC757vwqd6uG5L3TApuAgCMJFRepXDJEFQiELvSO0ngSDSHK/HOVPFlaIxvQXcunXAAw/olweAQ4eAb7+NHI8Zo2zXpFMefcjrrwf27/ekKplwerCcgHHYITkUksXnkL97mrxSB44NRapbDPFQnHkg0V1wDL1tBKGGS07C+eLsM7b9iDmWY68qRKG77Q537mpB4FIOEukFGboQBJEscE9Bb75pGQbm2mv5qvrsM+mPt982LKNMz5AXMM7VMPlZ3UBQ0fz2W+Rvs4WNtJn3YmZ+5ZfWcphkM7xcBggCw1fbKdweQZgh+DjCrycxtHPwDlJwpRFx9WwhUoqws5XpgkuxUNTbrF9xhfjPiCefBFq31r9mKlCzswoUBFOh1ldf8VfFw8dfZISbNeTDnU0AQUCncY3x+A/dDMuRACQF4ZAE14ff1UzBm+w4iuVPEGnMk89Yu33zhCgEYmOvEdrrjaXKVTdm4eBBqU6XhghHa/mMQ1koSDYs9ZD6YOhCEESKwDsJ/fabZ5N4WM5wJGS8vlDq0rxIqyEok2uafY5QCIcOAYtfWuK6zf9s7YjZszn65uE64EBdpnV4ZIKo51TcckGiu+AKWkd6Bym40gjmRTxjIql44/0sT+rhClFYGxFiGwntnS5I3ebguvlm6Q+LFWOvXvx94oI3JIAg4LNvMs2rEni0jERSwZFMpT4ouAiCqF/c91I1mje3Lse7Jjh6lL/tw3c/YF0I1lPp6efn4403xL/denBt25nNN3XT/F4voXUAQRDJgvDFl1zlXvuiTOVV5QUrXl1geE3pCe3FmBkKKuZbs/26IODnn103ZwtBYJ4o8QD+vF8EQaQuggDaQ3gEjZgEUQ9g+8Vk63uP5pgUiqzEjBRhysVaOHyfFaEQ8OSzGeYFLHjwQfH/3X+EsOtIHl/DXiBIubWsrCo4JiQSgKQeZ97Q2LJMfbC4SWUvqPrw+xCE12z5NRdbtliX4xXgZGUBH33EV7Z280+etKmk7YMX2r9JwUl/64Vnn+UoSHky6iU0zxAEkSwc2X2Iq9zgDUNw54P5luVOPtm6rvC8bLZHDyu4gkFg874y60rDLtgGLPnbiMhBMIge5T/oFxSEuMuNvWyPIjQRRPojgHJweQUpuAiiHsAOi4vdSWZxrZUKLgNrIaVgqdmQGq62X3nFogCHQOjll8X/L/0zV5P81JqHXmO8wiqeUHYkAEk5Lr+vkWWZ+qC4TOXlVn34fQgiUYTXBMr8m0Yo02WYwatQ51Eq3HYbX5s87NzJUSgBgjQi8dA8QxBEstBielfusjzbXMt9vIK8wGHDa+GgGHfcAdz51SDryt57z/Tyc19FjBBvvDsfH+xoql/Q40l51y5PqyMIgkhpY+JkgxRcaQS9FoQR4WfjSNDEk0oBT4jC3Xv5kjkOH25RwMbCM2QdMc4eVqtU2YPLAi4PLhpu05H6INhKZet0snwkiNizeLF1GV7PKy9lURbyMVsowysZwQTy4KqPkGCCIIhk4dARvv05wDcv8+SfDHNOj6cMr9XVivPjH39wVmbRue17I95nV99RaFzQY8OTHwwcxVRNetdcSu/BCILgg6QV3kES1zSCBHmEEX/s8eOtt8zL3PdoJHyh1zm4TJFWnVx1ey082rbN9DITBGzfDvzjO4vkXnZCFJJ5d1pRLxRc9MgSBGECjwDsjjv46nr86/bmBfbtA8C3GfRyzcKRkhEIhWi8rIeQhz5BEKmIz8exf+XYejMG/O9/wFnvTDcsU2ceNCUaG5o10z5yTd7e4uU6oD7sM4nY0yiPXA+TmZBAIQq9ghRcBFEP+Otj5ejf37zMaRdErJ+MFlNvv23D8oqTYC2/0spz42iL7Lo7d/tx6aXAG7+2Mq+HY0I6GvJTeo40pD5Y1qXyZ0zlvhNEqsAjh/rHP/jqWvLMbPMCQQ5XKom4K7hoc1ovIQEkQRCpCM/I9b//8dVlFcb3629tih1tTOBWCq5UzsFFHsKEF9DyNLkheYV3kIIrjYiJdw2RUFoX/Rq3tn793S+npDKzRv37371t98AB/knX88nZbx7GYcoZbXDDDRz1cHTs+k9H4owzOPtFpAz1QbC143BBortAEEQSEp76fCyO1htCuG3rsbeuDti925tmX3ie4zOSFUtakZ9hnE9GCQkgCYJIRXzMOw8uHt55B9j6E+dm3oZgy3QbnhAPLu+8Mb7fV+FJPUT9Zl9tdqK7QJhA60jvIAUXQSQx8ba2CC9izfJF8eShsENdXQKtSrKyvKmHU6j14YfeNEckD14puPpVfu9JPQRBEPHGt3NH/BqzsWD48UegpMSbZp9+hmPL5HGuDyKxNMjZy1WOQhQSBJGKeGkczVNX377ATTdzFKyrs9U5Mx3WZ1/6sWcPd1WeIAAQ/vPf+DZKECbsq82xLkQkDAEgNzuPIAUXQSQp558P/Li/NK5thsdVM6G9HQXX1q3WZR5+rthTD67fvt7NVxngnVUX5wcQBP6yRGpgpgy2A+OwoiQIgkg2vvwSeP6TSk/qsmMpnpThPMiDK63gtaitD57cBEGkHzbSXJnCmMeRhL78kqvC/fvF/0Mh47LfbA7guuu86hgnAhB67Y04N0oQRKoigHJweUUg0R0gvINxpdwmUoVNmwAgI65tRjy4jBeKv/3GX19NjXWZzT9ncsuEeMb9Bm2L+SoD4q7gItKPQ8FMT+oJhsjehCCI1EIQgNde866+a6/lKBQK4fBh4L9b23vXsEe0XTUi0V0gPIR3ZUehZQiCSEW8lB15uRXetiMTS86yFiIUFIjtmjVde1SIexqP7/4owc7DefFtlCCIlIVEid5BErU0gjwACLfweHBddZW3bWYEQjY8uDx+xj1ScB0+wFePIACf/5jvSZtEekEhjmIDzYoEEUM8Dsl3zz08bYbw4IPAvd/0965hgtDh5wPFXOXqBPN8rgRBEMmIVx5cgLeprlpPaIX/vF3EXT504JDhtWBdYnYCH+7ksPIlCIIA0KbIhgcBYQopuAiCkAl7UsUz9E+XFgc8DVFoC48SiuV0aM5VThCAjn8iC28iGvLgIggi1RBq6zyNyvfxx9Zl9uxh2MuXGokgXHE0FN8oCnZokr8z0V0gCCLF8dKDy8sc3QcP2dsTmYaJDYXi7sEFeBfCnqjflGfvS3QXiDjw5Iw7gYqKRHcjLaAQhQRByCQifURl8dHEpa3w0tyMg1deiWtzRApBHlyxgb5VgogdQ3vtxw97SuLa5imX0AaQIDJ9HkqTPSbLX4sjweRVDhIEIbLozIae1MMO7kdtbWIilIjKK+O2hWBihAyUm5HwAgqBXE847jigRYtE9yItIAVXGkHDH+GWYBD4z3+As96ZHr9GOUMcffYZsGu3x095nBVcBGHEnqM5ie5CWkIhCgkidny0Jb7KLbtkBoI4Wkfh44j0I56RFuxCuSQIon7BDuxHgwbJGYI/FIx/Di4AOBIkMSvhHppOCcIeNPKmEWOeOjnRXSDSgK++inODO3dyeXB16gQAmZ42feOjVZ7WRxBO2bKvPNFdIAiCSCv8QhAAKbiI9IO8AwiCSCZy4myn17HkZ3y2q5FlOSEYQtu2ceiQhvXvTot/o0TaQXM9QdiDgsMSBJFQhPfeT5i1559uSMCKlyAIgiCImMMY2b4SRLxJ5txlBEF4D0P8PTe5vVhDIbTL3hLTvhBErEhmb22CSEZIwUUQhEyiFE2JaDfTVxv/RgmCIAiCiAsH67IS3QWCiAkk9CIIIlk4eNiHH36Ib5uf76rmKicEQxCeeTbGvSGI2EA5uAjCHqTgIghCBU+c6mee8a49AYwrRCFBEIRdgiFa5hAEQRDpBYU1JggiWbjy3kpMnpzoXugjhATKY0SkLPTsEoQ9SPJDEISMEOKbRseP97hdmr0JgogBG9+bmuguEARBEARBOCbAgtj8/h+J7gZBpBw1+bsS3QWCcAx5axOEPUjBRRBEhARomp7YPwLr1sW9WVowEGlPjv9oortAEARBEARBuIAxAc2akjUgQdil9JOXKcwbQRBEPSGQ6A4QBJE8CMEQ4q33vual7sBLcW0SAMU0JtIfEoUQBEEQBEGkD9W5u7DtYEnM2ynLOYCdh/Ji3g5BxJKgwFAb8ie6GwThiP212YnuAkGkFOTBRRBEhFCIKwdXOhAkBReR5pCXIkEQBEEQRHpQVwdMa/ZR1Pnzp/3P03buvkvA1yuu9rROgkgEPR/diAUvHp/obhAEQRBxgBRcBEHIiB5c9QOBhj8izaHcdgRBEARBEOmBX8cRxcdCOG/6JwCATF+tJ+0EMhgy/PVnT0gQBEEQROpDEl6CICKEaDNDEARBmNO/9e+J7gJBEB7hZ0HdvwmCSCKkEBtm3vk3DnwAX8w613VTRUVA5tIFrushCIIgCMKC+hJCKw6QgosgiAi//ELjK0GkCSGBpngiNrxx3n8S3QWCIDyiUd5u+W8/I0Mngkg2GARDAZggQHbZ9zH3wak3n34TjjkGyOrQEj9+ssdlbQRBEARBEPGBpF8EQcgIt9ya6C4QBOERdQIlVSZiR+O8P1CevS/R3SAIwgXC1degOpeE2ASR0kjKLx9zH5u6WckeWZdWU+O6OoIgCIIgiLhACi6CICKEQintwVWYcSjRXSAIgqgXfP3hQTw6mowiCCJdWNvxxUR3gSAIC7QqLOW+jUEAKy+La38IgiAIgiCSAVJwEQQh4z6wRWLZW5uT6C4QBEHUC3JaN0amvy7R3SCSnExfbaK7QHByw+AHE90FgiCM4LBA9MKDy26bBEEQBEEQyUAg0R0gCCJ56PDQ+ah9LNG9IAiCIAgiHTgaykh0Fwhexo0Dbk90JwiCsIOg0GmJObg8VnIRBEEQBBE7yJjEM0jBRRCEzK4jecCRRPeCIAiCSAVoOU4QBEEQsUUp+xIEnZnXJwbl8bFQ7BomCIIgCIJIYihEYbpQSyFgCIIgCIIgCIJwAAmzCSJ5MXg/GYtcy/bX0WtMEARBEES9hBRc6UJtLY5t/n6ie0EQBEEQRD2BQiERBEEQRPzQzrqCACAkem5l+2tpXiYIgiAIol5CCi6CIAiCIIgUZduCMxLdBYIgUhhZHE6uHwSRvJi9n8OGAQDyB3aNX5sEQRAEQRBJBCm40gVBIHstgiAIgqhnVOXujX+jUlZ7kn0RROoTFGg7SBBeUJBxKDENFxfj9dcEDN4whOZlgiAIgiDqJbSjIQiCIAiCIAiCqG8IAmpDfvFvH20LCcIph49fg/4Nvo9J3UeCGfLfgqDWYIUVWgMGMu+VW6QtIwiCIAgiRaCdTLogCFELXrc0yNmD1R1e8rROgiAIgiDSA8r1QRApDmOoC9F2kCDcEvP50EDZJAjqa677IdC8ThAEQRBE6kE7mnRBECDAWwVXr4ofcNOg+z2tkyAIgiAID+nePf5tklU3QaQN+2uzEt0Fgkg5dv71n+oTVVWeG5uGyfLXyn97vd8nCIIgCIJIB0jBRRhCy2eCIAiCSHJat45bU2Mbf4bgspVAhhguiTGy9CaIVOOa/g9iY/cn5ePSrIPICxwGQMJzguCltKA26lwsZsRs/1HcOug+7vKe2p/EyZjlzSl/xpSmH8WlLYIgCIJIKshw1DNIwZUuCILni2ofC3lcI0EQBEEQnhLHRbGPheBjQmK8xgiC8IRRjb7Ayg6voFnBDgDA8xOvwZZ56xPcK4JIbfw+AUJNE8/rvXHgA1jc9s2kFIDdN+KvnqQzaF30m7i2IAiCIIj6BoUG9gxScKURXodFSL5lNEEQ8WL7gjNwepf/JLobBEFYEUehFwOA886TvcZonUAQqQeDgEadS7F57gage3eUZB1EefYBIBCgvHoEwYtm7vUxAUJuvvfNaN5J3TfUoxxc387ZoP5cFuuLea3eRbviXxy3F8bPQjTyEARBEAThikCiO0B4RAw8uBhZaRNEvaUg4zCy/HWJ7gZBEEkEYwJQWZnobhAE4ZaTTgJ++AFo0waoqwO+/RZo25ZCFBIELz61nXCsbE1kzyazBpQKLhf9aFm4A0Ce6lzzgt+xeV+F80o5CPhCMctfRhAEQRBJTRJ6aKcq5MGVLui4NY5q9LmrKn0Q6GUjiHoK5dYhCEILAzyzFCcIwh39Kr/HrBbv2b6PMQDZ2UDbtuLBqFHAypW05icIOwSi7YRjEWVIXo/7/VIbFu+p205IOTalxvH+9Etwi0kOMC9CC/pZCKE4Krh+WXA6RlR/Gbf2CIIgCIKIPaTgSiO0C97/HnOdq/pIwE0Q9RsKB0wQRBQkBCeIpGB2y/dwbo9/276PFNME4QGdO6uPBSEm6+aavF3iHzoKNT1cv98LF6oOS7IOoix7v2FxL3J2+1kIoTh6jzbI3UdyDoIgCIJIM0jBlS4IgudhRUiERRD1F3r/CSK1yPTVxrwNreCMdF0EkTg8F6bTC00Q/DRrpj5mzPN38tDSNRjR6CtgwQJDBZcXHlQy55wDNGwYdVpvZHhv2sVAx45ixBeX+FkIQSF2YqkMn3HIdWH5ipi1SxB26d/gu0R3gSAIImUhBVca4fU+19MFM0EQulTl7k7KzRUrLEh0FwiC4EESSucE4qDg0qwLPt9VFfM2CYIwxolOijwXCMIDdF4+rVOXU4ozD+CcCe8jOyApZgYPlq8dCaoVXVGhg30eKqpNBpieExoAJ5zgibzAxwRkmSih2hT9gq9nn+O4/qMnrIGfBfUvcnrGEUQ8eGPK5YnuAkEQRMpCCi7CEAphQhCxJ2nfsxh4hRIEETuaF+yIeRvaEWH7wSIAQHXurpi3TRCEGs/naPLgIgjnMIZrrwV+3XiD66r6Vm7BBZPe1b3292/7qY6jQgS6cSPTjgFS3i9dhgwBcnM9UXAxBlw74EGc3e1p3euFmYfRuug3V21ox0s5tcO0aa7qJQiCIAgiOSAFV7ogCNFJZydMcFWlnoXnwaVrXdVJEERqwCAkq+qNIAgNvx93Kk7p/HzM22FMUAnAxtV8BgDYMm895rZ8J+btEwShxomRTG3IRGhNEIQp1bm7cOyxiFYGMQa/H8gKGHsi2WLIEPH/3r1Ni0V1w83q3Y6CS8KTiC9VVWhWsBOX9HlMvw0IQLt27tshCIIgiCTh+Lav4ZvZGxPdjbSCFFzpguC9MFpvwRqPEEgEUe/o2zfRPYiCCe6TRhMEEQd8PpRnH4Dfg0TvVmj9O8qyxMTzfiagzkb+jPbF25IyNCtBpBIMApCVZfu+w3UZMegNQdQPJjT5FA89ZHydeZWIq1Ur4MorgeOPV52e3uwDdXuxNEdjDJg40bSNKA8yJ6xaZXrZ7wsBRUW2qy3MOIR+ld9HX+jUSXX43DHX4MROsTcSIghLGjdOdA8IgogThZmH0arodyBEcjevIAUXYQgFKiGI2EMRgQiCcEVxcdya0gq5wiF/fEwgrxCCiDMBn7MNcXXeHo97QhD1B18c4hvI3tIFBVEbhRaF6nDEPo1ntef7ikmTVIcntHsVj46+JdK+y++juKAOaNDAtIzTNs7u/jTenHoZMHSo+oLmSxrZ6Es0yt3tqA2C8JQePRLdA4Ig4oS8r/bKMIawVnAxxrIZY+8wxv7HGPuMMbZJOt+cMfY2Y+xbxtg/GGOZ0vks6fhb6XozRV1nS+e/YoyNjdmnqo/EIF+OJxZZBEGkJOTBRRApQk4OsHGjblhhr9GGKKwLRZaRpOAiiPjiZyHb3hvC8hVolLdb/yJj0eHOCYJQYRiST5obY51bV1s/g0bB5WWIQh16lP+Iac0/ko/thCgslby+lex6+RPxj/HjDe9zGgYxP3BE/EPnc8k1kmCRSCYSYPn68Khb494mQRAKaB7yDB4PriMARgiC0BVANwDjGGP9AFwG4BpBEFoB2AUg7D9/PIBd0vlrpHJgjHUAMAdARwDjANzMGCNpiIdEbUpdTpC0xSWI+gtjOmMKQRDJB2NATU18mtIcl2fvR/8G3wGwp+DSLk+mNP3IXccIop5Rk/cHBkjvHkEQ8WFOy3cwr1Uk3+RnM8/HX4fcG9c+6KT+SgxSSCVe5dO/xt6If429ybjA1KnAuHG6l5yEYP7k2E1Y0eEVICMD6NYtSn4YCu9xcnMBwHNDYYJIFWa0+DDRXSCIeolsnJpBocO9wlLBJYiEzW0ypH8CgBEAHpbO3wNgqvT3FOkY0vWRjDEmnX9AEIQjgiBsBvAtgD5efAgC+jm43Cq48vOiT44Z46pOgiBSgwAL0maPIAgVWi+x7EAd3phyOQCbCi7FiuXi3o/h4dG3edNBgqgn/Dj/bHQr3+p5vfHwBCWIVOX+kXdgcNW38nGHku1oVrBTPAjvu+Nsia0N3+dq+89xc5avTvxD+py8yqfcwFFk+oPmhQIB3dNOPLg6lW5Dhi8EXHcdkBct0wiF84aWlwOLFtmunyDShjiGOicIQkOrVkCHDonuRdrAlYOLMeZnjH0E4DcA/wXwHYDdgiBIKxxsBdBI+rsRgJ8AQLq+B0CZ8rzOPYRbYrCY9nXpZF2IIAj3JGMirjht0DN9tehVsSUubREEETvchCgM+EL4bs4GD3tDEPWAtWvl5cOqDi9hfqu3XVdJntsEYcGaNZG/zzsvslyWXkYvls/asINKMsMKJol4pBRQdiU7UCv+kZMjtc//gZ1+N64+o19cm6i+TkFAUFAoJAcMiENmNYJIUvqQzwFBJIwzzpDnKcI9XAouQRCCgiB0A9AYotdVu1h1iDG2nDH2HmPsvd9//z1WzaQlXm5Kx9V8imUruB4PgiBcEOtY/U54Y8plcVO69WuwGS9OvDoubREE4Y7N+8oNr5kpuMY2/gwbuj8lHzMAGDYMgOgtCgAtCnd40UWCqB906AB07iwfrmj/KtoU/equzmQ0tiGIZMLnA7p0iRxXV0dFOzgajK2g6sxuz+L1yZfJx4wB8czBBUCcv5s2BeA8P5YMh9bLdRuM6YQoVMs5nMpRijIPojp3l9OeEYSaRMzDNPcTBJEm2NJgCIKwG8CLAPoDKGaMhf3IGwP4Wfr7ZwA1ACBdLwKwU3le5x5lG7cLgtBLEIReFRUVdrpXv9ELUeiC6/r/A/37e1ghQRDGJNnCsn+D73U3g7GAQUhKJR9BpAxxDIf03u/NDK91Ld2K3HBCdw3dy3/Enzq+IB8zFrFOD/hib31OEGlH+L2nxNQEITOh5hPsXnxS7BrQWa/LuZykayXZh3Bpn0e5q5zW7EMw8M+D+ceOw4CG30e6lIg19Ny58ufNM5j3tXCFPzUYz5zk4AIArFpleCmoUWjxhGXvXbEZWf5a1bmN3Z/C+9MvcdY/gkgGfGTUThCJwE9hwT3HcjRjjFUwxoqlv3MAjAbwBURF17FSsUUAHpf+fkI6hnT9BUEQBOn8HMZYFmOsOYDWACJZWgnvcSE0N7w1yQTxBJEW1GMBFY0oBJEGZGTglsH/h98Wnm5YxGj5kOGzyMlBEIQhYeG2LSF39+4GldGMTKQ2Gb4gijIPx7VNrWLE7xNwVrdnue9/dMytqMlXewAxwPh9nDABaNFCPmxVqI54E+scXNoSYxp/jo+P3cRVfbviXzCrxXu2u+XIg6thQ6BbN8PLWg8uHt6Z9meUZe1XnfOzEBnqEakNKbgIIiFkawwmCPfwjGZVAF5kjH0M4F0A/xUE4d8AzgRwKmPsW4g5tu6Qyt8BoEw6fyqAswBAEITPADwI4HMAzwBYIwgCSTW8QhC4LI94oYUaQcSXWS3eQ1HmwUR3I0KcBF2U0J4gXCJtTGOROyc/w0JQOGqU+P/IkfAxAZn+OvPyEso1RsAXBM47D2jf3mk3CYLgpXt34PjjDS/TnEykMrF8fv8z4VouD65w+F07OJ2/n51wLZ6feI13IQodwBjQuXSbdTkABZlH8I9Rf7HdhmMPLrlxBkEj9ory4HKcH4zGTCLFIeMWgkgIpODyHksFlyAIHwuC0F0QhC6CIHQSBOEC6fz3giD0EQShlSAIMwVBOCKdPywdt5Kuf6+o62JBEFoKgtBWEISnY/ex6iGC4Klwiza4BBFf/jHqLxha9XWiuxE3BjT4Vv7baLwp1VhJEgQRe5rk78S3tz2PPYtPwqCGkfc0238UT467QV14xgxgwwZgyhRg7FhTMxul0E25l87wBYHqamDePI8+AUHUP7jX7c2bAxkZhpdjoSgniHgRy6d3dOMvdM/Lb154YhswAPjTn1y1pQzja0ZR5iHkZ2hCBMY4KkSBleGLAVyKN4O+O1IiWXwPsmLSzvd1zDFRp/wsRPoBwjs8epiE5Svi3iZBxIqXJl6J+0b81ZO6CjMOyX9PbvoRdp+buFzwWZyGoQQ/5I+aRni5nLWa5rqW/WRt2U0QROqyerWpV2iGz92E/PqUKwAYjzWDG36DAQ2+N7hKEISMxxvTbH8tWlYdRKEmzNOWuesxocmn6sI+H9Ckifj/9OnwtWllu73ybHeKbFubeIJIM0guRRARfCykCt/nOYFA1KkopTBj4rxoA603kW2UHlwxDFG4ee56HNPkExcN6GChYFrf7Slc0vsx7uoCLIjgspWWYde0IQq5IuEwphOSkvKIEikOhSgkkhBlONhGebtRlHnIpDQfT467Ac8dc4187GNCQpVMAQrT7zk0mqULHltrGVpZSQvf7mU/Yd+Sk1ReGPGmOneXdSGCSHKU26SksZzeuBFo1cpUaX5ipxewfcEZtqu+uPdjYpgXCcb0t5SvTL4STfN32q6fIAh3GFlK81hQM5/+GMag9jAJrzG+nn0OJjX52H4nCYJQ4cnqgbRlRIpjmrvKC1q3jjqlu4q12QfdfFAcdXi+bzBoMzxnNyvY6e7r9ftt37Kg9dtoX/ILfxO+kLheUbal7bQgRJSKkgzFqSTFzwQgP9/h3QShIRHzMGPo3+C7+LfrMb8sMM4DTKQW70y9BDNbvC8f+wzkRXbJ9NWpcj8netXrp6hpnkMKrjRC14LMIVa3+qRY2GEvjHhzSufn8L9jL0xI2wThOR06JLoHcaNn+Q+qMC9mIUu8zCtIEAQfRu8kl4KL85UNF2td9Bv8PlrcEwRBEO4xC9VZknUAgxt+47zyXr2AJUuiTru2MW3dOsqDy/bqV+GBEfc82gbhGFe2fxkjGynW+wzA2WcD5eW2qrebNsEX/vwDByoriSoX5cHFoyxkLKqcj4XAMqI9+wgiZfD58OqkK/DYmJsT3RNXNMjdl+guEB7RtOAP1UzmZyFP5jbG1NOBgMTadrnOL0lEQQqudEEQPA5RaO7BlQwJVUn0TaQNbdqIXlPJAsdM7+n717697unEjzIEkQJ4vDJnTL9Ot/M+q662LHN2t6dx/4i/4LOZ57tqiyDqC+H1etyF2gSRhPhM3oM/dXwRr0y+0nnlxxwD5OVFnR5c9S1O6vQ8EHQYaqikBEE9D65kZf589XGnTsDYsVHFNvZ4Cu2KNJ5XNTXAwoW2mpN/U05Not8XAtasAYYPNy3nOixkuD3OfGkEEVemTuUvyxj8PoGE7URSoTQm8Er27NN5xkMJjKCUDDL1dCOFVlOEKYIQ7e3gyoPL/GUz20DEC9rME2lFTU2iexBBGjusrBmdvIPascVAlg4gsQsOgkgZvFZwQdCNx+9jIeCUU5xXrBBOMabThiDgkj6PYU6r99ChZLvzdgiiHvHLwULvKiMhLZHiMBNlg5M16/UDHrDMOV2SdRDXDnhQrYCx+S7pKrgcvo+xzMGFqipgyBC+qpzs03WUWIwBWCHm2nxv2sXoVbHFtAo/CwHNm1t+FtmDSw5RyPfFaXvoZyHbbnwfH7sJNXl/2LqHqF/4mcvcPHaeSWk9btdbMhXJDRyR/17c5g1c1ueRBPaGMIJBQEgxJvtZyJMlKkP03JTB6tAkf6f7d84meYHD6G0xnxH2IQVXOuNGwWVxPdHaZlJuEelIMoTkK8vaH1Mhl1ZpZraYTpqcZASRAng1fvgUAkLlXOvzMaBdO8f1Rs3b2lwcTuM8DR3q7D6CSAPalfwKgHRTBAF4H93jT51eRJbPIgF9OApBr17OGmEsyqCLd58rgAHnn++s3RgTbdBm8Jks5n4fCwE9egCMoWfFj5bfjU9PyakzQDry4NKpx0norM6l20iSQejDGOpOWIn13Z82LLKs3at4dfLlnrZZX2hWEMnvPaz6K8xv/U4Ce0MYwZgQ5cHlSYhCnToCfgE/zFvvum677Fx0GrqX/xT3dtMdUnClC4IQLQzWscDmxWoASYZFWT2ai4l0J0ke5rO6PY2vZp/L3R9WWmK7jbqwxeSiRWIdRgUrK5NC4UcQSY/J+/rpsefbrw4CkJ0ddd5N6BI/CyHTr0nqG9DkrHCq4EqS8ZMgEkHAJ76Xnhh+0bvEzd+G35noLhA6mHpwxerxXrVK9G4ePdpxY65CFFZVqQ5djQWG351get1o/lYWL8064KxLNsvzrlVO7/JfnNzpOfmYawmi8/nDYzBBeILPB7/PfAdclHkI7Yt/MSkBIGTjufT5LEN6pgP/m3EBVrZ/RT7W8+YhkgMG9bzoVfhMMQeXEH2SSBtIwZVGRA3PjGHz3PV4b9rFmNL0I1t1Gb7n0gV5wOnd21a9XkGCbyIdSfQSqzDjMMqy+TagTheEdSE/MGUKUFkJAPh+XzmtKwiCE4YQfllwOnf5jqUOQ/317Al07ao6xeW5bfAy+1kI2f5aRTGBy4OrOJNvPFrT4UW0LvqVqyxBJBMvTbwSp3X5j+P7Ex1Rob6yoPXbie4CoYNZCH0GAWjc2Hadco1Gi9WsLNG72YVhqZchuRMisNULLajoy4Gla9FxQXf5WlXubu6q5c/D+f0GWIhLYLm200u4ZsBD8rET2cLK9i9jbOPPuPYxNwy4HwtbvykeDB1KsgzCMVzCfrtGY3PmpP0TWZR5KGrNVB/CMqYqQa0Hlwe/FYMAZjCXxHtMJuVqbCAFV7qgN4n5fGhWsBM9K37EY2NvsVWd2Qs3oeYTTGv2kdxGomA50RbmBJHKJDrnlKy4lt7rWEz0tSE/MGGCPGZ9s6eBYVma9glCDWNAg9x90Sc9xMcEICMDWL06+rxD/L4QAn5FDi4IQH6+6T2hZStQlbuHq/4bBz2ALqVbHfePIBLF0OpvcHHvx/HFrHMd3R9ehnslpKF5l0hlzDy4AAAFBbbrjIfQy58VsC4UDzxcTzAI8rohd9xQ0bhNYtuCM9GnYjNXPT4r7zEFl/V5BK9P0QndZnavnIOLD+XzsKH7UyjOOsR139pOL0XCozHm2GmdSE/O6PIsHh9zE5diims9bseDq55AyqzUgZWWyHKpO4bcg9KsA2mngKXnMTaQgitd0AtR6CYHl8kL9+T4GzGhyafiQVmZ4zYIglATchOixANkBVduLgDrNbaTIaY2pPbaMNvgJlrhRxApgccKLqaoU1mzKwVXVC4OAAMHAn37ysnjtesJ7o9FLqBEipPlr0PT/D8c3Rt2hKS3AGiQw6cQJ9IX5fwVdc3hHBYPRcRH//Ph8Yl/kY/dCL5i2l8bIQoZUxjM6hi06HZTpx7ZW0Vq22yv1Ln0Z7Qo3GF4XRdpEK3T7E900Xx+xgCccw53U8FQpO/kwUUoGdTwW0xu9jHXCxwUfGBFheaFBAF5gcO2+pDyHiVDhti+hd7C5IT5ffJ4ubTdG55t9ZgmlxcDoiOKxAkGABs3JqTtdIYUXGlE1JSUkeE4ITzXGDJxIjBggKP63cIgkEyLSAuUm9hELyuDAgPWrOGycHX6/rULxwwPb1TBDBfUUUp7gqjnaN8Vn0cxyVVtGAjW3Ci4AiwYfdLvB5YuFZPHA2KYp8suA664AmjRQuqL4yYJIqVwKlgKvyMNc/fwCUzNhGeMpaywR1i+AoMbfpvobhAJxtKDK34d4S87ZgxatQLalvwWuZ3zVr3X2ZXixGMPLrO+8LbkD+e4ysgAYG78Jndf+zm0x8ovThJuXvnxaFii8bxieblA48bc43edEBGk0h6H0KWBcWSTMFzPmyBg1+JTcFKn5z3oVIpgEVmKIXrMTHmlXprCWHRuylj8VgIAtG/veb08sHZtgZqahLSdzpCCK10QDBaRp5ziyMvKMLavcoE4aVJCQxQSRNogvVf/2drR02qXtn3NlgA8GPIBXbpwl7e70BCWr0D38p9U58w2qmTdSBDmXNL7MXn80JVbz5hhu07DFJwuNhZhAdVNA/9PrMtICFlcDBQWyjn6uEgGYSZBuMTpY5yZIWBxmzdQlGnPUtsIEvUQqYzZa+ToFcvOjqxFPQ8HHEKjRpG8YE7mWD3Dk4R4cOkVLcjHgbpMk6qkjmo6LCxfgXE1n8rHsgf4mDFAWZnptyR/hwb9DLAgDh+/RjwYMUI0pmnVCgBQG7IfJtLWI+HzoS5EchPCgiZNgJUrDS+/NPFKrO/+tPWzl5eHDF+IFDgW0BYieanTKrg8+K2UuSFlxowBjjkm7mFj6d2MDTTL1gdOPx2oqrJ1S4ZPx9oa0DF7SNysQIMCQZhzx9C/oSiTLzY8EG0pE1MFE2PoXvYjRjf6wjjiSexaJ4iUZ1m7V3Fmt2fleTmk9746mKOVyielNxdvVf834q/RdUr/r+74surYE4IG6xWCSAXKy13d7vMBdw27h/+GJJHmVGTvxRV9H050N4g0g8EbD66K7L14ZdIVQJs2MfO0mVDzKbZujdTtpNu6Cq4YrN0rsvebFzAIUTi/1TtY0f5l3VvMeqkM5Scb3ZaVARdeyBe+PKBRVinWNFn+OvHc7NnAmWfaNtZ1/P36/ZF9FiMpBqFG9f537274fAxs+B0KeQxamjYFJk605dmdJMsD53B8AGUReguTFwZBNQ9g1CjP6lUfQ/Ti7d/fcZ0ndnoeJ7R71X5fUv19S1JIwZUu6OXgClNaCowfb6s6QwVXmzbi/1L4IIIgPCBJZrhXfmkd1/Y+mHEx/tz3n4bXKXwHQajRfSPCCi69vBROFFxuNnyMYW6rd6NOv/1bc+d1WtG1KwAaL4gUpVDMo+H4vbMbSSHeJqoGNM7bjdO7/jfR3SDSDLMQhXbesaLMQxhcJYa8jNUbE5VDx8G76bkHl8F3N7Dhd/hj0Smma4rL+jyC9sXbVFUNq/4atw7+PwMFmF58RfGc0uBOFVWGMdMcXIwJYm5PKZyhSUHz65z3hQ+5cqb5/eTBRbgmPI4ZyurkggyYNIlUOAoYhGhb/ZLihPSFMIcxRBS5kyYBxx7LPYczmEcvUg7jgt5Jm/y5zz/RpfRn2/clifgv7aBZNl0QBNsT2KhGn6NTif7LGPAZDAwFBcC11wJnnCEeJ9KDiwYFgrDEzob+3d+aqY7Ncu7EIw8eLcoJggNpt+aV3NoXgxwm81q9Ey2I57G05BkFysvFdQlBpColJc7v9fJdZQxBITHJtr2A1gyE2fxl51WRyzoNxW/R2PDqL7HqsmbqW5SJ7zlzXuqFIY+FggsASrIOGt8nCFjX7T/IDdRad0Q67wt/XuV3LLUfVBisaNMm6Hqrh28HxNyehteNQxhe1Osxw/ss6+PB71eF3KIw7IQunGNObkateQF5b8DxnCVIqJbps/gMcYC8uJITBgG3DLoPm+euB/LzxRyxLnJBy/Uynd/c5fMf8AVxNJS6a+d0gxRc9ZiHR9+ma2kNmFiFMAbk5CRF7i2akIh0INHPcfOC33Fx78cAAOf1/LfqWkz7ptj4GrVDHhkEocbM4tlM6GOrDbEhZzdrwwIBeHnSlZjU9GPVusFyk1Jrc9Obk2OvPEEkC4wBF1wAluvwGfZyPU6WY/xkZSW6B4QODPDknZCVL0Y5ri07Yn7PTQPvtxtcRRefzvrZJyQgbG9YmK44pfoKQqGovxe1eRPTm32g/r2kv3MUijK/xujWLESh4b5FDlGo1zmR3MBRw3oN27PzaFRVqTzTksSZlkgSGAQxTFpRkXk56ZkzM0J11Yc44vf6M1i8kLqX6UVMWgoyj6BZwU71/MGB9nfO8NUZlvUpwhoLDlUjfibgSNDCa5iIG4nXUhDeYBai0IAMX9BwkegzSPzqllM7/xePjr7F0zoJgnBOadZBrO/+NAD7C1tHsrDzzhP/5xhbzIQKtw3+Oz6feZ6DDkT4/bhTMbuFvpKfIJIRBgGYODFyomVLMSE1vFMIq/Ju2b25Q4eoU70qtohrCqWCy2qsmTDBbssyKw3yfRBE0pKZKeaXcYLHHlzxIhYipbgaxUybFr+2CG4YBKCwECvav6zyDtjY/UnRk5gT2TNq6NC4yT+9ysFVnnsQ70272IMeKQh3rrKS/xajt1wSVi5r/xoeGXObuI7R8Lfhd+KNKZcB0AjBGePLwWXZOechCvXGGbOwiTInnIA6svInDGBMABYvti4HThmdrHROXg8uPQ/UeMIY2fUkK6rfRdo/8v5U2rlnUpOPcWbXZ+Rrut7SLh4EHxNwQrvXcOdQG/lwiZhBCq40wu76O8MXRFHmIXs3aV9+m4NB59Kf0aP8R3ttcnaFIAj7KIXZ5Zok0mZeFo7cxEeNAqqrxb85rHG0m9jwQvjXhafjhHavqSw8nVCefSDKMpSovwjLV6BPxWbTMp1Lt8apNyYoJ7916+SFf3gTe2jpGjTI2RNdlrd6hTWbapPAU9ewYWLidlV9Enas6hs3Bq65xtHG/JbB/4dNPZ/AvFZv87dHEAmG18CkffE2/G/GBZETXnpwxTE6g+fW5y1bUsivNEGevxzAmACMG4dbxz2OTH/Ek+mCXk+IluCc+JgAnH8+0LFjTJ4rvTW0cgzQ88zSQ/c9Ki9HzwqHe22jef6cc4AhQ4C5c/Wv6+bYUhwo1/xKD5Urr5TzEAIA/KICqDz7ANoXbxdPaUMUmnlwGV3Srml0CnItlzSfM1xfMCPb+t6SElXfabwi3PLz/HW65xvl7ZLn8/d3NIlnl2zh+TrA4iXWjiVA4iPpEPqofhfJsMLpb1WSdVDO+a6de716Bitz9mFJ2zc8qYtwBym40gUHIRT8TMCqDi+LsU1N6k1G8jKOJLoLBOEd0oJserMP4t502BJx24IzsKz9a6prvBtsbpQLT4W1utF6VBvPuEPxdgjLV6AyZ19MQjMQhNlT9ftxp+L2wX+PW1/0MNu7hQUn2YE6V4ITwxwmPNIfnw/o1cuyGFfv/H7HS5Bzez6JxW3edHYzQSQxjAFdyn5Wn5BQvi/ti7fhpE7P26s8jgouBojhmFIVsrKLGZ/PPB8PjbrN0b0+JojCsE2bVMoExqBWpPDUU1wMIDaegVY18q5xdcstWWLbI7Qsaz9mtnjPuECjRsD8+WIubhOU35WhgUyTJsCiRcCZZ0bXN2IE0KyZ6pRWKG22vuEWgLrwwFSFYQwruOYuML3nlyffj7qXwrATSri9UxiASy8FAFTn6RsDzGz+vvz3m79Ge0ga4ZXStTJnL1c5z98Ai3lZLwULTeXJCWMANmwQ55327SPneO5V/L2h+1M4s9uz0XVLKEMUOkYnPD+ROEjBlS44kAL5mICAL2Ru0ebxqC/AmwSBuYGjZHFBpB2eW/ONGsVdY1Xu3qiNstXr7+odLC8HTj8duDg6jMqQqq8BAD3KfwKDsYcVjQFEPCnIOIJADDz+svy1OKvb0/w3GLyYX+5uKP/tNITPk+NuwP0j/qrIV6F4xxxqm7TvqZ8F0an0Z4PSasjCmagv2F5uh5VRCqWUMg/flf0ewdK2rzurMw4wJgB5ea7rqcrdjS9mnSuGDjMos6bDi67bIeJDaNkKlGYfRLbfvof+9GYfYFm7iKFWlALhoou469JVHNmZA516T5u1r4NuiK+yMmDVKltt3zXsHjw46i+e7vsZA5AteTa1bau+OGAA0KJF9E25ucDZZwOIzP/aLpl7cHF8b5dcAlRVRd/Ls6fQdIb5xONgZXR9ShqUiTlgSKlFGKF9/jJ1lDEAgDZtgNJSw3f13B7/xjUDHpKvdyrhW297xTPjr8M/OdOReCETBIDjWr+JncedYjl+BYy+UyI5adJE9By2MS+VZB1Ah5Jt8vEJ7V5D66Lf5GMGqOZyMphOP0jBlUaYLpo0i/KyrP0GBTUToXazG0czh0Z5u3BVv4d0r2X7jZMFEkQqoXylPJ9iXb6vnsS5V6IVDrRuLSq6FP1kCOHlSVcBjOHsbk+j9oTV8ga+jifGve0u0WaT4CPA+DZGOf6jaCiFWPpx3ll4cNRtWN3hJcPyQxp+g0v7PMZVd6avzvC9fmRzd/lv+d21KbDuW7kZjfN361/s3l3/PC+SRfm+JSfhxn825rqF6/2UkwOry3o+fhFELJDmRdsGG+FxQDEeBEM+TRGbdfp8OKPLsyjJOmDvvgTCIKBd8a+GuXEA4MZBD6gEHt40TONLLHDztV7e7xF0L/9JPlYqfJGfD2RlcdflZyHF3CLhYVQTq/fdcjyQ+mIYacHii2xd9CuKMg/Kx/J86fQH6Nw5ugsQgAsvFI3Z2rSxXWVQ731mDOf0eApzWvLnUwvfJ/YJhuui49q8hbuG3m1ej0HEnLIyoF1ra9mE8l4SqxJmnNL5Obw6+XLjApZWqFJ0mOYfetgrc35beBrG1nyObM4UAl4pF3IDR1GafdByz6PrwUVvYlKi97vw/FZ/LDrV1HmDQVC9Ol7k4HK8NqB1ZEwgBVe6IAi2hucdi04zvPbJTHVs/7enXmrsauzIQo2PSU0+xqldntO9lu2vpTGBIDxALx51GKsxxat3UHfB4vOBMcDvExBcJlqiHg2SCziROHxM4FpcHzz+T+hQIuaOqMnfhZktPkBHj4Srmb6g4YtXqwjp6VS5I1s36rWxcCFfJXr3hnNzDRyInPPPRKAtR8gUxtQCSr0iCBl+H/H2/upW9iPO7/mvuLZJuOeZ8ddhcZvExM3/cta5XPkodQkLcpQGIm4fecZweb9HcW6PJ11WxNGUF2FhCMIEN/oopeBVnkucvqs6WEUBthT8hhVcDgXEX88+F1ObfSQfuzYI6dQJOE0tW2AQxLCQrVs7qjJkYNS2pO0buLDXE7rXrD6FmdK/JOsgFrfVD208t+U72L/kTzrtifXl5ABfvKkfLk6J/D0LAv44km9Znqg/aMeEvIyj6FbmIu+vXi5di7JudfgVOcYG9HroKZxcYeXBpZeDizx4khL9OZLvtzIzjtTWy4wa4+QfI28Hgg6f4yRNBZTqkIIrjYiJJ4Lfjz6VWxxbiNnhwl6P474Rf7Usd1GvxzC75Xs0KBBph9b62jUc72fAFzSMqX9ej3/jvWnRIQQBMYdfLJC7rNP3IyG1giscGsQNNIrUb8Y0/gxtin6xLCcsXwHGbCyuo47tP6vNCnaIiaIV+JixQufsbs/IfxsJhqww3WxmcyRR14E1qQHmzBHHmeOOA2pqOG9kltO8ygpP863He4lwUqcXsKL9K/FtlHBNx5JtKE2Qx1Lb4l9lobnt5XQ45n/Ys7JFi+hcNXb3BXEMUQjAWwWXSYhCALhr6D04rwcpoNOB4dVf4qd5Z0adVz1NjKnnXZvPmnLfK88ldiYVB++S8gnmWWuc0+PfaGqWZsAC5fjg2oMLAFq18nRNbeY1b/T9WAnz8x3m8M701yEv4ygAYNeRSGhVu2Gr5e/ZQ2Upkb648i6S3uVEhGDjGUXennopOnlk/Me7N9O+r+S9lby4+W2Uz7yVJ5gc5tfh3Der5fvWhYi4QgqudMGpJGfgQPPr0gJdrt3lZtTHQoYDVuui39AsP7JQN/pEazu9iJKsgzQpEemD9F71rtwSg6rN35OALwSMHat7rSDziOyJEnUfZ7g2FQbjlO67nJurOpzR/H3MaqFYRJxxhv32CULDP8fcgr6Vmz2pa0LNJ/hj0SkA7AmWGROABg2izm+euwG9K7aozvmYsddDA4WntZXnkx4lWQeQE857ogznA+DK1d/brk+Fw7WDlWJQJYTUlI23BxdZgaYm/hjk1bMFp6BzfM0n6hOjRkkXxov5dv70J5WnKIN+KC1Twmt+xaN859B78PwxV9urhwPP306TEIUA0KdyS1xDNRGxI8MXROP83fjg3MfQVmGgolpLap8Hm/tkpphrT+r0gv08boEARjf6HCOqv9SvX2/dq8wLwhGi8IJe/0KG0fjFMecqW5DXDAHvIiW41V+XZh/Ez/PXAYMHu2+TMcxo/j5O7/Jf40Lr1hnWq+f5AZhHwdBDHpPJSJfQYFuuZbWnlvYVybo27eOhzEN+oy3eKz1lH/mRJwcndnoehRmHTMtw/VZt25oqdbXvmWe/vxNPZYpiEBNIwZUu2AxRKLNwobmSq6TEaY908WninirRxkQ1Ilwky1+H2wf/zbO+EUTCkB78c3o8hQ+m8yfB9oIAC5lamhotuHnDtang3dCVlwOLFqlOPTz6dlzd/yGgqAi44AKgVSt7bevRtKn7OoiUxs8EQ8GFHkZT1MOjbsX9I/+Kkiwxp4X2Sbd8Vxo14m/fIOwIcy7LAyDGLTeag0+bYyNJtTYJuwtjFCtFITNS+FVWJsQEhuXmJKBVwg2WguRYY5GDa3LTj/DutEvw1PgbIyf//GegStXMAAAApaxJREFUpRTmMxAAunUDcnMxq+X7CC5bqa2aH521AIOA8mx7YYd48EroZub1Has2idhjppwNzwvdm/6h+k21j4ATQ48wSgHZ5f0exY2DHpDzSHLBGP7zTjHOUnhWqy/rCFoV3Y2l4v2xMTcD0Hpw+YD27YGMDFd1K383V4aol14KnHIKqtctAGbOjLps+5dlDA+Pvh1ndP2PcZmWLYGRI3UvyYqszEz1eZ9aqWrWPuCdwTCRfuiPCQ7foUsuAfJET0OuNU4insfGjT1bfcljjYPNDxnMJwfXDXgQOYGj8rFuiEKe3yovL7rc1Knyn4eCGdh+sEjRjke//8knA4358kvLkKFDTCAFVxrhyFqZMTFwtJaw98TYseLGWVneBVqNeoeSbeYJNHUID1qMAcvav+aqP06ItwKCSG+iLUk8nOx4QxTGa2HLMZEzCMCZZwKVlfoFLrtMYZXmRZdok1mf8bMQrhnwID6beT4wfLh54fXrDS/V5O9CYeZhR30wewJ1n08DhfTUZh/hibE3AVCEKHTykvTrZ/+eGGE1YmT5dZK6l5QAp5ziOEyjU3yanEKdS7di+wLyNE12EhG+R4WFB1fT/D/Qq+IH9UmNh7OS8OfRfTeskKM2iM/x8navYFSjL+zXw4HbvAdh/Cwkhj+dM8fWPqhZwQ5MavI/5w2TgDo28AqICgqAyZOj18wLFsh/usrBpaz3oouAjRtN3ztdqqttrehtrf/txO/VMKWZ+Nwr35eQwEQBnZvnWhNW2NUrUloKtGsHdOkCZGVFN2XwXdnJN6SL4gNU50ZCRAd8IaBDB2DgQNw++G9yuFO7IQrXdnwJZ3R5Vvx8BKFA76k0fYUmTDC+plDG21njeBX5gPc99GoPzhiAqiqgb19n9xJJhxt5WJ1i/8cyAmKkg06dAABHghmq6CjyXM/xIHQq+RnC8hXRFwYMEI3NGjZ03GfCO0jBlS44WcV37Kh/b1YWcNJJkb81nhQqGEOP8h/Qo/wH4zIKtF4fZVkHMKjhd1JVOh4hOl4aiZ6Iupf/lNgOEOmHl0nibWLlwWWEn4U866utenQKfz9nPW4d9HdvOkOkPWd3exovTbwSgPgcF2UeFkNxanN2KJk6FWja1Fqowhiwdq39TaLBS1Ab8gMAXpx4FQB1Di6t1VmWvw6Tmn4MQG25PrTqK/Rv8B1/X5YsUXTLwdpC68HldJxgzDLxfX4gkk9D7unkyUBpadwN48QcbZHjDF8QDXP3Gt9AJAUJV3DF6EHNDRzVH4fM2tO8rLcNuQ+N83d72zG5Kfefe1qzD3Fej3+L1uo6YV7N6FSyDU+Muxl+J+GWAbK8jRUGHjRKGBPE37ykJNpjSBHOTjAQc8xq8Z5lG6pwwBUV/LkjNRgZWujObIpnKh5bgU6lEc9sq7mWF+XnTYRnBNd4zqHg+s+Ea/HomFvl0wFfUJSNZGZiWfvXMK35RwA0IQo5FjtDrpiEy9ftiISYJQgJ7vclPE4MHQqceGJ0PUx7nLxzlaceXOefr5sreG3HF/HtnA0etUTEC10PLs5p6tweT2JF+5d1r4UEhuxAnZzH0877oVt26lRg/nzuOjQVOruPMIUUXGmErb1Wu3ZizH49Tj4ZaNaMu6r3p1+CGwc+wFXWz0Kuwx/IyQBd1kMQyYinG0Kfz7S+pvk7MLXZR6bvkt7cu7zdKziuzVvc3eha9hP2Lj6Ra5AKCn7uCT/82QoyDzuzVifqJW2KfkWnUjFXjepRKy+3vNcwxG74/M03A50727dK1FT89PjrAQBHQmI+jGHVX0cVM/f8ivz90qSr0b5YP5desmOmKOxVsQWX931EbzdveW+iOHqCwbqLSBh+Fkrss6KMksALxxzZKG+3faG1Qb2xEJB5sda5ot/DWNruDflYW+Owqq9U1rbKT3egTgw1Vrdstet+EB7i8wHXXce3p9UYQaieU5N35B+j/mLdDYUxiRuMPode7iZbzblVsA4fjjO7PotaaU4Kh1d2BVO/1bGU3RnVbZgTi9eQT/pecwJHVeGr/ZoxMPz12/6MbduKwlCSYRAaHIVk4/AEjPvqxiKPX17gMB4fI0ab8MxjjBm/kH4WQqbP2JCFQhQmlm5lP+I7TgUk72/Vs+JHrOn4ku618JohbLxl5wnUNaBo3Vr1zD886laUZ+/jq5AMpWICKbjSBcFmMumePSOLKzsvlwchCpWDkyp2Om8Xwn+0aeOqL47p0SMx7RLpCzPYoHtQr9m48NbUy8Qwn336iBO0Tpx7PdZ3fxo1+busC4a7AQEFmUesC2rhsOR1SzIKwYnYkumvUwt+160DxowRrSGNsJgnGQSga9doIcqECfZjcgMYV/MZAOBIUL1RZBCsBTXdukVZjcc7XJ8KFwt4M0Xhhu5PYWGbt+VjuaQ0nnplkc6LONqar2kyYphXhXCGjwlxf1ZkxowBJk3iK6sMvWOxFheWrzCeoznW8amy5VaFltEJd2S2ngomckwkzNGxwo9Ceo6DKo8h7/DKs9MoD5heaDvlOGTZuo0QhUvavK6+Nm0aMGMGGBP78cuC0zG+5lOrFrnwLAeXGVdcYfjxDX835bqJM0Sh8rMENJ6etscPstQnAFze9xH8U8qBZwlXYnrrMj3Kf+Sux/Ubm5cHbNpk2q3irEOY3EyMNhGPNAEMAmryd+GrWedEX3OST5zwlCx/HVoU7og679Xvoq1HO3bbmet58tnNaPEhd31EbKDVfbogCOaThNlCWBBQk/eH8XXGjDcNOoK2woxDmNfqbZ3CBoOIZH2S7a/lEjLIA9X8+XI81VgwruZT/GPk7TGrnyBklAqueDYLAZg3T1R2n356dMiMRo3075PeY88WhUYr4VmzRGtHq9thLsgCxLjJBAEAVbl71HNNy5bAjBmWVoeASd4HJgCrI54AcqmmTcXcHU5o1AgV2fs17cMwRKES+UrHjkDHjqrPW5hxiLsL8rrChXDG8TjBOO802JjHW3nNmKD6mnwsFAkFTSQtDAlUcHXrZjnuyO+5wXxshtN3IB4KcQa4tl7lHpaqqqQ2FcJrt785CaxjCu+zq/Lg8lBQ6ZWCy+g50/M0Ohr081esk5dKj0Z5u3DnsHvVJ7t3V3kQNcjd59njHBeD9MJC1An635VXHlyaP+HXKCR1FVw2w703yd+Jhjl7bN1DpDbl2fvRrvgX3WtR49dll4FlZbpuc3TjL/TzBsWC4cMto2EoQ7p7tfbimc/bFP/m6D4ithiN2fohCm2EE9Q516rwV1VoXrGcuTsuQ6R/lusCadLgNoCgdWRMIAUXAQD4cf7ZaGyi5DJ8ncMJqRUFVnd8CX8d8jf94iyksXAWxHCJEDX4UQOCzkpZHgtyc0WhZAx4fMxNeGT0rZjV8v2Y1E8QRnhqScSYaX2MwdxrZf16sMqK6PvsdsNmeRV5eZG/+/TR1Mv3Xc1u8S4+mnGh7jWy26pf7Fp0MoZXf21oVW31TNn2sNQmZjKq06DMvcPvwvYFZwAAzuz6DM7u/nREwWXSprxp9PuBE0+U59ZjmnyMnMBR486sXas6dLT59HDBHhZyvjxJzJk2r9XbCC5bydV+3HNwaRrVhjRSbpKI5GBJm9eRE+AzrkooTZpwGXtocfQOrFiBoNYTykE18UArmNB+XLnfHToAZ5+tutaldGtM+0bEB8N3123EERjPy3YwUtTpCfW0HtumLFokRjI5/XTTYqZrGg6jHruovJ5i6LFs9LsbCiD9CoWYhQfXxb0fQ8/yH9Gj/Ec5lFpA83uF9Ix/fD5k+03WVxq+mnUuHhsb7c1D+YLqJ1H7i6IisDatrW6KXYdcYCp7kP/wTuJRF5YfOvg+yIMrsSjnCSsPYCdPu/KR+GbOuWiiiW5gFt7ytsF/x+/HReZYQwOKMCHxOveegkIUxgRScKULgiAPAw1y9uDeYXfKLxkA8wFfulYnJbM3ymehey2s4FINSMbounZKdWb7a1EbsrZei8dENLnZx8gN1OpfpMGI8BClRwYuuMDjyl0ufAMBoKQkulrpHeSt3vYbo6x49mzRC+KUU4Djj9fUK5azWnBoPStUdSS7YJPwlOIs0XvJ6Hd36vGgvUtbv+ViVxDwztRLok7nZxxBw9y9AIA/9/0nVnV4RaHgMnizMjIgQL3ZC7f/73E3Gfdh2TKgc2d1v12ItvMzDiu7YB+Fom5I1Tfy6SgBlkEDcffg0oQoDGhi/iepHKJec+ewexHwhVw9567gfSg2bJCF0YJgcp/GW1T3HbBaw/boER8PLg88ZGSFgM8Hq5DMytzCP847C9cOeNBd47QXSCyS8YgqRKHZ62Txe63q8BJWd3hJPvbOg0v/vN66VRnKuyrXwrOnQQPgtNPE8OJ6cOz7cdFFaiMyD1CufWI55xlZyLsOUcgY1nd/GjmBWvh9ghxKLUcjE9Bt3+/HrkWn4Iq+D5v2PUx2oC5KcQYALXXCdRHpASlT1AoFr9YawRBnPV26qA59JvIBIj4o50Ivl1VR75rBD200Z7wz9RIsafs68gKRedly3SpFJaMQ2ImFvv00Iryx61z6s5iXQjlKmI0YYQFYeGOoMwCYWYqLbWvL67ennUiU9Wb56yIWGCYwJkRZYlpxXo9/2SpPEAmhQQOwfHebzRcnXoVTO/+XqyzPQltvPeDVxp+L4mLgxBNlT08l4QWEn5mLJ2ntSmjxSpEVOW/+TpgtdsP39q78Qe3lY7TrkkMUas4ffzxQUwNMnRp1i9M3VtdK2YpAAB8fuwkPehDiV6so1FVMGoQFeuu35q7btwNjUH1PfhZSHTMIlqFbiMTQsWR7orvgDTU1qrlS+d5PafoRnj/maq5qHviul6tubJO8Ts0IvxmmIdItEEz2LSqCQVWxvMCRmHqXEO4xm7OUv7ZhiEKL+VNL74ofcEGvJ+Rjn5WlNidGq1O9dXST/D8QWrYC383ZgPN7/ttdwzxzdkkJ0Lu3u3Y0xGt3oPUyDeM6RKFOuanNPsSUph9p2tepLxBAdqAOfSq3cHtsJ733MOE5xvsIvXP23yhtvrhEYKYIkMc+n8+z5z/I48HVo0eUoayPcnAlHHnMrqxUKTx1QxS6+a0MZOFGT0zvyh+i8ib7mGAeen7SJGD4cP7nmrSrMYEUXOmCXlJUzYvcseRn/dwb4US9JtYPgqasTEYGkKmOD8xgbA2hKxiXCucGjqr6YGRhz0pLI5aYHANDt7IfcX4v/o3C+9Mv4i5LEJ6gFYK6YECD73BFv0ewf8mfYjZxhheuyve5JOsAhlR9HVX2lM7P4eLej8ekH1V5omeLNjY+QVhRXXIIz4y/zrP6tK+ads588Pue5hVIHteCclmmDKkzfbp1J/r0ET04SkpwQa/HcVGvx+RLKit365oi3XJiheb3o3PpNlTm7LN/r4YXJl4te7at7vASlrV/NbqQViAlhyiMvweXclOjXe/4mACMGRPXPhF8rO7wEn6Yd1ZiO+GV6apiMFK+A/kZRzCi0VdcVWw7UBw5OPtsy3WJNu9uleR1at5Nsc4f55+tu3bgQTvOat95WcimCcXmydKIPLhiCk9EDwAagZjJbxKeJwxyV2k9cH0moYPtYDQPGRmKMQa0KNyBLH+d67YBg/le+bk8fo5VxkMxfEeMZBZcHlxmoRl1FFz/HHMrOpaqjSB0BZiZmcDKlRhS9Q1Cy1fh6Amr0KX0J+O2YKAoo3VC2mLXc9mJTCBDEz0Aa9ZwNOTRutkqhDoUn8nDEIVmRoTymDB+PJCd7VGLhFfIv0+TJoaGC04IT3PyUzZunPj/qFGqucnKaDtUFcl/64Ngng83JweYM4ffg4vWkTGBFFzpgiBEvyOaRdqnMy9Ax9Jt0fdKCiozDy7TGOdXX809IWo3EIwJgCDg9cmXoWPJtuiksYKAq/uJYUTC1nR29xt2rEMYQuhRbr4YJQjP8VDB5WMCfExAXsZRbdU6zXK0pZcHT/pfaSlZnbsbL0+6Kqrs6V3+gwlNPg03aNlcTd4f3C952ALbb2GBxUyu09Ii8ZRn70PnOOdDYQX5GFvzubgYtXMf5xOj9RD7aX+pSZ0Rnhp3PZ4ad714UKHIf1dSInpmTZ1qHaKQMZzT4yls6PG0vA5QzoN2nnlH74fUZnh8cTymMYb2Jb+gd+UPAICbBt2P4dU6gnA5F6h6DTOx6cem1bco+J27KzxlGRNUgoWAxoOrb+VmstZLUhgDSjIPJrobuvgUwiAZnvBjUAt9ZI+UkLVByLdzNuKvQ+4Fhg1ThfbTcna3p/HFrHNx34g7LeuM6qYHsy/X3qN1a1mwIYdXVrZtZo1r3riz+wguDtZlWhdizL5ATCcagFiV+vdsnLeL3+vHBCNvcV2hWgyeKeXnKs/eh6rc3eoCHOOBHeL1WhgJEA09uJRjpt9Eear8zRcsMCxWnr1f/0L37vKfGb6QpaCTJ3INkdy8OPEqvDAxev9rhNHc55W6J0qW1qUL0LWrJ3XzwpWDKzfXsxCFdSZG+owJwAkniLlMNfx3a3vy4Eow8v5UEGIbHrt1a+C664CZM1WntblcteRm1uHZCdcC4I9gxB0yk4gJ9O2nEeFFtPyiFhYqLpq8kH36ADBXYpkOOBkZ2uKmoR30JpIBDb8HY/ohB07p8jxennQl/j78TsP7zfA8DwdjGNjgW7QrTpOQNkRSYVf++c3sjcjyR2LDqzZ3jNlLWq3XHyH6XWYQgNWrVRO9UbflMpWVovWUCc8dcw0+mbmJu2/hui1zcHHXSCSCFgU78PGxF8a30VWrxATtp52mOm1lEGEY9chE2QSoc0iZMb7JZxjf5DOgRQtgzpzIhaZNxfdH8Q5xKag5rCnNcBQ+xCsljkU98lUDa+yWheZKqe/mbjS8VpJ1QPW3WVllf4qzD0NYvgKA5FkqfYY3p/wZL0y8GgiF8PbUSzGt2YeW9RH1AI53RZ5DHSi4lF4w8lkOgXZx1iEc3+51y+Y6l/6MdsW/WtZniEtpuHZ9r1vb6acDBQWqU6qx0+V41SBnj6v7CX0sBV1yfkmb3slDhgCTJ5tViW/nbMC1/R/0ZC4zmkO9CoFoiMYQ5s99HsX3czZg89wN6s8V9DacWbxyX7Yo3IGuZdEGqYYCSMZEy/3hw809uJQ5yQYPBlav1i3WqXQbtnOEYjVVcDVooC8IJUOYlKI48yAKpZyzbtANyWb1KNgxSrfbuAvMEgcwJoh7mnnzPAlRuL7bU1jd8WXD6z4IhqFYj4b8unIOIn7Ic7ggeJoPV3dfHvbgq6yMlFPMGVW5u3VDfI5p/AUA/nmb+3PQWB8TSMGVRoQtGQUwcQGvsCLSllFRVQVccQVemXQl3pp6qe6GM6ixjLbCX1Gme54xtTBaW1udQUiKIVXfoGn+TukemwouG5Mnb0z+16ZcgYdGuc8vQhAAXHlwtSpSC3G1r+ieo7mG9/ocWi2x4iKga1f4fRpvTL02wucvvBAoKjKtNy9wBEWZh/knfEGAsHwF5dJIccLPyOI2b6BZAV9y7XuG3YWFrd901mBmppir5rTTxP8VON1saZ9/7bxTrbWa1tKjh/p48WJRKHvllcA554gJ5SOVW3UmquxNg+7H21MvBYqLbW0f3AirwnfGdP0+d25UmOQwTfJ3ycomO5ze5T/4Y9Gp8jHvOKkdu/0sJAvSyrP3i7HcQyH0qdyCnMBR2/1S8tGMOCuE6wEJ2WfaUXDxEvZIqqxEqUJRK49RHgu0naL85E7DIlVk7wPOMg4tqX0nddcpGRnoV/m9rrDclFAIvy08DRu6P23vPoILXt2nUpDElYPL5wP69Ys6nacYkxvm7EV2wJsQgUYKDt15xak3oRWFhTiz27MoyDwihj5UGqaahVtyAHdoJpeUZB3ERzOiUwqYCiBnzlQbDukxcqTo8bJ8uXist7447jgAQMPcvZZjuJlnCdav1xeEktAzpWCMf61sVoo3LKsVbvJk630Os3X0l7PO1T1v9lkYBODUU4HSUk8UGhf3eRz9G3wvVR5dn/b7EFRzBpFInhp3Pe4adrd4kJdn6fnEvSc9/3z5T93h9MQTxRzvALJ8kbn+uzkb8aCJfJcBXIsTbk80igQQE0jBlS5oX5BZs3TfaMPXqLAQXcp+Rt/KLQYKLv4BRxuGUItPm3hV0c+jSmtTk/jk+gf68EyejaUk11aeIHKb55xDLs1ETHCyr7HyHjHCae6q8GKRZxFtx0o1ZFORrhyrTA3adfICDq3iy0VCxJ7wWH/XsHtEgSUHx7V5CzV5u5w1eKGxcsDSg8swtIjRDQrDE8MiAtCrF7BhQ+Rk+NkuKAAaN7bXJgB06iQq7yRP7sqcfehTuQXo2TPqE0xv9oFheBXH1pWLFzu7zwYCIIZQk2B6ni4GzGrxHnc7TsbJIVVfY1aL94GMDPyx6JSIIYL0e7gVZHQti29ITyJx2BZWDR8OrFwJrFuHLoMKcWipmH9DFqh7GJLMzTpYucZ3UouwfAUqcvYDzZvbbzv8R5MmwOTJeHPqZbisz6O266nI2R+d74TwBMu9m07+aO71s6bg5zPPw/TZGfLz7EZArMUohKJuG4sXi8oVr2nZMvL3jBlqY7Nhw0SZwQUXeNLUln3lntTjFNehobKyxJxFPaW8qXrj5cCB3NVFhYtTkp0NIaCjQNN5kE/p/Bz+MZIMa5MRu/Og0Th1WCfiipM5VveeGAnS2xp4cBsZrAPqsS8e8v1oBVeEloW/kzwvgQyp+gZN8neJThnHHONa4dmqUHoeq6rMC1ZUAPPm4dwe/8bycG5nnw85gVpTo2kv1wZE7CAFVxoRfuV0B2o7ySO1sw1jlkKuqFtMJotc/xFF1YLqZr9q0mNA//7mfeXYzSg180b8NP9sAJqBq7paP/cAY6LA0WbuFoIwxGUOLqfLAR7PBL3+KF+7a/o/aHE/P7Ii3YGCy7QPOguSlyZdLVVB9luJJlMxRtux/tWW7VL6E16ZdIX5TS1ayFZbejiN/619T7RPHNeTqowPz5H7w3SsWLtWVJhp3yUd743WRb/p57aCCwVX//6RfDdevGJLlkT+lnKoaAXL5/Z4EjcOvJ+rOjthlJVeMOZ1RtYzL0+6Cse1eQvIyEBJlpTb6dJLZU88x8pZot6h+6yavVQ+nygsKCgAVq5E9ogB0i0OPLis8v25pU4c+72ah+3kAwYgjpHSfMCV80lJOPefvbsITnjnYkMPLi0mIT7b9yuC74Slcng6L593o8+hKyjLyxND4g0eDCxa5K5ho887Zoy6XCAgei0pvcRd8MWsc3FBr8c9qcsUg5xC8nzrFRaRJ3T3IUuXisYs552HXuU/mObw1LU30Bnfu5f9iGnN3Yc2jnlozHqIjwncihozAbne/OVk/ezI6MJjr0HevVw8Qppqv/OwEvz96RfhnB5PUojCBCL/NitXArm5lgYKZs9VRfZefDMn4lHIE8Z/U69/RQwQ9ULXbtggj/Ezmr+PRW0cRo4xgrx1YwIpuNIFQZAFUbqThfRycs2/OslXrQTPvBNUSdYBZPqDandnxarg2Obv45NjN0lNCcCgQeJC0SGvT74MT46/gbu87MHVty+wfj3AGGpPWKVblsYkwguUeVoA/udqUMNv8PiYm8wLxciDS7n5P7nz8wCA/bVZumV5rV0YQmhWsFM6iEHs4m7ddE/HK18AYUymYjNmGs5F4qlx1wOIXugWZBzB4KpvzW+2eGYME9YLYWUN3/MsaLwR9TauU5p+ZNwvt8ntGdP/rOXlUU+8/A7o7NA9SfjrxkRz+XIx0XvfvsD06cApp8iX8jRh/rqX/4Q1HV/SrWZD96dw5PjVuKrfQ+ha9hMm1HwKANi24Ax0LPnZtAtDq/SVf1q0BjsAItb45eVAaanoVTdrFv7c91HsWXwSV71EjJk3L3Ft84QotEiAbUp2tiwIfuNXyYvDQ5Np7fzerexHbiUzA4ChQ8UuedQfbT3ab0wVNnX4cPFA2vMcCmbAFpq8XoS3mBlXKOdhw3J25tCwIleqyscEy5yxvNjy4Ap3YsECYMAAdw0rP5Pb9YQN2hX/ivE1n8W+oSVLgH798Mz46zCpyf8AKLw6vaRxY2DZMmCjdR5Omb59gSuuAKqr8cDIv+DL2fph3AB+h1q/z30wt0+O3YRFTkN7E4YwCNx7SbNIQV7kzirL2o+BDb+LLsc577teHkj9yTUJw62N+uQpuiEK1d95+E3qUf4TGNOJLEV4xosG0UHCaOdBq1HuaNB+9AvDZ0z7sEtrQfl0u3ai4al04uHRt4uGi0TSQwqudEHQTK7aAZ5nxpo8WYxLrrQil+qyM+kyJuhOML8tPE0MgagsC40Hl09Ap9JtAKRBz+eTBUROBNEdSrajKnevqCjTYWjVV/j9uEiuDT8LiUK0RYvkGOVRrqqk2SI8RLvY5V3s5QWOYnKzj80L6XkgKvDCgyvMEZ3QCoC0sJSEWIYIAkLLV6Emf5dxA7qdi5QzG+LojU1uMv11QEkJAD6rv/FNPosqe/vgv5nGzebFSqFj9CxpH9kuZVuR449s8PTm0MfG3mLckI6hiYysbDMuEsXGjcDUqcDAgcZvvY5nh5sE0J4oj3v2FK3ZGQPGjhU3HNLnDxhZqep8MX4WQqY/iFO7PIePZlyEhW3eBgBxfWBy+yfHbsJNnAJ7ANHSqi5dgNNPV4efHDkSGb4QCjOdJSWvUuRzK83yWJhX3zjnHHl+SniYGkHA34ffEXU6ajnv8L36cneVaKE6YoStPpmhXR+Pq/lMVDJzCOcFQAwjc/XVUdfmtXobj442GR9dwrKzIrl4JOF/JWd4XJnSUmDhQo97lp4cXLrW9j2jG3+Oac1MPFbCIQoVc7bKAMVsDjVCacgydar9+3UwskiP63iTa5yLNxZwhft3S04OMHgwxtZ8jsbhvUOs6NUrKl8rL36fIObfNEAI6TwHOgpJM4UBIHovWNGi8HduIy2CHztrcR8zTuOhJ9y3K3Lasei0SD4qDooyD6Ig45BpHwCgUd4uZPv5c8d2LduKn+ev070WEiIGeJ6FfJs71/CS9hNp93mMAaFl9vP1EtYMM4gOEkY1VzBmuQe3o+a3LGmg4IrCJOoLkZyQgiuNCAuizBbNZpbaOOYY0SJKZza19uDSnhCiklLKVlXatg0sy2ThuwvLM3ngNNikZ/nrUJ6tTMINcWPuZGNEEA7wM2ceXJablI0bgepqDGxg7NHCtbDUeXfkMUa6dmzz9zG75ftiWE9tGxCsLeS1QuFYeHDF4n7CEzJ9QaB3bwDmcdu1KL29upZtRXXeHtd9cWojq513bxn0f/hj0SlyyCOlUPqMLs/i1cmXK+5VMHiwqBThWFB3K/uJv4M1NaJFutncpqPgcrP1dKMc4yFgJESz+V6bGRnkBI7C7xOPh1nk7fvlYJFuiGe0bu2ZgPHqfg9i6/yzAADbF5yBP3V80ZN66y0ZNr12Ysz81u9EndN6tWrXDLa4/nrRm5AXaUwwWm8EmHrMkMP8VVbytxEIRCntWhTswLTmH/HXIWGl/NPdH0lj4qjGX+Lw8WvsNWhgPEeoyQnU2r6nZeEOPDrmVstyNw/8P5zV7WkAmtBcjOHuYXdhdYeXom/Svj+aNSiP8Rcv4TXFG1MuQyNFaNq4Lj/HjgU6dxZzS8WBbmUc4aI9JNZrDVNc7leMPLiaK8IafjD9Ikxu+rGhfOe6AQ/g05nWOdR8ebmu838S0YgeXHyYff+OIia4DOk/q8X72LvkZNHbGwYK+VGjsHX+Wbiy3yO2uma0JxMEJsv2eN/ci3s/hpGNvtC/WFkZycer68Gl8RLSjhdCdJ5uIj7EI6cVtzGJUc7shQvtrWkhelISiYMUXOmCIkShLpmZUjFnI3jQLEmqBjstMABo2lRxInK3WVJIvfJ62B04lVYlhsQ6JwFRr3DqwWW6AV+6VBRoM4bXplyB+0b8NapIcNlKZPqdJUeX3yvpXXho9O24uv9DkaTMemXNUC68mzXTj4OsR6dO4v/t25sKtvwspL+490V/i+XZ+3DDAGuPjePbvhafPANpTpfSnzCn5bvyZscwRKAOSsttq6cs238UW+aebR2i0CJEotH7qRUABxbNR/bw/vL8lheI5J7sWrYVg/RCiABiaKI1ayz7KSxf4SxUAmPRIQrDXdeRtITs5sVTYCefmi0EAWd0eRZdyrZy32I2rmq9UJS/pTxCjBqFFydFe5oo+W5vRbQHvMcwFhlTG+bupU25V5x3XmLa5fgBtWsEV94RvMZbYQV769amxeTuz5mDhjl70Kv8B+6uhBRhXJX7l8v6PIKVHV7hq+TEE1WH2j2OkWJO9bUrDrL81jl7ieRiabs3sLrDywDU4Y7BGBa1eQuTm/5PPjZEmvs8zRspEV5T9G/wvTondOPG3jWih/Qh/CwE5OeLeTnDIXNjDGMQw0WPGhXbhqTFiyehlJ3i0iBWd+/StCm+mHW+nOO4+9x2ouePwXPpZwJXbi0GAeNrPjM1fCTsw5jALV978sdOuvNSw5w96FJqvaY9qdPz3mvHw4Z1iOG6XUEIEVnbP0b9BQ+M/IvlPas7vGTfy1pCu2biCYVPxAfV3owj+pjZe6a9PVx3VCSuMHr5FYcMid4tlpUBF1wghvvmnNO+nH0uXlMYshLxhd7wNCK8SBIUE4dM9+5Au3aRl9bm5PiXIffinmF3GbetGHBMk1sOGhQ9YA0eDMyfD1x4IbAu4s4sC8YzM4G2bQ0HtaVtX8PYxvrxvrWCeC3y2fHjsbL9y+ImiVPBRRBe0LroN0ceXE/91Fn8o0cP9WRcXi7GfwciSdB13h3+3FgmHlxadBYjtq1zzjqL/0tYvFgMM7RsmamCP9NIaKXT34rsfVjb6SXLpntXbMH8VtHW9oQ9/jPhOsxq+b5uuCErlMow+RmfMkW3rI8JaFrwh+Wz5T7LgcSgQaKySmpvQ4+n8Omx58t9SRjMRNXjcYhCwxCCHnB5v0eRa+QVoPMbm/3sdwy5Fxf3fkw+fvj7HvLfcp7CwkLLPh3b4n1gxQqgY0fLsvIY7RSpPwm1XE8Hwg9GdTVYzx7mZWONgYW11vvF9vjhZM16zjmi8qhPH/Oqw6NJTg62L1wnh/7k6pZB/1Z3fBmN8nZbVzB/vuW7ZvTJjRRcRGqgncXCx6b7T7PfOfzuhUMUemjAqHxfHxt7Mx6efK94cNJJnrVhhqGnc6yZM0f0HIsDdgyjkg3tHD5t1F6gc2dk9esOf9tW4sljjzWtg/d5ZQyY2+pdvDYlft519QGfjRxci9vo50DbvnAdWhX9rntNybUDHrTVNysEQNyrSIalumtKD/N2Dq36Cuf3/Jc8Hrcu+s0yOgIgKhEv6v24o9DF2vdjb22OuoCHn4+wh960nOGrE6OA6RhdVuUaR2oxGgcNjcKaNwcmTQJOPjlybv58fXkzY8Dq1cDMmQBEI1OzEPHl2QdQknXQ8DoRW0jBlS5oPbi0I0YgAJxySlTiey4YwwntXhetxQ1DFGrV5jrlmjcXJ9EGDdTnfT5gyBDR/bNFC1FABI1ySpFYXtvOHUP/JgqWdJAFAxyT1y2D/w+X9HmM+7thvtRdUBPJwSOjb8Xdw+5RnbO9sdY+28pjDxNLM0USVvkV4XhXuD6PchFjZ2zKzRUTxUth4IwIW/UKy1eoQyoJ0VZ3ZgKSHuU/4E8dXwAA/HrIWuBNWKMyZACngmv9egDqcIYNcqT8Ay6taa3yHBg9nlZPbW6gFh1LtwNQfGYL7whDXG7GtPny5Pk7Jyeq7MhGX6J98TZH7eRZfJeO4R0jFGsN3XFICmnSrXwrJjaJ5DNsV/yrmOsL9kJVlWfvBwoKgBkzrAsvWSIaHjll1Spg1ixScKU6ymdZ573+YPpFOK3Lf1Xn/HYVXE7Gi/x8UXkkRyzQhzEBuOIKR0oiv8Kq1ml0CS1WnzTcTS8VGF71neBHa8gZNkzRE2QZCZ5Xtn8Z/SqlXDXSGjRc1kud56KlftmSu3PpNgz9q5S3jcNowgv8PhchTd3QqFHc2k2IB9f8+er/rTBYm4ZyIvuX1R1ewqP/LRS/txNOQKib2ujCLIIAzzdNkWdiA2NA26JfMIDDM85qj8GFk729IOCCXo+jUJFvSzytfnLM9mC6nk8VFQCA3hWb8fKkKy3f+U09/4VFJvJEIxiAFoUGoYttRnMa1PBb9b6GFFxJhZlzQvuSX6JS4BgRvt3Qg4sxYOJEoH17J910FhI7qhJ69mIBKbjSBSFiPWKegyu2PD3+eqzt+KL+5JuVJQ4ma9bIyVr9JolXtYkHdQeScFgrM3fjcBgzM2wq/AjCC5oX7EB+xhHVOd7H65we/xb/MArgDniaS04pVJPHGEkpEbnA8MO8szCq0efyKS5rc96QhCaYLTSUHlyq3tj0OGtZ+DuuH/gPAECn0m20WfQAOVTHiBFAx454YuzN1uEq8vKAm27CrJbvYX6rtyEsX4HmhTvFawbPvPxbWbxgx7V+C/kZh+18BHX9RjRrJv/pYyHg5puBadMAOAg5ZvbOc3A4qM47JACioljKg6bkkj6P4fNZmxy107xgB1bp5T9xi9HmvmtXcSyRlFM4/XRuJaJyUz+8OmJNyustc2DpWrQs3CEeSLkMTGFMXBPZRB6y6uqAkSO5rYYJA5J8Pde9/KcoDy5TLxUTYhGWqjDjsGNBvXLcU75lXs6r2lBQ4fcnKhThtdd61ibhEZrwk4YMHiwbu3C/zj4fbhn8f3h50pV4a+qlEQVXDJZ0mYvmYuBxLeXjuI3Z0pfhZT4xJ+3Hg4R4cA0ZAtx4I5/HNmCs4Dpmkvy3duzjXeoxQNdAKapcck93KQuDgNLsg3idwzPOx6mMlLEalBjDjObvY1zNp5ZVndPjKWT51euJqPB9ZgouvWuDBwMAKrL3Y0jVN5Z9sGMgq7rPZh4lJdp1/KSmH0f2NfPmkZIh0axdK/7PGHpXbMaQht/Ix3YwKm2o4DKA9w01fWxW8CnhaFCODaTgSiPClrxcL6adF4rDUiT8ko+r+QzFWYfkhVyGMt54uM0GDYDjjgMAZPsNQgxZXJPhEeCvXWv9eZX1cFrGxFu43aLA2nWdSC1shf9TsH3BGTinx5PigXaGVT7r4RCFLjbU4f4o47vLTRx/fCTxppS4vkn+LtVigktAPHcuUF0NrFzpuJ9mC40MX1AuoPouGjaMKmvWX+VGoHvZj/Y7SUQhC1+ys4ETT0TXsq0YWvW1YflMnzQvBAIY0/gL/H3EnZoKLcZvi7ngzG7P4sWJxrmWjJ4zyyn11FPFZxzSIlwx59hWcOmEEuSGsSgPLgBiOCEPPT4BINMfxM2DrPPZ2cboy161Crj++oigp7BQDgUoC7qHD9etR2mZqszZZxXmOIwqXGJZmai8XLaM48M4pFZsL5VDMyUFybS5tOqLIKBFwe8YzyHI0qs32yikJ08VOuuSH+edJebZkfqmoqAAT467AWd3e1q3vn+NvRF3D7tbPvZK6K+1RtcK95sX7sBV/R6K/qo5hMOECwxCB+sSfpY6dgSmTtUtonoeFyxAXXa+ZXXqCsQHINMfRN/KLVEeXJ7CGFBaKh96YM9ls3khMeNcPNqUc7cmSJyVkWFdJpzP0CDnWqi0XP5b+5V17qw4GDnSJIKAwLV3ZEKCwlWmOUa5HnXLeiE70nh+Pzz6dhzf9nWuW7VjXLQRiM5DJkVJUUbN0PZFDkke3kf066fbvixLsJmawUhJISxfYduDS2bgQGDoUOvGidgRCKgGujenXIanx18vHjidQzSTrKu8tXpoI5Hp0aOH5bjw9exzPOoQoYUUXOmCVYjCcLHwxBZrjyUp9M/2BeuwvttTOh0RX3pHCi6dEGxmnmBgzNo6w0iwp3dfAjYKP847C8vavxr3don4w7P4bZi7Fxk6z/zBpWvVz7KVAnj0aO5+KReI8t+NGom5866/HjjvPPm6UljMpeCqrBTvdxGyS5k7qUn+TsxtGcmPpUw8Lr/SbdoAs2dH5QkqzTpg2EbYi21eq7dRnbcnqWSjqYqPCbY8AHIDR43H4PXrrcdnp2E19RSkVvcoycoSw/Qi+p2wnVNH4Q3mhPtH3iHGwLdDMj3shlIeFj3myTlVJAYNEsetzp1lpTygFpApfw95HWLXG3bcOKBXL3v32EHqe0JCM6UpCXnEmfW6Xcl3czfi3J5P2m7m0dG34PoB/7B9nxk1+bv0L4wfD3TpgglNPsWI6i91i/Rr8D2qcqWwsoyphGp2hIVarO7M8IVwapfnHNcfpip3t6LNJBobE8CZXZ/BY2NuTmgfdENnAWL4zDDK90vrZSsZnwgsRuOpou2SEmDr1tg0o9cmQ4IUXPHwipCiSHQs2R4553J95DkbNwIXXKBScipRiTQ0gthRoxTXZ80CmjbFkjbRigzen5cx8EW1IWxh5+0yXe/ref/r/bjKh0ZWzlugHA9M+hNWBrw77RJ8PvM8PDL6VnHQWrFCf5yVZA5yrr9wJIjjjhPDv2m7AajPn3aaVc8BAJk+g1zaAIeCy0LBQR5cSYPfJ8Dvk34PKcoJL6qIMIg863Y9uCwdLPr2BUaPjqz9HEYxaF30m6NIHoQ1tDNOI3g2WfIY7rGCK6rtoUOBk09GWfaBSLxhnXqa5v9hWKfW2lR3/pGETgtavY0nx92gutRc4/EkLF+BAk3sYZk2bSJ/K/tZpzOhykH847hhyM5GrZ7lDJF2WOV2m99Kk8SdMTncTk6gVq3gMntG58zhyxUjobSAiRLmZ2WJG02p7WOafIpGebv0y8YIZZjSmwf9Hzb2iCjWlQtjITztzZoF5OWpEnA/O+Fa/MMkPF74O7hvxJ3RIY4IRzAmAJdeqj5n8sx0LjXJB9W0qUk70h8uw/t58TRrN1u2rctatRLzUl52mf3GGcPslu9hdst37d+bLNjxNJMVXAIwcqSo3DrnHDFU8rBhojAekdAr81q9jQlNPgEEAbsXnyR6pE+dKluw/rHoFLw08UpvPofTTfXpp8sWhLQvd4lyPEj0l8nhweWUac0/QgelENhrlH2bOlV+R3lzFqbSY/yXIfdiy9z1ie5G0tC66DeMbKSvyIwXhvsjI8FTRgawYYPo9Tt2LDB7NoAYDgHhHB9SvppGjWLUjg4Js01RGJDEjMaNge7dcXa3p3H0hFWicuvkk2Pfrh0KCkwt/qVHAj4WQp+KLeZ1MYY7h90bddrHQly/s88HMapNjx6WZes7V/Z7CENMokko0e5Z5EgTOoghCg0GmnPPjT6nNyh17Rp1ytLYKayI0gj7tV7O1Xl7AAC9Kn5A+5JfML35h2IfevRQjbO3Dvo7XpkUyb8Z8IWAK68UIxgAonyuSZPojwMAkyJhOdGmDVBUZN53AP7iAuOLFoZChmFaw3uJRK/90hkrhbpO1CEAolGAzXGKQRANCiRjUrlam0ZToxp9gYdG3Wb8XGRmAscei9xiKU1HOJJRVH8syMiQ1x6Et5CCK10IhWQPrpgKlA2Eg/0qv8eZXZ+JnGBMtogLGXiNbV9wBi7o9UR0ZdKAkqONE+yLdmsOK7iyA3WY0EQdtuX7uRvV/TFi3TqgZUv9ax06qA7HNP5M/lvPkqVhzh7jdlzAhJC+aziR0ug9llbvb1RINkHAB9Mvxlezzomu1Oy5r67m2/lK76MqB5fFbSd3fh5vTrmMq6xXKD24GICdhyOJm5sX7pDDujGoxxGl92fzgp0ozT5o2IbW24tycLnHB4E7Zs9V/R6KhC4wwmqj0ratZTtOPAi47gmHvdM8N6YeyEa0axcJfeMS3ZAk0YWcN+BSqRiFnUElvJ4I1IpKbcYi/wIBOQRWaZb43t834k45l1ZRppSLTfJIQYMGKMk6iN6VW/TbuvhiJ5/GPoq8YqE4eI+MafwZdh53SszbqffMnGl+PYFCGMtXTjsWWdygnTtVHlyAvZB2yno074PhN6bXPxMDCSV+JiDT7yJMbBrRNH8H+lV+H7sGOJ/5diW/4txwXlptFUZjZJMmQLduwPTpsgFDKFZikQYNgEsuUUU6iCvx1HJdeilw9tmi10esYQxYuRKMid6Z6NQp5cKNDhkC7P3kBwSXrcL81u+YFzZ4H5jJtejCLOW+o0SwtuNLkVxAFmgF6GYGkDVGht0DB0aUQ2bMmiVHSQIgv9shq3V8vhjGVZvDUzs0jGz0Jf5YpL/eqxMicqg2xb+KIYrDkZRYiCv/bFBHdsY1PNlY/7Qv3qbKOWpZvySvJNzRsvC36JOqOKsWKBVcTscoxX1OowFk+oM4tsUHlg/OW+8G8K1JalvLd/Kssww9ewl3kIIrjRBkBResQxQ6zbUhhQPQUpB5BH/u+0/1SXnS9amOwzTM3Wu4SZzb8p0ohZXsCaYkKyti/uSUli2NB7HJk+WNdrOCHXh2wvXyYKSn4Nq+cJ0675iHkAdX/SD8KK7t+CKWt3vF+oZQCC0Kd6BN8W/qCrwirODymXhwhVGE8Yq3KE5pvcYg4Pt9ovXo3sUnYm7Ld+X497KiThtWAdYKq94VP3jZZQIGSl6DR7g48yDyMkxCFJq1A0FUVIwbZ/teJUbKIDchSjyPD24GU6wTJLhCbLnJz+W1YN7m7//M+OuwssPLpmU6lGwXQ7yG0fY5N1e0KDSiVy9XFusV2XuxrJ39MMTxCFGY7a81VfynNIrfOeEhCtu3B665xrhsMlsZt2snCt7WrROPLcYL1RjI1DOvLzMATJjgyMuAS1lvxEknqZTHhDVb5m1Ax9Ltjo19nOYW1v7KWYEgNvWyCLvL8YKXNM6LjpLgFWVlfDmbvMIgJFnMKS1NvjCBSU5BPudvZGAsZBq+TUkyhZpOMNqoP1rsrMsZE1Q51owUXMLyFajK3av/M/DO7+3b60ZqUQnTL7ww+r7p04G2baMUXHrh+0qyNOs9qQ11rlpBdS3gC3I9XzExytIY9n4+axM2dI9EcTEMURiO0nTCCRQqziWfzzwPtwz6v+gLTsccG3tOYfkK3fOxdgxo0sTYPwIguW0iIQVXuiAI8qRhprEe2/hz9Ch3IKQ96SQxlEO+cSJfI8bXfIqhVV+pT5pN5Izh/0begWYFO1WnZat3Qb0x1gqd/CyICTWf6FatFeTpLmCU3gQZGXKS+gALin+PHQsAqA3qvz6uNtgGMB8zjjFPpCx6G8/wuSVtX496B6IIBMQYxccco6gg+vkLevBMKr1ODNcrgwYBNTUAYvMemKFc3Ad8EY/HgswjYn+POQaYOjXKE9Sn8kwzHpd+XXg6VrRXKxzd5AohRPQ2HkYCGfm505s/li61bqxvX9cZ3h3n4FLgOgeXByj7y7WvdqPg8tqDiyfBbxhBwNiaz5EbsIipDsnLK0ytcXndJ8Ch8uHZCdcCEMPC3D7k75blozxU4vDopKtIzCyMUNzQTqYZGeJa00vi8pAwMQRoeLcvjRdGTTMmqEKJKZ9rLz+/ofJebxGTlwf07YsDS9diXM2n0dfDt5Lntid8fOwm3Dfijpi3Y+fXCsyYgr9PfABYuTJm/Ykn7Yu3ieEjSbGR1lTm7KPf2CZaI2otPiZw51dkAHDqqbLBNbfC0QskjxV5D9yhg37ItIIC4KSTVEadgEn4PiXSRK4U2MvyM+m50yrOlBzX+k38uc+jAAw8uHj6EAoBK/QVGXrPvvK3i/qM/fqJ/w8eLP5fWQksXGjdB8IQHxOsPZZ4GDRIzMeel2ddVg/Fs9Cq8Df8fbiDNUbY66x/f2d9kLBUcNGYHTMsJRaMsRrG2IuMsc8ZY58xxk6Szp/PGPuZMfaR9G+C4p6zGWPfMsa+YoyNVZwfJ537ljF2Vmw+Uj1F4BtYruj3CN6ffon9l6pDBzGUgxGTJ0efk9roU7kFL026mr9Ngx2xoRBQIXg7od2r2DJ3PZ4cf6NpEzcMuB+ARsF1+eViaAVtMnllfOE5c2QvtqOhiKB0Qs0n+Gb2RsQMQSBLgHRFM4mHF3pci84bbxStxpTvn8575sWzo/TgMnwX8/LE+MfgcM32mKDCkyHgC+rnCRszJmpRXpgZyctn1uOoGPdDhrjsMQHoPOdSTiRTtM/4vHmyIYIXmD0HXsiKk8GDS9U+T4jEZPLgmjgRGD1aHmtct63dNOfmRt7vcMLsGDGm8RcAIu/BVf0eQvtikzxzGoJx8OBKhAI2HmT6g94rXzm5tv8/8O60S3Sv/Xbc6Vjf7Snda47w4DOGXyPuZzNsVW5oEIBIXiLGVK+p1fM2utHneO4YfU837ifVZD+SG6i1Frg1aCAb8xDOjH18EKzv4507vJpj2rUDrr5aFLClOpInw5X9HnE3f6cSCRrPXePiOf95/jqMafw5356LBKpc9KrYYmvd42OCuP/t1QtAdJ6rTF9txGPMruBe+5trf8O8PGDlSj5lHGM6Hlz8n7NOsd6U75PCKvqZYPh8tSv+BWd2exaAizWrlAdMVk4p0aQT0RL1GRcvFsd5M/cbwhZZ/jr9MUjzTNw48H45nYuwfEX03LRwoWhgYjRWrTfOf6q9xe8TrMO+6rFypZir2aVcgRRciYNnlKkDcJogCB0A9AOwhjEWHkmuEQShm/TvKQCQrs0B0BHAOAA3M8b8jDE/gJsAjAfQAcBcRT2EWwTBnseE1y/VMceI+SfKyoxj57tsk2cD9Zchf0fj/N2W5dZ2egmARrBXVGQYC/WZ8dfhgZF/VZ3rVLoNf+r4AgAgL+MIWhWJoTZi4X7NIKhiHxNpxOWXqw7Dr4mZNVRUYUDOJYNp06KKuQpjJQiY0/IdlVU1j7VVvOdtlQcXC2FczWfRhRiLUnBd2uefeHHiVQAUi2CdsKdy7aWlwKZNwLx5aevZEE+inpOpU8EK9ZMJy89deEF89tliKKuBA63b0W3MPoYeXHamX837kxAPLkV/TZXpBdJv4WYj6LWCKysLOPZYPgEzT9vaUGiXXgoMGACcf76uZ6Du+Ofy2Qo/A6d2eU4Mw8lJPJSjhuFdUpgr+j6MD6dflLCwfx1LtqGXQcjbkqyD+iGOtMZXvHgg9D0iGXR9PmsT3w1SX/WstQGdHFzKHJoWr1Kzgp3cXimG+yKj3106byks3LRJDjeXzJEj44WfYw7rXvajKoS79udrV7wdV/V7yFkHTH4E+RngHaPTRejEm483HQgLIrt0SWw/nMI7RivSRIxu9DkeHHUbqvP2gDGbhow0aJny7rRLbZXP8UtrtrBBtGbNVJO/S/QYKywENm2y5wXM81t1785tVBqWLYTDunUp26ouoCfUlz7XuT2elI1L/Cwk5hAqKgIQnaNaiXKPo9tPi8/48bGbVO9IOPIBAFHhpTTyDXuPG7QPQPw8Tj2ECF2y/bX6ykvN3LOm40sozT5geN2Spk2BKVPw3DHXYExjHTmPFwQCovG4y3mzLg4GiIQ+lt+8IAjbBUH4QPp7H4AvADQyuWUKgAcEQTgiCMJmAN8C6CP9+1YQhO8FQTgK4AGpLOEFyhCFPOVjsdgtLxeT6E6QnPnMNuNBE+G9Qd9kIZyLvmvnUC4vGcYwtuZzdC3bqmq7MOsIrh/4D7GIoniscmLUUojCtIMxISpkWnjha6rgatsWWLBAfW78eOCGG8Qky0quu0713F/Y63F8OetcW+/R/SPVLt48t8oL/jihDVFoJPjVKrhyA7VoUSgqp+VNh5l31qWXAg0bAoxRqCIP0BWem212BgyQN1Ro1kw0qOANO8j5zJv9rsrnbEjV17JVppsQhdkmCak9Rycvh6kH1yWXAJdd5i5hfCKFKXbb9vvFRNmMAVVVsbV8V/RNfg9atLDl/ZqfccTrXkURfl7P6fFvZPmTIKyfB7Qr/kU2SgoTz/Hcaig6vt3ruH7AA+JBONyQU4GMBwquRrm7MajhNwCAJW1ej1ww8qL0+4GTTzb24NKctmOgZ/Y7aesJz+06BU3PW/ZH8QF4w1ilLYsXI8tfh63zzzQt9sGMi7Gu63/kYx9TPx1Tmv4Pp3Z5Tv3b+Hy4f8Rf0Ltis3kfSGAfjVOFeCqycKG4TmnePNE9cQbvfuy44+RcnwUZhzGzxQfypQzGsY5Md0VngsgOh7eW1ouGa+r27SNGYxCNp2e3eNeTPvCuG0c1+gKtCn8FICq5FrV5S11g8WLgoouA226LnJOem7LsA6JxCYAGOXtV71uAhYzld0oFl858afVYdi7dphrjw5EPAABDh6pzG0r7QeUcnq5RCJKJTB+fBxegma4dzt0jG30ZpUhmMPAiTND6QM/ooGPJz5EDGo9jhq2dO2OsGYDuAMIZWNcyxj5mjN3JGAtLPxoB+Elx21bpnNF5wgsEge/97dtXFA527BjzLiEnxzjMg1kydoMPYpp/xSgZ9bBh4v8DBgBAlBdUZc4+436YdkLdDx7ByHdzNthrS4NyoLx+wAN4ZPStruojkhNLD65LLhFjfYdjRytRWPfJZGerFpTVubvRtvhX2xOrXSFOg9x9eGuqPSs4N6hCFLIgKnP24Z5hd0WVm9DkUwyr+kpXcE05teKP3lNl9GgyJgCLFsV2UVhcbHpZ+Zz1rdiMMY0/j/SNE+W8M6L6S8xo/oFJ6dhjauiRmWn5nRhSVSX+n2ZhQDx7/BSKB9kDomFDW8L++a3fxvFtX+Mq62dBjGr0ua0uApHn44Je/4q74UKskN/XBG18fRpDCxmpP43yduNPnV4UQ+Gdd554rahIFD796U/2GmvSRPxfKQSySWn2Qbw6+UoAENcPgJhzy8yLsn17w3WMyrCBaVbQFi+Y2VirvPLa5Mtx3YB/GBS08uDip16vGpo3F/NUZGWhUd5u43I6yhYG9d5JVwjp82FOq/eQG7AYd0jBFU1enrgHnjDBsmjKk5HhfJ2SDNTUiP238kCrqhKj5SB6HCzLOYhdi06OTf8IU7LCnqnS3FWZzSdbGlvzOe4cdg8+PfZ8/sYM5seAz2BNoeHyfo/imznnGhfw+aKjmGhyegnLV6BpwR/67eugHOeNvLo9IxAwlgkSMSM3cFQla+pdsRl/GXKvZX40N+O21mPMMFKLmVNFDNFbt3w68wIw8L2rhHO4RxnGWD6ARwCcLAjCXgC3AGgJoBuA7QCu8qJDjLHljLH3GGPv/f67geUdEY0goCTrIAALQduSJcCf/yzmmIg1jKkT9SoHmKIiMfzPlVdG3xe2CNFYq7Yo+B2rOryk39by5dFxeSdNAmbNEv9u2RK48kocCUY2+F/MOhdX9nuY73NYFrHeXLUo3GHdlkkXVnd4GRu7PwkA6Fz6M1oU0PuRjig9uLyyDG5esDNSP9P+YdUhsZyTnFp9K7fYvscpWg8uxoDjwpZpkyaJ/zOG+0bciRcm6ucEZACwQV8RrafE5vkKx9d8giFVX1sXrKd4blnn9+OliVdirF7oAp4fTBIeGFGn2Jwp+/77If2wilp+WXC66A0MAIKA5ydeg2HV8X8+lHNWzKwbTz4ZmDEDmDs3NvXzYNd7xYbHVuO8P6wLmaHom4+FRM/RmTMRlMayt3UMBLSeqe2Kf8Vfh/6Nq7kGOXvx32Ous91N5fORdt4qCsF4PPNGGiqVtUqooiK1h2r//tFe2laUlwPnnit6OHjASZ2exztT9fOHack0UHBpv2k7zxVvyaLMQ8gwErpZKETsKJlthYdPN3gVSzpzb8AXVIfK1ZuHJMXYy9vbOukdAMWzVR+FSXPnGqcNIJKHjAwxOsTq1c7rYAzFWYcMLweXKfLaeKQQLudU5KQknAYhL028En5fJHx63Qkr0bzAQN4jrfmUY11uoBYdS7cbN2CVg0tiSNU3GNjgW/PO2h0DzzxTjBZjZqQW9u7K2h9dv87ndZyDS2ctz6DjNcZYVF5dsn+ILcLyFcgO1GFE9Vf465B7AYgeVie0e93Ag0s6V1wMrFplv0GpTjkEYLt2+uXChjUJegD6Vm7R9WwXwuqX+rgmiRNcowxjLAOicus+QRAeBQBBEH4VBCEoCEIIwF8ghiAEgJ8BKE36GkvnjM6rEAThdkEQegmC0KtCJw8KYYAg4Kf5Z1uXYyz+yWaPO050yT72WPX5qiqVq7ZMaSlwwQWii7SC7EAdbh50v34bjImxjZU0a6a2GpTayvSJruTtin+1letCbkeHeLg/96z4ERf2fgKAKOSKRa4vIvEoPbiCnO7eVoyr+Qy1J4iLCFlRwzvhS4ngbQk2Bw2y0z1PUAonVZbEJSXAxImqsozBQMElRKzd9Tj99OjyFjw1/kbU5O2yLFdf0fsOXUXUCgQwtPob56HUAgHTV8ynUQyF+1+evd+67rIyNMjdF3n2EpUQ/corVVaUMcvjVFwMjBkjenMnihjE2Q//5l/NPhfn9/yXeNLJBkoZ3sUXAubNA3JzZW/tPjoGAma/1QntXsX0ZsbegDkBZ++ESsGVbsJ8Rd7VnMz4WXkajjGMiaGGvaZRI8/ehexAHXpX/sC1Fsn06YfNisrBZeP1MffgsghLtGaN+D0YCZI5PLi07aed0tcJc+aYX8/OVh2+PfVStCzcoQ6VGx7bNCEKuVi9Wvxd16yJulSdu9teXQSRCHw+5wLPmhrLQVQ1HoZCcg4mp/Rv8B1+P+5064JJiDKntCGcv8XQ6m9U9/h9gvGcIP1GjfN24fXJ3hichGlWsBOvTbnC0zrRooV+tBhAlNNJfDtnA85QhJ+VkT5veJxvU/QLupX9FF2OByFadmG0lxevmYdFJLynMPMwjm8nhrCWv3HG8N8J1+CEdq/K5eTfY82aSKQPB8j7WGVoWuXzUF0tdUwjH44jpp7tRMywXO0xxhiAOwB8IQjC1YrzyidyGoBPpb+fADCHMZbFGGsOoDWAdwC8C6A1Y6w5YywTwBypLOEFNsPlxZWBA4ErrogMNDw0aGDsZWa0SdEu7nSUZ+NrPlHFrObCaJGj+s5ji/Y39bNQzHJ9EYlF6cHlpSt/OHyAvOjj3exPnAjMmGHPun3hQpu9c0/YKkxYvoIv9Kjiva6TBMpm+xm/LwS0bq06xysw1n53fp7NVT1BN1y2mxFVJ/66U3Yedwqaazxle5T/KHsvMAiylWfD3L3WFY4YAfTpEwkxligFV0GBKlxvWsen79dPXINYWQmGDaoaNDAv17u3/KcPAs7q9gy2zD3b2W85YgQwdy5OaPcqVrV/WX4ZlJ7mWsx+q+XtXsX67k8bXs9xqPT1sZCceDytnpR4RTPQQV7T6T03ylDDiRojeOAQAHYr/wnX9o8OE5jpV8yBNoW6ZqWV477uXqhLF+Cqq8Q8prqVW/dFO7fE0/MvaRkwIBJKUw9pfA1vm/SU9/LYpnLr4lyntmsn/q46Id56ru6LoyesAmbP5quLIFKJ6mrgrLP4ytrw4Bpe/SWKMw+46FhysqnnE9i9+GSusrbtlqTxyvC2/v0BiD/DgIbfu2zMAV55jEyapFortyzcoZ7TNYTH9q9mn4fmhTujrh8NcuQL1FFwATD8TEeDEc/3xvVYyeBWka3HnUPvsSyjnM9HNf4SNw/6P3w9+xyxT24fdWmNrPUGDGgjBqxcKe7ZTjnFZYPe4zPJWUe4hycz+0AACwF8whj7SDq3HsBcxlg3iOP4FgArAEAQhM8YYw8C+BxAHYA1giAEAYAxthbAswD8AO4UBEEnhhDhiGT3v/XyJTaqq3HjyN+zZwNNm0YVeWr8je7aM7IUgQDceCPw1VfA7fabsIvfF6JNdRpg9guKHlyRyTvbfxRl2Qe8eZd69hQts3gIBIAhQxASOBIYJyPKsdHgXd5xOF88ZSK+9esIlvMCRxx1qVfFD3hr6mVgt99mXbge4mpskxROWos9xgySz+oQfg6KMg9h8z61JzljEL0XIHlwVYj5JLksQnNzgeOPjxwncN4O1pcEzH6/6EVuxYknAk89BRxzjHm5448H+0hcuvqYgEx/UMxFEGxsfp8egQAwbBj+cs87gK+7fPpw0HhpbvZbGSY2l8h26MGlfGvSylulpCRhTWeHlY1WCqwUV3DlBmpxUucXcPKbauWC9jlWPVcWQlizeVqZ28vwXTF10Q0LKPmfc1qLS2RlGV8z+M5DqnlI51k3yBet68Vn9LuOGoWMoUNd5aAjiKSjRPI+PvZYcS1hZ29o4VUvLF+BYIjhH9/3wvwXTtAtk3TG1Jxk+IJ80XsYsx9OT/oNdA3sLrwwKpeVCqO5nlOhExfOOw/44ANg7Fj96xwhCvVomLsX/xp7IyY9u9a4UDjUKuea6LBkKHZw6VrH0QsIfZa0fQNLX15kWsan8cjO8IXQuug36ZrLsWPQIOCzzxB8XP28ReWBKy8HTtAfvxKNae5rwjWWI7cgCK8JgsAEQegiCEI36d9TgiAsFAShs3R+siAI2xX3XCwIQktBENoKgvC04vxTgiC0ka6ZJ7og7BFHb6KEY2TR16sXMHw4sG6daBlthlU4DZ62FYkRGZMs+Vu1clavTfxMoKExTVGGKFRu/u8dfhc2z13vun5BYGLOOpuL5HDYLG7ivAgvyDisf8FIiaDoXzg0jqnwWEf4kukPyiFPzaB31R5GAkOuJ6pLF6BFC/0Npl1PAYvi2nCFtjHb7MaYoEEusXpLZSWweHF0cm0tjMmCUtWY4CaJcZ8+4vpFom3xr6jJ+0PMX6bB6Ld6cNRt6Fq61bQZpx5cjAmR0G3p/KjE8cPJAhcrYY3f5rwbazSh5pzw+3GnRp2zoySS3wGdvCAZCgteRwJYSfBr1h8KUajA5pyq/a7ChiglWQcwoIHk0VBeHilQUQFccQXaFZvkqOGBlFtEmtFuXDMxr2JYCcyT1iMst5g82TKXo98npF9IYiWLF5tfD4VU+977RvwV67o+a36P5BmsO/MoxzU9ahOkhLETXam6WozqohxPzdZN0rXSLHNPQMaAiU0/MS5w4oliuHNAtWZiMDZcnNbsQzw6+hbXyq22Rb+4uj9d2dTzCYxq9LnhdVmBo/N85AZspofRkpUFnHhiRAEtPQMBFkwur6hp0wwvkQdXbKEYZ2mIWXz6tCYQEBVXZskww9iJxa4cgJTChlWr5M1+rAWE2jGQQhQmN7NavIef5kUnluRBGaJQKQwIsJCYMN3lhOh0fJjZ4n0MaviNdcFIQ47acUqU90KzZuL/HKGIwu+vmUAsyvVd4sgJJhZnhCNcWcQHAsCZZ3oSc131PBjkbPMxAW9MuczZ415WBpx6KnD++Y776BRliMKY5eBKc7Q5LbziqXE34MvZ5wI+Hy7u/RjGNI4EOzD6rWa2+CCS6FyHGwbcj/tG3OGoP4LAVAo4ALim/4OO6koWdF9XQZBzVcaanPAG32gtGo5A0LVrXPrDjVJYYUMh2K8yEo6pPJsz7JWUJ+atqZdGh8Q57zxdwZzSgpcxACNHcvcRgOjd3qePdTkFaa301RAOMSTDEeECgPwlqTwi8vJkAfofi07FyEZfAvPnA23aqO8tLCQvOYJQcPAgsOkCpjK0xfLl5vlsiooi3gz5+ZFQ2SaYKe9T3jDKauBu316l4BrV6EsMbPCtftlwRJSmTYFzz3VmYGdiJPX7caciwyCfpe12wkydKoZp3riRr7yT9rKz8eO8szC31bvu2igtjbSjCBNu1n52oA7Tmn/kqtkPpl+Evwz5m6s6Ug3eFArn9nxS9sgyRec982o2jw5RmGRKo3HjgKVLdS/5bESVIexDEvJ0IZnDmHhFeCAoK3Nflx0Fl1KppRyMGjSQExozvetaPExyKCq4aGBMVooyD6Fx/m7LcnoKlfCGQkxUqyjr0WbC6VNzx9C/4aWJV+HsbsY5XlSEFc1Nmjhs0R4ZWgXU2rWiwnvePP0bwu/qkiXyuyQrRXQWZHohCnlJayvIGBD1W0rYscjXjo927q2TFs2q4VznmQgLU/s3+D7qGjdt27pKsuuUehOiMAaEx2jV8+HGg0tDdqAOuYFaICcH67s/jTkt35OvRf1WGgtBI8OXAQ2/Q03+Lued6tIFOO88+bOf3Pl553UlAbrPvCBEhziJEbIFq1JAqeTkk8X1pV0FTayYO1f9P2BLs/Pm1MvQNH+H4fXwczWk6uuo+vtWbsGStm/IuSRCAjO0Oj+72zM4vu1rACQL2VmzuPso3uQDjj/e1pxdnzy4TAVaZvsfKdfdLZ8PEY+zs4GNG6O/uyFDdG+vT98xQViRk6Pj3FteDqxebXzTmjV8Xl6AHF3AbIgPpLNhVMeOwOLFKgVXgAX1x6GuXdXKwkaNoopwrbHrDBRYgoDy7AP6OQrd0LOnGKbZrZe4WX86dULN2A7wLV1s7jFox0qke3d1vkevvg+pD4MbfiMbcBVkHK5Xc88vC06PeFJLTGzyMT6cfqF83LroV/lvs3WS/L011oRv79fPEwNUAKjT5KlPyjGpoEB9XFMDgPbdsYYUXETqsGGDmOjcyq2cBzsKLmXCby2CteeHjFXYRBMYBJWVjY8JpOBKYtx4RCgn7GObf4A5Ld8BoJi4nZgLn3GG/GeLwt/t388iSrdL+jzGd8+yZcD48eYbLg9Z0uYNvDDxqsiJggIxZKlRrPnworhfPwQLigGow7ZpcbMY0S6Q6c01p0HuPnw/x104TjchCr/a3VD+e0bz98MViv9LYWjntXobM1u8L1rCAqLnWAqhDlGYhJuCJCaQ6UObcNiS1q3F/3v08K6BGTNEhZIkEFAm4I6aW8aNA/r1kw9jti5golIhXZT1WQ7DNdphUMNvMLnpR7rXijMPAevXR29+w+Tmis+AnbVqLBk2DLj+eqB//8g5m2PewTqTtbTEy5OusixTmGkQjhjAyEZfYlOvfwFwN8/aEcDQWpyDpk2BGTOwr1Zajw0fDpSWcn93T467Ec9OuDZ2/SOIdMArYb8krzAT7vt9IXFeSDH8ir30RzMuxLCqr6ILjRgB5OWpFFx+X0hf0tO1q6zAD6P93vzacGR6kYaMFFwSDXL2oiJ7r/VvzKvE9CoHaWWl6GHVvn30NcbEHLj9+pmvZTp3NrwU5VHEjA1cvKBv5Wac3Pl57F18IloVOZCXuOSyPo84jgBkRFHmQeRy5AxvkLsvak4uzDyEbuVi+PP3p1+Er2efK1/rUGIcOlgAxJQx2t9KEDC35buY2ORj7v4bERWi0JdkIQr16NIFZ3R5Fuf2eDL5+5rCJMnOiXBNfcjBVVMjunoaWbzaQbMYMSUQEENIXXihYREu7xq3A1lNjRxruzp3t/3kp0TcKM46yFVO75EozDyMEdVfAgB6VvyI+0eKYaUqcvaJBZwouFq1AkaMgLB8BQY1/M7+/U4oKhJDIHi1iLYgO1CH4dVfWxcMo0iI3qL4DwDR7u5eof3FSKFggSCgeeHOqNN2hlDbOeMUDKmSQnEWF2NI1TdomLMn4iW9ejXQrRvuG3En2hb/CixZIoZUWptaoSr7VG7B/434KwB33on1Ed+qFfhqzQ1iXoA//Qk47TRvBT1jxojW1pJlrfJZtlK08wqMV7R/Wd5Ef3LsJqzq8JJ8rTjTOIScV5aXiSZTz0tUMbeWZe3HK5OucNXGpX3+iSv6PhLdzPIVYl6IcBjCVCE8Z4aNtQy8bYxY0PodzG/1tu61gF5YHIPIFNkWysmwwVm8LGTrk4V3FDqTcstCAy+vMWMiHnrSfbzjVeui39C3cnPkhNYqnCCIKITlK0wF22VZ++W/N89dj87hPJ6SR7rZCJqqoa3leaFhQ3Qt24rZLd8zzPGnXHvl2cgbFL3n05xZuRKYMEGdl8tig/PO1Evx6cwLjAusWwcMHCjuu8249lrgyiu9y0uYkQFcfLG4HnbK7Nm6OWdXd3gJfxt+l+4t7Yu3oXPpz3xGQJwyv+PbviavUQoyxfcm3iGIG+XtRkXOfuuCNvj5yf/h/3RClC9s/WbUuahnVzpzcqfn0L5YnY9sbccXcdQgrHdI8ImKXO1zHQrhzG7P4l/jbuL/AAbcM+xu/GvsjXIbRtFfkgrGcHm/R3Fmt2dJwRVDSEKeLqgUXCSsMiTsit2li+gNNns2331VVbLLvgrZg4sDFwOZcnEkLF+B0uyDUa65APD8MVc7boPwjmw/Z6xsHTL8ITw/8RrVuZ/nr0Pfyi3iQSISPmif3WRPOjFpkvG1uXPFsEWKxX15jijQlRVcMf58pFCIPad2eQ4buj8lH9sZfhvl7ZZDMpzY6UVsX7gu8kzk5ak8ZlBWJio49KwXk5iALyTHxc/xu0z4W99o1kzc0HfsKAr927SJzUZFqlOt4NIRKilCvxgJjOWzUk6nAAuhKPMQAKBN0a8ozxY31LUnrELPih/l+woyDqGfImRJuqwvs/TmaMW4n5dxBIOrDPJt1HdmzwZuvtl2uPCr+z+Ev4+4UzyQ8teGyfTrCCZ0vNtenXw5TuvyX6723DyrdjwVyYMrwr4lJ+Kqfg8bXm+lUX45Vg6WlDq7jyDSGZ11iDw+6exrvp+7AfcOE8fkZgU7I7KGggKgSxfTcdDPQsm/F9TBz0LAOefI89fKDq/gi1nn65YNr71enXw5Ar4Q97ygLRelDCwsBKZMEdeRK1eKMiaLcPoNcvehMmzoqkfLlqK3lGZujSInx9hz3Ck+n7WiyWyNnJUle4ev7/YUXpSisRzX5k1xn6Jz73vTL8HrUy7n69/FF1uXYQx/Hfo32VspTPVwgzzeHvH65Mtw86D7AIhKZqf5yh4dfQseHHVb9IVJk5A3qr9uHvF7h98NAGiYs0f2ZNSGOQ+PCdcMeEg0zALkZ5UxiLnhAdEob9kysd5hd2JtxxcdfQ47dC//CRObfiIfJ10OLitSqa8pRmrF1CGMScFFRkK49lrxu/L7DRP/2cLnw1+H3IuhVV8DGGQ+WHmh4FLUoedtMqKRjqs9EXd6lv/g/Gad56Q6b0/kwOm77ibUkfZeD/PNeE7btsDEicbXDTwtupf9iAY5e8UDve/Ywfs7vdkHUnXqe5PFgysvcBgH6iw2Q4nA4Lu2I7Cc1PRjTGr6MS7+cIJlvXp0K98KCEWRE2ma5/KD6RehU+nPABYkuiuEFmkcUim49N6Bnj1FT/Nbb7X2sFKEmPNLG1Nl3qmAL5Lfc3j1l3hBNraYL7afJsp53RCFinDUmbwJ3U34cX8pGpgJpFIZN3k7srNVYZMBjeVtOG/WjBnAoUPARx/Jl+x4oLvJW2p2p/YdTI83wgMYQ37GEV1BWhh5LSTNxUEbykFlSUlPTxCEEp29i6xE1ln/FmYejgitoQhdzRiwZg2Eh+4xbCpVDfV8TBA9QHdZ5yTNzxC9eMLzDq9CXvvNmO5duncX/xmh3X+nqlCcQwF25PjVCPhC8jrT7JPmhp9bq++jcWM+Dy6DelqfMhFC9xfBRgy3rsMBpdkHZO/AZgVi5BKevW7fyu/x/d5y/H64UKwn6wD2HDVIyQDj/LwA0KdyMx4fe4tUTiuv0OmL9rc8+eSIkefvv2PhY49Frmm/1+bNgXclJZ7HsuukzMGl/fyp+v6mGOTBlS6QgouPzExVaDLXMIbj270e8zi9svWPYmDsXv4TxjT+LKbt1jeiYj07QFi+QmVRYobuIiZWk5+b0J6ppOByIngTBHww42LkZRyVj5UsbvOG7SrbF2/DI2NuAwoKrMNVJIj9S0/SDw0VJ3YtOln/QgzyzjAI9t8t5XNg9HeK0738p4gFHpFcSF6mx7d7Xbay9uv9Vj6fKCBZt87QwphBEDf5incg/JePCSphVbiOFzSexEB0UudURTdE4YwZsqd+WLDlhunNPiTvHj1WrYoKMScrFNesAQYNEv8uKBDL2iSs2NJVBnNi6rmgeQfTJS+dFR1KtrmuQ6uAP1ynCJNVau6VFV4vH1q6xixiPEHUX3TWpiHBWMEFqJUxdZqcNmZL3ZT24AK4ZEFX938I383ZIB/zftxoDy4X31Oy5OF0y5gx4v8jRxoWyfQHVftjrr2y1b6Od99n9D0z5r3HmwI/ExytVN6aehlOV3izZ/qDUfPr17PP4QoH3LboV/lvbR26BrnK72rdOnUEE+337feLRscDB4qeX8NjoChMpRxcSlKprylGmoyaBDp1ki1A3FgtEjbh0Mz3rtiMZ8Zf50opoKfgqszZh2cnXK8uqAi5OKP5+3hv2sWeJHKsDwjLV6Bb2VbrgrHGSkHj1JNk2DBgwAAxZ4xdtM+1RTLchOJkwWCxa7lr2D2265UX5hUV1uEqEkgiZ4virEP6F6R34PXJl+H4tq/Jp91Z5Lt8LtLUg4tIYrKygLVrUZZ9AAvbiHkBTIX2LVuaP+dXXin/6WOCymtG+bfZW3Y0lOGJIUii0Q1RWFICnCnmJVNatTtBWL4C2YE6HHWRCzCtUApEdOZbWeGYY2yBbBc3sgM7Hly7jtjI6ZuinNL5OXw2c1O0cFLnSzb72rVroeHVX+GuoXeLB5s2Gd+oUIhm+evSRuZLEF7z2czzcVa3p+VjKwVXO0VeHa0BS3g9ISxfga9nn4P3p1+Ef465GUBy7WPsIO/NcnPF/fC6dWI+LB3yM46gReEO+Zh3H3E4qM5v5Uouly6DXYsWwPXXRzy0tWiez009n0Cnkp+t67Wa6HmNXvXqCStuHChyBzb4Fr8tPM2yXIAFMa3ZR7hFClMI8D9np3X5L/YsPgmA+D4q59dWhb+iddFvCm9p9XMUzs23a9HJ+HPff0batmuw07Kl+ljve5w0SQyfOXSo5VrQDUnpwaWFlFpxIU1GTQKNGonCayK+cCw8upf9hLE1n7tSCnB7fEgDZ/OC3/Hw6NvRs+LHhOXM6Fr2U0LadUNx1kHvKnOaQN5KEWoVX9uIjAxg0SJVvhZutBNyMntweaHg4l10mbRl9s7S8kaBXu6qBWK4vAENv0eFyxBfV/Z7KHLgxoNLKXht0UL8X5kgmiBiQefOqkOn3p+/HipUbfQzfEExp8TyFQCAEzu9gOeOET22osIfa5J/J7Ngq03RL/hi1rmW5eRwtFqkMcKrz6j0UJnY5GMcWrrGk3pThlatxP+VihEdYwHD3wMALrpIzJvLydGgGH3fVQ4uk1la68H1168GOW6Hl9zAETTN32FdMEb4WEhcO06eDADIzzjMJbzTov1FsgN1WNz2TdF7SxEiNIqzzpIty0k+RBDGdCjZjlLFXtZUWL5gATqXbpPXAXWCtEYIe3ApirYu+g09yn/C1Gb/AyCNg6nswQWIY5pWQG8C76c9pFFwuYrakS4hCgFzrznNuuDcnk8iOyDJzczkZ1YKLLN5xaR9DBzoyIM8TIYviIqc/ZblAr4QCjMPY2WHV+RzvK+V3yegMPOwVE8QHUu2oTp3FzJ8dWha8IeqrNKD69TO/8Xvx4m5o4uzDkWezzZtop5x3dCGZt+5HYWsV4ajUpsp4cGl7F+y9zWFIQVXOiFYx6wlPKa6WlQcVFWJxzqDlV7+LLvIC7JevcT/O3aUQ+mI14P65RHtbhwPOpb8jI9mXBT3dt3yyOhbsX3BGdYFeeCwGtK16grH4u7RIyqMD5YuFZPTJgJlDOtkVnA1aGD/Hh4Fl97727GjYZXyex8KRW0wQ2DOFZUek9D5omlTYPny6POdOiEcg0hpTeakr6d1eU6618EG0+cTE0B37QqMGhU5X1wMXHWVucU5QcQAXc8jBeGh6/s56zGj+fvy+fYl21XlAhohfX7GEYxs9CUA4ECtRggRDi0D4ImxN+GJsTcDCxbg2zkbcErn5+x+hJiS5a9Du+JfTcsIy1cYe2hJG2W7Y83T46/XPX80FEl17GehiMCmvrBmDXDCCcCUKZE1q47xz1+G/A3fz1mvX0dFBbB6NTB3LleT4Zx1Wit6O5hZMWs9uD6ecQHO6/Evx23xMLz6K2yZt8G6YIzwQRC9HSRhIYMgCu9s7mvO7PosrhvwQPQay6qejAyE/JyCSoKor0jvlTK3XUgTdlDF4MGqQ60HV1AvJHFAnNPMjEBO6/If7FtyIk+P445umGdJ0K6bmzPMeeep5oWTOj1vaLByz7C7xcg9Em7C5aaVgssMIyVWu3aiEb+WQYPEvVlZmf59K1eKMroFnPmFa2oif48aJd4XVsjFUJEb8AWBU08V971z5wIXX4zsBkV4aeKV0WVZECOqv0TdCSujrlXl7kGb4t/w84Kz8OvCM/C45Gmp58GV6a8Tc5hpDUwLC6NCGermo+3UCWjTBhg7NvqanefT7fc6YIC4H5cMATNM8n8mDaTgigsB6yJEqkEhCuNIdjZw+eURCxEdpQZjgphUccgQx834fdJv2qePOGE3bAjs2QPcfjuE5Ssw87/L8fDmnpHyimcgyho7DiTLkN2j/Ad8sEMtTCnP3ocdh/XjKRdlHkaRZA2j5cyuz2BUoy8w+qlTPO+nihkzxIVDx47i5HfXXcAHH4jX+vaNbdtmXHaZs/CG8WLjRuCtt0RXeLvwhDDQW4gsXaoK+6UqHt7Q6Czg6kK+iLI6wSR0tujd2zj5r/SbOM5hM3Ys8OyzDjsmkZdnnAA6P99d3Ymma1fgf6IlruEGkUgqXp98Gfo12GxaJqxMb164E03zIxac2hwQhqFfxo/HnvuNw8RNahoOedwDLQt3xGVD2bP8B/Rv8D1u/Mw6dr/rECWSMEk374AJ42r086EqQxSq1uanxHgdkSzk5orjPACcfz5w5IjumF+QeQQFmSZ5zxgDioqs21u9GsFLHgEAFGYahMDlwDREoeZdal64E+f3+jc2fcC39nhx4lUY/m973k+J9pqUFeKyZ4c0LyvXThzCqo6l29GxdDsgjLfdh0TsZQgiFZEVU61bY0T1l9JYWGF+U00NHh19K3YdzQUgeqXqGgmcfDJwszTXGrzzfiYgL+A+j2Us0FU2SZ/jjiH3SnP5UvX1CROA6mp53Lu492NY2PotQ4OVmvxdqs/vyoOrb1/gww8jx+kqFM/LUx83bQqcfbbx51240Lw+o72bEZWVotFiYWH0GsWGImbr/DPR+L7LRMWVwhBdyQW9HsfC1m+j+f2XiGvztm3Ff2EyMzG0+puo++4dfhdmt3wPvnWniwbQmzcD114re2CGKdGJRhTSMxbt0AF49FHx7+7dgdmzUXheJGTpixOvQvfyH0UP+o+ltf+cOeL+9zSDNYwdA+iASzXEcceJ/+8Qvdv7Vm4BWAd3dcYa5ZrJSc54ggtaLaYhlNQ6zuTmRgZpPQ8uCMBZZwG5ubhp4P9hebtXospYofICq6kRvcbKy+WcW0GNhZbSQknXAivGJIuS9f3pl6iOt8w9G1Oa/s9RXW2KfkUHjRW8KRYLounNPlAJIGUyMkTvrawsUXG6dCkwfjywIXHWuwDEvrRpI/7dpEli+6JHTQ0wc6Yzz6hweNfwYph3MZuXp/bsUaDc0Ghrq7OZl6Uo08PQmUpikezVDhwbNUe5swBg+vSIZ62N9gCIC/fmzfW9y9KF448H1q4FzjtP/EckPQMafm8pKFFevbD34/hpnphTSikg37v4RCxo/bY6xObGjcC4ccAxx6Bd8S9oUfC7eWc4ktB7xeyW7+GGgQ9wlW1d9JvhtcENv8H+JRZGGrKCi++DFWUeRH6GvlEMIIaoXtbuVfXJqVNFq+T6ht9vbNAQxuyB4nnYunZFXYkoyC3PPmCjc9qmnIUf5mFY9de279EqqONBfsZhWXCmVbDJ+wqlUDLGwpqCzCNomLMnpm0QREoje3BJ72d+Pp475ho8OvpW6/VvRQV6V/6AMY2/kE8tbfs6Hgt7ggDAJZfIxl1m46CPhcAYogTvyYCuB5f0vWX7a1GmN29oQqmt7/40avJ3cbU3sMG3WNr2ddv9lOneHThXEXY5w7lnclKTmQn8+c+R44KC+CvzGja0XqMYcG3/f2D34pPQKG83AOC5nztER+GRKM06iGYFO/HkuBtQka0Thv+EE3SVYxm+oPjelZWJso727cVQimZI76vSQEQAxHxoBQpj75UrgcJCPDr6Vnw3R5Q3dS79WTT6XrVKNHC+5hpruUE4sk11tXGZ8ePF59qtLIkx+V/tCatwepf/uKsvhrQr3o4XJl6lPkkKrphBCq4042/D78TFvR9PdDcIBfL87PdjdceXMUqxeOTFakN9df+H8OyEawEAzx1zDf4x8i/ihVatcMvg+3D/iL/YbtMNRuHATuvyH/Qo/yGufVHStOAPVx4r3ML29u0BQYCwfAUaGGzIHxlzG18i+4wMUSCWDEqlZctES7bVqxPdE2+ZOFG0SlwqWe3pCdJyjL0a9JDf2T59ooRltSE/t2TYz4LYvdietf9nM8/HjQPv1732yqQr8M3sjfhm9sao0Bd3DLkHS9u+hprC3Zja7EPd++OG9P24EusxjcUa72apTRvRKMFsgZ7qZGWJYR2qq83j4hOJ55xzHN2WG6hF4/zdANRriILMI+KroFRw1dQA06YBGRl4bOzN+HzW+eJ5I69hrSeHQ3gEYO2L+QxLXp18Oe4edrfh9YAviLyMo+aVSBvOHLNQRRI3D7oPP8w7G78uPN2wTHHWIdw+5O8AksezPWXhtAxuUrgbfSu/d9WU2bzz1m/NHder9Axc1eElvDDxKvSpMPfK1N4XL5TGktqQprJ1+qxZkZPSu8MVDjgspA2HlA7najMhKxDE9oXrrOsmiPqKtKZXhsYNy4ANWb8e6NdPHQJW2u8UZB7BlGYKo1CFt3+d4EvKHFw3DrwfXUqNc4DrenBpcwFpP5e0VnLycR8ZfSuWtX/N/o1KlHsR3pxSqUhJSeTvZPJU4/jhM/11hhGAlJzY83VMafoRAGBCk0/1P2bjxsDZZ+Pg0rW4sFdEputjghj2Wfk9ma2LRo4U320AhRkaj/asLN17y7IPoEXhDnQp/QkFYeMtn08MS8ij/GvcWFSErzcIOQ2IMq2VK737jX0+BHyiUj2pnhuJwoxDmNbsIwwPGzfNmiXmM02SVBXpCCm40owFrd9Gp9Jtie4GoUDemDIGjBvnLhazFmlR1qxgp2x1NbLRl5FnoEsXtCzcgfFNPvWuTQ6M5pc/9/knupUZLzzjgW7CzDAdOojxfHVgjHNxW1kJnHSSfPjV7HNx97C77HUyWSksFHNpKBdX6YDfLyolwxsHvR96/nzu6kY3+hwnd3peXIiOGKF64+8f8RfcNEhf+aSHE2vx5gU7kG0goB1c9S1aFf2OVkW/R72oS9u9gTuG/g0/nnQ1/jnmVu72hOUr0DhPxxtRh6fHX4/pzT7gqFRScKnCKtj8LhS/o67VJkGkAo0bc3tbluqEJgGgPx7MmSN6Ep15pup0hi8k5voaPDgSAsSAWIu3Di5di4lNP+Eq2zBnr8poZGP3J/Ha5MvlY65tL2P45NhNuGf4XcDcuXhn6iWGwrJVHV5BUeZhMZcBT9XhbyuZc1gmGjPhRNga2iKsTUHmEbw19TJX3TBT3B6uc25BH1Dky21d+BuGV3+Nt6f9GRf3fgz/HHMzHh6lP+86Cb0pLF+BwQ2jwxzxUpolejL4WAh9KyUlHGPoU7FZzNd32mnq9XJuLtCli3ne35kzRWFteDw78URR0DNvnnWHyNqZIMwpLwdatsS4mk8xvkYzbxqNrU2bAkuWiPu7FSvEqBQdzMN8PTP+OlzW59FIVA8N8rp95kx8euz5OLfHv+1+Eses6fgSbht8H24edJ/udTMPrqjjjRtFwx/JS6ZF4Q7ufoS/bk/CODMmjp2zZqWvB1eYsBK1U6fE9sMmBRn6ITn/N+MC5AUiiq/rRv1LNjwzJTMTOYHaiJIJ0hqySxd1OW0kBqXhquJ5mdT0Y/ww7yypHonSUlH2ocnFBwD/O/YiZPodPrtlZfF9TrV56pKM3YtPxsW9H4ucGDkSOOaYhPWnPkA5uAgixqiWlNOmYcpTq/DetIvR658ehJzTCkqUVkjLlomh7h59NO6Ww0aCaD8LmYZ+iQcNcvYaXGgghuwysTrhygHg96u0YUWZh9GphJTOKc2qVUC3btzFbxl8H1oW7gDazI9aeM1p9Z74h6CTNFcH20od2NtQ6QryOBaLC1u/iU/+aISPdoqehbyhccfVfIZHN0uhIM2EmR55iIQRBYskICNSFE7T4a5lW7Fn8Umqc/uX/Enfc6mqyjwXVKtWlsoEu+FW7aJUWGX46lAbMu6PNoxaWfYBtCn6VT7m8oJhLGIgFAqhd+UPrkPSRaqW6iGrTWPM5p6qKtGb0crAxgOvgqNB4+fajbGE0hNKOf2t7/604T2PjbkZvSu2WNbdqeRnfLqrERrm7JE9nZyGrH909C2yUiu4bJXq2ltTw2GkNEpExoA1a1Dx3KXGFY8apQ7rXF7OL+jJzQUOOc+rRhBpj88HrFuHQStW4KnxNwJQ5CDi8Wzo0UP8Z8HYms/FP/r0wbZnPsYvtz+Bj3bWYOnLiwBI487GjUDjxuj40EP4U+4L2HUkFzd8NsKy7i6lP+HjP2qs+2pCvwab0aviB6x+Ldow0cyDq2GuJB8Iy1ZqasR/En0qt9gOu+hZnlKDcPhpx1lnAd98Yy9/VqzRevhp+G7OBjQr2Kl7rUvZz+oQw7wGToEAMGcOVgUfwqhGX6DTw+frr0XHjQMOHAB69hSf1ZNPBq6+OqoYY0ATbVhNxsTyqY5y3ZiEHlxJ2KW0J7lVnoQ9ktBVnIj2wAj4QuhZ8aM3lWt/86wsoGVL0fqqV6+EWTVoP3NYsMSYdwJrXtZ3ewrruz0lH1/Q6wlsX3BGdMH8fFMLUQbBnqBr6lT5z54VPyK4bCX/vURiEQSc0eVZVOdKi0Gz90hn3A0ovTZhnM/j1cmXo9JI4eoC7oT0Rp/LwnoTAK7t/yBGVH8lHx81ETxrORzkKCv1bUT1l2gtCaltLxIV8c/9TKBVJpG6VFgkiA8zdCgKMw/L+TkBWIflM8LsfZGEPrUxVnApOXrCGtPrWsVDpq9OlQ/Udh4jaWznVRK8OPEqtaV6ixbRhXr1AoYMsdeP+oTVGN24cXQy+hhwOBixPn5/+kW4YUDE63pc489s1cUQeS6VczNv2MFjmnyC6rxIqOs5Ld/RDUH8ycwL8M3sjXhnWkTBZOpNJfHtnGhjuxaFO1RtKtfGViHPeldskUOme8ry5eK4c4bO+p0gCHOceFT07m1+3edD1dgu6F7+E4Yr9gPNCnaK76o0UJRnH8D1A/9h2dxfh9yLlyZFC+e9xMhA4dDSNRjY8DvxwEgJoTfwGQyGYeNEbXhXwoLCQlFZk+ReOUpaFO7Qlw/16QNAM9fX1Ymfj4fhw5HpD6Jj6XZk+WvRUc9YOiNDjMbQujV3f20ZzqbCvpm8uwkNqTN6ENaQgispyfTVcZcd3ehznNjpef7Km+vkAjjjDDHshwInXiBuUE6Hfx9+B16ddEXC+nJxn8dxZrdn5eNMfzBipaWHwXvEmICa/F3YMvdsADDMrSXToQNwVSShpFdW4ER8uLzfo8gNcAiGdZ4X2WJPWqAbKXUHNfwO/5txId6eamzx7MT6z/V6dPJklYLWso1hw3CgNpLH6d/jbsCSNuqkysWZB2TLRy6vD2kzPrHpJ/h69rkWhQ2YPRsYMECszisrSoJIBMOGifkPzeLaA+JG97zzuEMamqI3F7ZuLYYfqa4Grr8eR03e5U4lP2NVh5fc9aF9e+6iWsvsTL967cWYICbAbtwYOP5444pKSsRwtVIeEl6jnGHVX6MmXwrVWlEhhnBT9o8Jomd9OufQcIsXYW082Asdqov8RhXZ++W1gLB8BUY1/pK7nhM7PY+6ZWLO0juH3oPHxtwiX+MNO6hUih3X+k3cNPB+TG76P92yrYp+R43CSttIOXvb4L/LireWOmG35DZnzAAuuEDXItwIxiCHTPeUZs1ErxCOfF0EQWgoKrJ/z9KlEQ+Prl3F/w3G6PCwe2DpWqzq8LL9tmBPXmJFWNYwpOprOczwXUPvxjFN9EMeZwcUbRt57GzYEJ2PuW1b/falPT/tPdIAQcBNA/8PFdlq2dGEmk8iHn0nnigq5yR6V2wW35nzz0dFzn5VXVi2DDjhBPGY01Pt8PFr0WqZB+t6ACUGocwBANOnq4+vucaTNmOKUiCR7Ao5Wv/HBVJwEUQMWdbuVSxs/RZX2U4lP2Nlh1dw3YAH+RvIzgZuvDFyHDatNBngCzIOYVGbN/jbcIDSaros+4AqzNAV/R7B65Pd5Ufgpl07AEBh5mF8P2c9/r+9O4+Tojr3P/59elYYBhj2QYYdQRBkU0GFACK4gAsKroDgLipuUVzBuPyMGpNoEhOTeNUbE5NobhKNiZrExOQmmrhdjRrXaJS4xIgLLiDM+f1xqnuqu6uXmemZnh4+79eL13RXVVdVD9PVp85zzvOotjavl5014b60ZfHf6JDad9V43An611Hn6uOVp+hX+3xVPSs/atow3DDu1s3X5EJpCe7UXvygf4tenhixl0izFyEIfg3o+oEGdE0Pln5wtA9St/TmKCqQfENqTvqGhuiAc3W171DPsf/tat7zTwYO1OBu76ouqNkxvd/Lqg2K7T666DJJLXgftbXSwoVJi57eUN/8fQSN9fLY1o7f8AUyKS/39Q+HDMm+XSzmg09m0rp16etb2/l/1lm+w7u8XKqq0uYsszGXjHhEX57+48h1M+uf1wWT7olcl+T00/Ouf5g+g2uruoRqj8XkfCfERRclRtZGuuIKf1MfXC9um/Nd/Wqfr6ZvN3p0WorHxIy2AQPS0jsWtP5qZzNjhg9cbJdf6t6sChHgCs3g6l75SV4zoaKUW6Ni5vTtmbfq6O3/pDnbNc1yyHemdfhr65bZN6tX9cdZA8thqQGuLmU+UHf8Dn/QESP/qoWDowNlCePH+/TdpNUESo+Z/y67+uqWDR6Ixfwgk699zadql3xa0fnzpcMPT9o0PhCka/lnsl6hNLLNCI5Xlm1NqlNYCHfu9U1dMvUuSdLRo/8cXTMzdbZQpu+QhgYfnIi78sqmmlEpupX7mkxl1phW5xSl5+Rxv08aPCKF+rp22MEPngp8ZfoP9aVpd/gn9fX6w8Kr9c8j1uiZxWt9bW4zPzvyssv8zOR8jBzpaze10iuHn6fV47MMpJ8/P7muV2pAtyMKz+DqaDP/wg24+npp2rTincs2pIP9FQCl74tflL4w9WeSpBtnfk/jer2R1+seXXS5Fg17vPkHzKPRGr6+Tui1XjfPuqX5x2mGcKd5mTUm3WT3rv5Iuw14WXfM/aZ6VGYZRVIIobROw7r/J6+aP5J0zbQ701YnRmYHm8bMqUv5Z5rf8ExidO/6I8/x08/DLrkk8zHnzcvxBlAULWggnb7jr/XnA3xtisQNWra/t9AxoraqrYzfHPnP0q79XlZDzbsRW3rnT7xHjxx0eWI0WVSH3IljH/QPhg71o8emTk3clD4WBKKaTsrUUPOuzppwnzYsPz1p1X6Dn1Rtxadava5O//7hb6UZM/TQgVfq6cXr/FsLBbgn93lNvao2amxd03UwsTZXwGnBguzrm4FRlNjm1Nf7ovGSNCeofdHazn+zpMBNthSFMbmMM7bPGP9rXbbzz7Ie6td3BoH/mTPTC2tHSKQgDNK/VMS2qqZis54/9CJ/PvnOoo7Fkt7j+F7/StQceeqQS/TPI9boi7vc6YONY8b47/igrZH2+wil02MWdxZHHeWzDxSic6IAAa5+XT5Uj8qPtXHFqepR+am2NmY5rywp8+JBrGPH/G/a111ranllCyxLSgQKU+vGTuv/cqKN8L05N+nne38j8uX8pQKdQEWFr10XmlnS4v2EL2CLFqUNgku6Zlx6adPjPFPKrptyl+YPelq1lZuaXecqm6rYFjXmqp+9667Jz7P9vsLfL1nqQZbFnNzxJ/hfW1S6YpSODG2KxKCleEAo2G71+N9qRv2Lic9M/64fqqHbBu1ww2nJqQT79s2/zZNvuybT3+7220vyg7QrOlvazPDvsCOnK1y3jsFC7YQAV2fC6PQO4ZxzpHN2ui+5FkMeWtX5MWuWn60wcWLk6kydTEO6vaNlo/6sO+Z+M23drbNuih61nEE4x7BJ0uzZmlX/nHbq/XraTbYkHTw8ezCvR+XH6l6Ru6B0mW2NrGVw1MiImXMZGghHjHy4aV3ENh+uOE2zBz6f8Rzi729gzfvpubsjGi8LB/+ftGJF+lRwdAz1fqbQQwf+P31n5q3Zt3VOnx6zStdO/7Gm9fdF2VNzrkfW4MqzURv/237owC9qfK/1Gbfboe7NpNp+GTuey8ul887zo8fMNLH3a9qxbr0m9fEpPHTUUf5nZaX+ueqLumbanepZlfw5vHvvr6ss5lQ2f676LJkjxWLqWfWJ+lT7NAxVKanBXj78At2zz/Xp55LPd9b++0uShtX+W6eM+13u7TMco4IZXNgWTZ7sR28vWeKfFziVddYAV5Y2TaJjYMkSPbvkYp067rdp2+w5M2KkdRaJdsCIEZKa0h2N6vF28vp8RXQUlMe2qqHbBp1zWfemGaYDBiTSOKWlXz3zzMTDvGsjouj+sPBqvXjYhYn6dVlncI0cmTElaLYgVrk1+s9nXH2OGcqhWYeje76ln+x1Q+ZtzzpLGj487/pxqRJtlmzfmXmk2rlo8t16tg2yFQLIQyFmxOZj6tTk+5wWzBZbO+XutHuNbMb3el1njr8/fcXAgdK55yYuXZVlW3NfB4cMka65Rjr5ZGmffRLZX1rs5JNb93p0LEG7OfVe3sz57/6DDvILcvXptCa4kW/bvb5eWr5cOvvs5OUnneRnZHdGpZSiEO2CAFdnMmhQsc8AgaqyLYkp8fnK1BlUm0eQR4cf7juxunZNX7d4cdLT+A335Tv/VC8cdpFumX1zZLCpZ9Un+dUgkvSNPW7ThuVnqDpIf+IkqbZWDyy8Vv26fKgt4dGvQeeTlKHjP/DYosv1pQzpjcK2HHeyXjj0Iv1+4TWJZYNq3tV/z/kv/yScTqCx0Td+U9w256amJxGjP7pVbMp+DuFOrUzFaQOXTv2ZHzVbW8sXcUe1887SEUdo136v6Jgx/5tz86qyLUn/leWxrdL06Ym/pVc39kp+wdln5x3gMpOvvaP00dhhvas2Jj0fGEp7OKDL+zpuzB/8k5Qg+B/3v9oXpW9o8NNPZ8zwK2Ixn4qsGeI1RVJnS/Wo/NSnBenvUz5m+9yn2W8/afp0vXz4herX5cNmnY+k5AAXsC3q3r3puyZTbYkoeVyjsgW48grolJVpTM+3VJPyHVvWgjRFZeakq65KdAR0SUlF1OxBRGPHps0iTcwS22uv5A7+4He1JXWmz6BB0okntuz4aJnUWfQt0Kv6Y/Wpbko9nbNuZIbOp8g6W0Ha6pg5n35z7lz/Xbtunf++yyT0eYyZ00HDnsi8bXW1dO65aR27O0YVqY8671ijrw3SPyJN87Jl/ryj7jdS7Nb/5Vb3FQNopnPP9RlCCpDSLC/HHitX3b6pzGYPfE5fmn5H9MrhwxMDBipjW/IL9NfW+ppJBx6Y/d48nzbUTjvlHrCAkjas9t/a+/jBvu5tfJbibrslp6OM/x2tWOH76fKczRipOW333XZLnikm+e/rqVPze32BB8K1ufJy3yZZvrzYZ5IuyyxPtB0CXJ1Jnz7+wnr55cU+E7Qgx2qmWVYfrDg9vy+bTA2yuXNlQwYnnlbGtkhLl+r8Sb9smqacIYd0rkZhPCVZddkWda/8VJ8cc6qkHJ0BZ56ZCMZmGxVbHtuqZaMe0h/3vyrrOUjS8O7vaFSPtxLPkzrWwl/osZh0/vm+Mz9V/PcXBBOiTyo6LczW8O9p+vSs55r4f84RCEMRmUmf+1yLX15ujdLRRyeeP70hZRTlqFFJnVXZPmfh68KWDAGu1488V3s3PN20YMgQ7T/k//TRylMkSTfMuE03zvyef0/LloV2bupS/pnvCD7xRKlnz+QdV1UlHj635CLVd30v43kGu9PfDlmXNoMt4Ygjsr4+o1amrRpXt97/fggoY1vXnELzedSsPHDoE76YdoSYuaR6nGGJj2IwonXT1uQR32mf1Ayf3XN2uld/PegKScH3fvD+/rD/VZo/6OmkbZtdA8ssrQ5gmTX6zqsM57fFRbd9DhvxFx016uHmHR8ts+OO/mcBIytJgcsc7fuu5Zu0cYVvC0d+FwaDTJzk67QuXtwUSDLTj+d+S0Nr35GkpvbvpZfm/v5assR/vy9blhhcEx4U8+GK03RtfNBYuF28fLmOGPmw9h/yRGJRdVnQJog65u67+7ShOWxYfnpyuwRA+xg+XDr44Iz3rAVnlr2bIkN2mUi9euXeRrm/z2Pm9N7Rq2UmfRpuX6xcmf+5RBkc9Kek3i+h84rP4AotevnwC3XS+RF/q926pS+bNi1nXeucWhMca654n2Aeg1g6jJkzfWCvo+nf39dZO++8Yp/JNoUAV2czfHhycUAUx5FH+pQ1xx4bXeg9cPyYB/X4Ip+rOuu9a3NGbkSI19k5eezvdNqOD/gvgRUrmjaIT68OKbPGnIW143V1UoNzvauTZ5NM6L1ehw7/q39SXp4YwZqtQVxujaos26rdB7wUuT5mjTp8xF/8KOu0dSk7Xr3afy5OPtmnTgiOH2nGjPSp3XEZGsaJTq0DD/T/UoXT0CRe1PpRxmgnedZuk6Sder+W1qk1uc+r6a8L5WQvL8v8QbDQMVJnB4zv9bo2LD9d29W85ze5+mo/42DVKpkpUUw5cSP4uc8lBa2SZPowBh1Z2/d8O2MQPixrzcHBgxOzGZqllQGuvy3+gr7QzBm1QKc0apR0yCE+hVkmxx7rZ5XkESA4evSf9cCC6CLyMXMZWxD9u3zgjzFypKTMA3wSFi+O7FBqqHlXU4Lra2IPjY3aY8BLKosl77PFM6hCQa4ya4xOPxSkZGqMSu3WpYt+sOd3tdcgcrW1i8MO80GeEwpXxyXRxtt556TBK1HKrFE1FZt18tjf6dARf/V1NiLa2JlmMh8y/DF1CbIhJNq//frl11m9++7+XyA8eKZbxaamtomZbytMmyYNHKjb5tykGQNelCQ9s3ithnd/J/excmhOujEApS3rd/gxx/hZzxEm9v5ncs2tTPcoKarLc6cw7lH5qSSpZ7ze98CB6fW2mqtnT+nKK5PrjKFzC+5Bp/T5Z44N1er71TSrV/v6WUuXFna/2Rx9tB+sfc457XfMzmzKFF/7HO2GABfQFiorpR128DfDWaapD+/+jkZ0/3fu/bXyCzM+Wejre/xAC4c86fcXHoUakUu/zBpzF2aVdMfcb2rJiEcSz9cfeY7u2OtbSdt0q9ik2+d+p2lB0JmeabSzlGHka8jeg57W9/f8buJ5uKsgLTXS2LF+ZmNEoVdTyrZmUt++emzRZbp615T0B1OmSF//uu9AGDYssThRgHzMmOgClytXptdoIMDVOaR8Np84+LK0jtRf7nN9er2MyZN9+swrrtB23d7XC4demLw+GIVu5hJpPVNThppccidS96AuTMosjcT5ZOsgyxTgOuIIn+pEBZgAVV0tTZqUCLjnvcPWXP+YtQU0MfMdTUHB6Ug77+wDSi347PTv8r7eXX6GJH/dKa+M6Z1lZ+q7M2/RPg1PSZI2rjhVu/R7JSl98ropdyUG+/jXpnwv9+0rXXaZduv/onqF0rGa+X9f2/0Hqo2nOcxwLcs0myynBQsS6V4ypl3s1k06+ujoLr7Ro32nWrwOGtpWdbVvoxVw9HFiln5FRc7PRUO3DZJ8e3tMz7ekyy6T5s9v2qCxUSfu8HvNG/RM805i4ULfQRuehS1p8fBHdO++X4l8ScY2/OzZfn+hgW7x7+Ud6t5s3nkB2OaVZ0srHPSJjOz+VtqM76T7pfjs2xweOehyXTjpnrzPbeGQJ/XJylV5b59TXV1eNQjRSQQDsW6c+T39fcnF7XvssWP9gLT2THXXu7cPcpFmEyWKABfQ3k47LfEwZk5JY5yrq32B01SHHtqqQ9ZWbdaP534rfcWee0oTJkTWpYqZS/QT7bXdM/r1fl/W84delLbdwcMfT8wUUc+eGljzvh81lUdaxePG/DEpLUpYamM5PqJVks6feI8unnK3fxJ0NoSPlnOkdujcKsuC40yalLTJpD6vaVDNhogTK/cdDGvWJNLYbWrMMbK2okIaOVKXTf2plm7/kF9Gw6F0ZPtbjkpHkKJflw81uNu7yQvNfJqgIBXAyB4pge66Ot251zf1/Tnf9aO3167V92bflPQZTOpni/gMN20XnH+2FAeZZomatSgVR9ro9CuuSASqPr/Tfbpi5//Jf2eFGhEXFXwG0CrhwFHMnOqqPg4eN0qDBql39UdaOeZPmt7/ZUlSTUUoUB9cW2srN2lin9eT9hPlj/tfrR/PvbHp2ME3/6pxv4t+TSjY3+wUhRHKsg28mT5dO/d9NXrQzMqV7VcPBa2Xkuo93xpcn59wr/64/9Xp68Nf1lu26IYZ39fAmvfTt4sSf22PHtLatUkztCRpdI+3NC/D7MBbZ/+X7pj7TY3v1fTZ0jXXJNXCbdN6GxEz1wB0PlVluQdtvnDYxWmzYBJXxoYG6ZRT8krfP6XvP5PbEfK1tz89JjqIZSZVlzOoFC00YIC0dq1i5lTf9X3VlH+aeduOXL+qS/vWyQOKhQAX0N7GjUsULo9ZY3KXyzXXpNe9WL487Ya2ucx82pM0S5ZIq1ZFjkiNmUukKLxvv69qz+3+rlE93s5+oHB6vqgv+fhxgjy51+9+u67Y+aeRuwrP4Nq44lQtGPJk4vnp43+jXfu94p9MmCBde626VzQ1OPJJRfTFXe6UFNQkO/jgyM6n/l0/yL6T4P00upjKso1eC1ww+Zca3G2DdMYZ0XXA0DFlC7CMHetna+aQV5HjFIuGPa5ZA5/3f2cDB6pX9cdJn8FEh21DQ8b8zj+a+y3NGficTw2YLRgX1MKJFPVZjgrEZ7J8eVKdv6l9X9V5k36V/41AoQJchU4dASBJ+Ls3Jpf0Gd/aGPH5i7gGrJtyV3TaQzOZJV9LI5Mghvd54YWJ0bdZg1O5BPvMFSTbu+FpNR5/UsuPg44hntJakhYuTEsPnCb4++hd/VEiwJtRHgMtMif3TBefMRZll36v6ODhj+vJQ4LZkfPnp99jxAeJFbpfbtw4ae+9C7xTAB3RwG4f6MlDLsm5XWot4cQAmW7d/LUozxSFOvdcqbxc39jjNt025zu6edYtPsgWv75mm6XeHlpZWgIdTDCItHvlp9q4cnXm7erq/N9xhtr2RTV+fHqJEqATaqfqkwCixFJvYysq0u8y27OwZEjMGrUly8wkU6N08cXS/ff7BmnPnsk1OyZOlO6+WxoyRPrPf6SNG5tmgkyYIF1yibR2bcY0heHRYDUVm5NmhCSCX6tX+2PGYqqpjckdf4Lsxm/lDnCZ6ZyJ92lTY7nqKj+WdpqTHOQLHs8e+LzeWpqhHleK6rLc+cATClj8HO0gW2CkosLX28tR7yOffPEDu27Qvz6OSEOQITBl5nxw69RT09NlVFdLn36qxfHAdsoMxYTDDpM++CD7LK3Ua9KyZfkXc12wIPO2+d4Ajh0r/eY3LSvqHP6/I8AFtKlwACh1QExSTc+gtl+8dlXYvEHPaFr/f6Qtj9sa6iDb6swHI94J1QwKX6+6d/epEM9RM0IGmbW4jhdKz047SdddJ1VVac8br9bPX90pZ3rC/l1SBkXNmdP0+KSTpPfekzYnzzzIKcv33jvLzswdUAtbtCh9WfCetuaRkjwvkydLjz3m69kC2DY0Nmp8r3/l3ixlsF9a7a5jjpG+/OWkRY8uukyL7jtRr24M1ZgfPlwaO1YnbXkw+fXxdv7ChU3pai8O0srlU8ewUEaMkN56y7dP0Pl07x69vLxcuv76jpkxJBbzA06BTo4AF1BEZn40yOOLLm3qoG7r6c2hgulh29VsULfyTXru/QGSpLLU9ImSD1oFqsu2+M6pcMHtDaGRpA0NvhBrba30+uvSnXdKhx/etH6AP07UyNjG407w99zr1kl//7t0++1JTeDK2BZpjz18x3dc6PeWqJMRUXNLUuKG/qLJ8Rzec6K3k08vJ0k6OyLQFYw0m97/JZ/O0GjIdkr5BEYuvVS6KD2FZ9z4Xv/SS4ddEL0y+Ntdf9QaHf/gUfr232f4ZVdcIW3alHF2lUnS5z8fPeJx1SrpS1/Kfd6pteGynF/iRnTgwPSOvj339EGotJMsQLfyuHH+fbYkrWc48EeAC2hT8dpZW4870QeD3ODEukQ9oHHjpCOP9I/r6nyb5K67JEn/PGJNdGpgKTRjuumaEpNLvz727Zv13FokOHaXPAYqoBMJvltn1L+oxw6+XFL2bAp9qoP6cFdckT6CO95+vidL7ZigU2zmgBea/s5Dac1T9a7+qOnJ6NFZzy2j4Htxc3hA2zHHtGxfkq8tumFDxxzBDqBt5FlXOjWQnvg2j7fV6+ulc87Rlb//iR7412jd+/o4Te7zmvp3+VCvbuwjd3xoMGFUf0m/fv5nbW16QL89A1yHHCL16tXqDDzoQEaPlp57Tho0SDr22MzbRQzcAtB+6O0BiihmjdK6dZr4uR6+A1dKb7AVooM4PFMiSI+Y6vUj12jpqIeSzi0trZpzunXWTerf5X2N6/Wv9HNLLepdV+cblEOH+iKZEXWCUtMVSKHd1tdHNmArYlulpUuTF4beY8ycH6UyfXraa5MPkOF5quHDE0Xmk8yfL02YoD8dcJV+NPfbHTv3MpqvVy//c/Dg7NtJ/qYqniYwQ8284d3fiVwe/rtJPIqnOAh/ZlauTHqZmcv8t7v99oUbQZ3PNWjJkvzzey9e7APg+d74mfk0Yy2ZzRqL+c/pvHmFuZYCSBL+VMVnOMXM+TpG4RSF8fZEaidTqE3S0G1D5o9psCK+fvseb2pG/Yu+o6G+viml3JQp/hpzQfKAgr7x4ENLOKfNx56kbhWb8tue9ESdU6Y2nnN6d/kZ2m/wU/55tg6u+GzqqNTGs2ZJgwbpWzNv03OHrvUzw6Pqa555ZvLzCy/0nW4tEcwQO3jYYzpm9B99bdBddmnZviT/nUtwC9i2zJ3rf2aqNRlcO9NmcJnz167Fi5OWnzvxXk3p82ri+c/mf0MvH3Z+8j7rUjJeTJ2aPJA2VXvOqqmp8YN34veRKH2nny5ddZUfzEoddaDDYgYXUERl5vyXZJYRmgXplM0z8BJOIVRmTqN7vtm08vzzpV/8Qku3f1j7D/0/f+52cfIOqqqkNWuaNVOiIpajdlVw7uEUhZGvCb1Hk/OdXJl+d7l+p/mef02NnymTIz1ds/aJjuPSS306odTAbSZDh0o33ND8/2uz/D6ju+4q/eUviVScY3q8Kdl2zTtWSwSfl9p4nbvWXpPmzm26GW4PUWmZABTcPz4MZk8NHepT8yxY4K+Jkk8H3BrBdTU+Q/u5Q9f65fX1frZ3nFny9eWzz/TqEWvUr/pDSXu1+PAVzanhledodpSYLIHLRKpAs+z1Luvr/ezqqHZF166+8yxXm3L0aH/fcN11/nmmmq4HH+yzJ8RfE6W2Vjr7bO14zTX6zuf+W9LU7McGgFQHHeTvuzMNCAyunakzuGLmfIA+4r4i3CcxIKom9oEHSh9+KD3+uH++fHl6uvawjpg2DqUjFpN69Cj2WQDIgQAXUESRKXOc028XfEmfbKnU25/USlaAm81co4kTI6uaGp5l1qgR3d/x6QB69vS1tILtelRm6egeNqxZpzax9+u6a/7XtPDeU7JuZ6HaF5H960mzYHJ0wOf6fbR0enm2/RLgKj3l5c1PaZHr/3mffdKXnX56Ws75XEGkD44+zdepy3Z92JojeJyv4IbxNwu+rE9GTpCGHBW9HbMWgG3LqlXSV29MPB3f63X/YOed/c+JE6Vrr5XOPFNnTPi1Fg55UlL/3PutqkofnR2Y2vdVLR315/zPccAADe62oXXpiZo7O5sAV+eUz3fc9dfnbgdkC4AV0rx5fkbF00/7Wd2ZhDMUMNMZQHPFYn5gSybB/Uh4BlfPyo80vd/LklLKCcTrAkaUMEhSUyOdeKL0xhs+nXum4NaKFT7QnzJLDADQ+dDjChRRLLW4qiQ5p9kDn9e+g/+mo0f/uTA3m/F0IxMmRK+PSB0QM9d0kx4fadoGKfhi5rRgyFPpK+I33MGIq3jwbfn2f4reUejcGp1l/72lpm5LHdVVXZ1cqyzf/4NsAYV3MqSnw7Zj3jzpgAPSl48Z42dISprc55+qjOWu81JbuUmVZTkCWP3z6EjOx/Tp0tixqj9uoYavXdrymZEAOpcJE2QjR0iS3jzqbD15yKXS8cdLc0J1LWtqpLVrVRFr1A51b0bvJ16TK+7aa6NHW0+dqrqqj3Xr7JvzP8faWl8PNJ+ahJnk2/aJ1yRtTQ0jdFyZ0vCG/z4KWX+jEN+pZWW+7Z+hlicAtLmIwa9vLztbXzrvnYzXudTZXgmheuCS/KzYbMG1adN8armWpnEFAJQMAlxAEeWaiSSpMDN/5s/3NbCOOy56fUTqgDJrlM4913e+x1/XFueWom/1B75GwamnBgt8yqMu5ZslSTfPuiX6hcG57NL3H9q34W/ZDzJmjHTJJU3PozrSMtQqy+qzLIGJsWObvz90LvX1OTusThr7oDYdm302Y9723NMH1Naubd1+Kiqk1aulmTOzb3fKKf7zetZZrTsegJITr7+lsWPT2wbhOkJR7YaZM307JS7TbKtMbZhc6urap4N/+nTpW9+i7kZnc+KJvmZW1ACVYqGQPYBS0bOndNVVSZliYnLRNbOC+6SFQ57Unts9K02e3LRu/nw/I6u5GIAHANsEUhQCxRB08ESmKExViEZZLJY9PUmmGVxDh0pnnNH642cydqz0zDOJpxdPvlvnTfylNGqRT1EU3+aww3T9p7fr4sm/yLyv4Pf08EFXBs8zFLqN69ev6XGuQF2+/wfZUiANHOjzjNPxte3Klrs79W8w37+5bDnlKyqkfffNbz+FMGqUdNllwZPX2++4AIoukUY407Xr4IOlu+4qbJCgvWr5kX512zZpkv+XSRtkN8hp1Chfl7OZacFzKsZ7AdD59eihJcMf0YefVemXr41vGhSTKmhDzBr4vGYNfF5SKMC1++7MRgUAZESACyiiHpWf5N6oPUYdde8uKTnAVZahPlhBnXaaHxkbGNXjbVWXp9SuMJNmz1av229Xr+osReqnT5cefDD/Y4cDCrlqc+QKgC1YIL30UnIdgyiZCoGjczv1VP/3kW0WX2oHaj6f++uu6/ijEjv6+QEoiMQnPdNnft48aa+9Mq9vbhCpV6/2q6kxcqT0yivJA2OAuLb6nss2gMVMWrmyfY8JAK1w8PDHdfDwx7NvlO0alG2gIABgm0eACyiGXXfVi4ddoGGzI0ZepgaR2qODuKZGWrNGjQc+n1gUObKq0AGu0Hv7aOUp6lL2WdryvC1Z4msO/fjH+b+mrk765JPMdRXq6qQNG3xqmmzC9bqAVDvu6P9l09Dg/732Wv77jc9yBIAiiX9dJ9oM2QaEZPtu37Il87oobZAiOaP99/fpV1NrfwBtqRjBJgJcANrDfvtFL09Nvzptmv8O3ryZ2VsAgKyowQUUQ58+GnHzxYodF1GIvFjpQYYNU2PTGGz1rtqYvk0bBt+6ln/WtLtcI7mXLk1fVlEhjR/fvHO7/HJfeD7TtmvWSMuW+ZHnQFuKxaQLLsi9HemDAHRApuDalGtGdCY77+x/jhlTmBMqpKoqadYsX0cESFXo7+UxY3y7dMSIwu43m3i6z/ZK+wlg27Xnnj5oFSUc4Lr0UmmnnXwN4yFD2ufcAAAlixlcQLFkmn2ReqPcjrUf4sVf3fEnRG/QrVv7nMjIkZnXLVok7bFH9LrwiO58RqHm2qZnT5/vG2gP4UBrXV30Nlu3ts+5FAopCoFtgpmTTjih5Z/5ESOkK6/MPwUR1xZ0FPPmSf/7v9I++xRmf6ef7r/rWxosbonFi6WDDmrfYwLYNmXLQBG+N+/bt+3PBQDQadCKBTqarl2Tnw8Y0G6H3upydBgtWSJt2iQ9+aR/XogUQd26SRuD2WLnn+/3ma1WVbab73AwkM4vlKLPf1569FE/ujHKkCHS3/8u1da273k1kxOfP2BbYI0+6B6TkyZPzrF1DpkC+2E77CA9+2zrjwUUyoAB0g03FC5tpllxAk0EtwAUm5l00kl+wC/38gCAZqAlC3Q0M2ZIL73kO3FGjWrXlDjbdX0v+wbdu0urVvlR2lJhbobPOEO6+24/MyufAu7ZGruZamkBpWLkyOwzGBcs8NeEDl4LhkyKwDYimFXabv1QJ57oA1zhlMRAsbVnTTgA6Mw6+D0OAKBjIsAFdDQVFdJxxxXl0GdNuF8njf29fzJ0aO4XFKLY66BBvsMqX9k6Ebp39/vq3r315wV0RJWV0pw5xT6LnLY6OvuAbcIRR0gXS3b08vY5XnW1NGlS+xwLAAAUFjOzAABtgAAXgISymFNt5SZfA+OMMzJveO21Ph1gRxyxSscXUHQEuIBtQ8XwBs2YIVXtNqXYpwIAADq6jth/AAAoeQS4AKTbYYfss7NqatrvXFIx6gvo8HLW8wPQKZhJDz5Y7LMAAAAlYcSIYp8BAKATIsAFoLSUlRX7DADksKWRzykAAAAASVdcIa1f7wfSAgBQYMwPBpCuI6YOiDeGd9yxuOcBIKfN8QAXMy4BAACAbVvv3tKECcU+CwB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+      "text/plain": [
+       "<Figure size 2160x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "plt.plot(Y, color = 'red', linewidth=2.0, alpha = 0.6)\n",
+    "plt.plot(Y_pred, color = 'blue', linewidth=1)\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAPE:  2.0572089029888656 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "f8f3967282314d3995245835bdaa8418",
+   "translation_date": "2025-09-06T14:05:19+00:00",
+   "source_file": "7-TimeSeries/3-SVR/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/vi/7-TimeSeries/3-SVR/working/notebook.ipynb b/translations/vi/7-TimeSeries/3-SVR/working/notebook.ipynb
new file mode 100644
index 000000000..5394971a2
--- /dev/null
+++ b/translations/vi/7-TimeSeries/3-SVR/working/notebook.ipynb
@@ -0,0 +1,695 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fv9OoQsMFk5A"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Trong sổ tay này, chúng tôi sẽ hướng dẫn cách:\n",
+    "\n",
+    "- chuẩn bị dữ liệu chuỗi thời gian 2D để huấn luyện mô hình hồi quy SVM  \n",
+    "- triển khai SVR sử dụng kernel RBF  \n",
+    "- đánh giá mô hình bằng biểu đồ và MAPE  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Nhập các mô-đun\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('../../')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "M687KNlQFp0-"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import datetime as dt\n",
+    "import math\n",
+    "\n",
+    "from sklearn.svm import SVR\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from common.utils import load_data, mape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Cj-kfVdMGjWP"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8fywSjC6GsRz"
+   },
+   "source": [
+    "### Tải dữ liệu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "aBDkEB11Fumg",
+    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>load</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 00:00:00</th>\n",
+       "      <td>2698.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 01:00:00</th>\n",
+       "      <td>2558.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 02:00:00</th>\n",
+       "      <td>2444.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 03:00:00</th>\n",
+       "      <td>2402.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2012-01-01 04:00:00</th>\n",
+       "      <td>2403.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       load\n",
+       "2012-01-01 00:00:00  2698.0\n",
+       "2012-01-01 01:00:00  2558.0\n",
+       "2012-01-01 02:00:00  2444.0\n",
+       "2012-01-01 03:00:00  2402.0\n",
+       "2012-01-01 04:00:00  2403.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "energy = load_data('../../data')[['load']]\n",
+    "energy.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "O0BWP13rGnh4"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 486
+    },
+    "id": "hGaNPKu_Gidk",
+    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
+   },
+   "outputs": [],
+   "source": [
+    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IPuNor4eGwYY"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ysvsNyONGt0Q"
+   },
+   "outputs": [],
+   "source": [
+    "train_start_dt = '2014-11-01 00:00:00'\n",
+    "test_start_dt = '2014-12-30 00:00:00'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 548
+    },
+    "id": "SsfdLoPyGy9w",
+    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
+   },
+   "outputs": [],
+   "source": [
+    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
+    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
+    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
+    "plt.xlabel('timestamp', fontsize=12)\n",
+    "plt.ylabel('load', fontsize=12)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XbFTqBw6G1Ch"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Bây giờ, bạn cần chuẩn bị dữ liệu để huấn luyện bằng cách thực hiện lọc và chuẩn hóa dữ liệu của mình.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cYivRdQpHDj3",
+    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
+   },
+   "outputs": [],
+   "source": [
+    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
+    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
+    "\n",
+    "print('Training data shape: ', train.shape)\n",
+    "print('Test data shape: ', test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chuyển đổi dữ liệu để nằm trong khoảng (0, 1).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 363
+    },
+    "id": "3DNntGQnZX8G",
+    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "train['load'] = scaler.fit_transform(train)\n",
+    "train.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 206
+    },
+    "id": "26Yht-rzZexe",
+    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
+   },
+   "outputs": [],
+   "source": [
+    "test['load'] = scaler.transform(test)\n",
+    "test.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "x0n6jqxOQ41Z"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fdmxTZtOQ8xs"
+   },
+   "source": [
+    "Đối với SVR của chúng tôi, chúng tôi chuyển đổi dữ liệu đầu vào thành dạng `[batch, timesteps]`. Vì vậy, chúng tôi định hình lại `train_data` và `test_data` hiện có sao cho có một chiều mới đại diện cho các bước thời gian. Trong ví dụ của chúng tôi, chúng tôi chọn `timesteps = 5`. Vì vậy, đầu vào cho mô hình là dữ liệu của 4 bước thời gian đầu tiên, và đầu ra sẽ là dữ liệu của bước thời gian thứ 5.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Rpju-Sc2HFm0"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting to numpy arrays\n",
+    "\n",
+    "train_data = train.values\n",
+    "test_data = test.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting the timesteps\n",
+    "\n",
+    "timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "O-JrsrsVJhUQ",
+    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting data to 2D tensor\n",
+    "\n",
+    "train_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "exJD8AI7KE4g",
+    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
+   },
+   "outputs": [],
+   "source": [
+    "# Converting test data to 2D tensor\n",
+    "\n",
+    "test_data_timesteps=None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2u0R2sIsLuq5"
+   },
+   "outputs": [],
+   "source": [
+    "x_train, y_train = None\n",
+    "x_test, y_test = None\n",
+    "\n",
+    "print(x_train.shape, y_train.shape)\n",
+    "print(x_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8wIPOtAGLZlh"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EhA403BEPEiD"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model using RBF kernel\n",
+    "\n",
+    "model = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GS0UA3csMbqp",
+    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
+   },
+   "outputs": [],
+   "source": [
+    "# Fit model on training data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Rz_x8S3UrlcF"
+   },
+   "source": [
+    "### Dự đoán mô hình\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XR0gnt3MnuYS",
+    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
+   },
+   "outputs": [],
+   "source": [
+    "# Making predictions\n",
+    "\n",
+    "y_train_pred = None\n",
+    "y_test_pred = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_2epncg-SGzr"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scaling the predictions\n",
+    "\n",
+    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
+    "y_test_pred = scaler.inverse_transform(y_test_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xmm_YLXhq7gV",
+    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
+   },
+   "outputs": [],
+   "source": [
+    "# Scaling the original values\n",
+    "\n",
+    "y_train = scaler.inverse_transform(y_train)\n",
+    "y_test = scaler.inverse_transform(y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "u3LBj93coHEi",
+    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
+   },
+   "outputs": [],
+   "source": [
+    "# Extract the timesteps for x-axis\n",
+    "\n",
+    "train_timestamps = None\n",
+    "test_timestamps = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(25,6))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.title(\"Training data prediction\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "LnhzcnYtXHCm",
+    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 225
+    },
+    "id": "53Q02FoqQH4V",
+    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,3))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "clOAUH-SXCJG",
+    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DHlKvVCId5ue"
+   },
+   "source": [
+    "## Dự đoán toàn bộ tập dữ liệu\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cOFJ45vreO0N",
+    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
+   },
+   "outputs": [],
+   "source": [
+    "# Extracting load values as numpy array\n",
+    "data = None\n",
+    "\n",
+    "# Scaling\n",
+    "data = None\n",
+    "\n",
+    "# Transforming to 2D tensor as per model input requirement\n",
+    "data_timesteps=None\n",
+    "\n",
+    "# Selecting inputs and outputs from data\n",
+    "X, Y = None, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ESSAdQgwexIi"
+   },
+   "outputs": [],
+   "source": [
+    "# Make model predictions\n",
+    "\n",
+    "# Inverse scale and reshape\n",
+    "Y_pred = None\n",
+    "Y = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "id": "M_qhihN0RVVX",
+    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(30,8))\n",
+    "# plot original output\n",
+    "# plot predicted output\n",
+    "plt.legend(['Actual','Predicted'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AcN7pMYXVGTK",
+    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
+   },
+   "outputs": [],
+   "source": [
+    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "name": "Recurrent_Neural_Networks.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  },
+  "coopTranslator": {
+   "original_hash": "e86ce102239a14c44585623b9b924a74",
+   "translation_date": "2025-09-06T14:07:50+00:00",
+   "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/translations/vi/8-Reinforcement/1-QLearning/notebook.ipynb b/translations/vi/8-Reinforcement/1-QLearning/notebook.ipynb
new file mode 100644
index 000000000..dabb5722d
--- /dev/null
+++ b/translations/vi/8-Reinforcement/1-QLearning/notebook.ipynb
@@ -0,0 +1,411 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "17e5a668646eabf5aabd0e9bfcf17876",
+   "translation_date": "2025-09-06T15:07:06+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter và Sói: Giới thiệu về Học tăng cường\n",
+    "\n",
+    "Trong hướng dẫn này, chúng ta sẽ học cách áp dụng học tăng cường vào một bài toán tìm đường. Bối cảnh được lấy cảm hứng từ câu chuyện cổ tích âm nhạc [Peter và Sói](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) của nhà soạn nhạc người Nga [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Đây là câu chuyện về cậu bé tiên phong Peter, người dũng cảm rời khỏi nhà để đến một bãi trống trong rừng nhằm đuổi theo một con sói. Chúng ta sẽ huấn luyện các thuật toán học máy để giúp Peter khám phá khu vực xung quanh và xây dựng một bản đồ điều hướng tối ưu.\n",
+    "\n",
+    "Đầu tiên, hãy nhập một số thư viện hữu ích:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Tổng quan về Học Tăng cường\n",
+    "\n",
+    "**Học Tăng cường** (Reinforcement Learning - RL) là một kỹ thuật học cho phép chúng ta tìm hiểu hành vi tối ưu của một **tác nhân** trong một **môi trường** nào đó bằng cách thực hiện nhiều thí nghiệm. Một tác nhân trong môi trường này cần có một **mục tiêu**, được xác định bởi một **hàm phần thưởng**.\n",
+    "\n",
+    "## Môi trường\n",
+    "\n",
+    "Để đơn giản, hãy xem xét thế giới của Peter là một bảng vuông có kích thước `width` x `height`. Mỗi ô trên bảng này có thể là:\n",
+    "* **mặt đất**, nơi Peter và các sinh vật khác có thể đi lại\n",
+    "* **nước**, nơi rõ ràng bạn không thể đi qua\n",
+    "* **một cái cây** hoặc **cỏ** - nơi bạn có thể nghỉ ngơi\n",
+    "* **một quả táo**, đại diện cho thứ mà Peter rất vui khi tìm thấy để tự nuôi sống\n",
+    "* **một con sói**, thứ nguy hiểm và cần tránh xa\n",
+    "\n",
+    "Để làm việc với môi trường, chúng ta sẽ định nghĩa một lớp gọi là `Board`. Để tránh làm rối notebook này, chúng ta đã chuyển toàn bộ mã nguồn làm việc với bảng vào một module riêng biệt có tên `rlboard`, mà bây giờ chúng ta sẽ import. Bạn có thể xem bên trong module này để tìm hiểu thêm chi tiết về cách triển khai nội bộ.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "Bây giờ hãy tạo một bảng ngẫu nhiên và xem nó trông như thế nào:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Hành động và Chính sách\n",
+    "\n",
+    "Trong ví dụ của chúng ta, mục tiêu của Peter sẽ là tìm một quả táo, đồng thời tránh con sói và các chướng ngại vật khác. Định nghĩa các hành động đó dưới dạng một từ điển, và ánh xạ chúng tới các cặp thay đổi tọa độ tương ứng.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 2"
+   ]
+  },
+  {
+   "source": [
+    "Chiến lược của tác nhân (Peter) được định nghĩa bởi một cái gọi là **chính sách**. Hãy cùng xem xét chính sách đơn giản nhất được gọi là **đi ngẫu nhiên**.\n",
+    "\n",
+    "## Đi ngẫu nhiên\n",
+    "\n",
+    "Trước tiên, hãy giải quyết vấn đề của chúng ta bằng cách triển khai chiến lược đi ngẫu nhiên.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "# Let's run a random walk experiment several times and see the average number of steps taken: code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 4"
+   ]
+  },
+  {
+   "source": [
+    "## Hàm Thưởng\n",
+    "\n",
+    "Để làm cho chính sách của chúng ta thông minh hơn, chúng ta cần hiểu những nước đi nào \"tốt hơn\" so với những nước đi khác.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning\n",
+    "\n",
+    "Xây dựng một Q-Table, hoặc mảng đa chiều. Vì bảng của chúng ta có kích thước `width` x `height`, chúng ta có thể biểu diễn Q-Table bằng một mảng numpy với hình dạng `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Chuyển bảng Q-Table vào hàm `plot` để hiển thị bảng trên bảng:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "NameError",
+     "evalue": "name 'm' is not defined",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-8b8a9e4cb2b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mQ\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'm' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Bản chất của Q-Learning: Phương trình Bellman và Thuật toán Học\n",
+    "\n",
+    "Viết mã giả cho thuật toán học của chúng ta:\n",
+    "\n",
+    "* Khởi tạo Bảng Q Q với các giá trị bằng nhau cho tất cả các trạng thái và hành động\n",
+    "* Đặt tốc độ học $\\alpha\\leftarrow 1$\n",
+    "* Lặp lại mô phỏng nhiều lần\n",
+    "   1. Bắt đầu tại vị trí ngẫu nhiên\n",
+    "   1. Lặp lại\n",
+    "        1. Chọn một hành động $a$ tại trạng thái $s$\n",
+    "        2. Thực hiện hành động bằng cách di chuyển đến trạng thái mới $s'$\n",
+    "        3. Nếu gặp điều kiện kết thúc trò chơi, hoặc tổng phần thưởng quá nhỏ - thoát khỏi mô phỏng  \n",
+    "        4. Tính phần thưởng $r$ tại trạng thái mới\n",
+    "        5. Cập nhật Hàm Q theo phương trình Bellman: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Cập nhật tổng phần thưởng và giảm $\\alpha$.\n",
+    "\n",
+    "## Khai thác vs. Khám phá\n",
+    "\n",
+    "Cách tiếp cận tốt nhất là cân bằng giữa khám phá và khai thác. Khi chúng ta hiểu thêm về môi trường, chúng ta sẽ có xu hướng đi theo lộ trình tối ưu hơn, tuy nhiên, thỉnh thoảng vẫn nên chọn con đường chưa được khám phá.\n",
+    "\n",
+    "## Triển khai Python\n",
+    "\n",
+    "Bây giờ chúng ta đã sẵn sàng triển khai thuật toán học. Trước đó, chúng ta cũng cần một số hàm để chuyển đổi các số bất kỳ trong Bảng Q thành một vector xác suất cho các hành động tương ứng:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Chúng ta thêm một lượng nhỏ `eps` vào vector ban đầu để tránh chia cho 0 trong trường hợp ban đầu, khi tất cả các thành phần của vector đều giống nhau.\n",
+    "\n",
+    "Thuật toán học thực tế mà chúng ta sẽ chạy trong 5000 thí nghiệm, còn được gọi là **epochs**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "# code block 8"
+   ]
+  },
+  {
+   "source": [
+    "Sau khi thực hiện thuật toán này, Bảng Q nên được cập nhật với các giá trị xác định mức độ hấp dẫn của các hành động khác nhau tại mỗi bước. Hình dung bảng tại đây:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kiểm tra Chính sách\n",
+    "\n",
+    "Vì Q-Table liệt kê \"mức độ hấp dẫn\" của mỗi hành động tại mỗi trạng thái, nên rất dễ sử dụng nó để xác định cách điều hướng hiệu quả trong thế giới của chúng ta. Trong trường hợp đơn giản nhất, chúng ta chỉ cần chọn hành động tương ứng với giá trị cao nhất trong Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "# code block 9"
+   ]
+  },
+  {
+   "source": [
+    "Nếu bạn thử đoạn mã trên nhiều lần, bạn có thể nhận thấy rằng đôi khi nó chỉ \"đứng yên\", và bạn cần nhấn nút DỪNG trong notebook để ngắt nó.\n",
+    "\n",
+    "> **Nhiệm vụ 1:** Sửa đổi hàm `walk` để giới hạn độ dài tối đa của đường đi bằng một số bước nhất định (ví dụ, 100), và quan sát đoạn mã trên trả về giá trị này theo thời gian.\n",
+    "\n",
+    "> **Nhiệm vụ 2:** Sửa đổi hàm `walk` để nó không quay lại những nơi mà nó đã từng đi qua trước đó. Điều này sẽ ngăn `walk` lặp lại, tuy nhiên, tác nhân vẫn có thể bị \"mắc kẹt\" ở một vị trí mà nó không thể thoát ra được.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 5.31, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# code block 10"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26dab295a48>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 57
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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109.232964 214.756364 \r\nL 109.263403 214.085931 \r\nL 109.293843 201.703888 \r\nL 109.385161 202.919046 \r\nL 109.44604 214.756364 \r\nL 109.476479 192.192132 \r\nL 109.537358 214.735413 \r\nL 109.598237 214.756364 \r\nL 109.719994 198.100315 \r\nL 109.811312 214.756364 \r\nL 109.841752 210.230947 \r\nL 109.872191 214.735413 \r\nL 109.93307 208.63867 \r\nL 110.054828 214.756364 \r\nL 110.085267 200.509681 \r\nL 110.146146 208.052042 \r\nL 110.207025 214.756364 \r\nL 110.267904 213.268842 \r\nL 110.298343 193.470143 \r\nL 110.328782 214.756364 \r\nL 110.359222 209.811927 \r\nL 110.420101 214.756364 \r\nL 110.480979 214.044029 \r\nL 110.541858 214.756364 \r\nL 110.572298 212.032733 \r\nL 110.633176 214.756364 \r\nL 110.663616 214.756364 \r\nL 110.694055 199.441179 \r\nL 110.785374 202.981899 \r\nL 110.907131 214.756364 \r\nL 110.937571 211.110889 \r\nL 110.998449 214.756364 \r\nL 111.089768 209.916682 \r\nL 111.181086 214.756364 \r\nL 111.211525 203.882792 \r\nL 111.272404 207.842532 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115.016451 213.43645 \r\nL 115.046891 214.46305 \r\nL 115.07733 207.339708 \r\nL 115.168648 209.66527 \r\nL 115.229527 213.268842 \r\nL 115.290406 211.215644 \r\nL 115.320845 205.642677 \r\nL 115.351285 214.756364 \r\nL 115.381724 209.749074 \r\nL 115.533921 214.756364 \r\nL 115.564361 213.687862 \r\nL 115.655679 206.606423 \r\nL 115.746997 214.756364 \r\nL 115.716558 193.700604 \r\nL 115.777436 214.735413 \r\nL 115.838315 193.763457 \r\nL 115.868755 193.910114 \r\nL 115.899194 214.756364 \r\nL 115.990512 212.346998 \r\nL 116.051391 210.754722 \r\nL 116.11227 214.756364 \r\nL 116.142709 211.76037 \r\nL 116.203588 214.756364 \r\nL 116.234027 214.756364 \r\nL 116.264467 213.478352 \r\nL 116.294906 214.756364 \r\nL 116.325346 184.670721 \r\nL 116.386225 214.756364 \r\nL 116.416664 214.044029 \r\nL 116.447103 214.756364 \r\nL 116.477543 196.047117 \r\nL 116.538422 212.786969 \r\nL 116.5993 210.964232 \r\nL 116.690619 214.756364 \r\nL 116.812376 200.404926 \r\nL 116.903694 214.756364 \r\nL 116.934134 196.403284 \r\nL 116.995013 214.756364 \r\nL 117.055891 214.756364 \r\nL 117.086331 213.939274 \r\nL 117.11677 199.650689 \r\nL 117.177649 212.158439 \r\nL 117.208089 214.756364 \r\nL 117.238528 213.939274 \r\nL 117.268967 205.684579 \r\nL 117.329846 214.756364 \r\nL 117.360286 214.484001 \r\nL 117.421164 195.774754 \r\nL 117.451604 210.000486 \r\nL 117.512483 214.756364 \r\nL 117.542922 176.709339 \r\nL 117.63424 184.64977 \r\nL 117.725558 183.874583 \r\nL 117.755998 214.756364 \r\nL 117.786437 214.756364 \r\nL 117.877756 198.959306 \r\nL 117.908195 210.566163 \r\nL 117.938634 212.766018 \r\nL 117.969074 206.522619 \r\nL 118.029953 214.756364 \r\nL 118.060392 214.756364 \r\nL 118.18215 199.399277 \r\nL 118.273468 214.756364 \r\nL 118.303907 205.035098 \r\nL 118.364786 214.756364 \r\nL 118.456104 214.232589 \r\nL 118.486544 214.756364 \r\nL 118.516983 209.476711 \r\nL 118.577862 214.756364 \r\nL 118.608301 213.352646 \r\nL 118.66918 214.735413 \r\nL 118.69962 186.786773 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200.090661 \r\nL 122.595864 211.927978 \r\nL 122.687182 211.383252 \r\nL 122.717621 214.756364 \r\nL 122.7785 214.756364 \r\nL 122.80894 210.335702 \r\nL 122.900258 212.493655 \r\nL 122.930697 214.756364 \r\nL 123.022015 183.958387 \r\nL 123.052455 208.533915 \r\nL 123.082894 214.756364 \r\nL 123.113334 201.703888 \r\nL 123.143773 210.482359 \r\nL 123.174212 210.419506 \r\nL 123.265531 200.48873 \r\nL 123.29597 214.756364 \r\nL 123.326409 214.735413 \r\nL 123.356849 187.017234 \r\nL 123.417728 214.756364 \r\nL 123.569925 191.291239 \r\nL 123.691682 214.756364 \r\nL 123.722122 214.735413 \r\nL 123.783001 205.579824 \r\nL 123.81344 189.279942 \r\nL 123.874319 214.735413 \r\nL 123.935198 214.735413 \r\nL 123.996076 214.756364 \r\nL 124.026516 206.669276 \r\nL 124.148273 214.756364 \r\nL 124.178713 214.756364 \r\nL 124.239592 209.581466 \r\nL 124.270031 211.96988 \r\nL 124.300471 214.023078 \r\nL 124.33091 201.201064 \r\nL 124.422228 208.471062 \r\nL 124.513546 214.756364 \r\nL 124.543986 212.326047 \r\nL 124.604865 199.818297 \r\nL 124.635304 207.360659 \r\nL 124.726622 214.756364 \r\nL 124.787501 174.467582 \r\nL 124.81794 214.756364 \r\nL 124.909259 214.735413 \r\nL 124.939698 211.173742 \r\nL 124.970137 186.283949 \r\nL 125.031016 211.907027 \r\nL 125.091895 214.756364 \r\nL 125.152774 214.735413 \r\nL 125.274532 173.252424 \r\nL 125.36585 214.756364 \r\nL 125.396289 214.127834 \r\nL 125.426729 214.735413 \r\nL 125.457168 198.267923 \r\nL 125.518047 214.106882 \r\nL 125.548486 210.775673 \r\nL 125.609365 214.756364 \r\nL 125.639804 211.006134 \r\nL 125.731123 214.735413 \r\nL 125.761562 214.421148 \r\nL 125.822441 214.735413 \r\nL 125.913759 173.399081 \r\nL 125.974638 214.756364 \r\nL 126.035517 206.648325 \r\nL 126.065956 206.585472 \r\nL 126.187714 214.756364 \r\nL 126.248593 214.756364 \r\nL 126.309471 213.562156 \r\nL 126.339911 205.852187 \r\nL 126.37035 214.735413 \r\nL 126.40079 212.996479 \r\nL 126.431229 214.756364 \r\nL 126.461668 214.588756 \r\nL 126.522547 198.41458 \r\nL 126.552987 213.059332 \r\nL 126.705184 214.756364 \r\nL 126.735623 192.192132 \r\nL 126.796502 213.750715 \r\nL 126.91826 214.756364 \r\nL 126.979138 210.021437 \r\nL 127.009578 214.756364 \r\nL 127.040017 214.756364 \r\nL 127.131335 210.461408 \r\nL 127.192214 207.402561 \r\nL 127.253093 214.756364 \r\nL 127.283532 210.377604 \r\nL 127.344411 214.735413 \r\nL 127.374851 214.756364 \r\nL 127.40529 214.46305 \r\nL 127.466169 214.756364 \r\nL 127.527048 199.524983 \r\nL 127.557487 214.735413 \r\nL 127.587927 211.404203 \r\nL 127.618366 177.673086 \r\nL 127.648805 214.756364 \r\nL 127.679245 184.251701 \r\nL 127.801002 214.756364 \r\nL 127.861881 214.756364 \r\nL 127.953199 207.612071 \r\nL 128.014078 214.756364 \r\nL 128.044518 204.532273 \r\nL 128.074957 198.309825 \r\nL 128.105396 214.756364 \r\nL 128.166275 214.756364 \r\nL 128.257593 210.105241 \r\nL 128.348912 214.756364 \r\nL 128.501109 186.430606 \r\nL 128.531548 210.314751 \r\nL 128.592427 206.459766 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214.693511 \r\nL 130.236155 214.735413 \r\nL 130.297034 204.972245 \r\nL 130.388352 214.756364 \r\nL 130.449231 190.620806 \r\nL 130.47967 213.331695 \r\nL 130.540549 214.756364 \r\nL 130.601428 214.358295 \r\nL 130.692746 203.547576 \r\nL 130.753625 197.953658 \r\nL 130.814504 214.756364 \r\nL 130.844943 210.649967 \r\nL 130.905822 211.425154 \r\nL 130.936261 214.756364 \r\nL 130.99714 214.546854 \r\nL 131.058019 203.945645 \r\nL 131.088458 213.939274 \r\nL 131.149337 214.756364 \r\nL 131.179777 207.653973 \r\nL 131.240655 214.756364 \r\nL 131.51461 214.756364 \r\nL 131.54505 182.827033 \r\nL 131.605928 214.106882 \r\nL 131.636368 214.756364 \r\nL 131.758125 205.998844 \r\nL 131.788565 214.756364 \r\nL 131.819004 200.111612 \r\nL 131.849444 213.813568 \r\nL 131.879883 194.643399 \r\nL 131.910322 214.756364 \r\nL 131.940762 212.849822 \r\nL 131.971201 214.756364 \r\nL 132.092959 153.16041 \r\nL 132.184277 214.756364 \r\nL 132.214716 191.752161 \r\nL 132.306035 214.756364 \r\nL 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154.952954 214.756364 \r\nL 155.074712 200.823946 \r\nL 155.16603 214.756364 \r\nL 155.196469 214.735413 \r\nL 155.257348 214.735413 \r\nL 155.379106 197.869854 \r\nL 155.500863 214.756364 \r\nL 155.531303 209.141494 \r\nL 155.622621 211.634664 \r\nL 155.65306 209.204347 \r\nL 155.6835 214.756364 \r\nL 155.713939 211.383252 \r\nL 155.744379 213.813568 \r\nL 155.774818 195.167174 \r\nL 155.805257 214.756364 \r\nL 155.835697 213.729764 \r\nL 155.866136 214.756364 \r\nL 156.018333 198.770747 \r\nL 156.048773 210.398555 \r\nL 156.079212 135.854881 \r\nL 156.109651 214.756364 \r\nL 156.140091 195.984264 \r\nL 156.20097 214.756364 \r\nL 156.231409 210.754722 \r\nL 156.261848 183.706975 \r\nL 156.322727 214.756364 \r\nL 156.353167 214.756364 \r\nL 156.474924 196.466137 \r\nL 156.627121 214.756364 \r\nL 156.657561 214.756364 \r\nL 156.688 192.485446 \r\nL 156.748879 214.735413 \r\nL 156.779318 213.520254 \r\nL 156.840197 214.735413 \r\nL 156.870637 214.735413 \r\nL 156.901076 213.289793 \r\nL 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162.83676 214.756364 \r\nL 162.897639 198.016511 \r\nL 162.988958 214.756364 \r\nL 163.019397 209.392906 \r\nL 163.049836 209.351004 \r\nL 163.080276 211.508958 \r\nL 163.141155 204.951294 \r\nL 163.171594 214.735413 \r\nL 163.232473 214.756364 \r\nL 163.262912 211.299448 \r\nL 163.323791 210.649967 \r\nL 163.35423 214.756364 \r\nL 163.415109 201.641035 \r\nL 163.445549 214.735413 \r\nL 163.506427 214.756364 \r\nL 163.536867 213.729764 \r\nL 163.597746 214.756364 \r\nL 163.628185 214.756364 \r\nL 163.719503 191.815014 \r\nL 163.780382 214.756364 \r\nL 163.841261 214.023078 \r\nL 163.8717 202.688585 \r\nL 163.932579 208.743425 \r\nL 163.963019 214.756364 \r\nL 164.023897 212.912675 \r\nL 164.054337 202.939997 \r\nL 164.084776 214.756364 \r\nL 164.145655 205.097951 \r\nL 164.206534 214.756364 \r\nL 164.236973 202.541928 \r\nL 164.267413 189.971325 \r\nL 164.297852 214.756364 \r\nL 164.328291 205.852187 \r\nL 164.38917 214.756364 \r\nL 164.480489 212.870773 \r\nL 164.510928 212.59841 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168.589809 199.88115 \r\nL 168.650687 214.756364 \r\nL 168.772445 205.056049 \r\nL 168.833324 207.255904 \r\nL 168.955081 214.756364 \r\nL 169.076839 204.720832 \r\nL 169.168157 214.756364 \r\nL 169.198597 212.912675 \r\nL 169.289915 214.756364 \r\nL 169.320354 214.735413 \r\nL 169.442112 194.287232 \r\nL 169.56387 214.756364 \r\nL 169.685627 181.884238 \r\nL 169.807385 214.756364 \r\nL 169.837824 200.048758 \r\nL 169.898703 208.072993 \r\nL 169.959582 214.756364 \r\nL 169.990021 207.695875 \r\nL 170.020461 210.189045 \r\nL 170.111779 214.756364 \r\nL 170.142218 187.059136 \r\nL 170.203097 213.059332 \r\nL 170.233537 210.796624 \r\nL 170.263976 213.331695 \r\nL 170.324855 211.404203 \r\nL 170.385734 214.756364 \r\nL 170.446612 214.756364 \r\nL 170.477052 210.733771 \r\nL 170.537931 214.735413 \r\nL 170.629249 214.756364 \r\nL 170.659688 204.406567 \r\nL 170.720567 208.21965 \r\nL 170.842325 214.756364 \r\nL 170.933643 196.298529 \r\nL 170.964082 211.446105 \r\nL 170.994522 214.756364 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178.695692 214.756364 \r\nL 178.726131 205.077 \r\nL 178.78701 214.693511 \r\nL 178.847889 214.756364 \r\nL 178.878328 214.421148 \r\nL 178.908768 214.714462 \r\nL 178.939207 211.844174 \r\nL 178.969647 193.051123 \r\nL 179.030525 214.756364 \r\nL 179.060965 207.255904 \r\nL 179.121844 214.756364 \r\nL 179.213162 211.027085 \r\nL 179.274041 192.548299 \r\nL 179.30448 213.750715 \r\nL 179.334919 214.756364 \r\nL 179.426238 214.358295 \r\nL 179.456677 214.756364 \r\nL 179.487116 214.609707 \r\nL 179.547995 189.887521 \r\nL 179.578435 214.756364 \r\nL 179.639313 214.756364 \r\nL 179.669753 214.504952 \r\nL 179.700192 205.349363 \r\nL 179.730632 214.756364 \r\nL 179.761071 214.756364 \r\nL 179.882829 200.237318 \r\nL 179.913268 214.756364 \r\nL 180.004586 206.564521 \r\nL 180.065465 205.097951 \r\nL 180.095905 195.062419 \r\nL 180.126344 208.994837 \r\nL 180.156783 203.715184 \r\nL 180.278541 214.756364 \r\nL 180.400299 206.145501 \r\nL 180.430738 214.756364 \r\nL 180.461177 208.114895 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200.551583 \r\nL 184.266103 214.756364 \r\nL 184.296543 214.756364 \r\nL 184.387861 203.191409 \r\nL 184.509619 214.756364 \r\nL 184.540058 208.994837 \r\nL 184.600937 214.756364 \r\nL 184.631376 214.756364 \r\nL 184.661816 195.523342 \r\nL 184.722695 214.756364 \r\nL 184.783573 214.756364 \r\nL 184.93577 200.404926 \r\nL 184.96621 214.735413 \r\nL 185.057528 213.541205 \r\nL 185.118407 214.044029 \r\nL 185.240164 192.275936 \r\nL 185.331483 187.373401 \r\nL 185.361922 214.756364 \r\nL 185.392362 203.882792 \r\nL 185.45324 213.981176 \r\nL 185.48368 214.756364 \r\nL 185.544559 180.103402 \r\nL 185.574998 214.756364 \r\nL 185.605437 214.756364 \r\nL 185.757634 190.599855 \r\nL 185.788074 192.171181 \r\nL 185.818513 181.695679 \r\nL 185.909831 214.756364 \r\nL 185.940271 203.149507 \r\nL 185.97071 214.756364 \r\nL 186.031589 202.16481 \r\nL 186.062028 213.22694 \r\nL 186.092468 195.271929 \r\nL 186.153347 207.130198 \r\nL 186.214226 214.756364 \r\nL 186.244665 207.402561 \r\nL 186.305544 213.981176 \r\nL 186.396862 211.236595 \r\nL 186.427301 207.758728 \r\nL 186.457741 214.756364 \r\nL 186.48818 212.661263 \r\nL 186.51862 210.733771 \r\nL 186.609938 214.756364 \r\nL 186.640377 168.035624 \r\nL 186.701256 210.461408 \r\nL 186.762135 208.303454 \r\nL 186.792574 195.942362 \r\nL 186.823014 214.756364 \r\nL 186.853453 198.184119 \r\nL 186.975211 214.756364 \r\nL 187.00565 191.668357 \r\nL 187.066529 209.476711 \r\nL 187.096968 214.756364 \r\nL 187.188287 170.486891 \r\nL 187.310044 214.756364 \r\nL 187.340484 214.756364 \r\nL 187.431802 200.928701 \r\nL 187.462241 214.316393 \r\nL 187.492681 199.294522 \r\nL 187.553559 208.84818 \r\nL 187.614438 210.901379 \r\nL 187.644878 194.978615 \r\nL 187.705756 212.80792 \r\nL 187.736196 210.042388 \r\nL 187.857954 183.853632 \r\nL 187.827514 210.92233 \r\nL 187.888393 186.996283 \r\nL 187.949272 214.756364 \r\nL 187.979711 214.735413 \r\nL 188.010151 192.024524 \r\nL 188.04059 214.756364 \r\nL 188.071029 214.756364 \r\nL 188.162348 206.648325 \r\nL 188.192787 164.432051 \r\nL 188.253666 208.701523 \r\nL 188.284105 207.653973 \r\nL 188.314545 213.897372 \r\nL 188.344984 202.332418 \r\nL 188.405863 210.000486 \r\nL 188.436302 214.756364 \r\nL 188.466742 190.055129 \r\nL 188.52762 214.756364 \r\nL 188.55806 214.044029 \r\nL 188.588499 214.756364 \r\nL 188.710257 204.993196 \r\nL 188.801575 214.756364 \r\nL 188.832015 214.735413 \r\nL 188.862454 214.567805 \r\nL 188.892893 199.105963 \r\nL 188.984212 206.585472 \r\nL 189.136409 214.756364 \r\nL 189.166848 164.369198 \r\nL 189.227727 214.756364 \r\nL 189.319045 200.048758 \r\nL 189.440803 214.756364 \r\nL 189.471242 214.023078 \r\nL 189.501682 204.322763 \r\nL 189.56256 212.724116 \r\nL 189.623439 214.756364 \r\nL 189.653879 194.475791 \r\nL 189.714757 214.756364 \r\nL 189.836515 194.203428 \r\nL 189.866954 205.894089 \r\nL 189.927833 194.894811 \r\nL 189.958273 214.756364 \r\nL 189.988712 193.931065 \r\nL 190.049591 214.274491 \r\nL 190.08003 214.756364 \r\nL 190.11047 202.772389 \r\nL 190.201788 207.59112 \r\nL 190.323546 214.756364 \r\nL 190.353985 214.421148 \r\nL 190.384424 214.756364 \r\nL 190.414864 197.953658 \r\nL 190.475743 209.183396 \r\nL 190.62794 214.756364 \r\nL 190.658379 214.756364 \r\nL 190.688818 189.824668 \r\nL 190.749697 214.714462 \r\nL 190.810576 214.756364 \r\nL 190.932334 201.578182 \r\nL 191.023652 214.756364 \r\nL 191.084531 211.802272 \r\nL 191.175849 214.756364 \r\nL 191.236728 206.836884 \r\nL 191.267167 213.771666 \r\nL 191.328046 214.756364 \r\nL 191.419364 208.680572 \r\nL 191.480243 214.756364 \r\nL 191.541122 214.735413 \r\nL 191.571561 214.756364 \r\nL 191.63244 180.962393 \r\nL 191.662879 212.3889 \r\nL 191.693319 214.756364 \r\nL 191.815077 180.627177 \r\nL 191.936834 214.756364 \r\nL 191.967274 214.756364 \r\nL 191.997713 214.190687 \r\nL 192.028152 179.831039 \r\nL 192.058592 214.756364 \r\nL 192.089031 214.756364 \r\nL 192.14991 210.2938 \r\nL 192.180349 213.331695 \r\nL 192.271668 214.756364 \r\nL 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194.311108 208.931984 \r\nL 194.341547 208.177748 \r\nL 194.463305 214.756364 \r\nL 194.493744 194.203428 \r\nL 194.585063 195.586195 \r\nL 194.73726 214.756364 \r\nL 194.767699 212.053684 \r\nL 194.828578 214.756364 \r\nL 194.859017 214.756364 \r\nL 194.919896 191.144582 \r\nL 194.950335 195.020517 \r\nL 194.980775 214.756364 \r\nL 195.072093 202.479075 \r\nL 195.193851 214.756364 \r\nL 195.315608 201.515329 \r\nL 195.376487 201.431525 \r\nL 195.467805 214.756364 \r\nL 195.589563 203.966596 \r\nL 195.650442 196.382333 \r\nL 195.74176 214.756364 \r\nL 195.833078 206.606423 \r\nL 195.863518 213.562156 \r\nL 195.893957 214.756364 \r\nL 195.924397 213.43645 \r\nL 195.954836 211.425154 \r\nL 195.985275 212.640312 \r\nL 196.015715 194.685301 \r\nL 196.076594 214.756364 \r\nL 196.167912 214.756364 \r\nL 196.25923 192.736858 \r\nL 196.289669 206.711178 \r\nL 196.380988 202.79334 \r\nL 196.411427 214.756364 \r\nL 196.502745 214.756364 \r\nL 196.624503 197.932707 \r\nL 196.715821 214.756364 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200.520747 199.441179 \r\nL 200.612065 203.023801 \r\nL 200.703384 214.67256 \r\nL 200.672944 201.368672 \r\nL 200.733823 208.533915 \r\nL 200.764262 205.077 \r\nL 200.794702 214.756364 \r\nL 200.825141 195.104321 \r\nL 200.855581 160.87038 \r\nL 200.88602 214.756364 \r\nL 200.916459 207.423512 \r\nL 200.977338 214.735413 \r\nL 201.038217 177.777841 \r\nL 201.129535 212.17939 \r\nL 201.159975 210.126192 \r\nL 201.251293 214.232589 \r\nL 201.281732 184.545015 \r\nL 201.40349 213.604058 \r\nL 201.464369 208.911033 \r\nL 201.494808 198.393629 \r\nL 201.525248 214.756364 \r\nL 201.555687 214.756364 \r\nL 201.586126 214.421148 \r\nL 201.738323 146.89606 \r\nL 201.829642 214.756364 \r\nL 201.860081 208.63867 \r\nL 201.89052 206.040746 \r\nL 201.92096 212.053684 \r\nL 201.951399 212.493655 \r\nL 201.981839 214.756364 \r\nL 202.012278 214.735413 \r\nL 202.042717 142.140182 \r\nL 202.103596 159.843781 \r\nL 202.164475 214.756364 \r\nL 202.225354 211.844174 \r\nL 202.255793 157.664876 \r\nL 202.286233 214.756364 \r\nL 202.316672 213.85547 \r\nL 202.377551 205.642677 \r\nL 202.40799 214.756364 \r\nL 202.43843 214.484001 \r\nL 202.468869 188.588559 \r\nL 202.499309 214.756364 \r\nL 202.529748 214.484001 \r\nL 202.651506 198.330776 \r\nL 202.681945 214.756364 \r\nL 202.742824 212.849822 \r\nL 202.773263 182.994641 \r\nL 202.834142 190.055129 \r\nL 202.92546 214.756364 \r\nL 202.9559 214.735413 \r\nL 202.986339 170.067871 \r\nL 203.047218 214.756364 \r\nL 203.077657 208.869131 \r\nL 203.168976 211.089938 \r\nL 203.229854 214.337344 \r\nL 203.260294 211.739419 \r\nL 203.321173 211.948929 \r\nL 203.351612 193.449192 \r\nL 203.382051 214.756364 \r\nL 203.47337 202.311467 \r\nL 203.503809 203.547576 \r\nL 203.534248 214.735413 \r\nL 203.564688 165.66816 \r\nL 203.656006 192.715907 \r\nL 203.686445 193.009221 \r\nL 203.716885 214.400197 \r\nL 203.747324 206.627374 \r\nL 203.777764 170.549744 \r\nL 203.808203 214.756364 \r\nL 203.838643 214.756364 \r\nL 203.869082 208.198699 \r\nL 203.929961 209.288151 \r\nL 203.99084 214.756364 \r\nL 204.021279 210.964232 \r\nL 204.082158 176.353172 \r\nL 204.112597 214.756364 \r\nL 204.173476 213.918323 \r\nL 204.203915 214.756364 \r\nL 204.234355 210.482359 \r\nL 204.295234 214.567805 \r\nL 204.356112 211.96988 \r\nL 204.386552 214.756364 \r\nL 204.447431 186.975332 \r\nL 204.47787 211.446105 \r\nL 204.508309 212.870773 \r\nL 204.538749 209.2672 \r\nL 204.569188 207.989189 \r\nL 204.599628 190.159884 \r\nL 204.660507 214.756364 \r\nL 204.690946 214.756364 \r\nL 204.721385 211.907027 \r\nL 204.782264 214.379246 \r\nL 204.812704 192.213083 \r\nL 204.843143 214.756364 \r\nL 204.904022 205.91504 \r\nL 204.934461 205.244608 \r\nL 204.99534 214.756364 \r\nL 205.056219 211.488007 \r\nL 205.086658 213.771666 \r\nL 205.117098 213.352646 \r\nL 205.147537 171.052568 \r\nL 205.208416 207.549218 \r\nL 205.238855 213.394548 \r\nL 205.299734 204.86749 \r\nL 205.360613 214.442099 \r\nL 205.391052 157.74868 \r\nL 205.421492 214.756364 \r\nL 205.451931 214.756364 \r\nL 205.51281 202.688585 \r\nL 205.543249 213.373597 \r\nL 205.604128 211.865125 \r\nL 205.634568 214.756364 \r\nL 205.665007 199.734493 \r\nL 205.725886 214.756364 \r\nL 205.756325 213.310744 \r\nL 205.786765 193.428241 \r\nL 205.847643 214.756364 \r\nL 205.878083 212.849822 \r\nL 205.908522 212.975528 \r\nL 205.969401 188.777118 \r\nL 206.03028 199.650689 \r\nL 206.091159 214.756364 \r\nL 206.152037 205.077 \r\nL 206.212916 214.756364 \r\nL 206.243356 182.76418 \r\nL 206.304235 214.756364 \r\nL 206.425992 191.354092 \r\nL 206.456432 214.756364 \r\nL 206.51731 204.427518 \r\nL 206.54775 198.142217 \r\nL 206.578189 214.756364 \r\nL 206.669507 197.031814 \r\nL 206.760826 214.756364 \r\nL 206.791265 196.256627 \r\nL 206.852144 210.943281 \r\nL 206.882583 214.756364 \r\nL 206.913023 188.588559 \r\nL 207.004341 205.28651 \r\nL 207.03478 214.756364 \r\nL 207.06522 213.373597 \r\nL 207.095659 198.435531 \r\nL 207.156538 205.768383 \r\nL 207.278296 214.756364 \r\nL 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "## Bài tập\n",
+    "## Một thế giới Peter và Sói thực tế hơn\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb b/translations/vi/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
new file mode 100644
index 000000000..8903509fa
--- /dev/null
+++ b/translations/vi/8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb
@@ -0,0 +1,460 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "eadbd20d2a075efb602615ad90b1e97a",
+   "translation_date": "2025-09-06T15:15:50+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/assignment-solution.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter và Sói: Môi Trường Thực Tế\n",
+    "\n",
+    "Trong tình huống của chúng ta, Peter có thể di chuyển gần như không cảm thấy mệt mỏi hay đói. Trong một thế giới thực tế hơn, cậu ấy cần phải ngồi xuống nghỉ ngơi và ăn uống để duy trì sức khỏe. Hãy làm cho thế giới của chúng ta trở nên thực tế hơn bằng cách áp dụng các quy tắc sau:\n",
+    "\n",
+    "1. Khi di chuyển từ nơi này sang nơi khác, Peter sẽ mất **năng lượng** và tăng thêm **mệt mỏi**.\n",
+    "2. Peter có thể tăng năng lượng bằng cách ăn táo.\n",
+    "3. Peter có thể giảm mệt mỏi bằng cách nghỉ ngơi dưới gốc cây hoặc trên cỏ (tức là đi vào vị trí trên bảng có cây hoặc cỏ - ô màu xanh lá).\n",
+    "4. Peter cần tìm và tiêu diệt con sói.\n",
+    "5. Để tiêu diệt con sói, Peter cần đạt mức năng lượng và mệt mỏi nhất định, nếu không cậu ấy sẽ thua trong trận chiến.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math\n",
+    "from rlboard import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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o9rs6XrDuYvokSTj11FN53/vet83jQ0NDvPKVr2RoaIg///M/z1rp3V2Rtl77WK1WGR0dpV6vZ8t0Pv/5zxOGIa985StZsmTJNq99+umnc+KJJ2arDnrtbCQtRyG2U3dxMaSD/EEQ0Gk2CPd+Ce3xMcI998JoBVqnzQ8FiU5nox0KZ9MWorKgrE27yg6MtVgLnnVz1xja7DnagUos2jmMTW+6Y9m0/glWHHBAX+vjhepuCKy15sILL2TlypXbPL5s2TK+9KUvceihhwJkrUHP87j77ru55pprsudec8013HDDDbzrXe/Kxg//5m/+BoBHH32Uj370o2zevDl7vnOOf/qnf8quS5fNboXYAbpr71qtVrYrdbEywMDr3oxtNPFLgxCmrUPCCBsWsLmIOFcgyUW4MIJclD3HRIVnW5L5CB1FeFGEFxXxowJB9z5rSRbJFYr4YUCnNsvr/+g9/a6SF6Q70xzHMUuWLOHzn//8No8/8MADfPjDH2ZiYoJcLoe1lnK5zOjoKGeffTZr167NnttqtfjKV77C2rVryeVyVKtVfN9n8+bNnHrqqdxxxx3bvPbZZ59NoVDYZj/H314SJS1HIbZTd31edylJpVJhanqapQcfxgNXXUo0NU5x2e7gYhwaqxwolY0NWhyuu1LFpu1J7Vy69tE6lHNYB8o6YgfaOrR1GCw6Ac9Bo17n2m9cwJLVB1JavLhPNfHilEolpqamsg1oP/ShDxHHMV/4wheytYsPPvgghx9+eHY9dPdywsnJyee83kMPPcQxxxyTHVXRvS576+caY/jQhz7En/3Zn1GpVLLjLqIoes64o4SjENupu2UZsM2WZd7S3VjyjpN58utfYJ+TPkhh5T64dhNQOBwonu1au2cXbweFEhZQ9Tqq3cZaiNMnpN/DOpQF49Lu9fTYKLde+i12W30gH/rmv/XsEu4MZmZmGBkZYWpqisHBQeI45uyzzyaOY770pS9lddwrCOczPT0972NKKT74wQ/ymc98hmKxyOTkJFprwjCkXq+Ty+W2CUjpVguxnbo7xmzdOulOzix6/ZvZ98zPsf7SrzPz+KPkVqwkt9ueuLCACwsQFiBfIL/PaqJ9D8ArDbDljtt5+tabmH5yHflFSyms2J1weASvUMDLR/hRRK5UJCynLZ2bLrmY8u6788F//hpBEOyUx7JCuoFH97TGWq2WzTKfffbZPfdXfCHOPvvs7GOtNeeee27WYuzuBK6Uek4wgrQchdhu3VnW7h6B3S39K5UK09PTjBx9HElpALVhHQ986TP4+QIHnXM+aB9lNMnYJu479ywSpbFNy1UP3cfSQw7n2Ne+nrsu+BL16gzF3fdi/3e8h9zgADbu8OD113L31T8hqAzyh58/j+LS5QwuXsz09PROe8hWp9PJjrXtjil2z4zp+sQnPsEJJ5zAG9/4xp6XEz6fv/3bv+Wkk04il8vx13/911hr+fu//3vOOusswjDMTj7s7g702yQchdhOhUIhuy63e6hWuVymXq8zMDBArVZj8UtfwfTK/XjJMW+jMTXOvRd8ieZjDxEuW8HqT57LPl/4Rrbhqv7y5xnTipXv/BOWveUkisUi7U6Hh6+6nF/+5yUoP2T129/Nyf/2HQaHh0nm1ld2v+/OejTrNsugtjoB8B3veAfVahWAPffck8MPPzw7b3p7rFq1ijAMWbVqVfb9rrjiCs4444zsz92hjV7Xp++ctSpEH3W32oJ0xrU7sdDd9n9wcJCrf3U1m6qbaDfblMIijcP2p7V6L3CKG6/9Lnss2Z1jXnEMI0uXsfSw5Ty15h6eevIxVu1/MOPj4xQKBVa97URWn/Du7BK77i42YRgyNjbGyMgIY2NjlEolSqVSn2tl+3Vn/T3Po9FoZFfLdNceQvrLp9VqkSTJdr/+D3/4Q1796ldv0xLtXvLZbaF2W6O9jpqQcBRiO3UH8Lu7S9dqtbnFyU2uf+w6fnzfT7j9gTVsGt/MovwwJa9M0RSxbQsxPPTIg/zeEcdx6x23svgoxU9u/DH7jxqu/cE/MXDqXzIwtHibTVm7Y3HT09PZoumBgQGazSblcvk5u8nsLLonAXbPc4njmHa7vc0EU6PReMHjj9/85jdpNpscc8wx2eeSJGFmZoaBgQGq1SpKKcIwzI5l2HrcUcJRiO3UXfjtnMNai+/7bJrexMnf+gBrNzzC1PQ0rm3RHUVUKTBcGKLkl4l0nrwf8dKVhxKagJvHvo9/ZciFZ13G2MN3cfV3v8ijvzyCg456B7l8MTsuoPuG7bYgt94qrXu/tZ1lgqY7YxxFERMTE9nO3FtfreL7PieccMILajlCelzCcVttyuF5HiMjI9Tr9W2W8mx9hk323Bf21xJi17X11vzWWR6f+A1/8C/voDZbpxyWWZJfQlFFxK2ETaPP8MT69bzliLeyx8iebNmymfKeAdfc9lMafswFp1zAXstXEcVNVh/8Mm656m945JeXc9wf/y0Di1dmZ510zz7pTiJ0rwLpvrm3trPs71itVrOTAIeGhrKW49Zhf9ZZZ23zNQcddBCFQoG77rrrOX9vrTUvfelLcc5x9913A7B582Y+8IEPZM+J45jR0VEGBgaYnp7OWo69lvJIOAqxnaIoYnR0FIvlgZkH+MT3PsFsY5YDFh+AcQbbToh0RKFc4IClq3l609OMTY5RKPts4jfcN7uRoVW7c9+jj9Ky6REBzcWreNdHL+CXV5/Pvbd8g1u+dxb7vvSN7HvEnxB7OYwxjI+PMzIyQq1WY3h4mNHR0d+5s8xCls/nqdfreJ5HrVbL/h7vfe97WbNmzTYTMMuXL+eP//iPOeGEE1i5ciXnn38+zjmSJMnC1Pd9/sf/+B/Mzs5y6aWXctlll/HYY49lr6GU4rTTTst2AO+uOAB6LomScBRiO3W35n94yyN85AcfZu3m3+DHHo/Fa3FtsG3L37znC/z8vtvZY9EerN/wBCtekfDwk3eRW1zENIYYc9O857iTOOqgo5idnWVoaIhWq8UrjjuDJ+6+mMaWu7j32jtZ9dJ3ExbS85WHhoao1Wp4nsfExMQ23dKd8ZCtOI6zJTXdYYokSTjllFP41Kc+lYVjEARceumlHHbYYQRBQKPR4JxzzsnGJ7sHcVlrsdYyPDzMmWeeyVve8hbe/OY3ZwvDjTGcdtpp2ZGw3dfvnjn+2yQchdhOYRjy4FMP8+7vvZvBYJA3rHo9kYpo1BpEXo5yrsh/XPuvPLzpAV565AqGlg7xdG2SwSWL8HTAnsMjNNttPv2mz2CblsJQgampqXTNnwo5+dxHqNVq+J7BqTALgampKYaGhp69ZHHuypKdteXY6XQoFovZOdLdTWu7rbl8Ps/nPvc53v/+92fHKXTXJHqel21CsfVO3+12Ozv3+pBDDuHOO+/kuuuu41Of+lQ26909M6Y7ttk9svW3ty6TcBRiO7WSOn9x50fZf88Rdivvxt6De5M3Bf79+kuIw4Cley5jRFkOWLSY1XsfzNDqxXQSy0RjHGM9Dhw5nEW5JVSiAeI4zs6kcc7h+z5OBQRhekZNHMfouTdsLpfLWkmdTgfP87Ltu3bGheCFQoHZ2dlsrWj3XJ6ZmRl+//d/n5e//OXZ9datVit7rHtQVnftY7db3T1Eq91uMzAwQLvdZsWKFZx88skkScJ1112XPdYN5m4oyhUyQuwA1llOf8XHmI2n2eAeYLz5JFqXeO9b30RRD+DrIg8O3Mmh5lB2L+6DdoY4cTzw2B0ct+e7OHbPN7MkXLHNAuRuuHVnwLv3W28I2328+xiQPXdn1G0Bdxezd39RVCoV/vEf/5HBwUFmZ2ezM2K6B3FNTk4yMDBAvV4nDEMajUbWre7OQnevWJqdncXzPE4++WTe+c53Ui6XmZycpFwuZ+Oc3c1zf3vGWsJRiO2U94ocnH/93B9+n5naFJXKAPdsuYHb6j+m2piEnCLWVe6v3ka9VePUvc/hyFcfw7LibgwWhrOzk5vNJsViMT2Hplym2WxSqVSo1WrZeFp3prr73O66ytHRUcrlcl/r4sUoFovZ2s3JyUl838/GU0ulEpOTk9mVQEopfN9nbGwsO5CrO6HTPXq1+5qzs7MMDw9Tq9WoVCrEcZyN2VarVYaHh6nX65TL6VhuHMe9l/K0Wq1tZnQWmg0bNjA9Pb2gyzg+Po5zbkGXsVarUSqtJwzHf/eT+8Ta9oKuw5mZGWq1GuvWrdvqkwA5pmcb7MmR7OEfgfMc3Q3KHOmbVncMqqOYqs8wlX5RplarbXP/fDvLdJ8zNjaWlWlrmzdvZmpqakHX4/T0NBs2bMj+3GvXnW4dTE1NPeex7t/5+epp60O6ftvExMTvLGOr1cKrVqtcd911v/PJ/dKtyIVcxscff5wbb4yo17f0uyjzWrx4lM9+9pbnnLC2kFx99SwnnLBwf87GNBk+4udcdd1V/S7KvKJnIt7QfEPPbf8Xiqeeeorv8l3aj2zftdL/Ly2vLoczzjjDLWRr1651F110Ub+L8byuuOIKt3TpmrkN+Bbm7eUvP9dNTEz0u6rmZa1173znx/teT893y+XG3cvOe5ljAf+39Nal7sorr+z3j/N5XXjhha6yttL3unq+/44+/Wi3801xCSHE/wMSjkII0YOEoxBC9CDhKIQQPUg4CiFEDxKOQgjRg4SjEEL0IOEohBA9SDgKIUQPEo5CCNGDhKMQQvQg4SiEED1IOAohRA8SjkII0YOEoxBC9CDhKIQQPUg4CiFEDxKOQgjRg4SjEEL0IOEohBA9SDgKIUQPEo5CCNGDhKMQQvQg4SiEED1IOAohRA8SjkII0YOEoxBC9CDhKIQQPUg4CiFEDxKOQgjRg4SjEEL0IOEohBA9SDgKIUQPEo5CCNGDhKMQQvQg4SiEED1IOAohRA8SjkII0YM3PT3NVVdd1e9yzGtsbIzHH398QZfxgQceYPHiJ8nnR/tdlHkVCpu45ppryOfz/S7KvJJkhr32Wrg/Z8+rU9hUYK+r9up3UeZVXlfmgdoDKKX6XZR5rVu3juU3L2fowaF+F2VewXSA53kew8PD/S7LvDqdDvl8fkGXsVAocOaZJfbbb+GW8fLLAwYHBykUCv0uyrwGBz0uuWTh1uHsbI5zzz2Cxn/+db+LMq9w8BEKH6ou6PdLGIb81cBfsWR4Sb+LMq9/8/4Nr1Ao8OpXv7rfZZnXY489xsTExIIu4+joKEuWLOGoo47qd1HmdcMNN3DEEUcwODjY76L05JzjsssuW9A/54mJCZrNX7Jp08ItI8DKlWMLuh7vv/9+DjnkEFatWtXvoszre9/7now5CiFELxKOQgjRg4SjEEL0IOEohBA9SDgKIUQPEo5CCNGDhKMQQvQg4SiEED1IOAohRA8SjkII0YOEoxBC9CDhKIQQPUg4CiFEDxKOQgjRg4SjEEL0IOEohBA9SDgKIUQPEo5CCNGDhKMQQvQg4SiEED1IOAohRA8SjkII0YOEoxBC9CDhKIQQPUg4CiFEDxKOQgjRg4SjEEL0IOEohBA9SDgKIUQPEo5CCNGDhKMQQvQg4SiEED1IOAohRA8SjkII0YOEoxBC9CDhKIQQPUg4CiFEDxKOQgjRgzrppJPcIYcc0u9yzKtWqzE6OsrKlSv7XZR5PfPMM+RyOYaGhvpdlHk98sgj7L333vi+3++izOuee+7h0EMP7Xcx5tXpdLj11seZnNy/30WZVxhOcNhhLZYtW9bvosxr3bp1LF68mEKh0O+izOvee+/Fe+aZRVx55Yf7XZZ5lcvr+exnb+N973tfv4syr6uvvppFixbxspe9rN9Fmdc//MM/8P73v59KpdLvoszrQx/6Cz7/+YX7bzGXm2L1We/h/k98t99FmdeSO5fwsYkvcNxxx/W7KPO65JJLeO1rX7ugGzyf/exn8ZwztFoLt8XTbk8QhuGCbpVFUUSxWFzQZfR9n4GBAQYHB/tdlJ6cc8DC/rcIkPgJraFWv4sxr3axTdSMFvS/xTAMKZfLC7qMnufJmKMQQvTi9bsA/z+x1jIxMUEURXR952g4AAAgAElEQVQ6HQByuRzOOXK5HNVqFc/zaDQaDAwMMDExwejDD3Pt1y5gZmwUAAcc9d73ccu3v4VzYK3Dy0fsfvDBPHT77VgHDsXQsqW87zOfYWj33fF8H8/zsvHEZrNJoVBAKYVSql/VIcROTcJxB0qShDiOs5vWmjiOt3kMII5j2q0Wt3/73/npF84jbrfTbmX6P0/efRftdgdrHYl1WODBm26g3ekQW3C+T75c4Z4bb+ITF/wThxz7BpxzKKWy7xnHMZ7nSTgK8QJJt3oHajQalMtllFKEYYjneVhrsdZSq9UIwxClFOVymZu++XWu+avP4SUdQqPIe5q8b8h7msAl5Lf6XGg0gbKEniHnKXI4onzI0pV7cMk5/5u1v/gFWmtarRbNZhPP86jVanPjeEKIF0LCcQeKooiZmRmUUjSbzaz15nkexWKRZrOJUoo7rvhPbj7/i0S+Ie89ews9TeilYZh+rAmNIvQUoTHkPE1oNJ5ytGcmod1i2V4r+dY5n+PnP/4v8vk8hUKBOI4plUpoLT9eIV4o6VbvQDMzMwwNDdFsNomiiCRJsrHHer1OoVCg1Wjw5LU/wes0CDyNm+tLK6XSMUYc1kHiVHpvHbGzxAl4FtpolHXEzSZbfvMYQ0uXE5UrbHz4YWamp1FaE4YhExMTDA0N4XnyIxbihZCmxQ5ULpcZHR1Fa029XieOY3zfx/d9BgcHqdXrPHDZf7DhhmvI67RlWPANhcCkrUhfE3mGvG8o+Jq8pwl9Q+h55HxDzp9rWXqawGhM0uGRW29iaGSYX/3sWjbefz8DAwN0Oh1GRkYwxvS7SoTYaUk47kDNZpNSqYRzjiAI0FqTJAlJkqRhOTXB03esIeepdHzRT4Mu76n03p+7eZqcMWmX2jB3U+SMJmcUOa3IGfCNRruYB67/GfkgYM1VV1KdmcEYI2OOQrxIEo47ULvdJgxDnHMYY9BaY63FOUe73aa1ZTObbrs+HUucC8HIM+Q9L20lemn4pWOO6tnxRzM39mjSFmOwVVD62tCamSYfBvziJz9h0xNPYIyh0+lIOArxIkg47kDFYpHp6elsQqbT6WQhGfk+15/xp1ngRUYTzbUWI3/uYzM3OeObZydkfEPgmTQUdRqKgafwtCbQhpwGz2geXnMrL3vDG7jgQ6fRbrcpFosyISPEiyDvnh2oWq0yMjICpEGZy+WySZl1v7gNXZ8l9DSRbwiNIe8p8nMtw7yniXw1F5Iqm73OGU2oFTmjCAzkNGkoGoVvSEPSKFTc4fb/vIzBkWGeuP9+RkdHs3WVQojtJ+G4A4VhyMzMDEC2lEdrjTGG9Rd/jZxx6dhiGGRji9laxu4ynu6ki1FZFzuYC8nA03ieItDgG4WnFb5RBL5HGIYkzRa21eHqi75OuVyWCRkhXgQJxx2o3W6Tz+dxzqUXrs+NOSpnsVOjeCYdR4yKURqORs+tcZybmfbUVmOMc4Go5+673Wqt8bXGVxBohdHpeiwDGK14+LabSbDUmw2stf2uEiF2WhKOO1j3cr2tL9t7/Fv/jB3bRGQMURRSqFTmZqvnxhznutrR3Mx1zoOcp8l5msBLQ9A3Or1phZkbc/S0IjAajaNQLpDP5/E8zT3/dTU3f/vSflWBEP9fkBXCO1AQBNRqNYwxtNttALTWNDZuxEs6BJ4mXyoRVQZwzmKchWYd2i0skFiF1Q6dWIwCsOA0zth0cbjVWOPwncVqiK3KWoy5MCRW6frKxuwMYS4nEzJCvAgSjjtQvV5nZGSEarVKGIbEcUyn0yGfC/C0IhcVKC1Zjh9F+OUKRjmSzRtQXg6CgHZ1mtbYFkCjlMOStj4Tp0mcI9EQO4fRCqMNleVLaHViKoUiBCEFpwgKZdavXcvj991LY3aWQrnc30oRYicl4bgDVSoVtmzZQhRF1Gq1tNV47x2M3349xZElVPbZlyCXS8cMjcJTDtQKVC4C3yeJl9HZfRWtZp2phx+AOMYpR6AdNtYk2uInMLTvQSRBQCtJSJRHrBSdxNFKLPnKIM73uOU7l3LiGWdKOO6CZCemHUPCcQdKkgRgm+3DWmOj0GlTPvhwcvl8NnboafC1wisNoEivqe5Yi7ag7QBm0TI6iaNjHW1n6cTQThJiB+3E0k5g9ol1RIuX0tEGnYCyDs/C7qsPpDrbwM2VR+xaZPH/jiHhuAMlSUIURQDZhg9x0qG07wHkhkbSmWVt8DR4Jp1U8bVCo4idA+vAWqx1+Mmzf3bWMTcoiU53o0BZWLL6QGJrMdZhEjA2DViT2AV9kJYQOwMJxx0on8+zefNmSqUSrVYLYwxhGKJzITpfwOh0gkZr0FqlN6NRCrRNW37aOox12CTB2LmdwK2dC0cHzmHTbXtQcy1NZS06cRhr0dZirMJIOArxokg47kDT09MsWrSIRqNBoVDAWstoq0PSTtBBiA48lNagQBkFCqxWabfazXWHLCgLeq6VaJzDugSXqLnH3VwLErS1KAcqSTAOOhZ0ktCOHdrzQcaehHjBJBx3oEqlwjPPPEOlUmF2dpYgCFj2+uOYuvUatPEx+QilFSiF02BVGpCgcICzFuUUylqUc2jn0AlpQNq0Zajs3PMs6XPmQjJ2aZB6CTx51y8pDQ1RXKAnDQqxM5Bw3IHq9ToDAwM457IrZcjlKe13CFMP38/yN78dW6+m4agUVqVnxqAUOIdzDucU4OYCMg1AZUmD0aUb4ToLeq4L3nEO5SzaKqx1tKtV1t93D2/6yMeoLFrU5xoRYucl4bgDNZvNbJ1jEAQkSUK73WbR297NfZ/8Y9x/X8EeJ/wxtjaThaNO4zFtOZLuBt49bEsBxgFJgpo7iTB2Ds+llzZ1xyhjZzE2Pbhrzb98nSV77cOBbzhGZi2FeBEkHHegcrnM2NgYYRhSr9fRWuP7Pt7gMEf+65X8+sPv4pmfXs6yt7wTbTxwCa4TpynoHMp46CCHcukxCNoLSBJLXJ8hyBdJnIVOjEoSrEvD0aEwzjE9Ps5V//B3VJbvzsf//WKiYkE2nhDiRZBw3IFmZmYYHh5mdnaWKIqw1tJqtVBK0egkHPylf2Hdty9k8r5fYvIR4aKllPc/CKfSSZnGhieoPfYw1sY8c/c9/PzW63nJy45g0eqDUNqj02mTX7yCgb33JcHhEssjt93M9PgE7U7M3q84ind85rOEUZ6pqSmGh4flDBkhXiB55+xAhUKBqakpfN+n2WwCz653LBaLtIOAlR/7C8buWkPQaTC9ZTMT37+Y9tQkAwcfRuWlr0TNztJqNKjttR9r7vw5Bx77Vpav2p/RtQ9TzOdRxUEeve5qnrz/HoqLlhHtvR9LDzqUFQceyJJ99892Aa9UKnJttRAvgoTjDtRsNimXy9Tr9Wyj2ziOcc7RarUI8yFTs1MUDz+c2eoshYMVGx9/lEWVYRoOVHmA8OWvYcnAIMMzkxz8zG/IVyr4K/Zgn1WrmZ2dpVwuEyxdxkHv+QCdxDK8fAVoTRzH2WYXYRgyPT3N8PCwBKQQL5CE4w6k50JKKZWdHZNe5+q4/fHb2VzbzF9//6/ZOPY0ZV1kKBwmIGB8bIxWvUNztsFH3vERjn3VMUxHG7h93X/z8C9+wtnn/gurDz4KYwzWWooji8jlculO33N7Rna/P5CdYSPX2Arxwkk47kBhGNJoNPA8j06ng1KKR0Yf5Uf3/YivXf81xibGoO0oeSX2XrIPgQqIdMSqxfvgWR+VwIOPPcjPNl3K8qHdOOn3P8pjN/2A677/FZYs/XuGl+xJu93OgrG7NZpSCqUUQRAAz17GKOEoxAsnfa4dqFarUS6Xcc4RhiGb65s45Tun8M83/TOB9lk5vJLVy1YT6IC1Gx9lbGKMxQOLGSwOsWLRcqIwojq0nsaM43//wd9z4ts+yH4HHMLUpgf50TfO4P47fpLNhAdBQKfTIYqiLBQbjQb1eh1jDNVqVXYCF+JFkJbjDpRtWVaMeGjTg/ze148j50KWlZYS4BOSp6AjVg2vwsSaNfeswSjDkkUjfPeGi3nZW/Zm+s6YLdMN9tl9P5RSvO6ET/GaN53ID79+Gtd962NoDXuvfhn1uiaKIiYmJtLlQp5HsVjEGMPs7CwjIyMy3ijEiyDvnh2o0+ngBz63b7idP7z0DxmbHccmCQNhhU47JmcCXrLbgSwqLWL/PVbz2pcfxRP1+7lq8yUceuz+3PnwU9RLCe86/l34XrpxRKk8wOI9j+Dot3yCgajFnd8/lWu/+T5845EkCUEQZCHY6XRot9vpPpKNhiwCF+JFkJbjDmSt5dGJx/ir68/Dxo59hvchZwNmmzWiICLyCzy15Smerq0jt3gv1s88yG4HDbF5kyYhxx6Ll3PwkkP5s9f9L4BsT0iUYcVLjqNQGaI6U2Vo8XLc3HCi1jobc+yWYes/CyFeGAnHHahBndP+6xSGwiHe8pLjWb14NY8/vZ7Lbv0Oq/arkCvErL33GcyKGDOwD695zVEoz6NSWMKyaA+StuMtB7yDUKcTLr7vMzs7y+LFi8mVlrHfK9/PxPg4lUqF6enp7Mwa3/fxfT9rRdZqNQqFggSkEC+ChOMONFp/gj869jCUVmxq3cfjbi3xSscHVr6GkdwKNCG7rXyYkXA5Q7mleMrnV8/cwVBpkJzy+aOXnsryaHdyuRzOOeI4ZtGiRdRqNYIgYGJ8nCiKqFar5PN5Wq1WNgGUJAm1Wg1ID/qanJxkYGBArpAR4gWSd84OtGrgUP6k9UXCUsAdkz9mcbgnsW7ym9l7eKb9BFPNUUwUk8/7TLQ3sNjfjeP3OZF144/x8SP/F+1WuvynWq2ilCKXyzE+Ps7IyEg2ybJlyxaGhoaYmpra5oqcIAiIogjP87LLGIUQL5yE4w5kjGF4eBjf93lj/uT0k9qyvLCKyXgzzjo8AkDR6NSJTJl9ygei9jJ4xkfl0vHD7gJurTVBEGQz0UB2WWC5XMYYw9DQUPbc7lhj97lCiBdOHX/8ia5WW97vcszLmDaVyixBMNTvoswrjmcYGPCy82MWoi1btjA8PLygd+rZsOFpPG/h/luEhI5+Gn/xwj2CwtYtxbhIeQGfOjkxMUGxWMzW5y5ETz/9NN7s7O7ccsv5/S7LvCqV37DbbjfwwAMf7HdR5rX33j/kW99awitf+cp+F2Ve5513Hh/72McYGBjod1Hmdfrpp/PVr36138WY1+TkJBdeeCGf/vSn+12Uea1Zs4axsTHe/va397so87rooos49thj2WefffpdlHmdeeaZ3W71zjCrufDLuDPMDi/UMnbXZC7U8m1NyrhjLPQyyiJwIYToQcJRCCF6kHAUQogeJByFEKIHCUchhOhBwlEIIXqQcBRCiB4kHIUQogcJRyGE6EHCUQghepBwFEKIHiQchRCiBwlHIYToQcJRCCF6kHAUQogeJByFEKIHCUchhOhBwlEIIXqQcBRCiB4kHIUQogcJRyGE6EHCUQghepBwFEKIHiQchRCiBwlHIYToQcJRCCF6kHAUQogeJByFEKIHCUchhOhBwlEIIXqQcBRCiB4kHIUQogcJRyGE6EHCUQghepBwFEKIHiQchRCiBwlHIYTowVMqIQgm+12Oefl+FWNaC7qMxjSo1WpMTi7cMnY6Haanp/tdjOeVJMmCrsPp6Wk6nc6CLmO9XqfRaCzoMrZaLWZnZxd0GZMkQR133ElufPygfpdlXr5f48ADx9hzzz37XZR5bdq0iXvuydFsDva7KPMaHHyUo47aC9/3+12Ued13330cfPDB/S7GvDqdDuvXr2ffffftd1HmNTExQbvdZunSpf0uyrzWr1/Pg4sepFPo9Lso8xq5bwRe+9ozHLgFe6tU1rqLLrrILWRXXHGFW7p0Td/r6vluL3/5uW5iYqLfVTUva637+Mc/3u9iPK/x8XF33nnn9bsYz+vWW291V155Zb+L8bwuvPBCV1lbcSzg/44+/WgnY45CCNGD1+8CiP+3rLUkSYIxBmstSikAtNbZ48657HNJktCanWV68yYgbYcqrYgGBpidmMhe1wtyoBTtZjP7XBCGLN5jD6xz2fczxmSv3/3e3XshFhIJx11Mu91mfHyckZERJiYmKBQKJElCsVjEWkur1aLVapEkCZVKhXtu+Bm/vvz7/Pzy/8RaC86hg4BVr3oN919/HdZBYi2lJUtBGzY/9RSJVSjPY8nee/GHp5/Oy9/6NsqDA7RaLYaGhmg0GgCEYZiFshALjYTjLsQ5R6vVYmBggGazSblcJkkSPM+jVquhtUYplYXWY3f8nB//5acZ/c1vyCnAKEDhkph1t9xI6Gmcg9gqmmNbiK0jNIaOsuicT7te54p//Ecmt4xy4lmfJAxDpqam8DwP5xzVapVKpdLvahGiJwnHXYhSCt/3s1CqVqvk83mSJCGfz2OtJY5j2u02Exue4rKPnsLs6Ch5v9u6UzgHFpfeW0cCJIkjBmJria1DW4VSjjDwKQ8NcvfPrqU8OMDR730vlYEB2u02zjlyuVwfa0OI5yd9ml2Icw7nHEEQ4ObGAZ1zKKWw1qbdZtKxxv/67Kewk+NEnibvafKeIfI0kacoeIbQ04R+9zFN3kDe6PTzxqDjDjMbniCerVIZGuIH//BVHrnjDpIkycrR6SzcpRxCSMtxF9OdjOl0Ovi+n4VjHMfZx09e/9/UH36A0FNzvz3V3C0NWAvpWKNTJA5i7TBWYaxLb8qhE4dWsOWxR4nKFVYe+BLuu+EGVh95JP5cOHcnZoRYiKTluAvpdqtnZ2fJ5XLZOGOSJIRhSBAEWOd46KrvYzpN8sYQeoa8b8j7mtBL/xzN3actRkXoKUKjyXmanElvgVEERuFheereu/Fx3PL9yzGdNp7n4XkexWKx31UixLwkHHch1lqazSaLFy+mVqsxPDxMkiT4vs/U1BSNRoNHv3sxm39+Sxp+gSHyu91pTeRrokCR854NxDALSMgbRc7T+FqnYWkUvjGouMP911/LEce+nk+f8A5azSadTofx8fF+V4kQ85Ju9S5EKYUxhnq9ju/7NBoNlFLZhExrapLx+39NgMV4Cq3mbnNfbwGLxiYWCyTWEc/NVpvEESuHwqE8RSuxgME5C07jkoTJjRsplcrcd/PNHHbssQRB0L/KEOJ3kJbjLqYbkM65bI1hd8xx8rFH2HDNVXPd53QSJu/NTbJkEy+KvJ8+npt7XtqN1vhGkTOKQCsCY/C7H2vwjObBm2/EuJg1l3+fOI5lQkYsaBKOuxDnHEmSEEURnU6HMAyzWetGtcqjl36D0FPkPUXeM4Qm/Tjceoxx7pZ2qRU5rebGGdVcN1oRGEegScccjcLTCl9rfKXYvHYt5aFBbv/RVdlicCEWIgnHXUh3QmZsbIwoipienkZrTafToVSIqN55MzmTTrwUS0WiuaU6ka/nPlZbBaQhPzcJE3qGnGfIGUPgpa3GwGh8rdKbpykUI4JcQHV0lP++8OskzQZDQ0P9rhIh5iXhuAvpXiEzMjJCrVZjYGAAay2+7/P0bdfiach5mnzOJ1+Kng3BrQIx7U6nEzLdmWlfp93n7n1gFJ4GX6WtRk8pXLNJGObS5wQev77xRp58/PF+V4kQ85Jw3IUopQiCgKmpKfL5PNVqFa01cRyz/tv/mnWRC0NDRMVS2oo0mrzRRGbrcUdNYNhmyY4/d/O0xnRDcS4wPa3RCspDg+klitZy5+U/wDWlWy0WLpmt3oU452i325TLZWZmZigUCjSbTYwxmFaDQCtyviEqVwiKRbxyGQO0n16PSmISCzZJ6CQWlSgUDocCBxaFc5rEQWIsnlMk2uFpH0OCbwz5YpGhZUvYsnETOEtjttbvKhFiXhKOu5juom+tdXZFTOvpJ9FxG68QUVqxB4WRxVmrzygIZifxd98HBzQ2P01nZhrTaqFmZ+f2BoXYaRKVYBT4nsEUK/gYFu2+B816C1Uo0EkgP7KYwvAifvPAg/z4a1/joFe9SrYsEwuShOMuRGtNEASMjo6yaNEiJicnKZVKPPmDi4knxlh8yMvIDwykM8tmrkusQO29PyqXx+Lwon2JLQS1Kt7YKEFiacWWnHW0EksrdiTawxtZQgfF6FNPUlm2gra1mARUkuBHBZzx+fU1V6cbRAqxAEk47kKstbTbbYaHh6nX6wzM7ZDTnJlm4KDDyC9djq81XjcYTboEx6MCpDvvaGtR1uLCPHpwER1rCRNH2zk6ydzNOtqxxVioLN8NExXSa61tes11xzqW7FNgw/on+l0lQsxLwnEXorUml8sxNTXF0NAQ09PTRFG6xEYtXY4pFDFzEyhGg7dVUALpbhMWnLXYxOGsBQcusTjrUAkoa1HWgXUo6yjn88Qu/VgnCpMkGGvxLLLRrVjQJBx3Id1F4GEYEsdxtnVZklh8L8DkC2it0hllnR6HgFa4rcMx3dBxriVo0RY8m4Yj1uHmwtPNhWP3OcradMeexGGsJVFGwlEsaBKOu5juNmFbn+NSOvxVTFz1HSq77YHWCmXmQsuAU4pEdb82/TplFSqxaGfRjrS7bB3Gpl13tmo5KutQLg1HbR3GgYkta++5D5ML+1EFQvxfkXDchSil8DyParVKFEXUajU8z2P4NW9k03f+hXBkBUljOl39qtRcMCpcFo5zezA60rNksuBL93F0zuLNda9xoJ1Lu9mu23JUJNaiE8v4hif46D98FSWtR7FASTjuQqy1NBoNli1bxsTERHalTC4qMPTGt7P22//EXid/GGUUWLAKVLqMMdM9CTtd3OjQzmFc2p32umOLDlQ3HBOHcRbjHImFTifm3mv+m6kto+RHRvpUE0L8bhKOu5Buy7FWqz1ny7LlJ5xMa/0jbPzhd9jzpFNwWs+FoutuAj6Xkirrkpswws9HJO2YxtQ4aq5FiQPjHMo5lE0/1taROMfdV13Jlscf58Nf+zq77btvP6pBiP8rEo67kG632BiTHZcQx3G6CNvz2PMjn+bpi77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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "## Định nghĩa trạng thái\n",
+    "\n",
+    "Trong các quy tắc trò chơi mới, chúng ta cần theo dõi năng lượng và sự mệt mỏi ở mỗi trạng thái bàn cờ. Vì vậy, chúng ta sẽ tạo một đối tượng `state` để chứa tất cả thông tin cần thiết về trạng thái hiện tại của vấn đề, bao gồm trạng thái của bàn cờ, mức năng lượng và sự mệt mỏi hiện tại, và liệu chúng ta có thể đánh bại con sói khi ở trạng thái cuối hay không:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class state:\n",
+    "    def __init__(self,board,energy=10,fatigue=0,init=True):\n",
+    "        self.board = board\n",
+    "        self.energy = energy\n",
+    "        self.fatigue = fatigue\n",
+    "        self.dead = False\n",
+    "        if init:\n",
+    "            self.board.random_start()\n",
+    "        self.update()\n",
+    "\n",
+    "    def at(self):\n",
+    "        return self.board.at()\n",
+    "\n",
+    "    def update(self):\n",
+    "        if self.at() == Board.Cell.water:\n",
+    "            self.dead = True\n",
+    "            return\n",
+    "        if self.at() == Board.Cell.tree:\n",
+    "            self.fatigue = 0\n",
+    "        if self.at() == Board.Cell.apple:\n",
+    "            self.energy = 10\n",
+    "\n",
+    "    def move(self,a):\n",
+    "        self.board.move(a)\n",
+    "        self.energy -= 1\n",
+    "        self.fatigue += 1\n",
+    "        self.update()\n",
+    "\n",
+    "    def is_winning(self):\n",
+    "        return self.energy > self.fatigue"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(state):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(board,policy):\n",
+    "    n = 0 # number of steps\n",
+    "    s = state(board)\n",
+    "    while True:\n",
+    "        if s.at() == Board.Cell.wolf:\n",
+    "            if s.is_winning():\n",
+    "                return n # success!\n",
+    "            else:\n",
+    "                return -n # failure!\n",
+    "        if s.at() == Board.Cell.water:\n",
+    "            return 0 # died\n",
+    "        a = actions[policy(m)]\n",
+    "        s.move(a)\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 5, won: 1 times, drown: 94 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        elif z==0:\n",
+    "            n+=1\n",
+    "        else:\n",
+    "            s+=1\n",
+    "    print(f\"Killed by wolf = {w}, won: {s} times, drown: {n} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Hàm Thưởng\n",
+    "\n",
+    "### Tổng quan\n",
+    "Hàm thưởng là một phần quan trọng trong việc thiết kế hệ thống học tăng cường. Nó định nghĩa cách mà một tác nhân nhận được phản hồi từ môi trường để hướng dẫn hành vi của mình.\n",
+    "\n",
+    "### Cách hoạt động\n",
+    "Hàm thưởng thường được thiết kế để khuyến khích tác nhân đạt được mục tiêu cụ thể. Mỗi hành động mà tác nhân thực hiện sẽ dẫn đến một giá trị thưởng, giá trị này có thể là dương, âm hoặc bằng không.\n",
+    "\n",
+    "### Ví dụ\n",
+    "Dưới đây là một ví dụ đơn giản về cách định nghĩa hàm thưởng:\n",
+    "\n",
+    "```python\n",
+    "def reward_function(state, action):\n",
+    "    if state == \"goal_state\":\n",
+    "        return 10  # Thưởng cao khi đạt được mục tiêu\n",
+    "    elif action == \"invalid_action\":\n",
+    "        return -5  # Phạt khi thực hiện hành động không hợp lệ\n",
+    "    else:\n",
+    "        return 0  # Không thưởng hoặc phạt cho các hành động khác\n",
+    "```\n",
+    "\n",
+    "### Lưu ý\n",
+    "[!NOTE] Hàm thưởng cần được thiết kế cẩn thận để tránh việc khuyến khích hành vi không mong muốn. Ví dụ, nếu tác nhân nhận được thưởng cao khi thực hiện một hành động cụ thể, nó có thể lặp lại hành động đó mà không quan tâm đến mục tiêu dài hạn.\n",
+    "\n",
+    "### Các phương pháp phổ biến\n",
+    "- **Thưởng theo mục tiêu:** Tác nhân nhận được thưởng khi đạt được trạng thái mục tiêu.\n",
+    "- **Thưởng theo tiến trình:** Tác nhân nhận được thưởng dựa trên mức độ tiến bộ hướng tới mục tiêu.\n",
+    "- **Thưởng theo hành vi:** Tác nhân nhận được thưởng khi thực hiện hành vi mong muốn.\n",
+    "\n",
+    "### Cảnh báo\n",
+    "[!WARNING] Tránh thiết kế hàm thưởng quá phức tạp, vì điều này có thể làm cho việc học của tác nhân trở nên khó khăn và không hiệu quả.\n",
+    "\n",
+    "### Mẹo\n",
+    "[!TIP] Hãy thử nghiệm nhiều hàm thưởng khác nhau để tìm ra thiết kế phù hợp nhất với môi trường và mục tiêu của bạn.\n",
+    "\n",
+    "### Kết luận\n",
+    "Hàm thưởng đóng vai trò quan trọng trong việc định hình hành vi của tác nhân. Một hàm thưởng được thiết kế tốt sẽ giúp tác nhân học nhanh hơn và đạt được kết quả tốt hơn.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reward(s):\n",
+    "    r = s.energy-s.fatigue\n",
+    "    if s.at()==Board.Cell.wolf:\n",
+    "        return 100 if s.is_winning() else -100\n",
+    "    if s.at()==Board.Cell.water:\n",
+    "        return -100\n",
+    "    return r"
+   ]
+  },
+  {
+   "source": [
+    "## Thuật toán Q-Learning\n",
+    "\n",
+    "Thuật toán học thực tế hầu như không thay đổi, chúng ta chỉ sử dụng `state` thay vì chỉ vị trí trên bảng.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    s = state(m)\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = s.board.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        while True:\n",
+    "            a = random.choices(list(actions),weights=v)[0]\n",
+    "            dpos = actions[a]\n",
+    "            if s.board.is_valid(s.board.move_pos(s.board.human,dpos)):\n",
+    "                break \n",
+    "        s.move(dpos)\n",
+    "        r = reward(s)\n",
+    "        if abs(r)==100: # end of game\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kết quả\n",
+    "\n",
+    "Hãy xem liệu chúng ta đã thành công trong việc huấn luyện Peter để chiến đấu với con sói chưa!\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Killed by wolf = 1, won: 9 times, drown: 90 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [
+    "Chúng ta hiện thấy ít trường hợp chết đuối hơn nhiều, nhưng Peter vẫn không phải lúc nào cũng có thể giết được con sói. Hãy thử nghiệm và xem liệu bạn có thể cải thiện kết quả này bằng cách điều chỉnh các siêu tham số.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fe0987e7dd8>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/8-Reinforcement/1-QLearning/solution/notebook.ipynb b/translations/vi/8-Reinforcement/1-QLearning/solution/notebook.ipynb
new file mode 100644
index 000000000..bc8e66525
--- /dev/null
+++ b/translations/vi/8-Reinforcement/1-QLearning/solution/notebook.ipynb
@@ -0,0 +1,577 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "488431336543f71f14d4aaf0399e3381",
+   "translation_date": "2025-09-06T15:13:18+00:00",
+   "source_file": "8-Reinforcement/1-QLearning/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "# Peter và Chó Sói: Hướng dẫn cơ bản về Học tăng cường\n",
+    "\n",
+    "Trong hướng dẫn này, chúng ta sẽ học cách áp dụng học tăng cường vào một bài toán tìm đường. Bối cảnh được lấy cảm hứng từ câu chuyện cổ tích âm nhạc [Peter và Chó Sói](https://en.wikipedia.org/wiki/Peter_and_the_Wolf) của nhà soạn nhạc người Nga [Sergei Prokofiev](https://en.wikipedia.org/wiki/Sergei_Prokofiev). Đây là câu chuyện về cậu bé tiên phong Peter, người dũng cảm rời khỏi nhà để đến khu rừng trống nhằm đuổi theo con sói. Chúng ta sẽ huấn luyện các thuật toán học máy để giúp Peter khám phá khu vực xung quanh và xây dựng một bản đồ điều hướng tối ưu.\n",
+    "\n",
+    "Đầu tiên, hãy nhập một số thư viện hữu ích:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import math"
+   ]
+  },
+  {
+   "source": [
+    "## Tổng quan về Học tăng cường\n",
+    "\n",
+    "**Học tăng cường** (Reinforcement Learning - RL) là một kỹ thuật học cho phép chúng ta tìm hiểu hành vi tối ưu của một **tác nhân** trong một **môi trường** nào đó bằng cách thực hiện nhiều thử nghiệm. Một tác nhân trong môi trường này cần có một **mục tiêu**, được xác định bởi một **hàm phần thưởng**.\n",
+    "\n",
+    "## Môi trường\n",
+    "\n",
+    "Để đơn giản, hãy xem xét thế giới của Peter là một bảng vuông có kích thước `width` x `height`. Mỗi ô trên bảng này có thể là:\n",
+    "* **mặt đất**, nơi Peter và các sinh vật khác có thể đi lại\n",
+    "* **nước**, nơi rõ ràng bạn không thể đi qua\n",
+    "* **một cái cây** hoặc **cỏ** - nơi bạn có thể nghỉ ngơi\n",
+    "* **một quả táo**, đại diện cho thứ mà Peter rất vui khi tìm thấy để tự nuôi sống mình\n",
+    "* **một con sói**, thứ nguy hiểm và cần phải tránh xa\n",
+    "\n",
+    "Để làm việc với môi trường, chúng ta sẽ định nghĩa một lớp gọi là `Board`. Để tránh làm rối notebook này, chúng ta đã chuyển toàn bộ mã nguồn làm việc với bảng vào một module riêng tên là `rlboard`, mà bây giờ chúng ta sẽ import. Bạn có thể xem bên trong module này để tìm hiểu thêm chi tiết về cách triển khai nội bộ.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rlboard import *"
+   ]
+  },
+  {
+   "source": [
+    "Bây giờ hãy tạo một bảng ngẫu nhiên và xem nó trông như thế nào:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "width, height = 8,8\n",
+    "m = Board(width,height)\n",
+    "m.randomize(seed=13)\n",
+    "m.plot()"
+   ]
+  },
+  {
+   "source": [
+    "## Hành động và Chính sách\n",
+    "\n",
+    "Trong ví dụ của chúng ta, mục tiêu của Peter là tìm một quả táo, đồng thời tránh con sói và các chướng ngại vật khác. Để làm được điều này, anh ấy có thể đi xung quanh cho đến khi tìm thấy một quả táo. Vì vậy, tại bất kỳ vị trí nào, anh ấy có thể chọn một trong các hành động sau: lên, xuống, trái và phải. Chúng ta sẽ định nghĩa các hành động này dưới dạng một từ điển và ánh xạ chúng tới các cặp thay đổi tọa độ tương ứng. Ví dụ, di chuyển sang phải (`R`) sẽ tương ứng với cặp tọa độ `(1,0)`.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "actions = { \"U\" : (0,-1), \"D\" : (0,1), \"L\" : (-1,0), \"R\" : (1,0) }\n",
+    "action_idx = { a : i for i,a in enumerate(actions.keys()) }"
+   ]
+  },
+  {
+   "source": [
+    "Chiến lược của tác nhân (Peter) được định nghĩa bởi một cái gọi là **chính sách**. Hãy cùng xem xét chính sách đơn giản nhất được gọi là **đi ngẫu nhiên**.\n",
+    "\n",
+    "## Đi ngẫu nhiên\n",
+    "\n",
+    "Trước tiên, hãy giải quyết vấn đề của chúng ta bằng cách triển khai chiến lược đi ngẫu nhiên.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ],
+   "source": [
+    "def random_policy(m):\n",
+    "    return random.choice(list(actions))\n",
+    "\n",
+    "def walk(m,policy,start_position=None):\n",
+    "    n = 0 # number of steps\n",
+    "    # set initial position\n",
+    "    if start_position:\n",
+    "        m.human = start_position \n",
+    "    else:\n",
+    "        m.random_start()\n",
+    "    while True:\n",
+    "        if m.at() == Board.Cell.apple:\n",
+    "            return n # success!\n",
+    "        if m.at() in [Board.Cell.wolf, Board.Cell.water]:\n",
+    "            return -1 # eaten by wolf or drowned\n",
+    "        while True:\n",
+    "            a = actions[policy(m)]\n",
+    "            new_pos = m.move_pos(m.human,a)\n",
+    "            if m.is_valid(new_pos) and m.at(new_pos)!=Board.Cell.water:\n",
+    "                m.move(a) # do the actual move\n",
+    "                break\n",
+    "        n+=1\n",
+    "\n",
+    "walk(m,random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "Hãy thực hiện thí nghiệm bước đi ngẫu nhiên nhiều lần và xem số bước trung bình đã thực hiện:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 32.87096774193548, eaten by wolf: 7 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_statistics(policy):\n",
+    "    s,w,n = 0,0,0\n",
+    "    for _ in range(100):\n",
+    "        z = walk(m,policy)\n",
+    "        if z<0:\n",
+    "            w+=1\n",
+    "        else:\n",
+    "            s += z\n",
+    "            n += 1\n",
+    "    print(f\"Average path length = {s/n}, eaten by wolf: {w} times\")\n",
+    "\n",
+    "print_statistics(random_policy)"
+   ]
+  },
+  {
+   "source": [
+    "## Hàm Thưởng\n",
+    "\n",
+    "Để làm cho chính sách của chúng ta thông minh hơn, chúng ta cần hiểu những nước đi nào \"tốt hơn\" so với những nước khác.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "move_reward = -0.1\n",
+    "goal_reward = 10\n",
+    "end_reward = -10\n",
+    "\n",
+    "def reward(m,pos=None):\n",
+    "    pos = pos or m.human\n",
+    "    if not m.is_valid(pos):\n",
+    "        return end_reward\n",
+    "    x = m.at(pos)\n",
+    "    if x==Board.Cell.water or x == Board.Cell.wolf:\n",
+    "        return end_reward\n",
+    "    if x==Board.Cell.apple:\n",
+    "        return goal_reward\n",
+    "    return move_reward"
+   ]
+  },
+  {
+   "source": [
+    "## Q-Learning\n",
+    "\n",
+    "Xây dựng một Q-Table, hoặc mảng đa chiều. Vì bảng của chúng ta có kích thước `width` x `height`, chúng ta có thể biểu diễn Q-Table bằng một mảng numpy với hình dạng `width` x `height` x `len(actions)`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = np.ones((width,height,len(actions)),dtype=np.float)*1.0/len(actions)"
+   ]
+  },
+  {
+   "source": [
+    "Chuyển bảng Q-Table vào hàm vẽ để hiển thị bảng trên bảng:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p267d475764)\">\n    <image height=\"327\" id=\"image00b8757863\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Bản chất của Q-Learning: Phương trình Bellman và Thuật toán Học\n",
+    "\n",
+    "Viết mã giả cho thuật toán học của chúng ta:\n",
+    "\n",
+    "* Khởi tạo Bảng Q Q với các giá trị bằng nhau cho tất cả các trạng thái và hành động\n",
+    "* Đặt tốc độ học $\\alpha\\leftarrow 1$\n",
+    "* Lặp lại mô phỏng nhiều lần\n",
+    "   1. Bắt đầu tại vị trí ngẫu nhiên\n",
+    "   1. Lặp lại\n",
+    "        1. Chọn một hành động $a$ tại trạng thái $s$\n",
+    "        2. Thực hiện hành động bằng cách di chuyển đến trạng thái mới $s'$\n",
+    "        3. Nếu gặp điều kiện kết thúc trò chơi, hoặc tổng phần thưởng quá nhỏ - thoát khỏi mô phỏng  \n",
+    "        4. Tính phần thưởng $r$ tại trạng thái mới\n",
+    "        5. Cập nhật Hàm Q theo phương trình Bellman: $Q(s,a)\\leftarrow (1-\\alpha)Q(s,a)+\\alpha(r+\\gamma\\max_{a'}Q(s',a'))$\n",
+    "        6. $s\\leftarrow s'$\n",
+    "        7. Cập nhật tổng phần thưởng và giảm $\\alpha$.\n",
+    "\n",
+    "## Khai thác vs. Khám phá\n",
+    "\n",
+    "Cách tiếp cận tốt nhất là cân bằng giữa khám phá và khai thác. Khi chúng ta hiểu thêm về môi trường, chúng ta sẽ có xu hướng đi theo lộ trình tối ưu hơn, tuy nhiên, thỉnh thoảng vẫn nên chọn con đường chưa được khám phá.\n",
+    "\n",
+    "## Triển khai Python\n",
+    "\n",
+    "Bây giờ chúng ta đã sẵn sàng triển khai thuật toán học. Trước đó, chúng ta cũng cần một số hàm để chuyển đổi các số bất kỳ trong Bảng Q thành một vector xác suất cho các hành động tương ứng:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v"
+   ]
+  },
+  {
+   "source": [
+    "Chúng ta thêm một lượng nhỏ `eps` vào vector ban đầu để tránh việc chia cho 0 trong trường hợp ban đầu, khi tất cả các thành phần của vector đều giống nhau.\n",
+    "\n",
+    "Thuật toán học thực tế mà chúng ta sẽ chạy trong 5000 thí nghiệm, còn được gọi là **epochs**:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      ""
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from IPython.display import clear_output\n",
+    "\n",
+    "lpath = []\n",
+    "\n",
+    "for epoch in range(10000):\n",
+    "    clear_output(wait=True)\n",
+    "    print(f\"Epoch = {epoch}\",end='')\n",
+    "\n",
+    "    # Pick initial point\n",
+    "    m.random_start()\n",
+    "    \n",
+    "    # Start travelling\n",
+    "    n=0\n",
+    "    cum_reward = 0\n",
+    "    while True:\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        dpos = actions[a]\n",
+    "        m.move(dpos,check_correctness=False) # we allow player to move outside the board, which terminates episode\n",
+    "        r = reward(m)\n",
+    "        cum_reward += r\n",
+    "        if r==end_reward or cum_reward < -1000:\n",
+    "            print(f\" {n} steps\",end='\\r')\n",
+    "            lpath.append(n)\n",
+    "            break\n",
+    "        alpha = np.exp(-n / 3000)\n",
+    "        gamma = 0.5\n",
+    "        ai = action_idx[a]\n",
+    "        Q[x,y,ai] = (1 - alpha) * Q[x,y,ai] + alpha * (r + gamma * Q[x+dpos[0], y+dpos[1]].max())\n",
+    "        n+=1"
+   ]
+  },
+  {
+   "source": [
+    "Sau khi thực hiện thuật toán này, Bảng Q nên được cập nhật với các giá trị xác định mức độ hấp dẫn của các hành động khác nhau tại mỗi bước. Hình dung bảng tại đây:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 792x432 with 1 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"360.646265pt\" version=\"1.1\" viewBox=\"0 0 366.6475 360.646265\" width=\"366.6475pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;white-space:pre;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 360.646265 \nL 366.6475 360.646265 \nL 366.6475 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 33.2875 336.76814 \nL 359.4475 336.76814 \nL 359.4475 10.60814 \nL 33.2875 10.60814 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g clip-path=\"url(#p31952a86ca)\">\n    <image height=\"327\" id=\"image3fd90cd4cb\" 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "m.plot(Q)"
+   ]
+  },
+  {
+   "source": [
+    "## Kiểm tra Chính sách\n",
+    "\n",
+    "Vì Q-Table liệt kê \"sức hấp dẫn\" của mỗi hành động tại mỗi trạng thái, nên việc sử dụng nó để xác định cách điều hướng hiệu quả trong thế giới của chúng ta là khá dễ dàng. Trong trường hợp đơn giản nhất, chúng ta chỉ cần chọn hành động tương ứng với giá trị cao nhất trong Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ],
+   "source": [
+    "def qpolicy_strict(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = list(actions)[np.argmax(v)]\n",
+    "        return a\n",
+    "\n",
+    "walk(m,qpolicy_strict)"
+   ]
+  },
+  {
+   "source": [
+    "Nếu bạn thử đoạn mã trên nhiều lần, bạn có thể nhận thấy rằng đôi khi nó chỉ \"treo\" và bạn cần nhấn nút DỪNG trong notebook để ngắt nó.\n",
+    "\n",
+    "> **Nhiệm vụ 1:** Sửa đổi hàm `walk` để giới hạn độ dài tối đa của đường đi bằng một số bước nhất định (ví dụ, 100), và quan sát đoạn mã trên trả về giá trị này theo thời gian.\n",
+    "\n",
+    "> **Nhiệm vụ 2:** Sửa đổi hàm `walk` để không quay lại những nơi mà nó đã từng đi qua trước đó. Điều này sẽ ngăn `walk` lặp lại, tuy nhiên, tác nhân vẫn có thể bị \"mắc kẹt\" ở một vị trí mà nó không thể thoát ra được.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Average path length = 3.45, eaten by wolf: 0 times\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "def qpolicy(m):\n",
+    "        x,y = m.human\n",
+    "        v = probs(Q[x,y])\n",
+    "        a = random.choices(list(actions),weights=v)[0]\n",
+    "        return a\n",
+    "\n",
+    "print_statistics(qpolicy)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fbab852fd68>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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127.527048 214.756364 \nL 127.557487 211.325525 \nL 127.618366 214.756364 \nL 127.648805 204.39335 \nL 127.709684 214.61537 \nL 127.740124 211.20803 \nL 127.801002 214.685867 \nL 127.831442 214.521375 \nL 127.861881 212.030492 \nL 127.892321 214.756364 \nL 127.92276 214.756364 \nL 127.953199 214.098395 \nL 128.014078 214.756364 \nL 128.044518 214.756364 \nL 128.074957 209.563107 \nL 128.166275 210.644057 \nL 128.196715 210.409068 \nL 128.257593 213.933902 \nL 128.318472 212.077489 \nL 128.379351 201.502985 \nL 128.409791 210.57356 \nL 128.531548 214.756364 \nL 128.592427 213.087942 \nL 128.622866 208.411661 \nL 128.653306 214.756364 \nL 128.683745 214.450878 \nL 128.714185 214.756364 \nL 128.744624 209.210624 \nL 128.805503 214.756364 \nL 128.896821 212.594465 \nL 128.92726 213.651915 \nL 128.9577 214.756364 \nL 128.988139 208.85814 \nL 129.049018 214.756364 \nL 129.079457 214.756364 \nL 129.201215 209.093129 \nL 129.231655 214.756364 \nL 129.292533 214.121893 \nL 129.322973 207.659697 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133.949763 214.756364 \nL 133.980202 214.756364 \nL 134.132399 206.226264 \nL 134.254157 214.756364 \nL 134.284596 214.685867 \nL 134.315036 198.00165 \nL 134.345475 214.756364 \nL 134.375914 199.106098 \nL 134.436793 214.756364 \nL 134.528111 214.497876 \nL 134.558551 214.309885 \nL 134.58899 214.756364 \nL 134.649869 214.685867 \nL 134.680308 213.252434 \nL 134.710748 214.756364 \nL 134.741187 214.756364 \nL 134.802066 212.946949 \nL 134.832506 214.756364 \nL 134.954263 193.184376 \nL 135.076021 214.756364 \nL 135.10646 212.594465 \nL 135.167339 214.756364 \nL 135.289097 214.756364 \nL 135.502172 214.756364 \nL 135.532612 206.766738 \nL 135.593491 214.756364 \nL 135.62393 214.662368 \nL 135.65437 214.756364 \nL 135.745688 195.722257 \nL 135.806567 214.756364 \nL 135.867445 203.923372 \nL 135.989203 214.756364 \nL 136.050082 213.111441 \nL 136.080521 214.756364 \nL 136.110961 214.756364 \nL 136.202279 206.837235 \nL 136.293597 214.756364 \nL 136.324036 213.510922 \nL 136.384915 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138.150401 209.375116 \nL 138.21128 214.756364 \nL 138.363477 206.672743 \nL 138.454795 214.756364 \nL 138.485234 205.967776 \nL 138.546113 210.338571 \nL 138.576553 214.732865 \nL 138.637431 211.41952 \nL 138.667871 207.659697 \nL 138.69831 213.604918 \nL 138.789629 213.299432 \nL 138.820068 214.756364 \nL 138.911386 201.737974 \nL 139.002704 214.756364 \nL 139.033144 213.322931 \nL 139.094023 214.756364 \nL 139.124462 210.033085 \nL 139.21578 210.503063 \nL 139.276659 209.7276 \nL 139.337538 214.756364 \nL 139.398417 212.92345 \nL 139.428856 214.756364 \nL 139.459295 214.756364 \nL 139.520174 204.863328 \nL 139.611493 211.114034 \nL 139.641932 211.255028 \nL 139.672371 210.738052 \nL 139.702811 214.756364 \nL 139.794129 213.957401 \nL 139.824568 214.756364 \nL 139.855008 213.581419 \nL 139.885447 207.119222 \nL 139.915887 214.756364 \nL 139.946326 214.004399 \nL 140.037644 211.325525 \nL 140.128962 214.756364 \nL 140.159402 207.260215 \nL 140.220281 214.380381 \nL 140.25072 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151.300225 211.889498 \nL 151.330664 190.435005 \nL 151.361104 214.756364 \nL 151.391543 211.748505 \nL 151.421983 212.429973 \nL 151.452422 204.322853 \nL 151.482861 214.756364 \nL 151.513301 207.80069 \nL 151.665498 214.756364 \nL 151.726377 208.975635 \nL 151.756816 210.15058 \nL 151.787256 214.756364 \nL 151.817695 211.748505 \nL 151.848134 206.155767 \nL 151.909013 214.521375 \nL 151.969892 214.756364 \nL 152.000331 214.145392 \nL 152.06121 214.756364 \nL 152.182968 197.625667 \nL 152.243847 214.756364 \nL 152.304725 213.181937 \nL 152.335165 212.71196 \nL 152.365604 214.756364 \nL 152.426483 205.897279 \nL 152.456923 214.756364 \nL 152.548241 208.458659 \nL 152.57868 213.111441 \nL 152.60912 214.756364 \nL 152.700438 214.19239 \nL 152.730877 214.215889 \nL 152.822195 214.756364 \nL 152.852635 208.364663 \nL 152.913514 214.121893 \nL 152.943953 184.654276 \nL 153.065711 214.756364 \nL 153.09615 210.926043 \nL 153.157029 214.756364 \nL 153.187468 214.756364 \nL 153.248347 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154.952954 214.756364 \nL 155.013833 213.534421 \nL 155.044272 206.461253 \nL 155.105151 214.756364 \nL 155.16603 214.756364 \nL 155.196469 204.487345 \nL 155.257348 214.756364 \nL 155.379106 213.228935 \nL 155.439984 211.959995 \nL 155.561742 214.756364 \nL 155.622621 214.756364 \nL 155.713939 205.215811 \nL 155.805257 214.756364 \nL 155.835697 202.865921 \nL 155.896576 212.641463 \nL 156.018333 214.756364 \nL 156.048773 213.322931 \nL 156.109651 214.756364 \nL 156.140091 196.356727 \nL 156.261848 214.756364 \nL 156.322727 207.777191 \nL 156.353167 213.040944 \nL 156.414045 214.756364 \nL 156.566243 204.017367 \nL 156.657561 214.756364 \nL 156.688 214.239388 \nL 156.748879 214.756364 \nL 156.779318 208.035679 \nL 156.840197 207.80069 \nL 156.870637 214.756364 \nL 156.961955 202.912919 \nL 157.022834 214.756364 \nL 157.083712 214.356882 \nL 157.175031 200.539531 \nL 157.23591 214.756364 \nL 157.296788 207.847688 \nL 157.357667 214.756364 \nL 157.388107 204.463846 \nL 157.418546 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172.790447 211.466518 \nL 172.851326 214.756364 \nL 172.912204 214.756364 \nL 172.942644 208.623151 \nL 173.033962 211.772004 \nL 173.12528 214.756364 \nL 173.15572 212.030492 \nL 173.216598 214.756364 \nL 173.338356 214.709366 \nL 173.368796 202.724928 \nL 173.429674 214.756364 \nL 173.460114 214.756364 \nL 173.581871 205.709288 \nL 173.703629 214.756364 \nL 173.734068 214.756364 \nL 173.764508 214.544874 \nL 173.855826 214.662368 \nL 173.977584 214.756364 \nL 174.129781 210.855547 \nL 174.16022 204.134862 \nL 174.19066 214.638869 \nL 174.221099 214.215889 \nL 174.251538 210.620558 \nL 174.281978 214.756364 \nL 174.312417 214.756364 \nL 174.342857 214.756364 \nL 174.464614 204.745833 \nL 174.495054 207.871187 \nL 174.616811 214.756364 \nL 174.647251 214.709366 \nL 174.67769 214.145392 \nL 174.708129 214.709366 \nL 174.769008 203.429895 \nL 174.799448 214.756364 \nL 174.890766 214.756364 \nL 174.982084 213.416926 \nL 175.012524 214.756364 \nL 175.042963 211.560514 \nL 175.073402 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177.112843 214.756364 \nL 177.143282 214.027898 \nL 177.173721 208.270668 \nL 177.2346 208.811143 \nL 177.295479 214.756364 \nL 177.356358 212.241982 \nL 177.417237 214.756364 \nL 177.447676 214.568372 \nL 177.508555 208.106176 \nL 177.538994 214.756364 \nL 177.569434 212.359476 \nL 177.599873 212.335977 \nL 177.660752 214.756364 \nL 177.691191 200.022555 \nL 177.78251 214.756364 \nL 177.812949 213.087942 \nL 177.843388 214.756364 \nL 177.873828 214.638869 \nL 177.904267 196.427223 \nL 177.965146 214.756364 \nL 177.995585 208.599652 \nL 178.056464 209.892092 \nL 178.178222 214.756364 \nL 178.208661 214.756364 \nL 178.29998 208.482158 \nL 178.421737 214.756364 \nL 178.482616 211.443019 \nL 178.513055 214.756364 \nL 178.573934 212.030492 \nL 178.634813 213.158439 \nL 178.665252 214.756364 \nL 178.756571 205.380303 \nL 178.847889 214.756364 \nL 178.878328 207.683195 \nL 178.939207 214.756364 \nL 178.969647 214.756364 \nL 179.030525 210.879046 \nL 179.060965 214.19239 \nL 179.121844 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214.756364 \nL 191.023652 214.356882 \nL 191.054091 201.385491 \nL 191.084531 214.568372 \nL 191.14541 208.64665 \nL 191.267167 214.732865 \nL 191.328046 211.959995 \nL 191.358485 214.756364 \nL 191.388925 214.427379 \nL 191.449804 214.638869 \nL 191.541122 203.594387 \nL 191.571561 213.675414 \nL 191.63244 202.701429 \nL 191.662879 198.283636 \nL 191.784637 214.732865 \nL 191.815077 208.200171 \nL 191.845516 214.756364 \nL 191.906395 210.291573 \nL 191.936834 214.756364 \nL 191.967274 210.174079 \nL 191.997713 199.294089 \nL 192.058592 214.756364 \nL 192.119471 213.205436 \nL 192.14991 214.756364 \nL 192.210789 214.309885 \nL 192.241228 214.756364 \nL 192.271668 203.805877 \nL 192.332546 210.197577 \nL 192.454304 214.756364 \nL 192.606501 208.928637 \nL 192.728259 214.756364 \nL 192.789138 214.286386 \nL 192.819577 201.1975 \nL 192.850016 214.756364 \nL 192.880456 213.651915 \nL 192.941335 211.231529 \nL 192.971774 212.26548 \nL 193.032653 214.756364 \nL 193.15441 190.622996 \nL 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209.657103 \nL 208.404554 214.756364 \nL 208.434993 210.268074 \nL 208.465432 207.941683 \nL 208.495872 213.416926 \nL 208.556751 214.756364 \nL 208.58719 203.500392 \nL 208.678508 207.354211 \nL 208.708948 206.29676 \nL 208.800266 213.463924 \nL 208.830705 212.429973 \nL 208.861145 203.946871 \nL 208.891584 214.732865 \nL 208.952463 208.317666 \nL 208.982902 213.792909 \nL 209.013342 207.777191 \nL 209.043781 212.899951 \nL 209.135099 214.756364 \nL 209.165539 194.688305 \nL 209.226418 214.756364 \nL 209.287296 212.899951 \nL 209.317736 214.756364 \nL 209.348175 209.586606 \nL 209.409054 213.463924 \nL 209.439493 214.756364 \nL 209.469933 201.573482 \nL 209.530812 213.534421 \nL 209.561251 214.286386 \nL 209.591691 204.064365 \nL 209.652569 209.774597 \nL 209.713448 207.424708 \nL 209.774327 214.756364 \nL 209.804766 206.578747 \nL 209.865645 214.450878 \nL 209.896085 214.756364 \nL 209.987403 208.670149 \nL 210.017842 214.756364 \nL 210.10916 214.215889 \nL 210.1396 208.082677 \nL 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198.84761 \nL 212.118161 212.899951 \nL 212.148601 211.701507 \nL 212.17904 199.411583 \nL 212.20948 214.756364 \nL 212.239919 214.333383 \nL 212.300798 214.756364 \nL 212.331237 199.952058 \nL 212.392116 208.85814 \nL 212.422555 211.983494 \nL 212.452995 182.468878 \nL 212.483434 213.158439 \nL 212.513874 203.194906 \nL 212.544313 214.756364 \nL 212.635631 212.735458 \nL 212.666071 206.437754 \nL 212.72695 210.409068 \nL 212.757389 214.756364 \nL 212.818268 213.322931 \nL 212.848707 211.725006 \nL 212.879147 198.236639 \nL 212.909586 214.756364 \nL 212.940025 199.223592 \nL 213.000904 214.756364 \nL 213.061783 212.171485 \nL 213.092222 207.307213 \nL 213.122662 214.756364 \nL 213.153101 210.009586 \nL 213.183541 214.756364 \nL 213.244419 208.341165 \nL 213.274859 214.568372 \nL 213.335738 214.756364 \nL 213.396616 213.510922 \nL 213.427056 214.756364 \nL 213.457495 212.735458 \nL 213.487935 213.9809 \nL 213.518374 209.234123 \nL 213.579253 214.756364 \nL 213.609692 214.756364 \nL 213.761889 197.155689 \nL 213.883647 214.756364 \nL 213.914086 214.756364 \nL 213.944526 214.497876 \nL 214.005405 214.756364 \nL 214.035844 214.756364 \nL 214.066283 214.61537 \nL 214.127162 214.756364 \nL 214.188041 209.422114 \nL 214.309799 214.756364 \nL 214.370678 209.539608 \nL 214.401117 214.662368 \nL 214.431556 214.756364 \nL 214.522875 194.218327 \nL 214.553314 210.244575 \nL 214.614193 209.539608 \nL 214.675072 214.756364 \nL 214.76639 213.275933 \nL 214.857708 214.756364 \nL 214.827269 212.92345 \nL 214.888147 214.450878 \nL 214.918587 204.087864 \nL 215.009905 210.197577 \nL 215.101223 214.756364 \nL 215.131663 214.732865 \nL 215.192542 214.756364 \nL 215.25342 206.484752 \nL 215.28386 212.05399 \nL 215.314299 195.745755 \nL 215.375178 203.006915 \nL 215.496936 214.756364 \nL 215.527375 206.531749 \nL 215.588254 214.756364 \nL 215.618693 214.756364 \nL 215.649133 206.790237 \nL 215.710011 214.732865 \nL 215.77089 214.756364 \nL 215.831769 213.534421 \nL 215.862208 199.90506 \nL 215.923087 208.717147 \nL 216.014406 214.756364 \nL 216.044845 212.92345 \nL 216.105724 202.842423 \nL 216.136163 214.756364 \nL 216.227481 206.978229 \nL 216.257921 214.756364 \nL 216.349239 214.709366 \nL 216.440557 202.372445 \nL 216.410118 214.756364 \nL 216.470997 211.889498 \nL 216.531875 209.868593 \nL 216.592754 214.215889 \nL 216.684072 205.544796 \nL 216.775391 214.756364 \nL 216.80583 206.790237 \nL 216.866709 214.756364 \nL 216.927588 211.396021 \nL 216.958027 186.628183 \nL 216.988467 214.591871 \nL 217.018906 212.758957 \nL 217.049345 209.398615 \nL 217.110224 213.863406 \nL 217.262421 214.756364 \nL 217.292861 201.549983 \nL 217.353739 213.463924 \nL 217.384179 213.675414 \nL 217.414618 200.398537 \nL 217.445058 214.521375 \nL 217.475497 211.560514 \nL 217.505937 214.756364 \nL 217.597255 212.735458 \nL 217.658134 214.756364 \nL 217.719012 202.489939 \nL 217.779891 212.077489 \nL 217.84077 210.832048 \nL 217.932088 214.756364 \nL 217.962528 211.349023 \nL 218.023406 212.876452 \nL 218.114725 214.756364 \nL 218.145164 214.732865 \nL 218.206043 214.756364 \nL 218.297361 200.610027 \nL 218.388679 214.756364 \nL 218.419119 207.095723 \nL 218.540876 214.756364 \nL 218.571316 214.591871 \nL 218.632195 203.077411 \nL 218.662634 211.678008 \nL 218.693073 214.756364 \nL 218.723513 201.385491 \nL 218.784392 211.043538 \nL 218.87571 214.756364 \nL 218.936589 201.878968 \nL 218.967028 214.756364 \nL 218.997467 214.756364 \nL 219.027907 201.737974 \nL 219.088786 214.756364 \nL 219.149665 214.756364 \nL 219.180104 197.696164 \nL 219.240983 214.756364 \nL 219.271422 213.087942 \nL 219.301862 214.450878 \nL 219.332301 195.581263 \nL 219.39318 210.503063 \nL 219.423619 210.080083 \nL 219.454059 212.030492 \nL 219.514937 202.771926 \nL 219.545377 214.756364 \nL 219.575816 212.547467 \nL 219.636695 214.756364 \nL 219.697574 214.756364 \nL 219.819331 213.487423 \nL 219.91065 214.756364 \nL 219.971529 195.722257 \nL 220.001968 214.732865 \nL 220.032407 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228.068411 214.756364 \nL 228.09885 210.291573 \nL 228.159729 214.756364 \nL 228.190169 214.756364 \nL 228.220608 214.380381 \nL 228.251047 199.505579 \nL 228.281487 214.756364 \nL 228.311926 211.560514 \nL 228.342366 212.852953 \nL 228.372805 190.998978 \nL 228.433684 214.521375 \nL 228.464123 214.61537 \nL 228.494563 214.239388 \nL 228.555441 214.756364 \nL 228.61632 203.335899 \nL 228.677199 192.808393 \nL 228.738078 214.756364 \nL 228.890275 207.330712 \nL 228.920714 213.087942 \nL 228.951154 197.437676 \nL 228.981593 214.756364 \nL 229.012033 208.458659 \nL 229.042472 214.756364 \nL 229.13379 213.322931 \nL 229.225108 214.756364 \nL 229.255548 213.440425 \nL 229.285987 214.756364 \nL 229.346866 203.335899 \nL 229.377305 208.43516 \nL 229.468624 214.756364 \nL 229.529502 191.844938 \nL 229.559942 196.192234 \nL 229.6817 214.756364 \nL 229.712139 213.181937 \nL 229.773018 214.732865 \nL 229.864336 213.228935 \nL 229.894775 214.40388 \nL 229.925215 214.732865 \nL 229.955654 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231.72114 210.221076 \nL 231.782019 214.756364 \nL 231.812458 213.76941 \nL 231.903776 200.892014 \nL 231.934216 212.382975 \nL 231.964655 214.756364 \nL 232.055973 213.910403 \nL 232.086413 214.168891 \nL 232.177731 214.239388 \nL 232.20817 196.450722 \nL 232.23861 214.756364 \nL 232.329928 214.286386 \nL 232.360367 214.732865 \nL 232.451686 200.633526 \nL 232.421246 214.756364 \nL 232.482125 208.43516 \nL 232.543004 214.756364 \nL 232.603883 213.745911 \nL 232.664761 213.275933 \nL 232.72564 197.155689 \nL 232.786519 206.132268 \nL 232.877837 214.756364 \nL 232.847398 198.518625 \nL 232.908277 213.252434 \nL 232.999595 214.756364 \nL 233.030034 211.278527 \nL 233.121353 213.275933 \nL 233.24311 200.563029 \nL 233.27355 210.479564 \nL 233.303989 214.756364 \nL 233.395307 212.570966 \nL 233.425747 208.200171 \nL 233.456186 186.581185 \nL 233.517065 211.20803 \nL 233.547504 214.756364 \nL 233.608383 209.140127 \nL 233.638823 207.871187 \nL 233.669262 188.249607 \nL 233.730141 214.756364 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237.839461 199.881561 \nL 237.8699 214.756364 \nL 237.90034 214.685867 \nL 238.052537 203.382897 \nL 238.143855 214.756364 \nL 238.174294 205.615292 \nL 238.235173 211.137533 \nL 238.296052 214.756364 \nL 238.326491 212.547467 \nL 238.356931 198.84761 \nL 238.38737 214.756364 \nL 238.41781 205.967776 \nL 238.539567 214.756364 \nL 238.661325 198.072146 \nL 238.691764 214.756364 \nL 238.783082 212.241982 \nL 238.813522 200.093051 \nL 238.874401 214.756364 \nL 238.90484 203.547389 \nL 239.057037 214.756364 \nL 239.087476 207.213217 \nL 239.148355 211.090536 \nL 239.270113 214.756364 \nL 239.361431 191.539453 \nL 239.42231 214.756364 \nL 239.483189 205.87378 \nL 239.544068 214.756364 \nL 239.604946 210.009586 \nL 239.635386 214.427379 \nL 239.665825 201.385491 \nL 239.696265 214.756364 \nL 239.726704 211.41952 \nL 239.787583 214.450878 \nL 239.818022 209.445613 \nL 239.878901 214.756364 \nL 240.000659 209.375116 \nL 240.031098 191.492455 \nL 240.091977 214.662368 \nL 240.122416 214.61537 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254.733332 214.756364 \nL 254.763771 214.756364 \nL 254.794211 198.730115 \nL 254.885529 206.813736 \nL 254.946408 214.756364 \nL 254.976847 213.487423 \nL 255.007287 141.345807 \nL 255.068165 214.756364 \nL 255.098605 213.886904 \nL 255.159484 196.239232 \nL 255.189923 212.664962 \nL 255.220362 210.808549 \nL 255.250802 212.735458 \nL 255.281241 212.030492 \nL 255.311681 214.662368 \nL 255.34212 201.925966 \nL 255.402999 214.756364 \nL 255.463878 211.513516 \nL 255.494317 199.599575 \nL 255.555196 214.756364 \nL 255.616075 214.756364 \nL 255.646514 213.275933 \nL 255.676954 213.698913 \nL 255.707393 201.033007 \nL 255.737832 214.732865 \nL 255.768272 211.067037 \nL 255.798711 214.145392 \nL 255.829151 194.194828 \nL 255.85959 214.756364 \nL 255.890029 214.756364 \nL 255.920469 214.756364 \nL 255.950908 191.210468 \nL 256.011787 212.71196 \nL 256.042226 208.811143 \nL 256.103105 212.946949 \nL 256.133545 214.756364 \nL 256.163984 214.638869 \nL 256.224863 167.923061 \nL 256.255302 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(lpath)"
+   ]
+  },
+  {
+   "source": [
+    "Điều chúng ta thấy ở đây là ban đầu độ dài trung bình của đường đi tăng lên. Điều này có thể là do khi chúng ta chưa biết gì về môi trường - chúng ta dễ bị mắc kẹt trong các trạng thái xấu, như nước hoặc sói. Khi chúng ta học được nhiều hơn và bắt đầu sử dụng kiến thức này, chúng ta có thể khám phá môi trường lâu hơn, nhưng vẫn chưa biết rõ vị trí của những quả táo.\n",
+    "\n",
+    "Khi học đủ, việc đạt được mục tiêu trở nên dễ dàng hơn đối với tác nhân, và độ dài đường đi bắt đầu giảm. Tuy nhiên, chúng ta vẫn mở rộng khám phá, vì vậy thường đi lệch khỏi đường đi tốt nhất và thử nghiệm các lựa chọn mới, khiến đường đi dài hơn mức tối ưu.\n",
+    "\n",
+    "Điều chúng ta cũng quan sát được trên biểu đồ này là tại một số điểm, độ dài tăng đột ngột. Điều này cho thấy tính chất ngẫu nhiên của quá trình, và rằng tại một số thời điểm chúng ta có thể \"làm hỏng\" các hệ số trong Q-Table bằng cách ghi đè chúng với các giá trị mới. Điều này lý tưởng nên được giảm thiểu bằng cách giảm tốc độ học (tức là, về cuối quá trình huấn luyện, chúng ta chỉ điều chỉnh các giá trị trong Q-Table bằng một giá trị nhỏ).\n",
+    "\n",
+    "Nhìn chung, điều quan trọng cần nhớ là sự thành công và chất lượng của quá trình học phụ thuộc đáng kể vào các tham số, như tốc độ học, sự giảm tốc độ học và hệ số chiết khấu. Những tham số này thường được gọi là **siêu tham số**, để phân biệt với **tham số** mà chúng ta tối ưu trong quá trình huấn luyện (ví dụ: các hệ số trong Q-Table). Quá trình tìm giá trị tốt nhất cho các siêu tham số được gọi là **tối ưu hóa siêu tham số**, và nó xứng đáng là một chủ đề riêng.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "## Bài Tập\n",
+    "#### Một Thế Giới Peter và Con Sói Thực Tế Hơn\n",
+    "\n",
+    "Trong tình huống của chúng ta, Peter có thể di chuyển gần như không bị mệt mỏi hay đói. Trong một thế giới thực tế hơn, cậu ấy phải ngồi xuống nghỉ ngơi thỉnh thoảng và cũng cần ăn uống để duy trì sức khỏe. Hãy làm cho thế giới của chúng ta thực tế hơn bằng cách thực hiện các quy tắc sau:\n",
+    "\n",
+    "1. Khi di chuyển từ nơi này sang nơi khác, Peter mất **năng lượng** và tăng thêm **mệt mỏi**.\n",
+    "2. Peter có thể tăng năng lượng bằng cách ăn táo.\n",
+    "3. Peter có thể giảm mệt mỏi bằng cách nghỉ ngơi dưới gốc cây hoặc trên cỏ (tức là đi vào vị trí trên bảng có cây hoặc cỏ - ô màu xanh lá).\n",
+    "4. Peter cần tìm và tiêu diệt con sói.\n",
+    "5. Để tiêu diệt con sói, Peter cần đạt mức năng lượng và mệt mỏi nhất định, nếu không cậu ấy sẽ thua trong trận chiến.\n",
+    "\n",
+    "Hãy chỉnh sửa hàm thưởng ở trên theo các quy tắc của trò chơi, chạy thuật toán học tăng cường để tìm chiến lược tốt nhất để chiến thắng trò chơi, và so sánh kết quả của việc đi ngẫu nhiên với thuật toán của bạn về số lượng trò chơi thắng và thua.\n",
+    "\n",
+    "> **Note**: Bạn có thể cần điều chỉnh các siêu tham số để làm cho nó hoạt động, đặc biệt là số lượng epochs. Vì thành công của trò chơi (đánh bại con sói) là một sự kiện hiếm gặp, bạn có thể mong đợi thời gian huấn luyện lâu hơn.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/8-Reinforcement/2-Gym/notebook.ipynb b/translations/vi/8-Reinforcement/2-Gym/notebook.ipynb
new file mode 100644
index 000000000..482a4fce0
--- /dev/null
+++ b/translations/vi/8-Reinforcement/2-Gym/notebook.ipynb
@@ -0,0 +1,390 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.4 64-bit ('base': conda)"
+  },
+  "interpreter": {
+   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
+  },
+  "coopTranslator": {
+   "original_hash": "f22f8f3daed4b6d34648d1254763105b",
+   "translation_date": "2025-09-06T15:18:43+00:00",
+   "source_file": "8-Reinforcement/2-Gym/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Trượt ván CartPole\n",
+    "\n",
+    "> **Vấn đề**: Nếu Peter muốn thoát khỏi con sói, cậu ấy cần di chuyển nhanh hơn nó. Chúng ta sẽ xem cách Peter học trượt ván, đặc biệt là giữ thăng bằng, bằng cách sử dụng Q-Learning.\n",
+    "\n",
+    "Đầu tiên, hãy cài đặt gym và nhập các thư viện cần thiết:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 1"
+   ]
+  },
+  {
+   "source": [
+    "## Tạo môi trường cartpole\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 2"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Để xem môi trường hoạt động như thế nào, hãy chạy một mô phỏng ngắn trong 100 bước.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 3"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [
+    "Trong quá trình mô phỏng, chúng ta cần lấy các quan sát để quyết định cách hành động. Thực tế, hàm `step` trả về cho chúng ta các quan sát hiện tại, hàm phần thưởng, và cờ `done` cho biết liệu có nên tiếp tục mô phỏng hay không:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "#code block 4"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 5"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 6"
+   ]
+  },
+  {
+   "source": [
+    "Hãy cùng khám phá phương pháp rời rạc hóa khác sử dụng các thùng:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 7"
+   ]
+  },
+  {
+   "source": [
+    "Hãy cùng chạy một mô phỏng ngắn và quan sát những giá trị môi trường rời rạc đó.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -2, -2)\n(0, 1, -2, -5)\n(0, 2, -3, -8)\n(0, 3, -5, -11)\n(0, 3, -7, -14)\n(0, 4, -10, -17)\n(0, 3, -14, -15)\n(0, 3, -17, -12)\n(0, 3, -20, -16)\n(0, 4, -23, -19)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 8"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 9"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#code block 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 22.0, alpha=0.3, epsilon=0.9\n",
+      "5000: 70.1384, alpha=0.3, epsilon=0.9\n",
+      "10000: 121.8586, alpha=0.3, epsilon=0.9\n",
+      "15000: 149.6368, alpha=0.3, epsilon=0.9\n",
+      "20000: 168.2782, alpha=0.3, epsilon=0.9\n",
+      "25000: 196.7356, alpha=0.3, epsilon=0.9\n",
+      "30000: 220.7614, alpha=0.3, epsilon=0.9\n",
+      "35000: 233.2138, alpha=0.3, epsilon=0.9\n",
+      "40000: 248.22, alpha=0.3, epsilon=0.9\n",
+      "45000: 264.636, alpha=0.3, epsilon=0.9\n",
+      "50000: 276.926, alpha=0.3, epsilon=0.9\n",
+      "55000: 277.9438, alpha=0.3, epsilon=0.9\n",
+      "60000: 248.881, alpha=0.3, epsilon=0.9\n",
+      "65000: 272.529, alpha=0.3, epsilon=0.9\n",
+      "70000: 281.7972, alpha=0.3, epsilon=0.9\n",
+      "75000: 284.2844, alpha=0.3, epsilon=0.9\n",
+      "80000: 269.667, alpha=0.3, epsilon=0.9\n",
+      "85000: 273.8652, alpha=0.3, epsilon=0.9\n",
+      "90000: 278.2466, alpha=0.3, epsilon=0.9\n",
+      "95000: 269.1736, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "#code block 11"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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108.563315 90.206475 \r\nL 108.618101 199.087184 \r\nL 108.645494 123.955477 \r\nL 108.675931 111.098714 \r\nL 108.75811 203.90847 \r\nL 108.855507 101.456142 \r\nL 108.870725 152.883193 \r\nL 108.8829 201.096053 \r\nL 108.885944 102.25969 \r\nL 108.931599 110.69694 \r\nL 108.965079 67.70714 \r\nL 108.998559 209.533304 \r\nL 109.016821 157.302705 \r\nL 109.086826 203.104922 \r\nL 109.083782 85.786962 \r\nL 109.117262 140.829978 \r\nL 109.19031 17.083636 \r\nL 109.181179 201.096053 \r\nL 109.226834 167.347051 \r\nL 109.333363 211.542173 \r\nL 109.239009 110.69694 \r\nL 109.342494 210.738625 \r\nL 109.406411 121.946608 \r\nL 109.455109 139.222882 \r\nL 109.564681 208.729756 \r\nL 109.540332 117.527095 \r\nL 109.567725 166.141729 \r\nL 109.659035 74.537295 \r\nL 109.640773 211.542173 \r\nL 109.680341 152.883193 \r\nL 109.76252 211.140399 \r\nL 109.741214 110.69694 \r\nL 109.789913 152.883193 \r\nL 109.856874 212.747494 \r\nL 109.890354 91.81357 \r\nL 109.981664 210.336851 \r\nL 110.00297 190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 191.855255 \r\nL 111.330008 138.821109 \r\nL 111.339139 143.642395 \r\nL 111.336096 115.518226 \r\nL 111.34827 139.222882 \r\nL 111.357401 98.241951 \r\nL 111.366532 197.881863 \r\nL 111.457842 138.017561 \r\nL 111.466973 212.747494 \r\nL 111.479148 95.027761 \r\nL 111.570458 170.159468 \r\nL 111.655681 98.241951 \r\nL 111.582633 182.212683 \r\nL 111.683074 159.713348 \r\nL 111.743947 107.080976 \r\nL 111.71351 191.051707 \r\nL 111.750034 176.989623 \r\nL 111.774384 195.069446 \r\nL 111.765253 126.36612 \r\nL 111.789602 135.205144 \r\nL 111.850475 102.25969 \r\nL 111.823082 210.336851 \r\nL 111.899174 123.955477 \r\nL 111.978309 195.47122 \r\nL 111.923523 103.465011 \r\nL 112.008746 119.535965 \r\nL 112.030052 207.122661 \r\nL 112.066576 56.457472 \r\nL 112.118318 171.364789 \r\nL 112.191366 76.947938 \r\nL 112.185279 197.881863 \r\nL 112.230934 115.92 \r\nL 112.276589 207.524435 \r\nL 112.337462 101.054368 \r\nL 112.349637 153.284967 \r\nL 112.370942 74.135521 \r\nL 112.367899 203.506696 \r\nL 112.459209 153.284967 \r\nL 112.489645 199.488958 \r\nL 112.556606 78.555033 \r\nL 112.562693 130.383858 \r\nL 112.599217 78.153259 \r\nL 112.571824 191.453481 \r\nL 112.666178 102.25969 \r\nL 112.772706 209.533304 \r\nL 112.769663 17.083636 \r\nL 112.778794 159.713348 \r\nL 112.812274 79.358581 \r\nL 112.803143 211.140399 \r\nL 112.894453 127.169667 \r\nL 112.976632 211.943947 \r\nL 112.912715 82.974545 \r\nL 113.004025 154.088514 \r\nL 113.070986 90.206475 \r\nL 113.067942 212.345721 \r\nL 113.110553 168.552373 \r\nL 113.171427 204.310244 \r\nL 113.159252 93.822439 \r\nL 113.210994 126.36612 \r\nL 113.226213 46.8149 \r\nL 113.256649 203.506696 \r\nL 113.311435 139.222882 \r\nL 113.408833 207.926208 \r\nL 113.390571 44.404257 \r\nL 113.421007 168.552373 \r\nL 113.436226 94.224213 \r\nL 113.497099 192.257029 \r\nL 113.530579 136.812239 \r\nL 113.603627 205.917339 \r\nL 113.588409 32.351042 \r\nL 113.634064 158.106253 \r\nL 113.637108 72.528426 \r\nL 113.734505 190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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117.676054 183.418004 \r\nL 117.779538 123.955477 \r\nL 117.785626 176.587849 \r\nL 117.803888 17.083636 \r\nL 117.870848 77.349712 \r\nL 117.873892 27.93153 \r\nL 117.943896 178.998492 \r\nL 117.974333 86.188736 \r\nL 117.977377 193.864124 \r\nL 117.989551 17.083636 \r\nL 118.086949 163.329313 \r\nL 118.120429 21.904922 \r\nL 118.175215 197.078315 \r\nL 118.190433 188.641064 \r\nL 118.233045 205.515565 \r\nL 118.223914 34.359911 \r\nL 118.284787 130.785632 \r\nL 118.318267 78.956807 \r\nL 118.324355 190.649933 \r\nL 118.394359 100.25082 \r\nL 118.497844 200.292506 \r\nL 118.415665 99.447273 \r\nL 118.503931 107.482749 \r\nL 118.506975 108.688071 \r\nL 118.531324 211.943947 \r\nL 118.589154 34.761685 \r\nL 118.616547 100.25082 \r\nL 118.680464 197.078315 \r\nL 118.665245 21.503149 \r\nL 118.726119 141.231752 \r\nL 118.796123 64.091175 \r\nL 118.805254 195.47122 \r\nL 118.835691 132.392727 \r\nL 118.939175 163.731086 \r\nL 118.89352 80.965676 \r\nL 118.945263 149.267228 \r\nL 118.966568 21.503149 \r\nL 119.033529 212.747494 \r\nL 119.051791 169.35592 \r\nL 119.143101 203.90847 \r\nL 119.127883 45.609579 \r\nL 119.155276 163.329313 \r\nL 119.252673 17.083636 \r\nL 119.161363 209.13153 \r\nL 119.270935 82.170998 \r\nL 119.307459 194.265898 \r\nL 119.334852 74.537295 \r\nL 119.383551 133.196275 \r\nL 119.392682 31.949268 \r\nL 119.435293 190.649933 \r\nL 119.487035 123.553703 \r\nL 119.59052 200.292506 \r\nL 119.55704 75.340843 \r\nL 119.596607 134.80337 \r\nL 119.630088 189.444612 \r\nL 119.605738 93.822439 \r\nL 119.703136 122.750155 \r\nL 119.706179 122.348381 \r\nL 119.71531 133.999823 \r\nL 119.748791 201.096053 \r\nL 119.727485 67.70714 \r\nL 119.824882 143.642395 \r\nL 119.919236 106.277428 \r\nL 119.858363 203.104922 \r\nL 119.934454 146.856585 \r\nL 120.034895 204.310244 \r\nL 119.958804 84.179867 \r\nL 120.037939 125.160798 \r\nL 120.089681 86.59051 \r\nL 120.044026 207.524435 \r\nL 120.114031 119.937738 \r\nL 120.153598 202.703149 \r\nL 120.168817 61.278758 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121.611515 180.203814 \r\nL 121.614558 68.108914 \r\nL 121.635864 80.563902 \r\nL 121.745436 203.90847 \r\nL 121.644995 74.537295 \r\nL 121.751523 181.810909 \r\nL 121.763698 44.404257 \r\nL 121.842833 190.24816 \r\nL 121.861095 138.419335 \r\nL 121.903707 206.319113 \r\nL 121.918925 34.359911 \r\nL 121.955449 107.482749 \r\nL 121.958493 37.975876 \r\nL 121.991973 208.729756 \r\nL 122.061977 119.535965 \r\nL 122.098501 185.0251 \r\nL 122.138069 28.735078 \r\nL 122.171549 153.284967 \r\nL 122.275034 76.14439 \r\nL 122.177637 195.069446 \r\nL 122.281121 150.070776 \r\nL 122.372431 95.831308 \r\nL 122.293296 201.497827 \r\nL 122.393737 98.241951 \r\nL 122.463741 206.720887 \r\nL 122.427217 86.992284 \r\nL 122.503309 94.224213 \r\nL 122.515484 198.283636 \r\nL 122.57027 44.002483 \r\nL 122.615925 167.748825 \r\nL 122.701147 69.314235 \r\nL 122.722453 194.667672 \r\nL 122.731584 77.751486 \r\nL 122.768108 199.890732 \r\nL 122.780283 54.448603 \r\nL 122.847243 161.722217 \r\nL 122.880724 50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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125.656548 212.345721 \r\nL 125.738727 132.392727 \r\nL 125.811775 195.872993 \r\nL 125.805688 47.216674 \r\nL 125.817862 130.383858 \r\nL 125.820906 33.958137 \r\nL 125.924391 194.667672 \r\nL 125.991351 17.083636 \r\nL 125.970046 202.301375 \r\nL 126.046137 122.750155 \r\nL 126.137447 186.230421 \r\nL 126.122229 70.519557 \r\nL 126.155709 132.794501 \r\nL 126.16484 85.786962 \r\nL 126.177015 195.069446 \r\nL 126.259194 131.187406 \r\nL 126.271369 203.104922 \r\nL 126.353548 85.786962 \r\nL 126.37181 178.998492 \r\nL 126.453989 86.992284 \r\nL 126.472251 209.533304 \r\nL 126.55443 81.769224 \r\nL 126.584866 133.196275 \r\nL 126.633565 199.087184 \r\nL 126.603128 63.689401 \r\nL 126.694438 104.268559 \r\nL 126.706613 209.13153 \r\nL 126.7127 70.117783 \r\nL 126.807054 158.9098 \r\nL 126.886189 78.153259 \r\nL 126.840534 201.497827 \r\nL 126.916626 132.392727 \r\nL 127.001849 201.497827 \r\nL 126.931844 82.974545 \r\nL 127.023154 138.017561 \r\nL 127.074897 102.661463 \r\nL 127.111421 205.917339 \r\nL 127.13577 107.080976 \r\nL 127.150988 168.954146 \r\nL 127.199687 74.135521 \r\nL 127.248386 141.231752 \r\nL 127.26056 193.864124 \r\nL 127.312303 88.599379 \r\nL 127.354914 184.623326 \r\nL 127.370132 44.806031 \r\nL 127.418831 195.872993 \r\nL 127.464486 155.69561 \r\nL 127.50101 210.336851 \r\nL 127.546665 41.59184 \r\nL 127.567971 136.410466 \r\nL 127.589276 62.082306 \r\nL 127.59232 204.310244 \r\nL 127.677543 115.518226 \r\nL 127.695805 77.349712 \r\nL 127.74146 185.0251 \r\nL 127.759722 17.083636 \r\nL 127.851032 68.510687 \r\nL 127.878425 199.087184 \r\nL 127.887556 17.083636 \r\nL 127.963647 145.24949 \r\nL 128.061045 41.59184 \r\nL 128.064088 212.747494 \r\nL 128.073219 42.395388 \r\nL 128.109743 184.623326 \r\nL 128.1067 17.083636 \r\nL 128.185835 143.240621 \r\nL 128.201053 89.001153 \r\nL 128.252796 195.47122 \r\nL 128.295407 111.098714 \r\nL 128.3228 189.042838 \r\nL 128.383673 41.190067 \r\nL 128.404979 152.481419 \r\nL 128.462809 32.351042 \r\nL 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 182.212683 \r\nL 140.649651 195.069446 \r\nL 140.613127 74.135521 \r\nL 140.652694 174.57898 \r\nL 140.704437 51.636186 \r\nL 140.674 201.096053 \r\nL 140.76531 133.598049 \r\nL 140.832271 195.872993 \r\nL 140.847489 76.14439 \r\nL 140.874882 188.23929 \r\nL 140.887057 55.252151 \r\nL 140.963148 203.90847 \r\nL 140.987498 135.205144 \r\nL 141.087939 206.720887 \r\nL 141.002716 22.70847 \r\nL 141.09707 189.042838 \r\nL 141.197511 62.885854 \r\nL 141.194467 194.265898 \r\nL 141.206642 74.537295 \r\nL 141.218816 183.819778 \r\nL 141.319257 136.812239 \r\nL 141.352738 17.083636 \r\nL 141.419698 199.087184 \r\nL 141.498834 17.083636 \r\nL 141.489703 211.140399 \r\nL 141.532314 80.965676 \r\nL 141.608406 199.890732 \r\nL 141.611449 17.887184 \r\nL 141.641886 78.956807 \r\nL 141.675366 210.336851 \r\nL 141.672323 17.083636 \r\nL 141.751458 115.92 \r\nL 141.787982 17.083636 \r\nL 141.839724 205.917339 \r\nL 141.864074 99.849047 \r\nL 141.964515 189.846386 \r\nL 141.897554 48.020222 \r\nL 141.973646 169.757694 \r\nL 141.98582 37.574102 \r\nL 142.064956 184.221552 \r\nL 142.089305 38.779424 \r\nL 142.159309 194.265898 \r\nL 142.119742 31.949268 \r\nL 142.201921 134.80337 \r\nL 142.241488 46.413126 \r\nL 142.232357 203.506696 \r\nL 142.265838 93.420665 \r\nL 142.320624 199.087184 \r\nL 142.332798 48.823769 \r\nL 142.37541 111.098714 \r\nL 142.478894 199.890732 \r\nL 142.396715 48.020222 \r\nL 142.484982 120.741286 \r\nL 142.488025 21.503149 \r\nL 142.503244 201.497827 \r\nL 142.59151 74.939069 \r\nL 142.597597 203.506696 \r\nL 142.701082 53.645055 \r\nL 142.704126 146.454812 \r\nL 142.743693 44.002483 \r\nL 142.801523 186.230421 \r\nL 142.813698 87.394058 \r\nL 142.868484 199.890732 \r\nL 142.850222 52.841508 \r\nL 142.926313 193.46235 \r\nL 142.944575 65.698271 \r\nL 142.965881 193.864124 \r\nL 143.035885 142.437073 \r\nL 143.066322 194.265898 \r\nL 143.072409 66.501818 \r\nL 143.142414 93.822439 \r\nL 143.215462 202.301375 \r\nL 143.233724 80.965676 \r\nL 143.255029 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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198.68541 \r\nL 167.330434 17.083636 \r\nL 167.406525 97.03663 \r\nL 167.479573 17.083636 \r\nL 167.513054 202.703149 \r\nL 167.516097 70.92133 \r\nL 167.549578 209.935078 \r\nL 167.625669 125.160798 \r\nL 167.689586 203.104922 \r\nL 167.637844 44.002483 \r\nL 167.735241 160.91867 \r\nL 167.850901 29.538625 \r\nL 167.771765 197.881863 \r\nL 167.853944 87.394058 \r\nL 167.860032 195.069446 \r\nL 167.863075 42.395388 \r\nL 167.963516 84.983415 \r\nL 167.972647 190.24816 \r\nL 168.030477 54.448603 \r\nL 168.070045 87.795831 \r\nL 168.076132 209.935078 \r\nL 168.179617 17.083636 \r\nL 168.210053 185.426874 \r\nL 168.292232 103.866785 \r\nL 168.316582 206.720887 \r\nL 168.347018 34.359911 \r\nL 168.410935 171.364789 \r\nL 168.496158 17.083636 \r\nL 168.490071 201.899601 \r\nL 168.532682 112.705809 \r\nL 168.544857 200.694279 \r\nL 168.623992 33.15459 \r\nL 168.63921 108.286297 \r\nL 168.733564 59.269889 \r\nL 168.721389 200.694279 \r\nL 168.748782 74.939069 \r\nL 168.815743 193.46235 \r\nL 168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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17.083636 \r\nL 174.148247 207.122661 \r\nL 174.178684 195.872993 \r\nL 174.23347 40.788293 \r\nL 174.19999 200.694279 \r\nL 174.303474 55.653925 \r\nL 174.306518 208.729756 \r\nL 174.364348 17.083636 \r\nL 174.413046 103.063237 \r\nL 174.443483 190.649933 \r\nL 174.431308 49.225543 \r\nL 174.525662 122.348381 \r\nL 174.574361 17.083636 \r\nL 174.562186 181.409135 \r\nL 174.63219 144.847716 \r\nL 174.711326 194.265898 \r\nL 174.708282 56.055698 \r\nL 174.7235 102.25969 \r\nL 174.753937 28.333304 \r\nL 174.763068 161.320443 \r\nL 174.833072 110.69694 \r\nL 174.912208 50.832639 \r\nL 174.857422 207.524435 \r\nL 174.936557 115.518226 \r\nL 174.988299 203.104922 \r\nL 175.024823 40.788293 \r\nL 175.043085 181.810909 \r\nL 175.046129 47.216674 \r\nL 175.155701 75.742616 \r\nL 175.240924 201.497827 \r\nL 175.189181 39.181197 \r\nL 175.268317 130.785632 \r\nL 175.36267 29.136851 \r\nL 175.280491 166.543503 \r\nL 175.377889 125.562572 \r\nL 175.426587 24.315565 \r\nL 175.396151 199.890732 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 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188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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17.083636 \r\nL 193.238126 211.542173 \r\nL 193.283781 119.535965 \r\nL 193.286824 194.265898 \r\nL 193.320305 17.083636 \r\nL 193.390309 174.57898 \r\nL 193.45727 39.582971 \r\nL 193.408571 197.881863 \r\nL 193.502925 56.859246 \r\nL 193.52423 188.641064 \r\nL 193.554667 17.083636 \r\nL 193.618584 144.847716 \r\nL 193.712938 17.083636 \r\nL 193.685545 192.257029 \r\nL 193.7312 62.082306 \r\nL 193.743374 19.494279 \r\nL 193.749462 138.821109 \r\nL 193.758593 25.119113 \r\nL 193.807291 205.113792 \r\nL 193.789029 17.083636 \r\nL 193.868165 46.8149 \r\nL 193.968606 178.194945 \r\nL 193.883383 17.083636 \r\nL 193.977737 59.269889 \r\nL 194.011217 191.051707 \r\nL 194.062959 34.359911 \r\nL 194.081221 120.741286 \r\nL 194.1634 17.083636 \r\nL 194.154269 162.123991 \r\nL 194.190793 129.178537 \r\nL 194.291234 184.623326 \r\nL 194.22123 17.083636 \r\nL 194.297322 178.596718 \r\nL 194.379501 17.083636 \r\nL 194.306453 209.533304 \r\nL 194.406894 111.500488 \r\nL 194.482985 184.221552 \r\nL 194.449505 24.315565 \r\nL 194.49516 102.661463 \r\nL 194.556033 17.083636 \r\nL 194.537771 203.506696 \r\nL 194.601688 176.989623 \r\nL 194.604732 185.0251 \r\nL 194.632125 17.083636 \r\nL 194.665605 116.723548 \r\nL 194.686911 17.083636 \r\nL 194.67778 207.122661 \r\nL 194.775177 41.190067 \r\nL 194.872575 181.409135 \r\nL 194.793439 17.083636 \r\nL 194.884749 123.955477 \r\nL 194.988234 48.823769 \r\nL 194.976059 196.274767 \r\nL 194.997365 75.742616 \r\nL 195.067369 209.935078 \r\nL 195.036933 17.083636 \r\nL 195.106937 208.729756 \r\nL 195.201291 23.110244 \r\nL 195.222596 60.475211 \r\nL 195.295644 17.083636 \r\nL 195.313906 192.658803 \r\nL 195.329125 80.563902 \r\nL 195.383911 198.283636 \r\nL 195.420435 17.083636 \r\nL 195.438697 140.02643 \r\nL 195.542181 17.083636 \r\nL 195.469133 193.060576 \r\nL 195.548269 133.999823 \r\nL 195.551312 202.301375 \r\nL 195.645666 17.083636 \r\nL 195.654797 62.48408 \r\nL 195.71567 17.083636 \r\nL 195.700452 192.257029 \r\nL 195.74002 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51.636186 \r\nL 330.930568 164.13286 \r\nL 330.994485 63.689401 \r\nL 330.970136 197.881863 \r\nL 331.043184 113.911131 \r\nL 331.101013 59.269889 \r\nL 331.13145 192.257029 \r\nL 331.143625 140.02643 \r\nL 331.213629 200.292506 \r\nL 331.22276 49.627317 \r\nL 331.234935 174.980754 \r\nL 331.317114 38.779424 \r\nL 331.262328 199.488958 \r\nL 331.344507 81.36745 \r\nL 331.435817 199.890732 \r\nL 331.444948 50.832639 \r\nL 331.454079 185.0251 \r\nL 331.551476 43.198936 \r\nL 331.536258 208.729756 \r\nL 331.566694 101.456142 \r\nL 331.578869 193.864124 \r\nL 331.633655 76.14439 \r\nL 331.64583 93.018891 \r\nL 331.648873 21.503149 \r\nL 331.664092 202.703149 \r\nL 331.755402 46.8149 \r\nL 331.816275 210.336851 \r\nL 331.831493 46.011353 \r\nL 331.868017 153.284967 \r\nL 331.962371 17.083636 \r\nL 331.910629 201.096053 \r\nL 331.977589 82.974545 \r\nL 332.059768 194.265898 \r\nL 331.989764 58.064568 \r\nL 332.090205 170.963016 \r\nL 332.117598 23.512018 \r\nL 332.141947 188.23929 \r\nL 332.202821 102.661463 \r\nL 332.236301 199.488958 \r\nL 332.306305 31.547494 \r\nL 332.358048 17.083636 \r\nL 332.415877 204.712018 \r\nL 332.522406 17.083636 \r\nL 332.491969 209.533304 \r\nL 332.531537 43.60071 \r\nL 332.635021 209.533304 \r\nL 332.543711 17.083636 \r\nL 332.644152 87.795831 \r\nL 332.711113 44.002483 \r\nL 332.662414 199.890732 \r\nL 332.735462 117.928869 \r\nL 332.738506 196.274767 \r\nL 332.750681 17.083636 \r\nL 332.841991 81.769224 \r\nL 332.863296 192.257029 \r\nL 332.930257 17.083636 \r\nL 332.954606 193.864124 \r\nL 333.039829 80.965676 \r\nL 333.042873 17.083636 \r\nL 333.061135 206.720887 \r\nL 333.149401 39.582971 \r\nL 333.161576 180.605588 \r\nL 333.240711 17.083636 \r\nL 333.258973 87.795831 \r\nL 333.262017 17.083636 \r\nL 333.344196 193.46235 \r\nL 333.365501 76.546164 \r\nL 333.462899 206.319113 \r\nL 333.371589 17.083636 \r\nL 333.475073 121.14306 \r\nL 333.578558 47.618448 \r\nL 333.560296 195.872993 \r\nL 333.587689 55.653925 \r\nL 333.685086 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Từ biểu đồ này, không thể xác định được điều gì, vì do tính chất của quá trình huấn luyện ngẫu nhiên, độ dài của các phiên huấn luyện thay đổi rất nhiều. Để hiểu rõ hơn về biểu đồ này, chúng ta có thể tính **trung bình động** qua một loạt các thí nghiệm, chẳng hạn 100. Điều này có thể được thực hiện một cách thuận tiện bằng cách sử dụng `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "#code block 12"
+   ]
+  },
+  {
+   "source": [
+    "## Thay đổi siêu tham số và xem kết quả hoạt động\n",
+    "\n",
+    "Bây giờ sẽ rất thú vị khi thực sự xem cách mô hình đã được huấn luyện hoạt động. Hãy chạy mô phỏng, và chúng ta sẽ sử dụng cùng chiến lược chọn hành động như trong quá trình huấn luyện: lấy mẫu dựa trên phân phối xác suất trong Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code block 13"
+   ]
+  },
+  {
+   "source": [
+    "## Lưu kết quả thành ảnh GIF động\n",
+    "\n",
+    "Nếu bạn muốn gây ấn tượng với bạn bè, bạn có thể gửi cho họ ảnh GIF động của cây sào cân bằng. Để làm điều này, chúng ta có thể gọi `env.render` để tạo một khung hình ảnh, sau đó lưu những khung hình đó thành ảnh GIF động bằng thư viện PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, khuyến nghị sử dụng dịch vụ dịch thuật chuyên nghiệp bởi con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/8-Reinforcement/2-Gym/solution/notebook.ipynb b/translations/vi/8-Reinforcement/2-Gym/solution/notebook.ipynb
new file mode 100644
index 000000000..c4157ca25
--- /dev/null
+++ b/translations/vi/8-Reinforcement/2-Gym/solution/notebook.ipynb
@@ -0,0 +1,522 @@
+{
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 4,
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "interpreter": {
+   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+  },
+  "coopTranslator": {
+   "original_hash": "5c0e485e58d63c506f1791c4dbf990ce",
+   "translation_date": "2025-09-06T15:21:42+00:00",
+   "source_file": "8-Reinforcement/2-Gym/solution/notebook.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+  {
+   "source": [
+    "## Trượt băng CartPole\n",
+    "\n",
+    "> **Vấn đề**: Nếu Peter muốn thoát khỏi con sói, cậu ấy cần phải di chuyển nhanh hơn nó. Chúng ta sẽ xem cách Peter học trượt băng, đặc biệt là giữ thăng bằng, bằng cách sử dụng Q-Learning.\n",
+    "\n",
+    "Đầu tiên, hãy cài đặt gym và nhập các thư viện cần thiết:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Requirement already satisfied: gym in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.18.3)\n",
+      "Requirement already satisfied: Pillow<=8.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (7.0.0)\n",
+      "Requirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.4.1)\n",
+      "Requirement already satisfied: numpy>=1.10.4 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.19.2)\n",
+      "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.6.0)\n",
+      "Requirement already satisfied: pyglet<=1.5.15,>=1.4.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from gym) (1.5.15)\n",
+      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
+      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "!pip install gym \n",
+    "\n",
+    "import gym\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "source": [
+    "## Tạo môi trường cartpole\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env = gym.make(\"CartPole-v1\")\n",
+    "print(env.action_space)\n",
+    "print(env.observation_space)\n",
+    "print(env.action_space.sample())"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Discrete(2)\nBox(-3.4028234663852886e+38, 3.4028234663852886e+38, (4,), float32)\n0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Để xem môi trường hoạt động như thế nào, hãy chạy một mô phỏng ngắn trong 100 bước.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "for i in range(100):\n",
+    "   env.render()\n",
+    "   env.step(env.action_space.sample())\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: You are calling 'step()' even though this environment has already returned done = True. You should always call 'reset()' once you receive 'done = True' -- any further steps are undefined behavior.\u001b[0m\n  warnings.warn(colorize('%s: %s'%('WARN', msg % args), 'yellow'))\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [
+    "Trong quá trình mô phỏng, chúng ta cần lấy các quan sát để quyết định cách hành động. Thực tế, hàm `step` trả về cho chúng ta các quan sát hiện tại, hàm phần thưởng, và cờ `done` cho biết liệu có nên tiếp tục mô phỏng hay không:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   print(f\"{obs} -> {rew}\")\n",
+    "env.close()"
+   ],
+   "cell_type": "code",
+   "metadata": {},
+   "execution_count": 4,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[ 0.03044442 -0.19543914 -0.04496216  0.28125618] -> 1.0\n",
+      "[ 0.02653564 -0.38989186 -0.03933704  0.55942606] -> 1.0\n",
+      "[ 0.0187378  -0.19424049 -0.02814852  0.25461393] -> 1.0\n",
+      "[ 0.01485299 -0.38894946 -0.02305624  0.53828712] -> 1.0\n",
+      "[ 0.007074   -0.19351108 -0.0122905   0.23842953] -> 1.0\n",
+      "[ 0.00320378  0.00178427 -0.00752191 -0.05810469] -> 1.0\n",
+      "[ 0.00323946  0.19701326 -0.008684   -0.35315131] -> 1.0\n",
+      "[ 0.00717973  0.00201587 -0.01574703 -0.06321931] -> 1.0\n",
+      "[ 0.00722005  0.19736001 -0.01701141 -0.36082863] -> 1.0\n",
+      "[ 0.01116725  0.39271958 -0.02422798 -0.65882671] -> 1.0\n",
+      "[ 0.01902164  0.19794307 -0.03740452 -0.37387001] -> 1.0\n",
+      "[ 0.0229805   0.39357584 -0.04488192 -0.67810827] -> 1.0\n",
+      "[ 0.03085202  0.58929164 -0.05844408 -0.98457719] -> 1.0\n",
+      "[ 0.04263785  0.78514572 -0.07813563 -1.2950295 ] -> 1.0\n",
+      "[ 0.05834076  0.98116859 -0.10403622 -1.61111521] -> 1.0\n",
+      "[ 0.07796413  0.78741784 -0.13625852 -1.35259196] -> 1.0\n",
+      "[ 0.09371249  0.98396202 -0.16331036 -1.68461179] -> 1.0\n",
+      "[ 0.11339173  0.79106371 -0.1970026  -1.44691436] -> 1.0\n",
+      "[ 0.12921301  0.59883361 -0.22594088 -1.22169133] -> 1.0\n"
+     ]
+    }
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "[-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38]\n[4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(env.observation_space.low)\n",
+    "print(env.observation_space.high)"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discretize(x):\n",
+    "    return tuple((x/np.array([0.25, 0.25, 0.01, 0.1])).astype(np.int))"
+   ]
+  },
+  {
+   "source": [
+    "Hãy cùng khám phá phương pháp rời rạc hóa khác sử dụng các thùng:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Sample bins for interval (-5,5) with 10 bins\n [-5. -4. -3. -2. -1.  0.  1.  2.  3.  4.  5.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_bins(i,num):\n",
+    "    return np.arange(num+1)*(i[1]-i[0])/num+i[0]\n",
+    "\n",
+    "print(\"Sample bins for interval (-5,5) with 10 bins\\n\",create_bins((-5,5),10))\n",
+    "\n",
+    "ints = [(-5,5),(-2,2),(-0.5,0.5),(-2,2)] # intervals of values for each parameter\n",
+    "nbins = [20,20,10,10] # number of bins for each parameter\n",
+    "bins = [create_bins(ints[i],nbins[i]) for i in range(4)]\n",
+    "\n",
+    "def discretize_bins(x):\n",
+    "    return tuple(np.digitize(x[i],bins[i]) for i in range(4))"
+   ]
+  },
+  {
+   "source": [
+    "Hãy chạy một mô phỏng ngắn và quan sát những giá trị môi trường rời rạc đó.\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(0, 0, -1, -3)\n(0, 0, -2, 0)\n(0, 0, -2, -3)\n(0, 1, -3, -6)\n(0, 2, -4, -9)\n(0, 3, -6, -12)\n(0, 2, -8, -9)\n(0, 3, -10, -13)\n(0, 4, -13, -16)\n(0, 4, -16, -19)\n(0, 4, -20, -17)\n(0, 4, -24, -20)\n"
+     ]
+    }
+   ],
+   "source": [
+    "env.reset()\n",
+    "\n",
+    "done = False\n",
+    "while not done:\n",
+    "   #env.render()\n",
+    "   obs, rew, done, info = env.step(env.action_space.sample())\n",
+    "   #print(discretize_bins(obs))\n",
+    "   print(discretize(obs))\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Q = {}\n",
+    "actions = (0,1)\n",
+    "\n",
+    "def qvalues(state):\n",
+    "    return [Q.get((state,a),0) for a in actions]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# hyperparameters\n",
+    "alpha = 0.3\n",
+    "gamma = 0.9\n",
+    "epsilon = 0.90"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "0: 108.0, alpha=0.3, epsilon=0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "def probs(v,eps=1e-4):\n",
+    "    v = v-v.min()+eps\n",
+    "    v = v/v.sum()\n",
+    "    return v\n",
+    "\n",
+    "Qmax = 0\n",
+    "cum_rewards = []\n",
+    "rewards = []\n",
+    "for epoch in range(100000):\n",
+    "    obs = env.reset()\n",
+    "    done = False\n",
+    "    cum_reward=0\n",
+    "    # == do the simulation ==\n",
+    "    while not done:\n",
+    "        s = discretize(obs)\n",
+    "        if random.random()<epsilon:\n",
+    "            # exploitation - chose the action according to Q-Table probabilities\n",
+    "            v = probs(np.array(qvalues(s)))\n",
+    "            a = random.choices(actions,weights=v)[0]\n",
+    "        else:\n",
+    "            # exploration - randomly chose the action\n",
+    "            a = np.random.randint(env.action_space.n)\n",
+    "\n",
+    "        obs, rew, done, info = env.step(a)\n",
+    "        cum_reward+=rew\n",
+    "        ns = discretize(obs)\n",
+    "        Q[(s,a)] = (1 - alpha) * Q.get((s,a),0) + alpha * (rew + gamma * max(qvalues(ns)))\n",
+    "    cum_rewards.append(cum_reward)\n",
+    "    rewards.append(cum_reward)\n",
+    "    # == Periodically print results and calculate average reward ==\n",
+    "    if epoch%5000==0:\n",
+    "        print(f\"{epoch}: {np.average(cum_rewards)}, alpha={alpha}, epsilon={epsilon}\")\n",
+    "        if np.average(cum_rewards) > Qmax:\n",
+    "            Qmax = np.average(cum_rewards)\n",
+    "            Qbest = Q\n",
+    "        cum_rewards=[]"
+   ]
+  },
+  {
+   "source": [],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814bb79788>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    },
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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190.649933 \r\nL 110.042537 102.25969 \r\nL 110.112542 169.35592 \r\nL 110.212983 195.872993 \r\nL 110.139935 103.465011 \r\nL 110.216026 145.651264 \r\nL 110.243419 95.831308 \r\nL 110.270812 211.542173 \r\nL 110.325598 112.705809 \r\nL 110.34386 197.881863 \r\nL 110.352991 107.080976 \r\nL 110.43517 127.973215 \r\nL 110.471694 116.723548 \r\nL 110.444301 191.453481 \r\nL 110.474738 148.061907 \r\nL 110.483869 212.345721 \r\nL 110.523437 66.903592 \r\nL 110.58431 125.562572 \r\nL 110.684751 208.327982 \r\nL 110.599528 113.911131 \r\nL 110.699969 168.552373 \r\nL 110.769974 93.822439 \r\nL 110.727362 196.274767 \r\nL 110.812585 133.999823 \r\nL 110.897808 212.345721 \r\nL 110.843022 55.653925 \r\nL 110.922157 129.982084 \r\nL 110.967812 109.893392 \r\nL 110.961725 176.989623 \r\nL 110.998249 119.937738 \r\nL 111.001292 209.13153 \r\nL 111.049991 109.491619 \r\nL 111.107821 112.705809 \r\nL 111.202174 206.319113 \r\nL 111.217393 162.927539 \r\nL 111.235655 100.25082 \r\nL 111.250873 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190.649933 \r\nL 113.743636 158.508027 \r\nL 113.801466 196.274767 \r\nL 113.758854 49.627317 \r\nL 113.850164 150.874324 \r\nL 113.862339 94.224213 \r\nL 113.898863 210.336851 \r\nL 113.956693 115.116452 \r\nL 113.984086 194.667672 \r\nL 114.002348 86.188736 \r\nL 114.069308 161.320443 \r\nL 114.102789 199.087184 \r\nL 114.139313 70.117783 \r\nL 114.163662 134.401596 \r\nL 114.215404 82.170998 \r\nL 114.251928 203.90847 \r\nL 114.273234 158.106253 \r\nL 114.32802 109.089845 \r\nL 114.358457 195.47122 \r\nL 114.382806 133.196275 \r\nL 114.422374 198.68541 \r\nL 114.449767 113.509357 \r\nL 114.486291 127.973215 \r\nL 114.56847 17.083636 \r\nL 114.538033 202.301375 \r\nL 114.592819 111.098714 \r\nL 114.620212 213.551042 \r\nL 114.611081 59.269889 \r\nL 114.702391 180.605588 \r\nL 114.711522 82.572772 \r\nL 114.708478 203.90847 \r\nL 114.815007 132.794501 \r\nL 114.827181 191.051707 \r\nL 114.903273 56.859246 \r\nL 114.921535 177.793171 \r\nL 114.982408 70.519557 \r\nL 114.930666 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50.430865 \r\nL 122.853331 187.837517 \r\nL 122.956815 72.528426 \r\nL 122.962903 205.917339 \r\nL 123.02682 64.492949 \r\nL 123.069431 175.784302 \r\nL 123.121173 73.733747 \r\nL 123.154654 209.935078 \r\nL 123.179003 150.47255 \r\nL 123.18509 200.694279 \r\nL 123.200309 60.073437 \r\nL 123.282488 151.677871 \r\nL 123.285531 57.26102 \r\nL 123.361623 207.524435 \r\nL 123.395103 101.054368 \r\nL 123.489457 197.480089 \r\nL 123.422496 71.323104 \r\nL 123.504675 163.329313 \r\nL 123.547287 90.206475 \r\nL 123.586854 193.060576 \r\nL 123.614247 133.999823 \r\nL 123.647728 75.742616 \r\nL 123.723819 203.104922 \r\nL 123.763387 81.36745 \r\nL 123.836435 114.714678 \r\nL 123.897308 197.480089 \r\nL 123.942963 17.083636 \r\nL 124.028186 198.68541 \r\nL 124.052535 136.410466 \r\nL 124.152976 65.296497 \r\nL 124.107321 200.292506 \r\nL 124.165151 95.429534 \r\nL 124.171238 110.69694 \r\nL 124.177326 78.956807 \r\nL 124.232112 211.140399 \r\nL 124.286898 166.543503 \r\nL 124.393426 94.625987 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132.498711 84.179867 \r\nL 132.568715 196.274767 \r\nL 132.516973 70.519557 \r\nL 132.61437 176.186075 \r\nL 132.626545 80.162129 \r\nL 132.653938 203.506696 \r\nL 132.733073 105.875654 \r\nL 132.775685 207.926208 \r\nL 132.800034 66.903592 \r\nL 132.842645 109.089845 \r\nL 132.882213 194.265898 \r\nL 132.906562 74.939069 \r\nL 132.943086 139.222882 \r\nL 132.955261 48.020222 \r\nL 133.013091 185.426874 \r\nL 133.052658 84.581641 \r\nL 133.156143 212.747494 \r\nL 133.089182 34.359911 \r\nL 133.165274 159.311574 \r\nL 133.174405 85.786962 \r\nL 133.250497 191.855255 \r\nL 133.268759 170.159468 \r\nL 133.271802 211.943947 \r\nL 133.350938 54.046829 \r\nL 133.375287 117.125322 \r\nL 133.472684 45.609579 \r\nL 133.387462 212.345721 \r\nL 133.484859 105.875654 \r\nL 133.490946 188.23929 \r\nL 133.548776 17.083636 \r\nL 133.594431 131.58918 \r\nL 133.679654 19.092506 \r\nL 133.664435 192.658803 \r\nL 133.700959 170.963016 \r\nL 133.704003 201.497827 \r\nL 133.761833 56.859246 \r\nL 133.810531 173.775432 \r\nL 133.868361 52.439734 \r\nL 133.898798 185.0251 \r\nL 133.920103 89.402927 \r\nL 133.971846 204.712018 \r\nL 133.993151 76.546164 \r\nL 134.032719 195.872993 \r\nL 134.111854 32.351042 \r\nL 134.148378 114.312905 \r\nL 134.157509 60.475211 \r\nL 134.203164 184.221552 \r\nL 134.242732 97.840177 \r\nL 134.321867 203.104922 \r\nL 134.267081 54.046829 \r\nL 134.352304 198.283636 \r\nL 134.382741 17.887184 \r\nL 134.46492 135.205144 \r\nL 134.53188 31.145721 \r\nL 134.486225 194.667672 \r\nL 134.574492 113.911131 \r\nL 134.592754 199.488958 \r\nL 134.662758 33.15459 \r\nL 134.68102 115.92 \r\nL 134.684064 25.922661 \r\nL 134.735806 184.623326 \r\nL 134.787548 82.974545 \r\nL 134.790592 204.310244 \r\nL 134.842334 65.698271 \r\nL 134.89712 125.964346 \r\nL 134.95495 43.60071 \r\nL 134.991474 203.104922 \r\nL 135.009736 79.760355 \r\nL 135.098002 203.506696 \r\nL 135.085828 50.029091 \r\nL 135.119308 113.107583 \r\nL 135.204531 46.011353 \r\nL 135.201487 158.9098 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32.351042 \r\nL 136.601574 148.061907 \r\nL 136.616792 17.083636 \r\nL 136.653316 211.140399 \r\nL 136.711146 138.017561 \r\nL 136.772019 188.641064 \r\nL 136.74767 64.492949 \r\nL 136.817674 136.008692 \r\nL 136.90594 69.716009 \r\nL 136.881591 182.212683 \r\nL 136.924202 111.500488 \r\nL 136.978988 195.47122 \r\nL 137.009425 56.055698 \r\nL 137.033774 76.546164 \r\nL 137.097691 51.234412 \r\nL 137.106822 195.47122 \r\nL 137.152477 17.083636 \r\nL 137.219438 93.822439 \r\nL 137.2377 206.319113 \r\nL 137.286399 22.306696 \r\nL 137.325966 68.912461 \r\nL 137.395971 17.887184 \r\nL 137.347272 191.453481 \r\nL 137.435538 42.797162 \r\nL 137.459888 193.060576 \r\nL 137.548154 87.795831 \r\nL 137.599896 66.903592 \r\nL 137.581634 211.542173 \r\nL 137.621202 141.633525 \r\nL 137.721643 209.935078 \r\nL 137.691206 17.083636 \r\nL 137.72773 114.312905 \r\nL 137.809909 193.46235 \r\nL 137.840346 66.501818 \r\nL 137.898176 205.515565 \r\nL 137.952962 166.141729 \r\nL 138.004704 29.940399 \r\nL 137.974267 202.301375 \r\nL 138.065577 105.072106 \r\nL 138.141669 199.488958 \r\nL 138.102101 73.733747 \r\nL 138.147756 113.911131 \r\nL 138.196455 17.083636 \r\nL 138.214717 207.926208 \r\nL 138.257328 56.055698 \r\nL 138.327333 200.694279 \r\nL 138.269503 17.083636 \r\nL 138.3669 191.855255 \r\nL 138.369944 71.724878 \r\nL 138.439948 197.480089 \r\nL 138.479516 107.884523 \r\nL 138.48256 190.649933 \r\nL 138.500822 45.609579 \r\nL 138.592132 148.865455 \r\nL 138.598219 22.306696 \r\nL 138.601263 210.738625 \r\nL 138.704747 119.535965 \r\nL 138.723009 210.336851 \r\nL 138.786926 91.81357 \r\nL 138.814319 165.739956 \r\nL 138.875193 80.162129 \r\nL 138.844756 194.265898 \r\nL 138.923891 154.892062 \r\nL 138.987808 213.149268 \r\nL 138.993896 75.742616 \r\nL 139.036507 191.453481 \r\nL 139.060856 71.323104 \r\nL 139.146079 183.418004 \r\nL 139.173472 67.305366 \r\nL 139.152166 196.274767 \r\nL 139.276957 143.642395 \r\nL 139.359136 195.069446 \r\nL 139.286088 25.520887 \r\nL 139.383485 125.562572 \r\nL 139.416965 66.100044 \r\nL 139.39566 206.720887 \r\nL 139.477839 72.528426 \r\nL 139.569149 17.083636 \r\nL 139.590454 204.310244 \r\nL 139.593498 206.319113 \r\nL 139.596542 164.13286 \r\nL 139.64524 17.083636 \r\nL 139.706114 189.042838 \r\nL 139.709157 111.500488 \r\nL 139.773074 17.083636 \r\nL 139.721332 195.872993 \r\nL 139.800467 132.392727 \r\nL 139.897865 197.480089 \r\nL 139.82786 89.804701 \r\nL 139.906996 171.766563 \r\nL 139.910039 58.868115 \r\nL 139.937432 197.881863 \r\nL 140.019612 79.760355 \r\nL 140.113965 195.872993 \r\nL 140.110922 17.083636 \r\nL 140.138315 172.168337 \r\nL 140.141358 74.135521 \r\nL 140.241799 213.952816 \r\nL 140.247887 101.456142 \r\nL 140.263105 208.729756 \r\nL 140.323978 19.494279 \r\nL 140.357459 101.857916 \r\nL 140.360502 103.465011 \r\nL 140.363546 77.751486 \r\nL 140.467031 212.747494 \r\nL 140.372677 46.413126 \r\nL 140.473118 127.571441 \r\nL 140.537035 65.296497 \r\nL 140.488336 195.872993 \r\nL 140.576603 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163.440628 100.652594 \r\nL 163.468021 203.90847 \r\nL 163.519763 56.055698 \r\nL 163.5502 197.881863 \r\nL 163.574549 17.083636 \r\nL 163.604986 203.90847 \r\nL 163.662815 71.724878 \r\nL 163.690208 206.720887 \r\nL 163.684121 17.083636 \r\nL 163.772387 103.866785 \r\nL 163.854566 32.752816 \r\nL 163.805868 199.488958 \r\nL 163.878916 133.196275 \r\nL 163.979357 195.872993 \r\nL 163.976313 30.743947 \r\nL 163.988488 113.509357 \r\nL 164.09806 193.46235 \r\nL 164.031099 17.083636 \r\nL 164.101103 135.606918 \r\nL 164.201544 31.145721 \r\nL 164.113278 207.926208 \r\nL 164.210675 71.323104 \r\nL 164.31416 207.122661 \r\nL 164.216763 18.690732 \r\nL 164.323291 140.428204 \r\nL 164.390252 21.904922 \r\nL 164.344597 204.310244 \r\nL 164.423732 126.36612 \r\nL 164.499824 198.283636 \r\nL 164.472431 34.761685 \r\nL 164.533304 129.178537 \r\nL 164.606352 201.899601 \r\nL 164.591134 47.216674 \r\nL 164.609396 197.078315 \r\nL 164.694618 18.690732 \r\nL 164.655051 206.319113 \r\nL 164.718968 191.453481 \r\nL 164.725055 25.520887 \r\nL 164.764623 203.506696 \r\nL 164.822452 191.855255 \r\nL 164.82854 211.943947 \r\nL 164.871151 17.083636 \r\nL 164.913762 175.382528 \r\nL 165.020291 17.083636 \r\nL 164.980723 203.104922 \r\nL 165.023334 95.429534 \r\nL 165.07812 189.444612 \r\nL 165.126819 17.083636 \r\nL 165.132906 76.546164 \r\nL 165.199867 205.515565 \r\nL 165.166387 17.083636 \r\nL 165.239435 132.794501 \r\nL 165.263784 17.083636 \r\nL 165.291177 188.23929 \r\nL 165.349007 118.330643 \r\nL 165.419011 207.926208 \r\nL 165.358138 28.735078 \r\nL 165.458579 129.982084 \r\nL 165.476841 20.699601 \r\nL 165.470753 195.069446 \r\nL 165.571194 69.314235 \r\nL 165.671635 195.47122 \r\nL 165.629024 30.743947 \r\nL 165.680766 82.974545 \r\nL 165.756858 17.083636 \r\nL 165.711203 196.676541 \r\nL 165.769033 118.330643 \r\nL 165.772076 200.694279 \r\nL 165.842081 17.083636 \r\nL 165.878605 103.465011 \r\nL 165.963827 17.083636 \r\nL 165.933391 200.694279 \r\nL 165.988177 51.234412 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168.764001 39.984745 \r\nL 168.861398 144.044169 \r\nL 168.940533 35.163459 \r\nL 168.964883 184.221552 \r\nL 168.97097 88.599379 \r\nL 169.037931 199.087184 \r\nL 169.010538 41.190067 \r\nL 169.083586 150.874324 \r\nL 169.092717 48.421996 \r\nL 169.174896 193.46235 \r\nL 169.199245 86.59051 \r\nL 169.284468 196.676541 \r\nL 169.2449 56.055698 \r\nL 169.308817 91.81357 \r\nL 169.366647 63.689401 \r\nL 169.397083 197.881863 \r\nL 169.409258 78.153259 \r\nL 169.506655 191.855255 \r\nL 169.439695 23.913792 \r\nL 169.521874 136.008692 \r\nL 169.534048 30.743947 \r\nL 169.57666 207.524435 \r\nL 169.634489 36.36878 \r\nL 169.710581 194.265898 \r\nL 169.646664 17.083636 \r\nL 169.747105 191.051707 \r\nL 169.847546 27.529756 \r\nL 169.862764 50.430865 \r\nL 169.877983 195.47122 \r\nL 169.957118 17.083636 \r\nL 169.972336 129.982084 \r\nL 170.027122 17.083636 \r\nL 170.036253 193.864124 \r\nL 170.084952 73.331973 \r\nL 170.151913 207.926208 \r\nL 170.121476 17.083636 \r\nL 170.194524 141.231752 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126.767894 \r\nL 171.515476 194.667672 \r\nL 171.491126 32.752816 \r\nL 171.536781 186.632195 \r\nL 171.573305 37.975876 \r\nL 171.555043 193.060576 \r\nL 171.649397 112.304035 \r\nL 171.743751 203.104922 \r\nL 171.737663 28.333304 \r\nL 171.752882 82.572772 \r\nL 171.755925 17.083636 \r\nL 171.853323 197.480089 \r\nL 171.862454 87.394058 \r\nL 171.956807 17.083636 \r\nL 171.938545 182.614457 \r\nL 171.962895 144.445942 \r\nL 171.981157 197.078315 \r\nL 172.038986 17.083636 \r\nL 172.063336 39.984745 \r\nL 172.069423 83.778093 \r\nL 172.072467 17.083636 \r\nL 172.084641 205.917339 \r\nL 172.185082 127.169667 \r\nL 172.197257 34.359911 \r\nL 172.248999 193.060576 \r\nL 172.294654 109.089845 \r\nL 172.297698 200.694279 \r\nL 172.328135 17.083636 \r\nL 172.401183 174.57898 \r\nL 172.504667 17.083636 \r\nL 172.462056 197.881863 \r\nL 172.513798 84.983415 \r\nL 172.516842 83.376319 \r\nL 172.599021 191.051707 \r\nL 172.58989 17.083636 \r\nL 172.62337 127.973215 \r\nL 172.644676 17.083636 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196.676541 \r\nL 176.921028 98.241951 \r\nL 177.009294 208.327982 \r\nL 176.933202 17.083636 \r\nL 177.027556 137.214013 \r\nL 177.033643 17.083636 \r\nL 177.036687 208.327982 \r\nL 177.137128 99.447273 \r\nL 177.191914 212.747494 \r\nL 177.167565 34.761685 \r\nL 177.249744 183.819778 \r\nL 177.350185 17.083636 \r\nL 177.316704 199.890732 \r\nL 177.362359 86.59051 \r\nL 177.417145 207.122661 \r\nL 177.411058 37.975876 \r\nL 177.444538 119.134191 \r\nL 177.535848 17.083636 \r\nL 177.481062 203.506696 \r\nL 177.55411 83.778093 \r\nL 177.602809 201.096053 \r\nL 177.566285 17.083636 \r\nL 177.663682 152.883193 \r\nL 177.691075 26.324435 \r\nL 177.748905 201.899601 \r\nL 177.776298 133.999823 \r\nL 177.79456 61.278758 \r\nL 177.800647 201.096053 \r\nL 177.873695 125.160798 \r\nL 177.876739 209.13153 \r\nL 177.971093 41.59184 \r\nL 177.986311 184.623326 \r\nL 178.065447 17.083636 \r\nL 178.016748 205.113792 \r\nL 178.095883 83.376319 \r\nL 178.114145 192.257029 \r\nL 178.168931 17.083636 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205.515565 \r\nL 179.581193 41.190067 \r\nL 179.599455 197.078315 \r\nL 179.608586 37.172328 \r\nL 179.693808 189.444612 \r\nL 179.699896 17.083636 \r\nL 179.775987 205.113792 \r\nL 179.80338 92.617118 \r\nL 179.903821 207.122661 \r\nL 179.845992 17.083636 \r\nL 179.912952 198.283636 \r\nL 179.992088 33.15459 \r\nL 179.943389 202.703149 \r\nL 180.022524 42.395388 \r\nL 180.083398 202.301375 \r\nL 180.049917 17.083636 \r\nL 180.13514 99.045499 \r\nL 180.211232 193.060576 \r\nL 180.162533 21.904922 \r\nL 180.22645 189.042838 \r\nL 180.28428 17.083636 \r\nL 180.244712 193.864124 \r\nL 180.339066 94.224213 \r\nL 180.418201 178.194945 \r\nL 180.402983 17.083636 \r\nL 180.451681 123.553703 \r\nL 180.527773 197.480089 \r\nL 180.53386 37.975876 \r\nL 180.576472 17.083636 \r\nL 180.564297 196.676541 \r\nL 180.628214 73.331973 \r\nL 180.661694 201.096053 \r\nL 180.734742 19.494279 \r\nL 180.74083 191.855255 \r\nL 180.819965 39.984745 \r\nL 180.853445 113.107583 \r\nL 180.862576 190.24816 \r\nL 180.868664 19.494279 \r\nL 180.953886 105.072106 \r\nL 181.039109 17.083636 \r\nL 180.999541 191.855255 \r\nL 181.063458 70.92133 \r\nL 181.130419 188.641064 \r\nL 181.090851 17.083636 \r\nL 181.17303 70.117783 \r\nL 181.218685 17.083636 \r\nL 181.206511 199.087184 \r\nL 181.276515 149.267228 \r\nL 181.309995 37.975876 \r\nL 181.376956 199.087184 \r\nL 181.486528 17.083636 \r\nL 181.404349 199.488958 \r\nL 181.489572 28.735078 \r\nL 181.556532 199.488958 \r\nL 181.498703 17.083636 \r\nL 181.602187 153.284967 \r\nL 181.638711 24.315565 \r\nL 181.635668 213.551042 \r\nL 181.708716 78.555033 \r\nL 181.769589 199.890732 \r\nL 181.733065 41.190067 \r\nL 181.821331 163.731086 \r\nL 181.912641 17.083636 \r\nL 181.833506 211.542173 \r\nL 181.933947 89.804701 \r\nL 181.958296 37.574102 \r\nL 181.949165 168.954146 \r\nL 181.970471 52.03796 \r\nL 182.061781 196.676541 \r\nL 182.064825 17.083636 \r\nL 182.080043 97.438404 \r\nL 182.128742 17.083636 \r\nL 182.168309 197.480089 \r\nL 182.186571 149.267228 \r\nL 182.26875 196.274767 \r\nL 182.238314 17.083636 \r\nL 182.271794 119.937738 \r\nL 182.366148 192.658803 \r\nL 182.378322 17.083636 \r\nL 182.411803 205.515565 \r\nL 182.487894 172.971885 \r\nL 182.515287 17.083636 \r\nL 182.497025 211.140399 \r\nL 182.60051 90.206475 \r\nL 182.688776 191.855255 \r\nL 182.703995 28.333304 \r\nL 182.707038 88.599379 \r\nL 182.798348 193.060576 \r\nL 182.810523 17.083636 \r\nL 182.850091 203.506696 \r\nL 182.920095 83.778093 \r\nL 182.938357 179.400266 \r\nL 183.026623 17.083636 \r\nL 183.124021 192.257029 \r\nL 183.136195 162.123991 \r\nL 183.163588 19.896053 \r\nL 183.236636 194.667672 \r\nL 183.245767 31.547494 \r\nL 183.27316 19.494279 \r\nL 183.358383 198.68541 \r\nL 183.431431 17.083636 \r\nL 183.467955 173.775432 \r\nL 183.541003 195.47122 \r\nL 183.489261 19.494279 \r\nL 183.556221 179.80204 \r\nL 183.632313 17.083636 \r\nL 183.589702 203.90847 \r\nL 183.668837 103.465011 \r\nL 183.723623 212.747494 \r\nL 183.738841 21.904922 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185.315461 191.453481 \r\nL 185.382421 198.283636 \r\nL 185.428076 17.083636 \r\nL 185.528518 201.497827 \r\nL 185.540692 155.293836 \r\nL 185.558954 193.060576 \r\nL 185.647221 17.083636 \r\nL 185.732443 211.943947 \r\nL 185.759836 119.937738 \r\nL 185.814622 195.47122 \r\nL 185.848103 17.083636 \r\nL 185.869408 127.571441 \r\nL 185.930282 17.083636 \r\nL 185.884627 179.400266 \r\nL 185.969849 52.841508 \r\nL 185.972893 196.274767 \r\nL 185.97898 17.083636 \r\nL 186.079421 141.633525 \r\nL 186.182906 17.083636 \r\nL 186.170731 196.676541 \r\nL 186.192037 31.547494 \r\nL 186.246823 197.480089 \r\nL 186.204212 17.083636 \r\nL 186.304653 138.821109 \r\nL 186.362482 213.149268 \r\nL 186.417268 17.083636 \r\nL 186.469011 202.703149 \r\nL 186.52684 50.430865 \r\nL 186.545102 199.087184 \r\nL 186.566408 21.503149 \r\nL 186.657718 171.364789 \r\nL 186.752072 17.083636 \r\nL 186.666849 206.319113 \r\nL 186.76729 95.027761 \r\nL 186.855556 183.819778 \r\nL 186.797727 17.083636 \r\nL 186.873818 80.965676 \r\nL 186.980347 17.083636 \r\nL 186.965128 209.533304 \r\nL 186.98339 25.119113 \r\nL 187.089919 196.676541 \r\nL 187.050351 17.083636 \r\nL 187.092962 156.900931 \r\nL 187.129486 17.083636 \r\nL 187.19036 194.667672 \r\nL 187.202534 74.939069 \r\nL 187.254277 205.917339 \r\nL 187.211665 17.083636 \r\nL 187.31515 120.339512 \r\nL 187.327325 211.943947 \r\nL 187.333412 17.083636 \r\nL 187.424722 178.596718 \r\nL 187.500814 17.083636 \r\nL 187.433853 209.935078 \r\nL 187.540381 42.395388 \r\nL 187.613429 198.68541 \r\nL 187.607342 17.083636 \r\nL 187.656041 172.971885 \r\nL 187.662128 17.083636 \r\nL 187.710827 188.23929 \r\nL 187.768656 73.331973 \r\nL 187.7717 189.846386 \r\nL 187.853879 17.083636 \r\nL 187.878228 57.26102 \r\nL 187.957364 17.083636 \r\nL 187.936058 203.90847 \r\nL 187.978669 93.420665 \r\nL 188.039543 199.087184 \r\nL 188.009106 17.083636 \r\nL 188.091285 166.945277 \r\nL 188.133896 195.872993 \r\nL 188.118678 17.083636 \r\nL 188.152158 78.956807 \r\nL 188.155202 17.083636 \r\nL 188.200857 196.676541 \r\nL 188.255643 188.641064 \r\nL 188.368259 17.083636 \r\nL 188.301298 209.935078 \r\nL 188.380433 20.297827 \r\nL 188.471743 189.444612 \r\nL 188.413914 17.083636 \r\nL 188.490005 88.599379 \r\nL 188.584359 17.083636 \r\nL 188.496093 198.283636 \r\nL 188.59349 167.347051 \r\nL 188.602621 189.444612 \r\nL 188.611752 103.465011 \r\nL 188.614796 17.083636 \r\nL 188.715237 199.890732 \r\nL 188.721324 58.868115 \r\nL 188.785241 192.658803 \r\nL 188.782197 42.797162 \r\nL 188.830896 120.339512 \r\nL 188.83394 25.119113 \r\nL 188.910031 194.265898 \r\nL 188.937424 188.641064 \r\nL 188.943512 190.24816 \r\nL 188.949599 153.686741 \r\nL 189.007429 74.135521 \r\nL 189.034822 198.283636 \r\nL 189.05004 124.759024 \r\nL 189.08352 197.881863 \r\nL 189.120044 17.083636 \r\nL 189.159612 155.293836 \r\nL 189.171787 17.083636 \r\nL 189.202223 193.060576 \r\nL 189.269184 156.900931 \r\nL 189.272228 192.257029 \r\nL 189.360494 49.627317 \r\nL 189.372669 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190.60231 187.435743 \r\nL 190.644921 26.726208 \r\nL 190.711882 169.35592 \r\nL 190.7271 19.896053 \r\nL 190.809279 190.24816 \r\nL 190.827541 91.411796 \r\nL 190.885371 17.887184 \r\nL 190.873196 205.917339 \r\nL 190.894502 157.704479 \r\nL 190.90972 190.649933 \r\nL 190.961463 27.127982 \r\nL 190.964506 119.535965 \r\nL 191.067991 17.083636 \r\nL 190.979725 201.497827 \r\nL 191.077122 31.547494 \r\nL 191.089297 17.083636 \r\nL 191.095384 157.302705 \r\nL 191.125821 89.804701 \r\nL 191.147126 185.0251 \r\nL 191.162345 17.083636 \r\nL 191.235393 78.956807 \r\nL 191.293222 17.083636 \r\nL 191.311484 210.738625 \r\nL 191.344965 26.726208 \r\nL 191.36627 186.230421 \r\nL 191.454537 22.306696 \r\nL 191.45758 89.001153 \r\nL 191.54889 17.083636 \r\nL 191.494104 206.319113 \r\nL 191.564109 160.115122 \r\nL 191.570196 198.68541 \r\nL 191.673681 17.083636 \r\nL 191.682812 89.804701 \r\nL 191.701074 202.703149 \r\nL 191.69803 17.083636 \r\nL 191.792384 109.089845 \r\nL 191.834995 17.083636 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+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "plt.plot(rewards)"
+   ]
+  },
+  {
+   "source": [
+    "Từ biểu đồ này, không thể rút ra bất kỳ điều gì, vì do tính chất của quá trình huấn luyện ngẫu nhiên, độ dài của các phiên huấn luyện thay đổi rất nhiều. Để hiểu rõ hơn về biểu đồ này, chúng ta có thể tính **trung bình động** qua một loạt các thí nghiệm, chẳng hạn 100. Điều này có thể được thực hiện một cách thuận tiện bằng cách sử dụng `np.convolve`:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x2814cc63488>]"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    },
+    {
+     "output_type": "display_data",
+     "data": {
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110.421698 \r\nL 116.97379 100.967522 \r\nL 116.979883 101.257861 \r\nL 117.092611 107.246111 \r\nL 117.120031 106.3388 \r\nL 117.135264 104.941542 \r\nL 117.184011 116.434143 \r\nL 117.187058 116.440191 \r\nL 117.260178 117.625744 \r\nL 117.244945 114.298938 \r\nL 117.287598 115.678051 \r\nL 117.336345 105.95773 \r\nL 117.403372 110.669696 \r\nL 117.406419 110.669696 \r\nL 117.500866 98.608515 \r\nL 117.421652 111.812908 \r\nL 117.58008 100.30821 \r\nL 117.613593 105.570611 \r\nL 117.66234 97.441108 \r\nL 117.677574 97.586278 \r\nL 117.756787 86.80138 \r\nL 117.829908 93.442893 \r\nL 117.988335 105.752073 \r\nL 117.991382 103.889062 \r\nL 118.076689 113.905771 \r\nL 118.088876 110.488234 \r\nL 118.119343 112.61739 \r\nL 118.161996 108.54659 \r\nL 118.180276 108.570784 \r\nL 118.183323 108.334884 \r\nL 118.229023 113.137581 \r\nL 118.253397 115.901854 \r\nL 118.28691 109.919653 \r\nL 118.326517 111.982272 \r\nL 118.442291 101.83854 \r\nL 118.366124 113.542846 \r\nL 118.460571 103.495894 \r\nL 118.463618 103.876965 \r\nL 118.524552 97.997592 \r\nL 118.673839 87.884104 \r\nL 118.536738 98.348419 \r\nL 118.698213 90.708864 \r\nL 118.85664 106.768261 \r\nL 118.859687 106.683579 \r\nL 118.899294 103.949549 \r\nL 118.932807 110.960036 \r\nL 118.966321 120.595674 \r\nL 119.051628 113.433969 \r\nL 119.094281 114.480401 \r\nL 119.139982 110.814866 \r\nL 119.152168 111.65564 \r\nL 119.267942 95.953119 \r\nL 119.207009 113.349287 \r\nL 119.286222 100.290063 \r\nL 119.465977 111.050767 \r\nL 119.469023 109.998286 \r\nL 119.511677 108.117129 \r\nL 119.548237 112.677877 \r\nL 119.660964 116.845457 \r\nL 119.612217 110.578965 \r\nL 119.673151 115.974439 \r\nL 119.761505 114.2445 \r\nL 119.694478 118.496762 \r\nL 119.779785 116.101462 \r\nL 119.804158 118.285056 \r\nL 119.880326 114.861471 \r\nL 119.990006 111.776615 \r\nL 119.926026 119.198415 \r\nL 120.00524 112.865388 \r\nL 120.03266 113.343238 \r\nL 120.063127 109.217999 \r\nL 120.081407 109.804727 \r\nL 120.224601 96.805991 \r\nL 120.300768 90.636279 \r\nL 120.346468 94.773615 \r\nL 120.349515 95.160734 \r\nL 120.407402 88.482929 \r\nL 120.413495 87.751031 \r\nL 120.462242 91.186714 \r\nL 120.486615 90.866131 \r\nL 120.489662 92.075878 \r\nL 120.510989 86.692503 \r\nL 120.599343 91.985147 \r\nL 120.718163 95.729316 \r\nL 120.64809 86.166262 \r\nL 120.727303 94.767566 \r\nL 120.840031 89.263216 \r\nL 120.858311 89.946723 \r\nL 120.995412 97.525791 \r\nL 120.904011 87.956689 \r\nL 121.001505 97.283841 \r\nL 121.022832 96.461213 \r\nL 121.031972 98.58432 \r\nL 121.175166 108.607077 \r\nL 121.178213 107.609035 \r\nL 121.303127 96.309994 \r\nL 121.312267 97.380621 \r\nL 121.315313 97.846374 \r\nL 121.379294 93.823964 \r\nL 121.40976 95.475269 \r\nL 121.525534 103.653161 \r\nL 121.458507 92.196853 \r\nL 121.586468 102.36478 \r\nL 121.592561 101.052204 \r\nL 121.662635 106.048461 \r\nL 121.693102 103.066434 \r\nL 121.711382 106.314606 \r\nL 121.790596 102.721656 \r\nL 121.796689 103.362822 \r\nL 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107.699766 \r\nL 123.639932 97.652814 \r\nL 123.655166 101.052204 \r\nL 123.7161 94.555861 \r\nL 123.74352 95.922875 \r\nL 123.75266 94.912736 \r\nL 123.82578 104.022134 \r\nL 123.83492 101.602639 \r\nL 123.920227 107.445719 \r\nL 123.947647 103.03619 \r\nL 123.950694 102.89102 \r\nL 123.972021 106.223874 \r\nL 123.975068 106.169436 \r\nL 124.051235 109.012342 \r\nL 123.999441 104.143109 \r\nL 124.084748 107.246111 \r\nL 124.124355 105.068566 \r\nL 124.103028 108.165519 \r\nL 124.194429 107.088844 \r\nL 124.200522 107.482012 \r\nL 124.206615 106.15129 \r\nL 124.212709 106.786407 \r\nL 124.310203 103.75599 \r\nL 124.255362 108.849026 \r\nL 124.328483 104.475789 \r\nL 124.343716 103.18136 \r\nL 124.39551 109.078878 \r\nL 124.401603 108.921611 \r\nL 124.407697 109.242194 \r\nL 124.41379 110.373308 \r\nL 124.48691 103.489845 \r\nL 124.489957 103.719697 \r\nL 124.648385 112.502464 \r\nL 124.50519 103.374919 \r\nL 124.654478 111.558861 \r\nL 124.718458 107.7784 \r\nL 124.748925 112.829095 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129.230596 86.450553 \r\nL 129.264109 92.577924 \r\nL 129.34637 97.979446 \r\nL 129.306763 91.229055 \r\nL 129.38293 95.426879 \r\nL 129.398163 89.015218 \r\nL 129.498704 93.043676 \r\nL 129.559638 87.914347 \r\nL 129.605338 92.432754 \r\nL 129.651038 89.67453 \r\nL 129.721112 95.838193 \r\nL 129.803372 85.779143 \r\nL 129.836886 92.813824 \r\nL 129.861259 93.442893 \r\nL 129.882586 86.093678 \r\nL 129.97094 76.657648 \r\nL 129.900866 88.755122 \r\nL 130.004453 77.673836 \r\nL 130.028827 76.331016 \r\nL 130.07148 87.206645 \r\nL 130.242095 120.027092 \r\nL 130.245141 118.883881 \r\nL 130.333495 129.826047 \r\nL 130.373102 121.980835 \r\nL 130.528483 98.935146 \r\nL 130.531529 99.298071 \r\nL 130.601603 94.682884 \r\nL 130.635117 88.277272 \r\nL 130.699097 99.419045 \r\nL 130.702144 99.11056 \r\nL 130.711284 102.945459 \r\nL 130.796591 94.440935 \r\nL 130.799638 95.106296 \r\nL 130.827058 91.356079 \r\nL 130.918458 91.725052 \r\nL 130.921505 92.825922 \r\nL 130.979392 87.593764 \r\nL 131.025092 91.537541 \r\nL 131.079932 89.015218 \r\nL 131.098213 93.563868 \r\nL 131.119539 93.430796 \r\nL 131.131726 94.99137 \r\nL 131.186566 88.343808 \r\nL 131.22008 91.622223 \r\nL 131.226173 90.835887 \r\nL 131.27492 95.106296 \r\nL 131.360227 98.560125 \r\nL 131.317574 93.104164 \r\nL 131.390694 96.406774 \r\nL 131.402881 94.31996 \r\nL 131.451628 100.205381 \r\nL 131.485141 99.122657 \r\nL 131.488188 99.709385 \r\nL 131.555215 93.787671 \r\nL 131.567402 94.07801 \r\nL 131.674036 80.359475 \r\nL 131.713643 81.000641 \r\nL 131.80809 90.212867 \r\nL 131.747156 79.506603 \r\nL 131.838557 85.767046 \r\nL 131.841603 85.724705 \r\nL 131.847697 86.65621 \r\nL 131.984797 98.475442 \r\nL 131.987844 96.727357 \r\nL 132.060964 107.391281 \r\nL 132.088385 102.388975 \r\nL 132.091431 102.31639 \r\nL 132.106665 104.318522 \r\nL 132.112758 102.739802 \r\nL 132.243765 122.204638 \r\nL 132.246812 120.341627 \r\nL 132.252905 118.02496 \r\nL 132.277279 123.831748 \r\nL 132.350399 123.620042 \r\nL 132.356493 123.795456 \r\nL 132.359539 123.081705 \r\nL 132.40524 128.344106 \r\nL 132.49664 117.365648 \r\nL 132.499687 117.795108 \r\nL 132.56976 112.175832 \r\nL 132.825682 93.406601 \r\nL 132.575854 113.028704 \r\nL 132.831775 95.172832 \r\nL 132.837869 95.553902 \r\nL 132.840915 94.434886 \r\nL 132.871382 91.610126 \r\nL 132.947549 95.753511 \r\nL 132.996296 88.827707 \r\nL 133.06637 93.557819 \r\nL 133.300964 105.425441 \r\nL 133.087697 92.100073 \r\nL 133.319244 105.032273 \r\nL 133.361898 94.828054 \r\nL 133.441112 100.277966 \r\nL 133.450252 100.973571 \r\nL 133.456345 98.493589 \r\nL 133.462439 95.795852 \r\nL 133.520326 106.375093 \r\nL 133.553839 103.090629 \r\nL 133.556886 102.897069 \r\nL 133.575166 107.990106 \r\nL 133.578213 107.736059 \r\nL 133.715313 120.964647 \r\nL 133.733593 119.053246 \r\nL 133.767107 129.033662 \r\nL 133.840227 118.248764 \r\nL 133.855461 119.295195 \r\nL 133.879834 113.016606 \r\nL 133.882881 113.167825 \r\nL 134.071775 97.955251 \r\nL 134.093102 101.723614 \r\nL 134.105289 103.27814 \r\nL 134.190596 100.011822 \r\nL 134.196689 97.646765 \r\nL 134.294183 101.772004 \r\nL 134.29723 101.40908 \r\nL 134.464797 76.923792 \r\nL 134.571431 72.012218 \r\nL 134.540964 77.135498 \r\nL 134.583618 73.234063 \r\nL 134.623225 75.756386 \r\nL 134.601898 71.963828 \r\nL 134.690252 73.923619 \r\nL 134.699392 72.012218 \r\nL 134.778606 82.464435 \r\nL 134.888286 90.624182 \r\nL 134.827353 81.974488 \r\nL 134.900473 88.712781 \r\nL 135.037574 80.123574 \r\nL 135.04062 80.395767 \r\nL 135.071087 84.696419 \r\nL 135.110694 79.028753 \r\nL 135.165534 84.000815 \r\nL 135.189908 82.319266 \r\nL 135.226468 87.80547 \r\nL 135.272168 85.482755 \r\nL 135.290448 85.017002 \r\nL 135.305682 89.214826 \r\nL 135.360522 87.865957 \r\nL 135.366615 89.505165 \r\nL 135.439736 85.452511 \r\nL 135.467156 84.490762 \r\nL 135.494576 88.997071 \r\nL 135.531136 88.960779 \r\nL 135.66519 109.992238 \r\nL 135.668237 108.87927 \r\nL 135.683471 107.548548 \r\nL 135.747451 117.293063 \r\nL 135.765731 113.506554 \r\nL 135.768778 113.597285 \r\nL 135.780964 111.208034 \r\nL 135.927205 89.487019 \r\nL 136.055166 70.481887 \r\nL 136.070399 73.572792 \r\nL 136.149613 84.871833 \r\nL 136.186173 76.107213 \r\nL 136.19836 73.959911 \r\nL 136.274527 85.355732 \r\nL 136.518262 107.076746 \r\nL 136.283667 83.033017 \r\nL 136.530448 104.070524 \r\nL 136.637082 98.269785 \r\nL 136.652316 98.638758 \r\nL 136.676689 103.489845 \r\nL 136.731529 94.89459 \r\nL 136.835117 84.279056 \r\nL 136.874724 88.035322 \r\nL 136.87777 89.202728 \r\nL 136.956984 82.077316 \r\nL 136.969171 82.416045 \r\nL 136.984404 81.351468 \r\nL 137.042291 89.359996 \r\nL 137.109318 93.775574 \r\nL 137.084945 85.912215 \r\nL 137.158065 90.787497 \r\nL 137.267746 95.112344 \r\nL 137.182439 89.553555 \r\nL 137.298213 91.92466 \r\nL 137.404846 79.464262 \r\nL 137.423127 83.940327 \r\nL 137.46578 87.678447 \r\nL 137.490154 82.10756 \r\nL 137.535854 85.083538 \r\nL 137.593741 83.97662 \r\nL 137.566321 87.684495 \r\nL 137.599834 86.438456 \r\nL 137.694281 94.840151 \r\nL 137.627254 85.688412 \r\nL 137.721701 90.231014 \r\nL 137.724748 88.984974 \r\nL 137.797869 95.100247 \r\nL 137.825289 91.622223 \r\nL 137.931923 95.735364 \r\nL 137.916689 91.513346 \r\nL 137.941063 94.138498 \r\nL 138.069023 79.252556 \r\nL 137.956296 95.330099 \r\nL 138.093397 81.726489 \r\nL 138.227451 93.182797 \r\nL 138.291431 96.71526 \r\nL 138.239638 91.622223 \r\nL 138.340178 93.769525 \r\nL 138.343225 93.588063 \r\nL 138.361505 98.445199 \r\nL 138.373692 99.794067 \r\nL 138.449859 92.590021 \r\nL 138.492512 87.055427 \r\nL 138.556493 92.807776 \r\nL 138.775854 109.865214 \r\nL 138.788041 107.850985 \r\nL 138.794134 107.965911 \r\nL 138.797181 107.554597 \r\nL 139.083569 87.255035 \r\nL 139.098802 88.132102 \r\nL 139.202389 93.340065 \r\nL 139.129269 84.817394 \r\nL 139.22067 93.049725 \r\nL 139.24809 91.283494 \r\nL 139.269416 94.731274 \r\nL 139.403471 107.0586 \r\nL 139.290743 93.21909 \r\nL 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170.165829 75.901556 \r\nL 170.29379 93.079969 \r\nL 170.181063 75.877361 \r\nL 170.299883 92.674703 \r\nL 170.30293 91.646418 \r\nL 170.37605 104.288279 \r\nL 170.449171 107.87518 \r\nL 170.488778 107.433622 \r\nL 170.662439 82.627751 \r\nL 170.671579 83.825401 \r\nL 170.808679 77.704079 \r\nL 170.738606 85.609779 \r\nL 170.814773 78.302904 \r\nL 170.930547 81.944244 \r\nL 170.87266 75.381364 \r\nL 170.939687 81.750684 \r\nL 171.034134 75.042635 \r\nL 170.948827 82.240632 \r\nL 171.052414 79.56709 \r\nL 171.073741 82.216437 \r\nL 171.122488 76.675794 \r\nL 171.149908 78.08515 \r\nL 171.174281 82.77897 \r\nL 171.244355 77.788762 \r\nL 171.262635 78.956168 \r\nL 171.372316 67.941418 \r\nL 171.396689 69.090678 \r\nL 171.427156 73.542548 \r\nL 171.491136 66.036065 \r\nL 171.503323 68.044246 \r\nL 171.576443 61.596292 \r\nL 171.509416 68.141026 \r\nL 171.649564 63.634717 \r\nL 171.673937 64.705343 \r\nL 171.734871 56.7694 \r\nL 171.756198 54.670488 \r\nL 171.801898 63.749643 \r\nL 171.844552 56.714962 \r\nL 171.850645 57.259348 \r\nL 171.865878 54.694683 \r\nL 171.920719 45.476408 \r\nL 171.881112 55.051559 \r\nL 171.999932 49.92223 \r\nL 172.006026 50.260959 \r\nL 172.012119 50.176277 \r\nL 172.08524 55.807651 \r\nL 172.124846 53.230889 \r\nL 172.127893 52.41431 \r\nL 172.201014 60.834152 \r\nL 172.341161 81.702294 \r\nL 172.344208 81.508735 \r\nL 172.438655 74.655516 \r\nL 172.459982 78.569049 \r\nL 172.493495 87.32762 \r\nL 172.542242 78.242417 \r\nL 172.569662 78.780754 \r\nL 172.618409 73.221965 \r\nL 172.688483 75.925751 \r\nL 172.697623 76.591112 \r\nL 172.712856 71.050468 \r\nL 172.721996 72.901382 \r\nL 172.731136 69.725795 \r\nL 172.813397 75.187805 \r\nL 172.831677 72.502165 \r\nL 172.84691 75.11522 \r\nL 172.920031 67.530103 \r\nL 172.926124 68.0261 \r\nL 172.980964 64.215396 \r\nL 173.023618 70.608911 \r\nL 173.029711 70.252035 \r\nL 173.057131 73.385281 \r\nL 173.102832 65.800165 \r\nL 173.133299 68.709607 \r\nL 173.279539 75.429754 \r\nL 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176.935559 68.594681 \r\nL 177.060473 60.26557 \r\nL 177.09094 61.687023 \r\nL 177.20062 66.937327 \r\nL 177.170154 60.332106 \r\nL 177.206714 65.370704 \r\nL 177.212807 64.663002 \r\nL 177.249367 71.510172 \r\nL 177.252414 70.748032 \r\nL 177.307254 78.284758 \r\nL 177.365141 73.760303 \r\nL 177.447402 68.897118 \r\nL 177.432168 73.832888 \r\nL 177.477869 71.153297 \r\nL 177.480915 71.885194 \r\nL 177.560129 65.933237 \r\nL 177.566222 66.423185 \r\nL 177.572316 65.158999 \r\nL 177.645436 72.695725 \r\nL 177.657623 72.514263 \r\nL 177.791677 82.754775 \r\nL 177.80691 79.954209 \r\nL 177.82519 78.333148 \r\nL 177.876984 84.720614 \r\nL 177.880031 83.468526 \r\nL 177.989711 88.62205 \r\nL 177.931824 79.083191 \r\nL 177.995805 86.904208 \r\nL 177.998851 87.351815 \r\nL 178.047598 78.89568 \r\nL 178.050645 79.766699 \r\nL 178.193839 59.001384 \r\nL 178.218213 61.953168 \r\nL 178.227353 63.543986 \r\nL 178.279146 54.083761 \r\nL 178.3218 60.229278 \r\nL 178.34008 57.688808 \r\nL 178.410154 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2RSJUUQNMRFNnx3VXAMvfLq2vmVNty2nZP2WQDaBdlGOrSSlDNXn7j+OdXz0mJPXRwvHDmptr7hE1tIvnoWRUkWnBpgN4Zq41+7b8nXFiWtebV1BiZR5pywHjlAdHDQLZjLhueEdH+9nFiu9PJoCZRBQPz8PhMyHE90S0CcAnRPQkgDUAZkj9ZwB4n4jy4dHoLwvCuJl6iMvBpF4Kj57CjkOl2FfirzEfL3eWJ/3Vn5zZXZXeGYDH5FFRXYN4xQPOivBRImu61wzr6Gekj1Q9qpmUH753uya6faxE18pU1wg8Omujo7eitTZz3euR1sBYXOq9zew+bOx91ad9uuF2tzAV9kKI9QD6a7TvgMd+r24vB3CJK6NjogorZhw91HZXZaTq6Bd+QnWtwMCsZv47OpSFTic2m6uKYFTXCnR/aB7OPK2pt00vmZgVIkGzt4a7I3t/2S5NW74V1u62J+yzmjf0VpRSYpbBVK94ydnPGXsCZSu+G8GEI2iZkBHIBNYuxY/vVGUNXltSp3nLppKaCNRyZW1Pr1C3HY6drLKl2RYdc/5QCRR54nXFTnfs2E4FPQD8bjOZml49XrM6uQePOxtjqBzTWdgzISOQGBKl//oJHdOMlmdHuMW/m7VVNxYdw3u/+04SGj3fLvivvepNbmImGJduCzyKN1jojd0sqZ3Te223hKNTWNgzIcOJfVkIgQWbDvjYQ4+d0hb2WrbZUCv7ZRXVPuMwE3p6ZE2b7edi+MrifNw6uovlYwSiDQfKGoXppEDDw+qKGctDORxbqEs/ymjlt1GiDnjL23/c0kR/qBzGQpucgYlpnMi9GUt34snZmzFU4XJ47os/W95/9e7QRine9fk6n3Ut3+zz+xj7yiv91pVFU37NP+xXNtHtZ9nbV2fbLgiixU6FgB/1/E946fL+OHSiAteGyPMkGHyrk4ZZDy0XUk1Y2DPRhmyzlwXUF6sKMbxLC7Q2yC//5GxPJkK7Hiwyf/0w8CyZgaBVg9aozi3g67depkoE5nISTT9G92gVlOPeLvnmbztYig6q9MKRhtIcGIr8NWzGYaIOWdjLybnu/nxdRL/Ou4GWZm9nolrd1T/rpfvCKJjeIR+v2I1/zrPmW+8WRoF1fTVcQ5XC/sUfjdNx6GHnIREqMw4Le8YV9L7cQghvoJGydJ1snlDmG49GtD4WI+28TJUnXy3MQ6FpntFW3ze+PmKU0fLu8d392pR1g79Y5Z8iwwr/WWg9QV+o8kSysGcCZs6GInR6YI5m6t73ft+FSS8txW/5h/Dh8rqMk3IIuhJ19Gm0onYRnbOhCFnTZuO6mSv9hItSTt00srPfHMRelc9+kt1K4xo8MLEnLh/YPuDjuMnoHi2Dclx1MRjA15/eaaUxW8Le0Rnsw8KeCZj7vlwPAPhcQwvK2++pCrXzcJmpa5q60He0otbW5XmFHzcfRO5e3wee0uX0tSXbsXiLb8Ku3YoH7PIHxmDj4+NxaXZggjopIQ4jutpPTtiiUbJ5J4cESyAmJ/gLe6UHlds1hLXQ8y5zGxb2TMDILmk/aFSRypVqdC7bcSTkbpCRipFZQZ2Qy+wzi1MYfFulpSAxPk63Lm2wCaY1wqkLqxmZGs4BynkRvYLnbtKicfAekkpY2DOuoZViYIOkqS42KPWmh7KIRTRhJOzV+ffNkqZZqVyVZrH8nbKEoLqwuRXcKkqjRbA07OYabyNK000gZSz1yGru643EZhwmIjlUWoGsabPx3bp9OF5ehSe/3+Td1qJRku5+aSkJttMl9Htcv6B1faZWCNTUCgx5ZqFfmcRyVU1X2fU0EBIs2vHH9apzuxwaAamUlVRVh+5txWmJQquo8+4EWrrRKizsGVtskbT36XPz8MycPLy1tK56U28DL47z+7aJWjPOZzcOsdW/tLwaldW1KDpW7p3vkLFSaMMuWsLkvDNa+7UpTUiRVkk0GLni9dIvh8JOr4Rz4zARyQGpCtDeklPe/N1yHdbzemfins/XadaF/WHj/rDZkoON3QIpLy3K934Wai2y3IWShWkpviYYLcVeK9mX+s2rbXqDgMcSyXRt1UizPdiafbhgYc/oIoTw8xzZXHTcuyxPmsn/5+Xux+erCjXrwhYcPomhnVtonqf/4z/gjk/WaG6rDxxxULSi1yPzAfhPPBpVTDJiYu86TT1O9fDR0uzj4wjjT/eNllULuQYabolGhLgoWMDozTEEazJYD9bsmbDT+9EfcI6qJux36+oKM/+oKshcWmGcKEqtccocPVmFb2zmHXGbu8d1c7yvmxPJLR16ZijNLuoHtJawv/Ss9pg6vJNPmzrRl16qXz3s9jdjapDz6Dx/SV/N9mCbG9VzW2yzZ8JGaUU1sqbNRmlFtd9k0sET+vnU1bnLlfbnsT1bRrQZZ4jOW4cVurdu7No4lmwtNu+kgZHASIj33fbjnWdjcKfmGNixGWZMyfa2b1NFMyfpZH/Uw+lbiR7BNKe0SkvGsC6ee948Vd+xIBh8fP1g/H1snXLB3jhM2DjjH/N1tyl/fyO66gvI8qoa9Hh4nnd9cKfmPtkcI4U2kp91IIXJu7RshC9vHurWkByhHL16cjVBdW2dM+ps1crUCEoTHQAkxYfXLuM0VYEVFtw50rusNnsFkxFdW6Brq8b429iu3jZOl8BEPOkNPRqRlqDUCg5atsN9jwqnvHbFAKx8cKz3XSM+gB9cw6QEDOgQmjqiehjJq8Nllbhy8GnedaVwaZWmn3HULH97sLGS5O2/l/tVTLWE0qQYaHyAnYLh708d5F1u38wzAc6aPVNvsPK6LSBw48hOpv1CxYiuGchonOy1z8bZ+CXcf14Pv7Zwuyoanb/kZBUenNTT9jG3mAR0BZvINfoBtymKyDx0fi9Hx/j8xqF47YoBIXuzYGHP4EhZJY7rlPpzyk9b/CNmX9Dw0gkWPTPTDLfLNm55HsHOJNmNIzujkyLaNBIwG32lg0T46RpRtI0tRuO6QSA1i43o0lLb5RKw7hlz1zj/bJl2ad0kBRPOMC5k4yamwp6I2hPRYiLaTEQbiehvUvujRLSXiNZKfxMV+9xPRPlEtIWIxgfzApjAGfDEAvR59Afb+2nlapf52ydrfdZnLN2JvSXuFsD+6e5RutvMfrPqH7WVH/n401uhhzQZG2la51drjMvfNUryCOnbbJQ1/EPfNn5tTtIoOMXK/OzI7vYTtv2osNeridbAP8CaZl8N4C4hRE8AgwHcQkTye8uLQoh+0t8cAJC2XQbgdAATAPyPiOw57DJhxWjiVcncXP/EZ3ocOO5+3pQsA+3a6oSrnOjKimb/+pXZmHfH2dYGF2TsCt24OELB9Em2NNLxp3t895VvMWYxBdcN74ibRna2NTYtRvdoaclmn5aSiB1Pe/VM3DyqMz5Q2MXVPOTAnBUtmAp7IUSREGK1tHwCwGYAbQ12mQzgEyFEhRBiJ4B8AAPdGCzjLsdOVaGi2j88f3An37woe46c9EbORhp6KRrMZL0s2+VJSLtm02BUiLLD97cNd/2Y6jmVoV1a4Pf7R/t4rsjLKYnaouOh83uhUbI13U7P5XFsz1Z4++qz0L6ptfKFSpv39+v3GZp/AvG6knnnmrP82sxKTQLASw4nk93Cls2eiLIA9AcgV564lYjWE9HbRCTXMmsLYI9it0IYPxyYMNH3sR9w0f9+865nTZuNmb8V+PUb8exiDHp6YQhHZh09bwyt6M9hXeoeYuq6n+GeYLVLexfruA7q2AyAtsDKbNLAR0C2TW+AgumTsPyBsbqBaLLw7WpgGwf0I3RrpNw0vTVKBpqx58gpNG2o7zfvxl0epfE5vXlVXbyC3ptxqs2IZLexLOyJqBGALwHcIYQ4DuBVAJ0B9ANQBOAFuavG7n6PWiK6gYhyiCinuNhZIAnjnG2Sp8XGfb6+1f+YtTEcw3FMok7gT8Mk/4nEBIXLjSy/5PS/ekLg+hHBjeIMBKMso3aQg67s5P9q0iARt47u6tP2x/4enU42iZlNsOoVs1Gn4bBD4+QEpFp8s3CKlmKgDEB79A+na+53tgXtP5hYEvZElAiPoP9QCPEVAAghDgghaoQQtQDeRJ2pphCAslROOwB+sfBCiDeEENlCiOyMjPB+CLFIcWnwco87wUwL1EPrrXxsz5Z4WMMdTlkFSv7BJkuZD/Vs9r3bafvPy/t/fpO9jJdu4pYlSb72QLM9pjf0zCPEe4W9cX+9eRzZlVcvvYYR8fFkrNkH8Q1umuSSm9E4GZec2c5nW9v0Bq6nk7CLFW8cAjADwGYhxL8U7UqfoT8CyJWWZwG4jIiSiagjgK4AVrg3ZMYNjL54Rn7z/YMUPETkzJ6qtc9bU87yKxABAGWVdUFC8l7HpZJwejKgfVPjzI+h9E5R888/9XHlOHKErdF9//624XjtigGGx5Ft8LIZx+zhcUZbbfdYWaN/aFJPPGLRh13O0NmuaQOkN0zE+X0yNR/EWt/fyf38vY6ccOPZnbDj6YlIS0nEMxf19tlmN/VEMLAygmEArgQwWuVm+SwRbSCi9QDOAfB3ABBCbATwGYBNAOYBuEUIEfzaXowtWjXWj5w0ErqntzH2X7fCoxf0woWqH1gcEZo5yFGSoVP3VEuDG9WtpWK7578c6aun2evVVd15qMyvrbVBNGowOPO0puadLBAvmbeMhP0ZbZtY9gk/Q/qO9G9vPL42TbQfpLXSOFKTE3CtxehUeXK5b7t0EBFe/vMAnJXVzKdPzkNj0UfjTe2JC8+wdA4ziMj7oFMXjFGnrAgHVrxxlgohSAjRR+lmKYS4UgjRW2r/gxCiSLHPU0KIzkKI7kKIucG9BMYJRknJ9IT920t3ouRk4MFXU4ZmITXZ16ZeUV3ryMPFzmv5PEWNXHm/RtI4GupMniXreJ3IKGMH/jKog+WxuIHepdsVK3KQUdMAE4LJz4pBnZpj+QNjfNIua6FV/xUAaix+D/oqJnDJGySnzZJ7Ruk+uPXMRaMc+PDrYbVaWDAJ/wiYoCGEwKK8A5rBT0a/Jz0l5PHvN+H79UXaG21ARLhYZdOsrK7VtPEqPWiCid5Dw8yMfbKi7qX15lGB+5e7gd1H5l3juuH9qQP9NGG7KD25jHLuyOgJdavZLpVxFrKicEqnQLiTNMLPumQmA/yTzIUDFvZRRnVNLW7/eA027TuOV5dsx7Xv5uAJRZ1YGSNPCbVbYjDo38H3Fb9FoyTU1Aq/4tjn9vQtsBEIsouhFnoPuETJU2WgjiBUCqxQa29W71Oyib04MT4OI7oGrsUeVgVcDeroeVC/8mdtW79aB5FNHVZ81gHf5HVyLd+vdSKJzfLPTOrtb6IKdDJX77rDBQv7KGPHoTLMWrcPE1/6Bc/O2wIAmPn7LszLLUKZorjI+sJjeocIC/FxhFoh8CeVxq/8kVqN7NXjuhH6idi0BOel2e3RvFEyCqZPwmc6XjdWA4jcpGD6JM+CjixSm8OSwmRCaJqahILpkzCpT6amSadG9dok55dXpmA2QvnduELK6qlnSjMzmb94aT+seHCM3z5P/7G34wC2SX1Cl/fGCizsowy97/RNH6zGtK82eNeX79RPNzw3N3BTjV2ICEJ4XrevHprl0y5z7bCO+G3aaEfHv/+8HprauSwYSeOXcG4v/bcK2QunV6Z54I+ebdpNOiiCrNQvbZP7u+NtYkYPgyIu3Vt5Jm1lU9fAjs1QXeM70LvGdcODE3viAo2cPEr+fWk/AHXzLYDHx/+tq7LxmI6Pu555RyYpIQ4tVU4L8XGEPw/q4JPzvz7Dwj7KMHrz3H2kruqU0WTo6t0luttG92ipuy0QjpRVorSiGodKK5CSWKctKzWyuDhCG40i2B9ep58LRaZVWophGmOtj23fMf3EbfKErpXJxFJFXninWSO1JpCV9/rZi/Xty+eFKLOiUVKyW0d3wZc3D8V9E3rgzauy8f7UgX5BU8kJ8bj+7E5+DgK3j/EN3jq/TybuHtcN94yvy/NDRBjbq5WuKa2Rg8/9pMkDor7Bwj7qMJD2CsFkZQJN+xDByQkjuzJ+u3afz+u9cmJN71Vcfv0HgDeuPFP3HHYn6YwmCmWBVGthMvGEwnw2pJP2hLPaD1vtpy3nAFIH68go507BDLIAAB5KSURBVGDUIwpVJoi2Gg9imfg48rqKnturFZIT4i1PxGarXEwT4uNw6+iufh5dRlg1ZXXKqJv0VR5f+WCRuXxgB812La4ZlmWpXzBhYR9lGP2wjygKYzv10Q5iWVAvyrwvPpq9wcWdJgVR6b1yt0pLMfxstCbjurbUN0skeAOH7H0getegFkbqdXm/sxSTzMojpSrSQ6ifx6EoaD2sS3NcMeg0844K1MJezx1YmWbgkxsG2x8crJcenHVrnX1emcvmNI0gvWcu6o1bzrGWMtrouxQqWNhHGUZf6T1H6swSTos5Oy2IbQel0FAKLiOZNfOagbh9TFdN+/hzF/fBkM7NbQs9I/kgexPp+efroTeERJN6r17ThuLzUNqse2TWCZN+qijRUCj2fxrQznbFJav+9ACw4oEx+PLmoX4ZWa1iteyk8jNVmpMCVXL0ooVDSejKzjAhwaq72M/bIjf5nFJoLMqrq3hl5GqY1SIVd56rnYUxo7EnmMaugmv0WT5zUW9cMyzLtjlMT76ZuW2SV9YLRRvhg6mD8P6yAiQn1D10/tC3DVo1Tsalbyzz9gsWk/pkYvb6Ikdup2qFw+j+tkxLQcsAIpSdpOJQfm5OzZf5T52HgsMnDatjhQrW7KOI4+VVun7GMq8t2Q4AeOfXghCMKHDi4wj92ns0VacR57JA1hImsneIli+6kYxMSYzXDL03Q8scAAAX9PH1QFGfW09YDe/aAq9fme3XrpzIDmbuffnYTu6NOuulUVR3oITClKVFQnxcRAh6gDX7qMJKacHpc/NcqSQUKpQTl04LM8sapNbut47u6peq13u+IAiIjhrVtQZ0SEc7VcK1MT1bIS0lAcclTx5ZMFo1JyhTAARzmkWeS3fyWQWrxqwWgeamkYeqVaqxvsCaPeMKcnpbtyk8esorrALNJWXFnKFMtbxi5+HATqg5Bv+27q3T/K6tSYNEzL59hHf913zPWL5b55ctXJMmivsRTJlaG4Bm73TeyAlOFYVogoV9PWb5jsPImjYbBRoZGENF89QkbHlyQtBCw1ftOqpYc/aDlQWs2e99zcPn+nhjHCo1rrdqh/sm9ECvzDS0VmR6bCNNJv+hbxvNB5GWgDqh8Nm3SqVBYfhAkeW1k3kBdVCV1UnUYBMsxSXcsBkngjlwvBylFdW64ePfrPVoeb9uP2RYfDuYpCTGIzkh3kcMd2jW0CeAyy2cygLZVm8mkNRZH7cXlzo7oQY3j+rsnR8474zWmHBGa0zuV1etM2+/f6IsrdE6sWuv31NiOd+MXWQvIidmEqXb6gMTe0SMbXvtI+PCPYSgwMI+gpHrvnpzoahIkn5oVdXB09zMGCj5fSsF6ZmnNdUV9p0znD+UQq33bZVKN7rNq1f4B35pmVq0nk25e+1nT3QaQGeFJy48A+2aNsCo7vYjq5U2+xvOjux5pLQGHlHZKk07TXJ9gIV9PaZK0ozCWWJQjvRUCiYj7w+tdAdK1JWfOrZIDdjobJQmwYhQBsLIV9gzMw3T5c/Upcfb6J7BSXEBeIq7PDjJWjUpNS0b1x/BeU73lvjX//WNuORmdmCbfT1mzgZPwrJXFm8P2xjkPDZKsbS5SF8jNpuUO3bKtziK0uUwmP7iWgQrD5AW8gNySKfm6Bugq6kavaId4SZbSkx3ncVqVOGEiHDRgHY+8Qz1DRb29Ri5apS6xJ8dPphqnkTMCsrJxC0G5g8zd7uemZ5Iw2uHeQRAIAU1ZN95py8GoawbKrwTnYpGDWFvZ/Lwrauy8anD9AKhxEnAE2MfFvZRQEJ8nKWEXDJZ02Z7l4cHmCNexurP1UzwPj7Zk6L2ppGd0LRhok+6Y7s4qWmrxKzoh1WsJOHScmFs1rBu/PIEq53Sh2N7tcIgh+kFooV7xnc3TL0cS7Cwr8dcdlZ7AJ6MiP/7KT9o51ELXPm8SqxaWJSavVYmQNkdr2VaCtY8Mg7dFT9Uu5GgRcfKAQB7j+qnKjYiOdH5K/tfR3X2ZuDsoBM1q6QulqDug1SmIHj9yjPx0fWDcPc4a1kWGQ+3nNMF8+44O9zDiAh4grYekxAvuxQCz/+wNWjnaZPu682hLNwtY2RP3/T4eCzZUoybP1ztEwGqFU2qDqEH6kxEdmNwZBfQds2MJ4X1cKLZt01vgN5tm+DeCT0AeEwpfdqbF7/wPgT1iogTMLSzO29hkUboQqtiGxb2UUKz1CQcKXMvCEhJlSr4peRkFR6c2NNH6zZS7BsmJXiTWCknaLX20cqEKAfb2I24TEmMk87jzCbsRNj/qqqkNdag2pUdIiXgyE2i74oiG9NvMxG1J6LFRLSZiDYS0d+k9mZEtICItkn/m0rtREQvEVE+Ea0nosiquhtFyHKxvKomaIIe0M59f/3ZnXzyjJt5yshavFwrFAC+X+9f/vBsjcLX8gSeXWEve3uo3TmtEsoJWjPClcgrmLSV8gEp6xcwwcOKZl8N4C4hxGoiagxgFREtAHA1gIVCiOlENA3ANAD3ATgPQFfpbxCAV6X/jENOVlajYZL/rZJF39Nz8oJ6/gwL/tC5e40LmDeTik8r2bTPP0BIyzPj6Yt64+VF+bYLrjx6wem4emgWWjusAevWBK0bRGNul0m9M9HsuiQM6Rzbk8ihwvTbLIQoEkKslpZPANgMoC2AyQBmSt1mArhQWp4M4D3hYRmAdCKqv5EILiGEcJxqttcj83WOGciIrJNoISpJq/jGvRO64zmD2qhWldXOGY3w4qX9bGvaSQlx6NbKuSdGUnz99amuDxARhnZpEfL4iVjF1q+HiLIA9AewHEArIUQR4HkgAJAjUNoC2KPYrVBqUx/rBiLKIaKc4uLILaThFh3vn4O7PlsX8HFeX7IdP0gTpBVVdQWRz8pyVmbQCu2aNsDFOrVPZZR1YGW6t2qMS7L9PXdkjjtI6gUA/7msH2ZM8c/h7jaJCaETQt55BZ6tZIKEZWFPRI0AfAngDiGEUYIO7fxN6gYh3hBCZAshsjMygpOkKdL4yqSwiBHHpACqZ+bm4Yb3V/kdzzc7pDWmDDGvGdqjdWPExZGhhg5om1+ClcJ2cr+2GNPTnYlP7eN7gtQymzjz4nECK7dMsLEk7IkoER5B/6EQ4iup+YBsnpH+y/XjCgEo1bl2AKwl4Y5S7AQ86fHb9kM+6+8v2+V7DgensFLPU37FNnvV1hL2ai8eM+6T3BXDzX8u64+C6ZMcT+w6Qf74QlnQg4ktrHjjEIAZADYLIf6l2DQLwBRpeQqAbxXtV0leOYMBHJPNPbHKzsOB55tX1/h8+JvcgI95pgXTz+Yi/5c4rYhE2TWwqSKcv1xhZtIiSxVs9OeB1qNDo41mqZ5JcHWa5Wcu6o2+7cz99BnGDCveOMMAXAlgAxGtldoeADAdwGdENBXAbgCXSNvmAJgIIB/ASQDXuDriesjyHUcCPsb+4+V+2n2gNE4211wzNTxZtLR42TVQqZdWmKRevnNcd9z+8RrveiS5Ooaai/q3hRACF/b3nd66fGAHXB7DD0HGPUyFvRBiKfTjH8Zo9BcAbglwXFGFVlSomoMnypHRKNlrLlHb4P+7cBsOnnA3lXGDJHNvk/SG/vllxvVq7dcmW3mUJqvKamPNvqzCd4JWy6MnVoiLI8PJbIYJlNhVpUKIme03d+8xDHxqIT7PKfS2qQtnuCXo7SYH03IXvW10F782cqDZK/OZF0yf5GeqYhjGPfjXFQIqdDTcrGmzkTVtNj5cvhsAsKKgztwTjBzkfx/bDRdKpfCmnWdtMnScRri/VoBPqvSWoJxkNRP2IzSiZRmGCQ6cGycEmAm9j1d4hH2ipNl+tnIPdh1xPqk7Y0o2ps7M8WufOqIjGibG465x3ZCabH7rVz98LtIteqQkxMd5I2QfkiaP9R5yMpzHnGFCBwt7FymvqsHGfcf9wvorqqzViJWLNt/75fqAxqH0QR/ZLQNLtnqC1uLIo5VbEfRA4Pngza5blvWRlJaAYaIV/pW5yKOzNuJPr/6GXZKr5Wc5e5B/sNRUw5UJhqL77jVneZfdqmlqFbO5CiLCfRN6YNatw0M0IoaJXVizd5FNkk96yckqnNYcuPeL9UhOiMP1Izp5++wrOaVbdNvNHCEfXz8YLdOSfY7ptPC2Xc48rSlW7TqKszqalxS8eVTnEIyIYRgW9i6yvtCT+fFwWYU3VUBFda2PZp9g4F7oZhpbrUyCdo7fVueBZIUGUoUnKy6noeaFS/oikc1GTAzCwj4IrCw4iiGd6hKDKSdodxSXoWXjFM2UwMEOlQ+VEUf2l49EYf8nk4RuDBOtsIoTBI6dqsI9X9RluFSmDZCXH9JId/DubwX4dOVuW+d6aFJPy31DVQDjkQtOx9ndMnweeAzDhBfW7IPAR8t9BbZSs1+ytRiz1u7TzQh535cbbJ1rytAsPDl7s6W+ZgUwWqelYP9xT5FurefCm1dlW8rJ37FFKt67dqClMTEMExpY2IcApQviO78WAACSXIoWVdcm/UPfNo6PpXwWaAn7c12qp8owTOhhM04AlFVU47n5eag0CZoqq/Qv0lHpkj1bra2/eGk/x8dSeu6E2k2TYZjgwsI+AK6bmYNXFm/HN2uNi5L8ss3dbJVGaEWlWk0wdk4PZQFx14bEMEwEwMI+AH7fcRgAsGZ3ScjPTQTT6lEyP/x9JF68tK9pv39ccDoW3TUSDZPiMS1CCokwDOMObLN3gd0B5LExY94dIzDh37/4tV8x6DTLKXE7tkhFxxaputu/u3U45m/cj8T4OHTKaIRNj09wPF6GYSIT1uxdoE2TBkGrt9o8VTv75Ucr7LloGtG7XRPcPb67a8djGCbyYGHvAkO7NMftn6wx7+gAoarV3q99OoDgFfNmGCY6YWHvAmUVNZi9PjhldlMSfatJrd0T+vkBhmHqPyzsXeDRWRuDduyGiealA7+8eUjQzs8wTHTAwl6DaV+uxyWv/Wa5f3UQTSpWSvWVW8yXzzBM7MLeOBp8snKP4facgiO4+LXfvetxBITThB6MEoYMw0QXpmojEb1NRAeJKFfR9igR7SWitdLfRMW2+4kon4i2ENH4YA08nCgFPRCe8nqt0uoEfLdWjUJ+foZh6hdWNPt3AbwM4D1V+4tCiOeVDUTUC8BlAE4H0AbAj0TUTQhhrVRThPPIt7nomZnm1x7kzMSa3DWuzlXSzaInDMNEJ6bCXgjxMxFlWTzeZACfCCEqAOwkonwAAwH8brxbZLLrcBlOa14XjPTe77s0+wXTZq9HUUl5yM/JMEz9JZAJ2luJaL1k5pErbLcFoDR4F0ptfhDRDUSUQ0Q5xcXFAQwjeIx87iesL4xMV8fOLfUjYhmGYdQ4FfavAugMoB+AIgAvSO1a9gRNtVcI8YYQIlsIkZ2RkaHVJSLYfeSkX1v/Duk+6/dOCH30aeHRUz7rvds24XquDMPo4kjYCyEOCCFqhBC1AN6Ex1QDeDR5ZcKWdgD2BTbEyEOd+OyLnEJHx9kcQA6aU5W+0yDf3TYc93HyMoZhdHAk7IkoU7H6RwCyp84sAJcRUTIRdQTQFcCKwIYYXuS87ifKq3T77DjkLBFag6S6gKl5d4zwLmc09njaZDVvqLuv3IdhGMYKphO0RPQxgFEAWhBRIYB/ABhFRP3gMdEUALgRAIQQG4noMwCbAFQDuKW+e+I8Oz8Pk/pk4vI3l7l63LvHdfNZ79G6zstn5YNjTfcf2rm5q+NhGCa6seKNc7lG8wyD/k8BeCqQQUUSuw6fRL/Hf0DJSX3N3gm3ju7q1/bhdYOQf7DUrz37tKbI2XUUo3u0xKK8gwCAZAtpFBiGYWQ4XYIF3Bb0rdNSNNuHdWmBKUOz/Npfv/JMNE9Nwp3n1r0NWK0+xTAMA3C6BC+HSyuwcd9xnN0t+J5BN43s5F3u2z4dI7q0MOzfvFEyVj18rk9bYhw/pxmGsQ4Le4m/vLUceftPIPcxdzI8/HjnSGQ2ScGO4jJc8PJSn23tm9VNvH57yzBHx09MYGHPMIx1WGJIbDlwAgDw1OzNrhyvY4tUpCYnoHe7Jt422bumVxv/lAt2SWFhzzCMDVizB5C3/7g3v82+klPGnS2ilRxt0V2jcOxUFZqmJjk+7tpHzsX+4+WWUh8zDMPIsLAH8MmKugwPp6qC5ykaF0cBCXoASG+YhPSGgR2DYZjYI+aF/UPfbMAHy+qKd6/YeSSMo2EYhgkOMW8LUAp6hmGYaCXmhT3DMEwsENPCvromsNqt7107EL/ce45f+9y/jdDozTAMEz5i2mb/yKyNAe2vF4ClVc2KYRgmnMS0Zj9nQ5HjfXu3rfOfb8kZKBmGiXBiWrMPpHbsjKuzvctL7jkHOw6VYtJLS/GPC3r59f3qr0NRXROGQrUMwzASMS3sA6Fl47pkZg2S4nF6myYomD5Js++ADk012xmGYUJFTJtxGIZhYoWYFvbCoR1nbM+WLo+EYRgmuMS0sHfCiK4t8MaV2eYdGYZhIoiYFvZ6ev3401vp7pPVPBVxGknOGIZhIpmYFvYnyqs12+dvPKC7zxCu/cowTD0k5oR9Ta3A/mPleOTbXL9tZlWqLj6zHSb2zgzW0BiGYYJGzLle/nNeHt74eYfmtkbJxkW8OygqTDEMw9QnTDV7InqbiA4SUa6irRkRLSCibdL/plI7EdFLRJRPROuJaEAwB2+XlQVHdAU9ACSZFATp3yHd7SExDMOEBCtmnHcBTFC1TQOwUAjRFcBCaR0AzgPQVfq7AcCr7gzTHS557XfD7ftKyr3Lc24fgXeuPguXD+zgbRtuUhicYRgmUjEV9kKInwGoK3pMBjBTWp4J4EJF+3vCwzIA6URUb4zcKwrqLrNXmzSc06MlhnWpm5AlYi8chmHqJ04naFsJIYoAQPovRxm1BbBH0a9QavODiG4gohwiyikuLnY4DPdY8cAYzfZmXAKQYZgowG1vHC3VV9OdXQjxhhAiWwiRnZFh7AUTCL9tP4SPlu9GZbVx7vqWaSma7a2baLczDMPUJ5wK+wOyeUb6f1BqLwTQXtGvHYB9zocXOH9+czke+HoDKi0UKpHTFr91VV2EbAanL2YYJgpwKuxnAZgiLU8B8K2i/SrJK2cwgGOyuSfcHCmt1N3WOSMVABAvRcY2Ta0z3TROSQzuwBiGYUKAFdfLjwH8DqA7ERUS0VQA0wGcS0TbAJwrrQPAHAA7AOQDeBPAX4MyagekJOlfavfWjQEAXVs2AgA0aeAv4FulsYbPMEz9xTSoSghxuc4mvxlN4UkjeUuggwoGRsVD+rbz+M8/ceEZmNyvLbpIQl9GL089wzBMfSGq0yUcKq3wLg+dvki33/UjOgEAUhLjMbwr+9IzDBN9RLWwv+Kt5Zb6cRZLhmGinagW9nn7T4R7CAzDMBFBVAt7K2x8bHy4h8AwDBN0olbYV1vwq7+wXxukJsdc4k+GYWKQqBX2XR6ca9qndzvOYskwTGwQtcLeCuVVNeEeAsMwTEiIaWE/4YzW4R4CwzBMSIhpYS/046wYhmGiipgS9lufPM9nPS2FJ2cZhokNokra1dQKfLJyN/4vu71Pe3JCHH6+9xwkJcThgYk90CApAWN6tNRNa8wwDBNtRJWw/2LVHjz4dS5KTlZ52+4/rwduHNnZu37D2Z21dmUYholqosqMU1rh8a5Zs7sEAHDjyE4+gp5hGCZWiSphXyUFUv24+QAAYN2eknAOh2EYJmKIKmE/fW6ez3r+wbIwjYRhGCayiCphr0aZ4phhGCaWiWphzzAMw3iIamGf89DYcA+BYRgmIohqYd+iEdeNZRiGAaJY2PeQiogzDMMwUSzs371mYLiHwDAMEzEEFEFLRAUATgCoAVAthMgmomYAPgWQBaAAwP8JIY4GNkxz5HTF7Zs1wPSL+qB1E06FwDAMI+OGZn+OEKKfECJbWp8GYKEQoiuAhdJ60Bn+z0UAgPQGSRjWpUUoTskwDFNvCIYZZzKAmdLyTAAXBuEcfhwqrQQAbNh7LBSnYxiGqVcEKuwFgB+IaBUR3SC1tRJCFAGA9L9lgOewxcTeXJCEYRhGTaBZL4cJIfYRUUsAC4goz3QPCenhcAMAdOjQIcBh1PHipf1cOxbDMEy0EJBmL4TYJ/0/COBrAAMBHCCiTACQ/h/U2fcNIUS2ECI7IyMjkGEAAAZ08BQPT06ID/hYDMMw0YZjYU9EqUTUWF4GMA5ALoBZAKZI3aYA+DbQQVqhY4tGaM3FSBiGYTQJxIzTCsDXRCQf5yMhxDwiWgngMyKaCmA3gEsCH6YxczYU4cvVhcE+DcMwTL3FsbAXQuwA0Fej/TCAMYEMyi5//XB1KE/HMAxT74jaCFqGYRimDhb2DMMwMUC9F/alFdXhHgLDMEzEU++F/Wcr94R7CAzDMBFPvRf2S7YWe5ffuirboCfDMEzsEmgEbdhp3igJAPDtLcPQt316mEfDMAwTmdRrzf5kZTW+Wr0XANC+WcMwj4ZhGCZyqdfC/pFvN3qXGyXX+5cUhmGYoFGvhf1pCm0+KaFeXwrDMExQqdfq8G1juiI1OQF7S06FeygMwzARTb0W9gBw7fCO4R4CwzBMxMO2D4ZhmBiAhT3DMEwMwMKeYRgmBmBhzzAMEwOwsGcYhokBWNgzDMPEACzsGYZhYgAW9gzDMDEACSHCPQYQUTGAXQ53bwHgkIvDqQ/wNccGfM2xQSDXfJoQIsNKx4gQ9oFARDlCiJhKZM/XHBvwNccGobpmNuMwDMPEACzsGYZhYoBoEPZvhHsAYYCvOTbga44NQnLN9d5mzzAMw5gTDZo9wzAMYwILe4ZhmBigXgt7IppARFuIKJ+IpoV7PHYgovZEtJiINhPRRiL6m9TejIgWENE26X9TqZ2I6CXpWtcT0QDFsaZI/bcR0RRF+5lEtEHa5yUiotBfqT9EFE9Ea4joe2m9IxEtl8b/KRElSe3J0nq+tD1LcYz7pfYtRDRe0R5x3wkiSieiL4goT7rfQ6L9PhPR36XvdS4RfUxEKdF2n4nobSI6SES5irag31e9c5gihKiXfwDiAWwH0AlAEoB1AHqFe1w2xp8JYIC03BjAVgC9ADwLYJrUPg3AP6XliQDmAiAAgwEsl9qbAdgh/W8qLTeVtq0AMETaZy6A88J93dK47gTwEYDvpfXPAFwmLb8G4GZp+a8AXpOWLwPwqbTcS7rfyQA6St+D+Ej9TgCYCeA6aTkJQHo032cAbQHsBNBAcX+vjrb7DOBsAAMA5Cragn5f9c5hOt5w/xAC+KCHAJivWL8fwP3hHlcA1/MtgHMBbAGQKbVlAtgiLb8O4HJF/y3S9ssBvK5of11qywSQp2j36RfG62wHYCGA0QC+l77IhwAkqO8rgPkAhkjLCVI/Ut9ruV8kficApEmCj1TtUXuf4RH2eyQBliDd5/HReJ8BZMFX2Af9vuqdw+yvPptx5C+UTKHUVu+QXlv7A1gOoJUQoggApP8tpW5612vUXqjRHm7+DeBeALXSenMAJUKIamldOU7vtUnbj0n97X4W4aQTgGIA70imq7eIKBVRfJ+FEHsBPA9gN4AieO7bKkT3fZYJxX3VO4ch9VnYa9kl650fKRE1AvAlgDuEEMeNumq0CQftYYOIzgdwUAixStms0VWYbKs31wyPpjoAwKtCiP4AyuB59daj3l+zZEOeDI/ppQ2AVADnaXSNpvtsRtivsT4L+0IA7RXr7QDsC9NYHEFEifAI+g+FEF9JzQeIKFPangngoNSud71G7e002sPJMAB/IKICAJ/AY8r5N4B0IkqQ+ijH6b02aXsTAEdg/7MIJ4UACoUQy6X1L+AR/tF8n8cC2CmEKBZCVAH4CsBQRPd9lgnFfdU7hyH1WdivBNBVmuFPgmdiZ1aYx2QZaWZ9BoDNQoh/KTbNAiDPyE+Bx5Yvt18lzeoPBnBMeoWbD2AcETWVNKpx8NgziwCcIKLB0rmuUhwrLAgh7hdCtBNCZMFzvxYJIf4CYDGAi6Vu6muWP4uLpf5Car9M8uLoCKArPJNZEfedEELsB7CHiLpLTWMAbEIU32d4zDeDiaihNCb5mqP2PisIxX3VO4cx4ZzIcWFyZCI8XizbATwY7vHYHPtweF7L1gNYK/1NhMdWuRDANul/M6k/AXhFutYNALIVx7oWQL70d42iPRtArrTPy1BNEob5+kehzhunEzw/4nwAnwNIltpTpPV8aXsnxf4PSte1BQrvk0j8TgDoByBHutffwON1EdX3GcBjAPKkcb0Pj0dNVN1nAB/DMydRBY8mPjUU91XvHGZ/nC6BYRgmBqjPZhyGYRjGIizsGYZhYgAW9gzDMDEAC3uGYZgYgIU9wzBMDMDCnmEYJgZgYc8wDBMD/D9pwksMstgtRgAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "def running_average(x,window):\n",
+    "    return np.convolve(x,np.ones(window)/window,mode='valid')\n",
+    "\n",
+    "plt.plot(running_average(rewards,100))"
+   ]
+  },
+  {
+   "source": [
+    "## Thay đổi siêu tham số và xem kết quả hoạt động\n",
+    "\n",
+    "Bây giờ sẽ rất thú vị khi thực sự xem cách mô hình đã được huấn luyện hoạt động. Hãy chạy mô phỏng, và chúng ta sẽ sử dụng cùng chiến lược chọn hành động như trong quá trình huấn luyện: lấy mẫu dựa trên phân phối xác suất trong Q-Table:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obs = env.reset()\n",
+    "done = False\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   env.render()\n",
+    "   v = probs(np.array(qvalues(s)))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "env.close()"
+   ]
+  },
+  {
+   "source": [
+    "## Lưu kết quả dưới dạng ảnh GIF động\n",
+    "\n",
+    "Nếu bạn muốn gây ấn tượng với bạn bè, bạn có thể gửi cho họ hình ảnh GIF động của cây cân bằng. Để làm điều này, chúng ta có thể gọi `env.render` để tạo một khung hình ảnh, sau đó lưu những khung hình này thành ảnh GIF động bằng thư viện PIL:\n"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "360\n"
+     ]
+    }
+   ],
+   "source": [
+    "from PIL import Image\n",
+    "obs = env.reset()\n",
+    "done = False\n",
+    "i=0\n",
+    "ims = []\n",
+    "while not done:\n",
+    "   s = discretize(obs)\n",
+    "   img=env.render(mode='rgb_array')\n",
+    "   ims.append(Image.fromarray(img))\n",
+    "   v = probs(np.array([Qbest.get((s,a),0) for a in actions]))\n",
+    "   a = random.choices(actions,weights=v)[0]\n",
+    "   obs,_,done,_ = env.step(a)\n",
+    "   i+=1\n",
+    "env.close()\n",
+    "ims[0].save('images/cartpole-balance.gif',save_all=True,append_images=ims[1::2],loop=0,duration=5)\n",
+    "print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn thông tin chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/translations/vi/PyTorch_Fundamentals.ipynb b/translations/vi/PyTorch_Fundamentals.ipynb
new file mode 100644
index 000000000..240813b69
--- /dev/null
+++ b/translations/vi/PyTorch_Fundamentals.ipynb
@@ -0,0 +1,2828 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "gpuType": "T4",
+   "authorship_tag": "ABX9TyOgv0AozH1FKQBD+RkgT2bV",
+   "include_colab_link": true
+  },
+  "kernelspec": {
+   "name": "python3",
+   "display_name": "Python 3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "accelerator": "GPU",
+  "coopTranslator": {
+   "original_hash": "0ca21b6ee62904d616f2e36dc1cf0da7",
+   "translation_date": "2025-09-06T13:08:25+00:00",
+   "source_file": "PyTorch_Fundamentals.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "view-in-github",
+    "colab_type": "text"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EHh5JllMh1rG",
+    "outputId": "f55755ad-c369-414c-85ec-6e9d4f061a02",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    }
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'2.2.1+cu121'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 1
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(\"I am excited to run this\")"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "UPlb-duwXAfz",
+    "outputId": "cfd687e4-1238-49f4-ab6b-ee1305b740d2"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "I am excited to run this\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "print(torch.__version__)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "byWVlJ9wXDSk",
+    "outputId": "fd74a5c4-4d4a-41b2-ef3c-562ea3e4811f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "2.2.1+cu121\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "Osm80zoEYklS"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# scalar\n",
+    "scalar = torch.tensor(7)\n",
+    "scalar"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "-o8wvJ-VXZmI",
+    "outputId": "558816f5-1205-4de1-fe1f-2f96e9bd79e6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(7)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mCZ2tXC4Y_Sg",
+    "outputId": "2d86dbdc-56e1-45c6-d3dd-14515f2a457a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "scalar.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ssN00By0ZQgS",
+    "outputId": "490f40d1-5135-4969-a6d3-c8c902cdc473"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# vector\n",
+    "vector = torch.tensor([7, 7])\n",
+    "vector\n",
+    "#vector.ndim\n",
+    "#vector.item()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Bws__5wlZnmF",
+    "outputId": "944e38f9-5ba1-4ddc-a9c6-cfb6a19bb488"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([7, 7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "vector.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9pjCvnsZZzNG",
+    "outputId": "e030a4da-8f81-4858-fbce-86da2aaafe52"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([2])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Matrix\n",
+    "MATRIX = torch.tensor([[7, 8],[9, 10]])\n",
+    "MATRIX"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "a747hI9SaBGW",
+    "outputId": "af835ddb-81ff-4981-badb-441567194d15"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[ 7,  8],\n",
+       "        [ 9, 10]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XdTfFa7vaRUj",
+    "outputId": "0fbbab9c-8263-4cad-a380-0d2a16ca499e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "MATRIX[0]\n",
+    "MATRIX[1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TFeD3jSDafm7",
+    "outputId": "69b44ab3-5ba7-451a-c6b2-f019a03d0c96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Tensor\n",
+    "TENSOR = torch.tensor([[[1, 2, 3],[3,6,9], [2,4,5]]])\n",
+    "TENSOR"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ic3cE47tah42",
+    "outputId": "f250e295-91de-43ec-9d80-588a6fe0abde"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 2, 3],\n",
+       "         [3, 6, 9],\n",
+       "         [2, 4, 5]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 12
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Wvjf5fczbAM1",
+    "outputId": "9c72b5b8-bafe-4ae7-9883-b051e209eada"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 3, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mwtXZwiMbN3m",
+    "outputId": "331a5e36-b1b0-4a5f-a9b8-e7049cbaa8f9"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "TENSOR[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vzdZu_IfbP3J",
+    "outputId": "e24e7e71-e365-412d-ff50-fc094b56d2f3"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [3, 6, 9],\n",
+       "        [2, 4, 5]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "A8OL9eWfcRrJ"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor = torch.rand(3,4)\n",
+    "random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hAqSDE1EcVS_",
+    "outputId": "946171c3-d054-400c-f893-79110356888c"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.4414, 0.7681, 0.8385, 0.3166],\n",
+       "        [0.0468, 0.5812, 0.0670, 0.9173],\n",
+       "        [0.2959, 0.3276, 0.7411, 0.4643]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.ndim"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "g4fvPE5GcwzP",
+    "outputId": "8737f36b-6864-4059-eaed-6f9156c22306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XsAg99QmdAU6",
+    "outputId": "35467c11-257c-4f16-99aa-eca930bcbc36"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor.size()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cii1pNdVdB68",
+    "outputId": "fc8d2de6-9215-43de-99f7-7b0d7f7d20fa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([3, 4])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_image_tensor = torch.rand(size=(3, 224, 224)) #color channels, height, width\n",
+    "random_image_tensor.ndim, random_image_tensor.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "aTKq2j0cdDjb",
+    "outputId": "6be42057-20b9-4faf-d79d-8b65c42cc27e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([3, 224, 224]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "random_tensor_ofownsize = torch.rand(size=(5,10,10))\n",
+    "random_tensor_ofownsize.ndim, random_tensor_ofownsize.shape\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "IyhDdj-Pd6nC",
+    "outputId": "43e5e334-6d4d-4b67-f87d-7d364c6d8c67"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(3, torch.Size([5, 10, 10]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "UOJW08uOert_"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero = torch.zeros(size=(3, 4))\n",
+    "zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGvXtaXyefie",
+    "outputId": "d40d3e28-8667-4d2f-8b62-f0829c6162ad"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "zero*random_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "OyUkUPkDe0uH",
+    "outputId": "26c2e4be-36ba-4c6c-9a90-2704ec135828"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones = torch.ones(size=(3, 4))\n",
+    "ones\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "y_Ac62Aqe82G",
+    "outputId": "291de5d9-b9df-49de-c9d1-d098e3e9f4d8"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.],\n",
+       "        [1., 1., 1., 1.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TvGOA9odfIEO",
+    "outputId": "45949ef4-6649-4b6c-d6af-2d4bfb8de832"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ones*zero"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "--pTyge-fI-8",
+    "outputId": "c4d9bb7e-829b-43db-e2db-b1a2d64e61f0"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.],\n",
+       "        [0., 0., 0., 0.]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "qDcc7Z36fSJF"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "one_to_ten = torch.arange(start = 1, end = 11, step = 1)\n",
+    "one_to_ten"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "w3CZB4zUfR1s",
+    "outputId": "197fcba1-da0a-4b4a-ed11-3974bd6c01aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "ten_zeros = torch.zeros_like(one_to_ten)\n",
+    "ten_zeros"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "WZh99BwVfRy8",
+    "outputId": "51ef8bfb-6fa0-4099-ff66-b97d65b2ddea"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [],
+   "metadata": {
+    "id": "pGGhgsbUgqbW"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor = torch.tensor([3.0, 6.0,9.0], dtype = None, device = None, requires_grad = False)\n",
+    "float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JORJl4XkfRsx",
+    "outputId": "71114171-0f49-481f-b6fc-6cb48e2fb895"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3., 6., 9.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_32_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "6wOPPwGyfRLn",
+    "outputId": "f23776a1-b682-404a-9f67-d5bcb0402666"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float32"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor = float_32_tensor.type(torch.float16)\n",
+    "float_16_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tFsHCvmZfOYe",
+    "outputId": "d3aa305a-7591-47f5-97fd-61bff60b44bd"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.float16"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 31
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "float_16_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "TQiCGTPuwq0q",
+    "outputId": "98750fce-1ca3-4889-e269-8b753efdea96"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor = torch.tensor([3, 6, 9], dtype = torch.int32)\n",
+    "int_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5hlrLvGUw5D_",
+    "outputId": "41d890a0-9aee-446c-d906-631ce2ab0995"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9], dtype=torch.int32)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 33
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "int_32_tensor*float_32_tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ihApD9u3xTNW",
+    "outputId": "d295eed0-6996-4e0f-8502-ff4b55cd1373"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 9., 36., 81.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(0,100,10)"
+   ],
+   "metadata": {
+    "id": "utKhlb_KxWDQ"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p78D74E9Rj7Y",
+    "outputId": "781a1614-a900-41f5-9e5d-358f0b2390aa"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.min()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4BcSs5NeRkcj",
+    "outputId": "3f24a8dc-58e9-4a5f-9834-e85856a34f9d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.max()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hinqvXVLRm4q",
+    "outputId": "5c7d8a53-3913-4ac1-bba3-5ba8ff68250a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 38
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mean(x.type(torch.float32))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "k7okc0_vRpnB",
+    "outputId": "91e5494f-dc57-417c-ea4d-25dbc547c893"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 39
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.type(torch.float32).mean()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "29QcDTjHRq10",
+    "outputId": "62937c6c-78e0-49f2-dde3-1543ee8f7907"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(45.)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wlpY_G_sbdKF",
+    "outputId": "475d8258-af65-4011-a258-b93d4d8142d4"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(450)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmax()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "GT6HJzwhbk4n",
+    "outputId": "2e455c20-c322-4bcf-d07c-1259d3ccefc6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x.argmin()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "egL3oi2Mb19P",
+    "outputId": "f71fb32f-6338-44a3-b377-75bea0a3ab54"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 43
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "p2U8DZKib3DP",
+    "outputId": "b9f613b9-74e9-45f4-ed01-05babb6a6793"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 44
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[9]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "24qBFlGYcABe",
+    "outputId": "5813cfcb-7f63-4bd7-ee46-f95ccbfda939"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(90)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 45
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.arange(1, 10)\n",
+    "x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "0GPOxEzkcBHO",
+    "outputId": "aefbd903-4f4c-4d2c-c90f-eccd682fe018"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "torch.Size([9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 46
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped = x.reshape(1,9)\n",
+    "x_reshaped, x_reshaped.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "spmRgQjwddgp",
+    "outputId": "85a7c55c-2909-4ea2-fc68-386dddc65742"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]]), torch.Size([1, 9]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 47
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_reshaped.view(1,9)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "tH2ahWGydqqP",
+    "outputId": "65d92263-4fc4-434a-c06d-c5e08436f7fe"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 48
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked = torch.stack([x, x, x, x], dim = 1)\n",
+    "x_stacked"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "jgCeJcaud_-1",
+    "outputId": "7f293a37-6ef1-43b6-aee5-9d6d91c94f9e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 49
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "XhJHIK6cfPse",
+    "outputId": "06c47b89-3a9e-453e-bcc3-00cbcb0b8b49"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 50
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=1)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ej2c3Xxzf0tq",
+    "outputId": "94024061-eb37-446d-c4a8-e4d16cb6de81"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 52
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.squeeze()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "4DJYo1a0f5M0",
+    "outputId": "efca2b47-1b14-44de-9a9a-2c83629d153f"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 1, 1, 1],\n",
+       "        [2, 2, 2, 2],\n",
+       "        [3, 3, 3, 3],\n",
+       "        [4, 4, 4, 4],\n",
+       "        [5, 5, 5, 5],\n",
+       "        [6, 6, 6, 6],\n",
+       "        [7, 7, 7, 7],\n",
+       "        [8, 8, 8, 8],\n",
+       "        [9, 9, 9, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 53
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_stacked.unsqueeze(dim=-2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "J4iEjn2ah2HL",
+    "outputId": "22395593-7c16-4162-beae-dd2bbe7bda35"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[1, 1, 1, 1]],\n",
+       "\n",
+       "        [[2, 2, 2, 2]],\n",
+       "\n",
+       "        [[3, 3, 3, 3]],\n",
+       "\n",
+       "        [[4, 4, 4, 4]],\n",
+       "\n",
+       "        [[5, 5, 5, 5]],\n",
+       "\n",
+       "        [[6, 6, 6, 6]],\n",
+       "\n",
+       "        [[7, 7, 7, 7]],\n",
+       "\n",
+       "        [[8, 8, 8, 8]],\n",
+       "\n",
+       "        [[9, 9, 9, 9]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 55
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "tensor = torch.tensor([1, 2, 3])\n",
+    "tensor = tensor - 10\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cFfiD7Nth7Z_",
+    "outputId": "1139e1f8-fc1a-46ca-d636-f2bc4fd2eef6"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-9, -8, -7])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.mul(tensor, 10)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dyA7BM_GHhqE",
+    "outputId": "0e3b9671-d9e8-4a32-87bb-59bc05986142"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-90, -80, -70])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 9
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.sub(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "owtUsZ1KNegI",
+    "outputId": "189b7b23-0041-4e09-b991-cd209a48506a"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-109, -108, -107])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.add(tensor, 100)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "K5STXlQONsyc",
+    "outputId": "00cbb79a-0a1d-4e21-86ec-5c91c37a2d01"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([91, 92, 93])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.divide(tensor, 2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xqMGnzIUNvp0",
+    "outputId": "c894cf3e-f148-45f8-cfc8-d78740735306"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([-4.5000, -4.0000, -3.5000])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 13
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(tensor, tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ruGzKpV8NyBc",
+    "outputId": "fddb63bf-006f-48b6-ae28-287fbcda8bc5"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "8GS3r9yTeGfD",
+    "outputId": "c80b12ac-30b5-4f3d-c38c-9e41ba511b0e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 16
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "tensor@tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "QmuYHqXTemC0",
+    "outputId": "402fe3ba-70b5-4bb2-c83b-254db84ff810"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 622 µs, sys: 0 ns, total: 622 µs\n",
+      "Wall time: 516 µs\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 17
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "%%time\n",
+    "torch.matmul(tensor,tensor)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dGr1fzdNepd8",
+    "outputId": "97bd6c91-bc25-4b38-cdf5-f22dcdef243e"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "CPU times: user 424 µs, sys: 998 µs, total: 1.42 ms\n",
+      "Wall time: 1.43 ms\n"
+     ]
+    },
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(194)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.rand(3,2)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pGYDoK2gevfo",
+    "outputId": "2c8783d5-0453-47c5-c7ed-af10d25d6989"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5999, 0.0073],\n",
+       "        [0.9321, 0.3026],\n",
+       "        [0.3463, 0.3872]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.matmul(torch.rand(3,2), torch.rand(2,3))"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KGBGQoB8e2DP",
+    "outputId": "4c2ef361-a2d0-41ee-c328-3992cbbc138d"
+   },
+   "execution_count": null,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.3528, 0.1893, 0.0714],\n",
+       "        [1.2791, 0.7110, 0.2563],\n",
+       "        [0.8812, 0.4553, 0.1803]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch"
+   ],
+   "metadata": {
+    "id": "ib8DMtkBe_LJ"
+   },
+   "execution_count": 1,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x = torch.rand(2,9)"
+   ],
+   "metadata": {
+    "id": "nJo8ZBdrQY1b"
+   },
+   "execution_count": 2,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wi6oRv4MQfgf",
+    "outputId": "55c99f55-31f6-4cf5-ba4e-19a47c3a0167"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[0.5894, 0.4391, 0.2018, 0.5417, 0.3844, 0.3592, 0.9209, 0.9269, 0.0681],\n",
+       "        [0.0746, 0.1740, 0.6821, 0.6890, 0.0999, 0.7444, 0.2391, 0.4625, 0.8302]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "y=torch.randn(2,3,5)\n",
+    "y"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zpx8myAUQgoc",
+    "outputId": "07756d70-56bd-437c-c74e-9aecc1a77311"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[ 1.5552, -0.4877,  0.5175, -1.7958, -0.6187],\n",
+       "         [-0.3359, -1.9710,  0.0112, -1.7578, -1.5295],\n",
+       "         [ 0.0932,  1.4079,  0.9108,  0.3328, -0.6978]],\n",
+       "\n",
+       "        [[-0.9406, -1.0809, -0.2595,  0.1282,  1.6605],\n",
+       "         [ 1.1624,  1.0902,  1.7092, -0.2842, -1.3780],\n",
+       "         [-0.1534, -1.2795, -0.5495,  0.9902,  0.1822]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original = torch.rand(size=(224,224,3))\n",
+    "x_original"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "s4U-X9bJQnWe",
+    "outputId": "657a7a76-962c-4b41-a76b-902d0482266c"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[[0.4549, 0.6809, 0.2118],\n",
+       "         [0.4824, 0.9008, 0.8741],\n",
+       "         [0.1715, 0.1757, 0.1845],\n",
+       "         ...,\n",
+       "         [0.8741, 0.6594, 0.2610],\n",
+       "         [0.0092, 0.1984, 0.1955],\n",
+       "         [0.4236, 0.4182, 0.0251]],\n",
+       "\n",
+       "        [[0.9174, 0.1661, 0.5852],\n",
+       "         [0.1837, 0.2351, 0.3810],\n",
+       "         [0.3726, 0.4808, 0.8732],\n",
+       "         ...,\n",
+       "         [0.6794, 0.0554, 0.9202],\n",
+       "         [0.0864, 0.8750, 0.3558],\n",
+       "         [0.8445, 0.9759, 0.4934]],\n",
+       "\n",
+       "        [[0.1600, 0.2635, 0.7194],\n",
+       "         [0.9488, 0.3405, 0.3647],\n",
+       "         [0.6683, 0.5168, 0.9592],\n",
+       "         ...,\n",
+       "         [0.0521, 0.0140, 0.2445],\n",
+       "         [0.3596, 0.3999, 0.2730],\n",
+       "         [0.5926, 0.9877, 0.7784]],\n",
+       "\n",
+       "        ...,\n",
+       "\n",
+       "        [[0.4794, 0.5635, 0.3764],\n",
+       "         [0.9124, 0.6094, 0.5059],\n",
+       "         [0.4528, 0.4447, 0.5021],\n",
+       "         ...,\n",
+       "         [0.0089, 0.4816, 0.8727],\n",
+       "         [0.2173, 0.6296, 0.2347],\n",
+       "         [0.2028, 0.9931, 0.7201]],\n",
+       "\n",
+       "        [[0.3116, 0.6459, 0.4703],\n",
+       "         [0.0148, 0.2345, 0.7149],\n",
+       "         [0.8393, 0.5804, 0.6691],\n",
+       "         ...,\n",
+       "         [0.2105, 0.9460, 0.2696],\n",
+       "         [0.5918, 0.9295, 0.2616],\n",
+       "         [0.2537, 0.7819, 0.4700]],\n",
+       "\n",
+       "        [[0.6654, 0.1200, 0.5841],\n",
+       "         [0.9147, 0.5522, 0.6529],\n",
+       "         [0.1799, 0.5276, 0.5415],\n",
+       "         ...,\n",
+       "         [0.7536, 0.4346, 0.8793],\n",
+       "         [0.3793, 0.1750, 0.7792],\n",
+       "         [0.9266, 0.8325, 0.9974]]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted=x_original.permute(2, 0, 1)\n",
+    "print(x_original.shape)\n",
+    "print(x_permuted.shape)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DD19_zvbQzHo",
+    "outputId": "1d64ce1b-eb48-47e3-90b6-7f1340e7f2b2"
+   },
+   "execution_count": 9,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "torch.Size([224, 224, 3])\n",
+      "torch.Size([3, 224, 224])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NnPmMk4ZRF7w",
+    "outputId": "2cd5da7f-4a23-4a76-8c4a-bb982113f2a4"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 10
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Z0ylNoAARgTo",
+    "outputId": "ddca0298-cddf-4048-9b71-a791655e5bed"
+   },
+   "execution_count": 11,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.4549)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 11
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]=0.989"
+   ],
+   "metadata": {
+    "id": "RXw0xXsDRi4L"
+   },
+   "execution_count": 13,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_original[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1sFdV6wzRo3f",
+    "outputId": "1cf87d2c-6d88-453a-d136-0f625a2800f1"
+   },
+   "execution_count": 14,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 14
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x_permuted[0,0,0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "xTX-hx2SR1wp",
+    "outputId": "0d4908c4-c3bc-44e3-8ec6-1487104cc209"
+   },
+   "execution_count": 15,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(0.9890)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 15
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x=torch.arange(1,10).reshape(1,3,3)\n",
+    "x, x.shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "mZomOe7gR4Q8",
+    "outputId": "0b3c922f-ec11-46de-b8a5-9f9533d866ad"
+   },
+   "execution_count": 18,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(tensor([[[1, 2, 3],\n",
+       "          [4, 5, 6],\n",
+       "          [7, 8, 9]]]),\n",
+       " torch.Size([1, 3, 3]))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 18
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "3y7v4SQvSBs1",
+    "outputId": "8c53307d-e628-404d-db66-56c6bdffab7c"
+   },
+   "execution_count": 19,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([[1, 2, 3],\n",
+       "        [4, 5, 6],\n",
+       "        [7, 8, 9]])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 19
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "hf9uG4xLSNya",
+    "outputId": "3075bc42-9ffa-426b-8a86-95628ffcd824"
+   },
+   "execution_count": 21,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 21
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][0][0]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "zA4G2Se4SRB3",
+    "outputId": "324312d2-ed0a-49eb-f81f-e904e53992fe"
+   },
+   "execution_count": 22,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(1)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 22
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0][2][2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mwy3zmKKSdbk",
+    "outputId": "d35172c3-b099-40a6-ddf1-a453c2adfa44"
+   },
+   "execution_count": 23,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor(9)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 23
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[:,1,1]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fE3nCM1KS7XT",
+    "outputId": "01f5d755-9737-4235-9f73-dce89ff6ba16"
+   },
+   "execution_count": 24,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([5])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 24
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,0,:]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "luNDINKNTTxp",
+    "outputId": "091195ef-2f71-4602-e95f-529a69193150"
+   },
+   "execution_count": 25,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "x[0,:,2]"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "KG8A4xbfThCL",
+    "outputId": "5866bc41-9241-4619-be7b-e9206b3f80ab"
+   },
+   "execution_count": 26,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([3, 6, 9])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 26
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import numpy as np"
+   ],
+   "metadata": {
+    "id": "CZ3PX0qlTwHJ"
+   },
+   "execution_count": 27,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array = np.arange(1.0, 8.0)"
+   ],
+   "metadata": {
+    "id": "UOBeTumiT3Lf"
+   },
+   "execution_count": 28,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "RzcO32E9UCQl",
+    "outputId": "430def24-c42c-461f-e5e7-398544c695d3"
+   },
+   "execution_count": 29,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 4., 5., 6., 7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.from_numpy(array)\n",
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JJIL0q1DUC6O",
+    "outputId": "8a3b1d7c-4482-4d32-f34f-9212d9d3a177"
+   },
+   "execution_count": 32,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1., 2., 3., 4., 5., 6., 7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array[3]=11.0"
+   ],
+   "metadata": {
+    "id": "j3Ce6q3DUIEK"
+   },
+   "execution_count": 33,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "array"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dc_BCVdjUsCc",
+    "outputId": "65537325-8b11-4f36-fc73-e56f30d6a036"
+   },
+   "execution_count": 34,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array([ 1.,  2.,  3., 11.,  5.,  6.,  7.])"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VG1e_eITUta2",
+    "outputId": "a26c5198-23b6-4a6d-d73a-ba20cd9782b8"
+   },
+   "execution_count": 35,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([ 1.,  2.,  3., 11.,  5.,  6.,  7.], dtype=torch.float64)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.ones(7)\n",
+    "tensor, tensor.dtype\n",
+    "numpy_tensor = tensor.numpy()\n",
+    "numpy_tensor, numpy_tensor.dtype"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Swt8JF8vUuev",
+    "outputId": "c9e5bf6a-6d2c-41d6-8327-366867ffdd2d"
+   },
+   "execution_count": 37,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "(array([1., 1., 1., 1., 1., 1., 1.], dtype=float32), dtype('float32'))"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "random_tensor_A = torch.rand(3,4)\n",
+    "random_tensor_B = torch.rand(3,4)\n",
+    "print(random_tensor_A)\n",
+    "print(random_tensor_B)\n",
+    "print(random_tensor_A == random_tensor_B)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "uGcagTteVFTD",
+    "outputId": "49405790-08e7-4210-b7f1-f00b904c7eb9"
+   },
+   "execution_count": 38,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.9870, 0.6636, 0.6873, 0.8863],\n",
+      "        [0.8386, 0.4169, 0.3587, 0.0265],\n",
+      "        [0.2981, 0.6025, 0.5652, 0.5840]])\n",
+      "tensor([[0.9821, 0.3481, 0.0913, 0.4940],\n",
+      "        [0.7495, 0.4387, 0.9582, 0.8659],\n",
+      "        [0.5064, 0.6919, 0.0809, 0.9771]])\n",
+      "tensor([[False, False, False, False],\n",
+      "        [False, False, False, False],\n",
+      "        [False, False, False, False]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "RANDOM_SEED = 42\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_C = torch.rand(3,4)\n",
+    "torch.manual_seed(RANDOM_SEED)\n",
+    "random_tensor_D = torch.rand(3,4)\n",
+    "print(random_tensor_C)\n",
+    "print(random_tensor_D)\n",
+    "print(random_tensor_C == random_tensor_D)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "HznyXyEaWjLM",
+    "outputId": "25956434-01b6-4059-9054-c9978884ddc1"
+   },
+   "execution_count": 46,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
+      "        [0.3904, 0.6009, 0.2566, 0.7936],\n",
+      "        [0.9408, 0.1332, 0.9346, 0.5936]])\n",
+      "tensor([[True, True, True, True],\n",
+      "        [True, True, True, True],\n",
+      "        [True, True, True, True]])\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "!nvidia-smi"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vltPTh0YXJSt",
+    "outputId": "807af6dc-a9ca-4301-ec32-b688dbde8be8"
+   },
+   "execution_count": 2,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "Thu May 23 02:57:59 2024       \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
+      "|-----------------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                                         |                      |               MIG M. |\n",
+      "|=========================================+======================+======================|\n",
+      "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
+      "| N/A   60C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
+      "|                                         |                      |                  N/A |\n",
+      "+-----------------------------------------+----------------------+----------------------+\n",
+      "                                                                                         \n",
+      "+---------------------------------------------------------------------------------------+\n",
+      "| Processes:                                                                            |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
+      "|        ID   ID                                                             Usage      |\n",
+      "|=======================================================================================|\n",
+      "|  No running processes found                                                           |\n",
+      "+---------------------------------------------------------------------------------------+\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import torch\n",
+    "torch.cuda.is_available()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L6mMyPDyYh1j",
+    "outputId": "279c5dd8-c2a8-4fbd-f321-2f5d7c6e90e6"
+   },
+   "execution_count": 3,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 3
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "device"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "oOdiYa7ZYytx",
+    "outputId": "d73b04fc-8963-4826-9722-08d118d5ab91"
+   },
+   "execution_count": 5,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "'cuda'"
+      ],
+      "application/vnd.google.colaboratory.intrinsic+json": {
+       "type": "string"
+      }
+     },
+     "metadata": {},
+     "execution_count": 5
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "torch.cuda.device_count()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "vOdsazLqZFM5",
+    "outputId": "8189cd6a-9017-4663-a652-3e15c517d9c3"
+   },
+   "execution_count": 6,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 6
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor = torch.tensor([1,2,3], device = \"cpu\")\n",
+    "print(tensor, tensor.device)"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cdik9Vw3ZMv0",
+    "outputId": "044a68fd-83a1-409d-8e3b-655142ca0270"
+   },
+   "execution_count": 7,
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "tensor([1, 2, 3]) cpu\n"
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu = tensor.to(device)\n",
+    "tensor_on_gpu"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Zmp835rrZp-z",
+    "outputId": "37fa3413-18a3-47bf-ae51-5b36ff85a3ef"
+   },
+   "execution_count": 8,
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "tensor([1, 2, 3], device='cuda:0')"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 8
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_gpu.numpy()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 159
+    },
+    "id": "jhriaa8uZ1yM",
+    "outputId": "bc5a3226-1a12-4fea-8769-a44f21cdc323"
+   },
+   "execution_count": 10,
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "TypeError",
+     "evalue": "can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-e0c96c7436fd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtensor_on_gpu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first."
+     ]
+    }
+   ]
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "tensor_on_cpu = tensor_on_gpu.cpu().numpy()"
+   ],
+   "metadata": {
+    "id": "LHGXK3GgaOzL"
+   },
+   "execution_count": 12,
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "source": [],
+   "metadata": {
+    "id": "j-El4LlCajfq"
+   },
+   "execution_count": null,
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\n"
+   ]
+  }
+ ]
+}
\ No newline at end of file